Do people exaggerate how happy they are? Using a promise to induce truth-telling

Do people exaggerate how happy they are? Using a promise to induce truth-telling Abstract We investigate a novel approach to reduce measurement error in subjective well-being (SWB) data. Using a between-subject design, half of the subjects are asked to promise to answer the survey questions truthfully to make them commit to truth-telling. We find a statistically significant difference between mean stated well-being between the two samples (with and without a promise). People are consistently found to exaggerate their happiness and for several different aspects of life, without a promise. We then investigate to what extent the differences in stated well-being also affect the inference from regressions models on the determinants of SWB. The effect on the covariates are only weakly statistically significant and only for a few variables, if we compare the samples with and without the promise. Thus, this means that the policy implications based on an SWB study only marginally depends on whether we include a truth-telling question or not. 1. Introduction We have witnessed an increased use of subjective well-being (SWB) measures in economics; from 2000 to 2006, 157 papers and numerous books on the topic were published in economics literature (Krueger and Schkade, 2008). While most economists would probably agree that the information gained from subjective questions is interesting and important, the unwillingness to rely on such questions has historically marked an important difference between economists and other social scientists (Bertrand and Mullainathan, 2001). This attitude may, however, have shifted among economists during the last 10 years. Furthermore, we have seen an increased interest in incorporating findings from other disciplines, such as psychology, into economics (see, for example, Layard, 2006). One of the problems with SWB data is that it is prone to social desirability bias, i.e. the tendency of survey respondents to answer questions in a manner that will be viewed favourably by others and in line with certain social norms (Phillips and Clancy, 1972). Such social-image concerns are likely to be important to many people (Lacetera and Macis, 2010). Although not as extensively explored, it is also plausible that self-image concerns can bias survey responses, e.g. in an SWB context respondents may not want to admit even to themselves that they are unsatisfied with their life or some aspects thereof. In the words of Sandvik et al. (1993): ‘To claim to be happy may be the ultimate assertion of success in our society, and to admit unhappiness could be the single greatest summary of failure in life that an individual could concede.’ This type of misreporting creates a measurement error in the reported data, which can be handled either at the modeling stage,1 1 See, for example, Hausman (2001) for a review. The mismeasurement problem for linear models in econometrics is usually solved by using instrumental variables (Hausman, 2001). Hausman et al. (1998) offer methods that also deal with binary choice with misclassification and mismeasured discrete dependent variables with several categories. the data collection stage, or both. In an important contribution, Krueger and Schkade (2008) assessed the role of measurement error by correcting for attenuation for correlation coefficients between SWB variables and other variables, and they find substantial increases in correlation coefficients when correcting for attenuation. Furthermore, Benjamin et al. (2012) found that happiness data indeed could suffer from measurement error using a measurement-error-corrected regression. In this paper we investigate a novel approach to reduce measurement error in SWB surveys. We test a self-commitment mechanism where survey respondents are asked to promise to answer the survey questions truthfully. The main objective is to experimentally test whether making a promise affects responses to survey questions, in particular SWB questions. Subsequently, if we find differences, we investigate to what extent these differences affect the inference drawn from regressions models on the determinants of SWB. Traditionally, economists have been sceptical to asking respondents to tell the truth, primarily because there are no actual incentives for responding truthfully. However, empirical evidence suggests that a promise alone can indeed induce an emotional commitment to fulfill the promise (Ostrom et al., 1992; Braver, 1995; Ellingsen and Johannesson, 2004; Vanberg 2008; Carlsson et al., 2013; Kataria and Winter, 2013; Jacquemet et al., 2013).2 2 The reason why it works has recently been under scientific scrutiny. Charness and Dufwenberg (2006) found experimental evidence that a promise works because of guilt aversion: A guilt-averse person does not want to let down others’ expectations and is therefore committed to the promises made. An alternative explanation is that people may have a taste for keeping their word (see, for example, Ellingsen and Johannesson, 2004). Using a novel design, Vanberg (2008) found support for the latter explanation, i.e. people have preferences for promise keeping per se. This approach has been applied in, among other areas, experiments (Ellingsen and Johannesson, 2004; Jacquemet et al., 2013) and stated preference surveys on environmental problems (Carlsson et al., 2013). Yet as far as we know, it has not been used in SWB surveys. The literature on how to reduce measurement error at the data collection stage is rather extensive. One such area is survey research dealing with particularly sensitive topics such as racism, terrorism, corruption, illegal behaviour and drug use. Methods such as randomized response (Warner 1965; Greenberg et al., 1969) and item count techniques (Raghavarao and Federer, 1979) have been used in surveys with sensitive questions. Using randomized response, the respondent flips a coin and is instructed to answer either a sensitive or a non-sensitive yes/no question based on the outcome of the coin flip. Only the respondent knows which question he or she answered. This procedure hides individual answers but enables analysts to assess the true population proportions of yes/no replies because the noise probability is known. The drawback of the randomized response method is that it draws attention to the act of measurement itself. Respondents can become suspicious of intent and claims to anonymity and focus too much on how the method works instead of answering the questions. The item count method randomly splits the sample into two groups: control and treatment group. Both groups are asked insensitive questions, and then the treatment group is also asked a sensitive question, i.e. the question of interest. The respondents are then asked to reveal the number of ‘yes’ answers they have given. Respondent anonymity is assured whereas the number of people who answered ‘yes’ to the sensitive question can be mathematically deduced. While the item count method is straightforward with a low level of burden on the respondents, one major drawback of the method is the lack of power of the estimator for relatively high sample sizes (Droitcour et al., 1991). SWB data utilizes what in economics is known as experience utility, which is distinguished from what is known as decision utility. While decision utility is inferred from the decision-maker’s observed choices, experience utility is the satisfaction that is experienced once the decision is made.3 3 Benjamin et al. (2012) found that, using hypothetical survey questions, people’s choice and predicted SWB ranking of two alternatives usually coincide, albeit with some systematic deviations. If the choices are revealed in a market, the data is known as revealed preference data, while stated preference data represents choices made or stated in a constructed survey situation. Stated preference data is used frequently in economics to value public goods. Interestingly, both SWB data and stated preference studies deal with the same difficulties common to data based on subjective assessments. A number of approaches to reduce the so-called hypothetical bias, i.e. the difference between stated behaviour and behaviour if the choice situation would have been an actual one, have been suggested in the stated preference literature. One common approach is to use a cheap talk script, which aims to reduce hypothetical bias by informing respondents about the occurrence of hypothetical bias (Cummings and Taylor, 1999). The idea is that by making respondents aware of the problem, they will exert more effort when responding and in that way hypothetical bias will be reduced. The empirical support for a cheap talk script is mixed, and it is clear that the effect depends both on the context and on the formulation of the script (see, for example, List, 2001; Carlsson et al., 2005; Aadland and Caplan, 2006). Another approach that has been used is so-called inferred valuation (Lusk and Norwood, 2009a, 2009b; Carlsson et al., 2010), where respondents estimate other people’s valuations of goods.4 4 Other approaches to reduce hypothetical bias in the stated preference literature include expost calibration of willingness-to-pay estimates based on follow-up questions (see, for example, Champ et al., 1997; Johannesson et al., 1999; Champ and Bishop, 2001) and the time-to-think protocol (Whittington et al., 1992; Cook et al., 2007). Compared with the stated preference literature, surprisingly little attention has been given to reduce measurement error in SWB surveys. Layard (2006) argues that there is a need for an expanded model of happiness that incorporates findings from other disciplines, such as psychology. While his main focus was on theory, there seems also to be a need to look over how SWB data is collected, and as discussed, it seems possible to incorporate findings from neighbouring disciplines in the SWB literature. To simply rely on people truthfully reporting their happiness seems inappropriate and neglects developments in neighbouring disciplines. Understanding what affects SWB is important as it could help economists design policies that improve people’s well-being. In the late 1990s, economists started to present large-scale empirical analyses of determinants of well-being (Frey and Stutzer, 2002). Evidence suggests that poor health, divorce, unemployment, and lack of social relationships are important determinants of well-being (Dolan et al., 2008). While economists usually focus on determinants of SWB that can be categorized as actual observable life experiences, the approach has been somewhat more pluralistic in psychology. Here, the overall well-being is complemented with reported well-being in major domains of life, such as health, finances, social relationships, and sex life. The advantage of this life domain approach is that it better reflects subjective factors such as personal aspirations and norms that could affect the overall well-being. For example, an individual with high income and high financial aspirations could be less satisfied with life than someone with low income and low financial aspirations. We test whether making a promise affects responses to the overall well-being question as well as well-being in major domains of life. It helps us to gain a nuanced understanding of what aspects of life people in general are inclined to misrepresent when asked about their well-being. 2. Social desirability and self-image in a measurement-error framework One of the problems with SWB data is that it is prone to social desirability bias, i.e. the tendency of survey respondents to answer questions in a manner that will be viewed favourably by others and in line with some social norm (Phillips and Clancy, 1972). Self-image concerns may also bias the survey responses, e.g. respondents do not want to admit even to themselves that they are not satisfied with their life. In order to illustrate the potential problem with social desirability and self-image concerns, we use a measurement-error framework where the dependent and/or the independent variables in a regression model are observed with an error. The simplest case is a linear regression model with one independent variable and no intercept, which is also the standard textbook case (see, for example Greene, 2002). The observed dependent variable, y, is specified as y=y*+u, where y* is the true variable and u is a normally distributed error term, i.e. u∼N(μy,σ2y). Suppose that the observed independent variable is x, i.e. x=x*+v, where x* is the true variable and v∼N(μx,σx). Assume that u and v are independently distributed. If μ=0 the error term is a random error, and if μ≠0 the error is a systematic error. Two results are well-known in the literature. First, assuming that only y is measured with a random error does not result in biased parameter estimates since the measurement error is incorporated in the disturbance term. It will, however, increase the standard error of the estimated parameter, i.e. the parameter will be estimated with less precision. Second, if instead x is measured with a random error, the parameter estimates are inconsistent and biased towards zero (attenuation bias). Hausman (2001) calls this the ‘iron law of econometrics’—the magnitude of the estimate is usually smaller than expected. If both the dependent and the independent variable are mismeasured, the parameters are still—of course—biased and measured with less accuracy. Notably, Krueger and Schkade (2008) investigated correlations between life satisfaction and variables such as income with and without adjustment for attenuation bias due to measurement error and found a substantial increase for some of the correlations when adjusting for attenuation bias. The regression models used in the SWB literature deviate in many aspects from the simplifying assumptions we made above. First of all, a multiple regression framework, with an intercept, is used in the literature. Second, if social desirability and self-image are the reasons for measurement error, we would expect the dependent variable to be measured with a systematic rather than a random error. This is also true for independent variables with value-laden content such as life satisfaction in different domains, but perhaps not for objective variables that are merely counts of various types of individual characteristics. With objective variables we expect the problem of measurement error to be less severe, and if present we would expect it to be a random error. Also note that the measurement errors of the independent variables might be correlated with each other and the measurement error of the dependent variable. Third, the dependent variable is measured on an ordinal scale and should therefore be estimated with a non-linear model such as an ordered probit model. Hausman et al. (1998) showed that misclassificatio5 5 Misclassification means that the response is reported or recorded in the wrong category. of the dependent discrete variable causes inconsistent coefficient estimates if the measurement error is not taken into consideration in a standard framework (e.g. probit or logit). Relatively small amounts of misclassification of the dependent variable can lead to a large bias even with a large sample size. Hence, measurement bias causes severe problems. Exactly how these problems will manifest in our application is an empirical question. We will address the issue of measurement error in SWB data experimentally by comparing stated levels of well-being and coefficients of regressions models from two different survey versions, where only one of the versions include a short script and question asking if the respondents can answer the questions in the survey truthfully or not. Our expectation is that a truth-telling request reduces the reported SWB based on the expectation that people tend to exaggerate SWB measures. However, similar to all studies involving survey we data we face the problem of external validity. How does one really objectively validate SWB? Oswald and Wu (2010) suggest biological indicators such as blood pressure (see, for example, Blanchflower and Oswald, 2008) but they also point out that biological indicators are not unambiguous measures of happiness. As already mentioned, there is also an economic literature that truth-telling request does in fact induce more truth-telling. Hence, in the light of these findings we expect a treatment effect and the interpretation is that the truth-telling request will induce more truth-telling compared to the control treatment. 3. Survey design The questions used in this paper were administered to respondents as part of the thirteenth wave of the Citizen Panel (Martinsson et al., 2014). The Citizen Panel is an online panel survey administered by the Laboratory of Opinion Research (LORE), which was established in 2010 by the Multidisciplinary Opinion and Democracy (MOD) research group at the Faculty of Social Science, University of Gothenburg in Sweden. The survey was carried out from November 27 to December 21, 2014, and consisted of a set of core questions that were combined with some specific questions for the purpose of this study asked at the end of the survey. The Citizen Panel consists mainly of self-recruited respondents (85%). The remaining respondents (15%) comes from a probability-based recruitment from population samples. Overall, we consider the data to be of sufficient quality for the purpose of this paper where the main aim is to compare the difference between two treatments at which subjects were randomly allocated. However, there are of course reasons to be cautious, especially when looking beyond the differences between the two treatments in an attempt to interpret what affects SWB. The main feature of the experiment was that, based on random allocation, the subjects received either a survey version with a truth-telling request asking them to promise to tell the truth or a version without such a request. In all other respects, the two survey versions were identical. The truth-telling request read as follows: The questions that follow could by some people be perceived as sensitive and it can be difficult to give an honest answer even though the survey is anonymous. But it is very important that the answers to even the most sensitive issues are completely honest. The questions you will be asked are about how satisfied you are with your life as a whole and with various aspects of your life. They also deal with your health, and your income. Can you, hand on the heart, promise to answer the following questions honestly? Immediately after the request to promise to tell the truth, the survey consisted of questions about overall and domain-specific stated well-being. More specifically, the respondents were asked how satisfied they felt overall with their life, and subsequently with various aspects of their life on a scale from 0 to 10. Finally, they were asked questions about social trust, social interaction, health status and socio-economic characteristics. One obvious objection to methods attempting to reduce social desirability at the data collection stage is that survey respondents consciously or subconsciously change their behaviour to fit what they think is the purpose of the experiment, i.e. what is known as an experimenter demand effect. While this is generally true, we believe that it is of less concern in our setting since we do not say anything in the survey about the expected direction of a bias, something that is often done in for example stated preference surveys on public goods. Since we use a between-subject design the subjects do not know that we are observing how the truth-telling request affects them. Thus, it is hard for the subjects to know which researcher expectations to comply with beyond the simple request to answer truthfully. 4. Results 4.1 Descriptive results Table 1 reports descriptive statistics for the responses to the general and domain-specific well-being questions. The last columns report a test of the difference between the mean values with and without a promise, as well as the effect size. Table 1 Overall and domain-specific stated well-being, descriptive statistics, and test of difference between No Promise and Promise No promise Promise Difference Effect size Mean St.Dev. Obs. Mean St.Dev. Obs. Mean P-value (two-sided) Cohen's d Overall 6.94 1.99 1,782 6.58 2.10 1,700 0.36 <0.000 0.176 Financial situation 6.06 2.53 1,777 5.92 2.61 1,699 0.14 0.113 0.054 Spare time 6.55 2.15 1,761 6.25 2.22 1,688 0.30 <0.000 0.137 Work 6.12 2.55 1,506 5.97 2.64 1,427 0.15 0.127 0.056 Social life 6.88 2.18 1,769 6.52 2.25 1,695 0.36 <0.000 0.162 Sex life 5.30 2.94 1,648 4.83 2.99 1,629 0.47 <0.000 0.160 Family life 7.13 2.37 1,669 6.95 2.43 1,610 0.18 0.031 0.075 Relationships 7.35 2.59 1,401 7.05 2.73 1,335 0.30 0.006 0.112 Health 6.27 2.61 1,769 6.08 2.61 1,698 0.19 0.27 0.075 No promise Promise Difference Effect size Mean St.Dev. Obs. Mean St.Dev. Obs. Mean P-value (two-sided) Cohen's d Overall 6.94 1.99 1,782 6.58 2.10 1,700 0.36 <0.000 0.176 Financial situation 6.06 2.53 1,777 5.92 2.61 1,699 0.14 0.113 0.054 Spare time 6.55 2.15 1,761 6.25 2.22 1,688 0.30 <0.000 0.137 Work 6.12 2.55 1,506 5.97 2.64 1,427 0.15 0.127 0.056 Social life 6.88 2.18 1,769 6.52 2.25 1,695 0.36 <0.000 0.162 Sex life 5.30 2.94 1,648 4.83 2.99 1,629 0.47 <0.000 0.160 Family life 7.13 2.37 1,669 6.95 2.43 1,610 0.18 0.031 0.075 Relationships 7.35 2.59 1,401 7.05 2.73 1,335 0.30 0.006 0.112 Health 6.27 2.61 1,769 6.08 2.61 1,698 0.19 0.27 0.075 Notes: All variables range from 0 to 10. Source: Survey data, authors’ calculation Table 1 Overall and domain-specific stated well-being, descriptive statistics, and test of difference between No Promise and Promise No promise Promise Difference Effect size Mean St.Dev. Obs. Mean St.Dev. Obs. Mean P-value (two-sided) Cohen's d Overall 6.94 1.99 1,782 6.58 2.10 1,700 0.36 <0.000 0.176 Financial situation 6.06 2.53 1,777 5.92 2.61 1,699 0.14 0.113 0.054 Spare time 6.55 2.15 1,761 6.25 2.22 1,688 0.30 <0.000 0.137 Work 6.12 2.55 1,506 5.97 2.64 1,427 0.15 0.127 0.056 Social life 6.88 2.18 1,769 6.52 2.25 1,695 0.36 <0.000 0.162 Sex life 5.30 2.94 1,648 4.83 2.99 1,629 0.47 <0.000 0.160 Family life 7.13 2.37 1,669 6.95 2.43 1,610 0.18 0.031 0.075 Relationships 7.35 2.59 1,401 7.05 2.73 1,335 0.30 0.006 0.112 Health 6.27 2.61 1,769 6.08 2.61 1,698 0.19 0.27 0.075 No promise Promise Difference Effect size Mean St.Dev. Obs. Mean St.Dev. Obs. Mean P-value (two-sided) Cohen's d Overall 6.94 1.99 1,782 6.58 2.10 1,700 0.36 <0.000 0.176 Financial situation 6.06 2.53 1,777 5.92 2.61 1,699 0.14 0.113 0.054 Spare time 6.55 2.15 1,761 6.25 2.22 1,688 0.30 <0.000 0.137 Work 6.12 2.55 1,506 5.97 2.64 1,427 0.15 0.127 0.056 Social life 6.88 2.18 1,769 6.52 2.25 1,695 0.36 <0.000 0.162 Sex life 5.30 2.94 1,648 4.83 2.99 1,629 0.47 <0.000 0.160 Family life 7.13 2.37 1,669 6.95 2.43 1,610 0.18 0.031 0.075 Relationships 7.35 2.59 1,401 7.05 2.73 1,335 0.30 0.006 0.112 Health 6.27 2.61 1,769 6.08 2.61 1,698 0.19 0.27 0.075 Notes: All variables range from 0 to 10. Source: Survey data, authors’ calculation As discussed, the expected effect of the promise treatment is a reduction in stated well-being. This is confirmed for all nine well-being measures. Moreover, using a two-sided t-test we find that the differences are statistically significant at a 5% significance level for most of the differences. The difference in mean values is largest for the sex life domain. The largest effect size is found for the overall well-being measure, with a Cohen’s d of 0.18. Consequently, although there is a robust effect of the promise treatment, the effect sizes are rather small.6 6 With a Cohen's d of 0.2, 58% of the treatment group would be above the mean of the control group, 92% of the two groups would overlap, and there would be a 56% chance that a person randomly picked from the treatment group would have a higher score than a person randomly picked from the control group, i.e. 0.56 is the probability of superiority (McGraw and Wong, 1992). However, as discussed above, a relatively small amount of misclassification of a discrete dependent variable can still lead to biased coefficient estimates in the regression analysis (Hausman et al., 1998). We also report descriptive statistics of the variables that will be used in the regression models. These include both objective variables that we do not expect to be affected by the promise treatment, and a set of more value-laden questions such as self-reported health. The variables are presented in Table 2. Table 2 Potential determinants of stated well-being, descriptive statistics, and test of difference between the treatments (with a promise and the control group without a promise) Variable Description No promise Promise Difference Mean Std dev Mean Std dev P-value (two-sided t/pr-test) Woman = 1 if subject is a woman 0.47 0.47 0.772 Age Age in years 48.1 14.2 47.6 14.1 0.319 No of children No. of children (< 18 years) living in household 1.42 1.28 1.40 1.28 0.547 Divorced = 1 if divorced 0.07 0.07 0.589 Unemployed = 1 if unemployed 0.03 0.04 0.463 University = 1 if university education (at least three years) 0.32 0.32 0.927 Income Individual monthly income before taxes in SEK 30 900 15 350 30 300 15 100 0.261 Reported health Self-reported health status, 1 = very poor; 5 = very good 3.71 0.98 3.65 1.00 0.075 Social trust Stated trust, 1 = low trust; 10 = high trust 6.61 2.20 6.51 2.23 0.179 Social interact. Low = 1 if interact with friends/ relatives less than once per month 0.10 0.11 0.417 Social interact. intermediate = 1 if interact with friends/ relatives at least once per month 0.42 0.43 0.882 Social interact. High = 1 if interact with friends/ relatives at least once per week 0.47 0.46 0.602 Variable Description No promise Promise Difference Mean Std dev Mean Std dev P-value (two-sided t/pr-test) Woman = 1 if subject is a woman 0.47 0.47 0.772 Age Age in years 48.1 14.2 47.6 14.1 0.319 No of children No. of children (< 18 years) living in household 1.42 1.28 1.40 1.28 0.547 Divorced = 1 if divorced 0.07 0.07 0.589 Unemployed = 1 if unemployed 0.03 0.04 0.463 University = 1 if university education (at least three years) 0.32 0.32 0.927 Income Individual monthly income before taxes in SEK 30 900 15 350 30 300 15 100 0.261 Reported health Self-reported health status, 1 = very poor; 5 = very good 3.71 0.98 3.65 1.00 0.075 Social trust Stated trust, 1 = low trust; 10 = high trust 6.61 2.20 6.51 2.23 0.179 Social interact. Low = 1 if interact with friends/ relatives less than once per month 0.10 0.11 0.417 Social interact. intermediate = 1 if interact with friends/ relatives at least once per month 0.42 0.43 0.882 Social interact. High = 1 if interact with friends/ relatives at least once per week 0.47 0.46 0.602 Source: Survey data, authors’ calculations. Table 2 Potential determinants of stated well-being, descriptive statistics, and test of difference between the treatments (with a promise and the control group without a promise) Variable Description No promise Promise Difference Mean Std dev Mean Std dev P-value (two-sided t/pr-test) Woman = 1 if subject is a woman 0.47 0.47 0.772 Age Age in years 48.1 14.2 47.6 14.1 0.319 No of children No. of children (< 18 years) living in household 1.42 1.28 1.40 1.28 0.547 Divorced = 1 if divorced 0.07 0.07 0.589 Unemployed = 1 if unemployed 0.03 0.04 0.463 University = 1 if university education (at least three years) 0.32 0.32 0.927 Income Individual monthly income before taxes in SEK 30 900 15 350 30 300 15 100 0.261 Reported health Self-reported health status, 1 = very poor; 5 = very good 3.71 0.98 3.65 1.00 0.075 Social trust Stated trust, 1 = low trust; 10 = high trust 6.61 2.20 6.51 2.23 0.179 Social interact. Low = 1 if interact with friends/ relatives less than once per month 0.10 0.11 0.417 Social interact. intermediate = 1 if interact with friends/ relatives at least once per month 0.42 0.43 0.882 Social interact. High = 1 if interact with friends/ relatives at least once per week 0.47 0.46 0.602 Variable Description No promise Promise Difference Mean Std dev Mean Std dev P-value (two-sided t/pr-test) Woman = 1 if subject is a woman 0.47 0.47 0.772 Age Age in years 48.1 14.2 47.6 14.1 0.319 No of children No. of children (< 18 years) living in household 1.42 1.28 1.40 1.28 0.547 Divorced = 1 if divorced 0.07 0.07 0.589 Unemployed = 1 if unemployed 0.03 0.04 0.463 University = 1 if university education (at least three years) 0.32 0.32 0.927 Income Individual monthly income before taxes in SEK 30 900 15 350 30 300 15 100 0.261 Reported health Self-reported health status, 1 = very poor; 5 = very good 3.71 0.98 3.65 1.00 0.075 Social trust Stated trust, 1 = low trust; 10 = high trust 6.61 2.20 6.51 2.23 0.179 Social interact. Low = 1 if interact with friends/ relatives less than once per month 0.10 0.11 0.417 Social interact. intermediate = 1 if interact with friends/ relatives at least once per month 0.42 0.43 0.882 Social interact. High = 1 if interact with friends/ relatives at least once per week 0.47 0.46 0.602 Source: Survey data, authors’ calculations. As expected, there are no statistically significant differences among the objective variables between the versions with and without a promise. However, for the more subjective question about self-reported health status, we do find a weak statistically significant difference at a 10% significance level. Self-reported health is higher without a promise. This is in line with Jurges (2007), who found that Danish and Swedish respondents tend to overrate their health status compared to diagnosed conditions and measurements. No statistically significant differences between the versions with and without a promise are found for the other questions of a more of subjective nature, such as social trust and social interaction. 4.2 Regression models So far we have confirmed a systematic effect on SWB by asking subjects to promise to tell the truth. In addition, we found a weak statistically significant difference in self-reported health status between the two survey versions, while for the other subjective and all objective questions we did not find any statistically significant differences. The next question is whether the differences between the two survey versions affect coefficient estimates—in terms of size and statistical significance—in regression models of SWB. In order to investigate this, we compare two regression models with the same model specification that only differ in whether data was collected using a survey with or without asking the respondents to promise to tell the truth. Since the data is ordinal, we estimate ordered probit models.7 7 As discussed by Ferrer-i-Carbonell and Frijters (2004), the empirical findings in SWB studies need not be sensitive to the choice between a standard ordinary least squares (OLS) model and a discrete model such as an ordered probit model. However, in our specific case we focus on a model that from a conceptual point of view is the more appropriate model because measurement errors are more problematic in a discrete model framework. The results are presented in Table 3. Table 3 Ordered probit models, stated (overall) SWB as dependent variable, with and without a promise to tell the truth No Promise Promise Difference Coeff (S.E.) Coeff (S.E.) P-value Two-sided chi-squared test Woman 0.152*** 0.153*** 0.960 (0.053) (0.053) Age −0.022* −0.015 0.727 (0.014) (0.014) Age2 0.0003** 0.0002* 0.809 (0.0001) (0.0001) No. of children 0.146*** 0.133*** 0.719 (0.023) (0.023) Divorced −0.335*** −0.589*** 0.089 (0.101) (0.104) Unemployed −0.885*** −0.489*** 0.087 (0.148) (0.147) University −0.151*** −0.104* 0.558 (0.057) (0.058) Income 0.026 0.073*** 0.083 (0.017) (0.018) Reported health 0.521*** 0.474*** 0.294 (0.029) (0.028) Social trust 0.091*** 0.097*** 0.743 (0.012) (0.012) Social interaction low −0.133 −0.379*** 0.067 (0.089) (0.088) Social interaction high 0.125*** 0.139*** 0.859 (0.055) (0.056) No. obs. 1659 1596 Pseudo R2 0.097 0.094 No Promise Promise Difference Coeff (S.E.) Coeff (S.E.) P-value Two-sided chi-squared test Woman 0.152*** 0.153*** 0.960 (0.053) (0.053) Age −0.022* −0.015 0.727 (0.014) (0.014) Age2 0.0003** 0.0002* 0.809 (0.0001) (0.0001) No. of children 0.146*** 0.133*** 0.719 (0.023) (0.023) Divorced −0.335*** −0.589*** 0.089 (0.101) (0.104) Unemployed −0.885*** −0.489*** 0.087 (0.148) (0.147) University −0.151*** −0.104* 0.558 (0.057) (0.058) Income 0.026 0.073*** 0.083 (0.017) (0.018) Reported health 0.521*** 0.474*** 0.294 (0.029) (0.028) Social trust 0.091*** 0.097*** 0.743 (0.012) (0.012) Social interaction low −0.133 −0.379*** 0.067 (0.089) (0.088) Social interaction high 0.125*** 0.139*** 0.859 (0.055) (0.056) No. obs. 1659 1596 Pseudo R2 0.097 0.094 Notes: *, **, and *** denote significance at 10%, 5%, and 1%, respectively for a two-sided test. Table 3 Ordered probit models, stated (overall) SWB as dependent variable, with and without a promise to tell the truth No Promise Promise Difference Coeff (S.E.) Coeff (S.E.) P-value Two-sided chi-squared test Woman 0.152*** 0.153*** 0.960 (0.053) (0.053) Age −0.022* −0.015 0.727 (0.014) (0.014) Age2 0.0003** 0.0002* 0.809 (0.0001) (0.0001) No. of children 0.146*** 0.133*** 0.719 (0.023) (0.023) Divorced −0.335*** −0.589*** 0.089 (0.101) (0.104) Unemployed −0.885*** −0.489*** 0.087 (0.148) (0.147) University −0.151*** −0.104* 0.558 (0.057) (0.058) Income 0.026 0.073*** 0.083 (0.017) (0.018) Reported health 0.521*** 0.474*** 0.294 (0.029) (0.028) Social trust 0.091*** 0.097*** 0.743 (0.012) (0.012) Social interaction low −0.133 −0.379*** 0.067 (0.089) (0.088) Social interaction high 0.125*** 0.139*** 0.859 (0.055) (0.056) No. obs. 1659 1596 Pseudo R2 0.097 0.094 No Promise Promise Difference Coeff (S.E.) Coeff (S.E.) P-value Two-sided chi-squared test Woman 0.152*** 0.153*** 0.960 (0.053) (0.053) Age −0.022* −0.015 0.727 (0.014) (0.014) Age2 0.0003** 0.0002* 0.809 (0.0001) (0.0001) No. of children 0.146*** 0.133*** 0.719 (0.023) (0.023) Divorced −0.335*** −0.589*** 0.089 (0.101) (0.104) Unemployed −0.885*** −0.489*** 0.087 (0.148) (0.147) University −0.151*** −0.104* 0.558 (0.057) (0.058) Income 0.026 0.073*** 0.083 (0.017) (0.018) Reported health 0.521*** 0.474*** 0.294 (0.029) (0.028) Social trust 0.091*** 0.097*** 0.743 (0.012) (0.012) Social interaction low −0.133 −0.379*** 0.067 (0.089) (0.088) Social interaction high 0.125*** 0.139*** 0.859 (0.055) (0.056) No. obs. 1659 1596 Pseudo R2 0.097 0.094 Notes: *, **, and *** denote significance at 10%, 5%, and 1%, respectively for a two-sided test. In the model based on data from the survey without a promise, we see that most coefficients are statistically significant and the signs are in line with what is typically found in SWB studies. Stated well-being is positively correlated with being a woman, age, number of children, health status, and social trust, and negatively correlated with age squared,8 8 A negative relationship between SWB and age and a positive relationship between SWB and age squared is in line with previous findings (see Dolan et al., 2008) suggesting higher levels of well-being in younger and older age. being divorced, unemployed, and a low level of social interaction. The exceptions are that individual income does not have a statistically significant effect on SWB and that university education has a statistically significant negative impact on SWB. The results are largely similar in terms of sign and statistical significance in the model based on data with a promise to tell the truth. However, at the 10% significance level there are four statistically significant differences between the two datasets. This provides some support that a measurement bias can lead to incorrect inferences in the regression analysis of SWB data. The estimated negative effect of being divorced is greater when using the data from the survey with a promise to tell the truth. In contrast, the estimated negative effect of being unemployed is smaller when using the data with a promise. Moreover, with a promise we observe that individual income has a positive and statistically significant effect on SWB. Note that the reported incomes and proportions of subjects who are divorced or unemployed do not differ between the two survey versions. Finally, the negative impact of having limited social interaction is only statistically significant in the version with a promise, and the difference between the two versions is statistically significant. To summarize, while we do not see much of a difference on most of the determinants of SWB in the samples with and without a promise, for a few variables the effect is weakly significant. Moreover, the differences between these point estimates are substantial. In previous studies it has been found that one effect of asking people to promise to tell the truth is that the underlying variance decreases (see, for example, Carlsson et al., 2013). In order to control for differences in variance between the treatments for SWB data, we estimate a heteroskedastic ordered probit model on the pooled data. We include a set of interaction terms for all independent variables in order to allow for a level difference between the two survey versions as well. The results are presented in Table A1 in the Appendix. The differences between the two survey versions remain the same as when comparing the two models in Table 3, and there is no statistically significant difference in variance between the two survey versions. Thus, in this specific case, there is no effect on the underlying variance when asking subjects to promise to tell truth. Thus, we have some evidence that a measurement bias can lead to incorrect inference in the regression analysis of SWB data. The coefficients are smaller in the version without a promise for three of the four coefficients where we observe a statistically significant difference between the two survey versions. Finally, we investigate whether the promise to tell the truth has similar effects on domain-specific SWB, such as health, finances, social relationships and sex life. This analysis might help us to gain a better understanding of what aspects of life people in general are inclined to misrepresent when asked about their well-being. We control for a set of individual characteristics and allow for a difference in variance between the two survey versions. Heteroskedastic ordered probit models for each of the eight domain-specific SWB-measures are used and the results are presented in Table 4. Table 4 Heteroskedastic ordered probit model, stated domain specific SWB as dependent variable, with and without a promise to tell the truth, standard errors in parentheses Finance Spare Work Social Sex Family Relation Health Woman 0.007 0.109*** 0.010 0.186*** 0.186*** 0.218*** 0.102** −0.052 (0.038) (0.038) (0.041) (0.038) (0.038) (0.039) (0.043) (0.037) Age 0.014 −0.015 0.016 −0.020** −0.015 −0.023** −0.039*** 0.0002 (0.009) (0.010) (0.011) (0.009) (0.010) (0.010) (0.012) (0.009) Age2 0.000002 0.0004*** 0.0003** 0.0003*** 0.0002 0.0002** 0.0004*** 0.0004 (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0009) N. child −0.033** 0.032 0.039 0.067*** 0.092*** 0.183*** 0.051*** 0.014 (0.017) (0.017) (0.018) (0.016) (0.017) (0.018) (0.019) (0.016) Divorced −0.365*** −0.419*** −0.295*** 0.276*** −0.616 −0.629*** −1.054*** −0.146 (0.073) (0.075) (0.085) (0.083) (0.081) (0.078) (0.150) (0.071) Unemp −1.034*** −0.064 −1.540*** −0.064 −0.038 −0.340*** −0.158 −0.013 (0.108) (0107) (0.132) (0.105) (0.111) (0.113) (0.134) (0.103) Univ 0.007 −0.155*** −0.019 −0.127*** −0.045 −0.061 −0.083* −0.056 (0.041) (0.041) (0.044) (0.041) (0.041) (0.043) (0.046) (0.040) Income 0.153*** 0.014 0.058*** 0.016 0.042*** 0.002 0.006 −0.003 (0.013) (0.013) (0.015) (0.013) (0.013) (0.013) (0.014) (0.013) Health 0.330*** 0.366*** 0.370*** 0.233*** 0.153*** 0.181*** 0.132*** 1.387 (0.020) (0.021) (0.023) (0.020) (0.020) (0.020) (0.023) (0.032) Trust 0.057*** 0.046*** 0.057*** 0.054*** 0.046*** 0.059*** 0.064*** 0.037 (0.009) (0.009) (0.010) (0.009) (0.009) (0.009) (0.010) (0.009) S int low −0.224*** −0.271*** −0.134* −0.610*** −0.114* −0.268*** −0.200*** −0.075 (0.039) (0.064) (0.070) (0.064) (0.066) (0.066) (0.070) (0.062) S int high 0.028 0.312*** 0.058 0.491*** 0.152*** 0.134*** 0.112** 0.093 (0.039) (0.040) (0.043) (0.040) (0.040) (0.041) (0.045) (0.038) Promise −0.186 −0.125*** −0.027 −0.154*** −0.142*** −0.064* −0.102** −0.050 (0.036) (0.037) (0.039) (0.036) (0.037) (0.038) (0.041) (0.035) Variance Promise 0.024 0.045* 0.043 0.021 0.006 0.046 0.032 −0.036 (0.027) (0.027) (0.029) (0.027) (0.029) (0.029) (0.034) (0.027) Nobs 3255 3230 2771 3243 3083 3076 2578 3247 Finance Spare Work Social Sex Family Relation Health Woman 0.007 0.109*** 0.010 0.186*** 0.186*** 0.218*** 0.102** −0.052 (0.038) (0.038) (0.041) (0.038) (0.038) (0.039) (0.043) (0.037) Age 0.014 −0.015 0.016 −0.020** −0.015 −0.023** −0.039*** 0.0002 (0.009) (0.010) (0.011) (0.009) (0.010) (0.010) (0.012) (0.009) Age2 0.000002 0.0004*** 0.0003** 0.0003*** 0.0002 0.0002** 0.0004*** 0.0004 (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0009) N. child −0.033** 0.032 0.039 0.067*** 0.092*** 0.183*** 0.051*** 0.014 (0.017) (0.017) (0.018) (0.016) (0.017) (0.018) (0.019) (0.016) Divorced −0.365*** −0.419*** −0.295*** 0.276*** −0.616 −0.629*** −1.054*** −0.146 (0.073) (0.075) (0.085) (0.083) (0.081) (0.078) (0.150) (0.071) Unemp −1.034*** −0.064 −1.540*** −0.064 −0.038 −0.340*** −0.158 −0.013 (0.108) (0107) (0.132) (0.105) (0.111) (0.113) (0.134) (0.103) Univ 0.007 −0.155*** −0.019 −0.127*** −0.045 −0.061 −0.083* −0.056 (0.041) (0.041) (0.044) (0.041) (0.041) (0.043) (0.046) (0.040) Income 0.153*** 0.014 0.058*** 0.016 0.042*** 0.002 0.006 −0.003 (0.013) (0.013) (0.015) (0.013) (0.013) (0.013) (0.014) (0.013) Health 0.330*** 0.366*** 0.370*** 0.233*** 0.153*** 0.181*** 0.132*** 1.387 (0.020) (0.021) (0.023) (0.020) (0.020) (0.020) (0.023) (0.032) Trust 0.057*** 0.046*** 0.057*** 0.054*** 0.046*** 0.059*** 0.064*** 0.037 (0.009) (0.009) (0.010) (0.009) (0.009) (0.009) (0.010) (0.009) S int low −0.224*** −0.271*** −0.134* −0.610*** −0.114* −0.268*** −0.200*** −0.075 (0.039) (0.064) (0.070) (0.064) (0.066) (0.066) (0.070) (0.062) S int high 0.028 0.312*** 0.058 0.491*** 0.152*** 0.134*** 0.112** 0.093 (0.039) (0.040) (0.043) (0.040) (0.040) (0.041) (0.045) (0.038) Promise −0.186 −0.125*** −0.027 −0.154*** −0.142*** −0.064* −0.102** −0.050 (0.036) (0.037) (0.039) (0.036) (0.037) (0.038) (0.041) (0.035) Variance Promise 0.024 0.045* 0.043 0.021 0.006 0.046 0.032 −0.036 (0.027) (0.027) (0.029) (0.027) (0.029) (0.029) (0.034) (0.027) Nobs 3255 3230 2771 3243 3083 3076 2578 3247 Notes: *, **, and *** denote significance at two-sided 10%, 5%, and 1% significance level, respectively. Table 4 Heteroskedastic ordered probit model, stated domain specific SWB as dependent variable, with and without a promise to tell the truth, standard errors in parentheses Finance Spare Work Social Sex Family Relation Health Woman 0.007 0.109*** 0.010 0.186*** 0.186*** 0.218*** 0.102** −0.052 (0.038) (0.038) (0.041) (0.038) (0.038) (0.039) (0.043) (0.037) Age 0.014 −0.015 0.016 −0.020** −0.015 −0.023** −0.039*** 0.0002 (0.009) (0.010) (0.011) (0.009) (0.010) (0.010) (0.012) (0.009) Age2 0.000002 0.0004*** 0.0003** 0.0003*** 0.0002 0.0002** 0.0004*** 0.0004 (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0009) N. child −0.033** 0.032 0.039 0.067*** 0.092*** 0.183*** 0.051*** 0.014 (0.017) (0.017) (0.018) (0.016) (0.017) (0.018) (0.019) (0.016) Divorced −0.365*** −0.419*** −0.295*** 0.276*** −0.616 −0.629*** −1.054*** −0.146 (0.073) (0.075) (0.085) (0.083) (0.081) (0.078) (0.150) (0.071) Unemp −1.034*** −0.064 −1.540*** −0.064 −0.038 −0.340*** −0.158 −0.013 (0.108) (0107) (0.132) (0.105) (0.111) (0.113) (0.134) (0.103) Univ 0.007 −0.155*** −0.019 −0.127*** −0.045 −0.061 −0.083* −0.056 (0.041) (0.041) (0.044) (0.041) (0.041) (0.043) (0.046) (0.040) Income 0.153*** 0.014 0.058*** 0.016 0.042*** 0.002 0.006 −0.003 (0.013) (0.013) (0.015) (0.013) (0.013) (0.013) (0.014) (0.013) Health 0.330*** 0.366*** 0.370*** 0.233*** 0.153*** 0.181*** 0.132*** 1.387 (0.020) (0.021) (0.023) (0.020) (0.020) (0.020) (0.023) (0.032) Trust 0.057*** 0.046*** 0.057*** 0.054*** 0.046*** 0.059*** 0.064*** 0.037 (0.009) (0.009) (0.010) (0.009) (0.009) (0.009) (0.010) (0.009) S int low −0.224*** −0.271*** −0.134* −0.610*** −0.114* −0.268*** −0.200*** −0.075 (0.039) (0.064) (0.070) (0.064) (0.066) (0.066) (0.070) (0.062) S int high 0.028 0.312*** 0.058 0.491*** 0.152*** 0.134*** 0.112** 0.093 (0.039) (0.040) (0.043) (0.040) (0.040) (0.041) (0.045) (0.038) Promise −0.186 −0.125*** −0.027 −0.154*** −0.142*** −0.064* −0.102** −0.050 (0.036) (0.037) (0.039) (0.036) (0.037) (0.038) (0.041) (0.035) Variance Promise 0.024 0.045* 0.043 0.021 0.006 0.046 0.032 −0.036 (0.027) (0.027) (0.029) (0.027) (0.029) (0.029) (0.034) (0.027) Nobs 3255 3230 2771 3243 3083 3076 2578 3247 Finance Spare Work Social Sex Family Relation Health Woman 0.007 0.109*** 0.010 0.186*** 0.186*** 0.218*** 0.102** −0.052 (0.038) (0.038) (0.041) (0.038) (0.038) (0.039) (0.043) (0.037) Age 0.014 −0.015 0.016 −0.020** −0.015 −0.023** −0.039*** 0.0002 (0.009) (0.010) (0.011) (0.009) (0.010) (0.010) (0.012) (0.009) Age2 0.000002 0.0004*** 0.0003** 0.0003*** 0.0002 0.0002** 0.0004*** 0.0004 (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0009) N. child −0.033** 0.032 0.039 0.067*** 0.092*** 0.183*** 0.051*** 0.014 (0.017) (0.017) (0.018) (0.016) (0.017) (0.018) (0.019) (0.016) Divorced −0.365*** −0.419*** −0.295*** 0.276*** −0.616 −0.629*** −1.054*** −0.146 (0.073) (0.075) (0.085) (0.083) (0.081) (0.078) (0.150) (0.071) Unemp −1.034*** −0.064 −1.540*** −0.064 −0.038 −0.340*** −0.158 −0.013 (0.108) (0107) (0.132) (0.105) (0.111) (0.113) (0.134) (0.103) Univ 0.007 −0.155*** −0.019 −0.127*** −0.045 −0.061 −0.083* −0.056 (0.041) (0.041) (0.044) (0.041) (0.041) (0.043) (0.046) (0.040) Income 0.153*** 0.014 0.058*** 0.016 0.042*** 0.002 0.006 −0.003 (0.013) (0.013) (0.015) (0.013) (0.013) (0.013) (0.014) (0.013) Health 0.330*** 0.366*** 0.370*** 0.233*** 0.153*** 0.181*** 0.132*** 1.387 (0.020) (0.021) (0.023) (0.020) (0.020) (0.020) (0.023) (0.032) Trust 0.057*** 0.046*** 0.057*** 0.054*** 0.046*** 0.059*** 0.064*** 0.037 (0.009) (0.009) (0.010) (0.009) (0.009) (0.009) (0.010) (0.009) S int low −0.224*** −0.271*** −0.134* −0.610*** −0.114* −0.268*** −0.200*** −0.075 (0.039) (0.064) (0.070) (0.064) (0.066) (0.066) (0.070) (0.062) S int high 0.028 0.312*** 0.058 0.491*** 0.152*** 0.134*** 0.112** 0.093 (0.039) (0.040) (0.043) (0.040) (0.040) (0.041) (0.045) (0.038) Promise −0.186 −0.125*** −0.027 −0.154*** −0.142*** −0.064* −0.102** −0.050 (0.036) (0.037) (0.039) (0.036) (0.037) (0.038) (0.041) (0.035) Variance Promise 0.024 0.045* 0.043 0.021 0.006 0.046 0.032 −0.036 (0.027) (0.027) (0.029) (0.027) (0.029) (0.029) (0.034) (0.027) Nobs 3255 3230 2771 3243 3083 3076 2578 3247 Notes: *, **, and *** denote significance at two-sided 10%, 5%, and 1% significance level, respectively. The most important result is that the coefficient of the Promise dummy variable is statistically significant at least the 10% level in five of the eight regression models and notably always with a negative sign. If we compare with the tests based on the raw data in Table 1, there are thus three cases that no longer show any statistically significant difference. These three cases were the ones with smaller effect sizes, and two of them were only statistically significant at the 10% level. The coefficient in the variance part of the estimated models is only statistically significant in one of the eight regression models. Thus, we can conclude that overall there is a similar effect for many of the domain-specific SWB questions as we observed for the general SWB question. The correlation between individual characteristics and SWB in the different domains is rather consistent across domains, although there is some variation. We have also estimated models where the dummy variable for Promise is interacted with the individual characteristics. In general, we do not find any consistent effect through any of the individual characteristics.9 9 Results are available upon request from the authors. 5. Conclusion Social-desirability and self-image concerns are factors that might affect how subjects respond to survey questions, in particular, value-laden questions about how happy you are or how satisfied you are with your life, for example. To admit to being unhappy could undeniably be very hard. We find a systematic effect on stated well-being of making the subjects promise to tell the truth. We find this for both overall well-being and for SWB in a number of specific domains. In general, we find that people are inclined to overstate their life satisfaction. The effect size of this difference in stated SWB with and without promise to tell the truth is certainly not large; in fact, it is rather small. However, even small differences in the dependent variable can in theory have important implications for the inferences we draw in our regression models on the effect of the independent variables. However, our findings do not suggest that this would be a widespread problem. While we do find differences in terms of coefficient sizes and statistical significance if we compare the two models using the samples with and without a promise to tell the truth, these differences are statistically significant only at the 10% level and only for a few independent variables (e.g. income, unemployment). To summarize, while we do find that people exaggerate the overall happiness, the statistically weak effects on the relationship between covariates and SWB means that the policy implications we would draw from a study would only marginally depend on whether or not we include a truth-telling question. To our knowledge, we are the first to test how promises to answer truthfully affect SWB data. Clearly, more evidence is needed to draw robust conclusions. We also see a need for more work in general to improve the measurement of SWB data. One interesting topic for future research would be to investigate whether the bias we identify in our cross-sectional study is less of a problem using longitudinal data. Supplementary material Supplementary material—the data and replication files—are available online at the OUP website. Funding This work was supported by the Laboratory of Opinion Research at the University of Gothenburg. Acknowledgements We are very grateful to Elina Lampi for generously sharing her expertise and collaborating with us at the design stage of this study. We have received valuable comments from the managing editor, an anonymous reviewer and an anonymous member of the editorial board. References Aadland D. , Caplan A. ( 2006 ) Cheap talk revisited: new evidence from CVM , Journal of Economic Behavior and Organization , 60 , 562 – 78 . 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Google Scholar CrossRef Search ADS Appendix Table A1 Heteroskedastic ordered probit model, stated SWB as dependent variable, with and without a promise to tell the truth Coeff (S.E) Woman 0.153*** (0.053) Age −0.022* (0.013) Age^2 0.0003** (0.0001) No. of children 0.147*** (0.023) Divorced −0.334*** (0.101) Unemployed −0.899*** (0.148) University −0.151*** (0.057) Income 0.026 (0.018) Reported health 0.524*** (0.029) Social trust 0.091*** (0.012) Social interaction low −0.135 (0.089) Social interaction high 0.123*** (0.055) Promise −0.461 (0.223) Woman × Promise 0.010 (0.076) Age × Promise 0.007 (0.019) Age^2 × Promise −0.00004 (0.0002) No. of children × Promise −0.009 (0.033) Divorce × Promise −0.274* (0.148) Unemployed × Promise 0.398* (0.211) University × Promise 0.043 (0.083) Income × Promise 0.049* (0.025) Health × Promise −0.035 (0.039) Social trust × Promise 0.010 (0.018) Social interaction low × Promise −0.252** (0.128) Social interaction high × Promise 0.217 (0.079) Variance: Promise 0.035 (0.027) No. obs. 3255 Coeff (S.E) Woman 0.153*** (0.053) Age −0.022* (0.013) Age^2 0.0003** (0.0001) No. of children 0.147*** (0.023) Divorced −0.334*** (0.101) Unemployed −0.899*** (0.148) University −0.151*** (0.057) Income 0.026 (0.018) Reported health 0.524*** (0.029) Social trust 0.091*** (0.012) Social interaction low −0.135 (0.089) Social interaction high 0.123*** (0.055) Promise −0.461 (0.223) Woman × Promise 0.010 (0.076) Age × Promise 0.007 (0.019) Age^2 × Promise −0.00004 (0.0002) No. of children × Promise −0.009 (0.033) Divorce × Promise −0.274* (0.148) Unemployed × Promise 0.398* (0.211) University × Promise 0.043 (0.083) Income × Promise 0.049* (0.025) Health × Promise −0.035 (0.039) Social trust × Promise 0.010 (0.018) Social interaction low × Promise −0.252** (0.128) Social interaction high × Promise 0.217 (0.079) Variance: Promise 0.035 (0.027) No. obs. 3255 Notes: *, **, and *** denote significance at two-sided 10%, 5%, and 1% significance level, respectively. Table A1 Heteroskedastic ordered probit model, stated SWB as dependent variable, with and without a promise to tell the truth Coeff (S.E) Woman 0.153*** (0.053) Age −0.022* (0.013) Age^2 0.0003** (0.0001) No. of children 0.147*** (0.023) Divorced −0.334*** (0.101) Unemployed −0.899*** (0.148) University −0.151*** (0.057) Income 0.026 (0.018) Reported health 0.524*** (0.029) Social trust 0.091*** (0.012) Social interaction low −0.135 (0.089) Social interaction high 0.123*** (0.055) Promise −0.461 (0.223) Woman × Promise 0.010 (0.076) Age × Promise 0.007 (0.019) Age^2 × Promise −0.00004 (0.0002) No. of children × Promise −0.009 (0.033) Divorce × Promise −0.274* (0.148) Unemployed × Promise 0.398* (0.211) University × Promise 0.043 (0.083) Income × Promise 0.049* (0.025) Health × Promise −0.035 (0.039) Social trust × Promise 0.010 (0.018) Social interaction low × Promise −0.252** (0.128) Social interaction high × Promise 0.217 (0.079) Variance: Promise 0.035 (0.027) No. obs. 3255 Coeff (S.E) Woman 0.153*** (0.053) Age −0.022* (0.013) Age^2 0.0003** (0.0001) No. of children 0.147*** (0.023) Divorced −0.334*** (0.101) Unemployed −0.899*** (0.148) University −0.151*** (0.057) Income 0.026 (0.018) Reported health 0.524*** (0.029) Social trust 0.091*** (0.012) Social interaction low −0.135 (0.089) Social interaction high 0.123*** (0.055) Promise −0.461 (0.223) Woman × Promise 0.010 (0.076) Age × Promise 0.007 (0.019) Age^2 × Promise −0.00004 (0.0002) No. of children × Promise −0.009 (0.033) Divorce × Promise −0.274* (0.148) Unemployed × Promise 0.398* (0.211) University × Promise 0.043 (0.083) Income × Promise 0.049* (0.025) Health × Promise −0.035 (0.039) Social trust × Promise 0.010 (0.018) Social interaction low × Promise −0.252** (0.128) Social interaction high × Promise 0.217 (0.079) Variance: Promise 0.035 (0.027) No. obs. 3255 Notes: *, **, and *** denote significance at two-sided 10%, 5%, and 1% significance level, respectively. © Oxford University Press 2018 All rights reserved This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Oxford Economic Papers Oxford University Press

Do people exaggerate how happy they are? Using a promise to induce truth-telling

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Oxford University Press
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0030-7653
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1464-3812
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10.1093/oep/gpy003
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Abstract

Abstract We investigate a novel approach to reduce measurement error in subjective well-being (SWB) data. Using a between-subject design, half of the subjects are asked to promise to answer the survey questions truthfully to make them commit to truth-telling. We find a statistically significant difference between mean stated well-being between the two samples (with and without a promise). People are consistently found to exaggerate their happiness and for several different aspects of life, without a promise. We then investigate to what extent the differences in stated well-being also affect the inference from regressions models on the determinants of SWB. The effect on the covariates are only weakly statistically significant and only for a few variables, if we compare the samples with and without the promise. Thus, this means that the policy implications based on an SWB study only marginally depends on whether we include a truth-telling question or not. 1. Introduction We have witnessed an increased use of subjective well-being (SWB) measures in economics; from 2000 to 2006, 157 papers and numerous books on the topic were published in economics literature (Krueger and Schkade, 2008). While most economists would probably agree that the information gained from subjective questions is interesting and important, the unwillingness to rely on such questions has historically marked an important difference between economists and other social scientists (Bertrand and Mullainathan, 2001). This attitude may, however, have shifted among economists during the last 10 years. Furthermore, we have seen an increased interest in incorporating findings from other disciplines, such as psychology, into economics (see, for example, Layard, 2006). One of the problems with SWB data is that it is prone to social desirability bias, i.e. the tendency of survey respondents to answer questions in a manner that will be viewed favourably by others and in line with certain social norms (Phillips and Clancy, 1972). Such social-image concerns are likely to be important to many people (Lacetera and Macis, 2010). Although not as extensively explored, it is also plausible that self-image concerns can bias survey responses, e.g. in an SWB context respondents may not want to admit even to themselves that they are unsatisfied with their life or some aspects thereof. In the words of Sandvik et al. (1993): ‘To claim to be happy may be the ultimate assertion of success in our society, and to admit unhappiness could be the single greatest summary of failure in life that an individual could concede.’ This type of misreporting creates a measurement error in the reported data, which can be handled either at the modeling stage,1 1 See, for example, Hausman (2001) for a review. The mismeasurement problem for linear models in econometrics is usually solved by using instrumental variables (Hausman, 2001). Hausman et al. (1998) offer methods that also deal with binary choice with misclassification and mismeasured discrete dependent variables with several categories. the data collection stage, or both. In an important contribution, Krueger and Schkade (2008) assessed the role of measurement error by correcting for attenuation for correlation coefficients between SWB variables and other variables, and they find substantial increases in correlation coefficients when correcting for attenuation. Furthermore, Benjamin et al. (2012) found that happiness data indeed could suffer from measurement error using a measurement-error-corrected regression. In this paper we investigate a novel approach to reduce measurement error in SWB surveys. We test a self-commitment mechanism where survey respondents are asked to promise to answer the survey questions truthfully. The main objective is to experimentally test whether making a promise affects responses to survey questions, in particular SWB questions. Subsequently, if we find differences, we investigate to what extent these differences affect the inference drawn from regressions models on the determinants of SWB. Traditionally, economists have been sceptical to asking respondents to tell the truth, primarily because there are no actual incentives for responding truthfully. However, empirical evidence suggests that a promise alone can indeed induce an emotional commitment to fulfill the promise (Ostrom et al., 1992; Braver, 1995; Ellingsen and Johannesson, 2004; Vanberg 2008; Carlsson et al., 2013; Kataria and Winter, 2013; Jacquemet et al., 2013).2 2 The reason why it works has recently been under scientific scrutiny. Charness and Dufwenberg (2006) found experimental evidence that a promise works because of guilt aversion: A guilt-averse person does not want to let down others’ expectations and is therefore committed to the promises made. An alternative explanation is that people may have a taste for keeping their word (see, for example, Ellingsen and Johannesson, 2004). Using a novel design, Vanberg (2008) found support for the latter explanation, i.e. people have preferences for promise keeping per se. This approach has been applied in, among other areas, experiments (Ellingsen and Johannesson, 2004; Jacquemet et al., 2013) and stated preference surveys on environmental problems (Carlsson et al., 2013). Yet as far as we know, it has not been used in SWB surveys. The literature on how to reduce measurement error at the data collection stage is rather extensive. One such area is survey research dealing with particularly sensitive topics such as racism, terrorism, corruption, illegal behaviour and drug use. Methods such as randomized response (Warner 1965; Greenberg et al., 1969) and item count techniques (Raghavarao and Federer, 1979) have been used in surveys with sensitive questions. Using randomized response, the respondent flips a coin and is instructed to answer either a sensitive or a non-sensitive yes/no question based on the outcome of the coin flip. Only the respondent knows which question he or she answered. This procedure hides individual answers but enables analysts to assess the true population proportions of yes/no replies because the noise probability is known. The drawback of the randomized response method is that it draws attention to the act of measurement itself. Respondents can become suspicious of intent and claims to anonymity and focus too much on how the method works instead of answering the questions. The item count method randomly splits the sample into two groups: control and treatment group. Both groups are asked insensitive questions, and then the treatment group is also asked a sensitive question, i.e. the question of interest. The respondents are then asked to reveal the number of ‘yes’ answers they have given. Respondent anonymity is assured whereas the number of people who answered ‘yes’ to the sensitive question can be mathematically deduced. While the item count method is straightforward with a low level of burden on the respondents, one major drawback of the method is the lack of power of the estimator for relatively high sample sizes (Droitcour et al., 1991). SWB data utilizes what in economics is known as experience utility, which is distinguished from what is known as decision utility. While decision utility is inferred from the decision-maker’s observed choices, experience utility is the satisfaction that is experienced once the decision is made.3 3 Benjamin et al. (2012) found that, using hypothetical survey questions, people’s choice and predicted SWB ranking of two alternatives usually coincide, albeit with some systematic deviations. If the choices are revealed in a market, the data is known as revealed preference data, while stated preference data represents choices made or stated in a constructed survey situation. Stated preference data is used frequently in economics to value public goods. Interestingly, both SWB data and stated preference studies deal with the same difficulties common to data based on subjective assessments. A number of approaches to reduce the so-called hypothetical bias, i.e. the difference between stated behaviour and behaviour if the choice situation would have been an actual one, have been suggested in the stated preference literature. One common approach is to use a cheap talk script, which aims to reduce hypothetical bias by informing respondents about the occurrence of hypothetical bias (Cummings and Taylor, 1999). The idea is that by making respondents aware of the problem, they will exert more effort when responding and in that way hypothetical bias will be reduced. The empirical support for a cheap talk script is mixed, and it is clear that the effect depends both on the context and on the formulation of the script (see, for example, List, 2001; Carlsson et al., 2005; Aadland and Caplan, 2006). Another approach that has been used is so-called inferred valuation (Lusk and Norwood, 2009a, 2009b; Carlsson et al., 2010), where respondents estimate other people’s valuations of goods.4 4 Other approaches to reduce hypothetical bias in the stated preference literature include expost calibration of willingness-to-pay estimates based on follow-up questions (see, for example, Champ et al., 1997; Johannesson et al., 1999; Champ and Bishop, 2001) and the time-to-think protocol (Whittington et al., 1992; Cook et al., 2007). Compared with the stated preference literature, surprisingly little attention has been given to reduce measurement error in SWB surveys. Layard (2006) argues that there is a need for an expanded model of happiness that incorporates findings from other disciplines, such as psychology. While his main focus was on theory, there seems also to be a need to look over how SWB data is collected, and as discussed, it seems possible to incorporate findings from neighbouring disciplines in the SWB literature. To simply rely on people truthfully reporting their happiness seems inappropriate and neglects developments in neighbouring disciplines. Understanding what affects SWB is important as it could help economists design policies that improve people’s well-being. In the late 1990s, economists started to present large-scale empirical analyses of determinants of well-being (Frey and Stutzer, 2002). Evidence suggests that poor health, divorce, unemployment, and lack of social relationships are important determinants of well-being (Dolan et al., 2008). While economists usually focus on determinants of SWB that can be categorized as actual observable life experiences, the approach has been somewhat more pluralistic in psychology. Here, the overall well-being is complemented with reported well-being in major domains of life, such as health, finances, social relationships, and sex life. The advantage of this life domain approach is that it better reflects subjective factors such as personal aspirations and norms that could affect the overall well-being. For example, an individual with high income and high financial aspirations could be less satisfied with life than someone with low income and low financial aspirations. We test whether making a promise affects responses to the overall well-being question as well as well-being in major domains of life. It helps us to gain a nuanced understanding of what aspects of life people in general are inclined to misrepresent when asked about their well-being. 2. Social desirability and self-image in a measurement-error framework One of the problems with SWB data is that it is prone to social desirability bias, i.e. the tendency of survey respondents to answer questions in a manner that will be viewed favourably by others and in line with some social norm (Phillips and Clancy, 1972). Self-image concerns may also bias the survey responses, e.g. respondents do not want to admit even to themselves that they are not satisfied with their life. In order to illustrate the potential problem with social desirability and self-image concerns, we use a measurement-error framework where the dependent and/or the independent variables in a regression model are observed with an error. The simplest case is a linear regression model with one independent variable and no intercept, which is also the standard textbook case (see, for example Greene, 2002). The observed dependent variable, y, is specified as y=y*+u, where y* is the true variable and u is a normally distributed error term, i.e. u∼N(μy,σ2y). Suppose that the observed independent variable is x, i.e. x=x*+v, where x* is the true variable and v∼N(μx,σx). Assume that u and v are independently distributed. If μ=0 the error term is a random error, and if μ≠0 the error is a systematic error. Two results are well-known in the literature. First, assuming that only y is measured with a random error does not result in biased parameter estimates since the measurement error is incorporated in the disturbance term. It will, however, increase the standard error of the estimated parameter, i.e. the parameter will be estimated with less precision. Second, if instead x is measured with a random error, the parameter estimates are inconsistent and biased towards zero (attenuation bias). Hausman (2001) calls this the ‘iron law of econometrics’—the magnitude of the estimate is usually smaller than expected. If both the dependent and the independent variable are mismeasured, the parameters are still—of course—biased and measured with less accuracy. Notably, Krueger and Schkade (2008) investigated correlations between life satisfaction and variables such as income with and without adjustment for attenuation bias due to measurement error and found a substantial increase for some of the correlations when adjusting for attenuation bias. The regression models used in the SWB literature deviate in many aspects from the simplifying assumptions we made above. First of all, a multiple regression framework, with an intercept, is used in the literature. Second, if social desirability and self-image are the reasons for measurement error, we would expect the dependent variable to be measured with a systematic rather than a random error. This is also true for independent variables with value-laden content such as life satisfaction in different domains, but perhaps not for objective variables that are merely counts of various types of individual characteristics. With objective variables we expect the problem of measurement error to be less severe, and if present we would expect it to be a random error. Also note that the measurement errors of the independent variables might be correlated with each other and the measurement error of the dependent variable. Third, the dependent variable is measured on an ordinal scale and should therefore be estimated with a non-linear model such as an ordered probit model. Hausman et al. (1998) showed that misclassificatio5 5 Misclassification means that the response is reported or recorded in the wrong category. of the dependent discrete variable causes inconsistent coefficient estimates if the measurement error is not taken into consideration in a standard framework (e.g. probit or logit). Relatively small amounts of misclassification of the dependent variable can lead to a large bias even with a large sample size. Hence, measurement bias causes severe problems. Exactly how these problems will manifest in our application is an empirical question. We will address the issue of measurement error in SWB data experimentally by comparing stated levels of well-being and coefficients of regressions models from two different survey versions, where only one of the versions include a short script and question asking if the respondents can answer the questions in the survey truthfully or not. Our expectation is that a truth-telling request reduces the reported SWB based on the expectation that people tend to exaggerate SWB measures. However, similar to all studies involving survey we data we face the problem of external validity. How does one really objectively validate SWB? Oswald and Wu (2010) suggest biological indicators such as blood pressure (see, for example, Blanchflower and Oswald, 2008) but they also point out that biological indicators are not unambiguous measures of happiness. As already mentioned, there is also an economic literature that truth-telling request does in fact induce more truth-telling. Hence, in the light of these findings we expect a treatment effect and the interpretation is that the truth-telling request will induce more truth-telling compared to the control treatment. 3. Survey design The questions used in this paper were administered to respondents as part of the thirteenth wave of the Citizen Panel (Martinsson et al., 2014). The Citizen Panel is an online panel survey administered by the Laboratory of Opinion Research (LORE), which was established in 2010 by the Multidisciplinary Opinion and Democracy (MOD) research group at the Faculty of Social Science, University of Gothenburg in Sweden. The survey was carried out from November 27 to December 21, 2014, and consisted of a set of core questions that were combined with some specific questions for the purpose of this study asked at the end of the survey. The Citizen Panel consists mainly of self-recruited respondents (85%). The remaining respondents (15%) comes from a probability-based recruitment from population samples. Overall, we consider the data to be of sufficient quality for the purpose of this paper where the main aim is to compare the difference between two treatments at which subjects were randomly allocated. However, there are of course reasons to be cautious, especially when looking beyond the differences between the two treatments in an attempt to interpret what affects SWB. The main feature of the experiment was that, based on random allocation, the subjects received either a survey version with a truth-telling request asking them to promise to tell the truth or a version without such a request. In all other respects, the two survey versions were identical. The truth-telling request read as follows: The questions that follow could by some people be perceived as sensitive and it can be difficult to give an honest answer even though the survey is anonymous. But it is very important that the answers to even the most sensitive issues are completely honest. The questions you will be asked are about how satisfied you are with your life as a whole and with various aspects of your life. They also deal with your health, and your income. Can you, hand on the heart, promise to answer the following questions honestly? Immediately after the request to promise to tell the truth, the survey consisted of questions about overall and domain-specific stated well-being. More specifically, the respondents were asked how satisfied they felt overall with their life, and subsequently with various aspects of their life on a scale from 0 to 10. Finally, they were asked questions about social trust, social interaction, health status and socio-economic characteristics. One obvious objection to methods attempting to reduce social desirability at the data collection stage is that survey respondents consciously or subconsciously change their behaviour to fit what they think is the purpose of the experiment, i.e. what is known as an experimenter demand effect. While this is generally true, we believe that it is of less concern in our setting since we do not say anything in the survey about the expected direction of a bias, something that is often done in for example stated preference surveys on public goods. Since we use a between-subject design the subjects do not know that we are observing how the truth-telling request affects them. Thus, it is hard for the subjects to know which researcher expectations to comply with beyond the simple request to answer truthfully. 4. Results 4.1 Descriptive results Table 1 reports descriptive statistics for the responses to the general and domain-specific well-being questions. The last columns report a test of the difference between the mean values with and without a promise, as well as the effect size. Table 1 Overall and domain-specific stated well-being, descriptive statistics, and test of difference between No Promise and Promise No promise Promise Difference Effect size Mean St.Dev. Obs. Mean St.Dev. Obs. Mean P-value (two-sided) Cohen's d Overall 6.94 1.99 1,782 6.58 2.10 1,700 0.36 <0.000 0.176 Financial situation 6.06 2.53 1,777 5.92 2.61 1,699 0.14 0.113 0.054 Spare time 6.55 2.15 1,761 6.25 2.22 1,688 0.30 <0.000 0.137 Work 6.12 2.55 1,506 5.97 2.64 1,427 0.15 0.127 0.056 Social life 6.88 2.18 1,769 6.52 2.25 1,695 0.36 <0.000 0.162 Sex life 5.30 2.94 1,648 4.83 2.99 1,629 0.47 <0.000 0.160 Family life 7.13 2.37 1,669 6.95 2.43 1,610 0.18 0.031 0.075 Relationships 7.35 2.59 1,401 7.05 2.73 1,335 0.30 0.006 0.112 Health 6.27 2.61 1,769 6.08 2.61 1,698 0.19 0.27 0.075 No promise Promise Difference Effect size Mean St.Dev. Obs. Mean St.Dev. Obs. Mean P-value (two-sided) Cohen's d Overall 6.94 1.99 1,782 6.58 2.10 1,700 0.36 <0.000 0.176 Financial situation 6.06 2.53 1,777 5.92 2.61 1,699 0.14 0.113 0.054 Spare time 6.55 2.15 1,761 6.25 2.22 1,688 0.30 <0.000 0.137 Work 6.12 2.55 1,506 5.97 2.64 1,427 0.15 0.127 0.056 Social life 6.88 2.18 1,769 6.52 2.25 1,695 0.36 <0.000 0.162 Sex life 5.30 2.94 1,648 4.83 2.99 1,629 0.47 <0.000 0.160 Family life 7.13 2.37 1,669 6.95 2.43 1,610 0.18 0.031 0.075 Relationships 7.35 2.59 1,401 7.05 2.73 1,335 0.30 0.006 0.112 Health 6.27 2.61 1,769 6.08 2.61 1,698 0.19 0.27 0.075 Notes: All variables range from 0 to 10. Source: Survey data, authors’ calculation Table 1 Overall and domain-specific stated well-being, descriptive statistics, and test of difference between No Promise and Promise No promise Promise Difference Effect size Mean St.Dev. Obs. Mean St.Dev. Obs. Mean P-value (two-sided) Cohen's d Overall 6.94 1.99 1,782 6.58 2.10 1,700 0.36 <0.000 0.176 Financial situation 6.06 2.53 1,777 5.92 2.61 1,699 0.14 0.113 0.054 Spare time 6.55 2.15 1,761 6.25 2.22 1,688 0.30 <0.000 0.137 Work 6.12 2.55 1,506 5.97 2.64 1,427 0.15 0.127 0.056 Social life 6.88 2.18 1,769 6.52 2.25 1,695 0.36 <0.000 0.162 Sex life 5.30 2.94 1,648 4.83 2.99 1,629 0.47 <0.000 0.160 Family life 7.13 2.37 1,669 6.95 2.43 1,610 0.18 0.031 0.075 Relationships 7.35 2.59 1,401 7.05 2.73 1,335 0.30 0.006 0.112 Health 6.27 2.61 1,769 6.08 2.61 1,698 0.19 0.27 0.075 No promise Promise Difference Effect size Mean St.Dev. Obs. Mean St.Dev. Obs. Mean P-value (two-sided) Cohen's d Overall 6.94 1.99 1,782 6.58 2.10 1,700 0.36 <0.000 0.176 Financial situation 6.06 2.53 1,777 5.92 2.61 1,699 0.14 0.113 0.054 Spare time 6.55 2.15 1,761 6.25 2.22 1,688 0.30 <0.000 0.137 Work 6.12 2.55 1,506 5.97 2.64 1,427 0.15 0.127 0.056 Social life 6.88 2.18 1,769 6.52 2.25 1,695 0.36 <0.000 0.162 Sex life 5.30 2.94 1,648 4.83 2.99 1,629 0.47 <0.000 0.160 Family life 7.13 2.37 1,669 6.95 2.43 1,610 0.18 0.031 0.075 Relationships 7.35 2.59 1,401 7.05 2.73 1,335 0.30 0.006 0.112 Health 6.27 2.61 1,769 6.08 2.61 1,698 0.19 0.27 0.075 Notes: All variables range from 0 to 10. Source: Survey data, authors’ calculation As discussed, the expected effect of the promise treatment is a reduction in stated well-being. This is confirmed for all nine well-being measures. Moreover, using a two-sided t-test we find that the differences are statistically significant at a 5% significance level for most of the differences. The difference in mean values is largest for the sex life domain. The largest effect size is found for the overall well-being measure, with a Cohen’s d of 0.18. Consequently, although there is a robust effect of the promise treatment, the effect sizes are rather small.6 6 With a Cohen's d of 0.2, 58% of the treatment group would be above the mean of the control group, 92% of the two groups would overlap, and there would be a 56% chance that a person randomly picked from the treatment group would have a higher score than a person randomly picked from the control group, i.e. 0.56 is the probability of superiority (McGraw and Wong, 1992). However, as discussed above, a relatively small amount of misclassification of a discrete dependent variable can still lead to biased coefficient estimates in the regression analysis (Hausman et al., 1998). We also report descriptive statistics of the variables that will be used in the regression models. These include both objective variables that we do not expect to be affected by the promise treatment, and a set of more value-laden questions such as self-reported health. The variables are presented in Table 2. Table 2 Potential determinants of stated well-being, descriptive statistics, and test of difference between the treatments (with a promise and the control group without a promise) Variable Description No promise Promise Difference Mean Std dev Mean Std dev P-value (two-sided t/pr-test) Woman = 1 if subject is a woman 0.47 0.47 0.772 Age Age in years 48.1 14.2 47.6 14.1 0.319 No of children No. of children (< 18 years) living in household 1.42 1.28 1.40 1.28 0.547 Divorced = 1 if divorced 0.07 0.07 0.589 Unemployed = 1 if unemployed 0.03 0.04 0.463 University = 1 if university education (at least three years) 0.32 0.32 0.927 Income Individual monthly income before taxes in SEK 30 900 15 350 30 300 15 100 0.261 Reported health Self-reported health status, 1 = very poor; 5 = very good 3.71 0.98 3.65 1.00 0.075 Social trust Stated trust, 1 = low trust; 10 = high trust 6.61 2.20 6.51 2.23 0.179 Social interact. Low = 1 if interact with friends/ relatives less than once per month 0.10 0.11 0.417 Social interact. intermediate = 1 if interact with friends/ relatives at least once per month 0.42 0.43 0.882 Social interact. High = 1 if interact with friends/ relatives at least once per week 0.47 0.46 0.602 Variable Description No promise Promise Difference Mean Std dev Mean Std dev P-value (two-sided t/pr-test) Woman = 1 if subject is a woman 0.47 0.47 0.772 Age Age in years 48.1 14.2 47.6 14.1 0.319 No of children No. of children (< 18 years) living in household 1.42 1.28 1.40 1.28 0.547 Divorced = 1 if divorced 0.07 0.07 0.589 Unemployed = 1 if unemployed 0.03 0.04 0.463 University = 1 if university education (at least three years) 0.32 0.32 0.927 Income Individual monthly income before taxes in SEK 30 900 15 350 30 300 15 100 0.261 Reported health Self-reported health status, 1 = very poor; 5 = very good 3.71 0.98 3.65 1.00 0.075 Social trust Stated trust, 1 = low trust; 10 = high trust 6.61 2.20 6.51 2.23 0.179 Social interact. Low = 1 if interact with friends/ relatives less than once per month 0.10 0.11 0.417 Social interact. intermediate = 1 if interact with friends/ relatives at least once per month 0.42 0.43 0.882 Social interact. High = 1 if interact with friends/ relatives at least once per week 0.47 0.46 0.602 Source: Survey data, authors’ calculations. Table 2 Potential determinants of stated well-being, descriptive statistics, and test of difference between the treatments (with a promise and the control group without a promise) Variable Description No promise Promise Difference Mean Std dev Mean Std dev P-value (two-sided t/pr-test) Woman = 1 if subject is a woman 0.47 0.47 0.772 Age Age in years 48.1 14.2 47.6 14.1 0.319 No of children No. of children (< 18 years) living in household 1.42 1.28 1.40 1.28 0.547 Divorced = 1 if divorced 0.07 0.07 0.589 Unemployed = 1 if unemployed 0.03 0.04 0.463 University = 1 if university education (at least three years) 0.32 0.32 0.927 Income Individual monthly income before taxes in SEK 30 900 15 350 30 300 15 100 0.261 Reported health Self-reported health status, 1 = very poor; 5 = very good 3.71 0.98 3.65 1.00 0.075 Social trust Stated trust, 1 = low trust; 10 = high trust 6.61 2.20 6.51 2.23 0.179 Social interact. Low = 1 if interact with friends/ relatives less than once per month 0.10 0.11 0.417 Social interact. intermediate = 1 if interact with friends/ relatives at least once per month 0.42 0.43 0.882 Social interact. High = 1 if interact with friends/ relatives at least once per week 0.47 0.46 0.602 Variable Description No promise Promise Difference Mean Std dev Mean Std dev P-value (two-sided t/pr-test) Woman = 1 if subject is a woman 0.47 0.47 0.772 Age Age in years 48.1 14.2 47.6 14.1 0.319 No of children No. of children (< 18 years) living in household 1.42 1.28 1.40 1.28 0.547 Divorced = 1 if divorced 0.07 0.07 0.589 Unemployed = 1 if unemployed 0.03 0.04 0.463 University = 1 if university education (at least three years) 0.32 0.32 0.927 Income Individual monthly income before taxes in SEK 30 900 15 350 30 300 15 100 0.261 Reported health Self-reported health status, 1 = very poor; 5 = very good 3.71 0.98 3.65 1.00 0.075 Social trust Stated trust, 1 = low trust; 10 = high trust 6.61 2.20 6.51 2.23 0.179 Social interact. Low = 1 if interact with friends/ relatives less than once per month 0.10 0.11 0.417 Social interact. intermediate = 1 if interact with friends/ relatives at least once per month 0.42 0.43 0.882 Social interact. High = 1 if interact with friends/ relatives at least once per week 0.47 0.46 0.602 Source: Survey data, authors’ calculations. As expected, there are no statistically significant differences among the objective variables between the versions with and without a promise. However, for the more subjective question about self-reported health status, we do find a weak statistically significant difference at a 10% significance level. Self-reported health is higher without a promise. This is in line with Jurges (2007), who found that Danish and Swedish respondents tend to overrate their health status compared to diagnosed conditions and measurements. No statistically significant differences between the versions with and without a promise are found for the other questions of a more of subjective nature, such as social trust and social interaction. 4.2 Regression models So far we have confirmed a systematic effect on SWB by asking subjects to promise to tell the truth. In addition, we found a weak statistically significant difference in self-reported health status between the two survey versions, while for the other subjective and all objective questions we did not find any statistically significant differences. The next question is whether the differences between the two survey versions affect coefficient estimates—in terms of size and statistical significance—in regression models of SWB. In order to investigate this, we compare two regression models with the same model specification that only differ in whether data was collected using a survey with or without asking the respondents to promise to tell the truth. Since the data is ordinal, we estimate ordered probit models.7 7 As discussed by Ferrer-i-Carbonell and Frijters (2004), the empirical findings in SWB studies need not be sensitive to the choice between a standard ordinary least squares (OLS) model and a discrete model such as an ordered probit model. However, in our specific case we focus on a model that from a conceptual point of view is the more appropriate model because measurement errors are more problematic in a discrete model framework. The results are presented in Table 3. Table 3 Ordered probit models, stated (overall) SWB as dependent variable, with and without a promise to tell the truth No Promise Promise Difference Coeff (S.E.) Coeff (S.E.) P-value Two-sided chi-squared test Woman 0.152*** 0.153*** 0.960 (0.053) (0.053) Age −0.022* −0.015 0.727 (0.014) (0.014) Age2 0.0003** 0.0002* 0.809 (0.0001) (0.0001) No. of children 0.146*** 0.133*** 0.719 (0.023) (0.023) Divorced −0.335*** −0.589*** 0.089 (0.101) (0.104) Unemployed −0.885*** −0.489*** 0.087 (0.148) (0.147) University −0.151*** −0.104* 0.558 (0.057) (0.058) Income 0.026 0.073*** 0.083 (0.017) (0.018) Reported health 0.521*** 0.474*** 0.294 (0.029) (0.028) Social trust 0.091*** 0.097*** 0.743 (0.012) (0.012) Social interaction low −0.133 −0.379*** 0.067 (0.089) (0.088) Social interaction high 0.125*** 0.139*** 0.859 (0.055) (0.056) No. obs. 1659 1596 Pseudo R2 0.097 0.094 No Promise Promise Difference Coeff (S.E.) Coeff (S.E.) P-value Two-sided chi-squared test Woman 0.152*** 0.153*** 0.960 (0.053) (0.053) Age −0.022* −0.015 0.727 (0.014) (0.014) Age2 0.0003** 0.0002* 0.809 (0.0001) (0.0001) No. of children 0.146*** 0.133*** 0.719 (0.023) (0.023) Divorced −0.335*** −0.589*** 0.089 (0.101) (0.104) Unemployed −0.885*** −0.489*** 0.087 (0.148) (0.147) University −0.151*** −0.104* 0.558 (0.057) (0.058) Income 0.026 0.073*** 0.083 (0.017) (0.018) Reported health 0.521*** 0.474*** 0.294 (0.029) (0.028) Social trust 0.091*** 0.097*** 0.743 (0.012) (0.012) Social interaction low −0.133 −0.379*** 0.067 (0.089) (0.088) Social interaction high 0.125*** 0.139*** 0.859 (0.055) (0.056) No. obs. 1659 1596 Pseudo R2 0.097 0.094 Notes: *, **, and *** denote significance at 10%, 5%, and 1%, respectively for a two-sided test. Table 3 Ordered probit models, stated (overall) SWB as dependent variable, with and without a promise to tell the truth No Promise Promise Difference Coeff (S.E.) Coeff (S.E.) P-value Two-sided chi-squared test Woman 0.152*** 0.153*** 0.960 (0.053) (0.053) Age −0.022* −0.015 0.727 (0.014) (0.014) Age2 0.0003** 0.0002* 0.809 (0.0001) (0.0001) No. of children 0.146*** 0.133*** 0.719 (0.023) (0.023) Divorced −0.335*** −0.589*** 0.089 (0.101) (0.104) Unemployed −0.885*** −0.489*** 0.087 (0.148) (0.147) University −0.151*** −0.104* 0.558 (0.057) (0.058) Income 0.026 0.073*** 0.083 (0.017) (0.018) Reported health 0.521*** 0.474*** 0.294 (0.029) (0.028) Social trust 0.091*** 0.097*** 0.743 (0.012) (0.012) Social interaction low −0.133 −0.379*** 0.067 (0.089) (0.088) Social interaction high 0.125*** 0.139*** 0.859 (0.055) (0.056) No. obs. 1659 1596 Pseudo R2 0.097 0.094 No Promise Promise Difference Coeff (S.E.) Coeff (S.E.) P-value Two-sided chi-squared test Woman 0.152*** 0.153*** 0.960 (0.053) (0.053) Age −0.022* −0.015 0.727 (0.014) (0.014) Age2 0.0003** 0.0002* 0.809 (0.0001) (0.0001) No. of children 0.146*** 0.133*** 0.719 (0.023) (0.023) Divorced −0.335*** −0.589*** 0.089 (0.101) (0.104) Unemployed −0.885*** −0.489*** 0.087 (0.148) (0.147) University −0.151*** −0.104* 0.558 (0.057) (0.058) Income 0.026 0.073*** 0.083 (0.017) (0.018) Reported health 0.521*** 0.474*** 0.294 (0.029) (0.028) Social trust 0.091*** 0.097*** 0.743 (0.012) (0.012) Social interaction low −0.133 −0.379*** 0.067 (0.089) (0.088) Social interaction high 0.125*** 0.139*** 0.859 (0.055) (0.056) No. obs. 1659 1596 Pseudo R2 0.097 0.094 Notes: *, **, and *** denote significance at 10%, 5%, and 1%, respectively for a two-sided test. In the model based on data from the survey without a promise, we see that most coefficients are statistically significant and the signs are in line with what is typically found in SWB studies. Stated well-being is positively correlated with being a woman, age, number of children, health status, and social trust, and negatively correlated with age squared,8 8 A negative relationship between SWB and age and a positive relationship between SWB and age squared is in line with previous findings (see Dolan et al., 2008) suggesting higher levels of well-being in younger and older age. being divorced, unemployed, and a low level of social interaction. The exceptions are that individual income does not have a statistically significant effect on SWB and that university education has a statistically significant negative impact on SWB. The results are largely similar in terms of sign and statistical significance in the model based on data with a promise to tell the truth. However, at the 10% significance level there are four statistically significant differences between the two datasets. This provides some support that a measurement bias can lead to incorrect inferences in the regression analysis of SWB data. The estimated negative effect of being divorced is greater when using the data from the survey with a promise to tell the truth. In contrast, the estimated negative effect of being unemployed is smaller when using the data with a promise. Moreover, with a promise we observe that individual income has a positive and statistically significant effect on SWB. Note that the reported incomes and proportions of subjects who are divorced or unemployed do not differ between the two survey versions. Finally, the negative impact of having limited social interaction is only statistically significant in the version with a promise, and the difference between the two versions is statistically significant. To summarize, while we do not see much of a difference on most of the determinants of SWB in the samples with and without a promise, for a few variables the effect is weakly significant. Moreover, the differences between these point estimates are substantial. In previous studies it has been found that one effect of asking people to promise to tell the truth is that the underlying variance decreases (see, for example, Carlsson et al., 2013). In order to control for differences in variance between the treatments for SWB data, we estimate a heteroskedastic ordered probit model on the pooled data. We include a set of interaction terms for all independent variables in order to allow for a level difference between the two survey versions as well. The results are presented in Table A1 in the Appendix. The differences between the two survey versions remain the same as when comparing the two models in Table 3, and there is no statistically significant difference in variance between the two survey versions. Thus, in this specific case, there is no effect on the underlying variance when asking subjects to promise to tell truth. Thus, we have some evidence that a measurement bias can lead to incorrect inference in the regression analysis of SWB data. The coefficients are smaller in the version without a promise for three of the four coefficients where we observe a statistically significant difference between the two survey versions. Finally, we investigate whether the promise to tell the truth has similar effects on domain-specific SWB, such as health, finances, social relationships and sex life. This analysis might help us to gain a better understanding of what aspects of life people in general are inclined to misrepresent when asked about their well-being. We control for a set of individual characteristics and allow for a difference in variance between the two survey versions. Heteroskedastic ordered probit models for each of the eight domain-specific SWB-measures are used and the results are presented in Table 4. Table 4 Heteroskedastic ordered probit model, stated domain specific SWB as dependent variable, with and without a promise to tell the truth, standard errors in parentheses Finance Spare Work Social Sex Family Relation Health Woman 0.007 0.109*** 0.010 0.186*** 0.186*** 0.218*** 0.102** −0.052 (0.038) (0.038) (0.041) (0.038) (0.038) (0.039) (0.043) (0.037) Age 0.014 −0.015 0.016 −0.020** −0.015 −0.023** −0.039*** 0.0002 (0.009) (0.010) (0.011) (0.009) (0.010) (0.010) (0.012) (0.009) Age2 0.000002 0.0004*** 0.0003** 0.0003*** 0.0002 0.0002** 0.0004*** 0.0004 (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0009) N. child −0.033** 0.032 0.039 0.067*** 0.092*** 0.183*** 0.051*** 0.014 (0.017) (0.017) (0.018) (0.016) (0.017) (0.018) (0.019) (0.016) Divorced −0.365*** −0.419*** −0.295*** 0.276*** −0.616 −0.629*** −1.054*** −0.146 (0.073) (0.075) (0.085) (0.083) (0.081) (0.078) (0.150) (0.071) Unemp −1.034*** −0.064 −1.540*** −0.064 −0.038 −0.340*** −0.158 −0.013 (0.108) (0107) (0.132) (0.105) (0.111) (0.113) (0.134) (0.103) Univ 0.007 −0.155*** −0.019 −0.127*** −0.045 −0.061 −0.083* −0.056 (0.041) (0.041) (0.044) (0.041) (0.041) (0.043) (0.046) (0.040) Income 0.153*** 0.014 0.058*** 0.016 0.042*** 0.002 0.006 −0.003 (0.013) (0.013) (0.015) (0.013) (0.013) (0.013) (0.014) (0.013) Health 0.330*** 0.366*** 0.370*** 0.233*** 0.153*** 0.181*** 0.132*** 1.387 (0.020) (0.021) (0.023) (0.020) (0.020) (0.020) (0.023) (0.032) Trust 0.057*** 0.046*** 0.057*** 0.054*** 0.046*** 0.059*** 0.064*** 0.037 (0.009) (0.009) (0.010) (0.009) (0.009) (0.009) (0.010) (0.009) S int low −0.224*** −0.271*** −0.134* −0.610*** −0.114* −0.268*** −0.200*** −0.075 (0.039) (0.064) (0.070) (0.064) (0.066) (0.066) (0.070) (0.062) S int high 0.028 0.312*** 0.058 0.491*** 0.152*** 0.134*** 0.112** 0.093 (0.039) (0.040) (0.043) (0.040) (0.040) (0.041) (0.045) (0.038) Promise −0.186 −0.125*** −0.027 −0.154*** −0.142*** −0.064* −0.102** −0.050 (0.036) (0.037) (0.039) (0.036) (0.037) (0.038) (0.041) (0.035) Variance Promise 0.024 0.045* 0.043 0.021 0.006 0.046 0.032 −0.036 (0.027) (0.027) (0.029) (0.027) (0.029) (0.029) (0.034) (0.027) Nobs 3255 3230 2771 3243 3083 3076 2578 3247 Finance Spare Work Social Sex Family Relation Health Woman 0.007 0.109*** 0.010 0.186*** 0.186*** 0.218*** 0.102** −0.052 (0.038) (0.038) (0.041) (0.038) (0.038) (0.039) (0.043) (0.037) Age 0.014 −0.015 0.016 −0.020** −0.015 −0.023** −0.039*** 0.0002 (0.009) (0.010) (0.011) (0.009) (0.010) (0.010) (0.012) (0.009) Age2 0.000002 0.0004*** 0.0003** 0.0003*** 0.0002 0.0002** 0.0004*** 0.0004 (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0009) N. child −0.033** 0.032 0.039 0.067*** 0.092*** 0.183*** 0.051*** 0.014 (0.017) (0.017) (0.018) (0.016) (0.017) (0.018) (0.019) (0.016) Divorced −0.365*** −0.419*** −0.295*** 0.276*** −0.616 −0.629*** −1.054*** −0.146 (0.073) (0.075) (0.085) (0.083) (0.081) (0.078) (0.150) (0.071) Unemp −1.034*** −0.064 −1.540*** −0.064 −0.038 −0.340*** −0.158 −0.013 (0.108) (0107) (0.132) (0.105) (0.111) (0.113) (0.134) (0.103) Univ 0.007 −0.155*** −0.019 −0.127*** −0.045 −0.061 −0.083* −0.056 (0.041) (0.041) (0.044) (0.041) (0.041) (0.043) (0.046) (0.040) Income 0.153*** 0.014 0.058*** 0.016 0.042*** 0.002 0.006 −0.003 (0.013) (0.013) (0.015) (0.013) (0.013) (0.013) (0.014) (0.013) Health 0.330*** 0.366*** 0.370*** 0.233*** 0.153*** 0.181*** 0.132*** 1.387 (0.020) (0.021) (0.023) (0.020) (0.020) (0.020) (0.023) (0.032) Trust 0.057*** 0.046*** 0.057*** 0.054*** 0.046*** 0.059*** 0.064*** 0.037 (0.009) (0.009) (0.010) (0.009) (0.009) (0.009) (0.010) (0.009) S int low −0.224*** −0.271*** −0.134* −0.610*** −0.114* −0.268*** −0.200*** −0.075 (0.039) (0.064) (0.070) (0.064) (0.066) (0.066) (0.070) (0.062) S int high 0.028 0.312*** 0.058 0.491*** 0.152*** 0.134*** 0.112** 0.093 (0.039) (0.040) (0.043) (0.040) (0.040) (0.041) (0.045) (0.038) Promise −0.186 −0.125*** −0.027 −0.154*** −0.142*** −0.064* −0.102** −0.050 (0.036) (0.037) (0.039) (0.036) (0.037) (0.038) (0.041) (0.035) Variance Promise 0.024 0.045* 0.043 0.021 0.006 0.046 0.032 −0.036 (0.027) (0.027) (0.029) (0.027) (0.029) (0.029) (0.034) (0.027) Nobs 3255 3230 2771 3243 3083 3076 2578 3247 Notes: *, **, and *** denote significance at two-sided 10%, 5%, and 1% significance level, respectively. Table 4 Heteroskedastic ordered probit model, stated domain specific SWB as dependent variable, with and without a promise to tell the truth, standard errors in parentheses Finance Spare Work Social Sex Family Relation Health Woman 0.007 0.109*** 0.010 0.186*** 0.186*** 0.218*** 0.102** −0.052 (0.038) (0.038) (0.041) (0.038) (0.038) (0.039) (0.043) (0.037) Age 0.014 −0.015 0.016 −0.020** −0.015 −0.023** −0.039*** 0.0002 (0.009) (0.010) (0.011) (0.009) (0.010) (0.010) (0.012) (0.009) Age2 0.000002 0.0004*** 0.0003** 0.0003*** 0.0002 0.0002** 0.0004*** 0.0004 (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0009) N. child −0.033** 0.032 0.039 0.067*** 0.092*** 0.183*** 0.051*** 0.014 (0.017) (0.017) (0.018) (0.016) (0.017) (0.018) (0.019) (0.016) Divorced −0.365*** −0.419*** −0.295*** 0.276*** −0.616 −0.629*** −1.054*** −0.146 (0.073) (0.075) (0.085) (0.083) (0.081) (0.078) (0.150) (0.071) Unemp −1.034*** −0.064 −1.540*** −0.064 −0.038 −0.340*** −0.158 −0.013 (0.108) (0107) (0.132) (0.105) (0.111) (0.113) (0.134) (0.103) Univ 0.007 −0.155*** −0.019 −0.127*** −0.045 −0.061 −0.083* −0.056 (0.041) (0.041) (0.044) (0.041) (0.041) (0.043) (0.046) (0.040) Income 0.153*** 0.014 0.058*** 0.016 0.042*** 0.002 0.006 −0.003 (0.013) (0.013) (0.015) (0.013) (0.013) (0.013) (0.014) (0.013) Health 0.330*** 0.366*** 0.370*** 0.233*** 0.153*** 0.181*** 0.132*** 1.387 (0.020) (0.021) (0.023) (0.020) (0.020) (0.020) (0.023) (0.032) Trust 0.057*** 0.046*** 0.057*** 0.054*** 0.046*** 0.059*** 0.064*** 0.037 (0.009) (0.009) (0.010) (0.009) (0.009) (0.009) (0.010) (0.009) S int low −0.224*** −0.271*** −0.134* −0.610*** −0.114* −0.268*** −0.200*** −0.075 (0.039) (0.064) (0.070) (0.064) (0.066) (0.066) (0.070) (0.062) S int high 0.028 0.312*** 0.058 0.491*** 0.152*** 0.134*** 0.112** 0.093 (0.039) (0.040) (0.043) (0.040) (0.040) (0.041) (0.045) (0.038) Promise −0.186 −0.125*** −0.027 −0.154*** −0.142*** −0.064* −0.102** −0.050 (0.036) (0.037) (0.039) (0.036) (0.037) (0.038) (0.041) (0.035) Variance Promise 0.024 0.045* 0.043 0.021 0.006 0.046 0.032 −0.036 (0.027) (0.027) (0.029) (0.027) (0.029) (0.029) (0.034) (0.027) Nobs 3255 3230 2771 3243 3083 3076 2578 3247 Finance Spare Work Social Sex Family Relation Health Woman 0.007 0.109*** 0.010 0.186*** 0.186*** 0.218*** 0.102** −0.052 (0.038) (0.038) (0.041) (0.038) (0.038) (0.039) (0.043) (0.037) Age 0.014 −0.015 0.016 −0.020** −0.015 −0.023** −0.039*** 0.0002 (0.009) (0.010) (0.011) (0.009) (0.010) (0.010) (0.012) (0.009) Age2 0.000002 0.0004*** 0.0003** 0.0003*** 0.0002 0.0002** 0.0004*** 0.0004 (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0009) N. child −0.033** 0.032 0.039 0.067*** 0.092*** 0.183*** 0.051*** 0.014 (0.017) (0.017) (0.018) (0.016) (0.017) (0.018) (0.019) (0.016) Divorced −0.365*** −0.419*** −0.295*** 0.276*** −0.616 −0.629*** −1.054*** −0.146 (0.073) (0.075) (0.085) (0.083) (0.081) (0.078) (0.150) (0.071) Unemp −1.034*** −0.064 −1.540*** −0.064 −0.038 −0.340*** −0.158 −0.013 (0.108) (0107) (0.132) (0.105) (0.111) (0.113) (0.134) (0.103) Univ 0.007 −0.155*** −0.019 −0.127*** −0.045 −0.061 −0.083* −0.056 (0.041) (0.041) (0.044) (0.041) (0.041) (0.043) (0.046) (0.040) Income 0.153*** 0.014 0.058*** 0.016 0.042*** 0.002 0.006 −0.003 (0.013) (0.013) (0.015) (0.013) (0.013) (0.013) (0.014) (0.013) Health 0.330*** 0.366*** 0.370*** 0.233*** 0.153*** 0.181*** 0.132*** 1.387 (0.020) (0.021) (0.023) (0.020) (0.020) (0.020) (0.023) (0.032) Trust 0.057*** 0.046*** 0.057*** 0.054*** 0.046*** 0.059*** 0.064*** 0.037 (0.009) (0.009) (0.010) (0.009) (0.009) (0.009) (0.010) (0.009) S int low −0.224*** −0.271*** −0.134* −0.610*** −0.114* −0.268*** −0.200*** −0.075 (0.039) (0.064) (0.070) (0.064) (0.066) (0.066) (0.070) (0.062) S int high 0.028 0.312*** 0.058 0.491*** 0.152*** 0.134*** 0.112** 0.093 (0.039) (0.040) (0.043) (0.040) (0.040) (0.041) (0.045) (0.038) Promise −0.186 −0.125*** −0.027 −0.154*** −0.142*** −0.064* −0.102** −0.050 (0.036) (0.037) (0.039) (0.036) (0.037) (0.038) (0.041) (0.035) Variance Promise 0.024 0.045* 0.043 0.021 0.006 0.046 0.032 −0.036 (0.027) (0.027) (0.029) (0.027) (0.029) (0.029) (0.034) (0.027) Nobs 3255 3230 2771 3243 3083 3076 2578 3247 Notes: *, **, and *** denote significance at two-sided 10%, 5%, and 1% significance level, respectively. The most important result is that the coefficient of the Promise dummy variable is statistically significant at least the 10% level in five of the eight regression models and notably always with a negative sign. If we compare with the tests based on the raw data in Table 1, there are thus three cases that no longer show any statistically significant difference. These three cases were the ones with smaller effect sizes, and two of them were only statistically significant at the 10% level. The coefficient in the variance part of the estimated models is only statistically significant in one of the eight regression models. Thus, we can conclude that overall there is a similar effect for many of the domain-specific SWB questions as we observed for the general SWB question. The correlation between individual characteristics and SWB in the different domains is rather consistent across domains, although there is some variation. We have also estimated models where the dummy variable for Promise is interacted with the individual characteristics. In general, we do not find any consistent effect through any of the individual characteristics.9 9 Results are available upon request from the authors. 5. Conclusion Social-desirability and self-image concerns are factors that might affect how subjects respond to survey questions, in particular, value-laden questions about how happy you are or how satisfied you are with your life, for example. To admit to being unhappy could undeniably be very hard. We find a systematic effect on stated well-being of making the subjects promise to tell the truth. We find this for both overall well-being and for SWB in a number of specific domains. In general, we find that people are inclined to overstate their life satisfaction. The effect size of this difference in stated SWB with and without promise to tell the truth is certainly not large; in fact, it is rather small. However, even small differences in the dependent variable can in theory have important implications for the inferences we draw in our regression models on the effect of the independent variables. However, our findings do not suggest that this would be a widespread problem. While we do find differences in terms of coefficient sizes and statistical significance if we compare the two models using the samples with and without a promise to tell the truth, these differences are statistically significant only at the 10% level and only for a few independent variables (e.g. income, unemployment). To summarize, while we do find that people exaggerate the overall happiness, the statistically weak effects on the relationship between covariates and SWB means that the policy implications we would draw from a study would only marginally depend on whether or not we include a truth-telling question. To our knowledge, we are the first to test how promises to answer truthfully affect SWB data. Clearly, more evidence is needed to draw robust conclusions. We also see a need for more work in general to improve the measurement of SWB data. One interesting topic for future research would be to investigate whether the bias we identify in our cross-sectional study is less of a problem using longitudinal data. Supplementary material Supplementary material—the data and replication files—are available online at the OUP website. Funding This work was supported by the Laboratory of Opinion Research at the University of Gothenburg. Acknowledgements We are very grateful to Elina Lampi for generously sharing her expertise and collaborating with us at the design stage of this study. We have received valuable comments from the managing editor, an anonymous reviewer and an anonymous member of the editorial board. References Aadland D. , Caplan A. ( 2006 ) Cheap talk revisited: new evidence from CVM , Journal of Economic Behavior and Organization , 60 , 562 – 78 . 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Google Scholar CrossRef Search ADS Appendix Table A1 Heteroskedastic ordered probit model, stated SWB as dependent variable, with and without a promise to tell the truth Coeff (S.E) Woman 0.153*** (0.053) Age −0.022* (0.013) Age^2 0.0003** (0.0001) No. of children 0.147*** (0.023) Divorced −0.334*** (0.101) Unemployed −0.899*** (0.148) University −0.151*** (0.057) Income 0.026 (0.018) Reported health 0.524*** (0.029) Social trust 0.091*** (0.012) Social interaction low −0.135 (0.089) Social interaction high 0.123*** (0.055) Promise −0.461 (0.223) Woman × Promise 0.010 (0.076) Age × Promise 0.007 (0.019) Age^2 × Promise −0.00004 (0.0002) No. of children × Promise −0.009 (0.033) Divorce × Promise −0.274* (0.148) Unemployed × Promise 0.398* (0.211) University × Promise 0.043 (0.083) Income × Promise 0.049* (0.025) Health × Promise −0.035 (0.039) Social trust × Promise 0.010 (0.018) Social interaction low × Promise −0.252** (0.128) Social interaction high × Promise 0.217 (0.079) Variance: Promise 0.035 (0.027) No. obs. 3255 Coeff (S.E) Woman 0.153*** (0.053) Age −0.022* (0.013) Age^2 0.0003** (0.0001) No. of children 0.147*** (0.023) Divorced −0.334*** (0.101) Unemployed −0.899*** (0.148) University −0.151*** (0.057) Income 0.026 (0.018) Reported health 0.524*** (0.029) Social trust 0.091*** (0.012) Social interaction low −0.135 (0.089) Social interaction high 0.123*** (0.055) Promise −0.461 (0.223) Woman × Promise 0.010 (0.076) Age × Promise 0.007 (0.019) Age^2 × Promise −0.00004 (0.0002) No. of children × Promise −0.009 (0.033) Divorce × Promise −0.274* (0.148) Unemployed × Promise 0.398* (0.211) University × Promise 0.043 (0.083) Income × Promise 0.049* (0.025) Health × Promise −0.035 (0.039) Social trust × Promise 0.010 (0.018) Social interaction low × Promise −0.252** (0.128) Social interaction high × Promise 0.217 (0.079) Variance: Promise 0.035 (0.027) No. obs. 3255 Notes: *, **, and *** denote significance at two-sided 10%, 5%, and 1% significance level, respectively. Table A1 Heteroskedastic ordered probit model, stated SWB as dependent variable, with and without a promise to tell the truth Coeff (S.E) Woman 0.153*** (0.053) Age −0.022* (0.013) Age^2 0.0003** (0.0001) No. of children 0.147*** (0.023) Divorced −0.334*** (0.101) Unemployed −0.899*** (0.148) University −0.151*** (0.057) Income 0.026 (0.018) Reported health 0.524*** (0.029) Social trust 0.091*** (0.012) Social interaction low −0.135 (0.089) Social interaction high 0.123*** (0.055) Promise −0.461 (0.223) Woman × Promise 0.010 (0.076) Age × Promise 0.007 (0.019) Age^2 × Promise −0.00004 (0.0002) No. of children × Promise −0.009 (0.033) Divorce × Promise −0.274* (0.148) Unemployed × Promise 0.398* (0.211) University × Promise 0.043 (0.083) Income × Promise 0.049* (0.025) Health × Promise −0.035 (0.039) Social trust × Promise 0.010 (0.018) Social interaction low × Promise −0.252** (0.128) Social interaction high × Promise 0.217 (0.079) Variance: Promise 0.035 (0.027) No. obs. 3255 Coeff (S.E) Woman 0.153*** (0.053) Age −0.022* (0.013) Age^2 0.0003** (0.0001) No. of children 0.147*** (0.023) Divorced −0.334*** (0.101) Unemployed −0.899*** (0.148) University −0.151*** (0.057) Income 0.026 (0.018) Reported health 0.524*** (0.029) Social trust 0.091*** (0.012) Social interaction low −0.135 (0.089) Social interaction high 0.123*** (0.055) Promise −0.461 (0.223) Woman × Promise 0.010 (0.076) Age × Promise 0.007 (0.019) Age^2 × Promise −0.00004 (0.0002) No. of children × Promise −0.009 (0.033) Divorce × Promise −0.274* (0.148) Unemployed × Promise 0.398* (0.211) University × Promise 0.043 (0.083) Income × Promise 0.049* (0.025) Health × Promise −0.035 (0.039) Social trust × Promise 0.010 (0.018) Social interaction low × Promise −0.252** (0.128) Social interaction high × Promise 0.217 (0.079) Variance: Promise 0.035 (0.027) No. obs. 3255 Notes: *, **, and *** denote significance at two-sided 10%, 5%, and 1% significance level, respectively. © Oxford University Press 2018 All rights reserved This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Oxford Economic PapersOxford University Press

Published: Feb 12, 2018

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