Simulating the Effects of the Tax Credit Program of the Michigan Economic Growth Authority on Job Creation and Fiscal Benefits

Abstract

This article simulates job and fiscal impacts of the Michigan Economic Growth Authority’s tax credit program for job creation, commonly called “MEGA.” Under plausible assumptions about how such credits affect business location decisions, the net costs per job created of the MEGA program are simulated to be of modest size. The job creation impacts of MEGA are simulated to be considerably larger than devoting similar dollar resources to general business tax cuts. The simulation methodology developed here is applicable to incentives in other states.

Keywords

incentives job creation fiscal impact business taxes

How can we estimate the job and fiscal impacts of a state’s economic development incentives? The impact of incentives is a pressing issue, both because of the need for jobs in the wake of the Great Recession and because annual incentive costs are in the tens of billions of dollars nationwide (Bartik, 2001; Peters & Fisher, 2004; Thomas, 2000).

This article illustrates a methodology for estimating employment and fiscal impacts of incentives. The incentive program considered is Michigan’s MEGA tax credit program, which was in existence from 1995 to 2011. MEGA is an acronym that stands for Michigan Economic Growth Authority. The MEGA program provided discretionary incentives to firms, mostly in the manufacturing sector and other export-base industries. These incentives were tied to the firm’s job creation. MEGA’s incentives were large (over $2,000 per year, per job created) and long-lasting (over 15 years on average).

This article’s estimates of the employment and fiscal impacts of MEGA are not only relevant to this specific program but also have broader implications. At least 24 states have tax incentives in which the amount of the incentive is directly tied to business job creation (Chirinko & Wilson, 2010a). This article’s methodology is also applicable to other incentives.

Estimating the impacts of state incentives is difficult. The main estimation issue is these incentives’ endogeneity. These incentives are awarded to individual firms or to geographic areas with some discretion. This discretion makes it difficult to find a suitable comparison group for evaluating these incentives’ impact. Incentives could be selectively awarded to firms or areas that are high growth, which would bias estimates toward finding positive impacts. Alternatively, the incentives could be selectively awarded to firms or areas that have problems, which would bias estimates toward finding negative impacts.

Rather than directly estimating incentive impacts, this article uses an indirect approach: We estimate incentive impacts using simulation methods. That is, we simulate the probability that an incentive will be decisive. The simulated probabilities are based on the incentive’s impacts on costs and on previous estimates of how business location and expansion decisions respond to costs. These previous estimates are based on the extensive literature on how state business growth is affected by business taxes and wages. This previous literature helps set a plausible range for how business location and expansion decisions respond to incentives. As we will argue, these estimates of business tax and wage effects are less subject to endogeneity biases than are direct estimates of responses to incentives. Probabilities of an incentive being decisive are then plugged into a state econometric model and a state fiscal impact model, which, respectively, generate estimated job impacts and fiscal impacts.

This simulation methodology is obviously useful when no direct estimates of causal impact are available for a particular state incentive program. But even if direct estimates are available, the identification of causal impact may rest on questionable assumptions. The simulation methodology will then provide a useful comparison.

The plan of the article is as follows. We begin by reviewing the previous research literature on state and local economic development incentives. We describe the MEGA program. We then describe our data and methodology and present our estimates. A conclusion summarizes the results and suggests implications for future research and policy.

Previous Research Literature on Economic Development Incentives

What does the research literature say about the effects of state and local economic development incentives? By “economic development incentives,” we mean cash (tax breaks or otherwise) or services that are at least somewhat customized to the needs of an individual business and are awarded with some discretion. The discretion may be over which firms receive incentives. In other cases, such as enterprise zones, all firms in a targeted geographic area receive incentives, but there is discretion over which geographic areas are chosen. Such incentives are intended to affect business location, expansion, opening, downsizing, or closing decisions.

A larger, related research literature considers the effects of more general state and local tax policy and public services. More general taxes and public services are distinguished from incentives by not being customized for individual businesses and by the lack of government administrative discretion in targeting benefits to specific firms. This more general research literature has been reviewed by Bartik (1991, 1992), Phillips and Goss (1995), and Wasylenko (1997).

The incentives literature has been most recently reviewed by Buss (2001). The following review focuses largely on highlights of the literature since Buss’s review.

The incentives literature has considered many incentive types. Some research has considered overall incentive levels or multiple incentives (Calcagno & Thompson, 2004; Chirinko & Wilson, 2010b; Gabe & Kraybill, 2002; Lee, 2008; Luger & Bae, 2005). Studies have examined whether large new branch plants, which typically receive incentives, lead to stronger local growth (Edmiston, 2004; Fox & Murray, 2004; Greenstone & Moretti, 2004).

Other studies have considered specific incentive types, including the following:

Enterprise zones (Busso, Gregory, & Kline, 2010; Elvery, 2009; Greenbaum & Landers, 2009; Hansen & Kalambokidis, 2010; Lynch & Zax, 2010; Neumark & Kolko, 2010; Papke, 1994; Peters & Fisher, 2002)

Tax increment financing districts (Byrne, 2010; Dye & Merriman, 1999; Mason & Thomas, 2010; Merriman, Skidmore, & Kashian, 2007, 2011; Weber, Bhatta, & Merriman, 2003)

Customized job training (Hollenbeck, 2008; Holzer, Block, Cheatham, & Knott, 1993; Hoyt, Jepsen, & Troske, 2008)

Manufacturing extension services (Jarmin, 1998, 1999; Manufacturing Extension Partnership, 2010)

Finally, some studies have examined tax credits tied to job creation (Chirinko & Wilson, 2010a; Faulk, 2002; Hicks & LaFaive, 2011). The MEGA credit is of this type.

Most incentive studies suggest that incentives are not cost-effective, either having no statistically significant effects or large costs per job created. However, there are some exceptions. For example, customized job training has some favorable evidence (Hollenbeck, 2008; Holzer et al., 1993; Hoyt et al., 2008). Manufacturing extension services have some support (Jarmin, 1998, 1999; Manufacturing Extension Partnership, 2010).

Of most relevance to the current study, job creation tax credits are supported by Faulk (2002), but not by Luger and Bae (2005) or Hicks and LaFaive (2011). Hicks and LaFaive looked at the same MEGA program that we examine, but used a quite different methodology, as we will explore later.

Our study explores an alternative to the methodology used in most incentive studies. Most studies have attempted to directly estimate either the growth of individual firms as a function of incentives or the growth of a geographic area as a function of incentives. In a few cases, the incentive’s specific provisions have been modeled (Chirinko & Wilson, 2010a; Lee, 2008). But in most cases, incentives have been measured by the dollar magnitude of incentives received by a firm or an area.

Other studies, rather than statistically explaining growth using incentives, have relied on interviews that ask incentive recipients how the incentive affected their location or expansion decisions (Hollenbeck, 2008; Manufacturing Extension Partnership, 2010).

Finally, a few studies have used simulation methods to model the effects of incentives (Chirinko & Wilson, 2010b; Luger & Bae, 2005). These studies have predicted incentive impacts by extrapolating from what we know about the response of firms to other cost factors. The present study falls into this category.

The interest in interview or simulation methods reflects the difficulties in estimating incentive effects. To see the likely biases in estimates of incentive effects, consider the following simple model:

G_{s t} = B_{0} + B_{x} * X_{s t} + B_{I} * (I n c e n t i v e s_{s t}) + e_{s t},

where G_st is the percentage growth in economic activity in some state (or area) s over a time period t. X _st is a vector of control variables for the state and time period. Incentives_st represents the incentives variable for that state and time period. This incentives variable frequently is the dollar volume of incentives for the state. e_st is the disturbance term.

A variant of Equation (1) uses individual firm data. The dependent variable would be growth or location of some individual firm in some area over a period of time, and we would try to control for firm-specific variables and incentives. Our notation would be modified by adding a firm-specific subscript to the dependent variable, the X variables, the Incentives variable, and the disturbance term.

The problem in estimating Equation (1) is that because the incentives are awarded with discretion, estimating this equation with ordinary least squares or similar methods will yield biased estimates. The incentives variable is likely to be correlated with the error term. If incentives are targeted at firms or areas that are already likely to grow, the estimation is biased toward finding that incentives have larger positive effects on growth. If incentives are randomly awarded to growing firms, this will also bias the estimation toward finding positive effects. If incentives are targeted at firms or areas that suffer from some disadvantage that impedes growth, then the estimation will be biased against finding that the incentive boosts growth.

This bias in incentive estimates is likely to be particularly large, because incentives have modest effects on growth compared with how they might be affected by growth. For example, the ordinary least squares estimate of coefficient B_I will be biased because it estimates the following coefficient:

B_{I} (biased) = B_{I} (true) + B_{g I} .

where B_gI is the coefficient on incentives that would arise if we regressed the Equation (1) error term (which includes unobservables shocking firm or area growth) on incentives and the observed variables X _st affecting growth. B_I(true) is likely to be small, because incentives are small compared with overall costs. But because incentives are awarded with discretion, they can be very responsive to growth. As a result, B_gI may be large in absolute value relative to B_I(true).

This bias problem is difficult to solve. One solution is to control for as many observable variables as possible in estimating incentives’ effects. For example, Papke (1994) controlled for previous area growth trends. However, no matter how many observable variables are controlled for, biases may still occur because of the remaining unobservables.

Another solution is finding instrumental variables that are correlated with incentive awards but uncorrelated with unobservables affecting growth. Good instruments may arise if incentives have been awarded for some exogenous reason that is unrelated to growth. For example, Holzer et al. (1993) compared the behavior of firms that received incentives versus firms that applied after the incentives were exhausted. Jarmin (1998, 1999) compared firms that were more or less likely to use incentives because of their distance to the organization providing customized services. Greenstone and Moretti (2004) compared counties in which a new plant was located versus counties that were runners-up. Busso et al. (2010) compared successful with unsuccessful applicants for federal Empowerment Zone status. But we cannot count on always having such exogenous variation in incentive awards.

Simply using variables we designate as instruments will not yield unbiased results if the instrumental variables are correlated with unobservables affecting growth. Most relevant for the current article, we will argue that some of the instruments used in Hicks and LaFaive’s (2011) estimates of the MEGA program’s effects are likely to be correlated with unobservables affecting area growth. If instruments are correlated with unobservables affecting growth, then the fitted values used in estimation will be correlated with the disturbance term. Estimates will then be biased even though we have used instrumental variable procedures.

This type of problem is common in nonexperimental data. When instruments are provided by natural variation rather than random assignment, it is frequently plausible that these instruments may be correlated with unobservable variables affecting a state or local area’s growth. One could even argue that without random assignment, it is impossible in principle to rule out some such correlation with unobserved variables. The world is complicated, and there may be various direct and indirect ways in which the interaction of different forces may lead to any proposed instrument being correlated with unobservables driving state growth. This then raises at least some doubt, and maybe a lot of doubt, about whether these more sophisticated estimation approaches are actually solving the bias problem.

This bias problem increases the interest in other alternatives for estimating incentive effects. One alternative is asking firms about incentive effects. However, firms may not give accurate responses, either because they are concerned about not receiving future incentives or because whoever answers the survey may not know what the firm might have done without the incentive.

Another alternative is simulations. Simulations assume we can use information from other research to determine how sensitive firms are to local costs. Extensive research has examined how business location and expansion are affected by state and local business taxes. State and local business tax rates are less endogenous than incentive awards, as tax rates are set by statute. State and local areas do not change their business tax rates every year. In contrast, the dollar value of incentive awards fluctuates quite a bit over time, as we will see for the MEGA program. Even though business tax rates may sometimes change, and may even change in response to the economy, business tax rates are also to some extent set based on political ideology. Political institutions, interests, and gridlock may restrict changes in business tax rates. Incentives allow for administrative discretion that may often evade political scrutiny. As opponents of incentives have pointed out, incentive offers and the dollar volume of incentives are often not fully publicly disclosed or transparent, which opponents have criticized as restricting accountability (LeRoy, 2007). This allows incentives more freedom to fluctuate and expand in response to economic development opportunities, which incentive advocates see as an advantage. All of this adds up to incentives by their very design being more endogenous than business tax rates. Incentives by definition are discretionary; business taxes are not.

State and local business taxes are a larger share of business costs than are incentives. Therefore, the true effect of business taxes on state or local growth is larger than for incentives. However, the absolute value of the bias term, which depends on how business taxes are affected by unobserved shocks to local growth, is of similar magnitude to the bias term for incentives (see Equation 2). Therefore, compared with incentives, for business taxes the bias is likely lower in percentage terms relative to the true coefficient.

Wages’ share of business costs exceeds the share of taxes; however, wages are highly endogenous in response to business growth. This bias has a known direction. Because higher growth raises wages, the negative effects of wages on business growth will be biased in a positive direction by the endogeneity of wages. Therefore, estimated effects of wages on business growth set a lower bound to the effects of local costs on business growth.

Description of the MEGA Program

The MEGA program was created in 1995 and terminated (except for existing contracts) in 2011. MEGA provided discretionary tax credits to employers that created or retained jobs in Michigan. The credit was based on the personal income taxes for the workers in those new or retained jobs. Credits were provided for up to 20 years. If the credit exceeds the business’s tax liability, the business receives a cash payment. Credits were not an entitlement going to all eligible businesses, but rather were awarded by a state board.¹ The Michigan Economic Development Corporation (MEDC), the state’s economic development agency, ran MEGA.

Eligibility for MEGA credits was restricted to industries in the state’s “export base” (industries that primarily sell their goods or services to nonstate buyers), to boost the program’s economic development effects. (See below for more on why this would be expected.) Retail businesses were generally excluded from MEGA. For a project to be eligible for MEGA, there were minimum job creation and retention requirements, with relaxed requirements for rural or high-tech projects.

The MEGA program was terminated by the Michigan Legislature in 2011, based on a recommendation by Governor Snyder, elected in 2010. The MEGA program termination was part of an overall fiscal package that included a business tax cut and increases in some household taxes. In addition, subsequent state action has replaced MEGA with an incentive fund, which can provide credits similar to MEGA.

Compared with most economic development programs, MEGA was generous. This single program provided a tax credit whose annual value per job-year, over the life of the program, averaged $2,294.² The annual per-job value of all incentives in leading industrial states is $1,247 (Bartik, 2005; Peters & Fisher, 2002). The MEGA incentive was provided for a time period that averaged 15.75 years over the life of the program.³

The MEGA program focused on Michigan’s traditional manufacturing base. Some 49% of the credits were in the motor vehicle and motor vehicle parts industries, and 31% in other manufacturing industries.⁴

Given Michigan’s high manufacturing wages and strong manufacturing supplier base, MEGA projects had high wages and multiplier effects. Average annual wages in MEGA projects were $75,627.⁵ Our simulations suggest that the multiplier effects of the direct job creation by MEGA amounted to 3.88 in 2007.⁶

Because of its generosity, MEGA was more likely to affect business location and expansion decisions than the typical state incentive package. Because of MEGA’s high wages and multipliers, MEGA projects had relatively large economic and fiscal effects. Therefore, the gross benefits of MEGA were likely to loom large compared with typical business incentives.

For example, a state business incentive program that was awarded to selected nonexport-base businesses would be expected to have far smaller net effects on a state’s economy. Nonexport-base businesses by definition sell to customers in the state. The size of the nonexport-base sector is therefore largely dictated by the size of demand for goods and services in the state. Even if the business incentive induced some nonexport-base businesses to expand, their increased sales would largely come at the expense of other nonexport-base businesses in that industry in the same state (e.g., if a Burger King is subsidized to expand, its expanded sales will lead to reduced sales at a nearby McDonald’s). The “multiplier” for incentives to nonexport-base businesses is likely to be far less than one, and may even be close to zero.

Even if a state business incentive program is targeted at export-base businesses, its multiplier effects will often be less than for MEGA. If the incentivized firms are in industries that lack a strong state supplier base, much of the increased activity in the incentivized firms will flow to out of state suppliers. If the incentivized firms are low-wage firms, expanded employment in incentivized firms will have fewer effects on boosting state wages and thereby consumer demand.

Data and Method

Outline of Our Approach

We obtained data from the MEDC on MEGA credits paid, and the job creation on which these credits were based, for each MEGA project for each tax year from 1996 to 2007. These data were used to simulate the MEGA program’s economic and fiscal impacts on the Michigan economy for each year from 1996 to 2007. We simulated the effects of the MEGA program using the Upjohn Institute’s version of the Regional Economic Models Inc. (REMI) model. We simulated the economic and fiscal effects of the MEGA program both by simulating the effects of any jobs created and by simulating the effects of how the MEGA program’s credits are financed.

The REMI model is a structural economic simulation model that incorporates input–output, computable general equilibrium, econometric, and economic geography methodologies. The model is constructed of thousands of simultaneous equations including estimating equations explaining state industry output, labor demand, capital demand, and productivity for 70 different industries, as well as equations explaining migration and labor force participation, and equations explaining wages and prices. The model elasticities are estimated using pooled time-series cross-section data on U.S. states. The model is then calibrated to a particular state (or local area) to match historical data. The model allows for the simulation of effects of a wide variety of shocks to different policy variables (e.g., business tax rates, public spending) or natural events (e.g., a plant chooses to locate or expand in a state). The REMI model has been extensively documented in a scholarly book, as well as in articles in the research literature (Fan, Treyz, & Treyz, 2000; Greenwood, Hunt, Rickman, & Treyz, 1991; Rickman & Treyz, 1993; Treyz, 1993; Treyz, Rickman, & Shao, 1991; Treyz & Treyz, 2004).

To derive the final simulation, we first did two preliminary simulations using the REMI model. First, we simulated the effects on the Michigan economy under the unrealistic assumption that 100% of the MEGA-subsidized jobs were actually induced by MEGA. This simulation used information on actual new or retained jobs associated with MEGA by year and by the 70 REMI industries. The analysis understates MEGA-associated jobs by only counting jobs that receive MEGA subsidies. For example, the analysis would not count jobs previously associated with MEGA if during some year they fell below the minimum job number requirement or if the subsidy period had expired.

Second, using the REMI model, we simulated the negative effects on the Michigan economy of financing the MEGA credits in two different ways. The first way of financing the cost of MEGA credits was by reducing government spending. These costs were lagged one year, because MEGA credits were typically paid with at least a 1-year lag.

Because of the REMI model structure, the negative effects of reduced government spending will be solely due to demand effects. The REMI model does not explicitly allow for government spending to alter public service quality in a way that affects business location decisions. It is unrealistic to assume that spending reductions will have no effect on public service quality and location decisions. However, one could argue that the effects in the short run and medium run will be modest. For example, changes in public school spending or infrastructure spending will only gradually affect the attractiveness of a state to business. Alternatively, one could argue that negative effects of public service cuts on business attraction can be incorporated by changes in the assumed effectiveness of MEGA incentives in inducing business location or expansion decisions. This is discussed below.

The second way of financing MEGA was to increase state business taxes. This increase in business taxes was entered into an adjusted REMI model. As described below, the business tax effect was adjusted to be consistent with the assumed effects of the MEGA credits on business location, expansion, and retention decisions.

To make this model realistic, we have to modify the unrealistic assumption that all MEGA-subsidized jobs would not have occurred but for the MEGA subsidy. We assumed that the proportion k of the MEGA-subsidized jobs would not have existed in Michigan but for the MEGA program. That is, if the program had never existed, we assumed that the proportion (1 − k) of MEGA-subsidized jobs would have been created or retained in Michigan anyway, whereas the proportion k of MEGA-subsidized jobs exist in Michigan because of the program. We will further discuss below how we chose this proportion k.

Our model simulations were then calculated by combining our two preliminary REMI model simulations with assumptions about the proportion k. That is, the net effects of MEGA on the state economy were calculated as a weighted sum of the two preliminary simulations. We summed k times the economic effects if 100% of MEGA-subsidized jobs were induced by MEGA, plus 1 times the economic effects of the assumed financing mechanism. The positive effects of the MEGA-subsidized jobs that were assumed to be actually induced by MEGA were combined with the negative effects of financing MEGA’s costs by reduced government spending or increased business taxes. These calculations yielded a variety of simulation estimates that vary with how MEGA was assumed to be financed, and with the proportion k.

The REMI model only permits alternative simulations of the future, not of the past. Therefore, we estimated the historical impact of MEGA from 1996 to 2007 by simulating MEGA’s impact in a simulated future from 2010 to 2021. The job impacts in this simulated future are assumed to be what would have occurred over history. This method of simulating historical impacts would be perfectly accurate under the unrealistic assumption that shocks to jobs by industry and shocks to real government spending have identical multiplier effects in the 1996 to 2007 period as in the 2010 to 2021 period. More realistically, multipliers have probably changed somewhat in Michigan’s economy. Our procedure is likely to understate the historical multiplier effects of MEGA-induced jobs, as the state’s supplier linkages and wages have declined over time because of globalization.

Our simulations resulted in predicted percentage effects on state personal income and population for each year from 1996 to 2007. As detailed below, we used these population and income impacts to calculate effects on state and local tax revenue and spending needs.

Incentive Effect Simulation

To provide plausible values for the parameter k, the probability that the MEGA credit was decisive in triggering job creation, we relied on the research literature on business location decisions. We used this literature’s consensus range for the elasticities of business growth with respect to state and local business taxes and with respect to wages.

We assumed state and local business activity depends primarily on business costs. Therefore, the impact of any local cost factor on any measure of business activity—employment, value-added, capital stock—will be proportional to the change in costs.

This assumption means that we treat factor substitution effects as being of secondary importance. As shown in Bartik (1991), with plausible elasticities, the effects on employment of some changes in factor prices will mostly be due to overall effects on business activity, not factor substitution. As shown in Wasylenko (1997) and Phillips and Goss (1995), in practice the measure of business activity does not matter much.

To measure how business activity responds to overall costs, we used estimates of the long-run elasticity of state- or metro-area business activity with respect to overall state and local business taxes. These long-run business elasticity estimates were developed in Bartik (1991), added to by Wasylenko (1997), and analyzed by Phillips and Goss (1995).

These literature reviews adjusted estimates from different studies to approximate long-run elasticities with respect to overall state and local business taxes. As outlined in Bartik (1991, Appendix 2.2), tax elasticities were measured by considering an equal percentage increase in all tax rates included in the study. This assumes that business location decisions are driven by business taxes, not household taxes, and that if only some taxes are included, the percentage change in the taxes included in the study will roughly reflect percentage variations in overall state and local business taxes. In practice, the tax measure does not make a significant difference to the estimated elasticity (Phillips & Goss, 1995).

Some studies have an explicit dynamic model of long-run business activity, but other studies have used as a dependent variable either gross new business activity (new branch plants, start-ups, gross investment) or net business activity change over some time period. In the former case, the elasticity of gross new business activity during the time period was assumed to represent the long-run business elasticity. Bartik (1991) showed that this is consistent with a model in which business death rates are constant and long-run adjustment occurs by shocks to gross new business activity. In the latter case, local business activity was assumed to adjust to the long-run equilibrium as estimated in Helms’s (1985) well-known study, which estimates 9% per-year adjustment toward the long-run equilibrium. Long-run business elasticity estimates do not vary much with how they are derived (Phillips & Goss, 1995).

Based on 57 studies, Bartik (1991) argued that a plausible range for the long-run elasticity of state or metro area business activity with respect to state and local business taxes is from −0.1 to −0.6, with a plausible “best guess” elasticity of −0.2 to −0.3. Wasylenko (1997) added in another 17 studies and argued for a best guess elasticity of −0.2. An elasticity of −0.2 is consistent with the meta-analysis in Phillips and Goss (1995). Their Table 3 predicts that a study that controlled for wages and area fixed effects, allowed public services to endogenously adjust, and looked at individual firms, would get an elasticity of −0.195. Because our REMI model estimates implicitly allowed public service quality to endogenously adjust because of changes in public spending, it could be argued that these endogenous adjustment elasticities are the most appropriate to use in our analysis.

Under some special assumptions, the elasticity could be closer to −0.6. With controls for public services, Phillips and Goss’s (1995) estimates yield a predicted elasticity of −0.488. Hines (1996) compared location decisions for foreign firms that can credit state and local taxes against their home country taxes versus firms that only take state and local taxes as a deduction. His estimates also implicitly held public services constant, as both types of foreign firms can use the same public services in a state but face different net tax rates. Hines’s estimated elasticity was about −0.6. This should be an upper bound, as foreign firms would be particularly footloose. Because our REMI model does not explicitly hold the quality of public services constant, these more negative elasticities are only appropriate if one is willing to assume that public service quality, from a business perspective, is not much affected in the short run or medium run by public spending cuts.

Bartik (1991) had suggested a lower bound to the long-run tax elasticity of −0.1. Wasylenko (1997) found that in many cases the median estimates cluster around −0.1.

Another possible lower bound is the elasticity of state and local business activity with respect to wages. Bartik (1991) reported a mean long-run wage elasticity of −0.67. Wages are 14 times the cost share of state and local business taxes. If impacts depend on costs, a business tax elasticity consistent with this wage elasticity would be around −0.05 (= −0.67 ÷ 14). This implied elasticity is a lower bound because estimated wage elasticities are biased toward zero. Wages will endogenously increase with local growth.

Based on this research literature, our simulations assumed a range of long-run business tax elasticities. We initially assumed a business tax elasticity of −0.2. We view this as an appropriate central case, given both the overall literature and that our model allowed public services to adjust. But we also considered three other possible values: −0.05, −0.1, and −0.6.

To translate these elasticities into parameter k, the probability of the MEGA incentive being decisive, we first determined what present value of cost per induced job is implied by these various elasticities. Suppose T is state and local business taxes per job, J is the original number of jobs in the state or local area, dT is a change in state and local business taxes per job, and dJ is the change in jobs induced by that tax change. We considered the cost of the lower business taxes in lost tax revenue, before accounting for the fiscal impact of the increase in jobs. (Fiscal impact was considered separately later.) The cost in lost tax revenues per induced job is shown as

C = J d T / d J .

The long-run elasticity of jobs with respect to taxes is defined as

E = (d J / J) / (d T / T) .

Combining these two equations yields

C = T / E .

The cost per induced job is equal to state and local business taxes per job, divided by the elasticity of business activity with respect to business taxes.

Based on Peters and Fisher (2002), average state and local business taxes per job in the United States are $4,631 (in 2011 dollars). This dollar amount, divided by the business tax elasticity, is the annual cost of inducing one job due to permanently lower business taxes.

But to evaluate MEGA, which had varying annual dollar incentives over time, we need to know the present value of foregone taxes needed to create jobs. Based on Poterba and Summers (1995), we assumed business executives use a 12% real discount rate. Although this is a figure from the mid-1990s, we know of no reason to think that business executives have become any more myopic or more farsighted since that time period.⁷ Therefore, the present value of the foregone business taxes needed to induce one job is the annual flow of state and local business taxes per job ($4,631), divided by the elasticity (−0.05, −0.1, −0.2, or −0.6), and then discounted at a real discount rate of 12%.

Table 1 shows implied annual values and present values of costs per job induced by lower business taxes. These numbers do not mean that if the business tax elasticity is −0.2, providing a new branch plant with a perpetual annual subsidy of $23,155 per job will induce 100% of the plant’s jobs. The tax studies behind these numbers were considering much smaller annual tax differences. We should not extrapolate these numbers to much larger tax differences. But the MEGA subsidies were small enough to be in the range of the cross-state tax variations in these tax studies.

Table 1.

Implications of the Business Tax Elasticity for Annual and Present Value Costs per Job, and for MEGA Effectiveness.

Elasticity	Annual costs per job ($)	Present value costs per job ($)	% of MEGA subsidies decisive
−0.05	92,619	864,445	2.06
−0.1	46,310	432,223	4.12
−0.2	23,155	216,111	8.25
−0.6	7,718	72,037	24.74

Note. MEGA = Michigan Economic Growth Authority. All dollar figures are in 2011 dollars. The elasticity is the long-run elasticity of state or local business activity with respect to total state and local business taxes. The column on annual costs per job uses this elasticity, as well as Peters and Fisher’s (2002) estimate of annual state and local business taxes per job, to calculate the ratio of forgone business taxes to jobs created due to business tax cuts. The column on present value of costs per job uses these annual costs, and Poterba and Summers’s (1995) estimate of business CEOs’ real discount rate, to calculate the ratio of present value, from a business perspective, of forgone tax revenues to jobs created from business tax cuts. The column on MEGA subsidies reports the estimated percentage of MEGA jobs receiving incentives that are assumed to be induced by the incentive. The decisive percentage combines information from the present-value costs of job creation column with information on the present value of the typical MEGA incentive.

As mentioned, MEGA averaged an annual subsidy of $2,294 per job for a period of 15.75 years. The present value at a 12% discount rate of this average MEGA subsidy is $17,818. For the MEGA program’s effects to be consistent with business tax elasticities, the present value of the MEGA credit, divided by the present value of the cost of inducing a job, must be equal to the probability k that the MEGA credit was decisive.⁸

The last column in Table 1 presents these calculated values for k. This percentage varies from 2.06% to 24.74%. At an elasticity of −0.2, 8.25% of MEGA credits were decisive.

Some simulations considered MEGA if it was financed by increased business taxes. These simulations used business tax elasticities consistent with the incentive elasticities. Some preliminary simulations using the REMI model examined how Michigan responded to changes in state business taxes. These default responses were consistent with a long-run business tax elasticity of −0.24. We adjusted these REMI model responses up or down to match the various elasticities assumed in our different scenarios.

Fiscal Effects Simulations

We made assumptions about how shocks to personal income and population would affect various categories of state and local revenue and spending. Some revenue categories were assumed to respond to income, such as individual income taxes, sales taxes, and corporate income taxes.

For personal income and sales tax revenue, we used long-run elasticities, adjusted for rate changes, as estimated by Bruce, Fox, and Tuttle (2006) for Michigan. The long-run elasticity is 1.40 for the personal income tax and 0.772 for the sales tax. For the main Michigan state business tax during this time period (the Single Business Tax and then the Michigan Business Tax), we assumed an elasticity with respect to personal income of 1.00.

Most other categories of revenue and spending were assumed to change by the same percentage as state population. One exception is that these shocks were assumed to leave welfare-related spending unaffected. The “welfare” categories are public welfare, health spending (but not hospital spending), and employment security administration. This is likely to understate fiscal benefits, as increases in state employment rates and wages may reduce state welfare spending.

One revenue category assumed to increase proportionally with population is property tax revenue. Because of Michigan’s Headlee Amendment to the state constitution, increases in property values require a readjustment of the property tax rate to yield the same real revenue, except for increases in assessments due to new development. We assumed that the percentage increase in property tax revenues due to new development is roughly equal to the percentage increase in Michigan population.

Baseline figures for each revenue and spending category come from the U.S. Census of Governments’ figures for Michigan general own-source revenue and expenditure from 1996 to 2007. (The census skipped 2001 and 2003, so these values were interpolated as a percentage of state personal income.) These baseline figures and the above-assumed elasticities yielded estimates of how net Michigan state and local government revenue and expenditure responds to shocks to state income and population. These estimates were combined with estimated income and population effects from the REMI model to generate net fiscal benefits or costs.

Results

MEGA Net Effects When Financed by Cuts in Government Spending

Table 2 presents MEGA’s net effects when financed by reduced government spending. As the table shows, by 2007, MEGA was awarding credits that were associated with over 60,000 jobs. These credits cost almost $100 million per year.

Table 2.

MEGA Job Effects, Net Fiscal Costs, and Net Costs per Job Under Various Assumptions About Business Responsiveness to Incentives.

	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	Present value of annual costs per present value of job year
Subsidized MEGA jobs (000s)	0.7	1.8	3.0	5.8	7.5	9.8	11.1	13.0	21.0	31.1	40.8	62.1
Gross MEGA credit costs ($M of 2011 dollars)	0.0	−1.3	−4.2	−8.1	−13.5	−16.9	−23.9	−26.8	−34.0	−51.1	−73.0	−90.7
Naïve fiscal costs per job subsidized ($)	0	−726	−1,399	−1,396	−1,814	−1,724	−2,156	−2,057	−1,615	−1,640	−1,787	−1,459	−1,653
k = 2.06%; elasticity = −0.05
Net job effects (000s)	0.0	0.1	0.1	0.1	0.1	0.2	0.1	0.2	0.7	1.1	1.5	3.1
Net fiscal effects ($M)	0.1	−1.1	−4.0	−7.6	−13.1	−16.2	−23.5	−26.2	−31.6	−47.0	−67.6	−79.7
Net fiscal effects per job created ($)	3,594	−21,391	−63,481	−59,907	−135,423	−97,124	−193,728	−141,820	−44,732	−41,350	−45,338	−25,566	−44,797
k = 4.12%; elasticity = −0.1
Net job effects (000s)	0.0	0.1	0.2	0.4	0.5	0.7	0.7	0.9	2.1	3.3	4.5	8.1
Net fiscal effects ($M)	0.1	−0.8	−3.4	−6.5	−11.8	−14.6	−21.8	−24.3	−27.7	−40.3	−58.1	−63.6
Net fiscal effects per job created ($)	3,594	−6,337	−16,284	−15,685	−25,094	−21,424	−30,149	−26,584	−13,157	−12,144	−12,982	−7,857	−12,916
k = 8.25%; elasticity = −0.2
Net job effects (000s)	0.1	0.3	0.5	1.0	1.2	1.7	1.9	2.4	4.9	7.7	10.4	18.0
Net fiscal effects ($M)	0.3	−0.3	−2.4	−4.5	−9.2	−11.2	−18.4	−20.4	−19.8	−26.7	−39.0	−31.3
Net fiscal effects per job created ($)	3,594	−901	−4,717	−4,469	−7,554	−6,574	−9,566	−8,624	−4,036	−3,480	−3,733	−1,734	−3,748
k = 16.85%; elasticity = −0.41
Net job effects (000s)	0.1	0.6	1.1	2.2	2.8	3.8	4.4	5.4	10.7	16.8	22.9	38.8
Net fiscal effects ($M)	0.5	0.9	−0.2	−0.1	−3.8	−4.2	−11.4	−12.4	−3.2	1.6	0.9	36.2
Net fiscal effects per job created ($)	3,594	1,504	−206	−53	−1,353	−1,093	−2,568	−2,298	−302	96	40	932	0
k = 24.73%; elasticity = −0.6
Net job effects (000s)	0.2	0.9	1.7	3.3	4.2	5.8	6.7	8.2	16.1	25.1	34.3	57.8
Net fiscal effects ($M)	0.8	2.1	1.8	3.9	1.2	2.2	−5.0	−5.1	11.9	27.5	37.4	97.9
Net fiscal effects per job created ($)	3,594	2,195	1,031	1,162	291	380	−736	−623	740	1,097	1,092	1,693	1,045

Note. MEGA = Michigan Economic Growth Authority. All dollar figures are in 2011 dollars. Negative dollar amounts indicate net fiscal costs; positive dollar amounts indicate net fiscal benefits. The first set of numbers simply looks at subsidized MEGA jobs and MEGA’s own costs. Other groups of rows count net job creation effects, allowing for only a portion of subsidized jobs to be induced and allowing for multiplier effects and the effects of financing MEGA by reduced government spending. Each group of rows varies in what elasticity of business activity with respect to taxes is assumed. The present value calculations are from a social perspective and therefore use a 3% social discount rate to express both costs and job-years in present-value terms as of 2011. The last column divides the present value of costs by the present value of job-years to get an average cost per job-year created. All numbers allow for cumulative effects of MEGA subsidies and government spending up to the year considered. Because MEGA’s subsidies are paid out the year after the subsidized jobs are created, the table combines direct MEGA effects with the subsidy costs lagged 1 year. For example, in 1996, when MEGA was started, there are some subsidized jobs created, but no spending offsets, because the subsidies are not paid until the following year. This can be seen in the second row in the table, where MEGA’s naïve fiscal costs are zero in 1996. This also explains why net fiscal effects per job created are all the same in 1996, as the only fiscal effects are due to direct jobs created, and these fiscal effects are the same per job created in all scenarios using our fiscal impact model.

Even without multiplier effects or fiscal benefits, if all MEGA credits were decisive, MEGA would have been a cheap job creation program. The average annual value of MEGA credit costs, divided by the average of job years subsidized, was $1,653. This is cheap given that the jobs pay almost $75,000 per year on average.

But this analysis doesn’t consider various negative and positive factors that affect MEGA’s impact. On the negative side, many projects subsidized by MEGA would have occurred without the MEGA subsidy. Also, financing MEGA by cuts in government spending would reduce employment in state and local government, with negative multiplier effects. On the positive side, MEGA projects had some multiplier effects. In addition, any job creation effects of MEGA provided fiscal benefits.

The remainder of the table presents these positive and negative effects for various business tax elasticities: −0.05, −0.1, −0.2, and −0.6. We also considered an elasticity that is consistent with MEGA having zero net present-value fiscal costs over the 1996 to 2007 period. This elasticity is −0.41, which implies a probability of being decisive of 17%.

These remaining table entries show the cumulative effects of all credits awarded up to each year considered. These cumulative effects reflect the effects of assuming that some proportion k of jobs subsidized in each year were due to the program, where subsidized jobs include both jobs newly subsidized this year and subsidized jobs from projects initiated in previous years. The cumulative effects also include both current and lagged multiplier effects of the induced jobs, as calculated from the REMI model simulations. Finally, these cumulative effects incorporate the dynamic effects of the series of government spending reductions needed to finance MEGA. These dynamic effects will be due to both the spending reduction in the year in question and any lagged effects of spending reductions in previous years, as simulated using the REMI model.^9,10

At one extreme, a −0.05 elasticity results in huge costs per job created. The average present value cost per job-year created is about $45,000. This seems excessive if there are any opportunity costs of labor.

At the other extreme, at a −0.6 elasticity, MEGA clearly was beneficial. The program in this scenario is estimated to make money for the state, almost $100 million in 2007.

Does this mean that we have no definite conclusion about MEGA’s effects? Only if we regard the entire range of elasticities as equally plausible. The results suggest that it does not take a sizable tax elasticity for MEGA to be a reasonable investment. For example, even if the elasticity is −0.1 and MEGA was only decisive 4% of the time, the program would have had net costs of job creation of only $13,000 per job. Given Michigan’s high unemployment during much of this period, the benefits of job creation probably exceeded these costs. And −0.1 is the minimum elasticity that is consistent with the research literature on state and local business taxation. Four percent decisiveness does not seem too high for a subsidy of over $2,000 per job, or about 3% of labor costs.

For the consensus business tax elasticity of −0.2, the case is even clearer. MEGA then has average costs of under $4,000 per job-year created.

Therefore, there is some uncertainty about whether MEGA passes a benefit–cost test. However, under most reasonable scenarios, the program does pass such a test.

These favorable results are for a program with unusually high multiplier effects. A state incentive program that targeted nonexport-base industries or lower-wage industries, or in a state with weaker supplier networks, would have less favorable results.

MEGA Net Effects When Financed by Increases in the State’s Business Tax

Table 3 presents simulated effects of financing MEGA by increasing the state’s business tax. MEGA’s annual costs only corresponded to modest percentage increases in the main state business tax. In 2007, eliminating the $91 million in MEGA credits (see Table 2) would have only allowed about a 4% cut in the main state business tax.

Table 3.

Estimated Job and Fiscal Effects of Substituting MEGA for an Overall Business Tax Cut.

	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007
% Increase in state business tax needed to finance MEGA, static calculation	0.00	0.06	0.18	0.34	0.57	0.71	1.01	1.13	1.43	2.15	3.08	3.83
Gross job effects of MEGA (000s)	0.1	0.3	0.6	1.2	1.5	2.0	2.4	2.9	5.6	8.7	11.9	19.9
Gross job effects of cutting state business tax (000s)	0.0	0.0	0.1	0.2	0.4	0.5	0.7	0.9	1.2	1.7	2.4	3.2
Net job effects of substituting MEGA for business tax cut (000s)	0.1	0.3	0.5	1.0	1.1	1.5	1.7	2.0	4.4	7.0	9.5	16.7
Fiscal effects of substituting MEGA for overall business tax cut ($M of 2011 dollars)	0.3	1.1	1.8	3.7	4.3	5.7	5.5	6.3	14.1	24.2	33.4	58.2

Note. MEGA = Michigan Economic Growth Authority. This table rests on the assumption of an elasticity of state business activity with respect to total state and local business taxes of −0.2. The overall business tax increase is set so that the static revenue raised from this tax increase would in each year exactly equal MEGA credit costs. However, because this substitution ends up leading to some job gain, there is some net fiscal gain. Gross effects of MEGA (or a state business tax cut) are hypothetical effects that would occur under the unrealistic assumption that MEGA (or a state business tax cut) was paid for by no obligation donations from outside Michigan—in other words, the state gets a free lunch. Net job effects are equal to gross gains from MEGA minus gross job losses from increasing state business taxes. The estimated effects for other elasticities would be multiples of the numbers here: that is, a −0.6 elasticity would yield numbers 3 times as great.

As Table 3 shows, MEGA is simulated to create many more jobs than an equivalent dollar cut in the main state business tax. For example, in 2007, the gross effect of the MEGA credit by itself is estimated to be the creation of a little fewer than 20,000 jobs. By “gross effect,” we mean the effect of the MEGA credit if it did not require any financing, under the unrealistic assumption that the MEGA credit’s costs were covered for free by some source outside the state. The equivalent cut in business taxes would only create a little more than 3,000 jobs. As a result, the net effect of substituting the MEGA credit for a cut in the overall business tax is simulated to be the creation of about 17,000 jobs.

Although the substitution of a MEGA tax credit for a business tax cut is a balanced budget change using static revenue estimation, our dynamic fiscal impact estimates suggest considerable fiscal benefits. By 2007, fiscal benefits are simulated to be over $58 million.

Table 3 is based on the assumption of a −0.2 business tax elasticity. What about other elasticities? Job and fiscal effects of both MEGA and business cuts vary proportionately with the business tax elasticity. Results for an elasticity of −0.6 are three times as great, results for a −0.05 elasticity are one fourth as great.

One could argue that the −0.6 elasticity might be more appropriate. As was discussed above, more negative elasticities are estimated when business location models hold public services constant in estimating the effects of business taxes. In the scenario of substituting MEGA for an overall business tax cut, public services are being held constant, as there is then no need to cut public spending. If the −0.6 elasticity is used, the net job effects of substituting MEGA for overall business tax cuts would be around 50,000 jobs in 2007, three times the figure shown in Table 3. The estimated fiscal benefits to the state would be about $175 million in 2007—again, three times the figure shown in Table 3. The estimates in Table 3 could be argued to be quite conservative.

Regardless of the business tax elasticity, the MEGA credit is estimated to be more effective than an overall business tax cut in creating jobs. The intuition behind this result is that MEGA was targeted, as opposed to an overall business tax cut. The targeting occurs in two ways. First, MEGA was only awarded to projects involving new investment decisions. In contrast, an across-the-board business tax cut goes to firms regardless of whether they are considering new investment.

Second, MEGA was targeted to high-wage, export-base businesses, whereas an across-the-board business tax cut is not targeted. MEGA had multiplier effects on nonexport-base businesses that do not receive the credit. In contrast, across-the-board business tax cuts go to many businesses that are not export base. Nonexport-base businesses will not respond much on net to a business tax cut, as their business activity is determined more by in-state demand. As explained previously, even if we successfully induce some nonexport-base businesses to expand, their increased sales will reduce sales at other state businesses in the same industry, with little net effect on the state’s economy.

A Comparison With the Hicks and LaFaive (2011) Results

Our results appear to contrast with Hicks and LaFaive (2011), whose study also analyzed the MEGA program. They concluded that MEGA had no statistically significant positive impact on a county’s overall employment level.

However, there is sufficient statistical uncertainty in their results that it is unclear whether our respective results are contradictory. Although their preferred point estimates (Table 2 2SLS results for county employment) imply that MEGA credits destroy jobs, the 95% confidence interval includes the possibility that MEGA creates jobs. The implied cost per job appears high: $192,264. (This takes their coefficient estimates plus 1.96 times the standard error, and then takes 1 over this result. In addition, we adjust to 2011 dollars.)

However, their cost estimates were for the total MEGA credits for a project. The average length of a MEGA contract was 15.75 years. If we divide these estimates by 15.75, we get an approximation of annual MEGA costs per job created. A $192,264 project cost implies an annual MEGA cost of $12,215 per job created.

In contrast, suppose we apply our model to the 1995 to 2002 time period considered by Hicks and LaFaive (2011). As shown in Table 4, the resulting annual cost per job created, assuming a −0.2 elasticity of business activity with respect to state and local business taxes, is $10,015. This is close to the “optimistic bound” implied by Hicks and LaFaive’s article. Our most pessimistic simulation, with a tax elasticity of −0.05, results in a MEGA cost per job-year created over this time period of $102,860. Therefore, our simulation results overlap with those implied by the Hicks and LaFaive estimates.

Table 4.

Comparing This Study With Hicks and LaFaive.

Source of estimate	MEGA annual credit costs per job-year generated
Hicks and LaFaive estimate	Jobs destroyed
Hicks and LaFaive “pessimistic” bound	Jobs destroyed
Hicks and LaFaive “optimistic” bound	$12,215
This study, −0.2 elasticity	$10,015
This study, −0.05 elasticity	$102,860
This study, −0.6 elasticity	$2,940

Note. MEGA = Michigan Economic Growth Authority. For sources, see text.

Of course, their point estimates suggest that MEGA did not create jobs. How can this be reconciled with our results? One possibility is that their point estimates are biased because the endogeneity of their MEGA credit variable is not corrected for by their instruments. Their regression explained county-level employment in a given year as a function of lagged employment in the county, current and lagged employment in surrounding counties, county labor-force participation rates, a recession dummy, an overall state time trend, county dummies, and the MEGA credit variable.

The endogeneity problem is that the MEGA credit variable may be correlated with unobserved factors affecting county growth. If MEGA credits were awarded randomly, we would expect MEGA credits to be positively correlated with unobserved factors affecting county growth. If MEGA credits were awarded more to distressed counties, then MEGA credits would be negatively correlated with unobserved factors affecting county growth.

Their instruments included county population, whether the MEGA credit program existed in the state during that year, and whether any MEGA credit was approved for that county during that year. (MEGA credits were sometimes approved in the same year in which the project started, and at other times approval would lag for up to 6 years.) Whether the MEGA credit program existed could be seen as exogenous to county growth trends. But it seems highly unlikely that county population or whether an MEGA credit was approved during that year for that county would be uncorrelated with unobserved factors affecting county growth trends. County population will respond to recent shocks to county employment growth, which may persist over time, and county population may affect employment. Whether MEGA’s discretionary authority to award credits is used to offset county economic distress is one of the main factors that may cause MEGA credits to be endogenous.

As a result, neither county population nor MEGA credit approval for the county during the year are convincing instruments. If either or both of these instruments are correlated with unobserved county trends affecting growth, as seems plausible, then the resulting instrumental variable estimates will yield biased estimates. This means that it is at the very least a good idea to see whether the Hicks and LaFaive (2011) estimates are consistent with alternative estimates, such as our estimates using simulation methods. As we have seen, our alternative estimates suggest more positive net impacts of the MEGA program than were found by Hicks and LaFaive, although the statistical uncertainty means that there is some overlap between the two sets of estimates under more extreme assumptions.

Conclusion

This article uses a simulation approach to examine the effects of Michigan’s MEGA tax credit. Under plausible assumptions, MEGA appears likely to have had large effects on job creation relative to its net fiscal costs. Moreover, MEGA appears to have provided greater job creation and fiscal benefits than did cutting overall state business taxes.

The former conclusion, that adopting MEGA and cutting government spending has relatively low costs per job, is likely to be specific to Michigan and MEGA’s design. The extremely high multiplier effects of MEGA may not be easily reproducible by other states’ business tax incentives. The latter conclusion, that a marginal export-base business incentive is more cost effective at creating jobs than overall state business tax cuts, is more robust. Simulation exercises such as this are useful for getting a range of plausible estimates of the effects of business tax incentives. In some cases, such as this one, even a plausible range may suggest whether a given policy proposal is sensible.

Simulation exercises complement but do not substitute for convincing direct estimates of the effectiveness of state business tax incentives. Studies that can plausibly identify the causal effects of business tax incentives can provide more precise and more convincing estimates. But such estimates require convincing instruments that shift business tax incentives exogenously, independent of unobservables affecting state or local growth. Such instruments are hard to find. Therefore, researchers and policymakers are likely to find a continuing usefulness for simulation-based estimates of incentive effects.

Footnotes

Acknowledgements

The authors thank the associate editor of this journal and three anonymous reviewers for helpful comments on a previous draft. The authors also thank Claire Black and Wei-Jang Huang for their assistance with this article, and Kevin Hollenbeck, Ben Jones, Eric Hanna, Michael Hicks, Michael LaFaive, and Theodore Bolema for comments on previous drafts. Michael Hicks also provided some additional useful information on the Hicks and LaFaive study. The authors would also like to acknowledge and thank the staff of the Michigan Economic Development Corporation (MEDC) for providing a portion of the data used in the analysis. No MEDC funding was received, however. The article was prepared by Upjohn Institute staff using funding from the Institute’s endowment. The report’s conclusions are those of the authors and should not be construed as official views of the Upjohn Institute or its Board of Trustees, or as views of those commenting on previous drafts.

Declaration of Conflicting Interests

The author(s) declared no conflicts of interest with respect to the authorship and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The article was prepared by Upjohn Institute staff using funding from the Institute’s endowment.

Notes

Author Biographies

Timothy J. Bartik is a senior economist at the W.E. Upjohn Institute for Employment Research. His research focuses on state and local economic development policies, local labor market policies, and labor demand policies, analyzed from a local, regional, state, and national perspective. His most recent book, “From Preschool to Prosperity,” was published in 2014.

George Erickcek is senior regional analyst at the W.E. Upjohn Institute for Employment Research. His research focuses on issues of regional economic development including tax policy, economic and fiscal impacts, program evaluations, and economic development strategies and analyses.

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