Gender and Productivity Differentials in Smallholder Groundnut Farming in Malawi: Accounting for Technology Differences

References

• Aguilar, A., Carranza, E., Goldstein, M., Kilic, T., & Oseni, G. (2015). Decomposition of gender differentials in agricultural productivity in Ethiopia. Agricultural Economics, 46(3), 311–334.
   Web of Science ®Google Scholar
• Aigner, D., Lovell, C., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37.
   Google Scholar
• Al-Hassan, S. (2012). Technical efficiency in smallholder paddy farms in Ghana: An analysis based on different farming systems and gender. Journal of Economics and Sustainable Development, 3(5), 91–105.
   Google Scholar
• Amsler, C., O’Donnell, C. J., & Schmidt, P. (2017). Stochastic metafrontiers. Econometric Reviews, 36(6–9), 1007–1020.
   Web of Science ®Google Scholar
• Austin, P. C. (2011). Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharmaceutical Statistics, 10(2), 150–161.
   PubMed Web of Science ®Google Scholar
• Baccouche, R., & Kouki, M. (2003). Stochastic production frontier and technical inefficiency: A sensitivity analysis. Econometric Reviews, 22(1), 79–91.
   Google Scholar
• Battese, G. E., Rao, D. S. P., & O’Donnell, C. J. (2004). A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. Journal of Productivity Analysis, 21(1), 91–103.
   Web of Science ®Google Scholar
• Battese, G. E., & Rao, P. D. S. (2002). Technology gap, efficiency, and a stochastic metafrontier function. International Journal of Business and Economics, 1(2), 87–93.
   Google Scholar
• Bellemare, M. F., & Wichman, C. J. (2020). Elasticities and the inverse hyperbolic sine transformation. Oxford Bulletin of Economics and Statistics, 82(1), 50–61.
   Web of Science ®Google Scholar
• BenYishay, A., Jones, M., Kondylis, F., & Mobarak, A. M. (2020). Gender gaps in technology diffusion. Journal of Development Economics, 143(102380), 1–26.
   Google Scholar
• Berge, E., Kambewa, D., Munthali, A., & Wiig, H. (2014). Lineage and land reforms in Malawi: Do matrilineal and patrilineal landholding systems represent a problem for land reforms in Malawi? Land Use Policy, 41, 61–69.
   Web of Science ®Google Scholar
• Bergman Lodin, J., Paulson, S., & Mugenyi, M. S. (2012). New seeds, gender norms and labor dynamics in Hoima District, Uganda. Journal of Eastern African Studies, 6(3), 405–422.
   Web of Science ®Google Scholar
• Bindlish, V., & Evenson, R. E. (1993). Evaluation of the performance of T&V extension in Kenya (World Bank Technical Paper No. 208). Washington D.C. World Bank.
   Google Scholar
• Bravo-Ureta, B. E. (2014). Stochastic frontiers, productivity effects and development projects. Economics and Business Letters, 3(1), 51–58.
   Google Scholar
• Bravo-Ureta, B. E., Higgins, D., & Arslan, A. (2020a). Irrigation infrastructure and farm productivity in the Philippines: A stochastic Meta-Frontier analysis. World Development, 135(105073), 1–15.
   Google Scholar
• Bravo-Ureta, B. E., Moreira, V. H., Troncoso, J. L., & Wall, A. (2020b). Plot‐level technical efficiency accounting for farm‐level effects: Evidence from Chilean wine grape producers. Agricultural Economics, 51(6), 811–824.
   Web of Science ®Google Scholar
• Bravo-Ureta, B. E., Solís, D., Moreira, V. H., Maripani, J. F., Thiam, A., & Rivas, T. (2007). Technical efficiency in farming: A meta-regression analysis. Journal of Productivity Analysis, 27(1), 57–72.
   Web of Science ®Google Scholar
• Burke, W. J., Hichaambwa, M., Banda, D., & Jayne, T. S. (2011, March). The cost of producing maize for smallholders in Rural Zambia (Working Paper No. 50). Lusaka, Zambia. Food Security Research Project.
   Google Scholar
• Carletto, C., Gourlay, S., & Winters, P. (2015). From guesstimates to GPStimates: Land area measurement and implications for agricultural analysis. Journal of African Economies, 24(5), 593–628.
   Web of Science ®Google Scholar
• Cavatassi, R., González-Flores, M., Winters, P., Andrade-Piedra, J., Espinosa, P., & Thiele, G. (2011). Linking smallholders to the new agricultural economy: The case of the plataformas de concertación in Ecuador. Journal of Development Studies, 47(10), 1545–1573.
   Web of Science ®Google Scholar
• Chikowo, R., Snapp, S., & Hoeschle-Zeledon, I. (2015). Groundnut production in Malawi: The cash ‘cow’ and butter that nourishes families (Brief No. 36). Africa RISING.
   Google Scholar
• Clinton Foundation. (2016). Anchor Farm Project: Malawi. Clinton Foundation Accessed7 6 2018. https://www.clintonfoundation.org/our-work/clinton-development-initiative/programs/anchor-farm-project-Malawi
   Google Scholar
• Dadzie, S. K. N., & Dasmani, I. (2010). Gender difference and farm level efficiency: Metafrontier production function approach. Journal of Development and Agricultural Economics, 2(12), 441–451.
   Google Scholar
• de La O Campos, A. P., Covarrubias, K. A., & Patron, A. P. (2016). How does the choice of the gender indicator affect the analysis of gender differences in agricultural productivity? Evidence from Uganda. World Development, 77, 17–33.
   Web of Science ®Google Scholar
• Deaton, A., & Cartwright, N. (2018). Understanding and misunderstanding randomized controlled trials. Social Science & Medicine (1982), 210, 2–21.
   PubMed Web of Science ®Google Scholar
• Desiere, S., & Jolliffe, D. (2018). Land productivity and plot size: Is measurement error driving the inverse relationship? Journal of Development Economics, 130, 84–98.
   Web of Science ®Google Scholar
• Doss, C. R. (2001). Designing agricultural technology for African women farmers: Lessons from 25 years of experience. World Development, 29(12), 2075–2092.
   Web of Science ®Google Scholar
• Doss, C. R. (2014). If women hold up half the sky, how much of the world’s food do they produce? In A. R. Quisumbing, R. Meinzen-Dick, T. L. Raney, A. Croppenstedt, J. A. Behrman, & A. Peterman (Eds.), Gender in Agriculture (Vol. 79, pp. 69–88). Springer Netherlands
   Google Scholar
• Edriss, A. ‑. K. (2005). Gender differential in groundnut productivity in Sub-Saharan Africa: The case of Lilongwe and Salima ADDs. Bunda Journal of Agriculture, Environmental Science and Technology, 3, 29–37.
   Google Scholar
• Exagris Africa. (2014). Procedures for using Exagris land. Exagris Africa: Exagris Africa.
   Google Scholar
• FAO. (2011a). The role of women in agriculture (ESA Working Paper 11–02). Rome. Author Accessed 12 9 2020. http://www.fao.org/3/am307e/am307e00.pdf
   Google Scholar
• FAO. (2011b). Women in agriculture: Closing the gender gap for development. The state of food and agriculture. FAO Accessed 2 9 2020. http://www.fao.org/publications/sofa/2010-11/en/
   Google Scholar
• FAO. (2015). National investment profile. Water for agriculture and energy Accessed 3 6 2021. Author. http://www.fao.org/fileadmin/user_upload/agwa/docs/NIP_Malawi_Final.pdf
   Google Scholar
• Fifty by 2030 Project. (2019). Paving the way for data-smart agriculture Accessed 8 6 2021. https://www.50x2030.org/about
   Google Scholar
• Fisher, M., & Kandiwa, V. (2014). Can agricultural input subsidies reduce the gender gap in modern maize adoption? Evidence from Malawi. Food Policy, 45, 101–111.
   Web of Science ®Google Scholar
• Frölich, M. (2007). Propensity score matching without conditional Independence assumption—with an application to the gender wage gap in the United Kingdom. The Econometrics Journal, 10(2), 359–407.
   Web of Science ®Google Scholar
• González-Flores, M., Bravo-Ureta, B. E., Solís, D., & Winters, P. (2014). The impact of high value markets on smallholder productivity in the Ecuadorean Sierra: A stochastic production frontier approach correcting for selectivity bias. Food Policy, 44, 237–247.
   Web of Science ®Google Scholar
• Gourlay, S., Kilic, T., & Lobell, D. B. (2019). A new spin on an old debate: Errors in farmer-reported production and their implications for inverse scale - Productivity relationship in Uganda. Journal of Development Economics, 141(102376), 1–35.
   Google Scholar
• Guiomar, N., Godinho, S., Rivera, M., Pinto-Correia, T., Machado, R., Czekaj, M., … Puchała, J. (2021). Assessing food availability: A novel approach for the quantitative estimation of the contribution of small farms in regional food systems in Europe. Global Food Security, 30, 100555.
   Google Scholar
• Hayami, Y., & Ruttan, V. W. (1970). Agricultural productivity differences among countries. The American Economic Review, 60(5), 895–911.
   Web of Science ®Google Scholar
• Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007). Matching as nonparametric preprocessing for Reducing model dependence in parametric causal inference. Political Analysis, 15(3), 199–236.
   Web of Science ®Google Scholar
• Horrell, S., & Krishnan, P. (2007). Poverty and productivity in female-headed households in Zimbabwe. The Journal of Development Studies, 43(8), 1351–1380.
   Web of Science ®Google Scholar
• Huang, C. J., Huang, T. ‑. H., & Liu, N. ‑. H. (2014). A new approach to estimating the metafrontier production function based on a stochastic frontier framework. Journal of Productivity Analysis, 42(3), 241–254.
   Web of Science ®Google Scholar
• Jayne, T. S., Chamberlin, J., & Headey, D. D. (2014). Land pressures, the evolution of farming systems, and development strategies in Africa: A synthesis. Food Policy, 48, 1–17.
   Web of Science ®Google Scholar
• Julien, J. C., Bravo-Ureta, B. E., & Rada, N. E. (2019). Assessing farm performance by size in Malawi, Tanzania, and Uganda. Food Policy, 84, 153–164.
   Web of Science ®Google Scholar
• Kagin, J., Taylor, J. E., & Yúnez-Naude, A. (2016). Inverse productivity or inverse efficiency? Evidence from Mexico. Journal of Development Studies, 52(3), 396–411.
   Web of Science ®Google Scholar
• Karagiannis, G., & Kellermann, M. (2019). Stochastic frontier models with correlated effects. Journal of Productivity Analysis, 51(2–3), 175–187.
   Web of Science ®Google Scholar
• Kilic, T., Palacios-López, A., & Goldstein, M. (2015). Caught in a productivity trap: A distributional perspective on gender differences in Malawian Agriculture. World Development, 70, 416–463.
   Web of Science ®Google Scholar
• Kinkingninhoun-Mêdagbé, F. M., Diagne, A., Simtowe, F., Agboh-Noameshie, A. R., & Adégbola, P. Y. (2010). Gender discrimination and its impact on income, productivity, and technical efficiency: Evidence from Benin. Agriculture and Human Values, 27(1), 57–69.
   Web of Science ®Google Scholar
• Kumbhakar, S. C., & Lovell, C. A. K. (2000). Stochastic frontier analysis. United Kingdom: Cambridge University Press.
   Google Scholar
• Lamb, R. L. (2003). Inverse productivity: Land quality, labor markets, and measurement error. Journal of Development Economics, 71(1), 71–95.
   Web of Science ®Google Scholar
• Lobell, D. B., Azzari, G., Burke, M., Gourlay, S., Jin, Z., Kilic, T., & Murray, S. (2020). Eyes in the sky, boots on the ground: Assessing satellite‐ and ground‐based approaches to crop yield measurement and analysis. American Journal of Agricultural Economics, 102(1), 202–219.
   Web of Science ®Google Scholar
• MacKinnon, J. G. (2013). Thirty years of heteroskedasticity-robust inference. In X. Chen & N. R. Swanson, Eds., Recent advances and future directions in causality, prediction, and specification analysis, pp. 437–461. Springer New York.
   Google Scholar
• Maertens, A., Mhango, W., & Michelson, H. (2020). The effect of demonstration plots and the warehouse receipt system on integrated soil fertility management adoption, yield and income of smallholder farmers. (Impact Evaluation Report No. 122). New Delhi. International Initiative for Impact Evaluation Accessed 3 6 2021. A study from Malawi’s Anchor Farms https://reliefweb.int/sites/reliefweb.int/files/resources/IE122-TW4.1018-ISFM-Malawi.pdf
   Google Scholar
• Meinzen-Dick, R., Brown, L. R., Feldstein, H. S., & Quisumbing, A. R. (1997). Gender, property rights, and natural resources. World Development, 25(8), 1303–1315.
   Web of Science ®Google Scholar
• Meinzen-Dick, R., Johnson, N., Quisumbing, A. R., Njuki, J., Behrman, J. A., Rubin, D., … Waithanji, E. (2014). The gender asset gap and its implications for agricultural and rural development. In A. R. Quisumbing, R. Meinzen-Dick, T. L. Raney, A. Croppenstedt, J. A. Behrman, & A. Peterman (Eds.), Gender in agriculture (Vol. 32, pp. 91–115). Springer Netherlands
   Google Scholar
• Mishra, A. K., Khanal, A. R., & Mohanty, S. (2017). Gender differentials in farming efficiency and profits: The case of rice production in the Philippines. Land Use Policy, 63, 461–469.
   Web of Science ®Google Scholar
• Morrison, A., Raju, D., & Sinha, N. (2007). Gender equality, poverty and economic growth. The World Bank.
   Google Scholar
• Mugisha, J., Sebatta, C., Mausch, K., Ahikiriza, E., Okello, D. K., & Njuguna, E. M. (2019). Bridging the gap: Decomposing sources of gender yield gaps in Uganda groundnut production. Gender, Technology and Development, 23(1), 19–35.
   PubMed Web of Science ®Google Scholar
• Naab, J. B., Tsigbey, F. K., Prasad, P., Boote, K. J., Bailey, J. E., & Brandenburg, R. L. (2005). Effects of sowing date and fungicide application on yield of early and late maturing peanut cultivars grown under rainfed conditions in Ghana. Crop Protection, 24(4), 325–332.
   Web of Science ®Google Scholar
• Njuki, E., & Bravo-Ureta, B. E. (2019). Examining irrigation productivity in U.S. agriculture using a single-factor approach. Journal of Productivity Analysis, 51(2–3), 125–136.
   Web of Science ®Google Scholar
• Njuki, E., Bravo-Ureta, B. E., & O’Donnell, C. J. (2018). A new look at the decomposition of agricultural productivity growth incorporating weather effects. PloS One, 13(2), 1–21.
   Web of Science ®Google Scholar
• Ñopo, H. (2008). Matching as a tool to decompose wage gaps. The Review of Economics and Statistics, 90(2), 290–299.
   Web of Science ®Google Scholar
• O’Donnell, C. J. (2016). Using information about technologies, markets and firm behaviour to decompose a proper productivity index. Journal of Econometrics, 190(2), 328–340.
   Web of Science ®Google Scholar
• O’Donnell, C. J. (2018). Productivity and efficiency analysis. In An Economic Approach to Measuring and Explaining Managerial Performance. Springer Singapore.
   Google Scholar
• O’Donnell, C. J., Rao, D. S. P., & Battese, G. E. (2008). Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Economics, 34(2), 231–255.
   Web of Science ®Google Scholar
• Ogundari, K. (2014). The paradigm of agricultural efficiency and its implication on food security in Africa: What does meta-analysis reveal? World Development, 64, 690–702.
   Web of Science ®Google Scholar
• Orr, A., Tsusaka, T., Kee-Tui, S. H., & Msere, H. (2016). What do we mean by ‘women’s crops’? Commercialisation, gender and the power to name. Journal of International Development, 28(6), 919–937.
   Web of Science ®Google Scholar
• Oseni, G., Corral, P., Goldstein, M., & Winters, P. (2015). Explaining gender differentials in agricultural production in Nigeria. Agricultural Economics, 46(3), 285–310.
   Web of Science ®Google Scholar
• Owusu, E. S., & Bravo-Ureta, B. E. (2019). Benefits and costs of groundnut and soybean production: A farm level analysis for Malawi. University of Connecticut Research Report.
   Google Scholar
• Owusu, V., Donkor, E., & Owusu-Sekyere, E. (2018). Accounting for the gender technology gap amongst smallholder rice farmers in Northern Ghana. Journal of Agricultural Economics, 69(2), 439–457.
   Web of Science ®Google Scholar
• Peterman, A., Behrman, J. A., & Quisumbing, A. R. (2014). A review of empirical evidence on gender differences in Nonland agricultural inputs, technology, and services in developing countries. In A. R. Quisumbing, R. Meinzen-Dick, T. L. Raney, A. Croppenstedt, J. A. Behrman, & A. Peterman (Eds.), Gender in agriculture (Vol. 80, pp. 145–186). Springer Netherlands
   Google Scholar
• Peterman, A., Quisumbing, A., Behrman, J., & Nkonya, E. (2011). Understanding the complexities surrounding gender differences in agricultural productivity in Nigeria and Uganda. Journal of Development Studies, 47(10), 1482–1509.
   Web of Science ®Google Scholar
• Polo, K. (2015). Linking smallholder and commercial farms: A strategy for tackling SDG 2. Devex Accessed 3 6 2021. https://www.devex.com/news/linking-smallholder-and-commercial-farms-a-strategy-for-tackling-sdg–2–86950
   Google Scholar
• Quisumbing, A. R. (1996). Male-female differences in agricultural productivity: Methodological issues and empirical evidence. World Development, 24(10), 1579–1595.
   Web of Science ®Google Scholar
• Quisumbing, A. R., Haddad, L., & Peña, C. (2001). Are women overrepresented among the poor? An analysis of poverty in 10 developing countries. Journal of Development Economics, 66(1), 225–269.
   Web of Science ®Google Scholar
• Quisumbing, A. R., Meinzen-Dick, R., Raney, T. L., Croppenstedt, A., Behrman, J. A., & Peterman, A., Eds. 2014. Gender in agriculture. Springer Netherlands.
   Google Scholar
• Rubin, D. B. (2001). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services & Outcomes Research Methodology, 2(3/4), 169–188.
   Google Scholar
• Saito, K. A., Mekonnen, H., & Spurling, D. (1994). Raising the productivity of women farmers in Sub-Saharan Africa (World Bank discussion paper No. 230). Washington, D.C. World Bank.
   Google Scholar
• Sell, M., Bäckman, S., TettehAnang, B., & Niemi, J. K. (2018). The unequal efficiency gap: Key factors influencing women farmer’s efficiency in Uganda. Cogent Food & Agriculture, 4(1), 1–18.
   Web of Science ®Google Scholar
• Sherlund, S. M., Barrett, C. B., & Adesina, A. A. (2002). Smallholder technical efficiency controlling for environmental production conditions. Journal of Development Economics, 69(1), 85–101.
   Web of Science ®Google Scholar
• Simonyan, J. B., Umoren, B. D., & Okoye, B. C. (2011). Gender differentials in technical efficiency among maize farmers in essien udim local government area, Nigeria. International Journal of Economics and Managment Sciences, 1(2), 17–23.
   Google Scholar
• Slavchevska, V. (2015). Gender differences in agricultural productivity: The case of Tanzania. Agricultural Economics, 46(3), 335–355.
   Web of Science ®Google Scholar
• Smith, J. (2018). It’s time for Malawi to quit tobacco. The Conversation Accessed 2 11 2019. https://theconversation.com/its-time-for-Malawi-to-quit-tobacco–96516
   Google Scholar
• Timothy, A. T., & Adeoti, A. I. (2006). Gender inequalities and economic efficiency: New evidence from cassava-based farm holdings in rural south-western Nigeria. African Development Review, 18(3), 428–443.
   Web of Science ®Google Scholar
• Tsusaka, T. W., Msere, H. W., Gondwe, L., Madzonga, O., Clarke, S., Siambe, M., & Okori, P. (2016a). Assessing the post-harvest constraints in smallholders’ groundnut production: A survey in central Malawi. Agricultural Science Research Journal, 6(9), 213–226.
   Google Scholar
• Tsusaka, T. W., Msere, H. W., Siambi, M., Mazvimavi, K., & Okori, P. (2016b). Evolution and impacts of groundnut research and development in Malawi: An ex-post analysis. African Journal of Agricultural Research, 11(3), 139–158.
   Google Scholar
• Udry, C., Hoddinott, J., Alderman, H., & Haddad, L. (1995). Gender differentials in farm productivity: Implications for household efficiency and agricultural policy. Food Policy, 20(5), 407–423.
   Web of Science ®Google Scholar
• USAID. (2018). Gender considerations for researchers working in groundnuts. United States Agency for International Development Accessed26 3 2018. https://pdf.usaid.gov/pdf_docs/PA00T3SR.pdf
   Google Scholar
• Wanjala, B. M. (2014). Gendered asset inequalities in Africa. Development, 57(3–4), 472–480.
   Google Scholar
• Wooldridge, J. M. (2005). Violating ignorability of treatment by controlling for too many factors. Econometric Theory, 21(5). doi:https://doi.org/10.1017/S0266466605050516
   Web of Science ®Google Scholar
• World Bank. (2012). World development report 2012 Accessed 2 11 2021. https://doi.org/https://doi.org/10.1596/978-0-8213-8810-5
   Google Scholar
• World Bank. (2020). Survey solutions. The World Bank Accessed27 11 2020. https://mysurvey.solutions/en/
   Google Scholar
• World Bank. (2021). Living standards measurement survey - integrated survey on agriculture (LSMS-ISA) Accessed28 6 2020. https://www.worldbank.org/en/programs/lsms/initiatives/lsms-ISA#acc5
   Google Scholar
• Zellner, A., Kmenta, J., & Dreze, J. (1966). Specification and estimation of cobb-douglas production function models. Econometrica, 34(4), 784.
   Web of Science ®Google Scholar