Growth, entrepreneurship, and risk-tolerance: a risk-income paradox

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Abstract

Recent papers have modeled the prevalence of risk-tolerance as shaped by growth, making testable predictions about the distribution of risk-tolerance across the globe. We test these predictions using a dataset containing a survey question capturing people’s risk-tolerance for representative samples from 78 countries. We find a negative between-country correlation between risk-tolerance and GDP per capita. Together with the positive within-country correlation between risk-tolerance and income, this results in a risk-income paradox. We further find a negative interaction effect of risk-tolerance and GDP on fertility. These findings provide support for endogenous-preference models of economic growth.

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Notes

  1. 1.

    Galor and Michalopoulos (2012) do not take a stand on the transmission mechanism, acknowledging specifically that the theory is compatible with either genetic or cultural transmission (see their footnote 4). Transmission of preferences must, however, be prevalently vertical from parents to children, given the importance of fertility in shifting the composition of the population in terms of risk-tolerant types. Doepke and Zilibotti (2014), on the other hand, focus specifically on the transmission mechanism, identified in conscious socialization decisions by parents, but do not explicitly model the fertility channel.

  2. 2.

    Most previous comparative datasets rely on student samples, thus potentially being affected by selection effects and not permitting to draw inferences on population-wide patterns (see Rieger et al. 2014; L’Haridon and Vieider 2019). An exception to this rule are the representative data described by Falk et al. (2018). We will compare our results to those based on the latter dataset below.

  3. 3.

    This possibility is explicitly acknowledged by the authors, with the consequence that “allowing for entrepreneurial activity to generate higher expected income, in an era in which the latter is converted into larger number of surviving offspring, would accentuate the evolutionary advantage of the growth-promoting type.” (Galor and Michalopoulos 2012, footnote 16).

  4. 4.

    The fact that we may not see convergence happening in reality may be due to convergence being conditional on a number of other factors, such as education and institutions; see e.g. Barro (1991) and Sala-i Martin (1996).

  5. 5.

    Klasing (2014) proposed a model making similar predictions, that is however more difficult to test empirically. We will thus focus here on the model by Doepke and Zilibotti.

  6. 6.

    Notice that this prediction is derived in equilibrium, where entrepreneurship fuels growth, and growth in turn creates incentives for parents to socialize children to be entrepreneurial. That also means that the prediction may not be expected to hold in some countries if the link between entrepreneurship and growth is broken while entrepreneurship itself is driven by other factors.

  7. 7.

    We are grateful to Matthias Doepke for pointing this out in private correspondence.

  8. 8.

    The choices were represented physically using colored balls and banknotes, and were shown to subjects one by one in individual interviews. At the end of the experiment, one of the decisions was randomly extracted to be played for real money—the standard procedure in this kind of task.

  9. 9.

    The RRP is defined as the certainty equivalent minus the expected value, normalised by the absolute expected value.

  10. 10.

    We use Pearson correlations throughout, since we are interested in the strength of the effects beyond mere ranks, and since the use of aggregate quantities lowers the incidence of outliers. Results using Spearman rank-correlations instead are qualitatively similar unless stated otherwise.

  11. 11.

    We included a brief literature review of the correlation of risk-tolerance and entrepreneurship above. In terms of the correlation between risk-tolerance and income, many studies have found a positive relationship between risk-tolerance and income (Dohmen et al. 2011; Gloede et al. 2015; Hopland et al. 2016; Vieider et al. 2018; Falk et al. 2018), several others have found no correlation (Binswanger 1980; Cardenas and Carpenter 2013; Noussair et al. 2014), while others still have observed mixed results (Booij et al. 2010; Tanaka et al. 2010; von Gaudecker et al. 2011). See Hopland et al. (2016) for a more in-depth review.

  12. 12.

    The question reads as follows: “On this card is a scale of incomes on which 1 indicates the ‘lowest income decile’ and 10 the ‘highest income decile’ in your country. We would like to know in what group your household is. Please, specify the appropriate number, counting all wages, salaries, pensions and other incomes that come in.”

  13. 13.

    While the models we test predict causality to run from risk-tolerance to income passing through the entrepreneurship decision, in reality the causality in the risk-income relationship could run in either direction, and may well constitute a self-reinforcing feedback cycle. Indeed, the clear causal direction emerges from the model due to the abstraction from intergenerational transmission of wealth and skills, in addition to preferences. While most of the literature in labor economics emphasizes the causality from risk-tolerance to income passing through job choice (e g. Bonin et al. 2007), the literature in development economics generally emphasizes the opposite direction of causality (see Haushofer and Fehr 2014, for a review). Making risk-tolerance the dependent variable allows us to examine the correlates of risk-tolerance, which is the truly novel measure at the center of our analysis. The correlation between income and risk-tolerance is unaffected if we take income as the dependent variable instead—a regression with income as the dependent variable is shown in the Online Appendix.

  14. 14.

    Our data indeed show a substantial correlation between income decile and education. In particular, each higher level of education results in a significantly higher response on the income decile scale—see online appendix for the regression result.

  15. 15.

    In particular, optimistic people may overestimate their position in the income distribution, while at the same time indicating overly high levels of risk-tolerance. If so, one might observe a correlation between income and risk-tolerance that is spurious. To the extent that the life satisfaction question captures such optimism, it will serve to purify the correlation between income and risk-tolerance we observe in the regression.

  16. 16.

    Notice how we take the contemporary EWP and the growth of the previous 20 years to test for the relationships predicted in the model. While risk-tolerance levels are determined by levels of these variables at the time parents educated their children, and thus generally earlier than we observe them, this strategy is legitimate in terms of the theory since the predictions are derived in equilibrium.

  17. 17.

    This reflects the absence of growth data for some countries and territories in the World Bank tables (Palestine, Taiwan, and Serbia and Montenegro), as well as the absence of wage premium data for Argentina.

  18. 18.

    This is not surprising. Taking an index of deaths per year out of 1000 people published by the World Bank, we find that the latter has a correlation of 0.89 with GDP per capita.

  19. 19.

    The number of countries is reduced to 76 inasmuch as the question about number of children has no entries for Hong Kong and the USA.

  20. 20.

    Technically, the pure effect of GDP p.c. captures the effect of GDP on the number of children for the mean level of risk-tolerance, since the latter is entered into the interaction as a z-score. That is, for an average level of risk-tolerance, GDP per capita per se has no predictive value for between-country differenes in fertility once cross-country variations in risk-tolerance are taken into account.

  21. 21.

    We assembled the income per capita data at the regional level from the gross-cell-product (GCP) data assembled and discussed by Nordhaus (2006). The graph excludes a few extreme outliers on the GCP scale, generally found in petrolium-producing regions. The online appendix provides details on the assembly of the GCP data, and a stability analysis.

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Correspondence to Ferdinand M. Vieider.

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The experimental validation contained in this paper was financed by the German Science Foundation (DFG) as part of project VI 692/1-1 on “Risk preferences and economic behavior: Experimental evidence from the field”. We are grateful to Matthias Doepke, Oded Galor, Thomas Dohmen, Thomas Epper, and to five anonymous referees for constructive and helpful comments. All errors remain our own.

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Bouchouicha, R., Vieider, F.M. Growth, entrepreneurship, and risk-tolerance: a risk-income paradox. J Econ Growth 24, 257–282 (2019). https://doi.org/10.1007/s10887-019-09168-0

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Keywords

  • Risk-tolerance
  • Development
  • Growth
  • Risk-income paradox

JEL Classification

  • D01
  • D03
  • D81
  • E03
  • O10