Financial Literacy and Savings Account Returns

Financial Literacy and Savings Account Returns Abstract Savings accounts are owned by most households, but little is known about the performance of households’ investments. We create a unique dataset by matching information on individual savings accounts from the DNB Household Survey with market data on account-specific interest rates and characteristics. We document heterogeneity in returns across households, which can be partly explained by financial sophistication. A one-standard deviation increase in financial literacy is associated with a 12% increase compared to the median interest rate. We isolate the usage of modern technology (online accounts) as one channel through which financial literacy has a positive association with returns. 1. Introduction Savings accounts typically represent the most common vehicle for household financial investment. In the DNB Household Survey (DHS) savings accounts are owned by 82% of all Dutch households and make up the largest part of their financial wealth (with an average share of 43%).1 This contrasts with much lower ownership rates of funds or directly held stocks.2 Still, although there exists a large literature documenting how households invest in funds and stocks and how these investments perform, much less is known about savings accounts. We make use of the fact that the DHS reports bank and account names for each savings account owned by a household member, as well as the respective invested amount. This information allows us to match individual accounts held by households in the DHS with market data on interest rates and a set of account characteristics. We document considerable heterogeneity in returns across households for such a widely held and virtually riskless asset. To understand such a difference in performance of what seems to be a relatively simple financial product, our study first points to characteristics of the market and products. There is a wide dispersion of interest rates across products even for the same invested amount. A comparison of individual products is also not straightforward, for example, as accounts differ in the applicable amount thresholds to earn a higher interest rate as well as in additional restrictions. Notably, this variation is not due to so-called “teaser rates” that are paid when an account is newly opened or when fresh money is transferred, as these rates are not considered in the analysis. The difference in account characteristics, for which we can control, and the variety of offers in the market suggest, in particular, a role for financial sophistication as an explanation for the observed heterogeneity in returns. This paper is the first, to our knowledge, to show that heterogeneity in returns of a widely held asset such as savings accounts is partly linked to investor financial literacy. We recover measures of financial literacy from a special module of questions that was part of the 2005 wave of the DHS.3 Even after accounting for a range of socioeconomic characteristics, account characteristics, as well as amount invested, we find that financial literacy has a significant relationship with households’ individual returns on savings accounts: a one-standard deviation higher advanced financial literacy is associated with an approximately 29 basis points higher interest rate, which represents an increase of 12% compared to the median interest rate of 2.5%. We also calculate the gains from moving a household in the lowest literacy quartile to the highest literacy quartile. Applying the estimated gains of literacy to the average savings volume and projecting this over 10 years, total gains in real terms would accumulate to €838. Our investigation of products and the market suggests that lack of information may prevent households from securing the highest possible interest rate for the invested amount.4 Even at a given bank, households may not choose the most preferable offer. In fact, one such channel that we can isolate is the ability and willingness (or the lack of it) to use a higher interest bearing online account. We also find some evidence to suggest that more literate households might be better able to identify accounts across banks that for a given volume and a given set of characteristics offer the highest return. From banks’ perspective, lack of knowledge and sophistication are in fact prerequisites to uphold price dispersion across banks as well as price discrimination across accounts. A common feature of existing studies on households’ investment decisions and on financial literacy, as reviewed in what follows, is the difficulty to both isolate the contribution of financial literacy, which requires specific survey questions, and measure asset returns at the same time. Our combined data contain both pieces of information, allowing us to assess the association of financial literacy with savings returns. Importantly, note that the starting point for our matching process of survey data and administrative market data is a nationally representative survey that contains detailed information on all savings accounts held by household members (some of which are held in different banks), as well as on all other financial assets. Moreover, given that our outcome of interest is the applicable interest rate obtained from administrative market data, it is less likely to correlate with literacy through household unobservables such as knowledge about realized returns or reporting bias. In any case, we take a number of steps in response to endogeneity concerns including a standard IV approach and an alternative identification method recently introduced by Lewbel (2012) that exploits information from the heteroscedastic structure of the data. A number of studies document significant variation in households’ financial literacy in various countries.5 As savings accounts arguably play an important role in other countries as well, we would suggest that our results are likely to be more widely applicable. In fact, to the extent that savings accounts represent the most important financial assets, also the respective economic implications should concern a large fraction of society in many countries. The remainder of the paper is organized as follows. Section 2 provides a brief overview of the related literature. Section 3 presents the data and the matching procedure we apply. Section 4 introduces the empirical specifications to uncover the link between financial literacy and returns from savings accounts. Section 5 presents the empirical results and robustness checks and assesses the economic implications for consumers. Section 6 concludes. 2. Related Literature A large and growing number of studies examine the implications of financial literacy for various economic and investment choices (Lusardi and Mitchell 2014 provide a thorough review). Much of the extant literature has focused on investments in stocks and other risky financial assets. In particular, using the same survey, van Rooij et al. (2011, 2012) find that financial literacy induces stockholding and boosts wealth accumulation, respectively. Related work shows that cognitive skills such as numeracy (Christelis et al. 2010) and IQ (Grinblatt et al. 2011) positively associate with stockholding (see also Yoong 2011; Arrondel et al. 2012). Calvet et al. (2009) construct instead a proxy of financial sophistication based on the relationship between low education, income, and wealth and the likelihood of household financial mistakes, such as under-diversification.6 Another group of studies have analyzed the role of literacy for the choice of debt products (e.g., Stango and Zinman 2009). Lusardi and Tufano (2015) design a special set of questions measuring knowledge about properties of debt products. They find that those with lower levels of debt literacy tend to incur higher fees and use high-cost borrowing. Campbell (2006) shows that less educated individuals with fix-interest rate mortgages fail to refinance on time during a period of falling interest rates. In a related vein, Agarwal et al. (2009) provide evidence that older individuals are less savvy with debt management as this is indicated by the higher interest rates at which they borrow and the higher fees they incur. In a recent study, Gerardi et al. (2013) show that individuals with low financial knowledge and numerical ability were significantly more likely to default on subprime mortgages during the Great Recession in the United States. Financial literacy plays also an important role for retirement preparation. When households earn higher returns on their investments, this provides another explanation, next to differences in savings rates, for differences in retirement savings, which have been explored widely (e.g., Lusardi and Mitchell 2007a,b, 2008; Banks et al. 2010; van Rooij et al. 2012). More recently, Clark et al. (2017) employ administrative data on investment performance of a retirement plan matched with employees’ financial knowledge and show that higher literacy contributes to more profitable investing. More generally, understanding the link between financial literacy and asset returns can offer insights on topical policy issues, such as the distribution of wealth. Lusardi et al. (2017) develop a model in which agents invest not only in financial assets, but also in financial knowledge acquisition. According to their estimates, financial literacy has profound welfare implications accounting for roughly 40% of wealth inequality. Our paper adds to this literature by focusing on a very basic asset held by the vast majority of the population. Our unique data set allows linking the returns that households make on their savings accounts with heterogeneity in their financial knowledge. Moreover, we provide a rough quantification of likely foregone gains due to limited literacy and shed light on possible channels through which less sophisticated households fail to obtain the highest possible returns on their savings accounts. 3. Data 3.1. Household Characteristics and the Use of Savings Accounts Our main data source is the DNB Household Survey (DHS) in 2005. The DHS is an annually conducted survey of around 2,000 Dutch households that is sponsored by the Dutch National Bank and maintained by CentERdata at Tilburg University. The survey provides extensive information on demographic characteristics, asset and debt holdings, housing, work, health and income, as well as economic and psychological concepts (the variables used in our analysis are reported in what follows). The survey is representative of the Dutch population and is conducted via the Internet.7 One key feature of the DHS is that it asks detailed information on all savings accounts held by a household, including bank and account name, as well as invested volume on each account.8 The survey asks to report invested amounts for each financial asset as of December 31st of the year preceding the interviews. We supplement the DHS data with information from a special module on financial literacy designed by van Rooij et al. (2011) and conducted over a random subsample of the 2005 survey. This module contains a series of questions about financial knowledge addressed to the person in charge of household finances.9 Questions from this module have been used to construct an index of basic and an index of advanced financial literacy (Online Appendix D provides the exact wording of these questions). The latter index aims to measure understanding of more advanced financial concepts and the relevant questions refer to differences between stocks and bonds, stock market functioning, the benefits of portfolio diversification, and the association between bond prices and interest rates. Both indices are derived by factor analysis and are normalized to mean zero and standard deviation one (cf., van Rooij et al. 2011). Table 1 presents summary statistics of the main household-level variables including demographics, financial literacy, income, and wealth for the sample later used in the regression analysis.10 The financial respondents in our sample are on average 50.5 years old, 41% have a college education, 55% are male, and 66% live in a couple household. Overall, 4% are self-employed, 2% are unemployed, 20% have other employment, and 23% are retired.11 Households exhibit considerable heterogeneity in more advanced financial knowledge. Instead, basic literacy does not vary over a significant part of the sample, given that 43% of households therein manage to answer all basic literacy questions correctly. Following earlier work using information from the same financial literacy module, we thus consider the index of advanced financial literacy as our baseline measure of financial knowledge.12 As an alternative to this measure we also construct a measure based on correct responses to the “Big-Three” questions on financial literacy (first designed by Lusardi and Mitchell 2011). This measure draws on three standard questions regarding interest compounding, inflation and risk diversification and has been used in a number of studies examining the role of financial knowledge for financial outcomes (Hastings et al. 2013 provide a review). Results on this alternative measure along with different functional forms of the advanced literacy index are presented in the robustness section. In the DHS, after checking accounts, which are owned by virtually all households, savings accounts represent the second most prevalent financial asset with an ownership rate of 82%. For comparison, only 20% invest in funds and only 12% hold stocks directly. On average, households invest 43% of their financial wealth in savings accounts and hold 21% in checking accounts. Apart from insurances, which account for 12%, all other financial assets have a far lower weight in household portfolios. Thus, in terms of both ownership and financial wealth invested, savings accounts are by far the most important financial asset for Dutch households. 3.2. Interest Rate Data on Savings Accounts We use data on annual interest rates for savings accounts of all Dutch banks from April 2004 to December 2004 provided by a major Dutch financial institution.13 The administrative market dataset covers in total 43 banks and 105 savings accounts. For each savings account, it contains the account name, the bank name, and the weekly interest rate for eleven different amount brackets ranging from €0–€1,000 to €45,000 or more.14 In addition, using information from the Dutch Internet comparison website “SpaarInformatie”, we supplement our data with information on various savings account restrictions, which we use as controls in our empirical specification in what follows. These account types can be roughly partitioned across two dimensions. First, accounts are either restricted or not. The information from the comparison website allows us to distinguish in total six main restrictions.15 These restrictions are not exclusive but can coincide for one account. Second, accounts are either Internet managed or not. Internet accounts are fully managed online by the depositor and provide very limited face-to-face customer services. In addition, they can be both restricted and unrestricted. In light of the research question of this paper, we thus treat online accounts separately from the remaining set of account restrictions. We relegate an overview of the various characteristics of individual savings accounts to Table A.1 in Appendix A. Table 2 provides summary statistics for the distribution of interest rates across different amount brackets using the household survey matched with the administrative market data. As could be expected, accounts typically pay higher rates for larger volumes. Even for a given volume, dispersion is quite high. For example, for savings accounts actually held by survey respondents, interest rates for volumes from €2,500 to €3,500 range from 1.00% to 4.00% with an interquartile range of 1.45%. Yet, the interquartile range reduces to 1.00% for volumes above €45,000. 3.3. Data Matching Procedure Interest rate data are matched with DHS data as follows. Given the availability of literacy data in the 2005 wave, which reports the holdings of financial assets as of December 31, 2004, we match interest rates for the last week of December 2004 to the DHS data based on bank and account name as well as account volume.16 Precisely, based on the volume invested by households in each of their individual accounts, we can assign the respective interest rate for the applicable volume bracket.17 We achieve a full match for 79% of all accounts held by households in the DHS.18 For each savings account held by a member of a household, our matched data ultimately contain the invested volume, account name, bank name, and the applicable interest rate.19 For the final estimation sample, we exclude accounts with very low volumes (i.e., below €50), which are quite likely to be inactive.20 Table 3 shows summary statistics of the account-level APR over the sample used in the estimation and by various socioeconomic and account characteristics as well as financial literacy. The mean is 2.31% and the median is 2.50%. Dispersion is quite high given an interquartile range of 1.55% (i.e., 155 basis points). The APR increases considerably with invested volume as well as advanced financial literacy, and decreases in age, whereas there is no strong association with education and net income. 4. Econometric Specification The preceding description of the market for savings accounts suggests various channels through which households may obtain a lower than the highest possible return on their savings account(s). First, although banks offer different interest rates even for accounts with similar characteristics, households may not shop successfully for the highest interest account. Second, even at a given bank, households may not choose the most preferable account for the amount that they save. Third, even for a given set of own savings accounts, households may fail to allocate their savings to the highest interest account, potentially foregoing higher interest for larger volumes. Although we cannot completely disentangle these different channels, we provide some evidence for their relative importance in Section 5. In this context, limited financial sophistication can explain why some households actually fail in obtaining the highest possible return on their savings (i.e., through any of the aforementioned channels). More specifically, limited knowledge about available savings products and their underlying properties may prevent households from shopping efficiently for the highest returns. Moreover, households are likely inattentive to the interest rates they earn on their savings accounts. Low literate households in particular may (wrongly) perceive the gains from switching as being minimal.21 In a related vein, less sophisticated households are likely to exhibit considerable inertia in the management of their savings accounts. This would corroborate existing evidence on less educated households responding slowly or staying inactive to financial incentives related to their retirement saving (e.g., Madrian and Shea 2001), portfolio investing (e.g., Brunnermeier and Nagel 2008; Bilias et al. 2010) and debt refinancing (Andersen et al. 2015). Our main aim is to provide an estimate of the relationship between financial literacy and savings account returns. To that effect, we estimate the following, account-level specification:22 \begin{equation}{r_{h\,s}} = {\beta _1}\ {\textit {FinLi}}{t_h} + {\beta _2}{X_h} + {\beta _3}{V_{h\,s}} + {\beta _4}{Z_s} + {\varepsilon _{h\,s}}, \end{equation} (1) where rh s represents the interest rate earned on account s held by household h. FinLith denotes the advanced financial literacy index of household h (i.e., the covariate of interest). The vector Xh contains a set of household demographics including age, gender, marital status, and the number of children as well as occupation status. Furthermore, we include region dummies to take into account any relevant regional disparities, for example, in density of bank branches or in local employment conditions.23 In addition, we take into account nine dummies, contained in Vh s, which take the value one if the account volume falls into one of the previously mentioned amount brackets over which interest rates can vary and are zero otherwise.24 We also include a set of dummies denoting various account restrictions and bank fixed effects specific to each account in Zs. When estimating the baseline specification in equation (1) one should take into account the potential endogeneity of financial literacy. This has been a common empirical challenge for studies using survey data to examine the effect of literacy on various economic outcomes. In our set-up, it should be noted that the outcome of interest is the applicable interest rate that is obtained from administrative market data. Thus, it is less likely to correlate with literacy through household-specific unobserved factors such as knowledge about realized returns or reporting bias. Nevertheless, measurement error in the advanced financial literacy index remains a valid concern, given that some of the correct responses are likely to result from guessing (cf., van Rooij et al. 2011), in which case our estimated effect of literacy from OLS will be biased toward zero. We take a number of steps in order to address the measurement error issue and potential endogeneity concerns. First, we use a standard instrumental variable approach. Second, we utilize an alternative identification approach introduced by Lewbel (2012) that generates instruments using heteroscedasticity in the error structure of a first stage regression. Third, we estimate a specification that is more resilient to the measurement error of literacy. In what follows, we provide details on each of these steps. In our first approach, we employ the instruments from two earlier studies using the same financial literacy index and data.25 A valid instrument should exhibit meaningful correlation with advanced financial literacy and affect the interest rate only through the literacy channel and not through other unobserved factors. Building on van Rooij et al. (2011), we use the financial condition of the oldest sibling as an instrument for advanced financial literacy.26 The financial condition of the oldest sibling is beyond a respondent's immediate control and can thus be seen as relatively exogenous with respect to the savings account choice. Moreover, the authors argue in favor of a learning channel according to which respondents tend to become more interested in learning about financial matters due to the negative financial condition of their siblings. If such a mechanism is at work, one should observe a higher literacy score (on average) among respondents who report their siblings being in worse financial situation than respondents who do not. Results from our first stage regressions show a positive association between the literacy score and having siblings in bad financial shape, thus providing support for such a learning channel.27 In addition, following van Rooij et al. (2012), we use as a second instrument the economics education of the respondent.28 Economics education at an early stage is expected to positively affect financial literacy but is also likely to determine a household's current economic situation. We have experimented with specifications that control for contemporaneous household resources (e.g., net income, net financial and net real wealth) in order to take into account a possible channel through which past economics education can influence current investment choices. As we discuss in what follows, our IV estimates are quite comparable across both a parsimonious specification that conditions only on advanced literacy and some very rich ones that take also into account numerous account and household characteristics. The fact that our IV approach does not depend on the various controls included in equation (1) provides some indirect support for the exogeneity of our instruments. Our second approach uses the method recently introduced in Lewbel (2012) and does not rely on the validity of the instruments employed in standard IV.29 Instead, Lewbel proposes to exploit variation on higher moment conditions of the error distribution from a first stage regression of the likely endogenous covariate on (a subset of) other covariates in the model. The method generates a set of instruments that can be used for identification under two assumptions: The errors from a first stage regression of the endogenous covariate on a (subset) of other covariates in the model should be heteroscedastic. In our context, \begin{equation} {\textit {FinLi}}{t_h} = {\gamma _2}\ {X}_{h}^{'} + {\omega _h}, \end{equation} (2) where ωh denotes the error term and $${X}_{h}^{'}$$ is a subset of the RHS variables in equation (1) including a constant. Natural candidates for $${X}_{h}^{'}$$ are variables that are predetermined relative to the outcomes. We use age, gender and family size indicators. Heteroscedasticity in equation (2) implies that $${\rm {Cov}}( {{X}_{h}^{'},\ \omega _h^2} ) \ne 0$$. This assumption can be tested on the basis of a Breusch–Pagan test for heteroscedasticity and is strongly supported in our data. $${X}_{h}^{'}$$ is assumed to satisfy $${\rm {Cov}} ( {{X}_{h}^{'},\ {\varepsilon _{h\,s}}{\omega _h}} ) = 0$$, where εh s are errors from equation (1). This condition holds even when the two error terms share a common unobserved factor component (and are thus correlated), as long as the product of their idiosyncratic error components is uncorrelated with $${X}_{h}^{'}$$.30 Given that the method generates a number of instruments, one can test for their joint validity using a standard over-identification test, under the assumption that one of them is predetermined. Under conditions (a) and (b), Lewbel shows that a set of valid instruments for estimating equation (1) can be generated as: $$({X}_{h}^{'} - \bar {X}^{^\prime} ){\hat{\omega }_h}$$, where $$\bar {X}^{^\prime} $$ is the mean of $${X}_{h}^{'}$$ and $${\hat{\omega }_h}$$ are estimated residuals from equation (2). The advantage of using this method in our context is twofold: first, it allows to compare our estimates from the standard IV approach with those obtained using the generated instruments from the Lewbel method; second, it makes possible to test for the validity of both external instruments used under the standard IV approach. Finally, we have estimated a specification that controls for literacy via dummies denoting quartiles of the underlying distribution. Such a specification is likely to be more robust to measurement error, compared to the baseline specification using a continuous variable, as it is resilient to measurement error within each quartile. As we show in the robustness section, the implied effects from the specification that conditions on literacy quartiles are quite comparable to the IV estimates from the baseline model that uses a continuous literacy indicator. 5. Results 5.1. Baseline Results on Financial Literacy In what follows, we first discuss the results on financial literacy followed by other covariates and a number of robustness checks we have performed. Subsequently, we discuss the role of online account usage. Finally, we evaluate possible economic implications for households. Table 4 presents results from the account-level regressions as in equation (1). Given that financial literacy and other background characteristics, used as controls in this specification, do not vary across accounts owned by the same household, we cluster standard errors at the household level.31 First, we present results from a parsimonious specification, OLS (1), that conditions only on advanced financial literacy. In the second specification, OLS (2), we consider in addition socioeconomic characteristics and account volume dummies, whereas in the third specification, OLS (3), we add as well account characteristics and bank fixed effects. Next to each of these three OLS specifications, we show results from their IV counterparts (i.e., IV (1), IV (2), and IV (3)). In all IV specifications, the F-statistics from the first stage regressions are above or slightly below 10 and the two instruments exhibit meaningful correlations with the advanced literacy index (results from the first stage regressions are shown in Table B.1 of Appendix B). Given that we employ two instruments for one potentially endogenous covariate, one can test for their statistical validity on the basis of a test for over-identifying restrictions. According to the Hansen J-test (reported at the bottom of the table), we fail to reject the null hypothesis that the instruments are jointly valid (p-values: 0.29, 0.60, and 0.45). Table 1. Summary statistics household-level variables. Variable Mean Std. Dev. 25th pct. Median 75th pct. N Number of accounts 1.97 1.26 1.00 2.00 2.00 854 Total account volume 20,008 42,590 1,850 8,000 23,393 854 Net income 29,094 44,247 17,241 25,330 35,959 799 Net financial wealth 45,681 119,568 4,158 17,511 46,195 854 Net real wealth 116,815 205,985 2,200 26,541 182,500 854 Advanced financial literacy  Index 0.11 0.94 − 0.18 0.48 0.78 854  Number of correct answers 6.39 2.83 5.00 7.00 9.00 854 Basic financial literacy  Index 0.12 1.06 − 0.15 0.49 0.79 854  Number of correct answers 4.13 1.05 4.00 4.00 5.00 854 Economics education  A lot 0.17 0.38 0.00 0.00 0.00 854  Some 0.36 0.48 0.00 0.00 1.00 854  Little 0.27 0.44 0.00 0.00 1.00 854  Hardly at all/DK/refusal 0.20 0.40 0.00 0.00 0.00 854 Financial situation oldest sibling  No sibling/DK/refusal 0.14 0.35 0.00 0.00 0.00 787  Worse 0.23 0.42 0.00 0.00 0.00 787  Better/same 0.62 0.49 0.00 1.00 1.00 787 Age 50.47 15.45 37.00 50.00 62.00 854 Education  Less than high school 0.27 0.44 0.00 0.00 1.00 854  High school 0.33 0.47 0.00 0.00 1.00 854  College 0.41 0.49 0.00 0.00 1.00 854 Male 0.55 0.50 0.00 1.00 1.00 854 Couple 0.66 0.47 0.00 1.00 1.00 854 Number of children 0.59 0.99 0.00 0.00 1.00 854 Occupation  Employed 0.51 0.50 0.00 1.00 1.00 854  Self-employed 0.04 0.19 0.00 0.00 0.00 854  Unemployed 0.02 0.15 0.00 0.00 0.00 854  Other employment 0.20 0.40 0.00 0.00 0.00 854  Retired 0.23 0.42 0.00 0.00 0.00 854 Variable Mean Std. Dev. 25th pct. Median 75th pct. N Number of accounts 1.97 1.26 1.00 2.00 2.00 854 Total account volume 20,008 42,590 1,850 8,000 23,393 854 Net income 29,094 44,247 17,241 25,330 35,959 799 Net financial wealth 45,681 119,568 4,158 17,511 46,195 854 Net real wealth 116,815 205,985 2,200 26,541 182,500 854 Advanced financial literacy  Index 0.11 0.94 − 0.18 0.48 0.78 854  Number of correct answers 6.39 2.83 5.00 7.00 9.00 854 Basic financial literacy  Index 0.12 1.06 − 0.15 0.49 0.79 854  Number of correct answers 4.13 1.05 4.00 4.00 5.00 854 Economics education  A lot 0.17 0.38 0.00 0.00 0.00 854  Some 0.36 0.48 0.00 0.00 1.00 854  Little 0.27 0.44 0.00 0.00 1.00 854  Hardly at all/DK/refusal 0.20 0.40 0.00 0.00 0.00 854 Financial situation oldest sibling  No sibling/DK/refusal 0.14 0.35 0.00 0.00 0.00 787  Worse 0.23 0.42 0.00 0.00 0.00 787  Better/same 0.62 0.49 0.00 1.00 1.00 787 Age 50.47 15.45 37.00 50.00 62.00 854 Education  Less than high school 0.27 0.44 0.00 0.00 1.00 854  High school 0.33 0.47 0.00 0.00 1.00 854  College 0.41 0.49 0.00 0.00 1.00 854 Male 0.55 0.50 0.00 1.00 1.00 854 Couple 0.66 0.47 0.00 1.00 1.00 854 Number of children 0.59 0.99 0.00 0.00 1.00 854 Occupation  Employed 0.51 0.50 0.00 1.00 1.00 854  Self-employed 0.04 0.19 0.00 0.00 0.00 854  Unemployed 0.02 0.15 0.00 0.00 0.00 854  Other employment 0.20 0.40 0.00 0.00 0.00 854  Retired 0.23 0.42 0.00 0.00 0.00 854 Notes: The sample consists of those households used in the regressions analysis. See Online Appendix D for details on the construction of all variables. All statistics use sample weights. The data are from the DNB Household Survey 2005. View Large Table 1. Summary statistics household-level variables. Variable Mean Std. Dev. 25th pct. Median 75th pct. N Number of accounts 1.97 1.26 1.00 2.00 2.00 854 Total account volume 20,008 42,590 1,850 8,000 23,393 854 Net income 29,094 44,247 17,241 25,330 35,959 799 Net financial wealth 45,681 119,568 4,158 17,511 46,195 854 Net real wealth 116,815 205,985 2,200 26,541 182,500 854 Advanced financial literacy  Index 0.11 0.94 − 0.18 0.48 0.78 854  Number of correct answers 6.39 2.83 5.00 7.00 9.00 854 Basic financial literacy  Index 0.12 1.06 − 0.15 0.49 0.79 854  Number of correct answers 4.13 1.05 4.00 4.00 5.00 854 Economics education  A lot 0.17 0.38 0.00 0.00 0.00 854  Some 0.36 0.48 0.00 0.00 1.00 854  Little 0.27 0.44 0.00 0.00 1.00 854  Hardly at all/DK/refusal 0.20 0.40 0.00 0.00 0.00 854 Financial situation oldest sibling  No sibling/DK/refusal 0.14 0.35 0.00 0.00 0.00 787  Worse 0.23 0.42 0.00 0.00 0.00 787  Better/same 0.62 0.49 0.00 1.00 1.00 787 Age 50.47 15.45 37.00 50.00 62.00 854 Education  Less than high school 0.27 0.44 0.00 0.00 1.00 854  High school 0.33 0.47 0.00 0.00 1.00 854  College 0.41 0.49 0.00 0.00 1.00 854 Male 0.55 0.50 0.00 1.00 1.00 854 Couple 0.66 0.47 0.00 1.00 1.00 854 Number of children 0.59 0.99 0.00 0.00 1.00 854 Occupation  Employed 0.51 0.50 0.00 1.00 1.00 854  Self-employed 0.04 0.19 0.00 0.00 0.00 854  Unemployed 0.02 0.15 0.00 0.00 0.00 854  Other employment 0.20 0.40 0.00 0.00 0.00 854  Retired 0.23 0.42 0.00 0.00 0.00 854 Variable Mean Std. Dev. 25th pct. Median 75th pct. N Number of accounts 1.97 1.26 1.00 2.00 2.00 854 Total account volume 20,008 42,590 1,850 8,000 23,393 854 Net income 29,094 44,247 17,241 25,330 35,959 799 Net financial wealth 45,681 119,568 4,158 17,511 46,195 854 Net real wealth 116,815 205,985 2,200 26,541 182,500 854 Advanced financial literacy  Index 0.11 0.94 − 0.18 0.48 0.78 854  Number of correct answers 6.39 2.83 5.00 7.00 9.00 854 Basic financial literacy  Index 0.12 1.06 − 0.15 0.49 0.79 854  Number of correct answers 4.13 1.05 4.00 4.00 5.00 854 Economics education  A lot 0.17 0.38 0.00 0.00 0.00 854  Some 0.36 0.48 0.00 0.00 1.00 854  Little 0.27 0.44 0.00 0.00 1.00 854  Hardly at all/DK/refusal 0.20 0.40 0.00 0.00 0.00 854 Financial situation oldest sibling  No sibling/DK/refusal 0.14 0.35 0.00 0.00 0.00 787  Worse 0.23 0.42 0.00 0.00 0.00 787  Better/same 0.62 0.49 0.00 1.00 1.00 787 Age 50.47 15.45 37.00 50.00 62.00 854 Education  Less than high school 0.27 0.44 0.00 0.00 1.00 854  High school 0.33 0.47 0.00 0.00 1.00 854  College 0.41 0.49 0.00 0.00 1.00 854 Male 0.55 0.50 0.00 1.00 1.00 854 Couple 0.66 0.47 0.00 1.00 1.00 854 Number of children 0.59 0.99 0.00 0.00 1.00 854 Occupation  Employed 0.51 0.50 0.00 1.00 1.00 854  Self-employed 0.04 0.19 0.00 0.00 0.00 854  Unemployed 0.02 0.15 0.00 0.00 0.00 854  Other employment 0.20 0.40 0.00 0.00 0.00 854  Retired 0.23 0.42 0.00 0.00 0.00 854 Notes: The sample consists of those households used in the regressions analysis. See Online Appendix D for details on the construction of all variables. All statistics use sample weights. The data are from the DNB Household Survey 2005. View Large Table 2. Distribution of interest rates across amount brackets. Volume Mean Std. Dev. Min. 25th pct. Median 75th pct. Max. N €0–€1,000 2.05 0.94 1.00 1.10 1.55 3.10 4.00 364 €1,000–€2,500 2.13 0.91 1.00 1.10 2.40 3.10 4.00 228 €2,500–€3,500 2.20 0.86 1.00 1.55 2.40 3.00 4.00 104 €3,500–€4,500 2.32 0.84 1.00 1.60 2.40 3.00 4.00 72 €4,500–€7,000 2.53 0.79 1.00 2.00 2.50 3.25 4.00 119 €7,000–€10,000 2.48 0.76 1.00 2.20 2.50 3.10 4.00 107 €10,000–€25,000 2.52 0.75 1.00 1.60 2.70 3.30 3.50 254 €25,000–€45,000 2.60 0.61 1.00 2.10 2.50 3.25 3.50 97 >€45,000 2.83 0.51 1.50 2.30 3.00 3.30 3.50 65 Volume Mean Std. Dev. Min. 25th pct. Median 75th pct. Max. N €0–€1,000 2.05 0.94 1.00 1.10 1.55 3.10 4.00 364 €1,000–€2,500 2.13 0.91 1.00 1.10 2.40 3.10 4.00 228 €2,500–€3,500 2.20 0.86 1.00 1.55 2.40 3.00 4.00 104 €3,500–€4,500 2.32 0.84 1.00 1.60 2.40 3.00 4.00 72 €4,500–€7,000 2.53 0.79 1.00 2.00 2.50 3.25 4.00 119 €7,000–€10,000 2.48 0.76 1.00 2.20 2.50 3.10 4.00 107 €10,000–€25,000 2.52 0.75 1.00 1.60 2.70 3.30 3.50 254 €25,000–€45,000 2.60 0.61 1.00 2.10 2.50 3.25 3.50 97 >€45,000 2.83 0.51 1.50 2.30 3.00 3.30 3.50 65 Notes: This table shows the distribution of the account-level APR across nine amount brackets. The calculation is based on the matched household-administrative data that provides information on the accounts actually used by households. We group together three amount brackets from €7,000 to €10,000 due to too few observations in the respective categories for used accounts and no offered account reaching a new volume threshold within this range. All statistics use sample weights. The data are as of the last week of December 2004. View Large Table 2. Distribution of interest rates across amount brackets. Volume Mean Std. Dev. Min. 25th pct. Median 75th pct. Max. N €0–€1,000 2.05 0.94 1.00 1.10 1.55 3.10 4.00 364 €1,000–€2,500 2.13 0.91 1.00 1.10 2.40 3.10 4.00 228 €2,500–€3,500 2.20 0.86 1.00 1.55 2.40 3.00 4.00 104 €3,500–€4,500 2.32 0.84 1.00 1.60 2.40 3.00 4.00 72 €4,500–€7,000 2.53 0.79 1.00 2.00 2.50 3.25 4.00 119 €7,000–€10,000 2.48 0.76 1.00 2.20 2.50 3.10 4.00 107 €10,000–€25,000 2.52 0.75 1.00 1.60 2.70 3.30 3.50 254 €25,000–€45,000 2.60 0.61 1.00 2.10 2.50 3.25 3.50 97 >€45,000 2.83 0.51 1.50 2.30 3.00 3.30 3.50 65 Volume Mean Std. Dev. Min. 25th pct. Median 75th pct. Max. N €0–€1,000 2.05 0.94 1.00 1.10 1.55 3.10 4.00 364 €1,000–€2,500 2.13 0.91 1.00 1.10 2.40 3.10 4.00 228 €2,500–€3,500 2.20 0.86 1.00 1.55 2.40 3.00 4.00 104 €3,500–€4,500 2.32 0.84 1.00 1.60 2.40 3.00 4.00 72 €4,500–€7,000 2.53 0.79 1.00 2.00 2.50 3.25 4.00 119 €7,000–€10,000 2.48 0.76 1.00 2.20 2.50 3.10 4.00 107 €10,000–€25,000 2.52 0.75 1.00 1.60 2.70 3.30 3.50 254 €25,000–€45,000 2.60 0.61 1.00 2.10 2.50 3.25 3.50 97 >€45,000 2.83 0.51 1.50 2.30 3.00 3.30 3.50 65 Notes: This table shows the distribution of the account-level APR across nine amount brackets. The calculation is based on the matched household-administrative data that provides information on the accounts actually used by households. We group together three amount brackets from €7,000 to €10,000 due to too few observations in the respective categories for used accounts and no offered account reaching a new volume threshold within this range. All statistics use sample weights. The data are as of the last week of December 2004. View Large Table 3. Distribution of account-level APR. Mean Std. Dev. 25th pct. Median 75th pct. N 2.31 0.86 1.55 2.50 3.10 1,410 Advanced literacy quartiles Volume quartiles  1 (low) 2.11  1 (low) 2.02  2 2.23  2 2.11  3 2.36  3 2.43  4 (high) 2.46***  4 (high) 2.59*** Age Education  18–30 years 2.58  Less than high school 2.27  31–40 years 2.33  High school 2.34  41–50 years 2.22  College 2.32  51–60 years 2.37  61 years and older 2.22*** Gender Married  Female 2.30  Single-person households 2.25  Male 2.32  Two-person households 2.34 Internet account Withdrawal costs/limitations  No 1.83  No 2.38  Yes 3.21***  Yes 1.67*** Minimum amount Salary account  No 2.28  No 2.30  Yes 2.54***  Yes 3.07*** Lowest balance bonus Individual ownership  No 2.20  No 2.34  Yes 2.61***  Yes 2.29 Balance growth bonus Joint ownership  No 2.28  No 2.28  Yes 3.40  Yes 2.37 Fixed monthly deposit Third party ownership  No 2.30  No 2.33  Yes 4.00  Yes 2.18 Mean Std. Dev. 25th pct. Median 75th pct. N 2.31 0.86 1.55 2.50 3.10 1,410 Advanced literacy quartiles Volume quartiles  1 (low) 2.11  1 (low) 2.02  2 2.23  2 2.11  3 2.36  3 2.43  4 (high) 2.46***  4 (high) 2.59*** Age Education  18–30 years 2.58  Less than high school 2.27  31–40 years 2.33  High school 2.34  41–50 years 2.22  College 2.32  51–60 years 2.37  61 years and older 2.22*** Gender Married  Female 2.30  Single-person households 2.25  Male 2.32  Two-person households 2.34 Internet account Withdrawal costs/limitations  No 1.83  No 2.38  Yes 3.21***  Yes 1.67*** Minimum amount Salary account  No 2.28  No 2.30  Yes 2.54***  Yes 3.07*** Lowest balance bonus Individual ownership  No 2.20  No 2.34  Yes 2.61***  Yes 2.29 Balance growth bonus Joint ownership  No 2.28  No 2.28  Yes 3.40  Yes 2.37 Fixed monthly deposit Third party ownership  No 2.30  No 2.33  Yes 4.00  Yes 2.18 Notes: This table shows summary statistics of the account-level APR over the full sample used in the regression analysis as well as averages of the account-level APR by various household- and account-level characteristics. All statistics use sample weights. The data are from the matched DNB Household Survey in 2005. Stars indicate whether the bivariate (or the joint for categorical variables) associations estimated from univariate regressions of the APR on the respective variable is statistically significant. ***p < 0.01. View Large Table 3. Distribution of account-level APR. Mean Std. Dev. 25th pct. Median 75th pct. N 2.31 0.86 1.55 2.50 3.10 1,410 Advanced literacy quartiles Volume quartiles  1 (low) 2.11  1 (low) 2.02  2 2.23  2 2.11  3 2.36  3 2.43  4 (high) 2.46***  4 (high) 2.59*** Age Education  18–30 years 2.58  Less than high school 2.27  31–40 years 2.33  High school 2.34  41–50 years 2.22  College 2.32  51–60 years 2.37  61 years and older 2.22*** Gender Married  Female 2.30  Single-person households 2.25  Male 2.32  Two-person households 2.34 Internet account Withdrawal costs/limitations  No 1.83  No 2.38  Yes 3.21***  Yes 1.67*** Minimum amount Salary account  No 2.28  No 2.30  Yes 2.54***  Yes 3.07*** Lowest balance bonus Individual ownership  No 2.20  No 2.34  Yes 2.61***  Yes 2.29 Balance growth bonus Joint ownership  No 2.28  No 2.28  Yes 3.40  Yes 2.37 Fixed monthly deposit Third party ownership  No 2.30  No 2.33  Yes 4.00  Yes 2.18 Mean Std. Dev. 25th pct. Median 75th pct. N 2.31 0.86 1.55 2.50 3.10 1,410 Advanced literacy quartiles Volume quartiles  1 (low) 2.11  1 (low) 2.02  2 2.23  2 2.11  3 2.36  3 2.43  4 (high) 2.46***  4 (high) 2.59*** Age Education  18–30 years 2.58  Less than high school 2.27  31–40 years 2.33  High school 2.34  41–50 years 2.22  College 2.32  51–60 years 2.37  61 years and older 2.22*** Gender Married  Female 2.30  Single-person households 2.25  Male 2.32  Two-person households 2.34 Internet account Withdrawal costs/limitations  No 1.83  No 2.38  Yes 3.21***  Yes 1.67*** Minimum amount Salary account  No 2.28  No 2.30  Yes 2.54***  Yes 3.07*** Lowest balance bonus Individual ownership  No 2.20  No 2.34  Yes 2.61***  Yes 2.29 Balance growth bonus Joint ownership  No 2.28  No 2.28  Yes 3.40  Yes 2.37 Fixed monthly deposit Third party ownership  No 2.30  No 2.33  Yes 4.00  Yes 2.18 Notes: This table shows summary statistics of the account-level APR over the full sample used in the regression analysis as well as averages of the account-level APR by various household- and account-level characteristics. All statistics use sample weights. The data are from the matched DNB Household Survey in 2005. Stars indicate whether the bivariate (or the joint for categorical variables) associations estimated from univariate regressions of the APR on the respective variable is statistically significant. ***p < 0.01. View Large Adding account and bank fixed effects into the third specification improves considerably, as expected, the fit of the model. In all three OLS specifications, the coefficient of advanced financial literacy is statistically significant (p-value < 0.01) and shows a positive association with the APR. The corresponding IV estimates remain statistically significant and suggest a slightly stronger relationship. Notably, the estimated magnitudes are more or less unaffected across all three specifications (i.e., the IV strategy works irrespective of the set of other covariates taken into account). According to the IV estimates, an assumed one-standard deviation increase in advanced financial literacy implies a roughly 29 basis points increase in the APR. This effect, estimated net of socioeconomic characteristics, account restrictions and bank fixed effects, is nontrivial as it corresponds to 12% of the median interest rate in our sample. As an alternative to the standard IV approach used previously, we also apply the identification method of Lewbel (2012) that, as discussed in Section 4, exploits variation from the second moments of the error distribution of the first stage regression in equation (2) to generate a set of instruments. For this, $${X}_{h}^{'}$$ comprises few predetermined covariates, namely age, gender and number of children. First, we estimate the first-stage regression in equation (2) and test for heteroscedasticity using a Breusch–Pagan test. According to the test results (chi2 = 71.1, p-value = 0.00) there is strong evidence for heteroscedasticity in the first stage regression. Following Lewbel, we generate instruments by taking the products of residuals from equation (2) with each of the aforementioned covariates, centered at their respective sample means. These generated instruments can be subsequently used either alone or in conjunction with the two external instruments used under the standard IV approach in order to identify equation (1). Table 5 summarizes the relevant results. In particular, IV (1) uses generated instruments from the Lewbel method only, whereas IV (2) uses the two external instruments employed in the standard IV approach alone. IV (3) uses the two external instruments supplemented with the generated instruments from the Lewbel method resulting in more efficient estimates than in the standard IV specification. Notably, the estimated coefficients on financial literacy suggest qualitatively similar effects and are statistically significant across all three specifications. Moreover, the generated instruments from the Lewbel method meet the exogeneity assumption as Hansen's J-statistic fails to reject the null of exogeneity with high confidence (p-value: 0.62). As discussed, one can use the instruments from the Lewbel method to test for the joint validity of the two external instruments employed under the standard IV estimation. This test is based on the difference in Hansen's J-statistics between the model using the generated instruments according to the Lewbel method only and the full model using the entire set of generated and external instruments. According to the resulting C-statistic of 7.2 (p-value: 0.21), one cannot reject the null hypothesis that the two external instruments employed in the standard IV approach are jointly valid. Results from this test lend some further support to the validity of the originally employed instruments. 5.2. Results on Other Covariates With reference to other covariates in the model, the account volume dummies show, as expected, a progressively stronger association with a higher interest rate, consistent with the notion that the benefits from shopping are higher for investors with larger volumes. Given that we control for bank fixed effects and account characteristics, which are highly significant, these differences do not seem to be solely attributable to choices of accounts with more restrictive characteristics.32 In addition, we estimate a strong negative association of the APR with age. For example, respondents above sixty earn about 54 basis points less on average as compared to the base category of young adults below thirty. This likely suggests a significant role for age-of-account-effects given that the age of an account and respondents’ age should be highly correlated. It may also reflect cohort effects as younger cohorts are more familiar with Internet use and technology in general and, as we show in what follows, those with Internet-managed accounts earn higher interest rates.33 Other covariates, such as education and gender (sometimes used as proxies for financial sophistication) and family size and employments status (that are likely to reflect liquidity needs) do not exhibit any significant association with the APR. In addition, we have estimated a richer specification controlling for household net income, net financial wealth (excluding savings accounts) and net real wealth through dummies denoting quartiles of the respective distributions. Notably, these additional controls of household resources are insignificant, whereas our baseline estimates of financial literacy and account volume remain unaffected (literacy estimates are 0.107 and 0.313, both significant at 1%-level, under the OLS and IV specifications, respectively). This suggests that financial wealth and other household resources do not associate with the APR when we control for invested volume and financial literacy.34 5.3. Robustness Checks In this section, we discuss numerous checks that we have performed in order to verify the robustness of our baseline findings at the account level. Due to space constraints, Table 6 summarizes results from some of these robustness checks, whereas the entire set of results discussed in what follows is available from the authors upon request. Panel A shows results from several variations of equation (1). OLS (1) and IV (1) exclude volume dummies from the baseline specification, which are potentially endogenous. The derived estimates are highly comparable to the baseline ones with a financial literacy coefficient of 0.14 and 0.32 in the OLS and IV specification, respectively. OLS (2) and IV (2) use only accounts from the financial respondent, for whom the financial literacy data is available. Recall that in our baseline estimation, we assign to each account held by any member of nonsingle households the financial literacy of the household's financial respondent. This might be problematic if household members differ significantly in their degree of literacy. Given that a significant fraction of accounts is held by the financial respondent our sample reduces by only 16%. Our estimates of literacy remain highly significant at 1%-level with a comparable coefficient of 0.29 in the IV specification. OLS (3) and IV (3) attach a higher weight to more important accounts by weighting each observation with its relative volume share within the household. OLS and IV estimates from these weighted regressions are 0.13 and 0.26, both significant at 1%-level, respectively. Thus, using volume weights leaves our main findings unaffected. Panel B shows estimates using different financial literacy measures or functional forms. OLS (1) and IV (1) use the continuous financial literacy index but excludes “don’t know”-answers from the instrument denoting economics education. The estimated financial literacy coefficient is 0.26 (significant at 1%-level). This suggests that correlation between this instrument and the financial literacy index is not just due to a correlation with “don’t know”-responses in the economics education question. OLS (2) and IV (2) use the standardized number of correct answers to the financial literacy questions instead of constructing an index based on factor analysis.35 We estimate a financial literacy coefficient of 0.32 (p-value: <0.01) in the IV-specification, which is highly comparable to our baseline results. OLS (3) and IV (3) use the standardized number of correct answers to the “Big-Three” financial literacy questions. As discussed, information from the three basic questions on interest compounding, inflation, and risk diversification, has been collected by various household surveys that, unlike the 2005 DHS, do not have a special literacy module. As a result, this information has been used to measure literacy in a number of studies (see Hastings et al. 2013). Using this measure, we obtain an OLS estimate of 0.06 (p-value: <0.01) and an IV estimate of 0.43 (p-value: <0.05). According to the Hansen J-test the two instruments used are jointly valid, their F-statistic from the first-stage regression is nevertheless below 10. In addition, we have estimated an OLS specification using dummies denoting financial literacy quartiles. As discussed, using quartiles partly accounts for the measurement error in the continuous financial literacy index used in the baseline specification.36 Households in the top advanced literacy quartile earn on average 29 basis points more compared to the lowest literacy quartile.37 The derived effect is highly comparable to the IV estimate from the baseline specification, given that the interquartile range of the literacy index equals roughly one standard deviation. This suggests that the financial literacy index may indeed suffer from measurement error that is taken into account by the standard IV estimation used for the literacy index. As Lusardi and Mitchell (2014) point out, the reported IV estimates of the impact of financial literacy are higher than the counterpart OLS ones in all empirical studies they review, consistent with the measurement error hypothesis. Furthermore, we estimate the same specification as in van Rooij et al. (2011, 2012), by controlling, in addition, for the basic financial literacy index that is deduced from answers to the five basic literacy questions asked in the survey (see Online Appendix D). As discussed, basic literacy does not vary considerably over the sample and controlling for this leaves our estimates of interest on advanced literacy unaffected with an OLS-estimate of 0.138 and an IV-estimate of 0.328. We have also accounted for a number of factors that may influence the APR. Given that these additional controls have some missing values that reduce our estimation sample by about 15%–20% in each case, we add one factor at a time.38 In a first step, we include a measure of risk aversion from the DHS, as used in a similar robustness check by van Rooij et al. (2011).39 The inclusion of risk aversion (that is itself insignificant) does not affect our estimate for advanced financial literacy. Second, although we control for employment status in our main specification, households frequently exposed to transitory income shocks might on average hold more liquid accounts with lower APRs. To this end, we include a dummy indicating whether households’ last year's income was unusually low. The inclusion of this additional variable, however, leaves our key estimate unaffected. Third, we also added hours worked to our specification to proxy for opportunity costs of shopping for higher rates. This variable has no significant impact on the APR and our estimates for literacy remain unaffected. Fourth, we include a categorical variable measuring the investment horizon of respondents to account for differences in patience of investors but find no significant effect of the investment horizon on the APR and our literacy estimate remains unaffected. Fifth, we add a dummy denoting whether the individual received financial advice that turns out insignificant and leaves our results unchanged. In addition, we estimate our specification in the subgroup of unrestricted accounts as, in particular, for balance growth bonus and lowest balance bonus accounts our data contains only a measure of the exact interest rate received. This considerably reduces the sample used in the estimation (647 observations) but leaves our literacy estimate broadly unaffected (0.27, p-value: <0.10 in the IV-specification). Finally, although in our estimation we take into account bank fixed effects, we examine whether our results are particularly sensitive to banks grouped together as “small banks”, as these banks are likely to be quite heterogeneous in the savings accounts they offer. To that effect, we have re-estimated our baseline specifications by excluding accounts held in these small banks and the results remain unaffected with a financial literacy coefficient of 0.29 (p-value: <0.01) in the IV-specification. 5.4. Online Banking Usage One possible channel through which literacy could positively associate with APRs is through households’ ability to use Internet accounts. According to the data, most banks offer a menu of both Internet-managed and regular accounts, thus, allowing for more literate households to achieve higher returns even within the same bank. As discussed in the data section, Internet accounts are fully managed online with limited customer services and in return typically offer higher interest rates. We re-estimate our baseline specification by adding a dummy denoting Internet managed accounts.40 Results are shown in Table 7. The Internet account dummy displays a strong positive association with the APR. For example, after accounting for various account restrictions and bank fixed effects, the estimated impact of having an Internet-managed account exceeds 130 basis points. The implied effect of literacy is still statistically significant, albeit quantitatively smaller by around a half. This suggests that a sizable part of the effect of advanced financial literacy on the APR derives from familiarity with new technologies and the willingness and ability to use self-managed online banking.41 Switching to Internet managed accounts can help households to earn higher interest on their savings account without necessarily having to switch banks. As discussed in Section 3, literacy may also associate with APRs through another two channels: shopping aptitude for the highest interest account across banks and optimal rebalancing among the currently held set of accounts. In the next section, we show that the latter channel is of no quantitative importance. Note that the remaining effect of advanced literacy that we estimate is net of various household and account characteristics, Internet-managed accounts, as well as fixed differences across banks. That is, there is still room for financial literacy to play a role as more literate households might be better able to identify accounts across banks that for a given volume and a given set of characteristics offer the highest return (i.e., above average differences in returns across banks that are absorbed by bank fixed effects). 5.5. Economic Relevance In order to assess the economic relevance of limited literacy for a typical household, we first re-estimate our baseline specification at the household level.42 We show estimated results in Table 8, in which specifications (1)–(3) represent the counterparts to those shown in Table 4. Table 4. OLS of account-level APR on financial literacy. OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.116*** 0.032 0.304*** 0.112 0.128*** 0.031 0.275** 0.125 0.127*** 0.022 0.288*** 0.099 Age dummies  31–40 years − 0.316*** 0.103 − 0.246** 0.118 − 0.241*** 0.079 − 0.197** 0.092  41–50 years − 0.474*** 0.099 − 0.424*** 0.106 − 0.372*** 0.074 − 0.357*** 0.078  51–60 years − 0.400*** 0.100 − 0.377*** 0.108 − 0.285*** 0.075 − 0.303*** 0.081  61 years and older − 0.541*** 0.123 − 0.564*** 0.135 − 0.491*** 0.090 − 0.547*** 0.099 Education dummies  High school 0.039 0.066 0.008 0.081 0.081 0.051 0.050 0.064  College − 0.083 0.064 − 0.134 0.085 0.016 0.051 − 0.050 0.068 Male − 0.025 0.056 − 0.068 0.073 0.011 0.041 − 0.047 0.060 Couple 0.014 0.059 − 0.023 0.062 0.066 0.053 0.021 0.056 Number of children 0.021 0.029 0.018 0.030 0.002 0.023 0.001 0.023 Occupation dummies  Employed 0.061 0.123 0.083 0.134 0.102 0.108 0.088 0.126  Self-employed 0.048 0.163 0.045 0.192 0.063 0.126 0.076 0.152  Unemployed 0.051 0.077 0.098 0.083 0.088 0.060 0.114* 0.065  Retired − 0.021 0.104 0.020 0.110 0.016 0.077 0.052 0.084 Volume dummies  €1,000–€2,500 0.108 0.084 0.124 0.088 0.111* 0.065 0.116* 0.069  €2,500–€3,500 0.185* 0.098 0.185* 0.102 0.078 0.076 0.074 0.077  €3,500–€4,500 0.271** 0.108 0.249** 0.113 0.219*** 0.079 0.216*** 0.082  €4,500–€7,000 0.460*** 0.090 0.396*** 0.095 0.320*** 0.066 0.297*** 0.070  €7,000–€10,000 0.466*** 0.095 0.461*** 0.098 0.339*** 0.078 0.338*** 0.083  €10,000–€25,000 0.503*** 0.076 0.447*** 0.082 0.474*** 0.059 0.434*** 0.063  €25,000–€45,000 0.637*** 0.084 0.601*** 0.091 0.622*** 0.075 0.610*** 0.080  €45,000 or more 0.862*** 0.088 0.817*** 0.093 0.753*** 0.081 0.724*** 0.083 Account characteristics  Minimum amount − 0.369*** 0.097 − 0.399*** 0.102  Lowest balance bonus − 0.328*** 0.104 − 0.345*** 0.112  Balance growth bonus 2.121*** 0.117 2.169*** 0.128  Fixed monthly deposit 1.396*** 0.076 1.358*** 0.097  Withdrawal costs/limitations − 0.266*** 0.060 − 0.301*** 0.064  Salary account 1.118*** 0.152 1.071*** 0.150  Joint ownership 0.001 0.048 0.005 0.051  Third party ownership − 0.040 0.063 − 0.030 0.071 Constant 2.315*** 0.027 2.268*** 0.036 2.272*** 0.124 2.258*** 0.136 2.380*** 0.104 2.397*** 0.116 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 1,410 1,306 1,410 1,306 Adjusted R-squared 0.01 − 0.02 0.12 0.10 0.47 0.45 Hansen J-test p-value 0.29 0.60 0.45 F-statistic first stage 11.54 9.74 10.44 Exogeneity test p-value 0.12 0.23 0.07 OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.116*** 0.032 0.304*** 0.112 0.128*** 0.031 0.275** 0.125 0.127*** 0.022 0.288*** 0.099 Age dummies  31–40 years − 0.316*** 0.103 − 0.246** 0.118 − 0.241*** 0.079 − 0.197** 0.092  41–50 years − 0.474*** 0.099 − 0.424*** 0.106 − 0.372*** 0.074 − 0.357*** 0.078  51–60 years − 0.400*** 0.100 − 0.377*** 0.108 − 0.285*** 0.075 − 0.303*** 0.081  61 years and older − 0.541*** 0.123 − 0.564*** 0.135 − 0.491*** 0.090 − 0.547*** 0.099 Education dummies  High school 0.039 0.066 0.008 0.081 0.081 0.051 0.050 0.064  College − 0.083 0.064 − 0.134 0.085 0.016 0.051 − 0.050 0.068 Male − 0.025 0.056 − 0.068 0.073 0.011 0.041 − 0.047 0.060 Couple 0.014 0.059 − 0.023 0.062 0.066 0.053 0.021 0.056 Number of children 0.021 0.029 0.018 0.030 0.002 0.023 0.001 0.023 Occupation dummies  Employed 0.061 0.123 0.083 0.134 0.102 0.108 0.088 0.126  Self-employed 0.048 0.163 0.045 0.192 0.063 0.126 0.076 0.152  Unemployed 0.051 0.077 0.098 0.083 0.088 0.060 0.114* 0.065  Retired − 0.021 0.104 0.020 0.110 0.016 0.077 0.052 0.084 Volume dummies  €1,000–€2,500 0.108 0.084 0.124 0.088 0.111* 0.065 0.116* 0.069  €2,500–€3,500 0.185* 0.098 0.185* 0.102 0.078 0.076 0.074 0.077  €3,500–€4,500 0.271** 0.108 0.249** 0.113 0.219*** 0.079 0.216*** 0.082  €4,500–€7,000 0.460*** 0.090 0.396*** 0.095 0.320*** 0.066 0.297*** 0.070  €7,000–€10,000 0.466*** 0.095 0.461*** 0.098 0.339*** 0.078 0.338*** 0.083  €10,000–€25,000 0.503*** 0.076 0.447*** 0.082 0.474*** 0.059 0.434*** 0.063  €25,000–€45,000 0.637*** 0.084 0.601*** 0.091 0.622*** 0.075 0.610*** 0.080  €45,000 or more 0.862*** 0.088 0.817*** 0.093 0.753*** 0.081 0.724*** 0.083 Account characteristics  Minimum amount − 0.369*** 0.097 − 0.399*** 0.102  Lowest balance bonus − 0.328*** 0.104 − 0.345*** 0.112  Balance growth bonus 2.121*** 0.117 2.169*** 0.128  Fixed monthly deposit 1.396*** 0.076 1.358*** 0.097  Withdrawal costs/limitations − 0.266*** 0.060 − 0.301*** 0.064  Salary account 1.118*** 0.152 1.071*** 0.150  Joint ownership 0.001 0.048 0.005 0.051  Third party ownership − 0.040 0.063 − 0.030 0.071 Constant 2.315*** 0.027 2.268*** 0.036 2.272*** 0.124 2.258*** 0.136 2.380*** 0.104 2.397*** 0.116 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 1,410 1,306 1,410 1,306 Adjusted R-squared 0.01 − 0.02 0.12 0.10 0.47 0.45 Hansen J-test p-value 0.29 0.60 0.45 F-statistic first stage 11.54 9.74 10.44 Exogeneity test p-value 0.12 0.23 0.07 Notes: The table reports OLS and IV estimates from regressions of the account-level APR on financial literacy and several other controls. The sample excludes accounts with volume below €50. All IV specifications use economics education and the financial situation of the oldest sibling as an instrument for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. *p < 0.1; **p < 0.05; ***p < 0.01. View Large Table 4. OLS of account-level APR on financial literacy. OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.116*** 0.032 0.304*** 0.112 0.128*** 0.031 0.275** 0.125 0.127*** 0.022 0.288*** 0.099 Age dummies  31–40 years − 0.316*** 0.103 − 0.246** 0.118 − 0.241*** 0.079 − 0.197** 0.092  41–50 years − 0.474*** 0.099 − 0.424*** 0.106 − 0.372*** 0.074 − 0.357*** 0.078  51–60 years − 0.400*** 0.100 − 0.377*** 0.108 − 0.285*** 0.075 − 0.303*** 0.081  61 years and older − 0.541*** 0.123 − 0.564*** 0.135 − 0.491*** 0.090 − 0.547*** 0.099 Education dummies  High school 0.039 0.066 0.008 0.081 0.081 0.051 0.050 0.064  College − 0.083 0.064 − 0.134 0.085 0.016 0.051 − 0.050 0.068 Male − 0.025 0.056 − 0.068 0.073 0.011 0.041 − 0.047 0.060 Couple 0.014 0.059 − 0.023 0.062 0.066 0.053 0.021 0.056 Number of children 0.021 0.029 0.018 0.030 0.002 0.023 0.001 0.023 Occupation dummies  Employed 0.061 0.123 0.083 0.134 0.102 0.108 0.088 0.126  Self-employed 0.048 0.163 0.045 0.192 0.063 0.126 0.076 0.152  Unemployed 0.051 0.077 0.098 0.083 0.088 0.060 0.114* 0.065  Retired − 0.021 0.104 0.020 0.110 0.016 0.077 0.052 0.084 Volume dummies  €1,000–€2,500 0.108 0.084 0.124 0.088 0.111* 0.065 0.116* 0.069  €2,500–€3,500 0.185* 0.098 0.185* 0.102 0.078 0.076 0.074 0.077  €3,500–€4,500 0.271** 0.108 0.249** 0.113 0.219*** 0.079 0.216*** 0.082  €4,500–€7,000 0.460*** 0.090 0.396*** 0.095 0.320*** 0.066 0.297*** 0.070  €7,000–€10,000 0.466*** 0.095 0.461*** 0.098 0.339*** 0.078 0.338*** 0.083  €10,000–€25,000 0.503*** 0.076 0.447*** 0.082 0.474*** 0.059 0.434*** 0.063  €25,000–€45,000 0.637*** 0.084 0.601*** 0.091 0.622*** 0.075 0.610*** 0.080  €45,000 or more 0.862*** 0.088 0.817*** 0.093 0.753*** 0.081 0.724*** 0.083 Account characteristics  Minimum amount − 0.369*** 0.097 − 0.399*** 0.102  Lowest balance bonus − 0.328*** 0.104 − 0.345*** 0.112  Balance growth bonus 2.121*** 0.117 2.169*** 0.128  Fixed monthly deposit 1.396*** 0.076 1.358*** 0.097  Withdrawal costs/limitations − 0.266*** 0.060 − 0.301*** 0.064  Salary account 1.118*** 0.152 1.071*** 0.150  Joint ownership 0.001 0.048 0.005 0.051  Third party ownership − 0.040 0.063 − 0.030 0.071 Constant 2.315*** 0.027 2.268*** 0.036 2.272*** 0.124 2.258*** 0.136 2.380*** 0.104 2.397*** 0.116 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 1,410 1,306 1,410 1,306 Adjusted R-squared 0.01 − 0.02 0.12 0.10 0.47 0.45 Hansen J-test p-value 0.29 0.60 0.45 F-statistic first stage 11.54 9.74 10.44 Exogeneity test p-value 0.12 0.23 0.07 OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.116*** 0.032 0.304*** 0.112 0.128*** 0.031 0.275** 0.125 0.127*** 0.022 0.288*** 0.099 Age dummies  31–40 years − 0.316*** 0.103 − 0.246** 0.118 − 0.241*** 0.079 − 0.197** 0.092  41–50 years − 0.474*** 0.099 − 0.424*** 0.106 − 0.372*** 0.074 − 0.357*** 0.078  51–60 years − 0.400*** 0.100 − 0.377*** 0.108 − 0.285*** 0.075 − 0.303*** 0.081  61 years and older − 0.541*** 0.123 − 0.564*** 0.135 − 0.491*** 0.090 − 0.547*** 0.099 Education dummies  High school 0.039 0.066 0.008 0.081 0.081 0.051 0.050 0.064  College − 0.083 0.064 − 0.134 0.085 0.016 0.051 − 0.050 0.068 Male − 0.025 0.056 − 0.068 0.073 0.011 0.041 − 0.047 0.060 Couple 0.014 0.059 − 0.023 0.062 0.066 0.053 0.021 0.056 Number of children 0.021 0.029 0.018 0.030 0.002 0.023 0.001 0.023 Occupation dummies  Employed 0.061 0.123 0.083 0.134 0.102 0.108 0.088 0.126  Self-employed 0.048 0.163 0.045 0.192 0.063 0.126 0.076 0.152  Unemployed 0.051 0.077 0.098 0.083 0.088 0.060 0.114* 0.065  Retired − 0.021 0.104 0.020 0.110 0.016 0.077 0.052 0.084 Volume dummies  €1,000–€2,500 0.108 0.084 0.124 0.088 0.111* 0.065 0.116* 0.069  €2,500–€3,500 0.185* 0.098 0.185* 0.102 0.078 0.076 0.074 0.077  €3,500–€4,500 0.271** 0.108 0.249** 0.113 0.219*** 0.079 0.216*** 0.082  €4,500–€7,000 0.460*** 0.090 0.396*** 0.095 0.320*** 0.066 0.297*** 0.070  €7,000–€10,000 0.466*** 0.095 0.461*** 0.098 0.339*** 0.078 0.338*** 0.083  €10,000–€25,000 0.503*** 0.076 0.447*** 0.082 0.474*** 0.059 0.434*** 0.063  €25,000–€45,000 0.637*** 0.084 0.601*** 0.091 0.622*** 0.075 0.610*** 0.080  €45,000 or more 0.862*** 0.088 0.817*** 0.093 0.753*** 0.081 0.724*** 0.083 Account characteristics  Minimum amount − 0.369*** 0.097 − 0.399*** 0.102  Lowest balance bonus − 0.328*** 0.104 − 0.345*** 0.112  Balance growth bonus 2.121*** 0.117 2.169*** 0.128  Fixed monthly deposit 1.396*** 0.076 1.358*** 0.097  Withdrawal costs/limitations − 0.266*** 0.060 − 0.301*** 0.064  Salary account 1.118*** 0.152 1.071*** 0.150  Joint ownership 0.001 0.048 0.005 0.051  Third party ownership − 0.040 0.063 − 0.030 0.071 Constant 2.315*** 0.027 2.268*** 0.036 2.272*** 0.124 2.258*** 0.136 2.380*** 0.104 2.397*** 0.116 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 1,410 1,306 1,410 1,306 Adjusted R-squared 0.01 − 0.02 0.12 0.10 0.47 0.45 Hansen J-test p-value 0.29 0.60 0.45 F-statistic first stage 11.54 9.74 10.44 Exogeneity test p-value 0.12 0.23 0.07 Notes: The table reports OLS and IV estimates from regressions of the account-level APR on financial literacy and several other controls. The sample excludes accounts with volume below €50. All IV specifications use economics education and the financial situation of the oldest sibling as an instrument for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. *p < 0.1; **p < 0.05; ***p < 0.01. View Large Table 5. OLS of APR on financial literacy and controls using the Lewbel (2012) method. IV(1): Lewbel IV(2): Standard IV(3): Combined Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.218** 0.100 0.300** 0.121 0.228*** 0.077 Age dummies  31–40 years − 0.163 0.116 − 0.147 0.123 − 0.161 0.117  41–50 years − 0.326*** 0.109 − 0.321*** 0.110 − 0.325*** 0.109  51–60 years − 0.233** 0.107 − 0.246** 0.109 − 0.235** 0.107  61 years and older − 0.370*** 0.105 − 0.378*** 0.105 − 0.371*** 0.105 Male − 0.087 0.072 − 0.126 0.078 − 0.091 0.065 Number of children 0.024 0.031 0.027 0.031 0.025 0.030 Constant 2.577 0.098 2.581 0.098 2.578 0.098 N 1,306 1,306 1,306 Hansen J-test 3.51 5.73 10.60 Hansen J-test p-value 0.62 0.22 0.39 F-statistic first stage 6.03 11.12 16.75 Exogeneity test p-value 0.30 0.20 0.16 IV(1): Lewbel IV(2): Standard IV(3): Combined Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.218** 0.100 0.300** 0.121 0.228*** 0.077 Age dummies  31–40 years − 0.163 0.116 − 0.147 0.123 − 0.161 0.117  41–50 years − 0.326*** 0.109 − 0.321*** 0.110 − 0.325*** 0.109  51–60 years − 0.233** 0.107 − 0.246** 0.109 − 0.235** 0.107  61 years and older − 0.370*** 0.105 − 0.378*** 0.105 − 0.371*** 0.105 Male − 0.087 0.072 − 0.126 0.078 − 0.091 0.065 Number of children 0.024 0.031 0.027 0.031 0.025 0.030 Constant 2.577 0.098 2.581 0.098 2.578 0.098 N 1,306 1,306 1,306 Hansen J-test 3.51 5.73 10.60 Hansen J-test p-value 0.62 0.22 0.39 F-statistic first stage 6.03 11.12 16.75 Exogeneity test p-value 0.30 0.20 0.16 Notes: The table reports IV estimates from regressions of the account-level APR on financial literacy and age, gender and number of children using: (1) generated instruments from the Lewbel method alone; (2) external instruments (economics education and the financial situation of the oldest sibling) alone; (3) both sets of generated instruments under the Lewbel method and external instruments. The sample excludes accounts with volume below €50. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. **p < 0.05; ***p < 0.01. View Large Table 5. OLS of APR on financial literacy and controls using the Lewbel (2012) method. IV(1): Lewbel IV(2): Standard IV(3): Combined Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.218** 0.100 0.300** 0.121 0.228*** 0.077 Age dummies  31–40 years − 0.163 0.116 − 0.147 0.123 − 0.161 0.117  41–50 years − 0.326*** 0.109 − 0.321*** 0.110 − 0.325*** 0.109  51–60 years − 0.233** 0.107 − 0.246** 0.109 − 0.235** 0.107  61 years and older − 0.370*** 0.105 − 0.378*** 0.105 − 0.371*** 0.105 Male − 0.087 0.072 − 0.126 0.078 − 0.091 0.065 Number of children 0.024 0.031 0.027 0.031 0.025 0.030 Constant 2.577 0.098 2.581 0.098 2.578 0.098 N 1,306 1,306 1,306 Hansen J-test 3.51 5.73 10.60 Hansen J-test p-value 0.62 0.22 0.39 F-statistic first stage 6.03 11.12 16.75 Exogeneity test p-value 0.30 0.20 0.16 IV(1): Lewbel IV(2): Standard IV(3): Combined Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.218** 0.100 0.300** 0.121 0.228*** 0.077 Age dummies  31–40 years − 0.163 0.116 − 0.147 0.123 − 0.161 0.117  41–50 years − 0.326*** 0.109 − 0.321*** 0.110 − 0.325*** 0.109  51–60 years − 0.233** 0.107 − 0.246** 0.109 − 0.235** 0.107  61 years and older − 0.370*** 0.105 − 0.378*** 0.105 − 0.371*** 0.105 Male − 0.087 0.072 − 0.126 0.078 − 0.091 0.065 Number of children 0.024 0.031 0.027 0.031 0.025 0.030 Constant 2.577 0.098 2.581 0.098 2.578 0.098 N 1,306 1,306 1,306 Hansen J-test 3.51 5.73 10.60 Hansen J-test p-value 0.62 0.22 0.39 F-statistic first stage 6.03 11.12 16.75 Exogeneity test p-value 0.30 0.20 0.16 Notes: The table reports IV estimates from regressions of the account-level APR on financial literacy and age, gender and number of children using: (1) generated instruments from the Lewbel method alone; (2) external instruments (economics education and the financial situation of the oldest sibling) alone; (3) both sets of generated instruments under the Lewbel method and external instruments. The sample excludes accounts with volume below €50. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. **p < 0.05; ***p < 0.01. View Large Table 6. Robustness checks. OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Panel A: Variations ofequation (1) Advanced financial literacy 0.136*** 0.025 0.317*** 0.104 0.127*** 0.024 0.286*** 0.102 0.126*** 0.022 0.260*** 0.09 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,410 1,306 1,177 1,091 1,410 1,306 Adjusted R-squared 0.42 0.39 0.49 0.46 0.46 0.43 Hansen J-test p-value 0.14 0.32 0.56 F-statistic first stage 10.43 11.97 13.49 Exogeneity test p-value 0.05 0.1 0.12 Panel B: Different literacy measures Advanced financial literacy 0.127*** 0.024 0.262*** 0.094 Number of correct answers 0.117*** 0.022 0.318*** 0.116 “Big-Three”-Index 0.060*** 0.023 0.428** 0.196 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,290 1,290 1,410 1,306 1,410 1,306 Adjusted R-squared 0.47 0.45 0.47 0.43 0.46 0.32 Hansen J-test p-value 0.48 0.46 0.46 F-statistic first stage 11.21 7.11 2.68 Exogeneity test p-value 0.10 0.05 0.02 OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Panel A: Variations ofequation (1) Advanced financial literacy 0.136*** 0.025 0.317*** 0.104 0.127*** 0.024 0.286*** 0.102 0.126*** 0.022 0.260*** 0.09 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,410 1,306 1,177 1,091 1,410 1,306 Adjusted R-squared 0.42 0.39 0.49 0.46 0.46 0.43 Hansen J-test p-value 0.14 0.32 0.56 F-statistic first stage 10.43 11.97 13.49 Exogeneity test p-value 0.05 0.1 0.12 Panel B: Different literacy measures Advanced financial literacy 0.127*** 0.024 0.262*** 0.094 Number of correct answers 0.117*** 0.022 0.318*** 0.116 “Big-Three”-Index 0.060*** 0.023 0.428** 0.196 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,290 1,290 1,410 1,306 1,410 1,306 Adjusted R-squared 0.47 0.45 0.47 0.43 0.46 0.32 Hansen J-test p-value 0.48 0.46 0.46 F-statistic first stage 11.21 7.11 2.68 Exogeneity test p-value 0.10 0.05 0.02 Notes: The table shows variations of the OLS (3) and IV (3) specifications of Table 4. Panel A: (1) estimates equation (1) excluding the nine volume dummies; (2) uses only accounts reported by the financial head; (3) weighs each observation by its volume share within the household. Panel B: OLS (1) and IV(1) use the reduced sample with excluded “don’t know”-answers from the instrument economics education; (2) uses the standardized number of correct answers to the financial literacy questions; (3) uses the standardized number of correct answers to the “Big-Three” questions only. IV uses economics education and the financial situation of the oldest sibling as instruments for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. **p < 0.05; ***p < 0.01. View Large Table 6. Robustness checks. OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Panel A: Variations ofequation (1) Advanced financial literacy 0.136*** 0.025 0.317*** 0.104 0.127*** 0.024 0.286*** 0.102 0.126*** 0.022 0.260*** 0.09 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,410 1,306 1,177 1,091 1,410 1,306 Adjusted R-squared 0.42 0.39 0.49 0.46 0.46 0.43 Hansen J-test p-value 0.14 0.32 0.56 F-statistic first stage 10.43 11.97 13.49 Exogeneity test p-value 0.05 0.1 0.12 Panel B: Different literacy measures Advanced financial literacy 0.127*** 0.024 0.262*** 0.094 Number of correct answers 0.117*** 0.022 0.318*** 0.116 “Big-Three”-Index 0.060*** 0.023 0.428** 0.196 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,290 1,290 1,410 1,306 1,410 1,306 Adjusted R-squared 0.47 0.45 0.47 0.43 0.46 0.32 Hansen J-test p-value 0.48 0.46 0.46 F-statistic first stage 11.21 7.11 2.68 Exogeneity test p-value 0.10 0.05 0.02 OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Panel A: Variations ofequation (1) Advanced financial literacy 0.136*** 0.025 0.317*** 0.104 0.127*** 0.024 0.286*** 0.102 0.126*** 0.022 0.260*** 0.09 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,410 1,306 1,177 1,091 1,410 1,306 Adjusted R-squared 0.42 0.39 0.49 0.46 0.46 0.43 Hansen J-test p-value 0.14 0.32 0.56 F-statistic first stage 10.43 11.97 13.49 Exogeneity test p-value 0.05 0.1 0.12 Panel B: Different literacy measures Advanced financial literacy 0.127*** 0.024 0.262*** 0.094 Number of correct answers 0.117*** 0.022 0.318*** 0.116 “Big-Three”-Index 0.060*** 0.023 0.428** 0.196 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,290 1,290 1,410 1,306 1,410 1,306 Adjusted R-squared 0.47 0.45 0.47 0.43 0.46 0.32 Hansen J-test p-value 0.48 0.46 0.46 F-statistic first stage 11.21 7.11 2.68 Exogeneity test p-value 0.10 0.05 0.02 Notes: The table shows variations of the OLS (3) and IV (3) specifications of Table 4. Panel A: (1) estimates equation (1) excluding the nine volume dummies; (2) uses only accounts reported by the financial head; (3) weighs each observation by its volume share within the household. Panel B: OLS (1) and IV(1) use the reduced sample with excluded “don’t know”-answers from the instrument economics education; (2) uses the standardized number of correct answers to the financial literacy questions; (3) uses the standardized number of correct answers to the “Big-Three” questions only. IV uses economics education and the financial situation of the oldest sibling as instruments for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. **p < 0.05; ***p < 0.01. View Large Table 7. OLS of APR on financial literacy and online banking. OLS IV Estimate SE Estimate SE Advanced financial literacy 0.041*** 0.011 0.124** 0.050 Internet account 1.326*** 0.03 1.308*** 0.034 Constant 1.765*** 0.063 1.776*** 0.073 Demographics and other controls Yes Yes Region dummies Yes Yes Account characteristics Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 Adjusted R-squared 0.84 0.83 Hansen J-test p-value 0.85 F-statistic first stage 10.10 Exogeneity test p-value 0.11 OLS IV Estimate SE Estimate SE Advanced financial literacy 0.041*** 0.011 0.124** 0.050 Internet account 1.326*** 0.03 1.308*** 0.034 Constant 1.765*** 0.063 1.776*** 0.073 Demographics and other controls Yes Yes Region dummies Yes Yes Account characteristics Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 Adjusted R-squared 0.84 0.83 Hansen J-test p-value 0.85 F-statistic first stage 10.10 Exogeneity test p-value 0.11 Notes: The table reports OLS and IV estimates from regressions of the account-level APR on financial literacy and several controls (used in the third baseline specification shown in Table 4) including, in addition, a dummy that represents Internet-managed accounts. The sample excludes accounts with volume below €50. IV uses economics education and the financial situation of the oldest sibling as instruments for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. **p < 0.05; ***p < 0.01. View Large Table 7. OLS of APR on financial literacy and online banking. OLS IV Estimate SE Estimate SE Advanced financial literacy 0.041*** 0.011 0.124** 0.050 Internet account 1.326*** 0.03 1.308*** 0.034 Constant 1.765*** 0.063 1.776*** 0.073 Demographics and other controls Yes Yes Region dummies Yes Yes Account characteristics Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 Adjusted R-squared 0.84 0.83 Hansen J-test p-value 0.85 F-statistic first stage 10.10 Exogeneity test p-value 0.11 OLS IV Estimate SE Estimate SE Advanced financial literacy 0.041*** 0.011 0.124** 0.050 Internet account 1.326*** 0.03 1.308*** 0.034 Constant 1.765*** 0.063 1.776*** 0.073 Demographics and other controls Yes Yes Region dummies Yes Yes Account characteristics Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 Adjusted R-squared 0.84 0.83 Hansen J-test p-value 0.85 F-statistic first stage 10.10 Exogeneity test p-value 0.11 Notes: The table reports OLS and IV estimates from regressions of the account-level APR on financial literacy and several controls (used in the third baseline specification shown in Table 4) including, in addition, a dummy that represents Internet-managed accounts. The sample excludes accounts with volume below €50. IV uses economics education and the financial situation of the oldest sibling as instruments for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. **p < 0.05; ***p < 0.01. View Large In all three cases the estimated effects of financial literacy from both OLS and the corresponding IV regressions are comparable to those derived at the account level. For example, according to the IV estimate in the full specification, IV (3), an assumed one-standard deviation increase in advanced financial literacy implies a 30 basis points increase in the weighted APR. Other covariates in the model display a similar pattern to the one described in Section 5.2. That is, age and savings wealth associate with the interest earned, whereas other socioeconomic characteristics such as gender, family size, employment status and education are statistically insignificant.43 It should be noted that the account-level specifications presented in Section 5.1 preclude the possibility to reallocate funds to the highest interest account within household as a channel for financial literacy to influence the APR, whereas such a mechanism could be at work in the household-level regressions. The comparable effect of financial literacy in both account- and household-level specifications suggest that this channel is likely to be of limited importance. This is supported by simple data inspection: although the median number of owned accounts is two, most households tend to concentrate their savings in one account that typically earns the highest interest.44 As a last step, we attempt to assess the economic relevance of our key findings for households. One should note that our calculations only consider the influence of literacy for choosing an account that yields higher returns after controlling for account characteristics and bank fixed effects, which take into consideration a nontrivial part of heterogeneity across accounts. Moreover, our calculations do not incorporate the positive influence of literacy on the propensity to save higher amounts and invest more efficiently in other assets such as stocks, mutual funds and retirement plans that, compared to savings accounts, are more complex but also yield higher returns. One should also bear in mind that our calculations implicitly assume a partial equilibrium framework. If the entire population was more financially literate, banks would most likely adapt the products they offer (see also footnote 46). As a result, the general equilibrium effects can be quite different to the approximated partial equilibrium effects. First, we estimate how much more a typical household in the lowest literacy quartile could have earned today on its savings accounts when moved to the highest literacy quartile (other things equal).45 We have to make several assumptions in order to perform such a counterfactual exercise. We consider a 10-year time horizon and suppose that earned returns are reinvested. For each year, we use as the baseline rate the median interest rate of households in the first literacy quartile, which is 2.1%. Using our preferred estimate from the IV (3) specification in Table 8, we calculate that a household, moved from the lowest to the highest literacy quartile, would earn 39 basis points more on average. We then apply this extra return to the average household savings volume.46 To be conservative, we assume that additional deposits invested by households as a percentage of total savings wealth over one year grow only by the annual inflation rate.47 In this set-up, a typical foregone gain accumulates to €838 in real terms over 10 years or 4.8% of the initially invested average amount. Table 8. OLS of household-level APR on financial literacy. OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.148*** 0.028 0.317*** 0.091 0.129*** 0.028 0.286*** 0.110 0.123*** 0.023 0.297*** 0.093 Age dummies  31–40 years − 0.326*** 0.102 − 0.264** 0.111 − 0.267*** 0.084 − 0.225** 0.092  41–50 years − 0.542*** 0.100 − 0.465*** 0.109 − 0.449*** 0.082 − 0.408*** 0.088  51–60 years − 0.407*** 0.102 − 0.359*** 0.112 − 0.301*** 0.084 − 0.291*** 0.092  61 years and older − 0.540*** 0.130 − 0.541*** 0.137 − 0.537*** 0.107 − 0.560*** 0.112 Education dummies  High school − 0.010 0.067 − 0.030 0.077 0.028 0.056 0.007 0.064  College − 0.051 0.067 − 0.097 0.084 0.006 0.057 − 0.060 0.074 Male − 0.023 0.058 − 0.067 0.073 0.021 0.048 − 0.028 0.062 Couple 0.018 0.061 − 0.016 0.065 0.109* 0.066 0.058 0.070 Number of children 0.008 0.030 0.005 0.031 − 0.003 0.025 − 0.002 0.026 Occupation dummies  Employed 0.053 0.130 0.084 0.138 0.081 0.112 0.078 0.128  Self-employed 0.191 0.149 0.212 0.168 0.181* 0.109 0.208* 0.126  Unemployed − 0.071 0.079 − 0.036 0.084 0.004 0.066 0.027 0.070  Retired − 0.078 0.111 − 0.037 0.113 0.020 0.092 0.045 0.094 Savings wealth quartiles  Second quartile 0.254*** 0.078 0.234*** 0.082 0.218*** 0.064 0.207*** 0.068  Third quartile 0.411*** 0.075 0.341*** 0.083 0.377*** 0.063 0.318*** 0.067  Fourth quartile 0.634*** 0.077 0.533*** 0.093 0.600*** 0.069 0.528*** 0.079 Account characteristics  Minimum amount − 0.204*** 0.070 − 0.251*** 0.078  Lowest balance bonus − 0.181** 0.075 − 0.207** 0.082  Balance growth bonus 0.779*** 0.143 0.843*** 0.156  Fixed monthly deposit 0.705*** 0.165 0.611*** 0.166  Withdrawal costs/limitations − 0.195*** 0.067 − 0.234*** 0.072  Salary account 1.032*** 0.120 0.998*** 0.120  Joint ownership − 0.071 0.058 − 0.055 0.062  Third party ownership − 0.115* 0.069 − 0.096 0.073 Constant 2.341*** 0.027 2.316*** 0.031 2.322*** 0.126 2.317*** 0.143 2.339*** 0.121 2.363*** 0.139 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 854 787 854 787 854 787 Adjusted R-squared 0.03 − 0.01 0.13 0.10 0.41 0.36 Hansen J-test p-value 0.14 0.35 0.41 F-statistic first stage 18.96 14.42 15.30 Exogeneity test p-value 0.08 0.13 0.05 OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.148*** 0.028 0.317*** 0.091 0.129*** 0.028 0.286*** 0.110 0.123*** 0.023 0.297*** 0.093 Age dummies  31–40 years − 0.326*** 0.102 − 0.264** 0.111 − 0.267*** 0.084 − 0.225** 0.092  41–50 years − 0.542*** 0.100 − 0.465*** 0.109 − 0.449*** 0.082 − 0.408*** 0.088  51–60 years − 0.407*** 0.102 − 0.359*** 0.112 − 0.301*** 0.084 − 0.291*** 0.092  61 years and older − 0.540*** 0.130 − 0.541*** 0.137 − 0.537*** 0.107 − 0.560*** 0.112 Education dummies  High school − 0.010 0.067 − 0.030 0.077 0.028 0.056 0.007 0.064  College − 0.051 0.067 − 0.097 0.084 0.006 0.057 − 0.060 0.074 Male − 0.023 0.058 − 0.067 0.073 0.021 0.048 − 0.028 0.062 Couple 0.018 0.061 − 0.016 0.065 0.109* 0.066 0.058 0.070 Number of children 0.008 0.030 0.005 0.031 − 0.003 0.025 − 0.002 0.026 Occupation dummies  Employed 0.053 0.130 0.084 0.138 0.081 0.112 0.078 0.128  Self-employed 0.191 0.149 0.212 0.168 0.181* 0.109 0.208* 0.126  Unemployed − 0.071 0.079 − 0.036 0.084 0.004 0.066 0.027 0.070  Retired − 0.078 0.111 − 0.037 0.113 0.020 0.092 0.045 0.094 Savings wealth quartiles  Second quartile 0.254*** 0.078 0.234*** 0.082 0.218*** 0.064 0.207*** 0.068  Third quartile 0.411*** 0.075 0.341*** 0.083 0.377*** 0.063 0.318*** 0.067  Fourth quartile 0.634*** 0.077 0.533*** 0.093 0.600*** 0.069 0.528*** 0.079 Account characteristics  Minimum amount − 0.204*** 0.070 − 0.251*** 0.078  Lowest balance bonus − 0.181** 0.075 − 0.207** 0.082  Balance growth bonus 0.779*** 0.143 0.843*** 0.156  Fixed monthly deposit 0.705*** 0.165 0.611*** 0.166  Withdrawal costs/limitations − 0.195*** 0.067 − 0.234*** 0.072  Salary account 1.032*** 0.120 0.998*** 0.120  Joint ownership − 0.071 0.058 − 0.055 0.062  Third party ownership − 0.115* 0.069 − 0.096 0.073 Constant 2.341*** 0.027 2.316*** 0.031 2.322*** 0.126 2.317*** 0.143 2.339*** 0.121 2.363*** 0.139 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 854 787 854 787 854 787 Adjusted R-squared 0.03 − 0.01 0.13 0.10 0.41 0.36 Hansen J-test p-value 0.14 0.35 0.41 F-statistic first stage 18.96 14.42 15.30 Exogeneity test p-value 0.08 0.13 0.05 Notes: The table reports OLS and IV estimates from regressions of the household-level APR on financial literacy and several controls. The sample excludes accounts with volume below €50. All IV specifications use economics education and the financial situation of the oldest sibling as instruments for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. *p < 0.1; **p < 0.05; ***p < 0.01. View Large Table 8. OLS of household-level APR on financial literacy. OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.148*** 0.028 0.317*** 0.091 0.129*** 0.028 0.286*** 0.110 0.123*** 0.023 0.297*** 0.093 Age dummies  31–40 years − 0.326*** 0.102 − 0.264** 0.111 − 0.267*** 0.084 − 0.225** 0.092  41–50 years − 0.542*** 0.100 − 0.465*** 0.109 − 0.449*** 0.082 − 0.408*** 0.088  51–60 years − 0.407*** 0.102 − 0.359*** 0.112 − 0.301*** 0.084 − 0.291*** 0.092  61 years and older − 0.540*** 0.130 − 0.541*** 0.137 − 0.537*** 0.107 − 0.560*** 0.112 Education dummies  High school − 0.010 0.067 − 0.030 0.077 0.028 0.056 0.007 0.064  College − 0.051 0.067 − 0.097 0.084 0.006 0.057 − 0.060 0.074 Male − 0.023 0.058 − 0.067 0.073 0.021 0.048 − 0.028 0.062 Couple 0.018 0.061 − 0.016 0.065 0.109* 0.066 0.058 0.070 Number of children 0.008 0.030 0.005 0.031 − 0.003 0.025 − 0.002 0.026 Occupation dummies  Employed 0.053 0.130 0.084 0.138 0.081 0.112 0.078 0.128  Self-employed 0.191 0.149 0.212 0.168 0.181* 0.109 0.208* 0.126  Unemployed − 0.071 0.079 − 0.036 0.084 0.004 0.066 0.027 0.070  Retired − 0.078 0.111 − 0.037 0.113 0.020 0.092 0.045 0.094 Savings wealth quartiles  Second quartile 0.254*** 0.078 0.234*** 0.082 0.218*** 0.064 0.207*** 0.068  Third quartile 0.411*** 0.075 0.341*** 0.083 0.377*** 0.063 0.318*** 0.067  Fourth quartile 0.634*** 0.077 0.533*** 0.093 0.600*** 0.069 0.528*** 0.079 Account characteristics  Minimum amount − 0.204*** 0.070 − 0.251*** 0.078  Lowest balance bonus − 0.181** 0.075 − 0.207** 0.082  Balance growth bonus 0.779*** 0.143 0.843*** 0.156  Fixed monthly deposit 0.705*** 0.165 0.611*** 0.166  Withdrawal costs/limitations − 0.195*** 0.067 − 0.234*** 0.072  Salary account 1.032*** 0.120 0.998*** 0.120  Joint ownership − 0.071 0.058 − 0.055 0.062  Third party ownership − 0.115* 0.069 − 0.096 0.073 Constant 2.341*** 0.027 2.316*** 0.031 2.322*** 0.126 2.317*** 0.143 2.339*** 0.121 2.363*** 0.139 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 854 787 854 787 854 787 Adjusted R-squared 0.03 − 0.01 0.13 0.10 0.41 0.36 Hansen J-test p-value 0.14 0.35 0.41 F-statistic first stage 18.96 14.42 15.30 Exogeneity test p-value 0.08 0.13 0.05 OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.148*** 0.028 0.317*** 0.091 0.129*** 0.028 0.286*** 0.110 0.123*** 0.023 0.297*** 0.093 Age dummies  31–40 years − 0.326*** 0.102 − 0.264** 0.111 − 0.267*** 0.084 − 0.225** 0.092  41–50 years − 0.542*** 0.100 − 0.465*** 0.109 − 0.449*** 0.082 − 0.408*** 0.088  51–60 years − 0.407*** 0.102 − 0.359*** 0.112 − 0.301*** 0.084 − 0.291*** 0.092  61 years and older − 0.540*** 0.130 − 0.541*** 0.137 − 0.537*** 0.107 − 0.560*** 0.112 Education dummies  High school − 0.010 0.067 − 0.030 0.077 0.028 0.056 0.007 0.064  College − 0.051 0.067 − 0.097 0.084 0.006 0.057 − 0.060 0.074 Male − 0.023 0.058 − 0.067 0.073 0.021 0.048 − 0.028 0.062 Couple 0.018 0.061 − 0.016 0.065 0.109* 0.066 0.058 0.070 Number of children 0.008 0.030 0.005 0.031 − 0.003 0.025 − 0.002 0.026 Occupation dummies  Employed 0.053 0.130 0.084 0.138 0.081 0.112 0.078 0.128  Self-employed 0.191 0.149 0.212 0.168 0.181* 0.109 0.208* 0.126  Unemployed − 0.071 0.079 − 0.036 0.084 0.004 0.066 0.027 0.070  Retired − 0.078 0.111 − 0.037 0.113 0.020 0.092 0.045 0.094 Savings wealth quartiles  Second quartile 0.254*** 0.078 0.234*** 0.082 0.218*** 0.064 0.207*** 0.068  Third quartile 0.411*** 0.075 0.341*** 0.083 0.377*** 0.063 0.318*** 0.067  Fourth quartile 0.634*** 0.077 0.533*** 0.093 0.600*** 0.069 0.528*** 0.079 Account characteristics  Minimum amount − 0.204*** 0.070 − 0.251*** 0.078  Lowest balance bonus − 0.181** 0.075 − 0.207** 0.082  Balance growth bonus 0.779*** 0.143 0.843*** 0.156  Fixed monthly deposit 0.705*** 0.165 0.611*** 0.166  Withdrawal costs/limitations − 0.195*** 0.067 − 0.234*** 0.072  Salary account 1.032*** 0.120 0.998*** 0.120  Joint ownership − 0.071 0.058 − 0.055 0.062  Third party ownership − 0.115* 0.069 − 0.096 0.073 Constant 2.341*** 0.027 2.316*** 0.031 2.322*** 0.126 2.317*** 0.143 2.339*** 0.121 2.363*** 0.139 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 854 787 854 787 854 787 Adjusted R-squared 0.03 − 0.01 0.13 0.10 0.41 0.36 Hansen J-test p-value 0.14 0.35 0.41 F-statistic first stage 18.96 14.42 15.30 Exogeneity test p-value 0.08 0.13 0.05 Notes: The table reports OLS and IV estimates from regressions of the household-level APR on financial literacy and several controls. The sample excludes accounts with volume below €50. All IV specifications use economics education and the financial situation of the oldest sibling as instruments for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. *p < 0.1; **p < 0.05; ***p < 0.01. View Large Second, we use our preferred estimate to gain insights on the fraction of interest earnings on savings accounts that is due to limited literacy. To this end, we calculate the extra interest rate that every household would have earned due to an assumed one standard deviation increase in financial literacy and divide it by the weighted APR that every household actually earns. We find that such an assumed increase in literacy implies an average gain of 14% on the interest earnings of savings accounts and that the implied gains are relatively higher for households with lower savings accounts wealth. In particular, the implied gain among households at the bottom quartile of the savings wealth distribution is 17% whereas the corresponding one for their counterparts at the top quartile is 11.5%. Finally, we also estimate the implied aggregate foregone gains for the overall Dutch population due to an assumed one standard deviation increase in financial literacy. Taking into account the total number of Dutch households in 2005 of 7.1 million and the participation rate of 82.4% in savings accounts observed in the DHS data, we calculate that aggregate foregone gains could amount to €350.5 million for the entire Dutch population in this year.48 6. Concluding Remarks We have constructed a unique dataset by matching the 2005 DNB Household Survey, which includes detailed information on individual savings accounts, various socioeconomic characteristics and financial literacy, with interest rate data on savings accounts from an administrative source, based on bank names, account names and account volume. Although savings accounts represent a relatively simple investment, say compared to direct stock holdings or retirement funds, more financially literate investors earn higher savings returns on average, controlling for demographics, account volume, and various account characteristics. We isolate one channel through which literacy positively associates with interest rates, namely familiarity with new technologies (online banking usage). This channel may lessen in importance over time, as Internet penetration and familiarity with online banking expands. Nevertheless, the increasing availability of more complicated financial products, some of which are exclusively managed online, and the need to compare efficiently among them underline the importance of financial sophistication coupled with technological competence. We also find evidence suggesting that more literate households are better able to identify higher interest bearing accounts across banks. Unlike stocks and funds, savings accounts are held by the overwhelming majority of households and have the highest share in household financial wealth on average. Our findings may encourage more research in order to understand household heterogeneity in seemingly “simple” and widely held financial assets. In this respect, it may be worth extending our research to countries with a varying degree of market competitiveness and product complexity, as well as less financially literate populations. Acknowledgments We are grateful to five anonymous referees and Claudio Michelacci (editor) for their constructive comments. We would like to thank Tabea Bucher-Koenen, Gabriel Fagan, Luigi Guiso, Arthur Kennickell, Annamaria Lusardi, Maarten van Rooij, and participants at the Annual Congress of the European Economic Association, Toulouse; NETSPAR International Pension Workshop, Amsterdam; Research in Behavioural Finance Conference, Rotterdam; HFCN meeting at the ECB, Frankfurt for helpful comments. We are also grateful to Rob Alessie, Annamaria Lusardi, and Maarten van Rooij for making the data from a special financial literacy module publicly available, to a major Dutch bank for providing interest rate data on savings accounts in the Netherlands, to CentERdata for providing additional information on financial product holdings and bank relationships of DNB survey participants, as well as to SpaarInformatie for providing data on restrictions of Dutch savings accounts. All three authors acknowledge financial support from the German Science Foundation (DFG) under the Leibniz grant and Inderst also from the DFG Research Group 1371. The opinions expressed in the paper are those of the authors and do not reflect the views of the European Central Bank, the Deutsche Bundesbank or the euro system. Notes The editor in charge of this paper was Claudio Michelacci. Footnotes 1 The picture is similar for most other Euro area countries according to the recent data from the Household Finance and Consumption Survey (see http://www.ecb.europa.eu/home/html/researcher_hfcn.en.html). 2 For a comparison, ownership rates (average shares) are 20% (6%) for funds and only 12% (3%) for directly held stocks. 3 These are the same questions as used in van Rooij et al. (2011, 2012). 4 Several studies cite information/search frictions as a source of price dispersion in retail financial markets net of product differentiation by firms. See, for example, Hortacsu and Syverson (2004) for S&P 500 index funds and Stango and Zinman (2016) for credit cards. As a result, firms might have an incentive to add complexity to their pricing structures in order to gain market power (Carlin 2009). 5 See, for instance, Lusardi and Mitchell (2014) for a recent review of comparable studies in the United States as well as Europe, Australia, and Japan. Earlier studies include Bernheim (1998) and Hilgert et al. (2003). 6 For an overview, see Campbell (2006) and Guiso and Sodini (2013). Moreover, several studies have documented how investment mistakes correlate with proxies for financial knowledge such as education (e.g., Calvet et al. 2007; Bilias et al. 2010). 7 The survey provides equipment to households without Internet access in order to compensate for this form of bias. See Teppa and Vis (2012) for a detailed description of the DHS. 8 In the regular panel, participants are provided with a list of seven possible answers when asked at which bank they hold each of their savings accounts: ABN Amro, Postbank, Rabobank, ING, Fortis, SNS Bank, and “Other”. In case participants indicate ownership in the category “Other”, they are further asked to provide the name of the bank. This latter information along with account names is not available in the public version of the dataset, but has been recovered from additional data that were made available to us by CentERdata. Appendix C provides more details. 9 Smith et al. (2010) have shown that this person is actually the most influential for households’ financial decisions. The remaining sociodemographic characteristics that we take into account refer also to this person. 10 Reported statistics in Tables 1–3 have been computed using sample survey weights and are representative of the Dutch population. 11 Other employment includes the following categories: works in own household, (partly) disabled, unpaid work, volunteers, other employment, students, and too young with no occupation yet. 12 As van Rooij et al. (2011) point out, the basic literacy questions in the DHS special module test for basic numerical skills and are thus more likely to proxy for cognitive abilities that typically depreciate at advanced ages. Our estimates for the advanced literacy index are insensitive to the inclusion of the basic literacy index in the estimated model (results from this specification are discussed in the robustness section). 13 The data contain information on standard interest rates (i.e., not “teaser” ones offered by banks over a short period in order to attract new customers). April 2004 is the first month of the administrative data that we have access to. It should be noted that we do not consider checking accounts. These are typically used in daily transactions and their interest rate is virtually zero, exhibiting only limited variation that may reflect differences in services offered (e.g., check issuance, ATM services) but is unlikely to be related to individual financial sophistication. Instead, the saving account products we examine are not intended for frequent transactions and cannot be used for borrowing (i.e., their interest rate is bounded at zero). 14 The exact amount brackets are €0–€1,000, €1,000–€2,500, €2,500–€3,500, €3,500–€4,500, €4,500–€7,000, €7,000–€8,000, €8,000–€9,000, €9,000–€10,000, €10,000–€25,000, €25,000–€45,000, and >€45,000. 15 (1) Accounts with minimum amount requirements offer either very low base rates or zero interest rate up to a certain volume threshold and higher rates above that threshold. (2) Accounts with lowest balance bonus give a bonus rate on the lowest account balance within a year or a quarter and yield a base rate on the remaining balance. (3) Accounts with balance growth bonus yield a bonus rate if the balance grows by a specified percentage amount per quarter or year. (4) Accounts with fixed monthly deposit require a specified absolute deposit automatically withdrawn from the checking account of the consumer each month. (5) Accounts with withdrawal limitations/fees limit the maximum amount that can be withdrawn per month or impose percentage fees for withdrawals (in most cases 1% of the withdrawn amount). (6) Salary accounts are linked to a checking account at the same bank. 16 Our results are robust when we use, instead, a (geometrically) weighted interest rate for each account over all weeks in 2004. Precisely, for 2004 we can use interest data from April 2004 to December 2004. Interest rate changes are relatively infrequent in this period. 17 We recover missing volumes of individual saving accounts following the procedure used by CentERdata for total savings volumes as described in Appendix C. 18 We do not find evidence that the sample used in the estimation differs in a systematic way from the entire sample of account owners. We provide a comparison of main demographics and bank characteristics for the two samples in Table A.2 of Appendix A. 19 In what follows, we present results only from the sample of those accounts with matched interest rate information. We have also imputed missing interest rates utilizing information on the bank names reported by each household. Results from the sample that incorporates these imputed cases along with details on the imputation procedure can be found in Deuflhard et al. (2013). Results from both samples are highly comparable. 20 This concerns around 6.8% of the accounts in the account-level sample. 21 Such a behavior would be consistent with existing evidence suggesting that many households do not understand interest compounding and its likely implications (e.g., Song 2015 finds evidence that learning about interest compounding results into a significant increase in pension contributions in China; Stango and Zinman 2009 show that those who fail to calculate interest rates out of a stream of payments borrow more and accumulate less wealth). 22 When we assess the economic relevance of our findings, we also discuss results from a household-level specification. This is possible to estimate by calculating a volume-weighted measure of APR per household and aggregating individual account characteristics at the household level. The results we find are similar. 23 We distinguish among the five main Dutch regions (the three largest cities, other West, North, East, and South). 24 As in Table 2, we group together three amount brackets from €7,000 to €10,000 due to too few observations in these categories and no account reaching a new volume threshold within this range. 25 Summary statistics on both instruments used can be found in Table 1. 26 Respondents were asked to indicate whether the financial situation of the oldest sibling is better, the same or worse compared to their own financial situation. 27 If the financial condition of the oldest sibling proxies instead for a common set of preferences or a family fixed effect, one would expect a negative correlation between the instrument and financial literacy in the first stage regression. 28 Specifically, respondents were asked how much of their past education was devoted to economics (i.e., “a lot”, “some”, “little”, and “hardly at all”). 29 A number of recent empirical studies use the Lewbel method as an alternative to the standard IV approach, see, for example, Emran and Hou (2013) and Chowdhury et al. (2014). 30 This can be seen as a relatively mild assumption compared to the exclusion restriction required under standard IV. For example, in our context it allows for unobserved factors such as general well-being and ability (or intention) to learn about finances to affect both interest rate earned and financial literacy. 31 The number of observations slightly changes from the OLS to the IV specifications due to some missing observations in the used instruments. 32 The estimated net effect of each account characteristic is hard to interpret, given that many of these restrictions typically coexist. The interpretation is further complicated by the fact that the composition of account restrictions partly varies by bank, and, as a result, the bank fixed effects already absorb a nontrivial fraction of the mean differences in account restrictions. 33 Our cross-sectional specification does not allow distinguishing between age, cohort, and time effects. Yet, one should note that the implied negative age profile that we estimate is at odds with the hump-shaped age profile derived in similar specifications modelling investments in information-intensive assets, such as stocks. The latter has been interpreted as evidence for both learning by experience and declining cognitive capacity. Given that we examine performance of a very basic asset, learning by experience may be less relevant in obtaining higher returns than factors that are more prominent among the young, such as familiarity with technology. 34 It is worth noting that most of the empirical household finance literature examines investment decisions in assets that are held by household sub-groups with selected characteristics (e.g., stockholding typically entails high participation and information costs and thus stocks are mostly held by wealthier, better educated, and more financially literate investors). We examine instead returns from an asset that has low participation requirements and is held by the vast majority of households in the sample (82.4%). We have estimated a probit model of the probability of owning a savings account and most of the factors that were taken into account (including financial literacy) turn out to be insignificant. Financial wealth was estimated to have a strong positive association with savings account ownership, though such an association is likely to be mechanical. 35 We standardize these measures by their respective mean and standard deviation. 36 Such a specification is not free of measurement error as it is still subject to misclassification across the four possible quartiles. 37 Obviously, we cannot easily instrument for advanced financial literacy when using quartiles due to the number of endogenous covariates. 38 See Online Appendix D for the exact wording of these questions. 39 Based on two gambles presented to survey participants in the DHS, this measure can take five possible outcomes from low to high risk aversion (including one category for those who answered “don’t know”). 40 We obtain similar results when using self-reported online banking use, instead, which is asked in the DHS, as this is highly correlated with having an internet account. 41 This is corroborated by results from a probit regression (see Online Appendix E) in which we estimate a significant positive association between financial literacy and the likelihood of owning an online managed account. 42 We calculate a household-specific APR as the volume-weighted average APR across all accounts that each household h owns. In this specification we include dummies denoting quartiles of total savings account volume (i.e., instead of account volume dummies used in the account level specification). Given that the unit of observation is the household, dummies for account characteristics take the value one if at least one of the savings accounts in a household is subject to the restriction in question, while bank fixed effects are volume-weighted. 43 As is the case with account-level regressions, we find no significant effects of dummies for income, financial and real wealth quartiles of the respective distributions when included in the household-level specifications. 44 For instance, 66% of households allocate more than 80% to a single account. 45 For our calculation, we assume more narrowly that such an increase in financial literacy takes place for a single household only. If, for instance, a publicly sponsored program lifts financial literacy for a larger fraction of households, however, our calculation represents only a partial equilibrium analysis in the following sense. Presently, as noted previously, price differentiation across banks but also across accounts at a given bank seems to be possible as consumers are sophisticated to a different degree. When more consumers become literate in this sense, there is less scope for such differentiation. In equilibrium, banks would react by adjusting their offers. One possibility, which can be supported by a formal analysis, is that as more households become willing and able to choose the best offer, offers would become more attractive across the board, in which case the general equilibrium effect of a financial literacy program would further enhance the benefits to, in particular, (newly) literate households. 46 Our calculations use moments on total savings volume and financial literacy from the entire sample of households. 47 This simplifies matters in the sense that additional deposits and inflation cancel out in the calculation of cumulative losses. 48 For comparison, the estimated aggregate interest earnings from all households’ savings accounts were €3.1 billion in 2005. We perform these calculations by aggregating over all households used in the estimation sample and then scale this number up to the total number of households owning a savings account in the Dutch population. 49 For example, some respondents report accounts which are not offered anymore by the reported bank in 2005 but were replaced by an account with a different name. 50 Details can be found in the documentation of the DHS 2005 wave (available at: https://www.dhsdata.nl/site/releases/sourcefile/49). 51 60% of those household members hold only one account and thus total volume and individual account volume are equivalent. 52 Note that in the last two cases, we only consider accounts that do not exceed the total number of accounts as originally stated by the respondent, for example, we only consider the first three reported accounts of a household that claims to have 3 accounts in total but reports four. The same approach is used in the DHS for the calculation of total savings wealth. Appendix A: Descriptive Statistics on Savings Accounts Table A.1. Summary statistics account-level variables. Variable Mean Std. Dev. 25th pct. Median 75th pct. APR 2.31 0.86 1.55 2.50 3.10 Account volume in € 10,973 26,756 857 3,500 12,750 Bank fixed effects  ABN AMRO 0.12 0.33 – – –  ING Bank 0.37 0.48 – – –  Rabobank 0.30 0.46 – – –  Fortis Bank 0.03 0.17 – – –  SNS Bank 0.03 0.17 – – –  Small Banks 0.15 0.36 – – – Ownership  Individual 0.52 0.50 – – –  Joint 0.39 0.49 – – –  Third party 0.09 0.29 – – – Account restrictions  Internet account 0.35 0.48 – – –  Minimum amount 0.13 0.33 – – –  Lowest balance bonus 0.28 0.45 – – –  Balance growth bonus 0.03 0.16 – – –  Fixed monthly deposit 0.01 0.10 – – –  Withdrawal costs/limitations 0.09 0.29 – – –  Salary account 0.02 0.15 – – – Variable Mean Std. Dev. 25th pct. Median 75th pct. APR 2.31 0.86 1.55 2.50 3.10 Account volume in € 10,973 26,756 857 3,500 12,750 Bank fixed effects  ABN AMRO 0.12 0.33 – – –  ING Bank 0.37 0.48 – – –  Rabobank 0.30 0.46 – – –  Fortis Bank 0.03 0.17 – – –  SNS Bank 0.03 0.17 – – –  Small Banks 0.15 0.36 – – – Ownership  Individual 0.52 0.50 – – –  Joint 0.39 0.49 – – –  Third party 0.09 0.29 – – – Account restrictions  Internet account 0.35 0.48 – – –  Minimum amount 0.13 0.33 – – –  Lowest balance bonus 0.28 0.45 – – –  Balance growth bonus 0.03 0.16 – – –  Fixed monthly deposit 0.01 0.10 – – –  Withdrawal costs/limitations 0.09 0.29 – – –  Salary account 0.02 0.15 – – – Notes: The sample consists of accounts used in the regression analysis. All statistics use sample weights. The data are from the matched DNB Household Survey 2005. View Large Table A.1. Summary statistics account-level variables. Variable Mean Std. Dev. 25th pct. Median 75th pct. APR 2.31 0.86 1.55 2.50 3.10 Account volume in € 10,973 26,756 857 3,500 12,750 Bank fixed effects  ABN AMRO 0.12 0.33 – – –  ING Bank 0.37 0.48 – – –  Rabobank 0.30 0.46 – – –  Fortis Bank 0.03 0.17 – – –  SNS Bank 0.03 0.17 – – –  Small Banks 0.15 0.36 – – – Ownership  Individual 0.52 0.50 – – –  Joint 0.39 0.49 – – –  Third party 0.09 0.29 – – – Account restrictions  Internet account 0.35 0.48 – – –  Minimum amount 0.13 0.33 – – –  Lowest balance bonus 0.28 0.45 – – –  Balance growth bonus 0.03 0.16 – – –  Fixed monthly deposit 0.01 0.10 – – –  Withdrawal costs/limitations 0.09 0.29 – – –  Salary account 0.02 0.15 – – – Variable Mean Std. Dev. 25th pct. Median 75th pct. APR 2.31 0.86 1.55 2.50 3.10 Account volume in € 10,973 26,756 857 3,500 12,750 Bank fixed effects  ABN AMRO 0.12 0.33 – – –  ING Bank 0.37 0.48 – – –  Rabobank 0.30 0.46 – – –  Fortis Bank 0.03 0.17 – – –  SNS Bank 0.03 0.17 – – –  Small Banks 0.15 0.36 – – – Ownership  Individual 0.52 0.50 – – –  Joint 0.39 0.49 – – –  Third party 0.09 0.29 – – – Account restrictions  Internet account 0.35 0.48 – – –  Minimum amount 0.13 0.33 – – –  Lowest balance bonus 0.28 0.45 – – –  Balance growth bonus 0.03 0.16 – – –  Fixed monthly deposit 0.01 0.10 – – –  Withdrawal costs/limitations 0.09 0.29 – – –  Salary account 0.02 0.15 – – – Notes: The sample consists of accounts used in the regression analysis. All statistics use sample weights. The data are from the matched DNB Household Survey 2005. View Large Table A.2. Comparison of full versus final sample across major demographics and account characteristics. Full sample (N = 2,337) Final sample (N = 1,410) Variable Mean Std. Dev. Mean Std. Dev. Age 49.94 15.61 50.74 15.33 Male 0.56 0.50 0.58 0.49 Couple 0.71 0.45 0.71 0.46 Number of children 0.68 1.04 0.65 1.04 Less than high school 0.25 0.43 0.24 0.42 High school 0.35 0.48 0.34 0.47 College 0.41 0.49 0.43 0.49 ABN AMRO 0.13 0.34 0.12 0.33 ING Bank 0.34 0.47 0.37 0.48 Rabobank 0.28 0.45 0.30 0.46 Fortis Bank 0.04 0.19 0.03 0.17 SNS Bank 0.04 0.19 0.03 0.17 Small Banks 0.17 0.38 0.15 0.36 Volume in € 9,636 23,804 10,973 26,756 Individual ownership 0.53 0.50 0.52 0.50 Joint ownership 0.36 0.48 0.39 0.49 Third party ownership 0.11 0.31 0.09 0.29 Full sample (N = 2,337) Final sample (N = 1,410) Variable Mean Std. Dev. Mean Std. Dev. Age 49.94 15.61 50.74 15.33 Male 0.56 0.50 0.58 0.49 Couple 0.71 0.45 0.71 0.46 Number of children 0.68 1.04 0.65 1.04 Less than high school 0.25 0.43 0.24 0.42 High school 0.35 0.48 0.34 0.47 College 0.41 0.49 0.43 0.49 ABN AMRO 0.13 0.34 0.12 0.33 ING Bank 0.34 0.47 0.37 0.48 Rabobank 0.28 0.45 0.30 0.46 Fortis Bank 0.04 0.19 0.03 0.17 SNS Bank 0.04 0.19 0.03 0.17 Small Banks 0.17 0.38 0.15 0.36 Volume in € 9,636 23,804 10,973 26,756 Individual ownership 0.53 0.50 0.52 0.50 Joint ownership 0.36 0.48 0.39 0.49 Third party ownership 0.11 0.31 0.09 0.29 Notes: The full sample contains all accounts held by households in the DNB Household Survey. The final sample contains only those accounts used in the estimation sample. All statistics use sample weights. View Large Table A.2. Comparison of full versus final sample across major demographics and account characteristics. Full sample (N = 2,337) Final sample (N = 1,410) Variable Mean Std. Dev. Mean Std. Dev. Age 49.94 15.61 50.74 15.33 Male 0.56 0.50 0.58 0.49 Couple 0.71 0.45 0.71 0.46 Number of children 0.68 1.04 0.65 1.04 Less than high school 0.25 0.43 0.24 0.42 High school 0.35 0.48 0.34 0.47 College 0.41 0.49 0.43 0.49 ABN AMRO 0.13 0.34 0.12 0.33 ING Bank 0.34 0.47 0.37 0.48 Rabobank 0.28 0.45 0.30 0.46 Fortis Bank 0.04 0.19 0.03 0.17 SNS Bank 0.04 0.19 0.03 0.17 Small Banks 0.17 0.38 0.15 0.36 Volume in € 9,636 23,804 10,973 26,756 Individual ownership 0.53 0.50 0.52 0.50 Joint ownership 0.36 0.48 0.39 0.49 Third party ownership 0.11 0.31 0.09 0.29 Full sample (N = 2,337) Final sample (N = 1,410) Variable Mean Std. Dev. Mean Std. Dev. Age 49.94 15.61 50.74 15.33 Male 0.56 0.50 0.58 0.49 Couple 0.71 0.45 0.71 0.46 Number of children 0.68 1.04 0.65 1.04 Less than high school 0.25 0.43 0.24 0.42 High school 0.35 0.48 0.34 0.47 College 0.41 0.49 0.43 0.49 ABN AMRO 0.13 0.34 0.12 0.33 ING Bank 0.34 0.47 0.37 0.48 Rabobank 0.28 0.45 0.30 0.46 Fortis Bank 0.04 0.19 0.03 0.17 SNS Bank 0.04 0.19 0.03 0.17 Small Banks 0.17 0.38 0.15 0.36 Volume in € 9,636 23,804 10,973 26,756 Individual ownership 0.53 0.50 0.52 0.50 Joint ownership 0.36 0.48 0.39 0.49 Third party ownership 0.11 0.31 0.09 0.29 Notes: The full sample contains all accounts held by households in the DNB Household Survey. The final sample contains only those accounts used in the estimation sample. All statistics use sample weights. View Large Appendix B: First Stage Regression Table B.1. First stage regressions, account-level APR. OLS (1) OLS (2) OLS (3) Estimate SE Estimate SE Estimate SE Financial situation oldest sibling  Worse 0.336*** 0.115 0.326*** 0.104 0.326*** 0.102  The same or better 0.107 0.109 0.150 0.097 0.154 0.096 Economics education  Some − 0.287*** 0.082 − 0.259*** 0.082 − 0.261*** 0.081  Little − 0.458*** 0.088 − 0.404*** 0.088 − 0.414*** 0.087  Hardly at all − 0.694*** 0.107 − 0.579*** 0.101 − 0.586*** 0.098 Age dummies  31–40 years − 0.199 0.148 − 0.189 0.147  41–50 years 0.011 0.123 0.023 0.121  51–60 years 0.264** 0.113 0.265** 0.112  61 years and older 0.399*** 0.149 0.420*** 0.148 Education dummies  High school 0.295*** 0.093 0.274*** 0.091  College 0.383*** 0.084 0.363*** 0.086 Male 0.316*** 0.072 0.323*** 0.072 Couple 0.029 0.073 0.064 0.079 Number of children − 0.019 0.039 − 0.019 0.040 Basic financial literacy Occupation dummies 0.140 0.213 0.125 0.216  Employed − 0.029 0.194 − 0.008 0.192  Self-employed − 0.186* 0.102 − 0.188* 0.100  Unemployed − 0.191 0.127 − 0.193 0.126 Volume dummies  €1,000–€2,500 0.026 0.083 0.015 0.083  €2,500–€3,500 0.184** 0.092 0.140 0.090  €3,500–€4,500 0.164 0.108 0.114 0.110  €4,500–€7,000 0.162 0.099 0.082 0.099  €7,000–€10,000 0.142 0.107 0.104 0.109  €10,000–€25,000 0.174** 0.085 0.132 0.082  €25,000–€45,000 0.139 0.106 0.081 0.109  €45,000 or more 0.271*** 0.103 0.213** 0.102 Constant 0.421*** 0.106 –0.206 0.171 –0.151 0.188 Region dummies Yes Yes Account characteristics Yes Bank fixed effects Yes N 1,306 1,306 1,306 Adjusted R-squared 0.07 0.20 0.20 F-statistic first stage 11.54 9.74 10.44 OLS (1) OLS (2) OLS (3) Estimate SE Estimate SE Estimate SE Financial situation oldest sibling  Worse 0.336*** 0.115 0.326*** 0.104 0.326*** 0.102  The same or better 0.107 0.109 0.150 0.097 0.154 0.096 Economics education  Some − 0.287*** 0.082 − 0.259*** 0.082 − 0.261*** 0.081  Little − 0.458*** 0.088 − 0.404*** 0.088 − 0.414*** 0.087  Hardly at all − 0.694*** 0.107 − 0.579*** 0.101 − 0.586*** 0.098 Age dummies  31–40 years − 0.199 0.148 − 0.189 0.147  41–50 years 0.011 0.123 0.023 0.121  51–60 years 0.264** 0.113 0.265** 0.112  61 years and older 0.399*** 0.149 0.420*** 0.148 Education dummies  High school 0.295*** 0.093 0.274*** 0.091  College 0.383*** 0.084 0.363*** 0.086 Male 0.316*** 0.072 0.323*** 0.072 Couple 0.029 0.073 0.064 0.079 Number of children − 0.019 0.039 − 0.019 0.040 Basic financial literacy Occupation dummies 0.140 0.213 0.125 0.216  Employed − 0.029 0.194 − 0.008 0.192  Self-employed − 0.186* 0.102 − 0.188* 0.100  Unemployed − 0.191 0.127 − 0.193 0.126 Volume dummies  €1,000–€2,500 0.026 0.083 0.015 0.083  €2,500–€3,500 0.184** 0.092 0.140 0.090  €3,500–€4,500 0.164 0.108 0.114 0.110  €4,500–€7,000 0.162 0.099 0.082 0.099  €7,000–€10,000 0.142 0.107 0.104 0.109  €10,000–€25,000 0.174** 0.085 0.132 0.082  €25,000–€45,000 0.139 0.106 0.081 0.109  €45,000 or more 0.271*** 0.103 0.213** 0.102 Constant 0.421*** 0.106 –0.206 0.171 –0.151 0.188 Region dummies Yes Yes Account characteristics Yes Bank fixed effects Yes N 1,306 1,306 1,306 Adjusted R-squared 0.07 0.20 0.20 F-statistic first stage 11.54 9.74 10.44 Notes: The table reports estimates from first-stage regressions used to estimate the IV models shown in Table 4. The instruments employed refer to economics education and the financial situation of the oldest sibling. The reference group for the first consists of those with a lot of education in economics, whereas the base category for the second regards those with no siblings and refusals. The data are from the matched DNB Household Survey 2005. Standard errors are clustered at the household level. *p < 0.1; **p < 0.05; ***p < 0.01. View Large Table B.1. First stage regressions, account-level APR. OLS (1) OLS (2) OLS (3) Estimate SE Estimate SE Estimate SE Financial situation oldest sibling  Worse 0.336*** 0.115 0.326*** 0.104 0.326*** 0.102  The same or better 0.107 0.109 0.150 0.097 0.154 0.096 Economics education  Some − 0.287*** 0.082 − 0.259*** 0.082 − 0.261*** 0.081  Little − 0.458*** 0.088 − 0.404*** 0.088 − 0.414*** 0.087  Hardly at all − 0.694*** 0.107 − 0.579*** 0.101 − 0.586*** 0.098 Age dummies  31–40 years − 0.199 0.148 − 0.189 0.147  41–50 years 0.011 0.123 0.023 0.121  51–60 years 0.264** 0.113 0.265** 0.112  61 years and older 0.399*** 0.149 0.420*** 0.148 Education dummies  High school 0.295*** 0.093 0.274*** 0.091  College 0.383*** 0.084 0.363*** 0.086 Male 0.316*** 0.072 0.323*** 0.072 Couple 0.029 0.073 0.064 0.079 Number of children − 0.019 0.039 − 0.019 0.040 Basic financial literacy Occupation dummies 0.140 0.213 0.125 0.216  Employed − 0.029 0.194 − 0.008 0.192  Self-employed − 0.186* 0.102 − 0.188* 0.100  Unemployed − 0.191 0.127 − 0.193 0.126 Volume dummies  €1,000–€2,500 0.026 0.083 0.015 0.083  €2,500–€3,500 0.184** 0.092 0.140 0.090  €3,500–€4,500 0.164 0.108 0.114 0.110  €4,500–€7,000 0.162 0.099 0.082 0.099  €7,000–€10,000 0.142 0.107 0.104 0.109  €10,000–€25,000 0.174** 0.085 0.132 0.082  €25,000–€45,000 0.139 0.106 0.081 0.109  €45,000 or more 0.271*** 0.103 0.213** 0.102 Constant 0.421*** 0.106 –0.206 0.171 –0.151 0.188 Region dummies Yes Yes Account characteristics Yes Bank fixed effects Yes N 1,306 1,306 1,306 Adjusted R-squared 0.07 0.20 0.20 F-statistic first stage 11.54 9.74 10.44 OLS (1) OLS (2) OLS (3) Estimate SE Estimate SE Estimate SE Financial situation oldest sibling  Worse 0.336*** 0.115 0.326*** 0.104 0.326*** 0.102  The same or better 0.107 0.109 0.150 0.097 0.154 0.096 Economics education  Some − 0.287*** 0.082 − 0.259*** 0.082 − 0.261*** 0.081  Little − 0.458*** 0.088 − 0.404*** 0.088 − 0.414*** 0.087  Hardly at all − 0.694*** 0.107 − 0.579*** 0.101 − 0.586*** 0.098 Age dummies  31–40 years − 0.199 0.148 − 0.189 0.147  41–50 years 0.011 0.123 0.023 0.121  51–60 years 0.264** 0.113 0.265** 0.112  61 years and older 0.399*** 0.149 0.420*** 0.148 Education dummies  High school 0.295*** 0.093 0.274*** 0.091  College 0.383*** 0.084 0.363*** 0.086 Male 0.316*** 0.072 0.323*** 0.072 Couple 0.029 0.073 0.064 0.079 Number of children − 0.019 0.039 − 0.019 0.040 Basic financial literacy Occupation dummies 0.140 0.213 0.125 0.216  Employed − 0.029 0.194 − 0.008 0.192  Self-employed − 0.186* 0.102 − 0.188* 0.100  Unemployed − 0.191 0.127 − 0.193 0.126 Volume dummies  €1,000–€2,500 0.026 0.083 0.015 0.083  €2,500–€3,500 0.184** 0.092 0.140 0.090  €3,500–€4,500 0.164 0.108 0.114 0.110  €4,500–€7,000 0.162 0.099 0.082 0.099  €7,000–€10,000 0.142 0.107 0.104 0.109  €10,000–€25,000 0.174** 0.085 0.132 0.082  €25,000–€45,000 0.139 0.106 0.081 0.109  €45,000 or more 0.271*** 0.103 0.213** 0.102 Constant 0.421*** 0.106 –0.206 0.171 –0.151 0.188 Region dummies Yes Yes Account characteristics Yes Bank fixed effects Yes N 1,306 1,306 1,306 Adjusted R-squared 0.07 0.20 0.20 F-statistic first stage 11.54 9.74 10.44 Notes: The table reports estimates from first-stage regressions used to estimate the IV models shown in Table 4. The instruments employed refer to economics education and the financial situation of the oldest sibling. The reference group for the first consists of those with a lot of education in economics, whereas the base category for the second regards those with no siblings and refusals. The data are from the matched DNB Household Survey 2005. Standard errors are clustered at the household level. *p < 0.1; **p < 0.05; ***p < 0.01. View Large Appendix C: Details on Data Processing Whereas the majority of survey respondents provide a bank name, the data on the names of savings accounts contain some typos, abbreviations, and few inconsistencies. We process this raw information in the DHS in the following way. Using the bank and account names from the market interest rate data as a reference for the correct spelling, we replace all incorrectly spelled names and abbreviations in the DHS by their proper name. We replace those cases in which participants report outdated names of accounts by the names of their successor accounts. Finally, we set all potential inconsistent cases to missing.49 As we later match the DHS and market data based on volume as well, we also recover missing volumes of individual savings accounts following the procedure used by the official provider of the DHS (CentERdata). CentERdata first recovers volumes for individual savings accounts (details follow) and then aggregates over all accounts of each household member yielding total savings volume per household member (i.e., at the individual level). Only the recovered volume of the latter is available in the public version of the dataset. However, we are able to recover the large majority of the inserted values for individual savings accounts by following the same process that CentERdata has applied to calculate total savings account volume per household member.50 First, if a respondent does not report the exact amount of a savings account, the respondent is asked to choose from a sequence of follow-up questions in the form of unfolding brackets. In this case, we use the mid-point of the bracketed answer or the lower bound in case of the highest open-ended category (€25,000 or more). This leaves 10.3% of accounts with missing volume. Second, for these missing cases, we use the average amount of this savings account over the last two years. This leaves 8.1% of accounts with missing volume. For the remaining individual household members with at least one account with unreported volume, an imputed value for total savings volume was used by CentERdata. This was derived from a regression of total savings volume on a large set of individual characteristics. We use this imputed value to recover the volume of individual savings accounts in the following way. If only one account of a household member is left with missing volume, we use the difference between the total savings volume and the sum of all reported account volumes of that individual to fill in the single missing volume.51 This still leaves few individual household members with more than one account with missing volume. For those household members, we distribute this difference equally across all savings accounts with remaining missing volume.52 References Agarwal Sumit , Driscoll John , Gabaix Xavier , Laibson David ( 2009 ). “The Age of Reason: Financial Decisions over the Lifecycle with Implications for Regulation.” Brookings Papers on Economic Activity , 40 , 51 – 101 . 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Google Scholar CrossRef Search ADS van Rooij Maarten , Lusardi Annamarie , Alessie Rob ( 2012 ). “Financial Literacy, Retirement Planning and Household Wealth.” Economic Journal , 122 , 449 – 478 . Google Scholar CrossRef Search ADS Yoong Joanne ( 2011 ). “Financial Illiteracy and Stock Market Participation: Evidence from the Rand American Life Panel.” In Financial Literacy: Implications for Retirement Security and the Financial Marketplace , edited by Mitchell Olivia S. , Lusardi Annamarie . Oxford University Press . Supplementary Data Supplementary data are available at JEEA online. © The Author(s) 2018. Published by Oxford University Press on behalf of European Economic Association. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the European Economic Association Oxford University Press

Financial Literacy and Savings Account Returns

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of European Economic Association. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
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1542-4766
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1542-4774
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10.1093/jeea/jvy003
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Abstract

Abstract Savings accounts are owned by most households, but little is known about the performance of households’ investments. We create a unique dataset by matching information on individual savings accounts from the DNB Household Survey with market data on account-specific interest rates and characteristics. We document heterogeneity in returns across households, which can be partly explained by financial sophistication. A one-standard deviation increase in financial literacy is associated with a 12% increase compared to the median interest rate. We isolate the usage of modern technology (online accounts) as one channel through which financial literacy has a positive association with returns. 1. Introduction Savings accounts typically represent the most common vehicle for household financial investment. In the DNB Household Survey (DHS) savings accounts are owned by 82% of all Dutch households and make up the largest part of their financial wealth (with an average share of 43%).1 This contrasts with much lower ownership rates of funds or directly held stocks.2 Still, although there exists a large literature documenting how households invest in funds and stocks and how these investments perform, much less is known about savings accounts. We make use of the fact that the DHS reports bank and account names for each savings account owned by a household member, as well as the respective invested amount. This information allows us to match individual accounts held by households in the DHS with market data on interest rates and a set of account characteristics. We document considerable heterogeneity in returns across households for such a widely held and virtually riskless asset. To understand such a difference in performance of what seems to be a relatively simple financial product, our study first points to characteristics of the market and products. There is a wide dispersion of interest rates across products even for the same invested amount. A comparison of individual products is also not straightforward, for example, as accounts differ in the applicable amount thresholds to earn a higher interest rate as well as in additional restrictions. Notably, this variation is not due to so-called “teaser rates” that are paid when an account is newly opened or when fresh money is transferred, as these rates are not considered in the analysis. The difference in account characteristics, for which we can control, and the variety of offers in the market suggest, in particular, a role for financial sophistication as an explanation for the observed heterogeneity in returns. This paper is the first, to our knowledge, to show that heterogeneity in returns of a widely held asset such as savings accounts is partly linked to investor financial literacy. We recover measures of financial literacy from a special module of questions that was part of the 2005 wave of the DHS.3 Even after accounting for a range of socioeconomic characteristics, account characteristics, as well as amount invested, we find that financial literacy has a significant relationship with households’ individual returns on savings accounts: a one-standard deviation higher advanced financial literacy is associated with an approximately 29 basis points higher interest rate, which represents an increase of 12% compared to the median interest rate of 2.5%. We also calculate the gains from moving a household in the lowest literacy quartile to the highest literacy quartile. Applying the estimated gains of literacy to the average savings volume and projecting this over 10 years, total gains in real terms would accumulate to €838. Our investigation of products and the market suggests that lack of information may prevent households from securing the highest possible interest rate for the invested amount.4 Even at a given bank, households may not choose the most preferable offer. In fact, one such channel that we can isolate is the ability and willingness (or the lack of it) to use a higher interest bearing online account. We also find some evidence to suggest that more literate households might be better able to identify accounts across banks that for a given volume and a given set of characteristics offer the highest return. From banks’ perspective, lack of knowledge and sophistication are in fact prerequisites to uphold price dispersion across banks as well as price discrimination across accounts. A common feature of existing studies on households’ investment decisions and on financial literacy, as reviewed in what follows, is the difficulty to both isolate the contribution of financial literacy, which requires specific survey questions, and measure asset returns at the same time. Our combined data contain both pieces of information, allowing us to assess the association of financial literacy with savings returns. Importantly, note that the starting point for our matching process of survey data and administrative market data is a nationally representative survey that contains detailed information on all savings accounts held by household members (some of which are held in different banks), as well as on all other financial assets. Moreover, given that our outcome of interest is the applicable interest rate obtained from administrative market data, it is less likely to correlate with literacy through household unobservables such as knowledge about realized returns or reporting bias. In any case, we take a number of steps in response to endogeneity concerns including a standard IV approach and an alternative identification method recently introduced by Lewbel (2012) that exploits information from the heteroscedastic structure of the data. A number of studies document significant variation in households’ financial literacy in various countries.5 As savings accounts arguably play an important role in other countries as well, we would suggest that our results are likely to be more widely applicable. In fact, to the extent that savings accounts represent the most important financial assets, also the respective economic implications should concern a large fraction of society in many countries. The remainder of the paper is organized as follows. Section 2 provides a brief overview of the related literature. Section 3 presents the data and the matching procedure we apply. Section 4 introduces the empirical specifications to uncover the link between financial literacy and returns from savings accounts. Section 5 presents the empirical results and robustness checks and assesses the economic implications for consumers. Section 6 concludes. 2. Related Literature A large and growing number of studies examine the implications of financial literacy for various economic and investment choices (Lusardi and Mitchell 2014 provide a thorough review). Much of the extant literature has focused on investments in stocks and other risky financial assets. In particular, using the same survey, van Rooij et al. (2011, 2012) find that financial literacy induces stockholding and boosts wealth accumulation, respectively. Related work shows that cognitive skills such as numeracy (Christelis et al. 2010) and IQ (Grinblatt et al. 2011) positively associate with stockholding (see also Yoong 2011; Arrondel et al. 2012). Calvet et al. (2009) construct instead a proxy of financial sophistication based on the relationship between low education, income, and wealth and the likelihood of household financial mistakes, such as under-diversification.6 Another group of studies have analyzed the role of literacy for the choice of debt products (e.g., Stango and Zinman 2009). Lusardi and Tufano (2015) design a special set of questions measuring knowledge about properties of debt products. They find that those with lower levels of debt literacy tend to incur higher fees and use high-cost borrowing. Campbell (2006) shows that less educated individuals with fix-interest rate mortgages fail to refinance on time during a period of falling interest rates. In a related vein, Agarwal et al. (2009) provide evidence that older individuals are less savvy with debt management as this is indicated by the higher interest rates at which they borrow and the higher fees they incur. In a recent study, Gerardi et al. (2013) show that individuals with low financial knowledge and numerical ability were significantly more likely to default on subprime mortgages during the Great Recession in the United States. Financial literacy plays also an important role for retirement preparation. When households earn higher returns on their investments, this provides another explanation, next to differences in savings rates, for differences in retirement savings, which have been explored widely (e.g., Lusardi and Mitchell 2007a,b, 2008; Banks et al. 2010; van Rooij et al. 2012). More recently, Clark et al. (2017) employ administrative data on investment performance of a retirement plan matched with employees’ financial knowledge and show that higher literacy contributes to more profitable investing. More generally, understanding the link between financial literacy and asset returns can offer insights on topical policy issues, such as the distribution of wealth. Lusardi et al. (2017) develop a model in which agents invest not only in financial assets, but also in financial knowledge acquisition. According to their estimates, financial literacy has profound welfare implications accounting for roughly 40% of wealth inequality. Our paper adds to this literature by focusing on a very basic asset held by the vast majority of the population. Our unique data set allows linking the returns that households make on their savings accounts with heterogeneity in their financial knowledge. Moreover, we provide a rough quantification of likely foregone gains due to limited literacy and shed light on possible channels through which less sophisticated households fail to obtain the highest possible returns on their savings accounts. 3. Data 3.1. Household Characteristics and the Use of Savings Accounts Our main data source is the DNB Household Survey (DHS) in 2005. The DHS is an annually conducted survey of around 2,000 Dutch households that is sponsored by the Dutch National Bank and maintained by CentERdata at Tilburg University. The survey provides extensive information on demographic characteristics, asset and debt holdings, housing, work, health and income, as well as economic and psychological concepts (the variables used in our analysis are reported in what follows). The survey is representative of the Dutch population and is conducted via the Internet.7 One key feature of the DHS is that it asks detailed information on all savings accounts held by a household, including bank and account name, as well as invested volume on each account.8 The survey asks to report invested amounts for each financial asset as of December 31st of the year preceding the interviews. We supplement the DHS data with information from a special module on financial literacy designed by van Rooij et al. (2011) and conducted over a random subsample of the 2005 survey. This module contains a series of questions about financial knowledge addressed to the person in charge of household finances.9 Questions from this module have been used to construct an index of basic and an index of advanced financial literacy (Online Appendix D provides the exact wording of these questions). The latter index aims to measure understanding of more advanced financial concepts and the relevant questions refer to differences between stocks and bonds, stock market functioning, the benefits of portfolio diversification, and the association between bond prices and interest rates. Both indices are derived by factor analysis and are normalized to mean zero and standard deviation one (cf., van Rooij et al. 2011). Table 1 presents summary statistics of the main household-level variables including demographics, financial literacy, income, and wealth for the sample later used in the regression analysis.10 The financial respondents in our sample are on average 50.5 years old, 41% have a college education, 55% are male, and 66% live in a couple household. Overall, 4% are self-employed, 2% are unemployed, 20% have other employment, and 23% are retired.11 Households exhibit considerable heterogeneity in more advanced financial knowledge. Instead, basic literacy does not vary over a significant part of the sample, given that 43% of households therein manage to answer all basic literacy questions correctly. Following earlier work using information from the same financial literacy module, we thus consider the index of advanced financial literacy as our baseline measure of financial knowledge.12 As an alternative to this measure we also construct a measure based on correct responses to the “Big-Three” questions on financial literacy (first designed by Lusardi and Mitchell 2011). This measure draws on three standard questions regarding interest compounding, inflation and risk diversification and has been used in a number of studies examining the role of financial knowledge for financial outcomes (Hastings et al. 2013 provide a review). Results on this alternative measure along with different functional forms of the advanced literacy index are presented in the robustness section. In the DHS, after checking accounts, which are owned by virtually all households, savings accounts represent the second most prevalent financial asset with an ownership rate of 82%. For comparison, only 20% invest in funds and only 12% hold stocks directly. On average, households invest 43% of their financial wealth in savings accounts and hold 21% in checking accounts. Apart from insurances, which account for 12%, all other financial assets have a far lower weight in household portfolios. Thus, in terms of both ownership and financial wealth invested, savings accounts are by far the most important financial asset for Dutch households. 3.2. Interest Rate Data on Savings Accounts We use data on annual interest rates for savings accounts of all Dutch banks from April 2004 to December 2004 provided by a major Dutch financial institution.13 The administrative market dataset covers in total 43 banks and 105 savings accounts. For each savings account, it contains the account name, the bank name, and the weekly interest rate for eleven different amount brackets ranging from €0–€1,000 to €45,000 or more.14 In addition, using information from the Dutch Internet comparison website “SpaarInformatie”, we supplement our data with information on various savings account restrictions, which we use as controls in our empirical specification in what follows. These account types can be roughly partitioned across two dimensions. First, accounts are either restricted or not. The information from the comparison website allows us to distinguish in total six main restrictions.15 These restrictions are not exclusive but can coincide for one account. Second, accounts are either Internet managed or not. Internet accounts are fully managed online by the depositor and provide very limited face-to-face customer services. In addition, they can be both restricted and unrestricted. In light of the research question of this paper, we thus treat online accounts separately from the remaining set of account restrictions. We relegate an overview of the various characteristics of individual savings accounts to Table A.1 in Appendix A. Table 2 provides summary statistics for the distribution of interest rates across different amount brackets using the household survey matched with the administrative market data. As could be expected, accounts typically pay higher rates for larger volumes. Even for a given volume, dispersion is quite high. For example, for savings accounts actually held by survey respondents, interest rates for volumes from €2,500 to €3,500 range from 1.00% to 4.00% with an interquartile range of 1.45%. Yet, the interquartile range reduces to 1.00% for volumes above €45,000. 3.3. Data Matching Procedure Interest rate data are matched with DHS data as follows. Given the availability of literacy data in the 2005 wave, which reports the holdings of financial assets as of December 31, 2004, we match interest rates for the last week of December 2004 to the DHS data based on bank and account name as well as account volume.16 Precisely, based on the volume invested by households in each of their individual accounts, we can assign the respective interest rate for the applicable volume bracket.17 We achieve a full match for 79% of all accounts held by households in the DHS.18 For each savings account held by a member of a household, our matched data ultimately contain the invested volume, account name, bank name, and the applicable interest rate.19 For the final estimation sample, we exclude accounts with very low volumes (i.e., below €50), which are quite likely to be inactive.20 Table 3 shows summary statistics of the account-level APR over the sample used in the estimation and by various socioeconomic and account characteristics as well as financial literacy. The mean is 2.31% and the median is 2.50%. Dispersion is quite high given an interquartile range of 1.55% (i.e., 155 basis points). The APR increases considerably with invested volume as well as advanced financial literacy, and decreases in age, whereas there is no strong association with education and net income. 4. Econometric Specification The preceding description of the market for savings accounts suggests various channels through which households may obtain a lower than the highest possible return on their savings account(s). First, although banks offer different interest rates even for accounts with similar characteristics, households may not shop successfully for the highest interest account. Second, even at a given bank, households may not choose the most preferable account for the amount that they save. Third, even for a given set of own savings accounts, households may fail to allocate their savings to the highest interest account, potentially foregoing higher interest for larger volumes. Although we cannot completely disentangle these different channels, we provide some evidence for their relative importance in Section 5. In this context, limited financial sophistication can explain why some households actually fail in obtaining the highest possible return on their savings (i.e., through any of the aforementioned channels). More specifically, limited knowledge about available savings products and their underlying properties may prevent households from shopping efficiently for the highest returns. Moreover, households are likely inattentive to the interest rates they earn on their savings accounts. Low literate households in particular may (wrongly) perceive the gains from switching as being minimal.21 In a related vein, less sophisticated households are likely to exhibit considerable inertia in the management of their savings accounts. This would corroborate existing evidence on less educated households responding slowly or staying inactive to financial incentives related to their retirement saving (e.g., Madrian and Shea 2001), portfolio investing (e.g., Brunnermeier and Nagel 2008; Bilias et al. 2010) and debt refinancing (Andersen et al. 2015). Our main aim is to provide an estimate of the relationship between financial literacy and savings account returns. To that effect, we estimate the following, account-level specification:22 \begin{equation}{r_{h\,s}} = {\beta _1}\ {\textit {FinLi}}{t_h} + {\beta _2}{X_h} + {\beta _3}{V_{h\,s}} + {\beta _4}{Z_s} + {\varepsilon _{h\,s}}, \end{equation} (1) where rh s represents the interest rate earned on account s held by household h. FinLith denotes the advanced financial literacy index of household h (i.e., the covariate of interest). The vector Xh contains a set of household demographics including age, gender, marital status, and the number of children as well as occupation status. Furthermore, we include region dummies to take into account any relevant regional disparities, for example, in density of bank branches or in local employment conditions.23 In addition, we take into account nine dummies, contained in Vh s, which take the value one if the account volume falls into one of the previously mentioned amount brackets over which interest rates can vary and are zero otherwise.24 We also include a set of dummies denoting various account restrictions and bank fixed effects specific to each account in Zs. When estimating the baseline specification in equation (1) one should take into account the potential endogeneity of financial literacy. This has been a common empirical challenge for studies using survey data to examine the effect of literacy on various economic outcomes. In our set-up, it should be noted that the outcome of interest is the applicable interest rate that is obtained from administrative market data. Thus, it is less likely to correlate with literacy through household-specific unobserved factors such as knowledge about realized returns or reporting bias. Nevertheless, measurement error in the advanced financial literacy index remains a valid concern, given that some of the correct responses are likely to result from guessing (cf., van Rooij et al. 2011), in which case our estimated effect of literacy from OLS will be biased toward zero. We take a number of steps in order to address the measurement error issue and potential endogeneity concerns. First, we use a standard instrumental variable approach. Second, we utilize an alternative identification approach introduced by Lewbel (2012) that generates instruments using heteroscedasticity in the error structure of a first stage regression. Third, we estimate a specification that is more resilient to the measurement error of literacy. In what follows, we provide details on each of these steps. In our first approach, we employ the instruments from two earlier studies using the same financial literacy index and data.25 A valid instrument should exhibit meaningful correlation with advanced financial literacy and affect the interest rate only through the literacy channel and not through other unobserved factors. Building on van Rooij et al. (2011), we use the financial condition of the oldest sibling as an instrument for advanced financial literacy.26 The financial condition of the oldest sibling is beyond a respondent's immediate control and can thus be seen as relatively exogenous with respect to the savings account choice. Moreover, the authors argue in favor of a learning channel according to which respondents tend to become more interested in learning about financial matters due to the negative financial condition of their siblings. If such a mechanism is at work, one should observe a higher literacy score (on average) among respondents who report their siblings being in worse financial situation than respondents who do not. Results from our first stage regressions show a positive association between the literacy score and having siblings in bad financial shape, thus providing support for such a learning channel.27 In addition, following van Rooij et al. (2012), we use as a second instrument the economics education of the respondent.28 Economics education at an early stage is expected to positively affect financial literacy but is also likely to determine a household's current economic situation. We have experimented with specifications that control for contemporaneous household resources (e.g., net income, net financial and net real wealth) in order to take into account a possible channel through which past economics education can influence current investment choices. As we discuss in what follows, our IV estimates are quite comparable across both a parsimonious specification that conditions only on advanced literacy and some very rich ones that take also into account numerous account and household characteristics. The fact that our IV approach does not depend on the various controls included in equation (1) provides some indirect support for the exogeneity of our instruments. Our second approach uses the method recently introduced in Lewbel (2012) and does not rely on the validity of the instruments employed in standard IV.29 Instead, Lewbel proposes to exploit variation on higher moment conditions of the error distribution from a first stage regression of the likely endogenous covariate on (a subset of) other covariates in the model. The method generates a set of instruments that can be used for identification under two assumptions: The errors from a first stage regression of the endogenous covariate on a (subset) of other covariates in the model should be heteroscedastic. In our context, \begin{equation} {\textit {FinLi}}{t_h} = {\gamma _2}\ {X}_{h}^{'} + {\omega _h}, \end{equation} (2) where ωh denotes the error term and $${X}_{h}^{'}$$ is a subset of the RHS variables in equation (1) including a constant. Natural candidates for $${X}_{h}^{'}$$ are variables that are predetermined relative to the outcomes. We use age, gender and family size indicators. Heteroscedasticity in equation (2) implies that $${\rm {Cov}}( {{X}_{h}^{'},\ \omega _h^2} ) \ne 0$$. This assumption can be tested on the basis of a Breusch–Pagan test for heteroscedasticity and is strongly supported in our data. $${X}_{h}^{'}$$ is assumed to satisfy $${\rm {Cov}} ( {{X}_{h}^{'},\ {\varepsilon _{h\,s}}{\omega _h}} ) = 0$$, where εh s are errors from equation (1). This condition holds even when the two error terms share a common unobserved factor component (and are thus correlated), as long as the product of their idiosyncratic error components is uncorrelated with $${X}_{h}^{'}$$.30 Given that the method generates a number of instruments, one can test for their joint validity using a standard over-identification test, under the assumption that one of them is predetermined. Under conditions (a) and (b), Lewbel shows that a set of valid instruments for estimating equation (1) can be generated as: $$({X}_{h}^{'} - \bar {X}^{^\prime} ){\hat{\omega }_h}$$, where $$\bar {X}^{^\prime} $$ is the mean of $${X}_{h}^{'}$$ and $${\hat{\omega }_h}$$ are estimated residuals from equation (2). The advantage of using this method in our context is twofold: first, it allows to compare our estimates from the standard IV approach with those obtained using the generated instruments from the Lewbel method; second, it makes possible to test for the validity of both external instruments used under the standard IV approach. Finally, we have estimated a specification that controls for literacy via dummies denoting quartiles of the underlying distribution. Such a specification is likely to be more robust to measurement error, compared to the baseline specification using a continuous variable, as it is resilient to measurement error within each quartile. As we show in the robustness section, the implied effects from the specification that conditions on literacy quartiles are quite comparable to the IV estimates from the baseline model that uses a continuous literacy indicator. 5. Results 5.1. Baseline Results on Financial Literacy In what follows, we first discuss the results on financial literacy followed by other covariates and a number of robustness checks we have performed. Subsequently, we discuss the role of online account usage. Finally, we evaluate possible economic implications for households. Table 4 presents results from the account-level regressions as in equation (1). Given that financial literacy and other background characteristics, used as controls in this specification, do not vary across accounts owned by the same household, we cluster standard errors at the household level.31 First, we present results from a parsimonious specification, OLS (1), that conditions only on advanced financial literacy. In the second specification, OLS (2), we consider in addition socioeconomic characteristics and account volume dummies, whereas in the third specification, OLS (3), we add as well account characteristics and bank fixed effects. Next to each of these three OLS specifications, we show results from their IV counterparts (i.e., IV (1), IV (2), and IV (3)). In all IV specifications, the F-statistics from the first stage regressions are above or slightly below 10 and the two instruments exhibit meaningful correlations with the advanced literacy index (results from the first stage regressions are shown in Table B.1 of Appendix B). Given that we employ two instruments for one potentially endogenous covariate, one can test for their statistical validity on the basis of a test for over-identifying restrictions. According to the Hansen J-test (reported at the bottom of the table), we fail to reject the null hypothesis that the instruments are jointly valid (p-values: 0.29, 0.60, and 0.45). Table 1. Summary statistics household-level variables. Variable Mean Std. Dev. 25th pct. Median 75th pct. N Number of accounts 1.97 1.26 1.00 2.00 2.00 854 Total account volume 20,008 42,590 1,850 8,000 23,393 854 Net income 29,094 44,247 17,241 25,330 35,959 799 Net financial wealth 45,681 119,568 4,158 17,511 46,195 854 Net real wealth 116,815 205,985 2,200 26,541 182,500 854 Advanced financial literacy  Index 0.11 0.94 − 0.18 0.48 0.78 854  Number of correct answers 6.39 2.83 5.00 7.00 9.00 854 Basic financial literacy  Index 0.12 1.06 − 0.15 0.49 0.79 854  Number of correct answers 4.13 1.05 4.00 4.00 5.00 854 Economics education  A lot 0.17 0.38 0.00 0.00 0.00 854  Some 0.36 0.48 0.00 0.00 1.00 854  Little 0.27 0.44 0.00 0.00 1.00 854  Hardly at all/DK/refusal 0.20 0.40 0.00 0.00 0.00 854 Financial situation oldest sibling  No sibling/DK/refusal 0.14 0.35 0.00 0.00 0.00 787  Worse 0.23 0.42 0.00 0.00 0.00 787  Better/same 0.62 0.49 0.00 1.00 1.00 787 Age 50.47 15.45 37.00 50.00 62.00 854 Education  Less than high school 0.27 0.44 0.00 0.00 1.00 854  High school 0.33 0.47 0.00 0.00 1.00 854  College 0.41 0.49 0.00 0.00 1.00 854 Male 0.55 0.50 0.00 1.00 1.00 854 Couple 0.66 0.47 0.00 1.00 1.00 854 Number of children 0.59 0.99 0.00 0.00 1.00 854 Occupation  Employed 0.51 0.50 0.00 1.00 1.00 854  Self-employed 0.04 0.19 0.00 0.00 0.00 854  Unemployed 0.02 0.15 0.00 0.00 0.00 854  Other employment 0.20 0.40 0.00 0.00 0.00 854  Retired 0.23 0.42 0.00 0.00 0.00 854 Variable Mean Std. Dev. 25th pct. Median 75th pct. N Number of accounts 1.97 1.26 1.00 2.00 2.00 854 Total account volume 20,008 42,590 1,850 8,000 23,393 854 Net income 29,094 44,247 17,241 25,330 35,959 799 Net financial wealth 45,681 119,568 4,158 17,511 46,195 854 Net real wealth 116,815 205,985 2,200 26,541 182,500 854 Advanced financial literacy  Index 0.11 0.94 − 0.18 0.48 0.78 854  Number of correct answers 6.39 2.83 5.00 7.00 9.00 854 Basic financial literacy  Index 0.12 1.06 − 0.15 0.49 0.79 854  Number of correct answers 4.13 1.05 4.00 4.00 5.00 854 Economics education  A lot 0.17 0.38 0.00 0.00 0.00 854  Some 0.36 0.48 0.00 0.00 1.00 854  Little 0.27 0.44 0.00 0.00 1.00 854  Hardly at all/DK/refusal 0.20 0.40 0.00 0.00 0.00 854 Financial situation oldest sibling  No sibling/DK/refusal 0.14 0.35 0.00 0.00 0.00 787  Worse 0.23 0.42 0.00 0.00 0.00 787  Better/same 0.62 0.49 0.00 1.00 1.00 787 Age 50.47 15.45 37.00 50.00 62.00 854 Education  Less than high school 0.27 0.44 0.00 0.00 1.00 854  High school 0.33 0.47 0.00 0.00 1.00 854  College 0.41 0.49 0.00 0.00 1.00 854 Male 0.55 0.50 0.00 1.00 1.00 854 Couple 0.66 0.47 0.00 1.00 1.00 854 Number of children 0.59 0.99 0.00 0.00 1.00 854 Occupation  Employed 0.51 0.50 0.00 1.00 1.00 854  Self-employed 0.04 0.19 0.00 0.00 0.00 854  Unemployed 0.02 0.15 0.00 0.00 0.00 854  Other employment 0.20 0.40 0.00 0.00 0.00 854  Retired 0.23 0.42 0.00 0.00 0.00 854 Notes: The sample consists of those households used in the regressions analysis. See Online Appendix D for details on the construction of all variables. All statistics use sample weights. The data are from the DNB Household Survey 2005. View Large Table 1. Summary statistics household-level variables. Variable Mean Std. Dev. 25th pct. Median 75th pct. N Number of accounts 1.97 1.26 1.00 2.00 2.00 854 Total account volume 20,008 42,590 1,850 8,000 23,393 854 Net income 29,094 44,247 17,241 25,330 35,959 799 Net financial wealth 45,681 119,568 4,158 17,511 46,195 854 Net real wealth 116,815 205,985 2,200 26,541 182,500 854 Advanced financial literacy  Index 0.11 0.94 − 0.18 0.48 0.78 854  Number of correct answers 6.39 2.83 5.00 7.00 9.00 854 Basic financial literacy  Index 0.12 1.06 − 0.15 0.49 0.79 854  Number of correct answers 4.13 1.05 4.00 4.00 5.00 854 Economics education  A lot 0.17 0.38 0.00 0.00 0.00 854  Some 0.36 0.48 0.00 0.00 1.00 854  Little 0.27 0.44 0.00 0.00 1.00 854  Hardly at all/DK/refusal 0.20 0.40 0.00 0.00 0.00 854 Financial situation oldest sibling  No sibling/DK/refusal 0.14 0.35 0.00 0.00 0.00 787  Worse 0.23 0.42 0.00 0.00 0.00 787  Better/same 0.62 0.49 0.00 1.00 1.00 787 Age 50.47 15.45 37.00 50.00 62.00 854 Education  Less than high school 0.27 0.44 0.00 0.00 1.00 854  High school 0.33 0.47 0.00 0.00 1.00 854  College 0.41 0.49 0.00 0.00 1.00 854 Male 0.55 0.50 0.00 1.00 1.00 854 Couple 0.66 0.47 0.00 1.00 1.00 854 Number of children 0.59 0.99 0.00 0.00 1.00 854 Occupation  Employed 0.51 0.50 0.00 1.00 1.00 854  Self-employed 0.04 0.19 0.00 0.00 0.00 854  Unemployed 0.02 0.15 0.00 0.00 0.00 854  Other employment 0.20 0.40 0.00 0.00 0.00 854  Retired 0.23 0.42 0.00 0.00 0.00 854 Variable Mean Std. Dev. 25th pct. Median 75th pct. N Number of accounts 1.97 1.26 1.00 2.00 2.00 854 Total account volume 20,008 42,590 1,850 8,000 23,393 854 Net income 29,094 44,247 17,241 25,330 35,959 799 Net financial wealth 45,681 119,568 4,158 17,511 46,195 854 Net real wealth 116,815 205,985 2,200 26,541 182,500 854 Advanced financial literacy  Index 0.11 0.94 − 0.18 0.48 0.78 854  Number of correct answers 6.39 2.83 5.00 7.00 9.00 854 Basic financial literacy  Index 0.12 1.06 − 0.15 0.49 0.79 854  Number of correct answers 4.13 1.05 4.00 4.00 5.00 854 Economics education  A lot 0.17 0.38 0.00 0.00 0.00 854  Some 0.36 0.48 0.00 0.00 1.00 854  Little 0.27 0.44 0.00 0.00 1.00 854  Hardly at all/DK/refusal 0.20 0.40 0.00 0.00 0.00 854 Financial situation oldest sibling  No sibling/DK/refusal 0.14 0.35 0.00 0.00 0.00 787  Worse 0.23 0.42 0.00 0.00 0.00 787  Better/same 0.62 0.49 0.00 1.00 1.00 787 Age 50.47 15.45 37.00 50.00 62.00 854 Education  Less than high school 0.27 0.44 0.00 0.00 1.00 854  High school 0.33 0.47 0.00 0.00 1.00 854  College 0.41 0.49 0.00 0.00 1.00 854 Male 0.55 0.50 0.00 1.00 1.00 854 Couple 0.66 0.47 0.00 1.00 1.00 854 Number of children 0.59 0.99 0.00 0.00 1.00 854 Occupation  Employed 0.51 0.50 0.00 1.00 1.00 854  Self-employed 0.04 0.19 0.00 0.00 0.00 854  Unemployed 0.02 0.15 0.00 0.00 0.00 854  Other employment 0.20 0.40 0.00 0.00 0.00 854  Retired 0.23 0.42 0.00 0.00 0.00 854 Notes: The sample consists of those households used in the regressions analysis. See Online Appendix D for details on the construction of all variables. All statistics use sample weights. The data are from the DNB Household Survey 2005. View Large Table 2. Distribution of interest rates across amount brackets. Volume Mean Std. Dev. Min. 25th pct. Median 75th pct. Max. N €0–€1,000 2.05 0.94 1.00 1.10 1.55 3.10 4.00 364 €1,000–€2,500 2.13 0.91 1.00 1.10 2.40 3.10 4.00 228 €2,500–€3,500 2.20 0.86 1.00 1.55 2.40 3.00 4.00 104 €3,500–€4,500 2.32 0.84 1.00 1.60 2.40 3.00 4.00 72 €4,500–€7,000 2.53 0.79 1.00 2.00 2.50 3.25 4.00 119 €7,000–€10,000 2.48 0.76 1.00 2.20 2.50 3.10 4.00 107 €10,000–€25,000 2.52 0.75 1.00 1.60 2.70 3.30 3.50 254 €25,000–€45,000 2.60 0.61 1.00 2.10 2.50 3.25 3.50 97 >€45,000 2.83 0.51 1.50 2.30 3.00 3.30 3.50 65 Volume Mean Std. Dev. Min. 25th pct. Median 75th pct. Max. N €0–€1,000 2.05 0.94 1.00 1.10 1.55 3.10 4.00 364 €1,000–€2,500 2.13 0.91 1.00 1.10 2.40 3.10 4.00 228 €2,500–€3,500 2.20 0.86 1.00 1.55 2.40 3.00 4.00 104 €3,500–€4,500 2.32 0.84 1.00 1.60 2.40 3.00 4.00 72 €4,500–€7,000 2.53 0.79 1.00 2.00 2.50 3.25 4.00 119 €7,000–€10,000 2.48 0.76 1.00 2.20 2.50 3.10 4.00 107 €10,000–€25,000 2.52 0.75 1.00 1.60 2.70 3.30 3.50 254 €25,000–€45,000 2.60 0.61 1.00 2.10 2.50 3.25 3.50 97 >€45,000 2.83 0.51 1.50 2.30 3.00 3.30 3.50 65 Notes: This table shows the distribution of the account-level APR across nine amount brackets. The calculation is based on the matched household-administrative data that provides information on the accounts actually used by households. We group together three amount brackets from €7,000 to €10,000 due to too few observations in the respective categories for used accounts and no offered account reaching a new volume threshold within this range. All statistics use sample weights. The data are as of the last week of December 2004. View Large Table 2. Distribution of interest rates across amount brackets. Volume Mean Std. Dev. Min. 25th pct. Median 75th pct. Max. N €0–€1,000 2.05 0.94 1.00 1.10 1.55 3.10 4.00 364 €1,000–€2,500 2.13 0.91 1.00 1.10 2.40 3.10 4.00 228 €2,500–€3,500 2.20 0.86 1.00 1.55 2.40 3.00 4.00 104 €3,500–€4,500 2.32 0.84 1.00 1.60 2.40 3.00 4.00 72 €4,500–€7,000 2.53 0.79 1.00 2.00 2.50 3.25 4.00 119 €7,000–€10,000 2.48 0.76 1.00 2.20 2.50 3.10 4.00 107 €10,000–€25,000 2.52 0.75 1.00 1.60 2.70 3.30 3.50 254 €25,000–€45,000 2.60 0.61 1.00 2.10 2.50 3.25 3.50 97 >€45,000 2.83 0.51 1.50 2.30 3.00 3.30 3.50 65 Volume Mean Std. Dev. Min. 25th pct. Median 75th pct. Max. N €0–€1,000 2.05 0.94 1.00 1.10 1.55 3.10 4.00 364 €1,000–€2,500 2.13 0.91 1.00 1.10 2.40 3.10 4.00 228 €2,500–€3,500 2.20 0.86 1.00 1.55 2.40 3.00 4.00 104 €3,500–€4,500 2.32 0.84 1.00 1.60 2.40 3.00 4.00 72 €4,500–€7,000 2.53 0.79 1.00 2.00 2.50 3.25 4.00 119 €7,000–€10,000 2.48 0.76 1.00 2.20 2.50 3.10 4.00 107 €10,000–€25,000 2.52 0.75 1.00 1.60 2.70 3.30 3.50 254 €25,000–€45,000 2.60 0.61 1.00 2.10 2.50 3.25 3.50 97 >€45,000 2.83 0.51 1.50 2.30 3.00 3.30 3.50 65 Notes: This table shows the distribution of the account-level APR across nine amount brackets. The calculation is based on the matched household-administrative data that provides information on the accounts actually used by households. We group together three amount brackets from €7,000 to €10,000 due to too few observations in the respective categories for used accounts and no offered account reaching a new volume threshold within this range. All statistics use sample weights. The data are as of the last week of December 2004. View Large Table 3. Distribution of account-level APR. Mean Std. Dev. 25th pct. Median 75th pct. N 2.31 0.86 1.55 2.50 3.10 1,410 Advanced literacy quartiles Volume quartiles  1 (low) 2.11  1 (low) 2.02  2 2.23  2 2.11  3 2.36  3 2.43  4 (high) 2.46***  4 (high) 2.59*** Age Education  18–30 years 2.58  Less than high school 2.27  31–40 years 2.33  High school 2.34  41–50 years 2.22  College 2.32  51–60 years 2.37  61 years and older 2.22*** Gender Married  Female 2.30  Single-person households 2.25  Male 2.32  Two-person households 2.34 Internet account Withdrawal costs/limitations  No 1.83  No 2.38  Yes 3.21***  Yes 1.67*** Minimum amount Salary account  No 2.28  No 2.30  Yes 2.54***  Yes 3.07*** Lowest balance bonus Individual ownership  No 2.20  No 2.34  Yes 2.61***  Yes 2.29 Balance growth bonus Joint ownership  No 2.28  No 2.28  Yes 3.40  Yes 2.37 Fixed monthly deposit Third party ownership  No 2.30  No 2.33  Yes 4.00  Yes 2.18 Mean Std. Dev. 25th pct. Median 75th pct. N 2.31 0.86 1.55 2.50 3.10 1,410 Advanced literacy quartiles Volume quartiles  1 (low) 2.11  1 (low) 2.02  2 2.23  2 2.11  3 2.36  3 2.43  4 (high) 2.46***  4 (high) 2.59*** Age Education  18–30 years 2.58  Less than high school 2.27  31–40 years 2.33  High school 2.34  41–50 years 2.22  College 2.32  51–60 years 2.37  61 years and older 2.22*** Gender Married  Female 2.30  Single-person households 2.25  Male 2.32  Two-person households 2.34 Internet account Withdrawal costs/limitations  No 1.83  No 2.38  Yes 3.21***  Yes 1.67*** Minimum amount Salary account  No 2.28  No 2.30  Yes 2.54***  Yes 3.07*** Lowest balance bonus Individual ownership  No 2.20  No 2.34  Yes 2.61***  Yes 2.29 Balance growth bonus Joint ownership  No 2.28  No 2.28  Yes 3.40  Yes 2.37 Fixed monthly deposit Third party ownership  No 2.30  No 2.33  Yes 4.00  Yes 2.18 Notes: This table shows summary statistics of the account-level APR over the full sample used in the regression analysis as well as averages of the account-level APR by various household- and account-level characteristics. All statistics use sample weights. The data are from the matched DNB Household Survey in 2005. Stars indicate whether the bivariate (or the joint for categorical variables) associations estimated from univariate regressions of the APR on the respective variable is statistically significant. ***p < 0.01. View Large Table 3. Distribution of account-level APR. Mean Std. Dev. 25th pct. Median 75th pct. N 2.31 0.86 1.55 2.50 3.10 1,410 Advanced literacy quartiles Volume quartiles  1 (low) 2.11  1 (low) 2.02  2 2.23  2 2.11  3 2.36  3 2.43  4 (high) 2.46***  4 (high) 2.59*** Age Education  18–30 years 2.58  Less than high school 2.27  31–40 years 2.33  High school 2.34  41–50 years 2.22  College 2.32  51–60 years 2.37  61 years and older 2.22*** Gender Married  Female 2.30  Single-person households 2.25  Male 2.32  Two-person households 2.34 Internet account Withdrawal costs/limitations  No 1.83  No 2.38  Yes 3.21***  Yes 1.67*** Minimum amount Salary account  No 2.28  No 2.30  Yes 2.54***  Yes 3.07*** Lowest balance bonus Individual ownership  No 2.20  No 2.34  Yes 2.61***  Yes 2.29 Balance growth bonus Joint ownership  No 2.28  No 2.28  Yes 3.40  Yes 2.37 Fixed monthly deposit Third party ownership  No 2.30  No 2.33  Yes 4.00  Yes 2.18 Mean Std. Dev. 25th pct. Median 75th pct. N 2.31 0.86 1.55 2.50 3.10 1,410 Advanced literacy quartiles Volume quartiles  1 (low) 2.11  1 (low) 2.02  2 2.23  2 2.11  3 2.36  3 2.43  4 (high) 2.46***  4 (high) 2.59*** Age Education  18–30 years 2.58  Less than high school 2.27  31–40 years 2.33  High school 2.34  41–50 years 2.22  College 2.32  51–60 years 2.37  61 years and older 2.22*** Gender Married  Female 2.30  Single-person households 2.25  Male 2.32  Two-person households 2.34 Internet account Withdrawal costs/limitations  No 1.83  No 2.38  Yes 3.21***  Yes 1.67*** Minimum amount Salary account  No 2.28  No 2.30  Yes 2.54***  Yes 3.07*** Lowest balance bonus Individual ownership  No 2.20  No 2.34  Yes 2.61***  Yes 2.29 Balance growth bonus Joint ownership  No 2.28  No 2.28  Yes 3.40  Yes 2.37 Fixed monthly deposit Third party ownership  No 2.30  No 2.33  Yes 4.00  Yes 2.18 Notes: This table shows summary statistics of the account-level APR over the full sample used in the regression analysis as well as averages of the account-level APR by various household- and account-level characteristics. All statistics use sample weights. The data are from the matched DNB Household Survey in 2005. Stars indicate whether the bivariate (or the joint for categorical variables) associations estimated from univariate regressions of the APR on the respective variable is statistically significant. ***p < 0.01. View Large Adding account and bank fixed effects into the third specification improves considerably, as expected, the fit of the model. In all three OLS specifications, the coefficient of advanced financial literacy is statistically significant (p-value < 0.01) and shows a positive association with the APR. The corresponding IV estimates remain statistically significant and suggest a slightly stronger relationship. Notably, the estimated magnitudes are more or less unaffected across all three specifications (i.e., the IV strategy works irrespective of the set of other covariates taken into account). According to the IV estimates, an assumed one-standard deviation increase in advanced financial literacy implies a roughly 29 basis points increase in the APR. This effect, estimated net of socioeconomic characteristics, account restrictions and bank fixed effects, is nontrivial as it corresponds to 12% of the median interest rate in our sample. As an alternative to the standard IV approach used previously, we also apply the identification method of Lewbel (2012) that, as discussed in Section 4, exploits variation from the second moments of the error distribution of the first stage regression in equation (2) to generate a set of instruments. For this, $${X}_{h}^{'}$$ comprises few predetermined covariates, namely age, gender and number of children. First, we estimate the first-stage regression in equation (2) and test for heteroscedasticity using a Breusch–Pagan test. According to the test results (chi2 = 71.1, p-value = 0.00) there is strong evidence for heteroscedasticity in the first stage regression. Following Lewbel, we generate instruments by taking the products of residuals from equation (2) with each of the aforementioned covariates, centered at their respective sample means. These generated instruments can be subsequently used either alone or in conjunction with the two external instruments used under the standard IV approach in order to identify equation (1). Table 5 summarizes the relevant results. In particular, IV (1) uses generated instruments from the Lewbel method only, whereas IV (2) uses the two external instruments employed in the standard IV approach alone. IV (3) uses the two external instruments supplemented with the generated instruments from the Lewbel method resulting in more efficient estimates than in the standard IV specification. Notably, the estimated coefficients on financial literacy suggest qualitatively similar effects and are statistically significant across all three specifications. Moreover, the generated instruments from the Lewbel method meet the exogeneity assumption as Hansen's J-statistic fails to reject the null of exogeneity with high confidence (p-value: 0.62). As discussed, one can use the instruments from the Lewbel method to test for the joint validity of the two external instruments employed under the standard IV estimation. This test is based on the difference in Hansen's J-statistics between the model using the generated instruments according to the Lewbel method only and the full model using the entire set of generated and external instruments. According to the resulting C-statistic of 7.2 (p-value: 0.21), one cannot reject the null hypothesis that the two external instruments employed in the standard IV approach are jointly valid. Results from this test lend some further support to the validity of the originally employed instruments. 5.2. Results on Other Covariates With reference to other covariates in the model, the account volume dummies show, as expected, a progressively stronger association with a higher interest rate, consistent with the notion that the benefits from shopping are higher for investors with larger volumes. Given that we control for bank fixed effects and account characteristics, which are highly significant, these differences do not seem to be solely attributable to choices of accounts with more restrictive characteristics.32 In addition, we estimate a strong negative association of the APR with age. For example, respondents above sixty earn about 54 basis points less on average as compared to the base category of young adults below thirty. This likely suggests a significant role for age-of-account-effects given that the age of an account and respondents’ age should be highly correlated. It may also reflect cohort effects as younger cohorts are more familiar with Internet use and technology in general and, as we show in what follows, those with Internet-managed accounts earn higher interest rates.33 Other covariates, such as education and gender (sometimes used as proxies for financial sophistication) and family size and employments status (that are likely to reflect liquidity needs) do not exhibit any significant association with the APR. In addition, we have estimated a richer specification controlling for household net income, net financial wealth (excluding savings accounts) and net real wealth through dummies denoting quartiles of the respective distributions. Notably, these additional controls of household resources are insignificant, whereas our baseline estimates of financial literacy and account volume remain unaffected (literacy estimates are 0.107 and 0.313, both significant at 1%-level, under the OLS and IV specifications, respectively). This suggests that financial wealth and other household resources do not associate with the APR when we control for invested volume and financial literacy.34 5.3. Robustness Checks In this section, we discuss numerous checks that we have performed in order to verify the robustness of our baseline findings at the account level. Due to space constraints, Table 6 summarizes results from some of these robustness checks, whereas the entire set of results discussed in what follows is available from the authors upon request. Panel A shows results from several variations of equation (1). OLS (1) and IV (1) exclude volume dummies from the baseline specification, which are potentially endogenous. The derived estimates are highly comparable to the baseline ones with a financial literacy coefficient of 0.14 and 0.32 in the OLS and IV specification, respectively. OLS (2) and IV (2) use only accounts from the financial respondent, for whom the financial literacy data is available. Recall that in our baseline estimation, we assign to each account held by any member of nonsingle households the financial literacy of the household's financial respondent. This might be problematic if household members differ significantly in their degree of literacy. Given that a significant fraction of accounts is held by the financial respondent our sample reduces by only 16%. Our estimates of literacy remain highly significant at 1%-level with a comparable coefficient of 0.29 in the IV specification. OLS (3) and IV (3) attach a higher weight to more important accounts by weighting each observation with its relative volume share within the household. OLS and IV estimates from these weighted regressions are 0.13 and 0.26, both significant at 1%-level, respectively. Thus, using volume weights leaves our main findings unaffected. Panel B shows estimates using different financial literacy measures or functional forms. OLS (1) and IV (1) use the continuous financial literacy index but excludes “don’t know”-answers from the instrument denoting economics education. The estimated financial literacy coefficient is 0.26 (significant at 1%-level). This suggests that correlation between this instrument and the financial literacy index is not just due to a correlation with “don’t know”-responses in the economics education question. OLS (2) and IV (2) use the standardized number of correct answers to the financial literacy questions instead of constructing an index based on factor analysis.35 We estimate a financial literacy coefficient of 0.32 (p-value: <0.01) in the IV-specification, which is highly comparable to our baseline results. OLS (3) and IV (3) use the standardized number of correct answers to the “Big-Three” financial literacy questions. As discussed, information from the three basic questions on interest compounding, inflation, and risk diversification, has been collected by various household surveys that, unlike the 2005 DHS, do not have a special literacy module. As a result, this information has been used to measure literacy in a number of studies (see Hastings et al. 2013). Using this measure, we obtain an OLS estimate of 0.06 (p-value: <0.01) and an IV estimate of 0.43 (p-value: <0.05). According to the Hansen J-test the two instruments used are jointly valid, their F-statistic from the first-stage regression is nevertheless below 10. In addition, we have estimated an OLS specification using dummies denoting financial literacy quartiles. As discussed, using quartiles partly accounts for the measurement error in the continuous financial literacy index used in the baseline specification.36 Households in the top advanced literacy quartile earn on average 29 basis points more compared to the lowest literacy quartile.37 The derived effect is highly comparable to the IV estimate from the baseline specification, given that the interquartile range of the literacy index equals roughly one standard deviation. This suggests that the financial literacy index may indeed suffer from measurement error that is taken into account by the standard IV estimation used for the literacy index. As Lusardi and Mitchell (2014) point out, the reported IV estimates of the impact of financial literacy are higher than the counterpart OLS ones in all empirical studies they review, consistent with the measurement error hypothesis. Furthermore, we estimate the same specification as in van Rooij et al. (2011, 2012), by controlling, in addition, for the basic financial literacy index that is deduced from answers to the five basic literacy questions asked in the survey (see Online Appendix D). As discussed, basic literacy does not vary considerably over the sample and controlling for this leaves our estimates of interest on advanced literacy unaffected with an OLS-estimate of 0.138 and an IV-estimate of 0.328. We have also accounted for a number of factors that may influence the APR. Given that these additional controls have some missing values that reduce our estimation sample by about 15%–20% in each case, we add one factor at a time.38 In a first step, we include a measure of risk aversion from the DHS, as used in a similar robustness check by van Rooij et al. (2011).39 The inclusion of risk aversion (that is itself insignificant) does not affect our estimate for advanced financial literacy. Second, although we control for employment status in our main specification, households frequently exposed to transitory income shocks might on average hold more liquid accounts with lower APRs. To this end, we include a dummy indicating whether households’ last year's income was unusually low. The inclusion of this additional variable, however, leaves our key estimate unaffected. Third, we also added hours worked to our specification to proxy for opportunity costs of shopping for higher rates. This variable has no significant impact on the APR and our estimates for literacy remain unaffected. Fourth, we include a categorical variable measuring the investment horizon of respondents to account for differences in patience of investors but find no significant effect of the investment horizon on the APR and our literacy estimate remains unaffected. Fifth, we add a dummy denoting whether the individual received financial advice that turns out insignificant and leaves our results unchanged. In addition, we estimate our specification in the subgroup of unrestricted accounts as, in particular, for balance growth bonus and lowest balance bonus accounts our data contains only a measure of the exact interest rate received. This considerably reduces the sample used in the estimation (647 observations) but leaves our literacy estimate broadly unaffected (0.27, p-value: <0.10 in the IV-specification). Finally, although in our estimation we take into account bank fixed effects, we examine whether our results are particularly sensitive to banks grouped together as “small banks”, as these banks are likely to be quite heterogeneous in the savings accounts they offer. To that effect, we have re-estimated our baseline specifications by excluding accounts held in these small banks and the results remain unaffected with a financial literacy coefficient of 0.29 (p-value: <0.01) in the IV-specification. 5.4. Online Banking Usage One possible channel through which literacy could positively associate with APRs is through households’ ability to use Internet accounts. According to the data, most banks offer a menu of both Internet-managed and regular accounts, thus, allowing for more literate households to achieve higher returns even within the same bank. As discussed in the data section, Internet accounts are fully managed online with limited customer services and in return typically offer higher interest rates. We re-estimate our baseline specification by adding a dummy denoting Internet managed accounts.40 Results are shown in Table 7. The Internet account dummy displays a strong positive association with the APR. For example, after accounting for various account restrictions and bank fixed effects, the estimated impact of having an Internet-managed account exceeds 130 basis points. The implied effect of literacy is still statistically significant, albeit quantitatively smaller by around a half. This suggests that a sizable part of the effect of advanced financial literacy on the APR derives from familiarity with new technologies and the willingness and ability to use self-managed online banking.41 Switching to Internet managed accounts can help households to earn higher interest on their savings account without necessarily having to switch banks. As discussed in Section 3, literacy may also associate with APRs through another two channels: shopping aptitude for the highest interest account across banks and optimal rebalancing among the currently held set of accounts. In the next section, we show that the latter channel is of no quantitative importance. Note that the remaining effect of advanced literacy that we estimate is net of various household and account characteristics, Internet-managed accounts, as well as fixed differences across banks. That is, there is still room for financial literacy to play a role as more literate households might be better able to identify accounts across banks that for a given volume and a given set of characteristics offer the highest return (i.e., above average differences in returns across banks that are absorbed by bank fixed effects). 5.5. Economic Relevance In order to assess the economic relevance of limited literacy for a typical household, we first re-estimate our baseline specification at the household level.42 We show estimated results in Table 8, in which specifications (1)–(3) represent the counterparts to those shown in Table 4. Table 4. OLS of account-level APR on financial literacy. OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.116*** 0.032 0.304*** 0.112 0.128*** 0.031 0.275** 0.125 0.127*** 0.022 0.288*** 0.099 Age dummies  31–40 years − 0.316*** 0.103 − 0.246** 0.118 − 0.241*** 0.079 − 0.197** 0.092  41–50 years − 0.474*** 0.099 − 0.424*** 0.106 − 0.372*** 0.074 − 0.357*** 0.078  51–60 years − 0.400*** 0.100 − 0.377*** 0.108 − 0.285*** 0.075 − 0.303*** 0.081  61 years and older − 0.541*** 0.123 − 0.564*** 0.135 − 0.491*** 0.090 − 0.547*** 0.099 Education dummies  High school 0.039 0.066 0.008 0.081 0.081 0.051 0.050 0.064  College − 0.083 0.064 − 0.134 0.085 0.016 0.051 − 0.050 0.068 Male − 0.025 0.056 − 0.068 0.073 0.011 0.041 − 0.047 0.060 Couple 0.014 0.059 − 0.023 0.062 0.066 0.053 0.021 0.056 Number of children 0.021 0.029 0.018 0.030 0.002 0.023 0.001 0.023 Occupation dummies  Employed 0.061 0.123 0.083 0.134 0.102 0.108 0.088 0.126  Self-employed 0.048 0.163 0.045 0.192 0.063 0.126 0.076 0.152  Unemployed 0.051 0.077 0.098 0.083 0.088 0.060 0.114* 0.065  Retired − 0.021 0.104 0.020 0.110 0.016 0.077 0.052 0.084 Volume dummies  €1,000–€2,500 0.108 0.084 0.124 0.088 0.111* 0.065 0.116* 0.069  €2,500–€3,500 0.185* 0.098 0.185* 0.102 0.078 0.076 0.074 0.077  €3,500–€4,500 0.271** 0.108 0.249** 0.113 0.219*** 0.079 0.216*** 0.082  €4,500–€7,000 0.460*** 0.090 0.396*** 0.095 0.320*** 0.066 0.297*** 0.070  €7,000–€10,000 0.466*** 0.095 0.461*** 0.098 0.339*** 0.078 0.338*** 0.083  €10,000–€25,000 0.503*** 0.076 0.447*** 0.082 0.474*** 0.059 0.434*** 0.063  €25,000–€45,000 0.637*** 0.084 0.601*** 0.091 0.622*** 0.075 0.610*** 0.080  €45,000 or more 0.862*** 0.088 0.817*** 0.093 0.753*** 0.081 0.724*** 0.083 Account characteristics  Minimum amount − 0.369*** 0.097 − 0.399*** 0.102  Lowest balance bonus − 0.328*** 0.104 − 0.345*** 0.112  Balance growth bonus 2.121*** 0.117 2.169*** 0.128  Fixed monthly deposit 1.396*** 0.076 1.358*** 0.097  Withdrawal costs/limitations − 0.266*** 0.060 − 0.301*** 0.064  Salary account 1.118*** 0.152 1.071*** 0.150  Joint ownership 0.001 0.048 0.005 0.051  Third party ownership − 0.040 0.063 − 0.030 0.071 Constant 2.315*** 0.027 2.268*** 0.036 2.272*** 0.124 2.258*** 0.136 2.380*** 0.104 2.397*** 0.116 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 1,410 1,306 1,410 1,306 Adjusted R-squared 0.01 − 0.02 0.12 0.10 0.47 0.45 Hansen J-test p-value 0.29 0.60 0.45 F-statistic first stage 11.54 9.74 10.44 Exogeneity test p-value 0.12 0.23 0.07 OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.116*** 0.032 0.304*** 0.112 0.128*** 0.031 0.275** 0.125 0.127*** 0.022 0.288*** 0.099 Age dummies  31–40 years − 0.316*** 0.103 − 0.246** 0.118 − 0.241*** 0.079 − 0.197** 0.092  41–50 years − 0.474*** 0.099 − 0.424*** 0.106 − 0.372*** 0.074 − 0.357*** 0.078  51–60 years − 0.400*** 0.100 − 0.377*** 0.108 − 0.285*** 0.075 − 0.303*** 0.081  61 years and older − 0.541*** 0.123 − 0.564*** 0.135 − 0.491*** 0.090 − 0.547*** 0.099 Education dummies  High school 0.039 0.066 0.008 0.081 0.081 0.051 0.050 0.064  College − 0.083 0.064 − 0.134 0.085 0.016 0.051 − 0.050 0.068 Male − 0.025 0.056 − 0.068 0.073 0.011 0.041 − 0.047 0.060 Couple 0.014 0.059 − 0.023 0.062 0.066 0.053 0.021 0.056 Number of children 0.021 0.029 0.018 0.030 0.002 0.023 0.001 0.023 Occupation dummies  Employed 0.061 0.123 0.083 0.134 0.102 0.108 0.088 0.126  Self-employed 0.048 0.163 0.045 0.192 0.063 0.126 0.076 0.152  Unemployed 0.051 0.077 0.098 0.083 0.088 0.060 0.114* 0.065  Retired − 0.021 0.104 0.020 0.110 0.016 0.077 0.052 0.084 Volume dummies  €1,000–€2,500 0.108 0.084 0.124 0.088 0.111* 0.065 0.116* 0.069  €2,500–€3,500 0.185* 0.098 0.185* 0.102 0.078 0.076 0.074 0.077  €3,500–€4,500 0.271** 0.108 0.249** 0.113 0.219*** 0.079 0.216*** 0.082  €4,500–€7,000 0.460*** 0.090 0.396*** 0.095 0.320*** 0.066 0.297*** 0.070  €7,000–€10,000 0.466*** 0.095 0.461*** 0.098 0.339*** 0.078 0.338*** 0.083  €10,000–€25,000 0.503*** 0.076 0.447*** 0.082 0.474*** 0.059 0.434*** 0.063  €25,000–€45,000 0.637*** 0.084 0.601*** 0.091 0.622*** 0.075 0.610*** 0.080  €45,000 or more 0.862*** 0.088 0.817*** 0.093 0.753*** 0.081 0.724*** 0.083 Account characteristics  Minimum amount − 0.369*** 0.097 − 0.399*** 0.102  Lowest balance bonus − 0.328*** 0.104 − 0.345*** 0.112  Balance growth bonus 2.121*** 0.117 2.169*** 0.128  Fixed monthly deposit 1.396*** 0.076 1.358*** 0.097  Withdrawal costs/limitations − 0.266*** 0.060 − 0.301*** 0.064  Salary account 1.118*** 0.152 1.071*** 0.150  Joint ownership 0.001 0.048 0.005 0.051  Third party ownership − 0.040 0.063 − 0.030 0.071 Constant 2.315*** 0.027 2.268*** 0.036 2.272*** 0.124 2.258*** 0.136 2.380*** 0.104 2.397*** 0.116 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 1,410 1,306 1,410 1,306 Adjusted R-squared 0.01 − 0.02 0.12 0.10 0.47 0.45 Hansen J-test p-value 0.29 0.60 0.45 F-statistic first stage 11.54 9.74 10.44 Exogeneity test p-value 0.12 0.23 0.07 Notes: The table reports OLS and IV estimates from regressions of the account-level APR on financial literacy and several other controls. The sample excludes accounts with volume below €50. All IV specifications use economics education and the financial situation of the oldest sibling as an instrument for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. *p < 0.1; **p < 0.05; ***p < 0.01. View Large Table 4. OLS of account-level APR on financial literacy. OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.116*** 0.032 0.304*** 0.112 0.128*** 0.031 0.275** 0.125 0.127*** 0.022 0.288*** 0.099 Age dummies  31–40 years − 0.316*** 0.103 − 0.246** 0.118 − 0.241*** 0.079 − 0.197** 0.092  41–50 years − 0.474*** 0.099 − 0.424*** 0.106 − 0.372*** 0.074 − 0.357*** 0.078  51–60 years − 0.400*** 0.100 − 0.377*** 0.108 − 0.285*** 0.075 − 0.303*** 0.081  61 years and older − 0.541*** 0.123 − 0.564*** 0.135 − 0.491*** 0.090 − 0.547*** 0.099 Education dummies  High school 0.039 0.066 0.008 0.081 0.081 0.051 0.050 0.064  College − 0.083 0.064 − 0.134 0.085 0.016 0.051 − 0.050 0.068 Male − 0.025 0.056 − 0.068 0.073 0.011 0.041 − 0.047 0.060 Couple 0.014 0.059 − 0.023 0.062 0.066 0.053 0.021 0.056 Number of children 0.021 0.029 0.018 0.030 0.002 0.023 0.001 0.023 Occupation dummies  Employed 0.061 0.123 0.083 0.134 0.102 0.108 0.088 0.126  Self-employed 0.048 0.163 0.045 0.192 0.063 0.126 0.076 0.152  Unemployed 0.051 0.077 0.098 0.083 0.088 0.060 0.114* 0.065  Retired − 0.021 0.104 0.020 0.110 0.016 0.077 0.052 0.084 Volume dummies  €1,000–€2,500 0.108 0.084 0.124 0.088 0.111* 0.065 0.116* 0.069  €2,500–€3,500 0.185* 0.098 0.185* 0.102 0.078 0.076 0.074 0.077  €3,500–€4,500 0.271** 0.108 0.249** 0.113 0.219*** 0.079 0.216*** 0.082  €4,500–€7,000 0.460*** 0.090 0.396*** 0.095 0.320*** 0.066 0.297*** 0.070  €7,000–€10,000 0.466*** 0.095 0.461*** 0.098 0.339*** 0.078 0.338*** 0.083  €10,000–€25,000 0.503*** 0.076 0.447*** 0.082 0.474*** 0.059 0.434*** 0.063  €25,000–€45,000 0.637*** 0.084 0.601*** 0.091 0.622*** 0.075 0.610*** 0.080  €45,000 or more 0.862*** 0.088 0.817*** 0.093 0.753*** 0.081 0.724*** 0.083 Account characteristics  Minimum amount − 0.369*** 0.097 − 0.399*** 0.102  Lowest balance bonus − 0.328*** 0.104 − 0.345*** 0.112  Balance growth bonus 2.121*** 0.117 2.169*** 0.128  Fixed monthly deposit 1.396*** 0.076 1.358*** 0.097  Withdrawal costs/limitations − 0.266*** 0.060 − 0.301*** 0.064  Salary account 1.118*** 0.152 1.071*** 0.150  Joint ownership 0.001 0.048 0.005 0.051  Third party ownership − 0.040 0.063 − 0.030 0.071 Constant 2.315*** 0.027 2.268*** 0.036 2.272*** 0.124 2.258*** 0.136 2.380*** 0.104 2.397*** 0.116 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 1,410 1,306 1,410 1,306 Adjusted R-squared 0.01 − 0.02 0.12 0.10 0.47 0.45 Hansen J-test p-value 0.29 0.60 0.45 F-statistic first stage 11.54 9.74 10.44 Exogeneity test p-value 0.12 0.23 0.07 OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.116*** 0.032 0.304*** 0.112 0.128*** 0.031 0.275** 0.125 0.127*** 0.022 0.288*** 0.099 Age dummies  31–40 years − 0.316*** 0.103 − 0.246** 0.118 − 0.241*** 0.079 − 0.197** 0.092  41–50 years − 0.474*** 0.099 − 0.424*** 0.106 − 0.372*** 0.074 − 0.357*** 0.078  51–60 years − 0.400*** 0.100 − 0.377*** 0.108 − 0.285*** 0.075 − 0.303*** 0.081  61 years and older − 0.541*** 0.123 − 0.564*** 0.135 − 0.491*** 0.090 − 0.547*** 0.099 Education dummies  High school 0.039 0.066 0.008 0.081 0.081 0.051 0.050 0.064  College − 0.083 0.064 − 0.134 0.085 0.016 0.051 − 0.050 0.068 Male − 0.025 0.056 − 0.068 0.073 0.011 0.041 − 0.047 0.060 Couple 0.014 0.059 − 0.023 0.062 0.066 0.053 0.021 0.056 Number of children 0.021 0.029 0.018 0.030 0.002 0.023 0.001 0.023 Occupation dummies  Employed 0.061 0.123 0.083 0.134 0.102 0.108 0.088 0.126  Self-employed 0.048 0.163 0.045 0.192 0.063 0.126 0.076 0.152  Unemployed 0.051 0.077 0.098 0.083 0.088 0.060 0.114* 0.065  Retired − 0.021 0.104 0.020 0.110 0.016 0.077 0.052 0.084 Volume dummies  €1,000–€2,500 0.108 0.084 0.124 0.088 0.111* 0.065 0.116* 0.069  €2,500–€3,500 0.185* 0.098 0.185* 0.102 0.078 0.076 0.074 0.077  €3,500–€4,500 0.271** 0.108 0.249** 0.113 0.219*** 0.079 0.216*** 0.082  €4,500–€7,000 0.460*** 0.090 0.396*** 0.095 0.320*** 0.066 0.297*** 0.070  €7,000–€10,000 0.466*** 0.095 0.461*** 0.098 0.339*** 0.078 0.338*** 0.083  €10,000–€25,000 0.503*** 0.076 0.447*** 0.082 0.474*** 0.059 0.434*** 0.063  €25,000–€45,000 0.637*** 0.084 0.601*** 0.091 0.622*** 0.075 0.610*** 0.080  €45,000 or more 0.862*** 0.088 0.817*** 0.093 0.753*** 0.081 0.724*** 0.083 Account characteristics  Minimum amount − 0.369*** 0.097 − 0.399*** 0.102  Lowest balance bonus − 0.328*** 0.104 − 0.345*** 0.112  Balance growth bonus 2.121*** 0.117 2.169*** 0.128  Fixed monthly deposit 1.396*** 0.076 1.358*** 0.097  Withdrawal costs/limitations − 0.266*** 0.060 − 0.301*** 0.064  Salary account 1.118*** 0.152 1.071*** 0.150  Joint ownership 0.001 0.048 0.005 0.051  Third party ownership − 0.040 0.063 − 0.030 0.071 Constant 2.315*** 0.027 2.268*** 0.036 2.272*** 0.124 2.258*** 0.136 2.380*** 0.104 2.397*** 0.116 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 1,410 1,306 1,410 1,306 Adjusted R-squared 0.01 − 0.02 0.12 0.10 0.47 0.45 Hansen J-test p-value 0.29 0.60 0.45 F-statistic first stage 11.54 9.74 10.44 Exogeneity test p-value 0.12 0.23 0.07 Notes: The table reports OLS and IV estimates from regressions of the account-level APR on financial literacy and several other controls. The sample excludes accounts with volume below €50. All IV specifications use economics education and the financial situation of the oldest sibling as an instrument for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. *p < 0.1; **p < 0.05; ***p < 0.01. View Large Table 5. OLS of APR on financial literacy and controls using the Lewbel (2012) method. IV(1): Lewbel IV(2): Standard IV(3): Combined Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.218** 0.100 0.300** 0.121 0.228*** 0.077 Age dummies  31–40 years − 0.163 0.116 − 0.147 0.123 − 0.161 0.117  41–50 years − 0.326*** 0.109 − 0.321*** 0.110 − 0.325*** 0.109  51–60 years − 0.233** 0.107 − 0.246** 0.109 − 0.235** 0.107  61 years and older − 0.370*** 0.105 − 0.378*** 0.105 − 0.371*** 0.105 Male − 0.087 0.072 − 0.126 0.078 − 0.091 0.065 Number of children 0.024 0.031 0.027 0.031 0.025 0.030 Constant 2.577 0.098 2.581 0.098 2.578 0.098 N 1,306 1,306 1,306 Hansen J-test 3.51 5.73 10.60 Hansen J-test p-value 0.62 0.22 0.39 F-statistic first stage 6.03 11.12 16.75 Exogeneity test p-value 0.30 0.20 0.16 IV(1): Lewbel IV(2): Standard IV(3): Combined Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.218** 0.100 0.300** 0.121 0.228*** 0.077 Age dummies  31–40 years − 0.163 0.116 − 0.147 0.123 − 0.161 0.117  41–50 years − 0.326*** 0.109 − 0.321*** 0.110 − 0.325*** 0.109  51–60 years − 0.233** 0.107 − 0.246** 0.109 − 0.235** 0.107  61 years and older − 0.370*** 0.105 − 0.378*** 0.105 − 0.371*** 0.105 Male − 0.087 0.072 − 0.126 0.078 − 0.091 0.065 Number of children 0.024 0.031 0.027 0.031 0.025 0.030 Constant 2.577 0.098 2.581 0.098 2.578 0.098 N 1,306 1,306 1,306 Hansen J-test 3.51 5.73 10.60 Hansen J-test p-value 0.62 0.22 0.39 F-statistic first stage 6.03 11.12 16.75 Exogeneity test p-value 0.30 0.20 0.16 Notes: The table reports IV estimates from regressions of the account-level APR on financial literacy and age, gender and number of children using: (1) generated instruments from the Lewbel method alone; (2) external instruments (economics education and the financial situation of the oldest sibling) alone; (3) both sets of generated instruments under the Lewbel method and external instruments. The sample excludes accounts with volume below €50. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. **p < 0.05; ***p < 0.01. View Large Table 5. OLS of APR on financial literacy and controls using the Lewbel (2012) method. IV(1): Lewbel IV(2): Standard IV(3): Combined Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.218** 0.100 0.300** 0.121 0.228*** 0.077 Age dummies  31–40 years − 0.163 0.116 − 0.147 0.123 − 0.161 0.117  41–50 years − 0.326*** 0.109 − 0.321*** 0.110 − 0.325*** 0.109  51–60 years − 0.233** 0.107 − 0.246** 0.109 − 0.235** 0.107  61 years and older − 0.370*** 0.105 − 0.378*** 0.105 − 0.371*** 0.105 Male − 0.087 0.072 − 0.126 0.078 − 0.091 0.065 Number of children 0.024 0.031 0.027 0.031 0.025 0.030 Constant 2.577 0.098 2.581 0.098 2.578 0.098 N 1,306 1,306 1,306 Hansen J-test 3.51 5.73 10.60 Hansen J-test p-value 0.62 0.22 0.39 F-statistic first stage 6.03 11.12 16.75 Exogeneity test p-value 0.30 0.20 0.16 IV(1): Lewbel IV(2): Standard IV(3): Combined Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.218** 0.100 0.300** 0.121 0.228*** 0.077 Age dummies  31–40 years − 0.163 0.116 − 0.147 0.123 − 0.161 0.117  41–50 years − 0.326*** 0.109 − 0.321*** 0.110 − 0.325*** 0.109  51–60 years − 0.233** 0.107 − 0.246** 0.109 − 0.235** 0.107  61 years and older − 0.370*** 0.105 − 0.378*** 0.105 − 0.371*** 0.105 Male − 0.087 0.072 − 0.126 0.078 − 0.091 0.065 Number of children 0.024 0.031 0.027 0.031 0.025 0.030 Constant 2.577 0.098 2.581 0.098 2.578 0.098 N 1,306 1,306 1,306 Hansen J-test 3.51 5.73 10.60 Hansen J-test p-value 0.62 0.22 0.39 F-statistic first stage 6.03 11.12 16.75 Exogeneity test p-value 0.30 0.20 0.16 Notes: The table reports IV estimates from regressions of the account-level APR on financial literacy and age, gender and number of children using: (1) generated instruments from the Lewbel method alone; (2) external instruments (economics education and the financial situation of the oldest sibling) alone; (3) both sets of generated instruments under the Lewbel method and external instruments. The sample excludes accounts with volume below €50. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. **p < 0.05; ***p < 0.01. View Large Table 6. Robustness checks. OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Panel A: Variations ofequation (1) Advanced financial literacy 0.136*** 0.025 0.317*** 0.104 0.127*** 0.024 0.286*** 0.102 0.126*** 0.022 0.260*** 0.09 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,410 1,306 1,177 1,091 1,410 1,306 Adjusted R-squared 0.42 0.39 0.49 0.46 0.46 0.43 Hansen J-test p-value 0.14 0.32 0.56 F-statistic first stage 10.43 11.97 13.49 Exogeneity test p-value 0.05 0.1 0.12 Panel B: Different literacy measures Advanced financial literacy 0.127*** 0.024 0.262*** 0.094 Number of correct answers 0.117*** 0.022 0.318*** 0.116 “Big-Three”-Index 0.060*** 0.023 0.428** 0.196 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,290 1,290 1,410 1,306 1,410 1,306 Adjusted R-squared 0.47 0.45 0.47 0.43 0.46 0.32 Hansen J-test p-value 0.48 0.46 0.46 F-statistic first stage 11.21 7.11 2.68 Exogeneity test p-value 0.10 0.05 0.02 OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Panel A: Variations ofequation (1) Advanced financial literacy 0.136*** 0.025 0.317*** 0.104 0.127*** 0.024 0.286*** 0.102 0.126*** 0.022 0.260*** 0.09 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,410 1,306 1,177 1,091 1,410 1,306 Adjusted R-squared 0.42 0.39 0.49 0.46 0.46 0.43 Hansen J-test p-value 0.14 0.32 0.56 F-statistic first stage 10.43 11.97 13.49 Exogeneity test p-value 0.05 0.1 0.12 Panel B: Different literacy measures Advanced financial literacy 0.127*** 0.024 0.262*** 0.094 Number of correct answers 0.117*** 0.022 0.318*** 0.116 “Big-Three”-Index 0.060*** 0.023 0.428** 0.196 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,290 1,290 1,410 1,306 1,410 1,306 Adjusted R-squared 0.47 0.45 0.47 0.43 0.46 0.32 Hansen J-test p-value 0.48 0.46 0.46 F-statistic first stage 11.21 7.11 2.68 Exogeneity test p-value 0.10 0.05 0.02 Notes: The table shows variations of the OLS (3) and IV (3) specifications of Table 4. Panel A: (1) estimates equation (1) excluding the nine volume dummies; (2) uses only accounts reported by the financial head; (3) weighs each observation by its volume share within the household. Panel B: OLS (1) and IV(1) use the reduced sample with excluded “don’t know”-answers from the instrument economics education; (2) uses the standardized number of correct answers to the financial literacy questions; (3) uses the standardized number of correct answers to the “Big-Three” questions only. IV uses economics education and the financial situation of the oldest sibling as instruments for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. **p < 0.05; ***p < 0.01. View Large Table 6. Robustness checks. OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Panel A: Variations ofequation (1) Advanced financial literacy 0.136*** 0.025 0.317*** 0.104 0.127*** 0.024 0.286*** 0.102 0.126*** 0.022 0.260*** 0.09 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,410 1,306 1,177 1,091 1,410 1,306 Adjusted R-squared 0.42 0.39 0.49 0.46 0.46 0.43 Hansen J-test p-value 0.14 0.32 0.56 F-statistic first stage 10.43 11.97 13.49 Exogeneity test p-value 0.05 0.1 0.12 Panel B: Different literacy measures Advanced financial literacy 0.127*** 0.024 0.262*** 0.094 Number of correct answers 0.117*** 0.022 0.318*** 0.116 “Big-Three”-Index 0.060*** 0.023 0.428** 0.196 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,290 1,290 1,410 1,306 1,410 1,306 Adjusted R-squared 0.47 0.45 0.47 0.43 0.46 0.32 Hansen J-test p-value 0.48 0.46 0.46 F-statistic first stage 11.21 7.11 2.68 Exogeneity test p-value 0.10 0.05 0.02 OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Panel A: Variations ofequation (1) Advanced financial literacy 0.136*** 0.025 0.317*** 0.104 0.127*** 0.024 0.286*** 0.102 0.126*** 0.022 0.260*** 0.09 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,410 1,306 1,177 1,091 1,410 1,306 Adjusted R-squared 0.42 0.39 0.49 0.46 0.46 0.43 Hansen J-test p-value 0.14 0.32 0.56 F-statistic first stage 10.43 11.97 13.49 Exogeneity test p-value 0.05 0.1 0.12 Panel B: Different literacy measures Advanced financial literacy 0.127*** 0.024 0.262*** 0.094 Number of correct answers 0.117*** 0.022 0.318*** 0.116 “Big-Three”-Index 0.060*** 0.023 0.428** 0.196 Demographics and other controls Yes Yes Yes Yes Yes Yes Region dummies Yes Yes Yes Yes Yes Yes Account characteristics Yes Yes Yes Yes Yes Yes Bank fixed effects Yes Yes Yes Yes Yes Yes N 1,290 1,290 1,410 1,306 1,410 1,306 Adjusted R-squared 0.47 0.45 0.47 0.43 0.46 0.32 Hansen J-test p-value 0.48 0.46 0.46 F-statistic first stage 11.21 7.11 2.68 Exogeneity test p-value 0.10 0.05 0.02 Notes: The table shows variations of the OLS (3) and IV (3) specifications of Table 4. Panel A: (1) estimates equation (1) excluding the nine volume dummies; (2) uses only accounts reported by the financial head; (3) weighs each observation by its volume share within the household. Panel B: OLS (1) and IV(1) use the reduced sample with excluded “don’t know”-answers from the instrument economics education; (2) uses the standardized number of correct answers to the financial literacy questions; (3) uses the standardized number of correct answers to the “Big-Three” questions only. IV uses economics education and the financial situation of the oldest sibling as instruments for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. **p < 0.05; ***p < 0.01. View Large Table 7. OLS of APR on financial literacy and online banking. OLS IV Estimate SE Estimate SE Advanced financial literacy 0.041*** 0.011 0.124** 0.050 Internet account 1.326*** 0.03 1.308*** 0.034 Constant 1.765*** 0.063 1.776*** 0.073 Demographics and other controls Yes Yes Region dummies Yes Yes Account characteristics Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 Adjusted R-squared 0.84 0.83 Hansen J-test p-value 0.85 F-statistic first stage 10.10 Exogeneity test p-value 0.11 OLS IV Estimate SE Estimate SE Advanced financial literacy 0.041*** 0.011 0.124** 0.050 Internet account 1.326*** 0.03 1.308*** 0.034 Constant 1.765*** 0.063 1.776*** 0.073 Demographics and other controls Yes Yes Region dummies Yes Yes Account characteristics Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 Adjusted R-squared 0.84 0.83 Hansen J-test p-value 0.85 F-statistic first stage 10.10 Exogeneity test p-value 0.11 Notes: The table reports OLS and IV estimates from regressions of the account-level APR on financial literacy and several controls (used in the third baseline specification shown in Table 4) including, in addition, a dummy that represents Internet-managed accounts. The sample excludes accounts with volume below €50. IV uses economics education and the financial situation of the oldest sibling as instruments for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. **p < 0.05; ***p < 0.01. View Large Table 7. OLS of APR on financial literacy and online banking. OLS IV Estimate SE Estimate SE Advanced financial literacy 0.041*** 0.011 0.124** 0.050 Internet account 1.326*** 0.03 1.308*** 0.034 Constant 1.765*** 0.063 1.776*** 0.073 Demographics and other controls Yes Yes Region dummies Yes Yes Account characteristics Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 Adjusted R-squared 0.84 0.83 Hansen J-test p-value 0.85 F-statistic first stage 10.10 Exogeneity test p-value 0.11 OLS IV Estimate SE Estimate SE Advanced financial literacy 0.041*** 0.011 0.124** 0.050 Internet account 1.326*** 0.03 1.308*** 0.034 Constant 1.765*** 0.063 1.776*** 0.073 Demographics and other controls Yes Yes Region dummies Yes Yes Account characteristics Yes Yes Bank fixed effects Yes Yes N 1,410 1,306 Adjusted R-squared 0.84 0.83 Hansen J-test p-value 0.85 F-statistic first stage 10.10 Exogeneity test p-value 0.11 Notes: The table reports OLS and IV estimates from regressions of the account-level APR on financial literacy and several controls (used in the third baseline specification shown in Table 4) including, in addition, a dummy that represents Internet-managed accounts. The sample excludes accounts with volume below €50. IV uses economics education and the financial situation of the oldest sibling as instruments for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. **p < 0.05; ***p < 0.01. View Large In all three cases the estimated effects of financial literacy from both OLS and the corresponding IV regressions are comparable to those derived at the account level. For example, according to the IV estimate in the full specification, IV (3), an assumed one-standard deviation increase in advanced financial literacy implies a 30 basis points increase in the weighted APR. Other covariates in the model display a similar pattern to the one described in Section 5.2. That is, age and savings wealth associate with the interest earned, whereas other socioeconomic characteristics such as gender, family size, employment status and education are statistically insignificant.43 It should be noted that the account-level specifications presented in Section 5.1 preclude the possibility to reallocate funds to the highest interest account within household as a channel for financial literacy to influence the APR, whereas such a mechanism could be at work in the household-level regressions. The comparable effect of financial literacy in both account- and household-level specifications suggest that this channel is likely to be of limited importance. This is supported by simple data inspection: although the median number of owned accounts is two, most households tend to concentrate their savings in one account that typically earns the highest interest.44 As a last step, we attempt to assess the economic relevance of our key findings for households. One should note that our calculations only consider the influence of literacy for choosing an account that yields higher returns after controlling for account characteristics and bank fixed effects, which take into consideration a nontrivial part of heterogeneity across accounts. Moreover, our calculations do not incorporate the positive influence of literacy on the propensity to save higher amounts and invest more efficiently in other assets such as stocks, mutual funds and retirement plans that, compared to savings accounts, are more complex but also yield higher returns. One should also bear in mind that our calculations implicitly assume a partial equilibrium framework. If the entire population was more financially literate, banks would most likely adapt the products they offer (see also footnote 46). As a result, the general equilibrium effects can be quite different to the approximated partial equilibrium effects. First, we estimate how much more a typical household in the lowest literacy quartile could have earned today on its savings accounts when moved to the highest literacy quartile (other things equal).45 We have to make several assumptions in order to perform such a counterfactual exercise. We consider a 10-year time horizon and suppose that earned returns are reinvested. For each year, we use as the baseline rate the median interest rate of households in the first literacy quartile, which is 2.1%. Using our preferred estimate from the IV (3) specification in Table 8, we calculate that a household, moved from the lowest to the highest literacy quartile, would earn 39 basis points more on average. We then apply this extra return to the average household savings volume.46 To be conservative, we assume that additional deposits invested by households as a percentage of total savings wealth over one year grow only by the annual inflation rate.47 In this set-up, a typical foregone gain accumulates to €838 in real terms over 10 years or 4.8% of the initially invested average amount. Table 8. OLS of household-level APR on financial literacy. OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.148*** 0.028 0.317*** 0.091 0.129*** 0.028 0.286*** 0.110 0.123*** 0.023 0.297*** 0.093 Age dummies  31–40 years − 0.326*** 0.102 − 0.264** 0.111 − 0.267*** 0.084 − 0.225** 0.092  41–50 years − 0.542*** 0.100 − 0.465*** 0.109 − 0.449*** 0.082 − 0.408*** 0.088  51–60 years − 0.407*** 0.102 − 0.359*** 0.112 − 0.301*** 0.084 − 0.291*** 0.092  61 years and older − 0.540*** 0.130 − 0.541*** 0.137 − 0.537*** 0.107 − 0.560*** 0.112 Education dummies  High school − 0.010 0.067 − 0.030 0.077 0.028 0.056 0.007 0.064  College − 0.051 0.067 − 0.097 0.084 0.006 0.057 − 0.060 0.074 Male − 0.023 0.058 − 0.067 0.073 0.021 0.048 − 0.028 0.062 Couple 0.018 0.061 − 0.016 0.065 0.109* 0.066 0.058 0.070 Number of children 0.008 0.030 0.005 0.031 − 0.003 0.025 − 0.002 0.026 Occupation dummies  Employed 0.053 0.130 0.084 0.138 0.081 0.112 0.078 0.128  Self-employed 0.191 0.149 0.212 0.168 0.181* 0.109 0.208* 0.126  Unemployed − 0.071 0.079 − 0.036 0.084 0.004 0.066 0.027 0.070  Retired − 0.078 0.111 − 0.037 0.113 0.020 0.092 0.045 0.094 Savings wealth quartiles  Second quartile 0.254*** 0.078 0.234*** 0.082 0.218*** 0.064 0.207*** 0.068  Third quartile 0.411*** 0.075 0.341*** 0.083 0.377*** 0.063 0.318*** 0.067  Fourth quartile 0.634*** 0.077 0.533*** 0.093 0.600*** 0.069 0.528*** 0.079 Account characteristics  Minimum amount − 0.204*** 0.070 − 0.251*** 0.078  Lowest balance bonus − 0.181** 0.075 − 0.207** 0.082  Balance growth bonus 0.779*** 0.143 0.843*** 0.156  Fixed monthly deposit 0.705*** 0.165 0.611*** 0.166  Withdrawal costs/limitations − 0.195*** 0.067 − 0.234*** 0.072  Salary account 1.032*** 0.120 0.998*** 0.120  Joint ownership − 0.071 0.058 − 0.055 0.062  Third party ownership − 0.115* 0.069 − 0.096 0.073 Constant 2.341*** 0.027 2.316*** 0.031 2.322*** 0.126 2.317*** 0.143 2.339*** 0.121 2.363*** 0.139 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 854 787 854 787 854 787 Adjusted R-squared 0.03 − 0.01 0.13 0.10 0.41 0.36 Hansen J-test p-value 0.14 0.35 0.41 F-statistic first stage 18.96 14.42 15.30 Exogeneity test p-value 0.08 0.13 0.05 OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.148*** 0.028 0.317*** 0.091 0.129*** 0.028 0.286*** 0.110 0.123*** 0.023 0.297*** 0.093 Age dummies  31–40 years − 0.326*** 0.102 − 0.264** 0.111 − 0.267*** 0.084 − 0.225** 0.092  41–50 years − 0.542*** 0.100 − 0.465*** 0.109 − 0.449*** 0.082 − 0.408*** 0.088  51–60 years − 0.407*** 0.102 − 0.359*** 0.112 − 0.301*** 0.084 − 0.291*** 0.092  61 years and older − 0.540*** 0.130 − 0.541*** 0.137 − 0.537*** 0.107 − 0.560*** 0.112 Education dummies  High school − 0.010 0.067 − 0.030 0.077 0.028 0.056 0.007 0.064  College − 0.051 0.067 − 0.097 0.084 0.006 0.057 − 0.060 0.074 Male − 0.023 0.058 − 0.067 0.073 0.021 0.048 − 0.028 0.062 Couple 0.018 0.061 − 0.016 0.065 0.109* 0.066 0.058 0.070 Number of children 0.008 0.030 0.005 0.031 − 0.003 0.025 − 0.002 0.026 Occupation dummies  Employed 0.053 0.130 0.084 0.138 0.081 0.112 0.078 0.128  Self-employed 0.191 0.149 0.212 0.168 0.181* 0.109 0.208* 0.126  Unemployed − 0.071 0.079 − 0.036 0.084 0.004 0.066 0.027 0.070  Retired − 0.078 0.111 − 0.037 0.113 0.020 0.092 0.045 0.094 Savings wealth quartiles  Second quartile 0.254*** 0.078 0.234*** 0.082 0.218*** 0.064 0.207*** 0.068  Third quartile 0.411*** 0.075 0.341*** 0.083 0.377*** 0.063 0.318*** 0.067  Fourth quartile 0.634*** 0.077 0.533*** 0.093 0.600*** 0.069 0.528*** 0.079 Account characteristics  Minimum amount − 0.204*** 0.070 − 0.251*** 0.078  Lowest balance bonus − 0.181** 0.075 − 0.207** 0.082  Balance growth bonus 0.779*** 0.143 0.843*** 0.156  Fixed monthly deposit 0.705*** 0.165 0.611*** 0.166  Withdrawal costs/limitations − 0.195*** 0.067 − 0.234*** 0.072  Salary account 1.032*** 0.120 0.998*** 0.120  Joint ownership − 0.071 0.058 − 0.055 0.062  Third party ownership − 0.115* 0.069 − 0.096 0.073 Constant 2.341*** 0.027 2.316*** 0.031 2.322*** 0.126 2.317*** 0.143 2.339*** 0.121 2.363*** 0.139 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 854 787 854 787 854 787 Adjusted R-squared 0.03 − 0.01 0.13 0.10 0.41 0.36 Hansen J-test p-value 0.14 0.35 0.41 F-statistic first stage 18.96 14.42 15.30 Exogeneity test p-value 0.08 0.13 0.05 Notes: The table reports OLS and IV estimates from regressions of the household-level APR on financial literacy and several controls. The sample excludes accounts with volume below €50. All IV specifications use economics education and the financial situation of the oldest sibling as instruments for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. *p < 0.1; **p < 0.05; ***p < 0.01. View Large Table 8. OLS of household-level APR on financial literacy. OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.148*** 0.028 0.317*** 0.091 0.129*** 0.028 0.286*** 0.110 0.123*** 0.023 0.297*** 0.093 Age dummies  31–40 years − 0.326*** 0.102 − 0.264** 0.111 − 0.267*** 0.084 − 0.225** 0.092  41–50 years − 0.542*** 0.100 − 0.465*** 0.109 − 0.449*** 0.082 − 0.408*** 0.088  51–60 years − 0.407*** 0.102 − 0.359*** 0.112 − 0.301*** 0.084 − 0.291*** 0.092  61 years and older − 0.540*** 0.130 − 0.541*** 0.137 − 0.537*** 0.107 − 0.560*** 0.112 Education dummies  High school − 0.010 0.067 − 0.030 0.077 0.028 0.056 0.007 0.064  College − 0.051 0.067 − 0.097 0.084 0.006 0.057 − 0.060 0.074 Male − 0.023 0.058 − 0.067 0.073 0.021 0.048 − 0.028 0.062 Couple 0.018 0.061 − 0.016 0.065 0.109* 0.066 0.058 0.070 Number of children 0.008 0.030 0.005 0.031 − 0.003 0.025 − 0.002 0.026 Occupation dummies  Employed 0.053 0.130 0.084 0.138 0.081 0.112 0.078 0.128  Self-employed 0.191 0.149 0.212 0.168 0.181* 0.109 0.208* 0.126  Unemployed − 0.071 0.079 − 0.036 0.084 0.004 0.066 0.027 0.070  Retired − 0.078 0.111 − 0.037 0.113 0.020 0.092 0.045 0.094 Savings wealth quartiles  Second quartile 0.254*** 0.078 0.234*** 0.082 0.218*** 0.064 0.207*** 0.068  Third quartile 0.411*** 0.075 0.341*** 0.083 0.377*** 0.063 0.318*** 0.067  Fourth quartile 0.634*** 0.077 0.533*** 0.093 0.600*** 0.069 0.528*** 0.079 Account characteristics  Minimum amount − 0.204*** 0.070 − 0.251*** 0.078  Lowest balance bonus − 0.181** 0.075 − 0.207** 0.082  Balance growth bonus 0.779*** 0.143 0.843*** 0.156  Fixed monthly deposit 0.705*** 0.165 0.611*** 0.166  Withdrawal costs/limitations − 0.195*** 0.067 − 0.234*** 0.072  Salary account 1.032*** 0.120 0.998*** 0.120  Joint ownership − 0.071 0.058 − 0.055 0.062  Third party ownership − 0.115* 0.069 − 0.096 0.073 Constant 2.341*** 0.027 2.316*** 0.031 2.322*** 0.126 2.317*** 0.143 2.339*** 0.121 2.363*** 0.139 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 854 787 854 787 854 787 Adjusted R-squared 0.03 − 0.01 0.13 0.10 0.41 0.36 Hansen J-test p-value 0.14 0.35 0.41 F-statistic first stage 18.96 14.42 15.30 Exogeneity test p-value 0.08 0.13 0.05 OLS (1) IV (1) OLS (2) IV (2) OLS (3) IV (3) Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Advanced financial literacy 0.148*** 0.028 0.317*** 0.091 0.129*** 0.028 0.286*** 0.110 0.123*** 0.023 0.297*** 0.093 Age dummies  31–40 years − 0.326*** 0.102 − 0.264** 0.111 − 0.267*** 0.084 − 0.225** 0.092  41–50 years − 0.542*** 0.100 − 0.465*** 0.109 − 0.449*** 0.082 − 0.408*** 0.088  51–60 years − 0.407*** 0.102 − 0.359*** 0.112 − 0.301*** 0.084 − 0.291*** 0.092  61 years and older − 0.540*** 0.130 − 0.541*** 0.137 − 0.537*** 0.107 − 0.560*** 0.112 Education dummies  High school − 0.010 0.067 − 0.030 0.077 0.028 0.056 0.007 0.064  College − 0.051 0.067 − 0.097 0.084 0.006 0.057 − 0.060 0.074 Male − 0.023 0.058 − 0.067 0.073 0.021 0.048 − 0.028 0.062 Couple 0.018 0.061 − 0.016 0.065 0.109* 0.066 0.058 0.070 Number of children 0.008 0.030 0.005 0.031 − 0.003 0.025 − 0.002 0.026 Occupation dummies  Employed 0.053 0.130 0.084 0.138 0.081 0.112 0.078 0.128  Self-employed 0.191 0.149 0.212 0.168 0.181* 0.109 0.208* 0.126  Unemployed − 0.071 0.079 − 0.036 0.084 0.004 0.066 0.027 0.070  Retired − 0.078 0.111 − 0.037 0.113 0.020 0.092 0.045 0.094 Savings wealth quartiles  Second quartile 0.254*** 0.078 0.234*** 0.082 0.218*** 0.064 0.207*** 0.068  Third quartile 0.411*** 0.075 0.341*** 0.083 0.377*** 0.063 0.318*** 0.067  Fourth quartile 0.634*** 0.077 0.533*** 0.093 0.600*** 0.069 0.528*** 0.079 Account characteristics  Minimum amount − 0.204*** 0.070 − 0.251*** 0.078  Lowest balance bonus − 0.181** 0.075 − 0.207** 0.082  Balance growth bonus 0.779*** 0.143 0.843*** 0.156  Fixed monthly deposit 0.705*** 0.165 0.611*** 0.166  Withdrawal costs/limitations − 0.195*** 0.067 − 0.234*** 0.072  Salary account 1.032*** 0.120 0.998*** 0.120  Joint ownership − 0.071 0.058 − 0.055 0.062  Third party ownership − 0.115* 0.069 − 0.096 0.073 Constant 2.341*** 0.027 2.316*** 0.031 2.322*** 0.126 2.317*** 0.143 2.339*** 0.121 2.363*** 0.139 Region dummies Yes Yes Yes Yes Bank fixed effects Yes Yes N 854 787 854 787 854 787 Adjusted R-squared 0.03 − 0.01 0.13 0.10 0.41 0.36 Hansen J-test p-value 0.14 0.35 0.41 F-statistic first stage 18.96 14.42 15.30 Exogeneity test p-value 0.08 0.13 0.05 Notes: The table reports OLS and IV estimates from regressions of the household-level APR on financial literacy and several controls. The sample excludes accounts with volume below €50. All IV specifications use economics education and the financial situation of the oldest sibling as instruments for advanced financial literacy. All IV estimates use two-stage least squares. The data are from the matched DNB Household Survey in 2005. Standard errors are clustered at the household level. *p < 0.1; **p < 0.05; ***p < 0.01. View Large Second, we use our preferred estimate to gain insights on the fraction of interest earnings on savings accounts that is due to limited literacy. To this end, we calculate the extra interest rate that every household would have earned due to an assumed one standard deviation increase in financial literacy and divide it by the weighted APR that every household actually earns. We find that such an assumed increase in literacy implies an average gain of 14% on the interest earnings of savings accounts and that the implied gains are relatively higher for households with lower savings accounts wealth. In particular, the implied gain among households at the bottom quartile of the savings wealth distribution is 17% whereas the corresponding one for their counterparts at the top quartile is 11.5%. Finally, we also estimate the implied aggregate foregone gains for the overall Dutch population due to an assumed one standard deviation increase in financial literacy. Taking into account the total number of Dutch households in 2005 of 7.1 million and the participation rate of 82.4% in savings accounts observed in the DHS data, we calculate that aggregate foregone gains could amount to €350.5 million for the entire Dutch population in this year.48 6. Concluding Remarks We have constructed a unique dataset by matching the 2005 DNB Household Survey, which includes detailed information on individual savings accounts, various socioeconomic characteristics and financial literacy, with interest rate data on savings accounts from an administrative source, based on bank names, account names and account volume. Although savings accounts represent a relatively simple investment, say compared to direct stock holdings or retirement funds, more financially literate investors earn higher savings returns on average, controlling for demographics, account volume, and various account characteristics. We isolate one channel through which literacy positively associates with interest rates, namely familiarity with new technologies (online banking usage). This channel may lessen in importance over time, as Internet penetration and familiarity with online banking expands. Nevertheless, the increasing availability of more complicated financial products, some of which are exclusively managed online, and the need to compare efficiently among them underline the importance of financial sophistication coupled with technological competence. We also find evidence suggesting that more literate households are better able to identify higher interest bearing accounts across banks. Unlike stocks and funds, savings accounts are held by the overwhelming majority of households and have the highest share in household financial wealth on average. Our findings may encourage more research in order to understand household heterogeneity in seemingly “simple” and widely held financial assets. In this respect, it may be worth extending our research to countries with a varying degree of market competitiveness and product complexity, as well as less financially literate populations. Acknowledgments We are grateful to five anonymous referees and Claudio Michelacci (editor) for their constructive comments. We would like to thank Tabea Bucher-Koenen, Gabriel Fagan, Luigi Guiso, Arthur Kennickell, Annamaria Lusardi, Maarten van Rooij, and participants at the Annual Congress of the European Economic Association, Toulouse; NETSPAR International Pension Workshop, Amsterdam; Research in Behavioural Finance Conference, Rotterdam; HFCN meeting at the ECB, Frankfurt for helpful comments. We are also grateful to Rob Alessie, Annamaria Lusardi, and Maarten van Rooij for making the data from a special financial literacy module publicly available, to a major Dutch bank for providing interest rate data on savings accounts in the Netherlands, to CentERdata for providing additional information on financial product holdings and bank relationships of DNB survey participants, as well as to SpaarInformatie for providing data on restrictions of Dutch savings accounts. All three authors acknowledge financial support from the German Science Foundation (DFG) under the Leibniz grant and Inderst also from the DFG Research Group 1371. The opinions expressed in the paper are those of the authors and do not reflect the views of the European Central Bank, the Deutsche Bundesbank or the euro system. Notes The editor in charge of this paper was Claudio Michelacci. Footnotes 1 The picture is similar for most other Euro area countries according to the recent data from the Household Finance and Consumption Survey (see http://www.ecb.europa.eu/home/html/researcher_hfcn.en.html). 2 For a comparison, ownership rates (average shares) are 20% (6%) for funds and only 12% (3%) for directly held stocks. 3 These are the same questions as used in van Rooij et al. (2011, 2012). 4 Several studies cite information/search frictions as a source of price dispersion in retail financial markets net of product differentiation by firms. See, for example, Hortacsu and Syverson (2004) for S&P 500 index funds and Stango and Zinman (2016) for credit cards. As a result, firms might have an incentive to add complexity to their pricing structures in order to gain market power (Carlin 2009). 5 See, for instance, Lusardi and Mitchell (2014) for a recent review of comparable studies in the United States as well as Europe, Australia, and Japan. Earlier studies include Bernheim (1998) and Hilgert et al. (2003). 6 For an overview, see Campbell (2006) and Guiso and Sodini (2013). Moreover, several studies have documented how investment mistakes correlate with proxies for financial knowledge such as education (e.g., Calvet et al. 2007; Bilias et al. 2010). 7 The survey provides equipment to households without Internet access in order to compensate for this form of bias. See Teppa and Vis (2012) for a detailed description of the DHS. 8 In the regular panel, participants are provided with a list of seven possible answers when asked at which bank they hold each of their savings accounts: ABN Amro, Postbank, Rabobank, ING, Fortis, SNS Bank, and “Other”. In case participants indicate ownership in the category “Other”, they are further asked to provide the name of the bank. This latter information along with account names is not available in the public version of the dataset, but has been recovered from additional data that were made available to us by CentERdata. Appendix C provides more details. 9 Smith et al. (2010) have shown that this person is actually the most influential for households’ financial decisions. The remaining sociodemographic characteristics that we take into account refer also to this person. 10 Reported statistics in Tables 1–3 have been computed using sample survey weights and are representative of the Dutch population. 11 Other employment includes the following categories: works in own household, (partly) disabled, unpaid work, volunteers, other employment, students, and too young with no occupation yet. 12 As van Rooij et al. (2011) point out, the basic literacy questions in the DHS special module test for basic numerical skills and are thus more likely to proxy for cognitive abilities that typically depreciate at advanced ages. Our estimates for the advanced literacy index are insensitive to the inclusion of the basic literacy index in the estimated model (results from this specification are discussed in the robustness section). 13 The data contain information on standard interest rates (i.e., not “teaser” ones offered by banks over a short period in order to attract new customers). April 2004 is the first month of the administrative data that we have access to. It should be noted that we do not consider checking accounts. These are typically used in daily transactions and their interest rate is virtually zero, exhibiting only limited variation that may reflect differences in services offered (e.g., check issuance, ATM services) but is unlikely to be related to individual financial sophistication. Instead, the saving account products we examine are not intended for frequent transactions and cannot be used for borrowing (i.e., their interest rate is bounded at zero). 14 The exact amount brackets are €0–€1,000, €1,000–€2,500, €2,500–€3,500, €3,500–€4,500, €4,500–€7,000, €7,000–€8,000, €8,000–€9,000, €9,000–€10,000, €10,000–€25,000, €25,000–€45,000, and >€45,000. 15 (1) Accounts with minimum amount requirements offer either very low base rates or zero interest rate up to a certain volume threshold and higher rates above that threshold. (2) Accounts with lowest balance bonus give a bonus rate on the lowest account balance within a year or a quarter and yield a base rate on the remaining balance. (3) Accounts with balance growth bonus yield a bonus rate if the balance grows by a specified percentage amount per quarter or year. (4) Accounts with fixed monthly deposit require a specified absolute deposit automatically withdrawn from the checking account of the consumer each month. (5) Accounts with withdrawal limitations/fees limit the maximum amount that can be withdrawn per month or impose percentage fees for withdrawals (in most cases 1% of the withdrawn amount). (6) Salary accounts are linked to a checking account at the same bank. 16 Our results are robust when we use, instead, a (geometrically) weighted interest rate for each account over all weeks in 2004. Precisely, for 2004 we can use interest data from April 2004 to December 2004. Interest rate changes are relatively infrequent in this period. 17 We recover missing volumes of individual saving accounts following the procedure used by CentERdata for total savings volumes as described in Appendix C. 18 We do not find evidence that the sample used in the estimation differs in a systematic way from the entire sample of account owners. We provide a comparison of main demographics and bank characteristics for the two samples in Table A.2 of Appendix A. 19 In what follows, we present results only from the sample of those accounts with matched interest rate information. We have also imputed missing interest rates utilizing information on the bank names reported by each household. Results from the sample that incorporates these imputed cases along with details on the imputation procedure can be found in Deuflhard et al. (2013). Results from both samples are highly comparable. 20 This concerns around 6.8% of the accounts in the account-level sample. 21 Such a behavior would be consistent with existing evidence suggesting that many households do not understand interest compounding and its likely implications (e.g., Song 2015 finds evidence that learning about interest compounding results into a significant increase in pension contributions in China; Stango and Zinman 2009 show that those who fail to calculate interest rates out of a stream of payments borrow more and accumulate less wealth). 22 When we assess the economic relevance of our findings, we also discuss results from a household-level specification. This is possible to estimate by calculating a volume-weighted measure of APR per household and aggregating individual account characteristics at the household level. The results we find are similar. 23 We distinguish among the five main Dutch regions (the three largest cities, other West, North, East, and South). 24 As in Table 2, we group together three amount brackets from €7,000 to €10,000 due to too few observations in these categories and no account reaching a new volume threshold within this range. 25 Summary statistics on both instruments used can be found in Table 1. 26 Respondents were asked to indicate whether the financial situation of the oldest sibling is better, the same or worse compared to their own financial situation. 27 If the financial condition of the oldest sibling proxies instead for a common set of preferences or a family fixed effect, one would expect a negative correlation between the instrument and financial literacy in the first stage regression. 28 Specifically, respondents were asked how much of their past education was devoted to economics (i.e., “a lot”, “some”, “little”, and “hardly at all”). 29 A number of recent empirical studies use the Lewbel method as an alternative to the standard IV approach, see, for example, Emran and Hou (2013) and Chowdhury et al. (2014). 30 This can be seen as a relatively mild assumption compared to the exclusion restriction required under standard IV. For example, in our context it allows for unobserved factors such as general well-being and ability (or intention) to learn about finances to affect both interest rate earned and financial literacy. 31 The number of observations slightly changes from the OLS to the IV specifications due to some missing observations in the used instruments. 32 The estimated net effect of each account characteristic is hard to interpret, given that many of these restrictions typically coexist. The interpretation is further complicated by the fact that the composition of account restrictions partly varies by bank, and, as a result, the bank fixed effects already absorb a nontrivial fraction of the mean differences in account restrictions. 33 Our cross-sectional specification does not allow distinguishing between age, cohort, and time effects. Yet, one should note that the implied negative age profile that we estimate is at odds with the hump-shaped age profile derived in similar specifications modelling investments in information-intensive assets, such as stocks. The latter has been interpreted as evidence for both learning by experience and declining cognitive capacity. Given that we examine performance of a very basic asset, learning by experience may be less relevant in obtaining higher returns than factors that are more prominent among the young, such as familiarity with technology. 34 It is worth noting that most of the empirical household finance literature examines investment decisions in assets that are held by household sub-groups with selected characteristics (e.g., stockholding typically entails high participation and information costs and thus stocks are mostly held by wealthier, better educated, and more financially literate investors). We examine instead returns from an asset that has low participation requirements and is held by the vast majority of households in the sample (82.4%). We have estimated a probit model of the probability of owning a savings account and most of the factors that were taken into account (including financial literacy) turn out to be insignificant. Financial wealth was estimated to have a strong positive association with savings account ownership, though such an association is likely to be mechanical. 35 We standardize these measures by their respective mean and standard deviation. 36 Such a specification is not free of measurement error as it is still subject to misclassification across the four possible quartiles. 37 Obviously, we cannot easily instrument for advanced financial literacy when using quartiles due to the number of endogenous covariates. 38 See Online Appendix D for the exact wording of these questions. 39 Based on two gambles presented to survey participants in the DHS, this measure can take five possible outcomes from low to high risk aversion (including one category for those who answered “don’t know”). 40 We obtain similar results when using self-reported online banking use, instead, which is asked in the DHS, as this is highly correlated with having an internet account. 41 This is corroborated by results from a probit regression (see Online Appendix E) in which we estimate a significant positive association between financial literacy and the likelihood of owning an online managed account. 42 We calculate a household-specific APR as the volume-weighted average APR across all accounts that each household h owns. In this specification we include dummies denoting quartiles of total savings account volume (i.e., instead of account volume dummies used in the account level specification). Given that the unit of observation is the household, dummies for account characteristics take the value one if at least one of the savings accounts in a household is subject to the restriction in question, while bank fixed effects are volume-weighted. 43 As is the case with account-level regressions, we find no significant effects of dummies for income, financial and real wealth quartiles of the respective distributions when included in the household-level specifications. 44 For instance, 66% of households allocate more than 80% to a single account. 45 For our calculation, we assume more narrowly that such an increase in financial literacy takes place for a single household only. If, for instance, a publicly sponsored program lifts financial literacy for a larger fraction of households, however, our calculation represents only a partial equilibrium analysis in the following sense. Presently, as noted previously, price differentiation across banks but also across accounts at a given bank seems to be possible as consumers are sophisticated to a different degree. When more consumers become literate in this sense, there is less scope for such differentiation. In equilibrium, banks would react by adjusting their offers. One possibility, which can be supported by a formal analysis, is that as more households become willing and able to choose the best offer, offers would become more attractive across the board, in which case the general equilibrium effect of a financial literacy program would further enhance the benefits to, in particular, (newly) literate households. 46 Our calculations use moments on total savings volume and financial literacy from the entire sample of households. 47 This simplifies matters in the sense that additional deposits and inflation cancel out in the calculation of cumulative losses. 48 For comparison, the estimated aggregate interest earnings from all households’ savings accounts were €3.1 billion in 2005. We perform these calculations by aggregating over all households used in the estimation sample and then scale this number up to the total number of households owning a savings account in the Dutch population. 49 For example, some respondents report accounts which are not offered anymore by the reported bank in 2005 but were replaced by an account with a different name. 50 Details can be found in the documentation of the DHS 2005 wave (available at: https://www.dhsdata.nl/site/releases/sourcefile/49). 51 60% of those household members hold only one account and thus total volume and individual account volume are equivalent. 52 Note that in the last two cases, we only consider accounts that do not exceed the total number of accounts as originally stated by the respondent, for example, we only consider the first three reported accounts of a household that claims to have 3 accounts in total but reports four. The same approach is used in the DHS for the calculation of total savings wealth. Appendix A: Descriptive Statistics on Savings Accounts Table A.1. Summary statistics account-level variables. Variable Mean Std. Dev. 25th pct. Median 75th pct. APR 2.31 0.86 1.55 2.50 3.10 Account volume in € 10,973 26,756 857 3,500 12,750 Bank fixed effects  ABN AMRO 0.12 0.33 – – –  ING Bank 0.37 0.48 – – –  Rabobank 0.30 0.46 – – –  Fortis Bank 0.03 0.17 – – –  SNS Bank 0.03 0.17 – – –  Small Banks 0.15 0.36 – – – Ownership  Individual 0.52 0.50 – – –  Joint 0.39 0.49 – – –  Third party 0.09 0.29 – – – Account restrictions  Internet account 0.35 0.48 – – –  Minimum amount 0.13 0.33 – – –  Lowest balance bonus 0.28 0.45 – – –  Balance growth bonus 0.03 0.16 – – –  Fixed monthly deposit 0.01 0.10 – – –  Withdrawal costs/limitations 0.09 0.29 – – –  Salary account 0.02 0.15 – – – Variable Mean Std. Dev. 25th pct. Median 75th pct. APR 2.31 0.86 1.55 2.50 3.10 Account volume in € 10,973 26,756 857 3,500 12,750 Bank fixed effects  ABN AMRO 0.12 0.33 – – –  ING Bank 0.37 0.48 – – –  Rabobank 0.30 0.46 – – –  Fortis Bank 0.03 0.17 – – –  SNS Bank 0.03 0.17 – – –  Small Banks 0.15 0.36 – – – Ownership  Individual 0.52 0.50 – – –  Joint 0.39 0.49 – – –  Third party 0.09 0.29 – – – Account restrictions  Internet account 0.35 0.48 – – –  Minimum amount 0.13 0.33 – – –  Lowest balance bonus 0.28 0.45 – – –  Balance growth bonus 0.03 0.16 – – –  Fixed monthly deposit 0.01 0.10 – – –  Withdrawal costs/limitations 0.09 0.29 – – –  Salary account 0.02 0.15 – – – Notes: The sample consists of accounts used in the regression analysis. All statistics use sample weights. The data are from the matched DNB Household Survey 2005. View Large Table A.1. Summary statistics account-level variables. Variable Mean Std. Dev. 25th pct. Median 75th pct. APR 2.31 0.86 1.55 2.50 3.10 Account volume in € 10,973 26,756 857 3,500 12,750 Bank fixed effects  ABN AMRO 0.12 0.33 – – –  ING Bank 0.37 0.48 – – –  Rabobank 0.30 0.46 – – –  Fortis Bank 0.03 0.17 – – –  SNS Bank 0.03 0.17 – – –  Small Banks 0.15 0.36 – – – Ownership  Individual 0.52 0.50 – – –  Joint 0.39 0.49 – – –  Third party 0.09 0.29 – – – Account restrictions  Internet account 0.35 0.48 – – –  Minimum amount 0.13 0.33 – – –  Lowest balance bonus 0.28 0.45 – – –  Balance growth bonus 0.03 0.16 – – –  Fixed monthly deposit 0.01 0.10 – – –  Withdrawal costs/limitations 0.09 0.29 – – –  Salary account 0.02 0.15 – – – Variable Mean Std. Dev. 25th pct. Median 75th pct. APR 2.31 0.86 1.55 2.50 3.10 Account volume in € 10,973 26,756 857 3,500 12,750 Bank fixed effects  ABN AMRO 0.12 0.33 – – –  ING Bank 0.37 0.48 – – –  Rabobank 0.30 0.46 – – –  Fortis Bank 0.03 0.17 – – –  SNS Bank 0.03 0.17 – – –  Small Banks 0.15 0.36 – – – Ownership  Individual 0.52 0.50 – – –  Joint 0.39 0.49 – – –  Third party 0.09 0.29 – – – Account restrictions  Internet account 0.35 0.48 – – –  Minimum amount 0.13 0.33 – – –  Lowest balance bonus 0.28 0.45 – – –  Balance growth bonus 0.03 0.16 – – –  Fixed monthly deposit 0.01 0.10 – – –  Withdrawal costs/limitations 0.09 0.29 – – –  Salary account 0.02 0.15 – – – Notes: The sample consists of accounts used in the regression analysis. All statistics use sample weights. The data are from the matched DNB Household Survey 2005. View Large Table A.2. Comparison of full versus final sample across major demographics and account characteristics. Full sample (N = 2,337) Final sample (N = 1,410) Variable Mean Std. Dev. Mean Std. Dev. Age 49.94 15.61 50.74 15.33 Male 0.56 0.50 0.58 0.49 Couple 0.71 0.45 0.71 0.46 Number of children 0.68 1.04 0.65 1.04 Less than high school 0.25 0.43 0.24 0.42 High school 0.35 0.48 0.34 0.47 College 0.41 0.49 0.43 0.49 ABN AMRO 0.13 0.34 0.12 0.33 ING Bank 0.34 0.47 0.37 0.48 Rabobank 0.28 0.45 0.30 0.46 Fortis Bank 0.04 0.19 0.03 0.17 SNS Bank 0.04 0.19 0.03 0.17 Small Banks 0.17 0.38 0.15 0.36 Volume in € 9,636 23,804 10,973 26,756 Individual ownership 0.53 0.50 0.52 0.50 Joint ownership 0.36 0.48 0.39 0.49 Third party ownership 0.11 0.31 0.09 0.29 Full sample (N = 2,337) Final sample (N = 1,410) Variable Mean Std. Dev. Mean Std. Dev. Age 49.94 15.61 50.74 15.33 Male 0.56 0.50 0.58 0.49 Couple 0.71 0.45 0.71 0.46 Number of children 0.68 1.04 0.65 1.04 Less than high school 0.25 0.43 0.24 0.42 High school 0.35 0.48 0.34 0.47 College 0.41 0.49 0.43 0.49 ABN AMRO 0.13 0.34 0.12 0.33 ING Bank 0.34 0.47 0.37 0.48 Rabobank 0.28 0.45 0.30 0.46 Fortis Bank 0.04 0.19 0.03 0.17 SNS Bank 0.04 0.19 0.03 0.17 Small Banks 0.17 0.38 0.15 0.36 Volume in € 9,636 23,804 10,973 26,756 Individual ownership 0.53 0.50 0.52 0.50 Joint ownership 0.36 0.48 0.39 0.49 Third party ownership 0.11 0.31 0.09 0.29 Notes: The full sample contains all accounts held by households in the DNB Household Survey. The final sample contains only those accounts used in the estimation sample. All statistics use sample weights. View Large Table A.2. Comparison of full versus final sample across major demographics and account characteristics. Full sample (N = 2,337) Final sample (N = 1,410) Variable Mean Std. Dev. Mean Std. Dev. Age 49.94 15.61 50.74 15.33 Male 0.56 0.50 0.58 0.49 Couple 0.71 0.45 0.71 0.46 Number of children 0.68 1.04 0.65 1.04 Less than high school 0.25 0.43 0.24 0.42 High school 0.35 0.48 0.34 0.47 College 0.41 0.49 0.43 0.49 ABN AMRO 0.13 0.34 0.12 0.33 ING Bank 0.34 0.47 0.37 0.48 Rabobank 0.28 0.45 0.30 0.46 Fortis Bank 0.04 0.19 0.03 0.17 SNS Bank 0.04 0.19 0.03 0.17 Small Banks 0.17 0.38 0.15 0.36 Volume in € 9,636 23,804 10,973 26,756 Individual ownership 0.53 0.50 0.52 0.50 Joint ownership 0.36 0.48 0.39 0.49 Third party ownership 0.11 0.31 0.09 0.29 Full sample (N = 2,337) Final sample (N = 1,410) Variable Mean Std. Dev. Mean Std. Dev. Age 49.94 15.61 50.74 15.33 Male 0.56 0.50 0.58 0.49 Couple 0.71 0.45 0.71 0.46 Number of children 0.68 1.04 0.65 1.04 Less than high school 0.25 0.43 0.24 0.42 High school 0.35 0.48 0.34 0.47 College 0.41 0.49 0.43 0.49 ABN AMRO 0.13 0.34 0.12 0.33 ING Bank 0.34 0.47 0.37 0.48 Rabobank 0.28 0.45 0.30 0.46 Fortis Bank 0.04 0.19 0.03 0.17 SNS Bank 0.04 0.19 0.03 0.17 Small Banks 0.17 0.38 0.15 0.36 Volume in € 9,636 23,804 10,973 26,756 Individual ownership 0.53 0.50 0.52 0.50 Joint ownership 0.36 0.48 0.39 0.49 Third party ownership 0.11 0.31 0.09 0.29 Notes: The full sample contains all accounts held by households in the DNB Household Survey. The final sample contains only those accounts used in the estimation sample. All statistics use sample weights. View Large Appendix B: First Stage Regression Table B.1. First stage regressions, account-level APR. OLS (1) OLS (2) OLS (3) Estimate SE Estimate SE Estimate SE Financial situation oldest sibling  Worse 0.336*** 0.115 0.326*** 0.104 0.326*** 0.102  The same or better 0.107 0.109 0.150 0.097 0.154 0.096 Economics education  Some − 0.287*** 0.082 − 0.259*** 0.082 − 0.261*** 0.081  Little − 0.458*** 0.088 − 0.404*** 0.088 − 0.414*** 0.087  Hardly at all − 0.694*** 0.107 − 0.579*** 0.101 − 0.586*** 0.098 Age dummies  31–40 years − 0.199 0.148 − 0.189 0.147  41–50 years 0.011 0.123 0.023 0.121  51–60 years 0.264** 0.113 0.265** 0.112  61 years and older 0.399*** 0.149 0.420*** 0.148 Education dummies  High school 0.295*** 0.093 0.274*** 0.091  College 0.383*** 0.084 0.363*** 0.086 Male 0.316*** 0.072 0.323*** 0.072 Couple 0.029 0.073 0.064 0.079 Number of children − 0.019 0.039 − 0.019 0.040 Basic financial literacy Occupation dummies 0.140 0.213 0.125 0.216  Employed − 0.029 0.194 − 0.008 0.192  Self-employed − 0.186* 0.102 − 0.188* 0.100  Unemployed − 0.191 0.127 − 0.193 0.126 Volume dummies  €1,000–€2,500 0.026 0.083 0.015 0.083  €2,500–€3,500 0.184** 0.092 0.140 0.090  €3,500–€4,500 0.164 0.108 0.114 0.110  €4,500–€7,000 0.162 0.099 0.082 0.099  €7,000–€10,000 0.142 0.107 0.104 0.109  €10,000–€25,000 0.174** 0.085 0.132 0.082  €25,000–€45,000 0.139 0.106 0.081 0.109  €45,000 or more 0.271*** 0.103 0.213** 0.102 Constant 0.421*** 0.106 –0.206 0.171 –0.151 0.188 Region dummies Yes Yes Account characteristics Yes Bank fixed effects Yes N 1,306 1,306 1,306 Adjusted R-squared 0.07 0.20 0.20 F-statistic first stage 11.54 9.74 10.44 OLS (1) OLS (2) OLS (3) Estimate SE Estimate SE Estimate SE Financial situation oldest sibling  Worse 0.336*** 0.115 0.326*** 0.104 0.326*** 0.102  The same or better 0.107 0.109 0.150 0.097 0.154 0.096 Economics education  Some − 0.287*** 0.082 − 0.259*** 0.082 − 0.261*** 0.081  Little − 0.458*** 0.088 − 0.404*** 0.088 − 0.414*** 0.087  Hardly at all − 0.694*** 0.107 − 0.579*** 0.101 − 0.586*** 0.098 Age dummies  31–40 years − 0.199 0.148 − 0.189 0.147  41–50 years 0.011 0.123 0.023 0.121  51–60 years 0.264** 0.113 0.265** 0.112  61 years and older 0.399*** 0.149 0.420*** 0.148 Education dummies  High school 0.295*** 0.093 0.274*** 0.091  College 0.383*** 0.084 0.363*** 0.086 Male 0.316*** 0.072 0.323*** 0.072 Couple 0.029 0.073 0.064 0.079 Number of children − 0.019 0.039 − 0.019 0.040 Basic financial literacy Occupation dummies 0.140 0.213 0.125 0.216  Employed − 0.029 0.194 − 0.008 0.192  Self-employed − 0.186* 0.102 − 0.188* 0.100  Unemployed − 0.191 0.127 − 0.193 0.126 Volume dummies  €1,000–€2,500 0.026 0.083 0.015 0.083  €2,500–€3,500 0.184** 0.092 0.140 0.090  €3,500–€4,500 0.164 0.108 0.114 0.110  €4,500–€7,000 0.162 0.099 0.082 0.099  €7,000–€10,000 0.142 0.107 0.104 0.109  €10,000–€25,000 0.174** 0.085 0.132 0.082  €25,000–€45,000 0.139 0.106 0.081 0.109  €45,000 or more 0.271*** 0.103 0.213** 0.102 Constant 0.421*** 0.106 –0.206 0.171 –0.151 0.188 Region dummies Yes Yes Account characteristics Yes Bank fixed effects Yes N 1,306 1,306 1,306 Adjusted R-squared 0.07 0.20 0.20 F-statistic first stage 11.54 9.74 10.44 Notes: The table reports estimates from first-stage regressions used to estimate the IV models shown in Table 4. The instruments employed refer to economics education and the financial situation of the oldest sibling. The reference group for the first consists of those with a lot of education in economics, whereas the base category for the second regards those with no siblings and refusals. The data are from the matched DNB Household Survey 2005. Standard errors are clustered at the household level. *p < 0.1; **p < 0.05; ***p < 0.01. View Large Table B.1. First stage regressions, account-level APR. OLS (1) OLS (2) OLS (3) Estimate SE Estimate SE Estimate SE Financial situation oldest sibling  Worse 0.336*** 0.115 0.326*** 0.104 0.326*** 0.102  The same or better 0.107 0.109 0.150 0.097 0.154 0.096 Economics education  Some − 0.287*** 0.082 − 0.259*** 0.082 − 0.261*** 0.081  Little − 0.458*** 0.088 − 0.404*** 0.088 − 0.414*** 0.087  Hardly at all − 0.694*** 0.107 − 0.579*** 0.101 − 0.586*** 0.098 Age dummies  31–40 years − 0.199 0.148 − 0.189 0.147  41–50 years 0.011 0.123 0.023 0.121  51–60 years 0.264** 0.113 0.265** 0.112  61 years and older 0.399*** 0.149 0.420*** 0.148 Education dummies  High school 0.295*** 0.093 0.274*** 0.091  College 0.383*** 0.084 0.363*** 0.086 Male 0.316*** 0.072 0.323*** 0.072 Couple 0.029 0.073 0.064 0.079 Number of children − 0.019 0.039 − 0.019 0.040 Basic financial literacy Occupation dummies 0.140 0.213 0.125 0.216  Employed − 0.029 0.194 − 0.008 0.192  Self-employed − 0.186* 0.102 − 0.188* 0.100  Unemployed − 0.191 0.127 − 0.193 0.126 Volume dummies  €1,000–€2,500 0.026 0.083 0.015 0.083  €2,500–€3,500 0.184** 0.092 0.140 0.090  €3,500–€4,500 0.164 0.108 0.114 0.110  €4,500–€7,000 0.162 0.099 0.082 0.099  €7,000–€10,000 0.142 0.107 0.104 0.109  €10,000–€25,000 0.174** 0.085 0.132 0.082  €25,000–€45,000 0.139 0.106 0.081 0.109  €45,000 or more 0.271*** 0.103 0.213** 0.102 Constant 0.421*** 0.106 –0.206 0.171 –0.151 0.188 Region dummies Yes Yes Account characteristics Yes Bank fixed effects Yes N 1,306 1,306 1,306 Adjusted R-squared 0.07 0.20 0.20 F-statistic first stage 11.54 9.74 10.44 OLS (1) OLS (2) OLS (3) Estimate SE Estimate SE Estimate SE Financial situation oldest sibling  Worse 0.336*** 0.115 0.326*** 0.104 0.326*** 0.102  The same or better 0.107 0.109 0.150 0.097 0.154 0.096 Economics education  Some − 0.287*** 0.082 − 0.259*** 0.082 − 0.261*** 0.081  Little − 0.458*** 0.088 − 0.404*** 0.088 − 0.414*** 0.087  Hardly at all − 0.694*** 0.107 − 0.579*** 0.101 − 0.586*** 0.098 Age dummies  31–40 years − 0.199 0.148 − 0.189 0.147  41–50 years 0.011 0.123 0.023 0.121  51–60 years 0.264** 0.113 0.265** 0.112  61 years and older 0.399*** 0.149 0.420*** 0.148 Education dummies  High school 0.295*** 0.093 0.274*** 0.091  College 0.383*** 0.084 0.363*** 0.086 Male 0.316*** 0.072 0.323*** 0.072 Couple 0.029 0.073 0.064 0.079 Number of children − 0.019 0.039 − 0.019 0.040 Basic financial literacy Occupation dummies 0.140 0.213 0.125 0.216  Employed − 0.029 0.194 − 0.008 0.192  Self-employed − 0.186* 0.102 − 0.188* 0.100  Unemployed − 0.191 0.127 − 0.193 0.126 Volume dummies  €1,000–€2,500 0.026 0.083 0.015 0.083  €2,500–€3,500 0.184** 0.092 0.140 0.090  €3,500–€4,500 0.164 0.108 0.114 0.110  €4,500–€7,000 0.162 0.099 0.082 0.099  €7,000–€10,000 0.142 0.107 0.104 0.109  €10,000–€25,000 0.174** 0.085 0.132 0.082  €25,000–€45,000 0.139 0.106 0.081 0.109  €45,000 or more 0.271*** 0.103 0.213** 0.102 Constant 0.421*** 0.106 –0.206 0.171 –0.151 0.188 Region dummies Yes Yes Account characteristics Yes Bank fixed effects Yes N 1,306 1,306 1,306 Adjusted R-squared 0.07 0.20 0.20 F-statistic first stage 11.54 9.74 10.44 Notes: The table reports estimates from first-stage regressions used to estimate the IV models shown in Table 4. The instruments employed refer to economics education and the financial situation of the oldest sibling. The reference group for the first consists of those with a lot of education in economics, whereas the base category for the second regards those with no siblings and refusals. The data are from the matched DNB Household Survey 2005. Standard errors are clustered at the household level. *p < 0.1; **p < 0.05; ***p < 0.01. View Large Appendix C: Details on Data Processing Whereas the majority of survey respondents provide a bank name, the data on the names of savings accounts contain some typos, abbreviations, and few inconsistencies. We process this raw information in the DHS in the following way. Using the bank and account names from the market interest rate data as a reference for the correct spelling, we replace all incorrectly spelled names and abbreviations in the DHS by their proper name. We replace those cases in which participants report outdated names of accounts by the names of their successor accounts. Finally, we set all potential inconsistent cases to missing.49 As we later match the DHS and market data based on volume as well, we also recover missing volumes of individual savings accounts following the procedure used by the official provider of the DHS (CentERdata). CentERdata first recovers volumes for individual savings accounts (details follow) and then aggregates over all accounts of each household member yielding total savings volume per household member (i.e., at the individual level). Only the recovered volume of the latter is available in the public version of the dataset. However, we are able to recover the large majority of the inserted values for individual savings accounts by following the same process that CentERdata has applied to calculate total savings account volume per household member.50 First, if a respondent does not report the exact amount of a savings account, the respondent is asked to choose from a sequence of follow-up questions in the form of unfolding brackets. In this case, we use the mid-point of the bracketed answer or the lower bound in case of the highest open-ended category (€25,000 or more). This leaves 10.3% of accounts with missing volume. Second, for these missing cases, we use the average amount of this savings account over the last two years. This leaves 8.1% of accounts with missing volume. For the remaining individual household members with at least one account with unreported volume, an imputed value for total savings volume was used by CentERdata. 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Journal of the European Economic AssociationOxford University Press

Published: Apr 23, 2018

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