Brake segment for agglomeration policy: Engineers as human capital

Kuchiki, Akifumi Article Brake segment for agglomeration policy: Engineers as human capital Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Kuchiki, Akifumi (2024) : Brake segment for agglomeration policy: Engineers as human capital, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 12, Iss. 7, pp. 1-28, https://doi.org/10.3390/economies12070163 This Version is available at: https://hdl.handle.net/10419/329089 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Economies 12: 163. https://doi.org/ 10.3390/economies12070163 Academic Editor: António Cardoso Marques Received: 30 March 2024 Revised: 15 May 2024 Accepted: 21 May 2024 Published: 27 June 2024 Copyright: © 2024 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article Brake Segment for Agglomeration Policy: Engineers as Human Capital Akifumi Kuchiki Institute for International Trade and Investment, Tokyo 104-0045, Japan; [email protected] Abstract: A “segment” is a component of the organization of an agglomeration. The organization of agglomeration is formed by the construction of segments. Manufacturing agglomeration segments can be divided into four main categories: human resources including engineers, physical infrastructure, institutions, and living environment. Each segment then has a specific function in the process of building industrial agglomeration. We focus on the process of building segments in agglomeration formation. We define a “brake segment” as a segment that has a “function” to decelerate the speed of the process. The purpose of this paper is to identify the existence of this brake segment in the process of constructing the segments of the manufacturing agglomeration. We obtained the following three results. First, a modified version of the spatial economic model yields that the number of agglomerated firms is inversely related to the wages of skilled workers. Second, a factor analysis of the data on investment environment costs indicates that in the case of the manufacturing industry, the number of agglomerated firms are inversely related to the wages of engineers. Third, the factor analysis of the six countries in the JBIC survey reveals that the segment that poses the investment issue in foreign direct investment in India is engineers as human capital. We conclude that engineers as human capital are a brake segment. The implication is that the sustained development of “engineers” as human capital is essential for the success of manufacturing industry agglomeration. Keywords: brake segment; function; engineers; human capital; spatial economics 1. Introduction Special economic zones (SEZs) are strongly positive in attracting foreign direct investment (FDI) in China, according to UNCTAD (2019). The notion of industrial hubs by Oqubay and Lin (2020) is a generic term for agglomerations of economic activity that have developed since the Industrial Revolution. Special economic zones (SEZs), industrial parks, and export processing zones (EPZs) have been widely used to catch up and transform the economies of newly industrializing economies in East Asia. However, Zheng and Aggarwal (2020) found that India could not match the scale and success of China’s SEZs. The literature on industrial hubs has focused on different types of industrial hubs: industrial districts, EPZs and SEZs, and small industrial clusters, according to Oqubay (2020) as follows. First, FIAS World Bank’s occasional paper by FIAS (2008) views SEZs as ‘a tool to enhance industry competitiveness’ and defines ‘a geographically delimited area, offering certain incentives to businesses which physically locate in the zones’ (Farole and Akinci (2011); Zeng (2010)). Second, New Structural Economics by Lin (2012), focuses on a developing country strategy oriented to a latent comparative advantage. The construction of an agglomeration is not instantly completed, but a sequential process of building the segments. It took more than ten years to construct the manufacturing agglomerations. Third, Kanai and Ishida (2000) called research on the dynamic process of agglomeration building as ‘process analysis’. Kuchiki and Tsuji (2011) and Fujita and Kuchiki (2006) use a flowchart approach to industrial agglomeration. The flowchart Economies 2024,12, 163. https://doi.org/10.3390/economies12070163 https://www.mdpi.com/journal/economies Economies 2024,12, 163 2 of 28 approach analyzes the process of constructing agglomeration segments. The flowchart approach was applied to many cases all over the world, as follows: the Malaysian electronics industry by Meyanathan (2011), the Wuhan Optical Valley industry in China by Hu and Liu (2011), the automobile industry in China by He (2011), the Rio de Janeiro Software industry by Botelho et al. (2010), the Austin Technopolis in the US (2008), the Industrial Cluster Plan promoted by the Ministry of Economy, Trade and Industry Japan. Macasaquit (2008) applied it to industrial agglomeration in the Philippines, and Mitra and Mehta (2011) applied this approach to industrial agglomeration in India. They analyze the process of constructing agglomeration segments. In economic theory, Krugman (1991) built the prototype model to examine where economic activity occurs and why. Fujita et al. (1999) extended the prototype model to establish spatial economics. Using a Hotelling-type framework in central place theory, Henkel et al. (2000) obtained the breaking conditions of agglomeration equilibria on spatial allocation decisions in which consumers are active in a marketplace. Helpman and Krugman (1985) provided a new trade theory in spatial economics, in which the equilibrium number of firms is derived based on a general equilibrium model. By analyzing data on industrial hubs based on a model of spatial economics, the following three conclusions were drawn. First, Kuchiki (2021) used the model of Henkel et al. (2000) to derive the segment that is the master switch in the construction of tourism industrial agglomeration. It is the introduction of both infrastructure that reduces transport costs and heterogeneous goods with low elasticity of substitution. Second, Kuchiki and Sakai (2023) used the hybrid model of Krugman (1991) and Alonso (1964) to derive the segment that is the master switch of urban agglomeration. It is the introduction of both infrastructure that reduces commuter costs and heterogeneous goods with low elasticity of substitution. Third, Kuchiki (2023) used Helpman and Krugman’s (1985) model to derive the segment that is the “accelerator” in the construction of manufacturing agglomerations. It is the “leased” industrial park. The “segments” constitute the organization of industrial agglomeration. The organization of agglomerations is formed by building segments. The segments comprise four major categories: human resources, physical infrastructure, institutions, and living conditions, according to Kuchiki (2023). Table 1illustrates some of the investment survey items for the manufacturing industry. The major category of human resources consists of segments: general workers, general office staff, engineers, and section chief staff, or managers. Each segment has a “function”. Its function includes the role of a master switch or accelerator. Ports and roads have the function of master switches for industrial agglomeration policy. Industrial parks have the function of accelerators. We focus on the process of building segments in agglomeration formation. This paper understands the transition process as a process of constructing segments of an agglomeration and clarifies the importance of the “process of policy implementation”. The “sequencing economics” of the construction of segments is then addressed. This paper discusses segments that function to decelerate the process of building segments toward industrial agglomeration. This paper defines the segment as the “brake segment”. India has not succeeded in establishing export-oriented SEZs through the introduction of foreign direct investment. Therefore, it is conjectured that some segments function to bake the process of establishing industrial agglomeration. No study has taken up a case study to examine whether brake segments exist and what they are from the viewpoint of theoretical and quantitative analysis when constructing segments of manufacturing agglomerations. The purpose of this paper is to identify the existence of this brake segment. We will compare India with countries in East Asia, including Vietnam and Thailand, for the purpose of examining in this hypothesis. India did not succeed in introducing exportoriented foreign capital and switched to foreign capital for the domestic market, while Vietnam and Thailand succeeded in introducing foreign capital. Economies 2024,12, 163 3 of 28 Table 1. Segments and investment-related costs. Category Segment Segments Function Human resoruce worker (general laborer) W1 engineer Brake W2 middle management (section chief) W3 staff (general office work) W4 manager (section chief) W5 Physical infrastructure Industrial zone Accel Z1 Industrial zone Accel Z2 Offices Z3 Electricity P1 Water P2 Gas P3 Ports Master switch C1 Master switch C2 Road Master switch Airport Master switch Institutions Deregulation Master switch Preferential treatments Master switch One-stop services Master switch Laws and regulations Living conditions Housing International schools Hospitals Entertainment & shopping Source: Author’s based on Japan External Trade Organization (JETRO 2022). The results of this paper were derived in the following three steps. First, a modified version of the spatial economic model yields that the number of agglomerated firms is inversely related to the wages of skilled workers. Second, a factor analysis of the data on investment environment costs indicates that in the case of the manufacturing industry, skilled workers are engineers. In other words, the number of agglomerated firms are inversely related to the wages of engineers. Third, the factor analysis of the six countries in the JBIC survey reveals that the segment that poses the investment issue in foreign direct investment in India is engineers as human capital. A regression analysis of the factor score data confirms this fact. This paper concludes that engineers as human capital is a brake segment. The implication is that sustained development of “engineers” as human capital is essential for the success of manufacturing industrial agglomeration. To avoid pedaling the brakes in the transition process to an agglomeration equilibrium, it is necessary to sustainably develop engineers as human capital. Sequencing economics in architecture theory related to agglomeration is applied to sequencing the segments of an agglomeration in terms of ‘economies of sequence’. The concept of ‘economies of sequence’ is defined as the selection and sequencing of any two segments from among the entire group of segments of an industrial agglomeration toward the efficient building of the agglomeration, according to Kuchiki (2021). This paper is to link the theory of spatial economics with the practice of sequencing economics. Industrial agglomeration consists of organizations, and organizations consist of segments. Sequencing economics is used to embody the theoretical model of spatial economics into agglomeration policy. The “function” of the segments, which are the building blocks of agglomeration, is clarified. The “design” of agglomeration policy is essential for policy makers to design its construction process. The flowchart approach has so far arrayed segments in the process of building industrial agglomeration. In this approach, the concept of “economies of sequence” was introduced to introduce the perspective of “efficiency” of segment construction. In order to consider efficiency, the “function” of the segment was identified. Previously, master switches and accelerators were identified as examples of “functions”. In addition to these, this paper identifies the engineers as human capital segment Economies 2024,12, 163 4 of 28 as a segment of the “brake” function. In sequencing economics, identifying the function of a segment leads to efficient implementation of the agglomeration policy. Therefore, sequencing economics is useful for policy makers to implement agglomeration policies. Section 2presents preceding studies on the function of segments such as master switch. Section 3provides an overview of industrial zones in Vietnam and Thailand and special economic zones in India. In Section 4, we explain the materials and methods. In Section 5, we obtain the brake segment using factor analysis and regression analysis. Section 6 concludes this paper. 2. Literature Review on Sequencing Economics On one hand, Fujita et al. (1999) established in spatial economics ‘the study of where and why economic activity takes place’. The results can be used in sequencing economics as location conditions in economic decision-making. On the other hand, Kuchiki (2023) proposed sequencing economics as an architectural theory of agglomeration. Kuchiki (2023) analyzed special economic zones (SEZs) as agglomerations from the perspective of both spatial economics and sequence economics. The process of constructing an agglomeration identified by Kuchiki (2023) is as follows. The first step in building agglomerations is to determine where to locate them. Spatial economics determines the conditions for this location. Next, the segments of areas determined are constructed to satisfy those conditions. The analysis of the construction process addressed by Kuchiki (2023) is in the following way. The organization of industrial agglomeration consists of segments. Each of these segments has a function. Kuchiki and Sakai (2023) identified (1) master switches and (2) accelerators as examples of sequencing economics, respectively. Kuchiki and Sakai (2023) found that the segment that reduces transport costs is the master switch and that the segment that reduces fixed costs is the accelerator. This paper finds out which are (3) the “brake” segments that decelerate the construction process of the agglomeration segment. Figure 1illustrates the relationship that the master switch, to efficiently construct the segments that make up the agglomeration in sequencing, is the segment that satisfies the symmetry breaking condition. Economies 2024, 12, x FOR PEER REVIEW 4 of 30 The flowchart approach has so far arrayed segments in the process of building industrial agglomeration. In this approach, the concept of “economies of sequence” was introduced to introduce the perspective of “efficiency” of segment construction. In order to consider efficiency, the “function” of the segment was identified. Previously, master switches and accelerators were identified as examples of “functions”. In addition to these, this paper identifies the engineers as human capital segment as a segment of the “brake” function. In sequencing economics, identifying the function of a segment leads to efficient implementation of the agglomeration policy. Therefore, sequencing economics is useful for policy makers to implement agglomeration policies. Section 2 presents preceding studies on the function of segments such as master switch. Section 3 provides an overview of industrial zones in Vietnam and Thailand and special economic zones in India. In Section 4, we explain the materials and methods. In Section 5, we obtain the brake segment using factor analysis and regression analysis. Section 6 concludes this paper. 2. Literature Review on Sequencing Economics On one hand, Fujita et al. (1999) established in spatial economics ‘the study of where and why economic activity takes place’. The results can be used in sequencing economics as location conditions in economic decision-making. On the other hand, Kuchiki (2023) proposed sequencing economics as an architectural theory of agglomeration. Kuchiki (2023) analyzed special economic zones (SEZs) as agglomerations from the perspective of both spatial economics and sequence economics. The process of constructing an agglomeration identified by Kuchiki (2023) is as follows. The first step in building agglomerations is to determine where to locate them. Spatial economics determines the conditions for this location. Next, the segments of areas determined are constructed to satisfy those conditions. The analysis of the construction process addressed by Kuchiki (2023) is in the following way. The organization of industrial agglomeration consists of segments. Each of these segments has a function. Kuchiki and Sakai (2023) identified (1) master switches and (2) accelerators as examples of sequencing economics, respectively. Kuchiki and Sakai (2023) found that the segment that reduces transport costs is the master switch and that the segment that reduces fixed costs is the accelerator. This paper finds out which are (3) the “brake” segments that decelerate the construction process of the agglomeration segment. Figure 1 illustrates the relationship that the master switch, to efficiently construct the segments that make up the agglomeration in sequencing, is the segment that satisfies the symmetry breaking condition. Figure 1. “Master Switch” for agglomeration policy. Source: Author’s Illustration. Figure 1. “Master Switch” for agglomeration policy. Source: Author’s Illustration. 2.1. The Segments of Master Switch Kuchiki (2021) used a symmetry breaking condition derived from the Henkel et al. (2000) model in central place theory. This model is a “partial equilibrium” analysis of tourism industrial agglomeration in spatial economics. Kuchiki (2021) found that in sequencing the tourism agglomeration segments, the master switch was to give first priority to the opening of Universal Studios Japan, which reduces the elasticity of substitution among differentiated goods, and second priority to the construction of Kansai International Airport, which reduces transportation costs. The opening of Universal Studios Japan increased the number of foreign passengers at Kansai International Airport with a two-year lag. Subsequently, the increase in the number of Economies 2024,12, 163 5 of 28 foreign passengers was lagged by some years, leading to an increase in the number of arrivals and departures at Kansai International Airport, as well as an increase in the number of foreign tourists in Osaka Prefecture. The economy is a symmetric equilibrium in which manufacturing is equally divided between the two regions as Fujita et al. (1999) defined in Section 5. It found the conditions of the symmetry breaking. Kuchiki and Sakai (2023) used symmetry breaking conditions derived from a “general equilibrium” model in a monocentric city setting as a master switch. Krugman (1991) was used to derive the condition of master switch. As shown in Figure 2, when a stable symmetric equilibrium is broken, then the construction of segments of an agglomeration equilibrium begins. Kuchiki and Sakai (2023) present the conditions for the master switch to be turned on and identify what segments satisfy those conditions. The condition is that there is a critical value (threshold) for transport costs, and it is necessary to construct segments that reduce transport costs below the critical value. The segments are illustrated as roads, ports, simplification of investment procedures, etc. Economies 2024, 12, x FOR PEER REVIEW 5 of 30 2.1. The Segments of Master Switch Kuchiki (2021) used a symmetry breaking condition derived from the Henkel et al. (2000) model in central place theory. This model is a “partial equilibrium” analysis of tourism industrial agglomeration in spatial economics. Kuchiki (2021) found that in sequencing the tourism agglomeration segments, the master switch was to give first priority to the opening of Universal Studios Japan, which reduces the elasticity of substitution among differentiated goods, and second priority to the construction of Kansai International Airport, which reduces transportation costs. The opening of Universal Studios Japan increased the number of foreign passengers at Kansai International Airport with a two-year lag. Subsequently, the increase in the number of foreign passengers was lagged by some years, leading to an increase in the number of arrivals and departures at Kansai International Airport, as well as an increase in the number of foreign tourists in Osaka Prefecture. The economy is a symmetric equilibrium in which manufacturing is equally divided between the two regions as Fujita et al. (1999) defined in Section 5. It found the conditions of the symmetry breaking. Kuchiki and Sakai (2023) used symmetry breaking conditions derived from a “general equilibrium” model in a monocentric city setting as a master switch. Krugman (1991) was used to derive the condition of master switch. As shown in Figure 2, when a stable symmetric equilibrium is broken, then the construction of segments of an agglomeration equilibrium begins. Kuchiki and Sakai (2023) present the conditions for the master switch to be turned on and identify what segments satisfy those conditions. The condition is that there is a critical value (threshold) for transport costs, and it is necessary to construct segments that reduce transport costs below the critical value. The segments are illustrated as roads, ports, simplification of investment procedures, etc. Figure 2. Manufacturing agglomeration policy. Source: Author’s illustration. 2.2. The Segments of Accelerator Figure 2 shows the flow from the master switch through the accelerator to the anchor firm. The conditions for the accelerator segment were derived from Helpman and Krugman’s (1985) model of spatial economics. According to the new trade theory of spatial economics, the number of firms in an agglomeration is inversely related to its fixed costs. The main accelerator segment of agglomeration after the master switch is turned on is the formation of segments that reduce firms’ fixed costs. "Master switch": The first order segments Reduction in transportation costs : Down "Accelerator": Industrial Zones Introduction of differentiated goods: Down Anchor firm (An assemble firm) "Brake segment": Engineers Manufacturing agglomeration Figure 2. Manufacturing agglomeration policy. Source: Author’s illustration. 2.2. The Segments of Accelerator Figure 2shows the flow from the master switch through the accelerator to the anchor firm. The conditions for the accelerator segment were derived from Helpman and Krugman’s (1985) model of spatial economics. According to the new trade theory of spatial economics, the number of firms in an agglomeration is inversely related to its fixed costs. The main accelerator segment of agglomeration after the master switch is turned on is the formation of segments that reduce firms’ fixed costs. Kuchiki (2023) identifies the accelerator required for the process of agglomeration formation. In the case of manufacturing agglomeration formation, the accelerator segment is specifically industrial parks. The function of this segment is to speed up the process of building the segments of the manufacturing industry agglomeration. 2.3. Brake Segment Segments that function to decelerate the process of building segments for industrial agglomeration are defined as “brake segments”. In the following, we identify the segments that serve as brakes in the segment building process. We then empirically confirm that engineers as human capital is a brake segment. Economies 2024,12, 163 6 of 28 3. Industrial Agglomerations in Vietnam, Thailand, and India India has not succeeded in manufacturing agglomeration and is implementing agglomeration policies through the Make in India policy. Many east-Asian countries, including China, have succeeded in establishing manufacturing agglomerations. In 1981, China and India had approximately the same GDP per capita: USD 275 for India and USD 288 for India; in 2023, India’s GDP per capita is USD 2612 and China’s is USD 12,541 1 . One can presumably attribute this difference in part to the success of the Special Economic Zone (SEZ) policy that was launched in China in 1979, based on a chronological and statistical analysis by Kuchiki (2023). Zheng and Aggarwal (2020) conclude that India has failed to match the size and success of China’s SEZs in attracting FDI. Ahluwalia et al. (2018) found that India lost its comparative advantage in labor-intensive production in the early stages of development despite its relative abundance of unskilled labor. With regard to the use of SEZs to establish industrial agglomeration, there are differences in the effectiveness of SEZs in India and China. Two Asian countries that have typically succeeded in using the sequencing economics of agglomeration are Vietnam and Thailand. These countries have been successful in manufacturing agglomeration through the use of ODA as well as the introduction of foreign capital. Sequencing has been optimal in the construction of master switches and acceleration segments. In the case of these countries, the sequence of ODA implementation along with FDI was optimal. NESDB (2016) of the Thai government identified the results of the Eastern Seaboard Development Program agglomeration with Japanese ODA. Lecler (2002) described the automobile industrial agglomeration in the Eastern Seaboard Development Program, while Shimomura (2000) presented the Japanese contribution to the program and a third-party evaluation. Watanabe (2004) concluded that the combination of FDI and ODA was effective in promoting automobile accumulation in Thailand. The automobile agglomeration is called “Detroit in East Asia”. Similarly, Tran et al. (2003) positively evaluated the impact of Japanese ODA and FDI on industrial agglomeration in northern Vietnam. FDI was in the industrial agglomeration in the north of the country. Mitsui (2004) evaluated that the construction of National Highway 5 with ODA was effective for the agglomeration of FDI. ODA provided the master switch and accelerator through optimal sequencing. This section presents the segment sequencing. The section highlights successful cases of the introduction of export-oriented foreign direct investment in Vietnam and Thailand, in contrast to unsuccessful cases in India. The process of building master switches and accelerator segments in the process of building industrial agglomeration in Vietnam and Thailand will be illustrated. In this context, the process of developing engineers as human capital with a control function in the Vietnamese process will be highlighted. For the SEZs in India, we will focus on the process of shifting from export-oriented to domestic market-oriented foreign investment without much success in introducing foreign capital. The cases of Vietnam and Thailand in Kuchiki (2007) are reviewed in these subsections (1) and (2), as shown in Figures 3and 4. The segments of industrial agglomeration have a master switch and an accelerator pedal as functions. These cases are reinterpreted with respect to the function of the segments. In this section, on the one hand, Vietnam and Thailand implemented the policy of fostering engineers as human capital. On the other hand, India has implemented agglomeration policies using Special Economic Zones, but has not sufficiently trained engineers as human capital as a segment that constitutes the investment environment in its agglomeration policies. In the next section, we will theoretically demonstrate that agglomeration does not progress when there is a lack of engineers as human capital based on a model of spatial economics. Thus, we theoretically confirm that the failure of India’s manufacturing agglomeration was due to the lack of engineers as human capital. Economies 2024,12, 163 7 of 28 Economies 2024, 12, x FOR PEER REVIEW 7 of 30 The cases of Vietnam and Thailand in Kuchiki (2007) are reviewed in these subsections (1) and (2), as shown in Figures 3 and 4. The segments of industrial agglomeration have a master switch and an accelerator pedal as functions. These cases are reinterpreted with respect to the function of the segments. In this section, on the one hand, Vietnam and Thailand implemented the policy of fostering engineers as human capital. On the other hand, India has implemented agglomeration policies using Special Economic Zones, but has not sufficiently trained engineers as human capital as a segment that constitutes the investment environment in its agglomeration policies. In the next section, we will theoretically demonstrate that agglomeration does not progress when there is a lack of engineers as human capital based on a model of spatial economics. Thus, we theoretically confirm that the failure of India’s manufacturing agglomeration was due to the lack of engineers as human capital. Figure 3. The electronics industry agglomeration in northern Vietnam. Source: Tran et al. (2003) based on JBIC-IDCJ (2003). Note: ODA is official development assistance. Year ODA (1) Highway Route 5 1993, 1995 210 (1) Haiphong Port 1995, 1999 173 (2) Thanglong Industrial Park 1997 (2) Nomura Haiphong Industrial Zone 1997 (1) Institutional Reform 1999 200 (3) Canon 2001 Total amount 583 100 suppliers 2008 30 thousand employees (Unit: million $) Figure 3. The electronics industry agglomeration in northern Vietnam. Source: Tran et al. (2003) based on JBIC-IDCJ (2003). Note: ODA is official development assistance. Economies 2024, 12, x FOR PEER REVIEW 8 of 30 Figure 4. Eastern Seaboard. Source: Kuchiki and Tsuji (2011) based on Shimomura (2000). 3.1. Agglomerations in Northern Vietnam This subsection identifies the role of the “master switch” and accelerator in the establishment of industrial agglomeration through Japanese official development assistance in the case of industrial agglomeration in northern Vietnam2. 3.1.1. Roads and Ports: Reduction of Transport Costs as Master Switch The construction and renovation of National Highway No. 5 and Haiphong Port were effective as master switches for the building of industrial agglomeration. Figure 3 shows that JPY (Japanese Yen) 21 billion was provided for the construction of National Highway 5 and JPY 17.3 billion for the renovation of Haiphong port in 1993 and 1995. Loan contract agreement Loan completion Loan executed executed Water/Aqueduct Project 7/1982 4/1987 36.06 36.06 (1) Leamchabang Port 9/1984 6/1993 31.78 11/1986 11/1993 48.42 2/1990 9/1995 58.68 10/1985 10/1992 25.76 9/1987 9/1992 19.89 184.53 (2) Leamchabang Industrial Estate 10/1985 10/1992 25.76 9/1987 9/1992 19.89 45.65 Railway Projects Siracha/Leamchabang 9/1988 7/1996 9.2 Sattahip/Maptaphut 9/1988 1/1997 28.26 Khlong19/Kengkoi 2/1990 12/1999 72.98 110.44 Road Projects Chonburi/Pattaya 11/1988 3/1994 40.74 Bangkok/Chonburi 12/1990 4/1993 134.35 Outer Bangkok Ring 12/1990 4/1993 128.28 303.37 680.05 (3) Mitsubishi Motors Established in 1990 Figure 4. Eastern Seaboard. Source: Kuchiki and Tsuji (2011) based on Shimomura (2000). Economies 2024,12, 163 8 of 28 3.1. Agglomerations in Northern Vietnam This subsection identifies the role of the “master switch” and accelerator in the establishment of industrial agglomeration through Japanese official development assistance in the case of industrial agglomeration in northern Vietnam2. 3.1.1. Roads and Ports: Reduction of Transport Costs as Master Switch The construction and renovation of National Highway No. 5 and Haiphong Port were effective as master switches for the building of industrial agglomeration. Figure 3shows that JPY (Japanese Yen) 21 billion was provided for the construction of National Highway 5 and JPY 17.3 billion for the renovation of Haiphong port in 1993 and 1995. 3.1.2. Institutions: Reduction of Transport Costs as Master Switch In April 1999, Japanese Minister of Finance Miyazawa pledged JPY 20 billion to support a private sector development program. The loan was agreed upon and implemented in September 1999. According to the JBIC-IDCJ survey (2003), as shown in Table 2and Figure 5, private companies evaluate the effect of institutional change on transportation cost reduction from four main perspectives 3 . First, the approval system for new business was abolished and changed to a registration system. Second, sub-licences are abolished. Changes in company formation streamlined administrative procedures. Third, the collateral and access to banks are improved. Lastly, trade was liberalized. The number of restricted or prohibited industries decreased from 400 to 250. Table 2. Benefit of Well-developed Infrastructure. Type Production Market-Local Market-Export Import Hai Phong Port Highway No. 5 Example 1 Hanoi Haiphong Haiphong B B Canon 2 Hanoi Local Haiphong Haiphong B B TOTO 3 Hanoi Local Haiphong B C Vietnam Float Glass 4 Hanoi Via internet CAD technology Yabashi 5 Hanoi Noi Bai Haiphong C C Sumitomo Bakelite 6 Haiphong Haiphong Haiphong B As’ty 7 Haiphong Local Haiphong B C San Miguel Yamamura 8 Haiphong Local Haiphong Haiphong A (inc.local distribution by ship) B Han-Viet Heavy Industry & Construction 9 Haiphong Noibai Noibai B ESTELL 10 Vinh Phuc Local Haiphong C C Honda Note: A, B and C show frequency of use. A: extremely frequent, B: very frequent, C: frequent. Source: Kuchiki (2007) based on JBIC-IDCJ (2003). 3.1.3. Thang Long Industrial Park and Nomura Haiphong Industrial Zone as Accelerator Both Thang Long Industrial Park (TLIP) and Nomura Haiphong Industrial Zone (NHIZ) served as accelerators to help investors reduce their initial fixed costs. The development area is 121 hectares for TLIP and 180 hectares for NHIZ. Economies 2024,12, 163 15 of 28 JBIC (2007–2022) Survey Report on Overseas Business Operations by Japanese Manufacturing Companies from 2007 to 2022 aimed to research and analyze the current status and future prospects for overseas business development of Japanese manufacturing companies. The companies targeted in this survey are Japanese manufacturing companies which have three or more overseas affiliates. This paper uses the data of the details of both issues and promising reasons, and, in particular, focuses on the details of issues. Factor analysis is applied to the top ten countries from 2007 to 2022. The six countries are India, Vietnam, Indonesia, Thailand, the US, and China. For example, regarding (Japan Bank for International Cooperation) (JBIC 2007–2022), the number of surveyed companies is 946, and the number of respondents is 531. This paper provides a factor analysis regarding the reasons for the high potential and the reasons for the issues in investing in the promising investment countries. China was the first promising country in 2018. Appendix Aillustrates the statistics of promising and issue items in Tables A1 and A2, respectively. Among the issue items in Table A2, which are China’s challenges, the issue item with the highest number of firms is (o) Intense competition with other companies with 132 firms, and the item with the second-highest number of firms is (m) Rise in labor costs with 129 firms. However, it is noteworthy that the issue items with consistently high loading scores in the factor analysis are institutional reasons such as (h) Insufficient protection of intellectual property rights and (i) Foreign exchange and remittance restrictions. We use high potential, or promising reasons, which are conditions that promote industrial agglomeration in each country. Appendix Bshows the factor loadings as follows: The first factor, ML1, represents ‘FDI-led agglomeration’: (o) Local logistics services (1.1); (n) Local physical infrastructure (1); (r) Stable political and social conditions (1); (l) Profitability of local market (0.9); and (m) A base for product development (0.7). The second factor, ML2, represents ‘Human resources of low-wage labor’: (b) Lowwage labor (0.7); (a) Excellent human resources (0.5); and (f) Risk diversification receptacle for other countries (0.6). The third factor, ML3, represents ‘Export processing zone’: (p) Preferential tax incentives for investment (0.9); (q) Stable policies to attract foreign investment (0.9); (h) An export base to Japan (0.8); (g) An export base to third countries (0.6); and (d) A supple base to assemble makers (0.4). The fourth factor, ML4: ‘Raw material procurement’: (i) Advantage in procurement of raw materials (0.6); and (c) Cheap parts and raw materials (0.5). Here, the values of parentheses are factor loadings (Kuchiki (2023), pp. 10–11). This paper focuses on the issues that hinder industrial agglomeration. Regarding the factor loadings of the issues of investment, ML1 of Institutional Issues consists of (h) Insufficient protection of intellectual property rights (1), (i) Foreign exchange and remittance restriction (1), (b) Unclear operation of legal system (0.9), and (p) Difficulty in collecting payments (0.9). In terms of the operation of the legal system, they are (f) Strengthening of taxation (0.9), (j) Import regulations and customs procedures (0.8), (e) Strengthening taxation (0.8), (g) Investment licensing procedures are complicated and unclear (0.8), (m) Rise in labor costs (0.8), (d) Operation of the tax system is unclear (0.7), (q) Difficulty in raising funds (0.5), and (n) Labour issues’ (0.5). ML 2 of Industrial Agglomeration consist of (r) Underdevelopment of local supporting industries (1.1), (a) Underdeveloped legislation (1), (t) Underdeveloped infrastructure (0.9), (o) Intense competition with other companies ( − 0.9), (v) Lack of information on investing countries (0.9), and (s) Lack of currency and price stability (0.7). ML 3 of Human Capital of Engineers and Managers consists of (k) Difficulty in securing local engineers (0.8) and (l) Difficulty in securing management-level personnel (0.8). ML 4 of Insecurity and Social Instability consists of (u) Insecurity and social instability (0.8) and (c) Complexity of the tax collection system (0.5). Economies 2024,12, 163 16 of 28 The factors that this paper will pay particular attention to in the next section are ML 3, Engineers and Managers. In particular, engineers as capital stock as a fixed factor of production in the manufacturing industry. 5. Empirical Analysis of Brake Segments In this section, a factor analysis using JETRO data divided the investment environment segments into three factors: The first factor is workers (general labor) and staff (general office work). The second factor is middle management (section chief) and managers (section chief). The third factor is engineers (intermediate technician) for W2. Furthermore, a factor analysis was conducted on the investment issues of FDI in the six most promising investment destinations for Japanese firms using JBIC data: the six countries are India, Vietnam, Thailand, Indonesia, China, and the United States. As discussed in Sections 3.3.3 and 3.3.4, among these countries, India has been braking because it is not as agglomerated as other countries in terms of exports. The results of the factor analysis and regression analysis proved the hypothesis that the issue in India’s investment environment from 2007 to 2021, or a brake segment, is the “engineers” segment. Hence, we can conclude that the brake segment is engineers as human capital. A summary of the results obtained by factor analysis reveals that only India has a factor score of more than 1 for having issues with engineers from 2007 to 2021 (not including 2022. For the other countries, the factor score exceeds 1 for Vietnam only for 2007 and 2008, and for Thailand only for 2020. In other words, we can conclude that engineers are the factor that poses a challenge to the establishment of industrial agglomeration in India. 5.1. Factor Analysis of Workers, Engineers, and Managers Industrial agglomeration is basically formed at the city level. In most countries, the leading cities drive the economic growth of the country at the national level. Kuchiki (2020) in the Oxford Handbook of Industrial Hubs and Economic Kuchiki (2023) showed this fact for China and ASEAN, respectively. Therefore, we will conduct a factor analysis on a city-by-city basis. As shown in Appendix C, the conclusion obtained in this section is that investmentrelated costs can be categorized into three factors. The first factor of ML 1 is workers (general labor) in W1 and staff (general office work) in W4. The second factor of ML 2 is middle management (section chief) for W3 managers and managers (section chief) for W5. The third factor of ML 3 is engineers (intermediate technician) for (W2). The definition of engineers (intermediate technician) (W2) does not include so-called skilled workers. Therefore, the paper supposes that ML 3 of “engineers” belongs to skilled labor as the human capital of fixed costs in the manufacturing industry. Integrating this result with Result 1 yields the following result: Result 2. The number of manufacturing agglomerations is inversely related to the shortage of engineers as “human capital”. 5.2. Promising Factors of Investment As shown in Table 5a, we conclude the characteristics of the factor scores for each country in the following. Vietnam has high scores for Factor 2, ranging from 1.13 to 3.09 between 2007 and 2022, and is promising with respect to inexpensive labor. Thailand has high scores for Factor 3, ranging from 0.5 to 2.44 between 2007 and 2022, and is promising in terms of foreign investment in EPZs, etc. The US has high scores for Factor 1, ranging from 0.97 to 3.18 between 2007 and 2022, and is promising in terms of industrial agglomeration. China has high scores for Factor 4, ranging from 0.77 to 2.15 between 2007 and 2015, and is promising for investment in cheap raw materials and cheap parts. Economies 2024,12, 163 17 of 28 Table 5. (a) Factor scores of investment promising reasons by year by coutry by factor analysis. (b) Factor scores of investment issues by year by coutry by factor analysis. (a) India Vietnam Thailand Indonesia China U.S. Agglo FDI Worker Materi Agglo FDI Worker Materi Agglo FDI Worker Materi Agglo FDI Worker Materi Agglo FDI Worker Materi Agglo FDI Worker Materi 2007 −0.77 −0.52 0.96 0.37 -0.32 1.28 3.09 −1.85 0.17 2.08 0.43 0.37 −0.90 −0.57 0.57 2.67 −0.88 0.59 −0.07 2.16 2.76 −1.06 −0.17 −0.61 2008 −0.74 −0.70 0.60 −0.12 −0.58 1.00 2.24 −1.53 0.11 1.63 0.08 −0.13 −0.93 0.66 0.43 1.76 −0.71 0.16 −0.20 2.10 2.05 −1.48 −0.24 −0.99 2009 −0.85 −0.83 0.43 0.27 −0.68 0.67 1.72 −0.69 0.21 2.26 −0.07 0.39 −0.84 0.21 0.22 1.09 −0.75 0.26 −0.25 2.00 1.42 −1.54 −0.44 −1.21 2010 −0.85 −0.68 0.56 0.76 −0.60 0.51 1.89 −0.23 0.02 2.06 −0.34 1.46 −0.82 −0.24 0.70 1.26 −0.43 0.10 −0.68 2.03 1.75 −1.33 −0.47 −0.91 2011 −0.79 −0.60 0.35 0.48 −0.84 0.29 1.64 0.64 0.17 2.45 −0.49 1.09 −0.71 −0.08 0.52 0.78 −0.55 0.19 −0.84 1.66 1.95 −0.86 −0.63 −0.65 2012 −0.89 −0.71 0.13 0.67 −0.88 0.35 1.52 0.03 0.36 2.42 −0.37 0.15 −0.79 −0.35 0.39 0.23 −0.53 −0.13 −1.01 1.28 2.04 −1.15 −0.43 −0.83 2013 −0.80 −0.59 −0.02 0.25 −0.62 −0.03 1.92 −0.55 0.70 2.30 −0.30 −0.24 −0.62 −0.26 0.26 0.20 −0.38 −0.26 −1.48 0.93 2.40 −1.09 −0.25 −0.52 2014 −0.80 −0.80 0.00 0.34 −0.62 0.08 1.49 −0.36 0.36 2.34 −1.21 0.70 −0.64 −0.52 −0.14 −0.27 −0.21 −0.37 −1.23 0.77 3.19 −1.16 0.15 −0.68 2015 −0.79 −0.45 −0.17 0.32 −0.12 −0.16 1.85 −1.07 −0.13 1.52 −0.61 1.02 −0.60 −0.60 0.15 0.29 −0.17 −0.58 −1.58 1.03 2.23 −0.97 −0.48 −0.68 2016 −0.74 −0.63 −0.29 0.14 −0.34 −0.40 1.27 −0.96 −0.17 1.48 −0.95 −0.10 −0.74 −0.46 −0.21 −0.02 −0.25 −0.58 −1.38 0.36 2.41 −1.06 −0.37 −0.58 2017 −0.73 −0.62 −0.05 0.04 −0.28 0.00 1.72 −0.87 0.19 1.57 −0.70 −0.79 −0.65 −0.40 −0.12 0.33 −0.20 −0.45 −1.40 0.63 1.63 −1.11 −0.87 −0.29 2018 −0.68 −0.59 −0.29 0.08 −0.33 0.24 1.60 −0.59 0.33 1.16 −0.42 −0.92 −0.61 −0.51 −0.42 −0.26 0.02 −0.44 −1.33 0.49 1.60 −0.80 −0.77 −0.84 2019 −0.70 −0.55 −0.17 0.21 −0.12 0.12 1.36 −1.18 0.42 0.99 −0.79 −0.70 −0.60 −0.37 −0.14 −0.84 0.10 −0.41 −1.54 0.08 0.97 −0.87 −1.07 −0.79 2020 −0.85 −0.53 0.00 0.31 −0.38 −0.01 1.14 −0.93 0.24 0.51 −0.42 −0.92 −0.77 −0.31 −0.16 −0.27 −0.03 −0.50 −1.66 0.61 1.80 −0.80 −0.79 −0.67 2021 −0.87 −0.55 −0.15 0.24 −0.27 0.48 1.60 −1.79 0.32 1.90 −0.57 −1.51 −0.75 0.11 0.03 −0.18 0.23 −0.31 −1.50 0.00 1.50 −1.00 −0.66 −1.05 2022 −0.73 −0.46 0.01 −0.43 0.09 0.76 2.24 −1.50 0.47 1.95 0.07 −1.02 −0.49 −0.13 0.15 −0.28 0.24 −0.33 −1.46 0.04 1.91 −0.80 −0.66 −0.73 (b) India Vietnam Thailand Indonesia China U.S. Engi Agglo Insti Secu Engi Agglo Insti Secu Engi Agglo Insti Secu Engi Agglo Insti Secu Engi Agglo Insti Secu Engi Agglo Insti Secu 2007 1.20 −0.77 −0.44 −0.09 0.73 0.36 0.15 −0.08 0.65 0.11 0.31 0.53 −0.81 0.90 0.41 1.59 0.12 −1.11 1.27 −0.08 0.89 −1.26 −1.16 −1.01 2008 0.80 −0.70 −0.43 0.27 1.75 2.23 −0.41 −1.65 0.03 0.50 −0.49 0.78 −1.01 1.67 −0.17 1.10 −0.44 0.37 2.43 0.21 −0.52 −1.43 −1.13 −1.15 2009 0.86 −0.68 −0.84 −0.31 1.01 1.40 −0.49 −0.59 0.34 0.83 −0.15 0.30 −1.24 1.40 −0.33 0.96 −0.40 0.13 2.21 0.36 −0.52 −1.22 −1.31 −1.48 2010 1.20 −0.78 −0.70 −0.16 0.94 1.16 −0.21 −0.70 0.67 0.67 −0.36 0.87 −1.47 1.28 −0.05 1.08 −0.91 −0.35 1.97 −0.17 −0.76 −1.36 −1.35 −1.57 2011 0.98 −0.59 −0.96 0.25 0.59 0.86 −0.55 −0.84 0.27 0.03 −0.66 −0.29 −1.52 1.23 1.08 1.37 −0.33 −0.38 2.11 −0.10 −1.26 −1.26 −1.37 −1.48 2012 1.23 −0.72 −0.78 0.12 −0.38 1.47 0.05 −0.41 −0.18 0.62 −0.24 0.16 −1.52 1.23 1.08 1.37 −0.91 −0.31 2.29 0.45 −1.56 −1.40 −1.25 −1.62 2013 0.82 −0.75 −0.84 −0.32 0.26 1.48 −0.45 −0.94 0.06 0.62 −0.32 0.09 −1.58 1.39 0.07 1.01 −0.53 −0.41 2.21 0.18 −1.06 −1.21 −1.23 −1.35 2014 1.40 −0.87 −0.82 −0.46 0.45 1.12 −0.36 −0.67 0.47 0.56 −0.22 0.40 −1.78 1.29 1.01 1.89 −0.46 −0.60 1.94 0.32 −1.51 −1.43 −1.20 −1.42 2015 1.02 −0.69 −0.70 0.14 0.94 1.36 −0.02 −0.47 0.47 0.73 0.27 0.48 −1.67 1.37 0.28 1.67 −0.24 −0.75 2.10 0.60 −1.20 −1.33 −1.07 −1.35 2016 1.46 −0.68 −0.66 −0.33 0.68 0.62 −0.13 −0.90 0.24 0.65 0.58 0.94 −1.71 1.30 0.55 2.12 −0.18 −0.78 1.84 0.00 −0.60 −1.28 −1.25 −1.48 2017 1.54 −0.70 −0.68 0.18 0.53 0.76 −0.16 −0.37 −0.05 0.38 0.42 0.53 −1.83 1.25 0.35 2.01 −0.19 −0.85 1.62 0.03 −0.64 −1.29 −1.30 −1.29 2018 1.73 −0.77 −0.52 −0.03 0.84 0.70 −0.05 −0.51 0.86 0.31 0.51 1.06 −1.33 1.31 0.81 2.32 −0.46 −0.73 1.96 0.47 −0.29 −1.28 −1.09 −1.10 2019 1.41 −0.86 −0.73 −0.17 0.94 0.74 −0.05 −0.84 0.03 0.26 0.19 0.41 −1.27 1.04 0.41 1.05 −0.07 −0.85 1.42 −0.26 −0.14 −1.31 − 1.08658875 −1.02 2020 0.96 −0.96 −0.75 −0.48 0.69 0.47 −0.23 −1.27 0.54 0.34 −0.13 0.12 −1.07 1.11 0.29 1.17 0.32 −0.99 1.29 0.03 0.06 −1.24 −1.01 −0.90 2021 1.24 −0.84 −0.67 −0.66 0.50 0.26 −0.24 −0.92 1.11 0.50 0.05 0.20 −1.10 0.70 0.08 1.39 −0.26 −1.03 1.40 −0.07 0.30 −1.23 −1.17 −1.03 2022 0.91 −1.02 −0.64 −0.19 0.17 0.46 0.39 −0.41 0.39 0.60 0.57 0.89 −1.27 1.14 0.45 1.95 −0.13 −1.06 1.53 0.31 0.78 −1.17 −1.18 −0.69 Note: Agglo = Agglomeration, FDI = Foreign Direct Investment-led, Worker = Workers, Materi = Rww materials. Note: fa(p33,nfactors = X, fm = “ml”,rotate = “promax”) $scores by Program R. X = Number of eigne values greater than 1. Engi = Engineers, Inst = Institutions, Secu = Security. fa(p33,nfactors = X, fm = “ml”,rotate = “promax”) by Program R. X = Number of eigne values greater than 1. Source: Author’s calculation. Economies 2024,12, 163 18 of 28 When a factor score is 0.7, the variable has a strong relationship with the factor. When a factor score is 1.0, the variable is completely related to the factor. Finally, India, however, is promising for investment in terms of engineers as human capital only in 2007 and 2008, but not in other areas. 5.3. Issues of Investment as “Brake Segment” In this subsection, we reach the main conclusion of this paper that “engineers” are a brake segment as human capital for industrial agglomeration policies in the manufacturing industry. Factor scores of India are high at the factor consisting of both the items of difficult to secure technical/engineering staff and difficult to secure management-level staff. 5.3.1. Factor 1: Institutions As shown in Figure 6, institutional issues were identified as the number one factor for investment issues in promising countries. These include ‘Insufficient protection of intellectual property rights’ (1), ‘Foreign exchange and remittance restriction’ (1), ‘Unclear operation of legal system’ (0.9), and ‘Difficulty in collecting payments’ (0.9). In terms of the operation of the legal system, ‘Investment licensing procedures are complicated and unclear’ (0.8) and ‘Operation of the tax system is unclear’ (0.7) were cited. ‘Difficulty in raising funds’ and ‘Labour issues’ (0.5) were also cited. Economies 2024, 12, x FOR PEER REVIEW 20 of 30 5.3. Issues of Investment as “Brake Segment” In this subsection, we reach the main conclusion of this paper that “engineers” are a brake segment as human capital for industrial agglomeration policies in the manufacturing industry. Factor scores of India are high at the factor consisting of both the items of difficult to secure technical/engineering staff and difficult to secure management-level staff. 5.3.1. Factor 1: Institutions As shown in Figure 6, institutional issues were identified as the number one factor for investment issues in promising countries. These include ‘Insufficient protection of intellectual property rights’ (1), ‘Foreign exchange and remittance restriction’ (1), ‘Unclear operation of legal system’ (0.9), and ‘Difficulty in collecting payments’ (0.9). In terms of the operation of the legal system, ‘Investment licensing procedures are complicated and unclear’ (0.8) and ‘Operation of the tax system is unclear’ (0.7) were cited. ‘Difficulty in raising funds’ and ‘Labour issues’ (0.5) were also cited. Figure 6. Factor loadings of investment issues. Note: The numbers above the arrows are factor loadings. Source: Author’s calculation. 5.3.2. Factor 2: Industrial Agglomeration The following industrial agglomeration factors were identified: ‘Underdevelopment of local supporting industries’ (1.1); ‘Underdeveloped legislation’ (1); ‘Underdeveloped infrastructure’ (0.9); ‘Lack of information on investing countries’ (0.9) and ‘Lack of currency and price stability’ (0.7). Figure 6. Factor loadings of investment issues. Note: The numbers above the arrows are factor loadings. Source: Author’s calculation. Economies 2024,12, 163 19 of 28 5.3.2. Factor 2: Industrial Agglomeration The following industrial agglomeration factors were identified: ‘Underdevelopment of local supporting industries’ (1.1); ‘Underdeveloped legislation’ (1); ‘Underdeveloped infrastructure’ (0.9); ‘Lack of information on investing countries’ (0.9) and ‘Lack of currency and price stability’ (0.7). 5.3.3. Factor 3: Human Capital of Engineers ‘Difficulty in securing local engineers’ (0.8), and ‘Difficulty in securing managementlevel personnel’ (0.8). 5.3.4. Factor 4: Insecurity and Social Instability The issue of ‘insecurity and social instability’ (0.8) is significant. Note that ‘complexity of the tax collection system’ (0.5) is included in Factor 4. Next, this section examines the characteristics of each country’s factor score regarding issues in investment. As shown in Table 5b, India alone has high scores for Factor 3, ranging from 0.80 to 1.73 between 1.2 in 2007 and 0.91 in 2022, indicating issues for engineers and managers. Vietnam has high scores for Factor 2, ranging from 0.86 to 2.23 between 2.23 in 2007 and 1.36 in 2018, indicating that industrial agglomeration was the issue. Thailand has no high scores for all factors. Indonesia has high scores for both Factor 1 and Factor 4. Factor 1 ranged from 0.96 to 2.32 between 1.1 in 2007 and 1.59 in 2022, and Indonesia faced issues related to institutions and instability, such as social unrest and the tax collection system. Factor 4 ranged from 0.7 to 1.67 between 1.67 in 2007 and 0.9 in 2022, and Indonesia faced the problem of security. China has high scores for Factor 1, ranging from 1.27 to 2.43, indicating issues with the legal system. The US has no high scores for all factors between 2007 and 2018. Table 6is summary by combining the results of a and Table 5a,b India shows no promising item for investment and has issues with engineers and managers. Vietnam has issues with engineers only in 2007 and 2008 but has an abundance of low-wage labor and has been a good recipient of China’s relocation. Thailand has no investment issues and is promising for the introduction of foreign direct investment. China has no promising points and has issues with its legal system. Indonesia has the issues of both institutions and security. The US has no investment issues and is promising for industrial agglomeration. Thus, the issues related to investment in India are engineers and managers. The issues of engineers are the factors that hinder the industrial agglomeration of SEZs in India and are the “brake segment” for building industrial agglomeration. The summary of issues and promising reasons by factor analysis in Table 6gives policy recommendations as of 2022 to promote manufacturing agglomeration to the five target countries other than India. China has an issue on institutions but does not have an issue on engineers as human capital. Therefore, institutional reform is needed. Indonesia has both issues of institutions and security, so institutional reform and improvement of security are needed. The US and Thailand will have no issues in 2022. In the US, the agglomeration item is promoting, and in Thailand, the FDI-led item is promoting. Both countries should continue developing engineers as human capital in order to promote the agglomeration of manufacturing industries. In Vietnam, the workers item is promoting, and the issue of engineers as human capital was resolved in 2022. Therefore, it is necessary to continue the current development of engineers as human capital. As a whole, the results in Table 6 indicate that the condition for the continuation of the process of industrial agglomeration in any country is the development of engineers as human capital. Economies 2024,12, 163 20 of 28 Table 6. Summary of issues and promising reasons by factor analysis. Economies 2024, 12, x FOR PEER REVIEW 22 of 30 Table 6. Summary of issues and promising reasons by factor analysis. Issues Promising Items Year Engineers Agglomeration Institutions Security Agglomeration FDI-led Workers Raw Materials India 2007 1.2 2021 1.24 2022 0.91 Vietnam 2007 1.74 2.23 3.09 2008 1.01 1.4 2.24 2011 1.47 1.63 2014 1.36 2022 2.24 Thailand 2007 2.07 2018 1.15 2020 1.11 0.99 2021 1.9 2022 1.94 Indonesia 2007 1.67 1.09 2. 7 2010 1.23 1.07 1.26 2022 0.9 1.59 China 2007 2.42 2.15 2015 1.02 2016 2022 1.26 U.S. 2007 2.75 2022 1.9 Source: Author’s. Next, the relationship between the “industrial agglomeration” factor of the promising reasons in Table 5a and the “four factors” of the issues in Table 5b will be regressed. The industrial agglomeration factor of the promising reasons for the investment environment in Table 5a is the objective variable, and the factor scores of the issues of “engineers”, “agglomeration”, “safety”, and “institutions” in Table 5b are the independent variables. The results are as follows: Agglomeration = 0.01007 − 0.49339 Engineers − 0.34550 Agglomeration issue (0.158) (−6.744) *** (−4.479) *** −0.28121 Institutions − 0.41943 Security (−3.781) *** (−4.404) *** (Adjusted R-squared is 0.6059, F-statistic is 37.52 on 4 and 91 DF, and p-value is 2.2 × 10^16 and *** is significant at the 0 percent level. The numbers in parentheses are t-values). The factor scores for the investment environment issues of engineers, agglomeration, safety, and institutions are negatively related to the factor scores for the promising reasons for industrial agglomeration. The coefficients for all independent variables are negative, indicating that these issues have a negative impact on agglomeration building. The Source: Author’s. Next, the relationship between the “industrial agglomeration” factor of the promising reasons in Table 5a and the “four factors” of the issues in Table 5b will be regressed. The industrial agglomeration factor of the promising reasons for the investment environment in Table 5a is the objective variable, and the factor scores of the issues of “engineers”, “agglomeration”, “safety”, and “institutions” in Table 5b are the independent variables. The results are as follows: Agglomeration = 0.01007 −0.49339 Engineers −0.34550 Agglomeration issue (0.158) (−6.744) *** (−4.479) *** −0.28121 Institutions −0.41943 Security (−3.781) *** (−4.404) *** (Adjusted R-squared is 0.6059, F-statistic is 37.52 on 4 and 91 DF, and p-value is 2.2 ×1016 and *** is significant at the 0 percent level. The numbers in parentheses are t-values). Economies 2024,12, 163 21 of 28 The factor scores for the investment environment issues of engineers, agglomeration, safety, and institutions are negatively related to the factor scores for the promising reasons for industrial agglomeration. The coefficients for all independent variables are negative, indicating that these issues have a negative impact on agglomeration building. The coefficient for engineers was 0.493, the highest of the four factors. In other words, the issue of engineers was confirmed to have a negative impact on the promising reasons for industrial agglomeration. In summary, first, this paper applied a spatial economic model to obtain Result 1. In the “manufacturing” sector, engineers and so-called skilled workers are included in the category of skilled labor. Engineers can be assumed to be human capital as a fixed factor of production rather than a variable factor of production. Therefore, the shortage of engineers in the manufacturing sector can be a brake on agglomeration. Second, factor analysis using JETRO data found that investment-related costs can be categorized into three factors. The first factor of ML 1 is workers (general labor) in W1 and staff (general office work) in W4. The second factor of ML 2 is middle management (section chief) for W3 managers and managers (section chief) for W5. The third factor of ML 3 is engineers (intermediate technician) for (W2). The definition of engineers (intermediate technician) (W2) does not include so-called skilled workers. The paper supposes that ML 3 of “engineers” belongs to skilled labor as the human capital of fixed costs in the manufacturing industry. Integrating this result with Result 1 yields the following Result 2: the number of manufacturing agglomerations is inversely related to the shortage of engineers as “human capital”. Third, the factor analysis based on the manufacturing survey reveals that the factors that make up ML 3, where the factor scores for investment challenges in India are higher, are (k) Difficulty in securing local engineers (0.8) and (l) Difficulty in securing managementlevel personnel (0.8). In other words, India’s investment challenge is the shortage of managers and engineers. Taking the above three points together, we can judge that engineers as human capital belong to a brake segment. It is noted that we do not rule out the possibility that other segments, including managers, belong to a brake segment. In conclusion, the segment of “engineers” as human capital is one of the “brake segments”. 6. Conclusions and Summary India has not matched the size and success of East Asian industrial hubs, including China. Therefore, it is necessary to identify the missing segments of the investment environment for the introduction of foreign direct investment. The “segments” constitute the organization of agglomerations. Each segment then has a specific function in the process of building industrial agglomeration. We focus on the process of building segments in agglomeration formation. We define a “brake segment” as a segment that has the “function” of decelerating the speed of the process of building segments. This paper identifies brake segments in the process of constructing segments of industrial agglomeration. A variant model of spatial economics and factor analysis of investment environment cost data yielded the result that the number of agglomerated firms is inversely related to the wages of engineers. Factor analysis and regression analysis of the six most promising investment destinations for Japanese firms using JBIC data were conducted on the investment challenges of foreign direct investment. The six countries are India, Vietnam, Thailand, Indonesia, China, and the US. Only India has a braking segment, due to the fact that it is the only one of these countries that has not increased its agglomeration as much as the others. As a result of the analyses, India’s issue for the investment environment from 2007 to 2021 was the “engineer” segment. Hence, the conclusion of this paper is that the “brake segment” is “engineers as human capital”. As a policy recommendation, this paper provides steps that can be used for industrial agglomeration policies, as shown in Figure 2. First, the construction of agglomeration begins with an onset of the master switch. The master switch for manufacturing agglomer- Economies 2024,12, 163 22 of 28 ation is to develop transportation infrastructure that reduces transport costs and attracts firms that produce heterogeneous goods with low elasticity of substitution. Second, the accelerator segment of agglomeration is the construction of industrial parks. Third, identifying the braking segment is an essential proposition for policy makers in regions where the process of segment building is not yet in progress. Since the segment that acts as a brake in the agglomeration building process is engineers as human capital, the implication of this paper is to implement human resource development for engineers from “the initial stage”. The process of building industrial agglomeration requires “the continuous development” of engineers as human capital. If the development of human capital becomes a bottleneck, the process of building industrial agglomeration will likely come to a standstill. This approach can be used to determine the sequencing of official development assistance (ODA) in developing countries. First, ODA can be effective in building segments that will be turned on by a master switch to initiate an agglomeration policy. Second, human resource development of engineers needs to be implemented and continued from the beginning. Policy makers need to keep these two points in mind to initiate and continue the process of building the manufacturing agglomeration. Table 7summarizes the contributions of this paper. We linked spatial and sequence economics for tourism, urban agglomeration, and manufacturing. We focused on the segments that constitute the organization of agglomerations. We then identified the functions of the segments and specified master switch and accelerator segments. This paper identified the brake function. Table 7. Linking spatial economics and sequencing economics. Agglomeration Location Function Segment Spatial Economics Sequencing Economics Tourism industry Osaka, Japan (1) Master switch commuter costs Henkel et al. (2000) Kuchiki (2021) Urban agglomeration Sapporo, Japan (1) Master switch commuter costs Krugman (1991), Alonso (1964) Kuchiki and Sakai (2023) Manufacturing Industrial hubs, China (2) Accel industrial zones Helpman and Krugman (1985)Kuchiki (2023) Manufacturing Industrial hubs, India “(3) Brake” “engineers” Helpman and Krugman (1985)“This paper” Source: Author’s. The contribution of identifying segment functions can be explained below. Descriptive case studies on industrial agglomeration are numerous. The Oxford Handbook of Industrial Hubs and Economic Development by Oqubay (2020) provides a comprehensive analysis of the factors behind the success and failure of industrial agglomeration in Asia, Latin America, and Africa. The Oxford Handbook of Industrial Hubs and Economic Development by Lin and Oqubay provides a comprehensive analysis of the factors behind the success and failure of industrial agglomeration in Asia, Latin America, and Africa. Oqubay (2020) pointed out that Taiwan successfully pioneered an export processing zone at Kaohsiung Harbour, which aimed to attract investment and develop the manufacturing sector, especially for exports. Pietrobelli (2020) found that most examples in Latin America managed to create the conditions at the local level for private–private, public–private, and public–public collaboration. Oqubay and Kefale (2020) conclude that in Africa, effective mechanisms to develop production linkages remain weak in Ethiopia. Ahluwalia et al. (2018) argue that India lost its comparative advantage in labor-intensive production in the early stages of development due to very restrictive labor regulations in the formal sector and strong trade unions. Descriptive case studies on industrial agglomeration are numerous in this way. However, this paper identified the factors that pose issues to industrial agglomeration from the following different perspectives. This paper contributes to the analysis of the Economies 2024,12, 163 23 of 28 process of industrial agglomeration formation posed by Kanai and Ishida (2000). The process of agglomeration formation proceeds through the construction of the segments that structure the agglomeration. This paper identifies the role, or function, of these segments, and, in particular, identified segments that function to decelerate the speed of the process. In particular, the paper contributes to providing a means by which the theory of “spatial economics” can be used in industrial agglomeration policy. The location theory of spatial economics presents the breaking conditions from symmetric equilibrium to agglomeration equilibrium. However, the process of transition to an agglomeration equilibrium does not initiate unless those conditions are satisfied. This allows policy makers in industrial agglomeration to specifically determine the sequence of policy implementation. This paper presented successful examples of segment sequencing in the case of official development assistance (ODA) in Vietnam and Thailand. There are four issues to be addressed in the future of this paper. First, the most important task is to identify the segment with the function of innovation activity. This paper focuses on the process of agglomeration. Fujita and Kuchiki (2006) defined industrial clusters as consisting of two components: agglomeration and innovation activation. The study of the function of segments for innovation activation is essential under the fourth industrial revolution. Second, the number of case studies needs to be increased and inductive conclusions strengthened. There are similar research issues for other industry agglomerations. Knowledge industry, tourism industry, and urban agglomeration are some possible examples. We need to increase the number of case studies in order to attain conclusions, especially with regard to the empirical study. Third, although factor analysis was used as the statistical method, it would be worth exploring other statistical methods to identify the functions of segments. Fourth, the validity of the spatial economic model on which the conclusions of this paper are based also needs to be reexamined. With regard to the theoretical model, we need to consider cases in which the assumed assumptions change. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: https://www.jbic.go.jp/ja/information/press/press-2022/1216-017128.html (accessed on 25 September 2023). https://www.jetro.go.jp/world/reports/2010/07000312.html (accessed on 25 September 2023). Acknowledgments: We would like to thank Hideyoshi Sakai, Katsumi Nakayama, and referees for their comments on the draft of this paper. Conflicts of Interest: The author declares no conflict of interest. Appendix A Table A1. Promising items of investment. China 2018 Survey Item # of Companies Unit: % a Excellent human resources 25 11.3 b Cheap labor 29 13.1 c Inexpensive parts and raw materials 16 7.2 d A supply base for assembly manufacturers 53 24.0 e Industrial agglomeration 49 22.2 f Hubs of risk diversification in other countries 5 2.3 Economies 2024,12, 163 24 of 28 Table A1. Cont. China 2018 Survey Item # of Companies Unit: % g An export base to Japan 10 4.5 h An export base to third countries 23 10.4 i Advantages of raw material procurement 9 4.1 j Current size of local market 141 63.8 k Future growth potential of local markets 161 72.9 l Profitability of local market 18 8.1 m Product development hubs 16 7.2 n Well developed local infrastructure 30 13.6 o Well developed local logistics services 18 8.1 p Preferential tax treatment for investment 9 4.1 q Stable policies to attract foreign investment 1 0.5 r Stable political and social conditions 6 2.7 Total 221 100.0 Note: 1. # oc companies = number of companies. 2. China is the first promising country in 2018 to invest. 3. For the factor analysis in this paper, 16 data sets from 2007 to 2022 were used for each of the tables of promising investment items and investment issues for China. For India, 16 data sets from 2007 to 2022 were also used. Similarly, 16 datasets were used for each of the other countries in Thailand, Vietnam, Indonesia, and the United States. Thus, the total number of data observed in each table used in the factor analysis is 96. Source: JBIC (2018). Table A2. Issues of investment. China 2018 Survey Item # of Companies Unit: % a Underdeveloped legislation 18 8.1 b Unclear operation of legal system 99 44.8 c Insecurity and social instability 18 8.1 d Operation of the tax system is unclear 39 17.6 e Strengthening taxation 53 24.0 f Control of foreign currency 45 20.4 gComplicated and unclear investment licensing procedures 33 14.9 h Insufficient protection of intellectual property rights 79 35.7 i Foreign exchange and remittance restriction 62 28.1 j Import regulations and customs procedures 53 24.0 k Difficulty in securing local engineers 39 17.6 l Difficulty in securing management-level personnel 43 19.5 m Rise in labor costs 129 58.4 n Labour issues 41 18.6 o Intense competition with other companies 132 59.7 p Difficulty in collecting payments 52 23.5 q Difficulty in raising funds 11 5.0 r Underdevelopment of local supporting industries 9 4.1 s Lack of currency and price stability 14 6.3 t Underdeveloped infrastructure 11 5.0 u Complexity of the tax collection system 33 14.9 v Lack of information on investing countries 6 2.7 Total 221 100.0 Note: 1. # of companies = number of companies. 2. China is the first promising country in 2018 to invest. 3. For the factor analysis in this paper, 16 data sets from 2007 to 2022 were used for each of the tables of promising investment items and investment issues for China. For India, 16 data sets from 2007 to 2022 were also used. Similarly, 16 datasets were used for each of the other countries in Thailand, Vietnam, Indonesia, and the United States. Thus, the total number of data observed in each table used in the factor analysis is 96. Source: JBIC (2018).