• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Martine Beijing October 2008

Martine Beijing October 2008






Total Views
Views on SlideShare
Embed Views



1 Embed 5

http://www.efdinitiative.org 5


Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

    Martine Beijing October 2008 Martine Beijing October 2008 Presentation Transcript

    • Risk Aversion: Experimental Evidence from South African Fishing Communities
      • Kerri Brick, Justine Burns and Martine Visser*
      • *Environmental Policy Research Unit, University of Cape Town
    • Objectives and relevance
      • Estimate the risk attitudes of a large sample of individuals from nine rural fishing communities along the west coast of South Africa
      • ML estimation allows us to consider the effects of observable demographic characteristics in the estimation model
      • Relate derived risk attitudes to subjects’ admitted risk attitudes, inclination to poach and overfish and attitudes to fishing rights such as quotas or permits
      • 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
      • 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)
    • Elicitation procedure
      • Employ a multiple price list (MPL) experimental measure for risk aversion
      • Each subject was presented with eight pairs of lotteries (lottery A and lottery B)
      • For each pair, respondents had to choose lottery A or lottery B
      • 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
    • Sample description
      • 569 individuals, of whom 337 have consistent preferences (a unique switch point)
      • Of the whole sample:
        • Just over 60% were male
        • On average, participants were 41 years old and had lived in their respective communities for most of their lives
        • Majority reported Afrikaans as their home language
        • 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
        • 66% of participants classified themselves as Coloured, while the remaining majority classified themselves as Black or “Other”
        • 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
        • Mean wage income for employed individuals (after tax) was R920 per month
        • Mean monthly household per capita income for the entire sample was R330.48 compared with mean monthly household per capita expenditures of R379.93
      • 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.
    • Preliminary Risk Profiles
    • Preliminary Risk Profiles
    • Preliminary Risk Profiles * All respondents: not only those involved in the fishing industry
    • Preliminary Risk Profiles
    • Preliminary Risk Profiles Do you think that commercial and small-scale commercial quotas are allocated fairly?
    • Preliminary Risk Profiles
    • Estimating Risk Attitudes
      • Estimate a CRRA Utility Function using Maximum Likelihood
      • Dependent variable:
        • Subjects’ choices
      • Independent variables:
        • Age; Age squared
        • Gender
        • Education
        • Income per capita
        • Subject’s perceived financial status
        • Whether fishing is a primary source of household income; whether subject is involved in the fishing industry
        • Employment status
        • Whether the subject is a quota holder
        • Whether the subject is a permit holder
    • Estimating Risk Attitudes
      • Independent variables continued:
        • Number of times the subject has been charged or arrested for violating fishing regulations
        • Whether the subject thinks that commercial and small-scale commercial quotas are allocated fairly
        • Whether the subject thinks that officials who allocate the quotas are corrupt
        • Whether the subject belong to a social group such as a Fishers Association