1. Introduction

OJBM

Open Journal of Business and Management

2329-3284

Scientific Research Publishing

10.4236/ojbm.2019.74140

OJBM-96023

Articles

Business&Economics

Impact of Transport Infrastructure on Trade: Evidence from the Chinese Inland Provinces under “One Belt, One Road”

Jinhui

¹Gwang-Nam

Rim

²^*Chol-Ju

Faculty of Economics, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea

Center for North and South Korea Studies, Yanbian University, Yanji, China

06082019

07042030204612, October 201926, October 2019 29, October 2019

2014

This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/

This paper discusses the impact of transport infrastructure on trades in the Chinese provinces directly affecting by “One Belt, One Road” initiative. That is why infrastructure is of significance in “One Belt, One Road” initiative and one of important purpose for initiative is to promote the unimpeded trade in China. In this paper, authors analyzed the impact of railways and highways which are the key elements of transport infrastructure on total value of exports and imports in the Chinese inland provinces directly affecting by “One Belt, One Road” and correlations between individual elements of transport infrastructure based on data from National Bureau of Statistics of China and from some articles related to “One Belt, One Road” and correlation and regression analysis methods. The conclusion is that railways, highways, and port have strong correlation with Gross Regional Products, and effects of elements of transport infrastructure are different among inland provinces affected by “One Belt, One Road” and this needs rational management of transport infrastructure in promoting trade according to provinces.

Transport Infrastructure (TI) Trade Total Value of Exports and Imports (TVEI) “One Belt One Road” (OBOR) Chinese Economy

1. Introduction

In 2013, what is important in “One Belt, One Road” (OBOR) initiative proposed by China is the Land Road “Silk Road Economic Belt”. The OBOR initiative aims to increase the integration among countries in Asia as well as in Africa and Europe, and this will be accompanied by trade promotion in China [1] . The key element in OBOR initiative is the infrastructural connectivity, and transport infrastructure such as railways, roads, ports, and airports will be a particular focus in inland provinces [2] . These infrastructural elements will stimulate the economic growth in China and affect the majority of the Chinese provinces. Furthermore, some of the Chinese provinces will be directly affected by the initiative, since the New Silk Roads will go through the provinces. However, there exists many studies on effects of infrastructure on the Chinese economy, but it is seen that there is a few studies in quantitative studies on the impact of infrastructural projects according to OBOR on trade. Some scholars mentioned the impact of infrastructure on foreign trade. Under OBOR, transport infrastructure (TI) affects positively in promoting the foreign trade in states along the line [2] . Infrastructure will likely play a fundamental role in fostering regional cooperation and development, especially at the early stage of the Initiative. A large number of projects are already being considered to connect various sub-regions, including high-speed railroads, oil and gas pipelines and telecom and electricity links [3] . In his/her study, Ylander [4] discussed various variables representing the impact of OBOR, and analyzed their effects on GRP (Gross Regional Products) in the Chinese provinces directly affected by OBOR. However, the variables such as labor productivity, unemployment rate, and exports included in his/her study are all related to infrastructure. Also, he/she did not study the impact of infrastructure on trade considering the features of economic development in specific provinces, and furthermore, did not discuss the impact of correlations between elements of infrastructure on trade. From the limitations of previous studies, we raise the research problems as follows: 1) Are there correlations of individual elements of TI such as railways and highways with total value of exports and imports (TVEI), 2) If so, how do TI’s elements affect the TVEI according to provinces, and 3) what is the correlation between TI’s elements. Our study is based on that what is of significant in OBOR is infrastructural connectivity and infrastructure has its effect on trade. This will probably give the significant implications for strategic management in infrastructural investment under OBOR. From research problems and reasons, the paper aims to analyze the impact of specific elements of TI on trade in the Chinese inland provinces directly affected by OBOR as well as the impact of correlations between elements of TI. Based on research purpose, our papers are organized as follows.

In first section, authors discuss the successes of previous studies which analyzed the impact of infrastructure on economic growth and trade. Second section analyzes the correlation of individual elements of TI such as railways and highways with total value of exports and imports (TVEI) and constructs the regression models and conducts forecasts using the data from 2009 to 2017 and regression analysis method. The rest of paper discusses the results, discussions, conclusion, limitations and future research.

2. Previous Study on Impact of Infrastructure on Economic Growth and Trade

Many scholars studied the impact of infrastructural investment on economic growth based on premise that economic growth and trade are correlated. Previous researchers demonstrated the effects of infrastructural investment on economic performance in different regions and countries using long-term time series data and production function. For example, discussing the impact of public investment in infrastructure on economic performance. Aschauer [5] estimated that elasticity of output with respect to public investment in infrastructure is between 0.34 and 0.39. Some authors proved the strong positive impact of public capital in international or regional level using the production function approach [6] [7] [8] [9] , and others demonstrated the productivity of infrastructural investment using the cost function approach [10] [11] . On the other hand, some authors judged the positive impact of public capital on output in different countries using VAR (Vector Auto Regression) approach [12] [13] .

In some studies, the impact of the individual elements of infrastructure on economy was illustrated. For example, some researchers demonstrated the positive impact of energy infrastructure on output/growth [14] [15] and others tried to analyze the impact of water and sanitation on economy in various aspects [16] [17] . In addition, researchers analyzed the positive impact of telecommunication infrastructure on economic growth [18] [19] (for example, Zhan-Wei Qiang & Pitt 2004; Chakraborty & Nandi 2011) and investigated the impact of transport infrastructure on economy in various aspects [20] [21] [22] . Pereira & Andraz [23] summarized the positive impact of infrastructure investment on economic growth through the survey of previous studies on impact of infrastructure.

On the other hand, there exist few studies of impact of infrastructure on international trade in international level. Typically, Nordås and Piermartini [24] studied the correlation between infrastructure and trade. They studied the impact of quality of infrastructure on country’s performance of trade, and analyzed its effect in total bilateral trade and in automobiles, clothing, and textile sectors. Improving the TI has positive effect in bilateral trade by reducing trade cost [25] And TI investment enables to reduce the business cost according to distance and to improve the ability of firms to compete in world markets [26] Lawrence and Martin [27] investigated the quality of infrastructure on export, focusing on Sub-Sahara Africa. According to him/her, improving the quality of infrastructure has positive impact on export by lowering the transport cost faced by exporter. Discussing the close interrelations between infrastructure and trade, Behrens [28] demonstrated that countries with better infrastructure and larger volumes of interregional trade may experience a more balanced spatial development. Discussing the relation between infrastructure, trade cost, distance, and transport cost, some scholars demonstrated that TI has the positive and significant impact on trade by reducing the transport distance and cost [29] [30] [31] [32] [33] . Besides, other scholars demonstrated that Belt and Road economies located along the corridors where infrastructure projects are built experience the largest gains, and shipment times along these corridors decline by up to 11.9 percent and trade costs by up to 10.2 percent [34] .

Some Chinese scholars studied the impact of infrastructure on economic growth and trade in China.

In typical, Ni [35] analyzed the impact of transport infrastructure on economic growth in China. He studied the effect of investment in transport infrastructure on China’s economic growth and demonstrated that there is long-term stable equilibrium relationship between infrastructure investment in economic growth，and the transportation infrastructure’s positive spillovers to economic growth using data from the year 1978 to 2011. Yingying et al. [36] investigated the relationship between infrastructure capital and China’s regional economic growth for the period 1990-2013 and using a vector error correction model, found mixed support across time period and region for the contribution of infrastructure investment to economic development.

Based on the C-D production function model, Xinmin et al. [37] explored the influence of China’s TI investment on economic growth with the aggregation data and panel data, and showed that China’s investment in TI construction has a long-term and stable effect on economic growth with contribution rate of 12%. Ylander [4] conducted the regression analysis including various variables such as railways, highways, unemployment rate, labor productivity, and exports which affect the GRP (Gross Regional Products) in order to prove that OBOR and its infrastructural projects influence the GRP in China. According to him/her, OBOR has the positive effect on GRP in selected Chinese provinces.

Also, there exists a few studies of impact of TI on foreign trade under OBOR. According to Xiaodan [38] , TI projects promote the efficiencies of export and total trade, and states and regions along the line reduce the cost of bilateral trade, push ahead the exchange among regions, and enhance their export efficiency and efficiency of exports and imports among through participation in TI projects under OBOR. Zaiyong, et al. [39] demonstrates that three infrastructures such as energy, transport, and communication network and their interaction have positive effects on imports and exports of countries along the line and intraregional bilateral trade. In China, TI, export, labor productivity, and employment rate have positive effects on economic growth [4] .

As seen from analysis on previous studies, infrastructure has positive effects on economic growth and trade. However, it cannot be seen that in terms of individual province, TI has positive effect on trade. It is our review that there are various factors affecting the trade due to features of economic development in individual provinces, and thus, promotion of trade does not entirely depend on individual TI.

This paper focuses on analyzing the impact of infrastructural projects under OBOR on regional TVEI, and thus, discusses the influences of TI on region’s TVEI in the Chinese provinces directly affected by OBOR.

3. Material and Methods3.1. Data Description

According to National Development and Reform Commission et al. [1] , the initiate roads of economic corridors will have a direct effect on five Chinese inland provinces since the route will go through these provinces. The inland route will go through Shaanxi, Gansu, Ningxia Autonomous Region (Hereafter, Ningxia), Qinghai, and Xinjiang Uygur Autonomous Region (Hereafter, Xinjiang). Therefore, authors conduct the analysis based on data from year 2009 to 2017 for five Chinese inland provinces. Data are from the National Bureau of Statistics of China and various articles. Important elements of TI under study are railways and highways in terms of physical measurements. The reasons of selecting the data for period of 2009-2017 are concerned to facts that in general, many studies were conducted based on data before 2009, and 2009 is the postperiod of 2008 world financial crisis. Also, the reason of selecting the railways and highways as important elements of TI is concerned to facts that infrastructural elements are very complicated and diverse, and data of infrastructure related investment, in particular, data of infrastructural investment under OBOR are limited. Furthermore, considering that infrastructural investment is not basis for economic development in given period due to lags, using the data of physical infrastructure is acceptable.

Under above premise on data collection, authors collected the data regarding TVEI, length of railways, and length of highways for five Chinese inland provinces directly affecting by OBOR as follows (See Table 1).

Table 1 Province- and year-specific TVEI, length of railways, and length of highways

Province	Indicator	Year
Province	Indicator	2009	2010	2011	2012	2013	2014	2015	2016	2017
Shaanxi	TVEI^a (1000 USD)	8,405,392	12,101,680	14,647,271	14,799,030	20,128,062	27,364,485	30,498,504	29,947,223	40,202,798
	Length of railways in operation^b (km)	3300	4100	4100	4100	4400	4500	4500	4600	5000
	Length of highways (km)	144,100	147,500	152,000	161,400	165,200	167,100	170,100	172,500	174,400
Gansu	TVEI (1000 USD)	3,865,554	7,402,953	8,728,579	8,900,750	10,236,106	8,640,615	7,952,016	6,832,980	4,826,333
	Length of railways in operation (km)	2400	2400	2400	2500	2600	3400	3800	4100	4700
	Length of highways (km)	114,000	118,900	123,700	131,200	133,600	138,100	140,100	143,000	142,300
Qinghai	TVEI (1000 USD)	586,785	788,961	923,817	1,157,470	1,402,742	1,717,888	1,934,472	1,529,204	655,751
	Length of railways in operation (km)	1700	1900	1900	1900	1900	2100	2300	2300	2300
	Length of highways (km)	60,100	62,200	64,300	66,000	70,100	72,700	75,600	78,600	80,900

Ningxia	TVEI (1000 USD)	1,202,479	1,959,989	2,285,746	2,216,710	3,217,686	5,435,212	3,739,255	3,252,489	5,039,517
	Length of railways in operation (km)	900	1200	1300	1300	1300	1300	1300	1300	1400
	Length of highways (km)	21,800	22,500	24,500	26,500	28,600	31,300	33,200	33,900	34,600
Xinjiang	TVEI (1000 USD)	13,947,831	17,130,110	22,819,672	25,170,060	27,561,391	27,672,315	19,669,397	17,637,744	20,568,530
	Length of railways in operation (km)	3700	4200	4300	4700	4700	5500	5900	5900	5900
	Length of highways (km)	150,700	152,800	155,200	165,900	170,200	175,500	178,300	182,100	185,300

Source: National Bureau of Statistics of China [40] . Note: ^aData of import and export data are from the general administration of customs. Data of 1978 were from the Ministry of Foreign Trade, and data since 1980 are from Customs statistics. ^bLength of Railways in Operation refers to the total length of the trunk line for passenger and freight transportation in full operation or temporary operation calculated the actual length of the period between the two stations. Any full or partial lane and above lines, in calculated in the actual length of the first line; tracks, station lines, segments, branch lines and special purpose lines and does not calculate shipping connecting lines of business mileage.

These data show the specific features according to provinces. In other words, inland provinces include three data. That is why under OBOR land roads are linked through the railways and highways.

Putting these into graphs, features according to provinces seem to be more apparent, and thus, it can conclude that the specifics of individual provinces must consider rightfully under study.

Picturing the relationship between TVEI and lengths of railways and highways, it is as follows (Figures 1-5).

Graphic description indicates the features of trade development in the Chinese inland provinces directly affecting by OBOR more clearly. It shows that there are few differences between growths of TEVI, length of railways in operation, and length of highways according to provinces under study over period from 2009 to 2017.

For comparative consideration, calculating the province- and indicator-specific averages and putting them into graph, it is as follows (Figure 6).

It shows that all infrastructural elements grow, but the growth of TVEI is not proportional to them. In other words, while some provinces indicate the proportional relation, others—reverse relation. This enables us to analyze the impact of TI on TVEI in provinces under study.

3.2. Methods

Section 3.1 indicates that interrelations between three indicators emerge differently according to provinces as a result of description. This study aims to reveal the influences of TI’s elements on TVEI according to provinces, and thus, it is important to analyze the influences of railways and highways on TVEI in line with province-specific features and to reveal the interrelation between two elements of TI.

For this, first of all, it is important to understand the variation of individual values compared to average values according to provinces and indicators. That is why primary data and putting them into graphs only indicate the general features in interrelations between three indicators but not concrete features. Therefore,

we calculate descriptive statistics such as maximum, minimum, mean, and standard deviation of each indicator according to provinces. Next, we calculate the correlation coefficients to reveal the correlation between three indicators. This is significant procedure in revealing what degree of correlation between three indicators there exist. As a result of correlation analysis, correlation degree of factor indicators with result indicator and correlation between two factor indicators are revealed. Finally, in order to analyze the influences of TI elements on TVEI in provinces under study, we conduct the linear multiple regression analysis and use the statistical package SPSS. Conducting the linear multiple regression analysis is based on assumption that TVEI and diverse TI elements are in linear relation and these elements affect the TVEI diversely. Also, the degree of changes in TVEI according to changes of diverse TI elements can easily be estimated by drawing regression models through regression analysis. SPSS enables us to conduct regression analysis by treating large panel data conveniently.

As seen above, considering the features of economic development in provinces under study, regression analysis is conducted according to provinces, and based on them, regression equations are constructed. For regression analysis, correlation matrix and regression models are constructed, and in turn, statistical forecasts can be conducted. TVEI is selected as dependent variable and length of railways and length of highways are selected as independent variables.

3.2.1. Calculation of Descriptive Statistics and Correlation Coefficients

First of all, let’s calculate the descriptive statistics and correlation coefficients regarding each province, respectively using SPSS. Descriptive statistics represent major features of every independent variables and dependent variable, and correlation coefficients enable to reveal the influences of each independent variable on TVEI and correlation between independent variables.

Descriptive statistics and correlations regarding inland provinces are as follows (See Table 2 and Table 3).

Table 2 Descriptive statistics

Province^a	Variable	Minimum	Maximum	Mean	Standard deviation
Shaanxi	Length of railways in operation (km)	3300	5000	4333.33	471.699
	Length of highways (km)	144,100	174,400	1.62E5	11,154.421
	TVEI (100 thousand USD)	12102	402028	2.08E5	122,932.946
Gansu	Length of railways in operation (km)	2400	4700	3144.44	880.499
	Length of highways (km)	1.14E5	1.43E5	1.3166E5	10,586.20696
	TVEI (100 thousand USD)	38,656	102,361	7.49E4	20,394.143
Qinghai	Length of railways in operation (km)	1700	2300	2033.33	223.607
	Length of highways (km)	60,100.00	80,900.00	7.01E4	7405.5918
	TVEI (100 thousand USD)	5868	19345	1.19E4	4841.537

Ningxia	Length of railways in operation (km)	900	1400	1255.56	142.400
	Length of highways (km)	21,800.00	34,600.00	2.8544E4	4964.653
	TVEI (100 thousand USD)	12025	54352	3.15E4	14,134.048
Xinjiang	Length of railways in operation (km)	3700	5900	4977.78	842.285
	Length of highways (km)	150,700	185,300	1.68E5	13,048.382
	TVEI (100 thousand USD)	139,478	276,723	2.14E5	48,104.223

Note: ^anumber of observations = 9, Source: Own calculation.

Table 3 Correlation coefficientsa

Province^b	Independent Dependent	Length of railways in operation (km)	Length of highways (km)	Correlation between independent variables
Shaanxi	TVEI (100 thousand USD)	0.823**	0.928**	0.893**
Gansu	TVEI (100 thousand USD)	−0.330	0.205	0.837**
Qinghai	TVEI (100 thousand USD)	0.517	0.469	0.943**
Ningxia	TVEI (100 thousand USD)	0.675*	0.833**	0.691*
Xinjiang	TVEI (100 thousand USD)	0.203	0.285	0.966**

Note: **Correlation is significant at the 0.01 level (2-tailed). ^aPearson Correlation, ^bnumber of observations = 9, Source: Own calculation.

Diverse descriptive statistics and correlations show the features in impacts of individual elements of infrastructure on TVEI according to provinces. In other words, in general, railways in operation and highways have different correlations with TVEI in all inland provinces. For example, Shaanxi has the highest correlations of railways and highways with TVEI (0.823 and 0.928), Gansu—the lowest correlation of railways (−0.03), and Xinjiang has lower correlations of two independent variables with TVEI. Correlation coefficients are statistically significant at the one percent.

3.2.2. Construction and Analysis of Regression Models

Given that correlations of TI elements with TVEI are revealed, linear regression models are constructed and analyzed in provinces under study.

Construction and analysis of regression models are conducted according to each province, considering province-specific features. Results of construction and analysis are presented in Appendix. As a result of regression analysis, significance and availability of regression models were tested, and thus, based on them, influences of TI elements on TVEI according to provinces can be analyzed, and province-specific TVEIs are estimated.

As a result, regression equations according to provinces can be described as follows.

Inland provinces

Shaanxi:

y = − 1.457 E 6 − 6.998 x 1 + 10.488 x 2 ;

Gansu:

y = − 209921.285 − 38.688 x 1 + 3.087 x 2 ;

Qinghai:

y = − 10163.314 + 14.578 x 1 − 0.108 x 2 ;

Ningxia:

y = − 49228.595 + 18.885 x 1 + 1.997 x 2 ;

Xinjiang:

y = − 310552.721 − 62.760 x 1 + 0.150 x 2 ;

where x 1 —length of railways in operation (km).

x 2 —length of highways (km).

y —TVEI (100 thousand USD).

Constructing the regression models, significance provability, contribution degree, and standard error of estimate are calculated, and they are meaningful values. For example, for Shaanxi, significance provability is 0.03, contribution degree (R²)—0.861, and standard error of estimate −53,001.881 respectively. This shows that regression equation is significant and statistically meaningful, x 1 and x 2 explain 86.1% of changes in TVEI, and error interval of TVEI is in ±53,001.881 (100 thousand USD).

4. Results and Discussion

We gathered primary data in terms of TVEI, length of railways, and length of highways according to provinces under study and put them into graphs. As a result, we found the general features of interrelations between three indicators according to provinces. From Figures 1-6, we found that while railways and highways grew systematically in all provinces and growth of highways is faster than those of railways, growth of TVEI is different among provinces. However, we cannot understand how railways and highways affect the TVEI with such graphic description. Therefore, we calculated descriptive statistics and correlation coefficients of each indicator according to provinces. According to calculation of descriptive statistics, it is revealed that variations of individual indicators compared to average values in terms of standard deviation are very different according to provinces and indicators. In detail, while Shaanxi has the largest variation, Qinghai—the smallest in terms of TVEI. On the other hand, Gansu has the largest variations and Ningxia—the smallest in terms of both railways and highways. This shows that there are differences in growth according to provinces and indicators. At the same time, these may be indirect marks of correlations between three indicators. We calculated the correlation coefficients in order to reveal correlations of railways and highways. As a result, railways in operation and highways have different correlations with TVEI in all inland provinces. For example, Shaanxi has the highest correlations of railways and highways with TVEI (0.823 and 0.928), Gansu—the lowest correlation of railways (−0.03), and Xinjiang has lower correlations of railways and highways with TVEI.

We conducted the regression analysis in order to analyze the influences of individual TI elements and to estimate future TVEI in the Chinese inland provinces directly affecting by OBOR. As a result, it is seen that contributions of TI elements to TVEI are diverse and standard errors of estimate are relatively large in inland provinces. Results of regression analysis are given in Appendix A. Interpretations on results of regression analysis in provinces under study were discussed in subsection 3.2.2.

5. Conclusions

This study aims to analyze and forecast the influences of individual TI elements such as railways and highways on TVEI in the Chinese inland provinces directly affecting by OBOR, and to help in rational management related to infrastructural investment under execution of OBOR initiative. Therefore, authors conducted the correlation and regression analysis using panel data of TI elements from 2009 to 2017 according to provinces. Its conclusion is that TI elements contribute in promoting trade in some extend under OBOR, and these contributions are differ among individual inland province. Another conclusion is that individual TI elements affect TVEI differently according to correlations between them. This study can contribute to making decision of ways of investment in infrastructure for ensuring purposeful TVEI in future under execution of OBOR.

Our study has some limitations. First, we conducted analysis using data regarding some elements of infrastructure, that is, railways and highways due to limit of data. Second, we did not ensure the correctness in analysis enough due to catching analysis period of 9 years. Finally, comparability of data is not ensured. This means that scales of infrastructural elements and TVEI rely on populations and areas of provinces. From this, in future research, it is necessary to select data regarding all elements of infrastructure, take long-term analysis period as possible, and ensure the comparability of data in various aspects. Also, it is desirable to analyze based on standardization of data of dependent and independent variables in order to ensure comparability. Such analysis can enhance the correctness and reliability of analysis and help in practical decision-making.

Acknowledgements

We would like to thank the Chief of Center for North and South Korea Studies, Professor. Doctor. Can-Kui Piao at Yanbian University for insightful comments and materials related to “One Belt, One Road”.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

Cite this paper

Li, J.H., Rim, G.-N. and An, C.-J. (2019) Impact of Transport Infrastructure on Trade: Evidence from the Chinese Inland Provinces under “One Belt, One Road”. Open Journal of Business and Management, 7, 2030-2046. https://doi.org/10.4236/ojbm.2019.74140

Appendix A: Results of Regression Analysis

Table A1 Contribution of independent variables to dependent variableb

Model	R	R²	Adjusted R²	Standard error of estimate
Shaanxi	0.928^a	0.861	0.814	53,001.881
Gansu	0.938^a	0.880	0.840	8164.055
Qinghai	0.520^a	0.270	0.027	4775.666
Ningxia	0.844^a	0.713	0.617	8744.134
Xinjiang	0.402^a	0.162	−0.118	50,856.252

Note: ^aPredictors: (Constant), length of highways (km), length of railways in operation (km); ^bDependent Variable: TVEI (100 thousand USD). Source: Compiled by author.

Table A2 Test of significanceb

Model		Sum of Squares	Degree of freedom	Mean Square	F	Significance
Shaanxi	Regression Residual Total	1.040E11 1.686E10 1.209E11	2 6 8	5.202E10 2.809E9	18.519	0.003^a
Gansu	Regression Residual Total	2.927E9 3.999E8 3.327E9	2 6 8	1.464E9 6.665E7	21.961	0.002^a
Qinghai	Regression Residual Total	5.068E7 1.368E8 1.875E8	2 6 8	2.534E7 2.281E7	1.111	0.389^a
Ningxia	Regression Residual Total	1.139E9 4.588E8 1.598E9	2 6 8	5.697E8 7.646E7	7.451	0.024^a
Xinjiang	Regression Residual Total	2.994E9 1.552E10 1.851E10	2 6 8	1.497E9 2.586E9	0.579	0.589^a

Note: ^aPredictors: (Constant), length of highways (km), length of railways in operation (km); ^bDependent Variable: TVEI (100 thousand USD). Source: Compiled by author.

Table A3 Regression coefficientsa

Model		Unstandardized Coefficients		Standardized Coefficients	t	Significance	95% Confidence Interval for B
Model		B	Standard Error	Beta	t	Significance	Lower Bound	Upper Bound
Shaanxi	(Constant) length of highways (km) length of railways in operation (km)	−1.457E6 −6.998 10.488	315,898.881 87.929 3.728	−0.027 0.952	−4.611 −0.080 2.814	0.004 0.939 0.031	−2.230E6 −222.154 1.367	−683,735.040 208.157 19.609
Gansu	(Constant) length of highways (km) length of railways in operation (km)	−209,921.285 −38.688 3.087	50,903.989 5.983 0.498	−1.670 1.602	−4.124 −6.467 6.204	0.006 0.001 0.001	−334,478.858 −53.326 1.870	85,363.712 −24.049 4.305

Qinghai	(Constant) length of highways (km) length of railways in operation (km)	−10,163.314 14.578 −0.108	16,075.344 22.703 0.686	0.673 −0.166	−0.632 0.642 −0.158	0.551 0.545 0.880	−49,498.264 −40.974 −1.786	29,171.636 70.131 1.569
Ningxia	(Constant) length of highways (km) length of railways in operation (km)	−49,228.595 18.885 1.997	27,452.731 30.031 0.861	0.190 0.702	−1.793 0.629 2.319	0.123 0.553 0.060	−116,403.008 −54.600 −0.110	17,945.817 92.369 4.105
Xinjiang	(Constant) length of highways (km) length of railways in operation (km)	−310,552.721 −62.760 0.150	513,292.556 82.767 0.056	−1.099 0.909	−0.605 −0.758 0.929	0.567 0.477 0.388	−1.567E6 −265.283 −8.107	945,428.917 139.762 18.039

^aDependent Variable: TVEI (100 thousand USD). Source: Compiled by author.

Table A4 Estimation of TVEI using regression equationa.

Province	Indicator	Case Number
Province	Indicator	1	2	3	4	5	6	7	8	9
Shaanxi	Standard residual	0.992	−0.933	0.712	−1.120	−0.827	0.176	0.173	−0.392	1.220
	TVEI (100 thousand USD)	84,054	12,102	146,473	147,990	201,281	273,645	304,985	299,472	402,028
	Predicted value	31,501.90	61,562.14	108,757.74	207,344.12	245,098.68	264,325.88	295,789.62	320,260.78	337,388.48
	Residual	5.255E4	−4.946E4	3.771E4	−5.935E4	−4.382E4	9.319E3	9.195E3	−2.079E4	6.464E4
Gansu	Standard residual	−1.288	1.192	1.001	−1.150	0.052	0.187	0.483	−0.563	0.087
	TVEI (100 thousand USD)	38656	74030	87286	89008	102361	86406	79520	68330	48263
	Predicted value	49,167.93	64,295.14	79,113.64	98,398.79	101,939.28	84,881.60	75,580.96	72,927.54	47,553.99
	Residual	−1.051E4	9.734E3	8.172E3	−9.391E3	421.777	1.525E3	3.939E3	−4.598E3	709.343
Qinghai	Standard residual	−0.469	−0.608	−0.278	0.250	0.857	0.965	0.874	0.093	−1.684
	TVEI (100 thousand USD)	5868	7890	9238	11,575	14,027	17,179	19,345	15,292	6558
	Predicted value	8105.34	10,793.39	10,565.75	10,381.48	9937.05	12,570.90	15,172.23	14,847.04	14,597.73
	Residual	−2.237E3	−2.904E3	−1.328E3	1.193E3	4.090E3	4.608E3	4.172E3	444.999	−8.040E3
Ningxia	Standard residual	0.081	0.140	−0.160	−0.696	−0.031	1.888	−0.485	−1.202	0.466
	TVEI (100 thousand USD)	12,025	19,600	22,857	22,167	32,177	54,352	37,393	32,525	50,395
	Predicted value	11,312.58	18,376.17	24,259.59	28,254.55	32,449.26	37,842.47	41,637.68	43,035.92	46,322.61
	Residual	712.210	1.224E3	−1.402E3	−6.087E3	−272.404	1.651E4	−4.245E3	−1.051E4	4.073E3
Xinjiang	Standard residual	−1.300	−.263	0.745	0.656	0.707	1.198	−0.155	−0.926	−0.662
	TVEI (100 thousand USD)	139,478	171,301	228,197	251,701	275,614	276,723	196,694	176,377	205,685
	Predicted value	205,605.43	184,653.82	190,296.11	218,327.84	239,681.50	215,792.90	204,593.49	223,464.16	239,355.26
	Residual	−6.613E4	−1.335E4	3.790E4	3.337E4	3.593E4	6.093E4	−7.900E3	−4.709E4	−3.367E4

^aDependent Variable: TVEI (100 thousand USD). Source: Compiled by author.

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