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

2017 CE Survey

Microdata Users’ Workshop

Sampling Methods and

Derivation of Sampling Weights

Brian T. Nix

Division of Price Statistical Methods

Bureau of Labor Statistics

July 20, 2017
2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Overview

• History and Concepts

• Sample Selection

• Define PSUs

• Stratify and Select a Sample of PSUs

• Stratify and Select a Sample of Households

• Weighting the households (CUs)
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History of Sample Redesigns

• New sample of geographic areas and addresses selected 

every decade (2010)

• 1980 Census-Based Sample Design (1986–1995)

• 1990 Census-Based Sample Design (1996–2004)

• 2000 Census-Based Sample Design (2005–2014)

• 2010 Census-Based Sample Design (2015–2024?)
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Concepts

 Target Population:

U.S. non-institutional civilian population

 Consumer Unit

 person or a group of persons in a household related by 

blood, marriage, adoption, or other legal arrangements

 OR who are unrelated but pool their incomes to make 

joint expenditure decisions

 Same as households approximately 98% of time
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Concepts (continued)

 Old (pre-2010 census) Sampling Frame:

List of Households from which we draw our sample

 Unit Frame: Regular households (80%)

 Area Frame: Rural households (10%)

 Permit Frame: New construction (9%)

 Group Quarters: (<1%)

 Since 2015: Census Master Address File (MAF)

 (based on 2010 Census, with biannual updates from the 

United States Postal Service)

 Group Quarters (<1%)
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Sampling Process
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Sample Selection – Overview

• Geographic areas are randomly selected 

to represent the total U.S. 

• Households are randomly selected to 

represent the geographic areas

• Guiding principle:

“Randomness ensures representativeness.”
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PSU Definitions

• PSU: Primary Sampling Unit

 Counties are geographically grouped together to become units 

for sample selection

• CBSA: Core Based Statistical Areas (~old MSA)

 Counties are grouped together into geographic entities called 

core based statistical areas (CBSA’s) by Office of Management 

and Budget

 Metropolitan – one or more counties centered around urban 

area of > 50,000 people

 Micropolitan – one or more counties centered around urban area 

of 10,000 - 50,000 people

• Over 3,140 counties and county-equivalents in the U.S.

• Over 900 CBSAs defined by OMB
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Selection of PSUs

(2010-Census Design)

PSU 

class

Description CBSA/

Non-CBSA

Population

Total Examples

S SelfRepresentingMetropolitan

(urban)

More Than 

2,500,000

S11A

S49D

Boston MA

Seattle WA

N Non-SelfRepresentingMetro- or 

Micropolitan

(urban)

Less Than 

2,500,000 Topcoded

R Rural 

(also Not SelfRepresenting)

Non-CBSA 

(rural) Topcoded
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2010 Census-based Sample Selection

CPI – 75 PSUs; CE – 91 PSUs

PSU 

Size

Region/Division Total

Northeast Midwest South West

01 02 03 04 05 06 07 08 09

S 1 2 2 2 5 0 2 2 7 23

N 2 4 8 4 12 6 8 4 4 52

R 1 1 2 2 2 2 2 3 1 16

Total 4 7 12 8 19 8 12 9 12 91
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The Four Census Regions
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The Nine Census Divisions
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2010 Census-based Sample Selection

CPI – 75 PSUs; CE – 91 PSUs

PSU 

Size

Region/Division Total

Northeast Midwest South West

01 02 03 04 05 06 07 08 09

S 1 2 2 2 5 0 2 2 7 23

N 2 4 8 4 12 6 8 4 4 52

R 1 1 2 2 2 2 2 3 1 16

Total 4 7 12 8 19 8 12 9 12 91
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Hypothetical PSU Selection
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Hypothetical PSU Selection

LaGrange

Columbus

Valdosta

Moultrie

Augusta
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Hypothetical PSU Selection (continued)

CBSA 2010 

Population

Probability 

of Selection

Augusta, GA-SC 564,873 0.92208

Jessup, GA 30,099 0.04913

Fitzgerald, GA 17,634 0.02879

Total 612,606 1.00000

CBSA

2010 

Population

Probability 

of Selection

Columbus, GA-AL 294,865 0.47829

Valdosta, GA 139,588 0.22642

LaGrange, GA 67,044 0.10875

Moultrie, GA 45,498 0.07380

Douglas, GA 42,356 0.06870

Thomaston, GA 27,153 0.04404

Total 616,504 1.00000
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Hypothetical PSU Selection (continued)

CBSA 2010 

Population

Probability 

of Selection

 Augusta, GA-SC 564,873 0.92208

Jessup, GA 30,099 0.04913

Fitzgerald, GA 17,634 0.02879

Total 612,606 1.00000

CBSA

2010 

Population

Probability 

of Selection

Columbus, GA-AL 294,865 0.47829

Valdosta, GA 139,588 0.22642

 LaGrange, GA 67,044 0.10875

Moultrie, GA 45,498 0.07380

Douglas, GA 42,356 0.06870

Thomaston, GA 27,153 0.04404

Total 616,504 1.00000
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Number of Households

 Allocate Target Sample to PSUs

 Target size: ~7,000 interviewed households

• Based on Finite Budget

• For Diary Survey per year

• For Interview Survey per quarter

 6,600 to Households used jointly by CE and CPI

• for CPI cost-weight calculations

• 23 Self-Representing PSUs 

• 52 Non-Self-Representing PSUs

 400 to CE Households

• 16 Rural PSUs
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Number of Households

(continued)

 Target Sample Size

 7,000 interviewed households per year (Diary)

 7,000 interviewed households per quarter (Interview, 

interviews #2-5 only)

 Target Sample Yield

 14,000 weekly diaries per year (=7,000 x 2)

 28,000 quarterly interviews per year (=7,000 x 4)
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Number of Households

(continued)

 Local Target Sample Size

 Allocate 7,000 interviewed households to individual 

PSUs, proportional to each stratum’s population

 Minimizes CE’s nationwide variance
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Translate Addresses into Interviewed 

Households

 80% “eligibility” rate: (most of the missing 20% 

are unoccupied)

 70% response rate

 56% “participation” rate (0.56 = 0.80×0.70)
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Translate Interviewed Households into 

Addresses (continued)

PSU

Interviewed

households Addresses %

S11A Boston 169 322 52

S12A New York City 195 286 68

S12B Philadelphia 220 420 52

S35A Washington, DC 212 335 63

S35C Atlanta 182 291 63

etc. etc. etc.

Total 7,000 12,000
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Select a Random Sample of Households 

(Mechanics)

• Sort households from poor to rich based on 

information from Decennial Census and ACS:

• Number of people in household

• Tenure (owner, renter)

• Market value of home (owners)

• Monthly rent (renters)
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Select a Random Sample of Households 

(Continued)

• Compute the sampling interval for each PSU

• Sampling interval = (# addresses in sampling 

frame) ÷ (# addresses in CE sample)

• Typical sampling intervals:

• Every 1,000th address (N and R PSUs)

• Every 5,000th address (S PSUs)
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Select a Random Sample of Households 

(Continued)

• --- D --- I --- D --- I --- D --- I --- D --- I 

--- D --- I --- D --- I --- etc.

• D=Diary, I=Interview

• Each “D” and “I” has enough 

sample to cover the next 10 years
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Weighting Process
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Weighting Process

Base Weight Calculation: Real-World Example

(Self-Representing PSU)

 S49A (Los Angeles): population 12,828,837

 MAF counts 4,500,000 housing units

 470 addresses needed for each survey

 based on estimated response rates specific to Los Angeles

 proportional to 12,000 addresses needed for all PSUs

 “Take Every” = 4,500,000 / 470 ≈ 9,575

(every 4,788th address when considering both surveys)

 Stratum population also 12,828,837

(self-representing PSU)

 PSU Weight = 1 (for any self-representing PSU)

 Base Weight = “Take Every” ∗ PSU Weight

≈ 9,575 ∗ 1 ≈ 9,575
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Weighting Process

Base Weight Calculation: Hypothetical Example

(Non-Self-Representing PSU)

 PSU Population 538,200

 MAF counts 224,250 housing units

 115 addresses needed for each survey  based on estimated response rates specific to this PSU

 proportional to 12,000 addresses needed for all PSUs

 “Take Every” = 224,250 / 115 ≈ 1,950

(every 975th address when considering both surveys)

 Stratum population 2,800,000

 PSU Weight = 2,800,000 / 538,200 ≈ 5.2025

 Base Weight = “Take Every” ∗ PSU Weight

≈ 1,950 ∗ 5.2025 = 10,145
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Weighting Process (Continued)

 Base Weight (~10,000)

9,999 CUs + Self

 Weighting Control Factor (~1.00)

Apartment Building instead of a House

 Non-interview Adjustment Factor (~1.50)

Type A: Refusal to Participate

 Calibration Adjustment Factor (~1.15)

Adjusts sample estimate to CPS Totals
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Weighting Process (Continued)

• Final Weight

• Variable FINLWT21

• Base Weight * Weighting Control Factor *

Non-interview Adjustment Factor *

Calibration Adjustment Factor

• ~15,000 to 20,000
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Conclusion

Both Sample Design and Weighting 

Work Together to Produce:

 Best Estimates of U.S. Expenditures

 Subject to Allotted CE Budget
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Any Questions?
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Contact Information

Brian T. Nix

Mathematical Statistician

Statistical Methods Division

www.bls.gov/cex

202-691-6877

Nix.Brian@bls.gov