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Non-Parametric Demand Analysis with an Application to the Demand for Fish

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  • Joshua D. Angrist
  • Kathryn Graddy
  • Guido W. Imbens

Abstract

Instrumental variables (IV) estimation of a demand equation using time series data is shown to produce a weighted average derivative of heterogeneous potential demand functions. This result adapts recent work on the causal interpretation of two-stage least squares estimates to the simultaneous equations context and generalizes earlier research on average derivative estimation to models with endogenous regressors. The paper also shows how to compute the weights underlying IV estimates of average derivatives in a simultaneous equations model. These ideas are illustrated using data from the Fulton Fish market in New York City to estimate an average elasticity of wholesale demand for fresh fish. The weighting function underlying IV estimates of the demand equation is graphed and interpreted. The empirical example illustrates the essentially local and context-specific nature of instrumental variables estimates of structural parameters in simultaneous equations models.

Suggested Citation

  • Joshua D. Angrist & Kathryn Graddy & Guido W. Imbens, 1995. "Non-Parametric Demand Analysis with an Application to the Demand for Fish," NBER Technical Working Papers 0178, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberte:0178
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    References listed on IDEAS

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    Cited by:

    1. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
    2. Pettersson Lidbom, Per, 2003. "Does the Size of the Legislature Affect the Size of Government? Evidence from a Natural Experiment," Research Papers in Economics 2003:18, Stockholm University, Department of Economics.
    3. Heckman, James J. & Vytlacil, Edward J., 2000. "The relationship between treatment parameters within a latent variable framework," Economics Letters, Elsevier, vol. 66(1), pages 33-39, January.
    4. Daron Acemoglu & Joshua Angrist, 1999. "How Large are the Social Returns to Education? Evidence from Compulsory Schooling Laws," NBER Working Papers 7444, National Bureau of Economic Research, Inc.
    5. Tan, Reynold D. & Pabuayon. Isabelita M. & Rola, Agnes C. & Sumalde, Zenaida M. & Yorobe, Jose M., 2017. "Economic, Socio-Demographic and Psychographic Determinants of Milkfish Consumer Demand, Iloilo Province, Philippines," Journal of Economics, Management & Agricultural Development, Journal of Economics, Management & Agricultural Development (JEMAD), vol. 3(2), December.
    6. Arild Aakvik & James J. Heckman & Edward J. Vytlacil, 2000. "Treatment Effects for Discrete Outcomes when Responses to Treatment Vary Among Observationally Identical Persons: An Application to Norwegian ..," NBER Technical Working Papers 0262, National Bureau of Economic Research, Inc.
    7. Corts, Kenneth S., 1998. "Conduct parameters and the measurement of market power," Journal of Econometrics, Elsevier, vol. 88(2), pages 227-250, November.
    8. Card, David, 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica, Econometric Society, vol. 69(5), pages 1127-1160, September.
    9. Behrman, Jere R., 1996. "Measuring the effectiveness of schooling policies in developing countries: Revisiting issues of methodology," Economics of Education Review, Elsevier, vol. 15(4), pages 345-364, October.

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    More about this item

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco

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