How Do Rising Labor Costs Affect China’s Industrialization?

China’s uninterrupted high-speed economic growth depends on promoting the process of industrialization.Based on provincial data during 2003-2016,this paper empirically estimates the impact of labor costs on China’s industrialization and examines its mechanism and path.The results show that the rising labor costs such as wages and social security premiums may lead to China’s“de-industrialization”.With endogeneity excluded,the conclusion still holds.The following policy suggestions are proposed:increasing the effective labor supply in the industrial sector to slow down the increase in wages,implementing social insurance premium reform to reduce social security premium rate,reversing the situation of investment“fl eeing from the real economy and into the virtual economy”in a multi-pronged manner,and optimizing the combination of labor,capital and technology in various ways.

Agricultural Land Use Responses to the Rising Labor Opportunity Cost in Sui County,China

Rising labor opportunity cost is one of the most important characteristics of farmers' economic environment in present China,and brings great agricultural land use changes correspondingly.Based on the stratified random sample of rural households in Sui County,this paper estimates the opportunity cost of farm labors,and then analyzes its effect on farmers' agricultural land use decisions.The findings show that the households with higher labor opportunity cost are more likely to reduce labor intensity and increase labor-saving input in their land use decisions.From the descriptive analysis,we also find these households always prefer grain crop planting,reduce yield-raising input or even rent out their cultivated land.As labor and yield-raising input are two essential positive factors for grain production,continuous declining of labor input and yield-raising input caused by rising labor opportunity cost may influence the grain yield per unit area in the future.

Will Cleaner Air Reduce CorporateLabor Cost?

With China’s listed companies from 2005 to 2015 as samples, this paperinvestigates how air quality influences labor cost based on city-level air quality data. Ourstudy finds a significant negative correlation between air quality and corporate employeecompensation - such a negative correlation is particularly strong in regions whereemployees are more rights-conscious and for companies that are non-labor-intensive. Byadjusting employee compensation according to air quality, firms will increase their futurecorporate value. In addition, air quality has significantly differentiated effects on employeecompensation for firms of different ownership nature and different level of cash on hand.Further research reveals that the effects mainly stem from poor air quality. In general,extreme air quality changes will lead to a difference of 14,210 yuan in the annual averagecompensation to employees from sample companies, or a labor cost difference of around23 trillion yuan for all companies nationwide during the sample period. Our researchconclusions have broadened the scope of research on how air quality influences firmbehavior, and provide empirical reference for employee motivation and cost management,as well as empirical evidences for China’s policy principle that “lucid waters and lushmountains are invaluable assets”.

The Impact of Accounting Information Quality on Corporate Labor Investment Efficiency:Evidence from China

As an important part of the enterprise information system,accounting information plays a significant role in enterprise management decision-making,This study investigates the impact of accounting information quality on corporate labor investment efficiency.Using a sample of Chinese listed firms,we show that higher accounting information quality is associated with higher labor investment efficiency(i.e.,a lower deviation of labor investment from the expected employment level justified by economic fundamentals).Firms with higher accounting information quality reduce underinvestment and overinvestment in labor by alleviating financial constraints and agency conflicts.Cross-sectional tests indicate that this effect is more pronounced among non-state-owned enterprises and firms with higher labor adjustment costs.The robustness test shows that our results are robust to alternative proxies,endogeneity concerns,and controls for non-labor investments.This study sheds light on how enterprise information systems influence management decision-making practices from the perspective of accounting information quality.

Firms Quasi-dynamics in Chinese Manufacturing Industry

We explore firm quasi-dynamics(entry,exit,and growth)in Chinese manufacturing industry and investigate how these dynamics vary across regions.Our results show that relative to provinces with less developed economies,in provinces with more developed economies(i)there is a higher proportion of new firms;(i)new firms are smaller and more labor-intensive;(ii)firms exit at a quicker rate,and surviving firms grow faster.These results point toward cross-region differences in market efficiency in terms of how much it costs a firm to enter or exit the market.Our findings shed light on how firms should adapt their strategies across regions and how the government should create sound policies on industrial upgradingand relocation.

Active Anomaly Detection Technology Based on Ensemble Learning

Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detection schemes are unsupervised methods,such as anomaly detection methods based on density,distance and clustering.In total,unsupervised anomaly detection methods have many limitations.For example,they cannot be well combined with prior knowledge in some anomaly detection tasks.For some nonlinear anomaly detection tasks,the modeling is complex and faces dimensional disasters,which are greatly affected by noise.Sometimes it is difficult to find abnormal events that users are interested in,and users need to customize model parameters before detection.With the wide application of deep learning technology,it has a good modeling ability to solve linear and nonlinear data relationships,but the application of deep learning technology in the field of anomaly detection has many challenges.If we regard exceptions as a supervised problem,exceptions are a few,and we usually face the problem of too few labels.To obtain a model that performs well in the anomaly detection task,it requires a high initial training set.Therefore,to solve the above problems,this paper proposes a supervised learning method with manual participation.We introduce the integrated learning model and train a supervised anomaly detection model with strong stability and high accuracy through active learning technology.In addition,this paper adopts certain strategies to maximize the accuracy of anomaly detection and minimize the cost of manual labeling.In the experimental link,we will show that our method is better than some traditional anomaly detection algorithms.