THE CASINO EFFECT:
DO CASINOS SPUR GROWTH AND WHICH COMMUNITIES BENEFIT?
Chad Cotti
Department of Economics
University of South Carolina
Abstract: There has been a dramatic increase and spread of “Las Vegas” style casinos in
the United States over the last 20 years. Using data on employment from across the US,
this research finds that, in general, counties experience an increase in employment after a
casino opens when compared with counties without casinos. The effect on industries
related to casinos is somewhat mixed, but in general mildly positive, which would
suggest that casinos provide a positive employment spillover into the surrounding local
community. Intertemporal estimation suggests that the casino effect grows over time and
provide evidence that the results are not influenced by endogenous casino location.
Estimates on how the overall effects vary across different population densities show that
low-density communities see much larger employment growth than more densely
populated areas. Finally, additional estimation finds little impact on employment levels in
neighboring counties, although there are some small effects in certain sectors.
*Contact Cotti at 920-203-4660 or [email protected]. I would like to thank Scott
Adams, Scott Drewianka, John Heywood, James Peoples, McKinley Blackburn and Keith
Bender for their helpful suggestions. I would also thank David Mustard and Earl Grinols
for their help with the data. Dain Johnson provided valuable research assistance.
I. Introduction
There has been a dramatic increase and spread of “Las Vegas style” casinos in the
United States over the last 20 years. Many communities see casinos as the best option for
strong economic growth and development. Yet, the impact of casino introduction on local
communities and surrounding areas is unclear and has become a point of much debate
across the country. Do casinos create jobs and growth, or do they simply replace jobs in
other industries, such as entertainment? Are the effects consistent across all
communities, or does the size of the community seem to affect the economic payoff?
These are questions that have not been fully addressed in the literature surrounding the
effect of casinos on local economies. Moreover, having a strong understanding of the
nature of these economic effects of casinos is particularly important when one considers
the recent literature, which finds evidence that casino introduction may lead to significant
increases in crime (e.g. Grinols and Mustard, 2006).
In this paper, I attempt to add to this discussion by comparing changes in
countywide and industry specific employment in counties where casinos have opened (or
in counties that are adjacent to a county where a casino has opened) to changes in
counties without a casino. Understanding how casino introduction affects employment in
the community as a whole, and, maybe more importantly, in related industries, is key to
evaluating the general impact of a casino on a local economy. Further, an evaluation of
how these effects may vary across different population densities will provide valuable
insight into how the effects vary across localities and should lead to a better
understanding of previous research.
2
Current studies into the impact of casinos on employment and crime have added
a great deal to our understanding of the social and economic outcomes surrounding the
introduction of casinos into local municipalities. Yet, further research on how these
effects vary across heterogeneous communities may alter the interpretation of the
findings significantly or at least provide a deeper insight into causal relationships.
Specifically, an analysis of how outcomes vary between rural and urban communities and
across different related industries should lead to a more detailed understanding of the
economic impacts of casino introduction on host communities and the surrounding
populations. This paper makes use of a national sample in order to comprehensively add
to this on going discussion.
The primary finding is that casino introduction significantly increases
employment in host counties relative to counties without a casino. As anticipated,
analysis of related industries suggests that these positive effects are primarily focused in
the entertainment sector of a local economy, of which the casino is a part. A detailed
breakdown of selected sub-sectors of the local economy provides additional insight into
how the impact of casinos varies across potentially related industries. Separation of the
treatment counties by population density indicates that the increase in employment is
strongest in low-density rural areas. Further, employment in counties neighboring those
with casinos seems to be affected very little, suggesting that gains in employment in host
counties do not come at the expense of neighboring communities.
Section II discusses the recent history of casinos in the US, as well as the existing
literature that evaluates their impact on surrounding communities. Section III discusses
3
the methodology. Section IV summarizes the data. Section V presents the results and
discusses the findings. Section VI concludes.
II. Background
A. Legislation
With the exception of Nevada in the 1930’s and Atlantic City, NJ in 1978, casinos
had no presence in the United States until Congress passed the Indian Gaming Regulatory
Act (IGRA) in 1988. The IGRA formally recognized that, because tribes are sovereign
bodies, states have limited regulatory authority to govern them. Further, the IGRA
opened the door for formalized Indian casinos by defining regulations that allowed for
three classes of gaming to exist on tribal lands. Class I gaming permits traditional Indian
games only. Class II allows for bingo or similar gaming to be operated by a tribe as long
as the gaming takes place within a state that already permits that type of gaming for any
purpose. Class III allows for the management and establishment of full-scale “LasVegas” style casinos with the negotiated permission to allow these games within the
state.1 The significance of the IGRA cannot be understated in this discussion. As of 2003,
tribal casinos existed in 25 states across the country; ranging from one location in Maine,
Nebraska, Texas, and Wyoming, to dozens of establishments in California and Oklahoma
(Ader, 2003).
The second major shift in legislative politics began in the early 1990s with the
legalization of commercial casinos in several states. Beginning with riverboat casinos,
which opened in the state of Iowa in 1991, similar casino provisions have led to an influx
1
For a more detailed summary into the Indian Gaming Regulatory Act please see cited
work by Evans and Topoleski (2002).
4
of commercial casinos throughout the country. If we exclude Nevada, by 2003 there were
well over 100 state regulated commercial casinos operating in 9 states. Taken
collectively, it is obvious that casino introduction has become a widespread phenomenon
in the United States and requires comprehensive analysis.
B. Predictions
Due to the status quo illegality that characterizes organized gambling, casino
opening is subject to governmental approval and, as such, very political by nature. The
typical motivation used in support of the opening of a casino, whether it is tribal or
commercial, is economic development. Strong supporters of casinos claim that casinos
create jobs, spur economic development, increase the standard of living, and may even
lower crime in surrounding areas. Specifically, they feel that the opening of a casino will
create a new entertainment center for their community that will attract consumers from a
wide range of surrounding areas, which, subsequently, will generate new jobs, promote
the construction of infrastructure, and energize the local economy. In particular, it is easy
to suspect that the hospitality industry may benefit from a positive business spillover.
For example, casinos began opening in Tunica County, MS in 1991 and employment
levels grew noticeably in the subsequent years. In particular, the service sector in Tunica
grew over 1000% from 1992-2001 (Garrett, 2004).
The story of Tunica highlights the potential for new jobs and the creation of
tourism, which is the primary advantage to local communities cited by proponents of
casino introduction, whether it is tribal or otherwise. According to the 2004 report on
Indian Gambling, tribal casinos also show these gains as they now employ nearly a
5
quarter million people in the US nationwide (Meister, 2004). This is a considerable
number of employees, which is suggestive of a positive relationship between economic
development and casino introduction.
On the other hand, those in opposition to casinos feel that they will increase
several societal ills, such as crime and gambling addiction, which may lead to increased
domestic problems. Moreover, opponents feel that, rather than creating more jobs,
casinos act as substitute to other pre-existing forms of entertainment. Thus, casinos may
not actually create more jobs, but instead they may “cannibalize” jobs from other related
sectors of the local economy. It is not difficult to imagine that a casino opens and several
of the existing service/entertainment establishments, such as bars, restaurants, theaters,
bowling centers, etc., lose business and therefore jobs through substitution and increased
competition for disposable income.
Overall, it is unclear what outcome to anticipate. Employment could increase
following the casino introduction, reflecting positive impacts on communities.
Alternatively, if employment does not change very much overall, but falls in other related
sectors of the economy, it may not be obvious that casinos create many jobs. Moreover,
the results may differ for urban and rural areas. The introduction of a casino into a
predominantly rural area may spur more development and job creation then in urban
areas because such casinos may act as a “destination” and draw people to a location that
previously had very little tourist appeal. So, following a casino’s opening one might
expect an increase in demand for hotels, bars, and restaurants that would not have
otherwise existed, such as been observed in Tunica County, MS. While in urban areas,
the entertainment and hospitality sectors may already be substantial, and, hence, a casino
6
would provide little noticeable benefit for a community as a whole. Further, in urban
areas a larger portion of the casino patrons will likely come from the existing residents of
the community. Thus, a net positive flow of dollars into a city may be less obvious. For
example, in Detroit, MI casino openings in the late 1990s led to 6000 new jobs (Sabar,
2004), but it is unclear that these jobs did not come at the expense of employment in
related sectors. Moreover, these 6000 additional jobs are a much smaller piece of the
overall labor market in Detroit then they would be to a county similar to Tunica and,
hence, would have a substantially smaller effect on the local economy. Regardless, it is
clear that research on both the effects of casinos on related industries and how the “casino
effect” differs between urban and rural areas is needed.
C. Past Research
There is a strong body of empirical research that evaluates the economic and
social effects of casinos on their surrounding communities, but the majority of the work
comes from case studies of particular regions.2 The preponderance of these studies focus
on the impacts of commercial casinos on employment, with mixed results. An early study
by Grinols (1994) finds that casino introduction in Illinois had for the most part little
effect on overall employment in the area. However, Siegel and Anders (1999) find
empirical evidence that riverboat gambling in Missouri acted as a substitute to the
entertainment and services industries, supporting the concept that casinos “cannibalize”
jobs from other industries and therefore provide very little job creation. On the other
2
There also exists a slew of non-peer reviewed reports and analysis from consulting
firms, policy groups, and government agencies, which provides a great deal of
information and construct to the discussion that is not formally reviewed in detail here.
7
hand, work by Garrett (2004) investigates employment effects in six counties from the
Midwest and Mississippi using a forecasting model. His analysis predicts what the level
of household employment would have been in these casino counties if a casino had not
opened and then compares it to the observed household employment following the casino
opening. He finds that in most counties casino introduction resulted in increased
employment, although these results seem to be much stronger in rural areas. This result is
casually suggestive that employment effects may differ across population densities.
Two more recent studies of casinos are Grinols and Mustard (2006) and Evans
and Topoleski (2002). The Evans and Topoleski paper focuses on both the economic and
social impacts of casinos on tribal communities and the surrounding area. They employ a
difference-in-difference fixed effects analysis using data encompassing all native tribes in
the lower 48 states. Their results are somewhat mixed, finding an increase in jobs per
adult and a decrease in county level mortality rates. Both outcomes are indicative of a
positive impact on the local community. They also isolate negative social outcomes, such
as increased violent crime, larceny, and auto theft.3
Grinols and Mustard (2006) primarily focus on social effects of casinos.
Specifically, they address how several different measures of crime (assault, robbery,
burglary, etc.) are related to casino openings.4 Their findings indicate that casino
introduction dramatically increases most forms of crime, particularly noting that the
negative impact of crime increases over time. Alternatively, preliminary research by
3
These results are consistent with the contemporary study by Grinols and Mustard
(2006).
4
Their treatment group included both tribal and commercial casinos.
8
Navin and Sullivan (2006) find evidence that the effects of crime on a community are not
consistent across urban and rural areas.
Empirically, I add to the discussion of the economic impact of casinos by
undertaking a national difference-in-difference study of how the impact of casino
introduction varies across local communities and related sectors. In addition to
addressing the impact of casinos on the host community, this paper also endeavors to
analyze the effect on related industries, such as the entertainment and hospitality sectors,
which has not been studied in great detail or in a comprehensive manner to this point.
Further, I hope to contribute to the literature by empirically estimating if the casino effect
varies across different population densities, and if impacts spillover into neighboring
counties.
III. Methodology
In this section, I describe in detail the statistical model that is used in this analysis.
As outlined below, the primary data set includes employment data for all U.S. counties
for the years 1990 – 1996. During this time frame, several counties saw casinos open
within their borders, while many did not. So, to estimate the impact of casinos on
employment at many different industrial levels, a “difference-in-difference” estimator
was utilized. This model compares the employment outcomes of counties before and after
a casino opens (the treatment group) with the employment outcomes over the same
period in counties where a casino did not open during that time (the control group).
Moreover, because it may take some time for the full impact of a casino opening to be
realized, an alternative specification was under taken to measure the effect of casino
9
introduction over time. Specifically, two different fixed effects regression models were
utilized in this analysis, although different variations were employed to estimate results
across heterogeneous treatment groups.
The first method captures the basic impact of casinos on counties and assumes
that the effect is time-invariant:
(1)
Yit = i + tCit + γ’Xct + it.
where X is a vector of county-specific demographic characteristics, it is the
idiosyncratic error, and Yit is the dependent variable, log employment, in county i during
quarter t. Cit is a casino dummy that is equal to one if county i has a casino in operation at
time t and zero otherwise. Fixed effects for county and quarter are captured by i and t,
respectively. The county fixed effects are imperative in this context, as they help control
for the potential nonrandom selection of casino introduction into a county.
The second model allows for effects to vary over time:
(2)
Yit = i + t'Lit + γ’Xct + it
where Lit is a vector of casino opening dummy variables which includes three
years of leads and five years of lags, set to capture any intertemporal effects created by a
casino opening.5 Even though we may not anticipate any strong lead effects from casino
5
Unlike the other lags, which correspond to the exact number of years the casino has
been open, lag 5 indicates that a casino has been open five years or more.
10
openings, the inclusion of leads will help to discover if any issues of endogeneity are
present in the analysis.6 This issue is discussed further in the results section.
Lastly, all standard errors where corrected to allow for non-independence of
observations from the same county through clustering. This follows the work of Arellano
(1987) and Bertrand et al. (2004). Without such corrections, the standard errors would
likely be understated and significance perhaps overstated.
IV. Data
As a way of alleviating cited weaknesses in the previous literature, Grinols and
Mustard (2006) utilize data from all 3165 counties in the United States. They cite the
prevalence of small sample sizes or geographically narrow subsets as a need for more
comprehensive research. In order to alleviate similar concerns in this employment based
study, the Quarterly Census of Employment and Wages (QCEW) was utilized as the
primary source of relevant data. The QCEW is appropriate for this policy analysis, as it
contains nationwide county-level panel data on employment levels across all North
American Industrial Coding System (NAICS). I extract quarterly data for every county
from January 1990 to December 1996.7 This data period is useful because it allows for
identification of the effects of casino introduction during the time span that encompasses
the majority of casino openings in the United States.
6
This follows similar works that investigates this issue (e.g. Evans and Topoleski, 2002;
Grinols and Mustard, 2006).
7
The QCEW data begins in 1990. Information on the timing of casino introduction was
obtained via Earl Grinols, Baylor University, and David Mustard, University of Georgia,
who gathered the information by contacting state gaming authorities and by consulting
both Casino: The International Casino Guide and select casino websites, such as
www.casinocity.com. This data source runs through 1996, so the sample for this analysis
was restricted to this year.
11
The QCEW classifications are finely disaggregated and easily allow identification
of a wide range of industrial sectors and sub-sectors. Data on sectors and sub-sectors that
one may suspect are related to casinos were isolated for this analysis. The inclusion of
analysis on related industries permits a more detailed understanding of the overall impact
of a casino on a community and has frequently been left out of other employment studies.
The industrial sectors included in this paper are: Total, all industries; Arts, entertainment,
and recreation; Performing arts and spectator sports; Museums, zoos, and parks; Other
recreation (which includes golf, skiing, marinas, fitness centers, and bowling);
Accommodation and food services; Accommodations; Food service and drinking places;
Full service restaurants; Limited service eating places; Drinking places, alcoholic
beverages; and Hotels/motels, excluding casino hotels. Overall, this sample contained
over 600,000 county-quarter observations.
As with any analysis of this type, the identification of a treatment group of
counties that opened a casino during the designated time frame is a necessity. Utilizing
information on the timing of casino introduction, which was obtained by Earl Grinols and
David Mustard, a total of 161 casino openings, encompassing both commercial and tribal
casinos, were isolated for this analysis.8 A secondary treatment group of neighbor
counties, defined as all counties that border a county with a casino, was also identified.
Atlantic County, NJ and all counties in Nevada were excluded from the analysis due to
the unique nature of the casino industry in these areas. The remainder of U.S. counties
composed the control group in all analyses.
8
Since American Indian casinos take on several different classes of operation, Indian
casinos are treated as opening when they receive a Class III license, which is the level of
operation most consistent with typical “Las Vegas” style casinos.
12
In order to control for time-varying characteristics within the sample, a number of
county level variables were included. These consist of U.S. Census Bureau data on total
county population, population density per square mile, and population distributions by
race, age, and sex.9 Of course, the inclusion of more covariates would be ideal, but
because many of the controls which could be useful in this type of model are potentially
impacted by the intervention, they can not be added. That said this is only a limitation to
the degree that the fixed-effects model is unable to deal with differences between the
treatment and control groups.
Table 1 provides some summary statistics on casino and non-casino counties.
Casino counties are defined as having a casino in operation for at least one quarter during
the sample period, while non-casino counties do not have a casino present at any time
during the sample period.
V. Results
A. Basic Effects
1. Overall Employment and Super-Sectors
The top panel of Table 2 presents the standard employment effects of casino
introduction on a county as a whole, as well as two related super-sectors: the arts,
entertainment, and recreation super-sector, which is referred to as entertainment, and the
accommodation and food services super-sector, which is referred to as hospitality.
Estimates suggest that a casino increases total employment in a county by an average of
8.1% relative to counties without a casino. This result suggests that, on average, casinos
9
Census data was provided by Earl Grinols, Baylor University, and David Mustard,
University of Georgia.
13
play a significant role in increasing employment and promoting economic development
in a county.
I would like to point out that certain estimates presented in Table 2, and
subsequent tables in the paper may seem quite large, but these estimates need to be
evaluated with respect the range of populations in the treatment group. For example, an
8% average increase in total employment is completely reasonable when one considers
that fully 25% of the counties in the treatment group have a total employment (NAICS
10) of less than 4100 workers prior to a casino opening. Therefore, estimates are
influenced strongly by the low population counties, which may have 15 or 20 percent
increases in employment following a casino opening. This is an issue which is taken up in
more detail later in the paper.
From a policy and economic growth perspective, it is critical that an analysis of
the effects of casinos should address the potential spillover effects of casino introduction
on related industries. Previous studies have found that casinos “cannibalize” jobs from
other forms of entertainment and recreation, while providing little economic benefit to
the surrounding hotel and restaurant industries (Siegel & Anders, 1999; NGISC, 1999).
Still other research finds results that casinos may promote growth in other industries,
such as hotels, restaurants, bars, and other forms of entertainment (e.g. Taylor at el,
2000). As discussed earlier, one goal of this research is to comprehensively isolate how
casino introduction impacts related industries.
Analysis of the entertainment and hospitality super-sectors find results in favor of
a positive “spillover” story. Estimates, detailed in Table 2, suggest that casinos increase
employment in the entertainment sector by 54% relative to the control group. This is
14
definitely a significant increase, but again these results are tempered when one considers
that the casino itself is captured in these estimates, and a great deal of casinos locate in
rural counties which typically have very few entertainment jobs to speak of prior to the
casino opening (over 30% have less than 200 workers in the entertainment sector prior to
casino entry). Estimates for the hospitality sector show a positive point estimate of 2.6%,
although they do fall short of significance at the .10 level.
The basic empirical specification described in Section III controls for county
characteristics by adding certain covariates to the model, but if casinos are more likely to
locate in states whose economies are improving, then estimates in the top panel of Table
2 may overstate the actual effect of casinos on employment. Subsequently, to examine
the sensitivity of the results to more complete controls for time effects, state-specific
year-effects were added to the model. As detailed in the in the bottom panel of Table 2, it
is evident that these estimates are not appreciably different from the results provided
above.
2. Sub-Sector Analysis
In order to more accurately differentiate if there is a positive effect on
employment within the related super-sectors, rather than an internal tradeoff, a detailed
analysis of sub-sectors within the entertainment and hospitality industries was
undertaken.
Results, provided in Table 3, tell a relatively intuitive story about how casino
introduction may affect related sub-sectors of the local economy. Specifically, estimates
do not provide any proof of a decline in employment in any of the three non-gambling
15
related entertainment sub-sectors (NAICS code 711, 712, and 7139).10 To the contrary,
estimates from the performing arts and spectator sport sector show an increase in
employment of approximately 20%. Of course, it is likely the case that this estimate
captures increases in employment directly associated with the performing arts activities at
the casino itself. Moreover, it is unclear if such gains are present in the pre-existing or
non-casino related performing arts industry. As is the case in many sectors, further
estimation at finer levels of sectoral disaggregation would be useful, but is unattainable
due to data suppression.11 Estimates from analysis of both the museum, zoos, and parks
sector, as well as the other recreational centers sector (which includes golf courses, skiing
resorts, marinas, fitness centers, and bowling alleys) do not show the presence of a strong
casino effect, as estimates are small and insignificantly different from zero. That said, it
is noteworthy that in both cases they have non-trivial negative coefficients. In any case,
there is no strong evidence to suggest that there is any negative spillover or businessstealing effect present within the entertainment industry. On the other hand, the
aforementioned earlier work on this topic has found both positive and negative effects of
casino introduction on the hospitality industry. So it is possible that substitution or
complimentary effects of casinos may differ within the hospitality sector.
Estimates of the impact of casino opening on sub-sectors of the hospitality
industry are detailed in Table 3 and seem to suggest that casinos either have little effect
or possibly act as a mild stimulant to employment growth in these sub-sectors. For
10
An analysis of the gambling sector specifically (NAICS 7132) would be insightful, but
such estimation is prevented by suppression of the data at very fine levels of
disaggregation where a small number of establishments are present.
11
The BLS suppresses the QCEW data fields when the number of establishments is too
low to guarantee anonymity of the firm(s).
16
example, there is strong growth in the accommodations sector, with a 6.4% increase in
employment over the control counties. It needs to be pointed out, however, that due to the
frequency of casino-owned hotels, it is difficult to determine if these results actually
reflect gains to firms outside of the casino “complex.” An analysis of the hotel/motel
industry excluding casino hotels is necessary to determine if a true spillover benefit is
present. Fortunately, the QCEW data contains sufficient detail to separate casino hotels
from the rest of the hotel/motel sector and allow for a more detailed analysis. Estimates
for the hotel industry, excluding casino hotels, show a marginally significant 3.8%
increase in employment, indicative that the surrounding accommodation industry does
benefit directly from the casino’s presence. Similar industry level estimation of the bar
and restaurant sector demonstrate no large impact on employment levels, as the point
estimate is -1.3% and is not statistically significant. Yet, as confirmed by analysis of bars
and restaurants separately, there are consistent negative estimates throughout the bar and
restaurant sector, which may suggest a mild amount of demand substitution between
bars/restaurants and casinos. That said, in general, these findings, coupled with those
from the analysis of the entertainment sub-sectors, provide no strong evidence to suggest
that casinos harm related industries through substitution, and, further, they indicate that
certain industries may benefit from casino introduction.
B. Time Effects
It is easy to anticipate that it may take a few years before the full effect of casino
introduction may be realized. For example, consumers need to be informed, casino
management needs time to ascertain how to best market their product, and, particularly in
17
rural areas, an infrastructure to support the casino must be developed. Equation (2)
attempts to capture the effects of casinos over time. Coefficient estimates and standard
errors for this specification are detailed in Table 4.
Estimates are consistent with the results estimated with Equation (1), and indicate
that casino introduction increases employment in the county overall, as well as in the
entertainment sector.12 More specifically, this estimation provides evidence that the
effect of casinos on employment seems to change over time. Graphically, the results
show a clear change in the trend in employment relative to the control group following a
casino opening (see Figure 1). For example, overall county employment only increased
an estimated 2.1% in the year in which a casino opened, but 3 years after opening, was
10.2% greater. This growth in employment is indicative of the type of development many
communities point to when promoting the benefits of casinos for local economies.
Of course, endogeneity is always a concern when conducting analysis of this type,
because casino opening may be correlated with non-random growth in the treatment
group. Specifically, estimated results would overstate the true impact of casinos on
employment if industrial sectors in host counties are growing faster than average for this
time period. Likewise, the results will be biased downward if employment was growing
slower then the control group. In order to determine if there is any evidence to suggest
that the estimates are bias due to trending in the treatment group, the aforementioned
intertemporal analysis also included three year leads. If employment in counties where
casinos opened was growing faster then in the control group we would expect the
12
Similar analysis was also conducted on sub-sectors of both the entertainment and
hospitality super-sectors. The results were consistent with those reported in the timeinvariant analysis.
18
coefficients of the lead dummies to be positive and statistically significant. The results,
presented in Table 4, show no strong evidence for trending in either direction in the
entertainment sector or for employment overall. This is further supported as we are
unable to reject the null hypothesis that the leads are all jointly equal to zero.13 On the
other hand, there is some evidence to suggest that employment in the hospitality sector
maybe trending downward relative to the control group, which would suggest that
estimates of the effects of casinos on employment are biased downward; a point to be
aware of when evaluating the hospitality results. Overall, it is my hope that this test,
taken in conjunction with the earlier test for model sensitivity to state-specific trends, will
alleviate any strong concerns that the reported positive estimates are being driven by
casinos endogenously locating in areas which are positively trending with regards to the
control group.
C. Population Density Effects
The basic effects generated thus far are from a nationwide sample, but the data
allow us to say more about how the outcomes may differ across regions. Specifically,
several past studies have remarked that the effects of casino introduction may vary by
population density (e.g. Evans and Topoleski, 2002), while other research has casually
observed that rural communities seemed to realize greater gains than urban areas (e.g.
Garrett, 2005). There has been little work done at a comprehensive level, however, that
provides empirical results to speak to a difference in rural versus urban effects. It is
13
The p-values for these Wald tests are reported in the last row of Table 4.
19
important to capture such differences in order to truly assess the policy implications of
the aforementioned results.
In order to estimate how employment effects differ by population density the
sample was separated into thirds according to county population density. Counties were
placed into the bottom-third density group if their population densities were less then 32
people per mile, which was the 33rd percentile for counties in the treatment group. Topthird counties had a population density that exceeded 202 people per mile, which was the
66th percentile for counties in the treatment group.14 I estimate results consistent with the
methodology outlined in equation (1); except the treatment and control groups are
separated by density and all analysis is conducted on high-density (top-third) and lowdensity (bottom-third) counties separately. All “middle” density or middle-third counties
were excluded from this analysis.
The results for these two estimations are presented in Table 5 and are quite clear.
Total employment effects are only strong in the low-density counties, with a 14.5%
increase, versus a statistically insignificant 0.8% increase in high-density areas.15 The
large disparity between groups suggests that casinos have a much stronger positive effect
on a county, as a whole, if they are located in a rural area. The findings are different for
the entertainment sector, where the employment effects are positive and significant across
both population densities. Again this is not surprising as the casinos themselves are part
of the entertainment sector. Differences in the hospitality sector are noteworthy as well,
14
Treatment group counties were utilized in determining the separation points in order to
provide a relatively balanced sample size of treatment counties in each stratum.
15
Estimation of density effects with an intertemporal model consistent with equation (2)
provided very similar results, and, as in earlier estimation, the effect of the casino on
employment grew with time.
20
with low population density areas seeing in increase in employment of 5.0%, while high
density areas realize no significant effect. This result is consistent with the idea that
casinos provide some range of fixed employment growth, which is not strongly correlated
with the population base, and therefore, will provide relatively more growth to rural
communities.
D. Neighbor Effects
In their study on crime, Grinols and Mustard (2006) test the hypothesis that crime
could be attracted to the casino county from neighboring border counties, and, hence, not
actually create “new” crime. This hypothesis is also worth investigating with regard to
employment as well. If casinos cause a significant increase employment in the host
county but cause significant job loss in the surrounding counties, then the benefit of the
casino could be significantly over-stated. In order to identify how casinos affect counties
that neighbor counties with casinos, an analysis consistent with equation (1) was
undertaken. All counties that border counties with casinos are considered “neighbors”
and considered the treatment group, with all non-border non-casino counties utilized as
the control.
Results of this analysis are detailed in Table 6, and it is evident that the effect of
casino introduction on overall employment in neighboring counties is insignificant and
basically zero, but this is not to say that there is no fringe effect. While the results show
no impact on overall employment, analysis of related sectors does provide interesting
outcomes. The entertainment sector in a neighboring county realizes an increase in
employment, while the hospitality sector may be negatively affected in a small way.
21
Estimates show a 5.6% increase in entertainment employment and 1.3% decreases in
hospitality employment respectively. More detailed analysis of the hospitality industry
finds that the losses in employment are centered in the bar and restaurant industries, with
the accommodations sector actually showing an increase in employment of 3.1%.
These results are largely suggestive that employment in neighboring counties is
on the whole not largely affected. Although, they do indicate that there is a mild gain for
the entertainment and accommodations sector, which is consistent with the notion that
casinos lead to more tourism in an area. Conversely, results also indicate that restaurants
and bars are somewhat negatively impacted in neighboring counties, which may imply
that local residents are spending more time in the casino county than they were prior to
the casino opening. Apart from this suggestive evidence, there is little to support the
notion that employment gains in the host county are coming at the expense of
neighboring border counties.
VI. Conclusion
Prior to 1988 casinos were an isolated phenomenon in the United States. But
following changes in state and federal legislation in the late 1980s and early 1990s, the
door was opened for casinos to locate across the country. This paper sought estimates of
the effect of casino openings on overall employment in host counties, as well as in
several related industries, as a means of gauging the impact of casinos on economic
development. The main finding is that, overall, casinos lead to more employment, and as
such, likely due lead to some economic growth. Moreover, the results do not provide
strong evidence to suggest that this increase in jobs is offset through substitution of jobs
22
in other related industries, as has been suggested in prior research. To the contrary, some
related industries see an increase in employment, which could be indicative that these
firms benefit from some complementary demand, maybe through increased tourism, etc.
Intertemporal analysis tells a similar story and indicates that this effect grows, at least
initially, over time.
Further investigation on how the impact of casinos varied between high and low
population density communities found that the effects are much stronger in the lowdensity areas. Particularly, estimates show that low population density areas benefit from
more than high-density areas, which only realized gains in the entertainment sector.
These results are an important finding for policymakers to consider as they decide
whether to utilize a casino as a means of spurring economic growth in their communities.
If very dense communities see a much smaller increase in jobs per capita, then the overall
effectiveness of casinos as an economic stimulus measure is questionable.
Exploration into how employment in neighboring counties was affected by the
introduction of a casino suggests that overall employment is not affected significantly,
but there is evidence to suggest mild positive spillovers in the entertainment and
accommodations industries and possibly a small negative impact on restaurants and bars.
Overall, the findings in this paper indicate that casinos provide an economic boost
to host communities, with few negative affects on employment. Of course, this is not to
say that casinos have no down side. From a policy perspective, the gains to employment
and economic development need to be balanced against the previously documented
increases in crime that communities tend to see following a casino opening (Grinols &
Mustard, 2006). Ultimately, as is evident by their strong revenues, casinos are supplying
23
an important product to consumers, and, hence, are not going to be a passing fad in
American culture. For this reason, the economic and social effects of casinos on local
communities will continue to be an important issue of future research.
24
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Navin, John, and Timothy Sullivan, “Casinos and Crime: A tale of Two States.” Working
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2000.
26
Table 1-Summary Statistics: Casino vs. Non-casino Counties
Casino County
Variable
Non-Casino County
Mean
Median
Std. Dev.
Mean
Median
Std. Dev.
151,900
41,900
296,658
77,170
22,286
266,278
Population Density
(Pop/sq. mile)
218
39
501
221
39
1,466
Total Employment
(NAICS 10)
55,856
11,576
126,371
27,434
5,394
110,008
Employment
Entertainment
(NAICS 71)
1,573
634
2,684
767
160
2,665
Employment Hospitality
(NAICS 72)
7,322
2,710
13,427
3,990
1144
11,208
Population
27
Table 2 – Effects of Casino Introduction on Log Employment, Overall and Super-sectors
Time Invariant Method
Employment Effects
Estimate
Robust
Std. Error
t
Sample Size
County, Total
(NAICS 10)
0.081
0.016
4.93
87,172
Entertainment
(NAICS 71)
0.536
0.063
7.90
43,379
Hospitality
(NAICS 72)
0.026
0.021
1.27
51,134
Sector
Employment Effects
with State-Specific Time trends
Estimate
Robust
Std. Error
t
Sample Size
County, Total
(NAICS 10)
0.075
0.017
4.55
87,172
Entertainment
(NAICS 71)
0.478
0.057
8.37
43,379
Sector
Hospitality
0.036
0.020
1.82
51,134
(NAICS 72)
Note: Reported are coefficient estimates from a fixed effects model. Standard errors are
corrected to allow for non-independence of observations from the same county through
clustering.
28
Table 3– Effects of Casino Introduction on Log Employment, Detailed Analysis of the
Entertainment and Hospitality Industries, Time Invariant Method
Employment Effects
Estimate
Robust
Std. Error
t
Performing Arts/Spec. Sports
(NAICS 711)
0.201
0.080
2.52
5,997
Museums/Zoos/Parks
(NAICS 712)
-0.032
0.058
-0.55
11,624
Other Recreation
(NAICS 7139)
-0.015
0.046
-0.31
23,190
Accommodations
(NAICS 721)
0.064
0.028
2.26
37,603
Hotels, non-casino
(NAICS 72111)
0.038
0.023
1.66
20,553
Bars and Restaurants
(NAICS 722)
-0.013
0.011
-1.25
67,769
Full Service Restaurant
(NAICS 7221)
-0.006
0.013
-0.42
57,601
Limited Service Restaurant
(NAICS 7222)
-0.013
0.018
-0.75
59,601
Sector
Sample Size
Bars
-0.007
0.026
-0.30
19,419
(NAICS 7224)
Note: Reported are coefficient estimates from a fixed effects model. Standard errors are
corrected to allow for non-independence of observations from the same county through
clustering.
29
Table 4 - Effects of Casino Introduction on Log Employment, Overall and Related Supersectors, Time Variant Method
Employment
Effect
County, Total
(NAICS 10)
Entertainment
(NAICS 71)
Hospitality
(NAICS 72)
Lead 3
-0.014
(0.010)
-0.012
(0.054)
-0.002
(0.019)
Lead 2
-0.016
(0.011)
0.029
(0.061)
-0.030
(0.017)
Lead 1
-0.019
(0.016)
0.060
(0.066)
-0.047
(0.020)
Open
0.021
(0.016)
0.277
(0.070)
-0.019
(0.022)
Lag 1
0.068
(0.018)
0.607
(0.086)
0.002
(0.029)
Lag 2
0.092
(0.021)
0.734
(0.098)
0.020
(0.036)
Lag 3
0.102
(0.026)
0.758
(0.111)
0.002
(0.033)
Lag 4
0.129
(0.038)
0.803
(0.113)
0.016
(0.052)
Lag 5 +
0.126
(0.038)
0.771
(0.127)
0.043
(0.073)
Sample Size
87,172
43,379
51,114
P-Values on Wald test,
leads are jointly 0
0.4055
0.4796
0.0568
Note: Reported are coefficient estimates from a fixed effects model, with standard errors
in parentheses. The standard errors are corrected to allow for non-independence of
observations from the same county through clustering. Estimates provided in bold are
significant at the .05 level or greater.
30
Table 5 – Effects of Casino Introduction by Population Density, Overall and Related
Super-sectors
Employment Effects
Sector
Low Population Density
Counties
High Population Density
Counties
Estimate
n
Estimate
n
County, Total
(NAICS 10)
0.145
(0.035)
37,468
0.008
(0.008)
12,156
Entertainment
(NAICS 71)
0.443
(0.122)
7,309
0.460
(0.082)
11,557
Hospitality
(NAICS 72)
0.050
(0.038)
11,842
-0.010
(0.013)
11,695
Note: Reported are coefficient estimates from a fixed effects model. Standard errors are
in parentheses and have been corrected to allow for non-independence of observations
from the same county through clustering. Estimates in bold are significant at the .05
level or greater.
31
Table 6 – Effects of Casino Introduction on Log Employment in Neighboring Counties
Employment Effects
Sector
Estimate
SE
t
Sample Size
County, Total
(NAICS 10)
0.003
0.004
0.66
84,873
Entertainment
(NAICS 71)
0.056
0.016
3.49
41,684
Hospitality
(NAICS 72)
-0.013
0.007
-1.90
49,246
Accommodations
(NAICS 721)
0.031
0.013
2.38
35,858
Bars and Restaurants
-0.017
0.007
-2.29
65,593
(NAICS 722)
Note: Reported are coefficient estimates from a fixed effects model. Standard errors are
corrected to allow for non-independence of observations from the same county through
clustering.
32
Figure 1 - Employment Estimates, Before and After Casino Opening
% Change in Employment over Control
90
80
70
60
50
40
30
20
10
0
-10
Lead3
Lead2
Lead1
Overall
OPEN
Lag1
Entertainment
Lag2
Lag3
Lag4
Lag5
Hospitality
33