1

Geoffrey D. Paulin

paulin.geoffrey@bls.gov

Geoffrey D. Paulin is a senior economist in the 

Division of Consumer Expenditure Surveys, U.S. 

Bureau of Labor Statistics.

Parvati Krishnamurty

krishnamurty.parvati@bls.gov

Parvati Krishnamurty is a senior economist in the 

Division of Consumer Expenditure Surveys, U.S. 

Bureau of Labor Statistics.

Consumer Expenditure Survey Methods 

Symposium and Microdata Users’ Workshop, July 

21–24, 2020

The Consumer Expenditure Surveys (CE) program collects 

expenditure, demographic, and income data from families 

and households. The CE program held its annual Survey 

Methods Symposium and Microdata Users’ Workshop from 

July 21 to 24, 2020, to address CE-related topics in survey 

methods research, to provide free training in the structure 

and uses of the CE microdata, and to explore possibilities 

for collaboration. Economists from the CE program, staff 

from other U.S. Bureau of Labor Statistics offices, and 

research experts in a variety of fields—including academia, 

government, and private industry—gathered virtually to 

explore better ways to collect CE data and to learn how to 

use the microdata once they are produced. The experience 

was unique for presenters and attendees alike in that this 

was the first time either event was held online, in whole or 

in part.

The Consumer Expenditure Surveys (CE) are the most 

detailed source of data on expenditures, demographics, 

and income that the federal government collects directly 

from families and households (or, more precisely, 

“consumer units”).1 In addition to publishing standard 

expenditure tables twice a year, the U.S. Bureau of Labor 

Statistics (BLS) CE program releases annual microdata on 

the CE website from its two component surveys (the 

Quarterly Interview Survey and the Diary Survey).2 

Researchers use these data in a variety of fields, including academia, government, and various private industry 

areas, such as market research.

In July 2006, the CE program office conducted the first in a series of annual workshops in order to achieve three 

goals: (1) to help users better understand the structure of the CE microdata; (2) to provide training in the uses of 

the surveys; and (3) to promote awareness, through presentations by current users and interactive forums, of the 

different ways in which the data are used and thus provide opportunities to explore collaboration. In 2009, the 

June 2021
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workshop expanded from 2 days to 3 days to include presentations from data users not affiliated with BLS. This 

expansion allowed users to showcase their experiences with the public use microdata (PUMD) files (https:// 

www.bls.gov/cex/pumd.htm), to discuss problems and successes using the data, and to seek comment and 

guidance from CE program staff in completing their work.

In every year from 2012 onward, a 1-day symposium has preceded the workshop. The purpose of the symposium 

is to support the CE Gemini Redesign Project (Gemini Project), a major initiative to redesign the CE (for more 

information, go to https://www.bls.gov/cex/geminiproject.htm).

In addition to the CE program staff, workshop speakers have included economists from BLS regional offices and 

researchers not affiliated with BLS. Similarly, symposium speakers have included CE program staff, other BLS 

national office staff, and speakers from outside BLS. This article describes the 2020 Survey Methods Symposium, 

conducted on July 21, 2020, and the 2020 Microdata Users’ Workshop, conducted July 22–24, 2020.

For the first time, in whole or in part, both events were held online, rather than at the BLS national office in 

Washington, D.C. The CE program made this decision because of the continuing COVID-19 pandemic. As a result, 

to minimize potential disruption due to possible technological failures or other, unanticipatable, problems, both 

events were streamlined. For example, the Symposium, which usually features several speakers outside the CE 

program, instead consisted of only two presentations, both from CE staff. While the workshop maintained its 

tradition of featuring outside (non-CE program) speakers, BLS speakers, which usually include staff from several 

programs, were limited to members of one branch (Information and Analysis) of the CE program. The one 

exception was an overview of CE data presented by the CE program director.

Survey methods symposium

The symposium began with a presentation on the Gemini redesign titled “Gemini Redesign: Past, Present, and 

Future” by Parvati Krishnamurty from the CE program at BLS. The presentation outlined the original plans for the 

redesign and recent modifications made to the redesign plan for implementation. The redesign plan was intended 

to be implemented as a whole, but because of budget constraints, the plan will instead be implemented in phases. 

Therefore, the plan was modified to move to a phased implementation of key design elements into the CE surveys. 

The phased implementation plan is to retain the design elements that have been effective during field tests, which 

include a streamlined questionnaire with less expenditure detail, records focus (including a targeted incentive for 

record use), online diaries, and token incentives.3 These elements will be implemented directly into the CE 

Diary and Interview surveys. Other design elements such as a single sample design, two interview structure, and 

two wave design could be tested and implemented in future years, pending changes to requirements or funding 

availability. Dr. Krishnamurty provided more detail about the Large Scale Online Diary Feasibility Test (LSF), which 

was fielded from October 2019 to March 2020. She also provided a high-level overview of the streamlined 

questionnaire design and plans for releasing the new sections of the streamlined questionnaire in three phases 

starting in 2023. Dr. Krishnamurty mentioned future enhancements that are being explored by the CE, including 

new technologies such as receipt scanning and geolocation, self-administered interviews, adaptive design, split 

questionnaire design, single sample design, and gold-standard interviews.

The second presentation by Laura Erhard from the CE program was titled “Going Online: Results from the CE’s 

LSF.” The presentation summarized the test design, the online diary design, and some preliminary results from the 

first 3 months of unprocessed data. Procedural changes had to be made to the LSF in March 2020 when the 
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pandemic made in-person visits impossible, but otherwise fielding went smoothly. The overall response rate was 

47.2 percent, and the rate of online diary placement was lower than expected, despite screening respondents for 

internet access and frequency of use. Barriers to online diary placement reported by field representatives include 

language issues, lack of technological savviness, and lack of connectivity. The LSF included two experiments: an 

advance postcard and a token incentive. While there was a small and nonsignificant increase in response rates 

from a $5 token incentive, there was a large but nonsignificant increase in response rates from advance postcards 

in the preliminary data.4 In general, respondents reported positive experiences with the online diary. One area of 

concern was the large number of failed respondent logins to the online diary. Although the online diary has been 

used as a contingency measure in the CE Diary Survey during the COVID-19 pandemic, the CE staff is conducting 

data analysis of the online diary and will consider its use for production in 2022. Additionally, CE and Census staff 

are working on making improvements to the online diary design, protocols, and training based on lessons learned 

from the LSF.

Microdata users’ workshop

Meet with an expert: Beginning with the 2017 workshop, the CE organizers have included a feature called the 

“Meet with an expert” program. The purpose of the program is to provide an opportunity for attendees to have indepth, one-on-one meetings with members of the CE staff, during which the attendees can ask questions and 

receive comments and other guidance about the projects in which they are engaged.5 With the workshop shifting 

online this year, the meetings were reimagined. Instead of conducting meetings, attendees met with their experts 

by phone, calling a prearranged toll-free number at their appointed times. In addition, several of those who were 

waitlisted because of unusually high demand scheduled their meetings for the week following the workshop.

The program has proven beneficial to attendees and to CE staff, who learn more about how researchers are using 

the data and about factors related to data, documentation, etc., that can be improved. Despite the differences in 

the mode of meeting (i.e., by phone instead of in person), the program was just as successful at the 2020 

workshop. During the feedback session, those who participated in this program unanimously praised the 

experience both for the content of the meeting and the quality of information received. As a result, the program will 

be continued for the 2021 Microdata Users’ Workshop. Attendees are able (and encouraged) to arrange meetings 

via the registration form or email.

Day one

The first session of the 2020 workshop consisted of presenters from the CE program. After welcoming remarks by 

Scott Curtin, chief of the Branch of Information and Analysis (BIA), Adam Safir provided an overview of the CE, 

featuring topics including how the data are collected and published. Economist Bryan Rigg (BIA) then presented 

an introduction to the microdata, including how they can be used in research, and the types of documentation 

about them available to users. Mr. Curtin completed the session with a description of the data file structure and 

variable naming conventions.

Afterward, attendees received their first practical training with the data. In this session, led by senior economist 

Aaron Cobet (BIA), they learned basic data manipulation, including how to compute means from the microdata for 

consumer units with different characteristics (e.g., by number of children present).6 As expected, the 

circumstances of the workshop offered new challenges for this training session and subsequent training sessions: 

When conducted in person, members of CE staff circulate among attendees to offer help. In addition, some 
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attendees choose to work together on the projects. While these features were obviously not available for the online 

workshop, attendees were able to submit questions via an online chat feature or send email to CE staff to receive 

an answer directly or arrange a phone call with CE staff.

The afternoon activities included presentations from researchers not affiliated with the CE program. Summaries of 

the papers presented by outside researchers are included at the end of this report.

The first speaker, data scientist Aaron R. Williams (Urban Institute, Income and Benefits Policy Center), spoke 

about his use of CE microdata to study income and expenditures by low-income families with at least one member 

age 50 or older. The work was coauthored with Damir Cosic (Senior Research Associate, Urban Institute, Income 

and Benefits Policy Center), who attended the 2019 workshop.7

The second presentation was codelivered by Casey Goldvale (policy analyst) and Vincent Palacios (senior policy 

analyst) of the Georgetown Center on Poverty and Inequality. They described their work investigating costs 

beyond tuition (e.g., housing) for older (age 25 to 45) college students.

Following this presentation, self-directed practical training resumed with projects, introduced by economist Jimmy 

Choi (BIA), involving the integration of data from the Diary and Interview Surveys, a practice used in production of 

CE tables, and finding detailed information about education expenditures.8 Attendees also learned how to 

integrate results from the Interview and Diary Surveys to match expenditure categories in CE published tables. 

After this session, the workshop concluded for the day.9

Day two

To open the second day, Mr. Cobet explained the need to balance confidentiality concerns of respondents with the 

usefulness of the data to researchers. Because U.S. Code Title 13 requires confidentiality of response, information 

that might identify specific respondents must be removed from the CE data before they are released publicly. 

Some identifiers are direct, such as names and addresses. Others are not direct, such as extremely high 

expenditures or make and model of automobile(s) owned.

Mr. Cobet explained the methods used to produce the CE microdata files to address these disclosure concerns. 

The first method, called topcoding, involves reported values for income or expenditures that exceed a certain 

threshold, called the critical value. These top-coded values are replaced by an average of all values exceeding this 

critical-value threshold and then flagged as topcoded (or bottom-coded, in the case of large income losses).10 He 

also explained recoding, in which data are either made less precise (e.g., if the owned automobile was produced in 

1999, the year is replaced with the decade of manufacture [1990s in this example]) or changed in another way 

(e.g., state of residence is changed to a nearby state) to preserve both comparability and confidentiality.

Mr. Cobet next explained suppression, in which reported values are removed from the data set. In some cases, 

only specific information is suppressed on a record (e.g., details of a specialized mortgage). In other cases, the 

entire record is removed (e.g., report of a purchase of an airplane).11 Finally, Mr. Cobet talked about methods to 

eliminate reverse engineering, a process through which the user could deduce protected information from other 

information provided in the publicly available files.12

Next, Mr. Choi presented a brief description of experimental weights for estimating state-level expenditures with 

the use of the CE microdata. He noted that weights for New Jersey, California, Florida, New York, and Texas were 

available (https://www.bls.gov/cex/csxresearchtables.htm#stateweights).13 Mr. Choi also presented the criteria 
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used by the CE program to assess the feasibility of devising weights for other states (sample size, confidentiality 

concerns, and long-term retention of the state under study in the CE sample).

Concluding the session, Dr. Geoffrey Paulin, senior economist in the CE program (BIA), described the correct use 

of sample weights in computing consumer unit population estimates. His talk started with an overview of the 

computation of the weights.14 Following this, he introduced the procedures needed to get consumer-unitpopulation weighted averages for expenditures; that is, instead of computing mean expenditures from the sample 

itself, how to apply weights to estimate mean expenditures for the consumer unit population as a whole.15 Finally, 

he noted that the proper use of weights requires a special technique, called balanced repeated replication (BRR). 

BRR accounts for sample design effects in order to produce correct estimates of variances for weighted means, 

regression parameters, etc. Without BRR, these estimates can be biased or otherwise incorrect when computed 

for CE data. Next, he provided an example of BRR he derived from a question that arose during the talk. This led 

into a practical training session, instructed by Mr. Curtin, devoted to computing weighted results in two projects: 

one related to computing results for collection year estimates and the other for calendar year estimates. The 

distinction is that collection year refers to the date on which the respondent reported the expenditures to the 

interviewer, while calendar year refers to the period in which the expenditures actually occurred. For example, for a 

person participating in the Interview Survey in January 2018 who reports expenditures that occurred during the 

final 3 months of 2017 (i.e., October, November, or December), the expenditure collection year is 2018, while the 

expenditure calendar year is 2017.

Presentations by non-CE staff researchers continued in a themed session during the afternoon. Each of the 

speakers described their work with race and ethnicity variables in the CE microdata. The first presenter, Ziyao 

Tian, a Ph.D. candidate in sociology at Princeton University, discussed expenditures on higher education for AsianAmerican families. The second, copresented by Reginald Noël (research economist/data scientist) and Whitney 

Hewitt Noël (public health researcher/health equity advocate), both of the Noël Collective, discussed the 

intersectionality of sex and race in both income and expenditure patterns. Serving as a moderator of the 

discussion, Dr. Paulin briefly described his own work with the Diary Survey to explore food expenditures by race 

and ethnicity. He noted the detailed information on geographic origin included in race (Asian) and ethnicity 

(Hispanic) categories within the CE data for users interested in studying expenditures by the communities within 

these broader groups.16 He also pointed out the benefit of having these characteristics available for each member 

of the consumer unit, which he applied to his own research. For example, there is no attempt to identify a “decision 

maker” in the consumer unit, so the relationship of race or ethnicity to expenditure patterns is unclear when 

members of the consumer unit identify with different races or ethnicities.17

The last session of the day continued practical training. Dr. Paulin described the proper methods for analyzing CE 

income data, which are multiply imputed when missing. This presentation led into more self-directed practical 

training, in which attendees applied the methods described.

Day three

The final day started with a set of presentations from outside researchers who use CE microdata. The first of these 

presenters was Dr. Constantin Burgi, professor of economics at St. Mary’s College of Maryland. Dr. Burgi 

discussed his work examining how average consumer expectations differ when reporting households are weighted 

by actual expenditures, as opposed to households having equal weight, in computing the average.
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The second speaker, Dr. Ensieh Shojaeddini, a researcher on fellowship at the Environmental Protection Agency, 

used CE data in the construction of demand systems to estimate effects of regulation.

The final speakers in this session were Dr. David King, an assistant professor of urban planning at Arizona State 

University, Tempe, and Dr. Jonathan Peters, a professor of finance and data analytics at The City University of 

New York. The presenters noted several changes in the last decade that affect transportation expenditures for 

consumers (the rise of rideshare services, online shopping, and, most recently, the COVID-19 pandemic), and 

want to see how these changes will continue to affect these expenditures in the future. They proposed a plan for 

studying patterns using CE data and other sources, particularly once the CE data for 2020 are released.

Following a break, Dr. Paulin described work in progress within the CE program to impute data for assets owned 

and liabilities owed when the holding, but not specific value, of either is reported. Next, supervisory economist 

Brett Creech (BIA) provided a sneak peek of developments for CE publications and microdata. Starting with those 

recently implemented, such as the release of free PUMD covering 1980 to 1995 in early 2020,18 he described 

changes scheduled or under consideration for future releases.19 Those releases scheduled include new tables 

showing expenditures in 2019 at more refined geographic levels (census division in addition to current census 

region) and a new column on the generational tables (first published officially to reflect 2016 data) showing 

expenditures for the post-Millennial generation (i.e., those born in 1997 or later).20 He also noted the addition of a 

new question (July 2018 for the Interview Survey and January 2019 for the Diary Survey) that asks whether 

anyone in the consumer unit has previously served in the U.S. military. He stated that tables showing expenditures 

by veteran status will be published as soon as sample size permits. In addition, he announced the inclusion of a 

special question, starting in June 2020, regarding the receipt and use of the 2020 economic stimulus payments. 

Both microdata and published tables will include information collected from the special question.21

To conclude the workshop, David Biagas (BLS) led attendees in a feedback session. During the feedback session, 

attendees had the opportunity to provide comments on what they found most (or least) useful about the workshop, 

and to make suggestions for future events. Many comments were positive, with attendees liking the progressive 

nature of the workshop (i.e., starting with the most basic information about the data collection and file structures 

and ending with the most technical topics) and praising the “Meet with an expert” program. Workshop attendees 

also provided suggestions on what could be improved. These comments were especially important given the 

delivery of the workshop online this year, for the first time ever. Because of the ongoing COVID-19 pandemic, the 

workshop will be conducted online again in 2021.

Symposium and workshop of 2021

The next Survey Methods Symposium is scheduled for July 20, 2021, in conjunction with the 16th annual 

Microdata Users’ Workshop (July 21–23). Both events will be held online. Although the symposium and 

workshop remain free of charge to all participants, advance registration is required (https://data.bls.gov/cgibin/forms/cex-registration). For more information about these and previous events, visit the CE website 

(https://www.bls.gov/cex/) and look for the left navigation bar, titled “CE WORKSHOP AND SYMPOSIUM.” 

For direct access to this information, the link is https://www.bls.gov/cex/csxannualworkshop.htm. The link to 

the combined agendas for the 2020 symposium and workshop (https://www.bls.gov/cex/ce-2020-combined-
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agenda.pdf) is also available on this webpage. Workshop presentations are available in an online zip file 

(https://www.bls.gov/cex/ws2020-presentations.zip).

Highlights of workshop presentations

The following are highlights of the papers presented during the workshop, listed in the order of presentation. They 

are based on summaries written by the respective authors.

Aaron R. Williams, Data Scientist, Income and Benefits Policy Center (Urban Institute), “Lifetime Income & Costs 

of the LI50+” (Interview Survey), day one.

A primary mission of the AARP Foundation is to mitigate, and eventually eliminate, poverty among older 

Americans. An important concern for the Foundation in addressing poverty among seniors is to select the 

target population that maximizes the impact of their effort. Our report, which focused on households below 

250 percent of the Federal Poverty Guideline with at least one member age 50 or older, helped the AARP 

Foundation 1) identify demographic groups that are most vulnerable and 2) identify the groups that 

represent the biggest share of the vulnerable population. To identify the most vulnerable population—those 

in high need—we relied on household spending rather than income because it is measured more 

accurately than income and represents a better measure of personal well-being. We selected the bottom 

expenditure quartile—25 percent of LI50+ who had the lowest annual expenditures adjusted for household 

size—and analyzed the composition of this group and the likelihood of being in high need among the 

general population. Through this work, we developed a customized version of the R package library 

(cepumd) by Arcenis Rojas, we created a custom mapping of Universal Classification Codes to a custom 

hierarchy of grouped expenditures that matched the interests and needs of the AARP Foundation, and we 

created a detailed profile of the consumption and income of the LI50+ with extensive data visualization and 

tables.

We used a heavily functional approach in R to analyze the data and built a process with version control 

that proved useful for this analysis and hopefully future analyses.

Casey Goldvale, Policy Analyst, and Vincent Palacios, Senior Policy Analyst, Georgetown Center on Poverty & 

Inequality, “Costs Beyond Tuition: Estimating older college students’ basic needs with Consumer Expenditure 

Survey (PUMD) (Interview Survey), day one.

Though estimating the “cost of attendance” is key in determining student financial aid for higher education, 

there are no standardized measurement methods and estimates can vary wildly across colleges located 

within a few miles of each other. There is also evidence that “cost of attendance” may be severely 

underestimated for older students who are more likely to have dependents and be financially independent. 

We use the Consumer Expenditure Surveys (CE) to estimate average spending on components of an 

adequate living standard among older undergraduate students’ households nationally. Using UCC codes 

from MTBI data files, we have adapted FMLI and MEMI samples and variables to be comparable to cost 

categories defined in U.S. student financial aid policy and the Census Bureau and BLS basic needs and 
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poverty measurement methodologies. We also focus on equity by incorporating race/ethnicity, gender, and 

other demographic and geographic characteristics for older students and their households. To ensure 

adequate sample sizes, we pooled multiple years of data to increase sample size and adjusted the 

sampling weights accordingly. To our knowledge, this is the first time the CE has been used to study the 

older student population and is one of few studies beyond Geoffrey Paulin’s 2001 paper using CE 

microdata to estimate college students’ cost of living.22

Ziyao Tian, Ph.D. Candidate (Sociology), Princeton University, “How Expensive Is the Battle of Tiger Mothers? 

Understanding Race and Class behind the Educational Expenditure of Asian Americans” (Interview Survey), day 

two.

Social stratification scholars have been trying to understand the superior academic achievement of Asian 

Americans by examining the roles of family socioeconomic status (SES), culture, and the intersection of 

the two factors. Yet, the role of expenditure on education as an important mechanism linking social class 

and culture remains unexplored. Previous studies demonstrate that superior academic achievement is 

partly driven by Asian Americans’ high expectations of education across families of different SES origins. In 

other words, family SES has a weaker predicting power of educational expectations for Asian Americans 

than for Whites. Beyond this psychological-attitude channel, we use the Consumer Expenditure Surveys 

(CE) data from 2009 to 2019 to examine whether Asian Americans’ expenditure on education is also 

universally higher and less sensitive to SES. Preliminary results show that Asian Americans, on average 

and across SES distribution, spend more dollars, as well as a higher proportion of their spending budget, 

on education than their non-Hispanic White counterparts. The difference is primarily a result of Asian 

families’ high spending on college tuition. The racial gap in college tuition is more pronounced among 

lower-SES families than among higher-SES families. Further explorations of the gap in college tuition 

suggest that the difference is mainly driven by more college students from less advantaged Asian families, 

rather than a greater tendency to provide stronger college tuition support when having a college student at 

home.

Reginald Noël, Research Economist/Data Scientist, and Whitney Hewitt Noël, Public Health Researcher/Health 

Equity Advocate, Noël Collective, “Gender Economics, Race, and Intersectionality: Using CE Microdata to examine 

inequalities among adult women and men in the U.S. by race and ethnicity, 2016 through 2018 

combined” (Interview Survey), day two.

This working paper explores the issues of gender economics, with an intersectional dimension of race and 

ethnicity. Comparative analysis from two different datasets, the American Community Survey and the CE, 

show similar persistent inequalities in income, stratified by binary sex and race. Specifically, adult men had 

higher salaries and wages than adult women. In addition, adult Asian and White populations had higher 

salaries and wages than the adult Native, Black, and Hispanic populations. Moreover, the Consumer 

Expenditure Interview Survey data allowed for a deep examination of household spending, scarcity, 

resource allocation, and consumer patterns among the different cohorts. The data illustrated 

socioeconomic inequalities faced by women as compared with men, including the Gender Pension Gap, 

Pink Tax (higher prices for goods marketed to women, such as razors, that are actually or nearly identical 

to versions marketed to men), health care costs, educational attainment, occupation, and marital status. All 
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these factors depicted microeconomic inequalities faced by intersected subpopulations, which disserves 

not only these population groups but also the U.S. economy as a whole.

Constantin Burgi, Ph.D., Assistant Professor (Economics), St. Mary’s College of Maryland, “Predicting consumer 

expenditure based on the variables available in the Consumer Expectation Survey of the NY Fed” (Interview 

Survey), day three.

The aim of this work is to check how the mean household expectations from the New York Fed’s 

Consumer Expectations Survey change when households are weighted using consumer expenditure, as in 

the Consumer Price Index, instead of equal weights. In order to do so, it is necessary to impute the 

consumer expenditure of the households in the Consumer Expectation Survey. Variables that are available 

in both the CE and the Consumer Expectations Survey are made comparable and a (weighted) OLS 

regression is then used to impute the consumer expenditure. It is found that the consumption-weighted 

consumer expectations are around 0.7 percentage points lower than the equally weighted consumer 

expectations.

Ensieh Shojaeddini, Ph.D., Oak Ridge Institute for Science and Education fellow at U.S. Environmental Protection 

Agency, “Consumer Demand Estimation for Heterogeneous U.S. Households” (Interview Survey), day three.

The specification of the consumer demand system is important for estimating the economy-wide impacts of 

environmental regulation. First, it plays a key role in determining the baseline in a dynamic context. 

Second, it defines the final good demand curves that help determine the ability to control pollution on the 

extensive margin through the output effect. In this role, the specification of consumer demand also helps 

determine the share of abatement costs borne by factors or production relative to consumers. Finally, it 

plays an important role in determining tax interaction effects.

In computable general equilibrium (CGE) models, household behavior is typically governed by a constant 

elasticity of substitution (CES) utility function, though it fails to realistically capture well-known patterns of 

consumer behavior. In addition, only a few CGE models econometrically estimate their own elasticities, 

which are limited to a representative national-level household. We empirically estimate several flexible 

consumer demand systems for the U.S. economy for use in a CGE model with regional and household 

income disaggregation. As part of this evaluation, we consider tradeoffs between different specifications 

regarding complexity, regularity, the ability to capture cross-price elasticities, Engel curve flexibility, and the 

number of commodities that can be reasonably accommodated.

David A. King, Ph.D., Assistant Professor of Urban Planning, Arizona State University, Tempe, Arizona, and 

Jonathan Peters, Ph.D., Professor of Finance and Data Analytics, The City University of New York, “Household 

Transportation Spending Trends from 2010 to 2020 - Early Indications of the impact of cultural shifts and pandemic 

related household activity on transportation spending patterns” (Interview Survey), day three.

The last 10 years have been a time of radical change in household consumption as it relates to 

transportation spending. First, we experienced the massive growth in for-hire vehicle services such as 

Uber and Lyft that disrupted and transformed traditional taxi services in many cities. Second, we observed 

a decline in private vehicle ownership in several cities, with corresponding growth in car sharing services. 

Further, the growth in e-bicycles and scooter services, as well as the potential growth for autonomous 

vehicles, have made the last decade a time of revolution in the transportation sector. Now, further changes 
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are being wrought by the COVID-19 pandemic, reversing many trends in transportation use. Transit 

systems reeled from the needs for enhanced sanitation and social distancing, and ridership caps were 

instituted on many mass transit systems. Demand for gasoline and diesel collapsed. Online shopping and 

at-home consumption skyrocketed. What is still an open question in all of this is, are these changes 

temporary and will they reverse when the pandemic moderates, or will they result in a long-term reversal of 

the recent trends and usher in a 21st century wave of automobile use and reliance on personal instead of 

shared transportation services? These changes have the potential to disrupt many policy initiatives in 

terms of infrastructure investment; for example, a shift away from federal funding and a general movement 

to local funding sources such as tolls or parking fees.

When available, the authors will utilize new data collected on post-COVID-19 consumption from outside 

sources and compare these sources with BLS CE data to examine how household consumption may have 

shifted during this period (2010–20). The authors also plan to project what may happen in transportation 

consumption over the next five years (2021–25).

Workshop presenters

Staff of the CE program

Choi, Jimmy. Economist, Branch of Information and Analysis, BIA: practical training leader, day one; 

presenter, day two.

Cobet, Aaron. Senior Economist, BIA: practical training leader, day one; presenter, day two.

Creech, Brett. Supervisory Economist, Chief, Publications and Tables Production Section, BIA: 

presenter, day three.

Curtin, Scott. Supervisory Economist, Chief, BIA: emcee, days one, two, and three; practical training 

leader, day two.

Paulin, Geoffrey. Senior Economist, BIA: introducer of speakers, commentator, days one, two, and 

three; practical training leader, day two; presenter, days two and three.

Rigg, Bryan. Economist, BIA: presenter, day one.

Safir, Adam. Chief, Division of Consumer Expenditure Surveys: presenter, day one.

Other BLS speakers

Biagas, David. Research Psychologist, Office of Survey Methods Research: feedback coordinator, 

day three.

Non-BLS speakers
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Burgi, Dr. Constantin. Assistant Professor of Economics, St. Mary’s College of Maryland, “Predicting 

consumer expenditure based on the variables available in the Consumer Expectation Survey of the 

NY Fed” (Interview Survey); day three. First-time attendee and presenter (2020).

Goldvale, Casey. Policy Analyst, Georgetown Center on Poverty & Inequality, “Costs Beyond Tuition: 

Estimating older college students” basic needs with Consumer Expenditure Survey 

(PUMD)” (Interview Survey); day one. Former attendee (2019) and first-time presenter (2020).

King, Dr. David (Ph.D.). Assistant Professor of Urban Planning, Arizona State University (Tempe), 

“Household Transportation Spending Trends from 2010 to 2020 - Early Indications of the impact of 

cultural shifts and pandemic related household activity on transportation spending 

patterns” (Interview Survey); day three. First-time attendee and presenter (2020).

Noël, Reginald. Research Economist/Data Scientist, Noël Collective, “Gender Economics, Race, 

and Intersectionality: Using CE Microdata to examine inequalities among adult women and men in 

the U.S. by race and ethnicity, 2016 through 2018 combined” (Interview Survey); day two. First-time 

attendee and presenter (2020).

Noël, Whitney Hewitt. Public Health Researcher/Health Equity Advocate, copresenter with Reginald 

Noël; day two. First-time attendee and presenter (2020).

Palacios, Vincent. Senior Policy Analyst, Georgetown Center on Poverty & Inequality, copresenter 

with Casey Goldvale; day one. Former attendee (2019) and first-time presenter (2020).

Peters, Dr. Jonathan (Ph.D.). Professor of Finance and Data Analytics, The City University of New 

York, copresenter with David King; day three. Prior presenter (2014, and 2017 through 2019); 

returning presenter (2020).

Shojaeddini, Dr. Ensieh (Ph.D.). Oak Ridge Institute for Science and Education fellow at U.S. 

Environmental Protection Agency, “Consumer Demand Estimation for Heterogeneous U.S. 

Households” (Interview Survey); day three. Former attendee (2019) and first-time presenter (2020).

Tian, Ziyao. Ph.D. Candidate (Sociology), Princeton University, “How Expensive Is the Battle of Tiger 

Mothers? Understanding Race and Class behind the Educational Expenditure of Asian 

Americans” (Interview Survey); day two. First-time attendee and presenter (2020).

Williams, Aaron R. Data Scientist, Income and Benefits Policy Center (Urban Institute), “Lifetime 

Income & Costs of the LI50+” (Interview Survey); day one. First-time attendee and presenter (2020).

SUGGESTED CITATION

Geoffrey D. Paulin and Parvati Krishnamurty, "Consumer Expenditure Survey Methods Symposium and Microdata 

Users’ Workshop, July 21–24, 2020," Monthly Labor Review, U.S. Bureau of Labor Statistics, June 2021, https:// 

doi.org/10.21916/mlr.2021.14.
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NOTES

1 Although a household refers to all people who live together in the same living quarters, “consumer unit” refers to the people living 

therein who are a family, or others who share in specific financial arrangements. For example, two roommates living in an apartment 

constitute one household. However, if they are financially independent, they each constitute separate consumer units within the 

household. Similarly, although families are related by blood, marriage, or legal arrangement, unmarried partners who live together and 

pool income to make joint expenditure decisions constitute one consumer unit within the household. For a complete definition, see the 

CE glossary at https://www.bls.gov/cex/csxgloss.htm. For more information on households and families, see https://www.census.gov/

glossary/#term_Household and https://www.census.gov/glossary/#term_Familyhousehold.

2 The Quarterly Interview Survey is designed to collect data on expenditures for big-ticket items (e.g., major appliances or 

automobiles) and recurring items (e.g., payments for rent, mortgage, or insurance). In the Interview Survey, participants are visited 

once every 3 months for four consecutive quarters. In the Diary Survey, on the other hand, participants record expenditures daily for 2 

consecutive weeks. This survey is designed to collect expenditures for small-ticket and frequently purchased items, such as detailed 

types of food (e.g., white bread, ground beef, butter, or lettuce). The CE microdata for both surveys may be downloaded from the CE 

website at https://www.bls.gov/cex/pumd_data.htm.

Data from the Diary and Interview Surveys are published twice a year in various standard tables. One set describes expenditures that 

occurred within the calendar year of interest (e.g., January through December 2018 for the most recent set available as of the writing 

of this report). The other set provides a midyear update to expenditures, ranging from July of the earlier year to June of the later year 

(e.g., July 2017 through June 2018 for the most recent set available as of the writing of this report). The single-year series is available 

from 1984 onward. The midyear updates are available from July 2011 to June 2012 onward. Each set includes information on 

expenditures by age of reference person, composition of consumer unit, income of consumer unit, and other demographics. For a 

complete list, see https://www.bls.gov/cex/tables.htm.

3 Token incentives are being tested in the LSF prior to potential implementation in the CE.

4 Since the symposium, analysis of LSF data from October through February indicates that postcards have no impact on response 

rates.

5 Attendees were able to sign up for a meeting by checking a box on their registration forms. They could also sign up via e-mail 

throughout the virtual workshop, replacing the option to do so at the registration desk for previous in-person workshops previously. 

However, the main benefit—both to attendees and CE staff members—of advance registration was to allow the meetings coordinator 

time to find the most appropriate expert, and time for the expert to investigate the question or prepare other information (handouts, 

etc.) before the meeting to optimize the quality of the session.

6 The projects in this series built on each other, progressing from basic computation to more complicated use of the data, which 

involved finding and merging results from the FMLI, MEMI, and MTBI files. The FMLI files include general characteristics of the 

consumer unit (e.g., region of residence, number of members, etc.) and summary variables (e.g., total educational expenditures). The 

MEMI files contain information on each individual member of the consumer unit (e.g., each member’s age, race, educational 

attainment, etc.). The MTBI files include expenditures for specific educational expenses (e.g., expenditures on “College tuition,” 

“Elementary and high school tuition,” “Test preparation, tutoring services,” “School books, supplies, equipment for vocational and 

technical schools,” etc.).

7 In an email exchange with the author of this workshop report, Dr. Cosic states, “...my attendance of the 2019 workshop was 

instrumental in our successful completion of the project that Aaron presented. I think this is an excellent example of the success of 

your workshop.” (email from Damir Cosic to Geoffrey Paulin, August 2, 2020).

8 Specifically, attendees learned how to access the EDA files to ascertain for what type of school or facility (college or university, 

elementary through high school, child daycare center, etc.) certain education expenditures were incurred, and whether the 

expenditures were for a member of the consumer unit or a gift to someone outside of it.
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9 From 2012 until 2019, the first day of the workshop ended with a networking opportunity, where attendees could meet each other 

and informally discuss questions with CE staff. (Prior to 2012, this event was held on the second day of the workshop, to maximize 

overlap in attendance between newer and more experienced users.) However, with the delivery of the workshop online, this activity 

was unfeasible, due to limitations of the software approved for delivering the workshop.

10 For example, suppose the threshold for a particular income or expenditure is $100. On two records, the reported values exceed 

this: $200 on record A and $600 on record B. In this case, the value is topcoded to $400 (the average of $200 and $600) and the 

reported amounts are replaced with $400. An additional variable, called a “flag,” is coded to notify the data user that the $400 values 

are the result of topcoding, not actual reported values.

11 For details on topcoding and suppression, including specific variables affected and their critical values, see https://www.bls.gov/

cex/pumd_disclosure.htm#Basic.

12 For example, suppose a respondent reports values for two sources of income: (1) wages and salaries and (2) pensions. Further 

suppose the following: The reported value for wages and salaries exceeds the critical value, and is therefore replaced by the 

topcoded value of $X; the reported value for pension income, $Y, is below the critical value for this income source; and the value for 

total income is shown to be $X + $Y + $Z. Because this respondent only has two sources of income reported and pension income is 

not topcoded, the reported value for wages and salaries is $X + $Z. To prevent this, total income must be computed after each 

individual component has been topcoded as needed. Therefore, in this example, total income is $X + $Y and the actual reported 

value of wages and salaries cannot be “reverse engineered.”

13 Weights for the first three states (New Jersey, California, and Florida) are available for 2016 onward; for the latter two (New York 

and Texas), they are available for 2017 onward.

14 Traditionally, preceding this talk, a member of the Statistical Methods Division delivers a detailed explanation of the computation of 

the weights. However, as noted earlier, the workshop planners cut several detailed presentations due to the uncertainties of the firstever online workshop.

15 For example, suppose the sample consists of two consumer units, one of which represents 10,000 consumer units in the 

population (i.e., itself and 9,999 others like it) and another that represents 20,000 consumer units in the population. If the first spent 

$150 and the second spent nothing (i.e., $0), the sample mean expenditure is $75. However, the population-weighted mean is $50, or 

[($150 x 10,000)+($0 x 20,000)]/(10,000 + 20,000).

16 That is, in addition to asking the respondent the race of each member of the consumer unit, if Asian, the Interview and Diary 

Surveys both ask about geographic origin: Chinese, Filipino, Japanese, Korean, Vietnamese, Asian Indian, or other Asian (listed in 

the order of appearance in the questionnaire). If the respondent reports that a member is Hispanic, the interviewer asks whether the 

member is Mexican, Mexican American, Chicano, Puerto Rican, Cuban, or other Spanish (again, listed in the order of appearance in 

the questionnaire).

17 Even if a question were asked about “decision making,” it is not clear that the answer would be meaningful in a “real world” 

context. For example, in married couples, it is likely that at least some decisions are made jointly, and in those that are not, it is not 

clear who makes the decisions. For example, if only one spouse purchases the groceries, which spouse is it? Furthermore, that 

spouse will almost certainly take into account preferences of the other spouse. If the purchasing pattern therefore reflects the tastes 

(literally) of both spouses in food consumption, and these tastes are influenced by the different racial or ethnic backgrounds of each 

spouse, then the relationship of expenditure to race or ethnicity is diluted within such families. Therefore, comparing consumer units in 

which all members share the same race and ethnicity makes the comparisons across racial and ethnic groups much clearer.

18 Available at https://www.bls.gov/cex/pumd_data.htm.
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19 Prior to February 2020, free PUMD were available from 1996 onward. The release of data from 1980 to 1995 allows users to 

obtain these data for all years in which CE data were collected on a continual basis. Prior to 1980, they were collected approximately 

every 10 years (1972–73, 1960–61, etc.).

20 While a table showing expenditures for the “post-Millennial” generation for July 2018 through June 2019 is available, the 2019 

table will be the first standard (i.e., calendar year) table published to feature expenditures for this group.

21 This question is predated by similar questions added regarding earlier stimulus payments. The first was added in response to 

payments made in 2001; the second was added in response to payments made in 2008. (See https://www.bls.gov/cex/anthology11/

csxanth5.pdf.) In addition, CE collected information on the special $250 payment made in 2009 to most Social Security recipients and 

other eligible persons. (See https://www.bls.gov/opub/btn/archive/how-consumers-used-the-2009-economic-recovery-paymentsof-250.pdf.)

22 See “Expenditures of college-age students and nonstudents,” Monthly Labor Review, July 2001, pp. 46–50, https://www.bls.gov/

opub/mlr/2001/07/ressum1.pdf.

RELATED CONTENT

Related Articles

Consumer Expenditure Survey Methods Symposium and Microdata Users’ Workshop, July 16–19, 2019, Monthly Labor Review, April 

2020.

Consumer Expenditure Survey Methods Symposium and Microdata Users’ Workshop, July 17–20, 2018, Monthly Labor Review, May 

2019.

Consumer Expenditure Surveys Methods Symposium and Microdata Users’ Workshop, July 18–21, 2017, Monthly Labor Review, 

June 2018.

Related Subjects

Survey methods Expenditures Sampling Consumer expenditures Survey procedures