Shelter from the Storm? Household-Level Impacts of, and Responses to, the 2015 Floods in Malawi

Abstract

As extreme weather events intensify due to climate change, it becomes ever more critical to understand how vulnerable households are to these events and the mechanisms households can rely on to minimize losses effectively. This paper analyzes the impacts of the floods that occurred during the 2014/15 growing season in Malawi, using a two-period panel data set. The results show that maize yields and value of production per capita were lower for all households, particularly for those located in moderate and severe flood areas. However, drops in food consumption expenditures were less dramatic, and calories per capita were higher. Only the food consumption score, which is a measure of dietary diversity, was significantly lower, particularly for households located in areas of severe flooding. Although access to social safety nets increased food consumption outcomes, particularly for those located in areas of moderate flooding, the proportion of households with access to certain safety net programs was lower in 2015 compared with 2013. The latter finding suggests that linking these programs more closely to disaster relief efforts could substantially improve welfare outcomes during and after a natural disaster. Finally, potential risk-coping strategies, proxied by access to off-farm income sources, having financial accounts, and social networks, were generally ineffective in mitigating the negative impacts of the floods.

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Notes

  1. 1.

    The specific programs captured under “school feeding programs” were the School Feeding Programme, free distribution of Likuni Phala to children and mothers, and supplemental feeding for malnourished children at a nutritional rehabilitation unit. The most frequent of these three is by far the School Feeding Programme.

  2. 2.

    Fafchamps et al. (1998) use panel data collected in six villages in Burkina Faso, where the sampled villages experienced at least two years of extremely low rainfall compared with the long-term average. The authors find the expected negative impacts of low rainfall on the value of crop production, though the authors do not report the size of these impacts.

  3. 3.

    Natural disasters of all kinds can push the near poor into poverty (De la Fuente and Dercon 2008). A comparative study on mobility into and out of poverty in 15 countries of Africa, South Asia, East Asia and Latin America with about 9000 household interviews found that natural disasters (along with health adversities and death) were the second most important reason why people became poor (Narayan et al. 2009).

  4. 4.

    For instance, Kazianga and Udry (2006) and Wineman et al. (2017) find limited or no role for livestock as a risk-coping mechanism, but Miura et al. (2016) and Lybbert et al. (2004) find that livestock sales can offset crop losses, at least for households with larger herds to start with.

  5. 5.

    See, for instance, Wineman et al. (2017) and Arouri et al. (2015).

  6. 6.

    For details on the PSNP see Country Spotlight 4. Ethiopia: Deaths from Droughts or Derg?

  7. 7.

    FIAS 2015 was implemented with technical support from the World Bank Living Standards Measurement Study (LSMS), the World Bank Poverty and Equity Global Practice, and LEAD Analytics, and with the World Bank funding from the Global Facility for Disaster Reduction and Recovery (GFDRR), the Disaster Risk Financing and Insurance team, the Finance and Markets Global Practice, and the Global Solutions Group on Managing Risks within the Poverty and Equity Global Practice.

  8. 8.

    IHPS was implemented by the NSO, with financial and technical support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) program. In 2013, the IHPS was implemented from April to December 2013, with the objective of tracking and resurveying 3246 households across 204 enumeration areas (EAs). The anonymized, unit-record data and documentation from the IHPS 2013 can be accessed through www.worldbank.org/lsms.

  9. 9.

    FIAS was implemented on a computer-assisted personal interviewing (CAPI) platform that was designed using the World Bank Survey Solutions CAPI software (www.worldbank.org/capi). The FIAS CAPI experience was a key input into the design and implementation of the Fourth Integrated Household Survey (IHS4) and Panel Subcomponent later in 2016/17, also using a Survey Solutions-powered CAPI platform.

  10. 10.

    We calculate the flowering season rainfall as the cumulative rainfall over the last dekad in December through the third dekad in January.

  11. 11.

    Wu et al. (2014) provide details of the simulation model, which combines a land surface model with river tracing/water flow model, using satellite-based precipitation data.

  12. 12.

    The description can be found at: https://lta.cr.usgs.gov/srtmgl3.html.

  13. 13.

    Full regression reports for value of production per capita are reported in the online appendix, table A1.

  14. 14.

    Value of food consumption per capita is perhaps a more accurate way to describe the variable; however, we retain food expenditures per capita since it is more widely used in the literature.

  15. 15.

    The p-values for the Kolmogorov-Smirnov tests are (.114) for the low flood, (.003) for the moderate flood, and (.029) for the severe flood categories.

  16. 16.

    Another potential explanation is that food aid deliveries kept the market prices of maize flour in check. The WFP Malawi country office provided us with district-level data on food aid deliveries over the period January – July 2015. The simple correlation coefficient between calories of food aid delivered and unrefined maize flower prices in 2015 is significant but fairly low, at −.22. In many districts, households fell into all three flood categories, meaning that the food aid delivery data may be too coarse to adequately capture local market price effects. We believe being able to document the impact of food aid deliveries on local prices may show important indirect impacts on consumption, and would hope that such data will be made available on a more disaggregated scale in the future.

  17. 17.

    We considered using in our regressions the flood affectedness PCA index itself, in addition to the dichotomous variables for the flood categories. The index did not perform as well as the dichotomous variables, particularly when we included the interaction terms with our social safety net variables. As our results highlight, the interaction terms indicate that impacts on consumption outcomes are inconsistent with a linear specification of flood intensity.

  18. 18.

    The index is based on (i) the dichotomous variables for whether the household has any bed, table, chair, or other living room furniture; any of fan, air conditioner, clock or solar panel; any of radio or tape/CD/DVD player; any of sewing machine, washing machine, iron; any of TV, VCR, computer, satellite dish, or generator; any mobile phone., and (ii) the dichotomous variables for whether the household’s dwelling has improved walls; improved roof; improved floor; improved lighting fuel; electrification; access to an improved drinking water source; access to an improved latrine; insecticide treated .bed nets. The number of dwelling rooms per capita is also included in the index.

  19. 19.

    The implements include hand hoes, slashers, axes, knapsack sprayers, panga knives, and sickles.

  20. 20.

    Financial institutions include any of banks, credit unions, micro finance institutions, post offices, village savings organizations, or another financial institution.

  21. 21.

    In cases where we had very few FIAS households in a district due to households moving between survey rounds, households were matched to their district from the previous round. There were 25 households that moved to districts that had 5 or fewer surveyed households located in the new district. We ran the regressions dropping these households; results are nearly identical in terms of signs and significance of coefficients, and thus we include the full sample in our analysis. These results are available upon request.

  22. 22.

    With standard errors clustered at the EA-level, following each estimation of Eq. 1 with an alternative dependent variable, we test whether the household-level inter-annual averages included in the vector X are jointly statistically significant. This is known as the Mundlak (1978) test, and in each instance, as reported in the online appendix table A3, we find that the coefficients are not jointly statistically significant in the food expenditures per capita and food consumption score equations, providing support for the use of the correlated random effects model instead of the fixed effects estimation. The joint test for calories per capita gives a p-value very close to .1 (.097). The results from the fixed effects estimations, i.e. the estimations of Eq. 1 with household-level fixed effects but net of the vector M, are provided in the online appendix table A4, which highlights the similarities with respect to the findings from the correlated random effects models, even for the calorie per capita equation. Finally, we performed a number of robustness checks, including, among others, omission of insignificant variables and exclusion of variables with relatively high correlations with household wealth. The results were very robust to these sensitivity analyses, which are available upon request.

  23. 23.

    The full regression results are reported in the online appendix table A2. All dependent variables are in natural logarithms.

  24. 24.

    The full set of results from the IV regressions are found in the online appendix table A5.

  25. 25.

    The IV estimations are net of the analysis of log calories per capita, since at 10% statistical significance, we reject the null hypothesis that the IVs are orthogonal to the error term.

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Acknowledgements

The authors would like to thank Heather G Moylan for her inputs into the survey design and the field staff training, and (in alphabetical order) Simone Balog, Ruth Hill, two anonymous reviewers, and the participants of the World Bank Global Disaster Reduction and Recovery Facility (GFDRR) seminar (October, 2016 - Washington, DC), and the World Bank LSMS International Conference: The Use of LSMS Data for Research, Policy, and Development (February, 2017 - Dar es Salaam, Tanzania) for their comments on the earlier versions of this paper.

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McCarthy, N., Kilic, T., de la Fuente, A. et al. Shelter from the Storm? Household-Level Impacts of, and Responses to, the 2015 Floods in Malawi. EconDisCliCha 2, 237–258 (2018). https://doi.org/10.1007/s41885-018-0030-9

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Keywords

  • Extreme weather
  • Floods
  • Household welfare
  • Malawi
  • Sub-Saharan Africa

JEL Codes

  • D60
  • I38
  • Q12
  • Q54

MSC Codes

  • 62P20
  • 62P12