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The Effect of the Conservation Reserve Program on Rural Economies: Deriving a Statistical Verdict from a Null Finding


  • Jason P Brown
  • Dayton M Lambert
  • Timothy R Wojan


This article suggests two methods for deriving a statistical verdict from a null finding, allowing economists to more confidently conclude when “not significant” can be interpreted as “no substantive effect.” The proposed methodologies can be extended to a variety of empirical contexts where size and power matter. The example used to demonstrate the methods is the Economic Research Service’s 2004 Report to Congress that was charged with statistically identifying any unintended negative employment consequences of the Conservation Reserve Program (CRP). The report failed to identify a statistically significant negative long-term effect of the CRP on employment growth, but the authors correctly cautioned that the verdict of “no negative employment effect” was only valid if the econometric test was statistically powerful. We replicate the 2004 analysis and use new methods of statistical inference to resolve the two critical deficiencies that preclude estimation of statistical power: (a) positing a compelling effect size, and (b) providing an estimate of the variability of an unobserved alternative distribution using simulation methods. We conclude that the test used in the report had high power for detecting employment effects of -1% or lower resulting from the CRP, equivalent to job losses reducing a conservative estimate of environmental benefits by one-third.

Suggested Citation

  • Jason P Brown & Dayton M Lambert & Timothy R Wojan, 2019. "The Effect of the Conservation Reserve Program on Rural Economies: Deriving a Statistical Verdict from a Null Finding," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(2), pages 528-540.
  • Handle: RePEc:oup:ajagec:v:101:y:2019:i:2:p:528-540.

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    References listed on IDEAS

    1. Deirdre N. McCloskey & Stephen T. Ziliak, 1996. "The Standard Error of Regressions," Journal of Economic Literature, American Economic Association, vol. 34(1), pages 97-114, March.
    2. Kevin Hoover & Mark Siegler, 2008. "Sound and fury: McCloskey and significance testing in economics," Journal of Economic Methodology, Taylor & Francis Journals, vol. 15(1), pages 1-37.
    3. Sullivan, Patrick & Hellerstein, Daniel & Hansen, LeRoy T. & Johansson, Robert C. & Koenig, Steven R. & Lubowski, Ruben N. & McBride, William D. & McGranahan, David A. & Roberts, Michael J. & Vogel, S, 2004. "The Conservation Reserve Program: Economic Implications for Rural America," Agricultural Economics Reports 33987, United States Department of Agriculture, Economic Research Service.
    4. Christensen, Ronald, 2005. "Testing Fisher, Neyman, Pearson, and Bayes," The American Statistician, American Statistical Association, vol. 59, pages 121-126, May.
    5. Luther Tweeten, 1983. "Hypotheses Testing in Economic Science," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 65(3), pages 548-552.
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    Cited by:

    1. Carlos J. O. Trejo-Pech & Karen L. DeLong & Dayton M. Lambert & Vasileios Siokos, 2020. "The impact of US sugar prices on the financial performance of US sugar-using firms," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 8(1), pages 1-17, December.

    More about this item


    Hypothesis testing; Monte Carlo; power analysis;

    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes


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