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


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


  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:

  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.


  1. Abramovitz M (1986) Catching up, forging ahead, and falling behind. J Econ Hist 46:385–406

    Article  Google Scholar 

  2. Akerlof GA, Rose AK, Yellen JL (1998) Job switching and job satisfaction in the U.S. Labor Market. Brook Pap Econ Act 1998(2):495–582

    Google Scholar 

  3. Baier S, Mulholland S, Tamura R, Turner C (2007) Education and income of the States of the United States: 1840–2000. J Econ Growth 12(2):101–158

    Article  Google Scholar 

  4. Baldwin J, Gorecki PA (1991) Entry, exit and productivity growth. In: Geroski PA, Schwalbach J (eds) Entry and market contestability: an international comparison. Blackwell, Oxford

    Google Scholar 

  5. Ball VE, Gollop F, Kelly-Wawke A, Swinand G (1999) Patterns of state productivity growth in the U.S. Farm Sector: linking state and aggregate models. Am J Agric Econ 81(1):164–179

    Article  Google Scholar 

  6. Ball VE, Bureau J-C, Butault J-P, Nehring R (2001) Levels of farm sector productivity: an international comparison. J Prod Anal 15:5–29

    Article  Google Scholar 

  7. Ball VE, Hallahan C, Nehring R (2004) Convergence of productivity: an analysis of the catch-up hypothesis within a panel of states. Am J Agric Econ 86(5):1315–1321

    Article  Google Scholar 

  8. Baltagi B (2005) Econometric analysis of panel data, 3rd edn. Wiley, West Sussex

    Google Scholar 

  9. Baltagi B, Li Q (1995) Testing AR(1) against MA(1) disturbances in an error component model. J Econom 68:133–151

    Article  Google Scholar 

  10. Barro R, Sala-i-Martin X (1992) Convergence. J Polit Econ 100(2):223–251

    Article  Google Scholar 

  11. Basu S, Fernald JG (2001) Why is productivity procyclical? Why do we care? In: Hulten C, Dean E, Harper M (eds) New developments in productivity analysis. The University of Chicago Press, Chicago, pp 225–302

    Chapter  Google Scholar 

  12. Baumol W (1986) Productivity growth, and welfare: what the long run data show. Am Econ Rev 76(5):1072–1085

    Google Scholar 

  13. Baumol W, Wolff E (1988) Productivity growth, convergence, and welfare: reply. Am Econ Rev 78(5):1155–1159

    Google Scholar 

  14. Breitung J (2000) The local power of some unit root tests for panel data. Adv Econom 15:161–177

    Article  Google Scholar 

  15. Breusch T, Pagan A (1980) The Lagrange multiplier test and its applications to model specification in econometrics. Rev Econ Stud 47:239–253

    Article  Google Scholar 

  16. Cungun A, Swinnen JFM (2003) Transition and total factor productivity in agriculture 1992–1999. Working paper, Research Group on Food Policy, Transition & Development (PRG-Leuven), Katholieke Universiteit Leuven, 2003/2

  17. Daveri F, Jona-Lasinio C (2007) Off-shoring and productivity growth in the italian manufacturing industries. Working Paper 53, Luiss Lab of European Economics (LLEE)

  18. Davidson R, MacKinnon R (1993) Estimation and inference in econometrics. Oxford University Press, Oxford

    Google Scholar 

  19. Di Liberto A, Mura R, Pigliaru F (2008) How to measure the unobservable: a panel technique for the analysis of TFP convergence. Oxf Econ Pap 60(2):343–368

    Article  Google Scholar 

  20. Dollar D, Wolff EN (1994) Capital intensity and TFP convergence by industry in manufacturing. In: Baumol W, Nelson R, Wolff E (eds) Convergence of productivity: cross-national studies and historical evidence. Oxford University Press, New York, pp 197–224

    Google Scholar 

  21. Dowrick S, Nguyen D (1989) OECD comparative economic growth 1950–85: catch-up and convergence. Am Econ Rev 79(5):1010–1030

    Google Scholar 

  22. Dumagan J, Ball VE (2009) Decomposing growth in revenue and cost into price, quantity, and multifactor productivity contributions. Appl Econ 41:2943–2953

    Article  Google Scholar 

  23. Eisfeld AL, Rampini AA (2006) Capital reallocation and liquidity. J Monet Econ 53:369–399

    Article  Google Scholar 

  24. Escribano A, Stucchi R (2008) Catching up in total factor productivity through the business cycle: evidence from Spanish manufacturing surveys. Working Paper 08-51, Universidad Carlos III de Madrid

  25. Evenson RE, Huffman WE (2001) Structural and productivity change in US Agriculture, 1950–1982. Agric Econ 24:127–147

    Article  Google Scholar 

  26. Foote CL (1998) Trend employment growth and the bunching of job creation and destruction. Q J Econ 113:809–834

    Article  Google Scholar 

  27. Foster L, Haltiwanger J, Krizan CJ (1998) Aggregate productivity growth: lessons from microeconomic evidence. Working Paper, NBER, 6803

  28. Fujita S (2008) Creative destruction and aggregate productivity growth. Bus Rev (Federal Reserve Bank of Philadelphia) 2008(3):12–20

    Google Scholar 

  29. Geroski P, Walters CF (1995) Innovate activity over the business cycle. Econ J 105(431):916–928

    Article  Google Scholar 

  30. Gómez V, Maravall A (1997) Programs TRAMO and SEATS: instructions for the user (Beta Version: June 1997). Working Paper 97001, Ministerio de Economia y Hacienda Espana

  31. Gopinath M, Upadhyay MP (2002) Human capital, technology, and specialization: a comparison of developed and developing countries. J Econ 75(2):161–179

    Article  Google Scholar 

  32. Groth C, Nuñez S, Srinivasan S (2006) Productivity growth, adjustment costs and variable factor utilization: the UK case. Working Paper 295, Bank of England

  33. Hansen LP (1982) Large sample properties of generalized methods of moments estimators. Econometrica 50(4):1029–1054

    Google Scholar 

  34. Harris CD (1954) The market as a factor in the localization of industry in the United States. Ann Assoc Am Geogr 44:315–348

    Google Scholar 

  35. Hart OD (1985a) Monopolistic competition in the spirit of Chamberlin: a general model. Rev Econ Stud 52:529–546

    Article  Google Scholar 

  36. Hart OD (1985b) Monopolistic competition in the spirit of Chamberlin: special results. Econ J 95:1251–1271

    Google Scholar 

  37. Hausman JA (1978) Specification tests in econometrics. Econometrica 46(6):1251–1271

    Article  Google Scholar 

  38. Helpman E, Melitz M, Rubinstein Y (2008) Estimating trade flows: trading partners and trading volumes. Q J Econ 123(2):441–487

    Google Scholar 

  39. Im KS, Pesaran MH, Shin Y (2003) Testing for unit roots in heterogeneous panels. J Econom 115:53–74

    Article  Google Scholar 

  40. Jorgenson D, Griliches Z (1967) The explanation of productivity change. Rev Econ Stud 34:249–283

    Article  Google Scholar 

  41. Jovanovic B, MacDonald G (1994) Competitive diffusion. J Polit Econ 102(1):24–52

    Article  Google Scholar 

  42. Kleibergen F, Paap R (2006) Generalized reduced rank tests using the singular value decomposition. J Econom 133:97–126

    Article  Google Scholar 

  43. Levin A, Lin CF, Chu C (2002) Unit root test in panel data: asymptotic and finite sample properties. J Econom 108:1–25

    Article  Google Scholar 

  44. Levinson J, Petrin A (2003) Estimating production functions using inputs to control for unobservables. Rev Econ Stud 70(2):317–341

    Article  Google Scholar 

  45. Maravall A (2005) An application of TRAMO SEATS automatic procedure; direct vs indirect adjustment. Working Paper 0524, Banco de Espana

  46. Martin W, Mitra D (1999) Productivity growth and convergence in agricultural and manufacturing. Policy Research Working Paper 2171, World Bank

  47. McCunn A, Huffman WE (2000) Convergence in U.S. productivity growth for agriculture: implications of interstate research spillovers for funding agricultural research. Am J Agric Econ 82:370–388

    Article  Google Scholar 

  48. Parman JM (2009) Good schools make good neighbors: human capital spillovers in early 20th century agriculture. Working Paper (June 15). Available at SSRN:

  49. Phillips PCB, Perron P (1988) Testing for unit roots in time series regression. Biometrika 75:335–346

    Article  Google Scholar 

  50. Quah D (1993a) Empirical cross-section dynamics in economic growth. Eur Econ Rev 37(2/3):427–443

    Google Scholar 

  51. Quah D (1993b) Galton’s fallacy and test of convergence analysis. Scand J Econ 95(4):427–443

    Article  Google Scholar 

  52. Rucker R, Sumner D (1997) Agriculture and business cycles. In: Glasner D (ed) Business cycles and depressions: an encyclopedia. Garland publication, New York, pp 10–12

    Google Scholar 

  53. Sargan JD (1958) The estimation of economic relationships using instrumental variables. Econometrica 26:393–415

    Article  Google Scholar 

  54. Theil H (1954) Linear aggregation of economic relations. Amsterdam, North Holland

  55. Wolff E (1991) Capital formation and productivity convergence over the long term. Am Econ Rev 81(3):565–579

    Google Scholar 

  56. Wooldridge J (2002) Econometric analysis of cross section and panel data. MIT Press, Cambridge

    Google Scholar 

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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).

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  • Agriculture
  • Convergence
  • Total factor productivity

JEL Classification

  • Q1
  • R3
  • O4