Do Industrial Parks Improve the Performance of Their Tenant Firms in Korea? : Focused on the Small and Medium-Sized Manufacturing Firms

1. Backgrounds

The Korean economy has maintained its rapid growth due to 'industrial parks'. Industrial parks have been positioned as the foundation of development of local economies and key bases of the national economy in Korea. The number of industrial parks has been steadily growing since the early 2000s, and there are currently 1,082 industrial parks in operation.

The effectiveness of industrial clusters such as industrial parks and science parks is a controversial subject that has seen intense criticism and discussion. Though many studies have confirmed that industrial clusters can be effective tools for enhancing management and innovative performance of tenant firms, several other studies have found that industrial clusters have weak or insignificant impact (Massey et al., 1991; Westhead, 1997; Bakouros et al., 2002; Hansson et al., 2005).

For instance, Massey et al. (1991) described science parks as being high-tech fantasies that actually had only a marginal effect of promoting technology transfer, linking universities to industry, and enhancing the performance and growth of NTBFs (New Technology Based Firms). Westhead's (1997) survey on NBTFs on and off a science park concluded that there was no significant differences in terms of R&D intensity. These findings indicate the need for this study on the performance of industrial parks in Korea.

2. Purpose

Do industrial parks, which were established by the government for political reasons, improve the performance of their tenant firms? This paper answers this question by verifying if the innovative performance and management of such firms, measured in terms of their net profit, operating profit, total sales per worker, and patents, are affected by their location inside the industrial parks in Gyeonggi-do, Chungcheong-do, and Gangwon-do. There is some evidence of regional differences in the performances of park firms (Jin and Hur, 2014). The key issues are (1) whether park firms outperform off-park firms; and (2) whether the performances of park firms have a relation with the circumstances of the surrounding city (for example, administrative district and distance from Seoul).

For private and public sector bodies, a clear indication of the return on their investment is required. The demonstration of the effectiveness of industrial parks plays a key role in attracting tenants and talented people to work for the tenants, and in building local support and understanding of the park's activities (Monck and Peters, 2009). Therefore, empirically verifying if the management and innovative practices of such on-park firms are better than that of off-park firms would be a step toward the further development of industrial parks.

This paper begins by reviewing the existing literature on industrial parks. Section 3 presents the status of existing industrial parks. Based on the review of literature, hypotheses and study methods are derived, which are formally tested in Section 4. Next, the data upon which the tests were conducted are described, and the results are presented. The final section presents the conclusions and speculates on some policy implications.

1. Definition

The industrial park refers to a parcel of land, developed and managed to be used by industries according to a comprehensive plan established for an industrial location. As a means of policy implementation, the industrial park is created to attract factories and service facilities supporting various industries, in order to foster the manufacturing industry and the knowledge-based high-tech industry(｢Industrial Sites and Development Act｣ article 2).

The applicable law defines an industrial park as any plot of land to be designated and developed under a comprehensive plan to collectively install factories; the facilities related to the knowledge industry, the cultural industry, the information and communications industry and the recycling industry; resources warehousing facilities; logistics facilities; and educational, research, business, support, data processing, and distribution facilities thereto; residential, cultural, environmental, and green areas and parks; and medical, tourism, sports, and welfare facilities; in order to enhance the functions.

The purpose of developing industrial parks can be summarized mainly into three categories. First, the industrial park is developed to save the expenditure (of infrastructure) required for establishment of factories by the individual firms. Second, the effect of exchanges and cooperation can be maximized through industry clustering and enterprises can save related costs. Third, the industrial park is developed to promote efficient management of the environment of the country (KICOX. 2011).

Industrial parks in Korea can be classified according to the sponsorship, the location, or the function they perform. In terms of the function, the industrial parks are divided into traditional industrial parks, science parks, and business parks. Depending on the main actor developing the industrial parks, the parks are classified into government-owned parks and private-owned parks.

Korea is mainly classifying its industrial parks into national industrial parks, local industrial parks, and urban high-tech industrial parks and agricultural industrial parks based on the ｢Industrial Sites and Development Act｣. The classification method reflected the main actor of development and the purpose of development on a mixed basis. Table 1 shows the authority holder and purpose of industrial parks by type.

Table 1.

Types of Industrial Parks in Korea

2. State

Table 2 shows some industrial park statistics as of mid-2015. According to the latest research by KICOX, there are currently 1,082 industrial parks in Korea, which comprise 41 national industrial parks, 566 local industrial parks, 14 urban high-tech industrial parks, and 461 agricultural industrial parks. Of these, 12.2% (132), 13.31% (144), 9.89% (107), and 6.38% (69) are accounted by the Gyeonggi-do, Chungnam-do, Chungbuk-do, and Gangwon-do regions, respectively. Even though local industrial parks outnumber all other types of industrial parks, the national industrial parks accounted the largest proportion of industrial parks, occupying 57.38%.

Table 3 shows the construction completion periods of the national and local industrial parks by region. In the last five years, more national and local industrial parks were developed than ever before. The number of industrial parks in 2015 doubled from that in 2011. Additionally, the average age of industrial parks decreased sharply. Excluding the industrial parks constructed in the last five years, the ratio of the deteriorated (above 20 years old) industrial parks is about 25%. The average ages of local industrial parks in zones B (17), C (16), and A (13.6) show that such parks are the oldest.

Table 2.

Operation Status of Industrial Parks by Region and Type

Table 3.

Construction Completion Periods and Age of Industrial Parks by Zone

1. Data

This study verified whether industrial parks in different areas enhance the management and innovation performance of their tenant firms. We initially limited this study to national and local industrial parks (see Table 4).

Study areas were divided into four zones: zone A, B, C, and D to account for the fact that some study firms are affected by the degree of their agglomeration outside the park; therefore, the zones were categorized according to the degree of their agglomeration. Zones A and B in the capital regions (Gyeonggi-do) have a higher degree of agglomeration than zones C and D in the non-capital regions (Gangwon-do and Chungcheong-do).

The capital regions are large and dense and are more different from one another than they were in the past. This is true for the non-capital regions as well. Therefore, the study areas need to be split. There are several ways of dividing the capital regions. Many capital region studies based on comparison of capital and non-capital region have been carried out based on an area classification (for example, physical geography, siㆍgun unit, and regulation districts). However, these authors argue that there is a need for a comprehensive reflection on this matter. Therefore, the study areas were divided into two administrative areas, and each administrative area was further divided into two parts based on the distance between Seoul City and each firm’s site.

The average distance to other sites is a significant variable for analyzing the productivity of a firm (Duranton and Overman, 2005; Park et al., 2009). Additionally, Kwak and Ko (2005) showed the region-wise difference in performances of industrial parks.

Table 4.

Study Regions

2. Hypothesis

This paper incorporates further insights from the literature on management and innovative performance of on- and off-park firms. The results in the existing literature are replicated using micro-level data to determine whether tenancy in industrial parks enhances the performance of on-park firms. We also drew the variables in response to the extant studies using the micro-level data. Ferguson and Olofsson (2004), Squicciarini (2008 and 2009), Siegel et al. (2003), and Choi and Kim (2010) used micro-level data of on- and off-park firms. The extant literature suggests that industrial clusters influence various broadly defined dimensions of the performance of firms. Particularly, few studies have explained the differences in the survival, employment growth, and research and development (R&D) activity of firms located in industrial clusters and comparable firms outside the industrial clusters.

• ▪ H1: Tenant firms of industrial parks perform better than comparable firms outside the industrial parks.

3. Variables

• ▪ Study Periods : 2011, 2012, and 2013
• ▪ Study Areas : Gyeonggi-do, Chungcheong-do, and Gangwon-do
• ▪ Study Objects : Small and Medium-Sized Manufacturing Firms

This study is based on data from Kis-Value, KICOX (Korea Industrial Complex Cor.), and KIPRIS (Korea Intellectual Property Rights Information Service). The study analyzes small and medium-sized manufacturing (external auditing) firms located in the study areas between 2011 and 2013. The external audit makes the financial statements of the firms credible and reliable. The monetary unit of this study is '100 million won' (see Table 5).

The management performance is measured by the net income per worker, operating profit per worker, and total sales per worker.

Net income (NI1p) is calculated by taking revenues and adjusting for the cost of doing business, depreciation, interest, taxes and other expenses.

Operating profit (Op1p) is the profit earned from a firm's normal core business operations. This value does not include any profit earned from the firm's investments and the effects of interest and taxes.

Total sales (Sa1p) is an important metric in analyzing a business. and is the total amount of sales in a given period. Total sales is typically formulated as total number of units sold times price per unit.

For the innovation performance results of R&D activities, a two-year lag was applied because of the time lag effects of the input and output. Thus, the number of patents was recorded from 2013 to the first half of 2015 (accumulated). This data comes from KIPRIS.

d_OnOff variables refer to the on-park sample firms inside the parks (d_OnOff=1) or outside the parks (d_OnOff=0) that began to locate in such industrial parks before each fiscal year. This data comes from KICOX.

d_Tech_i is a dummy regressor that denotes the technological industry level. It covers four categories of tech industries : high-, medium- high-, medium-low-, and low-tech (The Bank of Korea, 2012).

Age reflects the age of the firms at each fiscal year. In this study, the firms were assumed to have been established on Jan. 1 of the corresponding year, unless otherwise specified by the firm.

The LeRATE, CI1p, TanA, Oc, and Td variables refer to the labor equipment ratio, capital intensity per worker, tangible asset, owner's capital, and total debt, respectively. They control the management status and the financial structure of the firms.

Labor equipment ratio (LeRATE) is an index which represents how much one employee uses a company's facilities, that is, utilization of capital facilities by one employee.

Capital intensity per worker (CI1p) is the amount per one worker of fixed or real capital present in relation to other factors of production, especially labor. At the level of either a production process or the aggregate economy, it may be estimated by the capital to labor, in this study.

Tangible assets (TanA) are those that have a physical substance, such as currencies, buildings, real estate, vehicles, inventories, equipment, art collections, and precious metals.

Owner’s capital (Oc) is the equity account that shows the owners’ stake in the business. In other words, this account shows the how much of the company assets are owned by the owners instead of creditors.

Total debt (Td) presents the amount of money borrowed by one party from another. We could know the firms' condition that it is to be paid back at a later date, with interest.

Table 5.

Using Variables (Unit : 100 million won, Number, ㎞)

4. Methods

The regression analysis and PSM (Propensity Score Matching) methods were used to verify that industrial parks in different areas enhance the management and innovation performance of their tenant firms. To achieve the objective of this study, equation (1) was estimated separately for each zone.

If industrial parks manage to successfully accomplish their supporting mission, the on-park firms should improve their performance by locating inside parks and outperforming their matched off-park firms. The determined multiple regression model can be written as follows:

Matching has become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. It applies for all situations where one has a treatment, a group of treated individuals and a group of untreated individuals.

The greatest challenge in evaluating any intervention or program is obtaining a credible estimate of the counterfactual: what would have happened to participating units if they had not participated? Without a credible answer to this question, it is not possible to determine whether the intervention actually influenced participant outcomes or is merely associated with successes (or failures) that would have occurred anyway. One feasible solution to this problem is to estimate the counterfactual outcome based on a group of non-participants and calculate the impact of the intervention as the difference in mean outcomes between groups.

Of fundamental interest in all program evaluation efforts is whether a particular intervention, as designed, is effective in accomplishing its primary objectives. A well-designed intervention (or 'treatment') is typically based on theory or research evidence that articulates how the intervention's core mechanisms will work to achieve its goals and produce the desired outcomes.

The main pillars of this model are individuals, treatment and potential outcomes. In the case of a binary treatment the treatment indicator D_i equals one if individual i receives treatment and zero otherwise. The potential outcomes are then defined as Y_i(D_i) for each individual i, where i= 1, ..., N and N denotes the total population. The treatment effect for an individual i can be written as:

The fundamental evaluation problem arises because only one of the potential outcomes is observed for each individual i. The unobserved outcome is called counterfactual outcome. Hence, estimating the individual treatment T_i is not possible and one has to concentrate on average treatment effects. The parameter that received the most attention in evaluation literature is the 'average treatment effect on the treated' (ATT), which is defined as:

ATT can be estimated by the difference between the mean observed outcomes for treated and untreated.

As the counterfactual mean for those being treated E[Y(0) D=1] is not observed, one has to choose a proper substitute for it in order to estimate ATT. Using the mean outcome of untreated individuals E[Y(0) D=0] is in non-experimental studies usually not a good idea, because it is most likely that components which determine the treatment decision also determine the outcome variable of interest. Thus, the outcomes of individuals from treatment and comparison group would differ even in the absence of treatment leading to a 'self-selection bias'. For ATT it can be noted as:

The difference between the left hand side of equation (4) and τ_ATT is the so-called 'self-selection bias'. The true parameter τ_ATT is only identified, if :

Propensity score matching methods provide a way to select control observations that are similar to individuals who received a particular treatment.

To empirically test the effects of industrial parks on management and innovative performances of their tenants, nearest neighbor matching (with replacement) were developed.

Nearest neighbor matching is one of the most straightforward matching procedures. An individual from the comparison group is chosen as a match for a treated individual in terms of the closest propensity score (or the case most similar in terms of observed characteristics). Variants of nearest neighbor matching include 'with replacement' and 'without replacement', where, in the former case, an untreated individual can be used more than once as a match and, in the latter case, is considered only once.

1. Descriptive Statistics

Table 6 shows the descriptive statistics of firm variables by the location of the firm inside or outside industrial parks. The sample size is 3,544 (on-park: 1,120; off-park: 2,424), 3,650 (on-park: 1,203; off-park: 2,447), and 3,362 (on-park: 1,216; off-park: 2,146) in 2011, 2012, and 2013, respectively. Approximately 72% of sample firms are in the capital regions.

The on-park firms perform better than off-park firms do with regard to their net income per worker, operating profit per worker, and the number of patents in 2011, 2012, and 2013.

The off-park firms perform better than on-park firms do with regard to their total sales per worker in 2011, 2012, and 2013.

Table 6.

Descriptive Statistics of Variables by On/Off Park

2. Regression Results

Table 7 shows the regression results of β₁ in entire study area and each zone categorized by firm location inside and outside industrial parks. The regression results show a significant difference in the performances in the net income per worker and the number of patents.

The difference in net income per worker between the on- and off-park firms in entire study area is 0.13 in 2012, and the difference in the number of patents between the on- and off-park firms is 0.71 in 2013.

Table 7.

On- and Off-Park Firms' Regression Results

In the regression results by zone, the difference in net income per worker between the on- and off-park firms in zone A was 0.18 in 2012 and 0.23 in zone B in 2013. This difference was the highest observed difference in performance. Therefore, on-park firms performed better than comparable firms in the above-mentioned sectors did. However, in zone D in 2011, net income per worker of off-park firms was 0.13 higher than that of on-park firms. Therefore, in terms of the number of patents, hypothesis is proved true in zones A and C.

The differences in the number of patents between on- and off-park firms in zone A were 0.49 in 2012 and 0.66 in 2013. In zone C, the differences in the number of patents between the on- and off-park firms was 1.17 in 2011, 1.29 in 2012, and 1.78 in 2013, which is consistently higher than the differences in other zones.

3. Propensity Score Matching Results

The results of the matching are interpreted with respect to the net income, operating profit, total sales per worker, and the number of patents in three years, from 2011 to 2013. The on-park firms are the treated group, and the off-park firms are the untreated group.

Table 8 presents the differences in ATT (Average Treatment Effect on the Treated) of on/off-park firms' performances.

In the entire area, on-park firms had 0.71 more patents than off-park firms did in 2013. Additionally, on-park firms had 0.32 more operating profit per worker than the off-park firms did in the same year.

In zone A in 2011, net income per worker of on-park firms was 0.12 higher than that of off-park firms.

In zone C, the difference in the number of patents between the on- and off-park firms was 1.7 in 2012, and 2.08 in 2013. In this area, patents of on-park firms was more than that of off-park firms.

However, in the PSM results by each zone, all performance is insignificant, and therefore, we cannot find any evidence that the industrial parks improve the performances of their tenant firms in each zone.

Table 8.

On- and Off-Park Firms' PSM Results

1. Findings

This paper used the Kis-Value data of firms located in capital(Gyeonggi-do) and non-capital (Chungcheong-do and Gangwon-do) regions to analyze and identify the impact of industrial parks based on local characteristics and the performance of the park tenant firm.

The regression analysis and PSM methods were used to determine (1) whether park firms outperform off-park firms; and (2) whether performances of park firms are related to the circumstances of the surrounding city.

Contrary to expectations, there is no evidence that suggests that industrial parks improve the performance of their tenant firms.

The analysis results of the location effects, which show differences related to the external areas of the industrial parks, prove that the hypothesis is right only in patent of zones A and C. The industrial parks show only partial success in zones A and C, where the numbers of patents of the on-park firms are significantly higher than those of off-park firms.

In the results of regression analysis, zone C has large difference between on- and off-park firms in the number of patents.

In the PSM analysis, there is no significant difference between two groups. No significant difference is observed in the total sales and operating profit per worker.

Only in whole areas and zone C, the on-park firms perform better than off-park firms do in terms of patents, and operating profit per worker in the year 2013.

2. Policy Implications

The number of industrial parks in Korea has increased in the last five years, and the number of industrial parks in the study areas has doubled since 2011. Moreover, several studies that focus on the regeneration and restructuring of industrial parks have conducted by academics and the government. Current industry policy related to industrial parks is limited to providing physical locations (Kim, 2011). Central and local government could not find a motive for additional development and support of industrial parks, without reviewing the efficiency of existing parks. Before discussing the regeneration and restructuring of industrial parks, the various aspects of the performance of the tenants of the existing parks need to be evaluated.

The results of this study can be attributed to (1) the absence of differences between internal and external circumstances of industrial parks, (2) the decrease of large firms in industrial parks, and (3) small and deteriorated industrial parks.

First, the study results show no difference between internal and external circumstances of industrial parks. This is because in the capital regions, the processes of urbanization and industry agglomeration have been underway for a long time (Choi and Kim, 2010). The same is true of the non-capital regions.

Second, the number of large firms has decreased in each park in the non-capital regions, after the deregulation in the capital regions. Since 2008, the number of large firms decreased more by 4.1 firms on average as compared to small- and medium- sized firms in each park in non-capital regions (Hong, 2015). Since agglomerative spillovers exist within the parks, with information apparently flowing from big companies to the other tenants of the industrial clusters (Squicciarini, 2009), it is obvious that the performances of small and medium-sized firms in the park might be poorer than before.

Third, existing parks are very small and deteriorated because of their limited land, poor conditions, and aging infrastructure (lack of support facilities and infrastructure). The total area of industrial parks is the statistically significant variable that represents the internal conditions of the park itself (Jin and Hur, 2014). On average, parks in the non-capital regions are approximately one-tenth the size of parks in the capital regions (see Table 2), and are limited to traditional low-tech manufacturing industries. Moreover, the percentage of deteriorated parks is high (see Table 3). This also affects the performance of the tenants off the parks and leads to decrease in firm size. The deterioration of industrial parks leads to the exit of large firms and worsen the performance of the tenant firms. The worsening performance of tenant firms is synthesized by the chain reaction of deterioration of the industrial parks and the decreasing size of the tenant firms (Jang, 2011; Chun, 2016).

The limitations of this study are as follows.

First, the results of this study are limited to Gyeonggi-do and its neighboring areas because other regions (for example, Seoul, Gyeongsang-do, and Jeolla-do) were excluded from the study areas. Therefore, zones C and D do not represent the non-capital region. The results of this study should be interpreted as being applicable to a limited area.

Second, this study was based on information from the external financial audit firms of small and medium-sized firms. Therefore, the results of this study cannot be applied to tiny or big firms, as they are likely to display different tendencies. Industrial parks should be further studied with more sample firms to check if the results of this study truly mirror the influence of industrial parks on the performance of their tenant firms.