Economic impacts of the maritime silk road initiative on the Suez Canal

Figures & data

Figure 1. Share of China’s BRI investment in China’s outward FDI (2005–2022).

Source: Authors’ calculations using the China Global Investment Tracker, American Enterprise Institute and Heritage Foundation, retrieved September 1, 2023, at https://www.aei.org/chinglobal-investment-tracker/.

Figure 2. China’s BRI investment by sector (2013–2022) ($m).

Source: Authors’ calculations using the China Global Investment Tracker, American Enterprise Institute and Heritage Foundation, retrieved September 1, 2023, at https://www.aei.org/chinglobal-investment-tracker/.

Figure 3. China’s BRI investment by region (2013–2022) ($m).

Source: Authors’ calculations using the China Global Investment Tracker, American Enterprise Institute and Heritage Foundation, retrieved September 1, 2023, at https://www.aei.org/chinglobal-investment-tracker/.

Figure 4. China’s share of global trade before and after the BRI.

Source: Authors’ calculations using Clarksons Research, Shipping Intelligence Network Timeseries, and IMF databases, July 2023.

Table 1. Cape of good hope vs. Suez canal BRI distance savings.

Origin	Destination	Distance (Nautical Miles)			Time (Days)
		Suez	Cape	Saving	Suez	Cape	Saving	Saving %
Shanghai	Piraeus	7,844	14,253	6,409	23	43	20	47
Shanghai	Constanza	8,233	14,768	6,535	24	44	20	45
Shanghai	Trieste	8,545	14,420	5,875	25	42	17	40
Shanghai	Rotterdam	10,525	13,843	3,318	31	41	10	24
Shanghai	Hamburg	10,778	14,096	3,318	32	42	10	24
Shanghai	ST Petersburg	11,767	15,085	3,318	35	45	10	22

Source: Authors’ calculation using data collected at https://sea-distances.org/.

Figure 5. Share of the SC transit numbers by vessel type in 2022.

Source: Authors’ calculations using data collected by the Suez Canal Authority in (Canal Authority, 2023).

Figure 6. Share of the SC cargo trade by vessel type in 2022.

Source: Authors’ calculations using data collected by the Suez Canal Authority in (Canal Authority, 2023).

Table 2. Variables considered, their indicators, and sources.

Variable	Indicator	Unit	Symbol	Source
SC Cargo Trade	It is an annual indicator of the volume of global seaborne trade passing through the Suez Canal.	m. tons	SC	The Suez Canal Authority
China’s seaborne trade	It measures the volume of China’s goods loaded and unloaded annually in the china’s seaports.	m. tons	CT	Clarksons Research.
China BRI Investment Projects	It is used as a proxy for the actual BRI projects that China is investing in. According to BRI average data estimates for the period (2013–2022), it is a combination of the actual BRI data over the period (2013–2022) and roughly 64% of China’s outward FDI during the period (1990–2012). Chinese investments in the world’s energy, transportation, metals, technology, and real estate markets are included.	m. US$	BRI	The Heritage Foundation, the American Enterprise Institute, and the World Bank
SC New Development Projects	It includes Suez Canal double parts, new Suez Canal, and other development projects.	Kilometers	NDP	The Suez Canal Authority

Table 3. Variable descriptive statistics.

Variable	SC	CT	BRI	NDP
Mean	576.8	1,511.2	35,343.4	86.1
Median	565.2	1,246.9	10,979.1	79.0
Maximum	1,088.9	3,498.4	126,840.0	113.3
Minimum	271.9	135.6	531.2	77.0
Std. Dev.	267.4	1,172.7	42,864.2	14.7
Skewness	0.3	0.4	1.1	1.3
Kurtosis	1.8	1.6	2.6	2.8
Jarque-Bera	2.5	3.4	6.4	9.6
Probability	0.00	0.00	0.0	0.01

Source: Authors’ analysis.

Table 4. ADF test results.

Variable	Variable	ADF Test Statistic	p-value	Variable	ADF Test Statistic	p-value
SC	SC	1.1275	0.9969	D(SC)	−5.2603	0.0002*
CT	CT	−2.7413	0.0082*
BRI	BRI	−1.2792	0.6269	D(BRI)	−5.7294	0.0000*
NDP	NDP	−0.4384	0.8905	D(NDP)	−5.6100	0.0001*

Note: D: the first difference level. * Estimated significance is at the 1% level. Automatic lag length selection uses the Schwarz Information Criterion (SIC)..

Table 5. Selection criteria for the VAR lag order.

Lag	LogL	LR	FPE	AIC	SC	HQ
0	56.47903	NA	2.22E–08	−3.43194	−3.1984	−3.35723
1	178.4357	195.1306*	3.56e-11*	−9.895712*	−8.494515*	−9.447457*
2	196.5193	22.90592	6.58E–11	−9.43462	−6.86576	−8.61282

*indicates lag order selected by the criterion. LR: sequential modified LR test statistic (each test at the 5% level). FPE: Final prediction error. AIC: Akaike information criterion. SC: Schwarz information criterion. HQ: Hannan-Quinn information criterion.

Figure 7. The inverse roots of the AR characteristic polynomial.

Table 6. The VAR model results.

Variable	D(LSC)	LCT	D(LBRI)	D(LNDP)
D(LSC) (−1)	0.69	0. 30	0.04	0.55
	(0.00)	(0.07)	(0.00)	(0.07)
LCT (−1)	0.23	0.90	0.41	0.31
	(0.00)	(0.00)	(0.01)	(0.01)
D(LBRI) (−1)	0.05	0.25	0.66	0.10
	(0.01)	(0.06)	(0.03)	(0.03)
D(LNDP) (−1)	0.03	0.03	0.02	0.77
	(0.01)	(0.05)	(0.09)	(0.00)
C	0.05	0.27	−0.33	0.39
	(0.03)	(0.21)	(0.81)	(0.04)

Note: The P-values are enclosed by parenthesis. (−1) in the parentheses represents the lag length.

Figure 8. Short-run causal relations between SC cargo trade, China’s seaborne trade, China’s BRI, and SC development projects.

Note: Statistically significant at the 10% level.

Figure 9. Response of SC cargo trade to China’s seaborne trade.

Figure 10. Response of SC cargo trade to China BRI investment.

Figure 11. Response of SC cargo trade to SC new development projects.

Canal Authority, S., (2023). Canal characteristics. Retrieveddate September 1, 2023. Retrieved from https://www.suezcanal.gov.eg/English/Pages/default.aspx

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