CN-China Methodology
Updated over a week ago

Population Data

In order to build our population projections, we gathered information from the statistical yearbooks published on the cities’ individual statistical webpages. Using historical trends, and assuming the current activity prevails, we were able to establish a reasonable expectation for predicting future population values.

We used two models for these population projections: The Monte Carlo and the Autoregression Integrated Moving Average (ARIMA) models. In each of China’s metro areas, we ran multiple configurations of each of the two models to find out which model (and which configuration within that model) was most accurate for that metro.

Lightcast covers the following metropolitan areas:

  1. Beijing

  2. Shanghai

  3. Chengdu

  4. Shenzhen

  5. Tianjin

  6. Chongqing

  7. Guangzhou

  8. Hangzhou

  9. Jinan

  10. Dongguan

  11. Shenyang

  12. Qingdao

  13. Nanchang

The limited amount of markets is dependent on the availability of labor market data that is available for both the public and private sectors. Many cities in China do not report private sector employment, which forms a substantial portion of the economy.

Labor Market Information (LMI) Data

Lightcast's labor market data for China is created using staffing patterns derived from the 2010 census applied to industry data for each city derived from statistical yearbooks.

Note that China uses its own occupational classification system called the Standard Occupation System of The Republic of China.

Finally, to compile supply projections, we took the expected population growth rate from the models mentioned above and applied that same growth rate across the LMI in each of Chinas’ cities. Once we had the projections, we mapped all the elements of China’s classification system to occupation groups.

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