Association for Public Policy Analysis & Management

Rising housing costs in metropolitan areas nationwide have drawn attention to many cities’ increased need for affordable housing development. One federal policy instrument critical to spurring this development is the Low-Income Housing Tax Credit (LIHTC) program, which gives states $8 billion annually to fund tax credits for housing construction and has helped fund 2.6 million housing units over the past three decades. A growing literature has explored the LIHTC program’s effect on neighborhoods, in particular examining outcomes such as nearby housing values, neighborhood income levels, and private real estate development. Although results have been mixed with regard to some of these outcomes, the research has found clear evidence of shifts in neighborhood residential populations as a result of LIHTC developments. However, little is known about the program’s effect on schools, one of the most important public institutions in a neighborhood and one particularly sensitive to changes in neighborhood demographics.

In this paper, I study the impact that the construction of LIHTC-funded developments has on nearby schools and school districts. Specifically, I look at impacts to local school enrollment, student composition, and student diversity with respect to both racial and socioeconomic subgroups. I also estimate low-income housing’s effect on district-level racial and socioeconomic segregation across schools, as well as the share of students in private or charter schools. Additionally, I estimate the effects on district-level student homelessness. Building on the results of previous research (e.g. Diamond and McQuade 2017) that found heterogeneous neighborhood responses depending on the original racial and socioeconomic makeup of the neighborhood, I examine whether impacts to schools and school districts are dependent on their baseline characteristics or achievement levels.

The estimation of low-income housing’s effects on nearby schools is complicated by the fact that the location of LIHTC developments is endogenous to neighborhood contexts and, by extension, to school characteristics. To account for this, I follow the example of Freedman and McGavock (2015) and exploit quasi-random variation in the location of developments that results from a program rule on the census tract-level tax incentives available to developers. By instrumenting the construction of LIHTC units in a tract with its identification as a low-income tract eligible for a larger tax credit, I am able to estimate plausibly causal impacts of the housing development.

My paper uses data from the LIHTC database, which includes information on all LIHTC-funded housing developments including their exact location, along with tract-level Census data on neighborhood characteristics and data from HUD used to identify tracts eligible for higher tax incentives. I combine this with data from the National Center for Education Statistics’ (NCES) Common Core of Data and EdFacts data on student homelessness. To link LIHTC developments with the schools most affected by their construction, I use the NCES School Attendance Boundary database to link schools’ catchment areas with their corresponding census tracts.