“You reap what you sow”: Do active labour market policies always increase job security? Evidence from the Youth Guarantee

Youth is the best time to be rich, and the best time to be poor.

Euripides, 416 b.C.

Abstract

The paper uses non-experimental longitudinal data to study the effects of participation in the Youth Guarantee programme aimed at fighting youth inactivity in the European Union territory. Particularly, this analysis questions the value of active labour market policy as a valid instrument to help individuals otherwise isolated from the labour market and, thus, at risk of deterioration of human capital overcome their condition of occupational inactivity. A difference-in-differences model is exploited in this regard to investigate whether there exists an advantage for participants of the Youth Guarantee in terms of employment and job stability. Results show that participants are 7.4 and 4.4 percentage points more likely to, respectively, become employed and be offered an open-ended contract. An assessment of profiling is also provided.

Appendices

Appendix

Profiling and the Youth Guarantee

On the occasion of signing the service pact with the job centre of reference, individuals are ‘profiled’ according to their degree of risk of remaining inactive. The indicator of profiling is computed statistically for each youth and is considered itself an active labour market policy (ALMP). The profiling is based on a series of individual characteristics such as the individual’s presence in Italy; her level of education; her situation of employment 1 year before the start of the Youth Guarantee; and other local features including entrepreneurial density and variation in the unemployment rate of the area of origin. The indicator ranges from 0 to 1. Lower and higher values of the profiling indicator signal, respectively, a higher and lower probability for the individual to be reintegrated in the labour market. A youth’s profiling indicator is low if its value falls between 0.000 and 0.250000; medium if it falls between 0.250001 and 0.50000; high if it falls between 0.50001 and 0.750000; and finally, very high if the indicator’s value falls between 0.750001 and 1.

While participation in ALMPs may or may not have beneficial effects for participants, the threat caused by their mere existence may have an impact on the occupational prospects of the individuals involved. The study by Black et al. (2003), for instance, examined the consequences of profiling on unemployment insurance claimants. In particular, they found that the former reduced both the number of weeks of benefit receipt and the amount received. At the same time, the activity led to a significant increase in earnings for the treated individuals in the year after their claim for unemployment benefits, suggesting an anticipated entry in the labour market. Similar results were observed by Bergemann et al. (2008, 2011) for Germany and by Blasco and Rosholm (2011) for Denmark. Scholars agree on the fact that systems that guarantee active labour market policies usually increase the effort in job search so as to avoid actual participation in ALMPs. On this subject, there might be valid reasons for individuals to be significantly influenced by the presence of ALMPs in their labour market. Heckman and Rubinstein (2001), for instance, associated this threat effect to the fear of having to renounce alternative activities or of being stigmatised. As a result, individuals may accept non-quality jobs rather than attend active measures that demand a long-term commitment. The fear of producing a negative signal to external subjects such as potential employers has been widely discussed in the literature. Spence (1973), before all, defined the job market as a market where signaling is paramount, due to the lack of information on job candidates. On the one hand, the employer lacks the necessary knowledge about the real skills of the job candidates. On the other hand, the individuals have to select the information they aim to signal at a certain cost. This is particularly true for the more disadvantaged subjects who compete with better educated and, sometimes, more productive individuals. As observed by Connelly and Certo (2011), inferior signalers may take the risk of producing false signals, or cheat, for they are aware of their chances being lower anyway. In this regard, Hopkins (2012) shed light on the stronger signals sent by high-quality workers to firms. This would explain the different degree of difficulty for low- and high-profiled individuals to get a job in the first place.

A comparison between individuals who are and who are not profiled would be more useful in order to identify the potential ex-ante effect of profiling in the Youth Guarantee. On this occasion, however, we will present an analysis that focuses exclusively on the participants of the Youth Guarantee. This allows us to exploit the information relevant for the individuals on their profiling and the duration of their training in the selected firms. In particular, we aim to investigate how the different types of profiling indicators with which NEETs are assessed influence their job opportunities. For the purpose of this analysis, we retain only those individuals who participate in the programme of the Youth Guarantee and, thus, who are profiled before starting their on-the-job training experience. The Youth Guarantee expects participants not to be engaged in employment, education, or training, and to be younger than 30 years old. In order not to produce biased estimates, associated to the duration of training, individuals who are still participating and who have 2100 as their year end date are dropped. As regards the most relevant controls, we account for the nationality of the participating individuals, their age, and their gender. A dummy ${IT}_{{i}}$ for whether the individual observed is Italian or not is included in the model. Indeed, in contrast to Northern countries like Denmark or the Netherlands, job vacancies in the Italian labour market are likely to require candidates to have a perfect knowledge of the Italian language. To verify this, it is sufficient to check some of the job offers promoted online by Italian firms or the number of blogs that provide suggestions for foreigners on what actions to take in order to find a job in Italy. With regard to the average age of the labour force in Italy, the fact that there is a tendency on the part of individuals to live with their parents long after reaching their majority, developed a labour market where firms are used to older candidates. Recent statistics by Eurostat show that the average age at which Italians left their family nest in 2017 was about 30.1 years old, in comparison with Germans and Swedes who moved out at, respectively, 23.7 and 21. This justifies the need to consider the individuals’ ${AGE}_{{i}}$ as well. Because of the traditional history of female discrimination in the Italian labour market, we also account for a dummy ${FEMALE}_{i}$. On this subject, the use of illegal undated letters of resignation that employers obliged female employees to sign, so as to prevent the costs of maternity leave, only stopped recently, with the 2014–2015 Jobs Act.

In this analysis, we are particularly interested in investigating whether the profiling of an individual can actually influence employers when opting for a candidate rather than for another one. Additionally, we are curious about understanding whether attending the programme for a longer duration can work as a remedial to the potential ‘stigma’ brought by the initial assessment of the individual. The longer a disadvantaged youth attends the programme, the more likely it could be for her to acquire and develop any lacking skill or, vice versa, to fall victim to potential locking-in effects. Using standard OLS, we first look at the association of the different types of profiling ${PROF}_{i}$ with the two labour outcomes of interest ${{Y}}_{{i}}$; namely, the likelihood of the participants to become employed and to be offered an open-ended contract. In particular, we include the ${LOW}_{i}$, ${MEDIUM}_{i}$, and ${VHIGH}_{i}$ dummies for having, respectively, a low, medium, and very high profiling and use the dummy of high profiling as the base category. We then incorporate ${DUR}_{i}$ in the model, or the duration of the internship measured in intervals of 100 days, to see whether there exists any compensation for having a particularly risky profile. A stronger commitment in the programme, or simply more time, may help the more vulnerable individuals overcome their occupational prospects. A distinction is made between short, medium, and long terms since the implementation of the Youth Guarantee, intended as the years 2015, 2016, and 2017.

$$\begin{aligned} Y_i = \alpha + \beta AGE_i + \theta _f FEMALE_i + \delta IT_i + \sum _{m=1}^{3} \theta _p PROF_{m,i} + \omega DUR_i + \epsilon _i \end{aligned}$$

(14)

The descriptive statistics of Table 16 show that individuals are on average about 24.2 years old, with male participants being slightly younger. As regards the nationality of participating individuals, Italians make up the majority of the sample analysed. NEETs participate in the on-the-job training for 324 days averagely, with a variation of less than 2 months. In respect to the profiling, we observe that 21.8% of the individuals are assessed with a low profiling; 29.3% with a medium profiling; 32.7% with a high profiling; and 16.3% with a very high profiling. In particular, individuals who are at a low risk of staying unemployed are 25.1 years old on average, while NEETs with a high profiling are usually 23.1 years old. Conversely, there is no particular pattern when trying to understand the relationship between the type of profiling assessed and the gender of the individual. Indeed, women are the minority in both low- and very high-profiling categories with a proportion of, respectively, 44.1% and 40.1%. On the contrary, Italians are the predominant nationality in all the categories. Indeed, they make up 90% of the low-risk group and 79.9% of the high-risk group. As regards participation in the on-the-job training, individuals usually attend the programme for about 11 months, with low and medium profiles participating slightly longer. In terms of the relationship between the occupational prospects of the NEETs and their profiling, there is a moderate contrast between low and high profiles. Table 17, in particular, shows that employed NEETs are assessed with a low profiling 54.4% of the time and with a very high profiling 31% of the time. The gap is also present to a modest degree when looking at the type of individuals who are offered an open-ended contract, independent of becoming employed. While we only find 12.1% of the NEETs with a very high profiling ending up with an open-ended contract, the proportion increases up to 26.1% for those assessed with a low profiling.

Table 16 Descriptive statistics for individuals registered at Agency of Labour of Trento, 2014–2017

Table 17 Descriptive statistics for individuals registered at Agency of Labour of Trento by profiling, 2014–2017

As regards our identification strategy, the OLS estimates of Table 18 show that an increase in age corresponds to an increase in the probability for participants to exit from their unemployment condition in both short and medium terms. For Italian participants, there is an additional comparative advantage in the medium term of about 4.3 percentage points, significant at 10% level. The training programme appears to be more beneficial for female participants too, compared to their male colleagues.⁶⁴ At least in the short term, women are 3.6 percentage points more likely to become employed. However, no such advantage is observed in the following periods. This is not particularly surprising given that the phenomenon of undated letters of resignation preventing maternity leave stopped with the 2014–2015 Jobs Act. While the latter might help explain the positive change in job opportunities observed for women in 2015, it did not guarantee its stability over time. Similarly, the fact that older and Italian job candidates are slightly favoured confirms the hypothesis of the existing literature of a labour market in Italy that is used to older employees and that prefers compatriot individuals. In regard to our main covariate of interest, we are interested in understanding how much of the variation observed in the labour outcomes is explained by being assessed with a certain type of profiling. Table 18 shows that being assessed with a low profiling corresponds to a positive change in the probability to be offered a job of, respectively, 24.9, 15.3, and 13.3 percentage points in the short, medium, and long terms. Compared to high profiles, a similar pattern is also found for individuals assigned with a medium profiling. Having a very high profiling, on the other hand, contributes to explaining a negative variation in the probability to become employed by 6.7 percentage points in the long term. Estimates are similar as regards job stability. Table 19 shows that being assessed with a low or medium profiling indicator contributes to a positive variation in the probability of participants to be offered an open-ended contract. In the long term, for instance, the change is equal to, respectively, 13.8 and 7.3 percentage points. Results seem to suggest a greater difficulty for high profiles to produce positive signals to the potential employers in the labour market.

Table 18 OLS: explaining the probability to become employed through profiling

Table 19 OLS: explaining the probability to be offered an open-ended contract through profiling

In this regard, we also take into account the possibility for participants to experience locking-in effects during their internship at the selected firms. Table 18 shows that attending the programme at the firm for 100 additional days corresponds to a reduction in the probability to become employed of 3.8 and 3.3 percentage points in, respectively, the medium and long terms. The latter supports the theory by Cerulli-Harms (2017) on the risks of becoming ‘eternal interns’. Results do not differ as regards the probability of the participants to be offered an open-ended contract. Indeed, a longer participation in the programme does not appear to be remedial for the candidates. The assignment of a low or medium profiling, on the other hand, contributes to explaining part of the positive variation in this labour outcome. However, and as anticipated by the weak correlation coefficients in Table 20, findings are not powerful in terms of the extent to which profiling explains the labour outcomes of the individuals.⁶⁵ The latter is in line with the alternative parallel trends assumption provided in Sect. 6.2 according to which results are not driven by specific profiling types but solely by participation. Nevertheless, individuals who are profiled with a certain type of profiling may experience an emotional shock that increases awareness on their condition. This would then lead them to accept any job offer they receive in line with the idea that the latter is the best they can get anyway. One may consider whether individuals fear social stigma with respect to participation in active labour market policies or social pressure on the part of their families and peers. Further research should investigate the nature of the question.

Table 20 Correlation matrix for labour outcomes and profiling