One Job, One Vote? The Electoral Returns to Patronage
Ernesto Dal Bó, Professor
Business, Haas School
Closed. This professor is continuing with Fall 2023 apprentices on this project; no new apprentices needed for Spring 2024.
Who gets patronage jobs in the public sector? Do patronage employees reciprocate by mobilizing votes for their patrons? We study these questions in the context of the archetypical patronage organization in US history: New York City’s Tammany Hall. We are digitizing and linking newly collected archival records on the organization’s personnel, the universe of city employees, and individual-level voter registry data. Complete lists of applicants, appointments, and the results of civil service exams allow us to identify who receives patronage jobs and how their characteristics differ from applicants who would have been selected for the job if civil service rules had been followed. We plan to test whether patronage appointees respond by mobilizing votes for Democratic politicians in their neighborhood. To alleviate concerns of selection, we isolate exogenous variation in the likelihood of getting a patronage job induced by turnover in Democratic party leaders.
Role: The goal is to match undergraduates to tasks based on interest and skills. We expect undergraduates to mainly contribute to the empirical and data-intensive aspects of the project. We use Python, R, Stata, and GIS applications like QGIS or ArcGIS.
Other tasks include, for example:
1) Collecting and digitizing novel historical data
2) Web scraping and Optical Character Recognition of historical records
3) Working with geo-spatial data and digitized maps
4) Combining data from the census and other sources to construct new data sets
5) Analyzing the data using econometrics methods
6) Visualizing the results in graphs and maps
Qualifications: We are especially looking for one or more undergraduates with GIS experience to create new shape-files of district boundaries for historical New York City. But these skills can also be learned while working on the project.
We also have openings to work on other tasks that do not require GIS skills. Data analysis skills, for example coding skills in Python, R or Stata, are valuable for those tasks.
Day-to-day supervisor for this project: Lukas Leucht, Ph.D. candidate
Hours: 6-8 hrs
Engineering, Design & Technologies Social Sciences