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

Maddalena Davoli; Jia Hou

doi:10.1515/ger-2020-0027

Published by De Gruyter May 8, 2021

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

Maddalena Davoli and Jia Hou

From the journal German Economic Review

https://doi.org/10.1515/ger-2020-0027

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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.

Keywords: financial literacy determinants; socialist education; German reunification; DiD

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

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

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.

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

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

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.

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

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

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

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 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

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

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

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.

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Published Online: 2021-05-08

Published in Print: 2021-11-30

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

Abstract

Acknowledgment

Appendix A The data

A.1 Panel on household finances

A.2 Our sample

Appendix B Further tables and figures

References

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