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Spring/Summer 2019   

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Evaluating Place-Based Incentives

Highlights

      • The nonrandom selection of Empowerment Zones and Enterprise Communities presented evaluation challenges in past studies because isolating the effect of the designation on housing prices and job growth was difficult.
      • Evaluations of Opportunity Zones can employ the same indicators for neighborhood change used to evaluate Empowerment Zones and Enterprise Communities such as job and business creation; changes in land prices, home values, and rents; and vacancy and poverty rates.
      • Administrative and survey data at the census tract level are widely available and can support the effective evaluation of Opportunity Zones.


Although the Opportunity Zones initiative is new, place-based incentives are not. In the United Kingdom (UK), Margaret Thatcher’s government introduced Enterprise Zones in 1981 to mixed success.1 Although the UK government phased out the program in 1999, it was revived in 2012 and 2016.2,3 UK Enterprise Zones were originally formed from “vacant, unoccupied, or deteriorating industrial land.”4 Over the next 10 years, new rules exempted Enterprise Zone properties from property taxes and the Development Land Tax, and developers in Enterprise Zones received a 100 percent tax deduction for spending on buildings and became exempt from some permitting requirements and data collection.5 To measure the effect of the UK Enterprise Zones, researchers counted jobs and firms, examined land prices, and surveyed managers.6 In general, the researchers found that only 25 percent of new jobs in the zones were attributable to the designation; the rest were merely relocations. In addition, researchers estimated the program cost per job at £23,000 to £50,000.7 In the United States, several states tested the Enterprise Zone concept, and Empowerment Zones were explored at the national level. This article reviews evaluations of the impacts of some of these place-based interventions and discusses findings that could inform the evaluation of Opportunity Zones.

Old multistory buildings along a street in Waco, Texas, with a sign on one building that says “Waco”.
A study of Texas Enterprise Zones found positive effects on employment growth. benedek / iStock.com

State Enterprise Zones

Early adopters of state enterprise zones in the United States included Indiana and New Jersey.8 The state enterprise zones typically offered relief or complete exemption from property taxes along with wage tax credits in return for data reporting.9 Papke presents results from some studies of these early efforts, finding that the cost per job for most of these programs ranged from $4,500 to $13,000 annually; in some cases, the cost reached $30,000 to more than $100,000 per zone resident job.10

In the case of Texas, Freedman found that enterprise zones had positive effects on employment growth.11 One important feature of the Texas study design that offers insights to researchers studying Opportunity Zones was that enterprise zone assignment in Texas was automatic; any area meeting the standards received the designation. This rule allowed researchers to compare nearby census tracts that were nearly identical before designation. For example, researchers could compare a tract with a poverty rate higher than 21 percent, which qualifies for the designation, against a nearby tract with a poverty rate of 19 percent, which does not. Although the difference between poverty rates of 19 and 21 percent may not be economically significant, the assignment of EZ status would be.

Freedman found that housing prices in zones that barely achieved enterprise zone designation increased by 10 percent more than those in zones that barely missed designation, and home vacancy rates in marginally qualified zones were 4 percent lower than those in zones that marginally did not qualify.

Empowerment Zones

Jack Kemp, first as a congressman and later as HUD Secretary from 1989 to 1992, strongly advocated for creating a federal program based on the enterprise zones. Although no such program was created under his watch, in 1994 the federal government designated the first Empowerment Zones (EZs), which included significant tax breaks with a large federal block grant to six urban and three rural targeted communities. Unlike enterprise zones, prospective EZs had to compete for the designation. (See “Place-Based Tax Incentives for Community Development,” for more information on EZs, Enterprise Communities, and Renewal Communities.)12

Philadelphia skyline as seen from across the river in Camden, New Jersey.
The Philadelphia-Camden bistate Empowerment Zone was one of the six Round I urban zones designated in 1994.

Considerable research has examined the effects of EZs on various outcomes. Early HUD-sponsored research compared areas awarded EZ designation with those not awarded EZ designation. It used as metrics job creation by zone employers, business formation and expansion in zones, employment of zone residents by zone businesses, and business ownership by zone residents. The study’s findings on the impact of the EZ program were inconclusive. Specifically, the researchers found that job growth in EZs outperformed that in comparison areas and that the number of new resident- and minority-owned businesses increased within the zones, but they could not determine a general trend of economic improvement from these numbers. In addition, job growth was correlated with EZ activity in only three of the six EZs. In the remaining three, job growth could have occurred because of other incentives or trends.13

Using mostly decennial census data, Busso, Gregory, and Kline examined the effects of EZs on economic indicators. In particular, the authors studied the estimated changes in rents and home prices in EZs relative to a comparison group of rejected EZ tracts and future applicant EZ tracts.

Using different estimation strategies, the authors found that EZ designation was correlated with an increase in home values of approximately 30 percent between 1990 and 2000, as self-reported in the decennial census, whereas rents increased by only 2 to 3 percent during the same period. The authors found this result striking and looked more closely at new residents or those who had last moved less than five years prior. These residents’ responses indicated that in EZs, home prices were 15 to 20 percent higher, and rents 4 to 6 percent higher, than those in comparison tracts. Although these gaps were smaller, a sizable difference still existed between the effect of EZs on owner-occupied housing and the effect on rental housing. The authors speculated that longtime owners who have not experienced a recent market transaction may greatly overestimate the effect of EZ designation or the resulting neighborhood changes on the value of their house. On the other hand, renters who moved to the EZ more recently saw greater rent increases, suggesting that rents do rise, although only over a longer period.14

EZ Designation Was Not Random

EZs and Enterprise Communities (ECs) were not randomly chosen. Their selection was based on applications, which encouraged localities to provide regulatory relief and take additional action. Therefore, any effect of EZs and ECs on the housing market is either indirect or a consequence of an optional state action, which makes isolating the effect of EZ designation difficult. For example, a locality might have chosen to invest in sewer and transportation upgrades in certain areas to win EZ designation. Any housing development occurring there is more likely the result of local government investment rather than federal tax relief. On the other hand, designation, or the competition for designation, often will spur localities to take actions that may be beneficial and long overdue.

Hanson critiqued previous studies that had assumed that EZ designation was random.15 Cities submitted applications, and EZ designations were assigned based on the merits of the applications. Many zones qualified for the designation, but few were approved. The zones that were designated were probably the ones most likely to show significant improvement without the EZ designation.

Hanson noted that the runner-up applicants for EZ designation were also highly qualified areas. Although they were probably less well positioned for wage growth or poverty reduction, they were more promising in those areas than a random area in the same city as an EZ with similar demographic statistics.

By comparing runner-up areas to the selected areas, Hanson found that EZ designation raised median property values by more than $100,000, which is both statistically and economically significant. Other studies showed large increases in employment and reductions in poverty in EZs but did not correct for the endogenous, or nonrandom, selection of EZs described above. For example, Busso, Gregory, and Kline found that employment in EZs increased between 12 and 21 percent and wages increased by 8 to 13 percent.16 An evaluation commissioned by HUD showed similar results.17 However, this study found that, after accounting for the nonrandom selection of EZs, EZs may have had zero effect on employment rates and slightly increased poverty rates.

Some EZ Designations Came Later

Krupka and Noonan estimated a simultaneous equations model based on the different rounds of EZ designations and hedonic analysis of housing markets.18 Hedonic analysis takes the characteristics of an item into account when determining its price; for example, hedonic analysis in housing will estimate house prices based on characteristics such as square footage, number of bathrooms, year of construction, school district, and distance to public transportation, among others.

Across six different formulations of the analysis, the authors found that EZs increased home prices between 10 and 40 percent. Because they used the hedonic approach, the authors could note several potential explanations. One possibility is that density decreased in EZ neighborhoods. The authors hypothesize that local governments spent grant money to demolish housing, which would increase the median value of the remaining homes. The authors note, however, that a more likely explanation is that the demand for commercial real estate increased, because commercial real estate in EZs would qualify for tax cuts.

Photo shows a five-story building with retail on the ground floor and residences above.
Future evaluations of Opportunity Zones could examine neighborhood impact, including changes in home values and rents, using administrative datasets.

These explanations have radically different implications. If the demolition hypothesis is correct, then median home prices may have increased, but the realities for people living in the remaining homes would not have changed. A national program to demolish vacant houses would increase median home prices in the absence of any tax or regulatory change, but such a program is unlikely to deliver real economic benefits to the country. If the commercial real estate hypothesis is correct, then economic activity may indeed have increased within EZs. Further study is needed to see whether the increased economic activity is primarily the result of existing businesses and business activity relocating to EZs. If the goal of the EZ designation was to improve outcomes for EZ residents, then it would matter whether those relocating firms brought their employees with them or hired locally.

What About Renters?

Reynolds and Rohlin examined block group data in EZs and ECs.19 Rather than examining means and medians, the authors plotted the distributions of rents and home values. Although the distribution of rent changed, the mean and median changed little; the percentage of households paying $600 or more per month in rent greatly increased, and the percentage of households paying $350 to $550 per month decreased. Although EZs were intended to benefit residents of low-income communities, this finding appears to show that EZ designation substantially increased their rents. These changes, however, occurred over a 20-year period, and some of this increase may be attributable to inflation. The remainder of the distribution, however, remains stable. In addition, the home value distribution showed that post-EZ home values were much more heavily concentrated over $100,000, and even over $300,000.

Evaluating Opportunity Zones

As the research above shows, future evaluators of Opportunity Zones have two major tasks:

  • Identify a comparison set of census tracts. To qualify as an Opportunity Zone, a census tract must be either a low-income community as defined by the Internal Revenue Service or contiguous to a low-income community. However, the governors of each state selected only a subset of census tracts meeting the criteria for a low-income community. Moreover, each governor likely used different criteria in his or her selection. In other words, as with EZs, the selection of Opportunity Zones was not random. Any evaluation will have to carefully consider how to adjust for this nonrandom selection. Based on past methods, techniques such as propensity score matching of nearby tracts or comparing tracts that were narrowly selected for designation with those that were narrowly rejected may be options.

  • Identify data that can be used to track neighborhood change. The research on enterprise zones and EZs focused on employment rates among area residents; the creation of jobs and businesses in the area; changes in land prices, home values, and rents; and changes in vacancy and poverty rates. These variables will likely be the same ones examined for Opportunity Zones.

Researchers may want to examine administrative data in addition to American Community Survey data. Administrative data are more available than they have ever been. Administrative data have two big advantages over survey data: they are not subject to sampling error, and the data can generally be obtained and analyzed much more quickly. These data also have two major flaws: they are not being collected for statistical purposes, so they may be biased, and only a small set of data points are available.

The administrative data points that HUD makes available capture the annual mobility of assisted housing tenants; because these data are constantly updated as tenants move, they can be an early indicator of neighborhood change. In addition, data from the U.S. Postal Service are updated every quarter and can capture changes in long-term vacant addresses, increases in total residential and business addresses (a sign of building permit activity), and changes in active addresses (a sign of residential and business lease-up).

Other administrative datasets that HUD and other researchers have used to measure neighborhood change include Home Mortgage Disclosure Act data, which can show change in mortgage activity, characteristics of those applying for and receiving mortgages as well as the amount borrowed; county records data, which can show property sales transactions, changes in property values, and foreclosure activity at the census tract level; and data on employment from unemployment insurance records.

New sources of data should also be explored, such as “scraping” the Internet to gather information on advertised rents; using posts from social media sites such as Twitter to measure levels of happiness, sadness, and fear at small-area geographies; and collecting credit card company data that could show changes in retail purchasing patterns or even the creation of retailers at the neighborhood level.

One important source of data will be Qualified Opportunity Funds — in particular, the activities in which they are investing and the location of those investments. At the time of publication, final regulations on reporting requirements had not yet been issued. If investment data are made available, they will help researchers understand the impacts of specific types of investments. For example, if research indicates that the number of residential units in an Opportunity Zone has increased, it would be helpful to know whether Qualified Opportunity Funds were investing in residential property developments in neighborhoods that reflect this impact.

Both survey and administrative data at the census tract level are more available now than in the past. With careful controls to identify comparison neighborhoods and data on investments to help explain any findings (or nonfindings) of impact, effectively evaluating the impacts of Opportunity Zones should be possible.

— Daniel Marcin, HUD Staff





  1. PA Cambridge Economic Consultants Ltd. 1987. An Evaluation of the Enterprise Zone Experiment, London: Department of the Environment, HMSO.
  2. Leslie E. Papke. 1993. “What Do We Know about Enterprise Zones?” in Tax Policy and the Economy 7, James M. Poterba, ed. Cambridge, MA: MIT Press, 48.
  3. “About Enterprise Zones,” HM Government website (enterprisezones.communities.gov.uk/about-enterprise-zones/). Accessed 28 May 2019.
  4. Papke, 46.
  5. Papke, 46–7.
  6. Papke, 47.
  7. Papke, 48.
  8. Papke, 50, 61.
  9. Papke, 49.
  10. Papke, 61.
  11. Matthew Freedman. 2013. “Targeted Business Incentives and Local Labor Markets,” Journal of Human Resources 48:2, 311–44.
  12. Michael J. Rich and Robert P. Stoker. 2006. “Lessons and Limits: Tax Incentives and Rebuilding the Gulf Coast after Katrina,” Washington, DC: Brookings Institution.
  13. Scott Hebert, Avis Vidal, Greg Mills, Franklin James, and Debbie Gruenstein. 2001. “Interim Assessment of the Empowerment Zones and Enterprise Communities (EZ/EC) Program: A Progress Report,” U.S. Department of Housing and Urban Development, Office of Policy Development and Research.
  14. Matias Busso, Jesse Gregory, and Patrick Kline. 2013. “Assessing the Incidence and Efficiency of a Prominent Place Based Policy,” American Economic Review 103:2, 897–947.
  15. Andrew Hanson. 2009. “Local employment, poverty, and property value effects of geographically-targeted tax incentives: An instrumental variables approach,” Regional Science and Urban Economics 39:6, 721–31.
  16. Busso et al., 921–2.
  17. Hebert et al.
  18. Douglas J. Krupka and Douglas S. Noonan. 2009. “Empowerment zones, neighborhood change and owner-occupied housing,” Regional Science and Urban Economics 39:4, 386–96.
  19. C. Lockwood Reynolds and Shawn M. Rohlin. 2015. “The effects of location-based tax policies on the distribution of household income: Evidence from the federal Empowerment Zone program,” Journal of Urban Economics 88, 1–15.

 

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