| Home | E-Submission | Sitemap | Editorial Office |  
Journal of Korean Society for Quality Management > Volume 52(2); 2024 > Article
글로벌 위기에서 산업 클러스터의 품질이 생산 탄력성에 미치는 영향



This paper aims to verify the difference in production resilience between local clusters and regions without clusters before and after a major crisis. Furthermore, this paper aims to identify the clusters’ quality factors that impact clusters’ shock vulnerability and resilience.


Utilizing open-source data from the US Cluster Mapping platform, this paper compares regions with industrial clusters to those without using the Differences-in-Differences (DID) estimator. It considers the regions with industrial clusters as a treatment group and others as the control group, comparing the period before and after the pandemic. Additionally, the paper examines which cluster factors make a difference in economic resilience during the crisis using Regression Discontinuity Design (RDD).


The study finds that regions with industrial clusters show higher production resilience compared to regions without clusters. Moreover, the number of establishments, annual payrolls, and employment can have a positive impact on resilience during the pandemic shock.


Though clusters could be vulnerable during the global crisis, industrial clusters can contribute to regional economic development and production resilience in the long-term aspect. Thus, it is required to construct a high-quality local cluster and support it during the economic crisis in the long-term aspect.

1. Introduction

After the spread of the global pandemic, the world has faced extra-ordinal crises that severely affect the global economy. The prolonged COVID-19 caused long-term blockades in numerous nations and led to production deterioration by paralyzing the global supply chain. However, compared to the early stage of the global pandemic crisis, when the World Health Organization (WHO) declared a Public Health Emergency of International Concern (PHEIC) on January 30, 2020, as the fatality rate decreased, WHO declared the end of the PHEIC on May 5, 2023 (Schell et al., 2020; Burki, 2023). Likewise, as COVID-19 has been mitigated and society has recovered its normal life as before the emergence of COVID-19 corresponding to the governmental effort to re-activate the economy, the regional economy also has been restored gradually as the normal production and business activity has been re-operated (Quy and Dung, 2023; Park and Park, 2024). However, the global supply chain which is considered as transmuted into the recovery period encounters critical geopolitical threats evoked by the Ukraine crisis (Alam et al., 2023; Handfield et al., 2020; Panwar et al., 2022). Likewise, as the uncertainty of the global supply chain has increased, restoring global production is highlighted as an assignment to stimulate the depressed world economy. Strategies such as digitization, re-shoring, and others have been proposed through various studies for responding to external risks over the supply chain (Alam et al., 2023). Therefore, as one of the strategies to recover from the shock of the pandemic, developing a sustainable entrepreneurial ecosystem has been proposed (Meng et al., 2022). As these global crises impede the local industry and economy seriously, governments are struggling to overcome those external risks, and to overcome the crisis, the importance of industrial clusters increases as a method to enhance resilience.
The industrial cluster is the set of interconnected firms, suppliers, and institutions within a specific field. This geographical concentration of establishments is expected to have mutual beneficiary within the industry by improving productivity and performing as an entrepreneurial incubation through cooperation and knowledge sharing. Likewise, though industrial clusters can expect agglomeration advantages to enhance local competitiveness and shock resilience from the paralysis of the global supply chain, the study on the effect of the industrial cluster on production resilience during the global crisis is limited despite there are studies about enhancing industrial efficiency (Pai and Lim, 2023). Thus, this paper aims to examine the impact of the industrial cluster on local production resilience under the exogenous crisis focusing on the COVID-19 case. Thus the research questions of this paper are as follows.
RQ 1) Whether the industrial cluster denotes a difference in regional productivity when comparing before and after the global pandemic.
RQ 2) Which quality factors of cluster significantly impact the shock and resilience competence?
To verify those assumptions, this paper utilizes the open-source data published by the US Cluster Mapping platform supported by the United States Economic Development Administration and Harvard Business School. Therefore, consider the regions obtaining industrial clusters as a treatment group and others as the control group, comparing the period before the pandemic and after the pandemic using the Differences-in-Differences (DID) estimator. After verifying whether the cluster contributes to production resilience, using the relatedness between clusters, this paper defines types of clusters based on the supply chain connectivity and identifies whether the types of the cluster and business make a difference in economic resilience under the crisis using Regression Discontinuity Design (RDD). Considering the recent dynamic industrial environment, this paper expects to provide significant implications on further cluster formation by identifying whether the cluster contributes to production resilience compared to the non-cluster area and which factors contribute to it.

2. Conceptual Background

2.1 Global Crisis and Global Value Chain

In December 2019, COVID-19 was first identified and rapidly spread throughout the world. Governments implemented global lockdowns to prevent the epidemic from spreading, which hampered the movement of people and goods. This restriction in the supply chain severely undermined productivity and deteriorated the economy of many regions.
In addition to COVID-19, other unpredictable geopolitical issues have arisen, such as the emergence of m-pox and the Russian-Ukraine war. The vulnerability of the hyper-connected global value chain, which developed during the globalization trend before COVID-19, has been proposed.
Likewise, as international threats such as the current prolonged trade war between the United States and China have increased, the recent business environment is represented as the Volatility, Uncertainty, Complexity, and Ambiguity (VUCA) era (Arunmozhi et al, 2021; Schell et al., 2020). Thus, referring to the current supply chain deterioration crisis caused by COVID-19, continuous studies are required to enhance resilience against further external threats.
Previous studies have studied the quality of the supply chain. Jeon and Yoo (2019) measured the efficiency of supply chain quality management using the Data Envelopment Analysis (DEA) model focusing on the domestic defense industry which should be inelastic from the externalities. Furthermore, Park, Soo, and Kim (2011) have examined that supply chain quality management with parent companies and cooperative companies can enhance the corporations' performance using the structural equation model. According to Park et al. (2011), the supply chain quality management infrastructural can impact pre-production and post-production processes, and the post-production process including production, storing, and service can lead to the performance of the business.
However, studies on industrial clusters and their performance measurement are limited and this causes skeptic concerns over the effectiveness of policies incubating the industrial clusters. Nonetheless, the role of industrial clusters and their supply chain within the local economy is expected as a method to mitigate the shock from the global crisis and enhance resilience from previous studies (Dai et al., 2021). Thus, this paper aims to measure the impact of the shock on the industrial cluster and its resilience.

2.2 Industrial Clusters

After Marshall insists on the agglomerate advantages of industrial concentration, to enhance industrial competitiveness, various types of clusters have emerged (Marshall, 2009; Gordon and McCann, 2000). Compared to the clusters formed by the physical proximity of the business, industrial clusters are generated by the conglomeration of interconnected businesses, suppliers, institutions, and others that are specialized in the specific industry.
U.S. Cluster Mapping categorizes the U.S. Clusters into traded and local clusters representatively, since balancing the traded and local clusters is significant to the policymaker as local clusters mitigate the external shock and intensify the local resilience, however, traded clusters improve local competitiveness and expand its economic size by serving several other markets (Delgado et al., 2014; Bell, 2005). Though, to mitigate the shock from the collapse of the global supply chain, the role of the local cluster has been highlighted as it has a short supply chain that serves the local market with less impact from external shock and responds promptly (Simmons et al., 2022; Hofe and Chen, 2006; Kim and Kim, 2019). However, despite its vulnerability to the effect of the supply chain, the traded clusters can also denote rapid resilience through active transactions with other clusters. However, in this paper, regarding that both types of clusters can contribute to regional economic development and production resilience by facilitating knowledge spillover, network effects, and other agglomeration effects, this paper verifies whether the region that has superior clusters has more resilience than other regions regardless of the type of the cluster.
Also, the study of Lu et al. (2013) has utilized R&D personnel, funds, institution, innovation consciousness, enterprise concentration, assets, technological purchase, digestion, and retrofit concentration with transportation conditions as quality factors of cluster explaining location quotient. However, the study of Slaper et al. (2018) utilizes population, wage, intensity, diversity, resource independence, education level, and others as quality factors that improve gross domestic production. Likewise, the cluster quality factor that can contribute to the performance of the cluster can be different, this paper adopts the indexes provided by the US Cluster Mapping data platform. Refer to the previous studies, this paper regards the industrial clusters’ quality factors as the degree of industrial concentration index (QP), locational quotient (LQ) which refers to the distinctive industrial concentration to the national averages, number of establishments of the local clusters (EST), wage and annual payroll which implies the attractive work environment deriving employees can impact on the productivity resilience of clusters, specifically the GDP contribution of local cluster and its manufacturing sectors, exports, and employment performance of clusters.

2.3 Production Resilience

Resilience refers to the capacity of the system to withstand external shocks or stresses and recover from the pre-crisis level. Due to global crises such as COVID-19, various local economies have undertaken excessive damage and policymakers have struggled to explore ways to mitigate the damage and recover the local economy rapidly as Figure 1. Thus, the role of the industrial clusters is highlighted as a way to recover the economic level by recovering the production level. Production implies the overall creation of goods and services using the resources of capital, labor, materials, and others. Thus, though the definition or measurement to measure production or economic resilience varies, referring to the Mendoza‐Velázquez and Rendón‐Rojas' study on industrial resilience, this paper defines resilience as the inherent ability to withstand adverse shock and recover the pre-crisis status (2021). Also, to measure the resilience of the regional unit, Mardaneh, Jain, and Courvisanos (2016) and Mardaneh et al. (2020) utilize the production growth or employment before and after the unit-wide shock or treatment. Thus, this paper regards the gross domestic production (GDP) of regions and employment as an index of resilience.

3. Methodology

3.1 Data Description

This study utilizes the open-source big data published by the US Cluster Mapping (USCM) platform supported by the United States Economic Development Administration and Harvard Business School. Since the global crisis from COVID-19 has been evoked in December 2019, its impact has been denoted after 2020. Thus, This paper regards 2020 to the present as the post-COVID era. However, as USCM provides data from 1998 to 2020, to measure the recent shock and resilience effect of regions, this paper retrieves the cluster feature from USCM, this study additionally collects GDP, GDP contribution of the manufacturing industry, location quotient (LQ), employment, export, and import data from Bureau of Economic Analysis (BEA) and BLS stands for the Bureau of Labor Statistics (BLS). These data sets from BEA and BLS are collected from 2015 to 2022 to measure the production resilience of the regions. However, this paper utilizes the time period from 2018 and 2020 to measure the shock of COVID-19 and 2020 to 2022 to measure resilience on the state level.

3.2 Difference-in-Difference (DID)

Difference-in-differences (DID) is a statistical method to compare the difference in outcomes before and after the treatment (Callaway and Sant’Anna, 2021). Thus, DID can also be utilized in measuring the impact of the policy or events by comparing the control group and treatment group affected by events. In DID analysis, the Ordinary Least Squares (OLS) are used to estimate the causal effect by regressing the outcome variables on the dummy variables and their interaction term. of treatment and period as following equation 1. Referring to the OLS model, yit implies the dependent variable for individual i by time t. γs(i) denotes the vertical intercept of the DID graph as Figure 2 and λt indicates the time trend (Bertrand et al., 2004).
Thus, δ is the treatment effect and ε implies the residual term (Bertrand et al., 2004).
(Equation 1)
To measure the effect of COVID-19 on production resilience through DID methods, this paper mines data for three years before and after the starting year of the pandemic, 2019. Thus, From 2018 to 2020 becomes the pre-COVID era, and from 2020 to 2022 becomes the post-covid era. Also, Model 1 is for measuring the shock of the crisis which analyzes 2018 and 2020. In Model 2, 2020 and 2022 data sets are used to measure the recovery of the regions. In addition to the period, the type of clusters is considered as treatment which can cause differences in dependent variables as Figure 3.

3.4 Regression Discontinuity Design (RDD)

The Regression Discontinuity Estimator (RDD) is a statistical method used to estimate causal effects in situations where treatment or intervention is assigned based on a continuous variable (Lee, 2016). It is similar to DID in the aspect of estimating casual effect, compared to DID which utilizes the binary or categorical variables, RDD can be applied to continuous variables. RDD compares the outcomes around the threshold value for continuous variables to measure the treatment and intervention effect (Hahn et al., 2001). To measure the impact of the test, the initial model of RDD can follow Equation 2.
(Equation 2)
The outcome (yi) can be denoted through the sum of β coefficient, variable (X), and a dummy variable for treatment (Z). β1 implies the linear pretest coefficient, β2 mean difference for treatment, β3 linear interaction, β4 quadratic pretest coefficient, and β5 quadratic interaction. i denotes the control and treatment statements as 0 and 1. Thus, ε denotes the residual same as DID (Hahn et al., 2001).
In this study, RDD can be used to estimate the causal effect of the cluster factors on production resilience for regions by the period, while DID could be used to estimate the causal effect of the pandemic on production resilience for regions that are assigned to different treatment groups.

4. Result

4.1 Shock Vulnerability of Cluster versus Non-cluster

To measure the shock effect of COVID-19 thoroughly, Model 1 compares 2018 and 2020 which are immediately before and after the emergence of the global pandemic. As Table 3, the pandemic has significant impacts on the overall GDP, export, import, and employment changes of the regions. To compare the non-cluster and regions with clusters, the shock effect is regarded as more significant than non-cluster regions and this paper assumes that the shock on the supply chain has spread by businesses and the physical concentration of the cluster can provide agglomerate advantages, however, it can also cause agglomerate damage. However, as in Table 3, since the adjusted R-squared value of GDP and Employment is higher than other models having 56% and 66.5% of explanation power, the model using the other performance index such as GDP of the manufacturing sector, export, and import denotes low explanation power by having 27.5%, 19%, and 17.8%.
Though the RDD model of Model 1 denotes low explanation power from 10.3% to 13% (Table 4), the establishment, annual payroll, and wage indicate a positive impact. However, the QP and LQ, implying the industrial concentration degree and differentiation indicate a negative impact on shock.

4.3 Production Resilience of Cluster versus Non-cluster

For production resilience, it indicates superior explanatory power near 70% to 97%. Though import of the region is not significantly influenced by the interaction of type of cluster and COVID impact under the 95% confidence level, COVID-19 itself and whether it is a cluster or not have an impact on import. Thus, for all GDP, GDP, export, and employment of the manufacturing sector denote significant impact. As Table 5, the treatment which implies whether it is a cluster or not has a negative impact on resilience, however, as indicated on interaction, under the global crisis, the cluster can have a significantly positive impact on resilience. Thus, it implies that the regions with clusters have a significant impact on the index related to production resilience, especially during the global crisis and a paralyzed value chain like COVID-19. Furthermore, rather than employment, in various dependent variables the amount of establishment of the business, average payroll level, and the location quotients which imply the comparative competitiveness with other clusters tend to influence production resilience on the confidence level of 95%. However, rather than the qp and lq which denote the distinctive competitiveness or concentration intensity of the industry, the index related to the working environment such as wage and annual payroll has a positive impact on resilience (Table 6). Thus, since the number of established companies denotes a positive impact on resilience, this paper assumes that the variety and amount of the company can enhance the quality of the cluster by enhancing resilience. However, instead, the lq and qp which have a negative impact on both shock and resilience can imply that clusters with high lq and qp are less vulnerable to shock but can be also slow to recover from the shock.

5. Conclusion

This paper aims to analyze the impact of global crises on the production resilience of industrial clusters. Furthermore, this paper aims to provide insights into components of industrial clusters that can have a positive impact on regional productivity and economy. Based on the study, this paper expects to provide useful insights for policymakers and researchers.
Referring to the results, industrial clusters are more affected by global crises than non-clustered regions, however, they also have a more positive impact on production resilience. This is assumed that the cluster promotes interaction and collaboration among industries, and facilitates knowledge and technology sharing, performing as a factor in promoting economic recovery and growth.
These results suggest that industrial clusters provide effective production resilience, however, also vulnerable to external crises. Thus, this study has implications for verifying the shock and resilience of the clusters quantitatively, and though it is limited to the quantitative cluster features in USCM, it identifies which components significantly influence the production indicators.
Considering the results of this study, it is important for the government and businesses to actively promote the formation of clusters, and effectively manage, and support them. Though clusters seem vulnerable and ineffective to the crisis, industrial clusters can contribute to regional economic development and production resilience in the long-term aspect, which can strengthen the sustainability and competitiveness of the local economy. Thus, while constructing the cluster, it is important to consider the method to attract company and employees to the cluster as it on the RDD result. Furthermore, based on the cluster strategy of the policy decision-maker maker whether to form an elastic cluster that can recover from the shock rapidly or form an inelastic cluster that can mitigate the external shock, the policymakers can support expanding the size of the cluster by forming superior work environment or specializing cluster by enhancing concentration of industry and competitive differences with other clusters.
Also, this paper has implications for examining the impact of the cluster under the global crisis. Though this paper focuses on the pandemic issue caused by COVID-19, under a similar situation that paralyzed the supply chain, the cluster that has high lq and qp can mitigate the impact of the shock, or conversely, a cluster that has a large cluster with lots of establishment and employees but has low lq and qp can be vulnerable to the shock but recover faster than other regions. Thus, supporting clusters during the crisis situation can prevent the collapse of the regional economy of the cluster and recover from the shock rapidly.
Thus, through additional research, policymakers can form and support the Korean industrial cluster. In particular, by presenting specific measures such as improving the employment environment, attracting companies, and strengthening industrial concentration, the resilience of domestic industrial clusters could be increased and the sustainability and competitiveness of the local economy could be intensified.
However, due to the limitations of the data and analysis methods used in this paper, the generalization of the research results can be restricted. Primarily, research methodology using secondary data is very useful in terms of reducing research costs and time efficiency, but it must be used carefully considering the limitations and accuracy of the data. This study reflects these aspects and suggests that future research requires in-depth analysis using more diverse data sources. Additionally, as this paper presents the analysis results for specific industries and regions, extensive study is required to generalize the results for other industries and regions. To expand this study to the Korean industrial cluster, since the data used in this study is focused on clusters in the United States, and there are limitations in generalizing it to domestic industrial complexes. Therefore, additional research tailored to domestic industrial complexes is needed. For example, in Korea, there is a need to collect and analyze similar data from specific industrial complexes to examine the impact of cluster qualitative factors on production resilience.
Also, though the analysis denotes that there is no significant difference between traded and local clusters, however, this result should be re-analyzed since this paper utilizes the secondary dataset and the types of the cluster are pre-defined. Thus, to attain thorough insight, it is required to pre-defined the features of the traded and local cluster through the statistical standards such as the amount of the transaction with other locals or nations and re-conduct the analysis. Furthermore, this paper only focuses on each index of the cluster and its impact on productivity. Among the dependent variables, Gross Domestic Product (GDP) can be used as an indicator of a region's overall economic performance, but it may not be the best indicator to measure production resilience specifically. GDP measures the total value of goods and services produced in a region over a specific time period. While it can be influenced by a region's ability to maintain or recover production capacity, it is also influenced by other factors such as consumption, investment, and government spending. Therefore, while GDP can provide some insight into a region's economic health, it may not capture the specific factors that contribute to production resilience. However, due to the limitation of the dataset which makes it hard to measure the total production and its sales by cluster unit, this paper utilizes GDP which reflects the production of local partially. Thus, future studies on other types of clusters, not only the innovative or cooperative clusters but also the traded and local clusters reflecting more performance indexes that can have a significant impact on productivity, should be continued.
Furthermore, to enhance the practical implication of this study, future studies could be conducted on the Korean industry to diagnose the quality of the cluster, collecting data considering the characteristics of domestic industrial clusters should be a prerequisite. For example, factors of a cluster can be evaluated using data such as the number of companies in an industrial complex, number of employees, and annual salary. Additionally, regional GDP and manufacturing GDP data can be used to analyze the impact of clusters on regional economic recovery as it is collected in USCM.
Applying this study to the Korean case is significant since Korea attains various and superior industrial clusters such as the Seoul Digital Industrial Cluster, Daegu Seongseo Industrial Cluster, and others, which have become the base of a variety of companies and have a positive impact on the local economy through geographical advantages and network effects, it is hard to measure the resilience and economic effect of the cluster in a simple and efficient way. Though RDD and DID are simple analyzing methods, they can provide the effectiveness of clusters and provide insights to policymakers to design policies to enhance the local economy and clusters. Thus, as it is on USCM, constructing a database that can track the economic status based on the economic statistical area can activate studies on incubating and accelerating Korean industrial clusters and local clusters.


Alam, M. M., Aktar, M. A., Idris, N. D. M., and Al-Amin, A. Q. 2023. World energy economics and geopolitics amid COVID-19 and post-COVID-19 policy direction. World Development Sustainability 2: 100048.
crossref pmc
Arunmozhi, Manimuthu, Kiran Kumar, R., and Srinivasa, B. A. 2021. Impact of COVID-19 on global supply chain management Managing supply chain risk and disruptions: Post COVID-19. Cham. Springer. International Publishing. pp 1-18.

Bell, G. G. 2005. Clusters, networks, and firm innovativeness. Strategic Management Journal 26(3):287-295.
Bertrand, M., Duflo, E., and Mullainathan, S. 2004. How much should we trust differences-in-differences estimates? The Quarterly Journal of Economics 119(1):249-275.
Bureau of Economic Analysis 2023. Employment.

Bureau of Economic Analysis 2023. Gross Domestic Production (GDP).

Bureau of Economic Analysis 2023. International Trade & Investment.

Bureau of Labor Statistics 2023. Employment and Wages.

Burki, T. 2023. WHO ends the COVID-19 public health emergency. The Lancet Respiratory Medicine.
crossref pmid
Callaway, B., and Sant’Anna, P. H. 2021. Difference-in-differences with multiple time periods. Journal of Econometrics 225(2):200-230.
Courvisanos, J., Jain, A. & K., and Mardaneh, K. 2016. Economic resilience of regions under crises: A study of the Australian economy. Regional Studies 50(4):629-643.
Dai, R., Mookherjee, D., Quan, Y., and Zhang, X. 2021. Industrial clusters, networks and resilience to the Covid-19 shock in China. Journal of Economic Behavior & Organization 183: 433-455.
Delgado, M., Porter, M.E., and Stern, S. 2014. “Defining Clusters of Related Industries.”.
Gordon, I. R., and McCann, P. 2000. Industrial clusters: complexes, agglomeration and/or social networks? Urban studies 37(3):513-532.
crossref pdf
Hahn, J., Todd, P., and Van der Klaauw, W. 2001. Identification and estimation of treatment effects with a regression-discontinuity design. Econometrica 69(1):201-209.
Handfield, R. B., Graham, G., and Burns, L. 2020. Corona virus, tariffs, trade wars and supply chain evolutionary design. International Journal of Operations & Production Management 40(10):1649-1660.
Jeon, G., and Yoo, H. 2019. An efficiency analysis of supply chain quality management using the multi-stage DEA model: focused on the domestic defense industry companies. Journal of Korean Society for Quality Management 47(1):163-186.

Kim, S., and Kim, M. S. 2019. A study on the effects of regional context on entrepreneurial orientation. Journal of the Korean Society for Quality Management 47(4):847-859.

Lee, M. J. 2016. Matching, regression discontinuity, difference in differences, and beyond. Oxford University Press.

Lu, H., Zhou, Y., and Tang, J. 2013 November; Influence factors of industrial cluster development. In 2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering. 1: 222-226. IEEE.
Mardaneh, Karim K., Jain, Ameeta, and Courvisanos, Jerry 2020. A guide to patterns of regional economic resilience. Handbook on regional economic resilience. Edward Elgar Publishing 126-142.

Marshall, A. 2009. Principles of economics: unabridged. eighth edition. Cosimo, Inc..

Mendoza‐Velázquez, A., and Rendón‐Rojas, L. 2021. Identifying resilient industries in Mexico’s automotive cluster: Policy lessons from the great recession to surmount the crisis caused by COVID 19. Growth and Change 52(3):1552-1575.
crossref pmid pmc pdf
Meng, S., Gao, X., and Duan, L. 2022. Facing the COVID-19 pandemic and developing a sustainable entrepreneurial ecosystem: the theory and practice of innovation and entrepreneurship policies in China. International Journal of Environmental Research and Public Health 19(14):8797.
crossref pmid pmc
Pai, H. S., and Lim, C. K. 2023. Derivation of Profit Curve by Cubic Cost Function and Mathematical Verification of Industry Life Cycle: Focused on All Industries in Korea. Journal of Korean Society for Quality Management 51(4):481-496.

Panwar, R., Pinkse, J., and De Marchi, V. 2022. The future of global supply chains in a post-COVID-19 world. California Management Review 642: 5-23.
crossref pdf
Park, J. S., and Park, H. 2024. Understanding Post-Pandemic Travel Intention: Boredom as a Key Predictor. Journal of Korean Society for Quality Management 52(1):1-21.

Park, J. Y., Oh, S. J., and Kim, S. W. 2011. Causal Relationship of Infra, Process and Firm Performance on Supply Chain Quality Management. Journal of Korean Society for Quality Management 39(4):464.

Quy, N. T. T., and Dung, N. T. 2023. Vietnamese Labor Market Situation After the Covid-19 Pandemic. European Journal of Development Studies 3(3):66.
crossref pdf
Schell, D., Wang, M., and Huynh, T. L. D 2020. This time is indeed different: A study on global market reactions to public health crisis. Journal of Behavioral and Experimental Finance 27: 1-8.
crossref pmid pmc
Simmons, Richard, Culkin, Nigel., and Davies, Virginia. 2022. Crisis Resilient Supply Chain Design-Post Pandemic, Post Ukraine Conflict Challenges And Opportunities. Post Ukraine Conflict Challenges And Opportunities.
Slaper, T. F., Harmon, K. M., and Rubin, B. M. 2018. Industry clusters and regional economic performance: A study across US metropolitan statistical areas. Economic Development Quarterly 32(1):44-59.
crossref pdf
U. S. Cluster Mapping http://clustermapping.us). Institute for Strategy and Competitiveness, Harvard Business School. Copyright © 2018 President and Fellows of Harvard College All rights reserved. Research funded in part by the U.S. Department of Commerce, Economic Development Administration..

Vom Hofe, R., and Chen, K. 2006. Whither or not industrial cluster: conclusions or confusions? Industrial Geographer 4(1).

Figure 1.
Scheme of Shock Impact and Recovery by Time
Figure 2.
Scheme of Difference-In-Difference Analysis
Figure 3.
Scheme of Shock Impact and Recovery by Time
Table 1.
Measurement Variables
Classification Variable Name Detail Reference
Dependent Variable GDP Gross Domestic Product BLS, 2023 BEA, 2023
GDP GDP of Manufacturing industry
Export Export of Manufacturing industry
Import Import of Manufacturing industry
Employment Employment of regions
Independent Variable QP Degree of industry concentration in a particular region USCM, 2023
LQ Location quotient, the degree of industry concentration in the region differs from the national average
AP Annual payroll
EST Number of establishments
WAGE The average annual wage for private employees
Table 2.
Descriptive Statistics
Cluster QP LQ AP EST Employment
Count 60000 Count 60000 Count 60000 Count 60000 Count 60000 Count 60000
Count_industry 67 Mean 104339.3 Mean 1.07 Mean 418224.55 Mean 510.66 Mean 2983.72
Count_sub_cluster 316 Median 9399.5 Median 0.79 Median 39836.50 Median 56 Median 1696.07
STDEV 512563.8 STDEV 2.23 STDEV 1951622.59 STDEV 2283.39 STDEV 3418.84
Count_state 32 Range 16367410 Range 121.56 Range 59745760 Range 78639 Range 17414.85
Count_traded 40800 Min. 0 Min. 0 Min. 0 Min. 0 Min. 274.02
Count_local 19200 Max. 16367410 Max. 121.56 Max. 59745760 Max. 78639 Max. 17688.87
WAGE GDP GDP_Manu Export Import
Count 60000 Count 60000 Count 60000 Count 60000 Count 60000
Mean 42004.48 Mean 453626 Mean 45605.78 Mean 18767.38 Mean 45677.61
Median 41413.54 Median 222601.6 Median 25934 Median 9574.70 Median 15645.6
STDEV 31224.20 STDEV 611468 STDEV 65856.30 STDEV 22079.66 STDEV 74901.36
Range 394005.39 Range 3561772 Range 422377.3 Range 117445.2 Range 448517.30
Min. 0 Min. 36330.4 Min. 1321.30 Min. 282.90 Min. 424.40
Max. 394005.39 Max. 3598103 Max. 423698.6 Max. 117728.1 Max. 448941.70
Table 3.
DID Model and Result Summary
DID Model Summary DID Result Summary
Dependent R2 Adjusted R2 F-stat P GDP Coeff. Std err t value p
GDP 0.560 0.560 8500 0.00 Intercept 0.070 0.002 40.170 0.00
GDP_manu 0.275 0.275 2530 0.00 Period −0.033 0.002 −13.410 0.00
Export 0.190 0.190 156 0.00 Treatment −0.034 0.002 −19.139 0.00
Import 0.179 0.178 1449 0.00 Interaction −0.022 0.003 −8.868 0.00
Employment 0.667 0.655 53.70 0.00
DID Result Summary
GDP_manu Coeff. Std err t value p Export Coeff. Std err t value p
Intercept 0.046 0.005 9.748 0.00 Intercept −0.147 0.007 −20.380 0.00
Period −0.038 0.007 −5.686 0.00 Period 0.139 0.010 13.610 0.00
Treatment −0.020 0.005 −4.053 0.00 Treatment 0.137 0.007 18.827 0.00
Interaction −0.045 0.007 −6.663 0.00 Interaction −0.237 0.010 −23.053 0.00
Import Coeff. Std err t value p Employment Coeff. Std err t value p
Intercept −0.255 0.009 −29.089 0.00 Intercept 0.029 0.001 27.438 0.00
Period 0.327 0.012 26.407 0.00 Period −0.036 0.002 −24.074 0.00
Treatment 0.288 0.009 32.593 0.00 Treatment −0.018 0.001 −16.830 0.00
Interaction −0.433 0.013 −34.606 0.00 Interaction −0.036 0.002 −23.486 0.00
Table 4.
RDD Model and Result Summary
RDD Model Summary RDD Result Summary
Dependent Adjusted R² F-stat P GDP Coeff. std err t value p
GDP 0.127 0.127 484.3 0.00 Const 257100 6797.58 37.819 0.00
GDP_Manu 0.104 0.103 384.8 0.00 QP −0.094 0.04 −2.337 0.02
Export 0.117 0.117 442.3 0.00 EST 36.261 2.763 13.123 0.00
Import 0.109 0.108 406.6 0.00 AP 0.067 0.012 5.482 0.00
Employment 0.130 0.130 498.1 0.00 WAGE 3.673 0.132 27.731 0.00
LQ −14390 1771.416 −8.124 0.00
RDD Model Summary
GDP_Manu Coeff. std err t value p Export Coeff. std err t value p
Const 26120 739.507 35.324 0.00 Const 15260 360.819 42.284 0.00
QP −0.027 0.004 −6.229 0.00 QP −0.009 0.002 −4.176 0.00
EST 3.025 0.301 10.062 0.00 EST 1.7156 0.147 11.697 0.00
AP 0.012 0.001 8.769 0.00 AP 0.0044 0.001 6.809 0.00
WAGE 0.354 0.014 24.591 0.00 WAGE 0.1916 0.007 27.251 0.00
LQ −15,700 1,943.579 −8.078 0.00 LQ −691.429 94.028 −7.353 0.00
Import Coeff. Std err t value p Employment Coeff. Std err t value p
Const 26030 878.33 29.638 0.00 Const 1863.395 38.68 48.175 0.00
QP −0.022 0.005 −4.162 0.00 QP −0.006 0 −2.481 0.01
EST 4.065 0.357 11.385 0.00 EST 0.204 0.016 12.977 0.00
AP 0.011 0.002 7.121 0.00 AP 0.003 0.069 5.045 0.00
WAGE 0.423 0.017 24.739 0.00 WAGE 0.0219 0.001 29.048 0.00
LQ −1509 228.889 −6.591 0.00 LQ −88 10.08 −8.726 0.00
Table 5.
DID Model and Result Summary
DID Model Summary DID Result Summary
Dependent R2 Adjusted R2 F-stat P GDP Coeff. Std err t value p
GDP 0.843 0.843 35780 0.00 Intercept 0.037 0.002 21.098 0.00
GDP_manu 0.704 0.704 15850 0.00 Period 0.101 0.002 40.351 0.00
Export 0.970 0.97 216700 0.00 Treatment −0.056 0.002 −31.520 0.00
Import 0.972 0.972 233300 0.00 Interaction 0.016 0.003 6.179 0.00
Employment 0.892 0.892 54850 0.00
DID Result Summary
GDP_manu Coeff. Std err t value p Export Coeff. Std err t value p
Intercept 0.008 0.004 1.922 0.06 Intercept −0.008 0.005 −1.647 0.10
Period 0.131 0.006 21.890 0.00 Period −0.907 0.007 −129.49 0.00
Treatment −0.065 0.004 −15.181 0.00 Treatment −0.100 0.005 −20.020 0.00
Interaction 0.056 0.006 9.272 0.00 Interaction 0.102 0.007 14.519 0.00
Import Coeff. Std err t value p Employment Coeff. Std err t value p
Intercept 0.072 0.070 1.027 0.30 Intercept −0.007 0.001 −5.914 0.00
Period 11.817 0.100 118.723 0.00 Period 0.045 0.002 26.631 0.00
Treatment −0.145 0.071 −2.034 0.04 Treatment −0.054 0.001 −44.791 0.00
Interaction 0.078 0.101 0.775 0.44 Interaction 0.053 0.002 31.272 0.00
Table 6.
RDD Model and Result Summary
RDD Model Summary RDD Result Summary
Dependent Adjusted R² F-stat P GDP Coeff. std err t value p
GDP 0.125 0.125 477.4 0.00 Const 281,700 7,458.236 37.766 0.00
GDP_Manu 0.102 0.102 377.6 0.00 QP −0.106 0.044 −2.394 0.02
Export 0.116 0.116 438.4 0.00 EST 39.610 3.032 13.065 0.00
Import 0.108 0.108 403.2 0.00 AP 0.073 0.013 5.498 0.00
Employment 0.130 0.129 496.0 0.00 WAGE 3.991 0.145 27.465 0.00
LQ −15,700 1,943.579 −8.078 0.00
RDD Model Summary
GDP_Manu Coeff. std err t value p Export Coeff. Std err t value p
Const 28,670 811.015 35.355 0.00 Const 7,868.08 275.901 28.518 0.00
QP −0.030 0.005 −6.221 0.00 QP −0.005 0.002 −2.877 0.00
EST 3.291 0.330 9.984 0.00 EST 0.884 0.112 7.882 0.00
AP 0.013 0.001 8.729 0.00 AP 0.002 0.000 4.660 0.00
WAGE 0.383 0.016 24.249 0.00 WAGE 0.099 0.005 18.483 0.00
LQ −1,277.8 211.347 −6.046 0.00 LQ −360.47 71.899 −5.014 0.00
Import Coeff. std err t value p Employment Coeff. Std err t value p
Const 29,940 1,002.9 29.848 0.00 Const 1,871.722 38.923 48.088 0.00
QP −0.024 0.006 −3.967 0.00 QP −0.001 0.000 −2.571 0.01
EST 4.649 0.408 11.402 0.00 EST 0.205 0.016 12.984 0.00
AP 0.012 0.002 6.894 0.00 AP 0.000 0.000 5.113 0.00
WAGE 0.484 0.020 24.769 0.00 WAGE 0.022 0.001 28.915 0.00
LQ −1,741.09 261.362 −6.662 0.00 LQ −88.542 10.143 −8.729 0.00
Editorial Office
13F, 145, Gasan digital 1-ro, Geumcheon-gu, Seoul 08506, Korea
TEL: +82-2-2624-0357   FAX: +82-2-2624-0358   E-mail: ksqmeditor@ksqm.org
About |  Browse Articles |  Current Issue |  For Authors and Reviewers
Copyright © The Korean Society for Quality Management.                 Developed in M2PI
Close layer
prev next