Improving the Consume

Improving the Consume

Expenditure Survey: A Vi

from the Research Commun

Orazio Attanasio

Chris Carroll

Thomas Crossley

Jonathan Parker Jonathan Parker

John Sabelhaus

Prepared for BLS CE Data User Prepared for BLS CE Data User s’ Forum

June 2010
Overview

• Chris Carroll will discuss

– CE’ l i i i h f h 

CE’s role in constructing weights for the 

– Use of non-CE data for improving CE m

• John Sabelhaus will discuss (w/ input from 

Attanasio, Thomas Cossley, and Jon Parker

– Joint distribution of consumption and inc

– Deteriorating ratio of CE/NIPA totals

– Using CE panel aspect to measure consum

Using CE panel aspect to measure consum

responses to tax rebates and other shocks
Expenditure Weights and the 

• CPI: a “Principal Economic Indicator” • With great power comes great respons

With great power comes great respons

– OMB Statistical Directive Number Three

• Timing etc of PEI Timing, etc of PEI s’• Requires ongoing comparison with external 

of accuracy, statistical rigor, etc; regular reviperformance compared to benchmarks etc
Charles Plosser

• Philly Fed President WSJ Interview Philly Fed President WSJ Interview

(2010/04):

– CPI substantially understates CPI substantially understates “true” infla

infla

• Housing overweighted• Falling gp , g housing prices, rents drag down CPI • If Fed believes “true” inflation higher, might 

– Does it matter if it’s true?

• No: Point is that doubts about CE weights ar• Yes: If housing weights wrong, others also w

are mismeasuring inflation
How To Fix?

• Unless CE data validated by multiple e

sources,yy credibility will always be und

• Pick measures of greatest importance• For CPI expenditure weights natural e

For CPI expenditure weights, natural e

metric is PCE expenditure weights

• PCE i h d i d f C R 

PCE weights derived from Census Ret
Retail Sales vs Alternativ
Regional Growth Rates, 20
Census vs State-Level Tax D
Implications of CPI Weight

• For macro policymakers, CPI credibili

means credibilit

yp 

 about ex

penditure w

• Natural external measure is BEA Natural external measure is BEA s’ PC

derived from Census Retail Sales data
The Joint Distribution o

Consumption and Incom

• Many CE research questions are based direc

the joint distribution of consumption and inc

– S i av

ing rates across groups an

d i t

ime

– Distribution of income versus consumption taxe

– Alternative measures of economic well-bein

g

• Three ways to measure ‘saving’ using CE

(1) Income minus taxes minus expenditures (1) Income minus taxes minus expenditures(2) Same as (1), but exclude Social Security and p

contributions from expenditures

(3) Change in assets minus change in liabilities
Joint Distribution of C/Y, C

• A l i h b d bli h d BLS t bl 

Ana

lys

is 

here 

base

d on publi

s

h

e

d BLS

t

able

spending by earnings quintile. BLS tables 

combine interview and diary to measure specombine interview and diary to measure spe

• What follows is based on means by income 

q intile; same res lts sho p at ho sehold q

uintile; same res

ults show u

p at ho

usehold 

(forthcoming book by Attanasio, Battistin, P

• In other words: outliers within quintiles (lik

very high spenders in the low income group

not what not what s’ driving the results driving the results
Figure 1. Cross Section Net Cash Flows as a Percent of Dispo

Income, 2008 Consumer Expenditure Survey

40.0%60.0%

Residual Cash Flow Residual Cash Flow Plus Pensions and Social Security Change in Assets Minus 

0.0%

20.0%

axes

-40.0%-20.0%

f Income Minus Ta

-80.0%-60.0%

Percent of

-120.0%-100.0%

Source: 

All Lowest Second Third Fourth 

Consumer Unit Quintile of Income
Reconciling C/Y by Incom

• Th i h ld i /i Theor

y: sav

i

n

g s

hould

increase w/inco

– People “smooth” temporary income fluctuations; some ho

in bottom 

q y yg g uintile this 

year usuall

y have hi

gher earnin

g

s

– Life cycle patterns; households save when middle aged/in

high, spend down assets when retired/income is low

• But theor

y cannot explain the ma

gnitu

– Income variability exists, but is simply not large enough– SCF ti f d bt t i i b tt i til i 13 5% 

SCF ratio o

f

d

ebt

to 

income 

in 

b

ottom qu

i

ntile 

is 13.5%; o

in bottom quintile even have credit cards, median balance 

– SCF wealth to income ratios for top quintile would be mu

if th ll d 40% if they really save

d 40% on average
Realistic Explanations Realistic Explanations

• Systematic under-reporting of consumption 

cogniti d ti b d i b t 

itive an

d time 

bur

dens; var

ies 

by ca

tego

overall C/Y way too low

• Also some under-reporting of income; those 

households (by construction) in bottom quin

– If true, survey-b d i i i b b

base

d

income stat

ist

ics may 

be b

because CE incomes match CPS for all but high

•

Some support 

f hi f C di S 

or this 

from 

Canadian 

Surve

Household Spending “balance edit” natural 

experiment
Income Distributions in CE and 

150000170000 2007 After tax income 5th -95th percentiles 110000130000

7000090000

CEX

500001000030000

-10000

‐10000 10000 30000 50000 70000 90000 110000 130000 150000 

CPS
Balance Edit in the Canadia

Survey of Household Spendi

• Canadian household budget survey based on recall, condu

face-to-face interviews. Until 2006, field recordin

g usin

g 

pencil.

• Field methodology included a data quality control measur

Field methodology included

a data quality control measur

the “balance edit”; identified households where expenditumore than 20% different from income + asset changes. 

• Th i t i i t t d t t t ll t dditi l The 

i

n

terv

iewer was 

ins

truc

t

e

d

to 

t

r

y

to collec

t additiona

l

information from such households in order to balance expwith income and changes in assets within 15%.

• At the processing stage, household records that were stiill 

balance” (more than 20%) were deemed unusable. Becauswas conducted in the field, it was not possible to examine 

of the edit in detail, althou

gh Statistics Canada reported th

the adjustment was to income and asset changes.
Balance Edit in the Canadian Su

of Household Spending (Cont

• In 2006 Statistics Canada adopted CAPI for the househ

In 2006, Statistics Canada adopted CAPI for the househ

budget survey (the Survey of Household Spending. In thyear of CAPI, the balance edit was not applied.

• Without the field balance edit, the number of unbalance

(>20%) records increased from 546 in 2005 to 4,300 (29

completed questionnaires ) Statistics Canada decided it completed questionnaires.) Statistics Canada decided it 

not discard this many records so unbalanced records inc

• The balance edit was reintroduced 

( ) within CAPI

) in 200

it is possible to infer something about the effect of the bedit by comparing 2006 data with data from 2005 and 2followin

g,( ) 

 slides, 2006 

(no balance edit

) is the line with 

dots.)
Effect of the Balance Edit, Saving 

05 0

%

-50 -100

1 2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 1

income vingtile

2005 2006 2007
Effect of the Balance Edit, First 5 Vin

20000Income (equiv. $)

20000Expenditure (equiv. $)

0 20

Saving

15000

15000

-20 0

10000

10000

-60 -40

5000

1 2

3

4

5

5000

1 2

3

4

5

-80

1 2

2005 2006 2007
Implications of C/Y Measurement Er

• W d ’t k h i i ith i We 

don’t

know 

how sav

ing var

ies with

inco

analysis of changes over time/groups is susp

• We don’t know how tax burdens would cha

under a consumption tax; but patterns of C/Y 

in CE data is still used in distributional anal

• We can t’ evaluate alternatives to CPS incom

evaluate alternatives to CPS incom

when measuring economic well-being acros

groups and time groups and time
Trends in CE/NIPA Aggreg

• Att i anas

i

o

/C l ross

l

e

y anal i fi B

ly

s

is confirms B

and other studies; ratio of CE total spe

t NIPA t to NIPA aggre

g

a

te measure h f ll as 

fallen 

steadily

• Also shows that U.K. EFS survey 

ex

p , erienced same decline, and res

pons

rates have fallen over time as well
Ratio to National Accounts in US and 

100% CE and EFS Coverage Rates

90%70%80%

age

60%

Cover 

40%50%

1970 1975 1980 1985 1990 1995 2000 2005 2010
Ratio to National Accounts and Resp

Ratio to National Accounts and Resp

Rates in US and UK

100%

CE and EFS Response and Coverage Rates

90%70%80%

age

60%

Covera 

40%50%

1970 1975 1980 1985 1990 1995 2000 2005 2010
Possible Explanations for the Dec

• CE l i b i l t 

CE samp

le 

is 

becom

i

n

g

less represen

t

a

of higher-income households over tim

• Composition of spending is shifting to

harder to measure 

goods and services

• Measuring any given spending categor

surve

y setti h b tti

n

g

has 

become more diffi 

diffic

(payment methods changed; people les

willi t th ti ll) illi

n

g

to answer the question well)
E l i i th A t T 

Exp

l

a

i

n

ing the 

Aggrega

te 

Tren

• Probably some combination of the thre

Probably some combination of the thre

explanations, sorting it out should be a 

point for the CE redesign process point for the CE redesign process

• Mixed evidence (comparing to CPS) o

whether CE representativeness is wors

– Sample characteristics seem to match CP– Trend analysis on total incomes limited b

CE began imputing missing income post-
Figure 2. Ratio of Average Income in the Consumer Expenditur

Survey to Average Income in Current Population Survey by Qu

90 00

100.00

Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1

80 0090.0070.0080.00

PercentRatio

60.00 50.00 

Source: BLS Web 

Year
Figure 3. Ratio of Average Expenditure to Average Income in th

Consumer Expenditure Survey by Quintile

175.00 

Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1 (Rig

150.00 125.00 

PercentRatio

75 00

100.00

50 0075.00

Source: BLS Web Site

50.00 

Year
Figure 4. Ratio of Average Expenditure in Consumer Expenditur

Survey to Average Income in the Current Population Survey by I Q i il 125.00 Income 

Q

uintile 

Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1 (Right Axis)

100.00 

PercentRatio

75.00 

Source: BLS Web Site

50.00 

Year
Implications of Trends Implications of Trends

• Inco e pu o s e oo e g o

m

e imputati

o

n

s mak

e it look lik

e all 

g

ro

are saving more (C/Y is lower) post 2000

• Concerns about the joint distribution of C/Y 

Concerns about the joint distribution of C/Y 

not changed (in fact, existed in 1972-73 as w

–

C spe d g e at ve to C S co e suggests co 

E spe

n

din

g r

e

lat

ive to

C

P

S inco

m

e suggests co

always problematic, only trend is in quintile five

– Supports increasingly unrepresentative sample i

• Lack of resolution means analysis of (for 

example) trends in spending inequality are s
T d i I lit Tren

ds 

in 

Inequalit

y

Source: Attanasio, Battistin, Padula, 2009
Consumption Change in CE P

• Many uses for panel dimension of CE—qua

growth variability is interesting in its own ri

b t l f ti t i b

u

t a

lso response o

f consumption 

to var

ious 

of shocks (especially policy changes)

• One example is the efficacy of fiscal stimulu

increasing consumption in times of recessio

wh t li i t i d b th hen mone

tary policy 

is cons

tra

ine

d

by the 

lower bound)
To What Extent Do Tax Reba

Stimulate Consumption Dema

• Much recent information from CE in 

pp ( ) a

pers: Parker 

(1999

); S

(1999, 2002); Barrow, McGranahan (2000); Hsieh (2003); St(2003); Johnson, Parker, Souleles (2006); Johnson, Parker, So(2009); Parker, Souleles, Johnson, McClelland (2010)

• PSJM (2010): 

- look at change in CE three-month spending for a household 

receive Economic Stimulus Payment (ESP) receive Economic Stimulus Payment (ESP)- compare among households that received at randomly differ- identifies effect on consumer demand of receipt of ESP- CE shows, in three months of arrival of check:

31 percent of ESP spent on broad nondurables (SE: 11 91 

p f p ( y )( 

ercent of ESP spent 

(extra mostly new cars

)

(SE: 3
Estimated Impact of Econo

Estimated Impact of Econo

Stimulus Payment (ESP
How to St d Effect of Ta Reba

to Study Effect of Tax Reba

• JPS (2006/2009) and PSJM (2010) use special questions adde• In 2001, BLS added questions rapidly in response to legislatio

authorizing rebate payments to households

- generally within scope of CE mission in that measures incom

generally within scope of CE mission in that measures incom

- but really more generally of use to policymakers, social scie

• In 2008, BLS also added subjective questions about In 2008, BLS also added subjective questions about what spe

what spe

on -- big step for CE as serving broader mission and doing grgood

• Al i d h l d f l i dd i k Alternative data has also proved very useful in addressing key 

questions: credit card data and Homescan data- CE data analysis has large statistical uncertainty despite sam

d t ueo poor measurement f o t t l/b d total/broad-category spending