FROM DATA TO DECISIONS: Neural network approaches to socio-labor regulation in the digital age

Abstract

As urban labor markets become increasingly complex due to the digital transformation of economic systems, traditional workforce governance tools are proving insufficient. This research presents a neural network-based strategy for improving socio-labor control in megacities by moving from raw, unstructured data to smart, flexible decision-making. The study combines advanced Big Data analytics and natural language processing (NLP) with more than 12 million data points from statistics agencies, digital platforms, and urban social media. Empirical tests conducted across four major Russian cities with 2,570 survey respondents demonstrate that this approach significantly enhances labor management outcomes: forecast accuracy for labor demand improved by 30%, crisis risks were reduced by an average of 23.45%, and operational load on employment services decreased by up to 30% under advanced digital maturity scenarios. These results are especially critical against the backdrop of Russia’s historically low unemployment rate of 2.9% in early 2024, a stark contrast to the global youth unemployment rate of 13% and the anticipated 23% job churn globally over the next five years. The study contributes to the emerging field of AI-driven urban governance and demonstrates the effectiveness of LSTM, CNN, and RNN neural architectures in resolving labor market complexities in digitally transforming megacities.