What has caused regional income inequality in China? Effects of 10 socioeconomic factors on per capita income

Li, Weiming; Cai, Zhaoyang; Cao, Shixiong

doi:10.1007/s10668-020-01218-7

Cited by 10 publications

(8 citation statements)

References 59 publications

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“…The ten counties of Nanling Yao ethnic group area in GDP per capita, fiscal income per capita, education, medical conditions and public service conditions in 2018 are below the national average. The proportion between the economic development level of most underdeveloped counties and the national average level have not changed, and many counties in ethnic group areas are still relatively poor (Li et al, 2021b ; Xu et al, 2021 ). Hence, it is of great significance to study the current situation of relative poverty in Nanling Yao ethnic group area from a multidimensional perspective to realizing common prosperity.…”

Section: Introductionmentioning

confidence: 99%

Investigating the multidimensional relative poverty in China: Evidence from Nanling Yao ethnic group area

Luo

Shu

Cai

2022

Environ Dev Sustain

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China has made remarkable achievements in solving absolute poverty and entered into the stage of solving relative poverty which takes on multidimensional characteristics. Solving multidimensional relative poverty is the key to promoting social equity and achieving coordinated development. Based on the data of ten counties in the Nanling Yao ethnic group area in China from 2011 to 2018, we used the entropy weight method to study the degree, main influencing dimensions and distribution of multidimensional relative poverty. We found that external risk contributed the most to multidimensional relative poverty, followed by internal risk, economic development opportunity and potential development opportunity. Counties with moderate multidimensional relative poverty accounted for the largest proportion, multidimensional relative poverty was not significantly alleviated from 2011 to 2018, but some counties have different multidimensional relative poverty levels. In order to solve the multidimensional relative poverty, it is necessary to establish a Nanling economic cooperation zone, develop characteristic industries and focus on supporting counties with deep multidimensional relative poverty.

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Section: Introductionmentioning

confidence: 99%

Investigating the multidimensional relative poverty in China: Evidence from Nanling Yao ethnic group area

Luo

Shu

Cai

2022

Environ Dev Sustain

Self Cite

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“…In addition, it is shown that the difference in per capital disposable income between regions has been yearly decreasing, and the size of the middle-income group has continued to expand, which may promote the conversion of residents' potential need into actual demand, and achieve the reduction of the regional differences. 40 In contrast, DECS declined between 2018 and 2020, but remained at a high level, with a significant decrease in regional differences. This may stem from the impact of COVID-19, with most cities taking measures such as regional lockdown, social distancing and restrictions on residents going outside to curb the spread of COVID-19.…”

Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Spatio-temporal Analysis of Demand for Elderly Care Services and Its Key Influencing Factors in Chinese Cities: A Web Search Engine Approach

Bian

Luo

et al. 2023

INQUIRY

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The demand for elderly care services (DECS) in Chinese Cities is one of the most concerned issues. The aim of this study was to understand the spatial and temporal evolution and external factors of DECS in Chinese cities and support the formulation of elderly care policies. We collected Baidu Index data for 287 prefecture-level and above cities and 31 provinces in China from January 1, 2012 to December 31, 2020. The Thiel Index was employed to describe the differences of DECS at different regional levels, and multiple linear regression was used to explore the external factors affecting DECS by calculating the variance inflation factor (VIF) to identify multicollinearity. The DECS of Chinese cities increased from 0.48 million in 2012 to 0.96 million in 2020, and the Thiel Index decreased from 0.5237 in 2012 to 0.2211 in 2020. Per capital GDP, number of primary beds, proportion of population aged 65 and over, number of primary care visits, and proportion of illiterate population over the age of 15 have significant influences on DECS ( P < .05). DECS was on the rise in Chinese cities, with significant regional differences. At the provincial level, regional differences were influenced by level of economic development, primary care provision, aging population, educational attainment, and health status. It is suggested to pay more attention to DECS in small and medium-sized cities or regions, to strengthen primary care, and to improve the health literacy and health status of the elderly population.

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“…We continued our analysis using a multivariate panel data regression model (Li et al, 2021) to empirically analyze the impacts of the in uencing factors on technological innovation. We used the following equation: 4Where D is the dependent variable (the comprehensive level of scienti c and technological innovation), a is the y-intercept, b K is the regression coe cient for variable K, X K is the value of explanatory variable K (i.e., the nine driving factors), and µ is the error term.…”

Section: Empirical Analysismentioning

confidence: 99%

What factors promote the development of technological innovation? An analysis of 286 Chinese cities

Yang

Cai

Cao

2023

Preprint

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Scientific discovery and technological innovation are the ultimate forces that promote socioeconomic development. However, researchers are not certain which factors play a leading role in scientific discovery and technological innovation and what differences in these factors exist between regions. In this study, we selected 286 Chinese cities at the same administrative level and developed a coupling model to link them within a comprehensive evaluation framework, then performed multiple-regression analysis and geographically weighted regression to identify the key driving factors for scientific discovery and technological innovation, their contributions to innovation, and their regional differences. We found that marketization (development of a market economy), population density, industrial upgrading, health care, and industrialization most strongly promoted Chinese innovation from 2007 to 2019, whereas the innovation level decreased with increasing environmental pollution. To promote scientific discovery and technological innovation, we should increase marketization, promote market-oriented reform of universities, sustainably increase population density, and upgrade the industrial structure. These results will provide important guidance for the formulation of China's future science and technology policy, but our method will also help other countries improve their own science and innovation by learning from China's experience.

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What has caused regional income inequality in China? Effects of 10 socioeconomic factors on per capita income

Cited by 10 publications

References 59 publications

Investigating the multidimensional relative poverty in China: Evidence from Nanling Yao ethnic group area

Investigating the multidimensional relative poverty in China: Evidence from Nanling Yao ethnic group area

Spatio-temporal Analysis of Demand for Elderly Care Services and Its Key Influencing Factors in Chinese Cities: A Web Search Engine Approach

What factors promote the development of technological innovation? An analysis of 286 Chinese cities

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