Inflation,technological-knowledge bias,and wages

A detailed examination of wage data points to a wage polarization trend vis-à-vis the distribution of qualifications. Theoretically terms, this points to the need for modeling focused on the relevance of the direction of technological knowledge. To this end, we branched production into routine and non-routine tasks. In this way, the results produced positive relationships between the relative supply of skilled workers and the skill premium and between automation and wage polarization.     

Does job mismatch affect wage earnings among business and management graduates in Vietnam?

This paper investigates the wage penalty resulting from horizontal job mismatch among wage earners with university degrees in the fields of business and management. The study utilizes pooled cross-sectional data from the Labour Force Surveys (LFS) in 2018, 2019 and 2020 in Vietnam. We employ instrumental variable (IV) estimators to address the potential endogeneity of job mismatch. Estimates from the ordinary least square (OLS) and IV methods show that job mismatch has a negative effect on wage earnings and that the penalty for such mismatches is nearly double for men than for women. Also, for mismatched graduates in the private sector, the wage penalty is nearly triple what it is for those in the public sector. Notably, we find that the wage penalty is substantial for those under 50, but less so for those over 50. This suggests that education-specific skills can be substituted by work experience to some extent.     

Does automation technology increase wage?

This paper examines the relationship between automation technology and wages. In the model, producers either choose automation or non-automation technology, whichever is more profitable. Furthermore, when producers introduce automation technology, they must pay fixed costs, which differ between industries. The main results of this paper indicate that the increased productivity of automation technology promotes automation, decreases labor income share, and also decreases wages when the level of automation diffusion is sufficiently high.     

Wage gap and stock returns: Do investors dislike pay inequality?

Recent research shows that a high wage-gap between managers and workers identifies better-performing firms, but the stock market does not seem to price this information. In this paper, we show that not all investors neglect pay inequality. Using a unique data set on German firms’ employee compensation, we find that the mispricing of the wage gap is driven by limits to arbitrage. Specifically, some investors seem to bid up low-wage-gap stocks for non-monetary reasons, thus exhibiting a preference for low pay-inequality. The results suggest that firms with equitable pay schemes are rewarded with a lower cost of capital.     

Group penalized logistic regressions predict up and down trends for stock prices

Stock prices are influenced by many economic factors, investors psychology and expectations, movement of other stock markets, political events, etc. Therefore, correctly predicting up and down trends for stock prices is an important puzzle in the financial field. In this paper we combine technical analysis with group penalized logistic regressions, and propose group SCAD/MCP penalized logistic regressions with technical indicators to predict up and down trends for stock prices. Firstly, we screen out 24 important technical indicators, divide them into the five different indicator groups, and construct group SCAD/MCP penalized logistic regressions for the three listed companies. Secondly, we apply the training set to learn the parameter estimators and the probability estimators for the two group penalized logistic regressions, adopt the test set to obtain confusion matrices and ROC(Receiver Operating Characteristic) curves to assess their prediction performances, and found that the AUC values to the three companies all exceed 0.78. Finally, we compare group SCAD/MCP penalized logistic regressions with SCAD/MCP penalized logistic regressions, and found that the two group penalized logistic regressions perform better than the two penalized logistic regressions in terms of prediction accuracy and AUC. Therefore, in this paper we develop a new prediction method by combining group SCAD/MCP penalized logistic regressions with technical indicators to improve the prediction accuracy and bring huge economic benefit for investors.     

城市群国土空间功能均衡发展研究 ——以京津冀地区为例

研究目的：以京津冀地区为例，探寻城市群地区县域国土空间均衡发展的演变趋势和实现路径，以优化国土空间开发保护策略，完善国土空间规划理论体系。研究方法：Zipf参数分析、泰尔指数、δ收敛和空间β收敛模型。研究结果：京津冀地区国土空间质量与均衡性演变趋势一致，呈现城镇空间和农业空间质量提升、生态空间质量下降的特征；非均衡性主要源于不同发展阶段区域间差异，而终极城镇化区域县域间国土空间功能差异最为显著，是实现城镇—农业—生态空间均衡的重点区域；不同类型国土空间功能收敛趋势差异明显，并因不同要素作用在不同发展阶段区域存在异质性特征，而表现出空间功能跃迁的路径差别。研究结论：考虑城市群地区县域间自然资源、经济产业和基础设施的差异状况，因地制宜实施国土空间发展策略，是区域间空间功能跃迁升级实现均衡发展的有效路径。     

Co-movement among oil,stock, bond,and housing markets: An analysis of U.S., Asian,and European economies

This study explores the co-movement among oil and the stock, bond, and housing markets of the U.S. and major developed countries across Europe and Asia. The results indicate that oil is long-run integrated with each asset class, and that the extent of convergence has increased after the onset of the 2007–2009 global financial crisis (GFC). Moreover, oil contributes most heavily toward the common trends, implying that oil is the “leader” sector that drives each asset class toward long-run equilibrium relationships. Short-run analyses indicate that oil shocks induce a negative response in stock and housing returns and a positive reaction in bond returns, showing a tendency to become more intense and persistent after the GFC. When oil shocks are disentangled, the results indicate that supply and demand have heterogeneous effects on the three global asset classes. Over the long-run, demand shocks make the most significant contribution to the common trends and “lead” the other asset classes, whereas supply shocks have either a negligible or a weaker impact. Over the short-run, demand shocks positively impact the stock and housing markets and negatively impact bonds, while supply shocks induce negative and weaker impacts on all three asset classes.     

政府推动的就近城镇化能够消除工资歧视吗

随着大中城市城镇化空间的逐渐饱和以及农民工回流现象的出现,就近城镇化成为推进我国新型城镇化进程的重要模式。文章基于2017年中国流动人口动态监测调查(CMDS)数据,研究政府推动的就近城镇化对农村劳动力工资收入的影响发现:政府通过征地、户口改革方式推动的就近城镇化并不能够消除工资歧视,实现就近城镇化的农村劳动力在其他条件相同的情况下其工资水平仍然低于城镇本地职工,这一结论在纠正自选择偏差和样本选择偏差后仍然成立。机制分析发现,城镇劳动力市场针对实现就近城镇化的农村劳动力在就业机会方面的歧视与隔离已不明显,同工不同酬成为实现就近城镇化者面临的主要歧视形式,而城乡劳动力之间教育回报率的差异是导致同工不同酬的主要原因。进一步讨论发现,就近城镇化对于农村劳动力参保城镇职工保险具有显著的提升作用,而自我雇佣的就业形式则有助于打破城镇劳动力市场对农村劳动力的收入歧视。文章拓展研究了就近城镇化对农村劳动力工资收入的影响,为政府进一步推进以县域城镇化为主要形式的就近城镇化提供了研究参考。未来应持续推进城乡基本公共服务均等化,进一步缩小城乡教育质量差异,加强农村劳动力"同工同酬"的保护力度,稳步推进农村劳动力的就近城镇化。     

制造业数字服务化研究的总体态势和热点趋势可视化分析

近年来，数字技术的快速发展掀起了数字服务化相关研究的热潮，把握数字服务化研究的总体态势和热点趋势，可为后续研究者提供引导和参考。针对Web of Science数据库611篇文献，借助CiteSpace进行可视化分析，探索数字服务化研究领域的总体态势和热点趋势。结果表明：数字服务化研究进入高速增长期，欧美发达国家引领了数字服务化领域的研究；数字服务化的研究热点主要包括数字服务化的概念、数字化与服务化关系及其内在机理、数字服务化的影响3方面；价值共创、工业互联网平台和产品服务系统代表了数字服务化领域的研究趋势。最后从数字服务化概念和机理、数字服务化与生态系统的作用关系以及基于中国情景的数字服务化3方面提出未来展望。