| 英文摘要: | Human migration attributable to climate events has recently received significant attention from the academic and policy communities 1, 2. Quantitative evidence on the relationship between individual, permanent migration and natural disasters is limited 3, 4, 5, 6, 7, 8, 9. A 21-year longitudinal survey conducted in rural Pakistan (1991–2012) provides a unique opportunity to understand the relationship between weather and long-term migration. We link individual-level information from this survey to satellite-derived measures of climate variability and control for potential confounders using a multivariate approach. We find that flooding—a climate shock associated with large relief efforts—has modest to insignificant impacts on migration. Heat stress, however—which has attracted relatively little relief—consistently increases the long-term migration of men, driven by a negative effect on farm and non-farm income. Addressing weather-related displacement will require policies that both enhance resilience to climate shocks and lower barriers to welfare-enhancing population movements. Donors spend approximately 4.6 billion dollars per year in emergency relief for natural disasters 1. Astonishing forecasts of the number of environmentally displaced persons are broadly based on measures of population exposure and ignore individual adaptation 2. Recent quantitative evidence suggests that individual, permanent migration increases with natural disasters and climate shocks, but not uniformly 3, 4, 5, 6, 7, 8, 9. Empirical work on the causes of migration has typically been limited to analysis of data covering only a few years, and can therefore conclude little about migration in the longer term. Using a unique, 21-year longitudinal survey (1991–2012), we examine the long-term migration of household members in response to states of extreme temperature and rainfall in rural Pakistan. Significantly expanding on previous studies of climate-induced migration, we allow climate effects to be time-varying, multidimensional, interactive, nonlinear, and heterogeneous, all while accounting for various spatial and temporal confounders. This approach reveals a complex migratory response that is not fully consistent with common narratives of climate-induced migration. Pakistan is highly vulnerable to climate change and involuntary displacement. In 2010 alone, floods in Pakistan affected 20 million people, destroying an estimated crop value of 1 billion US dollars 10. Some 14 million people relocated temporarily, and 200,000 moved to internal displacement camps funded by international donors 11. Uncharacteristically high temperatures (heat stress) also reduce population well-being by lowering agricultural yields. For example, the early maturity of wheat grains as a result of heat stress reduced Pakistani wheat yields by 13 per cent in 2010 12. However, Pakistan’s social protection programs and international relief efforts have been far more responsive to flood victims than heat stress victims, as in other parts of the developing world. This study aims to answer three unresolved questions in this literature. First, which weather patterns explain the long-term mobility patterns of men and women in Pakistan? Second, is there evidence that extreme rainfall and heat affect agricultural income—indicating a possible channel through which they impact migration? The channels through which disasters affect migration have rarely been addressed owing to data limitations 13. Third, are there barriers to weather-induced movement? Knowledge of what motivates migration and the barriers to adaptation through migration is important for designing appropriate policies that respond to natural disasters, migration, and displacement. To answer all of these questions, we construct a longitudinal survey based on the Pakistan Panel Survey (PPS) collected in 1986–1991 (ref. 14) and two tracking studies (Supplementary Methods). The heads of the 1991 PPS households or proxy respondents were resurveyed in 2001 and 2012 to track the movement of original, 1991 household members. The data collected from the PPS and the two tracking studies are used to build an individual-level panel of migrating and non-migrating household members over a 21-year period. We create a person-year dataset following previous work5, 6, 15, 16, 17, 18. As migration rates are very low for individuals younger than 15 or older than 39, individuals are included in the dataset, starting from baseline or when they reach age 15, and excluded after migrating or when they turn 40. This sample consists of 44,791 person-years, where 4,428 individuals are represented from 583 households. To answer the first question, we employ discrete-time event history models to measure individual responsiveness to weather variables, controlling for baseline (1991) household wealth and demographic characteristics, and for village and time fixed effects. (Explanatory variables are summarized in Supplementary Table 1.) We estimate the event history model as a logit model, analysing migration as a binary dependent variable. A household member is considered a migrant in year t if he was permanently not present in year t for reasons other than death. The individual is considered a within-village migrant if they moved elsewhere in the village, and an out-of-village migrant if they moved outside of the village (including abroad). The multinomial event history model, estimated as a multinomial logit, differentiates the impacts of weather anomalies on local (within-village) versus long-distance (out-of-village) moves, and gender-differentiated migration 5, 6: log(πrit/πsit) = αrt + αrv + βrXit, where πrit is the odds of moving distance r for individual i in year t, πsit is the odds of not moving, and parameters αrt and αrv are the baseline hazard of mobility in village v and year t, respectively, for the specific types of mobility r. X and βr vectors of controls and their corresponding parameters estimates. Inverse probability weights are used to correct estimates for individual attrition (Methods). From various secondary data sources (Supplementary Methods), we construct the key weather variables included in the analysis: cumulative rainfall over the monsoon period (June–September), average temperature over the Rabi season (November–April) when wheat is grown, a measure of flood intensity (the annual number of deaths caused by flooding)19, and a 12-month moisture index—the Standardized Precipitation Evapotranspiration Index20 (SPEI). All weather variables are measured at the village level, with the exception of flood intensity, which varies by province. Our preferred specifications use average weather values from year t and t − 1 to capture the weather preceding period t migration decisions. Trends seem stationary and migration corresponds with peaks in temperature (Supplementary Fig. 1). Table 1 presents results of migration responses to weather by gender using an event history model, and further by within-village and out-of-village moves using a multinomial event history model. We focus on the estimates of the weather parameters (though estimates of all coefficients are presented in Supplementary Table 2). Specifications A–C present results from linear and nonlinear specifications of the rainfall and temperature variables (Methods).
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- Walsh, D. Pakistan flood crisis as bad as African famines, UN says. The Guardian (27 January 2011); www.guardian.co.uk/world/2011/jan/27/pakistan-flood-crisis-african-famines (Accessed September 13, 2012).
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- Bie Lilleør, H. & Van den Broeck, K. Economic drivers of migration and climate change in LDCs. Glob. Environ. Change 21, S70–S81 (2011).
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