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Extreme Weather and Poverty Risk: Evidence from Multiple Shocks in Mozambique

Abstract

This paper investigates the effects of multiple weather shocks on household welfare in Mozambique, as well as some of the coping responses and price mechanisms at play. The analysis employs a triple-difference strategy that exploits variation in the shocks across space, time, and cropping cycles. The findings demonstrate high levels of vulnerability across various weather risks. Experiencing a cyclone, flood, or drought leads to a drop of up to 25–30% in per capita food consumption and around 0.4 fewer meals per day per person. Poverty increased by 12 and 17.5 percentage points in two of the three events analyzed. Human capital accumulation, as measured by school participation and morbidity, is disrupted. Households follow risk-coping strategies, such as increasing the labor supply of their children or selling assets, which entail partial protection in the aftermath of the shock at the cost of lower income growth in the future. In disentangling the channels, the paper shows that maize prices exhibit higher volatility in food markets that are spatially close to the most affected areas. The results are robust to several robustness checks, including analysis of bias from selective migration, and indicate that household welfare and economic mobility in low-income environments are constrained by uninsured weather risks.

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Notes

  1. 1.

    The 2014/15 survey was originally designed to interview over 11,000 households four times (once in each quarter) during a 12-month survey period starting in August 2014 and ending in July 2015. Yet, due to logistical and budget constraints, the survey was carried out only during three quarters: Q1 (August–October), Q2 (November–January) and Q4 (May–July).

  2. 2.

    For a disaster to be entered into this database, at least one of the following criteria must be fulfilled: 1) 10 or more people reported being killed, 2) 100 or more people being affected directly by the event, 3) declaration of a state of emergency by the corresponding authorities and 4) a formal request for international assistance.

  3. 3.

    We use only the between-district variation in growing cycles, which is substantially larger than the variation within districts. The standard deviations for the start and end dates of the growing periods are 72.8 and 226.7 days across districts (between-district variation) and 39.6 and 36.7 within districts (within-district variation).

  4. 4.

    According to the tropical cyclone intensity scale system, a Category 3 cyclone is a severe tropical cyclone with gusty winds over open flat land in the range of 165 to 224 km per hour.

  5. 5.

    Gust wind speed is the more accurate information that can be used to measure the intensity and timing of storms and hurricanes. Gusts winds are measured as the average wind speed over a 2–3 s period. Using gust windspeed for this study requires data on the exact time (day/h/min/s) of the maximum windspeed to accurately track the path of the storm across districts. Data with this fine temporal resolution are not available for Mozambique. A second option, followed in this study, relies on the daily average wind speed, provided by the African Flood and Drought Monitor. The indicator measures the accumulated daily intensity over the days of the duration of the storm as reported by the EM-DAT database.

  6. 6.

    Bias-correction and hybrid observational reanalysis methods are widely used to improve the accuracy of satellite data products by matching the moments of satellite observations with station observations where both types of data are available.

  7. 7.

    The SPI only measures water supply and does not account for evapotranspiration. Therefore, the SPI is more sensitive to soil moisture on shorter timescale. At longer timescales, the SPI is better able to capture abnormal wetness due to changes in groundwater and reservoir storage. SPI values for three months (SPI-1 or SPI-3) or less are deemed better for basic drought monitoring whereas values for six months or more (SPI-6 or SPI-12) are more useful for monitoring wider hydrological impacts.

  8. 8.

    Negative SPI indicates lack of precipitation, thus, a negative SPI with a large absolute value is associated with a more severe drought intensity. In order to be consistent with the other disasters interpretation (interpreting our coefficients as the impact of disasters as intensity increases) we multiplied the intensity by −1.

  9. 9.

    Using calorie-income elasticities in the 0.2–0.5 range as reported by Strauss & Thomas (1995) and Subramanian & Deaton (1996).

  10. 10.

    The poverty lines used in the analysis are 9.3 meticais in 2002/03 and 18.8 meticais in 2008/09 (World Bank 2018b).

  11. 11.

    The survey does not ask any information (geographic location, family or ethnic ties, etc.) about the households and the motives for the transfers.

  12. 12.

    The direction of these effects is in line with findings from previous literature (Baez et al. 2017b).

  13. 13.

    Results from the other outcomes are available from the authors upon request.

  14. 14.

    Household fixed effects estimation requires within-household variation of shock intensity over quarters. There is almost no variation of this short variation in the sample used for this analysis since there are only a few households that migrated between affected and non-affected districts in the period from Q1 to Q4.

  15. 15.

    A closer look at the data reveals that the average increase in consumption in the treated districts was largely driven by a major increase in consumption for households in the top (7th and 8th) deciles of the pre-shock distribution, something that requires further analysis.

References

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  14. Stanke C, Kerac M, Prudhomme C, Medlock J, Murray V. (2013). “Health effects of drought: a systematic review of the evidence.” PloS currents vol. 5. doi: https://doi.org/10.1371/currents.dis.7a2cee9e980f 91ad7697b570bcc4b004. PMID: 23787891; PMCID: PMC3682759

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  16. World Bank (2018b) Strong but not broadly shared growth – Mozambique poverty assessment. Policy Report, Washington D.C

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Acknowledgements

We thank comments received from Emmanuel Skoufias, Alejandro de la Fuente, Stephane Hallegatte, Ilan Noy and two anonymous referees. We also acknowledge useful feedback from participants at the CesIfo conference “Poverty, Inequality and their Association with Disasters and Climate Change” (Italy, June 5-6, 2019).

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Correspondence to Javier E. Baez.

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Baez, J.E., Caruso, G. & Niu, C. Extreme Weather and Poverty Risk: Evidence from Multiple Shocks in Mozambique. EconDisCliCha (2019) doi:10.1007/s41885-019-00049-9

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Keywords

  • Natural disasters
  • Long-term human welfare
  • Poverty
  • Human capital

JEL Classification

  • I3
  • J2
  • O1