<mark>in the urban sector. The model’s predictions thus suggest a more restrictive approach to rural-urban migration. Beyond their divergent support for migration, both the Lewis (1950s and 1960s) and HarrisTodaro (1970s and 1980s) schools of thought ignored rural-rural migration and return migration to rural areas, as well as the social relationships that surround migration decisions.</mark> In contrast, the new economics approach to migration, stemming from the works of Stark and Bloom (1985), Rosenzweig and Stark (1989), and Stark (1991), postulates that migration decisions are part of a collective household strategy to diversify family income and self-insure under missing and imperfect markets. Migration accordingly represents an important socio-economic decision made by households to not only maximize income, but also minimize risk, diversify income sources, and relax the constraints existing in the markets for factors of production (capital, credit, land, and labor) through remittances (Azam and Gubert 2006). In this view, migration can be international or internal – and go in any direction (rural-urban, urban-rural, etc.) – for various economic and non-economic purposes. Moreover, whether within or outside the country, economic migration requires an initial capital endowment to bear the costs of leaving the original residence and a potential period of unemployment at the destination. Networks thus play a strong role in lowering migration costs and risks, as largely evidenced in the international migration literature (Ilahi and Jafarey 1999; Massey et al. 1993; D. McKenzie and Rapoport 2007). Furthermore, remittances can take any form as part of the contractual arrangements between the individuals sent out for migration and the rest of their families.

In addition to the theoretical contributions, there is a considerable body of empirical work on the welfare effects of internal migration in developing countries. Much of this work, however, fails to establish a causal link between internal migration and welfare. Perhaps the only available experimental evidence of the impact of internal migration comes from Bangladesh, where Bryan et al. (2014) find large consumption gains among households randomly chosen to receive a small monetary incentive for migration transport costs. Recent work from Sub-Saharan Africa relies on panel data with tracking survey components to assess the welfare effects of moving. Beegle et al. (2011) exploit multiple rounds of panel data over a 13year period to examine the links between migration and consumption in Tanzania. The study is novel in its application of a rigorous individual tracking protocol: approximately 93 percent of baseline households (and 82 percent of non-deceased individuals) were re-interviewed at least more than a decade after the preceding survey. After accounting for individual and household fixed effects, the authors uncover a 36 percent impact on per capita consumption among migrants who left their original communities. Using a similar approach, de Brauw et al. (2013) find even larger welfare gains from migration in Ethiopia.

The available evidence from Uganda, meanwhile, relies primarily on cross-sectional data sets. Using one round of data from the 2002/03 UNHS, Herrin et al. (2009) observe a negative relationship between wealth accumulation and the number of moves undertaken by a household head in Uganda. They also find that long-distance moves are correlated with declines in asset values for migrating households. The authors suggest that this finding could be linked to regional and linguistic differences in Uganda that potentially raise barriers to wealth accumulation from migration. However, the reliance on cross-sectional data raises questions about the robustness of these findings. Strobl and Valfort (2015) combine 2002 census data with weather information to examine the impact of weather-induced migration on employment outcomes for non-migrants in Uganda. They uncover an adverse effect of migration on employment outcomes for residents in receiving communities—particularly in areas with fewer roads (a proxy for low capital mobility). Meanwhile, an unpublished analysis of the 2005/06 UNHS found a positive correlation between labor mobility and per capita expenditure (World Bank 2008).

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