Search

Residential amenities and access to employment are major factors in migration decisions. Yet traditional migration models are unable to reveal the causes (and effects) of migration because current data capture only movers who change their place of residence; depending on how far they move, these migrants may or may not also change their jobs. Migration flows thus could be categorized into two groups depending on whether the migrants also change their workplace. In this paper, we identify the number of movers who do and do not change their workplace by using overlapping county-to-county migration and commuting data. We refer to this as workplace-anchored and unanchored migration. Then we compare the local factors that affect both types of migration. Our analysis reveals that the most important local factors that separate workplace-anchored and unanchored migration are the poverty rate, commuting time, and age demographics of the origin and destination counties.

Labor Market Areas (LMAs) are distinctive communities of counties within a nation’s commuting network that are closely connected with one another than with other counties. The overlapping of communities within a hierarchical structure is a crucial feature of real-world networks, including commuting, yet existing methods are inadequate for modeling such overlapping because the concept is inconsistent with hierarchical ordering. Ahn et al. (2010) introduced the link community method to address this problem but did not consider weighted and directed links such as commuting flows. In this paper, we extend the link community method to accommodate directed and weighted networks using the idea that edges can be presented as vectors. We then apply our proposed method to U.S. commuting data. Results suggest that our new method reliably identifies the LMAs that we would expect to find. In our case, however, these LMAs can also overlap one another.

Agriculture has for the most part successfully met the food and material needs of people and industries. Generally, industries develop in rapid bursts that follow from economic innovations, instead of developing at a gradual and continuous pace. The development of agriculture is also closely linked to technological innovations and conditions elsewhere in the economy. However, few if any previous studies have examined how agriculture has reorganized in terms of industry input-output relationships. In this poster, We examine when and how agriculture has undergone changes within the US, through a comparative analysis of agricultural activities as reflected in input output tables over time.