Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter May 8, 2021

Financial literacy, institutions and education: Lessons from the German reunification

Maddalena Davoli and Jia Hou ORCID logo
From the journal German Economic Review

Abstract

A growing body of literature highlights the importance of financial literacy in affecting household choices. However, much fewer studies focus on understanding the determinants of different levels of financial literacy. Our paper contributes to filling this gap by analyzing a specific determinant, i. e., the educational system, to explain the heterogeneity of financial literacy scores across Germany. The results suggest that the lower financial literacy observed in East Germany can be partially attributed to the different institutional framework experienced during the Cold War, more specifically, to the socialist educational system experienced in East Germany, which affected specific cohorts of individuals. By exploiting the unique set-up of the German reunification, we identify education as a channel through which institutions and financial literacy are related in the German context. In support of this hypothesis, we find that individuals exposed to the Eastern educational system exhibit 12 % to 21 % lower financial literacy scores compared with the households in the control group, not exposed to such system.

JEL Classification: I25; P3; G53; D14

Funding statement: Jia Hou acknowledges financial support from the Leibniz Institute for Financial Research SAFE (Sustainable Architecture for Finance in Europe). Please find the funding information: https://safe-frankfurt.de/research/research-projects/research-projects-details/showproject/equity-of-opportunities-educational-achievement-and-financial-literacy.html.

Acknowledgment

We wish to thank Michael Berlemann, Kristof De Witte, Horst Entorf, Nghiem Giang, Cahit Guven, Majlinda Joxhe, Lennart Kraft, Jorge Quintana, and Jan Schnellenbach for suggestions and comments. We are also grateful to participants at the joint SAFE-Bundesbank seminar, where earlier results were presented. We also thank the editor and two anonymous referees for their helpful comments. We wish to thank all the participants in the 11 t h Workshop of Political Economy (Dresden), the Equity in Education Workshop (KU Leuven), the 4 t h Annual Workshop on Natural Experiments in History (Deakin Melbourne), the 6 t h Luxembourg Workshop on Household Finance and Consumption (Luxembourg) and the 27 t h Meeting of the Economics of Education Association. This paper uses data from the Deutsche Bundesbank Panel on Household Finances. The results published and the related observations and analysis may not correspond to results or analysis of the data producers.

Appendix A The data

A.1 Panel on household finances

The nationally representative Panel on Household Finances is a household level survey conducted by the Deutsche Bundesbank. The survey is part of a wider project, the Household Finance and Consumption Survey (HFCS), taking place in all Euro area countries to provide a system of harmonised national wealth surveys. All surveys part of the HFCS provide detailed information on economic and demographic characteristics of the households, including income, pensions, employment and consumption. For detailed information on the organizing network operating under the umbrella of the European Central Bank, see Eurosystem Household Finance and Consumption Network (2009).

The German part of the HFCS, the Panel on Household Finances (PHF throughout the paper) is a panel survey following individuals who were first interviewed in 2010–2011, a second time in 2013–2014 and finally in 2017–2018. All households have been re-contacted in later waves, and all individuals tracked, so that if households broke up or individuals deceased, refreshment samples and split-off households were added to the panel. The random sampling, which aims at overrepresenting wealthy households, was conducted in three stages. First, German municipalities are divided into three strata depending on the proportion of wealthy households. Then, streets in cities with more than 100,000 citizens are categorized according to the wealth of the households. Finally, 4,461 households above 18 year old are drawn.

To avoid biased means estimates, the Bundesbank computes survey weights to adjust for the unequal probability survey design and for unit non-response. Moreover, in order to deal with item non-response, the Bundesbank has implemented an iterative multiple imputation procedure (MI) of all PHF missing observations. More specifically, five multiple imputed datasets are generated following Rubin’s method (Rubin 1987), using variables that are correlated with the imputed variables which help explaining the non-response behavior. Continuous variables are imputed using linear stochastic regression models; binary variables are imputed using a linear probability model, and categorical variables are imputed by means of hot deck imputation. Because one single imputed data underestimates variances, the data are imputed five times, which is a generally accepted number of implications adopted by the HFCN when the rate of missing observations is low. A detailed description of the weighting and imputation procedure can be found in Knerr et al. (2014) and Zhu and Eisele (2013).

A.2 Our sample

All throughout our analysis, we made use of standardized survey weights in order to adjust the oversampling of wealthy households and to present results that are representative for the whole German population. As discussed in the main text, we use the second wave data throughout the empirical analysis and exclude observations older than 70 years old. Since the item non-response is relatively low for many variables, and especially low for the ones of interest in our analysis (such as demographic characteristics, financial literacy items and so on), the presence of missing observations does not affect our results and using all five imputations would only add computational burden to our estimation procedure. Hence, we employ only the first imputed dataset. Table A.2 presents the number of missing and imputed observations for the key variables used in our analysis.[17]

Table A.1

PHF Financial Literacy Questions.

Question Possible answers
Let us assume you have a balance of €100 in your savings account. This balance bears interest at an annual rate of 2 %, and you leave it there for 5 years. What do you think: how high is your balance after 5 years? Higher than €102; Exactly €102; Lower than €102; Don’t know; Refused to answer
Do you agree with the following statement: “The investment in the stock of a single company is less risky than investing in a fund with stock in similar companies”? I agree; I do not agree; Don’t know; Refused to answer
Let us assume that the interest paid on your savings account is 1 % per year and the inflation rate is 2 % per year. What do you think: After a year, will you be able to buy just as much, more or less than today with the balance in your savings account? More; Just as much; Less than today; Don’t know; Refused to answer

  1. Source: PHF, Bundesbank, 2014.

Table A.2

Missing and Imputed Observations for Key Variables.

Description No. of missing answers
Inflation question 0
Interest rate question 0
Risk-diversification question 0
Employment status 4
Number of children 3
Residence in 1989 0
Country of birth 0
Region of current residence 0
Age 0
Married 1
Education 2
Gender 0

  1. Source: PHF, Bundesbank, 2014. The number of missing values, i. e. the imputed information, refers to the whole sample, 4,112 observations in total.

Figure A.1 
Financial Literacy by Residential Location.

Figure A.1

Financial Literacy by Residential Location.

Table A.3

Financial Literacy Index over Other Demographic Characteristics.

West East


All Correct No. of Obs. All Correct No. of Obs.
Lower-level secondary school 54.8 % 560 43.0 % 72
(Hauptschule)
Mid-level secondary school 66.3 % 701 70.0 % 297
(Realschule)
University 81.6 % 1,221 77.8 % 220
(Oberschule& Gymnasium)
Male 70.4 % 1,418 76.4 % 315
Female 64.1 % 1,082 56.8 % 278
Self-employed 81.8 % 281 86.7 % 48
Not self-employed 66.1 % 2,219 65.3 % 545
Unemployed 42.1 % 86 47.4 % 54
Retired 55.6 % 485 59.7 % 129

  1. Note: Source: PHF Bundesbank, 2014. “West” and “East” refers to current residence of the household at the time of 2014 survey. Data are weighted.

Table A.4

Summary of Key Variables.

Mean Std. Min Max
All questions correct .67 .016 0 1
FL1 .89 .010 0 1
FL2 .90 .010 0 1
FL3 .77 .015 0 1
Current residence .22 .030 0 1
Residence in 1989 .21 .025 0 1
Age 47.67 .365 18 69
Female .46 .014 0 1
Self-employed .09 .009 0 1
Unemployed .05 .006 0 1
Retired .16 .010 0 1
East- West migration 0.08 0.012 0 1
Lower-level secondary school .31 .016 0 1
Mid-level secondary school .33 .015 0 1
University .35 .014 0 1
Married .47 .014 0 1
Self-Assessed Risk 3.91 .073 0 10
Self-Assessed Trust 5.30 .065 0 10
Self-Assessed Patience 4.65 .075 0 10
Log(income) 7.68 .021 5.6 11.9
Income from Financial Assets .32 .016 0 1
Having Loan .28 .013 0 1
N 3055

  1. Note: Source: PHF Bundesbank, 2014. Data are weighted.

We do not have precise information on the region where the respondents live, due to privacy issues. The data, however, provide anonymized indicators used in the sampling design stage to representatively classify households according to whether they live in large cities, small municipalities, wealthy small municipalities, wealthy street sections or other street sections. Basically, households leaving nearby share the same identifier, among the 342 available. To account for the possibility that households living in the same area share common characteristics (possibly also as far as concerns financial knowledge), we cluster the errors in our estimation process according to this indicator.[18]

Table A.3 reports summary statistics of the East/West divide in financial literacy over other individual characteristics for the second wave. West Germany scores better than East Germany over some of the employment variables, while middle-level education seems to be a particularly strong predictor of financial literacy in the East. Table A.4 presents basic descriptive statistics of all the variables that we use in the study, both to control for observable characteristics that are known to be associated with financial literacy, and to conduct subgroup analysis. To clarify, we only use the aggregated financial literacy indicator which captures whether a respondent answers all three financial literacy correctly or not as our dependent variable, except for results in the very first regression table. Besides, in all regression tables, unless otherwise specified, “East’’ refers to respondent residing in East Germany in 1989, instead of residence information at the time of survey, since we use residence at time of Berlin Wall fall as the proxy of exposure to socialist education.

Appendix B Further tables and figures

Figure B.1 
Educational System in the East before Reunification.

Figure B.1

Educational System in the East before Reunification.

Table B.1

Financial Literacy Determinants: All Regressors.

(Probit) (OLS)
All correct All correct
East in 1989 −0.077 −0.023
(−0.541) (−0.489)
Age −0.011 −0.005
(−0.309) (−0.439)
A g e 2 0.000 0.000
(0.509) (0.641)
Married and living together −0.125 −0.037
(−1.029) (−0.951)
Female −0.275*** −0.086***
(−2.862) (−2.694)
Lower level secondary school 0.436 0.164
(0.911) (0.947)
Mid-level secondary school 0.674 0.248
(1.435) (1.457)
Upper level secondary school 0.919* 0.319*
(1.926) (1.852)
Investment Behavior Risk Preference −0.123 −0.032
(−1.123) (−0.956)
Self-assessment: Risk −0.007 −0.001
(−0.283) (−0.138)
Self-assessment: Trust −0.013 −0.004
(−0.551) (−0.507)
Self-assessment: Patience −0.044** −0.014**
(−2.539) (−2.457)
1 if Regular Saving 0.193* 0.070*
(1.825) (1.911)
Log(income) 0.194* 0.058*
(1.784) (1.750)
Self-employment Income −0.113 −0.033
(−0.582) (−0.570)
Income from Financial Assets 0.345*** 0.103***
(3.259) (3.270)
Application for a Loan/Credit 0.105 0.033
(1.048) (1.023)
Self-employed 0.198 0.045
(0.880) (0.720)
Ownership of Private Business 0.175 0.057
(1.249) (1.386)
Unemployment Benefits 0.103 0.028
(0.565) (0.439)
Retired −0.196 −0.067
(−1.120) (−1.143)
Migration between East/West 0.022 −0.001
(0.114) (−0.016)
Panel household 0.103 0.028
(0.886) (0.741)
Constant −0.951 0.213
(−0.736) (0.500)
N 2137 2137
R 2 0.100
Weights YES YES

  1. Note: Source: PHF Bundesbank, 2014. The dependent variable is a dummy equal to one if answers to all three financial literacy questions were correct. Marginal effects from Probit estimation are presented. Results are weighted and errors are clustered at the municipalities level. t statistics in parenthesis. * p < 0.1, ** p < 0.05, *** p < 0.01.

Table B.2

Sample Means by Treatment and Control Groups.

Full West East


Control Treated Control Treated
Female 0.462 0.460 0.533 0.477 0.453
Self-employment 0.093 0.093 0.007 0.062 0.132
Unemployed 0.048 0.035 0.249 0.082 0.082
Observations 3,055 2,451 49 289 304

  1. Note: Source: PHF Bundesbank, 2014. “East” and “West” refer to the current residence of respondents. Results are weighted and errors are clustered at the municipalities level.

Table B.3

Determinants of Financial Literacy: Further Controls.

>=12 Years Old in 1989 >=18 Years Old in 1989 >=22 Years Old in 1989



All correct (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)
East 0.0589 0.0601 0.0520 0.0692 0.0662 0.0915** 0.0694 0.0878** 0.0941** 0.0903** 0.0492 0.0612 0.0464 0.0528 0.0496
(0.053) (0.093) (0.053) (0.053) (0.053) (0.045) (0.078) (0.045) (0.045) (0.045) (0.044) (0.069) (0.044) (0.045) (0.044)
Treat(1) 0.0726 0.240** 0.0715 0.204 0.188
(0.076) (0.110) (0.077) (0.139) (0.121)
East*Treat(1) −0.114* −0.0913 −0.105 −0.128* −0.121*
(0.069) (0.108) (0.068) (0.069) (0.068)
Treat(2) 0.0550 −0.0154 0.0615 0.0222 −0.0215
(0.064) (0.103) (0.065) (0.115) (0.095)
East*Treat(2) −0.209*** −0.141 −0.204*** −0.215*** −0.206***
(0.072) (0.098) (0.072) (0.073) (0.072)
Treat(3) −0.00960 0.00819 −0.00757 −0.0755 −0.103
(0.053) (0.089) (0.052) (0.102) (0.098)
East*Treat(3) −0.143** −0.144 −0.139** −0.150** −0.140**
(0.066) (0.097) (0.066) (0.066) (0.065)
N 3055 1337 3041 3055 3055 3055 1337 3041 3055 3055 3055 1337 3041 3055 3055
Marriage status YES YES YES
Number of kids YES YES YES
Risk, Patience, Trust YES YES YES
Cohort dummies: 3 years YES YES YES
Cohort dummies: 6 years YES YES YES

  1. Note: Source: PHF Bundesbank, 2014. The dependent variable is a dummy equal to one if all three financial literacy questions have been answered correctly. Marginal effects from Probit estimation are presented, the results are weighted and errors are clustered at the municipalities level. t statistics in parenthesis. * p < 0.1, ** p < 0.05, *** p < 0.01. T r e a t ( 1 ) is a dummy equal to 1 if the FKP is 37 years old (or older) at the time of the survey. T r e a t ( 2 ) and T r e a t ( 3 ) equal 1 if the FKP is 42 or 46 years old (or older), respectively, at the time of the survey. Respectively 15 and 8 dummies have been added to the fourth and fifth model for each DiD specification.

Figure B.2 
Real Marginal Effects of 
E
a
s
t
∗
T
r
e
a
t
(
2
)East\ast Treat(2) Term on Financial Literacy.

Figure B.2

Real Marginal Effects of E a s t T r e a t ( 2 ) Term on Financial Literacy.

Figure B.3 
Effects of the Treatment for Each 
C
o
h
o
r
tCohort.

Figure B.3

Effects of the Treatment for Each C o h o r t.

Figure B.4 
Marginal Effects of 
E
a
s
t
∗
C
o
h
o
r
tEast\ast Cohort Term on Financial Literacy.

Figure B.4

Marginal Effects of E a s t C o h o r t Term on Financial Literacy.

Table B.4

Determinants of Financial Literacy: FKP 32 to 70 Years Old.

All Correct 12 Years Old in 1989 18 Years Old in 1989 22 Years Old in 1989



Probit OLS Probit OLS Probit OLS
East 0.0671 0.0662 0.117** 0.119** 0.0558 0.0648
(0.084) (0.086) (0.057) (0.058) (0.055) (0.053)
Treat(1) 0.0856 0.0785
(0.090) (0.078)
East*Treat(1) −0.113 −0.0988
(0.101) (0.088)
Treat(2) 0.0531 0.0438
(0.078) (0.068)
East*Treat(2) −0.230*** −0.202***
(0.087) (0.071)
Treat(3) −0.0290 −0.0265
(0.055) (0.050)
East*Treat(3) −0.139* −0.138**
(0.073) (0.062)
N 2794 2794 2794 2794 2794 2794
R 2 0.101 0.106 0.104
All controls YES YES YES YES YES YES
Weights YES YES YES YES YES YES

  1. Note: Source: PHF Bundesbank, 2014. The dependent variable is a dummy equal to one if all three financial literacy questions were answered correctly. Marginal effects from Probit estimation are presented. “East” refers to respondents’ residence in 1989, a proxy of education system they were exposed to. The sample is restricted to individuals 32 to 70 years old at the time of the survey. T r e a t ( 1 ) is a dummy equal to 1 if the FKP is 37 years old (or older) at the time of the survey. T r e a t ( 2 ) and T r e a t ( 3 ) equal 1 if the FKP is 42 or 46 years old (or older), respectively, at the time of the survey. Results are weighted and errors are clustered at the municipalities level. t statistics in parenthesis. * p < 0.1, ** p < 0.05, *** p < 0.01.

Table B.5

Determinants of Financial Literacy: without East-West migrated Households.

12 Years Old in 1989 18 Years Old in 1989 22 Years Old in 1989



Probit OLS Probit OLS Probit OLS
East 0.023 0.020 0.077 0.077 0.034 0.039
(0.390) (0.333) (1.567) (1.537) (0.679) (0.796)
East*Treat(1) −0.041 −0.031
(−0.607) (−0.496)
Treat(2) 0.052 0.041
(0.777) (0.679)
East*Treat(2) −0.155** −0.135**
(−2.069) (−2.127)
Treat(3) −0.013 −0.014
(−0.237) (−0.288)
East*Treat(3) −0.083 −0.082
(−1.259) (−1.403)
N 2817 2817 2817 2817 2817 2817
R 2 0.100 0.103 0.101
All controls YES YES YES YES YES YES
Weights YES YES YES YES YES YES

  1. Note: Source: PHF Bundesbank, 2014. The dependent variable is a dummy equal to one if all three financial literacy questions were answered correctly. “East” refers to respondents’ residence in 1989, a proxy of education system they were exposed to. T r e a t ( 1 ) is a dummy equal to 1 if the FKP is 37 years old (or older) at the time of the survey. T r e a t ( 2 ) and T r e a t ( 3 ) equal 1 if the FKP is 42 or 46 years old (or older), respectively, at the time of the survey. The sample is restricted to individuals who did not change residence between 1989 and 2014. Results are weighted and errors are clustered at the municipalities level. t statistics in parenthesis. * p < 0.1, ** p < 0.05, *** p < 0.01.

Table B.6

DiD: Different Dependent Variables.

(1) (2) (3) (4)
Dependent variable: Number of At least one At least two At least one
Correct incorrect correct “don’t know”
East 0.472* 0.009 0.020 −0.051**
(1.950) (0.842) (0.769) (−1.986)
Treat(2) 0.228 −0.008 −0.014 −0.074**
(0.752) (−0.513) (−0.390) (−1.988)
East*Treat(2) −1.022*** −0.018 −0.083* 0.092
(−3.318) (−0.632) (−1.752) (1.511)
N 3055 3055 3055 3055
All controls YES YES YES YES
Weights YES YES YES YES

  1. Note: Source: PHF Bundesbank, 2014. The dependent variable is: in column (1) the cumulative number of correct responses to the financial literacy questions (value from 0 to 3); in column (2) the probably of answering incorrectly at least one question; in column (3) the probability of answering correctly at least two questions; in column (4) the probability of answering “I do not know” to at least one question. Coefficient reported in (1) is the predicted probability of answering correctly three questions from an Ordered Probit model, in (2) to (4) are the marginal effects from a Probit. “East” refers to respondents’ residence in 1989, a proxy of education system they were exposed to. T r e a t ( 2 ) is a dummy equal to 1 if the FKP is 42 years old (or older) at the time of the survey. Results are weighted and errors are clustered at the municipalities level. t statistics in parenthesis. * p < 0.1, ** p < 0.05, *** p < 0.01.

References

Agnew, J. R., H. Bateman, and S. Thorp. “Financial literacy and retirement planning in Australian.” Numeracy 6, no. 11 (2013). 10.2139/ssrn.2198641Search in Google Scholar

Alesina, A. and N. Fuchs-Schündeln. “Good-bye lenin (or not?): The effect of communism on people’s preferences.” The American Economic Review 97, no. 4 (2007): 1507–1528. 10.3386/w11700Search in Google Scholar

Behrman, J. R., O. S. Mitchell, C. Soo, and D. Bravo. “Financial literacy, schooling, and wealth accumulation.” The American Economic Review 102, no. 3 (2012): 303–304. 10.3386/w16452Search in Google Scholar

Bernheim, B. D. and D. M. Garrett. “The effects of financial education in the workplace: Evidence from a survey of households.” Journal of Public Economics 87, no. 7 (2003): 1487–1519. 10.1016/S0047-2727(01)00184-0Search in Google Scholar

Bernheim, B. D., D. M. Garrett, and D. M. Maki. “Education and saving: The long-term effects of high school financial curriculum mandates.” Journal of Public Economics 80, no. 3 (2001): 435–465. 10.3386/w6085Search in Google Scholar

Block, K.-D. and H.-W. Fuchs. The Eastern German education system in transition, 1993. Search in Google Scholar

Bonin, H. and R. Euwals (2002). “Participation behavior of East German women after German unification.” In 10th International Conference on Panel Data, Berlin, July 5–6, 2002 D1-1, International Conferences on Panel Data. 10.2139/ssrn.297087Search in Google Scholar

Brosig-Koch, J., C. Helbach, A. Ockenfels, and J. Weimann. “Still different after all these years: Solidarity behavior in East and West Germany.” Journal of Public Economics 95, no. 11 (2011): 1373–1376. 10.1016/j.jpubeco.2011.06.002Search in Google Scholar

Brown, M., C. Henchoz, and T. Spycher. “Culture and financial literacy.” Journal of Economic Behavior and Organization 150 (2018): 62–85. 10.2139/ssrn.2916636Search in Google Scholar

Bucher-Koenen, T. and B. Lamla-Dietrich. “The long shadow of socialism: Puzzling evidence on east-west German differences in financial literacy.” Economic Notes 47, no. 2-3 (2018): 413–438. 10.1111/ecno.12108Search in Google Scholar

Bucher-Koenen, T. and A. Lusardi. “Financial literacy and retirement planning in Germany.” Journal of Pension Economics & Finance 10, no. 4 (2011): 565–584. 10.3386/w17110Search in Google Scholar

Chen, H. and R. P. Volpe. “An analysis of personal financial literacy among college students.” Financial Services Review 7, no. 2 (1998): 107–128. 10.1016/S1057-0810(99)80006-7Search in Google Scholar

Chen, H. and R. P. Volpe. “Gender differences in personal financial literacy among college students.” Financial Services Review 11, no. 3 (2002): 289. Search in Google Scholar

Corsini, L. and L. Spataro. “Optimal decisions on pension plans in the presence of information costs and financial literacy.” Journal of Public Economic Theory 17, no. 3 (2015): 383–414. 10.1111/jpet.12121Search in Google Scholar

Delavande, A., S. Rohwedder, and R. Willis. Preparation for retirement, financial literacy and cognitive resources. Working Papers wp190, University of Michigan, Michigan Retirement Research Center, 2008, September. 10.2139/ssrn.1337655Search in Google Scholar

Eurosystem Household Finance and Consumption Network. Survey data on household finance and consumption: Research summary and policy use. ECB Occasional Paper (100), 2009. 10.2139/ssrn.1144504Search in Google Scholar

Fornero, E. and C. Monticone. “Financial literacy and pension plan participation in Italy.” Journal of Pension Economics and Finance 10, no. 04 (2011): 547–564. 10.1017/S1474747211000473Search in Google Scholar

Fuchs-Schündeln, N. and M. Haliassos. Does product familiarity matter for participation? Technical report, CEPR. Discussion Paper No. DP10632, 2015. 10.2139/ssrn.2651144Search in Google Scholar

Fuchs-Schündeln, N. and T. A. Hassan. “Natural experiments in macroeconomics.” In Handbook of Macroeconomics, Volume 2, 923–1012. Elsevier, 2016. 10.1016/bs.hesmac.2016.03.008Search in Google Scholar

Fuchs-Schündeln, N. and P. Masella. “Long-lasting effects of socialist education.” Review of Economics and Statistics 98, no. 3 (2016): 428–441. 10.1162/REST_a_00583Search in Google Scholar

Fuchs-Schündeln, N. and M. Schündeln. “Precautionary savings and self-selection: Evidence from the German reunification “experiment”.” The Quarterly Journal of Economics 120, no. 3 (2005): 1085–1120. 10.1162/003355305774268183Search in Google Scholar

Fuchs-Schündeln, N. and M. Schündeln. “On the endogeneity of political preferences: Evidence from individual experience with democracy.” Science 347, no. 6226 (2015): 1145–1148. 10.1126/science.aaa0880Search in Google Scholar

Hastings, J. S., B. C. Madrian, and W. L. Skimmyhorn. “Financial literacy, financial education, and economic outcomes.” Annual Review of Economics 5, no. 1 (2013): 347–373. 10.3386/w18412Search in Google Scholar

Hung, A., A. M. Parker, and J. Yoong. Defining and measuring financial literacy. Technical report, RAND Corporation. Working Papers – 708, 2009. 10.2139/ssrn.1498674Search in Google Scholar

Jappelli, T. “Economic literacy: An international comparison.” The Economic Journal 120, no. 548 (2010): F429–F451. 10.1111/j.1468-0297.2010.02397.xSearch in Google Scholar

Jappelli, T. and M. Padula. “Investment in financial literacy and saving decisions.” Journal of Banking & Finance 37, no. 8 (2013): 2779–2792. 10.1016/j.jbankfin.2013.03.019Search in Google Scholar

Kaiser, T. and L. Menkhoff. “Does financial education impact financial literacy and financial behavior, and if so, when?” World Bank Economic Review 31, no. 3 (2017): 611–630. 10.1093/wber/lhx018Search in Google Scholar

Knerr, P., F. Aust, N. Chudziak, R. Gilberg, and M. Kleudgen. Private haushalte und ihre finanzen (phf) 2. erhebungswelle. Technical report, infas Institut für angewandte Sozialwissenschaft GmbH, 2014. Search in Google Scholar

Lusardi, A.. Numeracy, financial literacy, and financial decision-making. Technical report, National Bureau of Economic Research, 2012. 10.3386/w17821Search in Google Scholar

Lusardi, A., P.-C. Michaud, and O. S. Mitchell. “Optimal financial knowledge and wealth inequality.” Journal of Political Economy 125, no. 2 (2017): 431–477. 10.3386/w18669Search in Google Scholar

Lusardi, A. and O. Mitchell. Financial literacy and retirement planning: New evidence from the Rand American Life Panel. Working Papers wp157, University of Michigan, Michigan Retirement Research Center, 2007. 10.2139/ssrn.1095869Search in Google Scholar

Lusardi, A. and O. S. Mitchell. “Planning and financial literacy: How do women fare?” American Economic Review 98, no. 2 (2008): 413–17. 10.3386/w13750Search in Google Scholar

Lusardi, A. and O. S. Mitchell. Financial literacy and planning: Implications for retirement well-being. In Financial Literacy: Implications for Retirement Security and the Financial Marketplace, 17. 2011. 10.3386/w17077Search in Google Scholar

Lusardi, A. and O. S. Mitchell. “The economic importance of financial literacy: Theory and evidence.” Journal of Economic Literature 52, no. 1 (2014): 5–44. 10.3386/w18952Search in Google Scholar

Lusardi, A., O. S. Mitchell, and V. Curto. “Financial literacy among the young.” Journal of Consumer Affairs 44, no. 2 (2010): 358–380. 10.1111/j.1745-6606.2010.01173.xSearch in Google Scholar

Marsh, H. W., O. Köller, and J. Baumert. “Reunification of East and West German school systems: Longitudinal multilevel modeling study of the big-fish-little-pond effect on academic self-concept.” American Educational Research Journal 38, no. 2 (2001): 321–350. 10.3102/00028312038002321Search in Google Scholar

Norton, E. C., H. Wang, and C. Ai. “Computing interaction effects and standard errors in logit and probit models.” Stata Journal 4, no. 2 (2004): 154–167. 10.1177/1536867X0400400206Search in Google Scholar

OECD. OECD/INFE high-level principles on national strategies for financial education. Technical report, 2012. Search in Google Scholar

Rooij, M. V., A. Lusardi, and R. Alessie. “Financial literacy and stock market participation.” Journal of Financial Economics 101, no. 2 (2011): 449–472. 10.1016/j.jfineco.2011.03.006Search in Google Scholar

Rubin, D. B. Multiple Imputation for Nonresponse in Surveys, Volume 81. John Wiley & Sons, 1987. 10.1002/9780470316696Search in Google Scholar

Sekita, S. “Financial literacy and retirement planning in Japan.” Journal of Pension Economics and Finance 10, no. 04 (2011): 637–656. 10.1017/S1474747211000527Search in Google Scholar

Spataro, L. and L. Corsini. “Endogenous financial literacy, saving, and stock market participation.” FinanzArchiv: Public Finance Analysis 73, no. 2 (2017): 135–162. 10.1628/001522117X14877521353555Search in Google Scholar

Stolper, O. A. and A. Walter. “Financial literacy, financial advice, and financial behavior.” Journal of Business Economics 87, no. 5 (2017): 581–643. 10.1007/s11573-017-0853-9Search in Google Scholar

Xu, L. and B. Zia. Financial literacy around the world. Policy Research Working Paper 6107, 2012. 10.2139/ssrn.2248863Search in Google Scholar

Zhu, J. and M. Eisele. Multiple imputation in a complex household survey-the German Panel on Household Finances (PHF): Challenges and solutions. PHF User Guide, 2013. Search in Google Scholar

Published Online: 2021-05-08
Published in Print: 2021-11-30

© 2021 Walter de Gruyter GmbH, Berlin/Boston