According to the literature on social networks 30-50% of employees search and find jobs through social networks. Intuitively, one can expect that immigrant workers have smaller social networks in the new destination country and by that smaller probability finding a job through their networks, but empirical evidence shows that immigrant workers are more likely to find a job through social contacts. The data of German Socio-Economic Panel (SOEP) from 2002 to 2008 show that nearly 41.21% of employed immigrants and 31.79% of employed Germans found their current job through relatives and friends. The main goal of this paper is to explain why migrant workers find jobs through social contacts more often than natives. Part of the different frequency of finding job through social contacts between natives and immigrants can be explained by individual characteristics, size and industry of firms where the individual is employed. Taking into consideration these aspects there is still significant difference: probability of finding job through relatives and friends is 34.40 % for migrants, and 27.19% for natives. I propose a model which tries to explain the reasons behind the difference and give a numerical example.
In the model firms do not observe real productivities of the applicants, instead they observe a noisy signal of the productivity, as well as the nationality of the applicant and whether the applicant found the vacancy through social contacts. The employer then forms unbiased expectations on the real productivities of applicants. The firms and the union bargain over the wage ex-ante. Then the firms set threshold values of productivity signals for the 4 groups of the applicants; natives (immigrants) who found the vacancy through social contacts or formal search channels. By this one gets the probabilities of being accepted after the match happens, which is simply the probability that the probability signal an individual generates is higher than the threshold value for the group.
The calibrated model shows that an immigrant is 4.06 times more likely to have a productivity signal higher than the threshold value when they are matched with the vacancy through contacts, while natives are 2.15 times more likely when matched with the vacancy through contacts. Since immigrant workers have low chances of being hired therefore they gain more from being matched to a job vacancy through social contacts. Native workers have good chances of being hired even if they are matched trough formal channels therefore they gain less from using their social contacts.
The role of doctoral recipients in promoting research and driving innovation is stressed in the literature (Stuen et al. 2012). In particular, the European Union policy agenda emphasizes the role of doctoral recipients since PhD education and training can help achieve the Lisbon goal of developing a competitive-knowledge based economy. However, in order to achieve these goals it is imperative that the doctoral recipients accept jobs that exploit their skills and training. A doctoral recipient would typically search for a job matching his/her education in their geographical area. When they cannot achieve this they could either accept a job requiring less education or skills; remain unemployed or outside the labor force, or widen the job search area (Büchel & van Ham 2003). If a doctoral recipient accepts a job that requires less education or skills than what they have acquired then they are mismatched for the job thus resulting in overeducation or overskilling. This labor market mismatch could be potentially costly both for the PhD recipient and society due to lower levels of productivity, lower job satisfaction and the inefficient use of investment made in education. (McGuinness 2006). Consequently, this has led to a growing literature documenting the incidence of labor market mismatch in the early career of young workers (Dolton & Vignoles 2000).
Using data from the Professional Integration Survey of PhDs administered by the Italian National Institute of Statistics (ISTAT) on Italian PhD graduates, this study contributes to the existing literature on job mismatch in several ways. First, we quantify the extent to which spatial mobility represents a strategy that reduces the risk of being overeducated or overskilled. Second, we use a new measure of overskilling where we first rely on standard measures based on workers' self-assessment and then exploit information on the R&D content of the job in order to capture the mismatch between the academic research training received before graduation and the actual job duties. Third, this study is important from the host country perspective since this group of tertiary education embodies high-skill labor and knowledge that could lead to higher innovative activities and knowledge creation in the host country (Bosetti et al. 2015; Chellaraj et al. 2008).
A clear pattern emerges from our analysis. Spatial mobility is found to reduce the probability of mismatch. Our estimates indicate that if a random individual from the population of PhDs chooses to migrate, he/she would face a 15.8 percent lower probability of getting a job for which the degree was formally required or at least useful at the hiring stage, a 20% lower probability to be overskilled and a 11.7% lower probability of being in a non-research-oriented job. These pieces of evidence broadly confirm what already found in the literature focusing on the role of spatial mobility in reducing the education-job mismatch of high-skill individuals and imply that PhD recipients enlarging their job-search area are able to transfer abroad, at least partially, their human capital. Since PhD holders possess a set of occupation-specific skills whose returns occur only in a limited set of activities, and since the geographic distribution of high-skill occupations requires individuals to move to the areas in which the job opportunities are located, individual mobility turns out to pay off in terms of lower chances to be mismatched in the labor market. Moreover, the estimated effects suggest that there are sizeable differences in the effects of migration on overskilling when we move from a self-assessment measure (20%) to our objective measure (11.7%). First, this result challenges the appropriateness of the use of measures for overskilling based on a subjective evaluation. Second, the result highlights a tendency of the Italian economy of being unable to employ all the potential scientific workforce trained in domestic institutions.