Study on the Evaluation of Employment Quality in China’s Provinces Based on Principal Tensor Analysis

Yingxue Pan () and Xuedong Gao ()
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Yingxue Pan: University of Science and Technology Beijing
Xuedong Gao: University of Science and Technology Beijing

A chapter in LISS 2022, 2023, pp 227-237 from Springer

Abstract: Abstract Employment is the biggest livelihood of the people, we must adhere to the employment-first strategy and active employment policy to achieve higher quality and fuller employment. This paper takes 30 provinces, autonomous regions and municipalities directly under the Central Government in China from 2011 to 2020 as the research sample. From the six dimensions of employment environment, employment status, employability, labor remuneration, social security, and labor relations, an evaluation system for measuring provincial employment quality is constructed. The employment quality index data is expressed in the form of space–time tensor, and four principal components are extracted by using the tensor-based principal component analysis method (modulo-k advocated quantitative analysis model). According to the coefficients of the four principal components of the employment quality data in each dimension, the comprehensive score of the employment quality of each province, autonomous region and municipality directly under the Central Government is calculated, and a visual analysis of the development and evolution process of the employment quality is carried out.

Keywords: Quality of employment; Spatiotemporal data; Principal tensor analysis (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-99-2625-1_17

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DOI: 10.1007/978-981-99-2625-1_17

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