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Paper ID:IJSREM66899

Digital Transformation and Economic Inequality in the IT Workforce: Task Displacement, Firm Rents, and the Narrowing of Entry Pathways.

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM66899

Volume:

10 |

8 |

Aug 2026

Pages:

1-17

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM66899

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Shaila Ashok Rane

Shaila Ashok Rane

Economics

Bharthi Vidhyapeeth Deemed University Pune

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DHM

Dr.VINOD H. MANE

Economics

Bharthi Vidhyapeeth Deemed University Pune

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Abstract

Research on technology and inequality has generally treated information technology workers as beneficiaries of skill-biased technological change rather than as a population within which inequality is itself being restructured. This paper argues that the assumption no longer holds, and supports the argument with a documentary qualitative analysis of thirty-four publicly available sources combined with descriptive quantitative evidence. We develop a framework in which IT work is a bundle of separable tasks priced by heterogeneous employers, and derive four propositions: automation of routine implementation widens dispersion within rather than between IT occupations; a growing share of that dispersion is attributable to employer identity rather than worker skill; geographic convergence at senior levels coexists with divergence at junior levels; and entry-level compression converts a distributional problem into a cohort problem. Thematic analysis of the source corpus yields six themes, of which three (task unbundling, broken apprenticeship, and attribution ambiguity) are salient across every source category while two (rent asymmetry and contractual distance) appear almost exclusively in academic and industry sources respectively, indicating a systematic blind spot in how the phenomenon is discussed. Descriptive evidence shows generative AI adoption among developers rising to 84 percent while trust in output accuracy fell to 29 percent, alongside relative employment declines of 9 to 20 percent for early-career workers in exposed roles with no corresponding senior effect. We specify an empirical design capable of testing the propositions, state falsification conditions, and identify the attribution problem posed by the coincidence of AI diffusion with a monetary tightening cycle that produced over 700,000 tech layoffs between 2022 and 2025. The framework carries an uncomfortable implication: interventions raising individual skill will not address inequality generated by employer identity or by the accident of entry timing.

KEYWORDS

Digital transformation; Economic inequality; IT workforce; Task displacement; Generative AI; Firm wage premia; Qualitative document analysis; Labour market entry

Cite This Article

Shaila Ashok Rane, Dr.VINOD H. MANE, "Digital Transformation and Economic Inequality in the IT Workforce: Task Displacement, Firm Rents, and the Narrowing of Entry Pathways.," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 8, pp. 1-17, Aug 2026. https://doi.org/10.55041/IJSREM66899

Full Article/Conclusion

The paper reports no causal estimates. The qualitative component is documentary rather than interview-based, which means it captures how the phenomenon is discussed rather than how it is experienced; a study drawing on interviews with junior developers, hiring managers, and capability centre leads would test the archetypes in Section 4.2 far more directly. The corpus was assembled purposefully and coded by one analyst without inter-rater checks, so the theme salience recorded in Figure 2 should be read as a structured reading rather than as a measurement. The framework has no dynamic supply response. If computing enrolment falls in response to visible entry difficulty, junior supply tightens and the long-run predictions change, possibly substantially. Building an equilibrium version with endogenous entry is the most important extension. The geographic analysis rests on industry sources with commercial interests in the trends they report. Consultancies selling capability centre services and staffing platforms selling contractor placement are not disinterested observers of capability centre growth or contractor share. Academic replication using firm filings and national statistics is needed before P3 should carry much weight. The distributional composition of the affected workforce is described but not modelled. Gender, ethnicity, disability, institutional pedigree, and immigration status all shape access to entry-level positions and to high-premium employers, and a framework about who loses from entry-level compression that does not model who was obtaining entry-level positions in the first place is incomplete in a way that matters. Digital transformation is no longer a process that happens to other occupations while IT workers collect the returns. It is restructuring the IT workforce internally, through at least four mechanisms that current designs cannot separate, and the resulting inequality is generated as much by employer identity and entry timing as by anything a worker knows how to do. The qualitative analysis adds a specific reason for the measurement gap: the two mechanisms most likely to determine who captures the returns are each visible to only one community of observers, and neither is positioned to measure the other's variable. Closing that gap requires data access that mostly sits inside firms, and the research community has not yet made a serious collective case for it.

References

Acemoglu, D., & Autor, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. In O. Ashenfelter & D. Card (Eds.), Handbook of Labor Economics (Vol. 4B, pp. 1043–1171). Elsevier. Acemoglu, D., & Restrepo, P. (2022). Tasks, automation, and the rise in U.S. wage inequality. Econometrica, 90(5), 1973–2016. Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30. Autor, D. H., Dorn, D., Katz, L. F., Patterson, C., & Van Reenen, J. (2020). The fall of the labor share and the rise of superstar firms. Quarterly Journal of Economics, 135(2), 645–709. Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. Quarterly Journal of Economics, 118(4), 1279–1333. Blinder, A. S. (2006). Offshoring: The next industrial revolution? Foreign Affairs, 85(2), 113–128. Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. Bresnahan, T. F., Brynjolfsson, E., & Hitt, L. M. (2002). Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence. Quarterly Journal of Economics, 117(1), 339–376. Brynjolfsson, E., Chandar, B., & Chen, R. (2025). Canaries in the coal mine? Six facts about the recent employment effects of artificial intelligence. Stanford Digital Economy Lab working paper. Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at work. NBER Working Paper No. 31161. National Bureau of Economic Research. Card, D., Cardoso, A. R., Heining, J., & Kline, P. (2018). Firms and labor market inequality: Evidence and some theory. Journal of Labor Economics, 36(S1), S13–S70. CompTIA. (2025). State of the tech workforce. Computing Technology Industry Association. Deepak Ranjan Sahoo, P. D. K. (2017). Emotional Intelligence: Managing Stress and Anxiety at Work Place. International Journal of Multifaceted and Multilingual Studies, 4(10), 88–93. Deepak Ranjan Sahoo, V. D. S. (2019a). A Study of Economic Literacy and sustainable development with context to India. COSMOS Bi-Annual Referred Journal, 10(1), 47–53.

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Publication Date

8/22/2026

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