Education, skills and a good job: A multidimensional econometric analysis
Introduction
There is a vast academic literature on the relation between labor market outcomes and education (see e.g. a literature review by Goldberg & Smith (2008)). A large number of empirical studies on this subject build upon the classic works of Becker, 1964, Mincer, 1974, with success in the labor market being traditionally measured by earnings, which in turn are directly associated with educational achievements such as years of schooling (see e.g., Heckman, Lochner, & Todd (2003)). In this study, we take a stand that (a) the direct relation between labor market outcomes and wages should be qualified to include skills and abilities, and (b) labor market success can hardly be described by a single measure. In what follows we elaborate on these two points, arguing for the need to go beyond traditional approaches to understand the relationship between education and a wide notion of work-related well-being.
The most dominant economic paradigm explaining labor market success, particularly in developing countries, can be represented by Mincer’s equation (or variants of it), which relates earnings to years of schooling. However, years of schooling or more generally, time spent in formal educational programs, reflect first-order educational attainments that influence other characteristics such as abilities or skills (Fasih, 2008). Even if the time dedicated to formal education gives important signals to employers about a person’s skills, it does not account for how these years have been effectively converted into skills by different individuals. It is these latter aspects of human capital that may transform into productivity and generate labor market returns.
The technology of skill formation pioneered and developed in Cunha and Heckman, 2007, Cunha and Heckman, 2008, Cunha et al., 2010 incorporates the above ideas, explicitly accounting for skills as mediators in the relationship between educational investments and labor market outcomes in adulthood. It has inspired a vast amount of applications using different outcome variables as the evaluative space of labor market outcomes (see e.g. Lin, Lutter, & Ruhm (2018) for the US, Lindqvist & Vestman (2011) for Sweden and Brunello & Schlotter (2011) for a set of European countries). In this paper, we propose to use as the outcome of interest a latent work-related wellbeing variable that encompasses multiple aspects of a good job.
Cunha and Heckman (2007)’s framework is flexible enough to allow for a wide definition of lifestyle outcomes in adulthood such as earnings or employment status (see e.g. Heckman et al., 2011, Heckman et al., 2018). This is particularly useful for the study of human development, the definition of which is largely inspired from the Capability Approach (cf. Sen, 1980, Sen, 1985, Sen, 1999). Several international initiatives such as the UNDP’s Human Development Index (UNDP, 1990), UN’s Sustainable Development Goals (UN, 2015), OECD’s Better Life Index,1 European Commission’s Going Beyond GDP2 have managed to convince policymakers around the world to adopt a broad vision of human well-being covering many dimensions and including manifold aspects within each dimension. Based on the same theoretical underpinning, we consider well-being in the work dimension (work-related well-being), which is itself one of the many dimensions of human well-being (Leßmann, 2012, Lugo, 2007), as a multifaceted notion going beyond earnings or any single indicator. The concept of Decent Work launched by ILO (Ghai, 2008) also fits in with this multifaceted vision as it defines a good job as having many characteristics in addition to earnings such as job stability, social protection, low physical stress and so on. In a similar spirit, the OECD brings job quality to the forefront of the policy debate, while considering three essential dimensions of workers’ well-being: earnings quality, labor market security and quality of the work environment (OECD, 2014). In spite of such initiatives and emphasis on a broad definition of a good job, studies relating education to such a multifaceted concept of a good job are still rare in the labor market literature. One can mention, for example, Muñoz de Bustillo, Fernández-Macías, Antón, and Esteve (2011), which posits that a better account of job quality should combine objective elements (e.g. earnings) with a subjective valuation of job attributes by the workers themselves.
The empirical operationalization of the technology of skill formation in its original formulation is a challenging endeavor as it is heavily demanding in terms of information. Longitudinal datasets at the individual level, covering early childhood to adolescence/adulthood, with extensive information on the type of investments made by parents on children’s education, constitute an ideal database for its practical application. This may be one of the reasons for the relative scarcity of such studies focusing on developing countries (see e.g. Laajaj & Macours (2017) for a discussion, and Sánchez, 2017, Villa, 2017 for empirical applications). Both these studies have only looked at the relationship between parental investment and cognitive/non-cognitive skills (and/or health) but none of them have related the latter to work-related outcomes. Furthermore, our idea of using a multifaceted concept of work-related well-being poses additional empirical challenges due to the abstract nature of this concept which cannot be directly observed using a single measure.
Our paper is an attempt to address the above two challenges. First, we examine how this framework can be operationalised with a ‘limited’ data set (possibly a single cross-section) as it is often the only information set available in a developing country. Nevertheless, investigating the connections between educational investments and skills on the one hand, and skills and work-related wellbeing on the other, is extremely important for developing countries because these are fundamental ingredients of the human development process at the country and individual levels. For our study, we have chosen Bolivia, the poorest country in South America in monetary terms, in which the World Bank conducted a national survey on work related information at the individual level, which also includes some past information on the educational paths of these individuals. It is the 2012 Survey Towards Employability and Productivity (STEP). Although relying on a single cross-section prevents us from fully identifying certain dynamic aspects of Cunha and Heckman (2007)’s framework, we believe that our methodology does provide a way for identifying one of the key aspects of this framework, namely the role of skills in the link between education and work-related wellbeing.
Second, drawing inspiration from the human development literature, we propose an outcome variable (work-related wellbeing) which is defined as a combination of multiple aspects of a job and hence not directly observable as such. Each of these aspects is specified as a latent variable and in turn measured by multiple indicators. The two are linked through measurement equations just as in the case of skills. Cunha and Heckman (2007) mention this possibility of having a latent outcome for anchoring skills (see page 898, Section 3.5 of their paper) giving the example of a variable measured by a binary indicator, whereas in our case we have more than one latent outcomes with multiple indicators for each.
To sum up, in this paper we adapt the technology of skill formation framework to a context with limited available information and relate the skills to an unobservable (latent) concept that encompasses multiple aspects of work-related well-being.
The paper is structured as follows: in Section 2 we explain our theoretical model. In Section 3 we specify the functional forms used for the empirical implementation of our framework, describe the data used and state the identification conditions. The empirical results for Bolivia are discussed in Section 4. We end the paper with some concluding remarks in Section 5.
Section snippets
The conceptual framework
Our model structure can be depicted by the diagram in Fig. 1 representing a two-step process to understand the connection between educational investments and work-related well-being. The first step focuses on the effect of investments on skill acquisition, as per the technology of skill formation, accounting for individual heterogeneity and the unobservable nature of skills. In the second step, these skills expand people’s realistic chances to increase their work-related well-being as described
The model
Let us now take the above model to the particular context of our empirical application on Bolivia. Based on the information available in our cross-sectional survey, we can consider 3 periods in the lifespan of an individual for the structural equations: the early childhood (pre-school) period is taken to be the first period of skill formation ; the period of formal training in school covering primary to tertiary levels is . The subsequent period corresponds to the period of the survey for
The relevance of a multidimensional approach to work-related well-being
Let us begin by briefly making a case for the relevance of adopting a multidimensional approach to gauge work-related well-being by showing some preliminary evidence.
A one dimensional monetary viewpoint implies focusing on say earnings as the outcome of interest.9 The results of this approach are given in panel (a) of Table 1. After correcting for
Concluding remarks
Understanding the nexus between education, skills and work-related well-being is fundamental in the quest to foster human development. Although rigorous studies are increasingly undertaken in developed countries to identify the connections involved, our knowledge of the same in the developing world is much less. This is largely due to the unavailability of appropriate longitudinal data in many developing countries. This study is an attempt to show that it is still possible to uncover some
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We are grateful to the Editor and to two anonymous referees whose valuable comments helped us to improve the paper substantially. We also thank participants at the 1st Cambridge Capability Conference, the 2017 Human Development and Capabilities Association Conference, the 6th Latin America & the Caribbean Association for the Study of Capabilities Conference, and OPHI and UPB Research Seminars for useful comments and suggestions.
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