On the Measurement of Federal
Taxes as Automatic Stabilizers
Hautahi Kingi and Kyle Rozema∗
September 2015
We develop the first policy relevant estimator for the size of the tax
system’s automatic stabilizers. The empirical measure estimates the
absorption effect of federal taxes relative to the response of aggregate
consumption to income fluctuations. Using the Survey of Consumer
Finances, we find that, on average between 1988 and 2009, the tax
system decreased the response of aggregate consumption to income
fluctuations by 19.5% (from 0.164 to 0.132). The tax system has played
a much larger role in stabilizing the macroeconomy than previously
thought.
JEL H2, E62, E63
Keywords: Automatic Stabilizers, Federal Income Taxes, Handto-Mouth, Liquidity Constraints, Marginal Propensity to Consume.
∗
Kingi: Department of Economics, Cornell University, Uris Hall, Ithaca, New York 14850, email: hrk55@cornell.edu, website: www.hautahikingi.com. Rozema: Northwestern University
School of Law, Levy Mayer Hall, 357 East Chicago Avenue, Chicago, Illinois 60611, e-mail:
kyle.rozema@law.northwestern.edu, website: www.kylerozema.com.
Introduction
The income tax system functions as an automatic stabilizer by partially
absorbing shocks to market income. Understanding the effectiveness of federal taxes as
automatic stabilizers is fundamental to the conduct of discretionary stabilization policy,
which targets the residual fluctuations left over after built-in stabilizers. Correctly
measuring the size of automatic stabilizers is therefore crucial for the design of both
automatic and discretionary stabilization policies.
In this article, we extend the current methods for estimating the size of
the tax system’s automatic stabilizers. We make three methodological contributions.
First, we propose a microsimulation estimator for the response of aggregate consumption to income fluctuations, which we herein refer to as the “consumption response.”
Second, we develop the first measure of the extent to which the tax system stabilizes
the consumption response. Finally, we incorporate empirical measures of the marginal
propensity to consume (MPC) from the consumption response literature into the estimation of automatic stabilizers.
We measure the automatic stabilization of disposable income using a concept we call the Normalized Disposable Income Change (NDC), which measures how
much aggregate disposable income changes in response to a change in aggregate market
income. The NDC is closely related to the automatic stabilization measure of the Normalized Tax Change (NTC) (Auerbach and Feenberg, 2000), which instead measures
how much aggregate tax revenue changes in response to a change in aggregate market
1
income (Slitor, 1948). Specifically, the NDC and NTC concepts are linked according
to Equation (1).
NDC = 1 − NTC
(1)
It is widely understood that because business cycle fluctuations are transitory, the naive interpretation of disposable income stabilization as demand stabilization
is inconsistent with rational, forward looking agents whose consumption decisions rely
upon a concept of permanent income. Translating disposable income stabilization into
demand stabilization requires adjusting for the MPC of each tax filer (Auerbach and
Feenberg, 2000).
The literature has thus far assumed that MPC adjustment does not affect
the simple mechanical relationship between the absorption effect of aggregate taxes
(the NTC) and the automatic stabilization of aggregate disposable income (the NDC)
in Equation (1) (Auerbach and Feenberg, 2000; Auerbach, 2009; Dolls et al., 2012).
This assumption is invalid. Equation (2) shows the relationship between the MPC
Adjusted NDC (ANDC) and the MPC Adjusted NTC (ANTC).
ANDC = MPC − ANTC
(2)
where MPC < 1 is the income-weighted average MPC in the economy, which can also
be interpreted as the baseline consumption response in the absence of a tax system. The
2
ANTC measures how much aggregate tax revenue that would have otherwise been spent
changes in response to a change in aggregate market income. The ANDC measures the
resulting change in aggregate consumption to a change in market income.
The goal of stabilization policy is to stabilize demand. The tax system
works to stabilize demand through stabilizing the baseline sensitivity of consumption
to changes in market income (i.e., MPC). The impetus for our new measure stems from
the breakdown of the 1-1 relationship between the ANTC and ANDC. Whereas the
NTC is a sufficient statistic for understanding the tax system’s impact on the response
of disposable income to market income changes, the ANTC does not fully describe the
tax system’s role in stabilizing the consumption response, because the consumption
response is also determined by the distribution of income and MPCs across tax filers
(Johnson et al., 2009), as captured by the MPC term in Equation (2). The automatic
stabilization literature has relied solely on information from the ANTC in estimating
the tax system’s role in demand stabilization. However, a sufficient statistic for the
tax system’s role in demand stabilization must incorporate the information from both
the ANTC and ANDC.
We develop a measure to capture the intuition that the change in the consumption response induced by the tax system should be measured against the baseline
consumption response. We therefore define the policy relevant measure of the tax
system’s built-in flexibility, what we refer to as the Normalized Automatic Stabilizer
3
(NAS), according to Equation (3).
NAS =
ANTC
MPC − ANDC
=
MPC
MPC
(3)
The NAS measures the extent to which the tax system reduces the sensitivity of consumption to income fluctuations. For the purpose of measuring the tax system’s role
as an automatic stabilizer, the NAS improves upon the ANTC by taking into account
whether the baseline consumption response is high or low. In this sense, the NAS,
unlike the ANTC, can be directly compared to equivalent measurements from the consumption response literature (Kniesner and Ziliak, 2002).
In addition to estimating the policy relevant automatic stabilizers, our empirical analysis employs updated methods of measuring the MPC of each household
that reflect the sizable fraction of wealthy households with nonzero MPC (Misra and
Surico, 2014; Campbell and Hercowitz, 2009). Kaplan et al. (2014) show that ignoring
wealthy “hand-to-mouth” households, which consume only from their income each period rather than using wealth to smooth consumption, leads to “a distorted view of the
effects of fiscal stimulus policies on aggregate consumption.” We show that this same
intuition applies to the measurement of automatic stabilizers. Moreover, we show that
these recent developments in the consumption response literature are also important
for the design of the microsimulation techniques used in the estimation of automatic
stabilizers. We leave our discussion of those details and their implications for Section
2.4.
4
Using microdata from the Survey of Consumer Finances from 1988 to 2009,
we first estimate that, on average, 74.7% of an increase in market income is reflected
in disposable income (the NDC). Equivalently, the tax system absorbs 25.3% of fluctuations in market income (the NTC). Next, we estimate that the tax system absorbed
3.2% of a change in aggregate market income that would have otherwise been spent
(ANTC). Finally, we estimate that 13.2% of an increase in aggregate income was reflected in consumption (ANDC). With the ANTC and ANDC estimates in hand, we
can identify the size of the tax code’s automatic stabilizers relative to the consumption
response (the NAS). Our main finding is that the tax system decreased the consumption
response from 16.4% to 13.2%, or by 19.5%.
Our findings have two important policy implications. First, we provide the
first estimates for which to measure the effectiveness of the tax system’s built-in stabilizers, which should be used in the design of policies that promote automatic stability.
Second, because discretionary stabilization policies target residual fluctuations left over
after automatic stabilizers, our estimates serve as an input into deciding if, when, and
what discretionary stabilization policies should be deployed.
The article proceeds as follows. We begin in Section 1 with a brief review
of the relevant literature on automatic stabilizers. Section 2 sets out the theoretical
framework behind our microsimulation empirical measures of the consumption response
and the tax system’s built-in flexibility. In Section 2.1, we reframe the discussion of
automatic stabilizers in terms of disposable income. In Section 2.2, we propose our
estimator for the consumption response. In Section 2.3, we develop the policy relevant
5
measure of the size of the tax system’s built-in flexibility. Section 2.4 is our discussion
of how we incorporate better empirical measures of the MPC into the estimation of
demand stabilization. Section 3 describes the data and presents our empirical results.
We discuss the importance of our results and conclude in Section 4.
1
Literature Review
Automatic stabilizers are the elements of fiscal policy that work to reduce
fluctuations in the business cycle without any discretionary action on behalf of the
government. They are the laws and regulations that make fiscal revenues and outlays
relative to total income change with the business cycle. Our work deals specifically with
the measurement of automatic stabilizers. The empirical literature in this area can be
divided into macro and micro studies. Macro approaches focus on simple aggregate
indicators such as the relationship between government size and output volatility (Gali,
1994; Fatas and Mihov, 2001). However, the endogeneity within macro-level aggregate
measures, which capture the effects of automatic stabilizers as well as behavioral and
general equilibrium effects, means that macro approaches are unable to isolate the
effects of automatic stabilizers.
Our work is closely related to microsimulation studies which overcome this
problem by holding discretionary policy changes constant and abstracting from general
equilibrium behavioral effects (Auerbach and Feenberg, 2000; Kniesner and Ziliak,
2002; Auerbach, 2009; Dolls et al., 2012). Although many elements of fiscal policy can
6
be considered automatic stabilizers, each of which operate through many channels, we
focus on one particular aspect of automatic stabilizers—the ability of the tax system
to absorb fluctuations in market income.
Slitor (1948) introduced the concept of a tax system’s “built-in flexibility”.
Built-in flexibility aims to capture the notion that an income tax system can provide
insurance against market income volatility by dampening the variability of disposable
income and hence consumption (Musgrave and Miller, 1948; Brown, 1955; Brown and
Kruizenga, 1959; Cohen, 1959; Pechman, 1973). To estimate built-in flexibility, Auerbach and Feenberg (2000) proposed the Normalized Tax Change (NTC) measure. To
construct the NTC, Auerbach and Feenberg (2000) first simulate a 1% change in aggregate income spread neutrally across the population by increasing income for each
tax filer by 1%. They then use a tax calculator to estimate the total tax liability before
and after the hypothetical income increase, and define the NTC according to Equation
(4).
T̂
−
T
i
i
i
NTC = P i Ŷi − Yi
P (4)
where T̂i is the amount of tax paid by tax filer i after the hypothetical increase in
income from Yi to Ŷi and Ti was the actual amount of tax paid by tax filer i. The NTC
measures the degree to which aggregate tax revenue fluctuates with aggregate market
income.
The NTC concept is closely related to the familiar public finance notion
7
of the elasticity of total tax revenue with respect to aggregate income, T,Y . This
is unsurprising given the fundamental relationship between a progressive tax system,
defined as T,Y > 1, and its ability to act as an automatic stabilizer (Hayes et al.,
1995; Ram, 1991; Fries et al., 1982). Auerbach and Feenberg (2000) point out that
T,Y is invariant to the proportion of income which is taken as taxes, and is therefore
less appropriate than the NTC in measuring the tax system’s role as an automatic
stabilizer. For example, a flat tax rate of 60% can have the same elasticity as a flat tax
rate of 10%, despite the former tax system being able to cushion fluctuations in market
income to a much higher degree. For the purpose of measuring the tax system’s role
as an automatic stabilizer, the NTC improves upon the elasticity notion by taking into
account whether the share of income taken as taxes is high or low. Slitor (1948) shows
P T
that the NTC is simply T,Y scaled by the average tax rate Pi Yii .
i
2
2.1
Theoretical Framework
Normalized Disposable Income Change
The automatic stabilization literature has largely concentrated on how the
tax system absorbs fluctuations in market income. We diverge from the literature
and introduce an additional measure of automatic stabilization. This divergence is
subtle, yet crucial for correctly understanding the MPC adjusted stabilization estimates
in terms of demand. We define the Normalized Disposable Income Change (NDC)
8
according to Equation (5).
P D̂
−
D
i
i
i
NDC = P i Ŷi − Yi
(5)
where Di = Yi − Ti is tax filer i’s actual disposable income and D̂i = Ŷi − T̂i is the
resulting disposable income with the hypothetical income change. The NDC measures
the degree to which aggregate disposable income fluctuates with aggregate market
income.
The NTC has conventionally been estimated in the literature because of its
1-1 relationship with the NDC, as given in Equation (1). However, the distinction is
crucial once we turn to demand stabilization.
2.2
Demand Stabilization
In order for aggregate demand to be stabilized, the cushioning effect of the
tax system on market income must be translated into a cushioning effect on household
consumption. A high reaction of consumption to transitory changes in current disposable income is inconsistent with rational, forward-looking behavior which implies
that current demand should depend on some permanent income concept (Auerbach
and Feenberg, 2000). A substantial empirical literature, however, documents a large
sensitivity of consumption to transitory changes in income (Shapiro and Slemrod, 2009;
Johnson et al., 2006; Shapiro and Slemrod, 2003; Souleles, 1999; Jappelli, 1990; Hall
and Mishkin, 1982). The literature attributes this discrepancy to the existence of
9
hand-to-mouth (HtM) households who have high MPCs. To translate disposable income stabilization to demand stabilization, the literature has therefore adjusted the
NTC to account for HtM households.
Equation (6) has been used to estimate the change in taxes relevant for
consumption, which we refer to as the MPC Adjusted NTC (ANTC).
MPC
T̂
−
T
i
i
i
i
P i Ŷi − Yi
P
ANTC =
(6)
where MPCi is the MPC for tax filer i for an increase in income over the relevant
range of Yi to Ŷi . The ANTC measures the change in aggregate taxes that would have
otherwise been spent in response to a change in market income.
Shifting the focus toward the effect of the tax system on demand stabilization, we define our measure of the consumption response as the MPC Adjusted NDC
(ANDC) according to Equation (7).
MPC
D̂
−
D
i
i
i
i
P i Ŷi − Yi
P
ANDC =
where, by definition,
(7)
MPC
D̂
−
D
is the aggregate change in consumption. The
i
i
i
i
P
ANDC measures the resulting change in aggregate consumption to a change in market
income.
The literature has assumed that the simple relationship between the NTC
and the NDC as shown in Equation (1) holds after adjusting for the MPC. Equation
10
(2), restated here, shows that this assumption is invalid, and that the distinction is not
trivial.
ANDC = MPC − ANTC
where MPC =
P
MPCi Yi
iP
i
Yi
is the income-weighted average MPC in the economy. Equa-
tion (2) is central to this article. First note that the income weighted average MPC,
MPC, can also be interpreted as the resulting change in aggregate consumption to a
change in market income in the absence of the tax system. Without taxes, households
simply react to an increase in market income by consuming the product of the income
change and their MPC. The difference between this and the actual resulting change
in consumption can therefore be thought of as the tax system’s role in reducing this
sensitivity, which is captured by the ANTC. We posit that the interpretation of the
ANTC throughout the literature is inconsistent with this distinction.1
2.3
Federal Taxes as Automatic Stabilizers of Demand
The goal of this article is to develop an estimator of a tax system’s ability
to stabilize demand. We are therefore interested in the extent to which the tax system reduces the response of consumption to income fluctuations. We develop such a
1
For example, Equation (7) on page 283 in Dolls et al. (2012) defines their MPC adjusted measure
as τ C = 1 − ANDC. However, their empirical results show that τ C < NTC, which is inconsistent with
the fact that ANDC < NDC if there is at least one household with a non-zero MPC. Specifically, with
at least one household that is not HtM, from Equation (1) it must be that 1 − ANDC > NTC. It
is therefore unclear what MPC adjusted measure Dolls et al. (2012) estimate, and how it should be
interpreted in light of the NTC.
11
measure using the intuition embedded in Equation (2), which demonstrates that the
effectiveness of a tax system as an automatic stabilizer is measured against the baseline
consumption response.
The tax system works to stabilize demand through stabilizing the sensitivity
of consumption to changes in market income. The ANTC embeds information regarding (1) the distribution of income (2) the distribution of HtM households, and (3) the
stabilizing power of the tax system. For instance, two economies with identical tax systems and income distributions can have differing ANTCs solely because of differences
in the prevalence of HtM households. A sufficient statistic for the tax system’s role
in demand stabilization must therefore incorporate the information from the ANTC
and the ANDC (or, equivalently, MPC). We define our measure of the tax system’s
automatic stabilizers as the NAS, restated here, according to Equation (3).
NAS =
MPC − ANDC
ANTC
=
MPC
MPC
The NAS measures the extent to which the tax system reduces the sensitivity of consumption to income fluctuations. The NAS captures the simple intuition
that a policy relevant measurement of the tax system’s automatic stabilizers should
compare the consumption response in the absence of the tax system (MPC) with the
actual consumption response that occurred in the presence of the tax system (ANDC),
normalized by the baseline consumption response (MPC).
Consider the following example, which demonstrates that neither the ANTC
12
nor the ANDC provide the sufficient information to know how the tax system stabilized
the consumption response. Suppose we want to compare the automatic stabilizers of
the European Union (EU) and the US as in Dolls et al. (2012). Dolls et al. (2012)
estimate that the NTC is approximately 38% in the EU and 32% in the US, and that
the ANTC is in the range of 4-22% in the EU and 6-17% in the US. Dolls et al. (2012)
conclude that it therefore follows that automatic stabilizers in European countries are
typically larger than in the US. However, this statement is only true if MPC = 1, in
which case the NAS reduces to the ANTC. However, we show that MPC in the US
is usually in the range of 10-40%. If MPC is, say, higher in Europe because of more
prevalent HtM households, then the tax system in the US may be more effective at
stabilizing the reaction of consumption to income fluctuations.
2.4
MPC Adjustment
Estimating the ANTC and ANDC according to Equations (6) and (7) re-
quires the identification of each household’s MPC. The automatic stabilization literature deals with this in two steps. The first is identifying HtM households. The second
is assuming that each HtM household has an MPC = 1 while all other households have
zero MPC.2 In other words, the literature has simplified MPC adjustment to HtM adjustment. We retain the assumption in the second step while modifying the approach
of the first.
2
P
P
The ANTC and ANDC under this assumption simplify to ANTC =
(D̂i −Di )
, where i ∈ HtM is the subset of HtM households.
(Ŷi −Yi )
i∈HtM
P
i
13
(T̂i −Ti )
and ANDC =
Ŷ
−Yi )
(
i
i
i∈HtM
P
Identifying HtM households has traditionally used information on net worth
(Zeldes, 1989; Runkle, 1991; Auerbach and Feenberg, 2000), which was consistent with
wealthy households exhibiting very low MPCs as in the vast majority of standard
consumption theory models (Aiyagari, 1994; Huggett, 1993; Krusell and Smith, 1998;
Deaton, 1991; Carroll, 1997; Heathcote et al., 2009). More recently, Jappelli et al.
(1998) and Dolls et al. (2012) have used more direct survey evidence from the Survey
of Consumer Finances to identify HtM households. Jappelli et al. (1998) defined a
household as HtM if (1) a credit application has been either rejected or not fully approved, or (2) a credit application has not been submitted because of concerns that the
application would be rejected. Both the net worth and credit application definitions
are misleading, however, because they miss the sizable fraction of wealthy HtM households widely documented in the literature (Campbell and Hercowitz, 2009; Broda and
Parker, 2014; Hsieh, 2003; Agarwal et al., 2007; Misra and Surico, 2014; Telyukova,
2013; Browning and Collado, 2001; Browning and Crossley, 2001).
In recent pioneering work, Kaplan and Violante (2014) (herein referred to as
KV) explain wealthy household HtM behavior by showing that many of these households choose to hold their wealth in the form of high return illiquid assets. These
wealthy households choose to consume a large portion of income fluctuations in order
to avoid the transaction costs associated with liquidating illiquid assets. The distinguishing factor between the KV approach and the Jappelli et al. (1998) approach is
that the former can account not only for households living at their credit limit, but
also for wealthy households who hold no liquid wealth. Indeed, KV find that a sizable
14
fraction of wealthy households are HtM because they do not hold liquid wealth.
The findings in KV have important implications for the estimation of automatic stabilizers because, as shown in Equations (6) and (7), only those households
identified as HtM influence demand stabilization. The findings in KV also have implications for the microsimulation methods used to estimate automatic stabilizers. In
particular, KV demonstrate that the MPC of wealthy households crucially depends
on the size of the income change. If the income fluctuation is sufficiently large, the
household optimally chooses to bear the liquidation costs in order to smooth consumption. Upon liquidation, these households have a zero MPC. This insight matters for
the estimation of automatic stabilizers because the counterfactual experiment that is
conducted determines how one should measure the MPC (or identify HtM households).
For instance, a 1% change in income in the Auerbach and Feenberg (2000) counterfactual experiment should measure MPCs differently than in the 5% counterfactual
experiment in Dolls et al. (2012).
So what is the best counterfactual and resulting MPC adjustment? We
leave the question of the appropriate counterfactual experiment to future research.
Our intention here is simply to bring to light the fact that the choice over the method
of measuring MPC (or identifying HtM households), which has been largely driven by
data availability, should not be independent of the choice over the size of an income
change in the simulation used to estimate automatic stabilizers. The reasoning in KV
suggests that a small increase in income should be paired with a definition of MPC
that captures wealthy HtM households, and a large increase should not.
15
3
Empirical Analysis
We use data from the Survey of Consumer Finances (SCF), a triennial survey
of U.S. households conducted by the Board of Governors of the Federal Reserve System
in cooperation with the Statistics of Income Division (SOI) of the Internal Revenue
Service (IRS). The SCF collects information on a broad array of assets, debts, liabilities,
and household demographics including wage and capital income, number of dependents,
and marital status. Each round of the SCF is nationally representative.
3.1
Identifying Hand-to-Mouth Households
Our first step in the empirical exercise is to identify HtM households. Fol-
lowing the approach in KV, we define a household to be HtM if it either has zero liquid
wealth or is at its credit limit. More formally, household i is HtM if either
0 ≤ mi ≤
yi
2fi
or
mi ≤ 0 and mi ≤
yi
− mi
2fi
where mi is the average balance of liquid assets over the past month, yi is monthly
labor income, mi is the credit limit, and fi is the pay period frequency.3 Each of these
expressions reflect the notion that households with sufficiently low liquid assets with
respect to their monthly income behave as HtM. As we discussed in the previous section,
the advantage of KV’s definition of HtM is that it is well rooted in economic theory:
3
Liquid wealth includes cash, money market, checking, savings, and call accounts. The SCF does
not record household cash holdings. We follow the procedure outlined in KV to impute cash holdings,
which relies on the strategy to identify revolving debt in Telyukova (2013).
16
households at kinks of their budget constraint have a high MPC out of a windfall gain
in income. We leave the detailed discussion of this intuition and identification of these
HtM households using the SCF to KV (see Appendix B of their paper).
To illustrate how the KV definition of HtM leads to the identification of
different HtM households than the net worth definition as in Zeldes (1989) (herein
referred to as ZS) and the credit application definition as in Jappelli et al. (1998)
(herein referred to as JT), Figure 1 plots the percentage of the population identified as
HtM under these definitions over time.4 Figure 1 confirms the results in KV, which were
based only on 2001 SCF data: the KV HtM definition identifies more households as
HtM than the ZS net worth and JT credit application definitions. Figure 1 shows that
the differences in identified HtM households between the HtM definitions in KV, JT,
and ZS are remarkably stable over time. Under the KV definition, 27.3% of households
are identified as HtM on average between 1988 and 2009, which is 14.9ppt higher than
the ZS net worth definition (12.7% of households are HtM) and 8.9ppt higher than
the JT credit application definition (18.6% of households are HtM). We also confirm
that the KV results for the higher mean income of HtM households carries over to
other years. In the KV definition, the mean income for HtM households is $38,002 on
average between 1988 and 2009, compared to $34,148 and $31,660 in the ZS net worth
and JT credit application definitions, respectively.
4
Due to differences in data limitations between datasets, the precise strategy to identify HtM
households using the net worth definition varies. To be able to better compare the percentage of
households as HtM in the net worth definition to those in the KV definition, we rely on the commonly
used net worth definition applied using the SCF rather than that using, for instance, the public use
IRS SOI data as in Auerbach and Feenberg (2000).
17
Figures 2 and 3 plot the percentage of households identified as HtM under
the three definitions over income and net worth, respectively. Although KV were particularly interested in the identification of wealthy HtM households, the KV definition
identifies more HtM households across the entire income and net worth distributions,
not just at the high ends. The ZS net worth definition identifies more HtM households
than the JT credit application definition at lower income and net worth levels, whereas
the opposite occurs for incomes over $12,500 and net worth over $10,000.
[Insert Figures 1, 2, and 3 about here]
Table 1 shows that the KV definition does not simply widen the set of HtM
households but also alters the composition. Under the assumption that the KV HtM
definition is the correct one, we find that 18.35% (15.05%) of households are subject to
Type I error under the JT credit application (ZS net worth) definition, whereas 9.66%
(0.36%) are subject to Type II error. More surprising is that the ZS net worth and
JT credit application definitions fail to identify the majority of households identified
as HtM in the KV definition. ZS identified 44.89% (12.26/27.31) and JT identified
32.8% (8.96/27.31) of all HtM households in the KV definition. In contrast to Figure
1, which shows that the JT credit application definition has a similar percentage of
HtM households to the KV definition than the ZS net worth definition, the composition
of HtM households in the ZS net worth definition is more closely aligned with the KV
definition.
[Insert Table 1 about here]
18
In sum, Figures 1, 2, and 3 show that the KV definition identifies more
HtM households than previous approaches in the automatic stabilization literature,
and Table 1 shows the composition of HtM households within each definition varies
considerably.
3.2
Automatic Stabilization Estimation
We estimate the NTC and NDC by running the SCF microdata through
TAXSIM—the NBER’s microsimulation tax model—to calculate each tax filer’s tax
liability with actual income and again after increasing all income items by 1%. We
choose a 1% increase in income as in Auerbach and Feenberg (2000) for two reasons.
First, a small increase in income is needed in order to be consistent with the HtM
definition in KV. Second, while we do not address the issue of the ideal income increase, which depends on the particular question at hand, we feel a relatively small
income change is nonetheless preferred because automatic stabilizers are concerned
with stabilizing the business cycle, which in general entails small income fluctuations.
Using the actual and counterfactual tax liabilities, we calculate the NTC
and NDC according to Equations (4) and (5). To translate these estimates into MPC
adjusted estimates that are relevant for demand stabilization, we invoke the common
assumption that HtM households have an MPC = 1 and non-HtM households have
an MPC = 0, and adjust for the MPC according to Equations (6) and (7) using the
HtM households identified in the KV definition. Based on the finding in the previous
19
section, we expect the KV HtM definition to significantly impact the estimated size of
automatic stabilizers because it identifies a larger percentage of HtM households and
the composition of HtM households is different.
Our NTC and NDC estimates, along with their MPC adjusted counterparts
(the ANTC and ANDC), are displayed in Figure 4.5 The left hand panel shows that
the change in aggregate tax revenue that is relevant for stabilizing aggregate demand
(ANTC) is substantially less than the change in aggregate tax revenue (NTC). In particular, on average between 1988 and 2009, the value of the ANTC is 14.2% of the
value of the NTC (3.6%/25.3%). This is to be expected because HtM households make
up less than 30% of total households and a likely lower proportion of aggregate income. The right hand panel of Figure 4 similarly shows that the change in aggregate
disposable income that is relevant for stabilizing aggregate demand (ANDC) is substantially less than the change in aggregate disposable income (NDC) – on average
17.8% (13.3%/74.7%).
Our estimates of the ANDC demonstrate that, on average, 13.2% of a change
in aggregate market income is consumed. To our knowledge, these are the first microsimulation estimates of the consumption response in the presence of the tax system.
To investigate how the ZS net worth and JT credit application HtM definitions change
5
The magnitude of Auerbach and Feenberg’s (2000) ANTC estimates are considerably larger
than our equivalent series, even in our net worth definition (see right hand side of Figure 5). Dolls
et al. (2012) correctly point out that this is largely a result of differences in the HtM definition within
the context of net worth. Auerbach and Feenberg (2000) estimate a cutoff ratio using the wealth to
disposable income ratio of ZS, and find a much larger percentage of the population to be HtM than
here and in Dolls et al. (2012) (in most years over 50%), which translates into larger ANTC estimates
(in most years in the range of 10-15%).
20
the estimated consumption response, the left hand side of Figure 5 plots the yearly
ANDC series under the three HtM definitions. On average, the ZS net worth and JT
credit application definitions result in the underestimation of the consumption response
by 8.9ppt and 3.47ppt, respectively. The right hand side of Figure 5 compares the estimated ANTC under the different HtM definitions. The HtM definition substantially
changes the estimates for the consumption response and the absorption effect of the
tax system.
[Insert Figures 4 and 5 about here]
Finally, we use the ANTC and ANDC estimates from the preferred KV
HtM definition to estimate the policy relevant measures for the tax system’s builtin stabilizers according to Equation (3). The yearly estimates for the Normalized
Automatic Stabilizer (NAS), which measures the change in the consumption response
induced by the tax system, are plotted in Figure 6. On average, the tax system
decreased the consumption response by 19.5%.
The policy importance of the NAS estimates can be seen by comparing the
estimates of the NAS (Figure 6) with both the ANTC and ANDC (Figure 5). These
figures demonstrate an important aspect of our theoretical section and the limitations of
past research that arise from relying on the ANTC as the measure of built-in flexibility
and from invoking the invalid assumption that ANTC = 1 − ANDC. On the one hand,
misinterpreting 1 − ANTC as the consumption response in the presence of the tax
system (the ANDC) significantly overestimates the consumption response to changes
21
in market income. On the other hand, misinterpreting ANTC as the NAS significantly
underestimates the stabilizing power of the tax system on an order of magnitude of six
(on average, 3.2% compared with 19.5%).
[Insert Figure 6 about here]
4
Discussion and Conclusion
Discretionary fiscal stabilization policies such as tax rebates are extremely
visible to citizens, policy makers, and academic researchers. For instance, an estimated
44%, 16%, and 15% of American households knew they would be receiving the 2008
federal tax rebate within one, two, and three months of the passage of the act (Broda
and Parker, 2014).
Much less salient are automatic stabilizers. Despite the design of better automatic stabilizers being one of the promising routes for improving macroeconomic policy
(Blanchard et al., 2010), the role automatic stabilizers play in the aggregate economy
has received very little attention since the original work of Slitor (1948) and Musgrave
and Miller (1948), as pointed out by Blanchard (2006). However, the measurement of
automatic stabilizers is just as important as the measurement of discretionary stabilizers because determining if, when, and what discretionary stabilization policies should
be deployed requires the accurate measurement of automatic stabilizers. This is because discretionary stabilization policies have to do “less work” in an economy with
strong automatic stabilizers. Dolls et al. (2012) find a negative correlation between the
22
size of automatic stabilizers and official stimulus programs (e.g., tax rebates), which
indicates that policy makers are indeed targeting the residual fluctuations.
In this article, we (1) incorporate empirical measures of the marginal propensity to consume (MPC) from the consumption response literature into the estimation
of automatic stabilizers, (2) propose a new empirical microsimulation measure for the
response of aggregate consumption to income fluctuations, and, most importantly, (3)
develop a microsimulation estimator for the effect of the tax system on the response of
aggregate consumption to income fluctuations that is relevant for policy making.
Our empirical measure of the tax system’s built-in flexibility improves upon
current measures in two ways. First, it fully appreciates the crucial distinction between
market income stabilization and demand stabilization. Second, it correctly describes
the tax system’s role in stabilizing the response of aggregate consumption to income
fluctuations. In sum, our measure of the tax system’s built-in stabilizers improves
upon previous measures by taking into account whether the response of aggregate
consumption to income fluctuations is high or low. In this sense, it can be directly
compared to equivalent measurements from the consumption response literature.
Using the Survey of Consumer Finances from 1988 to 2009, we estimate that
the tax system decreased the response of aggregate consumption to income fluctuations
by 19.5% – about six times larger than is suggested by previous estimators. The tax
system has played a much larger role in stabilizing the macroeconomy than previously
thought.
23
References
Agarwal, S., C. Liu, and N. Souleles (2007). The Reaction of Consumer Spending and
Debt to Tax Rebates–Evidence from Consumer Credit Data. Journal of Political
Economy 115 (6), 986–1019.
Aiyagari, R. (1994). Uninsured Idiosyncratic Risk and Aggregate Saving. Quarterly
Journal of Economics 109 (3), 659–684.
Auerbach, A. (2009). Implementing the New Fiscal Policy Activism. American Economic Review 99 (2), 543–49.
Auerbach, A. and D. Feenberg (2000). The Significance of Federal Taxes as Automatic
Stabilizers. Journal of Economic Perspectives 14 (3), 37–56.
Barro, R. and C. Sahasakul (1983). Measuring the Average Marginal Tax Rate from
the Individual Income Tax. Journal of Business 56 (4), 419–52.
Blanchard, O. (2006). Comments on ‘The Case Against the Case Against Discretionary
Policy’. In A. Blinder. (Ed.), The Macroeconomics of Fiscal Policy, Volume 18, pp.
62–67. MIT Press.
Blanchard, O., G. Dell’Ariccia, and P. Mauro (2010). Rethinking Macroeconomic
Policy. Journal of Money, Credit and Banking 42 (1), 199–215.
Broda, C. and J. Parker (2014). The Economic Stimulus Payments of 2008 and the
aggregate demand for consumption . Journal of Monetary Economics 68, S20–S36.
Brown, E. (1955). The Static Theory of Automatic Fiscal Stabilization. Journal of
Political Economy 63 (5), 427–440.
Brown, E. and R. Kruizenga (1959). Income Sensitivity of a Simple Personal Income
Tax. Review of Economics and Statistics 41 (1), 260–69.
Browning, M. and D. Collado (2001). The Response of Expenditures to Anticipated
Income Changes: Panel Data Estimates. American Economic Review 91 (3), 681–
692.
Browning, M. and T. Crossley (2001). Unemployment Insurance Benefit Levels and
Consumption Changes. Journal of Public Economics 80 (1), 1–23.
Campbell, J. and Z. Hercowitz (2009). Liquidity Constraints of the Middle Class.
Technical report.
Carroll, C. (1997). Buffer-Stock Saving and the Life Cycle/Permanent Income Hypothesis. Quarterly Journal of Economics 112 (1), 1–55.
24
Cohen, L. (1959). An Empirical Measurement of the Built-in Flexibility of the Individual Income Tax. American Economic Review, Papers and Proceedings 49 (2),
532–541.
Deaton, A. (1991). Saving and Liquidity Constraints. Econometrica 59 (5), 1221–1248.
Dolls, M., C. Fuest, and A. Peichl (2012). Automatic Stabilizers and Economic Crisis:
US vs. Europe. Journal of Public Economics 96 (3-4), 279–294.
Fatas, A. and I. Mihov (2001). Government Size and Automatic Stabilizers: International and Intranational Evidence. Journal of International Economics 55 (1),
3–28.
Fries, A., J. Hutton, and P. Lambert (1982). The Elasticity of the U.S. Individual
Income Tax: Its Calculation, Determinants and Behavior. Review of Economics and
Statistics 64 (1), 147–151.
Gali, J. (1994). Government Size and Macroeconomic Stability. European Economic
Review 38 (1), 117–132.
Hall, R. and F. Mishkin (1982). The Sensitivity of Consumption to Transitory Income:
Estimates from Panel Data on Households. Econometrica 50 (2), 461–481.
Hayes, K., P. Lambert, and D. Slottje (1995). Evaluating Effective Income Tax Progression. Journal of Public Economics 56 (3), 461–474.
Heathcote, J., K. Storesletten, and G. Violante (2009). Quantitative Macroeconomics
with Heterogeneous Households. Annual Review of Economics 1 (1), 319–354.
Hsieh, C. (2003). Do Consumers React to Anticipated Income Changes? Evidence
from the Alaska Permanent Fund. American Economic Review 93 (1), 397–405.
Huggett, M. (1993). The Risk-Free Rate in Heterogeneous-Agent Incomplete-Insurance
Economies. Journal of Economic Dynamics and Control 17 (5-6), 953–969.
Jappelli, T. (1990). Who is Credit Constrained in the U.S. Economy?
Journal of Economics 105 (1), 219–234.
Quarterly
Jappelli, T., J. Pischke, and N. Souleles (1998). Testing For Liquidity Constraints
In Euler Equations With Complementary Data Sources. Review of Economics and
Statistics 80 (2), 251–262.
Johnson, D., J. Parker, and N. Souleles (2006). Household Expenditure and the Income
Tax Rebates of 2001. American Economic Review 96 (5), 1589–1610.
Johnson, D., J. Parker, and N. Souleles (2009). The Response of Consumer Spending
to Rebates During an Expansion: Evidence From the 2003 Child Tax Credit. Report.
25
Kaplan, G. and G. Violante (2014). A Model of the Consumption Response to Fiscal
Stimulus Payments. Econometrica 82 (4), 1199–1239.
Kaplan, G., G. Violante, and J. Weidner (2014). The Wealthy Hand-to-Mouth. Working Paper 20073, National Bureau of Economic Research.
Kniesner, T. and J. Ziliak (2002). Tax Reform and Automatic Stabilization. American
Economic Review 92 (3), 590–612.
Krusell, P. and A. Smith (1998). Income and Wealth Heterogeneity in the Macroeconomy. Journal of Political Economy 106 (5), 867–896.
Mertens, K. (2013). Marginal Tax Rates and Income: New Time Series Evidence.
Working Paper 19171, National Bureau of Economic Research.
Misra, K. and P. Surico (2014). Consumption, Income Changes, and Heterogeneity: Evidence from Two Fiscal Stimulus Programs. American Economic Journal:
Macroeconomics 6 (4), 84–106.
Musgrave, R. and M. Miller (1948). Built-in Flexibility. American Economic Review 38 (1), 122–128.
Pechman, J. (1973). Responsiveness of Federal Income Tax to Changes in Income.
Brookings Papers on Economic Activity 4, 385–421.
Ram, R. (1991). Elasticity of Individual Income Tax in the United States: Further
Evidence from Cross-Section Data. National Tax Journal 44 (1), 93–99.
Runkle, D. (1991). Liquidity Constraints and the Permanent-Income Hypothesis: Evidence from Panel Data. Journal of Monetary Economics 27 (1), 73–98.
Saez, E. (2004). Reported Incomes and Marginal Tax Rates, 1960-2000: Evidence and
Policy Implications. In J. Poterba (Ed.), Tax Policy and the Economy, Volume 18,
pp. 117–174. MIT Press.
Shapiro, M. and J. Slemrod (2003). Consumer Response to Tax Rebates. American
Economic Review 93 (1), 381–396.
Shapiro, M. and J. Slemrod (2009). Did the 2008 Tax Rebates Stimulate Spending?
American Economic Review 99 (2), 374–79.
Slitor, R. (1948). The Measurement of Progressivity and Built-In Flexibility. Quarterly
Journal of Economics 62 (2), 309–313.
Souleles, N. (1999). The Response of Household Consumption to Income Tax Refunds.
American Economic Review 89 (4), 947–958.
26
Telyukova, I. (2013). Household Need for Liquidity and the Credit Card Debt Puzzle.
Review of Economic Studies 80 (3), 1148–1177.
Zeldes, S. (1989). Consumption and Liquidity Constraints: An Empirical Investigation.
Journal of Political Economy 97 (2), 305–346.
27
Tables and Figures
Table 1: Breakdown of Households Identified as Handto-Mouth under the Kaplan and Violante (2014) Wealthy
Hand-to-Mouth Definition and that in the Zeldes (1989)
Net Worth Definition and the Jappelli et al. (1998) Credit
Application Definition
HtMKV = 1
HtMKV = 0
HtMJT = 1
HtMJT = 0
8.96%
18.35%
9.66%
63.03%
HtMZS = 1
HtMZS = 0
12.26%
15.05%
0.36%
72.33%
Source: Own illustration using data from the Survey of Consumer Finances, 1988
to 2009. HtMKV = 1 (HtMKV = 0) denotes the set of households identified as
hand-to-mouth (not hand-to-mouth) under the definition in Kaplan and Violante
(2014). HtMJT = 1 (HtMJT = 0) denotes the set of households identified as
hand-to-mouth (not hand-to-mouth) under the definition in Jappelli et al. (1998).
HtMZS = 1 (HtMZS = 0) denotes the set of households identified as hand-tomouth (not hand-to-mouth) under the definition in Zeldes (1989). Each cell
indicates the portion of households who were identified as HtM = 1 and/or HtM =
0 in each of the corresponding columns and rows for the given definitions.
28
Figure 1: Percentage of Population Identified as Hand-to-Mouth Over Time under
Hand-to-Mouth Definitions in Kaplan and Violante (2014), Jappelli et al. (1998), and
Zeldes (1989)
Source: Own illustration using data from the Survey of Consumer Finances, 1988 to 2009. Zeldes (1989) define a
household as HtM based on net worth, which was consistent with the vast majority of standard consumption theory
models. Jappelli et al. (1998) define a household as HtM if (1) a credit application has been either rejected or not fully
approved, or (2) a credit application has not been submitted because of concerns that the application would be rejected.
Kaplan and Violante (2014) define a household to be HtM if it either has zero liquid wealth or is at its credit limit. The
distinguishing factor between the Kaplan and Violante (2014) definition and the Jappelli et al. (1998) definition is that
the former can account not only for households living at their credit limit, but also for wealthy households who hold no
liquid wealth.
29
Figure 2: Percentage of Households Identified as Hand-to-Mouth by Income under
Hand-to-Mouth Definitions in Kaplan and Violante (2014), Jappelli et al. (1998), and
Zeldes (1989)
Source: Own illustration using data from the Survey of Consumer Finances, 1988 to 2009. Zeldes (1989) define a
household as HtM based on net worth, which was consistent with the vast majority of standard consumption theory
models. Jappelli et al. (1998) define a household as HtM if (1) a credit application has been either rejected or not fully
approved, or (2) a credit application has not been submitted because of concerns that the application would be rejected.
Kaplan and Violante (2014) define a household to be HtM if it either has zero liquid wealth or is at its credit limit. The
distinguishing factor between the Kaplan and Violante (2014) definition and the Jappelli et al. (1998) definition is that
the former can account not only for households living at their credit limit, but also for wealthy households who hold no
liquid wealth.
30
Figure 3: Percentage of Households Identified as Hand-to-Mouth by Net Worth under
Hand-to-Mouth Definitions in Kaplan and Violante (2014), Jappelli et al. (1998), and
Zeldes (1989)
Source: Own illustration using data from the Survey of Consumer Finances, 1988 to 2009. Zeldes (1989) define a
household as HtM based on net worth, which was consistent with the vast majority of standard consumption theory
models. Jappelli et al. (1998) define a household as HtM if (1) a credit application has been either rejected or not fully
approved, or (2) a credit application has not been submitted because of concerns that the application would be rejected.
Kaplan and Violante (2014) define a household to be HtM if it either has zero liquid wealth or is at its credit limit. The
distinguishing factor between the Kaplan and Violante (2014) definition and the Jappelli et al. (1998) definition is that
the former can account not only for households living at their credit limit, but also for wealthy households who hold no
liquid wealth.
31
Figure 4: Comparison of Raw and MPC Adjusted Normalized Tax Change (NTC) and
Normalized Disposable Income Change (NDC) Automatic Stabilization Measures
Source: Own illustration using data from the Survey of Consumer Finances, 1988 to 2009. The NTC estimates how
much aggregate tax revenue changes in response to a change in aggregate market income. The MPC Adjusted NTC
(ANTC) estimates the change in aggregate taxes that would have otherwise been spent as a proportion of the change in
aggregate market income. The NDC estimates how much aggregate disposable income changes in response to a change
in aggregate market income. The MPC Adjusted NDC (ANDC) estimates the change in aggregate consumption as a
proportion of the change in aggregate market income.
32
Figure 5: Comparison of MPC Adjusted Normalized Tax Change (ANTC) and MPC
Adjusted Normalized Disposable Income Change (ANDC) under Hand-to-Mouth Definitions in Kaplan and Violante (2014), Jappelli et al. (1998), and Zeldes (1989)
Source: Own illustration using data from the Survey of Consumer Finances, 1988 to 2009. The ANTC estimates the
change in aggregate taxes that would have otherwise been spent as a proportion of the change in aggregate market
income. The ANDC estimates the change in aggregate consumption as a proportion of the change in aggregate market
income. Zeldes (1989) define a household as HtM based on net worth, which was consistent with the vast majority of
standard consumption theory models. Jappelli et al. (1998) define a household as HtM if (1) a credit application has
been either rejected or not fully approved, or (2) a credit application has not been submitted because of concerns that
the application would be rejected. Kaplan and Violante (2014) define a household to be HtM if it either has zero liquid
wealth or is at its credit limit. The distinguishing factor between the Kaplan and Violante (2014) definition and the
Jappelli et al. (1998) definition is that the former can account not only for households living at their credit limit, but
also for wealthy households who hold no liquid wealth.
33
Figure 6: Normalized Automatic Stabilizer (NAS)
Source: Own illustration using data from the Survey of Consumer Finances, 1988 to 2009. The NAS measures the
extent to which the tax system reduces the sensitivity of consumption to income fluctuations.
34