China Sub-Regional June 2017 Consolidation: Difference between revisions

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[[File:ChinaBeijingPopPyramid v728.png|900px|ChinaBeijingPopPyramid v728.png]]
[[File:ChinaBeijingPopPyramid v728.png|900px|ChinaBeijingPopPyramid v728.png]]


To address the lack of migration data and appropriately capture the impact of migration within China, migration rates are estimated. First, provinces with net-in migration and net-out migration are identified by looking at the population data from 2000 through 2014. This span of years is when internal migration in China increased significantly and it is where migration rates are most visible in graphs of population. Below is a table that summarizes the migration trends that are visible in the provincial population and the following section shows graphs of population with history and forecasts for each province in China for comparison. 
To address the lack of migration data and appropriately capture the impact of migration within China, migration rates are estimated. First, provinces with net inward migration and net outward migration are identified by looking at the population data from 2000 through 2014. This span of years is when internal migration in China increased significantly and it is where migration rates are most visible in graphs of population. Below is a table that summarizes the migration trends that are visible in the provincial population and the following section shows graphs of population with history and forecasts for each province in China for comparison. 


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[[File:ChinaPopSCE2.png|900px|ChinaPopSCE2.png]]
[[File:ChinaPopSCE2.png|900px|ChinaPopSCE2.png]]
The migration scenario also improved age-sex forecasts for provinces, such as Beijing, that have a substantial migrant worker population. Below is a comparison of the base case forecast of Beijing's population pyramid and the second scenario's forecast in 2060. It is clear that there will be a significant elderly population in Beijing by 2060, but the burden is more realistic in the scenario because of the influx of migrants. 
[[File:BeijingPopPyramidBase2060.png|350px]][[File:BeijingPopPyramidSCE2 2060.png|350px]]


=== Anhui ===
=== Anhui ===
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==== New Migrant Flow Estimate 2 ====
==== New Migrant Flow Estimate 2 ====


==== [[File:GuangxiMigStock2.png|900px|GuangxiMigStock2.png]] ====
[[File:GuangxiMigStock2.png|900px|GuangxiMigStock2.png]]


==== Migration Scenario 1 Results ====
==== Migration Scenario 1 Results ====
Line 1,422: Line 1,428:


= Economy =
= Economy =
China's provincial model GDP appears to be too high in the forecasts relative to the 186 model. This appears to be driven by productivity in the provincial model, specifically in Physical Capital, as documented below. There is positive contributions to growth from Physical Capital in the provincial model when it is negative in the 186 model. What is driving this divergence is unclear because there is so little provincial data in the Infrastructure module. There is only one series in China's Infrastructure module, which is ''ICTTelephoneLinesPer1000''. This series appears to be identical to the 186 model and does not appear to be the cause of this increased productivity in the provincial model. Value added appears to be inline with the 186 model as does government spending and both are normalized to the 186 model using ''ApplyMultAll''. [[File:2017-06-29.png|900px|2017-06-29.png]]
The following sections document the economy module as it appears after the final migration scenario. The documentation also breaks down what may be driving the observed divergence in forecast GDP growth between the provincial model and the 186 model. 


== China's GDP ==
== China's GDP ==
Line 1,431: Line 1,441:
=== Anhui's GDP ===
=== Anhui's GDP ===


Anhui's GDP is lower in the two scenarios because the population is forecast to decline due to migration. 
Anhui's GDP is lower in the two scenarios because the population is forecast to decline due to outward migration. 


[[File:AnhuiGDPSCE2.png|900px|AnhuiGDPSCE2.png]]
[[File:AnhuiGDPSCE2.png|900px|AnhuiGDPSCE2.png]]
Line 1,524: Line 1,534:


=== Shanghai's GDP ===
=== Shanghai's GDP ===
Like Beijing the three GDP forecasts are dramatically different due to variances in the working-aged population. The base case forecasts a rapidly aging population that is a drag on the economy because there is a shrinking working-aged population. The first scenario increases GDP by a significant margin due to a huge migrant population that improves the stock of labor. The second scenario shoots in the middle, producing a forecast of growing GDP by a decreasing rate due to the aging population. 


[[File:ShanghaiGDPSCE2.png|900px|ShanghaiGDPSCE2.png]]
[[File:ShanghaiGDPSCE2.png|900px|ShanghaiGDPSCE2.png]]
Line 1,536: Line 1,548:


=== Tianjin's GDP ===
=== Tianjin's GDP ===
Tianjin's population is aging, much like what was documented in Shanghai and Beijing. The inward migration is where much of the labor force comes from and therefore, the inward migration of the scenarios increases GDP forecasts substantially. The second scenario is a more cautious forecast and shows GDP growth at a decreasing rate due to the aging population that is supported by inward migration. 


[[File:TianjinGDPSCE2.png|900px|TianjinGDPSCE2.png]]
[[File:TianjinGDPSCE2.png|900px|TianjinGDPSCE2.png]]
Line 1,557: Line 1,571:
== China's GDP per Capita ==
== China's GDP per Capita ==


China's GDP per capita in the provincial model and in the scenario are significantly greater than the GDP per capita in the 186 version of IFs. This is expected because the total population is quite close in both models, whereas, the GDP is greater. 
China's GDP per capita in the provincial model and in the scenario are significantly greater than the GDP per capita in the 186 version of IFs. This is expected because the total population is close in the models but, the GDP is greater in the provincial model. 


[[File:ChinaGDPPCSCE.png|900px|ChinaGDPPCSCE.png]]
[[File:ChinaGDPPCSCE2 5.png|900px|ChinaGDPPCSCE2 5.png]]


=== Anhui's GDP per Capita ===
=== Anhui's GDP per Capita ===


== Value Added ==
[[File:AnhuiGDPPCSCE2 2.png|900px|AnhuiGDPPCSCE2 2.png]]


Value added in all sectors are relatively close in both models, but it is slightly lower in the provincial model overall. The disparity between the two model's GDP does not appear to be driven by value added. 
=== Beijing's GDP per Capita ===


=== Agriculture ===
[[File:BeijingGDPPCSCE2 5.png|900px|BeijingGDPPCSCE2 5.png]]


The provincial model has higher value added in agriculture historically, but is forecast to have slightly lower value added in the future. 
=== Chongqing's GDP per Capita ===


[[File:ChinaAgVadd v728.png|900px|ChinaAgVadd v728.png]]
[[File:ChongqingGDPPCSCE2 5.png|900px|ChongqingGDPPCSCE2 5.png]]


=== Energy ===
=== Fujian's GDP per Capita ===


Value added in energy is forecast to be lower in the provincial model until around 2075 when the two models are forecast to be about the same. 
[[File:FujianGDPPCSCE2 5.png|900px|FujianGDPPCSCE2 5.png]]


[[File:ChinaEnVadd v728.png|900px|ChinaEnVadd v728.png]]
=== Gansu's GDP per Capita ===


=== Materials ===
[[File:GansuGDPPCSCE2 5.png|900px|GansuGDPPCSCE2 5.png]]


Value added in materials is forecast to be lower in the provincial model by about a percentage point for all years. 
=== Guangdong's GDP per Capita ===


[[File:ChinaMatVadd v728.png|900px|ChinaMatVadd v728.png]]
[[File:GuangdongGDPPCSCE2 5.png|900px|GuangdongGDPPCSCE2 5.png]]


=== Manufacturing ===
=== Guangxi's GDP per Capita ===


Manufacturing value added is relatively close in both models, with the provincial model being slightly greater. 
[[File:GuangxiGDPPCSCE2 5.png|900px|GuangxiGDPPCSCE2 5.png]]


[[File:ChinaManufacturingVAdd v728.png|900px|ChinaManufacturingVAdd v728.png]]
=== Guizhou's GDP per Capita ===


=== Services ===
[[File:GuizhouGDPPCSCE2 5.png|900px|GuizhouGDPPCSCE2 5.png]]


The two models are close historically and are forecast to be identical after 2035. 
=== Hainan's GDP per Capita ===


[[File:ChinaSerVadd v728.png|900px|ChinaSerVadd v728.png]]
[[File:HainanGDPPCSCE2 5.png|900px|HainanGDPPCSCE2 5.png]]


=== ICT ===
=== Hebei's GDP per Capita ===


ICT forecasts are nearly identical. 
[[File:HebeiGDPPCSCE2 5.png|900px|HebeiGDPPCSCE2 5.png]]


[[File:ChinaICTVadd v728.png|900px|ChinaICTVadd v728.png]]
=== Heilongjiang's GDP per Capita ===


== MultiFactor Productivity ==
[[File:HeilongjiangGDPPCSCE2 5.png|900px|HeilongjiangGDPPCSCE2 5.png]]


Multifactor productivity is significantly larger in the provincial model than in the 186-model. This is the likely culprit for what is driving the disparity between the two model's GDP forecasts. 
=== Henan's GDP per Capita ===


=== Agriculture ===
[[File:HenanGDPPCSCE2 5.png|900px|HenanGDPPCSCE2 5.png]]


The provincial model is slightly lower than the 186-model until around 2040. After 2040, the provincial model is greater than the 186-model by an increasing margin. 
=== Hubei's GDP per Capita ===


[[File:ChinaMFPAg v728.png|900px|ChinaMFPAg v728.png]]
[[File:HubeiGDPPCSCE2 5.png|900px|HubeiGDPPCSCE2 5.png]]


=== Energy ===
=== Hunan's GDP per Capita ===


Multifactor productivity in energy is much greater in the provincial model.
[[File:HunanGDPPCSCE2 5.png|900px|HunanGDPPCSCE2 5.png]]


[[File:ChinaMFPEn v728.png|900px|ChinaMFPEn v728.png]]
=== Jiangsu's GDP per Capita ===


=== Materials ===
[[File:JiangsuGDPPCSCE2 5.png|900px|JiangsuGDPPCSCE2 5.png]]


Multifactor productivity in materials is much greater in the provincial model than in the 186-model. 
=== Jiangxi's GDP per Capita ===


[[File:ChinaMFPMat v728.png|900px|ChinaMFPMat v728.png]]
[[File:JiangxiGDPPCSCE2 5.png|900px|JiangxiGDPPCSCE2 5.png]]


=== Manufacturing ===
=== Jilin's GDP per Capita ===


The provincial model has much greater multifactor productivity in manufacturing than the 186-model. 
[[File:JilinGDPPCSCE2 5.png|900px|JilinGDPPCSCE2 5.png]]


[[File:ChinaMFPManufactures v728.png|900px|ChinaMFPManufactures v728.png]]
=== Liaoning's GDP per Capita ===


=== Services ===
[[File:LiaoningGDPPCSCE2 5.png|900px|LiaoningGDPPCSCE2 5.png]]


Multifactor productivity is much greater in the provincial model. 
=== Nei Mongol's GDP per Capita ===


[[File:ChinaMFPser v728.png|900px|ChinaMFPser v728.png]]
[[File:NeiMongolGDPPCSCE2 5.png|900px|NeiMongolGDPPCSCE2 5.png]]


=== ICT ===
=== Ningxia's GDP per Capita ===


Multifactor productivity is significantly greater in the provincial model. 
[[File:NingxiaGDPPCSCE2 5.png|900px|NingxiaGDPPCSCE2 5.png]]


[[File:ChinaMFPICT v728.png|900px|ChinaMFPICT v728.png]]
=== Qinghai's GDP per Capita ===


== GDS ==
[[File:QinghaiGDPPCSCE2 5.png|900px|QinghaiGDPPCSCE2 5.png]]


Government spending is a key driver in the model and it directly impacts multifactor productivity, which appears to be off. 
=== Shaanxi's GDP per Capita ===


=== Military ===
[[File:ShaanxiGDPPCSCE2 5.png|900px|ShaanxiGDPPCSCE2 5.png]]


Military spending is greater in the provincial model, why this is occurring is not clear because there is not any historical data for this series. 
=== Shandong's GDP per Capita ===


[[File:ChinaGDSMil v728.png|900px|ChinaGDSMil v728.png]]
[[File:ShandongGDPPCSCE2 5.png|900px|ShandongGDPPCSCE2 5.png]]


=== Education ===
=== Shanghai's GDP per Capita ===


The provincial model is initially greater than the 186-model and after 2055 the provincial model is lower. There is historical data in the provincial model for this series. 
[[File:ShanghaiGDPPCSCE2 5.png|900px|ShanghaiGDPPCSCE2 5.png]]


[[File:ChinaGDSEd v728.png|900px|ChinaGDSEd v728.png]]
=== Shanxi's GDP per Capita ===


=== Health ===
[[File:ShanxiGDPPCSCE 5.png|900px|ShanxiGDPPCSCE 5.png]]


The two models are initially close together, but over time the provincial model becomes greater than the 186-model by an increasing margin. There is historical data for this series. 
=== Sichuan's GDP per Capita ===


[[File:ChinaGDSHealth v728.png|900px|ChinaGDSHealth v728.png]]
[[File:SichuanGDPPCSCE2 5.png|900px|SichuanGDPPCSCE2 5.png]]


=== Research and Development ===
=== Tianjin's GDP per Capita ===


There is more investment in research and development in the provincial model, but the total amount is very small in both models. This means that the marginal difference is small, but the magnitude of difference is significant. There is historical provincial data for this series. 
[[File:TianjinGDPPCSCE2 5.png|900px|TianjinGDPPCSCE2 5.png]]


[[File:ChinaGDSR&D v728.png|900px|ChinaGDSR&D v728.png]]
=== Tibet's GDP per Capita ===


=== Infrastructure ===
[[File:TibetGDPPCSCE2 5.png|900px|TibetGDPPCSCE2 5.png]]


Infrastructure spending is close in the two models. There is not any historical provincial data for this series. 
=== Xinjiang's GDP per Capita ===


[[File:ChinaGDSInfra v728.png|900px|ChinaGDSInfra v728.png]]
[[File:XinjiangGDPPCSCE2 5.png|900px|XinjiangGDPPCSCE2 5.png]]


=== InfraOther ===
=== Yunnan's GDP per Capita ===


The provincial model has more InfraOther spending than the 186-model. 
[[File:YunnanGDPPCSCE2 5.png|900px|YunnanGDPPCSCE2 5.png]]


[[File:ChinaGDSInfraOther v728.png|900px|ChinaGDSInfraOther v728.png]]
=== Zhejiang's GDP per Capita ===


=== Other ===
[[File:ZhejiangGDPPCSCE2 5.png|900px|ZhejiangGDPPCSCE2 5.png]]


Other spending is significantly greater in the provincial model. 
== Value Added ==
 
Value added in all sectors are relatively close in both models, but it is slightly lower in the provincial model overall. The disparity between the two model's GDP does not appear to be driven by value added. 
 
=== Agriculture Value Added as a Percentage of GDP ===
 
The provincial model has higher value added in agriculture historically, but is forecast to have slightly lower value added in the future. 
 
[[File:ChinaAgVadd v728.png|900px|ChinaAgVadd v728.png]]
 
=== Energy Value Added as a Percentage of GDP ===
 
Value added in energy is forecast to be lower in the provincial model until around 2075 when the two models are forecast to be about the same. 
 
[[File:ChinaEnVadd v728.png|900px|ChinaEnVadd v728.png]]
 
=== Materials Value Added as a Percentage of GDP ===
 
Value added in materials is forecast to be lower in the provincial model by about a percentage point for all years. 
 
[[File:ChinaMatVadd v728.png|900px|ChinaMatVadd v728.png]]
 
=== Manufacturing Value Added as a Percentage of GDP ===
 
Manufacturing value added is relatively close in both models, with the provincial model being slightly greater. 
 
[[File:ChinaManufacturingVAdd v728.png|900px|ChinaManufacturingVAdd v728.png]]
 
=== Services Value Added as a Percentage of GDP ===
 
The two models are close historically and are forecast to be identical after 2035. 
 
[[File:ChinaSerVadd v728.png|900px|ChinaSerVadd v728.png]]
 
=== ICT Value Added as a Percentage of GDP ===
 
ICT forecasts are nearly identical. 
 
[[File:ChinaICTVadd v728.png|900px|ChinaICTVadd v728.png]]
 
== Government Consumption ==
 
Government spending is a key driver in the economy module and it directly impacts multifactor productivity. The graphs of GDS suggest that the 186 and provincial model have the same starting point but have different growth rates in the forecasts. 
 
=== Military ===
 
Military spending is greater in the provincial model, why this is occurring is not clear because there is not any historical data for this series. 
 
[[File:ChinaGDSMil v728.png|900px|ChinaGDSMil v728.png]]
 
=== Education ===
 
The provincial model is initially greater than the 186-model and after 2055 the provincial model is lower. There is historical data in the provincial model for this series. 
 
[[File:ChinaGDSEd v728.png|900px|ChinaGDSEd v728.png]]
 
=== Health ===
 
The two models are initially close together, but over time the provincial model becomes greater than the 186-model by an increasing margin. There is historical data for this series. 
 
[[File:ChinaGDSHealth v728.png|900px|ChinaGDSHealth v728.png]]
 
=== Research and Development ===
 
There is more investment in research and development in the provincial model, but the total amount is very small in both models. There is historical provincial data for this series. 
 
[[File:ChinaGDSR&D v728.png|900px|ChinaGDSR&D v728.png]]
 
=== Infrastructure ===
 
Infrastructure spending is close in the two models. There is not any historical provincial data for this series. 
 
[[File:ChinaGDSInfra v728.png|900px|ChinaGDSInfra v728.png]]
 
=== InfraOther ===
 
The provincial model has more InfraOther spending than the 186-model. 
 
[[File:ChinaGDSInfraOther v728.png|900px|ChinaGDSInfraOther v728.png]]
 
=== Other ===
 
Other spending is significantly greater in the provincial model. 


[[File:ChinaGDSOth v728.png|900px|ChinaGDSOth v728.png]]
[[File:ChinaGDSOth v728.png|900px|ChinaGDSOth v728.png]]
== MultiFactor Productivity ==
Multifactor productivity is significantly larger in the provincial model than in the 186-model, and therefore it is the likely culprit for what is driving the disparity between the two model's GDP forecasts. 
=== Agriculture MFP ===
The provincial model is slightly lower than the 186-model until around 2040. After 2040, the provincial model is greater than the 186-model by an increasing margin. 
[[File:ChinaMFPAg v728.png|900px|ChinaMFPAg v728.png]]
=== Energy MFP ===
Multifactor productivity in energy is much greater in the provincial model.
[[File:ChinaMFPEn v728.png|900px|ChinaMFPEn v728.png]]
=== Materials MFP ===
Multifactor productivity in materials is much greater in the provincial model than in the 186-model. 
[[File:ChinaMFPMat v728.png|900px|ChinaMFPMat v728.png]]
=== Manufacturing MFP ===
The provincial model has much greater multifactor productivity in manufacturing than the 186-model. 
[[File:ChinaMFPManufactures v728.png|900px|ChinaMFPManufactures v728.png]]
=== Services MFP ===
Multifactor productivity is much greater in the provincial model. 
[[File:ChinaMFPser v728.png|900px|ChinaMFPser v728.png]]
=== ICT MFP ===
Multifactor productivity is significantly greater in the provincial model. 
[[File:ChinaMFPICT v728.png|900px|ChinaMFPICT v728.png]]
== Growth Acounting ==
=== Capital ===
There is significantly more growth in capital in the materials sector in the provincial model, than there is in the 186 model. 
==== Capital in the Provincial Model ====
[[File:ChGACapitalProv.png|900px|ChGACapitalProv.png]]
==== Capital in the 186 Model ====
[[File:ChGACapital186.png|900px|ChGACapital186.png]]
=== Labor ===
The two models are quite close in the area of labor, but the provincial model is positive in the beginning, whereas the 186 model is not. 
==== Labor in the Provincial Model ====
[[File:ChGALaborProv.png|900px|ChGALaborProv.png]]
==== Labor in the 186 Model ====
[[File:ChGALabor186.png|900px|ChGALabor186.png]]
=== MultiFactor Productivity ===
MFP is greater in 2019 in the 186 model, but it is greater in all other years in the provincial model. Manufacturing is the key exception to this because it is greater in all years in the 186 model. 
==== MultiFactor Productivity in the Provincial Model ====
[[File:ChGAMFPProv.png|900px|ChGAMFPProv.png]]
==== MultiFactor Productivity in the 186 Model ====
[[File:ChGAMFP186.png|900px|ChGAMFP186.png]]
=== Technology Base MultiFactor Productivity ===
ICT is about the same in the two models, but it decreases at a slower rate in the 186 model. The remaining sectors are greater in the provincial model and decline over time, whereas the 186 model forecasts all other sectors (not ICT) to be about the same in all years. 
==== Technology Base MFP in the Provincial Model ====
[[File:ChGATechMFPProv.png|900px|ChGATechMFPProv.png]]
==== Technology Base MFP in the 186 Model ====
[[File:ChGATechMFP186.png|900px|ChGATechMFP186.png]]
=== Knowledge Base MultiFactor Productivity ===
There are differences between sectors in the provincial model, whereas the 186 model shows them all the be the same. The actual growth contributions are similar, but clearly there is a difference. The provincial model is greater in some of the earlier years where there is the large jump in provincial GDP. 
==== Knowledge Base MFP in the Provincial Model ====
[[File:ChGAKnowledgeMFPPRov.png|900px|ChGAKnowledgeMFPPRov.png]]
==== Knowledge Base MFP in the 186 Model ====
[[File:ChGAKnowledgeMFP.png|900px|ChGAKnowledgeMFP.png]]
=== Human Capital MultiFactor Productivity ===
Like what was documented in the area of Knowledge Capital, there is a difference among the sectors contribution to growth in the provincial. The 186 model has the same amount of growth contribution for all sectors in all years. On average the two models appear to be about the same. 
==== Human Capital MFP in the Provincial Model ====
[[File:CHGAHumanCapitalMFPProv.png|900px|CHGAHumanCapitalMFPProv.png]]
==== Human Capital MFP in the 186 Model ====
[[File:CHGAHumanCapitalMFP186.png|900px|CHGAHumanCapitalMFP186.png]]
=== Social Capital MultiFactor Productivity ===
Social Capital MFP is greater in the provincial model from the first year of forecasts through 2044, after that the 186 model is greater. Once again there is a difference between sectors in the provincial model, but not in the 186 model. 
==== Social Capital MFP in the Provincial Model ====
[[File:ChGASocialCapitalMFPProv.png|900px|ChGASocialCapitalMFPProv.png]]
==== Social Capital MFP in the 186 Model ====
[[File:ChGASocialCapitalMFP186.png|900px|ChGASocialCapitalMFP186.png]]
=== Physical Capital MultiFactor Productivity  ===
This area appears to be the area of greatest difference between the two models. Physical capital contributes to growth in the provincial model, whereas, in the 186 model it has a negative contribution. There is also a difference among sectors in the provincial model, that does not occur in the 186 model. 
==== Physical Capital MFP in the Provincial Model ====
[[File:ChGAPhysicalCapitalMFPProv.png|900px|ChGAPhysicalCapitalMFPProv.png]]
==== Physical Capital MFP in the 186 Model ====
[[File:ChGAPhysicalCapitalMFP186.png|900px|ChGAPhysicalCapitalMFP186.png]]
=== Correction MultiFactor Productivity ===
==== Correction MFP in the Provincial Model ====
[[File:CHGACorrectionMFPProv.png|900px|CHGACorrectionMFPProv.png]]
==== Correction MFP in the 186 Model ====
[[File:ChGACorrectionMFP186.png|900px|ChGACorrectionMFP186.png]]
== Foreign Direct Investment ==
Provincial FDI historically is quite close to the 186 model from 1973 through 1995. After 1995, there is a dramatic divergence. The provincial forecast initializes at 0 in 2014. This appears to be a major contributor to throwing off GDP in China. 
[[File:ChinaFDISCE2.png|900px]]

Latest revision as of 22:04, 3 July 2017

Overview

China's sub-regional model has a total of 63 preprocessor series. Although there are 652 preprocessor series in the IFs model, there are many that are not applicable to sub-regional models or do not significantly impact forecasts; only 355 preprocessor series are sought for sub-regionalization. Thus China's sub-regional model has nearly 18% coverage at this point. There are a 29 preprocessor series that have been identified that could be pulled into the model. If this data was to be pulled the the total coverage of the model would increase to nearly 26%. 

Below is a table that shows the module coverage for the China sub-regional model. Population has the most coverage, but this is also the fewest number of preprocessor series. The economy module has the second highest coverage, which is good. Population and Economy have  the greatest impact on the model's overall behavior and coverage in these modules is important. Health and education are areas of weakness for the China sub-regional model. In part the weakness is driven by the large number of series in those modules. There is more education data available that could be brought into the model, but some of the data requires historical age-sex cohorts to calculate rates with the data. Health has low coverage because there is not a lot of health data that is available for free and in English. It appears that much of China's health data is proprietary and not publicly available, but there may be alternative sources available if a researcher searched in Mandarin. 

IFs Module Preprocessor Series Pulled Total Preprocessor Series Module Coverage
Population 7 8 87.5%
Economy 24 55 43.6%
Health 5 57 8.8%
Education 8 91 8.8%
Agriculture 11 36 30.6%
Energy 7 66 10.6%
Infrastructure 1 21 4.8%
Environment 0 9 0.0%
Socio-Political
 0 12 0.0%
Total 63 355 17.7%
Potential Series 92 355 25.9%

Population

China's Population

The provincial model's population for China was rolled up in IFs and compared to other population forecasts for China, as shown below. It is clear that the population forecast from the sum of the provinces (IFs_WB) is in line with other forecasting models. Nanjing University's forecasting model is the most off from the rest of the forecasts, where population growth is forecast to continue to rise. 

ChinaPopVet 728.png

It was difficult to find provincial forecasts on China, but there was one paper that was found in Mandarin that had provincial population forecasts for China's provinces. The paper from Nanjing University compared its forecasts to several other population forecasting models of China and this data was used to compare the IFs provincial model to. As was previously stated, Nanjing University's model  has a steeper slope and forecasts population to continue to rise at the same rate, which all other forecasting models do not. This suggests that it might not be as good of a model as the others, but it gives some information about our forecasts. 

Migration

Migration rates for China's provinces are not publicly available, nor are there forecasts of China's migration rates. This means that the model is without migration, which is problematic because intranational migration is a salient issue that is shaping provincial populations. For instance, Beijing's population pyramid shows a population that has been shaped by migration because of the large young working population relative to the number of children. There are also visible jumps up and down in population in provinces that are clearly attributed to migration. 

ChinaBeijingPopPyramid v728.png

To address the lack of migration data and appropriately capture the impact of migration within China, migration rates are estimated. First, provinces with net inward migration and net outward migration are identified by looking at the population data from 2000 through 2014. This span of years is when internal migration in China increased significantly and it is where migration rates are most visible in graphs of population. Below is a table that summarizes the migration trends that are visible in the provincial population and the following section shows graphs of population with history and forecasts for each province in China for comparison. 

Net Inward Migration Net Outward Migration Flat Migration
Beijing Anhui Chongqing
Guangdong Gansu Fujian
Hebei Guizhou Hunan
Hubei Guangxi Jiangsu
Jiangxi Heilongjiang Liaoning
Shanghai Henan Nei Mongol
Tianjin Jilin Ningxia
Xinjiang Shaanxi Qinghai
Hainan Sichuan Shandong

Yunnan Shanxi

Tibet


Zhejiang

Estimation Methods

Below is a province-by-province comparison of migration and population. First is population by province in the base case, without migration that is compared to Nanjing University's provincial population forecasts.

Second is the first method of estimating the flow of migrants, this is the net number of new migrants in millions. To estimate the new migrants; historical crude birth rates, crude death rates, and population were used to calculate total births and deaths in millions starting in 1990. Births and deaths were then added to the previous year's population to generate an estimate of population without migration. For instance, population in 1990 for Anhui province is 54.7 million and births and deaths total to a net 0.84 million people in 1991. Then the population without migration in 1991 is estimated to be 55.54 million (54.7+0.84=55.54). These migration estimates are compared to a 5-year moving average of migrant flows from a paper that estimated subnational migration in China from 1990 through 2005. 

The third graph is a comparison of a second method of net migrant estimation with the first method and the paper's migrant flows. The second estimation method is a five-year moving average of the first method's net migrants.

Migration Scenario 1

A migration scenario was generated using the second method of net migrants estimation and population to calculate a net migration rate. The calculated migration rate for 2014 was used for 2014 in the scenario using the migrater parameter. Then all provinces were interpolated to 0 at the end of the time horizon in 2100. 

China's Population After Migration Scenario 1

After the migration scenario the total population in China is slightly higher than the 186 model and the base case. 

ChinaPopSCE.png

Migration Scenario 2

Building upon the first migration scenario, a second scenario was created. The second scenario includes India's final scenario migration data and China's in the same scenario run. Additionally, some province's migration rates were adjusted to improve model behavior because the inclusion of India's migration rates in the scenario run made the net inward migration in several Chinese provinces greater than it was in the original run. 

China's Population After Migration Scenario 2

The second scenario forecasts all of China's population to be greater than the first scenario, but after careful calibration it is still close. The first scenario forecasts population to be about 18 million greater than the 186 model forecasts in 2100. Whereas, the second scenario forecasts population to be about 36 million greater than the 186 model in 2100.

ChinaPopSCE2.png

The migration scenario also improved age-sex forecasts for provinces, such as Beijing, that have a substantial migrant worker population. Below is a comparison of the base case forecast of Beijing's population pyramid and the second scenario's forecast in 2060. It is clear that there will be a significant elderly population in Beijing by 2060, but the burden is more realistic in the scenario because of the influx of migrants. 


BeijingPopPyramidBase2060.pngBeijingPopPyramidSCE2 2060.png

Anhui

Population

Anhui's population takes a significant dip around 2008, which shows net-outward migration. 

Anhui Population

New Migrant Flow Estimate 1

AnhuiMigStock.png

New Migrant Flow Estimate 2

AnhuiMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

Anhui has significantly lower population forecasts after the migration scenario. 

AnhuiPopSCE.png

Migration Rate After Migration Scenario 1

The model pushes the rate to zero earlier than what is in the scenario. 

AnhuiMigRateSCE.png

Migration Scenario 2 Results

Anhui province's migration rates did not change in the second scenario.

Population After Migration Scenario 2

In the second scenario, population in Anhui is slightly higher. 

AnhuiPopSCE2.png

Migration Rate After Migration Scenario 2

AnhuiMigRateSCE2.png

Beijing

Population

Beijing has a large jump up in population from 2005 through 2014, which shows net-inward migration. 

ChinaBeijingPop v728.png

New Migrant Flow Estimate 1

BeijingMigstock.png

New Migrant Flow Estimate 2

BeijingMigstock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

Beijing's population is significantly higher after the senario and we may want to decrease the inward migration slightly. 

BeijingPopSCE.png

Migration Rate After Migration Scenario 1

The model pushes migration down more rapidly than what is in the scenario, which is probably a good thing being the initial migration rate for Beijing is so high. 

BeijingMigRateSCE.png

Migration Scenario 2 Results

Beijing's migration rate has been lowered in the second scenario from 3.56 in 2014 to 1.26 in 2014. 

Population After Migration Scenario 2

BeijingPopSCE2.png

Migration Rate After Migration Scenario 2

BeijingMigRateSCE2.png

Chongqing

Population

The World Bank data was interpolated in the model which fills the population back to 1960 smoothly. In reality the municipality did not bifurcate from Sichuan until 1997 and its borders were redefined several times between 1997 and 2008. Thus Nanjing University's historical data's strange behavior makes sense, but the forecasts look too high. 

ChinaChongqingPop v728.png

New Migrant Flow Estimate 1

The Chan paper's migrant stock estimates do not have data for Chongqing because the municipality did not come to be until 1997. 

ChongqingMigStock.png

New Migrant Flow Estimate 2

ChongqingMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

The population of Chongqing is greater in the scenario by about 5 million people at the end of the time horizon. 

ChongqingPopSCE.png

Migration Rate After Migration Scenario 1

ChongqingMigRateSCE.png

Migration Scenario 2 Results

Chongqing's migration rate was decreased from 0.43 to 0.33 in 2014 in the second scenario. 

Population After Migration Scenario 2

Despite the decline in migration rates in the second scenario, there is an increase in total population in Chongqing. 

ChongqingPopSCE2.png

Migration Rate After Migration Scenario 2

ChongqingMigRateSCE2.png

Fujian

Population

ChinaFujianPop v728.png

New Migrant Flow Estimate 1

FujianMigStock.png

New Migrant Flow Estimate 2

FujianMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

FujianPopSCE.png

Migration Rates After Migration Scenario 1

FujianMigRateSCE.png

Migration Scenario 2 Results

Fujian province's migration rates were not changed in the second scenario from the first. 

Population After Migration Scenario 2

Population looks to be the same in second scenario as in the first. 

FujianPopSCE2.png

Migration Rates After Migration Scenario 2

FujianMigRateSCE2.png

Gansu

Population

Gansu appears to have net-outward migration. 

ChinaGansuPop v728.png

New Migrant Flow Estimate 1

GansuMigStock.png

New Migrant Flow Estimate 2

GansuMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

Gansu's population is lower in the scenario by about 3.5 million. 

GansuPopSCE.png

Migration Rate After Migration Scenario 1

The model pushes the migration rates to zero more rapidly than what is in the scenario. 

GansuMigRateSCE.png

Migration Scenario 2 Results

Gansu's Migration rates did not change from the first scenario to the second scenario. 

Population After Migration Scenario 2

Although the second migration scenario's data did not change from the first scenario, population is slightly greater in the second scenario for Gansu. 

GansuPopSCE2.png

Migration Rates After Migration Scenario 2

GansuMigRateSCE2.png

Guangdong

Population

Guangdong experienced rapid inward migration in the mid to late 2000s.

ChinaGuangdongPop v728.png

New Migrant Flow Estimate 1

GuangdongMigStock.png

New Migrant Flow Estimate 2

GuangdongMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

There is a significant increase in population in Guangdong due to migration. By the end of the time horizon there are almost 40 million more people. 

GuangdongPopSCE.png

Migration Rates After Migration Scenario 1

GuangdongMigRateSCE.png

Migration Scenario 2 Results

Guangdong's migration rate was decreased in the second scenario from 0.83 in the first scenario to 0.43.

Population After Migration Scenario 2

Guangdong's population is slightly lower than the first scenario in the second, but  is significantly greater than the base case. 

GuangdongPopSCE2.png

Migration Rates After Migration Scenario 2

GuangdongMigRateSCE2.png

Guizhou

Population

Guizhou appears to have had significant net-outward migration.

ChinaGuizhouPop v728.png

New Migrant Flow Estimate 1

GuizhouMigStock.png

New Migrant Flow Estimate 2

GuizhouMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

GuizhouPopSCE.png

Migration Rates After Migration Scenario 1

The model pushes migration rates down lower than what are in the scenario, which is probably a good thing because there is a substantial change in population in the scenario. 

GuizhouMigRateSCE.png

Migration Scenario 2 Results

Guizhou's migration rates were not changed from the first scenario to the second scenario. 

Population After Migration Scenario 2

The second scenario has slightly larger population than in the first scenario. 

GuizhouPopSCE2.png

Migration Rate After Migration Scenario 2

GuizhouMigRateSCE2.png

Guangxi

Population

There is net-outward migration in Guangxi. 

ChinaGuangxiPopv728.png

New Migrant Flow Estimate 1

GuangxiMigStock.png

New Migrant Flow Estimate 2

GuangxiMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1GuangxiPopSCE.png
Migration Rates After Migration Scenario 1

The model pushes migration rates to zero before they reach zero in the migration scenario. GuangxiMigRateSCE.png

Migration Scenario 2 Results

The migration rates were not changed from the first scenario to the second. 

Population After Migration Scenario 2

Population after the second scenario is slightly greater than the first scenario. GuangxiPopSCE2.png

Migration Rates After Migration Scenario 2GuangxiMigRateSCE2.png

Hainan

Population

There appears to be net-outward migration in Hainan in the early years and then net-inward in the late-2000s. 

ChinaHainanPop v728.png

New Migrant Flow Estimate 1

HainanMigStock.png

New Migrant Flow Estimate 2

HainanMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

HainanPopSCE.png

Migration Rates After Migration Scenario 1

The model pushes migration rates to zero before they reach zero in the scenario. 

HainanMigRateSCE.png

Migration Scenario 2 Results

Hainan province's migration rates were not changed in the second scenario. 

Population After Migration Scenario 2

Population is slightly lower in the second scenario. 

HainanPopSCE2.png

Migration Rates After Migration Scenario 2

HainanMigRateSCE2.png

Hebei

Population

There appears to be net-inward migration into Hebei. 

ChinaHebeiPop v728.png

New Migrant Flow Estimate 1

HebeiMigStock.png

New Migrant Flow Estimate 2

HebeiMigStock2.png

Migration Scenario 1 Results 

Population After Migration Scenario 1

HebeiPopSCE.png

Migration Rates After Migration Scenario 1

HebeiMigRateSCE.png

Migration Scenario 2 Results

Hebei's migration rates were decreased slightly in the second scenario, from 0.19 in 2014 in the first scenario to 0.15 in the second. 

Population After Migration Scenario 2

Despite the decrease in migration rates, the total population is slightly greater in the second scenario than in the first. 

HebeiPopSCE2.png

Migration Rates After Migration Scenario 2

HebeiMigRateSCE2.png

Heilongjiang

Population

There is net-outward migration. 

ChinaHeilongjiangPop v728.png

New Migrant Flow Estimate 1

HeilongjiangMigStock.png

New Migrant Flow Estimate 2

HeilongjiangMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario1

HeilongjiangPopSCE.png

Migration Rates After Migration Scenario 1

HeilongjiangMigRateSCE.png

Migration Scenario 2 Results

Heilongjiang's migration rates were decreased slightly in the second scenario, from -0.21 to -0.31. 

Population After Migration Scenario 2

The second migration scenario has slightly lower population forecasts than the first scenario. 

HeilongjiangPopSCE2.png

Migration Rates After Migration Scenario 2

HeilongjiangMigRateSCE2.png

Henan

Population

Henan province appears to have net-outward migration. 

ChinaHenanPopVet v728.png

New Migrant Flow Estimate 1

HenanMigStock.png

New Migrant Flow Estimate 2

HenanMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

HenanPopSCe.png

Migration Rates After Migraion Scenario 1

HenanMigRateSCE.png

Migration Scenario 2 Results

Henan's migration did not change after the second scenario. 

Population After Migration Scenario 2 

Henan's population is slightly greater in the second scenario than it was in the first. 

HenanPopSCe2.png

Migration Rates After Migration Scenario 2

HenanMigRateSCE2.png

Hubei

Population

There are several other models that are compared here, the additional models were found in a paper that compared population models in China. IFs is in between all of the models, which is probably a good sign. There is a net-outward migration in the late 2000s. 

RTENOTITLE

New Migrant Flow Estimate 1

HubeiMigStock.png

New Migrant Flow Estimate 2

HubeiMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

HubeiPopSCE.png

Migration Rates After Migration Scenario 1

HubeiMigRateSCE.png

Migration Scenario 2 Results

Hubei's migration rates were lowered slightly in the second scenario from -0.25 to -0.35.

Population After Migration Scenario 2

HubeiPopSCE2.png

Migration Rates After Migration Scenario 2

HubeiMigRateSCE2.png

Hunan

Population

ChinaHunanPop v728.png

New Migrant Flow Estimate 1

HunanMigStock.png

New Migrant Flow Estimate 2

HunanMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

HunanPopSCE.png

Migration Rates After Migration Scenario 1

HunanMigRateSCE.png

Migration Scenario 2 Results

Hunan's migration rates are the same in the second scenario as they were in the first. 

Population After Migration Scenario 2

Despite the same migration rates going into the scenario, the second scenario forecasts slightly greater population than the first scenario. 

HunanPopSCE2.png

Migration Rates After Migration Scenario 2

HunanMigRateSCE2.png

Jiangsu

Population

ChinaJiangsuPop v728.png

New Migrant Flow Estimate 1

JiangsuMigStock.png

New Migrant Flow Estimate 2

JiangsuMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

JiangsuPopsCE.png

Migration Rates After Migration Scenario 1

JiangsuMigRateSCE.png

Migration Scenario 2 Results

The second scenario has the same migration rates for Jiangsu as were in the first scenario. 

Population After Migration Scenario 2

Population after the second scenario is slightly greater than in the first. 

JiangsuPopSCE2.png

Migration Rates After Migration Scenario 2

JiangsuMigRateSCE2.png

Jiangxi

Population

ChinaJiangxiPop v728.png

New Migrant Flow Estimate 1

JiangxiMigStock.png

New Migrant Flow Estimate 2

JiangxiMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

JiangxiPopSCE.png

Migration Rates After Migration Scenario 1

JiangxiMigRateSCE.png

Migration Scenario 2 Results

The same migration rates were used for Jiangxi in the second scenario as were in the first. 

Population After Migration Scenario 2

JiangxiPopSCE2.png

Migration Rates After Migration Scenario 2

JiangxiMigRateSCE2.png

Jilin

Population

There appears to be a small amount of outward migration. 

ChinaJilinPop v728.png

New Migrant Flow Estimate 1

JilinMigStock.png

New Migrant Flow Estimate 2

JilinMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

JilinPopSCE.png

Migration Rates After Migration Scenario 1

JilinMigRateSCE.png

Migration Scenario 2 Results

Jilin's migration rates were unchanged in this scenario. 

Population After Migration Scenario 2

JilinPopSCE2.png

Migration Rates After Migration Scenario 2

JilinMigRateSCE2.png

Liaoning

Population

ChinaLiaoningPop v728.png

New Migrant Flow Estimate 1

LiaoningMigStock.png

New Migrant Flow Estimate 2

LiaoningMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

LiaoningPopSCE.png

Migration Rates After Migration Scenario 1

LiaoningMigRateSCE.png

Migration Scenario 2 Results

Liaoning's migration rates were slightly lowered in the second scenario from 0.25 to 0.2 in 2014. 

Population After Migration Scenario 2

Despite the lowering of migration rates, the population is forecast to be greater in the second scenario than in the first. 

LiaoningPopSCE2.png

Migration Rates After Migration Scenario 2

LiaoningMigRateSCE2.png

Nei Mongol

Population

ChinaNeiMongolPop v728.png

New Migrant Flow Estimate 1

NeiMongolMigStock.png

New Migrant Flow Estimate 2

NeiMongolMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

NeiMongolPopSCE.png

Migration Rates After Migration Scenario 1

NeiMongolMigRateSCE.png

Migration Scenario 2 Results

Nei Mongol's migration rates were unchanged in the second scenario. 

Population After Migration Scenario 2

NeiMongolPopSCE2.png

Migration Rates After Migration Scenario 2

NeiMongolMigRateSCE2.png

Ningxia

Population

ChinaNingxiaPop v728.png

New Migrant Flow Estimate 1

NingxiaMigStock.png

New Migrant Flow Estimate 2

NingxiaMigStock2.png

Migration Scenario 1 Results

Population After Migraton Scenario 1

NingxiaPopSCE.png

Migration Rates After Migration Scenario 1

NingxiaMigRateSCE.png

Migration Scenario 2 Results

Ningxia's migration was unchanged from the first scenario in the second. 

Population After Migration Scenario 2

Population after the second scenario is greater than in the first scenario. 

NingxiaPopSCE2.png

Migration Rates After Migration Scenario 2

NingxiaMigRateSCE2.png

Qinghai

Population

ChinaQinghaiPop v728.png

New Migrant Flow Estimate 1

QinghaiMigStock.png

New Migrant Flow Estimate 2

QinghaiMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

QinghaiPopsSCE.png

Migration Rates After Migration Scenario 1

QinghaiMigRateSCE.png

Migration Scenario 2 Results

Qinghai's migration rates were unchanged in the second scenario from the first. 

Population After Migration Scenario 2

QinghaiPopsSCE2.png

Migration Rates After Migration Scenario 2

QinghaiMigRateSCE2.png

Shaanxi

Population

There is net-outward migration in Shaanxi. 

ChinaShaanxiPop v728.png

New Migrant Flow Estimate 1

ShaanxiMigstock.png

New Migrant Flow Estimate 2

ShaanxiMigstock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

ShaanxiPopSCE.png

Migration Rates After Migration Scenario 1

ShaanxiMigRateSCE.png

Migration Scenario 2 Results

Shaanxi's migration rates have not been changed in the second scenario from the first. 

Population After Migration Scenario 2

ShaanxiPopSCE2.png

Migration Rates After Migration Scenario 2

ShaanxiMigRateSCE2.png

Shandong

Population

ChinaShandongPop v728.png

New Migrant Flow Estimate 1

ShandongMigStock.png

New Migrant Flow Estimate 2

ShandongMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

ShandongPopSCE.png

Migration Rate After Migration Scenario 1

ShandongMigRateSCE.png

Migration Scenario 2 Results

Shandong's migration rates are unchanged in the second scenario from the first scenario. 

Population After Migration Scenario 2

ShandongPopSCE2.png

Migration Rates After Migration Scenario 2

ShandongMigRateSCE2.png

Shanghai

Population

There is significant in-migration into Shanghai. 

ChinaShanghaiPop v728.png

New Migrant Flow Estimate 1

ShanghaiMigStock.png

New Migrant Flow Estimate 2

ShanghaiMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

ShanghaiPopSCE.png

Migration Rates After Migration Scenario 1

ShanghaiMigRateSCE.png

Migration Scenario 2 Results

Shanghai's migration rates were lowered in the second scenario from 2.1 to 1 in 2014. 

Population After Migration Scenario 2

ShanghaiPopSCE2.png

Migration Rates After Migration Scenario 2

ShanghaiMigRateSCE2.png

Shanxi

Population

ChinaJiangxiPop v728.png

New Migrant Flow Estimate 1

ShanxiMigStock.png

New Migrant Flow Estimate 2

ShanxiMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

ShanxiPopSCE.png

Migration Rates After Migration Scenario 1

ShanxiMigRateSCE.png

Migration Scenario 2 Results

Shanxi's migration rates were lowered in the second scenario from 0.61 to 0.41 in 2014. 

Population After Migration Scenario 2

Despite the lowering of migration rates in the second scenario, population is greater in the second scenario. 

ShanxiPopSCE2.png

Migration Rates After Migration Scenario 2

ShanxiMigRateSCE2.png

Sichuan

Population

There is outward migration from Sichuan. 

ChinaSichuanPop v728.png

New Migrant Flow Estimate 1

SichuanMigStock.png

New Migrant Flow Estimate 1

SichuanMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

SichuanPopSCE.png

Migration Rates After Migration Scenario 1

SichuanMigRateSCE.png

Migration Scenario 2 Results

Sichuan's migration rates were unchanged from the first scenario in the second. 

Population After Migration Scenario 2

SichuanPopSCE2.png

Migration Rates After Migration Scenario 2

SichuanMigRateSCE2.png

Tianjin

Population

There is significant inward migration into Tianjin

ChinaTianjinPop v728.png

New Migrant Flow Estimate 1

TianjinMigStock.png

New Migrant Flow Estimate 2

TianjinMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

TianjinPopSCE.png

Migration Rates After Migration Scenario 1

TianjinMigRateSCE.png

Migration Scenario 2 Results

Tianjin's migration rates were lowered in the second scenario from 3.67 to 1.67 in 2014.

Population After Migration Scenario 2

TianjinPopSCE2.png

Migration Rates After Migration Scenario 2

TianjinMigRateSCE2.png

Tibet

Population

ChinaTibetPop v728.png

New Migrant Flow Estimate 1

TibetMigStock.png

New Migrant Flow Estimate 2

TibetMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

TibetPopSCE.png

Migration Rates After Migration Scenario 1

TibetMigRateSCE.png

Migration Scenario 2 Results

Tibet's migration rates were unchanged in the second scenario from the first. 

Population After Migration Scenario 2

Population in Tibet is slightly greater in the second scenario than in the first. 

TibetPopSCE2.png

Migration Rates After Migration Scenario 2

TibetMigRateSCE2.png

Xinjiang

Population

There is net-inward migration. 

ChinaXinjiangPop v728.png

New Migrant Flow Estimate 1

XinjiangMigStock.png

New Migrant Flow Estimate 2

XinjiangMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

XinjiangPopSCE.png

Migration Rates After Migration Scenario 1

XinjiangMigRateSCE.png

Migration Scenario 2 Results

Xinjiang's migration is unchanged in the second scenario after the first. 

Population After Migration Scenario 2

XinjiangPopSCE2.png

Migration Rates After Migration Scenario 2

XinjiangMigRateSCE2.png

Yunnan

Population

There is net-outward migration in Yunnan. 

ChinaYunnanPop v728.png

New Migrant Flow Estimate 1

YunnanMigStock.png

New Migrant Flow Estimate 2

YunnanMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

YunnanPopSCE.png

Migration Rates After Migration Scenario 1

YunnanMigRateSCE.png

Migration Scenario 2 Results

Yunnan's migration rates are the same in the second scenario as in the first. 

Population After Migration Scenario 2

YunnanPopSCE2.png

Migration Rates After Migration Scenario 2

YunnanMigRateSCE2.png

Zhejiang

Population

ChinaZhejiangPop v728.png

New Migrant Flow Estimate 1

ZhejiangMigStock.png

New Migrant Flow Estimate 2

ZhejiangMigStock2.png

Migration Scenario 1 Results

Population After Migration Scenario 1

ZhejiangPopSCE.png

Migration Rates After Migration Scenario 1

ZhejiangMigRateSCE.png

Migration Scenario 2 Results

Zhejiang's migration rates were lowered from 0.55 to 0.35 in the second scenario from the first in 2014. 

Population After Migration Scenario 2

ZhejiangPopSCE2.png

Migration Rates After Migration Scenario 2

ZhejiangMigRateSCE2.png

China's Crude Birth Rates

CBRs in the provincial model are quite close to the 186 model. There is a greater difference in the historical data than in the forecasts, but overall the two models are quite close. 

ChinaCBR v728.png

China's Crude Death Rates

Like CBRs, CDRs are quite close in the two models. There is a greater difference towards the end of the forecasts after 2065. 

ChinaCDR v728.png

China's Migration Rates

Migration rates that have been included in the model are historical and were found in a paper[1] that estimates sub-national migration in China. Chinese intranational migration has proven to be a difficult measure because there is a significant amount of illegal migration from rural areas to urban areas. 

There is only historical data available. Therefore the migration rates do not impact the forecasts because the model does not endogenously calculate migration forecasts. 

The two models appear to be different historically, but it should be noted that both models' migration rates are almost zero for all years. The difference between them is small in total, but the magnitude appears to be large. 

ChinaMigRate v728.png

China's Total Fertility Rates

There is only a single year of historical data for provincial TFR in China and unfortunately the data is from 2000, making this series outdated. For all provinces except Beijing, the 2000 historical data point is the same as the 2014 data point when the model initializes. Due to the outdated data being used for model initialization the provincial model's forecasts of TFR are slightly lower than the 186 model initially.

ChinaTFR v728.png

It is also important to note that provinces such as Beijing are outliers compared to the rest of the world due to the impact of the One Child Policy in China's urban areas. Unlike the rest of the world where TFR is forecast to decline to 1.9 and then remain at that level throughout the end of the time horizon, most provinces are forecast to have TFR increase to 1.9 and then remain at that rate through the end of the time horizon. Only Guizhou, Tibet, and Yunnan provinces have TFR above 1.9 historically that are forecast to decline to 1.9. 

There are no known forecasts to compare the provincial forecasts for TFR with. The model's behavior looks good and without an available data source to update the historical data there is nothing that can be reasonably done to get the provincial model to match the 186 version. 

China's Life Expectancy

Life expectancy in China's provincial model is in line with the 186 model historically and through the end of the time horizon in the forecasts. Male life expectancy is slightly lower in the provincial model than in the 186 model, but female life expectancy and total life expectancy are nearly identical in the two models. 

Total Life Expectancy 

ChinaLifExp v728.png

Life Expectancy Female

ChinaLifeExpFemale v728.png

Life Expectancy Male

ChinaLifeExpMale v728.png

Economy

China's provincial model GDP appears to be too high in the forecasts relative to the 186 model. This appears to be driven by productivity in the provincial model, specifically in Physical Capital, as documented below. There is positive contributions to growth from Physical Capital in the provincial model when it is negative in the 186 model. What is driving this divergence is unclear because there is so little provincial data in the Infrastructure module. There is only one series in China's Infrastructure module, which is ICTTelephoneLinesPer1000. This series appears to be identical to the 186 model and does not appear to be the cause of this increased productivity in the provincial model. Value added appears to be inline with the 186 model as does government spending and both are normalized to the 186 model using ApplyMultAll. 2017-06-29.png

The following sections document the economy module as it appears after the final migration scenario. The documentation also breaks down what may be driving the observed divergence in forecast GDP growth between the provincial model and the 186 model. 

China's GDP

Provincial GDP is significantly greater than the 186-model's GDP and after the migration scenario, this difference is greater in the latter years of forecasts. The second migration scenario forecasts lower GDP than in the first, but it is still significantly greater than the 186 model. 

ChinaGDPSCE2.png

Anhui's GDP

Anhui's GDP is lower in the two scenarios because the population is forecast to decline due to outward migration. 

AnhuiGDPSCE2.png

Beijing's GDP

Beijing's GDP varies greatly between the three versions. This is driven by the composition of the population in Beijing. Beijing's population in the base case is forecast to age rapidly due to the lack of migration that makes up much of Beijings workforce. This working-aged population is largest in the first scenario, and the second scenario is about in between the base case and the first scenario. BeijingGDPSCE2.png

Chongqing's GDP

ChongqingGDPSCE2.png

Fujian's GDP

FujianGDPSCE2.png

Gansu's GDP

GansuGDPSCE2.png

Guangdong's GDP

GuangdongGDPSCE2.png

Guangxi's GDP

GuangxiGDPSCE2.png

Guizhou's GDP

GuizhouGDPSCE2.png

Hainan's GDP

HainanGDPSCE2.png

Hebei's GDP

HebeiGDPSCE2.png

Heilongjiang's GDP

HeilongjiangGDPSCE2.png

Henan's GDP

HenanGDPSCE2.png

Hubei's GDP

HubeiGDPSCE2.png

Hunan's GDP

HunanGDPSCE2.png

Jiangsu's GDP

JiangsuGDPSCE2.png

Jiangxi's GDP

JiangxiGDPSCE2.png

Jilin's GDP

JilinGDPSCE2.png

Liaoning's GDP

LiaoningGDPSCE2.png

Nei Mongol's GDP

NeiMongolGDPSCE2.png

Ningxia's GDP

NingxiaGDPSCE2.png

Qinghai's GDP

QinghaiGDPSCE2.png

Shaanxi's GDP

ShaanxiGDPSCE2.png

Shandong's GDP

ShandongGDPSCE2.png

Shanghai's GDP

Like Beijing the three GDP forecasts are dramatically different due to variances in the working-aged population. The base case forecasts a rapidly aging population that is a drag on the economy because there is a shrinking working-aged population. The first scenario increases GDP by a significant margin due to a huge migrant population that improves the stock of labor. The second scenario shoots in the middle, producing a forecast of growing GDP by a decreasing rate due to the aging population. 

ShanghaiGDPSCE2.png

Shanxi's GDP

ShanxiGDPSCE2.png

Sichuan's GDP

SichuanGDPSCE2.png

Tianjin's GDP

Tianjin's population is aging, much like what was documented in Shanghai and Beijing. The inward migration is where much of the labor force comes from and therefore, the inward migration of the scenarios increases GDP forecasts substantially. The second scenario is a more cautious forecast and shows GDP growth at a decreasing rate due to the aging population that is supported by inward migration. 

TianjinGDPSCE2.png

Tibet's GDP

TibetGDPSCE2.png

Xinjiang's GDP

XinjiangGDPSCE2.png

Yunnan's GDP

YunnanGDPSCE2.png

Zhejiang's GDP

ZhejiangGDPSCE2.png

China's GDP per Capita

China's GDP per capita in the provincial model and in the scenario are significantly greater than the GDP per capita in the 186 version of IFs. This is expected because the total population is close in the models but, the GDP is greater in the provincial model. 

ChinaGDPPCSCE2 5.png

Anhui's GDP per Capita

AnhuiGDPPCSCE2 2.png

Beijing's GDP per Capita

BeijingGDPPCSCE2 5.png

Chongqing's GDP per Capita

ChongqingGDPPCSCE2 5.png

Fujian's GDP per Capita

FujianGDPPCSCE2 5.png

Gansu's GDP per Capita

GansuGDPPCSCE2 5.png

Guangdong's GDP per Capita

GuangdongGDPPCSCE2 5.png

Guangxi's GDP per Capita

GuangxiGDPPCSCE2 5.png

Guizhou's GDP per Capita

GuizhouGDPPCSCE2 5.png

Hainan's GDP per Capita

HainanGDPPCSCE2 5.png

Hebei's GDP per Capita

HebeiGDPPCSCE2 5.png

Heilongjiang's GDP per Capita

HeilongjiangGDPPCSCE2 5.png

Henan's GDP per Capita

HenanGDPPCSCE2 5.png

Hubei's GDP per Capita

HubeiGDPPCSCE2 5.png

Hunan's GDP per Capita

HunanGDPPCSCE2 5.png

Jiangsu's GDP per Capita

JiangsuGDPPCSCE2 5.png

Jiangxi's GDP per Capita

JiangxiGDPPCSCE2 5.png

Jilin's GDP per Capita

JilinGDPPCSCE2 5.png

Liaoning's GDP per Capita

LiaoningGDPPCSCE2 5.png

Nei Mongol's GDP per Capita

NeiMongolGDPPCSCE2 5.png

Ningxia's GDP per Capita

NingxiaGDPPCSCE2 5.png

Qinghai's GDP per Capita

QinghaiGDPPCSCE2 5.png

Shaanxi's GDP per Capita

ShaanxiGDPPCSCE2 5.png

Shandong's GDP per Capita

ShandongGDPPCSCE2 5.png

Shanghai's GDP per Capita

ShanghaiGDPPCSCE2 5.png

Shanxi's GDP per Capita

ShanxiGDPPCSCE 5.png

Sichuan's GDP per Capita

SichuanGDPPCSCE2 5.png

Tianjin's GDP per Capita

TianjinGDPPCSCE2 5.png

Tibet's GDP per Capita

TibetGDPPCSCE2 5.png

Xinjiang's GDP per Capita

XinjiangGDPPCSCE2 5.png

Yunnan's GDP per Capita

YunnanGDPPCSCE2 5.png

Zhejiang's GDP per Capita

ZhejiangGDPPCSCE2 5.png

Value Added

Value added in all sectors are relatively close in both models, but it is slightly lower in the provincial model overall. The disparity between the two model's GDP does not appear to be driven by value added. 

Agriculture Value Added as a Percentage of GDP

The provincial model has higher value added in agriculture historically, but is forecast to have slightly lower value added in the future. 

ChinaAgVadd v728.png

Energy Value Added as a Percentage of GDP

Value added in energy is forecast to be lower in the provincial model until around 2075 when the two models are forecast to be about the same. 

ChinaEnVadd v728.png

Materials Value Added as a Percentage of GDP

Value added in materials is forecast to be lower in the provincial model by about a percentage point for all years. 

ChinaMatVadd v728.png

Manufacturing Value Added as a Percentage of GDP

Manufacturing value added is relatively close in both models, with the provincial model being slightly greater. 

ChinaManufacturingVAdd v728.png

Services Value Added as a Percentage of GDP

The two models are close historically and are forecast to be identical after 2035. 

ChinaSerVadd v728.png

ICT Value Added as a Percentage of GDP

ICT forecasts are nearly identical. 

ChinaICTVadd v728.png

Government Consumption

Government spending is a key driver in the economy module and it directly impacts multifactor productivity. The graphs of GDS suggest that the 186 and provincial model have the same starting point but have different growth rates in the forecasts. 

Military

Military spending is greater in the provincial model, why this is occurring is not clear because there is not any historical data for this series. 

ChinaGDSMil v728.png

Education

The provincial model is initially greater than the 186-model and after 2055 the provincial model is lower. There is historical data in the provincial model for this series. 

ChinaGDSEd v728.png

Health

The two models are initially close together, but over time the provincial model becomes greater than the 186-model by an increasing margin. There is historical data for this series. 

ChinaGDSHealth v728.png

Research and Development

There is more investment in research and development in the provincial model, but the total amount is very small in both models. There is historical provincial data for this series. 

ChinaGDSR&D v728.png

Infrastructure

Infrastructure spending is close in the two models. There is not any historical provincial data for this series. 

ChinaGDSInfra v728.png

InfraOther

The provincial model has more InfraOther spending than the 186-model. 

ChinaGDSInfraOther v728.png

Other

Other spending is significantly greater in the provincial model. 

ChinaGDSOth v728.png

MultiFactor Productivity

Multifactor productivity is significantly larger in the provincial model than in the 186-model, and therefore it is the likely culprit for what is driving the disparity between the two model's GDP forecasts. 

Agriculture MFP

The provincial model is slightly lower than the 186-model until around 2040. After 2040, the provincial model is greater than the 186-model by an increasing margin. 

ChinaMFPAg v728.png

Energy MFP

Multifactor productivity in energy is much greater in the provincial model.

ChinaMFPEn v728.png

Materials MFP

Multifactor productivity in materials is much greater in the provincial model than in the 186-model. 

ChinaMFPMat v728.png

Manufacturing MFP

The provincial model has much greater multifactor productivity in manufacturing than the 186-model. 

ChinaMFPManufactures v728.png

Services MFP

Multifactor productivity is much greater in the provincial model. 

ChinaMFPser v728.png

ICT MFP

Multifactor productivity is significantly greater in the provincial model. 

ChinaMFPICT v728.png

Growth Acounting

Capital

There is significantly more growth in capital in the materials sector in the provincial model, than there is in the 186 model. 

Capital in the Provincial Model

ChGACapitalProv.png

Capital in the 186 Model

ChGACapital186.png

Labor

The two models are quite close in the area of labor, but the provincial model is positive in the beginning, whereas the 186 model is not. 

Labor in the Provincial Model

ChGALaborProv.png

Labor in the 186 Model

ChGALabor186.png

MultiFactor Productivity

MFP is greater in 2019 in the 186 model, but it is greater in all other years in the provincial model. Manufacturing is the key exception to this because it is greater in all years in the 186 model. 

MultiFactor Productivity in the Provincial Model

ChGAMFPProv.png

MultiFactor Productivity in the 186 Model

ChGAMFP186.png

Technology Base MultiFactor Productivity

ICT is about the same in the two models, but it decreases at a slower rate in the 186 model. The remaining sectors are greater in the provincial model and decline over time, whereas the 186 model forecasts all other sectors (not ICT) to be about the same in all years. 

Technology Base MFP in the Provincial Model

ChGATechMFPProv.png

Technology Base MFP in the 186 Model

ChGATechMFP186.png

Knowledge Base MultiFactor Productivity

There are differences between sectors in the provincial model, whereas the 186 model shows them all the be the same. The actual growth contributions are similar, but clearly there is a difference. The provincial model is greater in some of the earlier years where there is the large jump in provincial GDP. 

Knowledge Base MFP in the Provincial Model

ChGAKnowledgeMFPPRov.png

Knowledge Base MFP in the 186 Model

ChGAKnowledgeMFP.png

Human Capital MultiFactor Productivity

Like what was documented in the area of Knowledge Capital, there is a difference among the sectors contribution to growth in the provincial. The 186 model has the same amount of growth contribution for all sectors in all years. On average the two models appear to be about the same. 

Human Capital MFP in the Provincial Model

CHGAHumanCapitalMFPProv.png

Human Capital MFP in the 186 Model

CHGAHumanCapitalMFP186.png

Social Capital MultiFactor Productivity

Social Capital MFP is greater in the provincial model from the first year of forecasts through 2044, after that the 186 model is greater. Once again there is a difference between sectors in the provincial model, but not in the 186 model. 

Social Capital MFP in the Provincial Model

ChGASocialCapitalMFPProv.png

Social Capital MFP in the 186 Model

ChGASocialCapitalMFP186.png

Physical Capital MultiFactor Productivity 

This area appears to be the area of greatest difference between the two models. Physical capital contributes to growth in the provincial model, whereas, in the 186 model it has a negative contribution. There is also a difference among sectors in the provincial model, that does not occur in the 186 model. 

Physical Capital MFP in the Provincial Model

ChGAPhysicalCapitalMFPProv.png

Physical Capital MFP in the 186 Model

ChGAPhysicalCapitalMFP186.png

Correction MultiFactor Productivity

Correction MFP in the Provincial Model

CHGACorrectionMFPProv.png

Correction MFP in the 186 Model

ChGACorrectionMFP186.png

Foreign Direct Investment

Provincial FDI historically is quite close to the 186 model from 1973 through 1995. After 1995, there is a dramatic divergence. The provincial forecast initializes at 0 in 2014. This appears to be a major contributor to throwing off GDP in China. 

ChinaFDISCE2.png

  1. ↑ Kam Wing Chan, Forthcoming in; The Encyclopedia of Global Migration, edited by Immanuel Ness (Blackwell Publishing, 2017).
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