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School environment and risk preferences: Experimental evidence

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Abstract

Using a field experiment with high school students, we evaluate the development of risk preferences. Examining the impact of school characteristics on preference development reveals both peer and quality effects. For the peer effect, individuals in schools with a higher percentage of students on free or reduced lunches (hence a higher proportion of low-income peers with whom to interact) are significantly more risk averse. For the quality effect, individuals in schools with smaller class sizes and a higher percentage of educators with advanced degrees have higher, more moderate levels of risk aversion. We further discuss economic, cognitive and emotional development theories of risk preferences. Data show demographic-related patterns: girls are more risk averse on average, while taller and nonwhite individuals are more risk tolerant.

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Notes

  1. Further, children’s preferences often conform to standard theoretical predictions. For example, children’s decisions have been shown to conform to GARP as competently as adults by sixth grade (Harbaugh et al. 2001a), and to vary in expected or reasonable manners with changes in choice parameters (Eckel et al. 2010; Harbaugh et al. 2001b). Other studies examining the preferences of children have focused on the role of socio-economic or demographic differences between children (e.g., for time preference, see Castillo et al. 2008; for competition see Houser and Schunk 2009; Bartling et al. 2012).

  2. Considerable prior research examines the relationship between ethnicity and risk-taking for teens. Whites are more likely to engage in some risky behaviors, like smoking, while nonwhites are more likely to engage in others, like sexual intercourse or violence (Blum et al. 2000; Gruber 2001). There does not appear to be a stable pattern of differences in risk-taking across ethnic groups, but rather such differences vary by domain.

  3. Note that evidence suggests that adolescents and adults are able to similarly evaluate risks (Steinberg 2007 and references therein).

  4. Additionally, Problem Behavior Theory (PBT) integrates ideas from each, but is mainly a social development theory focusing on the influence of family structure and home environment (e.g., Jessor 1991). Our study collects data at the individual level, not at the family level, so we are unable to evaluate PBT in this setting.

  5. We are unable to examine psychobiological theories due to data limitations.

  6. The Adult Literacy and Life Skills Survey (ALL) is a large-scale co-operative effort undertaken by governments, national statistics agencies, research institutions and multi-lateral agencies designed to enable an international comparison of skills. The development and management of the study were coordinated by Statistics Canada and the Educational Testing Service (ETS) in collaboration with the other US and international statistical agencies. The ALL study builds on the International Adult Literacy Survey (IALS). The foundation skills measured in the ALL survey include prose literacy, document literacy, numeracy, and problem solving. In this study we used the numeracy measure, which consists of 40 problems developed to assess the ability to use mathematics in everyday life. The test was provided and scored by Statistics Canada. For more detail see Statistics Canada and OECD (2005).

  7. Several recent studies show a marked correlation between measures of cognitive ability (mathematical literacy or IQ) and risk and time preferences. Dohmen et al. (2010) address this relationship in a representative sample of one thousand German adults, and find that lower cognitive ability is associated with greater risk aversion. Burks et al. (2009) find a similar result for a sample of workers in a trucking firm.

  8. Similar procedures were followed in St. Cloud. The implementation differed slightly in that sessions were run over the period of only one week and gift cards were for local businesses.

  9. In some sessions the order of the dictator and ultimatum tasks was reversed.

  10. This risk-preference measure has been used in a number of studies with average CRRA estimates that are between 0.4–0.7. Estimates appear high here, but keep in mind that is assuming the CRRA functional form is correct. Our approach in this study is to classify subjects as relatively more or less risk averse.

  11. University students were recruited from large principles of economics classes at Virginia Tech. Six sessions were conducted in April and May of 2005 at the Lab for the Study of Human Thought and Action. The protocol was identical to that used in the high schools, including the stakes. The number of subjects was 91.

  12. An alternative approach would be to assume a specific functional form for the utility function of participants (such as CRRA) and estimate the relationship conditional on this assumption. Since we do not have data sufficient to test the CRRA assumption, we have presented the results in a less-structured, more general form.

  13. We also test for the impact of the school’s ethnic heterogeneity and being a member of the ethnic majority of the school, neither of which were found to be significant in any specification.

  14. A school’s student-teacher ratio might be related to the household income and wealth of families who are sending their children to the school. A similar argument can be made for the percentage of instructors holding advanced degrees, as the higher level of resources available to schools in wealthier areas would allow them to recruit more educated teachers. However, since many of these decisions are made at the district level, and we only include schools from two districts, we do not believe these variables accurately represent economic disadvantage in our sample. Class size is controlled by the district, since it sets catchment areas for each school and school classroom capacity. Teacher qualifications are likely driven by district-wide rewards for higher degrees and, within a district, a teacher’s ability to move between schools is limited by district policies.

  15. Note that we use the term peer effect to indicate the social and economic environment of the students’ peers at the school, rather than as the influences from students’ friends.

  16. For a subset of the population we also have census data for their zip code. The impact of low-income peers is not affected by including census data as a control for that subset of the sample.

  17. With one exception: the percent of families (but not individuals) in poverty is positive and statistically significant (but economically small, coef. = 0.0002). Including this variable does not alter any of the other results.

  18. Our results do not speak to students’ attitudes regarding “lifestyle” risks. Evidence suggests that greater lifestyle risk taking is associated with poverty (see, for example, Blum et al. 2000 and Males 2009).

  19. The results are not sensitive to specification as an ordered logit or to OLS, using gamble number (which would impose an underlying linear structure) or CRRA (from Table 1 above).

  20. This elicitation is similar to “multiple price list” elicitation methods such as that developed by Coller and Williams (1999). Alternatively, these choices can be used to estimate an individual discount rate. The results are not sensitive to alternative specifications of this variable. We use this measure for simplicity.

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Acknowledgments

We would like to thank Herbert Gintis, the Houston Independent School District, and ISD 742, without whom this project would not have been possible. Additionally, we thank the journal’s editor, an anonymous referee, Eugenia Toma, as well as participants in the ESA International Meetings and the Conference on Measuring Preferences in a Social Context at the Center for Behavioral and Experimental Economic Science (CBEES) at the University of Texas at Dallas for their valuable comments. Funding for this project was provided by the John D. and Catherine T. MacArthur Foundation, Network on the Nature and Origin of Preferences and Norms. Any errors remain our own.

Author information

Authors and Affiliations

  1. Texas A&M University, TAMU 4228, College Station, TX, 77845, USA

    Catherine C. Eckel

  2. Monash University, Clayton, Victoria, 3800, Australia

    Philip J. Grossman

  3. Academic Program Division, BASIS Educational Group, Tucson, AZ, USA

    Cathleen A. Johnson

  4. University of Massachusetts Amherst, 203 Stockbridge Hall, 80 Campus Center Way, Amherst, MA, 01003, USA

    Angela C. M. de Oliveira & Christian Rojas

  5. Rice University, MS 24, Houston, TX, 77251-1892, USA

    Rick K. Wilson

Authors
  1. Catherine C. Eckel
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  2. Philip J. Grossman
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  3. Cathleen A. Johnson
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  4. Angela C. M. de Oliveira
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  5. Christian Rojas
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  6. Rick K. Wilson
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Corresponding author

Correspondence to Angela C. M. de Oliveira.

Appendices

Appendix A Instructions (text only)

Task 1

  • For this task you will select from among six different gambles the one gamble you will play. The six different gambles are illustrated below.

  • Each gamble has two possible outcomes, Low or High.

  • For every gamble, each outcome is equally likely, or has a 50% chance of happening.

  • At the end of the study, if this task is randomly selected, you will roll a ten-sided die to determine which outcome will occur.

    • If you roll a 1, 2, 3, 4 or 5, you will receive the Low outcome.

    • If you roll a 6, 7, 8, 9 or 0, you will receive the High outcome.

  • You must select one and only one of these gambles. To select a gamble, put a mark (a large X) on the circle for the pair of outcomes that you select. Mark only one.

  • Your earnings for this task will be determined by:

    • which of the six gambles you select; and

    • whether you roll High or roll Low.

For example, say you select the $6, $42 gamble and you roll High (a 6, 7, 8, 9 or 0) with the 10-sided die, you will be paid $42. If you roll Low (a 1, 2, 3, 4 or 5), you will be paid $6.

Question:

Pretend you want to select the gamble for $10, $34. Mark your choice with an X on that pair of outcomes.

figure a
figure b

Note that if you choose the −$2, $54 gamble and you roll Low, $2 will be taken from your $15 participation fee.

Task 1

You must select one and only one of these gambles. To select a gamble, put a mark (X) on the pair of outcomes that you prefer. Mark only one.

Once you have finished with your decision, close your booklet.

If this task is selected as the one determining your actual earnings, we will have you roll a die to determine the outcome.

Appendix B

Table 6.

Table 6 Variable descriptions (in order of appearance)
Full size table

Appendix C

Table 7.

Table 7 Marginal effects for Table 3, Model 3
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Appendix D

Table 8.

Table 8 Marginal effects for Table 4
Full size table

Appendix E

Table 9.

Table 9 Marginal Effects for the Pooled Model in Table 5
Full size table

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Eckel, C.C., Grossman, P.J., Johnson, C.A. et al. School environment and risk preferences: Experimental evidence. J Risk Uncertain 45, 265–292 (2012). https://doi.org/10.1007/s11166-012-9156-2

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