The Gender Gap in Early‐Career Wage Growth

Manning,, Alan; Swaffield,, Joanna

doi:10.1111/j.1468-0297.2008.02158.x

The Gender Gap in Early‐Career Wage Growth

Manning,, Alan;Swaffield,, Joanna 2008-07-01 00:00:00 Abstract In the UK the gender pay gap on entry to the labour market is approximately zero but ten years after labour market entry, there is a gender wage gap of almost 25 log points. This article explores the reason for this gender gap in early‐career wage growth, considering three main hypotheses – human capital, job‐shopping and ‘psychological’ theories. Human capital factors can explain about 11 log points, job‐shopping about 1.5 log points and the psychological theories up to 4.5 log points depending on the specification. But a substantial unexplained gap remains: women who have continuous full‐time employment, have had no children and express no desire to have them earn about 8 log points less than equivalent men after 10 years in the labour market. Given the enormous literature on the gender pay gap – see Altonji and Blank (1999) for a relatively recent survey – one might wonder about the justification for yet another paper on the subject. The answer is that this article focuses on the gender gap in wage growth in the early years after labour market entry and argues that an understanding of this is necessary as it is the primary cause of the overall gender pay gap. The reason why this is particularly important in the UK can be explained very simply. Figure 1 presents the data the earnings‐experience profile in the UK for men and women together with the gender pay gap from one of the main data sets used in this article, the British Household Panel Survey (BHPS). Average log wages are normalised to be zero for men with zero years of labour market experience. One can see that the earnings of men and women are very similar on entry to the labour market but 10 years later a sizeable gender gap in earnings has emerged,1 a gap that reaches its maximum about 20 years after labour market entry before falling slightly in later years.2 Of course, cross‐section profiles like the ones presented in Figure 1 confound true life‐cycle effects and cohort effects. But, while cohort effects can probably explain some of the cross‐section profile, there is good reason to think that they are not the most important reason for the widening of the gender pay gap after labour market entry. To see this Figure 2 presents the gender earnings gap at different ages for different birth cohorts in the New Earnings Survey (NES).3 The NES contains no information on education (so we cannot compute experience) and Figure 2 simply presents age profiles. What one can see is that the gender pay gap is lower at all ages for younger cohorts but that the contribution of cohort is small relative to the life‐cycle effects. For example, Figure 2 suggests that the gender pay gap that can be expected at age 40 for those born around 1970 is likely to be smaller than for those born around 1960 but extrapolation of the profiles in Figure 2 would suggest that a very sizeable gender wage gap will still exist. And for women born after 1970 there is some evidence that the progress of women has stalled as the profiles of the gender pay gap are very similar for those born around 1970 and those born in the late 1970s – if this is true then there will be a gender pay gap for these women of 20 log points in their early 30s.4 So, young women entering the labour market today can still expect to earn much less than young men after 10 years in the labour market and this gender difference in early‐career wage growth needs explaining. Fig. 2. Open in new tabDownload slide The Evolution of the Gender Pay Gap for Different Birth CohortsNotes. Data come from the New Earnings Survey, 1975–2001. Earnings data are hourly earnings excluding over‐time. Fig. 2. Open in new tabDownload slide The Evolution of the Gender Pay Gap for Different Birth CohortsNotes. Data come from the New Earnings Survey, 1975–2001. Earnings data are hourly earnings excluding over‐time. Fig. 1. Open in new tabDownload slide The Earnings‐Experience Profile for Men and WomenNotes. Data come from the first 12 waves of the BHPS. Log hourly wages are normalised to be zero for men with zero years of labour market experience. Fig. 1. Open in new tabDownload slide The Earnings‐Experience Profile for Men and WomenNotes. Data come from the first 12 waves of the BHPS. Log hourly wages are normalised to be zero for men with zero years of labour market experience. Most papers on the gender pay gap focus on the level of pay and not the growth in pay. But as the level of pay at any level experience is simply the initial pay on labour market entry plus cumulated wage growth, any theory of the pay level must be, if only implicitly, also a theory of wage growth (and vice versa). So, theories to explain the level of pay should also be able to explain differences in wage growth. This article focuses on three theories of wage growth – the human capital hypothesis, job‐shopping and ‘psychological’ theories and asks how well they can do in explaining the gender gap in early‐career wage growth. The most common approach to explaining the gender pay gap is based on the human capital model developed by Becker (1993) and Mincer (1974) and applied to the gender pay gap in many papers; see Mincer and Polachek (1974), Mincer and Ofek (1982), Light and Ureta (1995),inter alia. This approach seeks to explain the gender pay gap by gender differences in human capital accumulation of which there are several dimensions. First, there are gender differences in accumulated work experience (which is assumed to be correlated with levels of human capital) because women are more likely to have intermittent labour market participation and are more likely to work part‐time. Secondly, the anticipation of future intermittence may affect current investments in human capital. The affected investments might be after labour market entry – see Ben‐Porath (1967) for a classic theoretical model of this – or prior to labour market entry where it may take the form of gender differences in the quantity or (more plausible these days for a country like the UK) type of education. Thirdly, Becker (1985) has argued that the greater domestic commitments of women (especially married women with dependent children) mean they put less energy into work and that translates into lower hourly wages. The estimates presented below find that the human capital hypothesis can explain a non‐trivial amount of the gender gap in early‐career wage growth but over 50% of the gender pay gap that emerges by 10 years after labour market entry cannot be explained in this way. The reason for this conclusion can be explained very simply. First, differences in actual labour market experience between young British men and women after 10 years in the labour market are simply not large enough to explain more than 6.5 of the 25 log‐point gender pay gap at that point in the life‐cycle. Secondly, gender differences in job‐related training do explain a part of the differential (primarily because young men are still much more likely than young women to go into apprenticeships) but, again, the estimated contribution is no more than 4.5 log points. And, thirdly, gender differences in the quantity of education are very small (and, in any case, Mincer's (1974) famous observation that the experience profiles are similar for different levels of education would suggest that any difference in the quantity of education should show up in a gender pay gap on labour market entry and not a gender gap in wage growth) and, while gender differences in the types of education and associated occupational choices are still large, we do not find that occupational differences contribute much to the gender gap in early‐career wage growth. The bottom line is that the human capital explanations can explain perhaps half of the 25 log point gender pay gap observed 10 years after labour market entry. The article then also investigates the job‐shopping hypothesis that an important part of early career wage growth is associated with moving from worse to better‐paying jobs, e.g. Topel and Ward (1992), who study only US men, find that something like one‐third of wage growth in the first 10 years after labour market entry can be put down to job mobility). Manning (2003a, ch. 7) has documented how women are more constrained in their opportunities to change jobs than men and are less concerned with money when they do. So we might expect to see women making fewer ‘good’ job changes, more ‘bad’ job changes and a lower return to job changes when they do. We investigate this hypothesis below, and find that it can explain only 1.5 log points of the gender pay gap after 10 years in the labour market. This is not because job mobility is unimportant but because gender differences among young workers in the incidence of and returns to job mobility are quite small. The article then considers a more recent but less well‐known theory of the gender pay gap – that has its roots in the sizeable psychological differences between men and women at the age they enter the labour market, e.g., in attitudes to risk‐taking, competition, self‐esteem and selflessness. These differences are surveyed in Croson and Gneezy (forthcoming) and Babcock and Laschever (2003); Gneezy et al. (2003) suggest that they might help to explain the gender pay gap. We use data on psychological attributes of men and women before labour market entry to examine the explanatory power of these ideas, finding that they can explain a few percentage points of the gender wage gap but that their impact is not enormous. The plan of the article is as follows. Section 1 describes the data used. Section 2 then presents ‘reduced‐form’ estimates of wage growth equations that confirm the presence of a gender gap in early‐career wage growth as suggested by Figure 1. Section 3 then investigates the extent to which this can be explained by differences in human capital considering the contribution of breaks in employment, of job‐related training, of the types of education and of occupational choice. Section 4 then considers the contribution of job mobility, both good and bad, to wage growth. Section 5 then investigates the importance of ‘personality’ for explaining the gender pay gap. Section 6 concludes. The bottom line is that a sizeable part of the gender pay gap after 10 years in the labour market remains unexplained: a woman who has done nothing but work full‐time ever since leaving education, has had no children and expresses no desire to have them in the future earns, on average, at least 8 percentage points less than an equivalent man. 1. The Data The main data used in the first part of this article come from the first 12 waves of the British Household Panel Study (BHPS) that cover the period 1991–2002 inclusive, though the Labour Force Survey (LFS) and the New Earnings Survey (NES) are sometimes used for supplementary evidence and consistency checks. The BHPS started in 1991 with some 5,500 households and 10,300 individuals drawn from 250 different areas of Great Britain. It has had a number of booster samples: approximately 1,000 households in 1997 that are part of the European Community Household Panel Survey (that include Northern Ireland and over‐sample low‐income households), approximately 3,000 Scottish and Welsh households that were added in 1999. It follows all individuals in sample households and those who end up in households with them as long as they remain in a household with an original sample member (in many ways, it can be thought of as the British PSID). From the BHPS the sample is constructed in the following way. First, the sample is restricted to those with no more than 10 years of potential labour market experience as our focus is on what happens to the gender pay gap in the years after labour market entry. Secondly, only those individuals for whom there is an uninterrupted set of interviews are included. Thirdly, anyone who has a period in employment when we cannot identify whether they are working for the same employer as at the previous interview is excluded. Finally, because of the difficulty in measuring earnings for the self‐employed, anyone who reported being self‐employed at any interview date is excluded: as few individuals move between employment and self‐employment (about 1.5% of employees become self‐employed in any year) this is a relatively minor restriction. In total about 5% of observations are excluded because of data problems and the final sample used for analysis is approximately 10,500 observations on wage growth for about 3,559 individuals. Wages are measured as an hourly rate, obtained by dividing usual weekly earnings by usual weekly hours. Those with hourly earnings below £1 or above £100 are excluded. As we do not want the measure of earnings growth on‐the‐job to include aggregate wage growth it is important to deflate wages: as Altonji and Williams (1998) point out, estimates of the return to job tenure can be sensitive to the way in which this is done.5 We experimented with the use of an external earnings index but average earnings growth in the BHPS deviates from this aggregate measure. As a result we estimated a simple earnings function of the log of hourly wages on a quartic in experience, regional, race and gender dummies and a set of wave dummies and then deducted the wave dummies to generate our adjusted wage series. As we are interested in how wages evolve, we need a measure of wage growth. In most studies of wage growth, the dependent variable is defined as the growth in wages between two fixed points in time, with this measure being undefined for those who are not in employment at either of the two points. This approach has the disadvantage that it under‐samples those individuals who have intermittent employment, a potentially serious problem when analysing the gender pay gap. For example, suppose we measure wages annually but some individuals have a year or more in which they are not in employment. If one only measures wage growth from one year to the next, one will drop two observations for such individuals because there will be two years in which annual wage growth is not defined. To deal with this problem, we define wage growth in the following way. For any year in which an individual is in employment, define the wage growth as the difference in log wages between the next year they are in employment and the log wage this year. Consequently there may be gaps between the two wage observations of any number of years. It is important to control for this ‘gap’ in what follows and we describe later how we do this. There remains a potential problem in that the gap between wage observations is censored at the top end by the latest year of observation being 2002 but this seems to be a small problem6 because long gaps between wage observations are relatively rare. The first two columns of Table 1 present the distribution of gaps between the wage observations in our data set for men and women. One observes, as one would expect, that women are more likely than men to have intermittent employment. But, what is also striking is the relatively short length of gaps between periods of employment; see also Myck and Paull (2001) for an analysis of gender differences in accumulated experience. Only 2% of the observations on female wage growth have a gap in employment of more than 2 years. Because the BHPS is a relatively short panel, there are many censored observations so, to get some idea of the uncensored distribution of the gap, the third and fourth columns of Table 1 report the distribution of the gap among those in waves 1–6. There are now more young women with longer gaps in employment, though it remains remarkable how rare are long breaks in employment. However these data are still censored: some women may never return to paid employment but some might have very long spells that are not observed in the BHPS. To get some idea of this, the final two columns of Table 1 present, for comparison, data on gaps between spells of employment from the Labour Force Survey (LFS). In the LFS those not currently in employment are asked when they last worked and we can use this information to work out the gap between employment spells for those who are employed in the subsequent quarter.7 Again, one sees that women are more likely to have gaps in employment than men but the average size of gaps and the gender differences are not large – the BHPS suggests an average gap for women that is 0.3 years above that for men and the LFS suggests a somewhat smaller gap of 0.25 years. And the very long gaps that contribute quite a lot to this overall gender difference are very rare among the young workers that are the focus of this article. For example, the work history data of the 1970 British Cohort Study suggests that, among those in employment at age 30, 23% of men have had a period of non‐employment between two periods of employment with an average of 0.3 gaps compared to 27% of women with an average of 0.35 gaps. Table 1 Distribution of Gap Between Successive Wage Observations Gap between wage obs (yrs) . BHPS (all waves) . BHPS (wave < = 6) . LFS . Men . Women . Men . Women . Men . Women . 1 95.6 93.0 94.7 91.5 96.6 92.7 2 3.1 4.8 3.3 5.2 2.4 3.1 3 0.9 0.9 1.1 1.1 0.4 1.1 4 0.2 0.4 0.4 0.8 0.2 0.6 5 0.1 0.2 0.1 0.4 0.1 0.5 6 0.1 0.3 0.1 0.5 0.1 0.4 7 0.0 0.1 0.0 0.3 0.0 0.3 8 0.7 0.1 0.2 0.2 0.0 0.3 9 0.0 0.0 0.0 0.0 0.0 0.2 10–14 0.1 0.7 15–19 0.0 0.0 0.0 0.1 0.0 0.1 20+ 0.0 0.0 No. of obs 5,344 5,756 2,709 2,990 37,384 34,595 Gap between wage obs (yrs) . BHPS (all waves) . BHPS (wave < = 6) . LFS . Men . Women . Men . Women . Men . Women . 1 95.6 93.0 94.7 91.5 96.6 92.7 2 3.1 4.8 3.3 5.2 2.4 3.1 3 0.9 0.9 1.1 1.1 0.4 1.1 4 0.2 0.4 0.4 0.8 0.2 0.6 5 0.1 0.2 0.1 0.4 0.1 0.5 6 0.1 0.3 0.1 0.5 0.1 0.4 7 0.0 0.1 0.0 0.3 0.0 0.3 8 0.7 0.1 0.2 0.2 0.0 0.3 9 0.0 0.0 0.0 0.0 0.0 0.2 10–14 0.1 0.7 15–19 0.0 0.0 0.0 0.1 0.0 0.1 20+ 0.0 0.0 No. of obs 5,344 5,756 2,709 2,990 37,384 34,595 Notes. BHPS sample is restricted to those with 10 or fewer years of potential labour market experience. LFS sample is from 1997–2002 and gives the distribution among those currently in employment who are not labour market entrants and who have less than 20 years of labour market experience. Open in new tab Table 1 Distribution of Gap Between Successive Wage Observations Gap between wage obs (yrs) . BHPS (all waves) . BHPS (wave < = 6) . LFS . Men . Women . Men . Women . Men . Women . 1 95.6 93.0 94.7 91.5 96.6 92.7 2 3.1 4.8 3.3 5.2 2.4 3.1 3 0.9 0.9 1.1 1.1 0.4 1.1 4 0.2 0.4 0.4 0.8 0.2 0.6 5 0.1 0.2 0.1 0.4 0.1 0.5 6 0.1 0.3 0.1 0.5 0.1 0.4 7 0.0 0.1 0.0 0.3 0.0 0.3 8 0.7 0.1 0.2 0.2 0.0 0.3 9 0.0 0.0 0.0 0.0 0.0 0.2 10–14 0.1 0.7 15–19 0.0 0.0 0.0 0.1 0.0 0.1 20+ 0.0 0.0 No. of obs 5,344 5,756 2,709 2,990 37,384 34,595 Gap between wage obs (yrs) . BHPS (all waves) . BHPS (wave < = 6) . LFS . Men . Women . Men . Women . Men . Women . 1 95.6 93.0 94.7 91.5 96.6 92.7 2 3.1 4.8 3.3 5.2 2.4 3.1 3 0.9 0.9 1.1 1.1 0.4 1.1 4 0.2 0.4 0.4 0.8 0.2 0.6 5 0.1 0.2 0.1 0.4 0.1 0.5 6 0.1 0.3 0.1 0.5 0.1 0.4 7 0.0 0.1 0.0 0.3 0.0 0.3 8 0.7 0.1 0.2 0.2 0.0 0.3 9 0.0 0.0 0.0 0.0 0.0 0.2 10–14 0.1 0.7 15–19 0.0 0.0 0.0 0.1 0.0 0.1 20+ 0.0 0.0 No. of obs 5,344 5,756 2,709 2,990 37,384 34,595 Notes. BHPS sample is restricted to those with 10 or fewer years of potential labour market experience. LFS sample is from 1997–2002 and gives the distribution among those currently in employment who are not labour market entrants and who have less than 20 years of labour market experience. Open in new tab One other peculiarity of the BHPS that is worth mentioning is that its basic measure of job tenure is tenure in a particular job with a particular employer so that within‐firm changes of job reset the tenure clock to zero. This is not the way that the job tenure variable is usually defined where it refers to job tenure with the same employer. However, the BHPS definition is retained here as job moves within employers (e.g. promotions) are as interesting and important as those between employers in understanding the emergence of the gender pay gap.8 The data are summarised in the Appendix in Table A1. 2. Reduced Form Estimates While the evidence presented in Figures 1 and 2 is strongly suggestive of a gender gap in wage growth in the years immediately after labour market entry, it is important to verify this using direct data on wage growth. Hence, this Section presents some ‘reduced‐form’ wage growth regressions to get some idea of what the data looks like and to confirm the existence of this gender gap in early‐career wage growth. Accordingly, we estimate the following ‘reduced‐form’ model for the wage growth between t and (t + g) of individual i where g is the gap between wage observations: (1) where e is years since leaving full‐time education i.e. potential experience. This model is estimated separately for men and women. The quadratic specification seems adequate to capture the variation in wage growth among the young workers in our sample but one should be wary of extrapolating into other parts of the life‐cycle; Murphy and Welch (1990) suggest a quartic for the level implying a cubic for the change if workers of all ages are in the sample. In specifying this equation we need to be careful about how we deal with individuals for whom the gap over which wage growth is measured is more than a year. To see why this matters suppose that the observed wage growth for all individuals (whatever the gap) was 5% but that men are always in employment (so ) but women are only in employment every two years (so ). After 2 years earnings for men would have grown by approximately 10% whereas those for women would have only grown by 5%. The gender pay gap would be widening but estimation of (1) that did not take account of differences in the gap would miss this. The simplest way of getting a gap‐adjusted measure of wage growth would be simply to divide the observed wage growth by the gap between wage observations to get an estimate of annualised wage growth. However the validity of this procedure is based on the implicit assumption that wage growth does not vary with experience, something we know to be untrue. So, a slightly more sophisticated approach is taken here to adjust for the gap between wage observations. If (1) is the wage growth for those for whom , then for someone with to return to employment at the same level of wages they must have expected wage growth between the two observations of: (2) the right‐hand side of which is simply two one‐period sets of wage growth. One can readily extend this formula to any value of g in which case it will be given by: (3) Using the specific functional form in (1), this can be written as: (4) Thus one can readily estimate wage growth on a consistent basis for individuals with different gaps by computing the ‘adjusted’ levels of experience in (4) and using these as regressors in a wage growth equation. Note that there is no constant in this regression – the constant in (1) gets multiplied by g. If wage growth does not vary with experience this approach is equivalent to the simple‐minded approach of just dividing wage growth by the interval between wage observations as the gap would be the only remaining regressor and the coefficient on it can be interpreted as annual wage growth. Table 2 reports estimates of the reduced‐form wage growth equations. The first two columns of Table 2 report estimates of (4) for men and women separately. We report the estimates of earnings growth at 0, 5 and 10 years of experience together with their standard errors. Taking the coefficients for men, the estimates suggest that a man can expect 14.5% annual wage growth on entry into the labour market, falling to 6.8% after 5 years and 1.3% after 10 years. For women (the second column) earnings growth on entry is lower than for men (at 12.0% per annum) and still lower after 10 years though the gap in wage growth narrows.9 These estimates are consistent with the finding that the gender pay gap begins to widen soon after labour market entry, as suggested in Figure 1.10 The bottom two rows of Table 2 report the gender pay gap implied by these differences in estimated wage growth 5 and 10 years after labour market entry. These are derived by cumulating the estimates of the gender difference in wage growth from the estimation of (4) assuming that there are no breaks in employment and (as is a good approximation) that gender differences in wages on labour market entry are zero. The estimates in columns 1 and 2 of Table 2 imply a gender pay gap of 14.4 log points after 5 years and 24.8 log points after 10 years. These estimates are not far out of line with those derived from estimates of wage levels. Table 2 ‘Reduced Form’Estimates of Wage Growth Equation . 1 . 2 . 3 . 4 . 5 . 6 . Men Women Men Women Men Women 0 yrs of experience 0.145 0.120 0.161 0.131 0.160 0.160 (0.013) (0.012) (0.013) (0.011) (0.019) (0.016) 5 yrs of experience 0.068 0.040 0.070 0.046 0.074 0.041 (0.005) (0.004) (0.004) (0.004) (0.008) (0.006) 10 yrs of experience 0.013 0.009 0.030 0.035 0.018 0.020 (0.010) (0.006) (0.007) (0.007) (0.015) (0.011) No. of obs 5,015 5,535 4,833 5,208 5,015 5,535 R2 0.076 0.051 0.077 0.058 0.020 0.015 Fixed Effects No No No No Yes Yes P = 1.00 P = 1.00 Sample All All Gap = 1 Gap = 1 All All Implied Gender Gap at 5 yrs 0.144 0.149 0.096 (0.044) (0.041) (0.062) Implied Gender Gap at 10 yrs 0.248 0.222 0.216 (0.054) (0.044) (0.066) . 1 . 2 . 3 . 4 . 5 . 6 . Men Women Men Women Men Women 0 yrs of experience 0.145 0.120 0.161 0.131 0.160 0.160 (0.013) (0.012) (0.013) (0.011) (0.019) (0.016) 5 yrs of experience 0.068 0.040 0.070 0.046 0.074 0.041 (0.005) (0.004) (0.004) (0.004) (0.008) (0.006) 10 yrs of experience 0.013 0.009 0.030 0.035 0.018 0.020 (0.010) (0.006) (0.007) (0.007) (0.015) (0.011) No. of obs 5,015 5,535 4,833 5,208 5,015 5,535 R2 0.076 0.051 0.077 0.058 0.020 0.015 Fixed Effects No No No No Yes Yes P = 1.00 P = 1.00 Sample All All Gap = 1 Gap = 1 All All Implied Gender Gap at 5 yrs 0.144 0.149 0.096 (0.044) (0.041) (0.062) Implied Gender Gap at 10 yrs 0.248 0.222 0.216 (0.054) (0.044) (0.066) Notes. These estimates are derived from the estimation of (4) where experience is modelled as a quadratic. Standard errors in parentheses. These are robust standard errors with clustering on the individual. Open in new tab Table 2 ‘Reduced Form’Estimates of Wage Growth Equation . 1 . 2 . 3 . 4 . 5 . 6 . Men Women Men Women Men Women 0 yrs of experience 0.145 0.120 0.161 0.131 0.160 0.160 (0.013) (0.012) (0.013) (0.011) (0.019) (0.016) 5 yrs of experience 0.068 0.040 0.070 0.046 0.074 0.041 (0.005) (0.004) (0.004) (0.004) (0.008) (0.006) 10 yrs of experience 0.013 0.009 0.030 0.035 0.018 0.020 (0.010) (0.006) (0.007) (0.007) (0.015) (0.011) No. of obs 5,015 5,535 4,833 5,208 5,015 5,535 R2 0.076 0.051 0.077 0.058 0.020 0.015 Fixed Effects No No No No Yes Yes P = 1.00 P = 1.00 Sample All All Gap = 1 Gap = 1 All All Implied Gender Gap at 5 yrs 0.144 0.149 0.096 (0.044) (0.041) (0.062) Implied Gender Gap at 10 yrs 0.248 0.222 0.216 (0.054) (0.044) (0.066) . 1 . 2 . 3 . 4 . 5 . 6 . Men Women Men Women Men Women 0 yrs of experience 0.145 0.120 0.161 0.131 0.160 0.160 (0.013) (0.012) (0.013) (0.011) (0.019) (0.016) 5 yrs of experience 0.068 0.040 0.070 0.046 0.074 0.041 (0.005) (0.004) (0.004) (0.004) (0.008) (0.006) 10 yrs of experience 0.013 0.009 0.030 0.035 0.018 0.020 (0.010) (0.006) (0.007) (0.007) (0.015) (0.011) No. of obs 5,015 5,535 4,833 5,208 5,015 5,535 R2 0.076 0.051 0.077 0.058 0.020 0.015 Fixed Effects No No No No Yes Yes P = 1.00 P = 1.00 Sample All All Gap = 1 Gap = 1 All All Implied Gender Gap at 5 yrs 0.144 0.149 0.096 (0.044) (0.041) (0.062) Implied Gender Gap at 10 yrs 0.248 0.222 0.216 (0.054) (0.044) (0.066) Notes. These estimates are derived from the estimation of (4) where experience is modelled as a quadratic. Standard errors in parentheses. These are robust standard errors with clustering on the individual. Open in new tab To check that these estimates are not sensitive to the treatment of those with gaps of more than 1 year between wage observations, columns 3 and 4 of Table 2 present the ‘reduced‐form’ wage growth equations restricting the sample to those for whom the gap between wage observations is only one year. One finds the same pattern of gender differences in wage growth. The implied gender pay gap after 10 years is lower for this sample at 22.2 log points suggesting that part of the overall gap can be explained by women having more intermittence but the contribution is not that large. One might also wonder whether the inclusion of other controls on individual characteristics alters these conclusions. The bottom line is that they do not have a big impact.11 One way to see the irrelevance of personal characteristics is to estimate wage growth equations including individual fixed effects. This is done in the final two columns of Table 2: the coefficients hardly change and the fixed effects are jointly insignificant from zero. We should not be surprised by this finding: if there were very significant permanent differences in wage growth across individuals one would expect to see the variance of earnings in a cohort rising explosively over the life‐cycle which it does not.12 Of course, this insignificance of fixed effects in a wage‐growth equation does not mean that individual fixed effects would not be very significant in a wage levels equation – rather, it is a validation of Mincer's conclusion that earnings profiles are parallel to each other (once one controls for gender). So, our basic data do contain the feature that there is a noticeable gender gap in wage growth from the start of labour market careers. Although this gender gap in wage growth eventually reverses there is a very large gender pay gap by the time it does and women never make up the ground lost in the first 10 years after labour market entry. One might be concerned that this conclusion applies only to the BHPS or is an artifact of the quadratic specification used. So, as a useful double‐check, Figure 3 presents data from the NES that has a large enough sample size to get precise estimates of wage growth for men and women at each age. This shows the same pattern as the BHPS. It is this gender gap in early‐career wage growth that this article seeks to explain. Fig. 3. Open in new tabDownload slide Gender Differences in Wage Growth: New Earnings Survey DataNotes. The NES does not contain information on education so cannot be used to compute years of experience. This is annual wage growth for the sample of workers observed in two adjacent years. Fig. 3. Open in new tabDownload slide Gender Differences in Wage Growth: New Earnings Survey DataNotes. The NES does not contain information on education so cannot be used to compute years of experience. This is annual wage growth for the sample of workers observed in two adjacent years. 3. Gender Differences in Human Capital Accumulation Human capital theories seek to explain the gender pay gap by gender differences in human capital accumulation. There is no doubt that human capital theory is of use in explaining at least some part of the gender pay gap – some researchers (O'Neill, 2003; Polachek, 2004) claim that it can explain virtually all of the US gender pay gap while others (Altonji and Blank, 1999; Blau and Kahn, 2004) find that there remains a sizeable unexplained component to the gender pay gap. There are several varieties of human capital explanations for the gender pay gap all of which have their root in ‘the lesser amount of time and energy that women can commit to labour‐market careers as a result of the division of labour within the family’ (O'Neill, 2003, p. 309). First, after labour market entry, the weaker labour market attachment of women means they tend to accumulate less labour market experience than men and, given that such experience is valuable, part of the difference in earnings can be explained as the result of this difference in actual labour market experience. Studies (Mincer and Polachek, 1974; Light and Ureta, 1995) sometimes find that the returns to actual experience are very similar for men and women though typically other measures of labour market history are also important in explaining the gender pay gap and a sizeable part of the gender pay gap remains unexplained even after controlling for differences in actual labour market experience.13 Secondly, there may be differences in human capital investments after labour market entry in anticipation of future labour market attachment. So, we might see men doing more job‐related training than women because they expect to spend longer in the labour market in later years. Thirdly, a similar argument might apply to human capital investments in education prior to labour market entry – men might make more investments than women in human capital that has a labour market pay‐off. We consider these three different human capital hypotheses in turn. 3.1. Gender Differences in Labour Market Experience First, consider whether gender differences in actual labour market experience (the results of more labour market intermittence and more part‐time working by women) can explain the gender gap in early‐career wage growth. In our framework labour market intermittence is captured by the gap between wage observations as a gap of more than one year must imply a period not in employment. As shown in Table 1, women have longer gaps between wage observations than men, implying that women do have weaker labour market attachment than men. It may be that the differences in the gap variable can explain the disadvantage of young women in wage growth. The specification of the wage‐growth equation used so far assumes that there is no wage penalty associated with having but there are good reasons to think there might be a penalty. Assume that each year in which one is out of employment reduces the level of wages when one returns by β5 log points.14 Then the equation for wage growth (4) will become: (5) so that a simple way to test for a penalty to intermittent employment is to include constants in the reduced‐form wage‐growth equations. This is done in the first and second columns of Table 3. These results suggest that each year out of employment reduces wages by 4.4% for men and 4.7% for women. The estimate for men is slightly lower but broadly consistent with the penalty for spells of unemployment found by Arulampalam (2001) and Gregory and Jukes (2001). Table 3 The Impact of Human Capital Variables on the Gender Gap in Wage Growth . 1 . 2 . 3 . 4 . 5 . 6 . 7 . 8 . 9 . 10 . 11 . 12 . Men Women Men Women Men Women Men Women Men Women Men Women 0 yrs of experience 0.151 0.130 0.172 0.141 0.173 0.141 0.153 0.138 0.118 0.088 0.159 0.150 (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.021) (0.013) (0.018) (0.015) 5 yrs of experience 0.074 0.046 0.077 0.056 0.063 0.043 0.059 0.040 0.049 0.026 0.058 0.047 (0.005) (0.004) (0.005) (0.004) (0.008) (0.006) (0.008) (0.007) (0.010) (0.004) (0.012) (0.008) 10 yrs of experience 0.023 0.032 0.028 0.046 −0.003 0.014 −0.004 0.010 −0.001 0.025 −0.010 0.025 (0.009) (0.008) (0.009) (0.008) (0.014) (0.012) (0.014) (0.012) (0.019) (0.004) (0.018) (0.014) Cost of gap > 1 0.044 0.047 0.046 0.059 0.054 0.066 0.054 0.067 0.119 0.093 0.072 0.049 (0.024) (0.015) (0.024) (0.015) (0.024) (0.016) (0.025) (0.017) (0.029) (0.021) (0.033) (0.017) Part‐Time −0.193 −0.060 −0.194 −0.062 −0.189 −0.060 −0.153 −0.066 −0.173 −0.058 (0.041) (0.011) (0.041) (0.012) (0.042) (0.012) (0.045) (0.014) (0.049) (0.016) Decades of job tenure −0.040 −0.041 −0.037 −0.045 −0.044 −0.068 −0.041 −0.032 (0.017) (0.015) (0.017) (0.015) (0.023) (0.021) (0.019) (0.016) Training in Past Year (yrs) 0.087 0.028 0.119 0.073 0.066 0.021 (0.058) (0.038) (0.091) (0.031) (0.064) (0.051) Training between wage obs (yrs) 0.107 −0.005 0.077 −0.039 0.114 −0.036 (0.063) (0.035) (0.081) (0.046) (0.062) (0.036) Housework (weekly hrs‐10)/10 −0.001 −0.003 (0.011) (0.006) Labour Market Motivation 0.005 0.004 (0.006) (0.005) No. of obs 5,015 5,535 5,015 5,535 5,015 5,535 4,830 5,306 2,505 3,127 3,622 3,923 R2 0.078 0.055 0.090 0.061 0.091 0.062 0.088 0.060 0.064 0.042 0.085 0.070 Controls for Initial Occupation No No No No No No No No Yes Yes No No P = 0.07 P = 0.14 Implied Gender Gap at 5 yrs 0.136 0.147 0.146 0.096 0.062 0.073 (0.042) (0.042) (0.046) (0.049) (0.095) (0.074) Implied Gender Gap at 10 yrs 0.220 0.183 0.183 0.138 0.124 0.057 (0.045) (0.048) (0.086) (0.091) (0.165) (0.132) . 1 . 2 . 3 . 4 . 5 . 6 . 7 . 8 . 9 . 10 . 11 . 12 . Men Women Men Women Men Women Men Women Men Women Men Women 0 yrs of experience 0.151 0.130 0.172 0.141 0.173 0.141 0.153 0.138 0.118 0.088 0.159 0.150 (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.021) (0.013) (0.018) (0.015) 5 yrs of experience 0.074 0.046 0.077 0.056 0.063 0.043 0.059 0.040 0.049 0.026 0.058 0.047 (0.005) (0.004) (0.005) (0.004) (0.008) (0.006) (0.008) (0.007) (0.010) (0.004) (0.012) (0.008) 10 yrs of experience 0.023 0.032 0.028 0.046 −0.003 0.014 −0.004 0.010 −0.001 0.025 −0.010 0.025 (0.009) (0.008) (0.009) (0.008) (0.014) (0.012) (0.014) (0.012) (0.019) (0.004) (0.018) (0.014) Cost of gap > 1 0.044 0.047 0.046 0.059 0.054 0.066 0.054 0.067 0.119 0.093 0.072 0.049 (0.024) (0.015) (0.024) (0.015) (0.024) (0.016) (0.025) (0.017) (0.029) (0.021) (0.033) (0.017) Part‐Time −0.193 −0.060 −0.194 −0.062 −0.189 −0.060 −0.153 −0.066 −0.173 −0.058 (0.041) (0.011) (0.041) (0.012) (0.042) (0.012) (0.045) (0.014) (0.049) (0.016) Decades of job tenure −0.040 −0.041 −0.037 −0.045 −0.044 −0.068 −0.041 −0.032 (0.017) (0.015) (0.017) (0.015) (0.023) (0.021) (0.019) (0.016) Training in Past Year (yrs) 0.087 0.028 0.119 0.073 0.066 0.021 (0.058) (0.038) (0.091) (0.031) (0.064) (0.051) Training between wage obs (yrs) 0.107 −0.005 0.077 −0.039 0.114 −0.036 (0.063) (0.035) (0.081) (0.046) (0.062) (0.036) Housework (weekly hrs‐10)/10 −0.001 −0.003 (0.011) (0.006) Labour Market Motivation 0.005 0.004 (0.006) (0.005) No. of obs 5,015 5,535 5,015 5,535 5,015 5,535 4,830 5,306 2,505 3,127 3,622 3,923 R2 0.078 0.055 0.090 0.061 0.091 0.062 0.088 0.060 0.064 0.042 0.085 0.070 Controls for Initial Occupation No No No No No No No No Yes Yes No No P = 0.07 P = 0.14 Implied Gender Gap at 5 yrs 0.136 0.147 0.146 0.096 0.062 0.073 (0.042) (0.042) (0.046) (0.049) (0.095) (0.074) Implied Gender Gap at 10 yrs 0.220 0.183 0.183 0.138 0.124 0.057 (0.045) (0.048) (0.086) (0.091) (0.165) (0.132) Notes. These estimates are derived from the estimation of (5) where experience is modeled as a quadratic and, where appropriate, a dummy variable for having intermittent employment, a part‐time dummy, years of job tenure and years of training are included. Standard errors in parentheses. These are robust standard errors with clustering on the individual. In columns 5 to 10 the estimates of the gender gap after 5 and 10 years are based on assuming no change in job so job tenure rises with experience. In columns 7 to 10 the estimates of the gender gap after 5 and 10 years are based on assuming no receipt of on‐the‐job training. Full‐time work is always assumed. In columns 9 and 10 where initial occupation is included as a control, the estimates of wage growth and the gender gap are based on the average using the distribution of initial occupation across both men and women with less than 10 years of experience. Using the male or the female distribution makes no difference to the results. The p‐value for a test of the hypothesis that occupational variables are jointly insignificant from zero is also reported. Open in new tab Table 3 The Impact of Human Capital Variables on the Gender Gap in Wage Growth . 1 . 2 . 3 . 4 . 5 . 6 . 7 . 8 . 9 . 10 . 11 . 12 . Men Women Men Women Men Women Men Women Men Women Men Women 0 yrs of experience 0.151 0.130 0.172 0.141 0.173 0.141 0.153 0.138 0.118 0.088 0.159 0.150 (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.021) (0.013) (0.018) (0.015) 5 yrs of experience 0.074 0.046 0.077 0.056 0.063 0.043 0.059 0.040 0.049 0.026 0.058 0.047 (0.005) (0.004) (0.005) (0.004) (0.008) (0.006) (0.008) (0.007) (0.010) (0.004) (0.012) (0.008) 10 yrs of experience 0.023 0.032 0.028 0.046 −0.003 0.014 −0.004 0.010 −0.001 0.025 −0.010 0.025 (0.009) (0.008) (0.009) (0.008) (0.014) (0.012) (0.014) (0.012) (0.019) (0.004) (0.018) (0.014) Cost of gap > 1 0.044 0.047 0.046 0.059 0.054 0.066 0.054 0.067 0.119 0.093 0.072 0.049 (0.024) (0.015) (0.024) (0.015) (0.024) (0.016) (0.025) (0.017) (0.029) (0.021) (0.033) (0.017) Part‐Time −0.193 −0.060 −0.194 −0.062 −0.189 −0.060 −0.153 −0.066 −0.173 −0.058 (0.041) (0.011) (0.041) (0.012) (0.042) (0.012) (0.045) (0.014) (0.049) (0.016) Decades of job tenure −0.040 −0.041 −0.037 −0.045 −0.044 −0.068 −0.041 −0.032 (0.017) (0.015) (0.017) (0.015) (0.023) (0.021) (0.019) (0.016) Training in Past Year (yrs) 0.087 0.028 0.119 0.073 0.066 0.021 (0.058) (0.038) (0.091) (0.031) (0.064) (0.051) Training between wage obs (yrs) 0.107 −0.005 0.077 −0.039 0.114 −0.036 (0.063) (0.035) (0.081) (0.046) (0.062) (0.036) Housework (weekly hrs‐10)/10 −0.001 −0.003 (0.011) (0.006) Labour Market Motivation 0.005 0.004 (0.006) (0.005) No. of obs 5,015 5,535 5,015 5,535 5,015 5,535 4,830 5,306 2,505 3,127 3,622 3,923 R2 0.078 0.055 0.090 0.061 0.091 0.062 0.088 0.060 0.064 0.042 0.085 0.070 Controls for Initial Occupation No No No No No No No No Yes Yes No No P = 0.07 P = 0.14 Implied Gender Gap at 5 yrs 0.136 0.147 0.146 0.096 0.062 0.073 (0.042) (0.042) (0.046) (0.049) (0.095) (0.074) Implied Gender Gap at 10 yrs 0.220 0.183 0.183 0.138 0.124 0.057 (0.045) (0.048) (0.086) (0.091) (0.165) (0.132) . 1 . 2 . 3 . 4 . 5 . 6 . 7 . 8 . 9 . 10 . 11 . 12 . Men Women Men Women Men Women Men Women Men Women Men Women 0 yrs of experience 0.151 0.130 0.172 0.141 0.173 0.141 0.153 0.138 0.118 0.088 0.159 0.150 (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.021) (0.013) (0.018) (0.015) 5 yrs of experience 0.074 0.046 0.077 0.056 0.063 0.043 0.059 0.040 0.049 0.026 0.058 0.047 (0.005) (0.004) (0.005) (0.004) (0.008) (0.006) (0.008) (0.007) (0.010) (0.004) (0.012) (0.008) 10 yrs of experience 0.023 0.032 0.028 0.046 −0.003 0.014 −0.004 0.010 −0.001 0.025 −0.010 0.025 (0.009) (0.008) (0.009) (0.008) (0.014) (0.012) (0.014) (0.012) (0.019) (0.004) (0.018) (0.014) Cost of gap > 1 0.044 0.047 0.046 0.059 0.054 0.066 0.054 0.067 0.119 0.093 0.072 0.049 (0.024) (0.015) (0.024) (0.015) (0.024) (0.016) (0.025) (0.017) (0.029) (0.021) (0.033) (0.017) Part‐Time −0.193 −0.060 −0.194 −0.062 −0.189 −0.060 −0.153 −0.066 −0.173 −0.058 (0.041) (0.011) (0.041) (0.012) (0.042) (0.012) (0.045) (0.014) (0.049) (0.016) Decades of job tenure −0.040 −0.041 −0.037 −0.045 −0.044 −0.068 −0.041 −0.032 (0.017) (0.015) (0.017) (0.015) (0.023) (0.021) (0.019) (0.016) Training in Past Year (yrs) 0.087 0.028 0.119 0.073 0.066 0.021 (0.058) (0.038) (0.091) (0.031) (0.064) (0.051) Training between wage obs (yrs) 0.107 −0.005 0.077 −0.039 0.114 −0.036 (0.063) (0.035) (0.081) (0.046) (0.062) (0.036) Housework (weekly hrs‐10)/10 −0.001 −0.003 (0.011) (0.006) Labour Market Motivation 0.005 0.004 (0.006) (0.005) No. of obs 5,015 5,535 5,015 5,535 5,015 5,535 4,830 5,306 2,505 3,127 3,622 3,923 R2 0.078 0.055 0.090 0.061 0.091 0.062 0.088 0.060 0.064 0.042 0.085 0.070 Controls for Initial Occupation No No No No No No No No Yes Yes No No P = 0.07 P = 0.14 Implied Gender Gap at 5 yrs 0.136 0.147 0.146 0.096 0.062 0.073 (0.042) (0.042) (0.046) (0.049) (0.095) (0.074) Implied Gender Gap at 10 yrs 0.220 0.183 0.183 0.138 0.124 0.057 (0.045) (0.048) (0.086) (0.091) (0.165) (0.132) Notes. These estimates are derived from the estimation of (5) where experience is modeled as a quadratic and, where appropriate, a dummy variable for having intermittent employment, a part‐time dummy, years of job tenure and years of training are included. Standard errors in parentheses. These are robust standard errors with clustering on the individual. In columns 5 to 10 the estimates of the gender gap after 5 and 10 years are based on assuming no change in job so job tenure rises with experience. In columns 7 to 10 the estimates of the gender gap after 5 and 10 years are based on assuming no receipt of on‐the‐job training. Full‐time work is always assumed. In columns 9 and 10 where initial occupation is included as a control, the estimates of wage growth and the gender gap are based on the average using the distribution of initial occupation across both men and women with less than 10 years of experience. Using the male or the female distribution makes no difference to the results. The p‐value for a test of the hypothesis that occupational variables are jointly insignificant from zero is also reported. Open in new tab But the implied gender pay gap after 10 years (for those without any intermittence) only falls from 24.8 to 22 log points, suggesting a contribution of intermittence of 2.8 log points (this is similar to the estimate from columns 3 and 4 of Table 2). Women accumulate less work experience than men, not just because they have more intermittence but also because they are more likely to work part‐time. For example, in our sample, 3.6% of the men are working part‐time (defined as less than 30 hours a week) and 19.2% of the women. The third and fourth columns of Table 3 include a dummy variable for whether the individual is currently working part‐time. The few men who are working part‐time have a very large penalty of 19.3 log points while the part‐time women have a pay penalty of 6.0%. The implied gender gap after 10 years for those who work continuously full‐time now falls to 18.3% suggesting a contribution of the gender difference in part‐time working of 3.7%. This is probably an over‐estimate as hourly earnings are obtained by dividing weekly earnings by reported hours, leading to a division bias problem that overstates the initital wages of those in part‐time employment. The estimates so far suggest that differences in actual work experience when young can explain at most 30% of the gender gap in early‐career wage growth. The explanation for this is simple: the gender gap in wage growth is largest when the gender gap in accumulated actual labour market experience is rather small. To illustrate this further Figure 4 presents data from the 1970 British Cohort Study, a longitudinal study of all children born in Britain in one week in 1970 and which collects monthly information on labour market status. Figure 4 shows the gender differences in actual experience (measured as months in work) over the first 10 years after labour market entry. Ten years after labour market entry the men have accumulated about 12 months more work experience than the women. The gap in full‐time experience is larger at 20 months. However, for the purpose of understanding the gender pay gap for those in work (as is the focus of this article) these differences are misleading as the pay differences relate to those in work and the gender differences in actual experience are much smaller for those in work. These differences are also plotted on Figure 4: after 10 years male workers have about 3 months more work experience than female workers and about 12 months more full‐time experience. These differences are simply not large enough to explain the magnitude of the gender pay gap after 10 years: for example, if one thinks of the male profile as being the ‘true’ one and women are one year behind men after 10 years this can only account for wage differentials of the order of 2 log points. The contribution is a bit larger if one recognises that the earnings profile is concave so it is not just the mean but also the variance that is of importance and women have slightly higher variance than men. After 10 years the gender difference in the variance of full‐time work experience among workers is about 1 year which adds at most an extra one log point to the predicted gender difference. These back‐of‐the‐envelope calculations are smaller than the regression estimates of Table 3 though, as we have already noted, the contribution of part‐time working is probably over‐estimated. Fig. 4. Open in new tabDownload slide Gender Differences in Accumulated Work ExperienceNotes. These figures come from the 1970 British Cohort Study. We thank Fernando Galindo‐Rueda for allowing us to use the work‐history data he had constructed. Fig. 4. Open in new tabDownload slide Gender Differences in Accumulated Work ExperienceNotes. These figures come from the 1970 British Cohort Study. We thank Fernando Galindo‐Rueda for allowing us to use the work‐history data he had constructed. The discussion so far has focused on the accumulation of general human capital but gender differences in the accumulation of specific human capital might also be important. Job‐specific human capital is often assumed to be measured by job tenure; see Mincer and Jovanovic (1981) for this theoretical argument and Jones and Makepeace (1996) for a British study in which this variable is important for explaining a gender gap. The fifth and sixth columns of Table 3 include job tenure in the current job as an extra control in the wage‐growth equations. We find that higher job tenure is associated with significantly lower wage growth. But, this does little to narrow the gender gap in wage growth on labour market entry and has no impact on the implied gender pay gap after 10 years. 3.2. Gender Differences in On‐The‐Job Training Of course, it may be differences in expected future labour market attachment that affect current human capital accumulation either before or after labour market entry. Let us start with the training after labour market entry. In the BHPS, respondents are asked about the total amount of job‐related training they have done over the previous year. We simply include a measure of the total years of training received as a control variable in our wage growth regression. We might expect that current job‐related training depresses current wages but raises future wages and wage growth. However, the strongest empirical effect of on‐the‐job training seems to come not from the measure of the training received between the two wage observations but from the measure of training received over the year prior to the initial wage observation. So, measures of training in both years are included in the wage growth regressions: the results are presented in columns 7 and 8 of Table 3. The return to a year of job‐related training for men is higher (at approximately 8–10%) than for women (approximately 3% for training received in the past year and zero for training between wage observations). The difference in the return to training is compounded by the fact that young men receive more on‐the‐job training than young women – for example among workers with less than 5 years of potential experience, the men receive on average 0.039 years of training per year and the women 0.032. This seems to be the result of the fact that men (especially those with little education) are much more likely than women to be in apprenticeships (which have a very large training component). Among men who leave school at 16 but are aged 20 or under, 25% are in apprenticeships compared to 10% of women (figures from the 2004 LFS). Figure 5 shows that the gender gap in training is entirely driven by the behaviour of the low‐educated: among college graduates young women receive more training than young men. When one cumulates this one can see that the predicted gender wage gap after 10 years falls to 0.138 from 0.183 when one controls for training, so something like 4.5 log points of the gap after 10 years can be explained by gender differences in the receipt and the return to on‐the‐job training. Fig. 5. Open in new tabDownload slide Gender Differences in On‐the‐Job TrainingNotes. This data comes from the Labour Force Survey for the years 2000–3. Fig. 5. Open in new tabDownload slide Gender Differences in On‐the‐Job TrainingNotes. This data comes from the Labour Force Survey for the years 2000–3. 3.3. Gender Differences in Occupation and Education Now let us consider gender differences in human capital accumulation before labour market entry. In terms of the quantity of education there are few gender differences (see the summary statistics in Table A1). But the type of education may be different, e.g., Brown and Corcoran (1997), Black et al. (2003) for the US and Machin and Puhani (2003) for the UK and Germany document that differences in subject of degree can explain a portion of the gender pay gap among college graduates. Unfortunately the BHPS does not contain detailed information on the subjects studied by college graduates or others. But it does contain information on the occupation of the first job that might be expected to capture much of gender differences in career choices. Columns 9 and 10 of Table 3 include dummy variables for the occupation in the first job so the assumption is that initial choice of occupation has a permanent effect on wage growth.15 Sample sizes preclude using more than 1‐digit occupation controls but there are sizeable gender differences even at this level in the type of job first done. Details are in Table A1 in the data Appendix but the big differences among young workers (with less than 10 years of potential experience) are that 38% of women first enter clerical and secretarial occupations compared to 16% of men, 20% of women enter personal and protective service occupations compared to 8% of men, 2% of women enter craft occupations compared to 23% of men and 7% of women enter elementary occupations compared to 15% of men. In producing the summary statistics on wage growth at different levels of experience and the gender gap we assume a distribution of first jobs equal to the average across men and women with less than 10 years of experience – evaluating at male or female characteristics makes no difference. For both men and women the coefficients on initial occupation are jointly not significantly different from zero though the rejection is marginal for men (the rather curious mix of managerial, professional, clerical and elementary occupations seem to have higher wage growth than the others). But, when it comes to explaining gender differences in wage growth, initial occupation has little explanatory power with the predicted gender pay gap after 10 years falling from 0.138 to 0.124 after controlling for initial occupation.16 However this evidence has only used 1‐digit controls for initial occupation and one might be concerned that more marked differences would emerge if a finer measure of occupation was used. That is not possible because of small sample sizes in the BHPS but we can get some idea of whether this is likely using the much larger sample sizes available in the NES where we can use 3‐digit occupation of which there are approximately 370. Once one goes to this level of disaggregation there is a problem that some occupations are so overwhelmingly dominated by one gender that one has very few observations on the other. To deal with this problem of matching men and women at the 3‐digit occupation level the sample is restricted to occupations that have at least 2% of the minority gender. This excludes from the sample 17.5% of men in 74 occupations (broadly, production managers in manufacturing and construction, senior police and fire officers, drivers of trains, boats, and planes, almost all of construction, almost anything that involves metal‐working, and refuse collection). 9.6% of women are excluded in 9 occupations (broadly, midwives, secretaries, dental nurses, childcare workers and beauticians). We take two approaches to controlling for occupation. In the first we simply include a dummy variable for each occupation in the gender‐specific wage growth equations effectively assuming that occupation only affects the level and not the life‐cycle profile of wage growth. The results are summarised in Panel (a) of Figure 6. The line marked ‘whole sample’ reproduces the raw gender gap in wage growth already shown in Figure 4. The line ‘matched sample’ reports the raw gap in wage growth for those in occupations that are not excluded from the analysis – one notices that the gender gap in wage growth is now much smaller for those aged 16–22. The most likely explanation for this finding is that many of the men who are excluded from the matched sample are in construction and/or metal‐working trades and the young men in particular are doing apprenticeships that are associated with fast wage growth. This is consistent with our earlier finding of gender differences in training receipt among this group. Fig. 6. Open in new tabDownload slide The Impact of Occupational Job Segregation on Gender Differences in Wage Growth.Notes. These data come from the New Earnings Survey for 1996–2001. Occupation is measured at the 3‐digit level. The matched sample excludes those men and women in occupations with very low representation of the opposite sex, as discussed in the text. The regression model simply includes dummy variables for each occupation in the wage growth equations. The reweighting model reweights observations to obtain the desired occupational distribution and computes the gender gap in wage growth for this distribution. Fig. 6. Open in new tabDownload slide The Impact of Occupational Job Segregation on Gender Differences in Wage Growth.Notes. These data come from the New Earnings Survey for 1996–2001. Occupation is measured at the 3‐digit level. The matched sample excludes those men and women in occupations with very low representation of the opposite sex, as discussed in the text. The regression model simply includes dummy variables for each occupation in the wage growth equations. The reweighting model reweights observations to obtain the desired occupational distribution and computes the gender gap in wage growth for this distribution. The remaining two lines in Figure 6(a) show the impact of adjusting the matched sample for differences in the gender distribution of occupation. We show two estimates – one evaluated at the male occupation distribution and one at the female occupation – though they are very similar and similar to the line that does not control for occupational segregation at all. Figure 6(a) suggests a sizeable gender pay gap in early‐career wage growth remains even after controlling for differences in occupation. However, the estimates that lie behind Figure 6(a) assume an impact of occupation on wage growth that can be modelled as a shifting intercept. This is quite restrictive e.g. it might be the case that women, anticipating future labour market withdrawal, go into occupations that offer high initial wage growth and slower wage growth later on. To allow for this possibility we also present – in Figure 6(b)– a reweighting estimator that uses the methodology of diNardo et al. (1996) to reweight the female (male) data to reproduce the male (female) occupational distribution. We then report weighted wage growth by age. The resulting estimates of the gender gap in wage growth are much noisier than the regression‐based estimates (unsurprisingly because less structure is put on the data) but one can still see that virtually all of the observations of the gender gap in wage growth up to the age of 35 are positive. So, differences in occupation do not seem to be able to explain much of the gender‐gap in early‐career wage growth once one has controlled for gender differences in training.17 This conclusion that occupational differences cannot explain much of the gender gap in early‐career wage growth can be reconciled with the findings of Machin and Puhani (2003) that field of study can explain a sizeable part of the gender wage gap among British college graduates. First, there is a small gender wage gap on entry to the labour market among graduates that can be explained almost entirely by differences in field of study but field of study seems to have little impact on wage growth. Secondly, gender differences in curriculum are larger for graduates than non‐graduates, as is found for the US by Brown and Corcoran (1997). 3.4. Gender Differences in Housework and Labour Market Motivation Becker (1985) suggested that part of the gender pay gap can be explained by the fact that women put less effort into paid work even when working the same number of hours because of greater domestic commitments. To investigate this we include as a control in our wage growth equation the number of hours of housework done per week (as expected women in our sample do 10 hours more on average than the men) and a composite measure of labour market motivation; as also used by Vella (1994), Swaffield (2000) and Chevalier (2004). The labour market motivation variable is constructed as a composite measure (standardised to have mean zero and variance one) of how much the respondent agrees or disagrees with 6 statements on the role of women in the labour market.18 To allay fears about how the responses might themselves be influenced by labour market outcomes, we construct this measure using only the earliest recorded response to each of these questions. As might be expected women have, on average, a more positive view about the role of women in the labour market than men and working women a more positive view than non‐working women. Columns 11 and 12 of Table 3 include both these variables in the wage growth regression. The sample size is lower because the question on housework is not asked every wave. Both of these variables have coefficients that are of the expected sign but are insignificantly different from zero for both men and women. The net effect of these variables on the predicted gender gap in wages is small after 5 years but appears large after 10 – however, this is more the result of the smaller sample than the variables themselves and the standard error on the estimate is very large. Hence, we find, with the variables available to us, little evidence for the Becker hypothesis being important. 3.5. Conclusions on the Human Capital Hypothesis The evidence presented here suggests that human capital factors can explain perhaps half of the gender gap in early‐career wage growth. Of the overall 25 log point gap implied after 10 years, 2.8 log points can be explained by differences in labour market intermittence, 3.7% by differences in part‐time working, 4.5% by differences in training (primarily because young men who leave school early are still more likely to do apprenticeships) and perhaps 1.5% by differences in occupational choice. Labour market intermittence and part‐time working is more important between 10 and 20 years after labour market entry for the reason that this is when most women have breaks in employment associated with childcare. The bottom line is that a woman who works continuously full‐time can expect to be 12% behind an equivalent man after 10 years in the labour market. 4. Job‐Shopping Models Part of the rapid wage growth in the years immediately after labour market entry may be the result of job mobility – finding a good match for one's skills or simply finding the good jobs (if, as evidence suggests, there is a sizeable amount of equilibrium wage dispersion). Theoretical analysis of the implications of job search models for the returns to experience and job tenure can be found in Burdett (1978) and Manning (1997, 2000). Topel and Ward (1992) found that for US men something like a third of wage growth in the first 10 years after labour market entry can be ascribed to job mobility. In this process of job‐shopping it seems plausible that there are gender differences. For example Manning (2003a, ch. 7) documents, using the BHPS, that women are more constrained in their job choices (primarily by family commitments), they travel less far to work, again, suggesting a narrower choice of jobs – see Manning (2003b), and their job changes are less motivated by money and are less likely to be ‘for a better job’. All of these factors might be expected to be potential explanations of the gender gap in early‐career wage growth. First, consider gender differences in job mobility. For current purposes we define job mobility rates as the percentage of those currently in employment for whom the next observation is non‐employment or a change in job. Note that a change in job need not be associated with a change in employer so a promotion will count as a job change; see Booth et al. (2003) for an analysis of promotions in the BHPS. Figure 7 presents the overall job mobility rates by experience for men and women. One sees the well‐known pattern that job mobility rates start off very high, then decline before flattening out and rising slightly for the older workers. Women have noticeably higher mobility rates than men between 10 and 25 years of experience i.e. in the prime child‐bearing years. Fig. 7. Open in new tabDownload slide Job Mobility Rates: TotalNotes. These data come from the BHPS. The mobility rate is measured as the fraction of workers who change jobs or leave a job for non‐employment in the coming year. Fig. 7. Open in new tabDownload slide Job Mobility Rates: TotalNotes. These data come from the BHPS. The mobility rate is measured as the fraction of workers who change jobs or leave a job for non‐employment in the coming year. It is likely that there are gender differences in the types of job mobility as well as the level. Although there is an argument (based on a frictionless competitive labour market) that there is no meaningful distinction between quits and lay‐offs (McLaughlin, 1991) most labour economists think there is a useful distinction between quits that are voluntary on the part of workers and lay‐offs that are not. Consistent with this, quits tend to be associated with wage gains while lay‐offs are associated with wage losses (Ruhm, 1991; Jacobson et al., 1993; Kletzer, 1998). The job history files of the BHPS record reasons for why jobs ended and Table 4 tabulates them. 47% of young men leave jobs because they are promoted or get a better job compared to 43% of women.19 15% of male jobs end in redundancy or dismissal compared with only 9% of women. 7% of female jobs end to have a baby or look after other family with, unsurprisingly, this proportion being close to zero for men. To summarise, male jobs are slightly more likely to end with a move to a better job, but men are more at risk of jobs ending because of redundancy. On the other hand, women's jobs are more likely to end to have a child. Table 4 Reasons Given for Jobs Ending Reason given . Men (%) . Women (%) . Promoted 19.1 16.7 Left For Better Job 28.0 26.9 Made Redundant 12.7 7.8 Dismissed or Sacked 3.1 1.4 Temporary Job Ended 10.5 9.8 Took retirement 1.9 2.9 Health Reasons 3.6 4.2 To Have Baby 0.0 3.7 Children/Home care 0.5 3.9 Moved Area 3.6 5.0 Other 17.1 17.1 No. of obs 3,611 3,749 Reason given . Men (%) . Women (%) . Promoted 19.1 16.7 Left For Better Job 28.0 26.9 Made Redundant 12.7 7.8 Dismissed or Sacked 3.1 1.4 Temporary Job Ended 10.5 9.8 Took retirement 1.9 2.9 Health Reasons 3.6 4.2 To Have Baby 0.0 3.7 Children/Home care 0.5 3.9 Moved Area 3.6 5.0 Other 17.1 17.1 No. of obs 3,611 3,749 Notes. Sample is restricted to BHPS from waves 5 on as extra categories are then added. Sample is those with between 0 and 10 years of labour market experience inclusive. Data refer to reasons given for job ending. Open in new tab Table 4 Reasons Given for Jobs Ending Reason given . Men (%) . Women (%) . Promoted 19.1 16.7 Left For Better Job 28.0 26.9 Made Redundant 12.7 7.8 Dismissed or Sacked 3.1 1.4 Temporary Job Ended 10.5 9.8 Took retirement 1.9 2.9 Health Reasons 3.6 4.2 To Have Baby 0.0 3.7 Children/Home care 0.5 3.9 Moved Area 3.6 5.0 Other 17.1 17.1 No. of obs 3,611 3,749 Reason given . Men (%) . Women (%) . Promoted 19.1 16.7 Left For Better Job 28.0 26.9 Made Redundant 12.7 7.8 Dismissed or Sacked 3.1 1.4 Temporary Job Ended 10.5 9.8 Took retirement 1.9 2.9 Health Reasons 3.6 4.2 To Have Baby 0.0 3.7 Children/Home care 0.5 3.9 Moved Area 3.6 5.0 Other 17.1 17.1 No. of obs 3,611 3,749 Notes. Sample is restricted to BHPS from waves 5 on as extra categories are then added. Sample is those with between 0 and 10 years of labour market experience inclusive. Data refer to reasons given for job ending. Open in new tab Not all job changes have reasons associated with them (approximately 12% do not). But, we can get an idea of the relative importance of different mobility rates for different sorts of job change by multiplying the overall mobility rate by the fraction of job changes due to particular reasons (this implicitly assumes that the job changes with no given reason are drawn at random). To prevent information overload we divide reasons for jobs ending into two categories: those that end with a move to a better job and those that do not. Figure 8 presents the experience profiles for changes to a better job for men and women: there are no large gender differences. Figure 9 presents the job mobility rates not to a better job: this is higher for women in the early years of the labour market, primarily because of the role played by family responsibilities. These figures suggest that over the first 10 years in the labour market men can expect 3 good job moves (compared to 2.9 for women) and 2 bad job moves (compared to 2.4 for women). Fig. 9. Open in new tabDownload slide Job Mobility Rates: not for Better JobNotes. As for Figure 7 but with mobility not being defined as being for a better job. Fig. 9. Open in new tabDownload slide Job Mobility Rates: not for Better JobNotes. As for Figure 7 but with mobility not being defined as being for a better job. Fig. 8. Open in new tabDownload slide Job Mobility Rates: for Better JobNotes. As for Figure 7 but with mobility defined as being for a better job. Fig. 8. Open in new tabDownload slide Job Mobility Rates: for Better JobNotes. As for Figure 7 but with mobility defined as being for a better job. Now let us consider the extent to which wage changes are related to different sorts of mobility. There is an existing literature on the impact of different sorts of moves on wage changes that starts with a series of papers in the early 1980s (Bartel, 1980, 1982; Borjas, 1981; Bartel and Borjas, 1981). More recent papers are Topel and Ward (1992) and, with a specific focus on gender differences, Loprest (1992), Crossley et al. (1994), Keith and McWilliams (1997, 1999) and Cobb‐Clark (2001). We examine the impact of job mobility by simply including dummy variables for different sorts of move.20 There are a large number of reasons for moves given in Table 4 and we aggregate these into four categories: moves for a better job (called ‘good moves’), moves because jobs ended in redundancy, dismissal or end of contract (called ‘bad’ moves), moves that end for family reasons (called ‘kid’ moves) and moves for other reasons (called ‘other’ moves). This is relatively crude: for example Gibbons and Katz (1991) provide evidence that those dismissed suffer a greater wage penalty than those made redundant through plant closure. But, we do not have enough data here to do a very fine disaggregation. The first two columns of Table 5 provide estimates of wage growth equations augmented by job mobility variables. For men a ‘good’ move is associated with a wage gain of 8.6% while for women it only produces a gain of 6.3%. This result that the returns to job mobility are lower for women is consistent with the evidence presented in Manning (2003a, ch. 7). ‘Bad’ moves are associated with significant wage losses; 3.9% for men but not for women. For kid moves, there is a very large pay penalty for the small number of men who make such a move but the most interesting finding is that there is no wage penalty associated with such moves for women (the coefficient is actually positive though insignificantly different from zero). This is different from the findings in Waldfogel (1998) or Joshi and Paci (1998) but they use data from a much earlier period and find that the wage penalty is smaller for women with maternity leave entitlements who return to the same employer. As these entitlements have been extended it is possible that now there is a relatively small penalty associated with having children if the woman returns to paid work quickly (there is a penalty to intermittence of employment as captured by the gap variable). Finally the ‘other’ move category has insignificant effects on wage growth. Table 5 The Impact of Job Mobility on Wage Growth . Men . Women . Men . Women . Men . Women . 0 yrs of experience 0.135 0.120 0.130 0.112 0.131 0.107 (0.014) (0.012) (0.014) (0.012) (0.014) (0.012) 5 yrs of experience 0.051 0.029 0.047 0.028 0.047 0.022 (0.008) (0.007) (0.008) (0.006) (0.008) (0.006) 10 yrs of experience −0.005 0.013 −0.001 0.013 −0.001 0.005 (0.015) (0.012) (0.014) (0.012) (0.014) (0.012) Cost of gap > 1 0.031 0.041 −0.002 0.033 −0.001 0.036 (0.037) (0.017) (0.037) (0.019) (0.036) (0.019) Part‐Time −0.193 −0.060 −0.194 −0.061 −0.194 −0.060 (0.043) (0.012) (0.043) (0.012) (0.043) (0.012) Decades of tenure −0.017 −0.031 −0.022 −0.032 −0.023 −0.033 (0.018) (0.015) (0.018) (0.015) (0.018) (0.015) Training in Past Year (yrs) 0.082 0.011 0.081 0.011 0.080 0.011 (0.058) (0.038) (0.059) (0.038) (0.059) (0.038) Training between wage obs (yrs) 0.085 −0.001 0.088 −0.002 0.087 −0.003 (0.066) (0.035) (0.066) (0.035) (0.065) (0.035) Good Move 0.086 0.063 0.087 0.063 0.087 0.063 (0.011) (0.010) (0.011) (0.010) (0.011) (0.010) Bad Move −0.039 0.013 0.008 0.029 (0.022) (0.023) (0.031) (0.027) Bad Move × Decades of experience −0.890 −0.277 −0.813 −0.143 (0.452) (0.240) (0.323) (0.202) Kid Move −0.143 0.041 −0.189 0.033 −0.186 0.035 (0.176) (0.052) (0.182) (0.053) (0.181) (0.053) Other Move −0.032 0.006 −0.037 0.004 −0.037 0.005 (0.021) (0.019) (0.021) (0.019) (0.021) (0.019) No. of Obs 4,762 5,142 4,762 5,142 4,762 5,142 R2 0.105 0.072 0.106 0.073 0.106 0.073 Gender Gap at 5 yrs 0.108 0.087 0.081 (0.052) (0.052) (0.052) Gender Gap at 10 yrs 0.155 0.127 0.122 (0.090) (0.091) (0.091) . Men . Women . Men . Women . Men . Women . 0 yrs of experience 0.135 0.120 0.130 0.112 0.131 0.107 (0.014) (0.012) (0.014) (0.012) (0.014) (0.012) 5 yrs of experience 0.051 0.029 0.047 0.028 0.047 0.022 (0.008) (0.007) (0.008) (0.006) (0.008) (0.006) 10 yrs of experience −0.005 0.013 −0.001 0.013 −0.001 0.005 (0.015) (0.012) (0.014) (0.012) (0.014) (0.012) Cost of gap > 1 0.031 0.041 −0.002 0.033 −0.001 0.036 (0.037) (0.017) (0.037) (0.019) (0.036) (0.019) Part‐Time −0.193 −0.060 −0.194 −0.061 −0.194 −0.060 (0.043) (0.012) (0.043) (0.012) (0.043) (0.012) Decades of tenure −0.017 −0.031 −0.022 −0.032 −0.023 −0.033 (0.018) (0.015) (0.018) (0.015) (0.018) (0.015) Training in Past Year (yrs) 0.082 0.011 0.081 0.011 0.080 0.011 (0.058) (0.038) (0.059) (0.038) (0.059) (0.038) Training between wage obs (yrs) 0.085 −0.001 0.088 −0.002 0.087 −0.003 (0.066) (0.035) (0.066) (0.035) (0.065) (0.035) Good Move 0.086 0.063 0.087 0.063 0.087 0.063 (0.011) (0.010) (0.011) (0.010) (0.011) (0.010) Bad Move −0.039 0.013 0.008 0.029 (0.022) (0.023) (0.031) (0.027) Bad Move × Decades of experience −0.890 −0.277 −0.813 −0.143 (0.452) (0.240) (0.323) (0.202) Kid Move −0.143 0.041 −0.189 0.033 −0.186 0.035 (0.176) (0.052) (0.182) (0.053) (0.181) (0.053) Other Move −0.032 0.006 −0.037 0.004 −0.037 0.005 (0.021) (0.019) (0.021) (0.019) (0.021) (0.019) No. of Obs 4,762 5,142 4,762 5,142 4,762 5,142 R2 0.105 0.072 0.106 0.073 0.106 0.073 Gender Gap at 5 yrs 0.108 0.087 0.081 (0.052) (0.052) (0.052) Gender Gap at 10 yrs 0.155 0.127 0.122 (0.090) (0.091) (0.091) Notes. These estimates are derived from the estimation of (5) where experience is modelled as a quadratic and, where appropriate, years of job tenure and a dummy variable for having intermittent employment is included. Standard errors in parentheses. These are robust standard errors with clustering on the individual. The estimates of the gender gap after 5 and 10 years are based on assuming no change in job so job tenure rises with experience and the job mobility variables are zero. Open in new tab Table 5 The Impact of Job Mobility on Wage Growth . Men . Women . Men . Women . Men . Women . 0 yrs of experience 0.135 0.120 0.130 0.112 0.131 0.107 (0.014) (0.012) (0.014) (0.012) (0.014) (0.012) 5 yrs of experience 0.051 0.029 0.047 0.028 0.047 0.022 (0.008) (0.007) (0.008) (0.006) (0.008) (0.006) 10 yrs of experience −0.005 0.013 −0.001 0.013 −0.001 0.005 (0.015) (0.012) (0.014) (0.012) (0.014) (0.012) Cost of gap > 1 0.031 0.041 −0.002 0.033 −0.001 0.036 (0.037) (0.017) (0.037) (0.019) (0.036) (0.019) Part‐Time −0.193 −0.060 −0.194 −0.061 −0.194 −0.060 (0.043) (0.012) (0.043) (0.012) (0.043) (0.012) Decades of tenure −0.017 −0.031 −0.022 −0.032 −0.023 −0.033 (0.018) (0.015) (0.018) (0.015) (0.018) (0.015) Training in Past Year (yrs) 0.082 0.011 0.081 0.011 0.080 0.011 (0.058) (0.038) (0.059) (0.038) (0.059) (0.038) Training between wage obs (yrs) 0.085 −0.001 0.088 −0.002 0.087 −0.003 (0.066) (0.035) (0.066) (0.035) (0.065) (0.035) Good Move 0.086 0.063 0.087 0.063 0.087 0.063 (0.011) (0.010) (0.011) (0.010) (0.011) (0.010) Bad Move −0.039 0.013 0.008 0.029 (0.022) (0.023) (0.031) (0.027) Bad Move × Decades of experience −0.890 −0.277 −0.813 −0.143 (0.452) (0.240) (0.323) (0.202) Kid Move −0.143 0.041 −0.189 0.033 −0.186 0.035 (0.176) (0.052) (0.182) (0.053) (0.181) (0.053) Other Move −0.032 0.006 −0.037 0.004 −0.037 0.005 (0.021) (0.019) (0.021) (0.019) (0.021) (0.019) No. of Obs 4,762 5,142 4,762 5,142 4,762 5,142 R2 0.105 0.072 0.106 0.073 0.106 0.073 Gender Gap at 5 yrs 0.108 0.087 0.081 (0.052) (0.052) (0.052) Gender Gap at 10 yrs 0.155 0.127 0.122 (0.090) (0.091) (0.091) . Men . Women . Men . Women . Men . Women . 0 yrs of experience 0.135 0.120 0.130 0.112 0.131 0.107 (0.014) (0.012) (0.014) (0.012) (0.014) (0.012) 5 yrs of experience 0.051 0.029 0.047 0.028 0.047 0.022 (0.008) (0.007) (0.008) (0.006) (0.008) (0.006) 10 yrs of experience −0.005 0.013 −0.001 0.013 −0.001 0.005 (0.015) (0.012) (0.014) (0.012) (0.014) (0.012) Cost of gap > 1 0.031 0.041 −0.002 0.033 −0.001 0.036 (0.037) (0.017) (0.037) (0.019) (0.036) (0.019) Part‐Time −0.193 −0.060 −0.194 −0.061 −0.194 −0.060 (0.043) (0.012) (0.043) (0.012) (0.043) (0.012) Decades of tenure −0.017 −0.031 −0.022 −0.032 −0.023 −0.033 (0.018) (0.015) (0.018) (0.015) (0.018) (0.015) Training in Past Year (yrs) 0.082 0.011 0.081 0.011 0.080 0.011 (0.058) (0.038) (0.059) (0.038) (0.059) (0.038) Training between wage obs (yrs) 0.085 −0.001 0.088 −0.002 0.087 −0.003 (0.066) (0.035) (0.066) (0.035) (0.065) (0.035) Good Move 0.086 0.063 0.087 0.063 0.087 0.063 (0.011) (0.010) (0.011) (0.010) (0.011) (0.010) Bad Move −0.039 0.013 0.008 0.029 (0.022) (0.023) (0.031) (0.027) Bad Move × Decades of experience −0.890 −0.277 −0.813 −0.143 (0.452) (0.240) (0.323) (0.202) Kid Move −0.143 0.041 −0.189 0.033 −0.186 0.035 (0.176) (0.052) (0.182) (0.053) (0.181) (0.053) Other Move −0.032 0.006 −0.037 0.004 −0.037 0.005 (0.021) (0.019) (0.021) (0.019) (0.021) (0.019) No. of Obs 4,762 5,142 4,762 5,142 4,762 5,142 R2 0.105 0.072 0.106 0.073 0.106 0.073 Gender Gap at 5 yrs 0.108 0.087 0.081 (0.052) (0.052) (0.052) Gender Gap at 10 yrs 0.155 0.127 0.122 (0.090) (0.091) (0.091) Notes. These estimates are derived from the estimation of (5) where experience is modelled as a quadratic and, where appropriate, years of job tenure and a dummy variable for having intermittent employment is included. Standard errors in parentheses. These are robust standard errors with clustering on the individual. The estimates of the gender gap after 5 and 10 years are based on assuming no change in job so job tenure rises with experience and the job mobility variables are zero. Open in new tab The inclusion of the job mobility variables essentially leaves the implied gender pay after 10 years of continuous employment slightly higher than it was without the job mobility variables (compare columns 1 and 2 of Table 5 with columns 7 and 8 of Table 3). But, the specification used here assumes that there is no heterogeneity in the returns to job mobility. In particular, the theory of job‐shopping would suggest that very young workers will not suffer much a of a wage loss when making a bad move because they have little to lose but older workers will face larger wage losses because they are more likely to have worked themselves into a good job.21 To test this hypothesis we also included an interaction of experience with a bad move – the results are reported in columns 3 and 4 of Table 5. For both men and women this interaction variable is negative though the coefficient is much larger for men (probably because older men have more to lose than older women). As predicted by the theory, the coefficient on the bad move variable itself is insignificantly different from zero (this is the estimate of the cost of a bad move for a worker just entering the labour market). Accordingly the fifth and sixth columns of Table 5 include only the interaction of bad moves with experience. The implied gender pay gap after 10 years for those who remain in the same job now falls to 0.122 suggesting a contribution of job mobility of 1.5 percentage points (comparing with columns 7 and 8 of Table 3). This modest contribution is not because job mobility has no effect on wage growth, just that the differences between young men and women are not that large. Hence, job‐shopping models do not appear to be able to explain more than a small part of the gender gap in early‐career wage growth. 5. Personality and the Gender Pay Gap There is an enormous literature in psychology on the size and nature of gender differences in personality, a literature that spills out into the popular imagination with books of the ‘men are from mars, women from venus’ variety. This literature – see Croson and Gneezy (forthcoming) for an excellent survey for economists – shows that by labour market entry, there are quite significant gender differences in personalities22 (something we confirm with our data below). Recently, economists have become interested in the possibility that these differences can help to understand differences in economic outcomes. There have been applications in a number of areas e.g. Barber and Odean (2001) suggest that the propensity of men to be more over‐confident leads them to be more active traders in the stock market, but the main areas of interest have been the labour market (Eckel and Grossman, 1998; Gneezy and Rustichini, 2004; Gneezy et al., 2003; Babcock and Laschever, 2003; Niederle and Westerlund, 2005). Most of these studies have been in experimental settings – while these can demonstrate the existence of significant differences in behaviour it is hard to translate the results into a portion of the gender pay gap that can be explained in this way. That is what we attempt here. One might hope that psychological differences could help to explain the gender pay gap because a number of recent studies have demonstrated intriguing correlations between earnings and ‘personality’ variables23 (Goldsmith et al., 1997, Feinstein, 2000; Bowles et al., 2001; Nyhus and Pons, 2004). A natural extension of this work is to investigate the hypothesis that psychological differences between men and women on entry into the labour market can explain part of the gender pay gap. The only paper to have tried to do this directly is Mueller and Plug (2004) who use data from 50‐year old high school graduates from Wisconsin. They investigate whether the ‘five‐factor’ model of personality structure can help to explain the gender pay gap in their data, finding only modest effects. But the personality data are contemporaneous with the earnings data (raising issues of reverse causation) and the personality traits used might not be the most important ones in the labour market, making this unlikely to be the last word on the subject. In our study we try to remedy both of these weaknesses. There are a very large number of potential personality traits that one might use to explain the gender pay gap. To put some structure on the issue, we use as a framework the survey by Croson and Gneezy (forthcoming) and the book by Babcock and Laschever (2003). Croson and Gneezy (forthcoming) identify a number of main areas in which men and women seem to have different preferences: Risk‐taking – men seem inclined to take more risks than women possibly because they are less risk‐averse and possibly because their assessment of risks is affected by the fact that men seem to have more self‐esteem Self‐esteem – men seem to have a higher opinion of their own abilities than do women Attitudes to competition – possibly because of the two previous factors but perhaps also because of some other factors, women seem more reluctant than men to enter competitive situations and their performance is affected differently when they do. Other‐regarding behaviour – women seem to be less selfish than men in a number of dimensions and care more about what others think about them. The specific argument put forward by Babcock and Laschever (2003) is that women are less effective than men in negotiation. This gender divide is related to more fundamental psychological differences between men and women, some of which are in the Croson and Gneezy list above (e.g. self‐esteem – see Babcock and Laschever, p. 72) but some that are slightly different e.g. Locus of Control (Babcock and Laschever, p. 23) – men are more likely to feel that their fate can be influenced by their actions. Ambition – men seem to be more career‐oriented than women (Babcock and Laschever, p. 30). In addition we add another set of factors that are sometimes argued to be important (Fisher, 1999). Social skills – women have better ‘soft’ skills then men. The addition of the last set of factors is potentially important because our exercise would have a bias to it if we only sought to model the aspects of personality that put women at a disadvantage in the labour market. The BHPS has collected data on youths living within sample households since wave 4 and asks them, among other things, the ‘psychological’ questions described in Table 6. The answers to these questions show that a higher incidence of lower self‐image and greater self‐doubt in young women as compared to young men. For example, only 13.9% of male youths agreed with the statement ‘I don't have much to be proud of’ compared to 17.5% of females. The lower self‐image for females is shown even more starkly in the responses to the statements ‘I certainly feel useless at times’– 29.1% of male youths agreed compared to 44.8% of females. In addition, 22.6% of male youths compared to 35.3% of females agreed with the statement ‘At times I feel I am no good at all’. These differences might be expected to produce wage differentials, e.g., Babcock and Laschever (2003) argue that low self‐esteem leads women not to negotiate wage rises for themselves. Unfortunately the BHPS panel is not long enough to provide a large enough sample of previously surveyed youths who are then observed earning in the labour market so that we cannot, as yet, use the BHPS for this purpose. Table 6 Gender Differences in Personality Among Teenagers: BHPS Data Percentages agreeing with the statement . Men . Women . Sample . I feel I have a number of good qualities 93.0 91.0 1,883 (1,882) I feel that I do not have much to be proud of 13.9 17.5 1,883 (1,882) I certainly feel useless at times 29.1 44.8 3,948 (3,753) I am able to do things as well as most other people 91.0 90.5 1,883 (1,882) I am a likeable person 93.0 93.6 3,948 (3,753) I can usually solve my own problems 90.2 86.0 1,883 (1,882) All in all, I am inclined to feel I am a failure 8.9 12.1 3,948 (3,753) At times I feel I am no good at all 22.6 35.3 3,948 (3,753) I feel left out of things when I am with friends 15.6 25.0 2,752 (2,679) Percentages agreeing with the statement . Men . Women . Sample . I feel I have a number of good qualities 93.0 91.0 1,883 (1,882) I feel that I do not have much to be proud of 13.9 17.5 1,883 (1,882) I certainly feel useless at times 29.1 44.8 3,948 (3,753) I am able to do things as well as most other people 91.0 90.5 1,883 (1,882) I am a likeable person 93.0 93.6 3,948 (3,753) I can usually solve my own problems 90.2 86.0 1,883 (1,882) All in all, I am inclined to feel I am a failure 8.9 12.1 3,948 (3,753) At times I feel I am no good at all 22.6 35.3 3,948 (3,753) I feel left out of things when I am with friends 15.6 25.0 2,752 (2,679) Notes. In the final column female sample sizes are shown in the parentheses. Rows 1, 2, 4 and 6 includes BHPS waves 9–11. Rows 3, 5, and 7 includes BHPS waves 4–11. Row 9 includes BHPS waves 7–11. ‘Agree’ includes strongly agree and agree. Open in new tab Table 6 Gender Differences in Personality Among Teenagers: BHPS Data Percentages agreeing with the statement . Men . Women . Sample . I feel I have a number of good qualities 93.0 91.0 1,883 (1,882) I feel that I do not have much to be proud of 13.9 17.5 1,883 (1,882) I certainly feel useless at times 29.1 44.8 3,948 (3,753) I am able to do things as well as most other people 91.0 90.5 1,883 (1,882) I am a likeable person 93.0 93.6 3,948 (3,753) I can usually solve my own problems 90.2 86.0 1,883 (1,882) All in all, I am inclined to feel I am a failure 8.9 12.1 3,948 (3,753) At times I feel I am no good at all 22.6 35.3 3,948 (3,753) I feel left out of things when I am with friends 15.6 25.0 2,752 (2,679) Percentages agreeing with the statement . Men . Women . Sample . I feel I have a number of good qualities 93.0 91.0 1,883 (1,882) I feel that I do not have much to be proud of 13.9 17.5 1,883 (1,882) I certainly feel useless at times 29.1 44.8 3,948 (3,753) I am able to do things as well as most other people 91.0 90.5 1,883 (1,882) I am a likeable person 93.0 93.6 3,948 (3,753) I can usually solve my own problems 90.2 86.0 1,883 (1,882) All in all, I am inclined to feel I am a failure 8.9 12.1 3,948 (3,753) At times I feel I am no good at all 22.6 35.3 3,948 (3,753) I feel left out of things when I am with friends 15.6 25.0 2,752 (2,679) Notes. In the final column female sample sizes are shown in the parentheses. Rows 1, 2, 4 and 6 includes BHPS waves 9–11. Rows 3, 5, and 7 includes BHPS waves 4–11. Row 9 includes BHPS waves 7–11. ‘Agree’ includes strongly agree and agree. Open in new tab For this reason we turn to another data set, the British Cohort Study (BCS) that also contains the type of data required to investigate the impact of personality on the gender wage gap. The BCS is a cohort of all individuals born in a single week in 1970; see Joshi and Paci (1998) for more description of this data. Information has been collected on the cohort at a variety of ages: here we use the latest information collected in 2000, for 30‐year‐old individuals that have been (roughly) 10 years in the labour market. In addition we use the detailed information collected before they entered the labour market – during their childhood and adolescence – on their personality. These personality controls are primarily constructed using the cohort members’ questionnaire responses at age 16. This survey contains hundreds of questions that one might conceivably use as a potential explanation of the gender pay gap. This embarrassment of riches presents some problems in deciding which variables to include and exclude. There are so many that one cannot include them all so selection is necessary. If a large number of variables are used there is an obvious danger that the exercise becomes a ‘fishing expedition’ in which one seeks those variables which, if only by chance, happen to be capable of ‘explaining’ the gender pay gap in our data. At the same time, restricting attention to a small set of variables which we have a priori reason to think might be important runs the risk of missing the variables that do matter, a particular danger when, as is the case here, our a priori views are not very strong and also of only estimating attenuated coefficients if there is measurement error in those variables. Our strategy tries to steer a middle course between these two dangers. We start by using a fairly long list of questions that seem most closely related to the factors identified in the existing literature as the main psychological differences between men and women – we then investigate which of these variables are significant in explaining the earnings of men and women separately and finally evaluate the contribution of these variables to the gender pay gap. By only considering the ability of these variables to explain the gender pay gap at the end we hope to minimise the fishing trip aspect of the exercise though variables will be retained based on their significance in explaining earnings. However, in the Appendix we report results from an alternative strategy in which we passively use a very large number of questions combined into modules (as was done in the original questionnaire). Although the primary aim of this Section is to investigate the impact of the personality variables on the gender wage gap, it is important to establish that our earlier findings about the gender pay gap in the BHPS are also found in the BCS. As the BCS only has information on earnings at a few points in time we focus on the gender pay gap in 2000 when the cohort members were aged 30 and had, on average, been in the labour market for approximately 10 years. But, we are estimating the level of the gender pay gap (which can be thought of as cumulated wage growth) rather than direct estimates of the gender gap in wage growth. Table 7 presents some estimates of the gender pay gap using the BCS 2000, We start with the estimation of equations similar to those we have used for the BHPS. The structure of the Table is as follows: in the second column we present the raw gender pay gap for the sample under consideration. The third column then reports the coefficient on a dummy variable for being male when other controls are introduced. The third (fourth) column then reports the results when separate earnings equations for men and women are estimated and the gender pay gap evaluated for a full‐time worker with 10 years of actual experience whose other characteristics match those of the average woman (man).24 Table 7 The Gender Gap at Age 30 (BCS wage data at age 30) Raw Log Wage gap . Wage Gap with controls . Wage Gap evaluated at 10 yrs of actual exp with: . Sample size . Female chars . Male chars . Women (Men) . 1. Basic equation without fertility or experience controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 2. Row 1 plus expected fertility controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 3. Row 2 plus actual experience 0.181 0.159 0.169 0.152 3,281 (0.016) (0.015) (0.002) (0.002) (3,681) 4. Row 2 plus actual full‐time and part‐time experience 0.181 0.119 0.127 0.115 3,281 (0.016) (0.016) (0.002) (0.003) (3,681) 5. Row 4 plus 1‐digit occupation 0.181 0.125 0.121 0.142 3,281 (0.016) (0.017) (0.002) (0.002) (3,681) 6. Basic equation with sample restricted to ‘always in FT employment’ with ‘no kids’ 0.081 0.110 0.121 0.115 1,310 (0.025) (0.023) (0.004) (0.004) (1,589) 7. Row 5 plus 1‐digit occupation 0.081 0.095 0.086 0.107 1,310 (0.025) (0.025) (0.005) (0.005) (1,589) Raw Log Wage gap . Wage Gap with controls . Wage Gap evaluated at 10 yrs of actual exp with: . Sample size . Female chars . Male chars . Women (Men) . 1. Basic equation without fertility or experience controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 2. Row 1 plus expected fertility controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 3. Row 2 plus actual experience 0.181 0.159 0.169 0.152 3,281 (0.016) (0.015) (0.002) (0.002) (3,681) 4. Row 2 plus actual full‐time and part‐time experience 0.181 0.119 0.127 0.115 3,281 (0.016) (0.016) (0.002) (0.003) (3,681) 5. Row 4 plus 1‐digit occupation 0.181 0.125 0.121 0.142 3,281 (0.016) (0.017) (0.002) (0.002) (3,681) 6. Basic equation with sample restricted to ‘always in FT employment’ with ‘no kids’ 0.081 0.110 0.121 0.115 1,310 (0.025) (0.023) (0.004) (0.004) (1,589) 7. Row 5 plus 1‐digit occupation 0.081 0.095 0.086 0.107 1,310 (0.025) (0.025) (0.005) (0.005) (1,589) Notes: The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, qualifications, marital status, ethnic, establishment size, whether a supervisor and future plans for (further) children. Average wage gaps are evaluated at the means of the full‐time only workers. Open in new tab Table 7 The Gender Gap at Age 30 (BCS wage data at age 30) Raw Log Wage gap . Wage Gap with controls . Wage Gap evaluated at 10 yrs of actual exp with: . Sample size . Female chars . Male chars . Women (Men) . 1. Basic equation without fertility or experience controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 2. Row 1 plus expected fertility controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 3. Row 2 plus actual experience 0.181 0.159 0.169 0.152 3,281 (0.016) (0.015) (0.002) (0.002) (3,681) 4. Row 2 plus actual full‐time and part‐time experience 0.181 0.119 0.127 0.115 3,281 (0.016) (0.016) (0.002) (0.003) (3,681) 5. Row 4 plus 1‐digit occupation 0.181 0.125 0.121 0.142 3,281 (0.016) (0.017) (0.002) (0.002) (3,681) 6. Basic equation with sample restricted to ‘always in FT employment’ with ‘no kids’ 0.081 0.110 0.121 0.115 1,310 (0.025) (0.023) (0.004) (0.004) (1,589) 7. Row 5 plus 1‐digit occupation 0.081 0.095 0.086 0.107 1,310 (0.025) (0.025) (0.005) (0.005) (1,589) Raw Log Wage gap . Wage Gap with controls . Wage Gap evaluated at 10 yrs of actual exp with: . Sample size . Female chars . Male chars . Women (Men) . 1. Basic equation without fertility or experience controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 2. Row 1 plus expected fertility controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 3. Row 2 plus actual experience 0.181 0.159 0.169 0.152 3,281 (0.016) (0.015) (0.002) (0.002) (3,681) 4. Row 2 plus actual full‐time and part‐time experience 0.181 0.119 0.127 0.115 3,281 (0.016) (0.016) (0.002) (0.003) (3,681) 5. Row 4 plus 1‐digit occupation 0.181 0.125 0.121 0.142 3,281 (0.016) (0.017) (0.002) (0.002) (3,681) 6. Basic equation with sample restricted to ‘always in FT employment’ with ‘no kids’ 0.081 0.110 0.121 0.115 1,310 (0.025) (0.023) (0.004) (0.004) (1,589) 7. Row 5 plus 1‐digit occupation 0.081 0.095 0.086 0.107 1,310 (0.025) (0.025) (0.005) (0.005) (1,589) Notes: The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, qualifications, marital status, ethnic, establishment size, whether a supervisor and future plans for (further) children. Average wage gaps are evaluated at the means of the full‐time only workers. Open in new tab The raw gender pay gap is approximately 18 log points for the wage levels as shown in the second column of the first row, somewhat lower than estimates from the BHPS.25 When controls for whether there are children in the household, marital status, race, tenure and qualifications are introduced (but not fertility intentions or actual labour market experience) the estimated gender wage gap falls to approximately 16 log points (row 1, column 3). The second row adds in the expected future fertility controls – a dummy for whether the individual is planning (further) children in the future but the impact of this control is negligible. In the third row a quadratic for total labour market experience is included. This has a relatively small impact on the wage gap estimates, reducing slightly the wage gap with controls and the wage gap evaluated with male characteristics. By comparison, the inclusion of full‐time and part‐time actual labour market experience in the row below (row 4) has a much greater impact on the wage gap estimates. The estimated gender wage gap with controls (column 3) falls by roughly 4 log points. The unexplained pay gap also falls by roughly 4 log points. The fourth row includes 1‐digit occupation controls – the unexplained gender gap rises when evaluated for the average man by almost 3 log points. The figures based on the female evaluated wage gaps are similar to the wage gap estimates (with controls) in the previous (third) column. All of these estimates suggest a pay gap in the region of 11–16% between British men and women aged 30 in 2000 who have spent all their careers in full‐time employment, an estimate similar to that derived from the BHPS earlier in the article. To illustrate this even more starkly, the fifth row restricts the sample to those individuals who at age 30 have not had any children and who have had every month in full‐time employment since leaving full‐time education. These are individuals whose commitment to the labour force cannot be questioned. The raw gender pay gap for this group is 8.1 log points but the adjusted gender pay gaps are larger at 11%, primarily because women in this group are better educated than the men. It should be noted that there is no correction for sample selection bias here ‐ such a correction is likely to make the adjusted gender pay gap even bigger as the ‘career’ women are likely to be much more positively selected than the ‘career’ men. The final row shows that the inclusion of 1‐digit occupation makes little difference to the conclusion for this group. These estimates are also in line with the unexplained gender pay gap reported in Dolton et al. (2002). Having established that the BHPS and BCS data lead to similar conclusions, we turn to the impact of personality variables on the gender pay gap – first, consider the pro‐active strategy. Table 8 provides the list of variables that we use, divided into six categories – risk aversion, competitiveness, self‐esteem, other‐regarding behaviour, career orientation and an ‘other’ category. It is very likely that some of these variables could arguably be placed in other categories from the one we have chosen but as this categorisation is mainly for ease of exposition this does not matter greatly. Table 8 also shows the gender differences in the responses to these questions. For the most part these differences are in line with what a reading of the literature on gender differences would suggest. Table 8 Gender Differences in Psychological Variables: BCS Data at Age 16 Exact Question . Responses . Gender Difference in Response [standard error] . Risk Aversion How often fasten seat belt in front seat? 0 = every time  1 = not every time −0.039 [0.010]** Done first‐aid course in past two years? 0 = yes  1 = no −0.023 [0.012] How interested are you in safety at home? −1 = very interested  0 = somewhat interested  1 = not interested −0.185 [0.019]** How interested are you in safety in traffic? −1 = very interested  0 = somewhat interested  1 = not interested −0.058 [0.019]** How interested are you in water safety? −1 = very interested  0 = somewhat interested  1 = not interested −0.03 [0.020] When did you last smoke? −1 = never  0 = 3 months ago or more  1 = more recently 0.101 [0.022]** How often drink alcohol in past year? 0 = never/special occasions  1 = no more than once a week  2 = more than once a week −0.14 [0.019]** Competitiveness Think fighting is wrong 0 = agree  1 = don't know  2 = disagree −0.302 [0.020]** No. of team sports participate in Count −0.101 [0.011]** No. of individual sports participate in Count 0.019 [0.008]* I am keen on sports 0 = does not apply  1 = applies somewhat  2 = applies very much −0.384 [0.020]** How often do you play card/board games? 0 = rarely/never  1 = sometimes −0.151 [0.013]** How often do you play electronic games? 0 = rarely/never  1 = sometimes −0.228 [0.012]** How often do you play sports? 0 = rarely/never  1 = sometimes −0.216 [0.013]** Self‐Esteem I am clever 0 = does not apply  1 = applies somewhat  2 = applies very much −0.182 [0.015]** In comparison with others my job prospects are −2 = much less  … 2 = much more −0.111 [0.026]** In comparison with others I get worried −2 = much more  … 2 = much less −0.337 [0.027]** Do you think of yourself as a worthless person 0 = not at all  1 = sometimes 0.086 [0.013]** Have you been feeling unhappy or depressed 0 = not at all  1 = sometimes 0.107 [0.014]** Have you been losing confidence in yourself 0 = not at all  1 = sometimes 0.115 [0.013]** Do you feel miserable or depressed 0 = sometimes  1 = rarely or never 0.199 [0.013]** Other‐regarding Behaviour Do you care what your mother thinks about you? 0 = cares a lot  1 = cares a little/not at all −0.139 [0.011]** Do you go out of your way to help others in trouble 0 = yes  1 = no −0.111 [0.013]** In your job how much does it matter to you to be able to help others −1 = very much, 0 = somewhat  1 = not at all −0.241 [0.016]** Are you interested in old people's needs? −1 = very interested…..  2 = not at all interested −0.459 [0.023]** Are you interested in handicapped people's needs? −1 = very interested ..  2 = not at all interested −0.52 [0.024]** Are you always nice to people 0 = no  1 = yes 0.031 [0.012]** I am helpful −1 = does not apply  0 = applies somewhat  1 = applies very much 0.103 [0.014]** Do you do volunteer/community work 0 = rarely/never  1 = sometimes/often 0.053 [0.011]** It is up to Africans to grow food for themselves 0 = disagree  1 = agree −0.172 [0.014]** I am always willing to help the teacher −1 = not true at all  0 = partly true  1 = very true 0.104 [0.016]** Career Orientation In your future job, how important is it to have high earnings? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.157 [0.016]** In your future job, how important is it to get ahead? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.069 [0.018]** Does getting married matter to you? −1 = matters very much  0 = matters somewhat  1 = doesn't matter 0.019 [0.021] Does having children of your own matter to you? −1 = matters very much 0 = matters somewhat 1 = doesn't matter −0.085 [0.020]** Women can do the same jobs as men 0 = agree fully 1 = agree partly 2 = disagree −0.412 [0.017]** Women's lib is a good thing −1 = agree fully 0 = agree partly 1 = disagree −0.272 [0.017]** Mother's attitudes to women's place in society −0.015 [0.014] Locus of Control Do you feel that wishing can make good things happen? 0 = yes 1 = don't know 2 = no −0.069 [0.029]** Is a high mark just a matter of luck for you? 0 = yes 1 = don't know 2 = no −0.146 [0.027]** Are tests just a lot of guess work for you? 0 = yes 1 = don't know 2 = no −0.113 [0.025]** Are you a person who believes that planning ahead makes things turn out better? 0 = yes 1 = don't know 2 = no 0.173 [0.031]** When bad things happen to you is it usually someone else's fault? 0 = yes 1 = don't know 2 = no 0.171 [0.027]** When nice things happen to you is it only good luck? 0 = yes 1 = don't know 2 = no 0.052 [0.026]** Do you think studying for tests is a waste of time? 0 = yes 1 = don't know 2 = no 0.079 [0.021]** Other variables I am violent 0 = does not apply 1 = applies somewhat/very much −0.12 [0.011]** I am reliable 0 = applies very much 1 = applies somewhat/does not apply −0.054 [0.013]** I am popular −1 = does not apply 0 = applies somewhat 1 = applies very much −0.029 [0.015]* I am friendly −1 = does not apply 0 = applies somewhat 1 = applies very much 0.099 [0.014]** I am lazy −1 = does not apply 0 = applies somewhat 1 = applies very much −0.057 [0.017]** I am punctual −1 = does not apply 0 = applies somewhat 1 = applies very much 0.021 [0.018] I am obedient −1 = does not apply 0 = applies somewhat 1 = applies very much 0.043 [0.015]** Exact Question . Responses . Gender Difference in Response [standard error] . Risk Aversion How often fasten seat belt in front seat? 0 = every time  1 = not every time −0.039 [0.010]** Done first‐aid course in past two years? 0 = yes  1 = no −0.023 [0.012] How interested are you in safety at home? −1 = very interested  0 = somewhat interested  1 = not interested −0.185 [0.019]** How interested are you in safety in traffic? −1 = very interested  0 = somewhat interested  1 = not interested −0.058 [0.019]** How interested are you in water safety? −1 = very interested  0 = somewhat interested  1 = not interested −0.03 [0.020] When did you last smoke? −1 = never  0 = 3 months ago or more  1 = more recently 0.101 [0.022]** How often drink alcohol in past year? 0 = never/special occasions  1 = no more than once a week  2 = more than once a week −0.14 [0.019]** Competitiveness Think fighting is wrong 0 = agree  1 = don't know  2 = disagree −0.302 [0.020]** No. of team sports participate in Count −0.101 [0.011]** No. of individual sports participate in Count 0.019 [0.008]* I am keen on sports 0 = does not apply  1 = applies somewhat  2 = applies very much −0.384 [0.020]** How often do you play card/board games? 0 = rarely/never  1 = sometimes −0.151 [0.013]** How often do you play electronic games? 0 = rarely/never  1 = sometimes −0.228 [0.012]** How often do you play sports? 0 = rarely/never  1 = sometimes −0.216 [0.013]** Self‐Esteem I am clever 0 = does not apply  1 = applies somewhat  2 = applies very much −0.182 [0.015]** In comparison with others my job prospects are −2 = much less  … 2 = much more −0.111 [0.026]** In comparison with others I get worried −2 = much more  … 2 = much less −0.337 [0.027]** Do you think of yourself as a worthless person 0 = not at all  1 = sometimes 0.086 [0.013]** Have you been feeling unhappy or depressed 0 = not at all  1 = sometimes 0.107 [0.014]** Have you been losing confidence in yourself 0 = not at all  1 = sometimes 0.115 [0.013]** Do you feel miserable or depressed 0 = sometimes  1 = rarely or never 0.199 [0.013]** Other‐regarding Behaviour Do you care what your mother thinks about you? 0 = cares a lot  1 = cares a little/not at all −0.139 [0.011]** Do you go out of your way to help others in trouble 0 = yes  1 = no −0.111 [0.013]** In your job how much does it matter to you to be able to help others −1 = very much, 0 = somewhat  1 = not at all −0.241 [0.016]** Are you interested in old people's needs? −1 = very interested…..  2 = not at all interested −0.459 [0.023]** Are you interested in handicapped people's needs? −1 = very interested ..  2 = not at all interested −0.52 [0.024]** Are you always nice to people 0 = no  1 = yes 0.031 [0.012]** I am helpful −1 = does not apply  0 = applies somewhat  1 = applies very much 0.103 [0.014]** Do you do volunteer/community work 0 = rarely/never  1 = sometimes/often 0.053 [0.011]** It is up to Africans to grow food for themselves 0 = disagree  1 = agree −0.172 [0.014]** I am always willing to help the teacher −1 = not true at all  0 = partly true  1 = very true 0.104 [0.016]** Career Orientation In your future job, how important is it to have high earnings? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.157 [0.016]** In your future job, how important is it to get ahead? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.069 [0.018]** Does getting married matter to you? −1 = matters very much  0 = matters somewhat  1 = doesn't matter 0.019 [0.021] Does having children of your own matter to you? −1 = matters very much 0 = matters somewhat 1 = doesn't matter −0.085 [0.020]** Women can do the same jobs as men 0 = agree fully 1 = agree partly 2 = disagree −0.412 [0.017]** Women's lib is a good thing −1 = agree fully 0 = agree partly 1 = disagree −0.272 [0.017]** Mother's attitudes to women's place in society −0.015 [0.014] Locus of Control Do you feel that wishing can make good things happen? 0 = yes 1 = don't know 2 = no −0.069 [0.029]** Is a high mark just a matter of luck for you? 0 = yes 1 = don't know 2 = no −0.146 [0.027]** Are tests just a lot of guess work for you? 0 = yes 1 = don't know 2 = no −0.113 [0.025]** Are you a person who believes that planning ahead makes things turn out better? 0 = yes 1 = don't know 2 = no 0.173 [0.031]** When bad things happen to you is it usually someone else's fault? 0 = yes 1 = don't know 2 = no 0.171 [0.027]** When nice things happen to you is it only good luck? 0 = yes 1 = don't know 2 = no 0.052 [0.026]** Do you think studying for tests is a waste of time? 0 = yes 1 = don't know 2 = no 0.079 [0.021]** Other variables I am violent 0 = does not apply 1 = applies somewhat/very much −0.12 [0.011]** I am reliable 0 = applies very much 1 = applies somewhat/does not apply −0.054 [0.013]** I am popular −1 = does not apply 0 = applies somewhat 1 = applies very much −0.029 [0.015]* I am friendly −1 = does not apply 0 = applies somewhat 1 = applies very much 0.099 [0.014]** I am lazy −1 = does not apply 0 = applies somewhat 1 = applies very much −0.057 [0.017]** I am punctual −1 = does not apply 0 = applies somewhat 1 = applies very much 0.021 [0.018] I am obedient −1 = does not apply 0 = applies somewhat 1 = applies very much 0.043 [0.015]** Notes. The sample size varies with the question but is generally in the region of 2500. Open in new tab Table 8 Gender Differences in Psychological Variables: BCS Data at Age 16 Exact Question . Responses . Gender Difference in Response [standard error] . Risk Aversion How often fasten seat belt in front seat? 0 = every time  1 = not every time −0.039 [0.010]** Done first‐aid course in past two years? 0 = yes  1 = no −0.023 [0.012] How interested are you in safety at home? −1 = very interested  0 = somewhat interested  1 = not interested −0.185 [0.019]** How interested are you in safety in traffic? −1 = very interested  0 = somewhat interested  1 = not interested −0.058 [0.019]** How interested are you in water safety? −1 = very interested  0 = somewhat interested  1 = not interested −0.03 [0.020] When did you last smoke? −1 = never  0 = 3 months ago or more  1 = more recently 0.101 [0.022]** How often drink alcohol in past year? 0 = never/special occasions  1 = no more than once a week  2 = more than once a week −0.14 [0.019]** Competitiveness Think fighting is wrong 0 = agree  1 = don't know  2 = disagree −0.302 [0.020]** No. of team sports participate in Count −0.101 [0.011]** No. of individual sports participate in Count 0.019 [0.008]* I am keen on sports 0 = does not apply  1 = applies somewhat  2 = applies very much −0.384 [0.020]** How often do you play card/board games? 0 = rarely/never  1 = sometimes −0.151 [0.013]** How often do you play electronic games? 0 = rarely/never  1 = sometimes −0.228 [0.012]** How often do you play sports? 0 = rarely/never  1 = sometimes −0.216 [0.013]** Self‐Esteem I am clever 0 = does not apply  1 = applies somewhat  2 = applies very much −0.182 [0.015]** In comparison with others my job prospects are −2 = much less  … 2 = much more −0.111 [0.026]** In comparison with others I get worried −2 = much more  … 2 = much less −0.337 [0.027]** Do you think of yourself as a worthless person 0 = not at all  1 = sometimes 0.086 [0.013]** Have you been feeling unhappy or depressed 0 = not at all  1 = sometimes 0.107 [0.014]** Have you been losing confidence in yourself 0 = not at all  1 = sometimes 0.115 [0.013]** Do you feel miserable or depressed 0 = sometimes  1 = rarely or never 0.199 [0.013]** Other‐regarding Behaviour Do you care what your mother thinks about you? 0 = cares a lot  1 = cares a little/not at all −0.139 [0.011]** Do you go out of your way to help others in trouble 0 = yes  1 = no −0.111 [0.013]** In your job how much does it matter to you to be able to help others −1 = very much, 0 = somewhat  1 = not at all −0.241 [0.016]** Are you interested in old people's needs? −1 = very interested…..  2 = not at all interested −0.459 [0.023]** Are you interested in handicapped people's needs? −1 = very interested ..  2 = not at all interested −0.52 [0.024]** Are you always nice to people 0 = no  1 = yes 0.031 [0.012]** I am helpful −1 = does not apply  0 = applies somewhat  1 = applies very much 0.103 [0.014]** Do you do volunteer/community work 0 = rarely/never  1 = sometimes/often 0.053 [0.011]** It is up to Africans to grow food for themselves 0 = disagree  1 = agree −0.172 [0.014]** I am always willing to help the teacher −1 = not true at all  0 = partly true  1 = very true 0.104 [0.016]** Career Orientation In your future job, how important is it to have high earnings? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.157 [0.016]** In your future job, how important is it to get ahead? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.069 [0.018]** Does getting married matter to you? −1 = matters very much  0 = matters somewhat  1 = doesn't matter 0.019 [0.021] Does having children of your own matter to you? −1 = matters very much 0 = matters somewhat 1 = doesn't matter −0.085 [0.020]** Women can do the same jobs as men 0 = agree fully 1 = agree partly 2 = disagree −0.412 [0.017]** Women's lib is a good thing −1 = agree fully 0 = agree partly 1 = disagree −0.272 [0.017]** Mother's attitudes to women's place in society −0.015 [0.014] Locus of Control Do you feel that wishing can make good things happen? 0 = yes 1 = don't know 2 = no −0.069 [0.029]** Is a high mark just a matter of luck for you? 0 = yes 1 = don't know 2 = no −0.146 [0.027]** Are tests just a lot of guess work for you? 0 = yes 1 = don't know 2 = no −0.113 [0.025]** Are you a person who believes that planning ahead makes things turn out better? 0 = yes 1 = don't know 2 = no 0.173 [0.031]** When bad things happen to you is it usually someone else's fault? 0 = yes 1 = don't know 2 = no 0.171 [0.027]** When nice things happen to you is it only good luck? 0 = yes 1 = don't know 2 = no 0.052 [0.026]** Do you think studying for tests is a waste of time? 0 = yes 1 = don't know 2 = no 0.079 [0.021]** Other variables I am violent 0 = does not apply 1 = applies somewhat/very much −0.12 [0.011]** I am reliable 0 = applies very much 1 = applies somewhat/does not apply −0.054 [0.013]** I am popular −1 = does not apply 0 = applies somewhat 1 = applies very much −0.029 [0.015]* I am friendly −1 = does not apply 0 = applies somewhat 1 = applies very much 0.099 [0.014]** I am lazy −1 = does not apply 0 = applies somewhat 1 = applies very much −0.057 [0.017]** I am punctual −1 = does not apply 0 = applies somewhat 1 = applies very much 0.021 [0.018] I am obedient −1 = does not apply 0 = applies somewhat 1 = applies very much 0.043 [0.015]** Exact Question . Responses . Gender Difference in Response [standard error] . Risk Aversion How often fasten seat belt in front seat? 0 = every time  1 = not every time −0.039 [0.010]** Done first‐aid course in past two years? 0 = yes  1 = no −0.023 [0.012] How interested are you in safety at home? −1 = very interested  0 = somewhat interested  1 = not interested −0.185 [0.019]** How interested are you in safety in traffic? −1 = very interested  0 = somewhat interested  1 = not interested −0.058 [0.019]** How interested are you in water safety? −1 = very interested  0 = somewhat interested  1 = not interested −0.03 [0.020] When did you last smoke? −1 = never  0 = 3 months ago or more  1 = more recently 0.101 [0.022]** How often drink alcohol in past year? 0 = never/special occasions  1 = no more than once a week  2 = more than once a week −0.14 [0.019]** Competitiveness Think fighting is wrong 0 = agree  1 = don't know  2 = disagree −0.302 [0.020]** No. of team sports participate in Count −0.101 [0.011]** No. of individual sports participate in Count 0.019 [0.008]* I am keen on sports 0 = does not apply  1 = applies somewhat  2 = applies very much −0.384 [0.020]** How often do you play card/board games? 0 = rarely/never  1 = sometimes −0.151 [0.013]** How often do you play electronic games? 0 = rarely/never  1 = sometimes −0.228 [0.012]** How often do you play sports? 0 = rarely/never  1 = sometimes −0.216 [0.013]** Self‐Esteem I am clever 0 = does not apply  1 = applies somewhat  2 = applies very much −0.182 [0.015]** In comparison with others my job prospects are −2 = much less  … 2 = much more −0.111 [0.026]** In comparison with others I get worried −2 = much more  … 2 = much less −0.337 [0.027]** Do you think of yourself as a worthless person 0 = not at all  1 = sometimes 0.086 [0.013]** Have you been feeling unhappy or depressed 0 = not at all  1 = sometimes 0.107 [0.014]** Have you been losing confidence in yourself 0 = not at all  1 = sometimes 0.115 [0.013]** Do you feel miserable or depressed 0 = sometimes  1 = rarely or never 0.199 [0.013]** Other‐regarding Behaviour Do you care what your mother thinks about you? 0 = cares a lot  1 = cares a little/not at all −0.139 [0.011]** Do you go out of your way to help others in trouble 0 = yes  1 = no −0.111 [0.013]** In your job how much does it matter to you to be able to help others −1 = very much, 0 = somewhat  1 = not at all −0.241 [0.016]** Are you interested in old people's needs? −1 = very interested…..  2 = not at all interested −0.459 [0.023]** Are you interested in handicapped people's needs? −1 = very interested ..  2 = not at all interested −0.52 [0.024]** Are you always nice to people 0 = no  1 = yes 0.031 [0.012]** I am helpful −1 = does not apply  0 = applies somewhat  1 = applies very much 0.103 [0.014]** Do you do volunteer/community work 0 = rarely/never  1 = sometimes/often 0.053 [0.011]** It is up to Africans to grow food for themselves 0 = disagree  1 = agree −0.172 [0.014]** I am always willing to help the teacher −1 = not true at all  0 = partly true  1 = very true 0.104 [0.016]** Career Orientation In your future job, how important is it to have high earnings? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.157 [0.016]** In your future job, how important is it to get ahead? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.069 [0.018]** Does getting married matter to you? −1 = matters very much  0 = matters somewhat  1 = doesn't matter 0.019 [0.021] Does having children of your own matter to you? −1 = matters very much 0 = matters somewhat 1 = doesn't matter −0.085 [0.020]** Women can do the same jobs as men 0 = agree fully 1 = agree partly 2 = disagree −0.412 [0.017]** Women's lib is a good thing −1 = agree fully 0 = agree partly 1 = disagree −0.272 [0.017]** Mother's attitudes to women's place in society −0.015 [0.014] Locus of Control Do you feel that wishing can make good things happen? 0 = yes 1 = don't know 2 = no −0.069 [0.029]** Is a high mark just a matter of luck for you? 0 = yes 1 = don't know 2 = no −0.146 [0.027]** Are tests just a lot of guess work for you? 0 = yes 1 = don't know 2 = no −0.113 [0.025]** Are you a person who believes that planning ahead makes things turn out better? 0 = yes 1 = don't know 2 = no 0.173 [0.031]** When bad things happen to you is it usually someone else's fault? 0 = yes 1 = don't know 2 = no 0.171 [0.027]** When nice things happen to you is it only good luck? 0 = yes 1 = don't know 2 = no 0.052 [0.026]** Do you think studying for tests is a waste of time? 0 = yes 1 = don't know 2 = no 0.079 [0.021]** Other variables I am violent 0 = does not apply 1 = applies somewhat/very much −0.12 [0.011]** I am reliable 0 = applies very much 1 = applies somewhat/does not apply −0.054 [0.013]** I am popular −1 = does not apply 0 = applies somewhat 1 = applies very much −0.029 [0.015]* I am friendly −1 = does not apply 0 = applies somewhat 1 = applies very much 0.099 [0.014]** I am lazy −1 = does not apply 0 = applies somewhat 1 = applies very much −0.057 [0.017]** I am punctual −1 = does not apply 0 = applies somewhat 1 = applies very much 0.021 [0.018] I am obedient −1 = does not apply 0 = applies somewhat 1 = applies very much 0.043 [0.015]** Notes. The sample size varies with the question but is generally in the region of 2500. Open in new tab As measures of attitudes towards risk, we use variables relating to whether the respondent normally wears a seat belt, whether they have taken a first aid course, their interest in safety at home, in traffic and in the water, and whether they smoke and drink alcohol.26 These variables are similar to others that have been used to proxy risk aversion in other studies and there does seem some link between them and more direct measures of risk aversion (Dohmen et al., 2005). Women are significantly more likely than men to wear a seatbelt, be interested in safety and drink less, all suggesting more risk aversion. However, young women smoke significantly more than young men. The next panel of Table 8 looks at variables to proxy for competitiveness. Here we use variables related to thinking that fighting (a form of conflict) can be exciting, the number of team and individual sports in which the respondent participates, the frequency of participation in sport and how often they play cards and electronic games. Again, we see significant gender differences of the type we would expect if women are less enthusiastic about competition – they play fewer sports and other games, enjoy them less and are less likely to think fighting can be exciting. The third panel of Table 8 looks at variables to proxy for self‐esteem. Here we use variables about whether the respondent has confidence in themselves, whether they think they are clever,27 whether they think they are worthless, whether they have been feeling unhappy or miserable and how they rate their job prospects. In all these dimensions women seem significantly less self‐confident than men and more prone to feeling unhappy or depressed. The fourth panel of Table 8 considers other‐regarding behaviour – whether the respondent cares about what their mother thinks about them, various measures of whether they want to help others, whether they do volunteer work and whether Africans should grow food for themselves. In all of these measures, women are significantly more likely than men to report caring about others. The fifth panel of Table 8 reports the measures we use to capture career orientation. We use questions about the importance of full‐time work, high wages, ‘getting ahead’ and also marriage and having children. We also include in this Section variables that capture attitudes to women's role in society generally – whether ‘women's lib’ is a good thing and also a composite measure of the mother's attitudes to work/home from the questionnaire at age 5. Among these variables we find women caring significantly less about work, more about children but also having more positive views about gender equality than men. The sixth panel of Table 8 reports the variables used to capture the ‘locus of control’ idea that women are less likely than men to think their destiny is in their hands. We use questions about whether things that happen are a matter of luck, the fault of others or whether outcomes can be influenced by one's own actions. Women are more likely to think that high marks are a matter of luck, that tests are guesswork, not to believe in planning ahead. But, at the same time women are less likely to think studying for tests is a waste of time, suggesting they are not always fatalistic about outcomes. Indeed the fact that girls are, on average, more diligent in their school work than boys, might be thought something of a problem for the locus of control idea. The final panel of Table 8 considers a collection of other traits that might be thought to affect employability – whether the individual is violent, reliable, popular, friendly, lazy, punctual and obedient. On most of these criteria women are significantly more likely than men to have the ‘desirable’ characteristic.28 The results in Table 8 show that it is very easy to find significant gender differences in personality traits along the lines suggested by the existing literature. But how much of the gender pay gap can be explained in this way? – this depends on whether these traits have a significant effect on wages. This is the question addressed in Table 9 where we report results from estimating separate male and female wage equations and decomposing the difference using the standard Oaxaca approach. We could include all the variables (and we do report a specification that does this) but requiring responses to all variables drastically reduces sample sizes. So, Table 9 first reports the results including the psychological variables one category at a time. Because the sample size varies according to the block considered we report the raw log wage gap in the sample considered, the portion of the wage gap that can be explained at male and female coefficients (using the standard Oaxaca approach), and the portion that can be explained by gender differences in the personality traits. Finally we also report a p‐value of an F‐test of the hypothesis that the psychological variables are jointly zero in both female and male wage equations. Table 9 The Impact of Psychological Variables On Wages at Age 30 . Raw Log Wage gap (standard error) . Explained Wage Gap . Contribution of psychological variables . p‐value for psych variables . Sample size . Female co‐efficients . Male co‐efficients . Female co‐efficients . Male co‐efficients . Female (Male) . Female (Male) . 1. Risk 0.168 0.071 0.067 0.014 0.005 0.48 1184 (0.030) (0.008) (0.009) (0.74) (952) 2. Competitiveness 0.166 0.075 0.098 0.017 0.037 0.022 1203 (0.030) (0.019) (0.019) (0.253) (963) 3. Self‐Esteem 0.179 0.085 0.087 0.018 0.034 0.108 1421 (0.030) (0.010) (0.010) (0.040) (1091) 4. Other‐regarding 0.195 0.095 0.031 0.009 −0.039 0.215 1382 (0.028) (0.014) (0.015) (0.001) (1041) 5. Career Orientation 0.178 0.109 0.055 0.043 0.002 0.030 1320 (0.029) (0.017) (0.015) (0.459) (1013) 6. Locus of Control 0.176 0.072 0.069 0.008 0.006 0.34 1507 (0.027) (0.008) (0.008) (0.897) (1183) 7. Other 0.182 0.061 0.046 −0.007 −0.011 0.103 1511 (0.027) (0.007) (0.006) (0.387) (1209) 8. All Variables 0.203 0.123 0.091 0.056 0.005 0.087 724 (0.041) (0.045) (0.040) (0.016) (536) 9. Selected Variables 0.177 0.114 0.076 0.043 0.009 0.001 1459 (0.027) (0.012) (0.013) (0.001) (1123) . Raw Log Wage gap (standard error) . Explained Wage Gap . Contribution of psychological variables . p‐value for psych variables . Sample size . Female co‐efficients . Male co‐efficients . Female co‐efficients . Male co‐efficients . Female (Male) . Female (Male) . 1. Risk 0.168 0.071 0.067 0.014 0.005 0.48 1184 (0.030) (0.008) (0.009) (0.74) (952) 2. Competitiveness 0.166 0.075 0.098 0.017 0.037 0.022 1203 (0.030) (0.019) (0.019) (0.253) (963) 3. Self‐Esteem 0.179 0.085 0.087 0.018 0.034 0.108 1421 (0.030) (0.010) (0.010) (0.040) (1091) 4. Other‐regarding 0.195 0.095 0.031 0.009 −0.039 0.215 1382 (0.028) (0.014) (0.015) (0.001) (1041) 5. Career Orientation 0.178 0.109 0.055 0.043 0.002 0.030 1320 (0.029) (0.017) (0.015) (0.459) (1013) 6. Locus of Control 0.176 0.072 0.069 0.008 0.006 0.34 1507 (0.027) (0.008) (0.008) (0.897) (1183) 7. Other 0.182 0.061 0.046 −0.007 −0.011 0.103 1511 (0.027) (0.007) (0.006) (0.387) (1209) 8. All Variables 0.203 0.123 0.091 0.056 0.005 0.087 724 (0.041) (0.045) (0.040) (0.016) (536) 9. Selected Variables 0.177 0.114 0.076 0.043 0.009 0.001 1459 (0.027) (0.012) (0.013) (0.001) (1123) Notes. The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, qualifications including detailed controls for the number of GCE and CSEs obtained, marital status, ethnic, establishment size, whether a supervisor and future plans for (further) children. Standard errors in parentheses. Open in new tab Table 9 The Impact of Psychological Variables On Wages at Age 30 . Raw Log Wage gap (standard error) . Explained Wage Gap . Contribution of psychological variables . p‐value for psych variables . Sample size . Female co‐efficients . Male co‐efficients . Female co‐efficients . Male co‐efficients . Female (Male) . Female (Male) . 1. Risk 0.168 0.071 0.067 0.014 0.005 0.48 1184 (0.030) (0.008) (0.009) (0.74) (952) 2. Competitiveness 0.166 0.075 0.098 0.017 0.037 0.022 1203 (0.030) (0.019) (0.019) (0.253) (963) 3. Self‐Esteem 0.179 0.085 0.087 0.018 0.034 0.108 1421 (0.030) (0.010) (0.010) (0.040) (1091) 4. Other‐regarding 0.195 0.095 0.031 0.009 −0.039 0.215 1382 (0.028) (0.014) (0.015) (0.001) (1041) 5. Career Orientation 0.178 0.109 0.055 0.043 0.002 0.030 1320 (0.029) (0.017) (0.015) (0.459) (1013) 6. Locus of Control 0.176 0.072 0.069 0.008 0.006 0.34 1507 (0.027) (0.008) (0.008) (0.897) (1183) 7. Other 0.182 0.061 0.046 −0.007 −0.011 0.103 1511 (0.027) (0.007) (0.006) (0.387) (1209) 8. All Variables 0.203 0.123 0.091 0.056 0.005 0.087 724 (0.041) (0.045) (0.040) (0.016) (536) 9. Selected Variables 0.177 0.114 0.076 0.043 0.009 0.001 1459 (0.027) (0.012) (0.013) (0.001) (1123) . Raw Log Wage gap (standard error) . Explained Wage Gap . Contribution of psychological variables . p‐value for psych variables . Sample size . Female co‐efficients . Male co‐efficients . Female co‐efficients . Male co‐efficients . Female (Male) . Female (Male) . 1. Risk 0.168 0.071 0.067 0.014 0.005 0.48 1184 (0.030) (0.008) (0.009) (0.74) (952) 2. Competitiveness 0.166 0.075 0.098 0.017 0.037 0.022 1203 (0.030) (0.019) (0.019) (0.253) (963) 3. Self‐Esteem 0.179 0.085 0.087 0.018 0.034 0.108 1421 (0.030) (0.010) (0.010) (0.040) (1091) 4. Other‐regarding 0.195 0.095 0.031 0.009 −0.039 0.215 1382 (0.028) (0.014) (0.015) (0.001) (1041) 5. Career Orientation 0.178 0.109 0.055 0.043 0.002 0.030 1320 (0.029) (0.017) (0.015) (0.459) (1013) 6. Locus of Control 0.176 0.072 0.069 0.008 0.006 0.34 1507 (0.027) (0.008) (0.008) (0.897) (1183) 7. Other 0.182 0.061 0.046 −0.007 −0.011 0.103 1511 (0.027) (0.007) (0.006) (0.387) (1209) 8. All Variables 0.203 0.123 0.091 0.056 0.005 0.087 724 (0.041) (0.045) (0.040) (0.016) (536) 9. Selected Variables 0.177 0.114 0.076 0.043 0.009 0.001 1459 (0.027) (0.012) (0.013) (0.001) (1123) Notes. The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, qualifications including detailed controls for the number of GCE and CSEs obtained, marital status, ethnic, establishment size, whether a supervisor and future plans for (further) children. Standard errors in parentheses. Open in new tab The first row of Table 9 reports the results when the ‘risk attitudes’ are included. For both men and women these variables are jointly insignificant (and none individually significant) and the portion of the gender pay gap that can be explained using them is small and insignificantly different from zero. We have been unable to find any variables representing attitudes to risk that are of much use in explaining the gender pay gap. The second row of Table 9 reports the results when the ‘competitiveness’ variables are included. These are jointly significantly different from zero for women but not for men with the variable for liking sport being significant for women and estimated to raise wages by 0.089. This finding that different variables are significant in male and female earnings functions will be a recurrent theme, indicating that (as is often claimed) different traits are regarded differently when displayed by a man and a women. Overall the gender differences in competitiveness are estimated to be capable of explaining 0.017 to 0.037 of the gender pay gap depending on whether male or female coefficients are used. The third row of Table 9 includes the self‐esteem variables. These are jointly significant for men but marginally so for women. The individually significant variables for women is whether they rated themselves as clever and for men it is those who report not getting worried about things who have significantly higher earnings. Overall the gender differences in competitiveness are estimated to be capable of explaining 0.018 to 0.034 of the gender pay gap depending on whether male or female coefficients are used. So far gender differences in personality traits have all been capable of explaining the gender pay gap. But the fourth row shows that the ‘other‐regarding’ traits may work in the opposite direction. These are jointly significant for men but not women, with the individually significant variables for men being that earnings are 0.11 higher for men who care a lot about their mothers and 0.12 lower for men who report they are always nice to others.29 The net effect is that, when evaluated at male coefficients, these traits are predicted to be to the advantage of women as they care more about their mothers. The fifth row of Table 9 investigates the role of the ‘career orientation’ variables which are jointly significant for women but not men. For women those who report having kids in later life as mattering very much to them have significantly lower earnings. This can explain 0.043 of the gender pay gap when evaluated at female coefficients but nothing when evaluated at male coefficients. The sixth row of Table 9 reports the results when the ‘locus of control’ variables are included. For both men and women these variables are jointly insignificant (and none individually significant) and the portion of the gender pay gap that can be explained using them is small and insignificantly different from zero. We have been unable to find any variables representing these ‘locus of control’ variables that are of much use in explaining the gender pay gap. The seventh row of Table 9 investigates the other personality traits, these not being significant for either men or women and the only individually significant variable being the finding that, for women, those who are punctual seem (oddly) to have lower earnings. The net effect of these variables on the gender pay gap is small but negative. In row 7 we include all the variables together except the ‘risk’ and ‘locus of control’ variables.30 These are jointly significant for men but for women only at the 10% level. Taken together these variables can explain 0.056 of the gender pay gap (about 25% of the total) at female coefficients but 0.005 at male coefficients. This result can be best understood in the next row where we restrict the included psychological variables to those found to be most significant in male or female wage equations. Because the number of variables is now manageable we report the estimates in Table 10. Looking at all the rows in Table 9 one can see that, generally, the psychological variables do help to explain the gender pay gap. There is a large range of estimates about their importance and these estimates are sensitive to the variables included. One can find specifications in which the psychological variables can explain about 5% of the gender pay gap but that is the upper end of what we have found. Table 10 Impact of Selected Variables on Male and Female Wages at Age 30 . Female . Male . I am keen on sports 0.076 0.026 [0.022] [0.025] I am clever 0.074 0.037 [0.034] [0.038] In comparison with others I get worried −0.005 0.035 [0.018] [0.020] Are you always nice to people −0.065 −0.088 [0.037] [0.043] Do you care what your mother thinks about you 0.009 −0.148 [0.042] [0.040] In your future job, how important is it to get ahead? 0.027 0.045 [0.025] [0.029] Does having children of your own matter to you? 0.035 0.007 [0.022] [0.026] Observations 1,459 1,123 R‐squared 0.24 0.24 . Female . Male . I am keen on sports 0.076 0.026 [0.022] [0.025] I am clever 0.074 0.037 [0.034] [0.038] In comparison with others I get worried −0.005 0.035 [0.018] [0.020] Are you always nice to people −0.065 −0.088 [0.037] [0.043] Do you care what your mother thinks about you 0.009 −0.148 [0.042] [0.040] In your future job, how important is it to get ahead? 0.027 0.045 [0.025] [0.029] Does having children of your own matter to you? 0.035 0.007 [0.022] [0.026] Observations 1,459 1,123 R‐squared 0.24 0.24 Notes. The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, detailed qualifications, marital status, ethnicity, establishment size, whether a supervisor and future plans for (further) children. Standard errors in parentheses. Details of the questions can be found in Table 8. Open in new tab Table 10 Impact of Selected Variables on Male and Female Wages at Age 30 . Female . Male . I am keen on sports 0.076 0.026 [0.022] [0.025] I am clever 0.074 0.037 [0.034] [0.038] In comparison with others I get worried −0.005 0.035 [0.018] [0.020] Are you always nice to people −0.065 −0.088 [0.037] [0.043] Do you care what your mother thinks about you 0.009 −0.148 [0.042] [0.040] In your future job, how important is it to get ahead? 0.027 0.045 [0.025] [0.029] Does having children of your own matter to you? 0.035 0.007 [0.022] [0.026] Observations 1,459 1,123 R‐squared 0.24 0.24 . Female . Male . I am keen on sports 0.076 0.026 [0.022] [0.025] I am clever 0.074 0.037 [0.034] [0.038] In comparison with others I get worried −0.005 0.035 [0.018] [0.020] Are you always nice to people −0.065 −0.088 [0.037] [0.043] Do you care what your mother thinks about you 0.009 −0.148 [0.042] [0.040] In your future job, how important is it to get ahead? 0.027 0.045 [0.025] [0.029] Does having children of your own matter to you? 0.035 0.007 [0.022] [0.026] Observations 1,459 1,123 R‐squared 0.24 0.24 Notes. The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, detailed qualifications, marital status, ethnicity, establishment size, whether a supervisor and future plans for (further) children. Standard errors in parentheses. Details of the questions can be found in Table 8. Open in new tab The approach we have taken so far can be criticised either as a fishing expedition in which we have sought out variables that are significant and reported specifications that maximise the importance of the psychological variables or as a very partial exercise in which many potentially relevant variables have not been considered. To allay some of these fears, the Appendix reports the results from a more passive approach in which we simply take batteries of questions as originally put into the age 16 questionnaire. For this purpose we use the segment on attitudes, divided into 21 modules, spanning a vast range of subjects. To this we add another module from another part of the questionnaire designed to measure self‐esteem as this has been argued to be important by Babcock and Laschever (2003) and others. In total there are 458 questions covering a very wide range of issues from those that have been deemed important in the literature of gender differences (e.g., self‐esteem, locus of control, attitudes to work), to issues that may be thought very tangential, e.g., the teenager's taste in soft drinks. The general description of the modules is listed in Table A2.31 The analysis of these questions is in the Appendix but the conclusion is similar, namely that gender differences in ‘personality’ variables can help to explain at most a few percentage points of the gender pay gap. A sizeable part of the gender pay gap remains unexplained. 6. Conclusions This article has argued that an understanding of the gender pay gap in Britain needs to focus on the explanation of the gender gap in early‐career wage growth that causes women who entered the labour market with the same average level of pay as men to be approximately 25 log points behind 10 years later. Human capital theory can explain at most half of this (primarily because of gender differences in the receipt of on‐the‐job training, and because of modest differences in accumulated labour market experience). Job‐shopping theories seem to be able to explain at most 1.5 log points and the ‘psychological’ differences appear capable of explaining up to 4.5 log points, though that is probably an upper bound. There remains a sizeable unexplained component. This gender gap in wage growth means that, although men and women have similar earnings when entering the labour market, the women will be something like 8% behind the men ten years later even if they have been in continuous full‐time employment, have had no children, do not want any and have the same personality as a man. London School of Economics University of York Submitted: 7 June 2005 Accepted: 29 March 2007 Footnotes 1 " This might be a finding specific to Britain as, for example, Kunze (2003, 2005) shows that for Germans with an apprenticeship, there is a sizeable gender gap on labour market entry that does not then change very much. 2 " This convergence in later years is less apparent in the BHPS than in other British data sets such as the Labour Force Survey. Note also that Kunze (2003, 2005) finds a different pattern for Germany with a pay gap on entry that remains approximately constant through the life‐cycle. 3 " The BHPS is too short a panel to follow cohorts through time. 4 " The flattening in the gender pay gap that one sees in the early 20s in Figure 2 is the result of the entry at that age into the labour market of college graduates for whom, on labour market entry, the gender pay gap is small. 5 " This is less important in the current context where we are primarily interested in the gender gap in wage growth than in the literature on the returns to job tenure as incorrect detrending probably has a very similar effect on both men and women. 6 " We did experiment with restricting the sample to those in the early waves when censoring is less problematic: this made no difference to the conclusions. 7 " Note that in a steady‐state it should not matter whether one looks forward as in the BHPS or backwards as in the LFS. 8 " Use of a more conventional job tenure measure does not make much difference to the results but does result in a large fall in the number of useable observations because the work history information needed to construct it is only available for a fraction of the sample. For this reason, we only report results using the BHPS job tenure variable. 9 " One might wonder whether these gender differences are significantly different from each other. At each individual level of experience the answer is often ‘no’ but one can easily reject the joint hypothesis that the returns to experience for men and women are equal in the first 10 years. 10 " Note that, averaging across men and women of all experience levels, there is no significant difference in wage growth – as found by Manning and Robinson (2004). However, that paper makes the (embarrassing) mistake of inferring from this fact that gender differences in wage growth cannot explain the gender pay gap. This is wrong because a male advantage in wage growth in early life off‐set by a female advantage in later life does not imply a zero gender pay gap. 11 " One proviso to this is that there is some evidence that, in the reduced form, earnings growth is lower in the early years for the best‐educated. But, this can be explained by the larger amount of on‐the‐job training received in the early years by the less‐educated and this is a variable for which we do control later in the article. 12 " See Mincer (1974) and, more recently, Heckman et al. (2003) for a discussion of the variance profile. 13 " And one should also note that actual labour market experience is endogenous so that part of the correlation of wages with actual experience may not be causal. 14 " In some of the literature, notably Mincer and Ofek (1982), there is a debate about whether it is the incidence or the length of the break in employment that is the more important. But, as Table 2 makes clear we do not really have sufficient variation in the employment gap in our data to explore this further. 15 " We did experiment with interacting first job with years of experience so the impact of initial job falls over time but the coefficients on these interactions were always insignificantly different from zero. 16 " One might be concerned that the sample sizes in columns 9 and 10 are considerably smaller than in columns 7 and 8 because initial job is missing for many observations. But, estimating the model of columns 7 and 8 using the restricted sample of columns 9 and 10 leads to a predicted wage gap after 10 years of 0.13 so the contribution of occupation is probably smaller than it appears from a simple inspection of Table 3. 17 " Though it may be that even the 3‐digit occupation is not fine enough – see Wood et al. (1993) for evidence that among lawyers, women are over‐represented in the lower paid fields. 18 " The six statements are ‘A pre‐school child suffers if mother works’, ‘A family suffers if mother works full‐time’, ‘A woman and family are happier if she works, ‘A husband and wife should both contribute’, ‘A full time job makes a woman independent’, ‘A husband should earn, wife stay at home’ 19 " There is also a question about why the new job is better, the reasons for which can be crudely divided into pecuniary and non‐pecuniary reasons. We did experiment with disaggregating the ‘left for better job’ category but there are many missing values and ambiguous answers (e.g. many saying they left for a better job because the new job is better) so this was not pursued. 20 " There are reasons to doubt whether this specification is adequate. For example, theory predicts some variation in the returns to job mobility both in observables (like experience, job tenure and the current level of wages) and unobservables (because individuals are less likely to leave jobs with high wage growth). However, experimentation with the specification did not lead to any substantive change in the results and we only report the simplest specification here. 21 " The theory might also be used to predict other interactions but, while we experimented with other interactions, we never found any to be significant. 22 " There is a heated debate about whether this is nature or nurture, a question we will avoid here. 23 " This research can be seen as one part of the renaissance in behavioural economics that seeks insights from psychology for application in economics. 24 " Our reason for showing this particular gender gap is that this is closest to the cumulated gap that has been the focus of attention in the BHPS data. 25 " It is worth noting that throughout this Section we use wage levels rather than wage growth because they are the only data available to us. But, as the gender gap is approximately zero on labour market entry this should not make a huge difference. 26 " We also experimented with measures of illegal drug use but these variables were never significant and low response rates meant their inclusion substantially reduced the sample size. 27 " This variable is, unsurprisingly, correlated with school attainment but even controlling for this, women are less likely to think they are clever. The regressions reported below contain detailed controls for education so we hope we are picking up the confidence not the ability part of this variable. 28 " Some authors (e.g. Fisher, 1999) have gone so far as to argue that women's advantage over men in skills like these that become more important over time mean the gender gap will fall over time and even reverse. 29 " These variables are only weakly positively correlated, something that might be found surprising. 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These questions cover a very wide range of differences between men and women along many different dimensions though as the questionnaire was not designed with a main purpose of investigating gender differences these questions are not tied so closely to the factors discussed above that have been thought to be the most important gender differences in personality – though there are sizeable gender differences in the response to most questions as we report below. The gender differences in the responses to these questions are summarised in Table A2. Table A2 details the different modules and the number of questions for which the coefficient is significantly different from zero at the 5% level. In 383 of the 458 questions in total, there are gender differences in responses that are significantly different from zero. There are too many individual questions to include all in an earnings function so we proceed by creating composite indices for each of the 22 modules. For each module we create a weighted average of the responses to create a single measure for each of them.32 For this to be a sensible procedure requires that the responses to questions within a module really measure the same thing. As a check on this we report Cronbach's alpha for each module in the fourth column of Table A2– a value above 0.7 is conventionally taken to mean that there is a valid underlying psychometric variable and most of the modules do show this level of congruence. Table A3 reports the coefficients on these modules in earnings functions for male and female workers using the BCS data. The first point to note is that most of the modules have estimated effects on wages that are insignificantly different from zero – out of 22 modules 2 are significantly different from zero for women and 1 for men. A joint hypothesis test finds one cannot reject the null hypothesis that all of the modules have no effect on wages. In spite of this the right‐hand panel of Table A3 shows that the total contributions of the modules to the gender pay gap is non‐trivial, being a significant 6.9 log points when evaluated at female coefficients and an insignificant 4.3 when evaluated at the male. One might conclude that a best estimate of the contribution of the personality variables to earnings is about 5 log points, though with a large standard error. It is the ‘my interests’ and ‘my point of view’ modules that seem most important in explaining the gender pay gap. The ‘my point of view’ module contains questions that seem most closely related to other‐regarding attitudes – women seem ‘softer’ and more tolerant in their attitudes than men and the labour market seems to punish such attitudes. The ‘my interests’ module is harder to interpret – the questions are mostly about interest in health issues with women being more interested than men and this being punished in the labour market for some reason. The results also suggest that some factors others have suggested are important, e.g., self‐esteem and locus of control, are not. It is also important to note that there remains a sizeable unexplained gender pay gap – the predicted wage gap between men and women who have worked continuously full‐time for 10 years is 9.3 log points when evaluated at average female characteristics and 7.4 log points when evaluated at the average male characteristics. Columns 3 and 4 of Table A3 show the results when we include only the modules that appear significant. The results are qualitatively very similar. Table A1 Summary Statistics for BHPS Data . Young Workers (≤ 10 yrs of potential experience) . Men . Women . Log Wage Growth 0.084 0.065 (0.306) (0.278) Potential experience (decades) 0.48 0.47 (0.28) (0.28) Job Tenure (decades) 0.15 0.13 (0.22) (0.19) Gap between wage obs 1.02 1.02 (0.18) (0.18) Part‐Time 0.036 0.196 Training in Past Year (yrs) 0.035 0.030 (0.106) (0.095) Training between wage obs (yrs) 0.030 0.030 (0.090) (0.099) Good Move 0.254 0.231 Bad Move 0.072 0.056 Kid Move 0.0005 0.010 Other Move 0.062 0.072 Manager 0.035 0.036 Professional occupations 0.060 0.049 Associate professional 0.104 0.104 Administrative and secretarial 0.156 0.378 Skilled trades 0.239 0.022 Personal services 0.083 0.200 Sales/customer services 0.081 0.109 Machine operatives 0.094 0.037 Elementary 0.147 0.068 Graduates 0.283 0.290 ‘A’ Level 0.396 0.430 GSCEs 0.286 0.267 No qualifications 0.036 0.012 Black 0.011 0.010 Asian 0.025 0.010 Married 0.442 0.543 Child in Household 0.253 0.259 No. of kids 0–2 0.098 0.070 No. of kids 3–4 0.051 0.066 No. of kids 12–15 0.107 0.099 No. of kids 16–18 0.028 0.031 Housework (weekly hrs‐10)/10 −0.442 0.594 Labour Market Motivation 0.005 0.119 . Young Workers (≤ 10 yrs of potential experience) . Men . Women . Log Wage Growth 0.084 0.065 (0.306) (0.278) Potential experience (decades) 0.48 0.47 (0.28) (0.28) Job Tenure (decades) 0.15 0.13 (0.22) (0.19) Gap between wage obs 1.02 1.02 (0.18) (0.18) Part‐Time 0.036 0.196 Training in Past Year (yrs) 0.035 0.030 (0.106) (0.095) Training between wage obs (yrs) 0.030 0.030 (0.090) (0.099) Good Move 0.254 0.231 Bad Move 0.072 0.056 Kid Move 0.0005 0.010 Other Move 0.062 0.072 Manager 0.035 0.036 Professional occupations 0.060 0.049 Associate professional 0.104 0.104 Administrative and secretarial 0.156 0.378 Skilled trades 0.239 0.022 Personal services 0.083 0.200 Sales/customer services 0.081 0.109 Machine operatives 0.094 0.037 Elementary 0.147 0.068 Graduates 0.283 0.290 ‘A’ Level 0.396 0.430 GSCEs 0.286 0.267 No qualifications 0.036 0.012 Black 0.011 0.010 Asian 0.025 0.010 Married 0.442 0.543 Child in Household 0.253 0.259 No. of kids 0–2 0.098 0.070 No. of kids 3–4 0.051 0.066 No. of kids 12–15 0.107 0.099 No. of kids 16–18 0.028 0.031 Housework (weekly hrs‐10)/10 −0.442 0.594 Labour Market Motivation 0.005 0.119 Notes. Sample is those with observations on wage growth aged 16–65 with less than 45 years of potential experience. Sample numbers for wage growth are 5,015 for men and 5,535 for women – some variables have smaller sample sizes because of non‐response. Means are reported and standard errors in parentheses. Standard errors not reported for binary variables. Open in new tab Table A1 Summary Statistics for BHPS Data . Young Workers (≤ 10 yrs of potential experience) . Men . Women . Log Wage Growth 0.084 0.065 (0.306) (0.278) Potential experience (decades) 0.48 0.47 (0.28) (0.28) Job Tenure (decades) 0.15 0.13 (0.22) (0.19) Gap between wage obs 1.02 1.02 (0.18) (0.18) Part‐Time 0.036 0.196 Training in Past Year (yrs) 0.035 0.030 (0.106) (0.095) Training between wage obs (yrs) 0.030 0.030 (0.090) (0.099) Good Move 0.254 0.231 Bad Move 0.072 0.056 Kid Move 0.0005 0.010 Other Move 0.062 0.072 Manager 0.035 0.036 Professional occupations 0.060 0.049 Associate professional 0.104 0.104 Administrative and secretarial 0.156 0.378 Skilled trades 0.239 0.022 Personal services 0.083 0.200 Sales/customer services 0.081 0.109 Machine operatives 0.094 0.037 Elementary 0.147 0.068 Graduates 0.283 0.290 ‘A’ Level 0.396 0.430 GSCEs 0.286 0.267 No qualifications 0.036 0.012 Black 0.011 0.010 Asian 0.025 0.010 Married 0.442 0.543 Child in Household 0.253 0.259 No. of kids 0–2 0.098 0.070 No. of kids 3–4 0.051 0.066 No. of kids 12–15 0.107 0.099 No. of kids 16–18 0.028 0.031 Housework (weekly hrs‐10)/10 −0.442 0.594 Labour Market Motivation 0.005 0.119 . Young Workers (≤ 10 yrs of potential experience) . Men . Women . Log Wage Growth 0.084 0.065 (0.306) (0.278) Potential experience (decades) 0.48 0.47 (0.28) (0.28) Job Tenure (decades) 0.15 0.13 (0.22) (0.19) Gap between wage obs 1.02 1.02 (0.18) (0.18) Part‐Time 0.036 0.196 Training in Past Year (yrs) 0.035 0.030 (0.106) (0.095) Training between wage obs (yrs) 0.030 0.030 (0.090) (0.099) Good Move 0.254 0.231 Bad Move 0.072 0.056 Kid Move 0.0005 0.010 Other Move 0.062 0.072 Manager 0.035 0.036 Professional occupations 0.060 0.049 Associate professional 0.104 0.104 Administrative and secretarial 0.156 0.378 Skilled trades 0.239 0.022 Personal services 0.083 0.200 Sales/customer services 0.081 0.109 Machine operatives 0.094 0.037 Elementary 0.147 0.068 Graduates 0.283 0.290 ‘A’ Level 0.396 0.430 GSCEs 0.286 0.267 No qualifications 0.036 0.012 Black 0.011 0.010 Asian 0.025 0.010 Married 0.442 0.543 Child in Household 0.253 0.259 No. of kids 0–2 0.098 0.070 No. of kids 3–4 0.051 0.066 No. of kids 12–15 0.107 0.099 No. of kids 16–18 0.028 0.031 Housework (weekly hrs‐10)/10 −0.442 0.594 Labour Market Motivation 0.005 0.119 Notes. Sample is those with observations on wage growth aged 16–65 with less than 45 years of potential experience. Sample numbers for wage growth are 5,015 for men and 5,535 for women – some variables have smaller sample sizes because of non‐response. Means are reported and standard errors in parentheses. Standard errors not reported for binary variables. Open in new tab Table A2 Gender Differences in Psychological Variables – Alternative Approach Title in Questionnaire . Type of question . Number of questions . Gender difference in composite score . In module . With signif gender diff . Cronbach's alpha . Coefficient . Standard error . What about work? Determinants of success in labour market 9 8 0.567 0.027 [0.013] Right and might Attitudes to law‐breaking and helping others 9 7 0.533 −0.152 [0.012] Have a drink Alcohol 17 17 0.725 0.197 [0.012] What's in a job Importance of different job characteristics 16 12 0.607 0.096 [0.010] Looking ahead What will be important as adult 15 13 0.661 0.02 [0.011] Up in smoke Smoking 17 14 0.746 −0.026 [0.012] Compared with others Physical/emotional attributes 28 21 0.822 −0.122 [0.011] Knowing myself Personal characteristics 27 23 0.724 −0.038 [0.010] How I feel Worries and anxieties 12 10 0.833 0.147 [0.016] At leisure Leisure activities 47 40 0.781 −0.036 [0.008] My interests Interests in physical/mental health topics 49 46 0.923 −0.289 [0.012] Fate and fortune Locus of control – belief in ability to influence outcomes 15 6 0.717 −0.025 [0.012] What I read Type of reading 25 24 0.817 0.224 [0.011] Me and the box Types of TV programmes watched 22 22 0.721 −0.101 [0.011] Feeling healthy Physical/mental health 22 18 0.839 −0.182 [0.013] My point of view Opinions on women's/gay/minority rights, drugs, death penalty etc 21 17 0.597 0.215 [0.009] Wotalotigot Material possessions, actual and desired 30 27 0.649 0.169 [0.008] Me and my family Activities done with family 15 13 0.757 −0.06 [0.013] Soft drink special Type of Soft drinks consumed 10 6 0.632 −0.085 [0.013] Home rule Parents expectations of behaviour within the home 23 19 0.743 −0.039 [0.011] What I eat How often eat different food items 18 14 0.718 −0.153 [0.011] Self‐esteem 11 6 0.702 −0.042 [0.014] Total 458 383 Title in Questionnaire . Type of question . Number of questions . Gender difference in composite score . In module . With signif gender diff . Cronbach's alpha . Coefficient . Standard error . What about work? Determinants of success in labour market 9 8 0.567 0.027 [0.013] Right and might Attitudes to law‐breaking and helping others 9 7 0.533 −0.152 [0.012] Have a drink Alcohol 17 17 0.725 0.197 [0.012] What's in a job Importance of different job characteristics 16 12 0.607 0.096 [0.010] Looking ahead What will be important as adult 15 13 0.661 0.02 [0.011] Up in smoke Smoking 17 14 0.746 −0.026 [0.012] Compared with others Physical/emotional attributes 28 21 0.822 −0.122 [0.011] Knowing myself Personal characteristics 27 23 0.724 −0.038 [0.010] How I feel Worries and anxieties 12 10 0.833 0.147 [0.016] At leisure Leisure activities 47 40 0.781 −0.036 [0.008] My interests Interests in physical/mental health topics 49 46 0.923 −0.289 [0.012] Fate and fortune Locus of control – belief in ability to influence outcomes 15 6 0.717 −0.025 [0.012] What I read Type of reading 25 24 0.817 0.224 [0.011] Me and the box Types of TV programmes watched 22 22 0.721 −0.101 [0.011] Feeling healthy Physical/mental health 22 18 0.839 −0.182 [0.013] My point of view Opinions on women's/gay/minority rights, drugs, death penalty etc 21 17 0.597 0.215 [0.009] Wotalotigot Material possessions, actual and desired 30 27 0.649 0.169 [0.008] Me and my family Activities done with family 15 13 0.757 −0.06 [0.013] Soft drink special Type of Soft drinks consumed 10 6 0.632 −0.085 [0.013] Home rule Parents expectations of behaviour within the home 23 19 0.743 −0.039 [0.011] What I eat How often eat different food items 18 14 0.718 −0.153 [0.011] Self‐esteem 11 6 0.702 −0.042 [0.014] Total 458 383 Open in new tab Table A2 Gender Differences in Psychological Variables – Alternative Approach Title in Questionnaire . Type of question . Number of questions . Gender difference in composite score . In module . With signif gender diff . Cronbach's alpha . Coefficient . Standard error . What about work? Determinants of success in labour market 9 8 0.567 0.027 [0.013] Right and might Attitudes to law‐breaking and helping others 9 7 0.533 −0.152 [0.012] Have a drink Alcohol 17 17 0.725 0.197 [0.012] What's in a job Importance of different job characteristics 16 12 0.607 0.096 [0.010] Looking ahead What will be important as adult 15 13 0.661 0.02 [0.011] Up in smoke Smoking 17 14 0.746 −0.026 [0.012] Compared with others Physical/emotional attributes 28 21 0.822 −0.122 [0.011] Knowing myself Personal characteristics 27 23 0.724 −0.038 [0.010] How I feel Worries and anxieties 12 10 0.833 0.147 [0.016] At leisure Leisure activities 47 40 0.781 −0.036 [0.008] My interests Interests in physical/mental health topics 49 46 0.923 −0.289 [0.012] Fate and fortune Locus of control – belief in ability to influence outcomes 15 6 0.717 −0.025 [0.012] What I read Type of reading 25 24 0.817 0.224 [0.011] Me and the box Types of TV programmes watched 22 22 0.721 −0.101 [0.011] Feeling healthy Physical/mental health 22 18 0.839 −0.182 [0.013] My point of view Opinions on women's/gay/minority rights, drugs, death penalty etc 21 17 0.597 0.215 [0.009] Wotalotigot Material possessions, actual and desired 30 27 0.649 0.169 [0.008] Me and my family Activities done with family 15 13 0.757 −0.06 [0.013] Soft drink special Type of Soft drinks consumed 10 6 0.632 −0.085 [0.013] Home rule Parents expectations of behaviour within the home 23 19 0.743 −0.039 [0.011] What I eat How often eat different food items 18 14 0.718 −0.153 [0.011] Self‐esteem 11 6 0.702 −0.042 [0.014] Total 458 383 Title in Questionnaire . Type of question . Number of questions . Gender difference in composite score . In module . With signif gender diff . Cronbach's alpha . Coefficient . Standard error . What about work? Determinants of success in labour market 9 8 0.567 0.027 [0.013] Right and might Attitudes to law‐breaking and helping others 9 7 0.533 −0.152 [0.012] Have a drink Alcohol 17 17 0.725 0.197 [0.012] What's in a job Importance of different job characteristics 16 12 0.607 0.096 [0.010] Looking ahead What will be important as adult 15 13 0.661 0.02 [0.011] Up in smoke Smoking 17 14 0.746 −0.026 [0.012] Compared with others Physical/emotional attributes 28 21 0.822 −0.122 [0.011] Knowing myself Personal characteristics 27 23 0.724 −0.038 [0.010] How I feel Worries and anxieties 12 10 0.833 0.147 [0.016] At leisure Leisure activities 47 40 0.781 −0.036 [0.008] My interests Interests in physical/mental health topics 49 46 0.923 −0.289 [0.012] Fate and fortune Locus of control – belief in ability to influence outcomes 15 6 0.717 −0.025 [0.012] What I read Type of reading 25 24 0.817 0.224 [0.011] Me and the box Types of TV programmes watched 22 22 0.721 −0.101 [0.011] Feeling healthy Physical/mental health 22 18 0.839 −0.182 [0.013] My point of view Opinions on women's/gay/minority rights, drugs, death penalty etc 21 17 0.597 0.215 [0.009] Wotalotigot Material possessions, actual and desired 30 27 0.649 0.169 [0.008] Me and my family Activities done with family 15 13 0.757 −0.06 [0.013] Soft drink special Type of Soft drinks consumed 10 6 0.632 −0.085 [0.013] Home rule Parents expectations of behaviour within the home 23 19 0.743 −0.039 [0.011] What I eat How often eat different food items 18 14 0.718 −0.153 [0.011] Self‐esteem 11 6 0.702 −0.042 [0.014] Total 458 383 Open in new tab Table A3 The Impact of Psychological Variables on Wages at Age 30 – Alternative Approach . Regression Coefficients . Contribution to Gender Pay Gap . 1 . 2 . 3 . 4 . 1 . 2 . 3 . 4 . Female . Male . Female . Male . Female coeffs . Male coeffs . Female coeffs . Male coeffs . What about work? 0.007 0.129 0.012 0.13 0.000 −0.003 0.000 −0.003 [0.044] [0.047] [0.043] [0.044] [0.006] [0.001] [0.001] [0.001] Right and might 0.037 0.001 0.006 0.000 [0.040] [0.041] [0.006] [0.006] Have a drink 0.019 −0.031 −0.004 0.007 [0.049] [0.048] [0.010] [0.010] What's in a job −0.035 −0.062 0.003 0.006 [0.052] [0.052] [0.005] [0.005] Looking ahead 0.017 0.004 0.000 0.000 [0.047] [0.051] [0.001] [0.001] Up in smoke 0.041 0.001 0.002 0.000 [0.045] [0.044] [0.002] [0.002] Compared with others 0.016 0.018 0.001 0.002 [0.052] [0.051] [0.005] [0.005] Knowing myself −0.074 −0.091 −0.003 −0.003 [0.063] [0.068] [0.002] [0.003] How I feel 0.039 −0.001 −0.005 0.000 [0.032] [0.040] [0.004] [0.005] At leisure 0.106 −0.038 0.003 −0.001 [0.073] [0.069] [0.002] [0.002] My interests 0.09 0.048 0.077 0.019 0.027 0.014 0.023 0.006 [0.047] [0.045] [0.043] [0.040] [0.014] [0.013] [0.013] [0.012] Fate and fortune 0.122 −0.023 0.135 0.001 0.001 0.000 0.000 0.000 [0.048] [0.053] [0.045] [0.049] [0.000] [0.000] [0.000] [0.000] What I read −0.069 0.038 −0.042 0.051 0.014 −0.008 0.009 −0.011 [0.050] [0.050] [0.049] [0.049] [0.010] [0.010] [0.010] [0.010] Me and the box 0.087 0.081 0.068 0.065 0.008 0.008 0.007 0.006 [0.055] [0.054] [0.053] [0.051] [0.005] [0.005] [0.005] [0.005] Feeling healthy 0.026 0.074 0.018 0.106 0.004 0.011 0.003 0.016 [0.048] [0.050] [0.039] [0.041] [0.007] [0.007] [0.006] [0.006] My point of view −0.099 −0.037 −0.113 −0.058 0.022 0.008 0.025 0.013 [0.064] [0.055] [0.061] [0.052] [0.014] [0.005] [0.014] [0.012] Wotalotigot −0.009 −0.071 0.001 0.010 [0.063] [0.065] [0.009] [0.010] Me and my family −0.044 0.021 −0.002 0.001 [0.044] [0.040] [0.002] [0.002] Soft drink special −0.006 −0.066 −0.001 −0.006 [0.043] [0.043] [0.004] [0.004] Home rule −0.107 −0.001 −0.006 −0.000 [0.048] [0.048] [0.003] [0.003] What I eat −0.024 −0.024 −0.004 −0.004 [0.044] [0.047] [0.007] [0.007] Self‐esteem 0.005 0.035 0.000 0.001 [0.034] [0.037] [0.001] [0.001] Observations 1,582 1,220 1,618 1,265 0.23 0.24 0.22 0.23 R‐Squared Total Explained 0.069 0.043 0.067 0.027 [0.027] [0.025] [0.023] [0.021] . Regression Coefficients . Contribution to Gender Pay Gap . 1 . 2 . 3 . 4 . 1 . 2 . 3 . 4 . Female . Male . Female . Male . Female coeffs . Male coeffs . Female coeffs . Male coeffs . What about work? 0.007 0.129 0.012 0.13 0.000 −0.003 0.000 −0.003 [0.044] [0.047] [0.043] [0.044] [0.006] [0.001] [0.001] [0.001] Right and might 0.037 0.001 0.006 0.000 [0.040] [0.041] [0.006] [0.006] Have a drink 0.019 −0.031 −0.004 0.007 [0.049] [0.048] [0.010] [0.010] What's in a job −0.035 −0.062 0.003 0.006 [0.052] [0.052] [0.005] [0.005] Looking ahead 0.017 0.004 0.000 0.000 [0.047] [0.051] [0.001] [0.001] Up in smoke 0.041 0.001 0.002 0.000 [0.045] [0.044] [0.002] [0.002] Compared with others 0.016 0.018 0.001 0.002 [0.052] [0.051] [0.005] [0.005] Knowing myself −0.074 −0.091 −0.003 −0.003 [0.063] [0.068] [0.002] [0.003] How I feel 0.039 −0.001 −0.005 0.000 [0.032] [0.040] [0.004] [0.005] At leisure 0.106 −0.038 0.003 −0.001 [0.073] [0.069] [0.002] [0.002] My interests 0.09 0.048 0.077 0.019 0.027 0.014 0.023 0.006 [0.047] [0.045] [0.043] [0.040] [0.014] [0.013] [0.013] [0.012] Fate and fortune 0.122 −0.023 0.135 0.001 0.001 0.000 0.000 0.000 [0.048] [0.053] [0.045] [0.049] [0.000] [0.000] [0.000] [0.000] What I read −0.069 0.038 −0.042 0.051 0.014 −0.008 0.009 −0.011 [0.050] [0.050] [0.049] [0.049] [0.010] [0.010] [0.010] [0.010] Me and the box 0.087 0.081 0.068 0.065 0.008 0.008 0.007 0.006 [0.055] [0.054] [0.053] [0.051] [0.005] [0.005] [0.005] [0.005] Feeling healthy 0.026 0.074 0.018 0.106 0.004 0.011 0.003 0.016 [0.048] [0.050] [0.039] [0.041] [0.007] [0.007] [0.006] [0.006] My point of view −0.099 −0.037 −0.113 −0.058 0.022 0.008 0.025 0.013 [0.064] [0.055] [0.061] [0.052] [0.014] [0.005] [0.014] [0.012] Wotalotigot −0.009 −0.071 0.001 0.010 [0.063] [0.065] [0.009] [0.010] Me and my family −0.044 0.021 −0.002 0.001 [0.044] [0.040] [0.002] [0.002] Soft drink special −0.006 −0.066 −0.001 −0.006 [0.043] [0.043] [0.004] [0.004] Home rule −0.107 −0.001 −0.006 −0.000 [0.048] [0.048] [0.003] [0.003] What I eat −0.024 −0.024 −0.004 −0.004 [0.044] [0.047] [0.007] [0.007] Self‐esteem 0.005 0.035 0.000 0.001 [0.034] [0.037] [0.001] [0.001] Observations 1,582 1,220 1,618 1,265 0.23 0.24 0.22 0.23 R‐Squared Total Explained 0.069 0.043 0.067 0.027 [0.027] [0.025] [0.023] [0.021] Notes. The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, detailed qualifications, marital status, ethnic, establishment size, whether a supervisor and future plans for (further) children. Standard errors in parentheses. Details of the questions can be found in Table A2. Open in new tab Table A3 The Impact of Psychological Variables on Wages at Age 30 – Alternative Approach . Regression Coefficients . Contribution to Gender Pay Gap . 1 . 2 . 3 . 4 . 1 . 2 . 3 . 4 . Female . Male . Female . Male . Female coeffs . Male coeffs . Female coeffs . Male coeffs . What about work? 0.007 0.129 0.012 0.13 0.000 −0.003 0.000 −0.003 [0.044] [0.047] [0.043] [0.044] [0.006] [0.001] [0.001] [0.001] Right and might 0.037 0.001 0.006 0.000 [0.040] [0.041] [0.006] [0.006] Have a drink 0.019 −0.031 −0.004 0.007 [0.049] [0.048] [0.010] [0.010] What's in a job −0.035 −0.062 0.003 0.006 [0.052] [0.052] [0.005] [0.005] Looking ahead 0.017 0.004 0.000 0.000 [0.047] [0.051] [0.001] [0.001] Up in smoke 0.041 0.001 0.002 0.000 [0.045] [0.044] [0.002] [0.002] Compared with others 0.016 0.018 0.001 0.002 [0.052] [0.051] [0.005] [0.005] Knowing myself −0.074 −0.091 −0.003 −0.003 [0.063] [0.068] [0.002] [0.003] How I feel 0.039 −0.001 −0.005 0.000 [0.032] [0.040] [0.004] [0.005] At leisure 0.106 −0.038 0.003 −0.001 [0.073] [0.069] [0.002] [0.002] My interests 0.09 0.048 0.077 0.019 0.027 0.014 0.023 0.006 [0.047] [0.045] [0.043] [0.040] [0.014] [0.013] [0.013] [0.012] Fate and fortune 0.122 −0.023 0.135 0.001 0.001 0.000 0.000 0.000 [0.048] [0.053] [0.045] [0.049] [0.000] [0.000] [0.000] [0.000] What I read −0.069 0.038 −0.042 0.051 0.014 −0.008 0.009 −0.011 [0.050] [0.050] [0.049] [0.049] [0.010] [0.010] [0.010] [0.010] Me and the box 0.087 0.081 0.068 0.065 0.008 0.008 0.007 0.006 [0.055] [0.054] [0.053] [0.051] [0.005] [0.005] [0.005] [0.005] Feeling healthy 0.026 0.074 0.018 0.106 0.004 0.011 0.003 0.016 [0.048] [0.050] [0.039] [0.041] [0.007] [0.007] [0.006] [0.006] My point of view −0.099 −0.037 −0.113 −0.058 0.022 0.008 0.025 0.013 [0.064] [0.055] [0.061] [0.052] [0.014] [0.005] [0.014] [0.012] Wotalotigot −0.009 −0.071 0.001 0.010 [0.063] [0.065] [0.009] [0.010] Me and my family −0.044 0.021 −0.002 0.001 [0.044] [0.040] [0.002] [0.002] Soft drink special −0.006 −0.066 −0.001 −0.006 [0.043] [0.043] [0.004] [0.004] Home rule −0.107 −0.001 −0.006 −0.000 [0.048] [0.048] [0.003] [0.003] What I eat −0.024 −0.024 −0.004 −0.004 [0.044] [0.047] [0.007] [0.007] Self‐esteem 0.005 0.035 0.000 0.001 [0.034] [0.037] [0.001] [0.001] Observations 1,582 1,220 1,618 1,265 0.23 0.24 0.22 0.23 R‐Squared Total Explained 0.069 0.043 0.067 0.027 [0.027] [0.025] [0.023] [0.021] . Regression Coefficients . Contribution to Gender Pay Gap . 1 . 2 . 3 . 4 . 1 . 2 . 3 . 4 . Female . Male . Female . Male . Female coeffs . Male coeffs . Female coeffs . Male coeffs . What about work? 0.007 0.129 0.012 0.13 0.000 −0.003 0.000 −0.003 [0.044] [0.047] [0.043] [0.044] [0.006] [0.001] [0.001] [0.001] Right and might 0.037 0.001 0.006 0.000 [0.040] [0.041] [0.006] [0.006] Have a drink 0.019 −0.031 −0.004 0.007 [0.049] [0.048] [0.010] [0.010] What's in a job −0.035 −0.062 0.003 0.006 [0.052] [0.052] [0.005] [0.005] Looking ahead 0.017 0.004 0.000 0.000 [0.047] [0.051] [0.001] [0.001] Up in smoke 0.041 0.001 0.002 0.000 [0.045] [0.044] [0.002] [0.002] Compared with others 0.016 0.018 0.001 0.002 [0.052] [0.051] [0.005] [0.005] Knowing myself −0.074 −0.091 −0.003 −0.003 [0.063] [0.068] [0.002] [0.003] How I feel 0.039 −0.001 −0.005 0.000 [0.032] [0.040] [0.004] [0.005] At leisure 0.106 −0.038 0.003 −0.001 [0.073] [0.069] [0.002] [0.002] My interests 0.09 0.048 0.077 0.019 0.027 0.014 0.023 0.006 [0.047] [0.045] [0.043] [0.040] [0.014] [0.013] [0.013] [0.012] Fate and fortune 0.122 −0.023 0.135 0.001 0.001 0.000 0.000 0.000 [0.048] [0.053] [0.045] [0.049] [0.000] [0.000] [0.000] [0.000] What I read −0.069 0.038 −0.042 0.051 0.014 −0.008 0.009 −0.011 [0.050] [0.050] [0.049] [0.049] [0.010] [0.010] [0.010] [0.010] Me and the box 0.087 0.081 0.068 0.065 0.008 0.008 0.007 0.006 [0.055] [0.054] [0.053] [0.051] [0.005] [0.005] [0.005] [0.005] Feeling healthy 0.026 0.074 0.018 0.106 0.004 0.011 0.003 0.016 [0.048] [0.050] [0.039] [0.041] [0.007] [0.007] [0.006] [0.006] My point of view −0.099 −0.037 −0.113 −0.058 0.022 0.008 0.025 0.013 [0.064] [0.055] [0.061] [0.052] [0.014] [0.005] [0.014] [0.012] Wotalotigot −0.009 −0.071 0.001 0.010 [0.063] [0.065] [0.009] [0.010] Me and my family −0.044 0.021 −0.002 0.001 [0.044] [0.040] [0.002] [0.002] Soft drink special −0.006 −0.066 −0.001 −0.006 [0.043] [0.043] [0.004] [0.004] Home rule −0.107 −0.001 −0.006 −0.000 [0.048] [0.048] [0.003] [0.003] What I eat −0.024 −0.024 −0.004 −0.004 [0.044] [0.047] [0.007] [0.007] Self‐esteem 0.005 0.035 0.000 0.001 [0.034] [0.037] [0.001] [0.001] Observations 1,582 1,220 1,618 1,265 0.23 0.24 0.22 0.23 R‐Squared Total Explained 0.069 0.043 0.067 0.027 [0.027] [0.025] [0.023] [0.021] Notes. The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, detailed qualifications, marital status, ethnic, establishment size, whether a supervisor and future plans for (further) children. Standard errors in parentheses. Details of the questions can be found in Table A2. Open in new tab © The Author(s). Journal compilation © Royal Economic Society 2008 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Economic Journal Oxford University Press http://www.deepdyve.com/lp/oxford-university-press/the-gender-gap-in-early-career-wage-growth-NRL8GlO1bW

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Publisher: Oxford University Press
Copyright: © The Author(s). Journal compilation © Royal Economic Society 2008
ISSN: 0013-0133
eISSN: 1468-0297
DOI: 10.1111/j.1468-0297.2008.02158.x
Publisher site: See Article on Publisher Site

Abstract

Abstract In the UK the gender pay gap on entry to the labour market is approximately zero but ten years after labour market entry, there is a gender wage gap of almost 25 log points. This article explores the reason for this gender gap in early‐career wage growth, considering three main hypotheses – human capital, job‐shopping and ‘psychological’ theories. Human capital factors can explain about 11 log points, job‐shopping about 1.5 log points and the psychological theories up to 4.5 log points depending on the specification. But a substantial unexplained gap remains: women who have continuous full‐time employment, have had no children and express no desire to have them earn about 8 log points less than equivalent men after 10 years in the labour market. Given the enormous literature on the gender pay gap – see Altonji and Blank (1999) for a relatively recent survey – one might wonder about the justification for yet another paper on the subject. The answer is that this article focuses on the gender gap in wage growth in the early years after labour market entry and argues that an understanding of this is necessary as it is the primary cause of the overall gender pay gap. The reason why this is particularly important in the UK can be explained very simply. Figure 1 presents the data the earnings‐experience profile in the UK for men and women together with the gender pay gap from one of the main data sets used in this article, the British Household Panel Survey (BHPS). Average log wages are normalised to be zero for men with zero years of labour market experience. One can see that the earnings of men and women are very similar on entry to the labour market but 10 years later a sizeable gender gap in earnings has emerged,1 a gap that reaches its maximum about 20 years after labour market entry before falling slightly in later years.2 Of course, cross‐section profiles like the ones presented in Figure 1 confound true life‐cycle effects and cohort effects. But, while cohort effects can probably explain some of the cross‐section profile, there is good reason to think that they are not the most important reason for the widening of the gender pay gap after labour market entry. To see this Figure 2 presents the gender earnings gap at different ages for different birth cohorts in the New Earnings Survey (NES).3 The NES contains no information on education (so we cannot compute experience) and Figure 2 simply presents age profiles. What one can see is that the gender pay gap is lower at all ages for younger cohorts but that the contribution of cohort is small relative to the life‐cycle effects. For example, Figure 2 suggests that the gender pay gap that can be expected at age 40 for those born around 1970 is likely to be smaller than for those born around 1960 but extrapolation of the profiles in Figure 2 would suggest that a very sizeable gender wage gap will still exist. And for women born after 1970 there is some evidence that the progress of women has stalled as the profiles of the gender pay gap are very similar for those born around 1970 and those born in the late 1970s – if this is true then there will be a gender pay gap for these women of 20 log points in their early 30s.4 So, young women entering the labour market today can still expect to earn much less than young men after 10 years in the labour market and this gender difference in early‐career wage growth needs explaining. Fig. 2. Open in new tabDownload slide The Evolution of the Gender Pay Gap for Different Birth CohortsNotes. Data come from the New Earnings Survey, 1975–2001. Earnings data are hourly earnings excluding over‐time. Fig. 2. Open in new tabDownload slide The Evolution of the Gender Pay Gap for Different Birth CohortsNotes. Data come from the New Earnings Survey, 1975–2001. Earnings data are hourly earnings excluding over‐time. Fig. 1. Open in new tabDownload slide The Earnings‐Experience Profile for Men and WomenNotes. Data come from the first 12 waves of the BHPS. Log hourly wages are normalised to be zero for men with zero years of labour market experience. Fig. 1. Open in new tabDownload slide The Earnings‐Experience Profile for Men and WomenNotes. Data come from the first 12 waves of the BHPS. Log hourly wages are normalised to be zero for men with zero years of labour market experience. Most papers on the gender pay gap focus on the level of pay and not the growth in pay. But as the level of pay at any level experience is simply the initial pay on labour market entry plus cumulated wage growth, any theory of the pay level must be, if only implicitly, also a theory of wage growth (and vice versa). So, theories to explain the level of pay should also be able to explain differences in wage growth. This article focuses on three theories of wage growth – the human capital hypothesis, job‐shopping and ‘psychological’ theories and asks how well they can do in explaining the gender gap in early‐career wage growth. The most common approach to explaining the gender pay gap is based on the human capital model developed by Becker (1993) and Mincer (1974) and applied to the gender pay gap in many papers; see Mincer and Polachek (1974), Mincer and Ofek (1982), Light and Ureta (1995),inter alia. This approach seeks to explain the gender pay gap by gender differences in human capital accumulation of which there are several dimensions. First, there are gender differences in accumulated work experience (which is assumed to be correlated with levels of human capital) because women are more likely to have intermittent labour market participation and are more likely to work part‐time. Secondly, the anticipation of future intermittence may affect current investments in human capital. The affected investments might be after labour market entry – see Ben‐Porath (1967) for a classic theoretical model of this – or prior to labour market entry where it may take the form of gender differences in the quantity or (more plausible these days for a country like the UK) type of education. Thirdly, Becker (1985) has argued that the greater domestic commitments of women (especially married women with dependent children) mean they put less energy into work and that translates into lower hourly wages. The estimates presented below find that the human capital hypothesis can explain a non‐trivial amount of the gender gap in early‐career wage growth but over 50% of the gender pay gap that emerges by 10 years after labour market entry cannot be explained in this way. The reason for this conclusion can be explained very simply. First, differences in actual labour market experience between young British men and women after 10 years in the labour market are simply not large enough to explain more than 6.5 of the 25 log‐point gender pay gap at that point in the life‐cycle. Secondly, gender differences in job‐related training do explain a part of the differential (primarily because young men are still much more likely than young women to go into apprenticeships) but, again, the estimated contribution is no more than 4.5 log points. And, thirdly, gender differences in the quantity of education are very small (and, in any case, Mincer's (1974) famous observation that the experience profiles are similar for different levels of education would suggest that any difference in the quantity of education should show up in a gender pay gap on labour market entry and not a gender gap in wage growth) and, while gender differences in the types of education and associated occupational choices are still large, we do not find that occupational differences contribute much to the gender gap in early‐career wage growth. The bottom line is that the human capital explanations can explain perhaps half of the 25 log point gender pay gap observed 10 years after labour market entry. The article then also investigates the job‐shopping hypothesis that an important part of early career wage growth is associated with moving from worse to better‐paying jobs, e.g. Topel and Ward (1992), who study only US men, find that something like one‐third of wage growth in the first 10 years after labour market entry can be put down to job mobility). Manning (2003a, ch. 7) has documented how women are more constrained in their opportunities to change jobs than men and are less concerned with money when they do. So we might expect to see women making fewer ‘good’ job changes, more ‘bad’ job changes and a lower return to job changes when they do. We investigate this hypothesis below, and find that it can explain only 1.5 log points of the gender pay gap after 10 years in the labour market. This is not because job mobility is unimportant but because gender differences among young workers in the incidence of and returns to job mobility are quite small. The article then considers a more recent but less well‐known theory of the gender pay gap – that has its roots in the sizeable psychological differences between men and women at the age they enter the labour market, e.g., in attitudes to risk‐taking, competition, self‐esteem and selflessness. These differences are surveyed in Croson and Gneezy (forthcoming) and Babcock and Laschever (2003); Gneezy et al. (2003) suggest that they might help to explain the gender pay gap. We use data on psychological attributes of men and women before labour market entry to examine the explanatory power of these ideas, finding that they can explain a few percentage points of the gender wage gap but that their impact is not enormous. The plan of the article is as follows. Section 1 describes the data used. Section 2 then presents ‘reduced‐form’ estimates of wage growth equations that confirm the presence of a gender gap in early‐career wage growth as suggested by Figure 1. Section 3 then investigates the extent to which this can be explained by differences in human capital considering the contribution of breaks in employment, of job‐related training, of the types of education and of occupational choice. Section 4 then considers the contribution of job mobility, both good and bad, to wage growth. Section 5 then investigates the importance of ‘personality’ for explaining the gender pay gap. Section 6 concludes. The bottom line is that a sizeable part of the gender pay gap after 10 years in the labour market remains unexplained: a woman who has done nothing but work full‐time ever since leaving education, has had no children and expresses no desire to have them in the future earns, on average, at least 8 percentage points less than an equivalent man. 1. The Data The main data used in the first part of this article come from the first 12 waves of the British Household Panel Study (BHPS) that cover the period 1991–2002 inclusive, though the Labour Force Survey (LFS) and the New Earnings Survey (NES) are sometimes used for supplementary evidence and consistency checks. The BHPS started in 1991 with some 5,500 households and 10,300 individuals drawn from 250 different areas of Great Britain. It has had a number of booster samples: approximately 1,000 households in 1997 that are part of the European Community Household Panel Survey (that include Northern Ireland and over‐sample low‐income households), approximately 3,000 Scottish and Welsh households that were added in 1999. It follows all individuals in sample households and those who end up in households with them as long as they remain in a household with an original sample member (in many ways, it can be thought of as the British PSID). From the BHPS the sample is constructed in the following way. First, the sample is restricted to those with no more than 10 years of potential labour market experience as our focus is on what happens to the gender pay gap in the years after labour market entry. Secondly, only those individuals for whom there is an uninterrupted set of interviews are included. Thirdly, anyone who has a period in employment when we cannot identify whether they are working for the same employer as at the previous interview is excluded. Finally, because of the difficulty in measuring earnings for the self‐employed, anyone who reported being self‐employed at any interview date is excluded: as few individuals move between employment and self‐employment (about 1.5% of employees become self‐employed in any year) this is a relatively minor restriction. In total about 5% of observations are excluded because of data problems and the final sample used for analysis is approximately 10,500 observations on wage growth for about 3,559 individuals. Wages are measured as an hourly rate, obtained by dividing usual weekly earnings by usual weekly hours. Those with hourly earnings below £1 or above £100 are excluded. As we do not want the measure of earnings growth on‐the‐job to include aggregate wage growth it is important to deflate wages: as Altonji and Williams (1998) point out, estimates of the return to job tenure can be sensitive to the way in which this is done.5 We experimented with the use of an external earnings index but average earnings growth in the BHPS deviates from this aggregate measure. As a result we estimated a simple earnings function of the log of hourly wages on a quartic in experience, regional, race and gender dummies and a set of wave dummies and then deducted the wave dummies to generate our adjusted wage series. As we are interested in how wages evolve, we need a measure of wage growth. In most studies of wage growth, the dependent variable is defined as the growth in wages between two fixed points in time, with this measure being undefined for those who are not in employment at either of the two points. This approach has the disadvantage that it under‐samples those individuals who have intermittent employment, a potentially serious problem when analysing the gender pay gap. For example, suppose we measure wages annually but some individuals have a year or more in which they are not in employment. If one only measures wage growth from one year to the next, one will drop two observations for such individuals because there will be two years in which annual wage growth is not defined. To deal with this problem, we define wage growth in the following way. For any year in which an individual is in employment, define the wage growth as the difference in log wages between the next year they are in employment and the log wage this year. Consequently there may be gaps between the two wage observations of any number of years. It is important to control for this ‘gap’ in what follows and we describe later how we do this. There remains a potential problem in that the gap between wage observations is censored at the top end by the latest year of observation being 2002 but this seems to be a small problem6 because long gaps between wage observations are relatively rare. The first two columns of Table 1 present the distribution of gaps between the wage observations in our data set for men and women. One observes, as one would expect, that women are more likely than men to have intermittent employment. But, what is also striking is the relatively short length of gaps between periods of employment; see also Myck and Paull (2001) for an analysis of gender differences in accumulated experience. Only 2% of the observations on female wage growth have a gap in employment of more than 2 years. Because the BHPS is a relatively short panel, there are many censored observations so, to get some idea of the uncensored distribution of the gap, the third and fourth columns of Table 1 report the distribution of the gap among those in waves 1–6. There are now more young women with longer gaps in employment, though it remains remarkable how rare are long breaks in employment. However these data are still censored: some women may never return to paid employment but some might have very long spells that are not observed in the BHPS. To get some idea of this, the final two columns of Table 1 present, for comparison, data on gaps between spells of employment from the Labour Force Survey (LFS). In the LFS those not currently in employment are asked when they last worked and we can use this information to work out the gap between employment spells for those who are employed in the subsequent quarter.7 Again, one sees that women are more likely to have gaps in employment than men but the average size of gaps and the gender differences are not large – the BHPS suggests an average gap for women that is 0.3 years above that for men and the LFS suggests a somewhat smaller gap of 0.25 years. And the very long gaps that contribute quite a lot to this overall gender difference are very rare among the young workers that are the focus of this article. For example, the work history data of the 1970 British Cohort Study suggests that, among those in employment at age 30, 23% of men have had a period of non‐employment between two periods of employment with an average of 0.3 gaps compared to 27% of women with an average of 0.35 gaps. Table 1 Distribution of Gap Between Successive Wage Observations Gap between wage obs (yrs) . BHPS (all waves) . BHPS (wave < = 6) . LFS . Men . Women . Men . Women . Men . Women . 1 95.6 93.0 94.7 91.5 96.6 92.7 2 3.1 4.8 3.3 5.2 2.4 3.1 3 0.9 0.9 1.1 1.1 0.4 1.1 4 0.2 0.4 0.4 0.8 0.2 0.6 5 0.1 0.2 0.1 0.4 0.1 0.5 6 0.1 0.3 0.1 0.5 0.1 0.4 7 0.0 0.1 0.0 0.3 0.0 0.3 8 0.7 0.1 0.2 0.2 0.0 0.3 9 0.0 0.0 0.0 0.0 0.0 0.2 10–14 0.1 0.7 15–19 0.0 0.0 0.0 0.1 0.0 0.1 20+ 0.0 0.0 No. of obs 5,344 5,756 2,709 2,990 37,384 34,595 Gap between wage obs (yrs) . BHPS (all waves) . BHPS (wave < = 6) . LFS . Men . Women . Men . Women . Men . Women . 1 95.6 93.0 94.7 91.5 96.6 92.7 2 3.1 4.8 3.3 5.2 2.4 3.1 3 0.9 0.9 1.1 1.1 0.4 1.1 4 0.2 0.4 0.4 0.8 0.2 0.6 5 0.1 0.2 0.1 0.4 0.1 0.5 6 0.1 0.3 0.1 0.5 0.1 0.4 7 0.0 0.1 0.0 0.3 0.0 0.3 8 0.7 0.1 0.2 0.2 0.0 0.3 9 0.0 0.0 0.0 0.0 0.0 0.2 10–14 0.1 0.7 15–19 0.0 0.0 0.0 0.1 0.0 0.1 20+ 0.0 0.0 No. of obs 5,344 5,756 2,709 2,990 37,384 34,595 Notes. BHPS sample is restricted to those with 10 or fewer years of potential labour market experience. LFS sample is from 1997–2002 and gives the distribution among those currently in employment who are not labour market entrants and who have less than 20 years of labour market experience. Open in new tab Table 1 Distribution of Gap Between Successive Wage Observations Gap between wage obs (yrs) . BHPS (all waves) . BHPS (wave < = 6) . LFS . Men . Women . Men . Women . Men . Women . 1 95.6 93.0 94.7 91.5 96.6 92.7 2 3.1 4.8 3.3 5.2 2.4 3.1 3 0.9 0.9 1.1 1.1 0.4 1.1 4 0.2 0.4 0.4 0.8 0.2 0.6 5 0.1 0.2 0.1 0.4 0.1 0.5 6 0.1 0.3 0.1 0.5 0.1 0.4 7 0.0 0.1 0.0 0.3 0.0 0.3 8 0.7 0.1 0.2 0.2 0.0 0.3 9 0.0 0.0 0.0 0.0 0.0 0.2 10–14 0.1 0.7 15–19 0.0 0.0 0.0 0.1 0.0 0.1 20+ 0.0 0.0 No. of obs 5,344 5,756 2,709 2,990 37,384 34,595 Gap between wage obs (yrs) . BHPS (all waves) . BHPS (wave < = 6) . LFS . Men . Women . Men . Women . Men . Women . 1 95.6 93.0 94.7 91.5 96.6 92.7 2 3.1 4.8 3.3 5.2 2.4 3.1 3 0.9 0.9 1.1 1.1 0.4 1.1 4 0.2 0.4 0.4 0.8 0.2 0.6 5 0.1 0.2 0.1 0.4 0.1 0.5 6 0.1 0.3 0.1 0.5 0.1 0.4 7 0.0 0.1 0.0 0.3 0.0 0.3 8 0.7 0.1 0.2 0.2 0.0 0.3 9 0.0 0.0 0.0 0.0 0.0 0.2 10–14 0.1 0.7 15–19 0.0 0.0 0.0 0.1 0.0 0.1 20+ 0.0 0.0 No. of obs 5,344 5,756 2,709 2,990 37,384 34,595 Notes. BHPS sample is restricted to those with 10 or fewer years of potential labour market experience. LFS sample is from 1997–2002 and gives the distribution among those currently in employment who are not labour market entrants and who have less than 20 years of labour market experience. Open in new tab One other peculiarity of the BHPS that is worth mentioning is that its basic measure of job tenure is tenure in a particular job with a particular employer so that within‐firm changes of job reset the tenure clock to zero. This is not the way that the job tenure variable is usually defined where it refers to job tenure with the same employer. However, the BHPS definition is retained here as job moves within employers (e.g. promotions) are as interesting and important as those between employers in understanding the emergence of the gender pay gap.8 The data are summarised in the Appendix in Table A1. 2. Reduced Form Estimates While the evidence presented in Figures 1 and 2 is strongly suggestive of a gender gap in wage growth in the years immediately after labour market entry, it is important to verify this using direct data on wage growth. Hence, this Section presents some ‘reduced‐form’ wage growth regressions to get some idea of what the data looks like and to confirm the existence of this gender gap in early‐career wage growth. Accordingly, we estimate the following ‘reduced‐form’ model for the wage growth between t and (t + g) of individual i where g is the gap between wage observations: (1) where e is years since leaving full‐time education i.e. potential experience. This model is estimated separately for men and women. The quadratic specification seems adequate to capture the variation in wage growth among the young workers in our sample but one should be wary of extrapolating into other parts of the life‐cycle; Murphy and Welch (1990) suggest a quartic for the level implying a cubic for the change if workers of all ages are in the sample. In specifying this equation we need to be careful about how we deal with individuals for whom the gap over which wage growth is measured is more than a year. To see why this matters suppose that the observed wage growth for all individuals (whatever the gap) was 5% but that men are always in employment (so ) but women are only in employment every two years (so ). After 2 years earnings for men would have grown by approximately 10% whereas those for women would have only grown by 5%. The gender pay gap would be widening but estimation of (1) that did not take account of differences in the gap would miss this. The simplest way of getting a gap‐adjusted measure of wage growth would be simply to divide the observed wage growth by the gap between wage observations to get an estimate of annualised wage growth. However the validity of this procedure is based on the implicit assumption that wage growth does not vary with experience, something we know to be untrue. So, a slightly more sophisticated approach is taken here to adjust for the gap between wage observations. If (1) is the wage growth for those for whom , then for someone with to return to employment at the same level of wages they must have expected wage growth between the two observations of: (2) the right‐hand side of which is simply two one‐period sets of wage growth. One can readily extend this formula to any value of g in which case it will be given by: (3) Using the specific functional form in (1), this can be written as: (4) Thus one can readily estimate wage growth on a consistent basis for individuals with different gaps by computing the ‘adjusted’ levels of experience in (4) and using these as regressors in a wage growth equation. Note that there is no constant in this regression – the constant in (1) gets multiplied by g. If wage growth does not vary with experience this approach is equivalent to the simple‐minded approach of just dividing wage growth by the interval between wage observations as the gap would be the only remaining regressor and the coefficient on it can be interpreted as annual wage growth. Table 2 reports estimates of the reduced‐form wage growth equations. The first two columns of Table 2 report estimates of (4) for men and women separately. We report the estimates of earnings growth at 0, 5 and 10 years of experience together with their standard errors. Taking the coefficients for men, the estimates suggest that a man can expect 14.5% annual wage growth on entry into the labour market, falling to 6.8% after 5 years and 1.3% after 10 years. For women (the second column) earnings growth on entry is lower than for men (at 12.0% per annum) and still lower after 10 years though the gap in wage growth narrows.9 These estimates are consistent with the finding that the gender pay gap begins to widen soon after labour market entry, as suggested in Figure 1.10 The bottom two rows of Table 2 report the gender pay gap implied by these differences in estimated wage growth 5 and 10 years after labour market entry. These are derived by cumulating the estimates of the gender difference in wage growth from the estimation of (4) assuming that there are no breaks in employment and (as is a good approximation) that gender differences in wages on labour market entry are zero. The estimates in columns 1 and 2 of Table 2 imply a gender pay gap of 14.4 log points after 5 years and 24.8 log points after 10 years. These estimates are not far out of line with those derived from estimates of wage levels. Table 2 ‘Reduced Form’Estimates of Wage Growth Equation . 1 . 2 . 3 . 4 . 5 . 6 . Men Women Men Women Men Women 0 yrs of experience 0.145 0.120 0.161 0.131 0.160 0.160 (0.013) (0.012) (0.013) (0.011) (0.019) (0.016) 5 yrs of experience 0.068 0.040 0.070 0.046 0.074 0.041 (0.005) (0.004) (0.004) (0.004) (0.008) (0.006) 10 yrs of experience 0.013 0.009 0.030 0.035 0.018 0.020 (0.010) (0.006) (0.007) (0.007) (0.015) (0.011) No. of obs 5,015 5,535 4,833 5,208 5,015 5,535 R2 0.076 0.051 0.077 0.058 0.020 0.015 Fixed Effects No No No No Yes Yes P = 1.00 P = 1.00 Sample All All Gap = 1 Gap = 1 All All Implied Gender Gap at 5 yrs 0.144 0.149 0.096 (0.044) (0.041) (0.062) Implied Gender Gap at 10 yrs 0.248 0.222 0.216 (0.054) (0.044) (0.066) . 1 . 2 . 3 . 4 . 5 . 6 . Men Women Men Women Men Women 0 yrs of experience 0.145 0.120 0.161 0.131 0.160 0.160 (0.013) (0.012) (0.013) (0.011) (0.019) (0.016) 5 yrs of experience 0.068 0.040 0.070 0.046 0.074 0.041 (0.005) (0.004) (0.004) (0.004) (0.008) (0.006) 10 yrs of experience 0.013 0.009 0.030 0.035 0.018 0.020 (0.010) (0.006) (0.007) (0.007) (0.015) (0.011) No. of obs 5,015 5,535 4,833 5,208 5,015 5,535 R2 0.076 0.051 0.077 0.058 0.020 0.015 Fixed Effects No No No No Yes Yes P = 1.00 P = 1.00 Sample All All Gap = 1 Gap = 1 All All Implied Gender Gap at 5 yrs 0.144 0.149 0.096 (0.044) (0.041) (0.062) Implied Gender Gap at 10 yrs 0.248 0.222 0.216 (0.054) (0.044) (0.066) Notes. These estimates are derived from the estimation of (4) where experience is modelled as a quadratic. Standard errors in parentheses. These are robust standard errors with clustering on the individual. Open in new tab Table 2 ‘Reduced Form’Estimates of Wage Growth Equation . 1 . 2 . 3 . 4 . 5 . 6 . Men Women Men Women Men Women 0 yrs of experience 0.145 0.120 0.161 0.131 0.160 0.160 (0.013) (0.012) (0.013) (0.011) (0.019) (0.016) 5 yrs of experience 0.068 0.040 0.070 0.046 0.074 0.041 (0.005) (0.004) (0.004) (0.004) (0.008) (0.006) 10 yrs of experience 0.013 0.009 0.030 0.035 0.018 0.020 (0.010) (0.006) (0.007) (0.007) (0.015) (0.011) No. of obs 5,015 5,535 4,833 5,208 5,015 5,535 R2 0.076 0.051 0.077 0.058 0.020 0.015 Fixed Effects No No No No Yes Yes P = 1.00 P = 1.00 Sample All All Gap = 1 Gap = 1 All All Implied Gender Gap at 5 yrs 0.144 0.149 0.096 (0.044) (0.041) (0.062) Implied Gender Gap at 10 yrs 0.248 0.222 0.216 (0.054) (0.044) (0.066) . 1 . 2 . 3 . 4 . 5 . 6 . Men Women Men Women Men Women 0 yrs of experience 0.145 0.120 0.161 0.131 0.160 0.160 (0.013) (0.012) (0.013) (0.011) (0.019) (0.016) 5 yrs of experience 0.068 0.040 0.070 0.046 0.074 0.041 (0.005) (0.004) (0.004) (0.004) (0.008) (0.006) 10 yrs of experience 0.013 0.009 0.030 0.035 0.018 0.020 (0.010) (0.006) (0.007) (0.007) (0.015) (0.011) No. of obs 5,015 5,535 4,833 5,208 5,015 5,535 R2 0.076 0.051 0.077 0.058 0.020 0.015 Fixed Effects No No No No Yes Yes P = 1.00 P = 1.00 Sample All All Gap = 1 Gap = 1 All All Implied Gender Gap at 5 yrs 0.144 0.149 0.096 (0.044) (0.041) (0.062) Implied Gender Gap at 10 yrs 0.248 0.222 0.216 (0.054) (0.044) (0.066) Notes. These estimates are derived from the estimation of (4) where experience is modelled as a quadratic. Standard errors in parentheses. These are robust standard errors with clustering on the individual. Open in new tab To check that these estimates are not sensitive to the treatment of those with gaps of more than 1 year between wage observations, columns 3 and 4 of Table 2 present the ‘reduced‐form’ wage growth equations restricting the sample to those for whom the gap between wage observations is only one year. One finds the same pattern of gender differences in wage growth. The implied gender pay gap after 10 years is lower for this sample at 22.2 log points suggesting that part of the overall gap can be explained by women having more intermittence but the contribution is not that large. One might also wonder whether the inclusion of other controls on individual characteristics alters these conclusions. The bottom line is that they do not have a big impact.11 One way to see the irrelevance of personal characteristics is to estimate wage growth equations including individual fixed effects. This is done in the final two columns of Table 2: the coefficients hardly change and the fixed effects are jointly insignificant from zero. We should not be surprised by this finding: if there were very significant permanent differences in wage growth across individuals one would expect to see the variance of earnings in a cohort rising explosively over the life‐cycle which it does not.12 Of course, this insignificance of fixed effects in a wage‐growth equation does not mean that individual fixed effects would not be very significant in a wage levels equation – rather, it is a validation of Mincer's conclusion that earnings profiles are parallel to each other (once one controls for gender). So, our basic data do contain the feature that there is a noticeable gender gap in wage growth from the start of labour market careers. Although this gender gap in wage growth eventually reverses there is a very large gender pay gap by the time it does and women never make up the ground lost in the first 10 years after labour market entry. One might be concerned that this conclusion applies only to the BHPS or is an artifact of the quadratic specification used. So, as a useful double‐check, Figure 3 presents data from the NES that has a large enough sample size to get precise estimates of wage growth for men and women at each age. This shows the same pattern as the BHPS. It is this gender gap in early‐career wage growth that this article seeks to explain. Fig. 3. Open in new tabDownload slide Gender Differences in Wage Growth: New Earnings Survey DataNotes. The NES does not contain information on education so cannot be used to compute years of experience. This is annual wage growth for the sample of workers observed in two adjacent years. Fig. 3. Open in new tabDownload slide Gender Differences in Wage Growth: New Earnings Survey DataNotes. The NES does not contain information on education so cannot be used to compute years of experience. This is annual wage growth for the sample of workers observed in two adjacent years. 3. Gender Differences in Human Capital Accumulation Human capital theories seek to explain the gender pay gap by gender differences in human capital accumulation. There is no doubt that human capital theory is of use in explaining at least some part of the gender pay gap – some researchers (O'Neill, 2003; Polachek, 2004) claim that it can explain virtually all of the US gender pay gap while others (Altonji and Blank, 1999; Blau and Kahn, 2004) find that there remains a sizeable unexplained component to the gender pay gap. There are several varieties of human capital explanations for the gender pay gap all of which have their root in ‘the lesser amount of time and energy that women can commit to labour‐market careers as a result of the division of labour within the family’ (O'Neill, 2003, p. 309). First, after labour market entry, the weaker labour market attachment of women means they tend to accumulate less labour market experience than men and, given that such experience is valuable, part of the difference in earnings can be explained as the result of this difference in actual labour market experience. Studies (Mincer and Polachek, 1974; Light and Ureta, 1995) sometimes find that the returns to actual experience are very similar for men and women though typically other measures of labour market history are also important in explaining the gender pay gap and a sizeable part of the gender pay gap remains unexplained even after controlling for differences in actual labour market experience.13 Secondly, there may be differences in human capital investments after labour market entry in anticipation of future labour market attachment. So, we might see men doing more job‐related training than women because they expect to spend longer in the labour market in later years. Thirdly, a similar argument might apply to human capital investments in education prior to labour market entry – men might make more investments than women in human capital that has a labour market pay‐off. We consider these three different human capital hypotheses in turn. 3.1. Gender Differences in Labour Market Experience First, consider whether gender differences in actual labour market experience (the results of more labour market intermittence and more part‐time working by women) can explain the gender gap in early‐career wage growth. In our framework labour market intermittence is captured by the gap between wage observations as a gap of more than one year must imply a period not in employment. As shown in Table 1, women have longer gaps between wage observations than men, implying that women do have weaker labour market attachment than men. It may be that the differences in the gap variable can explain the disadvantage of young women in wage growth. The specification of the wage‐growth equation used so far assumes that there is no wage penalty associated with having but there are good reasons to think there might be a penalty. Assume that each year in which one is out of employment reduces the level of wages when one returns by β5 log points.14 Then the equation for wage growth (4) will become: (5) so that a simple way to test for a penalty to intermittent employment is to include constants in the reduced‐form wage‐growth equations. This is done in the first and second columns of Table 3. These results suggest that each year out of employment reduces wages by 4.4% for men and 4.7% for women. The estimate for men is slightly lower but broadly consistent with the penalty for spells of unemployment found by Arulampalam (2001) and Gregory and Jukes (2001). Table 3 The Impact of Human Capital Variables on the Gender Gap in Wage Growth . 1 . 2 . 3 . 4 . 5 . 6 . 7 . 8 . 9 . 10 . 11 . 12 . Men Women Men Women Men Women Men Women Men Women Men Women 0 yrs of experience 0.151 0.130 0.172 0.141 0.173 0.141 0.153 0.138 0.118 0.088 0.159 0.150 (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.021) (0.013) (0.018) (0.015) 5 yrs of experience 0.074 0.046 0.077 0.056 0.063 0.043 0.059 0.040 0.049 0.026 0.058 0.047 (0.005) (0.004) (0.005) (0.004) (0.008) (0.006) (0.008) (0.007) (0.010) (0.004) (0.012) (0.008) 10 yrs of experience 0.023 0.032 0.028 0.046 −0.003 0.014 −0.004 0.010 −0.001 0.025 −0.010 0.025 (0.009) (0.008) (0.009) (0.008) (0.014) (0.012) (0.014) (0.012) (0.019) (0.004) (0.018) (0.014) Cost of gap > 1 0.044 0.047 0.046 0.059 0.054 0.066 0.054 0.067 0.119 0.093 0.072 0.049 (0.024) (0.015) (0.024) (0.015) (0.024) (0.016) (0.025) (0.017) (0.029) (0.021) (0.033) (0.017) Part‐Time −0.193 −0.060 −0.194 −0.062 −0.189 −0.060 −0.153 −0.066 −0.173 −0.058 (0.041) (0.011) (0.041) (0.012) (0.042) (0.012) (0.045) (0.014) (0.049) (0.016) Decades of job tenure −0.040 −0.041 −0.037 −0.045 −0.044 −0.068 −0.041 −0.032 (0.017) (0.015) (0.017) (0.015) (0.023) (0.021) (0.019) (0.016) Training in Past Year (yrs) 0.087 0.028 0.119 0.073 0.066 0.021 (0.058) (0.038) (0.091) (0.031) (0.064) (0.051) Training between wage obs (yrs) 0.107 −0.005 0.077 −0.039 0.114 −0.036 (0.063) (0.035) (0.081) (0.046) (0.062) (0.036) Housework (weekly hrs‐10)/10 −0.001 −0.003 (0.011) (0.006) Labour Market Motivation 0.005 0.004 (0.006) (0.005) No. of obs 5,015 5,535 5,015 5,535 5,015 5,535 4,830 5,306 2,505 3,127 3,622 3,923 R2 0.078 0.055 0.090 0.061 0.091 0.062 0.088 0.060 0.064 0.042 0.085 0.070 Controls for Initial Occupation No No No No No No No No Yes Yes No No P = 0.07 P = 0.14 Implied Gender Gap at 5 yrs 0.136 0.147 0.146 0.096 0.062 0.073 (0.042) (0.042) (0.046) (0.049) (0.095) (0.074) Implied Gender Gap at 10 yrs 0.220 0.183 0.183 0.138 0.124 0.057 (0.045) (0.048) (0.086) (0.091) (0.165) (0.132) . 1 . 2 . 3 . 4 . 5 . 6 . 7 . 8 . 9 . 10 . 11 . 12 . Men Women Men Women Men Women Men Women Men Women Men Women 0 yrs of experience 0.151 0.130 0.172 0.141 0.173 0.141 0.153 0.138 0.118 0.088 0.159 0.150 (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.021) (0.013) (0.018) (0.015) 5 yrs of experience 0.074 0.046 0.077 0.056 0.063 0.043 0.059 0.040 0.049 0.026 0.058 0.047 (0.005) (0.004) (0.005) (0.004) (0.008) (0.006) (0.008) (0.007) (0.010) (0.004) (0.012) (0.008) 10 yrs of experience 0.023 0.032 0.028 0.046 −0.003 0.014 −0.004 0.010 −0.001 0.025 −0.010 0.025 (0.009) (0.008) (0.009) (0.008) (0.014) (0.012) (0.014) (0.012) (0.019) (0.004) (0.018) (0.014) Cost of gap > 1 0.044 0.047 0.046 0.059 0.054 0.066 0.054 0.067 0.119 0.093 0.072 0.049 (0.024) (0.015) (0.024) (0.015) (0.024) (0.016) (0.025) (0.017) (0.029) (0.021) (0.033) (0.017) Part‐Time −0.193 −0.060 −0.194 −0.062 −0.189 −0.060 −0.153 −0.066 −0.173 −0.058 (0.041) (0.011) (0.041) (0.012) (0.042) (0.012) (0.045) (0.014) (0.049) (0.016) Decades of job tenure −0.040 −0.041 −0.037 −0.045 −0.044 −0.068 −0.041 −0.032 (0.017) (0.015) (0.017) (0.015) (0.023) (0.021) (0.019) (0.016) Training in Past Year (yrs) 0.087 0.028 0.119 0.073 0.066 0.021 (0.058) (0.038) (0.091) (0.031) (0.064) (0.051) Training between wage obs (yrs) 0.107 −0.005 0.077 −0.039 0.114 −0.036 (0.063) (0.035) (0.081) (0.046) (0.062) (0.036) Housework (weekly hrs‐10)/10 −0.001 −0.003 (0.011) (0.006) Labour Market Motivation 0.005 0.004 (0.006) (0.005) No. of obs 5,015 5,535 5,015 5,535 5,015 5,535 4,830 5,306 2,505 3,127 3,622 3,923 R2 0.078 0.055 0.090 0.061 0.091 0.062 0.088 0.060 0.064 0.042 0.085 0.070 Controls for Initial Occupation No No No No No No No No Yes Yes No No P = 0.07 P = 0.14 Implied Gender Gap at 5 yrs 0.136 0.147 0.146 0.096 0.062 0.073 (0.042) (0.042) (0.046) (0.049) (0.095) (0.074) Implied Gender Gap at 10 yrs 0.220 0.183 0.183 0.138 0.124 0.057 (0.045) (0.048) (0.086) (0.091) (0.165) (0.132) Notes. These estimates are derived from the estimation of (5) where experience is modeled as a quadratic and, where appropriate, a dummy variable for having intermittent employment, a part‐time dummy, years of job tenure and years of training are included. Standard errors in parentheses. These are robust standard errors with clustering on the individual. In columns 5 to 10 the estimates of the gender gap after 5 and 10 years are based on assuming no change in job so job tenure rises with experience. In columns 7 to 10 the estimates of the gender gap after 5 and 10 years are based on assuming no receipt of on‐the‐job training. Full‐time work is always assumed. In columns 9 and 10 where initial occupation is included as a control, the estimates of wage growth and the gender gap are based on the average using the distribution of initial occupation across both men and women with less than 10 years of experience. Using the male or the female distribution makes no difference to the results. The p‐value for a test of the hypothesis that occupational variables are jointly insignificant from zero is also reported. Open in new tab Table 3 The Impact of Human Capital Variables on the Gender Gap in Wage Growth . 1 . 2 . 3 . 4 . 5 . 6 . 7 . 8 . 9 . 10 . 11 . 12 . Men Women Men Women Men Women Men Women Men Women Men Women 0 yrs of experience 0.151 0.130 0.172 0.141 0.173 0.141 0.153 0.138 0.118 0.088 0.159 0.150 (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.021) (0.013) (0.018) (0.015) 5 yrs of experience 0.074 0.046 0.077 0.056 0.063 0.043 0.059 0.040 0.049 0.026 0.058 0.047 (0.005) (0.004) (0.005) (0.004) (0.008) (0.006) (0.008) (0.007) (0.010) (0.004) (0.012) (0.008) 10 yrs of experience 0.023 0.032 0.028 0.046 −0.003 0.014 −0.004 0.010 −0.001 0.025 −0.010 0.025 (0.009) (0.008) (0.009) (0.008) (0.014) (0.012) (0.014) (0.012) (0.019) (0.004) (0.018) (0.014) Cost of gap > 1 0.044 0.047 0.046 0.059 0.054 0.066 0.054 0.067 0.119 0.093 0.072 0.049 (0.024) (0.015) (0.024) (0.015) (0.024) (0.016) (0.025) (0.017) (0.029) (0.021) (0.033) (0.017) Part‐Time −0.193 −0.060 −0.194 −0.062 −0.189 −0.060 −0.153 −0.066 −0.173 −0.058 (0.041) (0.011) (0.041) (0.012) (0.042) (0.012) (0.045) (0.014) (0.049) (0.016) Decades of job tenure −0.040 −0.041 −0.037 −0.045 −0.044 −0.068 −0.041 −0.032 (0.017) (0.015) (0.017) (0.015) (0.023) (0.021) (0.019) (0.016) Training in Past Year (yrs) 0.087 0.028 0.119 0.073 0.066 0.021 (0.058) (0.038) (0.091) (0.031) (0.064) (0.051) Training between wage obs (yrs) 0.107 −0.005 0.077 −0.039 0.114 −0.036 (0.063) (0.035) (0.081) (0.046) (0.062) (0.036) Housework (weekly hrs‐10)/10 −0.001 −0.003 (0.011) (0.006) Labour Market Motivation 0.005 0.004 (0.006) (0.005) No. of obs 5,015 5,535 5,015 5,535 5,015 5,535 4,830 5,306 2,505 3,127 3,622 3,923 R2 0.078 0.055 0.090 0.061 0.091 0.062 0.088 0.060 0.064 0.042 0.085 0.070 Controls for Initial Occupation No No No No No No No No Yes Yes No No P = 0.07 P = 0.14 Implied Gender Gap at 5 yrs 0.136 0.147 0.146 0.096 0.062 0.073 (0.042) (0.042) (0.046) (0.049) (0.095) (0.074) Implied Gender Gap at 10 yrs 0.220 0.183 0.183 0.138 0.124 0.057 (0.045) (0.048) (0.086) (0.091) (0.165) (0.132) . 1 . 2 . 3 . 4 . 5 . 6 . 7 . 8 . 9 . 10 . 11 . 12 . Men Women Men Women Men Women Men Women Men Women Men Women 0 yrs of experience 0.151 0.130 0.172 0.141 0.173 0.141 0.153 0.138 0.118 0.088 0.159 0.150 (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.021) (0.013) (0.018) (0.015) 5 yrs of experience 0.074 0.046 0.077 0.056 0.063 0.043 0.059 0.040 0.049 0.026 0.058 0.047 (0.005) (0.004) (0.005) (0.004) (0.008) (0.006) (0.008) (0.007) (0.010) (0.004) (0.012) (0.008) 10 yrs of experience 0.023 0.032 0.028 0.046 −0.003 0.014 −0.004 0.010 −0.001 0.025 −0.010 0.025 (0.009) (0.008) (0.009) (0.008) (0.014) (0.012) (0.014) (0.012) (0.019) (0.004) (0.018) (0.014) Cost of gap > 1 0.044 0.047 0.046 0.059 0.054 0.066 0.054 0.067 0.119 0.093 0.072 0.049 (0.024) (0.015) (0.024) (0.015) (0.024) (0.016) (0.025) (0.017) (0.029) (0.021) (0.033) (0.017) Part‐Time −0.193 −0.060 −0.194 −0.062 −0.189 −0.060 −0.153 −0.066 −0.173 −0.058 (0.041) (0.011) (0.041) (0.012) (0.042) (0.012) (0.045) (0.014) (0.049) (0.016) Decades of job tenure −0.040 −0.041 −0.037 −0.045 −0.044 −0.068 −0.041 −0.032 (0.017) (0.015) (0.017) (0.015) (0.023) (0.021) (0.019) (0.016) Training in Past Year (yrs) 0.087 0.028 0.119 0.073 0.066 0.021 (0.058) (0.038) (0.091) (0.031) (0.064) (0.051) Training between wage obs (yrs) 0.107 −0.005 0.077 −0.039 0.114 −0.036 (0.063) (0.035) (0.081) (0.046) (0.062) (0.036) Housework (weekly hrs‐10)/10 −0.001 −0.003 (0.011) (0.006) Labour Market Motivation 0.005 0.004 (0.006) (0.005) No. of obs 5,015 5,535 5,015 5,535 5,015 5,535 4,830 5,306 2,505 3,127 3,622 3,923 R2 0.078 0.055 0.090 0.061 0.091 0.062 0.088 0.060 0.064 0.042 0.085 0.070 Controls for Initial Occupation No No No No No No No No Yes Yes No No P = 0.07 P = 0.14 Implied Gender Gap at 5 yrs 0.136 0.147 0.146 0.096 0.062 0.073 (0.042) (0.042) (0.046) (0.049) (0.095) (0.074) Implied Gender Gap at 10 yrs 0.220 0.183 0.183 0.138 0.124 0.057 (0.045) (0.048) (0.086) (0.091) (0.165) (0.132) Notes. These estimates are derived from the estimation of (5) where experience is modeled as a quadratic and, where appropriate, a dummy variable for having intermittent employment, a part‐time dummy, years of job tenure and years of training are included. Standard errors in parentheses. These are robust standard errors with clustering on the individual. In columns 5 to 10 the estimates of the gender gap after 5 and 10 years are based on assuming no change in job so job tenure rises with experience. In columns 7 to 10 the estimates of the gender gap after 5 and 10 years are based on assuming no receipt of on‐the‐job training. Full‐time work is always assumed. In columns 9 and 10 where initial occupation is included as a control, the estimates of wage growth and the gender gap are based on the average using the distribution of initial occupation across both men and women with less than 10 years of experience. Using the male or the female distribution makes no difference to the results. The p‐value for a test of the hypothesis that occupational variables are jointly insignificant from zero is also reported. Open in new tab But the implied gender pay gap after 10 years (for those without any intermittence) only falls from 24.8 to 22 log points, suggesting a contribution of intermittence of 2.8 log points (this is similar to the estimate from columns 3 and 4 of Table 2). Women accumulate less work experience than men, not just because they have more intermittence but also because they are more likely to work part‐time. For example, in our sample, 3.6% of the men are working part‐time (defined as less than 30 hours a week) and 19.2% of the women. The third and fourth columns of Table 3 include a dummy variable for whether the individual is currently working part‐time. The few men who are working part‐time have a very large penalty of 19.3 log points while the part‐time women have a pay penalty of 6.0%. The implied gender gap after 10 years for those who work continuously full‐time now falls to 18.3% suggesting a contribution of the gender difference in part‐time working of 3.7%. This is probably an over‐estimate as hourly earnings are obtained by dividing weekly earnings by reported hours, leading to a division bias problem that overstates the initital wages of those in part‐time employment. The estimates so far suggest that differences in actual work experience when young can explain at most 30% of the gender gap in early‐career wage growth. The explanation for this is simple: the gender gap in wage growth is largest when the gender gap in accumulated actual labour market experience is rather small. To illustrate this further Figure 4 presents data from the 1970 British Cohort Study, a longitudinal study of all children born in Britain in one week in 1970 and which collects monthly information on labour market status. Figure 4 shows the gender differences in actual experience (measured as months in work) over the first 10 years after labour market entry. Ten years after labour market entry the men have accumulated about 12 months more work experience than the women. The gap in full‐time experience is larger at 20 months. However, for the purpose of understanding the gender pay gap for those in work (as is the focus of this article) these differences are misleading as the pay differences relate to those in work and the gender differences in actual experience are much smaller for those in work. These differences are also plotted on Figure 4: after 10 years male workers have about 3 months more work experience than female workers and about 12 months more full‐time experience. These differences are simply not large enough to explain the magnitude of the gender pay gap after 10 years: for example, if one thinks of the male profile as being the ‘true’ one and women are one year behind men after 10 years this can only account for wage differentials of the order of 2 log points. The contribution is a bit larger if one recognises that the earnings profile is concave so it is not just the mean but also the variance that is of importance and women have slightly higher variance than men. After 10 years the gender difference in the variance of full‐time work experience among workers is about 1 year which adds at most an extra one log point to the predicted gender difference. These back‐of‐the‐envelope calculations are smaller than the regression estimates of Table 3 though, as we have already noted, the contribution of part‐time working is probably over‐estimated. Fig. 4. Open in new tabDownload slide Gender Differences in Accumulated Work ExperienceNotes. These figures come from the 1970 British Cohort Study. We thank Fernando Galindo‐Rueda for allowing us to use the work‐history data he had constructed. Fig. 4. Open in new tabDownload slide Gender Differences in Accumulated Work ExperienceNotes. These figures come from the 1970 British Cohort Study. We thank Fernando Galindo‐Rueda for allowing us to use the work‐history data he had constructed. The discussion so far has focused on the accumulation of general human capital but gender differences in the accumulation of specific human capital might also be important. Job‐specific human capital is often assumed to be measured by job tenure; see Mincer and Jovanovic (1981) for this theoretical argument and Jones and Makepeace (1996) for a British study in which this variable is important for explaining a gender gap. The fifth and sixth columns of Table 3 include job tenure in the current job as an extra control in the wage‐growth equations. We find that higher job tenure is associated with significantly lower wage growth. But, this does little to narrow the gender gap in wage growth on labour market entry and has no impact on the implied gender pay gap after 10 years. 3.2. Gender Differences in On‐The‐Job Training Of course, it may be differences in expected future labour market attachment that affect current human capital accumulation either before or after labour market entry. Let us start with the training after labour market entry. In the BHPS, respondents are asked about the total amount of job‐related training they have done over the previous year. We simply include a measure of the total years of training received as a control variable in our wage growth regression. We might expect that current job‐related training depresses current wages but raises future wages and wage growth. However, the strongest empirical effect of on‐the‐job training seems to come not from the measure of the training received between the two wage observations but from the measure of training received over the year prior to the initial wage observation. So, measures of training in both years are included in the wage growth regressions: the results are presented in columns 7 and 8 of Table 3. The return to a year of job‐related training for men is higher (at approximately 8–10%) than for women (approximately 3% for training received in the past year and zero for training between wage observations). The difference in the return to training is compounded by the fact that young men receive more on‐the‐job training than young women – for example among workers with less than 5 years of potential experience, the men receive on average 0.039 years of training per year and the women 0.032. This seems to be the result of the fact that men (especially those with little education) are much more likely than women to be in apprenticeships (which have a very large training component). Among men who leave school at 16 but are aged 20 or under, 25% are in apprenticeships compared to 10% of women (figures from the 2004 LFS). Figure 5 shows that the gender gap in training is entirely driven by the behaviour of the low‐educated: among college graduates young women receive more training than young men. When one cumulates this one can see that the predicted gender wage gap after 10 years falls to 0.138 from 0.183 when one controls for training, so something like 4.5 log points of the gap after 10 years can be explained by gender differences in the receipt and the return to on‐the‐job training. Fig. 5. Open in new tabDownload slide Gender Differences in On‐the‐Job TrainingNotes. This data comes from the Labour Force Survey for the years 2000–3. Fig. 5. Open in new tabDownload slide Gender Differences in On‐the‐Job TrainingNotes. This data comes from the Labour Force Survey for the years 2000–3. 3.3. Gender Differences in Occupation and Education Now let us consider gender differences in human capital accumulation before labour market entry. In terms of the quantity of education there are few gender differences (see the summary statistics in Table A1). But the type of education may be different, e.g., Brown and Corcoran (1997), Black et al. (2003) for the US and Machin and Puhani (2003) for the UK and Germany document that differences in subject of degree can explain a portion of the gender pay gap among college graduates. Unfortunately the BHPS does not contain detailed information on the subjects studied by college graduates or others. But it does contain information on the occupation of the first job that might be expected to capture much of gender differences in career choices. Columns 9 and 10 of Table 3 include dummy variables for the occupation in the first job so the assumption is that initial choice of occupation has a permanent effect on wage growth.15 Sample sizes preclude using more than 1‐digit occupation controls but there are sizeable gender differences even at this level in the type of job first done. Details are in Table A1 in the data Appendix but the big differences among young workers (with less than 10 years of potential experience) are that 38% of women first enter clerical and secretarial occupations compared to 16% of men, 20% of women enter personal and protective service occupations compared to 8% of men, 2% of women enter craft occupations compared to 23% of men and 7% of women enter elementary occupations compared to 15% of men. In producing the summary statistics on wage growth at different levels of experience and the gender gap we assume a distribution of first jobs equal to the average across men and women with less than 10 years of experience – evaluating at male or female characteristics makes no difference. For both men and women the coefficients on initial occupation are jointly not significantly different from zero though the rejection is marginal for men (the rather curious mix of managerial, professional, clerical and elementary occupations seem to have higher wage growth than the others). But, when it comes to explaining gender differences in wage growth, initial occupation has little explanatory power with the predicted gender pay gap after 10 years falling from 0.138 to 0.124 after controlling for initial occupation.16 However this evidence has only used 1‐digit controls for initial occupation and one might be concerned that more marked differences would emerge if a finer measure of occupation was used. That is not possible because of small sample sizes in the BHPS but we can get some idea of whether this is likely using the much larger sample sizes available in the NES where we can use 3‐digit occupation of which there are approximately 370. Once one goes to this level of disaggregation there is a problem that some occupations are so overwhelmingly dominated by one gender that one has very few observations on the other. To deal with this problem of matching men and women at the 3‐digit occupation level the sample is restricted to occupations that have at least 2% of the minority gender. This excludes from the sample 17.5% of men in 74 occupations (broadly, production managers in manufacturing and construction, senior police and fire officers, drivers of trains, boats, and planes, almost all of construction, almost anything that involves metal‐working, and refuse collection). 9.6% of women are excluded in 9 occupations (broadly, midwives, secretaries, dental nurses, childcare workers and beauticians). We take two approaches to controlling for occupation. In the first we simply include a dummy variable for each occupation in the gender‐specific wage growth equations effectively assuming that occupation only affects the level and not the life‐cycle profile of wage growth. The results are summarised in Panel (a) of Figure 6. The line marked ‘whole sample’ reproduces the raw gender gap in wage growth already shown in Figure 4. The line ‘matched sample’ reports the raw gap in wage growth for those in occupations that are not excluded from the analysis – one notices that the gender gap in wage growth is now much smaller for those aged 16–22. The most likely explanation for this finding is that many of the men who are excluded from the matched sample are in construction and/or metal‐working trades and the young men in particular are doing apprenticeships that are associated with fast wage growth. This is consistent with our earlier finding of gender differences in training receipt among this group. Fig. 6. Open in new tabDownload slide The Impact of Occupational Job Segregation on Gender Differences in Wage Growth.Notes. These data come from the New Earnings Survey for 1996–2001. Occupation is measured at the 3‐digit level. The matched sample excludes those men and women in occupations with very low representation of the opposite sex, as discussed in the text. The regression model simply includes dummy variables for each occupation in the wage growth equations. The reweighting model reweights observations to obtain the desired occupational distribution and computes the gender gap in wage growth for this distribution. Fig. 6. Open in new tabDownload slide The Impact of Occupational Job Segregation on Gender Differences in Wage Growth.Notes. These data come from the New Earnings Survey for 1996–2001. Occupation is measured at the 3‐digit level. The matched sample excludes those men and women in occupations with very low representation of the opposite sex, as discussed in the text. The regression model simply includes dummy variables for each occupation in the wage growth equations. The reweighting model reweights observations to obtain the desired occupational distribution and computes the gender gap in wage growth for this distribution. The remaining two lines in Figure 6(a) show the impact of adjusting the matched sample for differences in the gender distribution of occupation. We show two estimates – one evaluated at the male occupation distribution and one at the female occupation – though they are very similar and similar to the line that does not control for occupational segregation at all. Figure 6(a) suggests a sizeable gender pay gap in early‐career wage growth remains even after controlling for differences in occupation. However, the estimates that lie behind Figure 6(a) assume an impact of occupation on wage growth that can be modelled as a shifting intercept. This is quite restrictive e.g. it might be the case that women, anticipating future labour market withdrawal, go into occupations that offer high initial wage growth and slower wage growth later on. To allow for this possibility we also present – in Figure 6(b)– a reweighting estimator that uses the methodology of diNardo et al. (1996) to reweight the female (male) data to reproduce the male (female) occupational distribution. We then report weighted wage growth by age. The resulting estimates of the gender gap in wage growth are much noisier than the regression‐based estimates (unsurprisingly because less structure is put on the data) but one can still see that virtually all of the observations of the gender gap in wage growth up to the age of 35 are positive. So, differences in occupation do not seem to be able to explain much of the gender‐gap in early‐career wage growth once one has controlled for gender differences in training.17 This conclusion that occupational differences cannot explain much of the gender gap in early‐career wage growth can be reconciled with the findings of Machin and Puhani (2003) that field of study can explain a sizeable part of the gender wage gap among British college graduates. First, there is a small gender wage gap on entry to the labour market among graduates that can be explained almost entirely by differences in field of study but field of study seems to have little impact on wage growth. Secondly, gender differences in curriculum are larger for graduates than non‐graduates, as is found for the US by Brown and Corcoran (1997). 3.4. Gender Differences in Housework and Labour Market Motivation Becker (1985) suggested that part of the gender pay gap can be explained by the fact that women put less effort into paid work even when working the same number of hours because of greater domestic commitments. To investigate this we include as a control in our wage growth equation the number of hours of housework done per week (as expected women in our sample do 10 hours more on average than the men) and a composite measure of labour market motivation; as also used by Vella (1994), Swaffield (2000) and Chevalier (2004). The labour market motivation variable is constructed as a composite measure (standardised to have mean zero and variance one) of how much the respondent agrees or disagrees with 6 statements on the role of women in the labour market.18 To allay fears about how the responses might themselves be influenced by labour market outcomes, we construct this measure using only the earliest recorded response to each of these questions. As might be expected women have, on average, a more positive view about the role of women in the labour market than men and working women a more positive view than non‐working women. Columns 11 and 12 of Table 3 include both these variables in the wage growth regression. The sample size is lower because the question on housework is not asked every wave. Both of these variables have coefficients that are of the expected sign but are insignificantly different from zero for both men and women. The net effect of these variables on the predicted gender gap in wages is small after 5 years but appears large after 10 – however, this is more the result of the smaller sample than the variables themselves and the standard error on the estimate is very large. Hence, we find, with the variables available to us, little evidence for the Becker hypothesis being important. 3.5. Conclusions on the Human Capital Hypothesis The evidence presented here suggests that human capital factors can explain perhaps half of the gender gap in early‐career wage growth. Of the overall 25 log point gap implied after 10 years, 2.8 log points can be explained by differences in labour market intermittence, 3.7% by differences in part‐time working, 4.5% by differences in training (primarily because young men who leave school early are still more likely to do apprenticeships) and perhaps 1.5% by differences in occupational choice. Labour market intermittence and part‐time working is more important between 10 and 20 years after labour market entry for the reason that this is when most women have breaks in employment associated with childcare. The bottom line is that a woman who works continuously full‐time can expect to be 12% behind an equivalent man after 10 years in the labour market. 4. Job‐Shopping Models Part of the rapid wage growth in the years immediately after labour market entry may be the result of job mobility – finding a good match for one's skills or simply finding the good jobs (if, as evidence suggests, there is a sizeable amount of equilibrium wage dispersion). Theoretical analysis of the implications of job search models for the returns to experience and job tenure can be found in Burdett (1978) and Manning (1997, 2000). Topel and Ward (1992) found that for US men something like a third of wage growth in the first 10 years after labour market entry can be ascribed to job mobility. In this process of job‐shopping it seems plausible that there are gender differences. For example Manning (2003a, ch. 7) documents, using the BHPS, that women are more constrained in their job choices (primarily by family commitments), they travel less far to work, again, suggesting a narrower choice of jobs – see Manning (2003b), and their job changes are less motivated by money and are less likely to be ‘for a better job’. All of these factors might be expected to be potential explanations of the gender gap in early‐career wage growth. First, consider gender differences in job mobility. For current purposes we define job mobility rates as the percentage of those currently in employment for whom the next observation is non‐employment or a change in job. Note that a change in job need not be associated with a change in employer so a promotion will count as a job change; see Booth et al. (2003) for an analysis of promotions in the BHPS. Figure 7 presents the overall job mobility rates by experience for men and women. One sees the well‐known pattern that job mobility rates start off very high, then decline before flattening out and rising slightly for the older workers. Women have noticeably higher mobility rates than men between 10 and 25 years of experience i.e. in the prime child‐bearing years. Fig. 7. Open in new tabDownload slide Job Mobility Rates: TotalNotes. These data come from the BHPS. The mobility rate is measured as the fraction of workers who change jobs or leave a job for non‐employment in the coming year. Fig. 7. Open in new tabDownload slide Job Mobility Rates: TotalNotes. These data come from the BHPS. The mobility rate is measured as the fraction of workers who change jobs or leave a job for non‐employment in the coming year. It is likely that there are gender differences in the types of job mobility as well as the level. Although there is an argument (based on a frictionless competitive labour market) that there is no meaningful distinction between quits and lay‐offs (McLaughlin, 1991) most labour economists think there is a useful distinction between quits that are voluntary on the part of workers and lay‐offs that are not. Consistent with this, quits tend to be associated with wage gains while lay‐offs are associated with wage losses (Ruhm, 1991; Jacobson et al., 1993; Kletzer, 1998). The job history files of the BHPS record reasons for why jobs ended and Table 4 tabulates them. 47% of young men leave jobs because they are promoted or get a better job compared to 43% of women.19 15% of male jobs end in redundancy or dismissal compared with only 9% of women. 7% of female jobs end to have a baby or look after other family with, unsurprisingly, this proportion being close to zero for men. To summarise, male jobs are slightly more likely to end with a move to a better job, but men are more at risk of jobs ending because of redundancy. On the other hand, women's jobs are more likely to end to have a child. Table 4 Reasons Given for Jobs Ending Reason given . Men (%) . Women (%) . Promoted 19.1 16.7 Left For Better Job 28.0 26.9 Made Redundant 12.7 7.8 Dismissed or Sacked 3.1 1.4 Temporary Job Ended 10.5 9.8 Took retirement 1.9 2.9 Health Reasons 3.6 4.2 To Have Baby 0.0 3.7 Children/Home care 0.5 3.9 Moved Area 3.6 5.0 Other 17.1 17.1 No. of obs 3,611 3,749 Reason given . Men (%) . Women (%) . Promoted 19.1 16.7 Left For Better Job 28.0 26.9 Made Redundant 12.7 7.8 Dismissed or Sacked 3.1 1.4 Temporary Job Ended 10.5 9.8 Took retirement 1.9 2.9 Health Reasons 3.6 4.2 To Have Baby 0.0 3.7 Children/Home care 0.5 3.9 Moved Area 3.6 5.0 Other 17.1 17.1 No. of obs 3,611 3,749 Notes. Sample is restricted to BHPS from waves 5 on as extra categories are then added. Sample is those with between 0 and 10 years of labour market experience inclusive. Data refer to reasons given for job ending. Open in new tab Table 4 Reasons Given for Jobs Ending Reason given . Men (%) . Women (%) . Promoted 19.1 16.7 Left For Better Job 28.0 26.9 Made Redundant 12.7 7.8 Dismissed or Sacked 3.1 1.4 Temporary Job Ended 10.5 9.8 Took retirement 1.9 2.9 Health Reasons 3.6 4.2 To Have Baby 0.0 3.7 Children/Home care 0.5 3.9 Moved Area 3.6 5.0 Other 17.1 17.1 No. of obs 3,611 3,749 Reason given . Men (%) . Women (%) . Promoted 19.1 16.7 Left For Better Job 28.0 26.9 Made Redundant 12.7 7.8 Dismissed or Sacked 3.1 1.4 Temporary Job Ended 10.5 9.8 Took retirement 1.9 2.9 Health Reasons 3.6 4.2 To Have Baby 0.0 3.7 Children/Home care 0.5 3.9 Moved Area 3.6 5.0 Other 17.1 17.1 No. of obs 3,611 3,749 Notes. Sample is restricted to BHPS from waves 5 on as extra categories are then added. Sample is those with between 0 and 10 years of labour market experience inclusive. Data refer to reasons given for job ending. Open in new tab Not all job changes have reasons associated with them (approximately 12% do not). But, we can get an idea of the relative importance of different mobility rates for different sorts of job change by multiplying the overall mobility rate by the fraction of job changes due to particular reasons (this implicitly assumes that the job changes with no given reason are drawn at random). To prevent information overload we divide reasons for jobs ending into two categories: those that end with a move to a better job and those that do not. Figure 8 presents the experience profiles for changes to a better job for men and women: there are no large gender differences. Figure 9 presents the job mobility rates not to a better job: this is higher for women in the early years of the labour market, primarily because of the role played by family responsibilities. These figures suggest that over the first 10 years in the labour market men can expect 3 good job moves (compared to 2.9 for women) and 2 bad job moves (compared to 2.4 for women). Fig. 9. Open in new tabDownload slide Job Mobility Rates: not for Better JobNotes. As for Figure 7 but with mobility not being defined as being for a better job. Fig. 9. Open in new tabDownload slide Job Mobility Rates: not for Better JobNotes. As for Figure 7 but with mobility not being defined as being for a better job. Fig. 8. Open in new tabDownload slide Job Mobility Rates: for Better JobNotes. As for Figure 7 but with mobility defined as being for a better job. Fig. 8. Open in new tabDownload slide Job Mobility Rates: for Better JobNotes. As for Figure 7 but with mobility defined as being for a better job. Now let us consider the extent to which wage changes are related to different sorts of mobility. There is an existing literature on the impact of different sorts of moves on wage changes that starts with a series of papers in the early 1980s (Bartel, 1980, 1982; Borjas, 1981; Bartel and Borjas, 1981). More recent papers are Topel and Ward (1992) and, with a specific focus on gender differences, Loprest (1992), Crossley et al. (1994), Keith and McWilliams (1997, 1999) and Cobb‐Clark (2001). We examine the impact of job mobility by simply including dummy variables for different sorts of move.20 There are a large number of reasons for moves given in Table 4 and we aggregate these into four categories: moves for a better job (called ‘good moves’), moves because jobs ended in redundancy, dismissal or end of contract (called ‘bad’ moves), moves that end for family reasons (called ‘kid’ moves) and moves for other reasons (called ‘other’ moves). This is relatively crude: for example Gibbons and Katz (1991) provide evidence that those dismissed suffer a greater wage penalty than those made redundant through plant closure. But, we do not have enough data here to do a very fine disaggregation. The first two columns of Table 5 provide estimates of wage growth equations augmented by job mobility variables. For men a ‘good’ move is associated with a wage gain of 8.6% while for women it only produces a gain of 6.3%. This result that the returns to job mobility are lower for women is consistent with the evidence presented in Manning (2003a, ch. 7). ‘Bad’ moves are associated with significant wage losses; 3.9% for men but not for women. For kid moves, there is a very large pay penalty for the small number of men who make such a move but the most interesting finding is that there is no wage penalty associated with such moves for women (the coefficient is actually positive though insignificantly different from zero). This is different from the findings in Waldfogel (1998) or Joshi and Paci (1998) but they use data from a much earlier period and find that the wage penalty is smaller for women with maternity leave entitlements who return to the same employer. As these entitlements have been extended it is possible that now there is a relatively small penalty associated with having children if the woman returns to paid work quickly (there is a penalty to intermittence of employment as captured by the gap variable). Finally the ‘other’ move category has insignificant effects on wage growth. Table 5 The Impact of Job Mobility on Wage Growth . Men . Women . Men . Women . Men . Women . 0 yrs of experience 0.135 0.120 0.130 0.112 0.131 0.107 (0.014) (0.012) (0.014) (0.012) (0.014) (0.012) 5 yrs of experience 0.051 0.029 0.047 0.028 0.047 0.022 (0.008) (0.007) (0.008) (0.006) (0.008) (0.006) 10 yrs of experience −0.005 0.013 −0.001 0.013 −0.001 0.005 (0.015) (0.012) (0.014) (0.012) (0.014) (0.012) Cost of gap > 1 0.031 0.041 −0.002 0.033 −0.001 0.036 (0.037) (0.017) (0.037) (0.019) (0.036) (0.019) Part‐Time −0.193 −0.060 −0.194 −0.061 −0.194 −0.060 (0.043) (0.012) (0.043) (0.012) (0.043) (0.012) Decades of tenure −0.017 −0.031 −0.022 −0.032 −0.023 −0.033 (0.018) (0.015) (0.018) (0.015) (0.018) (0.015) Training in Past Year (yrs) 0.082 0.011 0.081 0.011 0.080 0.011 (0.058) (0.038) (0.059) (0.038) (0.059) (0.038) Training between wage obs (yrs) 0.085 −0.001 0.088 −0.002 0.087 −0.003 (0.066) (0.035) (0.066) (0.035) (0.065) (0.035) Good Move 0.086 0.063 0.087 0.063 0.087 0.063 (0.011) (0.010) (0.011) (0.010) (0.011) (0.010) Bad Move −0.039 0.013 0.008 0.029 (0.022) (0.023) (0.031) (0.027) Bad Move × Decades of experience −0.890 −0.277 −0.813 −0.143 (0.452) (0.240) (0.323) (0.202) Kid Move −0.143 0.041 −0.189 0.033 −0.186 0.035 (0.176) (0.052) (0.182) (0.053) (0.181) (0.053) Other Move −0.032 0.006 −0.037 0.004 −0.037 0.005 (0.021) (0.019) (0.021) (0.019) (0.021) (0.019) No. of Obs 4,762 5,142 4,762 5,142 4,762 5,142 R2 0.105 0.072 0.106 0.073 0.106 0.073 Gender Gap at 5 yrs 0.108 0.087 0.081 (0.052) (0.052) (0.052) Gender Gap at 10 yrs 0.155 0.127 0.122 (0.090) (0.091) (0.091) . Men . Women . Men . Women . Men . Women . 0 yrs of experience 0.135 0.120 0.130 0.112 0.131 0.107 (0.014) (0.012) (0.014) (0.012) (0.014) (0.012) 5 yrs of experience 0.051 0.029 0.047 0.028 0.047 0.022 (0.008) (0.007) (0.008) (0.006) (0.008) (0.006) 10 yrs of experience −0.005 0.013 −0.001 0.013 −0.001 0.005 (0.015) (0.012) (0.014) (0.012) (0.014) (0.012) Cost of gap > 1 0.031 0.041 −0.002 0.033 −0.001 0.036 (0.037) (0.017) (0.037) (0.019) (0.036) (0.019) Part‐Time −0.193 −0.060 −0.194 −0.061 −0.194 −0.060 (0.043) (0.012) (0.043) (0.012) (0.043) (0.012) Decades of tenure −0.017 −0.031 −0.022 −0.032 −0.023 −0.033 (0.018) (0.015) (0.018) (0.015) (0.018) (0.015) Training in Past Year (yrs) 0.082 0.011 0.081 0.011 0.080 0.011 (0.058) (0.038) (0.059) (0.038) (0.059) (0.038) Training between wage obs (yrs) 0.085 −0.001 0.088 −0.002 0.087 −0.003 (0.066) (0.035) (0.066) (0.035) (0.065) (0.035) Good Move 0.086 0.063 0.087 0.063 0.087 0.063 (0.011) (0.010) (0.011) (0.010) (0.011) (0.010) Bad Move −0.039 0.013 0.008 0.029 (0.022) (0.023) (0.031) (0.027) Bad Move × Decades of experience −0.890 −0.277 −0.813 −0.143 (0.452) (0.240) (0.323) (0.202) Kid Move −0.143 0.041 −0.189 0.033 −0.186 0.035 (0.176) (0.052) (0.182) (0.053) (0.181) (0.053) Other Move −0.032 0.006 −0.037 0.004 −0.037 0.005 (0.021) (0.019) (0.021) (0.019) (0.021) (0.019) No. of Obs 4,762 5,142 4,762 5,142 4,762 5,142 R2 0.105 0.072 0.106 0.073 0.106 0.073 Gender Gap at 5 yrs 0.108 0.087 0.081 (0.052) (0.052) (0.052) Gender Gap at 10 yrs 0.155 0.127 0.122 (0.090) (0.091) (0.091) Notes. These estimates are derived from the estimation of (5) where experience is modelled as a quadratic and, where appropriate, years of job tenure and a dummy variable for having intermittent employment is included. Standard errors in parentheses. These are robust standard errors with clustering on the individual. The estimates of the gender gap after 5 and 10 years are based on assuming no change in job so job tenure rises with experience and the job mobility variables are zero. Open in new tab Table 5 The Impact of Job Mobility on Wage Growth . Men . Women . Men . Women . Men . Women . 0 yrs of experience 0.135 0.120 0.130 0.112 0.131 0.107 (0.014) (0.012) (0.014) (0.012) (0.014) (0.012) 5 yrs of experience 0.051 0.029 0.047 0.028 0.047 0.022 (0.008) (0.007) (0.008) (0.006) (0.008) (0.006) 10 yrs of experience −0.005 0.013 −0.001 0.013 −0.001 0.005 (0.015) (0.012) (0.014) (0.012) (0.014) (0.012) Cost of gap > 1 0.031 0.041 −0.002 0.033 −0.001 0.036 (0.037) (0.017) (0.037) (0.019) (0.036) (0.019) Part‐Time −0.193 −0.060 −0.194 −0.061 −0.194 −0.060 (0.043) (0.012) (0.043) (0.012) (0.043) (0.012) Decades of tenure −0.017 −0.031 −0.022 −0.032 −0.023 −0.033 (0.018) (0.015) (0.018) (0.015) (0.018) (0.015) Training in Past Year (yrs) 0.082 0.011 0.081 0.011 0.080 0.011 (0.058) (0.038) (0.059) (0.038) (0.059) (0.038) Training between wage obs (yrs) 0.085 −0.001 0.088 −0.002 0.087 −0.003 (0.066) (0.035) (0.066) (0.035) (0.065) (0.035) Good Move 0.086 0.063 0.087 0.063 0.087 0.063 (0.011) (0.010) (0.011) (0.010) (0.011) (0.010) Bad Move −0.039 0.013 0.008 0.029 (0.022) (0.023) (0.031) (0.027) Bad Move × Decades of experience −0.890 −0.277 −0.813 −0.143 (0.452) (0.240) (0.323) (0.202) Kid Move −0.143 0.041 −0.189 0.033 −0.186 0.035 (0.176) (0.052) (0.182) (0.053) (0.181) (0.053) Other Move −0.032 0.006 −0.037 0.004 −0.037 0.005 (0.021) (0.019) (0.021) (0.019) (0.021) (0.019) No. of Obs 4,762 5,142 4,762 5,142 4,762 5,142 R2 0.105 0.072 0.106 0.073 0.106 0.073 Gender Gap at 5 yrs 0.108 0.087 0.081 (0.052) (0.052) (0.052) Gender Gap at 10 yrs 0.155 0.127 0.122 (0.090) (0.091) (0.091) . Men . Women . Men . Women . Men . Women . 0 yrs of experience 0.135 0.120 0.130 0.112 0.131 0.107 (0.014) (0.012) (0.014) (0.012) (0.014) (0.012) 5 yrs of experience 0.051 0.029 0.047 0.028 0.047 0.022 (0.008) (0.007) (0.008) (0.006) (0.008) (0.006) 10 yrs of experience −0.005 0.013 −0.001 0.013 −0.001 0.005 (0.015) (0.012) (0.014) (0.012) (0.014) (0.012) Cost of gap > 1 0.031 0.041 −0.002 0.033 −0.001 0.036 (0.037) (0.017) (0.037) (0.019) (0.036) (0.019) Part‐Time −0.193 −0.060 −0.194 −0.061 −0.194 −0.060 (0.043) (0.012) (0.043) (0.012) (0.043) (0.012) Decades of tenure −0.017 −0.031 −0.022 −0.032 −0.023 −0.033 (0.018) (0.015) (0.018) (0.015) (0.018) (0.015) Training in Past Year (yrs) 0.082 0.011 0.081 0.011 0.080 0.011 (0.058) (0.038) (0.059) (0.038) (0.059) (0.038) Training between wage obs (yrs) 0.085 −0.001 0.088 −0.002 0.087 −0.003 (0.066) (0.035) (0.066) (0.035) (0.065) (0.035) Good Move 0.086 0.063 0.087 0.063 0.087 0.063 (0.011) (0.010) (0.011) (0.010) (0.011) (0.010) Bad Move −0.039 0.013 0.008 0.029 (0.022) (0.023) (0.031) (0.027) Bad Move × Decades of experience −0.890 −0.277 −0.813 −0.143 (0.452) (0.240) (0.323) (0.202) Kid Move −0.143 0.041 −0.189 0.033 −0.186 0.035 (0.176) (0.052) (0.182) (0.053) (0.181) (0.053) Other Move −0.032 0.006 −0.037 0.004 −0.037 0.005 (0.021) (0.019) (0.021) (0.019) (0.021) (0.019) No. of Obs 4,762 5,142 4,762 5,142 4,762 5,142 R2 0.105 0.072 0.106 0.073 0.106 0.073 Gender Gap at 5 yrs 0.108 0.087 0.081 (0.052) (0.052) (0.052) Gender Gap at 10 yrs 0.155 0.127 0.122 (0.090) (0.091) (0.091) Notes. These estimates are derived from the estimation of (5) where experience is modelled as a quadratic and, where appropriate, years of job tenure and a dummy variable for having intermittent employment is included. Standard errors in parentheses. These are robust standard errors with clustering on the individual. The estimates of the gender gap after 5 and 10 years are based on assuming no change in job so job tenure rises with experience and the job mobility variables are zero. Open in new tab The inclusion of the job mobility variables essentially leaves the implied gender pay after 10 years of continuous employment slightly higher than it was without the job mobility variables (compare columns 1 and 2 of Table 5 with columns 7 and 8 of Table 3). But, the specification used here assumes that there is no heterogeneity in the returns to job mobility. In particular, the theory of job‐shopping would suggest that very young workers will not suffer much a of a wage loss when making a bad move because they have little to lose but older workers will face larger wage losses because they are more likely to have worked themselves into a good job.21 To test this hypothesis we also included an interaction of experience with a bad move – the results are reported in columns 3 and 4 of Table 5. For both men and women this interaction variable is negative though the coefficient is much larger for men (probably because older men have more to lose than older women). As predicted by the theory, the coefficient on the bad move variable itself is insignificantly different from zero (this is the estimate of the cost of a bad move for a worker just entering the labour market). Accordingly the fifth and sixth columns of Table 5 include only the interaction of bad moves with experience. The implied gender pay gap after 10 years for those who remain in the same job now falls to 0.122 suggesting a contribution of job mobility of 1.5 percentage points (comparing with columns 7 and 8 of Table 3). This modest contribution is not because job mobility has no effect on wage growth, just that the differences between young men and women are not that large. Hence, job‐shopping models do not appear to be able to explain more than a small part of the gender gap in early‐career wage growth. 5. Personality and the Gender Pay Gap There is an enormous literature in psychology on the size and nature of gender differences in personality, a literature that spills out into the popular imagination with books of the ‘men are from mars, women from venus’ variety. This literature – see Croson and Gneezy (forthcoming) for an excellent survey for economists – shows that by labour market entry, there are quite significant gender differences in personalities22 (something we confirm with our data below). Recently, economists have become interested in the possibility that these differences can help to understand differences in economic outcomes. There have been applications in a number of areas e.g. Barber and Odean (2001) suggest that the propensity of men to be more over‐confident leads them to be more active traders in the stock market, but the main areas of interest have been the labour market (Eckel and Grossman, 1998; Gneezy and Rustichini, 2004; Gneezy et al., 2003; Babcock and Laschever, 2003; Niederle and Westerlund, 2005). Most of these studies have been in experimental settings – while these can demonstrate the existence of significant differences in behaviour it is hard to translate the results into a portion of the gender pay gap that can be explained in this way. That is what we attempt here. One might hope that psychological differences could help to explain the gender pay gap because a number of recent studies have demonstrated intriguing correlations between earnings and ‘personality’ variables23 (Goldsmith et al., 1997, Feinstein, 2000; Bowles et al., 2001; Nyhus and Pons, 2004). A natural extension of this work is to investigate the hypothesis that psychological differences between men and women on entry into the labour market can explain part of the gender pay gap. The only paper to have tried to do this directly is Mueller and Plug (2004) who use data from 50‐year old high school graduates from Wisconsin. They investigate whether the ‘five‐factor’ model of personality structure can help to explain the gender pay gap in their data, finding only modest effects. But the personality data are contemporaneous with the earnings data (raising issues of reverse causation) and the personality traits used might not be the most important ones in the labour market, making this unlikely to be the last word on the subject. In our study we try to remedy both of these weaknesses. There are a very large number of potential personality traits that one might use to explain the gender pay gap. To put some structure on the issue, we use as a framework the survey by Croson and Gneezy (forthcoming) and the book by Babcock and Laschever (2003). Croson and Gneezy (forthcoming) identify a number of main areas in which men and women seem to have different preferences: Risk‐taking – men seem inclined to take more risks than women possibly because they are less risk‐averse and possibly because their assessment of risks is affected by the fact that men seem to have more self‐esteem Self‐esteem – men seem to have a higher opinion of their own abilities than do women Attitudes to competition – possibly because of the two previous factors but perhaps also because of some other factors, women seem more reluctant than men to enter competitive situations and their performance is affected differently when they do. Other‐regarding behaviour – women seem to be less selfish than men in a number of dimensions and care more about what others think about them. The specific argument put forward by Babcock and Laschever (2003) is that women are less effective than men in negotiation. This gender divide is related to more fundamental psychological differences between men and women, some of which are in the Croson and Gneezy list above (e.g. self‐esteem – see Babcock and Laschever, p. 72) but some that are slightly different e.g. Locus of Control (Babcock and Laschever, p. 23) – men are more likely to feel that their fate can be influenced by their actions. Ambition – men seem to be more career‐oriented than women (Babcock and Laschever, p. 30). In addition we add another set of factors that are sometimes argued to be important (Fisher, 1999). Social skills – women have better ‘soft’ skills then men. The addition of the last set of factors is potentially important because our exercise would have a bias to it if we only sought to model the aspects of personality that put women at a disadvantage in the labour market. The BHPS has collected data on youths living within sample households since wave 4 and asks them, among other things, the ‘psychological’ questions described in Table 6. The answers to these questions show that a higher incidence of lower self‐image and greater self‐doubt in young women as compared to young men. For example, only 13.9% of male youths agreed with the statement ‘I don't have much to be proud of’ compared to 17.5% of females. The lower self‐image for females is shown even more starkly in the responses to the statements ‘I certainly feel useless at times’– 29.1% of male youths agreed compared to 44.8% of females. In addition, 22.6% of male youths compared to 35.3% of females agreed with the statement ‘At times I feel I am no good at all’. These differences might be expected to produce wage differentials, e.g., Babcock and Laschever (2003) argue that low self‐esteem leads women not to negotiate wage rises for themselves. Unfortunately the BHPS panel is not long enough to provide a large enough sample of previously surveyed youths who are then observed earning in the labour market so that we cannot, as yet, use the BHPS for this purpose. Table 6 Gender Differences in Personality Among Teenagers: BHPS Data Percentages agreeing with the statement . Men . Women . Sample . I feel I have a number of good qualities 93.0 91.0 1,883 (1,882) I feel that I do not have much to be proud of 13.9 17.5 1,883 (1,882) I certainly feel useless at times 29.1 44.8 3,948 (3,753) I am able to do things as well as most other people 91.0 90.5 1,883 (1,882) I am a likeable person 93.0 93.6 3,948 (3,753) I can usually solve my own problems 90.2 86.0 1,883 (1,882) All in all, I am inclined to feel I am a failure 8.9 12.1 3,948 (3,753) At times I feel I am no good at all 22.6 35.3 3,948 (3,753) I feel left out of things when I am with friends 15.6 25.0 2,752 (2,679) Percentages agreeing with the statement . Men . Women . Sample . I feel I have a number of good qualities 93.0 91.0 1,883 (1,882) I feel that I do not have much to be proud of 13.9 17.5 1,883 (1,882) I certainly feel useless at times 29.1 44.8 3,948 (3,753) I am able to do things as well as most other people 91.0 90.5 1,883 (1,882) I am a likeable person 93.0 93.6 3,948 (3,753) I can usually solve my own problems 90.2 86.0 1,883 (1,882) All in all, I am inclined to feel I am a failure 8.9 12.1 3,948 (3,753) At times I feel I am no good at all 22.6 35.3 3,948 (3,753) I feel left out of things when I am with friends 15.6 25.0 2,752 (2,679) Notes. In the final column female sample sizes are shown in the parentheses. Rows 1, 2, 4 and 6 includes BHPS waves 9–11. Rows 3, 5, and 7 includes BHPS waves 4–11. Row 9 includes BHPS waves 7–11. ‘Agree’ includes strongly agree and agree. Open in new tab Table 6 Gender Differences in Personality Among Teenagers: BHPS Data Percentages agreeing with the statement . Men . Women . Sample . I feel I have a number of good qualities 93.0 91.0 1,883 (1,882) I feel that I do not have much to be proud of 13.9 17.5 1,883 (1,882) I certainly feel useless at times 29.1 44.8 3,948 (3,753) I am able to do things as well as most other people 91.0 90.5 1,883 (1,882) I am a likeable person 93.0 93.6 3,948 (3,753) I can usually solve my own problems 90.2 86.0 1,883 (1,882) All in all, I am inclined to feel I am a failure 8.9 12.1 3,948 (3,753) At times I feel I am no good at all 22.6 35.3 3,948 (3,753) I feel left out of things when I am with friends 15.6 25.0 2,752 (2,679) Percentages agreeing with the statement . Men . Women . Sample . I feel I have a number of good qualities 93.0 91.0 1,883 (1,882) I feel that I do not have much to be proud of 13.9 17.5 1,883 (1,882) I certainly feel useless at times 29.1 44.8 3,948 (3,753) I am able to do things as well as most other people 91.0 90.5 1,883 (1,882) I am a likeable person 93.0 93.6 3,948 (3,753) I can usually solve my own problems 90.2 86.0 1,883 (1,882) All in all, I am inclined to feel I am a failure 8.9 12.1 3,948 (3,753) At times I feel I am no good at all 22.6 35.3 3,948 (3,753) I feel left out of things when I am with friends 15.6 25.0 2,752 (2,679) Notes. In the final column female sample sizes are shown in the parentheses. Rows 1, 2, 4 and 6 includes BHPS waves 9–11. Rows 3, 5, and 7 includes BHPS waves 4–11. Row 9 includes BHPS waves 7–11. ‘Agree’ includes strongly agree and agree. Open in new tab For this reason we turn to another data set, the British Cohort Study (BCS) that also contains the type of data required to investigate the impact of personality on the gender wage gap. The BCS is a cohort of all individuals born in a single week in 1970; see Joshi and Paci (1998) for more description of this data. Information has been collected on the cohort at a variety of ages: here we use the latest information collected in 2000, for 30‐year‐old individuals that have been (roughly) 10 years in the labour market. In addition we use the detailed information collected before they entered the labour market – during their childhood and adolescence – on their personality. These personality controls are primarily constructed using the cohort members’ questionnaire responses at age 16. This survey contains hundreds of questions that one might conceivably use as a potential explanation of the gender pay gap. This embarrassment of riches presents some problems in deciding which variables to include and exclude. There are so many that one cannot include them all so selection is necessary. If a large number of variables are used there is an obvious danger that the exercise becomes a ‘fishing expedition’ in which one seeks those variables which, if only by chance, happen to be capable of ‘explaining’ the gender pay gap in our data. At the same time, restricting attention to a small set of variables which we have a priori reason to think might be important runs the risk of missing the variables that do matter, a particular danger when, as is the case here, our a priori views are not very strong and also of only estimating attenuated coefficients if there is measurement error in those variables. Our strategy tries to steer a middle course between these two dangers. We start by using a fairly long list of questions that seem most closely related to the factors identified in the existing literature as the main psychological differences between men and women – we then investigate which of these variables are significant in explaining the earnings of men and women separately and finally evaluate the contribution of these variables to the gender pay gap. By only considering the ability of these variables to explain the gender pay gap at the end we hope to minimise the fishing trip aspect of the exercise though variables will be retained based on their significance in explaining earnings. However, in the Appendix we report results from an alternative strategy in which we passively use a very large number of questions combined into modules (as was done in the original questionnaire). Although the primary aim of this Section is to investigate the impact of the personality variables on the gender wage gap, it is important to establish that our earlier findings about the gender pay gap in the BHPS are also found in the BCS. As the BCS only has information on earnings at a few points in time we focus on the gender pay gap in 2000 when the cohort members were aged 30 and had, on average, been in the labour market for approximately 10 years. But, we are estimating the level of the gender pay gap (which can be thought of as cumulated wage growth) rather than direct estimates of the gender gap in wage growth. Table 7 presents some estimates of the gender pay gap using the BCS 2000, We start with the estimation of equations similar to those we have used for the BHPS. The structure of the Table is as follows: in the second column we present the raw gender pay gap for the sample under consideration. The third column then reports the coefficient on a dummy variable for being male when other controls are introduced. The third (fourth) column then reports the results when separate earnings equations for men and women are estimated and the gender pay gap evaluated for a full‐time worker with 10 years of actual experience whose other characteristics match those of the average woman (man).24 Table 7 The Gender Gap at Age 30 (BCS wage data at age 30) Raw Log Wage gap . Wage Gap with controls . Wage Gap evaluated at 10 yrs of actual exp with: . Sample size . Female chars . Male chars . Women (Men) . 1. Basic equation without fertility or experience controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 2. Row 1 plus expected fertility controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 3. Row 2 plus actual experience 0.181 0.159 0.169 0.152 3,281 (0.016) (0.015) (0.002) (0.002) (3,681) 4. Row 2 plus actual full‐time and part‐time experience 0.181 0.119 0.127 0.115 3,281 (0.016) (0.016) (0.002) (0.003) (3,681) 5. Row 4 plus 1‐digit occupation 0.181 0.125 0.121 0.142 3,281 (0.016) (0.017) (0.002) (0.002) (3,681) 6. Basic equation with sample restricted to ‘always in FT employment’ with ‘no kids’ 0.081 0.110 0.121 0.115 1,310 (0.025) (0.023) (0.004) (0.004) (1,589) 7. Row 5 plus 1‐digit occupation 0.081 0.095 0.086 0.107 1,310 (0.025) (0.025) (0.005) (0.005) (1,589) Raw Log Wage gap . Wage Gap with controls . Wage Gap evaluated at 10 yrs of actual exp with: . Sample size . Female chars . Male chars . Women (Men) . 1. Basic equation without fertility or experience controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 2. Row 1 plus expected fertility controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 3. Row 2 plus actual experience 0.181 0.159 0.169 0.152 3,281 (0.016) (0.015) (0.002) (0.002) (3,681) 4. Row 2 plus actual full‐time and part‐time experience 0.181 0.119 0.127 0.115 3,281 (0.016) (0.016) (0.002) (0.003) (3,681) 5. Row 4 plus 1‐digit occupation 0.181 0.125 0.121 0.142 3,281 (0.016) (0.017) (0.002) (0.002) (3,681) 6. Basic equation with sample restricted to ‘always in FT employment’ with ‘no kids’ 0.081 0.110 0.121 0.115 1,310 (0.025) (0.023) (0.004) (0.004) (1,589) 7. Row 5 plus 1‐digit occupation 0.081 0.095 0.086 0.107 1,310 (0.025) (0.025) (0.005) (0.005) (1,589) Notes: The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, qualifications, marital status, ethnic, establishment size, whether a supervisor and future plans for (further) children. Average wage gaps are evaluated at the means of the full‐time only workers. Open in new tab Table 7 The Gender Gap at Age 30 (BCS wage data at age 30) Raw Log Wage gap . Wage Gap with controls . Wage Gap evaluated at 10 yrs of actual exp with: . Sample size . Female chars . Male chars . Women (Men) . 1. Basic equation without fertility or experience controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 2. Row 1 plus expected fertility controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 3. Row 2 plus actual experience 0.181 0.159 0.169 0.152 3,281 (0.016) (0.015) (0.002) (0.002) (3,681) 4. Row 2 plus actual full‐time and part‐time experience 0.181 0.119 0.127 0.115 3,281 (0.016) (0.016) (0.002) (0.003) (3,681) 5. Row 4 plus 1‐digit occupation 0.181 0.125 0.121 0.142 3,281 (0.016) (0.017) (0.002) (0.002) (3,681) 6. Basic equation with sample restricted to ‘always in FT employment’ with ‘no kids’ 0.081 0.110 0.121 0.115 1,310 (0.025) (0.023) (0.004) (0.004) (1,589) 7. Row 5 plus 1‐digit occupation 0.081 0.095 0.086 0.107 1,310 (0.025) (0.025) (0.005) (0.005) (1,589) Raw Log Wage gap . Wage Gap with controls . Wage Gap evaluated at 10 yrs of actual exp with: . Sample size . Female chars . Male chars . Women (Men) . 1. Basic equation without fertility or experience controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 2. Row 1 plus expected fertility controls 0.181 0.163 0.163 0.163 3,281 (0.016) (0.015) (0.015) (0.015) (3,681) 3. Row 2 plus actual experience 0.181 0.159 0.169 0.152 3,281 (0.016) (0.015) (0.002) (0.002) (3,681) 4. Row 2 plus actual full‐time and part‐time experience 0.181 0.119 0.127 0.115 3,281 (0.016) (0.016) (0.002) (0.003) (3,681) 5. Row 4 plus 1‐digit occupation 0.181 0.125 0.121 0.142 3,281 (0.016) (0.017) (0.002) (0.002) (3,681) 6. Basic equation with sample restricted to ‘always in FT employment’ with ‘no kids’ 0.081 0.110 0.121 0.115 1,310 (0.025) (0.023) (0.004) (0.004) (1,589) 7. Row 5 plus 1‐digit occupation 0.081 0.095 0.086 0.107 1,310 (0.025) (0.025) (0.005) (0.005) (1,589) Notes: The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, qualifications, marital status, ethnic, establishment size, whether a supervisor and future plans for (further) children. Average wage gaps are evaluated at the means of the full‐time only workers. Open in new tab The raw gender pay gap is approximately 18 log points for the wage levels as shown in the second column of the first row, somewhat lower than estimates from the BHPS.25 When controls for whether there are children in the household, marital status, race, tenure and qualifications are introduced (but not fertility intentions or actual labour market experience) the estimated gender wage gap falls to approximately 16 log points (row 1, column 3). The second row adds in the expected future fertility controls – a dummy for whether the individual is planning (further) children in the future but the impact of this control is negligible. In the third row a quadratic for total labour market experience is included. This has a relatively small impact on the wage gap estimates, reducing slightly the wage gap with controls and the wage gap evaluated with male characteristics. By comparison, the inclusion of full‐time and part‐time actual labour market experience in the row below (row 4) has a much greater impact on the wage gap estimates. The estimated gender wage gap with controls (column 3) falls by roughly 4 log points. The unexplained pay gap also falls by roughly 4 log points. The fourth row includes 1‐digit occupation controls – the unexplained gender gap rises when evaluated for the average man by almost 3 log points. The figures based on the female evaluated wage gaps are similar to the wage gap estimates (with controls) in the previous (third) column. All of these estimates suggest a pay gap in the region of 11–16% between British men and women aged 30 in 2000 who have spent all their careers in full‐time employment, an estimate similar to that derived from the BHPS earlier in the article. To illustrate this even more starkly, the fifth row restricts the sample to those individuals who at age 30 have not had any children and who have had every month in full‐time employment since leaving full‐time education. These are individuals whose commitment to the labour force cannot be questioned. The raw gender pay gap for this group is 8.1 log points but the adjusted gender pay gaps are larger at 11%, primarily because women in this group are better educated than the men. It should be noted that there is no correction for sample selection bias here ‐ such a correction is likely to make the adjusted gender pay gap even bigger as the ‘career’ women are likely to be much more positively selected than the ‘career’ men. The final row shows that the inclusion of 1‐digit occupation makes little difference to the conclusion for this group. These estimates are also in line with the unexplained gender pay gap reported in Dolton et al. (2002). Having established that the BHPS and BCS data lead to similar conclusions, we turn to the impact of personality variables on the gender pay gap – first, consider the pro‐active strategy. Table 8 provides the list of variables that we use, divided into six categories – risk aversion, competitiveness, self‐esteem, other‐regarding behaviour, career orientation and an ‘other’ category. It is very likely that some of these variables could arguably be placed in other categories from the one we have chosen but as this categorisation is mainly for ease of exposition this does not matter greatly. Table 8 also shows the gender differences in the responses to these questions. For the most part these differences are in line with what a reading of the literature on gender differences would suggest. Table 8 Gender Differences in Psychological Variables: BCS Data at Age 16 Exact Question . Responses . Gender Difference in Response [standard error] . Risk Aversion How often fasten seat belt in front seat? 0 = every time  1 = not every time −0.039 [0.010]** Done first‐aid course in past two years? 0 = yes  1 = no −0.023 [0.012] How interested are you in safety at home? −1 = very interested  0 = somewhat interested  1 = not interested −0.185 [0.019]** How interested are you in safety in traffic? −1 = very interested  0 = somewhat interested  1 = not interested −0.058 [0.019]** How interested are you in water safety? −1 = very interested  0 = somewhat interested  1 = not interested −0.03 [0.020] When did you last smoke? −1 = never  0 = 3 months ago or more  1 = more recently 0.101 [0.022]** How often drink alcohol in past year? 0 = never/special occasions  1 = no more than once a week  2 = more than once a week −0.14 [0.019]** Competitiveness Think fighting is wrong 0 = agree  1 = don't know  2 = disagree −0.302 [0.020]** No. of team sports participate in Count −0.101 [0.011]** No. of individual sports participate in Count 0.019 [0.008]* I am keen on sports 0 = does not apply  1 = applies somewhat  2 = applies very much −0.384 [0.020]** How often do you play card/board games? 0 = rarely/never  1 = sometimes −0.151 [0.013]** How often do you play electronic games? 0 = rarely/never  1 = sometimes −0.228 [0.012]** How often do you play sports? 0 = rarely/never  1 = sometimes −0.216 [0.013]** Self‐Esteem I am clever 0 = does not apply  1 = applies somewhat  2 = applies very much −0.182 [0.015]** In comparison with others my job prospects are −2 = much less  … 2 = much more −0.111 [0.026]** In comparison with others I get worried −2 = much more  … 2 = much less −0.337 [0.027]** Do you think of yourself as a worthless person 0 = not at all  1 = sometimes 0.086 [0.013]** Have you been feeling unhappy or depressed 0 = not at all  1 = sometimes 0.107 [0.014]** Have you been losing confidence in yourself 0 = not at all  1 = sometimes 0.115 [0.013]** Do you feel miserable or depressed 0 = sometimes  1 = rarely or never 0.199 [0.013]** Other‐regarding Behaviour Do you care what your mother thinks about you? 0 = cares a lot  1 = cares a little/not at all −0.139 [0.011]** Do you go out of your way to help others in trouble 0 = yes  1 = no −0.111 [0.013]** In your job how much does it matter to you to be able to help others −1 = very much, 0 = somewhat  1 = not at all −0.241 [0.016]** Are you interested in old people's needs? −1 = very interested…..  2 = not at all interested −0.459 [0.023]** Are you interested in handicapped people's needs? −1 = very interested ..  2 = not at all interested −0.52 [0.024]** Are you always nice to people 0 = no  1 = yes 0.031 [0.012]** I am helpful −1 = does not apply  0 = applies somewhat  1 = applies very much 0.103 [0.014]** Do you do volunteer/community work 0 = rarely/never  1 = sometimes/often 0.053 [0.011]** It is up to Africans to grow food for themselves 0 = disagree  1 = agree −0.172 [0.014]** I am always willing to help the teacher −1 = not true at all  0 = partly true  1 = very true 0.104 [0.016]** Career Orientation In your future job, how important is it to have high earnings? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.157 [0.016]** In your future job, how important is it to get ahead? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.069 [0.018]** Does getting married matter to you? −1 = matters very much  0 = matters somewhat  1 = doesn't matter 0.019 [0.021] Does having children of your own matter to you? −1 = matters very much 0 = matters somewhat 1 = doesn't matter −0.085 [0.020]** Women can do the same jobs as men 0 = agree fully 1 = agree partly 2 = disagree −0.412 [0.017]** Women's lib is a good thing −1 = agree fully 0 = agree partly 1 = disagree −0.272 [0.017]** Mother's attitudes to women's place in society −0.015 [0.014] Locus of Control Do you feel that wishing can make good things happen? 0 = yes 1 = don't know 2 = no −0.069 [0.029]** Is a high mark just a matter of luck for you? 0 = yes 1 = don't know 2 = no −0.146 [0.027]** Are tests just a lot of guess work for you? 0 = yes 1 = don't know 2 = no −0.113 [0.025]** Are you a person who believes that planning ahead makes things turn out better? 0 = yes 1 = don't know 2 = no 0.173 [0.031]** When bad things happen to you is it usually someone else's fault? 0 = yes 1 = don't know 2 = no 0.171 [0.027]** When nice things happen to you is it only good luck? 0 = yes 1 = don't know 2 = no 0.052 [0.026]** Do you think studying for tests is a waste of time? 0 = yes 1 = don't know 2 = no 0.079 [0.021]** Other variables I am violent 0 = does not apply 1 = applies somewhat/very much −0.12 [0.011]** I am reliable 0 = applies very much 1 = applies somewhat/does not apply −0.054 [0.013]** I am popular −1 = does not apply 0 = applies somewhat 1 = applies very much −0.029 [0.015]* I am friendly −1 = does not apply 0 = applies somewhat 1 = applies very much 0.099 [0.014]** I am lazy −1 = does not apply 0 = applies somewhat 1 = applies very much −0.057 [0.017]** I am punctual −1 = does not apply 0 = applies somewhat 1 = applies very much 0.021 [0.018] I am obedient −1 = does not apply 0 = applies somewhat 1 = applies very much 0.043 [0.015]** Exact Question . Responses . Gender Difference in Response [standard error] . Risk Aversion How often fasten seat belt in front seat? 0 = every time  1 = not every time −0.039 [0.010]** Done first‐aid course in past two years? 0 = yes  1 = no −0.023 [0.012] How interested are you in safety at home? −1 = very interested  0 = somewhat interested  1 = not interested −0.185 [0.019]** How interested are you in safety in traffic? −1 = very interested  0 = somewhat interested  1 = not interested −0.058 [0.019]** How interested are you in water safety? −1 = very interested  0 = somewhat interested  1 = not interested −0.03 [0.020] When did you last smoke? −1 = never  0 = 3 months ago or more  1 = more recently 0.101 [0.022]** How often drink alcohol in past year? 0 = never/special occasions  1 = no more than once a week  2 = more than once a week −0.14 [0.019]** Competitiveness Think fighting is wrong 0 = agree  1 = don't know  2 = disagree −0.302 [0.020]** No. of team sports participate in Count −0.101 [0.011]** No. of individual sports participate in Count 0.019 [0.008]* I am keen on sports 0 = does not apply  1 = applies somewhat  2 = applies very much −0.384 [0.020]** How often do you play card/board games? 0 = rarely/never  1 = sometimes −0.151 [0.013]** How often do you play electronic games? 0 = rarely/never  1 = sometimes −0.228 [0.012]** How often do you play sports? 0 = rarely/never  1 = sometimes −0.216 [0.013]** Self‐Esteem I am clever 0 = does not apply  1 = applies somewhat  2 = applies very much −0.182 [0.015]** In comparison with others my job prospects are −2 = much less  … 2 = much more −0.111 [0.026]** In comparison with others I get worried −2 = much more  … 2 = much less −0.337 [0.027]** Do you think of yourself as a worthless person 0 = not at all  1 = sometimes 0.086 [0.013]** Have you been feeling unhappy or depressed 0 = not at all  1 = sometimes 0.107 [0.014]** Have you been losing confidence in yourself 0 = not at all  1 = sometimes 0.115 [0.013]** Do you feel miserable or depressed 0 = sometimes  1 = rarely or never 0.199 [0.013]** Other‐regarding Behaviour Do you care what your mother thinks about you? 0 = cares a lot  1 = cares a little/not at all −0.139 [0.011]** Do you go out of your way to help others in trouble 0 = yes  1 = no −0.111 [0.013]** In your job how much does it matter to you to be able to help others −1 = very much, 0 = somewhat  1 = not at all −0.241 [0.016]** Are you interested in old people's needs? −1 = very interested…..  2 = not at all interested −0.459 [0.023]** Are you interested in handicapped people's needs? −1 = very interested ..  2 = not at all interested −0.52 [0.024]** Are you always nice to people 0 = no  1 = yes 0.031 [0.012]** I am helpful −1 = does not apply  0 = applies somewhat  1 = applies very much 0.103 [0.014]** Do you do volunteer/community work 0 = rarely/never  1 = sometimes/often 0.053 [0.011]** It is up to Africans to grow food for themselves 0 = disagree  1 = agree −0.172 [0.014]** I am always willing to help the teacher −1 = not true at all  0 = partly true  1 = very true 0.104 [0.016]** Career Orientation In your future job, how important is it to have high earnings? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.157 [0.016]** In your future job, how important is it to get ahead? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.069 [0.018]** Does getting married matter to you? −1 = matters very much  0 = matters somewhat  1 = doesn't matter 0.019 [0.021] Does having children of your own matter to you? −1 = matters very much 0 = matters somewhat 1 = doesn't matter −0.085 [0.020]** Women can do the same jobs as men 0 = agree fully 1 = agree partly 2 = disagree −0.412 [0.017]** Women's lib is a good thing −1 = agree fully 0 = agree partly 1 = disagree −0.272 [0.017]** Mother's attitudes to women's place in society −0.015 [0.014] Locus of Control Do you feel that wishing can make good things happen? 0 = yes 1 = don't know 2 = no −0.069 [0.029]** Is a high mark just a matter of luck for you? 0 = yes 1 = don't know 2 = no −0.146 [0.027]** Are tests just a lot of guess work for you? 0 = yes 1 = don't know 2 = no −0.113 [0.025]** Are you a person who believes that planning ahead makes things turn out better? 0 = yes 1 = don't know 2 = no 0.173 [0.031]** When bad things happen to you is it usually someone else's fault? 0 = yes 1 = don't know 2 = no 0.171 [0.027]** When nice things happen to you is it only good luck? 0 = yes 1 = don't know 2 = no 0.052 [0.026]** Do you think studying for tests is a waste of time? 0 = yes 1 = don't know 2 = no 0.079 [0.021]** Other variables I am violent 0 = does not apply 1 = applies somewhat/very much −0.12 [0.011]** I am reliable 0 = applies very much 1 = applies somewhat/does not apply −0.054 [0.013]** I am popular −1 = does not apply 0 = applies somewhat 1 = applies very much −0.029 [0.015]* I am friendly −1 = does not apply 0 = applies somewhat 1 = applies very much 0.099 [0.014]** I am lazy −1 = does not apply 0 = applies somewhat 1 = applies very much −0.057 [0.017]** I am punctual −1 = does not apply 0 = applies somewhat 1 = applies very much 0.021 [0.018] I am obedient −1 = does not apply 0 = applies somewhat 1 = applies very much 0.043 [0.015]** Notes. The sample size varies with the question but is generally in the region of 2500. Open in new tab Table 8 Gender Differences in Psychological Variables: BCS Data at Age 16 Exact Question . Responses . Gender Difference in Response [standard error] . Risk Aversion How often fasten seat belt in front seat? 0 = every time  1 = not every time −0.039 [0.010]** Done first‐aid course in past two years? 0 = yes  1 = no −0.023 [0.012] How interested are you in safety at home? −1 = very interested  0 = somewhat interested  1 = not interested −0.185 [0.019]** How interested are you in safety in traffic? −1 = very interested  0 = somewhat interested  1 = not interested −0.058 [0.019]** How interested are you in water safety? −1 = very interested  0 = somewhat interested  1 = not interested −0.03 [0.020] When did you last smoke? −1 = never  0 = 3 months ago or more  1 = more recently 0.101 [0.022]** How often drink alcohol in past year? 0 = never/special occasions  1 = no more than once a week  2 = more than once a week −0.14 [0.019]** Competitiveness Think fighting is wrong 0 = agree  1 = don't know  2 = disagree −0.302 [0.020]** No. of team sports participate in Count −0.101 [0.011]** No. of individual sports participate in Count 0.019 [0.008]* I am keen on sports 0 = does not apply  1 = applies somewhat  2 = applies very much −0.384 [0.020]** How often do you play card/board games? 0 = rarely/never  1 = sometimes −0.151 [0.013]** How often do you play electronic games? 0 = rarely/never  1 = sometimes −0.228 [0.012]** How often do you play sports? 0 = rarely/never  1 = sometimes −0.216 [0.013]** Self‐Esteem I am clever 0 = does not apply  1 = applies somewhat  2 = applies very much −0.182 [0.015]** In comparison with others my job prospects are −2 = much less  … 2 = much more −0.111 [0.026]** In comparison with others I get worried −2 = much more  … 2 = much less −0.337 [0.027]** Do you think of yourself as a worthless person 0 = not at all  1 = sometimes 0.086 [0.013]** Have you been feeling unhappy or depressed 0 = not at all  1 = sometimes 0.107 [0.014]** Have you been losing confidence in yourself 0 = not at all  1 = sometimes 0.115 [0.013]** Do you feel miserable or depressed 0 = sometimes  1 = rarely or never 0.199 [0.013]** Other‐regarding Behaviour Do you care what your mother thinks about you? 0 = cares a lot  1 = cares a little/not at all −0.139 [0.011]** Do you go out of your way to help others in trouble 0 = yes  1 = no −0.111 [0.013]** In your job how much does it matter to you to be able to help others −1 = very much, 0 = somewhat  1 = not at all −0.241 [0.016]** Are you interested in old people's needs? −1 = very interested…..  2 = not at all interested −0.459 [0.023]** Are you interested in handicapped people's needs? −1 = very interested ..  2 = not at all interested −0.52 [0.024]** Are you always nice to people 0 = no  1 = yes 0.031 [0.012]** I am helpful −1 = does not apply  0 = applies somewhat  1 = applies very much 0.103 [0.014]** Do you do volunteer/community work 0 = rarely/never  1 = sometimes/often 0.053 [0.011]** It is up to Africans to grow food for themselves 0 = disagree  1 = agree −0.172 [0.014]** I am always willing to help the teacher −1 = not true at all  0 = partly true  1 = very true 0.104 [0.016]** Career Orientation In your future job, how important is it to have high earnings? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.157 [0.016]** In your future job, how important is it to get ahead? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.069 [0.018]** Does getting married matter to you? −1 = matters very much  0 = matters somewhat  1 = doesn't matter 0.019 [0.021] Does having children of your own matter to you? −1 = matters very much 0 = matters somewhat 1 = doesn't matter −0.085 [0.020]** Women can do the same jobs as men 0 = agree fully 1 = agree partly 2 = disagree −0.412 [0.017]** Women's lib is a good thing −1 = agree fully 0 = agree partly 1 = disagree −0.272 [0.017]** Mother's attitudes to women's place in society −0.015 [0.014] Locus of Control Do you feel that wishing can make good things happen? 0 = yes 1 = don't know 2 = no −0.069 [0.029]** Is a high mark just a matter of luck for you? 0 = yes 1 = don't know 2 = no −0.146 [0.027]** Are tests just a lot of guess work for you? 0 = yes 1 = don't know 2 = no −0.113 [0.025]** Are you a person who believes that planning ahead makes things turn out better? 0 = yes 1 = don't know 2 = no 0.173 [0.031]** When bad things happen to you is it usually someone else's fault? 0 = yes 1 = don't know 2 = no 0.171 [0.027]** When nice things happen to you is it only good luck? 0 = yes 1 = don't know 2 = no 0.052 [0.026]** Do you think studying for tests is a waste of time? 0 = yes 1 = don't know 2 = no 0.079 [0.021]** Other variables I am violent 0 = does not apply 1 = applies somewhat/very much −0.12 [0.011]** I am reliable 0 = applies very much 1 = applies somewhat/does not apply −0.054 [0.013]** I am popular −1 = does not apply 0 = applies somewhat 1 = applies very much −0.029 [0.015]* I am friendly −1 = does not apply 0 = applies somewhat 1 = applies very much 0.099 [0.014]** I am lazy −1 = does not apply 0 = applies somewhat 1 = applies very much −0.057 [0.017]** I am punctual −1 = does not apply 0 = applies somewhat 1 = applies very much 0.021 [0.018] I am obedient −1 = does not apply 0 = applies somewhat 1 = applies very much 0.043 [0.015]** Exact Question . Responses . Gender Difference in Response [standard error] . Risk Aversion How often fasten seat belt in front seat? 0 = every time  1 = not every time −0.039 [0.010]** Done first‐aid course in past two years? 0 = yes  1 = no −0.023 [0.012] How interested are you in safety at home? −1 = very interested  0 = somewhat interested  1 = not interested −0.185 [0.019]** How interested are you in safety in traffic? −1 = very interested  0 = somewhat interested  1 = not interested −0.058 [0.019]** How interested are you in water safety? −1 = very interested  0 = somewhat interested  1 = not interested −0.03 [0.020] When did you last smoke? −1 = never  0 = 3 months ago or more  1 = more recently 0.101 [0.022]** How often drink alcohol in past year? 0 = never/special occasions  1 = no more than once a week  2 = more than once a week −0.14 [0.019]** Competitiveness Think fighting is wrong 0 = agree  1 = don't know  2 = disagree −0.302 [0.020]** No. of team sports participate in Count −0.101 [0.011]** No. of individual sports participate in Count 0.019 [0.008]* I am keen on sports 0 = does not apply  1 = applies somewhat  2 = applies very much −0.384 [0.020]** How often do you play card/board games? 0 = rarely/never  1 = sometimes −0.151 [0.013]** How often do you play electronic games? 0 = rarely/never  1 = sometimes −0.228 [0.012]** How often do you play sports? 0 = rarely/never  1 = sometimes −0.216 [0.013]** Self‐Esteem I am clever 0 = does not apply  1 = applies somewhat  2 = applies very much −0.182 [0.015]** In comparison with others my job prospects are −2 = much less  … 2 = much more −0.111 [0.026]** In comparison with others I get worried −2 = much more  … 2 = much less −0.337 [0.027]** Do you think of yourself as a worthless person 0 = not at all  1 = sometimes 0.086 [0.013]** Have you been feeling unhappy or depressed 0 = not at all  1 = sometimes 0.107 [0.014]** Have you been losing confidence in yourself 0 = not at all  1 = sometimes 0.115 [0.013]** Do you feel miserable or depressed 0 = sometimes  1 = rarely or never 0.199 [0.013]** Other‐regarding Behaviour Do you care what your mother thinks about you? 0 = cares a lot  1 = cares a little/not at all −0.139 [0.011]** Do you go out of your way to help others in trouble 0 = yes  1 = no −0.111 [0.013]** In your job how much does it matter to you to be able to help others −1 = very much, 0 = somewhat  1 = not at all −0.241 [0.016]** Are you interested in old people's needs? −1 = very interested…..  2 = not at all interested −0.459 [0.023]** Are you interested in handicapped people's needs? −1 = very interested ..  2 = not at all interested −0.52 [0.024]** Are you always nice to people 0 = no  1 = yes 0.031 [0.012]** I am helpful −1 = does not apply  0 = applies somewhat  1 = applies very much 0.103 [0.014]** Do you do volunteer/community work 0 = rarely/never  1 = sometimes/often 0.053 [0.011]** It is up to Africans to grow food for themselves 0 = disagree  1 = agree −0.172 [0.014]** I am always willing to help the teacher −1 = not true at all  0 = partly true  1 = very true 0.104 [0.016]** Career Orientation In your future job, how important is it to have high earnings? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.157 [0.016]** In your future job, how important is it to get ahead? −1 = doesn't matter  0 = matters somewhat  1 = matters very much −0.069 [0.018]** Does getting married matter to you? −1 = matters very much  0 = matters somewhat  1 = doesn't matter 0.019 [0.021] Does having children of your own matter to you? −1 = matters very much 0 = matters somewhat 1 = doesn't matter −0.085 [0.020]** Women can do the same jobs as men 0 = agree fully 1 = agree partly 2 = disagree −0.412 [0.017]** Women's lib is a good thing −1 = agree fully 0 = agree partly 1 = disagree −0.272 [0.017]** Mother's attitudes to women's place in society −0.015 [0.014] Locus of Control Do you feel that wishing can make good things happen? 0 = yes 1 = don't know 2 = no −0.069 [0.029]** Is a high mark just a matter of luck for you? 0 = yes 1 = don't know 2 = no −0.146 [0.027]** Are tests just a lot of guess work for you? 0 = yes 1 = don't know 2 = no −0.113 [0.025]** Are you a person who believes that planning ahead makes things turn out better? 0 = yes 1 = don't know 2 = no 0.173 [0.031]** When bad things happen to you is it usually someone else's fault? 0 = yes 1 = don't know 2 = no 0.171 [0.027]** When nice things happen to you is it only good luck? 0 = yes 1 = don't know 2 = no 0.052 [0.026]** Do you think studying for tests is a waste of time? 0 = yes 1 = don't know 2 = no 0.079 [0.021]** Other variables I am violent 0 = does not apply 1 = applies somewhat/very much −0.12 [0.011]** I am reliable 0 = applies very much 1 = applies somewhat/does not apply −0.054 [0.013]** I am popular −1 = does not apply 0 = applies somewhat 1 = applies very much −0.029 [0.015]* I am friendly −1 = does not apply 0 = applies somewhat 1 = applies very much 0.099 [0.014]** I am lazy −1 = does not apply 0 = applies somewhat 1 = applies very much −0.057 [0.017]** I am punctual −1 = does not apply 0 = applies somewhat 1 = applies very much 0.021 [0.018] I am obedient −1 = does not apply 0 = applies somewhat 1 = applies very much 0.043 [0.015]** Notes. The sample size varies with the question but is generally in the region of 2500. Open in new tab As measures of attitudes towards risk, we use variables relating to whether the respondent normally wears a seat belt, whether they have taken a first aid course, their interest in safety at home, in traffic and in the water, and whether they smoke and drink alcohol.26 These variables are similar to others that have been used to proxy risk aversion in other studies and there does seem some link between them and more direct measures of risk aversion (Dohmen et al., 2005). Women are significantly more likely than men to wear a seatbelt, be interested in safety and drink less, all suggesting more risk aversion. However, young women smoke significantly more than young men. The next panel of Table 8 looks at variables to proxy for competitiveness. Here we use variables related to thinking that fighting (a form of conflict) can be exciting, the number of team and individual sports in which the respondent participates, the frequency of participation in sport and how often they play cards and electronic games. Again, we see significant gender differences of the type we would expect if women are less enthusiastic about competition – they play fewer sports and other games, enjoy them less and are less likely to think fighting can be exciting. The third panel of Table 8 looks at variables to proxy for self‐esteem. Here we use variables about whether the respondent has confidence in themselves, whether they think they are clever,27 whether they think they are worthless, whether they have been feeling unhappy or miserable and how they rate their job prospects. In all these dimensions women seem significantly less self‐confident than men and more prone to feeling unhappy or depressed. The fourth panel of Table 8 considers other‐regarding behaviour – whether the respondent cares about what their mother thinks about them, various measures of whether they want to help others, whether they do volunteer work and whether Africans should grow food for themselves. In all of these measures, women are significantly more likely than men to report caring about others. The fifth panel of Table 8 reports the measures we use to capture career orientation. We use questions about the importance of full‐time work, high wages, ‘getting ahead’ and also marriage and having children. We also include in this Section variables that capture attitudes to women's role in society generally – whether ‘women's lib’ is a good thing and also a composite measure of the mother's attitudes to work/home from the questionnaire at age 5. Among these variables we find women caring significantly less about work, more about children but also having more positive views about gender equality than men. The sixth panel of Table 8 reports the variables used to capture the ‘locus of control’ idea that women are less likely than men to think their destiny is in their hands. We use questions about whether things that happen are a matter of luck, the fault of others or whether outcomes can be influenced by one's own actions. Women are more likely to think that high marks are a matter of luck, that tests are guesswork, not to believe in planning ahead. But, at the same time women are less likely to think studying for tests is a waste of time, suggesting they are not always fatalistic about outcomes. Indeed the fact that girls are, on average, more diligent in their school work than boys, might be thought something of a problem for the locus of control idea. The final panel of Table 8 considers a collection of other traits that might be thought to affect employability – whether the individual is violent, reliable, popular, friendly, lazy, punctual and obedient. On most of these criteria women are significantly more likely than men to have the ‘desirable’ characteristic.28 The results in Table 8 show that it is very easy to find significant gender differences in personality traits along the lines suggested by the existing literature. But how much of the gender pay gap can be explained in this way? – this depends on whether these traits have a significant effect on wages. This is the question addressed in Table 9 where we report results from estimating separate male and female wage equations and decomposing the difference using the standard Oaxaca approach. We could include all the variables (and we do report a specification that does this) but requiring responses to all variables drastically reduces sample sizes. So, Table 9 first reports the results including the psychological variables one category at a time. Because the sample size varies according to the block considered we report the raw log wage gap in the sample considered, the portion of the wage gap that can be explained at male and female coefficients (using the standard Oaxaca approach), and the portion that can be explained by gender differences in the personality traits. Finally we also report a p‐value of an F‐test of the hypothesis that the psychological variables are jointly zero in both female and male wage equations. Table 9 The Impact of Psychological Variables On Wages at Age 30 . Raw Log Wage gap (standard error) . Explained Wage Gap . Contribution of psychological variables . p‐value for psych variables . Sample size . Female co‐efficients . Male co‐efficients . Female co‐efficients . Male co‐efficients . Female (Male) . Female (Male) . 1. Risk 0.168 0.071 0.067 0.014 0.005 0.48 1184 (0.030) (0.008) (0.009) (0.74) (952) 2. Competitiveness 0.166 0.075 0.098 0.017 0.037 0.022 1203 (0.030) (0.019) (0.019) (0.253) (963) 3. Self‐Esteem 0.179 0.085 0.087 0.018 0.034 0.108 1421 (0.030) (0.010) (0.010) (0.040) (1091) 4. Other‐regarding 0.195 0.095 0.031 0.009 −0.039 0.215 1382 (0.028) (0.014) (0.015) (0.001) (1041) 5. Career Orientation 0.178 0.109 0.055 0.043 0.002 0.030 1320 (0.029) (0.017) (0.015) (0.459) (1013) 6. Locus of Control 0.176 0.072 0.069 0.008 0.006 0.34 1507 (0.027) (0.008) (0.008) (0.897) (1183) 7. Other 0.182 0.061 0.046 −0.007 −0.011 0.103 1511 (0.027) (0.007) (0.006) (0.387) (1209) 8. All Variables 0.203 0.123 0.091 0.056 0.005 0.087 724 (0.041) (0.045) (0.040) (0.016) (536) 9. Selected Variables 0.177 0.114 0.076 0.043 0.009 0.001 1459 (0.027) (0.012) (0.013) (0.001) (1123) . Raw Log Wage gap (standard error) . Explained Wage Gap . Contribution of psychological variables . p‐value for psych variables . Sample size . Female co‐efficients . Male co‐efficients . Female co‐efficients . Male co‐efficients . Female (Male) . Female (Male) . 1. Risk 0.168 0.071 0.067 0.014 0.005 0.48 1184 (0.030) (0.008) (0.009) (0.74) (952) 2. Competitiveness 0.166 0.075 0.098 0.017 0.037 0.022 1203 (0.030) (0.019) (0.019) (0.253) (963) 3. Self‐Esteem 0.179 0.085 0.087 0.018 0.034 0.108 1421 (0.030) (0.010) (0.010) (0.040) (1091) 4. Other‐regarding 0.195 0.095 0.031 0.009 −0.039 0.215 1382 (0.028) (0.014) (0.015) (0.001) (1041) 5. Career Orientation 0.178 0.109 0.055 0.043 0.002 0.030 1320 (0.029) (0.017) (0.015) (0.459) (1013) 6. Locus of Control 0.176 0.072 0.069 0.008 0.006 0.34 1507 (0.027) (0.008) (0.008) (0.897) (1183) 7. Other 0.182 0.061 0.046 −0.007 −0.011 0.103 1511 (0.027) (0.007) (0.006) (0.387) (1209) 8. All Variables 0.203 0.123 0.091 0.056 0.005 0.087 724 (0.041) (0.045) (0.040) (0.016) (536) 9. Selected Variables 0.177 0.114 0.076 0.043 0.009 0.001 1459 (0.027) (0.012) (0.013) (0.001) (1123) Notes. The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, qualifications including detailed controls for the number of GCE and CSEs obtained, marital status, ethnic, establishment size, whether a supervisor and future plans for (further) children. Standard errors in parentheses. Open in new tab Table 9 The Impact of Psychological Variables On Wages at Age 30 . Raw Log Wage gap (standard error) . Explained Wage Gap . Contribution of psychological variables . p‐value for psych variables . Sample size . Female co‐efficients . Male co‐efficients . Female co‐efficients . Male co‐efficients . Female (Male) . Female (Male) . 1. Risk 0.168 0.071 0.067 0.014 0.005 0.48 1184 (0.030) (0.008) (0.009) (0.74) (952) 2. Competitiveness 0.166 0.075 0.098 0.017 0.037 0.022 1203 (0.030) (0.019) (0.019) (0.253) (963) 3. Self‐Esteem 0.179 0.085 0.087 0.018 0.034 0.108 1421 (0.030) (0.010) (0.010) (0.040) (1091) 4. Other‐regarding 0.195 0.095 0.031 0.009 −0.039 0.215 1382 (0.028) (0.014) (0.015) (0.001) (1041) 5. Career Orientation 0.178 0.109 0.055 0.043 0.002 0.030 1320 (0.029) (0.017) (0.015) (0.459) (1013) 6. Locus of Control 0.176 0.072 0.069 0.008 0.006 0.34 1507 (0.027) (0.008) (0.008) (0.897) (1183) 7. Other 0.182 0.061 0.046 −0.007 −0.011 0.103 1511 (0.027) (0.007) (0.006) (0.387) (1209) 8. All Variables 0.203 0.123 0.091 0.056 0.005 0.087 724 (0.041) (0.045) (0.040) (0.016) (536) 9. Selected Variables 0.177 0.114 0.076 0.043 0.009 0.001 1459 (0.027) (0.012) (0.013) (0.001) (1123) . Raw Log Wage gap (standard error) . Explained Wage Gap . Contribution of psychological variables . p‐value for psych variables . Sample size . Female co‐efficients . Male co‐efficients . Female co‐efficients . Male co‐efficients . Female (Male) . Female (Male) . 1. Risk 0.168 0.071 0.067 0.014 0.005 0.48 1184 (0.030) (0.008) (0.009) (0.74) (952) 2. Competitiveness 0.166 0.075 0.098 0.017 0.037 0.022 1203 (0.030) (0.019) (0.019) (0.253) (963) 3. Self‐Esteem 0.179 0.085 0.087 0.018 0.034 0.108 1421 (0.030) (0.010) (0.010) (0.040) (1091) 4. Other‐regarding 0.195 0.095 0.031 0.009 −0.039 0.215 1382 (0.028) (0.014) (0.015) (0.001) (1041) 5. Career Orientation 0.178 0.109 0.055 0.043 0.002 0.030 1320 (0.029) (0.017) (0.015) (0.459) (1013) 6. Locus of Control 0.176 0.072 0.069 0.008 0.006 0.34 1507 (0.027) (0.008) (0.008) (0.897) (1183) 7. Other 0.182 0.061 0.046 −0.007 −0.011 0.103 1511 (0.027) (0.007) (0.006) (0.387) (1209) 8. All Variables 0.203 0.123 0.091 0.056 0.005 0.087 724 (0.041) (0.045) (0.040) (0.016) (536) 9. Selected Variables 0.177 0.114 0.076 0.043 0.009 0.001 1459 (0.027) (0.012) (0.013) (0.001) (1123) Notes. The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, qualifications including detailed controls for the number of GCE and CSEs obtained, marital status, ethnic, establishment size, whether a supervisor and future plans for (further) children. Standard errors in parentheses. Open in new tab The first row of Table 9 reports the results when the ‘risk attitudes’ are included. For both men and women these variables are jointly insignificant (and none individually significant) and the portion of the gender pay gap that can be explained using them is small and insignificantly different from zero. We have been unable to find any variables representing attitudes to risk that are of much use in explaining the gender pay gap. The second row of Table 9 reports the results when the ‘competitiveness’ variables are included. These are jointly significantly different from zero for women but not for men with the variable for liking sport being significant for women and estimated to raise wages by 0.089. This finding that different variables are significant in male and female earnings functions will be a recurrent theme, indicating that (as is often claimed) different traits are regarded differently when displayed by a man and a women. Overall the gender differences in competitiveness are estimated to be capable of explaining 0.017 to 0.037 of the gender pay gap depending on whether male or female coefficients are used. The third row of Table 9 includes the self‐esteem variables. These are jointly significant for men but marginally so for women. The individually significant variables for women is whether they rated themselves as clever and for men it is those who report not getting worried about things who have significantly higher earnings. Overall the gender differences in competitiveness are estimated to be capable of explaining 0.018 to 0.034 of the gender pay gap depending on whether male or female coefficients are used. So far gender differences in personality traits have all been capable of explaining the gender pay gap. But the fourth row shows that the ‘other‐regarding’ traits may work in the opposite direction. These are jointly significant for men but not women, with the individually significant variables for men being that earnings are 0.11 higher for men who care a lot about their mothers and 0.12 lower for men who report they are always nice to others.29 The net effect is that, when evaluated at male coefficients, these traits are predicted to be to the advantage of women as they care more about their mothers. The fifth row of Table 9 investigates the role of the ‘career orientation’ variables which are jointly significant for women but not men. For women those who report having kids in later life as mattering very much to them have significantly lower earnings. This can explain 0.043 of the gender pay gap when evaluated at female coefficients but nothing when evaluated at male coefficients. The sixth row of Table 9 reports the results when the ‘locus of control’ variables are included. For both men and women these variables are jointly insignificant (and none individually significant) and the portion of the gender pay gap that can be explained using them is small and insignificantly different from zero. We have been unable to find any variables representing these ‘locus of control’ variables that are of much use in explaining the gender pay gap. The seventh row of Table 9 investigates the other personality traits, these not being significant for either men or women and the only individually significant variable being the finding that, for women, those who are punctual seem (oddly) to have lower earnings. The net effect of these variables on the gender pay gap is small but negative. In row 7 we include all the variables together except the ‘risk’ and ‘locus of control’ variables.30 These are jointly significant for men but for women only at the 10% level. Taken together these variables can explain 0.056 of the gender pay gap (about 25% of the total) at female coefficients but 0.005 at male coefficients. This result can be best understood in the next row where we restrict the included psychological variables to those found to be most significant in male or female wage equations. Because the number of variables is now manageable we report the estimates in Table 10. Looking at all the rows in Table 9 one can see that, generally, the psychological variables do help to explain the gender pay gap. There is a large range of estimates about their importance and these estimates are sensitive to the variables included. One can find specifications in which the psychological variables can explain about 5% of the gender pay gap but that is the upper end of what we have found. Table 10 Impact of Selected Variables on Male and Female Wages at Age 30 . Female . Male . I am keen on sports 0.076 0.026 [0.022] [0.025] I am clever 0.074 0.037 [0.034] [0.038] In comparison with others I get worried −0.005 0.035 [0.018] [0.020] Are you always nice to people −0.065 −0.088 [0.037] [0.043] Do you care what your mother thinks about you 0.009 −0.148 [0.042] [0.040] In your future job, how important is it to get ahead? 0.027 0.045 [0.025] [0.029] Does having children of your own matter to you? 0.035 0.007 [0.022] [0.026] Observations 1,459 1,123 R‐squared 0.24 0.24 . Female . Male . I am keen on sports 0.076 0.026 [0.022] [0.025] I am clever 0.074 0.037 [0.034] [0.038] In comparison with others I get worried −0.005 0.035 [0.018] [0.020] Are you always nice to people −0.065 −0.088 [0.037] [0.043] Do you care what your mother thinks about you 0.009 −0.148 [0.042] [0.040] In your future job, how important is it to get ahead? 0.027 0.045 [0.025] [0.029] Does having children of your own matter to you? 0.035 0.007 [0.022] [0.026] Observations 1,459 1,123 R‐squared 0.24 0.24 Notes. The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, detailed qualifications, marital status, ethnicity, establishment size, whether a supervisor and future plans for (further) children. Standard errors in parentheses. Details of the questions can be found in Table 8. Open in new tab Table 10 Impact of Selected Variables on Male and Female Wages at Age 30 . Female . Male . I am keen on sports 0.076 0.026 [0.022] [0.025] I am clever 0.074 0.037 [0.034] [0.038] In comparison with others I get worried −0.005 0.035 [0.018] [0.020] Are you always nice to people −0.065 −0.088 [0.037] [0.043] Do you care what your mother thinks about you 0.009 −0.148 [0.042] [0.040] In your future job, how important is it to get ahead? 0.027 0.045 [0.025] [0.029] Does having children of your own matter to you? 0.035 0.007 [0.022] [0.026] Observations 1,459 1,123 R‐squared 0.24 0.24 . Female . Male . I am keen on sports 0.076 0.026 [0.022] [0.025] I am clever 0.074 0.037 [0.034] [0.038] In comparison with others I get worried −0.005 0.035 [0.018] [0.020] Are you always nice to people −0.065 −0.088 [0.037] [0.043] Do you care what your mother thinks about you 0.009 −0.148 [0.042] [0.040] In your future job, how important is it to get ahead? 0.027 0.045 [0.025] [0.029] Does having children of your own matter to you? 0.035 0.007 [0.022] [0.026] Observations 1,459 1,123 R‐squared 0.24 0.24 Notes. The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, detailed qualifications, marital status, ethnicity, establishment size, whether a supervisor and future plans for (further) children. Standard errors in parentheses. Details of the questions can be found in Table 8. Open in new tab The approach we have taken so far can be criticised either as a fishing expedition in which we have sought out variables that are significant and reported specifications that maximise the importance of the psychological variables or as a very partial exercise in which many potentially relevant variables have not been considered. To allay some of these fears, the Appendix reports the results from a more passive approach in which we simply take batteries of questions as originally put into the age 16 questionnaire. For this purpose we use the segment on attitudes, divided into 21 modules, spanning a vast range of subjects. To this we add another module from another part of the questionnaire designed to measure self‐esteem as this has been argued to be important by Babcock and Laschever (2003) and others. In total there are 458 questions covering a very wide range of issues from those that have been deemed important in the literature of gender differences (e.g., self‐esteem, locus of control, attitudes to work), to issues that may be thought very tangential, e.g., the teenager's taste in soft drinks. The general description of the modules is listed in Table A2.31 The analysis of these questions is in the Appendix but the conclusion is similar, namely that gender differences in ‘personality’ variables can help to explain at most a few percentage points of the gender pay gap. A sizeable part of the gender pay gap remains unexplained. 6. Conclusions This article has argued that an understanding of the gender pay gap in Britain needs to focus on the explanation of the gender gap in early‐career wage growth that causes women who entered the labour market with the same average level of pay as men to be approximately 25 log points behind 10 years later. Human capital theory can explain at most half of this (primarily because of gender differences in the receipt of on‐the‐job training, and because of modest differences in accumulated labour market experience). Job‐shopping theories seem to be able to explain at most 1.5 log points and the ‘psychological’ differences appear capable of explaining up to 4.5 log points, though that is probably an upper bound. There remains a sizeable unexplained component. This gender gap in wage growth means that, although men and women have similar earnings when entering the labour market, the women will be something like 8% behind the men ten years later even if they have been in continuous full‐time employment, have had no children, do not want any and have the same personality as a man. London School of Economics University of York Submitted: 7 June 2005 Accepted: 29 March 2007 Footnotes 1 " This might be a finding specific to Britain as, for example, Kunze (2003, 2005) shows that for Germans with an apprenticeship, there is a sizeable gender gap on labour market entry that does not then change very much. 2 " This convergence in later years is less apparent in the BHPS than in other British data sets such as the Labour Force Survey. Note also that Kunze (2003, 2005) finds a different pattern for Germany with a pay gap on entry that remains approximately constant through the life‐cycle. 3 " The BHPS is too short a panel to follow cohorts through time. 4 " The flattening in the gender pay gap that one sees in the early 20s in Figure 2 is the result of the entry at that age into the labour market of college graduates for whom, on labour market entry, the gender pay gap is small. 5 " This is less important in the current context where we are primarily interested in the gender gap in wage growth than in the literature on the returns to job tenure as incorrect detrending probably has a very similar effect on both men and women. 6 " We did experiment with restricting the sample to those in the early waves when censoring is less problematic: this made no difference to the conclusions. 7 " Note that in a steady‐state it should not matter whether one looks forward as in the BHPS or backwards as in the LFS. 8 " Use of a more conventional job tenure measure does not make much difference to the results but does result in a large fall in the number of useable observations because the work history information needed to construct it is only available for a fraction of the sample. For this reason, we only report results using the BHPS job tenure variable. 9 " One might wonder whether these gender differences are significantly different from each other. At each individual level of experience the answer is often ‘no’ but one can easily reject the joint hypothesis that the returns to experience for men and women are equal in the first 10 years. 10 " Note that, averaging across men and women of all experience levels, there is no significant difference in wage growth – as found by Manning and Robinson (2004). However, that paper makes the (embarrassing) mistake of inferring from this fact that gender differences in wage growth cannot explain the gender pay gap. This is wrong because a male advantage in wage growth in early life off‐set by a female advantage in later life does not imply a zero gender pay gap. 11 " One proviso to this is that there is some evidence that, in the reduced form, earnings growth is lower in the early years for the best‐educated. But, this can be explained by the larger amount of on‐the‐job training received in the early years by the less‐educated and this is a variable for which we do control later in the article. 12 " See Mincer (1974) and, more recently, Heckman et al. (2003) for a discussion of the variance profile. 13 " And one should also note that actual labour market experience is endogenous so that part of the correlation of wages with actual experience may not be causal. 14 " In some of the literature, notably Mincer and Ofek (1982), there is a debate about whether it is the incidence or the length of the break in employment that is the more important. But, as Table 2 makes clear we do not really have sufficient variation in the employment gap in our data to explore this further. 15 " We did experiment with interacting first job with years of experience so the impact of initial job falls over time but the coefficients on these interactions were always insignificantly different from zero. 16 " One might be concerned that the sample sizes in columns 9 and 10 are considerably smaller than in columns 7 and 8 because initial job is missing for many observations. But, estimating the model of columns 7 and 8 using the restricted sample of columns 9 and 10 leads to a predicted wage gap after 10 years of 0.13 so the contribution of occupation is probably smaller than it appears from a simple inspection of Table 3. 17 " Though it may be that even the 3‐digit occupation is not fine enough – see Wood et al. (1993) for evidence that among lawyers, women are over‐represented in the lower paid fields. 18 " The six statements are ‘A pre‐school child suffers if mother works’, ‘A family suffers if mother works full‐time’, ‘A woman and family are happier if she works, ‘A husband and wife should both contribute’, ‘A full time job makes a woman independent’, ‘A husband should earn, wife stay at home’ 19 " There is also a question about why the new job is better, the reasons for which can be crudely divided into pecuniary and non‐pecuniary reasons. We did experiment with disaggregating the ‘left for better job’ category but there are many missing values and ambiguous answers (e.g. many saying they left for a better job because the new job is better) so this was not pursued. 20 " There are reasons to doubt whether this specification is adequate. For example, theory predicts some variation in the returns to job mobility both in observables (like experience, job tenure and the current level of wages) and unobservables (because individuals are less likely to leave jobs with high wage growth). However, experimentation with the specification did not lead to any substantive change in the results and we only report the simplest specification here. 21 " The theory might also be used to predict other interactions but, while we experimented with other interactions, we never found any to be significant. 22 " There is a heated debate about whether this is nature or nurture, a question we will avoid here. 23 " This research can be seen as one part of the renaissance in behavioural economics that seeks insights from psychology for application in economics. 24 " Our reason for showing this particular gender gap is that this is closest to the cumulated gap that has been the focus of attention in the BHPS data. 25 " It is worth noting that throughout this Section we use wage levels rather than wage growth because they are the only data available to us. But, as the gender gap is approximately zero on labour market entry this should not make a huge difference. 26 " We also experimented with measures of illegal drug use but these variables were never significant and low response rates meant their inclusion substantially reduced the sample size. 27 " This variable is, unsurprisingly, correlated with school attainment but even controlling for this, women are less likely to think they are clever. The regressions reported below contain detailed controls for education so we hope we are picking up the confidence not the ability part of this variable. 28 " Some authors (e.g. Fisher, 1999) have gone so far as to argue that women's advantage over men in skills like these that become more important over time mean the gender gap will fall over time and even reverse. 29 " These variables are only weakly positively correlated, something that might be found surprising. 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Google Scholar Crossref Search ADS WorldCat Wood , Robert G. , Corcoran , Mary E. and Courant , Paul N. ( 1993 ). ‘Pay differences among the highly‐paid: the male‐female earnings gap in lawyers’ salaries’ , Journal of Labor Economics , vol. 11 , pp. 417 – 41 . Google Scholar Crossref Search ADS WorldCat Appendices Appendix An Alternative Approach to Investigating the Importance of ‘Personality’ Variables In the alternative approach we use the battery of questions contained in the age 16 questionnaire segment on attitudes plus one other battery of questions, designed to measure self‐esteem. In total there are 458 questions divided into 22 modules. These questions cover a very wide range of differences between men and women along many different dimensions though as the questionnaire was not designed with a main purpose of investigating gender differences these questions are not tied so closely to the factors discussed above that have been thought to be the most important gender differences in personality – though there are sizeable gender differences in the response to most questions as we report below. The gender differences in the responses to these questions are summarised in Table A2. Table A2 details the different modules and the number of questions for which the coefficient is significantly different from zero at the 5% level. In 383 of the 458 questions in total, there are gender differences in responses that are significantly different from zero. There are too many individual questions to include all in an earnings function so we proceed by creating composite indices for each of the 22 modules. For each module we create a weighted average of the responses to create a single measure for each of them.32 For this to be a sensible procedure requires that the responses to questions within a module really measure the same thing. As a check on this we report Cronbach's alpha for each module in the fourth column of Table A2– a value above 0.7 is conventionally taken to mean that there is a valid underlying psychometric variable and most of the modules do show this level of congruence. Table A3 reports the coefficients on these modules in earnings functions for male and female workers using the BCS data. The first point to note is that most of the modules have estimated effects on wages that are insignificantly different from zero – out of 22 modules 2 are significantly different from zero for women and 1 for men. A joint hypothesis test finds one cannot reject the null hypothesis that all of the modules have no effect on wages. In spite of this the right‐hand panel of Table A3 shows that the total contributions of the modules to the gender pay gap is non‐trivial, being a significant 6.9 log points when evaluated at female coefficients and an insignificant 4.3 when evaluated at the male. One might conclude that a best estimate of the contribution of the personality variables to earnings is about 5 log points, though with a large standard error. It is the ‘my interests’ and ‘my point of view’ modules that seem most important in explaining the gender pay gap. The ‘my point of view’ module contains questions that seem most closely related to other‐regarding attitudes – women seem ‘softer’ and more tolerant in their attitudes than men and the labour market seems to punish such attitudes. The ‘my interests’ module is harder to interpret – the questions are mostly about interest in health issues with women being more interested than men and this being punished in the labour market for some reason. The results also suggest that some factors others have suggested are important, e.g., self‐esteem and locus of control, are not. It is also important to note that there remains a sizeable unexplained gender pay gap – the predicted wage gap between men and women who have worked continuously full‐time for 10 years is 9.3 log points when evaluated at average female characteristics and 7.4 log points when evaluated at the average male characteristics. Columns 3 and 4 of Table A3 show the results when we include only the modules that appear significant. The results are qualitatively very similar. Table A1 Summary Statistics for BHPS Data . Young Workers (≤ 10 yrs of potential experience) . Men . Women . Log Wage Growth 0.084 0.065 (0.306) (0.278) Potential experience (decades) 0.48 0.47 (0.28) (0.28) Job Tenure (decades) 0.15 0.13 (0.22) (0.19) Gap between wage obs 1.02 1.02 (0.18) (0.18) Part‐Time 0.036 0.196 Training in Past Year (yrs) 0.035 0.030 (0.106) (0.095) Training between wage obs (yrs) 0.030 0.030 (0.090) (0.099) Good Move 0.254 0.231 Bad Move 0.072 0.056 Kid Move 0.0005 0.010 Other Move 0.062 0.072 Manager 0.035 0.036 Professional occupations 0.060 0.049 Associate professional 0.104 0.104 Administrative and secretarial 0.156 0.378 Skilled trades 0.239 0.022 Personal services 0.083 0.200 Sales/customer services 0.081 0.109 Machine operatives 0.094 0.037 Elementary 0.147 0.068 Graduates 0.283 0.290 ‘A’ Level 0.396 0.430 GSCEs 0.286 0.267 No qualifications 0.036 0.012 Black 0.011 0.010 Asian 0.025 0.010 Married 0.442 0.543 Child in Household 0.253 0.259 No. of kids 0–2 0.098 0.070 No. of kids 3–4 0.051 0.066 No. of kids 12–15 0.107 0.099 No. of kids 16–18 0.028 0.031 Housework (weekly hrs‐10)/10 −0.442 0.594 Labour Market Motivation 0.005 0.119 . Young Workers (≤ 10 yrs of potential experience) . Men . Women . Log Wage Growth 0.084 0.065 (0.306) (0.278) Potential experience (decades) 0.48 0.47 (0.28) (0.28) Job Tenure (decades) 0.15 0.13 (0.22) (0.19) Gap between wage obs 1.02 1.02 (0.18) (0.18) Part‐Time 0.036 0.196 Training in Past Year (yrs) 0.035 0.030 (0.106) (0.095) Training between wage obs (yrs) 0.030 0.030 (0.090) (0.099) Good Move 0.254 0.231 Bad Move 0.072 0.056 Kid Move 0.0005 0.010 Other Move 0.062 0.072 Manager 0.035 0.036 Professional occupations 0.060 0.049 Associate professional 0.104 0.104 Administrative and secretarial 0.156 0.378 Skilled trades 0.239 0.022 Personal services 0.083 0.200 Sales/customer services 0.081 0.109 Machine operatives 0.094 0.037 Elementary 0.147 0.068 Graduates 0.283 0.290 ‘A’ Level 0.396 0.430 GSCEs 0.286 0.267 No qualifications 0.036 0.012 Black 0.011 0.010 Asian 0.025 0.010 Married 0.442 0.543 Child in Household 0.253 0.259 No. of kids 0–2 0.098 0.070 No. of kids 3–4 0.051 0.066 No. of kids 12–15 0.107 0.099 No. of kids 16–18 0.028 0.031 Housework (weekly hrs‐10)/10 −0.442 0.594 Labour Market Motivation 0.005 0.119 Notes. Sample is those with observations on wage growth aged 16–65 with less than 45 years of potential experience. Sample numbers for wage growth are 5,015 for men and 5,535 for women – some variables have smaller sample sizes because of non‐response. Means are reported and standard errors in parentheses. Standard errors not reported for binary variables. Open in new tab Table A1 Summary Statistics for BHPS Data . Young Workers (≤ 10 yrs of potential experience) . Men . Women . Log Wage Growth 0.084 0.065 (0.306) (0.278) Potential experience (decades) 0.48 0.47 (0.28) (0.28) Job Tenure (decades) 0.15 0.13 (0.22) (0.19) Gap between wage obs 1.02 1.02 (0.18) (0.18) Part‐Time 0.036 0.196 Training in Past Year (yrs) 0.035 0.030 (0.106) (0.095) Training between wage obs (yrs) 0.030 0.030 (0.090) (0.099) Good Move 0.254 0.231 Bad Move 0.072 0.056 Kid Move 0.0005 0.010 Other Move 0.062 0.072 Manager 0.035 0.036 Professional occupations 0.060 0.049 Associate professional 0.104 0.104 Administrative and secretarial 0.156 0.378 Skilled trades 0.239 0.022 Personal services 0.083 0.200 Sales/customer services 0.081 0.109 Machine operatives 0.094 0.037 Elementary 0.147 0.068 Graduates 0.283 0.290 ‘A’ Level 0.396 0.430 GSCEs 0.286 0.267 No qualifications 0.036 0.012 Black 0.011 0.010 Asian 0.025 0.010 Married 0.442 0.543 Child in Household 0.253 0.259 No. of kids 0–2 0.098 0.070 No. of kids 3–4 0.051 0.066 No. of kids 12–15 0.107 0.099 No. of kids 16–18 0.028 0.031 Housework (weekly hrs‐10)/10 −0.442 0.594 Labour Market Motivation 0.005 0.119 . Young Workers (≤ 10 yrs of potential experience) . Men . Women . Log Wage Growth 0.084 0.065 (0.306) (0.278) Potential experience (decades) 0.48 0.47 (0.28) (0.28) Job Tenure (decades) 0.15 0.13 (0.22) (0.19) Gap between wage obs 1.02 1.02 (0.18) (0.18) Part‐Time 0.036 0.196 Training in Past Year (yrs) 0.035 0.030 (0.106) (0.095) Training between wage obs (yrs) 0.030 0.030 (0.090) (0.099) Good Move 0.254 0.231 Bad Move 0.072 0.056 Kid Move 0.0005 0.010 Other Move 0.062 0.072 Manager 0.035 0.036 Professional occupations 0.060 0.049 Associate professional 0.104 0.104 Administrative and secretarial 0.156 0.378 Skilled trades 0.239 0.022 Personal services 0.083 0.200 Sales/customer services 0.081 0.109 Machine operatives 0.094 0.037 Elementary 0.147 0.068 Graduates 0.283 0.290 ‘A’ Level 0.396 0.430 GSCEs 0.286 0.267 No qualifications 0.036 0.012 Black 0.011 0.010 Asian 0.025 0.010 Married 0.442 0.543 Child in Household 0.253 0.259 No. of kids 0–2 0.098 0.070 No. of kids 3–4 0.051 0.066 No. of kids 12–15 0.107 0.099 No. of kids 16–18 0.028 0.031 Housework (weekly hrs‐10)/10 −0.442 0.594 Labour Market Motivation 0.005 0.119 Notes. Sample is those with observations on wage growth aged 16–65 with less than 45 years of potential experience. Sample numbers for wage growth are 5,015 for men and 5,535 for women – some variables have smaller sample sizes because of non‐response. Means are reported and standard errors in parentheses. Standard errors not reported for binary variables. Open in new tab Table A2 Gender Differences in Psychological Variables – Alternative Approach Title in Questionnaire . Type of question . Number of questions . Gender difference in composite score . In module . With signif gender diff . Cronbach's alpha . Coefficient . Standard error . What about work? Determinants of success in labour market 9 8 0.567 0.027 [0.013] Right and might Attitudes to law‐breaking and helping others 9 7 0.533 −0.152 [0.012] Have a drink Alcohol 17 17 0.725 0.197 [0.012] What's in a job Importance of different job characteristics 16 12 0.607 0.096 [0.010] Looking ahead What will be important as adult 15 13 0.661 0.02 [0.011] Up in smoke Smoking 17 14 0.746 −0.026 [0.012] Compared with others Physical/emotional attributes 28 21 0.822 −0.122 [0.011] Knowing myself Personal characteristics 27 23 0.724 −0.038 [0.010] How I feel Worries and anxieties 12 10 0.833 0.147 [0.016] At leisure Leisure activities 47 40 0.781 −0.036 [0.008] My interests Interests in physical/mental health topics 49 46 0.923 −0.289 [0.012] Fate and fortune Locus of control – belief in ability to influence outcomes 15 6 0.717 −0.025 [0.012] What I read Type of reading 25 24 0.817 0.224 [0.011] Me and the box Types of TV programmes watched 22 22 0.721 −0.101 [0.011] Feeling healthy Physical/mental health 22 18 0.839 −0.182 [0.013] My point of view Opinions on women's/gay/minority rights, drugs, death penalty etc 21 17 0.597 0.215 [0.009] Wotalotigot Material possessions, actual and desired 30 27 0.649 0.169 [0.008] Me and my family Activities done with family 15 13 0.757 −0.06 [0.013] Soft drink special Type of Soft drinks consumed 10 6 0.632 −0.085 [0.013] Home rule Parents expectations of behaviour within the home 23 19 0.743 −0.039 [0.011] What I eat How often eat different food items 18 14 0.718 −0.153 [0.011] Self‐esteem 11 6 0.702 −0.042 [0.014] Total 458 383 Title in Questionnaire . Type of question . Number of questions . Gender difference in composite score . In module . With signif gender diff . Cronbach's alpha . Coefficient . Standard error . What about work? Determinants of success in labour market 9 8 0.567 0.027 [0.013] Right and might Attitudes to law‐breaking and helping others 9 7 0.533 −0.152 [0.012] Have a drink Alcohol 17 17 0.725 0.197 [0.012] What's in a job Importance of different job characteristics 16 12 0.607 0.096 [0.010] Looking ahead What will be important as adult 15 13 0.661 0.02 [0.011] Up in smoke Smoking 17 14 0.746 −0.026 [0.012] Compared with others Physical/emotional attributes 28 21 0.822 −0.122 [0.011] Knowing myself Personal characteristics 27 23 0.724 −0.038 [0.010] How I feel Worries and anxieties 12 10 0.833 0.147 [0.016] At leisure Leisure activities 47 40 0.781 −0.036 [0.008] My interests Interests in physical/mental health topics 49 46 0.923 −0.289 [0.012] Fate and fortune Locus of control – belief in ability to influence outcomes 15 6 0.717 −0.025 [0.012] What I read Type of reading 25 24 0.817 0.224 [0.011] Me and the box Types of TV programmes watched 22 22 0.721 −0.101 [0.011] Feeling healthy Physical/mental health 22 18 0.839 −0.182 [0.013] My point of view Opinions on women's/gay/minority rights, drugs, death penalty etc 21 17 0.597 0.215 [0.009] Wotalotigot Material possessions, actual and desired 30 27 0.649 0.169 [0.008] Me and my family Activities done with family 15 13 0.757 −0.06 [0.013] Soft drink special Type of Soft drinks consumed 10 6 0.632 −0.085 [0.013] Home rule Parents expectations of behaviour within the home 23 19 0.743 −0.039 [0.011] What I eat How often eat different food items 18 14 0.718 −0.153 [0.011] Self‐esteem 11 6 0.702 −0.042 [0.014] Total 458 383 Open in new tab Table A2 Gender Differences in Psychological Variables – Alternative Approach Title in Questionnaire . Type of question . Number of questions . Gender difference in composite score . In module . With signif gender diff . Cronbach's alpha . Coefficient . Standard error . What about work? Determinants of success in labour market 9 8 0.567 0.027 [0.013] Right and might Attitudes to law‐breaking and helping others 9 7 0.533 −0.152 [0.012] Have a drink Alcohol 17 17 0.725 0.197 [0.012] What's in a job Importance of different job characteristics 16 12 0.607 0.096 [0.010] Looking ahead What will be important as adult 15 13 0.661 0.02 [0.011] Up in smoke Smoking 17 14 0.746 −0.026 [0.012] Compared with others Physical/emotional attributes 28 21 0.822 −0.122 [0.011] Knowing myself Personal characteristics 27 23 0.724 −0.038 [0.010] How I feel Worries and anxieties 12 10 0.833 0.147 [0.016] At leisure Leisure activities 47 40 0.781 −0.036 [0.008] My interests Interests in physical/mental health topics 49 46 0.923 −0.289 [0.012] Fate and fortune Locus of control – belief in ability to influence outcomes 15 6 0.717 −0.025 [0.012] What I read Type of reading 25 24 0.817 0.224 [0.011] Me and the box Types of TV programmes watched 22 22 0.721 −0.101 [0.011] Feeling healthy Physical/mental health 22 18 0.839 −0.182 [0.013] My point of view Opinions on women's/gay/minority rights, drugs, death penalty etc 21 17 0.597 0.215 [0.009] Wotalotigot Material possessions, actual and desired 30 27 0.649 0.169 [0.008] Me and my family Activities done with family 15 13 0.757 −0.06 [0.013] Soft drink special Type of Soft drinks consumed 10 6 0.632 −0.085 [0.013] Home rule Parents expectations of behaviour within the home 23 19 0.743 −0.039 [0.011] What I eat How often eat different food items 18 14 0.718 −0.153 [0.011] Self‐esteem 11 6 0.702 −0.042 [0.014] Total 458 383 Title in Questionnaire . Type of question . Number of questions . Gender difference in composite score . In module . With signif gender diff . Cronbach's alpha . Coefficient . Standard error . What about work? Determinants of success in labour market 9 8 0.567 0.027 [0.013] Right and might Attitudes to law‐breaking and helping others 9 7 0.533 −0.152 [0.012] Have a drink Alcohol 17 17 0.725 0.197 [0.012] What's in a job Importance of different job characteristics 16 12 0.607 0.096 [0.010] Looking ahead What will be important as adult 15 13 0.661 0.02 [0.011] Up in smoke Smoking 17 14 0.746 −0.026 [0.012] Compared with others Physical/emotional attributes 28 21 0.822 −0.122 [0.011] Knowing myself Personal characteristics 27 23 0.724 −0.038 [0.010] How I feel Worries and anxieties 12 10 0.833 0.147 [0.016] At leisure Leisure activities 47 40 0.781 −0.036 [0.008] My interests Interests in physical/mental health topics 49 46 0.923 −0.289 [0.012] Fate and fortune Locus of control – belief in ability to influence outcomes 15 6 0.717 −0.025 [0.012] What I read Type of reading 25 24 0.817 0.224 [0.011] Me and the box Types of TV programmes watched 22 22 0.721 −0.101 [0.011] Feeling healthy Physical/mental health 22 18 0.839 −0.182 [0.013] My point of view Opinions on women's/gay/minority rights, drugs, death penalty etc 21 17 0.597 0.215 [0.009] Wotalotigot Material possessions, actual and desired 30 27 0.649 0.169 [0.008] Me and my family Activities done with family 15 13 0.757 −0.06 [0.013] Soft drink special Type of Soft drinks consumed 10 6 0.632 −0.085 [0.013] Home rule Parents expectations of behaviour within the home 23 19 0.743 −0.039 [0.011] What I eat How often eat different food items 18 14 0.718 −0.153 [0.011] Self‐esteem 11 6 0.702 −0.042 [0.014] Total 458 383 Open in new tab Table A3 The Impact of Psychological Variables on Wages at Age 30 – Alternative Approach . Regression Coefficients . Contribution to Gender Pay Gap . 1 . 2 . 3 . 4 . 1 . 2 . 3 . 4 . Female . Male . Female . Male . Female coeffs . Male coeffs . Female coeffs . Male coeffs . What about work? 0.007 0.129 0.012 0.13 0.000 −0.003 0.000 −0.003 [0.044] [0.047] [0.043] [0.044] [0.006] [0.001] [0.001] [0.001] Right and might 0.037 0.001 0.006 0.000 [0.040] [0.041] [0.006] [0.006] Have a drink 0.019 −0.031 −0.004 0.007 [0.049] [0.048] [0.010] [0.010] What's in a job −0.035 −0.062 0.003 0.006 [0.052] [0.052] [0.005] [0.005] Looking ahead 0.017 0.004 0.000 0.000 [0.047] [0.051] [0.001] [0.001] Up in smoke 0.041 0.001 0.002 0.000 [0.045] [0.044] [0.002] [0.002] Compared with others 0.016 0.018 0.001 0.002 [0.052] [0.051] [0.005] [0.005] Knowing myself −0.074 −0.091 −0.003 −0.003 [0.063] [0.068] [0.002] [0.003] How I feel 0.039 −0.001 −0.005 0.000 [0.032] [0.040] [0.004] [0.005] At leisure 0.106 −0.038 0.003 −0.001 [0.073] [0.069] [0.002] [0.002] My interests 0.09 0.048 0.077 0.019 0.027 0.014 0.023 0.006 [0.047] [0.045] [0.043] [0.040] [0.014] [0.013] [0.013] [0.012] Fate and fortune 0.122 −0.023 0.135 0.001 0.001 0.000 0.000 0.000 [0.048] [0.053] [0.045] [0.049] [0.000] [0.000] [0.000] [0.000] What I read −0.069 0.038 −0.042 0.051 0.014 −0.008 0.009 −0.011 [0.050] [0.050] [0.049] [0.049] [0.010] [0.010] [0.010] [0.010] Me and the box 0.087 0.081 0.068 0.065 0.008 0.008 0.007 0.006 [0.055] [0.054] [0.053] [0.051] [0.005] [0.005] [0.005] [0.005] Feeling healthy 0.026 0.074 0.018 0.106 0.004 0.011 0.003 0.016 [0.048] [0.050] [0.039] [0.041] [0.007] [0.007] [0.006] [0.006] My point of view −0.099 −0.037 −0.113 −0.058 0.022 0.008 0.025 0.013 [0.064] [0.055] [0.061] [0.052] [0.014] [0.005] [0.014] [0.012] Wotalotigot −0.009 −0.071 0.001 0.010 [0.063] [0.065] [0.009] [0.010] Me and my family −0.044 0.021 −0.002 0.001 [0.044] [0.040] [0.002] [0.002] Soft drink special −0.006 −0.066 −0.001 −0.006 [0.043] [0.043] [0.004] [0.004] Home rule −0.107 −0.001 −0.006 −0.000 [0.048] [0.048] [0.003] [0.003] What I eat −0.024 −0.024 −0.004 −0.004 [0.044] [0.047] [0.007] [0.007] Self‐esteem 0.005 0.035 0.000 0.001 [0.034] [0.037] [0.001] [0.001] Observations 1,582 1,220 1,618 1,265 0.23 0.24 0.22 0.23 R‐Squared Total Explained 0.069 0.043 0.067 0.027 [0.027] [0.025] [0.023] [0.021] . Regression Coefficients . Contribution to Gender Pay Gap . 1 . 2 . 3 . 4 . 1 . 2 . 3 . 4 . Female . Male . Female . Male . Female coeffs . Male coeffs . Female coeffs . Male coeffs . What about work? 0.007 0.129 0.012 0.13 0.000 −0.003 0.000 −0.003 [0.044] [0.047] [0.043] [0.044] [0.006] [0.001] [0.001] [0.001] Right and might 0.037 0.001 0.006 0.000 [0.040] [0.041] [0.006] [0.006] Have a drink 0.019 −0.031 −0.004 0.007 [0.049] [0.048] [0.010] [0.010] What's in a job −0.035 −0.062 0.003 0.006 [0.052] [0.052] [0.005] [0.005] Looking ahead 0.017 0.004 0.000 0.000 [0.047] [0.051] [0.001] [0.001] Up in smoke 0.041 0.001 0.002 0.000 [0.045] [0.044] [0.002] [0.002] Compared with others 0.016 0.018 0.001 0.002 [0.052] [0.051] [0.005] [0.005] Knowing myself −0.074 −0.091 −0.003 −0.003 [0.063] [0.068] [0.002] [0.003] How I feel 0.039 −0.001 −0.005 0.000 [0.032] [0.040] [0.004] [0.005] At leisure 0.106 −0.038 0.003 −0.001 [0.073] [0.069] [0.002] [0.002] My interests 0.09 0.048 0.077 0.019 0.027 0.014 0.023 0.006 [0.047] [0.045] [0.043] [0.040] [0.014] [0.013] [0.013] [0.012] Fate and fortune 0.122 −0.023 0.135 0.001 0.001 0.000 0.000 0.000 [0.048] [0.053] [0.045] [0.049] [0.000] [0.000] [0.000] [0.000] What I read −0.069 0.038 −0.042 0.051 0.014 −0.008 0.009 −0.011 [0.050] [0.050] [0.049] [0.049] [0.010] [0.010] [0.010] [0.010] Me and the box 0.087 0.081 0.068 0.065 0.008 0.008 0.007 0.006 [0.055] [0.054] [0.053] [0.051] [0.005] [0.005] [0.005] [0.005] Feeling healthy 0.026 0.074 0.018 0.106 0.004 0.011 0.003 0.016 [0.048] [0.050] [0.039] [0.041] [0.007] [0.007] [0.006] [0.006] My point of view −0.099 −0.037 −0.113 −0.058 0.022 0.008 0.025 0.013 [0.064] [0.055] [0.061] [0.052] [0.014] [0.005] [0.014] [0.012] Wotalotigot −0.009 −0.071 0.001 0.010 [0.063] [0.065] [0.009] [0.010] Me and my family −0.044 0.021 −0.002 0.001 [0.044] [0.040] [0.002] [0.002] Soft drink special −0.006 −0.066 −0.001 −0.006 [0.043] [0.043] [0.004] [0.004] Home rule −0.107 −0.001 −0.006 −0.000 [0.048] [0.048] [0.003] [0.003] What I eat −0.024 −0.024 −0.004 −0.004 [0.044] [0.047] [0.007] [0.007] Self‐esteem 0.005 0.035 0.000 0.001 [0.034] [0.037] [0.001] [0.001] Observations 1,582 1,220 1,618 1,265 0.23 0.24 0.22 0.23 R‐Squared Total Explained 0.069 0.043 0.067 0.027 [0.027] [0.025] [0.023] [0.021] Notes. The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, detailed qualifications, marital status, ethnic, establishment size, whether a supervisor and future plans for (further) children. Standard errors in parentheses. Details of the questions can be found in Table A2. Open in new tab Table A3 The Impact of Psychological Variables on Wages at Age 30 – Alternative Approach . Regression Coefficients . Contribution to Gender Pay Gap . 1 . 2 . 3 . 4 . 1 . 2 . 3 . 4 . Female . Male . Female . Male . Female coeffs . Male coeffs . Female coeffs . Male coeffs . What about work? 0.007 0.129 0.012 0.13 0.000 −0.003 0.000 −0.003 [0.044] [0.047] [0.043] [0.044] [0.006] [0.001] [0.001] [0.001] Right and might 0.037 0.001 0.006 0.000 [0.040] [0.041] [0.006] [0.006] Have a drink 0.019 −0.031 −0.004 0.007 [0.049] [0.048] [0.010] [0.010] What's in a job −0.035 −0.062 0.003 0.006 [0.052] [0.052] [0.005] [0.005] Looking ahead 0.017 0.004 0.000 0.000 [0.047] [0.051] [0.001] [0.001] Up in smoke 0.041 0.001 0.002 0.000 [0.045] [0.044] [0.002] [0.002] Compared with others 0.016 0.018 0.001 0.002 [0.052] [0.051] [0.005] [0.005] Knowing myself −0.074 −0.091 −0.003 −0.003 [0.063] [0.068] [0.002] [0.003] How I feel 0.039 −0.001 −0.005 0.000 [0.032] [0.040] [0.004] [0.005] At leisure 0.106 −0.038 0.003 −0.001 [0.073] [0.069] [0.002] [0.002] My interests 0.09 0.048 0.077 0.019 0.027 0.014 0.023 0.006 [0.047] [0.045] [0.043] [0.040] [0.014] [0.013] [0.013] [0.012] Fate and fortune 0.122 −0.023 0.135 0.001 0.001 0.000 0.000 0.000 [0.048] [0.053] [0.045] [0.049] [0.000] [0.000] [0.000] [0.000] What I read −0.069 0.038 −0.042 0.051 0.014 −0.008 0.009 −0.011 [0.050] [0.050] [0.049] [0.049] [0.010] [0.010] [0.010] [0.010] Me and the box 0.087 0.081 0.068 0.065 0.008 0.008 0.007 0.006 [0.055] [0.054] [0.053] [0.051] [0.005] [0.005] [0.005] [0.005] Feeling healthy 0.026 0.074 0.018 0.106 0.004 0.011 0.003 0.016 [0.048] [0.050] [0.039] [0.041] [0.007] [0.007] [0.006] [0.006] My point of view −0.099 −0.037 −0.113 −0.058 0.022 0.008 0.025 0.013 [0.064] [0.055] [0.061] [0.052] [0.014] [0.005] [0.014] [0.012] Wotalotigot −0.009 −0.071 0.001 0.010 [0.063] [0.065] [0.009] [0.010] Me and my family −0.044 0.021 −0.002 0.001 [0.044] [0.040] [0.002] [0.002] Soft drink special −0.006 −0.066 −0.001 −0.006 [0.043] [0.043] [0.004] [0.004] Home rule −0.107 −0.001 −0.006 −0.000 [0.048] [0.048] [0.003] [0.003] What I eat −0.024 −0.024 −0.004 −0.004 [0.044] [0.047] [0.007] [0.007] Self‐esteem 0.005 0.035 0.000 0.001 [0.034] [0.037] [0.001] [0.001] Observations 1,582 1,220 1,618 1,265 0.23 0.24 0.22 0.23 R‐Squared Total Explained 0.069 0.043 0.067 0.027 [0.027] [0.025] [0.023] [0.021] . Regression Coefficients . Contribution to Gender Pay Gap . 1 . 2 . 3 . 4 . 1 . 2 . 3 . 4 . Female . Male . Female . Male . Female coeffs . Male coeffs . Female coeffs . Male coeffs . What about work? 0.007 0.129 0.012 0.13 0.000 −0.003 0.000 −0.003 [0.044] [0.047] [0.043] [0.044] [0.006] [0.001] [0.001] [0.001] Right and might 0.037 0.001 0.006 0.000 [0.040] [0.041] [0.006] [0.006] Have a drink 0.019 −0.031 −0.004 0.007 [0.049] [0.048] [0.010] [0.010] What's in a job −0.035 −0.062 0.003 0.006 [0.052] [0.052] [0.005] [0.005] Looking ahead 0.017 0.004 0.000 0.000 [0.047] [0.051] [0.001] [0.001] Up in smoke 0.041 0.001 0.002 0.000 [0.045] [0.044] [0.002] [0.002] Compared with others 0.016 0.018 0.001 0.002 [0.052] [0.051] [0.005] [0.005] Knowing myself −0.074 −0.091 −0.003 −0.003 [0.063] [0.068] [0.002] [0.003] How I feel 0.039 −0.001 −0.005 0.000 [0.032] [0.040] [0.004] [0.005] At leisure 0.106 −0.038 0.003 −0.001 [0.073] [0.069] [0.002] [0.002] My interests 0.09 0.048 0.077 0.019 0.027 0.014 0.023 0.006 [0.047] [0.045] [0.043] [0.040] [0.014] [0.013] [0.013] [0.012] Fate and fortune 0.122 −0.023 0.135 0.001 0.001 0.000 0.000 0.000 [0.048] [0.053] [0.045] [0.049] [0.000] [0.000] [0.000] [0.000] What I read −0.069 0.038 −0.042 0.051 0.014 −0.008 0.009 −0.011 [0.050] [0.050] [0.049] [0.049] [0.010] [0.010] [0.010] [0.010] Me and the box 0.087 0.081 0.068 0.065 0.008 0.008 0.007 0.006 [0.055] [0.054] [0.053] [0.051] [0.005] [0.005] [0.005] [0.005] Feeling healthy 0.026 0.074 0.018 0.106 0.004 0.011 0.003 0.016 [0.048] [0.050] [0.039] [0.041] [0.007] [0.007] [0.006] [0.006] My point of view −0.099 −0.037 −0.113 −0.058 0.022 0.008 0.025 0.013 [0.064] [0.055] [0.061] [0.052] [0.014] [0.005] [0.014] [0.012] Wotalotigot −0.009 −0.071 0.001 0.010 [0.063] [0.065] [0.009] [0.010] Me and my family −0.044 0.021 −0.002 0.001 [0.044] [0.040] [0.002] [0.002] Soft drink special −0.006 −0.066 −0.001 −0.006 [0.043] [0.043] [0.004] [0.004] Home rule −0.107 −0.001 −0.006 −0.000 [0.048] [0.048] [0.003] [0.003] What I eat −0.024 −0.024 −0.004 −0.004 [0.044] [0.047] [0.007] [0.007] Self‐esteem 0.005 0.035 0.000 0.001 [0.034] [0.037] [0.001] [0.001] Observations 1,582 1,220 1,618 1,265 0.23 0.24 0.22 0.23 R‐Squared Total Explained 0.069 0.043 0.067 0.027 [0.027] [0.025] [0.023] [0.021] Notes. The basic equation for each row includes controls for whether there are any children in the household, quadratic in actual full‐time and part‐time labour market experience, age left full‐time education, quadratic for current tenure, detailed qualifications, marital status, ethnic, establishment size, whether a supervisor and future plans for (further) children. Standard errors in parentheses. Details of the questions can be found in Table A2. Open in new tab © The Author(s). Journal compilation © Royal Economic Society 2008

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The Economic Journal – Oxford University Press

Published: Jul 1, 2008

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The Gender Gap in Early‐Career Wage Growth

The Gender Gap in Early‐Career Wage Growth

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The Gender Gap in Early‐Career Wage Growth

The Gender Gap in Early‐Career Wage Growth

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