Empowerment Zones, neighborhood change and owner-occupied housing

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.

Full text

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IZA DP No. 3320
Empowerment Zones, Neighborhood Change and
Owner Occupied Housing
Douglas J . Krupka
Douglas S. Noonan
D I S C U S S I O N P A P E R S E R I E S
Forschungsinstitut
zur Zukunft der Arbeit
Institute for the Study
of Labor
January 2008

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Empowerment Zones, Neighborhood
Change and Owner Occupied Housing


Douglas J. Krupka
IZA

Douglas S. Noonan
Georgia Institute of Technology




Discussion Paper No. 3320
January 2008



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IZA Discussion Paper No. 3320
January 2008




ABSTRACT

Empowerment Zones, Neighborhood Change and
Owner Occupied Housing*

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 more willing to pay 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 indirect effects in a simultaneous equations setting,
compares indirect and full 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.


JEL Classification: R0, R21, R31, R38, R58

Keywords: economic development, empowerment zones, porperty values,
household mobility, sorting


Corresponding author:

Douglas J. Krupka
IZA
P.O. Box 7240
D-53072 Bonn
Germany
E-mail: dkrupka@iza.org




* The authors would like to thank John Winters for valuable, reliable research assistance, participants
in sessions at the annual APPAM meetings in 2006, the AREUEA annual meetings in 2008, and
participants in the Lincoln Institute of Land Policy “Impact of Public Policy on Land Values” workshop.
This research benefited from the support of the Lincoln Institute of Land Policy. This document
contains demographic data from Geolytics, Inc, East Brunswick, NJ.

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I. Introduction
Spatially targeted economic development policy has been a popular tool for addressing
the problem of entrenched concentrations of poverty in urban areas. Such spatially
targeted programs usually consist of tax incentives and other off-the-books expenditures.
Over the 1980’s many states created such programs, generically referred to as enterprise
zones,1 which provide economic incentives (usually through tax abatements) for
companies that create jobs in these depressed areas. While the popularity of such
programs is irrefutable, the efficacy of spatially targeted development incentives is not
well understood. While early case-study research suggested that the programs were
effective, more recent research has cast this early consensus into considerable doubt.
During the Clinton administration, the Federal Government created a similar
program, called Empowerment Zones (henceforth EZs), which coupled tax incentives and
wage credits with large amounts of federal funding for community development. This
program was continued during the early years of the Bush administration. At present, the
EZ initiative covers over 700 census tracts with a combined population of over 3 million
individuals in 31 communities (Greenbaum and Bondonio 2004). Although the
generosity of the program has varied over time, total incentives and grant expenditures
are valued at over 5 billion dollars, according to the HUD website. Despite, the extent of
the program, the literature on the effects of the EZ program is relatively undeveloped,
even compared to the more extensive literature on state enterprise zone programs.

1 Terminology in this field is unfortunately problematic, because the state-level programs have various
names. In this paper, we use the term enterprise zone to signify any of the various state programs and
Empowerment Zone (or EZ) to refer to the more generous federal program. A federal program called
enterprise communities also exists, but this program is more similar to the state programs than the federal
Empowerment Zone program.

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In this paper, we examine the effects of the federal program over a wide variety of
neighborhood-level indicators. We focus on the total effect of the Empowerment Zone
intervention, which likely includes not only direct effects, but several types of indirect
effects. This approach conceives of neighborhood outcomes as the result of a
complicated interplay between economic, demographic and housing market forces.
Recent researchers have had trouble finding significant direct effects of spatially targeted
economic development programs. By identifying both the direct effects and the indirect
effects, our approach offers EZ status its “best chance” to show some positive effect on
neighborhood quality.
Our results show that for our preferred measure of neighborhood quality (housing
values) EZ status appears to have had statistically significant and substantial positive
effects. The effects of EZ status on other neighborhood characteristics was more mixed.
The indirect effects vary somewhat depending on specification and estimation method,
but are generally either small or negative.
The rest of the paper is organized as follows. Section II reviews the literature on
state and federal spatially targeted economic development incentives. Section III lays out
a conceptual foundation for our empirical section, discusses the empirical specification,
and describes the data. Section IV presents and discusses the results. Section V
concludes.

II. Literature
Winnick (1966) lays out a very strong case against place-based policy. The primary
justification for spatially targeted economic development programs lies in the persistence

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of concentrations of poverty, mainly in urban areas.. Kain (1968) framed the problem in
terms of the spatial mismatch hypothesis (SMH), which posited that blacks were
prevented from commuting or moving to the suburbs, where their labor was demanded,
and that low-skill jobs were prevented from moving into the central city, where the low-
skill black population lived. The spatial mismatch of low-skill labor supply and low-skill
labor demand causes the location-constrained inner-city residents to experience adverse
labor market outcomes. Since that seminal paper, spatially-targeted policies have
become popular at many levels of government. While the SMH enjoyed several decades
of empirical support, more recent work taking into account the endogeneity of residence
choice has cast some doubt on the causal relationship between spatial mismatch and poor
central city labor market outcomes.2 Whether the SMH holds or not, it is widely
accepted by policy-makers, and spatially targeted economic incentives can be seen as an
attempt to correct for the cost differentials that keep businesses from locating in the inner
city.
Even in the absence of a causal effect of spatial mismatch, local jurisdictions may
wish to spur development within their boundaries to increase tax receipts. It is not far
fetched to believe that localized tax incentives could be beneficial for local jurisdictions,
even if they had no effect on the indigenous population. Bartik (1991) reviewed the
literature on the effects of local taxes on business activity and found that the elasticity of
business activity with respect to local tax rates lay somewhere between -1 and -3. If this
is true, decreasing local taxes (even in a small section of the jurisdiction) could be

2 Gurmu et al.(2006) uses panel data to control for individual-specific fixed effects, finding that access to
employment has little effect on employment outcomes for their sample of Atlanta-area TANF recipients.
Kling et al. (2004) use the random assignment of neighborhood achieved in the Moving To Opportunity
experiments to look at the effects of job access, and find that the experimental group (who were encouraged
to move to low-poverty neighborhoods) did not have better labor market outcomes.

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revenue-enhancing for local governments.3 These large elasticities suggest that the
effects of local tax incentives may be large, and that enterprise zones may be an effective
policy tool from a local perspective.
Research examining the effects of spatially targeted incentives has concentrated
on the various state programs. While many studies have found that enterprise zones have
faired well in terms of employment, Boarnet (2001) points to the many methodological
pitfalls inherent in straight comparisons of zones to non-zone areas. More rigorous
evaluations of the state programs have not been lacking. An extensive review of this
literature can be found in Peters and Fisher (2002). They find that while early
econometric studies of the effects of state enterprise zones usually found positive results,4
more recent results have been much less favorable.5 Peters and Fisher offer several
possible explanations for this set of findings. They suggest that the tax incentives are not
generous enough to overcome the substantial disadvantages associated with the targeted
areas. They also suggest that the administration of zones, which often put conditions on
the incentives that exist, may reduce their attractiveness. Bondonio and Greenbaum
(2007) suggests that the insignificant net effects mask countervailing positive effects on

3 These elasticity figures pertain to changes in business activity within a metropolitan area. Elasticities are
of much smaller magnitude (between -0.1 and -0.6) when comparing changes in business activity across
large areas. This implies that any tax advantages a jurisdiction might expect are coming primarily from
other near-by jurisdictions, not through the attraction of business from other parts of the country. Of
course, in the case of targeted incentives, the lower taxes may be drawing businesses away from other parts
of the same jurisdiction. Such possibilities complicate any kind of cost/benefit analysis of such programs.
In this paper we focus only on the local effects of the program, not the measurement of the benefits.
4 Erickson and Friedman (1990), Papke (1993), Papke (1994) are examples.
5 Boarnet and Bogart (1996), Greenbaum (1998) Greenbaum and Engberg (2000), Engberg and Greenbaum
(1999), Bondonio and Engberg (2000) and Peters and Fisher’s (2002) own analysis all point towards this
conclusion. Elvery (2004) is another very careful analysis that finds insignificant results of enterprise zone
status.

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new firms and negative effects on existing firms (who exit the zone), along with a
number of other interesting results.
The literature examining the effects of the federal Empowerment Zone program is
much less developed. Wallace (2003) examines the probability of an EZ applicant being
selected, while Greenbaum and Bondonio (2004) examine how the selection of federal
EZs has changed over the three rounds of the program. Oakley and Tsao (2006, 2007a,
b) use propensity score matching, as in much of the recent literature on the state
programs, to examine the effect of Chicago’s and some other Empowerment Zones on a
variety of socio-economic neighborhood outcomes. While they find some localized
effects (e.g. on poverty and related variables in the case of Chicago’s zone), they
characterize the effects as underwhelming. When pooling the four zones6, the
intervention had no significant effects on poverty, unemployment or average household
income.
While most of the studies mentioned above examine job creation or employment
outcomes, our primary variable of interest will be the value of owner-occupied housing in
a neighborhood. While we will also be examining the effect of EZ status on employment
outcomes of neighborhood residents, this more traditional variable takes a secondary
position in our analysis. This is because the empowerment zone program is supposed to
improve neighborhoods along a variety of dimensions (McCarthy 1998), not just improve
employment outcomes. As such, the general quality of life in a neighborhood should be
improved by the program. If the program is successful in making a neighborhood more
attractive, the price of housing should increase (Rosen 1974, Bartik and Smith 1987).

6 The other three zones were located in Baltimore, Detroit and New York City. The analysis of all four
zones is carried out in Oakley and Tsao (2006). The other two papers focus exclusively on Chicago.

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Our empirical approach will allow us to examine the effects of EZ status on other
variables of more traditional concern (employment outcomes, poverty, etc.), but housing
values will be the main variable of interest.

III. Empirical model and data
A. Empirical model
The empirical model used here follows closely on Noonan et al. (2007). We refer
the reader to that paper for the details of the model, and focus here on its highlights. The
hedonic approach generally uses cross-sectional data to predict housing prices. A
national database of individual home prices would be required to analyze a national
program such as Empowerment Zones in this way. Such a data base is not available, so
we are forced to use neighborhood averages as proxies for these individual values. The
use of aggregated data, even at the neighborhood level, limits our ability to infer price
effects at the individual level. Nonetheless, some hedonic research has shown that
estimates using aggregate data produce reasonably accurate results (Freeman 1979,
Nelson 1979, O’Byrne et al. 1985).7 Noonan et al. (2007) also find generally plausible
implicit prices in OLS estimations using aggregated data. Moreover, the median housing
value in a neighborhood is of considerable policy import. Learning more about the
effects of a policy on this neighborhood measure is informative, even if it does not
recover the true underlying hedonic price. The results based on such aggregate measures

7 See Shultz and King (2001) for additional review of the use of aggregated Census data in hedonics.
Greenstone and Gallagher (2005) use a similar data set for their analysis of superfund designation, although
they use the larger geography of the census tract.

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can be viewed in an epidemiological light; the effects of average policy exposure on
average outcomes, while perhaps not the ideal, are nonetheless interesting.
An advantage of our data is that these neighborhood averages can be observed
over time. One potential problem with a simple OLS approach to the hedonic equation in
levels is that some neighborhood characteristics will be unobserved and correlated with
the other variables of interest. This may be especially important in the context of EZs,
since EZ designation was not randomly distributed, but was targeted at distressed
neighborhoods (Greenbaum and Bondonio 2004). To mitigate this problem, we estimate
the model in first differences. This strategy purges our parameter estimate of bias from
the omission of time-invariant variables (Mendelsohn et al. 1992, Zabel 1999), and we
thus identify the parameters from within-neighborhood changes in neighborhood quality,
neighborhood demographic conditions, and housing structural characteristics. Our
primary equation of interest can be expressed as in equation 1,
(1)
,
ititMitNitSitEZit
M
&
N
&
S
&
ZE
&
P
&
10
ε
&
βββββ
+++++=
where t indexes time, i indexes neighborhoods,
, P is the median house
value value, S is a vector of structural characteristics of the neighborhood housing stock,
N is a set of neighborhood demographic characteristics and M is a vector of municipal
characteristics such as public services and taxes that may vary with time. Differenced out
of this equation are any time-invariant geographic factors that affect price (such as
distance to the CBD, metropolitan-wide factors, views or unobserved quality of the
neighborhood).
1,,
−
−=
t it iit
XXX&
8 Finally, the EZ variable allows the designation of a neighborhood as an

8 In the results reported below, we allow for some of these geographic factors to affect median price
appreciation.

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Empowerment Zone to have an independent effect on neighborhood attractiveness. Such
an effect is possible if EZ tax incentives increase employment in the area, or the federal
funds are used to improve neighborhood quality, or lower taxes.
It is likely that many of the variables in equation (1) are set simultaneously with
price, however, so that equation (1) is part of a larger system. If changes in
neighborhood quality also affect the types of housing and demographic characteristics, it
will be important to control for the simultaneity bias when estimating the direct and
indirect effects of federal intervention on home values, as is the goal of this paper. We
model the neighborhood housing stock as a partial adjustment process, with current levels
a function of lagged levels and other variables. For comparability to equation (1) and to
avoid problems associated with unobserved effects being correlated with independent
variables, we run all our regressions in first differences, as in equation (2):
(2)
.
ititM itP itN it EZt itSit
M
&
P
&
N
&
ZE
&
S
&
S
&
201
ε
&
γγγγγγ
++++++=
−
Here, the housing stock depends on its past levels, Empowerment Zone status,
neighborhood demographics, price and other considerations. The kind of housing built in
a neighborhood depends upon past levels because housing is a very durable asset, and
changes in the housing stock (at the aggregate level) will be gradual. These structural
characteristics might also depend on EZ designation if program funds are used to clear
abandoned housing or to subsidize construction of new housing. The housing stock may
also depend on the neighborhood demographics (if rich people demand different kinds of
housing than poor people), municipal-level variables (zoning restrictions, tax treatment)
and geographic variables (which are differenced out of equation (2)). Finally, the price of
housing may affect the kind of housing built because housing is produced using land and

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capital. Production theory suggests that if land becomes more expensive, some
substitution towards more capital would be expected. Since the value of a housing unit
(our price variable) includes the value of the land on which it sits, some effect should be
expected, although the sign depends on substitution elasticities in the production and
consumption of housing services.
We apply similar logic to the modeling of neighborhood demographic
characteristics. Neighborhood demographics follow a partial adjustment process, and we
difference the equation to control for unobserved fixed effects.
(3)
103
itN it EZitSitPitMitit
N
&
N
&
EZ
&
S
&
P
&
M
&
δδδδδδε
& .
−
=++++++
In equation (3), N follows a partial adjustment process, where lagged changes in
demographics are persistent because housing market frictions prevent neighborhood
demographics from reaching their equilibrium levels between periods. Demographic
groups’ differing demands for neighborhood quality may cause them to sort into
neighborhoods being improved by EZ programs according to their willingness to pay for
these attributes (Diamond and Tolley, 1982). Similar sorting according to municipal
characteristics would be expected. Similarly, changes in housing stock may attract
different types of residents, at least when the capital stock is somewhat inelastic. Finally,
the price level in a neighborhood could affect neighborhood demographics if certain
demographics are “priced out” of a neighborhood when prices increase.
The system of first-differenced equations (1)-(3) can be represented in matrix
notation as in equation (4).
(4)
1
12
13
1
γ
δ
−
0
S
N
&
1
δ
−
1
SN itEZM
PNitEZitStMit
PSitEZNtM
P
S
N
&
EZ
&
M
&
ββ
γ
−
β
γ
δ
⎢
⎣
β
γ
δ
⎢
⎣
ε
ε
&
& ε
⎢
⎣
γ
δ
⎢
⎣
−
−
⎡
⎢
⎢
⎢
⎣
⎤
⎥
⎥
⎥
⎦
−−
⎡
⎢
⎢
⎢
⎣
⎤
⎥
⎥
⎥
⎦
⎡
⎢
⎢
⎤
⎥
⎥
⎥
⎦
⎡
⎢
⎢
⎤
⎥
⎥
⎥
⎦
⎡
⎢
⎢
⎤
⎥
⎥
⎥
⎦
−=+++
&
&
&
&
⎡
⎢
⎢
⎤
⎥
⎥
⎥
⎦
.

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In this paper, we are specifically interested in the total effect of the EZ policy
intervention. System of equations (4) shows us that these effects depend on its direct
effect (
EZ
β
), and also on its indirect effects. Totally differentiating and dividing through
by yields:
ZEd&
(5)
.
1
γ
δ
−
/
/
/
1
δ
−
1
SN EZ
PN
PSEZ
dP dEZ
dS dEZ
dN dEZ
&
ββ
γ
−
β
γ
⎢
⎢
⎣
δ
⎡⎤
⎥
⎥
⎥
⎦
−−
⎡
⎢
⎢
⎢
⎣
⎤⎢
⎥
⎥⎢
⎥
⎦⎣
−=
⎢
&
&
&
&
&
EZ
⎡
⎢
⎤
⎥
⎥
⎥
⎦
The total effect in neighborhood housing prices due to the implementation of the
Empowerment Zone policy is thus available through the application of Cramer’s Rule:
(6)
()()
SNPPSNPPNS
SNEZEZ
−
SNEZ
δ
NSEZ
+
NEZS
−
EZ
ZE
&
d
P
&
d
δγδγβγδγβ
δγβγδβ
+
δγβ
−
δβγβ
1
β−++++
=
.
The direct effect on price is captured by the first term in the numerator. The next two
terms are the first-order indirect effects: ZE& ’s effect on P& through
and . The third
and fourth terms are the second-order indirect effects:
S&
N&
ZE& ’s effect on P& through
’s
effect on
and ’s effect on . The negative term corrects for double counting. The
denominator accounts for the bidirectional effects of
S&
N&
N&
S&
P& on
and and their effects
back on
S&
N&
P&. If there is no simultaneity in equation (4) this total derivative reduces to the
first three terms in the numerator.
As in Noonan et al. (2007), the system of equations is considerably more complex
because S, N and M are vectors. Hence, we assume that each variable in S depends on its
own lag; the vectors EZ, N, M and G; and the contemporaneous values of the other
variables in S. Likewise, each N variable depends on its own lag; the vectors EZ, S, M
and G; and the contemporaneous values of the other variables in N. The system in

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equation (4) thus models each and equation as dependent on that variable’s own
lagged difference,
S&
N&
ZE& , M& and the rest of the endogenous variables.
Intuitively, the suite of policies represented by the designation of an area as an
urban EZ is meant to have several effects on a neighborhood. On the one hand, if the
money is spent on beautification, increased police patrols, or improved social services,
there could be a direct improvement in the attractiveness of the neighborhood. Such
improvements would increase the demand for housing in the neighborhood, and increase
the price of housing there. On the other hand, a stated goal of the EZ program is to
improve the employment situation for zone residents. If the program is successful,
unemployment or poverty in the area may decrease. If high unemployment or poverty
decreases property values,9 then the EZ policy would have this indirect effect on housing
values through neighborhood composition. If program money is used to improve the
housing stock (demolition of abandoned properties), and that improvement effects prices
in the neighborhood (because the least valuable houses were demolished, or because the
abandoned houses had been driving prices down), then there will be an indirect effect of
the program on prices through improvements in the housing stock.
This paper tries to disentangle both the direct and indirect effects of EZ program
participation. To this end, the system of equations represented in (4) is estimated
simultaneously. To do so, we require at least one exogenous variable for each
endogenous variable in each equation. The partial adjustment theory used to generate the
empirical equations suggests the twice-lagged levels of each variable will be both

9 This effect could be either direct (people having a direct preference to live near more affluent people) or
indirect (decreased poverty leads to lower crime, which makes the neighborhood more attractive).

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exogenous and excludable in the context above. These excluded variables will be
sufficient to identify the system and allow estimation.10
A simpler method for obtaining indirect effects of the policy is available. In
estimating equation (1) with OLS, the coefficient βEZ represents the partial or direct effect
of the Empowerment Zone policy intervention on prices, holding other endogenous
variables constant. However, if equation (2) were estimated constraining βS and βN to be
zero (equivalently, omitting the endogenous variables from the regression), the returned
coefficient on the policy variable EZ will represent the effect of the policy intervention
holding nothing constant. In other words, estimation of a price equation containing only
the exogenous variables and EZ will return an unbiased estimate of the full effect
computed in equation (6). The difference between the direct and full effects is the
indirect effect. While this approach to the indirect effects makes it impossible to trace the
avenues by which the indirect effects are generated (through S or through N), it is simple
and probably more robust to misspecification than the systems approach. For that reason,
in this paper we will compute indirect and full effect by both methods.

B. Exogeneity of EZ
Up to this point, we have assumed that the designation of a neighborhood for EZ
status is exogenous. This is a dubious assumption. Greenbaum and Bondonio (2004)
show that EZs are less populated; are poorer; have more minorities, unemployment and

10 Specifically, in the basic model, there is one price variable P, seven demographic variables in N, and four
structure variables in S, leaving 12 equations in the system. Each N and S equation includes a lag of the
dependent variable in the partial adjustment model. Thus each S and N equation has 12 endogenous
variables, while the P equation has 11. Twice-lagged levels of P, N and S are available as excluded
instruments for each equation, leaving the S and N equations just-identified. The EZ, M and G vectors
serve as their own instruments.