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Indicator for Top-Coding Effects on a 

Household Survey Income Elasticity 

of Demand Estimates

Daniel K. Yang

Office of Survey Methods Research

U.S. Bureau of Labor Statistics

FCSM Government Advances in Statistical 

Programming Workshop

October 24, 2018
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Overview

 Introduction: Consumer Expenditure Surveys 

(CE) and Statistical disclosure limitation (SDL)

 Data utility and economics model

 Impact on Income Elasticity of Demand and 

Proportion Ratio Indicator:

 Log linear regression model for expenditures

 Propensity model for consumption

 Conclusion

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Consumer Expenditure Survey

 Consumer Expenditure Survey (CE) Collects 

information on the buying habits of U.S. 

consumers.

 CE’s goal: Provides data on expenditures, 

income, and consumer unit (families and 

single consumers) characteristics.

 Balance: confidentiality vs. satisfactory data 

utility.

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CE SDL Process

 CE microdata release requires statistical 

disclosure limitation (SDL).

 Objective: Conceal personally identifiable 

information (PII) to preserve the 

confidentiality and anonymity of survey 

participants.

 Production Process: さtop-codingざ and 

numerical impact.

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Top-coding

5

Olivia Rivers (F, 25), 

Salary: $542,508

Silver Spring, Montgomery 

County, MD (pop 71,452) Note: This example is 

completely fabricated. 

For illustration purposes 

only.
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Top-coding Illustration

6

 -

 100

 200

 300

 400

 500

 600

0 2 4 6 8 10 12 14 16 18 20

Extreme values 

Critical value 

i.e. Olivia Rivers

Source: Balancing Respondent Confidentiality and Data User Needs, Aaron E. Cobet, BLS, 2014 CE Microdata User’s Workshop.
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Top-coding Illustration (cont.)

7

 -

 100

 200

 300

 400

 500

 600

0 2 4 6 8 10 12 14 16 18 20

Top-coded values 

e.g. Replace Olivia’s 

Salary with the topcoded value: $246,921

Source: Balancing Respondent Confidentiality and Data User Needs, Aaron E. Cobet, BLS, 2014 CE Microdata User’s Workshop.
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Data Utility Measures

 Analysis-specific:

Compare regression coefficients from confidential 

data vs. top-coded data for the same analysis. 

Confidence Interval Overlap (IO) or Ellipsoid Overlap 

(EO).

 Global:

Compare propensity score percentiles.

Compare clusters in cluster analysis.

Compare Empirical Cumulative Density Functions 

ふCDF’sぶ.

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Expenditure Data and Demographics

 CE Data: 2008 public released micro data and confidential 

data.

 Expenditure outcomes: Utilities, Domestic Services,

Transportation, Shelter, Medical Supplies, Major Appliances, 

Other Vehicle, and New Cars and Trucks

 Covariates (adopted from Omori 2010): household (HH) 

income, family type (ref.: married couple), geographical region 

(ref.: Northeast), numbers of children (age 0-5, 6-12 and 12-

18), reference person's: education attainment (ref.: Less than 

HS), Occupation (ref.: Other), ethnicity (ref.: White), age.

13

ref.: reference level, HS: high schoolRef.: 




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Propensity Scores: Domestic 

Services
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Propensity Scores: Medical 

Supplies
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Propensity Scores Curve: New Cars 

and Trucks
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Income Elasticity (1)

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Income Elasticity (2)

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Income Elasticity of Demand

Expenditures Ratio of Top-coded over 

Confidential

Utilities 1.012

Domestic Services 1.034

Transportation 1.009

Shelter 1.006

Medical Supplies 1.176

Major Appliances 1.268

Other Vehicle (i.e. 

Motorcycle and 

Airplanes) 2.056

Cars and Trucks, 

New 1.922

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Income Elasticity of Demand

Expenditures Ratio of Top-coded over 

Confidential

Proportion Ratio 

Indicator (PRI) of 

Top-coding

Utilities 1.012 0.0026

Domestic Services 1.034 0.6840

Transportation 1.009 0.0021

Shelter 1.006 0.0037

Medical Supplies 1.176 0.2420

Major Appliances 1.268 0.2313

Other Vehicle (i.e. 

Motorcycle and 

Airplanes) 2.056 0.3882

Cars and Trucks, 

New 1.922 0.4313
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Summary

 No difference in log linear model between 

confidential and top-coded data.

 Differences from some of the propensity 

models. This translates into some differences 

in income elasticity of demand for some 

expenditures.

 Proportion Ratio Indicator (PRI) appears to 

reflect those differences.
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Acknowledgements and Disclaimer

 We would like to acknowledge our fellow 

researchers and managers from the 

program office. 

 The views expressed in this paper are 

those of the author(s) and do not 

necessarily reflect the policies of the 

Bureau of Labor Statistics.

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THANK YOU!
Contact Information

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Daniel K. Yang

Research Mathematical Statistician

Office of Survey Methods Research (OSMR) 

www.bls.gov/osmr/home.htm 

yang.daniel@bls.gov

Disclaimer: Any opinions expressed in this paper are 

those of the author(s) and do not constitute policy of 

the Bureau of Labor Statistics.