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Quantifying Heterogeneous Returns to Genetic Selection: Evidence from Wisconsin Dairies

In: Economics of Research and Innovation in Agriculture

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Listed:
  • Jared Hutchins
  • Brent Hueth
  • Guilherme Rosa
Abstract
Estimates of productivity growth in the dairy sector attribute as much as half of observed growth to genetic improvement. Unobserved match quality is an important determinate of genetic selection by dairy farmers that confounds attribution to genetic improvement alone. Using data from a large sample of Wisconsin dairy farms, and national-level data on sire rankings, we develop and estimate a model that accounts for selection behavior, and decompose total productivity change into separate effects for genetic improvement and endogenous selection. We find that selection accounts for as much as 75 percent of the total productivity improvement in our sample. Our results provide evidence for positive assortative matching, whereby farmers who adopt above-average yield genetics also perform better than average for their chosen genetics. Further, we find that management behavior accounts for a significant portion of within-herd cow-level heterogeneity, suggesting that dairy farmers manage their herds at the level of individual cows. Overall, our results indicate that a large portion of productivity growth in dairy farming can be explained by farmers’ ability to identify and select genetics well suited to their production environment.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Jared Hutchins & Brent Hueth & Guilherme Rosa, 2019. "Quantifying Heterogeneous Returns to Genetic Selection: Evidence from Wisconsin Dairies," NBER Chapters, in: Economics of Research and Innovation in Agriculture, pages 81-105, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberch:14297
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    References listed on IDEAS

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    1. Zvi Griliches & Jacques Mairesse, 1995. "Production Functions: The Search for Identification," NBER Working Papers 5067, National Bureau of Economic Research, Inc.
    2. Cornelissen, Thomas & Dustmann, Christian & Raute, Anna & Schönberg, Uta, 2016. "From LATE to MTE: Alternative methods for the evaluation of policy interventions," Labour Economics, Elsevier, vol. 41(C), pages 47-60.
    3. Yair Mundlak, 1961. "Empirical Production Function Free of Management Bias," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 43(1), pages 44-56.
    4. Jeffrey M. Wooldridge, 2005. "Fixed-Effects and Related Estimators for Correlated Random-Coefficient and Treatment-Effect Panel Data Models," The Review of Economics and Statistics, MIT Press, vol. 87(2), pages 385-390, May.
    5. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    6. James Heckman & Edward Vytlacil, 1998. "Instrumental Variables Methods for the Correlated Random Coefficient Model: Estimating the Average Rate of Return to Schooling When the Return is Correlated with Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 33(4), pages 974-987.
    7. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(2), pages 317-341.
    8. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    9. Jeffrey M. Wooldridge, 2015. "Control Function Methods in Applied Econometrics," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 420-445.
    10. Jeffrey D Michler & Emilia Tjernström & Simone Verkaart & Kai Mausch, 2019. "Money Matters: The Role of Yields and Profits in Agricultural Technology Adoption," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(3), pages 710-731.
    11. Tavneet Suri, 2011. "Selection and Comparative Advantage in Technology Adoption," Econometrica, Econometric Society, vol. 79(1), pages 159-209, January.
    12. Andrew Zeitlin & Stefano Caria & Richman Dzene & Petr Janský & Emmanuel Opoku & Francis Teal, 2010. "Heterogeneous returns and the persistence of agricultural technology adoption," CSAE Working Paper Series 2010-37, Centre for the Study of African Economies, University of Oxford.
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    More about this item

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
    • Q1 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture
    • 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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