Notice of Retraction: The research on cooperative relationship between higher vocational employment and regional economy: Based on cluster analysis with a specialty's panel data

2011 ◽  
Author(s):  
Mai Qiangsheng
Keyword(s):  
Cluster Analysis ◽  
Panel Data ◽  
Regional Economy ◽  
Cooperative Relationship

Analysis method ofrecruitment needs for the regional economy: Occupational section

2017 ◽  
pp. 142-149 ◽  
Author(s):  
E. Pitukhin ◽  
S. Shabaeva ◽  
I. Stepus ◽  
D. Moroz
Keyword(s):  
Cluster Analysis ◽  
Labor Market ◽  
Comparative Analysis ◽  
Regional Economy ◽  
Dimensional Space ◽  
Analysis Method ◽  
Scalar Form ◽  
Regional Labor Market ◽  
Regional Labor ◽  
Regional Specificity

The paper deals with comparative analysis of occupations in the regional labor market. Occupation is treated as a multi-dimensional space of characte- ristics, whereas a scalar form of a characteristic makes it possible to carry out a comparative analysis of occupations. Using cluster analysis of a pilot region indicators five meaningfully interpretable clusters of occupations were identified, reflecting their regional specificity.

scholarly journals Multivariable Panel Data Cluster Analysis using Ward Method Gross Enrollment Ratio (GER) Data in West Java in the Year 2015-2018

2020 ◽  
Vol 3 ◽  
pp. 291-296
Author(s):  
Reka Ramadhan ◽  
Asep Solih Awalluddin ◽  
Rini Cahyandari
Keyword(s):  
High School ◽  
Cluster Analysis ◽  
High Schools ◽  
Panel Data ◽  
Senior High School ◽  
West Java ◽  
Enrollment Rate ◽  
Minimum Number ◽  
Measure Of Similarity ◽  
Ward Method

The aim of this study is to determine cluster analysis for panel data with multivariable data structures. Choosing a Ward method Choosing a method in a cluster analysis hierarchical technique. Ward method is a method based on Sum Square Error (SSE) with a measure of homogeneity between two objects based on the minimum number of error squares. The measure of similarity used is the Euclidean distance squared. The Ward method is used to add variation between objects in one cluster and maximize variation with objects in another cluster. The steps of the analysis are described in the discussion of this study. The method of implementation uses education gross enrollment rate (GER) data in West Java Province in 2015-2018. The results of the study indicate that the grouping of education GER data in West Java in 2015-2018 using the Ward method produces four clusters. The first cluster consists of five regions, GER for Elementary school, junior and senior high school in the cluster are below the average APK in West Java. The second cluster consists of two regions, in contrast to the first cluster GER for elementary schools in this cluster according to the average GER in West Java but for junior and senior high school GER below the average GER in West Java. The third cluster consists of seven regions, the GER for elementary, junior and senior high schools in this cluster is above the average GER in West Java while the fourth cluster consists of five regions, the GER for elementary and junior high schools in this area is above the average GER in West Java.

Research on the Measurements of the Production Factors between the Hi-Tech Zones and the Hinterlands

2014 ◽  
Vol 556-562 ◽  
pp. 3043-3046
Author(s):  
Bing Zhao
Keyword(s):  
Panel Data ◽  
Regional Economy ◽  
Development Theory ◽  
High Tech ◽  
Production Factors ◽  
Clustering Quality ◽  
Balance Development

The regional transfer of production factors was the foundation of regional economy, which was connected with non-balance-development theory. The paper, from the angle of regional economy, made a deep study and established the transfer measurements of the production factors of the High-tech zones.Empirical study of the panel data of the High-tech Zone, results showed that the clustering rate in the zones is already good, but the clustering quality of production factors was not ideal.

The Cluster Analysis of Multivariable Panel Data and its Application

2012 ◽  
Vol 220-223 ◽  
pp. 2668-2671 ◽  
Author(s):  
Jin Hu Niu
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Panel Data ◽  
Distance Function ◽  
Experimental Analysis ◽  
Sum Of Squares ◽  
Productive Efficiency ◽  
Industrial Enterprises ◽  
Numeric Characteristic

The research on statistical analysis of multivariable panel data is a blank in China. The paper tries to do the basal study on cluster analysis of multivariable panel data. This paper firstly analyses the numeric characteristic and constructs anew the distance function of multivariable panel data and the function of sum of squares. On this base, the paper explains the arithmetic and process of cluster analysis. At last, an experimental analysis is done on productive efficiency of the industrial enterprises in China.

scholarly journals Effects of Post-Disaster Aid Measures to Firms: Evidence from Tohoku University Earthquake Recovery Firm Survey 2012–2015

2019 ◽  
Vol 14 (8) ◽  
pp. 1030-1046
Author(s):  
Yuzuru Isoda ◽  
Satoru Masuda ◽  
Shin-Ichi Nishiyama ◽  
◽  
◽  
...  
Keyword(s):  
Panel Data ◽  
Graduate School ◽  
Regional Economy ◽  
Disaster Resilience ◽  
Business Activity ◽  
Panel Survey ◽  
Disaster Aid ◽  
Source Of Information ◽  
Post Disaster ◽  
Firm Survey

Panel data of individual firms are a valuable source of information on the disaster resilience of the regional economy. Such data also helps to assess the effectiveness of government aids to recovery. Every year after the Great East Japan Earthquake 2011, from 2012 to 2015, Tohoku University’s Graduate School of Economics and Management conducted the Tohoku University Earthquake Recovery Firm Survey (TERFS) to obtain such information. The survey collected 25,826 responses over the 4-year period from a total of 11,090 firms in the east Tohoku region, the most severely affected region. Based on this survey, this paper assesses the effects of the conventional and new government recovery aid measures introduced to help firms affected by the disaster on the levels of business activity. The paper finds that group subsidy and debt reduction had important roles in the recovery of business activities, and demonstrates the importance of a panel survey in understanding and guiding policies for the resilience of the regional economy.

Are Merchandise Exports and FDI Inflows an Engine of Growth for ASEAN-10 Countries? New Evidence Using the Pooled Mean Group Estimation Method

2018 ◽  
Vol 12 (4) ◽  
pp. 414-430 ◽  
Author(s):  
Soo Khoon Goh ◽  
Koi Nyen Wong ◽  
Chee Lam Yew
Keyword(s):  
Panel Data ◽  
Regional Economy ◽  
Driving Forces ◽  
Early Stage ◽  
Regional Growth ◽  
Estimation Method ◽  
Jel Classification ◽  
Pooled Mean Group ◽  
Long Run ◽  
Short Run

The Association of Southeast Asian Nations (ASEAN) has made remarkable economic progress in terms of rapid economic growth and expanding export trade and foreign direct investment (FDI). Theoretically speaking, both merchandise exports and FDI can be regarded as the key driving forces behind the ‘economic miracle’ of the regional economy. The major contribution of this study is that it is the first effort to empirically analyse the short-run and long-run growth effects of merchandise exports and FDI on the ASEAN-10 countries using time-series panel data. In this regard, this study aims to ascertain whether the spectacular regional growth is export- and FDI-driven, based on the ASEAN-10 panel data spanning from 1970 to 2016 using the pooled mean group (PMG) method. The findings show that merchandise exports are a key source of growth for the regional economy, attributable to the joint liberalisation efforts of the member states to expand trade and FDI. The study does not find evidence of FDI-led growth because the bulk of the FDI was invested in only a few ASEAN countries, and the minor FDI-recipient countries are at an early stage to benefit from the growth impacts of FDI, owing to lower absorptive capacity. JEL Classification: C51, F21, F23, O19

Research on the Interaction between Resource Logistics Industrial Cluster and Regional Economic

2014 ◽  
Vol 1073-1076 ◽  
pp. 2372-2376
Author(s):  
Xiao Ye Zhou ◽  
Zhe Wang ◽  
Huan Sun
Keyword(s):  
Panel Data ◽  
Individual Difference ◽  
Data Model ◽  
Regional Economy ◽  
Industrial Cluster ◽  
Panel Data Model ◽  
Industry Cluster ◽  
Logistics Industry ◽  
Cluster Data ◽  
The Individual

As a hotspot in Econometrics, Panel Data can control the behavior of the individual difference better , and it can also provide more information, more variability, less multicollinearity, higher degree of freedom and more efficient. This paper takes Fuxin, Fushun, Jiaozuo, Zaozhuang, Liaoyuan five resource logistics industrial cluster data, constructs a panel data model, and analyzes the interaction of resources of logistics industry cluster and regional economy and logistics industry.

scholarly journals Research on Weighted Cluster Analysis Method of Panel Data

2021 ◽  
Vol 1848 (1) ◽  
pp. 012036
Author(s):  
Xiqin Ao ◽  
Yiwen Zhang ◽  
Yinyin Zhou ◽  
Deyu Xu
Keyword(s):  
Cluster Analysis ◽  
Panel Data ◽  
Analysis Method ◽  
Cluster Analysis Method

scholarly journals The Relationship Between Human Capital and the Regional Economy Productivity

JEJAK ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 138-152
Author(s):  
Hendarmin Hendarmin ◽  
Metasari Kartika
Keyword(s):  
Human Capital ◽  
Panel Data ◽  
Capital Investment ◽  
Regional Economy ◽  
Random Effect ◽  
Study Finding ◽  
Physical Capital ◽  
Productivity Level ◽  
Economic Productivity ◽  
The Impact

The aim of this study is to measure the impact of the human capital on the economic productivity level in provinces of Kalimantan Island. The data used in this study was a panel data from 5 provinces in Kalimantan Island within 2013-2017 time periods which its study finding was analyzed using panel data regression analysis with random effect approach. The study finding explains that the human capital, measured by the education level which is the average of years of education, the health level which is life expectancy and investment has a positive effect on the productivity level of the regional economy. The analysis result also shows that the health variable has a bigger magnitude compared to the education and physical capital investment variables. Based on the result, it can be concluded that the effect of human capital is very important in increasing economic productivity in the region.

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