Economic Review 33 (2015) 123–136

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China Economic Review

Economic spillover effects in the Bohai Rim Region of China: Is the economic growth of coastal counties beneficial for the whole area?

Caizhi SUN ⁎, Yudi YANG, Liangshi ZHAO

Center for Studies of Marine Economy and Sustainable Development, Normal University, 850 Huanghe Road, 116029, China article info abstract

Article history: This study uses panel data on the Bohai Rim Region of China to test for spatial autocorrelation, and Received 27 July 2014 measures economic spatial spillover effects with the space Durbin econometric model. We discuss Received in revised form 14 January 2015 whether the economic development of coastal counties benefits the whole area. To do this, we Accepted 15 January 2015 focus on the “distance from the coast” factor, which is influenced by transportation time. The Available online 20 January 2015 results indicate the presence of significant spatial autocorrelation in the Bohai Rim Region. Further, economic spatial spillover effects exist in this region. “Distance from the coast” exerts a JEL classification: significantly negative impact on the local GDP per capita but a significantly positive impact on R11 the GDP per capita of other districts. This means that the economic development of coastal Keywords: counties does not benefit the whole region. “Value of exports” exerts a significantly positive Bohai Rim Region influence on the local economy and no significant influence on other counties, while “foreign Economic spillover effects direct investment” exerts a significantly positive influence on the local economy and a significant- Coastal county economy ly negative influence on other counties. “Number of employees in units” exerts a significantly Space Durbin econometric model positive influence on the local economy and a significantly negative influence on the other Spatial autocorrelation counties. The factors “primary industry's share in GDP” and “tertiary industry's share in GDP” influence the local economy positively, but the former exerts no significant influence on other counties and the latter exerts a negative influence on other counties. “Rate of fixed asset invest- ment” influences the local economy negatively and has no significant effect on other counties. “Total retail sales of social consumer goods” has no significant influence on the local economy but a positive significant influence on the others. Finally, marine resource utilization and marine output can affect economic growth positively. On this basis, we propose policy suggestions for harmonious economic development in this region. © 2015 Elsevier Inc. All rights reserved.

1. Introduction

Since the start of China's open-door policy, the coastal areas of China have made remarkable achievements in economic and social development, relying on their great location advantages and abundant marine resources. However, with an enlargement of the economic gap between coastal and non-coastal areas, some scholars consider that the Chinese government may have been mistaken in its earlier belief that the eastern coastal areas would take the lead in development and trigger improvements in non-coastal areas (Bo, 2008; Peng, 2008). In recent years, the Chinese government has created a strategy for land–sea coordination, to strengthen the

⁎ Corresponding author. Tel.: +86 411 84258412; fax: +86 411 84258390. E-mail address: [email protected] (C. Sun).

http://dx.doi.org/10.1016/j.chieco.2015.01.008 1043-951X/© 2015 Elsevier Inc. All rights reserved. 124 C. Sun et al. / China Economic Review 33 (2015) 123–136 effective interaction between ocean and inland. Is the economic growth of coastal regions beneficial, then, for the whole area? Does the ocean significantly influence the development of coastal regions? How can complementarity between the economies of coastal and non-coastal regions be achieved, so that one may benefit from the economic development of the other? This study will try to solve the aforementioned problems by measuring the spatial spillover effects of coastal economic development. An economic spatial spillover effect is defined as the influence of the economic development of a region on regions near it. Starting in the 1950s, development economists have paid close attention to the theoretical study of the relationship between unbalanced develop- ment and economic growth, using growth pole theory, cumulative and circular causality theory, and regional economic growth propa- gation theory (Hirschman, 1958; Myrdal, 1957; Perroux, 1950). Since the 1980s, the new economic geography systematically explored the spillover mechanisms associated with economic growth by analyzing the externalities of spatial agglomeration (Baldwin, Martin, & Ottaviano, 2001; Krugman, 1991). Recently, empirical studies about economic spillover effects made progress in measuring spillover effects among nations and districts (Carlino & Defina, 1995; Conley & Ligon, 2002; Douver & Peeters, 1998; Ramajo, Marquez, Hewings, & Salinas, 2008; Sonis, Hewings, & Guilhoto, 1995). The above studies serve as significant references for the theories and methods we use in this study. Over the past few years, some scholars have devoted themselves to researching interregional and interprovincial economic spillover effects in China. Some of these studies divided China into three areas—east, central, and west. According to Li and Chen (2004), the economic growth of the eastern area produced a spillover effect on the development of the central and the western areas; however, this was disputed by Bo and An (2010). According to Brun, Combes, and Renard (2002), the beneficial effect exerted on non-coastal provinces by the growth of the coastal provinces was not enough to reduce the economic disparity between the two. Meanwhile, other studies divided China into more subareas. For instance, Groenewold's (2008)study, which divided China into six areas, concluded that the growth of the southeastern coastal provinces had no obvious spillover effects on other provinces. Chen (2007) study, which divided China into eight areas, pointed out that while the northern and eastern coastal areas had obvious spillover effects on other areas, the southern coastal area did not. Pan and Li (2007) and Peng (2008) concluded that the economic development of coastal areas had no obvious impetus on the growth of non-coastal areas, using an eight-region input–output table. In conclusion, these studies used different methods of dividing the study area and different ways of measuring spillover effects, and yielded mixed results. All of them have contributed greatly to the measurement of coastal economic spillover effects in China. Nonetheless, the goals of the above studies differ from those of this study, as we discuss below. First, earlier studies on economic spillover effects always involved all of China and focused on economic spillover between regions or provinces. However, few studies have concerned economic spillover between counties, particularly from coastal regions. According to this study's goals with respect to the ocean, it's appropriate to choose the Bohai Rim Region as our study area, for the following reasons. 1) As the largest marine economic zone in China, the Bohai Rim Region accounted for 35.8% of Chinese gross ocean product in 2012. Our choice is conducive to discussions on the economic spillover between counties in a coastal region and the link between ocean and regional economies. 2) Considering all coastal areas together may easily mask the developmental peculiarities of specific areas. This region has very serious problems in regional develop- ment, including industrial isomorphism, redundant construction, vicious competition, and uncoordinated development (Zhou and Shu, 2008). What we do in this study is a meaningful attempt to solve them. Consequently, we chose the Bohai Rim Region (rather than all of China) as our study area and try to offer some suggestions for the regional coordinated development by exploring the interactive mechanism among the various economic factors. Second, earlier studies have always looked at the regional or the provincial level. It is possible that the influence of factors affecting coastal economic development, such as marine resources and environments, may be felt inland. However, given that ocean-related statistical data from the State Oceanic Administration and the National Bureau of Statistics in China almost does not involve the non-coastal cites (SOAC, 2013), we believe that such marine influences do not extend to the whole province or to the whole region. Therefore, setting our study at the county level and having a scope of coastal cities gives us a larger sample and a more reasonable and precise explanation of the truth. Third, if one goal of this study is to explore the ocean's impact on regional development, then it is unsuitable to divide the research area into coastal versus non-coastal counties by virtue of administrative boundaries, as past studies did. Be- cause all the counties in this area can be influenced by the ocean, this study tries to address the defect by introducing the factor “distance from the coast,” which is influenced by transportation time. In this way, we can clearly test the impact of the ocean and discuss precisely whether the whole area benefits from the economic development of counties close to the ocean. In order to achieve the above goals, this study uses the spatial Durbin econometric model to measure economic spillover effects. This model includes spatial lags of the dependent and the explanatory variables, and has been proven to outperform the spatial lag and spatial error models (Sun, Zhao, Zou, & Zheng, 2014). Meanwhile, it can also measure the influence of local explanatory variables on the outcome variables of other counties, and its reliability has been verified in the spillover effects research in other professional fields (Tong, Yu, Cho, Jensen, & Ugarte, 2013; Yu, Jong, Storm, & Mi, 2013). Using panel data on the economic development of 192 districts in the Bohai Rim Region, we attempt to verify the spillover effects of coastal economic growth. We also try to find the economic interaction patterns among the counties, and analyze contradictions between regional competition and cooperation. Section 2 introduces the methods of the spatial autocorrelation test and the measure- ment of spillover effects. Section 3 introduces the study areas and database used. In Section 4, we analyze our results and do a discus- sion. Section 5 contains the conclusion and some suggestions. C. Sun et al. / China Economic Review 33 (2015) 123–136 125

2. Research methodology

2.1. Spatial autocorrelation test

This study uses the global Moran's I index and the local Moran's I index to test for spatial autocorrelation among the economies of the counties in the Bohai Rim Region (Moran, 1950). The expressions for them are as follows:

The global Moran's I index:

XnXn Wijziz j ’ ¼ i¼1 j≠i ð Þ Moran s I Xn Xn 1 σ 2 ; Wij i¼1 j≠i

where n is the number of observations, xi is the ith observation, zi is the standardized value of xi, Wij is the spatial weight, zi ¼ n n − xi x ¼ 1 ∑ σ 2 ¼ 1 ∑ ðÞ− 2 σ , x n xi,and n xi x . The expected value and variance of Moran's I can be calculated from the hypothetical i¼1 i¼1 spatial data distribution. The random distribution hypothesis is

1 EIðÞ¼ − ð2Þ n−1

hi hi 2− þ − þ 2 − 2− − þ 2 nn 3n 3 s1 ns2 3s0 kn n s1 2ns2 6s0 VarðÞ¼I ; ð3Þ 2ðÞ− ðÞ− ðÞ− s0 n 1 n 2 n 3 n n n n n n n n n 2 ¼ ∑ ∑ ¼ 1 ∑ ∑ð þ Þ2 ¼ ∑ð∑ þ ∑ Þ2 ¼ ∑ ðÞ− 4 = ∑ ðÞ− 2 where s0 Wij, s1 2 Wij W ji , s2 Wij W ji ,andk xi x xi x . i¼1 j¼1 i¼1 j¼1 i¼1 j¼1 j¼1 i¼1 i¼1 The original hypothesis is that there is no spatial autocorrelation. Hypothesis testing can be done using the following standardized statistic (Z) and the normal distribution table.

I−EIðÞ Z ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð4Þ VarðÞI

The global Moran's I index value, calculated by the line standardized weight matrix, is between −1 and 1. An index in the range [−1, 0) indicates negative spatial correlation, an index of 0 implies no spatial correlation, and an index in the range (0, 1] signifies positive spatial correlation. The expression for the local Moran's I index is:

Xn ðÞ¼ 0 : ð Þ Ii d zi W ijz j 5 j≠i

A positive local Moran's I index value indicates that regions with the same type of attribute value are adjacent to each other. If the index is negative, the adjacent regions have different types of attribute values. The greater the absolute value of the index, the greater the degree of proximity. Z-statistics can denote the obviousness of the local Moran's I index.

2.2. Spatial Durbin econometric model

If the economic development of different districts is spatially correlated, a spatial Durbin econometric model can be used to measure economic spillover effects in the Bohai Rim Region (Anselin, 1988). We apply the following spatial Durbin econometric model: ¼ ρ þ β þ θ þ ε ; ε ; σ 2 : ð Þ Yit WijY jt Xit WijX jt it N 0 I 6

The results obtained from the model represented by Equation (6) can be compared to (a) the results from a similar model, which omits the spatial lag of the explanatory variable from the right-hand side (Equation (7) below), and (b) the results from another 126 C. Sun et al. / China Economic Review 33 (2015) 123–136 model, which omits the spatial lags of both the dependent and the explanatory variables from the right-hand side (Equation (8) below). The model that omits a spatial lag of the explanatory variable can be written as: ¼ ρ þ β þ ε ; ε ; σ 2 : ð Þ Yit WijY jt Xit it N 0 I 7

The model that omits the spatial lags of the explanatory as well as the dependent variables can be written as: ¼ β þ ε ; ε ; σ 2 ; ð Þ Yit Xit it N 0 I 8 where Y, W, X, WX, WY,andε denote the GDP per capita, the spatial weight matrix, the factors influencing GDP per capita, the spatial lag of the influencing factors, the spatial lag of the GDP per capita, and the random disturbance term (which is independent of anything to do with the regions and periods), respectively. ρ denotes the coefficient on the spatial lags of GDP per capita. β denotes the coefficient on the factors influencing GDP per capita. θ denotes the coefficient on the spatial lags of these factors. In models containing spatial lags of the explanatory or dependent variables, the interpretation of the parameters becomes richer and more complicated than that seen in other models. LeSage and Pace (2009) provided some explanations about the parameters of the spatial Durbin econometric model in the form of a partial derivative matrix, and defined the terms “average total impact,”“average direct impact,” and “average indirect impact.” Taking these into account, Equation (6) can be rewritten as:

ðÞ−ρ ¼ β þ θ þ ε: ð Þ In W Y X WX 9

−1 Multiplying both sides by (In − ρW) ,weget:

Xk ¼ ðÞþ ðÞε; ð Þ Y Sr W xr VW 10 r¼1

−1 where Sr(W)=V(W)(Inβr + Wθr)and,V(W)=(In − ρW) . By expanding Equation (10), we obtain the following: 0 1 0 1 ðÞ ðÞ … ðÞ Y1 Sr W 11 Sr W 12 Sr W 1n x1r B C Xk B ðÞ ðÞ … ðÞ C B Y2 C ¼ B Sr W 21 Sr W 22 Sr W 2n C x2r þ ðÞε: ð Þ @ ⋮ A @ ⋮⋮⋮⋮A ⋮ VW 11 ¼ r 1 ðÞ ðÞ … ðÞ Yn Sr W n1 Sr W n2 Sr W nn xnr

The average total, direct, and indirect impact can be inferred from Equation (11), respectively, as:

ðÞ ¼ −1 −1 ðÞ ð Þ Mrtotal n ln Sr W ln 12

ðÞ ¼ −1 ðÞðÞ ð Þ Mrdirect n tr Sr W 13

ðÞ ¼ ðÞ − ðÞ ; ð Þ Mrindirect Mrtotal Mrdirect 14

T where ln =(1… 1)1× n. The average total impact is the average of all derivatives of yi with respect to xjr for any i and j. The average direct impact is the average of all own derivatives. The average of all derivatives (average total impact) less the average direct impact equals the average cross-derivative (the average indirect impact). Equation (9) relates to how changes in a single observation j influence all observations.

2.3. Spatial weight matrix

This study uses a spatial weight matrix based on the distance function (Sun and Zhao, 2013). This spatial weight matrix measures the degree of connection between two regions by using the spatial distance between them. This avoids the defects of the traditional spatial adjacency matrix, which neglects the connections between two non-adjacent regions. Spatial weight matrix elements based on the distance function are defined as follows:  ðÞ¼ ¼ 0 i j ; ð Þ Wij = ðÞ≠ 15 1 dij i j

where dij is the distance between the centers of gravity of regions i and j. All elements of the spatial weight matrix W need to be stan- dardized before use. C. Sun et al. / China Economic Review 33 (2015) 123–136 127

3. Description of study area and database

The Bohai Rim Region, located in the extreme north of the Chinese mainland coastline, comprises the entire coastal area of the and part of the coastal area of the . As Fig. 1 shows, the whole region has 17 coastal cities, which are Dandong, Dalian, , , , , , , Tianjin, Cangzhou, Binzhou, Dongying, Weifang, Yantai, Weihai, Qingdao, and Rizhao. These coastal cities, with an aggregate area of 174,617.2 km2 and a population of 85.61 million, contributed 11.7% of Chinese GDP in 2012. The distributions of GDP per capita, industrial structure, population, and employed persons in these 17 cities are described in Fig. 2. In past decades, although the economic development of the Bohai Rim Region occurred later than that of the Yangtze River Delta region and the Pearl River Delta region in China, it has shown a high rate of development by relying on an abundance of resources, including fisheries, ports, oil and gas, landscape, and sea salt. However, against the backdrop of macroeconomic adjustment and reforms in China, the Bohai Rim Region is currently in a critical period of economic transformation. Meanwhile, the development prospect of the whole region is being threatened by the overuse of resources, the deterioration of the ecological environment, and vicious competition originating from uncoordinated development. The 129 districts chosen as the study area in this study cover all the counties under the jurisdiction of the 17 aforementioned coastal cities. Furthermore, some counties under the jurisdiction of other non-coastal cities in this region are also near the coast, and so that they are absorbed into the study area. As can be seen from Fig. 3, the whole study area is located between the 35.1° and 42.3° north latitudes and the 115.7° and 125.7° east longitudes. In this study, data on GDP, population, and other factors have been taken from the statistical yearbooks, edited by the Tianjin Provincial Statistic Bureau, the Liaoning Provincial Statistic Bureau, the Provincial Statistic Bureau, and the Shandong Provincial Statistic Bureau. Data on road density are from the statistical bulletins of each county, from 2001 to 2012. Ocean-related data are from the State Oceanic Administration of China. Missing observations have been filled in with approximations of the relevant values of adjacent areas, or by fitting in forecast values. All variables are measured in current-year prices.

4. Empirical results

4.1. Spatial autocorrelation test

Using data on GDP per capita in 129 districts, we calculated the global Moran's I index value (2001–2012), using the method introduced in Section 2. Table 1 shows that the global Moran's I index for every year is above 0.01, at the 1% (2001–2009), 5% (2010), and almost 10% (2011, 2012) significance levels. This means that the economic level of counties in the Bohai Rim Region exhibits significantly positive spatial autocorrelation; the spatial distribution of a county economy is not completely random. There is a spatial agglomeration phenomenon among regions with similar levels of GDP per capita (i.e., the higher GDP per capita regions

Fig. 1. Location of 17 coastal cities in the Bohai Rim Region. 128 C. Sun et al. / China Economic Review 33 (2015) 123–136

Fig. 2. GDP per capita, industrial structure, population, and employed persons among the 17 coastal cities. Source: The Tianjin Provincial Statistic Bureau, the Liaoning Provincial Statistic Bureau, the Hebei Provincial Statistic Bureau, and the Shandong Provincial Statistic Bureau.

Fig. 3. Location of the study areas including 129 districts. C. Sun et al. / China Economic Review 33 (2015) 123–136 129

Table 1 The global spatial autocorrelation index of the county economy in the Bohai Rim Region. Source: Authors' own calculations, using MATLAB R2009a software. Year Moran's Iz-Statistic Year Moran's Iz-Statistic

2001 0.0347 3.7245 2007 0.0339 3.6478 2002 0.0351 3.7540 2008 0.0259 2.9546 2003 0.0333 3.6022 2009 0.0244 2.8253 2004 0.0322 3.5051 2010 0.0128 1.8077 2005 0.0300 3.3094 2011 0.0105 1.6039 2006 0.0306 3.3665 2012 0.0106 1.6117

are clustered together, as are the regions with low GDP per capita). Therefore, this study argues that a county economy in the Bohai Rim Region displays spatial correlation. It is, therefore, reasonable to measure the economic spillover effects among districts by using the space econometric model. To judge whether a local agglomeration phenomenon exists in the Bohai Rim Region, we use the local Moran's I index to find the degree of local space agglomeration (Anselin, 1995). In line with the local Moran's I index method discussed in Section 2,wedrawa LISA cluster map of the county economy in the Bohai Sea Ring Area for 2001 and 2012 (Fig. 4). Fig. 4 shows that the H–H areas are mainly concentrated in the northwestern regions of , the northeastern regions of Liaodong Bay, the western regions of the , the northern and southern coastal regions of the Shandong peninsula, and the Yellow River Delta. The L–L areas lie mainly on the Liaoning–Hebei and Shandong–Hebei borders. The H–LandL–H areas mainly lie in the regions between the H–HandL–L areas.

4.2. Measuring spillover effects

The spatial autocorrelation test demonstrates that the GDP per capita of districts in the Bohai Rim Region is positively spatially correlated. In particular, we find that H–H areas are mainly concentrated in coastal regions. We now examine the questions introduced at the beginning of Section 1. Are there significant spatial spillover effects in the Bohai Rim Region? Is the economic growth of coastal regions beneficial for the whole area? How do different factors influence the development of a county economy? These questions are explored further in the rest of this section.

Fig. 4. LISA cluster map of the county economy in the Bohai Rim Region. Note: “H–H” denotes a county with a high-level local economy and high-level economic counties adjacent. “L–L” denotes a county with a low-level local economy and low-level economic counties adjacent. “H–L” denotes a county with a high-level local economy and low-level economic counties adjacent. “L–H” denotes a county with a low-level local economy and high-level economic counties adjacent. Source: Authors' own calculations, using MATLAB R2009a software. 130 C. Sun et al. / China Economic Review 33 (2015) 123–136

4.2.1. Index selection (1) Our dependent variable is gross domestic product per capita (Y). This index stands for the level of economic activity of districts in the region (Huang, 2007). We use the natural logarithm of GDP per capita in the program in order to reduce the influence of heteroscedasticity. (2) The explanatory variable that measures geographical location is called “distance from the coast” (DFC). This variable is introduced to measure the relationship between the economic spillover effects and the distance from the coast, and to verify the impact of the ocean on the study area. The index includes two parts—spatial distance from the coast (D)and the influence of time (T). As regards spatial distance from the coast, the value of coastal units is taken to be 0, while the value of non-coastal units is the distance from the nearest coastal unit, calculated from the longitude and latitude of the geometric center points of the two units. With the development of transport, spatial distance in itself cannot give an accurate picture of the total geographical impact of economic activity. Therefore, this study considers an extra component, the influence of time, based on the spatial distance from the coast. This influence takes into account transport time, and it is represented by road density. As regards the influence of time, the value of coastal units is taken to be 0, and the value of non-coastal units is the average of road density values of all units that intersect the straight line between the two geometric center points of the non-coastal unit and the coastal unit nearest to it. Finally, the equation of distance from the coast (d) can be written as:

¼ = ; ¼ ; ð Þ da Dab Tab db 0 16

where a denotes non-coastal units, b denotes coastal units, Dab denotes the spatial distance from a to the nearest b,andTab denotes the associated time influence. (3) The explanatory variables measuring economic openness are “value of exports” and “foreign direct investment” (EXP and FDI, respectively). The existing literature supports close links among EXP, FDI, and economic growth (Wan and Li, 2013; Zhou and Lyu, 2014), and so these two variables are employed in this study. (4) The explanatory variable that measures the employment situation is the “number of employees in units” (NUE). This statistical indicator contains the employees in all state organs, political parties, social organizations, state-owned enterprises, and public institutions, and it can be used to explore the impact of public employment on economic spillover effects (Peng, 1998). As a meaningful indicator, the number of employees in private enterprises, of course, has been considered to have a strong relationship with economic growth (Wen and Li, 2012). Nevertheless, the statistics of counties are not to be published by the Chinese statistical system, and so only NUE is chosen in this study. (5) The truth that the change of industrial structure can affect the economy markedly has been verified (Gan, Zheng, & Yu, 2011). Hence, we used two indicators of primary industry's share in GDP (SPI) and the tertiary industry's share in GDP (STI)tostudy the impact of the industrial structure on economic spillover (Zhou, 2012). Primary industry in China includes agriculture, forestry, animal husbandry, and fisheries. Tertiary industry mainly refers to the service industry. (6) Investment and consumption are considered two important factors that push the economy forward. In this study, due to data availability, we use the rate of fixed asset investment (RFAI) and the total retail sales of social consumer goods (RSSC)to describe these two factors (Liu, 2000), where the rate of fixed asset investment refers to the ratio between fixed asset investment and GDP. Specific descriptive statistics of the variables are presented in Table 2.

4.2.2. Testing the explanatory variables for stationarity Because panel data are made up of time-series and cross-sectional data, using non-stationary time-series data could result in a spurious regression (Pan, 2012). Therefore, unit root tests need to be done to test the stationarity of the explanatory variables. EViews 6 software accommodates several different methods of unit root tests, including LLC, Breitung, IPS, ADF–Fisher, and PP–Fisher tests. Table 3 shows that the hypothesis of the existence of a unit root cannot be completely refuted for most of the explanatory variables, as long as we are considering their levels. However, when we consider the first differences of the explanatory variables, almost all of them pass the unit root test. The lone exception is one result obtained using the Breitung test. Therefore, all variables are integrated of order 1 and can be used for regression estimation.

Table 2 Specific descriptive statistics of variables. Source: Authors' own calculations, using Microsoft Office Excel 2003 software. DFC (km) EXP (108 dollars) FDI (104 dollars) NUE (104 persons) SPI (%) STI (%) RFAI (1) RSSC (108 yuan) Y (104 yuan)

Mean 56.78 9.47 17630.41 8.91 16.90 33.36 0.50 82.32 3.10 STDEV 69.03 32.61 67499.52 16.81 11.78 8.80 0.28 182.26 3.53 Min 0.00 0.00 0.00 0.45 0.13 11.30 0.02 2.04 0.17 Max 541.83 308.64 984100.00 159.39 74.12 80.31 2.58 2271.37 62.18 N 1548 1548 1548 1548 1548 1548 1548 1548 1548

Note: DFC denotes the distance from the coast; EXP denotes the value of exports; FDI denotes the foreign direct investment; NUE denotes the number of employees in units; SPI denotes the primary industry's share in GDP; STI denotes the tertiary industry's share in GDP; RFAI denotes the rate of fixed asset investment; RSSC denotes the total retail sales of social consumer goods; and Y denotes the GDP per capita. C. Sun et al. / China Economic Review 33 (2015) 123–136 131

Table 3 Panel unit root tests for the explanatory variables. Source: Authors' own calculations, using EViews 6 software. Level

LLC Breitung IPS ADF–Fisher PP–Fisher

Statistic Prob. Statistic Prob. Statistic Prob. Statistic Prob. Statistic Prob.

DFC −16.8580 0.000 6.66109 1.000 1.35004 0.912 3.04583 0.999 3.34841 1.000 EXP −19.8435 0.000 5.22756 1.000 −4.17478 0.000 −3.45842 0.000 0.49171 0.689 FDI −10.6249 0.000 7.60638 1.000 −0.49936 0.309 0.17564 0.570 0.69326 0.756 NUE −11.5977 0.000 10.6500 1.000 2.64812 0.996 3.45555 1.000 4.74523 1.000 SPI −17.3552 0.000 8.32538 1.000 −4.51736 0.000 −2.24122 0.013 −0.54940 0.292 STI −11.8966 0.000 2.76760 0.997 −0.95930 0.169 −0.22678 0.410 1.86856 0.969 RFAI −18.5109 0.000 0.64753 0.741 −6.36406 0.000 −6.11678 0.000 −1.05318 0.146 RSSC 5.63543 1.000 27.1497 1.000 20.9436 1.000 22.5083 1.000 27.1751 1.000

1st difference DFC −24.1437 0.000 −5.53311 0.000 −7.93844 0.000 −8.84198 0.000 −11.8451 0.000 EXP −32.1859 0.000 −10.4265 0.000 −13.0620 0.000 −15.3307 0.000 −20.4628 0.000 FDI −29.1245 0.000 −1.96558 0.025 −11.8490 0.000 −12.8386 0.000 −17.7520 0.000 NUE −25.2619 0.000 −0.80247 0.211 −9.24163 0.000 −11.6122 0.000 −17.0649 0.000 SPI −43.5885 0.000 −11.6700 0.000 −14.2923 0.000 −15.4160 0.000 −18.5283 0.000 STI −39.9513 0.000 −14.7343 0.000 −12.7647 0.000 −14.3081 0.000 −20.3793 0.000 RFAI −22.9443 0.000 −10.4075 0.000 −8.37159 0.000 −11.1367 0.000 −14.8747 0.000 RSSC −29.3927 0.000 −3.45982 0.000 −11.2581 0.000 −14.0647 0.000 −21.5759 0.000

Note: DFC denotes the distance from the coast (unit: km); EXP denotes the value of exports (unit: 108 dollars); FDI denotes the foreign direct investment (unit: 104 dollars); NUE denotes the number of employees in units (unit: 104 persons); SPI denotes the primary industry's share in GDP (unit: %); STI denotes the tertiary industry's share in GDP (unit: %); RFAI denotes the rate of fixed asset investment (unit: 1); and RSSC denotes the total retail sales of social consumer goods (unit: 108 yuan).

4.2.3. Regression results Table 4 displays the results using three kinds of models. In terms of the R2 value, the degree of fit in the spatial Durbin econometric model (6) is better than that in the other two models. Therefore, the use of the spatial Durbin econometric model (6) permits more accurate description of the fluctuation rules for each variable, and makes the conclusion more reliable. That the spatial autoregressive coefficients (ρ) estimated using models (7) and (6) are positive at the 1% significance level and are evidence of the existence of spatial spillover effects in the Bohai Rim Region. This makes it necessary to consider the spatial lags. Moreover, the spatial autoregressive

Table 4 Model regression results. Source: Authors' own calculations, using MATLAB R2009a and EViews 6 software. Spatial Durbin econometric model Model without spatial lags of the Model without spatial lags of the (6) explanatory variable (7) explanatory and the dependent variables (8)

Regression coefficient t-Statistic Regression coefficient t-Statistic Regression coefficient t-Statistic ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ DFC −0.0027 −14.9914 DFC −0.0034 −21.0067 DFC −0.0039 −17.7817 ⁎⁎⁎ ⁎⁎⁎ ⁎⁎ EXP 0.0031 5.0702 EXP 0.0028 4.4342 EXP 0.0021 2.4348 ⁎⁎⁎ ⁎⁎⁎ ⁎⁎ FDI 1.0E−6 2.8574 FDI 1.0E−6 3.9310 FDI 9.47E−7 2.5507 ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ NUE 0.0062 5.2788 NUE 0.0058 4.8156 NUE −0.0052 −3.2600 ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ SPI −0.0228 −22.7377 SPI −0.0229 −22.1797 SPI −0.0281 −20.4233 ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ STI −0.0147 −11.1214 STI −0.0142 −10.2017 STI −0.0165 −8.8568 ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ RFAI −0.4666 −10.5559 RFAI −0.3850 −8.8701 RFAI 0.3490 6.3880 ⁎⁎ ⁎⁎⁎ RSSC −0.0003 −2.2231 RSSC −0.0002 −1.5519 RSSC 0.0011 7.2004 ⁎⁎ WDFC 0.0065 2.1720 WEXP −0.0222 −1.6527 ⁎⁎⁎ WFDI −1.8E−5 −3.4972 ⁎⁎⁎ WNUE −0.3454 −6.6126 WSPI −0.0144 −0.4969 ⁎⁎⁎ WSTI −0.1242 −5.0564 WRFAI 0.0534 0.1004 ⁎⁎ WRSSC 0.0219 7.9492 ⁎⁎ ⁎⁎⁎ ρ 0.2420 2.5298 ρ 0.8230 38.3414 R2 0.7612 R2 0.7327 R2 0.5205 Like-lihood −784.86506 Like-lihood −883.16776 Like-lihood −1323.882

Note: DFC denotes the distance from the coast (unit: km); EXP denotes the value of exports (unit: 108 dollars); FDI denotes the foreign direct investment (unit: 104 dollars); NUE denotes the number of employees in units (unit: 104 persons); SPI denotes the primary industry's share in GDP (unit: %); STI denotes the tertiary industry's share in GDP (unit: %); RFAI denotes the rate of fixed asset investment (unit: 1); and RSSC denotes the total retail sales of social consumer goods (unit: 108 yuan). Variables beginning with ‘W’ correspond to calculations of independent variables with the spatial lag. ⁎⁎⁎ Refers to significance at the 1% level. ⁎⁎ Refers to significance at the 5% level. 132 C. Sun et al. / China Economic Review 33 (2015) 123–136 coefficient (ρ = 0.2420) estimated using the spatial Durbin econometric model (6) is significantly lower than the one (ρ = 0.8230) obtained using model (7). This means that neglecting the spatial lags of explanatory variables can result in an inflated estimation of economic spatial spillover effects. The regression results are very good in terms of R2 and likelihood ratio. However, the regression coefficients on the spatial lags of the explanatory variables in the spatial Durbin econometric model (6) and the other model (7) do not directly capture the impact of explanatory variables on the dependent variable. This needs to be reflected in the total, direct, and indirect effects, as shown in Table 5. The next part of this paper discusses the effects of different factors on the county economy.

(1) That the regression coefficient on the explanatory variable “distance from the coast” (β = −0.0039) in model (8) is significant- ly negative indicates that counties close to the coast experience more economic growth. This phenomenon can be attributed to the advantages of a coastal location: counties close to the coast are more affected by abundant marine resources that have been proved to play a basic role in the development of regional economy (Zhang, Han, Liu, & Liu, 2010). However, is the economic growth of coastal regions beneficial for the whole area? According to the results from the spatial Durbin econometric model (6) in Table 5, the explanatory variable “distance from the coast” has significantly negative direct effects on the dependent variable, but the indirect effects are significantly positive and the total effects are not significant. This shows that this kind of growth in counties close to the coast is not beneficial for the economic development of other counties, or for the area as a whole. This is mainly because coastal counties, by relying on their great competitiveness in terms of product sales and purchasing power, attract capital, technology, talents, and other resources away from other counties, so that these other counties are trapped in a process of endogenous “hollowing out,” and their economic development is restrained further (Bo, 2008). That also means that the belief that the coastal areas would take the lead in development and then trigger improvements in non-coastal areas, and thus achieve overall regional development, has not come true yet. (2) According to Table 5, the direct effects of value of exports on local economic growth are significantly positive, but indirect effects and total effects are not significant. Therefore, an increase in EXP can markedly promote local economic development; this has been expounded in the studies of Li (2010) and MaandGu(2012), inter alia. In addition, the reason why the spillover effects of EXP are not found in our study lies in the fact that exporting low-value-added goods produced by cheap labor in China can weaken the driving effects of EXP on economic growth (Ye and Fang, 2013).

Table 5 The total, direct, and indirect effects of the explanatory variables. Source: Authors' own calculations, using MATLAB R2009a software. Spatial Durbin econometric model (6) Model without spatial lags of the explanatory variable (7)

Total effects Regression coefficient t-Statistic Total effects Regression coefficient t-Statistic ⁎⁎⁎ DFC 0.0051 1.2541 DFC −0.0198 −7.2923 ⁎⁎⁎ EXP −0.0254 −1.3245 EXP 0.0160 3.9728 ⁎⁎⁎ ⁎⁎⁎ FDI −2.3E−5 −2.9800 FDI 6.0E−6 3.5157 ⁎⁎⁎ ⁎⁎⁎ NUE −0.4496 −6.5570 NUE 0.0335 3.6719 ⁎⁎⁎ SPI −0.0505 −1.2232 SPI −0.1311 −7.6035 ⁎⁎⁎ ⁎⁎⁎ STI −0.1837 −4.9000 STI −0.0817 −6.3779 ⁎⁎⁎ RFAI −0.5459 −0.7439 RFAI −2.2273 −4.9109 ⁎⁎⁎ RSSC 0.0286 6.7436 RSSC −0.0011 −1.4077

Direct effects Regression coefficient t-Statistic Direct effects Regression coefficient t-Statistic ⁎⁎⁎ ⁎⁎⁎ DFC −0.0027 −14.7913 DFC −0.0036 −21.0347 ⁎⁎⁎ ⁎⁎⁎ EXP 0.0030 4.8201 EXP 0.0029 4.5177 ⁎⁎⁎ ⁎⁎⁎ FDI 1.0E−6 2.6459 FDI 1.0E−6 4.0981 ⁎⁎⁎ ⁎⁎⁎ NUE 0.0052 4.2660 NUE 0.0060 4.8390 ⁎⁎⁎ ⁎⁎⁎ SPI −0.0229 −23.5282 SPI −0.0236 −22.0086 ⁎⁎⁎ ⁎⁎⁎ STI −0.0151 −11.3553 STI −0.0147 −10.5473 ⁎⁎⁎ ⁎⁎⁎ RFAI −0.4680 −10.8262 RFAI −0.3981 −8.7601 RSSC −0.0002 −1.6530 RSSC −0.0002 −1.4957

Indirect effects Regression coefficient t-Statistic Indirect effects Regression coefficient t-Statistic ⁎ ⁎⁎⁎ DFC 0.0079 1.9693 DFC −0.0162 −6.1408 ⁎⁎⁎ EXP −0.0284 −1.4871 EXP 0.0131 3.7600 ⁎⁎⁎ ⁎⁎⁎ FDI −2.3E−5 −3.1033 FDI 5.0E−6 3.3217 ⁎⁎⁎ ⁎⁎⁎ NUE −0.4548 −6.6626 NUE 0.0275 3.4182 ⁎⁎⁎ SPI −0.0276 −0.6744 SPI −0.1075 −6.3573 ⁎⁎⁎ ⁎⁎⁎ STI −0.1686 −4.5214 STI −0.0671 −5.5762 ⁎⁎⁎ RFAI −0.0779 −0.1072 RFAI −1.8292 −4.3844 ⁎⁎⁎ RSSC 0.0289 6.8358 RSSC −0.0009 −1.3823

Note: DFC denotes the distance from the coast (unit: km); EXP denotes the value of exports (unit: 108 dollars); FDI denotes the foreign direct investment (unit: 104 dollars); NUE denotes the number of employees in units (unit: 104 persons); SPI denotes the primary industry's share in GDP (unit: %); STI denotes the tertiary industry's share in GDP (unit: %); RFAI denotes the rate of fixed asset investment (unit: 1); and RSSC denotes the total retail sales of social consumer goods (unit: 108 yuan). ⁎⁎⁎ Refers to significance at the 1% significant level. ⁎ Refers to significance at the 10% level. C. Sun et al. / China Economic Review 33 (2015) 123–136 133

(3) Despite having three coefficients with small absolute values, the explanatory variable of FDI has significantly positive direct effects on economic growth and significantly negative indirect effects and total effects. On one hand, FDI can play a stimulative role in the local economy, but this kind of impact is becoming more slack (Yin, 2008; Chen and Xu, 2011). On the other hand, an earlier study on the effects of FDI on regional economic growth in China discovered that FDI has brought about regional economic disparity due in large part to the existence of regional circulation and accumulated effect (Wei, 2002). (4) That human capital has a significant influence on economic efficiency has been verified in a previous study (Herrerias, 2012). As regards the explanatory variable of the number of employees in units, the results obtained using the spatial Durbin econometric model (6) show that the direct effects are significantly positive, but the total and the indirect effects are significantly negative, and the negative indirect effects are stronger than the positive direct effects. This means that an increase in the number of employees in units can promote the development of the local economy, but is not beneficial for other counties or for the region as a whole. As Chen (2012) points out, economic growth can significantly boost talent aggregation. We therefore believe that counties at a high economic level are more appealing to human resources, and so high-quality talent from the surrounding counties will flock to the high-level counties. As a result, the local economy becomes more dynamic, but the loss of talent in other counties hinders their development. In addition, the increase in public employment described by this explanatory variable is not beneficial for the whole regional economy. (5) Traditionally, primary industry's share in value added should decline with economic growth. Thus, it should, in gener- al, be negatively correlated with economic activity. The regression coefficient on the explanatory variable SPI (β = −0.0281) estimated from model (8) and found in Table 4 illustrates this phenomenon. When model (6) is used, direct effects are still significantly negative, and indirect effects and total effects are not significant. This means that an increase in SPI can constrain the development of the local economy, and is also not beneficial for other counties or for the region as a whole. This is because when the share of primary industry in a county grows, the reduction in share belonging to secondary and tertiary industries will hurt the economy concurrently. Only an advanced industrial structure can promote economic growth (Zhou, 2012). (6) The regression coefficients on the tertiary industry's share in value added are significantly negative in all three models. Thus, if tertiary industry has a large share in GDP, it is not beneficial either for the local county or for the other counties in our sample. Although this result clearly contradicts the traditional rules of the evolution of industrial structure, similar results of a decrease in STI with an increase in economic development have been found for Tianjin, Shanxi, the Pearl River Delta, Jiangsu, and Zhejiang (Zhu, Chen, & Li, 2003; Zhao, 2014). Some scholars believe that the index STI can decrease for five years or more at atime(Peng, 2009). This phenomenon is directly related to the long-term demand structure in China, which involves more investment and less consumption. As seen in the industrial structure of the study area (Fig. 2), the fact that the city with a larger share of the secondary industry in GDP always has a higher GDP per capita explains why economic growth is driven mainly by secondary industry. That is also one of the reasons behind this phenomenon in the Bohai Rim Region. (7) The effect of fixed asset investment on economic development has been tested in previous studies (Liu and Li, 2006; Song, 2011). We find that the proportion of fixed asset investment in GDP has a negative direct effect on the local economy. However, the indirect and total effects are not significant. Thus, an increase in the index RFAI is not beneficial for local development, and it cannot make the economies of the whole region grow quickly. This is because Chinese economic growth has been accelerated by excessive fixed asset investment for a long time (Xie and Li, 2006). Furthermore, high investment and low consumption worsen this imbalance, hurting the economy (Wang, 2007). (8) Generally speaking, high consumption demand should stimulate the economy. Yet, according to models (7) and (6), the total retail sales of social consumer goods have not had a significant effect on local GDP per capita. Moreover, the regression coefficients all have small absolute values. However, the indirect effects and total effects are significantly positive. This means that the index RSSC has a tiny influence on the local economy; this finding is similar to that derived by Zhang and Shi (2011) on the relationship between the total retail sales of consumer goods and GDP. In addition, the strong indirect effects can be interpreted as a demonstration effect on surrounding areas in eastern China, as Zhao (2011) explains in his consumer behavior research on regional difference.

4.2.4. Discussion According to the direct effects results (Table 5), counties close to the coast experience more economic growth. Although the existing literature provides a reason for that, it is not sufficiently convincing in verifying that this kind of phenomenon is caused by the presence of the ocean. Furthermore, one of our goals in this study lies in discussion on the effect of the ocean on the development of the coastal region. Therefore, the next part of this section will examine the effects of marine resource utilization and marine output on economic growth. (1) Index selection The variable marine resource utilization (MRU) is introduced to measure the relationship between the marine resource and the

economic growth. In this study, the index MRU consists of marine catches (R1), mariculture production (R2), sea salt production (R3), output of the marine mining industry (R4), output of offshore crude oil (R5), output of offshore natural gas (R6), and volume of cargo handled at seaports (R7). The equation for MRU can be written as:

Xn ¼ = ; ðÞ¼ ; ; ; ; ¼ ; ð Þ MRU R j n j 1 2 n n 7 17 j¼1 134 C. Sun et al. / China Economic Review 33 (2015) 123–136

where Rj is the data normalized by using the method of the maximum and minimum value. The method is as follows: − x j min x j R ¼ ; ð18Þ j − max x j min x j

where xj is the original value, max(xj) is the original maximum value, and min(xj) is the original minimum value. The relationship between marine output and economic growth is also noteworthy. Hence, this study introduces the other in- dicator of gross ocean product that stands for marine output (MO). Along with GDP per capita, we use the natural logarithm of original value in the program. The data in this part are taken from the State Oceanic Administration of China. Statistics pertaining to cities or counties are not published by the office; the only provincial statistics available are those of Liaoning province, Shandong province, Hebei prov- ince, and Tianjin municipality. According to the degree of fit, we use the results with cross-sectional fixed effects. (2) Regression results The descriptive statistics, unit root tests, and regression results of MRU and MO are displayed in Table 6. The variables MRU and MO have significantly positive effects on local GDP per capita. Nonetheless, the regression coefficient of MRU is larger than that of MOn. These results highlight the advantageous effects of the ocean on the development of the regional economy. Particularly, marine resources play a momentous role in promoting economic development in the Bohai Rim Region.

5. Conclusions and some suggestions

5.1. Conclusions

Using panel data on the economic development of 129 districts in the Bohai Rim Region from 2001 to 2012, this study employs the spatial autocorrelation test method to test for the presence of spatial autocorrelation. The results show that the GDP per capita of counties in the Bohai Rim Region exhibit significantly positive spatial autocorrelation. The spatial distribution of economic develop- ment across counties is not completely random, but demonstrates spatial agglomeration; districts with similar values of GDP per capita tend to be clustered in the same region. Meanwhile, the local spatial autocorrelation is also significantly positive. The concen- tration of high-level areas, low-level areas, and the transition zones between them are clearly distinguishable. We measure the spatial spillover effects of geographical and economic factors using the space Durbin econometric model. We find that the whole region exhibits spatial economic spillover effects. The factor “distance from the coast” exerts a significantly negative impact on the local GDP per capita and a significantly positive impact on the GDP per capita of other districts. This implies that counties close to the coast grow quickly, but this kind of growth is not beneficial for other counties. With regard to indexes that measure open- ness, the “value of exports” exerts a significantly positive influence on the local economy and no significant influence on other counties, while “foreign direct investment” exerts a significantly positive influence on the local economy and a significantly negative influence on other counties. In addition, “number of employees in units” exerts a significantly positive influence on the local economy and a significantly negative influence on other counties. The factors “primary industry's share in GDP” and “tertiary industry's share in GDP” influence the local economy positively; the former exerts no significant influence on other counties, but the latter exerts a

Table 6 Descriptive statistics, unit root tests, and regression results of MRU and MO. Source: Authors' own calculations, using Microsoft Office Excel 2003 and EViews 6 software. Descriptive statistics

Mean STDEV Min Max N

MRU 0.243 0.163 0.051 0.701 48 MO 2129.969 2029.840 115.750 8972.100 48

Unit root tests (1st difference)

LLC Breitung IPS ADF–Fisher PP–Fisher ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ MRU −8.8473 −0.5750 −3.4843 −4.5518 −6.0165 ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ MO −6.1582 −4.3550 −3.3280 −2.9822 −4.5628

Regression results from model (8)

Regression coefficient t-Statistic R2 Likelihood F-statistic ⁎⁎⁎ MRU 1.6318 4.2464 ⁎⁎⁎ MO 0.4703 12.9476 ⁎⁎⁎ 0.9600 32.8830 201.7624

Note: MRU denotes the marine resource utilization (a dimensionless variable); and MO denotes the marine output (unit: 108 yuan). ⁎⁎⁎ Refers to significance at the 1% significant level. C. Sun et al. / China Economic Review 33 (2015) 123–136 135 negative influence on other counties. “Rate of fixed asset investment” influences the local economy negatively and has no significant effect on other counties. The final variable, “total retail sales of social consumer goods,” has no significant influence on the local economy, but a positive significant influence on those of other areas. In Section 4.2.4, some of the advantageous effects of the ocean on the development of the regional economy were verified.

5.2. Some suggestions

Based on the above conclusions, the economic development of coastal counties has not driven the growth of other counties. On the contrary, coastal counties have used their greater competitiveness in product sales and purchasing power to divert capital, technology, talent, and other resources away from other counties, which in turn become trapped in a process of endogenous “hollowing out” that further constrains their economic development. Therefore, to make the coastal counties generate more economic spillover benefits, it is necessary to encourage regional industrial collaboration and technological transfer to inland counties, and to improve the quality and efficiency of economic growth by integrating sea and land resources, against the backdrop of economic transformation and upgrades to the coastal region. Meanwhile, the current situation of vicious competition among administrative areas should be changed to strengthen communication among provinces, cities, and counties to realize bidirectional spillovers, using the characteristic advantages of various regions. Although the value of exports can accelerate local economic growth, this kind of effect is subsiding and has no spillover effects. Therefore, more high-value-added goods should be produced for export. Speeding up the rate at which the economic structure is adjusted and the economy transformed, of course, could support the upgrading of export products. Foreign direct investment is beneficial for the local economy, but it is a crucial factor that brings about regional economic disparity. The government should actively guide foreign investment toward non-coastal counties; meanwhile, strengthening infrastructure construction and improving the investment environment in non-coastal areas are good ways to resolve this problem. An increase in the number of employees in units can promote the development of the local economy, but is not beneficial for other counties. Therefore, the current decrease in the number of employees in state-owned firms and units is beneficial for the whole region. More of their employees should be transferred to key industries important to the national economy and people's livelihood (Liu and Kou, 2002). The result that primary industry's share in GDP has a negative effect on the local economy goes against the increase in local governments' investment in primary industry. However, due to the fundamental status of primary industry within the economy, counties that have characteristic advantages in primary industry should be encouraged to increase investments of capital and technology to improve the quality of primary industry. A large share of the tertiary industry in GDP is not beneficial either for the local county or for other counties in our sample. Therefore, a large share of the tertiary industry should not be emphasized blindly, even if its development needs to be encouraged continuously. It is critical to adjust the structure of investment and consumption immediately. An increase in the rate of fixed asset investment cannot make the local economy grow more quickly, because a high investment rate is associated with a low consumption rate, which is not beneficial for the local economy. It is necessary to make the fixed asset investment return to a normal range, for the sake of having healthy and balanced regional development. The reason why the total retail sales of social consumer goods have no significant influence on the local economy lies in the fact that social consumer goods constitute only a small part of final consumption (Zhang and Shi, 2011). If economic development needs to be driven by consumption, more attention should be paid to bulk commodity consumption and government consumption.

Acknowledgments

This study is supported by the MOE Project of Key Research Institute of Humanities and Social Sciences in Universities (No. 12JJD790032). The authors are grateful to Kai Liu, Xu Xi, and Xionghe Qin for their assistance in processing the figures and suggestions.

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