How do social networks influence the decision to migrate? Prior work suggests two distinct mechanisms that have historically been difficult to differentiate: as a conduit of information, and as a source of social and economic support. We disentangle these mechanisms using a massive “digital trace” dataset that allows us to observe the migration decisions made by millions of individuals over several years, as well as the complete social network of each person in the months before and after migration. These data allow us to establish a new set of stylized facts about the relationship between social networks and migration. Our main analysis indicates that the average migrant derives more social capital from “interconnected” networks that provide social support than from “extensive” networks that efficiently transmit information.
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Abstract -
Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty. Yet many poverty maps are out of date or exist only at very coarse levels of granularity. Here we develop microestimates of the relative wealth and poverty of the populated surface of all 135 low- and middle-income countries (LMICs) at 2.4 km resolution. The estimates are built by applying machine-learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, and topographic maps, as well as aggregated and deidentified connectivity data from Facebook. We train and calibrate the estimates using nationally representative household survey data from 56 LMICs and then validate their accuracy using four independent sources of household survey data from 18 countries. We also provide confidence intervals for each microestimate to facilitate responsible downstream use. These estimates are provided free for public use in the hope that they enable targeted policy response to the COVID-19 pandemic, provide the foundation for insights into the causes and consequences of economic development and growth, and promote responsible policymaking in support of sustainable development.more » « less
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Objective The objectives of this study were to examine (1) the linkage from airports to regional talent distribution and (2) the effect of talent on regional economic development.
Methods Using the data collected in Wisconsin at the municipal level, a subcounty level, in a region of the North Central United States from 1970 to 2010 and the American Community Survey 2006–2010 five‐year estimates, and random effects models and structural equation models, we employ descriptive and inferential statistics to examine the linkage from airports to talent to regional economic development.
Results We find that the farther a location is away from the airport, the lower its talent share tends to be, while greater passenger flow at the nearest airport increases a location's talent share. Given the quantity of passenger flow, a longer distance from the airport also reduces a location's talent share. The results furthermore suggest that economic development is impacted positively by passenger flow and talent share and negatively by distance to an airport.
Conclusion Our results underscore the intermediate role of talent between airports and regional economic development; building the linkage from airports to talent within the context of regional economic development provides important insights for local policy making aimed at attracting talented migrants.
