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The estimation of marginal utility of income for application to agricultural policy analysis

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  • E. Neal Blue
  • Luther Tweeten

Abstract

A quality‐of‐life index (QLI), a proxy measure of utility, is constructed by factor‐weighted and simple‐summation weighted aggregation of socio‐psychological measures of well‐being. The socio‐psychological measures were constructed from quality of life domains taken from selected years of the General Social Surveys [General Social Surveys, 1972–1993: Cumulative Code Book. Principal Investigator, James A. Davis; Director and Co‐Principal Investigator, Tom W. Smith — Chicago: National Opinion Research Center, 1993. (National Data Program for the Social Sciences Series, no. 13).]. The Quality of Life Indices (QLI) indices are regressed on selected socio‐demographic variables using quadratic. Cobb‐Douglas, square root, and semilog functional forms. QLI is much influenced by income, education, and health. As measured here, QLI is not much influenced by year of measurement, sector, or by region of residence. Much variability in the QLI is unique to individuals, and our results are suited to predict group, rather than individual well being. Practitioners computing the benefit–cost ratio for a public program, project, or policy can weight dollars by income groups with marginal utilities derived from this study. That methodology will matter: even the ‘conservative’ quadratic equation indicates that the marginal utility of income (MUI) for families with very low incomes is half as large as for families with median incomes.

Suggested Citation

  • E. Neal Blue & Luther Tweeten, 1997. "The estimation of marginal utility of income for application to agricultural policy analysis," Agricultural Economics, International Association of Agricultural Economists, vol. 16(3), pages 155-169, August.
    • Handle: RePEc:bla:agecon:v:16:y:1997:i:3:p:155-169
    DOI: 10.1111/j.1574-0862.1997.tb00451.x
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    Cited by:

    1. Agee Mark D & Crocker Thomas & Shogren Jason F, 2004. "An Economic Assessment of Parents' Self-Composure: The Case of Physical Child Abuse," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 4(1), pages 1-41, April.
    2. Tweeten, Luther G., 1997. "Agricultural Industrialization: For Better Or Worse?," Economics and Sociology Occasional Papers - ESO Series 28337, Ohio State University, Department of Agricultural, Environmental and Development Economics.
    3. Kula, E., 2002. "Regional Welfare Weights in Investment Appraisal - The Case of India," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 32(1), pages 1-16.
    4. David EVANS & Erhun KULA, 2009. "Social discount rates and welfare weights for public investment decisions under budgetary restrictions – the case of Cyprus," Departmental Working Papers 2009-19, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    5. Tweeten, Luther G., 1998. "Meeting Tomorrow'S Global Food Needs--A Moral Imperative," Economics and Sociology Occasional Papers - ESO Series 28335, Ohio State University, Department of Agricultural, Environmental and Development Economics.
    6. David Evans, 2004. "A social discount rate for France," Applied Economics Letters, Taylor & Francis Journals, vol. 11(13), pages 803-808.
    7. Abdul-Mohsen, Ashraf & Hitzhusen, Frederick J., 2006. "Environmental Injustice: An Ohio Case Study," 2006 Annual meeting, July 23-26, Long Beach, CA 21061, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    8. D. Evans & E. Kula & H. Sezer, 2005. "Regional welfare weights for the UK: England, Scotland, Wales and Northern Ireland," Regional Studies, Taylor & Francis Journals, vol. 39(7), pages 923-937.
    9. Erhun KULA, 2006. "Regional welfare weights," Departmental Working Papers 2006-32, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.

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