Monday, May 18, 2015

URSP Student Kyle Jackson Researches Labor Markets Disciplining Attorneys

My interest in this project began when I took ECON 415 – Law and Economics in the summer of 2014. Prof. Nick Bormann, a PhD candidate in Economics and the class’ instructor, spoke to his research interests throughout the class and I found that my interests aligned well with his. I knew I wanted to participate in URSP and was looking for a mentor so I approached him about the opportunity. It turned out that he had a research topic that he has been meaning to get to, but has not had the time or help to do so; my timing was perfect. There are so many professors out there who need help with research or know someone who does that you have a pretty good chance of getting into research as an undergraduate if you just ask!

The project I am working on will hopefully provide some important insight into why high involuntary employment and high fees exist in the legal industry. In order for this to happen, I have to collect data from multiple sources and conduct statistical analysis to make sense of the data. Our main quantitative methods are regressions; this is done to find correlations. Because of the nature of this research, I work from my desktop at home where I have two large screens for doing my data collection and analysis.

On a weekly basis, I am communicating with organizations, whether it be county courthouses or a bar association, to figure out what data they collect and can easily give to me. I also collect data from open sources where the information I want is not aggregated. For example, this week I have been working on developing a program (and also discovering how to do this!) in Python to parse HTML documents for information regarding attorney wages and employment in Florida by metropolitan area and year. I am doing this because it would be very time consuming to go to each webpage and table, pick out the information I want, and manually type it into an excel spreadsheet; why do that when I can program something to do that for me!

This work has immensely factored into my long-term professional development by providing me the ability to work with data and draw meaningful insights out of messy data. I am no longer given a clean dataset in class where I run one test and that is all; I have to now decide what data I want and whether I can process it in a way that would be beneficial to this research project. This has been an amazing experience so far and I actively encourage anyone interested in URSP to apply!