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Conference Paper: Is the spatial distribution of China’s population excessively unequal?: A crosscountry comparison
Title  Is the spatial distribution of China’s population excessively unequal?: A crosscountry comparison 

Authors  
Issue Date  2015 
Citation  The 62th Annual North American Meetings of the Regional Science Association International (NARSC 2015), Portland, OR., 1215 November 2015. How to Cite? 
Abstract  Is the spatial distribution of China`s population excessively unequal? So far, China has strictly controlled domestic migration to slow down the phase of urban growth. Such public action implicitly assumes that China`s population distribution in space is exceedingly unequal, so diseconomies of agglomeration dominate positive externalities from it. The validity of this assumption, however, is subject to scientific testing and requires empirical evidence that the spatial distribution of China`s population substantially deviates from a certain optimal range or a widely accepted reference level. Given the lack of such an empirical test, this study is motived to fill the gap.In detail, I test whether the spatial inequality of China`s population distribution deviates upward from that of other countries when controlling for a set of socioeconomic variables. For the purpose of this hypothesis testing, I estimate the following fixed effects model, and interpret regionspecific fixed effects (µ_i) as a systemic bias in spatial distribution of population:y_(i,t)=x_(i,t)^` ß+µ_i+e_(i,t) In the model, y_(i,t) and x_(i,t) are spatial inequality of population distribution and a vector of control variables for country i and time t, respectively, and ß and e_(i,t) refer to a vector of parameters to be estimated and error terms, respectively.Primary data for analysis (panel data for 65 countries) is 0.25°×0.25° global population grids for five years (1990, 1995, 2000, 2005, and 2010). I estimate the grids for China from China`s official census data and countylevel statistics, while for other countries I directly use the grids excerpted from the Gridded Population of the World version 3 (GPWv3) dataset (SEDAC, 2014). I consider two measures of spatial inequality of population distribution (y_(i,t)). One is the spatial Gini coefficient, measuring spatial inequality across cities; the other is Moran`s I index, measuring spatial inequality across clusters of cities. My preliminary analysis shows that the spatial Gini coefficient for China is not biased upward, while Moran`s I index is. In other words, the distribution of China`s population is not excessively unequal at the grid cell level, but those grid cells with high population counts tend to be highly agglomerated, compared with other countries. This results suggests that the spatial inequality of China`s population distribution is more obvious at the regional level than at the city (or county) level. 
Persistent Identifier  http://hdl.handle.net/10722/233629 
DC Field  Value  Language 

dc.contributor.author  Nam, K   
dc.date.accessioned  20160920T05:38:04Z   
dc.date.available  20160920T05:38:04Z   
dc.date.issued  2015   
dc.identifier.citation  The 62th Annual North American Meetings of the Regional Science Association International (NARSC 2015), Portland, OR., 1215 November 2015.   
dc.identifier.uri  http://hdl.handle.net/10722/233629   
dc.description.abstract  Is the spatial distribution of China`s population excessively unequal? So far, China has strictly controlled domestic migration to slow down the phase of urban growth. Such public action implicitly assumes that China`s population distribution in space is exceedingly unequal, so diseconomies of agglomeration dominate positive externalities from it. The validity of this assumption, however, is subject to scientific testing and requires empirical evidence that the spatial distribution of China`s population substantially deviates from a certain optimal range or a widely accepted reference level. Given the lack of such an empirical test, this study is motived to fill the gap.In detail, I test whether the spatial inequality of China`s population distribution deviates upward from that of other countries when controlling for a set of socioeconomic variables. For the purpose of this hypothesis testing, I estimate the following fixed effects model, and interpret regionspecific fixed effects (µ_i) as a systemic bias in spatial distribution of population:y_(i,t)=x_(i,t)^` ß+µ_i+e_(i,t) In the model, y_(i,t) and x_(i,t) are spatial inequality of population distribution and a vector of control variables for country i and time t, respectively, and ß and e_(i,t) refer to a vector of parameters to be estimated and error terms, respectively.Primary data for analysis (panel data for 65 countries) is 0.25°×0.25° global population grids for five years (1990, 1995, 2000, 2005, and 2010). I estimate the grids for China from China`s official census data and countylevel statistics, while for other countries I directly use the grids excerpted from the Gridded Population of the World version 3 (GPWv3) dataset (SEDAC, 2014). I consider two measures of spatial inequality of population distribution (y_(i,t)). One is the spatial Gini coefficient, measuring spatial inequality across cities; the other is Moran`s I index, measuring spatial inequality across clusters of cities. My preliminary analysis shows that the spatial Gini coefficient for China is not biased upward, while Moran`s I index is. In other words, the distribution of China`s population is not excessively unequal at the grid cell level, but those grid cells with high population counts tend to be highly agglomerated, compared with other countries. This results suggests that the spatial inequality of China`s population distribution is more obvious at the regional level than at the city (or county) level.   
dc.language  eng   
dc.relation.ispartof  Annual North American Meetings of the Regional Science Association International, NARSC 2015   
dc.title  Is the spatial distribution of China’s population excessively unequal?: A crosscountry comparison   
dc.type  Conference_Paper   
dc.identifier.email  Nam, K: kmnam@hku.hk   
dc.identifier.authority  Nam, K=rp01953   
dc.identifier.hkuros  265717   