Published Apr 24, 2019
Daryn Joy Go Michael Angelo Promentilla Kathleen Aviso Krista Danielle Yu


Economic sectors are highly interdependent, allowing them to promote sustainable development and inclusive growth by generating positive spillover effects from small investments in the economy. However, this interdependent nature can also generate negative spillover effects that lead to widespread inoperability and unemployment. While interdependence and the problem of scarce resources have led to the development of multiple sector prioritization tools, none of these tools have been able to wholly measure sector significance based on its multiple dimensions. Hence, this paper develops a composite sector prioritization index that identifies the key sectors based on five criteria of sector significance: degree of influence, structural significance, degree of interconnectedness, dependence on domestic economy, and contribution to risk of inoperability. The index is constructed using the Analytical Hierarchy Process, which shows the economy’s priorities and primary concerns in order to aid policymakers in investing in sectors that would generate the highest positive spillover effects to the economy. A case study from the Philippines is considered and the results show that much of the economy’s resources must be allocated towards the manufacturing, trade, and private services sectors.


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Input-Output Modelling, Analytic Hierarchy Process, Sector Prioritization, Sector Interdependence

Abdullah, L., Jaafar, S., & Che Taib, C.M.I. (2013). Ranking of human capital indicators using Analytic Hierarchy Process. Procedia - Social and Behavioral Sciences, 107. Doi: 10.1016/j.sbspro.2013.12.394.

Alauddin, M. (1986). Identification of key sectors in the Bangladesh economy: a linkage analysis approach. Applied Economics, 18(4), 421-442. Doi: https://doi.org/10.1080/00036848600000039

Asian Development Bank. (2013). The rise of natural disasters in Asia and the Pacific: Learning from ADB's experience, 1-9. Retrieved from https://www.adb.org/sites/default/files/evaluation-document/36114/files/rise-natural-disasters-asia-pacific.pdf. Doi: https://doi.org/10.1787/9789264041363-en

Ballester, R. E. D., Granadillos, J. R. G., Quintos, M. A., & Cruz, M. D. (2013). Evolution of the Philippine economy as seen through the time-series input-output tables (1961 to 2006). National Policy and Planning Staff-National Economic and Development Authority (NPPS-NEDA).

Barbarosoglu, G., & Yazgac, T. (1997). An application of the analytic hierarchy process to the supplier selection problem. Production and Inventory Management Journal, 38(1), 14.

Barker, K., & Santos, J. R. (2010). A risk?based approach for identifying key economic and infrastructure systems. Risk Analysis, 30(6), 962-974. Doi: https://doi.org/10.1111/j.1539-6924.2010.01373.x

Dedeke, N. (2013). Estimating the weights of a composite index using AHP: Case of the environmental performance index. British Journal of Arts & Social Sciences, 11, 199-221.

Dejuán, Ó., Lenzen, M., & Cadarso, M. Á. (Eds.). (2017). Environmental and economic impacts of decarbonization: Input-output studies on the consequences of the 2015 Paris Agreements. Routledge. Doi: https://doi.org/10.4324/9781315225937

Dhawan, S., & Saxena, K. (1992). Sectoral linkages and key sectors of the Indian economy. Indian Economic Review, 27(2), 195-210.

Dietzenbacher, E., Romero Luna, I., and Bosma, N.S. (2005) Using average propagation lengths to identify production chains in the Andalusian economy. Estudios de Economia Aplicada, 23, 405-422.

Drejer, I. (2002). Input-Output based measures of interindustry linkages revisited-A survey and discussion. In 14th International Conference on Input-Output Techniques, Montreal, Canada.

Eckstein, D., Künzel, V., & Schäfer, L. (2017). Global climate risk index 2018. Germanwatch, 33. Retrieved from http://germanwatch.org/en/download/20432.pdf

Eichhorn, W. (1976) Fisher’s tests revisited. Econometrica: Journal of the Econometric Society, 44, 247-256. Doi: https://doi.org/10.2307/1912721

Haimes, Y.Y. and Jiang, P. (2001). Leontief based model of risk in complex interconnected?infrastructures. Journal of Infrastructure Systems, 7, 1-12. Doi: https://doi.org/10.1061/(asce)1076-0342(2001)7:1(1)

Hewings, G. J. (1982). The empirical identification of key sectors in an economy: a regional perspective. The Developing Economies, 20(2), 173-195. Doi: https://doi.org/10.1111/j.1746-1049.1982.tb00444.x

Kurttila, M., Pesonen, M., Kangas, J., & Kajanus, M. (2000). Utilizing the analytic hierarchy process (AHP) in SWOT analysis—a hybrid method and its application to a forest-certification case. Forest Policy and Economics, 1(1), 41-52. Doi: https://doi.org/10.1016/s1389-9341(99)00004-0

Leontief, W. (1936). Quantitative input and output relations in the economic system of the United States. Review of Economics and Statistics, 18, 105-125. Doi: https://doi.org/10.2307/1927837

Lian, C. and Haimes, Y. Y. (2006). Managing the risk of terrorism to interdependent infrastructure systems through the dynamic inoperability input–output model. Systems Engineering, 9, 241–258. Doi:10.1002/sys.20051

Liu, F. H. F., & Hai, H. L. (2005). The voting analytic hierarchy process method for selecting supplier. International Journal of Production Economics, 97(3), 308-317. Doi: https://doi.org/10.1016/j.ijpe.2004.09.005

Miller, R.E. and Blair, P.D. (2009) Input-output analysis: Foundations and extensions. 2nd Ed. Cambridge, UK: University Press.

Niemira, M. P. (2001). An AHP-based composite cyclical-performance index. Indian Economic Review, 36(1), 241-250.

Nydick, R. L., & Hill, R. P. (1992). Using the analytic hierarchy process to structure the supplier selection procedure. Journal of Supply Chain Management, 28(2), 31-36. Doi: https://doi.org/10.1111/j.1745-493x.1992.tb00561.x

Okuyama, Y. & Yu, K.D. (2018). Return of the inoperability. Economic Systems Research. Doi:10.1080/09535314.2018.1510383.

Orencio, P. M., & Fujii, M. (2013). A localized disaster-resilience index to assess coastal communities based on an analytic hierarchy process (AHP). International Journal of Disaster Risk Reduction, 3, 62-75. Doi: https://doi.org/10.1016/j.ijdrr.2012.11.006

Organisation for Economic Co-operation and Development. (2011). OECD work on Sustainable Development [Brochure]. France.

Rasmussenís, P.N. (1957), Studies in inter-sectoral Relations. Amsterdam, North-Holland.

Pandian, G. S., Jawahar, N., & Nachiappan, S. P. (2013). Composite performance index for sustainability. IOSR Journal of Environmental Science, Toxicology and Food Technology, 3(1), 91-102. Doi: https://doi.org/10.9790/2402-03191102

Promentilla, M.A. (2006). AHP Calculator [Microsoft Excel spreadsheet]. De La Salle University, Manila, Philippines.

Resurreccion, J.Z., & Santos, J.R. (2012a). Integrated stochastic inventory and input-output models for enhancing disaster preparedness of disrupted interdependent sectors. Paper presented at The 20th International Input-Output Conference and the 2nd Edition of the International School of Input-Output Analysis, Bratislava, Slovakia. Doi: https://doi.org/10.1111/risa.12002

Resurreccion, J., & Santos, J. R. (2012b). Multiobjective prioritization methodology and decision support system for evaluating inventory enhancement strategies for disrupted interdependent sectors. Risk Analysis, 32(10), 1673-1692. Doi: https://doi.org/10.1111/j.1539-6924.2011.01779.x

Saaty, T. L., & Vargas, L. G. (1979). Estimating technological coefficients by the analytic hierarchy process. Socio-Economic Planning Sciences, 13(6), 333-336. Doi: https://doi.org/10.1016/0038-0121(79)90015-6

Saaty, T.L. (1980). Analytic Hierarchy Process. New York: McGraw-Hill.

Saaty T.L. (2006) The Analytic Network Process. In Decision Making with the Analytic Network Process. (International Series in Operations Research & Management Science, vol. 95), 1-26. Boston, MA: Springer. Doi: https://doi.org/10.1007/0-387-33987-6_1

Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98.

Saaty, T. (2010). Economic forecasting with tangible and intangible criteria: the analytic hierarchy process of measurement and its validation. Economic Horizons, 1, 5-45.

Saaty, T.L. (2012). Decision making for leaders: the analytic hierarchy process for decisions in a complex world (3rd revised edition). Pittsburgh: RWS Publications

Santos, J.R. and Haimes, Y.Y. (2004). Modeling the demand reduction input-output (I-O) inoperability due to terrorism of interconnected infrastructures. Risk Analysis, 24, 1437-1451. Doi: https://doi.org/10.1111/j.0272-4332.2004.00540.x

Silva, A. C., Belderrain, M. C. N., & Pantoja, F. C. M. (2010). Prioritization of R&D projects in the aerospace sector: AHP method with ratings. Journal of Aerospace Technology and Management, 2(3), 339-348. Doi: https://doi.org/10.5028/jatm.2010.02039110

Triantaphyllou, E., & Mann, S. H. (1995). Using the analytic hierarchy process for decision making in engineering applications: some challenges. International Journal of Industrial Engineering: Applications and Practice, 2(1), 35-44.

Tsekeris, T. (2017). Network analysis of inter-sectoral relationships and key sectors in the Greek economy. Journal of Economic Interaction and Coordination, 12(2), 413-435. Doi: https://doi.org/10.1007/s11403-015-0171-7

Yu, K.D.S., Tan, R.R., Aviso, K.B., Promentilla, M.A.B., & Santos, J.R. (2014). A vulnerability index for post-disaster key sector prioritization. Economic Systems Research, 26(1), 81-97. Doi: 10.1080/09535314.2013.872603

Zuhdi, U. (2017a). The ranks of Japanese industrial sectors: 2005-2011. Journal of Physics: Conference Series, 820(1), 012030. Doi: https://doi.org/10.1088/1742-6596/820/1/012030

Zuhdi, U. (2017b). An analysis of the characteristics of Japanese industrial sectors from 2005 through 2011. IOP Conference Series: Earth and Environmental Science, 88(1), 012027. Doi: https://doi.org/10.1088/1755-1315/88/1/012027