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When It Rains, It Pours? Analyzing the Rainfall Shocks-Poverty Nexus in the Philippines

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Abstract

Weather is an integral part of our life and weather shocks can have severe implications on welfare. Given evidence that points to climate change resulting in altered patterns of weather parameters and given that the Philippines is one of the most vulnerable countries to climatic shifts, this paper aims to contribute to poverty studies in the country by analyzing the poverty-rainfall shock nexus. The paper finds that rainfall shocks affect wages and income, which in turn, affect chronic total and chronic food poverty. Some policy directions are provided.

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Notes

  1. While evidence points to the adverse effects of weather shocks, there is a strand of literature at the macroceconomic level that emphasizes creative destruction. In this literature, extreme weather events have positive impact on capital and output growth. For example, Skidmore and Toya (2002) find that climatic disasters are associated with higher long run economic growth while geologic disasters are negatively associated with growth while Noy and Vu (2010) show that disasters that destroy more properties and capital boost the economy in the short-run.

  2. Mansur et al. (2008) used this formulation in the context of energy consumption.

  3. \(\bar{Q}_{x} (p_{x} ,b_{x} ,y,s,e) = - \frac{{\partial \bar{v}(p_{x} ,b_{x} ,y,s,e)/\partial p_{x} }}{{\partial \bar{v}(p_{x} ,b_{x} ,y,s,e)/\partial y}}\).

  4. OLS assumption \(\text{cov} (Y,\varepsilon ) = 0\) is violated.

  5. The authors would like to thank one of the referee for pointing this out.

  6. For completeness, all the results are presented but the analysis revolves around those that pass the endogeneity and identification tests.

  7. Specification 1 includes the proxy for rainfall shocks only and results show that rainfall shocks significantly explain chronic and transient poverty. Specification 2 enhances specification 1 by including the squared term of rainfall shocks to account for nonlinear effects. However, estimates pertaining to the squared term are not significantly different from zero and the results are not substantially different from specification 1.

  8. This is the basic political unit in the Philippines, equivalent to a village.

  9. https://psa.gov.ph/content/female-headed-families-have-more-income-male-headed-families-based-final-results-2009-family.

  10. As an illustration, Palawan province, located in Luzon’s Region 4A, has three stations, namely, Coron, Cuyo and Puerto Princesa. In this case, Puerto Princesa is chosen.

  11. For example, Mountain Province and the provinces of La Union and Ifugao are assigned the weather station in Baguio City, Benguet while Tarlac is assigned the weather station in Cabanatuan, Nueva Ecija.

  12. While three sets of estimations are done separately using weights in the 2003, 2006, and 2009 FIES, results presented here are estimates using the 2009 weights. Results across weights are relatively similar and estimates using the latest weights are presented due to space considerations. The full results are available from the corresponding author upon request.

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Authors and Affiliations

  1. Philippine Institute for Development Studies, Quezon City, Philippines

    Connie Bayudan-Dacuycuy & Lora Kryz Baje

  2. Economics Department, Ateneo De Manila University, Quezon City, Philippines

    Connie Bayudan-Dacuycuy

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  1. Connie Bayudan-Dacuycuy
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  2. Lora Kryz Baje
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Correspondence to Connie Bayudan-Dacuycuy.

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Appendix

Appendix

See Tables 5 and 6.

Table 5 Mapping of FIES provinces with the PAGASA weather stations
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Table 6 Endogeneity and heteroscedasticity test
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Bayudan-Dacuycuy, C., Baje, L.K. When It Rains, It Pours? Analyzing the Rainfall Shocks-Poverty Nexus in the Philippines. Soc Indic Res 145, 67–93 (2019). https://doi.org/10.1007/s11205-019-02111-1

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