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Time preferences and political regimes: evidence from reunified Germany

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Abstract

We use the separation and later reunification of Germany after World War II to show that a political regime shapes time preferences of its residents. Using two identification strategies, we find that former residents of the German Democratic Republic exhibit a significantly less pronounced present bias when compared with former residents of the Federal Republic of Germany, whereas measures of patience are statistically indistinguishable. Interpreting the years spent under the regime as a proxy for treatment intensity yields consistent results. Moreover, we present evidence showing that present bias predicts choices in the domains of health, finance, and education, thereby illustrating lasting repercussions of a regime’s influence on time preferences.

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Notes

  1. There is similar evidence, for example, regarding social preferences in Carlsson et al. (2014).

  2. The difference in the means of patience is smaller than the one for present bias, but in the anticipated direction. The same holds for the signs of the (insignificant) GDR regression coefficient in the estimation for the level of patience. Our data is thus consistent with the relatively greater variability of present bias across countries as reported in Wang et al. (2016).

  3. Rainer and Siedler (2009) primarily build on the German General Social Survey (ALLBUS).

  4. Present-biased preferences thus give special importance to immediate payoffs and allow for time-inconsistent choice (e.g., O’Donoghue and Rabin 1999).

  5. Pinger (2017) also finds that survey measures and experimental measures of present bias are significantly related. She uses the special experimental SOEP submodul 2006 and a survey measure of present bias that is not available in the main SOEP waves.

  6. Including the educational background of the parents is also important because own cognitive ability relates to time preferences, and is itself strongly influenced by parents (Anger and Heineck 2010; Anger and Schnitzlein 2017; Bishai 2004; Dohmen et al. 2010).

  7. Zumbuehl et al. (2013) and Dohmen et al. (2012), for example, provide strong empirical support for the cultural transmission approach.

  8. Evolutionary selection also plays a role in the formation of preferences but is less relevant for our context. The literature is surveyed by Robson and Samuelson (2011), for example.

  9. Delaney and Doyle (2012) present evidence on the relationship between psychometric measures proxying time preferences and socioeconomic differences that is consistent with the findings reported in Deckers et al. (2017).

  10. Non-parametric local mean smoothing is applied to estimate the changes by age.

  11. In this context, it is interesting that Chen (2013) documents that people who speak languages that do not delineate the future tense, and thus allowing “tomorrow” to be a continuation of “today” instead of forcing it to be a new day altogether, are more patient.

  12. The political regime of the GDR vigorously highlighted differences between the FRG and the GDR with respect to unemployment, for example. The salience of such differences for its residents was aided by the use of propaganda in school books or shows on television (e.g., Saunders2007).

  13. Later on, the US and the UK handed over small parts of their sectors to France (e.g., Burchardi and Hassan 2013).

  14. However, Schäfgen (1998: 58), for instance, asserts that the structure of society with respect to educational achievement and implied differences between social groups remained relatively stable and comparable in East and West Germany.

  15. Migration choices may be related to time preferences. For example, Goldbach and Schlüter (2018) present evidence that migration from developing countries is more likely for patient individuals.

  16. See, for example, Hunt (2006) and Fuchs-Schündeln and Schündeln (2009) for a description and analysis of migration after the end of the GDR.

  17. We incorporate 396 different counties in the estimating sample for our GRDD analysis.

  18. We exclude Berlin from our working sample for two reasons. First, we cannot calculate a reasonable Euclidian distance for people living in West Berlin (as there was both a border surrounding Berlin and the Berlin wall border). Second, we suspect strong selective migration in both parts of Berlin during the German separation for various reasons (e.g., political and cultural ones).

  19. For example, Becker et al. (2016) also assess the long-run effects of a border that existed in the past.

  20. We thereby lose about 4.4 % of our estimating sample (of which 3.6 % represent East-West movers). The results from our ordinary least squares regression exercises that we will report in Section 4.1 are robust to this change in the sample. Results are available upon request.

  21. In their study about work attitudes of females, Campa and Serafinelli (forthcoming) also apply a GRDD approach to the natural experiment represented by the German separation/reunification episode, similarly including donut specifications.

  22. The impressionable years hypothesis proposes that individuals are susceptible to attitude change during late adolescence and early adulthood and that susceptibility drops radically thereafter. Evidence confirming the hypothesis is presented, for example, in Krosnick and Alwin (1989). Giuliano and Spilimbergo (2014) provide a recent application in economics. The GDP data for the years 1947 to 1989 for East and West Germany and from 1990 to 1991 for West Germany are from Ritschl and Spoerer (1997). The GDP data from 1990 to 1991 for East Germany are from Sleifer (2006). These data are available at: histat.gesis.org/histat/. The data for the period 1992 to 2017 stem from the German Statistical Offices (see http://www.statistik-bw.de/VGRdL/tbls/?lang=en-GB).

  23. Note that we cannot use unemployment as an economic indicator, since unemployment was officially equal to zero in the former GDR.

  24. As a qualifier, we attest that home ownership may be a bad control variable. At least in the GDR, home ownership was related to the individual willingness to achieve this end, which might have been influenced by the level of self-control.

  25. The information about social ties stems from a question addressed at participants of the new East German SOEP sample in spring 1990. At the household level, participants were asked whether they have had relatives or friends in the former FRG.

  26. Regression results are available upon request.

  27. Remember that, in our GRDD estimations, we exclude survey respondents who moved from West to East or from East to West Germany within the relevant post-reunification period.

  28. Religion may be relevant to time preferences. For instance, Doepke and Zilibotti (2014) argue that religious beliefs such as Protestantism can be seen as complementary drivers of patience and work ethic.

  29. Regression results are available upon request.

  30. The psychological literature defines conscientiousness as the tendency to be organized, responsible, and hard working and agreeableness is defined as the tendency to act in a cooperative, unselfish manner (e.g., Almlund et al. 2011, p. 11).

  31. The SOEP comprises information about conscientiousness and agreeableness in 2005 and 2009 in the form of respondents’ self-positioning on scales ranging from 1 to 7 as an answer to how well specific statements describe their personality. We generate measures of agreeableness and conscientiousness by standardizing the sum of the scores of the dimension-specific questions whereby higher values indicate a stronger intensity of the particular trait (e.g., being more agreeable).

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Acknowledgments

We gratefully acknowledge the very helpful comments made by Daniel Harenberg, Mario Mechtel, Hannah Schildberg-Hörisch, Georg Weizsäcker, and participants of the 2017 German Economic Association Meeting in Vienna on earlier versions of the paper. We are also very thankful for the insightful suggestions received from three anonymous reviewers.

Author information

Authors and Affiliations

  1. Public Economics Group, University of Marburg, Am Plan 2, 35037, Marburg, Germany

    Tim Friehe

  2. CESifo, Munich, Germany

    Tim Friehe

  3. EconomiX, Paris, France

    Tim Friehe

  4. Department of Business and Economics, University of Applied Sciences Bielefeld, Interaktion 1, 33619, Bielefeld, Germany

    Markus Pannenberg

  5. IZA, Bonn, Germany

    Markus Pannenberg

  6. GLO, Maastricht, Netherlands

    Markus Pannenberg

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  1. Tim Friehe
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Correspondence to Tim Friehe.

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Appendices

Appendix A: Exploring the relationship of the SOEP proxy for present bias (impulsivity) and an experimental measure of present bias

Table 11 Correlation of impulsivity and experimental measure of present bias
Full size table

In 2006, experiments were conducted with a subsample of the SOEP to elicit their time preferences. In the experiment that we rely upon for our experimental measure of present bias, 526 individuals were asked to indicate their 20 choices from 20 sets of two alternatives, where the alternatives differed in their level of payout and their payout period. The difference in the payout period was fixed at one month. In a first task, participants were presented with the option of either receiving 200 euros immediately or some payment x > 200 euros in one month. In a second task, the decision was between 200 euros in 12 months and some payment x > 200 euros in 13 months. The level of x took on 20 values that were presented in an ascending order and the subjects were asked to state their choice for each of the 20 sets of two alternatives (Richter and Schupp 2014). After the experiment, a randomly selected subgroup of participants (11%) received 200 euros or a random x > 200 euros depending on their choice in the experiment.

The set of alternatives at which individuals switch from preferring the sooner payment to preferring the later payment gives us their switching point and is interpreted as a point of indifference. We observe valid switching points for 374 participants aged 18 to 80 years. These switching points allow us to calculate an experimental measure of present bias. For illustration, suppose that an individual is indifferent between the 200 euros immediately and x euros in one month (i.e., that u(200) = βδu(x), where we use the notation from Footnote 2) and that this individual is indifferent between 200 euros in 12 months and y euros in 13 months (i.e., that u(200) = δu(y)). For this individual, we can infer β = u(y)/u(x).

In a next step, we use an ordinary least squares specification to estimate the correlation between the experimental measure of present bias and our survey proxy for present bias (i.e., the SOEP measure for impulsivity) using age, age squared, and gender as additional covariates. For a subset of 322 of the 374 experiment participants with valid switching points and information about the place of residence in 1989, the SOEP survey in 2008 contains information on our proxy for present bias. We find a significant correlation between our survey measure for present bias and the experimental measure of present bias. The correlation remains significant and nearly unchanged when we add age, age squared, gender, and survey information on patience and risk (see Table 11 for complete results). Tobit and robust regression exercises yield similar results. Considering only subjects that were FRG residents in 1989, we find that the results from the pooled sample re-emerge. Considering only subjects that were GDR residents in 1989, we find that the sign and the size of the coefficient remain intact. However, these coefficients are not statistically significant, which may be related to the very small number of observations.

Appendix B: Robustness checks

Table 12 Taking account of East-West migration before 1989: ordinary least squares regressions
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Table 13 Taking account of East-West migration before 1989: GRDD regressions
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Table 14 Present bias and GDR treatment: GDR-states interactions
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Friehe, T., Pannenberg, M. Time preferences and political regimes: evidence from reunified Germany. J Popul Econ 33, 349–387 (2020). https://doi.org/10.1007/s00148-019-00728-7

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