Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Esta revista está autorizada por una licencia de atribución Creative Commons (CC BY-NC-SA 4.0) Attribution-Non Commercial 4.0 International. Para las licencias CC, el principio es el de la libertad creativa. Este sistema complementa el derecho de autor sin oponerse a este, conscientes de su importancia en nuestra cultura. El contenido de los artículos es responsabilidad de cada autor y no compromete, de ninguna manera, a la revista o a la institución. Se permite la divulgación y reproducción de títulos, resúmenes y contenido total, con fines académicos, científicos, culturales, siempre y cuando, se cite la respectiva fuente. Esta obra no puede ser utilizada con fines comerciales.
La revista no cobra a los autores por la presentación o la publicación de sus artículos
Resumen
Este documento estudia la evolución de la estructura de la economía colombiana empleando herramientas de la teoría de redes. Para lograr el objetivo se construyeron las matrices insumo-producto (MIP) actividad-actividad para 61 ramas en los años 2005 a 2021 y se emplean el diámetro y la densidad para caracterizar las redes. Adicionalmente, se emplean algoritmos de detección de comunidades para identificar las ramas de actividad económica que tienen una interconexión más fuerte cada año. Finalmente, se emplea, por primera vez en la literatura de análisis de redes, una metodología de análisis de estabilidad de clústeres para detectar cambios de un año a otro en la composición de las comunidades. Se encuentran siete comunidades y la existencia de cambios en la red que se reflejan en la MIP durante el periodo 2005-2021; sin embargo, no son sustanciales como para evidenciar fuertes cambios estructurales, al menos desde un punto de vista de comunidades.
Palabras clave:
Citas
Acemoglu, D., Carvalho, V., Ozdaglar, A., & Tahbaz-Salehi, A. (2012). The Network Origins of Aggregate Fluctuations. Econometrica, 80(5), 1977–2016. https://doi.org/10.3982/ECTA9623 . .
Alonso, J., & Carabali, J. A. (2019). Breve tutorial para visualizar y calcular métricas de redes (grafos) en R (para Económistas). Universidad Icesi.
Araújo, T., & Faustino, R. (2017). The topology of inter-industry relations from the Portuguese national accounts. Physica A: Statistical Mechanics and its Applications, 479, 236-248. https://doi.org/10.1016/j.physa.2017.03.018
Aroche-Reyes, F. (2003). A qualitative input-output method to find basic economic structures. Papers in Regional Science, 82, 581-590. https://doi.org/10.1007/S10110-003-0149-Z.
Barro, R. J., & Sala-i-Martin, X. (1992). Convergence. Journal of Political Economy, 100(2), 223-251. https://doi.org/10.1086/261816
Batool, F., & Hennig, C. (2021). Clustering with the Average Silhouette Width. Computational Statistics & Data Analysis, 158, 107190. https://doi.org/10.1016/j.csda.2021.107190
Battiston, S., Rodrigues, J., & Zeytinoglu, H. (2005). The network of inter-regional direct investment stocks across Europe. Advances in Complex Systems, 10, 29-51. https://doi.org/10.1142/S0219525907000933
Baumol, W. (2000a). What Marshall didn't know: On the twentieth century's contributions to economics. The Quarterly Journal of Economics, 115(Issue 1), 1-44, https://doi.org/10.1162/003355300554656
Baumol, W. (2000b). Leontief’s great leap forward: Beyond Quesnay, Marx and von Bortkiewicz. Economic Systems Research, 12(2), 141-152. https://doi.org/10.1080/09535310050005662
Beaton, M. K., Cebotari, A., Ding, X., & Komaromi, A. (2017). Trade integration in Latin America: A network perspective. IMF Working Papers. https://doi.org/10.2139/ssrn.3014078
Beygelzimer A, Kakadet S, Langford J, Arya S, Mount D, & Li S (2024). _FNN: Fast Nearest Neighbor Search Algorithms and Applications_. R package version 1.1.4, https://CRAN.R-project.org/package=FNN.
Blöchl, F., Theis, F., Vega-Redondo, F., & Fisher, E. (2011). Vertex centralities blancBlanco, G. (2017). Índice de complejidad económica para los departamentos de Colombia, evolución 2012 – 2015. Working paper Facultad de Ciencias Económicas; Universidad de Colombia: Bogotá, Colombia, https://repositorio.unal.edu.co/bitstream/handle/unal/63203/1015428688.2017.pdf?sequence=1
Brandes, U., Delling, D., Gaertler, M., Gorke, R., Hoefer, M., Nikoloski, Z., & Wagner, D. (2008). On modularity clustering. IEEE Transactions on Knowledge and Data Engineering, 20(2), 172-188. https://doi.org/10.1109/TKDE.2007.190689
Charrad, M., Ghazzali, N, Boiteau, V., & Niknafs A. (2014). NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. Journal of Statistical Software, 61(6), 1-36. URL http://www.jstatsoft.org/v61/i06/
Clemente, G., & Cornaro, A. (2019). A novel measure of edge and vertex centrality for assessing robustness in complex networks. Soft Computing, 24, 13687-13704. https://doi.org/10.1007/s00500-019-04470-w
Contreras, M. G. A., & Fagiolo, G. (2014). Propagation of economic shocks in input-output networks: A cross-country analysis. Physical Review E, 90(6), 062812. https://doi.org/10.1103/PhysRevE.90.062812
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695. https://igraph.org
DANE (2013). Metodología de la matriz insumo-producto (MIP). https://www.dane.gov.co/files/investigaciones/pib/especiales/metodologia_matriz_insumo_producto_07_13.pdf
DANE. (2020). Revisión 4 Adaptada para Colombia CIIU Rev. 4 A.C. (2020). https://www.dane.gov.co/files/sen/nomenclatura/ciiu/CIIU_Rev_4_AC2020.pdf
DePaolis, F., Murphy, P., & De Paolis M. C. (2022). Identifying key sectors in the regional economy: A network analysis approach using input-output data. Research Square. https://doi.org/10.21203/rs.3.rs-1666449/v1
Ding, J., & Lu, Y. (2015). Control backbone: An index for quantifying a node's importance for the network controllability. Neurocomputing, 153, 309-318. https://doi.org/10.1016/j.neucom.2014.11.024
Domínguez, A., Santos-Marquez, F., & Mendez, C. (2021). Sectoral productivity convergence, input-output structure and network communities in Japan. Structural Change and Economic Dynamics, 59, 582-599. https://doi.org/10.1016/j.strueco.2021.10.012
Dotta, V. (2021). Propagación de shocks económicos a través de redes insumo-producto : una aplicación para los países de América del Sur. Tesis de maestría. Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. https://hdl.handle.net/20.500.12008/31136
Drejer, I. (2000). Comparing patterns of industrial interdependence in national systems of innovation - A study of Germany, the United Kingdom, Japan and the United States. Economic Systems Research, 12, 377-399. https://doi.org/10.1080/09535310050120943
Fagiolo, G., Reyes, J., & Schiavo, S. (2008). On the topological properties of the world trade web: A weighted network analysis. Physica A: Statistical Mechanics and its Applications, 387(15), 3868-3873. https://doi.org/10.1016/j.physa.2008.01.050
Galili, T (2015). dendextend: an R package for visualizing, adjusting, and comparing trees of hierarchical clustering. Bioinformatics. DOI:10.1093/bioinformatics/btv428
García, A. (2013). Modelling linkages versus leakages networks: The case of Spain. Regional and Sectoral Economic Studies, 13, 43-54. https://pubs.acs.org/doi/suppl/10.1021/acs.est.5b05094/suppl_file/es5b05094_si_001.pdf
García, A., Morillas, A., & Ramos, C. (2008). Key sectors: A new proposal from network theory. Regional Studies, 42(7), 1013-1030. https://doi.org/10.1080/00343400701654152
Giuliani, E. (2013). Network dynamics in regional clusters: Evidence from Chile. Research Policy, 42, 1406-1419. https://doi.org/10.1016/J.RESPOL.2013.04.002
Graham, B., & De Paula, A. (2020). The econometric analysis of network data. Academic Press. https://doi.org/10.1920/wp.cem.2020.420
Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83(6), 1420-1443. https://doi.org/10.1086/226707
Ghosh, S., & Roy, J. (1998). Qualitative Input–Output Analysis of the Indian Economic Structure. Economic Systems Research, 10(3), 263–274. https://doi.org/10.1080/762947111
Guo, J., & Planting, M. A. (2000). Using input-output analysis to measure US economic structural change over a 24 year period. BEA Papers 0004, Bureau of Economic Analysis. https://www.bea.gov/system/files/papers/WP2000-1.pdf
Hausmann, R., & Hidalgo, C. A. (2011). The network structure of economic output. Journal of Economic Growth, 16, 309-342. https://doi.org/10.1007/s10887-011-9071-4
Hewings, G. J. D. (1982). The Empirical Identification of Key Sectors in an Economy: A Regional Perspective. The Developing Economies, 20(2), 173–195. https://doi.org/10.1111/j.1746-1049.1982.tb00444.x
Hidalgo, C. A., & Hausmann, R. (2010). Inferring macroeconomic complexity from country-product network data. AAAI Spring Symposium: Artificial Intelligence for Development. https://cdn.aaai.org/ocs/1183/1183-5889-1-PB.pdf
Hidalgo, C. A., Klinger, B., Barabási, A. L., & Hausmann, R. (2007). The product space conditions the development of nations. Science, 317(5837), 482-487. https://doi.org/10.1126/science.1144581
Hirschman, A. O. (1958). The Strategy of Economic Development. Yale University Press.
Holub, H., & Schnabl, H. (1985). Qualitative input-output analysis and structural information. Economic Modelling, 2, 67-73. https://doi.org/10.1016/0264-9993(85)90010-0
https://doi.org/10.1017/CBO9780511626982
Iori, G., Masi, G., Precup, O., Gabbi, G., & Caldarelli, G. (2008). A network analysis of the Italian overnight money market. Journal of Economic Dynamics and Control. https://doi.org/10.1016/J.JEDC.2007.01.032
Jankowska, A., Nagengast, A., & Perea, J. (2012). The product space and the middle-income trap: Comparing Asian and Latin American experiences. OECD Development Centre Working Papers, (311), OECD Publishing. https://doi.org/10.1787/5k9909j2587g-en
Jorgenson, D. W. (2016). Econometric general equilibrium modeling. Journal of Policy Modeling, 38(3), 436-447. https://doi.org/10.1016/j.jpolmod.2016.02.004
König, M., Battiston, S., Napoletano, M., & Schweitzer, F. (2008). On algebraic graph theory and the dynamics of innovation networks. Networks and Heterogeneous Media. https://doi.org/10.3934/NHM.2008.3.201
Laumas, P. S. (1975). Key sectors in some underdeveloped countries. Kyklos, 28(1). https://doi.org/10.1111/j.1467-6435.1975.tb01934.x
An, W. & Liu, Y (2023). _keyplayer: Locating Key Players in Social Networks_. R package version 1.0.4, https://CRAN.R-project.org/package=keyplayer
Lora, E. (2021). Forecasting formal employment in cities. Revista de Economía del Rosario, 24(1), 1-38. https://doi.org/10.12804/revistas.urosario.edu.co/economia/a.10029
Lora, E., & Prada, S. I. (2023). Técnicas de medición económica: metodología y aplicaciones en Colombia (6.ª ed.). Editorial Universidad Icesi. https://doi.org/10.18046/EUI/tme.6
Lorenz, J., Battiston, S., & Schweitzer, F. (2009). Systemic risk in a unifying framework for cascading processes on networks. The European Physical Journal B, 71, 441-460. https://doi.org/10.1140/epjb/e2009-00347-4
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K.(2022). cluster: Cluster Analysis Basics and Extensions. R package version 2.1.5. https://CRAN.R-project.org/package=cluster
McNerney, J., Fath, B. D., & Silverberg, G. (2013). Network structure of inter-industry flows. Physica A: Statistical Mechanics and its Applications, 392(24), 6427-6441. https://doi.org/10.1016/j.physa.2013.07.063
Miller, R. E., & Blair, P. D. (2009). Input-output analysis: Foundations and extensions (2nd ed.). Cambridge University Press.
Montresor, S., & Marzetti, G. V. (2009). Applying social network analysis to input–output based innovation matrices: An illustrative application to six OECD technological systems for the middle 1990s. Economic Systems Research, 21(2), 129-149. https://doi.org/10.1080/09535310902940228
Myers, C. A., & Shultz, G. P. (1951). The Dynamics of a Labor Market: A Study of the Impact of Employment Changes on Labor Mobility, Job Satisfactions, and Company and Union Policies. Prentice-Hall.
Newman, M. E. J., & Girvan, M. (2002). Mixing patterns and community structure in networks. arXiv: Statistical Mechanics of Complex Networks (pp. 66-87). Springer. https://doi.org/10.1007/978-3-540-44943-0_5
Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physics Review E, 69, 026113. https://doi.org/10.1103/PhysRevE.69.026113
Niño, J. O. P. (2020). Detección de comunidades en redes: algoritmos y aplicaciones. arXiv preprint arXiv:2009.08390.
O’Clery, N., Curiel, R. P., & Lora, E. (2019). Commuting times and the mobilisation of skills in emergent cities. Applied Network Science, 4(1), 1-27. https://doi.org/10.1007/s41109-019-0235-z
Pons, P., & Latapy, M. (2005). Computing communities in large networks using random walks. Computer and Information Sciences ISCIS 2005. Springer. https://doi.org/10.1007/11569596_31
Rasmussen, P. N. (1956). Studies in Inter-Sectoral Relations. E. Harck.
Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of community detection. Physical Review E, 74(1). https://doi.org/10.1103/PhysRevE.74.016110
R Core Team (2023). R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Rosenberg, A., & Hirschberg, J. (2007). V-measure: A conditional entropy-based external cluster evaluation measure. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (pp. 410-420). Association for Computational Linguistics. https://aclanthology.org/D07-1043
Santos, J. M., & Embrechts, M. (2009). On the use of the adjusted rand index as a metric for evaluating supervised classification. Artificial Neural Networks–ICANN 2009: 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II 19 (pp. 175-184). Springer. https://doi.org/10.1007/978-3-642-04277-5_18
Schnabl, H. (1994). The evolution of production structures, analyzed by a multi-layer procedure. Economic Systems Research, 6, 51-68. https://doi.org/10.1080/09535319400000004
Schultz, S. (1977). Approaches to identifying key sectors empirically by means of input‐output analysis. The Journal of Development Studies, 14(1), 77-96. https://doi.org/10.1080/00220387708421663
Slater, P. (1977). The determination of groups of functionally integrated industries in the United States using a 1967 interindustry flow table. Empirical Economics, 2, 1-9. https://doi.org/10.1007/BF01764717
Slater, P. (1978). The network structure of the United States input-output table. Empirical Economics, 3, 49-70. https://doi.org/10.1007/BF01764564
Sonis M., & Hewings, G. J. (1998). Economic complexity as network complication: Multiregional input-output structural path analysis. The Annals of Regional Science, 32, 407-436. https://doi.org/10.1007/S001680050081.
Sun, X., An, H., & Liu, X. (2018). Network analysis of Chinese provincial economies. Physica A: Statistical Mechanics and its Applications, 492, 1168-1180. https://doi.org/10.1016/j.physa.2017.11.045
Tsekeris, T. (2017). Network analysis of inter-sectoral relationships and key sectors in the Greek economy. Journal of Economic Interaction and Coordination, 12, 413-435. https://doi.org/10.1007/s11403-015-0171-7
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press.
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686.
Xing, L., Guan, J., & Wu, S. (2018). Measuring the impact of final demand on global production system based on Markov process. Physica A: Statistical Mechanics and its Applications, 502, 148-163. https://doi.org/10.1016/j.physa.2018.02.129
Xu, M., & Liang, S. (2019). Input–output networks offer new insights of economic structure. Physica A-statistical Mechanics and Its Applications, 527, 121178. https://doi.org/10.1016/J.PHYSA.2019.121178
Zhou, M., Wu, G., & Xu, H. (2016). Structure and formation of top networks in international trade, 2001-2010. Social Networks, 44, 9-21. https://doi.org/10.1016/j.socnet.2015.07.006
