Journal of Geographical Sciences >
Urbanization under globalization: How does the Belt and Road Initiative affect urbanization levels in participating countries
Ma Haitao (1979), PhD and Associate Professor, specialized in urban geography and planning. Email: maht@igsnrr.ac.cn 
Received date: 20220506
Accepted date: 20220926
Online published: 20221125
Supported by
The Third Xinjiang Scientific Expedition Program(2021xjkk0905)
Strategic Priority Research Program of the CAS, PanThird Pole Environment Study for a Green Silk Road(XDA20040402)
China’s Belt and Road Initiative (BRI) presents the world with a new era of inclusive globalization, which will shape urbanization patterns globally. This study considered the launch of BRI as a quasiexperiment, where we evaluated the BRI’s impact on urbanization by way of differenceindifferences (DID) and propensity score matching (PSM) methods. The results showed that the BRI exerted a significantly negative effect on urbanization in its first three years. Its positive effect emerged from the fifth year onwards, indicating that the BRI’s positive effect on urbanization required a period of practical experience. Heterogeneous analysis and placebo test were also conducted to verify the robustness of the model. The effects in lowincome countries were revealed to have been much greater than in highincome countries, meaning the BRI had been conducive to promote local urban growth in underdeveloped economies. Finally, the geographical detector model was introduced to discuss the influencing mechanism of urbanization in BRI and nonBRI countries, showing external factors were the prominent driving forces in BRI countries, whereas internal drivers played an important role in nonBRI countries. Our findings indicated that the implementation of the BRI contributed to building global economic growth and supporting a shared future.
MA Haitao . Urbanization under globalization: How does the Belt and Road Initiative affect urbanization levels in participating countries[J]. Journal of Geographical Sciences, 2022 , 32(11) : 2170 2188 . DOI: 10.1007/s1144202220421
Table 1 List of selected countries and their classifications of different income groups 
Income groups  Countries 

Lowincome  BurkinaFaso, Ethiopia, Gambia, Madagascar, Mozambique, Rwanda, Sudan, Syria*, Uganda 
Lower middleincome  Algeria, Angola, Bangladesh*, Belize, Bhutan*, Bolivia, Cambodia*, Cape Verde, Djibouti, Egypt*, El Salvador, Ghana, Haiti, Honduras, India*, Indonesia*, Iran*, Ivory Coast, Kenya, Kyrgyzstan*, Laos*, Mongolia*, Morocco, Nepal*, Nicaragua, Nigeria, Pakistan*, Papua New Guinea, Philippines*, Sri Lanka*, Swaziland, Tajikistan*, Tanzania, Tunisia, Ukraine*, Uzbekistan*, Vietnam*, Zambia, Zimbabwe 
Upper middleincome  Argentina, Albania*, Armenia*, Azerbaijan*, Belarus*, Bosnia and Herzegovina*, Botswana, Brazil, Bulgaria*, China*, Colombia, Costa Rica, Dominica, Ecuador, Georgia*, Guatemala, Guyana, Iraq*, Jamaica, Jordan*, Kazakhstan*, Lebanon*, Malaysia*, Mauritius, Mexico, Moldova*, Montenegro, Namibia, North Macedonia*, Panama, Paraguay, Peru, Romania*, Russia*, Serbia*, South Africa, Thailand*, Tonga, Turkey* 
Highincome  Cyprus*, Czech Republic*, Denmark, Estonia*, Finland, France, Germany, Greece*, Hungary*, Iceland, Ireland, Israel*, Italy, Japan, Korea, Latvia*, Lithuania*, Luxembourg, Malta, Netherlands, New Zealand, Norway, Oman*, Poland*, Portugal, Saudi Arabia*, Seychelles, Singapore*, Slovakia*, Slovenia*, Spain, Sweden, Switzerland, United Arab Emirates*, United Kingdom, United States, Uruguay 
Note: * means countries along the route at the very beginning of the initiative, that is, the BRI countries in this study. 
Table 2 Interaction types between two explanatory variables 
Type  Description  Interaction 

1  $q\left( {{X}_{1}}\cap {{X}_{2}} \right)<min\left( q\left( {{X}_{1}} \right),q\left( {{X}_{2}} \right) \right)$  Weaken, Nonlinear 
2  $min\left( q\left( {{X}_{1}} \right),q\left( {{X}_{2}} \right) \right)<q\left( {{X}_{1}}\cap {{X}_{2}} \right)<max\left( q\left( {{X}_{1}} \right),q\left( {{X}_{2}} \right) \right)$  Weaken, Univariate 
3  $max\left( q\left( {{X}_{1}} \right),q\left( {{X}_{2}} \right) \right)<q\left( {{X}_{1}}\cap {{X}_{2}} \right)<q\left( {{X}_{1}} \right)+q\left( {{X}_{2}} \right)$  Enhanced, Bivariate 
4  $q\left( {{X}_{1}}\cap {{X}_{2}} \right)=q\left( {{X}_{1}} \right)+q\left( {{X}_{2}} \right)$  Independent 
5  $q\left( {{X}_{1}}\cap {{X}_{2}} \right)>q\left( {{X}_{1}} \right)+q\left( {{X}_{2}} \right)$  Enhanced, Nonlinear 
Table 3 Descriptive statistics of the control variables 
Dimension  Variable  Definition  Code  Reference  Mean  Std. Dev. 

External drivers  Population flow  International inbound tourists  x11  Bjarnason et al. (2021); Muhammad et al. (2020)  1.39E+7  2.99E+7 
Cargo flow  Exports of goods and services (% of GDP)  x12  Inostroza and Zepp (2021); Yang and Hu (2019)  45.708  33.938  
Capital flow  Net inflows of foreign direct investment (% of GDP)  x13  Contractor et al. (2020); Razzaq et al. (2021)  6.132  14.577  
Internal drivers  Government factor  Gross fixed capital formation (% of GDP)  x21  Arvin et al. (2021); Li et al. (2018)  23.328  7.135 
Market factor  GDP per capita  x22  Fu et al. (2020); Wang and Li (2021)  16,629  20,993  
Technology factor  Patent applications per million people  x23  Gyedu et al. (2021); Smith and Thomas (2017)  202.567  512.237 
Table 4 Estimated results of urbanization based on DID and PSMDID 
Variable  DID  PSMDID  

Model (1)  Model (2)  Model (3)  Model (4)  
G_{i}  1.353  1.784  1.366  1.790 
(0.36)  (0.69)  (0.36)  (0.69)  
D_{t}  1.745^{***}  1.400^{***}  1.778^{***}  1.420^{***} 
(23.50)  (16.62)  (24.68)  (17.17)  
${{G}_{i}}\cdot {{D}_{t}}$  0.194^{*}  0.298^{***}  0.394^{***}  0.454^{***} 
(1.69)  (2.60)  (3.48)  (3.98)  
$lnx11$  1.185^{***}  1.213^{***}  
(8.90)  (9.15)  
$lnx12$  0.671^{***}  0.696^{***}  
(3.29)  (3.38)  
$lnx13$  0.075^{*}  0.066  
(1.73)  (1.56)  
$lnx21$  0.699^{***}  0.697^{***}  
(3.57)  (3.55)  
$lnx22$  0.947^{***}  0.639^{***}  
(4.64)  (3.10)  
$lnx23$  0.395^{***}  0.296^{***}  
(4.97)  (3.76)  
Intercept  61.486^{***}  38.722^{***}  61.475^{***}  41.499^{***} 
(25.07)  (13.12)  (25.31)  (14.04)  
Observations  1518  1518  1456  1456 
Note: lnx11, lnx12, lnx13, lnx21, lnx22, and lnx23 are the logarithms of x11, x12, x13, x21, x22, and x23 respectively. t statistics in parentheses. ^{*} p < 0.1, ^{**} p < 0.05, ^{***} p < 0.01. Models (1) and (2) show the estimation results based on ordinary DID. Models (3) and (4) show the estimation results based on PSMDID. Control variables for regression were not added in models (1) and (3); models (2) and (4) present the estimation results when control variables were added. 
Table 5 The samples selection of PSM 
Region  Off support  On support  Total 

NonBRI countries  13  867  880 
BRI countries  49  589  638 
Total  62  1456  1518 
Table 6 Balancing test of variables 
Variable  Mean  % bias  Reduction in % bias  Ttest  

Treated  Control  tvalue  $p>\left t \right$  
$lnx11$  Unmatched  15.47  14.879  35.7  6.85  0.000  
Matched  15.329  15.227  6.1  82.8  1.04  0.300  
$lnx12$  Unmatched  3.741  3.521  34.8  6.69  0.000  
Matched  3.693  3.641  8.4  75.9  1.59  0.112  
$lnx13$  Unmatched  1.125  1.012  9.6  1.84  0.066  
Matched  1.080  1.048  2.7  71.7  0.47  0.635  
$lnx21$  Unmatched  3.145  3.076  22.6  4.35  0.000  
Matched  3.132  3.134  0.6  97.5  0.10  0.919  
$lnx22$  Unmatched  8.868  8.989  9.3  1.76  0.079  
Matched  8.868  8.752  8.9  4.1  1.58  0.114  
$lnx23$  Unmatched  3.797  3.527  13.6  2.53  0.011  
Matched  3.661  3.654  0.3  97.5  0.06  0.950 
Table 7 The dynamic effect of BRI on urbanization 
Variable  DID  PSMDID  

Model (5)  Model (6)  Model (7)  Model (8)  
G_{i}  1.353  1.713  1.381  1.741  
(0.36)  (0.66)  (0.36)  (0.67)  
D_{t}  1.745^{***}  1.458^{***}  1.778***  1.473***  
(24.14)  (17.42)  (25.29)  (17.90)  
G_{i}·D_{t}  2015  0.934^{***}  0.816^{***}  1.075***  0.940*** 
(5.50)  (4.72)  (6.32)  (5.39)  
2016  0.644^{***}  0.554^{***}  0.813***  0.699***  
(3.80)  (3.23)  (4.81)  (4.06)  
2017  0.351^{**}  0.400^{**}  0.548***  0.555***  
(2.07)  (2.34)  (3.24)  (3.23)  
2018  0.051  0.261  0.275  0.427**  
(0.30)  (1.50)  (1.63)  (2.46)  
2019  0.254  0.016  0.004  0.193  
(1.50)  (0.09)  (0.02)  (1.10)  
2020  0.565^{***}  0.324^{*}  0.335**  0.130  
(3.33)  (1.88)  (1.99)  (0.75)  
Intercept  61.486^{***}  42.364^{***}  61.475***  44.681***  
(25.07)  (13.99)  (25.11)  (14.75)  
Control variable  No  Yes  No  Yes  
Observations  1518  1518  1456  1456 
Note: t statistics in parentheses. ^{*} p < 0.1, ^{**} p < 0.05, ^{***} p < 0.01. Models (5) and (6) show the estimation results based on ordinary DID. Models (7) and (8) show the estimation results based on PSMDID. Control variables were not added for regression in models (5) and (7), and models (6) and (8) present the estimated result of adding control variables. 
Figure 1 Results of heterogeneity analysis of income levelNote: The coefficients of dynamic effects from 2015 to 2020 in lowincome, lower middleincome, upper middleincome, and highincome countries and their 95% confidence intervals are plotted. The average effects of all countries involved in the initiative are also depicted using a red line. 
Table 8 Regression results of the parallel trends test 
Variable  Model (9)  Model (10)  Model (11)  Model (12) 

G_{i}  1.325  1.296  1.266  1.237 
(0.35)  (0.34)  (0.33)  (0.32)  
D_{t}  0.796^{***}  0.799^{***}  0.803^{***}  0.805^{***} 
(10.09)  (13.38)  (13.46)  (10.23)  
G_{i}·D_{t}  0.138  0.141  0.145  0.145 
(1.13)  (1.53)  (1.58)  (1.20)  
Intercept  61.327^{***}  61.167^{***}  61.005^{***}  60.842^{***} 
(24.85)  (24.79)  (24.72)  (24.65)  
Observations  690  690  690  690 
Note: t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. Models (9), (10), (11), and (12) are the placebo test results when the BRI was implemented in 2014, 2013, 2012, and 2011, respectively. 
Figure 2 Factor detector results for each driving factorNote: The explanatory power of factors significant at the level of 1% is highlighted in red. 
Figure 3 Matrix of interaction results of any two factors in BRI (a) and nonBRI countries (b)Note: The lower triangular matrix is the explanatory power of two factors. The upper triangular matrix represents the interaction type of two factors. 
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