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Marginal tax rates and state economic growth
Regional
EISEIVIER
Science
Marginal
and Urban
Economics
24 (1994)
687-705
tax rates and state economic John
K. Mullen”‘*,
Martin
growth
Williamsb
“Department of Economics and Finance, Clarkson University. Potsdam, NY 13699, USA hDepartment of Economics, Northern Illinois University, DeKalb, IL 60115, USA Received
May 1992, final version
received
April
1993
Abstract This paper analyzes the impact of state-local tax structures on state economic performance. Specifically, growth rates in Gross State Product over the 196946 period are related to several measures of a state’s marginal tax environment in addition to more traditional growth determinants. Estimates of marginal tax rates for individual states are derived and utilized alternately with other tax climate surrogates in explaining variations in economic growth. We report both output and productivity equations in order to distinguish separate impacts resulting from taxation; the endogeneity problem is also addressed in this fashion. The findings suggest that, after controlling for overall tax burdens, higher marginal tax rates impede output growth. Key words: Marginal JEL
classification:
tax rates;
Economic
growth;
Sub-national
public
finance
H70: 040
1. Introduction Considerable
uncertainty
persists
concerning
the
impact
of
state-local
taxes on sub-national economic growth. Private sector advocates have long maintained that high tax rates discourage economic development initiatives and depress economic growth rates. Public finance economists, however, maintain that the causal relationship between taxes and economic growth is much more complex. It is possible that higher taxes may actually be stimulative of economic activity if revenues are used to finance infrastructure improvements, encourage private sector development, or other* Corresponding
author.
0166.0462/94/$07.00 0 1994 Elsevier SSDI 0166-04622(94)02058-O
Science
B.V. All rights
reserved
688
J.K.
Mullen, M. Williams I Reg. Sci. Urban Econ. 24 (1994) 687-705
wise enhance public service levels. Furthermore, simply focusing on the correlation between tax rates and economic growth ignores the impact of economic activity levels on both of these variables. The present research is an attempt to augment our understanding of the influence of state-local tax structures and burdens on state economic growth. Such efforts are a prelude to formulating public policies that are effective in fostering regional economic development. The specific focus of this research concerns the role of state-local tax rates in explaining differential growth across states. A ‘supply-side’ hypothesis is explored alongside mainstream arguments by considering the impact of both marginal and average tax rates on economic performance. We take advantage of Gross State Product (GSP) figures from 1969 to 1986 together with data on state public and private capital stocks for this same period. State-local tax revenue data are used to compute average tax rates; these and GSP data also form the basis for statistical estimates of marginal tax rates over the period of analysis. These marginal tax rate estimates are then employed alongside several alternative measures of the marginal tax environment to assess their impact on state economic growth. The next section of the paper examines some relevant background literature and describes the causal growth factors that warrant consideration in the present analysis. This is followed first by a discussion of the empirical issues and experimental design, then by a presentation and discussion of the various statistical results. A final section summarizes our findings and offers some policy insights.
2. State-local
fiscal policies and economic growth
The degree to which states can use their fiscal policies to significantly influence economic activity remains an unsettled empirical issue. A sizable body of literature exists concerning the relationship between business activity and state-local taxes and public service levels. The more recent of this literature largely supports the contention that states (and their localities) can exert at least some modest influence on business location decisions via their choice of fiscal policies (e.g. Bartik, 1985, 1990; Helms, 1985; Papke and Papke, 1986 and Schmenner et al., 1987). A broader issue, however, concerns the impact of sub-national public sector activity on overall economic growth. Although some researchers appropriately have focused on growth rates (e.g. of income, employment, or capital stocks) instead of simply economic activity levels, few attempts have been made to explain statewide variations in the growth rates of a comprehensive measure of output such as GSP. Furthermore, little if any evidence exists concerning the
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impact of marginal tax rates on output growth at the regional level.’ The present research seeks to fill that void. A number of researchers have sought to explain differences in international growth rates by examining the role of taxation and public sector size. The basic premise underlying these analyses is that a larger public sector (and its higher tax rates) tends to discourage economic activity levels and growth rates. Grier and Tullock (1989) find that stronger growth in the government share of Gross Domestic Product (GDP) is associated with lower (five-year average) growth rates of GDP for a large sample of OECD countries. Scully (1989) reports results which similarly support the conclusion that, regardless of motivation, increases in the size of the government share of the economy adversely affect economic growth and the allocation of resources. Koester and Kormendi (1989) utilize data from 63 countries to examine the impact of average and marginal tax rates on both the level and growth of economic activity. Making sure to account for the relationship between per capita income and relative public sector size, they find little support for the hypothesis that economic growth is hampered by higher (average or marginal) tax rates. However, they do confirm that economic activity levels are depressed as a result of increases in marginal tax rates. Tax and expenditure considerations may be of even greater importance in explaining differential growth rates across sub-national regions than across countries. Clearly, factor mobility is much greater across states within a country. Thus, the intensity of competition for jobs and economic activity may allow certain state or (regional) economies to outperform national averages as resources re-locate in response to differential regional incentives. Typically, state-local governments offer numerous tax inducements to spur economic development. More generally, capital and labor migrate to those areas where tax burdens are perceived as being less onerous. Expenditures on certain public services may also encourage factor resource in-migration. Upgrading infrastructure or improving educational quality are examples of public spending that may induce differential growth at the state level. Although state-local public sector activities obviously are capable of having a substantial impact on regional economies, uncertainties abound given the complexity of these interrelationships. However, one important policy issue, begging for empirical evidence, concerns the impact of revenue-neutral reductions in marginal tax rates on economic growth. Similarly, the role of ‘predatory’ tax policies in stimulating local economies
I Papke (1989) has provided evidence of the influence on GSP levels within select manufacturing industries.
of effective
state business
tax burdens
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is not fully understood. As some analysts have argued, relative tax burdens may be more important than tax rates or tax levels per se in influencing industrial growth (e.g. see Benson and Johnson, 1986). Indeed, changes in relative tax burdens have been cited as perhaps most influential on the belief that resource owners assess the prevailing business climate from such comparisons. To gauge this, we need more compelling evidence on the role of the state-local tax environment in contributing to differential growth rates. Another growing body of research seeks to understand inter-regional growth rate differentials in the context of the convergence hypothesis. This hypothesis was originally developed to explain variations in international productivity performance and, subsequently, economic growth. Emphasizing the public goods aspect of technology, the theory suggests that productivity and income levels will converge as ‘laggards’ assimilate the storehouse of industrial and administrative technology initially developed by ‘leaders’ (see Baumol, 1986; DeLong, 1988, and Abramovitz, 1990, for some relevant discussion.) The convergence theory also has been invoked to explain the narrowing of regional income differentials within developed countries (see Hulten and Schwab, 1984; Olson, 1983, and Mullen and Williams, 1987, for some representative work). The conditions which foster convergence are more likely to exist on a regional basis because of greater factor mobility and resource re-allocation possibilities. This logic can be extended further to the state level, with the argument that resources and technology face even fewer impediments moving within rather than across states. In fact, the ‘catchingup’ hypothesis has been used to explain different growth rates across the United States. Yu et al. (1991) find that initial per capita income has been a significant determinant of variations in state personal income growth up through the 1970s. Though more recent data suggest a diminished role for ‘catching up’ as an explanation for inter-state variations in economic growth, this trend may simply reflect diminishing disparities in income levels. In sum, while the convergence hypothesis implies a tendency for an equalization of growth rates across regions to occur, it is quite likely that compensating differentials in per capita income levels would remain. However, the existence of persistent differential growth rates may be taken as an indication of the importance of fiscal and/or other policies in influencing economic development. It may be possible, for example, that a particular state consistently exhibits a stronger growth pattern because of conscious efforts to improve its tax environment or provide the type of ‘economic overhead capital’ that is conducive to private sector expansion. Evidence confirming such effects is decidedly mixed but essential to the formulation of sound policy prescriptions.
J.K.
3. Empirical
MulIen, M. Williams I Reg. Sci. Urban Econ. 24 (1994) 687-705
691
issues and design
In what follows we attempt to explain inter-state variations in GSP growth rates over the 1969 to 1986 period.’ We exploit earlier research efforts in designing an empirical model to test for tax structure effects on state growth. The model we develop is admittedly ad hoc, but we draw heavily upon economic theory as a guide. The specific theoretical construct underlying the empirical framework is the disequilibrium-adjustment model popularized in migration research. This model typically relates changes in the dependent variable over the period to levels of the explanatory variables at the beginning of the period. Plaut and Pluta (1983) employ such a model in their attempt to explain variations in state industrial growth between the 1967-72 and 1972-77 periods.’ But a more fully specified model also consider changes in the level of the independent variables. While we control for the impact of growth rates of the input levels themselves, we also consider the role of levels of certain variables (most notably real per capita income) in explaining variations in output growth. Our primary dependent variable is the (compound) annual growth rate of real (1982 dollars) GSP from 1969 to 1986. As noted, this approach represents a departure from existing efforts in that we are attempting to explain variations in a comprehensive measure of economic growth, rather than simply focusing on personal income, employment or the number of business ‘start-ups’. Our explanatory variables include tax rate measures, initial economic conditions, and input growth rates within each state. Some of the underlying hypotheses motivating the use of these regressors warrant further discussion. Our major preoccupation rests with determining the impact of the tax structure on state economic growth. Specifically, we are concerned with testing whether or not a higher marginal tax rate environment has a depressing effect on growth. Our inclusion of both marginal and average tax rates as regressors enables us to examine the supply-side theme that revenue-neutral increases in marginal tax rates have an adverse effect on output growth. Ideally, both sides of the budget equation should be considered because of the potentially positive impact of public spending on growth. Both Helms (1985) and Modifi and Stone (1990) present evidence
‘This sample period allows us to have a consistent set of measures, derived from various sources, for the numerous variables at the state level of disaggregation. More specifically, both public and private capital stock measures, at this level. are limited to this time period. 3 Their efforts, which did not include an explicit consideration of public expenditure impacts on economic growth, suggest that relative tax revenues are important in explaining variations in the growth rates of both employment and capital stock.
692
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that higher taxes used to augment spending on traditional services may actually stimulate income levels. In contrast, additional spending on social programs hampers economic performance. Yet it remains a difficult empirical task to distinguish revenue from expenditure effect. (This difficulty helps explain why earlier studies report tax rates as having either no statistical impact at all or a positive effect on economic growth.) In any case, statistical estimates of tax rate effects are highly sensitive to functional form, variable definitions, and the treatment of public sector spending. Note that our specification does not explicitly include a public expenditure variable because this complicates the attempt to disentangle average from marginal tax effects. Moreover, the possibility remains that some of the influence of government spending is embedded within the coefficient of the average tax rate variable.” Next, the convergence hypothesis implies that growth rates will be inversely related to income levels at the beginning of the period of analysis. Although by no means inevitable, higher initial wealth conditions should result in lower future growth rates. Furthermore, the stage of economic development (or wealth) tends to have a direct influence on the relative size of the public sector and is postulated to be positively associated with average tax rates. Therefore our inclusion of initial (real) per capita income controls for this relationship and provides a direct test of convergence amongst states. We expect output growth to be clearly linked to input growth rates. The size (and composition) of the labor force represents a major element in regional economic growth. We utilize the growth rate of the civilian labor force within the state to capture changes in this input. Similarly, we hypothesize that the growth rates for both the private and public capital stocks are instrumental in fueling state economic growth. It is easy to argue the case for the role of private capital in stimulating output growth. Moreover, recent evidence suggests that public capital (infrastructure or other ‘economic overhead capital’) is also an important factor in explaining interregional output and productivity differentials (see Aschauer, 1989; Duffy-Deno and Eberts, 1989; Munnell, 1990 for some relevant discussion). But this issue is further complicated if these two distinct types of capital are substitutes or complements in stimulating regional growth. Yet the nature of the association between public capital and state output remains an unsettled empirical issue (e.g. see Eisner, 1991). For empirical completeness, we provide results both with and without the public capital stock variable. The foregoing discussion is centered on input growth rates as regressors in the empirical specification of a model explaining variations in output ‘Admittedly. such an ad hoc treatment is a second-best (1990) for an excellent discussion of this and related issues.
solution.
See Modifi
and
Stone
J.K.
Mullen,
M. Williams
I Reg. Sci. Urban
Econ.
24 (1994) 687-705
693
growth. It is this type of specification which inherently generates a ‘productivity’ type equation. To deal with this problem, we also report ‘output’ equations with the input growth rates eliminated as explanatory variables. Obviously this approach heeds the suggestion that tax variables should have a stronger impact on output than on productivity growth because of the responsiveness of capital and labor inputs to the fiscal environment. A related issue here concerns the endogeneity of the input growth variables when output growth is the dependent variable. To overcome the controversy surrounding such a specification we employ an alternative approach to measure the impact of the tax environment on productivity. We draw on the growth-accounting framework common in the productivity literature. We measure productivity by subtracting the factor share-weighted input growth rates from the growth rate of GSP.5 This yields an indirect productivity measure that is regressed on the tax rate variables to discern their impact on output growth, after having accounted for input growth. Essentially, we report three variants of the basic specification: an output equation; a productivity equation; and a modified productivity equation. At this point, it is important to provide a further discussion of our tax variables. We define the average tax rate (ATR) as the sum of all ownsource state and local tax revenue as a percentage of GSP. This formulation is consistent with earlier research and is also representative to the ‘effort’ made by state-local governments in extracting revenue from the broadest definition of their tax base.‘j As for marginal tax rates, we follow the methodology employed by Koester and Kormendi (1989). Our marginal tax rates are derived by estimating the following regression equation, Eq. (l), over the 1969-86 period for each state: TAXREV,
= a, + a,GSP,
+ e, ,
(1)
where TAXREI/ = the sum of all own-source state-local tax revenue in year t. The coefficient of GSP represents an approximation to the (average) marginal tax rate for a given state, assuming a constant tax structure over the period. The estimates of marginal tax rates are generally consistent with typical perceptions concerning state-local tax systems. For example, the mean value for the marginal tax rate (MTR) is 8.28%, whereas the mean for the average tax rate is 8.47%. Although this implies that state-local tax ’ We assume that labor’s share of output is 60% and capital’s share is the remaining 40%. These figures represent a consensus of the tmmerous empirical estimates of such values for the aggregate U.S. economy (e.g. see Ghosh, 1991, p. 135). ‘This formulation coincides with the belief that the total tax bill, rather than its breakdown into individual levies, is important to decision-makers (see Benson and Johnson, 1986, p. 393. and ACIR, 1967, p. 62 for further discussion). Also, this overcomes problems caused by inter-state variations in the division of fiscal responsibilities between state governments and their localities.
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Mullen, M. Williams I Reg. Sci. Urban Econ. 24 (1994) 687-705
structures are, on average, slightly regressive, substantial differences between these rates exist within particular states. The intercept term is positive and significant in 20 of the 48 state equations. This finding may be suggestive that these states derive a substantial portion of their revenues from activity A further implication may be that the occurring outside their borders. overall tax structure is regressive within these 20 states. (The insignificance of the constant term in 19 of the equations implies a proportional tax structure within the state.) The correlation coefficient between ATR and MTR has a surprisingly low and insignificant value of 0.122. Using our definition of tax rates, high (average) tax burden states did not typically impose high incremental tax rates over the sample period. It is conceivable, of course, that states that have attempted to increase their average tax rates over the period would display higher marginal tax rates given our definition. Thus, we employ alternative measures to test whether the findings based on our definition are corroborated by other proxies for the marginal tax environment within each state.7 One such alternative is derived from marginal burden elasticity estimates generated by Phares (1980). This elasticity represents a measure of the degree of progressivity/regressivity of the state-local tax structure for 1976, defined as the percentage change in ‘tax effort’ arising from a percentage change in income. We convert these elasticity estimates to represent a marginal tax effort rate.# But while this variable is not directly comparable to our MTR measure, it is broadly representative of the nature of statelocal tax structures in 1976. Our second proxy measure of the marginal tax rate is based on the top legislated income tax brackets across states. These data are obtained from the 1981 edition (selected arbitrarily) of the Commerce Clearing House’s State Tax Handbook. Although there are obvious problems with this measure given the enormous diversity in underlying tax structures and the division of fiscal responsibilities across states, it does provide a rough approximation to the marginal tax environment within each state. Ideally, such a variable should encompass the multitude of state-local tax levies comparable to our estimates (MTR) of marginal tax rates. Finally, we compare the statistical findings based on the marginal tax environment variables with a closely-related tax concept. A number of researchers have suggested that changes in a state’s tax policies relative to other states may be more important than absolute levels of the tax burden (e.g. see Benson and Johnson, 1986; Canto and Webb, 1987). Accordingly,
’ This approach was suggested by an anonymous referee. ‘More specifically, the marginal tax effort rate is defined as d(T/Y)/dY, where T is total state-local tax revenue, Y is state personal income, and u represents mean personal income within the state.
J.K.
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6%
we estimate a model where MTR is replaced with a variable which captures the change in a state’s relative tax burden over the sample period. We define relative tax burden as a state’s average tax rate (ATR) divided by the sample mean ATR.” Changes in this variable over time (ATXBUR) reflect how a state’s tax environment has been altered relative to the U.S. average.“’ Table 1 presents a definition of the variables reported in our regression results. Most of these need no further elaboration at this point, although some of the data sources warrant a brief note.‘] GSP and personal income figures are obtained from the U.S. Department of Commerce (Bureau of Economic Analysis). All tax and expenditure variables are taken from the Government Finances series (Bureau of the Census). Of special interest are the estimates of total private and total public capital stocks for individual states over the 1969-86 interval. These data, provided by the Federal Reserve Bank of Boston,” allow us to explicitly consider the impact of these inputs on overall state economic growth.
4. Statistical
findings
In this section we report and discuss a number of cross-section regressions. We show results for the basic model, along with variants of it that are designed to test for robustness by considering both alternative definitions of the marginal tax variable and alternative time horizons (sub-periods). Furthermore, we undertake a regression diagnostics analysis to confirm that our findings are not largely due to the influential nature of a few prominent observations. Table 2 reports findings for the basic output and productivity equations as described above. It is immediately apparent that the results are generally supportive of the underlying hypotheses. All three of the basic equations are statistically significant. As expected, the percentage of explained variance is vastly greater in the productivity equation (II) where input growth rates appear as explanatory variables. The alternate productivity equation (III), based on the growth-accounting framework, generates results which explain variations in GSP growth net of input growth (NETGSPGR). These findings ’ This formulation is fundamentally the same as that used by Benson and Johnson (1986). Canto and Webb (1987) use a closely related measure based on (changes in) per capita tax revenue relative to the U.S. average. “’ By definition. this variable is expected to be closely related to our MTR estimates. In fact, the simple correlation coefficient between MTR and ATXBCJR is 0.635 and highly significant. I’ We offer a more complete citation in the references section. I’ We are indebted to Alicia Munnell and Leah Cook at FRB. Boston, for making these data available to us.
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Table 1 Variable names Name
(symbol)
GSPGR NETGSPGR
CLFGR CAPGR PIJBKGR YPC
ATR
MTR
ATXBUR
MB UR
CCHMTR
Mullen, M. Williams I Reg. Sci. Urban Econ. 24 (1994) 687-705
and definitions Definition Compound annual growth rate of real GSP, 1969-86 Compound annual growth rate of real GSP minus factor share-weighted growth in capital and labor inputs (i.e. GSPGR-(uGLFGR-PCAPGR) Compound growth rate of the civilian labor force, 1969-86 Compound growth rate for the private capital stock, 1969-86 Compound growth rate for the stock of public capital, 1969-86 Real per capita income at the beginning (1969) of the period (1969,1973 and 1979 for subperiod equations) Average tax rate, defined as the mean value (over the relevant period) of the sum of all own-source, state-local tax revenue as a percentage of GSP Marginal tax rate, defined as the coefficient of a regression of all state-local tax revenue on GSP; further explanation within the text Change in relative tax burden over the 1969-86 period, where relative tax burden is defined as ATR for state i + sample mean A TR Marginal burden (tax effort) rate, defined as the change in tax effort (total taxes + personal income) as average personal income changes, using estimates derived by Phares; this measure is a proxy for the degree of progressivityiregressivity of the state/local tax structure Highest statutory marginal tax rate as contained in Commerce Clearing House’s State Tax Handbook. 1981
are nearly identical both qualitatively and quantitatively to the direct specification. It is interesting to note the results for the marginal tax rate variable (MTR). It is negative and statistically significant in all three equations. This finding is consistent regardless of whether we focus on output or productivity growth. Clearly, strong support emerges for the contention that revenue-neutral increases in marginal tax rates have a perverse effect on economic growth. Next, the model controls for variations in revenue yield by including ATR, defined as the mean (average) tax rate over the period, as a regressor. Thus supply-side considerations appear to be important within the state-local sector. Note the magnitude of these
J.K. Table 2 Regression Dependent variable
Explanatory variables Intercept YPC ATR MTR
Mullen,
results,
M. Williams
basic output
and productivity
I GSP growth 1969-86 (GSPGR)
0.04605* (4.03) -0.00023 (0.19) -0.02167 (0.17) -0.17813* (3.13)
CLFGR CAPGR
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&on.
24 (1994) 687-705
697
equations” II GSP growth 1969-86 (GSPGR)
0.00441 (0.74) -0.00141* (2.56) -0.19277* (3.15) -0.11778* (4.62) 0.50511* (5.60) 0.53640” (5.18)
III Productivity growthb 1969-86 (NETGSPGR)
0.00429 (0.85) -0.00128* (2.39) 0.20241* (3.50) -0.11735* (4.70)
R’
0.192
0.850
0.462
F-value
3.49’
47.71*
12.59*
a Numbers in parentheses are absolute values of the t-statistics (*) (**) indicates significance at the 5% and 10% level respectively; (---) indicates that a particular variable is omitted from the equation. h Defined as GSP growth net of share-weighted growth in capital and labor inputs.
coefficients. Utilizing the output equation, the coefficient value of -0.1781 for MTR implies an elasticity (at the mean) of -0.5335. This suggests that lowering marginal tax rates may be a very effective way of encouraging growth within state borders.13 The average tax rate variable (ATR) is significantly positive in the productivity equations. Yet this result must be interpreted with caution. It suggests that higher average tax rates may actually be stimulative of economic activity. This positive effect, however, is not unusual when the empirical model does not appropriately distinguish between spending and tax impacts. Note though that we are more concerned with controlling for the effect of public sector size than in appropriately modeling the fiscal interaction that occurs.
“On the suggestion of an anonymous referee, we also estimated equations explaining variations in labor and capital growth rates on the basis of the tax variables. These regressions (not reported here) suggest that marginal tax rates have a negative. though generally statistically insignificant. impact on input growth rates.
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The findings in Table 2 also suggest that initial wealth conditions, as proxied by 1969 real per capita income (YPC69), have a depressing effect on productivity. This result tends to support the convergence hypothesis and confirms much of the existing empirical evidence in the productivity literature. On the other hand, it is interesting to note that initial wealth has an insignificant, though negative, coefficient value in the output equation. The implication here is that the leader-laggard phenomenon inherent in productivity patterns may not be a key element in explaining regional output variations. Finally, the input growth rate variables perform as expected in the direct productivity equation (II). We find that the growth rate in the civilian labor force and the growth rate of the private capital stock have a strong, direct influence on GSP growth. The influence exerted by the stock of public capital as an input in the production process is discussed in more general terms below. In view of the recent debate concerning the role of public capital stock, we provide a direct test of its impact on economic growth and productivity performance. Table 3 presents results where the growth of the public capital stock (PUBKGR) has been added to otherwise identical output and productivity equations. The coefficient for the public capital stock variable is positive and significant in all cases, bolstering arguments that infrastructure and other public investment spending help foster regional output growth and generate productivity gains. The findings regarding the other explanatory variables (in particular, those representing tax influences) are essentially unaltered, confirming the robustness of the model. Now, we turn to a fuller consideration of our state-by-state estimates of marginal tax rates by examining the empirical performance of related tax measures. Table 4 presents regression results for the output equation [analagous to Eq. (I) in Table 21 where MTR has been replaced by alternative tax variables serving as proxies for the marginal tax environment. The Table 4 results are qualitatively the same as those findings for MTR presented above. This pattern holds true regardless of whether taxes explain output or productivity (the latter equations are not reported here). Examining the individual equations, we conclude that the marginal tax effort or marginal burden rate (MBUR) has a significant negative impact on growth. That is, as the tax structure within a state becomes more progressive, economic growth is dampened. Also, we find that an increase in a state’s tax burden relative to the norm (as measured by ATXBZJR) tends to hamper output growth. This is a significant finding. For it appears that changes in a state’s relative tax burden, because they likely are correlated with perceptions of the prevailing business climate, have an important influence on the movement of resources and subsequent economic growth. Finally, note that the alternative marginal tax measure, based on top legislated income tax
J.K.
Mullen,
Table 3 Basic regression
results
Dependent variable:
Explanatory variables Intercept YPC ATR MTR
M. Williams
with public I GSP growth 1969-86 (GSPGR)
0.01832” (1.90) 0.00049 (0.53) 0.10402 (1.04) -0.22193* (5.19)
CLFGR CAPGR PUBKGR
R’ F-value
0.75999* (6.08)
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capital
stock
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699
variable”
II GSP growth 1969-86 (GSPGR)
0.00252 (0.44) -0.00109** (1.99) 0.19550* (3.35) -0.13807* (5.32) 0.41216* (4.31) 0.49831* (4.97) 0.20772* (2.24)
III Productivity growthh 1969-86 (NETGSPGR)
-0.00027
(0.05) -0.00116* (2.20) 0.22310* (3.86) -0.12455* (5.02)
0.12.506** (1.73)
0.565
0.867
0.497
13.98*
44.41*
10.61*
* Numbers in parentheses are absolute values of the r-statistics; (*) (**) indicates significance at the 5% and 10% level respectively; (---) indicates that a particular variable is omitted from the equation. ” Defined as GSP growth net of share-weighted growth in capital and labor inputs.
brackets (CCHMTR), is also negatively related to output growth. But it is not statistically significant. One explanation is that there exists enormous variation in the relative importance of the state income tax across statelocal tax systems. In general, the results in Table 4 tend to strengthen our initial findings on the impact of our marginal tax measure (M7’R) on interstate economic performance. Next, we assessed the robustness of our estimates over alternative time periods. To do this, we estimate the basic output equation over the three sub-periods which conform to peak-to-peak business cycles between 1969 and 1986. Recall that our estimate of MTR reflects a summary of the pattern of the marginal tax environment within a state over a lengthy time period, rather than being a literal description of the change in taxes for any given change in GSP over a short span of time. Thus our measure of MTR is as previously defined, but we substitute sub-period-specific values for average tax rates and initial wealth conditions. The results are presented in Table 5.
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Table 4 Regression (Dependent
results with alternative marginal tax burden variable = GSP growth, 1969-86)
Mullen, M. Williams I Reg. Sci. Urban Econ. 24 (1994) 687-705
Equation
(marginal
variables”
tax variable) t:.C.HMTR,
Explanatory variables Intercept YPC ATR Marginal tax variable
0.02086 (1.64) -0.00070 (0.56) -0.01196 (0.09) -6.36* (2.48)
RZ
0.133
F-value
2.42*
’ Numbers in parentheses are absolute at the 5% and 10% level respectively.
Table 5 Sub-period
estimates
of output
YPC ATR MTR
0.03459* (2.91) -0.00139 (0.99) 0.10320 (0.61) -0.05515 (1.19)
0.04356* (3.87) -0.00000757 (0.01) -0.18158 (1.26) -0.02098’ (3.07) 0.186
0.043
3.35*
0.65 values of the t-statistics;
(*) (**) indicates
significance
equations“
I 1969-73 Explanatory variables Intercept
III (ATXBUR)
0.07933* (4.83) -0.00249 (1.42) -0.02569 (0.16) -0.22291* (2.55)
R2
0.213
F-value
3.98*
a Numbers in parentheses are absolute at the 5% and 10% level respectively.
II 1973-79
III 1979-86
0.0839* (4.60) -0.00080 (0.47) 0.42551* (2.82) -0.13109** (1.66)
0.01545 (0.72) -0.0002 (0.11) 0.34496 (1.51) -0.25268* (2.67)
0.236
0.145
4.53* values of the t-statistics;
2.48* (*) (* *) indicates
significance
J.K.
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701
Once again, MTR is negative and statistically significant in all cases.14 On comparison, the somewhat lower coefficient value for MTR during the 1973-79 sub-period suggests that other forces may have exerted a more powerful influence on output growth over that time interval, but that marginal tax policies were still relatively important. The robustness of our estimates of the impact of marginal tax rates on economic growth across the various time periods also lends strong support to the validity of our findings. Finally, for empirical completeness, we undertook a regression diagnostic analysis of the results. Our purpose here is to identify the strength of the impact of individual observations on the empirical findings. We pinpoint those observations that are ‘outliers’ with respect to the independent variables. This is accomplished by computing critical values for the ‘hat diagonal’ matrix so that strongly influential observations may be identified.15 Our analysis of each of the three equations in Table 2 indicates that Arizona, Louisiana, Nevada, New York, South Dakota and Vermont are highly influential and therefore represent ‘leverage points’. However, this does not automatically imply the need to exclude these observations. So we rely on another diagnostic test (DFBETA) for further guidance. The DFBETA statistic captures the sensitivity of specific coefficient values to the exclusion of particular observations. This index involves computing the change in individual coefficient values as each observation (state) is deleted from the estimation process. Critical values for this statistic are used to determine whether the coefficients would change substantially if a particular observation were deleted. Our findings indicate that four of the above states have a substantial impact on a majority of the regression coefficients.” Next, we decided to eliminate these states as observations in generating empirical results. Then, we reestimate the three equations without Arizona, Louisiana, New York, and South Dakota.” The results (Table 6) are fundamentally unaltered. There is a slight drop in the percentage of explained variation in two of three equations, but the significance levels and magnitude of the coefficients for the other variables remain largely unaffected. In fact, the coefficient of MTR increases in size and significance even though New York is now excluded. Thus we can conclude that, although the estimates are sensitive to a few observations, the influence attributable to these specific states has a negligible impact on the overall findings.
I4 This outcome is qualitatively unchanged if we estimate a productivity instead of an output equation. Generally, the results prove to be very consistent. Is See Belsey et al. (1980) for a detailed discussion of this issue. ” In the interest of saving space we do not reproduce the results of the regression diagnostic analysis. They are available upon request from the authors. I’ Arizona had the highest GSP growth rate over the sample period. New York had both the highest average and marginal tax rate together with one of the lowest output growth rates.
702
J.K.
Mullen,
Table 6 Regression results. York. S. Dakota) Dependent variable:
Explanatory variables Intercept YPC ATR MTR
M. Williams
basic output
I Reg. Sci. Urban Econ.
and productivity
I GSP growth 1969-86 (GSPGR)
0.05713* (4.74) -0.00035 (0.32) -0.10868 (0.82) -0.20888* (3.58)
CLFGR CAPGR
equations”
24 (1994) 687-705
(excludes
II GSP growth 1969-86 (GSPGR)
0.01332** (1.94) -0.00133* (2.52) 0.11561”* (1.72) -0.12894* (4.49) 0.44901* (4.50) 0.51111* (4.59)
Arizona.
Louisiana,
New
III Productivity growthh 1969-86 (NETGSPGR)
0.01021** (1.77) -0.00134* (2.55) 0.14706* (2.32) 0.12511* (4.47)
R’
0.259
0.840
0.434
F-value
4.67*
39.78*
10.24*
a Numbers in parentheses are absolute values of the t-statistics; (*) (**) indicates significance at the 5% and 10% level respectively; (---) indicates that a partial variable is omitted from the equation. ’ Defined as GSP growth net of share-weighted growth in capital and labor inputs.
5. Summary
and conclusions
In this paper, we undertook a broader examination of inter-state variations in economic growth rates over the last two decades. Our goal has been to isolate causal forces which help explain differences in state economic growth, with a principal focus on marginal tax rates. Time-series regressions of tax revenues on GSP are used to derive estimates of marginal tax rates for individual states. We also consider the role of both private and public capital stocks along with other traditional growth determinants. The findings here lend general support to the ‘catching-up’ hypothesis, thereby co roborating some recent empirical work. Specifically, states with higher initial levels of per capita income have significantly lower rates of economic growth. As expected, labor force growth and increases in the stocks of both private and public capital have stimulative influences on interstate output growth. Previous research (e.g. Eberts, 1990) suggests that investment in infrastructure or other types of economic overhead capital
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may stimulate regional economic development. Our findings strengthen this contention. Our empirical results also indicate strong support for the ‘supply-side’ theme pertaining to marginal tax rates. We find that, for various measures of marginal taxes, higher marginal tax rates are associated with slower output growth. Furthermore, the magnitude of the coefficients suggests that lowering marginal tax rates can have a considerable positive impact on growth. Additional empirical support is marshalled from an analysis which considers changes in relative tax burdens. States with a deteriorating tax rate environment, in comparison to other states, have slower rates of economic growth. The policy implications of these results are straightforward. Creating a less confiscatory tax structure, while maintaining the same average level of taxation, enables sub-national governments to spur economic growth. Improving one’s relative tax environment also appears to be effective in generating a higher output growth rate. Thus predatory tax policies may remain viable development strategies for some states. The results here imply that changes in effective tax rates, rather than average tax rates per se, have an important influence on state economic growth. Indeed, average tax rates display a positive relationship with output growth, a result often found in earlier empirical work. The usual caveat is to exercise caution in interpreting this latter results because of the contemporaneous correlation with public expenditure impacts. Finally, we should emphasize that our results suggest that state (and local) tax policies exert a significant influence on relative economic performance. Long-term absolute growth within individual states is more likely to be dominated by federal fiscal policies and national economic forces over which states have little control.
Acknowledgements An earlier version of this paper American Meetings of the Regional Boston, MA, Nov. 1990.
was presented at the 37th North Science Association International,
References Abramovitz, M. 1990 The catch-up factor no. 1, l-18. Advisory Commission on Intergovernmental industrial location, Washington, DC.
in postwar Relations
economic (ACIR),
growth.
Economic
1967, State-local
Inquiry taxation
28, and
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Aschauer, D., 1989, Is public expenditure productive?, Journal of Monetary Economics 23, no. 2, 177-200. Bartik, T.J., 1985, Business location decisions in the United States: Estimates of the effects of unionization, taxes, and other characteristics, Journal of Business and Economic Statistics 3, no. 1, 14-22. Bartik, T.J., 1990, Small business start-ups in the United States: Estimates of the effects of characteristics of states, Southern Economic Journal 57, no. 2, 1004-1018. Baumol, W.J.. 1986, Productivity growth, convergence, and welfare: What the long-run data show, American Economic Review 76, no. 5, 1072-1085. Belsley, D.A., E. Kuh, and Welsch, R.E. 1980, Regression diagnostics, (John Wiley & Sons, New York). Benson, B.L. and R. Johnson, 1986, The lagged impact of state and local taxes on economic activity and political behavior, Economic Inquiry 24, no. 3, 389-401. Canto, V. and R. Webb, 1987 The effect of state fiscal policy on state relative economic performance, Southern Economic Journal, 54, no. 10, 186-202. Commerce Clearing House, 1981, State tax handbook (Commerce Clearing House Publishers, Inc., Chicago, IL). DeLong, J.B. 1988 Productivity growth, convergence, and welfare: Comnment, American Econ. Review, 78, no. 5, 1138-1154. Duffy-Deno, K. and R. Eberts, 1989 Public infrastructure and regional economic development: A simultaneous equations approach, Working Paper 8909, Federal Reserve Bank of Cleveland. Eberts, R., 1990, Public infrastructure and regional economic development, Economic Review 26, no. 1, 15-27. Eisner, R. 1991, Infrastructure and regional economic performance: Comment, New England Economic Review, Sept 1991 Oct., 47-58. Ghosh, S.K., 1991 Econometrics: Theory and applications. (Prentice-Hall, Inc., Englewood Cliffs, NJ). Grier, K. and G. Tullock, 1989, An empirical analysis of cross-national economic growth, 1951-80, Journal of Monetary Economics 24 no. 2, 259-276. Helms, L. Jay, 1985, The effect of state and local taxes on economic growth: A time series-cross section approach, Review of Economics and Statistics 67, no. 3, 574-582. Huhen, C.R. and R.M. Schwab, 1984, Regional productivity growth in U.S. manufacturing: 1951-78, American Economic Review 74, 152-162. Koester, R.B. and R.C. Kormendi, 1989, Taxation aggregate activity and economic growth: Cross-country evidence on some supply-side hypothesis, Economic Inquiry 27, 367-386. Modifi, A. and J.A. Stone, 1990, Do state and local taxes affect economic growth?, Review of Economics and Statistics 72, no. 4, 686-691. Mullen, J.K. and M. Williams, 1987, Technical progress in urban manufacturing: North-South comparisons, Journal of Urban Economics 21, no. 2., 194-204. Munnell, A., 1990, How does public infrastructure affect regional economic performance?, New England Economic Review, 11-32. Olson, M.. 1983, The South will fall again: The South as leader and laggard in economic growth, Southern Economic Journal 49, 917-932. Papke, L.E., 1989, Taxes and other determinants of gross state product in manufacturing: A first look, NTA-TIA Proceedings of the Eighty-Second Annual Conference, 274-282. Papke, J.A. and L.E. Papke, 1986, Measuring differential state-local tax liabilities and their implications for business investment location, National Tax Journal 39, no. 3. 357-366. Phares, D.. 1980, Who pays state and local taxes? (Oelgeschlager, Gunn and Hain, Publishers, Inc., Cambridge, MA). Plaut, T.R. and J.E. Pluta, 1983 Business climate, taxes and expenditures, and state industrial growth in the United States, Southern Economic Journal, 50, 99-119.
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Schmenner, R., J. Huber and Cook, 1987 Geographic differences and the location of new manufacturing facilities, Journal of Urban Economics 21, no. 1, 83-104. Scully. G.W.. 1989, The size of the state, economic growth and the efficient utilization of national resources, Public Choice 63. no. 2, 149-164. Bureau of the Census, Government finances in 1971U.S. Department of Commerce, 72. 1986-87, Series GF-72-5 through GF-87-5 (USGPO, Washington. DC). U.S. Department of Commerce, Bureau of Economic Analysis, 1989, State personal income, 1929-87 (USGPO, Washington, DC). U.S. Department of Commerce, Bureau of the Census, Statistical abstract of the United States (USGPO, Washington, DC). U.S. Department of Commerce (BEA), 1986, 1988, 1989. Survey of current business 66, 68. 69, (USGPO, Washington, DC). Yu, W., M.S. Wallace and C. Nardinelli, 1991, State growth rates: Taxes, spending, and catching up, Public Finance Quarterly 19, no. 1. 80-93.
EISEIVIER
Science
Marginal
and Urban
Economics
24 (1994)
687-705
tax rates and state economic John
K. Mullen”‘*,
Martin
growth
Williamsb
“Department of Economics and Finance, Clarkson University. Potsdam, NY 13699, USA hDepartment of Economics, Northern Illinois University, DeKalb, IL 60115, USA Received
May 1992, final version
received
April
1993
Abstract This paper analyzes the impact of state-local tax structures on state economic performance. Specifically, growth rates in Gross State Product over the 196946 period are related to several measures of a state’s marginal tax environment in addition to more traditional growth determinants. Estimates of marginal tax rates for individual states are derived and utilized alternately with other tax climate surrogates in explaining variations in economic growth. We report both output and productivity equations in order to distinguish separate impacts resulting from taxation; the endogeneity problem is also addressed in this fashion. The findings suggest that, after controlling for overall tax burdens, higher marginal tax rates impede output growth. Key words: Marginal JEL
classification:
tax rates;
Economic
growth;
Sub-national
public
finance
H70: 040
1. Introduction Considerable
uncertainty
persists
concerning
the
impact
of
state-local
taxes on sub-national economic growth. Private sector advocates have long maintained that high tax rates discourage economic development initiatives and depress economic growth rates. Public finance economists, however, maintain that the causal relationship between taxes and economic growth is much more complex. It is possible that higher taxes may actually be stimulative of economic activity if revenues are used to finance infrastructure improvements, encourage private sector development, or other* Corresponding
author.
0166.0462/94/$07.00 0 1994 Elsevier SSDI 0166-04622(94)02058-O
Science
B.V. All rights
reserved
688
J.K.
Mullen, M. Williams I Reg. Sci. Urban Econ. 24 (1994) 687-705
wise enhance public service levels. Furthermore, simply focusing on the correlation between tax rates and economic growth ignores the impact of economic activity levels on both of these variables. The present research is an attempt to augment our understanding of the influence of state-local tax structures and burdens on state economic growth. Such efforts are a prelude to formulating public policies that are effective in fostering regional economic development. The specific focus of this research concerns the role of state-local tax rates in explaining differential growth across states. A ‘supply-side’ hypothesis is explored alongside mainstream arguments by considering the impact of both marginal and average tax rates on economic performance. We take advantage of Gross State Product (GSP) figures from 1969 to 1986 together with data on state public and private capital stocks for this same period. State-local tax revenue data are used to compute average tax rates; these and GSP data also form the basis for statistical estimates of marginal tax rates over the period of analysis. These marginal tax rate estimates are then employed alongside several alternative measures of the marginal tax environment to assess their impact on state economic growth. The next section of the paper examines some relevant background literature and describes the causal growth factors that warrant consideration in the present analysis. This is followed first by a discussion of the empirical issues and experimental design, then by a presentation and discussion of the various statistical results. A final section summarizes our findings and offers some policy insights.
2. State-local
fiscal policies and economic growth
The degree to which states can use their fiscal policies to significantly influence economic activity remains an unsettled empirical issue. A sizable body of literature exists concerning the relationship between business activity and state-local taxes and public service levels. The more recent of this literature largely supports the contention that states (and their localities) can exert at least some modest influence on business location decisions via their choice of fiscal policies (e.g. Bartik, 1985, 1990; Helms, 1985; Papke and Papke, 1986 and Schmenner et al., 1987). A broader issue, however, concerns the impact of sub-national public sector activity on overall economic growth. Although some researchers appropriately have focused on growth rates (e.g. of income, employment, or capital stocks) instead of simply economic activity levels, few attempts have been made to explain statewide variations in the growth rates of a comprehensive measure of output such as GSP. Furthermore, little if any evidence exists concerning the
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24 (1994) 687-70.5
689
impact of marginal tax rates on output growth at the regional level.’ The present research seeks to fill that void. A number of researchers have sought to explain differences in international growth rates by examining the role of taxation and public sector size. The basic premise underlying these analyses is that a larger public sector (and its higher tax rates) tends to discourage economic activity levels and growth rates. Grier and Tullock (1989) find that stronger growth in the government share of Gross Domestic Product (GDP) is associated with lower (five-year average) growth rates of GDP for a large sample of OECD countries. Scully (1989) reports results which similarly support the conclusion that, regardless of motivation, increases in the size of the government share of the economy adversely affect economic growth and the allocation of resources. Koester and Kormendi (1989) utilize data from 63 countries to examine the impact of average and marginal tax rates on both the level and growth of economic activity. Making sure to account for the relationship between per capita income and relative public sector size, they find little support for the hypothesis that economic growth is hampered by higher (average or marginal) tax rates. However, they do confirm that economic activity levels are depressed as a result of increases in marginal tax rates. Tax and expenditure considerations may be of even greater importance in explaining differential growth rates across sub-national regions than across countries. Clearly, factor mobility is much greater across states within a country. Thus, the intensity of competition for jobs and economic activity may allow certain state or (regional) economies to outperform national averages as resources re-locate in response to differential regional incentives. Typically, state-local governments offer numerous tax inducements to spur economic development. More generally, capital and labor migrate to those areas where tax burdens are perceived as being less onerous. Expenditures on certain public services may also encourage factor resource in-migration. Upgrading infrastructure or improving educational quality are examples of public spending that may induce differential growth at the state level. Although state-local public sector activities obviously are capable of having a substantial impact on regional economies, uncertainties abound given the complexity of these interrelationships. However, one important policy issue, begging for empirical evidence, concerns the impact of revenue-neutral reductions in marginal tax rates on economic growth. Similarly, the role of ‘predatory’ tax policies in stimulating local economies
I Papke (1989) has provided evidence of the influence on GSP levels within select manufacturing industries.
of effective
state business
tax burdens
690
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M. Williams
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24 (1994) 6877705
is not fully understood. As some analysts have argued, relative tax burdens may be more important than tax rates or tax levels per se in influencing industrial growth (e.g. see Benson and Johnson, 1986). Indeed, changes in relative tax burdens have been cited as perhaps most influential on the belief that resource owners assess the prevailing business climate from such comparisons. To gauge this, we need more compelling evidence on the role of the state-local tax environment in contributing to differential growth rates. Another growing body of research seeks to understand inter-regional growth rate differentials in the context of the convergence hypothesis. This hypothesis was originally developed to explain variations in international productivity performance and, subsequently, economic growth. Emphasizing the public goods aspect of technology, the theory suggests that productivity and income levels will converge as ‘laggards’ assimilate the storehouse of industrial and administrative technology initially developed by ‘leaders’ (see Baumol, 1986; DeLong, 1988, and Abramovitz, 1990, for some relevant discussion.) The convergence theory also has been invoked to explain the narrowing of regional income differentials within developed countries (see Hulten and Schwab, 1984; Olson, 1983, and Mullen and Williams, 1987, for some representative work). The conditions which foster convergence are more likely to exist on a regional basis because of greater factor mobility and resource re-allocation possibilities. This logic can be extended further to the state level, with the argument that resources and technology face even fewer impediments moving within rather than across states. In fact, the ‘catchingup’ hypothesis has been used to explain different growth rates across the United States. Yu et al. (1991) find that initial per capita income has been a significant determinant of variations in state personal income growth up through the 1970s. Though more recent data suggest a diminished role for ‘catching up’ as an explanation for inter-state variations in economic growth, this trend may simply reflect diminishing disparities in income levels. In sum, while the convergence hypothesis implies a tendency for an equalization of growth rates across regions to occur, it is quite likely that compensating differentials in per capita income levels would remain. However, the existence of persistent differential growth rates may be taken as an indication of the importance of fiscal and/or other policies in influencing economic development. It may be possible, for example, that a particular state consistently exhibits a stronger growth pattern because of conscious efforts to improve its tax environment or provide the type of ‘economic overhead capital’ that is conducive to private sector expansion. Evidence confirming such effects is decidedly mixed but essential to the formulation of sound policy prescriptions.
J.K.
3. Empirical
MulIen, M. Williams I Reg. Sci. Urban Econ. 24 (1994) 687-705
691
issues and design
In what follows we attempt to explain inter-state variations in GSP growth rates over the 1969 to 1986 period.’ We exploit earlier research efforts in designing an empirical model to test for tax structure effects on state growth. The model we develop is admittedly ad hoc, but we draw heavily upon economic theory as a guide. The specific theoretical construct underlying the empirical framework is the disequilibrium-adjustment model popularized in migration research. This model typically relates changes in the dependent variable over the period to levels of the explanatory variables at the beginning of the period. Plaut and Pluta (1983) employ such a model in their attempt to explain variations in state industrial growth between the 1967-72 and 1972-77 periods.’ But a more fully specified model also consider changes in the level of the independent variables. While we control for the impact of growth rates of the input levels themselves, we also consider the role of levels of certain variables (most notably real per capita income) in explaining variations in output growth. Our primary dependent variable is the (compound) annual growth rate of real (1982 dollars) GSP from 1969 to 1986. As noted, this approach represents a departure from existing efforts in that we are attempting to explain variations in a comprehensive measure of economic growth, rather than simply focusing on personal income, employment or the number of business ‘start-ups’. Our explanatory variables include tax rate measures, initial economic conditions, and input growth rates within each state. Some of the underlying hypotheses motivating the use of these regressors warrant further discussion. Our major preoccupation rests with determining the impact of the tax structure on state economic growth. Specifically, we are concerned with testing whether or not a higher marginal tax rate environment has a depressing effect on growth. Our inclusion of both marginal and average tax rates as regressors enables us to examine the supply-side theme that revenue-neutral increases in marginal tax rates have an adverse effect on output growth. Ideally, both sides of the budget equation should be considered because of the potentially positive impact of public spending on growth. Both Helms (1985) and Modifi and Stone (1990) present evidence
‘This sample period allows us to have a consistent set of measures, derived from various sources, for the numerous variables at the state level of disaggregation. More specifically, both public and private capital stock measures, at this level. are limited to this time period. 3 Their efforts, which did not include an explicit consideration of public expenditure impacts on economic growth, suggest that relative tax revenues are important in explaining variations in the growth rates of both employment and capital stock.
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that higher taxes used to augment spending on traditional services may actually stimulate income levels. In contrast, additional spending on social programs hampers economic performance. Yet it remains a difficult empirical task to distinguish revenue from expenditure effect. (This difficulty helps explain why earlier studies report tax rates as having either no statistical impact at all or a positive effect on economic growth.) In any case, statistical estimates of tax rate effects are highly sensitive to functional form, variable definitions, and the treatment of public sector spending. Note that our specification does not explicitly include a public expenditure variable because this complicates the attempt to disentangle average from marginal tax effects. Moreover, the possibility remains that some of the influence of government spending is embedded within the coefficient of the average tax rate variable.” Next, the convergence hypothesis implies that growth rates will be inversely related to income levels at the beginning of the period of analysis. Although by no means inevitable, higher initial wealth conditions should result in lower future growth rates. Furthermore, the stage of economic development (or wealth) tends to have a direct influence on the relative size of the public sector and is postulated to be positively associated with average tax rates. Therefore our inclusion of initial (real) per capita income controls for this relationship and provides a direct test of convergence amongst states. We expect output growth to be clearly linked to input growth rates. The size (and composition) of the labor force represents a major element in regional economic growth. We utilize the growth rate of the civilian labor force within the state to capture changes in this input. Similarly, we hypothesize that the growth rates for both the private and public capital stocks are instrumental in fueling state economic growth. It is easy to argue the case for the role of private capital in stimulating output growth. Moreover, recent evidence suggests that public capital (infrastructure or other ‘economic overhead capital’) is also an important factor in explaining interregional output and productivity differentials (see Aschauer, 1989; Duffy-Deno and Eberts, 1989; Munnell, 1990 for some relevant discussion). But this issue is further complicated if these two distinct types of capital are substitutes or complements in stimulating regional growth. Yet the nature of the association between public capital and state output remains an unsettled empirical issue (e.g. see Eisner, 1991). For empirical completeness, we provide results both with and without the public capital stock variable. The foregoing discussion is centered on input growth rates as regressors in the empirical specification of a model explaining variations in output ‘Admittedly. such an ad hoc treatment is a second-best (1990) for an excellent discussion of this and related issues.
solution.
See Modifi
and
Stone
J.K.
Mullen,
M. Williams
I Reg. Sci. Urban
Econ.
24 (1994) 687-705
693
growth. It is this type of specification which inherently generates a ‘productivity’ type equation. To deal with this problem, we also report ‘output’ equations with the input growth rates eliminated as explanatory variables. Obviously this approach heeds the suggestion that tax variables should have a stronger impact on output than on productivity growth because of the responsiveness of capital and labor inputs to the fiscal environment. A related issue here concerns the endogeneity of the input growth variables when output growth is the dependent variable. To overcome the controversy surrounding such a specification we employ an alternative approach to measure the impact of the tax environment on productivity. We draw on the growth-accounting framework common in the productivity literature. We measure productivity by subtracting the factor share-weighted input growth rates from the growth rate of GSP.5 This yields an indirect productivity measure that is regressed on the tax rate variables to discern their impact on output growth, after having accounted for input growth. Essentially, we report three variants of the basic specification: an output equation; a productivity equation; and a modified productivity equation. At this point, it is important to provide a further discussion of our tax variables. We define the average tax rate (ATR) as the sum of all ownsource state and local tax revenue as a percentage of GSP. This formulation is consistent with earlier research and is also representative to the ‘effort’ made by state-local governments in extracting revenue from the broadest definition of their tax base.‘j As for marginal tax rates, we follow the methodology employed by Koester and Kormendi (1989). Our marginal tax rates are derived by estimating the following regression equation, Eq. (l), over the 1969-86 period for each state: TAXREV,
= a, + a,GSP,
+ e, ,
(1)
where TAXREI/ = the sum of all own-source state-local tax revenue in year t. The coefficient of GSP represents an approximation to the (average) marginal tax rate for a given state, assuming a constant tax structure over the period. The estimates of marginal tax rates are generally consistent with typical perceptions concerning state-local tax systems. For example, the mean value for the marginal tax rate (MTR) is 8.28%, whereas the mean for the average tax rate is 8.47%. Although this implies that state-local tax ’ We assume that labor’s share of output is 60% and capital’s share is the remaining 40%. These figures represent a consensus of the tmmerous empirical estimates of such values for the aggregate U.S. economy (e.g. see Ghosh, 1991, p. 135). ‘This formulation coincides with the belief that the total tax bill, rather than its breakdown into individual levies, is important to decision-makers (see Benson and Johnson, 1986, p. 393. and ACIR, 1967, p. 62 for further discussion). Also, this overcomes problems caused by inter-state variations in the division of fiscal responsibilities between state governments and their localities.
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structures are, on average, slightly regressive, substantial differences between these rates exist within particular states. The intercept term is positive and significant in 20 of the 48 state equations. This finding may be suggestive that these states derive a substantial portion of their revenues from activity A further implication may be that the occurring outside their borders. overall tax structure is regressive within these 20 states. (The insignificance of the constant term in 19 of the equations implies a proportional tax structure within the state.) The correlation coefficient between ATR and MTR has a surprisingly low and insignificant value of 0.122. Using our definition of tax rates, high (average) tax burden states did not typically impose high incremental tax rates over the sample period. It is conceivable, of course, that states that have attempted to increase their average tax rates over the period would display higher marginal tax rates given our definition. Thus, we employ alternative measures to test whether the findings based on our definition are corroborated by other proxies for the marginal tax environment within each state.7 One such alternative is derived from marginal burden elasticity estimates generated by Phares (1980). This elasticity represents a measure of the degree of progressivity/regressivity of the state-local tax structure for 1976, defined as the percentage change in ‘tax effort’ arising from a percentage change in income. We convert these elasticity estimates to represent a marginal tax effort rate.# But while this variable is not directly comparable to our MTR measure, it is broadly representative of the nature of statelocal tax structures in 1976. Our second proxy measure of the marginal tax rate is based on the top legislated income tax brackets across states. These data are obtained from the 1981 edition (selected arbitrarily) of the Commerce Clearing House’s State Tax Handbook. Although there are obvious problems with this measure given the enormous diversity in underlying tax structures and the division of fiscal responsibilities across states, it does provide a rough approximation to the marginal tax environment within each state. Ideally, such a variable should encompass the multitude of state-local tax levies comparable to our estimates (MTR) of marginal tax rates. Finally, we compare the statistical findings based on the marginal tax environment variables with a closely-related tax concept. A number of researchers have suggested that changes in a state’s tax policies relative to other states may be more important than absolute levels of the tax burden (e.g. see Benson and Johnson, 1986; Canto and Webb, 1987). Accordingly,
’ This approach was suggested by an anonymous referee. ‘More specifically, the marginal tax effort rate is defined as d(T/Y)/dY, where T is total state-local tax revenue, Y is state personal income, and u represents mean personal income within the state.
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6%
we estimate a model where MTR is replaced with a variable which captures the change in a state’s relative tax burden over the sample period. We define relative tax burden as a state’s average tax rate (ATR) divided by the sample mean ATR.” Changes in this variable over time (ATXBUR) reflect how a state’s tax environment has been altered relative to the U.S. average.“’ Table 1 presents a definition of the variables reported in our regression results. Most of these need no further elaboration at this point, although some of the data sources warrant a brief note.‘] GSP and personal income figures are obtained from the U.S. Department of Commerce (Bureau of Economic Analysis). All tax and expenditure variables are taken from the Government Finances series (Bureau of the Census). Of special interest are the estimates of total private and total public capital stocks for individual states over the 1969-86 interval. These data, provided by the Federal Reserve Bank of Boston,” allow us to explicitly consider the impact of these inputs on overall state economic growth.
4. Statistical
findings
In this section we report and discuss a number of cross-section regressions. We show results for the basic model, along with variants of it that are designed to test for robustness by considering both alternative definitions of the marginal tax variable and alternative time horizons (sub-periods). Furthermore, we undertake a regression diagnostics analysis to confirm that our findings are not largely due to the influential nature of a few prominent observations. Table 2 reports findings for the basic output and productivity equations as described above. It is immediately apparent that the results are generally supportive of the underlying hypotheses. All three of the basic equations are statistically significant. As expected, the percentage of explained variance is vastly greater in the productivity equation (II) where input growth rates appear as explanatory variables. The alternate productivity equation (III), based on the growth-accounting framework, generates results which explain variations in GSP growth net of input growth (NETGSPGR). These findings ’ This formulation is fundamentally the same as that used by Benson and Johnson (1986). Canto and Webb (1987) use a closely related measure based on (changes in) per capita tax revenue relative to the U.S. average. “’ By definition. this variable is expected to be closely related to our MTR estimates. In fact, the simple correlation coefficient between MTR and ATXBCJR is 0.635 and highly significant. I’ We offer a more complete citation in the references section. I’ We are indebted to Alicia Munnell and Leah Cook at FRB. Boston, for making these data available to us.
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Table 1 Variable names Name
(symbol)
GSPGR NETGSPGR
CLFGR CAPGR PIJBKGR YPC
ATR
MTR
ATXBUR
MB UR
CCHMTR
Mullen, M. Williams I Reg. Sci. Urban Econ. 24 (1994) 687-705
and definitions Definition Compound annual growth rate of real GSP, 1969-86 Compound annual growth rate of real GSP minus factor share-weighted growth in capital and labor inputs (i.e. GSPGR-(uGLFGR-PCAPGR) Compound growth rate of the civilian labor force, 1969-86 Compound growth rate for the private capital stock, 1969-86 Compound growth rate for the stock of public capital, 1969-86 Real per capita income at the beginning (1969) of the period (1969,1973 and 1979 for subperiod equations) Average tax rate, defined as the mean value (over the relevant period) of the sum of all own-source, state-local tax revenue as a percentage of GSP Marginal tax rate, defined as the coefficient of a regression of all state-local tax revenue on GSP; further explanation within the text Change in relative tax burden over the 1969-86 period, where relative tax burden is defined as ATR for state i + sample mean A TR Marginal burden (tax effort) rate, defined as the change in tax effort (total taxes + personal income) as average personal income changes, using estimates derived by Phares; this measure is a proxy for the degree of progressivityiregressivity of the state/local tax structure Highest statutory marginal tax rate as contained in Commerce Clearing House’s State Tax Handbook. 1981
are nearly identical both qualitatively and quantitatively to the direct specification. It is interesting to note the results for the marginal tax rate variable (MTR). It is negative and statistically significant in all three equations. This finding is consistent regardless of whether we focus on output or productivity growth. Clearly, strong support emerges for the contention that revenue-neutral increases in marginal tax rates have a perverse effect on economic growth. Next, the model controls for variations in revenue yield by including ATR, defined as the mean (average) tax rate over the period, as a regressor. Thus supply-side considerations appear to be important within the state-local sector. Note the magnitude of these
J.K. Table 2 Regression Dependent variable
Explanatory variables Intercept YPC ATR MTR
Mullen,
results,
M. Williams
basic output
and productivity
I GSP growth 1969-86 (GSPGR)
0.04605* (4.03) -0.00023 (0.19) -0.02167 (0.17) -0.17813* (3.13)
CLFGR CAPGR
I Reg. Sci. Urban
&on.
24 (1994) 687-705
697
equations” II GSP growth 1969-86 (GSPGR)
0.00441 (0.74) -0.00141* (2.56) -0.19277* (3.15) -0.11778* (4.62) 0.50511* (5.60) 0.53640” (5.18)
III Productivity growthb 1969-86 (NETGSPGR)
0.00429 (0.85) -0.00128* (2.39) 0.20241* (3.50) -0.11735* (4.70)
R’
0.192
0.850
0.462
F-value
3.49’
47.71*
12.59*
a Numbers in parentheses are absolute values of the t-statistics (*) (**) indicates significance at the 5% and 10% level respectively; (---) indicates that a particular variable is omitted from the equation. h Defined as GSP growth net of share-weighted growth in capital and labor inputs.
coefficients. Utilizing the output equation, the coefficient value of -0.1781 for MTR implies an elasticity (at the mean) of -0.5335. This suggests that lowering marginal tax rates may be a very effective way of encouraging growth within state borders.13 The average tax rate variable (ATR) is significantly positive in the productivity equations. Yet this result must be interpreted with caution. It suggests that higher average tax rates may actually be stimulative of economic activity. This positive effect, however, is not unusual when the empirical model does not appropriately distinguish between spending and tax impacts. Note though that we are more concerned with controlling for the effect of public sector size than in appropriately modeling the fiscal interaction that occurs.
“On the suggestion of an anonymous referee, we also estimated equations explaining variations in labor and capital growth rates on the basis of the tax variables. These regressions (not reported here) suggest that marginal tax rates have a negative. though generally statistically insignificant. impact on input growth rates.
698
J.K.
Mullen.
M. Williams
I Reg. Sci. Urban Econ.
24 (1994) 687-705
The findings in Table 2 also suggest that initial wealth conditions, as proxied by 1969 real per capita income (YPC69), have a depressing effect on productivity. This result tends to support the convergence hypothesis and confirms much of the existing empirical evidence in the productivity literature. On the other hand, it is interesting to note that initial wealth has an insignificant, though negative, coefficient value in the output equation. The implication here is that the leader-laggard phenomenon inherent in productivity patterns may not be a key element in explaining regional output variations. Finally, the input growth rate variables perform as expected in the direct productivity equation (II). We find that the growth rate in the civilian labor force and the growth rate of the private capital stock have a strong, direct influence on GSP growth. The influence exerted by the stock of public capital as an input in the production process is discussed in more general terms below. In view of the recent debate concerning the role of public capital stock, we provide a direct test of its impact on economic growth and productivity performance. Table 3 presents results where the growth of the public capital stock (PUBKGR) has been added to otherwise identical output and productivity equations. The coefficient for the public capital stock variable is positive and significant in all cases, bolstering arguments that infrastructure and other public investment spending help foster regional output growth and generate productivity gains. The findings regarding the other explanatory variables (in particular, those representing tax influences) are essentially unaltered, confirming the robustness of the model. Now, we turn to a fuller consideration of our state-by-state estimates of marginal tax rates by examining the empirical performance of related tax measures. Table 4 presents regression results for the output equation [analagous to Eq. (I) in Table 21 where MTR has been replaced by alternative tax variables serving as proxies for the marginal tax environment. The Table 4 results are qualitatively the same as those findings for MTR presented above. This pattern holds true regardless of whether taxes explain output or productivity (the latter equations are not reported here). Examining the individual equations, we conclude that the marginal tax effort or marginal burden rate (MBUR) has a significant negative impact on growth. That is, as the tax structure within a state becomes more progressive, economic growth is dampened. Also, we find that an increase in a state’s tax burden relative to the norm (as measured by ATXBZJR) tends to hamper output growth. This is a significant finding. For it appears that changes in a state’s relative tax burden, because they likely are correlated with perceptions of the prevailing business climate, have an important influence on the movement of resources and subsequent economic growth. Finally, note that the alternative marginal tax measure, based on top legislated income tax
J.K.
Mullen,
Table 3 Basic regression
results
Dependent variable:
Explanatory variables Intercept YPC ATR MTR
M. Williams
with public I GSP growth 1969-86 (GSPGR)
0.01832” (1.90) 0.00049 (0.53) 0.10402 (1.04) -0.22193* (5.19)
CLFGR CAPGR PUBKGR
R’ F-value
0.75999* (6.08)
I Reg. Sci. Urban
capital
stock
Econ.
24 (1994) 687-705
699
variable”
II GSP growth 1969-86 (GSPGR)
0.00252 (0.44) -0.00109** (1.99) 0.19550* (3.35) -0.13807* (5.32) 0.41216* (4.31) 0.49831* (4.97) 0.20772* (2.24)
III Productivity growthh 1969-86 (NETGSPGR)
-0.00027
(0.05) -0.00116* (2.20) 0.22310* (3.86) -0.12455* (5.02)
0.12.506** (1.73)
0.565
0.867
0.497
13.98*
44.41*
10.61*
* Numbers in parentheses are absolute values of the r-statistics; (*) (**) indicates significance at the 5% and 10% level respectively; (---) indicates that a particular variable is omitted from the equation. ” Defined as GSP growth net of share-weighted growth in capital and labor inputs.
brackets (CCHMTR), is also negatively related to output growth. But it is not statistically significant. One explanation is that there exists enormous variation in the relative importance of the state income tax across statelocal tax systems. In general, the results in Table 4 tend to strengthen our initial findings on the impact of our marginal tax measure (M7’R) on interstate economic performance. Next, we assessed the robustness of our estimates over alternative time periods. To do this, we estimate the basic output equation over the three sub-periods which conform to peak-to-peak business cycles between 1969 and 1986. Recall that our estimate of MTR reflects a summary of the pattern of the marginal tax environment within a state over a lengthy time period, rather than being a literal description of the change in taxes for any given change in GSP over a short span of time. Thus our measure of MTR is as previously defined, but we substitute sub-period-specific values for average tax rates and initial wealth conditions. The results are presented in Table 5.
700
J.K.
Table 4 Regression (Dependent
results with alternative marginal tax burden variable = GSP growth, 1969-86)
Mullen, M. Williams I Reg. Sci. Urban Econ. 24 (1994) 687-705
Equation
(marginal
variables”
tax variable) t:.C.HMTR,
Explanatory variables Intercept YPC ATR Marginal tax variable
0.02086 (1.64) -0.00070 (0.56) -0.01196 (0.09) -6.36* (2.48)
RZ
0.133
F-value
2.42*
’ Numbers in parentheses are absolute at the 5% and 10% level respectively.
Table 5 Sub-period
estimates
of output
YPC ATR MTR
0.03459* (2.91) -0.00139 (0.99) 0.10320 (0.61) -0.05515 (1.19)
0.04356* (3.87) -0.00000757 (0.01) -0.18158 (1.26) -0.02098’ (3.07) 0.186
0.043
3.35*
0.65 values of the t-statistics;
(*) (**) indicates
significance
equations“
I 1969-73 Explanatory variables Intercept
III (ATXBUR)
0.07933* (4.83) -0.00249 (1.42) -0.02569 (0.16) -0.22291* (2.55)
R2
0.213
F-value
3.98*
a Numbers in parentheses are absolute at the 5% and 10% level respectively.
II 1973-79
III 1979-86
0.0839* (4.60) -0.00080 (0.47) 0.42551* (2.82) -0.13109** (1.66)
0.01545 (0.72) -0.0002 (0.11) 0.34496 (1.51) -0.25268* (2.67)
0.236
0.145
4.53* values of the t-statistics;
2.48* (*) (* *) indicates
significance
J.K.
Mullen, M. Williams I Reg. Sci. Urban Econ. 24 (1994) 687-705
701
Once again, MTR is negative and statistically significant in all cases.14 On comparison, the somewhat lower coefficient value for MTR during the 1973-79 sub-period suggests that other forces may have exerted a more powerful influence on output growth over that time interval, but that marginal tax policies were still relatively important. The robustness of our estimates of the impact of marginal tax rates on economic growth across the various time periods also lends strong support to the validity of our findings. Finally, for empirical completeness, we undertook a regression diagnostic analysis of the results. Our purpose here is to identify the strength of the impact of individual observations on the empirical findings. We pinpoint those observations that are ‘outliers’ with respect to the independent variables. This is accomplished by computing critical values for the ‘hat diagonal’ matrix so that strongly influential observations may be identified.15 Our analysis of each of the three equations in Table 2 indicates that Arizona, Louisiana, Nevada, New York, South Dakota and Vermont are highly influential and therefore represent ‘leverage points’. However, this does not automatically imply the need to exclude these observations. So we rely on another diagnostic test (DFBETA) for further guidance. The DFBETA statistic captures the sensitivity of specific coefficient values to the exclusion of particular observations. This index involves computing the change in individual coefficient values as each observation (state) is deleted from the estimation process. Critical values for this statistic are used to determine whether the coefficients would change substantially if a particular observation were deleted. Our findings indicate that four of the above states have a substantial impact on a majority of the regression coefficients.” Next, we decided to eliminate these states as observations in generating empirical results. Then, we reestimate the three equations without Arizona, Louisiana, New York, and South Dakota.” The results (Table 6) are fundamentally unaltered. There is a slight drop in the percentage of explained variation in two of three equations, but the significance levels and magnitude of the coefficients for the other variables remain largely unaffected. In fact, the coefficient of MTR increases in size and significance even though New York is now excluded. Thus we can conclude that, although the estimates are sensitive to a few observations, the influence attributable to these specific states has a negligible impact on the overall findings.
I4 This outcome is qualitatively unchanged if we estimate a productivity instead of an output equation. Generally, the results prove to be very consistent. Is See Belsey et al. (1980) for a detailed discussion of this issue. ” In the interest of saving space we do not reproduce the results of the regression diagnostic analysis. They are available upon request from the authors. I’ Arizona had the highest GSP growth rate over the sample period. New York had both the highest average and marginal tax rate together with one of the lowest output growth rates.
702
J.K.
Mullen,
Table 6 Regression results. York. S. Dakota) Dependent variable:
Explanatory variables Intercept YPC ATR MTR
M. Williams
basic output
I Reg. Sci. Urban Econ.
and productivity
I GSP growth 1969-86 (GSPGR)
0.05713* (4.74) -0.00035 (0.32) -0.10868 (0.82) -0.20888* (3.58)
CLFGR CAPGR
equations”
24 (1994) 687-705
(excludes
II GSP growth 1969-86 (GSPGR)
0.01332** (1.94) -0.00133* (2.52) 0.11561”* (1.72) -0.12894* (4.49) 0.44901* (4.50) 0.51111* (4.59)
Arizona.
Louisiana,
New
III Productivity growthh 1969-86 (NETGSPGR)
0.01021** (1.77) -0.00134* (2.55) 0.14706* (2.32) 0.12511* (4.47)
R’
0.259
0.840
0.434
F-value
4.67*
39.78*
10.24*
a Numbers in parentheses are absolute values of the t-statistics; (*) (**) indicates significance at the 5% and 10% level respectively; (---) indicates that a partial variable is omitted from the equation. ’ Defined as GSP growth net of share-weighted growth in capital and labor inputs.
5. Summary
and conclusions
In this paper, we undertook a broader examination of inter-state variations in economic growth rates over the last two decades. Our goal has been to isolate causal forces which help explain differences in state economic growth, with a principal focus on marginal tax rates. Time-series regressions of tax revenues on GSP are used to derive estimates of marginal tax rates for individual states. We also consider the role of both private and public capital stocks along with other traditional growth determinants. The findings here lend general support to the ‘catching-up’ hypothesis, thereby co roborating some recent empirical work. Specifically, states with higher initial levels of per capita income have significantly lower rates of economic growth. As expected, labor force growth and increases in the stocks of both private and public capital have stimulative influences on interstate output growth. Previous research (e.g. Eberts, 1990) suggests that investment in infrastructure or other types of economic overhead capital
J.K.
703
Mullen, M. Williams I Reg. Sci. Urban Econ. 24 (1994) 687-705
may stimulate regional economic development. Our findings strengthen this contention. Our empirical results also indicate strong support for the ‘supply-side’ theme pertaining to marginal tax rates. We find that, for various measures of marginal taxes, higher marginal tax rates are associated with slower output growth. Furthermore, the magnitude of the coefficients suggests that lowering marginal tax rates can have a considerable positive impact on growth. Additional empirical support is marshalled from an analysis which considers changes in relative tax burdens. States with a deteriorating tax rate environment, in comparison to other states, have slower rates of economic growth. The policy implications of these results are straightforward. Creating a less confiscatory tax structure, while maintaining the same average level of taxation, enables sub-national governments to spur economic growth. Improving one’s relative tax environment also appears to be effective in generating a higher output growth rate. Thus predatory tax policies may remain viable development strategies for some states. The results here imply that changes in effective tax rates, rather than average tax rates per se, have an important influence on state economic growth. Indeed, average tax rates display a positive relationship with output growth, a result often found in earlier empirical work. The usual caveat is to exercise caution in interpreting this latter results because of the contemporaneous correlation with public expenditure impacts. Finally, we should emphasize that our results suggest that state (and local) tax policies exert a significant influence on relative economic performance. Long-term absolute growth within individual states is more likely to be dominated by federal fiscal policies and national economic forces over which states have little control.
Acknowledgements An earlier version of this paper American Meetings of the Regional Boston, MA, Nov. 1990.
was presented at the 37th North Science Association International,
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