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Federal Reserve Bank of Chicago

Revenue Bubbles and Structural
Deficits: What’s a state to do?
Richard Mattoon and Leslie McGranahan

REVISED
April 2012
WP 2008-15

Revenue Bubbles and Structural Deficits: What’s a state to do?

The past two recessions have both proved alarming to state government finances. In 2001, a
relatively shallow national recession led to a severe downturn in state revenues that took three
years to unwind. In the wake of the recent economic downturn, signs of fiscal stress are readily
apparent. In this paper, we investigate whether the revenue patterns surrounding these two
recessions are the result of state government revenues having grown more sensitive to economic
conditions. We find that the responsiveness of revenues to measures of business cycle conditions
has grown since the 1990s. We use data on state government revenues, state specific information
on economic conditions, and measures of state policy to examine fiscal performance and budgeting
practice over the economic cycle. Our findings suggest that increasing income cyclicality, in
particular of capital gains, have made state revenues more responsive to the business cycle since
the mid-1990s. We also find that changes in policy making have served to increase revenue
cyclicality.

Richard Mattoon
Leslie McGranahan
Federal Reserve Bank of Chicago 1
Corresponding author contact:
Rick Mattoon 312-322-2428
Rick.Mattoon@chi.frb.org

1

The opinions expressed in this paper are the authors’ and do not reflect the opinions of the Federal
Reserve Bank of Chicago or of the Federal Reserve System.

1

1. Introduction
The fiscal condition of the states has been garnering headlines as governments confront
difficult choices in their quest to close substantial budget gaps. Budget gaps can arise because
expenditures exceed anticipated levels or because revenues come in below forecasts. The 20072009 recession led to pressure on both sides of the government ledger -- substantial declines in
tax revenues were coupled with increasing expenditure pressures. While some areas of the
economy have been improving, state and local governments continue to struggle and cut over
230,000 jobs in 2011.
Throughout the recession, poor revenue numbers challenged state budgets. The deep
national recession was met with sharp declines in revenues. (Dadayan and Boyd 2009b).
Analysis of revenue patterns during the 2007-2009 recession point to declines in revenues that
were broadly analagous to those observed during the 2001 recession. In the 2001 recession, a
modest recession led to disproportionate drops in revenues
In this paper, we investigate the relationship between state government tax revenues and
economic conditions and ask whether changes in this relationship can explain the extent of the
weakness in state revenues during the past two downturns. We find that while government
revenues have always responded to economic conditions, in the period since the late 1990s, this
responsiveness has grown more pronounced. We find that this increasing responsiveness has
been concentrated in the individual income tax and is due partly to increasing cyclicality of
income, particularly investment income, and partly due to changes in state income tax policy
making.
2. Literature
Numerous researchers have investigated the business cycle properties of state tax
revenues, state tax bases, and state tax policy. Holcombe and Sobel (1997) provide a book
length treatment of the measurement and sources of cyclical variability. They found that among
the three major max bases, the corporate income tax was the most procyclical followed by the
general sales tax base and the individual income tax base. More recently, Dye (2004) offers a
2

summary of the research strands and methodological issues involved in estimating the short run
elasticity of revenues to the business cycle and the related long run responsiveness of revenues to
economic growth. He concludes that the methodology of Holcombe and Sobel is well suited to
researching revenue cyclicality. He also stresses the heterogeneity across states in economic
cycles and revenue patterns.
Interest in the business cycle properties of revenues increased in the wake of the 2001
recession as researchers questioned why a modest national recession had led to a severe drop in
revenues. Maag and Merriman (2003) note that the fiscal crisis following the 2001 recession
was characterized by a revenue drought rather than by a rapid expansion in expenditures. They
attribute some of this pattern to reluctance among policy makers to increase major taxes during
the 2001 budget crisis. Figure 1 displays net changes in the three major state tax revenue sources
(sales, personal income and business income) from Fiscal Years 1988 to 2012 and shows that tax
increases were modest in the period around the 2001 recession in comparison to period around
the 1990 recession. The authors suggest three factors contributed to this behavior: 1) new
political constraints; 2) new legal constraints; and 3) unusual access to and appeal of short-term
methods of coping. A second factor that has been proposed as an explanation for the 2001 crisis
is a pronounced drop in income tax revenues particularly due to declining capital gains income
(Sjoquist and Wallace (2003) and Fox (2003)). This in turn was blamed on the sharp drop in the
stock market. Sjoquist and Wallace (2003) further investigate the role of capital gains income
by testing whether states that experienced an increase in per capita capital gains larger than the
average state were more susceptible to the economic downturn. They find that states with the
more capital gains per capita in 1999 were more likely to face declines in tax revenues in
FY2001 to FY2002. Bruce, Fox and Luna (2009), Fox (2003) and Fox and Luna (2002) show
that erosion in the sales and corporate income tax base also exacerbated the fiscal crisis of 2001.
By reducing reliance on the portions of the tax base that hold up the best during downturns (such
as food and drugs), states exacerbated the effects of the business cycle on their revenues. This
caused the corporate and sales taxes to perform particularly poorly during the economic
slowdown.
We add to this discussion by returning to the Holcombe and Sobel (1997) framework and
asking whether something changed in the period surrounding the 2001 recession that led
3

revenues to be more responsive to changing economic conditions. We investigate three of the
explanations highlighted in the literature on the revenue drought following the 2001 recession:
changes in policy making, increased reliance on capital gains, and changes in the tax base
outside of capital gains.
Our paper proceeds as follows: In Section 3, we discuss the state level panel data on
quarterly revenue and economic conditions that we use to assess revenue cyclicality. In Section 4
we introduce our methodology that follows in the tradition of Holcombe and Sobel (1997). In
Section 5, we document that a substantial increase in revenue cyclicality in the personal income
tax occurred in the period leading up to the 2001 recession. Section 6 focuses on explanations
for the sources of this increased cyclicality and in particular examines changes in the dynamics
of personal income and in state policy making. In Section 7 we assess the relative magnitudes of
the role of changing income dynamics and changing tax policy. Section 8 concludes and
discusses options available to policy makers.
3. Data
We focus our investigation on the relationship between economic variables and state
revenue performance from 1980-2011. We choose this time span due to issues of data
availability.
For state revenues we use the U.S. Census Bureau’s quarterly data on state government
revenues for each state. The series is available from 1962 to 2011:Q3. The series originally
covered only general sales and gross receipts, motor fuel sales, individual income, and motor
vehicle taxes. In recognition of the changing revenue structure of the states, the series now
covers 25 revenue sources. The Data Appendix contains further information about this data
source. Throughout, we measure revenue in real per capita terms.
To measure state specific economic cycles we use the state coincident indexes produced
by the Federal Reserve Bank of Philadelphia. The coincident indexes use a dynamic singlefactor model to summarize state economic conditions into one single statistic. The indexes
combine data on four state-level indicators -- nonfarm payroll employment, average hours
worked in manufacturing, the unemployment rate, and wage and salary disbursements deflated
by the consumer price index. The trend for each state’s index is set to the trend of its gross
4

domestic product (GDP). As a result, long-term growth in the state’s index matches long-term
growth in its GDP and a one percentage point increase in the growth of the coincident index is
roughly equivalent to a one percentage point increase in GDP growth. The index is produced
monthly but for our purposes it has been converted to a quarterly series and is available from
1979-2011. 2 Our results are robust to using nonfarm payroll employment as an alternative
measure of state economic conditions and to analyzing the longer time series for which this
measure is available. 3
For our investigation of the role of tax bases, we rely on annual state specific data from
the Internal Revenue Service (IRS), Statistics of Income (SOI). The IRS releases total Adjusted
Gross Income (AGI) by state and breakdowns of this income into numerous different
subcomponents. Data on AGI is only currently available through 2009.
In Table 1, we display quarterly variable means for the state level data for 1980-2011:Q3:
columns 3 and 4 show growth rates, columns 5 and 6 show levels. We display data for total tax
revenues, for the three single largest state revenue sources; general sales, individual income
taxes, and corporation net income taxes, for revenues excluding these three sources, which we
label other revenues and for the coincident indicator. We also include the means of four
measures of annual real state per capita income and their growth rates based on the SOI data –
total AGI, wage and salary income, investment earnings (capital gains, interest and dividends)
and other income (primarily retirement income).
The sample sizes for the growth rate calculations differ slightly across the different
revenue sources due to missing data, because certain states do not levy some taxes or because
revenues are negative and thus we cannot compute growth rates, and because we delete some
observations. 4 In particular, we delete observations for a given tax that are based on changes
2

Our quarterly index is the average of the monthly indexes. For more information on the State Coincident Indexes
see Federal Reserve Bank of Philadelphia, 2011.
3
The Employment data is available for the whole time period for which the revenue data is available (1962-2011).
We prefer the coincident measure because it is a more comprehensive measure of state economic conditions.
4
In the absence of missing data we would have 6350 observations (50 states for 4 quarters in 31.75 years (19802011:Q3)). For total tax revenues, we lose 69 observations due to missing revenue data and four due to huge swings
in revenues – three of these in Alaska. For income tax revenues, we lose 64 due to missing data, twelve for negative
realizations of revenues, and 891 due to zero revenues (states that do not levy an income tax in a given quarter), 25
due to large swings in revenues, and 244 due to the deletion of NH and TN. In the regressions, we lose an
additional 8 observations due to missing data on the coincident indicators for some states in1979. Including the
observations with big swings does not alter our conclusions although the coefficients change modestly.

5

from the first quarter when a new tax was introduced. We also delete observations where
revenue growth was over 200% or below -200% from one year to the next. Most of these
observations are also related to the introduction of new taxes or to changes in the collection
cycle. For the individual income tax, we also delete observations for New Hampshire and
Tennessee because the tax base is far narrower in these than in other states. 5
The variable means show that revenues have been growing over the sample period, with
revenue growth averaging 1.5%. Sales tax revenue growth has been below individual income
tax revenue growth on average, while corporate income tax revenues have been shrinking. The
coincident indicators have been trending up, by 2.2% on average. Annual data on state per
capita income show income has been growing as well with other income growing faster than
wage and salary income and investment income.
In Table 2, we present similar information for one cross section of the data – 1995:Q1,
close to the midpoint of out sample. We provide this second table of means to highlight the
variation in revenue growth rates across states at a given point in time. For instance while
average total revenue growth was close to zero, the standard deviation was 0.12. There is
substantially less heterogeneity in revenue levels and in income amounts and growth rates.

4. Model
We are interested in assessing the relationship of tax revenues to economic conditions. A
basic model capturing this relationship is:

ln Ri ,t =
α i + β ln ECi ,t + ε i ,t

(1.1)

Equation (1.1) relates state revenues (R) in state i at time t to economic conditions (EC) in the
state at time t. The variable β represents the elasticity of revenues to economic conditions.
We do not estimate this equation, but instead estimate a transformed version of this
equation:

5

New Hampshire and Tennessee tax only dividend and interest income.

6

∆ ln Ri ,t + 4 = α + β∆ ln ECi ,t + 4 + ε i ,t + 4

(1.2)

Where ∆ ln Ri ,t + 4 is the log difference in revenues between period t and period t+4, and

∆ ln ECi ,t + 4 the log difference in economic conditions.
We choose this transformation for three reasons. First, estimates of β in equation (1.1)
will be biased if the measures of revenues or of economic conditions are non-stationary. By
using growth rates, we transform these non-stationary series to ones that are stationary.
Relatedly, the use of growth rates allows us to capture the short run, or cyclical, responsiveness
of revenues because we are abstracting away from the long term relationship between economic
conditions and revenues. Most previous researchers investigating the business cycle properties
of revenues have estimated equations in growth rates for these reasons. 6 Our third rational for
this transformation is the result of our use of quarterly revenue data. Quarterly revenue
collections vary dramatically and systematically across the quarters of the calendar year. For
instance sales tax receipts are particularly high in the fourth quarter due to holiday spending and
income tax revenues are particularly high in the second quarter due to final tax payments. By
calculating growth rates relative to the same calendar quarter one year prior, we minimize the
role of these seasonal variations.

An alternative method to dealing with this timing issue

would be to use annual revenue data. We choose to use quarterly data rather than annual data
because economic conditions often change within a calendar year and the quarterly data allow us
to exploit the within year variation.
The coefficient β in equation (1.2) represents the average responsiveness of state
government revenue growth to changes in state economic conditions or the economic condition
elasticity of state government revenues.

If economic growth increases by 1 percentage point,

we expect tax revenue growth to increase by β percentage points. We label this responsiveness
revenue cyclicality. Tax revenue is procyclical if β > 0 while tax revenue is countercyclical if

β <0.

6

This is the transformation used in Holcombe and Sobel (1997). It is also used in studies of business cycle revenue
responses with data other than that for U.S. states. See for example Priesmeier et. al. 2011. Holcombe and Sobel
(1997) also investigate an error correction model. As Dye (1994) points out, the results from the error correction
model are quite close to those from the log-difference model presented here.

7

5. Revenue Cyclicality
In Table 3, we show estimates of equation 1.2. Traditional OLS regression is not
appropriate in this context if errors are correlated within a given state over time or across states
at a point in time. We consider two potential standard error corrections. First we consider panel
corrected standard errors allowing for state specific AR(1) processes. This correction allows for
correlation across states a point in time as well as serial persistence within states. Second, we
use a cluster covariance matrix estimator following the work of Bester, Conley, and Hansen
(2009). In particular, we cluster the standard errors by region by half-decade. Bester, Conley,
and Hansen (2009) shows that inference based on clustering in this manner in the face of
potentially serially and spatially correlated data performs well. We choose the later standard
error correction because it captures the spatial nature of our data. In addition, the standard errors
from this procedure tend to be modestly larger than the panel corrected standard errors indicating
that this is a more conservative approach. The panel corrected standard errors lead to
substantively similar conclusions.
From the table we see that total tax revenue as well as revenue from each of the
component tax sources is responsive to changing economic conditions. The number 0.888 in the
first column indicates that a 1 percentage point increase in the growth of the coincident index is
related to a 0.9 percentage point increase in the growth of revenues. Corporate income tax
revenue is the most cyclically sensitive revenue source, followed by personal income tax
revenues, and sales tax revenues. Other tax revenues are the least responsive to economic
conditions.
We have established the link between revenues and economic conditions at the state
level. Next we turn to the issue of whether this relationship changed as we approached the 2001
recession. Rather than assume that a break occurred at a particular juncture, we allow the data
to tell us whether a break occurred and if so when. In particular, following Andrews (1993), we
use Quandt Likelihood Ratio tests (QLR) with 15% trimming to both determine whether there
was a break in the data and also to discover when the break occurred.

We look for a break

using the data on total tax revenues and allowing breaks to occur between one year and the next
(between the fourth quarter of one year and the first quarter of the next year). A break is defined
as a change in the average growth rate of revenues across all states as well as a change in the
cyclicality of revenues. In particular, we estimate the following equation:
8

∆ ln Ri ,t + 4= α1 + α 2break + β1∆ ln ECi ,t + 4 + β 2 break × ∆ ln ECi ,t + 4  + ε i ,t + 4

(1.3)

Where break=1 if the observation occurs after the hypothesized break time. The coefficient α 2
allows for a different level of revenue growth before and after the break while the coefficient β 2
allows for different cyclicality.

We jointly test the coefficients α 2 and β 2 . For total tax

revenues, the data point to a break occurring in 2000. Going forward, we assume that a break
occurred in 2000. 7 As a result, the version of equation (1.3) that we present is:

∆ ln Ri ,t + 4= α1 + α 2 ( post 1999) + β1∆ ln ECi ,t + 4 + β 2 ( post 1999) × ∆ ln ECi ,t + 4  + ε i ,t + 4 (1.4)
Table 4 shows estimates of α1 , α 2 , β1 ,and β 2 for total tax revenues and for the four
different subcategories of tax revenues.

We see that overall revenues are more sensitive to

economic conditions in the later period than they were in the earlier period. Prior to 2000, a one
percentage point increase in economic growth was related to a 0.7 percentage point increase in
tax revenue growth. In 2000 and after, a one percentage point increase in economic growth was
related to a 1.3 percentage point increase in revenue growth. While sales tax revenue cyclicality
and other tax revenue sensitivity has been essentially unchanged, individual income tax revenue
cyclicality has quadrupled. While a one percentage point change in the growth of the coincident
index was related to a 0.6 percentage point change in individual income tax revenue growth prior
to 2000, it corresponds to a 2.1 percentage point change during the 2000-2011:Q3 period. This
confirms the findings of other studies that a change in income tax receipts has dominated
revenue patterns (see Sjoquist and Wallace, 2003). We also find a large negative coefficient on
the post 2000 dummy in the individual income tax regression. In the later period, annual income
tax revenue growth rates declined by 3 percentage points relative to the earlier period. This may
be the result of legislated income tax reductions during the 1990s (see Figure 1) or national
changes in the income tax base that affected most states. Corporate income tax revenue shows a
similar pattern to individual income tax revenues – cyclicality has doubled. In the remainder of
7

The 1% threshold for the QLR statistic with 2 restrictions is 7.8, the 10% threshold is 5. The QLR for overall tax
revenue is 19.3 (in 2000). It is 36.6 for the income tax (in 2000). Quarterly data point to breaks for overall
taxation in 1999:Q4 and for the income tax in 2000:Q1. We don’t want to interpret this result as indication that
something specific occurred in 2000 because the underlying Wald test is above 7.8 for the entire period from 19912005 for the income tax and above 7.8 from 1998-2005 for overall tax revenues. The QLR for the sales tax is 8.4 in
1987. Insofar as there was a change in the sales tax it occurred earlier. The QLR is below 5 for other taxes.

9

this paper we focus on the growing sensitivity of individual income tax revenues because it is a
large revenue source (about five times corporate collections) that has grown far more sensitive.
In Table 5, we show that this increase in individual income tax cyclicality is robust to the
inclusion of state and date fixed effects in our regression. Inclusion of state dummies (column 2)
has a very small impact on our estimated coefficients. We fail to reject that the state fixed
effects have no effect on income tax revenue growth. We find similar results for the other types
of taxes. We estimate the remaining regressions in the paper without state fixed effects.
Including date fixed effects in the form of year-quarter dummies reduces our estimates of
cyclicality (column 3), but we continue to see an increase in cyclicality after 2000. When we
include the date fixed effects we are relying on cross state variation in economic conditions.
Going forward, we estimate our models without these date effects and interpret our coefficient
on the coincident index as measuring the effect of both national and state specific economic
conditions.
In Table 6, we show the cyclicality of individual income tax revenues separately for each
of the expansions and contractions over the past three decades based on National Bureau of
Economic Research (NBER) business cycle dates. We do this to investigate whether the change
in cyclicality occurred primarily in one phase of the business cycle. This table shows that the
increased responsiveness of income tax revenues to economic conditions occurred during both
the expansionary and contractionary periods since 2001:Q1.

6. Sources of Increasing Sensitivity
Having established that individual income tax sensitivity to business cycle conditions has
increased both during recent recessions and recent contractions, we seek to address why this
increase has occurred. Tax revenues derive from an intersection between the tax base and tax
rate. The base is measured by what and who we tax while tax rates are determined by state tax

10

policies. 8 In the next two sections, we separately investigate increased sensitivity of the income
tax base and the income tax rate.

Changes in the Base
There are two potential ways to think about the income tax base. We can think of the
base as the income of individuals who reside in the state or are otherwise obligated to pay taxes
to the state. We call this the exogenous base. Alternative, we can think about the base as the
income that policy makers subject to the tax. We call this the selected base. For example,
retirement income is not subject to income tax in Illinois. Retirement income is part of the
exogenous base, but not part of the selected base.
We investigate increasing cyclicality for both types of base measurement. In order to
measure the exogenous base, we use AGI per capita of state residents as reported to the IRS on
Federal returns and released to the public in the Statistic of Income data. We choose this
measure of the tax base because it is collected in a consistent manner across all states, is
available for a substantial enough period of time to address our question of interest, and contains
information about income earned from different sources. In particular, the data contains
information on wage and salary income, investment income, and other income separately.
However, this is not a perfect measure of the exogenous base for two reasons. First, it may
include some income reported to the IRS by state residents that the state cannot tax. This is the
case because a resident may owe taxes to a state different from the one in which he resides. This
is particularly true of states that do not have reciprocal agreements with other states. Second,
this measure of income may not be exogenous to the tax rate. Research on tax rates shows a
positive relationship between tax rates and Federal AGI indicating that state policy may
influence the reported base. (Bruce, Fox and Yang 2010). We use this measure of the base
keeping in mind these weaknesses.

8

Federal tax policies are also potentially quite important because states often piggyback on Federal decisions. We
label all of these state tax policies because states can choose how to incorporate Federal choices into their tax
systems.

11

As our measure of the selected base, we follow Bruce, Fox and Yang (2010) and
calculate the per capita base as income tax revenues per capita divided by the top marginal tax
rate. 9 Bruce, Fox and Yang (2010) justify using of the top marginal rate because in most states,
this rate kicks in at a fairly low level of income. There are some weaknesses with this measure
as well. First, it is not available for those states that lack an income tax. Second, we only
calculate one measure so we cannot break the base down by the different sources of income.
Finally, this measure of the base has a systematic relationship with economic conditions because
better economic conditions imply that more income is covered by the top marginal rate due to tax
progressivity. As a result, this measure understates the tax base to a greater degree when
incomes are low.
In this section, we ask whether growth in real AGI per capita or its components, or the
selected base have become more sensitive to economic conditions. To do so, we use various
measures of income as the dependent variable and estimate the following equation.

∆ ln Inci , y +=
α1 + α 2 ( post 1999) + β1∆ ln ECi , y +1 + β 2 ( post 1999) × ∆ ln ECi , y +1  + ε i , y +1 (1.5)
1
Inc refers to adjusted gross income or one of its components, or to the selected base. The
variable y refers to the year, as opposed to the quarterly t. We estimate this equation on annual
data because AGI is only available annually. We measure annual economic conditions based on
an annual version of the coincident index.
In Table 7 we show estimates of equation (1.5) for AGI, wage and salary income,
investment income, other income, and the selected base. Investment income includes capital
gains, interest and dividends. Other income is primarily comprised of retirement income. The
table shows that overall income cyclicality nearly doubled and that while wage and salary
income grew modestly more cyclical, investment income grew massively more cyclical. While
prior to 2000, a one percentage point increase in economic growth was related to a 0.5
percentage point increase in investment income growth, after 2000, it was related to a 5.6

9

We cannot use the state base as reported to tax authorities as is done in Bruce, Fox and Yang (2010) because the
data are not available for enough time or states.

12

percentage point increase. In the final column of the table, we show that the cyclicality of the
selected base also increased. 10
While some of the increase in cyclicality of investment income is probably due to
increasing cyclicality and volatility of stock market returns, some may be due to issues related to
the strategic timing of capital gains realizations. Capital gains are reported to the IRS and taxed
based on when they are realized rather than when they accrue. As a result, taxpayers have some
ability to time capital gains realizations at the most tax advantaged juncture. In particular,
taxpayers have an incentive to time gains when they face a lower tax rate which can occur either
because they are in a lower bracket in some years than others or because the entire capital gains
structure is lower in some years than in others. One factor that may have influenced the pattern
of capital gains income in our data series is the drop in tax rates in 2003. Insofar as this was
anticipated, taxpayers had an incentive to hold off on realizing gains until the lower tax rates
went into effect. This may partly explain the substantial drops in capital gains realizations in
2001 and 2002, which also corresponded to a period of economic weakness. Tax rates on
dividends fell at the same time giving shareholders an incentive to ask corporations to delay
dividends. We investigate this further by adding year fixed effects into the regressions
predicting our various measures of the base. We present results in Table 8. In the case of
investment income, these fixed effects control for federal capital gains policy, annual stock
market behavior, and other features that were consistent across states. These estimates depend
solely on cross sectional heterogeneity in changes in economic conditions. We now observe a
modest and insignificant increase in cyclicality, rather than the nearly 11 fold increase presented
earlier. This suggests that features that varied over time, including both federal tax policy and
other things such as stock market returns, rather than across states, explain a large portion, of the
increase in investment income cyclicality. 11
Our analysis of changes in the base confirms that increased cyclicality of income can
partly explain the increase cyclicality of income tax revenues. We next investigate the effects of
10

One issue with this equation is that wage and salary income is one of the components coincident index. To move
away from worry that we are regressing the growth of wage and salary income a transformed version of itself, we
also regressed our measures of base growth rates on growth rates of state employment and observed a similar pattern
of results.
11
We also investigated whether changes in the distribution of income affected revenue cyclicality by dividing states
into groups based on their income dispersion. We find some differences across groups of states but conclude that
the increase in cyclicality was a broad based phenomenon.

13

changes in income tax rate setting policy on revenue sensitivity. We conjecture that since 2000,
one of the reasons for the increase in business cycle sensitivity has been changes in policy
making as highlighted by Magg and Merriman (2003). Even if the income tax base had not
become more cyclical, if the method of determining tax rates had changed we could observe
changes in income tax revenue cyclicality.
Changes in Rates
In order to investigate whether tax rate setting policy has changed we look at income tax
policy parameters using data from three sources. First, we divide annual income tax revenues by
annual state AGI to find out the portion of resident income that is collected by the state. We
label this the average effective tax rate. Second, we use the measures of marginal tax rates
calculated from the TAXSIM model developed by Dan Feenberg at the NBER. The model
provides information on the maximum marginal state tax rate paid on wage income, long term
capital gain income and mortgage interest by state through 2011. The model also provides the
average (dollar weighted) marginal state tax rate on wage income, capital gains income, pension
income and other sources of income through 2011. The average marginal tax rate data is
provided in three ways – based on the actual state distribution of income in the year in question,
based on the state distribution of income in 1995, and based on the national distribution of
income in 1995. We use tax rates based on the 1995 national income distribution because we
believe it best isolates the role of state tax policy. 12

Third, we use data on the top marginal

income tax rate on the state’s tax schedule. 13 The top rate on the schedule and the top rate paid
differ because the top paid rate takes the deductibility of state taxes paid to federal authorities
into account.
We use these data to look at whether the determination of effective, average and
maximum state tax rates has changed since 1999. To do this, we use changes in the tax rate as
the dependent variable. We investigate the relationship between the change in economic
conditions and the change in tax rates, by estimating the following equation:

12

Data is available from the NBER at http://www.nber.org/~taxsim/marginal-tax-rates/.
This data was collected from The Tax Foundation (2011), the World Almanac and Book of Facts (Various Years),
and the Book of the States (Various Years).
13

14

∆ ln Ratei , y +=
α1 + α 2 ( post 1999) + β1∆ ln ECi , y +1 + β 2  post 1999 × ∆ ln ECi , y +1  + ε i , y +1 (1.6)
1
The results for our different measures of tax rates are presented in Table 9. In the first
column of Table 9, we show that effective tax rates went from being modestly countercyclical
prior to 2000 to very procyclical. Prior to 2000, a one percentage point increase in the coincident
index corresponded to a 0.5 percentage point drop in effective rates. After 2000, the same
increase in the coincident index corresponded to a 1.1 percentage point increase in rates.
Changes in effective rates cofound two phenomena; effective rates may change due to policy
decisions, alternatively rates may change because of changes in the distribution of income across
individuals or sources. For example, a transfer of one dollar from a poor household to a higher
income household that is taxed at a higher marginal rate would increase revenues but would not
increase AGI so it would lead to an increase in effective rates. In order to isolate the effects of
policy, in columns (2)-(6) we present rates that are not a function of changes in the distribution
of income. In Column (2), we present results based on changes in maximum marginal tax rates
on wages from the TAXSIM model. We find that rates were strongly countercyclical prior to
2000. When the economy was shrinking more quickly, legislators increased rates to stabilize
revenues. By contrast, when economic conditions improved more rapidly rates were reduced.
By contrast, beginning in 2000, rates became less countercylical. We discern a similar pattern
for average marginal rates on wages (Column 4), and top marginal rates (Column 7). For rates
on capital income (Columns(3) and (5)), we observe that rates were consistently countercyclical
before and after 2000. For pension income (Column 6) we find that average rates changed from
being countercyclical to being acyclical. 14
We can also see evidence of the change in policy making if we look at enacted revenue
changes as displayed in Figure 1. During the 1990-1991 recession, 13 states enacted revenue
increases while four states enacted decreases yielding a net $2.9 Billion increase in personal
income tax revenues for Fiscal Year 1991. (NASBO September 1990). By contrast, during the
2001 recession, three states increased personal income taxes and 12 states decreased them
leading to a net $0.7 Billion drop in revenues for Fiscal Year 2002 (NASBO December 2001).
The tax increases for FY2010 (NASBO December 2009) were also fairly dramatic, but they are
difficult to compare because the recession was much more severe. Our investigation of tax rates
14

There is no data on maximum marginal rates on pension income.

15

indicates that changes in tax policy also work in the correct direction to party explain the
increase in cyclicality since 1999. While policy had traditionally been strongly countercyclical,
in particular in terms of tax rates on wages and pensions, and would serve to dampen the
response of revenues to the economic cycle, policy became less countercyclical in the later
period.

7. Contributions of the Rate and the Base
From the previous two sections were learn that changes in the tax base, particular in capital
income, served to increase revenue cyclicality while changes in tax rate policy, particularly
pertaining to wage and pension income also served to increase revenue cyclicality. Both of these
can partially explain the changes in revenue cyclicality we have observed. In order to bring the
information in the previous two sections together, we perform some calculations where we
compare the magnitude of these two effects.
We can break annual revenue growth into that attributed to the base and that attributed to the
rate recognizing that average revenues are equal to the average base times the average rate and
taking advantage of the convenient properties of logarithms:

∆ ln Rt ,t +1 = ln ( Ratet +1 × Baset +1 ) − ln ( Ratet × Baset ) = ∆ ln Ratet ,t +1 + ∆ ln Baset ,t +1
Because revenue growth is equal to the sum of base growth and rate growth, we can divide
revenue growth into base and rate growth and as a result can divide revenue cyclicality and
increases in cyclicality into that attributable to the rate and the base. To do this we need a
measure of revenues, the rate, and the base that are consistent with one another. In other words,
we need a measure of revenues that is equal to a measure of the base multiplied by a measure of
the rate. However, the measures of revenues, rates, and bases used in the previous sections are
from different sources and not consistent with one another. We develop four combinations of
revenues, income tax rates, and the income tax base that are internally consistent to investigate
the relative contributions of the tax rate and tax base to revenue cyclicality.

16

For our first combination, we create simulated revenues using the NBER TAXSIM data and
the IRS SOI data. In particular, we generate an estimate of tax revenues by combining the SOI
data on income by source and the NBER estimates of average (dollar weighted) marginal tax
rates on income by source. We use the average marginal tax rates that are based on 1995
national income distributions to isolate the role of tax policy. We measure income tax revenues
derived from wage and salary income by multiplying wage and salary income per capita from
SOI by the average marginal tax rate on wage and salary income. We do this for wage income,
capital gains income, interest income, dividend income, and other income and generate an
estimate of total income tax revenues by summing across these sources. We multiply other
income by the tax rate on pensions assuming that most other income derives from pensions.
This gives us an estimate of tax revenues that measures what revenues would be if all income
reported to the IRS by state residents was taxed at its average marginal rate. This alternative
estimate of revenues is highly correlated (.95) with actually revenues but is higher in most cases
because average marginal tax rates are higher than average tax rates due to standard deductions,
personal exemptions, and tax rate progressivity. We come up with a single measure of the
average tax rate across all income sources by dividing our simulated revenues by AGI.
In the first three columns of tables 10 we show regression results based on these measures.
The first column shows estimates of the level and increase of cyclicality for estimated revenues,
the second for rates, and the third for the base (AGI). Using these estimates, we measure the
percent of the level of revenue cyclicality, both pre and post 2000, and the percent of the increase
in revenue cyclicality that is attributable to the base and the rate. We display these percentages
in Table 11.
First, from the first three columns of row 1 of Table 10, we note that the low level of revenue
cyclicality pre-2000 is the result of cyclical income being counteracted by countercyclical rates.
From the second row of Table 10, we see that the increase in cyclicality was due to both the rates
and the base. According to the second row of Table 11, 63% of the increase was due to changes
in the cyclicality of rates while 37% was due to changes in the cyclicality of the base. 15 In the
final row of Table 11, we show that post-2000 nearly all of the cyclicality was due to the base,
15

Average tax rates can change because of changes in policy or because changes in the distribution of income across
sources. In practice, changes in the distribution of income across sources has very little effect on the results. We
find nearly identical results if we fix the distribution of income across sources and only allow the rates to vary.

17

with rates being close to neutral. The drawback of this estimate is that the measures of revenue
cyclicality both before and after 2000 are lower than in actual data (in Column 4). This arises
because this simulation fails to take into account the increase in revenues that occurs during good
times because of the interaction between the rate and the base. In particular, when economic
conditions improve and incomes increase, average tax rates increase because of the progressivity
of the income tax. By measuring tax policy based on a fixed income distribution and by using
marginal rates rather than average rates, we lose this effect.
For our second set of estimates we use data on actual revenues as our revenue measure, data
on state AGI for our base measure, and data on the effective tax rate for our rate measure. (As
before, effective rates are equal to revenues divided by AGI). Results are presented in columns
4-6 of Table 10 and columns 3-4 of Table 11. The results here also point to countercyclical rates
being counteracted by a cyclical base prior to 2000 and the majority of the increase in cyclicality
being attributed to the increasing rate cyclicality. In contrast to the first simulation, both cyclical
rates and a cyclical base contribute post 2000. In this case, because we are using actual
revenues, we capture the increase in revenues in good times that results from tax progressivity.
However, we attribute all of the increase in cyclicality from this interaction to rates because of
our use of effective rates. As a result, this estimate overstates the role of policy because some of
the increase in rate cyclicality is a direct result of income cyclicality not a result of policy
choices.
Our third measure uses data on actual revenues as our measure of revenues, data on the
average marginal tax rates based on the NBER data as our rate measure (as in our first
combination), and calculates the base as revenues divided by the tax rate. We call the resulting
base the estimated base. The measures of cyclicality of actual revenues differs modestly in this
case (Column 7) from the previous example (Column 4) because the samples are slightly
different. We again find that prior to 2000, a cyclical base was counteracted by countercyclical
rates. We also see that both rate and base cyclicality increased post-2000. In this example we
attribute about two-thirds of the increase in cyclicality to the base. The base matters more in
this calculation than in the previous two because by keeping rates fixed at policy rates, this
calculation assigns the increase in revenues that occurs when the economy improves to the base.

18

Our final measure of revenues also uses data on actual revenues as our measure of revenues.
However, we use data on the top marginal rate as our measure of the tax rate, and data on the
selected base (revenues divided by the top marginal rate) as our base measure. Here we find an
even larger contribution of the base.
These four breakdowns reinforce the finding presented earlier that rate and base cyclicality
both contributed to increasing revenue cyclicality. They also highlight that the challenge in
assigning relative contributions to the rate and the base is how to account for the increase in
effective rates that occurs when income grows due to tax progressivity. Our preferred
breakdown is the third one because by using rates based on a fixed income distribution it isolates
the role of policy choices. In this case, the base measurement is a residual that captures the part
of revenue growth not captured by policy. Insofar as rates increase due to income changes, this
breakdown assigns the resulting revenue increase to the base. This breakdown attributes 69% of
the increase in cyclicality to the base and 31% to rates.
As an additional exercise, we take advantage of the fact that the NBER provides information
on marginal rates on different types of income and that the IRS data provides information on
income from different sources. Using this data, we investigate increases in the cyclicality of
revenues from three different sources – wages, investment income and other income. We
estimate revenues by source by multiplying NBER rates by source and SOI income by source.
We then break revenue cyclicality from these three sources into contributions due to the rate and
the base. We present our findings in Table 12 We find that increases in cyclicality occurred for
revenues from both wage and investment income. Revenues from other income work in the
direction opposite our main findings, namely that revenue cyclicality fell. When we divide the
growth in cyclicality into that due to rates and that due to the base, we find that for wage income,
the majority (82%) of the increase was due to rates while for investment income, the majority
(95%) of the increase was due to the base.

8. Conclusion
We find that state tax revenues have become far more sensitive to changing economic
conditions since 2000 and that increasing responsiveness in the individual income tax has been
19

an important source of this increase. We divide our discussion of individual income taxes into
investigations into changes in rates and changes in the base and confirm that both were important
contributors. In particular, personal income growth became more responsive to economic
conditions – especially investment income, but also wage and salary income. At the same time,
income tax rate policy transitioned from being countercyclical to being less countercyclical or
even modestly procyclical. Our preferred breakdown attributes 69% of the increase in cyclicality
to the base and 31% to rates. Further breakdowns suggest that most of the increase in the
cyclicality of the base was due to investment income while most of the increase in the cyclicality
of rates was due to takes on wages.
One question that remains is what states should do about this increase in cyclicality. The
state response depends on whether the increased cyclicality of revenues is likely to persist or
whether revenue responsiveness is anticipated to revert back to the patterns observed through the
mid-1990s. It is possible that the behavior of income in the years since 2000 has been
anomalous and is unlikely to persist. For instance, we may view the run up in the NASDAQ and
its subsequent decline as onetime events that influenced the cyclicality of income. In addition,
some of the elevated cyclicality of capital income may be due to strategic responses to changes
in tax policy that coincided with the economic cycle. Federal tax policy may again induce
strategic capital gains realizations, but these may not coincide with economic conditions in the
same manner. However, part of the increase in income cyclicality may be connected with
structural changes in labor markets that have led to increased income and wealth inequality and
increased income cyclicality at the top of the income distribution (Parker and Vissing-Jorgenson
2010). These trends are long standing and unlikely to reverse themselves. In keeping with this,
the state revenue response to the current recession has certainly been dramatic and has been more
consistent with the post-2000 than pre-2000 experiences. In other words, it does not appear that
any reversion to pre-2000 norms has occurred yet.
Given that states may continue to face these large swings the income tax base,
policymakers should consider ways to adapt. Policy makers could return to the pre-2000 method
of adjusting tax rates up during recessions and down during booms to moderate the effect of the
business cycle on revenues. Alternately, states could increase their reliance on Rainy Day Funds
by saving more during good times and draw down balances during recessions. Recognizing the
20

influence of capital gains on revenue fluctuations, Massachusetts recently arranged for excess
capital gains revenues to be transferred into the state’s rainy day fund (Ross 2009). Finally, the
Federal government could adjust grants to provide more help during recessions and fewer
resources during booms.

21

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Andrews, Donald W. K. 1993. “Tests for Parameter Instability and Structural Change With
Unknown Change Point,” Econometrica, 61(4), 821–856.
Boyd, Donald J., 2009 “What Will Happen to State Budgets When the Money Runs Out,” The
Nelson A. Rockefeller Institute of Government, Fiscal Features, February 19.
Bruce, Donald and Fox, William F., 2001, “State and Local Tax Revenue Losses From ECommerce: Updated Estimates,” State Tax Notes. October 15, pp 207-239.
Bruce, Donald, Fox, William F., and LeAnna Lunna 2009, “State and Local Government Sales
Tax Revenue Losses from Electronic Commerce”, University of Tennessee, April, 2009.
Bruce, Doanld, Fox, William F., and Zhou Yang 2010, “Base Mobility and State Personal
Income Taxes,” National Tax Journal, December 2010.
Congressional Budget Office. 2003 “H.R.2. Jobs Growth Tax Relief Reconciliation Act of
2003.” May 23.
Congressional Budget Office. 2001, “H.R. 1836. Economic Growth and Tax Relief
Reconciliation Act of 2001” June 4.
The Book of the States, Various Years, Lexington, KY: The Council of State Governments.
Dadayan, Lucy and Donald Boyd, 2009a, “April is the Cruelest Month: Personal Income Tax
Revenue Portend Deepening Troubles for Many States,” State Revenue Flash Report, June 18.
Dadayan, Lucy and Donald Boyd, 2009b, “State Tax Revenues Show Record Drop, For Second
Consecutive Quarter,” State Revenue Report, October.
Dye, Richard, “State Revenue Cyclicality,” National Tax Journal, March 2004.
Federal Reserve Bank of Philadelphia, “State Coincident Indexes,” Available on the Internet at
http://www.philadelphiafed.org/research-and-data/regional-economy/indexes/coincident/.
Fox, William F. 2003 “Three Characteristics of Tax Structures Have Contributed to the Current
State Fiscal Crises,” State Tax Notes. November 3, Vol.30, No.5. pp.369-378.
Hines, James R. 2010 “State Fiscal Policies and Transitory Income Fluctuations,” Brookings
Papers on Economic Activity, Fall.
Holcombe, Randall G. and Sobel, Russell S. 1997. Growth and Variability in State Tax
Revenue: An Anatomy of State Fiscal Crises. Westpot, CT: Greenwood Press.

22

Internal Revenue Service, Individual Income Tax Returns 2006, 2008, Publication 1304.
Interval Revenue Service, Statistics of Income Bulletin, Various Issues.
Maag, Elaine and Merriman, David. 2003 “Tax Policy Responses to Revenue Shortfalls,” State
Tax Notes. November 3, Vol.30, No.5. pp.393-404.
Mattoon, Richard, Vanessa Haleco-Meyer, and Taft Foster, “Improving the Impact of Federal
Aid to the States” Economic Perspectives, September 22, 2010.
National Conference of State Legislatures (NCSL). 2009. State Budget Update, November.
National Association of State Budget Officers, Fiscal Survey of the States, Various Issues
Ohlemacher, Stephen, 2009, “Tax Revenues Post Biggest Drop Since Depression,” August 3, AP
Online.
Parker Jonathan and Annette Vissing Jorgensen. 2010 "The Increase in Income Cyclicality of
High-Income Households and its Relation to the Rise in Top Income Shares" , Brookings Papers
on Economic Activity, Fall 2010, 1-55
Piketty, Thomas and Emmanuel Saez. 2003. “Income Inequality in the United States. 19131998.” Quarterly Journal of Economics 188, no. 1: 1-39.
Priesmeir, Christoph, Gerhard Kempkes and Gerrit B. Koester. 2011 “The Effects of The
Business Cycle on Profit Related Tax Revenues – Empirical Evidence on Tax Elasticities for
German Data 1991-2008,’ Deutsche Bundesbank.
Ross, Casey, 2009 “Lawmakers Look to Capital Gains Tax to Bolster Savings,” Boston Globe,
June 17.
Sjoquist, David and Wallace, Sally. 2003, “Capital Gains: Its Recent, Varied and Growing (?)
Impact on State Revenues,” State Tax Notes. November 3, Vol.30, No.5. pp.423-432.
Tax Foundation, 2011 “State Individual Income Tax Rates, 2000-2011.” Available on the
Internet at http://www.taxfoundation.org/taxdata/show/228.html.
U.S.
Census
Bureau,
State
Government
http://www.census.gov/govs/www/state.html

Finances,

various

years,

The World Almanac and Book of Facts, Various Years, New York: Funk.

23

Figure 1: Net Enacted Policy Changes in Current Dollars: FY1988-FY2012
$12.00

Change in Billions of Current Dollars

$10.00
$8.00
$6.00
Sales

$4.00

Personal Income

$2.00

Business Income
All Others

$$(2.00)
$(4.00)
$(6.00)
1985

1990

1995

2000

2005

2010

2015

Source: National Association of State Budget Officers, Fiscal Survey of the States, Various Issues. These
are enacted revenue changes for the current fiscal year based on mid-fiscal year data. In some cases
additional changes occur prior to the end of the fiscal year.

24

Table 1: Quarterly Variable Means, 1980-2011:Q3
Year Over Year Log Difference

Number of
Observations
(For Log
Differences)
Quarterly Data
Total State Tax Revenues Per
Capita
State Sales Tax Revenues Per
Capita
State Individual Income Tax
Revenues Per Capita
State Corporate Income Tax
Revenues Per Capita
Total State Other Revenues Per
Capita

Standard
Deviation

Mean

Level

Standard
Deviation

Mean

6277

0.015

0.124

506.073

240.353

5643

0.014

0.129

150.792

83.882

5114

0.019

0.220

148.446

107.896

5548

-0.033

0.403

33.660

55.131

6208

0.011

0.158

172.363

189.080

Coincident Indicator

6342

0.022

0.039

116.382

32.019

Annual Data
Adjusted Gross Income Per
Capita
Wage and Salary Income Per
Capita

1500

0.015

0.045

20324.920

5405.832

1500

0.010

0.029

15248.220

3506.290

Capital Gains, Interest and
Dividend Income Per Capita

1500

0.005

0.192

2337.604

1047.929

Other Income Per Capita

1484

0.054

0.314

2739.093

1528.054

**Dollar amounts are in $2007 dollars and calculations are based on 2007$. Level calcuations include zeros for states that
do not levy a given tax, outliers and all states with available data.

25

Table 2: Variable Means, 1995:Q1
Year Over Year Log
Difference

Number of
Observations
(For Log
Differences) Mean

Standard
Deviation

Level

Standard
Deviation

Mean

Quarterly Data
Total State Tax Revenues Per Capita

50

-0.003

0.122

466.26

118.833

State Sales Tax Revenues Per Capita
45
0.015
0.096
148.96
76.896
State Individual Income Tax
Revenues Per Capita
40
0.010
0.130
133.55
87.691
State Corporate Income Tax
Revenues Per Capita
45
0.010
0.310
26.81
18.958
Total State Other Revenues Per
Capita
50
0.000
0.174
156.94
95.145
Coincident Indicator
50
0.053
0.020
113.41
5.429
Annual Data
Adjusted Gross Income Per Capita
50
0.034
0.012
19622.55
3221.409
Wage and Salary Income Per Capita
50
0.021
0.012
14784.78
2559.495
Capital Gains, Interest and Dividend
Income Per Capita
50
0.136
0.044
1913.07
524.624
Other Income Per Capita
50
0.035
0.026
2924.69
441.434
**Dollar amounts are in $2007 dollars and calculations are based on 2007$. Level calcuations include zeros for
states that do not levy a given tax, outliers and all states with available data.

26

Table 3: Cyclical Responsiveness of State Tax Revenues: 1980-2011:Q3
(1)

VARIABLES
Year over Year Log Difference in Coincident Indicator
Constant

Observations
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Standard errors are clustered by region by half decade

(2)

(3)

(4)

(5)

Individual Corporate
Income
Income
Total
Sales Tax
Other Tax
Tax
Tax
Revenue Revenue Revenue Revenue Revenue
Per
Per
Per
Per
Per
Capita
Capita
Capita
Capita
Capita
0.888***
(0.0834)
-0.00406
(0.00377)
6,269

0.860*** 1.139*** 2.304*** 0.488***
(0.0718)
(0.178)
(0.291)
(0.0921)
-0.00483 -0.00489 -0.0832*** 0.000364
(0.00306) (0.00746) (0.00986) (0.00364)
5,635

5,109

5,543

6,200

27

Table 4: Revenue Cyclicality Before and After 2000

VARIABLES
Year over Year Log Difference in Coincident Indicator
Log Difference in Coincident Indicator 2000 and After
Dummy=1 if 2000 or Later
Constant

Observations
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Standard errors are clustered by region by half decade

(1)

(2)

(3)
(4)
(5)
Individual Corporate
Income
Income
Total
Other Tax
Sales Tax
Tax
Tax
Revenue Revenue Revenue Revenue Revenue
Per
Per
Per
Per
Per
Capita
Capita
Capita
Capita
Capita
0.714***
(0.0764)
0.610***
(0.106)
-0.00389
(0.00625)
-0.000651
(0.00414)

0.823***
(0.0958)
0.0761
(0.138)
-0.00427
(0.00578)
-0.00265
(0.00389)

0.638***
(0.111)
1.502***
(0.178)
-0.0298**
(0.0108)
0.0128
(0.00811)

1.853***
(0.173)
1.967***
(0.508)
0.00791
(0.0203)
-0.0820***
(0.00831)

0.544***
(0.127)
-0.0289
(0.189)
0.0139*
(0.00743)
-0.00600
(0.00496)

6,269

5,635

5,109

5,543

6,200

28

Table 5: Income Tax Revenue Cyclicality, With State and Date Fixed Effects
(1)

VARIABLES
Year over Year Log Difference in Coincident Indicator
Log Difference in Coincident Indicator 2000 and After
Dummy=1 if 2000 or Later
Constant

Observations
State Fixed Effects
Date Fixed Effects

(2)

(3)

(4)

Individual Individual Individual Individual
Income
Income
Income
Income
Tax
Tax
Tax
Tax
Revenues Revenues Revenues Revenues
0.638***
(0.111)
1.502***
(0.178)
-0.0298**
(0.0108)
0.0128
(0.00811)
5,109
No
No

0.682***
0.223
0.287
(0.0987)
(0.185)
(0.183)
1.502*** 1.019*** 1.073***
(0.164)
(0.276)
(0.320)
-0.0282***
(0.00932)
0.00760 0.0667*** 0.0570*
(0.0127) (0.0231) (0.0285)
5,109
Yes
No

5,109
No
Yes

5,109
Yes
Yes

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Standard errors are clustered by region by half decade

29

Table 6: Individual Income Tax Revenue Cyclicality during Expansions and Contractions

VARIABLES
Year over Year Log Difference in Coincident Indicator
Constant

Observations
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Standard errors are clustered by region by half decade

(1)
1980:12011:3
Entire
Period

(2)
1980:11980:3

(3)
1980:41981:2

(4)
1981:31982:4

(5)
1983:11990:2

(6)
1990:31991:1

(7)
1991:22000:4

(8)
2001:12001:4

(9)
2002:12007:3

(10)
2007:42009:2

(11)
2009:32011:3

Contraction

Expansion

Contraction

Expansion

Contraction

Expansion

Contraction

Expansion

Contraction

Expansion

1.139***
(0.178)
-0.00489
(0.00746)

0.497*
(0.207)
-0.00978
(0.0205)

-0.731
(0.784)
-0.0233
(0.0338)

0.0193
(0.726)
-0.0336
(0.0621)

0.394**
(0.154)
0.0401**
(0.0145)

0.570
(0.278)
-0.0123
(0.0178)

0.632***
(0.196)
0.0125
(0.00907)

2.014**
(0.347)
-0.0122
(0.0148)

2.299***
(0.257)
-0.0299**
(0.00963)

2.634***
(0.255)
-0.0297
(0.0181)

2.061***
(0.300)
0.0270*
(0.0125)

5,109

117

121

244

1,209

123

1,535

164

942

286

368

30

Table 7: Cyclicality the Income Tax Base
(1)

VARIABLES
Year over Year Log Difference in Annual Coincident Indicator
Log Difference in Annual Coincident Indicator 2000 and After
Dummy=1 if 2000 or Later
Constant

Observations
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Standard errors are clustered by region by half decade

(2)

(3)

(4)

(5)

Adjusted Wage and
Gross
Salary Investment
Income
Income
Income

Other
Income

Selected
Base

0.667***
(0.0427)
0.417***
(0.0802)
-0.0122**
(0.00454)
0.00192
(0.00348)

0.535***
(0.0244)
0.136**
(0.0493)
-0.00101
(0.00177)
-0.00216
(0.00153)

0.519*
(0.296)
5.105***
(0.427)
-0.143***
(0.0260)
0.0254
(0.0233)

2.822** 0.796***
(1.091)
(0.156)
-2.515** 1.510***
(1.104)
(0.258)
0.0366 -0.0454***
(0.0690) (0.0103)
-0.0173 0.0250***
(0.0688) (0.00714)

1,493

1,493

1,493

1,477

1,195

31

Table 8: Cyclicality of Adjusted Gross Income and Its Components Including Year Fixed Effects: 1980-2009
(1)

VARIABLES
Year over Year Log Difference in Annual Coincident Indicator
Log Difference in Annual Coincident Indicator 2000 and After
Constant

(2)

(3)

(4)

(5)

Other
Income

Selected
Base

0.581***
(0.100)
0.274
(0.304)
0.148***
(0.0287)

-0.621
(0.760)
1.107
(0.767)
-0.197***
(0.0397)

0.366**
(0.146)
0.887**
(0.370)
0.0144
(0.0160)

1,493

1,477

1,195

Adjusted Wage and
Investment
Gross
Salary
Income
Income
Income
0.499***
(0.0350)
0.0858
(0.102)
0.00284
(0.00330)

0.493***
(0.0348)
0.0466
(0.0964)
0.00178
(0.00468)

Observations
1,493
1,493
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Including Year Dummies, Standard errors are clustered by region by half decade

32

Table 9: Cyclicality of State Income Tax Parameters: 1980-2011

(1)

VARIABLES
Year over Year Log Difference in Annual Coincident Indicator
Log Difference in Annual Coincident Indicator 2000 and After
Dummy=1 if 2000 or Later
Constant

Observations
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Standard errors are clustered by region by half decade

(2)

(3)

(4)

(5)

(6)

(7)

Changes
Changes
Changes
Changes Changes
in
in
Changes
Top
in
in
in
Maximum
Average
in
Maximum Marginal Average Average Marginal Marginal
Average Marginal Rates on Marginal Rates on Rates on Rate on
Effective Rates on Capital Rates on Capital
Tax
Pension
Rates
Income
Wages
Wages
Income
Income Schedule
-0.466
(0.305)
1.609***
(0.329)
-0.0469***
(0.0113)
0.0354***
(0.0103)

-0.840***
(0.184)
0.644***
(0.195)
-0.0341***
(0.00911)
0.0338***
(0.00889)

-0.610***
(0.213)
0.0313
(0.375)
-0.0723***
(0.0174)
0.0631***
(0.0145)

-0.450***
(0.0995)
0.372***
(0.109)
-0.0243***
(0.00573)
0.0259***
(0.00551)

-0.453*
(0.226)
-0.0357
(0.312)
-0.0711***
(0.0178)
0.0590***
(0.0164)

-0.734***
(0.254)
0.846***
(0.300)
-0.0466***
(0.00966)
0.0460***
(0.00888)

-0.519**
(0.202)
0.302
(0.230)
-0.0130
(0.00988)
0.0133
(0.00969)

1,217

1,219

1,229

1,219

1,230

1,173

1,219

33

Table 10: Breakdowns of Tax Cyclicality
(1)

VARIABLES
Year over Year Log Difference in Annual Coincident Indicator
Coincident Indicator 2000 or After
Dummy=1 if 2000 or Later
Constant

Observations
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
standard errors are clustered by region by half decade

(2)
(3)
Average
Simulated NBER
Revenues Tax Rates State AGI
0.185*
(0.107)
0.874***
(0.127)
-0.0392***
(0.00682)
0.0295***
(0.00593)

-0.535***
(0.111)
0.553***
(0.121)
-0.0298***
(0.00566)
0.0290***
(0.00545)

1,247

1,247

(4)

(5)

(6)

(7)

Actual
Effective
Revenues Tax Rates State AGI

(8)
(9)
Average
Actual
NBER Estimated
Revenues Tax Rates
Base

(10)

(11)
Top
Actual
Marginal
Revenues
Rates

(12)
Selected
Base

0.720***
(0.0422)
0.320***
(0.0882)
-0.00937**
(0.00407)
0.000546
(0.00297)

0.268
(0.284)
1.920***
(0.314)
-0.0566***
(0.0131)
0.0359***
(0.0109)

-0.463
(0.306)
1.611***
(0.331)
-0.0472***
(0.0114)
0.0353***
(0.0103)

0.731***
(0.0399)
0.309***
(0.0871)
-0.00942**
(0.00409)
0.000602
(0.00299)

0.459**
(0.173)
1.656***
(0.210)
-0.0489***
(0.0113)
0.0288***
(0.00878)

-0.569***
(0.123)
0.511***
(0.134)
-0.0304***
(0.00645)
0.0303***
(0.00614)

1.027***
(0.125)
1.145***
(0.189)
-0.0185*
(0.00924)
-0.00153
(0.00551)

0.459**
(0.173)
1.656***
(0.210)
-0.0489***
(0.0113)
0.0288***
(0.00878)

-0.346***
(0.0896)
0.116
(0.143)
-0.00315
(0.00402)
0.00410
(0.00346)

0.804***
(0.177)
1.539***
(0.269)
-0.0457***
(0.0108)
0.0247***
(0.00797)

1,247

1,217

1,217

1,217

1,155

1,155

1,155

1,155

1,155

1,155

34

Table 11: Breakdowns of Tax Cyclicality: Contributions of Rate and Base

Pre-2000 Level
Increase
Post-2000 Level

Simulated Revenues
Rates
Base
-289%
389%
63%
2%

37%
98%

Actual
Revenues/State AGI
Rates
Base
-173%
273%
84%
52%

16%
48%

Actual
Revenues/NBER
Average Rates
Rates
Base
-124%
224%
31%
-3%

69%
103%

Actual Revenues/Top
Marginal Rates
Rates
Base
-75%
175%
7%
-11%

93%
111%

35

Table 12: Cyclicality of Revenues by Source of Income

VARIABLES
Year over Year Log Difference in Annual Coincident Indicator
Coincident Indicator 2000 or After
Dummy=1 if 2000 or Later
Constant

Observations
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Standard errors are clustered by region by half decade

(1)
(2)
Revenues
Tax Rate
from
on Wage
Wage
Income
Income

(3)

Wage
Income

-0.0245
-0.572*
0.548***
(0.306)
(0.301)
(0.0245)
0.601*
0.493
0.109*
(0.317)
(0.304)
(0.0564)
-0.0354** -0.0346** -0.000832
(0.0145) (0.0142) (0.00167)
0.0344** 0.0363** -0.00191
(0.0143) (0.0141) (0.00135)
1,189

1,189

1,189

(4)
(5)
(6)
Revenues Tax Rate
from
on
Investment Investment Investment
Income
Income
Income
0.105
-0.647***
0.751**
(0.387)
(0.144)
(0.314)
5.304***
0.262
5.042***
(0.428)
(0.181)
(0.389)
-0.159*** -0.0336*** -0.126***
(0.0304) (0.00857) (0.0272)
0.0478* 0.0360***
0.0118
(0.0274) (0.00754) (0.0240)
1,247

1,247

1,247

(7)
(8)
Revenues
Tax Rate
from
on Other
Other
Income
Income

(9)

Other
Income

2.789**
-0.738** 3.527***
(1.116)
(0.268)
(0.997)
-2.476**
0.848** -3.324***
(1.122)
(0.313)
(1.009)
0.0161 -0.0493*** 0.0654
(0.0596) (0.00982) (0.0600)
0.00270 0.0464*** -0.0437
(0.0594) (0.00909) (0.0598)
1,126

1,126

1,126

36

Data Appendix:
Quarterly Summary of State and Local Tax Revenues
1962: Q1- 1963:Q4: The Quarterly Summary of State and Local Tax Revenues was first collected by the
Census Bureau in 1962:Q1. For 1962: Q1-1963:Q4, state revenues are reported for five revenue
sources: General sales and gross receipts, Motor fuel sales, Individual income, Corporation net income,
and Motor vehicle and operators’ licenses. In each quarter, for 3-4 of the 36 states with both a
corporate and individual income tax, a breakdown between individual and corporate income taxes is not
available, only a combined income tax number.
1963: Q4 - 1976: Q4: Beginning in 1963: Q4, the corporation income tax number is no longer reported.
From 1963:Q4-1976:Q4, four revenue sources continue to be reported (General sales and gross receipts,
Motor fuel sales, Individual income, and Motor vehicle and operators’ licenses.) For a couple states, the
reported income tax number includes corporate income tax revenues as well:(Alabama: 1963:Q41969:Q1; Arizona: 1963:Q4;Georgia: 1963:Q4; Louisiana 1964:Q2-1964:Q4; Missouri: 1963:Q4-1969:Q1;
New Mexico: 1963:Q4-1967:Q1; North Dakota: 1963:Q4-1964:Q3; 1966:Q1-1968:Q1). By 1969: Q2 no
state income tax numbers include corporate income taxes.
1977:Q1-1992:Q2: In 1977:Q1 the survey was expanded to cover seven tax sources (General sales and
gross receipts, Motor fuel sales, Individual income, Motor vehicle and operators’ licenses, Corporate
Income, Alcoholic Beverages, Tobacco Product Sales). The survey also reports Total tax collections
which combines the seven listed sources and other tax revenue sources. This coverage continues
through 1992:Q2.
1992:Q3-1993:Q2: From 1992:Q3-1993:Q2 quarterly tax revenue data is not reported by the Census
Bureau due to “staff shortages”. However, the Census sent us unpublished data for 1992:Q3 and we

37

are able to back out approximations (and in some cases data) based on year-to-date and year-end totals
from the 1993 and 1994 releases for 1992:4 – 1993:Q2.
1993:Q3-1993:Q4: Published data for 1993:Q3 and 1993:Q4 is similar to the data available from
1977:Q1-1992:Q2 and covers seven sources and total revenues.
1994:Q1-present: From 1994:Q1-present, the Census Bureau reports data for 26 revenue sources. Data
for 22 of these 26 sources is also available in the 1992:Q3 data sent to us. This data is released
approximately 90 days after the quarter ends.

State Level IRS Data
We use IRS data on adjusted gross income, wage and salary income, dividend income, interest
income, and capital gains by state.
Tax Year 1979: Data by state available in Table 5 of the Summer 1981 Statistics of Income Bulletin.
Tax Year 1980: Data by state available in Table 4.1 of Individual Tax Returns, 1980.
Tax Year 1981: Data by state available in Table 4.1 of Individual Tax Returns, 1981.
Tax Year 1982-1985: Data by state for AGI, salary and wages, interest and dividends available in the
Statistics of Income Bulletin for one to three years later. Capital gains data by state was not published.
We are thankful to William Gentry for sending us this unpublished data.
Tax Year 1986-1995: Data by state is available in Historical Table 2 of the Statistics of Income Bulletin
from two or three years later (e.g. Tax Year 1987 is in Fall 1990, Tax Year 1990 is in Fall 1992). This table

38

has been in the Fall, Summer, Spring, and Winter Bulletin depending on the year. These Bulletins are
available from the IRS website at: http://www.irs.gov/taxstats/article/0,,id=117514,00.html.
Tax Year 1996-present: Data by state is available in electronic format from Historical Table 2 of the
Statistics of Income Bulletin. We obtained this data from the IRS website at:
http://www.irs.gov/taxstats/article/0,,id=171535,00.html.

39

Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
Firm-Specific Capital, Nominal Rigidities and the Business Cycle
David Altig, Lawrence J. Christiano, Martin Eichenbaum and Jesper Linde

WP-05-01

Do Returns to Schooling Differ by Race and Ethnicity?
Lisa Barrow and Cecilia Elena Rouse

WP-05-02

Derivatives and Systemic Risk: Netting, Collateral, and Closeout
Robert R. Bliss and George G. Kaufman

WP-05-03

Risk Overhang and Loan Portfolio Decisions
Robert DeYoung, Anne Gron and Andrew Winton

WP-05-04

Characterizations in a random record model with a non-identically distributed initial record
Gadi Barlevy and H. N. Nagaraja

WP-05-05

Price discovery in a market under stress: the U.S. Treasury market in fall 1998
Craig H. Furfine and Eli M. Remolona

WP-05-06

Politics and Efficiency of Separating Capital and Ordinary Government Budgets
Marco Bassetto with Thomas J. Sargent

WP-05-07

Rigid Prices: Evidence from U.S. Scanner Data
Jeffrey R. Campbell and Benjamin Eden

WP-05-08

Entrepreneurship, Frictions, and Wealth
Marco Cagetti and Mariacristina De Nardi

WP-05-09

Wealth inequality: data and models
Marco Cagetti and Mariacristina De Nardi

WP-05-10

What Determines Bilateral Trade Flows?
Marianne Baxter and Michael A. Kouparitsas

WP-05-11

Intergenerational Economic Mobility in the U.S., 1940 to 2000
Daniel Aaronson and Bhashkar Mazumder

WP-05-12

Differential Mortality, Uncertain Medical Expenses, and the Saving of Elderly Singles
Mariacristina De Nardi, Eric French, and John Bailey Jones

WP-05-13

Fixed Term Employment Contracts in an Equilibrium Search Model
Fernando Alvarez and Marcelo Veracierto

WP-05-14

1

Working Paper Series (continued)
Causality, Causality, Causality: The View of Education Inputs and Outputs from Economics
Lisa Barrow and Cecilia Elena Rouse

WP-05-15

Competition in Large Markets
Jeffrey R. Campbell

WP-05-16

Why Do Firms Go Public? Evidence from the Banking Industry
Richard J. Rosen, Scott B. Smart and Chad J. Zutter

WP-05-17

Clustering of Auto Supplier Plants in the U.S.: GMM Spatial Logit for Large Samples
Thomas Klier and Daniel P. McMillen

WP-05-18

Why are Immigrants’ Incarceration Rates So Low?
Evidence on Selective Immigration, Deterrence, and Deportation
Kristin F. Butcher and Anne Morrison Piehl

WP-05-19

Constructing the Chicago Fed Income Based Economic Index – Consumer Price Index:
Inflation Experiences by Demographic Group: 1983-2005
Leslie McGranahan and Anna Paulson

WP-05-20

Universal Access, Cost Recovery, and Payment Services
Sujit Chakravorti, Jeffery W. Gunther, and Robert R. Moore

WP-05-21

Supplier Switching and Outsourcing
Yukako Ono and Victor Stango

WP-05-22

Do Enclaves Matter in Immigrants’ Self-Employment Decision?
Maude Toussaint-Comeau

WP-05-23

The Changing Pattern of Wage Growth for Low Skilled Workers
Eric French, Bhashkar Mazumder and Christopher Taber

WP-05-24

U.S. Corporate and Bank Insolvency Regimes: An Economic Comparison and Evaluation
Robert R. Bliss and George G. Kaufman

WP-06-01

Redistribution, Taxes, and the Median Voter
Marco Bassetto and Jess Benhabib

WP-06-02

Identification of Search Models with Initial Condition Problems
Gadi Barlevy and H. N. Nagaraja

WP-06-03

Tax Riots
Marco Bassetto and Christopher Phelan

WP-06-04

The Tradeoff between Mortgage Prepayments and Tax-Deferred Retirement Savings
Gene Amromin, Jennifer Huang,and Clemens Sialm

WP-06-05

2

Working Paper Series (continued)
Why are safeguards needed in a trade agreement?
Meredith A. Crowley

WP-06-06

Taxation, Entrepreneurship, and Wealth
Marco Cagetti and Mariacristina De Nardi

WP-06-07

A New Social Compact: How University Engagement Can Fuel Innovation
Laura Melle, Larry Isaak, and Richard Mattoon

WP-06-08

Mergers and Risk
Craig H. Furfine and Richard J. Rosen

WP-06-09

Two Flaws in Business Cycle Accounting
Lawrence J. Christiano and Joshua M. Davis

WP-06-10

Do Consumers Choose the Right Credit Contracts?
Sumit Agarwal, Souphala Chomsisengphet, Chunlin Liu, and Nicholas S. Souleles

WP-06-11

Chronicles of a Deflation Unforetold
François R. Velde

WP-06-12

Female Offenders Use of Social Welfare Programs Before and After Jail and Prison:
Does Prison Cause Welfare Dependency?
Kristin F. Butcher and Robert J. LaLonde
Eat or Be Eaten: A Theory of Mergers and Firm Size
Gary Gorton, Matthias Kahl, and Richard Rosen
Do Bonds Span Volatility Risk in the U.S. Treasury Market?
A Specification Test for Affine Term Structure Models
Torben G. Andersen and Luca Benzoni

WP-06-13

WP-06-14

WP-06-15

Transforming Payment Choices by Doubling Fees on the Illinois Tollway
Gene Amromin, Carrie Jankowski, and Richard D. Porter

WP-06-16

How Did the 2003 Dividend Tax Cut Affect Stock Prices?
Gene Amromin, Paul Harrison, and Steven Sharpe

WP-06-17

Will Writing and Bequest Motives: Early 20th Century Irish Evidence
Leslie McGranahan

WP-06-18

How Professional Forecasters View Shocks to GDP
Spencer D. Krane

WP-06-19

Evolving Agglomeration in the U.S. auto supplier industry
Thomas Klier and Daniel P. McMillen

WP-06-20

3

Working Paper Series (continued)
Mortality, Mass-Layoffs, and Career Outcomes: An Analysis using Administrative Data
Daniel Sullivan and Till von Wachter
The Agreement on Subsidies and Countervailing Measures:
Tying One’s Hand through the WTO.
Meredith A. Crowley

WP-06-21

WP-06-22

How Did Schooling Laws Improve Long-Term Health and Lower Mortality?
Bhashkar Mazumder

WP-06-23

Manufacturing Plants’ Use of Temporary Workers: An Analysis Using Census Micro Data
Yukako Ono and Daniel Sullivan

WP-06-24

What Can We Learn about Financial Access from U.S. Immigrants?
Una Okonkwo Osili and Anna Paulson

WP-06-25

Bank Imputed Interest Rates: Unbiased Estimates of Offered Rates?
Evren Ors and Tara Rice

WP-06-26

Welfare Implications of the Transition to High Household Debt
Jeffrey R. Campbell and Zvi Hercowitz

WP-06-27

Last-In First-Out Oligopoly Dynamics
Jaap H. Abbring and Jeffrey R. Campbell

WP-06-28

Oligopoly Dynamics with Barriers to Entry
Jaap H. Abbring and Jeffrey R. Campbell

WP-06-29

Risk Taking and the Quality of Informal Insurance: Gambling and Remittances in Thailand
Douglas L. Miller and Anna L. Paulson

WP-07-01

Fast Micro and Slow Macro: Can Aggregation Explain the Persistence of Inflation?
Filippo Altissimo, Benoît Mojon, and Paolo Zaffaroni

WP-07-02

Assessing a Decade of Interstate Bank Branching
Christian Johnson and Tara Rice

WP-07-03

Debit Card and Cash Usage: A Cross-Country Analysis
Gene Amromin and Sujit Chakravorti

WP-07-04

The Age of Reason: Financial Decisions Over the Lifecycle
Sumit Agarwal, John C. Driscoll, Xavier Gabaix, and David Laibson

WP-07-05

Information Acquisition in Financial Markets: a Correction
Gadi Barlevy and Pietro Veronesi

WP-07-06

Monetary Policy, Output Composition and the Great Moderation
Benoît Mojon

WP-07-07

4

Working Paper Series (continued)
Estate Taxation, Entrepreneurship, and Wealth
Marco Cagetti and Mariacristina De Nardi

WP-07-08

Conflict of Interest and Certification in the U.S. IPO Market
Luca Benzoni and Carola Schenone

WP-07-09

The Reaction of Consumer Spending and Debt to Tax Rebates –
Evidence from Consumer Credit Data
Sumit Agarwal, Chunlin Liu, and Nicholas S. Souleles

WP-07-10

Portfolio Choice over the Life-Cycle when the Stock and Labor Markets are Cointegrated
Luca Benzoni, Pierre Collin-Dufresne, and Robert S. Goldstein

WP-07-11

Nonparametric Analysis of Intergenerational Income Mobility
with Application to the United States
Debopam Bhattacharya and Bhashkar Mazumder

WP-07-12

How the Credit Channel Works: Differentiating the Bank Lending Channel
and the Balance Sheet Channel
Lamont K. Black and Richard J. Rosen

WP-07-13

Labor Market Transitions and Self-Employment
Ellen R. Rissman

WP-07-14

First-Time Home Buyers and Residential Investment Volatility
Jonas D.M. Fisher and Martin Gervais

WP-07-15

Establishments Dynamics and Matching Frictions in Classical Competitive Equilibrium
Marcelo Veracierto

WP-07-16

Technology’s Edge: The Educational Benefits of Computer-Aided Instruction
Lisa Barrow, Lisa Markman, and Cecilia Elena Rouse

WP-07-17

The Widow’s Offering: Inheritance, Family Structure, and the Charitable Gifts of Women
Leslie McGranahan

WP-07-18

Demand Volatility and the Lag between the Growth of Temporary
and Permanent Employment
Sainan Jin, Yukako Ono, and Qinghua Zhang

WP-07-19

A Conversation with 590 Nascent Entrepreneurs
Jeffrey R. Campbell and Mariacristina De Nardi

WP-07-20

Cyclical Dumping and US Antidumping Protection: 1980-2001
Meredith A. Crowley

WP-07-21

The Effects of Maternal Fasting During Ramadan on Birth and Adult Outcomes
Douglas Almond and Bhashkar Mazumder

WP-07-22

5

Working Paper Series (continued)
The Consumption Response to Minimum Wage Increases
Daniel Aaronson, Sumit Agarwal, and Eric French

WP-07-23

The Impact of Mexican Immigrants on U.S. Wage Structure
Maude Toussaint-Comeau

WP-07-24

A Leverage-based Model of Speculative Bubbles
Gadi Barlevy

WP-08-01

Displacement, Asymmetric Information and Heterogeneous Human Capital
Luojia Hu and Christopher Taber

WP-08-02

BankCaR (Bank Capital-at-Risk): A credit risk model for US commercial bank charge-offs
Jon Frye and Eduard Pelz

WP-08-03

Bank Lending, Financing Constraints and SME Investment
Santiago Carbó-Valverde, Francisco Rodríguez-Fernández, and Gregory F. Udell

WP-08-04

Global Inflation
Matteo Ciccarelli and Benoît Mojon

WP-08-05

Scale and the Origins of Structural Change
Francisco J. Buera and Joseph P. Kaboski

WP-08-06

Inventories, Lumpy Trade, and Large Devaluations
George Alessandria, Joseph P. Kaboski, and Virgiliu Midrigan

WP-08-07

School Vouchers and Student Achievement: Recent Evidence, Remaining Questions
Cecilia Elena Rouse and Lisa Barrow

WP-08-08

Does It Pay to Read Your Junk Mail? Evidence of the Effect of Advertising on
Home Equity Credit Choices
Sumit Agarwal and Brent W. Ambrose

WP-08-09

The Choice between Arm’s-Length and Relationship Debt: Evidence from eLoans
Sumit Agarwal and Robert Hauswald

WP-08-10

Consumer Choice and Merchant Acceptance of Payment Media
Wilko Bolt and Sujit Chakravorti

WP-08-11

Investment Shocks and Business Cycles
Alejandro Justiniano, Giorgio E. Primiceri, and Andrea Tambalotti

WP-08-12

New Vehicle Characteristics and the Cost of the
Corporate Average Fuel Economy Standard
Thomas Klier and Joshua Linn

WP-08-13

6

Working Paper Series (continued)
Realized Volatility
Torben G. Andersen and Luca Benzoni

WP-08-14

Revenue Bubbles and Structural Deficits: What’s a state to do?
Richard Mattoon and Leslie McGranahan

WP-08-15

7