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How Productive is Rural Infrastructure? Evidence on Some Agricultural Crops in Colombia

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Abstract

This paper evaluates the role of rural infrastructure on the performance of some agricultural crops in Colombia. The study utilizes geo-referenced cross sectional data of four crops, coffee, rice, beans and plantains, collected for the majority of municipalities. Using genetic matching models, we find that both having access to irrigation and drainage systems and better infrastructure for marketing –rural roads and nearby retail and wholesale centers– significantly increase crop yield as well as planted and harvested areas. Results are robust to a suitable set of matching algorithms. The positive and significant impact on agricultural development provides support to reorient agricultural policy towards the supply of public goods that pushes up productivity.

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  • Ignacio Lozano-Espitia & Lina Ma. Ramírez-Villegas, 2016. "How Productive is Rural Infrastructure? Evidence on Some Agricultural Crops in Colombia," Borradores de Economia 948, Banco de la Republica de Colombia.
    • Handle: RePEc:bdr:borrec:948
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    1. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    2. Mendes, Sergio Magno & Teixeira, Erly Cardoso & Salvato, Marcio Antonio, 2008. "Effect of infrastructure investments on total factor productivity (TFP) in Brazilian agriculture," Working Papers in Applied Economics 53441, Universidade Federal de Vicosa, Departamento de Economia Rural.
    3. Fan, Shenggen & Zhang, Xiaobo, 2004. "Infrastructure and regional economic development in rural China," China Economic Review, Elsevier, vol. 15(2), pages 203-214.
    4. Timmer, C. Peter, 2002. "Agriculture and economic development," Handbook of Agricultural Economics,in: B. L. Gardner & G. C. Rausser (ed.), Handbook of Agricultural Economics, edition 1, volume 2, chapter 29, pages 1487-1546 Elsevier.
    5. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    6. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    7. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," Review of Economic Studies, Oxford University Press, vol. 65(2), pages 261-294.
    8. Roberto Steiner & Natalia Salazar Ferro & Alejandro Becerra, 2015. "La política de precios del café en Colombia," COYUNTURA ECONÓMICA, FEDESARROLLO, vol. 45(2), pages 101-136, December.
    9. Alexis Diamond & Jasjeet S. Sekhon, 2013. "Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 932-945, July.
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    More about this item

    Keywords

    Public Goods; Agricultural Productivity; Irrigation System; Road Maintenance; Treatment Effect Models;

    JEL classification:

    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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