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The moderating effect of gender Effect ofgender
on the relationship between
apprenticeship and self-
employment: evidence from a
developing country Received 21 October 2022
Revised 25March 2023
Ibrahim Mohammed 28May 2023Accepted 31May 2023
School of Hotel and Tourism Management, The Hong Kong Polytechnic
University, Hung Hom, China and Department of Economics and Actuarial
Science, University of Professional Studies, Accra, Ghana
Wassiuw Abdul Rahaman
Department of Economics, University of Ghana, Accra, Ghana
Alexander Bilson Darku
Department of Economics, University of Lethbridge, Lethbridge, Canada, and
William Baah-Boateng
Department of Economics, University of Ghana, Accra, Ghana
Abstract
Purpose – This study aims to examine the association between apprenticeship training and self-
employment and how gender moderates the association.
Design/methodology/approach – Secondary data from the World Bank’s Skills Towards Employment
and Productivity (STEP) survey on Ghana were analysed using a binary choice (logit regression) model. The STEP
survey drew its nationally representative sample from theworking-age population (15–64 years) in urban areas.
Findings – After controlling for several factors identified in the literature as determinants of self-
employment, the results indicate that completing apprenticeship training increases the likelihood of being
self-employed. However, women who have completed apprenticeship training are more likely to be self-
employed than men.
Originality/value – By examining the moderating effect of gender on the association between apprenticeship
training and self-employment, this study has offered new evidence that policymakers can use to promote self-
employment, especially amongwomen, to reduce the entrepreneurial gap betweenmen andwomen.
Keywords Youth unemployment, Informal training, Learning-by-doing, TVET,
Moderated relationship
Paper type Research paper
1. Introduction
Over the past three decades, there has been a growing interest amongst policymakers and
academics to understand the correlates of self-employment and entrepreneurship so that
Journal of Entrepreneurship in
Emerging Economies
© EmeraldPublishingLimited
2053-4604
JEL classification – J16, J24, J49, L26, M53 DOI 10.1108/JEEE-10-2022-0321
JEEE appropriate policy interventions can be put in place to promote them (Rico and Cabrer-
Borr
as, 2018). In a world development report on the changing world of work, the World
Bank (2019) noted that despite demographic transitions, structural change, technological
progress and global volatility, self-employment and traditional farming had remained the
dominant sectors employing most of the workforce. According to the International Labour
Organisation (ILO) statistics database, the self-employment rate for the world stood at
46.5% in 2019. Broken down by the ILO’s country classification scheme, the self-
employment rate for the same year constituted 12.2% of all employment in high-income
countries, 39.4% in upper-middle-income countries, 51.4% in middle-income countries,
54.0% in low- and middle-income countries, 63.5% in lower-middle-income countries and
80.3% in low-income countries. Across the sub-regions, the self-employment rate in Sub-
Saharan Africa has been the highest but on a downward trend, from a high of 81.7% in 1991
to a low of 75.0% in 2019 (ILO, 2021).
In a cross-country time-series panel data analysis, Baker et al. (2018) found that the
differences in the share of self-employment across European countries were related to policy
and institutional factors such as unemployment benefits, tax wedge and minimum wage.
Explaining international variations in self-employment rates, Parker and Robson (2004)
found that self-employment rates in Organisation for Economic Cooperation and
Development countries are negatively related to unemployment benefits and positively
related to tax rates. In many African countries, the declining trend in self-employment has
been linked to the youth’s inability to obtain employable skills (Filmer and Fox, 2014),
igniting policy discussion on how to equip the African youth with employable skills to enter
wage employment or self-employment. As the policy discussion on self-employment
continues, apprenticeship training has been identified as a viable solution. In line with this,
the World Bank (2019) observed that “International experience with the ‘productive
inclusion’ of poor vulnerable young people reveals that a wide array of programs is
available to connect them to wage employment and self-employment. Interventions may
include [. . .] coaching, apprenticeships, internships, and various forms of training” (p. 130).
In arriving at this recommendation, the World Bank suggests the need for more country-
specific studies because the empirical evidence on the outcomes of the various programmes
has been mixed depending on profiling, design and contexts.
In response to this clarion call, this study examines whether completing apprenticeship
training is associated with self-employment. Although self-employment is an opportunity
available to men and women, there appears to be a gender influence: men are more likely to
be self-employed than women (Blanchflower, 2000). In the labour market, Chung and van
der Lippe (2020) have cited flexibility and work–life balance as the concerns that make
employment gendered. Examining gender differences in solo self-employment, Bari et al.
(2021) found that self-employed women are more concerned about gendered flexibility – that
is, more likely to work reduced hours, to work from home and for reasons associated with
caring or family responsibilities. In this regard, the second objective of this study is to
investigate how the relationship between apprenticeship training and self-employment is
influenced by gender. The study context is Ghana. The choice of Ghana is firstly informed
by two characteristics of the country that typify many African countries:
(1) the sizeable but declining trend of self-employment rate; and
(2) the growing young adult unemployment and underemployment (Fox et al., 2016).
Consistent with the Sub-Saharan African trajectory, Ghana’s self-employment rate has been
declining continuously over the past three decades. From nearly 89.56% in 1991, the self-
employment rate declined to 86.89% in 2001 and further dipped to 79.19% and 72.25% in
2011 and 2019, respectively (ILO, 2021). The second reason for choosing Ghana is to offer Effect of
complimentary evidence to broaden the understanding and generalisation of a similar study gender
conducted by Hardy et al. (2019) on Ghana, which focused narrowly on a government-
sponsored apprenticeship programme to the neglect of the relatively common traditional
(private) apprenticeships.
In focusing the study on Ghana, it has departed from Hardy et al.’s (2019) study in three
significant respects. First, unlike Hardy et al.’s (2019) study, this study argues that the
unique nature of the government-sponsored apprenticeship programme that was the focus
of their research makes it a subsidised version of the traditional apprenticeship, which does
not lend the findings to generalisation even in Ghana. Thus, broadening the scope of the
current study to all forms of apprenticeship training, including the traditional
apprenticeships dominating Africa and other parts of the world, is a step in the right
direction. With this broader scope, the study can extend the generalizability of the findings
to the larger literature. Second, unlike Hardy et al.’s (2019) study, which is experimental and
has the advantages of establishing causal relationships and limiting the analysis to
transition from wage employment to self-employment, this study’s design is ex post facto,
which allows it to examine the determinants of self-employment in general and not
transitions into self-employment. Finally, and unique to this study, the moderating effect of
gender on the association between completing apprenticeship training and self-employment
is investigated to provide granular detail for gender-based policy options.
By pursuing the study objectives from these different perspectives, the findings make
two important contributions to literature and practice. First, it offers empirical evidence
from the Sub-Saharan African context to bolster the view that apprenticeship training is
positively associated with the probability of self-employment (a positive coefficient of 0.819
significant at a 1% level of significance). This indicates that Ghana, and Sub-Saharan
African countries with similar structures, can increase the self-employment rate by
promoting and supporting the youth to complete apprenticeship training. Second, the
significant interaction effect between gender and apprenticeship training shows a
moderation effect indicating that women apprentices are more likely to go into self-
employment than men (a significant coefficient of 0.565). This implies that apprenticeships
targeted at women can help to reduce the entrepreneurial gap between men and women and
impact their lives.
The ensuing sections discuss the literature review on apprenticeship and youth self-
employment and the methodology adopted for the study. The results are presented and
discussed to highlight the study’s contributions, with appropriate conclusions and
recommendations in the final section.
2. Literature review
2.1 Apprenticeship and youth unemployment
Available literature in Africa and many parts of the world, including Europe, indicates that
apprenticeships have existed for ages (Alet and Bonnal, 2011). However, it is only in the
recent three decades that increasing research attention has been paid to this ageless topic.
One reason for this renaissance has been linked to the rapid increase in youth
unemployment, especially after the Great Recession of the late 2000s (Markowitsch and
Wittig, 2022). In Europe, for example, many countries started to consider apprenticeships as
the policy option for solving youth unemployment and set up systems to improve and
diversify apprenticeships. Consequently, the European Alliance for Apprenticeships (EAfA)
was founded in 2013 to unite governments and key stakeholders around the tenets of
strengthening the quality, supply and overall image of apprenticeships across Europe.
JEEE In Africa, particularly Ghana, skills shortage has been identified as another reason for the
rise in policy interest in apprenticeships (Hardy et al., 2019; Ayentimi et al., 2018). One in every
five businesses in Ghana cites skills shortage as a major obstacle (Honorati and De Silva, 2016).
The skills deficit has been attributed to the limited schools’ capacity to absorb all pupils of
school-going age, and financial constraints or poor academic performance that block others
from the mainstream educational system (Ajayi, 2013). Therefore, apprenticeships are an
alternative to the formal educational system and address the skills gap challenge (Page et al.,
2020). In 2016/2017, approximately 22% of the working-age population in Ghana participated
in apprenticeship training (World Bank, 2020). The typical apprenticeships training has been in
the areas of auto electrician and mechanic, fashion design and dress-making, hair styling and
grooming, masonry, plumbing, carpentry, electrical wiring and repairs, metal fabrication and
welding, glazier work, transport and haulage, catering, etc.
As an instructional programme, apprenticeship training differs from other post-
secondary education programmes in many respects and offers several advantages (Lerman
et al., 2009). One distinctive feature of apprenticeship training is that it is a job with training;
that is, apprentices are engaged in the production process under a trainer and mentor at the
same time while acquiring adequate occupational competence for certification
(Ezenwakwelu et al., 2019). The advantages that come with this uniqueness are numerous.
Among others, it presents an opportunity to earn wages while learning, which can provide
some initial capital to enter into self-employment. As apprenticeship takes place in a real-
work context, it allows the apprentice to acquire not only professional or occupational skills
that bring theoretical and practical knowledge together, but also equips them with other
work-related skills such as communication, problem-solving, resource allocation, teamwork
and soft skills to deal with supervisors and co-workers (Ezenwakwelu et al., 2019).
2.2 Impact of apprenticeship on labour market outcomes and social considerations
Entrepreneurship researchers maintain that successful apprenticeship training increases the
probability of both wage employment and self-employment (World Bank, 2019; Odora and
Naong, 2014). The theoretical arguments that support the positive association between
apprenticeship training and self-employment are based on the human capital theory, which
recognises apprenticeship as one of the means of developing human capital for work. In
West Africa, it is estimated that 80% of the skills imparted in the informal economy are
transferred through apprenticeship (ILO, 2008). Several European studies have also shown
that the unemployment rate is lower among youngsters who enter the labour market as
apprentices. Bonnal et al. (2002), for example, found that apprentices are more likely to
experience a smoother transition to employment after graduation.
According to the World Bank’s Skills Towards Employment and Productivity (STEP)
survey, Ghana’s system of apprenticeships provides training to many people. Honorati and
De Silva (2016) estimated that nearly 40% of self-employed and 25% of wage employees in
Ghana have received apprenticeship training. A recent experiment conducted by Hardy et al.
(2019) evaluated the impact of a government-sponsored apprenticeship programme in
Ghana on youth employment transitions. They found that apprenticeships shift youth from
wage employment to self-employment albeit the income from self-employment does not
fully offset the loss of income fromwage employment.
Although apprenticeships have positive impacts on national economies, it is socially
perceived as “low status”, and associated with lower occupations than those associated with
higher education (Hogarth et al., 2012). In some countries, like Germany and Switzerland,
apprenticeship is highly regarded (Mazenod, 2016; Brockmann et al., 2010). However, in many
other countries, including South Africa and England, apprenticeship stigmatisation still exists
(Decker, 2019; Odora and Naong, 2014; Hogarth et al., 2012). According to Ryan and L†orinc Effect of
(2018), the phrase “looked down” was repeatedly mentioned by their interviewees to illustrate gender
the extent to which young people felt stigmatised about apprenticeship. In light of these
stigmatisations, there have been calls for more research on apprenticeships (Hogarth et al.,
2012) and some responses to these calls to give voice to the stigmatised (Ryan and L†orinc,
2018). This article responds to this call by contributing fresh evidence highlighting the
importance of apprenticeship as a panacea to youth unemployment andwomen empowerment.
2.3 Determinants of self-employment
Apart from apprenticeships that is linked to self-employment, the literature on the
determinants of self-employment has identified several other correlates of self-employment.
Synthesizing some of these studies, Georgellis and Wall (2005) note that the determinants
revolve around six themes, with the underlining model being the rational choice theory
which stipulates that a rational agent chooses self-employment if the expected utility of
doing so exceeds the expected utility of salaried employment. According to Georgellis and
Wall (2005), the first theme of the determinants revolves around using the expected earnings
differential between self-employment and salaried employment to predict whether to
become self-employed or not. This strand of the literature concludes that the higher the
earning differential, the more likely an individual will enter self-employment (Rees and
Shah, 1986; Fujii and Hawley, 1991; Taylor, 1996). The second theme emphasises the issue of
liquidity constraints (i.e. lack of sufficient start-up capital and/or insufficient access to credit
markets) and hypothesises that the probability of self-employment increases with lower
liquidity constraints (Evans and Jovanovic, 1989; Evans and Leighton, 1989; Lindh and
Ohlsson, 1996).
The third theme focuses on the intergenerational transfer of human capital and
entrepreneurial ability and predicts that offspring whose parents are self-employed have a
higher probability of becoming self-employed (Dunn and Holtz-Eakin, 2000; Taylor, 1996;
Hout and Rosen, 2000). The fourth theme draws attention to opportunities in the labour
market and highlights issues such as unemployment, low wages, job insecurity and ethnic
or racial discrimination as critical determinants of self-employment (Taylor, 1996; Fairlie
and Meyer, 1996; Borjas and Bronars, 1989). The penultimate theme examines the possible
effect of government policy on self-employment and argues that differential tax treatment or
government policy towards wage employment and self-employment may make one more
attractive (Bruce, 2000; Fan and White, 2003). The final theme considers non-pecuniary
benefits such as job satisfaction, life satisfaction, and independence as important in
choosing between salaried employment and self-employment (Taylor, 1996; Blanchflower
and Oswald, 1998; Blanchflower, 2000).
In a more recent literature survey, Simoes et al. (2016) focused on the individual
determinants of self-employment to identify 12 critical factors that were subsequently
grouped into seven categories as follows:
(1) basic individual characteristics (gender, age, marital status and children);
(2) family background (parents and spouse);
(3) personality characteristics;
(4) human capital (education and experience);
(5) health condition;
(6) nationality and ethnicity; and
(7) access to financial resources.
JEEE The empirical analysis in this study draws heavily on these classifications to explain a few
of the variables that are key to this study. The next section discusses the association
between self-employment and individual factors like gender, education and personality.
2.3.1 Gender. From most studies on the determinants of self-employment, gender has
emerged as a critical determinant with different theoretical perspectives to back the mixed
findings. On the one hand, the well-established facts that men are less risk averse (Croson
and Gneezy, 2009; Parker, 2008; Dohmen et al., 2011; Baffour et al., 2019), less satisfied with
their jobs (Clark, 1997), more prone to seek external finance, less discriminated against in the
credit market (Sena, et al., 2012), more likely to have more social capital (Koellinger et al.,
2013), driven by higher potential returns and more engaged in sectors with higher self-
employment rates (Leoni and Falk, 2010) have been used to support the argument that the
propensity to enter self-employment is greater for men than women (Stefanovi
c and Stoši
c,
2012). On the other hand, the fact that women have a higher likelihood of suffering employer
discrimination (Rosti and Chelli, 2005; Williams, 2012) and more desire for flexibility
(Wellington, 2006; Gurley-Calvez et al., 2009) have been used to support the argument that
women are more likely to enter self-employment, whereas in Germany, the rates of self-
employment do not differ much betweenmen andwomen (Georgellis andWall, 2005).
2.3.2 Education. From the theoretical standpoint, the relationship between education and
self-employment is inconclusive. The opposing views are that individuals with higher
educational levels are:

 less likely to be self-employed because they are more likely to have better job
opportunities in the wage-employment sector (Van der Sluis et al., 2008);

 more likely to be self-employed because they are more able to identify self-
employment opportunities; and

 more likely to be self-employed because they tend to have greater managerial ability
and skills which are needed to succeed in self-employment occupations (Lucas, 1978).
Mirroring these opposing views, the empirical studies have reported mixed results, although
most report a positive influence of education on self-employment (Borjas and Bronars, 1989;
Bates, 1995; Kim et al., 2006; Zissimopoulos et al., 2010).
2.3.3 Personality. Personality traits studies have investigated the relationship between
people’s innate or acquired characteristics and their attitude towards risks. They concluded
significant associations between some personality traits and risk aversion (Baffour et al.,
2019). Most studies on self-employment also conclude that the probability of entering self-
employment is lower for individuals with higher levels of risk aversion (Ekelund et al., 2005;
Ahn, 2010; Macko and Tyszka, 2009; Brown et al., 2011). Although there is no consensus on
the dimensions measuring personality traits in psychology, most empirical studies that have
linked self-employment to personality traits tended to use the big-five factor model and
other measures that capture grit (perseverance and passion for long-term goals),
overconfidence, optimism, need for achievement, need for autonomy, self-efficacy,
assertiveness and taste for variety as measures of personality traits (Koellinger et al., 2013;
Macko and Tyszka, 2009; Rauch and Frese, 2007; Croson and Minniti, 2012; Caliendo and
Kritikos, 2008; Åstebro and Thompson, 2011).
3. Methodology
3.1 Data
This study relies on data from the World Bank’s STEP survey on Ghana conducted in 2013.
The World Bank’s STEP surveys are designed to collect rich data sets to understand the
interplay among skills, employment and productivity. A detailed description of the design of Effect of
the survey, constructs and implementation standards for data quality is provided by Pierre gender
et al. (2014). Nevertheless, it is worth mentioning that the survey is nationally representative. It
draws its sample from the working-age population (15–64 years) in urban areas using a four-
stage sample selection process. In the first stage of the sample selection process, the primary
sampling units (PSUs) are uniquely defined by regions (stratified) and enumeration areas are
selected. In the second step, large PSUs are partitioned into smaller areas for random selection
and subsequent listing of the targeted respondents. The third step uses a systematic random
sampling method to select households. In the final step, individuals are chosen from the
sampled households using a random approach. At the end of the sampling process, 2,987 of the
targeted 3,000 individuals were sampled, representing a 99.6% response rate. However, based
on the selected variables, the eventual sample size used for this study is 1,236.
As far as the purpose of this study is concerned, the STEP data is themost comprehensively
available secondary in Ghana that allows a broad investigation of the association between
apprenticeship and self-employment. Another data set in Ghana that has been used by Hardy
et al. (2019) is experimental data conducted to evaluate the impact of government-sponsored
apprenticeship training programmes on youth unemployment in Ghana. However, this
experimental data, though not publicly available, focuses narrowly on government-sponsored
apprenticeships and do not include self-sponsored apprentices, who are in the majority.
Therefore, the STEP data were selected for its broader coverage. The survey provides data on a
wide range of issues, including the usual demographic characteristics of individuals, such as
gender, age and marital status. Human capital factors include education, experience,
apprenticeship, personality characteristics and access to financial resources. Guided by the
research objectives and the literature reviewed, 20 variables were selected from the STEP data
for the analysis. These variables are defined in Table 1.
3.2 Empirical model specification
As described in Table 1, the outcome variable in this study is self-employment, a binary
categorical variable. Appropriate to the objectives of this study, three logit models [i.e.
equations (1–3)] are estimated. The first model (Model 1) aimed to determine the effect of
gender (and other socio-demographic factors) on the probability of self-employment. Model 2 is
estimated by adding apprenticeship to Model 1 to capture the effect of completing an
apprenticeship on the likelihood of being self-employed. In the final model, the interactive effect
of gender and apprenticeship on the probability of self-employed is examined as Model 3
(controlling for other factors) to determine whether gender strengthens or weakens (i.e.
moderates) the relationship between apprenticeship and self-employment. The chosen logit
models have been applied frequently and widely in similar studies investigating the
determinants of self-employment (Belay, 2023; Safarov and Abdurazzakova, 2022; Bari et al.,
2021; Livanos, 2009). Compared to the probit model, the logit model is more robust to outliers
and can handle non-linear relationships between the independent and the dependent variables.
In a linearised form, the transformed logit models are specified as follows:
X
yi ¼ a0 þ a1genderi þ ajXji þ «i (1)
X
yi ¼ b0 þ b1genderi þ b2apprenticeshipi þ bjXji þ «i (2)
JEEE
Table 1.
Variable definitions
and measurement
Variable Definition Measurement
Dependent variable
Self_emp Self-employment dummy 1 if self-employed; 0 otherwise
Control and independent variables
Age How old respondent is in completed years Age in completed years
Age2 The squared of how old the respondent is in Age in completed years squared
completed years
Has_spouse Marital status Value is 1 if the respondent has a spouse, 0 otherwise
Hh_size Household size, excluding non-relatives Count of household members, excluding non-relatives
Tenure_diff Additional years required to do well on the job Difference between tenure and time needed to do a job well
Time_needed Time to learn to do a job well with experience Years required to learn to do the job well with experience and education needed
and education needed
Auto_avg Autonomy and repetitiveness of work Average of autonomy and repetitiveness at work scores
Think Cognition Frequency of tasks involving at least 30 min of thinking score
Ses Socioeconomic status at age 15 Value is 1 if the socioeconomic status is rated low; 2 if middle and 3 if high
Asset_index Asset wealth Constructed index for asset wealth based on dwelling characteristics and available assets
Shocks Negative economic shocks suffered before the Number of economic shocks before age 15
age of 15
Education (ISCED) Highest educational level completed 0 if ISCED is pre-primary; 1 if primary or first stage of basic; 2 lower secondary or second stage of
basic; 3 if upper secondary or post-secondary (non-tertiary); 5 if tertiary
Gender Sex of the respondent Value is 1 if female, 0 otherwise
Apprenticeship Has respondent completed apprenticeship Value is 1 respondent has completed an apprenticeship
training?
Risk Risk-taking propensity Average responses to the questions after reverse coding (responses range from 1 “almost never” to 4
“almost always”)
Grit_av Perseverance and passion for long-term goals Average responses to the questions on grit (responses range from 1 “almost never” to 4 “almost
always”)
Decision_av Decision-making Average responses to the questions on decision-making (responses range from 1 “almost never” to 4
“almost always”)
Hostile_av Hostile bias Average responses to the questions on hostile bias (responses range from 1 “almost never” to 4
“almost always”)
Discount Temporal preference Average responses to the questions on temporal preference in decision making (responses range from
1 “almost never” to 4 “almost always”)
Source:Authors’ own work
X
yi ¼ g0 þ g1genderi þ g2apprenticeshipi þ g3apprenticeshipi*genderi þ gjXji Effect of
X
þ þ gendergkZki «i (3)
where i represents individual and y is the dependent binary outcome variable which takes
the values of 1 if self-employed and 0 otherwise. Gender is a binary variable taking the value
of 1 if female and 0 if male.Apprenticeship is a dummy variable that takes the value of 1 if an
individual respondent has completed apprenticeship training or 0 otherwise. The as, bs and
gs are the vectors of unknown parameters. The remaining variables defined in Table 1 are
represented by two vectors, X and Z. The « is the error term.
The regression coefficients of Models 1–3 are estimated using the maximum likelihood
method and exponentiated to findmarginal effects. Since the dependent variable in the linearised
logit models is the log of the odds that an individual will be self-employed, a given slope
coefficient indicates the change in the probability of being self-employed as the corresponding
independent variable changes by a unit, or changes from the base category (Livanos, 2009).
4. Results
The descriptive statistics of the variables used in the analysis are presented in Table 2
according to gender. For the categorical variables, frequency is used while for the
continuous variables, mean, standard deviation, minimum and maximum are used.
Regarding gender, there were more females self-employed than males (i.e. 38% of the males
and 59% of the females). For apprenticeship, the proportion of males who completed
apprenticeship (i.e. 32%) is approximately equal to the proportion of females who completed
apprenticeship (i.e. 33%). The average household size and age were 3.4 (3.1 for males and 3.8
for females) and 34 years (35 years for males and 33 years for females), respectively. The
effects of these variables and other independent variables on self-employment are examined
through the logit models in the next section.
Table 3 displays the results of the econometric analysis which is organised into three
models. Model 1 shows the base results when apprenticeship training and personality traits
are not introduced. Model 2 augments Model 1 by introducing apprenticeship training,
whereas Model 3 adds the interactive term between apprenticeship training and gender to
Model 2 after incorporating the personality characteristics of the individuals. The
coefficients reported in Table 3 are marginal effects. From the results presented in Table 3,
the significant control variables maintain their signs across the three models, suggesting
that the results are robust. In particular, the significant positive coefficients of gender in all
three models (i.e. 0.195 in Model 1, 0.193 inModel 2 and 0.168 in Model 3 at a 1% significance
level) indicate that females are more likely to be self-employed than males. Specifically, the
coefficient of gender in Model 3 means that the probability of females being self-employed is
16.8% higher than the probability of males being self-employed, all other factors held
constant. Similarly, the coefficients of age (i.e. 0.034 in Model 1, 0.027 in Model 2 and 0.026 in
Model 3 at a 1% level of significance) and age squared (i.e. 0.0003 in Models 1 and 2 and
0.0002 in Model 3) confirm an inverse “U” relationship between self-employment and age,
corroborating the argument that although age positively influences the probability of self-
employment, a threshold exists above which the association is reversed to negative
(Levesque andMinniti, 2006). The significant positive coefficients of marital status (i.e. 0.062
in Model 1, 0.053 in Model 2 and 0.057 in Model 3 at a 5% level of significance) indicate that
having a spouse is associated with a higher probability of being self-employed. From the
coefficients of tenure_dif (i.e. 0.045 in Model 1, 0.049 in Model 2 and 0.046 in Model 3)
and time_needed (i.e. 0.017 in Model 2 and 0.016 in Model 3), it can be deduced that the
JEEE
Male Female All
Variable Category/statistic Freq. % Freq. % Freq. %
Categorical
Gender Male 695 100.00 – – 695 56.47
Female – – 538 100.00 538 43.53
Self_emp Yes 262 37.54 316 58.74 578 46.76
No 436 62.46 222 41.26 658 53.24
Completed apprenticeship Yes 224 32.09 179 33.27 403 32.61
No 474 67.91 359 66.73 833 67.39
Has_spouse Yes 339 48.57 271 50.37 610 49.35
No 359 51.43 267 49.63 626 50.65
ses Low 136 19.48 77 14.31 213 17.23
Medium 429 61.46 314 58.36 743 60.11
High 133 19.05 147 27.32 280 22.65
Education Pre-primary 15 2.15 7 1.30 22 1.78
Primary 48 6.88 32 5.95 80 6.47
Lower sec 239 34.24 233 43.31 472 38.19
Upper sec 231 33.09 163 30.30 394 31.88
Tertiary 165 23.64 103 19.14 268 21.68
Age Mean 35.03 33.30 34.28
Std dev 11.73 10.43 11.21
Min/max 15/64 15/64 15/64
Age_square Mean 1,365 1,217 1,300
Std dev 930.3 795.2 876.7
Min/max 225/4,096 225/,4096 225/4,096
Hh_size Mean 3.13 3.84 3.44
Std dev 2.26 2.22 2.27
Min/max 1/15 1/15 1/15
Tenure_diff Mean 2.67 2.79 2.72
Std dev 0.591 0.49 0.55
Min/max 1/3 1/3 1/3
Tenure_needed Mean 3.29 2.62 3.00
Std dev 1.34 1.37 1.39
Min/max 1/6 1/6 1/6
Auto_avg Mean 1.11 1.18 1.14
Std dev 0.71 0.73 0.72
Min/max 0/3 0/3 0/3
Think Mean 1.46 1.06 1.29
Std dev 1.17 0.73 1.14
Min/max 0/3 0/3 0/3
Asset_index Mean 0.26 0.39 0.32
Std dev 0.88 0.87 0.88
Min/max 1.96/2.59 1.85/2.84 1.96/2.84
Shocks Mean 0.78 0.66 0.73
Std dev 1.11 0.99 1.06
Min/max 0/8 0/6 0/8
Risk Mean 1.67 1.69 1.68
Std dev 1.13 1.09 1.11
Min/max 0/4 1/4 1/4
Grit_av Mean 2.86 2.76 2.81
Std dev 0.59 0.58 0.58
Table 2. Min/max 1/4 1/4 1/4
Descriptive results (continued )
Effect of
Male Female All
Variable Category/statistic Freq. % Freq. % Freq. % gender
Decsion_av Mean 3.09 2.96 3.03
Std dev 0.59 0.61 0.63
Min/max 1/4 1/4 1/4
Hostile_av Mean 2.21 2.22 2.22
Std dev 0.72 0.74 0.73
Min/max 1/4 1/4 1/4
Discount Mean 1.61 1.57 1.59
Std dev 1.02 0.95 0.99
Min/max 1/4 1/4 1/4
Source:Authors’ own work Table 2.
probability of being self-employed decreases the higher the additional years required by a
respondent to do well on the job.
Furthermore, the results indicate that the nature of work is associated with the
probability of being self-employed. According to the coefficients of “auto_avg” and “think”
in Model 3, individuals engaged in employment with higher autonomy and repetitiveness or
frequently involved in at least 30 min of thinking have 22.9% and 2.3% more chance of
being self-employed, respectively. The level of education is negatively associated with the
probability of being self-employed, suggesting that individuals with a higher level of
education are less likely to engage in self-employment, perhaps because they may prefer
paid employment for job security or they are more likely to have better job opportunities in
the wage-employment sector (Van der Sluis et al., 2008). Regarding risk aversion, the results
confirm that individuals who are more risk-averse are 2.3% less likely to be engaged in self-
employment. Grit has a significant positive coefficient of 0.049, meaning that individuals
with grit are 4.9% more likely to be self-employed. As reported in Wolfe and Patel’s (2016)
multi-country study, the positive association between grit and self-employment could be due
to several reasons, one of which is that grit can influence an individual’s perception of ability
or “can-do” spirit, which is important for self-employment. Another reason why grit might
be important is that it can influence individuals’ efforts towards skills development,
knowledge and competencies, which are relevant for self-employment.
Referring to the main variables of interest that address the objectives of this study, the
results in Models 2 and 3 show that the coefficients of apprenticeship (i.e. 0.131 in Model 2 and
0.094 in Model 3) are positive and significant at a 1% level of significance. This indicates that
completing apprenticeship training increases the probability of being self-employed by 13.1%
based on the results in Model 2. However, from the results in Model 3, the coefficient of the
interaction term between apprenticeship and gender (i.e. 0.086) is significant and positive at
10%, indicating that while completing an apprenticeship training can provide a positive
influence in general on all individuals’ likelihood of engaging in self-employment, it plays a
stronger role in females. It increases the probability of a female apprentice being self-employed
by 8.6%. Thus, this finding confirms the moderation effect of gender on the association
between completing apprenticeship training and being engaged in self-employment.
The predictive margins of the probability of being self-employed between female
apprentices and male apprentices versus their counterparts who have not completed
apprenticeship training are presented in Figure 1. This figure illustrates the differences
in the likelihood of self-employment for males and females if they have completed apprentices
or not {i.e. probability [No Apprenticeship and Male(No#Male)] = 0.362, probability
JEEE
Variable Model_1 dy/dx Model_2 dy/dx Model_3 dy/dx
Gender (female) 0.195*** (0.024) 0.193*** (0.023) 0.168*** (0.028)
Age 0.034*** (0.007) 0.027*** (0.007) 0.026*** (0.007)
Age-squared 0.0003*** (0.000) 0.0003*** (0.000) 0.0002*** (0.000)
Has_spouse 0.062** (0.008) 0.053** (0.027) 0.057** (0.027)
Hh_size 0.008 (0.005) 0.007 (0.006) 0.008 (0.006)
Tenure_dif 0.045* (0.025) 0.049** (0.025) 0.046* (0.025)
Time_needed 0.010 (0.010) 0.017* (0.010) 0.016* (0.010)
Auto_avg 0.232*** (0.016) 0.226*** (0.153) 0.229*** (0.0157)
Think 0.029*** (0.011) 0.024** (0.011) 0.023** (0.011)
Socioeconomic status (base category is high)
Low socioeconomic status (ses) 0.059 (0.038) 0.062* (0.038) 0.059 (0.037)
Middle socioeconomic status (ses) 0.062** (0.028) 0.063** (0.027) 0.057** (0.027)
Asset_index 0.002 (0.014) 0.001 (0.014) 0.003 (0.139)
Shocks 0.011 (0.011) 0.011 (0.011) 0.009 (0.011)
Educational level (base category is pre-primary)
Primary education 0.195** (0.088) 0.182** (0.092) 0.205** (0.092)
Secondary education 0.167** (0.072) 0.166** (0.589) 0.187** (0.077)
Post-secondary/non-tertiary 0.025*** (0.073) 0.206*** (0.079) 0.226** (0.077)
Tertiary education 0.509*** (0.076) 0.441*** (0.083) 0.448*** (0.082)
Apprenticeship 0.131*** (0.029) 0.094*** (0.036)
Gender apprenticeship 0.086* (0.049)
Risk 0.023** (0.010)
Grit_av 0.049** (0.199)
Decision_av 0.009 (0.018)
Hostile_av 0.002 (0.011)
Discount 0.024 (0.015)
Observations 1,236 1,236 1,236
Pseudo R2 0.321 0.334 0.344
Table 3.
Logit regression Notes: Standard errors are in parentheses; ***p< 0.01, **p< 0.05, *p< 0.1
output Source:Authors’ own work
[No Apprenticeship and Female(No#Female)] = 0.537, probability [Apprenticeship and Male
(Yes#Male)] = 0.458 and probability [Apprenticeship and Female(Yes#Female)] = 0.633}. The
results show that males or females who have completed apprenticeships have a higher
probability of being self-employed than those who have not. This conclusion is supported by
the upward slopes of the lines for males and females, meaning that males and females who
have not completed apprenticeship (No) have a lower probability of being self-employed as
compared to those who have completed (Yes). Comparing the relative slopes of the male and
female lines, it can be observed that the female line is slightly steeper than the male, indicating
that, female apprentices are more likely to be self-employed thanmale apprentices.
5. Discussion and implications
This study aimed to analyse the effect of completing an apprenticeship programme on the
probability of becoming and how this effect differs for males and females. Using logistic
regression, it is found that females are 19.5% more likely to be self-employed regardless of
whether they have apprenticeship training or not. However, males or females who have
completed apprenticeships are 9.4% more likely to be self-employed than those who have
not completed apprenticeship training. Furthermore, females who have completed an
Predictive Margins of Interaction between apprenticeship and gender with 95% CIs Effect of
gender
Figure 1.
Predicted
probabilities of self-
No Yes
Has completed an apprenticeship employment for
males and females
Male Female who either completed
apprenticeship
Source: training or did not Authors own work
apprenticeship training have 8.6% more chance of being self-employed than their
counterpart males who have also completed an apprenticeship training. These findings
address the study’s objectives and add fresh evidence to the existing literature regarding the
interactive effect of gender and apprenticeship on self-employment. This implies that
apprenticeships targeted at women can help to reduce the entrepreneurial gap between men
andwomen and impact their lives.
From previous studies, self-employment can provide many economic benefits, such as
job creation, reduction in unemployment, poverty alleviation and economic growth and
development (Brown et al., 2011). However, available statistics on self-employment rates
show a dwindling trend, prompting policymakers and academics to investigate factors
associated with self-employment. To contribute empirical evidence to this area, this study
focused on traditional apprenticeship training, which is widespread in West Africa (Monk
et al., 2008). The analysis found that approximately 47% of the respondents were self-
employed, 33% had completed apprenticeship training and women accounted for 44%.
Contrary to what most studies in advanced countries have found (Fossen, 2012; Georgellis
and Wall, 2000), women are found to be more likely to be self-employed than men in Ghana.
In line with Justo and DeTienne (2008), it is argued that Ghanaian women are more likely to
be self-employed because they are more prone to seek flexible employment due to their
caregiving responsibility and need to balance work and family responsibilities.
In line with the extant literature, the findings further revealed that age has an inverse U
relationship with self-employment (Fairlie, 1999; Dunn and Holtz-Eakin, 2000; Blanchflower,
2004; Georgellis et al., 2005a, 2005b; Parker, 2008; Andersson and Hammarstedt, 2010a;
Caliendo et al., 2014). This finding suggests that individuals’ attitudes towards self-
employment may be changing with age in a manner that there is a threshold age above
which the positive influence of age on self-employment is explained by the desire for
flexibility, accumulated human and social capital and wish to postpone retirement age (Kerr
and Armstrong-Stassen, 2011; Van Solinge, 2014) is reversed to negative because older
Probability of Self-employment
0.3 0.4 0.5 0.6 0.7
JEEE people might feel they have less time to recoup their investment and/or become weaker in
terms of physical andmental agility (Hintermaier and Steinberger, 2005).
Consistent with prior literature, which argues that self-employment is mainly associated
with risk-taking (Taylor, 1999) and risk aversion is negatively related to self-employment
(Ekelund et al., 2005), the findings from this study show that individuals who are more risk
averse are less likely to be self-employed. Furthermore, the result corroborates earlier
studies on the association between personality traits and self-employment (Caliendo et al.,
2014). Grit, an individual’s trait, is positively associated with self-employment, as reported
in other studies (Wolfe and Patel, 2016).
The findings for apprenticeship revealed that individuals who have completed
apprenticeship training are more likely to be self-employed than those who have not. As argued
in the literature review, this positive association could be due to twomain factors. First, because
apprenticeship takes place in a real-work context, it allows the apprentices to acquire
entrepreneurial skills to start and manage their businesses upon completion of the training.
Second, because apprenticeship provides an opportunity to earn wages while learning, it can
provide the apprentices with initial capital to enter self-employment (Ezenwakwelu et al., 2019).
In addition, the findings revealed that the association between apprenticeship and self-
employment is stronger for women than men, suggesting that apprenticeship training that is
targeted at womenmay bemore beneficial in producingmore self-employment thanmen.
These findings on apprenticeships are particularly important because they hold several
valuable insights and implications for policymaking. Before discussing the implications, it is
important to state that following the Great Recession of the late 2000s and the attendant youth
unemployment, there has been a reawakening of policy interest in apprenticeships. In 2013, the
EAfA was formed to unite governments and key stakeholders around the tenets of
strengthening the quality, supply and overall image of apprenticeships across Europe
(Markowitsch andWittig, 2022). Since then, the EAfA has been working to improve the overall
image of apprenticeships with renewed interest and commitment in recent years, leading to the
relaunch of apprenticeship support services in February 2023 (European Commission, 2023). In
Africa, the ILO has been working in the area of decent jobs for youth with a spotlight on the
quality of apprenticeships in five West African countries, Benin, Cote d’Ivoire, Mali, Niger and
Togo (Ganou et al., 2020). Through the Council for Technical and Vocational Education and
Training, the Government of Ghana has implemented the National Apprenticeship Programme
(NAP) in selected districts to provide options to the growing numbers of youth who are unable
to progress to secondary school (Hardy et al., 2019).
For policymakers, the findings of this study imply that self-employment can be promoted
through apprenticeship training programmes and not necessarily through the traditional
education system, especially when the apprenticeship is found to increase the probability of
self-employment and the level of formal education is negatively related to self-employment.
Based on this, policymakers in developing countries, especially Ghana, may want to, in the
immediate to short term, scale up the NAP or implement policies and programmes that will
provide physical, financial and moral support for informal apprenticeship training, given
that the formal Technical and Vocational Educational Training (TVET) may cost much
more. However, in the short to long term, policymakers may consider reviewing the current
educational system to improve the TVET or implement innovations in the higher
educational sector that allow students to gain the occupational competence that brings
theory and practice together in the apprenticeship setting.
Furthermore, the insight provided by the moderation effect of gender implies that female
apprentices are more likely to be self-employed than their male counterparts. This provides
an opportunity for women’s economic empowerment, especially when the evidence suggests
that female engagement in self-employment can offer substantial economic benefits in Effect of
under-developed and developed nations (Mammen and Paxson, 2000). In this regard, gender
policymakers can design and implement policies and programmes that will encourage
women’s participation in apprenticeship so that they can eventually become self-employed
and contribute to national development.
Apart from its practical importance, the study’s findings add valuable insights from a Sub-
Saharan African context to the empirical literature on self-employment, highlighting the role of
gender as an amplifier of the positive effect of apprenticeship on self-employment, especially for
women, suggesting an avenue for closing the entrepreneurial gap between men and women.
This contribution to the empirical literature on apprenticeships ties into the broader literature
on women’s empowerment through entrepreneurship and self-employment.
6. Conclusion
This study sought to analyse the determinants of self-employment, particularly the influence of
apprenticeship training on self-employment and how gender moderates the relationship. After
controlling for several relevant individual factors identified in the literature, the major findings
are that:

 completing apprenticeship training increases the likelihood of being self-employed
by 9.4 %; and

 the positive association between apprenticeship and self-employment is stronger for
women, an indication that gender moderates the relationship between apprenticeship
training and self-employment.
In view of these major findings, the study concludes that apprenticeship training in Ghana
should be promoted. To do this, it is recommended that appropriate policies must be put in
place to destigmatise, support andmake apprenticeship more attractive and effective.
In arriving at the above conclusion and recommendations, it is also important to
acknowledge the limitations of this study which exposes new opportunities for future research.
First, as a single-country study, it bears reminding that the generalisability of the findings to
other study contexts may be limited. So, future research can replicate this study in different
contexts to provide additional evidence on this subject. Second, as a non-experimental cross-
sectional study, it is impossible to interpret the result as causal but correlational, which is still
useful information for understanding the covariates of self-employment. Thus, it may be value-
adding for future research to use experimental data. Finally, the study considered gender as a
moderator of the relationship between apprenticeship and self-employment. However, it may
be helpful for future studies to explore other factors that might influence this relationship as
moderators or mediators. This research path will help uncover the nuances of how
apprenticeship influences self-employment and provide deeper understanding and policy
options for promoting self-employment through apprenticeship training.
Notwithstanding the study’s limitations, the insights offered by the findings can be used
to promote self-employment, especially among women, to reduce the entrepreneurial gap
between men and women and impact the lives of the underprivileged who are not able to
advance in formal education.
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Corresponding author
Wassiuw Abdul Rahaman can be contacted at: wabdulrahaman@ug.edu.gh
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