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Impact of Social Networks on the Labor Market Inequalities and School-to-Work Transitions

Year 2021, Volume: 11 Issue: 1, 38 - 50, 03.05.2021

Mahmut Özer Matjaz Perc

https://doi.org/10.2399/yod.21.868353
Cited By: 3

Abstract

Countries invest in education systems in order to increase the quality of their human capital. In this context, it is seen that especially after the expansion of the higher education systems, countries try to increase higher education graduation rates in order to improve the quality of human resources in the labor market. The ultimate goal of these efforts is to facilitate the transitions from school-to-work, and to increase social welfare by meeting the human resources needs of the labor market. The facilitation of school-to-work transitions has a direct impact on youth unemployment. School-to-work transitions are influenced not only by the quality of education from primary to higher education but also by the dynamics of the labor market. Social network analysis can provide important insights into this dynamics, and in doing so reveal that there are indeed many factors that play a key role in determining who gets a job and why, including, first and foremost, social contacts. An analysis of job search channels reveals that partners, friends, and relatives are those social contacts that are most decisive for employment outcomes. Research reveals that employers use social-contact-based reference channels much more frequently than formal channels for recruitment. Thus, employers frequently use such reference channels in recruitment. It has also been shown that the use of social-contact channels reduces employers' costs of finding suitable employees and increases productivity since employees hired through these channels also stay longer in their firms. We here explore the full potential of social network analysis to better our understanding of school-to-work transitions, to reveal in no uncertain terms the importance of social contacts, and to show how these insights can be leveraged to level the labor market for all involved. An important take-home message is that the labor market dynamics is strongly affected by the Matthew effect, such that the inequalities and the gaps between opportunities only grow and widen as the underlying social networks evolve. It is therefore important to mitigate these effects well before school-to-work transitions come into play, namely during the education. In particular, we assert that minimizing the inequalities during education should effectively mitigate the uneven impact of social networks on school-to-work transitions.

Keywords

Employment higher education inequality labor market Matthew effect social network

Supporting Institution

The Slovenian Research Agency (M. Perc)

Project Number

Grant Nos. P1-0403 and J1-2457

References

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Sosyal Ağların İşgücü Piyasası Eşitsizlikleri ve Okuldan İşe Geçişlere Etkisi

Year 2021, Volume: 11 Issue: 1, 38 - 50, 03.05.2021

Mahmut Özer Matjaz Perc

https://doi.org/10.2399/yod.21.868353
Cited By: 3

Abstract

Ülkeler insan kaynağı kalitesini artırmak için eğitim sistemlerine yatırım yapmaktadır. Bu bağlamda, özellikle yükseköğretim sistemlerinin genişlemesinden sonra ülkelerin, işgücü piyasasındaki insan kaynağı kalitesini artırmak için yükseköğretimden mezun olanların oranlarını artırmaya çalıştıkları görülmektedir. Bu çabaların nihai amacı, okuldan işe geçişleri kolaylaştırmak ve işgücü piyasasının insan kaynağı ihtiyaçlarını karşılayarak toplumsal refahı artırmaktır. Okuldan işe geçişin kolaylaştırılması genç işsizlik oranlarına doğrudan etki etmektedir. Okuldan işe geçiş ilkokuldan yükseköğretime kadar sadece eğitimin kalitesi ile değil, ayrıca işgücü piyasası dinamikleri ile de doğrudan ilişkilidir. İşgücü piyasalarında istihdam dinamiklerinin anlaşılmasında sosyal ağların analizlerinin kullanılmaya başlanması, istihdamı etkileyen beşeri sermayenin ötesinde işgücü piyasasında çok sayıda başka faktörün olduğunu ortaya koymuştur. İş arama kanalları arasında özellikle eş, dost, akraba, başka bir ifadeyle sosyal çevrenin çok daha önemli olduğu görülmektedir. Bu bakımdan, işverenler işe eleman alımında formal kanalların ötesinde referans kanallarını sıklıkla kullanmaktadır. Bu kanalların, işverenler açısından hem çalışan arama maliyetini düşürdüğü hem de çalışanların işletmelerde çalışma süresini uzattığı için verimliliği de artırdığı gösterilmiştir. Dolayısıyla okuldan işe geçişin dinamiklerini anlamada sosyal ağ analizleri önemli fırsatlar sunmaktadır. Bu nedenle bu çalışmada sosyal ağ modellerinin iş piyasalarında istihdam dinamiklerini ve eşitsizlikleri anlamada sunduğu imkânlar değerlendirilmekte, ağdaki temasların istihdamda ne kadar etkili olduğu ayrıntılı olarak ele alınmaktadır. Ayrıca, sosyal ağların oluşumunda ve genişlemesinde eşitsizlikleri artıran Matta etkisi değerlendirilmektedir. Diğer taraftan, Matta etkisi kendisini işgücü piyasalarından önce eğitimde gösterdiği için eğitimdeki eşitsizliklerin temel nedenleri ve çözüm yolları üzerinde durulmaktadır. Böylece, işgücü piyasalarında eşitsizliklerin etkilerini hafifletebilmek için eğitimdeki eşitsizliklerin azaltılmasının önemi vurgulanmaktadır.

Keywords

Eşitsizlik istihdam işgücü piyasası Matta etkisi sosyal ağ yükseköğretim

Project Number

Grant Nos. P1-0403 and J1-2457

References

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  • Ammermueller, A. (2013). Institutional features of schooling systems and educational inequality: Cross-country evidence from PIRLS and PISA. German Economic Review, 14(2), 190–213.
  • Arrow, K. J., & Borzekowski, R. (2004). Limited network connections and the distribution of wages. Washington, DC: U.S. Federal Reserve Board, Finance and Economics Discussion Series, No.2004-41.
  • Barabasi, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.
  • Barabasi, A. L., Albert, R., & Jeong, H. (1999). Mean-field theory for scale-free random networks. Physica A, 272, 173–187.
  • Barabasi, A. L., & Bonabeau, E. (2003). Scale-free networks. Scientific American, 288(5), 60–69.
  • Blossfeld, H. P., & Shavit, Y. (1991). Persistent barriers: Changes in educational opportunities in thirteen countries. EUI Working Papers in Political and Social Sciences, SPS No.92/16.
  • Bourdieu, P. (1973). Cultural reproduction and social reproduction. In Brown, R. K. (Ed.) Knowledge, educational and cultural change (pp. 71– 112). London: Tavistock.
  • Bourdieu, P., & Passeron, J. C. (1990). Reproduction in education, society and culture. London: Sage Publications.
  • Bowen, G. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.
  • Bölükbaş, S., & Gür, B. S. (2020). Tracking and inequality: The results from Turkey. International Journal of Educational Development, 78, Article: 102262.
  • Brunila, K., & Lundahl, L. (2020). Youth on the move: Tendencies and tensions in youth policies and practices. Helsinki: Helsinki University Press.
  • Burks, S. V., Cowgill, B., Hoffman, M., & Housman, M. (2015). The value of hiring through employee referrals. Querterly Journal of Economics, 130(2), 805–839.
  • Burroughs, N. A., & Plucker, J. A. (2014). Excellence gaps. In J. A. Plucker & C. M. Callahan (Eds.), Critical issues and practices in gifted education: What the research says (2nd ed., pp. 255–265). Waco, TX: Profrock Press.
  • Calvo-Armengol, A. (2006). Social networks and labour market outcomes. Els Opuscles del CREI, 17, 1-41.
  • Calvo-Armengol, A., & Jackson, M. O. (2004). The effects of social networks on employment and inequality. The American Economic Review, 94(3), 426–454.
  • Campbell, K. E., & Marsden, P. V. (1990). Recruitment and selection process: The organizational side of job searches. In R. L. Breiger (Ed.). Social mobility and social structure (pp. 59–79). Cambridge: Cambridge University Press.
  • Cingöz, Z. K., & Gür, B. S. (2020). The effect of economic, social and cultural status on academic achievement a comparison of PISA 2015 and TEOG 2017 results. İnsan ve Toplum, 10(4), 247–287.
  • Darling-Hammond, L. (2014). What can PISA tell us about U.S. education policy? New England Journal of Public Policy, 26(1), 1–14.
  • Darling-Hammond, L., & Sykes, G. (2003). Wanted: A national teacher supply policy for education: The right way to meet the “Highly qualified teacher” challenge. Education Policy Analysis Archives, 11(3), 1–53.
  • de Vaan, M., & Wang, D. (2020). Micro-structural foundations of network inequality: Evidence from a field experiment in professional networking. Social Networks, 63, 213–230.
  • Deissinger, T. (2015). The German dual vocational education and training system as ‘good practice’? Local Economy, 30(5), 557–567.
  • Deissinger, T. (2019). The sustainability of the dual system approach to VET. In D. Gulle & L. Unwin (Eds.). The Wiley handbook of vocational education and training (pp. 293–310). Hoboken, NJ: John Wiley & Sons, Inc.
  • Erra, R. G., Velazquez, J. L. P., & Rosenblum, M. (2017). Neural synchronization from the perspective of non-linear dynamics. Frontiers in Computational Neuroscience, 11, Article: 98.
  • Friedkin, N. (1980). A test of structural features of Granovetter’s strength of weak ties theory. Social Networks, 2, 411–422.
  • Gee, L. K., Jones, J., & Burke, M. (2017). Social networks and labor markets: How strong ties relate to job finding on Facebook’s social networks. Journal of Labor Market, 35(2), 485–518.
  • Gençoğlu, C. (2019). Milli bir destekleme ve yetiştirme sistemi modeli: İlkokullarda yetiştirme programı (İYEP). Milli Eğitim Dergisi, 48(1), 853–881.
  • Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380.
  • Granovetter, M. S. (1974). Getting a job: A study of contacts and careers. Chicago, IL: University of Chicago Press.
  • Granovetter, M. S. (1982). The strength of weak ties: A network theory revisited. In P. V. Marsden & N. Lin (Eds.). Social structure and network analysis (pp. 105–130). Beverly Hills, CA: Sage.
  • Granovetter, M. S. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91, 481–510.
  • Gür, B. S., Özoğlu, M., Akgeyik, T., Çetinkaya, E., Karagöl, E. T., Öztürk, M., … Çelik, Z. (2012). Türkiye’nin insan kaynağının belirlenmesi. Ankara: SETA Yayınları.
  • Hanushek, E. A., & Woessmann, L. (2006). Does educational tracking affect performance and inequality? Differences-in-differences evidence across countries. Economic Journal, 116(510), C63–C76.
  • Hirsch, E. D. (2007). The knowledge deficit: Closing the shocking education gap for American children. New York, NY: Houghton Mifflin.
  • Hurst, J. M. (2016). The impact of networks on unemployment. London: Palgrave Macmillan / Springer Nature.
  • Ioannides, Y. M., & Soetevent, A. R. (2006). Wages and employment in a random social network with arbitrary degree distribution. The American Economic Review, 96(2), 270–274.
  • Jacobs, B., & Wolbers, M. H. J. (2018). Inequality in top performance: An examination of cross-country variation in excellence gaps across different levels of parental socioeconomic status. Educational Research and Evaluation, 24(1–2), 68–87.
  • Katz, L. F., & Autor, D. H. (1999). Changes in the wage structure and earnings inequality. In O. Ashenfelter & D. Card. (Eds.) Handbook of labor economics (Vol. 3A, pp. 1463–1555). Amsterdam: Elsevier Science.
  • Kramarz, F., & Skans, O. N. (2014). When strong ties are strong: Networks and youth labour market entry. Review of Economic Studies, 81(3), 1164– 1200.
  • Li, W., Aste, T., Caccioli, F., & Livan, G. (2019). Early coauthorship with top scientists predicts success in academic careers. Nature Communications, 10, Article: 5170.
  • Lynch, L. M. (1989). The youth labor market in the eighties: Determinants of reemployment probabilities for young men and women. Review of Economics and Statistics, 71(1), 37–54.
  • Marks, G. N. (2005). Cross-national differences and accounting for social class inequalities in education. International Sociology, 20(4), 483–505.
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There are 86 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Conceptual Research
Authors

Mahmut Özer 0000-0001-8722-8670

Matjaz Perc 0000-0002-3087-541X

Project Number Grant Nos. P1-0403 and J1-2457
Publication Date May 3, 2021
Published in Issue Year 2021 Volume: 11 Issue: 1

Cite

APA Özer, M., & Perc, M. (2021). Impact of Social Networks on the Labor Market Inequalities and School-to-Work Transitions. Yükseköğretim Dergisi, 11(1), 38-50. https://doi.org/10.2399/yod.21.868353

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