Chapter 1. Theoretical framework, review of EU policies and case study
1.6 Thesis outline
Based on the theoretical framework and on the case study discussed in the previous sections of this chapter, in this section the structure of the thesis and the content of the next chapters is briefly outlined.
The thesis is divided in three chapters, plus this Introduction and the Conclusion chapter. The following chapter – number 2 – focuses on the labour market outcomes of the recipients of the M&B scheme. As previously mentioned, a priority objective of the European Union is to create “more and better jobs” by increasing individual
54 employability. However, especially due to a substantial lack of studies focusing on the labour market outcomes of FMS, there is no agreement in the literature on whether student mobility can actually improve individual career prospects (see for instance Messer and Wolter, 2007, Oosterbeek and Webbink, 2006, Rodrigues, 2013). Since SM enhances individual levels of human capital (Bracht et al., 2006, Konevas and Duoba, 2007, Murphy-Lejeune, 2002, Rodrigues, 2012) and spatial flexibility (Di Pietro, 2012, King and Ruiz-Gelices, 2003, Oosterbeek and Webbink, 2011), we also expect it to improve individual performance in the labour market. However, other strands of literature challenge this expectation, on the grounds that there are structural barriers at play in the labour markets (Constant and Massey, 2005), that human capital is not geographically transferable (Wiers-Jenssen and Try, 2005) and so on.
In order to contribute to this academic debate, we proxy more and better jobs through odds of employment and net monthly earnings, respectively, and compare the outcomes of the recipients of the M&B scheme to those of a suitable control group. The comparison is performed by Propensity Score Matching (Rosenbaum and Rubin, 1983), a technique which allows us to isolate the impact of the programme from other confounding factors and to identify its causal effect.
Chapter 3 focuses on the impact of SM schemes on the job matching of the recipients. There is evidence that more spatially mobile individuals are more likely to achieve a good job matching since they can access a larger number of spatially distributed job vacancies – especially if they access the dense labour markets of large urban areas (Büchel and Battu, 2003, Frank, 1978, Hensen et al., 2009, Jauhiainen, 2011, McGoldrick and Robst, 1996, Tselios, 2013, van Ham et al., 2001). However, there is a major gap in this literature concerning the extent to which better job matching can be achieved by artificially stimulating geographical mobility.
Therefore, Chapter 3 aims to contribute to this academic debate by assessing whether the recipients of the M&B programme are more likely than the control group to achieve a good job matching. We measure the level of both vertical and horizontal matching trough two proxies. Vertical matching is measured by comparing the individual level of education with that required for the employment at the time of our observation; horizontal matching is proxied by the individual satisfaction with the matching between the subject’s skills and job tasks.
55 In order to minimise the potential self-selection bias we rely on an Instrumental Variable (IV) approach, where the unobserved heterogeneity is controlled for by using mother’s level of education. Moreover, we control for current location in order to investigate whether the sending region (i.e., Sardinia) has been able to reap the returns to its investment in the M&B programme by achieving a good job matching of the recipients who return to Sardinia.
Both the literature and the EU acknowledge that SM can lead to brain drain from lagging to core regions (EC, 2001, Oosterbeek and Webbink, 2011). As a result, SM can lead lagging regions to lose an important asset for their development and economic growth: human capital (Fratesi and Riggi, 2007). Therefore, understanding what determines return migration to lagging regions by FMS would be extremely useful. This issue is related to the academic debate on the determinants of highly skilled individuals’ location decision, for which diverging opinions exist. Part of the literature maintains that highly skilled migration is mainly driven by economic factors; in contrast, another strand of literature tends to support the idea that amenities are the most dominant factors (exemples of this debate are Clark et al., 2002, Florida, 2002a, Glaeser, 2005b, Kemeny and Storper, 2012, Rodríguez-Pose and Ketterer, 2012, Scott, 2010, Storper and Scott, 2009). Yet other studies have emphasised the importance of social networks in different locations (Constant and Massey, 2003, Dahl and Sorenson, 2010b, Geddie, 2013, King, 2002, Massey et al., 1993, Vertovec, 2002). Yet another related aspect which has recently started to be investigated by the academic community concerns the nature of the decision-making process leading to the location decision (Carlson, 2013, Geddie, 2010, Mosneaga and Winther, 2012, Waters and Brooks, 2010).
Accordingly, Chapter 4 studies the determinants of M&B recipients’ location choice through the analysis of quantitative and qualitative data. First, relying on quantitative data, the impact of returning to Sardinia on the income of formerly mobile students is tested through an OLS regression. This analysis provides an assessment of the extent to which migration can be convenient from an economic viewpoint. Second, still using quantitative data, different potential drivers of location choice (economic factors, amenities and social networks) are regressed on a dummy accounting for return to Sardinia in order to detect their potential trade-offs and complementarities. Third, switching to the qualitative data, we explore the nature of the decision making process
56 (i.e., how the location decision occurs). In practice, the last empirical chapter relies on a mixed-methods approach, on the grounds that quantitative and qualitative methods are complementary and can provide a more comprehensive picture of a very complex phenomenon like the one at hand.
However, before we start off with the empirical chapters of this thesis, two remarks deserve to be made concerning the generalizability of our estimates and how the economic crisis might have influenced our results.
Concerning the first issue, Sardinia is characterized by very unique features and thus the results that have been observed in this study cannot be generalised to other contexts. In particular, Sardinia is an island and, therefore, its underlying patters of brain circulation are unique: its residents are less spatially flexible than those of other regions, inward highly skilled migration is more unlikely – as the psychic and economic costs to relocate in an island are very high – and so on. However, this does not imply that our research does not provide insights that can be useful for other regions managing similar programmes. In contrast, we believe that many problems identified in Sardinia are also relevant to other contexts, particularly other lagging regions engaged in the implementation of SM schemes. In this regard, the M&B programme can be considered an instance of a broader family of similar cases.
Concerning the second issue, we acknowledge that our findings may have been influenced by the economic crisis. In fact, recall that data collection was carried out between December 2011 and January 2012, coinciding with one of the worst economic crisis ever, similar in size only to the great recession of the 30s. The economic crisis, also known as the Great Recession, started in 2007 and peaked in 2009. However, after this phase, most European countries (particularly Italy) were hit by a second wave of recession, caused by government debts, which reached its peak in 2011-2012 – when the data for this work was collected. The potential impact of the crisis on the results of this research work is further discussed in the following chapters.