Applying data driven decision making to rank vocational and educational training programs with TOPSIS

Highlights

• A data-driven multi-criteria decision-making approach is presented.

• 8 different criteria were applied whose values were computed from a real dataset.

• The information is enriched by real data instead of being based on questionnaires.

• A new technique for assessing the influence of the different criteria is included.

Abstract

In this paper we present a multi-criteria classification of Vocational and Educational Programs in Extremadura (Spain) during the period 2009–2016. This ranking has been carried out through the integration into a complete database of the detailed information of individuals finishing such studies together with their labor data. The multicriteria method used is TOPSIS together with a new decision support method for assessing the influence of each criterion and its dependence on the weights assigned to them. This new method is based on a worst-best case scenario analysis and it is compared to a well known global sensitivity analysis technique based on the Pearson's correlation ratio.

Introduction

The 2008 financial crisis that hit the world's economies has had a particularly acute impact in Spain [1]. It is only since 2014 that Spain seemed to begin its recovery [2]. However, this recuperation is still far to be acceptable with regard to the labor landscape [3].

One of the main Spanish weaknesses that the crisis exposed was the so-called duality of the labor market. Thus, Spain is characterized by the existence of two very different types of workers. On one hand, long term workers on indefinite contracts, having both a very high job security and a very high cost for companies (especially in terms of dismissals) and usually with university studies even for jobs that do not require them. On the other hand, short term workers on temporary contracts or seasonal contracts with low wages and, in most cases, with very little training.

Another structural weakness of the Spanish economy unveiled during the years of the crisis was the fact that it had been relied heavily on two pillars: construction and tourism (and their associated services). This productive model had its main Achilles heel in the low level studies required in many of the jobs created in both sectors. Moreover, the relatively high wages that a worker could earn before the crisis, mainly in construction, led many young people to abandon their studies to work in these industries, without prior quality training. When the crisis arose and the destruction of employment reached unprecedented levels, Spain discovered that had to deal with a mass of unemployed, mostly young, people who, having no adequate training, had very difficult or even impossible reinstatement into the labor market.

This problem has been most pronounced in some regions of Spain as Extremadura. Extremadura is a European Union Objective 1 region located in western Spain that according to the Eurostat Regional Yearbook 2018,¹ its GDP per inhabitant in relation to the EU-28 average is 61.47%, it has 23.7% of unemployment rate and, even worse, its early leavers from education and training of young people rate is 20.9% and its young people neither in employment nor in education or training rate is 20%.

In other countries where the economic model was more diversified, with large sectors of skilled employment and better trained workers, the crisis was less intense, the employment destruction less acute, and the recovery was faster. One of the differences between Spain and, particularly, Extremadura with respect to those countries is the importance they give to Vocational Eucation and Training (hereinafter VET). For the European Union, VET should “prepare young people for entering and successfully and sustainably participating in the labor markets as well as to enable high potentials (e.g. migrants, refugees, low-skilled and unemployed, inactive groups, including women) to stay and/or (re-)enter the labor market” [4]. In Germany, for example, VET studies are closely linked to the labor market, so that the majority of VET students are also trained in companies where, in many cases, they end up working. This means that there is a quarry of workers with a specific qualification for the needs of the labor market.

However, in the collective imagination of Spanish families, VET has been considered for decades a second-rate training and has not been much appreciated. This vision, together with the high drop-out rates, has caused a very marked duality in training: on the one hand people who either did not finish more than compulsory education or, at best, have VET studies (which in this last case are seen wrongly as a low level education), or people with university education who, due to the high unemployment rates suffered in Spain during the crisis (still persisting), are hired in positions for which such studies are not really required, causing another of the many big problems of the Spanish labor market, which is the overqualification of workers [5].

In order to try to alleviate the mentioned problems, the Government of Extremadura, providing its historical data, asked for a scientific analysis about the real impact of VET studies on accessing to the labor market with a two-fold goal: increase the resources of those VET studies with higher employment rate and promote such studies among their unemployed citizens enhancing the image of the VET studies that really help to get a job.

To this end, the aim of this work is to determine the efficiency of VET studies and the evaluation of the performance of VET graduates from Extremadura in the different degrees of VET programs in the labor market. Thus, we illustrate a data-driven multi-criteria decision-making methodology with the aim of classifying the different degrees of VET programs according to some criteria related to labor insertion. Concretely, we have applied TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) [6] to this problem. TOPSIS is a well-known classical MCDM method, widely used by researchers and practitioners, that supports decision makers in performing analysis, comparisons and rankings to select the best alternative using a finite number of criteria. Moreover, since all criteria are weighted, their importance may be modified providing, thus, the flexibility for creating different rankings prioritizing different aspects. In our case, we have considered 8 different criteria whose values have been computed from a real dataset with more than 28,000 VET student records containing both educational and labor information. This is a key contribution for this work since the information obtained is enriched by real data instead of being based on questionnaires as similar TOPSIS approaches [7] or the REFLEX project in the field of higher education [8]. Furthermore, to the best of our knowledge, the relationship between VET programs and labor market has not been explored by previous works with such level of detail.

The rest of the article is organized as follows. Section 2 reviews some works related with predicting different outcomes using academic data, and some other works using TOPSIS as Multi-Criteria Decision Analysis method together with a weight assignment analysis, on different scientific domains. Section 3 describes the datasets used and the process applied to them for computing the different data used in our study. Section 4 describes the methodology followed to apply TOPSIS. Section 5 explains the influence of the criteria applied during the process. The results obtained and further considerations are detailed in Section 6. Finally, Section 7 concludes the paper.