Published at : 25 Oct 2018
Volume : IJtech Vol 9, No 5 (2018)
DOI : https://doi.org/10.14716/ijtech.v9i5.1862
|Ida Bagus Made Putra Jandhana||- Student
|Teuku Yuri M Zagloel||Universitas Indonesia|
|Rahmat Nurcahyo||Universitas Indonesia|
The metal industry in Indonesia is facing greater risks and challenges locally and globally. Therefore, decision makers in this industry should be able not only to be competitive in the market but also to be resilient in this dynamic environment. The purpose of this study is to assess and analyze the resilience of the metal sector in Indonesia through the Cobb-Douglas Production Function (CDPF). The analysis shows how labor expenses, capital investment, and Total Factor Productivity (TFP) contribute to sector growth. Shock, represented by the drastic changes in both global and local economies reflected in exchange rate shocks in 2008 and 2013, impacts not only sector growth but also the composition of labor expenses, capital investment, and TFP in the sector. Based on time series data, this study determines that the assessment is able to detect the structure of the sector’s production function, which becomes the building block of sector resilience after experiencing a shock. This study and its findings contribute to studies in resilience measurement in both the metal sector and other industries.
Assessment; Cobb-Douglas Production Function; Resilience; Sector
The metal sector is currently facing greater challenges not only due to its riskier and more complex operating environment but also due to its very competitive market. To maintain growth in this demanding environment, the sector must be resilient in facing various disruptions that may inhibit sector performance. One of many methods to analyze resilience is through assessing the growth of several variables described in Production Function theories, such as labor expenses, capital investment, and applied technology in production. Although there are other Production Function theories such as Constant Elasticity of Substitutions by Solow (1957) and Leontief Production Function by Leontief (1947), this research limits its investigation to exploring the use of the Cobb-Douglas Production Function (CDPF) by Cobb and Douglas (1928) as described by Baier et al. (2002).
Sector resilience is very important to national economies since it contributes to the sustainability of output for national economic growth (Mankiw, 2014). The resilience of the Indonesian metal sector had been tested over the past few decades. This includes a major financial crisis in 1997–1998, a global crisis in 2008, and local currency devaluation in 2013. The impact of these crises shows the weakness of the Indonesian economy and the importance of the industrial sector to the economy. Although there had been an increasing trend of average annual economic growth of 5.1% between 1999–2008 after experiencing the 2008 crisis, the growth of the industrial sector still shows a slowing trend with an average annual growth of 3.9% (Suryahadi et al., 2012).
Several studies have implemented resilience concepts in economics over the past few years. For example, Rose and Krausmann (2013), Kumar and Gregory (2013), and Dormady et al. (2018) elaborated on the framework for a resilience analysis of input-output production function at the firm level. These studies focused more on community resilience in disaster zones. Using the same framework as Dormady et al. (2018), this study proposes a different approach using CDPF to analyze and assess resilience at the sector level through its production structure when the sector experiences currency shock during an economic crisis. This paper adds to the previous study in assessing resilience by measuring the efficiency of the metal sector in Indonesia (Jandhana et al., 2017). Efficiency was measured by calculating the input and output of the metal sector and using the Data Envelopment Analysis (DEA) method. Despite its ability to assess industrial resilience through its efficiency value (Jandhana et al., 2017), this study found that DEA has several limitations. First, it is difficult to track the recovery trend in sector performance recovery and to trace the shift in input variables after the sector experiences economic shocks. Second, the input and output variables need to be analyzed prior to calculation so the results reflect the actual condition (Zbranek, 2013).
The concept of resilience has been implemented not only in economics fields but also in fields such as social sciences, environmental sciences, and others. Various definitions have been given (Kumar & Gregory, 2013; Righi et al., 2015), but most are related to safety attitudes or responses to risk events such as shocks that impact or cause critical or catastrophic states. Additionally, they are also associated with adapting to new environments and returning to normal or equilibrium states. According to Windle (2011), the word “resilience” comes from the Latin word “resilire,” which means “to leap back,” “to spring back,” or “to recover easily.” Historically, several scholars have been considered pioneers in resilience studies. Holling (1973) introduced the implementation of both resilience and stability concepts to describe the behavior of ecosystems. Resilience was also used by Garmezy (1985) in the field of developmental psychology to describe the stress-resistant abilities of children. In the engineering field, resilience is implemented for safety measures (Woods, 2003). Several studies associated with resilience had been reviewed, such as recovery management after local disasters (Cutter et al., 2010), the ability of a person to regain mental or physical health despite adversity (Herrman et al., 2011), risk and performance management associated with supply chain resilience (Mandal, 2014), various system safety and risk assessment studies (Righi et al., 2015), and the concept development for the infrastructure resilience (Vugrin & Camphouse, 2011).
There are two approaches to resilience studies in economics. The first approach is related to economic-based resilience, which describes how a local economy operates efficiently in a new equilibrium state after experiencing a shock by using its available resources. This concept was examined by Rose and Krausmann (2013). The second approach is engineering-based resilience, which was outlined by Bruneau et al. (2003). This engineering-based resilience focuses on investigating the properties of physical or social systems, such as robustness, redundancy, resourcefulness, and rapidity of response. Although the frameworks are helpful in providing guidance for analyzing the resilience characteristics of an object, they are not sufficient to analyze the structure of input-output production functions before and after an economic crisis.
Studies that investigate and assess industrial sector resilience through production function are still lacking. As described by Zagloel and Jandhana (2016), most performance-based indices such as the Global Competitive Index (GCI), World Competitiveness Yearbook (WCY), Competitiveness Industrial Performance (CIP), The Global Manufacturing Competitiveness Index (GMCI), and Purchasing Manager Index use Likert scales instead of employing methods that elaborate on the dynamic characteristics of sector resilience. The scale was also adopted to measure economic resilience by Briguglio et al. (2006). Although the Likert scale method has been applied in many performance assessments and measurements, the method may expose the studies to subjectivity by collecting biased answers if the questionnaires are not performed objectively by the surveyors (Hartley, 2013). In another study, Rose and Krausmann (2013) described the measurement framework for economic resilience in an area impacted by disasters. Although it did not explain any specific methods for measuring resilience at the sector level, the study suggested the use of a computable equilibrium model to measure economic resilience in the macro-economic setting. However, the computable equilibrium model is relatively difficult to trace and analyze (Taylor & von Amin, 2006). Accordingly, they suggested that more studies are needed to explore other resilience assessments and measurements in the economic sector as it experiences disruptions. Furthermore, Righi et al. (2015) also argued that further research should be conducted on resilience measurement and assessment by implementing the Resilience Engineering (RE) platform in other field of studies.
With this background, this study concludes that there is a research gap for exploration of a quantitative assessment of resilience in industrial sectors. Accordingly, this study proposes a method to analyze how the exchange rate shock changes sector resilience by examining any changes in production function described by the Cobb-Douglas Production Function (CDPF), such as production factors, labor, capital, and TFP values. This investigation is also intended to become a building block for the future design of a resilience-based indices measurement in different industrial setups. This study contributes to the development of measurement engineering that will be employed in a decision analysis with risk consideration. This paper includes several theories related to resilience studies and CDPF, the measurement methodology, the results of the calculation, a result discussion, and a conclusion.
Resilience assessment helps decision makers analyze policies in the industrial sector and their associated risks. As discussed previously, this study proposes a method to analyze resilience in the metal sector in Indonesia by observing changes in production function. In this study, the Cobb-Douglas Production Function (CDPF) was employed to explore any variations in production factors, such as labor, capital, and TFP values, which were examined both before and after an economic crisis. By using the data series as the input of CDPF, the study shows that resilience in the Indonesian metal sector can be investigated and analyzed further. The result also shows that shock, represented by a change in exchange rate, impacts not only sector growth but also the composition of labor expense, capital investment, and TFP in the sector. This study found contradictory results in the analysis of the correlation between currency shock toward capital investment and sector output. This finding suggests that there is a need for further investigations on analyzing the impact of various shocks toward production function and developing its measurement method. In doing so, the result of this research will become a building block for future research in developing a resilience-based measurement for the industrial sector.
The study also suggests that the sector’s resilience has improved as the industries are able to adapt to the new environment. Based on the findings, the drop in sector growth after the 2013 crisis was much shallower than the drop after the global crisis in 2008. Moreover, a positive trend in sector growth was seen after 2013. This study reflects an approximation of performance in the metal sector between 2005 and 2015, adjusted according to exchange rate shocks. This investigation did have some limitations, for example, labor expenses, capital investments, TFP, and exchange rate shocks were the only factors used to determine sector output. In reality, other factors also contribute to sector performance and the causes of shock that impact the sector. Nevertheless, the research findings are applicable to further studies of resilience capacity in other sectors.
|R5-IE-1862-20180830065520.jpg||Resilience Concept Development|
|R5-IE-1862-20180830065620.jpg||Output Labour and Capital|
Allen, R.G.D, 1968. Macroeconomic Theory: A Mathematical Treatment. London: McMillan
Almström, P., Kinnander, A., 2011. The Productivity Potential Assessment Method: Assessing and Benchmarking the Improvement Potential in Manufacturing Systems at Shop-floor Level. International Journal of Productivity and Performance Management, Volume 60(7), pp. 758–770
Baier, S., Dwyer G., Tamura, R., 2002. How Important are Capital and Total Factor Productivity for Economic Growth?. Federal Reserve Bank of Atlanta, Working Paper No. 2002-02
Bhanumurthy, K.V. 2004. Arguing a Case of Cobb-Douglas Production Function. Review of Commerce Studies. Delhi, India
Biddle, J., 2012. Retrospectives: The Introduction of the Cobb–Douglas Regression. Journal of Economic Perspectives, Volume 26(2), pp. 223–236
Briguglio, L., Cordina, G., Bugeja, S., Farrugia, N., 2006. Conceptualizing and Measuring Economic Resilience. Available online at www.researchgate.net, Accessed on May, 2016.
Bronfenbrenner, M., 1944. Production Functions: Cobb-Douglas, Interfirm, Intrafirm. Econometrica, Journal of the Econometric Society, Volume 12(1), pp. 35–44
Bruneau, M., Chang, S.E., Eguchi, R.T., Lee, G.C., O’Rourke, T.D., Reinhorn, A.M., Shinozuka, M., Tierney, K., Wallace, W., von Winterfeldt, D., 2003. A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities. Earthquake Spectra, Volume 19(4), pp. 733–752
Cobb, C.W., Douglas, P.H., 1928. A Theory of Production. The American Economic Review, Volume 18(1), pp. 139–165
Cutter, S. L., Burton, C. G., Emrich, C. T., 2010. Disaster resilience indicators for benchmarking baseline conditions. Journal of Homeland Security and Emergency Management, Volume 7(1), pp. 1–24
Dastkhan, H., Owlia, M.S., 2009. Study of Trends and Perspectives of Industrial Engineering Research. South African Journal of Industrial Engineering, Volume 20(1), pp. 1–12
de Menezes, A.G., 2005. Demand Shocks and Productivity Growth. Portuguese Economic Journal, Volume 4(1), pp. 15–45
Dormady, N., Roa-Henriquez, A., Rose, A., 2018. Economic Resilience of the Firm: A Production Theory Approach. Available online at www.researchgate.net, Accessed on May, 2016
Drig?, D.M., 2015. Formal Aspects of Resilience. Manager, Volume 22(1), pp. 322–328
Dutu, R., 2016. Why has Economic Growth Slowed Down in Indonesia? An Investigation into the Indonesian Business Cycle using an Estimated DSGE Model. Journal of Asian Economics, Volume 45, pp. 46–55
Garmezy, N., 1985. Stress-resistant Children: The Search for Protective Factors in Stevenson, JE (ed.) Recent Research in Developmental Psychopathology, pp. 213–233
Gelman, A., Hill, J., 2006. Data Analysis using Regression and Multilevel/Hierarchical Models. Cambridge University Press
Hallegatte, S., 2014. Economic Resilience: Definition and Measurement. The World Bank
Hartley, J., 2013. Some Thoughts on Likert-type Scales. International Journal of Clinical and Health Psychology, Volume 13, pp. 83–86
Herrman, H., Stewart, D.E., Diaz-Granados, N., Berger, E.L., Jackson, B., Yuen, T., 2011. What is Resilience? The Canadian Journal of Psychiatry, Volume 56(5), pp. 258–265
Holling, C.S., 1973. Resilience and Stability of Ecological Systems. Annual Review of Ecology and Systematics, pp. 1–23
Indonesia Statistics, 2017. Available online at https://www.bps.go.id/ Accessed on November 17, 2018
Jandhana, I.B.M.P., Zagloel, T.Y.M., Nurcahyo, R., 2017. Measuring Industrial Resiliency by using Data Envelopment Analysis Approach. In: 3rd International Conference on Business and Engineering Research (ICBER). Osaka, Japan 17th-18th November.
Karabarbounis, L., Neiman, B., 2014. The Global Decline of the Labor Share, Quarterly Journal of Economics, Volume 129(1), pp. 206–223
Kumar, M., Gregory, M., 2013. An Exploration of Risk Management in Global Industrial Investment. Risk Management, Volume 15(4), pp. 272–300
Leontief, W., 1947. Introduction to a Theory of the Internal Structure of Functional Relationships. Econometrica, Journal of the Econometric Society, Volume 15(4), pp. 361–373
Mandal, S., 2014. Supply Chain Resilience: A State-of-the-art Review and Research Directions. International Journal of Disaster Resilience in the Built Environment, Volume 5(4), pp. 427–453
Mankiw, N.G., 2014. Principles of Macroeconomics. USA: Cengage Learning
Righi, A.W., Saurin, T.A., Wachs, P., 2015. A Systematic Literature Review of Resilience Engineering: Research Areas and a Research Agenda Proposal. Reliability Engineering & System Safety, Volume 141, pp. 142–152
Rose, A., Krausmann, E., 2013. An Economic Framework for the Development of a Resilience Index for Business Recovery. International Journal of Disaster Risk Reduction, Volume 5, pp. 73–83
Solow, R.M., 1957. Technical Change and the Aggregate Production Function. The Review of Economics and Statistics, Volume 39(3), pp. 312–320
Suryahadi, A., Hadiwidjaja, G., Sumarto, S., 2012. Economic Growth and Poverty Reduction in Indonesia Before and After the Asian Financial Crisis. Bulletin of Indonesian Economic Studies, Volume 48(2), pp. 209–227
Tangen, S., 2005. Demystifying Productivity and Performance. International Journal of Productivity and Performance Management, Volume 54(1), pp. 34–46
Taylor, L., von Amin, R., 2006. Modelling the Impact of Trade Liberalisation. A Critique of Computable General Equilibrium Models. Oxlam International Research Report, July 2006
Vugrin, E.D., Camphouse, R.C., 2011. Infrastructure Resilience Assessment through Control Design. International Journal of Critical Infrastructures, Volume 7(3), pp. 243–260
Windle, G., 2011. What is Resilience? A Review and Concept Analysis. Reviews in Clinical Gerontology, Volume 21(2), pp. 152–169
Woods, D.D., 2003. Creating Foresight: How Resilience Engineering can Transform NASA’s Approach to Risky Decision Making. Work, Volume 4(2), pp. 137–144
Zagloel, Y., Jandhana, I.B.M.P., 2016. Literature Review of Industrial Competitiveness Index: Research Gap. In: Proceeding of Industrial Engineering and Operations Management (IEOM) Conference. Kuala Lumpur, Malaysia
Zbranek, P., 2013. Data Envelopment Analysis as a Tool for Evaluation of Employees’ Performance. Acta Oeconomica et Informatica, Volume 16(1), pp. 12–21