Food Security

, Volume 9, Issue 2, pp 281–300 | Cite as

Weather extremes and household welfare in rural Kenya

  • Ayala Wineman
  • Nicole M. Mason
  • Justus Ochieng
  • Lilian Kirimi
Original Paper
  • 251 Downloads

Abstract

Households in rural Kenya are sensitive to weather shocks through their reliance on rain-fed agriculture and livestock. Yet the extent of vulnerability is poorly understood, particularly in reference to extreme weather. This paper uses temporally and spatially disaggregated weather data and three waves of household panel survey data to understand the impact of weather extremes –including periods of high and low rainfall, heat, and wind– on household welfare. Particular attention is paid to heterogeneous effects across agro-ecological regions. We find that all types of extreme weather affect household well-being, although effects sometimes differ for income and calorie estimates. Periods of drought are the most consistently negative weather shock across various regions. An examination of the channels through which weather affects welfare reveals that drought conditions reduce income from both on- and off-farm sources, though households compensate for diminished on-farm production with food purchases. The paper further explores the household and community characteristics that mitigate the adverse effects of drought. In particular, access to credit and a more diverse income base seem to render a household more resilient.

Keywords

Food security Household welfare Kenya Resilience Weather shocks 

References

  1. Ahmed, S. A., Diffenbaugh, S., Hertel, T. W., Lobell, D. B., Ramankutty, N., Rios, A. R., & Rowhani, P. (2013). Climate volatility and poverty vulnerability in Tanzania. Global Environmental Change, 21(1), 46–55.CrossRefGoogle Scholar
  2. Argwings-Kodhek, G., Jayne, T. S., Nyambane, G., Awuor, T., & Yamano, T. (1998). How can micro-level household information make a difference for agricultural policy making? Selected examples from the KAMPAP survey of smallholder agriculture and non-farm activities for selected districts in Kenya. Nairobi: Tegemeo Institute of Agricultural Policy and Development. Available at http://tegemeo.org/images/downloads/publications/technical_reports/TR26.pdf. Cited 12 April 2016.
  3. Arouri, M., Nguyen, C., & Youssef, A. B. (2015). Natural disasters, household welfare, and resilience: evidence from rural Vietnam. World Development, 70, 59–77.CrossRefGoogle Scholar
  4. Auffhammer, M., Hsiang, S. M., Schlenker, W., & Sobel, A. (2013). Using weather data and climate model output in economic analyses of climate change. Review of Environmental Economics and Policy, 7(2), 181–198.CrossRefGoogle Scholar
  5. Baez, J., de la Fuente, A., & Santos, I. (2010). Do natural disasters affect human capital? An assessment based on existing empirical evidence. Discussion paper no. 5164. Bonn: Institute for the Study of Labor (IZA).Google Scholar
  6. Baez, J., Lucchetti, L., Genoni, M., & Salazar, M. (2015). Gone with the storm: rainfall shocks and household well-being in Guatemala. Policy research working paper 7177. Washington, D. C: World Bank.CrossRefGoogle Scholar
  7. Barrett, C., & Constas, M. (2014). Toward a theory of resilience for international development applications. Proceedings of the National Academy of Science, 111(40), 14625–14630.CrossRefGoogle Scholar
  8. Barrett, C., Marenya, P., Mcpeak, J., Minten, B., Murithi, F., Oluoch-Kosura, W., Place, F., Randrianarisoa, J., Rasambainarivo, J., & Wangila, J. (2006). Welfare dynamics in rural Kenya and Madagascar. Journal of Development Studies, 42(2), 248–277.CrossRefGoogle Scholar
  9. Béné, C., Wood, R. G., Newsham, A., & Davies, M. (2012). Resilience: new utopia or new tyranny? Reflection about the potentials and limits of the concept of resilience in relation to vulnerability reduction programmes. Working paper no. 405. Brighton: Institute for Development Studies.Google Scholar
  10. Burbidge, J. B., Magee, L., & Robb, A. L. (1988). Alternative transformations to handle extreme values of the dependent variable. Journal of the American Statistical Association, 83(401), 123–127.CrossRefGoogle Scholar
  11. Burgess, R., Deschenes, O., Donaldson, D., & Greenstone, M. (2011). Weather and death in India. Cambridge: Massachusetts Institute of Technology, Department of Economics. Mimeo.Google Scholar
  12. Cissé, J. D., & Barrett, C. B. (2016). Estimating development resilience: A conditional moments-based approach. Paper presented at the Centre for the Study of African Economies conference, 20–22 March, Oxford.Google Scholar
  13. Christiaensen, L., & Subbarao, K. (2005). Towards an understanding of household vulnerability in rural Kenya. Journal of African Economies, 14(4), 520–558.CrossRefGoogle Scholar
  14. Christiaensen, L., Hoffmann, V., & Sarris, A. (2007). Gauging the welfare effects of shocks in rural Tanzania. Policy research working paper no. 406. Washington, D. C: The World Bank.CrossRefGoogle Scholar
  15. Coates, J. (2013). Build it back better: deconstructing food security for improved measurement and action. Global Food Security, 2(1), 188–194.CrossRefGoogle Scholar
  16. Constas, M., Frankenberger, T., & Hoddinott, J. (2014). Resilience measurement principles. Food security information network technical series 1. Rome: Food and Agricultural Organization and World Food Programme.Google Scholar
  17. Cooper, P., Dimes, J., Rao, K., Shapiro, B., Shiferaw, B., & Twomlow, S. (2008). Coping better with current climatic variability in the rain-fed farming systems of sub-Saharan Africa: an essential first step in adapting to future climate change? Agriculture, Ecosystems, and Environment, 126(1–2), 24–35.CrossRefGoogle Scholar
  18. Davies, M., Béné, C., Arnall, A., Tanner, T., Newsham, A., & Coirolo, C. (2013). Promoting resilient livelihoods through adaptive social protection: lessons from 124 programs in South Asia. Development Policy Review, 31(1), 27–58.CrossRefGoogle Scholar
  19. Dell, M., Jones, B. F., & Olken, B. A. (2012). Temperature shocks and economic growth: evidence from the last half century. American Economic Journal: Macroeconomics, 4(3), 66–95.Google Scholar
  20. Dell, M., Jones, B. F., & Olken, B. A. (2014). What do we learn from the weather? The new climate-economy literature. Journal of Economic Literature, 52(3), 740–798.CrossRefGoogle Scholar
  21. Dercon, S., & Krishnan, P. (2000). Vulnerability, seasonality, and poverty in Ethiopia. Journal of Development Studies, 36(6), 25–53.CrossRefGoogle Scholar
  22. Dercon, S., Hoddinott, J., & Woldehanna, T. (2005). Shocks and consumption in 15 Ethiopian villages, 1999–2004. Journal of African Economies, 14(4), 559–585.CrossRefGoogle Scholar
  23. Fafchamps, M., Udry, C., & Czukas, K. (1998). Drought and savings in West Africa: are livestock a buffer stock? Journal of Development Economics, 55(2), 273–482.CrossRefGoogle Scholar
  24. Famine Early Warning Network (FEWSNET) (2011). Livelihoods zoning “plus” activity in Kenya. Available at http://www.fews.net/sites/default/files/documents/reports/KE_livelihood_profiles.pdf. Cited 12 April 2016.
  25. Food and Agricultural Organization of the United Nations (FAO) (2015). Crop calendar. Available at http://www.fao.org/agriculture/seed/cropcalendar. Cited 12 April 2016.
  26. Filmer, D., & Pritchett, L. (2001). Estimating wealth effects without expenditure data-or tears: an application to educational enrollments in states of India. Demography, 38(1), 115–132.PubMedGoogle Scholar
  27. Funk, C. C., Peterson, P. J., Landsfeld, M. F., Pedreros, D. H., Verdin, J. P., Rowland, J. D., Romero, B. E., Husak, G. J., Michaelsen, J. C., & Verdin, A. P. (2014). A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, doi:10.3133/ds832.
  28. Guerrero Compeán, R. (2013). Weather and welfare: health and agricultural impacts of climate extremes, evidence from Mexico. Working paper no. 391. Washington, D. C: Inter-American Development Bank.Google Scholar
  29. HarvestChoice (2015). Tropical Livestock Units. Available at http://harvestchoice.org/maps/total-livestock-population-tlu-2005. Cited 12 April 2016.
  30. Herrero, M., Ringler, C., van de Steeg, J., Thornton, P., Zuo, T., Bryan, E., Omolo, A., Koo, J., & Notenbaert, A. (2010). Climate variability and climate change and their impacts on Kenya’s agricultural sector. Nairobi: International Livestock Research Institute.Google Scholar
  31. Hirvonen, K. (2016). Temperature shocks, household consumption and internal migration: evidence from rural Tanzania. American Journal of Agricultural Economics98(4), 1230–1249.Google Scholar
  32. Hoddinott, J. (2006). Shocks and their consequences across and within households in rural Zimbabwe. Journal of Development Studies, 42(2), 301–321.CrossRefGoogle Scholar
  33. Hoddinott, J. (2014). Understanding resilience for food and nutrition security. 2020 resilience conference paper no. 8. Building resilience for food and nutrition security. Washington, D. C: International Food Policy Research Institute.Google Scholar
  34. Intergovernmental Panel on Climate Change (IPCC). (2014). Climate change 2014: impacts, adaptation, and vulnerability. Part a: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate. Cambridge: Cambridge University Press.Google Scholar
  35. Jodlowski, M., Winter-Nelson, A., Baylis, K., & Goldsmith, P. D. (2016). Milk in the data: food security impacts from a livestock field experiment in Zambia. World Development, 77, 99–114.CrossRefGoogle Scholar
  36. Kabubo-Mariara, J., & Karanja, F. (2007). The economic impact of climate change on Kenyan crop agriculture: a Ricardian approach. Global and Planetary Change, 57(3–4), 319–330.CrossRefGoogle Scholar
  37. Kazianga, H., & Udry, C. (2006). Consumption smoothing? Livestock, insurance, and drought in rural Burkina Faso. Journal of Development Economics, 79(2), 413–446.CrossRefGoogle Scholar
  38. Lobell, D., Banziger, M., Magorokosho, C., & Vivek, B. (2011). Nonlinear heat effects on African maize as evidenced by historical yield trials. Nature Climate Change, 1, 42–45.CrossRefGoogle Scholar
  39. Lukmanji, Z., Hertzmark, E., Mlingi, N., Assey, V., Ndossi, G., & Fawzi, W. (2008). Tanzania food composition tables. Dar es Salaam: Muhimbili University of Health and Allied Sciences, Tanzania Food and Nutrition Centre, and Harvard School of Public Health.Google Scholar
  40. Maystadt, J.-F., & Ecker, O. (2014). Extreme weather and civil war: does drought fuel conflict in Somalia through livestock price shocks? American Journal of Agricultural Economics, 96(4), 1157–1182.CrossRefGoogle Scholar
  41. Muyanga, M., Jayne, T. S., & Burke, W. J. (2013). Pathways into and out of poverty: a study of determinants of rural household wealth dynamics in Kenya. Journal of Development Studies, 49(10), 1358–1374.CrossRefGoogle Scholar
  42. National Oceanic and Atmospheric Administration (NOAA) (2015). National Weather Service Glossary. Available at http://w1.weather.gov/glossary/. Cited 12 April 2016.
  43. Ochieng, J., Kirimi, L., & Mathenge, M. (2016). Effects of climate variability and change on agricultural production: the case of small-scale farmers in Kenya. NJAS - Wageningen Journal of Life Sciences. doi:10.1016/j.njas.2016.03.005.Google Scholar
  44. Porter, C. (2012). Shocks, consumption and income diversification in rural Ethiopia. Journal of Development Studies, 48(9), 1209–1222.CrossRefGoogle Scholar
  45. Rienecker, M., Suarez, M., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M., Schubert, S., Takacs, L., & Kim, G. (2011). MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of Climate, 24(14), 3624–3648.CrossRefGoogle Scholar
  46. Republic of Kenya. (2007). Basic report on well-being in Kenya. Nairobi: Kenya National Bureau of Statistics.Google Scholar
  47. Rowhani, P., Lobell, D., Linderman, M., & Ramankutty, N. (2011). Climate variability and crop production in Tanzania. Agricultural and Forest Meteorology, 151(4), 449–460.CrossRefGoogle Scholar
  48. Schlenker, W., & Lobell, D. B. (2010). Robust negative impacts of climate change on African agriculture. Environmental Research Letters, 5(1), 014010.CrossRefGoogle Scholar
  49. Schlenker, W., & Roberts, M. (2009). Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proceedings of the National Academy of Sciences, 106(37), 15594–15598.CrossRefGoogle Scholar
  50. Skoufias, E. (2003). Economic crises and natural disasters: coping strategies and policy implications. World Development, 31(7), 1087–1102.CrossRefGoogle Scholar
  51. Skoufias, E., & Vinha, K. (2013). The impacts of climate variability on household welfare in rural Mexico. Population and Environment, 34(3), 370–399.CrossRefGoogle Scholar
  52. Thiede, B. C. (2014). Rainfall shocks and within-community wealth inequality: evidence from rural Ethiopia. World Development, 64, 181–193.CrossRefGoogle Scholar
  53. Thomas, T., Christiaensen, L., Do, Q. T., & Trung, L. D. (2010). Natural disasters and household welfare: evidence from Vietnam. Policy research working paper 5491. Washington, D. C: World Bank.CrossRefGoogle Scholar
  54. Thornton, P. K., Ericksen, P. J., Herrero, M., & Challinor, A. J. (2014). Climate variability and vulnerability to climate change: a review. Global Change Biology, 20(11), 3313–3328.PubMedPubMedCentralCrossRefGoogle Scholar
  55. Tukey, J. (1949). Comparing individual means in the analysis of variance. Biometrics, 5(2), 99–114.PubMedCrossRefGoogle Scholar
  56. United States Department of Agriculture (USDA) (2011). National Nutrient Database for Standard Reference. Available at http://ndb.nal.usda.gov/. Cited 10 August 2015.
  57. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). Cambridge: MIT Press.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht and International Society for Plant Pathology 2017

Authors and Affiliations

  1. 1.Department of Agricultural, Food, and Resource EconomicsMichigan State UniversityEast LansingUSA
  2. 2.AVRDC - The World Vegetable Center, Eastern and Southern AfricaArushaTanzania
  3. 3.Tegemeo Institute of Agricultural Policy and DevelopmentEgerton UniversityNairobiKenya

Personalised recommendations