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Has the belt and road initiative boosted the resident consumption in cities along the domestic route? – evidence from credit card consumption

Published Online: 05 Apr 2021
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Received: 04 Dec 2020
Accepted: 13 Jan 2020
Journal Details
License
Format
Journal
First Published
01 Jan 2016
Publication timeframe
2 times per year
Languages
English
Abstract

The Belt and Road (B&R) is a major initiative and vision for China and the world to jointly create prosperity. Based on the data of resident credit card consumption in 291 cities in China from 2011 to 2016, this article establishes a consumption relationship model for cities along the B&R to investigate the impact of the initiative on resident consumption. Through the DID model, there are the following findings. First, the B&R Initiative has a significant positive impact on the resident consumption in cities along the route. Second, the B&R Initiative has a significant effect on the promotion of various industries, and the crowding-out effect of government investment is relatively small. The research conclusions are of reference significance for policy formulation on the specific practice path and detailed measures of the B&R Initiative.

Keywords

Introduction

As pointed out in the report to the 19th National Congress of the Communist Party of China, the Belt and Road (B&R) foreign cooperation should achieve ‘the communication of policy, the connectivity of facilities, the smooth flow of trade, the integration of capital, and the understanding of the people’. The foreign cooperation in the B&R Initiative includes the traditional international trade as well as ethnic (non-governmental) economic activities such as industrial investment and cross-border consumption. The key cities, provinces and inter-provincial economic belts have benefitted from this initiative. Many domestic enterprises are gradually going abroad and entering the overseas market with great potential. At the same time, the resident consumption in various provinces and cities in China has also become the main content of ‘the smooth flow of trade’ and ‘the understanding of the people’ in the B&R Initiative. However, it is rare to study how the B&R Initiative affects the industrial economy and people's livelihood consumption in China in the academic circle, not to mention the empirical evidence based on the microcosm.

On the basis of existing research results and the micro-transaction data of domestic enterprises and residents, this article will conduct a comprehensive empirical study on the effect of the B&R Initiative for stimulating the domestic resident consumption. In terms of data, this article will use the transaction data of resident credit card consumption in desensitised areas and classify them according to the transaction attributes. In terms of methodology, this article will estimate the impact of the B&R Initiative on the livelihood consumption and industrial investment of relevant domestic cities, with reference to the Vision and Action to Promote the Joint Construction of the Silk Road Economic Belt and the 21st Century Maritime Silk Road (hereinafter referred to as the Vision and Action) and the double difference (DID) estimation method (Chen Qiang, 2014).

The rest of this article is arranged as follows. Section 2 discusses the literature review and research design. Section 3 discusses the model setting and data description. Section 4 discusses the empirical test. Section 5 provides the conclusion.

Literature review and research hypothesis
Literature review

The orderly promotion of the B&R Initiative not only brings countries along the route closer to China (Liu, H, 2016; Du, J and Zhang, Y, 2017; Zhang Yansheng, Wang Haifeng, Yang Kunfeng, 2017) but also has a significant driving effect on the domestic regional economy and industrial economy (Zhang Jun, 2014; Yang Jirui and Luo Zhigao, 2017; Liu Zhen, 2017). Related research can be roughly divided into two directions. First, the positive directions include the construction of global value chain (Research on Financial Issues, 2018(01): 43–49.ChenJianGongXiaoyingResearch on the construction of China's industry-led “Belt and Road” regional value chain[J]Research on Financial Issues2018014349' href="#j_amns.2021.1.00022_ref_006_w2aab3b7b1b1b6b1ab2b1b6Aa">Chen Jian and Gong Xiaoying, 2018), the distribution pattern of resources (Zhao Yabo, Liu Xiaofeng and Ge Yuejing, 2017), the international management methods (Fang Hui and Zhao Tian, 2017)), the location advantages (Journal of Yunnan University of Finance and Economics, 2017(06): 123–131.SunYanboAn Empirical Analysis of the Location Distribution and Political Risks of China's Investment in Countries along the “One Belt and One Road”[J]Journal of Yunnan University of Finance and Economics201706123131' href="#j_amns.2021.1.00022_ref_009_w2aab3b7b1b1b6b1ab2b1b9Aa">Sun Yanbo, 2017) and the cultural communication (Journal of Yunnan University of Finance and Economics, 2017(06): 123–131.SunYanboAn Empirical Analysis of the Location Distribution and Political Risks of China's Investment in Countries along the “One Belt and One Road”[J]Journal of Yunnan University of Finance and Economics201706123131' href="#j_amns.2021.1.00022_ref_009_w2aab3b7b1b1b6b1ab2b1b9Aa">Sun Ying, 2017). Second, the challenging directions include the trade protection (Yanwen, Wu, 2017), the financial risks (Li, W and Jin, D, 2018), the overseas political risks (Tang Lizhi and Liu Yu, 2017) and the environmental effects (Liu Naiquan and Dai Jin, 2017).

The B&R Initiative has become one of the hot topics in economic research in the past 3 years. The existing literature has conducted research from both international perspective and domestic perspective. In terms of domestic research, it can be divided into three levels, namely, specialised research involving key cities (He Yaoyin, 2015; Gao Xinfai and Yang Fang, 2015; Zhang Qi and Tong Jixin, 2016), provinces (Fujian Forum (Humanities and Social Sciences Edition), 2015(07): 160–166.HuangMaoxingJiPengThe realistic basis and strategic direction of Fujian's active integration into the 21st Century Maritime Silk Road[J]Fujian Forum (Humanities and Social Sciences Edition)201507160166' href="#j_amns.2021.1.00022_ref_020_w2aab3b7b1b1b6b1ab2b1c20Aa">Huang Maoxing and Ji Peng, 2015; Lu Wengang, Fujian Forum (Humanities and Social Sciences Edition), 2015(07): 160–166.HuangMaoxingJiPengThe realistic basis and strategic direction of Fujian's active integration into the 21st Century Maritime Silk Road[J]Fujian Forum (Humanities and Social Sciences Edition)201507160166' href="#j_amns.2021.1.00022_ref_020_w2aab3b7b1b1b6b1ab2b1c20Aa">Huang Xiaozhen and Liu Pei, 2015; Economic Geography, 2017(10): 43–48.OuyangLinHongMinChenZhengHunan's open economy level and contribution evaluation under the background of “One Belt One Road”[J]Economic Geography2017104348' href="#j_amns.2021.1.00022_ref_022_w2aab3b7b1b1b6b1ab2b1c22Aa">Ouyang Lin et al., 2017) and inter-provincial economic belts or urban cluster (Zhou Huan and Ma Naiyi, 2016; Lu Dadao, 2017; Qiu Shan, 2017). Based on the above research results, the selection of regions or industries is equivalent to correctly identifying, inheriting and applying the classical economic theories that have advantages and conform to the economic cycle.

With the continuous promotion of the B&R Initiative, the accumulation of relevant data has become more and more abundant. The research on the B&R Initiative is no longer limited to the model (Dong Suocheng et al., 2014), the logic (Lu Feng et al., 2015) or the basic architecture (Liu Qingcai and Zhi Jichao, 2016), but the in-depth measurement (He Chun et al., 2017), the empirical research (Journal of Yunnan University of Finance and Economics, 2017(06): 123–131.SunYanboAn Empirical Analysis of the Location Distribution and Political Risks of China's Investment in Countries along the “One Belt and One Road”[J]Journal of Yunnan University of Finance and Economics201706123131' href="#j_amns.2021.1.00022_ref_009_w2aab3b7b1b1b6b1ab2b1b9Aa">Sun Yanbo, 2017) and even the financial products designed based on data (Li, W and Jin, D, 2018). The research from the perspective of urban economics is as follows. Xiaofeng, Li and Song Qi (2016) studied and compared the advantages of service industries in cities along the route of the B&R Initiative. Academic Forum, 2017(05): 124–128.DengJianResearch on the development strategy of processing trade in China's border cities under the background of the “One Belt and One Road” ——Taking Chongzuo City, Guangxi, a border city adjacent to ASEAN as an example[J]Academic Forum201705124128' href="#j_amns.2021.1.00022_ref_031_w2aab3b7b1b1b6b1ab2b1c31Aa">Deng Jian (2017) proposed five major development measures for processing trade in border cities along the route of the B&R Initiative. Wang Lili and Xiao Wenwen (2018) studied the spatial network structure of the urban agglomeration along the B&R, which consists of five provinces in the northwest and four provinces in the southwest. Taking the China-Europe train as the research object, Wang Dongfang et al. (2018) studied the network structure of node cities along the route. The research from the perspective of industry research is as follows. International Trade Issues, 2016(07): 61–71.CaiZhonghuaWangYifanDongGuangweiResearch on the relationship between China's patent and export structure in the “Belt and Road” countries——Analysis based on industry-level similarity index[J]International Trade Issues2016076171' href="#j_amns.2021.1.00022_ref_034_w2aab3b7b1b1b6b1ab2b1c34Aa">Cai Zhonghua, Wang Yifan and Dong Guangwei (2016) studied the patent layout along the route of the B&R Initiative in several industries such as special (general) equipment manufacturing and chemical raw materials (products). Hu Angang et al. (2017) investigated eight representative central enterprises in the industry and provided solutions to common industry problems encountered in the construction of the B&R.

Through the above-mentioned literature review, notwithstanding the fact that it is based on the cross-sectional view of cities or regions, or the in-depth profile of industries, the research on the mechanism and effect of the B&R Initiative's driving economic development along the route has become increasingly abundant, and the research conclusions are fundamentally the same. According to the theoretical and practical results, there is no doubt that the B&R has made remarkable achievements, which also embodies the national will to explore the market vitality in the region and expand the opening-up to the outside world. However, the microscopic and detailed construction effect and empirical support still need to be further enriched.

Research hypothesis

According to the trend of the B&R, with the support of central cities along the route and key maritime ports, it aims to jointly create efficient transportation channel, international boutique tourism routes, and new tourism products to make the B&R node cities in China become commercial logistics hubs, key industries and cultural exchange bases. Under this guidance, the overall economic development of the cities along the route in China has been improved, and the consumption of livelihoods such as clothing, food, housing and transportation by residents of the cities along the route has also been driven, such as the new retail model arising from the B&R (Wang Juanjuan, 2017), the new processing trade strategy in border cities (Academic Forum, 2017(05): 124–128.DengJianResearch on the development strategy of processing trade in China's border cities under the background of the “One Belt and One Road” ——Taking Chongzuo City, Guangxi, a border city adjacent to ASEAN as an example[J]Academic Forum201705124128' href="#j_amns.2021.1.00022_ref_031_w2aab3b7b1b1b6b1ab2b1c31Aa">Deng Jian, 2017) and the “Silk Road Culture” of cities (Liao Qinghu et al., 2017). Therefore, it can be inferred that the B&R not only promotes the prosperity of trade but also focuses on certain industries in the cities along the domestic routes. Based on the above-mentioned analysis, this article puts forward the following research hypothesis: The B&R Initiative can drive the economic development of domestic cities along the route. However, the driving effect on different industries may be heterogeneous.

Model setting and data description
Measurement model setting

The construction of the B&R Initiative is both contemporary and regional. To control the grouping effect between node cities and non-node cities as well as the time effect of the B&R Initiative, the double difference method is adopted to examine the influence of the B&R Initiative on domestic node cities. The model is set as follows: consit=β0+β1Treati×OBORt+β12Treati+β13OBORt+β2GDPpcit+β3Exportit+β4FDIit+εitcon{s_{it}} = {\beta _0} + {\beta _1}Trea{t_i} \times OBO{R_t} + {\beta _{12}}Trea{t_i} + {\beta _{13}}OBO{R_t} + {\beta _2}GDPp{c_{it}} + {\beta _3}Expor{t_{it}} + {\beta _4}FD{I_{it}} + {\varepsilon _{it}} where i and t represent the city and year respectively, and cons represents the consumption of local residents. Treat is used to distinguish whether the B&R node cities, where Treat=1 means node cities, and Treat=0 means non-node cities. OBOR stands for the time node proposed by the B&R Initiative. Since President Xi Jinping proposed the strategic concept of the B&R Cooperation initiative in 2013, 2014 is set as the time node, so the variable OBOR before 2014 is 0, and the variable after that is 1. The coefficient of the cross-term Treat×OBOR is the focus of attention. The B&R construction has promoted the consumption growth of the node cities in China. GDPpc is the local GDP per capita; export is the total local exports and FDI is the actual foreign investment. The above-mentioned three variables control the impact of per capita economic output, trade volume and investment on local consumption.

Processing of related variables
Consumption of local residents

In the context of the new normal of economic development, the 13th Five-Year Plan needs to follow the first principle of ‘adhering to the dominant position of the people’ and proposes to ‘play the fundamental role of consumption in growth and focus on expanding household consumption’, to achieve the ‘the significant increase in the contribution of consumption to economic growth’ and the active implementation of the transition to the service sector. Throughout the entire 13th Five-Year Plan, the B&R is a national medium - and long-term development strategy, which has a profound impact on various service consumption fields. Therefore, the aggregate consumption and industry preference will be two important indicators of the quality of the local economy.

The B&R initiative

With reference to the common practice of DID and policy evaluation econometrics, this article sets dummy variables to represent the construction and implementation of the B&R Initiative. After President Xi Jinping proposed the B&R strategic concept, the industrial layout around the concept has been developed in an orderly manner. Therefore, this article constructs a dummy variable based on the time node of the proposed idea.

According to whether they are the node cities of B&R Initiative, the domestic cities are divided into two groups, among which the node cities are the treatment group, while the non-node cities are the control group. Before the B&R Initiative is proposed, neither the treatment group nor the control group was affected. After the B&R Initiative was proposed, the effect of policy influence of the treatment group may be more obvious than that of the control group. Based on the double differences of the four subsamples divided, the effect of the B&R Initiative on domestic node cities can be concluded.

Other control variables

Considering that local economic conditions may affect the implementation effect of the B&R construction, the international trade activities driven by the B&R construction are also economic factors that cannot be ignored. With the vigorous promotion of national policies, FDI in node cities along the B&R may increase significantly. Based on the above factors, local per capita GDP, export trade volume and FDI are selected as the control variables for the research.

Data sources

There are two main types of data used in this article. One is the micro-transaction data representing the consumption level and consumption categories of various domestic cities, and the other is the data representing the economic profile of these cities, such as economy, foreign trade and FDI. Among them, urban micro-transaction data refer to the data of bank card transaction and settlement after desensitisation. In the micro-transaction data with a total of more than 23 billion each year, each transaction contains more than 200 elements of variable information, such as transaction time, location, transaction amount, consumer merchant category, issuing bank and acquiring bank. This article collects and desensitises data and further classifies them by city and industry based on these data. In terms of industry classification, the seven major industries, namely clothing, food, housing, transportation, real estate, financial services and building materials, which are closely related to the lives of the general public, have been counted, covering the livelihood consumption field. In more detail, clothing refers to the shopping consumption in shopping malls, clothing stores and other places; food refers to the relevant consumption occurring in restaurants, bars, banquets and other dining places; accommodation refers to the consumption related to the accommodation places such as hotels and hotels as well as the service consumption of villas and vacation rooms; line refers to the consumption occurring in all kinds of gas stations; real estate refers to the new house purchase transaction of urban residents along the route; financial services refer to service consumption related to withdrawal and purchase of financial service products occurring at ATM and counter of financial institutions. The consumption of building materials refers to the consumption behaviour of all kinds of building materials wholesale market in the process of improving the housing quality.

The data representing the economic profile of cities are obtained from the Statistical Yearbook of Chinese Cities from 2012 to 2017, and the total export data come from China Regional Economic Statistics Yearbook. This article not only supplements some missing data by consulting various regional statistical yearbooks and government work reports but also compares and verifies the data from the CSMAR database, China Statistical Yearbook and National Prefecture-level City Financial and Economic Statistics to ensure the reliability of the data.

Of course, in addition to the two types of data mentioned above, the dummy variables constructed in this article rely on official documents related to the B&R Initiative, such as Will and Action, which are manually collated and encoded into the existing database. The total collected data from 291 domestic cities, with the time span from 2011 to 2016, covers the economic events that have immense influence on people's livelihood and consumption in China's economic development, including from rapid growth to slower growth, from total expansion to structural transformation, anti-corruption, monetary policy that changes from loose to tight and then to loose, the “Internet +” in industrial upgrading and the rise of the big data industry. A total of 2328 valid samples are obtained after eliminating the error values.

Descriptive statistics

Descriptive statistics for 2328 sample observations are listed in Table 1. Due to space limitations, this article cannot list the consumption data of each industry in detail. As shown in Table 1, the average value of household consumption is 37.99 billion yuan, and the maximum value is 119.543 billion yuan. According to the estimation of the 24 million permanent residents of Shanghai in 2015, the average annual credit card consumption per person is close to 50,000 yuan, with a minimum of 155 million yuan. According to an estimate of the 700,000 population of Changdu in the Tibet Autonomous Region in 2011, the average annual credit card consumption per person is only more than 200 yuan. The gap between the two cities is very wide. In addition, according to the value of the standard deviation, it can be seen that the dispersion of consumption values between different cities is relatively large. Among them, the city with the highest per capita GDP is Ordos, which is as high as 215,500 yuan. The city with the largest export volume is Shenzhen, which reached 1.57 trillion yuan in 2016. Tianjin ranks first in terms of foreign direct investment.

Descriptive statistics of variables

Variable nameNumber of observationsAverageStandard deviationMinimumMaximum
Cons2328379.91925.791.5511985.43
Treat23280.130.3301
OBOR23280.400.4901
GDPpc23284.313.210.5321.55
Export23280.702.470.011.57
FDI21998.7919.640.045188.67

Note: For the normative consideration, only two significant decimal places are allowed.

To observe the impact of the B&R Initiative on the local resident consumption more clearly, the test results of the difference in local resident consumption before and after 2014 are statistically analysed, as shown in Table 2. As can be seen from Table 2, the average value of credit card consumption of urban residents in the treatment group before 2014 was 97.51 billion yuan, but increased to 204.509 billion yuan after 2014, with the F value of 4.55. In other words, the credit card consumption of urban residents in the treatment group increased significantly after the B&R Initiative was proposed, while there was no significant difference in the control group. This preliminary statistical result is consistent with the hypothesis of this article.

Table of significant differences of key variables

Before 2014After 2014The F value
Resident card consumption MeanTreatment group975.102045.094.55***
Control group241.24293.690.92

Note: For the normative consideration, only two significant decimal places are allowed.

means significant at the 1% level.

Empirical test
Baseline regression

Table 3 reports the regression results of the B&R initiative on local resident consumption. It should be noted that the data of local consumption, GDP per capita, export trade volume, and FDI were logarithmised to make the data more stable.

Results of baseline regression

(1)(2)
Treat×OBOR1.055*** (0.0147)0.617*** (0.0414)
Treat0.185 (0.216)0.145 (0.114)
OBOR−0.0636 (0.148)−0.226 (0.193)
GDPpc1.515*** (0.0832)
Export0.477*** (0.0381)
FDI0.238*** (0.0283)
Constant19.31*** (0.0104)−5.538*** (0.876)
R-squared0.3090.208
N2,3282,199

Note: There are standard errors in brackets.

***, ** and * mean significant at the levels of 1%, 5% and 10%, respectively.

Column (1) of Table 3 is the estimated result of dummy variables. The coefficient of cross-term Treat×OBOR is 1.055, which is significant at the level of 1%, indicating that the B&R Initiative has a significantly higher driving effect on consumption in cities along the B&R Initiative than in cities outside the route. Column (2) is the re-estimation after adding control variables such as local per capita GDP GDPpc, total local exports, and actual foreign investment FDI. Among them, there is a significant positive correlation between per capita GDP, total local exports and FDI and local resident consumption. The cross-term Treat×OBOR coefficient becomes 0.617, which is slightly smaller than before but still significant at the level of 1%, indicating that the measurement pattern has no significant change.

Regression by industry

Considering that the driving effect of the B&R construction on residents’ consumption in cities along the line may vary with different industries, this article divides the sub-samples according to consumer consumption data classified and summarised by industry to re-examine the policy effect. As shown in Table 4, columns (1) to (7) respectively show the regression results of sub-samples of resident consumption in the seven industries of clothing, food, housing, transportation, real estate, financial services and building materials.

Regression results by industry

(1)(2)(3)(4)(5)(6)(7)
Treat×OBOR0.146** (0.0600)0.205*** (0.0583)0.361*** (0.0452)1.861*** (0.234)0.00338 (0.0612)0.465*** (0.0379)0.956*** (0.0998)
Treat−0.0527 (0.150)−0.118 (0.0831)0.212 (0.205)0.110 (0.0643)0.233 (0.0723)0.122 (0.0151)0.391 (0.0502)
OBOR0.0703 (0.115)0.134 (0.920)0.382 (0.633)0.946 (0.0108)0.0416 (0.0432)0.239 (0.0112)0.418 (0.206)
GDPpc0.512*** (0.122)0.724*** (0.118)0.539*** (0.0918)3.631*** (0.481)0.915*** (0.124)0.934*** (0.0769)2.188*** (0.203)
Export0.326*** (0.0546)0.391*** (0.0531)0.264*** (0.0412)1.413*** (0.231)0.317*** (0.0558)0.265*** (0.0345)0.473*** (0.0909)
FDI0.113*** (0.0411)0.129*** (0.0400)0.122*** (0.0310)0.652*** (0.168)0.187*** (0.0420)0.152*** (0.0260)0.292*** (0.0684)
Constant8.285*** (1.276)4.189*** (1.241)8.037*** (0.963)−47.99*** (5.153)5.807*** (1.303)7.219*** (0.806)−12.39*** (2.124)
R-squared0.1560.2430.2900.3330.2260.5110.401
N2,1992,1992,1992,1992,1992,1992,199

Note: There are standard errors in brackets.

***, ** and * mean significant at the levels of 1%, 5% and 10%, respectively.

According to the regression results mentioned above, the B&R Initiative has different driving effects on consumption in different industries. In detail, apart from the insignificant effect of the B&R initiative on real estate, it is significant in the six industries of residents’ clothing, food, housing, transportation, financial services and building materials, among which the significance level of the clothing industry is slightly lower. The possible reason for the insignificance of the real estate industry is that the real estate investment boom is sustained and widespread, which does not show a structural change due to the B&R Initiative. From the perspective of economic significance, among the six industries, the B&R Initiative has the greatest impact on the travel of urban residents along the route, while the impact on clothing and dining consumption is relatively small. Regardless of industries, the three control variables have a significant positive correlation with the consumption of residents in cities along the route.

Considering that different levels of local economic development may have different sensitivities to the construction of the B&R Initiative, all cities are divided into two groups based on the total GDP. In addition, a grouping dummy variable GDP50 is constructed, in which the top 50% cities set GDP50 as 1, and the bottom 50% cities set GDP50 as 0. Table 5 shows the estimated results of policy effects under the different levels of economic development.

Regression results by industry under different economic development levels

(1)(2)(3)(4)(5)(6)(7)
Treat×OBOR0.147** (0.0585)0.208*** (0.0568)0.368*** (0.0439)1.895*** (0.232)0.00256 (0.0608)0.471*** (0.0366)0.958*** (0.0975)
Treat0.219 (0.416)−0.0126 (0.202)0.0850 (0.227)0.429* (0.231)0.252 (0.237)0.209 (0.194)0.839 (0.526)
OBOR0.0208 (0.169)−0.0869 (0.151)−0.0535 (0.0869)0.0686 (0.0957)1.126 (0.742)0.396 (0.628)0.369 (0.243)
GDP500.278*** (0.0816)0.381*** (0.0792)0.280*** (0.0613)0.445 (0.336)0.247*** (0.0848)0.301*** (0.0510)0.517*** (0.136)
GDPpc0.477*** (0.118)0.701*** (0.114)0.505*** (0.0883)3.550*** (0.471)0.860*** (0.122)0.900*** (0.0735)2.117*** (0.196)
Export0.291*** (0.0540)0.350*** (0.0524)0.229*** (0.0406)1.311*** (0.231)0.279*** (0.0561)0.226*** (0.0337)0.404*** (0.0900)
FDI0.0976** (0.0400)0.117*** (0.0388)0.114*** (0.0300)0.665*** (0.165)0.183*** (0.0415)0.142*** (0.0250)0.273*** (0.0666)
Constant9.187*** (1.247)4.933*** (1.211)8.814*** (0.937)46.10*** (5.102)6.836*** (1.296)8.070*** (0.779)−10.76*** (2.078)
R-squared0.1700.2750.3160.3360.2290.5370.415
N2,1992,1992,1992,1992,1992,1992,199

Note: There are standard errors in brackets.

***, ** and * mean significant at the levels of 1%, 5% and 10%, respectively.

Regression results show that grouping does not change the significance of the Treat×OBOR coefficient. As for the economic grouping variable GDP50, all the six industries except travel industry are significantly positive, which indicates that the higher the level of economic development, the greater the promotion effect of this initiative on resident consumption. The insignificance of travel indicates that the travel livelihood consumption is not sensitive to the level of economic development. On the whole, the more developed a city is, the more likely its local resident consumption in various industries is to be positively affected.

Robustness test

To ensure the robustness of the estimated results, the model is re-estimated by replacing the consumption amount with the transaction amount of domestic urban resident credit card consumption. The setting form of the model and the processing method of the data remained unchanged. The robustness test for the baseline regression is shown in Table 6. Column (1) is the result of only estimating the dummy variables. The coefficient of the cross-term Treat×OBOR is 1.042, which is significant at the level of 1%. Column (2) is the re-estimate after adding control variables such as local GDP per capita (GDPpc), local export volume (Export) and foreign actual investment volume (FDI). The cross-term Treat×OBOR coefficient is 0.674, which is significant at the level of 1%. The levels of significance and measurement patterns are consistent with the results in Table 3, which shows that the estimated results are robust.

Robustness test results of different industries

(1)(2)
Treat×OBOR1.042*** (0.0143)0.674*** (0.0399)
Treat0.0903 (0.0670)0.0520 (0.209)
OBOR0.0681 (0.596)0.0583 (0.115)
GDPpc1.362*** (0.0801)
Export0.452*** (0.0367)
FDI0.221*** (0.0273)
Constant11.48*** (0.0101)−11.26*** (0.844)
R-squared0.3140.208
N2,3282,199

Note: There are standard errors in brackets.

***, ** and * mean significant at the levels of 1%, 5% and 10%, respectively.

For the sub-samples of different industries, it is also re-estimated by replacing the consumption amount with the transaction amount of domestic urban resident credit card consumption. The results are shown in Table 7. Columns (1)–(7) respectively show the regression results of the sub-samples of resident consumption in the seven industries of clothing, food, housing, transportation, real estate, financial services and building materials. Compared with Table 4, it is found that the policy variables have a significant effect on the consumption of real estate industry of urban residents along the route, but only at the edge of 10% level, which may be the reason for the data. The revaluation results of other industries are consistent with the previous ones, which show that the model is robust.

Robustness test results by sector

(1)(2)(3)(4)(5)(6)(7)
Treat×OBOR0.198*** (0.0575)0.473*** (0.0593)0.538*** (0.0473)1.947*** (0.220)0.120* (0.0637)0.299*** (0.0287)1.105*** (0.0906)
Treat0.195 (0.480)−0.854 (0.912)0.211 (1.087)0.421 (0.729)−0.0962 (0.005)−0.386 (0.255)−0.201 (0.163)
OBOR0.832* (0.401)0.0942 (0.891)1.383 (1.108)0.927 (0.723)0.0594 (0.077)0.0320 (0.194)0.419 (0.160)
GDPpc0.149 (0.115)0.965*** (0.119)0.740*** (0.0949)3.367*** (0.445)0.991*** (0.128)0.594*** (0.0576)1.826*** (0.182)
Export0.299*** (0.0525)0.397*** (0.0541)0.292*** (0.0432)1.272*** (0.216)0.290*** (0.0581)0.194*** (0.0262)0.434*** (0.0826)
FDI0.0852** (0.0391)0.162*** (0.0403)0.154*** (0.0322)0.606*** (0.156)0.225*** (0.0433)0.120*** (0.0195)0.227*** (0.0616)
Constant6.242*** (1.211)−5.477*** (1.249)−1.563 (0.996)−50.03*** (4.780)−6.441*** (1.341)5.099*** (0.605)−17.19*** (1.908)
R-squared0.1060.3480.3950.3400.2410.4480.420
N2,1992,1992,1992,1992,1992,1992,199

Note: There are standard errors in brackets.

***, ** and * mean significant at the levels of 1%, 5% and 10%, respectively.

Furthermore, the city groups are re-divided into three groups according to the economic volume of the city. In addition, two grouping dummy variables GDP35 and GDP70 are constructed. Among them, the GDP35 of the top 35% cities is 1, and the others are 0. The top 70% of cities have a GDP70 of 1 and the rest have a GDP of 0. Table 8 lists the estimated results of policy effects under different economic development levels. The regression results show that all the six industries except travel are significantly positive. On the whole, the higher the degree of economic development, the more likely the local resident consumption in various major industries is to be positively affected. After grouping, the significance of Treat×OBOR variable coefficients does not change, and there is no deviation in the direction, which is consistent with the estimation in Table 5.

Robustness test results for local economic scale

(1)(2)(3)(4)(5)(6)(7)
Treat×OBOR0.129** (0.0583)0.181*** (0.0561)0.351*** (0.0437)1.789*** (0.223)−0.0204 (0.0601)0.450*** (0.0364)0.931*** (0.0977)
Treat0.313 (1.088)0.108 (1.562)0.159 (1.533)0.403 (1.004)0.0137 (0.454)0.0534 (1.492)0.141 (0.920)
OBOR0.0157 (0.376)0.0187 (0.256)0.009 (0.215)0.055 (0.970)0.748* (1.853)0.0635 (0.265)0.251 (0.682)
GDP350.319*** (0.0729)0.458*** (0.0701)0.303*** (0.0547)1.968*** (0.287)0.379*** (0.0751)0.291*** (0.0455)0.474*** (0.122)
GDP700.341** (0.140)0.544*** (0.134)0.298*** (0.105)2.650*** (0.547)0.509*** (0.144)0.446*** (0.0872)0.454* (0.234)
GDPpc0.453*** (0.118)0.659*** (0.114)0.487*** (0.0885)3.231*** (0.456)0.811*** (0.122)0.867*** (0.0737)2.098*** (0.198)
Export0.285*** (0.0538)0.336*** (0.0518)0.226*** (0.0404)1.135*** (0.222)0.259*** (0.0555)0.219*** (0.0336)0.407*** (0.0902)
FDI0.102** (0.0397)0.122*** (0.0382)0.119*** (0.0298)0.626*** (0.158)0.182*** (0.0409)0.146*** (0.0248)0.286*** (0.0665)
Constant9.404*** (1.257)5.380*** (1.209)8.938*** (0.943)−41.21*** (4.940)7.467*** (1.295)8.384*** (0.785)−10.78*** (2.106)
R-squared0.1810.2970.3270.3880.2510.5440.416
N2,1992,1992,1992,1992,1992,1992,199

Note: There are standard errors in brackets.

***, ** and * mean significant at the levels of 1%, 5% and 10%, respectively.

Conclusion

The research results show that the B&R Initiative has a significant promoting effect on the consumption of relevant urban residents in China. However, this positive effect is different. After industry segmentation, the study found that the B&R Initiative has a significant driving effect on the consumption of livelihood and building materials for improving housing in cities along the route, while there is no significant difference in the investment of real estate.

The main contributions of this article are as follows. First, this article conducts empirical research on the driving effect of urban resident consumption on the B&R Initiative at the micro-level, and provides supplements in relevant aspects. Previous studies mainly focussed on macro-statistics, with a relatively broad measurement range, and the industries or regions covered were also relatively general. Based on the aggregated data of inter-bank transfer and clearing transactions, this article makes an empirical study and supplements the micro-level foundation, thus enriching the research on the B&R Initiative in driving domestic urban resident consumption. Second, this study examines the differences of the B&R Initiative in promoting consumption growth in different industries, providing a beneficial supplement to the policy effectiveness as well as strong policy significance for the research conclusion. While actively implementing the top-level design of the central government and local governments, residents and enterprises have the most direct and sensitive feelings at the micro level of the market. At present, there is no empirical evidence for the domestic cities along the route, especially for the industry segmentation in these cities. Therefore, the empirical conclusion of this article is helpful for the correct understanding of the policy effect of the B&R Initiative and has great reference value for the construction of the B&R Initiative by local governments.

This article examines the driving effect of the B&R Initiative on domestic cities from the micro level of urban resident consumption. The empirical tests in this article are all consistent, including the selection and treatment of indicator variables, the setting form of regression model and robustness tests. Through the quantitative analysis of the data of 291 cities in China from 2011 to 2016, it can be found that the B&R Initiative can significantly improve the consumption level of urban residents at all nodes in China. After subdividing different industries and levels of economic development, subsamples are studied again. The robustness of the regression model is further verified by variable substitution. After industry segmentation, the study shows that, except for real estate, the B&R Initiative has a significant and positive driving effect on the six industries including residents’ clothing, food, housing, transportation, financial services and building materials, especially in terms of travel. Furthermore, the higher the level of economic development of the city, the greater the B&R construction will promote the place.

The B&R Initiative, established in March 2015, is a major top-level design of national strategy, which has a profound impact on China's economy, politics and diplomacy. The data show that industrial growth has seen a significant jump in the frontier areas of the B&R Initiative, such as Xinjiang in the northwest and Fujian along the coast. The continuous advancement of the B&R construction not only makes full use of the relative excess production capacity in China but also activates and releases the effective consumption demand of urban residents, which is conducive to the construction of a new development pattern of ‘demand-led and supply-innovated’ and the guidance of the new normal of the economy.

Results of baseline regression

(1)(2)
Treat×OBOR1.055*** (0.0147)0.617*** (0.0414)
Treat0.185 (0.216)0.145 (0.114)
OBOR−0.0636 (0.148)−0.226 (0.193)
GDPpc1.515*** (0.0832)
Export0.477*** (0.0381)
FDI0.238*** (0.0283)
Constant19.31*** (0.0104)−5.538*** (0.876)
R-squared0.3090.208
N2,3282,199

Regression results by industry

(1)(2)(3)(4)(5)(6)(7)
Treat×OBOR0.146** (0.0600)0.205*** (0.0583)0.361*** (0.0452)1.861*** (0.234)0.00338 (0.0612)0.465*** (0.0379)0.956*** (0.0998)
Treat−0.0527 (0.150)−0.118 (0.0831)0.212 (0.205)0.110 (0.0643)0.233 (0.0723)0.122 (0.0151)0.391 (0.0502)
OBOR0.0703 (0.115)0.134 (0.920)0.382 (0.633)0.946 (0.0108)0.0416 (0.0432)0.239 (0.0112)0.418 (0.206)
GDPpc0.512*** (0.122)0.724*** (0.118)0.539*** (0.0918)3.631*** (0.481)0.915*** (0.124)0.934*** (0.0769)2.188*** (0.203)
Export0.326*** (0.0546)0.391*** (0.0531)0.264*** (0.0412)1.413*** (0.231)0.317*** (0.0558)0.265*** (0.0345)0.473*** (0.0909)
FDI0.113*** (0.0411)0.129*** (0.0400)0.122*** (0.0310)0.652*** (0.168)0.187*** (0.0420)0.152*** (0.0260)0.292*** (0.0684)
Constant8.285*** (1.276)4.189*** (1.241)8.037*** (0.963)−47.99*** (5.153)5.807*** (1.303)7.219*** (0.806)−12.39*** (2.124)
R-squared0.1560.2430.2900.3330.2260.5110.401
N2,1992,1992,1992,1992,1992,1992,199

Robustness test results by sector

(1)(2)(3)(4)(5)(6)(7)
Treat×OBOR0.198*** (0.0575)0.473*** (0.0593)0.538*** (0.0473)1.947*** (0.220)0.120* (0.0637)0.299*** (0.0287)1.105*** (0.0906)
Treat0.195 (0.480)−0.854 (0.912)0.211 (1.087)0.421 (0.729)−0.0962 (0.005)−0.386 (0.255)−0.201 (0.163)
OBOR0.832* (0.401)0.0942 (0.891)1.383 (1.108)0.927 (0.723)0.0594 (0.077)0.0320 (0.194)0.419 (0.160)
GDPpc0.149 (0.115)0.965*** (0.119)0.740*** (0.0949)3.367*** (0.445)0.991*** (0.128)0.594*** (0.0576)1.826*** (0.182)
Export0.299*** (0.0525)0.397*** (0.0541)0.292*** (0.0432)1.272*** (0.216)0.290*** (0.0581)0.194*** (0.0262)0.434*** (0.0826)
FDI0.0852** (0.0391)0.162*** (0.0403)0.154*** (0.0322)0.606*** (0.156)0.225*** (0.0433)0.120*** (0.0195)0.227*** (0.0616)
Constant6.242*** (1.211)−5.477*** (1.249)−1.563 (0.996)−50.03*** (4.780)−6.441*** (1.341)5.099*** (0.605)−17.19*** (1.908)
R-squared0.1060.3480.3950.3400.2410.4480.420
N2,1992,1992,1992,1992,1992,1992,199

Robustness test results of different industries

(1)(2)
Treat×OBOR1.042*** (0.0143)0.674*** (0.0399)
Treat0.0903 (0.0670)0.0520 (0.209)
OBOR0.0681 (0.596)0.0583 (0.115)
GDPpc1.362*** (0.0801)
Export0.452*** (0.0367)
FDI0.221*** (0.0273)
Constant11.48*** (0.0101)−11.26*** (0.844)
R-squared0.3140.208
N2,3282,199

Robustness test results for local economic scale

(1)(2)(3)(4)(5)(6)(7)
Treat×OBOR0.129** (0.0583)0.181*** (0.0561)0.351*** (0.0437)1.789*** (0.223)−0.0204 (0.0601)0.450*** (0.0364)0.931*** (0.0977)
Treat0.313 (1.088)0.108 (1.562)0.159 (1.533)0.403 (1.004)0.0137 (0.454)0.0534 (1.492)0.141 (0.920)
OBOR0.0157 (0.376)0.0187 (0.256)0.009 (0.215)0.055 (0.970)0.748* (1.853)0.0635 (0.265)0.251 (0.682)
GDP350.319*** (0.0729)0.458*** (0.0701)0.303*** (0.0547)1.968*** (0.287)0.379*** (0.0751)0.291*** (0.0455)0.474*** (0.122)
GDP700.341** (0.140)0.544*** (0.134)0.298*** (0.105)2.650*** (0.547)0.509*** (0.144)0.446*** (0.0872)0.454* (0.234)
GDPpc0.453*** (0.118)0.659*** (0.114)0.487*** (0.0885)3.231*** (0.456)0.811*** (0.122)0.867*** (0.0737)2.098*** (0.198)
Export0.285*** (0.0538)0.336*** (0.0518)0.226*** (0.0404)1.135*** (0.222)0.259*** (0.0555)0.219*** (0.0336)0.407*** (0.0902)
FDI0.102** (0.0397)0.122*** (0.0382)0.119*** (0.0298)0.626*** (0.158)0.182*** (0.0409)0.146*** (0.0248)0.286*** (0.0665)
Constant9.404*** (1.257)5.380*** (1.209)8.938*** (0.943)−41.21*** (4.940)7.467*** (1.295)8.384*** (0.785)−10.78*** (2.106)
R-squared0.1810.2970.3270.3880.2510.5440.416
N2,1992,1992,1992,1992,1992,1992,199

Regression results by industry under different economic development levels

(1)(2)(3)(4)(5)(6)(7)
Treat×OBOR0.147** (0.0585)0.208*** (0.0568)0.368*** (0.0439)1.895*** (0.232)0.00256 (0.0608)0.471*** (0.0366)0.958*** (0.0975)
Treat0.219 (0.416)−0.0126 (0.202)0.0850 (0.227)0.429* (0.231)0.252 (0.237)0.209 (0.194)0.839 (0.526)
OBOR0.0208 (0.169)−0.0869 (0.151)−0.0535 (0.0869)0.0686 (0.0957)1.126 (0.742)0.396 (0.628)0.369 (0.243)
GDP500.278*** (0.0816)0.381*** (0.0792)0.280*** (0.0613)0.445 (0.336)0.247*** (0.0848)0.301*** (0.0510)0.517*** (0.136)
GDPpc0.477*** (0.118)0.701*** (0.114)0.505*** (0.0883)3.550*** (0.471)0.860*** (0.122)0.900*** (0.0735)2.117*** (0.196)
Export0.291*** (0.0540)0.350*** (0.0524)0.229*** (0.0406)1.311*** (0.231)0.279*** (0.0561)0.226*** (0.0337)0.404*** (0.0900)
FDI0.0976** (0.0400)0.117*** (0.0388)0.114*** (0.0300)0.665*** (0.165)0.183*** (0.0415)0.142*** (0.0250)0.273*** (0.0666)
Constant9.187*** (1.247)4.933*** (1.211)8.814*** (0.937)46.10*** (5.102)6.836*** (1.296)8.070*** (0.779)−10.76*** (2.078)
R-squared0.1700.2750.3160.3360.2290.5370.415
N2,1992,1992,1992,1992,1992,1992,199

Table of significant differences of key variables

Before 2014After 2014The F value
Resident card consumption MeanTreatment group975.102045.094.55***
Control group241.24293.690.92

Descriptive statistics of variables

Variable nameNumber of observationsAverageStandard deviationMinimumMaximum
Cons2328379.91925.791.5511985.43
Treat23280.130.3301
OBOR23280.400.4901
GDPpc23284.313.210.5321.55
Export23280.702.470.011.57
FDI21998.7919.640.045188.67

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