Technical Paper Series

Congressional Budget Office

Washington, D.C.

Recent Literature on 

Taxable-Income Elasticities

December 2004

2004-16

Seth H. Giertz

Tax Analysis Division

Congressional Budget Office

Washington, D.C.

E-mail: Seth.Giertz@cbo.gov

Technical papers in this series are preliminary and are circulated to stimulate discussion

and critical comment. These papers are not subject to the Congressional Budget Office’s formal

review and editing processes. The analyses and conclusions expressed in them are those of the

authors and should not be interpreted as those of the Congressional Budget Office. References

in publications should be cleared with the authors. Papers in this series can be obtained at

www.cbo.gov/tech.cfm.

The author would like to thank Joel Slemrod, Jon Bakija, Jim Poterba, Emmanuel Saez, Bob

Williams, David Weiner, Bob Dennis, and Roger Hitchner for helpful comments.
Table of Contents

I. Introduction 1

II. Why the Elasticity of Taxable Income 4

III. Methodological Issues and Obstacles 6

Exogenous Income Shifts and Mean Reversion 7

Endogeneity of the Tax Rate 9

Institutional Factors: Contemporaneous Tax Policy Changes 10

Transitory versus Permanent Responses and Income Shifting 11

IV. Recent Empirical Work 12

Do Responses Depend on the Tax Change Examined? 13

Is There a Consensus on the Size of the Elasticity? 14

ERTA and TRA 86 15

OBRA 90 and OBRA 93 32

V. Conclusion 37

Table 1. Empirical Studies on the Elasticity of Taxable Income 40

Appendix: Highly Simplified Model for the Revenue-Maximizing Tax Rate 46
Recent Literature On Taxable-Income Elasticities

by Seth H. Giertz

Abstract

This paper reviews the literature on taxable-income elasticities, focusing primarily on empirical

studies examining the U.S. tax changes of 1981, 1986, 1990, and 1993 and the bracket creep of

the late 1970s and early 1980s. The paper first provides background on the importance of the

elasticity of taxable income both for forecasting income tax revenue and for assessing the

efficiency implications of tax rate changes. It then discusses the major methodological issues and

obstacles before delving into the literature. The paper emphasizes the different methodologies

employed and the sensitivity of estimates to an array of factors, including sample selection, the

tax reform under examination, and econometric techniques (or model specification). Although

it recognizes advances in the literature in recent years, the paper concludes that responses to tax

rate changes are far from fully understood and that there is much to be gained from continued

research on this topic.
1. Feldstein shows that deadweight loss = –0.5 $ tax_rate2 $ (1 – tax_rate)

-1,taxable_income $ taxable_income. However, there

are exceptions when a breakdown of the response can add insight into the efficiency implications. For example, suppose

tax rates rise and, in response, taxable income falls, but a portion of that drop in taxable income is due to increased

charitable contributions (and suppose those charities produce positive externalities). Or, suppose that a tax increase is

used to finance an underprovided public good. In instances such as those, where external costs or benefits are present,

assessing efficiency implications is more complex.

1

I. Introduction

Taxes can alter incentivesfor economic activity, in turn affecting income and reporting behavior.

From the government’s perspective, it is important to understand the relationship between taxes

and taxable income, because that relationship affects income tax revenue. The elasticity of

taxable income (ETI) measuresthe responsiveness ofreported taxable income to tax rate changes.

The ETI equals the percentage change in reported taxable income associated with a one-percent

increase in the net-of-tax rate, where the net-of-tax rate equals the share of the next dollar of

reported taxable income that is not taxed, or one minus the tax rate. The ETI is also key to

evaluating the efficiency implications of a tax change. Feldstein (1999) shows that, if accurately

estimated, the ETI can be used to calculate the deadweight lossfrom income taxation.1 Although

the variable isimportant, accurately estimating it is daunting. The tax code provides opportunities

to shift income between different tax bases (or to alter the timing of taxation), but reporting

requirements do not always allow analysts to observe the full spectrum of behavior. Likewise,

underlying economic conditions are complex and impactreported taxable income in waysthat are

not fully understood. Separating true responses to tax policy from responses to non-tax factors

is difficult.

The first modern studies to examine the broad range of responses to income taxation (Lindsey,
2. Based on IRS data available at www.irs.gov/pub/irs-soi/03db07co.xls.

2

1987, and Feldstein, 1995) found reported taxable income to be extremely responsive to the

across-the-board rate cuts of 1981 and 1986. Those findings were supported by anecdotal

evidence that some responses to taxation are large (and encompass more than labor supply

responses). In 1981 the top marginal individual income tax rate was 70 percent; five years later

the top rate had been more than cut in half, to 28 percent. Yet, despite substantial across-theboard rate cuts, economic growth and base-broadening pushed revenues from the corporate and

individual income tax up by nearly 15 percent in real terms from 1981 to 1990.2 And, despite a

much less progressive rate structure, the share of federal taxes paid by the highest income groups

increased substantially.

Since those seminal studies, much has been learned about the experience of the 1980s. It is now

recognized that factors other than tax policy played a role in those early estimates. And, contrary

to forecasts based on the 1980s experience, taxable income continued to grow rapidly for the

upper-income groups after their tax rates were raised in 1990 and 1993. That brought to the fore

the complexitiesinherent in measuring responsesto taxation. Subsequent research, by examining

the experience of the 1990s and by applying more sophisticated methods to the 1980s, has

concluded that the response of taxable income to tax rates, while sometimes substantial, is much

smaller than that found in the earliest studies.

Given the inherent complexities, the similarity in some of the recent estimates is surprising. For

example,severalstudies, using different methodologies and examining different tax changes, cite
3. Current annual taxable income and income tax revenue numbers are somewhat smaller, but roughly correspond to

those in the example.

3

a “best estimate” of the ETI of around 0.40. That might suggest that responses, if not perfectly

estimated, have at least been narrowed to a tight range. A closer examination of the literature

suggests that that may not be the case. In fact, some evidence suggests that responses may be

particularly sensitive to sample restrictions, econometric techniques, the period under

examination, and the past history of tax rate changes. Accurately estimating the ETI is especially

important because mis-estimation can profoundly affect both the estimated efficiency impact and

the tax revenue forecasts of a proposed change in tax rates. For example, suppose overall taxable

income were $5 trillion with a flat rate tax of 20 percent, yielding income tax revenue of $1

trillion.3 Now suppose the tax rate were raised by 10 percent (from 20 to 22 percent). If the ETI

were 0.40, that rate increase would cause reported taxable income to fall by $50 billion (= 0.40

* 10 percent * $5 trillion). The rate increase would also cause an additional deadweight loss of

$11.4 billion, while income tax revenues would increase by $89 billion. If, for comparison, the

ETI were 1.0, the same tax rate increase would cause taxable income to fall by $125 billion, or

2.5 times as much. Deadweight loss would rise by $26 billion, also 2.5 times as much. In

addition, the rate increase would cause tax revenues to increase by $72.5 billion, or 20 percent

less. If rates were raised more, revenues would rise at a decreasing rate, while the deadweight loss

would rise at an increasing rate.

Over the past two decades, the ETI has emerged as one of the key variables for assessing the

budgetary and efficiency implications of tax policy. But, because responses to taxation are so

complex and never occur in isolation, even estimates using the most sophisticated methods and
4. The term “compensated responses to tax rate changes” refers to the portion of the overall response attributable to

changes in relative prices (as opposed to the portion of the response due to changes in income). It is the distortion in

relative prices that leads to losses in efficiency.

4

the best available data are often inexact. 

II. Why the Elasticity of Taxable Income?

Because of its revenue and efficiency implications, the elasticity of labor supply is an important

variable in any examination of the impact of taxation. In a simplified model—where labor is the

only source ofincome and all income istaxed when earned—taxable income equalslaborincome,

and the ETI with respect to the tax rate is the same as the labor-supply elasticity. 

But the real world is much more complex. Slemrod (1998) notes that taxable income

encompasses more than just labor income, and that small (compensated) labor-supply elasticities

do not necessarily imply small efficiency costs.4 Income comes from many sources and those

sources are taxed differently, or sometimes not at all. Taxpayers have many opportunities for tax

deductions, exclusions, and credits; some of those opportunities allow income to escape the tax

base (that is, to go untaxed). Others allow taxpayers to shift when and under what base (for

example, individual versus corporate) income isreported and taxed. In that more realistic setting,

taxable income and laborincome (and their corresponding tax elasticities) can differ substantially.

As Slemrod (1998) and Slemrod and Yitzhaki (2000) report, people respond to income taxation

in many ways:

1) Real behavior involvesindividuals changing their consumption orthe amount they work,
5. Following Slemrod and Yitzhaki (2000), “avoidance” refers to avoiding the tax but not avoiding the activity. For

example, choosing leisure is one way to avoid paying income tax, but that decision falls under real substitution and

not avoidance, because the consumption bundle has changed as a result of the tax.

5

moving away from taxed goods or activitiestoward those that are untaxed or more lightly

taxed. The labor supply elasticity (which measures the trade-off between time spent on

labor and leisure) captures only a portion of that response.

2) Circumvention includes bypassing the tax system both illegally (by evasion) and legally

(by avoidance). In the case of evasion, income is concealed or at least is not reported to

the tax authorities. In the case of avoidance, income is shifted (intertemporally or between

sources) in order to receive more favorable tax treatment. Diverting income into a taxdeferred retirement account is an example of avoidance. Higher tax rates generally

increase the benefits from evasion and avoidance.5

3) Administration and compliance policy affect how people respond to taxes. Rigorous

enforcement and low compliance costs should limit evasion and lead to smaller income

responses of reportable taxable income to tax changes. In contrast, lax enforcement and

high compliance costs will tempt taxpayers to hide income, and thus will result in larger

changes in taxable income for a given change in tax rates. That implies that, instead of

structural parameters, taxable-income elasticities are endogenous and a function of

institutions.

Behavioral changes have efficiency implications. To assess them accurately requires
6

differentiating between real behavioral changes that affect resource allocation and accounting

maneuvers that simply re-label income. For example following the Tax Reform Act of 1986,

Subchapter S income increased nearly threefold as income was shifted from Subchapter C

corporations to Subchapter S corporations (because the 1986 act set the tax rate on Subchapter S

income below that on Subchapter C income). That shift of income was simply a transfer from one

tax base to another, but, because individuals do not report Subchapter C income, only half of the

picture was in view: the increase in Subchapter S income. Without information on the drop in

Subchapter C income, the relationship between marginal income tax rates and taxable personal

income is somewhat specious. 

In addition to behavioralresponses, the government’s costs of administering and enforcing the tax

system and compliance costs borne directly by taxpayers and employers affect tax system

efficiency. According to Slemrod and Yitzhaki (2000), tax administration and compliance costs

are nontrivial, and institutional factors both at home and abroad can be key in determining both

the efficiency costs of taxation and the size of elasticities.

III. Methodological Issues and Obstacles

The primary methodological objective is to devise a method for parsing the response of taxable

income to changes in taxes from the many other factors that also affect taxable income. This

section focuses on those complicating factors. Tax changes take place in a changing economic

environment; changesto that environment affect income growth. Adequately controlling for nontax-induced trends in taxable income poses a major challenge to estimating elasticities. In
6. See www.irs.ustreas.gov/pub/irs-soi/01in01ts.xls.

7

addition, a sound methodology must address several other important issues, including mean

reversion, tax-rate endogeneity, institutional changes (which often coincide with changes in the

rate structure), and the distinction between transitory (or temporary) and permanent (or longerterm) responses. Those complicating issues are discussed in more detail below. The following

section discusses methods for addressing them.

Exogenous Income Shifts and Mean Reversion

The distribution of reported income has widened over the past 30 years. That trend accelerated

in the 1980s, especially at the very top of the distribution. According to Piketty and Saez (2003),

the share of income reported by the top 10 percent of filersrose by more than one-third from, 32.9

percent in 1979 to 41.4 percent in 1988, but two-thirds of that increase went to the top 1 percent

of taxpayers. The share of income reported by the top one-half of one percent more than doubled,

the share reported by the top one-tenth of one percent nearly tripled, and the share reported by the

top one-hundredth of one percent more than quadrupled. Because people with the highest income

pay a disproportionate share oftaxes—the top 1 percent pay approximately one-third of allfederal

income taxes—their behavioris especially important.6 Not fully accounting for the portion of that

income growth that was unrelated to tax policy can result in large biases. For example, the 1980s

cuts in marginal tax rates were greatest at the top of the income distribution and thus inversely

correlated with the great income growth at the very top of the distribution. If the exogenous (nontax-related) portion of that income growth is not fully accounted for, that trend will bias ETI

estimates in a positive direction.
8

Mean reversion is another issue that complicates estimation. Over a person’s lifetime, income

often follows a general path with many fluctuations. After a period when income is particularly

high or low it will often revert to a more normal path. That phenomenon is especially pronounced

at the tails of the distribution. Those at the extreme top of the income distribution are often not

there for long, and will likely have a substantial drop in income—a drop that is unrelated to tax

policy. At the other extreme, those in school (or not employed) will often have large increases

in income upon entering the workforce. Not accounting for that mean reversion at the tails of the

distribution can substantially bias estimated elasticities. More specifically, not fully controlling

for mean reversion will erroneously count non-tax-related increases by those below their lifetime

path and decreases in taxable income by those above their lifetime path as responses to changes

in tax rates. Those factors will bias ETI estimates in opposite directions, depending on whether

tax rates are raised or lowered, but there is no reason to believe the biases will cancel each other

out. Partly for that reason, many studies exclude those with very low earnings. Those at the high

end cannot be so easily discarded, because they are responsible for a large share of both taxable

income and tax revenues.

One of the keysto successfully identifying taxable-income elasticitiesrests on the approach used

to control for non-tax-related changes to taxable income, including both exogenous shifts to the

income distribution and mean reversion. Mean reversion is certainly an important and complex

issue, but divergence in the income distribution seems to be more complicated and less well

understood. The fact that the exogenous income trend has persisted through periods of both

increases and decreases in the level and progressivity of tax rates suggests that it is, in large part,
9

not a direct response to tax changes. In addition, because the trend has been irregular,

distributional changes in years without tax changes may not provide useful measures of exogenous

shifts that occur during periods with tax changes.

It is also likely that the size of taxable income elasticities varies across the income distribution.

In general, estimated ETIs are larger for higher-income groups. People with higher incomes

generally have more opportunities to respond to tax changes. They generally rely less on wage

and salary income and have more control over the timing and source of their income than do other

groups. People with more modest incomes can alter their labor supply, but may have few other

alternatives altering their taxable income. If the ETI does in fact vary with income, a single

overall elasticity will not be applicable when considering the impact of rate changes that target

only part of the income distribution or that differ in magnitude across the distribution.

Endogeneity of the Tax Rate

Because ofthe federal tax system’s progressivity, it is almost axiomatic that a simple cross-section

regression will show a direct relationship between tax rates and taxable income. Even with

longitudinal data, an individual’stax rate rises with taxable income. In order to isolate the impact

of taxes on taxable income, tax rates should be imputed based on an instrumented (or exogenous)

measure oftaxable income. After instrumenting, the correct relationship between taxable income

and the tax rate should be achieved for each individual, but that method does not address the

cross-sectional correlation between taxable income and tax rates. Studies using cross-sectional
10

variation foridentification generallymust also include differencing methods (which transform the

key dependent variable to the change in the tax rate).

Institutional Factors: Contemporaneous Tax Policy Changes

Institutional changesto the tax system, taking effect contemporaneously with rate changes, could

affect reported taxable income, biasing estimated elasticities or at least complicating the

estimation. In fact, Slemrod (1996) shows that changes to the underlying tax base may result in

pre- and post-tax-change elasticities that differ substantially. Most regression techniques yield

a weighted average of the two elasticities. For more detail, see Section IV.

Most elasticity measures assume policiestoward tax evasion and avoidance as given, when in fact

those are choices that can change. But, in order to achieve a given objective (whether a revenue

goal, an improvement in the system’s efficiency, or altering the distribution of the tax burden),

it may be preferable to consider not simply changing tax rates (given the expected response), but

to alter both the rates and institutional factors.

The definition of taxable income itself may influence results. Changes to the tax system may alter

that definition. Using the concurrent definition for taxable income (that is, the definition that was

in effect when the income was received) will confound responses to tax rates with statutory

changes to the tax base. But even if a consistent measure is chosen, Slemrod (1998) shows that

estimates may depend on the definition used and that even a constant-law definition can yield

biased results.
11

Transitory versus Permanent Responses and Income Shifting

Permanent, or longer-term, behavioral responses to tax changes are of primary importance;

transitory responses are a lesser concern. For illustration, suppose that in 1986 it was well known

that the tax rates were set to fall beginning in 1987. That change could induce both short- and

long-term responses. In the short term, some may have delayed the receipt of income from

December of 1986 to January of 1987. That response would not have affected real economic

behavior and would not have impacted long-term taxable income. By contrast, a persistent change

in investment or labor market behavior would have represented a long-term response, affecting

the allocation of resources and taxable income for yearsto come. That is not to say that transitory

behavior is alwayssmall or trivial. For example, capital gains realizations rose by over 96 percent

from 1985 to 1986 in anticipation of less-favorable treatment of capital gainsset to begin in 1987.

Separating transitory from permanent responses is often difficult. Measuring changes in taxable

income in the year prior to and the years succeeding a tax change will likely yield a combination

of permanent and transitory responses. Phase-in periods and taxpayer expectations about future

tax legislation also matter. For example, if rate cuts phase in, people not only divert income (on

paper) to the future, but may also substitute leisure in the short term (while the rate cuts are in

progress) for work in the future (after the rates have been fully lowered). In that instance,

intertemporal substitution could result in a short-term understatement and a longer-term

overstatement of the ETI.
12

A related issue is the relationship between tax policy and long-term career and investment

decisions. Tax policy can affect investment in both human and physical capital, which over time

could influence taxable income. That long-run response is important in measuring the true

response to tax changes, but may not be fully observed for many years following a tax change,

leading to an understatement of the ETI.

In addition to intertemporal shifting of income, people may shift revenue from one tax base to

another or from outside the tax base to taxable income (or vice versa). Income shifting can occur

between different tax basesfor an individual or, as Carroll (1998) notes, between individuals. For

example, a highly paid lawyer may reduce his workload in response to a tax increase targeted at

high earners, but his reduction may shift business to lawyers in lower tax brackets. That point is

especially important when an analysis uses data that exclude portions of the income distribution.

In terms of tax revenue, it is important to account for gains and losses throughout the tax system,

as opposed to focusing on a limited set of sources.

IV. Recent Empirical Work

With a couple of exceptions, the ETI literature divides into two strains, one focusing on the tax

cuts of the 1980s (the Economic Recovery Act of 1981 and the Tax Reform Act of 1986) and the

other looking at the tax increases of the early 1990s (the Omnibus Budget Reconciliation Acts of

1990 and 1993). Over time, that literature has led to a clearer understanding of the fundamental

issues and to more sophisticated methods and better data for addressing them. Most of the papers

use individual-level panel data and employ a first-differences or differences-in-differences
13

methodology, while several use aggregated data and examine the relationship between tax rates

and the income (or share of income) accruing to various segments of the taxable income

distribution. Despite a good deal of research, the ETI is a far from settled issue. In many

instances estimates are not robust and the process underlying (or sources driving) the taxable

income response is not fully understood.

The remainder of this section is organized into four parts. The first lays out the rationale for

grouping the papers by the time period examined. The next part discusses a key point drawn from

the literature—namely, that it is premature to conclude that the ETI has been precisely estimated

(and hovers around 0.40). The final two parts discuss the literature covering the 1980s and 1990s,

respectively.

Do Responses Depend on the Tax Change Examined? 

The nature and circumstances surrounding a tax reform may influence the true elasticities, and

may also tend to impart upward bias in some instances and downward bias in others. Comparing

studies focusing on the same tax change makes it easier to evaluate the impact of other studyspecific factors (such as data and methodology) on the results.

The reform under consideration (or unique economic conditions) can be a key factor driving

results. For example, as discussed later in this section, Slemrod (1996) found that simply

including the TRA 86 experience increased measured responses for some income sources many

times compared with examining only the period from 1954 to 1985. In addition, the coefficient
14

on Slemrod’s control for exogenous changes in wage inequality often changes signs (as well as

changing greatly in absolute magnitude) when data from 1986 to 1990 are included.

The decision to group the papers by the reform examined is supported by two factors: First, the

actual elasticities may differ from one reform to the next and,second, the key non-tax factorsthat

affect taxable income, discussed in the previous section, may also depend on the reform. With

respect to the first point, identical changes in tax rates elicit smaller responses under a tough

enforcement regime with a broad tax base (and thus few opportunities to shelter income) than

under a regime with lax enforcement and a small base. With respect to the second point, suppose

that a law change affects both marginal rates and other aspects of the tax system, as typified by

TRA 86. That scenario may call for methods that would not be relevant to a case where only the

rates changed.

Is There a Consensus on the Size of the Elasticity?

Although the ETI literature appears to be moving toward a consensus, a deeper look at the

research suggests little agreement. Despite the complexities inherent in the estimation, several

recent articlessuggest a consensus value of about 0.40 (see Table 1). Carroll (1998), Saez (2003)

and Gruber and Saez (2000) all find an overall elasticity of 0.40, even though they examine

different tax changes and use alternative methodologies. A closer examination of the studies

suggests, however, that focusing on that single number masks considerable variation in the

estimates. In addition, Saez (2004) and Kopczuk (2003) suggest a great deal of uncertainty

surround their ETI estimates. In some cases the overall elasticities vary greatly depending on
15

specification. In other instances, breaking down the response by factors such as income source,

filing status, and income group paints a different picture.

Saez (2003), for example, finds elasticities ranging from -0.14 to 0.71 by separately analyzing four

income groups that are further divided by marital status and the decision to itemize deductions.

An elasticity of 0.40 applies only to the overall response for married taxpayers or to the overall

response for married and single taxpayers who itemize. The estimated overall elasticity is more

than 20 percent smaller: 0.31. The 0.38 overall elasticity found by Carroll (1998, for OBRA 90

and OBRA 93) falls to 0.33 when he includes taxpayers on the AMT and rises to 0.56 when he

restricts his comparison group, a sample of taxpayersfacing no change in rates, to taxpayers with

incomes above $75,000 (instead of above $50,000) and below the threshold where rates begin to

change. Gruber and Saez (2002) come to 0.40 by including a 10-piece spline of logged income

to control for heterogeneous movements in the income distribution (assumed to be unrelated to

tax changes). But Kopczuk (2003), using nearly the same data and methodology, finds the

elasticity falls by nearly 50 percent, to 0.21, when he uses the full sample instead of only

taxpayers with incomes greater than $10,000. Kopczuk provides additional evidence on the

sensitivity of estimates to sample selection and, furthermore, uses elaborate controls for

exogenous variations in income, which yield a wide range of taxable-income elasticities.

ERTA and TRA 86

ERTA (1981) and TRA 86 both substantially reduced marginal tax rates. ERTA reduced the top

rate from 70 percent to 50 percent, and TRA 86 dropped it further, to 28 percent. Both acts also
7. Specifically, in creating the counterfactual baseline, TAXSIM “ages” the data by adjusting the various income

sources by the corresponding changes to the National Income and Product Accounts.

8. For perspective, under the assumption that taxable income is an inverse function of the tax rate, under a single-rate

tax system tax revenue is maximized when the net-of-tax elasticity equals (1 – tax_rate)/tax_rate), which is equivalent

to an elasticity with respect to the tax rate of -1. Under that highly simplified model, an ETI of 0.40 would imply a

revenue-maximizing income tax rate of 70 percent, whereas an ETI of 3.0 would imply a revenue-maximizing tax rate

of 25 percent. (See Appendix I.)

9. Although most recent studies find small labor-supply elasticities (close to zero for prime-age males), Prescott (2004)

finds very large responses when examining the impact of marginal tax rates across countries over time.

16

cut rates across income distribution. Those two acts are sometimes examined together.

Lindsey (1987) examines ERTA, treating the act as a natural experiment. He uses IRS tax return

data from 1979 in conjunction with National Bureau of Economic Research’s(NBER) TAXSIM

model to make post-ERTA projections of the income distribution, assuming both pre-ERTA law

and the macroeconomic conditions of the intervening years.7 He then compares the

counterfactual projections against the actual post-ERTA distribution of income and finds large

taxable-income responses to the tax cuts. The consequent estimate of the overall ETI between

1.60 and 1.808 suggests that behavioral responses (to lower marginal tax rates) sharply reduce

revenue losses, more than the labor supply literature predicted.9 One important methodological

issue isthat Lindsey does not control for exogenousshiftsto the income distribution (which were

not widely recognized at the time) and thus attributes the secular income trend (especially at the

high end)to ERTA. That method yields estimated elasticities that are almost certainly overstated.

Feldstein (1995) follows a sample of married taxpayers over time, and, instead of running a

simulation to produce counterfactuals, uses a difference-in-differences approach to identify

responsesto TRA 86. Feldstein uses data from the Statistics of Income’s (SOI) Continuous Work

History Survey (CWHS) for years from 1985 and 1988. The sample includes married couples
10. The chief difference between Subchapter S and Subchapter C small businesses is that Subchapter S allows

shareholders to pass profits and losses through to their individual tax returns, whereas Subchapter C profits are taxed

at the corporate level and are often retained with the firm, thus not appearing on the stockholder’s individual tax return.

As mentioned earlier, after TRA 86, many who had used Subchapter C corporations switched to Subchapter S, which

were now taxed more favorably.

11. The sample is divided into three groups. Filers in the medium-income group have taxable income ranging from

$21,020 to $62,450. That is followed by the high-income group, which includes those with taxable incomes up to

$113,860. The highest-income group includes filers with taxable incomes above $113,860. Those ranges are

expressed in 1985 dollars. 

17

who were sampled in both 1985 and 1988 and who were under age 65 in 1988. Feldstein further

excludes filers who began reporting Subchapter S income between 1985 and 1988, as well as

those with 1985 marginal income tax rates below 22 percent.10 

Feldstein groups taxpayers by their 1985 marginal tax rates and inflates 1985 AGI to 1988 by

using the increase in personal income (17.4 percent) over the three years.11 He then uses a

difference-in-differences approach. For the first difference, he calculates both the percentage

change in mean taxable income and the percentage change in the net-of-tax rate from 1985 to

1988. The result of that first step shows the relationship between change in tax rate and change

in taxable income for each group. But, there may be other, non-tax, factors affecting taxable

income that are not taken into account. In an attempt to remove the influence of those non-tax

factors, Feldstein employs a second difference. For taxable income changes and tax rate changes

respectively, he subtractsthe percentage change for one of the two lower-income groupsfrom the

change for one of the two higher. If the influence of the non-tax factors is proportional across the

groups, that second difference will remove the non-tax component from the income measure,

leaving the portion that is a true response to the tax change. For the final step, Feldstein

calculates the elasticities by dividing the second difference for taxable income by the second

difference for the net-of-tax rates. 
12. An individual in 1985 with nominal Subchapter S income and substantial Subchapter C income would not be

excluded from the sample, but could bias results if he or she converted Subchapter C income to Subchapter S after TRA

86 took effect.

18

Feldstein’staxable-income elasticitiesfor the two highest-income groupsrange from 1.10 to 3.05

for AGI and 1.04 to 1.48 for AGI plus gross losses. The difference-in-differences approach

controls for unobserved factors that may have influenced the change in taxable income over the

period. But, as mentioned above, it implicitly assumes that those unobserved factors have a

proportional (or homogeneous) impact across the income distribution. If, as is now generally

believed, the secular upward trend in taxable income is much more pronounced at the upper end

of the distribution, then that approach leads to overestimates of the ETI (for periods when tax

rates are falling).

Sample size and the influence of outliers should not be overlooked. Feldstein’s estimates for the

top income group derive from just 57 observations. Slemrod (1996) reports that one filer in the

Feldstein’s study reported Subchapter S income of $5000 in 1985 and Subchapter S income of

more than $3 million in 1988.12 Slemrod claims that that one filer was responsible for close to

50 percent of the growth in net partnership and Subchapter S income after TRA 86—and that just

four filers accounted for close to 90 percent of the growth. 

Auten and Carroll(1995) begin their empirical analysis by employing Feldstein’smethodology,

but compare CWHS data for 1985 against those for 1989 rather than 1988, and generally find

large elasticities (from 0.46 to 3.04, but usually 2 or greater) that are consistent with Feldstein’s.

To test the sensitivity of their results, they explore several specifications, adding variables such
13. By contrast, the CWHS is a subsample of the SOI and represents a random sample of tax filers.

14. Moffitt and Wilhelm (2000), using data from 1983 and 1989 Survey of Consumer Finances (SCF), also find large

responses to TRA 86, with nearly all of the action at the high end. Using Feldstein’s (1995) methodology, they report

an elasticity of 1.83. Because the SCF does not provide information on taxable income, their focus is on AGI. Moffitt

and Wilhelm explore alternative specifications, which include adding independent variables to the model and various

instruments for the net-of-tax rate. Results from those techniques vary greatly. Estimated elasticities are often large,

but are generally smaller than those found using Feldstein’s (1995) methodology. For example, when including asset

values in the first-stage equation, they report a weighted elasticity of 0.65.

19

as age, age squared, and wealth to the model. They also expand the panel to include a large

number of high-income filers (from the SOI) and add state tax rates (as well as demographic

information) to the model. They find estimated ETIs that are quite sensitive to both income

definition and regression weights. Auten and Carroll seem most confident in an ETI estimate of

0.74, which is based on a constant law definition of taxable income and employs regression

weights (the analogous unweighted ETI estimate is 0.96) to control for the SOI’s nonrandom

sampling.13 With the SOI, people from the 1985 sample that have higher income are more likely

to be sampled in 1989 than those whose income declines. Auten and Carroll conclude by noting

that their results are “highly sensitive to specification decisions” and that results are conditional

on factors such as weighting, the definition of taxable income, and sample construction.14

Auten and Carroll (1999). The most recent research on taxable income in the 1980s not only

uses panel data,following the same people overtime, but also paysspecial attention to issuessuch

as mean reversion and secular income trends, which likely plagued some of the earlier studies.

Auten and Carroll focus on TRA 86 and draw a distinction between tax and non-tax factors that

have contributed to the rise in income inequality in recent decades. Their key dependent variable

is pre-tax taxable-income net of capital gains. They use SOI data and include an especially large
15. Due to the SOI’s sampling methods, those with rising incomes are more likely to be included in the sample than

those with declining incomes. To account for this non-random attrition, a weighted regression model is employed.

16. Auten and Carroll emphasize results for the gross-income models, which are generally quite similar to those in the

taxable-income models.

20

number of high-income filers.15 Filers whose marital status changes between 1985 and 1989 are

dropped, as are those for whom the secondary social security number changes. The sample

includes only those ages 25 to 55 who were at or above the 22 percent marginal tax bracket

($21,020 joint, $15,610 single) in 1985. Also excluded are AMT filers and those who report

Subchapter S income in 1989 but not in 1985.

Both constant-law gross and taxable income are calculated based on post-TRA 86 net of capital

gains. The income variables (after first-differencing) are regressed against changes in

instrumented marginal tax rates and against a set of demographic variables. The instrumented

change in tax rates is calculated by inflating 1985 income to 1989 and calculating the

corresponding tax rates under 1989 law. The actual change in tax rates is regressed against the

instrumented change in rates and some exogenous variables. Fitted values from that regression

are used in the final estimation. Non-tax control variables include a 1985-based wealth proxy,

the number of children, age and age-squared, and dummies for children away from home,

entrepreneurship, census region, and occupation (as a proxy for human capital).

Auten andCarroll are most confident in a weighted least-squares model that includes both tax and

non-tax factors (including occupational dummies), which yields a 0.55 ETI with respect to the

net-of-tax rate.16 Excluding occupation information reduces the elasticity only slightly, to 0.51,

and excluding all non-tax factors raises the elasticity to 0.75. (The unweighted models yield
18. It is not clear that that procedure will eliminate transitory influences on the elasticity. Nearly every year of the

1980s is likely to have some transitory responses, in part because of multiyear phase-in periods. Thus, comparing years

three years apart is unlikely to avoid transitory fluctuations.

19. An income effect variable is also discussed, but is left out of their most-preferred specification.

17. Broad income is defined as total income less capital gains and Social Security benefits. Taxable income is closest

to the 1990 definition of taxable income. The measures are consistent for all years.

21

much higher elasticities, but are most certainly biased by nonrandom attrition.)

Gruber and Saez (2000) use data from the SOI’s Continuous Work History File for years 1979

through 1990 to examine taxable income responses to ERTA and TRA 86. They calculate

constant-law income using 1990 law excluding capital gains and use NBER’s TAXSIM model

to estimate federal and state tax rates. Behavioral changes are measured between pairs of years

over three-year intervals. They use three-year intervals to focus on long-term responses instead

of year-to-year fluctuations.17 The natural log of the income growth is regressed against the

instrumented change in the after-tax rate (and yearfixed effects and dummiesfor maritalstatus).18

Gruber and Saez control for tax rate endogeneity by using instrumented tax rates calculated first

by inflating beginning-year income by the growth in broad income over the three-year interval.19

Counterfactual tax rates are then calculated by applying the future tax law (three years hence) to

the grossed-up beginning-year income. Next, the instrumented rates are imputed via two-stage

least squares (where the actual change in tax rates is regressed against the counterfactual change

in rates along with the other independent variables). Recognizing the possibility of mean

reversion and secular trends in income, they explore three specifications using: 

1) No controls other than scaling the data by the growth in broad income,

2) Log of initial period income as an independent variable, and
20. Including the log of base-year income in place of the spline imposes a single functional form between that variable

and the dependent variable. The spline allows for more flexibility, which should improve the model fit by accounting,

at least to some degree, for unobserved factors that have a heterogeneous impact on taxable-income growth across the

reported taxable-income distribution.

21. Within the 1980s, the income pattern of the top one percent is often quite different from the pattern of the top 5

percent or top 10 percent.

22

3) A 10-piece spline based on the log of initial-period income.

Regression results are dollar weighted. With dollar weighting, responses by high-income people

are the driving factor in determining the overall elasticity. Scaling income by the growth in broad

income isintended to control for inflation and exogenousincreasesin income. That assumes that

overall income growth is independent of tax rates (or at least that that response is trivial for most

of the distribution) and that exogenous growth impacts all taxpayers in the same manner.

Gruber and Saez control for mean reversion and exogenous income trends with independent

variables that significantly affect elasticity estimates. They are most confident in the estimated

ETI of 0.40 from the model that includes a 10-piece spline based on the natural log of initialperiod income. The spline allows the functional relationship between the dependent variable and

the independent variables to vary by decile,20 but assumes that the relationship between initial

income and income changes follows the same functional form within each decile.21

Gruber and Saez’s corresponding elasticity for broad income is much smaller, 0.12, suggesting

thatmuch ofthe taxable-income response comes through deductions, exemptions, and exclusions,

rather than through changes in labor supply. Elasticities vary greatly over the different income

groups—from 0.57 for people with incomes over $100,000 to 0.11 for those from $50,000 to

$100,000 and 0.18 for those from $10,000 to $50,000 range.
22. That finding can be compared with Long (1999), who uses 1991 public use SOI data and exploits cross-state

differences in marginal tax rates to produce taxable-income elasticities. Long reports overall elasticities between 0.19

and 0.82. Estimates are somewhat lower for married filers. The study contributes to the literature by using an

identification strategy that relies solely on cross-sectional variation in tax rates, and not tax changes, for people with

similar incomes. Like some of the other studies, Long shows that responses are quite sensitive to the model’s

specification. (The data for the study are restricted to filers with incomes between $0 and $200,000.)

23

Gruber and Saez use their preferred specification to estimate separate models with state rates and

with only federal rates. The authors find no statistically significant difference between the

coefficients on the state rate (0.63) and the federal rate (0.41).22

Kopczuk (2003) also uses CWHS data from 1979 to 1990 and tax rates from NBER’s TAXSIM

to examine the tax changes of the 1980s. He assumes that the ETI is not a structural parameter

but rather a function of the tax system’sstructure: The ETI is a function of the tax base—or, more

precisely, of taxable income divided by total income. Available deductions lower the cost of

shifting income outside the tax base; asthe tax base becomessmaller, responsesto changesin tax

rates increase. Kopczuk’s model treats mean reversion and divergence within the income

distribution as separate issues, each requiring its own variables to identify responses properly.

Kopczuk replicates Gruber and Saez’s work showing the sensitivity of their results to sample

selection. The chief difference between the two datasets is that Kopczuk uses the full sample,

while Gruber and Saez exclude taxpayers with less than $10,000 of income. (Also, Kopczuk

deflates his data by the CPI, whereas Gruber and Saez deflate by growth in broad income.) Using

the log of initial income to control for mean reversion, Kopczuk reports an elasticity of 1.44

compared with 0.61 for Gruber and Saez. Using Gruber and Saez’s preferred specification with

a 10-piece spline, Kopczuk finds an elasticity of 0.21, versus 0.40 for Gruber and Saez.
23. Kopczuk wants to compare sample restrictions used by Feldstein (1995), Auten and Carroll (1999), and Gruber

and Saez (2002). He restricts the analysis to TRA 86, a period covered in the other three papers.

24

To explore the sample selection issue further, Kopczuk performs a sensitivity analysis using data

from 1985 through 1988. He finds that, at least in that instance, relatively modest changes to the

sample can produce a wide range of reasonable values. He notes that Gruber and Saez restrict

theirsample to those earning more than $10,000 and that Auten and Carroll (as well as Feldstein)

exclude those with marginal tax rates below 22 percent.23 Kopczuk finds that, with no other

restriction, excluding taxpayers with incomes below $10,000 results in an elasticity of 0.347.

Excluding those with tax rates below 22 percent yields an elasticity of 0.85. Making no sample

restrictions gives an elasticity of 2.72. Increasing the exclusion to filers earning less than $30,000

lowers the elasticity to essentially 0, suggesting that mean reversion is important at the bottom

of the distribution. Those results show elasticities that are highly sensitive to sample selection.

As discussed in the previous section, mean reversion can bias ETI estimates if a portion of the

non-tax-related increase in income is mistakenly attributed to a response to the tax change. In

addition, nonrandom attrition at the low end of the income distribution may further bias ETI

estimates because people with rising incomes are likely to remain in the panel, while some with

falling incomes may disappear from the sample if they no longer file a tax return. One

interpretation of Kopczuk’sfinding isthat it is not possible to accurately estimate the true overall

ETI because estimates are highly sensitive to modest changesto the sample and because there are

no sound criteria for restricting the sample. Another, more optimistic interpretation is that better

methods are needed to controlformean reversion and nonrandom attrition—or that improvements

could result from the development of sound criteria for restricting the sample.
24. Corresponding elasticities for married filers are much lower. That finding contrasts with other studies, such as Saez

(2003), which find larger elasticities for married filers.

25. Kopczuk adds the direct coefficient on the log of the net-of-tax rate (0.44) to the product of one minus the tax

base (0.09) and the coefficient on the tax base control (1.04). That yields 0.53. The tax base control variable is

constructed by multiplying one minus the tax base times the log of the net-of-tax rate.

25

Kopczuk experiments with alternative controls, including specifications that control separately

for mean reversion and divergence in the income distribution. Controlling separately for those

two factors results in elasticities between 0.55 and 1.37.24 Kopczuk’s preferred specification,

which includes both a spline based on the log of 1979 income and a spline based on the difference

between the log of taxable income in the beginning year of each three-year period and the log of

1979 taxable income, yields an elasticity of 0.57.

Kopczuk repeats the analysis after adding information on the size of the tax base interacted with

the net-of-tax rate, and finds the coefficient on that variable to be statistically significant for all

specifications. As predicted, the elasticity falls as the tax base increases. Coefficients on the

direct tax elasticity still vary greatly depending on the other controls. His most preferred

specification, including the tax base effect, yields a direct tax elasticity of 0.44. Evaluated at the

average tax base, that is consistent with an elasticity of 0.53.25 Again, estimates are much larger

forsingle filersthan for married filers: The corresponding elasticity for married filers is just 0.09.

Kopczuk discounts his results for single filers, noting the lack of demographic information; he

suggests that younger filers entering the workforce may be biasing those estimates upward.

Slemrod (1996) addressessome ofthe non-tax factors, including changesin wage inequality, that

may have biased earlier studies. In addition, he highlights the importance of maintaining a
26. Using aggregated time-series data from 1954 to 1990, Feenberg and Poterba find the share of taxable income going

to the top one-half of one percent increasing, and find that that increase accelerated in the 1980s after ERTA and TRA

86, an apparent response to those tax cuts. Feenberg and Poterba, however, do not use a constant-law definition of

income. Slemrod (1996) builds directly on Feenberg and Poterba, using the same data (with adjustments to account

for changes to the definition of taxable income) and beginning with a sensitivity analysis of their results.

26

constant definition of taxable income—but also shows that the definition chosen can affect the

results. Slemrod focuses on the share of taxable income accruing to the top 1 percent of the

distribution, building on the research of Feenberg and Poterba (1993).26

Slemrod’s use of aggregated time-series data (which includes a share approach that focuses on

the share of income reported by various segments of the taxable income distribution) differs

greatly from the differencing approaches used with panel data. Using time-series data to examine

the impact of tax rates on taxable income (or taxable income shares) avoids problems associated

withmean reversion. In addition, the problem of transitory shifting, which can confuse short-term

responses with the permanent response, are less severe. The share approach has another

advantage over differencing techniques that treat the behavior of one income group as a

counterfactual in estimating the ETI.With panel data, iftheETIs orthe exogenoustaxable income

trends differ by income, differencing alone will result in biased ETI estimates,since differencing

implicitly assumes that the unobserved factors influencing taxable income are very similar and

affect taxable income in the same manner for both groups.

The use of aggregated time-series data does, however, have some drawbacks. The issue of

exogenous income trends that differ across the income distribution is not necessarily overcome

by using aggregated time-series data. In addition, panel data are superior for capturing shifting
27

that occurs within the income distribution. For example, suppose that, in response to a change

in tax rates, people at the top of the income distribution substitute taxable bonds for tax-exempt

bonds and those in lower tax brackets do the reverse, substituting taxable bonds for tax-exempt.

The net effect of that change on taxable income may be zero, but a share approach focusing on

the top portion of the income distribution may capture the drop in income but miss the

corresponding rise in other segments of the distribution. On top of that, rank reversal, in which

the composition of the segment of the income distribution under examination changes over time,

can potentially biasETIs estimated using aggregated time-series data. (Rank reversal is discussed

in more detail below.)

Slemrod illustrates the complicating factors inherent in estimating taxable-income elasticities,

showing that apparently innocuous assumptions can have an important impact on the estimates.

He uses both pre- and post-TRA 86 taxable-income definitions to show that constant-law

definitions yield positive but smaller growth in the income share for the top one-half of one

percent. For example, using concurrent taxable-income definitions, the share accruing to the top

one-half of one percent increases by 3.4 percentage points from 1984 to 1990. Using the 1990

definition, that increase falls to 1.9 percentage points.

Next, Slemrod regressesthe share oftaxable income received by the top one percent offilersfrom

each of several different sources (AGI, wages and salaries, capital gains, interest, and dividends)

against both tax and non-tax variables. The key explanatory tax variables are based on current,

lagged, and prospective tax rates for both realized capital gains and labor income. The non-tax
27. The measure is calculated as the difference between the log of the 90th and the 10th percentiles of male weekly

wages.

28. The increase from 1985 to 1990 in the share of wages earned by the top 0.5 percent and top 1 percent exceeded

the increases from 1973 to 1985, but the difference in earnings of the 90th and 10th percentiles actually fell between

1985 and 1990. So, with the post-TRA 86 data, an increase in wage inequality is actually associated with a decrease

in the concentration of taxable income held by the top 0.5 percent of the distribution.

28

factorsinclude a measure of wage inequality,27 the real level ofstock prices as a proxy for non-tax

influences on capital-gains realizations, and the nominal AAA bond interest rate.

Slemrod isolates the effects of TRA 86 by comparing regressions based on data for 1954 through

1985 and those based on 1954 through 1990. He finds that adding the years surrounding TRA 86

fundamentally alters the results. In addition to marked changes to the ETI, the coefficient on the

wage inequality variable is vastly different when the post-1985 data are included. With data from

1954 to 1985, the coefficient on the wage inequality variable is positive and often large; for that

same period, the “permanent” response to rate changes is extremely small. That suggests that

demand factors, not taxes, are the driving force behind the growth in taxable income. When the

1985 through 1990 data are added, the opposite is found: Taxes appear to be the driving force

behind the taxable income growth at the top of the distribution. With the addition of the 1985

through 1990 data, the coefficient on the concurrent labor tax variable is much larger (or more

negative, since Slemrod uses tax rates as opposed to net-of-tax rates).28

Slemrod concludes that even estimates based on a constant-law income definition may overstate

the taxable income gains accruing to the top 1 percent in response to TRA 86. Although real

responses may well be large, a significant portion of the increases is likely because of changes in

the tax structure that lead people to shift income and change their reporting behavior, and not
29.Slemrod cites another example of rank reversal that could result from not using a constant law definition of taxable

income. Slemrod notes that pre-TRA 86, 40 percent of realized capital gains were counted as taxable income, while

post-TRA 86 100 percent were included. Use of concurrent definitions of taxable income likely overstates the growth

in the share of income held by the top 1 percent, because those in the top 1 percent likely have a greater share of capital

gains than the rest of the distribution. In addition, some who derive a large fraction of their income from capital gains

would move into the top 1 percent. Thus, the cross-sectional correlation between realized capital gains and other sources

of income would also be spuriously correlated with the change in tax rates.

29

because of traditional behavioral responses to lower marginal tax rates. Shifting income from

corporate to individual sources plays an important role. Other factors include changing

demographics at the top ofthe distribution. Post-1986, the wealthiest taxpayers were younger and

thus more likely to have increasing incomes in the absence of rate changes.

Rank reversal, where, in the context of Slemrod’s paper, the composition of people with reported

taxable incomes in the top 1 percent changes over time and taxable-income sources are crosssectionally correlated, could also contribute to the results. For example, a positive correlation

exists between Subchapter S income and wage and salary income. As income is shifted to

Subchapter S after TRA 86, more filers with Subchapter S earnings move into the top 1 percent.

That is, the shifting of income from Subchapter C, which is not observed, to Subchapter S, which

is observed, resultsin a re-ranking of tax filers, thus altering the composition of the top 1 percent.

If those filersthat move into the top 1 percent as a result of the re-ranking also have higher wages

and salaries than the filers they replace, it may appear that the wage and salary increase is a

behavioral response. Insight into the impact of that correlation on the estimated elasticities could

be gained from repeating the analysis, while omitting Subchapter S income.29

Saez (2004) also employs a share analysis to aggregated time-series data. Saez uses aggregated

SOI data from 1960 to 2000 and a consistent definition of taxable income that excludes realized
30

capital gains. Average marginal tax rates for each group are estimated via TAXSIM, and ETIs

are generally weighted by income. Saez’s study is unique in that it does not focus primarily on

the tax cuts of 1980s or the tax increases of the 1990s, but examinesthe responsesto all of the tax

changes over the past four decades.

Using a simple model where the log of taxable income is regressed against the log of the net-oftax rate with no controls for non-tax related trends in taxable income, Saez reports a statistically

insignificant estimated ETI of -0.44 for the entire distribution of tax filers. Adding a time trend

raises the overall estimate ETI to 0. Including the time trend and the square of the time trend

raises the overall estimated ETI to a statistically insignificant 0.20. For the top 1 percent of the

taxable income distribution, the results are quite different. All three specifications yield much

larger estimated ETIs and allshow strong statisticalsignificance. With no controls, the estimated

ETI for the top 1 percent is 1.83. With the time trend, that number falls to 0.71. Adding the

square of the time trend lowers the ETI estimate further to 0.50. (For the bottom 99 percent of

the distribution, ETI estimates are negative, and not statistically different from 0, for each

specification.)

Saez reports estimated ETIs that vary greatly over some subsets of the 40 years examined. For

example, dividing the change in log income from 1981 to 1984 by the change in logged net-of-tax

rates between the same years yields an estimated ETI of 0.77. The same analysis, comparing

1985 to 1988, yields a much larger estimated ETI of 1.7. Comparing 1991 with 1994 yields an

estimated ETI of about 0. The variation in ETIs over time (and within the distribution) may be
30. Saez also tries a two-stage approach, where the top net-of-tax rate is used as an instrument for the tax rate variable.

For the most part, he concludes that the two-stage approach does not affect the results and, in the case where it does, he

prefers the OLS result because the instrument in that case is weak.

31

consistent with Kopczuk’s (2003) finding that the ETI depends on the size of the tax base (or the

availability of deductions).

Next, Saez employs a regression framework that uses taxable income shares to estimate ETIs for

different segments of the taxable-income distribution. Special attention is paid to the top 1

percent of filers. For the various taxable-income groups, Saez regresses the log of the group’s

share of taxable income against the log of the net-of-tax rate.30 Without any time trends, that

yields an estimated ETI of 1.58 for the top 1 percent. Including both the time trend and square

of the time trend yields an estimated ETI of 0.62. Saez expresses confidence in the 0.62 estimate

because that regression has an adjusted coefficient of determination of 0.98 and the fitted values

do an excellent job of tracking the trend in the share of income reported by the top 1 percent. (By

contrast, predicted values from the model with no time trends fit the data poorly.) By further

division of the taxable-income distribution, Saez shows that, even among the top 1 percent of the

distribution, estimated ETIs vary greatly by income. In fact, the same approach that yields 0.62

for the top 1 percent yields an estimated ETI of 1.09 for the top one-hundredth of one percent.

For those in the 90th to 95th percentiles, the same approach yields a negative, although statistically

insignificant, estimated ETI.

The analyses of the tax cuts in the 1980s developed methodologies to estimate taxable-income

elasticities and advanced the understanding of the key issues, but did not fully resolve the issue.
32

The large elasticities estimated by the earliest studies have been supplanted by lower estimates

in later studies that included more elaborate controls for important non-tax factors. The newer

estimates, however, are sensitive to sample selection and the choice of control variables. 

OBRA 90 and OBRA 93

In contrast with the rising incomes and declining tax rates of the 1980s, income and tax rates both

rose in the early 1990s. In the 1980s, high-income taxpayers, whose tax rates fell most,

experienced the greatest income gains. In the 1990s, the same group again experienced the

greatest income gains even though their tax rates increased.

The tax increases of the early 1990s provided an opportunity to use methodological advances in

a new context. The tax changes of OBRA 90 and 93 were smaller and more focused on highincome filersthan those ofthe 1980s, potentially focusing the effects more narrowly. At the same

time, the ETI cannot be estimated for lower-income taxpayers because their rates did not change.

Furthermore, any potential bias from the secular trend at the top of the distribution is reversed

from the 1980s, when the exogenous growth of income in the 1980s potentially biased estimates

of tax elasticities upward. In contrast, exogenous growth of incomes in the 1990s may have

hidden any drops in income induced by increased tax rates and thus may have biased elasticity

estimates downward.

Carroll (1998) uses SOI data for a group of taxpayers from 1989 through 1995 to analyze the
31. As with Auten and Carroll, a weighting procedure is used in the regression analysis to control for attrition bias.

32. Carroll uses an instrumental-variables approach to impute the tax rate (because the tax rate is a function of income,

which is not exogenous). The approach assigns tax rates based on the filer’s permanent income (defined as the average

income over the seven-year period).

33. Simulations suggest that the revenue gain from the 1993 tax increase may have been as much as 39 percent or $6.8

billion (or as little as 12 percent or $2.1 billion), less than what would have occurred absent behavioral responses.

34. As mentioned earlier, estimates from OBRA 90 and OBRA 93 correspond only to higher-income filers because

rates were raised only for that group.

33

responsiveness of taxable income to the tax increases of 1990 and 1993.31 The analysis includes

only taxpayers between ages 25 and 55 in 1989. People with income below $50,000 were not

affected by the two tax increases and were excluded, in part, because they were deemed a poor

control group in identifying the response of higher-income filers.

To control for tax-rate endogeneity and mean reversion, Carroll uses average income over the

seven yearsto generate a “synthetic” orinstrumented tax rate that varies only with exogenoustaxlaw changes.32 His model includes year and individual fixed effects, age, proxies for industry and

region, financial wealth, and information on children.

Carroll is most confident in the specification yielding a ETI of 0.38.33 (The corresponding

elasticity with respect to gross income is 0.32.) Excluding more returns at the low end of the

distribution by raising the exclusion to $75,000 increases the estimated elasticity to 0.56.

Restricting the sample probably raises Carroll’s estimate because he examines tax increases (as

opposed to decreases). That is consistent with mean reversion at the low end.34

Goolsbee (2000) examines high-income responses to OBRA 93 using panel data on compensation

for the five highest-paid employees for each firm in the Standard & Poor’s 500, Standard &
35. Goolsbee focuses on actual tax rates after concluding (from running a regression with tax rates based on permanent

income) that tax-rate endogeneity is a minor issue with this sample because nearly everyone is always in the top

bracket.

34

Poor’s Mid Cap 400, and the Standard & Poor’s Small Cap 600 from 1991 through 1995. The

sample includes 21,299 observations for executives with at least four years of observations.

Included variables are salary, bonuses, Long-Term Incentive Plan payouts, stock options

exercised, and other income. (Capital gains, other family income, and deductions are not

available. The estimation generally assumes that earners are married filing jointly and that the

spouses are not in the labor force.) The panel has a mean of $852,000, a median of $451,000, and

25th and 5th percentile values of $250,000 and $150,000, respectively.

In his standard specification, Goolsbee regresses income against both the net-of-tax rate and the

future year’s net-of-tax rate, along with year dummies and firm-specific information. Sources of

executive compensation vary greatly from year to year (and in some years, no income is reported

for some sources), which complicates matters. To address those issues, Goolsbee explores

various approaches, including regressions with linear fixed effects, first-differencing, and split

samples. He also divides responses into short-term (such as the timing of transactions) and more

permanent behavioral changes. 

In general, Goolsbee finds large responses to the current net of tax rate—ETIs often well above

one.35 But, significantly, much of that response is transitory—that is, a temporary shifting of

income into the relatively low-tax period. Longer-term (or permanent) elasticities are much

smaller—often close to 0, but as high as 0.40. Using the specification yielding the latter result,
35

Goolsbee divides hissample into income groups and findsthe largest permanent responses(0.56)

for those earning over $1 million. Among income sources, stock options are the most responsive

to tax rates.

Goolsbee uses few controls for exogenous changes to the income distribution, which could bias

responses down. However, his focus on high-income filers and the inclusion of firm-specific

variables may control for some of the exogenous trends in the income distribution. Also, the

dataset includes only observations appearing four or more times. If attrition is non-random, and

is more likely for those whose income falls, the estimates would be biased downward, because

tax rates are rising.

Sammartino and Weiner (1997) use data from the SOI’s Sales of Capital Assets(SOCA) panel,

a subset of the SOI that follows the same (mostly high-income) taxpayers from 1989 through

1994, to examine responses to the tax increases associated with OBRA 90 and OBRA 93. They

do not generate taxable income elasticities, but do examine income patterns surrounding OBRA

90 and OBRA 93. For the top 1 percent of the distribution, AGI plus gains fluctuates greatly from

year-to-year, at least in part because of tax changes, but the responses appear to be transitory, and

are much more modest when examined overseveral years. For example, average AGI less capital

gains for the top 1 percent rises by over $40,000 in 1992 (preceding OBRA 93), falls by nearly

$26,000 in 1993, but rises by close to $24,000 in 1994 and 1995 combined.

Saez (2003) examines responses to tax increases resulting from bracket creep (as opposed to
36

legislated tax changes) between 1979 and 1981, using data from the University of Michigan

Panel, a publicly available SOI-based dataset. Over those years, inflation was around 10 percent

a year, but the tax brackets were fixed in nominal terms (that is, not indexed). Saez compares

taxpayers with similar incomes (and thus the same tax rate) in period 1, but whose predicted

incomes (that year’s income grown at the inflation rate) put them into different brackets in a

subsequent year.

That approach hasthe advantage of examining tax changesthatresultfrom inflation, and not from

explicit legislation that often also altersthe structure ofthe tax system, obfuscating true responses

to the rate changes. However, because the tax changes were not legislated, many filers may not

have realized that their marginal tax rate had risen. That could be especially problematic when

applying those estimates to cases where rate changes are explicitly legislated. Furthermore, the

study cannot examine taxpayersin the top bracket because inflation cannot move them to a higher

tax rate.

Saez finds a statistically insignificant ETI of 0.311 for all earners, which he decomposes into

values of 0.42 for itemizers and about 0 for non-itemizers. The elasticities are much larger and

are statistically significantformarried taxpayers: 0.39 overall and 0.65 for itemizers. Saez argues

that itemized deductions are likely a key factor driving the taxable income estimates, which is

consistent with Kopczuk’sfindings on the tax base. He also emphasizes that the estimates capture

only the short-term response to the rate changes.
37

Studies examining responses to the tax increases of the early 1990s generally find smaller ETIs

than the earlier work looking at tax changes in the 1980s. Transitory changes are often large, but

permanent responses are much smaller. Unlike the 1980s, responses to the tax changes of the

1990s only apply to the top brackets, which were the only groups experiencing a tax change. The

1990s tax changes involved few changes to the structure of the tax system (in contrast to TRA

86), which make the estimation process somewhat less complex.

V. Conclusion

The ETI, which measures the responsiveness of reported income to changes to marginal tax

rates, is a key parameter for estimating the impact of income-tax-rate changes on tax revenues

and for measuring the efficiency implications of those rate changes. Studies have examined

the tax changes of 1981, 1986, 1990, and 1993, as well as the bracket creep of the late 1970s

and early 1980s, to estimate an overall ETI with respect to the net-of-tax rate. In addition,

some studies have separately estimated ETIs for different income groups. Estimated

elasticities appear to vary by income, with the highest-income households being the most

responsive. In terms of overall findings, several of the more recent studies have reached

similar conclusions, finding overall elasticities of about 0.40. A closer examination of the

literature, however, suggests a great deal of variation. Estimated elasticities are often quite

sensitive to sample selection, methodology, and the reform under consideration.

Research over the past two decades has greatly improved the understanding of the relationship

between taxes and income, but has also highlighted many complicating factors such as tax-rate
38

endogeneity, mean reversion, and exogenous distributional changes that differ greatly across

income classes. Those factors make parsing taxable-income elasticities from the array of nontax factors that also affect taxable income difficult, and current methods may still fail to

control fully for them.

The logical next steps, some of which are already under way, include a deeper examination

into the importance of the tax base, a decomposition of responses by income source and type

of deduction, extending the years of data to include both tax increases and decreases, more

work on the non-tax factors driving the heterogeneous income trends across the distribution,

and a closer examination of income shifting between the corporate and individual tax bases. 

One promising approach (see Kopczuk, 2003) involves including the tax base (or the

availability of deductions) in the model to see whether it is a determinant of the elasticity. 

Decomposing responses found in earlier research by the different sources of income would

improve our understanding of the process underlying the overall response. Evidence suggests

that particular income sources are driving the overall estimates. Separately calculating

elasticities for each component of taxable income would reveal which factors have the greatest

influence on overall responses. Conducting the analysis for different tax changes or over

longer periods would show whether the same factors always dominate or whether the

determining factors vary, depending on the nature of the tax change or other factors. Both the

direction of response and the robustness of estimates may differ, depending on whether tax

rates rise or fall. In addition to those issues, the exogenous income trends that differ across the

income distribution demand further investigation to determine, for example, whether controls
39

for the trends can explain income changes over years without tax changes or, perhaps more

accurately, over years when people seem to have adjusted to previous or anticipated tax

changes. Finally, the shifting of income between the corporate and individual tax bases is a

response to changing tax rates that is only partially understood and demands further research. 

Clearly, much work is still needed in order to better understand the process by which incomes

respond to tax changes.
40

Table 1. Empirical Studies on the Elasticity of Taxable Income

Paper Data Approach1 Focus

Tax Change(Years)

Permanentv. Transitory Best Estimate Comments

Auten & Carroll(1995) 

CWHS PS All income

groups

TRA 86 (1985,1989)

Permanent 0.46 to 3.04 Replicates Feldstein (1995

specifications.

Auten & Carroll(1999)

SOI PS Joint > $21k

Single> $15.6k

TRA 86 (1985,1989)

Permanent 0.45 to 1.13

0.57 bestestimate

Uses first-differences with ycharacteristics, such as oc

Carroll (1998) SOI PS > $50k OBRA 90 & 93

(1989-1995)

Permanent 0.4 Uses weighted least square

demographic/occupational 

Feldstein (1995) SOI/CWHS PS All income

groups

TRA 86 (1985,1988)

Permanent 1.04 to 3.05 Uses difference-in-differenc

Goolsbee(2000)

S&PEXECUCOMP

PS Upper Income OBRA 93

(1991-1995)

Both 0 to 0.40 Includes variables for curre

distinguish between the tra

Gruber & Saez(2002)

SOI/CWHS PS All income

groups

ERTA & TRA86 (1979-1990)

Permanent 0.4 ln(incomet/incomet+3) is re

marital status, and a 10-piebreakdown by income. 

Kopczuk (2003) SOI/CWHS PS All income

groups

ERTA & TRA86 (1979-1990)

Permanent 0.21 to 0.57 w/o

tax base effect 0.53 overall

Builds on Gruber and Saezmean reversion and exogetwo variables.

Lindsey (1987) SOI CS All income

groups

ERTA (1979) Not clear 1.6 to 1.8 Compares simulated count

Long (1999) SOI Public

Use File

CS $0k to $200k State variation

(1991)

More transitory 0.193 to 0.819 Uses differences in tax rate

Moffitt &Wilhelm (2000)

SCF PS All income

groups

TRA 86 (1983,1989)

Permanent 0 to 1.83 Uses SCF data and explor

a replication of Feldstein’s 

Saez (2003)3 SOI/Michigan

Panel

PS All income

groups

Bracket creep(1979-1981)

Transitory 0.311 Uses bracket creep from th

a natural experiment appro

Saez (2004) SOI TS All income

groups

1960-2000 Permanent 0 to 1.7 (pairs of

years)0.62 for top 1%

Uses aggregated time-serieSpecial attention is paid to trends are used to control f

Sammartino &Weiner (1997)

SOI/SOCA TS Top 1% OBRA 90 & 93

(1989-1994)

Both large transitory

small permanent

Calculates shares of incomAlso calculates the shares 

Slemrod (1996)2 SOI TS Top 1% TRA 86 (1954-

1990)

Both Regresses shares of incom

controls.

1. PS = panel following the same people; TS = time-series; CS = cross-sectional.2. This study builds on Feenberg and Poterba (1993) and uses essentially the same core dataset.3. Those starting in the top bracket are not impacted by bracket creep. For joint filers, this group has income of greater than $162, 400; $81,800.
41

Primary Articles

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Review of Economics and Statistics, November 1999, 81 (4), 681-693.

[3] Carroll, Robert, “Do Taxpayers Really Respond to Changes in Tax Rates? Evidence from

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[4] Feenberg, Daniel and James Poterba, “Income Inequality and the Incomes of Very High

Income Taxpayers: Evidence from Tax Returns,” Tax Policy and the Economy, J. Poterba, 

ed., MIT Press: Cambridge, 1993, 7, 145-177.

[5] Feldstein, Martin S., “Tax Avoidance and the Deadweight Loss of the Income Tax,”

Review of Economics and Statistics, November 1999, 81 (4), 674-680. 

[6] Feldstein, Martin S., “The Effect of Marginal Tax Rates on Taxable Income: A Panel Study

of the 1986 Tax Reform Act,” Journal of Political Economy, June 1995, 103 (3), 551-572. 

[7] Goolsbee, Austan, “What Happens When You Tax the Rich? Evidence from Executive

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[8] Gruber, Jon and Emmanuel Saez, “The Elasticity of Taxable Income: Evidence and

Implications,” Journal of Public Economics, April 2002, 84 (1), 1-32.

[9] Kopczuk, Wojciech, “Tax Bases, Tax Rates and the Elasticity of Reported Income,”

National Bureau of Economic Research Working Paper 10044,

www.nber.org/papers/w10044, October 2003.

[10] Lindsey, Lawrence, “Individual Taxpayer Response to Tax Cuts: 1982-1984, with

Implications for the Revenue Maximizing Tax Rate,” Journal of Public Economics, July

1987, 33 (2), 173-206.

[11] Long, James E., “The Impact of Marginal Tax Rates on Taxable Income: Evidence from

State Income Tax Differentials,” Southern Economic Journal, April 1999, 65 (4), 855-869.

[12] Moffitt, Robert and Mark Wilhelm, “Taxation and the Labor Supply Decisions of the

Affluent,” Does Atlas Shrug? The Economic Consequences of Taxing the Rich, Joel

Slemrod, ed., New York: Harvard University Press and Russell Sage Foundation, 2000.

[13] Piketty, Thomas and Emmanuel Saez, “Income Inequality in the United States, 1913-

1998,” Quarterly Journal of Economics, February 2003, 118 (1), 1-39.

[14] Saez, Emmanuel, “Reported Incomes and Marginal Tax Rates, 1960-2000: Evidence and

Policy Implications,” National Bureau of Economic Research Working Paper 10273,

www.nber.org/papers/w10273, January 2004.

[15] Saez, Emmanuel, “The Effect of Marginal Tax Rates on Income: A Panel Study of

‘Bracket Creep,’” Journal of Public Economics, 2003, 87, 1231-1258.
42

[16] Sammartino, Frank and David Weiner, “Recent Evidence on Taxpayers' Response to the

Rate Increases in the 1990s,” National Tax Journal, September 1997, 50 (3), 683-705.

[17] Slemrod, Joel, “Income Creation or Income Shifting? Behavioral Responses to the Tax

Reform Act of 1986,” American Economic Review, 1995, 85 (2), 175-180.

[18] Slemrod, Joel, “High Income Families and the Tax Changes of the 1980s: the Anatomy of

Behavioral Response,” Empirical Foundations of Household Taxation, M. Feldstein and J.

Poterba, eds., University of Chicago, 1996.

[19] Slemrod, Joel, “Methodological Issues in Measuring and Interpreting Taxable Income

Elasticities,” National Tax Journal, December 1998, 51 (4), 773-788.

[20] Slemrod, Joel and Shlomo Yitzhaki, “Tax Avoidance, Evasion, and Administration,”

National Bureau of Economic Research Working Paper No. w7473, January 2000.

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382.

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Handbook of Labor Economics, O. Ashenfelter and D. Card, eds., North-Holland,

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43

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46

Appendix I

Highly Simplified Model for the Revenue-Maximizing Tax Rate

maximize tax_revenue = tax_rate A taxable_income

0 _

_ _ _ __ set

dtax rate

dtaxable income taxable income tax rate

dtax rate

dtax revenue= + ⋅ =

1 _

_

_

_

_ ⇒ ⋅ = = − εtax rate dtax rate

dtaxable income

taxable income

tax rate

Note that dtax_rate = –1Md(1 – tax_rate) = –1Mdnet_of_tax_rate.

1 (1 _ ) d _ _ _

d _

_

_ 1 (1 _ ) tax rate

net of tax rate

taxable income

taxable income

tax rate

tax rate = − ⋅ − 































⇒− ⋅ −

(1 _ ) d _ _ _

d _

_

(1 _ ) _ tax rate

net of tax rate

taxable income

taxable income

tax rate

tax rate = − 













 ⋅ − ⇒ ⋅















 − ⇒ = tax rate

tax rate

ntr _

* 1 _

ε