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The Migration and Remittances Factbook 2011 updates the 2008 edition of the Factbook with additional data for 71 countries collected from various sources, including national censuses, labor force surveys, population registers, and other national sources. The Factbook attempts to present numbers and facts behind the stories of international migration and remittances, drawing on authoritative, publicly available data. It provides a snapshot of statistics on immigration, emigration, skilled emigration, and remittance flows for 210 countries and 15 regional and income groups. Some interesting facts emerge: •More than 215 million people, or 3 percent of the world population, live outside their countries of birth. Current migration flows, relative to population, are weaker than those of the last decades of the nineteenth century. •The top migrant destination countries are the United States, the Russian Federation, Germany, Saudi Arabia, and Canada. The top immigration countries, relative to population, are Qatar (87 percent), the United Arab Emirates (70 percent), Kuwait (69 percent), Andorra (64 percent), Cayman Islands (63 percent), and Northern Mariana Islands (62 percent). •The United States is likely to have seen the largest inflows of migrants between 2005 and 2010, despite the global financial crisis. The expansion of the European Union led to a surge of migrant flows to Spain, Italy, and the United Kingdom, with a large share from Eastern Europe. •The six Gulf Cooperation Council countries (Saudi Arabia, United Arab Emirates, Bahrain, Qatar, Oman, and Kuwait) have also seen a significant increase in migrant flows in the last few years, mostly from South Asia and East Asia. However, immigrant stocks in all regions started to plateau in 2009-10 because of the global financial crisis. •The volume of South–South migration is larger than migration from the South to the high-income countries belonging to the Organization of Economic Cooperation and Development (OECD). High-income non-OECD countries such as the Gulf countries are also major destinations for migrants from the South. South–South migration is significantly larger than South–North migration in Sub-Saharan Africa (73 percent) and Europe and Central Asia (61 percent). •According to available official data, the Mexico–United States corridor is the largest migration corridor in the world, accounting for 11.6 million migrants in 2010. Migration corridors in the Former Soviet Union — Russia–Ukraine, and Ukraine–Russia — are the next largest, followed by Bangladesh–India. In these corridors, natives became migrants without moving when new international boundaries were drawn. •Smaller countries tend to have higher rates of skilled emigration. Almost all physicians trained in Grenada and Dominica have emigrated abroad. St. Lucia, Cape Verde, Fiji, São Tomé and Principe, and Liberia are also among the countries with the highest emigration rates of physicians. •Refugees and asylum seekers made up 16.3 million or 8 percent of international migrants in 2010. The share of refugees in the population was 14.6 percent in the low-income countries—more than seven times larger than the share of 2.1 percent in the high-income OECD countries. Middle East and North Africa had the largest share of refugees and asylum seekers among immigrants (65 percent), followed by Sub-Saharan Africa (17 percent), South Asia (20 percent), and East Asia and Pacific (8.8 percent). •Worldwide remittance flows are estimated to have exceeded $414 billion in 2009, of which developing countries received $307 billion (This represents a small decline of 6 percent from the level in 2008). The true size, including unrecorded flows through formal and informal channels, is believed to be significantly larger. Recorded remittances are more than twice as large as official aid and nearly two-thirds of foreign direct investment (FDI) flows to developing countries.

One cannot study poverty in Tanzania without understanding the agricultural sector, which employs more than two-thirds of the population and accounts for nearly a quarter of national GDP. This thesis examines three themes that focus on the difficulties that rural Tanzanians face in achieving a reasonable livelihood: the adverse legacy of a failed historical policy, a difficult climate, and market failures. The first empirical chapter examines the legacy of the villagization program that attempted to transform the predominantly agricultural and rural Tanzania. Between 1971 and 1973, the majority of rural residents were moved to villages planned by the government. This essay examines if the programs e↵ects are persistent and have had a long-run legacy. It analyzes the impact of exposure to the program on various outcome measures from recent household surveys. The primary finding of this study is that households living in districts heavily exposed to the program have worse measures of various current outcomes. The second empirical chapter examines the role of reliability of rainfall, which is important in Tanzania as agriculture is predominantly rain-fed and a small fraction of plots are irrigated. This chapter investigates if households cope with this major risk to income by re-allocating their labor supply between agriculture, wage labor, and self-employment activities. This chapter combines data on labor allocation of households within and outside of agriculture from the National Panel Survey with high-resolution satellite-based rainfall data not previously used in this literature. The primary finding of this study is that households allocate more family labor to agriculture in years of good rainfall and more labor to self-employment activities in years of poor rainfall. Market failures are often cited as a rationale for policy recommendations and government interventions. The third chapter implements four tests of market failures suggested in the literature, all of which rely on the agricultural household model but di↵er in how market failures are manifested. The common finding of these tests is that market failures exist in agricultural factor markets in Tanzania, although significant heterogeneity exists. Markets are more likely to fail in rural areas, remote locations, and are more likely to affect female-headed households. Households are also more likely to face market failure when they try to supply labor to the market than when they try to hire labor from the market.

This paper uses data from Sri Lanka and Tanzania to evaluate the benefits of combining household surveys with geographically comprehensive geospatial indicators to generate small area estimates of non-monetary poverty. The preferred estimates are generated by utilizing subarea-level geospatial indicators in a household-level empirical best predictor mixed model with a normalized welfare measure. Mean squared errors are estimated using a parametric bootstrap procedure. The resulting estimates are highly correlated with non-monetary poverty calculated from the full census in both countries, and the gain in precision is comparable to increasing the size of the sample by a factor of three in Sri Lanka and five in Tanzania. The empirical best predictor model moderately underestimates uncertainty, but coverage rates are similar to standard survey-based estimates that assume independent outcomes across clusters. A variety of checks, including adding noise to the welfare measure and model-based and design-based simulations, confirm that the main results are robust. The results demonstrate that combining household survey data with subarea-level geospatial indicators can greatly increase the precision of survey estimates of non-monetary poverty at comparatively low cost.

This paper intends to inform the effort of the Sri Lankan government to reform the targeting efficacy of its social protection programs, in particular, Samurdhi, which currently distributes benefits based on self-reported income. The paper develops a proxy means test for Sri Lanka based on the Household Income and Expenditure Survey 2016 and evaluates its performance for targeting benefits of Samurdhi. The paper considers a range of models and policy parameters that could be applied depending on data availability and country preferences. The results indicate that switching to a proxy means test could considerably improve the targeting performance of Samurdhi and would significantly improve the poverty impact of the program. The analysis finds that the performance of the proposed proxy means test model suffers when the coefficients are estimated from samples smaller than 1,000 households. However, the analysis does not find a similar loss of model performance when the model is estimated from seasonal data, provided the sample size is sufficiently large. The proposed model could be applied to targeting a variety of safety net programs after validating and refining the model by conducting a pilot survey.