The Experts below are selected from a list of 135234 Experts worldwide ranked by ideXlab platform
Chen Zeng - One of the best experts on this subject based on the ideXlab platform.
-
urban Rural Income change influences of landscape pattern and administrative spatial spillover effect
Applied Geography, 2018Co-Authors: Chen Zeng, Yan Song, Yu LiuAbstract:Abstract China is experiencing unprecedented urbanisation, and urban–Rural livelihood is transforming in various ways, with urban–Rural Income being a representative aspect. In this context, we raised two issues that are related to our study on the changes in urban and Rural Income and the related influencing factors, that is, whether landscape pattern affects urban–Rural Income and whether different spatial adjacencies generate various impacts on urban–Rural Income. We incorporated landscape pattern indicators and the administrative spatial spillover effect into a spatial regression model to address these issues using the Wuhan agglomeration as an example. We used aggregation indexes (AI) and the proportion of other construction lands (CLP) to represent landscape patterns. Then, multiple strategies were used to accommodate different spatial adjacency situations at the county level by introducing the magnified spatial factor for strengthening specific scenarios of spatial interactions. Results revealed that AI and CLP showed a remarkable relationship with per capita urban disposable Income (UDI), but CLP was more powerful than AI in affecting per capita Rural net Income (RNI). Spatiotemporal differences were observed in urban–Rural Incomes, and an administrative spatial spillover effect was observed in the period of 2005–2015. The most powerful spatial interaction emerged when urban districts were neighbours for UDI and when a county-level city, a suburban district and a county were neighbours for RNI in 2005 and 2015. Coupled with urbanisation and urban–Rural integration, the administrative spillover effect weakened for UDI and functioned varyingly for RNI. These results reaffirmed the influence of urbanisation and economic development on urban–Rural Income, and regional disparity is expected be considered when corresponding policy implications are made in the future.
Yu Liu - One of the best experts on this subject based on the ideXlab platform.
-
urban Rural Income change influences of landscape pattern and administrative spatial spillover effect
Applied Geography, 2018Co-Authors: Chen Zeng, Yan Song, Yu LiuAbstract:Abstract China is experiencing unprecedented urbanisation, and urban–Rural livelihood is transforming in various ways, with urban–Rural Income being a representative aspect. In this context, we raised two issues that are related to our study on the changes in urban and Rural Income and the related influencing factors, that is, whether landscape pattern affects urban–Rural Income and whether different spatial adjacencies generate various impacts on urban–Rural Income. We incorporated landscape pattern indicators and the administrative spatial spillover effect into a spatial regression model to address these issues using the Wuhan agglomeration as an example. We used aggregation indexes (AI) and the proportion of other construction lands (CLP) to represent landscape patterns. Then, multiple strategies were used to accommodate different spatial adjacency situations at the county level by introducing the magnified spatial factor for strengthening specific scenarios of spatial interactions. Results revealed that AI and CLP showed a remarkable relationship with per capita urban disposable Income (UDI), but CLP was more powerful than AI in affecting per capita Rural net Income (RNI). Spatiotemporal differences were observed in urban–Rural Incomes, and an administrative spatial spillover effect was observed in the period of 2005–2015. The most powerful spatial interaction emerged when urban districts were neighbours for UDI and when a county-level city, a suburban district and a county were neighbours for RNI in 2005 and 2015. Coupled with urbanisation and urban–Rural integration, the administrative spillover effect weakened for UDI and functioned varyingly for RNI. These results reaffirmed the influence of urbanisation and economic development on urban–Rural Income, and regional disparity is expected be considered when corresponding policy implications are made in the future.
Yan Song - One of the best experts on this subject based on the ideXlab platform.
-
urban Rural Income change influences of landscape pattern and administrative spatial spillover effect
Applied Geography, 2018Co-Authors: Chen Zeng, Yan Song, Yu LiuAbstract:Abstract China is experiencing unprecedented urbanisation, and urban–Rural livelihood is transforming in various ways, with urban–Rural Income being a representative aspect. In this context, we raised two issues that are related to our study on the changes in urban and Rural Income and the related influencing factors, that is, whether landscape pattern affects urban–Rural Income and whether different spatial adjacencies generate various impacts on urban–Rural Income. We incorporated landscape pattern indicators and the administrative spatial spillover effect into a spatial regression model to address these issues using the Wuhan agglomeration as an example. We used aggregation indexes (AI) and the proportion of other construction lands (CLP) to represent landscape patterns. Then, multiple strategies were used to accommodate different spatial adjacency situations at the county level by introducing the magnified spatial factor for strengthening specific scenarios of spatial interactions. Results revealed that AI and CLP showed a remarkable relationship with per capita urban disposable Income (UDI), but CLP was more powerful than AI in affecting per capita Rural net Income (RNI). Spatiotemporal differences were observed in urban–Rural Incomes, and an administrative spatial spillover effect was observed in the period of 2005–2015. The most powerful spatial interaction emerged when urban districts were neighbours for UDI and when a county-level city, a suburban district and a county were neighbours for RNI in 2005 and 2015. Coupled with urbanisation and urban–Rural integration, the administrative spillover effect weakened for UDI and functioned varyingly for RNI. These results reaffirmed the influence of urbanisation and economic development on urban–Rural Income, and regional disparity is expected be considered when corresponding policy implications are made in the future.
Ximing Yue - One of the best experts on this subject based on the ideXlab platform.
-
Rural Income Volatility and Inequality in China
CESifo Economic Studies, 2009Co-Authors: John Whalley, Ximing YueAbstract:Current literature based on analyses of Rural Income volatility in China decompose poverty into chronic and transient components using longitudinal survey data and assesses the fraction of the Foster, Greer, and Thorbecke poverty gap attributable to mean Income over time being below the poverty line. Resulting estimates of 40--50% transient poverty point to the policy conclusion that poverty may be a less serious social problem than it appears in annual data due to Rural Income volatility. Here, we instead use a direct method to adjust Rural Income for volatility using a certainty equivalent Income measure and recomputed summary statistics for the distribution of volatility corrected Incomes, including the urban--Rural Income gap on which much of current poverty debate in China focuses. Available data indicate a growing urban--Rural Income gap (the ratio of mean urban to Rural Incomes) with a significant increase from around 1.8 in the late-1980s to over three today. These estimates do not take into account the higher volatility of Rural Incomes in China. Since an uncertain Income stream is worth less in utility terms than a certain Income stream, we argue that heightened Rural volatility increases the effective urban--Rural Income gap and intensifies not weakens poverty concerns. Using Chinese longitudinal Rural survey data for which current decompositions can be replicated, we make adjustments for certainty equivalence of Rural household Income streams, which not only widen the urban--Rural Income gap in China but also increase other distributional summary statistics. Depending upon values used for the coefficient of relative risk aversion, the measured urban--Rural Income gap increases by 20--30% using a certainty equivalent measure to adjust Rural Incomes for volatility. We also conduct similar analysis using consumption data, for which similar (but slightly larger) increases occur. (JEL codes: D00, D31, D81,G11, N55, O12; O15; R20) Copyright The Author 2009. Published by Oxford University Press on behalf of Ifo Institute for Economic Research, Munich. All rights reserved. For permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.
-
Rural Income Volatility and Inequality in China
Research Papers in Economics, 2006Co-Authors: John Whalley, Ximing YueAbstract:Available data indicates a growing urban-Rural Income gap (the ratio of mean urban to Rural Incomes) with a significant increase from around 1.8 in the late 1980's to over 3 today. These estimates do not take into account the higher volatility of Rural Incomes in China. Current literature based on analyses of Rural Income volatility in China decomposes poverty into chronic and transient components using longitudinal survey data and assesses the fraction of the Foster, Greer and Thorbecke poverty gap attributable to mean Income over time being below the poverty line. Resulting estimates of 40-50 % transient poverty point to the policy conclusion that poverty may be a less serious social problem than it appears in annual data due to Rural Income volatility. Here we use a direct method instead to adjust Rural Income for volatility using a certainty equivalent Income measure and recompute summary statistics for the distribution of volatility corrected Incomes, including the urban-Rural Income gap on which much of current poverty debate in China focuses. Since an uncertain Income stream is worth less in utility terms than a certain Income stream we argue that heightened Rural volatility increases the effective urban-Rural Income gap and intensifies not weakens poverty concerns. Using Chinese longitudinal Rural survey data for which current decompositions can be replicated, we make adjustments for certainty equivalence of Rural household Income streams which not only widen the urban-Rural Income gap in China but also increases other distributional summary statistics. Depending upon values used for the coefficient of relative risk aversion, the measured urban-Rural Income gap increases by 20-30% using a certainty equivalent measure to adjust Rural Incomes for volatility. We also conduct similar analyses using consumption data, for which slightly larger increases occur.
-
the urban Rural Income gap and inequality in china
Research Papers in Economics, 2006Co-Authors: Terry Sicular, Ximing Yue, Björn GustafssonAbstract:Using new household survey data for 1995 and 2002, we investigate the size of China's urban-Rural Income gap, the gap's contribution to overall inequality in China, and the factors underlying the gap. Our analysis improves on past estimates by using a fuller measure of Income, adjusting for spatial price differences and including migrants. Our methods include inequality decomposition by population subgroup and the Oaxaca- Blinder decomposition. Several key findings emerge.
Tsangyao Chang - One of the best experts on this subject based on the ideXlab platform.
-
Urbanization and the Urban–Rural Income Gap in China: A Continuous Wavelet Coherency Analysis
Sustainability, 2020Co-Authors: Yiguo Chen, Peng Luo, Tsangyao ChangAbstract:This study applies wavelet analysis to examine the relationship between the urbanization and the urban–Rural Income gap in 31 provinces in China over the period 1978–2019. We find three patterns of causality between urbanization and the urban–Rural Income gap. Empirical results show that urbanization does Granger-cause an urban–Rural Income gap, the urban–Rural Income gap does Granger-cause urbanization, and there exists a two-way causality between the urban–Rural Income gap and urbanization. Furthermore, these relationships mainly exist at high frequencies (short term). The results obtained by considering the resident population are more significant than those by the registered population. These results could help local governments develop fair policies for urban and Rural Income distribution in the process of urbanization of different provinces, promoting the coordinated development between urban and Rural areas.
