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

Sosyal Ağların İşgücü Piyasası Eşitsizlikleri ve Okuldan İşe Geçişlere Etkisi

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

• Albert, R. H., Jeong, H., & Barabasi, A. L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382.
• 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.
• Merton, R. K. (1968). The Matthew effect in science. Science, 159(3810), 53–63.
• Merton, R. K. (1973). The sociology of science: Theoretical and empirical investigations. Chicago, IL: University of Chicago Press.
• Montgomery, J. D. (1991). Social networks and labor-market outcomes: Towards and economic analysis. American Economic Review, 81(5), 1408– 1418.
• Montgomery, J. D. (1994). Weak ties, employment, and inequality: An equilibrium analysis. American Journal of Sociology, 99(5), 1212–1236.
• Myers, C. A., & Shultz, G. P. (1951). The dynamics of a labor market. New York, NY: Prentice-Hall.
• OECD (2018). Effective teacher policies: Insights from PISA. Paris: OECD Publishing.
• Onnela, J. P., Saramaki, J., Hyvönen, G., Szabo, D., Lazer, K., Kaski, J., … Barabasi, A. L. (2007). Structure and tie strength in mobile communication network. Proceedings of the National Academy of Sciences, 104(18), 7332–7336.
• Özer, M. (2018). The 2023 Education Vision and new goals in vocational and technical education. Journal of Higher Education and Science, 8(3), 425–435.
• Özer, M. (2019a). Background of problems in vocational education and training and its road map to solution in Turkey’s Education Vision 2023. Journal of Higher Education and Science, 9(1), 1–11.
• Özer, M. (2019b). Reconsidering the fundamental problems of vocational education and training in Turkey and proposed solutions for restructuring. İstanbul Üniversitesi Sosyoloji Dergisi, 39(2), 1–19.
• Özer, M. (2020a). Mesleki eğitimde paradigma değişimi: Türkiye’nin mesleki eğitim ile imtihanı. İstanbul: Maltepe Üniversitesi Kitapları.
• Özer, M. (2020b). The conflict between individualist vs structural approaches for issues in the transition from school-to-work. Bartın University Journal of Faculty of Economics and Administrative Sciences, 11(22), 330–345.
• Özer, M. (2020c). The new factors affecting the school-to-work transition for vocational education and training: New trends in global transformation. İnsan ve Toplum, 10(3), 1–27.
• Özer, M. (2020d). The paradigm shift in vocational education and training in Turkey. Gazi University Journal of Gazi Educational Faculty, 40(2), 357–384.
• Özer, M. (2020e). What PISA tells us about performance of education systems? Bartın University Journal of Faculty of Education, 9(2), 217–228.
• Özer, M. (2021). A new step towards narrowing the achievement gap in Turkey: “1,000 schools in vocational education and training” Project. Bartın University Journal of Faculty of Education, 10(1), 97–108.
• Özer, M., & Perc, M. (2020). Dreams and realities of school tracking and vocational education, Palgrave Communications, 6, 34.
• Özer, M., & Suna, H. E. (2019). Future of vocational and technical education in Turkey: Solid steps taken after Education Vision 2023. Journal of Education and Humanities, 10(20), 165–192.
• Özer, M., & Suna, H. E. (2020). The linkage between vocational education and labor market in Turkey: Employability and skill mismatch. Kastamonu Education Journal, 28(2), 558–569.
• Özer, M., Gencoğlu, C., & Suna, H. E. (2020). Türkiye’de eğitimde eşitsizlikleri azaltmak için uygulanan politikalar. Ondokuz Mayıs Üniversitesi Eğitim Fakültesi Dergisi, 39(2), 294–312.
• Perc, M. (2014). The Matthew effect in empirical data. Journal of Royal Society Interface, 11(98), 20140378.
• Raggat, P. (1988). Quality control in the dual system of West Germany. Oxford Review of Education, 14, 163–186.
• Rees, A. (1966). Information networks in labor markets. American Economic Review, 56, 559–566.
• Rees, A., & Shultz, G. P. (1970). Worker in an urban labor market. Chicago, LA: University of Chicago Press.
• Reichelt, M., Collischon, M., & Eberl, A. (2019). School tracking and its role in social reproduction: Reinforcing educational inheritance and the direct effects of social origin. The British Journal of Sociology, 70(4), 1–26.
• Reschly, A. (2010). Reading and school completion: Critical connections and Matta effects. Reading & Writing Quarterly, 26, 67–90.
• Rözer, J., & Van de Werfhorst, H. (2020). Three worlds of vocational education: Specialized and general craftsmanship in France, Germany, and The Netherlands. European Sociological Review, 36(5), 780–797.
• Sahlberg, P. (2011). PISA in Finland: An education miracle or an obstacle to change? Center for Educational Policy Studies Journal, 1(3), 119–140.
• Schott, T. (1998). Ties between center and periphery in the scientific world-system: Accumulation of rewards, dominance and self-reliance in the center. Journal of World Systems Research, 4, 112–144.
• Shafiei, M., Jafari, S., Parastesh, F., Özer, M., Kapitaniak, T., & Perc, M. (2020). Time delayed chemical synapses and synchronization in multilayer neuronal networks with ephaptic inter-layer coupling. Communications in Nonlinear Science and Numerical Simulation, 84, Article: 105175.
• Stadtfeld, C., Vörös, A., Elmer, T., Boda, Z., & Raabe, I. J. (2019). Integration in emerging social networks explains academic failure and success. Proceedings of the National Academy of Science, 116(3), 792–797.
• Stanovich, K. E. (1986). Matthew effect in reading: Some consequences of individual differences in acquisition of literacy. Reading Research Quarterly, 21, 360–406.
• Suna, H. E., Gür, B. S., Gelbal, S., & Özer, M. (2020a). Science high school students’ socioeconomic background and their preferences regarding their transition into higher education. Yükseköğretim Dergisi, 10(3), 356–370.
• Suna, H. E., Tanberkan, H., Eroğlu, E., Özer, M., & Gür, B. S. (2020b). Horizontal skills mismatch in vocational education in Turkey: The reasons for out-of-field employment. İstanbul Üniversitesi Sosyoloji Dergisi, 40(2), 931–955.
• Suna, H. E., Tanberkan, H., Gür, B. S., Perc, M., & Özer, M. (2020c). Socioeconomic status and school type as predictors of academic achievement. Journal of Economy Culture and Society, 61(1), 41–64.
• Suna, H. E., Tanberkan, H., & Özer, M. (2020d). Changes in literacy students in Turkey by years and school types: Performance of students in PISA applications. Journal of Measurement and Evaluation in Education and Psychology, 11(1), 76–97.
• Topa, G. (2011). Labor markets and referrals. In J. Benahhib, M. O. Jackson, & A. Bisin (Eds.) Handbook of social economics (Vol. 1, pp. 1193–1221). Amsterdam: Elsevier.
• Van der Leij, M., & Goyal, S. (2011). Strong ties in a small world. Review of Network Economics, 10(2), 1–20.
• Walberg, H. J., & Tsai, S. L. (1983). Matthew effects in education. American Educational Research Journal, 20(3), 359–373.
• Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442.
• Willis, C. L., & McNamee, S. J. (1990). Social networks of science and patterns of publication in leading sociology journals, 1960 to 1985. Science Communication, 11, 363–381.
• World Bank (2012). World development report 2013: Jobs. Washington, DC: World Bank.
• Zenou, Y. (2013). Spatial versus social mismatch. Journal of Urban Economics, 74, 113–132.
• Zenou, Y. (2015). A dynamic model of weak and strong ties in the labor market. Journal of Labor Economics, 33(4), 891–932.