4.3. Variable Description
The OFDI statistics of China’s provinces mainly began in 2003, so this paper selects 29 provinces in China from 2003 to 2016 (due to the lack of statistical data, excluding Tibet and Chongqing), constructs the measurement model of OFDI on the home country’s green technological progress, and carries out empirical research. On the basis of comprehensive consideration of the availability of China’s OFDI, FDI, total imports and R&D expenditures, this paper selects nine developed economies and 18 developing and transition economies according to the economic division criteria of the United Nations Conference on Trade and Development. Developed countries include: the United States, Australia, the Netherlands, the United Kingdom, Canada, Germany, France, Sweden, Japan. Developing and transition economies include Russia, Indonesia, Mexico, South Africa, South Korea, Kazakhstan, Vietnam, Pakistan, Zambia, Thailand, Singapore, Turkey, Mongolia, Malaysia, Iran, India, Argentina and Brazil. China has sustained investment in these countries, and its data can be obtained (Which is mentioned in variable construction). By the end of 2016, China’s OFDI stock to the above 27 countries accounted for 74.48% of the total OFDI stock, of which 9 developed countries accounted for 44.71%, 18 transition and developing countries accounted for 29.77% of the total OFDI stock.
In this paper, we use SBM model and GML index to calculate GTFP. Input variables: the same as most studies, choose labor, capital and energy consumption as input variables. Labor input is expressed by the sum of the end employment figures in various industries. Capital investment is obtained according to the calculation method of Zhu and Ye [
37]. Energy input is expressed in terms of total energy consumption in each province. Expected output variables: the actual GDP of each province is used. Unexpected outputs include wastewater, exhaust gas and solid waste, which are measured by provincial industrial wastewater discharge, industrial waste gas discharge and industrial solid waste production respectively. The above data are from the official statistical yearbook of China.
represents the domestic R&D capital stock in the period of province. Taking 2003 as the base period, the calculating formula is , in which is the R&D expenditure of province in 2003, is the depreciation rate 5%, is the average logarithmic growth rate of R&D expenditure of province from 2003 to 2012. The R&D capital stock in the last 12 years is calculated according to the perpetual inventory method. Provincial R&D expenditure data comes from the “China Statistical Yearbook of Science and Technology” and the consumer price index comes from the “China Statistical Yearbook”.
represents the foreign R&D capital stock acquired by
Province during the
period through investment in developed countries. According to Lichtenberg’s [
38] method, this paper firstly calculates the foreign R&D capital stock obtained from OFDI to developed countries at the national level in the
period:
, of which
is the sample of nine developed countries selected in this paper,
is the OFDI stock of China in the
period received from country
and
is the GDP of the
country in the
period. For the domestic R&D capital stock of country
in the
t period is
, its calculation method is the same as that of domestic R&D capital stock (
). Secondly, to calculate the foreign R&D capital stock obtained by each province by investing in developed countries:
, of which
is the OFDI stock of province
in the
period. Because the data of provincial investments in developed countries are not available, this paper calculates the proportion (
) of provincial investments in developed countries by the end of 2013 according to China’s “List of Overseas Investment Enterprises (Institutions)”. Using
approximation to calculate the investment stock of
province in developed countries in
period,
is the stock of the national investment in the developed countries in the period of
. The R&D expenditure of each country comes from UNESCO database and OECD database. Some missing data are estimated by the average value of last year or the ratio of R&D expenditure to GDP. The GDP and consumer price index of each country comes from the World Bank database. The data of foreign direct investment in China and other provinces are derived from China’s foreign direct investment statistics bulletin.
represents the foreign R&D capital stock acquired by Province during the period through investment in transition and developing countries. The calculation method is similar to . Firstly, we calculate the R&D capital stock of OFDI in transition and developing countries: , in which j = 1, 2,, 18 is the sample of 18 transition and developing countries. Then we get the foreign R&D capital stock obtained by the provinces through investment in the transition and developing countries: , is the proportion of provincial enterprises in the transition and developing countries by the end of 2013, is the national OFDI stock in the transition and developing countries in the period.
represents the foreign R&D capital stock acquired by foreign direct investment in the period of province. , , is the total foreign direct investment of country in China in the period, and is the proportion of foreign direct investment of province in China’s total foreign direct investment in the period. The calculation method of is the same as this. The data comes from the China Statistical Yearbook, and Chinese regional statistical yearbook.
Environmental regulation (
). There are two main ways to measure environmental regulation in existing literature [
39]. One is to use environmental taxes, emission reduction costs or pollution control investments to represent the severity of environmental regulation. The other is to estimate the environmental regulation by using the rate of pollution removal. At present, China has not made detailed statistics on emission reduction costs and investment in pollution control, and China’s environmental tax was only implemented in 2018. Therefore, this paper uses the second method to measure environmental regulation, but there are many kinds of pollution emissions in reality. Antiweiler et al. [
40] considers sulfur dioxide as a good research object for the following reasons. Firstly, sulfur dioxide is the main pollutant in the production process. It has a strong local effect. Secondly, it is harmful to the human body and is under the control of the government. Thirdly, there are many pollution reduction technologies in this area. Therefore, in the same way as Wang (2016), the sulfur dioxide removal rate is used as the surrogate variable of environmental regulation which is the same as the study of Wang et al. [
8]. The data comes from the China Environmental Statistical Yearbook and NBSC [
41].
represents the human capital stock in the period of province. According to the general method, this paper multiplies the population at each educational level by the number of years of study corresponding to each educational level for approximate calculation. The proportion of educational staff in each province is derived from the Yearbook of China’s labor statistics (According to macroeconomic growth theory, labor force is an important variable affecting total factor productivity and should be put into the model. But it is highly correlated with the stock of human capital which is concerned in this paper (the correlation coefficient is greater than 0.9), so we consider it in the robustness test).
Fiscal Decentralization (FD). As for the measurement of fiscal decentralization, the academic community has not yet reached a broad consensus. Considering that the empirical data selected in this paper is after the tax-sharing system in 1994, in order to accurately describe the changes in fiscal relations, this paper refers to the methods of Zhang and Gong [
42] to construct the index of fiscal decentralization. Economic growth is characterized by provincial GDP. The data comes from the China Statistical Yearbook. Descriptive statistics for each variable are shown in
Table 1.