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Is stock investment contagious among siblings?

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Abstract

A household may learn the importance of stock investment from its siblings. Using the logit and probit models, this paper tests empirically the learning hypothesis that a household is more likely to purchase stocks if its siblings have bought stocks, and finds evidence for the hypothesis. The estimates obtained in the analysis imply that a household’s probability of stock purchases increases by about 1–3 % points if a sibling has purchased stocks. The positive results are not due to alternative explanations, such as uncontrolled correlation of age effects, financial support from siblings, common shocks, or other unobservable correlations. Moreover, the analysis does not support the objection that the positive results may be due to the effects of genes or common parenting.

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Notes

  1. Evans and Honkapohja (2009) provide a comprehensive review of studies on the learning process in monetary policy.

  2. For example, using data on Swedish twins (from the Swedish Twin Registry) Calvet and Sodini (2014) reported that the adjusted R-squared in estimating the risky share of financial assets is higher for twins communicating more frequently, which suggests that communication between twins may be driving financial decisions. Using the same twin data, Cronqvist et al. (2015b) and Barnea et al. (2010) examined the financial behavior of twins, and the estimated residuals suggested the importance of communication between twins. Other studies that have examined the financial behavior of twins using these data include Cronqvist and Siegel (2015), Cesarini et al. (2010), and Cesarini et al. (2009).

  3. See, for example, Calvet and Sodini (2014) and Campbell (2006) for a comprehensive review of empirical and theoretical studies on standard portfolio theory.

  4. Wachter and Yogo (2010) provided a theoretical framework for this observation.

  5. For a comprehensive general literature review of social learning (not just in stock investment), see, e.g., Dahl et al. (2014) and Kaustia and Knupfer (2012).

  6. Related, Bursztyn et al. (2014) studied the impact of social learning in purchases of a financial product designed for a special experiment.

  7. In a related vein, Hong et al. (2005) reported that fund managers tend to purchase stocks that other fund managers in the same city have been buying.

  8. Related, Heimer (2014) reported that social interaction is associated with active portfolio management.

  9. Empirical studies on outcome-based learning in general are also few in number (exceptions include Conley and Udry 2010 and Munshi 2004), although there are numerous theoretical studies (including Cao et al. 2011 and Banerjee and Fudenberg 2004).

  10. In addition to the data set used for this paper (1999–2013), I could construct an alternative data set containing three survey waves, 1984–1989, 1989–1994, and 1994–1999, because the PSID reported wealth data every 5 years between 1984 and 1999. I use the data set containing data for the period 1999–2013 because it is much larger, comprising seven survey waves with over 10,000 observations. I cannot combine the alternative data set (1984–1999) with the current data set (1999–2013) because of the difference in the periods (5 years in the alternative data set against 2 years in the current data set).

  11. The table reports the 2-year lag because that is the variable used in the empirical analysis.

  12. In the PSID, stocks are defined as non-IRA (individual retirement accounts) stocks, including any shares of stock in publicly held corporations, mutual funds, and investment trusts, but not including any IRAs.

  13. This is the case unless both head and spouse were recorded in the 1968 survey.

  14. For additional details about the data structure, see Tokuoka (2013).

  15. Furthermore, to control for preferences, the analysis below adds dummies indicating the household head’s health conditions, whether the household head is now smoking or has ever smoked, and the number of cigarettes that the household head smokes per day.

  16. For example, Li (2014) and Hong et al. (2004) have run the logit or probit models to analyze social learning in stock investment.

  17. This is the approach taken by Hong et al. (2004) and other studies took a similar approach (e.g., Brown et al. 2008 created dummies for every percentile of the income distribution).

  18. The five regions are: northeast, north central, south, west, and others (Alaska and Hawaii). The eight occupation groups are: professional and technical workers; managers (not self-employed); managers (self-employed); clerical and sales workers; craftspersons; operatives and laborers; farmers and farm laborers; and service workers. The 12 industry dummies are: agriculture, forestry, and fishing; mining; construction; manufacturing; transportation, communications, and utilities; wholesale and retail trade; finance, insurance, and real estate; business and repair services; personal services; entertainment and recreation services; professional and related services; and public administration. The occupation and industry groupings are from Carroll and Samwick (1997).

  19. Shive (2010) also investigated the learning impact on stock purchases.

  20. This in fact may be an underestimate of the cumulative impact of siblings’ stock purchases because it measures only the lagged impact. The coefficient on \(DPurSib_{t}\) (third column in Table 2) infers that if a sibling purchases stocks, the probability of household i purchasing stocks increases by 0.1 % points.

  21. Of course, a sibling is more likely to give financial support to the household if it suffers severe financial difficulties (e.g., head’s unemployment). Such a household may not be in a financial position to purchase stocks, even after receiving financial support. That said, below I run regressions to test and deny formally the impact of financial support on stock purchases (one of the possible alternative explanations).

  22. The PSID reports only the total amount of financial support, which includes not only support to a sibling but also other kinds of support (e.g., support for elderly parents).

  23. The results are similar when including the dummy, which takes a value of one when \(DPurSib_{t-2}=1\) and returns from stocks have been in excess of 10 % (not reported here). Using an alternative threshold (e.g., 25 %) gives similarly weak results.

  24. We should not interpret this immediately as providing evidence for learning from parents because as noted in Introduction, the existence of various financial transfers from parents (e.g., inheritances), some of which are perhaps not reported in the survey, requires careful interpretation.

  25. The results are similar when specifying the dummy for parents’ stock purchases, instead of that for parents’ stock market participation.

  26. Barsky et al. (1997) report that (measured) risk tolerance is related to risky behaviors such as smoking.

  27. Cronqvist et al. (2015a) report that the extent of risk taking differs substantially between males and females.

  28. Ameriks and Zeldes (2004) report that the share of stocks in household portfolios declines after the age of 55 years when controlling for other factors.

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Acknowledgments

I am grateful to Christopher Carroll, Raphael Lam, Yasuhisa Ojima, Rui Ota, and those who participated in the seminars at Johns Hopkins University and Tokyo University on learning in financial decisions for insightful comments. I also thank anonymous referees for helpful comments. This paper is based on the research I started while working at the International Monetary Fund, but the views presented in this paper are those of the author, and should not be attributed to the International Monetary Fund or the Japanese Ministry of Finance.

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  1. Ministry of Finance, 3-1-1 Kasumigaseki, Chiyoda-ku, Tokyo, 100-8940, Japan

    Kiichi Tokuoka

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Correspondence to Kiichi Tokuoka.

Appendix: Detailed results of the baseline regression

Appendix: Detailed results of the baseline regression

Table 10 reports the results of Table 2 with a more comprehensive list of control variables. Aside from the key dummies (\(DEntrySib_{t}\), \(DEntrySib_{t-2}\), \(DPurSib_{t}\), and \(DPurSib_{t-2}\)), the lagged stock share in net worth and the dummy variable for white have a significant positive impact on stock purchases (third to fifth columns of Table 10). Education (dummy for head’s college degree) also has a positive impact on stock purchases, although the statistical significance is lower than the stock share and the dummy variable for white.

Table 10 Detailed results of the baseline regression (in Table 2)
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Tokuoka, K. Is stock investment contagious among siblings?. Empir Econ 52, 1505–1528 (2017). https://doi.org/10.1007/s00181-016-1120-6

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