are self-employment training programs effective self-employment training programs effective?...

Are Self-Employment Training Programs Effective?

Evidence from Project GATE

Marios Michaelides IMPAQ International

[email protected]

Jacob Benus IMPAQ International

[email protected]

April 2012

Abstract

We examine the efficacy of providing self-employment training to unemployed and other

individuals interested in self-employment using data from Project GATE. This experimental

design program offered self-employment training services to a random sample of individuals

who expressed a strong interest in self-employment. We find Project GATE was effective in

helping unemployed participants to start their own business, leading to significant impacts in

self-employment and overall employment soon after program entry. The program also helped

unemployed participants remain self-employed and avoid unemployment even five years after

program entry. However, the program was not effective in improving the labor market outcomes

of participants who were not unemployed.

Keywords: self-employment, entrepreneurship, small business, unemployment, workforce development,

training, SEA, Project GATE.

Introduction

Self-employment has historically played an important role in the U.S. economy. Many

workers in the U.S. view self-employment as an attractive alternative to salary employment

because it provides an opportunity for self-sufficiency, high earnings, and upward social mobility

(Bates, 1997; Fischer and Massey, 2000; Keister, 2000; Bucks et al., 2006). In fact, for the past

few decades, nearly 10 percent of U.S. workers have been self-employed (Bregger, 1996; Fairlie,

2004; Blanchflower, 2009). Self-employment is also important from a macroeconomic

perspective, since small businesses employ a large share of the workforce and produce important

innovations that contribute to the overall growth of the U.S. economy (Acs, 1999; Manser and

Picot, 1999; Lerner, 2002; Minniti and Bygrave, 2004; Davis et al., 2008).

To encourage the creation and growth of small businesses, U.S. policymakers have

established a variety of programs that provide self-employment training, technical support, and

financial assistance to unemployed and other individuals interested in self-employment

(Schreiner, 1999; Walker and Blair, 2002; Wandner, 2008). The objective of these programs is

to help aspiring business owners gain a better understanding of all aspects of starting and

operating a new business and obtaining access to start-up capital and credit. The expectation is

that self-employment programs will assist unemployed and other individuals in overcoming the

obstacles they face in pursuing self-employment, leading to the creation of viable small

businesses (Benus, 1994; Vroman, 1997; McKernan and Chen, 2005). Despite the growing

interest in self-employment programs, there is limited evidence on whether they are actually

effective in promoting self-employment participation and success.

This paper provides evidence on the efficacy of providing self-employment training services

without any financial support to unemployed and other individuals interested in self-

employment. For our analyses, we use data from Project GATE (Growing America Through

Entrepreneurship), the most recent experimental design self-employment program implemented

in the U.S. Project GATE offered an array of self-employment training services to a random

sample of individuals who expressed a strong interest in self-employment. Unlike most programs

implemented in the U.S. and elsewhere, Project GATE did not provide participants with any type

of financial support, and it accepted applications from all interested individuals – unemployed,

employed, self-employed, or not in the labor force. The program’s design provides a unique

opportunity to examine the efficacy of providing self-employment training without any financial

support to unemployed and other individuals interested in self-employment.

In the remainder of this paper, we examine the impact of Project GATE on the following

participant outcomes: likelihood of starting a new business, likelihood of self-employment in a

new business, self-employment, salary employment, overall employment, and earnings. Our

results show that Project GATE was effective in assisting unemployed participants start their

own business, leading to significant gains in self-employment and overall employment in the

first months following program entry. The program was also effective in assisting unemployed

participants to remain self-employed even 5 years after program entry. However, we find no

evidence that the program was effective in assisting non-unemployed participants improve their

labor market outcomes. Based on these results, we conclude that U.S. state workforce agencies

should consider adopting self-employment training programs targeting the unemployed as part of

their workforce development agenda.

1. Background

1.1. Self-Employment Programs in the U.S.

In the past 20 years, self-employment programs have received considerable attention from

various U.S. government agencies, including the U.S. Department of Labor (DOL). In the early

1990s, DOL funded two demonstration programs, the Washington Self-Employment and

Enterprise Development Program and the Massachusetts Enterprise Project. These experimental

design programs provided self-employment training and monetary assistance to unemployed

workers interested in self-employment (Benus et al., 1995). These programs were implemented

to assess if self-employment training combined with financial assistance is effective in

promoting the reemployment of unemployed workers through self-employment.

Following the success of these demonstrations, Congress authorized states to establish self-

employment assistance (SEA) programs for a 5-year period under the North America Free Trade

Agreement (NAFTA) Implementation Act of 1993. SEA programs provided states with the

ability to offer self-employment training to Unemployment Insurance (UI) recipients as a means

to promote their reemployment and facilitate their early exit from the UI system (Kosanovich et

al., 2002). SEA programs were permanently authorized by Congress in 1998. Since then, 11

states have passed SEA-enabling legislation; of these states, Delaware, Maine, Maryland,

Minnesota, Oregon, Pennsylvania, New Jersey, and New York implemented SEA programs as

part of their workforce development agenda. Unfortunately, participation in SEA programs in

these few states was limited by the fact that states lacked the necessary resources to provide

consistent and reliable self-employment training to interested individuals; thus, only between 1-2

percent of all UI claimants participated in SEA in recent years (Wandner, 2008).

In recent years, there has been a growing interest in understanding the potential benefits of

providing self-employment training to all interested individuals, not just unemployed workers. In

2002, DOL partnered with the Small Business Administration (SBA) to sponsor Project GATE

(Growing America Through Entrepreneurship), an experimental design demonstration program

that provided self-employment training to individuals who expressed a strong interest in self-

employment. Project GATE, implemented in Maine, Minnesota, and Pennsylvania, was designed

as an experiment to examine whether self-employment training is effective for unemployed and

other individuals (Bellotti et al., 2006; Benus et al., 2009). During the implementation of Project

GATE, DOL issued a directive to states to expand their Workforce Investment Act (WIA)

funded activities to provide entrepreneurship training to all customers of the state workforce

development system.1 These efforts show the commitment of policymakers to expand the role of

self-employment training in the U.S. workforce development system.

In addition, several programs have been established to assist aspiring and existing small

business owners overcome limited access to start-up capital and credit. According to the Aspen

Institute’s directory of U.S. microenterprise programs (Walker and Blair, 2002), more than 600

microenterprise programs in 2002 provided a combination of financing subsidies, assistance with

government procurement, and business counseling to small businesses. Among these were 55

Federal programs, of which 37 were supported by the SBA. The importance of microenterprise

programs in supporting small businesses in the U.S. economy has since grown. A recent Aspen

Institute report shows that, from 2002 to 2008, these programs experienced significant growth in

1 Title I of the WIA Act of 1998 allows states to “provide adults and dislocated workers occupational skills training,

including training for nontraditional employment, and entrepreneurial training.” U.S. Department of Labor Training

and Employment Guidance Letter No. 16-04 (February 2005) requested states to “encourage local workforce

investment boards to consider entrepreneurial training programs for WIA customers as part of their menu of services

and to explore the appropriate partnerships to support these training programs.” The same letter encouraged states

“to include entrepreneurial training providers on their eligible training provider lists.”

the number of participants (14 percent), the loan amounts disbursed (51 percent), and their total

microloan capital (85 percent).2 During the same period, SBA’s budget increased from $493

million in 2002 to $555 million in 2008. In fact, SBA’s budget has grown dramatically since

then; for 2011, SBA received a budget of nearly one billion dollars ($994 million) to support

small businesses, which is $170 million (21 percent) higher than the SBA’s 2010 budget.3

Despite the growing interest in self-employment programs, Benus et al. (1995) is the only

study that provides credible evidence on the efficacy of such programs in the U.S. Using data

from the Washington and Massachusetts experimental design demonstrations of the early 1990s,

that study shows that participants experienced significant gains in self-employment (44 percent),

overall employment (24 percent), and earnings (18 percent). The study concludes that providing

self-employment training and financial assistance to the unemployed is an effective

reemployment policy that should be widely adopted by state workforce agencies.

A study funded by DOL (Kosanovich et al., 2002) finds that SEA participants in Maine, New

Jersey, and New York were 55 percent more likely to enter self-employment and had 18 percent

higher total earnings than those who chose not to participate. However, the authors acknowledge

that, since the control group included individuals who declined SEA participation, these

differentials do not constitute reliable impact estimates. So, to date, there is no evidence whether

providing self-employment training without any financial support is an effective policy tool for

improving the labor market outcomes of unemployed or other individuals interested in self-

employment. Finally, there are no studies that provide evidence on the impacts of

microenterprise programs in the U.S., mainly due to lack of appropriate comparison groups

(McKernan and Chen, 2005).

2 U.S. Microenterprise Census Highlights, FY 2008 Data, The Aspen Institute, Washington, DC.

3 Source: Budget of the U.S. Government, Fiscal Year 2011, Office of Management and Budget.

1.2. Self-Employment Programs in Other Countries

Interest in self-employment programs has also grown in other developed and developing

countries. Several countries have established national programs to assist unemployed workers in

pursuing self-employment as part of their active labor market programs. The British Enterprise

Allowance Scheme, for example, provides a weekly allowance to unemployed individuals while

they attempt to start their own business; the Chomeurs Createurs program in France assists

unemployed individuals in starting businesses by providing them with start-up capital through

lump-sum payments in lieu of unemployment benefits (Benus et al., 1995; Meager, 1996). In

Germany, the Start-Up Subsidy and Bridging Allowance programs provide periodic payments to

unemployed individuals interested in starting their own business (Baumgartner and Caliendo,

2008; Caliendo and Künn, 2010). Similar programs have been implemented in Belgium,

Denmark, Italy, and Spain (Meager, 1996; Wandner, 2008).

Moreover, several countries have programs that provide a combination of financial assistance

and self-employment technical support to unemployed individuals, including Australia (Kelly et

al., 2002), Argentina (Almeida and Galasso, 2007), Poland and Hungary (O’Leary et al., 1998),

and Romania (Rodriquez-Planas, 2010). Furthermore, entrepreneurship education has been

incorporated in secondary and post-secondary school curricula in many European countries,

including Denmark, Germany, and the Netherlands (European Commission, 2008; Oosterbeek et

al., 2010).

Evaluations of self-employment assistance programs implemented in European and other

countries provide some promising evidence. Previous work shows that providing financial

support to unemployed workers interested in self-employment in Germany (Baumgartner and

Caliendo, 2008; Caliendo and Künn, 2010) and Sweden (Carling and Gustafson, 1999) was

effective in assisting them become self-employed and avoid unemployment. There is also

evidence that unemployed individuals who received a combination of financial assistance and

self-employment training in Argentina (Almeida and Gallasso, 2007), Romania (Rodriquez-

Planas, 2010), and New Zealand (Perry, 2006) experienced significant gains in self-employment

and overall employment. Other work shows that providing start-up capital and financial

assistance to small businesses in Spain (Cueto and Mato, 2006) and Australia (Kelly et al., 2002)

is associated with improved business sustainability. However, to our knowledge, there are no

evaluations of programs that provide self-employment training without any financial assistance

to unemployed and other individuals interested in self-employment.

1.3. The Role of Self-Employment Training

Many workers consider self-employment as an attractive option for improving their overall

earnings and socioeconomic status (Bates, 1997; Keister, 2000). Employed workers, for

example, may view self-employment as an alternative to salary employment or as a way to

supplement their salary income. Self-employment is particularly attractive for unemployed

workers, who may consider self-employment as their only option to exit unemployment or as a

way to avoid labor market discrimination (Meager, 1992; Bates and Servon, 2000; Glocker and

Steiner, 2007). In fact, previous research shows that unemployed workers are significantly more

likely than their peers to pursue and enter self-employment (Bates and Servon, 2000; Rissman,

2003; Glocker and Steiner, 2007).

Unemployed and other individuals interested in self-employment may be willing to work

hard and invest a substantial portion of their time in their efforts to successfully pursue self-

employment, but also face several obstacles. For example, many aspiring business owners lack

self-employment experience and business background, so they do not have an in-depth

understanding of all the different aspects of starting and operating a business. Previous work

shows that, controlling for human capital characteristics, individuals who lack self-employment

experience and business background are significantly less likely than their peers to enter self-

employment, remain self-employed, and achieve high earnings (Hout and Rosen, 2000; Dunn

and Holtz-Eakin, 2000; Fairlie and Robb, 2007). Another major impediment in pursuing self-

employment is limited access to start-up capital and business credit. Evidence suggests that,

accounting for individual skills and other pertinent characteristics, individuals with low access to

financing are much less likely than their peers to start and sustain their own business

(Blanchflower and Oswald, 1998; Fonseca et al., 2001; Cavalluzzo and Wolken, 2005).

Self-employment training services, such as training courses and individual business

counseling, are expected to help unemployed and other individuals interested in self-employment

gain a better understanding of the self-employment process, from the initial planning phase and

the actual start of business operations to implementing strategies to sustain and grow the

business. This process includes, but is not limited to, developing a comprehensive business plan,

obtaining information on credit options, applying for credit, hiring staff, producing marketing

materials, and dealing with various legal issues. So, although self-employment training is not

expected to eliminate the obstacles faced by aspiring business owners, it may enhance their

entrepreneurial and business skills, thus improving their chances of successfully pursuing self-

employment. Self-employment training may also improve the human capital of participants –

particularly the unemployed – and if their self-employment pursuit is unsuccessful, this may help

them to find salary jobs, avoid unemployment, and improve their earnings potential.

Based on the conclusions of previous research, we know that self-employment participation

and success are positively affected by individual human capital, self-employment experience and

business background, and access to credit. We also know that unemployed workers are more

likely than their peers to actively pursue self-employment as a means to avoid unemployment

and achieve self-sufficiency. However, very little is known about the role of self-employment

training in assisting unemployed and other individuals interested in self-employment to achieve

their goals. Is self-employment training an effective policy for promoting the reemployment of

unemployed individuals? Is self-employment training effective in assisting non-unemployed

individuals to improve their labor market outcomes? The objective of this paper is to address

these research gaps.

2. Project GATE Overview

2.1. Program Description

In 2002, DOL partnered with the SBA to sponsor Project GATE, an experimental design

demonstration program that offered an array of free training and counseling services to

individuals interested in self-employment. The objective of this demonstration program was to

assess the efficacy of offering free self-employment training through the public workforce

development system to all individuals interested in self-employment (Benus et al., 2009). DOL

implemented Project GATE from 2003 through 2005 in Pennsylvania, Minnesota, and Maine.4

Interested individuals registered for the program in designated One-Stop Career Centers in five

sites: Philadelphia, Pittsburgh, Minneapolis/St. Paul, rural Minnesota, and rural Maine.5 These

five sites were selected to include both urban and rural areas (Benus et al., 2009). Project GATE

4 For practical reasons, DOL wanted to implement Project GATE in states that had already passed SEA-enabling

legislation and were providing self-employment training through their workforce system. Maine, Pennsylvania, and

Minnesota were among the first states to do both – for a discussion, see Wandner (2010), pp. 289-334. 5 Project GATE was implemented in 21 One-Stop Career Centers (7 in Pittsburgh, 5 in Philadelphia, 4 in

Minneapolis/St. Paul, 2 in rural Minnesota, and 3 in Maine). One-Stop Career Centers were established under the

Workforce Investment Act of 1998 (Public Law 105-220) to provide the full range of available public workforce

development system services for job seekers, including UI application assistance, job training referrals, career

counseling, job listings, and labor market information.

included an outreach campaign to identify and recruit individuals interested in self-employment,

with One-Stop Career Centers serving as focal points of recruitment. Program sites advertised

the program through brochures, fliers, and posters. Additionally, the outreach campaign included

a website, a mass media marketing campaign (advertisements, media events, and public service

announcements), and networking with local organizations and government agencies.6

Following registration, individuals received an invitation to attend a mandatory orientation

meeting at a designated One-Stop Career Center. To ensure that all potential applicants received

consistent information, the orientation meeting included a video detailing the important features

of Project GATE. The video provided information on the benefits and risks of self-employment,

the application process, the random assignment process, and the services available through

Project GATE for those randomly selected to participate. Following the orientation meeting,

people wishing to participate in Project GATE had to complete an extensive application form.

The application form included demographic characteristics, employment status, and self-

employment experience. Further, the application form requested the applicant’s phone and

address, as well as the contact information of three alternate contacts (e.g., applicant spouse,

parents, brothers, and friends). The application also included an informed consent statement

indicating that the applicant understood that only half of the applicants would be selected to

receive program services and that selection would be based on a random process.

The required orientation and rigorous application process was designed to attract applicants

who were willing to make a substantial effort to reach the random selection phase. Thus, this

process ensured that program applicants had a strong interest in self-employment. A total of

4,198 individuals applied for Project GATE; 2,095 were randomly assigned to the treatment

group and 2,103 were assigned to the control group. Project GATE thus represents the largest

6 For more details on the outreach campaign for Project GATE, see Bellotti et al. (2006), pp. 37-52.

experimental design self-employment program implemented in the U.S., surpassing the 2,729

total applicants in the early 1990s demonstration programs in Washington and Massachusetts

(Benus et al., 1995). Project GATE applicants assigned to the treatment were offered program

services; those assigned to the control were not offered any program services but were free to

seek similar services elsewhere.

Program services were designed to help treatment group participants gain a better

understanding of the process of starting and operating a new business and to inform them of

various business financing options (Bellotti et al., 2006; Benus et al., 2009). Following random

assignment, treatment group members were invited to an individual assessment session with a

business counselor – nearly all treatment group members attended this session. The objective of

this assessment was for the counselor to recommend available program services that best met the

participant’s needs. Treatment group members were then offered an array of training courses

about the different aspects of starting and operating a business, including developing a business

plan, financing, marketing, hiring staff, and various legal issues. Advanced courses covered

topics such as business growth strategies, business planning, and customer relations.

Additionally, the program offered a business counseling session that provided an opportunity for

participants to meet one-on-one with a business counselor to discuss their business idea, receive

help producing or refining their business plan, and obtain information on various financing

sources, including the SBA MicroLoan program.

Findings from Project GATE’s implementation report (Bellotti et al., 2006) indicate that 90

percent of treatment group members attended the individual assessment session. Furthermore, 76

percent of all treatment group members received program services: 42 percent attended training

courses and received the business counseling session; 13 percent only attended training courses;

and 21 percent only received the business counseling session. The average treatment group

member received 13.0 hours of Project GATE services – 1.2 hours for the individual assessment,

10.5 hours for various training courses, and 1.3 hours for the business counseling session. It is

important to note that control group members were free to receive similar self-employment

training elsewhere. Indeed, 35 percent of control group members did so: 10 percent attended

training courses and received individual business counseling; 20 percent attended training

courses; and 5 percent received individual business counseling.

Local service providers, selected through a competitive process, provided Project GATE

services. Interested service providers submitted capabilities statements, exhibiting their

experience and capacity for providing self-employment services. Based on this information, five

SBA Small Business Development Centers and nine nonprofit community-based organizations

were selected to provide program services. These organizations provided experienced business

counselors, who conducted the individual assessment and business counseling sessions and the

training courses. The average Project GATE cost per treatment group participant was $859

(Benus et al., 2009), which includes the amounts paid to service providers for program services

and all other costs related to providing services to program participants.

2.2. Characteristics of Project GATE Applicants

Project GATE outreach efforts were designed to reach a broad group of individuals interested

in obtaining self-employment training services. However, since One-Stop Career Centers played

a major role in the outreach effort, customers of the public workforce development system

constituted a key target group for applicant recruitment. As a result, program applicants may not

be broadly representative of all individuals interested in self-employment. To shed light on the

representativeness of the Project GATE sample, Table 1 presents the characteristics of the U.S.

labor force, the self-employment population, and Project GATE applicants.7

As shown in Table 1, the gender distribution of applicants mirrored the gender distribution of

the U.S. civilian labor force in 2003: 54 percent men and 46 percent women. Women accounted

for 46 percent of program applicants and for 34 percent of the self-employed; a t-test comparison

indicated this difference was not statistically significant. The proportion of black applicants (31

percent) greatly exceeded the proportion of blacks in the labor force (11 percent) and in the self-

employed population (5 percent); these differences were statistically significant at the 5 percent

level. About one quarter (26 percent) of applicants had a high school diploma or lower education

compared to 64 percent of labor force participants and 60 percent of the self-employed. These

differences were statistically significant at the 1 percent level. Moreover, program applicants

were significantly more likely than labor force participants and self-employed workers to be in

the lowest household income bracket.

The employment status of program applicants is of particular interest. As shown in Table 1,

7 percent of the U.S. civilian force was unemployed in 2003 compared to 43 percent of Project

GATE applicants. The high proportion of unemployed applicants may be due to a variety of

factors, including the fact that One-Stop Career Centers were the focal point for outreach. This

is also an indication that unemployed individuals had a strong interest in pursuing self-

employment and in participating in self-employment training. The remaining program applicants

were employed in a salary job (28 percent), self-employed (16 percent), or not in the labor force

(13 percent) at the time of application.

7 The characteristics of the U.S. labor force and the self-employed population are from 2003 to correspond to the

start of the program’s implementation period.

Table 2 presents selected characteristics of applicants who were unemployed at the time of

application and of other applicants (employed, self-employed, or not in the labor force). There

were only minor differences in characteristics across the four groups, and t-test comparisons

showed there were no statistically significant differences. Notably, only about a quarter of

unemployed, employed, or not in the labor force applicants had previous self-employment

experience. This indicates that many applicants lacked self-employment background and

experience, which is widely cited as an important obstacle in pursuing self-employment (e.g.,

Dunn and Holtz-Eakin, 2000; Fairlie and Robb, 2007). Nearly half of all applicants had poor

credit histories and nearly half of all applicants received financial support from family members

while pursuing self-employment. Furthermore, most applicants had an annual household income

of less than $75,000 at the time of application. These figures suggest that many applicants had

limited access to credit at the time of application, a major impediment in starting and sustaining

a business (e.g., Blanchflower et al., 2003; Cavalluzzo and Wolken, 2005).

2.3. Random Assignment

Following application, applicants were randomly assigned to the treatment or to the control

group; of the 4,198 total applicants, 2,095 were assigned to the treatment and 2,103 were

assigned to the control. If random assignment (RA) was successfully implemented, there should

not be any treatment-control group differences in observable and unobservable characteristics at

the time of application. To establish that RA was successful, we calculated treatment-control

group differences in means for each observable applicant characteristic and used t-tests to assess

whether these differences were statistically significant. Our results show no statistically

significant differences in available characteristics between treatment and control group

members.8

Furthermore, we estimated a linear regression model where the dependent variable is the

likelihood of being assigned in the treatment group and the covariates include all available

applicant characteristics. If RA was successful, the estimated model parameters should not be

statistically significant, indicating there was no relationship between observed applicant

characteristics and treatment group assignment. Our regression results show that none of the

estimated parameters were statistically significant.9 Finally, to ensure that treatment and control

group members were identical in all observed characteristics and in the interactions of observed

characteristics, we used multivariate analysis of variance (MANOVA) tests. The MANOVA F-

statistic tests the null hypothesis that there were no treatment-control group differences in

characteristics and in the interactions of those characteristics.10

The MANOVA F-statistic was

0.89 with a p-value of .632, indicating that the null hypothesis cannot be rejected.11

These results

provide confidence that applicants were successfully randomized; thus any differences in post-

RA outcomes between treatment and control group members can be attributed to the program.

2.4. Applicant Responses to Follow-Up Surveys

Three follow-up surveys, conducted 6 months (Wave 1), 18 months (Wave 2), and 60 months

(Wave 3) after RA, documented applicants’ post-RA labor market outcomes. To trace applicants

for the follow-up surveys, program survey staff used the contact information of the applicant and

8 Similar results were obtained when we compared the treatment-control group characteristics separately for

unemployed and for non-unemployed applicants. Results are available upon request. 9 Note that similar results were obtained when we estimated this model separately for unemployed and for non-

unemployed applicants. Also, similar results were obtained using logistic and probit regressions to estimate these

models. Results are available upon request. 10

The MANOVA F-test can be produced based on four statistics: 1) Wilks’ lambda, 2) Lawley-Hotelling trace, 3)

Pillai’s trace, and 4) Roy’s largest root. The four methods produced identical F-statistics and p-values. 11

Similar results were obtained when we produced the MANOVA F-statistic separately for unemployed applicants

(p-value = .748) and for non-unemployed applicants (p-value = .613).

three alternate contacts provided in the application form. Two weeks prior to survey

implementation, survey notification letters were sent to applicants notifying them of the

upcoming phone survey and requesting their input on the most convenient times to reach them.

Applicants were given the choice of providing this information either by calling a toll-free

number or through mail. The letter also specified that those who took the phone survey would

receive a $15 incentive payment.

The surveys started within 2 weeks of sending the letters. When survey staff was unable to

reach the applicant, the alternate contacts were contacted. In cases where alternate contacts

provided a new applicant address and telephone number, the survey notification letter was sent to

the new address and survey staff used the new phone number for completing the survey. As a

result of this process, 82 percent of all applicants responded to the Wave 1 survey; 88 percent of

Wave 1 respondents responded to the Wave 2 survey; and 81 percent of Wave 2 respondents

responded to the Wave 3 survey for a 58 percent cumulative 60-month response rate.

While the response rate at each survey exceeded 80 percent, reasonable concerns remain

about potential cumulative non-response bias. A careful examination of applicant characteristics

shows that the characteristics of applicants at RA were nearly identical to the characteristics of

the respondents at each survey (see Appendix Table A). Therefore, survey non-response did not

lead to substantial changes in the characteristics distribution of program applicants.

Furthermore, we conducted MANOVA tests to assess whether there were disparities in

characteristics between treatment and control group respondents at each survey. We did not find

any significant treatment-control differences in the three follow-up surveys.12

These analyses

12

MANOVA F-statistics were produced for all applicants (p-values: Wave 1 = .789; Wave 2 = .777; and Wave 3=

.659), unemployed applicants only (p-values: Wave 1 = .664; Wave 2 = .479; and Wave 3 = .683), and other

applicants only (p-values: Wave 1 = .533; Wave 2 = .780; and Wave 3 = .775).

indicate that the treatment-control balance in observed characteristics was maintained for each

group of program applicants, mitigating concerns about survey non-response bias.13

Using responses to the three surveys, we constructed the following outcomes: started a new

business between RA and the time of the survey; self-employed at the time of the survey in a

business started within 6 months of RA; self-employed at the time of survey; employed in a

salary job at the time of survey; and employed (i.e., self-employed or employed in a salary job)

at the time of the survey. Table 3 presents these outcomes for unemployed and other applicants.

As shown in Table 3, 331 (22 percent) of unemployed applicants started a new business by

Wave 1, 468 (36 percent) started a new business by Wave 2, and 505 (47 percent) started a

business by Wave 3. As the last column of Table 3 shows, unemployed applicants were more

likely than non-unemployed applicants to start a new business by the time of each survey. As a

result, the unemployed were more likely than their peers to be self-employed in a new business

following program entry. For example, 21 percent of unemployed and 15 percent of other

applicants were self-employed at Wave 1 in a business started within 6 months of RA – the

difference was 6.5 percentage points and statistically significant at the 1 percent level. Similarly,

unemployed applicants were 6.4 and 4.6 percentage points more likely than their peers to be self-

employed in a new business at Wave 2 and Wave 3, respectively. These differences are

consistent with previous research that the unemployed are more likely than their peers to start a

new business and enter self-employment.

13

In general, if survey attrition is associated with post-program success among participants in a way that differs

from the attrition association in the control group, this may induce bias in program impact estimates. However, the

results of our tests show that attrition did not differ between the treatment and the control group based on participant

characteristics that predict post-program success. These results do not completely eliminate the possibility that there

is some sample attrition bias, but they do show that such bias is less plausible, since a mechanism that would induce

treatment-control differences in the association between attrition and outcome success, but not for characteristics

related to success, would be quite complex.

Table 3 also shows that, despite the fact that the unemployed were more likely than non-

unemployed applicants to start a new business after program entry, they remained less likely to

be self-employed at each survey. On the other hand, unemployed applicants were significantly

more likely than other applicants to be employed in a salary job at each survey. Notably, the

self-employment proportion of unemployed (28 percent) and non-unemployed applicants (43

percent) at Wave 3 much exceeded the U.S. self-employment proportion (10 percent).14

This

may be partly attributed to the fact that Project GATE applicants were much more interested than

average in starting their own business and becoming self-employed. Furthermore, about 76

percent of treatment group members received Project GATE services and 35 percent of control

group members received similar services elsewhere. These services may have helped them start

their own business and remain self-employed long after program entry. Finally, Table 3 shows

that unemployed applicants were 8.7 percentage points less likely to be employed (i.e., self-

employed or in a salary job) at Wave 1 relative to other applicants. This gap was lower at Waves

2 and 3.

Survey respondents also provided information on their personal earnings at the time of each

survey.15

Table 4 presents the average self-employment earnings, salary earnings, and total

earnings at each survey for all unemployed and non-unemployed applicants. Unemployed

applicants had an average of $1,821 in self-employment earnings at Wave 1; this increased to

$2,483 at Wave 2 and to $3,505 at Wave 3.16

As the last column of Table 4 shows, unemployed

14

Source: Tabulations of the American Community Survey, 2008 and 2009. 15

Unfortunately, participants did not provide information on their finances (e.g., accumulated debts, whether they

filed for bankruptcy, and business loans), so it is not possible to examine their overall financial situation following

program entry. The implication is that in analyzing the self-employment and total earnings of program applicants,

we cannot account for whether their pursuit of self-employment led to accumulated debts or other financial

obligations which would mitigate their well-being as measured by earnings variables. 16

These figures represent averages for all unemployed applicants. The average self-employment earnings for

unemployed applicants who were self-employed at each survey were much higher: $7,853 at Wave 1, $10,166 at

Wave 2, and $13,898 at Wave 3.

applicants had lower self-employment earnings at Wave 1 than other applicants, but there were

no statistically significant differences in self-employment earnings at Waves 2 and 3.17

Table 4

also shows that unemployed applicants experienced a substantial growth in salary earnings and

total earnings over time; at Wave 3, the average unemployed applicant had $40,472 salary

earnings and $43,978 total earnings. It is worth noting that the average total earnings for

unemployed applicants at Wave 3 were similar to the average total earnings of U.S. labor force

participants around the time Wave 3 was implemented,18

indicating that over time unemployed

applicants were able to achieve self-sufficiency. Separate analyses also show that unemployed

applicants who were self-employed at each survey had lower total earnings and a higher

coefficient of variation (standard deviation divided by the sample mean) than those who were not

self-employed at each survey. These analyses indicate that, among unemployed applicants, those

who were self-employed following program entry faced higher income risk than their peers.

Similar results were obtained for non-unemployed applicants.19

Finally, as seen in Table 4, there

were no statistically significant differences in salary earnings or in total earnings between

unemployed and other applicants at Wave 1. Interestingly, the unemployed experienced a steeper

growth in earnings over time; as a result, at Waves 2 and 3 they had higher salary earnings and

total earnings than other applicants.

17

Note also, that there were no statistically significant differences in self-employment earnings between

unemployed and other applicants who were self-employed at each survey. 18

The average total earnings per labor force participant in the U.S. from 2008 through 2009 were $44,057 (source:

tabulations of the American Community Survey, 2008 and 2009). 19

The mean (standard deviation) of total earnings for unemployed applicants who were self-employed at each

survey were: $16,654 (24,615) at Wave 1; $26,588 (32,037) at Wave 2; and $37,710 (46,622) at Wave 3. The mean

(and standard deviation) of total earnings for unemployed applicants who were not self-employed at each survey

were: $23,901 (28,578) at Wave 1; $32,780 (35,465) at Wave 2; and $42, 722 (39,683) at Wave 3.

3. Impact Analyses

The primary objective of this paper is to estimate Project GATE’s impacts on the post-RA

outcomes of unemployed and other participants. Our focus is to estimate the program’s intent-

to-treat effect on participant outcomes, that is, the effect of being offered Project GATE

services.20

Our impact analyses rely on linear regression models that estimate program impacts

for unemployed and other applicants, controlling for applicant characteristics that may affect

self-employment and other labor market outcomes. For each outcome, we estimated the

following model:

iiiiii eUNEMPTXTY

The dependent variable is the post-RA outcome for participant i ( iY ).21

The first control

variable is iT , a dummy indicating whether applicant i was in the treatment group. The model

also controls for a constant term, the program site, available characteristics capturing applicant

human capital, self-employment background, and access to credit ( iX ), and iUNEMP , a dummy

indicating whether applicant i was unemployed at application. The model also includes

the ii UNEMPT interaction, which indicates whether applicant i was in the treatment group and

unemployed at application. The parameter α is the baseline treatment effect, while the parameter

γ is the additional treatment effect for unemployed participants. We estimated each regression

model by weighted least squares, using survey non-response weights reported in the data to make

the estimation sample representative of all program applicants.22

20

From this point on, treatment effect or impact refers to the intent-to-treat effect. 21

For convenience, we used linear regression models to estimate program impacts on participant outcomes. For

dichotomous outcomes, we also estimated program impacts using logistic and probit models; these results are

discussed in the Sensitivity Analyses section. 22

The weights were designed to adjust for survey non-response for treatment and control group members based on

all observed characteristics at the time of application. Benus et al. (2009) provide a detailed description of the

methodology used to construct these weights. Using survey non-response weights in estimating program impacts is a

3.1. Impact Results

Table 5 reports the two parameters of interest, α and γ, for the following dependent variables:

likelihood of starting a new business, likelihood of self-employment in a business started within

6 months of RA, likelihood of self-employment, likelihood of employment in a salary job, and

likelihood of employment.23

The last column of Table 5 reports the treatment effect for

unemployed participants on each outcome (α + γ) and, where this effect is significant, the

treatment effect expressed as a percentage of the outcome’s control group mean for unemployed

participants.

As Table 5 shows, Project GATE significantly increased the likelihood of starting a new

business following RA for unemployed participants, but had no impact for other participants.

For example, the baseline treatment effect at Wave 1 was .021 but statistically insignificant,

whereas the interaction treatment effect for the unemployed was .080 and significant. The total

treatment effect for the unemployed was 10.1 percentage points and significant at the 1 percent

level. Dividing the total treatment effect by the control group mean likelihood of starting a new

business for unemployed participants (16.9 percent, see Table 3), we find that the program led to

a 60 percent increase in this outcome for unemployed participants. The program’s impact on the

likelihood of starting a new business for unemployed participants declined over time (8.8 and 6.5

percentage points by Wave 2 and Wave 3, respectively), but remained statistically significant.

This decline suggests that the program’s impact on starting a new business occurred within 6

months of RA.

widely used method for making the estimation sample representative of all program applicants (e.g., McConnell et

al., 2006; Trenholm et al., 2007). Note that we did not find any significant differences between impact estimates

based on weighted least squares models and impact estimates based on ordinary least squares models. 23

Complete regression results are available upon request.

As a result of the program’s large impact on starting a new business in the early months

following RA, the program was very effective in assisting unemployed participants to become

self-employed in a business started within 6 months of RA. Specifically, unemployed

participants experienced a 9.7 percentage-point (60 percent) increase in the likelihood of being

self-employed at Wave 1 in a business started within 6 months of RA. Moreover, the program

led to a 54 percent and a 53 percent increase in this outcome for unemployed participants at

Wave 2 and Wave 3, respectively. These results show that the program was not only effective in

assisting unemployed participants to become self-employed but also to sustain their new business

even 5 years after program entry. Thus, the program was effective in assisting the unemployed to

remain self-employed for long periods of time, avoid unemployment, and presumably reduce

their dependence on the UI system. In contrast, the program had no significant impact on these

outcomes for non-unemployed participants.

A number of underlying factors may explain the different program impacts for unemployed

and other participants. For example, the program may have helped participants learn more about

the potential rewards of self-employment which, compared to the time investment required and

associated risks, are typically low in the first few years of business ownership. This may have

discouraged many non-unemployed participants – particularly those employed in salary jobs –

from starting their own business and becoming self-employed following program entry. In

contrast, we would expect that this information had a less important deterrent effect on

unemployed participants, who probably had a lower opportunity cost to pursue self-employment

than their peers.24

Further, the program may have helped participants gain a better understanding

24

The view that unemployed workers may have a low opportunity cost to pursue self-employment is hardly a new

concept. Previous research suggests that, due to lack of acceptable employment options, unemployed workers may

view self-employment as the best alternative to return to productive employment (Meager, 1992; Bates and Servon,

2000; Rissman, 2003; Glocker and Steiner, 2007).

of the requirements and substantial time commitment needed to start and operate a business.

Even though both unemployed and non-unemployed participants had a strong interest in self-

employment at program entry, we would expect that the unemployed had more available time

than those employed in salary jobs or those who were already self-employed at program entry to

invest in the self-employment process (e.g., produce a business plan, secure financing, and start a

new business) or to participate in additional entrepreneurship services. This may partly explain

why the unemployed were more successful than their peers in starting and sustaining their own

business following program entry.

Due to the program’s impact on new business starts and on self-employment in a business

started within 6 months of RA, unemployed participants experienced significant increases in

self-employment, even 5 years after RA. As shown in Table 5, Project GATE increased self-

employment for the unemployed by 59 percent at Wave 1, by 24 percent at Wave 2, and by 16

percent at Wave 3. Notably, the program’s impact on self-employment declined over time,

reflecting the fact that, following Wave 1, the program had no effect on new business starts,

while the program’s effect on the likelihood of self-employment in a business started within 6

months of RA declined. Again, the baseline treatment effects were not statistically significant,

indicating that there was no impact on self-employment for non-unemployed participants.

There is no evidence the program had an effect on salary employment for unemployed or

other participants, but our results show that Project GATE led to a 9.5 percentage-point (or 11

percent) increase in the likelihood of employment at Wave 1 for unemployed participants. This

impact is directly tied to the program’s impact on the likelihood of self-employment at Wave 1

for unemployed participants. However, partly due to the declining program impact on self-

employment, the program did not have a statistically significant impact on total employment for

any participants at Waves 2 and 3.

Table 6 reports the treatment effects for self-employment earnings, salary earnings, and total

earnings.25

Our results show that Project GATE had a positive and statistically significant impact

on the Wave 1 self-employment earnings of unemployed participants. The program effects on

self-employment earnings for unemployed participants at Waves 2 and 3 had positive signs but

lacked statistical significance. The program’s effect on salary earnings for the unemployed had a

negative sign at Wave 1 and a positive sign at Waves 2 and 3, but these estimates were not

statistically significant. Finally, the program’s effect on total earnings for unemployed

participants had positive signs but lacked statistical significance. These results indicate that,

although Project GATE was effective in assisting unemployed participants in returning to

productive employment earlier than they would have in the absence of the program, the program

did not have a statistically significant effect on earnings after the first wave.

These results provide strong evidence that self-employment training is an effective policy

tool for assisting unemployed workers interested in self-employment to start their own business,

remain self-employed, and avoid unemployment for long periods after program entry. At the

time Project GATE’s implementation was completed in 2005, about 10 percent of U.S. workers

were self-employed. In fact, U.S. workers were more likely, on average, to be self-employed

and much more likely to be interested in self-employment relative to workers in other developed

countries, including Germany, France, Japan, and Great Britain (Blanchflower et al., 2001; Grilo

and Thurik, 2005).26

The effectiveness of Project GATE combined with the high interest in self-

25

Complete regression results are available upon request. 26

According to the 2004 Flash Eurobarometer Survey on Entrepreneurship, 67 percent of U.S. workers reported

they preferred self-employment over salary employment. Although interest in self-employment is likely inflated in

employment, suggests that self-employment training should be more widely adopted by state

workforce agencies and offered through DOL One-Stop Career Centers. In the past decade,

about 15 million workers a year received services from One-Stop Career Centers, including UI

recipients, WIA training participants, and Employment Service recipients (Wandner, 2008;

Jacobson, 2009). It is unknown how many of these workers would be interested in self-

employment training offered through the workforce development system. However, given the

high interest in self-employment in the U.S. and the fact that many unemployed workers may

view self-employment as an attractive reemployment option, we would expect that self-

employment programs would attract many DOL One-Stop Career Centers customers. For

example, according to DOL’s Unemployment Insurance Data Summary, there were more than

7.9 million new UI recipients in 2005. Even if a modest 2 percent of those recipients were

interested in self-employment training, there would be nearly 160,000 interested participants

from the UI system alone.

3.2. Sensitivity Analyses

Our impact analyses show that the program had positive impacts for unemployed participants

but no impacts for non-unemployed participants. However, it is possible that the program was

effective for one or more of the groups in the “other participants” category. To examine this

possibility, we re-estimated our regression models, allowing for differential treatment effects for

participants who were unemployed, self-employed, and not in the labor force. Our results

confirm that the program had no significant impacts on the labor market outcomes of any of

these groups.

surveys (see Blanchflower et al., 2001 for a discussion), the U.S. proportion was markedly higher than the one in

other developed countries such as Germany (46 percent), France (45 percent), and United Kingdom (46 percent).

There is a reasonable concern that program impacts may vary across key participant

characteristics associated with self-employment background (self-employment experience,

business plan), access to credit (bad/no credit history, income <$25,000), and human capital

(education, age, etc.). If such variation exists, the differential program impacts for unemployed

and non-unemployed participants may be due to the omission of interactions between the

treatment and key participant characteristics from the specification. To ensure that our analyses

were not ignoring important treatment interaction effects, we estimated several models that

included interactions between the treatment indicator and variables capturing self-employment

background, access to credit, and human capital. Our results provide no evidence that program

effects varied across these characteristics, providing further support to the conclusion that the

program was effective only for unemployed participants.

Another concern is that the use of linear regression models, instead of logistic or probit

models, to estimate program impacts for dichotomous outcomes may influence the impact

estimates. To eliminate these concerns, we estimated program impacts for dichotomous

outcomes using logistic and probit models. Our results show that the estimated treatment effects

for unemployed and other participants based on logistic and probit regression models are similar

in size and statistical significance to those produced using linear regression models.

Finally, an important concern about the validity of the impact analyses is whether sample

attrition may have caused some of the treatment-control group differences in outcomes for

unemployed participants. To mitigate these concerns, we showed that the characteristics

distribution of respondents to each survey reflected the characteristics distribution of all program

applicants and that there were no differences in characteristics between treatment and control

group respondents at each survey. To produce additional evidence that sample attrition did not

taint our estimates, we estimated program impacts at Wave 1 using data only for those who

responded to Wave 2, as well as program impacts at Wave 1 and at Wave 2 using data only for

those who responded to Wave 3. These results did not produce impacts that were statistically

different from those produced using the full sample of respondents, providing confidence that

our impact estimates are not tainted by an underlying relationship between sample attrition and

the outcomes of interest.

4. Conclusion

Project GATE was a demonstration program designed to offer an array of self-employment

training services through the U.S. workforce development system to individuals interested in

self-employment. The program, implemented from 2003 through 2005 in Maine, Minnesota, and

Pennsylvania, included an outreach campaign for recruiting applicants, with designated One-

Stop Career Centers serving as central points of recruitment. At the end of the recruitment

period, 4,198 individuals applied for program participation and were randomly assigned to the

treatment group or to the control group; only those in the treatment group were offered program

services. Depending on their needs, participants were referred to training courses to help them

understand the different aspects of starting and operating a business and an individual business

counseling session. Aside from these services, Project GATE did not offer any type of financial

support to program participants.

Using Project GATE data, we examined the impact of self-employment training on the

outcomes of unemployed and non-unemployed participants. Our analyses show that Project

GATE was effective in assisting unemployed participants start their own business and become

self-employed soon after random assignment. As a result, in the early months following program

entry, unemployed participants experienced significant gains in self-employment and in total

employment. The program also led to a substantial increase in the likelihood that unemployed

participants were self-employed in a new business even 5 years after program entry, indicating

that many of the businesses started by the unemployed were sustained for long periods of time.

Despite the program’s effect on the rapid reemployment of the unemployed through self-

employment, we found no evidence that the program led to significant impacts on total earnings.

Nevertheless, the average total earnings of unemployed applicants at 5 years after program entry

were similar to the U.S. average, indicating that they were able to achieve self-sufficiency

following program entry. Further, given the strong preference that program participants

expressed for self-employment, the higher levels of self-employment may well indicate greater

job satisfaction for program participants.

Our impact analyses for participants who were employed, self-employed, or not in the labor

force at the time of application, yielded substantially different results relative to the results for

the unemployed. We find no evidence that the program had significant impacts on new business

starts, self-employment, salary employment, or total employment for non-unemployed

participants. There is also no evidence the program led to a significant impact on total earnings

for these participants. Therefore, our analyses provide no evidence that self-employment

training is an effective intervention for employed, self-employed, or not in the labor force

individuals interested in self-employment.

This is the first paper that examines the efficacy of providing self-employment training

services without any financial support to unemployed and other individuals interested in self-

employment. Our results provide strong evidence that self-employment training is effective in

assisting unemployed individuals obtain a better understanding of the self-employment process

and become reemployed earlier than they would in the absence of such training. Perhaps more

importantly, self-employment training is effective in assisting unemployed individuals remain

self-employed, avoid unemployment, and presumably reduce their dependence on the UI system

for an extensive time period following program entry. Based on these results, we conclude that

offering self-employment training services through the U.S. public workforce development

system may be an effective policy tool for promoting the rapid reemployment of unemployed

individuals interested in self-employment. Although state workforce agencies should consider

adopting self-employment training programs targeting the unemployed as part of their workforce

development agenda, there may be no value in providing self-employment training to individuals

who are employed, self-employed, or not in the labor force.

References

Acs Z.J. (1999). Are Small Firms Important? Their Role and Impact. Kluwer Academic

Publishers, Boston, MA.

Almeida R. and Galasso E. (2007). Jump-Starting Self-Employment? Evidence Among Welfare

Participants in Argentina. IZA Discussion Paper No. 2902. Institute for the Study of Labor,

Bonn, Germany.

Bates T. (1997). Race, Self-Employment, and Upward Mobility: An Illusive American Dream.

Woodrow Wilson Center Press, Washington DC; Johns Hopkins University Press, Baltimore,

MD.

Bates T. and Servon L. (2000). Viewing Self-Employment as a Response to Lack of Suitable

Opportunities for Wage Work. National Journal of Sociology, Vol. 12, No. 2, pp. 23-55.

Baumgartner H.J. and Caliendo M. (2008). Turning Unemployment into Self-Employment:

Effectiveness of Two Start-Up Programmes. Oxford Bulletin of Economics and Statistics, Vol.

70, No. 3, pp. 347-373.

Bellotti J., McConnell S., and Benus J. (2006). Growing America Through Entrepreneurship:

Interim Report, U.S. Department of Labor, Washington, DC.

Benus J. (1994). Self-Employment Programs: A New Reemployment Tool. Entrepreneurship:

Theory and Practice, Winter 1994.

Benus J.M., Johnson T.B., Wood M., Grover N., and Shen T. (1995). Self-Employment

Programs: A New Reemployment Strategy: Final Report on the UI Self-Employment

Demonstration. Unemployment Insurance Occasional Paper No. 95-4. U.S. Department of

Labor, Washington DC.

Benus J., Shen T., Zhang S., Chan M., and Hansen B. (2009). Growing America Through

Entrepreneurship: Final Evaluation of Project GATE. U.S. Department of Labor, Washington

DC.

Blanchflower D.G. (2009). Minority Self-Employment in the United States and the Impact of

Affirmative Action. Annals of Finance, Vol. 5, No. 3, pp. 361-396.

Blanchflower D.G. and Oswald A.J. (1998). What Makes an Entrepreneur? Evidence on

Inheritance and Capital Constraints. Journal of Labor Economics, Vol. 16, No. 1, pp. 26-60.

Blanchflower D.G., Oswald A., and Stutzer A. (2001). Latent Entrepreneurship Across Nations.

European Economics Review, Vol. 45, No. 4-6, pp. 680-691.

Bregger J.E. (1995). Measuring Self-Employment in the United States. Monthly Labor Review,

Vol. 119, No. 1-2, pp. 3-9.

Bucks B.K., Kennickell A.B., and Moore K.B (2006). Recent Changes in U.S. Family Finances:

Evidence from the 2001 and 2004 Survey of Consumer Finances. Federal Reserve Bulletin,

Vol. 92, pp. A1-A38.

Caliendo M. and Künn S. (2010). Start-up Subsidies for the Unemployed: Long-Term Evidence

and Effect Heterogeneity. Journal of Public Economics, Vol. 95, No. 3-4, pp. 311–331.

Carling K. and Gustafson L. (1999). Self-employment Grants vs. Subsidized Employment: Is

There a Difference in the Re-unemployment Risk? Working Paper 1999:6, IFAU – Institute for

Labour Market Policy Evaluation, Uppsala, Sweden.

Cavalluzzo K. and Wolken J. (2005). Small Business Loan Turndowns, Personal Wealth, and

Discrimination. Journal of Business, Vol. 78. No. 6, pp. 2153-2178.

Cueto B. and Mato J. (2006). An Analysis of Self-Employment Subsidies with Duration Models.

Applied Economics, Vol. 38, No. 1, pp. 23-32.

Davis S. J., Haltiwanger J., and Jarmin R. (2008). Turmoil and Growth: Young Businesses,

Economic Churning, and Productivity Gains. Ewing Marion Kauffman Foundation Report,

June 2008.

Dunn T. and Holtz-Eakin D. (2000). Financial Capital, Human Capital, and the Transition to

Self-Employment: Evidence from Intergenerational Links. Journal of Labor Economics, Vol.

18, No. 2, pp. 282-305.

European Commission Report (2008). Entrepreneurship in Higher Education, Especially in

Non-Business Studies. Final Report of the Expert Group, March 2008.

Fairlie R.W. (2004). Self-Employed Business Ownership Rates in the United States: 1979-2003.

Research Summary No. 243. Small Business Administration, Washington, DC.

Fairlie R.W. and Robb A. (2007). Families, Human Capital, and Small Business: Evidence from

the Characteristics of Business Owners Survey. Industrial and Labor Relations Review, Vol.

60. No. 2, pp. 225-245.

Fischer M. and Massey D. (2000). Residential Segregation and Ethnic Enterprise in U.S.

Metropolitan Areas. Social Problems, Vol. 47, No. 3, pp. 408-424.

Fonseca R., Lopez-Garcia P., and Pissarides C.A. (2001). Entrepreneurship, Start-Up Costs, and

Employment. European Economic Review, Vol. 45, No. 4-6, pp. 692-705.

Glocker D. and Steiner V. (2007). Self-Employment: A Way to End Unemployment? Empirical

Evidence from German Pseudo-Panel Data. IZA Discussion Paper No. 2561. Institute for the

Study of Labor, Bonn, Germany.

Grilo I. and Roy T. (2005). Latent and Actual Entrepreneurship in Europe and the U.S.: Some

Recent Developments. International Entrepreneurship and Management Journal, Vol. 1, No. 4,

pp. 441-459.

Jacobson L. (2009). Strengthening One-Stop Career Centers: Helping More Unemployed

Workers Find Jobs and Build Skills. The Hamilton Project Policy Brief 2009-01.

Hout M. and Rosen H.S. (2000). Self-Employment, Family Background, and Race. Journal of

Human Resources, Vol. 35, No. 4, pp. 670-692.

Keister L.A. (2000). Wealth in America: Trends in Wealth Inequality. Cambridge University

Press, Cambridge, UK.

Kelly R., Lewis P., Mulvey C. and Dalzell B. (2002). A Study to Better Assess the Outcomes in

the New Enterprise Incentive Scheme. Working Paper, Centre for Labour Market Research,

University of Western Australia.

Kosanovich W.T., Fleck H., Yost B., Armon W. and Siliezar S. (2002). Comprehensive

Assessment of Self-Employment Assistance Programs. Employment and Training

Administration Occasional Paper No. 2002-01. U.S. Department of Labor, Washington DC.

Lerner J. (2002). Boom and Bust in the Venture Capital Industry and the Impact on Innovation.

Economic Review, Issue Q4, 2002, Federal Reserve Bank of Atlanta.

Manser M.E. and Picot G. (1999). The Role of Self-Employment in U.S. and Canadian Job

Growth. Monthly Labor Review, April 1999.

McConnell S., Stuart E., Fortson K., Decker P., Perez-Johnson I., Harris B., and Salzman J.

(2006). Managing Customers’ Training Choices: Findings from the Individual Training

Account Experiment. Mathematica Policy Research, Washington, DC.

McKernan S-M. and Chen H. (2005). Small Business and Microenterprise as an Opportunity-

and Asset-Building Strategy. Opportunity and Ownership Project Brief No. 3, The Urban

Institute, Washington, DC.

Meager N. (1992). Does Unemployment Lead to Self-Employment? Small Business Economics,

Vol. 4, No. 2, pp. 87-103.

Meager N. (1996). From Unemployment to Self-Employment: Labour Market Policies for

Business Start-Up. In: Schmid G., O’Reilly J., and Schömann K. (editors), International

Handbook of Labour Market Policy and Evaluation, Edward Elgar, Cheltenham, UK, pp. 489-

519.

Minniti M. and Bygrave W. D. (2004). National Entrepreneurship Assessment, United States of

America. Global Entrepreneurship Monitor 2003 Executive Report.

O’Leary C.J., Kolodziejczyk P., and Lazar G. (1998). The Net Impact of Active Labour

Programmes in Hungary and Poland. International Labour Review, Vol. 137, No. 3, pp. 321-

346.

Oosterbeek H., Praag M., and Ijsselstein A. (2010). The Impact of Entrepreneurship Education

on Entrepreneurship Skills and Motivation. European Economic Review, Vol. 54, No. 3, pp.

442-454.

Perry G. (2006). Are Business Start-Up Subsidies Effective for the Unemployed: Evaluation of

Enterprise Allowance. Working Paper. Auckland University of Technology.

Rissman E.R. (2003). Self-Employment as an Alternative to Unemployment. Working Paper

No. 2003-34. Federal Reserve Bank of Chicago.

Robb A.M. and Fairlie R.W. (2007). Access to Financial Capital among U.S. Businesses: The

Case of African American Firms. Annals of the American Academy of Political and Social

Science, Vol. 613, no. 1, pp. 47-72.

Rodriguez-Planas N. (2010). Channels Through Which Public Employment Services and Small

Business Assistance Programs Work. Oxford Bulletin of Economics and Statistics, Vol. 72,

No. 4, pp. 458-485.

Schreiner M. (1999). Self-employment, Microenterprise, and the Poorest. Social Service Review,

Vol. 73, No. 4, pp. 496-523.

Trenholm C., Devaney B., Fortson K., Quay L., Wheeler J., and Clark M. (2007). Impacts of

Four Title V, Section 510 Abstinence Education Programs. Mathematica Policy Research,

Princeton, NJ.

Vroman, W. (1997). Self-Employment Assistance: Revised Report. The Urban Institute,

Washington DC.

Walker B.A. and Blair A.K. (2002). 2002 Directory of U.S. Microenterprise Programs. The

Aspen Institute, Washington, DC.

Wandner S.A. (2008). Employment Programs for Recipients of Unemployment Insurance.

Monthly Labor Review, Vol. 131, No. 10, pp. 17-27.

Wandner S.A. (2010). Solving the Reemployment Puzzle: From Research to Policy. W.E.

Upjohn Institute for Employment Research, Kalamazoo, MI.

Table 1: Characteristics of U.S. Labor Force Participants,

Self-Employed Workers, and Project GATE Applicants

U.S. Civilian

Labor Force

in 2003†

Self-Employed

in 2003†

Project GATE

Applicants††

Total 140.1 million 13.8 million 4,198

Men 54% 66% 54%

Women 46% 34% 46%

Race: White 78% 86% 57%

Race: Black 11% 5% 31%

Race: Other 11% 9% 11%

Hispanic 13% 9% 5%

Married 58% 72% 44%

Child Under 18 44% 47% 46%

Age: Less 25 Yrs 13% 3% 4%

Age: 25-34 Yrs 23% 14% 21%

Age: 35-44 Yrs 26% 27% 33%

Age: 45-54 Yrs 23% 29% 31%

Age: 55+ Yrs 15% 27% 11%

Less than High School 12% 11% 4%

High School Diploma 52% 49% 22%

Associate Degree/Some College 8% 7% 37%

College Degree 18% 19% 18%

Post-Graduate Degree 10% 14% 19%

Born in U.S. 85% 84% 90%

Disabled 2% 3% 8%

Income: < $25,000 18% 17% 35%

Income: $25,000-$74,999 49% 43% 51%

Income: $75,000≥ 33% 40% 14%

Unemployed 7% -- 43%

Employed 83% -- 28%

Self-Employed 10% -- 16%

Not in the Labor Force -- -- 13%

Note: Reported are proportions of the U.S. civilian labor force in 2003 (ages 18 years old or older), self-employed

workers (ages 18 years old or older), and Project GATE applicants. Source: †American Community Survey, 2003;

†† Project GATE application data.

Table 2: Selected Characteristics of Project GATE Applicants

Unemployed Employed Self-Employed Not in the

Labor Force

Total Applicants 1,817 1,185 657 539

Treatment Group 49% 51% 49% 53%

Male 59% 45% 55% 50%

Race: White 65% 43% 61% 59%

Race: Black 26% 44% 26% 27%

Race: Other 9% 13% 12% 14%

Hispanic 5% 5% 4% 5%

Age: Less 25 Yrs 3% 8% 3% 4%

Age: 25-34 Yrs 19% 26% 17% 22%

Age: 35-44 Yrs 33% 33% 32% 31%

Age: 45-54 Yrs 34% 24% 34% 32%

Age: 55+ Yrs 12% 8% 14% 12%

Less than High School 3% 3% 2% 8%

High School Diploma 24% 21% 16% 27%

Associate Degree/Some College 36% 38% 38% 35%

College Degree 18% 20% 20% 14%

Post-Graduate Degree 19% 17% 24% 16%

Ever Self-Employed 25% 26% 100% 23%

Business Plan 20% 23% 29% 23%

Bad/No Credit History 44% 46% 42% 47%

Family Support 45% 43% 46% 48%

Income: < $25,000 33% 33% 40% 43%

Income: $25,000-$74,999 52% 56% 47% 43%

Income: $75,000 > 15% 11% 13% 14%

Site: Philadelphia 24% 38% 24% 27%

Site: Pittsburgh 16% 14% 11% 12%

Site: Minneapolis/St. Paul 42% 36% 47% 29%

Site: Rural Minnesota 6% 4% 4% 5%

Site: Maine 12% 8% 15% 27%

Note: Reported are proportions of Project GATE applicants.

Table 3: Post-Random Assignment Outcomes of Project GATE Applicants

Unemployed Other Difference

Started New Business

By Wave 1 331 (22%) 297 (15%) .069 [.013]***

By Wave 2 468 (36%) 465 (27%) .085 [.017]***

By Wave 3 505 (47%) 565 (41%) .058 [.020]***

Started New Business by Wave 1

Self-Employed at Wave 1 316 (21%) 285 (15%) .065 [.013]***



Self-Employed

At Wave 1 316 (21%) 858 (44%) -.228 [.016]***

At Wave 2 386 (29%) 794 (46%) -.169 [.018]***

At Wave 3 300 (28%) 594 (43%) -.154 [.019]***

Employed in Salary Job

At Wave 1 714 (48%) 658 (34%) .141 [.017]***

At Wave 2 577 (44%) 518 (30%) .137 [.017]***

At Wave 3 523 (49%) 522 (38%) .106 [.020]***

Employed

At Wave 1 1,030 (69%) 1,516 (78%) -.087 [.015]***

At Wave 2 963 (73%) 1,312 (76%) -.032 [.016]**

At Wave 3 823 (76%) 1,116 (81%) -.048 [.017]***

Note: Reported is the number of respondents with proportion of all respondents in parentheses. The last column

reports the means difference between unemployed and other applicants with standard errors in brackets – statistical

significance: **, *** = 5 percent, 1 percent. Wave 1 respondents = 3,449 (1,495 unemployed, 1,954 other); Wave 2

respondents = 3,038 (1,318 unemployed, 1,720 other); Wave 3 respondents = 2,450 (1,076 unemployed, 1,374

other).

Table 4: Post-Random Assignment Earnings of Project GATE Applicants

Unemployed Other Difference

Self-Employment Earnings

At Wave 1 $1,821 (8,781) $2,621 (13,258) -800 [396]**

At Wave 2 $2,483 (10,257) $2,812 (11,176) -329 [395]

At Wave 3 $3,505 (13,733) $3,729 (13,582) -224 [556]

Salary Earnings

At Wave 1 $20,709 (27,729) $21,458 (26,397) 740 [927]

At Wave 2 $27,990 (34,500) $23,409 (29,816) 4,581 [1,169]***

At Wave 3 $40,472 (44,275) $35,321 (39,536) 5,151 [1.697]**

Total Earnings

At Wave 1 $22,530 (28,122) $24,079 (28,544) -1,549 [975]

At Wave 2 $30,473 (34,500) $26,221 (30,689) 4,252 [1,195]**

At Wave 3 $43,978 (44,636) $39,051 (40,147) 4,927 [1,717]**

Note: Reported is the mean with standard deviation in parentheses. The last column reports the means difference

between unemployed and other applicants with standard errors in brackets – statistical significance: **, *** = 5

percent, 1 percent. Wave 1 respondents = 3,449 (1,495 unemployed, 1,954 other); Wave 2 respondents = 3,038

(1,318 unemployed, 1,720 other); Wave 3 respondents = 2,450 (1,076 unemployed, 1,374 other). Source: Project

GATE survey data.

Table 5: Regression-Adjusted Treatment Effects, Self-Employment, and Employment

Dependent Variable

Regression Parameters Treatment Effect,

Unemployed Applicants Treatment Treatment

x Unemployed

Started New Business

By Wave 1 .021 (.016) .080 (.026)*** .101 [.021]***

+60%

By Wave 2 .031 (.022) .057 (.034)* .088 [.026]***

+28%

By Wave 3 .010 (.028) .055 (.042) .065 [.031]**

+15%

Started New Business by Wave 1

Self-Employed at Wave 1 .022 (.016) .075 (.026)*** .097 [.020]***

+60%

Self-Employed at Wave 2 .017 (.015) .056 (.025)** .073 [.020]***

+54%

Self-Employed at Wave 3 .018 (.015) .034 (.024) .051 [.019]***

+53%

Self-Employed

At Wave 1 .011 (.019) .084 (.028)*** .095 [.020]***

+59%

At Wave 2 -.004 (.022) .066 (.033)** .062 [.026]**

+24%

At Wave 3 .004 (.026) .037 (.032) .041 [.026]*

+16%

Employed in Salary Job

At Wave 1 -.016 (.020) -.004 (.033) -.020 [.026]

--

At Wave 2 .009 (.022) -.040 (.035) -.031 [.028]

--

At Wave 3 -.029 (.027) -.022 (.042) -.051 [.032]

--

Employed

At Wave 1 -.005 (.018) .080 (.030)*** .075 [.024]***

+11%

At Wave 2 .005 (.021) .024 (.033) .029 [.025]

--

At Wave 3 -.025 (.022) .012 (.036) -.013 [.028]

--

Note: Reported are the regression parameters for Treatment and Treatment x Unemployed with standard errors in

parentheses; see Appendix for complete estimation results. The last column reports the treatment effect for the

unemployed with standard errors in brackets and, where statistically significant, the treatment effect as a percentage

of the control group mean. Statistical significance: *, **, *** = 10 percent, 5 percent, 1 percent.

Table 6: Regression-Adjusted Treatment Effects, Earnings

Regression Parameters Treatment Effect,

Unemployed Applicants Treatment Treatment x

Unemployed

Self-Employment Earnings

At Wave 1 -1,364 (607)** 2,302 (747)*** 938 [449]**

+67%

At Wave 2 -738 (526) 1,538 (760)** 800 [540]

--

At Wave 3 337 (679) 876 (1,042) 1,214 [796]

--

Salary Earnings

At Wave 1 38 (1,140) -778 (1,837) -740 [1,438]

--

At Wave 2 -157 (1,365) 222 (2,256) 64 [1,784]

--

At Wave 3 1,493 (2,039) -911 (3,260) 581 [2,507]

--

Total Earnings

At Wave 1 -1,326 (1,238) 1,524 (1,907) 197 [1,451]

--

At Wave 2 -895 (1,401) 1,759 (2,293) 864 [1,803]

--

At Wave 3 1,830 (2,059) -35 (3,257) 1,795 [2,489]

--

Note: Reported are the regression parameters for Treatment and Treatment x Unemployed with standard errors in

parentheses; see Appendix for complete estimation results. The last column reports the treatment effect for the

unemployed with standard errors in brackets and, where statistically significant, the treatment effect as a percentage

of the control group mean. Statistical significance: ** = 5 percent.

Appendix

Table A: Characteristics of Project GATE Applicants and Survey Respondents

Project GATE Applicants

All Respondents

to Wave 1

Respondents

to Wave 2

Respondents

to Wave 3

Total Applicants 4,198 3,449 3,038 2,450

Treatment Group 50% 51% 51% 52%

Unemployed 43% 43% 43% 44%

Employed 28% 27% 27% 26%

Self-Employed 16% 17% 17% 18%

Not in the Labor Force 13% 13% 13% 13%

Male 54% 53% 52% 52%

Race: White 57% 60% 61% 65%

Race: Black 31% 30% 29% 26%

Race: Other 11% 10% 10% 9%

Hispanic 5% 5% 5% 5%

Age: Less 25 Yrs 4% 4% 3% 3%

Age: 25-34 Yrs 21% 20% 19% 17%

Age: 35-44 Yrs 33% 32% 32% 31%

Age: 45-54 Yrs 31% 32% 34% 36%

Age: 55+ Yrs 11% 12% 12% 13%

Less than High School 4% 3% 3% 2%

High School Diploma 22% 21% 21% 19%

Associate Degree/Some College 37% 37% 36% 35%

College Degree 18% 19% 20% 22%

Post-Graduate Degree 19% 20% 20% 22%

Ever Self-Employed 37% 38% 39% 39%

Business Plan 23% 22% 22% 21%

Bad/No Credit History 45% 43% 42% 40%

Family Support 45% 45% 46% 46%

Income: < $25,000 35% 34% 33% 31%

Income: $25,000-$74,999 51% 52% 52% 53%

Income: $75,000 > 14% 14% 15% 16%

Note: Reported are proportions of Project GATE applicants.

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