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Productividad total de factores en la agricultura peruana: estimación y determinantes

Author

Listed:
  • Francisco B. Galarza

    (Universidad del Pacífico)

  • J. Guillermo Díaz

    (CENTRUM Catolica Graduate Business School)

Abstract

En este artículo, proponemos la estimación de la productividad agrícola usando datos microeconómicos para el Perú. El método consiste en la estimación de una función de producción agraria, que permite recuperar la productividad como un residuo, que constituye una aplicación directa de desarrollos metodológicos recientes en la estimación de funciones de producción con datos de panel (e.g., Gandhi et al., 2013), pero aplicado en el presente caso a datos de sección cruzada. Debido a la menor información disponible para la estimación con respecto al caso de datos de panel, en el presente estudio recurrimos a supuestos de formas funcionales. En particular, se escoge la función de elasticidad de sustitución constante, que permite una mayor flexibilidad que otras formas usadas previamente en la literatura empírica nacional, como la Cobb-Douglas. Encontramos que no hay sustento para la hipótesis de la existencia de retornos crecientes a escala; que la productividad está positivamente correlacionada con la edad, el sexo, y la educación; y negativamente relacionada con el tamaño de la unidad agropecuaria y el poder de mercado.

Suggested Citation

  • Francisco B. Galarza & J. Guillermo Díaz, 2015. "Productividad total de factores en la agricultura peruana: estimación y determinantes," Working Papers 2015-29, Peruvian Economic Association.
  • Handle: RePEc:apc:wpaper:2015-029
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    References listed on IDEAS

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    1. Nikita Céspedes & María E. Aquije & Alan Sánchez & Rafael Vera Tudela, 2016. "Productividad sectorial en el Perú: un análisis a nivel de firmas," Chapters of Books,in: Nikita Céspedes & Pablo Lavado & Nelson Ramírez Rondán (ed.), Productividad en el Perú: medición, determinantes e implicancias, edition 1, volume 1, chapter 3, pages 70-92 Fondo Editorial, Universidad del Pacífico.
    2. Gustavo Yamada (ed.), 2005. "Horas de trabajo: determinantes y dinámica en el Perú urbano," Books, Fondo Editorial, Universidad del Pacífico, edition 1, volume 1, number 05-13, June.
    3. Aguilar, Giovanna & Rendon, Si­lvio, 2008. "Matching bias in labor demand estimation," Economics Letters, Elsevier, vol. 100(2), pages 297-299, August.
    4. Céspedes, Nikita, 2012. "Remesas externas y crecimiento económico en el Perú," Revista Moneda, Banco Central de Reserva del Perú, pages 27-30.
    5. Nikita Céspedes & Nelson Ramirez-Rondán, 2014. "Total Factor Productivity Estimation in Peru: Primal and Dual Approaches," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 37(73), pages 9-39.
    6. Gustavo Yamada, 2005. "Horas de Trabajo: Determinantes y dinámica en el Perú Urbano," Working Papers 05-05, Centro de Investigación, Universidad del Pacífico.
    7. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", pages 129-137.
    8. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    9. MaCurdy, Thomas E, 1981. "An Empirical Model of Labor Supply in a Life-Cycle Setting," Journal of Political Economy, University of Chicago Press, vol. 89(6), pages 1059-1085, December.
    10. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588.
    11. César Carrera, 2016. "Long-run Money Demand in Latin American Countries: A Nonstationary Panel Data Approach," Monetaria, Centro de Estudios Monetarios Latinoamericanos, pages 121-152.
    12. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    13. Céspedes, Nikita, 2011. "Tendencia de las horas de trabajo en el mercado laboral peruano," Revista Moneda, Banco Central de Reserva del Perú, pages 13-17.
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    More about this item

    JEL classification:

    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services

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