On The Relative Distortions of State Sales and
Corporate Income Taxes
Donald Bruce
Center for Business and Economic Research
University of Tennessee
(dbruce@utk.edu)
John Deskins*
Department of Economics and Finance
Creighton University
(johndeskins@creighton.edu)
William Fox
Center for Business and Economic Research
University of Tennessee
(billfox@utk.edu)
June 2008
*Corresponding Address: John Deskins, College of Business Administration, Creighton
University, 2500 California Plaza, Omaha, NE 68178
1
On The Relative Distortions of State Sales and Corporate Income Taxes
Abstract: We use a 1985-2005 panel of state data to investigate the relative tax base
elasticities of two major US state taxes—corporate income taxes and sales taxes—with
respect to changes in their tax rates. We focus on these two taxes because they are
essentially flat-rate taxes at the state level, making it relatively straightforward to
construct estimates of their bases using aggregate collections and rate data. Building on
recent work on the elasticity of taxable income for the federal personal and corporate
income taxes, we estimate several regressions of alternative measures of state tax bases
on state tax rates and other controls. We find that both tax bases are responsive to
changes in tax rates, and that neither one is universally more elastic than the other. Our
estimated elasticities are a bit larger than estimates from recent federal studies, due in
large part to taxpayers’ ability to move taxable base across state lines without changing
federal taxable income.
JEL Codes: H2, H7
2
1. Introduction
The efficiency implications of tax policy rest fundamentally on the degree to
which government intervention distorts the behavior of firms and individuals. An
increasingly common way to quantify the overall distortions of particular tax policies is
to examine the responsiveness of the tax base with respect to the tax rate. Most prior
studies in this area have examined the responsiveness of taxable income to tax rates,
focusing on the federal personal income tax (see Feldstein, 1995, and Gruber and Saez,
2002, for example). Additional research has examined the effects of the federal corporate
income tax (Gruber and Rauh, 2005). This approach has the benefit of providing a broad
and more general means of capturing the overall welfare consequences of tax policy
relative to focusing on a particular aspect of the behavioral effect of taxes, such as labor
supply or savings behavior, since examination of the overall tax base change
encompasses all behavioral elements. 1
Only a very small number of studies have been identified that examine, wholly or
in part, the elasticity of taxable income for state-level taxes (Long, 1999, for example).
In this study, we investigate the relative distortions of state sales and corporate income
taxes by constructing a series of regression models to estimate the responsiveness of each
state tax base to changes in the respective tax rate. We focus on these two taxes because
in practice, they typically operate as essentially flat-rate systems at the state level. It is
therefore straightforward to calculate accurate approximations of their tax bases by
dividing aggregate collections by the tax rate. State individual income taxes, which are a
major source of state tax revenue, typically have progressive rate structures and also
typically involve tax bases that differ markedly from such available proxies as personal
3
income. Using a panel of state data spanning the years 1985 to 2005, we examine several
alternative measures of tax bases in order to provide a broad sense of the robustness and
reliability of our results.
A better understanding of the relative distortions caused by state sales and
corporate income taxes will aid in the design of more efficient tax systems on three
dimensions. First, it will provide information to help determine the optimal size of
government by providing a better understanding of the deadweight costs of taxation that
partially offset the marginal benefits of publicly-provided goods and services. Second,
the results will aid in the design of more efficient state tax portfolios in the choice of
whether to place a relatively heavier reliance on sales or corporate income taxes to fulfill
revenue requirements. Third, when compared with the available results from studies of
federal taxes, the results can speak to the debate concerning the optimal allocation of
taxing responsibilities of national versus sub-national governments in a federal system by
providing information on the relative distortions of taxes at these different levels of
government.
Analysis of taxable base elasticities at the state level adds several dimensions that
have not received significant attention at the national level. First, cross-state tax base
mobility can add an important cause of distortions. That is, in addition to all the ways in
which taxes may distort behavior when imposed at the national level, they can induce
taxpayers to shift potentially taxable bases, either by relocating real economic activity or
by engaging in tax planning, between states. The potential for cross-state mobility to
affect the elasticity is likely to vary between taxes on sales, individual income and
corporate income. An extensive literature has developed on how specific taxes affect
1
Feldstein (1995) makes an effective case for examining the responsiveness of taxable income.
4
business location (see Wasylenko, 1997), but this literature only accounts for part of the
overall taxable base elasticity. 2
Further, the measured base responses could depend on a set of tax-specific
institutional rules that determine which activity is taxable and where. The specific rules
can influence a taxpayer’s ability to engage in tax planning activities, and therefore the
taxable base elasticity, without affecting the mobility of real activity. The rules also
influence the base breadth, and the elasticity may differ across components of the base.
The standard determining nexus is a key aspect affecting the capacity to engage in tax
planning. Sales tax nexus requires physical presence. 3 On the other hand, essentially
every state uses either a “doing business” or an “earning income” concept to define nexus
for the corporate income tax (CIT). The CIT nexus standards generally do not require
physical presence for corporations to be taxable, though the issues continue to be
litigated. Further, states have been relatively aggressive in seeking to limit corporate tax
planning strategies. Some impose combined reporting rules, others assert nexus over
passive investment companies without physical presence and others disallow deductions
for some types of related companies without physical presence (Fox, Luna and Murray,
2005).
Our results indicate that taxable bases respond significantly to tax rates. More
specifically, we find that both the sales and the corporate income tax bases are responsive
to changes in statutory tax rates, and that neither tax base is universally more elastic than
the other. Furthermore, consistent with our expectations, our estimated elasticities for
2
3
The literature is more limited on individual taxes (for example, see Conway and Houtenville, 2001).
Quill v. North Dakota, 112 U.S. 298 (1992).
5
state sales and corporate income taxes are slightly larger than corresponding estimates for
federal personal and corporate income taxes from the earlier research noted above.
2. Earlier Research on Taxable Income Elasticities
Only a small number of studies have been identified that examine the
responsiveness of taxable bases to changes in tax rates for state sales, personal income, or
corporate income taxes. However, a large body of literature has developed recently on
the taxable income elasticity of the federal personal and corporate income taxes, both of
which are relevant to the present study.
Lindsey (1987) and Feldstein (1995) were among the first to estimate the
elasticity of taxable income with respect to marginal tax rates, focusing on the Economic
Recovery Tax Act of 1981 and the Tax Reform Act of 1986, respectively. Calculated
elasticities in both studies exceeded -1.0 in absolute value, suggesting that the overall
deadweight loss of the personal income tax (PIT) was substantial. More recent studies of
the same tax reforms using arguably better data sources and methods found smaller
elasticities, centering on a value of -0.7 (Navratil, 1995, and Auten and Carroll, 1999).
Studies of data surrounding the Omnibus Budget Reconciliation Act of 1993 by
Sammartino and Weiner (1997) and Goolsbee (2000) considered the timing of induced
changes in taxable income, revealing large short-term elasticities but smaller mid- or
long-term elasticities.
As data and methods have improved over time, consensus estimates on the
elasticity of taxable income have gradually fallen. Gruber and Saez (2002) examined
data from 1979 through 1990 and reported an overall elasticity of taxable income of -0.4
and an elasticity of adjusted gross income (AGI) of -0.2. Saez (2003) noted that the ideal
6
tax change for analyzing behavioral responses is mostly a rate change (with few other
rule changes), that affects similar taxpayers differently. His ideal case was the “bracket
creep” of the early 1980s, in which tax rates changed even though real income might
have remained constant. Using this to identify treatment and control groups, Saez (2003)
estimated an elasticity of taxable income of -0.3, with elasticities of AGI and wages
revealing that most of the response to tax rate changes involves reporting changes rather
than labor supply fluctuations.
Kopczuk (2005) placed this body of literature in perspective. Noting that most
tax reforms involve base (and other) changes in addition to tax rate changes, he
concluded that no permanent or structural elasticity of taxable income exists. The
elasticity varies over time as different policies change, and within a period of time for
varying types of taxpayers. Additional evidence on the variability of taxable income
elasticities has been presented in recent work by Giertz (2006) and Heim (2006).
While most of the available literature in this broad area has focused on the federal
PIT, some recent research has considered the taxable income elasticity for the federal
CIT. Specifically, Gruber and Rauh (2005) use micro-level data to estimate the
responsiveness of corporate income to changes in a constructed marginal effective
corporate income tax rate, at the federal level. Their primary specification estimates an
elasticity of taxable income for the federal CIT of -0.2.
Very little research has considered these issues with state-level taxes. Long
(1999) is perhaps the earliest exception. He examined changes in taxable income
reported on federal personal income tax returns in response to variations in state personal
income tax rates using micro-level data from 1991. He estimated a taxable income
7
elasticity ranging from around -0.2 to around -0.4, depending on income level, in
response to state marginal tax rate differentials.
In another recent state-level study, Bruce, Deskins, and Fox (BDF, 2007)
examined the elasticity of taxable income for state corporate income taxes as a function
of the CIT rate, holding constant state economic activity and other variables. BDF’s
approach focused on how firms respond to tax differentials through tax planning, which
does not account for the entire set of potential behavioral responses, but BDF employed a
similar methodology to that adopted here. The technique used by BDF isolated location
distortions (i.e., the movement of physical economic activity) from tax planning
activities, which they consider to be the myriad ways in which businesses shift taxable
income across state boundaries (or ensure it is not taxable anywhere) to reduce tax
liabilities without moving physical business activity. They found that the corporate
income tax base shrinks by around seven percent following a one-percentage-point
increase in the CIT rate, abstracting from locational distortions. The total response of
taxable income to the tax rate (tax planning distortions plus locational distortions) is
expected to be at least as great as the tax planning response found in BDF because of the
additional potential for physical economic activity to move between states. BDF did not
study state personal income or sales taxes.
We are sympathetic to the basic message of Kopczuk (2005), Giertz (2006), and
Heim (2006) that the pursuit of a single taxable income (or base) elasticity is perhaps a
fruitless exercise. With this in mind, we focus instead on a comparison of relative
taxable base elasticities across two major state taxes using aggregate data. This is
perhaps a more interesting policy issue at the state level since state governments rely on a
8
broader menu of tax instruments to fund public services. Better information about the
relative distortions of these alternative taxes might enable state policy makers to design
more efficient tax systems.
3. Empirical Design and Data
This study attempts to better understand the distortionary effects of state sales and
corporate income taxes from a broad perspective. Our basic approach is to estimate a
series of regressions of the tax base on the tax rate, for each tax, to produce an estimate of
the elasticity of taxable base and, correspondingly, an understanding of the relative
efficiency properties of each tax. Our panel regression models take the following forms:
Sales Tax Basei,t = α0 + α1 Sales Tax Ratei,t + α2 Populationi,t + S i + T t + εit,
CIT Basei,t = β0 + β1 CIT Ratei,t + β2 Populationi,t + S i + T t + μit,
where i and t are state and year indices, the Si terms are state fixed effects, the Tt terms
are year fixed effects, and the εit and μit terms represent well-behaved residuals. Our
panel of data covers all 50 states for the years 1985 through 2005. Note that population is
included as a control variable in each model in order to account for scale issues. We now
turn to a detailed description of all regression variables. 4
Tax Bases
We begin by describing our measures for the sales and corporate income tax
bases. For the sales tax base, our first measure is the actual sales tax base as reported by
state revenue departments. Unfortunately, we were only able to acquire a sufficiently
4
Summary statistics are presented in Appendix Table 1 and variable descriptions and source notes are
presented in Appendix Table 2.
9
long time series of sales tax base data for a subset of sales-taxing states. 5 Given the
limited number of observations on the actual tax base, we rely on the flat-rate nature of
most state sales taxes in calculating an alternative measure of the base as sales tax
revenues divided by the state general sales tax rate. 6 Of course, this approach may yield
some mismeasurement because several states use more than one tax rate. For example,
seven states tax food for consumption at home at a preferred rate and others levy higher
rates on hotel rooms or selected other transactions. Nonetheless, the correlation
coefficient between the state-reported sales tax base and our estimated measure is 0.91
among states with data for both measures, which provides considerable comfort in using
the estimated base.
We also attempted to gather corporate income tax base data from each state, but
had far less success than for the sales tax. 7 Thus we rely exclusively on two estimated
CIT base measures. Our first measure is CIT collections divided by the top marginal
state CIT rate for those states with a corporate income tax. 8 This approach is more likely
to suffer from division bias as well as measurement error for the CIT compared to the
sales tax since states depart from flat rates more frequently with the CIT. The
consequences are likely to be minor in this context for two reasons: First, the majority of
5
We were able to acquire actual sales tax base data from a) four states for the full period 1985 through
2005, b) 15 states for between six and 14 years, and c) three states for five years or less. Overall, we have
231 observations for actual sales tax bases reported by states.
6
Alaska, Delaware, Montana, New Hampshire, and Oregon have no broad based sales tax and are excluded
from the sales tax portion of this study (Federation of Tax Administrators, various years).
7
For the CIT, we were able to acquire actual base data for the full period 1985 through 2005 for only two
states; we were able to acquire data for five or more years but less than the entire study period from 8
states, and data for one to four year from three states. Overall, we have 133 observations for actual the CIT
base as reported by states.
8
Nevada and Wyoming have no broad business tax. Michigan imposed a single business tax until 2007
(sometimes described as a business activities tax or value added tax). Texas is currently phasing out a
franchise tax on earned surplus. South Dakota imposes a corporate income tax on banks. Washington
imposes a gross receipts tax termed the business and occupations tax. (Federation of Tax Administrators,
10
states (31 out of 44 that taxed corporate income in 2001) have a single rate. Second, the
threshold for the top bracket is relatively low in the 13 states with progressive rate
schedules, such that the majority of income falls into the top bracket. Indeed, a simple
correlation coefficient between the actual CIT base data that we were able to acquire and
our approximated measure is 0.96 among states with data for both measures. 9 As another
alternative CIT base measure, we divide federal CIT collections (reported for each state)
by the top marginal federal CIT rate. We use this measure purely for comparison
purposes, however, because differences between the federal and state CIT structures lead
us to strongly prefer base measures that are derived from state data.10
Tax Rates and Other Control Variables
For sales tax rates, we use the statutory general sales tax rate in each state.11 We
measure the CIT rate as the top statutory marginal CIT rate. The use of the top rate
potentially introduces measurement error since not every corporation is taxed at the
highest marginal rate. The effect of this will be the familiar attenuation bias: estimated
elasticities will be closer to zero than the true values. However, this error is likely to be
relatively small for the same reasons stated above. Also, we feel that the statutory top
marginal rate is the most appropriate measure since 1) most income is taxed at the top
2004) For the purposes of our analysis, Michigan, Texas, South Dakota, and Washington are treated as if
they have no CIT.
9
We estimate a parallel model that modifies our baseline to include the income threshold for the top CIT
bracket, since this top bracket threshold is directly correlated with the degree to which the tax base is
understated by our estimation method. Results for this alternative model, which can be found in Appendix
Table 3, are largely similar to the baseline results.
10
An important distinction is that federal CIT data are generally reported for the state from which the firm
files its return rather than the states where economic activity is located.
11
Significant variation exists in statutory CIT and sales tax rates, both between states and within states,
during the time frame of this analysis. For each of these taxes, over half of the states changed the rate at
least one time from 1985 to 2005.
11
rate, and 2) many firms likely view the top marginal rate as the most important policy
signal at the aggregate level.
In our baseline models we include only the tax rate and a population control, as
described in equations 1 and 2 above. However, we go on to estimate other models that
include several additional elements to account for differences in the defined base breadth.
For the sales tax, we include in these additional models several dummy variables that
denote exemptions of certain categories of purchases from sales taxation. In particular,
we include dummy variables that denote the presence of exemptions for a) groceries, b)
prescription drugs, c) farm equipment, and d) utilities. We also include four dummy
variables to denote when these four items are taxed at reduced rates. Complete data for
these exemptions are only available for the period 1994 through 2005. 12
In the more detailed CIT models, we include several elements of the CIT structure
that would be incorporated in an effective tax rate calculation. For example, we include
the sales factor weight in the state corporate income tax apportionment formula. The
apportionment formula uses a state’s share of the corporation’s national property, sales,
and payroll to distribute the corporation’s national profits to the state for tax purposes.
These three factors are added together using weights that the states have been varying as
economic development tools. In general, for given tax rates, locating relatively more
payroll and property in a state with a high sales factor weight while selling in other states
will reduce tax liability compared with locating the payroll and property in a state with a
low sales factor weight and higher weights on property and payroll factors (see Edmiston,
2002). We measure the sales factor weight through the use of three categorical variables
that denote a) an equally weighted formula in which the sales factor weight is one-third
12
(our reference category), b) a sales factor weight of 50 to 60 percent, and c) a sales factor
weight of greater than 60 percent.
States have aggressively increased the weight on the sales factor in the formula to
lessen origin-based taxation and increase destination taxation. This may reduce the
corporate income tax on many multistate (and presumably more mobile) firms without
affecting the tax liability of firms that produce and sell within a single state and therefore
do not apportion income. For example, in 1990, 32 of the 44 CIT states applied equal
weight to all three factors. By 2004, only 12 states applied equal weight to all factors,
while 23 states double-weighted the sales factor (a 50 percent weight), and the remainder
applied more than 50 percent weight to sales. 13
A dummy variable is used to control for the presence of a combined reporting
requirement to help explain state CIT base fluctuations. Combined reporting could
reduce economic activity in a state by driving away firms if such requirements effectively
raise the CIT burden by disallowing some tax planning opportunities, but it could also
broaden the base by lowering the potential for tax planning. Another dummy variable is
also included to identify whether states impose throwback rules, which are intended to
raise the CIT base by including in firms’ sales factor those transactions that cannot be or
are not taxed in the destination state. Throwback rules can create location distortions by
raising the origin component of the CIT. 14
Next we include the top marginal personal income tax (PIT) rate to allow for the
possibility of base shifting between the PIT and CIT. For example, a higher PIT rate may
12
Observations for 14 states are missing for 1996. We drop these observations from our analysis.
A significant amount of this variation occurred during the time period of this analysis. Indeed, 24 states
increased their sales factor weight at least once during this time period.
13
13
entice firms to hold more retained earnings, as opposed to paying higher dividends, thus
affecting the CIT base. Also, relative PIT and CIT rates might affect the selected
organizational forms of new firms, as firms choose between C-Corp, S-Corp, LLC,
partnership and other business structures. Our final CIT base determinant is a dummy
denoting whether states permit limited liability companies (LLCs; see Fox and Luna,
2005). The LLC structure can be preferred over the C-corporation structure because
LLCs also offer limited liability, but in many cases they are treated as pass-through
entities with the income taxed only under the PIT system. 15 Further, LLCs are often
exempt from some other corporate taxes, such as the corporate license tax in Louisiana.
In addition, single-member LLCs allow for several means of tax planning within broader
corporate structures. 16
5. Results and Discussion
Sales Tax Results. We begin our discussion of empirical results with the sales tax.
Note that tax base and tax rate measures enter our regressions in natural logs to facilitate
elasticity interpretations of coefficient estimates. Additionally, our estimates are not
directly comparable to earlier elasticity-of-taxable-income estimates, which typically
include the net-of-tax price rather than the tax rate itself. To facilitate comparisons to
earlier results, we multiply our elasticities by (τ-1)/τ, where τ is the average value of the
14
Combined reporting requirements and throwback rules are very common and visible policies that have
been used to offset the effects of tax planning but are not the only tools available to limit tax planning. It is
much more difficult to obtain reliable data on other policies for all states over a sufficient period of time.
15
The LLC structure also offers some advantages over S-corporations. For example, there is no limit on the
number of members of an LLC whereas an S corporation is limited to 100 shareholders (75 before 2005).
16
See Bruce, Deskins, and Fox (2007) for a full discussion of this issue.
14
particular tax rate used in our analysis. 17 We return to this below. Table 1 presents
results for our sales tax regressions. In the first column of the table, we present results
from our constructed sales tax base measure. Here we find that state sales tax bases
decline by 0.49 percent in response to a one-percent increase in the statutory sales tax
rate. Based on this estimate, a state that raises its sales tax rate from 6 percent to 7
percent (an increase of one percentage-point, or 16.7 percent) would experience a sales
tax base decline of around 8.8 percent. 18
In the second column of the table, we present results using data for the actual
sales tax base as reported by states. As previously stated, there are only 231 observations
for this model. Here the decline in the tax base associated with a one-percent increase in
the rate falls to 0.239 percent. In the third column, we present results from a third model
that utilizes our constructed tax base measure, but only uses those same 231 observations
that are used in the model presented in the second column to determine to what degree
this difference in the two elasticity estimates presented is due to the different set of
observations versus mismeasurement in our constructed base measure. In this model, we
estimate an elasticity of -0.328. We take this to indicate that the mismeasurement in our
constructed tax base measure, for reasons discussed above, yields an elasticity that is
around one-third higher than the true elasticity. Applying this to our original estimate of
-0.490 would yield an estimate of the true elasticity of about -0.357.
In table 2 we present results from the expanded sales tax model in which we
include the various exemption (and partial exemption) dummies, as described above to
17
Prior studies typically regress some form of the the log of the tax base on the log of the net of tax price
(one minus the tax rate). Since we regress the log of the base on the log of the tax rate itself, which we
view as more appropriate given our use of aggregate data, adjusting our estimated elasticities as described
in the text gives us equivalent net-of-tax price elasticities.
15
account for differences in breadth of the base. Here we present results for a model with
the inclusion of a) just the grocery and prescription drug exemptions, b) just the farm
equipment and utilities exemptions, and c) with all exemptions. We do this as a result of
limitations on data availability for the exemption categories. In all of these models, we
only use data for the period 1994 through 2005, as previously stated. For the models with
farm equipment and utilities exemptions, we lose observations for 14 states due to
missing data for 1996. In column four we present results from our baseline model,
without any exemptions but for the same estimation sample for comparison purposes. In
all of these models we find very similar elasticity estimates for the tax rate, ranging from
a low (in absolute value) of -0.332 to a high of -0.348. Further, the elasticity estimate in
our baseline model is virtually the same as in the models with the exemption dummies,
indicating that the inclusion of the dummies does not significantly change our elasticity
estimate. This suggests that base breadth has no effect on the taxable base elasticity.
Further, we conclude that the decline in our tax base elasticity estimate from our baseline
model in Table 1 to the results in Table 2 is due to the difference in the time period
considered in the two models (1985-2005 versus 1994-2005).
To investigate the possibility of a changing sales tax base elasticity over time, we
present an additional model in which we expand our baseline to include an interaction
between the sales tax rate and a time trend (which replaces the year fixed effects in order
to facilitate the interaction). 19 Results are presented in Table 3, and indicate that the base
18
Multiplying our baseline sales tax elasticity of -0.490 by (τ-1)/τ, which at our average sales tax rate of
5.03 percent would mean multiplying by 0.8, yields a corresponding net-of-tax price elasticity of -0.392.
19
The timed trend takes on the values 1 through 21 for the years 1985 through 2005, respectively.
16
elasticity has grown (in absolute value) over time. 20 The estimated annual taxable base
elasticities are shown graphically in Figure 1. As illustrated, the elasticity grew from just
over 0.4 (in absolute value) in 1985 to over 0.5 by 2003, an increase of about one-fourth.
Possible explanations for the more elastic sales tax bases include the growth of crossborder sales (especially via electronic commerce) and the general upward trend in state
sales tax rates.
Corporate Income Tax Results. Table 4 presents baseline results for the corporate
income tax. In the first column, where the state CIT base is calculated as state CIT
revenues divided by the top CIT rate, we find that a one-percent increase in the top CIT
rate is associated with a 0.447-percent drop in the CIT base. This estimated elasticity is
larger than the -0.20 estimate in the second column, which uses a base constructed from
federal data that are reported for each state. The elasticity estimate derived using state
data is very similar to the estimate of -0.48 from Bruce, Deskins, and Fox (2007), which
isolated the part of the tax base response that is associated with tax planning activity (and
which analyzed data for a smaller number of years). The similar elasticities indicate that
essentially all of the behavioral response to changes in the CIT rate are associated with
tax planning activity rather than locational distortions. These overall findings are
generally consistent with the literature that concludes that tax rates affect the location of
businesses, but the quantitative effects are very small (see Wasylenko, 1997). In addition,
our estimated CIT base elasticities are at least as large as the taxable income elasticity
20
We also estimated a model with a quadratic specification in which we included a) the time trend, b) the
time trend squared, c) the CIT rate multiplied by the time trend, and d) the CIT rate multiplied by the time
trend squared. Results from this model were virtually identical to the results presented and are available
from the authors upon request.
17
with the federal CIT of -0.2 that was reported in Gruber and Rauh (2005). 21 This is
consistent with the earlier hypothesis that state-level responses should be larger than their
federal counterparts due to the added dimension of mobility between the states.
Turning to the first column of Table 5, we present results for our expanded CIT
model in which we include several elements that define the tax base. In this model, the
taxable base elasticity is very close to the baseline estimate, indicating that as with the
sales tax, base breadth has little influence on the taxable base elasticity since adding these
elements in the model does not significantly change the baseline results. Other results
indicate that a double weighted sales factor weight used in the corporate income tax
apportionment formula increases the tax base, relative to states with an equally weighted
formula. Further, results indicate that a weight of more than 60 percent on the sales
factor also increases the tax base relative to an equally weighted formula. This is the
reverse of the expectation by some that higher weight on the sales factor lowers corporate
income taxes. In fact, greater weight on the sales factor changes the distribution of taxes
by lowering the liability of firms with relatively more sales than payroll and property, and
raising the liability of firms with relatively more payroll and property than sales. This is
potentially consistent with higher average tax liabilities. Further, the conclusion can still
be supportive of state efforts to seek economic development benefits from greater weight
on the sales factor. We discuss the apportionment formula further below.
We also find that a higher top marginal PIT rate reduces the CIT base. This runs
counter to our expectation that a higher PIT rate may enhance the CIT. State PIT rates
have fallen consistently over the past 20 years, and the result may evidence that states
21
Multiplying our baseline corporate income tax elasticity of -0.447 by (τ-1)/τ, which at the average CIT
rate of 7.55 percent would mean multiplying by 0.87, gives us a corresponding net-of-tax price elasticity of
18
have been more willing to reduce PIT rates in places where the CIT base has been more
stable.
In the second column of Table 5 we turn back to a consideration of state CIT
apportionment formulas. In particular, we investigate the extent to which the taxable
base elasticity depends on the apportionment formula. Thus we include an interaction
between the top CIT rate and the dummy variables denoting a double weighted sales
factor and a sales factor weight of more than 60 percent. Here we find that the taxable
base elasticity is closer to zero for states with double-weighted sales factors.
Specifically, when using our state measure of the CIT base, the estimated elasticity is
only about -0.349 for those states as compared to -0.595 in other states. This is expected
since greater weight on the sales factor reduces the origin component of the base.
In our final model, we consider whether the taxable base elasticity for the CIT has
changed over time, as was the case with the sales tax above. In Table 6 we present
results from an adaptation of our CIT model with the several other base controls to
include an interaction between the CIT rate and a time trend. 22 Results indicate that the
taxable base elasticity for the CIT has grown (in absolute value) substantially over time.
This changing effect is depicted in Figure 1. As illustrated, the CIT taxable base
elasticity grew (in absolute value) from around -0.4 in 1985 to nearly double its original
value by 2003. Several factors could have contributed to this growing distortionary effect
of the CIT rate, including an increased ability to move real economic activity to lower tax
jurisdictions as technology has expanded the ability to separate production and
about -0.389, still above that found by Gruber and Rauh (2005).
22
As in the corresponding sales tax base model, we replace the year fixed effects with a time trend. Also,
as in the sales tax model above, we estimated a model with a quadratic specification, and again, results did
not differ substantially from those presented.
19
consumption for both goods and services. Also, opportunities have expanded for using
tax planning to move bases to lower-tax (or no-tax) jurisdictions as state and local tax
practices at large accounting firms have grown rapidly and new business structures, such
as LLCs, have developed. The taxable base elasticity may continue to rise as
globalization becomes more of a reality.
6. Conclusions
We present a series of state-level panel regressions of the responsiveness of state
tax bases to changes in state tax rates. Our analysis, which focuses on state sales and
corporate income taxes, builds upon recent research on the elasticity of taxable income
with respect to changes in marginal tax rates. That research had its origins in the federal
PIT area, followed more recently by work on the federal CIT. Only a few studies have
explored these issues using state tax data.
Our analysis yields several interesting conclusions. First, state sales and
corporate income tax bases appear to be responsive to tax rate changes, with elasticity
estimates in the neighborhood of -0.3 to -0.35 for the sales tax and -0.4 to -0.6 for the
CIT. As expected, the CIT appears to exhibit relatively higher base elasticities than the
sales tax. The elasticities for both taxes are observed to be somewhat higher than those
found for the federal PIT and CIT using microdata. Specifically, when we convert our
tax rate estimates into net-of-tax price elasticities (as presented in prior studies), we find
ranges on the order of -0.24 to -0.28 for the sales tax and -0.35 to -0.52 for the CIT.
A second key finding is that both base elasticities have grown between 1985 and
2005, with the CIT base elasticity nearly doubling in size and the sales tax base elasticity
20
growing by about one-quarter. In fact, the two base elasticities were approximately the
same in 1985, but have moved apart with the more rapid changes in the CIT. It will be
important for future research to explore the possible causes for the growing elasticities of
these important state tax bases. A third key finding is that controlling for the breadth of
the base using characteristics of state tax rules such as sales tax exemption categories,
CIT apportionment formulas, and the like, does not appear to have much impact on the
estimated elasticities. In some cases, these policy decisions expand or contract the base
but do not alter the degree to which the measured base responds to rate changes. Other
policy decisions, such as combined reporting, are intended to reduce tax planning, but do
not appear to alter the underlying base elasticity. Still, the dollar base loss from a higher
rate will rise with policy choices that expand the base. The extent to which these policies
have other (non-base) impacts also deserves further attention.
A natural extension of the paper would be, of course, to include an analysis of
state personal income tax elasticities as well. This will be difficult, however, given that
the progressive nature of most state PIT structures complicates the process of calculating
PIT bases for each state. A fruitful approach might be to gather state-specific reported
PIT base values, but researchers will still face a difficult choice over which PIT rate to
use in empirical analysis. 23 Another possibility would be to replicate prior studies by
using microdata but instead focusing on state PIT rates.
23
Collection of state level PIT base data is also complicated since the authors found that most states do not
report (and in some cases even aggregate) base data.
21
References
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Income.” Review of Economics and Statistics 81(4): 681-693.
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Bruce, Donald, John Deskins, and William F. Fox. 2007. “On the Extent,
Growth, and Efficiency Consequences of State Business Tax Planning,” in Taxing
Corporate Income in the 21st Century, A. Auerbach, J. Hines, and J. Slemrod,
editors, Cambridge University Press.
Bruce, Donald, William F. Fox, and M. H. Tuttle. 2006. “Tax Base Elasticities: A
Multi-State Analysis of Long-Run and Short-Run Dynamics.” Southern
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Conway, Karen Smith, and Andrew J. Houtenville. 2001. “Elderly Migration Flows and
State Government Policy – Evidence from the 1990 Census Migration Flows.”
National Tax Journal 54 (1): 103-123.
Edmiston, Kelly. 2002. “Strategic Apportionment of the State Corporate Income Tax.”
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(3): 551-572.
Fox, William F., and LeAnn Luna. 2002. “State Corporate Tax Revenue Trends: Causes
and Possible Solutions.” National Tax Journal 55 (3): 491-508.
Fox, William F., and LeAnn Luna. 2005. “Do Limited Liability Companies Explain
Declining State Tax Revenues?” Public Finance Review November: 690-720.
Fox, William F., LeAnn Luna, and Matthew N. Murray. 2005. “How Should a
Subnational Corporate Income Tax on Multistate Business be Structured?”
National Tax Journal 58 (1): 139-59.
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Taxable Income for the 1980s and 1990s.” Working Paper, Congressional Budget
Office.
Goolsbee, Austan. 2000. “What Happens When You Tax the Rich? Evidence from
Executive Compensation.” Journal of Political Economy 108: 352-378.
22
Gruber, Jonathan, and Joshua Rauh. 2005. “How Elastic is the Corporate
Income Tax Base?” Presented at Taxing Corporate Income in the 21st Century,
co-sponsored by the Office of Tax Policy Research at the University of Michigan
and the Burch Center at the University of California, Berkeley.
Gruber, Jonathan, and Emmanuel Saez. 2002. “The Elasticity of Taxable Income:
Evidence and Implications.” Journal of Public Economics 84: 1-32.
Heim, Bradley. 2006. “The Elasticity of Taxable Income: Evidence from a New Panel
of Tax Returns.” Working Paper, Office of Tax Analysis, U.S. Department of the
Treasury.
Kopczuk, Wojciech. 2005. “Tax Bases, Tax Rates, and the Elasticity of Reported
Income.” Journal of Public Economics 89(11-12): 2093-2119.
Lindsey, Lawrence. 1987. “Individual Taxpayer Response to Tax Cuts, 1982-1984:
With Implications for the Revenue Maximizing Tax Rate.” Journal of Public
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Long, James E. 1999. “The Impact of Marginal Tax Rates on Taxable Income: Evidence
from State Income Tax Differentials.” Southern Economic Journal 65(4): 855869.
Navratil, J., 1995. Essays on the Impact of Marginal Tax Rate Reductions on the
Reporting of Taxable Income on Individual Income Tax Returns. Ph.D.
dissertation, Harvard University.
Saez, Emmanuel. 2003. “The Effect of Marginal Tax Rates on Taxable Income: A Panel
Study of Bracket Creep.” Journal of Public Economics 87(5-6): 1231-1258.
Sammartino, Frank, and David Weiner. 1997. “Recent Evidence on Taxpayers’
Response to the Rate Increases in the 1990s.” National Tax Journal 50: 683-705.
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Economics Literature.” New England Economic Review March/April, 37-42.
23
Table 1: Baseline Regression Results - Sales Tax
Variable
Ln (Sales Tax Rate)
Ln (State Sales Tax Revenues/Sales Tax Rate)
-0.490***
Tax Base Measure
Ln(State Reported Base)
-0.239***
Ln (State Sales Tax Revenues/Sales Tax Rate)
-0.328***
(0.054)
(0.093)
(0.105)
Population (millions)
0.024***
0.010
0.008
(0.007)
(0.007)
(0.008)
18.507***
18.423***
18.716***
(0.099)
(0.149)
(0.170)
945
0.866
231
0.958
231
0.945
Constant
Number of Observations
Within R-squared
Entries are fixed-effcts panel regression coefficients with standard errors in parentheses.
*, **, and *** denote statistical significance at the 10%, 5%, and 1% levels.
Regression includes state and year fixed effects.
24
Table 2: Regression Results - Sales Tax, Controlling for Various Exemptions
Variable
Ln (Sales Tax Rate)
Grocery Exemption
Partial Grocery Exemption
Prescription Drug Exemption
Partial Prescription Drug Exemption
Farm Equipment Exemption
Partial Farm Equipment Exemption
Utilities Exemption
Partial Utilities Exemption
Population (millions)
Constant
Number of Observations
Within R-squared
Tax Base Measure
Ln (State Sales Tax Revenues/Sales Tax Rate)
Model 1
Model 2
Model 3
Baseline
-0.348***
-0.332***
-0.344***
-0.342***
(0.076)
(0.077)
(0.077)
(0.077)
-0.036
-
-0.008
-
(0.026)
-
(0.030)
-
0.001
-
0.008
-
(0.034)
-
(0.035)
-
-0.192***
-
-0.205***
-
(0.036)
-
(0.040)
-
----
-
----
-
----
-
----
-
-
0.024
0.007
-
-
(0.022)
(0.022)
-
-
0.009
0.013
-
-
(0.015)
(0.015)
-
-
0.046***
0.043***
-
-
(0.015)
(0.015)
-
-
0.016
0.014
-
-
(0.015)
(0.014)
-
0.007
0.007
0.005
0.008
(0.007)
(0.008)
(0.007)
(0.007)
18.595***
18.341***
18.583***
18.369***
(0.141)
(0.138)
(0.143)
(0.137)
540
0.877
526
0.872
526
0.879
540
0.869
Entries are fixed-effcts panel regression coefficients with standard errors in parentheses.
*, **, and *** denote statistical significance at the 10%, 5%, and 1% levels.
Regressions include state and year fixed effects.
---- denotes insufficient variation.
25
Table 3: Regression Results - Sales Tax with Time Trend Interaction
Variable
Ln (Sales Tax Rate)
Tax Base Measure
Ln (State Sales Tax Revenues/Sales Tax Rate)
-0.412***
(0.058)
Ln (Sales Tax Rate)*Time Trend
-0.006*
(0.003)
Population (millions)
0.023***
Time Trend
0.058***
Constant
17.382***
(0.007)
(0.005)
(0.095)
Number of Observations
Within R-squared
945
0.857
Entries are fixed-effcts panel regression coefficients with standard errors in parentheses.
*, **, and *** denote statistical significance at the 10%, 5%, and 1% levels.
Regression includes state fixed effects.
26
Table 4: Baseline Regression Results - Corporate Income Tax
Variable
Ln (Top CIT Rate)
Tax Base Measure
Ln (State Revenues/CIT Rate) Ln (Federal Base)
-0.447***
-0.200**
(0.076)
(0.080)
Population (millions)
0.107***
0.056***
(0.0175)
(0.016)
Constant
15.902***
11.255***
(0.186)
(0.198)
924
0.519
924
0.773
Number of Observations
Within R-squared
Entries are fixed-effcts panel regression coefficients with standard errors in parentheses.
*, **, and *** denote statistical significance at the 10%, 5%, and 1% levels.
Regressions include state and year fixed effects.
All percentages are on a 0-100 scale.
27
Table 5: Regression Results - Corporate Income Tax, Controlling for Other Base-Defining Elements
Tax Base Measure
Ln (State Revenues/CIT Rate)
Model 1
Model 2
-0.530***
-0.595***
Variable
Ln (Top CIT Rate)
(0.076)
(0.081)
Sales Factor Appt: Double Weight
0.059**
-0.428**
(0.029)
(0.215)
Sales Factor Appt: Greater Than Double Weight
0.203***
0.316
(0.063)
(0.653)
-
0.246**
Ln(Top CIT Rate)*Double Weight Sales Factor
Ln(Top CIT Rate)*Greater Than Double Weight Sales Factor
Combined Reporting
LLC
Throwback Rule
-
(0.108)
-
-0.066
-
(0.329)
0.032
0.023
(0.046)
(0.046)
-0.009
-0.013
(0.042)
(0.042)
-0.052
-0.057
(0.054)
(0.055)
Ln (Top PIT Rate)
-0.038***
-0.035***
(0.008)
(0.008)
Population (millions)
0.099***
0.096***
(0.016)
(0.016)
Constant
16.096***
16.257***
(0.194)
(0.206)
924
0.539
924
0.542
Number of Observations
Within R-squared
Entries are fixed-effcts panel regression coefficients with standard errors in parentheses.
*, **, and *** denote statistical significance at the 10%, 5%, and 1% levels.
Regressions include state and year fixed effects.
All percentages are on a 0-100 scale.
28
Table 6: Regression Results - Corporate Income Tax with Time Trend Interaction
Tax Base Measure
Ln (State Revenues/CIT Rate)
-0.423***
Variable
Ln (Top CIT Rate)
(0.093)
Ln (Top CIT Rate)*Time Trend
-0.018***
(0.006)
Sales Factor Appt: Double Weight
0.069**
Sales Factor Appt: Greater Than Double Weight
0.194***
(0.032)
(0.070)
Combined Reporting
0.092*
(0.052)
LLC
0.188***
(0.035)
Throwback Rule
-0.082
(0.061)
Ln (Top PIT Rate)
-0.033***
Population (millions)
0.090***
Time Trend
0.047***
Constant
15.305***
(0.009)
(0.017)
(0.012)
(0.206)
Number of Observations
Within R-squared
924
0.413
Entries are fixed-effcts panel regression coefficients with standard errors in parentheses.
*, **, and *** denote statistical significance at the 10%, 5%, and 1% levels.
Regression includes state fixed effects.
All percentages are on a 0-100 scale.
29
Figure 1: Taxable Base Elasticity Over Time
0
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
-0.1
-0.2
Elasticity
-0.3
-0.4
CIT
Sales
-0.5
-0.6
-0.7
-0.8
-0.9
Year
30
Appendix 1: Summary Statistics
State CIT Revenues/Top CIT Rate (millions)
Federal Base Measure - CIT (millions)
State Sales Tax Revenues/Sales Tax Rate (millions)
State Reported Sales Tax Base (millions)
Top CIT Rate
Sales Tax Rate
Grocery Exemption*
Partial Grocery Exemption*
Prescription Drug Exemption*
Partial Prescription Drug Exemption*
Farm Equipment Exemption*
Partial Farm Equipment Exemption*
Utilities Exemption*
Partial Utilities Exemption*
Sales Factor Apportionment: Double Weighted
Sales Factor Apportionment: Greater Than Double Weight
Combined Reporting
Throwback Rule
LLC
Population (millions)
Threshold - Top CIT Bracket
Notes: All percentages are on a 0-100 scale.
All dollar amounts are expressed as current year dollars.
*Entries in 1985 column are for 1994.
31
Mean
4,124
32,145
33,100
62,400
7.39
4.56
0.56
0.04
0.96
0.02
0.24
0.22
0.20
0.18
0.14
0.02
0.20
0.56
0
4.75
225,000
1985
Std.Dev.
4,115
48,674
35,800
42,200
2.25
1.07
0.50
0.21
0.21
0.15
0.43
0.42
0.40
0.39
0.35
0.15
0.41
0.50
0
5.07
483,094
Mean
7,708
106,320
75,900
85,600
7.63
5.24
0.60
0.07
0.98
0.02
0.13
0.22
0.20
0.25
0.57
0.16
0.32
0.55
1
5.80
192,857
2005
Std.Dev.
12,600
133,574
76,800
89,800
1.63
1.00
0.50
0.25
0.15
0.15
0.34
0.42
0.40
0.44
0.5
0.37
0.47
0.50
0
6.45
249,227
Appendix 2: Data Descriptions and Source Notes
Variable
State CIT Revenues/Top CIT Rate
Federal Base Measure - CIT
State Sales Tax Revenues/ Sales Tax Rate
State Reported Sales Tax Base
Top CIT Rate
Sales Tax Rate
Grocery Exemption
Partial Grocery Exemption
Prescription Drug Exemption
Partial Prescription Drug Exemption
Farm Equipment Exemption
Partial Farm Equipment Exemption
Utilities Exemption
Partial Utilities Exemption
Sales Factor Apportionment: Double Weighted
Sales Factor Apportionment: Greater Than Double Weight
Combined Reporting
Throwback Rule
LLC
Population (millions)
Threshold - Top CIT Bracket
Definition
Corporate income tax (CIT) revenues divided by top marginal CIT rate. (1)
Federal CIT collections divided by top marginal federal CIT rate.(2)
Sales tax revenues divided by sales tax rate. (1)
State reported sales tax base. (3)
Highest marginal corporate income tax rate. (4)
General sales tax rate. (4)
1 if a state exempts groceries from sales taxation. (4)
1 if a state taxes groceries at a reduced sales tax rate. (4)
1 if a state exempts prescription drugs from sales taxation. (4)
1 if a state taxes prescription drugs at a reduced sales tax rate. (4)
1 if a state exempts farm equipment from sales taxation. (4)
1 if a state taxes farm equipment at a reduced sales tax rate. (4)
1 if a state exempts utilities from sales taxation. (4)
1 if a state taxes utilitiess at a reduced sales tax rate. (4)
1 if sales factor weight in CIT appt formula is between 34 and 60 percent. (4)
1 if sales factor weight in CIT appt formula is greater than 60 percent. (4)
1 if a state has a combined reporting requirement. (5)
1 if a state has a throwback rule. (5)
1 if a state allows LLCs. (6)
State population. (8)
Minimum income level for top CIT rate. (4)
Source Notes:
1. Authors' calculations based on data from State Government Finances , U.S. Census Bureau, various years,
and State Tax Handbook , Commerce Clearing House, various years.
2. Authors' calculations based on data from Statistics of Income, Tax Statistics , Internal Revenue Service, various years,
and Federal Tax Guide , Commerce Clearing House, various years.
3. Reported by state revenue departments.
5. State Tax Handbook , Commerce Clearing House, various years.
6. State Tax Handbook , Commerce Clearing House (various years) and various state revenue departments.
7. www.llcweb.com
8. Statistical Abstract of the United States , U.S. Census Bureau, various years.
32
Appendix 3: Corporate Income Tax Robustness Checks
Tax Base Measure
Ln (State Revenues/CIT Rate)
-0.530***
Variable
Ln (Top CIT Rate)
(0.076)
Threshold - Top CIT Bracket
(thousands)
Sales Factor Appt: Double Weight
-0.0001
0.059**
Sales Factor Appt: Greater Than Double Weight
0.203***
(0.002)
(0.029)
(0.063)
Combined Reporting
0.032
(0.046)
LLC
-0.009
Throwback Rule
-0.052
(0.042)
(0.055)
Ln (Top PIT Rate)
-0.038***
(0.008)
Population (thousands)
0.099***
Constant
16.101***
(0.016)
(0.194)
Number of Observations
Within R-squared
924
0.539
Entries are fixed-effcts panel regression coefficients with standard errors in parentheses.
*, **, and *** denote statistical significance at the 10%, 5%, and 1% levels.
Regression includes state and year fixed effects.
All percentages are on a 0-100 scale.
33