Demand-Driven Youth Training Programs: Experimental Evidence from Mongolia

Abstract

1. Introduction

Youth unemployment is a ubiquitous problem in most developed and developing countries. Over seventy-three million youth aged 15–24 are unemployed worldwide, and around 20 percent of the world's youth are neither employed nor enrolled in an education or training program (International Labour Organization 2017). For disadvantaged youth, who disproportionately lack skills and access to decent jobs that could lift them out of poverty, labor-market conditions are even more drastic (International Labour Organization 2015). In Mongolia, the share of young individuals who were neither employed nor enrolled in an education or training program topped 25 percent (Shatz et al. 2015).

This study assesses the effectiveness of a vocational-training program on labor-market outcomes of disadvantaged youth in Ulaanbaatar, the capital city of Mongolia, following a standard randomized controlled trial design (RCT). Vocational-training programs are the most widely used active-labor-market policy (ALMP) intended to mitigate unemployment among poor youth worldwide, but no evidence exists regarding their effectiveness in central Asia. The most recent meta-analyses (e.g., McKenzie 2017; Card, Kluve, and Weber 2018; Kluve et al. 2019) have revealed this important gap in the literature. Indeed, this large body of evidence shows modest (e.g., McKenzie 2017) to positive (e.g., Kluve et al. 2019) effects of vocational-training programs on employment and earnings of youth in developing settings.¹ A solid pattern that has emerged from this literature is the substantial heterogeneity of estimated treatment effects as a result of variations in country settings, program design, and profile of targeted groups.

Mongolia offers a new setting for an assessment of ALMPs because it is a “transitional country” – that is, one whose labor market rapidly transformed from a centrally planned to a free-market economy. In fact, Mongolia's economy shifted from a centralized, Communist system, in which youth unemployment was officially reported as being very low or nonexistent, to a free-market economy with a high share of youth not in employment, education, or training (NEET). Mongolia's is a traditional herding and agricultural economy that transitioned into a resource-based economy in a short period of time. Two demographic shifts have added pressure to the labor markets over the past 20 years. First, the share of working-age youth is relatively high because Mongolia has a population with an average age of 26. Second, sustained internal migration from rural areas to the city of Ulaanbaatar has doubled the population of the capital city over that period. As a result, the rate of youth labor-force participation in Ulaanbaatar is remarkably low at around 40 percent. Thus, assessing whether vocational-skills training would foster employment and wage growth for young, vulnerable, and unemployed individuals in such an environment is relevant to policy.

The Mongolian Vocational Skills-Training Program (MVTP) follows a market-based approach in program design that is intended to match the skills that training institutions offer with those that are in demand in the market. This is sometimes known as a “demand-driven” ALMP. Such programs have become widely used in several Latin American countries, and assessment of their effectiveness shows positive effects on employment and earnings, particularly for disadvantaged women (Betcherman et al. 2007; Attanasio, Kugler, and Meghir 2011). “Demand-driven” training programs are designed to combine in-class training in selected vocational skills – where proven demand exists for those skills – with on-the-job training in the form of internships (see, e.g., Ibarrarán and Rosas 2009; Galdo and Chong 2012). In contrast to RCT training interventions implemented over the last decade in developing settings, a distinctive difference of the MVTP initiative is its short duration and low cost. The per-trainee cost ranges from USD 90 to USD 140 for trainings that vary from 20 to 45 days.

Because lack of knowledge about market returns to training in a setting in transition from Communism to a market-based economy could lead participants to make uninformed choices, this study implements a complementary RCT design within a group of MVTP participants in which personalized weekly newsletters with information about the market returns to vocational training in Mongolia are distributed. The aim of the intervention was to shape choices made by MVTP participants regarding dropout or completion of vocational training. As shown Dammert and Galdo (2013) and Choe, Flores-Lagunes, and Lee (2015), lack of compliance with the treatment design is an important source of variation in the magnitude of treatment effects for training programs. Such a finding suggests the possible benefits of increased training exposure for potential dropouts. While providing information about market returns has been shown to be effective in education interventions in other developing settings (e.g., Jensen 2010; Dinkelman and Martínez 2013), to the best of the authors’ knowledge the approach has not been tested as a policy design in the context of an ALMP in a developing country.

The main results from this study point out positive and meaningful effects on skills matching and self-employment. The estimated impacts for employment and earnings, in contrast, are more ambiguous. The intent-to-treat estimated employment impacts are not statistically significant and range from −0.02 to 0.054, while the absolute change in monthly earnings ranges from USD 17 to USD 32, with statistical significance varying across model specifications and time span. In line with what has been reported in the literature, these mean effects hide important heterogeneities across the sample of interest, to the extent that positive and statistically significant effects on employment, earnings, and skill match are observed for relatively older, richer, and better-educated young people. Finally, provision of information on market returns to training positively affects the time of exposure to training (i.e., number of program days attended), but does not lead to higher employment or greater earnings.

This article adds to the present understanding of youth (un)employment, particularly in transitional countries. Its results are consistent with a broader body of micro-level evidence that suggests modest labor-market gains of vocational-training programs for youth. The study contributes to this evidence by assessing a low-cost, short-length training initiative, by providing some of the first evidence from central Asia, and by focusing attention on information constraints in returns to vocational training upon completion of training and employment. Indeed, these findings complement research regarding how providing information affects returns to formal education and school completion (e.g., Jensen 2010). Although there are limits to generalizability and long-term effects, these results provide insight into market-based approaches and information-based programs for ALMP in transitional settings.

This paper is organized as follows. Section 2 briefly describes the Mongolian labor market; section 3 provides details about the training design; section 4 develops the evaluation experimental design; section 5 discusses the data and baseline covariate distribution for treatment groups; section 6 presents the results; and section 7 provides the concluding remarks.

2. Institutional Setting

Mongolia is a small, transitional country with a population of slightly above 3 million living in a landlocked area located between Russia and China. Ethnic Mongols account for about 95 percent of the population. Mongolia is one of the least densely populated countries in the world, with almost half its population living in the city of Ulaanbaatar, the country's capital. Following the dismantling of the USSR in the early 1990s, Soviet assistance, which had accounted for almost one-third of Mongolia's GDP, disappeared almost overnight. Mongolia's economy changed dramatically from a centrally planned system to a market-based one. Mongolia, however, is resource-rich, with large deposits of copper, gold, coal, and uranium. Its mining sector accounts for almost one-fifth of GDP and 40 percent of exports, although it accounts for only 4.2 percent of employment. Mining has transformed Mongolia in a few decades from a traditional agricultural- and herding-based economy to a resource-based economy (World Bank Enterprise Survey 2013). Today, Mongolia has a per capita GDP of USD 13,000 (PPP), which makes its economy comparable to that of South Africa or Sri Lanka. Agriculture nonetheless remains the largest employer in the country and still absorbs 30 percent of the labor force (down from 46 percent in 2003), followed by services (17 percent), public administration (15 percent), and manufacturing (10 percent) (World Bank Enterprise Survey 2013).

Structural economic changes have been accompanied by two important demographic shifts that have imposed additional pressure on labor markets. Mongolia's population is relatively young: the median age is 26 years, while the youth share of the working-age population is close to 55 percent. Likewise, the clearly declining trend of employment in herding and agricultural is mirrored by strong internal migration from provinces to the city of Ulaanbaatar. Indeed, since the late 1990s, population growth in Ulaanbaatar has averaged 5.8 percent, twice the country's population growth rate. In 2013, 20 percent of the youth in Ulaanbaatar were new migrants who had moved to the city in the previous five years (Shatz et al. 2015).

Before the transition, the Mongolian labor market was characterized by high labor-force participation, which reached more than 75 percent in the early 1990s. According to official statistics, unemployment was nonexistent because a policy was in place that “everyone should work.” Only after 1992 did Mongolia begin measuring and reporting unemployment rates. Economic reforms implemented in the 1990s and afterward, including privatization and price liberalization, led to structural changes in Mongolia's economy, vastly transforming Mongolian labor markets. Indeed, labor-force participation began to fall consistently while the youth unemployment rates increased dramatically. In 2013, the country-level labor-force participation rate was 56 percent and 42 percent, respectively, for men and women aged 15–29. In Ulaanbaatar, the labor-participation rates of youth had fallen into the range of 30 percent for young women and 40 percent for young men (Shatz et al. 2015). Importantly, the share of inactive youth aged 15–29 has remained consistently above 20 percent. In 2013, the NEET rate for men and women aged 15–29 in Ulaanbaatar reached 13 and 27 percent. As in other developing settings, the Mongolian labor market shows stark gender disparities as a result of social norms and different employment frameworks for men and women (Pastore 2010; Schmillen and Sandig 2018). Social norms or deeply rooted expectations regarding the state's responsibility for providing jobs may have played some role in this emerging picture. In the study sample, for instance, measuring the subjective belief that it is a “government responsibility to provide jobs to us” resulted in a mean response of 85 points (on a scale of 0 to 100 points).

On the demand side, the role of labor-market regulations that directly affect the costs of hiring and firing may constitute a relevant barrier to entry. Lehmann and Muravyev (2012) described the drastic transformation of labor-market legislation frameworks in transitional countries and their subsequent role in youth unemployment. According to the World Bank Enterprise Survey (World Bank 2013), less than 3 percent of Mongolian firms reported labor-market legislation as a constraint on labor demand. Yet, a quarter of sampled firms stated that the main obstacle to hiring was the inadequacy of skills in the workforce and mismatching between prospective employees’ skills and market needs. If such mismatching exists, it is important to assess the effectiveness of training programs tailored toward providing the vocational skills that are in demand in the market. One potential barrier for such investments could be the existence of compressed salary scales – which are typical under centrally planned economies – because firms might be unable to reward applicants who have additional skills. The evidence, however, is not clear in this regard. In 2012, large and increasing labor-market returns were observed for each additional year of schooling.² Nonetheless, firm-level data showed that only 22.6 percent of Mongolian enterprises had a performance-based wage system, and only 7.9 percent had skill-based wage systems (Research Institute of Labor and Social Protection 2017). Furthermore, although there is no legislation that prevents firms from adopting new screening technologies in the labor markets, the awareness and utilization rate of government employment services is very low (less than 20 percent of idle youth in Ulaanbaatar indicated they were aware of employment websites, electronic job boards, and other types of public support; see Shatz et al. 2015).

3. The Mongolian Vocational-Training Program (MVTP)

The MVTP was introduced in 2003 to counteract high levels of unemployment and idleness. Its primary goal was to help those who were poor, unemployed, or vulnerable to unemployment to find jobs through a combination of vocational-skills training and internships in business firms. This program remains the oldest and most extensive labor-market policy in effect in Mongolia. The MVTP follows key aspects of the so-called demand-driven training approach, which is designed to align prospective employees’ skills with the real needs of local employers (Betcherman et al. 2007). The rationale for demand-driven training programs is twofold. First, private institutions can offer relevant, up-to-date training that decentralizes the traditional supply of vocational training by public institutions. Established training providers therefore usually bid to offer training in trades with high labor demand. Second, by combining traditional classroom education with on-the-job training in the form of internships, MVTP can provide training in trades that the market demands.³

The Employment Promotion Service Center (EPSC) and the Metropolitan Employment Department (MED), which are both within the Mongolian Ministry of Labor, oversaw the implementation of this training program. The MVTP is financed by the State Employment Promotion Fund, and according to administrative data from the Ministry of Labor the total number of participants was 8,000 by 2011, and total program expenditure was approximately 3.5 billion MNT (USD 2.1 million). The EPSC selects training institutions through a competitive bidding process. The selection criteria for training institutions include evidence of ability to provide adequate training, legal registration, curriculum quality, teaching quality, adequacy of training sites, and, importantly, the ability to place trainees in internship positions with registered private employers.

Although the MVTP began in 2003, its effectiveness has never been assessed. This paper focuses on the 2013 call that deliberately used an RCT to identify and measure MVTP impacts. In that year, the EPSC selected 47 training institutions in Ulaanbaatar that offered 141 courses in 6 family vocational skills, including construction, hairdressing, cooking, and heavy-machinery operation. The length of training varied from 20 to 45 days, depending upon vocation, with a minimum duration of 144 hours per course. According to the program's regulations, traditional classroom teaching did not exceed 30 percent of total hours, and practical, on-the-job training and internships accounted for the rest.

The MVTP tuition was set at between 140,000 MNT and 220,000 MNT per participant in 2013 (approximately USD 90–140). These fees were paid directly by MED to training providers. The program offered no additional benefits such as transportation, meals, or insurance to trainees. As a result of budgetary constraints, the official number of training slots was set to 1,400 in 2013. Comparative information on course length, costs, take-up rates, attrition rates, and treatment effects across 11 youth training RCT interventions in developing countries is reported in table 1. The length of training ranged from two-and-a-half months to two years for classroom training plus two to six months for internships. The cost of training ranged from USD $330 to USD $1,700 dollars per trainee, with the exception of Uganda (Bandiera et al. 2020). Compared to other youth vocational-training programs, then, the MVTP was shorter and less expensive.

The contractual framework for the market-based MVTP approach follows two levels. First, the EPSC signs contractual agreements with training providers regarding course length and content, timelines, and payments and internships for trainees. Second, individuals assigned to treatment are invited to sign an agreement with the corresponding labor office to secure participation. Up until Spring 2013, participants were required to sign a “trilateral” contract that involved a labor division district officer, an MED officer, and the beneficiary. This contract did not include any obligation on the trainees’ side to secure jobs after the completion of the classroom and internship phases. Subsequently, the MED changed to a “quadrilateral” agreement that added prospective employers as a fourth signatory. In practice, this meant that trainees needed to sign a job agreement with business firms to secure job placements after the completion of the program. Although these job agreements were not legally binding, the institutional change created an important slowdown in registration and enrollment, which in turn affected the program's take-up rates, as shown in section 5.

A Complementary Treatment Design

Because no information exists regarding the effectiveness of this ALMP, or any similar intervention in Mongolia, the average participant may not be aware of the labor-market benefits of completing training. Indeed, administrative data showed that dropout rates were particularly high for the MVTP. This lack of information about the benefits of vocational training is even more important in the context of Mongolia's very rapid transition from a state-controlled to a market-based economy. Less than 20 percent of unemployed Mongolian youth in Ulaanbaatar indicated they were aware of public-information sources regarding employment, while less than 5 percent made use of them in 2014 (Shatz et al. 2015).

Thus, inspired by the work of Jensen (2010) in the context of educational interventions, an information treatment has been added to the study's evaluation framework. Participants are randomly selected to receive weekly newsletters with information about labor-market outcomes among skilled and unskilled workers in Mongolia.⁴ The newsletters stated market wages for occupations in sectors that were similar to those of the trainees and compared wages for jobs filled by unskilled workers. This information was estimated based on the 2012 Mongolian Household Survey. Figure S1.1 in the supplementary online appendix shows a typical newsletter submitted to trainees.

This intervention combines two salient features of a relatively new stream of literature in development economics that has highlighted the role of framing and message repetition (e.g., Chong 2011). On the one hand, it provides (new) information on market returns to vocational training to participants who are typically information constrained. On the other hand, it provides repetitive (weekly) information to target individuals with potential limited attention or present-bias individuals who, given a choice between a payoff today (dropout from training for work) and a payoff in the future (complete training for subsequent work), will choose to have the payoff now (Gabaix and Laibson 2006).”

Information theory states that the value of information is determined by three important factors – credibility, novelty, and ability and willingness to act based on updated beliefs – all of which involve different forces and trade-offs (Hirshleifer and Riley 1992). Individuals process new information largely based on their prior beliefs. If individuals change behavior in response to new information, then that information has value to them. Thus, the value of these newsletters among participants could not be considered a given. On the one hand, disadvantaged participants do not have easy access to labor-market information, so it is arguably the case that information on training returns was new to the average participant. On the other hand, MVTP is a government program, and the erosion of trust in government on the part of disadvantaged populations may affect their perceptions of the credibility of information. Willingness to act based on new information could also be affected by commonly reported biases among unemployed individuals regarding their employment prospects (e.g., overoptimism). Such biases distort their behavior and may cause them to do little to leave unemployment (Spinnewijn 2015). The ways in which such factors interacted each other, then, would determine the value of the information to participants.

4. Evaluation Design

The randomization scheme for the two RCTs is covered in section 3 is depicted in the fig. S1.2 in the supplementary online appendix. Prospective trainees aged 15–30 were assessed at their local district office. After completing a short baseline questionnaire, each applicant was screened for eligibility by an administrative officer. All suitable applicants were then sent to a district labor office in which each applicant, alongside a labor officer, chose her preferred trade courses on a first-come, first-served basis. Individuals who chose unavailable trade courses were not included in the experiment unless they opted for an alternative available trade.⁵ At this stage, after prospective beneficiaries chose their courses, randomization at the individual level proceeded, and information from all district labor offices was pooled at the end of each day.

The random allocation of MVTP beneficiaries took place between August 26 and November 22, 2013. Each day during that period, the research team in coordination with MED officers, randomly assigned eligible applicants to either the treatment or control groups based on a 2:1 rule. As a result, the treatment group was made up of 774 trainees (65.2 percent), and 414 (34.8 percent) were assigned to the control group for a total of 1,188 participants.⁶ Individuals randomly assigned to the treatment group were notified by telephone or through in-house visits about the program benefits, timelines, and regulations in the days that followed. Individuals who chose unavailable trade courses were not included in the sampling design.

Individuals in the treatment group were distributed across 141 courses at 47 training centers. Class size varied highly across training courses and ranged from 3 to 30 participants. The average number of MED-funded students was 9.6 per class. Actual class sizes may have been slightly larger, however, because training institutions were entitled to recruit privately funded students if maximum class size was not reached. Though this happened occasionally, the significance was marginal, and most courses maintained small class sizes.

The second random allocation, that is, the newsletter treatment, was implemented after beneficiaries were assigned to a vocational-training treatment group. The allocation of weekly newsletters was implemented at the class level, rather than at an individual level, to prevent spillover effects. Overall, 101 classes were randomly assigned to the treatment-newsletter group and 40 classes to the control group. Thus, the final sample size was not known a priori and was determined after assigning classes to newsletter treatment and control groups.

The number of letters trainees received in the information-treatment group varied according to the length of training. Individuals assigned to the newsletter-treatment group received from 0 to 4 newsletters, with an average of 2.25. Across vocational trades, the number of letters varied from an average of 3.24 (cooking) to 1.66 (services). Because the total number of letters provided to participants varied depending on differences in the training, and because people who chose longer trainings may had unobservable characteristics that were correlated with training completion, the evaluation framework focused only on the comparison of the treatment and control information groups without considering the intensive margin of the treatment itself.

5. Data, Balancing Tests, and Take-Up Assessment

The empirical framework used in this study is based on individual-level survey data, including a baseline, collected in Fall 2013 and two follow-up surveys administered 6 and 18 months after training. The timeline of the intervention and data collection is illustrated in fig. 1. The evaluation data includes socio-demographic variables, participation in formal schooling and training, labor-market outcomes, and detailed information on participation in the MVTP. Attrition reached 5.4 percent at baseline, 9.6 percent at the first follow-up survey, and 15 percent at the second follow-up survey. As shown in table 1, and relative to similar interventions with similar time span for the measurement of treatment effects, the attrition rate in this study is moderate.⁷

Figure 1.

Timeline of the VT Intervention Mongolian VT Program, 2014–2016

Source: Authors' elaboration.

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Tables S1.1 and S1.2 in the supplementary online appendix provide results of tests for differences in baseline variables across treatment and control units for the 6- and 18-month follow-up samples conditional on response status. For most baseline covariates, no systematic differences are observed between individuals in the treatment and control groups after conditioning on response status. Only for a small number of baseline covariates is the null of equality of means by treatment status rejected, after conditioning on response status. Finally, table S1.3 in the supplementary online appendix reports the overall rate of attrition by treatment status. The null hypothesis that the overall attrition rate is of equal magnitude between treatment and control groups cannot be rejected.

Mean covariate balancing tests for the two experimental designs are shown in table 2, one for the allocation of training slots (left panel) and one for the allocation of weekly newsletters to individuals who received treatment (right panel). Baseline data show that the typical applicant is 23 years old, a woman (65 percent), and poor (82 percent lived in gers).⁸ Almost half of the participants are married (45 percent), have children (42 percent), and live with their parents (47 percent). On average, they show high levels of formal schooling (51 percent had completed high school) and prior labor experience (60 percent). The p-values for the coefficients of OLS models that regressed treatment status on baseline covariates (left panel) are above 0.10 in all cases, indicating that individuals in the treatment and control groups came from the same population. These baseline characteristics are broadly similar to those of poor individuals aged 15–30 in Ulaanbaatar as reported in the 2014 Mongolian socio-economic survey, which shows similar shares of idle youth, age mean composition, and average years schooling. The MVTP sample, however, shows a higher proportion of women (65 vs. 55 percent), a higher share of individuals living in gers (82 vs. 56 percent), smaller household size (4 vs. 6), and higher marriage rates (45 percent vs. 35 percent). Put differently, these differences suggest that the MVTP served, on average, a poorer set of Mongolian youth.

For the newsletter-treatment group, on the other hand, the right panel in table 2 shows that the p-values for most variables do not reject the equality of means between experimentally determined treatment groups. Equality is rejected regarding a few variables, with the exception of those related to chosen vocations. This is expected because the random allocation of MVTP participants to the newsletter-treatment group occurred at the course level rather than at the individual level.

Compared to other vocational-training programs for youth in developing countries (see table 1), administrative data reveal that lack of full compliance with the treatment is relatively high for the MVTP. Out of the 766 applicants randomly assigned to receive vocational-skills training, 439 (57 percent) did show up for training, while other studies have reported take-up rates from 77 to 99 percent. Table S1.4 in the supplementary online appendix gives detailed information on enrollment numbers.

Self-reported survey information indicated that, among those who did not take up the treatment, 35 percent cited family and personal commitments (e.g., household chores or pregnancy), 30 percent had started a new job right after enrollment, and 31 percent said they were not able to comply with contractual MVTP requirements (i.e., quadrilateral contracts). From a policy standpoint, then, empirical assessment of the determinants of take-up becomes essential to gain insight both into the operation of the program (e.g., targeting, eligibility rules, and institutional requirements) and the identification and estimation of the parameters of interest.

The results from linear-probability models are shown in table 3, where the dependent variable takes the value of 1 for those treatment-group individuals who received the treatment and 0 for those who did not. After including a rich set of socio-demographic and labor-market variables, MVTP institutional variables, and self-reported expectations regarding training and performance in the labor markets, results indicate that a handful of socio-demographic variables are statistically correlated with take-up decisions. On average, gender, household wealth, age, and formal schooling are important to take-up rates because women and individuals who were wealthier, older, and better-educated were more likely to participate in the program relative to male, poorer, younger, and less well-educated individuals. Importantly, findings show no meaningful statistical relationship between take-up and labor-market variables at baseline. This pattern ran counter to observations in other labor-market programs in which variables related to labor markets emerged as the main determinants of take-up.

Moreover, institutional variables related to the operation of the MVTP emerge as important take-up predictors. Individuals who were required to present quadrilateral contracts, for example, are 54 percentage points less likely to take up the treatment relative to individuals asked to present trilateral contracts. This institutional requirement constituted a critical barrier for take-up. Likewise, the likelihood of attending the training is statistically related to the vocational-skills courses that participants chose. Individuals who initially selected courses related to hairdressing and craftsmanship are less likely to attend training (−20 percentage points), while individuals who selected mechanical- and machinery-related courses are more likely to participate (+18 percentage points).

Results also show that take-up decisions are associated with self-reported expectations regarding training, labor-markets, and the role of government in facilitating jobs for youth (see table 3). Individuals who felt optimistic about getting a job or those who believed that the government should play a major role in helping youth find jobs took up the treatment disproportionately. In contrast, individuals who believe that getting a job is a personal responsibility are less likely to take up the treatment.

Overall, as shown by the p-values of joint significance at the bottom of table 3, socio-demographic and institutional variables (i.e., contractual agreements) are the most important predictors of take-up rates, while prior labor-market outcomes are not statistically associated with take-up decisions.

6. Empirical Framework and Findings

Table S1.5 in the supplemental online appendix shows the first-stage estimation results. The coefficient associated with the instrumental variable Z is statistically significant at the 1 percent level, and the resulting F-statistics confirm the relevance and strength of the instrument.

The main results for the vocational training intervention are presented in table 4. Four outcomes of interest are examined: employment in the week prior to the survey, monthly earnings, skills matching, and self-employment. Skills matching is defined by a dummy variable that takes the value of 1 if an individual self-reported using trained vocational skills at current work, 0 otherwise.¹⁰ Table S1.6 in the supplemental online appendix provides a full description of all outcome variables. The upper panel shows short-term (six-month) treatment effects, while the lower panel shows medium-term (eighteen-month) mean effects. Odd- and even-numbered columns show results for specifications without and with control variables. Robust standard errors are reported in parentheses.

Short-term point estimates (six months) show positive and statistically significant effects for self-employment and skills matching regardless of the econometric specification and parameter of interest. Relative to the mean of the control group, the magnitude of the treatment effects is somewhat large, particularly for the TOT point estimates (from 0.099 to 0.139 for skills matching and from 0.083 to 0.085 for self-employment). These results suggest that the vocational skills of MVTP participants are better aligned to occupations as compared to the skills of individuals in the control group. Similarly, and although the incidence of self-employment remained very low in comparison to what is commonly observed in other developing countries, these point estimates indicate that self-employment more than doubled among MVTP participants relative to nonparticipants. For the employment outcome, on the other hand, positive but imprecisely measured treatment effects are observed, ranging from 0.037 to 0.054 (ITT) and from 0.076 to 0.127 (TOT).¹¹ Finally, earnings effects are positive across all specifications, but their statistical relevance depends upon both the use of control variables and alternative specifications for the outcome itself. In this regard, significant and sizable impacts arise whenever LASSO control variables are included in the specification models, while imprecisely measured earnings effects emerge when the econometric specifications do not incorporate control variables (see table 4). The results of sensitivity tests that use the inverse hyperbolic sine transformation, trimming, and winsoring for earnings outcome are shown in table S1.7 in the supplemental online appendix.¹² While statistically significant results hold under trimming and winsoring approaches, imprecise point estimates emerge when the distribution of the outcome is smoothed out. All in all, the magnitude of the mean effects for monthly earnings is somewhat large (as high as 23 percent for the ITT) from a mean of the control group of approximately USD 110.¹³

Mean effects eighteen months after completion of training are presented table 4. Relative to short-term effects, a decay in the magnitude and statistical significance of treatment effects emerges for all outcome variables except self-employment. The effects on skills matching dissipate and become statistically not significant, while effects for the employment outcome become either negligible or negative. For monthly earnings, the point estimates from both ITT and TOT parameters are still in line with short-term findings because smaller but positive statistically significant effects emerge depending on the specification used. Self-employment, on the other hand, is the only outcome that shows steady positive and statistically relevant impacts over time, with sizable point estimates ranging from 0.04 to 0.092.

Sizable effects can be observed when comparing MVTP confidence intervals for earnings ITT treatment effects, which are measured in terms of percent gain relative to the control group and reported in footnote 13, against the corresponding findings that emerged from 11 RCT vocational-training programs that targeted youth worldwide (see table 1), somewhat sizable effects can be observed. This is particularly interesting given the short duration and low cost of this training initiative. As a result, and although there is uncertainty associated with the gains in earnings, a straightforward cost-benefit calculation reveals that it requires six to eight months of treatment effects to recoup the costs of the courses. This is plausible given the short-term impacts depicted in table 4.¹⁴

These findings, which are also in line with other studies that measured effects over a longer time span, found that treatment effects fell further over time. (e.g., Alzúa, Cruces, and Lopez 2016; Ibarrarán et al. 2019). Overall, results suggest that training helped trainees move to tailored jobs right after treatment ended, but over time the MVTP fell somewhat short of achieving its promises. This is plausibly explained by the short duration of employment that is likely secured at business firms that offered internships. However, extending these job contracts could have not been a priority for many business firms over time. Unlike most developing countries, employment in Mongolia is mostly formal, and thus extending job contracts to disadvantaged youth that participated in short vocational training courses might not be a priority for many business firms. This, in turn, might explain the consistency of positive impacts for self-employment.¹⁵

Heterogeneous Effects

A solid pattern in the existing training literature is the substantial heterogeneity in the impacts of training. To account for the heterogeneity of effects across subgroups of participants, the estimation framework and model specification given in equations (1) and (2) are applied after interacting the treatment status variable with baseline covariates of interest: gender (men vs. women), age (15–21 vs. 22–30), poverty status (poorest vs. less poor), and educational attainment (less than high school vs. high school vs. technical and university education).¹⁶ These policy variables, commonly used in the assessment of vocational-training programs worldwide, are related to the efficiency of the targeting approach. As before, the same four outcomes of interest are monitored, at 6 (table 5a) and 18 months (table 5b) after the intervention.