Martine Beijing October 2008


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Martine Beijing October 2008

  1. 1. Risk Aversion: Experimental Evidence from South African Fishing Communities <ul><li>Kerri Brick, Justine Burns and Martine Visser* </li></ul><ul><li>*Environmental Policy Research Unit, University of Cape Town </li></ul>
  2. 2. Objectives and relevance <ul><li>Estimate the risk attitudes of a large sample of individuals from nine rural fishing communities along the west coast of South Africa </li></ul><ul><li>ML estimation allows us to consider the effects of observable demographic characteristics in the estimation model </li></ul><ul><li>Relate derived risk attitudes to subjects’ admitted risk attitudes, inclination to poach and overfish and attitudes to fishing rights such as quotas or permits </li></ul><ul><li>Analyse the dynamic between the allocation of fishing rights, propensity to overfish or poach and risk preferences; this is relevant given that since 2005 certain fishing rights are allocated for periods of up to 15 years </li></ul><ul><li>Analyse the implication of granting long term fishing rights to females given their risk profile (assume a priori that females are more risk averse than males as per forthcoming slides) </li></ul>
  3. 3. Elicitation procedure <ul><li>Employ a multiple price list (MPL) experimental measure for risk aversion </li></ul><ul><li>Each subject was presented with eight pairs of lotteries (lottery A and lottery B) </li></ul><ul><li>For each pair, respondents had to choose lottery A or lottery B </li></ul><ul><li>Subjects also completed a detailed questionnaire that elicited information on their socio-economic background, employment activities, fishing experience and also included a range of attitudinal questions </li></ul>
  4. 4. Sample description <ul><li>569 individuals, of whom 337 have consistent preferences (a unique switch point) </li></ul><ul><li>Of the whole sample: </li></ul><ul><ul><li>Just over 60% were male </li></ul></ul><ul><ul><li>On average, participants were 41 years old and had lived in their respective communities for most of their lives </li></ul></ul><ul><ul><li>Majority reported Afrikaans as their home language </li></ul></ul><ul><ul><li>Educational attainments were low, with 14% of the sample having completed their primary schooling, 8% having completed high school and 6% having some form of tertiary qualification </li></ul></ul><ul><ul><li>66% of participants classified themselves as Coloured, while the remaining majority classified themselves as Black or “Other” </li></ul></ul><ul><ul><li>48% were employed at the time of the survey: of those employed, just over 50% reported fishing activities to be their primary source of income </li></ul></ul><ul><ul><li>Mean wage income for employed individuals (after tax) was R920 per month </li></ul></ul><ul><ul><li>Mean monthly household per capita income for the entire sample was R330.48 compared with mean monthly household per capita expenditures of R379.93 </li></ul></ul><ul><li>Source: Visser, M., and J. Burns (2007): “Bridging the Great Divide in South Africa: Inequality and Punishment in the Provision of Public Goods,” in Fairness, Reciprocity and Inequality: Experimental Evidence from South Africa , PhD Dissertation 162, Department of Economics, School of Business, Economics and Law, Goteborg University. </li></ul>
  5. 5. Preliminary Risk Profiles
  6. 6. Preliminary Risk Profiles
  7. 7. Preliminary Risk Profiles * All respondents: not only those involved in the fishing industry
  8. 8. Preliminary Risk Profiles
  9. 9. Preliminary Risk Profiles Do you think that commercial and small-scale commercial quotas are allocated fairly?
  10. 10. Preliminary Risk Profiles
  11. 11. Estimating Risk Attitudes <ul><li>Estimate a CRRA Utility Function using Maximum Likelihood </li></ul><ul><li>Dependent variable: </li></ul><ul><ul><li>Subjects’ choices </li></ul></ul><ul><li>Independent variables: </li></ul><ul><ul><li>Age; Age squared </li></ul></ul><ul><ul><li>Gender </li></ul></ul><ul><ul><li>Education </li></ul></ul><ul><ul><li>Income per capita </li></ul></ul><ul><ul><li>Subject’s perceived financial status </li></ul></ul><ul><ul><li>Whether fishing is a primary source of household income; whether subject is involved in the fishing industry </li></ul></ul><ul><ul><li>Employment status </li></ul></ul><ul><ul><li>Whether the subject is a quota holder </li></ul></ul><ul><ul><li>Whether the subject is a permit holder </li></ul></ul>
  12. 12. Estimating Risk Attitudes <ul><li>Independent variables continued: </li></ul><ul><ul><li>Number of times the subject has been charged or arrested for violating fishing regulations </li></ul></ul><ul><ul><li>Whether the subject thinks that commercial and small-scale commercial quotas are allocated fairly </li></ul></ul><ul><ul><li>Whether the subject thinks that officials who allocate the quotas are corrupt </li></ul></ul><ul><ul><li>Whether the subject belong to a social group such as a Fishers Association </li></ul></ul>