Project Title PIERI FINAL REPORT DRAFT 2 Presented to. Partnership for Economic Policy (PEP)

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1 Project Title PIERI FINAL REPORT DRAFT 2 Presented to Partnership for Economic Policy (PEP) Impact of a Short Term Vocational Training on Youth Unemployment: Evidence from Mongolia By Altantsetseg Batchuluun & Bayarmaa Dalkhjav Soyolmaa Batbekh Amartuvshin Sanjmyatav Tsogt-Erdene Baldandorj MONGOLIA 23 April

2 1. Introduction More than half of the population living in low and lower middle income countries are younger than 26 and a large number of them are lacking to have a decent job. 1 According to the Global Employment Trends for Youth 2015, in 2014, the global youth unemployment rate was 13.0 percent and since 1995 youth unemployment rate has been almost three times (between 2.7 and 2.9) higher than the adult unemployment rate. Youth unemployment increases probability of long-term scarring in person s future labor market outcome in terms of lower wage, higher unemployment and suboptimal investment in human capital (Mroz and Savage 2005, Bell and Blanchflower 2011). In the result, promoting youth employment is a top priority policy in most countries (ILO 2015). Mongolia has a young population as 54.5 percent were younger than 30 years and youth share (ages 15 to 29) was 26.9 percent in The labor market is characterized with extremely high youth unemployment rate. In 2014, youth unemployment rate was 17.4 percent which is by 4.4 points larger than the global rate of 13 percent. Moreover, Mongolia has a large number of inactive youth, who are not in education, employment or training (NEET). According to Shatz et al. (2015), one fourth of youth (ages 15 to 29) were NEET and the share seems to be higher if parents are unemployed, less educated and poorer. Hence, youth employment promotion is among the highest priorities in Mongolia. This paper examines the Vocational Training Program (VTP), a government program in Mongolia to promote employment among unemployed, youth not in education and poor by providing vocational skills. The Metropolitan Employment Department (MED) invited young unemployed or inactive youth to apply for the program. Applicants were randomly assigned among training and control groups. The government paid tuition fees (from $100 to $150) for the trainees, enrolled in days training to obtain his/her preferred vocational skill from 80 alternatives. Training classes were randomized into information treatment and control groups, where information treatment was a letter with labor market outcomes of skilled workers. Applicants were young unemployed, who on average reached grade 10 years, were from households with income less than $3 a day, had less than 2 years of work experience and were younger than Authors calculation using median age data for the regions in the World Health Statistics

3 There is an extensive literature on impact of Active Labor Market Programs (ALMP) in OECD countries. However, evidences are mixed from no impact to moderately positive impact on employment and earnings. According to Heckman et al. (1999), careful evaluations of these programs, including training, in the United States and Europe show at best moderate impacts and there is substantial heterogeneity in the impacts. Recently, Card et al. (2010) did a metaanalysis of ALMP evaluations using a sample of 199 program estimates from mostly OECD countries. The study indicates that classroom or on-the-job trainings tend to have zero short term impact and positive medium term impact. Youth targeting programs are less likely to have positive impacts and there is no systematic difference across gender. Hirshleifer et al. (2015) conducted a randomized experimental evaluation of vocational training programs of the Turkish National Employment Agency. The study examined short term, medium to long (3 years) term impact of the program combining experimental data with administrative data on formal employment. Participants were paid tuition of the training and received small stipend equal of 10 USD per day. Average training length was approximately 3 months. Estimations indicate no impact on employment and income in the short-run and long-run. The programs had only a short-run impact on job formality. On the other hand, experimental studies in developing countries are limited and existing studies are mostly from Latin American Countries (LAC). Ibarrarán and Shady (2008) and Card, Ibarrarán, Regalia, Rosas-Shady and Soares (2011) studied impact of youth (16-29 years old ) training program in Dominican Republic using randomized control trial. The training consists of components in technical and soft skills and internship and average length of the training is approximately 4 months. They found little evidence of a positive effect on employment outcomes but some evidence on earnings and formality (men 17% increase). Attanasio, Kugler and Meghir (2011) studied impacts of subsidized training program in Colombia for poor young (18-25 years old) unemployed living in urban areas. The program provides 6 month long training worth of US$750 per person. The study provided evidences on positive impacts on earnings, employment and job formality for both men and women. The program had substantial impact on women and cost benefit analysis provide the internal rate of return 21.6 percent for women under a pessimistic scenarios. The study suspect a lack of information and credit constraints to be likely causes for people not taking advantage of existing training opportunities. Alzua et al. (2015) studied the short, medium and long term effect of job training program for low income youth (18-30 years old) employment in Argentina using randomization method. The training consists of components on technical and life skills, basic 3

4 skills and internship and lasts approximately 7.5 months. Administrative data on formal employment, employment spells, and earnings were used to estimate the effects of the program and they found sizeable short term impact on employment and earnings. However, employment and earnings impact dissipated in the medium and long run. Cho et al. (2013) examine the effects of vocational and entrepreneurial training for Malawian youth and provide evidences of positive impacts on skills development, investment in human capital and well-being. However, there was no impact on labor market outcomes in the shortrun. An apprenticeship program, piloted by the Government of Malawi, provides on-the-job training for vulnerable youth. They provide extensive analysis for drop-out reasons. Maitra and Mani (2013) is an only experimental study on impact of vocational training in Asia. They examined effects of subsidized vocational training program in stitching and tailoring, targeted at young and poor women in India. 6 months-long program was organized by two NGOs. Participants of the program were required to deposit Rs 50 per month for continuing in the program and full participation will be paid back Rs 350. Their study indicates that the program has positive short-run and long-run impacts on employment, earnings and working hours. VTP in Mongolia has some distinctive features from the programs mentioned above. It is a very short term program of days and the program in general do not exclusively target youth or unemployed. Compared to other studies, the attrition rates for the first and second follow-up surveys was very low around 10%. Moreover, we have a randomized information treatment for trainees. We sent a letter with general information about labor market outcomes of skilled people in order to improve attendance. According to Jensen (2010), the perceived returns, which may affect schooling decisions, were inaccurate and extremely low and information on the higher measured return to secondary school increased schooling by more years. We have a lower than 10% drop-out rate among trainees. As mentioned earlier, we had information treatment for trainees. We found that information treatment significantly improves attendance and increases drop-out. Galdo and Chong (2012) examined the link between the quality of public-sponsored training programs and subsequent labor-market outcomes based on Youth Training Program in Peru. Trainees attending highquality training courses had higher earnings and better job than trainees attending low quality courses or non-participants. Expenditure per trainee was the most important characteristic. Hence, we have collected administrative data on the training quality. During the baseline data collection, the team collected additional information from training institutions. In total, 432 4

5 participants were trained in 47 training institutions 2, whereas 46 institutions provided information. The information is about the number of instructors, number of classrooms, average class size, expenditure per student and monthly salary of instructors etc. These 46 institutions trained 410 participants (94.9 percent) of our intervention. Participants applied for 66 different vocations. The most popular 5 vocations were beauty specialist, hairdresser, cook, sales assistant and childcare assistant. Average expenditure per trainee was approximately USD100. We estimated the effects of VTP on employment, earnings and job quality. VTP has a positive short term impact on employment, however the impact vanishes in the medium term. It has a positive impact on earnings both in the short term and medium term. Medium term impact on monthly earnings is positive and significant. However, VTP did not improve job quality both in the short term and medium term. Skill match could be an important indicator for the job quality. The study shows a better skill match among treatment 1 group. This paper has the following structure. In the second section, we describe the program design and implementation. In the third section, we discuss data collection and descriptive statistics of some main variables and the balance between the treatment and control groups. In the fourth section, we discuss the impacts of the vocational training on outcome variables. Finally, we make some concluding remarks. 2. Program description 2.1. Background Mongolia is a resource rich transition country sandwiched between Russia and China. In 1990, Mongolia transited from a centrally planned command economy to a market system. Before the transition, the labor market was characterized with extremely high labor force participation (75.8 in 1992) and no unemployment due to all should work policy. Only in 1992, Mongolia started to measure and report unemployment. After the economic reform, labor force participation has been falling and unemployment has been rising rapidly due to structural changes such as privatization, price liberalization etc. 3 In response to these changes, Mongolia adopted active labor market policies to promote employment and reduce unemployment. In 2 In 2013, 75 training institutions were selected by the Ministry of Labor. Altogether these institutions offer training on 79 different vocations. Those 432 applicants who were treated were trained by 47 training institutions out of these During the socialist period, there was no registered unemployed due to everybody should be employed policy. 5

6 2003, the government introduced a short-term vocational training program (VTP) and it is the oldest and the largest among the active labor market policies. Mongolia has a young population as 54.5 percent were younger than 30 years and youth share (ages 15 to 29) was 26.9 percent in The labor market is characterized with extremely high youth unemployment rate. According to Population Census in 2000 and 2010, youth unemployment was 22.8 percent and 20.0 percent respectively. The Government of Mongolia sat a target to reduce the youth unemployment to 2.5 percent by 2015 in its strategic document. 4 In 2014, youth unemployment rate was still very high at 17.4 percent, which is by 4.4 points larger than the global rate of 13 percent. Moreover, Mongolia has a large number of inactive youth, who are not in education, employment or training (NEET). According to Shatz et al. (2015), one fourth of youth (ages 15 to 29) were NEET and the share seems to be higher if parents are unemployed, less educated and poorer. Hence, youth employment promotion is among the highest priorities in Mongolia. Labor markets in Mongolia are characterized with dominant informal sector, underemployment and low productivity employment. However, labor market is one of the least studied areas in Mongolia. Most of the existing literature is limited by reports on surveys conducted by NSO and ILO. While these reports provide useful insights into the overall labor market performance in Mongolia, they suffer from the usual aggregation bias and do not control for individual level effects considered in this analysis. Pastore (2010) examined returns to education of young people in Mongolia using data from the School to Work Transition Survey. Education and experiences were important determinants of earnings 5. We will examine the individual level determinants of labor force participation and earnings in Mongolia using data from 2002 to In the fourth quarter of 2015, labor force participation rate was 60.5 and unemployment rate was 8.3. The labor market, in particular, impact of ALMP is one of the least studied areas in Mongolia. Moreover, the government acknowledged a lack of comprehensive study on labour 4 Comprehensive policy on National Development based on the MDGs, Government of Mongolia, In 2010, Banzragch (2010) studied education and labor market in Mongolia and Tajikistan. Return to schooling in Mongolia was estimated using data from the Living Standard Measurement Survey Estimated return to education was 5.6 percent to 6.5 percent for wage earners and higher participation was associated with higher education. 6

7 market and an importance of a research for better labor promotion policy in the fourth national report on Millennium Development Goals Implementation in There are a few surveys on employment such as labor market barometer surveys (The Central Labor Exchange), labor force surveys (National Statistical Office) and household income, and spending surveys (National Statistical Office). Although these surveys provide primary data and valuable information on employment and unemployment, there is no study on the impact of employment promotion policy Basic Design The short-term vocational training program (VTP) is the oldest and the largest among the active labor market policies in Mongolia. According to the Ministry of Labor, in 2011, the total number of participants was 8000 and total spending was approximately 3.5 billion MNT, equivalent to 2.1 million USD 6. The Government of Mongolia introduced the VTP for the first time in The Employment Promotion Service Center (EPSC) of the Ministry of Labor is responsible for overall design of the program and selection of training institutions. The VTP is implemented by local government institutions in all provinces of Mongolia, including the capital city, Ulaanbaatar. It is financed by the State Employment Promotion Fund and it is designed for people who are unemployed or vulnerable to unemployment, youth who are not enrolled in formal education and low income citizens. However, the VTP mainly focuses on unemployed people, in particular on young unemployed. The goal of the program is to promote employment by providing vocational skills through training. 7 In order to reach the goal, VTP is designed to provide unemployed trainees with most demanded vocational skills. The Labor Promotion Training Rule states that the program should select highly demanded vocations by local employers. In 2013, VTP provided 80 vocational skills such as heavy machinery operating, hairdressing, cooking, various types of construction work and so on. To provide training programs, the EPSC selects private training institutions through a competitive bidding process. Institutions should show their ability to provide an adequate training, when they submit their bid. According to the Labor Promotion Training Rule, selection criteria for training institutions are legal registration, curriculum quality, teaching 6 In 2013, actual spending was less than the originally planned spending of 4.4 billion MNT due to a budget review. 7 Labor Promotion Training Rule, Ministry of Labor 7

8 quality and adequacy of training places and ability to place trainees in internship positions at registered employers. The proposals are evaluated and revised by experts outsourced by EPSC. In 2013, 75 institutions were selected in VTP and approximately 15% were non-profit and 85% were for-profit institutions. The duration of the training varies from 20 days to 45 days depending on the type of vocation (minimum duration of 144 hours or 4 credit hours). Each training program consists of classroom training and a subsequent internship to provide on-thejob work experiences. According to the Labor Promotion Training Rule, classroom teaching or theory session should not exceed 30 percent of the 144 total hours. The second part of the training splits into practical training and internship. Compared to the training programs in Latin America (Alzua, Cruces and Lopez, 2015), (Card et al. 2011), VTP in Mongolia is much shorter and thus cheaper. In Ulaanbaatar, the Metropolitan Employment Department (MED) implements the VTP once a year, from April to November. The MED signs a contract with the selected training institutions and closely cooperates with them during the training. There were 5100 slots available for trainees, in VTP pays only tuition of a trainee and there is no additional benefits. In 2013, tuition fee was between 140,000 MNT (approximately, 100 USD) to 220,000 MNT (approximately, 150 USD). MED pays the tuition in four installments using certificate notes worth of 20,000 MNT and 10,000 MNT: Skill 1: 25% (At registration of the Metropolitan Employment Department) Skill 2: 25% (At the end of 1st week (or 2nd week)) Skill 3: 25% (At the end of 2nd week (or 4th week)) Skill 4: 25% (At the end of 3rd week (or 6th week)) These certificates become a valid payment instrument only with the trainee s signature. The MED is also responsible for monitoring of the courses. As mentioned earlier, unemployed and low income citizens are eligible for the VTP. An applicant should go to a respective Khoroo 8 office and apply for the program. A khoroo officer will screen the application for eligibility and send an eligible applicant to a district labor officer. At the district labor office, an eligible applicant should choose a training institution with available slots for the desired vocational skill from the list of selected institutions. Then, in order participate in the training, the applicant should sign a contract with the MED. Traditionally, participants were required to sign a trilateral contract (among a participant, a district officer and a MED officer). In spring 2013, the EPSC introduced a new type of contract, 8 Khoroo is an administrative unit and there are 158 khoroos in 9 districts of Ulaanbaatar. 8

9 which is a quadrilateral contract for some slots (among a participant, a district officer, a MED officer and an employer). Hence, there were two types of contract requirement. According to this new requirement, a participant must find an employer in order to sign a quadrilateral contract. Although, the EPSC attempted to improve trainees employment through a contract with employers, this new requirement made enrollment in the VTP harder for young unemployed with little work experience. In spring 2013, the MED observed no serious issues with this new requirement as enrolment was normal. However, in fall 2013, the enrolment became very slow for young applicants as most of the slots with trilateral contract requirement were filled in spring. The beneficiaries of the program are unemployed between 15 and 30 years old, who want to get vocational training. Participating in the training gives young unemployed an opportunity to improve their skills and competitiveness in labor market in the short term. Hence, young unemployed will benefit from the program by increasing their chance to obtain a job and increase their income. On top of the training, we introduced information treatment, which provides general information about a return to training and other useful information to increase job search intensity and learning effort. The research team prepared a letter with information on earnings and labor market variables and delivered the letter every week to trainees in randomly selected classes. 2.3.Cooperation with MED During the experimental study, we closely cooperated with the Metropolitan Employment Department (MED) the main implementing agency of VTP. Surprisingly, from the start of project implementation stage (after six weeks from the start of the project), we discovered two problems related to youth enrollment to the program from data collected from all applicants of the program and monitoring report on a flow of applicants and trainees. Although the program gives a priority to young unemployed under age 25, its share in a pool of applicants was under 22 percent; moreover, the take up rate among young unemployed was extremely low around 10 percent compared to other age groups. We sent a weekly report with this information to MED and organized a meeting with officials from MED and the Ministry of Labor to introduce these findings. Officials from both organizations found these findings to be serious issues for the program. 9

10 All parties discussed the problem and suspected one of the main reasons for low take up rate to be type of contract. Participants were required to sign either a trilateral contract (among a participant, a district officer and a MED officer) or a quadrilateral contract (including potential employer) depending on the class. The quadrilateral contract requirement was new to MED as it was introduced in spring Although, Employment Promotion and Study Center (EPSC) attempted to improve trainees employment through a contract with employers, meeting the new requirement may had been difficult for young unemployed who do not have well-built network on the job market. After many discussions with officials from MED and Ministry of Labor, MED immediately started to target young unemployed for the program in order to increase their participation. In other words, young unemployed were prioritized over older age groups. Moreover, MED looked for ways to make it easier for applicants to find private firms to sign the contract through coordination with labor exchange units. Starting from 2014, MED changed the contract type and participants have to sign trilateral contact only. Given the problems encountered during the implementation of the impact evaluation and findings from our study, the team can report the following policy recommendations for the MoL, EPSC and MED. The team can give some specific recommendations on increasing the take up rate for youth trainees beside the contract type. For instance, removing the quota by vocation might increase the take up rate for young participants. MoL and EPSC put limits on the number of slots on each vocation. On the other hand, some vocations are not very popular among young applicants while some are very popular. In addition, we noticed that there is a need to pay attention on the screening procedure for eligibility status in order to reach the more needy and poor people. Current eligibility status cannot distinguish some people with relatively moderate livelihood. For example, university students and women in housework were enrolled to the program due to their hobbies or to improve their skills just for housework. Although the share of these applicants is not very big, in terms of cost we could not underestimate the inefficiency caused by this problem. It is very important for MoL and MED to include graduates follow-up procedure in their internal monitoring and evaluation reporting. In particular, following up the graduates at least after 2 years might be important in order to examine their employment, job quality and earnings. As we found no significant impact on job formality and long run employment, regular 10

11 and thorough follow up studies can provide useful information to improve the program impact on these important outcome variables. The team and policy makers expect more specific policy recommendations from its cost benefit analysis. Recently the government implemented vocational training with duration of longer term, 2-5 months in order to improve the impact of vocational training. On the other hand, the impact of training might depend on its implementation design rather than its duration. We expect the cost benefit analysis of short term vocational training will show clear picture on which part the policy makers should focus on to improve the impact of training. Since the project showed from its start how important the impact evaluation to improve the program impacts, policy makers from the MoL and MED highly appreciated the RCT methodology and expressed their willingness to cooperate in the future to improve the format and design of VTP based on the final results of the study. The team will organize a national conference in cooperation with MoL and MED to disseminate the findings from the study. Additionally, government labor officials are also interested in using RCT in their other ALMPs. 9 Given this increased awareness of the importance of impact evaluation among the government officials, the team is hopeful for the practical application of the policy recommendations from this study Experimental design Before starting the experiment, we negotiated and agreed with MED on experimental design. As MED operates in UB city only, they recruit the trainees from UB city. However, applicants who are temporarily residing in UB can apply for the program. The participants allocated in the treatment group should have received the training funded by the Metropolitan Labor Department. According to our original plan, the baseline-survey sample size was : of which 1400 in treatment group and 700 in control group. We conducted power calculations to determine the sample size. Initially, the team proposed to test a hypothesis that the impact of short-term vocational training on employment is at least 3.0 percentage points. The MED and Ministry of Labor provided us with information on the 9 They want to improve their knowledge of impact evaluation knowledge. Our PEP mentor delivered training for officials from the MoL during her field visit in Mongolia. 10 MED told the team that they have 3000 quotas available for the remaining period of 2013 and the team can fill 1400 with randomly selected young (25 and younger) unemployed. 11

12 trainee s employment rate as well as the rate of drop-outs. Rough estimation on district data on the employment rate of trainees gave standard deviation of According to the MED, the drop-out rate is about 20 percent. However, we chose conservative drop-out rate at 30 percent. The sample size was set at 1400 and 700 for treatment and control groups (2100 in total) to detect 3 percentage point increase in employment with a power of 0.8 and drop-out rate of 30 percent using the sampsi command of Stata software. Due to budget review, the city declined the planned quotas and the sample size reduced from the original one. In order to increase the number of people being treated, the team agreed with mentors to train more participants using PEP fund in order. Under this plan, the team continued recruitment after 22nd of November, 2013, when the registration process by the Metropolitan Employment Department (MED) was finished. Registration for the additional training financed by PEP fund started on February 7, The training for the PEP funded trainees continued between February, 2014 and April, The team tried to mimic the original training program design for these additional applicants. However, the sample size is reduced from its planned level. Figure1 shows the planned size of sample, which is 2100, and its actual size, which is 1188 in total. Before starting our intervention, MED expressed its concern on filling the slots with young unemployed and the team did the following activities to promote its Short-Term Vocational Training Program (VTP): (i) prepared TV program about VTP and broadcasted via three TV channels, (ii) prepared radio advertisement and radio talk show about VTP, and broadcasted via four FM radios, and (iii) prepared two types of poster and distributed them to Khoroo offices. This promotion is financed by the PEP fund. The registration took place between August 26 and November 22, 12 weeks in total. During this registration, the team followed the regular registration process of the MED, which has the following three stage approvals: (i) Khoroo officials conduct screening of the eligibility of applicants and send them to the district labor divisions if they are eligible. The short-term vocational training program focuses on the following groups of population: (a) unemployed; (b) vulnerable to unemployment; (c) person with difficulty to find a job; and (d) school drop-out, who is in labor force. 12

13 (ii) (iii) District officials check the availability of slots on the applying vocation and provide the contract form if slot is available. Depending on both vocations and training institutions, some applicants have to sign an agreement with employer, while other applicants do not need to. There is no clear rule to distinguish whether it requires quadrilateral or trilateral contracts. Applicants find employer to sign on the contract if quadrilateral contract is required and then take it to the district labor division to be signed by district officer. 53.3% of treatment group participants were required to have a quadrilateral contract. Once the contract is signed by all parties employer, district officer and participants, applicants go to the MED office and receive the training certificate. MED office also should sign on the contract. The randomization was implemented after the first stage of the registration process. The randomization worked as follows. Khoroo officials, enumerators, were given an instruction to register applicants as a regular process. Along with registering, they had to ask young applicants between 15 and 30 years of age to give an interview for the baseline survey and tell them they may not be selected for the training. At the end of every workday, our workers at NUM called khoroo officials for the lists of applicants names. Based on those lists, individual level randomization was conducted on a daily basis and individuals were informed if they were selected to be trained or not. Randomization 11 was conducted with 1/3 probability of being selected into control group and 2/3 probability of being assigned into treatment. The advantage of this design is that it applies the process of registration in training, as it would take place in practice. During the registration process, we randomized 1188 applicants who met the selection criteria, with the distribution presented in Figure 1. As shown in the figure, 774 applicants (65.2%) were originally assigned to the treatment group, while 414 (34.8%) were assigned to the control group. Of the original treatment group, 342 applicants (44.2% of treatment group) did not show up for training, which means that they did not even start their training. We categorized reasons for no-show and presented in Table 1. Most of the participants (35.1%) did not show-up for personal reasons such as childcare (6.7%), pregnancy/birth (5.8%), not interested (6.7%) and so on. 30% had a job after registration\randomization (30.04%) and 31% did not show up due to VTP related reason. As mentioned above, 53.3% of the treatment group were required to have quadrilateral contract to start the training. This could be a hard 11 The team implemented randomization using software STATA and kept randomization protocol. 13

14 requirement for young unemployed who lack work experiences. 70% of no-shows were required a quadrilateral contract as opposed to 39% of trained participants. Hence, a quadrilateral contract may have resulted in this large number of no-shows. On the other hand, 432 applicants (55.8% of treatment group) started and received training. During the training, 30 students dropped out and out of them, 28 students dropped out in the first week of courses. 17 students out of them were in the second treatment group. However, 15 students dropped out before receiving the letter. Only 2 of them received one letter before dropping out. The experiment has two treatment groups and one control group. One treatment group received a short-term vocational training only and another treatment group received the training with a letter with general information about labor market variables. The second treatment was conducted among those who already were selected for training and started their training. The random assignment of the second treatment was done at a class level rather than an individual level, because of the small size of a class. Generally, the class sizes of short-term vocational training vary highly, from 3-30, across training institutions depending on the nature of the vocations and the demand for the vocations. The participants in our intervention studied in a class with 9.6 students on average. However, the number of students in one class, who were funded by MED, is very small due to the following reason. The reason is the on-going registration process of VTP by MED. Since applicants can apply any time of the year between May and November, it is impossible for training institutions to wait until the number of MED funded students to form one class. Therefore, training institutions recruit privately funded students in addition to MED funded students. In other words, they form a class from those students who pay for training by themselves and those who are MED funded. Thus, there were a small number of students funded by MED in a class. For the second treatment, we randomized each class with a chance of two third for being selected to treatment and one third for being selected to control. Those applicants in the second treatment group received a letter with information about labor markets in Mongolia, while they participated in training. We delivered those letters with the information every Friday. The information was about wage difference between unskilled and skilled workers in Mongolia. The applicants in the second treatment group received four letters at most. 14

15 Totally, 141 classes with 410 students were randomized for the second treatment 12. During the randomization of the second treatment, 291 students of 101 classes were assigned to the treatment group with information, while 119 students of 40 classes were selected to the treatment group without information. During the training, the team distributed a letter (see Appendix 3) or information on earnings to the students of selected classes for the second treatment. The information letters were delivered on every Friday during the training starting from the 1 st week to the 2 nd to the last week. The students received four letters at most. In our second intervention, 266 students received at least one letter, while 25 students did not receive any letter due to drop-outs in the beginning of courses and absences from classes. On average, students received 2.2 letters during the training Take-ups and no-shows As mentioned above, 44.2 percent of those originally assigned to the treatment group failed to show up for training. This evidence indicates that one of the main problems for short-term vocational training program might be low intake rate, while the officials of MED acknowledge that the number of people applying to it is high. Participants, who did not show up, stopped their enrollment process mostly after the second stage of registration process. Those no-shows may have some relation with change in type of contract mentioned before. The requirement to find an employer on the quadrilateral agreement was very difficult for applicants especially for young people who do not have well-built network on the job market. If the agreement imposes a barrier mostly for the applicants without well-built network on the job market, then the trained applicants may be different from the untrained applicants with some unobserved characteristics. As result, the quadrilateral agreement requirement may cause additional problem of self-selection in our research. Therefore, we need to consider whether the take-ups have different characteristics compared to no-shows. In order to check this, we created a dummy variable named take-up that takes the value of 1 if the individual participated in training and 0 if the individual did not show up for training. Using the baseline dataset, we regressed this dummy variable on the individual characteristics, a variable indicating the contract types, and dummy variables for economic sectors that the vocations are related. 12 First 22 students, who graduated before October 30, were not randomized in the second treatment. 15

16 We have administrative data on contract type by vocation and training institution, as well as data on the vocations for which the applicants applied. Based on this information, we created a variable contract type to indicate a contract type for each individual and it equals to 1 for trilateral contract and 0 quadrilateral contract. Table 10 shows the results from regressions of the probability of taking up for training on baseline characteristics. Take-ups were strongly correlated with schooling years, previous work experience, contract type and very optimistic expectation on getting a job and ambition to succeed in the labor market (at least 5% of significance level). According to the estimation results, having a trilateral contract has large and positive impacts on take-up rate for the training. It increases the rate by 0.5. Although it is related with their opinion about responsibility of government and own to get a job, the coefficients are close to zero. Due to these correlations, we cannot claim that take-up for training is random. To overcome this selection problem, we will estimate the impact of the program using instrumental variable regressions. We also include controls for sectors based on vocations the participants applied. We classified vocations into three sectors: a) service, b) industry and c) other (agriculture and so on). But sectors do not have impact on probability of take up. Due to ongoing registration process, applicants had to wait until the number of students reaches to fulfill one class. An average number of days from randomization date to course start date was 24.6 with standard deviation of In fall, average waiting days were 14.2 days with standard deviation of 17.5 and it ranges between 0 and 82 days. The average waiting days were 15.7 days for students with quadrilateral contracts and 11.6 days for those with trilateral contracts. Using t-test, there was a difference in waiting days between trilateral and quadrilateral contracts at significance level of 10 percent (p-value 0.08). Using the PEP fund, we trained 50 students, who applied for MED training in fall of 2013, in spring of Excluding these students, average waiting days in the spring of 2014 are 7.9 days with standard deviation of 7.1 days Data collection We conducted three surveys. The baseline survey collected information on the individuals before their participation in the program. The first follow-up survey collected information on individuals 6 months after the end of classroom for trainees or after the baseline survey for 16

17 control group. The second follow-up survey was conducted one year after the first follow-up survey. TableA1 shows data collection results of the baseline and two follow up surveys. The baseline data was collected for 12 weeks between August 26, 2013, and November 22, The additional baseline data collection funded by PEP was conducted between February 7, 2014 and March 11, During the baseline survey, there were 1188 eligible applicants for the program and we interviewed applicants (94.7%). The baseline sample includes 751 (66.8%) respondents in the treatment group and 374 (33.2%) respondents in the control group. The number of treated applicants is 432 and those participants completed the whole enrollment process including the quadrilateral contract. Out of total trainees, 30 participants dropped out after the training started. During baseline data collection, the team collected additional information from training institutions. In total, 432 participants were trained in 47 training institutions 14, whereas 46 institutions provided information. The information is about the number of instructors, number of classrooms, average class size, expenditure per student and monthly salary of instructors etc. These 46 institutions trained 410 participants (94.9 percent) of our intervention. Participants applied for 66 different vocations. The most popular 5 vocations were beauty specialist, hairdresser, cook, sales assistant and childcare assistant. The first follow-up survey was conducted between June, 2014 and November, 2014 (in 6-8 months after the baseline survey). During the first follow up survey, we collected information from 1075 (95.6% of 1125) participants, which means that attrition rate of the first follow up survey is 4.4%. The attrition rate of treatment and control groups are 4.4% and 4.5% respectively. Attrition rate is very low, compared to other studies in developed countries. However, we need to investigate whether individuals attrite differentially in terms of treatment assignment and their individual characteristics. In order to do this, we created a dummy variable named attrition that takes the value of 1 if the individual was not observed in the first followup and 0 otherwise. We used the mean difference test by attrition status (see Columns (5-8) in Table4). Attrition of the first follow-up survey is related to dwelling type, household size, having income per capita below poverty line, ambition to succeed in the labor market and 13 Baseline data were collected during the first and second follow-up surveys (respectively, 9 and 7 participants gave an interview by baseline questionnaires). 14 In 2013, 75 training institutions were selected by the Ministry of Labor. Altogether these institutions offer training on 79 different vocations. Those 432 applicants who were treated were trained by 47 training institutions out of these

18 opinion on importance of government to get a job at least 5% of significance level. However, the differences are close to zero. Although the attrition seems to be not random, the attrition rate is very low and it affected equally both treatment and control groups. We will estimate the effects of treatment by controlling those variables. The second follow-up interviews were carried out between June, 2015 and November, 2015 (in months after the baseline survey). The second follow up survey collected information on 1003 participants, which is 89.2% of Attrition rate of the second follow up survey is 10.8%, which is still small. Using mean difference tests in the first follow-up survey, we found that attrition of the second follow-up survey is correlated with several variables such as gender, dwelling type, marriage, work experience and opinion on importance of government to get a job at least 5% of significance level (see columns (9-12) of Table 4). In order to obtain an unbiased estimation of training impact, we will control all those characteristics in estimations. 3. Data Description and Baseline Comparison 3.1. Descriptive Statistics The baseline and follow-up surveys collected information on demographic characteristics, education, household income, general labor market information, their experience in labor market, and training. In demographic characteristics, we include age, gender, dwelling type, household size and marital status of the applicant. In addition to labor market outcome variables, we asked the applicants plan to complete training, expectation on getting a job and ambition to succeed in the labor market. Moreover, there are questions on how much applicants think the responsibility of the government and their own must take place for them to be employed. According to our baseline data, the average age of participants is 23 and 65% of them are female. Most of them (82.4 percent) live in ger area, while more than half of participants (54.7 percent) live in a household with income below the poverty line. One third of participants are married. Average schooling years are 10.8, which is almost high school year (10 years). Although 61.7 percent of participants have work experience, only 10.8 percent of them were employed in a week before baseline interview was conducted. About one third of participants were unemployed, which means that 58.9 percent of them were inactive. Out of those having a work experience, 46.9 percent signed a contract with their employer and only 35.3 percent 18

19 were paid their social benefits by employers percent of them have participated in shortterm vocational training before the intervention and duration of those training was 39.7 days on average. On average, they evaluated their chance to get job in the next six months as 78.3 percent and 68 percent of them had very optimistic expectation on it. About 90 percent of them told that they are very ambitious to succeed in the job market. The importance of own responsibility to get a job is about 85.8 percent on average and perceptions about government responsibility to get a job is 66.6 percent. There are no differences across treatment and control groups for these two variables Baseline and Balancing for treatments When randomization is successful, baseline characteristics of respondents should be balanced across treatment and control groups. Columns (1-4) of table 4 show baseline characteristics comparison between two groups. Baseline balancing is checked by differences in demographic characteristics, labor market outcomes, past training experiences, future plans of participants and contract type based on whole sample. Columns (1) and (2) report the pre-intervention means of the treatment and control groups, respectively. Columns (3) and (4) indicate the estimated difference between treatment and control, and p-values. Table 4 shows that treatment and control group characteristics are very similar for almost all variables. There are no significant differences at 5 percent significance level, except one variable, ambition in scale of 100. Variable, ambition is the only variable with a small p-value for balancing between treatment and control. However, similar variable, ambition in level, is not significant at 10 percent significance level. Thus, we claim that the treatment and control samples characteristics are balanced, which means that the random assignment was implemented successfully in creating comparable samples of participants in the treatment and control groups 15. In table 5, baseline characteristics of (i) participants who were assigned for training but not information treatment (training only), (ii) participants who were trained with information 15 Two variables measuring the ambition are both significant for baseline balancing for differences between trained only and control groups. Trained only group is already a self-selected one as no-how rate was very high. Therefore ambition of trained people is significantly higher than the ambition of people in control group. 19

20 treatment (treatment2) and (iii) participants in control group. It can be seen from table 5 that there are imbalances between training only, treatment 2 and control groups. This could be caused by difference in randomization design of these treatments. As mentioned earlier, randomization of treatment 2 was implemented at class level while randomization of the first treatment was implemented at individual level (see figure 2). Means of training only, treatment 2 and control groups are shown in Columns (1-3) respectively, and p-values for betweengroups difference tests in Column (5). Out of 21 covariates, the following variables are unbalanced between three groups (at 5% of significance level): age, schooling years, previous work experience, ambition to succeed in the labor market, and own/government responsibility to get a job. However, differences in these variables between the groups are very small or negligible, except one variable, work experience. Such as, average age differs by less than 1 year and average schooling years by 0.5 years. Nonetheless, the difference in previous work experience between three groups is considerable. For example, there is difference of 11 percentage points (the highest difference among groups comparisons) between training only and treatment 2, while difference between treatment 2 and control groups is 6 percentage points. We will use a variable, work experience as control variable to obtain unbiased results in impact of treatment 2. Although baseline characteristics are balanced between treatment (including both training only and treatment 2) and control groups, there is a little imbalance for some variables between three groups. The first reason of this problem might be that randomization for the treatment 2 was possible only for participants who did show up in the training. Given a quite high rate of no shows, baseline balancing in training only and treatment 2 looks less balanced for some variables. Second, it might be because of the randomization of treatment 2. In table 7, we checked a balance between treatment 2 and its control group (trained only). There are some imbalances between treatment 2 and trained only in terms of gender, monthly salary, job contract and importance of own responsibility to get a job as well as indicators of classes and institutions. 4. Estimations In this section, we will discuss about estimators of program effect. With full compliance, a difference in an outcome variable between treatment and control groups is the average treatment effect of the training program (ATE) and it can be interpreted as the average treatment on the treated (ATT) in the population of youth. Due to no-shows and little drop-outs 20

21 in our intervention, this effect will be an intention to treat (ITT) effect. By estimating ITT effects, we can evaluate the impact of offering the program to young people. We will use regression models in the following form: Y i = β 0 + β 1 AssignedToTraining + δx i + ε i where Y i denotes outcome variables such as employment, earnings, formality and AssignedToTraining takes one for the treatment group (assigned to training, therefore it includes participants in information treatment groups as well) and zero for control group. The estimate of β 1 in this regression equation indicates the effect of offering program to youth on an outcome variable, as an ITT effect. According to Alzua et al. (2015), this coefficient is a policy relevant parameter, which measures how much effect the program would bring if the policy makers offer the program. X i is a vector of individual characteristics such as gender, marital status, schooling years, number of children under one, income other than monthly wage and work experience from the baseline survey. In addition to these demographic variables, we add ambition in labor market and contract type, which are not balanced in baseline, and quarter dummy variables 16. We clustered standard errors by district and then by ger area within the district in estimations for employment, monthly earnings and skill match. Sample standard deviations for these outcome variables are different by district and further differs by ger area within the district. Given the geographical feature of UB city, individuals face different local labor market conditions depending on where they live. These differences can be captured by district and ger area variables in our database. Next, we can estimate the effects of receiving the treatment, which is the treatment on the treated (TOT) effect. In our case, a number of participants did not show up for the training after we assigned them to training group. In other words, they decided whether they take up the program or not when the program is available for them. Thus, receiving the treatment or not is 16 Due to the on-going registration process of the VTP, the participants applied for the training between August 2013 and march Therefore, we tracked and followed them between June and October 2014 for the first follow-up and June and October 2015 for the second follow-up. So the registration process continued for 2 subsequent seasons and so do the follow up surveys. In order to control for seasonal effect, we add variables for 3 rd and 4 th quarter of 2013 and the 1stquarter of

22 an endogenous variable. In order to solve self-selection problem, we will use random assignment as an instrument variable. To estimate TOT effects, we created a dummy variable trained, which takes the value of 1 if the individual receives training, without depending on assignment to treatment, and 0 otherwise. For this variable, the random assignment will be a very strong instrument, since it is unrelated with the outcome variables, but strongly related to whether participant receives training. F tests of the regression between variables for trained and random assignment are all reject null hypothesis. F test, for short term, from the first stage regression of TOT estimation of employment and wage is 36.9 and for medium term it is For formality estimation, F test of the first stage regression is 18 (for short term) and (for medium term). For skill match, it is for short term estimation and 42.8 for medium term estimation. In addition to above estimations, we will estimate the effect of training using a difference-indifferences (DID) estimator to control for time in-variant unobservables. We conducted pooled OLS and panel estimations with both random and fixed effects. 5. Impacts of the First Treatment: Employment, Wage, Job Quality Based on the first and second follow up data, we will analyze ITT and TOT effects of vocational training program in the short and medium terms. As we are interested in the impact of the program on employment, earnings and formality of job, first, we will see the basic comparison results for the outcome variables such as employment, labor force participation, monthly and hourly wages, weekly working hours, labor contract, social benefits paid by the employer and skill match. Table 6 shows the results. We define the employment as having a permanent job as of the week prior to the follow up survey interview. In addition, labor force participation is defined as being employed or being unemployed but looking for a job as of the week prior to the interview. We asked those participants who are employed about their salary in the last month and working hours in the last week. Hourly wage rate is calculated based on the monthly salary and weekly working hours. Monthly wage and hourly wage are coded as 0 for participants who were not working. Identification of job formality is quite challenging unless we refer to administrative data. For example, Alzua et al. (2015) used administrative data in their follow up data collection to define the percentage of participants who are employed in formal sector. In our case, we can define 22

23 number of indicators from the questionnaire to detect the job formality of participants as we asked participants several questions to define whether he/she works in formal sector or not. Questions include (i) if participant has signed a contract with his/her employer; (ii) if his/her employer pay the social security which is legal requirement from officially registered business entities; (iii) size of enterprise where the participant works; (iv) regular bonus in addition to the monthly salary. We choose two variables for job quality: formality based on whether social security paid by the employer or not and skill match. Moreover, skill match is defined as a dummy of whether participant s current position is related to his/her previous experience or not. Basic comparison results in short term We could not detect ITTs on most of the outcome variables are positive but the size is small and insignificant. So it is difficult to make conclusion from basic comparison results. During the first follow up survey, 49 percent of participants in treatment group were employed and this is higher by 3 percentage points than the percent of employed participants in control group. This difference in employment status is not significant (with p-value of 0.36). Participants in treatment group were earned higher monthly salary than participants in control group. Monthly earnings of treatment group participants are higher than monthly earnings of control group participants by 20% on average. This difference between treatment and control groups in monthly earnings (45,665 MNT, equal to 22 USD) is not significant though. Although weekly working hours for control group is longer than for treated (28.29 vs 26.87), hourly wage is higher for the latter (21 percent higher). This shows participants in treatment group works less hours but earns more than the controls on average. It can be seen from the table 6 that short-term impacts are not significant. Outcome variables of job formality (social security) do not show difference (insignificant) between treatment and control group. In treatment group, 24 percent of participants had signed contract with their employers, while in control group, 23 percent of participants had signed contract. Similarly, there is only 1 percentage point difference between treatment and controls in social security. Social security of 18 percent of participants in treatment group (17 of control group) were paid by their employers. According to skill match, treatment group participants 23

24 were doing better than the control group ones. 26 percent of treatment participants were recruited on the job related to their previous experience and this is 5 percentage points higher (insignificant though) than the control group s average. Basic comparison results in medium term In medium term, impacts were different from short term and they look quite mixed. Similar to the short term impacts, it is difficult to detect any impact based on simple mean difference analysis. ITTs on many outcome variables were negative but sizes were very small and statistically insignificant. According to the basic comparison, employment rate of the treatment group is lower by 3 percentage points than the control group mean. However, the difference is not significant. On the other hand, impact of monthly earnings is positive in medium term. Treatment participants earn 6.4 percent higher salary than control participants. Hourly rate of wage for treatment participants is 15.5 percent higher than the hourly rate of wage for control participants. On the other hand, weekly working hours for control group participants is longer than the hours for treatment group participants. According to the second follow up data, labor force participation, job contract and social security were lower for participants in treatment group than those in control group by 1%, 5% and 4% respectively. Although we could not detect any impact from basic comparison, we found positive significant impacts on employment and monthly earnings when we control for some socio-economic demographic characteristics and couple of variables that were not balanced in baseline. In the following sections, we will analyze treatment effects on variables of interest in more detail Employment The main expected outcome from the training program, implemented by MED, is employment improvement and it has a goal to promote employment by improving skill based on employers demand. We created a binary variable, which defines employment status and it takes the value of 1 if an individual has worked last week and 0 if the individual has not. 24

25 As shown in previous section, estimated effects from whole sample were positive but insignificant. In estimation, we interested in estimating the effect of VTP on employment outcomes of all participants who were randomized. Table 11 shows estimation results of ITT and TOT effects with control variables. As mentioned above regressions include control variables for gender, marital status, schooling years, work experience, number of children under one, income other than monthly wage 17, ambition in labor market and 3 party agreement slots share. In panel A of table 11, ITT estimate of short term impacts on employment are reported. In short term, we found 6.0 percentage points impacts of offering training on employment at 5 percent level of significance by ITT estimation. This result indicates that VTP was successful in increasing the employment of the trainees in short term. Treatment effect of 6 percentage point on employment represents 13 percent increase with respect to the control group s mean rate. In panel A of table 11, TOT estimates of short term effects of receiving training are shown. We found 14.0 percentage points increase in employment in the short run by TOT. TOT estimate is twice as high of the ITT point estimate. In column (2) of table 11, ITT and TOT estimates of employment in medium term are shown. In medium term, we did not find any significant impact on employment from both ITT and TOT estimations. According to our findings, the participants, offered or receiving training, get a job first compared to others in short term. However, both treatment and control groups are employed similarly in medium term. We did analyze the heterogeneity impact of employment by gender, age and ger area. There are evidences from existing evaluations of training programs in developing countries that claim different impacts by gender and age-group. For example, Attanasio et al (2011) found larger impact of training on young female and Alzua et al (2015) found substantially larger effect for men. We found significantly different impact by age group. Participants aged between 25 and 30, were little bit more likely (1 percentage point higher) to be employed when offered a training in short term. We could not detect any different impact by gender. We also did check 17 Income other than monthly wage is a dummy variable showing whether a participant has income sources other than monthly earnings. These sources can be child benefit, pocket money from parents, remittances, unemployment benefit and other type of social benefits etc. This variable is 1 if a participants has other income source and 0 if not. 25

26 for the difference by ger area and found a significantly different impact. In short term after the training, participants who live in ger area are more likely to be employed than those who live in apartment by 1 percentage point. In medium term, we found different effects for male, older and ger area participants. But sizes are small and insignificant. Male participants in treatment group are more likely to be employed (1 percentage points) than female participant. For age group, participants aged between 25 and 30 also have larger probability of being employed than young participants (3 percentage points). Finally, participants in ger area are more likely to be employed than participants who live in apartment (8 percentage points) Monthly earnings One of labor market s outcome variables in our research is monthly earnings. For the first follow-up data, the average monthly earnings are 280,721 MNT (137USD) for treatment group and 235,056 MNT (115 USD) for the control group. In panel B of table 11, short term and medium term ITT estimates of monthly earnings are shown. In short term, ITT effect of VTP on monthly earnings is positive at 5 percent level of significance. We found that VTP effectively increases the monthly earnings of trainees by 25% in short term. According to TOT estimation (shown in panel B of table 11) of short term monthly earnings is equivalent to almost 64.6% higher salaries than the mean salaries of control group. In medium term, the effect of VTP on monthly earnings is still significantly positive at 5 percent level. ITT and TOT effects in medium term are shown in panel B of table 11. Monthly salaries mean for treatment group participants are higher than the monthly salaries mean for control group participants by 16% in medium term. According to TOT estimation, the impacts of the treatment on earnings are larger (almost 40%) compared to ITT estimations and significance levels are similar to that of ITT. Participants in treatment group are employed first in short term and they are earning more in short and medium terms. We examined whether there is heterogeneous impact of the program on the monthly earning by gender, age groups and ger area. In both terms, there are no statistically significant differences by these factors. 26

27 5.3. Job Quality Like other developing countries, Mongolia is characterized with a large informal sector, where hiring and firing costs are low and jobs require low skills and long hours and do not provide any social benefits. Schneider (2002) estimated the average size of informal economy in developing countries to equal 41 percent of GNI. According to Morris (2002), an informal sector employment share in Ulaanbaatar was 26.9 percent in 2000 and two thirds of informal sector workers are aged between 20 and 40. Vocational training programs can provide trainees with an opportunity to improve job quality through skill development and information. In this research, we will measure job quality with two variables: social benefits paid by employer (formality) and skill match. Table 6 shows a mean difference comparison of the treatment and the control groups. There is no significant difference across the two groups. In the short term, formality measures tend to be higher (about 6.0 percent) for the treatment group. However, in the medium term control group participants are more employed in formal sector. In panel D of table 11, we present ITT estimation output for formality variables. We run regressions on formality controlling for gender, age, education, housing type, marital status, real estate ownership, household income, income other than monthly wage and some variables featuring the work place before the treatment. In baseline, we asked participants about their previous work experience. This allows us to control for the type of job (employee, selfemployed, employed without wage and etc), the size of employer they worked for, whether the previous employer paid for their social security and also paid benefit in addition to monthly wage. In both period, it is difficult to detect any impact on formality. As mentioned above, we found a significant positive impact on formality by OLS. But OLS estimation tends to be biased and inconsistent given the sample mean of formality which is close to zero. Another interesting dimension for job quality is a skill match. We have a variable on skill match, which tells if participant s current job is related to his/her previously obtained skills. ITT effects on skill match are shown in panel C of table 11. Regressions with controls have positive and significant ITT effect on the skill match in short term. The VTP increases the skill match by 4 percentage points. TOT effect is even higher (more than tripled) by 14 percentage points. In medium term, ITT and TOT effects on skill match become small and insignificant. 27

28 We did not find any significant differences related to skill match across gender and age group in short term. However, heterogeneity analysis provides a significant difference across ger area in medium term. On average, treatment participants who live in apartment have higher skill matching than treatment participants who live in ger area by 2 percentage points Panel estimation In addition to above estimations, we will estimate the effect of training using a difference-indifferences estimator to control for time in-variant unobservables. We conducted pooled OLS and panel estimations with both random and fixed effects. In table A7, results of OLS and random effect estimations are shown. OLS and random effect estimations of DID on employment is significant and difference on mean employment rate across treatment and control groups is positive in short run but negative in medium run. Mean rate of employment of participants in treatment group increased more than the mean rate of control group participants in short run (3 percentage points). In medium run, employment of control group participants increased more than the employment of treatment participants by 1 percentage point. Fixed effect estimation of employment is very small (negative) and insignificant (see table A9). We found positive but insignificant impact on monthly earnings from panel estimations (table A7). In short run, monthly earnings of treatment participants increased more than the monthly earnings of control group participants by MNT (21 USD). Increase in monthly salary is even larger for treatment group participants in medium run (49018 MNT which is equivalent to 24 USD). According to fixed effect estimation, monthly salary of treatment participants increases by 2353 MNT in each period. Results from panel estimations of formality are very small and insignificant. Therefore, it is difficult to detect any impact on formality after we control time-invariant unobservables (see table A8). Same for the skill match. We could define any significant impact of VTP on skill match from panel estimations (see table A7). The impacts of the second treatment: Attendance and Drop-outs One of the distinctive features of this study is a randomized information treatment (the second treatment) for trainees. For the second treatment, we randomized each class with a chance of 28

29 two third for being selected to treatment and one third for being selected to control. Trainees in the information treatment group received a letter with general information about measured labor market outcomes of skilled people, while participating in the training. According to MED, about 20 percent of participants in the VTP drop out on average. This high drop out rate may be caused by a lack of information about measured labor market outcome of skilled labor. Jensen (2010) showed that the perceived returns, which may affect schooling decisions, were inaccurate and extremely low. He found that information on the higher measured return to secondary school increased schooling by more years. Hence, information about measured labor market outcomes of skilled people may influence attendance directly and VTP impact indirectly. In this section, we will analyze the impacts of information treatment on trainees attendance. In particular, we will measure impacts of information treatment on class attendance and drop out. We define the drop-out as a dummy for whether participant dropped out without completing the training. Moreover, variable of attendance was measured with total days a participant attended the training. Table 9 presents the basic comparison results for the outcome variables: attendance and drop-out. Drop-out rate of participants in information treatment group was 5.8%, while drop-out rate in control group is 8.4%. However, the mean difference of 2.6% is not significant (with p-value of 0.34). Participants in information treatment group attended a class 26.9 days on average, which is by 3.3 days longer than the control group. This difference in attendance is statistically significant (with p-value 0.042). We can measure the intent-to-treat (ITT) effect of information treatment on an outcome of interest by estimating the following equation: Y i = α + βassigenedtoinformation + βx i + ε i (2) where Y i denotes outcome variables such as attendance and drop out and AssignedToInformation takes one for the information treatment group and zero for control group for information treatment (trained only). The estimate of β 1 in this regression equation indicates the effect of providing information for trainees on an outcome variable, as an ITT effect. X i is a vector of individual characteristics such as such as age, marital status, years of education, gender, housing condition, number of children and childcare. According to Galdo and Chong 29

30 (2012) trainees attending high-quality training courses had higher earnings and better job than trainees attending low quality courses or non-participants. Expenditure per trainee was the most important characteristic. Hence, we have collected administrative data on the training quality variables such as class size, cost per student, ownership of training building, full time and part time teachers, number of classrooms and etc. In the analysis training quality is measured with variables such as building ownership, large class and minimum cost per student. We define a class large if class size was larger than the institutional average. Building ownership is defined by a dummy for whether a training institution owns a training building. Table 13 reports the estimation results of above equation for two outcome variables. Drop-out is a binary variable and overall drop-out in our sample is low, less than 10%. Hence, we defined and estimated a probit model for drop-out. The column (2) and (3) show significant (at 5% significance level) impact of the second treatment on drop-out. The ITT for drop-out is 5.1%. In other words, delivering a letter with general information about labor market outcomes of skilled labor would reduce drop-out by 5.1% at the mean. There are no heterogeneous impacts across gender, age groups and housing condition. The column (1) show a significant impact of the second treatment on attendance. The ITT for attendance is 3.3 days. We find it economically significant as the treatment group will attend 13.8% more days than the control group. There is no heterogeneity in the impacts of information treatment. 6. Conclusion Mongolia is characterized with very high youth unemployment. In the beginning of 2000s, the government adopted vocational training program with a typical OECD format to tackle raising unemployment. However, Mongolian design of VTP is much shorter and thus cheaper than VTPs in other countries in particular, LAC. In order to evaluate the program impact, we conducted a field experiment between 2013 and The time period would allow us to examine short and medium term impacts. We estimated the effects of VTP on employment, earnings and job quality. VTP has a positive short term impact on employment, however the impact vanishes in the medium term. In particular, VTP successfully increases the employment rate 13 percent in short term. We found 30

31 significantly different impact on employment by age group. Participants aged between 25 and 30, were more likely (1 percentage point higher) to be employed when offered a training in short term. However, we could not detect any different impact by gender. We also found significantly different heterogeneity by ger area in short term. In short term after the training, participants who live in ger area are more likely to be employed than those who live in apartment. In medium term, we did not find significant heterogeneity across gender, age groups and ger area. On monthly earnings, VTP has positive and significant impacts in both terms. We found that VTP effectively increases the monthly earnings of trainees by 25% in short term and 16 percent in medium term. We examined whether there is heterogeneous impact of the program on the monthly earnings by gender, age groups and ger area. In both terms, there are no statistically significant differences by these factors. Job quality is measured by two variables formality and skill match. In both periods, it is difficult to detect any impact on formality. However, VTP increases the skill match by 4 percentage points in short term. In medium term, effect on skill match becomes small and insignificant. We did not find any significant differences related to skill match across gender and age group in short term. However, heterogeneity analysis provides a significant difference across ger area in medium term. On average, treatment participants who live in apartment have higher skill matching than treatment participants who live in ger area by 2 percentage points. One of the distinctive features of this study is a randomized information treatment (the second treatment) for trainees. Perceived returns to schooling (Jensen, 2010) affect schooling decisions and may be inaccurate and extremely low. Hence, providing information about measured labor market outcomes of skilled people may improve trainees attendance. We found an evidence on the positive impacts of information treatment. Information treatment increased attendance by 3.3 days and decreased drop-out by 5 percentage points. Hence, this could be a cheap way to improve overall impact of training through improved attendance. 31

32 References Alzúa, María Laura, Guillermo Cruces, and Carolina Lopez. "Long Run Effects of Youth Training Programs: Experimental Evidence from Argentina." (2016). Galdo, Jose, and Alberto Chong. "Does the quality of public-sponsored training programs matter? Evidence from bidding processes data." Labour Economics 19.6 (2012): Lehmann, Hartmut, and Jochen Kluve. Assessing active labour market policies in transition economies. The Labour Market Impact of the EU Enlargement. Physica-Verlag HD, Morris, Elizabeth. "The informal sector in Mongolia." ILO: Bangkok (2001). Schneider, Friedrich. "Size and measurement of the informal economy in 110 countries." Workshop of Australian National Tax Centre, ANU, Canberra Attanasio O., A. Kugler, and C. Meghir Effects of Youth Training in DevelopingCountries: Evidence from a Randomized Training Program in Colombia. American EconomicJournal- Applied Economics, 3(3): Card, D. J. Kluve, and A. Weber Active Labor Market Policy Evaluations: A Meta- Analysis, NBERWP Card, D., P. Ibarrarán, and J.M. Villa Building in an Evaluation Component for ActiveLabor Market Programs: A Practitioners Guide. IDB Technical Notes No 311. Washington,D.C.: Banco Interamericano de Desarrollo. Ibarraran, P and D. Rosas Schady, Evaluating the Impact of Job Training Programmesin Latin America: Evidence from IDB Funded Operations. Journal of DevelopmentEffectiveness. 1 (2): Ibarrarán, P., L. Ripani, B. Taboada, et al Youth Training in the Dominican Republic:New Evidence from a Randomized Evaluation Design. Documento mimeografiado List J. and I. Rasul Field Experiments in Labor Economics, NBER, WP WorldBank Impacts of Active Labor Market Programs: New Evidence from Evaluationswith Particular Attention to Developing and Transition Countries, SPDPS Heckman. J James, LaLonde. J Robert and Smith. A Jeffrey, The Economics and Econometrics of Active Labour Market Programs, Handbook of Labour Economics, Volume III,

33 Jonh.C Ham and Robert. J LaLonde, The Effect of Sample Selection and Initial Condition in Duration Model: Evidence on Experimental Data on Training, Econometrica, Volume 64, 1996 Ashenfelter, Orley Estimating the effects of training programs on earnings. Review of Economics and Statistics 60, no. 1:47 57 Kapsos, Steven. Global Employment Trends for Youth. United Nations, Department of Economic and Social Affairs, Population Division, Shatz, Howard J., Louay Constant, Francisco Perez-Arce, Eric Robinson, Robin Beckman, Haijing Huang, Peter Glick, and Bonnie Ghosh-Dastidar. Improving the Mongolian Labor Market and Enhancing Opportunities for Youth. Rand Corporation, World Health Statistics 2015, WHO, 2015 Global Employment Trends for Youth 2015: Scaling Up Investments in Decent Jobs for Youth, Geneva, ILO, 2015, Bell, David NF, and David G. Blanchflower. "Young people and the Great Recession." Oxford Review of Economic Policy 27.2 (2011): Mroz, Thomas A., and Timothy H. Savage. "The long-term effects of youth unemployment." Journal of Human Resources 41.2 (2006): Kluve, Jochen. "The effectiveness of European active labor market programs." Labour economics 17.6 (2010): Cho, Y., Kalomba, D., Mobarak, A. M., & Orozco, V. (2013). Gender differences in the effects of vocational training: Constraints on women and drop-out behavior. World Bank Policy Research Working Paper, (6545). Maitra, Pushkar, and Subha Mani. "Learning and earning: Evidence from a randomized evaluation in India." Fordham University, Department of Economics, Discussion Paper Series 2 (2013). Hirshleifer, Sarojini, et al. "The impact of vocational training for the unemployed: experimental evidence from Turkey." The Economic Journal (2015). Card, David, Jochen Kluve, and Andrea Weber. "Active labour market policy evaluations: a meta analysis*." The Economic Journal (2010): F452- F

34 Blattman, Christopher, Nathan Fiala, and Sebastian Martinez. "Generating skilled self-employment in developing countries: Experimental evidence from Uganda." Quarterly Journal of Economics, forthcoming (2013). The Law on Employment Promotion, 2012 Comprehensive Policy on National Development based on the MDG, 2008 The fourth national report on Millennium Development Goals Implementation, 2011, UNDP Population Census Report, 2000, 2010, NSO Labour Force Survey Report, 2004, 2009, 2010, NSO The Report of Labour Department of City Governor s Office Human Development Report: Mongolia, 2007, UNDP Consumer Confidence Survey Report, 2009, 2010, 2011, SES, National University of Mongolia Labour Barometer Survey, 2010, MCA 34

35 TABLES AND FIGURES Figure1. Planned sample size versus actual sample size Total Treatment Control Planned Actual Table 1. Reasons for No-show No.Obs Percent Personal reasons Got a job VTP related reasons Not eligible Other Total Table 2. Classification of treatment groups Assigned Training Received treatment Training with letters Controls Total Received training Received training and letters Did not receive either training or letter Total Table 3. Randomization of the Second Treatment Randomization of the second treatment Total Treatment Control Total class Total student Male Female

36 TABLE 4. BASELINE BALANCE AND ATTRITIONS Variables Summary Statistics: Mean, Differences and p-value Baseline Balance Attrition: Follow-up 1 Attirtion: Follow-up 2 Treatment Control Diff p- Missing Observed Diff p-value Missing Observed Diff p- value value (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Age Female Ger area Household size Married Education in years Income below poverty line Previous work experience Work experience in months Monthly salary 447, ,375 7, , ,060 95, , ,401 1, Job contract Social security Past experience on training Planned days to attend the training Plan to complete the training Expectation on getting a job in scale of Expectation on getting a job in level Ambition to succeed in LM in scale of Ambition to succeed in LM in level Importance of own responsibility to get a job Perception of government responsibility to get a job

37 Contract type Hotelling's T-squared

38 TABLE 5. BASELINE BALANCE BY TREATMENT 1, TREATMENT2 AND CONTROL Treatment 1 (T1) Summary Statistics: Mean, Differences and p-value Treatment 2 (T2) Baseline Control (C) T1 vs. C P-value T1 and T2 vs. C P-value Variables (1) (2) (3) (4) (5) Age Female Ger area Household size Married Education in years Income below poverty line Previous work experience Work experience in months Monthly salary 423, , , Job contract Social security Past experience on training Planned days to attend the training Plan to complete the training Expectation on getting a job in scale of Expectation on getting a job in level Ambition to succeed in LM in scale of Ambition to succeed in LM in level Importance of own responsibility to get a job Perception of government responsibility to get a job Contract type Oneway analysis's p-value between treatment 1, treatment 2 and control groups 38

39 TABLE 6. FOLLOW-UP SUMMARY STATISTICS Summary Statistics: Mean, Difference and p-value Follow-up 1 Follow-up 2 t=1 t=2 Outcome Variables Treatment Control Difference p-value No obs Treatment Control Difference p-value No obs (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Employment Labor force participation Monthly earnings 280, ,056 45, , ,000 19, Hourly wage 5,572 4, ,752 5, Weekly working hours Job contract Social security Skill match Formality

40 TABLE 7. BASELINE BALANCE BY TREATMENT 2 Variables Summary Statistics: Mean, Differences and p- value Treatment 2 (T2) Baseline Balance T2 Control Diff (T1) p-value (1) (2) (3) (4) Age Female Ger area Household size Married Education in years Income below poverty line Previous work experience Work experience in months Monthly salary 479, ,377 95, Job contract Social security Past experience on training Planned days to attend the training Plan to complete the training Expectation on getting a job in scale of Expectation on getting a job in level Ambition to succeed in LM in scale of Ambition to succeed in LM in level Importance of own responsibility to get a job Perception on government responsibility to get a job Contract type Days applicant waited Institution's experience Training tuition fee from MED Training tuition fee of institution Average cost per student Average class size of institution Average salary of teachers in an institution Number of full time teachers in an institution Hotelling's T-squared

41 TABLE 8. BASELINE BALANCE BY NO SHOWS, TREATED AND CONTROL GROUPS No shows (NS) Summary Statistics: Mean, Differences and p-value Treated (T) Baseline Control (C) NS vs. T P-value NS and T vs. C P-value Variables (1) (2) (3) (4) (5) Age Female Ger area Household size Married Education in years Income below poverty line Previous work experience Work experience in months Monthly salary 432, , , Job contract Social security Past experience on training Planned days to attend the training Plan to complete the training Expectation on getting a job in scale of Expectation on getting a job in level Ambition to succeed in LM in scale of Ambition to succeed in LM in level Importance of own responsibility to get a job Perception on government responsibility to get a job Contract type Oneway analysis's p-value between no shows, treated and control groups 41

42 TABLE 9. FOLLOW-UP SUMMARY STATISTICS FOR TREATMENT 2 Summary Statistics: Mean, Difference and p-value Follow-up 1 t=1 Outcome Variables Treatment Control Difference p-value No obs (1) (2) (3) (4) (5) Attendance Drop-out

43 TABLE 10. ESTIMATIONS: TAKE-UP RATE Variables OLS Probit: Margins Logit: Margins (1) (2) (3) Age (0.01) (0.01) (0.01) Female (0.05) (0.06) (0.06) Ger area (0.05) (0.06) (0.06) Household size (0.01) (0.02) (0.02) Married (0.04) (0.05) (0.05) Education in years 0.02** 0.02** 0.02** (0.01) (0.01) (0.01) Income below poverty line (0.04) (0.05) (0.05) Previous work experience 0.09* 0.11* 0.11* (0.05) (0.06) (0.07) Work experience in months (0.00) (0.00) (0.00) Monthly salary -0.00** -0.00* -0.00* (0.00) (0.00) (0.00) Service (0.13) (0.14) (0.15) Industry (0.13) (0.15) (0.15) Contract type 0.34*** 0.36*** 0.37*** (0.04) (0.04) (0.04) Past experience on training (0.05) (0.05) (0.06) Planned days to attend the training (0.00) (0.00) (0.00) Plan to complete the training (0.09) (0.11) (0.12) Expectation on getting a job in scale of (0.00) (0.00) (0.00) Ambition to succeed in the labor market in scale of ** 0.00** 0.00** (0.00) (0.00) (0.00) Importance of own responsibility to get a job -0.00* -0.00* -0.00* (0.00) (0.00) (0.00) Perception about government responsibility to get a job 0.00** 0.00** 0.00** (0.00) (0.00) (0.00) Constant -0.43* (0.26) Observations R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 43

44 TABLE 11. IMPACTS OF TRAINING ON LABOR MARKET OUTCOMES OLS Probit Short term Medium term Short term Medium term Dependent Variable t=1 t=2 t=1 t=2 (1) (2) (3) (4) Panel A: Employment Treatment group - ITT 0.06** ** (0.02) (0.04) (0.03) (0.05) Completed training - TOT 0.14** ** (0.06) (0.11) (0.06) (0.11) Control group mean Panel B: Monthly Earnings Treatment group - ITT 59,086.56** 50,265.98** (19,505.01) (20,749.36) Completed training - TOT 151,953.90** 126,808.21** (57,137.12) (55,034.93) Control group mean 530, ,667 Panel C: Skill Match Treatment group - ITT 0.05** *** 0.04 (0.02) (0.03) (0.02) (0.03) Completed training - TOT 0.14** *** 0.11 (0.05) (0.08) (0.05) (0.09) Control group mean Panel D: Social Security Treatment group - ITT 0.04** (0.02) (0.03) (0.01) (0.03) Completed training - TOT 0.10** (0.05) (0.06) (0.04) (0.06) Control group mean Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Notes: Regressions for employment, wage and skill match include controls for gender, age, age square, marital status, ger area dummy, number of children under 1, vocational or higher education dummy, schooling years, income other than wage, ambition in labor market, contract type and seasonal dummies. Regressions for social security include controls for age, age square, ger area dummy, gender, ownership of real estate, household income, income other than wage, schooling years, marital status, wage job, small sized enterprise, social security from baseline and taking a benefit. 44

45 TABLE 12. HETEROGENEITY IN EMPLOYMENT, WAGE AD SKILL MATCH BY GENDER, AGE GROUP AND LOCATION Short Term Medium Term VARIABLES Employment Wage Skill Match Social Security Employment Wage Skill Match Social Security (1) (2) (3) (4) (5) (6) (7) (8) Randomization 0.17*** 69, ** , *** 0.05 (0.05) (102,470.55) (0.07) (0.06) (0.06) (68,088.95) (0.06) (0.08) Randomization *Female , ,185.63* (0.06) (66,365.00) (0.04) (0.04) (0.06) (57,346.31) (0.08) (0.06) Female -0.12** -189,943.18** -0.06** *** -181,634.86*** (0.05) (62,226.14) (0.02) (0.04) (0.03) (24,276.83) (0.06) (0.05) Randomization *Older 0.08** 74,423.48* , (0.03) (37,560.23) (0.07) (0.04) (0.07) (54,640.31) (0.05) (0.06) Older -0.07** -28, , (0.03) (29,992.92) (0.06) (0.03) (0.05) (32,423.82) (0.06) (0.05) Randomization *Ger -0.14** 1, , * (0.06) (86,399.70) (0.07) (0.05) (0.09) (69,023.75) (0.07) (0.07) Ger 0.15* -28, * ** 60,827.42* 0.13*** 0.05 (0.07) (68,990.58) (0.05) (0.04) (0.06) (33,614.21) (0.04) (0.06) Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Notes: Regressions for employment, wage and skill match include controls for gender, age, age square, marital status, ger area dummy, number of children under 1, vocational or higher education dummy, schooling years, income other than wage, ambition in labor market, contract type and seasonal dummies. Regressions for social security include controls for age, age square, ger area dummy, gender, ownership of real estate, household income, income other than wage, schooling years, marital status, wage job, small sized enterprise, social security from baseline and taking a benefit. 45

46 TABLE 13. IMPACTS OF TREATMENT 2 ON OUTCOME VARIABLES Attendance Drop-out Dependent OLS OLS Margins (1) (2) (3) Treatment ** -0.08** -0.05** (1.66) (0.04) (0.03) Control group mean Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Notes: Regressions include controls for gender, age, age square, marital status, ger area dummy, ambition in labor market, schooling years, number of children under 1, class size, own building, expenditure per student in log and childcare. 46

47 APPENDIX TABLE A1: DATA COLLECTION RESULTS, BASELINE SURVEY Total Treatment Control Randomized Full data of Baseline Data missing during Baseline Percent of having data in Baseline 94.7% 97.0% 90.3% Full data of Follow up Data missing during Follow up Percent of having data in Follow up % 95.6% 95.5% Full data of Follow up Data missing during Follow up Percent of having data in Follow up % 89.1% 89.3% 47

48 TABLE A2. ITT ESTIMATIONS: EMPLOYMENT, WAGE AND SKILL MATCH Short Term Medium Term Employment Wage Skill Match Employment Wage Skill Match VARIABLES OLS ME OLS OLS ME OLS ME OLS OLS ME (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Randomization 0.06** 0.06** 59,086.56** 0.05** 0.05*** ,265.98** (0.02) (0.03) (19,505.01) (0.02) (0.02) (0.04) (0.05) (20,749.36) (0.03) (0.03) Age 0.09*** 0.10*** 60,453.23* 0.05* 0.07** 0.11*** 0.13*** 93,062.90*** (0.03) (0.03) (32,338.86) (0.03) (0.03) (0.03) (0.03) (17,843.37) (0.05) (0.07) Age^2-0.00*** -0.00*** -1, * -0.00*** -0.00*** -1,749.37*** (0.00) (0.00) (739.30) (0.00) (0.00) (0.00) (0.00) (355.88) (0.00) (0.00) Female -0.16*** -0.17*** -231,083.22*** -0.10*** -0.10*** -0.20*** -0.22*** -263,809.31*** -0.11*** -0.12*** (0.02) (0.02) (37,634.07) (0.01) (0.01) (0.03) (0.03) (30,174.99) (0.03) (0.03) Married ,805.67* 0.06* 0.06** * 31, ** 0.10*** (0.03) (0.03) (29,096.64) (0.03) (0.03) (0.03) (0.03) (20,245.15) (0.04) (0.04) Ger area , * 0.12* 67, (0.04) (0.05) (30,356.65) (0.03) (0.03) (0.06) (0.06) (38,629.79) (0.05) (0.04) Number of children under ** 0.08** -7, * 0.06** 0.07*** 0.08*** 29, (0.03) (0.03) (24,773.46) (0.03) (0.03) (0.02) (0.02) (17,138.84) (0.05) (0.04) Vocational or higher education 0.08** 0.08*** * 0.07* , (0.03) (0.03) (29,571.19) (0.03) (0.04) (0.04) (0.04) (45,820.49) (0.03) (0.04) Schooling years 0.01* 0.01* 12,639.74* 0.01* , ** 0.02** (0.01) (0.01) (5,907.60) (0.01) (0.01) (0.01) (0.01) (7,011.52) (0.01) (0.01) Income other than wage -0.27*** -0.28*** -156,823.26*** -0.13*** -0.13*** -0.20*** -0.22*** -149,892.29*** -0.12*** -0.13*** (0.02) (0.02) (25,690.41) (0.01) (0.02) (0.03) (0.03) (30,362.70) (0.03) (0.03) Ambition to succeed in LM in scale of ,269.82* ,018.22* (0.00) (0.00) (653.72) (0.00) (0.00) (0.00) (0.00) (490.95) (0.00) (0.00) Trilateral contract -0.06* -0.07* -33, ** -0.04** -0.09*** -0.10*** -74,527.85*** -0.04* -0.04* (0.03) (0.04) (37,653.06) (0.02) (0.02) (0.02) (0.02) (12,226.33) (0.02) (0.02) Third quarter , ** , ** 48

49 (0.06) (0.06) (39,783.50) (0.04) (0.04) (0.05) (0.05) (37,341.86) (0.03) (0.03) Fourth quarter , (0.05) (0.05) (43,260.06) (0.04) (0.04) (0.06) (0.06) (32,506.07) (0.05) (0.05) Constant -0.57** -554,213.06* *** -802,365.23*** (0.25) (270,804.57) (0.34) (0.26) (237,040.68) (0.55) Observations 1,048 1,048 1,043 1,048 1, R-squared Standard errors clustered by district and further by ger area within the district in parentheses *** p<0.01, ** p<0.05, * p<0.1 49

50 TABLE A3. ITT AND TOT ESTIMATIONS: SOCIAL SECURITY ITT Estimations TOT Estimations Short Term Medium Term Short term Medium term VARIABLES OLS ME OLS ME OLS ME OLS ME (1) (2) (3) (4) (5) (6) (7) (8) Randomization 0.04** (0.02) (0.01) (0.03) (0.03) Trained 0.10** (0.05) (0.04) (0.06) (0.06) Age ** 0.15*** ** 0.13*** (0.03) (0.01) (0.04) (0.05) (0.03) (0.03) (0.04) (0.04) Age^ ** -0.00*** ** -0.00*** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Ger area (0.03) (0.01) (0.03) (0.04) (0.03) (0.02) (0.04) (0.03) Female (0.02) (0.01) (0.03) (0.03) (0.02) (0.02) (0.03) (0.03) Ownership of real estate (0.02) (0.01) (0.03) (0.03) (0.02) (0.02) (0.03) (0.03) Household income 0.00** ** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Income other than wage, current (0.02) (0.01) (0.03) (0.04) (0.02) (0.02) (0.03) (0.03) Schooling years 0.01** *** 0.02*** 0.01* *** 0.02*** (0.00) (0.00) (0.00) (0.01) (0.00) (0.00) (0.00) (0.00) Married 0.05** ** 0.04** (0.02) (0.01) (0.03) (0.04) (0.02) (0.02) (0.03) (0.03) Wage job, current 0.46*** 0.48*** 0.46*** 0.40*** (0.03) (0.03) (0.03) (0.04) Small sized enterprise, current -0.21*** -0.03** -0.13*** -0.12*** -0.21*** -0.14*** -0.12*** -0.11*** 50

51 (0.03) (0.01) (0.03) (0.03) (0.03) (0.02) (0.03) (0.04) Social security from baseline 0.06** *** 0.13*** 0.06** *** 0.10*** (0.03) (0.01) (0.04) (0.04) (0.03) (0.02) (0.04) (0.03) Taking a benefit, current 0.14*** *** 0.48*** 0.14*** 0.04** 0.46*** 0.34*** (0.05) (0.01) (0.04) (0.04) (0.05) (0.02) (0.04) (0.03) Constant -0.53* -1.13*** -0.54* -1.13*** (0.31) (0.41) (0.31) (0.41) Observations R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 51

52 TABLE A4. TOT ESTIMATIONS: EMPLOYMENT, WAGE AND SKILL MATCH Short Term Medium Term Employment Wage Skill Match Employment Wage Skill Match VARIABLES OLS ME OLS OLS ME OLS ME OLS OLS ME (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Trained 0.14** 0.14** 151,953.90** 0.14** 0.13*** ,808.21** (0.06) (0.06) (57,137.12) (0.05) (0.05) (0.11) (0.11) (55,034.93) (0.08) (0.09) Age 0.09*** 0.09*** 56, * 0.06** 0.11*** 0.12*** 88,100.62*** (0.02) (0.03) (32,709.68) (0.03) (0.03) (0.03) (0.03) (19,581.08) (0.05) (0.07) Age^2-0.00*** -0.00*** -1, ** -0.00*** -0.00*** -1,646.42*** (0.00) (0.00) (748.10) (0.00) (0.00) (0.00) (0.00) (400.19) (0.00) (0.00) - 226,467.40*** -0.09*** -0.09*** -0.21*** -0.21*** - 261,826.87*** -0.11** -0.10*** Female -0.15*** -0.15*** (0.02) (0.02) (38,167.24) (0.01) (0.01) (0.03) (0.03) (32,113.48) (0.04) (0.03) Married ,241.76* 0.06* 0.06** * 28, ** 0.09*** (0.03) (0.03) (30,313.92) (0.03) (0.03) (0.03) (0.03) (20,793.26) (0.04) (0.03) Ger area , * 0.11** 76,228.01* (0.04) (0.04) (31,169.82) (0.03) (0.03) (0.06) (0.05) (38,477.14) (0.04) (0.05) Number of children under ** 0.07** -8, * 0.05* 0.07*** 0.08*** 29, (0.03) (0.03) (26,735.64) (0.03) (0.03) (0.02) (0.02) (21,102.18) (0.05) (0.04) Vocational or higher education 0.08** 0.08*** 7, ** 0.07** , (0.03) (0.03) (33,009.22) (0.03) (0.03) (0.04) (0.04) (44,954.57) (0.03) (0.03) Schooling years * 11,729.38* , ** 0.02** (0.01) (0.005) (6,448.52) (0.01) (0.01) (0.01) (0.01) (7,024.38) (0.01) (0.01) - 151,188.96*** -0.12*** -0.12*** -0.20*** -0.21*** - 139,437.76*** -0.11*** -0.11*** Income other than wage -0.27*** -0.25*** (0.02) (0.01) (26,039.82) (0.02) (0.02) (0.02) (0.02) (30,307.55) (0.03) (0.02) Ambition to succeed in LM in scale of * (0.00) (0.00) (633.10) (0.00) (0.00) (0.00) (0.00) (406.11) (0.00) (0.00) Trilateral contract -0.10** -0.10*** -69, *** -0.07*** -0.09** -0.09** - 104,392.71*** -0.07* -0.06* 52

53 (0.03) (0.03) (39,469.08) (0.02) (0.02) (0.04) (0.04) (18,839.07) (0.03) (0.04) Third quarter , , (0.06) (0.06) (45,974.45) (0.04) (0.04) (0.06) (0.06) (36,360.41) (0.03) (0.03) Fourth quarter , , (0.05) (0.05) (43,367.98) (0.04) (0.03) (0.06) (0.06) (33,184.09) (0.05) (0.05) Constant -0.53* -507,067.54* *** -750,358.65** (0.25) (283,645.82) (0.34) (0.28) (258,881.49) (0.56) Observations 1,048 1,048 1,043 1,048 1, R-squared Standard errors clustered by district and further by ger area within the district in parentheses *** p<0.01, ** p<0.05, * p<0.1 53

54 TABLE A5. HETEROGENEITY IN EMPLOYMENT, WAGE AD SKILL MATCH BY GENDER, AGE GROUP AND LOCATION Short Term Medium Term Employment Wage Skill Match Employment Wage Skill Match VARIABLES OLS ME OLS OLS ME OLS ME OLS OLS ME (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Randomization 0.17*** 0.19*** 69, ** 0.15** , *** 0.22*** (0.05) (0.06) (102,470.55) (0.07) (0.07) (0.06) (0.07) (68,088.95) (0.06) (0.04) Treatment1*Female , ,185.63* (0.06) (0.07) (66,365.00) (0.04) (0.05) (0.06) (0.07) (57,346.31) (0.08) (0.08) Female -0.12** -0.14*** -189,943.18** -0.06** -0.07** -0.18*** -0.20*** -181,634.86*** (0.05) (0.05) (62,226.14) (0.02) (0.03) (0.03) (0.03) (24,276.83) (0.06) (0.07) Treatment1*Older 0.08** 0.09** 74,423.48* , (0.03) (0.04) (37,560.23) (0.07) (0.07) (0.07) (0.08) (54,640.31) (0.05) (0.05) Older -0.07** -0.08** -28, , * (0.03) (0.04) (29,992.92) (0.06) (0.06) (0.05) (0.05) (32,423.82) (0.06) (0.07) Treatment1*Ger -0.14** -0.16** 1, , * -0.16** (0.06) (0.08) (86,399.70) (0.07) (0.09) (0.09) (0.10) (69,023.75) (0.07) (0.07) Ger 0.15* 0.17** -28, * 0.10* 0.16** 0.17** 60,827.42* 0.13*** 0.14*** (0.07) (0.08) (68,990.58) (0.05) (0.06) (0.06) (0.07) (33,614.21) (0.04) (0.03) married ,381.28** 0.06* 0.06** 0.07* 0.08* 36, ** 0.12*** (0.03) (0.03) (28,472.62) (0.03) (0.03) (0.04) (0.04) (23,110.81) (0.04) (0.04) Number of children under ** 0.09*** 3, ** 0.06** 0.09*** 0.10*** 44,912.11* (0.03) (0.03) (26,433.75) (0.03) (0.03) (0.02) (0.02) (20,704.36) (0.05) (0.05) Vocational or higher education 0.08** 0.09*** 6, ** 0.07** 0.07* 0.07* 20, (0.03) (0.03) (28,000.44) (0.03) (0.04) (0.04) (0.04) (42,590.31) (0.03) (0.04) Schooling years 0.01* 0.01** 14,555.94** 0.01* 0.01* * 9, ** 0.02** (0.01) (0.01) (5,839.23) (0.01) (0.01) (0.01) (0.01) (7,005.09) (0.01) (0.01) Income other than wage -0.27*** -0.28*** -159,579.45*** -0.13*** -0.13*** -0.20*** -0.21*** -144,755.55*** -0.11*** -0.12*** (0.02) (0.02) (27,635.29) (0.02) (0.02) (0.03) (0.03) (30,091.14) (0.03) (0.03) Ambition to succeed in LM in scale of * 1, (0.00) (0.00) (698.43) (0.00) (0.00) (0.00) (0.00) (567.56) (0.00) (0.00) 54

55 Trilateral contract -0.07* -0.08* -38, ** -0.04** -0.10*** -0.11*** -78,388.26*** -0.05* -0.04* (0.04) (0.04) (37,625.45) (0.02) (0.02) (0.02) (0.03) (14,147.00) (0.02) (0.03) Third quarter , * , * -0.07** (0.06) (0.07) (44,251.00) (0.05) (0.04) (0.06) (0.06) (39,572.74) (0.04) (0.04) Fourth quarter , , (0.05) (0.06) (44,799.67) (0.04) (0.04) (0.06) (0.06) (33,250.52) (0.05) (0.05) Constant 0.38*** 175, *** 295,972.70** 0.06 (0.12) (192,785.30) (0.13) (0.12) (100,261.29) (0.12) Observations 1,048 1,048 1,043 1,048 1, R-squared Standard errors clustered by district and further by ger area within the district in parentheses *** p<0.01, ** p<0.05, * p<0.1 55

56 TABLE A6. HETEROGENEITY IN SOCIAL SECURITY BY GENDER, AGE GROUP AND LOCATION Short term Medium Term VARIABLES OLS Margins OLS Margins (1) (2) (3) (4) Randomization (0.06) (0.02) (0.08) (0.09) Treatment1*Female (0.04) (0.02) (0.06) (0.06) Female (0.04) (0.01) (0.05) (0.05) Treatment1*Older (0.04) (0.02) (0.06) (0.06) Older (0.03) (0.01) (0.05) (0.05) Treatment1*Ger (0.05) (0.02) (0.07) (0.09) Ger (0.04) (0.02) (0.06) (0.06) Ownership of real estate (0.02) (0.01) (0.03) (0.03) Household income 0.00** (0.00) (0.00) (0.00) (0.00) Income other than wage, current (0.02) (0.01) (0.03) (0.04) Schooling years 0.01** *** 0.02*** (0.00) (0.00) (0.00) (0.01) Married 0.05** * 0.06* (0.02) (0.01) (0.03) (0.04) Wage job, current 0.46*** 0.48*** (0.03) (0.03) Small sized enterprise, current -0.21*** -0.03** -0.12*** -0.12*** (0.03) (0.01) (0.03) (0.03) Social security from baseline 0.06** *** 0.15*** (0.02) (0.01) (0.04) (0.04) Taking a benefit, current 0.13*** *** 0.49*** (0.05) (0.01) (0.04) (0.04) Constant -0.12** (0.06) (0.08) Observations R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 56

57 TABLE A7. PANEL ESTIMATIONS: EMPLOYMENT, WAGE AD SKILL MATCH Employment Wage Skill Match VARIABLES OLS RE OLS RE OLS RE (1) (4) (2) (5) (3) (6) Randomization*Period -0.04** -0.04** 5, , (0.02) (0.02) (11,406.54) (11,267.50) (0.03) (0.03) Randomization 0.07*** 0.07*** 37,734.96** 37,740.11*** 0.07* 0.07** (0.02) (0.02) (12,821.93) (12,816.30) (0.04) (0.03) Period 0.25*** 0.25*** 149,288.69*** 150,143.25*** 0.05*** 0.06*** (0.02) (0.02) (9,066.11) (9,122.54) (0.01) (0.01) Age 0.07*** 0.07*** 50,020.99*** 49,858.95*** (0.02) (0.02) (16,266.41) (16,155.21) (0.03) (0.03) Age^2-0.00*** *** ** *** (0.00) (0.00) (352.27) (350.17) (0.00) (0.00) Female *** *** - 187,214.87*** - 186,075.90*** -0.10*** -0.10*** (0.02) (0.02) (15,575.02) (15,294.30) (0.02) (0.02) Married * 30,196.10* 30,760.75** 0.08*** 0.08*** (0.02) (0.02) (15,485.69) (15,552.96) (0.02) (0.03) Ger area , , (0.03) (0.03) (17,337.74) (17,490.63) (0.04) (0.04) Number of children under *** 0.03*** 1, , * 0.06** (0.01) (0.01) (11,925.64) (12,087.35) (0.03) (0.03) Vocational or higher education 0.05** 0.05*** 9, , * 0.06* (0.02) (0.02) (19,251.39) (19,319.52) (0.03) (0.03) Schooling years 0.01* 0.01** 7,002.78* 6,981.87* 0.01** 0.01** (0.00) (0.00) (3,790.99) (3,793.00) (0.01) (0.01) Income other than wage *** *** -79,353.38*** -84,902.04*** -0.12*** -0.12*** (0.01) (0.01) (11,448.30) (10,867.64) (0.01) (0.01) Ambition to succeed in LM in scale of ** 0.00** ** ** (0.00) (0.00) (346.95) (346.81) (0.00) (0.00) Trilateral contract *** *** -44,885.90** -44,277.71*** -0.04** -0.04*** (0.01) (0.01) (16,277.82) (16,280.61) (0.01) (0.01) Third quarter , , * -0.06** (0.04) (0.04) (28,007.63) (28,011.53) (0.03) (0.03) Fourth quarter , , (0.03) (0.03) (22,689.33) (22,681.61) (0.03) (0.03) Constant *** *** - 608,332.64*** - 603,640.12*** (0.17) (0.16) (171,967.61) (169,975.74) (0.36) (0.37) Observations 3,121 3,121 3,105 3,105 2,027 2,027 R-squared Number of id 1,095 1,095 1,052 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 57

58 TABLE A8. PANEL ESTIMATIONS: SOCIAL SECURITY Social Security VARIABLES OLS RE (1) (2) Treatment1*Period (0.01) (0.01) Treatment (0.01) (0.01) Period (0.01) (0.01) Age 0.04** 0.04** (0.02) (0.02) Age^2-0.00** -0.00** (0.00) (0.00) Ger area (0.01) (0.01) Female (0.01) (0.01) Ownership of real estate (0.01) (0.01) Household income 0.00*** 0.00*** (0.00) (0.00) Income other than wage, current (0.01) (0.01) Schooling years 0.01*** 0.01*** (0.00) (0.00) Married 0.03** 0.03** (0.01) (0.01) Wage job, current 0.52*** 0.52*** (0.02) (0.02) Small sized enterprise, current -0.15*** -0.14*** (0.01) (0.01) Constant -0.61*** -0.60*** (0.18) (0.18) Observations 2,808 2,808 R-squared Number of id 1,056 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 58

59 TABLE A9. PANEL ESTIMATIONS, FIXED EFFECT: EMPLOYMENT, WAGE, SKILL MATCH AND SOCIAL SECURITY VARIABLES Employment Wage Skill Match Social Security (1) (2) (3) (4) Treatment1*Period , (0.02) (11,502.59) (0.02) (0.01) Period 0.19*** 106,714.68*** 0.05*** 0.11*** (0.01) (7,349.14) (0.01) (0.01) Childcare -0.49*** -217,054.47*** -0.30*** -0.18*** (0.03) (17,489.87) (0.04) (0.02) Income other than wage -0.06*** -93,890.90*** -0.10*** 0.01 (0.02) (15,558.13) (0.02) (0.01) Household income 0.00*** 0.20*** (0.00) (0.03) Constant 0.23*** 50,045.19* 0.32*** 0.09*** (0.02) (24,540.52) (0.02) (0.01) Observations 2,868 2,853 2,067 3,185 R-squared Number of id 1,097 1,095 1,073 1,119 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 59

60 TABLE A10. IMPACTS OF TREATMENT 2: ATTENDANCE AND DROP-OUT Attendance Drop-out Attendance Drop-out VARIABLES OLS OLS Margins OLS OLS Margins (1) (2) (3) (4) (5) (6) Randomization of Treatment ** ** -0.08** -0.05** (1.56) (0.03) (0.03) (1.66) (0.04) (0.03) Female (2.38) (0.04) (0.03) Age (2.57) (0.04) (0.03) Age^ (0.06) (0.00) (0.00) Married (1.69) (0.03) (0.03) Ger area (1.67) (0.03) (0.02) Ambition to succeed in LM in scale of ** (0.05) (0.00) (0.00) Schooling years ** -0.01*** (0.31) (0.01) (0.00) Number of children under ** -0.06* (1.54) (0.02) (0.03) Class size (1.58) (0.03) (0.02) Own building 3.02* (1.65) (0.02) (0.02) Expenditure per student, log (1.96) (0.03) (0.03) Childcare (1.49) (0.03) (0.03) Constant 23.64*** 0.08*** (1.29) (0.03) (37.06) (0.65) Observations R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 60

61 APPENDIX 3. Dear Ms. XXX According to official statistics in our country, people that complete vocational training courses improve substantially their welfare and labor market success. Think about these numbers: In 2012, people with vocational and professional skills made 35% more in salaries than people without those skills. In 2012, the average monthly salary of individuals with some vocational and professional skills was MNT, while the average monthly earnings of individuals without any professional and vocational skills were only MNT. In 2012, the majority of available jobs were taken for people with vocational and professional skills. In 2012, two out of three individuals with vocational and professional skills were employed while only half of people without vocational and professional skills were employed. These numbers suggest that the best investment you can do is to complete the vocation training. The benefits of vocational training will last for many years to come. Do you want to improve your chances of being successful in the labor market? Do you want to get a job? 61