APPRENTICING AMERICA: THE EFFECTS OF TAX CREDITS FOR REGISTERED
APPRENTICESHIP PROGRAMSA Thesissubmitted to the Faculty of the
Graduate School of Arts and Sciencesof Georgetown University
in partial fulfillment of the requirements for the
degree ofMaster of Public Policyin Public Policy

By

Colin John Becht, B.S.

Washington, DC
April 19, 2019

Copyright 2019 by Colin John Becht
All Rights Reservedii

APPRENTICING AMERICA: THE EFFECTS OF TAX CREDITS FOR REGISTERED
APPRENTICESHIP PROGRAMSColin John Becht, B.S.
Thesis Advisor: John J. Hisnanick, Ph.D.

ABSTRACT
Despite generally declining wages for workers without a college degree, apprenticeshipoffers a proven path to improved labor market outcomes. However, use of registeredapprenticeship programs in the United States remains far lower than in other nations with
developed economies. As the federal and state governments have considered options to increase
the use of apprenticeship, a frequently considered proposal has been to provide a tax credit toemployers who operate apprenticeship programs. Examining data on the share of apprentices in
the labor forces of states without and without apprenticeship tax credits between fiscal year 2011
and fiscal year 2017 using ordinary least squares regressions, this study finds that the existenceof an apprenticeship tax credit is associated with a statistically significant increase in the share ofapprentices in a state’s labor force of between 28.72 percent and 37.60 percent. Apprenticeshiptax credits therefore appear to be an effective policy option to expand use of a program withproven benefits for workers without a bachelor’s degree that could help reduce income
inequality. However, apprenticeship tax credits do not appear to significantly affect a state’s real
GDP per capita growth rate in the short run, indicating that states seeking an immediate
economic stimulus should pursue other policies instead of or in addition to implementing anapprenticeship tax credit.

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TABLE OF CONTENTSINTRODUCTION ...........................................................................................................................1
BACKGROUND .............................................................................................................................3
LITERATURE REVIEW ................................................................................................................4
METHODS AND DATA ..............................................................................................................11
RESULTS ......................................................................................................................................18
Univariate Results ......................................................................................................................20
Correlation Results.....................................................................................................................21
Multivariate Results ...................................................................................................................22
DISCUSSION AND CONCLUSION............................................................................................28
APPENDIX ....................................................................................................................................35
BIBLIOGRAPHY ..........................................................................................................................49

iv

LIST OF FIGURES
Figure 1: Share of Apprentices in States With and Without Apprentices Tax Credit ...................39
Figure 2: Estimated Effect of Implementing Apprentices Tax Credit ...........................................45

LIST OF TABLES
Table 1: Summary Statistics of Dummy Variable .........................................................................35
Table 2: Summary Statistics of Continuous Variables ..................................................................36
Table 3: Mean Apprenticeship Share in States With and Without Tax Credit ..............................37
Table 4: Univariate Regression Results of Apprenticeship Share of State Labor Force ...............38
Table 5: Mean Real GDP Per Capita Growth Rate in States With and Without Tax Credit .........40
Table 6: Univariate Regression Results of Real GDP Per Capita Growth Rate ............................41
Table 7: Correlation Matrix ...........................................................................................................42
Table 8: Multivariate Regression Results of Model 1 ...................................................................44
Table 9: Multivariate Regression Results of Model 2 ...................................................................46
Table 10: Multivariate Regression Results of Model 3 .................................................................47
Table 11: Multivariate Regression Results of Model 4 .................................................................48v

INTRODUCTION
Despite nine years of nearly consistent growth in real gross domestic product (U.S.
Department of Commerce, 2018), real average hourly wages have remained largely flat in the
United States, continuing a trend that dates back roughly 40 years (DeSilver, 2018). A specificpoint of concern in the labor market is the so-called polarization of employment, in which highwage and low-wage jobs have expanded at the expense of middle-wage jobs (Autor, Katz, andKearney, 2006). The results have been markedly deleterious for workers with less education,with real median hourly wages for workers with a high school degree falling by 14.3 percent
between 1979 and 2017 (Congressional Research Service, 2018).
Notwithstanding these larger trends in the labor market, verified strategies exist in which
workers with less than a bachelor’s degree can improve their earnings potential. Participation ina registered apprenticeship program in one such strategy. According to Reed et al. (2012),
participation in a registered apprenticeship program is associated with an average gain in annual
earnings of $5,839 nine years after enrollment and an estimated average increase in career
earnings of $98,718 compared to nonparticipants. Completion of a registered apprenticeshipprogram is associated with an average gain of $12,733 in the ninth year after enrollment and an
estimated $240,037 over a career. Reed et al. found similarly promising results for society as awhole, with an average social benefit of $59,606 nine years after enrollment compared to $718 ingovernment costs of overseeing each registered apprenticeship. Although it is difficult todetermine the net benefit or cost to employers, who must pay for apprentices’ training and wages
as well as the costs of establishing and managing the apprenticeship program, employers cangain from a better trained workforce and reduced recruiting costs to find skilled workers.

1

Despite the clear benefits of registered apprenticeship programs, they remain a small
fraction of the U.S. workforce, particularly compared to many other nations with developed
economies. Even after a 42 percent increase in the number of registered apprenticeships between
2013 and 2017 (U.S. Department of Labor, 2018), apprentices make up just 0.3 percent of the
U.S. labor force1 compared to 3.0 percent in Germany.2 With many prospective apprentices
experiencing waitlists for desired programs (National Joint Apprenticeship Committee, 2018),
the demand for more apprenticeships in the labor market is evident. As policymakers consider
strategies to increase wages and create more pathways to economic security that do not require afour-year college degree, ways to create more apprenticeship positions will likely be a significant
focus.
To address this policy issue, many state governments have sought to incentivize
businesses to create new apprenticeship programs or expand existing ones by offering tax credits
to employers. According to the U.S. Department of Labor (2018), 12 states offer a tax credit
related to operating an apprenticeship program or employing an apprentice. However, little is
known about the efficacy of these policies, a knowledge gap this research intends to fill.
Using the U.S. Labor Department’s data on the number of active apprentices in eachstate, this thesis investigates the following research hypotheses. Do states that implement tax
credits for registered apprenticeship programs generate a higher usage rate of apprenticeship
positions in their labor force compared to states that do not offer an apprenticeship-related tax
credit? And do states that implement tax credits for registered apprenticeship programs generate

The U.S. Department of Labor’s Bureau of Labor Statistics estimated the size of the U.S. labor force at the end of2017 at 160,597,000. The Department of Labor’s Employment and Training Administration found that there were533,607 apprentices in 2017. 533,607 divided by 160,597,000 equals 0.3 percent.
2
The Federal Statistical Office of Germany found that there were 1,300,000 apprentices in Germany in 2017. The
World Bank estimated the size of the German labor force in 2017 at 43,473,200. 1,300,000 divided by 43,473,200
equals 3.0 percent.
12

a higher growth rate in their real gross domestic product per capita compared to states that do notoffer an apprenticeship tax credit? The results of this research can help inform federal
policymakers considering adding an apprenticeship tax credit to the federal tax code, as well asstate legislatures studying whether to add or preserve their own employer tax credits forapprenticeship programs.

BACKGROUND
The registered apprenticeship system in the United States was created by the National
Apprenticeship Act of 1937 (Code of Federal Regulations). The Office of Apprenticeship in the
Department of Labor’s Employment and Training Administration oversees the registeredapprenticeship program in conjunction with State Apprenticeship Agencies that exist in 25 states
and the District of Columbia. To meet the standards of a registered apprenticeship, theapprenticeship must provide both on-the-job training and related technical instruction, among
other requirements. The on-the-job training must typically last at least 2,000 hours, and at least
144 hours per year of related technical instruction is recommended. Apprentices receive
progressively increasing wages to mark their increasing productivity over the duration of theirapprenticeship, and upon fulfilling the requirements of their apprenticeship, they receive anationally recognized certificate of completion. Sponsors of a registered apprenticeship, typically
employers, employer associations, or labor management organizations, 3 deliver theapprenticeship, connecting the apprentice with an employer and the related technical instruction.

3 Apprenticeship and labor management organizations have historically been closely linked, particularly in theskilled trades, as trade unions developed many early apprenticeship programs. By creating and controlling the rules
for the apprenticeship programs in their fields, trade unions established some control over the flow of workers into
the market for their respective crafts. In fields with equally powerful employer associations and labor management
organizations, the employer associations and trade unions mutually determined the rules and size of early American
apprenticeship programs (Motley, 1907).

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Employers cover the costs of the training and the wages paid to the apprentice. Registeredapprenticeships are designed to improve the productivity and the labor market value of theapprentices while allowing them to simultaneously earn money, a differentiating factor frommany other forms of human capital accumulation. Employers benefit from the apprentice’s labor,
which gets increasing skilled over the course of the apprenticeship, as well as access to a skilled
potential employee upon the completion of the apprenticeship.

LITERATURE REVIEW
Several studies have documented the benefits of the registered apprenticeship program,
both to the apprentice and to society. A difference-in-differences examination of apprentices inthe state of Washington found that one year after exiting the apprenticeship, participants
increased their average quarterly earnings by $2,370 compared to similar non-participants(Hollenbeck and Huang, 2006). The apprenticeship participants also had a higher hourly wage by$6.60, worked 33.5 more hours per quarter, and had a higher employment rate by 7.4 percentage
points. They also received significantly fewer benefits from Medicaid and food stamp programs.
These gains continued with only minor fade-out three years after apprentices exited theirprograms, though the apprenticeship participants did receive significantly more unemployment
compensation. Over a worker’s lifetime, apprenticeship participation was associated with a net
benefit to the worker of $220,873 and a net benefit to the public of $46,917. An examination of
apprentices across 10 different states found similar positive benefits for the participants andsociety. Reed et al. (2012) used a dosage model to find that apprenticeship participants earned anaverage of $6,595 more in annual earnings than similar non-participants in the sixth year after
enrollment in the apprenticeship. Completers of the apprenticeship earned $14,404 more than4

non-participants. In the ninth year after enrollment, participants earned an average of $5,839
more and completers earned $12,733 more. Over the course of the worker’s career, anapprenticeship participant would earn $98,718 more than a similar non-participant, and an
apprenticeship completer would earn $240,037 more. The increase in wages was statisticallysignificant for both men and women at the six-year and nine-year marks and over a career. The
social benefits of the apprenticeship exceed its social costs by $58,888 on average nine years
after enrollment and by $124,057 over the apprentice’s career. The benefits stem from increased
productivity and decreased use of social safety net programs, outweighing the costs ofadministering the apprenticeship and providing the related technical instruction.
Multiple studies of apprenticeship programs in other countries have found largely
positive results for apprenticeships. Male apprenticeship completers in Canada earn 17.3 percent
more in weekly wages than similar workers who did not participate in an apprenticeship(Boothby and Drewes, 2010). However, female apprenticeship completers earn 3.5 percent lessthan their non-apprenticeship-participating peers. In Germany’s apprenticeship programs, thereturns to apprenticeship for even the lowest ability of workers who leave school before college
is comparable to estimates of the returns to school (Clark and Fahr, 2001).
However, all of these studies suffer from the limitation that despite controls for preapprenticeship earnings, labor force participation, and demographic characteristics, it is possiblethat the apprenticeship participants were different in unobservable ways from non-participants.
Fersterer, Pischke, and Winter-Ebmer (2008) avoided this issue by examining how an event
unrelated to the apprentice – whether the apprentice’s employer remains in business – affects
how much of the apprenticeship that the apprentice completes. Using instrumental variable
estimation with the amount of time between when the apprentice joined the firm and when the5firm failed as an instrument of the apprentice’s apprenticeship duration, they found that
apprenticeship training raises wages by 3.8 percent per year of apprenticeship training in Austria.
Increased utilization of apprenticeship programs can also correspond with better labormarket statistics across a country. Across the European Union, regression analysis shows that a
one percentage point increase in the apprenticeship coverage rate in a country is associated with
a 0.95 percentage point increase in the youth employment rate and a 0.8 percentage point
decrease in the youth unemployment rate (Ecorys, Institute for Employment Studies, and Istituto
per la Ricerca Sociale, 2013).
While the benefits to the apprentices demonstrate that apprenticeships are an effective
human capital investment and the benefits to society and a nation’s economy validate
governments’ interest in expanding use of apprenticeships, that expansion may be contingent on
employers’ interest in hiring apprentices. A rigorous cost-benefit analysis of hiring apprentices
does not exist for employers in the United States. However, 97 percent of sponsors ofapprentices in the United States from a survey with a random stratified sample said they would
recommend the registered apprenticeship program, including 86 percent who would “strongly”
recommend it (Lerman, Eyster, and Chambers, 2009). Eighty percent said the registeredapprenticeship program helped to meet their demand for skilled workers. In evaluating problems
with the registered apprenticeship program, 55 percent identified apprentices’ failure to complete
the program, 54 percent identified competitor firms bidding away trained apprentices, 41 percent
identified the cost of experienced workers’ time to instruct apprentices, and 36 percent identified
the costs of the related technical instruction.
Several studies have examined the cost-benefit analysis for employers of apprentices inother countries. In European countries with “a well-functioning apprenticeship training system,”6

Muehlemann and Wolter (2014) found that firms that hire apprentices recoup the costs of theirinvestments by the end of the apprenticeship. Because apprentices often remain with thecompany after the conclusion of their apprenticeship, hiring apprentices can be an effective
strategy for a company to gain a supply of skilled labor. In Switzerland, firms reported €57,100
in benefits over the course of a three-year apprenticeship compared to €54,400 in costs(Muehlemann et al., 2010). Although German firms reported just €24,000 in benefits comparedto €46,600 in costs, the authors concluded that German firms must derive post-program benefits
that allow them to recoup their investment in training apprentices. Given the time that newly
hired employees, even skilled employees, take to reach full productivity, the costs of that periodof sub-full productivity exceed the net costs of apprenticeships for employers (Wagner, 1998).
This matches the findings of a study of apprenticeship in the United Kingdom in the engineering,
construction, business administration, and retail sectors (Hasluck and Hogarth, 2010). While theemployers’ total costs exceeded the benefits in all four sectors during the apprenticeship and in
the first year after the apprenticeship, the overall net benefits became positive by the second year
after the apprenticeship in retail and business administration and by the third year in engineeringand construction. Apprentices’ rapidly improving productivity helps their employers recoup theirinvestments quickly. Over the course of a three-year apprenticeship, the productivity of a Swissapprentice rises from 37 percent of a skilled workers in the apprentice’s first year to 75 percent
by the final year, while German apprentices improve from 30 percent of a skilled work’s
productivity to 68 percent (Muehlemann et al., 2010). A survey of Canadian employers foundthat the benefits of apprenticeships to employers exceed the costs by an average of C$74,206
over the duration of the apprenticeship without even including tax credits available to theemployer (Canadian Apprenticeship Forum, 2006). The net benefit to employers was positive in7

all 15 trades examined, ranging from C$8,250 for mobile crane operators to C$132,780 for
sprinkler system installers.
Nevertheless, the evidence of long-term benefits to employers who hire apprentices canonly stimulate increased usage of apprenticeship programs to the extent that employers recognize
their benefits. In a survey of Canadian employers in skilled trades, 13 percent of employers whoformerly hired apprentices but did not continue to do so said the training costs of apprenticeships
were too high or required too much time (Canadian Apprenticeship Forum, 2011). Twenty-five
percent of all the employers in the survey identified a perceived need for financial assistance assomething that could make apprentices a more attractive option.
Employers who participate in apprenticeship programs presumably determine that thebenefits of participation from access to a supply of skilled workers exceed the costs of setting up
their program, providing the training, and paying wages to apprentices. In contrast, employers
who do not participate in apprenticeship programs presumably determine that the costs exceed
the benefits. The costs of establishing an apprenticeship program and getting it registered withthe federal Office of Apprenticeship or a State Apprenticeship Agency create a fixed upfront costthat may serve as a barrier to employers hiring apprentices even if the long-term net benefits are
positive. Providing a tax credit for hiring apprentices increases the benefits to employers. Thisadditional benefit should, on the margin, make the net benefits of hiring an apprentice positive
for some employers, increasing employer participation in apprenticeship programs. If the net
social benefits of registered apprenticeships in the United States are sufficiently positive,
increasing the social costs of the registered apprenticeship by the value of the tax credit would
still leave the net social benefits positive. Increasing the number of apprentices would therefore
increase the total social benefits of apprenticeship in the United States.

8No rigorous studies exist that examine the effects of tax credits for employers who hire
apprentices. This study aims to fill that gap. However, many other studies have examined other
employer tax credits that could influence business decision-making and hiring. In perhaps theclosest parallel to tax credits for hiring apprentices, studies have assessed the effects of the Work
Opportunity Tax Credit (WOTC). The WOTC provides a tax credit to employers who hirecertain categories of disadvantaged workers, including ex-offenders, recipients of certain social
safety net programs, and some veterans. In 2007, the WOTC was expanded, to include increasing
the tax credit to $4,800 for employers who hired disabled veterans who were recently discharged
or unemployed for more than six months. Using a difference-in-differences model, Heaton(2012) found that this increased tax credit was associated with a statistically significant 1.8
percentage point increase in employment among the target group of disabled veterans. Because
the pre-credit earnings for this population were low, the tax credit was associated with a 39.9
percent increase in wage income. However, not all studies of the WOTC have produced aspromising results. Examining the WOTC among welfare recipients in Wisconsin, Hamersma(2008) found only short-term benefits that faded out quickly. Eligibility for the WOTC wasassociated with a 5.9 percentage point increase in the likelihood of being employed six months
after becoming eligible, but by one year after becoming eligible and in the second year of
eligibility, there was no statistically significant difference in the probability of employment. The
author remarks that a low take-up rate may contribute to the limited long-term employment
effects. It is worth noting that the WOTC is designed specifically to improve the labor market
outcomes of several types of hard-to-employ workers. Although some apprentices may alsocome from these groups of workers, apprentices represent a broader array of workers, including
those with greater employability.

9

Several other studies have examined the effects of tax credits on business behavior via
tax credits for research and development. In a close analog of what this study will attempt to
examine regarding apprenticeship tax credits, Wu (2005) assessed research and development tax
credits across 13 states over 17 years. The author found that the establishment of a state researchand development tax credit was associated with an increase in private per capita research anddevelopment spending of $75 to $118. Larger per capita numbers of doctoral degrees and state
expenditures on higher education were associated with larger increases in private research anddevelopment spending resulting from the tax credit. The size of the credit also appears to matter,as a study across all 50 states found that a one percentage point increase in a state’s effective
research and development credit rate was associated with a three-to-four percent increase inresearch and development spending within the state in the long run (Wilson, 2005). International
studies have likewise shown an increase in research and development spending associated with aresearch and development tax credit. Across nine countries in the Organization for Economic
Co-operation and Development, a 10 percent decrease in the cost of research and development
was associated with slightly more than a one percent increase in the level of research anddevelopment in the short run and slightly less than a 10 percent increase in the long run (Bloom,
Griffith, and Van Reenen, 2002). Researching the varying effects of research and development
tax credits across different sectors, Castellacci and Lie (2015) found in a meta-regression
analysis that the tax credits are most effective at stimulating increased private spending on
research and development at small and medium enterprises, firms in the service sector, and firms
in low-tech sectors.
Despite robust evidence of the positive personal and social benefits of apprenticeships
and of employers’ willingness to respond to targeted changes in tax policy related to business10

decisions, there is an absence of research into the effects of tax credits for employers who hire
apprentices. With 12 states currently offering a tax credit related to operating an apprenticeshipprogram or employing an apprentice, according the United States Department of Labor, the data
to evaluate apprenticeship tax credits clearly exists. Moreover, the existence of the employer tax
credits for apprenticeships demonstrates a palpable interest in the efficacy of such policies. As
the United States weighs policy solutions to increase the utilization of apprenticeship programs,
this study strives to provide valuable evidence regarding the effectiveness of a prominent option.

METHODS AND DATA
To examine the effects of employer tax credits for hiring apprentices on the use of
apprentices in a state’s labor force, I use an ordinary least squares regression with the share of astate’s labor force that is composed of apprentices, expressed as the number of active apprentices
per 1,000 workers, as my dependent variable. My independent variable of interest is a dummy
variable of whether the state had an employer tax credit related to hiring apprentices in effect for
at least six months of the fiscal year.4 If the upfront costs of hiring an apprentice and managing
an apprenticeship program demotivate employers from sponsoring apprenticeship programs, thenthe existence of a tax credit for hiring apprentices should increase the share of apprentices in theworkforce.
In this regression analysis, I control for the real gross domestic product per capita growthrate in the state, lagged by one year; the percentage of the state’s population with an associatedegree or higher; the percentage of the state’s labor force that works in a blue-collar field; the4

Because the apprenticeship tax credit must have been in effect for at least six months of the fiscal year, this meansthat Maryland’s apprenticeship tax credit is not counted for fiscal year 2017. Although the tax credit created by theMore Jobs for Marylanders Act of 2017 applied to the 2017 taxable year, it was not signed into law until April 11,
2017. This left employers with little time to alter their hiring plans in response to the tax credit in fiscal year 2017.

11

race and ethnicity proportions of the state’s population; the percentage of workers in the state’s
labor force who are members of a labor union; and the fiscal year. I control for GDP growth
because it is possible that higher GDP growth would be associated with a higher percentage ofapprentices in the state’s labor force due to employers being more willing to invest in an
apprentice when the economy is more robust. However, it is also possible that the relationshipcould be negative if the size of the workforce expands faster than the number of apprentices in a
growing economy. Regardless, the GDP data is lagged by one year because employers need time
to respond and adjust to economic indicators in their hiring practices. States with a higher
percentage of adults with an associate degree or more may have a lower percentage ofapprentices. Apprenticeship is an alternative path after high school that allows apprentices toenter the labor market while increasing their human capital. It may therefore be a substitute forother forms of postsecondary training, such as studying full-time at a two- or four-year college.
Although apprenticeships exist in newer, growing sectors of the labor force, such as healthcare,the most common professions for apprentices remain in blue-collar trades such as electricalwork, carpentry, and truck driving (U.S. Department of Labor, 2018). Thus, states with a higher
percentage of workers in blue-collar professions may have a higher share of apprentices. I
control for the racial and ethnic demographics of the state by including the percentage of thestate population that is black, the percentage that is Hispanic, and the percentage that is AsianAmerican. These variables allow me to determine if the racial configuration of the state
influences its use of apprenticeship programs. Modeling my regression with these control
variables reflects practices used to study the effects of employer tax credits for research anddevelopment (Wu, 2005). I also control for the percentage of workers in each state who are union
members because of the role unions can play as sponsors of apprenticeship programs. States with12

more robust union representation may have more established potential sponsors forapprenticeship programs, leading to greater use of apprentices. Lastly, controlling for the fiscal
year allows me to control for nationwide factors that may have affected the use of apprentices orthe creation of apprenticeship tax credits, such as changes in the federal government’s
investment in apprenticeship programs.
Model 1apprentsharelf =  0 + 1*apprenttaxcredit +  2*lagstateGDPgrowth +3*percentpostsecondary +  4*percentbc +  5*percentblack +6*percenthisp +  7*percentasian +  8*percentunion +  k*Fiscal Year
Dummies + In this model, the dependent variable apprentsharelf is the number of apprentices per
1,000 workers in a state’s labor force; apprenttaxcredit is the dummy variable of whether thestate has a tax credit for hiring apprentices; lagstateGDPgrowth is the real GDP per capita
growth rate in the state, lagged by one year; percentpostsecondary is the percentage of the state’s
population with an associate degree or higher; percentbc is the percentage of the state’s labor
force that is employed in blue-collar fields; percentblack is the percentage of the state’s
population that is black; percenthisp is the percentage of the state’s population that is Hispanic;
percentasian is the percentage of the state’s population that is Asian-American; percentunion is
the percentage of the state’s workers who are members of a union; and Fiscal Year Dummies
represent the dummy variables to control for each fiscal year in the model.
To further examine the effects of employer tax credits for hiring apprentices on the use of
apprentices in a state’s labor force, I use a second model utilizing ordinary least square
regression. This model is structured in the same way as Model 1 but excludes Hawaii from the13regression. Hawaii is an outlier in many of the variables included in Model 1, most notably in its
use of apprentices. Among all the states and years included in the data for this research, Hawaii’s
share of apprentices in its labor force in each year was higher than in any other state in any otheryear. In each year in the dataset, there were at least 10 apprentices for each 1,000 workers in
Hawaii’s labor force, reaching a high of 14.74 apprentices per 1,000 workers in 2017. The
District of Columbia in 2017 was the only other state to have 10 or more apprentices per 1,000
workers in any year in the dataset. To account for the outsized impact that Hawaii’s liberal use of
apprenticeship programs might have on Model 1, my second model removes Hawaii from thedataset used in the regression. The variables in the model remain the same.
Model 2*apprentsharelf =  0 + 1*apprenttaxcredit +  2*lagstateGDPgrowth +3*percentpostsecondary +  4*percentbc +  5*percentblack +6*percenthisp +  7*percentasian +  8*percentunion +  k*Fiscal Year
Dummies + *Hawaii excluded
To examine the effects of employer tax credits for hiring apprentices on the real grossdomestic product per capita growth rate, I use an ordinary least squares regression with a state’s
real GDP per capita growth rate in a given fiscal year as my dependent variable. As in Models 1
and 2, my independent variable of interest is a dummy variable of whether the state had an
employer tax credit related to hiring apprentices in effect for at least six months of the fiscal
year. Employer tax credits are typically believed to have a stimulative effect on the economy by
reducing costs on employers, allowing them to invest more in their businesses and expanding
their output.

14

This model uses the same control variables as Models 1 and 2 to account for the same
factors that might affects the involvement of apprentices in a state’s labor force and therefore inits economic output. The GDP per capita growth rate in a state one year prior should be highly
correlated with the state’s GDP per capita growth rate in a given year. States in which a higher
share of the population has a postsecondary degree may be more productive as a result of that
increased education, producing higher GDP growth. Controlling for the fiscal year is particularly
important for this model because there are many factors that could cross state lines that could
affect a state’s real GDP per capita growth rate and alter its willingness to implement anapprenticeship tax credit.
Model 3stateGDPgrowth =  0 +  1*apprenttaxcredit +  2*lagstateGDPgrowth +3*percentpostsecondary +  4*percentbc +  5*percentblack +6*percenthisp +  7*percentasian +  8*percentunion +  k*Fiscal Year
Dummies + 
Accounting for Hawaii’s role as an outlier in its use of apprenticeship programs, I
also use a model that is similar to Model 3 but excludes Hawaii from the dataset in the
regression. Although this model does not examine the share of apprentices in a state’s
labor force, Hawaii’s extensive use of apprentices could alter its GDP growth and exert
outsized weight on the results of the regression. This last model also uses the state’s real
GDP per capita growth rate as its dependent variable, whether the state has anapprenticeship tax credit in effect as its independent variable of interest, and the samegroup of control variables as Models 1, 2, and 3.

15

Model 4*
stateGDPgrowth =  0 +  1*apprenttaxcredit +  2*lagstateGDPgrowth +3*percentpostsecondary +  4*percentbc +  5*percentblack +6*percenthisp +  7*percentasian +  8*percentunion +  k*Fiscal Year
Dummies + *Hawaii excluded
The data to conduct these four regression analyses comes from a variety of sources within
the federal government. The share of apprentices in a state’s labor force is a self-calculated
statistic that uses the number of active apprentices in the state divided by the size of the state’s
labor force. The U.S. Department of Labor’s Office of Apprenticeship (OA) collects the annual
number of active apprentices in each state (U.S. Department of Labor, 2018). For 34 of thestates, this data comes from OA’s Registered Apprenticeship Partners Information Management
Data System (RAPIDS), which records individual data on apprentices. The remaining states do
not use RAPIDS but instead submit information on the number of apprentices in their state to
OA on a quarterly basis. The size of the state’s labor force comes from U.S. Census Bureau’s
Current Population Survey, which samples around 60,000 households nationwide and collects
data on a variety of aspects of the labor market, including labor force participation andemployment (U.S. Department of Commerce, 2018). I use the total nonfarm, seasonally adjusted,labor force from each state. Because OA presents its totals on active apprenticeships by fiscal
year, I use the size of the state’s labor force in September, the final month of the fiscal year. I
have independently identified states that have implemented a tax credit for hiring apprentices. To
do this, I began with OA’s list of “States that offer tax credits to employers for hiring
apprentices” and independently verified when those states implemented their tax credits (U.S.

16

Department of Labor, 2018). The data on states’ real GDP per capita growth rate comes from
calculations by the Bureau of Economic Analysis (U.S. Department of Commerce, 2018). The
percentage of each state’s population with an associate degree or higher and the racial and ethnic
demographics of the states are drawn from the U.S. Census Bureau’s American Community
Survey, a national monthly survey used to produce estimates of demographic information about
the United States, individual states, and smaller areas (U.S. Department of Commerce, 2018).
The Current Population Survey also provides the percentage of workers in each state who aremembers of unions. Lastly, the Occupational Employment Statistics survey offers data regarding
the percentage of workers in blue-collar fields in each state. This survey is conductedsemiannually by the Bureau of Labor Statistics and state workforce agencies and samples
200,000 establishments throughout the country. To determine the fields that I am considering theblue-collar sector, I identified the major occupation groups that hosted the most common
occupational titles among apprentices. These groups include the transportation and material
moving occupations; the construction and extraction occupations; the installation, maintenance,and repair occupations; and the production occupations. Summing up the employment
percentages of each of these four groups in each state produced the percentage of the state’s
labor force in blue-collar fields that I use in my analysis.
My sample includes data from all 50 states in addition to the District of Columbia. OA
provides data on the number of active apprentices for each fiscal year from 2011 to 2017. 5 This
leaves me with 357 state-years in my dataset. In 60 of those state-years, the state had a tax credit

5

OA did not report data on the number of active apprentices for the District of Columbia, Montana, and Rhode
Island in 2013 and the District of Columbia in 2012. For Montana and Rhode Island, I computed a figure for the
missing values by taking the respective averages of the states’ number of active apprentices in 2011, 2012, 2014,
and 2015. For the District of Columbia, I first computed a figure for 2013 by taking the average for the District in2011, 2014, and 2015. I then computed a figure for 2012 by taking the average for the District in 2011, 2013, and2014, using the aforementioned computed value for 2013 in this calculation.

17

for hiring apprentices in effect. The mean share of the apprentices in the workforce in my sample
is slightly more than three apprentices per 1,000 workers (Table 2). The mean GDP per capita
growth rate is 0.85 percent while the mean lagged GDP per capita growth rate is 0.90 percent.
The mean percentage of the state’s population with an associate degree or higher is 39.60 percent
while 21.96 percent work in a blue-collar field. The mean percentage of the population of thestates that is black is 11.18 percent, with 10.98 percent Hispanic and 3.89 percent AsianAmerican. The mean percentage of workers in a state who are members of a union is 10.25
percent.

RESULTSThis research evaluates state employer tax credits related to operating registeredapprenticeship programs and the effects those tax credits have on the use of apprenticeshipprograms in the United States. It also examines the extent to which those tax credits stimulate
economic growth. To conduct this analysis, I use state-level data from a variety of sources within
the federal government covering all 50 states and the District of Columbia from fiscal year 2011
to fiscal year 2017. Data on the number of active apprentices in each state comes from the U.S.
Department of Labor’s Office of Apprenticeship. The U.S. Census Bureau’s Current Population
Survey provides data on the size of state labor forces and the percentage of workers in each state
who are members of labor unions. I have independently verified states that have implemented a
tax credit related to hiring apprentices. The U.S. Bureau of Economic Analysis provides data on
the real gross domestic product per capita growth rate in each state, while the U.S. Census
Bureau’s American Community Survey provides the percentage of each state’s population withan associate degree or higher and the racial and ethnic demographics of each state. Lastly, the18Occupational Employment Statistics survey offers data for a self-calculated measure of thepercentage of workers in blue-collar fields in each state.
To conduct my analysis of the effects of the apprenticeship tax credits on the use of
apprenticeship programs, I use an ordinary least squares regressions with the share of a state’s
labor force that is composed of apprentices as my dependent variable. This variable expresses thenumber of active registered apprentices per 1,000 workers in the state. My independent variableof interest is a dummy variable reflecting whether the state had an employer tax credit related tohiring apprentices in effect in that year. Model 1 includes all states in each year from fiscal year
2011 to fiscal year 2017. To account for Hawaii’s considerably larger use of apprentices in its
labor force than in any other state, Model 2 excludes Hawaii from the regression. In both of these
regression analyses, I control for the real gross domestic product per capita growth rate in thestate, lagged by one year; the percentage of the state’s population with an associate degree orhigher; the percentage of the state’s labor force that works in a blue-collar field; the race andethnicity of the state’s population; and the percentage of workers in the state who are members ofa union. I also control for the fiscal year to account for any nationwide effects on the use of
apprenticeship tax credits or apprenticeship programs, such as increased investment from thefederal government in expanding apprenticeship. If upfront financial costs related to establishinga registered apprenticeship program and hiring apprentices create a barrier to employers
choosing to operate apprenticeship programs, then the existence of a tax credit for hiring
apprentices should increase the share of apprentices in the workforce.
To evaluate the effects of apprenticeship tax credits on economic growth, I use similar
ordinary least squares models to Models 1 and 2 with the same control variables. However, thedependent variable is the state’s real gross domestic product per capita growth rate. The

19

independent variable of interest remains whether the state had an employer tax credit related tooperating an apprenticeship program in effect in that year. Model 3 includes all states in eachyear from fiscal year 2011 to fiscal year 2017. Similar to Model 2, Model 4 excludes Hawaii
from the regression. To the extent that the tax credit incentivizes employers to hire apprentices toexpand their businesses, the tax credit should increase economic growth.
The data for my analysis comes from all 50 states and the District of Columbia for eachfiscal year from 2011 to 2017. During this period, most states did not offer an employer tax
credit related to operating an apprenticeship program. However, by 2017, 10 states had anapprenticeship tax credit in effect. Because the data covers every state, it includes a wide varietyof economies, workforces, and populations with different demographics. The period covered
begins shortly after the end of the Great Recession and continues through the economic recovery.

Univariate Results
Examining the share of apprentices in the labor force in states with and without anapprenticeship tax credit indicates that the tax credit may be associated with increased use of
apprenticeship programs. States with a tax credit for hiring apprentices did have a higher share ofapprentices in their labor force than states that did not have a tax credit (Table 3).
Additionally, a univariate OLS regression of the share of apprentices in the state’s labor
force on whether the state had a tax credit indicates a statistically significant increase in theapprenticeship share in states with a tax credit (Table 4).
A scatterplot and line of best fit of the share of apprentices per 1,000 workers in stateswith and without tax credits related to operating apprenticeship programs further illustrates that20

the apprenticeship share is higher in states that have an apprentices tax credit than in states that
do not (Figure 1).
However, analyzing the real GDP per capita growth rate in states with and without anapprenticeship tax credit indicates that the tax credit may be associated with decreased economic
growth. States with an apprenticeship tax credit had lower growth rates in their real GDP per
capita than states that did not have the tax credit (Table 5).
This aligns with a bivariate OLS regression of the state real GDP per capita growth rate
on whether the state had an apprenticeship tax credit, which found a statistically significant
decrease in the real GDP per capita growth rate in states with a tax credit (Table 6). Althoughthese results are counter to what economic theory would suggest, they may simply indicate thatstates with lower economic growth are more likely to implement an apprenticeship tax credit.

Correlation Results
A statistically significant positive correlation between states with an apprenticeship tax
credit and the share of apprentices in the state’s labor force provides further indication that the
tax credit appears to be associated with an increased use of apprenticeship (Table 7). Other
variables, such as the percentage of the state population with an associate degree or higher, thepercentage of Asian-Americans in the state population, and the percentage of workers who areunion members, are also positively correlated with an increased use of apprentices in the statelabor force. Surprisingly, the percentage of workers in the state employed in blue-collar fields isnegatively correlated with the share of apprentices in the state labor force. The percentage of
Hispanics in the state population is also negatively correlated with use of apprenticeship.

21

Consistent with the idea that states with low economic growth are more likely to
implement a tax credit for operating an apprenticeship program, the existence of anapprenticeship tax credit is negatively correlated with the real state GDP per capita growth rate
and the lagged real state GDP per capita growth rate. Apprenticeship tax credits appear to be acounter-cyclical policy measure. The existence of an apprenticeship tax credit is also negatively
correlated with the percentage of the state population with an associate degree or higher, perhaps
indicating that states that place more emphasis on traditional forms of postsecondary training areless likely to prioritize increasing apprenticeship programs.

Multivariate Results
The results of Model 1 indicate that the existence of an employer tax credit related tooperating a registered apprenticeship program does increase the share of apprentices in a state’s
labor force by a statistically significant amount. According to the model, the existence of the tax
credit increases the share of apprentices by 0.862 apprentices per 1,000 workers, controlling forthe state’s economic growth and other factors of the state’s labor force and population (Table 8).
Although this may appear to be a rather modest increase, because that the share of apprentices
currently in the labor force in U.S. states is so small, the increase associated with the tax credit
represents a sizeable jump. The average state has only 3.11 apprentices per 1,000 workers in its
labor force. Thus, an uptick in the share by 0.862 apprentices amounts to a 27.75 percent
increase. In a state as large as California, the results of Model 1 indicate that the introduction ofan apprenticeship tax credit could increase the number of active registered apprentices by morethan 14,500. The average share of apprentices in the labor force in states without anapprenticeship tax credit is 3.00 apprentices per 1,000 workers. According to the results of the22model, implementing an apprenticeship tax credit could increase that share by 28.72 percent(Figure 2).
These results provide evidence that the financial costs of operating an apprenticeshipprogram and hiring apprentices create a barrier that prevents some employers from creating anapprenticeship program. The financial incentive of the apprenticeship tax credit appears to makethe decision to implement or expand an apprenticeship program profitable for some employers,
leading them to do so and expanding the number of apprentices in the labor force.
The percentage of Asian-Americans in the state population is also associated with astatistically significant increase in the share of apprentices in the state labor force, according toModel 1. Each additional percentage point of Asian-Americans in a state’s racial demographics
is associated with 0.236 more apprentices per 1,000 workers (Table 8). However, this finding isheavily influenced by Hawaii, which has a considerably larger share of Asian-Americans in its
population than any other state as well as the largest share of apprentices in its labor force of any
state. Model 1 also indicates that the percentage of Hispanics in the state population is associatedwith a statistically significant decrease in the share of apprentices in the state labor force. Each
additional percentage point of Hispanics in a state’s ethnic demographics is associated with
0.068 fewer apprentices per 1,000 workers. This may indicate that Hispanic workers are lesslikely to seek out apprenticeships or that employers are less likely to offer apprenticeships to
Hispanic workers.
The percentage of workers in the state employed in blue-collar fields is also associatedwith a decrease in the share of apprentices in the state labor force, according to Model 1. An
additional percentage point of blue-collar workers is associated with 0.065 fewer apprentices per
1,000 workers. This is a surprising finding given that blue-collar fields include many of the most23

common professions for apprenticeships. It is possible that states with robust blue-collar
industries have infrequent shortages of full-time jobs and trained applicants and thus employers
feel less need to provide training through apprenticeship programs. Further research on theoccupational breakdown of state labor forces and apprenticeship positions may provide insights
into this topic.
Given the relatively high R2-statistic and F-statistic for this regression, Model 1 appears
to explain a sizeable amount of the variation in the share of apprentices among the different
states.
When Hawaii is removed in Model 2, several results change considerably. Most notably,
the increase in use of apprenticeship programs associated with apprenticeship tax credits
becomes stronger. According to Model 2, the existence of an apprenticeship tax credit isassociated with an increase in the share of apprentices in a state’s labor force by 1.129
apprentices per 1,000 workers, controlling for the state’s economic growth and other factors of
the state’s labor force and population (Table 9). Because Hawaii has such a strong use of
apprentices in its labor force and because it does not have an apprenticeship tax credit, it
noticeably decreases the relationship between apprenticeship tax credits and increased use of
apprenticeship programs. Removing Hawaii from the model in Model 2 thus makes the link
between the tax credits and a rise in the share of apprentices more robust. Based on the results ofModel 2, the average state without an apprenticeship tax credit could increase the share ofapprentices in its labor force by 37.60 percent by implementing the tax credit. This finding
provides further evidence to support the theory that reducing the financial costs of operating anapprenticeship program can generate a significant increase in the use of apprenticeship in the
U.S.

24

As expected, removing Hawaii from Model 2 reduces the relationship between thepercentage of a state’s population that is Asian-American and the share of apprentices in thestate’s labor force. In fact, the relationship is no longer statistically significant in Model 2. Thishighlight how much of an outlier Hawaii is in both of these two variables, allowing it to have asignificant effect on Model 1’s finding of a relationship between the two variables.
Model 2 surprisingly finds a significant, positive relationship between the percentage of astate’s population with a postsecondary degree and the share of apprentices in the state’s labor
force. Each additional percentage point of the population with a postsecondary degree isassociated with an increase of 0.061 apprentices per 1,000 workers in the state. Although
apprenticeship may be viewed as a substitute for other forms of postsecondary education, statesin which more of the population pursues a postsecondary degree may be more likely to seek out
all possible paths of postsecondary skills development. Furthermore, the related technicalinstruction of an apprenticeship could form the basis for earning an associate or bachelor’s
degree either as part of the apprenticeship or afterward. Additional research into the relationshipbetween apprenticeship and postsecondary degree attainment could provide more insight into
this finding.
Model 2 also finds a significant, positive relationship between the percentage of workers
in a state who are members of a union and the share of apprentices in the state’s labor force.
Each additional percentage point of union membership among workers in the state is associatedwith 0.102 more apprentices per 1,000 workers. This finding supports the important role unions
can play as sponsors of apprenticeship programs. In states where more workers are members ofunions, these stronger unions appear to be better able to organize apprenticeship programs andpartner with employers to hire and train the apprentices.

25

Although Model 2 does not have as high of an R2-statistic and F-statistic as Model 1, it
still explains a significant amount of the variation in the share of apprentices among different
states’ labor forces.
According to the results of Model 3, the existence of an employer tax credit related tooperating a registered apprenticeship program does not have a statistically significant effect on astate’s real GDP per capita growth rate when controlling for the state’s economic growth a year
earlier and other factors of the state’s labor force and population (Table 10). This finding is not
surprising given the relatively small share that apprentices comprise of a state’s labor force. To
the extent that the apprenticeship tax credit increases the hiring of apprentices or reduces
employers’ labor costs, this effect is likely too small to stimulate significant growth in the state’s
entire economy. In fact, because apprentices’ productivity improves rapidly over the course of
their apprenticeship (Muehlemann et al., 2010), an increase in the number of apprentices in astate’s labor force may initially decrease the average productivity of the state’s workers before
increasing it in the long run. Regardless, while apprenticeship tax credits do appear to increase
the use of apprentices, this increase does not appear to have a short-term stimulative effect on thestate’s economy.
The R2-statistic and F-statistic for Model 3 are both rather low, though the model isstatistically significant. This makes sense given that the control variables generally relate tofactors that might affect the use of apprenticeship programs in a state. Most of these variables do
not appear to have a statistically significant effect on the state’s economic growth. Only thestate’s real GDP per capita growth rate of the prior year has a statistically significant relationshipwith the state’s current-year real GDP per capita growth rate.

26

Similar to Model 3, Model 4 finds that the existence of an employer tax credit related tooperating a registered apprenticeship program does not have a statistically significant effect on astate’s real GDP per capita growth rate when controlling for the state’s economic growth a year
earlier and other factors of the state’s labor force and population (Table 11). This reinforces thetheory that apprentices constitute too small of a share of a state’s labor force to significantly
change the economic output of the state in the short run. Additionally, the savings to employers
from apprenticeship tax credits are too small of a share of their overall budget to drive asignificant increase in output. Although apprenticeship tax credits can alter the hiring approachof employers with regard to apprentices, the effect of this change on a state’s economic growthrate would likely not be felt until a much longer term.
As with Model 3, Model 4 has a low R2-statistic and F-statistic but remains a statisticallysignificant model. This again is largely due to the strong relationship between the real GDP per
capita growth rate in a state in the prior year to its real GDP per capita growth rate in the given
year.
The regressions for each of the four models do not include dummy variables for eachspecific state. Although including those variables could attempt to control for other factors
within states that affect the share of apprentices (such as the awareness of employers regarding
apprenticeship programs and the marketing efforts of states to promote apprenticeship to
employers and workers) or the economic growth rate, the models utilize state-level data for the
independent variables. Thus, including a dummy variable for each state would create an issue ofmulticollinearity.

27

DISCUSSION AND CONCLUSIONMy research evaluates the effectiveness of employer tax credits related to operating
registered apprenticeship programs, specifically examining the extent to which the existence ofapprenticeship tax credits increases the use of apprenticeship programs and increases economic
growth. To evaluate these questions, I use ordinary least squares regression analysis. In mymodels, the dependent variables are the share of active registered apprentices per 1,000 workers
in a state’s labor force and the state’s real gross domestic product per capita growth rate. The
independent variable of interest in the models is whether the state had an apprenticeship tax
credit in effect in that year.
The results of this research indicate that apprenticeship tax credits do increase the use ofregistered apprenticeship programs. The results from Model 1 indicate that the existence of anapprenticeship tax credit is associated with an increase in the share of apprentices in a state’s
labor force by 0.862 apprentices per 1,000 workers, controlling for other factors in the state’s
economy, demographics, and labor force characteristics. In the average state without anapprenticeship tax credit in 2017, this rise in the share of apprentices in the labor force would
amount to a 28.72 percent increase if a tax credit were implemented. The results from Model 2
indicate that the existence of an apprenticeship tax credit is associated with an increase in theshare of apprentices in a state’s labor force by 1.129 apprentices per 1,000 workers, controlling
for those same other factors in the state. Based on this larger effect, the share of apprentices in
the labor force of the average state without an apprenticeship tax credit in 2017 would increaseby 37.60 percent.
Apprenticeship tax credits do not appear to affect a state’s economic growth. According
to the results of Models 3 and 4, the existence of an apprenticeship tax credit is not associated28with a statistically significant change in a state’s real gross domestic product per capita growthrate, controlling for other factors in the state’s economy, demographics, and labor force
characteristics.
The finding that apprenticeship tax credits increase the use of apprenticeship programs
provides compelling support for the creation of these tax credits. As states and the federal
government consider policy options to raise the use of apprenticeship programs, my results
suggest that implementing an apprenticeship tax credit is an effective strategy. This link between
the tax credits and increased use of apprenticeships indicates that the costs to employers offorming a registered apprenticeship program, hiring apprentices, and providing training create
barriers that limit the use of apprenticeship programs in the United States. Apprenticeship tax
credits subsidize those costs and consequently appear to make the decision to create or expand an
apprenticeship program profitable for some employers. If every state that did not have anapprenticeship tax credit in effect in 2017 implemented one, the results from Model 1 indicate
this would increase the number of apprentices by nearly 120,000, a sizeable rise given that theUnited States had just 534,000 active apprentices in 2017.
The difference between the results of Model 1 and Model 2 illustrate the extent to which
Hawaii is an outlier in its use of apprenticeship programs. Of the 357 state-years in my dataset,
Hawaii accounted for each of the top seven figures in the share of apprentices in a state’s labor
force. By 2017, nearly 15 out of every 1,000 workers in Hawaii’s labor force were apprentices,more than four times the average share of apprentices in the other states that year. Hawaii hasachieved this extensive use of apprentices without implementing an apprenticeship tax credit.
Thus, when removing Hawaii from the regression analysis in Model 2, the model finds a stronger
relationship between the existence of an apprenticeship tax credit and the share of apprentices in29

a state’s labor force. Without including Hawaii, the use of apprenticeship in states without anapprenticeship tax credit is far lower. In fact, the average share of apprentices in states without
an apprenticeship tax credit drops from 3.002 to 2.766 apprentices per 1,000 workers if Hawaii is
not included. Because Model 2 removes Hawaii, the state with the highest use of apprentices anda state without an apprenticeship tax credit, from the analysis, it is not surprising that this model
finds the effects of apprenticeship tax credits on the use of apprenticeship programs to bestronger. Further research on the use of apprenticeship programs in Hawaii could provide insight
into what policies, economic conditions, labor market characteristics, or other factors explain thestate’s high share of apprentices in its labor force.
The lack of evidence indicating that apprenticeship tax credits increase states’ real grossdomestic product per capita growth rates suggests that the effects of an apprenticeship tax crediton the overall economy of a state are too small to provide a significant stimulative effect. Even
with the increased use of apprenticeship facilitated by the tax credit, apprentices still account fora very small share of a state’s labor force. Thus, changes in this share have little effect on theeconomic output of a state. State governments seeking policies to provide a short-run stimulation
to their economies should therefore pursue other policies instead of or in conjunction with anapprenticeship tax credit. Any sizeable effects of an apprenticeship tax credit on economic
growth would likely be in the long run due to the increased productivity of workers after they
have completed their apprenticeship.
An array of research has demonstrated that registered apprenticeships are profitable toapprentices by increasing their earnings and profitable to society by increasing tax revenue anddecreasing use of social safety net programs (Hollenbeck and Huang, 2006; Reed et al., 2012).
These findings have raised the question of why, if apprenticeship is so beneficial, is its use in the30

United States so low, particularly in comparison to other countries with similarly advanced
economies and labor forces. Without rigorous evidence of the costs and benefits to U.S.
employers of creating registered apprenticeship programs, speculation has frequently focused oneither that the financial costs of operating an apprenticeship program exceed its benefits or thatthe federal government’s regulations of the registered apprenticeship system create too large of aburden on employers (King, 2018). State governments have attempted to address the financial
costs on employers by implementing apprenticeship tax credits, but despite the increasingly
common use of these tax credits, there had been a dearth of rigorous evaluation of theireffectiveness. My research indicates that subsidizing the financial costs on employers byproviding them an apprenticeship tax credit can significantly increase the use of apprenticeshipprograms. Although this increase still leaves the use of apprenticeship in the U.S. far lower thanin other countries such as Germany, it nonetheless represents a sizeable spike in the share ofapprentices in the labor force in U.S. states.
My results demonstrate that state and federal policymakers seeking mechanisms toincrease the earnings and improve the labor market outcomes of workers without four-yearcollege degrees should clearly consider apprenticeship tax credits part of their policy arsenal.
Prior research has shown that apprenticeships improve earnings for workers in the short run andlong run and do so in a net beneficial way for society (Hollenbeck and Huang, 2006; Reed et al.,2012; Clark and Fahr, 2001; Fersterer, Pischke, and Winter-Ebmer, 2008; Ecorys, Institute for
Employment Studies, and Istituto per la Ricerca Sociale, 2013). This study reveals that
apprenticeship tax credits significantly increase the use of apprenticeship programs. Taken
together, the evidence indicates that apprenticeship tax credits effectively increase the use of a31

program with proven economic benefits for workers and society, improving labor market
outcomes for workers without requiring a bachelor’s degree.
In conducting my research, I use state-level data to control for a variety of state-specificfactors that might affect a state’s likelihood of implementing an apprenticeship tax credit and its
use of apprenticeship in its labor force. This use of state-level data compels that I not utilize state
fixed effects models because the inclusion of dummy variables for each state would introduce
issues of multicollinearity. As a result, I am unable to control for qualitative factors that might
impact the use of apprenticeship programs and the implementation of apprenticeship tax credits.
For example, South Carolina, in addition to offering an apprenticeship tax credit, authorizes its
state apprenticeship agency to provide robust technical assistance to employers to help them
design registered apprenticeship programs and to partner with the state’s technical colleges tohelp provide the related technical instruction to apprentices. This type of state assistance andother non-financial support for apprenticeships are not captured in my models. If states that offer
apprenticeship tax credits are more or less likely to have other qualitative factors that increase or
decrease the use of apprenticeship in those states, then my results could overestimate orunderestimate the effects of apprenticeship tax credits on the share of apprentices in a state’s
labor force.
Among the states that offered an apprenticeship tax credit in the years within my dataset,each state structured its tax credit slightly differently. The credits vary by the size of the credit,
the types of apprenticeships that are eligible (for example, specifically targeting youth
apprenticeships), and the occupational fields that are eligible (for example, specifically targetingapprenticeships in manufacturing). Due to the fact that no two credits are structured exactly thesame, I group all of the apprenticeship tax credits together to assess whether the policy idea as a32

whole is an effective method to increase the use of apprenticeship. However, this means that I
cannot evaluate varying levels of effectiveness among the different structures of the tax credits.
It is likely that the size of the tax credit would influence its effectiveness as would the otheraspects that determine eligibility; nevertheless, my models cannot evaluate these questions. The
take-up rate of the apprenticeship tax credits may also vary among the states that offer them. The
effort a state makes to market its tax credit and the ease with which it allows employers to claim
the credit would likely influence the percentage of eligible employers who benefit from thecredit. If few employers are aware of the tax benefits their state offers for operating anapprenticeship program, then they would not be incentivized to create an apprenticeship program
and thus the effects of the apprenticeship tax credit on the use of apprenticeship in the state
would be reduced. However, due to a lack of data on the take-up rate of the apprenticeship tax
credits, that factor is not included in my models. It is possible that the effects of apprenticeshiptax credits are larger than my research found in states with a high take-up rate.
Despite these limitations, the results of my research indicate that apprenticeship tax credits canbe part of a strategy to effectively improve the labor market outcomes of workers without four
years of full-time college study. By increasing the use of apprenticeship, a program with proveneconomic benefits to workers, apprenticeship tax credits expand the pathways for workers toincrease their earnings that do not require a bachelor’s degree. These results show that anapprenticeship tax credit provides policymakers with an option to address the persistent wage
stagnation of workers without a college degree. As more states implement apprenticeship tax
credits that are structured in different ways, evaluations of these different structures may bepossible to determine the most cost-effective way to increase the use of apprenticeship programs.

33

Rigorous evaluations of the costs and benefits to employers that operate apprenticeship programs
could also help determine the ideal size of the tax credit.

34

APPENDIX
Table 1: Summary Statistics of Dummy Variable
Variableapprenttaxcredit

Description of the
Variable
Whether state hasapprenticeship tax
credit in effect(1 = Yes)

Frequency of
Occurrence
.168

35

Standard Error.375

Table 2: Summary Statistics of Continuous Variables
Variable

Description of the
Variableapprentsharelf
Number of active
apprentices per
1,000 workers
stateGDPgrowth
Real GDP per
capita growth rate
lagstateGDPgrowth
Real GDP per
capita growth rate,
lagged one year
percentpostsecondary Percentage of state
population withassociate degree orhigherpercentbc
Percentage ofstate’s labor force
in blue-collar fields
percentblack
Percentage of state
population that isblack
percenthisp
Percentage of state
population that is
Hispanic
percentasian
Percentage of state
populations that is
Asian-American
percentunion
Percentage ofworkers in state
who are union
members

MeanMedian3.11

Standard
Error
2.19

2.55

Minimum,
Maximum
0.69, 14.74

0.85

1.99

0.90

-6.50, 19.50

0.90

2.11

0.90

-6.50, 19.50

39.60

5.96

39.59

26.40, 59.70

21.96

4.33

22.18

5.42, 31.86

11.18

10.87

7.30

0.40, 51.90

10.98

9.92

8.60

1.20, 48.20

3.89

5.47

2.50

0.60, 38.90

10.25

5.14

9.90

1.60, 24.70

36

Table 3: Mean Apprenticeship Share in States With and Without Tax Credit

Apprenticeship share of statelabor force(standard error)
Sample sizeState does not haveapprenticeship tax credit
3.00

State has apprenticeship tax
credit3.63(2.27)
297(1.62)
60

37

Table 4: Univariate Regression Results of Apprenticeship Share of State Labor Force
Variable

Coefficient

apprenttaxcredit(standard error)
Constant(standard error)

0.629(0.309)3.002(0.127)Significance
level
**

t-statistic

p-value

2.04

0.042

***

23.73

0.000

Significance-level: one-percent --***, five-percent -- **; ten-percent -- *38

Figure 1: Share of Apprentices in States With and Without Apprenticeship Tax Credits

39

Table 5: Mean Real GDP Per Capita Growth Rate in States With and Without Tax Credit

Real GDP per capita growthrate(standard error)
Sample sizeState does not haveapprenticeship tax credit
0.98

State has apprenticeship tax
credit0.21(2.05)
297(1.45)
60

40

Table 6: Univariate Regression Results of Real GDP Per Capita Growth Rate
Variable

Coefficient

apprenttaxcredit(standard error)
Constant(standard error)-0.773(0.278)0.980(0.114)Significance
level
***
***

t-statistic

p-value

-2.78

0.006

8.58

0.000

Significance-level: one-percent --***, five-percent -- **; ten-percent -- *41

Table 7: Correlation Matrix
apprent
sharelf
apprent
sharelf
stateGDP
growthapprent
taxcredit

1.000

stateGDP
growthapprent
taxcredit

lagstateGDP
growthpercent
post
secondary-0.070

1.000

0.108
**

-0.146***1.000

lagstateGDP
growthpercent
post
secondary
percentbc
percent
black
percent
hisp
percent
asian
percent
union
year 17-0.0480.229***-0.134
**

1.000

0.219
***

0.099
*

-0.321
***

0.091
*

1.000

-0.266
***
0.015

0.046

0.044

0.078

-0.144
***
0.038

0.177
***
-0.089*-0.076-0.129**-0.017-0.620***-0.019-0.119
**
0.031

0.029

year 16

0.002

year 15-0.052

year 14-0.096*
0.061

year 13
year 12-0.148
***
0.597
***
0.385
***
0.080

0.090
*

0.034
0.026
0.017

0.014

percentbc

percent
black

percent
hisp

percent
union

1.000
1.000
-0.118**-0.088*-0.250
***
0.009

1.000

0.226
***
0.314
***
-0.026-0.209***-0.364***-0.353***-0.355***-0.005-0.076

0.002

0.000

percent
asian

0.204
***
0.026

1.000
1.000

0.024

0.515
***
0.027

0.001

0.015

0.009

-0.026-0.115
**
0.130
**
0.082

0.009

-0.118
**
0.113

0.009

0.068

0.087

0.017

-0.000

0.009

0.005

-0.003-0.012

0.051

0.013

-0.0010.001-0.003-0.006-0.143***0.023-0.012-0.144
***
0.012

0.028

-0.000-0.002-0.007-0.0080.007-0.012-0.004-0.009-0.009-0.003-0.017-0.0130.007

Significance-level: one-percent --***, five-percent -- **; ten-percent -- *42

-0.025

Table 7: Correlation Matrix (cont.)

year
17
year
16
year
15
year
14
year
13
year
12

year
17
1.000

year
16

-0.167***-0.167***-0.167***-0.167***-0.167***1.000-0.167***-0.167***-0.167***-0.167
***

year
15

year
14

year
13

year
12

1.000
-0.167***-0.167***-0.167***1.000-0.167***-0.167***1.000-0.167***1.000

Significance-level: one-percent --***, five-percent -- **; ten-percent -- *43

Table 8: Multivariate Regression Results of Model 1
Variable

CoefficientSignificance
level
***

t-statistic

apprenttaxcredit
0.862
3.48
(standard error)(0.248)
lagstateGDPgrowth-0.039-0.94(standard error)(0.042)
percentpostsecondary 0.017
0.79(standard error)(0.022)
percentbc-0.065*-1.91(standard error)(0.034)
percentblack-0.003-0.34(standard error)
percenthisp-0.068***-6.47(standard error)(0.010)percentasian
0.236
***
12.81
(standard error)(0.018)percentunion
0.020
0.94
(standard error)(0.021)
year17
0.769
**
2.44
(standard error)(0.315)
year160.473
1.52(standard error)(0.311)
year150.143
0.45(standard error)(0.317)
year14-0.101-0.32(standard error)(0.318)
year13
0.767
**
2.45
(standard error)(0.313)
year12
0.925
***
2.97
(standard error)(0.312)
Constant
2.977
*
1.81
(standard error)(1.648)Significance-level: one-percent --***, five-percent -- **; ten-percent -- *N = 357, R2 = 0.5082, F-statistic = 25.24

44

p-value
0.001
0.348
0.430
0.057
0.730
0.000
0.000
0.349
0.015
0.129
0.651
0.751
0.015
0.003
0.072

Figure 2: Estimated Effect of Implementing Apprenticeship Tax Credit

45

Table 9: Multivariate Regression Results of Model 2
Variable

CoefficientSignificance
level
***

t-statistic

apprenttaxcredit
1.129
4.68
(standard error)(0.241)
lagstateGDPgrowth-0.034-0.84(standard error)(0.040)percentpostsecondary 0.061***
2.76(standard error)(0.022)
percentbc-0.028-0.86(standard error)(0.033)
percentblack
0.017
*
1.88
(standard error)
percenthisp-0.022*-1.77(standard error)(0.013)
percentasian-0.069-1.29(standard error)(0.053)
percentunion
0.102
***
4.18
(standard error)(0.024)
year17
0.894
***
2.93
(standard error)(0.306)
year160.587*
1.95(standard error)(0.301)
year150.141
0.46(standard error)(0.305)
year14-0.069-0.22(standard error)(0.307)
year13
0.756
**
2.51
(standard error)(0.302)
year12
0.927
***
3.08
(standard error)(0.301)
Constant-0.271-0.16(standard error)(1.667)Significance-level: one-percent --***, five-percent -- **; ten-percent -- *N = 350, R2 = 0.2671, F-statistic = 8.72

46

p-value
0.000
0.399
0.006
0.392
0.061
0.078
0.199
0.000
0.004
0.052
0.644
0.823
0.013
0.002
0.871

Table 10: Multivariate Regression Results of Model 3
Variable

CoefficientSignificancelevel

t-statistic

apprenttaxcredit-0.383-1.29(standard error)(0.297)
lagstateGDPgrowth
0.207
***
4.15
(standard error)(0.050)percentpostsecondary 0.032
1.23(standard error)(0.026)
percentbc
0.044
1.07
(standard error)(0.041)percentblack-0.014-1.32(standard error)(0.011)percenthisp
0.011
0.86
(standard error)(0.013)
percentasian
0.011
0.50
(standard error)(0.022)
percentunion-0.009-0.35(standard error)(0.025)
year170.230
0.61(standard error)(0.378)
year16-0.643*-1.72(standard error)(0.373)
year150.517
1.36(standard error)(0.380)
year140.525
1.37(standard error)(0.382)
year13-0.716*-1.91(standard error)(0.375)
year120.134
0.36(standard error)(0.374)
Constant-1.433-0.73(standard error)(1.975)Significance-level: one-percent --***, five-percent -- **; ten-percent -- *N = 357, R2 = 0.1405, F-statistic = 3.99

47

p-value
0.198
0.000
0.219
0.285
0.188
0.391
0.616
0.725
0.543
0.086
0.174
0.170
0.057
0.720
0.469

Table 11: Multivariate Regression Results of Model 4
Variable

CoefficientSignificancelevel

t-statistic

apprenttaxcredit-0.459-1.51(standard error)(0.304)
lagstateGDPgrowth
0.205
***
4.09
(standard error)(0.050)percentpostsecondary 0.020
0.70(standard error)(0.028)percentbc
0.033
0.79
(standard error)(0.042)
percentblack-0.020*-1.73(standard error)(0.012)
percenthisp-0.002-0.15(standard error)(0.016)
percentasian
0.098
1.46
(standard error)(0.067)
percentunion-0.032-1.04(standard error)(0.031)
year170.172
0.45(standard error)(0.385)
year16-0.718*-1.89(standard error)(0.373)
year150.477
1.24(standard error)(0.385)
year140.542
1.40(standard error)(0.387)
year13-0.721*-1.90(standard error)(0.380)
year120.115
0.30(standard error)(0.379)Constant-0.473-0.23(standard error)(2.101)Significance-level: one-percent --***, five-percent -- **; ten-percent -- *N = 350, R2 = 0.1459, F-statistic = 4.09

48

p-value
0.132
0.000
0.486
0.433
0.085
0.882
0.145
0.297
0.656
0.059
0.216
0.162
0.059
0.762
0.822

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