Accounting for TFP Growth in Global Agriculture - a Common-Factor-Approach-Based TFP Estimation

DOI 10.7160/aol.2020.120401
No 4/2020, December
pp. 3-13

Baráth, L. and Fertö, I. (2020) “Accounting for TFP Growth in Global Agriculture - a Common-Factor-Approach-Based TFP Estimation", AGRIS on-line Papers in Economics and Informatics, Vol. 12, No. 4, pp. 3-13. ISSN 1804-1930. DOI 10.7160/aol.2020.120401.

Abstract

There is no consensus about trends in agricultural productivity among agricultural economists. The aim of this paper is to contribute to the investigation of this issue by estimating a Total Factor Productivity (TFP) index for global agriculture and global agricultural regions. One of the biggest challenges with analysing global productivity trends is the lack of price data or cost shares, especially in developing countries. We apply recently introduced econometric models that permit accounting for technology heterogeneity and the time-series properties of data to estimate cost shares. Aggregate sectoral data from the USDA ERS database are investigated for the period 1990 to 2013. Although we used a different method, our results are in line with earlier findings that used USDA or FAO database. TFP growth has accelerated in world agriculture, largely due to better performance in transition countries. Although TFP growth has accelerated in world agriculture, it has slowed down in industrialized countries. TFP growth in the EU has increased, but at slower rate in recent years. In the Old Member States the growth rate has decreased, whereas in the New Member States it has increased. The results highlight that insufficient spending on productivity-enhancing agricultural R&D in industrialized countries may put future agricultural productivity growth at risk.

Keywords

Total Factor Productivity (TFP), agricultural productivity, heterogeneous technology, time series properties, cross sectional dependence.

References

  1. Alston, J. M., Babcock, B. A. and Pardey, P. G. (2010) "The Shifting Patterns of Agricultural Production and Productivity Worldwide", The Midwest Agribusiness Trade Research and Information Center, Iowa State UNiversity, Ames, Iowa. ISBN 978-0-9624121-8-9.
  2. Alston, J. M., and Pardey, P. G. (2014) “Agriculture in the Global Economy”, Journal of Economic Perspectives, Vol. 28, Bo. 1, pp. 121-146. ISSN 08953309. DOI 10.1257/jep.28.1.121.
  3. Bai, J. (2009) “Panel Data Models with Interactive Fixed Effects”, Econometrica, Vol. 77, No. 4, pp. 1229-1279. E-ISSN 1468-0262. DOI 10.3982/ECTA6135.
  4. Baltagi, B. H., Bresson, G. and Pirotte, A. (2007) “Panel Unit Root Tests and Spatial Dependence”, Journal of Applied Econometrics, Vol. 22, No. 2, pp. 339-360. E-ISSN 1099-1255. DOI 10.1002/jae.950.
  5. Banerjee, A. and Carrion-i-Silvestre, J. L. (2015) “Cointegration in Panel Data with Structural Breaks and Cross-Section Dependence”, Journal of Applied Econometrics, Vol. 30, No. 1, pp. 1-23. E-ISSN 1099-1255. DOI 10.1002/jae.2348.
  6. Baráth, L. and Fertő, I. (2017) “Productivity and Convergence in European Agriculture”, Journal of Agricultural Economics, Vol. 69, No. 1, pp. 228-248. E-ISSN 1477-9552. DOI 10.1111/1477-9552.12157.
  7. Cavalcanti, V. de V. , T., Mohaddes, K. and Raissi, M. (2011) “Growth, Development and Natural Resources: New Evidence Using a Heterogeneous Panel Analysis”, Quarterly Review of Economics and Finance, Vol. 51, No. 4, pp. 305-318. ISSN 1062-9769. DOI 10.1016/j.qref.2011.07.007.
  8. Chudik, A., Pesaran, M. H. and Tosetti, E. (2011) “Weak and Strong Cross-Section Dependence and Estimation of Large Panels”, Econometrics Journal, Vol. 14, No. 1. E-ISSN 1368-423X, ISSN 1368-4221. DOI 10.1111/j.1368-423X.2010.00330.x.
  9. Coakley, J., Fuertes, A.-M. and Smith, R. (2006) “Unobserved Heterogeneity in Panel Time Series Models”, Computational Statistics & Data Analysis, Vol. 0, No. 9, pp. 2361-2380. ISSN 0167-9473. DOI 10.1016/j.csda.2004.12.015.
  10. Coelli, T. J. and Rao, D. S. P. (2005) “Total Factor Productivity Growth in Agriculture: A Malmquist Index Analysis of 93 Countries, 1980-2000”, Agricultural Economics, Vol. 32, No. 1, pp. 115-134. E-ISSN. DOI 10.1111/j.0169-5150.2004.00018.x.
  11. Craig, B. J., Pardey, P. G. and Roseboom, J. (1997) “International Productivity Patterns: Accounting for Input Quality, Infrastructure, and Research”, American Journal of Agricultural Economics, Vol. 79, No. 4. pp. 1064-1076. E-ISSN 1467-8276. DOI 10.2307/1244264.
  12. Dias, A., Flavio, A. and Evenson, R. E. (2010) “Chapter 72 Total Factor Productivity Growth in Agriculture. The Role of Technological Capital”, Handbook of Agricultural Economics, Vol. 4, pp. 3769-3822. ISSN 1574-0072. DOI 10.1016/S1574-0072(09)04072-9.
  13. Eberhardt, M. and Bond, S. (2009) “Cross-Section Dependence in non-stationary Panel Models: A Novel Estimator”, Social Research, MPRA Paper,University Library of Munich, Germany.
  14. Eberhardt, M. and Teal, F. (2011) “Econometrics for Grumblers: A New Look at the Literature on Cross-Country Growth Empirics”, Journal of Economic Surveys, Vol. 25, No. 1, pp. 109-155. E-ISSN 1467-6419. DOI 10.1111/j.1467-6419.2010.00624.x.
  15. Eberhardt, M. and Teal, F. (2013a) “No Mangoes in the Tundra: Spatial Heterogeneity in Agricultural Productivity Analysis”, Oxford Bulletin of Economics and Statistics, Vol. 75, No. 6, pp. 914-939. E-ISSN 1468-0084. DOI 10.1111/j.1468-0084.2012.00720.x.
  16. Eberhardt, M. and Teal, F (2013b) “Structural Change and Cross-Country Growth Empirics”, World Bank Economic Review, Vol. 27, No. 2, pp. 229–271. E-ISSN 1564-698X, ISSN 0258-6770. DOI 10.1093/wber/lhs020.
  17. Eberhardt, M. and Vollrath, D. (2018) “The Effect of Agricultural Technology on the Speed of Development”, World Development, Vol. 109, pp. 483-496. ISSN 0305-750X. DOI 10.1016/j.worlddev.2016.03.017.
  18. Fuglie, K. and Wang, S. L. (2012) “Productivity Growth in Global Agriculture”, Population and Development Review, Vol. 39, No. 2, pp. 361-365. ISSN 00987921.
  19. Fuglie, K. (2015) “Accounting for Growth in Global Agriculture”, Bio-Based and Applied Economics., Vol. 4, No. 3, pp. 201-234. E-ISSN 2280-6172, ISSN 2280-6180. DOI 10.13128/BAE-17151.
  20. Fuglie, K. (2012) "Productivity Growth and Technology Capital in the Global Agricultural Economy", In "Productivity Growth in Agriculture: An International Perspective", CABI e-Book, Chater No. 16, 333 p. ISBN 9781845939212. DOI 10.1079/9781845939212.0335.
  21. Fuglie, K. and Heisey, P. W . (2007) "Economic Returns to Public Agricultural Research", Economic Brief, Nr. 10, USDA Economic Research Service.
  22. Jerzmanowski, M. (2007) “Total Factor Productivity Differences: Appropriate Technology vs. Efficiency”, European Economic Review, Vol 51, No. 8, pp. 2080-2110. ISSN 0014-2921. DOI 10.1016/j.euroecorev.2006.12.005.
  23. Kapetanios, G., Pesaran, M. H. and Yamagata, T. (2011) “Panels with Non-Stationary Multifactor Error Structures”, Journal of Econometrics, Vol. 160, No. 2, pp. 326-348. ISSN 0304-4076. DOI 10.1016/j.jeconom.2010.10.001.
  24. Ludena, C. E. (2010) “Agricultural Productivity Growth, Efficiency Change and Technical Progress in Latin America and the Caribbean”, Inter-American Development Bank. DOI 10.2139/ssrn.1817296.
  25. Lusigi, A., and Thirtle, C. (1997) “Total Factor Productivity and the Effects of R&d in African Agriculture”, Journal of International Development, Vol. 9, No. 4, pp. 529-538. E-ISSN 1099-1328. DOI 10.1002/(SICI)1099-1328(199706)9:4<529::AID-JID462>3.0.CO;2-U.
  26. Maddala, G. S., and Wu, S. (1999) “A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test”, Oxford Bulletin of Economics and Statistics, Vol. 61, No. S1, pp. 631-652. E-ISSN 1468-0084. DOI 10.1111/1468-0084.0610s1631.
  27. Mundlak, Y., Butzer, R. and Larson, D. F. (2012) “Heterogeneous Technology and Panel Data: The Case of the Agricultural Production Function”, Journal of Development Economics, Vol. 99, No. 1, pp. 139-149. ISSN 0304-3878. DOI 10.1016/j.jdeveco.2011.11.003.
  28. Nin-Pratt, A., Falconi, C. Ludena, C. L. and Martel, P. (2015) “Productivity and the Performance of Agriculture in Latin America and the Caribbean”, Inter-American Development Bank.
  29. Pedroni, P. (2007) “Social Capital, Barriers to Production and Capital Shares: Implications for the Importance of Parameter Heterogeneity from a non-stationary Panel Approach”, Journal of Applied Econometrics, Vol. 22, No. 2, pp. 429-451. E-ISSN 1099-1255. DOI 10.1002/jae.948.
  30. Pesaran, M. H. (20077) “A Simple Panel Unit Root Test in the Presence of Cross-Section Dependence”, Journal of Applied Econometrics, Vol. 22, No. 2, pp. 265-312. E-ISSN 1099-1255. DOI 10.1002/jae.951.
  31. Pesaran, M. H. and Smith, R. (1995) “Estimating Long-Run Relationships from Dynamic Heterogeneous Panels”, Journal of Econometrics, Vol. 68, No. 1, pp. 79-113. ISSN 0304-4076. DOI 10.1016/0304-4076(94)01644-F.
  32. Pesaran, M. H., Smith, L. V. and Yamagata, T. (2013) “Panel Unit Root Tests in the Presence of a Multifactor Error Structure”, Journal of Econometrics, Vol. 175, No. 2, pp. 94-115. ISSN 0304-4076. DOI 10.1016/j.jeconom.2013.02.001.
  33. Pesaran, M. H. (2004) “General Diagnostic Tests for Cross Section Dependence in Panels General Diagnostic Tests for Cross Section Dependence in Panels”, Cambridge Working Papers in Economics 0435, Faculty of Economics, University of Cambridge.
  34. Swinnen, J. and Vranken, L. (2010) “Reforms and Agricultural Productivity in Central and Eastern Europe and the Former Soviet Republics: 1989-2005”, Journal of Productivity Analysis, Vol. 33, pp. 241–258. E-ISSN 1573-0441, ISSN 0895-562X. DOI 10.1007/s11123-009-0162-6.

Full paper

  Full paper (.pdf, 577.26 KB).