Roughly one in ten Americans move every year, bringing significant social and economic impact to both the places they move from and places they move to. We show that migration intent mined from internet search\u00a0 queries can forecast domestic migration and provide new insights beyond government data. We extract from a major search engine (Bing.com) 120 million raw queries with migration intent from 2014 to 2016,\u00a0 including origin and destination geographies, and the specific intent for migration such as whether the potential migration is housing or employment related. Using these queries, we map U.S. state level migration\u00a0 flows, validate them against government data, and demonstrate that adding\u00a0 search query-based metrics explains variance in migration prediction above robust baseline models. In addition, we show that the
\nspecific migration intent extracted from these queries unpack the differential demands of migrants with different demographic backgrounds and geographic interests. Examples include interactions between age, education, and income, and migration attributes such as buying versus renting housing and employment in technology versus manual labor job sectors. We discuss how local government, policy makers, and computational social scientists can benefit from this information.<\/p>\n","protected":false},"excerpt":{"rendered":"
Roughly one in ten Americans move every year, bringing significant social and economic impact to both the places they move from and places they move to. We show that migration intent mined from internet search\u00a0 queries can forecast domestic migration and provide new insights beyond government data. We extract from a major search engine (Bing.com) 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