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Social networks and the labour market mismatch

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Abstract

This paper assesses the extent to which social contacts and ethnic concentration affect the education-occupation mismatch of natives and immigrants. Using Australian panel data and employing a dynamic random effects probit model, we show that social capital exacerbates the incidence of over-education, particularly for females. Furthermore, for the foreign born, ethnic concentration significantly increases the incidence of over-education. Using an Alternative Index, we also show that social participation, friends and support and ethnic concentration are the main contributors in generating a mismatch, while reciprocity and trust does not seem to have any effect on over-education for both, immigrants and natives. Finally, we show that social networks are more beneficial for the relatively better educated.

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Notes

  1. For a general discussion, see Winter (2000).

  2. See for instance, Calvo-Armengol and Jackson (2004, 2007), Calvo-Armengol and Zenou (2005) and Wahba and Zenou (2005).

  3. Using the same data set, Green et al. (2007) show that immigrants in Australia are much more likely to be overeducated than the natives and the difference is more pronounced for those coming from non-English speaking backgrounds.

  4. For a general literature review on the role of social networks in the labour market, see Ioannides and Loury (2004).

  5. See Granovetter (1973), Granovetter (1983) and Montgomery (1991).

  6. Similar results were found by Horváth (2014) and Griesshaber and Seibel (2015), who found that personal networks and social participation leads to lower levels of over-education.

  7. See also Catanzarite and Aguilera (2002) and Chiswick and Miller (2002, 2005).

  8. For more details about the structure of HILDA see Summerfield et al. (2012).

  9. For more details, see ANZSCO, first edition, Australian Bureau of Statistics, Canberra, Cat No. 1220.0.

  10. A similar type of index was used by Aguilera and Massey (2003) to study the effect of social contacts on Mexican immigrants.

  11. For instance, for the social participation index, if individuals respond to be both active members of clubs/associations and union members, the index takes the value of 2. If respondents report to be a member of only one of the two, the index takes the value of 1, and if individuals report to not being a member of any of the two, the index value is 0.

  12. The proportions of ethnic concentration for each ethnic group residing in a specific region have been merged into one single index.

  13. English proficiency includes those who state that English is the only language spoken at home or those who report to speak English very well. Approximately 67% report to speak only English at home, while 23% of those who do not speak only English report to know the language very well.

  14. English-speaking countries include New Zealand, UK, Ireland, Canada, USA and South Africa.

  15. In fact, individuals are likely to self-select themselves into specific types of networks according to their education level as well as according to gender and ethnicity (Rosenbaum et al. 1999; Smith 2000).

  16. The existence of state dependence in over-education has been supported by a number of studies (see Mavromaras and McGuinness 2012 for Australia; Cuesta and Budria 2012 for Germany; and Joona et al. 2012 for Sweden).

  17. The results of the robustness checks for different groups of migrants are not reported but are available upon request.

  18. As a robustness check, we have furthermore run separate estimates with social network by systematically adding personal and family characteristics, job characteristics and health status in order to investigate potential collinearity. Since the social capital index remains unchanged, we can conclude that collinearity is not an issue. To conserve space the results from these estimates are not presented in this paper but are available upon request.

  19. A possible explanation for the insignificant effect of host country language skills might be that 90% of the immigrant group have been classified as fluent English speakers.

  20. See Chiswick and Miller (2009)

  21. Note that this presents the principal components taken as an average over the 11-year period to illustrate an example on how the PCA index was created. In order to construct the PCA variable for the analysis, the principal components of each year have been captured and merged in order to capture each variation in the data for every wave, rather than the average.

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Acknowledgements

We would like to thank Massimiliano Tani, Guy Tchuente, an anonymous referee and the Editor-in-Chief Klaus Zimmermann for their detailed comments on an earlier draft. This paper uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views reported in this paper, however, are those of the authors and should not be attributed to either FaHCSIA or the Melbourne Institute.

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Authors and Affiliations

  1. School of Economics, University of Kent, Canterbury, Kent, CT2 7NP, UK

    Eleni Kalfa & Matloob Piracha

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  1. Eleni Kalfa
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Correspondence to Matloob Piracha.

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Responsible editor: Klaus F. Zimmermann

Appendices

Appendix 1

Table 11 ANZSCO—occupational breakdown—two-digit level and their corresponding skill requirements
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Appendix 2

Table 12 ANZSCO definition of skill levels—required formal education and years of relevant experience
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Appendix 3

Table 13 Social capital variables and definitions
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Appendix 4: Construction of the principal component analysis

The PCA is a statistical method which aims to reduce multicollinearity by using an orthogonal transformation to transform a set of explanatory variables into a set of principal components, which are uncorrelated one another. By that, it reduces the dimensionality of the data keeping as much of the variation as possible. Thus, the first principal component of the set of variables chosen has the largest variation available in the data. The following tables report the results obtained using eight variables related to social capital in order to construct the PCA index.

Table 14 shows the correlation between the variables used in the PCA, which verifies that the components of the PCA are sufficiently different from one another to relate to various dimensions of social capital. The eigenvalues and the cumulative proportion as shown in Table 15 indicate the variation that is accounted for from the eight variables chosen. As we can see, the first component accounts for 26% of the variation in the data. Since this is relatively low, a number of other variables should be chosen. Although there is no consensus on how many and which components should be considered, it is argued that those components with eigenvalues greater than one have a larger variation than the variance of the individual standardised x it variables (Manly 2004). The first three components seem to be more important as they seem to have a larger variation and are all greater than one.

Table 14 Pearson correlation coefficients between the social capital variables
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Table 15 Eigenvalues and cumulative proportion
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Table 16 reports the eigenvectors obtained which present the coefficients of the principal components at time t.Footnote 21 It is noticeable that the PC2t and PC3t seem to contain more relevant information where the PC2t is led by dcommunity and dclub (coefficients x 6t and x 7t ), while PC3t is led by dunion (coefficient x 8t ). In order to investigate which principal component is most suitable for the analysis and whether PC2t and PC3t are more relevant, the regressions have been re-estimated using all three components as well as each of the three at a time. However, since no effect is observed, the analysis has been conducted using the first principal component.

Table 16 Outcomes of the principal components (eigenvectors)
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The first principal component used as a proxy for social capital can be represented as the following regression:

PC1t  = 0.4376 x 1t  + 0.4679 x 2t  + 0.4618 x 3t  + 0.3843 x 4t   + 0.2885 x 5t  + 0.3043 x 6t  + 0.215 x 7t  + 0.0814 x 8t ,

where the first principal component PC1t is a function of 8 eigenvectors (its coefficients).

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Kalfa, E., Piracha, M. Social networks and the labour market mismatch. J Popul Econ 31, 877–914 (2018). https://doi.org/10.1007/s00148-017-0677-5

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