Skip to main content

Weather extremes and household welfare in rural Kenya


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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2


  1. 1.

    In this paper, we use the terms ‘weather shocks’, ‘weather extremes’ and ‘climate variability’ interchangeably. A ‘weather shock’ is considered here to be any unpredictable change in weather outcomes (Dell et al. 2014), and not a major climate event, such as a flood or hurricane. Thus, the weather shocks to be considered in this paper include the degree of exposure to rainy, dry, hot, or windy conditions (as defined in “Variables” section).

  2. 2.

    ‘Gridded’ data sets capture the spatial distribution of parameters by converting individual data points into a regular grid of estimated values across a surface.

  3. 3.

    These agro-ecological zones are based on temperature belts (the maximum temperature thresholds within which the most common crops of Kenya can be grown). The annual mean temperature for each major zone (for which such information is available) is as follows: Coastal lowland (> 24°C, with maximum <31°C), inner lowland (> 24°C, with maximum >31°C), lower midland (21–24°C), upper midland (18–21°C), lower highland (15–18°C), and upper highland (10–15°C).

  4. 4.

    This summary is based on the livelihood profiles provided by FEWSNET ( 2011), which describe a larger set of more narrowly defined livelihood zones.

  5. 5.

    Although not feasible with the TAPRA data set, further work on this topic may use higher frequency survey data from rural Kenya to measure household resilience to various weather shocks (Cissé and Barrett 2016), thus operationalizing the theory of development resilience put forth by Barrett and Constas (2014).

  6. 6.

    The NASA-MERRA data set is generated with version 5.2.0 of the Goddard Earth Observing System.

  7. 7.

    See Argwings-Kodhek et al. (1998) for a detailed description of the TAPRA sampling design.

  8. 8.

    Note that pastoralists are likely to be most vulnerable to shocks, such as climate variability (see Christiaensen and Subbarao 2005). However, just 8.4% of rural Kenyans reside in the arid north (Herrero et al. 2010), indicating that the TAPRA data set represents a large majority of the population of rural Kenya.

  9. 9.

    As the NASA-MERRA temperature data is derived from different meteorology sources before and after 2008, and trend analysis is not recommended over this break (Rienecker et al. 2011), we omitted year 2010 from this analysis.

  10. 10.

    Because land owned was not captured in 2000, this variable was imputed with a household regression.

  11. 11.

    Other costs associated with crop and livestock production were not captured in the TAPRA surveys and cannot be netted out.

  12. 12.

    Note that these estimates of calories and income are subject to recall error, particularly regarding food acquired outside the home and income sources that are less discrete (e.g., income from small businesses or intermittent farm labor).

  13. 13.

    The omission of calories derived from meat consumption likely results in underestimating the calories sourced from livestock/ livestock products, which is most likely to affect the estimates for relatively wealthy households. However, in a similar context, it has been found that a majority of the gains in dietary diversity that derive from livestock ownership come from milk consumption (Jodlowski et al. 2016), which is captured in our estimate of calories. Nevertheless, this should be regarded as a rough estimate, and the results interpreted accordingly.

  14. 14.

    Unfortunately, the TAPRA data set does not include enough information to derive measures of seasonal food availability, nor does it capture recent coping strategies or physical measurements of household members.

  15. 15.

    Robustness tests using weather outcomes over the entire year, rather than the main growing season, produce results generally consistent with those reported here.

  16. 16.

    In the U.S., the term ‘windy’ officially refers to winds of ≥20 miles per hour (8.9 m/s) (NOAA 2015). However, the NASA-MERRA data set provides daily average wind speeds, and we could find no reference for what counts as a ‘windy day’ in rural Kenya. Because the threshold used in this analysis is not derived from prior research, a sensitivity analysis was conducted with alternate definitions of ‘windy day’. To conserve space, results are not reported here, though they are robust to different wind speed cut-offs.

  17. 17.

    Note that this national poverty line is measured in per-adult-equivalent terms (Republic of Kenya 2007). When measured in per capita terms relative to the international extreme poverty line of USD $1.25 per day, the sample poverty rate is considerably higher at 44.2%.

  18. 18.

    A Tukey test is used for these comparisons across agro-ecological regions to correct for the heightened probability of a type 1 error (the incorrect rejection of a true null hypothesis) that accompanies multiple comparisons (Tukey 1949).

  19. 19.

    See Table 11 in the appendix for a sample of full regression results. Complete results for all regressions are available from the authors upon request.

  20. 20.

    We also considered lagged TLU as a candidate mitigating factor, thinking that livestock may be liquidated after a negative shock. However, results (not reported here) suggest that an increase in lagged TLU actually ‘amplifies’ household sensitivity to periods of drought. This pattern is consistent with Christiaensen and Subbarao (2005), who found that possession of livestock in rural Kenya was not effective in protecting households against covariate shocks. The authors attribute this to poorly integrated livestock markets resulting in low and unstable prices (and therefore reduced liquidity of livestock) in years of poor rainfall. In Somalia, Maystadt and Ecker (2014) also found that temperature shocks depressed local livestock prices as herders simultaneously flooded the market with animals for sale. In West Africa, several authors found that livestock were not used for consumption smoothing, on average (Fafchamps et al. 1998; Kazianga and Udry 2006). Although this topic merits further attention, it is beyond the scope of this paper to survey the robustness of this result across model specifications, and to ascertain precisely why, on average, TLU might heighten household sensitivity to low rainfall in our study population.

  21. 21.

    Although not reported here, this effect is statistically significant when income/ AE/ day (IHST), instead of poverty status, is used as the dependent variable.


  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.

    Article  Google 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 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.

    Article  Google 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.

    Article  Google 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.

    Book  Google 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.

    CAS  Article  Google 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.

    Article  Google 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.

    Article  Google 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.

  13. Christiaensen, L., & Subbarao, K. (2005). Towards an understanding of household vulnerability in rural Kenya. Journal of African Economies, 14(4), 520–558.

    Article  Google 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.

    Book  Google Scholar 

  15. Coates, J. (2013). Build it back better: deconstructing food security for improved measurement and action. Global Food Security, 2(1), 188–194.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  21. Dercon, S., & Krishnan, P. (2000). Vulnerability, seasonality, and poverty in Ethiopia. Journal of Development Studies, 36(6), 25–53.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  24. Famine Early Warning Network (FEWSNET) (2011). Livelihoods zoning “plus” activity in Kenya. Available at Cited 12 April 2016.

  25. Food and Agricultural Organization of the United Nations (FAO) (2015). Crop calendar. Available at 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.

    CAS  PubMed  Google 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 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.

  32. Hoddinott, J. (2006). Shocks and their consequences across and within households in rural Zimbabwe. Journal of Development Studies, 42(2), 301–321.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  42. National Oceanic and Atmospheric Administration (NOAA) (2015). National Weather Service Glossary. Available at 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  48. Schlenker, W., & Lobell, D. B. (2010). Robust negative impacts of climate change on African agriculture. Environmental Research Letters, 5(1), 014010.

    Article  Google 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.

    CAS  Article  Google Scholar 

  50. Skoufias, E. (2003). Economic crises and natural disasters: coping strategies and policy implications. World Development, 31(7), 1087–1102.

    Article  Google 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.

    Article  Google Scholar 

  52. Thiede, B. C. (2014). Rainfall shocks and within-community wealth inequality: evidence from rural Ethiopia. World Development, 64, 181–193.

    Article  Google 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.

    Book  Google 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.

    PubMed  PubMed Central  Article  Google Scholar 

  55. Tukey, J. (1949). Comparing individual means in the analysis of variance. Biometrics, 5(2), 99–114.

    CAS  PubMed  Article  Google Scholar 

  56. United States Department of Agriculture (USDA) (2011). National Nutrient Database for Standard Reference. Available at Cited 10 August 2015.

  57. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). Cambridge: MIT Press.

    Google Scholar 

Download references


The authors gratefully acknowledge financial support from USAID/Kenya for funding this study through the Tegemeo Agricultural Policy Research and Analysis (TAPRA) Project. They also wish to thank Jordan Chamberlin and Jenni Gronseth for their assistance. The views expressed in this study are those of the authors only.

Author information



Corresponding author

Correspondence to Ayala Wineman.

Ethics declarations

Conflicts of interest

The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.



Table 7 Tests for attrition bias
$$ {Y}_{it}=\alpha +\tau {R}_{i,t+1}+{\boldsymbol{W}}_{\boldsymbol{it}}\boldsymbol{\beta} +{\boldsymbol{Z}}_{\boldsymbol{it}}\boldsymbol{\delta} +{\mu}_i+{\theta}_t+{\varepsilon}_{it} $$

This is based on Eq. (1), which is introduced in “Identification strategy and econometric models” section. Y it = outcome variable for household i at time t, W it  = a vector of weather shocks, Z it = a vector of household characteristics, μ i = household fixed effects, θ t = time fixed effects, and ε it = a stochastic error term. Added to Eq. (1) is R i , t + 1, a binary indicator for whether household i remains in the panel at time t + 1. Thus, only years 2000 and 2004 are included in the regressions, which otherwise mirror those of Table 4 (odd columns). If the key coefficient (τ) is significant, it indicates attrition bias.

Table 8 Asset index weights, derived with principal component analysis
Table 9 Correlation matrix of weather shocks and seasonal weather outcomes (household level)
Table 10 Summary statistics of household welfare indicators and exposure to weather shocks, by year
Table 11 Effects of weather shocks on household welfare (FE full regression results)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wineman, A., Mason, N.M., Ochieng, J. et al. Weather extremes and household welfare in rural Kenya. Food Sec. 9, 281–300 (2017).

Download citation


  • Food security
  • Household welfare
  • Kenya
  • Resilience
  • Weather shocks