RESEARCH POSTER PRESENTATION DESIGN © 2012

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• Data on household (consumer unit) characteristics, expenditures, 

income, and taxes. See http://www.bls.gov/cex

• Two independent surveys: Quarterly Interview and Diary

• Quarterly Interview is a multi-wave recall survey 

covering mainly large and/or recurring expenditures 

(This is the CE data source for this study)

• Diary covers smaller, day-to-day expenditures over two 

weekly periods

• Paradata on contact attempts and other interview characteristics

• Internal geographic data exists for each household at the PSU, 

state, county, and tract levels

• 9,778 Households

Background Merging the PDB and CE

Higher response rates only reduce nonresponse bias if the added 

respondents reduce the contrast between respondents and nonrespondents. Information which is auxiliary to the survey and available 

on the complete sample can be used to help measure this contrast 

with Representivity (R) Indicators. 

As part of ongoing efforts to understanding nonresponse bias in the 

Consumer Expenditure Surveys (CE), we explored a potential source of 

auxiliary information that could be used to construct R Indicators – the 

Census Planning Database (PDB).

CE already has paradata on contact attempts and interview methods. 

We hope that using the PDB as an auxiliary data source will enable use 

to capture “neighborhood” effects in unit nonresponse.

Goal: To learn how to use the PDB with CE and to determine which 

variables in the PDB might be most useful in constructing R 

Indicators for the CE. 

The Consumer Expenditure Surveys (CE)

Initial list of PDB variables associated with CE Unit response status by 

variable selection method

Chart 2: Truncated Classification Tree Results Initial List of PDB Variables

Contact

Clayton Knappenberger, Economist, Branch of Production and Control, 

Consumer Expenditure Surveys, U.S. Bureau of Labor Statistics

Email: Knappenberger.clayton@bls.gov 

Telephone: 202-691-6236

The key used in this merge is an 11 digit track number made by 

concatenating STATE, COUNTY, and TRACT codes.

The 2015 PDB contains 5 year ACS estimates covering 2009 – 2013, so 

the appropriate CE equivalent are the 2013 files. However, the 2015 

PDB uses 2010 Census tract numbers while the 2013 CE files continue 

to use 2000 tract numbers. 

The 2010 Census Tract Relationship files are necessary for 

“translating” 2000 tract numbers in CE into 2010 tract numbers that 

exist in the PDB. For a summary of the steps taken see Chart 1.

The first selection step was to extract only the variables which began 

with “Pct”, “Med”, or “Avg” – we also extracted the Census Bureau 

constructed Low_Response_Score variable. Variables that were not 

considered include margin of error variables and geography variables. 

Our selection process made use of three main levels of analysis and 

four different methods. 

1. Univariate Method

A. Near-zero Variance: variables which have very small 

variances are unlikely to be useful in discriminating 

between groups. We identified variables that had 

greater than zero variance using the nearZeroVar

function in the R package caret. 

2. Bivariate Methods

A. The Pearson correlation with Unit Response was 

calculated for each variable in the PDB and those that 

were significant at the 5% level were identified.

B. Group means were computed by Unit Response for each 

PDB variable. A t-statistic was then calculated 

comparing the group means for each variable and 

variables for which the t-statistic was significant at the 

5% level were identified.

3. Multivariate Method

A. Using the DecisionTreeClassifier in Python’s Scikit-learn 

package, we fitted a classification tree for Unit 

Response of depth 5 on the 4,387 complete cases. 

Those variables that were selected by the decision tree 

were identified.

Because the decision tree was the only multivariate method used in this 

analysis, we used it as the basis for our initial list of PDB variables – see 

Chart 2. 

Variable Selection Methods

Clayton Knappenberger, Arcenis Rojas, Lucilla Tan

U.S. Bureau of Labor Statistics*

Hunting for Auxiliary Variables in the Census Planning Database Tract File

The Census Planning Database (PDB)

• Contains housing, demographic, socioeconomic, and census 

operational data from census and American Community Survey 

(ACS) databases to be used for survey and census planning. See 

http://www.census.gov/research/data/planning_database

• Variable names and descriptions can be found at 

https://www.census.gov/research/data/planning_database/2015/d

ocs/PDB_Tract_2015-07-28a.pdf

• Data exists at the census block group and tract levels

• 74,021 Tract level records

Census Relationship Files

• The Over Time (Comparability) files show relationships for the 

same type of geography over different periods of time. See 

http://www.census.gov/geo/maps-data/data/relationship.html

• Contains mapping files that link the 2000 Census geographies to the 

2010 Census geographies.

• Relationships can be many to many

Things to Watch For

1. Almost half of the households in the 2013 CE were assigned 2000 

tract numbers that mapped to more than one 2010 census tract. 

Those households had to be dropped from our study because they 

could not be reliably assigned PDB values. 

2. Tracts that are in the Relationship file but not in the 2015 PDB. 

Census occasionally changes tract numbers in between decennial 

censuses – the updated tract numbers get used in the PDB, but it 

seems that the Relationship file is not always updated to reflect 

the most recent geographies. Hence there were six tracts that 

appeared in CE that could not be initially mapped to 2010 tract 

numbers that were excluded from our study. 

3. Formatting issues in PDB and Relationship files:

A. Leading zeroes in the PDB and Relationship files. 

B. Dollar signs and commas in specific PDB variables.

C. Descriptive statistics – variable “Moved from a 

different household 1 year ago (%)” has max of 118,050

Number of 

2010 Tracts

Frequency Percent

Single Tract 4,547 53.17

Multiple Tracts 4,005 46.83

Mobile Homes 

(%) <= 2.574

N = 4,387

Vacant Homes 

(%) <= 7.235

N = 3,119

College Degree 

(%) <= 13.539

N = 1,268

Moved from a 

different 

household 1 year 

ago (%) <= 19.923

N = 329

Speak “Other Slavic 

Language” at Home 

(%) <= 0.089

N = 408

Employment (%) 

<= 96.371

N = 402

Population 

between 5 and 

17 (%) <= 12.690

N = 6

Speak Polish at 

Home (%) <= 0.710

N = 851

Householder moved 

into the current unit in 

the year 2010 or later 

(%) <= 0.198

Household language is 

Spanish and no one 

speaks English “very 

well” (%) <= 17.244

N = 2,790

Not US Citizen 

(%) <= 25.1428

N = 860

Living in Noninstitutional 

group quarters 

(%) <= 0.105

N = 2,649

Population that is 5 

years old or older 

(%) <= 87.003

N = 402

Moved from a 

different 

household 1 year 

ago (%) <= 3.524

N = 253

Housing units in a 

structure containing 2 to 

9 housing units (%) 

<=16.645

N = 76

For each split in a node, the black arrow 

points to the child for whom the parent’s 

threshold was met, while the red arrow 

points to the child for whom the threshold 

was not met.

Special Note: The variable 

pct_Diff_HU_1_yr_Ago_ACS_09_13 

appears twice in our classification tree –

as mentioned earlier this variable has a 

max of 118,050 %. When we emailed the 

PDB team at Census to ask them about 

this in January, they confirmed that it was 

an error and said that they would post a 

user note on the PDB website.

Programming and Package Notes

Python’s Scikit-learn package uses an optimized version of the CART 

algorithm for classification trees and offers users the option to use either 

the Gini impurity or the information gain for measuring a split’s quality. 

Our analysis uses the Gini impurity. One downside is that Scikit-learn does 

not provide a native Python means of viewing the tree and requires the 

user to install the Graphviz tool.

R’s rpart package provides a similar level of sophistication to what is 

available in Scikit-learn, and allows the user to view the tree natively in 

R’s plotting functions. 

SAS Enterprise Miner provides a means of performing classification tree 

analysis, but this capability does not appear to be available in SAS outside 

of Enterprise Miner. 

T

F

Predicting Unit Response

PDB variables 

(sorted alphabetically )

Variable Selection Method

Greater than 

zero-variance

Pearson 

Correlation

Group mean TTestClassification 

tree

avg_Tot_Prns_in_HHD_ACS_09_13

The average number of persons per ACS occupied housing unit.

 

pct_Age5p_OthSlavic_ACS_09_13

The percentage of the ACS population ages 5 years and over who 

speak English less than "very well" and speak some other Slavic 

language at home. Examples include Czech, Slovak, and Ukranian

  

pct_Age5p_Polish_ACS_09_13

The percentage of the ACS population ages 5 years and over who 

speak English less than "very well" and speak Polish at home.



pct_Civ_emp_16p_ACS_09_13

The percentage of ACS civilians ages 16 years and over in the labor 

force that are employed

 

pct_Civ_emp_65p_ACS_09_13

The percentage of ACS civilians ages 65 and over in the labor force 

that are employed

 

pct_College_ACS_09_13

The percentage of the ACS population aged 25 years and over that 

have a college degree or higher



pct_Diff_HU_1yr_Ago_ACS_09_13

The percentage of the ACS population aged 1 year and over that 

moved from another residence in the U.S. or Puerto Rico within the 

last year



pct_FRST_FRMS_CEN_2010

The percentage of all addresses in a 2010 Census mailback area for 

which the first Mailout/Mailback form mailed was completed and 

returned

 

pct_Females_ACS_09_13

The percentage of the ACS population that is female

 

pct_Females_CEN_2010

The percentage of the 2010 Census population that is female

 

pct_HHD_Moved_in_ACS_09_13

The percentage of all ACS occupied housing units where the 

householder moved into the current unit in the year 2010 or later

   

pct_MLT_U2_9_STRC_ACS_09_13

The percentage of all ACS housing units that are in a structure that 

contains two to nine housing units

   

pct_Mobile_Homes_ACS_09_13

The percentage of all ACS housing units that are considered mobile 

homes

   

pct_NH_Asian_alone_CEN_2010

The percentage of the 2010 Census population that indicate no 

Hispanic origin and their only race as "Asian Indian", "Chinese", 

"Filipino", "Korean", "Japanese", "Vietnamese", or "Other Asian"

 

pct_NON_US_Cit_ACS_09_13

The percentage of the ACS population who are not citizens of the 

United States

 

pct_Non_Inst_GQ_CEN_2010

The percentage of the 2010 Census population who live in group 

quarters and are primarily eligible, able, or likely to participate in 

labor force while residents.



pct_Pop_5yrs_Over_ACS_09_13

The percentage of the ACS population who are ages 5 years and 

over at time of interview

 

pct_Renter_Occp_HU_ACS_09_13

The percentage of ACS occupied housing units that are not owner 

occupied, whether they are rented or occupied without payment of 

rent

 

pct_Tot_Occp_Units_CEN_2010

The percentage of all 2010 Census housing units that are classified 

as the usual place of residence of the individual or group living in it

   

pct_Vacant_CEN_2010

The percentage of all 2010 Census housing units where no one is 

living regularly at the time of interview; units occupied at the time 

of interview entirely by persons who are staying two months or less 

and who have a more permanent residence elsewhere are classified 

as vacant



Note: 

* Significantly different from 0 at less than 5 percent level

* DISCLAIMER: The opinions expressed in this paper are those of the authors and 

do not represent official policy of the Bureau of Labor Statistics.