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.

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.

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.

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.

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.

Anhui

Population

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

New Migrant Flow Estimate 1

New Migrant Flow Estimate 2

Migration Scenario 1 Results

Population After Migration Scenario 1

Anhui has significantly lower population forecasts after the migration scenario.

Migration Rate After Migration Scenario 1

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

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.

Migration Rate After Migration Scenario 2