Munich Personal RePEc Archive Does China’s overseas lending favor One Belt One Road countries? Zhang, Yifei and Fang, Heyang Beijing Normal University-Hong Kong Baptist University, United International College, The Chinese University of Hong Kong 3 January 2020 Online at https://mpra.ub.uni-muenchen.de/97954/ MPRA Paper No. 97954, posted 05 Jan 2020 05:13 UTC
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Does China’s overseas lending favor One Belt One …1 “China’s commitment to building infrastructure in countries covered by its ‘One Belt, One Road’ initiative - a scheme
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Munich Personal RePEc Archive
Does China’s overseas lending favor One
Belt One Road countries?
Zhang, Yifei and Fang, Heyang
Beijing Normal University-Hong Kong Baptist University, United
International College, The Chinese University of Hong Kong
3 January 2020
Online at https://mpra.ub.uni-muenchen.de/97954/
MPRA Paper No. 97954, posted 05 Jan 2020 05:13 UTC
* Heyang is with The Chinese University of Hong Kong. Email: [email protected] is with Beijing
Normal University-Hong Kong Baptist University, United International College. Email: [email protected].
Does China's overseas lending favor the One Belt One Road countries?
Heyang Fang and Yifei Zhang∗
January 2020
Abstract
The One Belt One Road initiative is found to promote China’s overseas lending in the
belt road countries, especially for countries along the continental route. Such effect
strengthens and persists for at least three years. Our findings show that launching a
national strategy could be a decisive determinant of one country’s outbound loans.
JEL Code: F34, F42
Keywords: International lending, One Belt One Road
“China’s commitment to building infrastructure in countries covered by its ‘One Belt,
One Road’ initiative - a scheme to boost development along ancient ‘silk road’ trading
routes between China and Europe - is revealed by data showing that the lion’s share of Beijing’s recent overseas lending pledges have been in countries that lie along the
routes.”
Financial Times (June 18, 2015)
1. Introduction
Banks from developed countries often provide credits to developing countries
(Dymski, 2003), as marginal returns are usually higher in less developed regions
where 𝑌𝑖𝑡 is the logarithm of China’s total overseas lending to country i in year t. 𝑃𝑜𝑠𝑡𝑡 is a dummy variable and equals to 1 if t is after year 2014 and 0 otherwise. 𝑂𝐵𝑂𝑅𝑖 is an indicator variable and equals to 1 if the recipient country i is an OBOR
country and 0 otherwise. 𝛾𝑖𝑡 is a vector of country i’s year-varying controls such as
GDP, population, capital stock, exchange rate, etc. Note that model (1) does not include 𝑃𝑜𝑠𝑡𝑡 and 𝑂𝐵𝑂𝑅𝑖, as they are absorbed by the recipient country (𝜃𝑖) and the year fixed
effects (𝛼𝑡) respectively. The standard error is clustered at borrower country level to
account for potential serial correlations within that country. Moreover, loan
commitments could also be path-dependent, as loans to developing countries often
follow schedules spanning over years (Kraay, 2014). To alleviate such concern, we
include lagged loan amount in some specifications. We also present results
incorporating the lagged country controls.
To substantiate our argument that the change in China’s overseas lending is solely
due to the OBOR initiative, we adopt the following time-varying DD model that treats
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Capital Services (Ratio) 560 1.132 0.239 0.137 3.034
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Table 2 Parallel trend test. This table presents the results of the parallel trend test. The dependent variable is the logarithm of
China’s aggregate loan amount to the recipient country i. Each pre-shock year dummy is interacted
with the OBOR country dummy before the policy announcement. For brevity, we do not report the
estimate for 𝑃𝑜𝑠𝑡𝑡 × 𝑂𝐵𝑂𝑅𝑖. Country fixed effect is also included. Robust standard errors, clustered
at recipient country level, are reported in parentheses. *, **, and *** denote significance at the 10%,
5% and 1% level, respectively.
(1)
VARIABLES Loans
Year 2011𝑡 * 𝑂𝐵𝑂𝑅𝑖 -0.705
(0.564)
Year 2012𝑡 * 𝑂𝐵𝑂𝑅𝑖 -0.365
(0.467)
Year 2013𝑡 * 𝑂𝐵𝑂𝑅𝑖 -0.781
(0.568)
Constant 18.27***
(0.220)
Country FE Yes
Observations 839
Adjusted R-squared 0.698
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Table 3 The impact of the OBOR policy on China’s overseas lending. This table shows the DD results investigating the impact of the OBOR policy on China’s overseas lending. The dependent
variable is the logarithm of China’s aggregate loan amount to the recipient country i. Both country controls, country and
year fixed effects are included in all specifications. Column (1) to (4) are the DD results of all countries. Column (1) is the
baseline and column (2) adds the country lagged controls. Column (3) and (4) include the lagged loans up to 3 years with
and without lagged country controls. Columns (5) to (8) show the corresponding results for land belt countries. Robust
standard errors, clustered at recipient country level, are reported in parentheses. *, **, and *** denote significance at the
Table 4 The impact of signing the OBOR agreement on China’s overseas lending.
This table shows the time-varying DD results investigating the impact of the OBOR agreement
on China’s overseas lending. The dependent variable is the logarithm of China’s aggregate loan
amount to the recipient country i. 𝑇𝑟𝑒𝑎𝑡𝑝𝑜𝑠𝑡it is a dummy variable and equals 1 after country
i signs the agreement with China in year t, and 0 otherwise. 𝑇𝑟𝑒𝑎𝑡𝑝𝑜𝑠𝑡𝑖𝑡* 𝐿𝑎𝑛𝑑𝑖 is a dummy
variable and equals 1 after land-based country i signs the agreement with China in year t, and
0 otherwise. The country fixed effect, the year fixed effect, country controls and lagged
treatment variables 𝑇𝑟𝑒𝑎𝑡𝑝𝑜𝑠𝑡it up to three years are included in all specifications. Column (1)
presents the result for the OBOR countries and column (2) is for the land belt countries. Robust
standard errors, clustered at recipient country level, are reported in parentheses. *, **, and ***
denote significance at the 10%, 5% and 1% level, respectively.
(1) (2)
VARIABLES Loans Loans 𝑇𝑟𝑒𝑎𝑡𝑝𝑜𝑠𝑡𝑖𝑡 1.066*
(0.562) 𝑇𝑟𝑒𝑎𝑡𝑝𝑜𝑠𝑡𝑖𝑡* 𝐿𝑎𝑛𝑑𝑖 1.959*
(1.007) 𝑇𝑟𝑒𝑎𝑡𝑝𝑜𝑠𝑡𝑖𝑡 (-1) 0.298 0.539
(0.413) (0.946) 𝑇𝑟𝑒𝑎𝑡𝑝𝑜𝑠𝑡𝑖𝑡 (-2) -1.037 0.545
(0.699) (1.464) 𝑇𝑟𝑒𝑎𝑡𝑝𝑜𝑠𝑡𝑖𝑡 (-3) -0.135 0.0471
(0.672) (1.599)
Constant 3.994 199.2
(157.7) (337.3)
Country FE Yes Yes
Year FE Yes Yes
Country Controls Yes Yes
Country Lag Controls Yes Yes
Observations 560 280
Adjusted R-squared 0.812 0.826
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Figure 1 The trend of China’s over sea lending to the OBOR and the non-OBOR
countries.
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Figure 2 The dynamic effects of the OBOR initiative in China’s oversea loans.
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Appendix Table A Variable Definitions and Sources
Variable Sources Definition
Loan Amount (Log) Horn et al. (2019) Natural logarithm of the estimated total external debt owed to China (USD). GDP (log) Horn et al. (2019) Natural logarithm of Nominal GDP in USD. Population (log) Penn World Table 9.0 Natural logarithm of the country’s population. Capital Stock (log) Penn World Table 9.0 Natural logarithm of the capital stock at constant 2011 prices in USD. Depreciation Rate Penn World Table 9.0 Average depreciation rate of the capital stock. Exchange Rate Penn World Table 9.0 Official exchange rate (national currency / USD). Capital Services Penn World Table 9.0 Capital services at constant 2011 national prices (2011=1).
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Table A1 Loan recipient country list.
This table lists all the recipient countries in our analysis, as to their alphabetical orders.
Countries denoted by # and * are the land-road countries and the sea-belt countries
respectively.
Albania* Dominica Mauritius Tanzania
Algeria Ecuador Mexico Togo
Angola Egypt* Mongolia# Tonga#
Argentina Equatorial Guinea Montenegro* Turkey#
Armenia# Eritrea Morocco Turkmenistan#
Azerbaijan# Ethiopia Mozambique Uganda
Bahamas Fiji* Myanmar* Ukraine#
Bangladesh* Gabon Namibia Uruguay
Belarus# Ghana Nepal# Uzbekistan#
Benin# Guinea Niger Vanuatu
Bolivia Guyana Nigeria* Venezuela
Bosnia# India* Oman# Vietnam*
Botswana Indonesia* Pakistan# Yemen, Rep.*
Brazil Iran# Papua New Guinea Zambia
Bulgaria# Jamaica Peru Zimbabwe
Burkina Faso# Jordan# Philippines*
Burundi# Kazakhstan# Romania#
Cabo Verde# Kenya* Russia#
Cambodia* Kyrgyzstan# Rwanda
Cameroon Laos* Samoa
Central African Republic Lebanon# Senegal Chad Lesotho Serbia*
Chile Liberia Seychelles Colombia Macedonia, FYR# Sierra Leone
Comoros# Madagascar South Africa
Congo, Dem. Rep. Malawi# South Sudan#
Congo, Rep. Malaysia* Sri Lanka*
Costa Rica Maldives# Sudan#
Cote d'Ivoire# Mali Suriname
Djibouti Mauritania Tajikistan#
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Table A2 The signatory years of the One Belt One Road countries.
This table illustrates the signatory years of the recipient countries, as to their alphabetical orders and the news sources respectively.