Does Private Security Affect the Level of Crime? A test using state regulations as
instruments
Bruce L. Benson and Brian Meehan
Mailing Address: Department of Economics, Florida State University, Tallahassee, FL 32306.
Emails: bbenson@fsu.edu & bmeehan@fsu.edu
Abstract
Private security is employed to deter criminals from attacking specific targets, presumably not to
produce general deterrence. Indeed, private security generates negative spillovers if criminals
substitute non-protected targets for protected targets. Specific deterrence efforts may generate
positive spillovers too, however, by raising the expected cost of committing crimes, thereby
reducing crime for the community at large. The hypothesis that private security deters crime
within a community is tested. The demand for private security in an area is expected to be
simultaneously dependent on the level of crime, so an instrumental variable approach is
employed in a panel-data fixed-effect model using data from urban counties during 1998-2010.
Instruments for the amount of private security are statewide licensing regulations for the firms
specializing in providing security, since these regulations should influence entry at the local
level. Various types of violent and property crime are shown to be negatively and significantly
related to increases in private security. That is, private security has a general deterrence effect.
Key Words: private security, general deterrence, specific deterrence, instrumental variables,
licensing regulations
1 Introduction
Since Gary Becker's 1968 exploration of the incentive for criminal activity, economists have
been testing the deterrence hypothesis.1 Large numbers of studies have examined the impacts on
crime of both public policing and punishment, but relatively little work has considered the
potential impact of private investments in crime control. This relationship has not attracted much
research interest even though more is spent on private security than on public policing in the
United States (Ayres and Levitt 1998; Benson 1998, 79-80).2 In fact, private security employees
in the United States are estimated to outnumber public police by as much as three to one (Joh,
2004), and the market for security is growing rapidly throughout most of the world (Graduate
Institute of International Development Studies 2011, 101).3 Recent estimates report between
19.5 and 25.5 million formally employed private security personnel in seventy countries
(Graduate Institute of International Development Studies 2011, 101).4 The study presented below
1
See Tauchen (2010) for a review of relatively resent studies of this type and references to older reviews
of earlier work.
2
Private security involves a wide variety of personnel, of course, ranging from night watchmen to highly
trained/skilled security advisors, but it also includes substantial investments in capital, including alarms,
cameras, safes, and so on. See Benson (1998, 75-93; 2013) for more details.
3 The Graduate Institute of International Development Studies (2011, 101) estimated that the average
annual worldwide growth rate for private security employment has been between seven and eight percent
from the mid-1980s to the late 2000s. In the U.S. the number of private security firms increased 116
percent from 11,675 in 1988 to 25,249 in 2007, these firms increased their employment of security
personnel from 473,308 to 788,766 over the same period, roughly 66.6 percent (County Business
Patterns, various years). Note that these figures do not include in-house security by firms and
organizations that are not specialized in the provision of private security. Nonetheless, using these data,
the average annual growth rates, 4.1 percent and 3.5 percent, for the U.S. appear to be lower than those
for the world as a whole. This probably reflects the fact that the private security market grew relatively
rapidly in the U.S. during the 1960s, 1970s and 1980s, so it was already quite large by the mid to late
1980s. For instance, the number of private security firms in the U.S. was 1,988 in 1964 and they
employed 62,170 security personnel. Therefore the number of firms increased by 483 percent between
1964 and 1988, an average growth rate of over 20 percent, while employment by these firms increased by
661 percent, an annual average of over 27.5 percent. See Blackstone and Hakim (2010) for details about
the structure of the private security market in the U.S. as well as details about growth and diversity of
services.
4
Perhaps surprisingly, for instance, the country with the largest number of private security employees is
China with an estimated five million personnel, roughly two to two and a half times as many as the U.S..
considers the impact of one particular type of private crime-control investment - services
provided by firms specializing in the provision of private security. The question considered here
is, do these investments in private security actually reduce overall crime?5
The amount of crime and the demand for security is expected to be simultaneously
determined (Ehrlich 1973): while security investments are expected to reduce crime, demand for
and investments in security are simultaneously expected to depend on the level of crime in a
community. The empirical problem is how to separate the security-determines-crime
relationship from the crime-determines-security relationship. An exogenous shock not related to
crime but that affects the amount of security in an area, can help isolate the effect of security on
crime. Therefore, an instrumental variable approach has been used to isolate public-sector
institutions’ effects on crime in a number of studies of the public-security and crime relationship.
For instance, Levitt (1997, 2002) used gubernatorial and mayoral election cycles as well as
firefighter employment in metropolitan areas to instrument for public sworn police officers.
Klick and Tabarrok (2005) used terrorism alert levels post 9/11 to instrument for public police
levels in Washington DC, and Evans and Owens (2007) used federal grants from the Community
Oriented Policing Services program (COPS) as the exogenous instrument for changes in public
police officers. These studies all found a negative correlation between the instrumented public
police force and most types of crime (as have many others), but they did not consider the
potential impact of private security on crime.6
5
There is substantial evidence that private security reduces crimes against protected targets, as noted
below, but this specific deterrence may simply be a displacement effect as criminals shift to less wellprotected targets (Becker 1968, 201). As explained in Section 2 below, however, some private security
investments appear to generate general deterrence.
6
In the process of re-examining Lott and Mustard’s (1997) findings regarding the impact of right-to-carry
concealed handgun laws for potential missing variable bias, Benson and Mast (2001) added controls for
private security to the Lott-Mustard model, employing an instrumental-variable approach in cross-section
time-series pool of data from over 3000 U.S. counties. They found general deterrence impacts of private
This study uses state-level private security licensing regulations to instrument for private
security establishments in a sample of urban counties. The state-level regulations should not be
correlated with county-level crime, but they should be determinants of county-level entry into the
private security market. In fact, the approach taken in choosing the instruments was to ask
ourselves: if we were entrepreneurs wanting to start a private security firm, what license(s) must
we get and what start up costs would the licensing requirements impose?7 While requirements
vary considerably across states8 (and through time in several states), the most significant entry
requirements appear likely to be those setting bond/insurance levels, minimum law-enforcement
experience, minimum training, and testing.
Section 2 below briefly reviews research dealing with private security and deterrence in
order to place this study in context. Data employed to examine the private-security crime
relationship are described in Section 3. The econometric model is developed in Section 4, where
empirical results are also presented. Section 5 offers concluding comments.
2 Private Security and Crime
One reason for the relative lack of research on private security’s impact on crime probably is a
theoretical point raised by Becker (1968). The major intended benefit of investments in private
security is a localized or "specific" deterrence for an individual target (e.g., a person, home or
security for at least some crimes, as explained below, but data and specification issues (e.g., missing data,
weak instruments) that resulted from the focus on the Lott-Mustard model suggest that even more
significant impacts may occur.
7
If regulators are captured, a la Stigler (1971), various regulations should be negatively related to the
number of security firms as they limit competitive entry and create market power. If regulators serve the
public interest by preventing entry of unqualified and dishonest firms, thereby insuring quality, entry is
also limited. However, such public-interest regulation may enhance the attractiveness of and demand for
the regulated services, suggesting that some regulations could even be positively correlated with some
measures of the level of private security in a market [e.g., there could be fewer firms but more
employees].
8
In fact, some states do not have firm-level licensing requirements. If such a state has individual-level
requirements an entrepreneur entering the market would have to have an individual license, so these
regulations still apply. As explained below, however, there are states with no licensing requirements.
business establishment), a geographical area (e.g., neighborhood, private gated community,
shopping mall, or business district), or a specific type of activity (e.g., railroads in the U.S. are
protected by a private force of railroad police). Such focused specialization is intended to raise
the expected costs of crime against the specific protected entity, activity or area by reducing the
probability that a criminal will succeed in an attempted theft or violent attack of that target.9
Criminals deterred from victimizing one entity, activity or location because of private protection
efforts may, however, simply find another unprotected victim, activity or location. Therefore,
even though such investments substantially reduce offenses against specific targets, they may not
have large impacts on total offenses (Becker 1968, 201). While such negative geographic
spillovers clearly occur, 10 positive spillovers also are possible. Ayres and Levitt (1998) illustrate
this in their empirical analysis of the impacts of Lojack, a hidden radio transmitter installed in
cars, which can be remotely activated if the car is stolen.
9
A number of studies report significant specific deterrence. For instance, Hannan (1982, 91) found that
the presence of private guards in banks "significantly reduce the risk of robbery. Accepting point
estimates, the magnitude of this reduction is approximately one robbery attempt a year for those offices
which would have otherwise suffered a positive number of robbery attempts." Similarly but on a broader
scale, Clotfelter (1977, 874) considered the impact of private and public security services on the
manufacturing, wholesaling, and finance, insurance, and real estate sectors; his empirical results "indicate
that private protective firms are more effective than public police at protecting firms in these industries."
The same apparently is true for railroads, which have had their own private police force in the U.S. since
the end of WWI. Between the end of World War I and 1929, for instance, freight claim payments for
robberies fell by 92.7 percent, from $12,726,947 to $704,262 (Wooldridge 1970, 116; also see Dewhurst
1955). Residential communities protected by private security also enjoy much lower crime rates than
surrounding areas [e.g., see Donovan and Walsh (1986), Walsh, et al. (1992)].
10
For instance, changing behavior of bank robbers observed by FBI officials in the Washington
D.C./Maryland area illustrate the specific deterrent impact of private security equipment along with
negative geographic spillovers (John Jay College of Criminal Justice/CUNY 1997b, 8). Increased
security measures taken by banks in the urban and suburban areas of Washington D.C. and Maryland
resulted is a drop in bank robberies in these urban/suburban areas. However, this drop was accompanied
by a dramatic increase in robberies in small-town branches in rural Maryland. Bronars and Lott (1998)
report similar negative geographic spillovers due to right-to-carry concealed handguns. In this context,
however, it should be noted that increases in public policing efforts in one area also lead to increased
criminal activity in nearby areas (Rasmussen et al. 1993), so negative spillovers are not exclusive to
private security. Indeed, a reallocation of public police or prisons in order to increase efforts to control
one type of crime reduces efforts against other crimes, all else equal, and those crimes increase (Benson,
et al. 1998; Benson 2009, 2010, 2013). Thus, allocations of public security have negative impacts on
people who are relatively unprotected too.
Lojack greatly reduced the expected loss for car owners who use them, since 95 percent
of the cars equipped with these devises are recovered compared to 60 percent for non-Lojack
equipped cars. Therefore, the estimated mean loss per auto theft for cars equipped with Lojack is
about 25 percent of the expected mean loss for cars without this device. However, this direct
benefit for the individual using the device is only part of the total benefit arising from its
availability and use. There really is no visible indication that a vehicle is equipped with Lojack.
Therefore, a potential car thief does not know whether Lojack protects a potential target vehicle
or not. Ayres and Levitt (1998) found that Lojack had a significant crime reducing effect, as a
one percentage point increase in installations of the device in a market was associated with a 20
percent decline in auto thefts within large cities and a five percent reduction in the rest of the
state. Since other crime rates are not correlated with the drop in auto theft and installation of
Lojack, the obvious implication is that many potential auto thieves are aware of the increased
probability that they will be arrested, and are deterred as a consequence. Indeed, Ayres and
Levitt (1998, 75) concluded that “Lojack appears to be one of the most cost-effective crime
reduction approaches documented in the literature, providing a greater return than increased
police, prisons, job programs, or early education interventions.”
Because of the controversy surrounding firearms and violence, firearm ownership has
attracted more study than other specialized private forms of personal protection. Knowledge of
the potential dangers posed by armed victims generates considerable incentives to avoid victims
known to be or relatively likely to be armed, and criminals apparently respond to these
incentives. For example, consider the impact of publicized programs to provide training in
firearm use for potential victims. One such effort occurred in Orlando, Florida between October
1966 and March 1967. The program was designed to train women in the safe use of firearms
because of the sharp increase in rapes in the city during 1966, and it was widely publicized in
Orlando newspapers. Kleck and Bordua (1983), in their examination the consequences of this
program, reported that the rape rate in Orlando fell from a 1966 level of 35.91 per 100,000
inhabitants to only 4.18 in 1967. This was clearly not a part of any general downward trend
since the national rate was increasing, and rates in surrounding metropolitan areas, as well as in
Florida as a whole (excluding Orlando), were either constant or increasing over the same period.
Furthermore, this decrease did not reflect a continual downward trend for Orlando since the trend
had been erratic but upward for the previous several years. This program also had positive
spillovers for another type of crime. The Orlando burglary rate declined, not surprisingly, since
burglary probably was the most likely crime category other than rape in which a criminal might
confront an armed female victim.11
Similar deterrence impacts are suggested by Lott and Mustard (1977), Lott (1998), and a
number of subsequent studies regarding consequences of right-to-carry-concealed-handgun
legislation. Lott and Mustard employed a very large data set and controlled for a wide range of
socioeconomic factors as well as arrest rates. They found that violent crimes, including murder,
rape and robbery, are significantly deterred when citizens are allowed to carry concealed
handguns. Indeed, one set of estimates suggest that if the states that did not have right-to-carry
laws in 1992 had adopted such laws, approximately 1,570 murders, 4,177 rapes and more than
60,000 aggravated assaults would have been prevented. Follow-up studies by Lott and several
other researchers (e.g. Plassmann and Whitely (2003), Moody (2010)) support the general
deterrence conclusion using different specifications and data, controlling for other gun control
11
The Orlando example is not unique. Publicized training programs in the use of firearms have led to a
reduction in armed robberies in Highland Park, Michigan, drug store robberies in New Orleans, and
grocery store robberies in Detroit (Kleck and Bordua 1983). When potential criminals become aware that
potential victims might be willing and able to protect themselves with a gun, the increased perceived risk
of committing a crime leads to the abandonment of at least some potential criminal acts.
laws as well as endogeneity issues. The implication is that when potential criminals consider
committing confrontational crimes in states with right-to-carry laws they recognize that there is a
relatively high probability that a potential victim will be armed, raising the expected costs of
confrontational crimes so fewer occur. These findings remain quite controversial, and there have
been a number of studies with contradicting results (e.g., Dezhbakhsh and Rubin (1998); Ayres
and Levitt (2003)). Indeed, Moody and Marvel (2008) surveyed the academic literature on rightto-carry laws and found that ten empirical studies supported the hypothesis that these laws
reduce crime while eight found that the laws had no significant effect. Four years later, eighteen
peer-reviewed studies were found that supported the hypothesis, ten reported no significant
relationship, and results in one indicated that right-to-carry laws increase crime (Lott 2012).
Whether the right-to-carry impact is significant or not, the basis for the hypothesis is
essentially identical to the Ayres and Levitt (1998) Lojack study: when criminals realize that
there is a relatively high probability that a potential victim has a concealed firearm or that an
auto is equipped with the concealed Lojack devise, those criminals are less likely to attack
victims and steal cars. Other aspects of private security could have similar impacts. Is a store
equipped with hidden video cameras or plain clothes security officers or not? Is the bank teller
protected by bulletproof glass? Does the residence have an alarm system? If more stores and
homes employ difficult-to-observe security, the impact should be a general reduction in crime.
The Lojack and concealed-firearm deterrence results presumably should not hold for
highly visible investments in private security, and there are strong incentives to use highly
visible security in order to maximize the specific deterrence effect. Security guards in banks and
shopping malls wear uniforms, homes with alarm systems have signs posted in a prominent
places warning the criminal away, video cameras on ATMs are clearly visible, and so on. In
these cases, criminals may simply choose other less well-protected targets, so the impact of
specialized private security is simply a substitution of unprotected targets and no reduction in
overall crime. Given this reasonable argument, Macdonald, et al. (2012) findings regarding the
impact of the University of Pennsylvania's private police force on crime in the surrounding area
may be quite surprising: they found a 45-60 percent reduction in all crime incidence in the area
due to the extra private policing provided by the university.12 This positive spillover effect could
still be relatively localized, of course, as criminals may just move further away from the
university to attack other targets. On the other hand, similar localized spillovers from the very
large level of private security employment noted above could raise costs for many criminals by
making it relatively difficult to find soft targets worth attacking (Benson and Mast, 2001).
There are other reasons to expect general reductions in some kinds of crime as a
consequence of private security. For instance, shopping malls and districts often employ private
security. Indeed, one of the characteristics of the service they can offer is a relatively secure
environment for shopping, eating out, socializing, and so on. The retailers, restaurants, bars, and
other businesses willingly contribute to cover the costs of such security because the safe
environment attracts more potential customers, raising their expected demand and revenues.
Those customers actually pay for security, of course, because it is bundled with the goods or
12
The University of Pennsylvania is not the only institution of higher education with employing private
security. In fact, as Blackstone and Hakim (2010, 365) pointed out, a substantial portion of the large
increases in the use of private security over the last decade or two reflect responses to demands for
protection of colleges and universities (other major sources of increasing demand are hospitals, along
with hazardous facilities such as the 104 nuclear reactors and roughly 15,000 major chemical plants in the
U.S., particularly since September 11, 2001). In 2009, 25 percent of US universities had their own sworn
officer police departments (Blackstone and Hakim 2010, 365). Sworn university officers typically can
make arrests without waiting for the public police. Such sworn officers can also do complicated
investigations, requiring specialized knowledge of university-type issues. Rather than creating their own
private police forces, however, many of the over 3000 colleges and universities in the United States
contract with private security firms. For instance, one firm, Allied-Barton, provides security services to
90 US colleges and universities (Blackstone and Hakim 2010, 365). While security services were
widespread among colleges and universities before the Virginia Tech massacre in 2007 where a student
killed thirty-two students and faculty, many universities have increased security substantially since then.
services they purchase. If a mall or business district gains an advantage through such
investments, of course, competitive malls and business districts are likely to respond by making
such investments too. Competitive pressures lead to the spread of private security, making it
increasingly costly for potential criminals whose skill set leads them to search for targets among
retail firms, shoppers, restaurant and/or bar patrons (e.g., shop-lifters, pick-pockets, car thieves
robbers, rapists) find it increasingly costly to find vulnerable targets, and these rising search costs
could reduce overall crime – that is, produce general deterrence.13 The rapid expansion in the
use of private security14 suggests that this may well be occurring, and indeed, there is some
evidence supporting this hypothesis.
Zedlewski (1992) used a cross section of 1977 data from 124 SMSAs in an effort to
analyze the effect of both public police and private security on the overall safety environment of
communities. His control for private security was security employment by private security firms
in the SMSAs. He found a significant negative impact of private security employment on crime.
Zedlewiski (1992, 51) concluded "that greater levels of security personnel are associated with
reduced levels of community crime. It suggests that investments in private security produces
spillover benefits to the community at large. Crime is not only displaced; it is somewhat
discouraged." The finding was not extremely robust, however, as an alternative measure of
criminal activity was not related to private security employment. Furthermore, and perhaps more
significantly, Zedlewiski did not consider the endogeneity issue, and his study also probably
This process of generating general deterrence is quite different from the public criminal justice system’s
process. The public sector tends to focus on responding after a crime is committed (Sherman 1983;
Benson 2010; Benson 2011, 131-137), presumably under the assumption that spending resources on arrest
and punishment provides an effective general deterrent. Private security focuses on preventing crimes
against specific targets, but as such protection spreads, an unintended benefit can arises: criminals find
fewer and fewer attractive targets, and perceive a rising price of crime due to the increased chances of
being observed, so they are less likely to commit crimes in the first place.
14
See note 3.
13
suffers from substantial missing variable bias that often plagues cross-section analysis.
Therefore, his conclusions must be considered with considerable caution.
Benson and Mast (2001) examined the potential for missing-variable bias of Lott and
Mustard’s (1977) results regarding right-to-carry concealed handgun laws, but in doing so they
considered the potential deterrence impact of private security. The focus of the study was the
possibility that right-to-carry laws and resulting firearm ownership could correlate with other
potential private investments in security, resulting in Lott-Mustard estimates being biased
upwards. In order to examine this issue, Benson and Mast added data on private security
employment and private security firms from County Business Patterns, along with a number of
instrumental variables to the original Lott-Mustard data set.15 They used NRA membership and
lagged percentages of states Republican votes as instruments, along with lagged numbers of
firms and employees in 23 industries that were expected to be sources of heavy demand for
private security. County level weighted least square panel regressions were estimated with
county and time fixed effects, as in Lott and Mustard, to alleviate other potential sources of
missing variable bias. Benson and Mast did not find any evidence of bias in Lott and Mustard’s
coefficients by adding controls for private security, but they did discover some evidence of a
While the Census Bureau’s annual County Business Patterns reports private security employment, it
only reports employment by private security firms. These firms protect many residential and business
locations, but many other businesses and probably some residential communities choose to hire their own
security personnel in house. For instance, 56.5 percent of the security reported by 4000 companies
surveyed in 2005 was produced internally, by in-house security staff rather than through contracts with
specialized security providers (ASIS Foundation 2005). While these findings do not represent the total
percentage of private security personnel that are contract versus in house, they do illustrate that a
substantial portion of the total population of private security personnel are employed by firms that are not
specialized in security provision. After examining a number of different and at times conflicting sources
of information, Strom, et al (2010, 4-4) reported that about 60 percent of total security employment in the
U.S. is by specialized security firms and the remaining 40 percent is produced in house, although other
estimates suggest very different percentages (Benson 2013). Given the lack of jurisdiction-level data on
in-house security employment, the Benson-Mast study and this study are both limited to consideration of
that portion of private security that is provided by firms specializing in security services. Thus, results are
likely to be biased against finding any relationship.
15
general deterrence impact from private security as measured by the listed instruments used to
predict private security establishments and employment per 100,000 people. The strongest
results were for burglary and rape, as other crime rates tested with the instruments - Murder, total
violent crimes, robbery, assault, total property crime, larceny, auto theft - did not produce robust
results across plausible specifications. Since the primary purpose was to reconsider Lott and
Mustard’s results with additional controls for private security, not to explicitly test the privatesecurity deterrence hypothesis, there were some important aspects of this study that may mask
more significant deterrence relationships. Lott and Mustard 1977-1992 panel included data from
all 3,054 counties in the U.S. for instance, and most counties are rural with small populations.
Many have no private security firms at all, so a substantial portion of the observations were
zeros, and the distribution was highly skewed. In addition, the source of the security data,
County Business Patterns, indicates that the number of employees fall within a range when the
number of establishments are positive but small, rather than providing the actual employment
numbers.16 Therefore, using the Lott-Mustard sample of all counties meant that many
observations from small counties that were not zero had to be estimated [this was done by
multiplying the number of establishments in each of the size categories by the median number of
employees in the size category]. These data issues may have masked substantial variation.
Another issue had to do with the instruments employed. While some industries clearly
do demand more security than others, for instance, the location of firms from at least some of
those industries could attract more criminal activity to their area (i.e., “cause” local crime),
16
This is one reason for using a sample of urban-area counties. The problem also is partially mitigated by
using the County Business Pattern data on the number of security firms, rather than employment. There
also are other reasons for focusing on firm level data rather than employment data, as explained below,
although results using employment data are also noted.
making them inappropriate as instruments.17 Similarly, decisions to vote republican could be, at
least in part, determined by local crime. Finally, Glaeser and Glendon (1998: 462) concluded
that gun ownership tends to be most acceptable where people mistrust or are dissatisfied with
public justice and have a “tradition of private retribution,” suggesting that NRA membership
[and perhaps Republican votes] may reflect the relative portion of population that prefers to take
responsibility for their own protection. In this case, it was expected that NRA membership could
be highly correlated with private investments in crime prevention and detection, while not
actually affecting crime directly. If so, it could be a good instrument. In fact, however, NRA
membership was not related to the measures of private security employed by Benson and Mast.
The impact of such issues could be quite significant given the limited impact of private security
on many crimes found in Benson and Mast compared to the much stronger although relatively
localized relationships found in Macdonald, et al. (2012).
3 Data
A panel of 84 counties in 27 different states spanning 1998-2010 is used.18 Crime data were
collected from the FBI's Uniform Crime Reports. County level private security number-ofestablishments data were obtained from the U.S. Census Bureau's County Business Patterns
17
Furthermore, firms in some of these industries may be relatively more likely to employ in-house
security rather than contracting with security firms. See note 15. In fact, coefficients on many of the
industries’ employment and establishment values were negative, suggesting that they may tend to use inhouse security, and several negative coefficients were significant, particularly in the security employment
equation.
18
Various issues limited the size of the sample. While states’ current regulatory requirements are readily
available, for instance, as are dates when those regulations were established, finding out what the
regulations were prior to the change proved to be very difficult for some states. Crime data for some
counties were also not reported every year, and in some cases, there were so few observations that the
county was dropped from the analysis. In some cases where crime data are not available for all years in
the sample, the observations that are available are still included. This resulted in an unbalanced panel,
although but most counties in the panel are represented by a full time series. The goal was to find a set of
states that had changes in the collected regulations during the time frame, and a set of states with no
changes in these regulatory data that work as a control group. So, data was collected from the 17 states
that had changes and a full panel could be collected, and 10 that did not have regulatory changes but had
the full time panel.
(CBP) online database.19 The simple correlation statistic between private security firms and the
number of violent and property crimes are 0.8849 and 0.8303 respectively.20 These statistics
suggest that the simultaneity problem between number of crimes and measures of private
security may be substantial, as they are highly correlated. The following model attempts to
break the assumed endogeneity by exploring the effects of exogenous shocks to private security
in the form of regulatory changes, and the subsequent effect on crime. Regulation of licensing
for private security is done at the state level.
Private-security-regulations data were collected by examining and coding state statues
and administrative codes accessed through Lexis-Nexis and West Law Next databases.21 Since
the licensing is done at the state level, it is assumed to be an exogenous constraint to each
county. Regulations vary dramatically across states, as noted below. Over the sample dates
19
http://www.census.gov/econ/cbp/. Data on employment by firms specializing in the provision of
private security were also obtained, but there are several reasons for focusing on firms rather than their
employment. These reasons are noted as the data are discussed and the model is developed. Nonetheless,
empirical results using employment data rather than firm data were examined and are the results are noted
below. In this context, while employment data for large counties listed by the CBP generally reports
actual employment, some of the urban-area counties included in this analysis still have a relatively small
number of private security firms in some size categories (information about firms, including employment
are provided in categories based on firm size defined by numbers of employees; the categories are 0-19
employees, 20-99, 100-249, 250-499, 500-999, 1000-2499), and under these circumstances, CBP only
provides the range in which the employment value falls for that category. When such observations are
used, the mean of the reported range served as the data point. The potential for measurement error
problems exist with this range data, however, one reason for preferring firm level data. County Business
pattern annual employment and firm data is collected March 12 of every year. If a regulatory change
happened after March 12 of any given year then the change is recorded for the following year.
Private investigator data were also collected and tested but results are not reported here.
Employment levels and establishments are small relative to the private security data, however, and the
relationship between private investigators and crime is not as straightforward. After all, a substantial
portion of private investigation work arises because of civil disputes, insurance claims, divorces, and
other non-crime issues. Estimates using these data can be obtained upon request.
20
These statistics for private security employment are 0.8707 for violent crime and 0.8695 [see note 16 in
this regard.
21
http://www.lexisnexis.com/en-us/home.page and
https://1.next.westlaw.com/Session/SignOn.html?bhcp=1
(1998-2010), the regulatory criterion in 17 of the 27 states changed.22 Furthermore, a relatively
large number of counties in the sample turned out to be from states that had multiple changes in
the collected regulations. Regulatory data collected are expected to influence the cost of entry
into the private security market for individuals who do not need to work under the employment
of an existing firm (i.e., they may form their own firm).23 These data consist of:
• the bond/insurance requirement necessary to obtain a private security license;
• law-enforcement experience required (in years) to obtain a security license;
• training necessary (in hours) to qualify for a licensure;
• a dummy variable indicating whether a test must be taken to qualify for licensure.
If an agency license was necessary to practice as an individual entity in the state, then the agency
qualifications were collected. If a simple private security guard license was all that was
necessary, then this is the data collected.24 All data collected assumed an unarmed security
guard.25 These data are meant to establish a lower bound for entry into the marketplace.
22
State level private security regulation is not likely to respond to individual county crime levels from
year to year. A state may change private security regulations in response to a statewide change in crime
(and these changes might be driven by crime in a few large urban counties), but even this is not likely.
Regulations in some states have changed over time, but relatively infrequently (certainly not annually).
Thus the county level crime rate and the state imposed regulation are assumed to move independent of
each other.
23
In some states proprietary security services were not subject to the collected criteria, but the measured
regulations are applicable to all firms specializing in the provision of security.
24
An exception to this rule is Maryland where the licensing requirements applied to firms with 5 or more
employees. Since most of the firms in the state have more than 5 employees these was the data used, it
was assumed that this was practically the lower bound for entry.
25
Most private security personnel are not armed. Data quality varies considerably, but Cunningham and
Taylor (1985, 20) reported that only three percent of the uniformed security personnel employed by
Guardsmark, one of the largest national firms at the time, were armed, and they estimated that less than
ten percent of the entire private security force was armed. While customers frequently request armed
guards, security firms discourage many of these requests, both because weapons are generally not needed
and because the firms face higher insurance costs when more of their employees are armed. More
recently, the Graduate Institute of International Development Studies (2011, 111-116) provided estimates
from the 70 countries they examined and explained that
In practice, PSCs provide a number of services that do not require the use of firearms, such as risk
Individual licensing in the form of trainee licenses or security guard licenses that were
conditional on existing agency employment were not included. For the private security
bond/insurance requirement, the minimum required insurance or bond (in some states both were
required so the observation recorded was the summation of the two) was used as the data point.
The experience necessary was based on a 2,000-hour work year. The training criterion was the
combination of pre-license training and continuing education training required for licensure.
These licensing requirements vary dramatically across states and over time:26

At the end of the data period ten states did not require either insurance or a bond; twelve
required a bond but no insurance with bonds ranging from $2,500 to $50,000 and a
median of $10,000; nineteen required insurance but no bond with insurance ranging from
$50,000 to $2,000,000 and a median of $500,000; one required $300,000 in insurance
and a bond of $10,000 while another required $1,000,000 in insurance along with a
$5,000 bond; and the rest allowed licensees to choose either insurance or a bond, with
values ranging from a low of $5,000 in insurance or a $5,000 bond up to combinations of
$400,000 or $400,000 and $25,000 or $1,000,000.
analysis and advisory services. In non-conflict settings, PSCs are most likely to use arms when
guarding sensitive industrial, government, and bank sites, performing mobile patrols and
emergency interventions (in case an alarm system is activated), or protecting convoys (such as
cash-in-transit) and people (acting as bodyguards) …. it appears that PSCs worldwide hold
somewhere between 1.7 and 3.7 million legal firearms. While the dearth of information explains
such a broad range, this estimate remains significant in that PSCs hold only a small proportion of
the global firearm stockpile of at least 875 million units. PSC holdings are comparable to the
quantities of small arms held worldwide by gangs and armed groups (2 to 11 million units), but
much lower than those of law enforcement (26 million), armed forces (200 million), and civilians
(650 million).
The study reported a range of estimates of firearms per PSC personnel for several countries. For instance,
the ratio for Australia was between 0.02 and 0.15, while both India’s and China’s ratios were between
0.01 and 0.05. Their estimate for the U.S. ratio was estimated to be between 0.2 and 0.3, substantially
higher than Cunningham and Taylor (1985).
26
Meehan and Benson (2013) explore potential reasons for such variation.

During the data period, three states instituted insurance requirements and seven more
raised the amount of insurance required, while four eliminated bonding requirements
(three simultaneously increased the amount of insurance required) and two increased
their required bond.

At the end of the data period, seventeen states had no law-enforcement experience
requirement to get a firm license while four states required five years of experience, three
required four years, eleven required three years, eight required two years, and the
remainder required one year or less.

Over the data period, two states instituted experience requirements for the first time,
while one raised the amount of experience required and one lowered its requirement.

Only two states required law-enforcement experience for security employees, one
mandating four years while the other instituted a one-year requirement during the data
period.

Twenty-three states had no training requirement to get a license and twenty-nine had no
training requirement for private security employees.

States with training requirements for a license mandated between one hour and eighty
hours, while employee training requirements ranged from four to thirty-two hours in
states with these regulations.

Ten states instituted training requirements over the data period while seven instituted
training requirements for employees, and four raised the amount of training needed for a
license while two raised the hours required for employees and one eliminated the
employee training requirement.

Twenty states required tests to get a license (passing a test can substitute for the
experience requirement in two states) and eight states required tests for security
employees.

Eleven states did not require tests, experience or training while eleven required all three,
six states had experience requirements but no training or testing requirements, two had
training requirements but no experience or testing mandates, two states required tests but
no training or experience and the remainder had some combination of two of the three.

Eight states actually did not have any insurance, bonding, experience, training, or testing
requirements.
Data for control variables on income and population are from the Bureau of Economic
Analysis.27
4 Model
Two Stage Least Squares (2SLS) models were estimated with time and county level fixed
effects. The first stage tests the relationship between the regulatory barriers and private security
levels. The results from this first stage allow evaluation of the relationship between private
security and the regulations. The relationship between state regulations and county level private
security establishments is tested.28 Bootstrapped clustered standard errors were used to correct
for the downward bias of the normal OLS standard errors within panel estimation; see Cameron,
et al. (2008).
The first stage model for private security establishments is:
PSfirmit = β0PSbondit + β1PStrainit + β2PSexperit + β3PStestit +Xitφ+Γt +Υi +εit
27
(1)
http://www.bea.gov/
The estimated relationship between state regulations and county level private security employment are
also noted.
28
where PSfirmit is the number of private security establishments in county i, in year t, PSbondit is
the insurance and/or bond requirement in i and t, PSexperit is the experience requirement, Xit is a
matrix of county and time level controls for population, income and unemployment, Gamma is
the time fixed effect, Upsilon is the county level fixed effect, and varepsilon is the idiosyncratic
error.
The first stage results follow in Table 1. Note that the bond/insurance requirement
significant and negatively related to private security establishments. The coefficient appears to
be small, but the mean value of this requirement is $244,715.90 in this data sample. Therefore,
the estimation suggests that a $200,000 increase in this requirement is associated with an average
3.3 percent decrease in the number of private security firms in a county. The coefficient on the
test requirement also is negative and significant. Using the sample means to calculate average
county level effects, the introduction of a mandatory test to qualify for licensure is associated
with a 27.36% reduction in licensed private security establishments in a county, on average. The
experience requirement also has a negative sign but it is insignificant. In contrast, the training
requirement coefficient is positive coefficient, although it is not significantly different from zero.
Table 1: First Stage Results
Private Security Establishments
(bootstrapped clustered standard errors in parenthesis)
-.00000529***
Bond/Insurance
(.00000224)
Requirement
.153435
Training
(.1951731)
Requirement
-4.357442
Experience
(3.342791)
Requirement
-8.760086***
Test Requirement
(2.755347)
.0001026**
Population
(.0000461)
1.344183*
Unemployment
(.7201675)
Rate
.0008528*
Real Income Per
(.0004922)
.9002
1023
Capita
R2
N
*** significant at the 1% level, 2 tailed test
** significant at the 5 % level, 2 tailed test
* significant at the 10% level, 2 tailed test
Following Stock and Yogo (2001), an F-test of the joint significance of the four
regulations in the regression is used to test the strength of the instruments. The resulting Fstatistic is 14.06, confirming that the instruments are sufficiently strong, and in conjunction with
the results of the regression analysis,29 suggesting that the regulations are good candidates for
instrumenting for private security firms.30 Using these regulations as instruments for a two stage
least square fixed-effects model results in the following specification:
^
CrimeIncidenceit = β0PSfirmit + Xitφ + Γt + Υi + εit
(2)
Where CrimeIncidenceit is a crime statistic (property crime, violent crime, robbery etc.) from
county i, in year t, PSfirmit ‘hat’ is the predicted value of Private Security firms from the first
stage specification, and all other variables are consistent with the initial explanation for the first
29
The first stage model with private security employment, PSemployit , as the dependant variable
produced
Bond/Insurance Requirement
Training Requirement
Experience Requirement
Test Requirement
Population
Unemployment Rate
Real Income Per Capita
R2
N
Private Security Employment
(bootstrapped clustered standard errors)
-.0003465 (.0002134)
12.18353 (21.79907)
-124.383 (359.5759)
-416.1831** (202.7901)
.073703** (.0030928)
67.59894 (74.46391)
.0809355** (.0404941)
.8866
1023
** significant at the 5 % level, 2 tailed test
30
The F-stat for the regulations in the private security employment equation in note 26 is 6.79. Therefore,
as an instrument for employment, these regulations appear to be weak, providing another reason for
focusing on the firm-level relationships.
stage regression. Crime incidence is the crime measure as opposed to crime rates often have been
used in crime studies, in part because researchers have raised serious objections to empirically
modeling crime with crime rates. For instance, Chamlin and Cochran (2004, 127) explain that
The process of deflation, by partially controlling for the effects of population size on
crime prior to the estimation of any multivariate models, misspecifies the causal
relationship between population size and macro-level indicators of crime. It tends to
overestimate the effects of the social, economic, and political conditions, while it
simultaneously underestimates the importance of opportunities for social contacts (the
number of people in a geographic area) on variations in the level of crime.
In addition, Osgood (2000, 22) points out that
When Populations are small relative to offense rates, crime counts cannot be ignored.
Indeed for a population of a few thousand even a single arrest for rape or homicide may
correspond to a high crime rate. ....Crime rates based on small counts of crimes present
two serious problems for least squares analysis. First because the precision of the
estimated crime rate depends on population size, variation in population sizes across the
aggregate units will lead to violating the assumption of homogeneity of error variance.31
Second normal or even symmetrical error distributions of crime rates cannot be assumed
when crime counts are small.32
Thus, the specification choice made here is to include population as an explanatory variable, and
not to deflate the crime incidence by population.
We assume that the state-level private security regulations are not correlated with number
of public police in an individual county. Given that the instruments in this study are valid, they
should correct for endogeneity from simultaneity issues and endogeneity from omitted variable
bias, allowing the exclusion of public police.33
31
Fixed effects alleviate this problem.
There are a few counties in this sample with populations under 70,000 where a murder is not an every
year occurrence. The rare murder that does occur in these counties has relatively large ramifications for
the crime rates.
33
Because public police face the same simultaneity issues as private security services, the public police
would have to be instrumented in the crime determinant specification. Thus, the only way the estimation
of the current instruments was invalid based on public police omitted variable bias, is if they were
correlated with the instruments for public police. We have no reason to think that the state level private
security regulations used here are correlated to the instruments used in the literature for measuring public
police impact on crime.
32
Tables 2 and 3 report the two-stage-least-squares models for violent crimes and property
crimes respectively using the predicted value of private security firms. First, note that
coefficients on the population variable are significantly correlated with crime incidence at
customary levels in five of the regressions, and at the ten percent level in four more. Thus
supporting the Chamlin and Cochran (2004, 127) using crime rates rather than criminal the
process of deflation, by partially “misspecifies the causal relationship between population size
and macro-level indicators of crime” and “underestimates the importance of opportunities for
social contacts (the number of people in a geographic area) on variations in the level of crime.”
Table 2: Private Security and Violent Crime
Total Violent Crime
Murder
Rape
Robbery
Predicted
Security Firms
Population
Real Income per
capita
Unemployment
Rate
R2
F-Statistic
N34
-167.6306**
(85.40667)
.0124231**
(.0062802)
.0042999
(.0602394)
-3.337797
(83.17266)
.6424
34.40
1007
Assault
-1.18312
(.7751508)
.0001713*
(.0001015)
.0000073
(.0001015)
-2.478681
(1.794117)
-4.467077***
(1.064403)
.0005834***
(.0002121)
.0005603
(.0020199)
-4.108488
(3.403203)
-24.21878*
(13.42577)
.0030721***
(.0010483)
-.0153803
(.0146894)
-23.53413
(22.76447)
-139.0382*
(75.29738)
.0088362
(.0063561)
.0212743
.0559889
26.66543
(83.28753)
.7572
66.48
973
.8240
41.70
962
.8342
87.47
1007
.4638
21.18
1007
*** significant at the 1% level, 2 tailed test
** significant at the 5 % level, 2 tailed test
* significant at the 10% level, 2 tailed test
bootstrapped clustered standard errors in parenthesis
Table 3: Private Security and Property Crime
Total Property Crime
Larceny
Burglary
Auto Theft
Predicted
Security Firms
Population
Real Income
per capita
Unemployment
34
-324.5291***
(120.4619)
.0153386
(.018482)
-.3278099
(.2241548)
-507.8366
-118.4711
(84.47278)
.0022395
(.0117689)
-.2922219**
(.145554)
-291.9835
-71.6859***
(24.84459)
.002637
(.0034158)
-.0206175
(.0382955)
64.86634
-134.2103***
(46.76365)
.0104472*
(.0060081)
-.0150983
(.0732028)
-280.5452
Arson
-5.026057**
(2.031073)
.0006978
(.0005461)
.000363
(.0052023)
-18.18282
The difference in the number of observations comes from the inconsistency of reporting some types of
crimes; some counties do not record number of rapes, murders, arsons etc. for a given year but then do
report this data in other years.
Rate
R2
F-Statistic
N
(456.4784)
.0076
85.36
1007
(282.1506)
.4709
93.33
1007
(60.63142)
.1852
96.48
1007
(183.9019)
(12.64736)
.6678
32.26
1007
.6711
15.24
944
*** significant at the 1% level, 2 tailed test
** significant at the 5 % level, 2 tailed test
* significant at the 10% level, 2 tailed test
bootstrapped clustered standard errors in parenthesis
Now consider the relationship between private security and crime.35 Negative and
significant (at customary levels) effects of private security result in six of the ten crime
categories - total violent crime, rape, total property crime, burglary, and auto theft. Results for
two other crime categories, robbery and assault, suggest negative relationships too, although they
are only significant at the 10% level. Only murder and larceny coefficients are not significantly
different from zero (they do, however, have the expected negative signs). Using the sample
means for both security firms and crimes, a ten percent increase in the number of predicted
private security firms is associated with around a 10.2 percent decrease in total violent crimes,36
and a 3.2 percent decrease in total property crime37 within a county, on average.
35
The model was also estimated in an OLS model with private security establishments and without using
the instruments. These results are included in the appendix. Relative to the OLS model, in the 2SLS
model the magnitudes of the coefficients increase substantially for all crimes except arson, in several
cases more than doubling. Five coefficients become significant at customary (5%) levels with the 2SLS
that are not in the OLS models (total property, burglary, arson, robbery and assault); and the population
becomes significant at customary levels in three equations (and at the ten percent level in two more),
supporting Chamlin and Cochran (2004) and the use of crime incidence over rates.
36 The results for violent crime using private security employment rather than private security firms are:
Total Violent Crime
Murder
Rape
Robbery
Assault
-2.8761*
-.0218554
-.657424**
-.3536018
-2.456348*
Predicted Security
(1.648222)
(.0164853)
(.0311023)
(.3004875)
(1.403757)
Employment
.0166683*
.0002124*
.0005536**
.0032378**
.0128692*
Population
Real Income per
capita
Unemployment
Rate
R2
F-Statistic
N
(.0087571)
.1044781
(.1208258)
-60.43493
(109.1884)
.5015
23.42
1007
(.0001237)
.0002124
.0011638
-2.822318
(1.904607)
.7200
65.19
973
(.0002565)
.0025642
(.0029821)
-5.883206
(4.350144)
.7925
35.90
962
(.0015336)
-.0060915
(.0213126)
-33.22437
(22.50662)
.7807
74.41
1007
(.0065725)
.110294
(.0885877)
-16.75771
(76.41336)
.3549
13.86
1007
4 Conclusions
If private security is even considered within the economics of crime literature, it is
assumed, explicitly in a few cases but implicitly in most, that it does not have a general
deterrence impact. The underlying argument is that private security hardens specific targets
causing criminals to shift to (substitute) softer targets with no net reduction in crime. A few
studies challenge this assumption, however, particularly for private security measures that are
known to exist but cannot be directly observed, such as Lojack (Aryes and Levitt 1998) and
concealed firearms (Lott and Mustard 1997, and others). Some studies also suggest the potential
for positive spillover effects of private security that is likely to be observable (Zedlewski 1992,
Benson and Mast 2001, MacDonald, et al. 2012), presumably because increases in private
security raise the cost of finding targets for potential criminals. There are potential problems
*** significant at the 1% level, 2 tailed test
** significant at the 5 % level, 2 tailed test
* significant at the 10% level, 2 tailed test
bootstrapped clustered standard errors in parenthesis
While these results are consistent with Table 2 with regard to coefficient signs, the private security
employment coefficients are generally less significant than the coefficients with private security firms.
Recall note 27, however, and recognize that the instruments are relatively weak for the private security
employment model. Therefore, less confidence can be placed on these results than those in Table 2.
37 The results for property crimes using private security employment are:
Total Property Crime
Larceny
Burglary
Auto Theft
Arson
-4.405359
-1.45774
-.9291213
-2.015156*
-.0399216
Predicted Security
(3.0171)
(1.657433)
(.6787538)
(1.221264)
(.0590796)
Employment
.0151526
.001084
.0022779
.0117675
.0004895
Population
Real Income per
capita
Unemployment
Rate
R2
F-Statistic
N
(.0222305)
-.2312082
(.2869304)
-682.9808
(469.5428))
.0639
80.05
1007
(.0124566)
-.2695534
(.1717044)
-364.2811
(291.5965)
.5247
90.02
1007
*** significant at the 1% level, 2 tailed test
** significant at the 5 % level, 2 tailed test
* significant at the 10% level, 2 tailed test
bootstrapped clustered standard errors in parenthesis
(.0043628)
-.0029616
(.0485503)
23.73423
(59.01626)
.3483
103.5
1007
(.0096305)
.0410348
(.107584)
-342.236*
(179.9584)
.5661
25.99
1007
(.0006949)
. -.0003975
(.006137)
-22.03035
(14.06271)
.6803
15.26
944
and/or limitations with these studies, however, so this study develops an instrumental variable
approach to isolate private-security effects on crime using state-level licensing requirements to
instrument county level-private security services for a sample of urban counties over the 19982010 period. The results provide additional support for the positive spillover effects of private
security by showing that private security has a negative effect on many types of crime. But these
findings are important on two dimensions, one that within our sample, evidence is provided
supporting the position that private security does appear have positive spillover deterrence
effects, which we are able to isolate away from the simultaneous link with crime. The second
important conclusion provides evidence that regulators should be careful about the unintended
consequences of these types of regulations, from a welfare perspective, if we assume that the
violent and property crimes reduce aggregate welfare, these type of regulations could reduce
overall welfare by increasing crime, as the under-investment in private security could result from
these barriers to entry which could in turn lead to more crime.
It appears that there is an under-investment in private security because providers are not
able to charge for all of the benefits they generate, as concluded by Ayres and Levitt (1998).
Given that greater investments in private security are desirable, then the relative effects of the
regulatory inputs revealed in the first stages regression presented above become relevant. Private
Security bond/insurance requirements and mandatory testing have negative relationships with the
number of private security establishments in a county. The political economy aspect of these
findings suggests that policymakers should be careful; these regulations apparently are
significant barriers to entry that result in relatively high incidence of most crimes.
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Appendix
Real Income Per Capita
(2005 dollars)
Population
Private Security Firms
Private Security
Employment
Violent Crime Incidence
Property Crime Incidence
PS Bond/Insurance
Requirement
PS Training Requirement
(hours)
PS Experience Requirement
(years)
Selected Summary Statistics
Mean
$36,534.83
Standard Deviation
$9,293.45
819,058.4
32.02
2,566.45
1,255,456
59.12
5,326.63
5,241.21
32,106.6
244,715.9
10,206.73
43,398.52
318,427.7
10.76
18.38
2.07
1.65
OLS results
Table 4: Private Security and Violent Crime with no IV
Total Violent Crime
Murder
Rape
Robbery
Private Security
Firms
Population
Real Income per
capita
Unemployment
Rate
R2
Wald stat
N
-113.5412**
(52.3561)
.0070847
(.0056572)
-.0410698
(.0410698)
-73.32722
(59.42744)
.4538
41.07
1007
Assault
-.73414916*
(.3968074)
.000127
(.0000795)
-.0003971
(.000658)
-3.026525
(2.27737)
-2.501688**
(1.115329)
.0003632
(.0002319)
-.0010861
(.0023108)
-6.558927
(4.113692)
-15.40923**
(6.723225)
.0022027
(.0013605)
-.0227697
(.0013605)
-32.93327
(23.21571)
-95.21712**
(44.7036)
.0045112
(.003927)
-.0154825
(.0394029)
-30.03718
(53.11568)
.7699
38.88
973
.8356
53.10
962
.8473
36.42
1007
.0144
29.93
1007
*** significant at the 1% level, 2 tailed test
** significant at the 5 % level, 2 tailed test
* significant at the 10% level, 2 tailed test
bootstrapped clustered standard errors in parenthesis
OLS results
Table 5: Private Security and Property Crime no IV
Private
Security Firms
Population
Real Income
Total Property Crime
Larceny
Burglary
Auto Theft
-156.3843*
(81.10056)
-.0012568
(.0165909)
-.4688482**
71.02784
(47.28651)
-.002443
(.0102973)
-.3320169**
-24.48375
(16.80303)
-.0020217
(.0027682)
-.0602102**
-60.87425***
(17.12822)
.0032091
(.0068377)
-.0766119
Arson
-5.208217
(1.534152)
.0007157
(.0005163)
.0005172
per capita
Unemployment
Rate
R2
Wald Stat
N
(.2090375)
-725.4089
(443.1602)
.6565
47.99
1007
(.133872)
-353.373
(285.599)
.6680
55.69
1007
*** significant at the 1% level, 2 tailed test
** significant at the 5 % level, 2 tailed test
* significant at the 10% level, 2 tailed test
bootstrapped clustered standard errors in parenthesis
(.0288412)
3.78878
(53.45736)
.7725
63.76
1007
(.058055)
-375.4389*
(206.3859)
.0829
63.33
1007
(.0051822)
-17.96131
(12.74587)
.6701
91.11
944