Risk aversion and the character of the individual’s place of residence

ABSTRACT

Most Israelis live in big and small cities and two neighborhood types that are unique to Israel – the moshav and the kibbutz – thus offering a natural experimental field for exploring the relationship between place of residence and general and financial risk aversion. Based on a sample of 528 questionnaires, this study found that independent of one’s place of residence respondents proved more risk averse in their general decision-making than with regard to financial issues. The kibbutz respondents demonstrated the highest average level of risk aversion, followed by small-city dwellers, big-city residents, and moshav members. The kibbutz level of risk aversion is inconsistent with the Cushion Hypothesis, while the lowest level of risk aversion demonstrated by moshav residents suggests that facing an independent lifestyle and significant dilemmas on a daily basis reduces not only the individual’s level of risk aversion in general, but also the level of intimidation associated with financial issues.

KEYWORDS:

• Risk aversion
• place of residence
• collective and individualistic cultures
• Cushion Hypothesis
• Israel

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. Schooley and Worden, “Risk Aversion Measures,” 5.‏

2. McInish, “Individual Investors and Risk-Taking,” 2; Morin and Suarez “Risk Aversion Revisited,” 38; and Palsson, “Does the Degree of Relative Risk Aversion Vary with Household Characteristics?” 17.

3. Grable and Joo, “Determinants of Risk Preference,” 9; Wang and Hanna, “Does Risk Tolerance Decrease with Age,” 8; and Grable, “Financial Risk Tolerance and Additional Factors,” 14.

4. Lewellen et al., “Some Direct Evidence on the Dividend Clientele Phenomenon,” 33; Bajtelsmit and Bernasek, “Why Do Women Invest Differently than Men?” 7; Powell and Ansic, “Gender Differences in Risk Behaviour in Financial Decision-Making,” 18; Grable, “Financial Risk Tolerance and Additional Factors,” 14; Grable and Joo, “A Cross-Disciplinary Examination of Financial Risk Tolerance,” 46; Halek and Eisenhauer, “Demography of risk aversion,” 68; and Weber, Siebenmorgen and Weber, “Communicating Asset Risk,” 25.

5. Hartog et al., “Linking Measured Risk Aversion to Individual Characteristics,” 55.

6. Friedman, “Risk Aversion and the Consumer Choice of Health Insurance Option,” 56; Cohn et al., “Individual Investor Risk Aversion and Investment Portfolio Composition,” 30; Riley and Chow, “Asset Allocation and Individual Risk Aversion,” 48; Schooley and Worden, “Generation X,” 5; Shaw, “An Empirical Analysis of Risk Aversion and Income Growth,” 14; and Wang and Hanna, “The Risk Tolerance and Stock Ownership of Business Owning Households,” 18.

7. Haliassos and Bertaut, “Why Do so Few Hold Stocks?” 105; Sung and Hanna, “Factors Related to Risk Tolerance,” 7; and Grable and Lytton, “Investor Risk Tolerance,” 9.

8. Hsee and Weber, “Cross-National Differences in Risk Preference and Lay Predictions,” 12; and Fan and Xiao, “Cross-Cultural Differences in Risk Tolerance,” 5.

9. Grable, Joo and Park, “Cross Cultural Risk-Tolerance Self-Evaluation Bias,” 69.

10. Marshall et al., “Extending Prospect Theory Cross-Culturally by Examining Switching Behavior,” 64; Cheung, Wu and Tao, “A Comparison Between Hong Kong and Mainland Chinese Undergraduate Students,” 22; and Eriksson and Simpson, “Emotional Reactions to Losing Explain Gender Differences,” 5.

11. Zhu et al., “Does Online Community Participation Foster Risky Financial Behavior?” 49.

12. Rosenboim, Shavit, and Shoham, “Financial Decision Making in Collective Society,” 39.

13. The Cronbach’s alphas for the decision making chapter vary between 0.605 and 0.701, and for the financial preferences chapter they vary between 0.794 and 0.865.

14. Gilliam, Chatterjee and Grable, “Measuring the Perception of Financial Risk Tolerance,” 21.

15. See note 12 above.

16. Ibid.

17. Grable and Joo, “Determinants of Risk Preference,” 9; and Marshall et al., “Extending Prospect Theory Cross-Culturally by Examining Switching Behavior,” 64.

18. Cohen’s d is defined as the difference between two means (H0 and H1), in units of (and thus divided by) the standard deviation. A higher d indicates a higher difference between the two means. For d>0.8, the difference between the means is considered significant.

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Notes on contributors

Tchai Tavor

Tchai Tavor is member of the Department of Economics and Management, The Max Stern Yezreel Valley Academic College, Yezreel Valley, Israel.