Association for Public Policy Analysis & Management

Demographers have realized the population structure change in China. Major argument is that China’s population is losing its demographic dividend, which contributed to economic growth in past decades. Cai argues that Chinese economic growth is losing its source from its demographic dividend and China’s economy development is reaching its Lewis turning point, which means labor supply is limited.

However, some scholars argue that population aging is not necessarily a negative factor for sector shifts, because population aging promotes sectoral shifts, and then improve economic growth.

This research aims to examine the effects of demographic transitions and relative political capacity on sectoral shifts in the local provinces of China. The source of data is from China’s statistical yearbook from 1993 to 2015.

Dependent Variables:

secgdp_percent: the secondary industry as share of total industrial GDP

tertgdp_percent: the tertiary industry as share of total industrial GDP

Independent Variables:

rpc: the description as above

rpc2: rpc x rpc

Control variables:

export_pert: the export as share of total industrial GDP

import_pert: the import as share of total industrial GDP

urbanpop_pert: the population of urban as share of total population in different provinces.

unemploy_town: the unemployment rate in town

local_gnp: total productivity value is divided by total population of the local provinces

totalraiseratio: the sum of young and old raise ratio

tour_fx: International Tourism Receipts, unit is million dollar

rmb_fx: the exchange rate of RMB

fisreven_pert: fiscal revenue of the local provinces as share of GDP

fisexpend_pert: fiscal expenditure of the local provinces as share of GDP

budget_deficit: the ratio fisexpend and fisreven. If the ratio is greater than 1, it means that the local governments have deficit problem and vice versa.

We apply 2SLS model to estimate the relationship between the secondary industry and rpc in the first stage. In the second stage, we put the predicted value of secondary industry and rpc into the equation and explore the correlation between tertiary industry, secondary industry, and rpc. In addition, we also apply k-Nearest Neighbours to impute the missing value.

The preliminary result shows that the secondary sector is negatively correlated with rpc. The tertiary sector is positively and negatively correlated with rpc and rpc2, respectively. It means that higher rpc promotes sectoral shifts in China, but the relationship will be “inversed-U” in the long-term. The interesting result is that international tourism receipts can increase the gdp of tertiary industry. This preliminary result basically answers our puzzle but we still try to figure out the adequate variables to estimate our puzzle. For example, we are going to consider that applying principal component analysis to explore the main components of the secondary and tertiary sectors and estimate the model again.