Stochastic frontiers, productivity effects and development projects

Authors

  • Boris E. Bravo-Ureta University of Connecticut, USA

DOI:

https://doi.org/10.17811/ebl.3.1.2014.51-58

Abstract

A common objective of many development projects is to promote output growth as well as better management in order to improve incomes and reduce poverty. In other words, the purpose is to induce upwards shifts in the production frontier (i.e., technological change) while also promoting better management (i.e., narrowing the gap from the frontier). Given the link between managerial performance and technical efficiency, stochastic production frontiers are well suited for the task from a methodological point of view. Despite this suitability, work linking stochastic frontiers with impact evaluation methods has just begun and a major hurdle is resolving biases that might arise from selection on observables and unobservables. This article provides an overview of how impact evaluation and stochastic frontiers, two well-established areas in applied econometrics, are being brought together to shed light on the productivity effects of agricultural development interventions. 

Author Biography

Boris E. Bravo-Ureta, University of Connecticut, USA

Professor of Agricultural and Resource Economics Web: http://are.uconn.edu/bbu.php Google scholar citations (as of September 2013): 1,839 Dr. Bravo-Ureta has been a Professor of Agricultural and Resource Economics at the University of Connecticut since 1980. He served as Executive Director of the Office of International Affairs from July 1998 to August 2008 where he had oversight for international activities at UConn. He also serves as the Director of the Learning for International Development (LID) Program since August 2008. His technical expertise is in production and development economics.

References

Angrist, J. and Pischke, J. (2009) Mostly harmless Econometrics, Princeton University Press, New Jersey.

Barrett, C. and Carter, M. (2010) The power and pitfalls of experiments in Development Economics: some non-random reflections, Applied Economics Perspectives and Policy, 32, 515-548.

Bradford, D, Kleit, A., Krousel-Wood, M. and Re, R. (2001) Stochastic frontier estimation of cost models within the hospital, Review of Economics and Statistics, 83, 302–309.

Bravo-Ureta, B.E., Greene, W. and Solís, D. (2012) Technical efficiency analysis correcting for biases from observed and unobserved variables: an application to a natural resource management project, Empirical Economics, 43, 55-72.

Bravo-Ureta, B.E., Solís, D., Cocchi, H. and Quiroga, R. (2006) The impact of soil conservation and output diversification on farm income in Central American hillside farming, Agricultural Economics, 35, 267-276.

Bravo-Ureta, B.E, Solís, D., Moreira, V., Maripani, J., Thiam, A. and Rivas, T. (2007) Technical efficiency in farming: a meta-regression analysis, Journal of Productivity Analysis, 27, 57-72.

Caliendo, M. and Kopeinig, S. (2008) Some practical guidance on the implementation of propensity score matching, Journal of Economic Surveys, 22, 31–72.

Cavatassi, R., González-Flores, M.M., Winters, P., Andrade-Piedra, J., Espinosa, P. and Thiele, G. (2011) Linking smallholders to the new Agricultural Economy: the case of the Plataformas de Concertación in Ecuador, Journal of Development Studies, 41, 62-89.

Cooper, W.W. and Lovell, C.A.K. (2011) History lessons, Journal of Productivity Analysis, 36, 193-200.

Dinar, A., Karagiannis, G. and Tzouvelekas, V. (2007) Evaluating the impact of agricultural extension on farm’s performance in Crete: a non-neutral stochastic frontier approach, Agricultural Economics, 36, 135-146.

Duflo, E., Glennerster, R. and Kremer, M. (2008) Using randomization in Development Economics research: a toolkit, in Schultz, T. and Strauss, J. (eds) Handbook of Development Economics, 3895-3962, Amsterdam: Elsevier.

Gertler, P.J., Martinez, S., Premand, P., Rawlings, L.B. and Vermeersch, C.M.J. (2011) Impact evaluation in practice, The World Bank, Washington D.C.

González-Flores, M., Bravo-Ureta, B.E., Solís, D. and Winters. P. (2014) The impact of high value markets on smallholder efficiency in the Ecuadorean sierra: a stochastic production frontier approach correcting for selectivity bias, Food Policy, forthcoming.

Fried, H.K., Lovell, C.A.K. and Schmidt, P. (eds) (2010) The measurement of productive efficiency and productivity growth, Oxford University Press: Oxford.

Greene, W. (2010) A stochastic frontier model with correction for sample selection, Journal of Productivity Analysis, 34, 15-24.

Heckman, J. (1979) Sample selection bias as a specification error, Econometrica, 47, 153–161.

Hoch, I. (1976) Production functions and supply applications for California dairy farms, Giannini Foundation Monograph, 36.

IEG (Independent Evaluation Group) (2011) Impact evaluations in Agriculture: an assessment of the evidence, Washington, DC: World Bank.

Kaparakis, E., Miller, S. and Noulas, A. (1994) Short run cost inefficiency of commercial banks: a flexible stochastic frontier approach, Journal of Money Credit and Banking, 26, 21–28.

Khandker, S.R., Koolwal, G.B. and Samad, H.A. (2010) Handbook on impact evaluation: quantitative methods and practices, The World Bank, Washington D.C..

Kumbhakar, S., Tsionas, M. and Sipilainen, T. (2009) Joint estimation of technology choice and technical efficiency: an application to organic and conventional dairy farming, Journal of Productivity Analysis, 31, 151–162.

Lai, H., Polachek, S. and Wang, H. (2009) Estimation of a stochastic frontier model with a sample selection problem, Working Paper, Department of Economics, National Chung Cheng University.

Maffioli, A., Ubfal, D., Vázquez-Baré, G. and Cerdán-Infantes, P. (2011) Extension services, product quality and yields: the case of grapes in Argentina, Agricultural Economics, 42, 727-734.

Martin, J.P. and Page Jr, J.M. (1983) The impact of subsidies on X-efficiency in LDC industry: theory and an empirical test, The Review of Economics and Statistics, 65, 608-617.

Mayen, C., Balagtas, J. and Alexander, C. (2010) Technology adoption and technical efficiency: organic and conventional dairy farms in the United States, American Journal of Agricultural Economics, 92, 181-195.

Mundlak, Y. (1961) Empirical production function free of management bias, Journal of Farm Economics, 43, 44-56.

Rahman S., Wiboonpongse, A., Sriboonchitta, S. and Chaovanapoonphol, Y. (2009) Production efficiency of jasmine rice producers in Northern and North-Eastern Thailand, Journal of Agricultural Economics, 60, 419-435.

Ravallion, M. (2008) Evaluating anti-poverty programs, in Schultz, T. and Strauss, J. (eds) Handbook of Development Economics, Amsterdam: Elsevier, 3787-3846.

Sipiläinen, T. and Oude Lansink, A. (2005) Learning in switching to organic farming, NJF-Seminar 369, NJF Report 1, Nordic Association of Agricultural Scientists.

Solís, D., Bravo-Ureta, B.E. and Quiroga, R. (2007) Soil conservation and technical efficiency among hillside farmers in Central America: a switching regression model, Australian Journal of Agricultural and Resource Economics, 51, 491-510.

Taylor, T.G. and Shonkwiler, J.S. (1986) Alternative stochastic specifications of the frontier production function, the analysis of agricultural credit programs and technical efficiency, Journal of Development Economics, 21, 149-160.

Taylor, T.G., Drummond, H.E. and Gomes, A.T. (1986) Agricultural credit programs and production efficiency: an analysis of traditional farming in Southeastern Minas Gerais, Brazil, American Journal of Agricultural Economics, 68, 110-119.

Terza, J. (1986) FIML, method of moments and two stage method of moments estimators for nonlinear regression models with endogenous switching and sample selection, Working Paper, Department of Economics, Penn State University.

Terza, J.V. (2009) Parametric nonlinear regression with endogenous switching, Econometric Reviews, 28, 555–580.

Todd, P.E. (2008) Evaluating social programs with endogenous program placement and selection of the treated, in Schultz, T. and Strauss, J. (eds) Handbook of Development Economics, Amsterdam: Elsevier, 3846-3894.

Triebs, T.P. and Kumbhakar, S.C. (2013) Production and management: does inefficiency capture management?, paper presented at the 13th EWEPA Meeting, Helsinki, Finland.

Villano, R., Bravo-Ureta, B.E., Solís, D. and Fleming, E. (2014) Modern rice technologies and productivity in The Philippines: disentangling technology from managerial gaps, manuscript.

Winters, P., Salazar, L. and Maffioli, A. (2010) Designing impact evaluations for agricultural projects, Strategy Development Division, Inter-American Development Bank, Washington D.C.

World Bank (2006) Impact evaluation: the experience of the Independent Evaluation Group of the World Bank, Independent Evaluation Group, World Bank.

Downloads

Published

31-03-2014

How to Cite

Bravo-Ureta, B. E. (2014). Stochastic frontiers, productivity effects and development projects. Economics and Business Letters, 3(1), 51–58. https://doi.org/10.17811/ebl.3.1.2014.51-58