Research on interval optimization of the proportion of female executives based on panel threshold model

2017 ◽  
Vol 30 (7) ◽  
pp. 1081-1092
Author(s):  
Xing Wang ◽  
Xuefeng Shao
Keyword(s):  
Regression Model ◽  
Market Value ◽  
Threshold Effect ◽  
Enterprise Management ◽  
Female Executives ◽  
Management Performance ◽  
Content Type ◽  
Optimal Interval ◽  
Threshold Regression Model ◽  
Panel Threshold Regression

Purpose This paper aims to seek the optimal proportion of female executives in corporate management teams, and to analyze the threshold effect of the proportion of female executives on the enterprise market value and enterprise management performance by using a panel threshold regression model. The purpose of this paper is to obtain the optimal interval, during which female executives exert positive effects on enterprise market value and enterprise management performance. Design/methodology/approach Based on the data of listed companies in SSE from 2003 to 2012, this paper conducts theoretical and empirical analysis by using a panel threshold regression model. Findings This paper proves that the proportion of female executives has a threshold effect on the enterprise market value and enterprise management performance. The results show that the proportion of female executives has an optimal interval. In other words, during the 53.8-68.4 percent interval, the proportion of female executives exerts the least negative effect on the enterprise market value and the most positive effect on the enterprise management performance. Originality/value In this paper, the non-linear relationship between female executives, enterprise market value and enterprise management performance has been verified, and the optimization interval of the female executives’ proportion has been figured out as well.

The threshold effect of firm size on technological innovation: examination of panel data from China

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaoxue Zhou ◽  
Yu Li ◽  
Yao Zhang
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Technological Innovation ◽  
Firm Size ◽  
Manufacturing Sector ◽  
Threshold Effect ◽  
Nonlinear Relationship ◽  
Threshold Regression ◽  
Content Type ◽  
Threshold Regression Model

PurposeThe purpose of this paper is to explore the threshold effect of firm size on technological innovation using panel data from 2007 to 2012 for listed enterprises in China's manufacturing sector.Design/methodology/approachConsidering the aim of research question is to examine the nonlinear relationship, this paper utilizes the threshold regression proposed by Hansen's (2000).FindingsBased on a threshold regression model using panel data from 2007 to 2012 for listed enterprises in China's manufacturing sector, we find a series of new results. This nonlinear relationship is under the restrictions and impacts of various factors, such as industry characteristics and government subsidies. The results suggest that the threshold regression model well explains the complicated nonlinear relationship and transition process, and it can also shed light on management practice and policy.Originality/valueThere are categorical arguments regarding why firm size is not as effective as before in explaining the monotonic principle of industrial innovation, especially for establishing an effective industrial policy in a particular situation. One of the important reasons is that we have begun to adopt a new perspective from the nonlinear view on the relationship between firm size and industrial innovation. In this study, we have examined the threshold effect of firm size on industrial technological innovation, which is the most representative nonlinear relationship.

scholarly journals Does the Level of Environmental Uncertainty Matter in the Effect of Returnee CEO on Innovation? Evidence from Panel Threshold Analysis

2019 ◽  
Vol 11 (9) ◽  
pp. 2645 ◽  
Author(s):  
Weili Huang ◽  
Guangqi Ma ◽  
Xuemeng Chen
Keyword(s):  
Theoretical Foundation ◽  
Environmental Uncertainty ◽  
Chief Executive ◽  
Threshold Value ◽  
Threshold Effect ◽  
Publicly Traded ◽  
Innovation Ability ◽  
Enterprise Innovation ◽  
Threshold Regression Model ◽  
Panel Threshold Regression

This study applies a panel threshold regression model to reconcile the inconsistent findings about returnee chief executive officer (CEO) effects on enterprise innovation using the environmental uncertainty as the threshold variable and panel data from 187 publicly traded electronic firms in China for the period 2012–2016. The empirical results suggest that a significant double threshold effect does exist, showing an inverted-U correlation between returnee CEO and innovation. Specifically, a returnee CEO significantly promotes innovation at moderate levels of environmental uncertainty but significantly hinders innovation when the environmental uncertainty surpasses the larger threshold value, which contradicts much of the previous literature. This research enriches the scholarship on returnee CEOs and lays a theoretical foundation that firms can use in corporate governance. If firms pay more attention to environmental uncertainty while formulating new CEO introduction policies, substantial innovation ability can be improved at moderate levels of environmental uncertainty.

scholarly journals Does the Economic Growth Improve Public Health? A Cross-Regional Heterogeneous Study in China

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiao-Tong Niu ◽  
You-Cai Yang ◽  
Yu-Cong Wang
Keyword(s):  
Public Health ◽  
Economic Growth ◽  
Health Utility ◽  
Threshold Value ◽  
Threshold Effect ◽  
Eastern Region ◽  
The Government ◽  
Threshold Regression Model ◽  
Panel Threshold Regression ◽  
Different Levels

On public health, the effect of economic growth in China is analyzed in this paper by using the panel threshold regression model. The empirical study from 2000 to 2017 shows that China's economic growth has a significant threshold effect on public health. After the threshold is exceeded, public health will be improved dramatically. The threshold effect is heterogeneous at the regional level. The eastern region has no threshold, and both central and western regions have a single significant threshold. However, the threshold value and threshold effect in the central and western regions are also different. The heterogeneity is caused by the different levels of regional economic development. Therefore, based on public health utility maximization, the government should make different health policies according to the characteristics of regional development.

scholarly journals The Linkage Mechanism between Environment-Related Rules and Environment-Related Efficiency of Industries in China: An Analysis Based on the Adaptive Semi-Parametric Panel Model

2021 ◽  
Vol 13 (11) ◽  
pp. 6203
Author(s):  
Di Wang
Keyword(s):  
Threshold Effect ◽  
Manufacturing Industries ◽  
Linkage Mechanism ◽  
Optimal Interval ◽  
Panel Model ◽  
Wide Range ◽  
The Government ◽  
National Industry ◽  
Threshold Regression Model ◽  
Roll Out

This study employed the adaptive semi-parameter model to determine the effects of environment-related rules (environment-related rules refer to laws and regulations in relation to environmental protection) on environment-related efficiency (environment-related efficiency refers to the environmental efficiency of a wide range of industries). In addition, the threshold regression model was employed to determine the industry threshold effect of environment-related rules on environment-related efficiency. The following conclusions were primarily drawn: (1) A U-shaped curve relationship was identified between the effect of environment-related rules and the environment-related efficiency of the broader national industry; (2) A threshold effect is exerted on environment-related efficiency, and under the effect exerted by environment-related rule below the lower limit of the optimal interval, environment-related rule policies cannot play a corresponding role, while the effect of environment-related rules is high. When the upper limit of the optimal interval is set, environment-related rules will exert extensively strong effects, which leads to the unsustainable development of the industry and distorts the industry’s development. The government is required to roll out different environment-related rule policies in accordance with the industry differences and the development stages of respective industries, fully exploit such environment-related rule policies for industries and technologies, optimize the environment-related rules system, and harmoniously advance industries, the economy, and the environment. Given the empirical results, it is critical to enhance the effect exerted by environment-related rules in the mining and manufacturing industries, elevate their technical level, and develop a positive relationship between the effect exerted by environment-related rules and environment-related efficiency enhancements. While China’s current environment-related rule policy imposes no discrimination between pollution-intensive industries and cleaner production industries, these industries should be treated differently in the days ahead.

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