Teknoloji Kullanım Düzeyinin Ücretler Üzerindeki Etkisi: Türkiye İmalat Sanayi Sektörü Örneği

Yıl 2024, Cilt: 14 Sayı: 2, 126 - 141, 15.05.2025

Ensar Balkaya , Harun Sıçrar

https://doi.org/10.32331/sgd.1699798

Cited By: 1

https://izlik.org/JA34WW69HZ

Öz

Ücret farklılıklarına neden olan etkenlerin belirlenmesi, ücretin çoğu durumda işgücünün tek gelir kaynağı olarak bireysel önemi ve gelir dağılımındaki adaletin bir göstergesi olması itibari ile oldukça önemlidir. Ücret farklılıklarına neden olan önemli dışsal etkenlerden biri de teknolojidir. Bu çalışmanın amacı, teknoloji faktörünün ücretler üzerindeki etkisini belirlemektir. Bu kapsamda, ücretleri etkileyen faktörlerin
belirlenmesi amacıyla beşeri sermaye özelliklerinin yanı sıra teknoloji faktörünü de hesaba katabilmek adına yüksek ve düşük teknolojili imalat sektöründe çalışanlara ait veriler kullanılmıştır. Veriler TÜİK tarafından gerçekleştirilen Kazanç Yapısı Araştırması-2018’den elde edilmiştir. Bu kapsamda imalat sanayinde çalışan bilim ve mühendislik alanında profesyonel meslek mensupları ile sabit tesis ve makine operatörlerine ait veriler ayırt edilmiştir. Çalışmanın amacı doğrultusunda, aynı mesleği icra eden çalışanlara ait saatlik ücretlerin bağımlı değişken ve beşeri sermaye özelliklerinin yanı sıra çalışanların yüksek ya da düşük teknolojili imalat sektöründe çalışma durumlarının bağımsız değişken olarak seçildiği iki meslek grubu için iki ayrı model geliştirilmiştir. Bu modeller, bağımlı değişkenin normal dağılıma uymaması
nedeni ile genelleştirilmiş doğrusal regresyon analizleri ile test edilmiştir. Elde edilen sonuçlara göre, çalışanlar aynı mesleği icra etse de beşeri sermaye faktörleri ve teknoloji kullanım düzeyi ücretlerde farklılığa neden olmakta ve yüksek teknolojili sektörde çalışma durumu iki ayrı meslek için ücretleri önemli düzeyde artırmaktadır.

Anahtar Kelimeler

Ücret , teknoloji , beşeri sermaye , genelleştirilmiş doğrusal regresyon

Kaynakça

• Acheson, J. and Collins, M. (2021). The Gender Pay Gap in Revenue. Administration. 69(3). 45–75. doi: 10.2478/admin-2021-0020
• Addison, J. T., Ozturk, O. D. and Wang, S. (2018). The Occupational Feminization of Wages. ILR Review. 71(1). 208–241. doi: 10.1177/0019793917708314
• Aksoylu, D. ve Karaalp-Orhan, H. S. (2018). Ücret Farklılıklarına Neden Olan Faktörler ve İşgücü Piyasasında Tabakalaşma: Ankara İli İnşaat Sektörü Üzerine Bir Alan Araştırması. Journal of Management and Economics Research. 16(2). 1–20. doi: https://doi.org/10.11611/yead.359930
• Allen, S. G. (2001). Technology and the Wage Structure. Journal of Labor Economics. 19(2). 440–483.
• Baigh, T. A., Yong, C. C. and Cheong, K. C. (2021). Existence of Asymmetry Between Wages and Automatable Jobs: A Quantile Regression Approach. International Journal of Social Economics. 48(10). 1443–1462. doi:10.1108/IJSE-02-2021-0085
• Bartel, A. P. and Lichtenberg, F. R. (1988). Technical Change, Learning and Wages. National Bureau of Economic Research Cambridge, Mass., USA.
• Bartel, A. P. and Sicherman, N. (1999). Technological Change and Wages: An Interindustry Analysis. Journal of Political Economy. 107(2). 285–325.
• Brewer, D. J., Hentschke, G. C. and Eide, E. R. (2010). Theoretical Concepts in the Economics of Education. Economics of Education. 3–8.
• Burnham, K. P. and Anderson, D. R. (2004). Multimodel Inference: Understanding AIC and BIC in Model Selection. Sociological Methods ve Research. 33(2). 261–304. doi: 10.1177/0049124104268644
• Cadil, J., Kopecky, M. and Jurcik, T. (2022). Job Grade Camouflage: When Low Gender Pay Gap Does Not Mean Equal Pay. International Journal of Economic Sciences. 11(2). 28–47.
• Cameron, A. C. and Trivedi, P. K. (2013). Regression Analysis of Count Data (Issue 53). Cambridge University Press.
• Cerejeira, J., Kızılca, K., Portela, M. and Sa, C. (2012). Minimum Wage, Fringe Benefits, Overtime Payments and the Gender Wage Gap. IZA Discussion Papers.
• Dattero, R., Galup, S. D. and Dhariwal, K. (2013). The Determinants of Information Technology Wages. In Enhancing the Modern Organization Through Information Technology Professionals: Research, Studies, and Techniques (pp. 32–49). IGI Global. doi:10.4018/jhcitp.2011010104
• Demirkıran, S., Yücel, M. A., Terzioğlu, M. K. ve Selvi, A. (2021). Dijital Dönüşüm Sürecinde Akıllı Yönetişim. Tesam Akademi Dergisi. 8(2). 489-519. doi: http://dx.doi.org/10.30626
• Echeverri-Carroll, E. and Ayala, S. G. (2009). Wage Differentials and the Spatial Concentration of High-Technology Industries. Papers in Regional Science. 88(3). 623–642. doi: https://doi.org/10.1111/j.1435-5957.2008.00199.x
• Entorf, H. and Kramarz, F. (1997). Does Unmeasured Ability Explain the Higher Wages of New Technology Workers? European Economic Review. 41(8). 1489–1509.
• Fairris, D. and Jonasson, E. (2008). What Accounts for Intra-Industry Wage Differentials? Results from a Survey of Establishments. Journal of Economic Issues. 42(1). 97–114. doi: 10.1080/00213624.2008.11507116
• Fossen, F. M. and Sorgner, A. (2022). New Digital Technologies and Heterogeneous Wage and Employment Dynamics in the United States: Evidence from Individual-Level Data. Technological Forecasting and Social Change. 175. 121381. doi: https://doi.org/10.1016/j.techfore.2021.121381
• Fox, J. (2015). Applied Regression Analysis and Generalized Linear Models. Sage publications.
• Gabriel, P. E. (2005). The Effects of Differences in Year-Round, Full-Time Labor Market Experience on Gender Wage Levels in the United States. International Review of Applied Economics. 19(3). 369–377. doi: 10.1080/02692170500119813
• Genz, S., Janser, M. and Lehmer, F. (2019). The Impact of Investments in New Digital Technologies on Wages–Worker-Level Evidence from Germany. Jahrbücher Für Nationalökonomie Und Statistik. 239(3). 483–521. doi:https://doi.org/10.1515/jbnst-2017-0161
• Goel, M. (2017). Offshoring–Effects on Technology and Implications for the Labor Market. European Economic Review. 98. 217–239. doi: https://doi.org/10.1016/j.euroecorev.2017.04.009
• Güven, A. ve Çağatay, K. (2019). Türkiye’de Riskli İşler İçin Telafi Edici Ücret Farklılığı Uygulanıyor mu? Emek Araştırma Dergisi. 4(2). 97–121.
• Harbi, S., Amamou, M. and Anderson, A. R. (2009). Establishing High-Tech industry: The Tunisian ICT experience. Technovation. 29(6–7). 465–480. doi: https://doi.org/10.1016/j.technovation.2008.11.001
• Harman, J. and Bartůsková, L. (2024). The Gender Pay Gap in the Visegrad Groups. Journal of Economic Studies. 51(4). 733–763. doi: 10.1108/JES-02-2023-0072
• Hellerstein, J. K., Neumark, D. and Troske, K. R. (1999). Wages, Productivity and Worker Characteristics: Evidence from Plant-Level Production Functions and Wage Equations. Journal of Labor Economics. 17(3). 409–446.
• Hijzen, A. (2007). International Outsourcing, Technological Change and Wage Inequality. Review of International Economics. 15(1). 188–205. doi:10.1111/j.1467-9396.2006.00623.x
• Hu, S., Chen, M., Liu, X. and Wang, H. (2024). Winner-Takes-All or Inclusive Growth? The Impact of Digitalization on Inter-Firm Wage Inequality: Evidence from China. Applied Economics. 1–16. doi: https://doi.or g/10.1080/00036846.2024.2369723
• Ismail, R., Farhadi, M. and Wye, C. (2017). Occupational Segregation and Gender Wage Differentials: Evidence from Malaysia. Asian Economic Journal. 31(4). 381–401. doi: 10.1111/asej.12136
• Jain, H. (2018). Technological Change, Skill Supply and Wage Distribution: Comparison of High-Technology and Low-Technology Industries in India. The Indian Journal of Labour Economics. 61. 299–320. doi: https://doi.org/10.1007/s41027-018-0130-7
• Kahyalar, N., Fethi, S., Katircioglu, S. and Ouattara, B. (2018). Formal and Informal Sectors: Is There Any Wage Differential? The Service Industries Journal. 38(11–12). 789–823. doi: 10.1080/02642069.2018.1482877
• Koirala, G. (2007). An Analysis of Labor Wage Differentials in Nepal. Journal of Asian Economics. 18(4). 636–648. doi:10.1016/j.asieco.2007.03.008
• Lee, N. and Clarke, S. (2019). Do Low-Skilled Workers Gain from High-Tech Employment Growth? High-Technology Multipliers, Employment and Wages in Britain. Research Policy. 48(9). 103803. doi: https://doi. org/10.1016/j.respol.2019.05.012
• López-Bazo, E. and Motellón, E. (2012). Human Capital and Regional Wage Gaps. Regional Studies. 46(10). 1347–1365.doi: 10.1080/00343404.2011.579092
• McCullagh, P. (2019). Generalized Linear Models. Routledge.
• McDevitt, C. L., Irwin, J. R. and Inwood, K. (2009). Gender Pay Gap, Productivity Gap and Discrimination in Canadian Clothing Manufacturing in 1870. Eastern Economic Journal. 35. 24–36.
• Mendoza Gonzalez, M. A., Cardero Garcia, M. E. and Ortiz Garcia, A. S. (2017). Some Stylized and Explanatory Facts About the Differential and Salary Discrimination by Sex in Mexico, 1987-2015. Investıgacıon Economıca. 76(301). 103–135.
• Mitková, L. (2018). Occupational Segregation and Gender Pay Gap in Slovakia. Proceedings of the 31st International Business Information Management Association Conference (IBIMA).
• Monti, H., Stinson, M. and Zehr, L. (2020). How Long Do Early Career Decisions Follow Women? The Impact of Employer History on the Gender Wage Gap. Journal of Labor Research. 41(3). 189–232. doi: https://doi. org/10.1007/s12122-020-09300-9
• Nelder, J. A. and Wedderburn, R. W. M. (1972). Generalized Linear Models. Journal of the Royal Statistical Society Series A: Statistics in Society. 135(3). 370–384.
• Oaxaca, R. L. and Ransom, M. R. (1994). On Discrimination and the Decomposition of Wage Differentials. Journal of Econometrics. 61(1). 5–21.
• Pagan Rodriguez, R. (2007). Wage Differentials Between Full-Time and Part-Time Employment. Revısta De Economıa Aplıcada. 15(43). 5–47.
• Papapetrou, E. (2008). Evidence on Gender Wage Differentials in Greece. Economic Change and Restructuring. 41. 155–166. doi:10.1007/s10644-008-9046-4
• Rattsø, J. ve Stokke, H. E. (2020). Private-public wage gap and return to experience: Role of geography, gender and education. Regional Science and Urban Economics. 84. 103571.doi: https://doi.org/10.1016/j. regsciurbeco.2020.103571
• Roberts, B. and Wolf, M. (2018). High-Tech Industries: An Analysis of Employment, Wages and Output. Beyond the Numbers. 7(7). 1–11.
• Shim, M. and Yang, H.-S. (2018). Interindustry Wage Differentials, Technology Adoption and Job Polarization. Journal of Economic Behavior and Organization. 146. 141–160. doi: https://doi.org/10.1016/j.jebo.2017.11.014
• Smelser, N. J. and Baltes, P. B. (2001). International Encyclopedia of the Social ve Behavioral Sciences (Vol. 11). Elsevier Amsterdam.
• Sun, H. and Kim, G. (2014). Longitudinal Evidence of Firm Size Effect on Wage Premium and Wage Differential in Korean Labor Market. International Journal of Economic Sciences. 3(3). 66–85.
• Yücel, M. A. ve Terzioğlu, M. K. (2023). Sürdürülebilir Kalkınma ve Eko-İnovasyon: Dinamik Mekânsal Etkileşim. Verimlilik Dergisi. 171-186. doi: 10.51551/verimlilik.1103725
• Wolter, S. C. and Ryan, P. (2011). Apprenticeship. In Handbook of the Economics of Education (Vol. 3, pp. 521–576). Elsevier. Doi: https://doi.org/10.1016/B978-0-444-53429-3.00011-9
• Wongmonta, S. (2023). Revisiting the Wage Effects of Vocational Education and Training (VET) Over the Life Cycle: The Case of Thailand. International Journal of Educational Development. 103. 102886. doi: https://doi.org/10.1016/j.ijedudev.2023.102886
• Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT Press.
• Wu, Z. (2005). Generalized Linear Models in Family Studies. Journal of Marriage and Family. 67(4). 1029–1047. doi: https://doi.org/10.1111/j.1741-3737.2005.00192.x