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Library of Congress

Expanded Academic ASAP

National Tax Journal, Sept 1993 46 n3 p331-344

What is the distributional burden of taxing consumption? (Symposium: New Directions in Tax

 Policy) John Sabelhaus.

Abstract: Shifts in tax burdens due to a consumption tax have been exaggerated, as shown by statistical analysis of

 the common data used in such estimates. A cash-basis burden measure is used, which consists of the ratio of realized

 cash tax liability to realized cash income. Results indicate that common estimates based upon residual methods are

 biased toward regressivity. The results are valid for sales taxes, value added taxes or excise taxes if these taxes use

 estimates of income and consumption distribution.

Full Text: COPYRIGHT National Tax Association 1993

There are several variants of consumption tax systems in use around the world and being discussed in the United

 States.(3) This paper focuses on a proposal to allow the deduction of new net saving from gross income to arrive at

 taxable income. The resulting tax base (income less saving) is current consumption, and, hence, this method of

 collecting revenues is sometimes referred to as a "consumed-income" tax. Partial deduction of new saving already

 exists in the United States, as employer contributions to pension funds, taxpayer contributions to some IRAs and

 401k plans, and all interest earned on these retirement accounts are excluded from the tax base.

The consumed-income tax has a desirable property with respect to distributional burden, because a multiple bracket

 structure is maintained. In fact, given estimates of how saving varies with income, a set of brackets and rates can be

 found that maintains the distributional burden currently in place. The exact set of brackets and rates needed to

 maintain the current distributional burden depend on how saving varies with income. Thus, point estimates of saving

 rates by income class would play a crucial role in any equity evaluation of a policy shift toward consumption taxes.

Two theoretically equivalent approaches can be used to estimate the joint distribution of income and saving using

 household-level data. First, expenditures and taxes can be subtracted from household income to create a residual

 measure of saving, similar to the approach in the National Income and Product Accounts (NIPA). A second approach

 is to measure net worth for a household at two points in time and then compute cash saving as the change in assets

 less the change in liabilities, adjusted for capital gains. Although these two measures are theoretically equivalent,

 actual estimates show very divergent results.

An example of how residual and net worth saving estimates diverge is found in Bosworth et al. (1991). They used the

 Survey of Consumer Finances (SCF) for 1983 and 1986 and found net worth saving rates ranging from -2.4 percent

 of income for the bottom income quintile up to 12.5 percent for the top three quintiles. In contrast, the same authors

 used data from the Consumer Expenditure Surveys (CEX) for 1982-1985 to create estimates of residual saving, and

 average saving varied from -92.1 percent of income in the bottom quintile to 25.8 percent in the top quintile. The

 SCF and CEX surveys show similar aggregate saving rates but yield very different conclusions about the

 distributional burden of a consumption tax.

This paper builds on the analysis described above but controls for possible differences in the surveys. Saving is

 measured using both the residual and net worth methods with one data set, the 1988-1989 CEX. The differences in

 the joint distribution of saving and income still exist when changes in net worth and residual measures are created for

 the same set of households. Stated differently, there are large statistical discrepancies in the survey data between

 residual and net worth saving measures. No final solution to the discrepancy problem is offered in this paper.

 Evidence is presented that the net worth saving rates, which indicate that a consumption tax is less regressive than

 previously thought, are more reasonable than the residual measures commonly used. The evidence implies that the

 current statistical basis for evaluating the distributional burden of consumption tax proposals is at least very

 questionable. More research is needed on this topic, or policy proposals may be rejected for the wrong reasons.
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RESIDUAL AND NET WORTH SAVING ESTIMATES

Cash-basis saving can be measured in two theoretically equivalent ways. The residual measure of saving is defined

 by

1 [S.sup.r] = Y + R - T - I - C

where Y is cash income, R is private transfers and other money received, T is the amount of personal taxes paid, I is

 social insurance contributions, and C is consumption. An alternative to the residual saving construct is a net worth

 saving measure, given by

2 [S.sup.nw] = [Delta]A - [Delta]L + [Delta]H

where [Delta]A is the change in nonhousing assets, [Delta]L is the change in nonhousing

 liabilities, and [Delta]H is the change in housing net worth. Housing wealth is distinguished

 from nonhousing wealth because of the differential treatment in the existing tax code, i.e., the

 fact that mortgage interest paid is excluded from the income tax base. If the components of each saving measure are

 conceptually consistent, then [S.sup.r] and [S.sup.nw] should be equal for any household. To the extent that they are

 not, define the statistical discrepancy as

3

D = [S.sup.r] - [S.sup.nw].

If the statistical discrepancy is zero, the household's books balance. Another way to say this is that sources of funds

 (Y + R + [Delta]L) are equal to the uses of funds (T + I + C + [Delta]A + [Delta]H).

The next section is devoted to estimates of residual and net worth saving across income

 groups. If the components of each saving measure are conceptually equivalent, then the statistical discrepancy should

 be zero for every household. Two possible problems with comparing residual and net worth based measures arise

 from the failure of surveys to be comprehensive and the treatment of capital gains. Some examples will help clarify

 exactly what is not measured in the survey data and when we can expect the alternative measures to be equivalent.

The survey data used in this paper do not include information about employer pension contributions or balances in

 pension funds. Therefore, it is impossible to include employer contributions or interest earned by pensions as part of

 household saving, as is done in the NIPA. In my notation, Y is missing employer contributions and imputed interest

 on pensions. This is consistent with [S.sup.r] and [S.sup.nw] being equal if we do not count the change in pension

 fund balance as part of [Delta]A, which we do not. The employer contributions and interest earned by pensions are

 certainly part of saving, but we cannot measure them.

A second example of what may be counted incorrectly in the survey is the purchase of consumer durables. If a

 household borrows money to buy a car, then residual saving (which has the purchase price of the car subtracted) and

 net worth saving (which has the value of the loan originated subtracted) will both be lower by the purchase price of

 the car. In economic terms, the household's residual saving should only fall by the "consumption flow" produced by

 the car during the survey year, and [Delta]A should include the undepreciated value of the car at year end. It can be

 argued that saving is mismeasured, but [S.sup.r] and [S.sup.nw] should be mismeasured by the same amount, that is,

 the statistical discrepancy should be zero.

These first two examples of what is not counted in the surveys and

 why the treatment should not affect the statistical discrepancy may give you the wrong idea about the goal of this

 paper. These transactions are treated incorrectly from the point of view of measuring saving, and the lack of
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 information needed to do the accounting of the transactions is a problem. However, employer pension fund

 contributions and interest earned on pension assets are already excluded from the tax base. Also, no consumptionbased tax proposal considers allowing subtraction of saving in the form of consumer durables saving from the tax

 base. Therefore, the improper treatment biases the saving rate estimates, but not conclusions about how the tax base

 would change.

Employer or government provided transfers for medical care or education are not covered by the survey either. This

 omission does not bias the estimates of saving levels, though it will affect saving rates. Saving levels are unaffected

 because the transfers are not counted as part of income or consumption.(4) If the level of saving is unaffected, then

 [S.sup.r] and [S.sup.nw] should still be equal. The saving rate is biased upward because the level of income, which

 should include the transfers, is lower.

Realized capital gains measured in the asset data may cause bias in the saving estimates but should not introduce a

 wedge between [S.sup.r] and [S.sup.nw]. As an example, assume a person sells $10,000 worth of stock during the

 survey, for which they had paid $5,000 in some year prior to the survey. There is a $5,000 capital gain which is not

 counted in the survey income measure, so any saving estimate will be biased down by $5,000. The impact of the

 stock sale on [S.sup.r] and [S.sup.nw] depends on what the person does with the proceeds of the sale. If the person

 deposits the $10,000 proceeds in a saving account, there are two offsetting changes in [Delta]A, so [S.sup.r] and

 [S.sup.nw] are not affected. If all other saving were zero, both [S.sup.r] and [S.sup.nw] would still register a value of

 zero, though each should be $5,000. If the person buys a car with the proceeds, C rises by $10,000, so [S.sup.r] falls

 by $10,000. The value of [S.sup.nw] will also fall by $10,000, because the drop in one [Delta]A category is ot offset

 by an increase in another. In this case, if all other saving is zero, true saving is -$5,000 but registered saving will be -

$10,000. Again, this example is not meant to defend the survey measure of saving on economic grounds. The example

 just points out that both measures of saving should be biased in the same way.

Only one type of economic event I can think of could cause [S.sup.r] and [S.sup.nw] to

 be different. If a household reports a positive [Delta]A that is due to unrealized capital gains, there will be no

 offsetting transaction in the residual saving calculation. The only asset category for which this may cause a problem

 is stocks and bonds, where households are asked how much the market value of stocks and bonds they are holding

 has changed in the last year. The way this question is asked could potentially cause problems with the statistical

 discrepancy, but its effect is attenuated by the empirical observation that most households hold little, if any, stocks

 and bonds.

HOW DOES SAVING VARY ACROSS INCOME GROUPS?

Average residual and net worth saving in the CEX survey are reasonably close for the entire sample but

 differ markedly across income quintiles. Table 1 presents consumer-unit averages for several components of the

 income, tax, consumption, and wealth change measures used to construct the saving estimates.(5) The bottom line

 ratios of residual and net worth saving to cash income are 4.4 and 2.4 percent for the entire sample. The two

 estimates, in turn, are similar to the NIPA rate computed using the residual method (adjusted for the omissions

 described in the last section), which was 2.6 percent in 1989.

 TABLE 1

 MEASURING SAVING USING THE RESIDUAL AND

 NET WORTH METHODS, CONSUMER

 EXPENDITURE SURVEY (1989)

 Consumer Unit

 Average

Cash income (Y) $32,978

 Wages and salaries 24,775

 Self-employment income 2,302

 Rent, interest, and divi-

 dends 1,473

 Government transfers 2,787

 Private transfers received 1,638

+ Other money receipts (R) 25

 Support paid to other CUs

 (outflow) -591

 Insurance refunds 149

 Inheritances and bequests 378

 Food stamp benefits 87

- Personal taxes (T) 4,368

- Social insurance taxes (I) 2,095

- Consumption (C) 25,081
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 Food 4,143

 Clothing 1,095

 Rent and utilities 3,389

 Out-of-pocket medical ex-

 penses 1,458

 Motor vehicles and parts 2,633

 Furniture and household

 equipment 777

 Housing interest 1,799

 Housing property taxes 586

 Housing intermediate

 goods 615

 Life insurance premiums

 paid 361

 Gifts and contributions to

 organizations 462

 Personal interest expense 571

 Other goods 3,264

 Other services 3,921

= Residual saving ([S.sup.r]) 1,457

Increase in nonhousing as-

 sets ([Delta]A) 1,258

 Private pension contribu-

 tions 216

 Deductible IRA/Keogh

 contributions 186

 Other retirement contri-

 butions 137

 Change in checking ac-

 counts -17

 Change in saving accounts 194

 Change in stocks and

 bonds 247

 Investments to own farm/

 business 294

- Increase in nonhousing li-

 abilities ([Delta]L) 462

 Vehicle loans originated 1,565

 Less: vehicle loan principal

 paid 1,163

 Change in other install-

 ment credit 113

 Less: change in other

 amount owed to CU 52

+ Change in housing net

 worth ([DELTA]H) -3

 Properties purchased 1,246

 Less: properties sold 506

 Additions and alterations 436

 Payment of mortgage

 principal 937

 Less: mortgage loans orig-

 inated 2,116

= Net worth saving ([S.sup.nw]) 792

Statistical discrepancy 665

Residual saving as a percent

 of cash income 4.4 percent

Net worth saving as a per-

 cent of cash income 2.4 percent

Discrepancy as a percent of

 cash income 2.0 percent

Sample size 5976

Source: Author's calculations using 1988-1989 CEXs.

The similarity in residual and net worth saving rates disappears when the sample is divided by income class. Table 2

 shows saving rate estimates by income quintiles, and the differences between residual and net worth saving are

 striking.(6) Residual saving is -72.5 percent of cash income in the bottom quintile, but net worth saving is estimated

 at -6.7 percent of income. At the top of the income distribution, residual saving is estimated at 16.8 percent of cash

 income, while net worth saving is only 5.7 percent. The residual and net worth estimates imply very different changes

 in distributional burden if the tax base is shifted toward consumption. Which, if either, of these sets of estimates is

 correct?
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The CEX data used to estimate saving by income quintile

 in Table 2 are often criticized on the grounds that the income information is poor. This criticism is consistent with the

 findings in Table 2. Any negative measurement error in the income data will be reflected in very low (e.g., negative)

 residual saving rates. It is possible to evaluate this critique by comparing the CEX income data to another benchmark.

 Table 3 presents a comparison of the CEX cash income distribution to the CPS/SOI income distribution developed by

 the Congressional Budget Office.(7)

The distribution of population across quintiles in Table 3 is expected to be even, but the results show

 that the top two quintiles are underrepresented in the CEX. The details show that the top 1 percent of the income

 distribution is effectively missing or topcoded in the CEX, the next four percentiles of the population have only 82.5

 percent (3.3 divided by the expected 4.0) represented, and the next five percentiles have only 92.0 percent (4.6

 divided by the expected 5.0) represented. The bottom of the income distribution is consistent with CPS tabulations. I

 find 12.6 percent of the population living below the poverty level in the CEX data, whereas Census reports 12.8

 percent.(8) The differences between the CEX and CPS/SOI income distribution are apparently concentrated at the top

 end and due to undersampling of the wealthy.

The CEX income data are consistent with the CPS/SOI up to the top percentiles of the distribution. This observation

 is due to the fact that the CEX and the CPS ask basically the same questions of basically the same people. The fact

 that CEX and CPS data are similar does not refute the point made above that Table 2 is consistent with income

 reporting errors in the data. The similarity just adds an interesting dimension to this research, because it suggests that,

 if income reporting is a problem in the CEX, then the CPS might have the same problems.

The fact that the top 1 percent of the distribution is missing complicates the search for
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 which piece(s) of the discrepancy puzzle are badly reported. We know that the sum of

 any income, consumption, or wealth variable in the CEX should not match the aggregate

 value of the variable from any other source, because the CEX sample is not

 representative. The approach of comparing CEX totals by income, consumption, and

 wealth change component will have to wait until further information about the top 1

percent can be used to aggregate totals to derive truncated sums the CEX sample should

 match. Though aggregate sums cannot be compared at this point, a good deal more can

 be learned by looking at the CEX more closely and a Canadian survey which does not

 have the discrepancy problem.

SAVING PROFILES IN THE CANADIAN FAMILY EXPENDITURE SURVEY

Some evidence about why residual and net worth saving diverge across income groups

 can be found by looking at tabulations from another survey, the Canadian Family

 Expenditure Survey (FEX). The FEX is similar to the CEX in its basic mission to collect detailed expenditure

 information for developing a consumer price index. It differs in the way the survey is conducted and the emphasis

 that Statistics Canada places on "balancing" residual and net worth saving measures for a households in the sample.

The Canadian FEX survey is conducted using only one interview, and respondents are asked about income,

 expenditure, and wealth change during the previous 12 months. In contrast, the CEX interviewers visit respondents

 for each of 4 consecutive quarters and ask about the previous 3 months.(9) The FEX interviewers do a rough

 balancing calculation while at the respondent's home, and central office personnel call on a respondent if their

 calculations show large discrepancies between the sources and uses of funds. In contrast, each quarter of CEX data is

 processed independently, and the Bureau of Labor Statistics explicitly states that microlevel consistency is not a goal

 of the survey.

This alternative approach to collecting, processing, and reconciling the data leads to much less discrepancy between

 residual and net worth saving estimates across income groups, as the results in Table 4 indicate. The saving rate

 estimates in the FEX are close to the CEX residual measure at the top and close to the CEX net worth measure at the

 bottom. Unfortunately, this suggests multiple sources of error in the CEX saving estimates, i.e., too little income at

 the bottom and too little positive asset change at the top. This comparison is a little ambiguous, however, because the

 CEX is missing the top of the income distribution.

The lack of discrepancy in the FEX is partially manufactured, however, as Statistics Canada procedures call for any

 households with large discrepancies (greater than 10 percent of sources or use of funds) to be dropped from

 tabulations and the public-use data. In 1986, about 5 percent of the FEX sample was excluded on this basis. It would

 be useful to look at these observations, but the point that the FEX is much cleaner can be made by noting that only

 about 40 percent of the CEX sample meets the FEX discrepancy criteria.

CAN THE DISCREPANCIES BE RESOLVED?

The Canadian FEX data show much less discrepancy

 between residual and net worth saving across income

 groups than the CEX data. Is there any systematic bias

 causing discrepancies in the CEX? The Canadian FEX

 data excludes households with extreme discrepancies, and

 one could take that approach with the CEX. Before that

 solution is invoked, however, it is interesting to consider

 subsamples of the population whose discrepancies are

 expected to be smaller.

One subset of the CEX sample is considered in Table 5.

 Any household that changed income quintile during the
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 survey year is omitted.(10) The statistical discrepancy for

 the omitted group is still expected to be zero, but the

 subsetting exercise is based on the idea that the survey

 may have trouble reconciling sources and uses for

 households with large income swings. The extreme

 differences between residual and net worth saving are

 still apparent in Table 5, however. Residual saving is

 somewhat less negative in the bottom quintile, but the

 discrepancy is by no means eliminated.

I have not included tables on several other subsetting

 exercises that were done and are worth mentioning.

 Households where members other than the head and spouse were working, where self-employment income was

 present, where the head was over a critical age, where properties were bought or sold, or where one or more imputed

 variables existed were all excluded as a test of whether systematic discrepancies were specific to certain groups. The

 reduction of statistical discrepancy in each case was less than that reported in Table 5.

Table 6 reports the results of applying the Canadian approach to the CEX data. Households are excluded from the

 sample if their statistical discrepancies are large. Because the CEX and FEX surveys are fundamentally different, I

 excluded households if the absolute value of their statistical discrepancy exceeded 25 percent of the greater of

 sources or uses of funds, rather than 10 percent, as in the FEX. Even this less restrictive criteria excludes 45 percent

 of the sample, a much larger fraction than the 5 percent dropped from the FEX.

The omission of high-discrepancy observations in the CEX dramatically flattens estimated residual saving profiles.

 Saving rates are estimated to range from -4.6 percent in the lowest quintile to 11.4 percent in the highest. These

 estimates do not differ much from the FEX estimates nor do they differ much from the Bosworth et al. (1991)

 estimates using the SCF.

Some concern about the saving estimates in Table 6 arises, because the remaining sample is clearly no longer

 representative. The fraction of population estimated to be in the bottom quintile falls dramatically to 12.5 percent.

 Accepting the saving rates in Table 6 as point estimates implicitly involves rejecting the CPS income distribution,

 because the CEX incomes match the CPS incomes and we are throwing out a disporportionate number of

 observations in the lowest quintile. Without stronger evidence about why we should exclude low-income households,

 I can only argue that the CEX residual saving rates estimated using a sample dominated by discrepancies should be

 treated with skepticism.

The last few tables are meant to illustrate an aspect of measuring saving in these data sets which researchers should

 keep in mind should they go searching for point estimates of saving rates. Table 7 shows the distribution of CEX and

 FEX observations by residual saving, net worth saving, and discrepancy, all relative to cash income. The most

 important point in Table 7 is that there is a great deal of inherent variability in these types of saving measures. The
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 FEX, which forces sources and uses of funds to be equal, shows that 12.3 percent of the population saves less than

 -25 percent of their cash income, while 11.3 percent save more than +25 percent of their cash income. This occurs

 even though there is virtually no discrepancy in those ranges.

The CEX shows more dispersion than the FEX, as 23.0 percent of households save less than -25 percent of their cash

 income, while 23.5 percent save more than +25 percent. The dispersion is larger than in the FEX by a factor of about

 two in each tail. This is meant to illustrate that excluding people because their saving rates are "unreasonable" is not

 ultimately the solution to the discrepancy problem. The pure financial measure of saving, which, for example, treats

 durable expenditures as consumption, leads to highly dispersed saving rates.

Tables 8A through 8C and 9A through 9C further refine the observations about Table 7. The residual, net worth, and

 discrepancy distributions for the FEX and CEX are presented by quintiles. The Table 8 group, for example, shows

 how much the bottom quintile is dominated by discrepancy. Table 8A shows that 54.4 percent of the bottom quintile

 is in a residual saving class of less than -25 percent of income, but Table 8C shows that this is because 56.5 percent

 of the bottom quintile is in a discrepancy class of less than -25 percent of income. Table 9A shows, by contrast, that

 only 19.6 percent of the FEX bottom quintile saves less than -25 percent of their income on a residual basis.

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Conclusions

This project set out to estimate the joint distribution of consumption and income for the purpose of assessing the

 distributional burden of taxes and found that the estimates are dominated by unresolved statistical discrepancy.

 However, the circumstantial evidence suggests that analysis of consumption taxation based on residual measures of

 saving should be treated with skepticism. This critique applies to any studies of excise, VAT, or sales tax burdens
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 which rely on an estimated distribution of consumption and income. It is reasonable to infer that existing studies

 using the residual method to compute saving are biased toward determining that consumption taxes are more

 regressive than what is probably the case.

ENDNOTES

 I am grateful to Frank Sammartino for help with the CBO tax model data and to seminar participants at the

 Congressional Budget Office for useful comments and ideas. Opinions here do not necessarily reflect those of the

 Congressional Budget Office.

(1) See Davies et al. (1984), Fullerton and Rogers (1991), and Poterba (1989).

(2) For examples of tax burden estimates, see Pechman (1985), Kasten et al. (1993), and Congressional Budget

 Office (1987, 1988, 1992a, 1992c).

(3) For a discussion of the various types of consumption taxes, see, for example, Gravelle (1992), Aaron and Galper

 (1985), and Bradford (1986).

(4) Medical expenditures, for example, are measured as consumption only if they are "out of pocket." For a

 discussion of how these omitted transactions affect the NIPA saving measure, see the Appendix to Bosworth et al.

 (1991).

(5) Consumer units are the basic sampling unit in the CEX. A consumer unit is similar to the basic Census sampling

 units of households but adjusted for whether individuals share resources. In 1989, there were 93.4 million households

 and 95.8 million consumer units. See the Appendix for a discussion of how the CEX data are used in this paper.

(6) The allocation of households across quintiles is based on Adjusted Family Income (AFI), which is an

 equivalence-scale adjusted measure of income. The quintile breaks are from the Congressional Budget Office tax

 simulation data base, which combines Current Population Survey (CPS) and Statistics of Income (SOI) information

 into one consistent data base. The CPS/SOI data set is the basis for CBO studies of tax burden (1987, 1988) and

 changes in distribution income over time (1992b).

(7) The sampling basis for the CEX has to be adjusted somewhat to make this comparison. When the Census and

 Congressional Budget Office estimate income distribution, individuals living in households, who are not related to

 the household head, are treated as separate units. I split out unrelated individuals in order to create Table 3, which is

 why the population distribution does not match that in Table 2.

(8) This finding that poverty rates in the CEX are similar to poverty rates in the CPS implies that some critical

 analysis of U.S. poverty estimates may be possible. In particular, the percent of people living under the income

 poverty line (which is not justified by net worth change, such as borrowing) is only 5.6%. This estimate should be

 treated cautiously until a more detailed comparison of the CPS poor and CEX poor can be undertaken.

(9) The survey only covers certain topics during certain interviews. For example, income is asked about at the first

 and last interview and financial wealth change is asked about at only the last interview. The Appendix has some

 details about how this distribution of questions affects the sample selection process.

(10) Respondents are asked at the first and fourth interviews about income during the prior 12 months. The change in

 income quintile is based on the change between the first and fourth interviews.

REFERENCES

Aaron, Henry J. and Harvey Galper. Assessing Tax Reform. Washington, D.C.: The Brookings Institute, 1985.

Bradford, David F. Untangling the Income Tax.

 Cambridge: Harvard University Press, 1986.
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Bosworth, Barry, Gary Burtless, and John Sabelhaus.

 "The Decline in Saving: Evidence from Household

 Surveys." Brookings Papers on Economic Activity (1)

 (1991).

Congressional Budget Office. The Changing Distribution

 of Federal Taxes: 1975-1990. Washington, D.C.: U.S.

 Government Printing Office, October, 1987.

Congressional Budget Office. The Changing Distribution

 of Federal Taxes: A Closer Look at 1980. Washington,

 D.C.: U.S. Government Printing Office, July, 1988.

Congressional Budget Office. Effects of Adopting a

 Value Added Tax. Washington, D.C.: U.S.

 Government Printing Office, February, 1992a.

Congressional Budget Office. "Measuring the

 Distribution of Income Gains." Staff Memorandum.

 Washington, D.C.: U.S. Goverment Printing Office,

 March, 1992b.

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Congressional Budget Office. "Distributional Effects of

 Substituting a Flat-Rate Income Tax and a Value Added Tax for Current Federal Income, Payroll, and Excise Taxes."

 Staff Memorandum. Washington, D.C.: U.S. Government Printing Office, April, 1992c.

Congressional Budget Office. "New Tax Proposals:

 Flat, VAT, and Variations." Congressional Research

 Service Report for Congress No. 92-386 S. Washington,

 D.C.: U.S. Government Printing Office, April, 1992.

Davies, James, France St. Hilaire, and John Whalley.

 "Some Calculations of Lifetime Tax Incidence."

 American Economic Review 74, No. 4 (September,

 1984).
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Fullerton, Don and Diane Lim Rogers. "Lifetime Versus Annual Perspectives on Tax

 Incidence." National Tax Journal XLIV (No. 3, 1991).

Kasten, Richard, Frank Sammartino, and Eric Toder. "Trends in Federal Tax Progressivity, 1980-93." In Tax

 Progressivity and Income Inequality, edited by Joel Slemrod. New York, Cambridge University Press, 1993, in press.

Pechman, Joseph A. Who Paid the Taxes, 1966-85. Washington, D.C.: Brookings Institution, 1987.

Poterba, James M. "Lifetime Incidence and the Distributional Burden of Excise

 Taxes." American Economic Review 79, No. 2 (May, 1989).

 Article A14760477

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