Production networks and volatility in the colombian manufacturing industry
ilustraciones, gráficas, tablas
- Autores:
-
Taborda Martínez, Jennifer
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/80592
- Palabra clave:
- 330 - Economía
Input–Output Tables and Analysis
Network Formation and Analysis: Theory
Business Fluctuations • Cycles
Transactional Relationships • Contracts and Reputation • Networks
Aggregate volatility
Production networks
Diversification
Input-output linkages
Volatilidad agregada
Redes de producción
Diversificación
Tablas insumo-producto
Comportamiento económico
Economic behaviour
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.eng.fl_str_mv |
Production networks and volatility in the colombian manufacturing industry |
| dc.title.translated.spa.fl_str_mv |
Redes de producción y volatilidad en la industria colombiana |
| title |
Production networks and volatility in the colombian manufacturing industry |
| spellingShingle |
Production networks and volatility in the colombian manufacturing industry 330 - Economía Input–Output Tables and Analysis Network Formation and Analysis: Theory Business Fluctuations • Cycles Transactional Relationships • Contracts and Reputation • Networks Aggregate volatility Production networks Diversification Input-output linkages Volatilidad agregada Redes de producción Diversificación Tablas insumo-producto Comportamiento económico Economic behaviour |
| title_short |
Production networks and volatility in the colombian manufacturing industry |
| title_full |
Production networks and volatility in the colombian manufacturing industry |
| title_fullStr |
Production networks and volatility in the colombian manufacturing industry |
| title_full_unstemmed |
Production networks and volatility in the colombian manufacturing industry |
| title_sort |
Production networks and volatility in the colombian manufacturing industry |
| dc.creator.fl_str_mv |
Taborda Martínez, Jennifer |
| dc.contributor.advisor.spa.fl_str_mv |
Hoyos Gómez, Nancy Milena |
| dc.contributor.author.spa.fl_str_mv |
Taborda Martínez, Jennifer |
| dc.subject.ddc.spa.fl_str_mv |
330 - Economía |
| topic |
330 - Economía Input–Output Tables and Analysis Network Formation and Analysis: Theory Business Fluctuations • Cycles Transactional Relationships • Contracts and Reputation • Networks Aggregate volatility Production networks Diversification Input-output linkages Volatilidad agregada Redes de producción Diversificación Tablas insumo-producto Comportamiento económico Economic behaviour |
| dc.subject.jel.eng.fl_str_mv |
Input–Output Tables and Analysis Network Formation and Analysis: Theory Business Fluctuations • Cycles Transactional Relationships • Contracts and Reputation • Networks |
| dc.subject.proposal.eng.fl_str_mv |
Aggregate volatility Production networks Diversification Input-output linkages |
| dc.subject.proposal.spa.fl_str_mv |
Volatilidad agregada Redes de producción Diversificación Tablas insumo-producto |
| dc.subject.unesco.spa.fl_str_mv |
Comportamiento económico |
| dc.subject.unesco.eng.fl_str_mv |
Economic behaviour |
| description |
ilustraciones, gráficas, tablas |
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2021 |
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2021-10-21T15:29:20Z |
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2021-10-21T15:29:20Z |
| dc.date.issued.none.fl_str_mv |
2021-10-08 |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/80592 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/80592 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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eng |
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eng |
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Acemoglu, D., Carvalho, V. M., Ozdaglar, A., & Tahbaz-Salehi, A. (2012). The Network Origins of Aggregate Fluctuations. Econometrica, 80(5), 1977–2016. https://doi.org/10.3982/ECTA9623 Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2017). Microeconomic Origins of Macroeconomic Tail Risks. American Economic Review, 107(1), 54–108. https://doi.org/10.1257/aer.20151086 Aizenman, Joshua, & Marion, N. (1999). Volatility and Investment: Interpreting Evidence from Developing Countries. Economica, 66(262), 157–1179. https://doi.org/10.1111/1468-0335.00163 Aizenman, Joshua, & Pinto, B. (2005). Managing Economic Volatility and Crises (Joshua Aizenman & B. Pinto (eds.)). Cambridge University Press. https://doi.org/10.1017/CBO9780511510755 Atalay, E. (2017). How Important Are Sectoral Shocks? American Economic Journal: Macroeconomics, 9(4), 254–280. https://doi.org/10.1257/mac.20160353 Badinger, H. (2010). Output volatility and economic growth. Economics Letters, 106(1), 15–18. Baldwin, R., & Weder di Mauro, B. (2020). Economics in the Time of COVID-19 (VoxEU.org (ed.)). CEPR Press. Blöchl, F., Theis, F. J., Vega-Redondo, F., & Fisher, E. O. (2011). Vertex centralities in input-output networks reveal the structure of modern economies. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 83(4 Pt 2), 046127. http://www.ncbi.nlm.nih.gov/pubmed/21599260 Breen, R., & Garcia-Penalosa, C. (2005). Income Inequality and Macroeconomic Volatility: An Empirical Investigation. Review of Development Economics, 9(3), 380–398. https://doi.org/10.1111/j.1467-9361.2005.00283.x Calderon, C., & Yeyati, E. L. (2009). Zooming in : from aggregate volatility to income distribution. April. http://documents.worldbank.org/curated/en/2009/04/10421833/zooming-aggregate-volatility-income-distribution Carvalho, V. M., & Tahbaz-Salehi, A. (2019). Production Networks: A Primer. In Annual Review of Economics (Vol. 11, Issue 1). https://doi.org/10.1146/annurev-economics-080218-030212 Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2007). Power-law distributions in empirical data. https://doi.org/10.1137/070710111 Clauset, A., Young, M., & Gleditsch, K. S. (2007). On the Frequency of Severe Terrorist Events. Journal of Conflict Resolution, 51(1), 58–87. https://doi.org/10.1177/0022002706296157 del Rio-Chanona, R. M., Mealy, P., Pichler, A., Lafond, F., & Farmer J., D. (2020). Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective. Covid Economics, 1(6), 65–103. Gabaix, B. Y. X., Basu, S., Bénabou, R., Blanchard, O., Caballero, R., Canning, D., Caplin, A., Chaney, T., Chari, V. V, Christiano, L., Comin, D., Davis, D., Durlauf, S., Edmans, A., Eichenbaum, M., Engel, E., & Fernald, J. (2011). The Granular Origins of Aggregate Fluctuations. Econometrica, 79(3), 733–772. https://doi.org/10.3982/ECTA8769 Gillespie, C. S. (2015). Fitting heavy tailed distributions: The powerlaw package. Journal of Statistical Software, 64(2), 1–16. https://doi.org/10.18637/jss.v064.i02 Gonçalves, J., Matsushita, R., & Da Silva, S. (2020). The asymmetric Brazilian input–output network. Journal of Economic Studies. https://doi.org/10.1108/JES-05-2020-0225 Hakura, D. (2007). Output Volatility and Large Output Drops in Emerging Market and Developing Countries. IMF Working Papers, 07(114), 1. https://doi.org/10.5089/9781451866780.001 Jaimovich, N., Pruitt, S., & Siu, H. E. (2013). The Demand for Youth: Explaining Age Differences in the Volatility of Hours. American Economic Review, 103(7), 3022–3044. https://doi.org/10.1257/aer.103.7.3022 Joya, O., & Rougier, E. (2019). Do (all) sectoral shocks lead to aggregate volatility? Empirics from a production network perspective. European Economic Review, 113, 77–107. https://doi.org/10.1016/j.euroecorev.2019.01.004 Laursen, T., & Mahajan, S. (2005). Volatility, income distribution and poverty. In J. Aizenman & B. Pinto (Eds.), Managing Economic Volatility and Crises: A Practitioner’s Guide. (pp. 101–136). Cambridge University Press. Long, J. B., & Plosser, C. (1983). Real Business Cycles Author. Journal of Political Economy, 91(1), 39–69. Lucas, R. E. (1977). Understanding Business Cycles. In Carnegie–Rochester Conference Series on Public Policy (No. 5) 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 Mundt, P. (2021). The formation of input–output architecture: Evidence from the European Union. Journal of Economic Behavior and Organization, 183, 89–104. https://doi.org/10.1016/j.jebo.2020.12.031 Newman, M. (2018). Networks (Second Edi). Oxford University Press. https://global.oup.com/academic/product/networks-9780198805090?cc=co&lang=en&# OECD/World Trade Organization. (2019). Promoting economic diversification and structural transformation through industrialisation. In Aid for Trade at a Glance 2019: Economic Diversification and Empowerment (pp. 81–108). OECD Publishing. https://doi.org/10.1787/785f021c-en Ramey, G., & Ramey, V. A. (1995). Cross-Country Evidence on the Link Between Volatility and Growth. The American Economic Review, 85(5), 1138–1151. http://www.jstor.com/stable/2950979 Romero, P. P., López, R., & Jiménez, C. (2018). Sectoral networks and macroeconomic tail risks in an emerging economy. PLoS ONE, 13(1), 1–17. https://doi.org/10.1371/journal.pone.0190076 Sahay, R., & Goyal, R. (2006). Volatility and Growth in Latin America: An Episodic Approach. IMF Working Papers, 06(287), 1. https://doi.org/10.5089/9781451865479.001 Szirmai, A. (2012). Industrialisation as an engine of growth in developing countries, 1950–2005. Structural Change and Economic Dynamics, 23(4), 406–420. https://doi.org/10.1016/j.strueco.2011.01.005 |
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Universidad Nacional de Colombia |
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Bogotá - Ciencias Económicas - Maestría en Ciencias Económicas |
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Escuela de Economía |
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Facultad de Ciencias Económicas |
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Bogotá, Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Hoyos Gómez, Nancy Milena12999640324d4aba16dd48e0d34cd393Taborda Martínez, Jenniferc7d4c61bcd810431239a537890d7add22021-10-21T15:29:20Z2021-10-21T15:29:20Z2021-10-08https://repositorio.unal.edu.co/handle/unal/80592Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasOutput volatility is a useful indicator to assess the stability growth, investment, and employment of any economy. Previous research has shown that an input-output structure dominated by few sectors, enables the transmission of sector-level shocks via central activities to the rest of the economy, amplifying the effect of microeconomic shocks into aggregate output volatility. This paper studies the structure of the industrial input-output network in Colombia between 1982 and 2012 to understand its role as a source of industrial output volatility. We build a series of unique input-output networks at the product level, based on industrial survey data on production at the plant level for Colombia. The richness of the data allows us to study the structural properties of the network by characterizing the distribution of first- and second-order degree sequences. The impact of the intersectoral network in the propagation of sector-level shocks into output volatility in the manufacturing industry is quite central. The input-output industrial network in Colombia is composed of few very central inputs providers connected among them and many other products with smaller importance as input suppliers. Such heterogeneous structure amplifies the impact of the intersectoral network on output volatility 3.3 times on average, versus the impact that a balanced structure would have on aggregate volatility.La volatilidad del producto es un indicador de la estabilidad de crecimiento, de la inversión y del empleo en una economía. Trabajos anteriores han mostrado que redes insumo-producto compuestas por algunos productos muy conectados y muchos otros con pocas conexiones entre sí, permiten la transmisión de choques sectoriales al resto de la economía, precisamente mediante la transferencia del choque entre cadenas de productos centrales que a la vez están conectados unos a otros, amplificando la magnitud del choque inicial y generando volatilidad agregada. Este artículo estudia la estructura de la red insumo-producto para Colombia entre 1982 y 2012, para entender su papel en la generación de volatilidad agregada del producto industrial. Para ello, se construyen una serie de redes insumo-producto a nivel de bienes, basadas en datos de la Encuesta Anual Manufacturera a nivel de establecimiento. La riqueza de los datos permite estudiar las propiedades estructurales de la red, mediante la caracterización de las secuencias de primer y segundo orden de la centralidad de los productos en la red. Se observa que la red de insumo-producto en la industria manufacturera en Colombia está compuesta por algunos pocos productos centrales que proveen de insumos a muchos sectores y que se encuentran conectados entre sí, y muchos otros productos que tienen una importancia menor en la provisión de insumos. Este tipo de estructura heterogénea tiene un impacto en la generación de volatilidad agregada que es 3,3 veces mayor al impacto que tendría una red insumo-producto en la cual la participación de todos los sectores es homogénea. En conclusión, la red intersectorial tiene un papel muy importante en la propagación de choques sectoriales y en consecuencia en la generación de volatilidad del PIB industrial. (Texto tomado de la fuente).Incluye anexosMaestríaMagíster en Ciencias Económicasxiii, 51 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ciencias Económicas - Maestría en Ciencias EconómicasEscuela de EconomíaFacultad de Ciencias EconómicasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá330 - EconomíaInput–Output Tables and AnalysisNetwork Formation and Analysis: TheoryBusiness Fluctuations • CyclesTransactional Relationships • Contracts and Reputation • NetworksAggregate volatilityProduction networksDiversificationInput-output linkagesVolatilidad agregadaRedes de producciónDiversificaciónTablas insumo-productoComportamiento económicoEconomic behaviourProduction networks and volatility in the colombian manufacturing industryRedes de producción y volatilidad en la industria colombianaTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAcemoglu, D., Carvalho, V. M., Ozdaglar, A., & Tahbaz-Salehi, A. (2012). The Network Origins of Aggregate Fluctuations. Econometrica, 80(5), 1977–2016. https://doi.org/10.3982/ECTA9623Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2017). Microeconomic Origins of Macroeconomic Tail Risks. American Economic Review, 107(1), 54–108. https://doi.org/10.1257/aer.20151086Aizenman, Joshua, & Marion, N. (1999). Volatility and Investment: Interpreting Evidence from Developing Countries. Economica, 66(262), 157–1179. https://doi.org/10.1111/1468-0335.00163Aizenman, Joshua, & Pinto, B. (2005). Managing Economic Volatility and Crises (Joshua Aizenman & B. Pinto (eds.)). Cambridge University Press. https://doi.org/10.1017/CBO9780511510755Atalay, E. (2017). How Important Are Sectoral Shocks? American Economic Journal: Macroeconomics, 9(4), 254–280. https://doi.org/10.1257/mac.20160353Badinger, H. (2010). Output volatility and economic growth. Economics Letters, 106(1), 15–18.Baldwin, R., & Weder di Mauro, B. (2020). Economics in the Time of COVID-19 (VoxEU.org (ed.)). CEPR Press.Blöchl, F., Theis, F. J., Vega-Redondo, F., & Fisher, E. O. (2011). Vertex centralities in input-output networks reveal the structure of modern economies. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 83(4 Pt 2), 046127. http://www.ncbi.nlm.nih.gov/pubmed/21599260Breen, R., & Garcia-Penalosa, C. (2005). Income Inequality and Macroeconomic Volatility: An Empirical Investigation. Review of Development Economics, 9(3), 380–398. https://doi.org/10.1111/j.1467-9361.2005.00283.xCalderon, C., & Yeyati, E. L. (2009). Zooming in : from aggregate volatility to income distribution. April. http://documents.worldbank.org/curated/en/2009/04/10421833/zooming-aggregate-volatility-income-distributionCarvalho, V. M., & Tahbaz-Salehi, A. (2019). Production Networks: A Primer. In Annual Review of Economics (Vol. 11, Issue 1). https://doi.org/10.1146/annurev-economics-080218-030212Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2007). Power-law distributions in empirical data. https://doi.org/10.1137/070710111Clauset, A., Young, M., & Gleditsch, K. S. (2007). On the Frequency of Severe Terrorist Events. Journal of Conflict Resolution, 51(1), 58–87. https://doi.org/10.1177/0022002706296157del Rio-Chanona, R. M., Mealy, P., Pichler, A., Lafond, F., & Farmer J., D. (2020). Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective. Covid Economics, 1(6), 65–103.Gabaix, B. Y. X., Basu, S., Bénabou, R., Blanchard, O., Caballero, R., Canning, D., Caplin, A., Chaney, T., Chari, V. V, Christiano, L., Comin, D., Davis, D., Durlauf, S.,Edmans, A., Eichenbaum, M., Engel, E., & Fernald, J. (2011). The Granular Origins of Aggregate Fluctuations. Econometrica, 79(3), 733–772. https://doi.org/10.3982/ECTA8769Gillespie, C. S. (2015). Fitting heavy tailed distributions: The powerlaw package. Journal of Statistical Software, 64(2), 1–16. https://doi.org/10.18637/jss.v064.i02Gonçalves, J., Matsushita, R., & Da Silva, S. (2020). The asymmetric Brazilian input–output network. Journal of Economic Studies. https://doi.org/10.1108/JES-05-2020-0225Hakura, D. (2007). Output Volatility and Large Output Drops in Emerging Market and Developing Countries. IMF Working Papers, 07(114), 1. https://doi.org/10.5089/9781451866780.001Jaimovich, N., Pruitt, S., & Siu, H. E. (2013). The Demand for Youth: Explaining Age Differences in the Volatility of Hours. American Economic Review, 103(7), 3022–3044. https://doi.org/10.1257/aer.103.7.3022Joya, O., & Rougier, E. (2019). Do (all) sectoral shocks lead to aggregate volatility? Empirics from a production network perspective. European Economic Review, 113, 77–107. https://doi.org/10.1016/j.euroecorev.2019.01.004Laursen, T., & Mahajan, S. (2005). Volatility, income distribution and poverty. In J. Aizenman & B. Pinto (Eds.), Managing Economic Volatility and Crises: A Practitioner’s Guide. (pp. 101–136). Cambridge University Press.Long, J. B., & Plosser, C. (1983). Real Business Cycles Author. Journal of Political Economy, 91(1), 39–69.Lucas, R. E. (1977). Understanding Business Cycles. In Carnegie–Rochester Conference Series on Public Policy (No. 5)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.2013.07.063Mundt, P. (2021). The formation of input–output architecture: Evidence from the European Union. Journal of Economic Behavior and Organization, 183, 89–104. https://doi.org/10.1016/j.jebo.2020.12.031Newman, M. (2018). Networks (Second Edi). Oxford University Press. https://global.oup.com/academic/product/networks-9780198805090?cc=co&lang=en&#OECD/World Trade Organization. (2019). Promoting economic diversification and structural transformation through industrialisation. In Aid for Trade at a Glance 2019: Economic Diversification and Empowerment (pp. 81–108). OECD Publishing. https://doi.org/10.1787/785f021c-enRamey, G., & Ramey, V. A. (1995). Cross-Country Evidence on the Link Between Volatility and Growth. The American Economic Review, 85(5), 1138–1151. http://www.jstor.com/stable/2950979Romero, P. P., López, R., & Jiménez, C. (2018). Sectoral networks and macroeconomic tail risks in an emerging economy. PLoS ONE, 13(1), 1–17. https://doi.org/10.1371/journal.pone.0190076Sahay, R., & Goyal, R. (2006). Volatility and Growth in Latin America: An Episodic Approach. IMF Working Papers, 06(287), 1. https://doi.org/10.5089/9781451865479.001Szirmai, A. (2012). Industrialisation as an engine of growth in developing countries, 1950–2005. Structural Change and Economic Dynamics, 23(4), 406–420. https://doi.org/10.1016/j.strueco.2011.01.005EstudiantesInvestigadoresPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/80592/5/license.txtcccfe52f796b7c63423298c2d3365fc6MD55ORIGINAL52987337.2020.pdf52987337.2020.pdfTesis de Maestría en Ciencias Económicasapplication/pdf904061https://repositorio.unal.edu.co/bitstream/unal/80592/6/52987337.2020.pdf8ebbef54881c889c95e9dbdc7352f840MD56THUMBNAIL52987337.2020.pdf.jpg52987337.2020.pdf.jpgGenerated Thumbnailimage/jpeg4171https://repositorio.unal.edu.co/bitstream/unal/80592/7/52987337.2020.pdf.jpg25eb0ddeb5165b928917457d2603f0caMD57unal/80592oai:repositorio.unal.edu.co:unal/805922023-07-29 23:04:06.06Repositorio Institucional Universidad Nacional de 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