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Determinants of Corporate Voting – Evidence from a Large Survey of German Retail Investors

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Schmalenbach Business Review Aims and scope

“Do not judge us, for our apathy is rational. It is simply too costly for retail investors like us to make intelligent voting decisions” (Gulinello 2010 , p. 547).

Abstract

Although corporate voting is a vital instrument of corporate governance, research on the determinants of voting turnout at an individual level is still scarce. This paper sheds first light on corporate voting from the individual investors’ perspective by analyzing a large and unique data set collected from a survey among almost 425,000 German retail investors of a German blue-chip company. The results of this case study provide unprecedented insights into potential factors influencing private shareholders’ decision to cast their votes in corporate elections. Drawing on the traditional rational choice model and the resource model of political voting, my findings indicate that particularly investors with better resources, i. e., particularly well-educated or rather sophisticated and more experienced retail investors, are more likely to use their corporate voting right. Overall, the results may offer new implications for explaining the voting turnout in German publicly listed companies.

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Notes

  1. Over the last three years (2013–2015) these attendance rates amounted to an average of 53%. See http://www.sdk.org/assets/Statistiken/HV-Praesenzen/praesenz-dax15.pdf.

  2. For a review of aggregate-level research, see Geys (2006). For an overview of individual-level studies, see Smets and van Ham (2013).

  3. On a macro-level, several studies have discussed aspects that might influence the costs and benefits of using corporate voting rights and, consequently, voting turnout at annual general meetings. For example, Hewitt (2011), p. 16, finds that voting turnout can be expected to be higher in countries with lower barriers to exercise voting rights. Also, the fraction of foreign investors of a company can be negatively correlated with voting turnout, if certain barriers of cross-border voting increase procedural costs of voting (e. g., Schouten 2009, p. 5).

  4. Even though the resource model of Brady et al. (1995) focuses on the resources “time, money and skills” it is reasonable not to limit the relevant resources to these three factors. Hence, I follow the resource perspective of Smets and van Ham (2013) who provide a slightly broader interpretation of the resource model.

  5. Regarding cognitive characteristics (e. g., political interest and political knowledge), the resource model overlaps with the behavioral/psychological model of voter turnout, which in addition includes ability as well as personal preferences and personality traits (Denny and Doyle 2012; Smets and van Ham 2013, p. 354). Provided that, as suggested by the psychological model, cognitive characteristics serve as personal resources that reduce the costs of voting, and personal preferences are positively connected to the intrinsic benefits of voting, one cannot draw a clear-cut line between the psychological model and the rational choice in combination with the resource model. Thus, “(…) the psychological model represents a wide variety of approaches to explaining voter turnout” (Smets and van Ham 2013, p. 354).

  6. In this respect, Gamble et al. (2015), p. 2604, find strong empirical evidence, that, for example, particularly older retail investors “who experience a decrease in cognition are more likely to stop managing their own finances and pass on this responsibility to their spouses, and they are more likely to get financial help from outside their households.”.

  7. In this respect, Verba et al. (1995), p. 359, also conclude that “the informed citizen will find it easier to vote”.

  8. See Smets and van Ham (2013), p. 354, who also subsume, among other things, political interest and political knowledge under cognitive characteristics.

  9. Even if this argumentation is based on the psychological model of voter turnout the corresponding assumption that cognitive characteristics serve as resources is obviously compatible with the resource model.

  10. Even though empirical results on political voting turnout suggest that young adults notoriously abstain from voting and that voting turnout increases with age (Smets and van Ham 2013, p. 349), I support the cognitive aging argumentation. In this context, one has to bear in mind that in many studies on political voting turnout it is not controlled for the potential influence of experience on turnout. As I explicitly distinguish between the influence of age and experience, I follow the approach of Korniotis and Kumar (2011), who “use age to capture the adverse effects of cognitive aging and use experience (…) to capture positive effects of experience” (Korniotis and Kumar 2011, p. 244).

  11. I intentionally avoided to survey retail investors who are members of any shareholder association as I expect these investors to be significantly more likely to engage in shareholder activism.

  12. For my translated version of the original German-language questionnaire see Figure A in the Appendix of this paper.

  13. For a discussion of the “social desirability bias”, see Nederhof (1985), p. 264.

  14. Table 2 provides information on observations lost because of implausible and incorrect values. In detail, 5394 observations are lost because, on the one hand, respondents did not answer the relevant questions validly. For example, with regard to their dividend preference 222 respondents did not enter a permitted value in the relevant field of the questionnaire. Likewise, 1845 observations are lost because survey participants did not answer question Q18 concerning their risk attitude correctly as they did not enter one of the three valid numbers (1, 2 or 3) in the provided field of the questionnaire. On the other hand, it is considered implausible if respondents answered to be younger than 15 years and older than 100 years (45 observations lost). In analogy, 917 observations are lost because respondents stated to be directly invested in 0, more than 1000 or a negative number of companies. Furthermore, 871 observations are deleted as the specified investment experience (in years) exceeded the retail investor’s age.

  15. I cannot rule out that this specific variable is also a proxy for the investor’s basic interest in economic issues. However, this would be still in line with the resource model of voting since a psychological reasoning suggests that cognitive characteristics including, e. g., economic interest serve retail investors as resources, which lower the costs of voting (Smets and van Ham 2013, p. 354).

  16. See European Central Bank (2013), p. 16 table B3.

  17. However, it is also possible that this level of fear stated by the investor might capture fear in general rather than her fear of being exploited.

  18. In contrast to the lottery choices introduced by Holt and Laury (2002), for reasons of simplicity respondents had to choose between a sure payoff and a higher (uncertain) expected payoff (see question Q18 of the questionnaire in the Appendix). While I cannot distinguish between risk-neutral and risk-seeking investors, a respondent’s level of risk-aversion is revealed by stating a preference for the sure payoff or by being indifferent between the two choices.

  19. Differences between the number of observations of some variables result from missing values due to item non-response or “cannot say”-answers.

  20. See http://www.sdk.org/assets/Statistiken/HV-Praesenzen/praesenz-dax13.pdf.

  21. One could also perform an independent two-sample t‑test, which yields nearly the same results as the test on the equality of two proportions (prtest command in stata). That is because if N is sufficiently large the t probability distribution and the binomial distribution are approximated to the normal distribution.

  22. See the dummy variable descriptions in Table 5.

  23. As SOPHISTICATED and FINANCAL_EDU are highly correlated with each other (correlation coefficient of about 0.4, see Table A in the Appendix), I have to consider the possible impact of multicollinearity. For this purpose, in a first step, I excluded FINANCIAL_EDU from models 2 and 4, while in a second step, I dropped SOPHISTICATED. I compared the corresponding coefficient estimates with those of models 2 and 4, including both variables simultaneously. However, while the coefficient estimates of both variables only change slightly when I consider their joint effect simultaneously, I neither observe a change in the signs nor the level of statistical significance of both coefficients.

  24. For the issue that the high correlation between SOPHISTICATED and FINANCIAL_EDU might cause a multicollinearity problem see footnote 23.

  25. To determine the weights, the raking mechanism procedure survwgt implemented in STATA was applied. For a detailed description of the raking technique (also known as iterative proportional fitting or multiplicative weighting) see Bethlehem (2009), pp. 260 ff.

  26. See in the context of political voting Smets and van Ham (2013), p. 354.

  27. See Rowley (2002), p. 16.

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Authors and Affiliations

  1. Chair of International Accounting, Ruhr-University Bochum, Universitätsstraße 150, 44780, Bochum, Germany

    André Schmidt

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Correspondence to André Schmidt.

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First, I would like to thank Tom Jungius for his great support. Furthermore, I would like to thank two anonymous reviewers and the editors, as well as my paper discussant at the 39th EAA Annual Congress, Luminita Enache, and the other congress participants for their valuable comments on earlier versions of this paper.

Appendix

Appendix

Table A.1 Correlation Matrix
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Fig. A.1
figure 1

Survey Questionnaire (Translation of the Original German-language Questionnaire)

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Fig. A.1
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Survey Questionnaire (Translation of the Original German-language Questionnaire)

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Schmidt, A. Determinants of Corporate Voting – Evidence from a Large Survey of German Retail Investors. Schmalenbach Bus Rev 18, 71–103 (2017). https://doi.org/10.1007/s41464-016-0024-5

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