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Economic Change and Restructuring

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03-11-2023

Impact of fiscal expenditure stress on green transformation risk: evidence from China education authority reform

Authors: Fei Peng, Shibiao Zhou, Tao Ding, Huaqing Wu

Published in: Economic Change and Restructuring | Issue 6/2023

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Abstract

Ongoing fiscal support is a crucial assurance for achieving green development, while the increasingly prominent fiscal expenditure stress has been subjected to a great challenge in achieving green transformation. Based on China’s education authority reform as a quasi-natural experiment, this study uses green total factor productivity to represent urban green development, which has been widely used in the literature, and reveals how local government fiscal expenditure stress affects green total factor productivity. It is found that local fiscal stress significantly decreases the green total factor productivity of cities. This effect is particularly pronounced for resource-based cities, cities with laxer environmental regulations, and cities under greater pressure to promote and develop their economies. Moreover, it is confirmed that it is not the revenue-raising effect but rather the expenditure reduction effect that is at play. Specifically, local governments have reduced environmental protection expenditures and green subsidies, leading to a decline in the green innovation capacity of cities and corporations. Finally, based on the above findings, policy implications are provided to reduce the risk of green transformation from the perspective of policymakers.

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Data source: The World Bank. Available at https://data.worldbank.org.cn/indicator/NY.GDP.MKTP.KD.ZG.

Data source: The World Bank. Available at https://data.worldbank.org.cn/indicator/EN.ATM.CO2E.KT.

Data source: China National Bureau of Statistics. The results are calculated by the authors.

The calculation of GTFP requires that there are no missing values for the input and output data for each city. Therefore, we had to exclude those cities with missing values.

We also examine the relationship between education expenditure and GTFP in Appendix B.

In fact, this policy was proposed by the central government in July 2010. For a local government, its budgeted expenditures have already been prepared in the previous year (2009), and there is little possibility for the local government to immediately change the scale and structure of its fiscal expenditures in the current year.

The ML index has the following advantages over parametric methods: first, it does not need to give the specific statistical distribution of decision-making units (DMUs). Second, it can handle small sample data and categorical variables. Third, it does not need to introduce time trends into the data analysis and can thus avoid the phenomenon of smoothing productivity changes that is inherent in most parametric methods.

Following Young (2003), the fixed assets stock of the first period is estimated to be 10 times the fixed asset investment in 2007. The calculations are as follows:

$${K}_{2007}={I}_{2007}*10$$

$${K}_{t+1}={K}_{t}*(1-\theta )+{I}_{t+1}$$

Specifically, ${K}_{2007}$ is the fixed capital stock in the base year, 2007; ${I}_{2007}$ is the total fixed assets investment of the whole city in 2007; ${K}_{t+1}$ and ${K}_{t}$ are the fixed capital stock in year t + 1 and t;$\theta$ is the depreciation rate; and ${I}_{t+1}$ is the total fixed assets investment of the whole city in year t + 1.

The reason why we select 9.6% as the depreciation rate is that Zhang et al. (2004) estimated physical capital stock and concluded that the depreciation rate is 9.6%. Moreover, most of the recent literature has 9.6% as the depreciation rate for China (Liu et al. 2022; Wang et al. 2022).

GDP per capita pressures = GDP per capita of prefecture-level cities ranked one place ahead in the same province/GDP per capita of this prefecture-level city; similarly, fixed asset investment pressures = fixed asset investment of prefecture-level cities ranked one place ahead in the same province/fixed asset investment of this prefecture-level city.

We do so because local governments do not know where their city ranks this year in terms of economic development compared to other cities in the province, and local governments’ behavior in the current year is heavily influenced by the previous year’s economic development pressures. This approach of lagging one period can avoid the effects of endogeneity and yield a reasonable result.

Firm-level control variables include firm size, leverage, cash ratio, city GDP, and city fiscal expenditure size. Specifically, firm size is measured by the logarithm of total assets, leverage is measured by the ratio of total liabilities to total assets, cash ratio is measured by the ratio of total liabilities to total assets, city GDP is measured by the logarithm of GDP and city fiscal expenditure size is measured by the logarithm of fiscal expenditure.

The pilot area includes the provinces of Guangdong, Liaoning, Hubei, Shaanxi and Yunnan and the cities of Tianjin, Chongqing, Shenzhen, Xiamen, Hangzhou, Nanchang, Guiyang and Baoding.

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Title: Impact of fiscal expenditure stress on green transformation risk: evidence from China education authority reform
Authors: Fei Peng
Shibiao Zhou
Tao Ding
Huaqing Wu
Publication date: 03-11-2023
Publisher: Springer US
Published in: Economic Change and Restructuring / Issue 6/2023
Print ISSN: 1573-9414
Electronic ISSN: 1574-0277
DOI: https://doi.org/10.1007/s10644-023-09567-9

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