Many unemployed workers in the United States and other developed countries view self-employment as an attractive reemployment option, especially those who face the prospect of long-term unemployment (Dennis, 1996; Bates, 1997; Thurik et al., 2008; von Greiff, 2009). Lack of labor market options notwithstanding, the unemployed face lower opportunity costs and are more willing than employed workers to invest the time and undertake the risk to start a business (Meager, 1992; Bates and Servon, 2000; Grilo and Thurik, 2005; Glocker and Steiner, 2007). Over the past three decades, U.S. policymakers have been concerned that although many skilled unemployed workers aspire to become business owners, they may lack the business background and financing access needed to succeed. For this reason, they have supported entrepreneurship training programs to help unemployed workers overcome these obstacles and start their own businesses, thereby reducing the amount of time they remain unemployed (Wandner, 2010, pp. 289-340).
This article examines whether government-sponsored entrepreneurship training can help unemployed workers interested in self-employment to improve their labor market outcomes and serve as an effective policy for combating unemployment at different stages of the business cycle. There is a distinction in the use of the terms “self-employment” and “entrepreneurship.” Entrepreneurship is usually used to describe activities to start a business and make a profit, whereas self-employment is usually used to describe when individuals work for themselves, either as contractors/freelancers or by owning a business. We use the term “self-employment” to mean both.
Mostly because of DOL’s involvement, the five programs had a very similar design. Each program randomly selected which applicants would be offered training and which applicants would be excluded from the program. The programs followed similar applicant recruitment and random assignment procedures, offered the same types of services to participants, and collected similar information on participants’ labor market outcomes following program entry. These common features enable us to examine the causal effects of each program on participant outcomes and assess whether training effects vary when implemented in different state economies and at different stages of the business cycle.
This study is not the first to provide experimental evidence on the effects of entrepreneurship training programs in the U.S. context. Prior work shows that training can help unemployed workers collecting unemployment insurance (UI) benefits to start their own businesses and improve their reemployment outcomes (Benus et al., 1995). Studies that pooled data from the same Maine, Minnesota, and Pennsylvania programs considered here show that training can help nascent entrepreneurs to start a business in the context of a relatively strong economy (Michaelides and Benus, 2012; Fairlie et al., 2015; Michaelides, 2020).
Our study contributes to this literature in various ways. This study is the first to provide experimental evidence on the effects of entrepreneurship training in a weak economy. Many factors could influence the relative efficacy of entrepreneurship training during a recession. There is an expanded pool of unemployed workers during recessions who may consider entrepreneurship as an alternative to salary employment, including high-skill workers who would not have employability issues under normal circumstances (Aaronson et al., 2004; Constant and Zimmerman, 2004; Alba-Ramirez, 2006; Koellinger and Thurik, 2012; Michaelides and Mueser, 2013). Recessions also bring reduced opportunity and business start-up costs which, combined with fewer job options, may encourage more workers than usual to start a business, thereby increasing demand for training (Rissman, 2003; Rampini, 2004; Mandelman and Montes-Rojas, 2009; Biehl et al., 2014). On the other hand, training may be less valuable in a weak economy when business opportunities are limited and securing start-up financing may prove to be very challenging (Block et al, 2010; Fairlie, 2013; Kennickell et al
Our study also makes different methodological choices than did prior studies, including studies of the same Maine, Minnesota, and Pennsylvania programs considered here. First, we privilege local average treatment effect (LATE) estimates over intent-to-treat (ITT) effect estimates. We believe LATE estimates are more policy-relevant in this context because many applicants do not comply with the treatment protocol. In particular, using data on services received by treatment cases from the government program and substitute services received by control cases from the community, we find that many treatment cases do not receive the services offered by the government program and that many control cases receive similar services from the community after they are denied government services. Thus, our discussion focuses on LATEs, which estimate the effect of training for compliers, that is, those who would not have received training in the absence of the government program.
Second, instead of pooling data from all programs operating in the same period (a strategy used by prior work to evaluate the Maine, Minnesota, and Pennsylvania programs), we consider each program separately. Our results validate this choice, as we identify different patterns across states both in the proportion of compliers and in the effects of training on participant outcomes, highlighting heterogeneity across state contexts not identified by prior work.
Third, in addition to considering program effects on the likelihood of an individual becoming self-employed (a key outcome of interest both here and in past work), our analyses explicitly distinguish between self-employment in own business and independent self-employment. Self-employment in own business indicates that the individual started a business and was the sole (or primary) operator of the business, while independent self-employment indicates that the individual was self-employed as a contractor/freelancer but did not own a business. There are many reasons why self-employed workers may find it in their best interest to start a business, including to improve their chances of securing credit, limit their liability for their business debts, obtain tax flexibility, hire employees, make their services and products more attractive to customers, and potentially achieve higher earnings (Kugler et al., 2017). The distinction between self-employment in own business and independent self-employment enables us to identify program mechanisms not previously considered, including that: (1) training may not increase overall self-employment but may help compliers start their own business instead of becoming self-employed as independent contractors and (2) training may increase salary employment and earnings by nudging compliers to consider salary employment instead of independent self-employment.
Our analyses begin by examining whether the programs increased receipt of entrepreneurship training. Inspired by studies that show that new (or existing) government programs may displace alternatives that are available in the community (e.g., Kline and Walters, 2016), we examine whether, in the absence of a government option, unemployed workers could avail themselves of entrepreneurship training available in the community. If community options are available, then similarly focused government programs may displace those options and or have limited value in improving training access. Using program data, which report services received by treatment cases from government programs and substitute services received by control cases from the community, we compare training receipt rates (and types of services received) between treatment and control cases.
We then present LATEs on labor market outcomes for each program, including self-employment (in own business, contractor, and overall), salary employment, monthly earnings (self-employment, salary, and total), and the number of employees hired to support self-employment activities. In addition, we undertake analyses to identify whether training effects varied based on individual traits, particularly based on characteristics that capture human capital (such as education and age), entrepreneurship skills (such as self-employment experience and experience working in a family business), and financing access (such as credit history and household income). These analyses identify whether policymakers can improve program efficacy by targeting training to individuals who are most likely to benefit from the treatment.
We organize the remainder of the article as follows. Section 1 describes the policy context and evidence on the efficacy of U.S. entrepreneurship training programs. Section 2 discusses the operational details of each program, including the application process, training offered to participants, applicants’ characteristics, and the context of operation. Section 3 presents the methodology and results. Section 4 summarizes the findings and conclusions.
In the early 1990s, U.S. policymakers recognized that many unemployed workers were interested in self-employment instead of finding regular jobs, but public employment offices did not offer any services to help them start their own businesses. In 1992, DOL implemented two demonstration programs—the Washington Self-Employment and Enterprise Development Project and the Massachusetts Enterprise Project—which offered entrepreneurship training and monetary assistance to unemployed workers who were collecting UI benefits (Benus et al., 1995). The results of these demonstrations convinced Congress to authorize states to create self-employment assistance (SEA) programs for a 5-year trial period under the North America Free Trade Agreement Implementation Act of 1993. SEA programs offered participants a financial stipend to allow them to devote their time to entrepreneurship activities instead of searching for a job and training and technical assistance to support their business start-up efforts.
SEA programs were permanently authorized in 1998 but ultimately fell short of accomplishing their objectives. The programs’ reach was restricted by a congressional mandate that only UI recipients at high risk of exhausting UI were eligible for SEA. SEA programs were required to use statistical profiling mechanisms developed under the Worker Profiling and Reemployment Services (WPRS) program to identify eligible participants. Only UI recipients with high profiling scores (i.e., high predicted probability of exhausting UI benefits based on observed characteristics) were eligible for SEA. For a discussion of WPRS profiling mechanisms, see Dickinson
Policymakers recognized these limitations and shifted their attention to programs that would have sufficient resources and be available to all workers, not only those collecting UI benefits. In 2003, DOL funded the Project “Growing America Through Entrepreneurship” (GATE) demonstration programs to test this new approach. Project GATE programs operated through public employment offices in Maine, Minnesota, and Pennsylvania, and (in contrast to the early 1990s demonstrations and the SEA programs operating at the time) accepted applications from all interested workers. The program offered participants training workshops covering topics related to the business start-up process and business counseling to help them develop and implement viable business plans (Bellotti et al., 2007). While Project GATE programs were operating, DOL issued directives to state workforce agencies to encourage the use of Workforce Investment Act (WIA) funds to support entrepreneurship training activities. U.S. Department of Labor Training and Employment Guidance Letter No. 16-04, February 2005 (
Efforts to promote entrepreneurship training for unemployed workers were intensified during the Great Recession. In 2008, DOL awarded GATE II grants to workforce agencies in Alabama, North Carolina, Virginia, and Minnesota to operate programs that replicated the Project GATE design (Davis et al., 2017). In 2012, DOL awarded grants to workforce investment boards in Florida and Virginia to implement entrepreneurship training programs. DOL also encouraged the use of WIA funds for entrepreneurship training activities U.S. Workforce Innovation and Opportunity Act of 2014, Public Law 113-128. (
What research has been done on entrepreneurship training in the U.S. has largely examined programs that operated in periods of moderate unemployment. A government study found that the early-1990s demonstration programs in Massachusetts and Washington helped participants to become self-employed, leading to positive short-term effects on overall employment rates (Benus et al., 1995). Effects on earnings were mixed. The Washington program increased self-employment earnings, but this effect was offset by a reduction in wage and salary earnings, while the Massachusetts program had no effects on either type of earnings.
The most recent experimental evidence derives from the Project GATE experiments (Michaelides and Benus, 2012; Fairlie et al., 2015; Michaelides, 2020). Pooling data from the three Project GATE programs, these studies showed that, at 6 months after program entry, treatment cases had higher self-employment and total employment rates than control cases. Effects mainly occurred for participants who were unemployed at program entry; there were no effects for participants who were employed in salary jobs or who were already self-employed at program entry. These results confirmed findings of an earlier government study (Benus
The current literature provides no evidence on the efficacy of entrepreneurship training during recessions and no formal theoretical models of how the effectiveness of training may vary with the business cycle. This is a conspicuous evidence gap given the policymaker’s support that entrepreneurship training programs received during the Great Recession. Conceptually, demand for training may be higher during recessions, when business start-up costs and individual opportunity costs tend to be low, and thus more unemployed workers than usual may decide to pursue self-employment (Rissman, 2003; Rampini, 2004; Alba-Ramirez, 2006; Mandelman and Montes-Rojas, 2009; Biehl et al., 2014). Also, the socioeconomic profile of the unemployed population is affected by prevailing economic conditions, with relatively more educated and experienced workers becoming unemployed during recessions than during good economic times (Koellinger and Thurik, 2012; Michaelides and Mueser, 2013). It is thus likely that, during recessions, there would be relatively more skilled unemployed workers who might be willing to pursue self-employment and, with the right assistance, succeed. On the other hand, during recessions, start-up financing is scarce for many reasons, including that credit institutions are reluctant to invest in start-ups or because declining home values limit the ability of aspiring entrepreneurs to use home equity to finance their business (Block et al, 2010; Fairlie, 2013; Kennickell et al, 2014). Thus, entrepreneurship training may be less valuable in a weak economy because participants would not be able to secure start-up financing even if training helps them develop a viable business plan and provides them information on various financing options.
Our article provides empirical evidence to fill this literature gap. We investigate and compare the effects of government-sponsored entrepreneurship training during a period of moderate unemployment and a severe recession and assess whether entrepreneurship training is a viable policy option to combat unemployment at different stages of the business cycle.
DOL implemented Project GATE in Maine, Minnesota, and Pennsylvania because these states had SEA-enabling legislation that allowed them to legally support entrepreneurship training activities. The Pennsylvania program operated in Philadelphia and Pittsburgh, the Minnesota program operated in Minneapolis/St. Paul and Duluth, and the Maine program operated in Portland, Lewiston, and Bangor. Program recruitment in each state followed a straightforward sequence. Interested individuals who were at least 18 years old could register at designated public employment offices, by calling a toll-free number or through the program’s website. To boost registration, the programs implemented a marketing campaign that included brochures and posters at program sites, fliers, media events, public service announcements, and networking with local government agencies and community-based organizations (Benus et al., 2010). Registered individuals were invited to an orientation meeting to obtain information about program services. Those wishing to participate were required to complete an application form and sign an informed consent statement that program participation was not guaranteed and that participants would be randomly selected from the pool of all applicants. Program registrations started in September 2003 and were done on a rolling basis through July 2005.
Individuals who completed the application form and signed the informed consent statement (hereafter, As noted in the implementation report of the Project GATE programs (Benus To minimize the likelihood that control individuals would reapply for the program and get assigned to the treatment group under the new application, program staff checked the information on new applications (e.g., name, address, and social security number) against old applications and disqualified those who had applied for the program in the past.
To ensure that training met high-quality standards, DOL used a competitive process to select training providers with experience in providing small-business counseling. Through this process, Small Business Development Centers (SBDCs), SBDCs were created by the Small Business Administration to provide technical assistance services to nascent entrepreneurs and small businesses. SBDCs offered similar services with those offered by Project GATE and at no charge, and thus, program applicants (particularly control cases) could on their own initiative access free workshops and business counseling at local SBDCs. At the time, SBDCs operated in each of the programs’ areas of operation – one in each Portland, Lewiston, and Bangor (Maine); two in Minneapolis/St. Paul and one in Duluth (Minnesota); and two in each
In 2008, DOL awarded GATE II grants to the Alabama, North Carolina, Virginia, and Minnesota workforce agencies. The only distinction with Project GATE was that GATE II programs accepted applications only from unemployed workers, while Project GATE programs accepted applications from all individuals, regardless of employment status. GATE II operated statewide in North Carolina, Alabama, and Minnesota, while the Virginia program operated in Northern Virginia and Richmond. There were notable implementation differences across GATE II programs. Alabama and North Carolina accepted applications from all unemployed workers (18+ years old); Virginia and Minnesota accepted applications only from older unemployed workers (45+ years old). North Carolina and Virginia used random assignment to determine participation and conducted follow-up surveys to collect outcomes information; Alabama and Minnesota offered training to all applicants and did not collect follow-up outcomes. Thus, our GATE II analyses consider only the North Carolina and Virginia programs.
North Carolina and Virginia replicated Project GATE’s recruitment and service delivery process. Similar to Project GATE, GATE II programs used a mass media marketing campaign to boost enrollment and accepted registrations on a rolling basis from January 2009 through December 2010. Individuals who registered for GATE II were invited to an orientation to receive program information. Those who remained interested in the program were required to complete the application form and sign the informed consent statement. Applicants were then randomly assigned to treatment and control groups. Treatment individuals were offered program services, while those in the control were informed that they were not selected for the program and had no access to program services Similar to Project GATE programs, GATE II programs implemented checks to ensure that individuals could not reapply for the program and get assigned to the treatment group after they were initially denied services. At the time, 10 SBDCs operated statewide in North Carolina and eight SBDCs operated in Northern Virginia and Richmond. Note that GATE II applicants (particularly those assigned to the control group) could, on their own initiative, obtain similar services as those offered by the GATE II programs at local SBDCs.
Project GATE recruitment began in September 2003 and continued through July 2005, a period of moderate and declining unemployment while GATE II recruitment was done from January 2009 through December 2010, near the depth of the Great Recession. As such, economic conditions differed across states when their respective programs were operating (see Figure A1
in Appendix). Pennsylvania, Minnesota, and Maine had low unemployment and increasing self-employment rates, while North Carolina and Virginia had high (and increasing) unemployment and declining self-employment rates. When programs were operating, earnings for self-employed and wage and salary workers followed positive trends in the Project GATE states and declining trends in GATE II states. There were also notable differences across states over the business cycle, including that self-employment rates were higher than the national rate in Minnesota and Maine and lower than the national rate in Pennsylvania and Virginia. States also differed in the characteristics of unemployed workers (see Table A1 in Appendix).
Table 1 summarizes applicant characteristics based on information from the application form. Among Project GATE programs, Pennsylvania attracted much higher proportions of female, nonwhite, and younger applicants. Minnesota and Maine applicants had similar characteristics, except that more Minnesota applicants had a college degree. The majority of applicants had no self-employment experience and no business plan, although many had work experience in an industry related to their business idea and in a family business. Many applicants had bad or no credit histories and low annual household incomes, suggesting limited access to financing.
Characteristics of program applicants
Project GATE | GATE II | ||||
---|---|---|---|---|---|
Pennsylvania | Minnesota | Maine | North Carolina | Virginia | |
Number of applicants | 722 | 869 | 226 | 1,175 | 435 |
Male | 0.546 | 0.627 | 0.628 | 0.558 | 0.506 |
Female | 0.454 | 0.373 | 0.372 | 0.442 | 0.494 |
White | 0.414 | 0.777 | 0.889 | 0.600 | 0.513 |
Nonwhite | 0.586 | 0.223 | 0.111 | 0.400 | 0.487 |
Married | 0.341 | 0.514 | 0.443 | 0.517 | 0.503 |
Disabled | 0.061 | 0.073 | 0.115 | 0.057 | 0.032 |
18–25 years old | 0.039 | 0.017 | 0.018 | 0.024 | – |
25–34 years old | 0.226 | 0.165 | 0.173 | 0.146 | – |
35–44 years old | 0.313 | 0.345 | 0.305 | 0.275 | – |
45–54 years old | 0.314 | 0.354 | 0.381 | 0.317 | 0.503 |
55+ years old | 0.108 | 0.119 | 0.124 | 0.237 | 0.497 |
Missing | – | 0.121 | |||
No high school diploma | 0.042 | 0.022 | 0.062 | 0.066 | 0.009 |
High school diploma | 0.298 | 0.167 | 0.301 | 0.359 | 0.103 |
Some college | 0.381 | 0.358 | 0.336 | 0.243 | 0.297 |
College degree | 0.280 | 0.454 | 0.301 | 0.212 | 0.591 |
Self-employment experience | 0.226 | 0.257 | 0.332 | 0.313 | 0.441 |
Family business | 0.639 | 0.768 | 0.748 | 0.491 | – |
Relevant work experience | 0.796 | 0.829 | 0.810 | 0.373 | 0.885 |
Business plan | 0.235 | 0.181 | 0.181 | 0.116 | 0.053 |
Bad/no credit history | 0.573 | 0.331 | 0.429 | 0.534 | 0.205 |
Family financial support | 0.418 | 0.480 | 0.473 | 0.521 | 0.559 |
Household income | |||||
Less than $25,000 | 0.425 | 0.229 | 0.407 | 0.489 | 0.308 |
$25,000–$49,999 | 0.352 | 0.327 | 0.367 | 0.310 | 0.308 |
$50,000–$74,999 | 0.134 | 0.216 | 0.146 | 0.129 | 0.140 |
$75,000+ | 0.089 | 0.228 | 0.080 | 0.072 | 0.244 |
Differences across GATE II programs are more pronounced, which is expected given that Virginia recruited only older unemployed workers. North Carolina applicants were younger and less educated. A large proportion of Virginia applicants had self-employment experience and good credit histories, and many more applicants in Virginia than in North Carolina had household incomes of at least $50,000. North Carolina applicants were older and less educated and had lower household incomes compared to applicants in the Project GATE states. Virginia applicants were older and relatively more of them had a college education and good credit than did Project GATE applicants.
Characteristics comparisons between applicants (Table 1) and the unemployed population (see Table A1 in Appendix) indicate that, in all five states, nonwhites, workers 35–54 years old, college-educated unemployed workers, and individuals with $25,000–49,000 annual household income were overrepresented among applicants. These comparisons suggest that unemployed workers with high levels of human capital and labor market experience were more likely to apply for training. Except for the fact that men were overrepresented in Project GATE states but not in GATE II states (and some other minor differences), there is limited evidence of systematic differences in observed applicant characteristics over the business cycle.
Except in North Carolina, all programs randomly assigned about half of the applicants to the treatment; the North Carolina program assigned about 75% (see Table A2 in Appendix, top panel). To assess whether treatment and control groups were balanced in observed characteristics, we estimated regression models in which the dependent variable is an indicator that equals 1 if the individual was assigned to the treatment and zero otherwise. Control variables include those in Table 1 and a constant term. Regression results (see Table A3 in Appendix) show that of the 100 estimated parameters, only one was statistically significant at the 5% level or less and another four were statistically significant at the 10% level. These comparisons indicate that random assignment created treatment and control groups that were generally balanced, with some differences possibly attributed to chance.
All programs administered follow-up surveys, asking applicants about the training they had received since program entry (including program training received from the community for treatment cases and similar training received from the community for control cases) and their labor market outcomes at the time of the survey. The data made available to us for the analyses did not include any administrative data sources, such as state Unemployment Insurance (UI) data and state UI wage records. Thus, our analyses of program outcomes rely exclusively on the survey data collected by the programs.
The fact that many applicants did not respond to the survey and response rates varied by characteristics raises concerns that the treatment–control balance in characteristics may not have been maintained among respondents, which could lead to biased estimates of program effects. Regression models that estimate treatment assignment based on individual characteristics using survey respondents only (see Table A5 in Appendix) show that survey attrition did not cause imbalanced treatment and control samples among respondents, mitigating concerns that estimated effects would be biased. Another concern is that survey attrition caused some differences in characteristics between program applicants (Table 1) and survey respondents (see Table A6 in Appendix). The implication is that the outcomes of respondents may not be representative of the outcomes for the entire applicant population. To mitigate concerns that estimated effects may be affected by survey attrition (aside from having larger standard errors), we estimate effects using regression models that control for individual characteristics.
The policymakers’ motivation for supporting entrepreneurship training was to provide unemployed workers with access to services that would not be otherwise available to them. The expectation was that services would help them overcome obstacles associated with starting and operating a new business, thereby expediting their start-up efforts and reducing the amount of time they remained unemployed. Conceptually, we can identify three potential mechanisms through which entrepreneurship training may affect the outcomes of participants. First, training would educate participants about the different aspects of starting and running a business, helping them to overcome lack of business background. Second, business counseling and financial advice would assist participants to produce viable business plans and secure start-up financing. Third, business counselors may influence the decision of participants to pursue self-employment by encouraging those who lack a viable business idea (or those whom they believed they would not succeed) to consider salary job alternatives. While the first two mechanisms should manifest as improved self-employment rates (and, potentially, improved self-employment earnings), the latter mechanism may reduce self-employment rates and earnings and improve salary employment outcomes instead.
Our analyses first examine program effects on training take-up. Using survey responses, we identify which treatment cases received training from the program and which control cases received similar training from the community. We also measure the number of workshops and business counseling sessions completed by treatment and control individuals. To estimate the effects on training receipt, we estimate regression models of the following form:
The dependent variable (
Second, we examine whether entrepreneurship training led to improved labor market outcomes for participants. Using survey responses, we measure the employment situation of applicants at the time of the survey, including whether they were self-employed in their own business or self-employed as contractors/freelancers but did not own a business, employed in wage and salary jobs, and employed in any capacity. Information on self-reported earnings is used to measure self-employment, wage and salary, and total monthly earnings at the time of the survey. We also measure whether those who became self-employed hired employees to support their self-employment activities and the number of employees they hired.
Random assignment allows us to estimate each program’s ITT effects by fitting model [1] for each outcome of interest. As shown below, in all five experiments, there was substantial control group substitution into training provided in the community, while there was program dropout with many treatment individuals not receiving training. The implication is that ITT effects can severely underestimate the value of receiving training for those who complied with the treatment protocol. Thus, we focus our discussion on LATE estimates, which measure the effects of training for compliers, that is, those who would not have received training in the absence of a government program.
Following Bloom (1984), under the assumption that outcomes are only affected by training and are independent of program assignment, we can estimate LATEs using program assignment as an instrument for the likelihood of receiving training. The first- and second-stage equations are as follows:
The dependent variable in the first-stage equation (
Third, we examine whether program effects varied based on observed characteristics that measure human capital, entrepreneurship skills, and access to financing. For example, to estimate whether program effects varied based on self-employment experience, we separated the sample for each program into those who had self-employment experience and those who did not and then estimated the LATEs separately for each group. We perform this exercise for gender, race, age, education, self-employment experience, credit history, and household income. The discussion of the results in Section 5 focuses only on characteristics for which there is evidence of differential program effects.
Figure 1 presents training take-up rates. Most treatment cases—ranging from 64.3% in North Carolina to 87.4% in Minnesota—received at least one type of training (workshops and or counseling). Many of these participated in both workshops and counseling. Many control cases used training options available in the community, but overall control group training take-up rates were lower than the treatment rates. Moreover, treatment cases participated on average in more workshops and counseling sessions than did control cases (see Table A7 in Appendix). Separate analyses of the marginal and joint distributions of counseling sessions and workshops received (see Tables A8 and A9 in Appendix) show that about half (or more) of the Pennsylvania, Minnesota, and Virginia treatment cases participated in at least four workshops, compared with more than a third in North Carolina and about one-quarter in Maine. Relatively more GATE II treatment cases received at least three counseling sessions than did Project GATE treatment cases.
Table 2 presents the effects on training take-up based on model [1]. All programs led to substantial increases in any training receipt ranging from 22.4 percentage points in North Carolina to 42.5 percentage points in Maine. In all states, except Maine, effects were mostly because of increased take-up of both services types; in Maine, the effect was mostly due to increased take-up of counseling only. All programs increased the number of workshops and counseling sessions, with relatively low effects in Maine.
There are two key takeaways from these analyses. One, the five programs increased training take-up (and the quantity of training received), suggesting that government-sponsored programs can be valuable in providing entrepreneurship services to unemployed workers who would not otherwise receive such services. At the same time, there was substantial variation across states in the compliance with the treatment protocol, so the proportion of compliers (measured as the program effect on overall training receipt) ranged widely across programs. Two, there was variation across programs in the level of services received by participants. It appears that Maine participants received a lighter dose of the treatment than participants in other programs, as they participated in relatively fewer workshops. Moreover, GATE II participants exhibited a higher preference for (or GATE II programs provided more emphasis on) business counseling, suggesting that GATE II participants received a heavier dose of the treatment than did Project GATE participants.
Survey responses are used to construct outcome measures at the time of the survey, including three measures of self-employment status:
Program effects on training take–up
Project GATE | GATE II | ||||
---|---|---|---|---|---|
Pennsylvania | Minnesota | Maine | North Carolina | Virginia | |
Any training | 0.425 (0.042)*** | 0.391 (0.033)*** | 0.279 (0.064)*** | 0.224 (0.039)*** | 0.361 (0.050)*** |
Workshops only | 0.100 (043)** | –0.004 (0.035) | 0.021 (0.063) | –0.052 (0.034) | –0.169 (0.045)*** |
Counseling only | 0.047 (0.022)** | 0.084 (0.025)*** | 0.192 (0.063)*** | 0.062 (0.023)*** | 0.061 (0.029)** |
Workshops plus counseling | 0.278 (0.036)*** | 0.311 (034)*** | 0.066 (0.071) | 0.205 (0.035)*** | 0.460 (0.045)*** |
Number of workshops | 3.8 (0.4)*** | 2.8 (0.3)*** | 0.3 (0.7) | 1.6 (0.2)*** | 3.4 (0.3)*** |
Number of counseling sessions | 0.9 (0.1)*** | 0.9 (0.2)*** | 0.5 (0.3)* | 1.5 (0.4)*** | 2.6 (0.3)*** |
***
Table 3 summarizes outcome measures for treatment and control cases; the control means represent average outcomes in the absence of the offer of the government program. In the Project GATE programs, 16.8–25.8% of control individuals were self-employed in their own businesses and 6.6–15.4% were self-employed as contractors. GATE II control cases were less likely than those in Project GATE to be self-employed in their own business, indicating that it was harder to start a business during the recession. Differences in monthly average earnings reflect differences in labor market conditions and employment rates across the five states.
Labor market outcomes
Project GATE | GATE II | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Pennsylvania | Minnesota | Maine | North Carolina | Virginia | ||||||
Treatment | Control | Treatment | Control | Treatment | Control | Treatment | Control | Treatment | Control | |
Self-employment | ||||||||||
| 0.203 | 0.168 | 0.343 | 0.235 | 0.253 | 0.258 | 0.164 | 0.117 | 0.129 | 0.114 |
| 0.104 | 0.066 | 0.121 | 0.144 | 0.089 | 0.154 | 0.129 | 0.092 | 0.208 | 0.152 |
| 0.307 | 0.235 | 0.463 | 0.379 | 0.342 | 0.412 | 0.293 | 0.209 | 0.337 | 0.266 |
Regular employment | 0.473 | 0.509 | 0.382 | 0.420 | 0.494 | 0.351 | 0.418 | 0.474 | 0.420 | 0.500 |
Any employment | 0.780 | 0.743 | 0.846 | 0.799 | 0.835 | 0.763 | 0.710 | 0.684 | 0.757 | 0.766 |
Self-employment earnings | 112 (525) | 97 (515) | 417 (1,286) | 329 (1,303) | 425 (1,451) | 154 (589) | 152 (748) | 87 (372) | 68 (330) | 148 (836) |
Regular earnings | 1,739 (2,464) | 1,636 (2,302) | 2,305 (4,315) | 2,422 (3,650) | 2,232 (352) | 1,389 (1,713) | 504 (1,658) | 453 (859) | 823 (1,610) | 1,480 (6,366) |
Total earnings | 1,851 (2,493) | 1,733 (2,302) | 2,722 (4,383) | 2,750 (3,735) | 2,657 (3,180) | 1,542 (1,752) | 656 (1,779) | 540 (894) | 891 (1,678) | 1,628 (6,391) |
Has employees | 0.058 | 0.062 | 0.084 | 0.075 | 0.152 | 0.063 | 0.035 | 0.020 | 0.017 | 0.025 |
Number of employees | 0.18 (0.94) | 0.16 (0.88) | 0.32 (1.33) | 0.27 (1.32) | 0.41 (1.14) | 0.21 (1.41) | 0.14 (1.13) | 0.06 (.47) | 0.02 (0.18) | 0.04 (0.31) |
Table 4 presents LATE parameters. See Table A10 in Appendix for corresponding ITT effects.
Local average treatment effects on labor market outcomes
Project GATE | GATE II | ||||
---|---|---|---|---|---|
Pennsylvania | Minnesota | Maine | North Carolina | Virginia | |
Self-employment | |||||
| 0.071 (.086) | .264 (0.089)*** | 0.254 (0.252) | 0.184 (0.133) | 0.019 (0.102) |
| 0.107 (0.063)* | −0.066 (0.069) | −.388 (.217)* | 0.115 (0.117) | 0.113 (0.111) |
| 0.179 (0.098)* | 0.198 (0.097)** | −0.133 (0.281) | 0.299 (0.160)* | 0.132 (0.131) |
Regular employment | −0.096 (0.111) | −0.128 (0.098) | 0.479 (0.287)* | −0.247 (0.180) | −0.181 (0.151) |
Any employment | 0.083 (0.095) | 0.070 (0.076) | 0.346 (0.262) | 0.052 (0.164) | −0.049 (0.131) |
Self-employment earnings | 24 (116) | 131 (253) | 901 (661) | 290 (249) | −208 (188) |
Regular earnings | 207 (528) | −800 (797) | 3,086 (1,628)* | 181 (559) | −1,871 (1,414) |
Total earnings | 231 (532) | −669 (796) | 3,987 (1,755)** | 471 (599) | −2,078 (1,430) |
Has employees | −0.003 (054) | −0.008 (0.054) | 0.412 (0.194)** | 0.056 (0.063) | −0.025 (0.045) |
Number of employees | 0.08 (0.21) | 0.02 (0.26) | 0.75 (0.74) | 0.42 (0.37) | −0.07 (0.08) |
Sample size |
***
The North Carolina GATE II program increased any self-employment by 29.9 percentage points, an effect that is attributed to an increase in both types of self-employment. Results show a reduction in regular employment and increases in individual earnings and employment of other workers, although estimates lack statistical significance. In Virginia, we estimate a positive effect on overall self-employment, driven mostly by an increase in independent self-employment. At the same time, training reduced salary employment, salary earnings, and total earnings.
Previous work suggests that individuals with high levels of education and labor market experience (Fairlie and Meyer, 2000; Hout and Rosen, 2000; Reynolds et al., 2004) and those with self-employment experience or experience working in a family business (Hout and Rosen, 2000; Fairlie and Robb, 2007; Edelman et al., 2010) are more likely to possess the skills and know-how to start successful businesses. There is also evidence that financing constraints may explain why many start-up efforts are unsuccessful and many new businesses fail soon after their creation (Blanchflower et al., 2003; Cavalluzzo and Walken, 2005; Asiedu et al., 2012; Loftstrom and Bates, 2013).
It is thus possible that training may dissuade those who lack human capital and entrepreneurship skills from pursuing self-employment, convincing them to pursue regular employment instead. In contrast, individuals with an entrepreneurship background may have a frame of reference that allows them to derive different benefits from training and make better use of business counseling than those who lack this background. Also, workshops and business counseling may be less beneficial for individuals who have limited access to start-up financing, even in cases when they have a promising business plan and the skills to execute it.
Consider, for example, the results in Maine, where training increased both self-employment in own business and employment in regular jobs, suggesting that training helped some compliers to start a business and may have persuaded others to pursue regular employment instead. It is possible that this heterogeneity occurred based on observed factors. Thus, aggregate effect estimates may hide heterogeneity in training effects based on observed characteristics associated with human capital, business background, and financing access. Effects heterogeneity is an important policy concern: if only individuals with certain traits are likely to succeed if they receive training, policymakers and practitioners can improve program cost-effectiveness by targeting workers with characteristics associated with improved training success.
To investigate effects heterogeneity, we estimated LATEs based on gender, race, age, education, self-employment experience, credit history, and household income. We did not find evidence of differential effects based on gender, race, and household income. Here, we discuss results for four characteristics for which there is evidence of heterogeneity in training effects— self-employment experience, college education, credit history, and age. Note that limited sample sizes do not allow us to estimate with high accuracy effects based on pairwise combinations of characteristics. Separate analyses that attempted such combinations lacked statistical power and are therefore omitted from the paper. Note that, in general, self-employment experience was highly correlated with age (45+ years old) but had low or no correlation with good credit and education (see Table A11 in Appendix). Our analyses test multiple null hypothesis for differential treatment effects based on observed characteristics across the five programs without adjusting inference to account for the possibility that we may falsely reject a true null. Because of the large number of tests performed, many estimates would fail an inference adjustment correction (e.g., using the Bonferroni correction or the Benjamini-Hochberg procedure). Thus, nominally significant results should be interpreted with caution.
Local average treatment effects by selected characteristics, Project GATE
By self-employment experience | By college degree | |||
---|---|---|---|---|
No experience | With experience | No degree | With degree | |
Self-employment | ||||
| −0.017 (0.102) | 0.320 (0.213) | 0.070 (0.110) | 0.099 (0.160) |
| 0.109 (0.066)* | 0.146 (0.170) | 0.161 (0.084)* | 0.067 (0.106) |
| 0.092 (0.113) | 0.466 (0.247)* | 0.232 (0.130)* | 0.165 (0.171) |
Regular employment | −0.028 (0.132) | −0.205 (0.255) | −0.129 (0.156) | −0.057 (0.16) |
Any employment | 0.064 (0.113) | 0.260 (0.224) | 0.102 (0.135) | 0.108 (0.146) |
Self-emp. Earnings | 3 (120) | 151 (349) | 91 (128) | 25 (241) |
Regular earnings | 12 (652) | 673 (1,034) | −64 (685) | 768 (946) |
Has employees | −0.045 (0.059) | 0.102 (0.158) | −0.060 (0.077) | 0.106 (0.081) |
Number of employees | −0.10 (0.25) | 0.72 (0.51) | −0.00 (0.03) | 0.38 (0.41) |
Self-employment | ||||
| 0.172 (0.103)* | 0.498 (0.183)*** | 0.169 (0.108) | 0.425 (0.151)*** |
| −0.045 (0.083) | −0.121 (0.134) | −0.046 (0.091) | −0.092 (0.108) |
| 0.127 (0.118) | 0.376 (0.183)** | 0.122 (0.122) | 0.333 (0.160)** |
Regular employment | −0.148 (0.123) | −0.150 (0.173) | −0.220 (0.126)* | −0.024 (0.158) |
Any employment | −0.022 (0.092) | 0.226 (0.134)* | −0.107 (0.104) | 0.309 (0.120)** |
Self-emp. Earnings | 154 (343) | 252 (295) | 226 (258) | 122 (480) |
Regular earnings | −1,290 (1,091) | 559 (836) | −459 (787) | −1,093 (1,516) |
Has employees | 0.005 (0.068) | −0.024 (0.096) | 0.068 (0.073) | −0.076 (0.083) |
Number of employees | 0.08 (0.33) | 0.07 (0.46) | 0.44 (0.38) | −0.34 (0.36) |
Self-employment | ||||
| −0.002 (0.222) | 0.364 (0.273) | 0.250 (0.186) | −0.072 (0.224) |
| −0.123 (0.236) | −0.438 (0.247)* | −0.095 (0.162) | −0.409 (0.204)** |
| −0.125 (0.271) | −0.074 (0.261) | 0.155 (0.196) | −0.482 (0.228)** |
Salary employment | 0.528 (0.266)* | 0.083 (0.333) | 0.221 (0.201) | 0.624 (0.292)** |
Any employment | 0.403 (0.235)* | −0.009 (0. 0.354) | 0.376 (0.283) | 0.142 (0.314) |
Self-Emp. Earnings | −128 (607) | 4,237 (3,752) | 1,168 (728) | 957 (2,523) |
Salary earnings | 7,025 (3,139)** | −4,925 (3,084) | 1,885 (1,425) | 9,280 (4,961)* |
Has employees | 0.120 (0.216) | 0.531 (0.310)* | 0.605 (0.191)*** | 0.181 (0.224) |
Number of employees | 0.14 (0.12) | 1.92 (0.94)* | 0.98 (0.46)** | 0.27 (0.41) |
By Credit Access | By Age | |||
Bad Credit | Good Credit | <45 Years | 45+ Years | |
Self-employment | ||||
| 0.199 (0.131) | −0.032 (0.117) | 0.177 (0.128) | −0.007 (0.123) |
| 0.024 (0.094) | 0.223 (0.083)*** | 0.072 (0.093) | 0.129 (0.090) |
| 0.223 (0.145) | 0.192 (0.130) | 0.249 (0.146)* | 0.122 (0.140) |
Salary employment | −0.077 (0.171) | −0.135 (0.137) | −0.116 (0.175) | −0.069 (0.150) |
Any employment | 0.146 (0.147) | 0.057 (0.119) | 0.133 (0.146) | 0.052 (0.135) |
Self-Emp. Earnings | 131 (157) | −21 (170) | 63 (176) | 13 (160) |
Salary earnings | 97 (738) | −50 (718) | −769 (923) | 946 (563) |
Has employees | 0.046 (085) | −0.022 (0.069) | −0.046 (0.083) | 0.018 (0.074) |
Number of employees | 0.45 (0.33) | −0.10 (0.27) | −0.07 (0.31) | 0.11 (0.30) |
Self-employment | ||||
| −0.063 (0.150) | 0.375 (0.107)*** | 0.324 (0.153)** | 0.235 (0.109)** |
| −0.077 (0.142) | −0.079 (0.078) | −0.086 (0.121) | −0.070 (0.083) |
| −0.140 (0.185) | 0.296 (0.113)*** | 0.238 (0.167) | 0.165 (0.118) |
Salary employment | 0.113 (0.206) | −0.209 (0.111)* | −0.363 (178)** | 0.023 (0.116) |
Any employment | −0.027 (0.160) | 0.087 (0.086) | −0.125 (0.131) | 0.188 (0.096)* |
Self-Emp. Earnings | 147 (284) | 70 (328) | 999 (437)** | −415 (328) |
Salary earnings | −1,010 (1,309) | −619 (978) | −2,096 (1,510) | −92 (875) |
Has employees | −0.132 (0.107) | 0.025 (0.063) | 0.027 (0.096) | −0.049 (0.065) |
Number of employees | −0.29 (0.37) | 0.12 (0.33) | −0.30 (0.45) | 0.10 (0.33) |
Self-employment | ||||
| 0.461 (0.220)** | −0.068 (0.341) | 0.191 (0.267) | 0.157 (0.332) |
| −0.403 (0.238)* | −0.138 (0.309) | −0.003 (0.291) | −0.524 (0.318) |
| 0.058 (0.255) | −0.235 (0.352) | 0.189 (0.295) | −0.367 (0.334) |
Salary employment | −0.054 (0.259) | 0.596 (0.315)* | 0.259 (0.249) | 0.495 (0.301) |
Any employment | 0.034 (0.239) | 0.361 (0.290) | 0.448 (0.230)* | 0.128 (0.286) |
Self-Emp. Earnings | 2,158 (1,888) | 611 (1,099) | 354 (776) | 1,557 (1,405) |
Salary earnings | 1,466 (3,238) | 5,004 (2,402)** | 4,209 (1,845)** | 3,990 (3,365) |
Has employees | 0.597 (0.329)* | 0.143 (0.376) | 0.464 (,260)* | 0.221 (0.433) |
Number of employees | 0.92 (0.52)* | 0.31 (0.46) | 1.07 (1.08) | 0.51 (1.3) |
Effects by selected characteristics, GATE II
By self-employment experience | By college degree | |||
---|---|---|---|---|
No self-employment experience | Self-employment experience | No college degree | College degree | |
Self-employment | ||||
| 0.262 (0.178) | 0.015 (0.197) | 0.068 (0.140) | 0.263 (0.242) |
| 0.161 (0.140) | 0.074 (0.203) | 0.145 (0.122) | 0.111 (0.410) |
| 0.423 (0.209)** | 0.089 (0.248) | 0.213 (0.167) | 0.374 (0.330) |
Salary employment | −0.343 (0.243) | −0.070 (0.256) | −0.262 (0.194) | 0.172 (0.244) |
Any employment | 0.080 (0.223) | 0.018 (0.231) | −0.048 (0.181) | 0.546 (0.309) |
Self-Emp. earnings | 292 (327) | 291 (382) | 0.299 (253) | 1,028 (1,707) |
Salary earnings | 547 (857) | −227 (577) | 476 (694) | −1,194 (1,688) |
Has employees | 0.143 (0.159) | −0.005 (0.094) | 0.081 (0.070) | 0.033 (0.047) |
Number of employees | 0.29 (0.17) | 0.05 (0.07) | 0.48 (0.44) | −0.31 (1.83) |
Self-employment | ||||
| 0.200 (0.114)* | −0.271 (0.259) | −0.067 (0.212) | 0.073 (0.125) |
| 0.128 (0.091) | 0.004 (0.319) | −0.171 (0.250) | 0.159 (0.133) |
| 0.328 (0.140)** | −0.266 (0.348) | −0.237 (0.310) | 0.232 (0.156) |
Salary employment | −0.250 (0.185) | 0.078 (0.328) | −0.210 (0.347) | −0.159 (0.173) |
Any employment | 0.078 (0.167) | −0.188 (0.259) | −0.448 (0.335) | 0.073 (0.151) |
Self-Emp. earnings | 148 (87)* | −647 (620) | −509 (354) | −102 (245) |
Salary earnings | −1,849 (2,159) | −720 (830) | −9,395 (5,619)* | 111 (603) |
Has employees | 0.037 (0.060) | −0.163 (0.094)* | 0.009 (0.088) | −0.039 (0.055) |
Number of employees | 0.06 (0.08) | −0.41 (0.24)* | −0.15 (0.21) | −0.06 (0.08) |
By Credit | Access | By Age | ||
Bad Credit | Good Credit | <45 Years | 45+ Years | |
Self-employment | ||||
| 0.310 (0.205) | 0.039 (0.181) | 0.377 (0.274) | 0.090 (0.165) |
| 0.039 (0.187) | 0.145 (0.150) | 0.241 (0.224) | 0.063 (0.147) |
| 0.349 (0.253) | 0.184 (0.206) | 0.618 (0.327)* | 0.153 (0.202) |
Salary employment | −0.440 (0.304) | −0.069 (0.226) | −0.267 (0.344) | −0.261 (0.228) |
Any employment | −0.091 (0.281) | 0.115 (0.194) | 0.351 (0.330) | −0.108 (0.207) |
Self-Emp. earnings | 303 (464) | 277 (261) | 672 (536) | 47 (289) |
Salary earnings | −62 (1,114) | 257 (476) | 821 (1,440) | −135 (500) |
Has employees | −0.098 (0.097) | 0.170 (0.088)* | 0.125 (0.120) | 0.027 (0.082) |
Number of employees | −0.29 (0.29) | 0.76 (0.63) | 1.23 (1.02) | 0.06 (0.34) |
Self-employment | ||||
| 0.217 (0.424) | −0.015 (0.099) | – | 0.019 (0.102) |
| −0.721 (0.654) | 0.227 (0.114)** | – | 0.113 (0.111) |
| −.504 (0.670) | 0.213 (0.133) | – | 0.132 (0.131) |
Salary employment | 0.442 (0.686) | −0.246 (0.154) | – | −0.181 (0.151) |
Any employment | −0.062 (0.465) | −0.013 (0.133) | – | −0.036 (0.130) |
Self-Emp. earnings | −616 (858) | −141 (188) | −208 (188) | |
Salary earnings | 198 (1,210) | −2,013 (1,572) | −1,871 (1,414) | |
Has employees | −0.037 (0.062) | −0.014 (0.051) | – | −0.025 (0.045) |
Number of employees | −0.08 (0.09) | −0.05 (0.08) | – | −0.07 (.08) |
In Pennsylvania, training increased self-employment in own business and any self-employment for those with self-employment experience but had much smaller or no effects for those with no self-employment experience. The increase in self-employment for experienced individuals (although partly offset by a reduction in regular employment) yielded a 26.0 percentage points increase in any employment. There is limited evidence that effects varied based on education, age, and credit access. Similarly, in Minnesota, training increased self-employment in own business by 49.8 percentage points and any self-employment by 37.6 percentage points for those with self-employment experience. Effects for those with no self-employment experience were much smaller. Training also increased self-employment in own business for individuals with a college degree or with good credit but had much lower effects for those with no college degree or bad credit.
The Maine results show different effects based on observed characteristics, although small sample sizes limit statistical power. Effects on regular employment were large and statistically different from zero for those with no self-employment experience, a college degree, and good credit. These groups experienced large gains in regular earnings. On the other hand, training increased self-employment in own business and reduced contractor self-employment for those with self-employment experience, no college degree, and bad credit. These individuals were also more likely to hire employees to support their business operations.
In North Carolina, training had much higher effects on self-employment for individuals with no self-employment experience than for experienced individuals. Training increased overall self-employment for experienced individuals by 42.3 percentage points, an effect that was due to increases in both types of self-employment. This effect was largely offset by a reduction in regular employment. Training seems to have been more effective for individuals under 45 years old—in this case, training increased self-employment by 61.8 percentage points, any employment by 35.1 percentage points, and the likelihood of hiring employees by 12.5 percentage points.
The Virginia results reveal effects not identified by the aggregate results. Training increased overall self-employment for those with no self-employment experience by 32.8 percentage points, an effect that was largely offset by a reduction in regular employment. Training also yielded small positive effects on self-employment earnings and large negative effects on total earnings for inexperienced individuals. There is also evidence that training may have increased self-employment rates and reduced earnings for individuals with a college degree or good credit history, although estimates for these groups lacked statistical significance.
Although a full cost–benefit analysis is beyond the scope of this study, we outline some of the benefits (and costs) for compliers and discuss considerations for assessing the costs and benefits from the society’s perspective. From the compliers’ perspective, an important program benefit is that they were able to access free training through the programs, so that they enjoyed the benefits of training (which they would not have received in the absence of the program) without incurring the associated pecuniary costs. Benefits for compliers would also include any improvements in earnings resulting from training participation. Effects on earnings are ambiguous. We estimate positive effects on self-employment, regular, and total earnings in Pennsylvania and North Carolina and negative effects in Minnesota and Virginia. In Maine, we estimate large and positive effects on regular and total earnings. Note that, on average, treated individuals in the three Project GATE programs were able to achieve self-sufficiency, with the average annualized total earnings in Pennsylvania ($20,676), Minnesota ($31,080), and Maine ($27,588) exceeding the federal poverty limits. The federal poverty limit in 2008 ranged from $10,400 for a one-person household to $21,200 for a four-person household (source: The federal
Self-employed workers may derive much greater levels of job satisfaction than those employed in regular jobs because they value the day-to-day activities, flexibility, and financial independence associated with self-employment (Douglas and Shepherd, 2002
Overall, our findings suggest that compliers in at least four of the five programs may have experienced positive welfare gains. A more complete analysis would take into account the personal costs of participating in the programs (including pecuniary costs such as travel costs and lost wages and nonpecuniary costs, such as loss of leisure) and other potential benefits deriving from self-employment, such as the employment of family members and business tax write-offs.
Assessing the costs and benefits of the program from the society’s perspective is complicated by lack of appropriate data and other considerations. Program benefits from the society’s perspective would include any welfare gains for participants as well as any savings in UI and other social assistance benefits because participants were able to secure employment. Another potential society benefit is that the self-employment activities of compliers led to the creation of new jobs and the employment of other workers. The Maine and North Carolina results provide evidence that such spillover employment effects are possible. Also, to the extent that the self-employment activities of compliers created new markets and revenue streams, then society benefits would include the expansion of the tax base. Society’s costs include the cost of recruiting and providing services to participants, estimated at $1,258 in Pennsylvania, $1,027 in Minnesota, and $2,342 in Maine (Benus et al., 2010). GATE II costs have not been made public. Society’s costs would also include any displacement effects occurring because training helped individuals to start businesses at the expense of interested unemployed workers with similar business ideas and no access to training. If displacement effects exist in this context, we would expect them to be relatively more important during recessions.
Our findings show that government-sponsored entrepreneurship training programs increased training take-up among unemployed workers at both stages of the business cycle. Evidently, program participants during the recession received a higher dose of the intervention than did participants in a good economy, suggesting that demand for training may be higher in a weak economy. At the same time, the finding that many control individuals received similar training from the community—both before and during the recession—suggests that government-sponsored training partially crowded out training options available in the community.
Analyses show that training helped compliers in Pennsylvania and Minnesota to start businesses and become self-employed. These effects were accompanied by reductions in wage and salary employment, indicating that training essentially aided the business start-up efforts of those who would have found regular jobs in the programs’ absence. These effects occurred primarily for individuals with self-employment experience. A potential explanation is that these individuals derive higher benefits from training and counseling or simply that they are more committed to starting a business when the economy is relatively strong, compared with individuals who have little practical understanding of the demands of running a business and who may prefer pursuing (attractive) salary employment options.
In Maine, training reduced contractor and overall self-employment and increased regular employment and earnings. Further analyses reveal that these effects occurred mainly for individuals with no self-employment experience, suggesting that training persuaded those who lacked relevant experience but presumably had good labor market alternatives to pursue regular employment instead of becoming self-employed as independent contractors. The same analyses indicate that training increased self-employment in own business for those with self-employment experience, an effect that was accompanied by a reduction in independent self-employment and an increase in the employment of other workers. These results suggest that training helped those with prior self-employment experience (who would otherwise be independently self-employed) to start a business and hire employees to support those businesses.
Impact disparities across Project GATE states are in some ways consistent with the observed patterns in services received. Maine participants were at least as likely as Minnesota and Pennsylvania participants to receive counseling but much less likely to receive multiple workshop training sessions. This pattern suggests that the Maine program may have provided more emphasis on advising participants about the pros and cons of self-employment and whether they should pursue self-employment when attractive regular jobs are available. As a result, Maine participants who realized during counseling that self-employment may not be the best option for them exited the program earlier (and thus received a lower treatment dose) than their Pennsylvania and Minnesota peers.
Earlier studies of Project GATE pooled data from all three states and did not distinguish between self-employment in own business and contractor self-employment (Michaelides and Benus, 2012; Fairlie et al., 2015; Michaelides, 2020). Those studies showed that training had small impacts on self-employment rates and no impacts on total employment. However, they missed underlying impact differences across states and did not identify the mechanisms discussed above, including that training was more effective for experienced individuals or that training may have persuaded others to pursue regular employment instead.
Analyses of the North Carolina and Virginia GATE II programs provide benchmark evidence about the efficacy of entrepreneurship training during recessions. Training increased self-employment rates, but effects were largely offset by reductions in regular employment. Further analyses suggest that training helped those with no self-employment experience to become self-employed but had no effects for those with prior experience. Positive effects on self-employment for inexperienced individuals were partly offset by reductions in regular employment. Training also increased the employment of other workers in North Carolina, suggesting that inexperienced individuals who became self-employed as a result of the program were likely to hire workers to support their self-employment activities.
These findings indicate that the primary effect of entrepreneurship training during a recession is to help unemployed workers with no self-employment experience to overcome the knowledge gap associated with starting a business. It is possible that, in a weak economy, self-employment attracts relatively more workers with the skills and motivation to become business owners but who would face high opportunity costs to pursue self-employment in a good economy (when attractive regular jobs are available). Among these are individuals who had not engaged in self-employment activities in the past, but lack of attractive salary options during a recession may push them to consider self-employment; our findings suggest that these individuals may derive substantial benefits from training. This finding is consistent with the fact that, on average, GATE II participants participated in multiple workshops and completed more counseling sessions than did Project GATE participants, revealing a higher need for training support.
Our findings lead to four general conclusions. First, government-sponsored entrepreneurship training programs may offer the opportunity to many unemployed workers to receive training that they would not have otherwise received. However, before implementing such programs, policymakers should carefully assess whether a government option would displace programs that are already available in the community. Second, in a strong economy, training may help individuals with prior self-employment experience to start their own business instead of finding a salary job while it may persuade those with attractive salary job alternatives to pursue salary employment instead. If both mechanisms are in effect, it is possible that training may lead to improved overall employment rates and earnings. Third, during recessions, training may help those who had not previously engaged in self-employment to become self-employed and, potentially, hire other workers to support their self-employment activities. However, in most cases, these effects occur for those who would have otherwise found salary jobs and are accompanied by close-to-zero or negative earnings effects. Finally, our findings provide weak evidence that entrepreneurship training can improve overall employment, so it is unlikely that it represents an effective policy for combating unemployment, particularly in the context of a weak labor market.