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State productivity growth in agriculture: catching-up and the business cycle

Abstract

This paper examines the relation between the business cycle and convergence in levels of agricultural productivity across the 48 contiguous states. First, we find evidence of convergence in total factor productivity levels across the different phases of the business cycle, but the speed of convergence was greater during periods of contraction in economic activity than during periods of expansion. Second, we find that technology embodied in capital was an important source of productivity growth in agriculture. As with the rate of catch-up, the embodiment effect was much stronger during low economic activity phases of the business cycle.

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Fig. 1

Notes

  1. 1.

    Ball et al. (2001), in a study of agriculture in twelve OECD countries, found evidence of convergence in levels of productivity. Moreover, the dispersion of their relative levels (as measured by the coefficient of variation) decreased over time.

  2. 2.

    Their tests for convergence are conditional on these variables. In the growth literature, this is referred to as conditional convergence. However, for simplicity of exposition we use the term convergence.

  3. 3.

    Dumagan and Ball (2009) provide a decomposition of changes in revenue into its components. This decomposition reveals that productivity growth in agriculture accounted for nearly two-thirds of the growth in revenue over the postwar period. The authors conclude that policy should focus more on measures to foster productivity growth (e.g., public funding of research) than often adopted price support programs to enhance growth in income.

  4. 4.

    Overshooting of prices refers to temporary changes beyond long-run equilibrium levels.

  5. 5.

    A particular case in which this can happen is when we only consider export markets where we observe zero trade flows between specific pairs of countries (see Helpman et al. 2008).

  6. 6.

    If most exiting farms were concentrated in states with lower initial aggregate productivity the bias would be negative (i.e., biased towards β-convergence). If most exiting farms were concentrated in the states with higher initial aggregate productivity, the bias would be positive (i.e., biased against β-convergence). Finally, if there were no statistically significant differences in the exit rates between the most productive states and the less productive states the results would be unbiased.

  7. 7.

    Ball et al. (2004) also allowed for embodiment of technology in materials inputs, but the estimated effect was not statistically significant. They attributed this result to adjusting the input indexes for quality change.

  8. 8.

    The capital intensities are defined over machinery and equipment and non-residential structures.

  9. 9.

    We use the term ‘spillovers’ because our measures of educational attainment and worker experience pertain to the total workforce in each state as opposed to the agricultural workforce in that state.

  10. 10.

    In the most basic specification, only the initial and final periods are considered. We, on the other hand, construct growth rates for overlapping periods. The advantage of using overlapping periods is that the estimates are less sensitive to starting and ending dates.

  11. 11.

    The production accounts are available electronically at: http://www.ers.usda.gov/data-products/agricultural-productivity-in-the-us.aspx.

  12. 12.

    It is likely that at least some technological innovation is embodied in materials inputs such as fertilizers and pesticides. However, the input quantities are measured implicitly using hedonic price indexes; they are adjusted for changes in input quality. The resulting input measures will be uncorrelated with changes in productivity (see Jorgenson and Griliches 1967; Ball et al. 2004).

  13. 13.

    Baier et al. (2007) use a perpetual inventory method to construct average years of schooling and experience of the work force for each state. The data span the years 1840–2000. Estimates for the years 2001–2004 were extrapolated using TRAMO. TRAMO is a program for MLE of regression models with general non-stationary errors, outliers, and long sequences of missing observations (see Gómez and Maravall 1997; Maravall 2005).

  14. 14.

    A complete description of methods and data used to construct the market accessibility and domestic and external demand variables is provided in an appendix available from the authors.

  15. 15.

    We perform the Baltagi and Li (1995) test and Wooldridge (2002) test since both tests can be applied under very few maintained assumptions.

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Correspondence to V. Eldon Ball.

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The views expressed in this article are those of the authors and should not be attributed to the Economic Research Service or the US Department of Agriculture.

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Ball, V.E., San-Juan-Mesonada, C. & Ulloa, C.A. State productivity growth in agriculture: catching-up and the business cycle. J Prod Anal 42, 327–338 (2014). https://doi.org/10.1007/s11123-013-0352-0

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Keywords

  • Agriculture
  • Convergence
  • Total factor productivity

JEL Classification

  • Q1
  • R3
  • O4