training for success: targeting and incentives in apprenticeship … · 2019-11-15 · garments,...

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 [email protected].

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)

mailto:[email protected]

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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.

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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

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Table 6: First stage – Apprenticeship duration

Table 5: First stage - Successfully completed apprenticeship?(1) (2) (3) (4) (5) (6) (7) (8)


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.


Table 6: First stage - Apprenticeship duration(1) (2) (3) (4) (5) (6) (7) (8)


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.


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.

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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)


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)


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.


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)


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.


Table 11: Extensive Margin of Labor Supply in Agriculture(1) (2) (3) (4) (5) (6) (7) (8) (9)


VARIABLESWorking in agriculture

(0/1)

Working in agriculture

(0/1)


(0/1)


(0/1)


(0/1)


(0/1)


(0/1)


(0/1)


(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.


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)


VARIABLESHours 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.


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)


VARIABLES

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.


Table 14: Intensive Margin of Labor Supply - Self Employment(1) (2) (3) (4) (5) (6) (7) (8) (9)


VARIABLES

Hours worked in

own business (month)

Hours worked in


Hours worked in


Hours worked in


Hours worked in


Hours worked in


Hours worked in


Hours worked in


Hours worked in


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.


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)


VARIABLES

Hours worked in agriculture

(month)


(month)


(month)


(month)


(month)


(month)


(month)


(month)


(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.


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)


VARIABLESEarnings

from working (month)

Earnings from working

(month)


(month)


(month)


(month)


(month)


(month)


(month)


(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.


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)


VARIABLESWage 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.


Table 18: Earnings from Self Employment(1) (2) (3) (4) (5) (6) (7) (8) (9)


VARIABLESBusiness

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.


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)


VARIABLESDurable

assets index (z score)

Durable assets index

(z score)


(z score)


(z score)


(z score)


(z score)


(z score)


(z score)


(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.


Table 21: Consumption expenditure(1) (2) (3) (4) (5) (6) (7) (8) (9)


VARIABLESConsumption Expenditure

(Cedi)

Consumption Expenditure

(Cedi)


(Cedi)


(Cedi)


(Cedi)


(Cedi)


(Cedi)


(Cedi)


(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.


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)


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.


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)


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)



Table 24: Fertility(1) (2) (3) (4) (5) (6) (7) (8) (9)


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)



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)


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.


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)


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.


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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.

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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.

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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.

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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.

Monk, Courtney; Sandefur, Justin; and Teal, Francis. “Does Doing an Apprenticeship Pay Off? Evidence from Ghana.” CSAE Working Paper Series 2008-08 (2008).

Teal, Francis. “Are apprenticeships beneficial in sub-Saharan Africa?” IZA World of Labor 2016: 268 (2016).

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World Bank. 2016. "Expanding Job Opportunities in Ghana" World Bank, Washington DC.

World Development Indicators 2017. The World Bank, Washington, DC.

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

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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

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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?

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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.


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.

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Attrition Analysis

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Attrition Analysis with Baseline Characteristics

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