Working Paper Series

Congressional Budget Office

Washington, D.C.

The Elasticity of Taxable Income During the 1990s:

A Sensitivity Analysis

Seth H. Giertz

Tax Analysis Division

Congressional Budget Office

Washington, D.C.

Seth.Giertz@cbo.gov

January 2006

CBO Working Paper 2006-03

Congressional Budget Office working papers in this series are preliminary and are circulated

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authors. Papers in the series can be obtained at www.cbo.gov/publications.
The Elasticity of Taxable Income During the 1990s: A Sensitivity Analysis

Seth H. Giertz

Abstract

This paper examines alternative methodologies for measuring responses to the 1990 and 1993

federal tax increases. The methodologies build on those employed by Gruber and Saez (2002),

Carroll (1998), and Auten and Carroll (1999). Internal Revenue Service tax return data for the

project are from the Statistics of Income, which heavily oversamples high-income filers.

Special attention is paid to the importance of sample income restrictions and methodology.

Estimates are broken down by income group to measure how responses to tax changes vary

by income. In general, estimates are quite sensitive to a number of different factors. Using an

approach similar to Carroll’s yields elasticity of taxable income (ETI) estimates as high as

0.54 and as low as 0.03, depending on the income threshold for inclusion into the sample.

Gruber and Saez’s preferred specification yields estimates for the 1990s of between 0.20 and

0.30. Yet another approach compares behavior in a year before a tax change to behavior in a

year after the tax change. That approach yields estimated ETIs ranging from 0 to 0.71. The

results suggest tremendous variation across income groups, with people at the top of the

income distribution showing the greatest responsiveness. In fact, the estimates suggest that the

ETI could be as high as 1.2 for those at the very top of the income distribution. The major

conclusion, however, is that isolating the true taxable income responses to tax changes is

extremely complicated by a myriad of other factors and thus little confidence should be

placed on any single estimate. Additionally, focusing on particular components of taxable

income might yield more insight.

The author wishes to thank David Weiner, Ed Harris, Bob Williams, Bob Dennis, Bruce

Vavrichek, and Emmanuel Saez for comments and for assistance with the construction of the

data.
3

I. Introduction

In recent years, the elasticity of taxable income (ETI) has come to be considered the single

most important parameter for measuring the implications of a change in tax rates on both

efficiency and tax revenue. However, the economic magnitude of the ETI, the best method for

measuring it, and the composition of the responses underlying the ETI are far from settled

issues. In fact, as Kopczuk (2005) and Slemrod and Kopczuk (2002) posit, the ETI is not itself

a structural parameter but is a function of many factors, including the institutional features of

a tax system, which establish rules for deductions and credits, as well as policies toward

evasion.

This paper examines alternative methodologies for measuring responses to the 1990 and 1993

federal tax increases. The methodologies build on those employed by Gruber and Saez (2002),

Carroll (1998), and Auten and Carroll (1999). Internal Revenue Service tax return data for the

project are from the Statistics of Income, which heavily oversamples high-income filers.

Those data are very similar to data used by Carroll and Auten and Carroll and are a more

extensive version of data used by Gruber and Saez. The paper pays special attention to the

importance of sample income restrictions and methodology and breaks responses down by

income group to measure how responses to tax changes vary by income.

In general, estimates are quite sensitive to a number of different factors. Using an approach

similar to Carroll’s yields ETI estimates as high as 0.54 and as low as 0.03, depending on the

income threshold for inclusion into the sample. Gruber and Saez’s preferred specification

yields estimates for the 1990s of between 0.20 and 0.30. Yet another approach compares

behavior in a single pair of years, before and after tax changes. That approach yields

estimated ETIs ranging from 0 to 0.71. Those widely differing results suggest tremendous

variation across income groups, with people at the top of the income distribution showing the

greatest responsiveness. In fact, the estimates suggest that the ETI could be as high as 1.2 for

those at the very top of the income distribution. The major conclusion is that isolating the true

taxable income responses to tax changes is extremely complicated by a myriad of other

factors and thus little confidence should be placed on any single estimate. Additionally,

focusing on particular components of taxable income might yield more insight.

II. Issues

Although taxes affect income, so do many other economic factors. Controlling for non-taxinduced trends in taxable income is a major obstacle to accurately estimating elasticities. The

more rapid growth of income at the top of the income distribution has been especially

pronounced over the 1990s. The underlying causes of that rapid growth are not well

understood and the choice of methods to control for it has a great impact on estimated

responses.

1 One approach explored in this paper, and developed by Gruber and Saez (2002),

uses first differences (where the base, or initial year, and the subsequent year are generally

three years apart) and includes a variable that is a piece-wise function of income in order to

control for non-tax-related income trends.2 A second approach, used by Carroll (1998), uses

1 For example, see Kopczuk (2005).

2 See Giertz (2004).
4

year-to-year differences (or changes) and includes more extensive demographic information –

such as geographic and occupational information – in an attempt to capture the non-taxrelated income trends. A third approach uses control variables similar to those used by Gruber

and Saez but compares years before and after a tax change (for the 1990s, that involves

differencing observations that are six years apart) in order to focus on long-term behavioral

responses to tax changes.

Controlling for Exogenous Trends in Income

Over the past 30 years, the distribution of reported income has widened. That trend

accelerated in the 1980s and early 1990s, especially at the top of the distribution.3 Because

people with the highest income pay a disproportionate share of taxes—the top 1 percent pay

approximately one-third of all federal income taxes—their behavior is especially important

for tax revenue.4 Not fully accounting for the portion of that income growth that is unrelated

to tax policy can result in large biases. For example, increases in marginal tax rates during the

1990s were targeted at taxpayers at the top of the income distribution and thus directly

correlated with the rapid growth of their incomes. Not fully accounting for the exogenous

(non-tax-related) portion of that income growth will bias ETI estimates downward. Although

changes to the income distribution are widely documented and theories such as shifts in the

returns to education and the market for superstars help explain the phenomenon,5 the

underlying driving factors are not well understood, nor are the year-to-year deviations from

that trend. 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, 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.

Controlling for Mean Reversion

Mean reversion also complicates estimation. Over a person’s lifetime, income often follows a

general path (for example, age-earnings profiles are well-known), with many fluctuations.

After income has been particularly high or low, it will often revert to that general path. That

reversion is especially pronounced at the tails of the distribution. People at the high end of the

income distribution are often not there for long and will probably have a substantial drop in

income that is unrelated to tax policy. At the other extreme, students will often have large

increases in income when they enter the workforce. Estimating the ETI without fully

controlling for mean reversion will result in a spurious correlation between non-tax-related

increases (by people below their lifetime path) and decreases (by those above their lifetime

path) in taxable income as responses to changes in tax rates. Those factors bias ETI estimates

in opposite directions. Because the 1990s tax increases only targeted upper-income groups,

mean reversion at the bottom of the distribution will not directly contaminate the elasticity

3 According to Piketty and Saez (2003), the share of income reported by the top 10 percent of filers rose by more

than a third, from 32.9 percent in 1979 to 41.4 percent in 1998; two-thirds of that increase went to the top 1

percent of taxpayers. The share of income reported by the top one-half of 1 percent more than doubled; the share

reported by the top one-tenth of 1 percent nearly tripled; and the share reported by the top one-hundredth of 1

percent more than quadrupled.

4 See Internal Revenue Service (2004).

5 See Rosen (1981).
5

estimate. It could, however, indirectly affect the coefficients on the control variables in the

estimation. Mean reversion at the top of the distribution is still important for the 1990s and, in

the absence of adequate controls, will result in an upward bias for the estimated elasticities.

Research into the ETI is also complicated by the fact that the ETI appears to rise with

income.6 If so, 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 across the

distribution. In addition, a meaningful average overall ETI must take into account the

correlation between income and the elasticities. The average response of all filers may poorly

assess how taxable income or tax revenue as a whole will respond. For example, suppose

there are two types of people, that tax rates are the sole determinant of income, and that

between the base and subsequent years the net-of-tax rates rise by 10 percent for all filers.7

Now, suppose that half of the population is in the first group and that each filer in that group

has a base-year taxable income of $10,000 and a subsequent-year taxable income of $10,100,

while those in the second group have a base-year taxable income of $1,000,000 and a

subsequent-year taxable income of $1,090,000. In that instance, the low-income filers have an

ETI of 0.1 and the high-income filers have an ETI of 0.9.8 Under that scenario, the average

individual ETI equals 0.5. But the ETI that accurately reflects the change in overall taxable

income is 0.892 because the 0.9 applies to a base that is 100 times as large as the base for the

group with the ETI of 0.1. That 0.892 is the dollar-weighted ETI.

III. Data and Methods

This paper uses data on individual tax returns from the Statistics of Income (SOI) for 1985

and 1988 to 2001 – although the primary analysis focuses on the period from 1989 to 1995.

The SOI is a stratified random sample of tax filers, compiled by the Internal Revenue Service,

and includes all information reported on filers’ tax returns, plus additional demographic

information. The SOI data encompass the Continuous Work History Survey (CWHS), used by

Gruber and Saez (2002).9 The CWHS is a subset of filers from the SOI who are followed

from year to year. Although the CWHS contains detailed and accurate information, it is

deficient in two important respects. First, although the CWHS sample is quite large (for some

years, more than 20,000 tax returns), relatively few returns are from the very top of the

income distribution. If high-income taxpayers dominate an estimate, that estimate using the

CWHS will depend heavily on just a few filers.10 That shortcoming of the CWHS can be

overcome by moving to the full SOI (as was done in Carroll [1998] and in Auten and Carroll

[1999]), which heavily over-samples high-income filers. Second, the CWHS (and the full

6 People with higher incomes generally have more opportunities to respond to tax changes (see Saez, 2004).

They generally itemize their tax returns, 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 change their

labor supply but may have few other alternatives for altering their taxable income.

7 The net-of-tax rate equals one minus the marginal tax rate; i.e., the share of the next dollar of earnings that is

not taxed.

8 The low-income filers’ ETI is calculated by dividing their 1 percent increase in taxable income by the 10

percent increase in the net-of-tax rate. The high-income filers’ ETI is calculated by dividing their 9 percent

increase in taxable income by the 10 percent increase in the net-of-tax rate.

9 Gruber and Saez use a publicly released version of the CWHS and their data span the 1980s. Giertz (2005) also

applies Gruber and Saez’s methodology to a proprietary version of the CWHS data for both the 1980s and 1990s.

10 See Giertz (2004).
6

SOI) includes only people who file returns and are listed as the primary filers. Thus, attrition

is an issue. For example, suppose two individuals have identical incomes in year one, but in

year two one person’s income falls below the filing threshold while the other person

experiences an increase in income. The data will only include the person whose income rose,

thus providing a misleading picture of income growth between the two years.

The analysis relies on three core samples constructed from the SOI. Construction of each

sample conformed to the three different methodologies explored in the next section (see Table

1).

• The sample used with Gruber and Saez’s approach includes only observations paired

over three-year intervals from 1988 to 2001 – although much of the analysis uses only

years 1989 to 1995. For example, a paired observation from base year 1992 would

contain the change in that variable between years 1992 and 1995. Additionally, only

filers with more than $10,000 in total real income (in 1992 dollars) are included in the

sample. Both federal and state tax rates are calculated using the Congressional Budget

Office’s (CBO’s) internal tax calculators.

• For the approach followed by Carroll, each paired observation represents the one-year

change in the particular variable. For inclusion into that sample, the filer must be

married and appear (with the same spouse) in each year from 1989 to 1995 and have a

minimum of $50,000 in total income in each (every) year. Thus, year-to-year changes

are included only for those meeting the income criterion who also appear in each of

the seven years. Federal tax rates are calculated on the basis of average taxable

income. The portion of the payroll tax used to finance Medicare is also included in the

analysis. As with Carroll, state tax rates are not included.

• The third approach examines behavior in only two periods: one well prior to the tax

change and the other after taxpayers have had the opportunity to respond. That

approach, of comparing a year prior to and after a tax change, has been employed by

Auten and Carroll (1999). This paper uses that same general approach, but the analysis

employs Gruber and Saez’s variable construction and not Auten and Carroll’s method.

The base year is 1989 (before the 1990s tax changes have taken place) and the

subsequent year is 1995 (by which time taxpayers are assumed to have adjusted to the

tax changes). For comparison, the same technique is applied to the Tax Reform Act of

1986 (TRA-86) using years 1985 and 1989 – the years used by Auten and Carroll.

Both federal and state tax rates are calculated using CBO’s internal tax calculators.

Gruber and Saez’s Approach

For base years 1989 to 1992, the sample constructed for Gruber and Saez’s approach includes

over 157,000 paired observations. The SOI sample is rich in high-income filers, containing

26,128 returns with taxable income greater than $1,000,000 (see Table 2). In contrast, the

CWHS sample used by Gruber and Saez includes just 25 such returns.11 For the SOI, 5,775

returns report taxable income over $5,000,000, compared to just two for the CWHS.

Estimated elasticities are generated with respect to two different income measures. The first

measure is gross income, which equals total income minus Social Security benefits and

capital gains. The second income measure is taxable income, which equals gross income

11 That is based on incomes in 1992 dollars, as adjusted by rates of growth in gross income.
7

minus exemptions and standard or itemized deductions. Both measures use a 1990 constantlaw definition of income, meaning that income measures are constructed in an identical

manner for all years. Following Gruber and Saez, income measures are adjusted by the

growth in average gross income, using 1992 as the base.12 The sample includes only returns

with gross income greater than $10,000 in the base year and positive income in the

subsequent year. Without those restrictions, the SOI sample would be about 20 percent larger.

Mean taxable base-year income is $826,100 and gross income is $971,100 after adjusting by

the growth rate in gross income (see Table 2). Individual marginal tax rates (both state and

federal) are imputed using CBO’s internal tax calculators.13 The mean base-year net-of-tax

rate is 69.6 percent when taking into account both federal and state taxes.

The methodology follows directly from Gruber and Saez (2002). The income growth rate

equals

α ξ + ⋅ β + ⋅β + ⋅ β + ⋅ β + ε 















−

− = + ⋅ 













 + +

1 2 3 4

3 3 ( ) 1

1 ln ln mars year spline income demog

taxrate

taxrate

income

income t

t

t

t

t .

14

The dependent variable is log of income in the subsequent year (incomet+3) divided by income

in the base year (incomet), where the subsequent year is three years after the base year. The

key independent variable equals the log of the net-of-tax rate in the subsequent year divided

by the net-of-tax rate in the base year. To avoid endogeneity between the tax rate and income,

an instrument is used in place of the actual log change in the net-of-tax rate.15 The coefficient

on that variable, ξ, represents the ETI. Control variables include year-fixed effects, dummies

for marital status (mars), and a 10-piece spline of the log of individual base-year income. The

spline is intended to control for mean reversion and for non-tax-related income trends that

have differed across the reported income distribution in recent decades. Alternative

specifications explored by Gruber and Saez include a model with the log of base-year income

in place of the spline and a specification that excludes the income control altogether.16 Gruber

and Saez also weight their regressions by income. That places much more emphasis on

responses at the top of the income distribution and is designed to yield results that are more

indicative of the overall income response (and tax revenue implications) resulting from a

change in tax rates. Note that if responses do not vary by income, then weighting will not

affect ETI estimates. In addition to extending the years of data and adding more high-income

filers, this analysis also adds a richer set of demographic information (demog), including age,

sex, itemization status, and state-fixed effects.

12 The adjustment for gross income growth is analogous to adjustments that transform nominal dollars into real

dollars. Thus, for this paper, each individual's reported income is multiplied by the ratio of average gross income

in 1990 over the average gross income in the year of the observation.

13 Jon Bakija of Williams College designed the state tax calculator used by CBO. Gruber and Saez used the

National Bureau of Economic Research’s (NBER’s) TAXSIM model to impute their federal and state marginal

tax rates.

14 Note that all variables are for individuals. For simplicity, though, the equation omits subscripts denoting the

individual.

15 The instrument is constructed by inflating base-year income by the growth in mean gross income over the

three-year interval. Next, the tax calculator computes counterfactual tax rates on the basis of the inflated income

measure. Finally, two-stage least squares (2SLS) is employed, where, in the first stage, the log change in the

actual net-of-tax rate is regressed against the imputed log change in the net-of-tax rate, along with the other

independent variables.

16 In addition, Gruber and Saez include a variable to separate the income effect from the substitution effect. They

conclude that the income effect is not important and thus exclude it from most of their analysis.
8

Carroll’s Approach

The data for Carroll’s approach are composed of 5,143 filers (30,858 paired observations of

successive years) who are followed from 1989 to 1995. Mean taxable income (in 1992 CPIadjusted dollars) is $1,223,800, and mean gross income is $1,398,600. The mean net-of-tax

rate is 64.7 percent.

Carroll’s approach involves estimating

ln(Incomet /Incomet-1) = 0 + 1ln[(1–t)/(1–t-1)] + X + t + t ,

where the dependent variable is the log change in income between year (t – 1) and t. The key

independent variable is the log change in the net-of-tax rate. The model includes year (t) and

state-fixed effects, age, and children, as well as proxies for financial wealth and

entrepreneurship. Carroll also included occupational information, which CBO does not have.

To control for tax-rate endogeneity, average income over the seven years is used to generate a

“synthetic” tax rate that varies only with exogenous tax law changes. That is, the marginal tax

rate is calculated for each year on the basis of the individual’s average income. The Medicare

portion of the FICA tax, the cap for which was increased in the early 1990s, is also included

in the tax rate imputations. Earning splits are imputed on the basis of W-2 forms for 1995.17

That synthetic tax rate is the key independent variable in the first-stage regression, which is

used to produce the instrumented net-of-tax rates.

Approach Comparing End Years

Data for 1989 and 1995 are used to examine the tax changes of the early 1990s. For

comparison, data from 1985 and 1989 are used to estimate ETIs for TRA-86. For the 1990s,

the sample includes 31,940 filers with incomes greater than $10,000 in 1989. The sample used

to evaluate TRA-86 includes 37,673 filers.

The model looks very similar to that used by Gruber and Saez and discussed above. The main

difference is that this approach uses only one paired observation covering six years per

individual, as opposed to a series of three-year time spans. Thus, the model can be written

α ξ + ⋅ β + ⋅ β + ⋅ β + ⋅ β + ε 















−

− = + ⋅ 













 1 2 1989 3 4

1989

1995

1989

1995 ( ) 1

1 ln ln mars year spline income demog

taxrate

taxrate

income

income .

The model for TRA-86 is identical, except the base year is 1985 (instead of 1989) and the

subsequent year is 1989 (instead of 1995). As with Gruber and Saez’s analysis, the equations

are estimated via two-stage least squares, where the tax rate for the subsequent year is

instrumented first by inflating base-year income by the growth in gross income between the

base and subsequent year. That income measure is used to impute marginal tax rates on the

basis of the law in the subsequent year. The imputed tax rate is the key independent variable

in the first-stage regression.

17 W-2 information was not available for other years. Given the size of the sample, imputing splits on the basis of

one year should have little impact on the estimates, although if there was a shift in the distribution of earnings

among households – or a change in the distribution of earnings within the household in response to changes in

tax rates – then that approach could bias estimates.
9

Sample Weighting

While some of the regressions are weighted by income, all regressions are weighted to adjust

for the SOI’s non-random sampling properties. In contrast to the CWHS, which is designed to

be random, selection into the full SOI is conditional on several factors, including income.

Sampling probabilities reach 100 percent for very high-income filers. The sample is also

constructed such that once a filer is sampled, he will continue to be sampled in all subsequent

years, so long as his income increases (and his other characteristics, such as filing status, do

not change). In fact, the probability that one is observed in two different years is simply the

minimum of the sampling probabilities for the two years. Without weighting, that sampling

strategy raises the potential for spurious correlation between the dependent variable

[ln(incomet/incomet-1)] and the independent variables, including the tax variable. To avoid that

possibility, (paired) observations from the full SOI are weighted by the reciprocal of their

probability of appearing in the sample. That strategy is discussed in Imbens and Lancaster

(1996) and in Auten and Carroll (1999), the latter of whom employ the strategy using SOI

data.18

IV. Results

The three approaches yield a range of estimates of the ETI that are highly sensitive to a

number of factors, including the choice of income cutoff for inclusion into the sample, the

identification strategy, and the weights used in the regressions. Estimated responses are

generally much stronger at the very top of the income distribution, but they are often not

robust. The variation in responses by income and across time periods may truly reflect the fact

that behavioral responses differed across those factors. But, the fact that estimates vary greatly

both within and across methodologies cautions against placing too much emphasis on any

particular result.

Results from Gruber and Saez’s Approach

Using the CWHS, which contains few filers from the very top of the income distribution,

Gruber and Saez (2002) report an overall estimated ETI of 0.40 for the 1980s. Applying the

same methodology and similar data, Giertz (2005) obtains a similar result for the 1980s, but

finds that his results are very sensitive to income-weighting the regressions and that just a few

primarily very high-income filers drive the overall result. Employing the same approach to

similar data from the CWHS, Giertz finds a much smaller estimated ETI of 0.20 for the 1990s

and an estimated ETI of 0.30 for the full 1979-2001 period.

Here, using the full SOI for the same set of years (1989 to 1995) as Carroll, the methodology

of Gruber and Saez (which compares behavior over three-year intervals) results in an incomeweighted estimated ETI of 0.30 for Gruber and Saez’s most preferred specification, which

includes a 10-piece spline of logged base-year income (see Table 3). The corresponding gross

income elasticity is 0.20. Both estimates show strong statistical significance. With no controls

for base-year income, estimates are much larger – 1.4 for taxable income.

18 Additionally, income-weighted results for the full SOI are produced by simply multiplying income by the

weights used to adjust for the SOI’s sampling strategy.
10

Those estimated elasticities decline by about one-third when the period is extended to include

1988 and 1996 to 2001. The estimated ETI falls to 0.20 and the estimated gross income

elasticity drops to 0.13, but both remain highly significant (see Table 4). Those estimates are

very similar to the statistically insignificant estimates reported by Giertz (2005), examining

the same years with the same methodology but using CWHS data rather than the full SOI.

Because the added years do not include any federal tax changes, those additional years

probably affect estimates by altering the estimated baseline income trend. For example, if the

income growth for upper-income filers (relative to lower-income filers) moderated during

those years, then the spline coefficients might be affected. That could result in a flatter

baseline income trend, which would result in lower estimated responses.

The much larger estimates without income controls suggest that mean reversion dominates the

secular increase in income at the top of the income distribution. In a period like the early

1990s, falling incomes (due to mean reversion) at the top of the income distribution are

directly but spuriously correlated with the also falling net-of-tax rate. Without adequate

controls, at least a portion of that spurious correlation will be reflected in the estimated ETI.

By contrast, in a period in which the net-of-tax rate is rising, such as the 1980s, that spurious

correlation may bias estimates downward because the falling incomes at the top are inversely

correlated with the rise in the net-of-tax rate. That possibility is supported by Giertz (2005),

who finds that excluding income controls greatly increases estimated responses for the 1990s,

but substantially lowers them for the 1980s. Note that very few middle-income filers will

move to the very top of the income distribution. But, incomes for most of those at the very top

of the distribution will eventually fall to more moderate levels (independent of taxes). Thus,

mean reversion at the top of the income distribution is an important phenomenon.

Additionally, the fact that Gruber and Saez’s approach weights regressions by base-year

income makes mean reversion at the top of the income distribution potentially even more

problematic.

Results from Carroll’s Approach

Carroll’s model yields ETI estimates ranging from 0.54 to 0.03, depending on the income

cutoff and whether his full model or only year-fixed effects are used (see Table 5). Using

Carroll’s income cutoff of $50,000, the estimated ETI is 0.46 when including only year-fixed

effects and 0.54 for the full model. Those estimates are substantially higher than the 0.31 and

0.38 reported by Carroll. In contrast, the estimates for gross income are smaller than those

found by Carroll: 0.18 (versus 0.26) with just year-fixed effects and 0.27 (versus 0.32) for the

full model (see Table 6). Only one of the gross income elasticities shows strong statistical

significance.

Those estimates are quite sensitive to the income cutoff used for inclusion into the sample.

The $50,000 cutoff results in 5,143 filers – substantially more than the 4,233 in Carroll’s

sample, which used the same cutoff.19 Raising the cutoff to $61,500 results in a sample size

19 The income cutoffs are based on gross income. It is not clear which measure was used by Carroll, but

assuming a cutoff based on gross income results in a sample that is substantially larger than Carroll’s.

Conversely, using a cutoff based on taxable income results in a sample of 3,716 individuals, substantially smaller

than Carroll’s sample of 4,233 individuals.
11

that is very close to Carroll’s and somewhat smaller estimated ETIs: 0.48 for the full model.

The estimated gross income elasticities are a little larger and closer to the core gross income

elasticities reported by Carroll: 0.21 (versus 0.26) with just year-fixed effects and 0.29 (versus

0.32) for the full model.

Increasing the income cutoff further yields smaller estimated ETIs, a finding that contrasts

with Carroll’s substantially larger estimates when he raised the sample income cutoff from

$50,000 to $75,000. As the income cutoff is raised to $75,000, gross income elasticity

estimates are more stable than the corresponding ETI estimates. Once the income cutoff

reaches $100,000 both taxable and gross income elasticities are small (often very close to zero

and not statistically different from zero). While some of the samples yield statistically

significant results, the sensitivity of the results to the sample cutoff makes it difficult to place

very much confidence in any single estimate.

The same analysis was repeated using individuals’ average (instead of annual) gross income

over the 1989-1995 period as the criterion for inclusion into the sample. Using the $50,000

cutoff, the average income measure yields a sample size somewhat closer to Carroll’s,

although the resulting estimates are just as sensitive and exhibit similar patterns as the income

cutoff is raised. For the $50,000 to $75,000 income cutoffs, the estimated ETIs for the full

model are somewhat smaller than those reported in Table 5; for the $100,000 cutoff, the

estimated ETI is larger than the corresponding estimate in Table 5.

Altering the income cutoff for inclusion into the sample primarily affects the counterfactual

group (i.e., the group not facing a tax change). For a couple of the paired years (1991-92 and

1994-95), the tax schedule did not change, so observations for those years serve primarily to

identify the non-tax-related trend in income. Additionally, the 1990 and 1993 tax increases

were targeted at upper-income filers; many filers experienced no statutory change in tax rates

over the entire period. Those lower-income filers also serve to identify the non-tax-related

income trend, assuming trends are the same across incomes. Over that period, income growth

has been slower for low-income groups than for higher-income groups. Thus, as the income

cutoff for inclusion into the sample is raised, the estimated income trend, in the absence of

any changes in tax law, may rise. If that phenomenon dominates, then estimated elasticities

would be expected to rise (as found by Carroll) as the income cutoff increases over a period

when tax rates were rising (i.e., where the net-of-tax rate is falling) as in the first half of the

1990s. In contrast, mean reversion may also be strong for lower-income filers. For the 1990s,

the impact of mean reversion at the bottom of the distribution would have the opposite effect

on estimated ETIs, raising the estimates. If that phenomenon dominates, then estimated

elasticities would be expected to fall as the income cutoff is raised – which is what this paper

found.

When the analysis is repeated with various subsets of years, substantial responses are

generated when including only years 1991 and 1993, although statistical significance for most
12

of those estimates is poor (see Table 7 and Table 8).20 For some of the income cutoffs,

estimated elasticities are extremely close to the estimates from the corresponding model that

includes all of the years; for others, the estimates are different. Given that most of the dropped

observations do not contain tax changes, it is not clear which estimates are more believable.

On the one hand, including the other years may help to identify the control variables. On the

other hand, they may bias the estimates if the income growth in those other years (when tax

rates are not changing) is very different from the non-tax-related income growth in the years

in which tax rates did change.

Results from the Approach Comparing End Years

A question, aside from the robustness of the estimates, is whether the estimates capture

longer-term behavioral responses, as opposed to shorter-term income shifting. Comparing

adjacent years, à la Carroll, may yield estimates that reflect transitory responses and not

fundamental shifts in behavior. For example, Goolsbee (2000), examining the behavior of

high-paid executives, finds very large transitory responses to the Omnibus Budget

Reconciliation Act (OBRA) of 1993, but only modest longer-term responses. Although

Gruber and Saez compare behavior over three-year intervals, many years that make up each

paired observation probably contain transitory shifting. Thus, long-run estimated elasticities

based on that approach may still be contaminated by short-run income shifting. One way to

focus on longer term responses is to compare behavior a couple of years before a tax change

to behavior a couple of years after the change. Auten and Carroll (1999) used that basic

approach to examine responses to the Tax Reform Act of 1986.

The present analysis, in addition to estimating ETIs for the 1990s by comparing end years,

also applies the same approach to generate ETI estimates for TRA-86 for purposes of

comparison. The TRA-86 estimates are not intended to be a pure replication of Auten and

Carroll’s approach because, although the years used are the same, the identification strategy

(including the variables used to control for non-tax-induced changes to income) and data

construction are different. In any event, using years 1985 and 1989, Auten and Carroll’s

approach yields a person-weighted estimated ETI of 0.32 using the set of control variables

from Gruber and Saez’s most preferred specification (see Table 9). By contrast, Auten and

Carroll report an estimated ETI of 0.51 to 0.75 depending on the set of control variables that

they include. Note that Auten and Carroll restrict their sample to filers ages 25 to 55 in 1985

with at least $21,020 in taxable income if married and $15,610 in taxable income if single.

This paper excludes from the sample only those filers with less than $10,000 in gross income

in 1985 and with zero or negative gross or taxable income in 1989. (Those are the same

sample restrictions imposed by Gruber and Saez.)

Applying the same approach using years 1989 and 1995 yields a slightly negative personweighted estimated ETI (-0.08) that is not statistically different from zero. When including the

log of base-year income, instead of a 10-piece spline, the estimated ETI falls to -0.57. The

corresponding estimate for TRA-86 is 1.33. The difference in the two results suggests that the

20 The 1991 observations measure changes from 1990 to 1991, and the 1993 observations measure changes from

1992 to 1993.
13

log of base-year income fails to fully control for the secular rise in income. That failure would

bias the estimated ETI upward for TRA-86 and downward for the early 1990s. The 10-piece

spline may control more effectively for the secular income trend. If the model is properly

identified, it suggests that there was no response to the 1990s tax increases but a substantial

(although at the low end of the range reported in the literature) response to TRA-86. Of

course, with so many other factors moving simultaneously, it is quite possible that even the

model with the 10-piece spline is poorly identified.

Reestimating the different specifications using income-weighted two-stage least squares, as

used by Gruber and Saez, results in a modest increase in the estimated ETI for TRA-86 (using

Gruber and Saez’s most preferred set of control variables). The fact that the estimated ETIs

for TRA-86 are almost identical with and without income weighting suggests that the ETI for

the time period is relatively constant across the income distribution. That finding is not

consistent with Giertz (2005), Gruber and Saez (2002), and others who report estimated ETIs

for the 1980s that are much larger for upper-income filers. By contrast, the estimated ETI for

the 1990s rises from slightly negative to 0.42 with income weighting. That estimate is still not

statistically different from zero, however.

For TRA-86, raising the threshold for inclusion into the sample from $10,000 to $75,000 of

base-year income lowers the person-weighted ETI to 0.08 but raises the income-weighted

estimate to 0.40 (although still not statistically different from zero). It is possible that mean

reversion at the bottom of the income distribution was positively correlated with the increase

in net-of-tax rates after TRA-86 and that raising the cutoff for inclusion into the sample thus

removes much of that mean reversion. By contrast, income weighting may more than offset

that effect by placing greater weight on filers with more taxable income, who probably have

larger ETIs.

For the 1990s, raising the income cutoff leads to much larger person-weighted and incomeweighted ETI estimates. Because the 1990s tax increases affected primarily upper-income

filers, raising the cutoff for inclusion into the sample may simply change the pool of filers

who do not experience a tax change. Reducing the sample may eliminate much of the mean

reversion at the bottom of the distribution – i.e., those with low income in 1989 who

experienced a large non-tax-related increase in income between 1989 and 1995 – resulting in

a slower estimated secular trend in taxable income.

Heterogeneous Responses by Income

A number of studies have found that estimated ETIs vary greatly by income, with the largest

responses for very high-income filers. [For example, see Saez (2004).] This analysis of the

1990s tax changes, employing Gruber and Saez’s methodology, finds that raising the sample

cutoff from $10,000 to $100,000 has a positive but only a modest impact on the estimated

ETI, raising it from 0.20 to 0.23. In contrast, raising the cutoff to $500,000 has an enormous

effect, increasing the (income-weighted) estimated ETI to over 1.2.

Note that changing the income restriction for the sample also affects the identification

strategy. With the unrestricted sample, identification is based at least partly on lower-income
14

filers who experience a different (or no) change in tax rates. Restricting the sample to those

filers with over $500,000 in income removes most differences in (instrumented) net-of-tax

rates. Identification comes from comparing behavior over years in which tax rates change to

behavior over years with the same tax rates. Whether that hampers or improves the

identification process depends on how well the control variables, such as the spline, capture

non-tax-related changes to income that vary across the income distribution.

Person-Weighted Estimates by Income Quantile

As an alternative to income-weighted two-stage least squares and to further explore how

responses vary by income, the analysis employed two person-weighted approaches to estimate

ETIs separately by income quantile: a single-equation least squares framework and an

absolute deviation method with separate regressions for each quantile.

The first approach estimates separate ETIs by quantile within a single equation framework by

interacting the log change in the net-of-tax rate with quantile dummies, defined using baseyear taxable income.21 All estimates use Gruber and Saez’s most preferred specification,

which includes a 10-piece spline. If the ETI is uncorrelated with taxable income within each

quantile and the model is otherwise properly identified, then person-weighted estimated ETIs,

by quantile, would represent a viable alternative to income-weighted 2SLS. If that supposition

(that the ETI is uncorrelated with taxable income within each quantile) is correct, then

unweighted and income-weighted quantile elasticity estimates would be identical because the

elasticity, while varying by income, remains constant within each income quantile.

The person-weighted estimates vary greatly by quantile, with by far the largest elasticities for

those at the very top of the income distribution (see Panel 1 of Table 10).22 Estimates are

positive for all quantiles above the 30th percentile and almost always show strong statistical

significance above the 50th percentile. For the top one-half of 1 percent of filers, the

estimated ETI is 0.99.23 Interestingly, the estimated ETI is just 0.16 for the 95th percentile to

the 99.5th percentile and just 0.21 for the 90th percentile to the 95th percentile. For deciles

four to nine, estimated ETIs range from a high of 0.68 to a low of 0.05. For the bottom five

deciles, estimates are either the wrong sign or not statistically different from zero. Little

significance can be placed on the estimates for the lower quantiles because those filers

generally experienced no change in (federal) tax rates.

The second alternative approach uses quantile regressions to estimate ETIs. The quantile

regression is a more general form of the median regression (or least absolute deviations

regression). Each regression uses the same data (and sample restrictions) as used with Gruber

and Saez’s approach applied to the 1990s. The quantile regression is based on minimizing the

21 While estimates are based on the full SOI, income cutoffs for the various quantiles are based on the CWHS

sample, which represents a random sample of all tax filers.

22 The first nine dummies represent the first nine deciles, or tenths, of the income distribution. The 10th dummy

applies to filers between the 90th and 95th percentiles. The 11th dummy applies to those between the 95th and

99.5th percentiles. The final quantile dummy applies to filers in the top one-half of 1 percent.

23 Reported taxable income at the 99.5th percentile (based on the sample used in the full regression analysis,

which excludes filers with less than $10,000 in base-year gross income) is about $203,000; the minimum level of

reported gross income in the top one-half of 1 percent of the reported taxable-income distribution is about

$218,300. That is after incomes have been adjusted by growth in gross income (and expressed in 1992 dollars).
15

weighted absolute value of the residuals, where one weight applies to values with positive

residuals and one minus that weight applies to values with negative residuals. The weight

simply indicates the point in the income distribution around which the sum of absolute

residuals is minimized. In the special case of the median regression, the weight equals 0.5.

Estimates from the person-weighted quantile regressions are substantially larger than those

from the single-equation model for the 90th percentile to the 99.5th percentile but are

somewhat smaller (than those from the single-equation model) for deciles six through nine

(see Panel 2 of Table 10). Estimates from the quantile regressions also show strong statistical

significance, as measured by Koenker-Bassett standard errors. For the top one-half of 1

percent of filers, the estimated ETI is 0.87. As the focal point moves down the income

distribution, the estimated ETI falls but remains above 0.5 for the top three deciles and above

0.4 throughout the distribution.

V. Conclusion

This paper builds on Gruber and Saez (2002), Carroll (1998), and Auten and Carroll (1999)

by applying their (or parts of their) methodologies to SOI data for the 1990s. Many of the

estimates are in line with the recent literature, but estimates vary greatly depending on

methodology, time period analyzed, income group, and sample income cutoffs.

Applying Gruber and Saez’s approach to the 1990s yields an estimated ETI of between 0.2

and 0.3, depending on whether the sample extends to 1995 or to 2001. The additional years of

data do not contain a federal tax change, so the difference between the estimates probably

results from the impact that observations from the second half of the 1990s have on the

estimated baseline income trend. When the sample is restricted to filers with more than

$500,000 in income, the estimated ETI is quite large, 1.2. In addition to income-weighting

estimates, as done by Gruber and Saez, the analysis used two person-weighted approaches to

estimate ETIs for segments of the income distribution. For the top one-half of 1 percent, the

estimated ETI is between 0.88 and 0.99. Estimates for the remainder of the distribution are

substantially smaller and vary greatly depending on the methodology employed. Little stock

can be placed in estimates outside the upper quantiles because, during the 1990s, only upperincome groups experienced a change in federal tax rates.

After replicating Carroll’s approach using similar data, the analysis conducts a number of

checks for robustness. Carroll’s approach shows great sensitivity to the income cutoff for

inclusion into the sample. It appears that estimated responses are driven by just a couple of

years of the data. Additionally, it appears that the approach is probably picking up more of a

transitory (as opposed to a permanent) response. Comparing behavioral changes several years

apart, as Auten and Carrroll did for TRA-86, suggests that income-weighted responses to the

1990s tax changes were larger than the responses to TRA-86; although, for the personweighted estimates, estimated responses are heavily dependent on the income cutoff for the

sample. Additionally, those estimates are sensitive to factors such as income weighting and

generally show very low levels of statistical significance.

The findings that very high-income filers respond more to tax changes than do those with

lower incomes and that estimates for the 1990s are generally smaller than comparable
16

elasticities for the 1980s are consistent with the previous literature. However, the estimates

are highly sensitive to an array of factors, including sample income cutoffs, the

methodological approach, and tweaks to each of the core methodologies. That sensitivity

warrants caution against placing too much emphasis on any particular estimate. Additionally,

the results reinforce the inherent complexities associated with identifying the ETI. Recent

literature on the ETI highlights the factors that complicate estimation, including recognizing

both the many ways that individuals can respond to tax changes and the many (often

changing) non-tax factors that also affect taxable income. Employing a reduced-form

approach that attempts to separate the tax from the non-tax factors has proven difficult. In

particular, mean reversion and exogenous trends in income that fluctuate over time and affect

different segments of the income distribution differently are especially problematic because

the underlying causes for those phenomena are not well understood. Those observations

suggest that a narrower focus, for example on particular sources of taxable income, may prove

more fruitful to understanding what drives the overall ETI estimates. Additionally, a rigorous

decomposition of the various sources may aid in understanding how the various complexities

have hampered other ETI estimates.
17

References

Auten, Gerald, and Robert Carroll, “The Effect of Income Taxes on Household Behavior,”

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

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

1993 Act,” Office of Tax Analysis Working Paper 78, U.S. Department of the

Treasury, 1998.

Feldstein, Martin, “Tax Avoidance and the Deadweight Loss of the Income Tax,” Review of

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

Giertz, Seth, “Recent Literature on Taxable-Income Elasticities,” Congressional Budget

Office, Technical Paper 2004-16, December 2004, www.cbo.gov/ftpdocs/60xx/

doc6028/2004-16.pdf.

——, “A Sensitivity Analysis of the Elasticity of Taxable Income,” Congressional Budget

Office, Working Paper 2005-01, February 2005, www.cbo.gov/ftpdocs/60xx/

doc6077/WP-2005-01.pdf.

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

Compensation,” Journal of Political Economy, 2000, 108 (2), 352-378.

Gruber, Jonathan, and Emmanuel Saez, “The Elasticity of Taxable Income: Evidence and

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

Imbens, Guido, and Tony Lancaster, ‘‘Efficient Estimation and Stratified Sampling,’’ Journal

of Econometrics, 1996, 74, 289-318.

Internal Revenue Service, “Individual Income Tax Returns with Positive Adjusted Gross

Income (AGI)” Unpublished Statistics of Income data, September 2004,

www.irs.gov/pub/irs-soi/02in01ts.xls.

Koenker, Roger, and Kevin Hallock, “Quantile Regression,” Journal of Economic

Perspectives, 2001, 15 (4), 143-156.

Kopczuk, Wojciech, “Tax Bases, Tax Rates and the Elasticity of Reported Income,” Journal

of Public Economics, December 2005, 89 (11-12), 2093-2119.

Piketty, Thomas, and Emmanuel Saez, “Income Inequality in the United States, 1913–1998,”

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

Rosen, Sherwin, “The Economics of Superstars,” American Economic Review, December

1981, 71 (5), 845-858.

Saez, Emmanuel, “Reported Incomes and Marginal Tax Rates, 1960–2000: Evidence and

Policy Implications,” In Poterba, J. (Ed.), Tax Policy and the Economy 18

(Cambridge, Mass.: MIT Press, 2004), 117-173.

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.
18

Slemrod, Joel, “High income families and the tax changes of the 1980s: The Anatomy of

Behavioral Response,” In Feldstein, M., Poterba, J. (Eds.), Empirical Foundations of

Household Taxation (Chicago: University of Chicago Press, 1996), 169-189.

——, “Methodological Issues in Measuring and Interpreting Taxable Income Elasticities,”

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

Slemrod, Joel, and Wojciech Kopczuk, “The Optimal Elasticity of Taxable Income,” Journal

of Public Economics, April 2002, 84 (1), 91-112.
19

Table 1. Comparing the Three Samples

Methodological Approaches

Gruber and

Saez Carroll End Years

Years 1989-1995a 1989-1995 1989, 1995

Paired Observations 157,083 30,858 31,940

Differencing: Years from Base to

Subsequent Observation 316

Sample Income Cutoff $10,000 $50,000 $10,000

Source: Estimates are based on Statistics of Income data.

Note: Income and tax liabilities are expressed in 1992 dollars.

a. Gruber and Saez’s approach is applied to observations paired over three-year intervals

from 1988 to 2001, although much of the analysis focuses on the 1989-1995 period.
20

Table 2. SOI Summary Statistics

SOI Carroll

Paired Observationsa 157,083 30,858

Returns with Base-Year Taxable Income Greater Than:

$1,000,000 26,128 7,006$5,000,000 5,775 1,822

Mean Taxable Incomeb $826,100 $1,223,800

Mean Gross Incomeb $971,100 $1,398,600Mean Federal Tax Rate 25.6 35.3Mean State Tax Rate 4.8 n.a.Mean Net-of-Tax Rate 69.6 64.7Mean Federal Tax Liabilityb $114,400Mean State Tax Liabilityb $56,700 n.a.

Source: Estimates are based on Statistics of Income data from 1989to 1995. Filers with less than $10,000 of gross income are excluded.a. Sample sizes are for the taxable income regressions.b. Income and tax liabilities are expressed in 1992 dollars, asadjusted by the growth in gross income.Note: n.a. = not applicable.
21

Table 3. Full SOI Taxable and Gross-Income Elasticities for the 1990s:

Gruber and Saez’s Approach for 1989 to 1995

No Income Controls

Base-Year Income

Controls Spline Income Controls

Gross

Income

Taxable

Income

Gross

Income

Taxable

Income

Gross

Income

Taxable

Income

ln((1 - mtrt+3)/(1 - mtrt)) 0.977 1.409 0.143 -0.025 0.201 0.30

(0.085) (0.130) (0.066) (0.097) (0.070) (0.131)

Married 0.111 0.003 0.138 0.059 0.139 0.073

(0.018) (0.020) (0.015) (0.017) (0.016) (0.018)

Single 0.017 -0.048 0.014 -0.022 0.017 -0.011

(0.017) (0.019) (0.015) (0.016) (0.015) (0.017)

Age 0.024 0.038 0.037 0.054 0.037 0.06

(0.006) (0.007) (0.005) (0.006) (0.005) (0.006)

Age Squared/10 -0.007 -0.01 -0.009 -0.012 -0.009 -0.013

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Age Cubed/100 0.005 0.006 0.006 0.007 0.006 0.008

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Sex 0.004 0.001 0.001 -0.001 0.001 -0.001

(0.002) (0.002) (0.001) (0.001) (0.001) (0.001)

Itemizer Dummy -0.008 -0.053 -0.102 -0.134 -0.095 -0.133

(0.008) (0.009) (0.009) (0.009) (0.010) (0.009)

ln(income) -0.113 -0.127

(0.006) (0.006)

Decile 1 -0.015 -0.58

(0.404) (0.022)

Decile 2 -0.203 -0.168

(0.032) (0.024)

Decile 3 -0.07 -0.175

(0.031) (0.029)

Decile 4 -0.169 -0.174

(0.040) (0.036)

Decile 5 -0.111 -0.088

(0.041) (0.030)

Decile 6 -0.063 -0.06

(0.028) (0.025)

Decile 7 -0.078 -0.043

(0.023) (0.032)

Decile 8 -0.102 -0.155

(0.031) (0.050)

Decile 9 -0.288 -0.333

(0.070) (0.116)

Decile 10 -0.088 -0.006

(0.069) (0.092)

Constant -0.366 -0.446 0.671 0.613 -0.184 4.519

(0.095) (0.108) (0.101) (0.108) (0.370) (0.212)

Observations 123,163 163,364 123,163 157,083 123,163 157,083

Source: Estimates are based on Statistics of Income data for years 1989 to 1995.

Estimates are from 2SLS regressions. The income range is $10,000 and above. Regressions are weighted by the

inverse of sampling probabilities and by income (see Section III). Robust standard errors are in parentheses.
22

Table 4. Full SOI Taxable and Gross-Income Elasticities for the 1990s:

Gruber and Saez’s Approach for 1988 to 2001

No Income Controls

Base-Year Income

Controls Spline Income Controls

Gross

Income

Taxable

Income

Gross

Income

Taxable

Income

Gross

Income

Taxable

Income

ln((1 - mtrt+3)/(1 - mtrt)) 0.478 0.745 0.133 0.144 0.125 0.198

(0.044) (0.071) (0.038) (0.058) (0.037) (0.060)

Married 0.091 0.013 0.139 0.082 0.137 0.092

(0.010) (0.011) (0.009) (0.010) (0.010) (0.011)

Single 0.017 -0.024 0.029 0.002 0.029 0.014

(0.009) (0.011) (0.009) (0.010) (0.009) (0.010)

Age 0.016 0.028 0.028 0.043 0.028 0.05

(0.006) (0.007) (0.006) (0.006) (0.006) (0.006)

Age Squared/10 -0.006 -0.008 -0.007 -0.01 -0.007 -0.011

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Age Cubed/100 0.004 0.006 0.005 0.006 0.005 0.007

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Sex 0.004 0.002 0.001 -0.002 0.001 -0.001

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Itemizer Dummy 0.012 -0.023 -0.096 -0.127 -0.083 -0.119

(0.005) (0.005) (0.006) (0.006) (0.006) (0.006)

ln(income) -0.107 -0.12

(0.004) (0.004)

Decile 1 -0.025 -0.554

(0.022) (0.014)

Decile 2 -0.19 -0.201

(0.021) (0.014)

Decile 3 -0.102 -0.17

(0.018) (0.017)

Decile 4 -0.153 -0.147

(0.020) (0.020)

Decile 5 -0.075 -0.021

(0.020) (0.016)

Decile 6 -0.033 -0.053

(0.013) (0.016)

Decile 7 -0.094 -0.097

(0.015) (0.022)

Decile 8 -0.139 -0.204

(0.026) (0.039)

Decile 9 -0.311 -0.314

(0.064) (0.079)

Decile 10 -0.095 -0.08

(0.046) (0.049)

Constant -0.218 -0.304 0.776 0.72 0.08 4.472

(0.096) (0.104) (0.100) (0.103) (0.10) (0.150)

Observations 323,776 449,584 323,776 449,584 323,776 449,584

Source: Estimates are based on Statistics of Income data for years 1988 to 2001.

Estimates are from 2SLS regressions. The income range is $10,000 and above. Regressions are weighted by the

inverse of sampling probabilities and by income (see Section III). Robust standard errors are in parentheses.
23

Table 5: Estimated Taxable Income Elasticities

Gross Income >50,000 >61,500 >75,000 >100,000 >150,000

(1) (2) (1) (2) (1) (2) (1) (2) (1) (2)

ln((1 - mtrt)/(1 - mtrt-1)) 0.458 0.539 0.412 0.477 0.366 0.412 0.107 0.101 0.035 0.031

(0.203) (0.213) (0.165) (0.173) (0.174) (0.183) (0.183) (0.195) (0.320) (0.350)

1991 Dummy 0.019 0.02 0.015 0.016 0.001 0.003 -0.024 -0.022 -0.081 -0.079

(0.009) (0.009) (0.011) (0.011) (0.013) (0.013) (0.018) (0.018) (0.026) (0.027)

1992 Dummy 0.023 0.024 0.034 0.038 0.041 0.045 0.044 0.048 0.042 0.045

(0.008) (0.008) (0.010) (0.010) (0.011) (0.011) (0.015) (0.015) (0.022) (0.022)

1993 Dummy 0.004 0.008 0.009 0.016 0.002 0.009 -0.049 -0.044 -0.104 -0.101

(0.008) (0.008) (0.010) (0.011) (0.015) (0.015) (0.017) (0.018) (0.041) (0.044)

1994 Dummy 0.016 0.02 0.017 0.024 0.001 0.009 -0.004 0.003 -0.065 -0.06

(0.008) (0.008) (0.010) (0.010) (0.012) (0.012) (0.015) (0.015) (0.021) (0.021)

1995 Dummy 0.025 0.031 0.04 0.048 0.047 0.057 0.02 0.028 0.002 0.009

(0.008) (0.008) (0.011) (0.011) (0.014) (0.014) (0.016) (0.016) (0.022) (0.022)

ln(financial asset income) -0.001 0.001 0.001 -0.001 -0.004

(0.001) (0.001) (0.002) (0.002) (0.002)

Age 0.002 0 -0.001 -0.011 -0.015

(0.002) (0.003) (0.004) (0.006) (0.007)

Age Squared 0 0 0 0.001 0.001

(0.000) (0.000) (0.000) (0.001) (0.001)

Entrepreneur Dummy 0.023 0.015 0.01 0.007 0.011

(0.005) (0.005) (0.006) (0.008) (0.010)

Kids 0.003 0.002 0.002 -0.001 -0.007

(0.002) (0.002) (0.003) (0.003) (0.003)

Children Away from Home -0.005 0.008 0 -0.002 -0.014

(0.010) (0.012) (0.013) (0.015) (0.033)

Children Away from HomeConstant 0.015 -0.029 0.016 0.006 0.025 0.126 0.044 0.403 0.076 0.64

(0.006) (0.099) (0.007) (0.128) (0.009) (0.106) (0.011) (0.137) (0.016) (0.186)

Observations 30,858 30,858 25,356 25,356 21,936 21,936 18,606 18,606 15,252 15,252

Source: Estimates are based on Statistics of Income data for 1989 to 1995.Estimates are from 2SLS regressions. Specification (1) refers to the model with only year dummies. Specification (2) refers to the full model. Regressionsare weighted by the inverse of sampling probabilities (see Section III). Robust standard errors are in parentheses.
24

Table 6: Estimated Gross Income Elasticities

Gross Income >50,000 >61,500 >75,000 >100,000 >150,000

(1) (2) (1) (2) (1) (2) (1) (2) (1) (2)

ln((1 - mtrt)/(1 - mtrt-1)) 0.176 0.273 0.205 0.292 0.181 0.255 -0.027 0.01 -0.015 0.025

(0.152) (0.159) (0.135) (0.142) (0.144) (0.152) (0.153) (0.164) (0.249) (0.274)

1991 Dummy -0.003 -0.001 -0.004 -0.002 -0.016 -0.014 -0.031 -0.028 -0.077 -0.073

(0.006) (0.006) (0.007) (0.007) (0.010) (0.010) (0.014) (0.014) (0.020) (0.021)

1992 Dummy 0.016 0.019 0.021 0.025 0.022 0.026 0.025 0.03 0.02 0.024

(0.005) (0.005) (0.007) (0.007) (0.009) (0.009) (0.012) (0.012) (0.019) (0.019)

1993 Dummy -0.012 -0.007 -0.01 -0.002 -0.02 -0.011 -0.064 -0.056 -0.107 -0.097

(0.005) (0.005) (0.007) (0.007) (0.011) (0.011) (0.014) (0.015) (0.031) (0.033)

1994 Dummy 0.003 0.008 0.003 0.011 -0.013 -0.004 -0.021 -0.013 -0.078 -0.072

(0.005) (0.005) (0.007) (0.007) (0.009) (0.010) (0.012) (0.012) (0.018) (0.018)

1995 Dummy 0.014 0.022 0.025 0.035 0.027 0.038 0.012 0.02 -0.009 -0.001

(0.006) (0.006) (0.008) (0.008) (0.011) (0.011) (0.013) (0.013) (0.019) (0.018)

ln(financial asset income) 0.001 0.002 0.003 0.001 -0.001

(0.001) (0.001) (0.001) (0.002) (0.002)

Age -0.002 -0.004 -0.008 -0.013 -0.016

(0.002) (0.002) (0.003) (0.005) (0.006)

Age Squared 0 0 0.001 0.001 0.002

(0.000) (0.000) (0.000) (0.001) (0.001)

Entrepreneur Dummy 0.02 0.014 0.011 0.008 0.009

(0.003) (0.004) (0.005) (0.006) (0.009)

Kids 0.002 0.002 0.002 -0.002 -0.006

(0.001) (0.002) (0.002) (0.003) (0.003)

Children Away from Home -0.009 0.003 0.004 0.001 -0.014

(0.006) (0.007) (0.009) (0.011) (0.034)

Constant 0.016 0.036 0.018 0.089 0.03 0.265 0.045 0.42 0.077 0.602

(0.004) (0.081) (0.005) (0.105) (0.007) (0.083) (0.008) (0.113) (0.013) (0.165)

Observations 30,858 30,858 25,356 25,356 21,936 21,936 18,606 18,606 15,252 15,252

Source: Estimates are based on Statistics of Income data for 1989 to 1995.Estimates are from 2SLS regressions. Specification (1) refers to the model with only year dummies. Specification (2) refers to the full model. Regressionsare weighted by the inverse of sampling probabilities (see Section III). Robust standard errors are in parentheses.
25

Table 7. Estimated Taxable Income Elasticities: Paired Years 1991 and 1993

Gross Income >50,000 >61,500 >75,000 >100,000 >150,000

(1) (2) (1) (2) (1) (2) (1) (2) (1) (2)

ln((1 - mtrt)/(1 - mtrt-1)) 0.313 0.215 0.414 0.342 0.366 0.133 0.224 -0.085 0.158 -0.396

(0.178) (0.196) (0.157) (0.176) (0.165) (0.182) (0.188) (0.221) (0.318) (0.363)

1991 Dummy 0.016 0.017 0.005 0.008 0 0 0 0 0.015 0.066

(0.008) (0.008) (0.010) (0.010) (0.000) (0.000) (0.000) (0.000) (0.030) (0.034)

ln(financial asset income) -0.005 -0.006 -0.013 -0.015 -0.021

(0.002) (0.002) (0.003) (0.005) (0.006)

Age 0.004 0.001 0.006 -0.024 -0.024

(0.006) (0.007) (0.011) (0.013) (0.016)

Age Squared -0.001 0 -0.001 0.003 0.003

(0.001) (0.001) (0.001) (0.001) (0.002)

Entrepreneur Dummy 0.011 0.013 0.019 -0.015 -0.01

(0.011) (0.011) (0.013) (0.016) (0.021)

Kids 0.001 0.002 -0.005 -0.001 -0.002

(0.004) (0.004) (0.005) (0.007) (0.007)

Children Away from Home -0.008 0.014 -0.016 -0.015 -0.069

(0.032) (0.021) (0.020) (0.020) (0.035)

Constant 0.018 0.054 0.025 0.162 0.026 0.113 0.022 0.809 -0.014 0.835

(0.006) (0.147) (0.007) (0.182) (0.008) (0.269) (0.011) (0.316) (0.038) (0.455)

Observations 10,286 10,286 8,452 8,452 7,312 7,312 6,202 6,202 5,084 5,084

Source: Estimates are based on Statistics of Income data for 1989 to 1995.Estimates are from 2SLS regressions. Specification (1) refers to the model with only the year dummy. Specification (2) refers to the full model. Regressionsare weighted by the inverse of sampling probabilities (see Section III). Robust standard errors are in parentheses.
26

Table 8. Estimated Gross Income Elasticities: Paired Years 1991 and 1993

Gross Income >50,000 >61,500 >75,000 >100,000 >150,000

(1) (2) (1) (2) (1) (2) (1) (2) (1) (2)

ln((1 - mtrt)/(1 - mtrt-1)) 0.159 0.183 0.207 0.23 0.185 0.089 0.058 -0.143 0.043 -0.418

(0.137) (0.148) (0.130) (0.144) (0.138) (0.153) (0.156) (0.183) (0.240) (0.288)

1991 Dummy 0.01 0.008 0.006 0.004 0 0 0 0 0.025 0.068

(0.005) (0.006) (0.007) (0.007) (0.000) (0.000) (0.000) (0.000) (0.024) (0.027)

ln(financial asset income) -0.001 -0.002 -0.007 -0.011 -0.017

(0.001) (0.002) (0.003) (0.004) (0.005)

Age 0.003 0 -0.001 -0.024 -0.025

(0.004) (0.005) (0.009) (0.011) (0.014)

Age Squared 0 0 0 0.002 0.003

(0.000) (0.001) (0.001) (0.001) (0.002)

Entrepreneur Dummy 0.017 0.016 0.021 0.001 0.003

(0.007) (0.008) (0.010) (0.013) (0.018)

Kids 0 0.002 -0.004 0 -0.001

(0.002) (0.003) (0.004) (0.005) (0.006)

Children Away from Home -0.018 0.002 -0.009 -0.008 -0.05

(0.024) (0.013) (0.013) (0.014) (0.031)

Constant 0.004 -0.003 0.009 0.085 0.014 0.167 0.015 0.702 -0.023 0.752

(0.004) (0.104) (0.005) (0.131) (0.006) (0.211) (0.009) (0.261) (0.028) (0.375)

Observations 10,286 10,286 8,452 8,452 7,312 7,312 6,202 6,202 5,084 5,084

Source: Estimates are based on Statistics of Income data for 1989 to 1995.Estimates are from 2SLS regressions. Specification (1) refers to the model with only the year dummy. Specification (2) refers to the full model. Regressionsare weighted by the inverse of sampling probabilities (see Section III). Robust standard errors are in parentheses.
27

Table 9: SOI Taxable Income Elasticities for TRA-86 and for the 1990s Using Observations Pre and Post Reform

Person-Weighted Income-Weighted

No Income

Controls

Base-Year

Income

Controls

Spline

Income

Controls

No Income

Controls

Base-Year

Income

Controls

Spline

Income

Controls

TRA-86: All Filers with More than $10,000 in Base-Year Gross Incomeln((1 - mtr1989)/(1 - mtr1985)) -2.099 1.328 0.318 -0.166 0.992 0.34

(0.109) (0.155) (0.165) (0.093) (0.153) (0.180)

Observations 38,676 37,673 37,673 38,676 37,673 37,673TRA-86: All Filers with More than $75,000 in Base-Year Gross Incomeln((1 - mtr1989)/(1 - mtr1985)) -0.432 0.077 0.081 -0.093 0.463 0.399

(0.121) (0.208) (0.208) (0.161) (0.292) (0.321)

Observations 17,392 17,238 17,238 17,392 17,238 17,238OBRA-90 and OBRA-93: All Filers with More than $10,000 in Base-Year Gross Income

ln((1 - mtr1995)/(1 - mtr1989)) 1.408 -0.565 -0.084 1.803 -0.059 0.424

(0.390) (0.154) (0.178) (0.278) (0.167) (0.264)

Observations 33,023 31,940 31,940 33,023 31,940 31,940OBRA-90 and OBRA-93: All Filers with More than $75,000 in Base-Year Gross Income

ln((1 - mtr1995)/(1 - mtr1989)) 1.244 -0.155 0.307 1.911 0.208 0.707

(0.281) (0.325) (0.355) (0.324) (0.354) (0.433)

Observations 15,785 15,646 15,646 15,785 15,646 15,646Source: Estimates are based on Statistics of Income data for years 1985, 1989, and 1995.Estimates are from 2SLS regressions. Regressions are weighted by the inverse of sampling probabilities and byincome (see Section III). Robust standard errors are in parentheses.
28

Table 10. Full SOI Person-Weighted Taxable-Income Elasticities by Income Quantile

Panel 11 Panel 22

Quantile Dummies Interacted

with Net-of-Tax Rates Quantile Regressions

Percentiles 1988 to 1998 Percentile 1988 to 1998

99.5 to 100 0.987 99.5th 0.871

(0.245) (0.031)

95 to 99.5 0.16 95th 0.693

(0.098) (0.027)

90 to 95 0.211 90th 0.644

(0.167) (0.028)

80 to 90 0.678 80th 0.581

(0.175) (0.030)

70 to 80 0.636 70th 0.522

(0.112) (0.029)

60 to 70 0.543 60th 0.482

(0.108) (0.034)

50 to 60 0.595 50th 0.457

(0.127) (0.040)

40 to 50 0.201 40th 0.457

(0.143) (0.048)

30 to 40 0.052 30th 0.470

(0.256) (0.058)

20 to 30 -0.502 20th 0.469

(0.221) (0.076)

10 to 20 -1.019 10th 0.411

(0.233) (0.102)

0 to 10 -2.608

(0.258)

Observations 449,584 449,584

Source: Estimates are based on Statistics of Income data for 1979 to

2001.

1 Robust standard errors are in parentheses. Estimated quantile

ETIs are generated by interacting quantile dummies with the

net-of-tax rate. Estimates are based on the specification with the

10-piece spline and with full demographic information, except for

state dummies, which do not affect the estimates. Regressions

are weighted by the inverse of observation sampling

probabilities.

2 Koenker–Bassett standard errors are in parentheses. Each

estimate represents a separate quantile regression. Estimates

are based on the specification with the log of base-year income

control and with full demographic information, except for state

dummies, which do not affect the estimates. Regressions are

not weighted by income but are weighted by the inverse of

observation sampling probabilities.