Women’s Empowerment in Action: Evidence from a Randomized Control Trial in Africa¤ Oriana Bandiera, Niklas Buehren, Robin Burgess, Markus Goldstein, Selim Gulesci, Imran Rasul and Munshi Sulaimany July 2017 Abstract Women in developing countries are disempowered: high youth unemployment, early mar- riage and childbearing interact to limit their investments into human capital and enforce dependence on men. We evaluate a multi-faceted policy intervention attempting to jump- start adolescent women’s empowerment in Uganda, a context in which 60% of the population are aged below twenty. The intervention aims to relax human capital constraints that ado- lescent girls face by simultaneously providing them vocational training and information on sex, reproduction and marriage. We …nd that four years post-intervention, adolescent girls in treated communities are 48% more likely to engage in income generating activities, an impact almost entirely driven by their greater engagement in self-employment. Teen preg- nancy falls by 34%, and early entry into marriage/cohabitation falls by 62%. Strikingly, the share of girls reporting sex against their will drops by close to a third and aspired ages at which to marry and start childbearing move forward. The results highlight the potential of a multi-faceted program that provides skills transfers as a viable and cost-e¤ective policy intervention to improve the economic and social empowerment of adolescent girls over a four year horizon. JEL Classi…cations: I25, J13, J24. ¤ We thank all those at BRAC Uganda, and Agnes Katushabe, Upaasna Kaul, Nuarpear Lekfuangfu, Irene Lumala and Georgina Shiundu for excellent research assistance. We have bene…ted from discussions with Pedro Carneiro, Lucia Corno, Bruno Crepon, Nathan Fiala, Louise Fox, Emanuela Galasso, Maria Guadalupe, Khondoker Ariful Islam, Adriana Kugler, Brendon McConnell, David McKenzie, Silvana Tenreryo, Michele Tertilt, Marcos Vera-Hernández, Abebual Zerihun and numerous seminar and conference participants. We are grateful to the Africa Gender Innovation Lab , Bank-Netherlands Partnership Programme, Mastercard, Nike, the Gender Action Plan of the World Bank, the Improving Institutions for Pro-Poor Growth program of DfID and the International Growth Centre for …nancial support. The views presented in this paper are the authors’ and do not represent those of the World Bank or its member countries, or DfID. All errors remain our own. y Bandiera: LSE, o.bandiera@lse.ac.uk; Buehren: World Bank, nbuehren@worldbank.org; Burgess: LSE, r.burgess@lse.ac.uk; Goldstein: World Bank, mgoldstein@worldbank.org; Gulesci: Bocconi, se- lim.gulesci@unibocconi.it; Rasul: UCL, i.rasul@ucl.ac.uk; Sulaiman: BRAC, munshi.slmn@gmail.com. 1 1 Introduction Women’s empowerment has three key dimensions: political, economic, and control over one’s body. In today’s developed countries, the historic process of economic empowerment, and to a lesser extent, control over the body, mostly preceded universal su¤rage [Fernandez 2014]. This is almost entirely reversed in many developing countries today: universal su¤rage for women was often achieved at independence, yet empowerment along economic and reproductive dimensions has progressed more slowly and might be reversing in some countries [Doepke et al. 2012]. In these countries, female labor force earnings are strikingly low and the majority of women marry and have children at far younger ages relative to their contemporaries in developed nations [World Bank 2007, Doepke et al. 2012]. The type of technological advances that drove demand for female labor in the developed nations have spread less far in the developing world [Goldin 2006], access to contraceptive methods, which enable control over reproduction and facilitate human capital investment, is more limited [Goldin and Katz 2002] and violence towards women is more prevalent and acceptable [Anderson and Ray 2010, 2012, Doepke et al. 2012]. Many women in these countries appear trapped in an equilibrium where the phenomena of low human capital investment, restricted access to labor markets and limited control over their bodies reinforce each other, leading to dependence on men. The key question is then whether jump-starting women’s human capital accumulation can set them on a trajectory towards a better equilibrium, or whether such circumstances are maintained by binding social norms or low aspirations, that cannot easily be shifted or relaxed by public policy [Field et al. 2010]. This is the research question at the heart of our analysis. We evaluate a multi- faceted program that provides adolescent girls with an opportunity to simultaneously accumulate two types of human capital: vocational skills to enable them to start small-scale income generating activities, and life skills to help to make informed choices about sex, reproduction and marriage. Targeting adolescents is important: as dependence on parents comes to a close during ado- lescence, there is a central tension between whether women are able to delay childbearing and undertake human capital investments critical to pursuing some form of career, or become depen- dent on men (either as a wife or via temporary relationships). A lack of future labor market opportunities can reduce the incentives for young girls to invest in their human capital [Jensen 2012], leading to early marriage and childbearing, and potentially increasing their dependency on older men [Dupas 2011]. In turn, teen pregnancy and early marriage are likely to have a decisive impact on the ability of young girls to accumulate human capital, and limit their future labor force participation [Field and Ambrus 2008, Bruce and Hallman 2008].1 Economic empowerment and social empowerment, in its most basic form as having control over the body, thus interact in 1 Baird et al. [2011] document that marriage and schooling are mutually exclusive activities in Malawi, and Ozier [2011] provide similar evidence from Kenya. In Bangladesh, Field and Ambrus [2008] show that each additional year that marriage is delayed is associated with .3 additional years of schooling and 65% higher literacy rates. 2 a powerful way during adolescence. Hence interventions targeted towards adolescent girls might have higher returns than later timed interventions [Heckman and Mosso 2014]. Helping young women out of this low-empowerment equilibrium has become a priority for policy makers in developing countries because of burgeoning youth populations and concerns over youth unemployment.2 This is true throughout Sub-Saharan Africa and especially in Uganda, the focus of this study. Uganda has the second lowest median age of all countries and the highest child dependency ratio as shown in Figure 1A [UNAIDS 2010]. For those in the labor force, Figure 1B shows women tend to have higher unemployment rates than men, and this is especially pronounced in the youngest age cohorts. Finally, as Figure 1C highlights, relative to their contemporaries in richer economies, the fertility rate of Ugandan women is three to four times higher and the gap is most pronounced among adolescents aged 15 to 19.3 Against this background, the multifaceted program we evaluate aims to break the vicious circle between low labor force participation and high fertility by kick-starting human capital accumu- lation along two dimensions through the simultaneous provision of: (i) ‘hard’ vocational skills to enable adolescent girls to start small-scale income generating activities; (ii) ‘soft’ life skills to build knowledge enabling girls to make informed choices about sex, reproduction and marriage. The intervention is delivered from designated ‘adolescent development clubs’ rather than in schools, and can thus reach school drop-outs as well as girls currently enrolled in school. The program was developed in another country, Bangladesh, where female disempowerment is also a major issue. Since 1993 in Bangladesh, BRAC, one of the world’s largest NGOs, has established 9 000 clubs that have reached over one million adolescent girls. We worked with BRAC to evaluate the program in an African setting where women face similar challenges to those in Bangladesh. The program has proved to be transportable across countries, and also scalable and cost e¤ective: to date BRAC has started 1200 clubs in Uganda, reaching 50 000 girls.4 We collaborated with BRAC to randomly assign clubs across communities. We surveyed and 2 The number of young people in the developing world is increasing: one billion people on the planet are aged between 15 and 24 and reside in a developing country, an increase of 17% since 1995. Nowhere is this phenomenon more pronounced than in Sub-Saharan Africa, where 60% of the population is now aged below twenty [World Bank 2009]. Youths face severe economic challenges, as they account for most of the region’s poor and unemployed: in sub-Saharan Africa, 60% of the total unemployed are aged 15-24, and on average 72% of the youth population live on less than $2 per day. The continued rise in the numbers of young people in the global population has led policy makers to consider responses to what has now become termed the ‘youth bulge’ [World Bank 2007]. The central policy challenge is to provide increasing numbers of young people the skills and job opportunities to enable them to lead ful…lled and economically self-reliant lives in adulthood. A parallel set of concerns are that ever rising numbers and proportions of youth will be a key factor driving alienation, social unrest and demands for political reforms, as has been observed throughout North Africa and the Middle East recently [Fuller 1995, Goldstone 2002]. 3 Demographic and Health Survey data indicates 38% of the 52 million women aged 20-24 in developing countries were married before age 18 [Mensch et al. 2005] and these girls are often subject to unprotected sex. Girls aged 15-24 are almost 8 times more likely than men to be HIV positive in Sub-Saharan Africa [Bruce and Hallman 2008, UNAIDS 2010, Dupas 2011]. Unprotected heterosexual intercourse together with the onward transmission of HIV to newborn and breast-fed babies is responsible for the vast majority of new HIV infections in the region. 4 The program has also started in Tanzania, where 200 clubs have over 7 000 adolescents enrolled in them, 120 clubs have been set up in South Sudan. Ongoing pilots are taking place in Afghanistan and Liberia. 3 tracked a representative sample of almost 5 000 adolescent girls at baseline, midline (two-years post intervention) and endline (four years post-intervention). Club participation is voluntary and unrelated to other BRAC activities. The take-up rate is 21%, suggesting that a sizeable share of eligible girls have latent demand for the combined vocational and life skills on o¤er, and are not held back from participating by social norms or their own weak aspirations over women’s labor force participation, teen childbearing and marriage. Our results show that four years post-intervention, the bundled provision of hard vocational and soft life skills through the program leads to substantial advances in economic empowerment and control over the body for adolescent girls in treated communities relative to girls in control communities. ITT estimates imply girls in treated communities are 48 percentage point (pp) more likely to engage in income generating activities relative to girls in control communities, corresponding to a 48% increase over baseline levels, that is driven predominantly by additional engagement in self-employment activities (by 51%). Despite school-enrolled girls being eligible for the program, we …nd no reduction in school enrollment among eligibles (at either midline or endline). Hence, economic gains from the program do not come at the cost of girl’s lowering their investment in formal education. The program signi…cantly improves control over the body: there is a 34% reduction in rates of early (teen) pregnancy, and a 62% reduction in rates of marriage/cohabitation. Most dramatically, the share of adolescent girls reporting having had sex unwillingly in the past year is 53pp lower in treatment vs. control communities, starting from a baseline level of 17%. This is perhaps the clearest marker that the bundled provision of life skills and vocational training successfully improves the adolescent girls’ relationship quality. Finally, we evaluate changes in girls’ expectations for ages at marriage, childbearing and fer- tility, as well as aspirations for their own daughters (and sons). The overall picture from these aspirations related outcomes are that although the program impacts most dimensions in the short term, these tend to die out by endline. There are however two notable dimensions of aspira- tions that do not die out, and relate closely to the earlier documented impacts: these relate to adolescent girl’s views on ideal ages at marriage for women, and the most suitable age to start childbearing. On both dimensions, these shifts in girls aspirations endure and we record impacts that are statistically signi…cant at endline. Our paper contributes to the literature evaluating the impact of human capital interventions targeted to youth. The evaluation helps …ll two gaps in the literature: (i) to study the impact of a bundled provision of hard and soft skills, in the form of vocational and life skills training, in contrast to many earlier interventions that focus on one dimension in isolation; (ii) to study the impact of such a bundled skills intervention that targets adolescents in a critical stage of the life cycle as they transit from school to work [Heckman and Kautz 2014]. A number of meta-analyses and systematic reviews of the literature have pointed to the low or short-lived returns in low- income settings to standalone vocational skills training programs [Card et al. 2010, Blattman and 4 Ralston 2015, McKenzie 2017]. Similarly, standalone school-based sex education programs have met with, at best, rather mixed success [Gallant and Maticka-Tyndale 2004, Paul-Ebhohimhen et al. 2008, Cornish and Campbell 2009, McCoy et al. 2010, Groh et al. 2012, De Walque 2014]. As in our setting, some of the more promising life skills interventions have been those delivered outside of school environments [Dupas 2011]. Our …ndings complement a small body of research using large-scale randomized control trials to provide evidence on the interlinkages between economic and reproductive challenges that ado- lescent girls face in developing countries. The main body of evidence built up along these lines relates to the impacts of (un)conditional cash transfers on risky behaviors, where conditionality often relates to school attendance. For example, Baird et al. [2011] …nd a conditional cash transfer of $10 per month conditional on school attendance for adolescent girls in Malawi led to signi…cant declines in early marriage, teenage pregnancy and self-reported sexual activity after a year, while an unconditional cash transfer had generally weaker impacts. Baird et al. [2014] report bene…cial impacts on the economic and social empowerment of adolescent girls in Malawi that have dropped out of formal schooling from a cash transfer conditioned on school attendance.5 This branch of work sheds light on the direct e¤ect of resources, rather than skills, on economic and social empowerment. Perhaps closest to our evaluation in terms of another multi-faceted intervention targeting adolescents is the program evaluated by Du‡o et al. [2015]: they investigate a school-based HIV prevention program in Kenya coupled with subsidies to attend school, and present evidence highlighting the joint determination of schooling and pregnancy outcomes for adolescent girls. They show the e¢cacy of providing adolescent girls information on how to reduce their exposure to pregnancy risks, is larger when reinforced by program components that simultaneously empower girls to lead economically independent lives. Relative to the earlier literature, our results highlight the potential of a multi-faceted program that provides bundled hard and soft skills as a viable and cost-e¤ective alternative to direct (un)conditional cash transfers, in promoting the economic and social empowerment of adolescent girls over a four year horizon. The paper is organized as follows. Section 2 details the intervention and its implementation. Section 3 describes the research design, data and estimation strategy. Section 4 presents estimates of the program’s two- and four-year impacts on adolescent girls’ economic empowerment, control over the body, expectations and aspirations. Section 5 discusses the cost e¤ectiveness of the intervention, and we conclude by highlighting the broader implications of our …ndings for policies and future research designed to address the economic and reproductive challenges facing the burgeoning number of young women in the developing world today. 5 Baird et al. [2014] provide a systematic review of the e¤ects of cash transfer programmes on schooling out- comes, using data covering 35 studies. They …nd that both conditional cash transfers (CCTs) and unconditional cash transfers (UCTs) improve the odds of being enrolled in and attending school compared to no cash transfer programme. The e¤ect sizes for enrolment and attendance are always larger for CCTs compared to UCTs, but the di¤erence is not statistically signi…cant. 5 2 Background The Empowerment and Livelihood for Adolescents (ELA) program is designed to improve the lives of adolescent girls through the simultaneous provision of two types of skills: vocational and life skills. The program is implemented by the NGO, BRAC Uganda. In contrast to school- based information campaigns on adolescent health, the ELA program operates outside of schools, through development clubs that are in a …xed meeting place in the community. Clubs are open …ve afternoons per week and timed so that girls enrolled in school can attend. Club activities are led by a female mentor. Mentors are selected from within the community, are slightly older than the target population of girls, and receive small lump-sum payments for their work. They are trained during a week-long initiation program, as well as monthly refresher courses. Using locally hired mentors ensures the program is scalable (as evidenced by its spread across countries, and also its expansion within Uganda). Moreover, the fact that mentors are close in age to mentees and have often successfully confronted challenges related to economic and social empowerment, is thought to help facilitate the transfer of knowledge. Indeed, existing work emphasizes that school-based interventions designed to socially empower adolescent girls may have limited impact because youth are uncomfortable discussing such matters with teachers [Gallant and Maticka-Tyndale 2004, Ross et al. 2006]. Club participation is voluntary and unrelated to participation in other BRAC activities. El- igibility is based on gender and age: girls aged between 14 and 20 are permitted to participate. Given the di¢culties of verifying ages in the …eld and the demand for vocational and life skills from other girls, in practice some girls outside of this age range also attend the clubs and receive skills training. In addition, the clubs also host popular recreational activities such as reading, staging dramas, singing, dancing and playing games. As such, outside of school hours, the clubs serve as a protected local space in which adolescent girls can meet, socialize, privately discuss issues of concern and to continue to develop their skills. The vocational skills and life skills training are provided in the …rst two years of the interven- tion. After this adolescent girls are free to use the clubs as a safe, social space but do not receive further training. Vocational skills training comprises a series of courses on income generating ac- tivities. Although many of the skills are applicable for either wage or self-employment, more focus is placed on the adolescent girls establishing small-scale enterprises of their own. Courses relating to a broad range of income generating activities are provided including hair-dressing, tailoring, computing, agriculture, poultry rearing and small trades operation. The vocational training modules are taught by entrepreneurs engaged in the respective activi- ties or by hired professionals as well as BRAC’s own agriculture and livestock program sta¤. These courses are supplemented by …nancial literacy courses covering budgeting, …nancial services and accounting skills. The process of matching girls to income generating activities is partly demand- driven, but account is also taken of the girl’s educational level, the local business environment 6 and demand for such services (so that all girls in a community are not provided the same voca- tional skill). The overarching aim of the vocational skills component of the program is to aid the economic empowerment of adolescent girls.6 The key topics covered in the life skills training sessions include sexual and reproductive health, menstruation and menstrual disorders, pregnancy, sexually transmitted infections, HIV/AIDS awareness, family planning, rape; other sessions cover enabling topics such as management skills, negotiation, con‡ict resolution and leadership; a …nal class of life skills training focuses on providing girls with legal knowledge on women’s issues such as bride price, child marriage and violence against women. These life skills training sessions are conducted either by the trained mentors and/or BRAC’s own professional sta¤. The overarching aim of the life skills component of the program is to socially empower girls by enhancing the control that adolescent girls have over their own bodies, and to enable them to act on improved knowledge of reproductive health. Two further points are of note. First, given the age range of targeted girls, some are enrolled in school, others have graduated, while others have dropped out. Although the clubs operate outside of school times, emphasis is placed on ensuring that girls enrolled in school do not reduce their educational investments in order to engage in club activities. We later provide evidence the program had no adverse impact on girls’ contemporaneous investments into formal educational. 3 Design, Data and Estimation 3.1 Research Design We evaluate the ELA program using a randomized control trial. BRAC has established branch o¢ces throughout Uganda, ten of which were chosen for the evaluation. Five branches are located in the urban or semi-urban regions of Kampala and Mukono; the others are located in the mostly rural region around Iganga and Jinja. In each branch, …fteen communities with the potential to host an ELA club were identi…ed. From this list, ten communities in each branch o¢ce were randomly assigned to receive the treatment, i.e. to set up a club and deliver the ELA program, with the remaining …ve communities assigned as controls. In each treated community, a single club was opened. The research design thus delivers 100 treatment and 50 control communities, strati…ed by branch o¢ce.7 6 The vocational skills provided overlap those studied in the literature on stand-alone business skills training [Field et al. 2010, Karlan and Valdivia 2011, Drexler et al. 2014]. However, it is useful to stress the key di¤erences between this intervention and the kind of business training/entrepreneurship skills program reviewed by McKenzie and Woodru¤ [2013], that have been found to have relatively weak impacts even among those self-selected into micro-entrepreneurship: (i) it targets adolescent girls, the majority of whom do not engage in self-employment activities at baseline; (ii) it has an intense training period lasting far longer than a few weeks; (iii) the training covers general business skills as well as technical knowledge and sector speci…c content; (iv) it bundles vocational skills with life skills. 7 For expositional ease we refer to communities as the unit of randomization. For the rural branches these correspond to villages. For the branches located in urban or semi-urban regions of Kampala and Mukono, the 7 We present two- and four-year impacts of the program. The vocational and life skills training are provided in the …rst two years of the intervention. The two-year e¤ects capture the immediate post-training impact of the program and the four-year e¤ects the longer term impact. When the evaluation was originally designed, the intention was that after two-years half of the 100 treated communities would be randomly assigned to additionally o¤er micro…nance to participating older adolescents in order to capitalize on their newly acquired skills. During the …rst two years post- intervention, BRAC sta¤, mentors and adolescent club participants were unaware of the potential future o¤er of micro…nance. In practice what occurred was that two-years post-intervention, a very limited o¤er of micro…nance was actually made to age-eligible girls in treated communities: the terms of micro…nance on o¤er did not di¤er from other pre-existing sources available to girls in the communities, and unsurprisingly, we …nd an almost zero take-up of micro…nance. Hence when examining four-year impacts we continue to compare outcomes between the original set of treated communities (with and without micro…nance) to control communities. We later provide evidence con…rming the future o¤er of micro…nance does not drive any of the two-year …ndings. 3.2 Data, Attrition and Descriptives 3.2.1 Surveys An initial census of all adolescent girls in the 150 evaluation communities was conducted in early 2008. This revealed that around 130 eligible adolescent girls resided in the average community, and was used to draw a random sample of around 40 girls to survey in each. The baseline sur- vey was administered to adolescent girls from March to June 2008. ELA clubs were established between June and September 2008, midline surveys were …elded from March to June 2010, and endline surveys were …elded May to July 2012. Each survey covers topics related to: (i) the vo- cational skills component, such as …nancial literacy, analytical ability, labor market and income generating activities; (ii) the life skills component, such as engagement in sex, childbearing and marriage/cohabitation, HIV related knowledge; (iii) other margins such as educational invest- ments, time use, expenditures, and further measures of economic and social empowerment. At baseline 5 966 adolescents were surveyed: 3 964 (2 002) from treatment (control) commu- nities. Despite the high degree of geographic mobility of adolescent girls in Uganda, 4 888 (3 522) adolescents were tracked to midline (endline) follow-up, corresponding to a two-year (four-year) tracking-rate of 82% (59%), that is comparable to rates from studies in similar contexts [Du‡o et al. 2015, Friedman et al. 2016]. randomized units often correspond to smaller urban areas or slums. 8 3.2.2 Attrition Table A1 shows correlates of attrition. We …rst consider attrition at midline: Column 1 shows that residing in a treated community does not predict attrition over this time frame. Column 2 shows this to be robust within branch, and Column 3 shows that the result holds conditioning on indi- vidual characteristics, measured at baseline. Moreover, none of these characteristics: age, current enrollment in school, being married/cohabiting or having children, themselves predict attrition. Column 4 examines how individual characteristics di¤erentially relate to attrition between treated and control communities. We …nd no evidence that adolescent girls in treated communities are di¤erentially likely to attrit along these dimensions. We next examine whether and how attrition varies between: (i) midline and endline (Columns 5-8); (ii) baseline and endline, the vast majority of who are also observed at midline (Columns 9-12). Reiterating the earlier results, we see that treatment does not predict attrition, nor do individual characteristics of girls at baseline, and nor is there much evidence of di¤erential attrition by these characteristics in treated communities relative to control communities. Undoubtedly the relatively high attrition rate at endline reduces the precision of the treatment e¤ect estimates. The evidence in Table A1 does not shed light on whether attrition is likely to upwards or downwards bias our estimates. To address issues of selective attrition, we therefore present Lee bounds estimates of all the midline and endline impacts [Lee 2009], where the bounds assume the tracked sample is either entirely negatively or entirely positively selected. 3.2.3 Baseline Characteristics Table 1 shows characteristics of adolescent girls at baseline, by treatment status. It does so for those girls tracked to endline and used to estimate endline ITT e¤ects. The …rst panel shows adolescent girls in our sample are on average aged 16, and just under 70% are enrolled in school full-time. We next present an overall index of ‘gender empowerment’, scaled from 0 to 100. This is based on multiple questions asked to girls relating to gender roles in labor markets, education and household chores. A higher index value corresponds to girls believing that tasks should be gender neutral.8 In control communities, the index average is just 32 (out of 100), suggesting norms biased against women, as held by adolescent girls themselves, are highly prevalent. Our analysis examines whether the multi-faceted ELA intervention changes norms and behaviors from this baseline, through its relaxation of human capital constraints. 8 The empowerment index is a variable that cumulates the number of times a respondent answers “Both/Same” to the following questions: “Who should earn money for the family?”, “Who should have a higher level of education in the family?”, “Who should be responsible for washing, cleaning and cooking?”, “If there is no water pump or tap, who should fetch water?”, “Who should be responsible for feeding and bathing children?”, “Who should help the children in their studies at home?” and “Who should be responsible for looking after the ill persons?” The other possible answers given to the respondent were “Male” and “Female”. The index is then re-scaled such that 100 indicates that the respondent answered that both sexes should be responsible for the mentioned activities. 9 The second panel focuses on economic empowerment, …rst presenting evidence on girls’ own assessment of their entrepreneurial ability: this is based on an index scaled to run from 0 to 100, constructed from 10 underlying questions.9 The average score is around 70, suggesting most girls are con…dent about having the necessary business-related skills pre-intervention. Despite this con…dence, only 6% of girls report being self-employed in control communities (the type of income generating activity the program fosters by relaxing vocational skills constraints), and rates of wage employment are even lower (36%) at baseline. On anxieties related to the transition into the labor market, around 60% of girls worry they will not …nd a job in adulthood. These statistics illustrate that economic empowerment is extremely low among our sampled girls.10 The third panel shows that despite their young age, 11% of girls already have at least one child and around 12% of them are already married or in a cohabiting relationship. The data also illustrates how high the incidence of girls having sex against their will is in the communities we study. In control communities at baseline 17% report having had sex unwillingly in the past year. This signals a striking lack of control that adolescent girls have over their bodies, a fact associated with low economic empowerment, early childbearing and marriage in our sample. On life skills at baseline, we see that one in four of them incorrectly answer a very basic question related to pregnancy knowledge, that asks whether “A woman cannot become pregnant at …rst intercourse or with occasional sexual relations”. Girls score around 38 on a 0-6 scale of HIV knowledge on average, yet there is considerable variation in this metric: at the tails of the knowledge distribution, 52% of girls correctly answer all the questions and 20% provide no correct answers. Only 45% of adolescent girls report always using a condom if they are sexually active and only 20% report using some other form of contraceptive. These self-reports help explain why teenage pregnancies are common in these communities.11 The …nal row reveals that adolescent girls believe that women should get married at around 24 years of age: clearly observed behavior departs signi…cantly from these expressed ideals, suggesting the presence of binding constraints. Table A2 presents a complete set of balance checks for both this endline sample, and the baseline 9 The entrepreneurial index consists of cumulative ranks (scaled from one to ten with ten being the highest) of the following activities: “Run your own business”, “Identify business opportunities to start up new business”, “Obtain credit to start up new business or expand existing business”, “Save in order to invest in future business op- portunities”, “Make sure that your employees get the work done properly”, “Manage …nancial accounts”, “Bargain to obtain cheap prices when you are buying anything for business (inputs)”, “Bargain to obtain high prices when you are selling anything for business (outputs)”, “Protect your business assets from harm by others”, “Collecting the money someone owes you”. 10 The rates of self-employment reported in our baseline match closely with those from the nationally repre- sentative Uganda National Household Survey 2005/2006. There we …nd that among those in the labor force, self-employment rates for 12-20 years olds are 7%. 11 The HIV knowledge index is based on the number of statements correctly identi…ed as true or false. The statements are: (i) “A person who has HIV is di¤erent from a person who is ill with AIDS”; (ii) “During vaginal sex, it is easier for a woman to receive the HIV virus than for a man”; (iii) “Pulling out the penis before a man climaxes keeps a women from getting HIV during sex”; (iv) “A woman cannot get HIV if she has sex during her period”; (v) “Taking a test for HIV one week after having sex will tell a person if she or he has HIV”; (vi) “A Pregnant woman with HIV can give the virus to her unborn baby”. 10 sample of 5 966 interviewed girls irrespective of whether they attrit or not. Table A2 shows that on most dimensions in the estimation sample, treatment and control groups are balanced. In this sample, the null of equal means is rejected for only one out of the twenty-one outcomes considered. In all cases the normalized di¤erences are small relative to the sample variation, and well below the rule of thumb value of 25 [Imbens and Wooldridge 2009]. Moreover, there are no large di¤erences between the characteristics of girls in the estimation and baseline samples. This con…rms what was earlier suggested by the attrition analysis, that there is not strong evidence of attrition being predicted by observables. 3.2.4 Club Participation Table 2 documents participation in the ELA clubs at midline and endline. The …rst row shows that until midline, in treated communities the ELA club participation rate is 21%: recall this is the period over which the vocational skills and life skills training are all provided. There is no drop o¤ in continued participation to endline, suggesting there is some value to girls being able to enjoy the safe space the clubs provide. Appendix Table A3 shows characteristics of participants and non-participants in treatment communities (as measured at midline, once all training had been delivered and using the sample of girls tracked to endline). On nearly all dimensions, ELA participants do not signi…cantly di¤er from non-participants. Hence, participants do not appear to be strongly negatively or positively selected on the various measures of economic empowerment and control over the body.12 The practicalities of program implementation lead to possible non-compliance with the research design: an adolescent girl resident in a control community wishing to attend a club in a treated community is always able to do so.13 As Table 2 shows, 47% of girls in control communities (77 girls) have ever participated in ELA club activities by midline. More then 75% of the girls that initially did attend from control communities had dropped out by six months prior to midline, and by endline, only 8% of girls in control communities report ever having attended an ELA club. The remaining rows in Table 2 report statistics conditional on club participation in treated communities. We focus on treatment communities as the number of regular participants from control communities is negligible. We see that the majority of adolescents who have ever par- ticipated in ELA club activities continue to be engaged through to midline. Nearly half of all participants have attended club meetings one or twice a week over the …rst two years of the club’s operation. Hence, the intervention amounts to a considerable time investment for participants, and it is plausible that such an intense intervention permanently shifts the level of human capital accumulated, which, in turn, drives the economic and social empowerment of treated girls. 12 There is a nominal fee due for club attendance but in practice this is often waived (and this is common knowledge). Hence binding credit constraints are unlikely to drive non-participation. 13 In some urban areas, the distance to the nearest club can be similar in treatment and control communities. In rural locations, most clubs are located in the center of treatment locations. 11 By midline, 53% (85%) of club participants have taken part in the vocational skills (life skills) training. The majority (51%) report having received both forms of training; we therefore infer that 33% take-up only life skills training, and 1% take-up only vocational skills training. Revealed preference therefore suggests the two training components are complementary for the majority.14 3.3 Estimation As club participation is voluntary, we focus on intent-to-treat (ITT) impacts throughout, estimated using the following OLS ANCOVA speci…cation for the impact on outcome  for adolescent  in community  , separately for midline ( = 1) and endline ( = 2),  =  +    +  0 +  0 +   (1)  equals one if individual  is in a community assigned to be treated and zero otherwise.  1 and  2 are the coe¢cients of interest from the midline and endline speci…cations, measuring the ITT impact of the ELA program at midline and endline respectively.  0 controls for the adolescent’s age at baseline ( = 0), and we also include a series of dummies for our randomization strata (i.e. branch) [Bruhn and McKenzie 2009].  0 is the outcome at baseline, and  is a disturbance term clustered by community  . To account for attrition, we bound the treatment estimates using the trimming procedure proposed by Lee [2009]. This can be performed for the midline and endline samples separately, hence the motivation for using the speci…cation above for the midline and endline samples, rather than pooling the data waves into a single speci…cation. In the Appendix we present a robustness check on our main results from running such a pooled speci…cation. The Lee bounds at midline (endline) are calculated based on girls tracked to midline (midline and endline).15 As Lee [2009] discusses, using covariates to trim the samples yields tighter bounds. In our setting, sample sizes dictate that we cannot use any covariates to perform the trimming. Hence when showing the Lee bounds estimates, was also present comparable ITT estimates that do not condition on covariates: these ITT estimates are always guaranteed to lie within the estimated Lee bounds (unlike the ITT estimates from (1) that condition on other baseline covariates).16 14 This variation in skills training is not driven by supply side constraints. In nearly all treated communities we observe: (i) some eligible girls taking-up a component and other girls not doing so; (ii) the vast majority of eligible girls report life and livelihood skills training as being available even if they don’t themselves take-up the course(s). In addition, we do not …nd school enrolment at baseline to be a signi…cant determinant of enrolment in the vocational training component: this is as expected given clubs operate out of school hours. 15 The procedure trims observations from above (below) in the group with lower attrition, to equalize the number of observations in treatment and control groups. It then re-estimates the program impact in the trimmed sample to deliver the lower (upper) bounds for the true treatment e¤ect (as well as standard errors for each bound). The bounding procedure relies on the assumptions that treatment is assigned randomly and that treatment a¤ects attrition in only one direction so there are no heterogeneous e¤ects of the treatment on attrition/selection: this is in line with the evidence provided in Tables A1 and A2. 16 To check whether the midline impacts pick up anticipation e¤ects of the future o¤er of micro…nance, we focus 12 4 Results 4.1 Economic Empowerment Table 3 summarizes the ITT treatment impacts of the program on outcomes related to economic empowerment. We show midline and endline impacts. To benchmark the magnitude of each, Col- umn 1 shows the level (and standard deviation) of the outcome at baseline in control communities. Column 2 then shows the number of adolescents in the sample used to estimate the midline and endline ITTs respectively. Columns 3 and 4 report the ITT estimates from (1) at midline and endline (so conditional on a full set of baseline covariates); Columns 5 and 6 report unconditional ITT estimates to maintain consistency with the Lee bounds treatment e¤ect estimates (and their associated standard error) shown in Columns 5 and 6. Row 1 shows the impact on girl’s self-reported entrepreneurial skills. The midline ITT estimate shows an increase of 8% over its baseline value, and at endline this is sustained at a 3% increase. Figures 2A and 2B presents spider graphs showing the midline and endline ITT impacts (and their associated 95% con…dence interval) for each component of the entrepreneurial skills score. Strikingly, the program increases entrepreneurial skills on all ten dimensions at midline: girls in treatment communities perceive themselves as having better entrepreneurial skill than girls in control communities in terms of being able to run a business, identifying business opportunities, obtaining and managing capital, managing employees, bargaining over input and output prices, protecting assets and collecting debts. Hence relative to girls in control communities at midline, this is a major shift upward in the treated girls’ self-perceived ability to run small businesses. We next analyze whether this translates into actual labor market activities of adolescent girls. We …nd that eligible girls are 68pp (49pp) more likely to be engaged in any income generating activity at midline (endline), a 66% (49%) increase over the baseline mean. Improvements in hu- man capital related to entrepreneurial ability do therefore get re‡ected in economically signi…cant improvements in labor force participation. Labor force participation is a major driver of women’s empowerment across the world. Rows 3 and 4 show this increase is entirely driven by adolescent girls engaging in self-employment activities. At midline, rates of self-employment are double those in control communities, and at endline these rates remain 50% higher. Even taking into account selective attrition, the Lee bounds estimates remain well away from zero: the lower bound endline estimate still corresponds to a 48% increase in self-employment over baseline levels. Given the multiple outcomes considered, we summarize the impacts of the program on economic empowerment by constructing an overall index of outcomes, where the subcomponents are those in Rows 1 to 4: we convert each subcomponent into a z-score, average across subcomponents and on the sample of 100 treated communities and then estimate whether the future random assignment to micro…nance predicts outcomes in the …rst two years of the program. Reassuringly, for nearly all outcomes, we …nd no signi…cant ITT anticipation impacts of future assignment to micro…nance. 13 then re-construct a z-score of the average. The …nal row in Table 3 shows the ITT e¤ect estimates on this economic empowerment index: at midline the ITT impact shows an e¤ect size of 238 (from a baseline level of 0 by construction), that is signi…cant at the 1% level; by endline this falls to .139, but the impact remains signi…cant at the 1% level. Columns 5 and 6 show both impacts to be robust to selective attrition: all four of the Lee bounds estimates remain signi…cantly di¤erent from zero. The Lee bounds estimates at endline suggest: (i) if attrited girls are negatively selected, then the upper bound ITT estimate on the index of economic empowerment is 185; (ii) if attrited girls are positively selected, then the lower bound ITT estimate on the index of economic empowerment is 096; (iii) both bounds are signi…cantly di¤erent from zero at conventional levels.17 Overall, these results suggests that an intense, bundled-skills intervention such as ELA has quantitatively signi…cant impacts on adolescent girls’ economic empowerment. The documented impacts are encouraging relative to the impact evaluations of other programs delivering standalone entrepreneurship training – see for example Field et al. [2010], Karlan and Valdivia [2010], Bruhn et al. [2012], Drexler et al. [2014], and Fairlie et al. [2015], or the review of such evidence in McKenzie and Woodru¤ [2013].18 This is despite the fact that other programs are often speci…- cally targeted towards those who have self-selected to be small-scale entrepreneurs. Our evidence suggests that bundling the provision of hard and soft skills that simultaneously tackle economic and social constraints adolescent girls face, can lead to signi…cant improvements in business skills and engagement in self-employment even among girls who ex ante, might not consider themselves 17 In Table A4 we examine related impacts for total earnings, albeit with the caveat that earnings are di¢cult to measure precisely in low-income settings especially when generated through self-employment. The results in the top panel of Table A4 suggest that by endline, annual earnings of girls increase threefold. the point estimate is UGX85K, corresponding to US$50 in 2008 prices, which is economically signi…cant. We have also explored treatment e¤ects on earnings from self and wage-employment separately (results not shown). As expected, earnings from self- employment signi…cantly increase, while there is no impact on earnings from wage-employment. Estimating midline (endline) ITT impacts on annual earnings from self-employment from a Tobit speci…cation we …nd that: (i) on the extensive margin, adolescent girls are 45pp (36pp) more likely to have some earnings from self-employment, corresponding to a 102% (79%) increase over baseline levels; (ii) on the intensive margin, self-employment earnings increase by nearly …ve times their baseline level at midline (and by more than six times their baseline value at endline). On the intensive margin we …nd the proportionate impact on earnings from self-employment to be larger than on hours worked in self-employment, indicating the marginal product of labor for adolescent girls in self-employment rises as a consequence of the combined hard and soft skills provided by the program. 18 Field et al. [2010] evaluate the provision of basic …nancial literacy training to female entrepreneurs in India. Only a socially unrestricted sub-group bene…ted in terms of business income and borrowings. Drexler et al. [2014] …nd that teaching accounting principles to micro-borrowers in the Dominican Republic has no impact on the way they run their business or business outcomes. However, simple rule-of-thumb style training does a¤ect …nancial record keeping. Karlan and Valdivia [2010] investigate the impact of an intense training intervention of up to two years, that delivered training on business practices to clients of a Peruvian Micro…nance institution. Despite improving business knowledge, the intervention failed to impact business outcomes. Fairlie et al. [2015] …nd that providing entrepreneurs training has no long-run measurable impact on business operations. Two studies have however found more substantial evidence of the e¤ectiveness of such interventions: Bruhn et al. [2012] suggests granting small and medium enterprises in Mexico access to consulting services, that are much more costly than the forms of business intervention described above, does have large positive impacts on …rm pro…ts, but not on em- ployment. Calderon et al. [2013] report large impacts on pro…ts from self-employment among female entrepreneurs in rural Mexico from a business skills intervention. A key channel for the impact is changes in product mix o¤ered by entrepreneurs. 14 as being on the margin of being an entrepreneur. The second natural point of comparison is with the literature evaluating standalone vocational training interventions. Such hard skills interventions are often found to have limited impacts in developed [Blundell et al. 2004, Card et al. 2010] and developing countries [Card et al. 2011, Groh et al. 2012]. Among studies …nding impacts, Attanasio et al. [2012] show that for women, the likelihood of being employed increases by 61pp. This impact is slightly larger than those we …nd for the ELA intervention, although as we discuss later, the ELA program is signi…cantly cheaper, and designed to be scalable in the context of Sub-Saharan Africa. Finally, we address concerns of adverse e¤ects on contemporaneous schooling investments be- cause the program targets girls of school going age. Table A4 examines education related impacts. Row 2 con…rms the program does not signi…cantly increase drop out rates, either at midline or endline. Hence the increased rates of self-employment documented above do not come at the ex- pense of school enrolment. Indeed, as the …nal two rows show (taking as given the program has no direct impact on school enrolment): (i) among those in school, the ITT estimate implies the ELA program marginally increases their hours of study at midline; (ii) among those that have dropped out of school at baseline, the program motivates a signi…cantly higher proportion of dropped out girls to consider going back to school. In short, the evidence suggests the program increases the value attached to formal education in treated communities. 4.2 Control Over the Body Table 4 shows the program impacts on control over the body for adolescent girls, as measured through outcomes such as childbearing, marriage and sex. Rows 1 and 2 cover the critical issues of whether the program a¤ects early childbearing and marriage, two of the most signi…cant roadblocks to adolescent girls acquiring human capital and fully participating in labor markets. The program has a strong negative impact on early childbearing: the ITT impact at midline in Column 3 shows the probability of having a child is 27pp lower in treated communities than control communities: given that at baseline 113% of girls have at least one child, this is a 24% drop in fertility rates over a two year period. If we consider that fertility rates rise between baseline and endline from 105% to 123% in control communities as girls get older, the ITT estimate implies this natural rate of increase is eliminated in treatment communities where adolescent girls largely forego reproduction once the program is o¤ered. These trends continue to endline, at which point girls in treated communities are 38pp less likely to have a child than girls in control communities. Delaying the onset of marriage is an important mechanism through which adolescent girls can improve their long term earnings potential [Field and Ambrus 2008, Baird et al. 2011]. Along this margin the program also has noteworthy impacts: the midline ITT estimate shows girls in treated communities to be 69pp less likely to be married/cohabiting at follow up, corresponding to 53% of the baseline mean. Again in control communities marriage rates for adolescent girls rise naturally 15 from 12% to 18% from baseline to follow-up, and the evidence suggests this is almost entirely prevented from happening by the program in treatment communities. Again this divergence in trends continues to endline, at which point they are 8pp less likely to be married or cohabiting. In Row 3 we see the rate of adolescents who report having had sex unwillingly during the past year is 61pp lower in treated communities at midline and 53pp lower at endline. Starting from a baseline of 17% in control communities, this corresponds to a near 30% reduction in the incidence of such events by endline. This impact is likely a direct result of three program features: (i) girls being able to act on speci…c soft skills accumulated through the life skills sessions on negotiation, rape and legal rights, as well as improved knowledge of reproductive health; (ii) the additional vocational skills provided raise girls’ engagement in and earnings from self-employment, and such economic empowerment likely reinforces girls’ control over their bodies [Baird et al. 2011, 2014]; (iii) the fact that the clubs provide a safe location for girls, especially in the after-school period in the afternoon when their parents might not be back from work. The program also signi…cantly improves girl’s health related knowledge, both in terms of a basic question related to pregnancy (Row 4) and as measured by a HIV-related knowledge index (Row 5). In terms of sexual behaviors, in Row 6 we see that condom use increases by midline among sexually active girls: the percentage of girls who always use a condom when having intercourse is 13pp higher, although again this impact dies out by endline. On the other hand, both Lee bounds estimates at endline (that do not control for any covariates) remain signi…cantly di¤erent from zero.19 Row 7 shows that among the sexually active there is little evidence that other forms of contraceptive use increase. This is reassuring because although girls are encouraged to use various forms of contraception, there is limited availability of such alternatives in these communities. Hence the results do not seem to re‡ect girls merely repeating what they have been taught in life skills courses, or experimenter demand e¤ects.20 Aggregating all these margins of control over the body into a single index, we see the ITT e¤ect size is to increase the index by 535 at midline, and by 269 at endline, which are both larger point estimates relative to the earlier documented impact on economic empowerment index in Table 3. Moreover, all the Lee bounds estimates on the index are signi…cantly di¤erent from zero at midline and endline. 19 As argued in Dupas [2011], childbearing is not a perfect proxy for the incidence of risky sex because: (i) adoles- cent girls in long-term relationships are more likely to get pregnant than girls in several short-term relationships; (ii) teenage girls might be more likely to abort if the father is a teenage boy who cannot provide economic support; (iii) adolescent girls might be more likely to engage in anal sex with partners to avoid pregnancy, and this is especially risky for HIV transmission. The concern that such changes in behavior might be driving fertility drops is partly ameliorated by the increased self-reported condom usage. 20 A recent trend in the literature examining interventions to reduce risky behaviors has been towards the collection of bio-markers rather than relying on self-reports that are often argued to be more unreliable. Corno and de Paula [2014] test this claim by developing and calibrating a model of STIs: they identify conditions under which self-reports can be more reliable than bio-markers, where these conditions depend on the prevalence of STIs and properties of the epidemiological model of infection. 16 Comparing our …ndings to the literature, we note …rst that meta-analyses generally report weak impacts of standalone HIV-education programs, irrespective of whether they are delivered via classroom-based courses [Gallant and Maticka-Tyndale 2004, McCoy et al. 2010, Du‡o et al. 2015] or peer-provided courses [Cornish and Campbell 2009]. There are two recent studies that …nd impacts of standalone education programs that are worth comparing to. First, Arcand and Wouabe [2010] use a regression discontinuity design to estimate the impacts of a school-based HIV prevention course in Cameroon. Their estimated impacts on childbearing and condom usage are slightly above the ITT estimates we …nd. Second, Dupas [2011] uses an RCT design to evaluate the e¤ectiveness of the Kenyan national HIV curriculum relative to an intervention providing information on the relative risk of HIV infection by the partner’s age. She …nds that exposure to this curriculum causes a 28% reduction in teenage pregnancies over a one-year period, and the key mechanism relates to how risks are presented to adolescents. 4.3 Aspirations We complete our analysis by considering ITT e¤ects on girl’s perception of gender roles, and their aspirations related to marriage and childbearing: these serve as markers for the program potentially impacting deep rooted social norms about girls’ role in society and lifetime opportunities, which might be far harder to shift than the accumulation of human capital as focused on so far. The results are in Table 5: the …rst outcome is an aggregate gender empowerment index which re‡ects how girls perceive their role in various tasks related to the labor market and in the household. The other outcomes are girls’ aspirations over ages at marriage for themselves and their children, desired fertility and aspirations over age at …rst childbirth. The overall picture from these aspirations related outcomes are that although the program has impacts on most dimensions in the short term, these tend to nearly always die out by endline. This is best illustrated in the …nal row where all outcomes are incorporated into an aspirations index z-score: at midline this index signi…cantly increases by 259 but by endline, there is no statistical di¤erence between adolescent girls in treatment and control communities. While these results help reassure against the concern that the earlier results are driven merely by reporting bias or experimenter demand e¤ects, they also serve to highlight the great challenge in being able to permanently shift aspirations, even when girls’ economic and social empowerment have improved in treated communities. There are however two notable dimensions of aspirational changes that do persist, and these both relate closely to the earlier documented impacts in terms of control over the body. The …rst is shown in Row 2: girl’s views on ideal ages at marriage for women in society as a whole. Adolescent girls in treated communities report signi…cantly higher ages of 77 and 23 years at midline and endline. As not all ages of marriage are logically feasible, an appropriate way to benchmark these impacts is relative to the standard deviation of baseline responses (rather than their mean value). 17 The ITT impacts then correspond to a shift in expectations on age at marriage for women of around 25% (8%) of a standard deviation at midline (endline). If unmarried at follow-up, we also asked girls about their expected age at the time of their own marriage : the di¤erence between girls in treatment and control communities is almost one year (not shown).21 The second longer lasting dimension along which aspirations are shifted relate to childbearing: Row 5 shows there are signi…cant increases in what girls report being the most suitable age for women to have their …rst child at both midline and endline: the ITT estimates are 619 and 272 respectively, corresponding to 20% and 9% of the baseline standard deviation, respectively. Both longer lasting changes in aspirations related to age at marriage and at …rst child were earlier picked up in actual behaviors, where we documented signi…cant reductions in fertility and marriage among treated girls relative to controls. In order to more permanently shift some of the other dimensions, one avenue for future interventions to consider is to also target fathers and other men in the communities. 4.4 Robustness and Heterogeneous Impacts In Table A5 we present midline and endline ITT estimates based on the following speci…cation that pools both post-intervention survey waves: X  =  +   ( £  ) +  0 +  0 + 1 +   (2)  where  ( = 1 2) is a survey wave dummy, and the coe¢cients of interest are ( 1 , 2 ). Focusing on the overall indices of economic empowerment, social empowerment and aspirations, Table A5 shows the quantitative and qualitative pattern of results remains the same as discussed earlier. We have also probed whether subsamples of adolescent girls drive the core impacts documented earlier. Table A6 presents results on impact heterogeneity along the following dimensions: (i) rural versus urban households; (ii) rich versus poor households, as de…ned by whether the household’s asset values at baseline are above or below the median for all households; (iii) girls aged above 16 at baseline versus older girls at baseline. We see that for indices related to economic and social empowerment, the impacts are largely homogeneous across rural and urban areas, rich and poor households, and young and old girls.22 This implies, for example, that relaxing human capital skills constraints can lead to behavioral change with regards to sex, childbearing and marriage among adolescent girls of all ages from 12 to 20. This might not have been the case for older girls if such 21 Adolescent girls were also asked who they thought would be involved in deciding their marriage partners (not shown). We …nd that among treated girls there is a signi…cant reduction in the likelihood they report the choice will be made by them alone, and a corresponding increase of similar magnitude in the likelihood they report decisions over marriage partners will be made in conjunction with their parents. This might be taken as tentative evidence that higher quality marriage partners being sought, as well as changes in the timing of marriage. 22 The results for younger girls are especially encouraging given the conventional wisdom that girls aged 10-14, particularly those out of school, face the greatest economic challenges and health challenges arising from unsafe sexual behavior in this context [UNICEF 2003]. 18 behavior were habitual for example, or if younger girls particularly lacked bargaining power or negotiation skills in their relationships with men. Similarly being rural or urban, richer or poorer, younger or older does not seem to constrain adolescent girls from bene…tting economically from the program. For the aspirations index, along most dimensions except the urban-rural divide, the program shifts impacts at midline but these die out by endline. The lower part of Table A6 examines heterogeneous impacts on education related outcomes. We see these ITT impacts are also similar across the three dimensions considered at midline and endline. In particular, the …nding that the program does not encourage girls to drop out from schooling applies equally to rural and urban areas, rich and poor households, and young and old girls. This is again encouraging: if, for example, girls were especially myopic, the incentives to drop out of school in the presence of the program might be higher in rural areas where the returns to education are limited due to a lack of labor market opportunities. 5 Cost E¤ectiveness The ELA program has proved to be transportable across countries (with modi…cation), having started in Bangladesh, been tailored to other contexts in South Asia and East Africa, and is now being piloted in Liberia and Sierra Leone. In Uganda, the scalability and potential cost e¤ectiveness of the program has also been demonstrated through its expansion to over 1200 clubs.23 Given the gains to adolescent girls accrue through channels of economic and social empower- ment, many of the gains are unpriced and other will be realized over the life cycle, as vocational and life skills are accumulated, entry into self-employment is accelerated, and marriage and child bearing are delayed. It is precisely events such as getting married or having children during adolescence which interrupt human capital accumulation and thus permanently and signi…cantly adversely a¤ect the lifetime earnings potential of women across the developing world. There is also a literature which suggests that having sex against one’s will seriously lowers lifetime incomes [MacMillan 2011]. Monetizing all these gains in a sensible way is beyond the scope of this paper, but they are likely to be substantial. Given this, we do not attempt to calculate the internal rate of return of the program. Rather, we conduct the more modest task of describing the program cost structure and using the endline results to gauge how large the bene…ts would have to be for 23 Adoho et al. [2014] evaluated the ELA program in Liberia: their independent replication of the short run (6-month) impacts …nd that adolescent girls in treated communities increased employment by 47% and earnings by 80% relative to girls in control communities. Their impact evaluation documents positive e¤ects on a variety of empowerment measures, including access to money, self-con…dence, and anxiety about circumstances and the future. The evaluation found no net impact on fertility or sexual behavior, suggesting those channels might take longer to work through. Another replication pilot study was also attempted in Tanzania [Buehren et al. 2017] but this failed due to implementation issues. For example, in Tanzania it turned out to be far harder to secure a safe space in communities. In many cases, an arrangement with local schools or church had to be found to share space, and this limited BRAC’s ability to decide on the timing of club activities. In addition, the donated club houses were often insecure. The provision of materials for the life skills training and quality of vocational training were other implementation challenges. The impact evaluation in Sierra Leone is ongoing. 19 the program to be cost e¤ective at endline. Table 6 categorizes the program’s …xed and variable costs, where variable costs depend on the number of participating girls. Depending on whether the costs are incurred once only or recur each month, we list the amounts in Column 1 or 2 respectively. Columns 3 and 4 then split each cost into its …rst year, and subsequent year components respectively. All costs are in 2008 US$. Rows 1 to 3 show the costs associated with the initial program investment of setting up a program o¢ce, training of program sta¤ and program manual development. The second set of …xed costs in Rows 4 to 14 comprise all cost items that are necessary to provide the infrastructure for the ELA clubs to function (irrespective of the number of actual club participants). Finally, Rows 15 to 19 detail the variable costs of the program. Summing across all costs in the 100 treated communities, Row 20 shows that in year one, the program costs $365 690. This falls to $232 240 in year two onwards as some set-up costs are not recurring. This somewhat overestimates the total program costs because some of these resources would have been put to another overlapping use in the absence of the program. However, as it is impossible to accurately measure what fraction of these costs would still have be reallocated to other uses, we include them all as program costs and so bias the results against yielding a positive net gain. Our pre-baseline census listing of all households revealed that around 130 eligible adolescent girls resided in the average community. Given the bene…ts we document relate to ITT estimates of residing in a community that is o¤ered the ELA program (and we have no reliable way to estimate spillover e¤ects), we use this number of eligible girls to calculate the per girl cost of the program. Hence in the fourth panel of Table 6, Rows 21 and 22 show the average …xed and variable costs per eligible girl. The overall cost per eligible is shown in Row 23. Given our ITT estimates are measured four-years after the baseline, we focus on the fourth year per-girl cost of $179. To put the cost estimate in context, we note that $179 corresponds to less than 1% of household annual incomes at baseline. If the per girl bene…ts to an adolescent girl residing in a community that is o¤ered the ELA program are larger than this, it would suggest the program is sustainable from the social planner’s perspective.24 As mentioned above, monetizing the bene…ts of the program is not straightforward because the main gains will be over the life cycle or unpriced and so hard to value. The impacts of the program can be crudely monetized using the ITT estimate on annual earnings at endline. The …nal row of Table 6 shows this endline increase of $50 (taken from Table A4, and is signi…cantly di¤erent from zero). This more than o¤sets the per girl program cost. Even if the bene…ts of the program outweigh its costs, the question of whether the same resources could be spent more e¤ectively remains open. As discussed earlier, the bundled ELA intervention appears to improve outcomes at least as well as single-pronged interventions that have focused on classroom-based education courses designed to reduce risky behaviors, or exclusively 24 We do not factor in the opportunity cost of time of attending the ELA clubs. We do however know that attendance does not come at the cost of reduced participation in formal schooling, as shown in Tables A4 and A5. 20 on vocational training designed to improve labor market outcomes among youth. However, one class of vocational training programs that has met with some success are the Jovenes programs implemented throughout Latin America. For example, Attanasio et al. [2012] …nd that for the Jovenes program in Colombia, among women, the likelihood to be employed increases by 61pp, which is a larger impact than we …nd for the bundled ELA intervention. However the costs per trainee of the Jovenes programs vary from $600 to $2000 per participant served [World Bank 2009]. These costs are still an order of magnitude larger than the $179 per eligible girl of the ELA program, or given a 21% take-up rate, a cost of $85 per participating adolescent girl. Another approach to understand whether the ELA program is socially bene…cial is to consider the impacts of providing unconditional cash transfers in a similar setting. This is precisely what is considered in Blattman et al. [2014], who present evidence from the Youth Opportunities program (YOP) using a randomized control trial in which youth were given unconditional and unsupervised cash transfers.25 They …nd that almost 80% of youth chose to spend these transfers on acquiring vocational skills and tools, and that the resultant increase in earnings imply an annual return on capital of 35% on average. There are of course many di¤erences between the treated individuals in the ELA and YOP programs: the YOP targets both genders and those aged 16 to 35; individuals form groups to apply for the unconditional transfers; the per person transfer $374. Although the ELA program can be thought of as a constrained version of such unconditional cash transfers, even if the rates of return through labor market outcomes alone are half as much, this still compares favorably with regards to other formal sector …nancial investment opportunities available in Uganda in mid-2008 when the ELA program was initiated.26 In summary, our results highlight the potential of a multi-faceted program that provides bun- dled hard and soft skills, as a viable and cost-e¤ective alternative to direct (un)conditional cash transfers, in promoting the economic and social empowerment of adolescent girls over a four year horizon. 6 Conclusions Developing countries face enormous challenges stemming from rapid population growth and a rising proportion of young people entering the labor market. For women in developing countries, 25 Similarly, Baird et al. [2012] report that the provision of unconditional cash transfers via lotteries, to girls aged 13-22 and enrolled in school at baseline in Malawi, signi…cantly reduced the prevalence of HIV and herpes simplex virus 2 [HSV-2] after 18 months. These e¤ects were also supported by self-reported sexual behaviors. To gauge the cost per treated girl, we note that monthly cash transfers valued at between $4 and $10 were provided to girls along with monthly transfers of between $1 and $5 to their guardians. 26 For example, the International Financial Statistics of the IMF state that the deposit rate in the formal sec- tor in Uganda (i.e. the rate paid by commercial banks for savings deposits) was 10.7% in 2008, 9.75% in 2009 and 7.69% in 2010. An alternative investment would have been to buy a two-year Uganda Treasury bond auc- tioned at the end of May 2008. It sold at a discount and yielded 14.45% according to the Bank of Uganda (http://www.bou.or.ug/bou/collateral/tbond_forms/2008/May/tbond_28May2008.html). 21 these challenges are coupled with a lack of empowerment: they lag behind their contemporaries in richer nations on many relevant dimensions of female empowerment but most strikingly so as regards economic empowerment and control over the body. Yet e¤ectively facing each challenge requires us to think jointly about economic and reproductive issues [Du‡o 2012]. A lack of future labor market opportunities can reduce the incentives for young girls to invest in their human capital leading to early marriage and childbearing, and potentially increasing their dependency on older men. At the same time, teen pregnancy and early marriage are likely to have a decisive impact on the ability of young girls to accumulate human capital and limit their future labor force participation. In this paper we evaluate an attempt to jump-start female economic and social empowerment in the world’s second youngest country: Uganda. We examine the impacts of a multifaceted program that provides adolescent girls an opportunity to simultaneously relax constraints related to two types of human capital: hard vocational skills to enable them to start small-scale income generating activities, and soft life skills to enable them to make informed choices about sex, reproduction and marriage. The ideas which underpin the program were developed in Bangladesh where the program has achieved signi…cant scale. Our evidence suggests these ideas can be e¤ectively transported (with modi…cation) from South Asia to a setting in Sub-Saharan Africa. Engaging in economic activities and delaying childbearing and marriage is likely to have a major impact on the life trajectories of adolescent girls. For example, such delays have been shown, in other contexts, to improve marriage quality, increase decision-making within households and reduce exposure to domestic violence [Goldin and Katz 2002, Jensen and Thornton 2003, Field and Ambrus 2008]. Alongside economic empowerment they are fundamental to improving women’s lives. Africa has been a laggard relative to other developing regions in terms of how quickly it is converging to the low fertility, late marriage and high career participation norms that characterize women’s lives in developed nations. There is thus a case to be made for cost-e¤ective programs like this to help women in Africa accelerate convergence towards these norms. What our results suggest is that such progress is possible. The impacts found over a four-year period suggest that the poor life circumstances that adolescent girls …nd themselves in at baseline will not necessarily be maintained by binding social norms. The external validity of our results are currently being researched as ELA-style programs have been piloted in a range of Sub-Saharan countries. The program o¤ers some promise to policy makers, this being a low cost and scalable intervention that enables adolescent girls to improve their life outcomes. As this research agenda expands, an obvious direction for future work is to unbundle the intervention and separate out the relative importance of vocational skills, life skills and the provision of a safe space. A second important direction for future work to take is to study in more detail the impacts such programs have on interactions between men and adolescent girls. Indeed, in related work combining this intervention with a lab experiment, we …nd the ELA program led to signi…cant 22 increases in adolescent girl’s competitiveness [Buehren et al. 2016]. 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[61] wooldrige.j.m (2002) Econometric Analysis of Cross Section and Panel Data, MIT Press: Cambridge MA. 28 Table 1: Characteristics of Adolescent Girls, By Treatment Status Sample: Adolescent Girls Tracked to Endline (N = 3474) Means, standard errors in parentheses, standard deviations in brackets (4) Normalized (1) Treatment (2) Control (3) Difference Difference 16.3 16.3 -.014 -.004 Age [2.68] [2.76] (.162) .689 .685 .004 .006 Currently enrolled in school [yes=1] [.463] [.465] (.031) 28.6 31.7 -3.13 -.091 Gender empowerment [0-100 score] [24.2] [24.5] (2.04) 69.6 71.8 -2.17 -.063 Entrepreneurial ability [0-100 score] [24.3] [24.7] (1.97) .070 .059 .011 .032 Self-employment [yes=1] [.255] [.235] (.010) .057 .036 .021** .070 Wage employment [yes=1] [.231] [.186] (.010) Never worry to get a good job in .397 .389 .007 .010 adulthood [yes=1] [.489] [.488] (.031) .114 .113 .0006 .001 Has child(ren) [yes=1] [.318] [.317] (.022) .101 .129 -.027 -.061 Married or cohabiting [yes=1] [.302] [.335] (.019) Had sex unwillingly in the past year .198 .174 .023 .042 [yes=1] [.399] [.380] (.029) .729 .737 -.008 -.013 Pregnancy knowledge [0-1 score] [.445] [.441] (.030) 3.83 3.78 .045 .026 HIV knowledge [0-6 score] [1.23] [1.23] (.081) If sexually active, always uses condom .423 .446 -.023 -.033 [yes=1] [.495] [.498] (.051) If sexually active, uses other .187 .203 -.016 -.029 contraceptives [yes=1] [.390] [.403] (.045) 23.9 23.9 .035 .008 Suitable age at marriage for a woman [3.07] [3.08] (.217) Notes: *** denotes significance at 1%, ** at 5%, and * at 10%. The sample is based on those adolescent girls who are observed at baseline, midline and endline and where information on their age and the outcome at endline is available (N = 3474). The standard errors on the differences are estimated from running the corresponding least squares regression and allowing for the errors to be clustered by community. The normalized difference is computed following Imbens and Wooldridge [2009]. The gender empowerment index is a variable that cumulates the number of times a respondent answers "Both/Same" to the following questions: "Who should earn money for the family?", "Who should have a higher level of education in the family?", "Who should be responsible for washing, cleaning and cooking?", "If there is no water pump or tap, who should fetch water?", "Who should be responsible for feeding and bathing children?", "Who should help the children in their studies at home?" and "Who should be responsible for looking after the ill persons?" The other possible answers given to the respondent were "Male" and "Female". The index is then rescaled such that 100 indicates that the respondent answered that the female should (at least partly) be responsible for all the activities. The entrepreneurial ability index is the cumulative and rescaled score aggregating the self-assessed ranks to the following activities (where 10 was the highest rank and 1 the lowest): "Run your own business", "Identify business opportunities to start up new business", "Obtain credit to start up new business or expand existing business", "Save in order to invest in future business opportunities", "Make sure that your employees get the work done properly", "Manage financial accounts", "Bargain to obtain cheap prices when you are buying anything for business (inputs)", "Bargain to obtain high prices when you are selling anything for business (outputs)", "Protect your business assets from harm by others", "Collecting the money someone owes you". The pregnancy knowledge index equals 1 if the respondent correctly identifies the statement "A woman cannot become pregnant at first intercourse or with occasional sexual relations" as true or false. The HIV knowledge index is based on the number of statements correctly identified as true or false. The relevant statements are, "A person who has HIV is different from a person who is ill with AIDS", "During vaginal sex, it is easier for a woman to receive the HIV virus than for a man", "Pulling out the penis before a man climaxes keeps a women from getting HIV during sex", "A women cannot get HIV if she has sex during her period", "Taking a test for HIV one week after having sex will tell a person if she or he has HIV." and "A Pregnant woman with HIV can give the virus to her unborn baby". Variables indicating suitable ages were trimmed at 15 years or younger. Table 2: Participation in ELA Clubs Means, standard errors in parentheses, standard deviations in brackets Midline Sample (N = 4831) Endline Sample (N = 3474) Normalized Normalized Treatment Control Difference Treatment Control Difference Difference Difference (1) (2) (3) (4) (5) (6) (7) (8) Have heard about club [yes=1] .590 .400 .189*** .272 .795 .613 .182*** .287 [.492] [.490] (.036) [.404] [.487] (.042) Have ever participated in club activities, .207 .047 .159*** .349 .246 .083 .163*** .318 conditional on having heard about club [yes=1] [.405] [.212] (.016) [.431] [.276] (.024) Continued participation, conditional on ever .629 .356 having participated [yes=1] [.483] [.479] Attend(ed) club meetings at least 3 times a week, .273 .421 conditional on ever having participated [yes=1] [.446] [.494] Attend(ed) club meetings 1 or 2 times a week, .494 .337 conditional on ever having participated [yes=1] [.500] [.473] Received vocational skills training, conditional on .526 .602 ever having participated [yes=1] [.500] [.490] Received life skills training, conditional on ever .846 .758 having participated [yes=1] [.361] [.428] Received life and vocational skills training, .507 .569 conditional on ever having participated [yes=1] [.500] [.496] Notes: *** denotes significance at 1%, ** at 5%, and * at 10%. Columns 1-4 relate to outcomes measured at midline: the sample covers those adolescent girls tracked from baseline to midline and where information on their age is available (N = 4831). Columns 5-8 relate to outcomes at endline: the sample covers those adolescent girls tracked from baseline to midline and endline and where information on their age is available (N = 3474). The standard errors on the differences are estimated from running the corresponding least squares regression allowing for the errors to be clustered by community. The normalized difference is computed following Imbens and Wooldridge (2009). The indicators for having received vocational skills and/or life skills are elicited from respondents' declarations to having participated in the corresponding training sessions at least very few times. Training area examples mentioned for the vocational training include training in hair-dressing, computer and poultry rearing. Training area examples mentioned for the life skill training include learning about pregnancy or HIV. Table 3: Economic Empowerment Coefficients, standard errors in parentheses, standard deviations in brackets Lower and upper Lee bounds and associated standard errors shown in smaller font in Columns 5 and 6 (1) Baseline (2) Sample Size at (3) ITT Midline, (4) ITT Endline, (5) ITT Midline, (6) ITT Endline, Outcome Levels, Control Midline/Endline With Controls With Controls No Controls No Controls 1. Entrepreneurial ability [0-100 score] 71.8 4,797 / 3,455 5.63*** 1.80* 5.76*** 1.29 [24.7] (.982) (.951) (2.17) (1.80) 4.82*** 7.31*** .279 2.13** (.931) (1.12) (.964) (.962) 2. Any IGA [yes=1] .102 4,831 / 3,474 .068*** .049** .070*** .050** [.302] (.016) (.020) (.019) (.024) .049*** .076*** .044*** .068*** (.016) (.012) (.017) (.023) 3. Self-employment [yes=1] .063 4,831 / 3,474 .059*** .032* .061*** .033* [.243] (.012) (.017) (.013) (.019) .039*** .065*** .030** .054** (.015) (.010) (.014) (.023) 4. Wage employment [yes=1] .040 4,831 / 3,474 .009 .018 .008 .017 [.196] (.007) (.012) (.009) (.014) -.017 .009 .015 .039* (.015) (.007) (.011) (.023) Economic Empowerment Index .023 4,831 / 3,474 .238*** .139*** .245*** .130* [1.03] (.042) (.052) (.058) (.067) .179*** .286*** .096** .185*** (.045) (.037) (.046) (.061) Notes: *** denotes significance at 1%, ** at 5%, and * at 10%. Standard errors are clustered by community. In Columns 3 and 4, the control variables include the adolescent girl's age and a series of indicator variables for branch areas (the randomization strata). The entrepreneurial ability index is the cumulative and rescaled score aggregating the self-assessed ranks to the following activities (where 10 was the highest rank and 1 the lowest): "Run your own business", "Identify business opportunities to start up new business", "Obtain credit to start up new business or expand existing business", "Save in order to invest in future business opportunities", "Make sure that your employees get the work done properly", "Manage financial accounts", "Bargain to obtain cheap prices when you are buying anything for business (inputs)", "Bargain to obtain high prices when you are selling anything for business (outputs)", "Protect your business assets from harm by others", "Collecting the money someone owes you". The Economic Empowerment Index is based on the entrepreneurship ability and the indicators for any IGA, self-employment and wage employment. The index is constructed by converting each component into a z-score, averaging these and taking the z-score of the average. z-scores for each component are computed using means and standard deviations in control communities at baseline and the z-score averaged by treatment group is imputed for missing values. The Lee bounds in Column 5 are estimated considering girls included in the midline as the selected sample. The lower and upper bounds in Column 6 are estimated considering girls included in the midline and endline as the selected sample. Table 4: Control Over the Body Coefficients, standard errors in parentheses, standard deviations in brackets Lower and upper Lee bounds and associated standard errors shown in smaller font in Columns 5 and 6 (1) Baseline (2) Sample Size at (3) ITT Midline, (4) ITT Endline, (5) ITT Midline, (6) ITT Endline, Outcome Levels, Control Midline/Endline With Controls With Controls No Controls No Controls .113 4,806 / 3,415 -.027*** -.038*** -.031* -.040* 1. Has child(ren) [yes=1] [.317] (.010) (.013) (.017) (.022) -.054*** -.029*** -.043*** -.020 (.016) (.010) (.013) (.023) 2. Married or cohabiting .129 4,713 / 3,263 -.069*** -.080*** -.071*** -.082** [yes=1] [.335] (.013) (.015) (.018) (.032) -.094*** -.069*** -.086*** -.070*** (.017) (.011) (.016) (.023) 3. Had sex unwillingly in the .174 1,847 / 1,655 -.061** -.053** -.071*** -.031 past year [yes=1] [.380] (.028) (.025) (.024) (.027) -.102*** -.067*** -.033* -.023 (.033) (.019) (.018) (.038) 4. Pregnancy knowledge [0-1 .737 4,750 / 3,386 .048** .025 .058** .029 score] [.441] (.021) (.016) (.026) (.034) .051*** .077*** .009 .035** (.015) (.018) (.023) (.016) 3.79 4,831 / 3,474 .471*** .109** .507*** .115 5. HIV knowledge [0-6 score] [1.26] (.047) (.045) (.079) (.078) .452*** .611*** .036 .160*** (.053) (.070) (.081) (.061) 6. If sexually active, always .446 1,781 / 1,630 .130*** .035 .194*** .089** uses condom [yes=1] [.498] (.038) (.039) (.030) (.039) .192*** .198*** .081** .099*** (.027) (.031) (.032) (.034) 7. If sexually active, uses .203 1,781 / 1,630 .028 -.019 .047* -.042 other contraceptives [yes=1] [.403] (.031) (.049) (.028) (.037) .046** .053* -.048* -.030 (.020) (.032) (.029) (.036) -.018 4,831 / 3,474 .535*** .269*** .540*** .265*** Control Over the Body Index [1.03] (.038) (.034) (.052) (.060) .521*** .611*** .193*** .305*** (.033) (.041) (.064) (.047) Notes: *** denotes significance at 1%, ** at 5%, and * at 10%. Standard errors are clustered by community. In Columns 3 and 4, the control variables include the adolescent girl's age and a series of indicator variables for branch areas. The pregnancy knowledge index equals 1 if the respondent correctly identifies the statement "A women cannot become pregnant at first intercourse or with occasional sexual relations" as true or false. The HIV knowledge index is based on the number of statements correctly identified as true or false. The relevant statements are "A person who has HIV is different from a person who is ill with AIDS", "During vaginal sex, it is easier for a woman to receive the HIV virus than for a man", "Pulling out the penis before a man climaxes keeps a women from getting HIV during sex", "A woman cannot get HIV if she has sex during her period", "Taking a test for HIV one week after having sex will tell a person if she or he has HIV." and "A Pregnant woman with HIV can give the virus to her unborn baby". The Control Over the Body Index is based on all the listed outcomes. The index is constructed by converting each component into a z-score, averaging these and taking the z-score of the average. z-scores for each component are computed using means and standard deviations in control communities at baseline and the z-score averaged by treatment group is imputed for missing values. The Lee bounds in Column 5 are estimated considering girls included in the midline as the selected sample. The lower and upper bounds in Column 6 are estimated considering girls included in the midline and endline as the selected sample. Table 5: Aspirations Coefficients, standard errors in parentheses, standard deviations in brackets Lower and upper Lee bounds and associated standard errors shown in smaller font in Columns 5 and 6 (1) Baseline (2) Sample Size at (3) ITT Midline, (4) ITT Endline, (5) ITT Midline, (6) ITT Endline, Outcome Levels, Control Midline/Endline With Controls With Controls No Controls No Controls 1. Gender empowerment 32.9 4,831 / 3,474 2.86*** -2.25 2.63** -2.56 index [0-100 score] [24.4] (.932) (1.59) (1.26) (2.08) 1.10 3.45*** -3.24*** -.969 (.996) (.789) (1.08) (1.53) 2. Suitable age at marriage 23.9 4,790 / 3,457 .770*** .231* .826*** .176 for a woman [3.08] (.116) (.132) (.145) (.252) .679*** 1.01*** -.006 .320** (.116) (.134) (.188) (.153) 3. Suitable age at marriage 28.0 4,789 / 3,453 .693*** .199 .747*** .139 for a man [3.74] (.125) (.149) (.198) (.366) .546*** 1.01*** -.109 .363* (.135) (.163) (.238) (.189) 4. Preferred number of 4.11 4,774 / 3,416 -.279*** .013 -.296*** .028 children [1.43] (.052) (.052) (.089) (.086) -.394*** -.247*** -.053 .137 (.051) (.048) (.072) (.084) 5. Suitable age for women 23.5 4,781 / 3,445 .619*** .272* .681*** .277 to have her first baby [3.20] (.110) (.158) (.168) (.213) .538*** .880*** .101 .431*** (.107) (.118) (.152) (.144) 6. Preferred age at which 24.8 4,757 / 3,380 .718*** .123 .749*** .059 daughter(s) get married [2.64] (.118) (.116) (.126) (.209) .605*** .928*** -.101 .184 (.109) (.119) (.156) (.136) 7. Preferred age at which 28.4 4,761 / 3,378 .120 .025 .185 -.014 son(s) get married [3.13] (.113) (.116) (.167) (.311) -.003 .431*** -.204 .185 (.126) (.156) (.184) (.195) -.015 4,831 / 3,474 .269*** .059 .290*** .045 Aspirations Index [.967] (.038) (.045) (.055) (.095) .233*** .361*** -.014 .106* (.038) (.043) (.060) (.060) Notes: *** denotes significance at 1%, ** at 5%, and * at 10%. Standard errors are clustered by community. In Columns 3 and 4, the control variables include the adolescent girl's age and a series of indicator variables for branch areas. The gender empowerment index is the sum of the answers to the following questions: "Who should earn money for the family?", "Who should have a higher level of education in the family?", "Who should be responsible for washing, cleaning and cooking?", "If there is no water pump or tap, who should fetch water?", "Who should be responsible for feeding and bathing children?", "Who should help the children in their studies at home?" and "Who should be responsible for looking after the ill persons?" where answers are coded as 1 if the respondent chooses "both" and zero otherwise. The other possible answers given to the respondent were "Male" and "Female". The index is then rescaled such that 100 indicates that the respondent answered that the female should (at least partly) be responsible for all the activities. All variables indicating ages are trimmed at 15 years or younger. The Aspirations Index is based on all the listed outcomes. The index is constructed by converting each component into a z-score, averaging these and taking the z-score of the average. z-scores are computed using means and standard deviations in control at baseline. The Lee bounds in Column 5 are estimated considering girls included in the midline as the selected sample. The lower and upper bounds in Column 6 are estimated considering girls included in the midline and endline as the selected sample. Table 6: Cost Effectiveness, in 2008 US$ (1) Non- (2) Recurring (3) Year (4) Year Two Recurring Monthly One Onwards A. Fixed (1) Office Space & Equipment 10 Branch Offices 4,000 4,000 Costs (2) Program Assistant Training 10 Assistants 2,250 2,250 Training & Operational Material (3) 2 Manuals 4,000 4,000 Development Program Management (4) 2 Coordinators 780 9,360 9,360 Compensation (5) Program Assistant Compensation 10 Assistants 1,690 20,280 20,280 (6) Adolescent Leader Compensation 100 Adolescent Leaders 1,200 14,400 14,400 (7) Adolescent Leader Training 100 Adolescent Leaders 22,500 22,500 Adolescent Leader Training (for (8) 20 Adolescent Leaders 4,500 4,500 Replacements) (9) Adolescent Leader Refreshers 100 Adolescent Leaders 400 4,800 4,800 (10) Club Rent 100 Clubs 1,000 12,000 12,000 (11) Club Materials 100 Clubs 42,000 42,000 (12) Club Materials (Replenishment) 100 Clubs 16,800 16,800 (13) Branch Office Overhead 10 Branch Offices 800 9,600 9,600 (14) Country Office Overhead 1 Country Office 4,000 48,000 48,000 B. Variable (15) Financial Literacy Courses 2,500 Members 12,500 12,500 12,500 Costs (16) Livelihood Training (Year 1) 2,000 Members 100,000 100,000 (17) Livelihood Training Inputs (Year 1) 2,000 Members 60,000 60,000 (18) Livelihood Training (Year 2) 1,000 Members 50,000 50,000 (19) Livelihood Training Inputs (Year 2) 1,000 Members 30,000 30,000 C. Total (20) ELA Program Costs for the 100 studied Communities 365,690 232,240 Costs D. Yearly Assuming 130 potential girl (21) Fixed Costs 14.9 10.7 Per Unit attendees per club Average (22) Variable Costs 13.3 7.12 Costs (23) Total Costs 28.1 17.9 E. Yearly (24) ITT Impact of ELA on Individual Annual Earnings 50 Benefits Notes: The exchange rate used to convert monetary values is based on January 2008 at which point $1 was worth approximately UGX1700. The yearly costs shown in Columns 3 and 4 are obtained by multiplying Column 2 times 12 (months) and adding Column 1 for all fixed and variable cost categories applicable to the respective year of operation. The yearly total cost of the ELA Program stated in row 20 is the summation of all individual cost items applicable to the respective year. The yearly benefits shown in row 24 are based on the endline ITT impact estimates on annual earnings. Table A1: Attrition OLS estimates Standard errors clustered by community Tracked Between Baseline and Tracked Between Baseline, Tracked Between Baseline and Outcome: Midline Midline and Endline Endline (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Treatment .023 .024 .026 -.019 -.011 -.011 -.010 -.080 -.013 -.013 -.014 -.110 (.030) (.027) (.027) (.100) (.043) (.024) (.024) (.110) (.045) (.022) (.022) (.104) Age -.0004 -.0009 -.0009 -.002 -.002 -.004 (.002) (.004) (.003) (.005) (.002) (.004) Currently enrolled in school .017 -.015 .003 -.027 -.007 -.038 [yes=1] (.015) (.030) (.020) (.031) (.019) (.030) .012 .024 .012 .021 .005 -.002 Married or cohabiting [yes=1] (.018) (.033) (.022) (.039) (.021) (.035) Has child(ren) [yes=1] .021 .004 .014 -.027 .016 -.034 (.020) (.033) (.026) (.046) (.025) (.044) Treatment x age .0007 .002 .003 (.005) (.006) (.005) Treatment x currently .046 .043 .045 enrolled in school [yes=1] (.039) (.042) (.040) Treatment x married or -.022 -.013 .013 cohabiting [yes=1] (.040) (.047) (.044) Treatment x has child(ren) .024 .059 .072 [yes=1] (.043) (.056) (.054) Mean of Dependent Variable: .819 .590 .647 Branch Dummies No Yes Yes Yes No Yes Yes Yes No Yes Yes Yes Observations 5,966 5,966 5,661 5,661 5,966 5,966 5,661 5,661 5,966 5,966 5,661 5,661 Notes: *** denotes significance at 1%, ** at 5%, and * at 10%. The dependent variable in Columns 1-4 is a dummy that is equal to one if the adolescent girl is tracked between the baseline survey and the midline survey, and zero otherwise. The dependent variable in Columns 5-8 is a dummy that is equal to one if the adolescent girl is tracked between the baseline, midline and endline surveys, and the dependent variable in Columns 9-12 is a dummy that is equal to one if the adolescent girl is tracked between the baseline and endline surveys (but is not necessarily observed at midline). The standard errors are clustered by community. There are ten branch dummies controlled for in Columns 2-4, 6-8 and 10-12. Table A2: Balance at Baseline Means, standard errors in parentheses, standard deviations in brackets Estimation Sample (N = 3474) Baseline Sample (N =5966) (4) Normalized (6) Normalized (1) Treatment (2) Control (3) Difference (5) Difference Difference Difference 16.3 16.3 -.014 -.004 -.063 -.015 Age [2.68] [2.76] (.162) (.150) .689 .685 .004 .006 -.010 -.016 Currently enrolled in school [yes=1] [.463] [.465] (.031) (.027) 69.6 71.8 -2.17 -.063 -2.48 -.071 Entrepreneurial ability [0-100 score] [24.3] [24.7] (1.97) (1.64) .070 .059 .011 .032 .011 .032 Self-employment [yes=1] [.255] [.235] (.010) (.010) .057 .036 .021** .070 .020** .067 Wage employment [yes=1] [.231] [.186] (.010) (.009) Never worry to get a good job in .397 .389 .007 .010 .024 .035 adulthood [yes=1] [.489] [.488] (.031) (.026) Expenditure on goods in the last 12,345 11,916 429 .016 1,269 .048 month [UGX] [18,842] [18,850] (1,113) (1,020) Notes: *** denotes significance at 1%, ** at 5%, and * at 10%. In Columns 1-4 the estimation sample covers those adolescent girls tracked from baseline to midline and endline and where information on their age is available (N = 3474). In Columns 5 and 6 the baseline sample covers all adolescent girls surveyed at baseline (N = 5966). The standard errors on the differences are estimated from running the corresponding least squares regression and allowing for the errors to be clustered by community. The normalized difference is computed following Imbens and Wooldridge [2009]. The Entrepreneurial ability index is the cumulative and rescaled score aggregating the self-assessed ranks to the following activities (where 10 was the highest rank and 1 the lowest): "Run your own business", "Identify business opportunities to start up new business", "Obtain credit to start up new business or expand existing business", "Save in order to invest in future business opportunities", "Make sure that your employees get the work done properly", "Manage financial accounts", "Bargain to obtain cheap prices when you are buying anything for business (inputs)", "Bargain to obtain high prices when you are selling anything for business (outputs)", "Protect your business assets from harm by others", "Collecting the money someone owes you". The top 1% outliers of the expenditure variable have been removed. All monetary variables are deflated and expressed in terms of the price level in January 2008 using the monthly consumer price index published by the Uganda Bureau of Statistics. Table A2 (continued): Balance at Baseline Means, standard errors in parentheses, standard deviations in brackets Estimation Sample (N = 3474) Baseline Sample (N = 5966) (4) Normalized (6) Normalized (1) Treatment (2) Control (3) Difference (5) Difference Difference Difference .114 .113 .0006 .001 .003 .008 Has child(ren) [yes=1] [.318] [.317] (.022) (.017) .101 .129 -.027 -.061 -.020 -.046 Married or cohabiting [yes=1] [.302] [.335] (.019) (.014) Had sex unwillingly in the past year .198 .174 .023 .042 .054** .102 [yes=1] [.399] [.380] (.029) (.022) .729 .737 -.008 -.013 -.004 -.006 Pregnancy knowledge [0-1 score] [.445] [.441] (.030) (.025) 3.83 3.78 .045 .026 .055 .032 HIV knowledge [0-6 score] [1.23] [1.23] (.081) (.076) If sexually active, always uses .423 .446 -.023 -.033 -.006 -.009 condom [yes=1] [.495] [.498] (.051) (.039) If sexually active, uses other .187 .203 -.016 -.029 -.016 -.032 contraceptives [yes=1] [.390] [.403] (.045) (.026) Gender empowerment index [0-100 28.6 31.7 -3.13 -.091 -2.81 -.081 score] [24.2] [24.5] (2.04) (2.02) 23.9 23.9 .035 .008 .115 .026 Suitable age for marriage for a woman [3.07] [3.08] (.217) (.201) 27.8 28.0 -.199 -.038 -.112 -.021 Suitable age for marriage for a man [3.71] [3.74] (.192) (.186) 4.19 4.11 .076 .036 .120 .057 Preferred number of children [1.53] [1.43] (.106) (.101) Suitable age for women to have the 23.7 23.5 .204 .045 .165 .036 first baby [3.21] [3.20] (.257) (.251) Preferred age at which daughter(s) get 25.0 24.8 .138 .035 .192 .048 married [2.83] [2.64] (.157) (.163) Preferred age at which son(s) get 28.5 28.4 .090 .020 .192 .042 married [3.18] [3.13] (.163) (.165) Notes: *** denotes significance at 1%, ** at 5%, and * at 10%. In Columns 1-4 the estimation sample covers those adolescent girls tracked from baseline to midline and endline and where information on their age is available (N = 3474). In Columns 5 and 6 the sample covers all adolescent girls surveyed at baseline (N = 5966). The standard errors on the differences are estimated from running the corresponding least squares regression using the baseline data only and allowing for the errors to be clustered by community. The normalized difference is computed following Imbens and Wooldridge [2009]. The pregnancy knowledge index equals 1 if the respondent correctly identifies the statement "A women cannot become pregnant at first intercourse or with occasional sexual relations" as true or false. The HIV knowledge index is based on the number of statements correctly identified as true or false. The relevant statements are "A person who has HIV is different from a person who is ill with AIDS", "During vaginal sex, it is easier for a woman to receive the HIV virus than for a man", "Pulling out the penis before a man climaxes keeps a women from getting HIV during sex", "A woman cannot get HIV if she has sex during her period", "Taking a test for HIV one week after having sex will tell a person if she or he has HIV." and "A Pregnant woman with HIV can give the virus to her unborn baby". The gender empowerment index is a variable that cumulates the number of times a respondent answers "Both/Same" to the following questions: "Who should earn money for the family?", "Who should have a higher level of education in the family?", "Who should be responsible for washing, cleaning and cooking?", "If there is no water pump or tap, who should fetch water?", "Who should be responsible for feeding and bathing children?", "Who should help the children in their studies at home?" and "Who should be responsible for looking after the ill persons?" The other possible answers given to the respondent were "Male" and "Female". The index is then rescaled such that 100 indicates that the respondent answered that the female should (at least partly) be responsible for all the activities. All variables indicating ages are trimmed at 15 years or younger. Table A3: Participants and Non-participants Sample: Adolescent Girls Tracked to Endline (N = 3474) Means, standard errors in parentheses, standard deviations in brackets (2) Non (4) Normalized (1) Participants (3) Difference Participants Difference 16.3 16.3 .010 .003 Age [2.82] [2.69] (.156) Currently enrolled in school .690 .687 .003 .004 [yes=1] [.463] [.464] (.029) Gender empowerment index [0- 28.1 29.9 -1.73 -.051 100 score] [23.4] [24.5] (1.42) Entrepreneurial ability [0-100 69.6 70.5 -.879 -.026 score] [23.8] [24.5] (1.45) .068 .066 .003 .008 Self-employment [yes=1] [.253] [.248] (.010) .048 .050 -.002 -.006 Wage-employment [yes=1] [.215] [.218] (.008) Satisfaction with 1.08 1.30 -.220 -.093 earnings/income [0-6 score] [1.61] [1.72] (.141) Never worry to get a good job in .405 .392 .013 .019 adulthood [yes=1] [.491] [.488] (.026) .105 .115 -.010 -.023 Has child(ren) [yes=1] [.307] [.319] (.017) .094 .113 -.019 -.044 Married or cohabiting [yes=1] [.293] [.317] (.014) Had sex unwillingly in the past .203 .187 .017 .029 year [yes=1] [.404] [.390] (.038) Pregnancy knowledge [0-1 .760 .727 .033 .054 score] [.428] [.446] (.023) 3.84 3.81 .033 .019 HIV knowledge [0-6 score] [1.29] [1.22] (.063) If sexually active, always uses .465 .425 .040 .057 condom [yes=1] [.501] [.495] (.050) Number of Observations 676 2606 Notes: *** denotes significance at 1%, ** at 5%, and * at 10%. The sample is based on those adolescent girls observed at baseline, midline and endline (N = 3474). The standard errors on the differences are estimated from running the corresponding least squares regression allowing for the errors to be clustered by community. The normalized difference is computed following Imbens and Wooldridge [2009]. The gender empowerment index is a variable that cumulates the number of times a respondent answers "Both/Same" to the following questions: "Who should earn money for the family?", "Who should have a higher level of education in the family?", "Who should be responsible for washing, cleaning and cooking?", "If there is no water pump or tap, who should fetch water?", "Who should be responsible for feeding and bathing children?", "Who should help the children in their studies at home?" and "Who should be responsible for looking after the ill persons?" The other possible answers given to the respondent were "Male" and "Female". The index is then rescaled such that 100 indicates that the respondent answered that the female should (at least partly) be responsible for all the activities. The entrepreneurial ability index is the cumulative and rescaled score aggregating the self-assessed ranks to the following activities (where 10 was the highest rank and 1 the lowest): "Run your own business", "Identify business opportunities to start up new business", "Obtain credit to start up new business or expand existing business", "Save in order to invest in future business opportunities", "Make sure that your employees get the work done properly", "Manage financial accounts", "Bargain to obtain cheap prices when you are buying anything for business (inputs)", "Bargain to obtain high prices when you are selling anything for business (outputs)", "Protect your business assets from harm by others", "Collecting the money someone owes you". The index for satisfaction with earnings/income is the reversed and rescaled respondent's self- assessment on a 7 point score (where originally "1" is completely happy and "7" is not at all happy). The pregnancy knowledge index equals 1 if the respondent correctly identifies the statement "A woman cannot become pregnant at first intercourse or with occasional sexual relations" as true or false. The HIV knowledge index is based on the number of statements correctly identified as true or false. The relevant statements are "A person who has HIV is different from a person who is ill with AIDS", "During vaginal sex, it is easier for a woman to receive the HIV virus than for a man", "Pulling out the penis before a man climaxes keeps a women from getting HIV during sex", "A women cannot get HIV if she has sex during her period", "Taking a test for HIV one week after having sex will tell a person if she or he has HIV." and "A Pregnant woman with HIV can give the virus to her unborn baby". Table A4: Welfare and Education Coefficients, standard errors in parentheses, standard deviations in brackets Lower and upper Lee bounds and associated standard errors shown in smaller font in Columns 5 and 6 (1) Baseline (2) Sample Size at (3) ITT Midline, (4) ITT Endline, (5) ITT Midline, (6) ITT Endline, Outcome Levels, Control Midline/Endline With Controls With Controls No Controls No Controls 1. Annual Earnings [UGX] 27,443 4,824 / 3,466 5,189 84,732** 9,090 83,429 [139,968] (17,498) (41,793) (19,284) (58,519) -46,438*** 11,605 77,138** 202,552*** (15,822) (13,813) (38,356) (69,032) .685 4,831 / 3,475 -.018 .001 -.013 .0010 2. Currently enrolled in school [yes=1] [.465] (.017) (.020) (.025) (.027) -.022 .004 -.013 .011 (.015) (.017) (.022) (.020) 3. If enrolled, hours spent on going to and attending 61.1 3,243 / 1,972 1.59* 1.69 2.36* 1.63 school, homework and study per week [20.3] (.892) (1.18) (1.24) (1.61) 1.31 4.16*** -.229 2.69** (.918) (1.17) (1.87) (1.29) 4. If dropped out, plan to start/go back to school .573 1,537 / 1,393 .076** .044 .102*** -.008 [yes=1] [.496] (.037) (.043) (.035) (.043) .071** .139*** -.023 .011 (.033) (.035) (.035) (.038) Notes: *** denotes significance at 1%, ** at 5%, and * at 10%. Standard errors are clustered by community. In Columns 3 and 4, the control variables include the adolescent girl's age and a series of indicator variables for branch areas. The top 1% outliers of the income variables have been removed. The Lee bounds in Column 5 are estimated considering girls included in the midline as the selected sample. The lower and upper bounds in Column 6 are estimated considering girls included in the midline and endline as the selected sample. In January 2008 $1 was worth approximately UGX1700. Table A5: Pooled ANCOVA Specification Coefficients, standard errors in parentheses (2) ITT, (3) ITT, (1) Sample Size With controls With controls Outcome at midline at endline 6,948 .196*** .140** Economic Empowerment Index (.052) (.053) 6,948 .437*** .269*** Control Over the Body Index (.043) (.043) 6,948 .254*** .049 Aspirations Index (.054) (.060) Notes: *** denotes significance at 1%, ** at 5%, and * at 10%. Standard errors are clustered by community. The control variables include the adolescent girl's age and a series of indicator variables for branch areas. The Economic Empowerment Index is based on the entrepreneurship ability and the indicators for any IGA, self-employment and wage employment outcomes in Table 3. The Control Over the Body Index is based on all the listed outcomes in Table 4. The Aspirations Index is based on all the listed outcomes in Table 5. Each index is constructed by converting each component into a z-score, averaging these and taking the z-score of the average. z-scores are computed using means and standard deviations in control at baseline. Table A6: Impact Heterogeneity Coefficients, standard errors in parentheses Above Median HH Below Median HH Rural Urban Younger than 16yrs Older than 16yrs Asset Value Asset Value (1) ITT, (2) ITT, (3) ITT, (4) ITT, (5) ITT, (6) ITT, (7) ITT, (8) ITT, (9) ITT, (10) ITT, (11) ITT, (12) ITT, Midline Endline Midline Endline Midline Endline Midline Endline Midline Endline Midline Endline .179*** .106 .301*** .180** .260*** .127** .214*** .151** .229*** .191*** .251*** .101 Economic Empowerment Index (.048) (.071) (.069) (.076) (.055) (.064) (.053) (.060) (.045) (.049) (.057) (.075) .526*** .318*** .534*** .211*** .516*** .331*** .539*** .214*** .553*** .139*** .509*** .355*** Control Over the Body Index (.053) (.048) (.054) (.049) (.045) (.050) (.046) (.041) (.042) (.043) (.051) (.048) .270*** .119* .257*** -.021 .216*** .049 .295*** .055 .243*** .015 .281*** .078 Aspiration Index (.061) (.061) (.047) (.066) (.048) (.052) (.051) (.057) (.061) (.063) (.045) (.053) Currently enrolled in school [yes=1] -.029 .014 -.010 -.013 -.025 .00009 -.011 .003 -.023 -.030 -.018 .026 (.022) (.026) (.027) (.030) (.021) (.028) (.023) (.025) (.017) (.025) (.024) (.025) If enrolled, hours spent on going to and attending 2.16 .052 .969 3.55* 1.85 2.70 1.25 .613 1.39 -.037 1.78 5.03** school, homework and study per week (1.44) (1.31) (1.02) (1.97) (1.30) (1.77) (.942) (1.35) (.957) (1.16) (1.57) (2.33) If dropped out, plan to start/go back to school .036 .068 .139** .009 .040 .027 .100** .055 .292* .327 .062 .033 [yes=1] (.050) (.060) (.053) (.048) (.068) (.073) (.041) (.046) (.165) (.221) (.038) (.044) Notes: *** denotes significance at 1%, ** at 5%, and * at 10%. Standard errors are clustered by community. The control variables throughout include the adolescent girl's age and a series of indicator variables for branch areas. The Economic Empowerment Index is based on the entrepreneurship ability and the indicators for any IGA, self-employment and wage employment outcomes in Table 3. The Control Over the Body Index is based on all the listed outcomes in Table 4. The Aspirations Index is based on all the listed outcomes in Table 5. Each index is constructed by converting each component into a z-score, averaging these and taking the z-score of the average. z-scores are computed using means and standard deviations in control at baseline. Figure 1B: Unemployment Rates (%), by Age and Figure 1A: Female Population by Age, 2010 Gender, Uganda 2005/6 Notes: The data source is the Uganda National Household Survey (UNHS). Unemployment is Notes: The data stems from the 2010 UN World Population Prospects data base. More developed defined as those who actively wanted a job but did not participate in any employment activities, regions comprise Europe, Northern America, Australia/New Zealand and Japan. inclusively self-employment and agricultural works). The UNHS is a nationally representative sample of 7246 households. Figure 1C: Age-Specific Fertility Rate, 1995-2010 Notes: The data stems from the 2010 UN World Population Prospects data base. The fertility rate is measured by the number of births per 1,000 women. More developed regions comprise Europe, Northern America, Australia/New Zealand and Japan. Figure 2: The ITT Impact of the ELA Program on Entrepreneurship Measures A. Midline B. Endline Notes: Adolescent girls were asked to rank their ability on how well they can do the following activities on a scale of 1 to 10, 1 means they cannot do this activity and 10 is they definitely can (clockwise, beginning with the spoke on top): "Run your own business", "Identify business opportunities to start up new business", "Obtain credit to start up new business or expand existing business", "Save in order to invest in future business opportunities", "Make sure that your employees get the work done properly", "Manage financial accounts", "Bargain to obtain cheap prices when you are buying anything for business (inputs)", "Bargain to obtain high prices when you are selling anything for business (outputs)", "Protect your business assets from harm by others", and "Collecting the money someone owes you".