Tracking China’s Fiscal Sustainability: A Time-Frequency Perspective

ABSTRACT

This paper evaluates China’s fiscal sustainability by examining national and regional revenue-expenditure nexuses from a time-frequency perspective. Our analysis is novel since research on China in this respect remains limited, and existing literature largely neglects time-frequency dependencies. We employ wavelet techniques to identify the time-frequency relationship between government revenue and expenditure from 1952 to 2020. We find that the national and regional revenue-expenditure nexuses are frequency-dependent and time-varying, providing a possible explanation for the mixed patterns emerging from the analyses based on traditional methods. In addition, the medium- and long-term national and regional revenue-expenditure nexuses in recent years predominantly support the institutional separation hypothesis. These findings indicate that China’s fiscal risk is rising and urge for fundamental reforms to safeguard budgetary sustainability.

KEYWORDS:

• Fiscal sustainability
• wavelet analysis
• revenue-expenditure nexus
• time-frequency decomposition
• China

JEL:

• E6
• H5
• H6
• H7

Acknowledgments

We thank the editor in Chief, Paresh Kumar Narayan, an anonymous Subject Editor, and two anonymous referees for their helpful comments and suggestions. However, all errors remain our own. Ding Liu would like to thank the financial support from the Humanity and Social Science Foundation of China’s Ministry of Education for Young Researcher (22YJC790074) and Project No. 2022CDJSKJC12 supported by the Fundamental Research Funds for the Central Universities. Weihong Sun would like to thank the financial support from the National Social Science Fund of China (Grant NO. 21xSH005).

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Supplementary data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/1540496X.2023.2202792.

Notes

1. China alone accounted for 26% of the $28 trillion global debt surge in 2020.

2. Please refer to the IMF Fiscal Monitor, October 2021: Strengthening the Credibility of Public Finances.

3. In 2009, the central government implemented a five-year pilot bond issuance program, under which the Ministry of Finance issued RMB 200 billion municipal bonds on behalf of local governments.

4. According to Goldman Sachs, China’s hidden local government debt, mostly incurred by LGFVs, has swelled to 53 trillion yuan, more than half the size of the economy at the end of 2020, up from 16 trillion yuan in 2013.

5. The Eastern region includes Beijing, Tianjing, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan; the Central region contains Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan; and the Western region consists of Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Inner Mongolia, Guangxi, Ningxia, and Xinjiang. We focus on mainland China, so Hong Kong, Macao, and Taiwan are not considered.

6. The official introduction of this database can be accessed via https://www.wind.com.cn/en/edb.html.

7. These advantages enable researchers to detect novel patterns otherwise hard to uncover. Therefore, wavelet methods are getting increasingly popular in economics (e.g., Liu et al. 2023; Liu, Sun, and Zhang 2020). Please refer to Aguiar-Conraria and Soares (2014) for a comprehensive review.

8. In the online appendix, we perform robustness checks using traditional cointegration and Granger causality methods on the same data used in our wavelet analysis. Overall, the robustness tests show that our benchmark results are robust, and wavelet methods uncover new insights not revealed by traditional methods.

9. Wavelet coherency ranges from blue (low) to red (high). Dark lines represent regions of statistically significant powers at 5%, while gray lines delimit regions significant at 10%. A black dashed line indicates the cone of influence affected by edge effects, requiring caution in interpreting the evidence. The phase-difference is represented by a thick red line.

10. China’s growth rate has declined from 10.6% in 2010 to 6% in 2019. The COVID-19 pandemic has exacerbated the slowdown: China’s economy only grows 2.2% in 2020, rebounds to 8.4% in 2021, but falls to 3% in 2022.

11. Liu, Su, and Jiang (2014) apply a frequency-based unit root test to annual regional data over 1978–2011, and find that the Eastern and Central regions’ deficit – GDP ratio is stationary, while the deficit – GDP ratios in the Western region are not stationary.

Additional information

Funding

The work was supported by the National Office for Philosophy and Social Sciences [21xSH005]; Humanity and Social Science Foundation of China’s Ministry of Education for Young Researcher [22YJC790074]; Fundamental Research Funds for the Central Universities [2022CDJSKJC12].