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Productivity Dispersion and Persistence Among the World's Most Numerous Firms

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  • Casey C. Maue
  • Marshall Burke
  • Kyle J. Emerick

Abstract

A vast firm productivity literature finds that otherwise similar firms differ widely in their productivity and that these differences persist through time, with important implications for the broader macroeconomy. These stylized facts derive largely from studies of manufacturing firms in wealthy countries, and thus have unknown relevance for the world's most common firm type, the smallholder farm. We use detailed micro data from over 12,000 smallholder farms and nearly 100,000 agricultural plots across four countries in Africa to study the size, source, and persistence of productivity dispersion among smallholder farmers. Applying standard regression-based approaches to measuring productivity residuals, we find much larger dispersion but less persistence than benchmark estimates from manufacturing. We then show, using a novel framework that combines physical output measurement, estimates from satellites, and machine learning, that about half of this discrepancy can be accounted for by measurement error in output. After correcting for measurement error, productivity differences across firms and over time in our smallholder agricultural setting closely match benchmark estimates for non-agricultural firms. These results question some common implications of observed dispersion, such as the importance of misallocation of factors of production.

Suggested Citation

  • Casey C. Maue & Marshall Burke & Kyle J. Emerick, 2020. "Productivity Dispersion and Persistence Among the World's Most Numerous Firms," NBER Working Papers 26924, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:26924
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    Cited by:

    1. Porteous, Obie, 2022. "Reverse Dutch disease with trade costs: Prospects for agriculture in Africa's oil-rich economies," Journal of International Economics, Elsevier, vol. 138(C).
    2. Bruno Morando, 2023. "Subsistence Farming and Factor Misallocation: Evidence from Ugandan Agriculture," The World Bank Economic Review, World Bank, vol. 37(4), pages 570-598.
    3. Carletto,Calogero & Dillon,Andrew S. & Zezza,Alberto, 2021. "Agricultural Data Collection to Minimize Measurement Error and Maximize Coverage," Policy Research Working Paper Series 9745, The World Bank.
    4. Bruno Morando & Carol Newman, 2021. "Capital Misallocation, Agricultural Subsidies and Productivity: A European Perspective," Trinity Economics Papers tep0221, Trinity College Dublin, Department of Economics.
    5. Bruno Morando, 2022. "Aggregate productivity and inefficient cropping patterns in Uganda," Journal of Productivity Analysis, Springer, vol. 58(2), pages 221-237, December.
    6. Bruno Morando, 2021. "Market access and inefficient cropping patterns in Uganda," Economics Department Working Paper Series n309-21.pdf, Department of Economics, National University of Ireland - Maynooth.

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    More about this item

    JEL classification:

    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

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