Tuesday, March 7, 2017

New era of segregation

Summary of 7 min video below: segregation caused by artificial intelligence, e.g., software that matches job applicants to firms, and potential mates, is exacerbated by housing restrictions. http://marginalrevolution.com/marginalrevolution/2017/03/new-era-segregation.html

this is related to our blogging about how zoning restricts housing supply which raises house prices.  


  1. “If an asset is mobile, then in long-run equilibrium, the asset will be indifferent about where it is used; that is, it will make the same profit no matter where it goes” (Froeb, McCann, Shor, & Ward, 2016, p. 317). Labor, in particular, is considered a highly mobile asset so it would seem that in the face of rising housing prices, that the indifference principle would hold true as people migrate away from areas of high prices. Given the information in the video, this is exactly what is happening. The technology discussed is just an accelerant.

    Cowen (2017) suggests changes in housing restrictions to spur the construction of housing that would allow for lower-income families to stay in those areas. Another important consideration are wages. “At the core of this displacement crisis is income inequality driven by declining real wages—in other words, a labor question brought on by the reorganization of work. What is widely viewed as a housing crisis, then, is actually an income crisis” (Chapple, 2017).

    Lifting restrictions to create housing geared toward lower-income and moderate-income families only works if those families can actually afford it. Per Chapple (2017), in this situation we’re dealing with “an income crisis, necessitating intervention in the labor market”.


    Chapple, K. (2017). Income Inequality and Urban Displacement: The New Gentrification. New Labor Forum (Sage Publications Inc.), 26(1), 84-93. doi:10.1177/1095796016682018

    Cowen, T. (2017, March 7). The new era of segregation. Retrieved March 18, 2017, from Marginal Revolution: http://marginalrevolution.com/marginalrevolution/2017/03/new-era-segregation.html

    Froeb, L. M., McCann, B. T., Shor, M., & Ward, M. R. (2016). Managerial Economics: A Problem Solving Approach. Boston, Massachusetts: Cengae Learning.

  2. There is a new type of segregation driven by technology and big data that changes the definition of what we would see as segregation. When we think of have segregation is defined with think of an action with intent behind it of setting something apart and it can have a negative connotation. What big data is doing today is matching people, buying patterns and product suggestions to make decisions extremely easy for today’s consumers. It sounds like a positive thing, taking the work out of decisions and delivering exactly what you would like, a product, house and even a human from a dating site. Algorithms help us match on what we like and who is like us. When big data matches us to like-minded people or people “just like us” we are staying with what is familiar to us.
    In 2014, the Federal Trade Commission held a public workshop, Big Data: A tool for Inclusion or Exclusion. Many of the positive benefits in healthcare, educational, and employment opportunities were discussed but risks were of a big concern. This big data could be used to exclude the less fortunate population, restricting them from credit or employment opportunities. In January 2016 report, when the report was published from the Big Data workshops, the FTC advised companies to question their data integrity. Collection of data can incorrectly exclude certain populations resulting in discrimination. An example included in the report showed higher prices for lower income communities when orders are placed online. In the higher income communities, consumers have access to physical stores and they realize the competitive nature of competition between retail and online stores. This is lost in the poorer communities and they pay higher prices.
    Big data has its benefits in all sectors but the data has to be used carefully. When data is collected, or interpreted incorrectly biases can occur and it could be breaking the law. Companies should always review the laws to make sure their use of big data does not violate any anti-discrimination laws.