Abstract: I identify neighborhoods within cities that have consumption bundles identical to remote rural neighborhoods via unsupervised machine learning on 127 "in-person" varieties using cellphone flow data and establishment-matched credit/debit card transaction data. To explain the phenomena, I construct a novel quantitative spatial model with commuting, in-person travel for the 127 varieties, non-homothetic preferences, and firm entry conditions. Utilization of the model requires new estimates of several sets of parameters, including variety travel sensitivities and income elasticities. I then test four separate counterfactuals with the same taxation procedure: a general reduction to income inequality; a place-based reduction to income inequality; a subsidy to business operating costs; and a subsidy to business entry costs. I find operating cost subsidies to be both the least disruptive to "wealthy" workers' consumption bundles and most beneficial to poor urban or rural workers.
Abstract: I analyze neighborhood-level per-capita consumption bundles using the highly granular details of cellphone flow data. I show that while cities do have more consumption varieties available than rural communities, several urban neighborhoods have consumption bundles as if they were located in remote, rural region. Neighborhood groupings are identified using an unsupervised machine learning clustering algorithm on per-capita consumption patterns on over 100 consumption industries. The "correct" number of neighborhood groupings is determined using a statistic-based selection procedure. I then utilize establishment-matched electronic transaction data to provide new consumption-price elasticity of substitution estimates that utilizes establishment distance variation for identification for the industries of study. I conclude the paper with a discussion on when granularity, heterogeneity, and the spatial dimension play an important role using a model, patterns in the data, and borrowed estimates of income elasticity and consumption travels sensitivity from my job market paper and the literature.
Abstract: I examine the relationship between the United States' labor market shift towards high-skilled workers and the increase in city-level income inequality that started in the 1980s. I use the Dotcom bubble as a natural experiment, which is a shock to a subset of high-skilled workers. Using a differential exposure difference-in-differences model, I show that a one percent increase in exposure to the bubble led to a 6% increase in wage inequality and a 9% increase in total personal income inequality at the metropolitan-level and a within metropolitan 5% increase in household inequality at the census tract-level. 50% of the estimate disparity can be explained by capital income. A decomposition suggests that the increase in income inequality was driven more by computer-related employment than wages and that there were spillovers to other industries. Tightly estimated null migration results suggest occupational switching occurred. The null migration result likely explains the tightly estimated null housing market results. The last two findings contradict the literature, which instead uses general shocks to high-skilled labor demand, providing a useful avenue for future research.
(Previous name: John Julian Patrick Wade)
Wade, John J. P. (2016). "The Effect of Health Insurance Expansion on Mortgage Performance," (Resting Paper).
Gilbert, Ben and Wade, John J. P. (2016) "Health Care and the Housing Crisis," Social Science Research Network. available at ssrn.com