Faculty Profile

Adam H. Langley
July 1, 2014

Adam H. Langley is a senior research analyst in the Department of Valuation and Taxation at the Lincoln Institute of Land Policy. Previously, Langley worked for the New York State Assembly. He earned his B.A. in political studies from Bard College and an M.A. in economics from Boston University.

Langley’s research has covered a range of issues related to state and local public finance, with a particular focus on the property tax. He has coauthored three Lincoln Institute Policy Focus Reports: Property Tax Circuit Breakers: Fair and Cost-Effective Relief for Taxpayers (2009), Payments in Lieu of Taxes: Balancing Municipal and Nonprofit Interests (2010), and Rethinking Property Tax Incentives for Business (2012). He has also led several projects to provide data on the Lincoln Institute’s website, including creation of the Fiscally Standardized Cities (FiSCs) database and a dataset with extensive information on nonprofits that make payments in lieu of taxes and the localities that receive them.

His articles have appeared in journals such as Regional Science and Urban Economics, Public Finance and Management, and Publius: The Journal of Federalism. His research has also been covered by more than a hundred news outlets, including The New York Times, The Wall Street Journal, The Economist, Governing, and The Boston Globe.

Land Lines: What projects have you been working on recently as a senior research analyst at the Lincoln Institute?

Adam Langley: I have been working on several projects related to local government finances. One major project has been the creation of the Fiscally Standardized Cities (FiSCs) database. This subcenter on the Lincoln Institute’s website allows users to make meaningful comparisons of local government finances at the city level for 112 of the largest U.S. cities over the past 35 years. I drew on this data in a recent paper on municipal finances during the Great Recession, which I presented at Lincoln’s 9th annual Land Policy Conference on June 2, 2014. I am also creating a summary table that describes state programs for property tax exemptions and credits, drawing information from Lincoln’s Significant Features of the Property Tax subcenter. I plan to use that table to estimate tax expenditures for these programs in all 50 states.

Land Lines: You’ve worked on several projects to provide data on the Lincoln Institute’s website. What motivates this focus on data?

Adam Langley: These data projects go to the core of Lincoln’s mission to inform decision making on issues related to the use, regulation, and taxation of land. Lincoln’s databases have been used by policymakers to help guide their decisions, by journalists to provide broader context in their stories, and by researchers for their own projects. Providing data that is freely accessible and easy to use greatly magnifies the potential reach of Lincoln’s work on land policy issues, because it empowers other analysts to undertake new research in this area.

It is also essential for Lincoln’s reputation that we base our policy recommendations on high-quality analysis and good data. To impact policy decisions, it’s critical that our research be widely viewed as objective, nonpartisan, and evidence-based.

Land Lines: You say that Fiscally Standardized Cities allow for meaningful comparisons of local government finances at the city level. What’s wrong with simple comparisons of city governments?

Adam Langley: The service responsibilities for city governments vary widely across the country. While some municipalities provide a full array of public services for their residents, others share these responsibilities with a variety of overlying independent governments. Because of these differences in local government structure, comparing city governments alone can be very misleading.

For example, consider a comparison of Baltimore and Tampa. The city government in Baltimore spends three times more per capita than the city government in Tampa—$5,594 versus $1,829 in 2011. However, the difference is almost entirely due to the fact that the City of Tampa splits the provision of local services with overlying Hillsborough County and an independent school district, whereas Baltimore has no overlying county government and the schools are part of the city government itself. Once all overlying governments are accounted for in the FiSC methodology, per capita expenditures for residents in the two cities are nearly identical—$6,083 in Baltimore versus $6,067 in Tampa.

Land Lines: Can you explain the methodology used to create Fiscally Standardized Cities?

Adam Langley: FiSCs are constructed by adding together revenues for each city government plus an appropriate share from overlying counties, independent school districts, and special districts. County revenues are allocated to the FiSC based on the city’s share of county population, school revenues are allocated based on the percentage of students in a school district who live in the central city, and special district revenues are allocated based on the city’s share of residents living in the district’s service area. Thus FiSCs provide a full picture of revenues raised from city residents and businesses, whether collected by the city government or a separate overlying government. These allocations are made for more than 120 categories of revenues, expenditures, debt, and assets. The FiSC methodology was developed with Andrew Reschovsky, a Lincoln Institute fellow, and Howard Chernick, a professor at Hunter College of the City University of New York. We calculate the estimates using fiscal data for individual governments provided by the U.S. Census Bureau, and we will update the FiSC database as data for additional years become available.

Land Lines: Why is it important to compare local government finances at the city level?

Adam Langley: Many people want to know how their city compares to other cities, but it’s critical to account for differences in local government structure when making these comparisons. The FiSC database does account for these differences. Thus, it can be used to compare property tax revenues in two cities, rank all cities by their school spending, investigate changes in public sector salaries over time, or see which cities are most reliant on state aid to fund their budgets.

In a separate project with Andrew Reschovsky and Richard Dye, we’re using the FiSC methodology to estimate pension costs and liabilities for all local governments serving each city. Media coverage sometimes creates the impression that all public pension plans face serious challenges, but in fact there is a great deal of variation around the country. In order to investigate these differences, it’s essential to have comparable data on pension costs for all local governments serving each city. For example, initial estimates show that on average the annual required contribution (ARC) for local pension plans in 2010 was equal to 4.9 percent of general revenues for the 112 FiSCs. However, ARC was more than 10 percent of revenues in both Chicago (11.7 percent) and Portland, Oregon (10.9).

Land Lines: Did revenue declines vary much across cities during the Great Recession?

Adam Langley: Yes, revenue declines ranged widely across the 112 FiSCs during and after the recession. Accounting for inflation and population growth, only eight FiSCs avoided revenue declines entirely through 2011. I calculated changes in real per capita revenues from each FiSC’s peak through 2011: About a third experienced declines of 5 percent or less (41 FiSCs), another third saw declines between 5 and 10 percent (34 FiSCs), and about a quarter had declines exceeding 10 percent (29 FiSCs). FiSCs with very large revenue declines include Las Vegas (20.2 percent), Riverside (18.0 percent), and Sacramento (18.0 percent).

Land Lines: Have local government revenues recovered much since the end of the recession?

Adam Langley: Not really, because revenue changes lagged behind economic changes by several years during and after the recession. Real per capita local government revenues were stable through 2009, declined slightly in 2010, and fell more significantly in 2011. The latest year with comprehensive data is 2011, so I tied together several different data sources to estimate revenues through 2013. Those data suggest that revenues hit bottom in 2012, when they were 5 to 6 percent below 2007 levels. That means revenues did not bottom out until three years after the recession officially ended. Revenues started to recover in 2013 but remained more than 4 percent below pre-recession levels.

This lag is driven by changes in intergovernmental aid and property taxes, which together fund almost two-thirds of local governments’ budgets. The American Recovery and Reinvestment Act provided states with about $150 billion in federal stimulus between 2009 and 2011, and there were additional stimulus funds provided directly to local governments. Most stimulus funds were gone by 2012, however, which led to the largest cuts in state spending in at least 25 years. Moreover, changes in property taxes typically lag behind changes in housing prices by two to three years, due to the fact that property tax bills are based on assessments from prior years, there are delays in reassessing properties, and other factors. That lag means that property taxes actually grew through 2009, did not fall until 2011, and then hit their trough in 2012.

Land Lines: Can you elaborate on your work describing property tax exemption and credit programs?

Adam Langley: I’m nearly finished with the first stage of this project, which entails creating a summary table on states’ exemption and credit programs. The table contains data for 167 programs, with 18 variables describing the key features of each program. There is information on the value of exemptions expressed in terms of market value; criteria related to age, disability, income, and veteran status; the type of taxes affected; whether tax loss is borne by state or local government; local options; and more. Once that table is completed, I will write a policy brief to outline key features of these programs. All of this information is drawn from the table on Residential Property Tax Relief Programs in Lincoln’s Significant Features of the Property Tax subcenter of the website. The original Residential Relief table provides detailed descriptions of each program, while the summary table should be most useful for users who want to make quick comparisons of states or for researchers who want to conduct quantitative analysis.

In the second stage of this project, I will estimate tax expenditures for these property tax relief programs. Despite the prevalence of these programs and their often large impacts on property tax burdens, there are no comprehensive estimates of their costs. Using data from the summary table and microdata from the American Community Survey, I will estimate for each state the percentage of residents who are eligible for property tax relief programs, the total cost of tax relief programs, the average benefit for beneficiaries, and the percentage of residents eligible and their average benefit by income quintile. These estimates will provide valuable new information on the impacts of property tax relief programs in the United States.