AREG protein, human

Survey data are derived from the World Bank’s African Migration and Remittances Surveys (AMRS), which collected standardized retrospective data on international and internal migration for ~2,000 households per country. Retrospective household-level data have a long history of successful use for investigating the determinants of migration in the developing world (Massey and Espinosa 1997 ; Smith and Thomas 2003 ). In the case of AMRS, households reported the destination of all departed household members and return migrants, as well as the timing and motivation of each move. To limit errors due to retrospection and whole-household mobility, we use data on migrants who departed in the year of data collection and in the five years prior (2004–09). In Uganda, Nigeria and Senegal the household sample is nationally representative. In Burkina Faso and Kenya, 10 provinces¹ and 17 districts respectively were included as the most important sources of migrants identified by census data (Table 1; Supplement F2). In all countries, disproportionate sampling was then used within the sample areas to oversample enumeration areas that were more important sources of migration as measured by census data, and two-phase sampling was used within enumeration areas to select households, oversampling those that had sent migrants (Gray & Bilsborrow 2013 (link); Plaza et al. 2011 ). We use survey weights in all analyses to account for this sampling design. Interviews took place in late 2009 in Kenya, Burkina Faso and Senegal and early 2010 in Uganda; a small number of moves which took place in Uganda in 2010 are consolidated with those of 2009.
We use AMRS to create a household-year dataset on 9,812 households over a 6 year period (2009-2004) with information on migration and control variables (Table 1). Migration is measured as the number of migrants ages ten or older sent by the household in year t, a count which is subsequently decomposed by migrant destination, gender and reported motivation. Included control variables are standard for the literature on the determinants of migration (Massey and Espinosa 1997 ) and include household composition and characteristics of the household head at the beginning of 2004 (Table 2). These values were estimated by adding household members born before 2004 to migrants who departed during the study period and adjusting ages appropriately. Potential control variables which were likely to have been measured with error for 2004 were excluded. Given that very few households had heads under age 20 at the time of the survey, 272 households with heads under age 25 at the time of the survey were excluded from the analysis as unlikely to have been in existence in 2004. Data on one or more control variables are missing for 313 households. These values are interpolated to the country median and this interpolation was accounted for through the inclusion of missing indicators in the regression analysis. Table 2 also documents considerable heterogeneity across the five study countries, ranging from Kenya with the highest level of education, smallest household size, lowest rate of previous migration, lowest proportion rural, and lowest proportion working in the primary sector, to Burkina Faso at the opposite end of the spectrum.
Households in AMRS are linked to climate by their district-level administrative unit of residence, for a total of 140 such units. For comparison purposes, we chose two high-quality datasets that are available at high spatial resolution and extracted them as spatial means for these units: (1) the Climatic Research Unit’s (CRU) time-series 3.21 containing high-resolution monthly precipitation and temperature; and (2) monthly mean land surface temperature and total surface precipitation from the NASA Modern Era-Retrospective Analysis for Research and Applications (MERRA). CRU is a monthly global dataset at 0.5° resolution (~50km at the equator) created by interpolating data from weather stations (UEACRU et al. 2013 ). Derived from a network of over 4000 stations, including a large number in Sub-Saharan Africa, CRU data are considered to provide reliable climate information in Africa (e.g., Zhang et al. 2013 ). MERRA is a reanalysis product, produced through the integration of observed data, including satellite data, with numerical models. The MERRA data are available sub-daily at 0.5° × 0.67° lat-long resolution (Rienecker et al. 2011 ).
From these climate data sources, we extract the following values as spatial means at the district-year scale: the total annual precipitation, the month in which 50% annual precipitation was reached, and the mean annual temperature. Temperature and precipitation anomalies were also defined relative to a 1981–2010 base period. District-level units were defined as districts in Kenya and Uganda, provinces in Burkina Faso and departments in Senegal. Temperature anomalies have been shown to have robust negative effects on agricultural output across Sub-Saharan Africa, while precipitation usually but not always has positive effects (Seo et al. 2009 ). Because previous studies have shown that climate can have lagged effects on migration for at least two years (Bohra-Mishra et al. 2014 (link)), we average annual climate values across year t and t-1 as well as test for longer lags (Table 3). Substantial differences in climate anomalies extracted from CRU and MERRA (Table 3) reinforce the importance of using data from multiple climate sources (Auffhammer et al. 2013 ).
Negative binomial regression is then used to model the number of migrants per household-year as a function of climate variables and controls. This modelling approach has been widely used across the quantitative social sciences to model count outcomes (Cameron and Trivedi 2013 ), including migration (Taylor et al. 2003 ). Our core specification of climate includes linear measures of annual precipitation and temperature anomalies derived from CRU and averaged over years t and t-1 (Table 3). We also test additional plausible specifications of climate as described below. Predictors include climate variables, a set of socio-demographic controls, district-level fixed effects, a quadratic time trend (to account for potential retrospective reporting biases), and, for a small fraction of households, indicators for missing values on one or more control variables. Standard errors are corrected for clustering at the district-level unit. Socio-demographic control variables include the number of migrants sent before 2004, rural versus urban location, and various demographic characteristics of the household and household head estimated for 2004 (Table 2). The inclusion of district-level fixed effects allows each district to have a baseline rate of migration and accounts for all time-invariant district-level factors as long as these effects are linear. To account for potential errors of retrospection we allow for a quadratic time trend by including both linear and squared terms for the year (VanWey 2005 ). With inclusion of the time trend, the effects of climate variables are statistically identified by local deviations of climate from the national-scale trend.