Greening monetary policy: evidence from the People’s Bank of China

ABSTRACT

In June 2018, the People’s Bank of China (PBoC) decided to include green financial bonds into the pool of assets eligible as collateral for its Medium Term Lending Facility (MLF). The PBoC also gave green financial bonds a ‘first-among-equals’ status. We measure the impact of the policy on the yield spread between green and non-green bonds. Using a difference-in-differences approach, we show that the policy increased the spread by 46 basis points. Our approach differs from the literature in that we match bonds under review with non-green bonds with similar characteristics and issued by the same firm, which allows for highly relevant firm fixed effects. We also specifically investigate the impact on green bonds. The granularity of the data (daily) also allows us to conduct a dynamic analysis by dividing the sample into weekly, monthly and quarterly observations. Our results also show that the impact of the reform starts to materialize after three weeks, has a maximum effect after three months, and has a persistent effect over six months.

Key policy insights

• The PBoC was one of the first central banks to have a policy specifically targeting green bonds in 2018.

• Including green financial bonds as collateral for monetary policy increased the spread by 46 basis points compared to before the policy reform.

• These findings show that a central bank can actively influence market rates for green projects compared with non-green projects by accepting green bonds into the pool of eligible collateral and that giving them preferential status.

• It is especially relevant for countries with shallow bond markets to help develop nascent green bond markets, as was the case in China before the reform studied here.

KEYWORDS:

• People’s Bank of China
• central bank collateral framework
• green bonds
• bond yields
• greenium

Acknowledgements

The views expressed in this paper do not represent the opinion of the Banque de France or the Eurosystem. We thank Damien Bonnot for excellent research assistance. For comments and discussions, we thank Zeynep Alraqeb, Pauline Bacos, Rafael Cezar, Chiara Colesanti Senni, Ugo Dubois, Hanming Fang, Sam Foxall, Simon Hinrichsen, Walter Jansson, Jens van ‘t Klooster, Rémy Lecat, Lukas Leucht, Jean-Stéphane Mésonnier, Pierre Monnin, Dongyang Pan, Naelle Verniest, Adrian von Jagow, Pierre-François Weber, participants at the SERMI-BdF workshop, E-axes seminar, and members of the Berkeley research Fika.

Disclosure statement

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

Notes

1 Note that these companies are either small or ‘micro’, which usually involves one employee working on a freelance basis.

2 Traditionally, the method is used with two time periods. Here, we test the impact over a one-year time period with different length of time sub-periods, up to a week.

3 See, for example, Larcker and Watts (2020); Larsson (2019); Alessi et al. (2019); Partridge and Medda (2020); Partridge and Romana (2020); Zerbib (2019).

4 On the market neutrality of the Swiss National Bank and European Central Bank, see also van ’t Klooster and Fontan (2020).

5 Christine Lagarde is quoted saying, ‘In the face of what I call the market failures’ we have to ask ‘whether market neutrality should be the actual principle that drives our monetary-policy portfolio management’. https://www.bloomberg.com/news/articles/2020-10-14/lagarde-says-ecb-needs-to-question-market-neutrality-on-climate.

6 See the announcement by the SNB chairman here: https://www.snb.ch/en/mmr/speeches/id/ref_20201217_tjn.

7 https://www.iea.org/data-and-statistics.

8 Note that corporate bonds also underwent a reform, and AA and AA+ corporate bonds were also included in the list of eligible collaterals for the MLF facility. Prior to the reform, the MLF operations accepted government securities, central bank bills, China Development Bank bonds, policy financial bonds, local government debts, and AAA-rated corporate bonds as collaterals.

9 http://www.pbc.gov.cn/en/3688110/3688172/4048314/3711516/index.html.

10 Series that we removed either presented an insufficient number of data points or were extremely nonlinear or flat, suggesting that they did not reflect real market prices.