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Price Incentives, Non‐price Factors and Agricultural Production in Sub‐Saharan Africa: A Cointegration Analysis

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  • Rainer Thiele

Abstract

This paper deals with the question of how responsive farmers in Sub-Saharan Africa (SSA) are to changes in incentives. Employing Johansen's multivariate cointegration approach and covering the period 1965-99, it investigates for ten selected SSA countries the long-run effect of pricing policies,macroeconomic distortions, and certain non-price factors on agricultural production. I turns out that - in those cases where cointegration relationships are found - estimated supply elasticities tend to lie between 0.20 and 0.50. Among the non-price factors, drought episodes have significantly impaired agricultural growth in six out of ten sample countries. Technical progress as measured by a simple deterministic trend has only had a minor impact on output.
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  • Rainer Thiele, 2003. "Price Incentives, Non‐price Factors and Agricultural Production in Sub‐Saharan Africa: A Cointegration Analysis," African Development Review, African Development Bank, vol. 15(2‐3), pages 425-438.
    • Handle: RePEc:adb:adbadr:860
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    Cited by:

    1. Francesco Caracciolo & Fabio Gaetano Santeramo, 2013. "Price Trends and Income Inequalities: Will Sub-Saharan Africa Reduce the Gap?," African Development Review, African Development Bank, vol. 25(1), pages 42-54, March.
    2. Didier Yélognissè Alia & Anne Floquet & Epiphane Adjovi, 2017. "Heterogeneous Welfare Effect of Cotton Pricing on Households in Benin," African Development Review, African Development Bank, vol. 29(2), pages 107-121, June.
    3. Magrini, Emiliano & Morales-Opazo, Cristian & Balie, Jean, 2014. "Supply response along the value chain in selected SSA countries: the case of grains," 2014: Food, Resources and Conflict, December 7-9, 2014. San Diego, California 197193, International Agricultural Trade Research Consortium.
    4. Kariuki, George Mbugua & Njaramba, Jennifer & Ombuki, Charles, 2021. "Tea Production Response to Climate Change in Kenya: An Autoregressive Distributed Lag Approach," African Journal of Economic Review, African Journal of Economic Review, vol. 10(1), December.
    5. Johnson, Michael E. & Takeshima, Hiroyuki & Gyimah-Brempong, Kwabena, 2013. "Assessing the potential and policy alternatives for achieving rice competitiveness and growth in Nigeria:," IFPRI discussion papers 1301, International Food Policy Research Institute (IFPRI).
    6. Olubode-Awosola, O.O. & Oyewumi, Olubukola Ayodeju & Jooste, Andre, 2006. "Vector error correction modelling of Nigerian agricultural supply response," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 45(4), pages 1-16, December.
    7. Donato, Romano & Carraro, Alessandro, 2015. "Modelling Acreage, Production and Yield Supply Response to Domestic Price Volatility," 2015 Fourth Congress, June 11-12, 2015, Ancona, Italy 207278, Italian Association of Agricultural and Applied Economics (AIEAA).
    8. Anbes Tenaye, 2020. "New Evidence Using a Dynamic Panel Data Approach: Cereal Supply Response in Smallholder Agriculture in Ethiopia," Economies, MDPI, vol. 8(3), pages 1-24, July.
    9. Bingxin Yu & Shenggen Fan, 2011. "Rice production response in Cambodia," Agricultural Economics, International Association of Agricultural Economists, vol. 42(3), pages 437-450, May.
    10. Olubode-Awosola, O.O. & van Schalkwyk, H.D. & Jooste, A., 2008. "Mathematical modeling of the South African land redistribution for development policy," Journal of Policy Modeling, Elsevier, vol. 30(5), pages 841-855.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices

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