Skip to main content
SpringerLink
Log in

Revisiting the Impact of US Uncertainty Shocks: New Evidence from China’s Investment Dynamics

  • RESEARCH ARTICLE
  • Published:
Open Economies Review Aims and scope Submit manuscript

Abstract

This paper investigates the impact of US uncertainty shocks on China’s macroeconomy with a focus on the dynamic response of investment. Using a structural vector autoregression (VAR) model, we find that the wait-and-see mechanism of aggregate investment in the face of heightened US uncertainty disappears in China. Robust evidence confirms that the increase in state-owned enterprises’ investment in response to heightened uncertainty explains this puzzle, while private-owned enterprises’ investment decreases as expected. We apply regime-dependent local projections to link uncertainty shocks with credit regimes to explore whether the impact of US uncertainty shocks on investment in China has varied over time in connection with the states of bank loans. The empirical results support a positive response of state-owned enterprises’ investment to increased US uncertainty during the tightening of medium- and long-term bank loans but a negative reaction when short-term bank loans are tightening. Finally, we show that economic policy uncertainty conveying political signals leads to a decline in state-owned enterprises’ investment. Overall, this paper provides richer empirical evidence on the investment-uncertainty nexus.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data Availability

All data included in this article are available upon request by contact with the corresponding author.

Notes

  1. For example, Carrière-Swallow and Céspedes (2013), Choi (2018), and Bhattarai et al. (2020) select 20, 18, and 15 EMEs, respectively, but China is excluded.

  2. State-owned enterprises (SOEs) in China refer to firms owned by all citizens of China and controlled by central and local governments. Usually, the objectives of SOEs go beyond profits, including resources, employment, and foreign policy.

  3. In China, some SOEs controlled by the central government (e.g., State Grid Corporation of China) and most SOEs controlled by the local governments are nonlisted firms. In addition, we have the macro level investment of private-owned enterprises (POEs) in the subsequent analysis.

  4. The financial cycle view is related to but virtually different from the financial frictions view. In the financial frictions view, when financial contracts are subject to imperfect information, a rise in uncertainty increases the external finance premium and the cost of capital and further declines firms’ investment, where credit aggregates propagate uncertainty shocks to the real economy.

  5. Bank loans are the most important source of external funds used to finance businesses in China. Over the period 2016–2018, bank loans dominate the external financing in SOEs and POEs, around 70%.

  6. I also consider economic policy uncertainty in the further discussion section.

  7. Chan (2017) develops an efficient Markov Chain Monte Carlo (MCMC) algorithm to estimate the hyperparameters in three equations. A significant feature in this approach is when \({\mathrm{\alpha }}_{\mathrm{t}}=0\) for all t, the model degenerates to a standard time-varying parameter regression with stochastic volatility. If we relax this assumption and allow \({\mathrm{\alpha }}_{\mathrm{t}}\ne 0\), then the model allows an extra channel of persistence.

  8. In the subsequent analysis, we use US uncertainty (\({\mathrm{VIX}}^{\mathrm{US}}\)) and estimated China’s uncertainty (\({\mathrm{SV}}^{\mathrm{China}}\)).

  9. The one-year benchmark lending rate is regarded as the policy rate in China. A recent reform that the People’s Bank of China (PBC) announced the new formation mechanism of loan prime rate (LPR) in August 2019 shows that one-year benchmark lending rate will be an ideal proxy of the policy rate in China.

  10. The aggregate investment is decomposed into SOEs’ investment, POEs’ investment, households’ investment, and others (joint ventures for example), whereas SOEs’ and POEs’ investments dominate compared to other types of investments and, on average, account for around 70% of aggregate investment over the sample period.

  11. In general, government investment injecting into SOEs is to achieve specific government projects, e.g., infrastructure investment. Besides, the SOEs’ investment excluding government investment mainly incorporates fixed-asset investment and overseas direct investment, etc.

  12. All bank loans are end-of-quarter financial institution loans outstanding.

  13. Website: https://www.frbatlanta.org/cqer/research/china-macroeconomy.aspx?panel=2. For more details on this dataset, referring to Chang et al. (2016).

  14. The real bank lending rate is the difference between nominal bank lending rate and inflation rate.

  15. Given that we have two types of bank loans (short-term or long-term), so we have two kinds of indicator functions. In Sections 4 and 5, we present more details on constructing this indicator.

  16. Later, we use a smooth transition function to identify the regimes of bank loans to test the robustness of regime-dependent results and find that the empirical conclusions do not depend on a particular identification of credit regimes.

  17. This is also known as a financial frictions view.

  18. If this conjecture is right, then investment will have a larger positive response to US uncertainty shocks after keeping China’s uncertainty constant.

  19. Also see Fig. 2.

  20. We use the HP filter to detrend all series of investment and bank loans. The smoothing parameter in the HP filter takes 1600 for quarterly data.

  21. Uribe and Yue (2006) point out that researchers typically define the business cycle as movements in time series of frequencies ranging from 6 to 32 quarters.

  22. It is meaningless to compare the magnitude of variance decomposition in different models.

  23. Cheng et al. (2018) and Alessandri and Mumtaz (2019) use the financial condition index as a real-time indicator of financial distress to identify credit regimes. However, this type of index is not available in China.

  24. Claessens et al. (2012) employ another way to find the financial cycle through an extended BBQ algorithm introduced by Harding and Pagan (2002) and identify the turning points in the log-level variable. Because the financial cycle has a much lower frequency and the average length of the financial cycle is 15 to 20 years (Borio 2014), so we cannot capture enough variations in credit regimes via the extended BBQ algorithm.

  25. The correlation between two cyclical series is -0.206.

  26. There is a negative correlation between short-term and long-term loans in China in sharp contrast to the positive one in the US (Chang et al. 2016).

  27. This is known as the soft budget constraint. Xu (2011) mentions that the soft budget constraint is a major moral hazard problem in all centralized economies and transition economies.

  28. Applying this criterion to capture uncertainty related to economic policy, Baker et al. (2016) construct the EPU index based on the newspaper coverage frequency. Specifically, Baker et al. (2016) perform systematic searches of leading newspapers to obtain a monthly count of articles that contain the following trio of terms: [“uncertainty” or “uncertain”], [“economic” or “economy”] and [“congress”, “deficit”, “Federal Reserve”, “legislation”, “regulation”, or “White House”].

  29. An et al. (2016) collect data on changes of government officials in 277 Chinese cities.

References

  • Aastveit KA, Natvik GJ, Sola S (2017) Economic uncertainty and the influence of monetary policy. J Int Money Finance 76:50–67

    Article  Google Scholar 

  • Aikman D, Haldane AG, Nelson BD (2015) Curbing the credit cycle. Econ J 125(585):1072–1109

    Article  Google Scholar 

  • Albrizio S, Choi S, Furceri D, Yoon C (2020) International bank lending channel of monetary policy. J Int Money Finance 102:102124

  • Alessandri P, Mumtaz H (2019) Financial regimes and uncertainty shocks. J Monet Econ 101:31–46

    Article  Google Scholar 

  • Alfaro I, Bloom N, Lin X (2018) The finance uncertainty multiplier. Tech. rep, National Bureau of Economic Research

  • An H, Chen Y, Luo D, Zhang T (2016) Political uncertainty and corporate investment: Evidence from China. J Corp Finance 36:174–189

    Article  Google Scholar 

  • Auerbach AJ, Gorodnichenko Y (2012) Fiscal multipliers in recession and expansion. In: Fiscal policy after the financial crisis. University of Chicago Press, pp 63–98

  • Bachmann R, Bayer C (2011) Uncertainty business cycles - Really? Tech. rep, National Bureau of Economic Research

  • Baker SR, Bloom N, Davis SJ (2016) Measuring economic policy uncertainty. Q J Econ 131(4):1593–1636

    Article  Google Scholar 

  • Basu S, Bundick B (2017) Uncertainty shocks in a model of effective demand. Econometrica 85(3):937–958

    Article  Google Scholar 

  • Bernanke BS (1983) Irreversibility, uncertainty, and cyclical investment. Q J Econ 98(1):85–106

    Article  Google Scholar 

  • Bhattarai S, Chatterjee A, Park WY (2020) Global spillover effects of US uncertainty. J Monet Econ 114:71–89

    Article  Google Scholar 

  • Bloom N (2009) The impact of uncertainty shocks. Econometrica 77(3):623–685

    Article  Google Scholar 

  • Bloom N (2014) Fluctuations in uncertainty. J Econ Perspect 28(2):153–176

    Article  Google Scholar 

  • Bloom N (2017) Observations on uncertainty. Aust Econ Rev 50(1):79–84

    Article  Google Scholar 

  • Bloom N, Floetotto M, Jaimovich N, Saporta-Eksten I, Terry SJ (2018) Really uncertain business cycles. Econometrica 86(3):1031–1065

    Article  Google Scholar 

  • Borio C (2014) The financial cycle and macroeconomics: What have we learnt? J Bank Finance 45:182–198

    Article  Google Scholar 

  • Caggiano G, Castelnuovo E, Figueres JM (2017a) Economic policy uncertainty and unemployment in the United States: A nonlinear approach. Econ Lett 151:31–34

    Article  Google Scholar 

  • Caggiano G, Castelnuovo E, Figueres JM (2020) Economic policy uncertainty spillovers in booms and busts. Ox Bull Econ Stat 82(1):125–155

    Article  Google Scholar 

  • Caggiano G, Castelnuovo E, Pellegrino G (2017b) Estimating the real effects of uncertainty shocks at the zero lower bound. Eur Econ Rev 100:257–272

    Article  Google Scholar 

  • Caldara D, Fuentes-Albero C, Gilchrist S, Zakrajšek E (2016) The macroeconomic impact of financial and uncertainty shocks. Eur Econ Rev 88:185–207

    Article  Google Scholar 

  • Carrière-Swallow Y, Céspedes LF (2013) The impact of uncertainty shocks in emerging economies. J Int Econ 90(2):316–325

    Article  Google Scholar 

  • Chan JC (2017) The stochastic volatility in mean model with time-varying parameters: An application to inflation modeling. J Bus Econ Stat 35(1):17–28

    Article  Google Scholar 

  • Chang C, Chen K, Waggoner DF, Zha T (2016) Trends and cycles in China’s macroeconomy. NBER Macroecon Annu 30(1):1–84

    Article  Google Scholar 

  • Chen K, Ren J, Zha T (2018) The nexus of monetary policy and shadow banking in China. Am Econ Rev 108(12):3891–3936

    Article  Google Scholar 

  • Chen K, Zha T (2018) Macroeconomic effects of China’s financial policies. Tech. rep, National Bureau of Economic Research

  • Cheng CHJ, Chiu CWJ, Hankins WB, Stone AL (2018) Partisan conflict, policy uncertainty and aggregate corporate cash holdings. J Macroecon 58:78–90

    Article  Google Scholar 

  • Choi S (2013) Are the effects of bloom’s uncertainty shocks robust? Econ Lett 119(2):216–220

    Article  Google Scholar 

  • Choi S (2017) Variability in the effects of uncertainty shocks: New stylized facts from OECD countries. J Macroecon 53:127–144

    Article  Google Scholar 

  • Choi S (2018) The impact of US financial uncertainty shocks on emerging market economies: an international credit channel. Open Econ Rev 29(1):89–118

    Article  Google Scholar 

  • Choi S, Shim M (2019) Financial vs. policy uncertainty in emerging market economies. Open Econ Rev 30(2):297–318

    Article  Google Scholar 

  • Choi S, Yoon C (2019) Uncertainty, financial markets, and monetary policy over the last century. Technical report. Yonsei University, Yonsei Economics Research Institute

  • Christiano LJ, Motto R, Rostagno M (2014) Risk shocks. Am Econ Rev 104(1):27–65

    Article  Google Scholar 

  • Claessens S, Kose MA, Terrones ME (2012) How do business and financial cycles interact? J Int Econ 87(1):178–190

    Article  Google Scholar 

  • Claessens S, Kose MA (2018) Frontiers of macrofinancial linkages. BIS Paper (95)

  • Colombo V (2013) Economic policy uncertainty in the US: Does it matter for the Euro area? Econ Lett 121(1):39–42

    Article  Google Scholar 

  • Diks C, Panchenko V (2006) A new statistic and practical guidelines for nonparametric Granger causality testing. J Econ Dyn Control 30(9–10):1647–1669

    Article  Google Scholar 

  • Diks C, Wolski M (2016) Nonlinear Granger causality: Guidelines for multivariate analysis. J Appl Economet 31(7):1333–1351

    Article  Google Scholar 

  • Drehmann M, Yetman J (2018) Why you should use the hodrick-prescott filter-at least to generate credit gaps. BIS Working Paper

  • Engle RF, Granger CW (1987) Co-integration and error correction: representation, estimation, and testing. Econometrica 55(2):251–276

    Article  Google Scholar 

  • Fontaine I, Didier L, Razafindravaosolonirina J (2017) Foreign policy uncertainty shocks and US macroeconomic activity: Evidence from China. Econ Lett 155:121–125

    Article  Google Scholar 

  • Fontaine I, Razafindravaosolonirina J, Didier L (2018) Chinese policy uncertainty shocks and theworld macroeconomy: Evidence from STVAR. China Econ Rev 51:1–19

    Article  Google Scholar 

  • Gilchrist S, Zakrajšek E (2012) Bank lending and credit supply shocks. Springer

    Book  Google Scholar 

  • Harding D, Pagan A (2002) Dissecting the cycle: a methodological investigation. J Monet Econ 49(2):365–381

    Article  Google Scholar 

  • Jones PM, Olson E (2015) The international effects of US uncertainty. Int J Financ Econ 20(3):242–252

    Article  Google Scholar 

  • Jordà Ò (2005) Estimation and inference of impulse responses by local projections. Am Econ Rev 95(1):161–182

    Article  Google Scholar 

  • Kang SH, Tiwari AK, Albulescu CT, Yoon SM (2019) Exploring the time-frequency connectedness and network among crude oil and agriculture commodities. Energy Econ 84:104543

  • Leduc S, Liu Z (2016) Uncertainty shocks are aggregate demand shocks. J Monet Econ 82:20–35

    Article  Google Scholar 

  • Lin JY, Cai F, Li Z (1998) Competition, policy burdens, and state-owned enterprise reform. Am Econ Rev 88(2):422–427

    Google Scholar 

  • Liu G, Zhang C (2019) Economic policy uncertainty and firms’ investment and financing decisions in China. China Econ Rev 63:101279

  • Motegi K, Sadahiro A (2018) Sluggish private investment in Japan’s Lost Decade: Mixed frequency vector autoregression approach. N Am J Econ Finance 43:118–128

    Article  Google Scholar 

  • Mumtaz H, Theodoridis K (2015) The international transmission of volatility shocks: An empirical analysis. J Eur Econ Assoc 13(3):512–533

    Article  Google Scholar 

  • Nakamura E, Steinsson J (2018) Identification in macroeconomics. J Econ Perspect 32(3):59–86

    Article  Google Scholar 

  • Naughton B (2017) Is China socialist? J Econ Perspect 31(1):3–24

    Article  Google Scholar 

  • Naughton BJ (2018) The Chinese economy: Adaptation and growth. MIT Press

  • Pastor L, Veronesi P (2012) Uncertainty about government policy and stock prices. J Finance 67(4):1219–1264

    Article  Google Scholar 

  • Pastor L, Veronesi P (2017) Explaining the puzzle of high policy uncertainty and low market volatility. VOX Column 25

  • Popp A, Zhang F (2016) The macroeconomic effects of uncertainty shocks: the role of the financial channel. J Econ Dyn Control 69:319–349

    Article  Google Scholar 

  • Prasad ES, Rajan RG (2006) Modernizing China’s growth paradigm. Am Econ Rev 96(2):331–336

    Article  Google Scholar 

  • Qin D, Cagas MA, Quising P, He X (2006) How much does investment drive economic growth in China? J Policy Model 28(7):751–774

    Article  Google Scholar 

  • Ramey VA (2016) Macroeconomic shocks and their propagation. Handbook of macroeconomics, vol 2

  • Shi J, Zhang X (2018) How to explain corporate investment heterogeneity in china’s new normal: Structural models with state-owned property rights. China Econ Rev 50:1–16

    Article  Google Scholar 

  • Song L, Yang J, Zhang Y (2011a) State-owned enterprises’ outward investment and the structural reform in China. China World Econ 19(4):38–53

    Article  Google Scholar 

  • Song Z, Storesletten K, Zilibotti F (2011b) Growing like China. Am Econ Rev 101(1):196–233

    Article  Google Scholar 

  • Stock JH, Watson MW (2012) Disentangling the channels of the 2007–2009 recession. Tech. rep, National Bureau of Economic Research

  • Stockhammar P, Österholm P (2017) The impact of US uncertainty shocks on small open economies. Open Econ Rev 28(2):347–368

    Article  Google Scholar 

  • Uribe M, Yue VZ (2006) Country spreads and emerging countries: Who drives whom? J Int Econ 69(1):6–36

    Article  Google Scholar 

  • Wang Y, Chen CR, Huang YS (2014) Economic policy uncertainty and corporate investment: Evidence from China. Pac Basin Finance J 26:227–243

    Article  Google Scholar 

  • Wu J, Zhang J, Wu Y, Kong D (2019) When to go abroad: economic policy uncertainty and Chinese firms’ overseas investment. Accounting and Finance

  • Xu C (2011) The fundamental institutions of China’s reforms and development. J Econ Lit 49(4):1076–1151

    Article  Google Scholar 

  • Zhang G, Han J, Pan Z, Huang H (2015) Economic policy uncertainty and capital structure choice: Evidence from China. Econ Syst 39(3):439–457

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank Dr. Jaroslav Horvath and participants at UNH economics seminar for their extensive and valuable comments. We also thank the valuable comments of two anonymous referees. All remaining errors are our own.

Funding

This study was funded by National Social Sciences Funds (20BTJ015).

Author information

Authors and Affiliations

  1. Policy and Economics Institute, China Academy of Information and Communications Technology, Beijing, China

    Meng Yan

  2. School of Economics and Management, Northeast Normal University, Changchun, China

    Kai Shi

Authors
  1. Meng Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kai Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meng Yan.

Ethics declarations

Competing Interests

No potential conflict of interest was declared by the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, M., Shi, K. Revisiting the Impact of US Uncertainty Shocks: New Evidence from China’s Investment Dynamics. Open Econ Rev (2023). https://doi.org/10.1007/s11079-023-09734-5

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11079-023-09734-5

Keywords

JEL Classification

Search

Navigation