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Weather extremes and household welfare in rural Kenya

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.

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Fig. 1
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Notes

  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.

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Acknowledgments

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.

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Correspondence to Ayala Wineman.

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Appendix

Appendix

Table 7 Tests for attrition bias
$$ {Y}_{it}=\alpha +\tau {R}_{i,t+1}+{\boldsymbol{W}}_{\boldsymbol{it}}\boldsymbol{\beta} +{\boldsymbol{Z}}_{\boldsymbol{it}}\boldsymbol{\delta} +{\mu}_i+{\theta}_t+{\varepsilon}_{it} $$
(4)

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)

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Wineman, A., Mason, N.M., Ochieng, J. et al. Weather extremes and household welfare in rural Kenya. Food Sec. 9, 281–300 (2017). https://doi.org/10.1007/s12571-016-0645-z

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Keywords

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