Meta-regression Analysis of Technical (In)Efficiency in Agriculture: a Regional Approach

Authors

DOI:

https://doi.org/10.17059/ekon.reg.2021-3-14

Keywords:

agriculture, technical efficiency, farms inefficiency, meta-regression, parametric and non-parametric methods, regional analysis

Abstract

While scientific studies have not reached a consensus on the methodology for examining Technical Efficiency (or Inefficiency), the influence of regions appears to be important for efficiency scores. Therefore, this research aims to investigate the empirical procedures for the achievement of more robust results in the analysis of productive efficiency, as well as to evaluate the effect of the location of farms on such efficiency. The goal was to check whether the most developed regions are the most efficient. Meta-regression analysis provides an adequate method for an accurate assessment of both situations. This technique was applied based on a database of 166 observations on the agricultural sector from countries around the world, published in the period 2010-2017. The criteria used for the database collection and for the conceived model were not previously used and, thereby, enrich the discussion on the topic. The procedure aims to check the variation in the Mean of Technical Inefficiency and conduct an analysis using Quasi-Maximum Likelihood Estimation. The regressions showed that the Mean of Technical Inefficiency could be mainly explained by data, variables, employed empirical models and the region of study. The studies that focus on farms of developed countries present the lowest Mean of Technical Inefficiency, while studies for developing or low-income countries exhibit the opposite. Therefore, for future research on productive analysis, we suggest empirical procedures aimed at achieving robust results that take into account specific regional characteristics of farms.

Author Biographies

Micael Queiroga dos Santos, Centre for Transdisciplinary Development Studies; University of Tras-os-Montes e Alto Douro

PhD Student in Development, Societies and Territories; Junior Research Associate, Centre for Transdisciplinary Development Studies; Teaching Assistant in Economics, Department of Economics, Sociology and Management, University of Trás-os-Montes e Alto Douro; https://orcid.org/0000-0002-0684-3899 (Quinta de Prados, Pólo II — ECHS, 5000–801, Vila Real, Portugal; e-mail: micaels@utad.pt).

Ana Alexandra Marta-Costa, Centre for Transdisciplinary Development Studies; University of Tras-os-Montes e Alto Douro

PhD in Agri-social Sciences; Senior Research Associate, Centre for Transdisciplinary Development Studies (CETRAD); Assistant Professor, Department of Economics, Sociology and Management, University of Trás-os-Montes e Alto Douro; https://orcid.org/0000-0001-9247-9167; Researcher ID: N-1923–2018 (Quinta de Prados, Pólo II — ECHS, 5000–801, Vila Real, Portugal; e-mail: amarta@utad.pt).

Xose Anton Rodriguez, University of Santiago de Compostela

PhD in Economics; Professor, Department of Quantitative Economics, University of Santiago de Compostela; https://orcid.org/0000-0002-4741-7538 (Avda Xoán XXII, s/n, Santiago de Compostela, Spain; e-mail: xoseanton.rodriguez@usc.es).

References

Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253–290. DOI: 10.2307/2343100.

Coelli, T., Rao, D. S. P., O’Donnell, C. J. & Battes, G. E. (2005). An introduction to efficiency and productivity analysis (2nd Edition). U.S.: Springer. DOI: 10.1007/b136381.

Aparicio, J., Borras, F., Pastor, J. T. & Vidal, F. (2013). Accounting for slacks to measure and decompose revenue efficiency in the Spanish Designation of Origin wines with DEA. European Journal of Operational Research, 231(2), 443–451. DOI: 10.1016/j.ejor.2013.05.047.

Bravo-Ureta, B. E., Solís, D., Moreira López, V. H., Maripani, J. F. & Thiam, A. (2007). Technical Efficiency in Farming: A Meta-Regression Analysis. Journal of Productivity Analysis, 27(1), 57–72. DOI: 10.1007/s11123–006–0025–3.

Anang, B. T., Bäckman, S. & Rezitis, A. (2016). Does Farm Size Matter? Investigating Scale Efficiency of Peasant Rice Farmers in Northern Ghana. Economics Bulletin, 36(4), 2275–2290.

Alvarez, A. & Orea, L. (2001). Different Approaches to Model Multi-Species Fisheries Using a Primal Approach. Efficiency Series Paper, 3(200), 17. Retrieved from: https://www.unioviedo.es/oeg/ESP/esp_2001_04.pdf (Date of access: 08.05.2019).

Coelli, T. (1995). Recent Developments in Frontier Modelling and Efficiency Measurement. Australian Journal of Agricultural and Resource Economics, 39(3), 219–245. DOI: 10.1111/j.1467–8489.1995.tb00552.x

Cullinane, K., Wang, T. F., Song, D. W. & Ji, P. (2006). The Technical Efficiency of Container Ports: Comparing Data Envelopment Analysis and Stochastic Frontier Analysis. Transportation Research Part A: Policy and Practice, 40(4), 354–374.

Resti, A. (2000). Efficiency Measurement for Multi-Product Industries: A Comparison of Classic and Recent Techniques Based on Simulated Data. European Journal of Operational Research, 121(3), 559–578. DOI: 10.1016/S0377–2217(99)00054–5.

Wadud, A. & White, B. (2000). Farm Household Efficiency in Bangladesh: A Comparison of Stochastic Frontier and DEA Methods. Applied Economics, 32(13), 1665–1673. DOI: 10.1080/000368400421011.

Kalaitzandonakes, N. G. & Dunn, E. g. (1995). Technical Efficiency, Managerial Ability and Farmer Education in Guatemalan Corn Production: A Latent Variable Analysis. Agricultural and Resource Economics Review, 24(1), 36–46. DOI: 10.1017/S1068280500003592.

Sharma, K. R., Leung, P. S. & Zaleski, H. M. (1999). Technical, Allocative and Economic Efficiencies in Swine Production in Hawaii: A Comparison of Parametric and Nonparametric Approaches. Agricultural Economics, 20(1), 23–35. DOI: 10.1111/j.1574–0862.1999.tb00548.x.

Solis, D. A. (2005). Household Productivity and Investment in Soil Conservation: Evidence from Small-Scale Hillside Farmers in Central America. Ph.D. Dissertation. Connecticut: University of Connecticut.

Mareth, T., Thomé, A. M. T., Cyrino Oliveira, F. L. & Scavarda, L. F. (2016). Systematic Review and Meta-Regression Analysis of Technical Efficiency in Dairy Farms. International Journal of Productivity and Performance Management, 65(3), 279–301. DOI: 10.1108/IJPPM-02–2015–0027.

Ogundari, K. (2014). The Paradigm of Agricultural Efficiency and Its Implication on Food Security in Africa: What Does Meta-Analysis Reveal? World Development, 64(1920), 690–702. DOI: 10.1016/j.worlddev.2014.07.005.

Djokoto, J. G., Srofenyoh, F. Y. & Arthur, A. A. A. (2016). Technical Inefficiency Effects in Agriculture — A Meta-Regression. Journal of Agricultural Science, 8(2), 109–121. DOI: 10.5539/jas.v8n2p109.

Sickles, R. C. & Zelenyuk, V. (2019). Measurement of productivity and efficiency. Cambridge, UK: Cambridge University Press, 626. DOI: 10.1017/9781139565981.

Färe, R., Grosskopf, S. & Lovell, C. A. K. (1983). The Structure of Technical Efficiency. The Scandinavian Journal of Economics, 85(2), 181–190. DOI: 10.2307/3439477.

Färe, R., Grosskopf, S. & Lovell, C. A. K. (1985). The Measurement of Efficiency of Production. Dordrecht: Springer, 216. DOI: 10.1007/978–94–015–7721–2

Färe, R., Grosskopf, S. & Lovell, C. A. K. (1994). Production Frontiers. Cambridge, UK: Cambridge University Press, 312. DOI: 10.1016/0954–349X(95)90003-T.

Haghiri, M., Nolan, J. F. & Tran, K. C. (2004). Assessing the Impact of Economic Liberalization across Countries: A Comparison of Dairy Industry Efficiency in Canada and the USA. Applied Economics, 36(11), 1233–1243.

Thiam, A., Bravo-Ureta, B. E. & Rivas, T. E. (2001). Technical Efficiency in Developing Country Agriculture: A Meta-Analysis. Agricultural Economics, 25(2–3), 235–243. DOI: 10.1016/S0169–5150(01)00081–0.

Ramanathan, R. (2003). An introduction to data envelopment analysis: a tool for performance measurement. New Delhi: SAGE Publications, 201. DOI: 10.1177/097206341001300107.

Simar, L. & Wilson, P. W. (2000). Statistical Inference in Nonparametric Frontier Models: The State of the Art. Journal of Productivity Analysis, 13(1), 49–78. DOI: 10.1023/A:1007864806704.

Simar, L. & Wilson, P. W. (2007). Estimation and Inference in Two-Stage, Semi-Parametric Models of Production Processes. Journal of Econometrics, 136(1), 31–64. DOI: 10.1016/j.jeconom.2005.07.009.

Johnson, A. L. & Kuosmanen, T. (2012). One-Stage and Two-Stage DEA Estimation of the Effects of Contextual Variables. European Journal of Operational Research, 220(2), 559–70. DOI: 10.1016/j.ejor.2012.01.023.

Banker, R. D. & Natarajan, R. (2008). Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis. Operations Research, 56(1), 48–58. DOI: 10.1287/opre.1070.0460.

Aigner, D., Lovell, C. A. K. & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37. DOI: 10.1016/0304–4076(77)90052–5.

Meeusen, W. & van Den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2), 435–444. DOI: 10.2307/2525757.

Banker, R. D. & Maindiratta, A. (1992). Maximum Likelihood Estimation of Monotone and Concave Production Frontiers. Journal of Productivity Analysis, 3(4), 401–415.

Fan, Y., Li, Q. & Weersink, A. (1996). Semiparametric Estimation of Stochastic Production Frontier Models. Journal of Business & Economic Statistics, 14(4), 460–468.

Kneip, A. & Simar, L. (1996). A General Framework for Frontier Estimation with Panel Data. Journal of Productivity Analysis, 7(2–3), 187–212.

Kumbhakar, S. C., Parmeter, C.F. & Zelenyuk, V. (2017). Stochastic Frontier Analysis: Foundations and Advances. Working Papers, 2017–10. University of Miami, Department of Economics, 103.

Kuosmanen, T. & Kortelainen, M. (2012). Stochastic Non-Smooth Envelopment of Data: Semi-Parametric Frontier Estimation Subject to Shape Constraints. Journal of Productivity Analysis, 38(1), 11–28. DOI: 10.1007/s11123–010–0201–3.

Noh, H. (2014). Frontier Estimation Using Kernel Smoothing Estimators with Data Transformation. Journal of the Korean Statistical Society, 43(4), 503–512. DOI: 10.1016/j.jkss.2014.07.005.

Parmeter, C. F. & Racine, J. S. (2013). Smooth Constrained Frontier Analysis. In: Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis: Essays in Honor of Halbert L. White Jr. (pp. 463–488). New York: Springer. DOI: 10.1007/978–1-4614–1653–1_18.

Parmeter, C. F. (2018). Estimation of the Two-Tiered Stochastic Frontier Model with the Scaling Property. Journal of Productivity Analysis, 49(1), 37–47. DOI: 10.1007/s11123–017–0520–8.

Greene, W. H. (2008). The econometric approach to efficiency analysis. In: H. O. Fried, C. A. K. Lovell, S. S. Schmidt (Eds.), The measurement of productive efficiency and productivity growth (pp. 68–119). Oxford, UK: Oxford University Press. DOI: 10.1093/acprof:oso/9780195183528.003.0002.

Lovell, C. A. K. (1993). Production Frontiers and Productive Efficiency. In: H. O. Fried, S. S. Schmidt (Eds.), Measurement of Productive Efficiency: Techniques and Applications (pp. 3–67). Oxford, UK: Oxford University Press.

Schmidt, P. & Sickles, R. C. (1984). Production frontiers and panel data. Journal of Business & Economic Statistics, 2(4), 367–374. DOI: 10.1080/07350015.1984.10509410.

Lovell, C. A. K. (1996). Applying efficiency measurement techniques to the measurement of productivity change. Journal of Productivity Analysis, 7, 329–340. DOI: 10.1007/BF00157047.

Kumbhakar, S. C. (2001). Estimation of profit functions when profit is not maximum. American Journal of Agricultural Economics, 83(1), 1–19. DOI: 10.1111/0002–9092.00133.

Shephard, R. W. (1953). Cost and production functions. Princeton, N. J.: Princeton University Press, 104. DOI: 10.1017/S1373971900103956.

Shephard, R. W. (1970). Theory of cost and production functions. Princeton, N. J.: Princeton University Press, 308. DOI: 10.1017/S002205070007457X.

Chambers, R. G., Chung, Y. & Färe, R. (1996). Benefit and distance functions. Journal of Economic Theory, 70(2), 407–419. DOI: 10.1006/jeth.1996.0096.

Chambers, R. G., Chung, Y. & Färe, R. (1998). Profit, directional distance functions, and Nerlovian efficiency. Journal of optimization theory and applications, 98(2), 351–364. DOI: 10.1023/A:1022637501082.

Chambers, R. G., Fāure, R. & Grosskopf, S. (1996). Productivity growth in APEC countries. Pacific Economic Review, 1(3), 181–190. DOI: 10.1111/j.1468–0106.1996.tb00184.x.

Luenberger, D. G. (1992). Benefit functions and duality. Journal of Mathematical Economics, 21(5), 461–481. DOI: 10.1016/0304–4068(92)90035–6.

Luenberger, D. G. (1995). Microeconomic theory. New York: Mcgraw-Hill.

Costa, L., Lansink, A. O. & Silva, E. (2010). Animals’ health control efficiency in Northwest Portugal: A two-stage DEA approach. Acta Agriculturae Scandinavica, Section C — Food Economics, 7(2–4), 197–208. DOI: 10.1080/16507541.2010.531941.

Ball, V. E., Fare, R., Grosskopf, S. & Nehring, R. (2001). Productivity of the US agricultural sector: the case of undesirable outputs. In: C. R. Hulten, E. R. Dean, M. J. Harper (Eds.), New developments in productivity analysis (pp. 541–586). Chicago: University of Chicago Press.

Chen, P.-C., Yu, M.-M., Chang, C.-C., Hsu, S.-H. & Managi, S. (2015). The enhanced Russell-based directional distance measure with undesirable outputs: numerical example considering CO2 emissions. Omega, 53, 30–40. DOI: 10.1016/j.omega.2014.12.001.

Chung, Y. H., Färe, R. & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51(3), 229–240. DOI: 10.1006/jema.1997.0146.

Djokoto, J. G. (2015). Technical Efficiency of Organic Agriculture: A Quantitative Review. Studies in Agricultural Economics, 117(2), 61–71. DOI: 10.7896/j.1512.

Rasmussen, S. (2010). Scale Efficiency in Danish Agriculture: An Input Distance-Function Approach. European Review of Agricultural Economics, 37(3), 335–367. DOI: 10.1093/erae/jbq023.

Sipiläinen, T., Kumbhakar, S. C. & Lien, G. (2014). Performance of Dairy Farms in Finland and Norway from 1991 to 2008. European Review of Agricultural Economics, 41(1), 63–86. DOI: 10.1093/erae/jbt012.

Tsionas, E. g., Kumbhakar, S. C. & Malikov, E (2015). Estimation of Input Distance Functions: A System Approach. American Journal of Agricultural Economics, 97(5), 1478–1493. DOI: 10.1093/ajae/aav012.

Alston, J. M., Marra, M. C., Pardey, P. G. & Wyatt, T. J. (2000). A Meta‐analysis of the Returns to Agricultural R&D. Australian Journal of Agricultural and Resource Economics, 44(2), 185–215.

McDonald, J. (2009). Using Least Squares and Tobit in Second Stage DEA Efficiency Analyses. European Journal of Operational Research, 197(2), 792–98. DOI: 10.1016/j.ejor.2008.07.039.

Ramalho, E. A., Ramalho, J. J. S. & Henriques, P. D. (2010). Fractional Regression Models for Second Stage DEA Efficiency Analyses. Journal of Productivity Analysis, 34(3), 239–255. DOI: 10.1007/s11123–010–0184–0.

Papke, L. E. & Wooldridge, J. M. (1996). Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics, 11(6), 619–632. DOI: 10.1002/(SICI)1099–1255(199611)11:6<619::AID-JAE418>3.0.CO;2–1

Nelson, J. P. & Kennedy, P. E. (2009). The Use (and Abuse) of Meta-Analysis in Environmental and Natural Resource Economics: An Assessment. Environmental and Resource Economics, 42(3), 345–377. DOI: 10.1007/s10640–008–9253–5.

Espey, M., Espey, J. & Shaw, W. D. (1997). Price Elasticity of Residential Demand for Water: A Meta‐analysis. Water Resources Research, 33(6), 1369–1374. DOI: 10.1029/97WR00571.

Stanley, T. D. & Doucouliagos, H. (2015). Neither Fixed nor Random: Weighted Least Squares Meta‐analysis. Statistics in Medicine, 34(13), 2116–2127. DOI: 10.1002/sim.6481.

Stanley, T. D. (2008). Meta‐regression Methods for Detecting and Estimating Empirical Effects in the Presence of Publication Selection. Oxford Bulletin of Economics and Statistics, 70(1), 103–127. DOI: 10.1111/j.1468–0084.2007.00487.x.

Alem, H. (2018). Effects of model specification, short-run, and long-run inefficiency: an empirical analysis of stochastic frontier models. Agricultural Economics, 648(11), 508–516. DOI: 10.17221/341/2017-AGRICECON.

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28.09.2021

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dos Santos, M. Q., Marta-Costa, A. A., & Rodriguez, X. A. (2021). Meta-regression Analysis of Technical (In)Efficiency in Agriculture: a Regional Approach. Economy of Regions, 17(3), 917–928. https://doi.org/10.17059/ekon.reg.2021-3-14

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Vol. 17 No. 3 (2021)

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Research articles

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Copyright (c) 2021 Micael Queiroga dos Santos, Ana Alexandra Marta-Costa, Xose Anton Rodriguez

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