Evaluating decentralized policies: How to compare the performance of state and local economic development programs across different regions* September 8, 2000 Paper to be presented at the Fourth EES Conference. Lausanne, October 12-14th, 2000 DRAFT Daniele Bondonio Heinz School of Public Policy and Management Carnegie Mellon University Pittsburgh, PA 15213-3890 E-mail:
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[email protected] Abstract This paper features an empirical analysis of the U.S. Enterprise Zone (EZ) programs to show how impact evaluations can be performed on decentralized economic development policies with heterogeneous characteristics implemented in different regions. The evaluation design developed in the paper is a comparative model that operationalizes the differences in the decentralized policy implementations and controls for the differences in the pre-designation characteristics of the targeted areas. The results of the analysis provide important evidence that could help to effectively refine future geographically-targeted economic development initiatives. These results show that the evaluation design proposed in the paper is an effective tool to turn the heterogeneity of decentralized economic development programs from a threat to the validity of the analysis into a great opportunity for testing a number of policy implementation features that would not be otherwise possible to empirically evaluate in unitarian-types of programs. (*)
The work that provides the basis for this paper was supported by funding under the Doctoral Dissertation Research Grant from the U.S. Department of Housing and Urban Development, and under the Doctoral Dissertation Research Improvement Grant from the Geography and Regional Science Program, National Science Foundation. The author is solely responsible for the accuracy of the statements and interpretations contained in the paper. Such interpretations do not necessarily reflect the views of the U.S. Government or of the National Science Foundation.
1. INTRODUCTION This paper proposes an empirical analysis of the U.S. Enterprise Zone (EZ) programs to show how impact evaluations can be performed on decentralized economic development policies with heterogeneous characteristics implemented in different regions. The U.S. EZ programs were initiated autonomously by a number of states, rather than by the Federal government as a policy initiative providing tax and other business incentives aimed at encouraging businesses to relocate to (or to avoid leaving) economically depressed areas. By the time that the Federal government implemented (at the beginning of 1994) a spatially-targeted economic development policy referred to as the “empowerment zone/enterprise community” program, almost forty states had passed their own versions of EZ programs, with the first programs being implemented in the early 1980’s. Because the states autonomously initiated the EZ policies with the involvement of county and local governments of the targeted areas, a large variety of programs emerged. Thus, the U.S. EZ programs represent an excellent case study to illustrate the challenges and the opportunities that decentralized interventions pose to evaluators. States’ EZ programs vary in the type and the monetary generosity of the incentives offered to zone businesses, the criteria for selecting the targeted areas designated as EZ, and the business eligibility rules to receive the EZ incentives. Evaluation of single decentralized implementations of such programs, in the form of evaluation of single specific state EZ programs (e.g. Alm and Hart 1997, Boarnet and Bogart 1996, Dowall 1996, Papke 1993, 1994, and U.S. General Accounting Office 1988, Rubin M. 1990, Rubin M.M. 1991 and Wilder and Rubin 1988) is not a satisfactory option as the external validity of the results is compromised by the wide heterogeneity of the policies implemented by the decentralized state administrations. For the U.S. EZ programs, positive or negative findings from the experience of one state are difficult to generalize to other places or times, since, for example, it is not possible to disentangle whether these results were determined by some specific policy features of the local implementation of the program or by its relatively low or high monetary generosity. More informative than single case studies can be comparative evaluations of more than one decentralized program implementation. For the case of the U.S. EZ programs, such types of evaluations are studies that compare outcomes from multiple state programs. These types of comparative evaluation are generally not easy to implement due to the difficulties in gathering the required data about the location of the target areas, the date of designation of the local
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programs, the specific local policies and the outcome measures used to assess the success of the program. If carefully implemented, however, comparative evaluations can take advantage of the heterogeneity of the local decentralized implementations of the program (that can be thought of as a natural experiment) to provide valuable evidence to help refining future economic development interventions. This is because decentralized programs often provide variations of specific policy implementation features that can be incorporated in the analysis to assess their specific contribution to the effectiveness of the program intervention. In this paper I develop a comparative evaluation that investigates the impact of selected EZ policy features on a number of business outcomes in zone areas. The focus of the policy debate on enterprise zones is whether these development tools are effective in arresting and reversing urban decline. The diversity of state policies and the availability of U.S. Census Bureau establishment-level manufacturing data (i.e. the Census Longitudinal Research Database LRD) provide a unique opportunity to estimate the impact of some key specific decentralized program features on a variety of business outcomes: total employment (to measure which type of incentives or program features best create and retain jobs), total value of shipments (to assess whether plant output has been affected), total payroll/number of employees (to investigate, for example, whether higher-paying jobs are replacing lower-paying jobs), expenditures on new buildings and machinery (to assess whether zone incentives increase investments). By using the establishment-level data provided by the Census LRD, I am able to decompose the average impact of specific policy features into changes attributed to new firms, ongoing firms and firms that have closed. This distinction allows me to investigate whether any specific decentralized EZ policy feature has a different impact in attracting new start-up establishments versus contrasting the decline of existing businesses. Knowing whether certain decentralized EZ policies are more appropriate to incentive new business creation, and on the other hand, whether other EZ policies are better suited to retain existing businesses, can provide very insightful findings to reshape future geographicallytargeted economic development efforts. This is because the different EZ policy features implemented by the individual states and specific local target sites might be appropriate in different circumstances. For example, policy features that are found to be effective in attracting new establishment start-ups can be better directed to support the development efforts applied to areas with substantial expansion potential such as newly equipped industrial park sites or
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underutilized land sites at the edge of the urban texture, rather than being directed to reverse the decline of inner city business district sites. On the other hand, policy features that are effective in retaining existing zone businesses, instead, can be directed to support efforts to save central business districts, rather than to boost the economic growth of sites in areas specifically set apart to host new industrial development. To provide this type of evidence, I construct the analysis setting apart and operationalizing the main decentralized EZ policy differences across states and I control for the monetary generosity of the incentives awarded to zone businesses as a development tool to boost zone employment. Since local areas can be designated as EZs only if they show signs of economic distress, the data to evaluate EZ programs are non-experimental and non-random by nature. This poses the challenge to control for selection bias in retrieving the impact estimates. The econometric methods that I adopted in this paper address the selection bias issue by estimating the designation probability of each area based on pre-designation characteristics. Differences in these characteristics are then controlled for by includin g the predicted probability of zone designation in a regression of employment growth on indicators of zone status and program features. Finally, a number of different econometric specifications are also implemented to test the robustness of the results through a fairly extensive sensitivity analysis. The results of the analysis show that the heterogeneity of decentralized policy interventions offers a great opportunity to test a number of policy implementation features that would not be otherwise possible to empirically evaluate in unitarian-types of interventions. The policy features tested in this paper provides findings that lead to the proposal of relevant policy recommendations that could help to effectively refine future geographically-targeted economic development initiatives. The remainder of the paper is organized as follows. Sections 2 and 3 illustrate the EZ programs and the single EZ policy features analyzed in the paper. Section 4 describes the data used in the analysis. Section 5 is devoted to investigate the effectiveness of EZ policies in attracting new zone business start-ups and retaining existing zone establishments and to describe the method of the analysis. Section 6 illustrates the results of the analysis. Section 7 discusses the major findings of the paper and provide policy recommendations. Section 8 contains concluding remarks.
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2. STATES’ EZ PROGRAMS Among the more than forty states that have implemented a version of EZ policies, I sampled eleven states for the analysis. The choice of the particular states included in the analysis is the result of a decision based on a number of criteria: ease of gathering geographic and policy information about the program; limited territorial extension of the program (i.e. the sample investigated is limited to states that do not designate hundreds and hundreds of zones), longevity of the EZ program. In addition to these factors, the analysis is limited to the states that implement competitive EZ programs, where communities have to compete and participate into a selection process to gain the state resources needed to implement an EZ program. Following is a brief description of each of the EZ programs in the data sample used in the analysis. - California is the only state that implemented two EZ programs that stayed in place from 1986 to 1996 before being jointed into a single program. These two programs, which differ because of the type of incentives awarded to zone businesses, targeted areas composed by a residential community with high unemployment and poverty rates and low income levels and by land available for commercial and industrial development. - Connecticut’s EZs are designed to attract primarily manufacturers and R&D facilities. Wholesale distribution operations and certain other service industries are also eligible for some Enterprise Zone benefits. Zone areas have to encompass census tracts that meet poverty and unemployment distress criteria. No deadline for zone designation is set. - District of Columbia’s EZ program consists of three zones with different targets. One zone is targeted for the development and rehabilitation of affordable rental and ownership housing for low –to moderate-income people. A second zone is targeted for business and commercial development. A third zone is finally property of the government and is targeted for a number of different uses among which the development of a production and technical employment center and the development of office space and light industrial use. - Florida implemented three EZ programs. The first program was implemented in 1982 and lasted until the end of 1986. The second program was implemented in 1987 and lasted until 1994. The third program, to date still in operation, was implemented in 1995. Florida’s second EZ program is the one analyzed in this paper. EZ’s of Florida’s second program had to have not less than forty per cent of the land area available for commercial and industrial uses, and
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not less than forty per cent available for residential uses. Zones of this program were predominantly dilapidated, deteriorated or slum structures in areas with faulty or inadequate street or lot layout. - Indiana’s EZs are urban areas suitable for a mix of commercial, industrial and residential uses. EZs are designated if the proposed area meets poverty or unemployment criteria. - Kentucky EZ program targets area for industrial, commercial and light industrial development. A couple of Kentucky zones include residential areas. Zone designation lasts for twenty years. - Maryland’s EZs consists mainly in industrial parks, and areas readily available for retail and commercial development. These development areas has to be adjacent (or partially located) in areas with high economic distress. Zone designation lasts for ten years. - New Jersey’s EZ program targets areas that are portions of qualifying municipalities. Municipalities that qualify for the program are those that meet high unemployment or other distress criteria. Designated areas inside the qualifying municipalities include about thirty per cent of the municipality’s land area and the central business district of the municipality. - New York EZ program focus on areas that are characterized by persistent and pervasive poverty, high unemployment, limited job creation and abandoned industrial and commercial facilities. Emphasis of the program is posed on the fact that unemployed and needy people of the local area will be able to fill jobs made available in the zone. Each zone has to contain a Census tract area with high unemployment and poverty rates. EZs’ actual boundaries have to be set to allow at least twenty five per cent of the total land area to be used for industrial or commercial development, and other vacant, abandoned or otherwise available. Designated areas expire after ten years. - Pennsylvania EZ program targets areas in distressed urban, suburban and rural communities. The Pennsylvania program seeks to aid local government to improve local business climate and to sustain their efforts to respond to business expansion opportunities and constraints. Pennsylvania’s zones also provide support to local government to form partnerships with the private sector to promote new investment. - Virginia’s EZs must include a residential area in order to meet distress criteria relating to poverty or unemployment. Areas proposed for enterprise zone designation should also include significant investment opportunities (e.g. undeveloped land, vacant buildings and underutilized
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facilities) capable of attracting private sector investment into the area. Size limits have been established to assure that a zone will be large enough to have a significant impact on the community in which it is located yet small enough so that results of the program can be readily measured. Virginia’s Enterprise zones are in effect for a period of twenty years from the date of zone designation.
3. EZ POLICY FEATURES To effectively evaluate decentralized program interventions, it is crucial to sort out and correctly operationalize the heterogeneous policy characteristics implemented in the different regions targeted by the program implementation. For the case of the U.S. EZs, these differences are the single policy features implemented by each state to create their own version of EZ program. In this paper, the state-specific policy features that I sort out and operationalize are related to the following factors: the monetary value of zone incentives, the zone designation process, the business requirements for receiving zone incentives and the portion of state land covered by zones. Table 1. summarizes the distribution by state of the policy features operationalized in the paper.
3.1 Monetary value of zone incentives As noted by Fisher and Peters (1996), any positive outcome from geographically-targeted economic development programs can be expected only if program incentives influence the investment patterns of expanding or relocating firms. To compare the efficacy of decentralized EZ policy designs in promoting local employment and other business outcomes, it is crucial to properly account for the monetary generosity of the incentives awarded to zone businesses by EZ programs. Negligible success of an EZ program, in fact, can be also due to a limited monetary commitment to the program by the state, instead to a relatively non-efficacy of the program features per se, as a local economic development tool. To develop a measure of monetary generosity of EZ incentives, I adopt the “hypothetical firm” approach developed by Fisher and Peters (1998). Fisher’s and Peters’s “hypothetical firm” model (the Tax and Incentive Model –TAIM-) fully incorporates both tax and non-tax incentives by adapting to economic development analysis an approach first developed to study the
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relationship between tax burdens and economic growth (e.g. Papke 1987, 1991, and Tannenwald 1996). The TAIM algorithm works, first, by constructing the financial and tax statements, the balance sheets, and operating ratios of various hypothetical firms, each of them representing the characteristic of a typical firm in different fast-growing industries. Each firm’s liabilities in terms of federal and State taxes are then calculated based on the appropriate tax laws. As a next step, the opening of a new plant in a specific location is assumed. Appropriate changes to the firms’ assets, revenues, and costs are then taken into account, net to the impact on the firms’ State and federal taxes of the economic development incentives offered at the new plant location (further details on the “hypothetical firm” approach and the TAIM algorithm are provided in Fisher and Peters 1996, 1998 and Peters and Fisher 1997). To determine the monetary value of a State’s EZ incentives, I estimate the difference between the TAIM internal rate of return of the investment in the new plant made by a typical firm in an EZ area and the internal rate of return of the same investment made in a non-EZ area within the same State. This within -State differential estimate is motivated by the fact that development incentives are most likely to influence business location decisions at the margin, as tie-breakers between similar and spatially adjacent areas (e.g. Bostic 1996, Wilder and Rubin 1996 and Bartik 1991). This is because the magnitude of the variation in labor, tax, and other business costs, as well as of the variation in revenue potential, across different Regions and States is larger than the variation in development incentives, making businesses regional and State location decisions primarily dependent from these first types of variation (Wilder and Rubin 1996). Thus, EZ incentives are very unlikely to influence businesses’ location decisions across States, while they might influence businesses’ location decisions between similar and adjacent areas within the same State. TAIM estimates are two-digit SIC industry specific. To utilize TAIM estimates in assessing the impact of EZ programs on total employment, a nonindustry specific index has to be constructed. Evidence from other studies, reviewed by Wilder and Rubin (1996), shows that existing zone businesses are more likely than others to take advantage of EZ incentives. To combine the TAIM industry specific figures into a single incentive value estimate per each EZ program, therefore, I weight each two-digit SIC industry specific estimate by the State proportion of establishments operating in that same industry prior to the EZ program start.
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3.2 Zone designation process States’ EZ policies adopt a variety of requirements to select the areas to be designated as EZs. In many states minimal thresholds (concerning unemployment, income and or education levels, percentage of vacant building and population decrease) have to be met by local communities in order to be eligible for EZ designation. Eligible local communities are then required to submit a formal application for EZ designation. Finally, EZ status is typically awarded to a sub-sample of the eligible local communities that applied for EZ designation. A distinctive feature of this common designation process, that has been regarded as potentially important for the effectiveness of EZ programs, is the provision of a strategic business plan among the application requirements (Bostic 1996). California program officials, interviewed by Bostic, for example, revealed that the business planning portion of the application process was important to organize local development resources in a more productive way. This requirement appeared to them to be beneficial by itself for local economic development, even apart from the actual designation of an area as EZ. As pointed out by Wilder and Rubin (1996), the body of knowledge about EZ programs lacks attention to the planning process as a policy implementation requirement. An additional policy feature that I exploit in this paper is therefore the presence of a business planning requirement in the zone designation application process.
3.3 Business requirements for receiving zone incentives State enterprise zone programs often tie incentives awarded to zone businesses to specific requirements. The two most common requirements of this type are the provision that ties EZ incentives to the number of new jobs created by zone firms and the provision that ties EZ incentives to the size of firms’ capital investment in the zone. As pointed out by Papke (1993), zone incentives may have also an impact on factor prices. Incentives that reduce the price of capital goods may increase production and employment by lowering costs, but they may also have a substitution effect by inducing businesses to substitute capital for labor. Programs that tie incentives awarded to zone businesses to the number of new jobs created, therefore, might be more effective in promoting local employment growth than the programs that tie incentives to capital investments. Wilder and Rubin (1996) suggested that the state programs not tying their major incentives directly to employment may compromise the objective of inducing employment
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growth in the targeted areas. Thus, job and capital requirement variables are introduced in the analysis to investigate any potential substitution effect induced by enterprise zone programs that more heavily subsidizes capital over labor.
3.4 Portion of state area covered by zones In previous enterprise zone studies (Erickson and Friedman 1990a, 1990b) it has been suggested that EZ programs might be more successful if they restrict the number of designated zones. This is because a more competitive zone selection process can allow program officials to better evaluate the potential comparative advantage of the different eligible areas. In this way, program officials would be able to designate the areas that have developed the strongest local support for economic growth. A more conservative attitude in the designation process is also considered beneficial to facilitate a closer monitoring and evaluation of the implementation of the program, allegedly improving its ultimate efficacy (Wilder and Rubin 1996). To investigate the degree by which the impact of EZ programs on desirable business outcomes is due to their zone land coverage, I include in the analysis a policy variable that estimates the percentage of states land covered by EZ areas.
4. DATA The data I used in this paper were collected from various documents and sources provided by states EZ program and economic development offices, the Census Bureau and the Department of Housing and Urban Development. Zone location and designation dates information are obtained from interviews and questionnaires from state EZ and local development administrators, complemented, in some cases, by U.S. Department of Housing and Urban Development data. In order to evaluate the EZ programs with Census outcome data, I map EZ locations in terms of U.S. postal ZIP code. Zip code areas are encoded as ZIP zones if they encompass any significant portion of an actual EZ area. While in some cases zone location information in terms of ZIP areas was provided directly by state or local officials, in many other cases, census tracts and ZIP code areas location information was retrieved from EZ maps using the software Arc View GIS Version 3.0a. Table 2. illustrates the programs’ starting dates and the number of EZs and zone-ZIPs tabulated by State. Since ZIP code areas changes over time a specific feature of the Census LRD data is
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exploited to trace historic ZIP code area changes from 1977 to 1992 (the time span of the data used in the analysis). To retrieve pre-designation demographic, income, poverty, unemployment and population density information, I make use of the 1980 Decennial Census STF3a files. These data, recorded by Census tracts, needed to be allocated to ZIP code areas to be used together with the LRD data. This allocation was performed using the MABLE GEOCORR 1 geographical correspondence engine that determines the degree of overlap between different spatial units. Business outcome data to measure the impact of EZ policies come form the U.S. Bureau of Census’ Longitudinal Research Database (LRD). The LRD, which includes data on U.S. manufacturing plants with five or more employees, was developed by the Census Bureau in order to better investigate changes in the U.S. manufacturing sector over time. The LRD data allows me to track manufacturing establishments by assigning each plant a unique identification number. The data available for each establishment include location, output quantities, and detailed information on the factors of production, such as the levels of capital, labor, energy and materials used as inputs. The LRD data is composed by the quinquennial Census of Manufactures (CM) and the Annual Survey of Manufacturers (ASM). The LRD data contains CM data from 1963, 1967, 1972, 1977, 1982, 1987, and 1992 on 300,000-400,000 plants, and ASM data from 1972 to 1995 for a probability sample of 50,000 to 70,000 plants each year. The plants with the larger number of employees, at least 250, are generally included with certainty in the ASM panel. For the establishments with a smaller number of employees, the probability of inclusion in the ASM panel is proportional to the number of employees. For the analysis of this paper, I use the CM panel of years 1977, 1982, 1987, 1992. The exclusive use of the CM panel has the advantage (over the use of the ASM panel) that it allows to measure with certainty changes over time occurring in each and every establishment included in the geographic sample analyzed. This is important since EZ program might attract into zone areas establishment of a small size, so that the use of the ASM panel would be not optimal because of the occurrence of measurement errors due of the non-inclusion in the sample of some of the small establishments.
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MABLE/GEOCORR is available on the World-Wide Web at http://plue.sedac.ciesin.org/plue/geocorr/.
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5. ENTERPRISE ZONES AND BUSINESS OUTCOMES The aim of the evaluation case proposed in this paper is to evaluate Enterprise Zone (EZ) policies against their immediate goal of influencing business decisions to retain existin g firms and to attract new ones. Successful EZ policies should lead to both a growth in business formation and a decrease in business exit inside zone areas relative to excluded areas. In addition, EZ policies should bring more business activity to the new and existing zone establishments. Empirical evidence of such increment in business activity would be found in increased employment, sales, payroll, and capital expenditures. Total employment is used as the outcome variable of the evaluation to measure whether zones create and retain jobs. Total dollar value of shipments provides a measure of whether plant output has been affected. Total payroll/number of employees is used to provide an indication of how earnings have changed. This is important to measure, because earnings could increase even as employment falls if higher-paying jobs are replacing lower-paying jobs. Expenditures on new buildings and machinery are an indicator of whether zone incentives encourage increased capital investments. To enhance the external validity of the research it is crucial to include in the analysis the EZ policy variables illustrated in section 3. Since each state’s EZ program adopt very different incentive packages, positive or negative findings from the experience of one state are difficult to generalize to other places or times, since it would not be possible to disentangle whether these results were determined by some specific feature of that program rather than by a generalized failure of EZ policies to improve local economic conditions. In order to be able to analyze the marginal contribution of specific policy features to EZs’ performances, it is crucial to extend the data sample to a significant number of state programs. This is because EZ policy features, by the most part, have variation across states, but not within zones of the same state. The eleven-states sample used in this paper has an adequate dimension to provide enough variation for the analysis. The use of this large data set, however, poses a restriction to the analysis: data on the monetary value of the incentives cannot be used as the monetary value-estimates from the TAIM algorithm are obtainable only for five of the eleven states in the data sample and only for only sixteen two-digit sic code industry sectors. To use the monetary value of the incentives as one of the policy features investigated, one would have to restrict the analysis to only five states, and to few SIC code industry sectors. Not controlling for the monetary value of the incentives can be a limitation of the analysis from a theoretical point of view, but in light of the findings of
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Bondonio and Engberg (2000), the gain in external validity and in the precision of the impact estimates on the other policy variables that can be achieved by using a larger data sets largely compensates the loss of the monetary value variable. Nevertheless to check the robustness of the results obtained with the larger sample of states, the analysis is also replicated for the smaller sample of states for which the monetary value of zone incentives can be estimated.
5.1 Are there EZ’s Policy Features that can Help in Attracting new Businesses Versus Retaining the Existing Ones? The immediate goal of most state EZ programs is to modify businesses location decisions. Attracting and retaining businesses and jobs is a goal of most EZ programs to contrast the impoverishment of the inner cities in favor of suburbs. As well documented by the U.S. department of Housing and Urban Development (1997), despite the overall economy has been booming since 1991, inner city business districts have still been losing jobs. Although, attracting new business and jobs is certainly the most headline grabbing goal of EZ programs, retaining businesses and jobs is also stated as an important goal. This is especially true when the legislator has in mind to revitalized down-sloped trended inner city business districts. Investigating whether certain EZ policy features are more appropriate to incentive new business creation, and on the other hand, whether other EZ policy features are better suited to retain existing businesses, can be very beneficial to reshape future geographically-targeted economic development efforts. For example, capital is a primary concern for start-up businesses and new branches of existing businesses. Thus, it can be argued that capital and finance incentives for the few first years of a start-up business plan are typically more attractive than other types of tax incentives, since new businesses do not expect to make a profit in the first years of operation. In a survey study of a number of zones in four states, Sheldon and Elling (1989) found that new firms reported to be significantly affected by EZ program services (e.g. technical assistance and streamlines regulations), while expanding businesses reported to be affected more by financial assistance (e.g. low-rate financing, venture capital and fee waivers). If some policy features are found to be particularly effective in attracting new start-up establishments, those features should be directed primarily to support economic development initiatives targeting areas such as newly equipped industrial park sites or underutilized land sites at the edge of the urban texture, rather than to support initiatives aimed at reversing the decline
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of inner city business district sites. On the other hand, if other policy features are found to be effective in retaining existing zone businesses, those features should be directed to support future interventions aimed at saving existing central business districts, rather than aiming at boosting the economic growth of sites in areas specifically set apart to host new industrial development. To address these types of research question, the Census Bureau LRD establishment-level data provides an excellent source of information. Using the Census of Manufactures (CM) portion of the Census Bureau LRD one can calculate for each business establishment the growth rate between the closest available times prior and post-intervention for employment, shipments, earnings, and capital expenditures. These growth rates can be then decomposed into changes due to three “types” of establishments: births, deaths, and ongoing establishments.
5.2 Establishment classifications Births are defined as establishments that have positive employment in the post-intervention time, but had zero employment prior to the program intervention. Deaths are defined as establishments that have zero employment in the current year, but had positive employment in the previous year. Establishments are classified as “ongoing” if they have positive employment levels, both before and after the program intervention. The intersection among the four growth measures and the three different “types” of firm identify 12 data cells. The data inside of each of the different cells are then geographically aggregated to the ZIP area level. As a result, information on the pre-post intervention changes for the four outcomes variables of interest for the evaluation can be sorted out for, new, on-going, and vanishing establishments.
5.3 Method of analysis To estimate the impact of different EZ policy features for each type of establishments, the analysis is implemented using a conditioning on a propensity score approach (Engberg and Greenbaum 1998, Bondonio 1998, Bondonio and Engberg 2000). This approach is chosen for the following reasons. First, data are available for both target and excluded ZIP code areas located within the eleven states considered for the analysis. Second, the selection process that is followed to designate EZ areas in the eleven states under consideration is strictly based on the policy guidelines and designation criteria set in the state EZ legislations. As described in section 3, the zone designation process is based on minimal thresholds (concerning unemployment, income
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and or education levels, percentage of vacant building and population decrease) that have to be met by local communities in order to be eligible for EZ designation. Eligible local communities are then required to submit a formal application for EZ designation. EZ status is finally typically awarded to the eligible communities that submit all of the required application documents and plans in a timely and complete way. This type of zone selection process leaves very little room to self –selection into treatment based on different economic growth potential of the eligible local communities. Selection into treatment based on discretionary judgments of states’ program administrators is also not a serious concern for the analysis. The designation rules that supervise the zone selection process do not leave much room for selection decisions based on growth potential of eligible areas that are unobservable by non-zone administrators. Indeed, in more than one occasion it has been reported by states’ zone officials that tie-breaking decisions have been made by literally flipping a coin. One final reason to use the conditioning on the propensity score approach is the limited panel of years (1977, 1982, 1987 and 1992) offered by the Census of Manufactures (CM) portion of the LRD data which does not unable to efficiently implement most of the methods relying on selection on unobservables such as, for example, the random growth rates approach (Heckman and Hotz 1989, Papke 1993, 1994, Boarnet and Bogart 1996, Bondonio 1998, Bondonio and Engberg 2000).
5.3.1 Modeling zone designation The propensity score approach that I use here is a model that estimates a separate probit regression for each of two state clusters. The eleven states included in the data sample are clustered based on the criteria mentioned in the EZ state legislations to select zone areas (Table 3). This clustering solution is preferred to the less restrictive specification in which a separate probit regression is used for each state included in the analysis because among the 11 states included in the analysis, a few of them have a too small number of ZIP zones, so that separate estimation for each state would be unfeasible for the entire data sample. The first cluster considered in the analysis includes states (California, Connecticut, District of Columbia, Kentucky New Jersey, Pennsylvania) for which official zone selection guidelines include primarily income, unemployment or poverty indicators. The second cluster includes states (Florida, Indiana, Maryland, New York, Virginia) for which official zone selection
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guidelines include also criteria based on land availability or buildings vacancy, in addition to unemployment, income or poverty indicators (Table 4). Each one of the two probit regressions (one for each cluster of states) expresses zone designation as a function of five pre-designation variables derived from 1980 Decennial Census data. These variables are used to capture ZIP areas’ poverty, unemployment and income characteristics along with few basic demographic characteristics. In addition to these Decennial Census variables, the two probit regressions express zone designation as a function also of predesignation growth in employment, new capital expenditure and value of shipments. These growth variables are based on 1982-1977 Census of Manufacturing data and are added to the probit specifications specifically to control for the possibility that EZ programs targeted slow (or faster) growing areas, following unofficial policy guidelines. The probit specification for the second cluster of states, finally, includes also two 1980 Census housing market variables. This is because these states have specific policy selection guidelines that include also housing condition indicators. The two probit specifications are illustrated in equations 1 and 2:
P(EZi=1) = ? (X80iα + GROW iβ + δj),
(1)
i = ZIP areas ⊂ cluster I (j=CA, CT, DC, KY, NJ, PA);
P(EZi=1) = ? (X80iα + GROW iβ + HOUS80iδ + δj),
(2)
i = ZIP areas ⊂ cluster II (j=FL, IN, MD, NY, VA).
In equations (1) and (2), EZi equals 1 if ZIP i is ever a zone in any year from 1982 to 1992, and 0 otherwise; X80 i are the set of 1980 Census variables capturing unemployment, poverty, per capita income and some demographic characteristics of each ZIP i in the data sample; GROWi are the variables expressing the 1977-82 growth in employment and in the establishments located in ZIP i; HOUS80i, are the 1980 Census variables expressing ZIP i’s characteristics of the housing market; δj, finally, is a set a state dummy variables. Table 5 illustrates the complete set of independent variables included in equations (1) and (2), along with their means and standard deviations sorted by zone ZIPs and non-zone ZIPs.
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5.3.2 Modeling zone outcomes The predicted probabilities from equations (1) and (2) are then included in a set of regressions of an outcome variable growth rates [measured over the two 5-year covered by the available CM data (i.e. 1992-87 and 1987-82)]2 on a set of state dummies (αj), a year dummy (1987t), and a set of EZ status variables and interaction terms between an EZ status variable and a policy variable: Ln(Yit/Yit-5) = αj + β1987t +∑ c δcPRci + λ{EZit*[(t- tdi)/5]} +
∑pol θpol{EZit*[(t-tdi)/5]* polit} + uit;
(3)
i =ZIP; t = 1987, 1992; j = state; c = cluster of states (c=I, II); pol = policy variable [pol=buspl, job, cap, land (var. definition in Table 1)]; t di = time of zone designation (t- t di)
=
(t- tdi)
if (t- tdi)
