- Original article
- Open Access
The long-term earnings consequences of general vs. specific training of the unemployed
© Stenberg and Westerlund. 2015
- Received: 29 April 2015
- Accepted: 30 October 2015
- Published: 13 November 2015
Training programs for the unemployed typically involve training specific skills in demand amongst employers. In 1997, Swedish unemployed could also choose general schooling at the upper secondary level. This offers a unique opportunity to assess the theoretically ambiguous long-term relative earnings of general vs. specific training for unemployed. Analyzing detailed administrative data 1990–2010, we find 1) that specific training is associated with higher earnings in the short run, 2) that earnings converge 5–7 years post program and 3) that individuals act on their comparative advantages. When we extrapolate our estimates to life-time earnings, there is overall a relative advantage of specific training. However, for females with limited prior education, we find a relative life-time earnings advantage of general training.
JEL-codes: I21, J62, J68
- Active labor market programs
- Adult education
- Vocational training
Governments in most OECD countries offer training programs for the unemployed, typically oriented toward vocational/specific skills. The consensus view seems to be that vocational/specific training is a more efficient measure for unemployed individuals than are courses providing general/theoretical skills. In the short run, learning a branch specific skill is presumed to better enhance re-entry into employment. General training, without an obvious connection to a labor market branch, may have less of an impact. However, in the long run, if general skills increase the ability to learn new tasks, this could make workers less sensitive to changes in the demand for skills. Earlier studies of adults in general education have reported average earnings returns which still increase eight to ten years after enrolment (Jacobson et al. 2003, 2005, Stenberg 2011). As program effects vary between individuals and over time, these estimates are not directly comparable with evaluations of vocational training programs, but they raise the question of whether the long-term effects of general training would catch up with or exceed the earnings effects of specific training. 1 In addition, an expansion of the menu of programs could also potentially enhance efficiency by allowing individuals to act on their comparative advantages. Some economists have suggested that governments should stimulate adults to enroll in formal schooling during economic downturns (e.g., Heckman and Urzua 2008, Pissarides 2011), but there is an almost complete lack of empirical research on this topic. It is therefore unclear whether skill adjustments among the unemployed should involve a larger element of general training.2
The purpose of this article is to evaluate the relative earnings association of general versus specific training for the unemployed. In the spring of 1997, the Swedish government announced the Adult Education Initiative (AEI henceforth) which targeted the same groups of the unemployed as did the traditional vocational/specific training program. AEI enabled unemployed adults aged 25–55 to attend a year of full-time schooling at the upper secondary level, with financial support equal to a maintenance of unemployment benefits. The AEI started in August 1997 and attracted large numbers. We study a sample comprising the unemployed individuals in the spring of 1997. Of these, many individuals enrolled in either the AEI or the largest vocational training program in Sweden (Arbetsmarknadsutbildning), which we will refer to as “Labor Market Training” (LMT).
We explore exceptionally rich population register data which includes annual earnings from 1990 until 2010, providing a follow-up period of 13 years. The empirical strategy is based on difference-in-differences propensity score matching, which explicitly takes into account heterogeneous treatment effects and individual time invariant (fixed) unobserved characteristics. It also enables us to consider the possibility that results are affected by individuals acting on their comparative advantages in practical/theoretical skills. We achieve this by altering between defining AEI as treatment and LMT as counterfactual state, or vice versa. The intuition is simply that by defining AEI (LMT) as treatment, propensity score matching estimates will mirror the relative program effects for individuals with a relatively high probability to enroll AEI (LMT). The overall findings convey similar implications across a wide set of specifications. Our preferred estimates are based on more than 100 covariates and are robust, e.g., when we check for potential bias by including measures of cognitive and non-cognitive skills (males born 1953 or later) and for “parallel trends” by controlling for dynamic factors (changes) prior to program enrolment. In contrast, the results are sensitive with respect to the length of the follow up period and to the assumed counterfactual state, LMT versus the AEI.
Research comparing general and specific training for the unemployed is scant. Stenberg (2007) is a study similar to the present one, reporting individual fixed effects regression results of average post-program earnings up and until 2003 (six years after enrolment). However, the analyses did not address shape of the trajectories or the longer perspective, instead focusing on cumulative post-program earnings. The estimates indicated throughout that LMT was associated with higher relative earnings, thus corroborating the consensus view regarding outcomes as the LMT individuals’ earnings exceeded those of participants in the AEI.
The main contribution of this study is the estimation of the long-term relative earnings impact of general versus specific training of the unemployed 13 years post enrolment. The length of the observation period makes it possible to examine the theoretically ambiguous issue of long-term earnings implications. Specifically, we study if the earlier reported short-term earnings advantage of LMT remains over time, whether trends converge or whether the long-term earnings are more in favor of general training. The results provide unique information on whether an expansion of active labor market programs to include general schooling yields scope for efficiency gains. This is of clear policy relevance. A second contribution is that we allow estimates to vary according to individuals’ comparative advantages, as our empirical strategy considers heterogeneous program effects and interchangeably models the counterfactual state as LMT or AEI. Our results first confirm earlier findings from studies referred to on vocational or general education for adults. For the case of LMT in Sweden, these results represent a minor contribution given the length of the evaluation. Turning to relative program estimates, the results indicate that specific training outperforms general training in the short run (5–7 years). In the longer perspective, 7–13 years after program enrolment, the estimates tend to converge toward zero. However, we also find evidence revealing substantial heterogeneity. In particular, we find for females with limited education and for residents in a metropolitan labor market region (Stockholm) that an expansion of labor market programs to include general training of the unemployed might enhance efficiency. Finally, we also find indications that vocational training may be a way to compensate for low levels of non-cognitive skills or, conversely, that non-cognitive skills are an important complement to skills obtained in general training.
The distinction between specific and general skills made by Becker (1964) has often been used to formulate hypotheses on differences in expected short-term and long-term labor market outcomes (e.g. Brunello 2003, Hanushek et al. 2011, Kreuger and Kumar 2004a, 2004b, Shavit and Müller 1998). In the short run, specific skills are assumed to be instantly in demand in the labor market, and to yield short-term average earnings returns which exceed those of general skills. General skills instead enhance the ability to learn, at the expense of a more sluggish transition from training into employment.
In a longer perspective, business cycle fluctuations and technological changes may influence the relative payoff of the different types of human capital. First, by definition, the degree of transferability between employers is lower for specific skills. If the business cycle generates structural changes which force individuals to switch careers, there is a risk attached to investments in specific skills. Relatedly, technological changes could create an advantage for general skills if they enhance the ability to learn new skills. Employers could be more likely to offer further training to these individuals, who then become even less sensitive to changes. In sum, the long-run relative earnings implications are ambiguous, and the time frame emerges as an important aspect to appropriately analyze the impact of general vs. specific skills.
We expect individuals’ comparative advantages to affect the choice of investment in specific or general human capital. From this follows two crucial implications. On the one hand, labor market efficiency and societal benefit may be enhanced when program options are increased. On the other hand, it also implies that program types may attract individuals with different characteristics. The latter potentially (but not necessarily) constitutes a source of endogeneity bias in our estimates. This is discussed in detail in Section 5.
In Sweden, compulsory school is nine years with very limited tracking. For the sample we study, it was followed by two- or three-year programs at the upper secondary school. The two-year programs were mainly vocational, but also included business, social science and technology. The three-year programs were all theoretical and provided eligibility for higher studies.
A notable characteristic of the Swedish educational system is the prevalence of adults in formal education. Since 1969, Swedish municipalities have been obliged by law to offer schooling to adults who wish to re-enroll at the compulsory or upper secondary level. The courses offered are primarily theoretical, with only a limited supply of vocational courses, and are provided by institutes known as Komvux. Participants at Komvux are aged 20 years or older and may be drop-outs from compulsory or upper secondary school. Compared with continental Europe, there is a relatively modest gap in the educational content between the vocational and theoretical programs at the upper secondary level. Many individuals therefore enroll in Komvux to change the direction of their studies. Others enroll to complete a three-year upper secondary diploma and/or to improve grades, potentially to qualify for higher education. Those registered in Komvux are eligible to apply for study allowances that amount to about €1000 per month (2010 values) of which two-thirds is a loan to be repaid over 25 years.
Vocational courses for adults are mainly offered as active labor market programs. The content of the LMT is typically highly varied, with the five largest sectors represented being technology and science, health care, administration, manufacturing and service (AMS 1999). Importantly, prior to the early 1990s, Komvux enrolment was rarely offered to unemployed individuals. This is partly explained by the fact that UI benefits are more generous than are study allowances (and do not require repayment) and that this would have generated incentives for individuals to register as unemployed before enrolling in Komvux.
The LMT and AEI partly targeted the same groups of the unemployed and prioritized those individuals in a weak position in the labor market. The choice of program was a joint decision between the individual and a case worker at the employment office, with the preferred program usually available if individuals met the formal criteria of being 25–55 years old and eligible for UI benefits. The financial support for the participants in each program was equal to the level of the individuals’ UI benefits, and a six-month training period in either program qualified the individual for a new 300-day benefit period. The average program duration in the LMT was 141 days. AEI participants were offered one year of full time studies, but enrollees in 1997 were in 1998 offered a prolonged special grant for education and training (equal to their UI) for the school year 1998–1999, which approximately 35 percent of the individuals accepted. The costs of each type of program were reported as SEK 85,000 (1 SEK ≈ .11 €) per year for the LMT and SEK 34,000 per year for the AEI. This would correspond to similar costs per participant. To simplify the analysis, we will disregard the direct program costs when assessing the relative payoff of the programs.3
This study is based on annual population register data for 1990–2010, which encompasses all individuals residing in Sweden. Our overall sample is restricted to individuals who in 1997 were aged 25–55, received UI benefits and were registered as unemployed for at least one day between the 1st of January and the 30th of June. The unemployment registers provide information on the day of enrolment in the LMT and the end date of this registration. We define the LMT participants as those enrolled in May or later in 1997, to make the timing of the programs reasonably similar. The courses at Komvux are usually ongoing from August until December (autumn semester) and/or from January until the beginning of June (spring semester). To define AEI participants, we set the twofold condition that individuals were registered in Komvux in the autumn semester of 1997 and that they received the special grant for education and training (Särskilt utbildningsbidrag, UBS) that was introduced in 1997 specifically for the AEI. This helps us distinguish between participants in the AEI and participants in the regular Komvux program, who attended the same courses (and in the same classrooms). Individuals who in 1997 were registered in both LMT and AEI are excluded, as are those attending vocational courses within the AEI and those who were registered in any of the two programs in 1996. With these restrictions, the number of observations are 15,129 (LMT), 16,099 (AEI) and 95,939 individuals with “no program”. This is the sample used in the analyses unless stated otherwise.
Sample means by program status 1997
Yrs of sch
No upp sec sch
2-yr upp sec
Vocational 2 years
Business 2 years
Social sci 2 years
Technology 2 years
Business 3 years
Inland of Norrland
Parent > 0 1990
Parent > 0 1995
Sick > 0 1990
Sick > 0 1995
Social welf. > 0 1990
Social welf. > 0 1995
UI > 0 1990
UI > 0 1995
Days unempl 1995
Max unempl 1995
No unempl 1995
No earn 1995
Content of general training within the AEI. Credits expressed in years of full-time studies
Total registered course credits at Komvux (years)
Total completed course credits at Komvux (years)
Fraction completing zero credits
Fraction completing credits > 0 but < .25 years of AE
Fraction completing credits > .25 but < .5 years of AE
Fraction completing credits > .5 but < 1 year of AE
Fraction completing more than 1 year of AE credits
Proportion registered in compulsory level courses
Registered compulsory credits, average
Completed compulsory credits, average
Completed compulsory credits, if registered at level
Proportion registered in upper secondary level courses
Registered upper secondary credits, average
Completed upper secondary credits, average
Completed upper secondary credits, if registered at level
Proportions in type of upper secondary course registration
- Social sciences
- Natural sciences
- Human sciences (e.g., foreign languages)
- Computer sciences
- Health-related subjects (e.g., nursing)
- Vocational courses
Proportion completing some tertiary level education
Completed tertiary education, average
Completed tertiary education, if registered at level
Total adult education completed (years)
In this section we describe our empirical strategy, difference-in-differences propensity score matching (DID-PSM) (e.g. Smith and Todd 2005). Matching estimators generally take into account that treatment effects are heterogeneous by explicitly seeking to compare comparable individuals. Below, we first describe the case of a conventional average treatment effect on the treated (ATT) of a particular program. We then define the relative ATT, which directly compares participants in the AEI and LMT. The interpretation of the relative ATT estimates is discussed in Section 5.3.
5.1 Difference-in-differences propensity score matching
To give estimates of the ATT a causal interpretation, one needs to assume: i) that P(X) < 1; ii) that program participation does not affect the earnings of other individuals and; iii) conditional on the covariates in X, that the mechanisms behind enrolment decisions are independent of potential future earnings changes under non-treatment. The crucial assumption is iii because it is not possible to rule out that there are remaining unobserved factor(s) which may correlate with both participation and future earnings. 6 The DID-PSM estimator adjusts for time invariant unobserved heterogeneity affecting earnings. Our balancing tests encompass a rich set of covariates that include age, regional employment levels, dummies for region of residence (23 categories), employment sector (seven categories), prior education level (six categories) and educational track (six categories), number of children at home (six categories), age of children (6 categories), indicators of marital status or divorce, pre-treatment annual earnings trajectories for 1990–1995 (1996 with our extended model, see below), and four different types of social insurance benefits in 1990–1995 (1996) related to unemployment insurance, parental leave, sick-leave and social welfare, applying both dummy variables (zero earnings, incidence of the various benefits) and continuous measures of amounts. We further balance on days registered as unemployed each year in 1992–1995 (1996) and on indicator variables if either zero days or the maximum number of days (365/366). In total, our balancing tests encompass at least 132 variables.
Our main concerns regarding sources of potential bias are differences in unobserved ability and in time-varying unobserved factors (see Biewen et al. 2014 for an extensive discussion on specification issues). As a check for ability bias in our estimates, for males born 1953 or later, we compare the results when including and excluding test scores relating to cognitive and non-cognitive skills. These checks indicate that abilty bias is not a major concern, as the change in estimation results corresponds to less than .5 percentage points of annual earnings (this is the case also for our relative ATT).
Regarding time-varying unobserved factors, changes in motivation or health may not be captured by our covariates.7 A common critique of difference-in-difference estimators is that a temporary earnings drop in the year prior to program enrolment among the treated generates an upward bias because the earnings level does not reflect the individual’s true productivity (Ashenfelter 1978). The baseline model we use in the results section, unless otherwise stated, does not consider covariates recorded in 1996, with pre-program earnings defined as the average of the annual earnings in 1993–1995. A contrasting approach is to assume that changes occurring shortly prior to program imply changes with permanent effects which must be controlled for (e.g., Heckman and Smith 1999, Heckman et al. 1999). We applied extended versions of our estimation models to consider changes in transfers and earnings 1995–1996.8 If our estimates are affected by diverging parallel trends, or time-varying unobserved characteristics, one would expect results to systematically change by model specifications.9 However, the different specifications generally yield negligible differences in (relative) ATT estimates. In Sections 6, 7 and 8, the extended model results are reported when relevant. Overall, the stability of our findings with respect to the extended model specification and the check for potential ability bias indicate support for our empirical strategy.10
5.2 Relative program effects
The estimated ATT for AEI and LMT differ in two dimensions; the type of treatment and the group of participants. If program effects are heterogeneous across individuals, then the ATTs for different participant groups are not directly comparable (i.e. ATT may be different from the average treatment effect, ATE). To directly compare AEI and LMT, one may estimate a relative ATT by applying the same reasoning as in the case of the ATT discussed above, but consider D = 1 the treatment and D = 0 the alternative treatment (instead of “no treatment”). We thereby obtain an estimate of relative program effects for comparable program participants. To give a hypothetical example, if the program effects are correlated with say, age, separate estimates of ATT for the AEI and the LMT may differ only because of participants’ different age structure. The relative ATT would correct this potential flaw by comparing ΔY of program participants of the same age, where the age variable has been balanced between the two groups.
Probit model estimates (marginal) of the relative probability of enrolment in AEI
Less than 9 years
9 years of sch
2-yr upp sec
Social sci 2 years(d)
Vocation 2 years(d)
Technology 2 years(d)
Business 3 years(d)
12 years of sch
Business 3 years
15 years of sch
Parent > 0 1990(d)
Parent > 0 1991(d)
Parent > 0 1993(d)
Parent > 0 1994(d)
Parent > 0 1995(d)
Zero earn 1991(d)
Zero earn 1993(d)
Zero earn 1994(d)
Unemp ben 1990
Unemp ben 1991
Unemp ben 1993
Unemp ben > 0 1991(d)
Unemp ben > 0 94(d)
Unemp ben > 0 1995(d)
Days unemp 1992
Days unemp 1993
Days unemp 1994
Days unemp 1995
Max unemp 1992(d)
Max unemp 1993(d)
Max unemp 1994(d)
Max unemp 1995(d)
No unemp 1992(d)
No unemp 1993(d)
No unemp 1995(d)
Sick leave 1990
Sick leave 1992
Sick leave 1994
Sick leave 1995
Sick > 0 1990(d)
Sick > 0 1991(d)
Sick > 0 1993(d)
Sick > 0 1995(d)
Social welf 1992
Social welf 1993
Social welf 1994
Social welf 1995
Social welf > 0 1990(d)
Social welf > 0 1995(d)
5.3 Comparative advantages
The Pr[AEI] vs Pr[LMT] approaches are asking two different questions. Assuming that all individuals in our sample have decided to enroll in a program, and that they choose freely between only two existing programs, the Pr[AEI] case evaluates the relative ATT for the AEI participants had they enrolled LMT. With Pr[LMT], one instead evaluates the relative ATT for LMT participants had they enrolled AEI. Thus, if individuals act on their comparative advantages, one could expect the relative estimate to indicate a more beneficial effect of the program considered as “treatment” (D = 1). However, hypothetically, an ability (e.g. reading skills or practical handiness) which generates a comparative advantage may also affect earnings independently of a program, to yield bias in our relative estimates. Therefore, results are presented below for both Pr[AEI] and Pr[LMT].
6.1 Evaluating programs separately
For completeness, Additional file 1: Tables S4 and S5 provide estimates from simple OLS regressions assessing each program compared with “no program” (1) without control variables and (2) with individual and year fixed effects. The fixed effects models yield, at the end of the observation window, higher parameters of the AEI. However, the impression from Fig. 4 is that specific training is associated with higher short-run estimates and that the long-run estimates of general training only in a few cases (females) catch up with those of specific training. As discussed in Section 5.2, the separate program estimates of the ATT are not necessarily comparable. We therefore turn to the evaluation of relative ATT as our main question of interests is whether changing the program content for individuals from LMT to the AEI, or vice versa, could enhance the discounted value of earnings streams.
6.2 Relative program effects
When we switch between matching on Pr[LMT] and Pr[AEI], the estimates change in the expected direction (as described in Section 5.3), favoring the program chosen as the “treatment” indicator. This is consistent with that individuals act on their comparative advantages. The relative treatment effects estimated from Pr[AEI] tend to converge for males, while the estimates for females are significantly above zero from 2003 and onward. These findings are shifted downward when the matching is based on estimates of Pr[LMT], positive but closer to zero for females and often significantly below zero for males. Robustness checks for parallel trends via the extended model corroborate the results.
To gauge the long term implications of the estimates, we extrapolate the last estimate (from 2010) into future years. We assume a two percent discount rate (base year is 1997) and that everyone retires at age 65, accounting for the age structure of the samples (the cohorts retire gradually between 2007 and 2037). This framework is used repeatedly below to assess what the estimates imply for the net benefits.16
For males, even if we extrapolate the largest estimate into future years, the present value of the estimated relative payoff of AEI vs LMT would still not cover the initial relative earnings loss during 1998–2004 (recall that the direct costs are approximated as equal for the two programs). For the sample of females, extrapolation of the estimates based on Pr[AEI] implies that the initial relative earnings losses (costs) in 1998–2002 are recovered by approximately 2020.17 The youngest cohort in the sample is then 48 years old, and about half of the individuals are still below age 65. However, the estimates based on Pr[LMT] do not support a conclusion that the initial earnings drop for enrollees in AEI is recovered before the last cohort retires. Thus, the results so far provide no robust evidence for the hypothesis that general training programs would be relatively more beneficial in the long term.
The results for females with prior completion of a two-year upper secondary program indicate positive relative earnings estimates of the AEI from 2003 to 2010, a result that also holds when the matching is based on Pr[LMT]. In both cases, the estimates imply that the initial relative earnings losses are recovered around 2020, but this does not hold when applying the extended model specification. Turning to females with no secondary education, at the bottom of Fig. 8, the estimates are positive and statistically significant almost throughout from 2003 and onward, regardless of specification used. The accumulated net present values implied by the estimates indicate that the initial earnings disadvantage is already recovered within or just beyond our observation window. This result is very stable as it holds whether the matching is based on Pr[AEI] or Pr[LMT], and whether one employs the extended model specification. We also checked if the results reflect fertility decisions by conditioning on samples to have two children, most often signaling completed fertility, or to have zero children, but the overall implications remain robust. Thus, for this particular subgroup, expanding the menu of labor market programs to include general training appears associated with substantial efficiency gains.
While this last result seems relatively compelling, it may be difficult to generalize because 1997 was the first year of a reform. One could imagine that an inherent demand made individuals with the highest gains from the AEI more likely to enroll. To check this, we estimated the corresponding relative program effects for participants without upper secondary school in 1998, 1999, 2000 and 2001, using data of the same quality as described in Section 4. The business cycle recovered quickly in 1997–2000 and further contributed to generating a different composition of the samples. In six cases out of eight, these estimates imply a similar recovery of initial earnings losses of AEI. The exceptions are when employing Pr[LMT] in the 1999 sample or the 2001 sample. We made a similar examination of the results for Stockholm residents, which hold only in five of 16 cases, with the Pr[AEI] set-up for males in 2000 and 2001 and with both set-ups for females in 1998 and 2001.
A principal contribution of this study is to provide empirical evidence on long term earnings associated with general training as an alternative to vocational/specific training. Heterogeneity among the unemployed, and in labor market demand for skills, implies that variety in the supply of training may allow individuals to capitalize on comparative advantages and improve the benefits of investments. With data on earnings 13 years post-enrolment showing differences between long-term and short-term outcomes, our analyses underscore the need for long follow-up periods to appropriately assess such programs. We also find strong indications that individuals tend to act on their comparative advantages. Characteristics predicting enrolment in general or specific training tend to be associated with estimated relative treatment effects that favor the chosen type of training. Methodologically, robustness checks for ability bias and time-varying characteristics prior to the program confirm our main findings.
For females with limited prior schooling and for participants in the metropolitan labor market of Stockholm, we find that general training is associated with earnings that exceed those of specific training. These findings are in line with the hypothesis that general training better enhances labor market prospects in the long run, by providing skills which make individuals less sensitive to labor market-related changes. Nevertheless, most of our estimates imply that vocational/specific training is associated with more favorable earnings trajectories. Therefore, arguments in favor of theoretical/general training programs must be based on the heterogeneity of the unemployed. As has been suggested earlier, theoretical programs may be especially appropriate in periods of high unemployment when opportunity costs are low and high numbers in specific training programs may inflict lower marginal returns.
Our study makes a distinct contribution compared with previous research, but there are some important caveats and we would like to point out four of these. First, the program costs are based on rough approximations and are assessed as equal on average. Second, the comparison between the two programs disregards outside alternatives, e.g., other programs. Third, other goals for policy (equity, democracy, etc.) are not considered. Fourth, general equilibrium effects are not considered. One might think here of costs associated with general training because, in the presence of labor market frictions, firms have incentives to offer not only specific training but also general education (Acemoglu and Pischke 1999). As in the case of specific training, increased public supply of general training may be associated with a deadweight loss due to crowding out of firms’ investments in general skills.
The results from evaluations of specific training for the unemployed in Sweden have differed across decades, with positive effects in the 1980s, zero or negative effects for participants at the start of the 1990s, and positive effects again in the late 1990s and early 2000s (e.g., Andrén and Gustafsson 2005, Calmfors et al. 2002, Axelsson and Westerlund 2005, Stenberg and Westerlund 2004, de Luna et al. 2008). The results at the start of the 1990s have usually been ascribed to the economic recession’s effect on employment prospects and/or the large scale of labor market training programs at the time.
A few studies compare the economic efficiency between other training programs, job search assistance, public employment, and/or wage subsidies (Lechner et al. 2011, Kluve 2010, Card et al. 2010; for Sweden, see Forslund et al. 2011 and Forslund et al. 2013).
The average costs of the LMT would be SEK 33,300 [(141/360)*85000] compared with SEK 45,900 for the AEI if one assumes 1.35 years in Komvux on average. Our decision to disregard the differences is based on the fact that drop- outs complicate this calculation (completion rates at Komvux are below 60 percent), as does the fact that vocational programs vary greatly in their costs and we do not have access to information at the individual level. The implications of our estimates in the empirical section must be considered with this reservation about the costs in mind.
About 14 percent of enrollees in AEI had completed at least a three-year upper secondary school program. As explained in Section 3, enrolment may be motivated by individuals’ desires to redirect their studies or improve their grades. They may also have a diploma obtained in a foreign country.
Cognitive skills are based on test scores of inductive, verbal, technical, and spatial skills. Non-cognitive scores are determined by a certified psychologist and measure social skills, leadership qualities, emotional stability and persistence. The scores are available for a subsample of 97,027 males born 1953 or later.
In the case under study, assumption ii can also be questioned because both training programs are large. However, Dahlberg and Forslund (2005) find no displacement effects of Swedish training programs in 1987–1996. One may note that they report substantial displacement effects of subsidized employment, as do Crépon et al. (2013) of job search assistance programs. Regarding positive externalities, Albrecht et al. (2009) argue that the returns to society of the AEI were higher than the individual earnings return by a factor of 1.5.
For some of the unemployed, program participation seems to be motivated primarily by avoidance of an active job search and/or to qualify for another period of UI benefits (Stenberg and Westerlund 2008, p63).
For our extended model, the balancing concerns an additional 26 variables. We follow Heckman and Smith (1999) to control for nine different transitions in labor force status 1995–1996 between outside the labor force, employment and unemployment. Also included are levels 1996 and changes in the amounts of earnings and social insurance benefits in 1995–1996 and regarding sick-leave or social welfare also for 1996–1997 (we then assume that program choice does not cause transfers to change).
This is consistent with findings from studies assessing non-experimental estimates based on data of high quality. Card et al. (2010) conclude that “The absence of an ‘experimental’ effect suggests that the research designs used in recent non-experimental evaluations are not significantly biased relative to the benchmark of an experimental design” (F475, their quotation marks). Of course, this is not to say that adequate experimental data is not preferred. Nevertheless, when good non-experimental data is available, it is unreasonable to abstain from studying important research questions while waiting for the uncertain event of future access to relevant experimental data.
Balancing the samples was at times difficult with one-to-one matching without “trimming” the samples (excluding treated participants). Therefore, the results presented are based on four-to-one matching, overall similar to the one-to-one matching estimates, but avoiding trimming. For the balancing tests, we set the threshold at p-values of .05. As we balance on more than 120 variables, we allowed one variable to be unbalanced by accident. Our checks indicate that this has no bearing on the estimated results.
A conventional OLS estimator is perfectly symmetrical and switching between AEI and LMT indicators just switches the sign of the coefficient. The asymmetry in Figure 3 arises because, in the case where AEI is the treatment, matched LMT comparisons will to a greater extent be drawn from the side of the probability distribution where AEI participation is more likely. This is exacerbated by that matching is performed “with replacement” (to minimize bias). Thus, a matched comparison is always re-inserted (“replaced”) into the pool of potential comparisons.
The Probit estimates and the balancing tests behind the results in Fig. 4 are available on request.
In Addiitional file 1: Table S6, we also present results from OLS regressions (1) without controls and (2) controlling for individual and year fixed effects. Compared with the OLS fixed effects, the full PSM model yields relative estimates which at the end of the observations window are slightly more to the advantage of LMT.
We fully acknowledge that this may be developed, but leave it for future research. Our priority is to keep the discussion intelligible, and, because we are in relatively unexplored territory, to establish the qualitative results rather than to pin down the precise estimates.
The magnitude of the loss for females in 1998–2002 is about SEK 80,000, only about half the amount for the males.
The results obtained when conditioning samples on age, 25–42 and 43–55, and on whether annual earnings in 1995 were above or below median, did not differ in any important way from the overall results just presented. The working paper version of this article contains details on these analyses (Stenberg and Westerlund 2014).
We are grateful for valuable suggestions and comments on previous versions from Xavier de Luna, Anders Forslund, Helena Holmlund and seminar participants at IFAU, Uppsala, and the Workshop on Economics of Education at University of Barcelona. Financial support from IFAU, the Institute for Evaluation of Labour Market and Education Policy and from the Swedish Research Council (grant 2006-21576-36119-66) is gratefully acknowledged.
Responsible editor: Sara De la Rica.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Acemoglu D, Pischke JS (1999) Beyond Becker. Training in Imperfect Labour Markets. Econ J 109(453):F112–F142View ArticleGoogle Scholar
- Albrecht JW, Van den Bergh G, Vroman SB (2009) The aggregate labour market effects of the Swedish Knowledge Lift program. Rev Econ Dyn 12(1):129–146View ArticleGoogle Scholar
- AMS (1999) Uppföljning av kursdeltagare som slutat yrkesinriktad arbetsmarknadsutbildning andra kvartalet 1998. Prora, Vol. 1Google Scholar
- Andrén T, Gustafsson B (2005) Income effects from labour market training programs in Sweden During the 80s and 90s. Int J Manpow 25(8):688–713View ArticleGoogle Scholar
- Ashenfelter O (1978) Estimating the Effeect of Training Program son Earnings. Rev Econ Stat 60:47–57View ArticleGoogle Scholar
- Axelsson R, Westerlund O (2005) Kunskapslyftets effekter på årsarbetsinkomster – Nybörjare höstterminen 1997. Umeå Economic Studies, Vol. 647Google Scholar
- Becker G (1964) Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education., NBER (3:e upplagan 1993)Google Scholar
- Biewen M, Fitzenberger B, Osikominu A, Paul M (2014) The Effectiveness of Public-Sponsored Training Revisited: The Importance of Data and Methodological Choices. J Labor Econ 32(4):837–897View ArticleGoogle Scholar
- Brunello G (2003) On the Complementarity between Education and Training in Europe. In: Lucifora C (ed) Checchi, C. Education, Training and Labour Market Outcomes in Europe. MacMillanGoogle Scholar
- Caliendo M, Kopeinig S (2008) Some Practical Guidance for the Implementation of Propensity Score Matching. J Econ Surv 22(1):31–72View ArticleGoogle Scholar
- Calmfors L, Forslund A, Hemström M (2002) Does Active Labor Market Policy Work? Lessons from the Swedish Experiences, IFAU Working Paper 2002., p 4, UppsalaGoogle Scholar
- Card D, Kluve J, Weber A (2010) Active Labor Market Policy Evaluations: A Meta-Analysis. Econ J 120(548):F452–F477View ArticleGoogle Scholar
- Chabé-Ferret S (2015) Analysis of the Bias of Matching and Differene-in-Difference Under Alternative Earnings and Selection Processes. J Econ 185(1):110–123View ArticleGoogle Scholar
- Crépon B, Duflo E, Gurgand M, Rathelot R, Zamora P (2013) Do Labor Market Policies have Displacement Effects? Evidence from a Clustered Randomized Experiment. Q J Econ 128(2):531–580View ArticleGoogle Scholar
- Dahlberg M, Forslund A (2005) Direct Displacement Effects of Active Labor Market Programs. Scand J Econ 107(3):475–494View ArticleGoogle Scholar
- de Luna X, Forslund A, Liljeberg L (2008) Effekter av yrkesinriktad arbetsmarknadsutbildning för deltagare under perioden 2002–04, IFAU Rapport 2008., p 1, UppsalaGoogle Scholar
- de Luna X, Waernbaum I, Richardson T (2011) Covariate Selection for the Non-Parametric Estimation of an Average Treatment Effect. Biometrika 98(4):861–875MathSciNetView ArticleMATHGoogle Scholar
- Flores C, Flores-Lagunes A, Gonzalez A, Neumann T (2012) Estimating the Effects of Length of Exposure to Instruction in a Training Program: The Case of Job Corps. Rev Econ Stat 94(1):153–171View ArticleGoogle Scholar
- Forslund A, Fredriksson P, Vikström J (2011) What Active Labour Market Policy Works in a Recession? IFAU Working Paper 2011:2, UppsalaGoogle Scholar
- Forslund A, Liljeberg L, von Trott zu Solz L (2013) Job Praftice: An Evaluation and a Comparison with Vocational Labour Market Market Training Programmes, IFAU Working Paper 2013., p 6, UppsalaGoogle Scholar
- Hanushek E, Woessman L, Zhang L (2011) General Education, Vocational Education, and Labor market Outcomes over the Life-Cycle. IZA discussion paper 6083View ArticleGoogle Scholar
- Heckman J, Smith J (1999) The Pre-Programme Earnings Dip and the Determinants of Participation in a Social Programme. Implications for Simple Programme Evaluation Strategies. Econ J 109:313–348View ArticleGoogle Scholar
- Heckman J, Urzua S (2008) The Option Value of Educational Choices and the Rate of Return to Educational Choices. University of Chicago, MimeoGoogle Scholar
- Heckman J, LaLonde R, Smith J (1999) The Economics and Econometrics of Active Labor Market Programs. In: Ashenfelter O, Card D (eds) Handbook of Labor Economic, Volume 3A, Ch. 31Google Scholar
- Jacobson LS, LaLonde RJ, Sullivan DG (2003) Should We Teach Old Dogs New Tricks? The Impact of Community College Retraining on Older Displaced Workers. Federal Reserve Bank of Chicago WP 2003–25Google Scholar
- Jacobson LS, LaLonde RJ, Sullivan DG (2005) The Returns to Community College Schooling for Displaced Workers. J Econ 25:271–304MathSciNetView ArticleGoogle Scholar
- Kluve J (2010) The Effectiveness of European Active Labour Market Programs. Labour Econ 17:904–918View ArticleGoogle Scholar
- Kluve J, Schneider H, Uhlendorff A, Zhong Z (2012) Evaluating Continuous Training Programmes by Using the Generalized Propensity Score. J R Stat Soc 175(2):587–617View ArticleGoogle Scholar
- Kreuger D, Kumar K (2004a) Skill-Specfic rather than General Education: A Reason for US-Europe Growth Differences? J Econ Growth 9:167–207View ArticleGoogle Scholar
- Kreuger D, Kumar K (2004b) US-Europe Differences in Technology-Driven Growth: Quantifying the role of Education. J Monet Econ 51:161–190View ArticleGoogle Scholar
- Lechner M, Miquel R, Wunsch C (2011) Long-Run Effects of Public Sector Sponsored Training in West Germany. J Eur Econ Assoc 9(4):742–784View ArticleGoogle Scholar
- Pissarides C (2011) Regular Education as a Tool of Counter-Cyclical Employment Policy. Nordic Econ Policy Rev 1:207–232Google Scholar
- Rosenbaum P, Rubin D (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70(1):41–55MathSciNetView ArticleMATHGoogle Scholar
- Shavit Y, och Muller W (1998) From School to Work. Oxford University press, Cary, North Carolina.Google Scholar
- Smith J, Todd P (2005) Does matching overcome LaLonde’s critique of non-experimental estimators? J Econ 125(1–2):305–353MathSciNetView ArticleGoogle Scholar
- Stenberg A (2007) Comprehensive Education or Vocational Training for the Unemployed? Int J Manpow 28(1):42–61MathSciNetView ArticleGoogle Scholar
- Stenberg A (2011) Using Longitudinal Data to Evaluate Publicly Provided Formal Education for Low Skilled. Econ Educ Rev 30(6):1262–1280View ArticleGoogle Scholar
- Stenberg A, Westerlund O (2004) Does Comprehensive Education Work for the Long-term Unemployed? Umeå Economic Studies 641Google Scholar
- Stenberg A, Westerlund O (2008) Does Comprehensive Education Work for the Unemployed? Labour Econ 15(1):54–67View ArticleGoogle Scholar
- Stenberg A, Westerlund O (2014) The Long Term Consequences of General vs. Specific Training for the Unemployed? IZA Discussion Paper 8668Google Scholar
- Stenberg A, de Luna X, Westerlund O (2014) Does Formal Education for Older Workers Increase Earnings? Evidence Based on Rich Data and Long-term Follow-up. Labour 28(2):163–189View ArticleGoogle Scholar