Abstract
The environmental migration literature presents conflicting results: While some research finds that natural disasters induce international migration, other work discovers a dampening effect. We construct an innovative longitudinal provincial dataset for the Philippines, a country prone to natural disasters and a major exporter of labor. Using a comprehensive list of weather shocks, it is possible to identify major channels behind those conflicting findings. Filipinos are more likely to work abroad when they experience less-intense tropical cyclones and storm warnings but are more likely to stay when very intense storms occur or are forecasted.
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Notes
Authors’ computation from SOF datasets and IOM website.
A panel dataset at the household level is not available. Therefore, we opted to construct a longitudinal dataset at the provincial level.
For missing values, we use the values from the closest survey year.
The available dataset limits us to regional variables for education and health infrastructure.
Using the average exchange rate in 2019 (PHP 50.60 to USD 1).
The migrant’s network refers to family and friends who have already migrated to the migrant’s destination area and make it easier for the migrant to adjust. In some cases, the network provides financial assistance.
The PSGC Codes Legend Spreadsheet contains the PSGC followed for the analysis. To ensure matching of the LFS using the PSGC provincial codes across time (2005–15), we assumed regional demarcations (17) prior to the formation of the Negros Island Region. As per the PSGC Summary of Changes (March 2017) spreadsheet of PSA, it was only in 2015 that a province was transferred to another region.
Migration variable is transformed into a logarithmic form, in particular, log(x + 1) to accommodate for zero values. This transformation applies to all our log-transformed variables.
Time-invariant variables (historical migration rate, and provincial and major island dummies) are also included but only for RE and OLS models.
We also estimate all our migration equations (1–4), using random effects (RE) and ordinary least squares (OLS). Individual weather events and their various combinations are also used. For brevity, we only focus on the more apt econometric method (FE) and regression results using complete weather shocks.
This is similar to saying that a PHP 3.2 million increase in total damages could result to an increase of only about 1 international migrant.
Applying the equation dlog(y)/dx = (β1 + 2 β2) × 100 if y = β1x + β2 x (Wooldridge 2006).
Applying the equation \( x=\left|\raisebox{1ex}{${\beta}_1$}\!\left/ \!\raisebox{-1ex}{$2{\beta}_2$}\right.\right| \) to compute for the turning point (Wooldridge 2006).
Similar to migration strategies, we also estimate the income equations using RE and OLS. We also consider individual weather shocks and their different combinations but we only discuss the more appropriate FE results using complete weather shocks for brevity.
1 metric ton = 1000 kg.
The 2010 average monthly farmgate price of rice was PHP 14.40/kg.
We also estimate the second-stage equations using other instrumental variable methods, such as instrumental variable random effects (IVRE) and two-stage least squares (2SLS). Similar to migration and income regressions, we focus on IVFE, assuming that the fixed effects and all independent variables, including weather shocks, are correlated.
There has been a series of drought-causing El Niño events in the Philippines. PAGASA (2011) identified six of these events, starting in the 1960s.
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Acknowledgments
The authors would like to thank the three anonymous reviewers for their helpful comments and suggestions, the editor-in-chief Dr. Klaus F. Zimmermann, Louisa Camille Poco for her able assistance, and the participants at the University of the Philippines seminar in 2018.
Funding
This study was funded by the University of the Philippines Office of the Vice President for Academic Affairs through its Balik-PhD research grant [OVPAA-BPhD-2016-02].
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Pajaron, M.C., Vasquez, G.N.A. Weathering the storm: weather shocks and international labor migration from the Philippines. J Popul Econ 33, 1419–1461 (2020). https://doi.org/10.1007/s00148-020-00779-1
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DOI: https://doi.org/10.1007/s00148-020-00779-1