Comparing factors affecting Chinese institutional investors’ willingness to pay for green bonds in Beijing, Shanghai, and Shenzhen

Figures & data

Figure 1. GB issuance amount in 2022. Prepared by authors based on CBI data.

Figure 2. GB issuance in 5 Asian countries and Hong Kong. Prepared by authors based on CBI data.

Figure 3. China and its neighboring countries. Prepared by authors.

Table 1. Summary of the China interbank bond market transaction amount in 2022

	Bond Cash Transaction (MIL RMB)	Bond Repo Transaction (MIL RMB)	Bond Outright Transaction (MIL RMB)	Total (MIL RMB)	Percentage of the total
Banks	164,791,697.52	1,454,369,258.10	4,440,604.60	1,623,601,560.22	61.22%
Security houses	127,355,220.68	148,308,404.20	4,292,321.30	279,955,946.18	10.56%
Credit unions	3,758,276.09	33,920,802.50	1,713,639.50	39,392,718.09	1.49%
Insurance companies	1,013,032.16	23,889,589.10	0.00	24,902,621.26	0.94%
Overseas institutions	11,301,187.46	4,465,618.90	55,083.00	15,821,889.36	0.60%
Others	50,662,934.08	617,357,525.80	321,459.40	668,341,919.28	25.20%
Total	358,882,347.98	2,282,311,198.60	10,823,107.80	2,652,016,654.38	100.00%

*These previous studies are related to finance but do not focus on GBs.

Figure 4. Distribution of Chinese GB issuers in 2021. Prepared by authors based on data on CBI data.

Table 2. Methods and variables in the previous literature

Previous Literatures	Methods	Variables
Zerbib (2016)	Matching method	Rating, Sector, Currency, Maturity, Issue amount
Baker et al. (2018)	Matching method Pooled regression model	Rating, Maturity, Insured, Taxable, Taxable AMT, Taxable State, Bond Size, Bank Qualified, New Money, General Obligation, Callable, Puttable Certified Green, Any Certified Green
Hu et al. (2022)	Matching method	Issuer, Bond’s seniority, Credit rating, Maturity type, Coupon type, Currency and Amount issued
0Larcker and Watts (2019)	Matching method	Aggregate rating, Callable, Certification, Coupon, Deal size, Credit rating, Green rating spread, Yield Institutional ownership, Issue among, Large issuer Long-term rating, Number of trades, Offering year, Placebo bond, Price distortion, Refunding, Turnover, Underwriter’s discount, Years to call, Years to maturity
Zhang (2020)	Matching method CAPM Differential in differential	Issuer type, Green bond country, Bond type, Firm characteristic, Stock market reaction, Corporations or financials, Yield spread, Institutional ownership, Stock liquidity
Karpf and Mandel (2017)	Matching method Neumark method Svensson method Oaxaca-blinder decomposition	Days to maturity, Amount, Principal amount at issuance, Number of transactions, Rating, Benchmark treasury rate, Broker transaction, Actual amount of total outstanding debt, Population of the states
Bachelet et al. (2019)	Matching method	Rating agency, Amount, Bond type, Issuer, Country risk, Coupon rate, Currency, Date, Ask price and bid price, Credit rating, Time to maturity, Liquidity
Kapraun et al. (2021)	Matching method	Size of the bond, Label certification, Credit rating, Maturity, Currency
Agliardi and Agliardi (2021)	Matching method Two-factor structural model	Issued amount, Issue type, Maturity
Wang et al. (2020)	Matching method	Credit rating, Issue size, Maturity, Callable/puttable, Credit enhancement, Enterprise bond, Difference of issue plan amount and actual issued amount, Financial leverage, ROA, Tangible assets, Listed company, State-owned company
Hu et al. (2022)	Matching method	Coupon, Yield, Yield spread, Maturity, Issue year, Amount issued

Figure 5. Flowchart of questionnaire survey.

Table 3. The selection of variables

Variable	Name	Symbol	Explanation of meaning	Previous literature using the variables
Dependent variable		WTP_ordi
Core explanatory variables	Respondent’s gender	gender	It is“1” indicates males, and “0” represents females.	Hassan Al-Tamimi and Anood Bin Kalli (2009) * Charness and Gneezy (2012) *
	Respondent’s age	age	“1” for ages 20–29, “2” for ages 30–39, “3” for ages 40–49, and “4” for ages 50–59.	Korniotis and Kumar (2011) *
	GB investment experience	gbexp	If invested in GBs in the past, it is “1”; if not invested in GBs in the past, it is ”0.”	Gambetti and Giusberti (2019) *
	Respondent’s total investing amount	invest	from RMB 1 to RMB 1 million, it is represented as “1”. If the respondent’s total investment amount is from RMB 1 million to RMB 3.5 million, it is represented as “2”. If the respondent’s total investment amount is from RMB 3.5 million to RMB 6.5 million, it is represented as “3”. If the respondent’s total investment amount is over RMB 6.5 million, it is represented as “4”.	Irwin and Sanders (2011) *
	Respondent’s environmental awareness	env	If the answer is “very concerned,” it is represented as ”5.” If the answer is “concerned a little,” it is represented as ”4.” If the answer is “cannot say either,” it is represented as ”3.” If the answer is “not concerned very much,” it is represented as ”2.” If the answer is “not concerned at all,” it is represented as ”1.”	Aruga (2022)
	Reside in Shanghai	shang	If resides in Shanghai, it is set to “1”; otherwise, it is set to ”0.	Chen et al. (2011), Zhou et al. (2019)
	Reside in Shenzhen	shen	If resides in Shenzhen, it is set to “1”; otherwise, it is set to ”0.	Chen et al. (2011), Zhou et al. (2019)
Control variables	GB issuer’s credit rating/credibility	credit	It is “1” if a Chinese institutional investor considers the issuer’s credit rating as a criterion for GB investment and “0” otherwise.	Bachelet et al. (2019), Fatica et al. (2021), Larcker and Watts (2019), Li et al. (2020), Sangiorgi and Schopohl (2021), Zenno and Aruga (2023), Zerbib (2019)
	Use of the fund of the GB	usage	It is “1” if an institutional investor considers the use of the GB fund as a criterion for GB investment, and “0” otherwise.	Dou et al. (2019), Fatica et al. (2021), Zenno and Aruga (2023)
	Amount of GB issued and the liquidity of the bond	liquidity	It is “1” if an institutional investor considers the issue amount of GB as a criterion for GB investment, and “0” otherwise.	Bachelet et al. (2019), Fatica et al. (2021), Larcker and Watts (2019), Sangiorgi and Schopohl (2021), Zenno and Aruga (2023)
	Redemption term of GB	term	It is “1” if a Chinese investor considers the redemption term of GB as a criterion for GB investment, and “0” otherwise.	Bachelet et al. (2019), Dou et al. (2019), Fatica et al. (2021), Larcker and Watts (2019), Li et al. (2020), Zenno and Aruga (2023), Zerbib (2019)
	GB issuer’s industrial type	issuer	It is “1” if a respondent considers the issuer’s business sector as a criterion for GB investment, and “0” otherwise.	Bachelet et al. (2019), Dou et al. (2019), Larcker and Watts (2019), Sangiorgi and Schopohl (2021), Zenno and Aruga (2023), Zerbib (2019)
	Currency of the GB	currency	It is “1” if an investor considers the type of currency as a criterion for GB investment, and “0” otherwise.	Bachelet et al. (2019), Sangiorgi and Schopohl (2021), Zenno and Aruga (2023)
	GB with the proof of the label	label	It is “1” if a respondent considers GB with the certification label as a criterion for GB investment, and “0” otherwise.	Bachelet et al. (2019), Larcker and Watts (2019), Li et al. (2020), Sangiorgi and Schopohl (2021), Wang et al. (2020), Zenno and Aruga (2023)
	Pre-explanation or post-report	maintenance	It is coded as “1” if an investor considers GB issuance with pre-explanation or post-report as a criterion for GB investment, and “0” otherwise.	Sangiorgi and Schopohl (2021), Zenno and Aruga (2023)

*These previous studies are related to finance but do not focus on GBs.

Table 4. Summary of demographic statistics and environmental concerns of survey responses

Variables	Beijing		Shanghai		Shenzhen		Total
	Samples Frequency	Percentage (%)	Samples Frequency	Percentage (%)	Samples Frequency	Percentage (%)	Samples Frequency	Percentage (%)
Respondent’s gender: gender
Male=1	301	54.53	321	59.12	297	53.51	919	55.70
Female=0	251	45.47	222	40.88	258	46.49	731	44.30
Respondent’s age (year): age
Age (20–29) =1	44	7.97	42	7.73	49	8.83	135	8.18
Age (30–39) =2	239	43.30	245	45.12	254	45.77	738	44.73
Age (40–49) =3	233	42.21	221	40.70	218	39.28	672	40.73
Age (50–59) =4	36	6.52	35	6.45	34	6.13	105	6.36
GB investment experience: gbexp
yes=1	253	45.83	201	37.02	239	43.05	693	41.97
no=0	299	54.17	342	62.98	316	56.94	958	58.03
Respondent’s total investing amount: invest
RMB 1–RMB 1 million=1	87	15.67	82	15.10	77	13.87	246	14.91
RMB 1 million–RMB 3.5 million=2	167	30.25	159	29.28	183	32.97	509	30.85
RMB 3.5 million–RMB 6.5 million=3	192	34.78	193	35.54	179	32.25	564	34.18
RMB 6.5 million and above=4	106	19.20	109	20.07	116	20.97	331	20.06
Respondent’s Environmental Concerns: env
Very concerned=5	196	35.51	172	31.68	182	32.79	550	33.33
Concerned a little=4	303	54.89	312	57.46	314	56.58	929	56.30
Cannot say either=3	42	7.61	48	8.84	42	7.57	132	8.00
Not concerned very much=2	11	1.99	10	1.84	254	2.52	35	2.12
Not concerned at all=1	0	0.00	1	0.18	49	0.54	4	0.24
Total	552	33.45	543	32.90	555	33.63	1650	100.00

Table 5. Summary of survey responses for GB investment decision criteria

Variables Name		Beijing		Shanghai		Shenzhen		Total
		Samples Frequency	Percentage (%)	Samples Frequency	Percentage (%)	Samples Frequency	Percentage (%)	Samples Frequency	Percentage (%)
credit	yes=1 no=0	486 66	88.04 11.95	453 90	83.43 16.57	497 58	89.55 10.45	1436 214	87.03 12.97
usage	yes=1 no=0	408 144	73.91 26.09	404 139	74.40 25.60	396 159	71.35 28.65	1208 442	73.21 26.79
liquidity	yes=1 no=0	429 123	77.72 22.28	410 133	75.51 24.49	417 138	75.14 24.86	1256 394	76.12 23.88
term	yes=1 no=0	387 165	70.11 29.89	394 149	72.56 27.44	379 176	68.29 31.71	1160 490	70.30 29.70
issuer	yes=1 no=0	453 99	82.07 17.93	427 116	78.64 21.36	447 108	80.54 19.46	1327 323	80.42 19.58
currency	yes=1 no=0	345 207	62.50 37.50	343 200	63.17 36.83	345 210	62.16 37.84	1033 617	62.61 37.39
label	yes=1 no=0	418 134	75.72 24.28	420 123	77.35 22.65	432 123	77.84 22.16	1270 380	76.97 23.03
maintenance	yes=1 no=0	416 136	75.36 26.64	417 126	76.80 23.20	400 155	72.07 27.93	1233 417	74.73 25.27

Table 6. Distributions of the bid responses in Beijing, Shanghai, and Shenzhen

Beijing (n = 600)
1st bid	2nd bid (B^l/B^u)	y/y	y/n		n/y	n/n	Total Respondents
0.50%	+0.25%/+0.75%	57	39		19	5	120
		47.50%	32.50%		15.83%	4.17%	100.00%
0.25%	±0.00%/+0.50%	79	23		14	4	120
		65.83%	19.17%		11.67%	3.33%	100.00%
±0.00%	−0.25%/+0.25%	91	15		14	0	120
		75.83%	12.50%		11.67%	0.00%	100.00%
−0.25%	−0.50%/±0.00%	39	34		33	14	120
		32.50%	28.33%		27.50%	11.67%	100.00%
−0.50%	−0.75%/−0.25%	31	33		31	25	120
		25.83%	27.50%		25.83%	20.83%	100.00%
Total respondents		297	144		111	48	600
		49.50%	24.00%		18.50%	8.00%	100.00%
Shanghai(n=600)
1^st bid	2nd bid (Bl/Bu)	y/y	y/n	n/y		n/n	Total Respondents
0.50%	+0.25%/+1.00%	89	12	14		5	120
		74.20%	10.00%	11.70%		4.20%	100.00%
0.25%	±0.00%/+0.50%	76	19	17		8	120
		63.30%	15.80%	14.20%		6.70%	100.00%
±0.00%	−0.25%/+0.25%	61	32	18		9	120
		50.80%	26.70%	15.00%		7.50%	100.00%
−0.25%	−0.50%/±0.00%	43	27	40		10	120
		35.80%	22.50%	33.30%		8.30%	100.00%
−0.50%	−1.00%/-0.25%	55	23	17		25	120
		45.80%	19.20%	14.20%		20.80%	100.00%
Total respondents		324	113	106		57	600
		54.00%	18.80%	17.70%		9.50%	100.00%
Shenzhen (n = 600)
1st bid	2nd bid (Bl/Bu)	y/y	y/n		n/y	n/n	Total Respondents
0.50%	+0.25%/+0.75%	63	30		16	11	120
		52.50%	25.00%		13.33%	9.17%	100.00%
0.25%	±0.00%/+0.50%	79	20		18	3	120
		65.83%	16.67%		15.00%	2.50%	100.00%
±0.00%	−0.25%/+0.25%	92	11		16	1	120
		76.67%	9.17%		13.33%	0.83%	100.00%
−0.25%	−0.50%/±0.00%	52	26		33	9	120
		43.33%	21.67%		27.50%	7.50%	100.00%
−0.50%	−0.75%/−0.25%	30	34		35	21	120
		25.00%	28.33%		29.17%	17.50%	100.00%
Total respondents		316	121		118	45	600
		52.67%	20.17%		19.67%	7.50%	100.00%

Table 7. Ordered probit regression results

Variable Symbol	Ordered probit regression results
Gender	0.011 (0.052)
Age	0.014 (0.035)
Gbexp	0.166*** (0.055)
Invest	0.014 (0.067)
Env	0.127*** (0.039)
shang	0.115* (0.064)
shen	0.034 (0.063)
credit	0.253*** (0.080)
usage	0.038 (0.060)
liquidity	0.195*** (0.064)
term	0.027 (0.058)
issuer	0.097 (0.066)
maintenance	0.012 (0.064)
currency	0.076 (0.055)
label	0.020 (0.062)

Standard errors are reported in parentheses.

Dependent variables:WTP_ordi

No. of observations:1650.

*** p<0.01, * p<0.1

Table 8. The results of the robustness test

Variable Symbol	Heteroskedastic ordered probit model results
Gender	0.004 (0.040)
Age	0.006 (0.027)
Gbexp	0.133*** (0.045)
Invest	0.014 (0.051)
Env	0.095*** (0.035)
Shang	0.087* (0.052)
Shen	0.035 (0.047)
Credit	0.210*** (0.067)
Usage	0.014 (0.047)
Liquidity	0.160*** (0.054)
Term	0.012 (0.045)
Issuer	0.078 (0.052)
Maintenance	0.005 (0.047)
Currency	0.058 (0.043)
Label	0.023 (0.048)

Standard errors are reported in parentheses.

Dependent variables:WTP_ordi

No. of observations:1650.

*** p<0.01, * p<0.1

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