REVENUE VOLATILITY: THE DETERMINANTS AND CONSEQUENCES
by
SUNJOO KWAK
A Dissertation submitted to the
Graduate School-Newark
Rutgers, The State University of New Jersey
in partial fulfillment of the requirements
for the degree of
Doctor of Philosophy
School of Public Affairs and Administration
written under the direction of
Frank J. Thompson
and approved by
________________________________________
________________________________________
________________________________________
________________________________________
Newark, New Jersey
October, 2011
2011
Sunjoo Kwak
ALL RIGHTS RESERVED
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ABSTRACT OF THE DISSERTATION
Revenue Volatility: The Determinants and Consequences
by Sunjoo Kwak
Dissertation Director:
Frank J. Thompson
In response to the growing concerns over the recurring state fiscal crises, this
dissertation aims to shed light on the determinants and consequences of revenue volatility.
To this end, the dissertation specifically addresses two questions. First, it examines how
the composition of tax bases varies across states and what effects tax base composition
has on the cyclical volatility of tax revenues. With particular focus on two major revenue
sources relied upon by state governments, general sales tax and individual income tax,
this study develops a measure of revenue volatility and investigates the questions using
pooled OLS on state panel data over the sample period from 1992 to 2007. Overall, the
empirical analysis finds that there exists a wide variation in both sales and individual
income tax across states. Regression results indicate that tax base composition
significantly affects revenue volatility, with economic structure and demographic-
economic characteristics being controlled for. Specifically, tax exemptions for household
necessities (food and clothing) and producer goods are found to have statistically
significant effects on sales tax volatility. On the other hand, exemptions for Social
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Security benefits, public pensions, and long-term capital gains, along with deduction for
local tax property tax paid, are significantly related to income tax volatility.
Second, this dissertation examines how cyclical changes in tax revenues affect
state fiscal behavior in terms of the level of spending and taxation, using a panel data set
for state governments over the period of 1992 to 2007. Specifically, the study tests fixed
effects models that explain own-source expenditure and overall tax rate as a function of
revenue gap, the cyclical component of state tax revenue. Regression results reveal that
cyclical changes in tax revenues are positively related to changes in own-source
expenditures, whereas they are negatively related to changes in tax rates, suggesting the
relationship between revenue volatility and fiscal instability. Based on these findings, the
dissertation concludes by discussing the dynamics of state fiscal behavior over the
business cycle and suggesting spending-smoothing rules as a policy solution to structural
budget deficits and fiscal crises.
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TABLE OF CONTENTS
CHAPTER 1 GENERAL INTRODUCTION AND RESEARCH MOTIVATION…1
CHAPTER 2 TAX BASE COMPOSITION AND REVENUE VOLATILITY……7
2.1 Introduction……………………….……………………………………......….7
2.1.1 Previous Studies……….……………………………………........….9
2.2 Conceptual Framework………………………………………………………18
2.3 Data and Methods…………………………………………………………....30
2.3.1 Variables and Data Sources………………………………………..31
2.3.2 Models and Estimation Methods………………………………....72
2.4 Results and Discussion…………………………………………………...….74
CHAPTER 3 REVENUE VOLATILITY AND FISCAL INSTABILITY…………83
3.1 Introduction………………………….……………………………………….83
3.2 Literature Review……………………………….……………………………90
3.3 Conceptual Framework……………………………………………………109
3.3.1 The Rationale for the Revenue-Spending Hypothesis....................109
3.3.2 The Mechanisms of the Revenue-Spending Relationship…..........112
3.3.3 Other Relevant Factors…………….……………………..............119
3.4 Data and Methods…………………………………………………………..124
3.4.1 Variables and Data Sources………………………………………124
3.4.2 Models and Estimation Methods……………………………......130
3.5 Results and Discussion………………………………………………...….133
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CHAPTER 4 POLICY IMPLICATIONS…………………………………………151
4.1 Implications for Revenue Stability………………………………………...151
4.2 Implications for Fiscal Stability……………………………………………158
CHAPTER 5 CONCLUSION………………………………………….…………......167
5.1 Summary of Findings and Contributions.......................................................167
5.2 Limitations…………………………………..……………………………170
5.3 Directions for Future Research………………………………………….…171
REFERENCES………………………………..………………………………………174
APPENDICES……………………………………………………….……….………186
CURRICULUM VITAE………………………..……………………….……………191
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LIST OF TABLES
Table 2.1 Sales Tax Treatment of Utility Services, Automotive Services, and Finance,
Insurance, and Real Estate.................................................................................................25
Table 2.2 Cyclical Volatility of General Sales Tax and Individual Income Tax by State
(1992–2007 Average)…………………………………………………………………....41
Table 2.3 Sales Tax Treatment of Food and Clothing and 1992−2007 Major Changes...46
Table 2.4 Sales Tax Treatment of Services……………………………………………47
Table 2.5 Sales Tax Treatment of Producer Goods and 1992−2007 Major Changes…...51
Table 2.6 Sales Tax Treatment of Utilities for Industrial Use…………………………..53
Table 2.7 Income Tax Treatment of Retirement Incomes and 1992−2007 Major
Changes..............................................................................................................................57
Table 2.8 Income Tax Treatment of Long-Term Capital Gains and 1992−2007 Major
Changes..............................................................................................................................63
Table 2.9 Deduction for Federal Income Tax Paid and 1992−2007 Major Changes……63
Table 2.10 Deduction for Local Property Tax Paid……………………………………64
Table 2.11 Personal Exemptions and 1992−2007 Major Changes…………….………..66
Table 2.12 Variable Descriptions and Data Sources………………………………….....69
Table 2.13A Descriptive Statistics for Sales Tax Model...................................................71
Table 2.13B Descriptive Statistics for Income Tax Model………………………………71
Table 2.14 Regression Results for Sales Tax Volatility……….……………………..74
Table 2.15 Regression Results for Income Tax Volatility………..…………………….75
Table 3.1 Variable Descriptions and Data Sources.........................................................129
Table 3.2 Summary Statistics..........................................................................................130
Table 3.3 Regression Results for Own-Source Expenditure...........................................137
Table 3.4 Regression Results for Overall Tax Rate.........................................................138
Table 4.1 Correlation between % Share of Fiscal Reserves and Revenue Volatility...163
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Table A.1 Long-Run Income Elasticity of General Sales Tax and Individual Income Tax
by State (1992−2007)…………………………………………………………………186
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LIST OF FIGURES
Figure 2.1 Plots from Two Hypothetical Regressions of Tax Revenue…………………35
Figure 2.2 Illustration of Orthogonal Deviation Calculation…………………………37
Figure 2.3A Box Plot of General Sales Tax Volatility…………………………………..43
Figure 2.3B Box Plot of Individual Income Tax Volatility……………………………44
Figure 3.1 Box Plot of Expenditure Gap by State……………………………………...135
Figure 3.2 Annual Percentage Changes in Overall Tax Rate by State…………………136
Figure 3.3 Regression of Aggregate Federal Grants on Year (1992–2007)……………144
Figure 4.1 Private Sector Participants in an Employment-Based Retirement Plan by Plan
Type, 1979–2008 (Among those who have a retirement plan)…………………………155
Figure 4.2 The Dynamics of State Fiscal Behavior over the Business Cycle…………159
1
CHAPTER 1
GENERAL INTRODUCTION AND MOTIVATION FOR RESEARCH
With increasing fiscal stress, long-term strategic fiscal planning and management
have grown in importance for state governments over the past decades. On the revenue
side, an ever-present anti-tax sentiment along with growing skepticism towards big
government, as shown by recent conservative political movements, have thwarted the
attempts from states to raise taxes. To make matters worse, state revenue bases have
steadily eroded (Lav, McNichol, and Zahradnik 2005): (1) the U.S. economy‘s shift from
goods to services have reduced sales tax revenues, because most states levy sales taxes
mainly on tangible goods not on services; (2) the rapid growth of e-commerce has
considerably eroded sales tax bases as states‘ ability to tax interstate sales has been
impaired; (3) with the baby boom generation beginning to retire, income tax revenues are
expected to decline significantly over the next decades as states provide income tax
preferences for the elderly, and also sales tax revenues are predicted to diminish as
elderly people spend less on taxable goods. On the expenditure side, state spending needs
have substantially increased due to growing health care and education costs since the
1980s (Lav, McNichol, and Zahradnik 2005)1 and the large influx of immigrants since
the 1970s (U.S. Census Bureau 1993).
Weakening revenue-raising capacities and increased spending needs have
combined to create structural budget deficits (Hovey 1998; Behn and Keating 2005),
which, in turn, have brought about fiscal crises whenever a recession hit. In particular, the
1 Lav, McNichol, and Zahradnik (2005) note that pressures to improve public education stem from three
fronts: public demands, court challenges, and student sub-populations with special needs (including special
education students, low-income students, and students with limited English proficiency).
2
state fiscal crises of the early 2000s that began with the 2001 national recession clearly
show how prevalent and severe fiscal problems were across states. According to the
National Bureau of Economic Research (NBER) business cycle dating protocol, the 2001
recession lasted for just 8 months, while the duration of the ones that took place between
1919 and 1945 and between 1945 and 2001 were 18 and 10 months, respectively. Also,
National Income and Product Accounts (NIPA) data indicate that GDP stayed even
during the 2001 recession (0.08% increase in real dollars), while it declined 2.64% and
1.36% during the early 1980s and 1990s.
Although the recession was never severe in terms of both length and magnitude
compared to prior ones, fiscal difficulties that states experienced during the recession and
subsequent years were far more severe than expected.2 Budget problems did not go away
and continued to distress states even as the economy improved. Using NIPA data, Knight,
Kusko, and Rubin (2003) analyze how the aggregate budget balance of state and local
governments (excluding social insurance funds) has changed relative to GDP since 1970.
From this analysis, they find that aggregate state/local deficit in 2002 as a percent of GDP
was the largest since 1970. In response to the fiscal crises, despite tax increases, states
enacted budget cuts even in major programs such as health and education to close budget
gaps for the years 2001 through 2003. The Center for Budget and Policy Priorities (CBPP)
reports that 34 states cut eligibility for public health insurance, causing 1.2 million to 1.6
million low-income people to lose health coverage, and at least 23 states cut eligibility
for child care subsidies or limited access to child care. The Center goes on to report that
34 states cut real per-pupil aid to school districts for K-12 education over the period
2 Sheffrin (2004: 205–206) and Behn and Keating (2005: 1) provide specific examples of state fiscal crises
and the resulting budgetary and political chaos.
3
2002–2004, and spending cuts in higher education led to double-digit increases in college
and university tuitions and reduced course offerings.
The fact that a brief and shallow economic recession left states reeling leads us to
the conclusion that state fiscal problems are not just cyclical and temporary but structural
and chronic in nature. From a broader perspective, some researchers have consistently
pointed to policymakers‘ myopic and opportunistic attitude to state finance and the
resulting poor fiscal planning and management as the underlying cause of the structural
fiscal problems. For example, Knight, Kusko, and Rubin (2003) provide useful insights to
the dynamics of structural deficits by examining contributing factors to the 2001 fiscal
crises using state/local aggregate data. Specifically, they decompose the sharp decline in
state/local budgets into three components: macroeconomy, capital gains realizations, and
policy factors. The results from the analysis reveal that most of the 2001/2002 budget
deficit stems from policy factors such as ―the relatively rapid increases in state and local
consumption spending between 1998 and 2001, and the return of double-digit growth in
Medicaid outlays after a quiescent period in the mid- to late 1990s, a series of tax
reductions between 1995 and 2001.‖ The authors then conclude, ―The bottom line of this
analysis is that neither the cyclical weakness in the economy, when measured relative to
its potential level, nor the direct effects of capital gains realizations, when measured
relative to their longer-run trend, account for very much of the deficit in 2002. The
implication is that the current deficit is structural for the most part and thus unlikely to be
eliminated in the absence of significant budgetary actions by these governments.‖ Taking
a step further, Edwards, Moore, and Kerpen (2003) and Schunk and Woodward (2005)
argue that blinded by large budget surpluses that the extraordinary economic boom of the
4
1990s brought, many states have made unsustainable spending increases and tax cuts
without serious consideration of their long-term fiscal impacts.
Although the lack of a long-term perspective had brewed up structural problems,
states, once again, responded to the early 2000s fiscal crises with short-term stopgap
measures (Bruce, Fox, and Tuttle 2006). States are currently undergoing another
recession that came along with the downfall of the financial market—which is said to be
one of the worst since the Great Depression. Whatever the cause, this unprecedented
economic crisis should be much harsher for states that have neglected to make efforts to
fix structural problems embedded in their fiscal systems, content with revenue growth
that economic expansion in the mid-2000s brought.
In light of the structural fiscal problems that have recurred across states over
multiple economic cycles, the general purpose of this dissertation is to examine how
fiscal problems arise over time and seek ways to restore fiscal sanity to states. In doing so,
this study brings a business cycle perspective to discussions of state fiscal policy.3 This
approach is considered critical in looking into fiscal issues, because a business cycle is
the most fundamental factor that explains the time dynamics of fiscal condition; therefore,
without a clear understanding of it, optimal fiscal policy is difficult. In the business cycle
framework, economies are assumed to swing back and forth between expansion and
contraction, thus giving rise to the issue of revenue volatility (or stability). As will be
discussed later, this study assumes that revenue availability induces spending, especially
3 While the term business cycle is commonly used, in recent years there has been a debate among
economists over the appropriateness of the term. Most notably, Milton Friedman reasons that in modern
economies, shifts between economic upturns and downturns result mostly from adjustments in monetary
policies primarily involving interest rate and credit. But in this article, the terms business cycle and
economic fluctuation are interchangeably used, because the primary purpose is to observe and explain
cyclical fluctuations in tax revenues, not to discuss the nature of those fluctuations.
5
in the public sector where its budgetary resources are likely to suffer from ―the tragedy of
the commons.‖ This assumption leads naturally to the hypothesis that states with a more
volatile revenue base will likely see larger fluctuations in spending and tax adjustments
(i.e. fiscal instability) over the business cycle as they make larger spending increases and
tax cuts during good times and, as a result, larger spending cuts and tax increases during
bad times. This relationship, in turn, highlights the necessity of an empirical investigation
into what factors determine the cyclical volatility of tax revenues.
An in-depth analysis of these causal links centering on revenue volatility is
particularly relevant and timely, considering the fact that state fiscal environments are
increasingly volatile and unpredictable with trade liberalization and advances in
transportation and communications technology, thus closely interweaving not only state
but national economies. In light of the importance and relevance of the subject matter to
state finance, the specific aim of this study is to empirically examine the determinants
and consequences of revenue volatility.
The present study is organized as follows. First, with a particular focus on general
sales and individual income tax, Chapters 2 examines how tax base composition affects
the cyclical volatility of tax revenues. Based on a discussion of the relative sensitivity of
industrial sectors and tax bases—taxable incomes and purchases—to the business cycle,
the second section develops a conceptual framework. The third section summarizes data
on how states tax specific types of incomes and purchases, and discusses empirical
methods. The last section presents and discusses regression results.
Chapter 3 examines how cyclical changes in tax revenues are related to spending
and tax adjustments. The second section reviews relevant literature with a focus on three
6
strands of research: what is so-called the ―tax-spend debate,‖ one on the effects of fiscal
institutions and rules, and one on the cyclicality of fiscal policy. The third section
provides a conceptual discussion of the commons nature of public budgetary resources
and the mechanisms through which revenue availability induces spending. The fourth
section develops a theoretical model, which explains fiscal policy as a function of
revenue gap (the cyclical component of tax revenue), federal grants (for ―flypaper
effects‖), debt, fiscal institutions/rules, partisan control, divided government, election
years (for ―political business cycles‖), and demographic-socioeconomic characteristics.
The rest of the chapter covers empirical analysis.
Based on empirical findings from these analyses, Chapter 4 discusses policy
implications. Specifically, the first section discusses the implications of tax exemptions
for tax base components under study for revenue volatility as well as other policy
considerations such as tax equity, economic neutrality and efficiency, and revenue
adequacy. The second section discusses the spending-smoothing approach as a solution to
revenue volatility and the resulting fiscal instability, and in doing so, compares it to the
―starve-the-beast‖ approach that argues for deficit reductions for tax cuts. Lastly, Chapter
5 concludes the dissertation, presenting a summary of the findings, contributions to the
literature, and directions for future research.
7
CHAPTER 2
TAX BASE COMPOSITION AND REVENUE VOLATILITY
2.1 Introduction
Revenue volatility, defined as the extent to which revenue fluctuates over the
course of the business cycle, is a serious concern particularly for state policymakers and
fiscal administrators operating within the context of balanced budget requirements. It
makes it hard to make accurate forecasts for future revenues and the establishment of
long-term fiscal plans for the stable operation of public programs and services. With the
global economy being liberalized and more tightly interwoven, the tax environment of
governments has been increasingly volatile and uncertain over the past thirty years, and
as a result, fiscal planning and management have become more challenging particularly
for state governments operating under the institutional constraints of balanced budget
requirements.
In the aftermath of the financial and economic crisis that began in late 2008, once
again, states with volatile revenue bases are experiencing severe budget problems. Recent
Census Bureau data on annual changes in state tax collections offers us a glimpse of the
prevalence and extent of revenue volatility. According to the data, despite tax increases,
states‘ total revenues fell, on average, by 8.9% (in real terms) from 2008 to 2009, with
only five states seeing slight increases. Sixteen states posted revenue declines of more
than 10%, and among them, Arizona and South Carolina are the most serious, reporting a
19.7% and 16.8% drop, respectively, in total tax collection. When the data are
8
disaggregated by type of tax, state revenue volatility is much more apparent. With more
than half of states recording double-digit percent declines, Arizona and South Carolina
each saw a 53.9% and 34.7% drop, respectively, in individual income tax.
The situation is more serious in the case of corporate income tax. Most states,
except only a few, reported double-digit percent drops in corporate income tax, and at the
top end of the list, Michigan, Oregon, and New Mexico's corporate income taxes
plummeted by more than half of what they collected in the previous year. As for general
sales tax, while the situation is a bit better compared to income taxes, the actual one
should be worse than it looks, considering the tax increases that have been enacted since
the recession began.
The important implication of revenue volatility for state finance is that in the
absence of adequate fiscal reserves, it is hard for states with volatile revenue bases to
avoid massive spending cuts and tax hikes in times of economic crisis when governments‘
countercyclical fiscal actions are needed more than ever. Another, maybe more important,
implication is that such procyclical austerity measures affect real economies, reducing
households and businesses' propensity to consume and consequently creating the vicious
circle of economic recession. A simple comparison of the data presented above with data
on state fiscal actions in the following year offers us some insight into the fiscal
consequences of revenue volatility. According to a fiscal survey of states conducted by
the Center for Budget and Policy Priorities (Johnson, Oliff, and Williams 2011), fifteen
states (e.g. Arizona, California, Florida, Georgia, Idaho, Maine, Maryland, Massachusetts,
Michigan, Ohio, Rhode Island, South Carolina, Utah, Virginia, and Washington) have
enacted budget cuts for all major state services (e.g. health care, services to the elderly
9
and disabled, K-12 education, and higher education) since 2008, and a closer inspection
of the data tells us that most of the states with across-the-board budget cuts are the ones
that faced sharp revenue falls in the previous year.4
In response to the increasing volatility of government revenues and the growing
concerns over its adverse effects on fiscal and policy stability, empirical research has
been done on revenue volatility. The next section reviews the existing literature to survey
what has been done and how previous work can be improved upon, and based on the
literature review, derives specific research questions for empirical analysis.
2.1.1 Previous Studies
Groves and Kahn (1952) is often cited as one of the earliest works on revenue
growth and volatility. Viewing revenue stability as a special case of adequacy, in their
seminal work, they stress that government tax systems should be stable to provide
approximately constant real revenues over a period of time. Based on the norm of
stability, they estimate how responsive (income-elastic) state and local tax revenues are
to income changes across time using a log-log regression. In the statistical analysis, they
find that state and local tax systems are more stable (less income-elastic) than the federal
tax system, while most state income taxes are less stable (more income-elastic).
Fox and Campbell (1984) renew interest in the issue by questioning the existing
conceptualization of revenue stability. They argue that revenue stability is a concept
concerned with short-run fluctuations in revenues over the business cycle; therefore,
4 According to the Census Bureau data, with Alaska excluded, Arizona, California, Florida, Georgia, Idaho,
Maine, Maryland, Massachusetts, Michigan, Ohio, Rhode Island, South Carolina, Utah, Virginia, and
Washington ranked 1, 4, 12, 11, 5, 24, 36, 10, 32, 22, 27, 2, 8, 6, and 19th respectively in revenue fall.
10
Groves and Kahn's estimates of revenue stability developed on the basis of long-run
measures are not appropriate for explaining the short-run dynamics of revenues. They
define a stable tax as one that is less sensitive to economic fluctuations. Fox and
Campbell (1984) analyze the income elasticities of ten categories of sales taxable in
Tennessee using an elasticity model regressing consumption expenditure on economic
conditions (position in the business cycle, interest rate, and inflation rate) as the
determinants of people's marginal propensity to consume (MPC). From the analysis, they
find that sales of durable goods are highly procyclical detracting from the tax‘s stability,
whereas those of nondurable goods and services are relatively countercyclical mitigating
the instability. Noting that it is not only politically difficult but economically undesirable
to reduce the instability simply by shifting the focus of sales taxation from durable to
nondurable goods, they conclude that the instability could be eased by expanding the
taxation of services.
Otsuka and Braun (1999) revisit Fox and Campbell‘s work using a random
coefficient model as an alternative to the fixed coefficient model. In this analysis, the
authors confirm the conclusion of Fox and Campbell (1984) that sales of durable goods
such as automobiles are generally variable over the business cycle, whereas service such
as utilities and lodging are countercyclical. Based on these findings, they conclude that
with information on revenue growth and variability on hand, the optimality of a tax
portfolio can be adjusted through the composition of tax bases.
Dye and McGuire (1991) extend the literature by investigating the trade-off
relationship between revenue growth and stability. Pointing out that the conclusions of
previous studies that sales taxes are less income responsive and more cyclically stable
11
than income taxes are too broad and general, they argue that responsiveness and stability
characteristics may depend on the specific structural characteristics of taxes—on what
components tax bases have. They estimate the trend rate of growth and cyclical
variability of several components of state general sales and individual income tax bases
(total personal consumption expenditures, representative broad/narrow base, food for
home consumption, motor vehicle fuels, household utilities, telephone services, personal
consumer services, personal business services, and recreation services) using national
aggregate time series data. From this analysis, the authors discover that for some tax
bases, growth rate and variability are negatively related. Based on these findings, they
argue that the commonly assumed trade-off relationship between these behavioral
properties is not always true, concluding that state tax systems can be better optimized in
terms of growth and stability through proper designing of the tax structures.
Sobel and Holcombe (1996) bring important methodological improvements to the
estimation of revenue volatility. They develop an estimation model for the short-run
income elasticity of tax bases using the log changes of the variables as opposed to the
logs as in the standard elasticity model, and apply the model to major state tax bases (e.g.
individual income, corporate income, retail sales, nonfood retail sales, and motor fuel
usage) approximated using national aggregate time series data. The results show that the
long- and short-run elasticity are 1.215 and 1.164 for individual income tax; 0.670 and
3.369 for corporate income tax; 0.660 and 1.229 for retail sales; 0.701 and 1.612 for non-
food retail sales; 0.996 and 0.729 for motor fuels usage. In this analysis, the authors find
that corporate income taxes are the most volatile over the business cycle, while motor
fuel taxes are the most stable. Another important finding from this study is that while
12
corporate tax bases and retail sales have nearly the same long-run growth potential, the
latter is much more stable, suggesting that substantial variety in revenue growth and
volatility exist across tax base components. As an extended effort, Holcombe and Sobel
(1997) estimate long- and short-run income elasticity for major state tax bases (individual
income, corporate income, retail sales, nonfood, retail sales, and motor fuel usage) using
combined and state-level data for the fifty states. In addition to the previous findings,
they discover that food exemption makes retail sales tax bases as variable as income tax
bases.
More recently, Bruce, Fox, and Tuttle (2006) bring a fresh perspective to the
subject matter by examining cross-state variations in the long-run income elasticities of
general sales and individual income tax bases. Their study is distinguished from previous
ones in that it uses actual tax bases or revenues, not proxy measures and attempts to
explain variations in the long-run growth rates as a function of structural features of the
state taxes, demographic characteristics, political circumstances, and economic structure.
In this analysis, they find that public and private pension exemptions have adverse effects
on the long-run income elasticity of individual income tax revenues. As for sales tax,
however, any main independent variables were not found to have expected effects.
Building on Sobel and Holcombe's estimation methods, Felix (2008) examines the
growth and stability characteristics of the tax revenue sources—general sales, personal
income, corporate income, selective sales, and severance tax—of seven states in the
Tenth Federal Reserve District—Colorado, Kansas, Missouri, Nebraska, New Mexico,
Oklahoma, and Wyoming for the sample period of 1967–2007. His empirical results are
generally consistent with those of previous studies; elasticity estimates exhibit that
13
individual income taxes have grown fastest, whereas corporate income taxes have grown
relatively slowly while fluctuating widely over the business cycle, and that sales taxes
have been the most stable revenue source.
In a study of North Carolina's tax system, Wagner (2005) examines the
composition of the state's revenue and the long- and short-run elasticity of various
revenue sources. Based on the estimates, he concludes that more reliance on the
individual income tax will enhance the state's revenue-raising capacity over the long run
but may add to the cyclical variability of the state's revenue, while less reliance on the
corporate income tax and more reliance on motor fuel taxes will enhance both the long-
and short-run stability. Pointing out that in response to economic downturns,
policymakers often adopt procyclical fiscal measures (i.e. spending cuts and tax increases)
in an attempt to meet the requirement of a balanced budget, Wagner discusses the role of
rainy-day funds and savings in mitigating the revenue impacts of economic downturns,
and argues that rainy-day funds should be governed by strict deposit and withdrawal rules.
Cornia and Nelson (2010) highlight the importance of considering economic
conditions and tax portfolios in determining the growth rate and volatility of state tax
revenues. Using 1989–2009 state data and simple graphical constructs, they conduct
various comparative analyses of the long-run growth rate and short-run volatility—in
percent changes—of state economies and tax revenues and state tax portfolios. In the
analyses, they find that wide variations in these respects exist among states, confirming
the stylized fact, as suggested by Groves and Kahn (1952), that there is a trade-off
between revenue growth and volatility. Their finding suggests that in the short run, states
14
cannot alter the underlying structure of the economy but can mitigate the impacts of the
business cycle on their fiscal conditions by making changes to their tax portfolios.
Building on modern portfolio theory (Markowitz, 1952), some studies estimate
the overall volatility of a revenue portfolio and examine whether revenue portfolio
diversification contributes to revenue stability. White (1983) defines revenue instability
as potential variability in tax revenue, and develops a measure of the concept based on
residual variance from a levels regression of tax revenue on time period. In addition, he
develops a measure of overall instability in the entire tax system using the variance of
each tax and the covariance between the taxes. Using data on Georgia's seven major taxes
(e.g. personal income, corporation, sales, alcoholic beverages, motor vehicle, tobacco,
and motor fuel) for the period of 1970–1981, he examines the instability of each tax and
the entire tax structure. In the analysis, he finds that personal income, corporation income,
and sales tax exhibit the highest growth rates among the seven major taxes, while
corporation income, alcoholic beverage, and individual income tax are the most unstable,
thus suggesting that taxes with higher growth rates are less stable. Using quadratic
programming, he also develops a set of feasible tax structures to minimize overall
revenue instability for any given growth rate.
Garrett (2006) examines state tax revenue variability using a volatility model
based on Markowitz‘s portfolio theory (1952), which evaluates how well a state‘s tax
portfolio is structured in terms of revenue variability through a comparison of the actual
tax structure with the structure where overall variance is minimized. In an application of
the model to state revenue data (on individual income taxes, corporate income taxes,
general sales taxes, and excise taxes) over the period of 1977 to 2000, he finds that in
15
Arkansas, Iowa, Louisiana and West Virginia, the actual tax revenue shares in some
states are very close to the variance minimizing shares. He also finds that in many states,
the actual shares of excise tax revenue and sales tax revenue, considered less sensitive to
the business cycle, are below the variance minimizing shares, and argues that states have
shifted towards more volatile revenue sources.
In a similar vein, Hou and Seligman (2007) recognize the recent trend of local
governments shifting away from property taxes towards sales taxes in designing their tax
portfolios, and raise the question, ―What impacts such a shift has on revenue growth and
volatility?‖ Specifically, they examine the effects of the adoption of LOST (Local Option
Sales Tax)—which allows a local government to substitute sales tax (up to 1%) for a
portion of a property tax—and SPLOST (Special-Purpose Local Option Sales Tax)—
which allows a local government to increase sales tax (up to 1%) for the purpose of
capital project financing—by Georgia local governments on the overall long- and short-
run elasticity of their own-source revenues. In an empirical analysis using a long panel
data set, they find that the adoption of LOST increases the short-run volatility of overall
revenues. Their findings suggest that sales tax tends to be a more volatile revenue source
than property tax.
Yan (2010) makes the case for revenue diversification. Specifically, she
investigates the impacts of revenue diversification and economic stability on revenue
stability using state panel data over the period of 1986–2004. Following White (1983),
she defines revenue instability as the short-run variability of the tax portfolio around its
expected growth rate and measures it by the portfolio standard deviation. Results suggest
16
that while revenue diversification enhances revenue stability, the effect depends on
economic stability.
In sum, the revenue volatility literature has focused predominantly on the
estimation of revenue volatility or stability, which has generated two approaches: one [e.g.
Sobel and Holcombe (1996)] focuses on the estimation of the cyclical volatilities (or
short-run income elasticities) of the individual components of tax bases (i.e. potentially
taxable incomes and sales) using national aggregate data,5 while the other [e.g. White
(1980)] is mainly concerned with the effect of tax portfolio structure on the overall
cyclical volatility of the revenue that the tax system generates. The former has
contributed especially to our understanding of the cyclical patterns of individual tax bases,
while the latter has been useful in assessing and designing the optimality of tax portfolio
structure in terms of growth and stability.
Although each approach has contributed in its own way to our understanding of
revenue volatility, little has been revealed as to whether tax policy and structure for
individual taxes indeed matter for their cyclical volatilities, more specifically, how
structural features of individual taxes vary across governments and how they affect the
cyclical volatilities of the tax revenues in particular economic environments. These
questions are particularly important for state governments, because they rely on various
revenue sources and each of the sources varies widely across states in base composition
5 For example, Dye and McGuire (1991) estimate the growth and variability of a state sales tax using
national aggregate time series data on home consumption, motor vehicle fuels, household utilities,
telephone services, personal consumer services, personal business services, and recreation services. For a
state income tax, out of the belief that the more important source of variation is tax rate structures rather
than the definition of the tax base, they uses national aggregate data on household money income by
income range.
17
and in the economic environment in which it operates. This implies that taxes, even if
they are the same kind, could exhibit varying degrees of cyclical volatility depending on
how their bases are composed and in what economic environment they operate. Hence, it
may be an oversimplification to generalize sales taxes as a volatile revenue source and
income taxes as stable, concluding that shifting from income tax to sales tax will
contribute to revenue stability.
Despite their importance and relevance, however, the questions have received
virtually no empirical investigation. This deficiency may be due in large part to the sheer
complexity of state tax systems and the resulting empirical challenges. As will be
discussed later, the empirical examination of the given questions requires panel data on
how states treat potentially taxable incomes and sales of interest in taxation, which, as
legal provisions, are hard to collect. Another empirical challenge is that revenue volatility
is hard to measure. For the accurate measurement of it, tax revenues should be adjusted
for tax rate changes, which add to difficulty in data collection. One way to remove the
effects of rate changes on revenue outcomes is to use tax bases,6 not revenues.
78 While
this method is conceptually simple and straightforward, the difficulty of collecting tax
rate data makes it hard to implement.
6 Actual tax bases are obtained using specific tax data for each state—by dividing actual revenues by tax
rates, and thus should be distinguished from the proxy measures of tax bases based on national aggregate
data that most previous studies have used. 7 Wagner (2005) provides an illustrative example regarding the usefulness of using tax bases as follows:
―The revenue generated from a general sales tax depends on (1) the sales tax rate and (2) the tax base.
Policy makers frequently change sales tax rates (especially during downturns), so examining how sales tax
revenue changes over time is not particularly insightful. A rate increase will ―bump‖ revenue beyond where
it would have been in the absence of the rate change. However, examining how a tax base changes with the
state‘s economic activity reveals how revenue from a given tax would fluctuate if the tax rate remained
constant.‖ 8 In this article, therefore, revenue volatility and tax base volatility are interchangeably used.
18
Dealing with these empirical challenges, this study answers the following
questions:
1. How does the composition of tax bases vary across states?
2. What effects does tax base composition have on the cyclical volatility of tax
revenues?
In doing so, this study focuses on two major revenue sources relied upon by most
state governments: general sales tax and individual income tax. Specifically, the study
empirically investigates the questions using state panel data over the sample period from
1992 to 2007.9 The cross-state heterogeneity of tax base composition, economic structure,
and demographic characteristics provides a natural laboratory for empirical analysis.
economic characteristics provides a natural laboratory for empirical analysis, and the
sample period is sufficiently long for the given questions, covering approximately two
business cycles—two troughs (in 1992 and 2001) and two peaks (in 2000 and 2007). The
rest of the chapter is organized as follows: the next section provides a conceptual
discussion of factors that affect cyclical fluctuations in tax revenues, and Section 2.3
discusses data and methods. Section 2.4 then presents and discusses analysis results.
2.2 Conceptual Framework
9 Using panel data has considerable merit for answering the given questions. It solves the "small N"
problem which is common in empirical studies taking states as the unit of analysis. A small sample size can
lead to large sampling variances and ultimately the violation of OLS assumptions. As a result, the problem
escalates as the number of explanatory variables increases. Adding a time-series dimension reduces the
problem of small sample size by multiplying the number of observations.
19
The cyclical volatility of taxes stems mainly from two sources. First, most
fundamentally tax revenues are directly affected by economic fluctuations. Generally,
aggregate output fluctuates around the long-term growth trend within the context of the
business cycle—shifting between periods of economic expansion and periods of
contraction. The fluctuation of ups and downs in output leads to ups and downs in
employment, income, and consumption, which, in turn, result in fluctuations in various
types of tax revenues.10
Given the overarching influences of business cycles on government revenues and
finances, it is important to discuss the different cyclical patterns of outputs by sector. The
degree of cyclical fluctuations in output should be affected by the sectoral composition of
the economy. The most common typology of output includes goods and services. Many
economists have observed that the output of services tends to be less sensitive to the
business cycle than that of goods and manufactures. They explain that this difference
comes from a difference in ―storability‖ between goods and services. Except perishable
food such as fruit, vegetables, and meat, most goods and manufactures can be stored for
long period of time, even though they differ in the extent. Storability has a significant
influence on cyclical fluctuations in output, because it affects the rate of purchase.
Stressing the difference between consumption and the rate of purchase, Fuchs
explains that ―In the case of consumer durable goods, true consumption (i.e. the use of
the goods or of their services) depends upon the stocks in the hands of consumers, not on
10
Economists observe that although output and consumption follow the same cyclical patterns, generally
they are rarely equal over a given period. Huffman (1994) finds that aggregate consumption tends to
fluctuate less than do aggregate output. It is not surprising, given that rational consumers tend to save and
invest in good times for future bad times. He explains that assuming that income changes may be
permanent in the long term but transitory and soon reversed in the short term, a rational consumer rarely
changes consumption by a change in income; he or she smoothes consumption over the business cycle
through saving or investment.
20
the rate of purchase of new goods. The latter, which is comparable to investment in
capital goods, may evidence wide cyclical swings in response to changes in availability
of credit, expectations, and other investment determinants, while the true consumption
rate remains relatively stable. … In the case of services, consumption and output must
coincide; inventories are nonexistent.‖ In short, while goods and services might be
similar in the rate of consumption, they greatly differ in the rate of purchase. This
difference has significant implications particularly for sales tax, because the tax is
realized when purchase, not consumption, takes place.
Another related characteristic of goods and manufactures is that most of them are
repairable. Durable goods do not quickly wear out, and are consumed not in one use but
gradually over time. This means that in the case of durables, product lives can be
prolonged, and as a result, new purchases can be delayed to some degree. This
phenomenon is more likely in recession, thereby deepening cyclical declines in aggregate
consumption and output. As the economy slows and economic uncertainty increases,
more consumers are likely to delay the new purchase of products until the lives of
existing ones come to an end. For example, in the face of recession, it is more likely that
consumers will put on hold their purchases of goods such as automobiles, furniture, and
appliances in anticipation of a further decline in the economy, deciding to persevere a bit
further with existing ones. The implication is that nondurable goods may be closer to
services in this regard.
Given the different cyclical behavior of goods and services output, it is likely that
states with larger goods-producing industries will see greater cyclical fluctuations in their
tax revenues than those with larger service-producing industries. According to the NIPA
21
definition, goods-producing industries are again divided into a number of sectors:
agriculture, mining, construction, and durable and nondurable manufacturing. It is a
widely accepted fact that input inventory (i.e. materials) investment tends to be more
volatile and procyclical than output inventory investment [see Iacoviello and Schiantarelli
(2007)]. Following this assumption, among goods-producing industries, particularly
agriculture and mining industries, will likely contribute to revenue volatility. Meanwhile,
nondurable goods, as noted above, are similar to services in terms of storability and
durability. Hence, the relative size of nondurable goods will likely contribute to stability
in aggregate output.
Cyclical fluctuations in tax revenues are also expected to be affected by tax policy
factors. States do not tax every sale and income; they levy taxes selectively on specific
types of sales and incomes, which constitute tax bases. States differ widely in tax base
composition as they allow varying levels of tax exemption for different types of sales and
incomes.
What is important to note here is that potentially taxable purchases and incomes
differ in sensitivity to changes in aggregate output—though they, for the most part,
behave in a procyclical manner. Some types of consumption and income fluctuate more
than aggregate output, whereas some others are relatively less sensitive to the business
cycle. Given the varying degrees of sensitivity of potential base components to the
business cycle, the cyclical volatility of taxes is likely to vary depending on what
components are included to or excluded from the tax bases; in other words, what types of
purchases and incomes are taxable.
22
For analytical purposes, therefore, it is useful to discuss the cyclical behavior of
each tax base‘s potential components listed above. To begin, in the case of sales tax, with
tangible goods dominating most states‘ sales tax bases, wide variations in base
composition are observed in the level of tax exemption for purchases of (1) goods used in
manufacturing (e.g. direct materials, machinery/equipment, and utilities)—so-called
producer goods, (2) (nonprepared) food,11
(3) clothing (including footwear), and (4)
services.
First, in light of the above discussion on a difference between goods and services,
exempting producer goods, the consumption of which tends to be sensitive to the
business cycle, from taxation is expected to dampen cyclical amplitudes in the sales tax
revenue. In other words, other factors being equal, states that offer a lower level of tax
exemption for producer goods are likely to see greater cyclical fluctuations in their sales
tax revenues. The favorable tax treatment of producer goods is offered exclusively to
manufacturing businesses. Hence, even if a state grants a high level of sales tax
exemption for a wide range of business purchases of manufacturing inputs, the policy
will not greatly affect the state's sales tax base if the state has a small manufacturing base
(e.g. Hawaii and Delaware). In other words, the effect of tax exemption for producer
goods on revenue outcomes will likely be greater in states with a larger manufacturing
sector.
For the task of predicting the effects of sales tax exemption, an understanding of
income elasticity of demand is necessary. Intuitive reasoning and empirical studies on
income elasticity of demand suggest that the more necessary a good is, the less sensitive
11
Prepared food or food marketed for immediate consumption generally does not qualify for sales tax
exemption.
23
the demand for the good is to economic changes, as people attempt to purchase it no
matter how tough the economy is. Given the notion of income elasticity of demand, it is
only logical to assume that exempting food that is the most basic necessity for everyone
from taxation will widen cyclical swings in the sales tax revenue; in other words, holding
other factors fixed, states that offer a higher level of tax exemption for food will likely
see greater cyclical fluctuations in their sales tax revenues.
Predicting the effect of tax exemption for clothing and footwear is a little bit
tricky. While clothing and footwear are generally classified as nondurable, they are much
more durable and also repairable compared to other typical nondurable goods such as
food and household goods (e.g. cosmetics, soap and light bulbs). Given the mixed
characteristics between clothing and footwear, it seems reasonable to hypothesize that
tax-exempting clothing will make the sales tax revenue more variable as opposed to food.
Meanwhile, predicting the impact of sales tax exemption for services is not
straightforward. As discussed, the consumption of services is considered less sensitive to
economic changes as compared to goods. It can therefore be reasonably assumed that
other things being held constant, the more services a state taxes, the more stable its sales
tax revenue will be throughout a certain business cycle. But another important
consideration is that services vary in income elasticity. This implies that the effect of
exemption for services on revenue volatility may differ depending on which services are
taxable. Some services such as investment counseling, swimming pool cleaning, and
private limo may be considered luxuries for average consumers, which will likely be
relatively sensitive to the business cycle compared to services that are accessible to more
24
average people such as repair services where the demand for it may rather increases
during a recession.
Given the variation in income elasticity that may exist across services, it may not
be appropriate to treat them as a homogeneous group and assume that tax-exempting
more services will increase revenue volatility. One simple way to take the possible
variation into account is to classify services in terms of income elasticity and estimate
their effects by category. But classifying services is such a task that is worth another
separate empirical study. Estimating the effect of each service without classifying (i.e.
including all services in the model), while possible theoretically, is not even practical,
given the large number of potentially taxable services.12
Alternatively, this study assumes
that states seek to be cost efficient in taxation, and in doing so, have a tendency to tax
services that are less income elastic (more stable). Expanding tax bases do not only bring
increased revenues to states; it takes costs as well. Administrative systems and a
professional workforce to operate them are required for proper and effective taxation.
Common sense and intuition suggest that it is more cost efficient for a state to expand its
sales tax base by incorporating services that are more universally consumed by a broader
range of people, and such services are likely to be more of a necessity (or less of a luxury)
that is less income elastic.
Mazerov (2009) discusses challenges facing states that attempt to expand their
sales tax base, one of which lends support to this assumption. He notes that ―State
revenue departments may not be equipped to integrate numerous new services and the
merchants selling them into their sales tax administration systems in a short period of
12
The Federation of Tax Administrators (FTA) periodically conducts a survey on state sales taxation of
services, and its 2007 update examines the taxable status of 168 services.
25
time. These factors likely explain why all the states that have expanded their taxation of
services in recent years did so incrementally, a few services at a time.‖ FTA survey
results on state sales taxation of services lend plausibility to states‘ tendency towards less
income elastic services.
Table 2.1 Sales Tax Treatment of Utility Services, Automotive Services, and Finance,
Insurance, and Real Estate
Services Total Number of States that Tax
Utility Services (for residential use)
Intrastate telephone & telegraph 41
Interstate telephone & telegraph 27
Cellular telephone services 44
Electricity 22
Water 12
Natural gas 22
Other fuel (including heating oil) 23
Automotive Services
Automotive washing and waxing 21
Automotive road service and towing services 19
Auto service. except repairs, incl. painting & lube 25
Parking lots & garages 21
Automotive rustproofing & undercoating 25
Finance, Insurance and Real Estate
Service charges of banking institutions 3
Insurance services 6
Investment counseling 6
Loan broker fees 3
Property sales agents (real estate or personal) 5
Real estate management fees (rental agents) 5
Real estate title abstract services 5
Tickertape reporting (financial reporting) 8
Investment counseling 6
Source: 2007 FTA Survey of State Sales Taxation of Services.
Table 2.1, even without statistical analysis, clearly shows that more states tax
services such as telephone services that are generally considered a necessity in modern
26
times and as automotive services that are also more of a necessity in the context of
American life, should be consumed by more people and thus less income elastic rather
than financial services. In light of states‘ tendency towards less income elastic services,
tax exemption for services is likely to have a nonlinear effect on revenue volatility. Put
differently, sales tax volatility will increase as the level of tax exemption for services
increases, but the effect will decrease once it goes over a certain point.
The same logic is applied to the explanation of income tax volatility. Most states
offer income tax preferences for the elderly, but the extent widely varies from state to
state. With most kinds of earned incomes (such as wages, salaries, and tips; interest and
dividends; capital gains) being taxable, states exhibit wide variations (1) in the level of
tax exemption for (1-a) pensions and retirement incomes, (1-b) long-term capital gains;
(2) in the level of tax deduction for (2-a) federal income tax paid and (2-b) local property
tax paid, and (3) in the level of personal exemption.
Pensions and retirement incomes are largely divided into three categories: Social
Security benefits, public, and private pensions. Pensions are generally defined as
financial arrangements in which participants receive payments upon retirement. Pensions
and retirement incomes are usually paid in regular installments and thus considered the
most stable source of income for retirees. Given the general nature of retirement incomes,
allowing taxpayers to exclude them from income tax bases is expected to exert an adverse
impact on income tax stability.
When an investor sells a capital asset such as stocks, bonds, and real estate, the
difference between the purchase price and the selling price arises, which is referred to as
27
a capital gain or a loss. Capital gains or losses realized on the sale of assets held more
than one year are considered ―long-term.‖ As with the federal government that favorably
treats long-term capital gains realizations by imposing a lower tax rate—the maximum
tax rate for net long-term capital gains income was reduced to 15% in 2003, an increasing
number of states have been providing preferential tax treatment for long-term capital
gains income. Capital gains realizations, whether long-term or short-term, have become
increasingly volatile over time. According to data released by the U.S. Treasury
Department, during the study period (1992 to 2007), net long-term capital gains of
individuals averaged 3.73% of GDP, ranging from 1.8% in 1992 to 6.12% in 2007. Given
the increasingly volatile nature of financial investment returns, it is assumed that the
exclusion of long-term capital gains realizations from taxation will decrease the cyclical
volatility of the income tax.
Given that investment gains are generally earned by high income people, the
effect of tax exemption for long-term capital gains income is likely to be greater in states
with a larger wealthy population. Investment can be seen as a type of activity of saving a
portion of disposable income or deferring consumption from high-earnings periods to
low-earnings periods. Investment opportunities should therefore be greater for higher
income earners. For example, the Minnesota House Research reports that in tax year
2007, about 24% of tax returns filed by Minnesota residents reported some capital gains
income and filers with incomes over $100,000 received over 86 percent of capital gain
income.
28
In addition to tax exemption for specific types of income, states also allow various
deductions, among which, this study focuses on deductions13
for federal income tax paid
and local property tax paid and personal exemption (including exemption for dependents).
Generally deductions and personal exemptions are likely to have adverse effects on
revenue stability by substantially reducing income tax bases. Taking them into account is
important, because they are allowed to relatively broad ranges of taxpayers when
compared to tax exemptions on pensions (only for retirees) and long-term capital gains
(only for investors).
Along with tax and economic structure, demographic and economic
characteristics may also potentially affect the cyclical volatility of both sales and
individual income tax. First, size might have an effect. Studies on the relationship among
region size, industrial diversification, and economic stability [see Kort (1981), Brewer
and Moomaw (1985), and Malizia and Shanzi Ke (1993)] suggest that the size of a
regional economy tends to be positively associated with industrial diversification and
economic stability. Thus, it is hypothesized that tax revenues will be relatively stable
over the business cycle in larger states.
Another potential factor that affects revenue volatility is population age
distribution. For income tax, two groups of population appear of particular relevance:
young (pre-college) and elderly population. States generally provide favorable tax
13
The vast majority of states allow taxpayers to choose between standard and itemized deduction. This
study focuses on the latter under the assumption that large portions of middle- and high-income taxpayers
choose itemization in their state income tax returns. Although state specific data are not available, IRS data
on federal tax returns warrant this approach. According to the IRS, in tax year 2007, 72%, 87%, and 94%
of tax returns filed by individuals with AGI over $75,000, $100,000, and $200,000, respectively, chose
itemized deduction.
29
treatment for taxpayers with dependent children, which reduces the tax bases and likely
increases income tax volatility. On the other hand, the major income source of the elderly
is retirement income such as Social Security benefits and pensions, which tends to be the
most stable, thereby increasing income tax stability.
As for sales tax, the proportion of the prime working-age and elderly population
may be relevant. Two well-known theories on consumption lend support to this idea.
Milton Friedman's permanent income theory (1956) argues that people are rational to
base their spending and saving decisions on permanent income as defined as the average
income that they assume they would be able to earn over their lifetime. According to the
theory, only changes in permanent income affects people's spending decisions. People
therefore do not respond sensitively to transitory income shocks, smoothing their
consumption over the business cycle. Modigliani and Brumberg's life-cycle theory of
consumption (1954; 1980) is closely related to the permanent income theory. According
to the life-cycle model, people make assumptions about their expected income over their
lifetime and base their spending decisions on the income expectations. The theory
suggests that especially working people do not reduce consumption too much in recession,
expecting that their disposable income will increase soon in the future. These theories
lead to the hypothesis that the relative size of the working-age population, who tends to
have a relatively high expectation of future permanent income, is likely to have a positive
effect on cyclical stability in sales tax collections. The other side of the theories also
suggests that people in old age have a low expectation of future income. The implication
is that the relative size of the elderly would likely exert a negative influence on sales tax
stability.
30
Lastly, income distribution may potentially exert influence on cyclical
fluctuations in both tax revenues. In particular, the proportion of the wealthy population
would likely have an impact on state finance, in light of the income group‘s relative
importance in the economy. Specifically, the income group is expected to contribute to
the cyclical volatility of both taxes, given that the income of high-ranking people tends to
be more sensitive to boom and bust cycles. A September 2009-Wall Street Journal article,
titled "Income Gap Shrinks in Slump at the Expense of the Wealthy," reports that the
income of the top 1 percent as a percent of the total U.S. personal income has fallen faster
than has that of any other income groups since the recession, and goes on to project that
the proportion of the very top income earners will drop from 23.5% in 2007 down to
between 15% and 19% in 2010. This may be in large part because, in the U.S.,
compensation systems for people in top management positions such as chief and senior
executives have shifted towards performance-based incentives. In such a system,
executive compensations are determined by overall organizational performance (for
example, stock prices and net profits). Thus, their incomes are likely to be more heavily
affected by economic cycles compared to that of middle- and low-ranking people.
Put together, the cyclical volatility of state general sales tax and income tax is
modeled as followed:
Revenue volatility = f (tax base composition, sector GSP
composition, demographic-economic characteristics)
2.3 Data and Methods
31
The main empirical goal of this study is to estimate the effect of tax base
composition on revenue volatility, with particular focus on two major state revenue
sources, general sales and individual income tax. Specifically, this Section develops two
separate models for sales tax and income tax on the basis of the conceptual discussion
above and presents estimation methods for estimating the models, and the following
section discusses results and implications.
2.3.1 Variables and Data Sources
The dependent variables in this study are the cyclical volatility of state general
sales tax and individual income tax, which is defined as the degree to which tax revenue
fluctuates around the long-term growth trend over the business cycle. Empirical studies
have attempted to develop a measure of the concept. White (1983) represents one of the
earliest attempts to measure the concept. Building on Harry Markowitz‘s portfolio theory,
he defines a stable tax structure as ―one that contains taxes that are not perfectly
correlated (i.e. one in which taxes do not move in exactly the same direction and
proportion).‖ He explains, ―If the revenue from one tax is down for some reason such as a
recession, then the decrease in the government's overall tax revenue is minimized because
other taxes have not experienced such a decrease in revenue.‖ In this view, estimating
revenue volatility involves calculating the variance of each tax—which represents the
degree of ―dispersion about the expected level of tax revenue‖—and the covariance
between the taxes. In mathematical terms, White‘s measure is expressed as follows:
32
where is the level of revenue from the th tax; is the level of revenue from the th
tax; is the correlation between the th and th tax; and are the standard deviation
of the th and th tax, respectively. And the formula for calculating the standard deviation
of each tax is:
where is the standard deviation of the th tax; is revenue from the th tax in period
; is expected revenue from the th tax in period ; is mean revenue of the th tax
for period 1 through m; is the number of time periods. This measurement method, as
originating from the portfolio selection approach, has been used in studies examining the
relationship between revenue portfolio diversification and volatility (see, for example,
Gentry and Ladd (1994) and Yan (2010)).
Another important approach is introduced by Sobel and Holcombe (1996).14
In
contrast to the portfolio approach, they employ parametric methods in estimating revenue
volatility. Arguing that the standard method using a regression of the log level of tax
14
An earlier study in this context is Williams, W., R. Anderson, D. Froehle, and K. Lamb, 1973, The
Stability, Growth, and Stabilizing Influence of State Taxes, National Tax Journal, 26, 267-274.
33
revenue on the log level of income only provides information about the long-run growth
rate of taxes, they develop a separate measure for short-run volatility. In the Augmented
Dickey-Fuller (ADF) test for the stationarity of the variables concerned, they find that the
variables are nonstationary in their regular or level form, thus suggesting that both
income and tax revenues systematically move upwardly together over time. Emphasizing
that revenue volatility is concerned with how much a tax base fluctuates around the long-
term trend over the business cycle, they argue that their change or first difference forms,
which are found to be stationary in the diagnostic tests, must be used for the estimation of
the short-run cyclical volatility of taxes. Therefore, the equation for the long-run growth
rate of a tax base is as follows:
where and denote the natural log of tax base and personal income of state
in year , and the regression coefficient represents the long-run growth rate of tax
base. Modifying the standard log model above, the equation for the short-run cyclical
volatility of a tax is:
where the regression coefficient represents the short-run cyclical volatility of tax base.
This log change model has been widely used by subsequent studies, which include Bruce,
Fox, and Tuttle (2006), Felix (2008), and Hou and Seligman (2007).
34
In addition to the log change model, Sobel and Holcombe (1996) use the error
correction model (Engle and Granger 1987) out of the recognition that the variables may
move up and down simply due to their tendency to move back towards the equilibrium.
To handle this so-called error correction bias, they propose a modified model by adding
an error correction term—the estimated error from the standard log model in the previous
time period—to the log change model. In a comparison of results from these two models,
Sobel and Holcombe find that there are only slight differences between estimates from
the log change model and ones from the error correction model.
In order to take full advantage of the panel structure of data used in this study in
determining the effect of tax base composition on revenue volatility, this study employs
the deviation-from-trend approach with some modifications as opposed to Sobel and
Holcombe‘s parametric method that estimates the average change in tax base for every
one-unit change in income over a given period. Specifically, revenue volatility is
measured by first regressing tax base on income based on the assumption that there is a
long-term relationship between the variables, and then calculating the absolute deviations
of annual tax bases from the fitted regression line (trend line) as a percent of the mean of
the sample tax bases. While this method is useful in that it allows for panel data analysis
and widely used in the field of public finance,15
it suffers from the problem of
measurement bias due to the nonstationarity of tax revenues. Sobel and Holcombe
correctly point out that the volatility measure based upon residual variances may be
incorrect because tax revenues tend to be not trend stationary but systematically trending,
usually upward. Ordinary least squares (OLS) estimates are obtained by minimizing the
15
For example, see Aisen and Veiga (2008) which examines the relationship between political instability
and inflation volatility using standard deviation.
35
sum of the squared vertical deviations of observations from the fitted regression line,
which are exaggerated as the slope is steeper (e.g. as the long-run rate of growth is
higher)—because the trend components of revenues are added on top of the cyclical ones.
This concern is easily illustrated through graphics.
Figure 2.1 Plots from Two Hypothetical Regressions of Tax Revenue
Figure 2.1A
Figure 2.1B
36
Figure 2.1 contrasts the plots from two hypothetical regressions of tax revenue on
income. As portrayed in Figure 1a and 1b, suppose that they have the same short-run
cyclical volatility but different long-run growth rates. Contrasting the regression plots
clearly shows that using conventional residuals (vertical deviations) leads one to a faulty
conclusion that Figure 2A has a greater cyclical volatility than Figure 2B—even though
they have the same. In generalized terms, this shows that the higher the rate of growth (i.e.
the steeper the slope is), the more incorrect the estimate becomes. Such an exaggeration
is particularly problematic within this study, given the wide cross-state variations in the
long-run growth rate of sales tax and income tax as shown in Appendix A16
[see also
long-run income elasticity estimates reported by Bruce, Fox, and Tuttle (2006)]. To
resolve this problem, this study measures revenue volatility using orthogonal (or
16
This study conducted a preliminary study to estimate the long-run elasticity of sales and income tax bases
with respect to income. Appendix A presents the estimation method used and the results. Results indicate
that the long-run growth rates of sales and income tax bases range from .16 to 1.85 and from .24 to 2.06,
respectively. Consistent with Bruce, Fox, and Tuttle‘s findings, results also suggest that income tax has a
higher growth potential than sales tax: the means of the long-run elasticities are .83 and 1.27, respectively.
This means that sales tax fails to grow in tandem with personal income in the long run, whereas income tax
grows more than personal income.
37
perpendicular) deviations as opposed to vertical deviations used in OLS regression.17
The procedure for calculating orthogonal deviations is as follows:
Figure 2.2 Illustration of Orthogonal Deviation Calculation
As an example, Figure 2.2 illustrates how the orthogonal deviation of tax revenue
for year from the long-run trend is calculated. As the first step, including three
hypothetical straight lines over Observation and its fitted regression line (
) creates two right triangles, T1 (filled with lines) and T2 (filled with dots). Suppose that
Line 1 goes through Observation , forming a right angle with the fitted line; Line 2 goes
through Observation , forming a right angle with X Axis; Line 3 forms a right angle
with Line 2, and the length (Side b) between the crossing point of Line 3 and the fitted
17
As a side note, the estimation method that uses orthogonal deviations is called the ―orthogonal distance
regression (ODR).‖ Boggs and Rogers (1990) note that ODR is used to solve the computational problem
concerned with ―finding the maximum likelihood estimators of parameters in measurement error models in
the case of normally distributed errors.‖ See Boggs, Byrd, and Schnabel (1987) for a more detailed
discussion of an algorithm for finding the solution of this problem. As a recent example using ODR in the
public finance literature, see Alm and James (2011).
38
line and the crossing point with Line 3 and Line 2 becomes one unit of the regressor,
income.
The sides of T1 and T2 have the same ratio, since they have the same interior
angles. The ratio of Side a to b equals that of Side d to e (the solution in this case);
therefore, the length of Side e is obtained by obtaining those of the remaining ones [Side
e = (b*d)/a]. Side d is the vertical residual for Observation , which is obtained by
subtracting the fitted value from the actual value for Observation [Side d =
]. Side c equals the slope coefficient , since Side b is 1. By the Pythagorean
theorem, therefore, . Expressed in mathematical terms, the
orthogonal deviation of Observation from the long-run trend is as follows:
The value obtained from this formula represents the absolute magnitude of the
deviation. Therefore, for valid cross-state comparisons, it needs to be adjusted for the
overall revenue size, which is approximated by the mean of the sample tax revenues.
Converting the adjusted into percentage form, the final formula for the size-
adjusted orthogonal deviation of tax revenue in time period is:
39
The estimation of revenue volatility requires two data sets: tax revenue and
income. Data on personal income are obtained from the U.S. Bureau of Economic
Analysis (BEA) website. As noted above, this study uses actual tax base as opposed to
tax revenue collection to remove the effects of tax rate changes on revenue outcomes.
This approach has an empirical merit, given the fact that states frequently change tax
rates. The problem that stems from revenue adjustments is especially troubling in the case
of income taxes that use inflation indexation in determining tax brackets. In order to
prevent inflation from causing indirect tax increases, what is called bracket creep, some
states apply annual indexation, and in this case, rate changes can be effectively said to
occur annually. Using tax base, however, allows one not to adjust for tax rate changes.
Tax revenue is obtained simply by multiplying tax base by tax rate. Hence, if there has
been no change in tax rate over a given period of time, mathematically the cyclical
volatility of tax revenue should be exactly the same as that of tax base from which the
revenue was collected.
Further, the measurement of tax base requires two data sets: tax revenue and rate.
Tax liability is determined by multiplying tax base (i.e. taxable incomes and purchases)
by tax rate; therefore, a state‘s tax base is obtained by dividing its tax revenue by tax rate.
Data on general sales tax and individual income tax revenues are drawn from the State
Government Finance series annually published by the U.S. Census Bureau, while each
state‘s annual sales tax rates are collected from the State Tax Reporter series published by
the Commerce Clearing House.
40
As for income tax, unlike sales tax for which flat tax rates are used, many states
use a progressive tax system with multiple tax brackets and rates, which makes it
extremely difficult to measure income tax bases. One conceivable way might be to use
top-bracket tax rates. But this method may yield incorrect estimates if changes in income
thresholds by bracket are not taken into account. Recognizing the potential problem with
using top-bracket tax rates, Bruce, Fox, and Tuttle (2006), which examine factors that
affect the long-run income elasticity of state income taxes using actual revenues but, not
bases, control for the effects of tax structures by including top-bracket income thresholds
and tax rates in their regressions.
This strategy, though reasonable, has the drawback that complicates the
interpretation of the analysis results, thereby making it difficult to derive meaningful
policy implications. To avoid this complexity, this study uses estimates of state average
marginal income tax rates published by the National Bureau of Economic Research
(NBER). Using a simulation program called TAXIM, the NBER calculates state income
tax liabilities from a sample of actual tax returns provided for public use by the Statistics
of Income Division of the Internal Revenue Service, and then derive the average marginal
income tax rate of each state by year and income type. Estimates take into account most
important features of state tax codes (including not only basic tax structures such as tax
rates and income thresholds by bracket, exclusions, deductions, exemptions, and credits,
but also maximum tax, minimum tax, alternative taxes, earned income credit, etc). Given
the comprehensiveness of the criteria, the estimates are well suited to the purpose of
adjusting tax revenues for changes in tax rates.
41
Using these formula and data, revenue volatility is calculated by state and tax.
Table 2.2 and Figure 2.3 present results and box plots, respectively.
Table 2.2 Cyclical Volatility of General Sales Tax and Individual Income Tax (1992–
2007 Average)
State General Sales Tax Individual Income Tax
Alabama 2.2571 3.4970
Alaska No ST No IIT
Arizona 2.1672 6.2014
Arkansas 1.8684 2.8338
California 1.3903 3.8986
Colorado 3.5431 4.8916
Connecticut 7.9598 3.9747
Delaware No ST 3.7310
Florida 2.7079 No IIT
Georgia 2.8197 2.4480
Hawaii 1.0402 4.1101
Idaho 1.5371 5.3365
Illinois 1.3802 3.3534
Indiana 2.1929 4.3725
Iowa 6.2776 5.0190
Kansas 3.1904 5.2838
Kentucky 1.4970 3.3227
Louisiana 3.4298 6.8230
Maine 2.4067 3.8517
Maryland 2.5864 2.1075
Massachusetts 3.7362 2.6426
Michigan 3.7522 6.5320
Minnesota 1.3302 3.4061
Mississippi 2.9523 4.4432
Missouri 3.3067 4.4272
Montana No ST 4.0113
Nebraska 3.5007 4.3833
Nevada 4.4378 Excluded
New Hampshire No ST No IIT
New Jersey 2.1755 3.7979
New Mexico 6.2798 4.7430
New York 1.8327 3.3910
North Carolina 3.5819 2.3265
North Dakota 3.2119 3.7530
Ohio 2.1886 2.5736
42
Oklahoma 2.5487 5.3811
Oregon No ST 3.4284
Pennsylvania 2.2939 2.2313
Rhode Island 1.9911 2.9956
South Carolina 2.2065 4.0264
South Dakota 2.0903 No IIT
Tennessee 2.0234 Excluded
Texas 2.0771 No IIT
Utah 2.9019 3.9343
Vermont 2.9579 4.7145
Virginia 3.9833 3.8062
Washington 1.8746 No IIT
West Virginia 2.3075 2.1022
Wisconsin 1.7642 4.1890
Wyoming 3.8190 No IIT
Mean 2.8306 4.0558
Std. Dev. 1.3643 1.4866
Min 1.0402 2.1022
Max 7.9598 10.5244
43
Figure 2.3A Box Plot for General Sales Tax Volatility
05
10
15
20
05
10
15
20
05
10
15
20
05
10
15
20
05
10
15
20
05
10
15
20
05
10
15
20
AL AZ AR CA CO CT FL
GA HI ID IL IN IA KS
KY LA ME MD MA MI MN
MS MO NE NV NJ NM NY
NC ND OH OK PA RI SC
SD TN TX UT VT VA WA
WV WI WY
Sale
s T
ax V
ola
tilit
y
Graphs by State
44
Figure 2.3B Box Plot for Individual Income Tax Volatility
05
10
15
20
25
05
10
15
20
25
05
10
15
20
25
05
10
15
20
25
05
10
15
20
25
05
10
15
20
25
AL AZ AR CA CO CT DE
GA HI ID IL IN IA KS
KY LA ME MD MA MI MN
MS MO MT NE NJ NM NY
NC ND OH OK OR PA RI
SC UT VT VA WV WI
Incom
e T
ax V
ola
tilit
y
Graphs by State
45
Mainly three groups of factors are used to explain revenue volatility: economic
structure, tax base composition, and demographic-economic characteristics. Starting from
the model of sales tax volatility, the effect of tax base composition is captured by
including the level of tax exemption by category of purchases discussed above in the
conceptual discussion: food, clothing, services, and producer goods. First, the level of
sales tax exemption for food and clothing (separately) is measured by 100 minus tax rate
for each category as a percent of general sales tax rate: 100 indicates full exemption, 0
indicates no exemption.18
Data on the sales taxation of food and clothing were collected
from the State Tax Reporter series (See Table 2.3 for a summary).
18
Revenue volatility, as described above, is expressed as a percentage. Thus, for more intuitive and
straightforward interpretation of analysis results, percent forms are used instead of 0-to-1 ratios. For
example, if a given state, where the general sales tax rate is 5%, applies a reduced sales tax rate of 3% for
food, then the level of tax exemption is 40 (100 - 3/5*100).
46
Table 2.3 Sales Tax Treatment of Food and Clothing and 1992−2007 Major Changes
State General Rate Food Clothing
As of Jan 2007 As of Jan 2007 Changes As of Jan 2007 Changes
Alabama 4%
Arizona 5.6%
Arkansas 6% Full
California 6.25% Full
Colorado 2.9% Full
Connecticut 6% Full
Florida 6% Full
Georgia
4% Full No exemption
prior to 1997
1997: 2%
1998: 1%
1999−2007:
Full
Hawaii 4%
Idaho 6%
Illinois 6.25% 1%
Indiana 6% Full
Iowa 5% Full
Kansas 5.3%
Kentucky
6% Full No exemption
prior to 2004
2004−2007:
Full
Louisiana 4% Full
Maine 5% Full
Maryland 6% Full
Massachusetts 5% Full Full (up to
$175)
Michigan 6% Full
Minnesota 6.5% Full Full
Mississippi 7%
Missouri
4.225% 1.225% No exemption
prior to 1998
1998−2007:
1.225%
Nebraska 5.5% Full
Nevada 6.5% Full
New Jersey 7% Full Full
New Mexico
5% Full No exemption
prior to 2005
2005−2007:
Full
New York
4% Full Full (up to
$110)
No exemption
prior to 2006
2006−2007:
47
Full (up to
$175)
North Carolina
4.25% Full No exemption
prior to 2002
2002−2007:
Full
North Dakota 5% Full
Ohio 5.5% Full
Oklahoma 4.5%
Pennsylvania 6% Full Full
Rhode Island 7% Full Full
South Carolina 6%
South Dakota 4%
Tennessee
7% 5.5% No exemption
prior to 2002
2002−2007:
5.5%
Texas 6.25% Full
Utah 4.65% 2.75% No exemption
prior to 2007
Vermont
6% Full Full (up to
$110)
No exemption
prior to 2000
2000−2007:
Full (up to
$110)
Virginia
4% 1.5% No exemption
prior to 2000
2000−2004:
3%
2005−2007:
1.5%
Washington 6.5% Full
West Virginia 6%
Wisconsin 5% Full
Wyoming
4% Full Temporary
exemption for
Jul 2006 – Jun
2008
Sources: State Tax Reporter series published by the Commerce Clearing House
Notes: Full denotes full exemption.
Table 2.4 Sales Tax Treatment of Services
State Total Number of Services Taxed Percent of Services Taxed
FY 1996 FY 2004 FY 2007 FY 1996 FY 2004 FY 2007
Alabama 32 37 37 80.5 78.0 78.0
Arizona 57 58 72 65.2 65.5 57.1
Arkansas 65 72 55 60.4 57.1 67.3
California 13 23 21 92.1 86.3 87.5
48
Colorado 14 14 14 91.5 91.7 91.7
Connecticut 87 80 79 47.0 52.4 53.0
Florida 64 64 63 61.0 61.9 62.5
Georgia 34 36 36 79.3 78.6 78.6
Hawaii 157 160 160 4.3 4.8 4.8
Idaho 29 30 94 82.3 82.1 44.0
Illinois 17 17 29 89.6 89.9 82.7
Indiana 22 23 17 86.6 86.3 89.9
Iowa 94 94 24 42.7 44.0 85.7
Kansas 76 71 74 53.7 57.7 56.0
Kentucky 26 29 28 84.1 82.7 83.3
Louisiana 58 55 55 64.6 67.3 67.3
Maine 27 24 25 83.5 85.7 85.1
Maryland 39 39 39 76.2 76.8 76.8
Massachusetts 20 19 18 87.8 88.7 89.3
Michigan 29 26 26 82.3 84.5 84.5
Minnesota 61 67 66 62.8 60.1 60.7
Mississippi 70 74 72 57.3 56.0 57.1
Missouri 28 28 26 82.9 83.3 84.5
Nebraska 49 76 77 70.1 54.8 54.2
Nevada 11 15 18 93.3 91.1 89.3
New Jersey 50 55 74 69.5 67.3 56.0
New Mexico 152 156 158 7.3 7.1 6.0
New York 74 56 57 54.9 66.7 66.1
North Carolina 28 30 30 82.9 82.1 82.1
North Dakota 25 27 26 84.8 83.9 84.5
Ohio 52 68 68 68.3 59.5 59.5
Oklahoma 32 32 32 80.5 81.0 81.0
Pennsylvania 61 55 55 62.8 67.3 67.3
Rhode Island 28 29 29 82.9 82.7 82.7
South Carolina 32 34 35 80.5 79.8 79.2
South Dakota 141 146 146 14.0 13.1 13.1
Tennessee 71 67 67 56.7 60.1 60.1
Texas 78 81 83 52.4 51.8 50.6
Utah 54 57 58 67.1 66.1 65.5
Vermont 23 29 32 86.0 82.7 81.0
Virginia 18 18 18 89.0 89.3 89.3
Washington 152 157 158 7.3 6.5 6.0
West Virginia 110 110 105 32.9 34.5 37.5
Wisconsin 69 74 76 57.9 56.0 54.8
Wyoming 63 62 58 61.6 63.1 65.5
Average 55.4 57.2 57.6 66.2 66.0 65.7
Sources: 1996/2004/2007 Federation of Tax Administrators (FTA) Survey on State Sales Taxation of
Services
49
The level of tax exemption for services is measured by the number of exempt
services as a percent of the total number of taxable services. Data on the taxation of
services were drawn from the Survey of State Services Taxation series published by the
Federation of Tax Administrators (FTA). The survey develops a list of feasibly taxable
services, and examines the taxable status of each service by state. One limitation of the
FTA survey data is that the survey has been updated periodically, not annually. The first
survey was conducted in 1990, and updated in 1996, 2004, and 2007. Among the series,
the 1996, 2004, and 2007 update are used, with each one covering the previous years.
Substituting them for missing years is considered acceptable, given the fact that states
rarely changed their tax policies on the taxation of services over the study period. The
sales taxation of services by state is summarized in Table 2.4,19
which reveals that there
has been little change.
Producer goods are divided into three categories: direct materials,
machinery/equipment, and utilities for industrial use (electricity, natural gas, and fuel).
The level of tax exemption for each category is measured separately in the same way as
food and clothing, and then weighted according to the proportion that each category
makes up of the total manufacturing cost, and finally combined into a composite index.
Weight percentages for each category were obtained from the manufacturing industry
database jointly developed by the NBER and Census Bureau's Center for Economic
19
In the survey, services are classified into eight categories, which include utilities, personal services,
business services, admissions/amusements, professional services, fabrication, repair, and installation, and
other services.
50
Studies (CES).20
Data on the taxation of direct materials and machinery/equipment were
collected from the State Tax Reporter series (See Table 2.5 for a summary), while data on
utilities were drawn from the aforementioned FTA survey series21
(See Table 2.6 for a
summary).
20
The database covers manufacturing industries in two versions: a Standard Industrial Classification (SIC)
version for 1958–1996 and a North American Industry Classification System version for 1997–2005.
Among the two, I use the latter one that matches my study period. 21
As noted above, the survey is conducted not annually but periodically. Thus, this deficiency is handled
by using the 1996, 2004, and 2007 results for the years 1992–1996, 1997–2004, and 2005–2007,
respectively.
51
Table 2.5 Sales Tax Treatment of Producer Goods and 1992−2007 Major Changes
State General Sales
Tax Rate
Materials Machinery and Equipment
As of Jan 2007 As of Jan 2007 Changes As of Jan 2007 Changes
Alabama 4% Full 1.5%
Arizona 5.6% Full Full
Arkansas
6% Full Partial
exemption for
new and
expanded
industries
California
6.25% Full exemption
prior to 2004
Partial
exemption for
certain start-
ups prior to
2004
Colorado 2.9% Full Full
Connecticut 6% Full Full
Florida
6% Full Partial
exemption for
new and
expanded
industries
Georgia
4% Full Full No exemption
prior to 1995
1995−2007:
Full
Hawaii 4% 1.5%
Idaho 6% Full Full
Illinois 6.25% Full Full
Indiana 6% Full Full
Iowa 5% Full Full
Kansas 5.3% Full Full
Kentucky
6% Full Partial
exemption for
new and
expanded
industries
Louisiana 4% Full
Maine 5% Full Full
Maryland 6% Full Full
Massachusetts 5% Full Full
Michigan 6% Full Full
Minnesota 6.5% Full
Mississippi 7% Full 1.5%
Missouri 4.225% Full Full
Nebraska 5.5% Full
Nevada 6.5% Full
52
New Jersey 7% Full Full
New Mexico 5% Full
New York 4% Full Full
North Carolina 4.25% Full 1%
North Dakota
5% Full Partial
exemption for
new and
expanded
industries
Ohio 5.5% Full Full
Oklahoma 4.5% Full Full
Pennsylvania 6% Full Full
Rhode Island 7% Full Full
South Carolina 6% Full Full
South Dakota 4% Full
Tennessee 7% Full Full
Texas 6.25% Full Full
Utah
4.65% Full Partial
exemption for
new and
expanded
industries
Vermont 6% Full Full
Virginia 4% Full Full
Washington
6.5% Full Full No exemption
prior to 1996
1996−2007:
Full
West Virginia 6% Full Full
Wisconsin 5% Full Full
Wyoming 4% Full
Sources: State Tax Reporter series published by the Commerce Clearing House
Notes: Full denotes full exemption.
53
Table 2.6 Sales Tax Treatment of Utilities for Industrial Use
State Tax Rate
1996 2004 2007
Gene
ral
Electri
city
Natu
ral
gas
Fue
l
Gene
ral
Electri
city
Natu
ral
gas
Fue
l
Gene
ral
Electri
city
Natu
ral
gas
Fue
l
Alabama 4 4 4 4 4 4 4 4 4 4 4 4
Arizona 5 5 5 5 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6
Arkansas 4.5 4.5 4.5 4.5 6 6 6 6 6 6 6 6
Californi
a
6 0 0 7.2
5
6 0 0 7.2
5
6.25 0 0 7.2
5
Colorado 3 0 0 0 2.9 0 0 0 2.9 0 0 0
Connecti
cut
6 6 6 6 6 6 6 6 6 6 6 6
Florida 6 7 6 6 6 7 6 6 6 7 6 6
Georgia 4 4 4 4 4 4 4 4 4 4 4 4
Hawaii
4 5.885 5.88
5
5.8
85
4 5.885 5.88
5
5.8
85
4 5.885 5.88
5
5.8
85
Idaho 5 0 0 0 6 0 0 0 6 0 0 0
Illinois
6.25 5 5 6.2
5
6.25 5 5 6.2
5
6.25 5 5 6.3
Indiana 5 0 0 0 6 0 0 0 6 0 0 0
Iowa 5 5 5 5 5 5 5 5 5 5 5 5
Kansas 4.9 4.9 4.9 4.9 5.3 5.3 5.3 5.3 5.3 5.3 5.3 5.3
Kentucky 6 6 6 6 6 6 6 6 6 6 6 6
Louisiana 4 4 4 4 4 3.9 3.9 4 4 3.9 3.9 4
Maine 6 6 6 6 5 5 5 5 5 5 5 5
Maryland 5 5 5 5 5 5 5 5 5 5 5 5
Massachu
setts
5 5 5 5 5 5 5 5 5 5 5 5
Michigan 6 6 6 6 6 6 6 6 6 6 6 6
Minnesot
a
6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5
Mississip
pi
7 1.5 1.5 1.5 7 7 7 7 7 7 7 7
Missouri
4.22
5
4.225 4.22
5
4.2
25
4.22
5
4.225 4.22
5
4.2
25
4.22
5
4.225 4.22
5
4.2
25
Nebraska 5 5 5 5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5
Nevada 6.5 0 0 0 6.5 0 0 0 6.5 0 0 0
New
Jersey
6 0 0 0 6 6 6 0 7 7 7 0
New
Mexico
5 5 5 5 5 5 5 5 5 5 5 5
New
York
4 4 4 4 4.25 0 0 0 4 0 0 0
North
Carolina
4 3 3 1 4.5 3 0 4.5 4.25 3 0 4.5
North 5 0 2 0 5 0 2 0 5 0 2 0
54
Dakota
Ohio 5 0 0 5 6 0 0 6 5.5 0 0 5.5
Oklahom
a
4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5
Pennsylv
ania
6 6 6 6 6 6 6 6 6 6 6 6
Rhode
Island
7 7 7 7 7 7 7 7 7 7 7 7
South
Carolina
5 0 0 0 5 0 0 0 5 0 0 0
South
Dakota
4 4 4 4 4 4 4 4 4 4 4 4
Tennesse
e
6 6 6 6 7 1 1 1 7 1 1 1
Texas
6.25 6.25 6.25 6.2
5
6.25 6.25 6.25 6.2
5
6.25 6.25 6.25 6.2
5
Utah
4.87
5
0 0 0 4.75 0 0 0 4.75 0 0 0
Vermont 5 5 5 5 6 6 6 6 6 6 6 6
Virginia 3.5 0 0 4.5 3.5 0 0 5 4 0 0 5
Washingt
on
6.5 3.873 3.85
2
6.5 6.5 3.873 3.85
2
6.5 6.5 3.873 3.85
2
6.5
West
Virginia
6 4 4.29 5 6 4 4.29 5 6 4 4.3 5
Wisconsi
n
5 5 5 5 5 5 5 5 5 5 5 5
Wyoming 4 4 4 4 4 4 4 4 4 4 4 4
Sources: 1996/2004/2007 Federation of Tax Administrators (FTA) Survey on State Sales Taxation of
Services
55
To take sector GSP composition into account, the percent share of GSP by sector
(which are agriculture, mining, construction, and durable manufacturing, nondurable
manufacturing, and service sector) is included. Data on GSP by sector were drawn from
the BEA website. To observe the decreasing effect of services exemption, a quadratic
term in the variable is also included. For the interaction effect of the level of tax
exemption for producer goods and the percent share of manufacturing sector GSP
(durable and nondurable, separately), an interaction term between the two variables is
included as well.
To control for demographic and economic factors, six variables are included:
population; the proportion of the population aged 17 and under, the proportion of the
population aged 65 and over (for population age structure); per capita personal income;
the proportion of the population with federal adjusted gross income (FAGI) over
$200,000, and the proportion of the population below the federal poverty level (for
income distribution). Data for all the variables, except the proportion of the wealthy
population whose data source is the Internal Revenue Service (IRS) website, were
collected from the Statistical Abstract of the United States series published by the U.S.
Census Bureau and the BEA website.
For the model of income tax volatility, the effect of tax base composition is
captured by including mostly three groups of variables: (1) tax exemptions for Social
Security benefits, pensions, and long-term capital gains, (2) deductions for federal
income tax and local property tax paid, and (3) personal exemptions. First, tax exemption
for Social Security benefits is measured as a dummy variable, with 1 indicating full
56
exemption, 0 no or partial exemption.22
Pensions are again divided into private and public
pensions.23
Tax exemption for public and private pensions is also measured separately as
a dummy variable. Three groups are employed: no, partial and full exemption (1 if yes, 0
otherwise), with no exemption dropped as the base group. Data on the income tax
treatment of pensions were collected from the State Tax Reporter series (See Table 2.7
for a summary).
22
No and partial exemption for Social Security benefits are lumped into the same category, based on the
assumption that the effect of partial exemption would be minimal. With the vast majority of states allowing
taxpayers to fully exclude Social Security benefits from taxable incomes, states with partial exemption for
Social Security benefits provide favorable tax treatment only to low-income people (for example,
exclusively to taxpayers with FAGI less than $50,000 in Connecticut and with FAGI less than $25,000 in
Montana). 23
Public pension incomes include military, federal, state, and local government pension incomes, among
which, the study focuses on ones from states and locals. Generally there is little policy difference among
government levels and state/local government employees account for the majority of public employees.
According to 2008 Census data on public employment, the number of federal and state/local government
full-time employees is 2,518,101 (14%) and 14,857,827 (86%) respectively.
57
Table 2.7 Income Tax Treatment of Retirement Incomes and 1992−2007 Major
Changes
State Social
Security
benefit
exclusion
Private pension
exclusion
Public
pension
exclusion
As of
January
2007
Changes As of January
2007
Changes As of
January
2007
Changes
Alabama Full Defined benefit
plans only
Full
Arizona Full None $2,500
Arkansas Full $6,000 $6,000
California Full None None
Colorado None $24,000 (age
65−)
$20,000 (age
55−64)
Applicable to
qualified
pensions and
annuities, IRA
distributions,
Keogh plans,
and Social
Security
benefits
FY
1992−1999:
$20,000 (age
55−)
FY
2000−2007:
$24,000 (age
65−)
$20,000 (age
55−64)
Same as
private
Connecticut Full only
for
taxpayers
with AGI
less than
$50,000
None until
FY 1998
None None
Delaware Full $12,500 (age
60−)
$2,000 (−age
59)
FY
1992−1998:
$3,000 (age
60−)
$2,000
(−age 59)
FY 1999:
$5,000 (age
60−)
$2,000
(−age 59)
FY
2000−2007:
$12,500 (age
60−)
Same as
private
58
$2,000
(−age 59)
Georgia Full $30,000 FY
1992−1994:
$11,000
FY
1995−1998:
$12,000
FY 1999:
$13,000
FY 2000:
$13,500
FY 2001:
$14,000
FY 2002:
$14,500
FY
2003−2005:
$15,000
FY 2006:
$25,000
FY 2007:
$30,000
Same as
private
Hawaii Full Full for
noncontributory
plans; Only
distributions
attributable to
employer
contributions
for contributory
plans
Full
Idaho Full None $21,900 –
Social
Security
benefit
(age 65−)
only for
public
safety
officers
FY
1992−2007:
Amount
annually
adjusted for
inflation
Illinois Full Full Full for
qualified
retirement
plans
Indiana Full None None
Iowa 32% None until
FY 2006
$6,000 (age
55−)
FY 1992–
1994: None
FY
Same as
private
59
1995−1997:
$3,000
FY
1998−2000:
$5,000
FY
2000−2007:
$6,000
Kansas Full only
for
taxpayers
with AGI
less than
$50,000
None until
FY 2006
None Full
Kentucky Full $41,100 FY
1992−1994:
None
FY 1995:
$6,250
FY 1996:
$12,500
FY 1997:
$18,750
FY 1998:
$35,000
FY
1999−2005:
Amount
annually
adjusted for
inflation
with 1998 as
the base year
FY
2006−2007:
$41,100
$41,110
Full for
benefits
earned
before
1998
FY
1992−1997:
Full
FY
1998−2007:
Same as
private (Full
for benefits
earned
before 1998)
Louisiana Full $6,000 (age
65−)
Full
Maine Full $6,000 – Social
Security benefit
Applicable only
to 401(a), 403,
457(b) plans
FY
1992−1999:
None
FY
2000−2007:
$6,000 –
Social
Security
benefit
Applicable
only to
$6,000 –
Social
Security
benefit
FY
1992−1999:
None
FY
2000−2007:
$6,000 –
Social
Security
benefit
60
401(a), 403,
457(b) plans
Maryland Full $23,600 –
Social Security
benefit
Not applicable
to IRA
distributions
and Keogh
plans
FY
1992−2007:
Amount
annually
adjusted for
inflation
$23,600 –
Social
Security
benefit
FY
1992−2007:
Amount
annually
adjusted for
inflation
Massachusetts Full None Full
Michigan Full $42,240 (age
65−)
FY
1992−2007:
Amount
annually
adjusted for
inflation
Full
Minnesota None None None
Mississippi Full Full Full
Missouri 20% None until
FY 2006
None $6,000
Montana Full only
for
taxpayers
with AGI
less than
$25,000
$3,600 for
taxpayers with
AGI less than
$30,000
Same as
private
Nebraska None None None
New Jersey Full $15,000 (age
62−)
FY
1992−1999:
$7,500
FY 2000:
$9,735
FY 2001:
$11,250
FY 2002:
$13,125
FY
2003−2007:
$15,000
Same as
private
New Mexico None None None
New York Full $20,000 (age
59.5−)
Full
North Carolina Full $2,000 $4,000
North Dakota None None $5,000 –
Social
Security
benefit
only for
61
public
safety
officers
Ohio Full Retirement
income credit
$0−$500: None
$500−$1,500:
$25
$1,500−$3,000:
$50
$3,000−$5,000:
$80
$5,000−$8,000:
$130
$8,000−: $200
Same as
private
Oklahoma Full $10,000
Taxpayers with
AGI less than
$50,000
FY
1992−2003:
None
FY 2004:
$5,500
Taxpayers
with AGI
less than
$37,500
FY 2005:
$7,500
Taxpayers
with AGI
less than
$37,500
FY 2006:
$10,000
Taxpayers
with AGI
less than
$37,500
FY 2007:
$10,000
Taxpayers
with AGI
less than
$50,000
$10,000 FY
1992−2004:
$5,500
FY 2005:
$7,500
FY
2006−2007:
$10,000
Oregon Full 9%
Taxpayers with
household
income less than
$22,500
Same as
private
Pennsylvania Full Full Full
Rhode Island None None None
62
South Carolina Full $10,000 (age
65−)
$3,000 (−age
64)
Same as
private
Utah None $7,500 (age
65−)
$4,800 (−age
64)
Same as
private
Vermont None None None
Virginia Full $12,000 (age
65−)
$6,000 (age 62−
age 64)
Same as
private
West Virginia None $8,000 (age
65−)
$2,000
(Full for
public
safety
workers)
Wisconsin None None None
Sources: State Tax Reporter series published by the Commerce Clearing House
Notes: Exemption amounts are for single filers.
63
The level of tax exemption for long-term capital gains is measured as the percent
of long-term capital gains excluded from taxable income, and data for the variable were
from the State Tax Reporter series (See Table 2.8 for a summary).
Table 2.8 Income Tax Treatment of Long-Term Capital Gains and 1992−2007
Major Changes
State As of 2007 Changes
Arizona 30% of net capital gains excluded (from 1999) Fully taxable prior to 1999
Montana 30% of net capital gains excluded 15% in 2005 and 2006. Fully taxable
prior to 2005 (a)
New Mexico 50% of net capital gains excluded 10% in 2003; 20% in 2004; 30% in
2005; 40% in 2006; and 50% in 2007.
Fully taxable prior to 2003
North Dakota 30% of net capital gains excluded (from 2002) Fully taxable prior to 2002
South
Carolina
44% of net capital gains excluded (from 2001) Fully taxable prior to 2001
Vermont 40% of net capital gains excluded (from 2006) Fully taxable prior to 2006
Wisconsin 60% of net capital gains excluded
Sources: State Tax Reporter series published by the Commerce Clearing House
Notes:
(a) Montana allows taxpayers a tax credit against income tax in an amount equal to 2% (1% for the 2005
and 2006 tax years) of the taxpayers' net capital gains. The exclusion portion was calculated based on
Montana's top income tax rate of 6.9% for the taxable years involved.
Deductions for federal income tax and local property tax paid are measured
separately as a dummy variable, using three groups: no, partial and full deduction (1 if
yes, 0 otherwise), with no deduction dropped as the base group. Data for the variables
were from the State Tax Reporter series (See Table 2.9 and Table 2.10 for a summary).
Table 2.9 Deduction for Federal Income Tax Paid and 1992−2007 Major Changes
State As of 2007 Changes
Alabama Full deduction
Iowa Full deduction
Louisiana Full deduction
Missouri Partial deduction (up to $5,000)
Montana (a) Partial deduction (up to $5,000) (from 2005) Full deduction (until 2004)
North Dakota* Optional
64
Oklahoma (b) Optional
Oregon Partial deduction (up to $3,000)
Utah Partial deduction (up to 50%)
Sources: State Tax Reporter series published by the Commerce Clearing House
Notes:
Deduction amounts are for single filers.
(a) Taxpayers are allowed to choose between standard and itemized deduction. As of 2004, for taxpayers
with income under $129,000, full deductions are allowed, and then deduction amounts phase out as
incomes increase.
(b) North Dakota and Oklahoma provide two options: one is to opt for the deductions and be subject to
higher tax rates, and the other is to opt out of the deductions and subject to lower tax rates. These states are
coded as 0 (no deduction) on the assumption that revenue losses due to deductions are generally offset by
gains due to higher tax rates.
Table 2.10 Deduction for Local Property Tax Paid
State Amounts (As of 2007)
Alabama Full
Alaska No individual income tax
Arizona Full
Arkansas Full
California Full
Colorado Full
Connecticut Tax credit of up to $500
Delaware Full
Florida No individual income tax
Georgia Full
Hawaii Full
Idaho Full
Illinois 5% of property tax paid
Indiana Deduction of up to $2,500
Iowa Full
Kansas Full
Kentucky Full
Louisiana Deduction of up to $75K of assessed property value
Maine Full
Maryland Full
Massachusetts* Tax credit only to seniors, the blind, surviving spouses and minor
children, homeowners facing hardships, and certain disabled veterans
who meet financial, residency, and other eligibility requirements
Michigan Tax credit of up to $1,200. Taxpayers with household income over
$82,650 are not eligible for the credit.
Minnesota Full
Mississippi Tax credit of up to $300
Missouri Full
Montana Full
Nebraska Full
Nevada No individual income tax
New Hampshire Partial individual income tax only on income from interest and
65
dividends
New Jersey Deduction of up to $10K; $5K for taxpayers with gross income of
150K-250K; None for over 250K
New Mexico Full
New York Full
North Carolina Full
North Dakota Full
Ohio* Tax credit only to senior citizens and the disabled
Oklahoma Full
Oregon Full
Pennsylvania* Tax credit only to seniors and disabled residents
Rhode Island Full
South Carolina Full
South Dakota No individual income tax
Tennessee Partial individual income tax only on income from interest and
dividends
Texas No individual income tax
Utah Full
Vermont Full
Virginia Full
Washington No individual income tax
West Virginia Tax credit of up to $500
Wisconsin Tax credit of up to $300 for property tax paid of $2,500 or more
Wyoming No individual income tax
Sources: State Tax Reporter series published by the Commerce Clearing House
Notes:
Deduction amounts are for single filers.
While there have been some changes in partial exemption amounts, there has been virtual no change in the
status of full or partial exemption. For this reason, data only for 2007 are presented.
*States allowing the deductions only to a few minority groups such as seniors and disabled residents are
coded as 0 (no deduction), since their effects on the income taxes are assumed to be relatively minimal
compared to states doing so to all taxpayers.
Personal exemptions are measured by the sum of personal and dependent
exemption. Demographic data from the U.S Census Bureau 2000 report indicate that the
average household and family size are 2.59 and 3.14, respectively. Based on this statistic,
personal exemptions are calculated on the assumption that taxpayers, on average, claim
personal exemptions for themselves and three dependents. Data for the variables were
also collected from the State Tax Reporter series (See Table 2.11 for a summary).
66
Table 2.11 Personal Exemptions and 1992−2007 Major Changes
State Personal exemption amount Dependent exemption amount
As of 2007 Changes As of 2007 Changes
Alabama 1,500 500
Arizona 2,100 2,300 2,300 (2000–)
2,100 (–1999)
Arkansas 22 (Tax credit) 21 (2006–)
20 (–2005)
22 (Tax credit) 21 (2006–)
20 (–2005)
California 94 Indexed for
inflation
294 Indexed for
inflation
Colorado 799 (a) Tied to federal tax
system (b)
799 (a) Tied to federal tax
system (b)
Connecticut 12,750 (c) 12,625 (2004–
2006)
12,500 (2001–
2003)
12,250 (2000)
12,000 (–1999)
0
Delaware 110 (Tax credit) 110 (2000–)
100 (–1999)
110 (Tax credit) 110 (2000–)
100 (–1999)
Georgia 2,700 3,000 3,000 (2003–)
2,700 (1998–2002)
2,500 (1995–1997)
2,000 (–1994)
Hawaii 1,040 1,040
Idaho 3,400 Tied to federal tax
system (b)
3,400 Tied to federal tax
system (b)
Illinois 2,000 2,000 (2001–)
1,650 (2000)
1,300 (1999)
1,000 (–1998)
2,000 2,000 (2001–)
1,650 (2000)
1,300 (1999)
1,000 (–1998)
Indiana 1,000 1,000 1,000 (2000–)
2,500 (–1999)
Iowa 40 (Tax credit) 40 (1998–)
20 (–1997)
40 (Tax credit) 40 (1995–)
15 (–1994)
Kansas 2,250 2,250 (1998–)
2,000 (–1997)
2,250 2,250 (1998–)
2,000 (–1997)
Kentucky 20 (Tax credit) 20 (Tax credit)
Louisiana 4,500 1,000
Maine 2,850 2,850 (2000–)
2,750 (1999)
2,400 (1998)
2,100 (1997)
2,000 (–1996)
2,850 2,850 (2000–)
2,750 (1999)
2,400 (1998)
2,100 (1997)
2,000 (–1996)
Maryland 2,400 2,400 (2002–)
2,100 (2001)
1,850 (1999–2000)
1,750 (1998)
2,400 2,400 (2002–)
2,100 (2001)
1,850 (1999–2000)
1,750 (1998)
67
1,200 (–1997) 1,200 (–1997)
Massachusetts 4,125 4,125 (2007)
3,850 (2006)
3,575 (2005)
3,300 (2002–2004)
2,200 (–2001)
1,000
Michigan 3,300 3,300 (2006–2007)
3,200 (2005)
3,100 (2003–2004)
2,900 (2001–2002)
2,800 (2000)
2,595 (1999)
2,539 (1998)
2,500 (1997)
2,400 (1995–1996)
2,100 (1994)
2,000 (–1993)
3,300 3,300 (2006–2007)
3,200 (2005)
3,100 (2003–2004)
2,900 (2001–2002)
2,800 (2000)
2,595 (1999)
2,539 (1998)
2,500 (1997)
2,400 (1995–1996)
2,100 (–1994)
2,000 (–1993)
Minnesota 3,400 Tied to federal tax
system (b)
3,400 Tied to federal tax
system (b)
Mississippi 6,000 1,500
Missouri 2,100 2,100 (1999–)
1,200 (–1998)
1,200 1,200 (1999–)
400 (–1998)
Montana 1,980 Indexed for
inflation
1,980 Indexed for
inflation
Nebraska 111 (Tax credit) Indexed for
inflation
111 (Tax credit) Indexed for
inflation
New Jersey 1,000 1,500
New Mexico 3,400 Tied to federal tax
system (b)
3,400 Tied to federal tax
system (b)
New York 0 1,000
North Carolina 2,500 2,500
North Dakota 0 0
Ohio 1,450 Annually adjusted
by $50
1,450 Annually adjusted
by $50
Oklahoma 1,000 1,000
Oregon 165 (Tax credit) Indexed for
inflation
165 Indexed for
inflation
Pennsylvania 0 0
Rhode Island 0 0
South Carolina 3,400 Tied to federal tax
system (b)
3,400 Tied to federal tax
system (b)
Utah 2,550 Tied to federal tax
system (b): 75% of
federal personal
exemption
2,550 Tied to federal tax
system (b): 75% of
federal personal
exemption
Vermont 0 0
Virginia 900 900 (2005–2007)
800 (–2004)
900 900 (2005–2007)
800 (–2004)
68
West Virginia 2,000 2,000
Wisconsin 700 700 (2001–2007)
600 (2000)
0 (–1999)
700 700 (2001–2007)
600 (2000)
0 (–1999)
Sources: State Tax Reporter series published by the Commerce Clearing House
Notes:
Exemption amounts are for single filers and in constant dollars. They are converted to 2007 chained dollars
for use in regression analysis.
Tax credit amounts are converted to taxable income amounts by dividing tax credit amounts by top-bracket
tax rates.
(a) Colorado individual income tax system is tied to the federal income tax system: in the State, a person‘s
tax liability is determined by multiplying his or her federal taxable income by the income tax rate (for
example, 4.63% in 2007). This implies that personal exemptions are already reflected in the federal taxable
income which is the starting point for determining the State taxable income. Hence, Colorado personal
exemptions are calculated by multiplying federal personal exemptions by the federal average marginal
income tax rates, which are obtained from NBER TAXSIM data programs.
(b) Federal personal exemptions are $2,300 in 1992; $2,350 in 1993; $2,450 in 1994; $2,500 in 1995;
$2,550, in 1996; $2,650 in 1997; $2,700 in 1998; $2,750 in 1999; $2,800 in 2000; $2,900 in 2001; $3,000
in 2002; $3,050 in 2003; $3,100 in 2004; $3,200 in 2005; $3,300 in 2006; and $3,400 in 2007.
(c) Connecticut tax system does not differentiate between personal and dependent exemption.
69
To take demographic and economic factors into account, the same variables as in
the sale tax model are included, except that for age structure, the proportion of the prime
working-age (25–54 years old)24
population is included instead of the young population.
The data cover the period from 1992 to 2007, and the sample includes 45 states
for sales tax, with five states without sales tax—Alaska, Delaware, Montana, New
Hampshire, and Oregon—excluded, and 41 states for income tax, with five states without
income tax—Alaska, Florida, Nevada, South Dakota, Texas, Washington, and
Wyoming—and two states only taxing income from interest and dividends—New
Hampshire and Tennessee—excluded. All the monetary figures were transformed into
2007 chained dollars using the Census Bureau CPI deflator. The number of people with
income over $200,000 generally trends upward over time as the economy grows. To take
this account, as with monetary figures, the proportion of the wealthy population was also
adjusted for inflation. Table 2.12 presents variable descriptions and data sources, and
Table 2.13 presents summary statistics.
Table 2.12 Variable Descriptions and Data Sources
Variables Descriptions Data sources
stvol Cyclical volatility of general sales tax: Absolute
orthogonal deviation of sales tax base from the
trend line as % of the sample mean
State Government Finance series
State Tax Reporter series
BEA website
itvol Cyclical volatility of individual income tax:
Absolute orthogonal deviation of income tax base
from the trend line as % of the sample mean
State Government Finance series
State Tax Reporter series
NBER TAXSIM website
BEA website
svc Level of tax exemption for services:
Number of services tax exempt as % of total
number of "feasibly-taxable" services (Higher
value indicates higher level of exemption.)
FTA survey series (1996, 2004,
2007)
24
While there is no definite consensus on the range of prime working-age, literature (e.g. the Occupational
Outlook Handbook series published by the U.S. Bureau of Labor Statistics) generally refers to population
25 to 54 years old as prime working-age group.
70
food Level of tax exemption for food:
100 - (tax rate for food as % of general sales tax
rate)
State Tax Reporter series
clth Level of tax exemption for clothing:
100 - (tax rate for clothing as % of general sales
tax rate)
State Tax Reporter series
prod Level of tax exemption for producer goods:
Weighted sum of exemptions for categories of
producer goods
Exemption by category = 100 - (tax rate for each
category as % of general sales tax rate)
State Tax Reporter series
NBER manufacturing industry
database
ss Full exemption for Social Security benefits:
1 if full exemption; 0 if no or partial exemption
State Tax Reporter series
prv_pt Partial exemption for public pensions:
1 if yes; 0 otherwise
State Tax Reporter series
prv_fl Full exemption for public pensions
1 if yes; 0 otherwise
State Tax Reporter series
pbl_pt Partial exemption for private pensions:
1 if yes; 0 otherwise
State Tax Reporter series
pbl_fl Full exemption for private pensions:
1 if yes; 0 otherwise
State Tax Reporter series
ltcg Level of tax exemption for long-term capital
gains:
% of LTCG excluded
State Tax Reporter series
fit_pt Partial deduction for federal income tax paid:
1 if yes; 0 otherwise
State Tax Reporter series
fit_fl Full deduction for federal income tax paid:
1 if yes; 0 otherwise
State Tax Reporter series
lpt_pt Partial deduction for local property tax paid:
1 if yes; 0 otherwise
State Tax Reporter series
lpt_fl Full deduction for local property tax paid:
1 if yes; 0 otherwise
State Tax Reporter series
pers Dollar amount of personal exemption for self + 3
× (dollar amount of personal exemption for
dependent)
State Tax Reporter series
pop Population Statistical Abstract of the United
States series
under17 % of population aged 17 or under Statistical Abstract of the United
States series
over65 % of population aged 65 or over Statistical Abstract of the United
States series
pcpi Per capita personal income (in thousands) BEA website
weal % of population with FAGI over $200,000 IRS tax statistics
poor % of population below federal poverty level Statistical Abstract of the United
States series
agrGSP % of agriculture sector GSP BEA website
minGSP % of mining sector GSP BEA website
consGSP % of construction sector GSP BEA website
durGSP % of durable manufacturing sector GSP BEA website
71
ndurGSP % of nondurable manufacturing sector GSP BEA website
servGSP % of service sector GSP BEA website
Table 2.13A Descriptive Statistics for Sales Tax Model
Variable Mean Std. Dev. Min Max
stvol 2.839866 2.642922 0.00473 19.97958
food 67.55925 23.30801 4.487179 93.125
clth 63.48253 46.53473 0 100
svc 10.69444 29.42785 0 100
prod 83.81822 18.34428 1.720711 100
pop 6049965 6268548 466251 3.64E+07
workage 42.738 1.828713 37.28627 47.33019
over65 12.61292 1.966556 4.3 18.6
pcpi 33414.73 5597.793 21651.25 56510
weal 1.911954 0.822291 0.777973 5.421682
poor 12.885 3.52463 5.7 26.4
agrGSP 1.886038 1.980807 0.119016 11.92524
minGSP 2.128385 4.619672 0.009717 32.95344
consGSP 4.587076 1.007394 2.686019 10.35751
durGSP 8.740904 4.224646 0.485976 23.28784
ndurGSP 6.297988 3.400311 1.030329 18.97705
servGSP 63.71253 5.024774 45.57038 74.33044
Table 2.13B Descriptive Statistics for Income Tax Model
Variable Mean Std. Dev. Min Max
itvol 4.123569 3.643723 .003412 25.90669
ss .6341463 .4820363 0 1
prv_pt .3932927 .4888536 0 1
prv_fl .1219512 .3274792 0 1
pbl_pt .3978659 .489831 0 1
pbl_fl .2682927 .4434089 0 1
ltcg 3.060976 11.62752 0 60
fit_pt .097561 .2969465 0 1
fit_fl .0945122 .2927632 0 1
lpt_pt .2195122 .414232 0 1
lpt_fl .7073171 .4553413 0 1
pers 8448.938 4469.436 0 17733.6
pop 5511600 5890694 572751 3.64e+07
under17 25.36267 2.007615 21.02874 36.07281
over65 12.63453 2.037031 4.3 18.6
pcpi 33282.03 5726.689 21651.25 56510
weal 1.892837 .8452066 .777973 5.421682
poor 12.82409 3.597709 5.7 26.4
agrGSP 1.875769 1.846979 .1190157 11.92524
minGSP 1.551481 2.84 .0097172 16.46646
consGSP 4.474374 .7891637 2.686019 7.4177
durGSP 9.051881 4.332315 .4859761 23.28784
ndurGSP 6.568565 3.422808 1.030329 18.97705
72
servGSP 63.778 4.892006 49.76388 76.20969
2.3.2 Models and Estimation Methods
Based on the variables discussed above, the model for sales tax volatility is
specified as follows:
(1) = + + + + + +
+ 7 · + γ + + + ,
where the and subscript refer to the panel (state) and the time period (year),
respectively; denotes the cyclical volatility of general sales tax; , , ,
and are tax exemption for food, clothing, services, and producer goods, respectively;
and indicate the share of durable and nondurable manufacturing sector
GSP, respectively; V, W, and Z are a matrix of demographic-economic characteristics, the
share of GSP by sector, and year dummies, respectively; is the error term.
In addition, the model for income tax volatility is specified as follows:
(2) = + + + + + +
+ + + + + +
+ γ + + + ,
73
where denotes the cyclical volatility of individual income tax; , , ,
and , and is tax exemption for Social Security benefits; partial and full
exemption for private pensions; and partial and full exemption for public pensions,
respectively; is tax exemption for long-term capital gains; is the proportion of
the population with FAGI over $200,000; , , and indicate partial and full
deduction for federal income tax paid and full deduction for local property tax paid,
respectively; is personal exemption; V, W, and Z are a matrix of demographic-
economic characteristics, the share of GSP by sector, and year dummies, respectively;
is the error term.
The models are estimated using the pooled OLS estimator, and year dummies are
included to control for year-specific unobserved heterogeneity (Podesta, 2000). The data
indicate that the cross-time variations in the key variables for tax base composition are
not large, whereas the cross-state variations are wide. Given the data structure, the fixed
or random effects estimator, which is based on the within transformation, is not
appropriate; instead the pooled OLS estimator with year dummies is used. At this
juncture, it is important to assume that the various sectors of state economies are closely
interrelated under the roof of the U.S. national economy. As a result, state economies
move together and share the same business cycle characteristics. Under this assumption,
variations in cyclical fluctuations in tax revenues across states are largely attributed to
variations in economic and revenue structures.
For OLS estimators to be consistent, homoskedasticity and no serial correlation
assumption should hold. To diagnose the presence of heteroskedasticity and serial
correlation in the errors, two tests were performed: Breusch-Pagan/Cook-Weisberg test
74
for heteroskedasticity and Wooldridge test for serial correlation. Test results indicate that
both are present: all the null hypotheses of homoskedasticity and no serial correlation are
strongly rejected by each test (See Appendix B for detailed results). In correcting for
them, this study uses clustered robust standard errors which are robust to disturbances
being heteroskedastic and serially correlated.
2.4 Results and Discussion
Table 2.14 and 2.15 report the results of regression analyses for sales tax and
income tax volatility. Overall, the models perform well: most of the key variables for tax
base composition are statistically significant with the expected signs, and the regressions
explain 19% and 31%, respectively, of the variations in the dependent variables. Before
interpreting the regression results, it should be noted that most of the key variables
including the dependent variables were measured in percentage form. This means that the
coefficients on them represent percentage point changes in the dependents, not
percentage changes as in log functions.
Table 2.14 Regression Results for Sales Tax Volatility
Explanatory variables Coef. Clustered Robust
Std. Err.
Exemption for food 0.009117** 0.004503
Exemption for clothing -0.01559*** 0.005589
Exemption for services -0.00196 0.04146
(Exemption for services)^2 1.84E-05 0.000352
Exemption for producer goods -0.0356* 0.020508
Exemption for producer goods × % of durable
manufacturing sector GSP
0.000368 0.002043
Exemption for producer goods × % of
nondurable manufacturing sector GSP
0.006251* 0.003619
75
% of agriculture sector GSP 0.044363 0.083519
% of mining sector GSP 0.117755* 0.066276
% of construction sector GSP -0.05013 0.177785
% of durable manufacturing sector GSP 0.044331 0.207352
% of nondurable manufacturing sector GSP -0.42853 0.345164
% of services sector GSP 0.03807 0.084465
Population -1.40E-07*** 3.05E-08
% of population aged 25 to 54 -0.22149* 0.11963
% of population aged 65 or over 0.071406 0.06356
Per capita personal income 5.91E-05 0.00011
% of population with AGI over $200,000 1.297343* 0.664692
% of population below poverty level 0.095143* 0.054081
Constant 7.238988 10.05118
Number of observations 720
Number of states 45
R-squared 0.1876
Note: Year effects not reported.
***p<0.01; **p<0.05; *p<0.1
Table 2.15 Regression Results for Income Tax Volatility
Explanatory variables Coef. Clustered Robust
Std. Err.
Full exemption for Social Security benefits 0.684174* 0.35612
Partial exemption for private pensions -0.50986 0.433281
Full exemption for private pensions -0.24972 0.580233
Partial exemption for public pensions 0.626986 0.476724
Full exemption for public pensions 1.062584*** 0.357041
Exemption for long-term capital gains -0.08036* 0.041498
Exemption for long-term capital gains × % of
population with AGI over $200,000
0.063927** 0.024151
Partial deduction for federal income tax paid -0.1362 0.570368
Full deduction for federal income tax paid 0.523886 0.508445
Partial deduction for local property tax paid 1.239711** 0.485865
Full deduction for local property tax paid 2.080549*** 0.500427
Personal exemption 4.74E-05 4.04E-05
% of agriculture sector GSP 0.290092** 0.114027
% of mining sector GSP 0.463241*** 0.155492
% of construction sector GSP 0.139126 0.33256
% of durable manufacturing sector GSP 0.201583* 0.115118
% of nondurable manufacturing sector GSP 0.095391 0.093654
% of services sector GSP 0.087271 0.118443
Population 2.60E-08 2.70E-08
% of population aged 17 or under 0.118373 0.133713
% of population aged 65 or over -0.12376* 0.064133
76
Per capita personal income 0.00013 0.000134
% of population with AGI over $200,000 -0.21125 0.625919
% of population below poverty level -0.15787** 0.077824
Constant -13.0187 9.667144
Number of observations 656
Number of states 41
R-squared 0.3085
Note: Year effects not reported.
***p<0.01; **p<0.05; *p<0.1
Starting from the analysis for sales tax volatility, the results show that sales tax
exemptions for food and clothing have statistically significant effects on sales tax
volatility. The OLS coefficients on the variables indicate that exemption for food has a
positive effect, whereas exemption for clothing exerts a negative impact on the dependent
variable. Specifically, other things being equal, changes from no to full exemption for
food and clothing are predicted to lead to, on average, a .9 percentage point increase and
a 1.6 percentage point decrease in sales tax volatility, respectively [.009*100 = .9%;
0.016*100 = 1.6%]. These results suggest that the consumption of food is relatively
stable over the course of the business cycle, whereas that of clothing, as expected, is
sensitive to economic changes. Table 2.3 shows that only a small number of states are
offering sales tax exemption for clothing, whereas relatively many states are exempting
food from sales taxation. In light of the results, when it comes to household necessities,
state sales tax bases can be said to be, on average, prone to cyclical volatility.
On the other hand, the results indicate that exemption for services in both linear
and quadratic form is not statistically significant. This result implies that it is not entirely
correct to say that incorporating more services into a sales tax base would lead to
increased sales tax stability, generalizing services as a stable tax base. Although, in
theory, the consumption of services is considered relatively stable compared to that of
77
goods, it appears that there exists quite a difference in necessity and income elasticity
across services. Further, the statistical insignificance of services exemption suggests that
a more sophisticated measure of state sales taxation of services needs to be developed to
produce more detailed results. This study uses the number of services taxable to measure
the level of tax exemption for services, which does not capture possible differences in
income elasticity among services. Although this study included a quadratic function of
services exemption based on the assumption that state sales taxation tends towards less
income elastic services, it turns out to be not sophisticated enough to produce significant
results.
The regression reveals that exemption for producer goods on sales tax volatility
has a negative and statistically significant impact on sales tax volatility. The slope
coefficient on the variable indicates that a one percentage point increase in producer
goods exemption lowers sales tax volatility by .036 percentage points. For example, in
the case of Minnesota, one of states granting the lowest level (approximately 60%) of tax
exemption for producer goods, sales tax volatility is predicted to decrease by 1.44
percentage points if the state raises the current level to full exemption like most other
states.
The study also finds that there is an interaction effect between producer goods
exemption and nondurable manufacturing sector. The different signs of the coefficients
on producer goods exemption and the interaction term imply that the GSP share of the
nondurable manufacturing sector moderates the effect of producer goods exemption on
sales tax volatility. Taking the partial derivative of Equation (1) tells us that the partial
effect of producer goods exemption on sales tax volatility is now -0.036 +
78
0.006* [ = + ]. That is, as increases,
the effect of producer goods exemption on income tax volatility decreases. A further
analysis by group—states with the share of nondurable manufacturing sector GSP above
the state average (about 6.3%) and those below the average—confirms this conclusion:
the coefficient on producer goods exemption remains the same (negative) for the below-
the-average group, whereas the sign is flipped (positive) for the above-the-average group.
This indirectly shows that the consumption of nondurable goods, as discussed earlier,
tends to be less sensitive to economic fluctuations. On the other hand, the interaction term
between producer goods exemption and durable manufacturing sector GSP share is not
statistically significant. This result may be due in part to differences among durable
manufacturing industries in income elasticity of demand.
In contrast to the variables for tax base composition, all the variables for
economic structure, except for mining sector, are not statistically significant. Consistent
with prevailing notions that commodity prices are highly volatile, the results indicate that
the relative size of mining sector has an effect in increasing cyclical swings in sales tax
revenues. According to the coefficient estimate, a one percentage point increase in the
GSP share of mining sector is predicted to add to sales tax volatility by .118 percentage
points. Taken together, the results suggest that with tax structure taken into account,
economic structure is not a strong predictor of sales tax volatility, highlighting the
relative importance of policy factors in this respect.
As for the variables for demographic and economic characteristics, the results
confirm that population is statistically significant and dampens sales tax fluctuations. The
coefficient on the variable indicates that an increase in population by 100,000 leads to
79
about 0.14% point decrease in sales tax volatility. This result provides evidence for the
relationship among region size, industrial diversification, and economic stability. For
example, an average difference between New Jersey and Rhode Island in population over
the sample period is 7304864 [8346873 - 1042009], which, according to the estimated
coefficient, is predicted to make a difference of about 1.02 percentage points (higher in
Rhode Island) [7304864*0.00000014] in sales tax volatility.
The regression also reveals that age structure and income distribution significantly
affect sales tax volatility. The results confirm that the prime working-age population
exerts a negative and statistically significant influence on cyclical swings in sales tax
revenues. In other words, sales tax volatility is lower in states with a larger prime
working-age population, suggesting that this economically active population group does
not respond sensitively to recession-driven transitory income shocks. The coefficient on
the variable indicates that a one percentage point increase in the prime working-age
population causes sales tax volatility to decrease by .221 percentage points. This result
provides evidence in support of the life-cycle theory of consumption and the permanent
income theory, suggesting that in the interest of stable sales tax revenue, states also need
to keep an eye on the changing age structure of the population.
As for income distribution, the results display that the wealthy population adds to
cyclical fluctuations in sales tax revenues. The coefficient on the variable indicates that a
one percentage point increase in the proportion of the population with FAGI over $200K
results in a 1.30 percentage point increase in sales tax volatility. Summary statistics
indicate that the minimum and maximum of the variable are about 0.77 and 5.42,
respectively. According to the coefficient estimate, this difference, holding other factors
80
constant, is predicted to make a difference of about 6 percentage points [(5.42-0.77)*1.3
= 6.045] in sales tax volatility.
Turning to the analysis for income tax volatility, the results show that full
exemptions for Social Security benefits and public-sector pensions have positive and
statistically significant effects on income tax volatility. The coefficients on the variables
indicate that income tax volatility is .699 and 1.2 percentage points higher in states
allowing full exemption for Social Security benefits and public pensions than states
allowing no exemption. This implies that tax revenues from these income sources are
relatively stable over the business cycle, therefore excluding them from the list of taxable
incomes amplifies income tax volatility.
By contrast, both partial and full exemption for private-sector pensions and partial
exemption for public-sector pensions are shown to be not statistically significant. Such
different results between private and public pensions exemption in terms of statistical
significance may be due in part to the heterogeneous nature of private pensions.
Retirement plans are largely classified into two types according to how they are funded
and how the benefits are determined: defined benefit and defined contribution. Private
pensions are relatively mixed in this respect, compared to public pensions in which a
defined benefit plan is the dominant form of retirement plan. This issue will be later
discussed in more details.
As expected, exemption for long-term capital gains is found to lessen income tax
volatility. The coefficient indicates that for the same level of other factors, a ten
81
percentage point increase in exemption for LTCG lowers income tax volatility by 0.87
percentage points. The regression also reveals that there is an interaction effect between
LTCG exemption and the wealthy population. As predicted, the partial effect of LTCG
exemption on income tax volatility is moderated by the proportion of the wealthy
population. Specifically, taking the partial derivative of Equation (2) with respect to
gives: -0.087 + 0.066* [ = + ], suggesting that the negative
effect of LTCG exemption on income tax volatility is smaller in states with a larger
wealthy population. This result, as discussed earlier, is thought to be attributable to states‘
practices of not allowing this preferential tax treatment to high-income people.
Among the other tax structure variables, both partial and full deduction for local
property tax paid have relatively strong effects on income tax volatility, whereas both
partial and full deduction for federal income tax paid and personal exemption are not
statistically significant. The estimated coefficients on deductions for local property tax
paid suggest that states allowing partial and full deduction for local property tax paid are,
on average, 1.58 and 2 percentage points higher in income tax volatility than states
allowing no deductions.
As for the control variables, the results exhibit that agriculture and mining sector
are positively associated with income tax volatility. According to the coefficients on them,
a one percent increase in the GSP share of agriculture and mining sector increases income
tax volatility by .308 and .453, respectively. These results, once again, confirm the
inherent volatility of commodity-producing industries. Consistent with the theoretical
discussions, durable manufacturing sector is also found to be positively related to the
dependent variable. The coefficient indicates that a one percent increase in the GSP share
82
of durable manufacturing sector is predicted to increase income tax volatility by .215
percentage points. Lastly, among the demographic-economic variables, the low-income
and elderly population are shown to have statistically significant effects. A one
percentage point increase in the population groups decreases income tax volatility by .11
and .163 percentage points.
83
CHAPTER 3
REVENUE VOLATILITY AND FISCAL INSTABILITY
“Expenditures rise to meet income.”
C. Northcote Parkinson (1960)
3.1 Introduction
Fiscal stability characterizes a government‘s ability to maintain adequate levels of
funding for its programs and services especially during periods of recession. Stable fiscal
policies are the ones that are consistent and predictable over the course of the business
cycle, and ―budgeting processes thrive on stability (Caiden 1981).‖ In recent years,
achieving this fiscal principle has become challenging with increasingly volatile and
unpredictable economic environments. Gaps between the peaks and troughs of businesses
cycles are becoming wide, while the turnarounds and intervals becoming rapid irregular.
Governments and private businesses more frequently face economic fluctuations and
upheavals. The increasing volatility of fiscal environments has significant implications
for state governments, because they operate under legal constraints such as balanced
budget requirements (BBRs) and tax and expenditure limitations (TELs).
In response to increasingly volatile fiscal environments, scholars have
consistently argued for countercyclical fiscal policy. Hou (2006: 735) explicitly argues
that ―state governments can smooth the fluctuation of economic cycles by curbing
spending during booms in order to accumulate more reserves for expenditure during
84
cyclical downturns (See also Hou and Moynihan 2007).‖25
In a similar vein, Dothan and
Thompson (2006) contend that governments should smooth spending over the business
cycle by balancing their budgets in a present-value sense—in a way that matches the
present value of future outlays to that of future receipts plus net assets (or liabilities). In
the broader context, modern economic theory generally holds that governments should
keep the economy from overheating to head off inflation during expansionary periods and,
conversely, stimulate demands and encourage supplies to help the economy recover
during recessionary periods. Tax smoothing models (Barro 1979; Lucas and Stokey 1983)
suggest that tax rates be held constant over the business cycle so as not to distort
economic agents‘ decisions. Based on these theoretical discourses, fiscal instability is
defined as ―procyclicality in conducting fiscal policy‖ (i.e. spending increases and tax
cuts during booms and spending cuts and tax increases during recessions) in this study.26
Together, these arguments highlight the importance of smoothing spending over
the business cycle through countercyclical fiscal policy. This can be interpreted as
ensuring the ―affordability and sustainability‖ of government programs and services
(Shah 2005: xiii) and maintaining sustainable levels of spending and some form of
savings in times of plenty beyond merely balancing budgets. This approach makes sense,
because by nature, the economy swings back and forth between boom and bust.
Following a boom, governments invariably face a recession with revenue shortfalls and
expenditure overruns. Consequently, the ones that implemented unsustainable spending
25
He takes a step further, adding that the strong version of this policy involves ―raising taxes during boom
years and lowering them during recessions.‖ 26
Readers should note that here ―procyclicality‖ is a relative concept in this study. Empirical studies on the
cyclicality of fiscal policy at the international level have been consistent to find that developed countries
are by and large countercyclical in conducting fiscal policy, whereas developing countries procyclical. As
will be shown later, U.S. states as subnational units of a developed country are generally countercyclical.
Therefore, when a state is said to be procyclical, the term procyclical, precisely speaking, means ―relatively
procyclical‖ compared to other states, and concerns policy adjustments rather than fiscal outcomes.
85
increases and tax cuts during the preceding boom inevitably experience greater hardship
during the subsequent recession, as suggested by the popular adage, ―There‘s no such a
thing as a free lunch (Friedman 1975).‖
Despite the normative significance and practical virtue of fiscal stability and
spending-smoothing as a strategy for it, state governments have failed to put the principle
into practice. Edwards, Moore, and Kerpen (2003) report that state general fund spending
in real dollars grew 18.1% from 1990 to 2001, and more importantly, that its annual
growth rate was 2.9% greater than inflation and particularly rapid in the late 1990s when
the economy was at its peak, posting 7% in 1999, 6.6% in 2000, and 8% increase in 2001.
Similarly, Crain (2003) reports that annual state spending growth rate was approximately
1% higher on average than income growth during the 1990s when the nation experienced
the longest economic boom in history. Schunk and Woodward (2005) observe that
aggregate state general fund spending grew 18.4% faster than inflation plus population
growth between 1995 and 2001. The upturn years of the 1980s and the 2000s also show a
similar trend. Moore (1991) reports that state nominal expenditures increased 8.5% per
year between 1980 and 1989, outpacing inflation by 3.5%, while Stansel and Mitchell
(2008) discover that despite the 2001 recession and budget problems in subsequent years,
they rose 0.8% higher than income from 2000 to 2007.
Meanwhile, some studies point to tax cuts during expansionary periods. In a
report on state tax policy, Johnson (2002) reports that from 1994 to 2001, nearly every
state enacted tax cuts and most of them were substantial and permanent. According to his
report, aggregate net tax cuts in 43 states during that period were about 8.2 percent of
aggregate state tax revenues. In a summary of National Conference of State Legislatures
86
(NCSL) data, Knight, Kusko, and Rubin (2003: 432–435) observe that a series of tax cuts
enacted between 1995 and 2001 reduced state collections by about 36 billion dollars (8
percent) below baseline levels, arguing that they were partly responsible for the state
fiscal crises of the early 2000s.
Further, these studies suggest that rapid and sustained revenue growth and the
resulting large surplus revenue during booms tend to induce unsustainable spending
increases and tax cuts, creating structural budget deficits and ultimately causing fiscal
crises. In generalized terms, it can be said that revenue availability induces spending.
Moore (1991) observes that most of the states that experienced the most severe budget
deficits in the early 1990s recession were those that saw rapid revenue growth during the
preceding boom period. In case studies of states with severe budget deficits, Edwards,
Moore, and Kerpen (2003) observe that as the economy expanded, states incrementally
added new costly recurring spending commitments by expanding existing programs or
launching new programs in major policy areas such as education, health care, welfare,
and corrections without offsetting cuts in other areas. Based on this observation, they
argue that ―the structural problem that faces state budgets is that revenues rise too quickly
during economic booms and cause politicians to overspend.‖ They go on to argue that if
states had limited their spending growth to inflation while conducting tax policies such as
rebates in a flexible and timely fashion, they would have easily overcome recession-
driven revenue shortfalls. Similarly, Schunk and Woodward (2005) observe that during
the expansion phases of the late 1990s, nearly every state created ongoing programs
(annualizations) and permanent tax cuts using one-time (non-recurring) surplus funds,
assuming that revenues would continue to grow to provide funding for the new and
87
expanded programs. With regard to tax policy, Johnson (2002) argues that states were
able to enact large tax cuts in the 1990s maintaining spending growth at about the same
pace as in the previous decade, because of higher-than-expected tax revenues—due to
high capital gains realizations and corporate profits—and the resulting record surpluses.
In a bit broader context, Kaminski, Reinhart, and Vegh (2004) suggest that the roots of
most government fiscal crises often lie in governments themselves that go through bouts
of high spending and borrowing when the times are favorable and financial resources are
plentiful.
If it is true that cyclical increases in tax revenues induce spending growth (i.e.
spending increases and tax cuts), it would be reasonable to assume that state fiscal
conditions may differ depending on the extent of revenue fluctuations over the business
cycle. Government revenues and expenditures generally grow along their long-term trend
lines, while, at the same time, fluctuating over the short term through boom-bust cycles.
As the economy expands and contracts, revenues move in a procyclical manner.
Conversely, demands for many types of expenditures move in a countercyclical manner,
amplifying cyclical swings in budget balances. Demands for public services rise during
recessions and fall during booms (for example, more students are likely to choose public
schools, and more people to find municipal service facilities during recessions), and
claims for Medicaid and welfare benefits move up and down over the business cycle.
What should be noted here is that state tax revenues vary widely in cyclical volatility, as
found in Chapter 2. Given the wide cross-state variation in revenue volatility, it would be
logical to hypothesize that spending increases and tax cuts during booms will be larger in
states with a more volatile revenue base.
88
This line of reasoning is consistent with Parkinson‘s second law: ―Expenditure
rises to meet income,‖ which is one of the variants of Parkinson‘s famous first law,
―Work expands so as to fill the time available for its completion.‖ In his book The Law
and the Profits (1960), Parkinson illustrates how manifest his law is in our everyday life
using an example of households. He notes that the law applies not only to individuals but
also to organizations, arguing that bureaucracies, whether public or private, tend to spend
whatever is made available. According to him, their spending propensities usually distort
the budgetary process in such a way that they determine the levels of expenditures that
can be financed and then justify their budget requests. He argues that such a perverse
budgetary behavior is particularly likely in government organizations where there is
virtually no notion of profit, consequently, no incentive for saving and surpluses.
In a 1976 news article ―Parkinson Revisited,‖ Milton Friedman introduces as a
modern illustration of the law a comparative study of the fiscal systems of Vermont and
New Hampshire conducted by Campbell and Campbell (1976). The two states are in stark
contrast in that Vermont has both income tax and general sales tax, whereas New
Hampshire has neither. He summarizes that as Parkinson‘s law suggests, Vermont‘s
expenditures rose to meet its income in an inefficient manner: expenditures were much
higher (50%) in Vermont than in New Hampshire but it is doubtful that services in
Vermont were proportionately better than those in New Hampshire. Friedman goes on to
contend, ―Therefore, the only way to cut government waste and extravagance is to cut
government income (Friedman 1983).‖
This revenue-spending hypothesis naturally extends to recessionary periods. The
economy generally goes through a cycle of expansion and contraction, and unlike the
89
federal government, states operate under balanced budget requirements. Given the
inherent tendency of the economy to revert to its trend rate of growth (Steindl 2007: 189)
and the legal constraints under which states operate, spending cuts and tax increases are
inevitable for the states that have increased spending at unsustainable rates during booms,
without borrowing, off-budget spending (spending by means of off-budget enterprises
such as public corporations and quasi-governmental units), or ―creative accounting.‖
Based on this logic, it is also assumed that spending cuts and tax increases during
recessions will be larger in states with a more volatile revenue base.
Although there has been ample anecdotal evidence suggesting the association
between cyclical changes in tax revenues and policy adjustments (for the level of
spending and taxation) and further between revenue volatility and fiscal instability,
systematic empirical research is scant. This deficiency may be due in large part to the
difficulty of measuring the cyclical components of tax revenues (also called revenue
gap27
in this study) and quantifying tax and spending adjustments. This dissertation fills
this gap in the literature by empirically examining how cyclical changes in tax revenues
affect state fiscal behavior in terms of the level of spending and taxation, using a panel
data set for U.S. states over the period of 1992 to 2007. To this end, the present study
answers the following specific questions:
1. Are cyclical changes in tax revenues positively related to changes in
expenditures over the business cycle?
27
As will be discussed later, the way the concept is measured is, in essence, the same as output gap,
commonly used in the economics and business literature, and ―expenditure gap‖ often used in the public
finance literature (See Hou 2005) in that the measurement involves separating the trend and cyclical
component of tax revenue. In keeping with the existing terms, this study terms the concept ―revenue gap.‖
90
- Do cyclical increases in tax revenues during expansions lead to increases in
expenditures?
- Do cyclical decreases in tax revenues during recessions lead to decreases in
expenditures?
2. Are cyclical changes in tax revenues negatively related to changes in tax rates
over the business cycle?
- Do cyclical increases in tax revenues during expansions lead to decreases in tax
rates?
- Do cyclical decreases in tax revenues during recessions lead to increases in tax
rates?
The cross-state heterogeneity of revenue volatility, political-institutional
environments, and demographic-economic characteristics provides a natural laboratory
for the empirical analysis of the chosen question. The sample period—beginning after the
1990–1991 recession ending before the 2008 recession—is considered balanced, with
two peaks and troughs included. The rest of the chapter is organized as follows: the next
section reviews previous studies, and Section 3.3 provides a conceptual discussion as to
how revenue availability induces spending in the public sector. Section 3.4 discusses
other relevant factors that influence state fiscal behavior, and Section 3.5 presents data
and methods for empirical analysis. Lastly, Section 3.6 discusses analysis results.
3.2 Literature Review
91
To date, a vast body of literature has been established on the behavior of fiscal
policy, and along the way, various theoretical and methodological approaches have been
developed. In light of the research questions raised, this literature review focuses
primarily on empirical studies that explored factors that explain the level of spending and
taxation. Upon review of relevant literature, three strands of research stand out.
The first is literature known as the "tax-spend debate" or the "revenue-expenditure
nexus." This literature has emerged since 1980s in response to the growing concerns over
government expansion and budget deficits. The tax-spend literature has attempted to
determine the intertemporal relationship between tax and spending adjustments in
generation of budget deficits. Along the way, four main hypotheses have been developed.
First, the tax-spend hypothesis suggests a causal relation running from revenues to
expenditures (i.e. the hypothesis that changes in revenues would lead to changes in
expenditures). The tax-spend hypothesis has again diverged into two different views. The
conventional tax-spend hypothesis, advanced by Friedman (1978), holds that raising
taxes in an attempt to reduce budget deficits would only result in increases in
expenditures. Based on this view, the proponents argue that in order to rein in
government spending and bring down budget deficits, we should first curtail resources
available by cutting taxes.
Buchanan and Wagner (1977; 1978) set forth an alternative view, which
postulates that raising taxes would lead to decreases in expenditures due to fiscal illusion.
The central theme of the fiscal-illusion view is that how citizens are taxed (e.g. direct vs.
indirect taxation through borrowing and inflation; a single tax vs. a variety of smaller
92
taxes) can affect taxpayer perceptions of the price of public output and the size of the
public sector. According to this view, tax hikes increase the perceived prices of public
goods, which, in turn, make taxpayers hostile towards the attempts from governments to
increase spending. Conversely, tax cuts decrease the perceived price of government,
leading taxpayers to be favorable to spending increases.
Second, the spend-tax hypothesis postulates a causal relation running from
expenditures to revenues. This view, supported by Peacock and Wiseman (1979), holds
that temporary increases in expenditures for dealing with social disturbances or crises
would lead to permanent tax increases, as the social problems that have been treated as
peripheral policy issues become national policy agendas due to increased attention and
eventually lead to the creation of specific public programs that require annual
appropriations. This hypothesis is consistent with the ratchet theory of government
spending growth that ―temporary crises (such as severe economic crises and wars) cause
government spending to rise and to remain permanently higher than if the crises had not
occurred (Holcombe 1993).‖ This view is also consistent with the Ricardian equivalence
proposition that debt financing today would lead to increased tax liabilities in the future
(Barro 1979).
Third, the fiscal synchronization hypothesis suggests a bidirectional causal
relation between revenues and expenditures. This view assumes that governments make
decisions about revenues and expenditures simultaneously based on information obtained
through the comparison of the costs and benefits of government programs (Musgrave
1966; Meltzer and Richard 1981).
93
Lastly, the institutional separation hypothesis negates any type of causal relation
between revenues and expenditures. This view holds that decisions on revenues are
independent from decisions on expenditures due to the institutional separation of the
taxation and allocation process (Wildavsky 1988; Baghestani and McNown 1994; Hoover
and Sheffrin 1992).
Reflecting the variety of theoretical perspectives, empirical studies have yielded
mixed results. The tax-spend literature is vast, so this review focuses only on U.S.
evidence. First, at the federal level, evidence supporting the conventional (Friedman-type)
tax-spend hypothesis is found by Blackley (1986), Bohn (1991), Mounts and Sowell
(1997), Koren and Stiassny (1998), Garcia and Henin (1999), and Chang, Liu, and
Caudill (2002). Evidence supporting the fiscal illusion (Buchanan-Wagner-type) tax-
spend hypothesis is relatively scant. Niskanen (2005) and Romer and Romer (2007)
provide support for this view. Empirical studies in support of the spend-tax hypothesis
include von Furstenberg, Green, and Jeong (1985), Anderson, Wallace, and Warner
(1986), Joulfaian and Mookerjee (1991), Ross and Payne (1998), and Islam (2001). The
fiscal synchronization hypothesis is supported by Manage and Marlow (1986), Miller and
Russek (1989), Jones and Joulfaian (1991), Hasan and Sukar (1995), and Owoye (1995).
Evidence for the institutional separation hypothesis is found by Ram (1988a), Hoover and
Sheffrin (1992) and Baghestani and McNown (1994).
For the state level, there have been far fewer studies. von Furstenberg, Green, and
Jeong (1985) examine intertemporal relations between tax initiatives and expenditures at
the aggregate state-local level (and also at the federal level) in the United States over the
period of 1955–1982 (quarterly data), using vector autoregressive models [following
94
Sims (1980)] with potential GDP and grants. Their analysis finds evidence in support of
the spend-tax hypothesis. Using annual data covering the period of 1952 to 1982, Marlow
and Manage (1987; 1988) and Chowdhury (1988) conduct causality tests [following
Granger (1969)] with only revenue and expenditure variables for the aggregate state and
local level, separately. Their model for the aggregate state level provides support for the
tax-spend hypothesis, whereas the model for the aggregate local level supports the
institutional separation hypothesis (in Marlow and Manage) and the fiscal
synchronization hypothesis (in Chowdhury). In Granger-causality tests [following
Guilkey and Salemi (1982)] using annual and quarterly aggregate state-local data over the
period of 1929–1983 and 1947–1983, respectively, Ram (1988b) finds evidence in favor
of the spend-tax hypothesis. Joulfaian and Mookerjee (1990a) investigate the revenue-
expenditure nexus for Massachusetts over the period of 1955–1986 (annual data), using
VAR [following Sims (1980)]. Their model incorporating personal income and federal
grants along with revenues and expenditures lends support to the tax-spend (Friedman-
type) hypothesis. They also find that federal grants do not have a significant effect on
state expenditures. Another study by Joulfaian and Mookerjee (1990b) for the aggregate
state-local level employs the same estimation methods [following Sims (1980)] with
annual data over the period of 1960–1986, and finds evidence supporting the tax-spend
hypothesis. Payne (1998) undertakes extensive research for all U.S. states over the period
of 1942 to 1992, using Engle-Granger (1987) cointegration procedure and error
correction models with only revenue and expenditure variables. Results indicate that the
tax-spend, spend-tax, and fiscal synchronization hypothesis are supported for twenty-four,
95
eight, and eleven states, respectively. The remaining five states failed the diagnostic tests
for error correction modeling.28
Methodologically, the tax-spend debate has relied on the concept of Granger
causality. Following Granger (1969) and Sims (1972), early studies employed bivariate
vector autoregressive (VAR) models of revenue and expenditure. Recognizing the
possibility of omitted variable bias, the literature has shifted towards a multivariate
framework. The cointegration approach introduced by Engle and Granger (1987) and
Johansen (1988) brought another important development to causality analysis.
Recognizing the possibility of spurious regression between two time series processes
with a unit root, this approach involves determining cointegration and performing a
causality test using an error-correction model.
While the literature has grown significantly in theoretical elaboration and
methodological rigor, some empirical shortcomings need to be addressed. The first
relates to model specification. Placing a primary focus on the temporal relationship
between revenue and expenditure, the tax-spend debate has paid relatively little attention
to other potentially relevant factors such as fiscal institutions and political institutions
(Payne 2003). This is likely to lead to biased and inconsistent results especially at the
28
Specifically, the tax-spend hypothesis is supported for Arkansas, California, Connecticut, Florida,
Georgia, Idaho, Iowa, Kansas, Kentucky, Maryland, Michigan, Missouri, Nevada, New Hampshire, North
Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, South Carolina, Tennessee, Washington,
and West Virginia; the spend-tax hypothesis supported for Alabama, Delaware, Mississippi, New York,
Rhode Island, Utah, Wisconsin, and Wyoming; the fiscal synchronization hypothesis supported for Arizona,
Colorado, Illinois, Indiana, Massachusetts, Minnesota, Montana, Nebraska, Texas, Vermont, and Virginia.
In the case of Louisiana, Maine, and New Mexico, the coefficients were found to be not statistically
significant, while in the case of New Jersey and South Dakota, they were omitted from error-correction
modeling given that the respective time series were found to be not cointegrated [a linear combination of
the two series was not I(0)].
96
state level, because states operate under various fiscal control mechanisms and tend to be
more susceptible to local politics in the budgetary process.
The second shortcoming concerns the endogeneity of variables such as revenue
and expenditure. Fiscal policy, as hypothesized in this study, is likely to be affected by
cyclical factors in the economy; therefore, controlling for them is critical in explaining
either tax and spending adjustments. Recognizing this concern, some studies include
macroeconomic controls such as GDP, inflation rate, and interest rate (Payne 2003: 314)
to capture the effects of the business cycle. A study by Joulfaian and Mookerjee (1991)
represents one of the rare attempts to demonstrate how sensitive the results of Granger-
causality tests are to the inclusion of macroeconomic controls. Using time series data for
22 industrial countries, they compare the results of bivariate models of revenue and
expenditure with those of multivariate models incorporating output (GDP) gap and
inflation rate. In the baseline model, they find evidence in support of the spend-tax
hypothesis, but in the extended model, discover that decisions on taxation are
independent from the allocation of expenditures. While including macroeconomic
controls reduces the endogeneity problem to some degree, it is still a roundabout way.
The mixed and conflicting results observed in the literature may be a reflection of the
lack of consistency of this indirect method.
The second branch of literature on fiscal behavior and policy deals with the
problem of omitted variable bias by examining how fiscal institutions and rules affect
fiscal policy and whether they work as intended. Poterba (1994) examines how fiscal
rules (no deficit carry-over rules and tax and expenditure limitations) and political
97
circumstances affect the way states respond to deficit shocks. One of the interesting
features that sets his study apart from the previous studies is that it takes macroeconomic
effects into account. Recognizing the overarching impacts of the macroeconomy on
expenditure demands and revenue conditions, he develops a measure of fiscal stress by
calculating a difference between expenditure shock (actual expenditure – forecast
expenditure – spending adjustment) and revenue shock (actual expenditure – forecast
expenditure – tax adjustment). With these explanatory variables of interest, Poterba
analyzes panel data on 27states with an annual budget cycle over the period of the late
1980s economic recession using the fixed effects estimator. From this analysis, the author
finds that states (1) with greater deficit shocks, (2) with more stringent fiscal rules, and (3)
where one party controls both the governorship and the lower house in the legislature
tend to react to fiscal shocks more swiftly by making adjustments to spending and taxes.
Alt and Lowry (1994) investigate how political and institutional factors affect
state spending and taxing levels. They model (1) revenues as a function of party control
(unified Republic/Democratic control of government, split branch, and split legislature)
and fiscal rule (no deficit carry-over balanced budget provision) along with lagged
revenue, aggregate personal income, federal government contribution, and lagged surplus,
and (2) expenditures as a function of revenue, unemployment rate, and lagged surplus.
They test the models using state panel data from 1968 to 1987 and employing three-stage
OLS procedures and fixed effects estimators. From the analysis, Alt and Lowry find that
states where unified party control exists and deficit carry-over is prohibited tend to react
more quickly to deficit shocks in downturn years compared to states with divided
government, especially, split legislature.
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Bohn and Inman (1996) take on the question of whether balanced budget
requirements of varying stringency across states have positive effects in limiting deficits.
In modeling state deficits, they employ a host of balanced budget provisions as the key
explanatory variables of interest. They test the model using panel data from 47 states
(excluding Alaska, Hawaii, and Wyoming) for the sample period of 1970–1991. The
analysis finds that stringent end-of-year balance requirements have significant effects on
general fund balances through spending cuts rather than tax increases.
Endersby and Towle (1997) examine the effects of legal and political controls on
state per capita expenditures and per capita debts. Specifically, their model includes
gubernatorial veto (line-item veto and item reduction veto) and recision (a governor's
power to withhold budgetary appropriations without legislative approval) power,
legislative budget responsibility, debt limitation, deficit carry-over, override (percentage
vote necessary to override a governor's veto), and a host of electoral and political factors.
They analyze data for all fifty states for three years, 1988, 1990, and 1992 using the
pooled cross-sectional OLS and Generalized Least Squares (GLS) estimator. From the
analysis, Endersby and Towle find that political and electoral factors play more
significant roles in limiting spending and minimizing debt, whereas legal restrictions,
except deficit carry-overt provisions that have positive and statistically significant effects
on spending and debt levels, exert limited influences.
Smith and Hou (2008) investigate the effects of budgetary institutions and rules
on state spending behavior using a long-panel analysis (1950 to 2004). They pointing out
that very little of the variance (or lack thereof) attributable to BBRs" have been found due
to "the absence of analyses of panel data over multiple economic cycles and across state
99
budgetary reforms. Unlike the previous studies that have relied mostly on measures
developed by the United States Advisory Commission on Intergovernmental Relations
(ACIR), the National Association of State Budget Officers (NASBO), and the National
Conference of State Legislatures (NCSL), they use a more detailed and comprehensive
set of balanced budget rules which was developed by the same researchers (2006). Along
with the fixed effects estimator, their study employs a Maximum Likelihood Estimation
(MLE) estimator in an attempt to take into account a state-specific intercept composed of
both a fixed and a random component. In the analysis, they find that technical provisions
such as no deficit carry-over that take a financial management approach are more
effective in limiting spending than balanced budget provisions, such as ―Governor must
submit a balanced budget‖ and ―Legislature must pass a balanced budget,‖ that focus on a
political control mechanism.
Hou and Duncombe (2008) examine whether fiscal institutions affect state saving
behavior. In their model, the dependent variable is total saving [general fund balance
(GFB) plus budget stabilization fund (BSF)] as a percent of general fund expenditure, and
the independent variables include two fiscal rules, budget stabilization funds (BSF) and
balanced budget requirements (BBR). In the analysis of a panel of all 50 states covering
three business cycles from 1972 to 2003 using the fixed effect estimator, they find that
more stringent BSF, such as 4–7% BSF balance cap; 7–12%; no limit on BSF balance,
and BBR provisions, such as a limit on debt for deficit reduction and no deficit carry-over,
tend to have positive effects on state savings.
Mullins and Joyce (1996) look into the fiscal impacts of TELs in the context of
state-local relations. They deal with the questions of whether TELs have had effects in
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altering revenue systems away from broad-based taxes such as property, sales, and
income taxes toward narrow-based revenue sources such as user fees and whether TELs
have played a role in shifting responsibilities for expenditures from local to state
governments. They employ four dependent variables, which include (1) changes in the
relative size of the state-local public sector, (2) the shifts in reliance among various
revenue sources, (3) the changes in the state "share" of total state and local revenue, and
(4) changes in the state share of expenditure responsibility. They test their model by
analyzing state panel for the sample period of 1970 to 1990 with the fixed effects
estimator. From the analysis, they discover that TELs do not have substantial impacts on
aggregate state-local spending (measured as the ratio of total state-local general
expenditure to gross state product), but they lead to increased centralization in service
delivery and the shift of locals toward narrow-based revenue sources decreasing local
responsiveness.
Bails and Tieslau (2000) add to the literature by examining the effects of fiscal
institutions on state and local spending. Fiscal institutions examined in their study include
TELs, line item veto power, and BBRs, as a budgetary constraint mechanism; term limits
for state legislatures, bill introduction limits, and the length of the budget cycle as an
administrative constraint mechanism; initiative procedures and state referenda as a direct
democracy mechanism. Their analysis uses a panel data set of 49 states (excluding
Alaska) obtained at five year intervals from 1969 through 1994. The ten fiscal discipline
mechanisms are measured as a binary dummy variable (present or not), and the
dependent variable is real per capita state expenditure. They use a random-effects (RE)
estimator for the reason that five key variables are time invariant in each state over the
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sample period. Their analysis finds that the presence of TELs, citizen initiative
procedures, and term limits is effective in limiting government spending.
Knight and Levinson (1999) examine the effects of RDFs on state saving behavior.
They use three measures of state RDFs, which include the presence of RDF, RDF balance,
and RDF legal provisions obtained from The National Council of State Legislatures
(NCSL) (deposit by formula, deposit of year-end surplus, 5-9% limit, over 9% limit, no
limit, withdrawal by formula, and withdrawal by shortfall), with total balances as the
dependent variable. They analyze state panel data set for the sample period of 1984–
1997—during which 27 states adopted RDFs—employing the cross-sectional OLS and
fixed effects estimator. The results reveal that states with RDFs have higher total
balances than states without RDFs and also have higher balances after adoption than
before adoption.
In three closely related studies, Hou (2005 and 2006) analyzes state panel data for
the years 1979 to 1999 to examine the effects of fiscal reserves (BSF and GFS,
collectively and individually) on state own-source expenditures during downturn years.
He uses gaps between expected and actual expenditures (expressed as a percent of
expected expenditures) as the dependent variable, which is obtained using the residual-
from-trend approach. He also employs the Heckman sample selection model as a method
to extract periods of economic downturn from upturn. Data on fiscal reserves are from
two sources: the Fiscal Survey of the States and the Comprehensive Annual Financial
Report. From the analysis, he finds that fiscal reserves (particularly BSF) have positive
effects on state own-source expenditures during downturn years, and based on the
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findings, concludes that fiscal reserves help ease the negative impacts of recessions on
state spending and maintain fiscal stability over the business cycle.
Hou and Moynihan (2007) explores the effects of BSF and GFS defined as
"countercyclical fiscal capacity" on state reactions to revenue shortfalls using a 1985–
2003 state panel data set. Three dependent variables are used: budget cuts, revenue
actions, and net revenue changes. The results indicate that fiscal reserves as a total are
effective in reducing the levels of all the dependent variables, in other words, in
countering revenue shocks and increasing fiscal stability during recession periods.
While this line of research provides a broad understanding of how and when fiscal
institutions work, as in the tax-spend literature, improvements in model specification are
needed. Although fiscal institutions and rules are certainly a crucial factor in explaining
state fiscal behavior, it is undeniable that the most fundamental factor is how much
revenue is available and how economic and fiscal conditions are. Therefore, failure to
take the cyclical position of state finance into account may lead to omitted variable bias
and ultimately incorrect statistical inferences. A few studies have made conscious efforts
to account for the effects of cyclical factors. Alt and Lowry (1994) include a lagged
surplus in modeling state expenditures. Smith and Hou (2008) take account of revenue-
raising mechanisms by incorporating binary variables for the presence of three major
state tax instruments (general sales, individual income, and corporate income tax).
Perhaps the most impressive attempt is Poterba (1994), which develops a measure of
fiscal shock (revenue and expenditure, separately) and uses it as the key explanatory
variable in explaining state fiscal reactions during downturn years. While the inclusion of
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such variables certainly reduces the problem of omitted variable bias to some degree, as
pointed out in the review of the tax-spend literature, they are still a roundabout way. In
contrast to these previous studies, this study deals with this concern in a more direct and
straightforward way by developing a measure of revenue gap, the cyclical component of
tax revenue.
Finally, the third stream of literature relates to the cyclicality of fiscal policy.
Based on the idea that in order to maintain economic stability and minimize distortionary
costs associated with government interventions, fiscal policy should be countercyclical,
as suggested by Keynesian economics (i.e. decreases in spending and increases in tax
rates during expansions and the opposite policy adjustments during recessions), or neutral
over the business cycle, as proposed by Barro (1979),29
this empirical literature has
attempted to determine and explain the cyclical patterns of fiscal policies. Along the way,
general consensus has been established that fiscal policy tends to be relatively procyclical
in developing countries, compared to industrialized countries such as G7 countries where
it tends to be either countercyclical or acyclical.
As an early attempt at this research program, Gavin and Perotti (1997) compare
the cyclical volatilities—measured as average standard deviations—of fiscal outcomes
such as budget balances, revenues, and expenditures in 13 major Latin American
economies with those in industrialized countries over the period of 1968 to 1995. In this
29
With Keynesian prescriptions, a correlation between government spending and output and a correlation
between tax rates and output are expected to be negative and positive, respectively. With Barro‘s
prescriptions, theoretically, no correlations are expected. Even if a government follows Barro‘s prescription
by holding constant tax rates and discretionary spending as a share of GDP over the cycle, a pattern in
fiscal outcomes is likely to be countercyclical because of automatic stabilizers and progressivity in tax rates
(Alesina, Campante, and Tabellini 2008).
104
analysis, they find that fiscal policy is more procyclical in Latin America than in
industrial economies, arguing that this phenomenon arises because of binding borrowing
constraints.
Using data for 104 countries over the period of 1960–2003, Kaminski, Reinhart,
and Vegh (2004) examine the cyclical properties of fiscal policy in developing countries.
Recognizing the endogeneity of fiscal outcomes such as fiscal balance and tax revenues,
they define the cyclicality of fiscal policy in terms of policy instruments such as tax rates.
The cyclical position of the economy is measured using three approaches: the
nonparametric approach, which involves dividing the sample into good times where
annual real GDP growth is above the median and bad times where growth falls below the
median; the ubiquitous Hodrick-Prescott (HP) filter; and the bandpass filter developed in
Baxter and King (1999), which involve decomposing each time series into its stochastic
trend and cyclical component. By comparing the cyclical behavior of the fiscal policy
indicators in good and bad times (using correlations), they discover that fiscal policy is
generally either countercyclical or acyclical in OECD countries, whereas it is
predominantly procyclical in developing countries.
Talvi and Vegh (2005) point to the dysfunctional political systems that pervade
developing countries. Specifically, using a sample of 56 countries (20 industrial countries
and 36 developing countries) over the period of 1970–1994, they tackle the questions of
how countries conduct fiscal policies over the business cycle and whether tax base
variability has an influence on procyclical fiscal policy. For the first question, the cyclical
components of fiscal variables (government consumption, revenues, and inflation tax
rates) are first isolated from the trend line using the Hodrick-Prescott filter, and the
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cyclical volatility and the procyclicality of the fiscal variables are measured by the
standard deviation and the correlation coefficient, respectively. In the analyses, they find
evidence confirming the stylized fact that fiscal policy in developing countries is more
procyclical, compared to industrial countries. The authors then develop an optimal fiscal
policy model in which running budget surpluses becomes costly as they create pressures
to increase public spending. According to this model, due to political distortions,
developing countries that face large fluctuations in the tax bases find it optimal to run a
procyclical fiscal policy, even though it is suboptimal for the society as a whole. They
assess their model through a cross-country regression, which finds that output volatility is
associated with procyclicality in fiscal policy.
In a similar vein, Alesina, Campante, and Tabellini (2008) provide a political
economy explanation for the procyclicality of fiscal policy, with a special focus on
corruption. In a political agency framework, they argue that when faced with corrupt
governments that can appropriate public money for political rents in the form of favors
paid to special interests, voters, who are poorly informed about fiscal policies, will
rationally demand higher government spending or lower taxes during booms. The authors
test this model using data on 87 countries going from 1960 to 1999. Specifically, they
estimate a cross-country regression in which the dependent variable is the
countercyclicality of fiscal policy, while the independent variables are control of
corruption, real per capita income, the state of democracy, the relative size of government,
and credit constraints. The countercyclicality of fiscal policy is measured by first
regressing the central government‘s overall budget surplus as a percentage of GDP on
output gap, terms of trade, and a lagged budget surplus and then obtaining the coefficient
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on output gap. In the test, they find that there is a strongly significant relationship
between control of corruption and the procyclicality of fiscal policy.
Battaglini and Coate (2008) develop a dynamic political economy model for the
behavior of fiscal policy over the business cycle in which elected representatives attempt
to target public spending to their own home districts (i.e. pork-barrel spending).
Barseghyan, Battaglini, and Coate (2010) assess the quantitative predictions of the model
by conducting a set of numerical experiments calibrated to the U.S. economy using data
from 1979 to 2009. As predicted by their theoretical model, the results show that
government spending increases during booms and decreases during recessions, whereas
tax rates fall during booms and increase during recessions.
The literature on the cyclicality of fiscal policy is of most relevance to this study
in that it most explicitly addresses the fiscal implications of the business cycle. However,
with most studies focusing on the comparison of developing and industrial countries,
very few have paid attention to differences that may exist across subnational government
units. Although, as an industrialized country, the United States has been found to be
countercyclical in conducting fiscal policy, it is reasonable to assume that there may be a
difference in the countercyclicality across states, given the wide cross-state variation in
tax base volatility, as found in Chapter 2. Furthermore, it may be more necessary to look
into the issue at subnational levels, because subnational governments tend to be more
vulnerable to ―capture‖ by special interests compared to the central government. In this
regard, Sturzenegger and Werneck (2006) argue that political distortions are more likely
in ―subnational government, usually suspected of being subject to a higher degree of
cronyism and corruption than the national government.‖
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This study is in contrast to previous research in several important ways. First, it
introduces a new variable, revenue gap (the cyclical component of tax revenue) in
explaining state fiscal behavior. As discussed in Introduction, how much revenue is
available and how fiscal condition is, which will most likely be affected by the business
cycle, may be the most fundamental factors that affect fiscal policy. Therefore, failure to
take the cyclical positions of state economies and finances into account may lead to
omitted variable bias and ultimately incorrect statistical inferences. As reviewed above, a
few studies attempt to take this factor into account. For example, Kaminski, Reinhart, and
Vegh (2004) and Talvi and Vegh (2005) do so by using output gap, which, however, may
produce biased and inconsistent results, because the cyclical component of output could
differ substantially from that of tax revenue, as suggested by the results of Chapter 2. In
light of these empirical shortcomings in previous research, this study takes the cyclical
positions of state finances into account in a more direct and straightforward way by
measuring revenue gaps using the actual tax bases of four major state revenue sources,
general sales tax, individual income tax, corporate income tax, and selective sales tax on
motor fuel.
Second, to take full advantage of the information that revenue gap contains about
the cyclical positions of state economies and finances, this study divides the sample into
two subgroups, upturn and downturn years, and performs separate analyses for them in
addition to an analysis using the entire sample. This attempt stands out in comparison to
some studies (Poterba 1994; Hou 2005; 2006; and Hou and Moynihan 2007) that focus
only on downturn years. As will be discussed in more details in the next section, this
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study sees that the root cause of recent state fiscal crises lies in unsustainable spending
growth during upturn periods (Knight, Kusko and Rubin 2003); therefore, it may be more
useful and relevant in understanding the dynamics of fiscal problems to look into states‘
fiscal behavior during both boom and bust phases of the business cycle rather than
recessions only. Furthermore, dividing the sample using revenue gap has a
methodological merit as well. Hou (2005; 2006) separates downturn years from upturn
years using the Heckman sample selection model (Heckman 1979) based on the
dependent variable, expenditure gap, which, as a type of endogenous sample selection,
may suffers from sample bias. By contrast, this study uses sample selection based on the
independent variable, which, as a type of endogenous sample selection, does not cause
bias or inconsistency in OLS (Wooldridge 2002).
Third, this study employs tax rates alongside expenditures as the dependent
variable in explaining the level of spending. While tax policy, as a half part of fiscal
policy, requires a close examination, there have been relatively few attempts at the
taxation side due to difficulty in data collection. Regarding the use of fiscal balances as
an alternative, Kaminski, Reinhart, and Vegh (2004) correctly point out that fiscal policy
should be defined ―in terms of policy instruments as opposed to outcomes,‖ suggesting
that tax policy should be defined in terms of tax rates as opposed to fiscal balances which
are simply differences between revenues and expenditures. However, they use inflation
tax rates as a proxy for the reason that there is no systematic data on tax rates.30
By
contrast, this study collects annual tax rate data for three major state taxes, sales,
individual income, and corporate income tax, and obtains overall tax rate for each state.
30
See also Talvi and Vegh (2005) as an example.
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3.3 Conceptual Framework
As discussed in Introduction, this study views procyclical fiscal policy during
recessionary periods (i.e. spending cuts and tax increases) as the consequence of
unsustainable spending increases and tax cuts during the preceding expansionary periods.
Thus, the following conceptual discussion focuses primarily on the spending propensities
of government institutions during booms.31
Specifically, this section discusses the
fundamental nature of public budgetary resources that brings about demands and
pressures for spending, the mechanisms through which revenue availability induces
spending, and other relevant factors that may potentially affect the level of government
spending.
3.3.1 The Rationale for the Revenue-Spending Hypothesis
Most fundamentally, the effect of revenue gap on fiscal policy arises from the
―commons‖ nature of government budgetary resources. Public production is justified
over private production for certain goods and services on the basis of market failures. In
particular, nonexcludability, a situation where buyers cannot prevent nonbuyers from
using what they pay for, inhibits the exchange of resources through a free market, and
this phenomenon is particularly evident in services such as national defense and
environmental protection. In practice, public production involves collecting taxes from
individual entities, lumping them together, and allocating them in the form of budgets. In
31
tax cuts are viewed as another form of spending in this study
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doing so, the logical linkage between who pays and who receives is blurred, and
government revenues essentially become a common pool of resources.
In the absence of a market-exchange mechanism, the process of allocating
financial resources essentially becomes value-based and thus political. Describing ―a
budget as a representation of monetary terms of government activity,‖ Wildavsky (1988),
―If politics is regarded in part as conflict over whose preferences will prevail in the
determination of public policy, then a budget records the outcome of this struggle.‖ What
is important here is that such a political struggle takes place over limited resources. The
implication is that one‘s gain leads to someone else‘s loss, therefore the policy and
budgetary process becomes contentious among competing demands. As a result, people
as potential users of the budgetary commons and their political agents have a tendency to
obtain as larger portions of the common pool as possible by overstating the needs for and
the importance of policy programs that would benefit themselves, while, at the same time,
trying to evade contributions to public funds. In other words, the ―up for grabs‖ nature of
government budgetary resources motivates potential users to seek the maximum
extraction of resources out of the common pool for themselves as an individual rather
than the optimal utilization of limited resources for the society as a whole.
Brubaker (1997) describes this phenomenon as ―the tragedy of the public
budgetary commons,‖ noting, ―Exploiters act they do because each one knows that if he
does not exploit the resource, someone else will.‖ As described in Garrett Hardin‘s
influential article, The Tragedy of the Commons, the commons refers to resources that are
communally owned for use by a community as a whole. The basic idea of the tragedy of
the commons is that if the commons is open to all individuals of a community—who
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presumably only pursue their own self-interests without consideration of the long-term
interests of their community, acting independently—without any proper intervention and
regulation by either themselves or the third party, then the commons will be ruined and
depleted in the end. In a parable about the grazing of animals on a common pasture,
Hardin notes, ―Therein is the tragedy. Each man is locked into a system that compels him
to increase his herd without limit in a world that is limited. Ruin is the destination toward
which all men rush, each pursuing his own interest in a society that believes in the
freedom of the commons (Hardin 1968).‖
Given the commons nature of government resources, demands and pressures for
spending are likely to increase particularly in times of plenty when budget surpluses are
generated, ultimately causing government spending to grow more than proportionally
relative to an increase in income. In this regard, Posner and Gordon (2001) explicitly
address the politics of budget surpluses. They argue that the idea of running a budget
surplus is less likely to gain popular support in times of financial abundance, because
benefits achieved by maintaining surpluses are generally perceived as vague and remote
as they are diffused across the society as a whole and over time. On the other hand,
benefits achieved by increasing spending are tangible and immediate in nature, as they
are concentrated on specific population groups. With such perceptions prevailing, they
argue, large budget surpluses make it increasingly difficult for politicians to turn away
from the policy demands of their constituencies that have been restrained over periods of
recession.
Political economy models provide more systematic explanations for such political
distortions. In addition to Talvi and Végh (2005), Alesina, Campante and Tabellini
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(2008), and Battaglini and Coate (2008) reviewed above, Lane and Tornell (1998) and
Tornell and Lane (1998; 1999) put forth a dynamic common pool model in which
competition for a common pool of funds among multiple interest groups interacts with
revenue windfalls to lead government spending to increase more than proportionally
relative to an increase in income. This political distortion in conducting fiscal policy has
been famously dubbed the ―voracity effect.‖ In a similar vein, Ilzetzki (2010) suggests
that a political friction, where alternating governments disagree on the desired
distribution of public expenditures, leads to suboptimal fiscal policy.
3.3.2 The Mechanisms of the Revenue-Spending Relationship
While the idea of the public budgetary commons is intuitively persuasive, it
provides little information on specific institutional mechanisms through which revenue
availability induces spending. Public choice theory remedies this deficiency by offering
rational political explanations for the fiscal and budgetary behavior of policymakers and
program administrators operating under institutional constraints and the influence of
special interest groups. The basic assumption underlying the theory is that actors in
public decision-making settings are motivated by their own self-interests and their
behaviors are driven by the goal of utility maximization, just as people in free markets are
so. As such, voters and interest groups seek to gain policy favors; politicians to be elected
or reelected to office; and bureaucrats to advance their own careers. Under this
assumption, the theory has sought to explain the inefficiency of public decision-making
and the expansion of the public sector, and the efforts have generated various models
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including budget-maximizing bureaucracy, vote-maximizing politicians, and interest
group influence. Public choice models suggest that in a political system of representative
democracy, government institutions, the executive and legislative branch, generally have
strong spending propensities and often interact to lead to the oversupply of public goods
beyond the levels that are socially desirable—economically optimal and financially sound.
First, on the legislative side, spending propensities arise as politicians converge
on median voter preferences. The ultimate goal of politicians is to maximize votes to stay
in office, and to this end, they need to represent the interests of voters by supplying
public goods and services in line with their preferences. According to the median voter
theorem, vote maximization can best be met by focusing on median voters‘ preferences.
Public choice theory suggests that expenditures are likely to increase beyond optimal
levels as politicians attempt to accommodate median voter preferences and please self-
interested majorities on different issues.
This type of bias towards inefficiency is particularly likely in social welfare
policy areas involving minority groups. Many important social issues and problems are
concerned with minority groups who generally lack the ability to mobilize political
support from the society at large and gain majority voting support in the legislative
process governed by majority rule. In the public choice view, the significance of
minority-related issues and problems makes politicians to act on them, and in negotiation
and bargaining processes, political compromises are made to transform them into ones
that can obtain majority support. This eventually leads to original plans expanding into
large-scale policy programs for all. Dunleavy and O‘Leary (1987: 109) present a useful
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example as to how politicians‘ attempt to accommodate median voter preferences pushes
up government budgets artificially. The authors note:
For example, suppose it becomes clear that the very poorest groups in society
cannot acquire decent accommodation in the housing market without some
element of state subsidy. Although this economic need can be met by a small and
carefully targeted program of transfer payments, in political terms this solution is
likely to be a non-starter, because it attracts support only from the small minority
who would benefit but imposes costs on everyone else in the society. To increase
support for the issue, politicians must try to construct a vote-winning package of
generalized housing subsidies, perhaps linking housing subsidies for the poor
with tax exemptions for younger middle-class homeowners in a structure which
is ‗fair‘ (i.e. gives some benefits) to enough groups to ensure success. Effectively
this electoral logic means that many of the most efficient and carefully targeted
social policies get rules out of consideration, pushing the government into a
position where it can remedy many genuine social evils only by adding
economically and socially unnecessary private goods provisions into its programs
in order to secure median voter support.
Inefficiency due to politicians‘ tendency towards median voter preferences is
closely related to what is called universalism in distributive politics. Not all winning
coalitions are dominant in negotiation and bargaining processes. Many barely win
majority support, and high-profile and controversial issues often face stiff oppositions
and long debates even after going through legislative approvals. According to the theory,
rational politicians tend to prefer to avoid ―hard-ball‖ coalition politics, and their
preferences often produces ―universalistic‖ coalitions, transforming original policy
schemes designed for small and targeted areas or population groups into large-scale
social policies that will serve the interests of all members of the legislature and society.
This strategy is assumed to be a rational choice for politicians in terms of vote
maximization, because it prevents vote losses that would be caused by excluding
particular groups of constituents in distributing policy benefits.
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Policy activism is another source of bias towards inefficiency in public service
provision. In modern politics, it is essential for politicians to build an image as a policy
activist who is attentive and responsive to public concerns. To earn such a political image,
they need to constantly ―get things started‖ by bringing social problems and issues to the
media and public attention and developing them into major policy agendas through
awareness-raising activities. In doing so, politicians tend to prefer initiating new policy
programs and projects rather than improving existing ones. Generally, it is very hard to
evaluate the policy outcome of a certain government program, hence questioning the
effectiveness of a policy program is controversial at best. Rather it is much more
effective for politicians in building a policy-activist image to find out new issues, set new
policy agendas, and initiate new programs and projects rather than follow through on
existing programs.
In the public choice view, politicians‘ spending propensities also arise from
electoral interactions between politicians and interest groups. Politicians wish to bring
benefits to their constituents and home districts in return for political support in elections,
while interest groups continuously strive for policy gains and government favors. The
convergence of the two political groups‘ interests is particularly apparent in pork barrel
projects and distributive policies. Strong incentives for pork barrel projects are created
despite their inherent inefficiency, because by nature the benefits are concentrated in
specific local areas or groups of people, whereas the costs are spread among all tax
payers.
Logrolling (or vote-trading) has long been cited as a legislative strategy
commonly used by politicians and interest groups to win majority support for such
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localized projects. Logrolling involves two or more policy groups with different interests
working together in the legislative process to achieve both groups‘ policy objectives by
trading votes with each other (i.e. voting for each other‘s policy options). In legislative
processes, issues are considered under majority rule not individually but in combination.
It is therefore critical for legislators to come up with packages of policy measures and
form winning coalitions. Public choice theorists argue that majority coalitions usually
dominate policymaking processes using their voting power and push through inefficient
pork barrel projects, passing the production costs on to the general public.
As described, legislators‘ spending propensities basically stem from their desires
to represent the interests of their constituencies. But the role as a representative is not the
only one for them to perform; they are also expected to keep their governments
financially sound by cutting budgets proposed by executives. Although both roles are
equally important, they are likely to be inclined towards the narrowly defined role as a
representative of their home districts in real-world politics. While legislators are well
aware of their role as a budget cutter for the interest of the general public, it is politically
rational for them to prevent budget cuts from affecting their constituencies who hold
control over their political futures.
Of course, not all legislative members put the role of a spender first over that of a
budget cutter. Jacobsen (2006) explains legislators‘ different spending preferences
according to positions and ranks within parties and committees. Political parties, as in
most other formal organizations, operate in the context of formal structure. Individual
politicians‘ roles and responsibilities are defined along the hierarchical dimension, and
their behavioral patterns are shaped depending on their positions and ranks. According to
117
the author, legislators at leadership positions such as party leaders and executives and
committee (or subcommittee) chairs or on budget or finance committees, such as the
Appropriations Committee and the Finance Committee at the federal level, have
responsibility for balancing different demands within budget constraints. Furthermore,
they generally have broader constituencies and, consequently, broader perspectives on
government policies and finances acting more responsibly for the long-term interests of
the general public. Meanwhile, members on substantive standing committees have
limited roles and responsibilities in specific areas. As a result, they tend to ―fight for
single causes and act in a way that is more parochial,‖ paying less attention to the overall
fiscal health of the government.
Although certain legislative members at leadership positions may be more
conservative in spending decisions and attempt to tone down expenditure demands from
constituency-oriented politicians, the weak leadership of American political parties (Lee
and Joyce 2008) is likely to make it difficult for them to control their party members.
Recognizing legislators‘ inclination towards a parochial protector of their constituents‘
interests and weak party leadership, Lee and Joyce (2008) note that ―While in years past
legislatures sometimes had a reputation for being budget cutters, more recently their role
has been to represent constituents who would be harmed if proposed budget cuts were
implemented.‖ Given such a dysfunctional political system in representative democracy,
it is predictable that a legislature‘s ability and will to keep diverse sources of spending
propensities in check will weaken particularly in times of plenty.
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Second, spending propensities come from the executive side as well. In
Niskanen‘s budget-maximizing model, rational bureaucrats are expected to seek to
maximize the budgets of their bureaus or programs and projects over which they have
responsibilities in an attempt to advance their own careers and improve compensations
and benefits ranging from additional staff, capital, and office perquisites to public
reputation, power, and patronage. In this view, although legislators have the formal
authority to monitor their administrative agents, it is likely that the monitoring power is
not exercised properly due to information asymmetry inherent in the relationship between
bureaus and their political overseers, as suggested by the principal-agent model. That is,
bureaucrats have an advantage in bargaining processes with their political supervisors
because the latter lacks information on the actual costs of producing services. As a result,
they have a superior bargaining power in budget decision making over their political
supervisors.
Niskanen (1991) later modifies his original theory, pointing to particular types of
budgets as the object of budget maximization. He argues that in the bilateral monopoly
relation between bureaus and their political sponsors (i.e. where bureaus are a monopoly
supplier of public services, while legislators are a monopoly buyer of those services on
behalf of citizens as the ultimate consumer of the services), bureaucrats try to maximize
their bureaus‘ discretionary budgets rather than budgets as a whole. The author goes on
to argue that budgets of this particular type are shared by both bureaucrats and their
political superiors to serve their own interests.
Another modification is that political officials are not necessarily passive in the
review of bureaus‘ budget proposals. They do make efforts to obtain more information on
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administrative activities and production costs and also employ bargaining techniques. But
he argues that incentives for legislators to make conscientious efforts for bureau
monitoring generally do not exceed incentives to serve ―their political interests of
committee members, subject to the approval of the committee proposal by the whole
legislature,‖ because the benefits achieved by careful monitoring and detailed reviews are
a public good that is shared by the legislature as a whole and citizens at a large. Based on
these ideas, Niskanen concludes that public production becomes inefficient, as bureaus
gain too large budgets and produce too small outputs in terms of the demands expressed
by the political sponsors.
3.3.3 Other Relevant Factors
Revenue gap certainly is not the only factor that affects state fiscal behavior in
terms of expenditures and tax rates. Upon review of relevant literature, seven groups of
factors appear of particular relevance.
First, the so-called ―flypaper effect‖ may be an important factor in explaining
state fiscal behavior. Theories of intergovernmental grants advanced by Bradford and
Oates (1971a; 1971b) and expanded on by Gramlich and Galpher (1973) and Gramlich
(1977) argue that grants to lower-level governments would result in increases in recipient
government expenditures equivalent to increases in income, since the recipients have the
same propensity to spend out of their own-source revenues or grants (Aragon 2009: 1).
Inconsistent with these theoretical predictions, however, empirical studies have found
evidence that grants lead to greater increases in government expenditures than increases
120
in income. This phenomenon has been widely called the flypaper effect that ―money
sticks where it lands.‖ The notion suggests that grant-receiving governments use the
additional revenues to expand public programs rather than substitute them for their own-
source revenues through tax cuts. Volden (1999) argues that this stimulative effect on
spending is particularly true for matching grants, because programs under this funding
scheme are perceived less expensive by the receiving governments as portions of the
costs of the programs are paid by the granting governments. Martell and Smith (2004)
suggest that the flypaper effect tends to last even after grants are no longer available.
Empirical results indicate that the relationship between federal grants and state
expenditures is asymmetric; states tend to decrease expenditures when a grant is
withdrawn less than they had increased expenditures when a grant was awarded. That is,
when a decrease in grants occurs, that money is ―replaced‖ by other revenue—what is
called ―fiscal replacement‖ occurs.
Second, borrowing may affect expenditures and tax rates. Obviously, debt
financing provides states that are required to balance their budgets with financial aids
particularly during recessions when the legal requirements become challenging to meet.
Observing that states and localities substantially increased both long- and short-term
debts in 2002 and early 2003, Knight, Kusko, and Rubin (2003: 435–437) note that debt
measures are used to finance capital projects to promote economic recovery in the middle
and long term and reduce budget shortfalls in the short term, scaling down states‘
spending cuts and tax increases and increasing general fund balances to which balanced
budget requirements apply mainly. One of the short-term measures used to close budget
gaps is to generate one-time revenues, one of which is revenues obtained from the so-
121
called ―tobacco settlement.‖ According to a report issued by the U.S. Government
Accountability Office (GAO), over the period from 2001 to 2003, fourteen states have
sold bonds backed by payments from tobacco companies (in other words, securitized
future tobacco revenue streams), and the tobacco revenues generated during this period
totaled more than 10 billion dollars. Further, Behn and Keating (2004) point out that the
financial practice of securitization involves not only tobacco funds but also future
revenue streams in general such as revenues from an increase in sales tax. In light of the
above discussion, it is hypothesized that expenditures will be higher, while tax rates will
be lower in states with larger debts.
Third, fiscal institutions and rules are expected to affect state fiscal behavior. In
their seminal work The Calculus of Consent, Buchanan and Tullock (1962) argue that
institutions and rules defining the way collective choices are made have important effects
on policy outcomes. This view has been widely applied to state budgetary processes to
create various fiscal control mechanisms, among which, this study focuses on balanced
budget requirements (BBRs), tax and expenditure limitations (TELs), gubernatorial line
item veto, and budget stabilization funds (or rainy-day funds). As discussed in the
literature review, empirical studies suggest that these fiscal institutions, when properly
implemented, have positive effects in restraining government spending and minimizing
deficits and debts across business cycles.
Another commonly cited fiscal institution is biennial budgeting. The literature
suggests contrasting views on its effect on fiscal process and outcomes. One view argues
that biennial budget cycle leads to more deliberative and meaningful budgeting by
bringing a longer-term perspective to the budgetary process and by allowing more time
122
for legislative oversight (Fisher 1997). The other view holds that a two-year cycle could
contribute to spending growth by weakening legislative control of spending in the
appropriation process (Greenstein and Horney 2000). Supporting this view, Kearns (1994)
finds empirical evidence that biennial budgeting has a positive effect on state spending.
The fourth factor to be considered is partisan control. The most common way of
modeling party ideologies is the left-right political spectrum. In the United States, the
Republican Party, conventionally considered a right wing and characterized as fiscal
conservatism, is expected to be more hostile towards the expansion of the public sector
than the Democratic Party positioned as a left wing and characterized as social liberalism.
In light of these prevailing notions about American political parties and their policy
stances, it is hypothesized that gubernatorial and legislative control by the Republican
Party will exert a negative influence on government spending.
Fifth, divided government may also affect state fiscal behavior. Divided
government refers to a situation in which one party control the executive branch and
another party controls one or both chambers of the legislature. While the variable is
assumed to affect fiscal behavior and policy, whether the effects are positive or negative
is not clear. Conventional wisdom holds that divided government tends to restrain
spending by creating political gridlock between the executive and the legislative branch
which serves as a check on the abuse of power and limits spending. In keeping with the
conventional wisdom, Niskanen (2003) makes the case for divided government as a
political mechanism for fiscal restraint. Presenting as evidence an annual percentage
increase in real federal spending by administration (from Eisenhower to Clinton), he
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finds that the rate of growth of real (inflation-adjusted) federal spending is usually lower
with divided government.
By contrast, Gould (2009) finds evidence that runs counter to the gridlock
hypothesis. In an empirical study of state expenditures, he finds that divided government
has a positive effect on per-capita expenditures. Emphasizing that not only the presence
of divided government but also its degree is an important consideration, he explains that
when the gridlock caused by political division is not hostile but friendly, the government
loses the ability to make "tough choices" between partisan spending priorities and ends
up accommodating the expenditure demands of both parties. His reasoning is, indeed,
consistent with policy universalism described above based on the public choice
explanation of legislative behavior. In light of the contrasting views, this study leaves the
prediction of the effect of divided government on spending open for empirical analysis.
Sixth, electoral cycles might affect state fiscal decisions. Theoretical reasoning
and empirical evidence (Nordhaus 1975; Tufte 1978) suggest that political business
cycles exist, in other words, incumbent politicians are likely to increase expenditures in
periods of election races in an attempt to attract votes. Although there have been many
studies that find evidence against the political cycle hypothesis since Richards (1986)32
and Keech (1989),33
there is no reason not to assume the general existence of political
business cycles, in light of the political benefits that such strategic political behavior will
bring to incumbents seeking reelection at least in the short run.
32
Richards (1986) suggests that political business cycles may have existed for some time, but they have
gradually disappeared since the mid 1970s. 33
Keech and Pak (1989) find no strong evidence that electoral cycles contribute to the growth of
government spending, and reason that politicians have given up electoral cycles because they have found
them not as helpful for their electoral prospects.
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Lastly, demographic and socioeconomic characteristics such as population age
distribution and income distribution might potentially influence policy and budget
outcomes. A public budget is a reflection of institutional judgments of the community‘s
values, preferences, and priorities (Wildavsky 1988). Hence, fiscal policy decisions may
depend on what characteristics the population has. Based on prevailing notions that age
and economic minorities tend to lean towards liberal fiscal policy, expenditures and tax
rates are hypothesized to be higher in states with more underage youths, seniors in
retirement, and people in poverty.
Put together, state fiscal behavior in terms of the level of spending and taxation is
modeled as follows:
State expenditures / tax rates = f (revenue availability, federal grants, borrowing,
fiscal institutions/rules, partisan control, divided government, electoral cycles,
demographic-socioeconomic characteristics)
3.4 Data and Methods
For the empirical analysis of the proposed research questions, this Section
develops econometric models based on the above conceptual discussion; presents
estimation methods; and discusses results.
3.4.1 Variables and Data Sources
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Two dependent variables are employed as an indicator of state fiscal policy: total
own-source expenditure and overall tax rate. First, own-source expenditure (OSE) is
obtained by deducting revenue from the federal government from state general
expenditure. Following previous studies (White 1983; Dye and McGuire 1991; Braun and
Otsuka 1998; Hou 2005; Hou 2006), OSE is measured as expenditure gap, that is, the
deviation of OSE from the trend line. The way the measure is calculated is essentially the
same as revenue volatility34
in Chapter 2. It involves separating the trend and cyclical
component of OSE by detrending the original time series. Specifically, expenditure gap is
obtained by (1) regressing OSE on income,35
(2) obtaining the orthogonal deviation of
OSE from the trend line—unlike the conventional method that uses the residual (actual
expenditure - trend expenditure), and (3) dividing the deviation by the sample mean,
which is expressed as a percentage. Using expenditure gap has two merits. One is that it
better captures the effects of legislated adjustments to spending by detrending time series
on expenditures, and the other is that it allows for the more intuitive and meaningful
interpretation of regression results as the measure has a percentage point interpretation
along with the independent variable, revenue gap, as will be specified next. Data on own-
source expenditure and personal income were obtained from the State Government
Finances series and the BEA website, respectively. The second dependent variable,
overall tax rate, is measured by the weighted sum of the tax rates of three major state
revenue sources, general sales tax, individual income tax, and corporate income tax. In
34
The only difference is that the latter uses absolute values. 35
Wagner‘s Law suggests that economic development leads to an increase in government spending in the
long run as it increases government activities (i.e. increases in social and welfare functions and long-term
investment projects). Under the assumption that the level of government spending is associated with the
degree of industrialization and economic development—commonly approximated by income, own-source
expenditure is detrended using a levels regression of expenditure on income.
126
the case of income taxes with progressive tax rate structure, top-bracket tax rates are used.
Annual tax rates for each tax were collected from the State Tax Reporter series.
The independent variable of primary interest is revenue availability over the
business cycle, which is measured as revenue gap, the cyclical component of tax revenue.
State tax systems consist of various revenue sources, among which, this study focuses on
four major ones: general sales, individual income, corporate income, and motor fuel tax.
Revenue from these revenue sources, on average, accounts for about 80 percent of total
state revenue over the sample period. Specifically, therefore, revenue gap is measured by
the weighted sum of the cyclical components of the four major tax revenues. The cyclical
component of each tax revenue is measured by the deviation of a tax base from the trend
line as a percent of the sample mean, in the same way as revenue volatility in Chapter
2—using tax bases, which are unaffected by tax rate changes, instead of actual tax
revenues.
As noted in Chapter 2, tax base is obtained by dividing tax revenue by tax rate.
General sales and individual income tax bases obtained in Chapter 2 are used again for
this analysis. For corporate income tax bases, top bracket tax rates are used, and for
motor fuel tax bases, annual motor fuel consumption is used as a proxy. Data on tax
revenues by type were obtained from the State Government Finances series, while data
on tax rates were collected from the State Tax Reporter series. Data on annual motor fuel
consumption were drawn from the Highway Statistics series published by the Federal
Highway Administration (FHA). Given that the budgetary process requires political
consensus among policymakers or support from members in the society, it may take
fiscal years for budget surpluses or deficits to lead to specific fiscal actions such as
127
spending increases or cuts. It is also plausible that the cyclical component of tax revenue
may have effects on spending in a quadratic manner. Thus, three lags and a quadratic of
the variable are added.
To capture the flypaper effect, per capita federal grant is included. As with
revenue gap, it may take time for additional resources from the federal government to
lead to increases in expenditures; therefore, a lag of per capita federal grant is also
included. Data for the variable were from the State Government Finance series. For
borrowing, total debt as a share of GSP is included with a lag, and data for the variable
were also from the State Government Finance series.
For fiscal institutions, six variables are employed: no deficit carry-over; revenue
limitation; expenditure limitation; line item veto; budget stabilization fund; and biennial
budget cycle. Each of them is measured as a dummy variable: 1 if in place, and 0
otherwise. Balanced budget requirement systems comprise a group of legal provisions,
among which, this study focuses on a no deficit carry-over provision that has been found
to be most relevant in restraining the rate of growth in state spending.36
Data on fiscal
institutions were collected from the Budget Processes in the States series published by
the National Association of State Budget Officers (NASBO).
To capture the effect of partisan control, four dummy variables are used:
Republican majority in the Senate (with Democratic majority in the Senate as the base
36
Hou and Smith (2006) define BBRs as ―a system of interrelated rules of a political and/or technical
nature governing the executive preparation, legislative review, and implementation phases of the budget
cycle,‖ and put forth an analytical framework that categorizes BBR systems as ―from a procedural rule
through two technical rules.‖ Based on this framework, Smith and Hou (2008) examine the effects of BBR
systems on state spending behavior. In the analysis, they conclude that ―balanced budget requirements
governing the political process of public budgeting are ineffective in restraining state spending. In the
interest of forcing a state government to restrain expenditures, legislators must look to technical provisions
that govern the financial management systems under which the budget is constructed and executed (Smith
and Hou 2008: 23).‖
128
group), Republican majority in the House (with Democratic majority in the House as the
base group), Republican Governor, and Independent Governor (with Democratic
Governor as the base group). Divided government is also measured as a dummy variable,
with 1 indicating yes and 0 otherwise. To control for the effects of electoral cycles,
gubernatorial election years are included as a dummy variable along with a lag and a lead:
1 indicates yes, and 0 indicates otherwise. Data for partisan control, divided government,
and election cycles were obtained from the Book of the States series published by the
Council of State Governments (CSG).
Lastly, the effects of demographic and socioeconomic characteristics are captured
by age structure and income distribution, specifically, the proportion of the population
aged over 65 and the population under 17, and the proportion of the population below the
federal poverty level and the population with federal adjusted gross income (FAGI) over
$200,000. In keeping with the norms of econometric analysis in the field of public
budgeting and finance, total population and per capita personal income are also included.
All population-related data were drawn from the Statistical Abstract of the United States
series published by the U.S. Census Bureau, except data on the proportion of the
population with FAGI over $200,000, which were obtained by modifying data on federal
income tax returns from the Internal Revenue Service (IRS) tax statistics. Finally, data
for personal income per capita were from the website of the BEA.
The data covers 49 states (excluding Alaska)37
and 16 fiscal years from 1992 to
2007. As a note on data format, all monetary figures are converted into 2007 chained
dollars. The proportion of the wealthy population is also adjusted for inflation, since the
37
Alaska was excluded because of its unique economic and fiscal structure (due to its heavy reliance on the
oil industry) that may adversely affect mean-based statistical analysis.
129
original IRS data do not take inflation into account in categorizing tax returns by income
level. Variable definitions and data sources are summarized in Table 3.1, and descriptive
statistics are presented in Table 3.2.
Table 3.1 Variable Descriptions and Data Sources
Variables Descriptions Data sources
ose Own-source expenditure: Orthogonal
deviations from the trend line as % of the
sample mean
State Government Finance series
taxrat Overall tax rate: Weighted sum of tax
rates for general sales, individual income,
and corporate income tax
State Government Finance series
revgap Cyclical component of tax revenue:
Weighted sum of the orthogonal
deviations of tax bases (general sales,
individual income, corporate income, and
motor fuel sales tax) from the trend lines
as % of the sample mean
State Government Finance series
State Tax Reporter series
NBER TAXSIM website
BEA website
Federal Highway Administration website
grant Per capita revenue from the federal
government
State Government Finance series
debt Total debt as % of GSP State Government Finance series
nodef No deficit carry-over provision: 1 if in
place; 0 otherwise
Budget Processes in the States series
revlim Revenue limitation: 1 if in place; 0
otherwise
Budget Processes in the States series
explim Expenditure limitation: 1 if in place; 0
otherwise
Budget Processes in the States series
liv Line item veto: 1 if in place; 0 otherwise Budget Processes in the States series
bsf Budget stabilization fund: 1 if in place; 0
otherwise
Budget Processes in the States series
bien Biennial budget cycle: 1 if in place; 0
otherwise
Budget Processes in the States series
repsen Republican majority in Senate: 1 if yes; 0
otherwise
The Book of the States series
rephou Republican majority in House: 1 if yes; 0
otherwise
The Book of the States series
repgovn Republican governor: 1 if yes; 0
otherwise
The Book of the States series
indgovn Independent governor: 1 if yes; 0
otherwise
The Book of the States series
divgov Divided government: 1 if yes; 0 otherwise The Book of the States series
eltyr Gubernatorial election year: 1 if yes; 0
otherwise
The Book of the States series
pop Population Statistical Abstract of the United States
130
series
under17 % of population aged 17 or under Statistical Abstract of the United States
series
over65 % of population aged 65 or over Statistical Abstract of the United States
series
pcpi Per capita personal income (in thousands) BEA website
weal % of population with FAGI over
$200,000
IRS tax statistics
poor % of population below federal poverty
level
Statistical Abstract of the United States
series
Table 3.2 Summary Statistics
Variables Mean Std. Dev. Min Max
ose -0.0069 5.287894 -20.008 19.75871
taxrat 6.176218 1.302307 3 10.36349
revgap 0.320603 4.159046 -12.9676 23.04729
grant 1240.116 412.3397 571.3274 4060.431
debt 6.88963 3.876823 0.866069 22.77977
nodef 0.718112 0.450206 0 1
revlim 0.105867 0.307864 0 1
explim 0.446429 0.497439 0 1
liv 0.839586 0.367227 0 1
bsf 0.9225 0.26755 0 1
bien 0.420918 0.494022 0 1
repsen 0.487245 0.500156 0 1
rephou 0.44898 0.497708 0 1
repgovn 0.542092 0.498543 0 1
indgovn 0.019133 0.137079 0 1
divgov 0.563776 0.496233 0 1
eltyr 0.265306 0.441778 0 1
pop 5684498 6138458 466251 3.64E+07
under17 25.39138 1.986387 21.02874 36.07281
over65 12.64841 1.913482 4.3 18.6
pcpi 33458.15 5531.674 21651.25 56510
weal 1.906012 0.805704 0.777973 5.421682
poor 12.74222 3.513371 5.3 26.4
3.4.2 Models and Estimation Methods
Specifically, this study estimates the following regression models for state fiscal
policy:
131
(1) / = + + + +
+ +
+ +
+ + + + +
+ + + + + +
+ + + + +
+ + + γ + + ,
where the and subscript refer to panel (state) and time period (year), respectively;
and denote total own-source expenditure and overall tax rate; is the
cyclical component of tax revenue, measured by the weighted sum of the deviations of
major tax bases from the trend line as a percent of the sample mean; is per capita
revenue from the federal government; is total debt as a share of GSP; ,
, , , , and denote no deficit carry-over provision, revenue
limitation, expenditure limitation, line item veto, budget stabilization fund, and biennial
budgeting, respectively; , , , and refer to Republican
majority in the Senate, Republican majority in the House, Republican Governor, and
Independent Governor, respectively; represents divided government;
denotes election years; V and Z are a matrix of socioeconomic characteristics and year
dummies,38
respectively; is the error term.
A common concern with panel data models is that there may be unobserved
effects correlating with explanatory variables. It can never be assumed for sure that there
exists no association between certain unobserved factors (e.g. cultural factors) and state
38
A full set of year dummies—all years but the first—are included to control for state trends in own-source
expenditure and overall tax rate.
132
spending. To remove time-constant unobserved factors, this study employs a fixed effects
model.39
Modifying Equation (1), the fixed effects models are as follows:
(2) / = + + + +
+ +
+ +
+ + + + +
+ + + + + +
+ + + + +
+ + + γ + + + ,
where is the state fixed effect and is the idiosyncratic error (or time-varying error);
other terms are defined the same as in Equation (1); time-constant effects are cancelled
out in the fixed effects model. For the fixed effects estimation, the study assumes that the
time-varying explanatory variables and the individual fixed effect are not correlated with
the error term in any time periods for any states in the data. This assumption is sufficient
for the consistency of the fixed effects estimators where N is relatively large while T is
small (Wooldridge 2002).
Regressions using the entire sample provide an overall but not detailed picture of
fiscal behavior over the course of the business cycle. Another useful way of gaining a
39
Two methods are available for estimating unobserved effects panel data models: the fixed effects and the
random effect model. To determine which one is appropriate for the given data, the Hausman test was
conducted. Test results indicate that it cannot be assumed that the unobserved factors are uncorrelated with
the explanatory variables, suggesting that fixed effects is appropriate in this case:
Test: Ho: difference in coefficients not systematic
chi2(38) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 191.93
Prob>chi2 = 0.0000
133
sense of it is to look at it by cyclical position. For this purpose, this study divides the
study sample into two subgroups, upturn and downturn years, based on revenue gap
(upturn if revenue gap is above zero, and downturn if below zero), and run separate
regressions for each subgroup.
As in Chapter 2, diagnostic tests were performed for the presence of
heteroskedasticity and serial correlation in the errors using modified Wald test (for
heteroskedasticity) and Wooldridge test (for serial correlation). The tests find that the
errors here are heteroskedastic and serially correlated. In correcting for the problems, this
study uses clustered robust standard errors which are reportedly robust to
heteroskedasticity and serial correlation.
3.5 Results and Discussion
Before discussing regression results, it is useful to see how widely the dependent
variables, own-source expenditure and overall tax rate, vary across states. Summary
statistics in Table 2 indicate that the mean and standard deviation of OSE are -0.0069 and
5.287894, respectively, with the minimum and maximum being -20.008 and 19.75871.
This suggests that with positive OSE gaps during upturn years almost fully offset by
negative OSE gaps during downturn years, as indicated by the approximately zero mean,
there is a substantial variation in OSE across states. Figure 3.1 provides a visual
representation of this variation. Expenditures in New Jersey, Ohio, and Georgia are
shown to fluctuate most widely over the business, whereas ones in Virginia, Utah, and
Hawaii are the most stable.
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Meanwhile, Figure 3.2 shows that with most annual changes in overall tax rate
being zero or small, some states enacted large tax cuts. An interesting observation is that
large changes (more than 10 percent) in overall tax rate are all positive in sign (i.e. tax
increases) and were made in economically tough times. Graphics by state reveal that
Indiana, New Jersey, and Tennessee enacted large tax hikes following the 2001 recession,
while Wyoming, Michigan, Vermont, and Rhode Island in the aftermath of the 1990–
1991 recession. On the other hand, tax cuts are relatively small in size, except for two
states, Kansas and Hawaii which enacted a nearly 10 percent tax cut in 1998 and 1999,
respectively, when the economy was at its peak. Overall, these results suggest that when
it comes to large tax cuts, state tax policy was procyclical over the sample period.
135
Figure 3.1 Box Plot of Expenditure Gap by State
-20
-10
010
20
-20
-10
010
20
-20
-10
010
20
-20
-10
010
20
-20
-10
010
20
-20
-10
010
20
-20
-10
010
20
AL AK AZ AR CA CO CT DE
FL GA HI ID IL IN IA KS
KY LA ME MD MA MI MN MS
MO MT NE NV NH NJ NM NY
NC ND OH OK OR PA RI SC
SD TN TX UT VT VA WA WV
WI WY
Expe
nd
iture
Gap
136
Figure 3.2 Annual Percentage Changes in Overall Tax Rate by State
-10
010
20
30
-10
010
20
30
-10
010
20
30
-10
010
20
30
-10
010
20
30
-10
010
20
30
-10
010
20
30
1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005
1990 1995 2000 2005 1990 1995 2000 2005
AL AK AZ AR CA CO CT DE
FL GA HI ID IL IN IA KS
KY LA ME MD MA MI MN MS
MO MT NE NV NH NJ NM NY
NC ND OH OK OR PA RI SC
SD TN TX UT VT VA WA WV
WI WYAnn
ua
l C
han
ge
s in O
vera
ll T
ax R
ate
(%
)
YearGraphs by State
137
Table 3.3 Regression Results for Own-Source Expenditure
Explanatory
variables
Entire sample Upturn years Downturn
years
Coef. Clustered
Robust Std.
Err.
Coef. Clustered
Robust Std.
Err.
Coef. Clustered
Robust Std.
Err.
revgap 0.140728* 0.074844 -0.08245 0.360614 -0.12894 0.565706
revgap_1 0.206678*** 0.059937 0.276177** 0.105922 0.109673 0.133672
revgap_2 0.152506** 0.067194 0.116481 0.104047 0.240948* 0.131712
revgap_3 0.207201** 0.082126 0.246823** 0.09577 0.266712* 0.135387
revgap2 0.00073 0.00788 0.019651 0.033347 -0.03116 0.044702
revgap2_1 -0.01817*** 0.005975 -0.02162* 0.012405 -0.01764 0.017733
revgap2_2 -0.01364 0.009715 -0.01029 0.020864 -0.02038** 0.00956
revgap2_3 -0.00638 0.011458 -0.00344 0.015417 -0.00371 0.018617
grant 0.007986*** 0.002286 0.005407 0.003601 0.012288*** 0.003772
grant_1 0.002357* 0.001338 0.000907 0.002492 0.00518* 0.002786
debt 0.585109** 0.27289 0.48046 0.384622 0.476312 0.667204
debt_1 -0.21148 0.320162 -0.5577 0.373779 0.328163 0.506066
nodef 3.433418** 1.695718 5.449216* 3.019447 -0.25955 2.568337
revlim 3.582779*** 1.191588 2.450598* 1.382282 5.736686** 2.823636
spdlim 0.905916 0.961293 0.316687 2.252248 1.634223 2.438122
liv -1.05108 1.780661 -0.18865 1.635543 -5.80484 3.603278
bsf -1.45635 1.349294 -2.01099 2.226945 -3.15117 1.997457
bien -2.86484*** 1.017006 -3.03458** 1.340435 -3.34809 4.965941
repsen 0.052127 0.673867 1.030087 1.149071 -1.40425 1.406549
rephou -0.76934 0.908064 0.67241 0.907488 -1.07495 1.402015
repgovn -0.76386 0.596234 -1.04083 1.16738 -0.26981 0.741225
indgovn 3.302643 2.424698 3.161515 2.944127 2.839553 4.024915
divgov -1.06311** 0.468083 -1.10612 0.804678 -0.70992 0.901887
eltyr 0.224332 0.497814 0.156877 0.79641 1.091207 1.354092
eltyr_lg 0.15972 0.408047 -0.19284 0.727649 1.241845 1.446606
eltyr_ld -0.25762 0.291272 -0.37224 0.606004 0.612076 0.991889
pop 9.75E-09 6.84E-07 5.11E-07 8.86E-07 -2.09E-06 1.30E-06
under17 -0.21708 0.529245 -0.41037 0.66557 -0.74724 0.867867
over65 0.688738 0.444442 1.086779** 0.502784 -0.69684 1.133569
pcpi -0.00156*** 0.000428 -0.00168*** 0.000591 -0.0024*** 0.000661
weal 2.266504 2.126179 0.091506 2.642696 9.835721*** 3.154147
poor 0.2215 0.149888 0.183162 0.141164 0.427334 0.515932
_cons 28.58141 21.34315 44.49318 30.18426 78.44587 36.84793
Number of
observations
580 338 242
Number of
states
49 49 49
R-squared
(within)
.6001 .6008 .5671
Note: Year effects not reported.
***p<0.01; **p<0.05; *p<0.1
138
Table 3.4 Regression Results for Overall Tax Rate
Explanatory
variables
Entire sample Upturn years Downturn
years
Coef. Clustered
Robust
Std. Err.
Coef. Clustered
Robust
Std. Err.
Coef. Clustered
Robust
Std. Err.
revgap -0.00257 0.004038 -0.0119 0.015192 -0.00064 0.016268
revgap_1 0.002245 0.002723 0.00449 0.004653 0.000974 0.005067
revgap_2 -0.00083 0.004411 0.001735 0.003813 0.003687 0.006497
revgap_3 0.005716 0.004154 0.005487 0.004777 0.011785 0.0087
revgap2 -0.00085*** 0.000308 -0.00053 0.001317 -0.00018 0.001419
revgap2_1 -0.00105*** 0.000332 -0.00078 0.000545 -0.00168*** 0.000618
revgap2_2 -0.00159*** 0.000431 -0.00194*** 0.000502 -0.00118* 0.000662
revgap2_3 -0.00167*** 0.00057 -0.00324*** 0.000812 -0.00095 0.000802
grant 4.86E-05 8.61E-05 7.71E-05 9.73E-05 9.26E-07 0.000137
grant_1 5.92E-06 8.98E-05 -0.00017 0.000159 4.47E-06 8.37E-05
debt 0.030581 0.024808 0.084093*** 0.01695 0.012189 0.026385
debt_1 -0.02988 0.039204 -0.02987 0.030798 -0.02096 0.045858
nodef 0.483379*** 0.105265 0.33781** 0.13188 0.602802*** 0.081071
revlim 0.119832 0.15526 0.102934 0.089928 -0.09264 0.265432
spdlim 0.222629 0.214246 0.183884 0.217971 0.502359 0.337519
liv -0.19096*** 0.064705 -0.19492** 0.092133 -0.49427*** 0.090433
bsf -0.09407 0.221345 -0.27245 0.299961 -0.07655 0.222464
bien -0.03788 0.046136 -0.11069 0.092042 -0.02263 0.145025
repsen -0.08446* 0.047438 -0.07399 0.04895 -0.05635 0.062844
rephou -0.03931 0.04343 -0.01067 0.058165 -0.00486 0.072761
repgovn 0.013755 0.047806 0.01872 0.049658 -0.05466 0.045343
indgovn 0.204424 0.14384 0.202546 0.166271 0.036646 0.094493
divgov -0.02215 0.034659 0.018838 0.029708 -0.01088 0.035949
eltyr -0.02756* 0.015328 -0.01921 0.022867 -0.00411 0.030017
eltyr_lg -0.00759 0.018063 -0.03637 0.022639 0.038317 0.034519
eltyr_ld -0.01811 0.017569 -0.06146** 0.024413 0.057727 0.038261
pop 1.98E-08 4.76E-08 3.01E-08 4.65E-08 -3.25E-08 6.70E-08
under17 0.055569 0.037423 0.013088 0.060696 0.031884 0.050961
over65 0.026444 0.03401 0.013871 0.026605 0.016575 0.041748
pcpi -2.65E-06 2.39E-05 -2.4E-05 2.92E-05 1.81E-05 4.35E-05
weal -0.02133 0.135119 0.083799 0.153344 -0.051 0.217659
poor 0.007655 0.006392 0.001508 0.006453 0.020933 0.024996
_cons 4.23275 1.933999 6.484937 3.016582 4.782678 2.439343
Number of
observations
580 338 242
Number of
states
49 49 49
R-squared
(within)
.2194 .4138 .2911
Note: Year effects not reported.
***p<0.01; **p<0.05; *p<0.1
139
Table 3.3 and 3.4 report the results of regression analyses for own-source
expenditure (OSE) and overall tax rate by sample—the entire sample and two subgroups
(upturn and downturn years). Overall, both models perform well. The key variable,
revenue gap, in linear and/or quadratic form, is statistically significant in the expected
direction across the models, which explain the 60.01% and 21.94% in a regression using
the entire sample period, 60.08% and 41.38% in a regression using the upturn-year
sample, and 56.71% and 29.11% in a regression using the downturn-year sample, of the
(within) variation in own-source expenditure and overall tax rate, respectively.
Starting from the analysis for OSE, the results show that, as expected, there is a
positive relationship between revenue gap and own-source expenditure. The results of the
regression using the entire sample, in Column 1 of Table 3.3, report that all the revenue
gap variables, , , _2, and , are statistically significant
(at the 10%, 1%, 5%, and 5% level, respectively) with positive signs. This means that
revenue gap affects OSE both contemporaneously and with lags. Specifically, the slope
coefficients indicate that, holding all other independent variables fixed, a one percentage
point increase in current, one-, two-, and three-year lagged revenue gap increases own-
source expenditure, on average, by .141, .207, .153, and .207 percentage points. The
combined effect of revenue gap is substantial in magnitude. The sum of the coefficients
is .708, which means that another percentage point on revenue gap is associated with .708
of a percentage point on OSE, or much more than half a percentage point over years.
Together, these results suggest that spending policy is more procyclical (or less
140
countercyclical) in states with a larger revenue gap (in other words, more unstable in
states with a more volatile revenue base).
The results also show that the quadratic term in one-year lagged is
statistically significant. This implies that OSE depends on one-year lagged , but it
does so in a quadratic fashion. The sign of the coefficient on the first lag is positive,
whereas that of the coefficient on its quadratic is negative. This means that one-year
lagged has a diminishing effect on OSE. For example, in one-year lagged
going from 0 to 1, OSE is predicted to increase by .189 percentage points [about
]; in one-year lagged going from 10 to 11,
OSE is predicted to decrease by .218 percentage points [about
112 102 ]. The effect of one-year lagged
becomes zero at 11.5; before this point, the variable has a positive effect on OSE; after
this point, its effect turns negative.
Moving on to Column 2 and 3 of Table 3.3, the regressions using subgroups,
upturn and downturn years, produce less statistically significant but still theoretically
consistent results. The results in Column 2 indicate that one- and three-year lagged
are statistically significant and positively associated with OSE. The coefficients
indicate that during upturn years, other things being equal, a one percentage point
increase in one- and three-year lagged increases OSE on average by .276
and .247 percentage points, respectively, with a lag of one and three years. Put together,
has the combined effect of increasing OSE by .523 percentage points with lags.
As in the regression using the entire sample, one-year lagged during booms has a
decreasing effect on OSE. Increases in revenue gap from 0 to 1 and from 10 to 11 are
141
predicted to result in a .254 percentage point increase and a .186 percentage point
decrease in OSE.
Meanwhile, the results in Column 3 of Table 3.3 report that during downturn
years, two- and three-year lagged are statistically significant and positively
linked to OSE. A one percentage point decrease in two- and three-year lagged
decreases OSE by .241, and .267 percentage points, respectively. The results also report
that the quadratic term in two-year lagged is statistically significant and
negatively related to OSE. This implies that the marginal effect of two-year lagged
on OSE decreases as the variable increases. All other factors being held fixed,
two-year lagged decreasing from 0 to -1 and from -10 to -11 during downturn
years leads to OSE going down by about .221 and up by .179 percentage points,
respectively.
In sum, all the revenue gap variables in linear form that are statistically significant
have positive effects on own-source expenditure, even though some of them have
diminishing effects. These results clearly show that revenue gap affects state spending
policy in a procyclical fashion over phases of the business cycle. Cyclical increases in tax
revenues induce increases in spending, while cyclical decreases in tax revenues lead to
decreases in spending. In other words, states with a more volatile revenue base tend to
increase spending during expansions and decrease it during contractions at faster rates.
Further, the results can be also interpreted as indicating that volatile states are likely to
adopt unsustainable spending increases during booms overheating the economy and
disruptive spending cuts during recessions deepening economic downturn.
142
The results also suggest that revenue gap affects OSE even contemporaneously,
suggesting that cyclical changes in tax revenues lead to mid-year budget adjustments.
Although this result is from the regression using the entire sample, not particularly
downturn years, the covariance between the two variables is thought to reflect the
covariance between cyclical decreases in revenue gap and mid-year budget cuts rather
than the one between cyclical increases in revenue gap and mid-year budget increases,
considering the urgency of balancing year-end budgets during downturn years and
business cycle asymmetry (Sichel 1993).
As mentioned in the Methods section, this study divides the sample into upturn
and downturn years based on the sign of revenue gap (i.e. years that revenue gap is
positive and years that revenue gap is negative). In the regressions using subgroups, this
method causes the loss of data on variations in the variables between years when the sign
of revenue gap is flipped. The effect of revenue gap on OSE should be much larger when
revenue gap changes from positive to negative rather than from negative to positive,
considering that balanced budget requirements are concerned with keeping their accounts
not in deficit rather than maintaining certain levels of balances.
Another explanation is related to asymmetry in business cycles. Empirical studies
on the cyclical behavior of macroeconomic variables suggest that contractions tend to be
more rapid and steeper than expansions (in other words, the economy tends to gradually
expand over a long period of time, but rapidly contract over a relatively short period).
This implies that changes in revenue gap and OSE between years when the economy
turns downward tend to be much larger than between years when the economy turns
upward. Therefore, the contemporaneous effect of revenue gap on OSE can be interpreted
143
as an indication that in response to negative revenue shocks, states tend to make mid-year
budget cuts in an attempt to balance their budgets.
As for the control variables, federal grants are found to be statistically significant
in the regressions using the entire sample and downturn years, as indicated in Column 1
and 3 of Table 3.3. Consistent with the theoretical prediction, the results show that
federal grants have effects in increasing own-source expenditure. The coefficients on
indicate that over the entire sample period and during contractions, other things
being equal, a one dollar increase in current per capita federal grant increases OSE
by .008 and .012 percentage points, respectively. The regressions also find that federal
grants affect OSE not only contemporaneously but with a lag. The coefficients on lagged
indicate that a one dollar increase in lagged per capita federal grant results in
a .002 and .005 percentage point increase in OSE, respectively. Together, these results
serve as evidence in support of the flypaper effect hypothesis. States tend to use federal
grants to increase expenditures rather than substitute the additional revenues from outside
for own-source revenues.
By contrast, federal grants during upturn years, both current and lagged, are not
statistically insignificant. That is, the positive relationship between federal grants and
OSE only holds during contractions. What is important here is that federal grants tend to
move in a countercyclical fashion. The federal government usually increases funding to
states and locals during recessions as it attempts to stimulate the economy, while
decreasing (or at best stagnating) during growth periods. The regression diagnostic plot in
Figure 3.3 from a simple regression of aggregate federal grants to states on year clearly
illustrates the countercyclical movement of federal grants. The implication is that the
144
effects of federal grants on OSE during expansions and contractions are not equal.
Although an increase in federal grants during downturns increases OSE, a decrease
during upturns do not significantly lower it. This means that federal grants only increase
OSE over the business cycle.
Figure 3.3 Regression of Aggregate Federal Grants on Year (1992–2007)
Meanwhile, the results display that borrowing is statistically significant and
positively associated with OSE, but only in the regression using the entire sample. As
with the results for current revenue gap in the regression using the entire sample, this
result appears to be attributable to the covariance between debt and OSE when the
economy shifts from above to below the trend line, given the necessity of debt financing
during downturn years. The coefficient on current indicates that a one percentage
point increase in debt as a share of GSP increases OSE by .585 percentage points. This
result is not surprising, because borrowing, though it should be paid off sometime in the
40
00
00
040
10
00
040
20
00
040
30
00
0
Co
mpo
ne
nt p
lus r
esid
ual
1990 1995 2000 2005 2010var2
145
future, provides financial relief at least in the short term to states required to balance their
budgets.
As for the fiscal institution controls, no deficit carry-over and revenue limitation
rules, among the fiscal control mechanisms, produce statistically significant but
counterintuitive results across the regressions. The coefficients on these legal provisions
indicate that on average, OSE is 3.43 and 3.58 percentage points higher, respectively, in
states with no deficit carry-over and revenue limitation rules in place than states without
them. While these results do not justify claims that these rules contribute to spending
growth and thus should be removed, they certainly suggest that the rules do not work as
intended.
On the other hand, as a fiscal institution, biennial budgeting is statistically
significant (at the 1% and 5% significance level) with the expected sign in the regressions
using the entire sample and upturn years. The coefficients indicate that a biennial budget
cycle exerts a negative influence on OSE. Holding other factors constant, OSE is
predicted to be, on average, 2.86 (in the entire-sample regression) and 3.03 (in the upturn-
year regression) percentage points lower in states budgeting on a biennial basis than
states budgeting on an annual basis. In stark contrast to the findings of Kearns (1994) that
biennial budgeting leads to spending growth, these results provide empirical evidence
supporting the proponents‘ claim that a biennial budget cycle contributes to restraining
state spending (particularly during expansions, as suggested by the result that the
negative relationship only holds for upturn years) by leading budget makers to be prudent
in conducting fiscal policy and prepare for hard times ahead with a long-term view of
their fiscal conditions.
146
Among the political controls, only divided government is found to be statistically
significant at the 5% level. Its coefficient indicates that OSE is 1.03 percentage point
lower on average in states where the executive or legislative branch is divided in terms of
partisan control than a state unified. This result suggests that political division and
deadlock within a government as a whole or a legislature lead to lower spending, which
can be interpreted as evidence supporting Niskanen‘s view (2003). On the other hand, the
statistically insignificant coefficients on the variables relating to partisan control suggest
that an important political factor in terms of spending restraint is whether there is political
check and balance in the budgetary process rather than which party, among Democratic
and Republican, controls the government. Meanwhile, election year and its lag and lead
are all shown to be statistically insignificant, and these results invalidating the political
business cycle hypothesis at least on the expenditure side.
For the demographic and socioeconomic controls, per capita personal income is
found to be statistically significant (at the 1% level) and negatively associated with OSE
across all the regressions. Holding other factors constant, a one hundred dollar increase in
per capita personal income is predicted to lead to a .002 percentage point decrease in
OSE (in all the regressions). These results confirm that overall, state spending is
countercyclical in absolute terms. More importantly, the results that even with such a
strong macroeconomic predictor controlled for, revenue gap exerts consistently positive
and statistically significant effects on OSE clearly show that revenue gap is a valid and
robust predictor of state spending policy.
Lastly, the proportions of the elderly and wealthy population are found to be
statistically significant in the regressions for upturn and downturn years, respectively.
147
The coefficient on the elderly population indicates that a one percentage point increase in
the elderly population causes OSE to increase by 1.09 percentage points during upturn
periods. This makes intuitive sense, because the population group has relatively high
demands for public services. Meanwhile, the coefficient on the wealthy population
indicates that a one percentage point decrease in the elderly population results in a large
(9.84% percentage point) decrease during downturn periods. This result is also intuitively
sensible, because even a little decrease in the wealthy population means large decreases
in tax revenues.
Turning to the results for overall tax rate in Table 3.4, the first result worth
pointing out is the less statistically significant coefficients on the revenue gap variables.
While the statistically significant ones have the expected (negative) signs suggesting that
states, on average, tend to conduct tax policy in a procyclical manner (i.e. tax cuts during
booms and tax increases during recessions), no current revenue gap is statistically
significant. This shows that the effects of revenue gap on tax rates are relatively small
and limited compared to those on spending.
Column 1 of Table 3.4 reports the results of the regression using the entire sample.
All the quadratic terms are statistically significant with the negative signs. This suggests
that the negative effects of revenue gap on tax rates increase as the variable escalates.
Specifically, in current, one-, two-, and three-year lagged going from 0 to 1,
overall tax rate is predicted to decrease by about .001, .001, .002, and .002 percentage
points, respectively, and in going from 10 to 11, overall tax rate is to be down
by .021, .021, .042, and .042 percentage, respectively. An interesting result worth
148
pointing out is that as in the spending policy model, revenue gap contemporaneously
affects tax rates. Following the same logic as discussed earlier for the contemporaneous
effect of revenue gap on OSE, this result is judged to be attributable in large part to the
covariance between revenue gap and overall tax rate when the economy falls into
recession. The result can therefore be interpreted as indicating that a rapid decrease in
revenue gap tends to lead to mid-year tax increases. Together with the results for OSE,
these results clearly show that revenue gap contemporaneously affects the level of both
spending and taxation in a procyclical fashion, when budget balance figures turn from
blue to red, that is, when countercyclical fiscal policy is more needed than ever.
The regressions using subgroups provide a more detailed picture of state fiscal
behavior on the taxation side. The results in Column 2 of Table 3.4 report that the
quadratic terms in two- and three-year lagged are statistically significant and
negatively related to overall tax rate. Their coefficients indicate that a first one percentage
point increase in revenue gap during upturn years leads to a .002 and .003 percentage
point decrease in overall tax rate, respectively, with a lag of two and three years.
Similarly, in the regression using downturn years, two quadratic terms in one- and two-
year lagged revenue gap are statistically significant. The non-zero coefficients mean that
a first one percentage point decrease in revenue gap during downturn years increases
overall tax rate by .002 and .001 percentage points, respectively, with a lag of one and
two years.
Taken together, the results for the relationship between revenue gap and overall
tax rate lead to the conclusion that the tax smoothing hypothesis does not hold at least at
the state level. That is, states do not hold their tax rates constant over the business cycle.
149
While revenue gap has effects on tax rates in a quadratic fashion, the results certainly do
not support the tax policy model that is widely considered optimal among economists.
Moving on to the institutional controls, no deficit carry-over rules are found to be
statistically significant consistently across the regressions. The slope coefficients indicate
that having this fiscal rule in place increases overall tax rate by .483, .338, and .603
percentage points, respectively, over the business cycle and during upturn and downturn
periods. While these results certainly lead to the conclusion that no deficit carry-over
provisions contribute to balancing budgets by leading states to raise tax rates, the fact that
they only have a positive effect on overall tax rate over phases of the business cycle
suggests that they are used as a tool for merely balancing budgets rather than fiscal
restraint or stabilization. This interpretation makes more sense, when considered together
with the earlier results that no deficit carry-over rules do not play any significant role in
curbing spending throughout the business cycle.
Another fiscal control mechanism that is statistically significant is line-item veto.
The non-zero coefficients on this variable (in all the regressions) indicate that overall tax
rate is .191, .195, and .494 percentage points lower, on average, in states granting the
gubernatorial authority to line-item veto appropriations bills. These results may not make
logical sense, because a line-item veto is, in essence, a fiscal control mechanism
concerned with appropriations, not taxes (Gouras 2011). One possible explanation is that
the adoption of a line-item veto law increases governors‘ influences across various
aspects of the fiscal process, thereby leading the fiscal policy into a direction that they
prefer, probably tax cuts. Governors‘ constituency is generally state residents as a whole,
and as a result, they are likely to attempt to accommodate the median voter‘s preference
150
through tax cuts, benefits from which are spread across the entire state population, rather
than spending increases, benefits from which are, in many cases, concentrated on
particular population groups. While legislators still have the last say in making fiscal
decisions, they would likely find it politically disadvantageous to object to governors‘
proposal for tax cuts, considering general public sentiment against government expansion.
Among the variables for partisan control, only Republican control of the Senate is
statistically significant (in the entire-sample regression). As expected, the variable has a
negative effect on overall tax rate. Overall tax rate is .084 percentage points lower in
states where the Republican Party controls the Senate than states where the Democratic
Party does so.
Another statistically significant political control is gubernatorial election year (in
the regressions using the entire and upturn-year sample). The results indicate that election
years exert negative effects on overall tax rate contemporaneously and with a lead. The
coefficient on current election year suggests that overall tax rate is .028 percentage points
lower, on average, in an election year. The results suggest that election years lead to
larger tax cuts when the economy is in an expansion phase. Overall tax rate is .061
percentage points lower, on average, with an election one year ahead. These results can
be interpreted as evidence showing that political business cycles exist, in other words,
with elections ahead, politicians have a tendency to offer generous tax policies in an
attempt to attract votes.
151
CHAPTER 4
POLICY IMPLICATIONS
Based on the results of the empirical analyses in Chapter 2 (on the determinants of
revenue volatility) and Chapter 3 (on its consequences), this chapter discusses policy
implications, which are organized into two sections. The first discusses what policy
choices are available for states to reduce sales and individual income tax volatility, given
other considerations such as equity and efficiency, and what implications current or
future changes in tax environments have. The second section discusses what approach
needs to be taken to maintain fiscal stability during expansions and thus avoid fiscal crisis
during recessions.
4.1 Implications for Revenue Stability
The results of the empirical analyses in Chapter 2 on the relationship between tax
base composition and revenue volatility reveal that sales and individual income tax
volatility are significantly affected by how their tax bases are composed. These results
suggest that this behavioral property should be incorporated as an important dimension,
along with other commonly cited principles such as equity, efficiency, and revenue
adequacy, in designing tax structure. With this general implication, several specific
policy implications are drawn from the analysis results of Chapter 2.
First, the analysis results suggest that while food and clothing fall into the same
category as household necessities, different tax treatments need to be applied. The
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analysis finds that the consumption of food is stable over the business cycle, whereas that
of clothing is volatile. First, in the case of clothing, preferential tax treatment seems less
controversial at least in terms of equity and revenue volatility, given the current state
practices of limiting exemptions to purchases under certain prices (usually under $200).
The issue of food exemption, however, involves conflicting interests and policy goals.
Some may support this policy on the grounds that it makes sales tax less regressive and
more equitable, but some others may oppose it based on its negative effect on revenue
stability. Such a conflict could be resolved to some degree by selectively granting tax
exempt status to particular groups of purchases. Bahl and Hawkins (1997) provide useful
insights in this regard. In a study of Georgia‘s sales tax system, recognizing that
preferential tax treatment is applied not only to purchases of bread and milk but also
lobster and filet mignon, in which they calculate the specific dollar amounts of tax reliefs
that households by income group receive from food exemption. They find that the tax
benefits that average poor and wealthy households receive are respectively $68 and $166.
Their study suggests that states may be able to move closer to the efficiency frontier in
terms of tax equity and revenue volatility by focusing their food exemption programs on
purchases disproportionately made by lower-income people.
Second, the results suggest that expanding services taxation does not necessarily
reduce sales tax revenue volatility. There has been an assertion that broadening sales tax
bases with more services may make the sales tax revenues fluctuate less over the business
cycle. The reasoning is that, as noted earlier, purchases of big-ticket consumer goods
such as automobiles and household appliances, which account for a large portion of state
sales tax revenues, tend to be highly procyclical and volatile, whereas those of services
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stable. The statistical insignificance of services exemption and its quadratic, however,
implies that there exists a considerable variation in cyclical volatility among services,
with states exhibiting no noticeable tendency in taxing services, thus suggesting that
relating more services to less sales tax volatility is not perfectly valid.
Third, the results confirm that exempting business purchases from sales taxation
is beneficial in terms of revenue volatility, reinforcing the case against levying a sales tax
on business-to-business transactions. Scholars and experts have argued for this tax policy
on the grounds that it eliminates ―tax pyramiding‖ and reduces allocative inefficiency
while boosting economic development. Tax pyramiding occurs as businesses pass the
additional costs caused by sales taxes on production inputs into the selling prices of their
goods. This business practice effectively leads to households, which are final consumers
of the products, paying additional taxes for the inputs already taxed (Chamberlain and
Fleenor, 2006). Taxing business purchases may also lead to inefficiency in resource
allocation by inducing ―vertical integration.‖ Vertically integrated companies in a supply
chain are not subject to a sales tax on production inputs. Consequently, taxing business
purchases artificially creates a market environment that induces businesses to opt for self-
supply or in-house production rather than outsourcing in an attempt to gain competitive
advantage in competition with single-process independents in the same industries (Perry,
1988). Another argument against sales tax on business purchases is that taxation on
production inputs has adverse effects on economic development. While some argue that
the negative effects of taxing production inputs have been somewhat exaggerated, broad
taxation certainly lowers the price competitiveness of businesses (Mazerov, 2009). Along
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with these reasons, the result of this study that taxing producer goods increases sales tax
volatility provides additional support for producer goods exemption.
Fourth, this study finds that only particular types of retirement incomes contribute
to the stability of income tax bases. The results indicate that the exclusion of public
pensions, the majority of which are DB plans, detracts from revenue stability, whereas
the exclusion of private pensions, which are mixed in terms of the way contributions and
benefits are determined, does not make a significant difference in income tax volatility. A
further discussion of a changing trend in pension plans appears useful. As the term
suggests, a DB plan is a type of plan in which, upon retirement, a certain amount of
benefit is guaranteed through a lump sum or in monthly payment according to a set
formula usually based on the employee‘s salary and length of service. On the other hand,
in a DC pension plan, the most well-known example of which is 401(k), an employee
makes a certain amount of contributions—usually with employer matching
contributions—to his or her retirement account during employment. Directed by the
participant, the contributions are invested in financial securities such as stocks, bonds,
money market instruments, and mutual funds. In exchange for being given more
discretion over where their contributions are invested, the employee assumes more
responsibility for funding. That is, while the contribution is defined, the benefit is not
guaranteed but depends on the account balance which is the sum of the contributions and
returns on the investments.
A noticeable change in this respect is that unlike the public sector where a DB
pension plan has been the dominant form of pension plan, the private sector has
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significantly shifted from DB to DC pension plan over the past decades.40
In the case of
the public sector, numbers have not changed much. According to data from the Bureau of
Labor Statistics‘ Employee Benefits Survey, 84% of state and local government workers
have access to defined benefit pension plans in 2010, and among those with access to DB
pension plans, nearly all workers (94%) chose to participate in the plans. Foster (1997)
reports that in 1993–1994, 91% of state and local government workers participated in the
DB pension plans. Given the historical comparison, there has been no significant change
in the public sector‘s orientation towards DB pension plans.
Figure 4.1 Private Sector Participants in an Employment-Based Retirement Plan by
Plan Type, 1979–2008 (Among those who have a retirement plan)
Source: Employee Benefit Research Institute estimates. Available at
http://www.ebri.org/publications/benfaq/index.cfm?fa=retfaq14
40
This study represents policy experiments for testing the consequences of complicated tax policies in a
given economic and demographic environment. As found, how tax bases are composed is an important
factor in determining the cyclical volatility of two major state revenue sources. However, constantly
changing tax environments may potentially outdate the current results. Thus, in order for the empirical
findings here to be of more relevance in the future, there needs to be careful consideration of changes in
state tax environments.
0%
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50%
60%
70%
80%
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Defined Benefit Only
Defined Contribution Only
Both DB & DC
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Meanwhile, according to the BLS survey data, only 20% of private-sector
workers have access to the DB pension plans in 2010. The Employee Benefit Research
Institute (EBRI) provides historical data on the share of pension plans by type in the
private industry, which clearly show a trend in the private industry towards DC pension
plans. As shown in Figure 4, the proportion of private-sector workers with only a 401(k)-
type defined contribution plan among those who had a retirement plan rose from 38% in
1990 (17% in 1980) to 67%, whereas that of private-sector workers with only a DB plan
fell from 29% (60% in 1980) to 7%. In brief, public and private pensions are contrasted
in terms of retirement plan type: public pensions are dominated by stable DB plans,
whereas private pensions are characterized by volatility-prone DC plans.
Such a shift in the private sector has important implications for income tax
volatility. According to the general distribution rules set by the Internal Revenue Service
(IRS), minimum required distributions (MRD) from a qualified 401(k) plan are
determined by dividing the fair market value (FMV) of the retirement plan at year end by
the applicable distribution period, which normally is the life expectancy of the account
owner after retirement. An important point here is that, as suggested earlier by the results
on capital gains exemption, investment returns on which benefits from DC plans are
based are closely linked to economic fluctuations. The implication is that with the first
generation of workers who have widely adopted 401(k) plans since the 1980s beginning
to retire (Browning 2011), private pension income may likely become less stable in the
future, thereby making the effect of tax exemption for private pension income on income
tax volatility less positive. This is particularly likely, due to the growing volatility of the
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U.S. and global financial market, which was witnessed by the 2008 financial meltdown
and the subsequent economic crisis.
The fifth policy discussion emerges from the findings on long-term capital gains
exemption. How capital gains income should be treated in individual income tax has been
one of the major policy debates at both the federal and state level. Supporters for
preferential tax treatment for capital gains argue that it encourages private investment and
potentially promotes business activities. On the other end, opponents emphasize potential
revenue losses that this policy will bring to already fiscally strained governments. With
increasingly volatile financial markets, what impacts capital gains exemption has on
income tax volatility certainly brings another important dimension to this policy
discussion. This policy is likely to raise concerns about tax equity, because its benefits,
without question, go disproportionately to high-income people with more savings and
financial resources. But the policy is expected to gain more political support if states
continue to maintain the current practices of limiting this special tax treatment only to
taxpayers in low- and middle-income brackets.
The last policy discussion relates to the structure of the U.S. manufacturing sector.
According to Census data, U.S. manufacturing industries have steadily declined over the
past decades. The GDP for the durable and nondurable manufacturing sector GDP has
respectively fallen about 6.2% (12.8 to 6.6%) and 3.3% (8.5 to 5.2%) from 1980 to 2010.
Although the trend has been so, it certainly will not go unchanged. While economists
generally suggest that with relatively labor-intensive industries moving their production
bases to low-wage countries such as China and India, the nondurable manufacturing
sector will not improve anytime soon (Ezell and Atkinson 2011), some economists are
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carefully predicting a turnaround in the durable manufacturing sector such as the capital-
and technology-intensive industries such as the auto industry (Krugman 2011). A shift of
the economy from nondurable to durable manufacturing may potentially increase the
cyclical volatility of state tax bases in general, as suggested by the conceptual discussion
earlier. In particular, this has implications for sales tax. Producer goods will become more
durable and consequently their positive effect on sales tax volatility will then be greater
than now.
4.2 Implications for Fiscal Stability
The major finding of the empirical analysis in Chapter 3 is that cyclical changes
in tax revenues are positively related to expenditures and negatively related to tax rates.
This implies that fiscal policy is more likely to be unsustainable (during booms) and
disruptive (during recessions) in states with a more volatile revenue base. Based on these
findings, Figure 4.2 provides a simplified illustration of how structural budget deficits
and fiscal crises arise over phases of the business cycle. Dotted lines represent what
would have been original expenditure and revenue if there had not been spending and tax
adjustments, while solid lines actual expenditure and revenue. Lines that go through
expenditure and revenue lines represent long-run growth trend. According the results,
expenditure is adjusted upward, while revenue is adjusted downward during the
expansion phase. While structural imbalance occurs as much as the sum of the gap
between actual and original expenditure and the one between actual and original revenue,
it is obscured by cyclical budget surplus that is as much as the sum of the gap between
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original and trend expenditure and the one between original and trend revenue. The
effects of the spending and tax adjustments, though, continue through the contraction
phase. Actual expenditure is higher than original, while actual revenue is lower than
original. In other words, budget deficit is amplified as structural deficit is added to
cyclical deficit. While cyclical deficit during the contraction is generally offset by
cyclical budget surplus during the preceding expansion, structural deficit remains, forcing
the state, which is required to balance its budget, into spending cuts and tax increases (or
borrowing).
Figure 4.2 The Dynamics of State Fiscal Behavior over the Business Cycle
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These findings naturally lead to two strategies for fiscal stability: reducing the
cyclical volatility of tax revenue by adjusting the tax structure or restricting the ability to
spend surplus revenue by means of spending rules combined with budget stabilization
funds or tax rebates. The former was extensively discussed in the previous section;
therefore this section focuses on the latter solution. While both strategies are certainly
plausible, the former is relatively difficult to implement, because revenue stability is not
the only principle for state tax systems to pursue. There are other principles such as
equity (again, vertical and horizontal equity), economic neutrality/efficiency, revenue
adequacy, and administrative simplicity. With each of them being of equal importance,
one principle often conflicts with the others. The difficulty of designing optimal tax
structure brings the ex-post solution using spending rules to the fore.
The basic idea of this approach is to smooth spending over the course of the
business cycle by offsetting revenue shortfalls in times of scarcity with revenue windfalls
in times of plenty. In practice, this approach can be embodied by establishing rules which
prescribe desired rates of annual spending growth and saving. The spending-smoothing
approach is justified in part by the difficulty of accurately forecasting future economic
conditions and receipts and outlays. Dothan and Thompson (2006) express skepticism
about the formulation of fiscal policy based upon revenue forecasting. They note that ―the
standard approach to revenue forecasting is inherently flawed. States generally use
econometric models to forecast revenues. These models are often quite complex, with
scores of exogenous and endogenous variables and constants. … As forecasts, they are
not significantly better than naive extrapolations and are sometimes worse.‖
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The idea of smoothing spending is certainly not new. Broadly, research on TELs
and budget stabilization funds can be grouped under this theme. Schunk and Woodward
(2005) represent one of the well developed articulations of the necessity of spending
stabilization rules as a fundamental solution to recurring state fiscal crises. Using
aggregate state spending and revenue data for the period of 1992–2002, they analyze
―what would have happened in the 1990s if states had a spending limit combined with a
rainy-day balance rule.‖ From this simulation, they find that states could have maintained
stable growth in spending throughout the recession of the early 2000s and avoided the
severe fiscal crises if they had only invested part of surplus revenues in a rainy-day fund.
Based on this finding, they conclude:
The key to any successful spending rule is an accompanying rule governing the
use of surplus revenue. Under the stability principle advanced here, surplus
revenue is one-time money that cannot be counted on in the future. Instead,
surplus revenues should be used for adding to rainy-day funds, for one-time
capital expenditures, or for temporary tax relief.
Once consensus is built on this approach, the next question naturally is how much
of surplus revenue should be saved for rainy days? This study finds that there is a wide
variation in revenue volatility across states, suggesting that a one-size-fits-all solution
cannot exist as in most other areas of public policy; that is, a certain level of rainy day
fund balance, say five percent of general fund expenditure, may be adequate for some
states, but not for others. Describing the widely accepted target of five percent as
―oversimplified,‖ Joyce (2001) compares the actual size of rainy day funds in each state
to the volatility of fiscal environments (share of revenue from corporate tax, economic
environment, reliance on federal aid and gambling revenues, and Medicaid expenditures)
162
as a proxy for the optimal size. In this analysis, he finds that there is little relationship
between actual and optimal balances, and based on this finding, concludes that states fail
to take into account the unique characteristics of fiscal environments in terms of cyclical
volatility in determining the adequacy of rainy days funds. Dothan and Thompson (2006)
expand on the literature by introducing the present-value balance approach. Correctly
pointing out that ―an arbitrary, one-size-fits-all solution (such as the five-percent rule)
ignores variations in revenue codes and consequently differences in growth trends and
revenue volatility,‖ they propose an optimal spending rule based upon a present-value
balance, which is identified by ―modeling revenue growth as a Wiener process (Brownian
motion) with drift, a continuous-time, continuous-state Markov process.‖
While ―a rather roundabout way,‖ as Dothan and Thompson call, assessing the
adequacy of rainy day funds using the standard deviation of actual revenues is certainly
worthwhile at least as a preliminary study to provide rough estimates prior to more
sophisticated analyses using mathematical modeling. Thus, this study replicates Joyce‘s
analysis using its own volatility measure and data. Following Hou (2006), the level of
fiscal reserves is measured by the percentage share of the sum of general fund balance
(GFB) and budget stabilization funds (BSF) of OSE. Revenue volatility is measured by
the mean of the absolute values of revenue gaps over the sample period.
Table 4.1 presents the result of a correlation analysis between fiscal reserves and
revenue volatility. The correlation coefficient is nearly zero (-.0843) and not even
positive. This implies that there is virtually no correlation between the variables,
confirming Joyce‘s finding that states fail to reflect the volatility of fiscal environments
in determining the size of fiscal reserves.
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Table 4.1 Correlation between % Share of Fiscal Reserves and Revenue Volatility
State % of Fiscal Reserves Revenue Volatility
Alabama 2.409073 2.852564
Arizona 6.033182 3.159395
Arkansas 0.217075 2.424032
California 6.167943 2.982762
Colorado 7.068583 4.199058
Connecticut 4.574547 3.8783
Delaware 16.44397 3.854388
Florida 5.875159 2.660813
Georgia 9.049477 2.996147
Hawaii 4.693555 1.473511
Idaho 4.132171 3.624568
Illinois 2.778668 2.920007
Indiana 8.02592 3.943351
Iowa 6.053224 4.402094
Kansas 8.319192 4.49367
Kentucky 3.327303 126.3827
Louisiana 2.554394 3.827134
Maine 2.839488 3.276741
Maryland 6.512641 2.124943
Massachusetts 9.014068 2.851664
Michigan 3.282884 4.678667
Minnesota 17.53485 2.64163
Mississippi 4.483664 2.482862
Missouri 4.099988 4.037358
Montana 4.802539 4.563871
Nebraska 9.677273 1.693929
Nevada 6.821588 3.702078
New Hampshire 1.520214 3.887869
New Jersey 6.378742 2.083997
New Mexico 6.923473 5.544979
New York 2.488961 3.159573
North Carolina 4.204438 2.781249
North Dakota 5.002338 4.851521
Ohio 3.425808 1.44793
Oklahoma 5.383904 4.644167
Oregon 4.74402 3.285877
Pennsylvania 2.34455 1.829702
Rhode Island 3.00542 1.648559
South Carolina 4.780872 2.905602
South Dakota 3.401749 1.863492
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Tennessee 3.154332 2.517853
Texas 7.722879 1.879281
Utah 2.843235 4.359538
Vermont 0.598717 3.408754
Virginia 4.473652 3.635443
Washington 4.388638 1.710954
West Virginia 5.146307 3.817349
Wisconsin 2.582509 2.952727
Wyoming 9.629311 3.345789
Corr. Coef. -.0843
This cursory analysis partly explains the insignificant effects of most of fiscal
control mechanisms, particularly tax and expenditure limitation mechanisms and budget
stabilization funds which are considered of most relevance in terms of achieving fiscal
sustainability during booms, on spending and tax policy. Taken together, these results
suggest that while having such mechanisms in place will certainly provide an institutional
basis for fiscal control and bring renewed interest in fiscal sustainability to policymakers
and the public, they would likely be ineffective unless they specifically lead to the states
building adequate amounts of budget reserves according to the cyclical volatility of fiscal
environments in general and tax revenues in particular. In this regard, Schunk and
Woodward (2005) emphasize that:
Notably, TELs have not been designed to fund rainy-day accounts.
Overwhelmingly, TELs are oriented toward fiscal restraint, not stability. …
Indeed, of varying TELs in place, the chief failure is that they do not build rainy-
day and other stabilization funds.
From this point of view, the spending-smoothing approach needs to be
distinguished from what is so-called ―starve-the-beast‖ approach. The basic idea of this
approach is that a government has a tendency to attempt to eat away whatever is available.
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Therefore, the most effective way to restrain government spending and reduce a deficit is
to cut resources (i.e. taxes) available to the government (Friedman 2003). In response to
the criticism that tax cuts will add to budget deficits hindering economic growth, starve-
the-beast proponents argue that although increased deficits may adversely affect the
economy in the short term, they will ultimately have positive effects in the long term as
they lead to increased public concerns about fiscal health and eventually elected officials
acting on the deficits by cutting unnecessary spending (Templeman 2006).
These two approaches, though they may look similar, in essence differ in that the
―starve-the-beast‖ approach is concerned with fiscal restraint and discipline, whereas the
spending-smoothing approach with fiscal stability and predictability over the business
cycle; the former focuses on reducing deficits and ultimately making governments small,
whereas the latter on preventing unsustainable spending increases and tax cuts during
upturn years and disruptive spending cuts and tax increases during downturn years; the
former is aimed at bringing down the trend line itself, whereas the latter at smoothing out
the actual line—in this view, the trend line is adjusted only when structural imbalances
occur; and the former, as suggested from the term ―starve,‖ is reactive and destructive in
nature, whereas the latter proactive and constructive. From this point of view, the
spending-smoothing approach should not be confused with either conservative fiscal
policies based on neoclassical economics or liberal ones based on Keynesian economics.
That one must live within his or her means is an undisputable principle beyond the left-
right paradigm, which applies to any economic entity, whether individual or corporate,
living in an environment of limited resources. And the most rational way for governments
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to achieve this canon in an uncertain and unpredictable fiscal environment would be to
smooth spending over the business cycle by maintaining sustainable fiscal policy.
At this juncture, it is important to note that many states are currently facing
structural budget deficits and chronic fiscal stress (Behn and Keating 2005) and must
deal with the existing fiscal problems anyhow through either spending cuts or tax
increases (or both) apart from enforcing spending stabilization rules. While either way
certainly will contribute to reducing structural imbalances, the results of this study
suggest that tackling the spending side would be desirable over tapping into the revenue
side if budget deficits are moderate. The first reason is that structural deficits are likely to
have stemmed from the spending side rather than the revenue side. As discussed earlier,
the American political system based on electoral districts creates incentives for
politicians, whether an incumbent or a challenger, to attempt to maximize votes through
specific spending programs whose benefits are concentrated in particular constituencies
rather than tax cuts, if not targeted at particular taxpayers or income brackets, whose
benefits are spread out across all members. A more important reason is that tax increases,
whether through increasing tax rates or broadening tax bases, will amplify cyclical
increases in tax revenues, particularly in states with progressive income tax, during the
following expansion period, inducing expenditure demands again in the absence of strict
spending rules.
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CHAPTER 5
CONCLUSION
5.1 Summary of Findings and Contributions
Motivated by recurring state fiscal crises, this dissertation examined how the
composition of general sales and individual income tax bases varies across states, how
widely each tax revenue fluctuates over the business cycle, how tax base composition
affects the cyclical volatility of the tax revenues, and what effects revenue volatility has
on state fiscal policy, using pooled OLS and fixed effects regressions on panel data over
the period from 1992 to 2007. The empirical findings of this study are summarized as
follows:
There is a substantial variation in the cyclical volatility of sales tax and
income tax across the states, much of which is explained by a variation in the
composition of the tax bases.
Tax exemptions for household necessities (food and clothing) and producer
goods exert statistically significant effects on sales tax volatility, while
exemption for services has no significant impacts.
The prime working-age population has a strong effect on cyclical fluctuations
in sales tax revenue, providing evidence in support of the life-cycle hypothesis
of consumption and the permanent consumption hypothesis.
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The preferential tax treatment of Social Security benefits and pubic pensions
increases income tax volatility, but private pensions have no effect.
The exclusion of long-term capital gains from income taxation reduces
income tax volatility, and the negative effect of this exclusion is weaker in
states with a larger wealthy population as the special tax treatment is generally
offered only to low- and middle-income taxpayers.
The effect of economic structure, measured by sector GSP composition, is not
as strong especially in the sales tax volatility model, highlighting the relative
importance of tax structure in terms of revenue volatility.
Revenue gap has a positive effect on OSE and a negative effect on overall tax
rate. In other words, cyclical upturns in tax revenues during expansion periods
induce spending increases and tax cuts, while cyclical downturns in tax
revenues during contraction periods cause spending cuts and tax increases.
Taken together, these results suggest that revenue volatility is significantly
related to fiscal instability over the business cycle.
With revenue gap having strong effects on the levels of spending and tax rates,
biennial budgeting and divided government tend to play a significant role in
restraining spending growth during booms, whereas most of the fiscal control
mechanisms are mostly ineffective.
Tax cuts (but not spending increases) tend to be enacted ahead of
gubernatorial elections, suggesting that political business cycles do exist.
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Overall, this study contributes to the literature on state fiscal policy and behavior
in four important ways.
First, this study develops a measure of revenue volatility and gap using
orthogonal deviation as opposed to the standard technique using vertical
deviations, based on the concern that there exists a wide cross-state variation
that exists in long-run revenue growth and this could potentially cause using
vertical deviation to produce biased and invalid estimates.
Second, this study is among the few to estimate revenue volatility using actual
tax bases by state as opposed to national aggregates. The use of actual tax
bases is meaningful in that it makes possible a cross-sectional analysis of what
factors determine revenue volatility by tax type and what consequences
overall revenue volatility has on fiscal and policy stability.
Third, this study introduces a new variable, revenue gap which represents the
cyclical position of state finance, in explaining state fiscal behavior. With
other relevant factors controlled for, this variable is found to have strong
effects on own-sources expenditures and tax rates.
Lastly, to take full advantage of the information that revenue gap contains,
this study divides the sample into two subgroups, upturn and downturn years,
and estimates the models separately for each sample in addition to the entire
sample. These additional regressions provide a more detailed picture of state
fiscal behavior by revealing how it changes over phases of the business cycle.
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5.2 Limitations
This study has several limitations. First, some of the measures used in the
regressions in Chapter 2 potentially limit the validity of the results. In the regression for
sales tax volatility, the level of tax exemption for services was measured by the number
of services exempt as a percentage of feasibly taxable services. While this measure is
certainly useful and relevant, its failure to take into account the difference in income
elasticity of demand that exists across services undermines the validity of the measure.
To resolve this potential problem, this study included the quadratic in the original model
and additionally employed an auxiliary model in which the variable was measured by
category. But it should be acknowledged that these methods are still the second best. The
same concern applies to the measurement of economic structure. Although the GDP
shares of economic sectors are widely used in the public finance literature as measures to
capture the effects of economic structure, more sophisticated measures are warranted to
better control for the structural characteristics of state economies, considering that there
may be differences in sensitivity to the business cycle even among industries in the same
category.
Second, fiscal control mechanisms, particularly tax and expenditure limitations, in
the regressions for state fiscal policy need to be better measured. This study included the
presence of a tax and spending limitation provision to capture the effects of TEL
mechanisms. Whether or not tax and spending limits are in place, though commonly used
in the fiscal institution literature, are too rough to capture the differing impacts of TEL
mechanisms, considering that they vary in stringency across states. Limiting spending
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and revenue growth to population growth plus inflation is considered the most stringent,
while restricting to certain percents of personal income more flexible. Besides limit
formulas, there are other important dimensions that characterize the stringency of tax and
spending limits such as how it is codified (i.e. is it statutory or constitutional?); what type
of approval is required to override limits (i.e. is it voter or legislative approval?); and how
surplus revenue is treated (i.e. should it be it returned to taxpayers or reserved in rainy
day funds?).
Third, the panel data set used in this study covers a relatively short time period
(16 years and roughly two business cycles), which limits the validity of the results on the
effects of fiscal institutions on state fiscal policy. As part of budgetary reform, fiscal
institutions and rules have been adopted and amended over decades, and this implies that
only a small number of groups in the panel have a variation in the related variables and,
further, that the effects of fiscal institutions may not have been accurately estimated.
5.3 Directions for Future Research
This study suggests several directions for future research. First, future research
needs to look into the determinants of corporate income tax volatility. While this study
focused only on sales tax and individual income tax, as one of the major state revenue
sources, corporate income tax certainly is worth a separate examination, considering
increasingly complex and controversial issues surrounding its structure [e.g. an
apportionment formula (sales-only or three-factor, property, payroll, and sales, formula),
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deductions for dividend received and foreign royalty, and the reporting method of income
tax returns (combined or separate)].
Second, it would be interesting to look into what categories of state spending and
what taxes are particularly affected by cyclical changes in tax revenues. Analyses using
total spending and overall tax rate obscure variations that may exist across spending
categories and taxes. Further analyses by spending category and tax will possibly reveal
what categories of spending are the main driver of spending growth during expansionary
periods and subject to larger cuts during recessions; and what taxes are preferred for rate
cuts during upturn years and are more frequently used to reduce budget gaps during
downturn years.
Another intriguing question to ponder would be what factors lead some states to
choose spending increases over tax cuts during booms (tax increases over spending cuts
in the face of a deficit during recessions) and some others to do the opposite. Immediate
intuition suggests that with surplus revenue in hand, states with more conservative voters
would likely prefer tax cuts to spending increases, and in the face of a budget deficit,
spending cuts to tax increases. Empirically, such a political factor could be accounted for
by including a broad range of voter preferences.
Lastly, future research needs to be done on factors that influence policy choices
regarding tax base composition. In Chapter 2, the study develops the causal system that
links tax base composition—which is considered exogenous—to revenue volatility. A
natural question that follows is, ―What factors determine state policy decisions in
designing their tax bases?‖ More specific questions are, ―What factors make some states
to tax a wide range of services and some others to exempt capital gains to a great extent?‖
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While one may be intuitive to presume that political factors such as voter preferences will
have influences on policy choices concerning tax structure, these questions certainly
require separate empirical investigations.
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REFERENCES
Aisen, Ari, and Francisco José Veiga. 2008. Political Instability and Inflation Volatility.
Public Choice 135(3-4): 207–223.
Alesina, Alberto, Filipe R Campante, and Guido Tabellini. 2008. Why Is Fiscal Policy
Often Procyclical? Journal of the European Economic Association 6(5): 1006–
1036.
Alt, James E, and Robert C Lowry. 1994. Divided Government, Fiscal Institutions, and
Budget Deficits: Evidence from the States. The American Political Science
Review 88(4): 811–828.
Alm, James, and Janet Rogers. 2011. Do State Fiscal Policies Affect State Economic
Growth? Public Finance Review 39(4): 483–526.
Anderson,William, Myles S Wallace, and John T Warner. 1986. Government Spending
and Taxation: What Causes What? Southern Economic Journal 52(3): 24–31.
Aragon, Fernando M. 2009. The Flypaper Effect Revisited. LSE STICERD Research
Paper No. EOPP004. Available at SSRN: http://ssrn.com/abstract=1546898.
Baghestani, Hamid, and Robert McNown. 1994. Do Revenues or Expenditures Respond
to Budgetary Disequilibria? Southern Economic Journal 61(2): 311–322.
Bahl, Roy W, and Richard Hawkins. 1997. The Sales Tax in Georgia: Issues and Options.
Fiscal Research Program, School of Policy Studies, Georgia State University
Bails, Dale, and Margie A Tieslau. 2000. The Impact of Fiscal Constitutions on State and
Local Expenditures. Cato Journal 20(2): 255–277.
Barro, Robert J. 1979. On the Determination of the Public Debt. Journal of Political
Economy 87(5): 940–971.
Barro, Robert J. 1990. On the Predictability of Tax-Rate Changes. In Macroeconomic
Policy, ed. Robert J. Barro, 268–297. Cambridge, MA: Harvard University Press.
Baxter, Marianne, and Robert G King. 1999. Measuring Business Cycles: Approximate
Band-Pass Filters for Economic Time Series. Review of Economics and Statistics
81(4): 575–593.
Behn, Robert D, and Elizabeth K Keating. 2004. Facing the Fiscal Crises in State
Governments: National problem; National responsibilities. Faculty Research
Working Papers Series. John F. Kennedy School of Government.
Behn, Robert D, and Elizabeth K Keating. 2005. The Fiscal Crisis of the States:
Recession, Structural Spending Gap, or Political Disconnect? Taubman Center,
Kennedy School of Government, Harvard University.
175
Blackley, Paul R. 1986. Causality between Revenues and Expenditures and the Size of
the Federal Budget. Public Finance Quarterly 14(2): 139–156.
Boggs, Paul T, Richard H Byrd, and Robert B Schnabel. 1987. A Stable and Efficient
Algorithm for Nonlinear Orthogonal Distance Regression. Society for Industrial
and Applied Mathematics (SIAM) Journal of Scientific and Statistical Computing
8(6): 1052–1078.
Boggs, Paul T, and Janet E Rogers. 1990. Orthogonal Distance Regression. In Statistical
Analysis of Measurement Error Models and Applications: Proceedings of the
AMS-IMS-SIAM Joint Summer Research Conference (held June 10-16, 1989), eds.
Philip J Brown and Wayne A Fuller, 183–194. Providence, RI: American
Mathematical Society.
Bohn, Henning. 1991. Budget Balance through Revenue or Spending Adjustments?
Journal of Monetary Economics 27(3): 333–359.
Bohn, Henning, and Robert P Inman. 1996. Balanced-Budget Rules and Public Deficits:
Evidence from the U.S. States. Carnegie-Rochester Conference Series on Public
Policy 45: 13–76.
Bradford, David F, and Wallace E Oates. 1971a. The Analysis of Revenue Sharing in a
New Approach to Collective Fiscal Decisions. Quarterly Journal of Economics
85(3): 416–439.
Bradford, David F, and Wallace E Oates. 1971b. Towards a Predictive Theory of
Intergovernmental Grants. American Economic Review 61(2): 440–448.
Braun, Bradley M, and Yasuji Otsuka. 1998. The Effects of Economic Conditions and
Tax Structures on State Revenue Flows. International Advances in Economic
Research 4(3): 259–269.
Brewer, H L, and Ronald L Moomaw. 1984. A Note on Population Size, Industrial
Diversification and Regional Economic Instability. Urban Studies 22(4): 349–354.
Browning, E S. 2011. Retiring Boomers Find 401(k) Plans Fall Short. Wall Street
Journal. Available at
http://online.wsj.com/article/SB1000142405274870395960457615279274870735
6.html?mod=WSJ_hp_MIDDLENexttoWhatsNewsTop.
Brubaker, Earl R. 1997. The Tragedy of the Public Budgetary Commons. The
Independent Review 1(3): 353–370.
Bruce, Donald, William F Fox, and Markland Tuttle. 2006. Tax Base Elasticities: A
Multistate Analysis of Long Run and Short Run Dynamics. Southern Economic
Journal 73(2): 315–341.
Buchanan, James M, and Gordon Tullock. 1962. The Calculus of Consent. Ann Arbor,
MI: University of Michigan Press.
176
Buchanan, James M, and Richard E Wagner. 1977. Democracy in deficit: The Political
Legacy of Lord Keynes. New York, NY: Academic Press.
Buchanan, James M, and Richard E Wagner. 1978. Dialogues Concerning Fiscal
Religion. Journal of Monetary Economics 4(3): 627–636.
Caiden, Naomi. 1981. Public Budgeting amidst Uncertainty and Instability. Public
Budgeting and Finance 1(1): 6–19.
Campbell, Colin Dearborn; Campbell, Rosemary G. 1976. A Comparative Study of the
Fiscal Systems of New Hampshire and Vermont, 1940–1974: A Report to the
Wheelabrator Foundation, Inc. Hampton, NH: The Foundation.
Catao, Luis A, and Bennett W Sutton. 2002. Sovereign Defaults: The Role of Volatility.
IMF Working Papers 02/149. International Monetary Fund.
Center for Budget and Policy Priorities. 2005. A Brief Overview of State Fiscal
Conditions and the Effects of Federal Policies on State Budgets. Available at
http://www.cbpp.org/cms/index.cfm?fa=view&id=1329.
Center for Budget and Policy Priorities. 2010. An Update on State Budget Cuts.
Available at http://www.cbpp.org/cms/index.cfm?fa=view&id=1214 &
http://www.cbpp.org/cms/?fa=view&id=711.
Chamberlain, Andrew, and Patrick Fleenor. 2007. Tax Pyramiding: The Economic
Consequences of Gross Receipts Taxes. Washington, DC: Tax Foundation.
Chang, Tsangyao, Wen Rong Liu, and Steven B Caudill. 2002. Tax-and-Spend, Spend-
and-Tax, or Fiscal Synchronization: New Evidence for Ten Countries. Applied
Economics 34(12): 1553–1561.
Chowdhury, Abdur R. 1988. Expenditures and Receipts in State and Local Government
Finances: Comment. Public Choice, 59(3): 277–285.
Cornia, Gary C, and Ray D Nelson. 2010. State Tax Revenue Growth and Volatility.
Regional Economic Development 6(1): 23–58.
Crain, W Mark. 2003. Volatile States: Institutions, Policy, and the Performance of
American State Economies. Ann Arbor, MI: University of Michigan Press.
Dothan, Michael U, and Fred Thompson. 2006. Optimal Budget Rules: Making
Government Spending Sustainable Through Present-Value Balance. Available at
http://ssrn.com/abstract=939815.
Dunleavy, Patrick, and Brendan O'Leary. 1987. Theories of the State. Basingstoke, UK:
Macmillan Education.
Dye, Richard F, and Therese J McGuire. 1991. Growth and Variability of State
Individual Income and General Sales Taxes. National Tax Journal 44(1): 55–66.
177
Edwards, Chris, Stephen Moore, and Phil Kerpen. 2003. States Face Fiscal Crunch after
1990s Spending Surge. Cato Briefing Paper No. 80. Washington, DC: Cato
Institute.
Eichengreena, Barry, and Tamim Bayoumib. 1994. The Political Economy of Fiscal
Restrictions: Implications for Europe from the United States. European Economic
Review 38(3-4): 783–791.
Endersby, James W, and Michael J Towle. 1997. Effects of Constitutional and Political
Controls on State Expenditures. Publius 27(1): 83–98.
Engle, Robert F, and Clive W J Granger. 1987. Co-integration and Error Correction:
Representation, Estimation, and Testing. Econometrica 55(2): 251–276.
Ezell, Stephen J, and Robert D Atkinson. 2011. The Case for a National Manufacturing
Strategy. The Information Technology and Innovation Foundation.
Felix, R Alison. 2008. The Growth and Volatility of State Tax Revenue Sources in the
Tenth District. Economic Review 93(3): 63–88.
Fiorito, Riccardo. 1997. Stylized Facts of Government Finance in the G7. IMF Working
Paper 97/42.
Fiorito, Riccardo, and Tryphon Kollintzas. 1994. Stylized Facts of Business Cycles in the
G7 from a Real Business Cycles Perspective. European Economic Review 38(2):
235–269.
Fisher, Louis. 1997. Biennial Budgeting in the Federal Government. Public Budgeting
and Finance 17(3): 87–97.
Foster, Ann C. 1997. Public and Private Sector Defined Benefit Pensions: A Comparison.
Compensation and Working Conditions.
Fox, William E, and Charles Campbell. 1984. Stability of the State Sales Tax Income
Elasticity. National Tax Journal 37(2): 201–212.
Friedman, Milton. 1975. There's No Such Thing as a Free Lunch. La Salle, IL: Open
Court Publishing Co.
Friedman, Milton. 1978. The Limitations of Tax Limitation. Policy Review 5(Summer):
7–14.
Friedman, Milton. 1983. Bright Promises Dismal Performance: An Economist's Protest.
San Diego, CA: Harcourt Brace Jovanovich.
Friedman, Milton. 2003. What Every American Wants. Wall Street Journal (January 15):
A10.
Gamage, David S. 2010. Preventing State Budget Crises: Managing the Fiscal Volatility
Problem. California Law Review 98(3): 101–163.
178
Garcia, Sophie, and Pierre-Yves Henin. 1999. Balancing Budget through Tax Increases
or Expenditure Cuts: Is It Neutral? Economic Modeling 16(4): 591–612.
Garrett, Thomas A. 2006. Evaluating State Tax Revenue Variability: A Portfolio
Approach. FRB of St. Louis Working Paper No. 2006-008A. Available at SSRN:
http://ssrn.com/abstract=881778.
Gavin, Michael, and Roberto Perotti, 1997a. Fiscal Policy in Latin America. NBER
Macroeconomics Annual 12: 11–61.
Gavin, Michael, and Ricardo Perotti. 1997b. Fiscal Policy and Saving in Good Times and
Bad Times. In Promoting Savings in Latin America, eds. Ricardo Hausmann and
Helmut Reisen. Inter-American Development Bank/OECD.
Gavin, Michael, Ricardo Hausmann, Roberto Perotti, and Ernesto Talvi. 1996. Managing
Fiscal Policy in Latin America. Working Paper. Inter-American Development
Bank.
Gentry, William M, and Helen F Ladd. 1994. State Tax Structure and Multiple Policy
Objectives. National Tax Journal 47(4): 747–772.
Gildenhuys, J.S.H. 1997. Public Financial Management. Pretoria, South Africa: J.L. van
Schaik Publishers.
Gould, Brandon. 2009. Divided Governments and Tough Choices: An Empirical Study of
the Effect of Divided Government on State Expenditures. Presidential Fellows
Program Draft Paper. Center for the Study of the Presidency. Available at
http://64.177.72.250/pubs/Fellows_2009/drafts_Domestic_Challenges.pdf.
Gouras, Matt. 2011. GOP Questioning Mont. Gov's Line-Item Veto on Tax.
Businessweek. Available at
http://www.businessweek.com/ap/financialnews/D9N2019O0.htm.
Gramlich, Edward M. 1977. Intergovernmental Grants: A Review of the Empirical
Literature. In The Political Economy of Fiscal Federalism, ed. Wallace E Oates,
219–240. Lexington, MA: Lexington Books.
Gramlich, Edward M, and Harvey Galper. 1973. State and Local Fiscal Behavior and
Federal Grant Policy. Brookings Papers on Economic Activity 1: 15–65.
Granger, Clive W J. 1969. Investigating Causal Relationships by Econometric Models
and Cross Spectral Methods. Econometrica 37(3): 424–438.
Greenstein, Robert, and James Horney. 2000. Biennial Budgeting: Do the Drawbacks
Outweigh the Advantages? Center on Budget and Policy Priorities. Available at
http://www.cbpp.org/cms/?fa=view&id=1243.
Groves, Harold M, and C Harry Kahn. 1952. The Stability of State and Local Tax Yields.
American Economic Review 42(1): 87–102.
179
Guilkey, David K, and Michael K Salemi. 1982. Small Sample Properties of Three Tests
of Granger-Causal Ordering in a Bivariate Stochastic System. Review of
Economics and Statistics 64(4): 668–680.
Hardin, Garrett. 1968. The Tragedy of the Commons. Science 162: 1243–1247.
Hasan, Seid Y, and Abdul Hamid Sukar. 1995. Short and Long-run Dynamics of
Expenditures and Revenues and the Government Budget Constraint: Further
Evidence. Journal of Economics 22(2): 35–42.
Holcombe, Randall G. 1993. Are There Ratchets in the Growth of Federal Government
Spending? Public Finance Review. 21(1) 33–47.
Holcombe, Randall G, and Russell S Sobel. 1997. Growth and Variability in State Tax
Revenue: An Anatomy of State Fiscal Crises. Greenwood Press: Westport, CT.
Holcombe, Randall G, and Jeffrey A Mills. 1994. Is Revenue-Neutral Tax Reform
Revenue Neutral? Public Finance Quarterly 22(1): 65–85.
Hoover, Kevin D, and Steven M Sheffrin. 1992. Causation, Spending, and Taxes: Sand in
the Sandbox or Tax Collector for the Welfare State? American Economic Review
82(1): 225–48.
Hou, Yilin. 2005. Fiscal Reserves and State Own-Source Expenditure in Downturn Years.
Public Finance Review 33(1): 117–144.
Hou, Yilin, 2006. Budgeting for Fiscal Stability over the Business Cycle. Public
Administration Review 66(5): 730–741.
Hou, Yilin, and Jason S Seligman. 2007. Lost Stability? Consumption Taxes and the
Cyclical Volatility of State and Local Revenues. Available at SSRN:
http://ssrn.com/abstract=1029697.
Hou, Yilin, and Daniel L Smith, 2006. Informal Norms as a Bridge between Formal
Rules and Outcomes of Government Financial Operations: Evidence from State
Balanced Budget Requirements. Journal of Public Administration Research and
Theory 20(3): 655–678.
Hou, Yilin, and William D Duncombe. 2008. State Saving Behavior: Effects of Two
Fiscal and Budgetary Institutions. Public Budgeting and Finance 28(3): 48–67.
Hou, Yilin, and Donald P Moynihan. 2008. The Case for Countercyclical Fiscal Capacity.
Journal of Public Administration Research and Theory 18(1): 139–159.
Hovey, Hal. 1998. The Outlook for State and Local Finances: The Dangers of Structural
Deficits to the Future of American Education. Washington, DC: National
Education Association.
180
Huang, Chao-His, and Kenneth S Lin. 1993. Deficits, Government Expenditures, and Tax
Smoothing in the United States: 1929–1988. Journal of Monetary Economics
31(3): 317–339.
Huffman, Gregory W. 1994. A Primer on the Nature of Business Cycles. FRB of Dallas
Economic Review (First Quarter): 27–41.
Iacoviello, Matteo M, Fabio Schiantarelli, and Scott D Schuh. 2010. Input and Output
Inventories in General Equilibrium. FRB International Finance Discussion Paper
No. 1004. Available at SSRN: http://ssrn.com/abstract=1708202.
Ilzetzki, Ethan. 2010. Rent-Seeking Distortions and Fiscal Procyclicality. Journal of
Development Economics 96(1): 30-46.
Ilzetzki, Ethan, and Carlos A Vegh. 2008. Procyclical Fiscal Policy in Developing
Countries: Truth or Fiction? NBER Working Paper No. 14191.
Islam, Muhammad Q. 2001. Structural Break, Unit Root, and the Causality between
Government Expenditures and Revenues. Applied Economics Letters 8(8): 565–
567.
Jacobsen, Dag Ingvar. 2006. Public Sector Growth: Comparing Politicians‘ and
Administrators‘ Spending Preferences. Public Administration 84(1): 185–204.
Johansen, Soren. 1988. Statistical Analysis of Cointegration Vectors. Journal of
Economic Dynamics and Control 12(2-3): 231–254.
Jones, Jonathan D, and David Joulfaian. 1991. Federal Government Expenditures and
Revenues in the Early Years of the American Republic: Evidence from 1792–
1860. Journal of Macroeconomics 13(1): 133–155.
Johnson, Nicholas. 2002. The State Tax Cuts of the 1990s, the Current Revenue Crisis,
and Implications for Services. Center on Budget and Policy Priorities. Available
at http://www.cbpp.org/cms/index.cfm?fa=view&id=1379.
Joulfaian, David, and Rajen Mookerjee. 1990a. The Government Revenue-Expenditure
Nexus: Evidence from a State. Public Finance Quarterly 18(1): 92–103.
Joulfaian, David, and Rajen Mookerjee. 1990b. The Intertemporal Relationship between
State and Local Government Revenues and Expenditures: Evidence from OECD
Countries. Public Finance 45(1): 109–117.
Joulfaian, David, and Rajen Mookerjee. 1991. Dynamics of Government Revenues and
Expenditures in Industrial Economies. Applied Economics 23(12): 1839–1844.
Joyce, Philip G. 2001. What's So Magical about Five Percent? A Nationwide Look at
Factors That Influence the Optimal Size of State Rainy Day Funds. Public
Budgeting and Finance 21(2): 62–87
181
Kaminski, Graciela, Carmen Reinhart, and Carlos Vegh. 2004. When It Rains It Pours:
Procyclical Capital Flows and Macroeconomic Policies. In NBER
Macroeconomic Annual 2004, eds. Mark Gertler and Kenneth Rogoff. Cambridge,
MA: MIT Press.
Kearns, Paula S. 1994. State Budget Periodicity: An Analysis of the Determinants and the
Effect on State Spending. Journal of Policy Analysis and Management 13(2):
331–362.
Keech, William R, and Kyoungsan Pak. 1989. Electoral Cycles and Budgetary Growth in
Veterans' Benefit Programs. American Journal of Political Science 33(4): 901–
911.
Knight, Brian G, Andrea L Kusko , and Laura Rubin. 2003. Problems and Prospects for
State and Local Governments. State Tax Notes 29(6): 427–439.
Knight, Brian, and Arik Levinson. 1999. Rainy Day Funds and State Government
Savings. National Tax Journal 52(3): 459–472.
Koren, Stefan, and Alfred Stiassny. 1995. Tax and Spend, or Spend and Tax? An
International Study. Journal of Policy Modeling 20(2): 163–191.
Kort, John R. 1981. Regional Economic Instability and Industrial Diversification in the
U.S. Land Economics, 57(4): 596–608.
Krugman, Paul. Making Things in America. New York Times. Available at
http://www.nytimes.com/2011/05/20/opinion/20krugman.html.
Lane, Philip, and Aaron Tornell. 1998. Why aren‘t Savings Rates in Latin America
Procyclical? Journal of Development Economics 57(1): 185–199.
Lav, Iris J, Elizabeth McNichol, and Robert Zahradnik. 2005. Faulty Foundation: State
Structural Budget Problems and How to Fix Them. Center on Budget and Policy
Priorities. Available at www.cbpp.org/5-17-05sfp.pdf.
Lee, Robert D, and Philip G Joyce. 2007. Public Budgeting Systems (8th ed). Sudbury,
MA: Jones and Bartlett Publishers.
Lucas, Robert E, Jr., and Nancy L Stokey. 1983. Optimal Fiscal and Monetary Policy in
an Economy without Capital. Journal of Monetary Economics 12(1): 55–93.
Malizia, Emil E, and Shanzi Ke. 1993. The Influence of Economic Diversity on
Employment and Stability. Journal of Regional Science 3(2): 221–235.
Manage, Neela, and Michael L Marlow. 1986. The Causal Relation between Federal
Expenditures and Receipts. Southern Economic Journal 52(3): 617–629.
Markowitz, Harry. 1952. Portfolio Selection. Journal of Finance 7(1): 77–91.
Marlow, Michael L, and Neela Manage. 1987. Expenditures and Receipts: Testing for
Causality in State and Local Government Finances. Public Choice 5(3): 243–255.
182
Marlow, Michael L, and Neela Manage. 1988. Expenditures and Receipts in State and
Local Government Finances: Reply. Public Choice 59(3): 287–290.
Martell, Christine R, and Bridget M Smith. 2004. Grant Levels and Debt Issuance: Is
There a Relationship? Is There Symmetry? Public Budgeting and Finance 24(3):
65–81.
Mazerov, Michael. 2009. Expanding Sales Taxation of Services: Options and Issues.
Center on Budget and Policy Priorities. Available at http://www.cbpp.org/files/8-
10-09sfp.pdf.
Meltzer, Allan H, and Scott F Richard. 1981. A Rational Theory of Size of Government.
Journal of Political Economy 89(5): 914–927.
Mikesell, John L. 2001. Sales Tax Incentives for Economic Development: Why Shouldn't
Production Incentives be General? National Tax Journal 54(3): 557–567.
Miller, Stephen M, and Frank S Russek. 1989. Co-Integration and Error-Correction
Models: The Temporal Causality between Government Taxes and Spending.
Southern Economic Journal 57(1): 221–229.
Mitchell, Wesley C. 1941. What Happens During Business Cycles: A Progress Report.
National Bureau of Economic Research.
Modigliani, Franco, and Richard H Brumberg. 1954. Utility Analysis and the
Consumption Function: An Interpretation of Cross-Section Data. In Post-
Keynesian Economics, ed. Kenneth K. Kurihara, 388–436. New Brunswick, NJ:
Rutgers University Press.
Modiglinai, Franco, and Richard H Brumberg. 1980. Utility analysis and aggregate
consumption functions: an attempt at integration. In The Collected Papers of
Franco Modigliani: Volume 2, The Life Cycle Hypothesis of Saving, ed. Andrew
Abel, 128–197. Cambridge, MA: MIT Press.
Moore, Stephen. 1991. State Spending Splurge: The Real Story behind the Fiscal Crisis
in State Government. Cato Policy Analysis 152. Cato Institute.
Mounts, W Stewart, and Clifford Sowell. 1997. Taxing, Spending, and the Budget
Process: The Role of Budget Regimes in the Intertemporal Budget Constraint.
Swiss Journal of Economics and Statistics 133(3): 421–439.
Mullins, Daniel R, and Philip G Joyce. 1996. Tax and Expenditure Limitations and State
and Local Fiscal Structure: An Empirical Assessment. Public Budgeting and
Finance 16(1): 75–101.
Musgrave, Richard. 1966. Principles of Budget Determination. In Public Finance:
Selected Readings, eds. Helen A Cameron and William L Henderson. New York,
NY: Random House.
183
Niskanen, William A. 1991. A Reflection on Bureaucracy and Representative
Bureaucracy. In The Budget-Maximizing Bureaucrat: Appraisals and Evidence,
eds. Andre Blais and Stephane Dion. Pittsburgh, PA: University of Pittsburgh
Press.
Niskanen, William A. 2003. A Case for Divided Government. Cato Policy Report
(March/April): 2.
Niskanen, William A. 2005. ―Starving the Beast‖ Will Not Work. In Cato Handbook on
Policy (6th ed): 113–116. Cato Institute.
Nordhaus, William. 1975. The Political Business Cycle. Review of Economic Studies
42(2): 169–190.
Tufte, Edward R. 1978. Political Control of the Economy. Princeton, NJ: Princeton
University Press.
Otsuka, Yasuji, and Bradley M Braun. 1999. The Random Coefficient Approach for
Estimating Tax Revenue Stability and Growth. Public Finance Review 27(6):
665–676.
Owoye, Oluwole. 1995. The Causal Relationship between Taxes and Expenditures in the
G7 Countries: Cointegration and Error Correction Models. Applied Economics
Letters 2(1): 19–22.
Parkinson, C Northcote. 1960. The Law and the Profits. New York, NY: Ballantine
Books.
Payne, James E. 1995. The Tax-Spend Debate: Time Series Evidence from State Budgets.
Public Choice 95(3-4): 307–320.
Payne, James E. 2003. A Survey of the International Empirical Evidence on the Tax-
Spend Debate. Public Finance Review 31(3): 302–324.
Perry, Martin K. 1988. Vertical Integration: Determinants and Effects. In Handbook of
Industrial Organization, eds. Richard Schmalensee and Robert D Willig: Ch.4.
Amsterdam, NY: North-Holland.
Peacock, Alan T, and Jack Wiseman. Approaches to the Analysis of Government
Expenditure Growth. Public Finance Review 7(1): 3–23.
Posner, Paul L, and Bryon S Gordon. 2001. Can Democratic Governments Save?
Experiences of Countries with Budget Surpluses. Public Budgeting and Finance
21(2): 1–28.
Poterba, James M. 1994. State Responses to Fiscal Crises: The Effects of Budgetary
Institutions and Politics. Journal of Political Economy 102(4): 799–821.
Ram, Rati. 1988a. A Multicountry Perspective on Causality between Government
Revenue and Government Expenditure. Public Finance 43(2): 261–269.
184
Ram, Rati. 1988b. Additional Evidence on Causality between Government Revenue and
Government Expenditure. Southern Economic Journal 54(3): 763–769.
Richards, Daniel J. 1986. Unanticipated Money and the Political Business Cycle. Journal
of Money, Credit, and Banking 18(4): 447–457.
Romer, Christina D, and David H Romer. 2007. Do Tax Cuts Starve the Beast? The
Effect of Tax Changes on Government Spending. NBER Working Paper No.
13548.
Ross, Kevin L, and James E Payne. 1998. A Reexamination of Budgetary Disequilibria.
Public Finance Review 26(1): 67–79.
Savage, Jaivies D. 1992. California's Structural Deficit Crisis. Public Budgeting &
Finance 12(2): 82–97.
Schunk, Donald, and Douglas Woodward. 2005. Spending Stabilization Rules: A
Solution to Recurring State Budget Crises? Public Budgeting and Finance 25(4):
105–124.
Shah, Anwar. 2005. Fiscal Management. Washington, D.C.: World Bank.
Sheffrin, Steven M. 2004. State Budget Deficit Dynamics and the California Debacle.
Journal of Economic Perspectives 18(2): 205–226.
Sichel, Daniel E. 1993. Business Cycle Asymmetry: A Deeper Look. Economic Inquiry
31(2): 224–236.
Sims, Christopher A. 1972. Money, Income, and Causality. American Economic Review
62(4): 540–552.
Sims, Christopher A. 1980. Macroeconomics and Reality. Econometrica, 48(1): 1–48.
Smith, Daniel, and Yilin Hou. 2008. State Spending Behavior under Heterogeneous
Balanced Budget Requirement Systems: A Long-Panel Study. Unpublished paper
available on the Social Science Research Network.
Sobel, Russell S, and Randall G Holcombe. 1996. Measuring Measuring the Growth and
Variability of Tax Bases over the Business Cycle. National Tax Journal 49(4):
535–552.
Stansel, Dean, and David T Mitchell. 2008. State Fiscal Crises: Are Rapid Spending
Increases to Blame? Cato Journal 28(3): 435–448.
Steindl, Frank G. 2007. What Ended the Great Depression? It Was Not World War II.
The Independent Review 12(2): 179–198.
Strazicich, Mark C. 1997. Does Tax Smoothing Differ by the Level of Government?
Time Series Evidence from Canada and the United States. Journal of
Macroeconomics 19(2): 305– 326.
185
Sturzenegger, Federico, and Rogério L F Werneck. 2006. Fiscal Federalism and
Procyclical Spending: The Cases of Argentina and Brazil. Económica 52(1-2):
151–194.
Tempelman, Jerry H. 2006. Does ‗Starve the Beast‘ Work? Cato Journal 26(3): 559–571.
Tornell, Aaron, and Philip Lane. 1998. Are Windfalls a Curse? Journal of International
Economics 44(1): 83–112.
Tornell, Aaron, and Philip Lane. 1999. The Voracity Effect. American Economic Review
89(1): 22– 46.
U.S. Census Bureau. 1993. We the American… Foreign Born. U.S. Department of
Commerce Economics and Statistics Administration. Available at
http://www.census.gov/apsd/wepeople/we-7.pdf.
Volden, Craig. 1999. Asymmetric Effects of Intergovernmental Grants: Analysis and
Implications for U.S. Welfare Policy. Publius 29(3): 51–73.
von Furstenberg, George M, R Jeffery Green, and Jin-Ho Jeong. 1985. Have Taxes Led
Government Expenditures? The United States as a Test Case. Journal of Public
Policy 5(3): 321–348.
Wagner, Gary A. 2006. Financing Government: Revenue Variability and the Role of
Rainy Day Funds. Popular Government 71(3): 24–30.
White, Fred C. 1983. Trade-Off in Growth and Stability in State Taxes. National Tax
Journal 36(1): 103–114.
Wildavsky, Aaron. 1988. The New Politics of the Budgetary Process. Glenview, IL: Scott,
Foresman.
White, Fred C. 1983. Trade-off in Growth and Stability in State Taxes. National Tax
Journal 36(1): 103–114.
Wooldridge, M. Jeffrey. 2002. Econometric Analysis and Cross Section and Panel Data.
Cambridge, MA: MIT Press.
Yan, Wenli. The Impact of Revenue Diversification and Economic Base on State
Revenue Stability. Journal of Public Budgeting, Accounting & Financial
Management. (Forthcoming)
186
Appendix A The Long-Run Income Elasticity of State Income Tax and Sales Tax
Revenues
Technically, elasticity is the ratio of the percent change in one variable to the
percent change in another variable. Following the standard estimation method of constant
elasticity, the following model is used to estimate the long-run elasticity of tax revenue
with respect to income:
where and denote the natural log of tax base and personal income of state
in year , and the regression coefficient represents the long-run income elasticity of
the tax revenue. Results are as follows:
Table A.1 Long-Run Income Elasticity of General Sales Tax and Individual Income
Tax by State (1992−2007)
State Sales Tax Income Tax
Alabama 0.8049 1.2059
Alaska No ST No PIT
Arizona 0.6640 1.0507
Arkansas 0.7880 1.1909
California 0.6947 1.8087
Colorado 0.8750 1.1957
Connecticut 0.2577 2.0579
Delaware No ST 1.3813
Florida 0.9193 No PIT
Georgia 0.7128 1.2476
Hawaii 1.4442 1.7838
Idaho 0.7558 1.0436
Illinois 0.8074 1.1424
Indiana 0.9179 0.2403
187
Iowa 0.1609 0.6652
Kansas 0.5756 1.7413
Kentucky 0.9828 1.0368
Louisiana 1.8479 1.2752
Maine 1.3640 1.4567
Maryland 0.7817 1.1274
Massachusetts 0.9473 1.3459
Michigan 0.3732 1.6766
Minnesota 0.5565 1.0089
Mississippi 0.8740 1.2627
Missouri 0.3331 1.2667
Montana No ST 1.2181
Nebraska 1.1694 1.0851
Nevada 0.9163 No PIT
New Hampshire No ST Excluded
New Jersey 0.8747 1.0068
New Mexico 0.2555 1.5367
New York 0.8245 1.5739
North Carolina 0.6669 1.1775
North Dakota 0.7387 1.1955
Ohio 1.3709 1.7949
Oklahoma 0.5950 1.7278
Oregon No ST 1.1884
Pennsylvania 0.9954 1.0314
Rhode Island 1.6021 1.3656
South Carolina 0.9384 0.9141
South Dakota 1.1725 No PIT
Tennessee 0.9119 Excluded
Texas 0.6914 No PIT
Utah 0.8251 1.2134
Vermont 0.3755 1.2793
Virginia 0.5900 1.3314
Washington 0.6496 No PIT
West Virginia 0.3351 0.9679
Wisconsin 0.9898 1.2564
Wyoming 1.2269 No PIT
Mean .8257 1.2702
Std. Dev. .3547 .3310
Min .1609 .2403
Max 1.8479 2.0579
188
Appendix B Results of Diagnostic Tests for Revenue Volatility Models
The results of diagnostic tests for the sales tax volatility model are as follows:
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of stvol
chi2(1) = 243.29
Prob > chi2 = 0.0000
Wooldridge test for autocorrelation in panel data
H0: no first-order autocorrelation
F( 1, 44) = 27.612
Prob > F = 0.0000
The results of diagnostic tests for the income tax volatility model are as follows:
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of itvol
chi2(1) = 146.32
Prob > chi2 = 0.0000
189
Wooldridge test for autocorrelation in panel data
H0: no first-order autocorrelation
F( 1, 40) = 14.667
Prob > F = 0.0004
190
Appendix C Results of Diagnostic Tests for Fiscal Policy Model
The results of diagnostic tests for the fiscal policy model are as follows:
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model
H0: sigma(i)^2 = sigma^2 for all i
chi2 (49) = 881.10
Prob>chi2 = 0.0000
Wooldridge test for autocorrelation in panel data
H0: no first-order autocorrelation
F( 1, 47) = 45.836
Prob > F = 0.0000
191
CURRICULUM VITAE
SUNJOO KWAK
1976 Born February 27 in Incheon, Republic of Korea
2002 Bachelor of Public Administration, Hankuk University of Foreign Studies, Seoul,
Republic of Korea
2005 Master of Public Administration, Hankuk University of Foreign Studies, Seoul,
Republic of Korea
2011 Ph. D. in Public Administration, Rutgers University, Newark, New Jersey