Working Paper - Employment Concentration and its Effect on American Cities

Before starting this project, I had assumed that cities with robust public rail transport would have lower rents. I thought that more transit options would lead to more living spaces within commuting distance of jobs. The assumption I didn’t realize I had made, however, was that jobs would be concentrated in the center of cities (at the central business district, CBD). This may be true for New York, but it’s not for Los Angeles.

Keeping that distorted assumption in mind, I investigated the following:

  1. Which cities have the most centralized and concentrated job centers?
  2. How significantly does the size of a city’s job center affect its home values, rent prices, and income levels?
  3. Does employment density have any bearing on income segregation?

I was using CPI’s rent and home value data for this project initially, but the American Community Survey has far more locality-based attribute data. Also, using the Tidycensus library in R, I can retrieve this data much quicker than I can BLS data. 

Though I can’t share the full project yet, I can share some neat maps!

This is a LISA (local indicators of spatial association) map built using Local Moran’s I. The map shows every block group that is a part of the New York City metropolitan statistical area (defined by Census, a region that consists of a city and surrounding communities that are linked by social and economic factors). The darkest red indicates high global employment density surrounded by similarly high employment-dense neighbors. Pink indicates high global employment density but surrounded by less dense block groups. 

The paper isn’t compete yet, but feel free to check out code for the project on GitHub