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

Model-Assisted State 

Expenditure Estimates

Clayton Knappenberger

Yezzi Angi Lee

U.S. Bureau of Labor Statistics

Division of Consumer Expenditure Surveys
2 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Outline

◼ Consumer Expenditure Surveys (CE)

◼ Project Goal

Existing Products

Provide Additional States

◼ Model-Assisted Method

Why use MAEs? 

Auxiliary Data Used

◼ Models Explored

Cross-validated Errors

◼ Results/Comparisons/Limitations
3 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Consumer Expenditure Surveys

◼ Two surveys providing data on expenditures, 

income, and demographics of US consumers

Quarterly Interview Weekly Diary

Large purchases Small purchases

Recurring payments Frequent spending

Three-month recall Contemporaneous

Rotating panel Rotating panel

Four waves Two waves
4 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Project Goal

◼ CE Sample is meant to represent the US noninstitutional civilian population

◼ Currently publish

4 Regions, 9 Divisions, 5 States, and 23 major 

urban areas

◼ Users frequently ask us for States

Can machine learning help us? 
5 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Existing State-level products

◼ CE currently provides estimates for 5 States 

Large and representative samples 

California

Texas

Florida

New Jersey

New York
6 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Provide Additional States!

◼ Feasibility study using Gradient Boosting 

Machines 

Ohio

Pennsylvania

North Carolina
7 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Model-Assisted Method

$ $

$ $

$ $

$

$

$

$

◼ Using a model to combine sample data with 

auxiliary data from areas not sampled
8 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Model-Assisted Method

$ $ $

$ $

$ $

$

$

$

◼ Model predicts expenditures for each area in 

the auxiliary data giving us total coverage
9 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Model-Assisted Method

1. Predicted Expenditures (m) 

2. Number of HH (N) in the tract (i)

3. Reported Expenditures (y) 

4. Selection probability (π)

5. Survey correction 

1 2

4

3

$

5
10 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Why Use MAEs?

◼ Best of both worlds!

Unbiased estimate (if either term is unbiased)

More precise than just the survey estimate

◼ Doesn’t depend too much on mෝ

Breidt and Opsomer (2017) show a range of 

Machine Learning models can work for this
11 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Auxiliary Data Used

◼ Consumer 

Expenditures Survey

◼ Census Planning Database

Merge on tract

◼ Quarterly Census of 

Employment and Wages

Merge on county
12 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Auxiliary Data Continued

Dataset N. Obs. N. Vars. Unit of Observation

CEQ 2017 29,872 N.A. Consumer Unit

CEQ 2018 28,244 N.A. Consumer Unit

CEQ 2019 26,462 N.A. Consumer Unit

CEQ 2020 25,087 N.A. Consumer Unit

PDB 2019 72,893 124 Census Tract

PDB 2020 72,893 124 Census Tract

PDB 2021 72,893 124 Census Tract

QCEW 2017 3,190 44 U.S. County

QCEW 2018 3,191 44 U.S. County

QCEW 2019 3,191 44 U.S. County

QCEW 2020 3,192 44 U.S. County
13 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Models Explored

◼ Models

Gradient Boosting Machines 

Lasso

K-Nearest Neighbors

◼ Evaluation metrics

Cross-validation RMSE

Comparison to existing estimates
14 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Cross-Validation Errors

2017 5-fold Cross-Validation RMSE

Model Total Food Housing Transport Health Entertain

GBM $13,251.65 $1,182.30 $3,587.82 $5,590.90 $1,346.13 $2,075.71 

Lasso $14,004.36 $1,320.54 $3,957.22 $5,580.79 $1,445.78 $2,068.02 

KNN $13,551.45 $1,219.20 $3,661.10 $6,307.92 $1,396.20 $2,380.56 

2018

Model Total Food Housing Transport Health Entertain

GBM $11,299.43 $1,263.33 $3,585.88 $5,679.56 $1,358.37 $2,580.32

Lasso $12,479.09 $1,446.52 $3,972.41 $5,661.81 $1,469.81 $2,574.83

KNN $11,639.90 $1,297.21 $3,693.67 $6,458.11 $1,414.44 $2,904.76

2019

Model Total Food Housing Transport Health Entertain

GBM $11,435.33 $1,337.12 $3,675.72 $5,777.95 $1,510.45 $1,789.47

Lasso $12,433.45 $1,502.79 $3,928.52 $5,761.16 $1,615.87 $1,795.59

KNN $11,860.80 $1,380.31 $3,837.14 $6,569.00 $1,569.00 $1,992.28
15 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Results
16 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

State Weights Comparison

Total Food Housing Transport Health Entertain

California

2017 107.62% 104.64% 107.75% 113.14% 110.17% 110.17%

2018 104.88% 101.37% 107.19% 105.78% 103.94% 114.34%

2019 107.85% 103.83% 111.99% 112.95% 104.68% 113.00%

Florida

2017 107.62% 100.54% 107.29% 116.21% 106.49% 143.19%

2018 105.50% 100.52% 107.57% 116.70% 111.17% 103.90%

2019 101.66% 98.70% 103.12% 115.58% 105.75% 98.85%

New Jersey

2017 90.29% 93.52% 91.38% 87.95% 92.40% 89.57%

2018 93.57% 95.06% 93.38% 104.06% 101.43% 106.38%

2019 97.31% 100.16% 96.54% 101.20% 97.91% 102.36%

New York

2017 111.40% 101.23% 103.51% 115.79% 99.98% 113.55%

2018 97.81% 96.33% 98.59% 108.40% 94.23% 95.10%

2019 98.89% 102.11% 100.06% 103.91% 103.33% 94.65%

Texas

2017 103.48% 100.94% 104.16% 105.21% 106.34% 99.99%

2018 99.76% 100.80% 99.81% 102.52% 99.79% 100.85%

2019 99.05% 101.93% 101.78% 98.43% 97.91% 108.19%
17 — U.S. B UREAU OF LABOR STATISTICS TATISTICS • bls.gov bls.gov

Limitations

◼ Models aren’t very accurate (high RMSE)

◼ High year-to-year volatility (weird results)

◼ Lack of auxiliary data

◼ We didn’t calculate variances
Contact Information

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

U.S. Bureau of Labor Statistics

Division of Consumer Expenditure Surveys

www.bls.gov/cex