1 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Distribution of U.S. Personal Consumption Expenditures 

Using Consumer Expenditure Survey Data 

Preliminary Results for 2019

Thesia I. Garner, Bob Martin, Brett Matsumoto, and Scott Curtin

OECD Expert Group on Disparities within a National Accounts Framework 

15 – 16 June 2022 

14.45 – 15:45 Session 3. Exercise, Country Experiences 

27 June 2022 version
2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Overview of Research

◼ Problem: NIPA Personal Consumption Expenditures (PCE) available only for total population (U.S. 

“residents” and non-profit institutions serving households); no household characteristics available

◼ Potential solution: prototype created to assign 2019 PCE aggregates (excluding NPISHs) to 

consumer units represented by the Consumer Expenditure Survey (CE) such that distributional 

analysis possible

◼ Potential drawbacks

 Assumptions made regarding scaling up unlikely to reflect reality

 Assumptions regarding imputations (focus today on those for health care expenditures)

 Assumption regarding spending for whom (population represented by people living in U.S. households 

versus U.S. “residents” versus U.S. households)

 PCE, as currently defined, does not equal “final consumption” as defined in 2013 OECD Framework 

◼ Work being done in coordination with BEA 

 Focus thus far is only PCE with no reference to income

 Prototype, as currently designed, not comparable to work of other countries

◼ Results by percentiles and for select demographic groups
3 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Consumer/Consumption Expenditures

◼ Value of goods and services purchased by and 

on behalf of U.S. “residents” (persons in 

households) and by non-profit institutions 

serving households (NPISHs)

◼ Classification by type of product 

 Durable goods (4 subcategories)

 Non-durable goods (4 subcategories)

 Services (7 subcategories)

◼ https://apps.bea.gov/iTable/index_nipa.cfm (see 

Table 2.3.5)

◼ https://www.bea.gov/resources/methodologies/nip

a-handbook/pdf/chapter-05.pdf 

◼ Out-of-pocket spending by civilian noninstitutionalized population

◼ Categorized by BLS expenditure use/purpose 

(not based on COICOP), e.g.,

 Food

 Housing

 Apparel

 Education

 Health

 Transportation

◼ https://stats.bls.gov/cex/

◼ https://stats.bls.gov/cex/cecomparison/pce_profile

.htm

Personal Consumption Expenditures (PCE) Consumer Expenditure Survey

Reference for comparison: Passero, William, Thesia I. Garner, and Clinton McCully. 2014. "Understanding the Relationship: CE Survey and PCE." In 

Improving the Measurement of Consumer Expenditures, 181-203. University of Chicago Press.
4 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

U.S. Consumer Expenditure Survey Design

◼ Current design in effect since 1979 (first data 1980)

◼ Designed to collect large expenditure items and those less 

frequently purchased

◼ Personal interview

◼ Consumer units (CUs) staggered entrance to survey

 New CUs enter the sample every month

 While others continue and participate in up to 4 consecutive 3-month 

periods (or interviews)

 Reference periods can overlap calendar year

◼ Quarterly (3-month recall) expenditures

◼ Each quarterly record of expenditures when weighted 

represents those for U.S. non-institutionalized population 

in a calendar year

◼ Sample size of completed quarterly interviews ~ 5,000 

(20,000 for the year)

◼ With global questions, represents ~ 95% of total spending

◼ Current design in effect since late 1979 (first data 1980)

◼ Designed to collect smaller expenditure items and those 

frequently purchased

◼ Diary recordkeeping

◼ CUs staggered entrance to survey

 New CUs enter sample every week 

 Participate up to 2 consecutive weeks

 Reference periods can overlap calendar years

◼ Weekly expenditures

◼ Each weekly record of expenditures when weighted 

represents those for U.S. non-institutionalized population 

in a calendar year

◼ Average sample size of completed weekly diaries is 221 

(11,500 for the year)

Interview Diary

CE Survey is sponsored by the BLS with data collected under contract by the U.S. Census Bureau.
5 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Method to Produce PCE-defined Consumption Expenditures

◼ Start with CE

 Use Interview as base (represents ~ 95% of total expenditures)

 Apply statistical matching to impute the remaining 5% from the Diary as only source (e.g., non-prescription 

drugs) or better measured (e.g., apparel)

 To represent 2019 annual estimates, use Interviews with reference period November 2018 – February 2020

 Sample of CUs with 2 to 4 quarterly reports which are annualized with weights calibrated to reflect U.S. 2019 

population

◼ Organize CE to match PCE category definitions with adjustments

 Omit household-to-household sales of vehicles

 Allocate CE defined health care expenditures to PCE categories 

◼ Augment CE health expenditures with administrative data 

 Since CE only includes out-of-pocket spending for health insurance and health care goods and services 

 PCE also includes expenditures by employers and the government on behalf of consumers

 Without imputations, assume missing health care expenditures distributed identically to the non-missing values

◼ Scale up CE to PCE major product aggregates using proportional allocation for remaining gap (after health 

care expenditure allocations and imputations)
6 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Method to Produce Distributions of PCE

◼ Focus on aggregate PCE and product type (durables, non-durables, services) - exclude expenditures by 

NPISHs

◼ Consumer Expenditure Survey values, with allocations and imputations, based on PCE definitions

◼ Preliminary annual estimates for 2019

◼ Divide CU expenditures by cu size to derive equivalized PCE

◼ Rank CUs by equivalized PCE (population weighted, as opposed to household weighted)

◼ PCE distributed implicitly across the population (by weighting by people living in CUs)

◼ Ranking by equivalized PCE at the CU observation unit level ensures that all members of the CU are in the 

same percentile group

◼ Shares and rank percentiles based on equivalized values (population weighed) unless otherwise noted

NOTE: Differs from other NIPA consumption expenditure-based analyses in that we rank by equivalized PCE, not equivalized NIPA income

(per BEA preference) & ranking is of people not households (following methods in economics literature on inequality) and fact that 

PCE measures goods and services purchased by “persons” (see BEA NIPA Chapter 5).
7 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Structure of U.S. Health Care System – Need for Imputations 

Health Care Provision

Public

Medicaid

Other

Medicare

Private

Group (i.e., 

employer)

Non-group

Private (not 

subsidized)

Private 

(subsidized –

ACA)
8 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Imputations Assigned to Consumer Units with Different 

Health Insurance Types by Decile – 1

Preliminary Estimates 2019

NOTE: ranking of eq. PCE expenditures after scaling up 

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90-100%

Share of Consumer Units

Decile of Equivalized Expenditure

Medicare Medicaid Employer
9 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Imputations Assigned to Consumer Units with Different 

Health Insurance Types by Decile – 2

Preliminary Estimates 2019

NOTE: ranking of eq. PCE expenditures after scaling up 

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90-100%

Share of Consumer Units

Decile of Equivalized Expenditure

Indiv. Subsidized CHIP IHS CHAMP Other Mil.
10 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Impact of Imputations: Shares of CE Spending (before 

Scaling-up) on PCE-defined Health Care Services by Decile 

Preliminary Estimates 2019

NOTE: ranking of eq. PCE expenditures after scaling up 

90.6% 88.5% 85.3% 81.9% 79.1% 75.6% 74.8% 72.1% 67.5%

58.5%

9.4% 11.5% 14.7% 18.1% 20.9% 24.4% 25.2% 27.9% 32.5%

41.5%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90-100%

Share of CE Health Care Services Spending

Decile of Equivalized Expenditure

Out-of-pocket

Imputed
11 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Allocation of CE Expenditures to PCE Categories

CE Health Care Expenditures “Augmented”

PCE Services

PCE Net Health 

Insurance

PCE Health 

Care 

PCE Nondurables
12 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Impact of Adjustments: Imputed Share of CE Spending 

(before scaling-up) by PCE Health Categories

NOTE: Adjustments=imputations + allocations
13 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Most Health Care Spending Not in CE but Is Added at the 

Consumer Level Preliminary Estimates 2019

CE to PCE Ratios

PCE Category

Diary imputed to 

Interview before 

adjustments (all 

out-of-pocket)

+ Adjustment to split CE 

OOP health insurance 

premium into PCE 

categories

+ With imputations 

for value of benefits 

(before scaling up)

Pharmaceuticals and 

other medical goods 0.16 0.25 0.65

Health care services 0.05 0.20 0.80

Health insurance 

premium (net for split) 1.85 0.19 0.79

NOTE: ranking of eq. PCE expenditures after scaling up 
14 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

CE/PCE Ratios After All Adjustments to CU Expenditures 

but before “Scaling up” to PCE Totals

Preliminary Estimates 2019

CE/PCE before scaling up CE/PCE published CE/PCE published 

“comparable”

PCE less final Cons. Exp. of NPISHs 0.70 0.60 0.73

Durable goods 0.55 0.59 0.65

Nondurable goods 0.63 0.52 0.53

Services 0.75 0.63 0.87

Source for published CE/PCE: https://stats.bls.gov/cex/cecomparison/pce_profile.htm

NOTE: Adjustments=imputations + allocations
15 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

0-20%, 9.2%

20-40%, 13.6%

40-60%, 17.4%

60-80%, 22.4%

80-99%, 33.1%

Top 1%

4%

Shares of “Equivalized PCE” by Distribution of Population 

Preliminary Estimates 2019

For example...

33% of Equivalized PCE accounted for by 

the population within 80-99th

equivalized CE-PCE expenditure 

percentile range

NOTE: Shares and rank percentiles based on CExp 

scaled up to PCE, referred to a “Equivalized PCE”
16 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Composition of PCE by Quintile

Preliminary Estimates 2019 

For example...

23.5% of PCE

accounted for by 

households in the 

bottom quintile, is 

for Health Care

4.2% 5.6% 7.7% 10.8% 15.6%

28.9% 26.0% 24.0% 21.7% 16.1%

19.8% 18.3% 18.6% 18.9% 19.3%

23.5% 23.9% 20.5% 17.6%

12.3%

23.6% 26.1% 29.2% 31.0%

36.7%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0-20% 20-40% 40-60% 60-80% 80-100%

Distribution of Eq. CE-PCE within Decile

Quintile of Population based on Rank of Eq. PCE

Other Services

Health Care Services

Housing (including Utilities) Services

Non-Durable Goods

Durable Goods

NOTE: ranking of eq. PCE expenditures after scaling up 
17 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Equivalized PCE: Aggregate Inequality Indices & Lorenz

Curve 

Preliminary Estimates 2019

Aggregate Inequality Indices

Gini 0.28

Thiel 0.14

Mean log dev. 0.13

90/10 ratio 3.40

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Cum .Share of Equivalized Expenditure

Decile of Population Based on Rank of Equivalized CE-PCE

Perfect equality

Total
18 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Equivalized PCE: Concentration Curves by PCE Product 

Preliminary Estimates 2019

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Cum .Share of Equivalized Expenditure

Decile of Population Based on Rank of Equivalized CE-PCE

Perfect equality

Health Services

Nondurables

Housing (including Utilities)

Services

Other Services

Durables
19 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Example of Impact: Health Care Services Share of Eq. PCE 

before Adjustments and after Scaling-up by Decile

Preliminary Estimates 2019

NOTE: ranking of eq. PCE expenditures after scaling up 

4.4%

7.1% 8.4% 9.5% 10.1% 10.3% 11.0% 11.6% 12.7% 14.9%

0.1% 0.4% 1.2% 1.7% 2.8% 3.7% 6.0%

9.5%

15.2%

59.4%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

1 2 3 4 5 6 7 8 9 10

Share of Eq. PCE

Decile of Equivalized Expenditures

Health care Before allocation and imputations
20 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Equivalized PCE: Concentration Curves for Health Care

Preliminary Estimates 2019

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Cum .Share of Equivalized Expenditure

Decile of Population Based on Rank of Equivalized CE-PCE

Perfect equality

Health Services

Health Services (prior to

imputations)
21 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Quintile Membership by Demographic Characteristics of 

Consumer Unit (CU) 

Preliminary Estimates 2019

44

37

0

10

20

30

40

50

60

70

80

90

100

Single Married, no

others

Single Parent Married w/

others

Other

Percent of Populaton within Group

By Consumer Unit Type

80-100%

60-80%

40-60%

20-40%

0-20%

37

57

28 25

0

10

20

30

40

50

60

70

80

90

100

Own. w/ Mort. Own. No Mort. Renter Other

Percent of Populatoni within Group

By Housing Tenure

80-100%

60-80%

40-60%

20-40%

0-20%

“Other”: own but do not know if have mortgage; rent without payment of rent; student housing
22 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Quintile Membership by Demographic Characteristics of 

Reference Person

Preliminary Estimates 2019

36 36

26 22

0

10

20

30

40

50

60

70

80

90

100

White, non-Hisp. Black, non-Hisp. Hisp., any Other

Percent of Population within Group

By Race

80-100%

60-80%

40-60%

20-40%

0-20%

47

48

0

10

20

30

40

50

60

70

80

90

100

< H.S. H.S. Some Coll. Assoc. Bach. >= MS

Percent of Population withing Group

By Education

80-100%

60-80%

40-60%

20-40%

0-20%
23 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Summary

◼ Distributional PCE accounts and inequality

 Refinements to CE allocations and adjustments

– Imputations for health care expenditures based on CU characteristics, e.g., disability 

status, number of children

– Add pareto adjustment to upper tail of the distribution 

• Should the upper tail of consumption expenditure distribution reflect upper tail of income distribution?

• Should the adjustment be based on something else?

 Update to more recent time periods

 Add rankings by equivalized income and for households

◼ Forthcoming BLS Working Paper

◼ Progress posted on https://www.bls.gov/cex/consumption-home.htm
24 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Contact

Thesia I. Garner

Chief Researcher, Division of Price and Index Number Research (DPINR)

Bureau of Labor Statistics

Washington, DC 20212

Garner.Thesia@BLS.gov