Lomonosov Moscow State University, Moscow, Russian Federation.
PLoS One. 2024 Jul 8;19(7):e0304730. doi: 10.1371/journal.pone.0304730. eCollection 2024.
In recent years, with the continuous evolution of the global economy and the adjustment of industrial structures, the understanding of the role played by human capital in the process of economic development has become particularly important. However, existing research on the impact of human capital on economic growth often adopts traditional regression methods, failing to comprehensively consider the heterogeneity and nonlinear relationships in the data. Therefore, to more accurately understand the influence of human capital on economic growth at different stages, this study employs Bayesian quantile regression method (BQRM). By incorporating BQRM, a better capture of the dynamic effects of human capital in the process of industrial structure upgrading is achieved, offering policymakers more targeted and effective policy recommendations to drive the economy towards a more sustainable direction. Additionally, the experiment also examines the impact of other key factors such as technological progress, capital investment, and labor market conditions on economic growth. These factors, combined with human capital, collectively promote the upgrading of industrial structure and the sustainable development of the economy. This study, by introducing BQRM, aims to fill the research gap regarding the impact of human capital on economic development during the industrial structural upgrading process. In the backdrop of the ongoing evolution of the global economy and adjustments in industrial structure, understanding the role of human capital in economic development becomes particularly crucial. To better comprehend the direct impact of human capital, the experiment collected macroeconomic data, including GDP, industrial structure, labor skills, and human capital, from different regions over the past 20 years. By establishing a dynamic panel data model, this study delves into the trends in the impact of human capital at various stages of industrial structure upgrading. The research findings indicate that during the high-speed growth phase, the contribution of human capital to GDP growth is 15.2% ± 2.1%, rising to 23.8% ± 3.4% during the period of industrial structure adjustment. Technological progress, capital investment, and labor market conditions also significantly influence economic growth at different stages. In terms of innovation improvement, this study pioneers the use of BQRM to gain a deeper understanding of the role of human capital in economic development, providing more targeted and effective policy recommendations. Ultimately, to promote sustainable economic development, the experiment proposes concrete and targeted policy recommendations, emphasizing government support in training and skill development. This study not only fills a research gap in the relevant field but also provides substantive references for decision-makers, driving the economy towards a more sustainable direction.
近年来，随着全球经济的不断演变和产业结构的调整，人们对人力资本在经济发展过程中所起作用的认识变得尤为重要。然而，现有的关于人力资本对经济增长影响的研究往往采用传统的回归方法，未能全面考虑数据中的异质性和非线性关系。因此，为了更准确地了解人力资本在不同阶段对经济增长的影响，本研究采用了贝叶斯分位数回归方法（BQRM）。通过引入 BQRM，可以更好地捕捉人力资本在产业结构升级过程中的动态效应，为政策制定者提供更有针对性和有效的政策建议，以推动经济向更可持续的方向发展。此外，实验还检验了技术进步、资本投资和劳动力市场条件等其他关键因素对经济增长的影响。这些因素与人力资本一起，共同推动产业结构升级和经济的可持续发展。本研究通过引入 BQRM，旨在填补人力资本对产业结构升级过程中经济发展影响的研究空白。在全球经济不断演变和产业结构调整的背景下，理解人力资本在经济发展中的作用变得尤为重要。为了更好地理解人力资本的直接影响，实验收集了过去 20 年来自不同地区的宏观经济数据，包括 GDP、产业结构、劳动力技能和人力资本。通过建立动态面板数据模型，本研究深入探讨了人力资本在产业结构升级各个阶段的影响趋势。研究结果表明，在高速增长阶段，人力资本对 GDP 增长的贡献为 15.2%±2.1%，在产业结构调整阶段上升至 23.8%±3.4%。技术进步、资本投资和劳动力市场条件也在不同阶段对经济增长产生显著影响。在创新改进方面，本研究率先采用 BQRM 来深入了解人力资本在经济发展中的作用，提供更有针对性和有效的政策建议。最终，为了促进可持续经济发展，实验提出了具体而有针对性的政策建议，强调政府在培训和技能发展方面的支持。本研究不仅填补了相关领域的研究空白，还为决策者提供了实质性的参考，推动经济向更可持续的方向发展。
