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Peer effects in risk preferences: Evidence from Germany

  • S.I.: Recent Developments in Financial Modeling and Risk Management
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Abstract

This study uses data from the German Socio-Economic Panel to analyze peer effects in risk preferences. Empirical evidence on the impact of peer groups on individual willingness to take risks (‘peer effects’) is very limited so far as causality is hard to establish. To establish a causal relationship between individual and community risk preferences, we use an instrumental variables approach where we track the impact of the East–West migration after the German reunification. We find strong support for peer effects in risk preferences. Peer effects seem particularly relevant for women, less educated individuals, the young population, parents, and married individuals. Individuals with higher social interaction tend to have stronger peer effects. Our findings shed light on the origin and stability of risk tolerance and, more generally, on the determinants of economic preferences.

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Fig. 1

Source: SOEP v32.1, SOEP (2016)

Fig. 2

Source: SOEP v32.1, 2004–2015

Fig. 3

Source: SOEP v32.1, 2004–2015

Fig. 4

Source: SOEP v32.1, 2004–2015

Fig. 5

Source: SOEP v32.1, 2004–2015

Fig. 6

Source: SOEP v32.1, 2004–2015

Fig. 7

Source: SOEP v32.1, 2004–2015

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Notes

  1. See, for instance, Mossin (1968), Barsky et al. (1997), and Dohmen et al. (2011). Recent research has focused on factors changing risk attitudes of individuals, such as macroeconomic conditions and major life events (see, e.g., Malmendier and Nagel 2011; Hoffmann et al. 2013; Browne et al. 2019; Görlitz and Tamm 2019).

  2. Note that we document the existence of peer effects despite the higher time stability.

  3. From the erection of the Berlin wall in 1961 until the fall of the Berlin wall in 1989, Germany was physically and ideologically divided into the Federal Republic of Germany (i.e., West Germany) and the German Democratic Republic (GDR; i.e., East Germany). The fall of the Berlin wall in 1989 paved the way for German reunification, which formally took place on October 3, 1990. Before the fall of the Berlin wall, GDR citizens were usually not allowed to migrate to West Germany. Note that we refer to former western or former eastern federal states as western or eastern federal states throughout this article.

  4. We restrict our sample to ‘native’ individuals, that is, those individuals who have lived in the same federal state in West Germany at least one year prior to and all years after the fall of the Berlin wall. Thus, native individuals have lived in the same western community over the entire panel. This ensures that the results are not contaminated by an individual and her new community members sharing the same background.

  5. See Ahern et al. (2014) and Balsa et al. (2015) for experimental studies on peer effects in academic settings with students.

  6. See Goebel et al. (2019) for more information on the German SOEP.

  7. Due to interpolation, decimal numbers are also allowed. For reliability analysis, Cronbach’s alpha was calculated to assess the internal consistency of the subscale for sociability, which consists of seven SOEP questions. According to Field (2009), the internal consistency lies within an acceptable range, with a Cronbach’s alpha for sociability of 0.511.

  8. Note that Germany was divided into the Federal Republic of Germany (former West Germany) and the GDR (former East Germany) from 1961 to 1989.

  9. The Berlin wall physically and ideologically divided Berlin from 1961 to 1989 and thus a differentiation between former East and West Germans regarding this particular federal state is impossible.

  10. Note that we drop information on 19 individuals that lived in two different East German federal states during this period.

  11. See Sect. 4.1 for details.

  12. Note that the term ‘mover’ refers to former East Germans and West Germans moving from any one to another federal state, while the term ‘native’ covers only West Germans who have lived in the same federal state all their lives.

  13. Our example is very close to the one presented by Brown et al. (2008) to point out the differences to their approach.

  14. The median value is 4 for movers and 5 for natives.

  15. Jaeger et al. (2010) investigate the 2004 and 2006 waves of the SOEP.

  16. Note that Saarland and Hamburg are not presented in this figure as we do not include them in our analyses for having too few incoming movers.

  17. See Angrist and Pischke (2009) and Wooldridge (2015) for more information on the instrumental variables and two stage least squares (2SLS) estimation. Please refer to Angrist and Krueger (2001) for a brief exposition of the history and applications of the instrumental variable.

  18. It is noteworthy that a more detailed structural analysis would come with the challenge that for geographically smaller regional levels, the case numbers in the regions become too low to allow for statistically significant conclusions (Knies and Spiess 2007). See https://www.diw.de/en/diw_02.c.222519.en/regional_data.html for more information.

  19. This may be due to Germans being less willing to move within Germany. Note that our instrumental variable is constructed by adding up all non-natives’ (lagged) average WTR of eastern federal states for each region and dividing it by the number of incoming movers. However, there are many regions in the postal code and county code analyses that do not have incoming movers. Thus, only a small number of observations could be studied due to the instrument having missing values for a large amount of observations.

  20. Clustered standard errors account for possible correlations within a cluster and asymptotically equal unclustered standard errors. Since we cannot rule out that clustered standard errors are necessary, we include them to err on the side of caution.

  21. Note that particularly East Germany has low numbers of church members.

  22. Before the fall of the Berlin wall, GDR citizens were usually not allowed to migrate to West Germany. Even though every citizen had the right to apply for a permit to leave the GDR, applying for such permit usually had severe political repercussions from close observance from the Staatssicherheit (i.e., the national intelligence agency), job loss, and denial of higher education for the whole family to several years in jail. Despite this, around 250,000 GDR citizens migrated to West Germany between 1961 (erection of the Berlin wall) and 1989 (fall of the Berlin wall).

  23. Note that we use this expression synonymously to \( WTR\_community \) throughout this article.

  24. Risk preferences were not surveyed before 2004 and in 2005 and 2007. Data for 2004, however, is not part of the regression but used to derive lagged variables.

  25. Inflation-adjusted income is defined as the natural logarithm of monthly real after tax household income adjusted for inflation.

  26. The dummy variable urban is 1 if a person lives in an urban area, and 0 otherwise (definition according to the German Federal Office for Building and Regional Planning).

  27. Berlin, Hamburg, Munich, Cologne, and Frankfurt are the largest German cities in terms of population size in 2015.

  28. In cooperation with the state governments, German companies have extensive trainee programs where school graduates enroll in a two-year to three-year trainee program. Several weeks of instruction in a public specialized school are followed by several weeks of training on the job.

  29. Students who graduate with the Abitur are allowed to enroll at a university in Germany. An Abitur is comparable to the A-levels in the United Kingdom and the Baccalauréat in France.

  30. The main difference between the lowest and the medium school degree in Germany is related to the fact that most white-collar positions require a medium school degree, whereas certain blue-collar workers only need to have the lowest school degree.

  31. Natives are the focus of our analysis, while information on East Germans is used to construct (1) the average risk tolerance in a western federal state and (2) the instrument.

  32. Note that all regression tables including covariates are listed in the Appendix.

  33. See Coudert and Gex (2008).

  34. When we use clustered standard errors at the federal state level, we obtain similar coefficients for peer effects and the interaction term. Note that the interaction term becomes significant at the 5% level. However, we prefer the more cautious approach with higher standard errors and a higher number of clusters. See Cameron and Miller (2015) for details.

  35. Note that for both sub-analyses, we find the instrument to be highly significant in the first stage.

  36. We include attend church (see Brown et al. 2008) and visit neighbors and friends (see Hong et al. 2004) to construct the sociability index. Since church attendance is less frequent in Germany, our definition of sociability here also involves information on how regularly people visit friends and neighbors to account for their local environment. Both activities refer to a person’s social activities which are important when it comes to sociability.

  37. Note that the instrumental variable is found to be significant at the 1% level in the first-stage regression. This is also the case for the other robustness checks in this section.

  38. However, people with children do not necessarily have to be (more) sociable. It is therefore crucial to differentiate between parenthood and sociability in this approach.

  39. This is supported by the fact that the majority of children in Germany is born in families where parents are married (Statistisches Bundesamt (Destatis) 2017).

References

  • Ahern, K. R., Duchin, R., & Shumway, T. (2014). Peer effects in risk aversion and trust. The Review of Financial Studies, 27(11), 3213–3240.

    Google Scholar 

  • Angrist, J. D., & Krueger, A. B. (2001). Instrumental variables and the search for identification: From supply and demand to natural experiments. Journal of Economic Perspectives, 15(4), 69–85.

    Google Scholar 

  • Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton: Princeton University Press.

    Google Scholar 

  • Arrow, K. J. (1962). The economic implications of learning by doing. The Review of Economic Studies, 29(3), 155–173.

    Google Scholar 

  • Balsa, A. I., Gandelman, N., & González, N. (2015). Peer effects in risk aversion. Risk Analysis, 35(1), 27–43.

    Google Scholar 

  • Banerjee, A., Chandrasekhar, A. G., Duflo, E., & Jackson, M. O. (2013). The diffusion of microfinance. Science, 341, 1236498.

    Google Scholar 

  • Barsky, R. B., Juster, F. T., Kimball, M. S., & Shapiro, M. D. (1997). Preference parameters and behavioral heterogeneity: An experimental approach in the Health and Retirement Study. The Quarterly Journal of Economics, 112(2), 537–579.

    Google Scholar 

  • Booth, A., & Nolen, P. (2012a). Choosing to compete: How different are girls and boys? Journal of Economic Behavior & Organization, 81(2), 542–555.

    Google Scholar 

  • Booth, A. L., & Nolen, P. (2012b). Gender differences in risk behaviour: Does nurture matter? The Economic Journal, 122(558), F56–F78.

    Google Scholar 

  • Brown, J. R., Ivković, Z., Smith, P. A., & Weisbenner, S. (2008). Neighbors matter: Causal community effects and stock market participation. The Journal of Finance, 63(3), 1509–1531.

    Google Scholar 

  • Browne, M. J., Jaeger, V., Richter, A., & Steinorth, P. (2019). Family changes and the willingness to take risks. Working paper.

  • Bursztyn, L., Ederer, F., Ferman, B., & Yuchtman, N. (2014). Understanding mechanisms underlying peer effects: Evidence from a field experiment on financial decisions. Econometrica, 82(4), 1273–1301.

    Google Scholar 

  • Cai, J., De Janvry, A., & Sadoulet, E. (2015). Social networks and the decision to insure. American Economic Journal: Applied Economics, 7(2), 81–108.

    Google Scholar 

  • Cameron, A. C., & Miller, D. L. (2015). A practitioner’s guide to cluster-robust inference. Journal of Human Resources, 50(2), 317–372.

    Google Scholar 

  • Card, D., Mas, A., Moretti, E., & Saez, E. (2012). Inequality at work: The effect of peer salaries on job satisfaction. The American Economic Review, 102(6), 2981–3003.

    Google Scholar 

  • Cárdenas, J.-C., Dreber, A., von Essen, E., & Ranehill, E. (2012). Gender differences in competitiveness and risk taking: Comparing children in Colombia and Sweden. Journal of Economic Behavior & Organization, 83(1), 11–23.

    Google Scholar 

  • Charness, G., & Gneezy, U. (2012). Strong evidence for gender differences in risk taking. Journal of Economic Behavior & Organization, 83(1), 50–58.

    Google Scholar 

  • Cialdini, R. B., & Goldstein, N. J. (2004). Social influence: Compliance and conformity. Annual Review of Psychology, 55, 591–621.

    Google Scholar 

  • Cialdini, R. B., & Trost, M. R. (1998). Social influence: Social norms, conformity and compliance. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The Handbook of Social Psychology (pp. 151–192). Boston: McGraw-Hill.

    Google Scholar 

  • Cohn, R. A., Lewellen, W. G., Lease, R. C., & Schlarbaum, G. G. (1975). Individual investor risk aversion and investment portfolio composition. The Journal of Finance, 30(2), 605–620.

    Google Scholar 

  • Coudert, V., & Gex, M. (2008). Does risk aversion drive financial crises? Testing the predictive power of empirical indicators. Journal of Empirical Finance, 15(2), 167–184.

    Google Scholar 

  • Dohmen, T., Falk, A., Huffman, D., Sunde, U., Schupp, J., & Wagner, G. G. (2011). Individual risk attitudes: Measurement, determinants, and behavioral consequences. Journal of the European Economic Association, 9(3), 522–550.

    Google Scholar 

  • Duflo, E., & Saez, E. (2002). Participation and investment decisions in a retirement plan: The influence of colleagues’ choices. Journal of Public Economics, 85(1), 121–148.

    Google Scholar 

  • Falk, A., & Ichino, A. (2006). Clean evidence on peer effects. Journal of Labor Economics, 24(1), 39–57.

    Google Scholar 

  • Fehr, E., & Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. The Quarterly Journal of Economics, 114(3), 817–868.

    Google Scholar 

  • Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7(2), 117–140.

    Google Scholar 

  • Field, A. (2009). Discovering statistics using SPSS. London: Sage Publications.

    Google Scholar 

  • Friedman, M., & Savage, L. J. (1948). The utility analysis of choices involving risk. Journal of Political Economy, 56(4), 279–304.

    Google Scholar 

  • Georgarakos, D., Haliassos, M., & Pasini, G. (2014). Household debt and social interactions. The Review of Financial Studies, 27(5), 1404–1433.

    Google Scholar 

  • Goebel, J., Grabka, M. M., Liebig, S., Kroh, M., Richter, D., Schröder, C., et al. (2019). The German socio-economic panel (SOEP). Journal of Economics and Statistics, 239(2), 345–360.

    Google Scholar 

  • Görlitz, K., & Tamm, M. (2019). Parenthood, risk attitudes and risky behavior. Journal of Economic Psychology. https://doi.org/10.1016/j.joep.2019.102189.

    Article  Google Scholar 

  • Guiso, L., Sapienza, P., & Zingales, L. (2004). The role of social capital in financial development. The American Economic Review, 94(3), 526–556.

    Google Scholar 

  • Gutek, B. A., Searle, S., & Klepa, L. (1991). Rational versus gender role explanations for work-family conflict. Journal of Applied Psychology, 76(4), 560–568.

    Google Scholar 

  • Halek, M., & Eisenhauer, J. G. (2001). Demography of risk aversion. Journal of Risk and Insurance, 68(1), 1–24.

    Google Scholar 

  • Heimer, R. Z. (2014). Friends do let friends buy stocks actively. Journal of Economic Behavior & Organization, 107, 527–540.

    Google Scholar 

  • Hoffmann, A. O., Post, T., & Pennings, J. M. (2013). Individual investor perceptions and behavior during the financial crisis. Journal of Banking & Finance, 37(1), 60–74.

    Google Scholar 

  • Hong, H., Kubik, J. D., & Stein, J. C. (2004). Social interaction and stock-market participation. The Journal of Finance, 59(1), 137–163.

    Google Scholar 

  • Hoxby, C. M. (2000). Peer effects in the classroom: Learning from gender and race variation. NBER Working Paper No. 7867. https://www.nber.org/papers/w7867.pdf.

  • Hunt, J. (2006). Staunching emigration from East Germany: Age and the determinants of migration. Journal of the European Economic Association, 4(5), 1014–1037.

    Google Scholar 

  • Jaeger, D. A., Dohmen, T., Falk, A., Huffman, D., Sunde, U., & Bonin, H. (2010). Direct evidence on risk attitudes and migration. The Review of Economics and Statistics, 92(3), 684–689.

    Google Scholar 

  • Knies, G., & Spiess, C. K. (2007). Regional data in the German socio-economic panel study (SOEP). DIW Data Documentation 17.

  • Lahno, A. M., & Serra-Garcia, M. (2015). Peer effects in risk taking: Envy or conformity? Journal of Risk and Uncertainty, 50(1), 73–95.

    Google Scholar 

  • Lavy, V., & Schlosser, A. (2011). Mechanisms and impacts of gender peer effects at school. American Economic Journal: Applied Economics, 3(2), 1–33.

    Google Scholar 

  • Lin, F.-T. (2009). Does the risk aversion vary with different background risk of households. International Research Journal of Finance and Economics, 34(34), 69–82.

    Google Scholar 

  • Lönnqvist, J.-E., Verkasalo, M., Walkowitz, G., & Wichardt, P. C. (2015). Measuring individual risk attitudes in the lab: Task or ask? An empirical comparison. Journal of Economic Behavior & Organization, 119, 254–266.

    Google Scholar 

  • Malmendier, U., & Nagel, S. (2011). Depression babies: Do macroeconomic experiences affect risk taking? The Quarterly Journal of Economics, 126(1), 373–416.

    Google Scholar 

  • Manski, C. F. (2000). Economic analysis of social interactions. Journal of Economic Perspectives, 14(3), 115–136.

    Google Scholar 

  • Mata, R., Frey, R., Richter, D., Schupp, J., & Hertwig, R. (2018). Risk preference: A view from psychology. Journal of Economic Perspectives, 32(2), 155–172.

    Google Scholar 

  • Mossin, J. (1968). Aspects of rational insurance purchasing. Journal of Political Economy, 76(4), 553–568.

    Google Scholar 

  • Rabin, M. (1993). Incorporating fairness into game theory and economics. The American Economic Review, 83(5), 1281–1302.

    Google Scholar 

  • Riley, W. B., & Chow, K. V. (1992). Asset allocation and individual risk aversion. Financial Analysts Journal, 48(6), 32–37.

    Google Scholar 

  • Sacerdote, B. (2001). Peer effects with random assignment: Results for Dartmouth roommates. The Quarterly Journal of Economics, 116(2), 681–704.

    Google Scholar 

  • Scherer, C. W., & Cho, H. (2003). A social network contagion theory of risk perception. Risk Analysis, 23(2), 261–267.

    Google Scholar 

  • Shiller, R. J. (1984). Stock prices and social dynamics. Brookings Papers on Economic Activity, 2, 457–498.

    Google Scholar 

  • SOEP. (2016). Socio-Economic Panel (SOEP). Data for years 1984–2015, version 32.1. Data set. Retrieved from https://doi.org/10.5684/soep.v32.1.

  • Statistisches Bundesamt (Destatis). (2017). Kinderlosigkeit, Geburten und Familien. Ergebnisse des Mikrozensus 2016 [online]. https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Haushalte-Familien/Publikationen/Downloads-Haushalte/geburtentrends-tabellenband-5122203169014.pdf?__blob=publicationFile. Accessed 20 Sept 2019.

  • Trautmann, S. T. (2009). A tractable model of process fairness under risk. Journal of Economic Psychology, 30(5), 803–813.

    Google Scholar 

  • Trogdon, J. G., Nonnemaker, J., & Pais, J. (2008). Peer effects in adolescent overweight. Journal of Health Economics, 27(5), 1388–1399.

    Google Scholar 

  • Vieider, F. M., Lefebvre, M., Bouchouicha, R., Chmura, T., Hakimov, R., Krawczyk, M., & Martinsson, P. (2015). Common components of risk and uncertainty attitudes across contexts and domains: Evidence from 30 countries. Journal of the European Economic Association, 13(3), 421–452.

    Google Scholar 

  • Viscusi, W. K., Phillips, O. R., & Kroll, S. (2011). Risky investment decisions: How are individuals influenced by their groups? Journal of Risk and Uncertainty, 43, 81–106.

    Google Scholar 

  • Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Mason: Cengage Learning.

    Google Scholar 

  • Zimmerman, D. J. (2003). Peer effects in academic outcomes: Evidence from a natural experiment. The Review of Economics and Statistics, 85(1), 9–23.

    Google Scholar 

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Acknowledgements

We are grateful for helpful comments from participants at the 2017 annual meeting of the American Risk and Insurance Association, the 2017 annual meeting of the European Group of Risk and Insurance Economists, the 2018 European Conference on Operational Research, the 2018 CEAR/MRIC Behavioral Insurance Workshop, and the Chubb Research Seminar at St. John's University. We are indebted to Thomas Berry-Stölzle, Irina Gemma, Verena Jäger, Richard Peter, David Pooser, Casey Rothschild, and two anonymous reviewers for valuable comments. Sophie-Madeleine Roth thanks the W. R. Berkley Corporation for supporting her work on this paper within the Visiting Scholars Program at the Maurice R. Greenberg School of Risk Management, Insurance and Actuarial Science at St. John’s University.

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  1. Maurice R. Greenberg School of Risk Management, Insurance and Actuarial Science, Tobin College of Business, St. John’s University, New York City, USA

    Mark J. Browne & Annette Hofmann

  2. Institute for Risk Management and Insurance, Munich Risk and Insurance Center, Munich School of Management, Ludwig-Maximilians-Universität München, Munich, Germany

    Andreas Richter & Sophie-Madeleine Roth

  3. Institute for Risk Management and Insurance, Hamburg Business School, Universität Hamburg, Hamburg, Germany

    Petra Steinorth

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Appendix

Appendix

1.1 Results for covariates

See Tables 11, 12, and 13.

Table 11 OLS regression results including covariates
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Table 12 First-stage regression results (2SLS estimation) including covariates
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Table 13 Second-stage regression results for a full sample (1–4) and a subsample of males (4.1) and females (4.2) (2SLS estimation) including covariates
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Browne, M.J., Hofmann, A., Richter, A. et al. Peer effects in risk preferences: Evidence from Germany. Ann Oper Res 299, 1129–1163 (2021). https://doi.org/10.1007/s10479-019-03476-9

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