Empirical Study of Chinese Urban-Rural Income Gap and Heavy Industrial Relations

Rural areas in Romania are underdeveloped, with the main economic activity being agriculture. Urban-rural income gap and poverty levels are indicative of an underdeveloped rural area. Urban-rural absolute income gap for average monthly income increased from 352 RON in 2007 to 663 RON in 2017. Moreover, the work poverty rate is higher in rural areas than in urban areas. Economic rural development can be achieved by improvements of the labour market and introduction of new value-added products. Agricultural and non-agricultural activities are dependent on each other for a successful rural development leading to poverty alleviation. An industry that combines the two types of economic activities is agriculture biomaterial industry. In this paper, the authos investigates the factors influencing rural poverty and analyses the current stage of the bioplastics market in Romania and its economic implications. Bioplastics industry can reduce urban-rural income gaps and poverty in rural areas.

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How does the urban–rural income gap affect the quality of China’s economic growth?

China Political Economy ◽

10.1108/cpe-09-2018-005 ◽

2018 ◽

Vol 1 (1) ◽

pp. 136-148 ◽

Cited By ~ 1

Author(s):

Baoping Ren ◽

Xiaojing Chao

Keyword(s):

Economic Growth ◽

Industrial Structure ◽

Empirical Test ◽

Theoretical Interpretation ◽

Content Type ◽

Income Gap ◽

Rural Income ◽

Urban Rural ◽

Growth Quality

Purpose Based on the theoretical definition of the quality of economic growth as well as the availability and reliability of the given data, the purpose of this paper is to build an evaluation system of a regional economic growth quality on three levels: conditions, processes and results. Design/methodology/approach From the perspective of economic quality, this paper offers a theoretical interpretation on how the urban–rural income gap affects the quality of economic growth and takes an empirical test on the sample panel data from 30 provinces and regions through difference GMM and system GMM models. Findings The results show that the excessively large income gap will influence economic growth in terms of the foundation, operation and the outcome, thereby, restricting the quality of economic growth. In addition, investments in human and physical capital and improvements in terms of transport infrastructure, industrial structure and economic openness play an active role in economic growth quality, whereas government expenditure scale, financial development and the deviation of industrial structure have a negative effect. Originality/value There has been a substantial amount of experience and evidence on the research about the issue of China’s income distribution and the quantity of economic growth, whereas there are relatively fewer discussions about the income distribution and the quality of economic growth. This paper, based on what has been mentioned above, tries to give a theoretical interpretation and an empirical test to describe the relationship between urban–rural income gap and the quality of economic growth from the quality point of view.

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Spatial Pattern and Driving Mechanism of Urban–Rural Income Gap in Gansu Province of China

Land ◽

10.3390/land10101002 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1002

Author(s):

Ping Zhang ◽

Weiwei Li ◽

Kaixu Zhao ◽

Sidong Zhao

Keyword(s):

Spatial Pattern ◽

Development Planning ◽

Gansu Province ◽

Floating Population ◽

Driving Mechanism ◽

County Level ◽

Income Gap ◽

Rural Income ◽

Group Matrix ◽

Urban Rural

The urban–rural income gap is a principal indicator for evaluating the sustainable development of a region, and even the comprehensive strength of a country. The study of the urban–rural income gap and its changing spatial patterns and influence factors is an important basis for the formulation of integrated urban–rural development planning. In this paper, we conduct an empirical study on 84 county-level cities in Gansu Province by using various analysis tools, such as GIS, GeoDetector and Boston Consulting Group Matrix. The findings show that: (1) The urban–rural income gap in Gansu province is at a high level in spatial correlation and agglomeration, leading to the formation of a stepped and solidified spatial pattern. (2) Different factors vary greatly in influence, for example, per capita Gross Domestic Product, alleviating poverty policy and urbanization rate are the most prominent, followed by those such as floating population, added value of secondary industry and number of Internet users. (3) The driving mechanism becomes increasingly complex, with the factor interaction effect of residents’ income dominated by bifactor enhancement, and that of the urban–rural income gap dominated by non-linear enhancement. (4) The 84 county-level cities in Gansu Province are classified into four types of early warning zones, and differentiated policy suggestions are made in this paper.

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Rural - urban income gap and labour market in Romania

SHS Web of Conferences ◽

10.1051/shsconf/20219501003 ◽

2021 ◽

Vol 95 ◽

pp. 01003

Author(s):

Irina Băncescu

Keyword(s):

Labour Market ◽

Rural Areas ◽

Reverse Flow ◽

Income Effect ◽

Gap Effect ◽

Migration Flow ◽

Income Gap ◽

Rural Income ◽

Urban Rural ◽

Urban Income

Rural – urban income gap is an important social-economic development indicator for a society. A large rural - urban income gap within a society reveals a split of it into two distinct societies, one extremely poor compared to the other. In Romania, almost half of the resident population lives in rural areas (46.02% in 2019), while the urban-rural migration flow is higher than the traditional reverse flow since 1997. However, the country is characterized by regional economic disparities, Bucharest-Ilfov region being the most economically developed. Labour market in rural areas is underdeveloped, rural population being highly depended on subsistence agriculture. Furthermore, rural areas have a low level of income and living standards. In this paper, we analysis the rural-urban labour market dynamics and rural-urban income gap using a LMDI (logarithmic mean Divisia index) decomposition for 2005-2019 period. Factors such as income gap effect and structural rural income effect are considered. Results show that the total urban–rural income gap has decreased with 8.91%, while structural rural income effect contributed with an increase of only 0.63%, the income gap effect (of different employed population groups) being of -9.49%.

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