Having learned some of the basics of Stata, this week I got to get more into the data analysis part of my internship. I’ve performed t-tests, ran regressions, and made data tables with Bureau data. I’ve also been learning more about how to interact properly with the company server. In terms of my independent project, I finally had the chance to consolidate and compare all of the sources I have read thus far. Here are some of the more interesting findings:
Researchers tend to agree that consumption and expenditure, especially on non-durable goods, tend to increase in the days after paychecks/government aid is distributed. The cause for this increase, however, is less universally agreed upon. Studies, or at least those I have read about so far, tend to point to one or more of the following three causes: impatience, present bias, and/or liquidity constraints. These are not mutually exclusive and are believed by many economists to interact to cause this change. The trouble is that impatience and present bias are quite similar and so are very difficult to differentiate between.
This spike also seems to apply more to low income individuals. Most studies on changes in consumption as well as payment distribution frequency tend to focus on low income groups as they are more impacted by these factors. They also tend to be the recipients of government aid, upon which many of these studies are done. This makes it difficult to generalize any findings to the greater population.
Last week, I also began reading some papers about payday loans and whether they should be more strictly regulated or not. I plan to continue reading about this topic this week as well as think more about how I want to structure my final paper.