sustainability 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. 8 n o Subsequently, each city may be characterised by the vector kc = kc0,n n = 1 ::: N and kp = n o j k0 n = 1 ::: N , where N is a predetermined number of iterations and N Z and N > 1. The p,n j 2 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].