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Federal grants account for a quarter of all public goods and services provided by state and local governments. Beyond redistribution goals, these transfers primarily aim to incentivize spending on specific areas such as education, healthcare, or infrastructure. The efficacy of federal grants depends on how they affect state and local funding decisions. Under basic assumptions of fiscal behavior, unmatched federal grants might displace some of the state and local funds that would have been otherwise spent on the targeted category. However, empirical evidence often contradicts this prediction, giving rise to the notion of the flypaper effect: money sticks where it hits.
My research is the first to study these questions in the context of federal funding for public transit in the U.S. Public transit systems, while administered locally, receiving substantial funding from both federal, state, and local sources. In 2019, capital expenditures (facilities, rail infrastructure, and rolling stock) were funded with 45% of federal funds, 23% of state funds, and 32% of local funds. I leverage the American Recovery and Reinvestment Act of 2009 (ARRA) as a useful setting for studying an increase in federal funding for transit. ARRA allocated $6 billion for public transit through a pre-existing formula with relatively stable local inputs, meaning funding amounts were based on time-constant differences between locations rather than dynamic shocks. Unlike regular federal programs, ARRA did not require matching funds from state and local governments. These characteristics allow me to employ a Difference-in-Differences model with continuous treatment to estimate the causal effect of ARRA.
I find that each $1 of ARRA transit funding per capita led to a $3.9 of additional capital transit expenditures in the following 11 years. This increase operates through two key channels. First, there is an initial increase in federally-funded expenditures with no displacement of existing state and local funds, a phenomenon known as the flypaper effect. Second, there is a significant crowding-in of additional state and local investment beyond that. This crowding-in effect is observed only in large cities and is more pronounced in those with existing rail systems. This suggests that factors such as political influence, large upfront costs, and cost overruns might be driving this effect. A set of covariates, used to control for demographic, economic, and Great Recession-related local characteristics, does not alter this conclusion.