Policy Design from a Network Perspective: Targeting a Sector, Cascade of Links, Network Resilience

Temel, Tugrul and Phumpiu, Paul (2023): Policy Design from a Network Perspective: Targeting a Sector, Cascade of Links, Network Resilience.

Abstract

Drawing on input-output data, a computational methodology is proposed to: (i) characterize the upstream and/or downstream network of a targeted (or prioritized) sector i, (ii) uncover the cascade of layers of links in the network constructed, and (iii) measure the degree of network resilience using edge betweenness centrality measure of edges between communities. These objectives are accomplished through three complementary algorithms. The implementation of the algorithms is illustrated using Turkiye’s 2018 input-output production network. Ways to design policies are discussed from a network perspective. The key findings are three-fold. First, in network-based policy design, it is highly critical to consider the interdependencies of regulated and seemingly competitive sectors. Efficiencies gained in liberalized markets via pro-competitive PMR can easily be wasted before final consumers benefit from them as regulated industries may exercise their market power to confiscate part of the efficiency gain created in competitive markets. Improved competition in a single market may not generate the desired outcome even if competition policies perfectly support that market because benefits from competition may not spread over the rest of the network due to disruptions in the cascade of interdependencies concerned. Second, a network-based policy design should start with the identification of the “dominant” source and the “subordinate” sink sector(s), and those in between. The source−sink structure of Turkiye’s manufacturing network illustrates that the manufacturing sector is the most dominant, whereas telecommunications and transport, energy and construction sectors are the potential sinks where large chunk of input flow ends up. Agriculture, finance and oil extraction-mining seem to be interactive sectors. Third, the cascade of three layers of links are identified, and the upstream network of the manufacturing sector is found to have a mediocre level of resilience against the complete disruption of the intermediate layer of the network.

• Item Type: MPRA Paper
• Language: English
• Keywords: graph theory; input-output production network; network resilience; systemic risk; policy plan- ning; technological change
• Subjects: C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C67 - Input-Output Models; D - Microeconomics > D2 - Production and Organizations > D24 - Production ; Cost ; Capital ; Capital, Total Factor, and Multifactor Productivity ; Capacity; O - Economic Development, Innovation, Technological Change, and Growth > O2 - Development Planning and Policy > O21 - Planning Models ; Planning Policy; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O33 - Technological Change: Choices and Consequences ; Diffusion Processes

References

[1] Daron Acemoglu, Vasco M Carvalho, Asuman Ozdaglar, and Alireza Tahbaz-Salehi. The network origins of aggregate fluctuations. Econometrica, 80(5):1977–2016, 2012. [2] Daron Acemoglu, Asuman Ozdaglar, and Alireza Tahbaz-Salehi. Cascades in networks and aggregate volatility. Technical report, National Bureau of Economic Research, 2010. [3] Philippe Aghion and Mark Schankerman. On the welfare effects and political economy of competition-enhancing policies. The Economic Journal, 114(498):800–824, 2004. [4] Petra Ahrweiler, Andreas Pyka, and Nigel Gilbert. Simulating knowledge dynamics in innovation networks (skin). Technical report, Volkswirtschaftliche Diskussionsreihe/Institut f ̧r Volkswirtschaftslehre der Universitâ° t Augsburg, 2004. [5] Pol Antràs, Davin Chor, Thibault Fally, and Russell Hillberry. Measuring the upstreamness of production and trade flows. American Economic Review, 102(3):412–416, 2012. [6] Enghin Atalay. How important are sectoral shocks? American Economic Journal: Macroeconomics, 9(4):254– 280, 2017. [7] Enghin Atalay, Ali Hortacsu, James Roberts, and Chad Syverson. Network structure of production. Proceedings of the National Academy of Sciences, 108(13):5199–5202, 2011. [8] Nihat Ay and Daniel Polani. Information flows in causal networks. Advances in complex systems, 11(01):17–41, 2008. [9] Guglielmo Barone and Federico Cingano. Service regulation and growth: evidence from oecd countries. The Economic Journal, 121(555):931–957, 2011. [10] Saki Bigio and Jennifer Laâo. Distortions in production networks. The Quarterly Journal of Economics, 135(4):2187–2253, 2020. [11] Béla Bollobás. Graph theory: an introductory course, volume 63. Springer Science & Business Media, 2012. [12] Romain Bouis, Mr Romain A Duval, and Johannes Eugster. Product market deregulation and growth: New country-industry-level evidence. International Monetary Fund, 2016. [13] R. Bourles, G. Cette, J. Lopez, J. Mairesse, and G. Nicoletti. Do product market regulations in upstream sectors curb productivity growth: Panel data evidence for oecd countries. techreport 791, OECD, 2010. [14] Stefano Breschi and Franco Malerba. Clusters, networks and innovation. Oxford University Press, 2005. [15] Paolo Buccirossi, Lorenzo Ciari, Tomaso Duso, Giancarlo Spagnolo, and Cristiana Vitale. Competition policy and productivity growth: An empirical assessment. Review of Economics and Statistics, 95(4):1324–1336, 2013. [16] Vasco M Carvalho. Aggregate fluctuations and the network structure of intersectoral trade. The University of Chicago, 2008. [17] Vasco M Carvalho. From micro to macro via production networks. Journal of Economic Perspectives, 28(4):23– 48, 2014. [18] Neil M Coe and Timothy G Bunnell. Spatializing knowledge communities: towards a conceptualization of transnational innovation networks. Global networks, 3(4):437–456, 2003. [19] Murat Ucer Daron Acemoglu. The ups and downs of turkish growth, 2002-2015: Political dynamics, the european union and the institutional slide. NBER Working Papers, (21608), 2015. [20] Bruno Ricardo Delalibera, Pedro Cavalcanti Ferreira, Diego Braz Pereira Gomes, and Johann Rodrigues de Souza Soares. Tax reforms and network effects. 2023. [21] Erik Dietzenbacher. In vindication of the ghosh model: a reinterpretation as a price model. Journal of regional science, 37(4):629–651, 1997. [22] Klaus Fichter. Innovation communities: the role of networks of promotors in open innovation. R&d Manage- ment, 39(4):357–371, 2009. [23] Santo Fortunato. Community detection in graphs. Physical Review E, 486(3-5):75–174, 2010. [24] Santo Fortunato, Vito Latora, and Massimo Marchiori. Method to find community structures based on infor- mation centrality. Physical Review, 70(056104), 2004. [25] Peter N Gal and Alexander Hijzen. The short-term impact of product market reforms: A cross-country firm- level analysis. International Monetary Fund, 2016. [26] Clara Granell, Richard K. Darst, Alex Arenas, Santo Fortunato, and Sergio Gomez. Benchmark model to assess community structure in evolving networks. In arXiv:1501.05808v2 [physics.soc-ph], 2015. [27] Vladimír Holy` and Karel Šafr. Disaggregating input–output tables by the multidimensional ras method: a case study of the czech republic. Economic Systems Research, 35(1):95–117, 2023. [28] Darko Hric, Richard K Darst, and Santo Fortunato. Community detection in networks: Structural communities versus ground truth. Physical Review E, 90(6):062805, 2014. [29] Boyan Jovanovic. Micro shocks and aggregate risk. The Quarterly Journal of Economics, 102(2):395–409, 1987. [30] William Q Judge, Gerald E Fryxell, and Robert S Dooley. The new task of r&d management: creating goal-directed communities for innovation. California Management Review, 39(3):72–85, 1997. [31] Vasileios Kandylas, S Phineas Upham, and Lyle H Ungar. Finding cohesive clusters for analyzing knowledge communities. Knowledge and Information Systems, 17(3):335–354, 2008. [32] Nur Keyder. Turkiye’nin kriz deneyimleri: 1994, 2000-001, 2008-009 ve 2018-022 krizleri. Iktisat ve Toplum, (141):4–13, 2022. [33] Yusoon Kim, Yi-Su Chen, and Kevin Linderman. Supply network disruption and resilience: A network struc- tural perspective. Journal of operations Management, 33:43–59, 2015. [34] Nobuhiro Kiyotaki and John Moore. Credit cycles. Journal of political economy, 105(2):211–248, 1997. [35] Ernest Liu. Industrial policies in production networks. The Quarterly Journal of Economics, 134(4):1883–1948, 2019. [36] Anthony L Loviscek. Industrial cluster analysis-backward or forward linkages? The Annals of Regional Science, 16(3):36–47, 1982. [37] Leonard H Lynn, John D Aram, and N Mohan Reddy. Technology communities and innovation communities. Journal of Engineering and Technology Management, 14(2):129–145, 1997. [38] Ronald E. Miller and Peter D. Blair. Input-Output Analysis: Foundations and Extensions. Cambridge Univer- sity Press, 2 edition, 2009. [39] Mark EJ Newman. Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems, 38(2):321–330, 2004. [40] Mark EJ Newman and Michelle Girvan. Finding and evaluating community structure in networks. Physical review E, 69(2):026113, 2004. [41] Ezra Oberfield. A theory of input–output architecture. Econometrica, 86(2):559–589, 2018. [42] Supun Perera, H Niles Perera, and Dharshana Kasthurirathna. Structural characteristics of complex supply chain networks. In 2017 Moratuwa Engineering Research Conference (MERCon), pages 135–140. IEEE, 2017. [43] Anton Pichler, Marco Pangallo, R Maria del Rio-Chanona, François Lafond, and J Doyne Farmer. Forecasting the propagation of pandemic shocks with a dynamic input-output model. Journal of Economic Dynamics and Control, 144:104527, 2022. [44] Mason A Porter, Jukka-Pekka Onnela, and Peter J Mucha. Communities in networks. Notices of the AMS, 56(9):1082–1097, 2009. [45] Andreas Pyka. Avoiding evolutionary inefficiencies in innovation networks. Prometheus, 32(3):265–279, 2014. [46] Frank Schweitzer, Giorgio Fagiolo, Didier Sornette, Fernando Vega-Redondo, Alessandro Vespignani, and Douglas R White. Economic networks: The new challenges. science, 325(5939):422–425, 2009. [47] Bodo E Steiner and Jolene Ali. Regional food clusters and government support for clustering: Evidence for a’dynamic food innovation cluster’in alberta, canada? 2009. [48] Kozo Sugiyama, Shojiro Tagawa, and Mitsuhiko Toda. Methods for visual understanding of hierarchical system structures. IEEE Transactions on Systems, Man, and Cybernetics, 11(2):109–125, 1981. [49] United Nations, European Commission, International Monetary Fund, Organisation for Economic Co-operation and Development, and World Bank. System of National Accounts 2008. United Nations, New York, 2009. [50] Stephan M Wagner and Nikrouz Neshat. Assessing the vulnerability of supply chains using graph theory. International journal of production economics, 126(1):121–129, 2010. [51] Nina Weitz, Henrik Carlsen, Måns Nilsson, and Kristian Skånberg. Towards systemic and contextual priority setting for implementing the 2030 agenda. Sustainability science, 13:531–548, 2018. [52] Hao Xiao, Tianyang Sun, Bo Meng, and Lihong Cheng. Complex network analysis for characterizing global value chains in equipment manufacturing. PloS one, 12(1):e0169549, 2017.