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Training for success: targeting and incentives in apprenticeship training in Ghana
Isaac Mbiti, University of Virginia
Jamie McCasland, University of British Columbia
Morgan Hardy, New York University Abu-Dhabi
Kym Cole, Innovations for Poverty Action
Mark Enriquez, University of Virginia
Isabelle Salcher, New York University
Grantee Final Report
Accepted by 3ie: November 2019
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Note to readers
This impact evaluation has been submitted in partial fulfilment of the requirements of grant TW1.1063 issued under Social Protection Thematic Window. The report is technically sound, and 3ie is making it available to the public in this final report version as it was received. No further work has been done.
All content is the sole responsibility of the authors and does not represent the opinions of 3ie, its donors or its board of commissioners. Any errors and omissions are the sole responsibility of the authors. All affiliations of the authors listed in the title page are those that were in effect at the time the report was accepted. Despite best efforts in working with the authors, 3ie could not replicate the results. Any comments or queries, including data or codes, should be directed to the corresponding author, Isaac Mbiti at imm9v@virginia.edu.
The 3ie technical quality assurance team comprises Francis Rathinam, Thomas de Hoop, Heather Lanthorn, Kanika Jha Kingra, an anonymous external impact evaluation design expert reviewer and an anonymous external sector expert reviewer, with overall technical supervision by Marie Gaarder.
3ie received funding for the Social Protection Thematic Window from the UK aid through the Department for International Development.
Suggested citation: Mbiti, I, McCasland, J, Hardy, M, Cole, K, Enriquez, M and Salcher, I, 2019. Training for success: targeting and incentives in apprenticeship training in Ghana, 3ie Grantee Final Report. New Delhi: International Initiative for Impact Evaluation (3ie)
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Summary
Youth unemployment and underemployment are among the most pressing policy issues of our time, particularly in developing regions such as sub-Saharan Africa. Typical policy responses to address youth unemployment include active labor market programs such as job placement, and training programs. Previous program evaluations have focused extensively on formal vocational training programs, with limited research on apprenticeship programs. These studies have shown mixed results in their effectiveness in increasing youth employment ranging from no change to an 8% increase (Mckenzie, 2017).
This project evaluates how access to on-the-job apprenticeship training in informal sector trades affects youth labor market participation, earnings, and other life outcomes for Ghanaian youth. Youth unemployment in Ghana, as is the case in many countries in Africa, is extremely high (World Bank, 2016), and youth in Africa transition from school to work at relatively slow rates (Filmer and Fox, 2014). The inability to obtain marketable (or appropriate) skills has been cited as a major impediment to the employability and productivity of youth (Filmer and Fox, 2014).
Apprenticeships are common in Ghana, and are responsible for providing a significant fraction of the population with skills (Filmer and Fox, 2014). However, there is limited evidence on their ability to improve the labor market outcomes of youth, especially in the context of a government sponsored active labor market program.
We partner with the Ghanaian government to evaluate the National Apprenticeship Program, which placed youth applicants into apprenticeships with small informal sector firms (micro-enterprises). As apprenticeships typically require the upfront payment of a training fee, many youth may be locked out of training opportunities due to credit constraints. The National Apprenticeship Program (NAP) recruited youth interested in training within one of five trades: garments, cosmetology, carpentry, welding, and masonry. Selected applicants were offered apprenticeships in a firm close to their house, within the trade of their choice, and the program paid their fees. We employ an oversubscription design, randomly allocating limited slots within each program district and trade. We collect data from 32 districts in all ten regions of Ghana, following program applicants from baseline, in 2012, through to endline, in 2017, four years after the apprenticeship training commenced.
Compliance with treatment assignment was far from 100%. Like many youth programs in Sub-Saharan Africa, compliance of the treatment group was low. In addition, many control applicants found alternative paths to apprenticeship training. Nonetheless, our first stage estimates show that the program increased access to apprenticeship training, as well as the duration and completion of training, although by moderate amounts.
Our intent to treat estimates show that (access to) the program reduces wage employment and work in agriculture for both men and women, which leads to decreased earnings from wage employment. For women, training increases self-employment, though self-employment earnings effects are insignificant. However, among compliers (or those that took up training) in the treatment group, one-third of men and one-fifth of women were still in an apprenticeship1. Therefore the results presented here are very short run estimates of the 1 The research team was very concerned about collecting data “too early” but was unable to postpone the endline further due to donor imposed timeline constraints.
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labor market returns to apprenticeship training. Additional data would be needed to better gauge the returns once participants have had time to fully transition into the labor market. Despite the short run nature of the data, we do find some promising emerging patterns. Apprenticeship training increases craft and general technical job skills utilized in self-employment. Given the importance of skills in the labor market, this is an encouraging sign. We also see evidence of increases in other measures of wellbeing, and delayed marriage and fertility, especially among women.
Overall, our results suggest that apprenticeships can provide youth with skills. Moreover, the program is already shifting youth into self-employment. Although we do not see any earnings gains in the short run, the long run effects may be different especially given the increase in skills and the emerging patterns on migration, fertility, and durable assets among women. Focusing on the skills outcomes, and assuming a scenario in which the bulk of the program costs were the intended training fees paid to trainers, the program raised test scores by 0.42SD per US$100. Compared to other programs that promoted access to schooling, NAP was more cost effective than a conditional cash transfer program in Malawi, but less cost-effective compared to a girls’ secondary school scholarship program in Kenya (Kremer et al, 2013). A further round of data would be needed to better assess the impact of the program on the labor market.
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Contents
Summary ..............................................................................................................................ii Contents .............................................................................................................................. iv Abbreviations and Acronyms ............................................................................................ vi 1. Introduction .................................................................................................................. 1 2. Intervention, Theory of change, and Research Hypotheses ........................................ 3
2.1 Intervention ................................................................................................................. 3 2.2 Theory of Change ....................................................................................................... 5 2.3 Hypotheses ................................................................................................................. 6
3. Context ............................................................................................................................ 7 4. Timeline ........................................................................................................................... 9 5. Evaluation: Design, Methods, and Implementation ...................................................... 9
5.1 Evaluation Design ....................................................................................................... 9 5.2 Implementation .......................................................................................................... 10 5.3 Data .......................................................................................................................... 10
6. Programme or Policy: Design, Methods, and Implementation .................................. 11 7. Impact Analysis and Results of the Key Evaluation Questions ........................................ 13
7.1 Empirical Strategy ..................................................................................................... 13 7.2 Summary Statistics .................................................................................................... 13 7.3 Balance Table of Baseline Characteristics ................................................................ 15 7.4 First Stage ................................................................................................................. 18 7.5 Apprenticeship Characteristics .................................................................................. 22 7.6 Treatment Effects (Intent-to-Treat) ............................................................................ 22 7.7 Mechanisms .............................................................................................................. 36
8. Discussion ..................................................................................................................... 38 9. Specific Findings for Policy and Practice ................................................................... 40 10. References ................................................................................................................... 42 11. Appendices .................................................................................................................. 45
Appendix A: Field notes and other information from formative work ................................ 45 Appendix B: Sample design ............................................................................................ 45 Appendix C: Survey instruments ..................................................................................... 46 Appendix D: Pre-Analysis Plan ........................................................................................ 46 Appendix E: Sample size and power calculations ............................................................ 46 Appendix F: Monitoring plan ............................................................................................ 46 Appendix G: Descriptive Statistics ................................................................................... 46 Appendix H: Results ........................................................................................................ 46 Appendix I: Cost Data ..................................................................................................... 47 Appendix J: Do files ........................................................................................................ 47 Appendix K: Challenges and Lessons ............................................................................. 47 Appendix L: Craftskills Questions .................................................................................... 47 Appendix M: Variable definitions ..................................................................................... 47 Appendix N: Deviations from the PAP ............................................................................ 48 Appendix O: Attrition Analysis ........................................................................................ 50
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List of figure and tables
Figure 1: Theory of Change. ................................................................................................. 6 Figure 2: Project Timeline ..................................................................................................... 9 Table 1: Summary Statistics for Covariates and Outcomes ................................................. 15 Table 2: Comparison of Baseline Characteristics by Treatment/Control .............................. 17 Table 3: First Stage ............................................................................................................. 18 Table 4: First stage – Ever started an apprenticeship? ........................................................ 20 Table 5: First stage – Successfully completed apprenticeship? ........................................... 20 Table 6: First stage – Apprenticeship duration .................................................................... 21 Table 7: Apprenticeship characteristics .............................................................................. 22 Table 8: Extensive Margin of Labor Supply ......................................................................... 25 Table 9: Extensive Margin of Labor Supply – Wage Employment ....................................... 25 Table 10: Extensive Margin of Labor Supply – Self Employment ........................................ 26 Table 11: Extensive Margin of Labor Supply in Agrictulture................................................. 26 Table 12: Intensive Margin of Labor Supply ........................................................................ 27 Table 13: Intensive Margin of Labor Supply – Wage Employment....................................... 28 Table 14: Intensive Margin of Labor Supply – Self Employment .......................................... 28 Table 15: Intensive Margin of Labor Supply in Agriculture ................................................... 29 Table 16: Labor Earnings .................................................................................................... 30 Table 17: Earnings from Wage Employment ....................................................................... 31 Table 18: Earnings from Self Employment .......................................................................... 31 Table 19: Labor Earnings in Agriculture .............................................................................. 32 Table 20: Durable household assets ................................................................................... 33 Table 21: Consumption expenditure .................................................................................... 33 Table 22: Marriage .............................................................................................................. 34 Table 23: Fertility ................................................................................................................ 35 Table 24: Fertility ................................................................................................................ 35 Table 25: Craftskills ............................................................................................................ 36 Table 26: Job skills ............................................................................................................. 37 Table 27: Migration ............................................................................................................. 38
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Abbreviations and Acronyms
BECE Basic Education Certificate Examination
COTVET Council for Technical and Vocational Education and Training
GES Ghana Education Service
GLSS Ghana Living Standards Survey
ITT Intent To Treat
JHS Junior High School
SHS Senior High School
LATE Local Average Treatment Effect
MCP Master Craftsperson
NAP National Apprenticeship Program
OLS Ordinary Least Squares
PAP Pre-Analysis Plan
PI Principal Investigator
RCT Randomized Control Trial
TVET Technical and Vocational Education and Training
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1. Introduction
Youth unemployment is a pressing concern for governments in developing countries. Youth
in Ghana, as elsewhere, often face unique challenges transitioning into the labor market.
Recent data from Ghana show that youth ages 15-24 are much less likely to be working than
adults 25-65 years, where just over fifty percent of young people are working (52%), compared
to the majority of other adults (89%) (GLSS survey 2014). Although the lower youth labor
force attachment reflects the fact that young people are still in school, policymakers and
researchers are increasingly concerned by the growing share of young people who are neither
in school nor at work, compared to other adults.2
These employment challenges are in part driven by human capital constraints. Filmer and Fox
(2014) argue that human capital is a key facilitator for youth in their efforts towards obtaining
productive work. Although Ghana has made significant progress in improving educational
access, less than one-third of young people 15-24 years have any senior secondary schooling
(29%), just higher than the rate for ages 25-34 (25%) but much higher than the oldest cohort
(13% for ages 35-65). Skills are rarely reported as the most important obstacle for businesses;
nonetheless, nearly 20% of firms do report it as a major obstacle (World Bank 2016).
The skills deficit in Ghana is in part driven by an education system where large numbers of
students fail to progress beyond critical junctures, such as the end of Junior High School.
Compulsory education in Ghana consists of six years of Primary School and three years of
Junior High School (JHS). Upon completing Junior High School, young people can choose to
continue their studies by attending a Senior High School (SHS), a Secondary Technical
School, or a Technical Institute (Gondwe and Walenkamp 2011). Access to these institutions
is based upon the performance in the Basic Education Certificate Examination (BECE), which
is taken at the end of Junior High School. While the government has made some efforts to
increase the number of Senior High Schools in the country, there are still too few places
(relative to applicants) and there is substantial variation in the quality of schooling (Ajayi 2013).
This is reflected in the gap between primary and secondary enrollment, where the gross
primary enrollment rate was over 100%, while the secondary enrollment rate was
2 Among 15-24 year olds, 34% are working and not in school, 18% are working and also in school, 32% are in school and not working, and 16% are neither in school or working. Among adults 25-65 years, these statistics are 88%, 1%, 1% and 10%, respectively (GLSS Survey, 2014).
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approximately 60% (World Bank Development Indicators 2017). Limited capacities at
government Senior High Schools combined with costly fees in informal training prevent many
young people from furthering their education and improving their skills.
Job training programs have the potential to provide skills to young people, especially those
who are locked out of the mainstream education system. Traditional approaches such as the
provision of training through public vocational institutions are often criticized for their inability
to provide market-ready skills (Johanson and Van Adams 2004 and Blattman and Ralston
2015). In contrast, apprenticeship training within informal sector firms is a promising avenue
that utilizes the large informal private sector to effectively deliver skills training to youth. By
providing on-the-job training, apprenticeships could overcome both the skill-mismatch and the
lack of relevant employment experience that impede youth in the labor market. In addition, by
partnering with informal sector firms, apprenticeships are potentially better placed to prepare
youth to transition into the informal sector which accounts for about 85% of jobs in Ghana
(World Bank 2016).
Despite the fact that apprenticeship is common in Ghana (Fox and Filmer, 2014; and World
Bank, 2016), there is little evidence on whether they work or could work better. In addition, as
apprenticeships require upfront fees, many youth may not be able to access training due to
lack of fees (Frazer, 2006 and Teal, 2016). In general, a small number of studies have
examined the returns to apprenticeships (for example Acemoglu and Pischke 1998 and
Fersterer, Pischke, and Winter-Ebmer 2008); these studies have generally found positive
impacts of the on-the-job training on individual participants and have also highlighted the
potential for firms to benefit from providing such training. Despite this promising evidence,
there is limited rigorous research in developing countries (let alone Ghana), where both
participants and training programs are potentially more heterogeneous, and where
informational asymmetries between young workers and hiring firms might be even larger than
in developed nations. Previous observational studies have examined the process of
apprenticeship training (Frazer, 2006), as well as the heterogeneous returns to training using
a control function approach (Monk, Sandefur, and Teal, 2008). We are only aware of two
recent RCTs on apprenticeships in Africa. Cho et al. (2015) evaluate a 3-month apprenticeship
program in Malawi and find no improvements in labor market outcomes. Alfonsi et al. (2017)
compares an on-the-job training program to a formal vocational training program in Kampala,
Uganda. They find that both forms of training increase labor market outcomes, but individuals
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assigned to formal vocational training had greater earnings growth due to their acquisition of
“transferable skills” rather than “firm specific skills”, further highlighting the importance of skills.
In this project, we conduct a randomized control trial to evaluate the National Apprenticeship
Programme (NAP) in Ghana. The NAP Program is a nationwide program implemented by the
Council for Technical and Vocational Education and Training (COTVET), a government
agency, in collaboration with District Officials from the Ghana Education Service (GES). The
program provides youth who were unable to progress to Senior High School an alternative
path to acquiring marketable skills through an apprenticeship. By eliminating the fee barrier,
the program may be especially important for youth from disadvantaged backgrounds.
The NAP scaled up in the past two years to about 5,000 beneficiaries and is one of the largest
youth employment programs in Ghana. Among publicly funded programs targeted to
vulnerable youth, major programs include: the recently re-designed National Youth
Employment Program, Youth in Agriculture, Youth in Cocoa, the Rural Enterprise Program
and the NAP (Avura and Ato 2016). None of them have been rigorously evaluated so far,
hence the strategic relevance of providing rigorous impact evaluation evidence to the
Ghanaian government, which is currently developing a new set of programs to address youth
unemployment.
2. Intervention, Theory of change, and Research Hypotheses 2.1 Intervention
NAP offers access to apprenticeship training in block-laying, welding, carpentry, garments,
and cosmetology. Participants were be matched with a local master-craftsperson within
walking distance of their home and obtained skills through learning by doing in an unstructured
environment similar to a traditional apprenticeship program. Although the training was
intended to last for one year, it essentially functioned as a traditional apprenticeship with
training timelines of around 3 years (or longer). The program also planned to provide
participants with a tool kit relevant to their trade (e.g. a sewing machine for garment makers).
However, most toolkits were never delivered. The choice of these five trades was determined
by COTVET. To our knowledge, these five trades were not chosen in response to analysis or
predictions of market demand, but rather reflected the presence of strong and active trade
associations in these fields. Further, COTVET tried to be sensitive to gender equity concerns
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by including a mixture of female dominated trades (garments and cosmetology) as well as
male dominated trades (block-laying, welding, and carpentry).
COTVET also selected the 78 program districts across all 10 regions of Ghana to ensure
national equity. As the Northern region of Ghana is disadvantaged and marginalized relative
to the Southern region, the NAP program purposely provided relatively more opportunities for
youth in the North. The program was intended to target youth between the ages of 15 and 30.
However, due to the decentralized nature of the implementation, it was difficult for COTVET
to enforce these age limits.
Starting with the entire list of 78 program districts, we chose a set of 32 districts for the
evaluation using population weighted random sampling, stratified by north and south, resulting
in a representative set of evaluation districts across all 10 regions, a request of our government
partners. In these 32 evaluation districts, the individuals applied to the trade of their choice
within their home district, and as discussed below in more detail, a subset of applications was
randomly assigned to treatment and control within district and trade.
Following Ghana’s decentralized model of educational delivery, recruitment was conducted
by local district TVET coordinators of the Ghana Education Service (GES), and other local
officials. In order to apply, applicants submitted a formal application to the district office, and
attended an interview with a panel of district officials. Due to political considerations detailed
below, district officials were given the opportunity to hand-pick applicants for approximately
16% of the slots. Officials could also outright reject an applicant. The remaining eligible
applicants were then placed in the random lottery. The construction trades were less
oversubscribed than those in garments and cosmetology, and generally the program received
more interest from women than men.
Once randomized into treatment, both treatment and those hand-picked “priority” applicants
were invited to attend “matching meetings”, where firm owners introduced themselves, and
apprentices were given the opportunity to list firms with which they were willing and able to
train, a function both of geographic feasibility and idiosyncratic preference. Within these
preference sets, apprentices were randomly assigned to a firm, with apprentices who only
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listed one firm being assigned to that firm. This matching meeting was designed to promote
high take up (and minimize drop-out) as we wanted to make sure that apprentices could easily
walk to their training location. Moreover, we wanted apprentices to be “excited” about working
with their trainer.
The program was meant to pay trainers 150 Ghana Cedis to train an apprentice. This was
equivalent to the traditional apprenticeship entrance fee (about 150 Ghana Cedis at the time
of our baseline survey). However, this fee never materialized due to the government’s fiscal
crisis. In order to facilitate a complementary study, Innovations for Poverty Action paid trainer
approximately 100GHC partway through the training as part of another program evaluation
designed to study trainer incentives. There was no subsidy given to apprentices by the
program, however, firm owners typically paid apprentices small wages or “chop money” (about
20 Ghana Cedi/month in our firm owner midline surveys), which increased with seniority and
varied with firm productivity/revenues. On paper, the NAP training period was supposed to
last for one year, but in practice, trainers generally kept their apprentices for 18 months to
almost 4 years depending on district and trade. The length of training was ultimately decided
by each individual trainer. As most trainers considered one year to be too short, they pushed
back on COTVET’s suggested duration. Since the program was decentralized COTVET could
not enforce the one year training term.
2.2 Theory of Change
We outline our theory change in Figure 1 below. The theory is rooted in the standard human
capital model in economics (Becker 1993). The theory postulates that skills deficits are a
major impediment to youth employment and livelihood. Consequently, alleviating these
constraints through apprenticeship training will improve youth labor market outcomes such as
employment, and earnings. As training is provided by firms that are mostly in the informal
sector, the training is arguably more appropriate, as it can better prepare youth for work in the
fast growing informal sector. By improving youth livelihoods, the program can also impact
other aspects such as migration, fertility (number of children), and also potentially shift youth
out of low productivity agriculture into more productive sectors.
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Figure 1: Theory of Change.
2.3 Hypotheses
Following our theory of change, we generate several hypotheses which our evaluation is
designed to test using the RCT. We focus on human capital (or skills) as the main mechanism
or driver of our primary outcomes of interest (i.e. labor market outcomes). We also examine
secondary outcomes that are related such as migration, fertility, and asset accumulation. We
discuss our hypotheses in the context of the following research questions:
• Does NAP increase the training of youth? This is also referred to the as the “first stage”.
Here we examine the extent to which individuals in the treatment group are able to get
more training relative to their peers in the control group. If fees are barriers to training,
then the NAP program should promote access to training as it eliminates fees for
participants.
• Does NAP increase the skills of youth? This is similar in spirit to the “First stage” but
examines the extent to which individuals in the treatment group obtain specific skills
such as better business practices, and craft specific skills.
Inputs
•Recruitment and screening of Trainers
•Recruitment and screening of youth
•Design process to pair trainers and apprentices
•Govt collaboration
Activities
•Training of youth•Regular
monitoring of Trainers and apprentice progress
Outputs
•Youth trained in a trade and acquire skills•Youth gain wrok experience and other wage or self employment relevant skills such as how to run a business
Ouctomes
•Increase in human capital including trade skills and management skills•Youth labor market outcmoes improve. •Occupational choice may change
•Other outcomes, such as consumption and assets, fertility, migration improve / change.
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• Does NAP increase the labor supply, earnings, and job skill content of youth? This is
the main question of interest. If individuals get more skills and training, then the human
capital model suggests there should be a corresponding increase in labor market
outcomes such as earnings and quality of the job (measured by job skill content). If the
program increases wages of youth, the impact of the program on labor supply (e.g.
hours worked) is ambiguous. Standard models of labor supply, postulate that there is
a tradeoff between labor and leisure. Thus depending on an individual’s preferences
for leisure, wage increases could increase or decrease work hours depending on the
magnitude of income and substitution effects. The program could also allow individuals
to reallocate their labor across different sectors of employment. Economic theory
suggests that individuals will choose to work in fields where they have the greatest
comparative advantage. The effect of the program on occupational choice is an
empirical question.
• Does NAP increase other measures of material well-being/livelihood? The program
could also have effects on other outcomes such as consumption, fertility, and
migration.
• Do the effects of NAP vary by sub-group? Specifically, we examine if the effects differ
by gender, urban- rural location, and by trade3.
3. Context
Over the past twenty years, Ghana has been one of the fastest growing economies in sub-
Saharan Africa (World Bank 2016). Poverty rates have declined, and other markers of
economic well-being have generally increased (World Bank 2016). Despite the high growth
rate, Ghanaian youth still face a myriad of challenges in the transition from school to work.
Data from the Ghana Demographic and Health survey shows that net enrollment rates in
primary school have increased from 60 percent in 2003 to 70 percent in 2014 (Ghana
Statistical Service, 2014), suggesting that access to schooling may still be a constraint. In
addition, secondary school net enrollment rates are lower with just over 50% of secondary
school aged children attending secondary school in 2015 (World Bank Development Indicators
2017). In order to increase access to secondary education, the Ghanaian government recently
introduced a free secondary school policy. While this will alleviate financial barriers, other
barriers such as low grades on the junior secondary school exit exam (BECE) will continue to
lock out many students from progressing beyond junior secondary.
3 We may be underpowered for some of sub-group analysis.
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Given the large numbers of youth that are unable to progress to secondary schooling, training
programs in Ghana, provide youth with a potentially promising avenue to increase their skills.
Although enrollment in vocational education has tripled over the past two decades (faster than
the population growth rate), there are still many barriers to accessing training. Overall, less
than 10 percent of 15-35 year olds have attended a training program. This is in part driven by
credit constraints which prevent youth from accessing training. For example, our baseline data
show that apprentice training costs on average 150 Ghanaian Cedis, which had to be paid
upfront. Despite these financial barriers, apprenticeship training in particular is an important
avenue for providing youth with skills. In urban areas, 40 percent of self-employed and 25
percent of wage employed workers had undertaken and apprenticeship (World Bank 2016).
Despite the recent economic growth experienced in Ghana, the growth in employment
opportunities have been concentrated in the informal sector, mostly driven by self-employment
as opposed to wage employment. Moreover, job growth has been concentrated in sectors with
low productivity (World Bank 2016). Of the 12 million Ghanaians actively engaged in the labor
market, less than 3 million are wage workers (World Bank 2016). The data further show that
among non-agricultural workers, 88% of males, and 95% of females are in the informal sector
(World Bank Development Indicators 2017). Most of these informal sector jobs are “low skill”,
in that less than 50 percent of informal sector jobs require reading or writing, and just over
10% require the use of a computer. These fractions are significantly lower among self-
employed workers in the informal sector (World Bank 2016).
The NAP program was conceived by COTVET as a potential policy solution to address the
growing numbers of youth who were unable to progress to secondary school. As youth who
are unable to complete secondary school are typically confined to low productivity (and low
paying) jobs, the program could uplift youth by providing them an avenue to increase their
human capital. By eliminating fee barriers, COTVET hoped the program would enable youth
across Ghana to access training opportunities. Moreover, by focusing their efforts on recruiting
youth who were unable to progress to senior secondary school, COTVET hoped to empower
relatively disadvantaged youth. As the demographic projections suggest that the population
share of youth will soon peak, there is an urgent policy need to implement programs that can
help Ghana reap the benefits of the “demographic dividend” (World Bank 2016 and The
Economist 2014).
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4. Timeline
The timeline of program and evaluation activities is summaried in Figure 2 below. In our
analysis we primarily utilize data from the endline survey collected in 2017, complemented by
baseline measures for heterogeneity and balance analysis.
Note that apprentice placement occurred between October 2013 and January 2014, between
42 and 52 months before endline survey data collection. Traditional apprenticeships vary in
length, but are typically three years, and sometimes longer. We find that almost a quarter of
treatment compliers were still in training at the time of the endline survey, suggesting even
longer training durations, especially among men.
Figure 2: Project Timeline
5. Evaluation: Design, Methods, and Implementation
5.1 Evaluation Design
BASELINE DATA / APPRENTICE APPLICATION
RANDOMIZATION
FIRM- WORKER PLACEMENT
MEETINGSTRAINING
COMMENCESMIDLINE SURVEYS
WITH FIRMS ENDLINE SURVEY
Aug- Dec 2012 Jan-Feb 2013 May-Oct 2013 Oct-2013-Jan2014
Jun 2014-Aug2015
Aug 2017-Dec2017
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We use a randomized-control trial to rigorously evaluate the effectiveness of the National
Apprenticeship Programme (NAP) in Ghana. NAP offered training in tailoring, hairdressing
and cosmetology, masonry, carpentry and welding. In our sample districts, 3,927 youth
applied to the program and were placed into one of three categories by the committees: (1)
priority applicants, whose place in the program was guaranteed (329), (2) control applicants,
who were randomly assigned to the control group (1,568), and (3) treatment applicants, who
were randomly assigned to the treatment group until all spaces in the program were occupied
(2,031). The randomization was stratified by choice of training and district, and was conducted
electronically but announced locally in conjunction with district officials. Treatment apprentices
were then placed with one of 1,187 small firm owners who requested access to apprentices
through the program.
5.2 Implementation
The 2012 elections resulted in a change in the political regime and this delayed the
implementation of the program. The program was finally launched in late 2013. Treatment
group applicants were informed by phone and were invited to a series of “matching meetings”,
where prospective training providers introduced themselves and their firms. Potential trainers
described the location of their shops, their experiences training apprentices, a summary of
their firm, and any trade specializations. Potential apprentices then completed a preference
sheet, expressing the set of trainers they could feasibly train with, based on distance. This
feasible set was defined as “trainers within walking distance”. Apprentices were then randomly
assigned to a provider in their feasible set. Training began in late 2013 and lasted between
two and three years. 20 additional apprentices “gatecrashed” these matching meetings in
districts and trades that were undersubscribed and joined the sample at this juncture, leading
to an endline sample of 3,947. These “gatecrashers” are not included in the analysis.
5.3 Data
5.3.1 Apprentice Baseline Survey
The apprentice baseline collected the following information: Personal details and contact
information; Education and training history; Family details; Cognitive assessments (digits
recall, ravens, math and literacy); Non-cognitive indicators (self-esteem); risk preferences,
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Social networks credit accessibility, and job search; labor market outcomes, labor market
expectations, assets and socio-economic status, health. Excluding the “gatecrashers”, 97% of
the sample participated in the baseline survey.
5.3.2 Apprentice Endline Survey
The endline survey was launched in August 2017 and continued through December 2017. We
analyze attrition in more detail below, but summary numbers indicate we interviewed 87% of
the target sample (excluding the gatecrashers, but including the priority sample), and that we
do not see differential attrition from the sample by treatment or control.
The endline apprentice survey collects education and training history; family details (e.g.
fertility and marriage); Non-cognitive indicators (self-esteem and happiness); migration
history, social networks credit accessibility, and job search; labor market outcomes (working
vs not working, formal vs informal employment, occupation, wage earnings, earnings from
self-employment (i.e. profits), assets and socio-economic status, health. Among the self-
employed we will collect information on revenues, sales, firm assets, firm hiring, and firm
management practices. By using comparable measures and instruments to other impact
evaluations we will be able to better compare the results of our study to other studies and data
in Ghana.
6. Programme or Policy: Design, Methods, and Implementation
NAP was designed by COTVET and implemented through a collaboration of COTVET, the
local officials of the Ghana Education Service (GES), and trade associations whose members
(generally) provided the training. The program aimed to place 5000 junior secondary school
leavers in apprenticeship training across 78 beneficiary districts. NAP was first launched in
2011. This first phase of the program covered only four trade areas: cosmetology, garments,
auto mechanics, and electronics. This list of trades was reviewed by COTVET and revised to
cosmetology, garments, welding, block-laying, and carpentry for the second phase in 2012
12
(our evaluation cohort). COTVET had also hoped to offer ICT as a trade but due to the lack of
trainers, this was not implemented.
Actual program implementation was decentralized, with local officials, led by the district TVET
coordinator of GES. Officials distributed application forms, and materials and also convened
interview panels. To complete an application, applicants filled out the form, and also obtained
a letter of support from their parent or guardian. They would then have to appear for an in
person interview with the local interview panel which had the authority to reject or prioritize
candidates. In evaluation districts, local officials could only prioritize a maximum of 16% of the
slots and the remaining slots would be randomly allocated among the remaining eligible
candidates. As COTVET does not collect information from non-evaluation districts we are not
able to compare the selection process between evaluation and non-evaluation districts.
Candidates that were selected to attend training were contacted by phone and were informed
of the next steps. Given the decentralized nature of the program, there was no consistent
process of recruiting trainers or of placing apprentices to firms. Through several discussions
with district officials, we collaborated with COTVET to develop a systematic recruitment and
placement procedure that was heavily informed by successful practices used in different
districts in the first phase of NAP. Training firms were recruited through trade associations and
coordinated by local TVET officials. Interested firms and selected apprentices were then
invited to attend a matching meeting in their local area which would facilitate the placement of
trainees to firms. As the NAP did not provide any transport subsidy to apprentices, we made
sure to prioritize placements that were convenient for trainees. Practically, each firm provided
their location and apprentices were asked to list the set firms that were in walking distance.
As there was excess demand for apprentices, the placements were randomized conditional
on the feasible set stated by the apprentice.
The list of matches was then provided to COTVET who were supposed to organize the
disbursement of training tools to each trainee. One of the innovations in the NAP program was
to provide tools to each trainee that they could use during their training, and subsequently in
their own business. However, this phase of the program was not implemented well, with few
toolkits actually reaching trainees. In addition, the fiscal crisis in Ghana severely limited
COTVETs ability to fulfill its monetary promises to trainers. Although COTVET intended NAP
13
to only be a one year training program, due to lack of monitoring, and a lack of a formal
syllabus, the trainers treated NAP as a traditional apprenticeship, which can last up to 3 years.
Syllabus materials through a revised skills qualification framework were finally released in
2014 in garments, cosmetology and welding. Block-laying and carpentry syllabi were released
in later years.
7. Impact Analysis and Results of the Key Evaluation Questions
7.1 Empirical Strategy
We estimate the treatment effects of our main randomization by comparing the outcomes of
the treatment group to the outcomes of the control group in an OLS framework. Our primary
specification is as follows:
𝑌𝑌𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖 + 𝜂𝜂𝑖𝑖 + 𝜎𝜎𝑠𝑠 + 𝜖𝜖𝑖𝑖𝑖𝑖 (1)
where 𝑌𝑌𝑖𝑖𝑖𝑖 is the outcome of interest, 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖 is a binary treatment indicator, 𝜂𝜂𝑖𝑖 are endline
survey month fixed effects, 𝜎𝜎𝑠𝑠 are strata fixed effects (district by trade) and 𝜖𝜖𝑖𝑖𝑖𝑖 is an error term.
Standard errors are robust. The coefficient 𝛽𝛽1 can be interpreted as the intent-to-treat estimate
of the program impact. Although this estimate does not account for imperfect compliance with
the treatment assignment, it is arguably more policy relevant as it reflects the actual behavior
of respondents in response to the program.
7.2 Summary Statistics
Table 1 below shows summary statistics. All our analysis excludes priority applicants and the
20 people who “gatecrashed” the matching meetings to enter later experimental parts of the
study. Panel A shows summary statistics of baseline covariates, while summary statistics
related to the randomization and outcomes can be found in Panels B and C respectively. Our
sample is predominately female (75 percent), was aged 23 at baseline and had completed 7.5
years of schooling. The education levels of both mothers and fathers are lower than the
schooling of our primary respondents, with a gender gap in years of schooling between
mothers and fathers. One third of our sample was married at baseline and 44 percent already
had children. 28 percent had started an apprenticeship, 21 percent owned a business and
14
only 6.5 percent worked in a wage job. In our main randomization, 57 percent out of a total of
3,125 individuals were assigned to the treatment group, i.e. were offered a NAP
apprenticeship. Garment-making and cosmetology were the two most popular trades which is
not surprising given the gender composition of our sample. Out of 3,125, 44 percent expressed
interest in an apprenticeship in garment-making, 35 percent were interested in cosmetology,
10 percent in welding, while the remaining split equally among masonry (block-laying) and
carpentry. At endline, 70 percent had started an apprenticeship which lasted 1.75 years on
average, while only 30 percent had completed an apprenticeship. 70 percent reported some
form of labor market activity, which encompasses owning a business (30 percent of
respondents), wage employment (15 percent of respondents), farming, apprenticeship, and
unpaid work. Overall, respondents spent about 120 hours a month working in the labor market,
unconditional on working. Their average earnings from wage employment and business profits
were both around 40 GHC per month, while total earnings were 96 GHC per month. 33 percent
of respondents are married at endline and 70 percent have children.
15
Table 1: Summary Statistics for Covariates and Outcomes
7.3 Balance Table of Baseline Characteristics
Table 1: Summary Statistics for Covariates and Outcomes(1) (2) (3) (4) (5)N Mean SD Min Max
Panel A: Baseline CovariatesFemale (0/1) 3,044 0.750 0.433 0 1Age (yrs) 3,030 23.38 5.458 6 55Years of schooling 2,959 7.497 3.170 0 19HH size (adults+children) 2,888 7.018 4.318 1 71Mother: years of schooling 2,534 3.493 4.628 0 21Father: years of schooling 2,262 5.998 5.866 0 21Vocabulary score (z-score) 2,258 -0.239 0.990 -2.200 1.514Math score (z-score) 2,936 -0.117 1.022 -2.396 1.610Digits (z-score) 3,049 -0.126 0.960 -2.709 2.515Ravens (z-score) 3,043 -0.0772 0.972 -1.769 2.566Married (0/1) 3,024 0.326 0.469 0 1Children (0/1) 3,125 0.443 0.497 0 1Health index 3,042 1.717 0.607 1 4Bicep relaxed 3,049 26.83 4.969 2.700 44Started an apprenticeship (0/1) 3,049 0.277 0.448 0 1Wage job (0/1) 3,049 0.0653 0.247 0 1Own business (0/1) 3,048 0.214 0.410 0 1Wage job earnings 198 52.46 70.40 0 600Business profits 538 58.23 124.5 0 2,000Assetscore (z-score) 2,917 -0.103 1.004 -2.927 1.686Rural 2,983 0.250 0.433 0 1
Panel B: RandomizationTreatment 3,125 0.565 0.496 0 1Cosmetology 3,125 0.348 0.476 0 1Garments 3,125 0.439 0.496 0 1Blocklaying 3,125 0.0586 0.235 0 1Welding 3,125 0.0950 0.293 0 1Carpentry 3,125 0.0592 0.236 0 1
Panel C: Outcome VariablesStarted apprenticeship? (0/1) 3,125 0.693 0.461 0 1Completed apprenticeship? (0/1) 3,125 0.302 0.459 0 1Apprenticeship duration (months) 3,125 21.61 23.34 0 255.7Working (0/1) 3,125 0.717 0.450 0 1Wage job (0/1) 3,125 0.149 0.357 0 1Own business (0/1) 3,125 0.298 0.457 0 1Hours worked (month) 3,125 121.4 105.7 0 672Hours worked in agriculture (month) 3,125 14.75 44.83 0 336Hours worked in wage job (month) 3,125 26.50 71.77 0 448Hours worked in own business (month) 3,125 46.13 85.48 0 476Earnings from working (month) 3,125 96.42 172.9 -55 1,000Earnings fom work in agriculture (month) 3,125 12.79 48.46 0 700Wage job earnings (month) 3,125 37.85 115.9 0 700Business profits (month) 3,125 40.68 100.8 0 600Durable assets index (z score) 3,125 -0.0174 1.000 -1.460 3.171Consumption Expenditure (Cedi) 3,118 37.22 60.09 0 1,103Married? (0/1) 3,125 0.328 0.470 0 1Children? (0/1) 3,125 0.715 0.451 0 1Number of children 3,121 1.501 1.387 0 9
Summary statistics for all individuals that we have currently surveyed. Gatecrashers and individuals assigned topriority have been excluded.
16
In order to provide evidence for the internal validity of our randomization, we test whether our treatment and control groups are similar on observable characteristics on average. Using baseline survey data, we calculate the groups’ means for characteristics including (1) demographics such as gender and age, (2) proxies for ability such as a math and Ravens matrix score, (3) proxies for health, (4) measures of education and labor market activity, and (5) indicators for marriage and fertility. For a given characteristic, an OLS regression with assignment to treatment as the independent variable has been used to test whether the difference in means between treatment and control group is statistically significant. As opposed to a simple t-test, this regression framework allows us to control for district x trade fixed effects, the stratification unit of our randomization. In addition, we perform an F-test to test whether characteristics of individuals assigned to treatment are jointly different from characteristics of the control group.
Table 2 below shows that baseline characteristics are indeed balanced. We reject the null hypothesis of equal means only in two out of 20 cases which is still consistent with random selection. Mothers of individuals assigned to treatment tend to have 1/3 years less of schooling, while individuals assigned to treatment on average tend to have scored lower on the vocabulary test. Each of these differences is only significant at the 10 percent level, however. Moreover, we cannot reject the null hypothesis that treatment individuals overall differ from control individuals at any conventional significance level. Thus, we take these results as evidence for the internal validity of our randomization.
17
Table 2: Comparison of Baseline Characteristics by Treatment/Control
Table 2: Comparison of Baseline Characteristics by Treatment/Control
Variable Observations Mean Control Group
Mean Treatment Group
Treatment Coefficient
Standard errors
(1) Female (0/1) 3,485 0.851 0.678 -0.00708 (0.00721)
(2) Age (yrs) 3,468 23.13 23.39 0.0455 (0.190)
(3) Years of schooling 3,387 7.245 7.557 0.0918 (0.113)
(4) HH size (adults+children) 3,299 6.695 7.193 0.0828 (0.134)
(5) Mother: years of schooling 2,900 3.831 3.228 -0.339* (0.173)
(6) Father: years of schooling 2,596 6.228 5.642 -0.216 (0.231)
(7) Vocabulary score (z-score) 2,556 -0.302 -0.190 0.0798* (0.0413)
(8) Math score (z-score) 3,346 -0.148 -0.106 0.0183 (0.0370)
(9) Digits (z-score) 3,490 -0.159 -0.108 0.0341 (0.0340)
(10) Ravens (z-score) 3,486 -0.113 -0.0873 0.0182 (0.0337)
(11) Married (0/1) 3,465 0.317 0.319 -0.00273 (0.0159)
(12) Children (0/1) 3,600 0.458 0.419 -0.0129 (0.0173)
(13) Health index 3,484 1.749 1.701 -0.0210 (0.0220)
(14) Bicep relaxed 3,492 26.74 26.76 0.0374 (0.169)
(15) Started an apprenticeship (0/1) 3,492 0.257 0.286 -0.000238 (0.0150)
(16) Wage job (0/1) 3,492 0.0537 0.0748 -0.00255 (0.00793)
(17) Own business (0/1) 3,490 0.187 0.228 0.0199 (0.0143)
(18) Wage job earnings 228 48.68 51.83 2.333 (17.28)
(19) Business profits 605 56.78 60.96 -8.907 (8.770)
(20) Assetscore (z-score) 3,345 -0.0780 -0.126 0.0278 (0.0285)
F-test 1,510 1.227 0.231Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Balanced baseline covariates by T/C are tested via OLS regressions for a sample of 3,600 individuals (gatecrashers andindividuals assigned to priority have been excluded). Each row corresponds to such a regression. District x TradeFixed Effects have been included and standard errors are robust. F-test statistic and corresponding p-value are reported. Wage job earnings and business profits excluded in F-test.
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7.4 First Stage
The first stage results from our evaluation are given in Table 3 below. We focus on three
different, but related measures of program take-up. First, we examine differences in the
probability of ever starting an apprenticeship. As column (1) indicates, the NAP program
increased apprenticeship take-up by 13 percentage points, where we treat the NAP program
as equivalent to other types of apprenticeships offered in the market. Since 62 percent of the
control group also had started an apprenticeship, this corresponds to a 21 percent increase in
training probability induced by NAP. Second, we examine the impact of being offered a NAP
apprenticeship on the probability of successful apprenticeship completion. We find that
apprenticeship completion increased by almost 10 percentage points as indicated in column
(2). With 25 percent of the control group also having completed their apprenticeship, this
translates into a 40 percent increase in completion rates induced by NAP. Third, we examine
the impact of a NAP offer on the apprenticeship duration. The estimate in column (3) indicates
that the NAP program increased training time by almost 4 months which represents a 20
percent increase relative to the control group.
Table 3: First Stage
Table 3: First stage(1) (2) (3)
Entire Sample Entire Sample Entire SampleVARIABLES Started
apprenticeship? (0/1)
Completed apprenticeship?
(0/1)
Apprenticeship duration (months)
Treatment 0.130*** 0.0985*** 3.745***(0.0173) (0.0171) (0.819)
Observations 3,125 3,125 3,125Strata FE Yes Yes YesMonth FE Yes Yes YesStandard errors Robust Robust RobustMean Control Group 0.624 0.247 18.78Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1The sample comprises all individuals we have currently surveyed. Gatecrashers and individualsassigned to priority have been excluded from this analysis. Estimation via OLS with treatmentassignment as the independent variable. DistrictxTrade Fixed Effects have been included andstandard errors are robust.
19
Tables 4-6 examine whether our first stage results differ by urban/rural, gender and their
interaction for each of our three take-up measures.4 The availability and attractiveness of labor
market options other than an apprenticeship might differ based on whether an individual lives
in an urban or rural area, while males and females might be interested in inherently different
trades which could influence the probability of starting or completing an apprenticeship as well
as the training duration.
We indeed find heterogeneity in take-up for all three first stage measures. The 13 percentage
point increase in the probability of having started an apprenticeship masks urban-rural
differences which seem to be driven by women. The NAP offer increased apprenticeship take-
up by 20 percentage points for individuals living in a rural area at baseline, whereas it
increased take-up by only 11 percentage points for individuals in urban areas. This magnitude
in urban-rural difference holds true for the subsample of women, but not for the subsample of
men. For men, NAP increases the probability of having started an apprenticeship by
approximately 16 percentage points, regardless of whether they lived in a rural or urban area
at baseline. Our second take-up measure reveals that NAP increases the probability of having
successfully completed an apprenticeship more for women than for men, where the probability
increase for men is even statistically indistinguishable from zero. In addition, with a 14
percentage point increase relative to a 10 percentage point increase, NAP raises the
probability of training completion more for females in rural than in urban areas. For males, the
estimated increase in completion probability is larger when the respondent lived in an urban
area and even slightly negative for respondents in rural areas, although both estimates are
statistically not different from zero. Estimates point in similar directions for our third measure
of program take-up. Females tend to do longer apprenticeships and females in rural areas in
particular. The estimated increase in apprenticeship duration is positive for men in urban
areas, while negative for men in rural areas. Yet, both estimates are again statistically
indistinguishable from zero.
4 Urban/rural is defined based on the respondent’s town at baseline. Out of our sample of 3,125 respondents, unambiguous information has only been available for 2,983. Urban/rural is available for 733 out of 769 Males and for 2,250 out of 2,356 Females. We have refrained from imputing missing and ambiguous values which slightly reduces our sample size however.
20
These first stage results suggest that limiting the analysis of results to solely the full sample
would mask important heterogeneities in treatment effects. Therefore, we will examine our full
sample results for heterogeneities along these dimensions in the subsequent analysis.
Table 4: First stage – Ever started an apprenticeship?
Table 5: First stage – Successfully completed apprenticeship?
Table 4: First stage - Ever started an apprenticeship?(1) (2) (3) (4) (5) (6) (7) (8)
Male Male Female FemaleUrban Rural Urban Rural
Treatment 0.109*** 0.201*** 0.149*** 0.128*** 0.158** 0.162* 0.105*** 0.209***(0.0206) (0.0387) (0.0450) (0.0187) (0.0625) (0.0847) (0.0218) (0.0438)
Observations 2,237 746 769 2,356 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 0.641 0.559 0.604 0.627 0.618 0.565 0.644 0.557Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Dependent variable: Have you ever started an apprenticeship? The sample comprises all individuals we have currentlysurveyed. Gatecrashers and individuals assigned to priority have been excluded from this analysis. Estimation via OLSwith treatment assignment as the independent variable. DistrictxTrade Fixed Effects have been included and standarderrors are robust. Urban/rural as of baseline in 2012.
Urban Rural Male Female
21
Table 6: First stage – Apprenticeship duration
Table 5: First stage - Successfully completed apprenticeship?(1) (2) (3) (4) (5) (6) (7) (8)
Male Male Female FemaleUrban Rural Urban Rural
Treatment 0.0918*** 0.109*** 0.0485 0.109*** 0.0601 -0.0192 0.0990*** 0.144***(0.0205) (0.0369) (0.0454) (0.0186) (0.0597) (0.0815) (0.0219) (0.0416)
Observations 2,237 746 769 2,356 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 0.262 0.190 0.254 0.246 0.252 0.242 0.264 0.178Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Dependent variable: Did you successfully complete your apprenticeship? The sample comprises all individuals we havecurrently surveyed. Gatecrashers and individuals assigned to priority have been excluded from this analysis. Estimation via OLS with treatment assignment as the independent variable. DistrictxTrade Fixed Effects have been included and standard errors are robust. Urban/rural as of baseline in 2012.
Urban Rural Male Female
Table 6: First stage - Apprenticeship duration(1) (2) (3) (4) (5) (6) (7) (8)
Male Male Female FemaleUrban Rural Urban Rural
Treatment 3.589*** 4.697** -1.196 4.742*** 1.717 -1.935 3.980*** 6.446***(0.933) (1.991) (3.015) (0.803) (3.481) (6.251) (0.945) (1.924)
Observations 2,237 746 769 2,356 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 18.95 17.47 30.24 16.83 29.18 30.63 17.50 14.25Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Dependent variable: Apprenticeship duration in months from start until completion date; quite date or date of endline survey.The sample comprises all individuals we have currently surveyed.Gatecrashers and individuals assigned to priority have been excluded from this analysis. Estimation via OLS with treatment assignment as the independent variable. DistrictxTrade FixedEffects have been included and standard errors are robust. Urban/rural as of baseline in 2012.
Urban Rural Male Female
22
7.5 Apprenticeship Characteristics
In Table 7 below we want to compare the characteristics of apprenticeships undertaken by
treatment group with those of the control group. A comparison of apprenticeship
characteristics by subgroups is provided in the Appendix. The underlying assumption that we
try to validate is that NAP trainers are similar to other training providers in Ghana. In the
following, we focus on trainer characteristics such as firm size, availability of tools and practice
materials, distance, as well as post-training outcomes such as providing testimonials (a
reference letter). The most significant differences are found on paid entrance and exit fees
which is not surprising as NAP trainers were not supposed to charge fees, although some
NAP treatment apprentices may have trained with non-NAP trainers. They pay a roughly 60
Cedi lower entrance fee which represents a nearly 40 percent fee reduction. In addition,
treatment apprentices are more likely to have taken an exam upon apprenticeship completion
and to be working with gas-powered machinery. Nonetheless, these findings overall suggest
that training providers across treatment and control assignment were generally similar.
Table 7: Apprenticeship characteristics
7.6 Treatment Effects (Intent-to-Treat)
Table 7: Apprenticeship characteristics(1) (2) (3) (4) (5) (6) (7) (8) (9)
VARIABLES Total firmsize
Entrance fee (Cedi)
Exit fee (Cedi)
Toolkit (0/1)
Testimonial (0/1)
Exam (0/1)
Gas-powered
machinery (0/1)
Travel time (min)
Practice materials
(0/1)
Treatment -0.299 -60.69*** -38.09*** -0.0495** -0.0544 0.0962*** 0.0471** -1.262 0.00298(0.243) (8.431) (12.03) (0.0228) (0.0344) (0.0337) (0.0199) (1.057) (0.0226)
Observations 2,161 2,114 1,674 2,167 941 941 2,167 2,146 2,167Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 4.130 161.1 118.5 0.475 0.603 0.546 0.661 26.50 0.601Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Respondents have been asked about exit fee; testimonial and exam conditional on their apprenticeship being completed or terminated.The sample comprises all individuals we have currently surveyed. Gatecrashers and individuals assigned to priority have been excludedfrom this analysis. Estimation via OLS with treatment assignment as the independent variable. DistrictxTrade Fixed Effects have beenincluded and standard errors are robust. Urban/rural as of baseline in 2012.
23
In the following tables we present the results on labor market outcomes, assets, and
consumption as well as on marriage and fertility. This analysis is guided by our Pre-Analysis
plan, although we do not include all outcomes specified in the PAP. Deviations from the PAP
are discussed in the appendix. In our regression analysis we focus on Intent-to-Treat
specifications, i.e. we measure the impact of offering a NAP apprenticeship on outcomes. We
include strata fixed effects (district x trade) as well as month fixed effects in all our
specifications. To be consistent with our analysis presented earlier, we estimate treatment
effects for our full sample as well as separately by urban/rural, gender and their interaction.
As a significant fraction of compliers are still undergoing training (33% of men and 20% of
women), the results should be considered very short run and preliminary5.
7.6.1 Labor Market
Labor Supply: Extensive Margin
On the extensive margin of labor supply (Tables 8-11), we find a shift from wage to self-
employment as well as a transition out of the agricultural sector. For overall labor market
participation (Table 8), which comprises wage employment, self-employment, farming, unpaid
work and apprenticeships, we obtain negative point estimates for the full sample and nearly
all subsamples. These estimates are more negative for men than for women, although always
statistically insignificant. When we decompose overall labor market participation and focus
only on wage employment as the outcome variable (Table 9), the estimated treatment
coefficients are similar in sign and magnitude. We find a significant reduction in wage
employment for our full sample (at the 1 percent level), which seems to be attributable to males
and females in rural areas. In rural areas, males assigned to treatment are 12.5 percentage
points less likely to work at a wage job, while treatment females are 6.1 percentage points
less likely to have a wage job which are significant at the 10 and 5 percent level respectively.
However, taking self-employment as the outcome variable (Table 10) yields different insights.
For the full sample, we estimate the treatment effect to amount to a 2.9 percentage point
higher probability of owning a business which is significant at the 10 percent level. In particular,
women seem to be more likely to be a business owner (3.6 percentage points, 10 percent
level). This probability is slightly higher for females in rural than in urban areas (6.2 vs. 4.5
percentage points), but only the coefficient for the urban subsample is significant. Point
5 We have not yet accounted for multiple testing in our analysis.
24
estimates are positive for men in rural areas while negative for men in urban areas, yet both
are insignificant. Interestingly, the probability of working in agriculture has declined for the full
sample as well as for nearly all subsamples and especially for males (Table 11). Males in rural
areas are 17.1 percentage points less likely to work in agriculture, while the point estimate for
males in urban areas is -10.6 percentage points, both significant at the 10 percent level.
Females in urban areas are only 3.5 percentage points less likely to do agricultural work (5
percent level), while female in rural areas are slightly more likely to work in agriculture
(insignificant).
In sum, we find that while wage employment and agricultural work have declined for both men
and women, the increases in self-employment are concentrated among women.
25
Table 8: Extensive Margin of Labor Supply
Table 9: Extensive Margin of Labor Supply – Wage Employment
Table 8: Extensive Margin of Labor Supply(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLES Working (0/1)
Working (0/1)
Working (0/1)
Working (0/1)
Working (0/1)
Working (0/1)
Working (0/1)
Working (0/1)
Working (0/1)
Treatment -0.0240 -0.0529 -0.0201 -0.0275 -0.00275 -0.0420 -0.102 -0.0243 0.0311(0.0174) (0.0367) (0.0193) (0.0206) (0.0386) (0.0545) (0.0651) (0.0222) (0.0458)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 0.712 0.848 0.689 0.722 0.663 0.829 0.871 0.706 0.613Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Working includes wage job; own business; own farm; unpaid work and apprenticeship. Time period: one month prior to endline survey.The sample comprises all individuals we have currently surveyed. Gatecrashers and individuals assigned to priority have been excludedfrom this analysis. Estimation via OLS with treatment assignment as the independent variable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
Table 9: Extensive Margin of Labor Supply - Wage Employment(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLESWage job
(0/1)Wage job
(0/1)Wage job
(0/1)Wage job
(0/1)Wage job
(0/1)Wage job
(0/1)Wage job
(0/1)Wage job
(0/1)Wage job
(0/1)
Treatment -0.0352*** -0.0642 -0.0320** -0.0235 -0.0730*** -0.0140 -0.125* -0.0248 -0.0614**(0.0127) (0.0437) (0.0130) (0.0154) (0.0265) (0.0598) (0.0724) (0.0158) (0.0270)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 0.147 0.269 0.127 0.144 0.143 0.260 0.258 0.127 0.115Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Time period: one month prior to endline survey. The sample comprises all individuals we have currently surveyed. Gatecrashersand individuals assigned to priority have been excluded from this analysis. Estimation via OLS with treatment assignment as theindependent variable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
26
Table 10: Extensive Margin of Labor Supply – Self Employment
Table 11: Extensive Margin of Labor Supply in Agriculture
Labor Supply: Intensive Margin
Table 10: Extensive Margin of Labor Supply - Self Employment(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLESOwn
business (0/1)
Own business
(0/1)
Own business
(0/1)
Own business
(0/1)
Own business
(0/1)
Own business
(0/1)
Own business
(0/1)
Own business
(0/1)
Own business
(0/1)
Treatment 0.0292* -0.0105 0.0359* 0.0350 0.0138 -0.0568 0.0293 0.0451* 0.00621(0.0177) (0.0377) (0.0197) (0.0214) (0.0371) (0.0513) (0.0726) (0.0233) (0.0434)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 0.300 0.198 0.317 0.315 0.248 0.211 0.177 0.329 0.265Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Time period: one month prior to endline survey. The sample comprises all individuals we have currently surveyed. Gatecrashersand individuals assigned to priority have been excluded from this analysis. Estimation via OLS with treatment assignment as theindependent variable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
Table 11: Extensive Margin of Labor Supply in Agriculture(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLESWorking in agriculture
(0/1)
Working in agriculture
(0/1)
Working in agriculture
(0/1)
Working in agriculture
(0/1)
Working in agriculture
(0/1)
Working in agriculture
(0/1)
Working in agriculture
(0/1)
Working in agriculture
(0/1)
Working in agriculture
(0/1)
Treatment -0.0388*** -0.129*** -0.0248** -0.0456*** -0.0274 -0.106* -0.171* -0.0353** 0.0170(0.0123) (0.0429) (0.0123) (0.0137) (0.0340) (0.0551) (0.0916) (0.0139) (0.0341)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 0.147 0.315 0.119 0.133 0.194 0.276 0.403 0.112 0.142Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Working in agriculture includes own farm and working as a skilled worker or laborer either for a wage or unpaid. Time period: one month prior toendline survey. The sample comprises all individuals we have currently surveyed. Gatecrashers and individuals assigned to priority have beenexcluded from this analysis. Estimation via OLS with treatment assignment as the independent variable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
27
The results on the intensive margin of labor supply (Tables 12-15), or number of hours worked,
are in line with our extensive margin estimates for wage employment, self-employment and
agricultural work, but are a bit more mixed for total hours worked. Table 13 suggests a
reduction in hours worked in a wage job for the full sample and most subsamples. Table 14
indicates an increase in hours worked in own business in particular for women while Table 15
shows that hours worked in agriculture have declined across all subsamples. Note that, in
order to get cleaner ITT effects, 0’s have been put for respondents who did not report a given
labor market activity. This makes all hours “unconditional on working”.6 These results are
robust to winsorizing hours at the 99% level to control for outliers.
Table 12: Intensive Margin of Labor Supply
6 886 (28%) 0’s have been put for total hours worked; 2,658 (85%) for hours in wage employment; 2,195 (70%) for hours in self-employment; and 2,701 (86%) for hours in agricultural work.
Table 12: Intensive Margin of Labor Supply(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLESHours worked (month)
Hours worked (month)
Hours worked (month)
Hours worked (month)
Hours worked (month)
Hours worked (month)
Hours worked (month)
Hours worked (month)
Hours worked (month)
Treatment -1.004 7.458 -2.985 -3.259 9.058 1.400 20.29 -4.000 7.249(4.003) (9.417) (4.374) (4.872) (8.312) (13.37) (16.36) (5.205) (9.611)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 118.2 139.1 114.7 122.9 100.9 148.9 117.1 119.2 96.94Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Hours are unconditional. Working includes wage job; own business; own farm; unpaid work and apprenticeship. The sum of all hoursworked is considered. Time period: one month prior to endline survey. The sample comprises all individuals we have currently surveyed.Gatecrashers and individuals assigned to priority have been excluded from this analysis. Estimation via OLS with treatment assignment as the independent variable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis. Urban/rural as ofbaseline in 2012.
Full sample Male Female Urban Rural
28
Table 13: Intensive Margin of Labor Supply – Wage Employment
Table 14: Intensive Margin of Labor Supply – Self Employment
Table 13: Intensive Margin of Labor Supply - Wage Employment(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLES
Hours worked in wage job (month)
Hours worked in wage job (month)
Hours worked in wage job (month)
Hours worked in wage job (month)
Hours worked in wage job (month)
Hours worked in wage job (month)
Hours worked in wage job (month)
Hours worked in wage job (month)
Hours worked in wage job (month)
Treatment -6.091** -3.466 -7.010** -4.659 -12.57** 5.672 -12.92 -6.113* -13.69**(2.625) (8.566) (2.726) (3.238) (5.224) (11.47) (14.34) (3.388) (5.356)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 26.61 41.02 24.17 26.77 24.58 40.92 35.73 24.76 21.85Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Hours are unconditional. Time period: one month prior to endline survey. The sample comprises all individuals we have currently surveyed. Gate-crashers and individuals assigned to priority have been excluded from this analysis. Estimation via OLS with treatment assignment as theindependent variable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
Table 14: Intensive Margin of Labor Supply - Self Employment(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLES
Hours worked in
own business (month)
Hours worked in
own business (month)
Hours worked in
own business (month)
Hours worked in
own business (month)
Hours worked in
own business (month)
Hours worked in
own business (month)
Hours worked in
own business (month)
Hours worked in
own business (month)
Hours worked in
own business (month)
Treatment 5.151 -3.059 6.390* 5.706 6.126 -13.30 9.299 7.841* 5.276(3.345) (8.304) (3.641) (4.123) (6.502) (12.06) (13.15) (4.375) (7.568)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 45.75 38.35 47 49.30 33.02 46.27 23.66 49.73 35.32Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Hours are unconditional. Time period: one month prior to endline survey. The sample comprises all individuals we have currently surveyed. Gate-crashers and individuals assigned to priority have been excluded from this analysis. Estimation via OLS with treatment assignment as theindependent variable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
29
Table 15: Intensive Margin of Labor Supply in Agriculture
Earnings
As with labor supply, we present results on total earnings (Table 16) which includes wage
income, business profits, farm profits and apprenticeship earnings, followed by separate
estimates on wage income (Table 17), business profits (Table 18), and income from
agricultural work (Table 19) respectively. All earnings are “unconditional on working”, meaning
that we again put 0’s for those who did not report a given labor market activity.7 Our results
on labor earnings are roughly in accordance with our results on labor supply above. Total labor
earnings are estimated to decrease by 10 GHC based on treatment for the full sample,
significant at the 10 percent level. While estimated treatment effects are negative for all
subsamples, this seems to be at least in part attributable to the shift from wage work into lower
paying self-employment. The reductions in earnings are especially pronounced among rural
treatment males (-86.7 GHC, 10 percent level).
Consistent with a reduction in labor supply in the wage sector, we observe a negative
treatment effect on wage job earnings. For the full sample, illustrated in Table 17, treatment
7 897 (29%) 0’s have been put for total earnings; 2,662 (85%) for earnings from wage employment; 2,199 (70%) for profits from self-employment; and 2,810 (90%) for earnings from agricultural work.
Table 15: Intensive Margin of Labor Supply in Agriculture(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLES
Hours worked in agriculture
(month)
Hours worked in agriculture
(month)
Hours worked in agriculture
(month)
Hours worked in agriculture
(month)
Hours worked in agriculture
(month)
Hours worked in agriculture
(month)
Hours worked in agriculture
(month)
Hours worked in agriculture
(month)
Hours worked in agriculture
(month)
Treatment -4.496*** -9.845 -3.744** -5.800*** -3.137 -10.94 -9.380 -4.560*** -0.862(1.574) (6.200) (1.475) (1.751) (4.153) (8.475) (11.01) (1.616) (4.351)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 15.53 32.43 12.66 14.02 20.80 31.02 36.55 11.60 16.94Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Hours are unconditional. Working in agriculture includes own farm and working as a skilled worker or laborer either for a wage or unpaid. Timeperiod: one month prior to endline survey. The sample comprises all individuals we have currently surveyed. Gatecrashers and individualsassigned to priority have been excluded from this analysis. Estimation via OLS with treatment assignment as the independent variable.DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
30
assignment is estimated to cause an 11 GHC decrease in wages, significant at the 1 percent
level. This treatment effect is slightly less negative for females, but considerably more negative
for treatment males in rural areas (-54.2 GHC, 10 percent level). While treatment females were
more likely to own a business and work longer hours in their own business, we do not observe
significantly higher business profits although the estimated treatment effect is positive for
women overall and women in urban areas. On the other hand, the estimated treatment effect
for the full sample is close to 0 and negative for women in rural areas and men. In line with a
transition out of agricultural work both on the extensive and intensive margin, we estimate a
reduction in agricultural earnings for the full sample and nearly all subsamples. The treatment
effect for the full sample, shown in Table 19, amounts to -4.5 GHC and is significant at the 1
percent level which is of similar magnitude as the treatment effect for urban females (-4.3
GHC, significant at the 5 percent level). With -16.4 GHC (significant at the 10 percent level)
the estimated treatment effect is more negative for urban males.
Table 16: Labor Earnings
Table 16: Labor Earnings(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLESEarnings
from working (month)
Earnings from working
(month)
Earnings from working
(month)
Earnings from working
(month)
Earnings from working
(month)
Earnings from working
(month)
Earnings from working
(month)
Earnings from working
(month)
Earnings from working
(month)
Treatment -10.04* -50.44** -4.680 -4.151 -28.36* -22.88 -86.70* -2.784 -16.42(5.781) (25.39) (5.147) (6.537) (14.63) (33.64) (48.14) (6.023) (13.39)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 86.98 184.1 70.49 84.48 88.63 170 193.5 72.31 62.92Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Earnings are unconditional and have been winsorized. Working includes wage job; own business; own farm; unpaid work and apprenticeship. The sumof all earnings is considered. Time period: one month prior to endline survey. The sample comprises all individuals we have currently surveyed.Gatecrashers and individuals assigned to priority have been excluded from this analysis. Estimation via OLS with treatment assignment as theindependent variable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
31
Table 17: Earnings from Wage Employment
Table 18: Earnings from Self Employment
Table 17: Earnings from Wage Employment(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLESWage job earnings (month)
Wage job earnings (month)
Wage job earnings (month)
Wage job earnings (month)
Wage job earnings (month)
Wage job earnings (month)
Wage job earnings (month)
Wage job earnings (month)
Wage job earnings (month)
Treatment -10.99*** -32.95* -8.780*** -9.272** -17.01** -15.01 -54.19* -9.554** -9.176(3.689) (16.90) (3.107) (4.318) (7.841) (22.82) (29.63) (3.832) (5.609)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 34.98 87.65 26.04 34.29 31.68 82.50 82.90 27.43 19.13Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Earnings are unconditional and have been winsorized. Time period: one month prior to endline survey. The sample comprises all individuals wehave currently surveyed. Gatecrashers and individuals assigned to priority have been excluded from this analysis. Estimation via OLS withtreatment assignment as the independent variable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis.Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
Table 18: Earnings from Self Employment(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLESBusiness
profits (month)
Business profits (month)
Business profits (month)
Business profits (month)
Business profits (month)
Business profits (month)
Business profits (month)
Business profits (month)
Business profits (month)
Treatment 0.847 -4.751 2.509 4.578 -7.451 -5.091 -7.408 6.374 -9.502(3.626) (12.87) (3.632) (4.434) (7.512) (17.35) (23.04) (4.489) (7.336)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 38.80 50.71 36.78 40.10 33.38 48.54 48.71 38.89 29.62Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Earnings are unconditional and have been winsorized. Time period: one month prior to endline survey. The sample comprises all individuals wehave currently surveyed. Gatecrashers and individuals assigned to priority have been excluded from this analysis. Estimation via OLS withtreatment assignment as the independent variable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis.Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
32
Table 19: Labor Earnings in Agriculture
7.6.2 Assets and Consumption
We next present results on durable household assets (Table 20) and consumption expenditure
(Table 21) which serve as proxies for evaluating whether the program has been welfare
enhancing for individuals who were offered a NAP apprenticeship. Durable household assets
encompass a radio, TV, car, motorbike and fridge owned by the household the respondent
lives in, with the restriction to be functioning at the time of the endline survey. Previous
literature constructed similar indices based on household-owned durable assets for measuring
poverty and carrying out welfare analysis more generally (ex. Booysen et al. 2008; Filmer and
Pritchett 1998).8 Table 20 indicates that although the overall treatment effect on durable
household assets is positive, it is only significantly so for females while being negative and
significant for males. Even though the effect turns insignificant for most subsamples, the sign
remains unchanged for males and females in urban and rural areas. The most negative
treatment effect is obtained for urban males which is also significant at the 10 percent level.
8 For instance, Filmer and Scott (2012) found that economic gradients in education and health outcomes are similar when these are based on an asset index or on per capita expenditure – a more direct measure of household economic status.
33
Table 20: Durable household assets
Consumption expenditure is the sum of the respondent’s expenditures on phone credit,
personal items and eating out during the week prior to the endline survey. Table 21 suggests
a positive treatment effect in urban areas (+4.7 GHC, 10 percent level) while being assigned
to treatment is associated with lower consumption in rural areas (-4.0 GHC, insignificant). This
is true for both men and women, and treatment effects for men are larger in absolute values
however.
Table 21: Consumption expenditure
Table 20: Durable household assets(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLESDurable
assets index (z score)
Durable assets index
(z score)
Durable assets index
(z score)
Durable assets index
(z score)
Durable assets index
(z score)
Durable assets index
(z score)
Durable assets index
(z score)
Durable assets index
(z score)
Durable assets index
(z score)
Treatment 0.0534 -0.177* 0.0895** 0.0549 -0.0170 -0.202* -0.142 0.0809 0.0194(0.0378) (0.0954) (0.0413) (0.0456) (0.0799) (0.121) (0.183) (0.0492) (0.0896)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group -0.0670 0.126 -0.0998 -0.0431 -0.130 0.173 0.0418 -0.0738 -0.172Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Durable assets include: radio; TV; bicycle; car; motorbike and fridge. Must be working at the time of the endline survey. The z-score has been obtainedfrom a PCA analysis. The sample comprises all individuals we have currently surveyed. Gatecrashers and individuals assigned to priority have beenexcluded from this analysis. Estimation via OLS with treatment assignment as the independent variable. DistrictxTrade Fixed Effects have been included.Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
Table 21: Consumption expenditure(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLESConsumption Expenditure
(Cedi)
Consumption Expenditure
(Cedi)
Consumption Expenditure
(Cedi)
Consumption Expenditure
(Cedi)
Consumption Expenditure
(Cedi)
Consumption Expenditure
(Cedi)
Consumption Expenditure
(Cedi)
Consumption Expenditure
(Cedi)
Consumption Expenditure
(Cedi)
Treatment 2.580 -0.170 2.863 4.686* -4.032 13.14 -16.56 3.360 -0.679(2.211) (9.237) (2.076) (2.783) (4.686) (15.81) (12.81) (2.445) (4.685)
Observations 3,118 766 2,352 2,234 742 492 238 1,742 504Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 33.81 54.87 30.23 32.51 38.05 49.79 68.29 30.05 30.64Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Consumption expenditure is the sum of expenditures on phone credit; personal items and eating out. The reference period for these expenditures is theweek prior to the endline survey. The sample comprises all individuals we have currently surveyed. Gatecrashers and individuals assigned to priorityhave been excluded from this analysis. Estimation via OLS with treatment assignment as the independent variable. DistrictxTrade Fixed Effects have beenincluded. Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
34
7.6.3 Marriage and Fertility
We hypothesize that an apprenticeship delays marriage and delays and reduces fertility. Table
22 suggests that the marriage hypothesis does not always hold true. Females and females in
urban areas in particular who were offered a NAP apprenticeship tend to have a higher
probability of being married at endline. Treatment females are 3 percentage points more likely
to be married and treatment females in urban areas even 5.3 percentage points, significant at
the 10 percent and 5 percent level respectively. Treatment females in rural areas are less
likely to be married and so are treatment males in urban areas, but both estimates are
statistically insignificant. Interestingly, the estimated treatment effects go in opposite directions
for urban and rural males and females.
Table 22: Marriage
Table 23 below suggests that our hypothesis of delayed fertility holds true overall and for most
subsamples. Individuals who were offered a NAP apprenticeship are 2.9 percentage points
less likely to have children. Among those who live in urban areas, treatment individual are 3.8
percentage less likely to be childless. Perhaps surprisingly, although treated urban females
are more likely to be married (column 8 in Table 23), they are 3.5 percentage points less likely
to have kids (significant at 10 percent level). Moreover, being offered a NAP apprenticeship
Table 22: Marriage(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLES Married? (0/1)
Married? (0/1)
Married? (0/1)
Married? (0/1)
Married? (0/1)
Married? (0/1)
Married? (0/1)
Married? (0/1)
Married? (0/1)
Treatment 0.0227 -0.0192 0.0302* 0.0418** -0.00627 -0.0325 0.0170 0.0527** -0.0126(0.0164) (0.0410) (0.0179) (0.0192) (0.0367) (0.0512) (0.0836) (0.0206) (0.0403)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 0.306 0.350 0.299 0.286 0.381 0.293 0.484 0.286 0.356Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1The sample comprises all individuals we have currently surveyed. Gatecrashers and individuals assigned to priority have been excluded fromthis analysis. Estimation via OLS with treatment assignment as the independent variable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
35
increases the probability of having children only for males in rural areas, but this effect is
statistically insignificant.
Table 23: Fertility
Table 24 below provides suggestive evidence that the NAP apprenticeship offer reduced
fertility. The estimated treatment effects suggest that males and urban females have less
children. Only rural females are estimated to have more children. However, all estimates are
statistically indistinguishable from zero.
Table 24: Fertility
Table 23: Fertility(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLESChildren?
(0/1)Children?
(0/1)Children?
(0/1)Children?
(0/1)Children?
(0/1)Children?
(0/1)Children?
(0/1)Children?
(0/1)Children?
(0/1)
Treatment -0.0293* -0.0304 -0.0277 -0.0384** -0.00122 -0.0550 0.0466 -0.0350* -0.00995(0.0165) (0.0497) (0.0171) (0.0193) (0.0372) (0.0674) (0.0937) (0.0198) (0.0395)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 0.750 0.533 0.787 0.754 0.759 0.480 0.645 0.793 0.787Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1The sample comprises all individuals we have currently surveyed. Gatecrashers and individuals assigned to priority have been excluded fromthis analysis. Estimation via OLS with treatment assignment as the independent variable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
Table 24: Fertility(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLES Number of children
Number of children
Number of children
Number of children
Number of children
Number of children
Number of children
Number of children
Number of children
Treatment -0.0550 -0.191 -0.0254 -0.0650 0.0436 -0.224 -0.104 -0.0352 0.0998(0.0520) (0.167) (0.0538) (0.0607) (0.129) (0.235) (0.366) (0.0619) (0.132)
Observations 3,121 768 2,353 2,233 746 491 241 1,742 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 1.550 1.250 1.600 1.527 1.622 1.131 1.500 1.583 1.652Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1The sample comprises all individuals we have currently surveyed. Gatecrashers and individuals assigned to priority have been excluded fromthis analysis. Estimation via OLS with treatment assignment as the independent variable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
36
7.7 Mechanisms
In this section we try to provide evidence for the theory of change of this impact evaluation,
namely that through training in a trade youth acquire skills which improve their employability
and labor market outcomes. In addition, we want to explore to which extent labor market and
material well-being outcomes can be explained by migration patterns.
7.7.1 Skills
In order to test whether youth acquired skills in their trade of interest, we administered a short
trade-specific test that was developed in conjunction with industry experts. While all estimated
treatment effects are positive, the significant effects seem to be driven by females. Females
who were offered a NAP apprenticeship tend to score almost 4 percentage points higher in
both urban and rural areas which corresponds to a 13 percent increase relative to the control
group. Estimated treatment effects for males are positive but statistically insignificant.
Converting these effects to standard deviations, the program led to a 0.16SD increase in skills
for the whole sample.
Table 25: Craftskills
Table 25: Craftskills(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLESCraftskills
scoreCraftskills
scoreCraftskills
scoreCraftskills
scoreCraftskills
scoreCraftskills
scoreCraftskills
scoreCraftskills
scoreCraftskills
score
Treatment 0.0318*** 0.0197 0.0338*** 0.0357*** 0.0374** 0.0111 0.0529 0.0374*** 0.0397**(0.00726) (0.0176) (0.00791) (0.00861) (0.0164) (0.0233) (0.0350) (0.00922) (0.0182)
Observations 3,125 769 2,356 2,237 746 492 241 1,745 505Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group 0.325 0.346 0.322 0.327 0.321 0.339 0.360 0.325 0.311Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Respondents have been asked 9 questions to assess their craftskills of which an average score has been computed. The sample comprises all individuals we have currently surveyed. Gatecrashers and individuals assigned to priority have been excluded from this analysis. Estimation via OLS with treatment assignment as the independent variable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
37
In addition to trade related skills, we want to examine whether the NAP program increased the
use at one’s job of general skills such as reading, writing, measuring, calculating, operating
machines, directing, using a phone, using a computer, learning, thinking in the respondent’s
main work activity. As with craft skills, estimated treatment effects are always positive, but only
significant for females. However, in contrast to craft skills, the treatment effect is twice as large
for treatment females in rural areas relative to treatment females in urban areas. Point
estimates for males in rural areas are also larger relative to males in urban areas, but both
estimates are insignificant. Overall, the job skills suggests that women in the treatment group
were able to move into better quality jobs (measured by skill content).
Table 26: Job skills
7.7.2 Migration
Table 27 below shows that individuals who were offered the NAP program are more likely to
have migrated since our baseline in 2012. Except for males who lived in an urban area in
2012, all estimated treatment effects are positive. Not too surprisingly, the estimated
probability of migration is higher for rural than for urban areas.
Table 26: Jobskills(1) (2) (3) (4) (5) (6) (7) (8) (9)
Male Male Female FemaleUrban Rural Urban Rural
VARIABLES Jobskills (z-score)
Jobskills (z-score)
Jobskills (z-score)
Jobskills (z-score)
Jobskills (z-score)
Jobskills (z-score)
Jobskills (z-score)
Jobskills (z-score)
Jobskills (z-score)
Treatment 0.122*** 0.142 0.120*** 0.120*** 0.218*** 0.165 0.238 0.111** 0.213**(0.0375) (0.0899) (0.0410) (0.0460) (0.0784) (0.133) (0.164) (0.0486) (0.0912)
Observations 2,657 711 1,946 1,903 632 453 223 1,450 409Strata FE Yes Yes Yes Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes Yes Yes YesStandard errors Robust Robust Robust Robust Robust Robust Robust Robust RobustMean Control Group -0.161 0.224 -0.234 -0.154 -0.226 0.251 0.161 -0.216 -0.342Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1Jobskills include: read; write; measure; calculate; operate machines; direct; use phone; use computer; learn; think; required training. Thesevariables have been added up and then standardized. The sample comprises all individuals we have currently surveyed. Gatecrashers andindividuals assigned to priority have been excluded from this analysis. Estimation via OLS with treatment assignment as the independentvariable. DistrictxTrade Fixed Effects have been included. Robust standard errors in paranthesis. Urban/rural as of baseline in 2012.
Full sample Male Female Urban Rural
38
Table 27: Migration
8. Discussion
In this section, we discuss possible mechanisms by which we may be observing the results
above, and the magnitude of our findings. Pinning down these mechanisms is beyond the
scope of this report.
To begin with earnings, arguably the most salient summary measure of program effects, we
estimate a 10 GhC reduction, 11% of the control group mean of 87 GhC. For mean, we
estimate an approximately 50 GhC reduction in earnings, which is approximately 27% of the
control group mean, a large and meaningful effect.
One key mechanism to explain negative, large, and significant earnings estimates for men is
that 33% of the compliers are still in their low paid apprenticeships. Additional evidence for
this mechanism can be seen in Table 5 columns 3, 5, and 6, where men in the treatment group
are not significantly more likely to have completed an apprenticeship despite similar first stage
magnitudes to women in starting an apprenticeship. Medium and long-term follow-up could
provide further insight into whether earnings trajectories change over time for men in the
sample across treatment and control groups. Another key finding to keep in mind for men is
39
that the point estimate on craft-skills gained is near zero, reflecting their on-going training
status.
For women, 20% of compliers were still in training at endline. As fewer women were still in
training, we observed more promising patterns in their outcomes. This suggests we may more
optimistic that medium and long-term follow-up could show positive effects as women have
already moved into self-employment and seen an increase in durable assets. As women
assigned to NAP already have large and significant point estimates on both craft and job skills,
these skills may enable them to follow a steeper employment and earnings trajectory over
time.
With respect to marriage, fertility, and migration, we find that women are more likely to marry
and migrate but less likely to have children. Though exploration of these phenomena in detail
and in the context of culturally informed qualitative work is outside the scope of this report, we
hypothesize that women may be less likely to have children outside marriage, and may be
using skills training as a way to become a more desirable partner (hence increasing marriage
rates). Migration for women tracks with marriage, and women typically move to the family
home of their partners. Migration is also driven by human capital and is an important outcome.
40
9. Specific Findings for Policy and Practice
In this report, we present findings from our impact evaluation of the NAP program in Ghana.
First, we find a relatively modest first stage on starting an apprenticeship, evidence perhaps
that program recruitment was not particularly well targeted. The implementation delays
spurred by political transition were likely an important contributing factor to our modest first
stage on starting an apprenticeship.
However, the point estimate on apprenticeship completion relative to the control group mean
is quite large, at a 40% increase, suggesting that NAP apprentices may be able to overcome
barriers to apprenticeship completion faced by those in the control group. Alternatively, the
program could simply have enabled NAP apprentices to get into an apprenticeship earlier, and
thus are more likely to have completed by the endline survey.
Apprenticeships appear to move participants from wage employment and agriculture to self-
employment. As consequence this reduces earnings in wage employment and agriculture,
with limited impacts on self-employment earnings. However, there are encouraging signs of
earnings growth in self-employment particularly in urban areas, and especially for women who
applied to study cosmetology (results not shown). Overall, the program does not appear to
have a sizeable or significant average effect on youths’ total labor market participation or labor
income. The program does increase other outcomes such as migration, fertility and asset
accumulation, especially among women, which is encouraging. However, given the short-run
nature of the data collection, with a significant fraction of compliers still in training, we cannot
definitively assess the impacts of the program on the labor market. We postulate that we see
more encouraging results among women as they were less likely to be in training during the
survey. Additional data collection will be needed to better assess the impact of NAP on youth
employment and earnings.
In addition to the findings, implementation challenges and mitigation strategies offer lessons
for practice. The COTVET-suggested solution to allow for district officials to hand-select some
applicants was an effective compromise. The delay brought about by an election and political
transition may have reduced take up, but may have also retained only those most interested
in the program (or perhaps those who are most credit constrained), an issue we plan to
41
investigate further. The matching meetings were logistically effective in matching apprentices
to firm owners who were willing to train and employ them. In addition, skills assessment and
testing was a useful check on craft skills development built into the experimental study. In
general, while implementation did not go smoothly, the program continued and demand for it
from both apprentices and firms was high enough for it to go forward. A final implementation
constraint was the toolkit distribution problems, a supply-chain issue in government that could
merit its own analysis elsewhere.
Overall, given the short-run nature of our findings, it may be premature to provide definitive
extensive policy recommendations. However, given the critical role of skills in determining the
employability and productivity of youth, we can evaluate the cost-effectiveness of NAP in
promoting skills. The program raised test scores by 0.42SD per US$100, assuming the bulk
of the program costs were the training fees (GHC 150 or approximately US$37.5). Compared
to other programs that promoted access to schooling, NAP was more cost effective than a
conditional cash transfer program in Malawi, but less cost-effective compared to a girls’
secondary school scholarship program in Kenya (Kremer et al, 2013). The program is also
significantly cheaper than the training programs reviewed in McKenzie (2017), which ranged
from US$13 in India to US$1700 in Turkey. Additional rounds of data will be needed to
definitively assess the labor market impacts and the cost-effectiveness of the program.
42
10. References
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Acemoglu, Daron and Pischke, Jörn-Steffen. “Beyond Becker: Training in Imperfect Labour Markets” The Economic Journal 109.453 (1999): 112-142.
Ajayi, Kehinde. School Choice and Educational Mobility: Lessons from Secondary School Applications in Ghana. Boston University Working Paper, 2013
Alfonsi, L., Bandiera, O., Bassi, V., Burgess, R., Rasul, I., Sulaiman, M., & Vitali, A. (2017). Tackling Youth Unemployment: Evidence from a Labor Market Experiment in Uganda (No. eopp64). STICERD LSE Working Paper
Attanasio, Orazio, Adriana Kugler, and Costas Meghir. "Subsidizing vocational training for disadvantaged youth in Colombia: Evidence from a randomized trial." American Economic Journal: Applied Economics 3.3 (2011): 188-220. Avura, Francis and Ato Ulzen-Appiah. 2016 (forthcoming). "An Inventory of Youth Employment Programs in Ghana".
Becker, Gary Human Capital A Theoretical and Empirical Analysis, With Special Reference to Education. 1993 University of Chicago Press
Blattman, Christopher, Nathan Fiala, and Sebastian Martinez (2014). "Generating skilled self-employment in developing countries: Experimental evidence from Uganda." The Quarterly Journal of Economics, 129(2) (2014) 697-752.
Blattman, Christopher and Laura Ralston, “Generating employment in poor and fragile states: Evidence from labor market and entrepreneurship programs” World Bank Working Paper, 2015
Booysen, Frikkie, Servaas van der Berg, Ronelle Burger, Michael von Maltitz, and Gideon du Rand. “Using an Asset Index to Assess Trends in Poverty in Seven Sub-Saharan African Countries,” World Development, 36(4) (2008), p.1113-1130.
43
Cho, Yoonyoung, Davie Kalomba, A. Mushfiq Mobarak, and Victor Orozco (2015) "Gender Differences in the Effects of Vocational Training: Constraints on Women and Drop-out Behavior," World Bank Working Paper WPS6545.
De Mel, Suresh, David McKenzie, and Christopher Woodruff. "Returns to capital in microenterprises: evidence from a field experiment." The Quarterly Journal of Economics (2008): 1329-1372.
De Mel, Suresh, David McKenzie, and Christopher Woodruff. "One-time transfers of cash or capital have long-lasting effects on microenterprises in Sri Lanka." Science 335.6071 (2012): 962-966.
Demographic and Health Survey Ghana [Dataset]. (2014).
The Economist. “The Dividend is Delayed” May 8, 2014. London.
Fafchamps, Marcel, David McKenzie, Simon Quinn, and Christopher Woodruff. "Microenterprise growth and the flypaper effect: Evidence from a randomized experiment in Ghana." Journal of development Economics 106 (2014): 211-226.
Fersterer, Josef, Jörn‐Steffen Pischke, and Rudolf Winter‐Ebmer. "Returns to apprenticeship training in Austria: Evidence from failed firms." The Scandinavian Journal of Economics 110.4 (2008): 733-753.
Filmer, Deon and Lant H. Pritchett. “Estimating Wealth Effects Without Expenditure Data or Tears: An Application to Educational Enrollments in States of India,” Demography, 38(1) (2001), p.115-132.
Filmer, Deon and Kinnon Scott. “Assessing Asset Indices,” Demography, 49(1) (2012), p.359-392.
Frazer, Garth. “Learning the master's trade: Apprenticeship and human capital in Ghana” Journal of Development Economics 81.2 (2006): 259-298.
44
Ghana Statistical Service - GSS, Ghana Health Service - GHS, and ICF International. 2015. Ghana Demographic and Health Survey 2014. Rockville, Maryland, USA: GSS, GHS, and ICF International.
Ghana Statistical Service, Ghana Living Standards Survey 6 (GLSS 6): Main Report,
Accra, Ghana, available online at http://www.statsghana.gov.gh/surveys.html (2014).
Gondwe, Mtinkheni, and Jos Walenkamp. "Alignment of higher professional education with the needs of the local labour market: The case of Ghana." The Hague: NUFFIC and The Hague University of Applied Sciences (2011).
Hicks, Joan Hamory, Michael Kremer, Isaac Mbiti, and Edward Miguel. "Vocational education in Kenya: Evidence from a randomized evaluation among youth." UC-Berkeley Working paper (2012).
Johanson, Richard K., and Avril V. Adams.2004. “Skills Development in Sub-Saharan Africa.” World Bank Publications, The World Bank: number 15028.
Karlan, Dean, Ryan Knight, and Christopher Udry. "Consulting and capital experiments with microenterprise tailors in Ghana." Journal of Economic Behavior & Organization 118 (2015): 281-302.
Kremer, Michael, Conner Brannen, and Rachel Glennerster. "The challenge of education and learning in the developing world." Science 340.6130 (2013): 297-300.
McKenzie, David. "How effective are active labor market policies in developing countries? A critical review of recent evidence." The World Bank Research Observer 32.2 (2017): 127-154.
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11. Appendices
Appendix A: Field notes and other information from formative work
The endline survey was first launched in Akuapim North, a district in the South of Ghana. The
24 Southern districts were divided among initially four teams, each composed of five surveyors
and one team leader. In line with our stratification in the randomization, these teams moved
district by district while aiming for balanced survey completion rates by treatment assignment
status. Since tracking of respondents was a major challenge in this project, a specialized
tracking team was also deployed. This tracking team was formed by drawing four surveyors
who showed outstanding tracking successes from the southern teams and re-organizing the
remaining teams slightly. This tracking team was led by a Senior Field Manager (Snr. FM) who
was in contact with numerous TVET district coordinators. The Snr. FM met with each TVET
coordinator in order to gather up-to-date information about respondents.
In order to adapt to slower productivity rates and to different completion rates within
districts, the teams of surveyors have were re-shuffled several times. This helped in ensuring
that each surveyor could work proactively and efficiently. In total, the Southern field team was
composed of 20 surveyors, 4 team leaders, and 2 auditors (in charge of doing the back-
checks).
The endline survey was organized differently in the North of Ghana. The North of
Ghana is characterized by a large variety of local languages, so that surveyors tend to stay
within their assigned districts. 14 surveyors are divided into eight teams to cover 8 northern
districts (1 team of surveyors per district). In addition, for every 2 teams of surveyors (4
surveyors total), 1 team leader was supervising their work and ensuring protocols were being
followed. Finally, 2 auditors were hired in the North in order to run the back-checks. Those 2
auditors could speak all of the Northern languages, which made it easier for them to back-
check surveys from all of the 8 targeted Northern districts.
Appendix B: Sample design
46
From the set of 78 program districts, we randomly selected a set of 32 evaluation districts
where we would conduct the study. The choice of the evaluation districts was done randomly,
and weighted by population in order to ensure a representative set of districts. In our sample
districts, 3,927 youth applied to the program (and enrolled in our study) and placed into one
of three categories by the committees: (1) priority applicants, whose place in the program was
guaranteed (329), (2) control applicants, who were randomly assigned to the control group
(1,568), and (3) treatment applicants, who were randomly assigned to the treatment group
until all spaces in the program were occupied (2,031). The randomization was stratified by
choice of training and district, and was conducted electronically but announced locally in
conjunction with district officials. Treatment apprentices were then placed with one of 1,187
small firm owners who requested access to apprentices through the program. An additional
20 apprentices who were not in our baseline participated in the matching meetings.
Appendix C: Survey instruments Attached
Appendix D: Pre-Analysis Plan Attached
Appendix E: Sample size and power calculations
As is typical of youth job training programs, the take up rate is moderate. However, power
calculations reveal that despite a 45-50% treatment take up, sample size (3,599 in the main
randomization) will allow us to detect a 3.5% increase in employment, including both self-
employment and wage employment, and earnings gains of 18% (equivalent to approximately
$13 per year) at 95% confidence with statistical power of 80%. This compares favorably to the
sample sizes used in recent youth training programs such as Cho et al (2015) in Malawi, Hicks
et al (2012) in Kenya, and Attanasio et al (2011) in Colombia. Given that the training period in
our study is much longer (3 years compared to 3 months to 1 year) we can be fairly confident
that we are appropriately powered to detect meaningful effects.
Appendix F: Monitoring plan Attached
Appendix G: Descriptive Statistics Attached
Appendix H: Results Attached
47
Appendix I: Cost Data Attached
Appendix J: Do files Attached
Appendix K: Challenges and Lessons Attached
Appendix L: Craftskills Questions Attached
Appendix M: Variable definitions
Variables and Definitions
Variable Variable Definition
Started Apprenticeship Have you ever started an apprenticeship? (Yes/No). Determines whether the participant ever started an apprenticeship.
Completed Apprenticeship
Did you successfully complete your apprenticeship? (Yes/No) Determines whether the participant finished an apprenticeship.
Apprenticeship Duration
Difference between the month and year started and month and year ended.
Apprenticeship Characteristic (Firm Size)
Count of apprentices, paid workers, and unpaid workers employed at the firm.
Apprenticeship Characteristic (Entrance Fee)
What fees did you pay, if any, in Ghana cedis to enter your apprenticeship? Surveyor clarifies if amount is greater than 500.
Apprenticeship Characteristic (Exit Fee)
What fees did you pay, if any, in Ghana cedi to exit your apprenticeship? Surveyor clarifies if amount is greater than 500.
Apprenticeship Characteristic (Testimonial)
Did you receive a testimonial for your apprenticeship? (Yes/No) A testimonial is a reference letter
Apprenticeship Characteristic (Exam)
Did you take an exam after completing your apprenticeship? What type of exam (Trade Association/NAP/Government)? Did you pass the exam?
48
Apprenticeship Characteristic (Gas-powered Machines)
Was/is electrical or gas-powered machinery used in your master craftsperson’s business?
Apprenticeship Characteristic (Travel Time)
How many minutes did it take you to travel to your apprenticeship each day, on average?
Apprenticeship Characteristic (Practice Materials)
Did you have any practice materials during your apprenticeship? Where did you get these practice materials (Government/Employer/Family or Friend/Myself)
Durable Assets Index Does your household own a: working radio, working television, working bicycle, working car, working motorbike, refrigerator, freezer? Yes/No for each item.
Consumption Expenditure
Sum total of cedi spent last week on: phone credit, personal items for yourself (clothes, jewelry, hair, makeup, shavers, body spray, etc.), eating out at restaurants/bars/etc.
Married What is your current relationship status? (Single, Boyfriend/Girlfriend, Married/engaged, Polygamous)
Children Do you have any biological children?
Number of Children How many biological children do you have with any partner, in total?
Craftskills Score The craft skills score is derived from a series of 9 questions created through collaboration with trade associations intended to measure familiarity with a trade. For example, a question for block laying asks participants to identify a block laying tool.
Job Skills Score The job skills score is derived from a series of questions including the number of new designs created in the past month to a test of salesmanship. The sales test asks the participant to attempt to sell a pen to the surveyor for 2 Ghana cedi with two minutes of preparation time.
Migration Our records say that in 2012 you could get to your house with the following directions. Do you still live at the same place? What region do you live in? What district do you live in?
Appendix N: Deviations from the PAP
As our analysis of the program effects is still on-going, we discuss discrepancies between our
submitted PAP and our analysis presented above in this section. In particular, we discuss
discrepancies in terms of outcome variables, heterogeneity analysis and estimation
specifications of the main randomization.
49
Outcomes of interest as specified in the PAP can be grouped in six categories: (1) Labor
supply and earnings, (2) job characteristics, (3) human capital, (4) other material well-being
outcomes, (5) marriage, fertility, and gender outcomes, and (6) mental health. While the
analysis presented above examines current labor supply and labor earnings as stated in the
PAP, we do not exploit the full retrospective panel yet. Current employment and earnings are
arguably most policy relevant and while stacked outcomes would add power to the estimation,
they would also introduce noise due to recall error. Moreover, in our analysis we studied labor
supply and earnings overall, where wage employment, self-employment, farming, unpaid
work, and apprenticeship work are captured, as well as separately for wage employment, self-
employment and agricultural work (either own farm, paid farm work or unpaid farm work). The
PAP only explicitly mentions overall labor supply and earnings, but since apprenticeships can
lead to shifts between wage employment, agricultural work and self-employment, we deem it
important to add this granularity. Other outcomes such as the business assets of self-
employed respondents have been analyzed but omitted here, whereas firm size and formality
of the work setting are yet to be inspected. Analysis of non-cognitive/business skills and
managerial skills as part of the human capital section have not been completed. Our analysis
of respondents’ other material well-being has been limited to effects on a measure of
consumption and an index of durable household assets, leaving loan access and savings for
analysis yet to come. We presented results on our hypothesis that training will delay marriage,
and delay and reduce fertility as stated in the PAP. However, the marriage, fertility and gender
outcomes will still need to be complemented by evidence on relationship quality, relationship
search and female autonomy.
Our heterogeneity analysis to date has focused on differential treatment effects by gender,
urban/rural and the interaction of gender and urban/rural, as presented here, as well as by
trade and the interaction of gender and trade. In our view, these dimensions were a natural
starting point for our heterogeneity analysis. As stated in the PAP, further dimensions of
heterogeneity that we are interested in are cognitive ability, non-cognitive ability, educational
attainment and credit access (all measured at baseline).
Consistent with our PAP, our primary ITT specification includes follow up month and strata
fixed effects. In contrast to our PAP, standard errors are robust instead of clustered at the
apprentice level. Moreover, we limited our current analysis to ITT effects as this measure of
50
treatment effect is more policy relevant. Although outcomes such as wage employment and
self-employment might be jointly determined which would lend itself for a multinomial logit
specification, we follow our PAP and focus on OLS ITT regressions for now.
A single PAP has been submitted for two combined projects. 3ie funding has supported the
evaluation of the main randomization of apprenticeships, so that the analysis of the match and
incentives randomization has been omitted from this report.
Appendix O: Attrition Analysis
The tables below provide results from our analysis of attrition from the study. We define
attrition as an inability to contact a participant for the follow-up survey conducted at the end
of the trial. We find that in the full sample, as well as when we break down the sample into
urban and rural, there is not statistically significant attrition in our treatment groups. We do
find a small, but statistically significant decrease in attrition of the “priority” group that lived in
rural areas compared to non-priority participants.
When we examine attrition by baseline characteristics, we find certain characteristics are
related to attrition from the study. We find that women in urban districts are 6.11 percentage
points more likely than men to drop from the study. It seems likely that this subgroup drives
the marginally statistically significant result that women in all districts are more likely to have
dropped from the study. Those with more years of schooling and a higher Ravens score
were less likely to have dropped from the study, though the difference was small (0.6
percentage points per year of schooling and 1.31 percentage points per 1 point increase in
z-score). This result holds true for urban districts in the case of education, though the point
estimate is smaller at 0.498 percentage points per year of schooling. Finally, we find a small
and only marginally statistically significant result for married participants. Married participants
are 2.82 percentage points less likely to drop from the study than unmarried participants.
Overall, we see no significant differences in attrition from our treatment group, even when
we control for baseline characteristics.
51
Attrition Analysis
52
Attrition Analysis with Baseline Characteristics
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