The impact of manufacturing credentials on earnings 

and the probability of employment

Maggie R. Jones 

Vanessa Brown (NSC), Gardner Carrick (NAM), 

Nick Pharris-Ciurej, John Voorheis, and Caroline Walker 

FCSM

October 25, 2022

Any views expressed are those of the authors and not those of the U.S. Census Bureau. The Census Bureau's Disclosure Review Board and Disclosure 

Avoidance Officers have reviewed this data product for unauthorized disclosure of confidential information and have approved the disclosure avoidance 

practices applied to this release. DRB approval number CBDRB-FY2020-CES010-007 and CBDRB-FY2022-CES010-001.
Industry credentials

• What they’re not

• Licenses from state government agencies

• Certificates from a community college (although some CCs coordinate with industry 

organizations on approved training)

• A degree

• What they are

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

or standards organization (e.g., in manufacturing or health care)

• Training provided by an entity, sometimes accredited by the organization (sometimes 

a CC)

• Attainment achieved via a test or demonstration

• Sometimes time-limited, often tiered/tied to other credentials from the same org

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Project background

• Partners

• U.S. Census Bureau

• National Association of Manufacturers and the Manufacturing Institute (NAM)

• National Student Clearinghouse (NSC)

• Pilot involving data sharing between NAM-NSC, and then Census

• Linking select manufacturing credentials data to NSC post-secondary education data

• Linking NSC and credential data to demographic and labor force data held at the U.S. 

Census Bureau 

• Combined data provides opportunities to understand:

• the intersection of industry credentials with traditional education programs and 

pathways

• the employment patterns and earnings trajectory of industry credential-holders 

versus comparable non-credentialed workers

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Data

• Credential earners from third-party providers were matched to their 

NSC post-secondary data

• Credential data from four organizations

• Credential data coverage: 2005 to 2018

• NSC post-secondary data: late 1960s to 2018

• NSC-credential data linked to data at Census Bureau 

• Demographic data from various Title 13 sources (ACS, decennial Census, etc.)

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

• Comparison group developed from ACS samples (all ACS workers 18-64 in 

2016, 2017, and 2018 linked to W2 from 2014-2018)

• Individuals without credentials but otherwise similar to credentialled group

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Methods

• Summary comparisons within the credentialled group to uncover 

patterns of credential attainment by demographic group/industry 

• Coarsened exact matching to an ACS comparison group (using cem

command in Stata)

• Takes the idea of an exact match (X) but bins categories to reduce the number 

of cells (X*)

• Allows the user to predefine categories (or bins) on which exact matches must 

be made (e.g., industry categories) 

• Prunes both treatment and control units (so results are interpretable as a 

treatment effect on the matched subsample)

• Regression analysis proceeds on processed data (outcomes are 

earnings and 0/1 employment)

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Summary Results

• Focus on the timing of the last credential earned

• May only be separated by three months

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

• Earnings trajectories by manufacturing credential and work in a 

manufacturing industry

• Subgroup analysis of student outcomes by age group, race, gender, 

industry of employment, and educational degree

• Only presenting a small subset of results in the interest of time

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Match rates and observation count

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Source: NAM-NSC, decennial 2010, and 2005-2018 ACS, Form 1040, and Form W-2 data. Approved for release under CBDRBFY2021-CES010-029.

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

PIK rate on full NSC-NAM data transfer 0.5766

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
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CEM results

• Three samples: all workers, associate’s or less, and a pre/post group 

isolated to those attaining first cred in 2016/2017

• Earnings/employment measured in 2018

• Matches made on X*; same controls used in post-match regressions

• X* includes: age, age squared, race/ethnicity, gender, education (4 

categories or associates/no associates), industry (5 categories), family 

structure (married/children), employed in 2014, 5 bins of first-period 

earnings, employer 2017 payroll

• Bins were chosen to balance match with the retention of project obs. 

In the rebalance, we retain between 95% and 98%. 

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Pre-earnings control check

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Earnings

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Full matched sample Associates degree sample Assoc. and post 2017

Has a credential -1,165*** 1,468*** 1,993***

(88.46) (83.68) (128.3)

Observations 6,410,000 4,140,000 3,138,000

R-squared 0.3956 0.3775 0.2509

Source: NAM-NSC, decennial 2010, and 2005-2018 ACS, Form 1040, and Form W-2 data. Approved for release under CBDRB-FY2022-

CES010-001.
Employment

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Full matched sample Associates degree sample Assoc. and post 2017

Has a credential 0.006** 0.007*** 0.013***

(0.001) (0.001) (0.002)

Observations 6,410,000 4,140,000 3,138,000

R-squared 0.4066 0.4048 0.3658

Source: NAM-NSC, decennial 2010, and 2005-2018 ACS, Form 1040, and Form W-2 data. Approved for release under CBDRB-FY2022-

CES010-001.
Summary conclusions

• Workers with credentials have better employment outcomes than 

comparable workers without credentials

• Results hold for earnings when restricting to the group with an associate’s degree or 

less (since a BA/BS is a rarer event among project obs)

• Results are stronger when we limit comparison to a per/post setting

• More credentials equals better outcomes, conditional on any credential

• Earnings continue to improve for credential holders as much as 5 years from 

the last credential attainment; older workers recoup declining earnings

• Credential attainment may level playing field somewhat between white 

workers and historically disadvantaged workers 

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Lessons Learned (to date)

• The universe of credentialing entities is large and varied

• The nature and types of credentials is constantly evolving

• Additional research needs to occur on harmonization and 

categorization of credentials

• Increasingly credentials are a key component of the educational 

process (alone or in combination with traditional credentials, such as a 

college degree)

• Generation of regular data releases will require the production of 

numerous statistics and flexibility in reporting

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Thank you!

Maggie R. Jones

margaret.r.jones@census.gov

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