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Tobin Pre-Doctoral Fellowship

Research in Household Finance and Real Estate



Paul Goldsmith-Pinkham and Cameron LaPoint



Paul Goldsmith-Pinkham and Cameron LaPoint are looking to recruit a highly skilled and intellectually curious individual to join the Tobin Pre-Doctoral Fellowship Program to work on several research projects at the intersection of household finance, real estate, and urban economics.

The prospective fellow will evenly split their time between two sets of projects.

The first set of projects, supervised by Paul Goldsmith-Pinkham, revolves around consumer bankruptcy and housing choice. Consumer bankruptcy in the United States varies significantly by geography and demographics. We use administrative data on consumer bankruptcy filings to study why, holding fixed most economic characteristics, Black Americans’ bankruptcy filings tend to be dismissed in court more often. This project will involve combining bankruptcy filing records and geospatial data on bankruptcy courts and lawyers. It will also involve econometric research on the challenges involved with identifying race and ethnicity based on name and location.

The research on housing choice will involve questions of housing risk and transactions. This involves several potential projects, but the first will involve questions of the risk associated with selling a home. Namely, how often do potential transactions fall through? How costly is it? What do sellers do to avoid it? We will use housing listing and transaction data to study this aspect of the real estate market.

Sample tasks in this set of projects will involve data merges based on geography, clean-up of existing datasets to construct a new panel dataset on failed housing sales, and statistical regression analysis.

The second set of projects, supervised by Cameron LaPoint, examines the role of mortgage and tax delinquencies as a vehicle for neighborhood amenity and demographic change. It has been alleged in the popular media that the involvement of large institutional investors in distressed housing markets has accelerated gentrification in major cities, amplified wealth inequality, and allowed certain companies to greatly expand their real estate holdings by converting formerly blighted housing into commercial buildings. This research will evaluate the validity of this narrative using a combination of databases containing housing transaction records, mortgage loan originations, tax payment histories, and private equity real estate deals.

Sample tasks the pre-doctoral fellow will perform under this project set include assigning coordinates to property addresses, fuzzy merging based on names and address information between datasets, and constructing a new database of distressed home sale records to inform policy debates related to affordable housing provision.



Applicants should be completing or have completed a bachelor's or master's degree. Applicants need not hold an Economics degree, but a working knowledge of economics/finance principles is necessary to carry out the research underlying these projects.

As many of the research tasks will involve extensive work merging large datasets and spatial mapping techniques, we are especially interested in candidates with prior experience using statistical software such as Stata and programming languages such as R. Additionally, experience using geocoding tools like ArcGIS/Google API, webscraping, and interests or coursework in applied econometrics are a big plus.

Preference will be given to applicants with a strong interest in pursuing a PhD in Economics or a related discipline.



Applicants interested in this position should include several coding samples in their application file (ideally in R or Stata), and/or a link to a GitHub account, if available. As part of the application review process, short-listed candidates may also be asked to complete a sample exercise.