Why Understanding Who Benefits From Incentive Programs Is Important

This column by Jim Landers, associate professor of clinical public affairs and Enarson fellow, John Glenn College of Public Affairs, The Ohio State University, examines how evaluators can better understand who benefits from incentives to help determine whether targeted incentives are achieving their goals; reaching intended beneficiaries; and have unintended or disproportionate impacts. Landers provides examples of researchers examining incentives programs along four different units of measurements: taxpayer or household income group; geographic unit; business/firm; and demographic group.

This piece was originally published by The Pew Charitable Trusts in an August 2022 newsletter for tax incentive evaluators and scholars.

Evaluation Perspectives: Understanding Who Benefits From Incentive Programs Is Important.

Why and how should we do it?

Jim Landers
Associate Professor of Practice in Public Affairs, Enarson Fellow
John Glenn College of Public Affairs
The Ohio State University

Who benefits from incentive programs?

While it has not always been the traditional focus of incentive evaluations, evaluating who benefits from incentive programs can be a critical part of understanding whether programs are achieving their goals. Recent conversations among state evaluation staff, including some held at the Fall 2021 National Conference of State Legislatures (NCSL)/Pew Roundtable on Evaluating Economic Development Incentives, have highlighted important questions to consider when incorporating this type of analysis. These include:

• Why should evaluators examine the distributional impacts of incentive programs?
• How can evaluators best measure the distribution of incentive program dollars and/or benefits?
• What are the challenges of developing and using more granular (or disaggregated) distributional analyses? And when are such analyses most appropriate?
• What positive and/or normative economic observations could result from distributional analyses?

This analysis addresses several of the questions raised during these conversations.

Why should evaluators examine the distributional impacts of incentive programs?

Examining the distributional impacts of incentives isn’t a new concept – public policy discussions have long emphasized the economic implications of how public expenditures and taxes are distributed (e.g., tax policy, K-12 funding). Thus, presenting information about how incentive dollars are distributed across income strata, demographic groups, industries, business types, or geographies within a state can be seen as a logical extension of policy discussions that are already occurring. More specifically, such analyses can help determine whether targeted incentives are (1) achieving their goals; (2) reaching intended beneficiaries; or (3) have unintended or disproportionate impacts.

 Are targeted incentives achieving their goals?

The Kansas Legislative Division of Post Audit (LPA) conducted an evaluation of the state’s STAR bonds financing program. The program’s broadly defined purpose is the “promotion, stimulation, and development of the general and economic welfare of the state of Kansas, including Kansas communities.” STAR bonded districts are created collaboratively between state and local governments. The analysis found that only three of the 16 STAR bond attractions reviewed by LPA met tourism-related program goals and LPA provided recommendations to better achieve such goals.

Are programs reaching intended beneficiaries?

The Maryland Department of Legislative Services (DLS) conducted evaluations of the state’s enterprise zone tax credit and One Maryland program. DLS found that the most distressed neighborhoods received $1 for enterprise zones for every $3 to $4 of One Maryland program funding provided to the least distressed neighborhoods. DLS recommended that the General Assembly establish new criteria to include multiple measures of need and work to prevent areas that are not economically distressed from qualifying for credits.

Do programs have unintended or disproportionate impacts?

Determining distributional impacts shouldn’t be limited to programs with social and economic equity goals (e.g., the earned income tax credit or EITC). To do this would be to severely limit the ability to understand the performance of other economic development incentives for job retention, job creation, and business investment. As stewards of public resources, policymakers should be aware of where funds end up. Analyzing an incentive’s distributional impacts is a critical component to understanding if programs are beneficial to the communities or constituencies that policymakers represent. This type of analysis may reveal inequities in program design or administration that—however unintentional—disproportionately limit which individuals, businesses, or communities receive incentive benefits. Therefore, lawmakers may learn from distributional analysis how to more effectively and efficiently tailor existing incentive programs.

How Can Evaluators Measure Who Benefits?

When analyzing and measuring who benefits from incentive programs, evaluators can consider at least four distributional groups:

1. taxpayer or household income group
2. geographic unit (e.g., neighborhood, community, city, county, or region)
3. business/firm (e.g., firm employment, firm wage rate, firm capitalization or revenue, firm industry sector)
4. demographic group

Income distribution

Standard measures of tax equity are one starting point for evaluating the income distribution of incentive program benefits. The ability-to-pay principle suggests that the tax burden should depend on a person’s taxpaying capacity (e.g., current income, assets). This principle leads to two related concepts of tax equity: horizontal equity and vertical equity.

Horizontal equity holds that taxpayers with similar taxpaying capacities should incur similar tax burdens. Vertical equity holds that taxpayers with different taxpaying capacities should incur different tax burdens. Horizontal and vertical equity principles can be applied to examine the impact of subsidies on effective tax rates (tax liability divided by income). For instance, one could examine the extent to which similar businesses or households have different effective tax rates because one business or household received a tax incentive and the other did not. Or, one could examine the extent that a tax incentive program changes effective tax rates of businesses or households as income of these entities change.

Accessing administrative data is one challenge to measuring the distribution of tax incentives. Return-level income tax data, for example, would include counts of taxpayers claiming a tax incentive, the incentive amount claimed, the taxpayer income, and tax liability measures. Evaluations have shown how valuable administrative data can be when used to evaluate the effectiveness of certain incentives.

Evaluators in Indiana used income tax return data and parcel-level property tax data to generate informative distributional presentations on several occasions. (See 2014, 2019, and 2020 evaluations.) The evaluations report counts of incentive claims, incentive amounts, tax liability impacts of incentives, and other measures by income class. The presentations highlight the income groups claiming the bulk of incentives and the reduction in tax liability due to the incentive. Potentially, these data could be used to also measure the average incentive as a share of income for each income class. This measure, much like a tax burden measure, could demonstrate distributional characteristics of vertical equity.

Not all administrative tax data includes measures of individual or household income. Some important incentives are provided via deductions, credits, and abatements against local property taxes and property tax records typically include detailed information about the property, its value and tax liability, and tax incentives received by the property owner. However, these records generally don’t include property owner income. Consequently, evaluators examining the income distribution of property tax incentives may have to rely on alternative measures of economic well-being (e.g., market value or assessed value of property), or develop indirect research methods.

In an evaluation of housing tax expenditures, Washington, D.C.’s chief financial officer’s Office of Revenue Analysis (ORA) used both median home value and the variation in this measure across city wards to construct a distribution of the homestead deduction by median home value.

Geographic distribution

Geographic data can provide a feasible and convenient way to analyze the distribution of incentives (e.g., by neighborhood, county, or region) and different demographic and economic measures (e.g., race, ethnicity, income by neighborhood, county, or region). Besides the practicality of using geographic data, policymakers are also often interested in knowing what is going on in their districts and how their constituents are affected by incentive programs. In addition, policymakers may want to know if every neighborhood in a local jurisdiction or every region in a state has a relatively equal shot at receiving incentives. Consequently, analyzing the geographic distribution of incentives can be a powerful way of informing these types of questions. In the examples below, presentations by geographic unit provide a rich source of distributional information by incorporating socioeconomic measures from other databases (such as census data) by different geographic levels of analysis,  county-level employment, occupational, or income data.

In the review of Washington, D.C.’s housing tax expenditures, ORA used effective property tax rates (e.g., property tax divided by taxable value) as well as assessed value comparisons to analyze the equity implications of the city’s homestead deduction. The report includes two particularly interesting figures: 1) the distribution of properties claiming the homestead deduction and 2) the assessed value of properties claiming the homestead deduction by city council ward. Likewise, the evaluation of the city’s earned income tax credit also presents the geographic distribution (by neighborhood and city council ward) of households claiming the credit, credits claimed, and mean and median credit. (ORA’s Charlotte Otabor presented these findings at the 2021 NCSL / Pew roundtable.)

Colorado’s recent evaluation of enterprise zones (EZs) includes geographic presentations of: 1) the distribution by county of certified EZ tax credits and investment in EZs and, 2) the distribution by EZ of new jobs by participating EZ businesses and employment of EZ residents by participating EZ businesses.

Distribution among businesses

Another challenge surrounds evaluating the distribution and equity of incentives to businesses, whether they are direct subsidies (e.g., training support or closing funds) or incentives provided through the tax system. What distributional measures can we use for businesses receiving incentives? Are there reasonable measures of economic well-being for businesses?

Recent research by Slattery and Zidar (2020) provides various approaches to examine the distribution of incentive dollars to businesses. Using multistate primary datasets, as well as secondary data from the Census Business Dynamics Statistics and the Compustat database, Slattery and Zidar evaluate the impacts of state corporate tax rate reductions, general tax credits, and firm-specific incentives on productive efficiency and equity.

Slattery and Zidar examine the distribution of firms receiving incentives by firm employment, profits, revenue, and value of capital stock as well as the distribution of incentives by industry sector. Such an analysis could be used to examine the reach of incentive programs in the business community and validate whether incentives are being allocated as intended. Slattery and Zidar also examine the distribution of incentive deal cost per job and wage levels by county. This approach highlights not only the distribution of incentive benefits between distressed and nondistressed areas, but also highlights the disadvantages that distressed areas may face when using incentives to compete for investment.

(The study is a must-read—particularly for authors’ discussion and examination of the distributional and equity implications of the different incentives.)

Demographic distribution

Examining how incentive benefits are distributed across demographic groups may be the hardest measure to tackle for various reasons. First, tax return data contains little or no demographic information. Aside from limited household characteristics (e.g., married or single, dependents, children), tax returns don’t include personal taxpayer characteristics like gender, race, or ethnicity. Personal information included on tax returns (e.g., taxpayer name and address) may be sparse and is often off-limits to most incentive evaluators. Even administrative data from incentive programs like applications and application processing data often don’t contain demographic information about the individual or business receiving an incentive. As in the evaluations of the D.C. homestead deduction and the D.C. EITC referenced earlier, evaluators may consider merging income tax return data or parcel-level property tax data containing a geographic identifier (e.g., zip code, county, census block, block groups, or tracts) with other data sets containing demographic measures by the geographic identifier. This might work particularly well with tax expenditures claimed by individual taxpayers or households since they are the direct, and perhaps only, beneficiary.

However, the ability to make conclusions about demographic information is especially limited when looking at businesses. Firm owners and employees may not live in the same location as where the business is located, so the demographic characteristics at that location are not meaningful. What is more, if the firm receiving the incentive is a public company, then the ownership can be widely distributed among different shareholders from various locations and economic circumstances.

Conclusion

There are many considerations and challenges for evaluators to examine when incorporating distributional analysis of incentives. For more information, please refer to the presentations from day 2 of the 2021 NCSL/Pew Roundtable on Evaluating Economic Development Tax Incentives. The Urban Institute in particular provides useful context regarding the ethical use of disaggregated data.