Empowerment Zones, neighborhood change and owner-occupied housing

Regional Science and Urban Economics (Impact Factor: 1.01). 07/2009; 39(4):386-396. DOI: 10.1016/j.regsciurbeco.2009.03.001
Source: RePEc

ABSTRACT This paper examines the effects of a generous, spatially targeted economic development policy (the federal Empowerment Zone program) on local neighborhood characteristics and on the neighborhood quality of life, taking into account the interactions amongst the policy, changes in neighborhood demographics and neighborhood housing stock. Urban economic theory posits that housing prices in a small area should increase as quality of life increases, because people will be willing to pay more to live in the area, but these changes in prices and quality of life will also affect the demographics of the population through sorting and the housing stock through reinvestment. Using census block-group level data, we examine how housing prices respond to the Empowerment Zone policy intervention. Changes in the other dimensions of neighborhood quality (demographics and housing stock characteristics) will also help determine the total -- or full -- effect on housing values of the policy intervention. This paper estimates these direct and full effects in a simultaneous equations setting, compares direct and indirect effects and examines the robustness of the effects to alternate estimation strategies. We find strong evidence for substantively large and highly significant direct price effects, while results suggest that the indirect effects are substantively small or even negative.

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Available from: Douglas Noonan, Aug 13, 2015
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    • "At first glance, our results may appear counter-intuitive; we show that a policy designed to strengthen local economies causes a decline in the number of new small establishments and has a statistically unimportant effect on all new establishments. Although our IV estimates suggest the effect of the program is positive, the null OLS finding seems to fit with the existing evidence in Krupka and Noonan (2009) and Hanson (2009) that the EZ tax incentives are capitalized into local property values. If property values reflect immediate capitalization of the tax incentives by the marginal land purchase, new establishments considering locating in the targeted area may not be able to afford the increased rents. "
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    ABSTRACT: ABSTRACT This paper examines how offering tax incentives in a local area affects the entry of new business establishments. We use the federal Empowerment Zone (EZ) program as a natural experiment to test this relationship. Using instrumental variables estimation, we find that the EZ wage tax credit is responsible for attracting about 2.2 new establishments per 1,000 existing establishments, or a total of 20 new establishments in EZ areas. New establishment growth is strongest in the retail (about 40 new establishments) and service (about five new establishments) sectors, and offset by declines or slower growth in other industries.
    Journal of Regional Science 07/2011; 51(3):427 - 449. DOI:10.1111/j.1467-9787.2010.00704.x · 2.00 Impact Factor
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    • "Both the HUD and Busso and Kline estimation strategies rely on the assumption that EZ designation did not depend on the economic outcomes that an area would have experienced had it not been awarded EZ status (i.e. that EZ designation is exogenous). Krupka and Noonan (2009) use future recipients of EZs as a control group to determine the effects of first round EZs on local property values. "
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    ABSTRACT: The federal Empowerment Zone (EZ) program is a set of tax incentives targeted to areas of select cities. I estimate the effect of the EZ program on employment, poverty, and property values by comparing areas that received an EZ to areas that applied (and qualified), but were rejected. Because of endogeneity concerns, I use political representation to instrument for EZ designation. OLS results show a positive and statistically significant effect of the program on employment and poverty. IV estimates suggest the program had no effect on employment and poverty, and instead had a large statistically significant effect on property values.
    Regional Science and Urban Economics 11/2009; 39(6-39):721-731. DOI:10.1016/j.regsciurbeco.2009.07.002 · 1.01 Impact Factor
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    • "Counterfactual statistical impact evaluation analyses focusing on the distant outcomes of type C should be performed mainly for programs targeting only specific geographic areas, such as, for example, the US state and federal Enterprise Zones (e.g. Krupka and Noonan 2009, Bondonio and Greenbaum 2007, O'Keefe 2004, Greenbaum and Engberg 2004, Peters and Fisher 2002, Bondonio and Engberg 2000, Engberg and Greenbaum 1999, Boarnet and Bogart 1996, Papke 1994), the " Zones Franches Urbaine " of France (Rathelot and Sillard 2008), the proposed " Zone Franche Urbane " of Italy, and, by some degree, the incentives cofunded by the EU structural funds in " Objective 2 areas " (Bondonio and Greenbaum 2006). In such cases the economic weight of the program incentives is not disproportionably small compared to the size of the economy of the local target areas, and appropriate comparison group statistical models are capable of identifying the program impact on the target areas outcomes, controlling for the major confounding factors. "
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    ABSTRACT: Although business incentive programs of different forms have been the bulk of local economic development policies in many industrialized countries for more than the last three decades, evaluating their impact on employment or local economic growth outcomes remains a challenging task due to the persisting lack of randomized experiments and the presence of many confounding factors which affect firms and economic growth outcomes. Moreover, much of the recent advancements in the statistical program evaluation methodology applicable to non-experimental settings do not make any direct reference to the specificities posed by business incentive policies. This paper aims at offering some clear guidance on how to choose the appropriate focus of the evaluation, the policy relevant evaluation parameters and empirical impact identification strategies when applying statistical methods attempting to estimate how much of the different outcomes between treatment and control groups are attributable to the program/s being evaluated. Each methodological option discussed in the paper is linked to the different features of commonly implemented US and EU policies and to whether or not the analysis focuses on outcomes recorded at a firm-level or at the level of the geographic areas in which the assisted firms are located.
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