Economic Complexity of the City Cluster in Guangdong–Hong Kong–Macao Greater Bay Area, China

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Article Economic Complexity of the City Cluster in Guangdong–Hong Kong–Macao Greater Bay Area, China

Ivan Lee 1,2 and Regina Fang-Ying Lin 1,*

1 School of Economics and Management, Harbin Institute of Technology, Shenzhen 518055, China; [email protected] 2 STEM, University of South Australia, Mawson Lakes SA 5095, Australia * Correspondence: [email protected]

Received: 5 June 2020; Accepted: 8 July 2020; Published: 13 July 2020

Abstract: With the rapid economic growth in China over the last two decades, exploring the changes in the Chinese economy has attracted great attention from the research community. Among diﬀerent economic clusters in China, the southern region represents the wealthiest region. Hence, it is essential to conduct an in-depth analysis to explore the region’s sustainability in its economy. This paper applies the economic complexity model to 22 major cities within the Guangdong–Hong Kong–Macao Greater Bay Area cluster. The study is based on seven industrial sectors. Revealed comparative advantage of diﬀerent product sectors, similarities of product sector specialisation, diversity of the economic composition, and the association to the geographical location are investigated in this paper.

Keywords: economic complexity; city clusters; Guangdong–Hong Kong–Macao Greater Bay Area; Chinese economy; revealed comparative advantage

1. Introduction City clusters are formed as a result of urban development, with regional economies combined to scale. Cities surrounding San Francisco, New York, and Tokyo are seminal examples of city clusters in the world. In practice, over 60% of the world’s economic activities take place in harbour cities, with 75% of large cities and 70% of capitals and populations situated within 100 km from the coastline. China, being the fastest growing economy over the last two decades, possesses multiple city clusters that attract domestic and foreign investments [1]. In Ren’s work on Urban China, in-depth insight on the formation of this mega-economy is covered, from aspects of governance, landscape, migration, inequality, and cultural economy [2]. Similar to most economies, Chinese city clusters are mostly geographically based to form a producer serving network [3]. Cities within close geographical proximity, such as cities in the Pearl River Delta (PRD) region, have attracted great attention from the research community [4]. PRD, the most representative emerging city cluster in Southern China following the strategic economic reform in 1993, is the largest mega-city region in the world. A study between 1993 and 2012 in the PRD has shown that emerging clusters have negative effects on other co-located industries’ or clusters’ total factor productivity, while mature clusters have positive effects [4]. Thus, it is of great interest to further explore the industrial composition and how that affects the economy over time, and this paper discusses this matter from the economic complexity perspective. City clusters may be formed by multiple factors, such as geographical proximity and the accessibility through transport infrastructure [5–9], thus spatial interaction among cities leads to a positive influence on urban growth [10]. Although, urbanisation through city clusters also brings in other challenges, such as environmental impacts [10,11]. At the same time, urban development,

Sustainability 2020, 12, 5639; doi:10.3390/su12145639 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 5639 2 of 14 housing policies, personal status, and family relationships lead to distinctive spatial patterns of ageing differentiation [12]. To evaluate the economic development within city clusters, export records are frequently used as a good estimate of economic productivity. Guangdong’s government provides city-level GDP records from different industrial sectors; these values are used to replace the export value by analysing the attributes of major Chinese cities in the PRD region and discussing their economic complexity. This paper calculates the revealed comparative advantage of each industrial sector of the PRD cities based on the city-level GDP figure and subsequently evaluates ubiquity and diversity of the PRD cities to reflect the city-level economic complexity. The economic complexity model [13,14], originally introduced by Harvard University, has found applications in profiling the competences of economies at the country level. Economic complexity builds on top of the analysis of diversity and ubiquity of an economy and obtains revealed comparative advantage in different industrial sectors to reflect the knowledge barrier of an industrial sector for an economy to possess. Of the two complementary measures of the knowledge capital, ubiquity measures how many products are produced by an economy and ubiquity measures how many economies export a product. The analysis includes normalisation such that small economies or industrial sectors are fairly considered. Such a model has been applied at the subnational level in Australia [15], Brazil [16], and Mexico [17]. These studies explore domestic trading patterns that are significantly different to international trades, such as service sectors and perishable foods. In addition to the application in economic analysis, the complexity model has been applied to analyse academic output among research institutes [18] and to predict the evolution of the research output by constructing the research space [19]. China is the second largest economy in terms of GDP. Although, according to the economic complexity analysis, the composition of China’s product sectors ranked 33 in the year range 2013–2017, according to the Observatory of Economic Complexity (https://atlas.media.mit.edu/en/), reflecting that its product sectors exhibit relative knowledge and skill deficiency. Although, such an impression is rapidly changing, especially with the recent boost in entrepreneurship in China, subject to the effect of localisation and urbanisation economies as discussed [20]. While the localisation and urbanisation economies form city clusters, there’s an imminent interest in exploring the economic complexity at the intra city-cluster level. Such a study will offer an insight into the regional economy to assist policy makers. The rest of this paper is organised as follows: An overview of the technical background of revealed symmetric comparative advantage is presented first, which leads to the modelling of economic complexity. Collection and pre-processing of the data used in this study is explained, and the result and the analysis are covered. Subsequently, we discuss the limitations of this study, followed by the concluding remarks.

1.1. Revealed Symmetric Comparative Advantage This paper investigates complexity modelling of major cities around Pearl River in Southern China, based on the GDP records of industrial sectors [13]. Let c denote a city, and p denote an industrial sector (or product). Let Xcp denote the GDP value of industrial sector p of city c, and the revealed comparative advantage (RCA) [21] can be obtained to reﬂect the degree of specialisation in an industry. Mathematically, RCAcp for city c and product p can be expressed as: P Xcp/ p Xcp RCAcp = P P . (1) c Xcp/ c,p Xcp

Normalisation of RCAcj makes a smaller scale city with fewer export values adequately assessed; likewise, a less popular industrial sector can be fairly compared. According to the economic complexity model [14], a city is known to have revealed comparative advantage for a product sector if its RCA value is equal to or larger than one. This approach faces a Sustainability 2020, 12, 5639 3 of 14 limitation that the ratio relative to the threshold is inconsistent. For example, when RCA is halved or doubled from reference point 1, the two operations yield different levels of loss/gain (i.e., 0.5 and 2), making it difficult to interpret the relative position against the reference point. Thus, there is a need for symmetry in RCA, such that the relative change from the reference point is comparable. The Revealed Symmetric Comparative Advantage (RSCA), proposed by Laursen [22], offers symmetry in RSCA values centred closed to zero: RCA 1 RSCA = − cp RCA + 1 P P P (2) Xcp c,p Xcp c Xcp p Xcp = P − P P . Xcp c,p Xcp + c Xcp p Xcp

The RSCA values across diﬀerent disciplines are used to construct the Mcp matrix, which are either 0’s or 1’s, to indicate whether a city is considered to possess RCA for an industrial sector: ( 1 if RSCAcp 0, Mcp = ≥ (3) 0 if RSCAcp < 0.

1.2. Economic Complexity

Based on Mcp, which indicates whether a city possesses revealed symmetric comparative advantage for an industrial sector, diversity Dc presents the degree of multi-specialisation in a city, which is deﬁned as the sum of all industrial sectors in which the city possesses RCA: X Dc = kc,0 = Mcp. (4) p

Ubiquity of an industrial sector, Up, is deﬁned as the number of cities that have RCA in that industrial sector: X Up = kp,0 = Mcp. (5) c

By applying the Method of Reﬂections, the value of kc,n can be expressed in terms of kp,n 1: − 1 X kc,n = Mcpkp,n 1. (6) k − c,0 p

Similarly, the value of kp,n can also be expressed in terms of kc,n 1: − 1 X kp,n = Mcpkc,n 1. (7) k − p,0 c

The economic complexity of a city is evaluated as the normalised diversiﬁcation, according to Hidalgo [13], where each diversiﬁcation value is normalised by subtracting the mean and then dividing by the standard deviation: kc,n kc,n kc0,n = − , (8) σ(Kc) where kc,n denotes the average of kc,n c with a given n, and σ denotes the standard deviation function. ∀ n o Subsequently, each city may be characterised by the vector kc = kc0,n n = 1 ... N and kp = n o | k0 n = 1 ... N , where N is a predetermined number of iterations and N Z and N > 1. The p,n | ∈ diversity and ubiquity values converge after several rounds of iteration, and when N = 18 the diversity value is chosen as the measure of economic complexity, according to Hidalgo [13]. Sustainability 2020, 12, 5639 4 of 14

2. Data Collection In this study, we investigate the city cluster around the Pearl River Delta region in the southern part of China and analyse its GDP-based economic complexity in between 2000 and 2015, covering 7 product sectors in Table1. In comparison to the study on the producer service linkage of 9 PRD cities [23], or the study on functional and sectoral divisions of labour between Hong Kong, 2 PRD cities, and 2 PRD regions [24], or 9 PRD municipalities for the study of knowledge-intensive business services [25], this study investigates the economic complexity of 21 PRD cities plus Hong Kong, oﬀering more ﬁne-grained analysis of the region and exploring more insights of economic interactions and sustainability.

Table 1. Classiﬁcation of industrial sectors. (Data source: Wind database and Hong Kong government statistics website).

Classification Industrial Sector c1 agriculture/forestry/livestock/fisheries c2 industrial c3 building and construction c4 wholesale and retail c5 transport and logistics c6 finance c7 real estate

The GDP record among the PRD cities were collected between 2000 and 2015. To give an overview of the GDP data, records from 2015 are included in Table2. It should be noted, among all GDP data missing records are observed, including Hong Kong’s c4 (wholesale and retail) in 2000, and Hong Kong’s c4 (wholesale and retail) and c7 (real estate) in 2015. While these are the ﬁrst and the ﬁnal year of the collected data, the GDP record of the matching product sector in adjacent years (2001 and 2014, respectively) are taken as the estimate of the missing entries.

Table 2. City-level GDP data in the PRD region in 2015.

City Abbrev c1 c2 c3 c4 c5 c6 c7 Guangzhou GU 226.84 5185.63 551.17 3099.92 1255.19 1628.71 1529.42 Shenzhen SZ 6.65 6742.98 479.72 2361.16 540.8 2501.57 1564.41 Shantou ST 96.71 877.22 86.56 341.47 42.4 50.95 82.92 Foushan FS 136.45 4675.14 166.36 643.29 270.47 341.73 628.94 Shaoguan ZK 151.68 360.33 71.64 146.14 84.13 50.69 55.69 Heyuan HU 94.01 334.67 35.85 108.04 22.59 43.89 57.31 Meizhou MJ 188.49 287.99 65 108.71 25.44 42.54 52.28 Huizhou HJ 151.54 1624.48 102.43 411.41 80.71 121.04 213.79 Shanwei SW 118.04 320.24 28.7 93.15 20.93 20.6 51.77 Dongguan TG 21.03 2840.35 88.81 905.37 205.9 401.37 529.25 Zhongshan ZS 66.48 1566.89 66.07 328.61 72.91 159.97 184.78 Jiangmen GM 174.5 1023.06 62.02 222.23 85.91 126.31 128.99 Yangjian EG 205.33 509.38 55 129.97 81.26 34.19 73.62 Zhanjiang JG 454.67 796.77 115.88 244.34 118.97 76.82 122.85 Maoming MM 387.41 901.03 99.71 293.41 80.14 62.5 122.4 Yuqing CG 288.29 930.28 61.17 199.15 56.77 54.91 53.25 Qingyuan FM 192.52 437.46 48.48 129.91 85.9 61.08 82.06 Chaozhou T5 64.27 454.05 30.23 107.6 22.97 40.02 39.64 Jieyang RU 167.69 1062.01 69.2 342.02 19.7 25.14 41.03 Yunfu ZM 149.11 265.83 37.9 63.07 22.78 35.38 34.12 Zhuhai 5C 45.11 894.07 119.95 249.24 46.51 146.8 160.32 Hong Kong HK 18.85 508.83 885.74 1495.18 1224.95 3316.8 869.9 Sustainability 2020, 12, 5639 5 of 14 Sustainability 2020, 12, x FOR PEER REVIEW 6 of 15

Economic Complexity in the Pearl RiverRiver DeltaDelta RegionRegion TheThe RSCA RSCA distribution distribution across across different different cities cities is illustrated is illustrated using using a heat a map heat as map shown as shown in Figure in 1.Figure Across1. different Across di productfferent sectors, product it sectors,is found itth isat foundmost cities that exhibit most cities strong exhibit revealed strong comparative revealed advantagecomparative in advantage c1 (agriculture/forestry/livestoc in c1 (agriculture/forestryk/fisheries),/livestock/fisheries), whereas whereasHong Kong, Hong Kong,Shenzhen, Shenzhen, and Dongguanand Dongguan significantly significantly fall fallbehind behind in this in this sector. sector. This This observation observation shows shows that that major major cities have limited space, thus resulting in high-cost operations for sustaining an agriculturalagricultural industry. On thethe contrary, for the c6 (finance(finance sector), Hong Kong is the only city among the list with strong revealedrevealed comparative advantage, whereas other cities havehave picked up in this sectorsector overover time.time. Among the PRD cities, Hong Kong retains its leadership in diversity, with revealed comparative Among the PRD cities, Hong Kong retains its leadership in diversity, with revealed comparative advantage in 5 out of 7 product sectors in all 4 sample years, which is ahead of other cities. The advantage in 5 out of 7 product sectors in all 4 sample years, which is ahead of other cities. The distribution pattern is also unique to most PRD cities other than Guangzhou. The finding supports that distribution pattern is also unique to most PRD cities other than Guangzhou. The finding supports Hong Kong’s functional and sectoral specialisation reveals complementarities over neighbouring cities, that Hong Kong’s functional and sectoral specialisation reveals complementarities over neighbouring whereas Guangzhou (and to a lesser extent Shenzhen) represents competition to Hong Kong, according cities, whereas Guangzhou (and to a lesser extent Shenzhen) represents competition to Hong Kong, to the study by Schiller et al. [24]. Hong Kong’s strength in c6 (finance sector) has consistently yielded according to the study by Schiller et al. [24]. Hong Kong’s strength in c6 (finance sector) has high revealed comparative advantage over PRD cities. While this sector has picked up among all PRD consistently yielded high revealed comparative advantage over PRD cities. While this sector has cities over time, it demonstrates a possible linkage that Hong Kong’s leadership helps to promote picked up among all PRD cities over time, it demonstrates a possible linkage that Hong Kong’s development in the finance sector among the PRD cities. leadership helps to promote development in the finance sector among the PRD cities.

(a)

Figure 1. Cont. Sustainability 2020, 12, 5639 6 of 14 Sustainability 2020, 12, x FOR PEER REVIEW 7 of 15

(b)

(c)

Figure 1. Cont. Sustainability 2020, 12, 5639 7 of 14 Sustainability 2020, 12, x FOR PEER REVIEW 8 of 15

(d)

Figure 1. Revealed Symmetric Comparative Advantage (RSCA) distribution across different years. (a) Figure 1. Revealed Symmetric Comparative Advantage (RSCA) distribution across different years. (a) 2000, (b) 2005, (c) 2010, (d) 2015. 2000, (b) 2005, (c) 2010, (d) 2015. In general, Figure1 shows that PRD cities are moving away from c1 (agriculture, forestry, livestock,In general, fisheries), Figure while 1 shiftingshows that the focusPRD towardscities are c6 moving (finance), away c7 (real from estate), c1 (agriculture, and c3 (building forestry, and construction).livestock, fisheries), Such findings while shifting suggest the that focus during towards the rapid-growth c6 (finance), phase c7 (real of PRD estate), cities, and the c3 agricultural (building industryand construction). is the least Such economically findings sustainablesuggest that sector, during whereas the rapid-growth finance, real phase estate, of and PRD building cities, andthe agricultural industry is the least economically sustainable sector, whereas finance, real estate, and construction are the most economically sustainable sectors. Such a finding aligns with Ren’s study building and construction are the most economically sustainable sectors. Such a finding aligns with of the rural-to-urban transition, yet the rural–urban dichotomy may need further adjustment for the Ren’s study of the rural-to-urban transition, yet the rural–urban dichotomy may need further evolving economy in China [2]. This is supported in our study that the role of agriculture-focused adjustment for the evolving economy in China. [2] This is supported in our study that the role of cities has been evolving with reduced emphasis over time but still holds a significant role amid agriculture-focused cities has been evolving with reduced emphasis over time but still holds a industrialisation and urbanisation. The RSCA shift could be influenced by both governmental policies significant role amid industrialisation and urbanisation. The RSCA shift could be influenced by both as well as private investments. The findings can be utilised by policy makers of future emerging governmental policies as well as private investments. The findings can be utilised by policy makers economies to learn from the experience in PRD city clusters, to position their strategic industrial focus of future emerging economies to learn from the experience in PRD city clusters, to position their over time. In addition, subject to the availability of similar datasets from other emerging city clusters, strategic industrial focus over time. In addition, subject to the availability of similar datasets from it would be an interesting future study to explore the differences in industrial shift, offering further other emerging city clusters, it would be an interesting future study to explore the differences in advice to policy makers about how the strategic industrial focus could be fine-tuned. industrial shift, offering further advice to policy makers about how the strategic industrial focus The similarity of RSCA distribution across different product sectors among PRD cities can be could be fine-tuned. found in Figure2. Euclidean distance of the Mcp values between each city pair is calculated, and links are generatedThe similarity if the of distance RSCA valuedistribution falls below across a givendifferent threshold. product Acrosssectors di amongfferent PRD years, cities it has can been be found in that Figure city pairs2. Euclidean sharing distance similar distributionof the Mcp values patterns between exhibit each a slightcity pair decreasing is calculated, trend, and with links 24 linksare generated in 2000, 17if the links distance in 2005, value 14 links falls inbelow 2010, a and give 16n threshold. links in 2015. Across The different linked city years, groups, it has or been the clustersfound that formed city pairs by these sharing links, simila includer distribution 5 clusters inpatterns 2000, 7 exhibit clusters a inslight 2005, decreasing 5 clusters trend, in 2010, with and 24 4 clusterslinks in in2000, 2015. 17 Therelinks isin no2005, clear 14 pattern links in showing 2010, an whetherd 16 links the in clusters 2015. The are expandinglinked city orgroups, diminishing. or the clustersSimilarities formed by across these dilinks,fferent include time 5 varies, clusters with in 2000, no common 7 clusters links in 2005, across 5 clusters all four in subplots2010, and in 4 Figureclusters2 .in The 2015. observation There is no reflectsclear pattern that theshowingMcp distributionwhether the clusters of each are city expanding varies, and or theydiminishing. may be groupedSimilarities in different across clusters different at di timefferent varies, times. with In betweenno common 2000 links and across 2005, 4all common four subplots links are in Figure found (Heyuan–Meizhou,2. The observation Qingyuan–Heyuan,reflects that the Mcp Qingyuan–Meizhou,distribution of each Yangjiang–Shanwei);city varies, and they inmay between be grouped 2005 and in 2010,different 3 common clusters links at different are found times. (Zhongshan–Foshan, In between 2000 Yunfu–Meizhou, and 2005, 4 common Yangjiang–Shanwei); links are found in (Heyuan– between 2010Meizhou, and 2015, Qingyuan–Heyuan, 5 common links Qingyuan–Meizhou, are found (Zhongshan–Foshan, Yangjiang–Shanwei); Yunfu–Meizhou, in between Jiangmen–Huizhou, 2005 and 2010, Chaozhou–Huizhou,3 common links are found Chaozhou–Jiangmen). (Zhongshan–Foshan, Among Yu thesenfu–Meizhou, common links,Yangjiang–Shanwei); 3 links have appeared in between twice 2010 and 2015, 5 common links are found (Zhongshan–Foshan, Yunfu–Meizhou, Jiangmen–Huizhou, Chaozhou–Huizhou, Chaozhou–Jiangmen). Among these common links, 3 links have appeared Sustainability 2020, 12, 5639 8 of 14 Sustainability 2020, 12, x FOR PEER REVIEW 9 of 15

(Yangjiang–Shanwei,twice (Yangjiang–Shanwei, Yunfu–Meizhou, Yunfu–Meizhou, Zhongshan–Foshan), Zhongshan–Foshan), and the and ﬁndings the findings demonstrate demonstrate that these that cities share closer similarities in terms of Mcp distribution over time. these cities share closer similarities in terms of Mcp distribution over time.

(a) 2000 (b) 2005

Figure 2. Mcp pattern similarity with d = 0.3 in different years. (a) 2000, (b) 2005, (c) 2010 and (d) 2015. Figure 2. Mcp pattern similarity with d = 0.3 in different years. (a) 2000, (b) 2005, (c) 2010 and (d) 2015 Figure3 shows the geographical distribution of economic complexity of the city cluster. Hong Kong—togetherFigure 3 shows with the two geographical super-cities, Shenzhendistribution and of Guangzhou economic complexity and their surrounding of the city cluster. cities—have Hong formedKong—together a cluster thatwith exhibits two super-cities, a high concentration Shenzhen and in combined Guangzhou economic and their complexity. surrounding Another cities—have cluster isformed formed a cluster by Shantou that exhibits and Chaozhou a high concentratio on the easternn in combined region of theeconomic Pearl River complexity. Delta. Another The remaining cluster citiesis formed either by possess Shantou low and economic Chaozhou complexity on the eastern or situated region far of apart, the thusPearl mostly River Delta. appear The as individualremaining nodescities either or with possess weak linkslow economic to the adjacent complexity cities. or situated far apart, thus mostly appear as individual nodesThe or overallwith weak economic links to complexity, the adjacent measured cities. in terms of normalised diversification as shown in Equation (8), is summarised in Table3. Hong Kong, holding the highest normalised diversification in 2000, has dropped its leading position in economic complexity, with a loss in its normalised diversity of 1.06. This is possibly due to the economic boost in other PRD cities. Hong Kong’s decline in its relative economic complexity is also reflected in Figure1. Shenzhen, one of the four super cities in China (Beijing, Shanghai, Shenzhen, Guangzhou), has been under rapid growth in economic complexity, with a gain of 2.16 between 2000 and 2015. On the contrary, the economic complexity of Guangzhou, another super city in China, remains almost unchanged with a slight 0.01 drop.

Sustainability 2020, 12, x FOR PEER REVIEW 9 of 15 twice (Yangjiang–Shanwei, Yunfu–Meizhou, Zhongshan–Foshan), and the findings demonstrate that these cities share closer similarities in terms of Mcp distribution over time.

(a) 2000 (b) 2005

Figure 3 shows the geographical distribution of economic complexity of the city cluster. Hong Kong—together with two super-cities, Shenzhen and Guangzhou and their surrounding cities—have formed a cluster that exhibits a high concentration in combined economic complexity. Another cluster is formed by Shantou and Chaozhou on the eastern region of the Pearl River Delta. The remaining Sustainabilitycities either2020 possess, 12, 5639 low economic complexity or situated far apart, thus mostly appear as individual9 of 14 nodes or with weak links to the adjacent cities.

Figure 3. Geographical distribution of economic complexity heat map among Pearl River Delta cities.

As shown in Table4, by averaging the normalised diversity throughout 16 years, Hong Kong has the highest average economic complexity, followed by Guangzhou, Dongguan, Shenzhen, and Zhuhai. The cities with the lowest average economic complexity are Huizhou, Chaozhou, Yunfu, Heyuan, and Jiangmen. In terms of volatility in economic complexity across 16 years, Shenzhen, Zhuhai, Foshan, Zhongshan, and Yangjiang have the highest standard deviation, whereas Huizhou, Shantou, Heyuan, Jieyang, and Jiangmen have the lowest standard deviation. Sustainability 2020, 12, 5639 10 of 14

Table 3. Economic complexity as the normalised diversiﬁcation.

City 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 GU 2.09 1.49 1.27 1.39 1.93 1.41 1.97 1.95 2.32 1.98 1.58 1.19 1.67 2.07 2.09 2.08 SZ 0.08 0.04 0.04 1.30 1.28 2.01 1.86 1.88 2.18 2.19 0.50 0.17 2.05 1.83 1.91 2.08 − − ST 0.26 0.37 0.41 0.53 0.19 0.34 0.39 0.39 0.37 0.40 0.11 0.03 0.12 0.07 0.02 0.02 − − − − − − − − − − − − − FS 0.01 0.34 0.47 0.14 0.07 0.38 0.09 0.14 0.07 0.58 1.67 1.79 1.08 0.95 0.72 0.39 − − − − − − − − − − − − ZK 0.36 0.31 0.32 0.26 0.67 0.48 0.57 0.57 0.56 0.21 1.13 1.04 0.53 0.39 0.06 0.10 − − − − − − − − − − HU 0.54 0.30 0.25 0.32 1.05 0.91 0.57 0.57 0.56 0.72 0.85 0.87 0.99 0.58 0.67 0.64 − − − − − − − − − − − − − − − − MJ 0.54 0.91 0.25 0.32 1.05 0.91 0.90 0.92 0.81 0.78 0.57 0.64 0.18 0.40 0.64 0.76 − − − − − − − − − − − − − HJ 0.47 0.67 0.75 0.71 0.67 0.48 0.51 0.48 0.51 0.85 0.85 0.87 0.99 0.96 0.90 0.76 − − − − − − − − − − − − − − − − SW 0.54 0.30 0.07 0.27 0.50 0.59 0.54 0.56 0.47 0.35 0.70 0.63 0.12 0.16 0.90 0.76 − − − − − − − − − − − − − − TG 2.09 1.49 1.77 1.63 1.28 2.01 1.86 1.88 1.60 1.05 0.41 0.03 1.04 0.92 1.37 1.54 − ZS 0.26 0.34 0.47 0.14 0.07 0.38 0.09 0.14 0.07 0.58 1.67 1.79 1.08 0.95 0.72 0.39 − − − − − − − − − − − − GM 0.26 0.37 0.41 0.53 0.19 0.42 0.51 0.48 0.51 0.85 0.85 0.87 0.99 0.96 0.90 0.76 − − − − − − − − − − − − − − − − EG 0.54 0.30 1.31 0.91 0.13 0.59 0.54 0.56 0.47 0.35 0.70 0.63 0.18 0.58 0.67 0.17 − − − − − − − − − − − JG 0.38 0.39 0.38 0.72 0.50 0.42 0.51 0.57 0.81 0.21 1.13 1.04 0.53 0.39 0.06 0.76 − − − − − − − − − − MM 0.26 0.39 0.38 0.72 0.61 0.59 0.54 0.56 0.47 0.29 0.47 0.34 0.04 0.24 0.43 0.64 − − − − − − − − − − CG 0.47 1.00 1.04 0.72 0.32 0.59 0.54 0.56 0.47 0.35 0.57 0.64 0.99 0.96 0.90 0.76 − − − − − − − − − − − − − − FM 0.54 0.30 0.25 0.32 1.05 0.91 0.57 0.57 0.56 0.72 0.18 1.04 0.53 0.39 0.06 0.10 − − − − − − − − − − − − T5 0.47 0.67 0.75 0.71 0.58 0.26 0.28 0.27 0.27 0.85 0.85 0.87 0.99 0.96 0.90 0.76 − − − − − − − − − − − − − − − − RU 0.26 0.37 0.41 0.53 0.19 0.26 0.39 0.39 0.27 0.39 0.24 0.37 0.34 0.96 0.04 0.16 − − − − − − − − − − − − − − ZM 0.93 1.00 1.04 1.29 1.05 0.91 0.90 0.92 0.81 0.78 0.57 0.64 0.18 0.40 0.64 0.76 − − − − − − − − − − − − − − 5C 0.00 0.10 0.12 0.11 0.07 0.22 0.13 0.15 0.06 1.31 1.67 1.79 0.27 0.67 0.66 0.75 − − − − HK 2.95 3.38 3.12 2.89 2.69 2.21 2.28 2.22 1.93 1.89 1.40 1.20 2.13 2.01 1.92 1.89 Sustainability 2020, 12, 5639 11 of 14

Table 4. Statistical analysis of economic complexity.

Mean Standard Median First Third Variance Standard Kurtosis Skewness Range Mini Max Sum City Error Quartile Quartile Deviation GU 1.78 0.09 1.94 1.47 2.07 0.12 0.35 1.25 0.34 1.13 1.19 2.32 28.48 − − SZ 1.27 0.24 1.85 0.14 2.02 0.94 0.97 1.23 0.75 2.69 0.50 2.19 20.24 − − − ST 0.23 0.05 0.30 0.39 0.06 0.04 0.20 1.28 0.36 0.64 0.53 0.11 3.70 − − − − − − − FS 0.47 0.16 0.37 0.78 0.01 0.40 0.64 0.09 0.89 2.17 1.79 0.38 7.53 − − − − − − ZK 0.05 0.14 0.29 0.50 0.26 0.32 0.57 0.06 1.02 1.80 0.67 1.13 0.84 − − − − − HU 0.65 0.06 0.61 0.86 0.56 0.06 0.24 0.71 0.03 0.80 1.05 0.25 10.39 − − − − − − − − MJ 0.40 0.15 0.59 0.83 0.23 0.37 0.61 0.24 1.15 1.96 1.05 0.91 6.34 − − − − − − HJ 0.71 0.05 0.73 0.86 0.51 0.03 0.18 1.40 0.08 0.52 0.99 0.47 11.43 − − − − − − − − SW 0.30 0.11 0.41 0.55 0.15 0.19 0.44 1.55 1.29 1.60 0.90 0.70 4.80 − − − − − − TG 1.37 0.15 1.52 1.05 1.79 0.34 0.58 0.98 1.09 2.12 0.03 2.09 21.91 − − ZS 0.49 0.16 0.37 0.78 0.04 0.39 0.63 0.16 0.87 2.17 1.79 0.38 7.78 − − − − − − − GM 0.62 0.07 0.52 0.86 0.42 0.07 0.26 1.45 0.00 0.80 0.99 0.19 9.86 − − − − − − − − EG 0.08 0.16 0.33 0.55 0.26 0.39 0.63 0.02 1.13 1.98 0.67 1.31 1.27 − − − − − JG 0.13 0.16 0.39 0.53 0.26 0.38 0.62 0.17 0.97 1.94 0.81 1.13 2.08 − − − − − − MM 0.17 0.11 0.34 0.55 0.27 0.20 0.45 1.31 0.55 1.33 0.72 0.61 2.71 − − − − − − CG 0.53 0.13 0.58 0.92 0.44 0.25 0.50 1.88 1.48 1.68 1.04 0.64 8.46 − − − − − − FM 0.25 0.14 0.31 0.57 0.06 0.30 0.55 0.76 0.90 2.09 1.05 1.04 4.05 − − − − − − T5 0.65 0.07 0.73 0.86 0.42 0.07 0.26 1.28 0.50 0.73 0.99 0.26 10.44 − − − − − − − − RU 0.32 0.06 0.36 0.39 0.26 0.06 0.24 3.29 0.57 1.12 0.96 0.16 5.17 − − − − − − − ZM 0.65 0.14 0.86 0.95 0.58 0.31 0.55 1.86 1.59 1.93 1.29 0.64 10.40 − − − − − − 5C 0.03 0.20 0.12 0.02 0.33 0.62 0.79 2.09 1.24 3.10 1.79 1.31 0.48 − − − HK 2.26 0.15 2.17 1.91 2.74 0.37 0.61 0.43 0.25 2.18 1.20 3.38 36.11 − Sustainability 2020, 12, 5639 12 of 14

3. Discussion It should be noted that Macau, a major city in Southern China within close geographical proximity, is excluded in this study. While Macau possesses a high GDP, its industrial composition significantly differs to the PRD city cluster, hence it is not typically considered as the same industrial ecosystem. However, it is unclear how cross-sectors influence the nearby economies, and further exploration may be of great interest. The analysis conducted in this paper should be taken with caution, as the small number of industrial sectors provide a very high-level abstraction of economic complexity modelling. The classification with seven product sectors used in this paper can be considered comparable to Tier 1 of the Standard International Trade Classification (SITC), which consists of 10 product sectors. In the study of the Atlas of Economic Complexity [14], a six-digit SITC code with over 1000 industry classifications have been used, which provides a more fine-grained product sector composition compared to this study. Collecting finer details of GDP records will be considered for future work following this study. Selection of the representative cities in the PRD region is also worth discussion. In comparison with the popular choice of nine PRD cities possessing the highest GDP [23], subsequently 12 PRD cities, according to the GDP together with Hong Kong, have been included in this study. As illustrated in Figure2, these smaller scale cities may possess unique patterns of industrial composition, representing three, one, three, and three isolated nodes in years 2000, 2005, 2010, 2015, respectively. These are comparable to three, two, three, and three isolated nodes from the list of the nine largest PRD cities, excluding Hong Kong. Such an observation indicates that the economic composition of smaller cities is not a replication of the larger ones. These smaller cities could, however, still be considered subsidiary as the result of industrial transformation in larger cities (for instance, offloading labour-intensive industries to smaller counterparts). Such practices may lead to satellite cities with complementing industrial sectors that demand different skillsets. Further investigations of such behaviour may require data set at finer details (i.e., further break down of industrial sectors), which could be a potential future study, subject to the availability of relevant datasets. A further limitation is the scale of the cities in the study. China, the country with the highest population in the world, hosts cities in much larger scales. For instance, the population of Shenzhen and Guangzhou combined has exceeded the entire population in Australia. Cities at such a scale could be subdivided, and intracity studies, such as the work investigated by Liu et al. [26] may help to give better insights or provide fair comparisons.

4. Conclusions This paper examines the economic complexity of the Pearl River Delta cities, using the GDP data between 2000 and 2016. Among 22 major cities in this region, it was found that Hong Kong, Guangzhou, and Dongguan are top-ranked cities in terms of economic complexity, whereas Yunfu, Heyuan, and Jiangmen represent the bottom three. In terms of the variation in economic complexity Shenzhen, Zhuhai, Foshan, Zhongshan, and Yangjiang were found to have the highest standard deviation across this period, showing the highest volatility of structural change in the economy. It was found that the distribution pattern of product specialisation of a city, defined as the number of product sectors in the city with revealed comparative advantage, has been changed over time. City pairs with the closest distribution of product sector specialisation are Yangjiang–Shanwei, Yunfu–Meizhou, and Zhongshan–Foshan. Hong Kong’s leadership in the finance sector correlates to the growth of the surrounding cities, demonstrating a possible linkage of this sector in the PRD city cluster. It was also found that during the rapid growth phase of PRD cities, the agricultural industry was found to be the least economically sustainable sector, whereas finance, real estate, and building and construction were the most economically sustainable sectors. Sustainability 2020, 12, 5639 13 of 14

Author Contributions: Conceptualization, R.F.-Y.L. and I.L.; methodology, I.L.; software, I.L.; validation, I.L. and R.F.-Y.L.; formal analysis, I.L.; investigation, R.F.-Y.L.; resources, I.L. and R.F.-Y.L.; data curation, R.F.-Y.L.; writing—original draft preparation, I.L.; writing—review and editing, I.L. and R.F.-Y.L.; visualization, I.L.; project administration, R.F.-Y.L.; funding acquisition, R.F.-Y.L. All authors have read and agreed to the published version of the manuscript. Funding: This research received fundings from Shenzhen Government (No. JCYJ20190806144609107, HA11409051 and (2018)725). Conﬂicts of Interest: The authors declare no conﬂict of interest.

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