Industrial Variety and Structural Change in Korean Regional Manufacturing,

Transcription

1 Growth and Change Vol. 48 No. 2 (June 2017), pp DOI: /grow Industrial Variety and Structural Change in Korean Regional Manufacturing, DONGHEON LEE ABSTRACT There has been increasing concern about the roles of industrial variety, particularly related and unrelated variety, in adaptive response of regional economies to recessionary shock. Using South Korean regional manufacturing data around the 1997 Asian financial crisis, this paper investigates how differently related and unrelated variety affect intrasectoral and intersectoral structural change in regional economy. The empirical findings reveal that regions with higher related variety adapt to recessionary shock mainly through intrasectoral structural change, but that related variety diminishes the opportunity for regional economies to pursue intersectoral reorganization. However, unrelated variety makes up for this negative effect as it increases intersectoral structural change. This study sheds light on the complementary roles of related and unrelated variety in regional adaptability and path creation or diversion. Introduction A n evolutionary approach to regional economic resilience has been attracting increasing attention in economic geography and regional studies (Boschma 2015; Christopherson, Michie, and Tyler 2010; Martin 2012; Martin and Sunley 2015). According to this approach, regional economic resilience is considered an adaptive process in which a regional economy seeks a new growth path by reorganizing itself autonomously from the external disturbance (Simmie and Martin 2010). This process is often prompted by external shocks, such as natural disasters, technological progress, and trade liberalization. However, the adaptive capability of a region s economy depends firmly on its pre-existing internal economic and social condition. Thus, as Martin (2012) insists, regional economic resilience is apparently a path-dependent adaptive process, and a region s industrial and institutional legacies shape the dimensions of adaptation, i.e., whether and to what extent regional economies respond toward resistance, renewal, or reorientation. 1 Among various components of a region s economic structure, related variety and unrelated variety have been claimed as crucial elements for regional economic resilience (Boschma 2015; Cho and Hassink 2009; Hassink 2010; Martin 2012; Martin and Sunley 2015; Pike, Dawley, and Tomaney 2010). They have been also identified as important causal factors enabling new path creation, branching out, and diversion over the long run (Essletzbichler 2015; Neffke, Henning, and Boschma 2011). However, against their rising popularity in academic and policy circles (Asheim, Boschma, and Cooke 2011), it is less clear how related and unrelated variety affect regional economic resilience and adaptation in response to sudden shocks in the relative short run. In order to know how they actually shape regional resilience and new path creation, what is required is a much clearer conceptualization and concrete empirical evidence (Martin and Sunley 2015). Dongheon Lee is a Doctoral Researcher in the Bartlett School of Planning, University College London, 14 Upper Woburn Place, London WC1H 0NN, UK. His address is: dongheon.lee@gmail.com. Submitted December 2013; revised April 2014; accepted August VC 2017 Wiley Periodicals, Inc

2 INDUSTRIAL VARIETY AND STRUCTURAL CHANGE 247 In this sense, this article intends to answer the question of how the existing internal structure of a local economy affects its diverse adaptations. In particular, the focus goes into the different roles of related and unrelated variety to determine the way in which regions adapt themselves to recessionary shock. In order to address this research question, the study empirically examines the different regional adaptations in South Korea (henceforth Korea) to the catastrophic recessionary shock triggered by the 1997 Asian financial crisis. Provided that intrasectoral and intersectoral structural changes represent two modes of regional adaptation, I will investigate how and to what extent related and unrelated variety can explain a regional economy s intrasectoral and intersectoral structural changes. The analysis narrows its focus onto structural change in the manufacturing sector during the period , because the annual Mining and Manufacturing Survey used in the analysis is the only available census data in Korea that covers detailed regional economic activities around the 1997 Asian financial crisis. The regional level dataset is constructed by aggregating plant-level survey data at 16 identical provincial-level regions and 221 municipal-level regions. 2 Measures on structural change are derived from Lilien (1982) and measures of structural variety follow Frenken, Van Oort, and Verburg (2007). This paper is organized as follows. The next two sections review relations between related and unrelated variety of industrial structure and adaptation in the evolutionary perspective and propose the hypotheses to be tested. The fourth section proposes a measurement index for structural change and related/unrelated variety. The fifth section provides a brief overview of the different adaptation processes of Korean regions around the Asian financial crisis. In the following section, analytical models between related/unrelated variety and structural change are estimated using panel data regression. The final section summerizes and concludes the paper. Theoretical Background The concept of related variety and unrelated variety has been underscored recently as an effective means through which a regional economic structure can explain the difference in economic performance (Bishop and Gripaios 2010; Boschma and Iammarino 2009; Frenken, Van Oort, and Verburg 2007). Related variety and unrelated variety are assumed to reflect both the particular mix of regional industrial activities and the relationships and interdependencies between them (Martin 2012:12). Accordingly, they are appraised to overcome the methodological dichotomy between Jacobs diversity and Marshallian sectoral specialization externality (Beaudry and Schiffauerova 2009; Boschma and Frenken 2011; Desrochers and Lepp al a 2010). Two varieties are considered to have different impacts on regional economic growth. Frenken, Van Oort, and Verburg (2007), who first raised the concept of related variety in the context of a regional economy, argued that related variety has a positive effect on regional economic growth, while unrelated variety dampens rising unemployment during a recessionary period. According to the authors and succeeding studies, regional economies with higher related variety might enjoy rapid employment growth due to the externalities effect such as knowledge spillovers between complementary industries ( complementary asset effect ) (Boschma and Iammarino 2009). Moreover, it is argued that an optimal level of related variety enables a regional economy to avoid cognitive lockin (Boschma, Minondo, and Navarro 2012:243). Meanwhile, a regional economy with higher unrelated variety gains a positive portfolio effect because unrelated variety prevents the diffusion of external asymmetric shocks in demand into other sectors. Besides, it is favorable for reallocating labor across unrelated sectors (Frenken, Van Oort, and Verburg 2007).

3 248 GROWTH AND CHANGE, JUNE 2017 On the other hand, several recent studies have investigated the role of industrial relatedness in the long-term evolutionary process of regional industry. They emphasize that emerging, seemingly new industries in a region do not start from scratch but are strongly rooted in the historical economic structure of a region (Neffke, Henning, and Boschma 2011:261). Frenken and Boschma (2007) described the process in which new variety emerges from technologically related industries in a region as regional branching. Neffke, Henning, and Boschma (2011) and Essletzbichler (2015), respectively using slightly different measures of cross-industry technology relatedness, demonstrated that industries more technologically related to existing local industrial portfolios are more likely to enter and less likely to exit regional economy over the long run. However, these researches on related and unrelated variety have not yet captured dynamic and evolutionary concepts, such as adaptation and adaptability, path creation and divergence (Martin and Sunley 2015). As discussed previously, the mix and relatedness of industries is important not only for stable economic growth and robustness against recessionary shock. They also can determine whether or not a regional economy can lock out from an existing regional development path and create a new growth path (Martin 2012:12). Pike, Dawley, and Tomaney (2010:65) argue that related variety informs mechanisms of adaptation and adaptability in its focus upon how existing paths are shaped and how paths are destroyed and created anew. Boschma (2015) further develops the significance of related and unrelated variety in regional adaptation. He asserts that related variety might enhance the recombination potential of a region to develop new growth paths, while unrelated variety will protect this potential of a region from idiosyncratic sectorspecific shocks. He suggests that it is necessary to conduct further empirical tests to establish whether the mixed effect that related and unrelated variety makes is beneficial to new growth path creation. Indeed, related and unrelated variety might impose the possibility, scope and direction of adaptive reorganization of a regional economy (Martin and Sunley 2015:28), which will be empirically investigated in the following sections. Hypotheses Based on the above theoretical background, this section proposes hypotheses about the role of related and unrelated variety in regional adaptation and path creation/diversion. For the purpose of empirical analysis, structural change is supposed to occur as a result of the adaptation of a regional economy to circumstantial change. Structural change is a reorganizational process in which the compositions of economic aggregates are changed (Kr uger 2008; Peneder 2003). In the course of structural change, economic resources, including labor and capital, are reallocated from more vulnerable and less productive industries to less vulnerable and more productive industries. Thus, it is reasonable to assume structural change occurs as a path-dependent adaptive result of a regional economy to recessionary shock. Structural change can be decomposed into two components: intrasectoral structural change (within-sector structural change) and intersectoral structural change (between-sector structural change). Intrasectoral structural change is defined as the reallocation of resources (e.g., labor) between industries within the same sector; intersectoral structural change is defined as the reallocation of resources across different sectors. Taken together, they can explain the direction and scale of regional adaptation. The combination of intrasectoral and intersectoral structural change provides a regional economy with diverse adaptive pathways, which is illustrated in Figure 1. When a regional economy goes mainly through intrasectoral structural change without enough intersectoral structural

4 INDUSTRIAL VARIETY AND STRUCTURAL CHANGE 249 Intersectoral structural change Low High High Gradual adaptation (Adjustment and upgrading) Great transformation? Intrasectoral structural change Low Lock-in Radical adaptation (Renewal) FIGURE 1. STRUCTURAL CHANGE AND ADAPTATION PATHWAYS. change, it follows the path of gradual adaptation through an adjustment and upgrading process because labor reallocation is mostly bounded within the same sector. On the other hand, when a regional economy undergoes significant intersectoral structural change but mild intrasectoral structural change, it implies that the regional economy follows a radical adaptation process that implies renewal of a regional economy. When both intrasectoral and intersectoral structural change are inactive, the regional economy might be locked-in to a rigid incumbent industrial structure (Cho and Hassink 2009; Hassink 2010). Finally, when both structural changes reach a significant level, the regional economy goes through overall transformation. However, not all these various trajectories of structural change guarantee the successful adaptation of a regional economy. For instance, radical structural change might sometimes lead to destructive results, and both low intrasectoral and low intersectoral change might come from the robustness and competitiveness of the existing economic structures. The research question about the effects of structural diversity on adaptive structural change is reconstructed into three testable hypotheses. The first hypothesis is concerned with Jane Jacobs s famous thesis (1969) on the merit of general industrial variety that increases a region s adaptive capacity. It is considered that higher industrial diversity is beneficial to overall adaptability as it enhance(s) robustness, and the scope for adaptive reorganization, whereas low diversity is harmful to adaptability as it reduces robustness by increasing vulnerability and limiting scope for recovery (Martin and Sunley 2015:28). Thus, we can draw the following hypothesis: H1 Higher industrial variety of existing economic structure promotes the overall structural reorganisation of a regional economy by increasing both intra- and inter-sectoral structural change. Conversely, lower variety of existing economic structure hinders the reallocation activities of a regional economy. The second hypothesis deals with related variety. When a regional economy has a higher related variety, economic activities of various industries are interrelated and complementarily linked, and therefore their performance is also very likely to become interrelated. As a result, a region s individual path trajectories tend to be mutually reinforcing to some degree due to mutual adaptation through interrelatedness and the interaction of economic agents. Martin and Sunley (2006: ) called this path interdependence or interpath coupling. 3 When recessionary shock hits a regional economy with higher related variety, this region is more likely to adjust its economy by reallocating resources within interrelated industries. Indeed, related variety might contribute to within-sector structural change through within-sector externalities (Bishop and Gripaios 2010:445). However, too much related variety might lead individual coupled paths to

5 250 GROWTH AND CHANGE, JUNE 2017 Intersectoral structural change Low High Intrasectoral structural change High When related variety increases When unrelated variety increases Low FIGURE 2. RELATED/UNRELATED VARIETY AND THE DIRECTION OF STRUCTURAL CHANGE. converge towards a rigid singular path by the intensive coupling mechanism. This reduces the opportunity for intersectoral structural change and hinders the emergence of a radically different path. Accordingly, a region with higher relative variety has a lower chance of radical adaptation through intersectoral structural change. Therefore: H2 Higher related variety of an existing economic structure promotes gradual adaptation by allowing intra-sectoral structural change while hindering inter-sectoral structural change. Finally, we can infer that unrelated variety increases intersectoral structural change. When a region has a narrow range of unrelated variety, it will not only be more susceptible to idiosyncratic sectorspecific shock, but will have fewer opportunities to re-orient its economy, and hence fewer alternative routes to recovery (Martin and Sunley 2015:27). On the contrary, a region with higher unrelated diversity, it will not only be more robust against sectorally differential idiosyncratic shocks ( a portfolio effect ), but will also more actively respond to unequal effects of technological progress and demand shift ( innovation and market opportunity effect ) (Frenken, Van Oort, and Verburg 2007; Martin and Sunley 2015:27). Thus: H3 Higher unrelated variety of existing economic structure offers more opportunities of inter-sectoral structural change. Conversely, lower unrelated variety deters inter-sectoral structural change. Figure 2 summarizes these hypotheses about related/unrelated variety and different adaptation paths. The hypotheses suggest that higher related variety leads the regional adaptation path toward the upper left quadrant in the diagram equivalent to gradual adaptation in Figure 1. On the other hand, unrelated variety leads the regional adaptation path to the right overall, which can make up for the negative effect of excessive related variety to reduce the potential of intersectoral structural change. Measurement Index Industrial variety indices. Measures for structural diversity, i.e., the so-called Jacobs variety, related variety and unrelated variety, follow the method of Frenken, Van Oort, and Verburg (2007). 4 As a Shannon (1948)-type entropy measure, the overall variety measure at industry level is decomposed into the weighted average of variety within each sector and variety among sectors. 5 Frenken, Van Oort, and Verburg (2007) named the former related variety and the latter unrelated variety. The relation between overall variety, related variety and unrelated variety is as follows:

6 INDUSTRIAL VARIETY AND STRUCTURAL CHANGE 251 X N i51 Overall Variety5Related Variety1Unrelated Variety 1 / i ln 5 XS X! / / s / s i ln 1 i / s 1 XS 1 / s ln i / s s51 i2s where / i is defined as the share of industry i in a region s total employment; / s is the share of sector s in a region s total employment; / s i is the share of industry i in sector s s employment. In the formulation, industries were defined as three-digit standard industrial classification (SIC) categories and sectors as categories equivalent to two-digit SIC (see Appendix A for details). However, these measures of variety depend on the size of regional employment: when a regional economy is large, the variety measures are also large. For the control of size effect of regions with different employment, all variety measures were normalized by dividing by the maximum value of variety, i.e., the natural log of regional total employment. Figure 3 presents the maps of overall variety, related variety and unrelated variety in 1995 at municipal level, i.e., city, county, and district (si-gun-gu in Korean). Structural change indices. Structural changes are measured by employing Lilien s (1982)-type indices. Originally invented to measure dispersion of employment reallocation rates across sectors and industries, the Lilien index is usually considered to reflect the ability of an economy to flexibly react and quickly adapt to changes in aggregate demand (Goschin et al. 2009:100). 6 The original Lilien s sigma (r) is used for measuring intersectoral structural change. It is defined as the weighted variance of the sectoral growth (or decline) rates of employment from period t21 to period t, as follows: s51 Intersectoral structural change5r s 5 X s!1 = E 2 s ðdlog E s 2Dlog EÞ 2 E 5 X s!1 = 2 ð/ st Þðlog / st 2log / st21 Þ 2 (A) Overall Variety (B) Related Variety (C) Unrelated Variety FIGURE 3. INDUSTRIAL VARIETY OF REGIONAL MANUFACTURING IN SOUTH KOREA, 1995.

7 252 GROWTH AND CHANGE, JUNE 2017 where E s and E are the employment of sector s and the total employment in a region; / s 5 E s E is the share of sector s of total employment in a region; and t21 andt imply base year and final year. D is the first difference operator. The value 0 indicates that no structural change occurs, as every sector increases or decreases its employment by the same proportion. On the contrary, a higher value of Lilien s sigma implies that the region experiences severe structural change, i.e., the relative employment of each sector has been changed highly unevenly. Similarly, structural change of regional employment at industry level can be defined as follows: Interindustrial structural change5r i 5 X i!1 = E 2 i ðdlog E i 2Dlog EÞ 2 E 5 X i!1 = 2 ð/ it Þðlog / it 2log / it21 Þ 2 where i stands for industry. Finally, through a slight modification of the original Lilien s sigma, the third index for intrasectoral structural change of a regional economy is defined as a weighted average of an individual sector s internal interindustrial structural change. That is: Intrasectoral structural change5 n s 5 X s E s n E s 5 X s / s n s where internal cross-industry structural change n s of individual sector s is measured as follows: n s 5 X i2s E s!1 = i Dlog E s i E 2Dlog E 2 2 s 2 X s i2s / s it!1 = log / s it 2log 2 2 /s it21 : In the index, E s i indicates the employment of industry i in sector s, and therefore / s it 5 Es i E s is the share of industry i in the sectoral employment of sector s. While the former two measures each capture the magnitude of overall employment reallocation across sectors and industries, respectively, the third measure captures the average magnitude of employment reallocation across industries in the same sector. These three indices quantify the scale of structural change as an outcome of adaptation. Particularly, intersectoral structural change r s and intrasectoral structural change n s measure how much regional economies adapt or structurally reorganise their economic structure, intersectorally and intrasectorally, respectively. Figure 4 shows that regions experienced different scales of structural change in the period when the Korean economy collapsed from recessionary shocks triggered by the financial crisis. Regional Disparities in Structural Change in Korean Manufacturing With the indices computed in the previous section, this section briefly explains structural change in regional manufacturing of the Korean economy since the 1990s. For the sake of simplicity in the explanation, the measures are calculated at 16 metropolitan city and province levels (si-do in Korean) (see Appendix B for details).

8 INDUSTRIAL VARIETY AND STRUCTURAL CHANGE 253 (A) Inter-industrial structural change (B) Intra-sectoral structural change (C) Inter-sectoral structural change FIGURE 4. STRUCTURAL CHANGE OF REGIONAL MANUFACTURING IN SOUTH KOREA, Figure 5 presents the trend and variation of structural change on a national scale. It shows there were huge structural changes in the late 1990s and mid-2000s, especially in 1997 when the Asian financial crisis hit the Korean economy. However, such structural changes during the crisis occurred primarily within the same sectors, while intersectoral structural change was relatively modest. This implies that Korean manufacturing during the crisis experienced significant industrial restructuring, but most of it was limited within the same sectors. Restructuring at the intersectoral level was rather stable and proceeded gradually despite the economic crisis. However, at the regional level, there was considerable variation in structural change across regions. Table 1 shows the different structural changes in manufacturing in 16 provincial-level regions in Korea. First, some relatively newly industrialized regions, such as Daejeon metropolitan city and Chungcheongnam-do province, went through significant structural changes in manufacturing during the whole period In contrast, old industrial regions and de-industrialized regions, such as the metropolitan cities of Seoul, Busan, and Incheon, experienced relatively less structural change. Second, each region shows different trajectories of intrastructural and interstructural change over time. Figure 6 illustrates the structural changes of three regions selected from Table 1. It shows that Chungcheongnam-do adapted toward diminishing intersectoral structural change while increasing intrasectoral structural change. Incheon seemed to be locked in decline. Meanwhile, Gangwon-do suffered from severe labor reallocation across industries and sectors throughout the whole period. The final finding is that the regional economies followed an irreversible recovery path. Table 1 shows that, although there was regional variation, in most regions the structural change over the decade almost reached the sum of individual structural changes in the periods , , and This implies that most crisis-affected regional economies transformed to other industrial structures rather than opting to restore the previous structure after the recessionary shock. The tendency toward transformation rather than restoration implies that external shock permanently affected the configuration of regional economies. In refining the concept of regional

9 254 GROWTH AND CHANGE, JUNE 2017 FIGURE 5. ANNUAL TREND OF STRUCTURAL CHANGE ON A NATIONAL SCALE ( ). economic resilience, Martin (2012) referred to the situation in which even temporary disturbances could permanently affect the structure and growth path of the economy as hysteresis. The case of restructuring Korean regional manufacturing after the crisis supports the proposal that even temporary shock may have an irreversible hysteretic effect on both the intersectoral and intrasectoral industrial composition of the economy. Regression Analysis Empirical model. To test the hypothesized relationships between the structural diversity and structural change of a regional economy, I constructed a regression model with a panel data set at 221 municipal levels, i.e., city, county, and district (si-gun-gu in Korean). The data covers four time periods, with the same 3-year intervals between 1992 and 2004: , , FIGURE 6. DIFFERENT REGIONAL TRAJECTORIES OF STRUCTURAL CHANGE.

10 INDUSTRIAL VARIETY AND STRUCTURAL CHANGE 255 TABLE 1. STRUCTURAL CHANGE OF REGIONAL MANUFACTURING IN SOUTH KOREA, (3-YEAR INTERVAL). (A) Interindustrial structural change Region Seoul Busan Daegu Incheon Gwangju Daejeon Ulsan Gyeonggi-do Gangwon-do Chungcheongbuk-do Chungcheongnam-do Jeollabuk-do Jeollanam-do Gyeongsangbuk-do Gyeongsangnam-do Jeju-do Mean (B) Intrasectoral structural change Region Seoul Busan Daegu Incheon Gwangju Daejeon Ulsan Gyeonggi-do Gangwon-do Chungcheongbuk-do Chungcheongnam-do Jeollabuk-do Jeollanam-do Gyeongsangbuk-do Gyeongsangnam-do Jeju-do Mean

11 256 GROWTH AND CHANGE, JUNE 2017 TABLE 1. CONTINUED (C) Intersectoral structural change Region Seoul Busan Daegu Incheon Gwangju Daejeon Ulsan Gyeonggi-do Gangwon-do Chungcheongbuk-do Chungcheongnam-do Jeollabuk-do Jeollanam-do Gyeongsangbuk-do Gyeongsangnam-do Jeju-do Mean Note: Boldfaced numbers indicate they are above the national average. Source: Mining and Manufacturing Survey, Statistics Korea , and These intervals capture the business cycle of the Korean economy around the economic crisis (Statistics Korea 2010). 7 A regression model is constructed as follows: Y rt 5b 0 1b 1 V rt 1b 2 X rt 1u rt : In the equation, Y rt represents structural change between period t and t13, which includes interindustrial structural change, intersectoral structural change, and intrasectoral structural change for a 3- year period. The explanatory variable V rt stands for industrial variety and its decomposition, i.e., related variety and unrelated variety, in year t. The control variable X rt is a vector of regional manufacturing characteristics in year t. It includes employment size, capital intensity, skill intensity, labor productivity, and its Theil inequality index (1967). Log employment is used to measure the size of regional manufacturing. Both capital intensity and skill intensity stand for the competitiveness of regional manufacturing in terms of input intensity. Capital intensity is measured as the log ratio of capital stock to its employees in regional manufacturing. Skill intensity is measured as the wage bill ratio of total payments made to nonproduction workers to total payments to production workers (Bernard, Jensen, and Schott 2006). Labor productivity is measured as the log ratio of total value added in regions to total employment. The labor productivity gap between firms, between industries and between sectors might affect labor reallocation after recessionary shock, as productivity differentials can be either pushing or pulling factors of firm entry/exit and labor reallocation (Aw, Chen, and Roberts 2001). To consider

12 INDUSTRIAL VARIETY AND STRUCTURAL CHANGE 257 the restructuring effect of productivity differentials, the model included the Theil inequality index of labor productivity as a control variable. Using the two-stage nested Theil decomposition method (Akita 2003), the labor productivity differential across plants in a region is firstly decomposed into the within-sector component (the differential between plants within the same sector) and the between-sector component (the differential across sectors). The within-sector component is further decomposed into the within-industry component (the differential between plants within the same industry) and the between-industry component (the cross-industry differential within the same sector). The error term u rt is decomposed into two fixed effects l r, k t and pure error part e rt. The year fixed effect l t measures unobserved heterogeneity, such as national economy-wide cyclical fluctuations, that changes over the year but not across regions. The regional fixed effect h r measures time-invariant region-specific heterogeneity. Use of the fixed-effect model is justified twofold. First, the result of the Hausman specification test (1978), where the null hypothesis that the preferred model is random effect is rejected in most model specifications, indicates that models with fixed effect are generally superior to their random counterpart (Table 2). Second, on a theoretical basis, the fixed-effect model is preferred to the random-effect model when observations correspond to a mutually exhaustive set of units, not a random sample drawn from the larger population (Wooldridge 2008). As my analysis uses the complete population of regions in Korea, the fixed-effect model is more appropriate. e rt is assumed as cluster-robust standard errors, which relaxes the strict assumption that regression errors are fully independent and identically distributed by allowing for the errors to be correlated within the same cluster of regions (Cameron and Trivedi 2009). Estimation results. Table 2 presents the results of the fixed-effect panel regression to estimate the effects of related and unrelated variety on different adaptations at si-gun-gu municipal level. In the table, columns (1) (5) provide the results for interindustrial structural change as the dependent variable. Model (1) is a base model that includes only overall industrial variety as an explanatory variable, and in model (2), all control variables are added to the base model (1). Model (3) is a base model in which related and unrelated variety are used instead of the overall industrial variety index as an explanatory variable. In models (4) and (5) all the control variables are added to the base model (3). The index for labor productivity gap in model (2) is divided into within-sector gap and betweensector gap in model (4), and within-sector gap is further divided into within-industry gap and between-industry gap in model (5). The estimation results for intrasectoral structural change as the dependent variable are shown in columns (6) (10), and the results for intersectoral structural change as the dependent variable are shown in columns (11) (15). Columns (6) (10) and columns (11) (15) follow the same model specifications as columns (1) (5). The results generally support the hypothesis that the pattern of structural change is guided by existing structural diversity within a region. Regressions (1) (2), (6) (7), (11) (12) show the result of testing the first hypothesis H1. We expected that higher industrial variety would yield higher structural change at industrial level, as it would be favorable to both intrasectoral and intersectoral structural change. However, the results do not support this hypothesis about the connection between so-called Jacobs industrial variety and structural change. The Jacobs variety per se did not have significant impacts on any kind of structural change. Moreover, the table shows there are no significant effects of both related and unrelated variety on industry-level structural change, as models (3) (5) show. This implies that both higher related and unrelated variety per se do not increase the chance of economic resources in a region being reallocated across industries.

13 258 GROWTH AND CHANGE, JUNE 2017 TABLE 2. REGRESSION RESULTS FOR STRUCTURAL CHANGE, (3-YEAR INTERVALS). Dependent variables Interindustrial structural change Intrasectoral structural change Intersectoral structural change (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) Industrial variety (0.306) (0.314) (0.182) (0.176) (0.278) (0.284) Related variety ** 0.622** *** ** ** (0.530) (0.515) (0.565) (0.320) (0.316) (0.366) (0.498) (0.482) (0.506) Unrelated variety ** 0.790** 0.784** (0.331) (0.341) (0.349) (0.171) (0.169) (0.177) (0.331) (0.348) (0.347) Employment (log) *** *** *** (0.033) (0.034) (0.034) (0.014) (0.014) (0.013) (0.033) (0.034) (0.033) Capital intensity (log) * (0.037) (0.037) (0.037) (0.023) (0.023) (0.023) (0.032) (0.032) (0.032) Skill intensity (0.037) (0.037) (0.037) (0.025) (0.024) (0.024) (0.051) (0.049) (0.049) Labor productivity (log) * 0.045* 0.045* (0.041) (0.040) (0.040) (0.027) (0.026) (0.026) (0.036) (0.036) (0.036) Labor productivity gap *** (0.188) (0.100) (0.276) Within sector labor productivity gap Within industry labor productivity gap Between industry labor productivity gap Between sector labor productivity gap *** (0.196) (0.098) (0.313) ** (0.283) (0.137) (0.298) (0.571) (0.353) (0.632) (0.511) (0.530) (0.357) (0.379) (0.545) (0.555) Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Hausman Chi ** 21.37** 13.81** 22.45** 22.97** 7.95* ** ** 12.51** 29.60*** 12.36** 26.48*** 25.97** Observations R Notes: Robust standard errors are shown in parentheses. ***, ** and * denote statistical significance at the 1, 5, and 10 percent levels.

14 INDUSTRIAL VARIETY AND STRUCTURAL CHANGE 259 Regressions (8) (10) show the effects of related variety and unrelated variety on intrasectoral structural change, and regressions (13) (15) show their effects on intersectoral structural change. They firmly support hypothesis 2 (H2) and hypothesis 3 (H3). In equations (8) (10), the coefficient on related variety is positive and statistically significant, but in (13) (15) it becomes negative. This suggests that related variety promotes intrasectoral structural change, while it reduces intersectoral structural change. The results strongly support H2. On the other hand, unrelated variety is shown to increase the chance of intersectoral structural change while it has no significant effect on intrasectoral structural change, which is in line with our expectations proposed in H3. In addition, it should be emphasised that related variety has a more influential role than unrelated variety in both intrasectoral and intersectoral structural change: the absolute values of the estimated coefficients of related variety are far larger than the ones of unrelated variety in columns (8) (10) and (13) (15). As for the control variables, regional labor productivity and its Theil gap show significant relationships with intrasectoral structural change in models (7), (9) (10). In particular, the labor productivity gap between plants generally decreases the chance for intrasectoral structural change. Moreover, these effects are mostly generated not from the between-sector productivity gap but from the withinsector productivity gap, specifically from the productivity differentials between plants within the same industry. This implies that productivity inequality between firms within the same sectors, especially within the same industry, has a negative effect on intrasectoral structural change. However, this effect was not observed in intersectoral structural change. Finally, the results show that the overall employment size of regional manufacturing is related to a reduction in intersectoral structural change. It implies that larger economies are more robust to radical changes in industrial structure. There is no statistically significant impact of region-specific capital intensity and skill intensity on structural change. 8 Conclusions This study examined the role of related and unrelated variety in structural change as an adaptational outcome of a regional economy to recessionary shock. Overall, both varieties increase the structural reorganization of a crisis-stricken regional economy. However, the effects of related and unrelated variety are different. Higher related variety leads regions to adapt to recessionary shock through intrasectoral reorganization, but diminishes intersectoral structural change. Conversely, unrelated variety increases intersectoral structural change. These results suggest that related and unrelated variety play distinctive but complementary roles in adaptation and new path creation. When regions have higher related variety, it increases intrasectoral labor reallocation. This implies that regions with high relative variety might choose to adapt gradually, rather than resort to structural change in a radical manner. As many scholars have claimed, this adjustment path seems stable and safe in a recessionary period. However, it entails the risk of falling into the trap of lock-in and path consolidation (Cho and Hassink 2009; Hassink 2010; Martin and Sunley 2006), because related variety also increases resistance to intersectoral structural change. By promoting cross-sectoral reorganization, unrelated variety counters this negative effect excessive related variety generates to regional adaptation and opens up the possibility for new path creation or diversion. In sum, it is when regions have sufficiently high and balanced related and unrelated variety that the regional economy can evolve successfully through adjustment and renewal strategies. Conversely, when both related and unrelated variety are low, it becomes very difficult for a regional economy to seek a new growth path.

15 260 GROWTH AND CHANGE, JUNE 2017 Last, as discussed briefly above, both gradual adaptation and radical adaptation do not guarantee the performance or qualitative aspect of structural change. When this study is linked with empirical studies to examine whether structural changes contribute to economic growth, we can grasp the comprehensive mechanism of related/unrelated variety and successful regional adaptation. Comparative case studies with more detailed regional industrial characteristics and historical perspectives could also provide ample explanation of the causal process of related/unrelated variety and regional adaptation. NOTES 1. In this article, regional adaptability is interpreted as a region s ability to cope with unpredictable changes in the environment by reorganizing its internal structure, whereas adaptation is understood as a process in which the region s adaptability is unevenly realized in response to a specific circumstantial change (Grabher 1993; Boschma 2015). 2. During the period under analysis ( ), detailed local administrative regions changed considerably, particularly at municipal level, and several local regions were divided or merged. Each year s plant data in each region were realigned and reaggregated at identical regions across years through regional correspondence matching. 3. Martin and Sunley (2006) claim that seemingly single regional path dependence is actually an ensemble of multiple related and unrelated path dependencies. 4. The entropy measure for related variety is widely used to measure the diversification strategy (related versus unrelated diversification) of firms in industrial economics and management studies (Jacquemin and Berry 1979; Palepu 1985; Robins and Wiersema 2003). It assumes significant relatedness and externality between industries within the same sector but no relatedness between sectors. However, this measure has a caveat that comes from its reliance on the nested structure of the standard industrial classification (SIC) code. First, as established on the basis of product relatedness, SIC classification does not capture technological similarities across industries and sectors (Bishop and Gripaios 2010). Moreover, even though interrelatedness and interaction may exist between sectors through input-output linkages, labor mobility and knowledge spillover, such relatedness between sectors is neglected in the measures. Besides the problem associated with the SIC system, Robins and Wiersema (2003) argue that entropy measures are sensitive to features of portfolio composition that may not be directly linked to portfolio relatedness. Despite these imperfections, however, the related and unrelated variety measures of Frenken, Van Oort, and Verburg (2007) are commonly used by virtue of the relative ease of data collection as well as decomposability. 5. The Shannon (1948) entropy measure is derived from Theil s inequality index. Thus, it is decomposed into a within part, an average of entropy within each subgroup with a weight of group share, and a between part, group level entropy. 6. Lilien (1982) developed an original index to test whether sectoral shifts in labor demand were responsible for a substantial fraction of unemployment fluctuations in the U.S. economy. Subsequently, some scholars contended that the Lilien index is inappropriate for measuring pure sectoral shift because it does not isolate pure sectoral shift from the influence of aggregate demand shocks (Abraham and Katz 1986; Mills, Pelloni, and Zervoyianni 1995). Nevertheless, it is still considered a useful measure to assess the magnitude of short-term reallocation (Prasad 1997). 7. The actual number of observations used for the regression is 865 for 219 regions. Two regions, Ongjin-gun and Hwacheongun, have no observations for the whole period, and seven cities and regions have missing data for some periods. 8. Two input intensities, capital intensity and skill intensity, and labor productivity might co-move. However, the results show that the effects of various varieties and productivity differentials to structural change are robust, regardless of whether the two factor intensities are excluded from all specifications (which is not reported here but results are available upon request). REFERENCES Abraham, K., and L. Katz Cyclical unemployment: Sectoral shifts or aggregate disturbances? Journal of Political Economy 94(3): Akita, T Decomposing regional income inequality in China and Indonesia using two-stage nested Theil decomposition method. Annals of Regional Science 37(1):

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