INTRODUCTION
Employment tax credits have become one of the primary tools of
state economic development policy, with just over one-half of the states
in the U.S. offering this form of tax incentive. A recurring question is
whether these credits create jobs that would not have been created in
their absence. (1) Few studies of this issue have focused on specific
state tax incentives as opposed to taxes in general. Yet, policy makers
must know such impacts before they can design effective incentive
programs. This paper provides estimates of such impacts by comparing the
employment change in eligible firms (2) that participate in employment
tax credit programs with eligible firms that do not participate in such
programs.
Numerous studies have shown that taxes, in general, have a small or
no effect on employment. Bartik (1991) and Wasylenko (1997) discuss
studies that have examined this relationship. The findings that taxes
have a very limited effect on employment may reflect limitations of the
tax measure. Since these studies address average effects of all programs
operating in a geographic area, the response of individual firms to
specific tax incentive programs cannot be teased out. To assess the
effect of these tax incentives on employment, firm-level data is needed
so that conclusions can be drawn about the effects of employment tax
credits on individual firms.
The scant evidence (consisting of one study) on state employment
tax credits suggests that these credits modestly influence firms'
employment decisions. Using data on business establishments applying for
Ohio's Job Creation Tax Credit, Gabe and Kraybill (1999) find that
the credit had a positive impact on job creation in Ohio between 1993
and 1995, with between 63 and 68 percent of new jobs (2764 to 3976 jobs)
occurring in firms that received the credit (among firms that were
eligible to take the credit).
Evidence from federal employment tax credits suggests much smaller
effects. These credits have had low participation rates and low impacts
on employment. Bishop and Montgomery (1993) estimate that less than 5
percent of firms participated in the Targeted Job Tax Credit program and
that at least 70 percent of the tax credits were for workers that would
have been hired in the absence of the credit. Perloff and Wachter (1979)
estimate that firms which knew about the New Job Tax Credit, a less
targeted employment tax credit program in place from 1977-78, created 3
percent more jobs than other firms.
The present analysis differs from previous studies on employment
tax credits in two ways. First, it uses firm-level data taken from
corporate income tax returns rather than survey data, which are subject
to respondent bias and which may inflate the estimates of the employment
impact. In addition to being more objective than surveys, these data
allow an examination of how firm characteristics affect program
participation and employment. Previous work on employment tax credit
programs, such as Hamermesh (1976, 1978, 1993), has focused on how wage
subsidies affect the quantity of labor demanded, (3) where an employment
tax credit serves to reduce wage payments. These studies focus on the
elasticity of demand for labor and ignore the problem of employers'
failure to participate in the subsidy program.
Second, the empirical model jointly estimates the decision to
participate in a tax credit program and the effect of such a tax credit
on employment. To achieve this end, a switching regression is used. (4)
The model incorporates the decision to participate in a tax credit
program into the firm's employment decision. Data from
Georgia's Job Tax Credit (JTC) are used to estimate the model. The
parameter estimates are then used to calculate the difference in the
employment change for participating and nonparticipating firms and
thereby provide estimates of the maximum possible effect of the credit
on employment in participating firms. The evidence presented in this
paper suggests that firms are creating jobs in response to state
employment tax credits. Firms taking Georgia's JTC between 1993 and
1995 created 23 to 28 percent more jobs than eligible firms not taking
the credit.
The remainder of the paper is organized as follows. The next
section presents a brief overview of Georgia's JTC program. The
following section provides a framework for analyzing the participation
decision and the employment impact of state employment tax credits. The
fourth section develops an empirical model and presents estimates of the
employment impact of state employment tax credits. The final section
offers conclusions.
GEORGIA'S JOB TAX CREDIT
The structure of the JTC influences its economic impact and
effectiveness in creating jobs. Some relevant economic features of
Georgia's Job Tax Credit include the following:
(1) Georgia's JTC is a tax credit available for the creation
of new full-time jobs. This provision may discourage over-time or
part-time work since the credit is not available for these types of
employment.
(2) The JTC is a credit against corporate income tax liability.
Firms with no tax liability cannot use the credit. All firms would
qualify for a credit against income tax withholdings, payroll, or the
social security contribution, for example.
(3) Fifty percent of a firm's income tax liability is the
maximum JTC a firm can take in any year.
(4) If the minimum number of jobs is not maintained, the firm does
not have to refund a portion of the previous years' credit back to
the state i.e., there are no clawback provisions.
(5) The minimum number of new jobs that a business establishment
must create to qualify for the JTC and the credit per job differs
depending on the Tier designation of the county in which the
establishment is located (Table A-1 in the Appendix). (5) Establishments
located in Tier 1 counties have to create fewer jobs to qualify for the
JTC, and the credit per job is higher relative to the other tiers.
Through this mechanism, the JTC targets business establishments in less
developed counties.
(6) An establishment must maintain a minimum increase of jobs for
two full years before it can take the JTC. A firm's increase in
employment is determined by the increase in the average monthly
employment over the firm's fiscal year. The firm supplies this
information on the tax credit schedule when claiming the credit.
(7) The JTC can be taken for five years if the jobs are maintained.
For example, if a firm in an eligible industry in a Tier 1 county chose
1992 as the base year, created 10 new full-time jobs in 1993 and
maintained them in 1994, it can claim a JTC of $20,000 on its 1994 tax
return as long as the firm has a state corporate income tax liability of
at least $40,000. If the establishment maintains these ten jobs, it can
continue to take the JTC through the 1998 tax year The firm must
maintain the minimum employment increase for seven years in order to
take the credit for five years. The above provisions reduce the
incentive for churning i.e., where establishments hire workers, fire
them, and then hire new workers to continuously take advantage of the
credit.
(8) With the exception of firms in Tier 1 counties, the JTC is
limited to certain industries, currently manufacturing and distribution,
warehousing, goods processing, tourism, research and development, and
information processing. In Tier 1 counties, firms in any industry could
take the JTC.
(9) The JTC is nonrefundable, but unused JTC can be carried forward
for up to ten years.
(10) Firms can file a Notice of Intent to maintain Tier status. For
example, a firm would file a Notice of Intent for a participating
establishment located in a Tier 1 county so that if the county is
classified as Tier 2 the following year, the firm can continue to claim
the Tier 1 credit amount. This provision allows the firm to claim the
same credit amount per job created regardless of changes in the Tier
designation of the county where the establishment is located. From 1993
to 1995, 29 establishments filed a Notice of Intent for the JTC in
Georgia.
(11) The JTC does not require firms to sign an a priori contract
guaranteeing the creation of a set number of jobs. Firms can track
employment and decide to claim the JTC after the legislated number of
jobs is created.
(12) Multi-establishment firms in qualifying industries may take
the JTC for jobs created in any single establishment that meets the job
creation criteria.
THEORETICAL FRAMEWORK
Faulk (1998) estimates that only 19 percent of eligible firms apply
for Georgia's JTC. The decision to participate in an employment tax
credit is related to the benefits and costs of participation, which can
be linked to certain characteristics of the firm and the structure of
the credit. (6)
The Benefit of Participation
The benefit of participation in an employment tax credit program is
the value of the tax savings. This is determined by the number of
eligible jobs, current and expected tax liability (or past tax liability
through net operating loss), the tax credit ceiling, and the discount
rate associated with future credits and any carryforward.
The number of creditable jobs and the credit rate determines the
maximum credit that a firm can potentially take. This maximum is limited
by certain constraints. An income tax credit, especially if it is
nonrefundable and nontradable, will have little value to firms with no
tax liability. More than 75 percent of corporations in Georgia have no
state corporate income tax liability in any given year. (7) The credit
ceiling limits the credit to half of tax liability, which also limits
the value of the credit. As a result of these constraints, the effective
(or average) credit per job may be different from the statutory credit
per job. (See Table A-1 for the statutory credit rates.) A firm can take
the credit for a number of years if the new jobs are maintained (five
years for Georgia's JTC) and can carry forward unused credit for up
to ten years, the discount rate associated with the present value of the
credit is also a determinant of participation.
The Costs of Participation
Firms incur costs when participating in a tax credit program. These
costs fall into six categories: (1) search costs, (2) compliance costs,
(3) costs associated with providing additional information to the
government, (4) stigma costs, (5) hiring costs, and (6) additional
federal tax liability.
Search costs associated with filing employment tax credits include
finding out about the credit and other tax abatement programs. Either
firms search for ways to reduce their tax liability, or the government
can develop a method to notify firms of their eligibility. Because
search activities are costly, firms may not continue to search until all
tax abatement options are known.
Compliance costs can be divided into two components: startup costs
and annual costs. Startup costs include the cost of learning about the
credit, training staff, and setting up new forms and systems to capture
the information necessary to claim the credit. Annual costs are the
year-to-year costs associated with claiming the credit. Firms that
participate in an employment tax credit program (or their designated tax
advisor) must obtain the necessary forms and gather information needed
to flu out the forms. There are also internal coordination costs: within
the firm, personnel in charge of hiring decisions need to coordinate
activities with personnel in charge of reducing tax liability. For
multi-establishment firms, the coordination costs both within and among
establishments may become quite substantial.
Costs associated with supplying additional information to the
Georgia Department of Revenue may prevent an eligible firm from applying
for the credit. Fear of audit (or other consequences of revealing
additional information to the Department of Revenue) may be a deterrent.
Additional personnel in the Georgia Department of Revenue view the
corporate income tax returns of firms that take the JTC. This additional
scrutiny may increase the expectation or probability of an audit. In a
tax evasion model, Rice (1992)shows that publicly traded companies are
more likely to over report income and suggests that these companies do
so to avoid audits.
Positive or negative stigma associated with taking the JTC may
explain in part why some eligible firms do not file for the credit. The
public scrutiny of firms participating in tax abatement programs has
increased over the past few years. Recent articles in the popular press
have severely criticized tax abatements as a form of corporate welfare.
For example, TIME magazine ran a four-part series on corporate welfare
(Barlett and Steele, 1998a-d). One might argue that this type of stigma
is not apparent with the JTC since minimizing corporate tax liability
may be viewed as a good business practice.
Hiring costs are another explanation of the lack of participation
in employment tax credit programs. New employees must be interviewed and
trained, and the appropriate paperwork must be completed. General
Accounting Office (1991) reports that employers participating in the
Targeted Job Tax Credit Program estimated that it cost between $600 and
$1000 to recruit and train a new employee in the late 1980s. Baron and
Bishop (1985) find that hiring costs are positively related to firm size
(measured as employment) and the number of establishments within the
firm. Because of these differential hiring costs, firms in different
industries and of different sizes may find it more or less advantageous
to participate in employment tax credit programs. In the case of the
JTC, such hiring costs might be larger than the potential credit and may
be compounded because the same "new workers" do not have to be
employed over the life of the credit. If labor turnover is high, the
firm has to replace workers in order to maintain eligibility for the
credit.
The deductibility of state corporate income tax liability from
federal corporate income tax liability dampens the value of the credit
and may be viewed as another cost. The magnitude of the increase in
federal income tax liability resulting from state income tax credits
depends on the firm's federal corporate tax rate.
The Employment Impact of Participation
It is the firm's responsiveness to a reduction in wages that
determines the effectiveness of an employment tax credit in changing a
firm's demand for labor. The level of tax liability and the credit
ceiling affect the employment impact of the credit. The credit is
limited to half of state income tax liability. Because of this credit
ceiling, the effective credit per job may be a small portion of the
statutory credit, and the reduction in labor costs attributable to the
credit may be small. Through this constraint, tax liability ultimately
dictates the degree to which the credit reduces the price of labor
relative to other factors of production. Changing these structural
aspects may enhance the effectiveness of the credit.
EMPIRICAL MODEL AND DATA
Although the benefits and costs of participation and the
theoretical determinants of the employment impact of tax credits can be
identified, these factors are hard to measure. In addition, it is
difficult to estimate the effects of economic development programs
because it is difficult to determine what would have happened without
the program, i.e., there is no counterfactual. Bartik (1991) suggests
using micro data on assisted businesses and a control group of
unassisted businesses to examine the effects of specific programs. This
study uses tax and employment data for eligible firms that participated
and did not participate in Georgia's Job Tax Credit program between
1993 and 1995. (8) The model variables can be linked to the benefits and
costs of participation.
To implement the model empirically, a switching regression model is
used. The switching regression model is a simultaneous system of three
equations: two employment equations and a participation equation that
serves as the "switch." Maddala (1983) provides an overview of
switching regression models. The equations of the switching regression
model are:
[1] [y.sub.1i] = [[beta].sub.1] [x.sub.i] + [u.sub.1i] Employment
equation for participants
[2] [y.sub.2i] = [[beta].sub.2] [x.sub.i] + [u.sub.2i] Employment
equation for eligible nonparticipants
[3] [y.sub.3i]* = [[gamma].sub.3] [z.sub.i] + [u.sub.3i]
Participation equation
where
[y.sub.3i] = 1 iff [y.sub.3i]* > 0
[y.sub.3i] = 0 otherwise.
The error structure for the switching regression model:
[4] [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
where [OMEGA] = Cov([u.sub.1i], [u.sub.2i], [u.sub.3i]) =
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
In equations [1] and [2], [y.sub.1i] and [y.sub.2i] are the annual
change in employment in eligible firms that participate and do not
participate in Georgia's JTC program. In equation [3], [y.sub.3i]*
is an unobserved latent variable representing a firm's propensity
to participate in the JTC program, and [y.sub.3i] is a dichotomous
variable that indicates a firm's participation choice. The
[x.sub.i] and [z.sub.i] are vectors of explanatory variables. As
discussed previously, the propensity to participate in the JTC program
is derived from the benefit of participation less any associated costs.
The advantages of this specification are that it treats participation as
endogenous and allows the effects of the explanatory variables to differ
for participating and nonparticipating firms. The employment equations
determine whether or not a firm's participation in the JTC program
affects the level of employment. The participation equation (equation
[3]) indicates a firm's decision to take the JTC, and this
information is used to test for and correct sample selection bias in the
employment equation.
Sample selection bias occurs because the same unobservable factors
that influence a firm's participation in the tax credit program may
influence the likelihood of being in the sample for which the estimates
are calculated. Under self-selection those firms that have a comparative
advantage in taking the JTC will participate in the program and thus
would benefit from it more than would a randomly selected firm with the
same characteristics. For example, participating firms may be more
likely to increase employment and therefore be more likely to be in the
sample.
Heckman's two-step method is used to estimate the model. In
short, this method consists of estimating the participation equation
with a probit model, using the estimates from the probit to calculate an
inverse mills ratio (IMR), and including the IMR as a regressor in each
of the employment equations. Heteroskedasticity results from the use of
Heckman's two-step method of estimation and is corrected using GLS
with the appropriate estimated variance.
Model Specification
The estimating equations are shown below. The variable sources and
definitions are shown in Tables A-2 and A-3 of the Appendix.
[5] EMPLOYMENT CHANGE = [[beta].sub.0] +[[beta].sub.1] INITIAL
EMPLOYMENT + [[beta].sub.2] PLANTS + [[beta].sub.3] PREVIOUS
PARTICIPATION + [[beta].sub.4] START UP + [[beta].sub.5] AGE +
[[beta].sub.6] RANK + [[beta].sub.7] IMR + [epsilon]
[6] PARTICIPATION = [[gamma].sub.0] + [[gamma].sub.1] TAX LIABILITY
+ [[gamma].sub.2] TIER 1 DUMMY + [[gamma].sub.3] TIER 2 DUMMY +
[[gamma].sub.4] INITIAL EMPLOYMENT + [[gamma].sub.5] PREVIOUS
PARTICIPATION + [[gamma].sub.6] EFFECTIVE JTC + [[gamma].sub.7]
HEADQUARTERS LOCATION + [[gamma].sub.8] MANUFACTURING DUMMY +
[[gamma].sub.9] START UP + [[gamma].sub.10] RANK*YEAR95 + [epsilon]
Equation [5] is estimated separately for participating and
nonparticipating firms. (9)
Data
Data from the firms' corporate income tax returns and
establishment-level data from the Georgia Department of Labor's
ES202 dataset were used to create the model variables. (10)
Nonparticipating firms were selected randomly from a list of eligible
firms identified using ES202 data. The dataset consists of 151 firms
that were eligible to take the JTC. (11) Seventy of the firms
participated in the JTC program, and 81 firms were eligible but did not
participate in the program. (12)
The average participating firm had an employment increase of 68
workers (Table 1). Employment increase ranged from -35 to 483. The -35
may seem counterintuitive. Consider a firm that increases employment by
50 workers but only needs to create 10 new jobs to qualify for the
credit. If this firm reduces employment by 35 workers, it still
qualifies to take a credit for 15 workers. Firms with an initial
employment of zero are startups. Startups represent 12 percent of the
sample of participating firms. For firms participating in the JTC
program, 58 percent had previously participated.
The summary statistics (Tables 1 and 2) show that the average
change in employment is larger for nonparticipating firms due, in part,
to one firm that had a large change in employment. On average initial
employment for nonparticipating firms is higher than for participating
firms. Only 6 percent of the nonparticipating firms had participated in
the JTC program previously while 37 percent of the sample are startups.
Just over a quarter of the firms in the sample have no tax liability.
As shown in Table 3, 46 percent of the firms in the sample
participated in Georgia's JTC program between 1993 and 1995. Half
of the firms in the sample had locations in Tier 1 counties, and just
over 56 percent of the firms were headquartered in Georgia. Just over a
third of the firms had participated in the JTC program in previous
years. The vast majority of the firms (89 percent) were in manufacturing
industries. A quarter of the sample was startups in the base year. The
average tax liability of firms in the sample is just over $302,000.
Employment Equation
Employment change is measured as the annual increase in employment
for the firm. For nonparticipating firms, this variable was calculated
from ES202 data. For participating firms, it was taken from the JTC
schedule included with the corporate income tax return. A more detailed
breakdown of the average and total employment change in participating
and nonparticipating firms is shown in Table 4. The data indicates that
the average and total employment change in both participating and
nonparticipating firms increased steadily from 1993 to 1995. (13)
Initial employment is included as a measure of firm size. The
relationship between employment change and firm size has been widely
debated in the literature. Wagner (1992) provides an overview. Larger
firms are expected to have a higher level of employment change relative
to smaller firms.
The number of plants is also a measure of firm size. Wasylenko
(1981) suggests that multi-establishment firms are more responsive to
tax incentives because they can locate capital (labor) intensive plants
in low tax jurisdictions.
The dummy variable for previous participation in the JTC program is
included to examine how taking the JTC in previous years affects current
employment. Of particular interest is whether firms that took the JTC in
the past continue to increase employment.
It might be argued that startup firms should be modeled separately
since the startup decision is different from the decision to expand
employment in an existing plant. However, given the number of
observations in the sample (39 of 151 firms are startups), a separate
model is not feasible. The dummy variable for startup firms is included
to distinguish between firms that startup in the base year and existing
firms that expand.
The age variable is included to determine if younger firms have a
greater change in employment than older firms do.
The rank variable measures a county's level of economic
development and differences in the value of the JTC. Bartik (1991)
suggests that jobs created in areas with high unemployment are more
valuable than jobs created in areas with low unemployment and argues
that for state and local incentives to produce national benefits, places
with higher unemployment should offer greater incentives than places
with low unemployment so that jobs are redistributed from places with
lower unemployment to places with higher unemployment. If the JTC
induces such a change, establishments located in less developed
counties--those counties with highest unemployment and poverty rates,
the lowest manufacturing wage and per capita income--would be more
likely to create jobs in response to the JTC than establishments in
other counties. If this is true, the rank variable will be positively
related to the change in employment.
The coefficient on the inverse mills ratio (Lambda) estimates the
covariance between the error terms of the participation equation and
each employment equation and indicates whether or not sample selection
bias is evident.
Participation Equation
As discussed earlier, a firm's tax liability determines, in
part, the benefit of participation. Firms with a larger tax liability
receive a larger effective credit and should be more likely to
participate. To provide information on the degree to which firms are
able to use the JTC, more detailed information on the available credit,
credit actually taken and the effective credit for participating firms
is shown in Table 5. The average JTC carryforward from previous years,
available credit in the current tax year, and JTC taken were $80,310,
$109,870, and $71,548, respectively. On average, firms are able to
utilize about 38 percent of the total available JTC in a given tax year.
Firms located in tier 1 counties have a larger statutory credit
rate and lower job creation criteria relative to tiers 2 and 3 and
therefore may be more likely to take the JTC. See Table A-1 in the
Appendix for credit amounts per job and job creation criteria. Five of
the firms in the sample contained establishments located in counties
that changed tiers during the study period. Four of these counties were
reclassified from tier 2 to tier 1, and one was reclassified from tier 1
to tier 2.
Smaller firms may be more likely to participate. First, firms
located in less developed counties are more likely to be smaller
relative to firms in more developed counties, e.g., the average size of
firms in the sample located in tier 1, 2 and 3 counties is 200, 304 and
661 workers, respectively. Second, smaller firms may have greater
incentive to participate in the tax credit program. While it is true
that smaller firms may have fewer resources to devote to finding tax
abatement, it is also true that abated taxes may be a larger proportion
of total costs. Also, smaller firms may face credit rationing or other
financial constraints that make tax credits more valuable to them. In
addition, larger firms may experience greater difficulty coordinating
information needed to claim the JTC, which increases the cost of taking
the credit. In a smaller firm, the same person is more likely to be in
charge of hiring and taxes, so coordination costs are lower. Gabe and
Kraybill (1998) and Pope and Kuhle (1996) find that smaller firms are
more likely to participate in an employment tax credit program.
Past participation should be a good predictor of current
participation. Since firms have already incurred the cost of finding out
about the credit and developing the appropriate systems to track
information necessary to claim the credit, they should continue to
participate.
As discussed earlier, the maximum JTC is limited to half of a
firm's tax liability, so the amount of the credit available to the
firm is not necessarily directly related to the number of jobs that are
creditable in each firm. For example, if a firm in a tier 1 county
created 15 jobs and is eligible to take the credit in 1995, the maximum
credit that the firm could potentially take is $37,500 (=2500*15). If
the firm has a corporate income tax liability of only $10,000, the firm
would take a credit for $5000 and carry forward the $32,500 difference.
The effective credit (5000/15 = 333) determines the benefit of the job
tax credit. A firm with a higher effective credit should be more likely
to take the credit since the benefit per job is higher. (14)
Whether a firm is headquartered in the state is used to measure the
likelihood that firms have information about tax abatement options and
thus face lower participation costs. Detailed information on how to file
for the JTC, the credit amounts, the tier structure, job creation
criteria, or qualifying industries is not readily available in the
Georgia corporate income tax form or instructions. Thus, being in-state
may imply better information via word of mouth, better-informed tax
advisors, etc.
The startup dummy and manufacturing dummy are included to determine
if these types of firms are more likely to participate in the tax credit
program. Startups may be more likely participate since they can take the
credit for their entire payroll. However, startups typically have low
tax liability for the first several years of operation, which may reduce
the likelihood of participation.
In 1995 tier 3 firms became eligible to take the JTC. The
interaction term is included to control for this change in credit
structure.
RESULTS
Determinants of the Change in Employment
The analysis of the employment impact of employment tax credits
seeks to determine if both the change in employment and the determinants
of employment change are different for participating and
nonparticipating firms. Tables 6 and 7 show the parameter estimates for
participating and nonparticipating firms, respectively.
Model estimates show a positive relationship between firm size and
the change in employment for both participating and nonparticipating
firms, indicating that the change in employment is greater in larger
firms. However, the magnitude of the effect differs between firms that
participate in the JTC and those that do not. Employment growth for
firms participating in the JTC program is about 60 percent larger than
it is for firms of similar size that do not participate. The magnitude
of the parameter estimate is larger for participating firms, suggesting
that size has a larger effect on employment in participating firms than
in nonparticipating firms. (15)
Being a startup in the base year is not a significant determinant
of the change in employment for firms that took the JTC. It is
significant for firms that did not take the JTC. Startups that did not
take the credit had an average employment change of 162.5 workers, while
startups that took the credit had an average employment change of 79.5
workers.
The coefficients on the IMR are negative but insignificant in both
samples indicating that sample selection bias does is not present. These
coefficients measure the covariance between the error terms in the two
employment equations. The insignificance of this coefficient for
nonparticipating firms provides some evidence that these firms did not
participate in the JTC program because they did not know about the
program and did not incur the search costs to find out about the credit
rather than their making a conscious decision not to participate. For
participating firms, the insignificance of the coefficient provides some
evidence that the tax credit was not a driving force behind an average
firm's employment decision. Using a similar model, Gabe and
Kraybill (1999) find a negative but significant coefficient on the
Inverse Mills Ratio for firms receiving Ohio's Job Creation Tax
Credit.
In sum, the determinants of the change in employment are different
for participating and nonparticipating firms. The differences in the
parameter estimates for the two sets of firms suggest that there are
structural differences in the growth patterns of participating and
nonparticipating firms. The coefficient on the inverse mills ratio is
measured with less precision but is smaller for nonparticipating firms
relative to participating firms. This suggests that the hiring decision
and participation have a lower correlation for non-participating firms
relative to participating firms. It may be that nonparticipating firms
do not know about the credit. An alternative explanation is that they
are unwilling to incur the search costs to find out about the credit;
perhaps because they believe that the cost of finding out about tax
abatement is substantial relative to the credit. In this case, the firm
has a higher tax liability than necessary as a result of not taking the
credit. Finally, a nonparticipating firm may know about the credit and
not take it because other participation costs are higher than the
credit.
Determinants of Participation
The parameter estimates for the participation equation are shown in
Table 8, the marginal effects (calculated at the variable mean), which
show how the probability of taking the JTC changes when firm
characteristics are slightly altered, are shown in Table 9. These
results generally support the hypothesis that participation depends on
the benefits and costs.
The probit model estimates show that tax liability is a
significant, positive but small influence on the firm's likelihood
of taking the JTC. As the marginal effects in Table 9 indicate, changes
in the pre-credit tax liability of the average firm has a relatively
small effect on a firm's probability of taking the JTC. According
to these results, a $10,000 increase in tax liability increases the
probability of filing for the JTC by 0.5 percentage point.
Even though the credit amount per job is higher and the job
creation threshold is lower in less developed counties, there is no
evidence that firms located in such counties are more likely to take the
credit. The tier level of the county in which the firm is located is not
a significant influence on a firm's likelihood of taking the JTC.
Even though 68 percent of participating firms are located in Tier 1
(poor) counties, the estimation results show that firms located in less
developed counties are not more likely to participate in the JTC program
when other factors are taken into account. (16)
Not unexpectedly, firms that previously took the JTC are more
likely to take the JTC in the current year. The marginal effects
indicate that previous participation has a relatively large effect on a
firm's probability of taking the credit. The model estimates
indicate that the difference in the parameter estimate for firms
previously participating in the JTC and those not participating is 1.68.
Firms headquartered in Georgia are more likely to take the JTC. As
the marginal effects indicate, changes in headquarters location has a
relatively large effect on a firm's probability of taking the
credit.
The manufacturing dummy is not a significant influence on a
firm's likelihood of taking the JTC. Even though 89 percent of the
sample are manufacturing firms, when other variables are taken into
account, this is not a significant determinant of participation. In
their study of Ohio's Job Creation Tax Credit, Gabe and Kraybill
(1998) use a larger dataset and include 18 industry dummies as
explanatory variables to determine if business establishments in certain
industries are more likely to receive a tax credit. None of the industry
variables are significant. For the Georgia and Ohio employment tax
credit programs, at least, industry does not appear to be a significant
influence on the likelihood of participation.
Jobs Attributable to the Employment Tax Credit
To evaluate the benefit of the JTC program, we would like to know
the number of jobs created as a result of the program. To estimate this,
we compare the employment change in firms that participate in the JTC
program with similar firms that do not participate in the program. A
portion of this difference in the employment change is attributable to
the JTC. (17) Maddala (1983) shows two methods of evaluating program
impact. Both methods are used here.
The first method is to compare the change in employment,
[y.sub.1i], for a participating firm i and the expected employment if
the firm had not participated. The last term in both equations is the
inverse mills ratio, and the [sigma] terms are the coefficients on the
inverse mills ratios. Under the normality assumption, the change in
employment due to participation is:
[7] [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
The change in employment potentially attributable to the JTC is the
summation over all participants. This calculation subtracts the
predicted employment change if participants had not participated from
the observed change in employment of participants. According to this
calculation, for the 1993-95 period the total number of new jobs
attributable to the JTC is 1870. This is 23.5 percent of the employment
change in participating firms. (18) For the firms in the sample, the tax
expenditure on the JTC over the 1993-95 period was just over $5 million,
so the tax expenditure per new job created is $2678 over the 1993-95
period.
Another method is to calculate the expected growth in employment
given that each type of firm participates in the tax credit program.
This calculation subtracts the predicted employment change for
nonparticipants if they had participated in the program from the
predicted employment change of participants.
[8] [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
With this calculation, the number of new jobs attributable to the
JTC is 2,196, which is 27.6 percent of the employment change in
participating firms. The tax expenditure per job is $2,280 over the
1993-95 period.
According to these estimates, between 1,870 and 2,196 jobs or
between 23.5 and 27.6 percent of the employment change in participating
firms can be attributed to the JTC. The cost per job is between $2,280
and $2,678. If all of the jobs that were credited (7,951 jobs) were
actually attributable to the JTC, then the cost per job is $630. Through
the JTC, the state reduced corporate income tax liability of
participating firms by just over $5 million between 1993 and 1995, but
$3.6 million to $3.8 million of this was a credit for jobs that would
have been created in the absence of the JTC program.
While the number of jobs attributable to the program is low, the
cost per job relative to the cost for other programs is also low. When
compared to some of the large incentive packages that states have
offered large corporations over the past several years, broadly applied
programs such as employment tax credits appear to be as effective in
terms of initial job creation as incentive packages offered to entice
large corporations to locate in a particular state. For example, the
incentive package that the state of Alabama offered Mercedes is
estimated to have cost just short of $170,000 per job for 1500 jobs.
(19)
CONCLUSIONS
This paper presents an analysis of the effect of a state job tax
credit on employment changes in eligible firms. Firm-level data from
corporate income tax returns of eligible firms are used in the analysis.
Tax liability, past participation, and headquarters location influence
participation in the JTC program. Firm size influences the employment
change of participating firms. The parameter estimates from a switching
regression model are used to estimate the employment impact of the tax
credit.
The evidence presented in this study suggests that firms are
creating jobs in response to tax incentives such as Georgia's JTC.
Firms taking the credit created 23.5 to 27.6 percent more jobs (1870 to
2196 jobs) than eligible firms not taking the credit between 1993 and
1995. The flip side of the issue is that 72.4 to 76.5 percent of the
employment change in participating firms would have been created in the
absence of the credit. Since the total tax expenditure on the JTC
program was just over $5 million during this period, the state gave up
$3.6 to $3.8 million on jobs that would have been created without the
credit.
APPENDIX
Acknowledgments
I wish to thank Roy Bahl, Chris Bjornson, Shit Gurmu, Florenz
Plassmann, Dave Sjoquist, Eric Schansberg, Mark Strazicich, Mary Beth
Walker, Douglas Holtz-Eakin, Therese McGuire, and two anonymous referees
for their helpful comments and discussions of earlier versions of this
paper. Any remaining errors are my own.
(1) A related issue is whether these credits actually lead to the
creation of new jobs or the redistribution of jobs from one geographic
area or firm to another. This issue is not addressed here.
(2) Eligible firms are those firms in qualifying industries that
create the minimum number of new jobs.
(3) Hamermesh (1978) discusses three types of subsidies for jobs:
employment subsidies, wage subsidies, and hiring subsidies. Employment
subsidies apply for the entire time a worker is with a firm. Wage
subsidies are a fixed percentage of wages, a flat dollar amount or a
fixed percentage of wages with a maximum. Hiring subsidies offset
training and hiring costs for the initial period of employment in a
firm. Employment tax credits are a form of wage subsidy.
(4) This approach assumes that firms not participating in the tax
credit program would have created more jobs if they had participated.
(5) Each year the Department of Community Affairs ranks the level
of economic development in each of Georgia's 159 counties and
assigns each county to one of three tiers. A county's level of
economic development is determined by its unemployment rate, average
manufacturing wage, poverty rate, and per capita income. Tier 1 counties
are the least developed, while tier 3 counties are the most developed.
(6) The following analysis assumes that the credit is applied only
to corporate tax liability and is nonrefundable, as is the case for the
majority of state employment tax credit programs.
(7) Annual Reports, Georgia Department of Revenue.
(8) For multi-establishment firms, data has been aggregated to the
firm level.
(9) Model identification deserves further explanation. The tax
liability variable and the previous JTC variable essentially identify
the model. Tax liability is statistically significant in the
participation equation and not included in the employment change
equations. The previous JTC variable is significant in the participation
equation and insignificant (as a nonlinear transformation of the
original variable through the IMR) in the employment equations. As such,
nonlinearity aids in the identification of the model. The author would
like to thank one of the anonymous referees for providing detailed
comments on this issue.
(10) The ES202 dataset contains information on monthly employment
levels, industry, unemployment tax payments, and total wage bill and
county for each business establishment in Georgia.
(11) Some of the firms are in the sample for more than one year
during the 1993-95 period.
(12) This paper addresses the impact of employment tax credits in
firms eligible (created the minimum number of jobs necessary to qualify)
to take the credit and thereby excludes firms that did not create enough
jobs to qualify for the credit. Due to the exclusion of these firms, the
estimates of the employment impact may be lower than they would be if
noneligible firms had been included in the analysis.
(13) The dramatic increase in the average employment change and
total employment change for nonparticipating firms between 1994 and 1995
is the result of one firm that grew by over 2,000 employees.
(14) The author would like to thank one of the anonymous referees
for suggesting the use of this variable.
(15) This might seem to conflict with the finding that smaller
firms are more likely to take the credit. However, even though larger
firms are less likely to take the credit, among firms taking the credit,
larger firms create more jobs than larger firms that do not take the
credit.
(16) Of the startups, 33 percent (13 firms) are located in Tier 1
counties.
(17) This is an estimate of the maximum (upper bound) effect of the
credit on employment in participating firms. In reality, only a portion
of the this difference is likely to be attributable to the credit since
many other factors influence employment decisions.
(18) The employment change is the number of new jobs reported by a
firm taking the JTC. Since some of these jobs would have been created in
the absence of the credit, only a portion of these jobs is actually
attributable to the JTC.
(19) However, because employment is more concentrated, the
multiplier effects associated with a large plant citing are likely to be
larger than those associated with a broadly applied employment tax
credit.
REFERENCES
Bartik, Timothy J. Who Benefits from State and Local Economic
Development Policies? Kalamazoo, MI: W.E. Upjohn Institute, 1991.
Barlett, Donald L., and James B. Steele. "Corporate
Welfare." Time (November 9, 1998a): 36-54.
Barlett, Donald L., and James B. Steele. "Fantasy Islands and
Other Perfectly Legal Ways That Big Companies Manage to Avoid Billions
in Federal Taxes." Time (November 16, 1998b): 79-93.
Barlett, Donald L., and James B. Steele. "Paying a Price for
Polluters." Time (November 23, 1998c): 72-82.
Barlett, Donald L., and James B. Steele. "The Empire of the
Pigs." Time (November 30, 1998d): 52-66.
Barton, John M., and John Bishop. "Extensive Search, Intensive
Search, and Hiring Costs: New Evidence on Employer Hiring
Activity." Economic Inquiry 23 No. 3 (July, 1985): 363-82.
Bishop, John H., and Mark Montgomery. "Does the Targeted Job
Tax Credit Create Jobs at Subsidized Firms?" Industrial Relations
32 No. 3 (Fall, 1993): 289-306.
Faulk, Dagney. "Georgia's Job Tax Credit: An Analysis of
the Characteristics of Eligible Firms." Fiscal Research Program
Report No. 8. Atlanta: Georgia State University, June 1998.
Gabe, Todd M., and David S. Kraybill. "Tax Incentives and
Offers in a State Economic Development Program." Review of Regional
Studies 28 No. 3 (1998): 1-14.
Gabe, Todd M., and David S. Kraybill. "The Effects of Tax
Incentives on Enterprise Growth." Department of Agricultural,
Environmental and Development Economics, The Ohio State University.
Mimeo, 1999.
General Accounting Office. Targeted Jobs Tax Credit Employer
Actions to Recruit, Hire, and Retain Eligible Workers Vary. GAO HRD
91-33, Washington, D.C., February 1991.
Hamermesh, Daniel S. Labor Demand. Princeton, NJ: Princeton
University Press, 1993.
Hamermesh, Daniel S. "Subsidies for Jobs in the Private
Sector." In Creating Jobs: Public Employment Programs and Wage
Subsidies, edited by John L. Palmer, 87-122. Washington, D.C.: Brookings
Institution, 1978.
Hamermesh, Daniel S. "Econometric Studies of Labor Demand and
Their Applications to Policy Analysis." Journal of Human Resources
11 No. 4 (Fall, 1976): 507-25.
Maddala, G. S. Limited Dependent and Qualitative Variables in
Econometrics. Cambridge: Cambridge UP, 1983.
Perloff, Jeffrey M., and Michael L. Wachter. "The New Jobs Tax
Credit: An Evaluation of the 1977-78 Wage Subsidy Program."
American Economic Review 69 No. 2 (May, 1979): 173-9.
Pope, Ralph A., and James L. Kuhle. "Tax Credits for Job
Creation and Job Retention in the California Economy." Public
Finance Quarterly, 24 No. 2 (April, 1996): 192-215.
Rice, Eric M. "The Corporate Tax Gap: Evidence on Tax
Compliance by Small Corporations." In Why People Pay Taxes Tax
Compliance and Enforcement, edited by Joel Slemrod, 125-61. Ann Arbor,
MI: Michigan University Press, 1992.
Wagner, Joachim. "Firm Size, Firm Growth, and Persistence of
Chance: Testing Gibrat's Law with Establishment Data from Lower
Saxony, 1978-89." Journal of Small Business Economics 4 No. 2
(June, 1992): 125-31.
Wasylenko, Michael. "Taxation and Economic Development: The
State of the Economic Literature." New England Economic Review
(March/April, 1997): 37-52.
Wasylenko, Michael. "The Location of Firms: The Role of Taxes
and Fiscal Incentives." In Urban Government Finance Emerging
Trends, Volume 20, Urban Affairs Annual Review, edited by Roy Bahl,
155-90. Beverly Hills, CA: Sage Publications, 1981.
Employment tax credits have become one of the primary tools of
state economic development policy. A recurring question is whether these
credits create jobs that would not have been created in their absence.
While numerous studies have shown that taxes, in general, have small or
insignificant effects on employment, this study focuses on a specific
tax incentive, state employment tax credits, and uses firm-level data
from state corporate income tax returns.
This paper discusses the benefits and costs of participation in
employment tax credit programs and provides estimates of the employment
impact of such credits by comparing the employment change in eligible
firms that participate in employment tax credit programs with eligible
firms that do not participate in such programs. The empirical model
jointly estimates the decision to participate in a tax credit program
and the effect of such a tax credit on employment using data from firms
eligible to take Georgia's Job Tax Credit (JTC).
Evidence presented in this study suggests that firms are creating
jobs in response to tax incentives such as Georgia's JTC. Results
from a switching regression model indicate that firms taking
Georgia's Jobs Tax Credit created 23 to 28 percent more jobs (1870
to 2196 more jobs on average) than eligible firms not taking the credit
between 1993 and 1995. The cost per job is $2280 to $2680 over the 1993
to 1995 period. While the maximum number of jobs potentially
attributable to the program is small, the cost per job is also low
especially when compared with firm--specific incentive packages.
Dagney Faulk Indiana University Southeast, New Albany, IN
47150-6405
TABLE A-1
GEORGIA'S JOB TAX CREDIT ELIGIBILITY REQUIREMENTS, MINIMUM JOB
CREATION CRITERIA, AND CREDIT AMOUNTS PER JOB CREATED
Tax Year Tier 1 Counties Tier 2 Counties Tier 3 Counties
1991 Jobs: 10 Not Eligible Not Eligible
Credit: $1,000
1992 Jobs: 10 Not Eligible Not Eligible
Credit: $1,000
1993 Jobs: 10 Jobs: 10 Not Eligible
Credit: $2,000 Credit: $1,000
1994 Jobs: 10 Jobs: 10 Not Eligible
Credit: $2,000 Credit: $1,000
1995 Jobs: 10 Jobs: 25 Jobs: 50
Credit: $2,500 Credit: $1,500 Credit: $500
1996 Jobs: 10 Jobs: 25 Jobs: 50
Credit: $2,500 Credit: $1,500 Credit: $500
1997 Jobs: 10 Jobs: 15 Jobs: 25
Credit: $2,500 Credit: $1,500 Credit: $500
TABLE A-2
DESCRIPTION OF VARIABLES USED IN THE EMPLOYMENT EQUATION
Variable Description
Employment Employment in period t less employment
Change in period t - 1
Initial Employment Employment level in the base year within
the state of Georgia
Plant Number of establishments within firm
(tax entity)
Previous JTC =1 if firm took JTC in a previous year
=0 otherwise
Startup =1 if the base year employment was zero
=0 otherwise
Age of firm Age measured as date of incorporation in
Georgia less the income tax year
Rank Tier ranking of county where firm is
located (This is an indicator of the level of
development of the county)
Lambda Inverse Mills Ratio
Variable Source
Employment Georgia Corporate Income Tax
Change Returns or ES202 data
Initial Employment Georgia Corporate Income Tax
Returns or ES202 data
Plant ES202 data
Previous JTC Georgia Corporate Income Tax
Returns
Startup Georgia Corporate Income Tax
Returns or ES202 data
Age of firm Georgia Corporate Income Tax
Returns
Rank Georgia Corporate Income Tax
Returns or ES202 data
Lambda Calculated from the
participation (probit) equation
TABLE A-3
DESCRIPTION OF VARIABLES USED IN THE PARTICIPATION EQUATION
Variable Description
Participation =1 if firm took the JTC (had a positive JTC)
Dummy =0 if firm did not take the JTC or claimed
zero JTC.
Tax Liability * Pre JTC tax liability on the Georgia
Corporate Income Tax return (in 10,000s).
Tier 1 Dummy =1 if located in a Tier 1 county.
=0 otherwise.
Tier 2 Dummy =1 if located in a Tier 2 county.
=0 otherwise.
Initial The number of employees in the base year
Employment **
Previous JTC =1 if firms took JTC previously
=0 otherwise
Effective JTC * The JTC that a firm claims (in participating
firms) or can potentially claim (in
nonparticipating firms) divided by the
number of credited or potentially credited
jobs (in 100s).
Headquarters =1 if firm's headquarters is in Georgia
Location =0 otherwise
Manufacturing =1 for manufacturing firms
Dummy =0 otherwise
Start up =1 if the base year employment was zero
=0 otherwise
Rank x Year 95 Interaction of the rank of the county
where firm is located and a year dummy
Variable Source
Participation Georgia Corporate Income
Dummy Tax Returns and ES202 data
Tax Liability * Georgia Corporate Income
Tax Returns
Tier 1 Dummy Georgia Corporate Income
Tax Returns or ES202 data
Tier 2 Dummy Georgia Corporate Income
Tax Returns or ES202 data
Initial Georgia Corporate Income
Employment ** Tax Returns or ES202 data
Previous JTC Georgia Corporate Income
Tax Returns
Effective JTC * Georgia Corporate Income
Tax Return and Author's
calculation
Headquarters Georgia Corporate Income
Location Tax Returns
Manufacturing Georgia Corporate Income
Dummy Tax Returns or ES202 data
Start up Georgia Corporate Income
Tax Returns or ES202 data
Rank x Year 95 Author's calculation
* Tax liability and the Effective JTC were scaled by 10,000 and
100, respectively so that their order of magnitude would be
similar to the other variables in the model.
** For multi-establishment firms, annual employment for all
establishments in a particular firm that participates
or is eligible to participate in the JTC program is used.
TABLE 1
SUMMARY STATISTICS OF VARIABLES USED IN THE EMPLOYMENT
EQUATION, PARTICIPATING FIRMS
Variable Mean Standard Deviation
Employment Change 68.2857 100.5274
Initial Employment 250.3714 352.3007
Plant 5.6857 9.7185
Previous JTC 0.5857 0.4961
Startup 0.1285 0.3371
Age 18.6428 17.2954
Rank 46.4142 39.3461
IMR 0.4851 0.4881
Obs. = 70
Variable Minimum Maximum
Employment Change -35 483
Initial Employment 0 1259
Plant 1 52
Previous JTC 0 1
Startup 0 1
Age 0 67
Rank 1 158
IMR 0.119E-13 1.70
Obs. = 70
TABLE 2
SUMMARY STATISTICS OF VARIABLES USED IN THE EMPLOYMENT
EQUATION, NONPARTICIPATING FIRMS
Variable Mean Standard Deviation
Employment Change 99.1604 240.1943
Initial Employment 344.6172 687.7551
Plant 3.2469 6.7943
Previous JTC 0.0617 0.2421
Startup 0.3703 0.4859
Age 19.8888 19.0794
Rank 74.8168 42.6097
IMR -0.4192 0.3992
Obs. = 70
Variable Minimum Maximum
Employment Change -15 2062
Initial Employment 0 4540
Plant 1 57
Previous JTC 0 1
Startup 0 1
Age 0 84
Rank 4 159
IMR -1.95 -0.214E-01
Obs. = 70
TABLE 3
SUMMARY STATISTICS OF VARIABLES USED IN THE PARTICIPATION EQUATION
Standard
Variable Mean Deviation
Participation Dummy 0.4635 0.5003
Tax Liability ($ ten thousands) 30.2178 82.2216
Tier 1 Dummy 0.5099 0.5015
Tier 2 Dummy 0.3509 0.4788
Initial Employment 300.9271 558.2
Previous JTC 0.3046 0.4617
Effective JTC (in hundreds) 5.7299 6.9469
Headquarters Location 0.5695 0.4967
Manufacturing Dummy 0.8940 0.3088
Start up 0.2582 0.4391
Rank * Year 95 46.2875 51.5242
Obs.= 151
Minimum Maximum
(Counts for (Counts for
Variable Dummy Variables) Dummy Variables)
Participation Dummy 0(81) 1(70)
Tax Liability ($ ten thousands) 0 517.41
Tier 1 Dummy 0(74) 1(77)
Tier 2 Dummy 0(98) 1(53)
Initial Employment 0 4540
Previous JTC 0(105) 1(46)
Effective JTC (in hundreds) 0 39.12
Headquarters Location 0(65) 1(86)
Manufacturing Dummy 0(16) 1(135)
Start up 0(112) 1(39)
Rank * Year 95 0 159
Obs.= 151
TABLE 4
EMPLOYMENT CHANGE BY YEAR AND PARTICIPATION STATUS
Average Employment Change
1993 1994 1995
Participating Firms 54.8 58.7 82.4
Nonparticipating Firms 27 35.1 123.3
Total Employment Change
1993 1994 1995
Participating Firms 877 1349 2554
Nonparticipating Firms 54 702 7276
TABLE 5
USE OF GEORGIA'S JTC BY PARTICIPATING FIRMS (IN DOLLARS)
Mean Std. Dev. Sum
Carryforward from Previous years 80,310 147,089 5,621,713
Available JTC in Current Year 109,870 143,406 7,690,917
Total JTC Available * 190,180 261,445 13,312,630
JTC Taken in Current Year 71,548 115,804 5,008,399
Effective Credit 682 639 47,764
Obs.=70
Min Max
Carryforward from Previous years 0 819,021
Available JTC in Current Year 0 834,332
Total JTC Available * 0 1,607,858
JTC Taken in Current Year 414 585,125
Effective Credit 6 3911
Obs.=70
* Carryforward from Previous years plus JTC available in current year.
TABLE 6
PARAMETER ESTIMATES OF THE
EMPLOYMENT MODEL, PARTICIPATING FIRMS
Parameter Standard
Variable Estimate Errors T-ratio
Intercept 69.7694 47.8099 1.459
Initial
Employment 0.1360 * 0.0365 3.724
Plant -0.9922 1.3467 -0.737
Previous JTC -46.3685 36.2462 -1.279
Startup 46.9534 36.1348 1.299
Age 0.6412 0.7367 0.870
Rank -0.3116 0.3203 -0.973
IMR -12.9749 35.0488 -0.370
Obs. = 70 R-sq. = .2627
Note: The final step of Heckman's procedure was
implemented using GLS.
** Significant at the 0.05 level in a two-tailed test.
TABLE 7
PARAMETER ESTIMATES OF THE
EMPLOYMENT MODEL, NONPARTICIPATING
FIRMS
Parameter Standard
Variable Estimate Errors T-ratio
Intercept 6.5957 92.9532 0.071
Initial
Employment 0.0803 ** 0.0444 1.810
Plant 2.5156 4.0337 0.624
Previous JTC -20.9658 165.7702 -0.126
Startup 116.3356 ** 63.1789 1.841
Age -1.9772 1.4655 -1.349
Rank 0.6978 0.7351 0.949
IMR -4.8330 113.171 -0.043
Obs. = 81 R-sq. = .1285
Note: The final step of Heckman's procedure was
implemented using GLS.
* Significant at the 0.10 level in a two-tailed test.
TABLE 8
PROBIT PARAMETER ESTIMATES OF THE
PARTICIPATION MODEL
Parameter Standard
Variable Estimate Errors T-ratio
Intercept -0.0038 0.9693 -0.004
Tax Liability 0.0126 ** 0.0056 2.231
Tier 1 Dummy -0.5197 0.8260 -0.629
Tier 2 Dummy -0.7742 0.6593 -1.129
Initial
Employment -0.0003 0.0004 -0.738
Previous JTC 1.6836 ** 0.3421 4.922
Effective JTC 0.0266 0.0246 1.079
Headquarters
Location 0.7904 ** 0.2841 2.781
Manufacturing
Dummy -0.4188 0.5118 -0.818
Start up -0.3862 0.3479 -1.110
Rank/Year 95
Interaction -0.0081 0.0052 -1.560
Obs.= 151
Goodness of Fit: The joint predictions for the model
were 71/81 for JTCD = 0 and 53/70 for JTCD = 1. The
total predictions were 88/81 for JTCD = 0 and 63/70
for JTCD = 1.
Log likelihood function = -60.84664
**Significant at the 0.05 level in a two-tailed test.
TABLE 9
MARGINAL EFFECTS OF PARTICIPATION
IN THE JTC PROGRAM
Effect on the
Probability of Standard
Variable Taking the JTC Errors T-ratio
Tax Liability 0.0050 ** 0.0022 2.243
Tier 1 Dummy -0.2072 0.3294 -0.629
Tier 2 Dummy -0.2967 0.2630 -1.128
Initial
Employment -0.0001 0.0001 -0.739
Previous JTC 0.6712 ** 0.1359 4.939
Effective JTC 0.0106 0.0098 1.078
Headquarters
Location 0.3151 ** 0.1133 2.782
Manufacturing
Dummy -0.1669 0.2041 -0.818
Start up -0.1539 0.1387 -1.109
Rank/Year 95
Interaction -0.0032 0.0021 -1.560
Note: Marginal effects are calculated at mean values
of the independent variables.
** Significant at the 0.05 level in a two-tailed test.