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 to lower financial literacy scores compared with the households in the control group, not exposed to such system.
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.
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 Workshop of Political Economy (Dresden), the Equity in Education Workshop (KU Leuven), the Annual Workshop on Natural Experiments in History (Deakin Melbourne), the Luxembourg Workshop on Household Finance and Consumption (Luxembourg) and the 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.
|Let us assume you have a balance of €100 in your savings account. This balance bears interest at an annual rate of , 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 per year and the inflation rate is 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|
Source: PHF, Bundesbank, 2014.
|Description||No. of missing answers|
|Interest rate question||0|
|Number of children||3|
|Residence in 1989||0|
|Country of birth||0|
|Region of current residence||0|
Source: PHF, Bundesbank, 2014. The number of missing values, i. e. the imputed information, refers to the whole sample, 4,112 observations in total.
|All Correct||No. of Obs.||All Correct||No. of Obs.|
|Lower-level secondary school||54.8 %||560||43.0 %||72|
|Mid-level secondary school||66.3 %||701||70.0 %||297|
|University||81.6 %||1,221||77.8 %||220|
|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|
Note: Source: PHF Bundesbank, 2014. “West” and “East” refers to current residence of the household at the time of 2014 survey. Data are weighted.
|All questions correct||.67||.016||0||1|
|Residence in 1989||.21||.025||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|
|Income from Financial Assets||.32||.016||0||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.
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
|All correct||All correct|
|East in 1989||−0.077||−0.023|
|Married and living together||−0.125||−0.037|
|Lower level secondary school||0.436||0.164|
|Mid-level secondary school||0.674||0.248|
|Upper level secondary school||0.919*||0.319*|
|Investment Behavior Risk Preference||−0.123||−0.032|
|1 if Regular Saving||0.193*||0.070*|
|Income from Financial Assets||0.345***||0.103***|
|Application for a Loan/Credit||0.105||0.033|
|Ownership of Private Business||0.175||0.057|
|Migration between East/West||0.022||−0.001|
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. *, **, ***.
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.
|>=12 Years Old in 1989||>=18 Years Old in 1989||>=22 Years Old in 1989|
|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|
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. *, **, ***. is a dummy equal to 1 if the FKP is 37 years old (or older) at the time of the survey. and 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.
|All Correct||12 Years Old in 1989||18 Years Old in 1989||22 Years Old in 1989|
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. is a dummy equal to 1 if the FKP is 37 years old (or older) at the time of the survey. and 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. *, **, ***.
|12 Years Old in 1989||18 Years Old in 1989||22 Years Old in 1989|
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. is a dummy equal to 1 if the FKP is 37 years old (or older) at the time of the survey. and 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. *, **, ***.
|Dependent variable:||Number of||At least one||At least two||At least one|
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. 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. *, **, ***.
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