Estimating the Distribution of 

Consumption

-based Taxes with 

Consumer Expenditure Survey

Ed Harris Ed HarrisKevin PereseCongressional Budget OfficeTax Analysis Division
Disclaimer

Analysis and conclusions presented are my own and should not be interpare my own and should not be interp

as those of the Congressional Budg

Office.
Uses of the CE

• Primary Use: Distribution of consum

Primary Use: Distribution of consumbased taxes 

– Excise taxes– Cap and Trade– Value-Added Tax?

• Estimated distributional effect of the

taxes depends critically on relations

between consumption and income observed in the CE
Average Excise Tax Rate By Inc

Quintile

3.0

Percent of Income

2 02.5

1.5

2.00.5

1.0

Midd

H

0.0

1979

980

981

982

983

984

985

986

987

988

989

990

991

992

993

994

995

996

1997

998

999

000

001

002

1

19

1

19

19

19

19

19

1

19

19

19

1

19

19

1

19

19

1

19

19

20

2

20
Average Gain or Loss in Households' Purchasing Power from the Gree

Cap-and-Trade Program in H.R. 2454: 2020 Policy Measured at 2010 LeIncome

Loss From Compliance 

Costs

Gain From Allowance 

Allocations and Other 

Transfers

Net Gai

Househo

P

Average Dollar Gain or Loss per Household

Lowest Quintile ‐430 555 

Second Quintile ‐560 410 

Middle Quintile           ‐685 375 Fourth Quintile           ‐825 455 

Highest Quintile          ‐1,400 1,235 Unallocated ‐120 130 

All Households Households ‐900 740 

Gain or Loss as

a Percentage of After‐Tax Income

Lowest Quintile -2.5 3.2 

Second Quintile -1.5 1.1 

Middle Quintile -1.3 0.7 Fourth Quintile -1.1 0.6 

Highest Quintile -0.7 0.6 

Unallocated -0 2. 0 2. 

All Households -1.2 1.0 

Source: Congressional Budget Office, "The Economic Effects of Legislation to Reduce Greenhouse Gas Emission

2009, Table2
Approach To Cap and Trade 

Estimates

• Estimate price effect of the cap and 

Estimate price effect of the cap and 

program on final goods 

• Impute expenditures by category fro

Impute expenditures by category fro

CE to CBO’s base distributional data

(which is based on income tax recor(which is based on income tax recor

supplemented with data from the CP

• A l i ff t t di t t

Apply price effec

t

to spending 

to est

the effect across income groups
Input-Output Model: Price Cha

Results

Food 0.5%Clothing 0 2%.Nondurables 0.4%Electricity 8 8%.Natural Gas 11.4%Gasoline 4 2%.All Expenditures 0.7%

Assumes Total allowance revenues of about 0.7% of GDP
CE and NIPA aggregate

CE and NIPA aggregate

• CE aggregates generally below NIPA CE aggregates generally below NIPA

aggregates

• Applying price increases from NIPA 

Applying price increases from NIPA 

I/O model to spending in the CE doe

yield the same revenue yield the same revenue

• Differential across expenditure categ•

Adjusting for these has distributiona

implications
Imputing Consumption: Prepa

the CE

• We convert 

q y uarterl

y cross-sections fro

Interview Survey to annual panel files

– Reweight complete and incomplete interv

• Adj t t f di di Adjus

tmen

ts 

for diary spending

• Adjustments for renters with no reporte

utility spending utility spending

• Pool multiple panels• Two Methods to im

p j ute from ad

justed 

– Hot deck imputation for most of sample– Regression imputation for high income 

households
Imputing Consumption:

Statistical Match SOI/CPS & 

• Hot deck routine with both rigid and flexible ma

cri i ter

a

– Fixed: Region– Flexible: Age (+/- 1 year increments)

Income (+/- 2% increments)Family Type (+/- 1 child only)

• For each record in base data file, match to a C

record within the same cell

• Carry over ratio of consumption to income, e pe d tu e s a es o d e e t te s xpe

n

ditu

r

e

s

h

a

res

of 

diff

e

r

e

n

t ite

m

s

• Applied to:

• Single households <$150,000 income• M

a ed ouse o ds $300,000 co e rried house

h

olds <$300,000 inco

m

e
Consumption to Income ratios, 200

BLS Published Income and Consumption by Income Class, 2004 BLS Published Income and Consumption by Income Class, 2004

Population 

Average 

Income 

Average 

Consumption 

Consumption/ 

Income 

< $5,000 4.553 $2,626 $17,029 6.49< $10,000 7.218 $7,856 $14,596 1.86< $15,000 8.950 $12,554 $19,444 1.55< $20,000 8.177 $17,427 $23,023 1.32

< $30,000 14.172 $24,892 $27,741 1.11

< $40,000 13.125 $35,107 $33,273 0.95

< $50,000 11.374 $45,052 $38,204 0.85

< $70,000 18.069 $59,920 $47,750 0.80

> $70,000 30.644 $118,332 $76,954 0.65

Total 116.282 $54,680 $43,395 0.79

NOTE:BLS consumption concept does NOT equal CBO consumption conceptConsumption and income data are constructed based on 

b th d di d t b

oth survey an

d diary 

d

ata.

Source: BLS, Consumer Expenditure Survey, 2004 Table 2. Income before taxes: Average annual expenditures and characteristics.
Consumption-to-Income Ratios by Pre-tax-Income Quintiles, CEX 1

250%300%

Quintile 1

200%150%

Quintile 2

100%

Quintile 3Quintile 4

50%

Quintile 5

0%

1994 1995 1996 1997 1998 1999 2000 2001 2002 

Source: BLS, Consumer Expenditure Survey, Table 1. Quintiles of income before taxes: Average annual expendicharacteristics, multiple years
Potential Adjustments That Re

C-I ratios at the Bottom

Adjustments Made:• D l rop very low-i d ncome recor

d

s

• Use income averaged between 1st and last interview

• Estimate income taxes based on reported income, use rat

consumption to after consumption to after

-tax income tax income

• Adjustments to consumption definitionEx

plored But Not Done:

• Limit to prime-age individuals• Cap consumption-income ratios unless observed dis-savi

explain

• Even with these adjustments, C-I ratios are quite high for 

the distribution

• Any adjustments to hit PCE totals exacerbate this problem Any adjustments to hit PCE totals exacerbate this problem
High Income Regression

• Both income and ex

penditure amounts 

top coded in CE

• Impute expenditure amounts based on 

regression models for high regression models for high

-income

households

• Need to extend analysis significantly b

Need to extend analysis significantly b

the income range covered in the CE

• Separate models for electricity, gasolin

oil t l d t t l dit il, na

tural gas, an

d

t

o

t

al expenditures

• Use regression results up to 1M in inco

after that hold C after that hold

C

-I ratio constant I ratio constant
Comparison of High Income 

CE SOI

Units above 100,000

Number of Units ( ) M 18.9 16.1

Average Income $164,000 $254,000Share of Income 43.2 51.2

Units

a

bove 150,000

Number of Units (M) 7.3 7.1

Average Income $236,000 $425,000

Share of Income 23 8. 37 7.Source: BLS Table 2301. Higher income before taxes: Average annual expenditures and characteristics, Consumer Expenditure Survey, 2006 and IRS Statistics of Income, Individual Income Tax Returns 2006 Table 1.2
Top Quintile Income and Consumption 

0.60 5

0.550.45

0.5

0.35

0.4 

0.3
High Income Regression

ln(Consumption) by ln(Pre-tax-income), CEX 2004

$13$14$15$10$11$12

umption)

$7$8$9

$10

ln(Consu

$5$6$7

10 10.5 11 11.5 12 12.5 

ln(pre-tax-income) $60,000 $160,000
High Income Regressions 

Projections

Ln(Consumption) = Ln(Pre-tax-Income)

$700,000$800,000$400 000$500,000$600,000

onsumption 

Predicted Consumption (Left Axis)

$200,000$300,000$400,000

Predicted C

Predicted Consumption-to-Income Ratio

$0

$100,000

$500,000 $1,000,000 $1,500,000 $2,000,000 $2,500,000

Predicted Consumption to Income Ratio

(Right Axis)

Pre-tax-income
High Income Regressions

Effect of Top-coding

ln(Expenditures)

= ln(Pre‐tax Income)

S C O i

50%60%

BL

S vs.

C

B

O estimates

40% 

ome Ratio

20%30%

Expenditures‐to‐Inco BLS

CBO

10% 

0% 

$500,000 $1,000,000 $1,500,000 $2,000,000 $2,500,000

Pre‐tax Income
High Income Regressions

Effect of Top-coding

ln(GasolineExpenditures)

= ln(Pre‐tax Income)

BLS CBO i

1.80%2.00%

BLS vs. CBO estimates

1.20%1.40%1.60%

come Ratio

0.60%0.80%1.00%

Expenditures‐to‐Inc

0.20%0.40%

BLS

CBO

0.00% 

$500,000 $1,000,000 $1,500,000 $2,000,000 $2,500,000

Pre‐tax Income
Final Consumption

-Income R

1.8 

1 21.41.6

0.8

1

1.2

Total Consumptio(left scale)

0.20.40.6

Energy Consumption 

(right scale)

0

Lowest 

Quintile 

Second 

Quintile 

Middle 

Quintile 

Fourth 

Quintile 

Highes

Quintil

(right scale)

Quintile Quintile Quintile Quintile Quintil
Evaluation

• Data from CE is very py valuable. Especially 

to the micro data. Only source for: 

– Detailed consumption– V i ti f ti b Variation of consumption by age, geographi 

c 

• But...

– Observed consumption-income pattern is diff

explain

– Differential reporting error across income gro

– Raises questions about the expenditure shar

derived from the CE derived from the CE
Suggested Improvement

Major• Top-down reconciliation of income and consumption 

the interview process

– Perhaps something like to the diary, where focus is tot

spending/saving

• High-Income oversampleMinor

– Pool all interviews for a CU create panel weights Pool all interviews for

a CU, create panel weights

– Impute from diary to interview, so one complete file– Continue research into reconciling differences with PC

• Provide cross-walk (j ) or adjustment factors) between NIPA P

UCC codes

– Study income misreporting with a one-time match to 

administrative records