The Labor Market Returns to Earning Industry Credentials

Caroline Walker

Center for Economic Studies

U.S. Census Bureau

Vanessa Brown (NSC), Gardner Carrick (NAM), Leah Clark (Census), Diana Gillum (NSC), Roberta 

Hyland (NSC), Maggie R. Jones (Census), Shannon Lee (NSC), Nikolas Pharris-Ciurej (Census), and 

John Voorheis (Census)

FCSM Conference

October 2023

1

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

the U.S. Census Bureau or the Internal Revenue Service. The Census Bureau has reviewed this data product 

for unauthorized disclosure of confidential information and has approved the disclosure avoidance practices 

applied to this release. DRB Approval Numbers: CBDRB-FY2020-CES014-001, CBDRB-FY2023-CES010-005, 

CBDRB-FY23-CES014-032, CBDRB-FY23-CES014-044.
Motivation

• Widening wage gap between high school graduates and those with a 

college degree (Autor 2014)

• Nominal costs of four-year degrees have risen dramatically

• Sub-baccalaureate options have emerged as an alternative by 

providing additional skills to students without high tuition costs

• We ask: What are the labor market returns to an industry credential?

2
What is an industry credential?

• What they are

• Formal recognition of training (or demonstration of skill) by an industry 

association, professional association, or standards organization

• Training provided by a community college or on-the-job

• Attainment achieved via a test or demonstration

• Often tiered/tied to other credentials from same org

• What they are not

• Licenses from state government agencies

• Certificates from community colleges

• A degree

3
Data

• Partnership between the U.S. Census Bureau, the National Student 

Clearinghouse (NSC), and the National Association of Manufacturers (NAM)

• Data acquired by NSC from industry credential providers

• Current: Manufacturing, welding, safety professionals, pharmacy technicians 

• Future: IT, trucking, human resources

• Credential data matched to their NSC post-secondary information

• Enrollment and completion from late 1960s to present

• NSC-credential data transferred to the Census Bureau then linked to

• Demographic data (ACS, Decennial Census)

• IRS earnings and employer data (Form W2, Form 1040, Business Register)

• Comparison group drawn from ACS sample

4
Methods: Summary Figures

• Earnings and employment trajectories before and after credential 

attainment by demographics/industry/level within credentialed group

• “Before and after credential” defined relative to year of last credential

• Getting as close as we can to labor market entry post-credential

• Multiple credentials may only be separated by a few months

Outcomes: 

• Average W2 earnings

• Employment (has W2 earnings, employed in a particular industry)

5
Wage Trajectories: Manufacturing

6

Note: White, Black, and Hispanic are mutually exclusive and defined as White non-Hispanic, Black non-Hispanic, and 

Hispanic any race. Source: NAM-NSC, Census 2000 and 2010, 2005-2018 ACS, and IRS W2 data. DRB approval number: 

CBDRB-FY202020-CES014-001.
Methods: Measuring Causal Effects

• Research paper uses coarsened exact matching (CEM) to construct an 

ACS comparison group

• Takes the idea of an exact match but bins characteristics

• Characteristics include age, race/ethnicity, gender, educational attainment, 

industry, family structure (married/children), average wages pre-credential, in 

labor force pre-credential, pre-credential firm characteristics (# employees, 

payroll) 

• Bins chosen to maximize match quality while retaining project 

observations

• Define placebo treatment for comparison group as ACS survey year

• Estimate event study and diff-in-diff regressions on matched sample

7
8

Source: NAM-NSC, Census 2010, and 2005-2018 ACS, Form 1040, and Form W-2 data. 

DRB approval number: CBDRB-FY23-CES014-032.

Event Study: Earnings
Event Study: Employment

9

Source: NAM-NSC, Census 2010, and 2005-2018 ACS, Form 1040, and Form W-2 data. 

DRB approval number: CBDRB-FY23-CES014-032.
Current work: Routinizing a Comparison Group

• CEM, diff-in-diff good for academic research but less scalable and 

requires significant tinkering

• Goal: Want simple, standardized process that is easily interpretable 

for both potential credential earners and credential providers

• Sample of ACS respondents in the occupation not enough

• Credential earners skewed by age, other characteristics

• Ambiguous whether ACS respondents have other credentials

• Difficult to assign an industry or occupation

10
Discussion and lessons learned

• Fruitful project generating estimates of academic, industry, and public 

interest

• Results in manufacturing sector suggest workers with industry 

credentials have better employment and wage outcomes than 

comparable workers without credentials

• Still working to establish a generalizable process for suitable 

comparison groups for each credential type

• Ability to paint clear picture depends on cooperation of credential 

providers

11
Thank you!

Caroline Walker

caroline.walker@census.gov

12
Appendix slides

13
Match rates

14

Match rates of project data to unique identifier, demographic data, and federal tax information

PIK rate on manufacturing credential records 0.5628

Fraction unique manufacturing learner IDs assigned PIK 0.5433

Percent of PIKed manufacturing cred students linked to ACS/Decennial 0.8203

Percent of PIKed manufacturing cred students ever linked to W2 0.8281

Percent of PIKed manufacturing cred students ever linked to 1040 0.8767

Source: NAM-NSC, decennial 2010, and 2005-2018 ACS, Form 1040, and Form W-2 data. Approved for release under CBDRB-FY2021-

CES010-029.
Balance

15

Unmatched Matched

Cred No cred P-value Cred No cred P-value

Birth year 1980 1971 0 1980 1980 0.01

Pre-wage 21820 25150 0 22480 23990 0

Firm emp 12900 11380 0 13230 13230 0.97

Firm pay 979,300 863,900 0 1,004,000 1,004,000 0.97

In LF 0.8199 0.7232 0 0.8408 0.8407 0.97

Male 0.8131 0.4977 0 0.8223 0.8218 0.86

White NH 0.5648 0.7136 0 0.6245 0.6241 0.92

Black NH 0.164 0.1071 0 0.1604 0.1608 0.89

Hispanic 0.1158 0.1183 0.17 0.1048 0.1049 0.97

Other race 0.1554 0.06095 0 0.1104 0.1103 0.97

Associates 0.1312 0.1601 0 0.1036 0.1033 0.91

N 30,000 1,210,000 25,000 328,000

Source: NAM-NSC, decennial 2010, and 2005-2018 ACS, Form 1040, Form W-2, and Business Register data. Approved for 

release under CBDRB-FY23-CES014-032
Regression specifications

Event study:

yit = ෍

t=−10

8

βt hascredi × τt + γXit + αi + yeart + εit

Differences-in-differences:

yit = α + β hascredi × postt + γ Xit+τt + yeart + εit

where t = years since treatment 

hascred = indicator for whether the worker ever earned a credential

post = indicator for whether the year is after the treatment year.

16
Diff-in-diff: Earnings

Associates or less Credentialed 2016-17

Unmatched CEM matched Unmatched CEM matched

Has cred X post 5522 1981 5431 878.5

82.86 87.86 158.7 149.3

Person-years 18600000 5299000 4736000 1551000

Persons 1240000 350000 300000 100000

R-squared 0.3867 0.4519 0.5324 0.5469

17

Source: NAM-NSC, decennial 2010, and 2005-2018 ACS, Form 1040, and Form W-2 data.

Approved under CBDRB-FY23-CES014-032.

Controls: male, age, age squared, Black, Hispanic, otherrace, household structure (4 categories), associates 

degree, industry (5 categories), region (4 categories), average wage pre-treatment, in labor force pretreatment, firm payroll, firm employment, treatment year, calendar year.
Diff-in-diff: Employment

Associates or less Credentialed 2016-17

Unmatched CEM matched Unmatched CEM matched

Has cred X post 0.1382 0.0714 0.1315 0.0568

0.0012 0.0016 0.0021 0.0028

Person-years 18600000 5299000 4736000 1551000

Persons 1240000 350000 300000 100000

R-squared 0.3914 0.3198 0.3962 0.3662

18

Source: NAM-NSC, decennial 2010, and 2005-2018 ACS, Form 1040, and Form W-2 data.

Approved under CBDRB-FY23-CES014-032.

Controls: male, age, age squared, Black, Hispanic, otherrace, household structure (4 categories), associates 

degree, industry (5 categories), region (4 categories), average wage pre-treatment, in labor force pretreatment, firm payroll, firm employment, treatment year, calendar year.