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

2018 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 19, 2018
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
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History of Sample Redesigns

• New sample of geographic areas selected every decade

• 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?)

• 2020 Census-Based Sample Design (2025–2034???)
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Concepts

 Target Population:

U.S. non-institutional civilian population

 Sampling Frame

 List of households from which we draw our sample

 Based on 2010 Census (“Master Address File”)

 Biannual updates from U.S. Postal Service

(twice a year)
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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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Selection of PSUs

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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The Four Census Regions
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The Nine Census Divisions
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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 (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 Addresses

 Local Target Sample Size

 Allocate 12,000 addresses to individual PSUs, 

proportional to each stratum’s population

 Minimizes CE’s nationwide variance
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Number of Addresses 

(continued)

Given the values of ppii and rrii for every index area i,

find the values of nnii that

Minimize �

ii=1

91

nniirrii

NNNN − ppiipp

2

Subject 

to: �

ii=1

91

nnii = 12,000

nnii ≥ 0, for ii = 1 tttt 91
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Translate Addresses into Interviewed 

Households

 83% “eligibility” rate: (most of the missing 17% 

are unoccupied)

 60% response rate

 50% “participation” rate (0.50 ≈ 0.83 × 0.60)
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Number of Households from 

Number of Addresses

PSU Population Addresses

Interviewed

Households

S11A Boston 4.6 million 193 80

S12A New York City 19.6 million 770 389

S12B Philadelphia 6.0 million 191 109

S35A Washington, DC 5.6 million 188 103

S35C Atlanta 5.3 million 170 95

etc. etc. etc.

Total 308 million 12,000 6,350
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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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Weighting Process
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Weighting Process

 Base Weight (~10,000)

Household + 9,999 others

 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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Base Weight

 PSU Population 538,200

 MAF counts 224,250 housing units

 115 addresses allocated for each survey

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

 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

A Hypothetical Example:

(Non-Self-Representing PSU)
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Final Weight

• Variable FINLWT21

• = Base Weight 

x Weighting Control Factor

x Non-Interview Adjustment Factor 

x 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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Contact Information

Brian T. Nix

Mathematical Statistician

Statistical Methods Division

www.bls.gov/cex

202-691-6877

Nix.Brian@bls.gov