“Building Evidence by Linkage to 

Data at the U.S. Census Bureau”

Discussant Comments

Ben Bolitzer, Department of Commerce

October 25, 2023

Any opinions and conclusions expressed herein are those of the authors and do not represent the 

views of the U.S. Department of Commerce
Linking Improves Administrative Data

“The information collected in administrative records is determined by 

the entity administering the program. In probability surveys, data 

collection is tailored to information needed to calculate statistics.” –

NAS, 2023

Linked data addresses some of limitations of strictly administrative 

data:

• Lack of data not essential to administrating the program –

demographics, etc.

• If the data is on program participants – what is the counterfactual?

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My Discussant Comments

•Overview of the papers

•Describe the ways each paper leverages linking to build better 

evidence than administrative data alone.

•Offer suggestions for future work – things to consider – things that I 

wondered about

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The Labor Market Returns to Earning Industry Credentials

•Goal: Estimate the labor market returns (employment, earnings) from an 

industry credential

• Background: Paper cites literature on both the growing prevalence of 

industry credentials, and the challenges of measuring industry credentials 

in existing “standard” data sources, although additional questions on 

credentials were added to the CPS in 2017

• Methods

• Graphical Analysis: Comparing the time series before and after receiving degree – stratified by educational attainment, and age at last credential

• Regression Modeling:

• (Matching) Construct a control sample with ACS of those not in the sample and without a 

college degree.

• (Regression) Include as co-variates variables included in the matching process 

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The Labor Market Returns to Earning Industry Credentials

•Data: Paper links administrative data from a collaboration between 

NAM and NSC. 

• Industry credentials from four organizations were linked with information on 

education attainment from NSC.

• Approximately 54 percent of the credentials were linked to an ID, of those 

about 85 percent were linked to survey (ACS), W2 and 1040 data.

• Findings:

• In both the graphical and regression analyses, they find that adding a 

credential increases earnings, and employment. The results are comparable 

between the methods – about $5,000 for earnings, and 13 percent for 

employment, depending on the group.

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Paper 1: Demonstration of the Value of Linked Data

Lack of data not essential to administrating the program

• Linking with the ACS data allowed to see the demographics of the 

program participants. Without this linking – the authors would not 

have been able to even do the graphical analysis. 

If the data is on program participants – what is the counterfactual?

• Survey data allowed the authors to develop the control group, 

based on ACS respondents. This was much stronger than if they had 

relied on simply the program data.

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Paper 1: Things I wondered about

• As the authors note, a limitation is that it’s unknown whether in the ACS data the survey 

participants have credentials.

• I think that a gap to understanding the external validity of the paper is that we are not given 

much information about the credential process itself. Are these programs “standard”

• The authors noted the “ashenfelter dip”, but it seems like there was a downward trend well before the three years. Was this dip examined?

• Whether it made sense to match on industry. It seems to me that part of the argument for 

credentials is that it would allow individuals to switch industries. By controlling for industry, are 

you understating the effect of the credential? On the other hand, you also want to match the 

distributions of the participants to the source industries to make them comparable.

• Even after the matching, there still was a gap in pre-participation earnings. As noted in the paper, does this imply that there is still unobserved heterogeneity?

• Standard errors -> it seems to me that after linking to the ACS data, you would have had the option of constructing standard errors using the ACS replicate weights. Is that something that 

you considered? 

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Methodology on Creating the U.S. Linked Retail Health Clinic 

(LiRHC) Database

•Goal: Describe the creation of a new linked retail health clinic data 

base, and report on geographic distribution

•Methods & Data: Linked data (2018-2020) from three sources:

• Convenient Care Association Membership - RHCs and other walk-in health 

care centers. 

• County Business Patterns Business Register (Census) - information related to 

payroll, employment size, and business characteristics. 

• National Plan and Provider Enumeration System (CMS) - data on individual 

health care providers and non-individual organizations.

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Methodology on Creating the U.S. Linked Retail Health Clinic 

(LiRHC) Database

• Linking:

• Employed a multistage matching process, whereby scores were given on 

different match criteria. 

• Finding: Geographic Distribution

• Almost half (47.0%) located in the South region, with majority (550 RHCs) 

found in the South Atlantic division. 

• Counts are consistent with other data sources.

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Paper 2: Things I wondered about

• The paper notes that “Some CBPBR locations matching to the CCA records had 

an unexpected NAICS code from industries unassociated with retail trade 

locations or healthcare services.” Were there any lessons from these cases?

• Are there challenges to expanding the time component – would the matching have to be year by year?

• Would it be possible to obtain summary statistics on how well the matching 

process “worked”? For example, X% of CCA records matched to at least one 

site.

• Would it be possible to provide information about what questions this new 

linked data would be able to provide? For example:

• Do the proportion of retail health clinics affect the price of medical service or the 

proportion of expenditure?

• What is the likelihood of RHC co-located with other health facilities? Are they adding to or replacing other providers?

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The Demographics of the Recipients of the First Economic 

Impact Payment

•Goal: To report on the demographics of receipt of the First Round 

Economic Impact Payments authorized under the CARES Act.

•Methods: Report on differences in the time of receipt by age, gender, 

income, number of children, income level

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The Demographics of the Recipients of the First Economic 

Impact Payment

•Data

• IRS records on receipt linked to Census data

• Findings

• Consistent with IRS operational decisions, lower income individuals and 

families with children received payments earlier than higher income or 

families without children, 

• While there was a near 90 percent receipt rate for most racial/ethnic 

subgroups – it was highest for White individuals and lowest for Hispanic and 

Some Other Race individuals.

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Paper 3: Demonstration of the Value of Linked Data

Lack of data not essential to administrating the program

• Linking with the census data allowed to see the demographics of 

the receipt of the credit. 

If the data is on program participants – what is the counterfactual

• I’m not sure that the “control group” language holds, but the study 

used the demographics within the program to develop comparison 

groups – without this it would not have been possible to understand 

equity issues in the timing of credits.

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Paper 3: Things I wondered about

• Not surprisingly, non-filers were the most likely to not receive a return 

within the first week by a wide margin (only 2 percent). This made me 

wonder of whether any other demographic gaps measured in the data 

could be explained by the percentage of non-filers in that group.

• In the paper, the authors state that “Many non-filers had no filing 

obligation in those years.” because of low income. Another reason for 

non-filing could be tax avoidance for higher income individuals.

• The authors present information on the percentage of eligible individuals 

that received the payment – it would also be interesting to see whether 

there were individuals – based on links to survey data – that would be 

estimated to be ineligible and received the payment.

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Conclusion

• All papers provided evidence of the value of linking administrative 

data with survey data –performing analyses that would not have 

been possible without the linked data.

• The linked data also provided important information on the 

demographics of program participants.

• I’d encourage researchers to consider how “generalizable” results 

are to other similar programs

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Methodology on Creating the U.S. Linked 

Retail Health Clinic (LiRHC) Database

• Retail health clinics (RHCs) are a relatively new type of health care setting and understanding the role they 

play as a source of ambulatory care in the United States is important. To better understand these settings, a 

joint project by the Census Bureau and National Center for Health Statistics used data science techniques to 

link together data on RHCs from Convenient Care Association, County Business Patterns Business Register, 

and National Plan and Provider Enumeration System to create the Linked RHC (LiRHC, pronounced “lyric”) 

database of locations throughout the United States during the years 2018 to 2020. The matching 

methodology used to perform this linkage is described, as well as the benchmarking, match statistics, and 

manual review and quality checks used to assess the resulting matched data. The large majority (81%) of 

matches received quality scores at or above 75/100, and most matches were linked in the first two (of eight) 

matching passes, indicating high confidence in the final linked dataset. The LiRHC database contained 2,000 

RHCs and found that 97% of these clinics were in metropolitan statistical areas and 950 were in the South 

region of the United States. Through this collaborative effort, the Census Bureau and National Center for 

Health Statistics strive to understand how RHCs can potentially impact population health as well as the 

access and provision of health care services across the nati

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The Demographics of the Recipients of the 

First Economic Impact Payment

Starting in April 2020, the federal government began to distribute Economic Impact Payments (EIPs) in 

response to the health and economic crisis caused by COVID-19. More than 160 million payments were 

disbursed. We produce statistics concerning the receipt of EIPs by individuals and households across key 

demographic subgroups. We find that payments went out particularly quickly to households with children and 

lower-income households, and the rate of receipt was quite high for individuals over age 60, likely due to a 

coordinated effort to issue payments automatically to Social Security recipients. We disaggregate statistics by 

race/ethnicity to document whether racial disparities arose in EIP disbursement. Receipt rates were high 

overall, with limited differences across racial/ethnic subgroups. We provide a set of detailed counts in tables 

for use by the public

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