Reference list

Reading List EFC Spring School

Please note that this is a non-exhaustive list of readings useful to engage with the Economic Fitness and Complexity literature. This list is constantly evolving; core readings highlighted in red will help you in the comprehension of  the EFC Spring School lectures and coding labs.

Methods

• Tacchella, A., Cristelli, M., Caldarelli, G. et al. (2012). A New Metrics for Countries' Fitness and Products' Complexity. Sci Rep 2, 723. https://doi.org/10.1038/srep00723

• Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. Proceedings of the national academy of sciences, 106(26), 10570-10575

• Cristelli M, Gabrielli A, Tacchella A, Caldarelli G, Pietronero L (2013) Measuring the Intangibles: A Metrics for the Economic Complexity of Countries and Products. PLoS ONE 8(8): e70726. https://doi.org/10.1371/journal.pone.0070726 

• Tacchella, A., Mazzilli, D., & Pietronero, L. (2018). A dynamical systems approach to gross domestic product forecasting. Nature Physics, 14(8), 861-865 

• Servedio, Vito D. P., Paolo Buttà, Dario Mazzilli, Andrea Tacchella, and Luciano Pietronero (2018). A New and Stable Estimation Method of Country Economic Fitness and Product Complexity Entropy 20, no. 10: 783. https://doi.org/10.3390/e20100783 

• Pietronero, L., Cristelli, M., Gabrielli, A., Mazzilli, D., Pugliese, E., Tacchella, A., & Zaccaria, A. (2017). Economic Complexity:" Buttarla in caciara" vs a constructive approach. arXiv preprint arXiv:1709.05272

• Mazzilli D., Mariani S. M., Morone F., & Patelli A., (2022). Fitness in the light of Sinkhorn, arXiv preprint arXiv:2212.12356

• Balland, P. A., Broekel, T., Diodato, D., Giuliani, E., Hausmann, R., O'Clery, N., & Rigby, D. (2022). Reprint of The new paradigm of economic complexity. Research Policy, 51(8), 104568

• Operti F., Pugliese E., Andrade J. S., Pietronero L, Gabrielli A., (2018). Dynamics in the Fitness - Income plane: Brazil states vs World countries, PLOS ONE, 14(5), e0217034,https://doi.org/10.1371/journal.pone.0197616

• Pugliese, E., Zaccaria, A. & Pietronero, L. (2016). On the convergence of the Fitness-Complexity algorithm. Eur. Phys. J. Spec. Top. 225, 1893–1911 . https://doi.org/10.1140/epjst/e2015-50118-1

• Balassa R. (1965). Trade Liberalisation and "Revealed" Comparative Advantage, 33(2), 99-123

Economic growth and development

• Tacchella, A., Cristelli, M., Caldarelli, G., Gabrielli, A., & Pietronero, L. (2012). A new metrics for countries' fitness and products' complexity. Scientific reports, 2(1), 1-7.

• Cristelli, M., Tacchella, A., & Pietronero, L. (2015). The heterogeneous dynamics of economic complexity. PloS one, 10(2), e0117174.

• Zaccaria, A., Cristelli, M., Tacchella, A., & Pietronero, L. (2014). How the taxonomy of products drives the economic development of countries. PloS one, 9(12), e113770.

• Pugliese, E., Chiarotti, G. L., Zaccaria, A., & Pietronero, L. (2017). Complex economies have a lateral escape from the poverty trap. PloS one, 12(1), e0168540.

• Castañeda, G., Pietronero, L., Romero-Padilla, J., & Zaccaria, A. (2022). The complex dynamic of growth: Fitness and the different patterns of economic activity in the medium and long terms. Structural Change and Economic Dynamics.

• Sbardella, A., Pugliese, E., Zaccaria, A., & Scaramozzino, P. (2018). The role of complex analysis in modelling economic growth. Entropy, 20(11), 883.

• Ferrarini, B., & Scaramozzino, P. (2016). Production complexity, adaptability and economic growth. Structural change and economic dynamics, 37, 52-6

• Justin Yifu Lin, New Structural Economics: A Framework for Rethinking Development, The World Bank Research Observer, Volume 26, Issue 2, August 2011, Pages 193–221, https://doi.org/10.1093/wbro/lkr007

• Lin, J., Cader, M., & Pietronero, L. (2020). What African Industrial Development Can Learn from East Asian Successes.

• Cristelli, M. C. A., Tacchella, A., Cader, M. Z., Roster, K. I., & Pietronero, L. (2017). On the predictability of growth. World Bank Policy Research Working Paper, (8117).

• Zaccaria, A., Mishra, S., Cader, M. Z., & Pietronero, L. (2018). Integrating services in the economic fitness approach. World Bank Policy Research Working Paper, (8485).

• Roster, K., Harrington, L., & Cader, M. (2018). Country case studies in economic fitness: Mexico and Brazil. Entropy, 20(10), 753.

• Andrea Morrison, Carlo Pietrobelli & Roberta Rabellotti (2008). Global Value Chains and Technological Capabilities: A Framework to Study Learning and Innovation in Developing Countries, Oxford Development Studies, 36:1, 39-58, DOI: 10.1080/13600810701848144 

• Cimoli, M., Dosi, G., and Stiglitz, J. E. (2009). Industrial policy and development: The political economy of capabilities accumulation. Oxford University Press, Oxford, New York.

Relatedness

• Hidalgo, C. A., Klinger, B., Barabási, A. L., & Hausmann, R. (2007). The product space conditions the development of nations. Science, 317(5837), 482-487

• Frank Neffke, Martin Henning & Ron Boschma (2011). How Do Regions Diversify over Time? Industry Relatedness and the Development of New Growth Paths in Regions, Economic Geography, 87:3, 237-265, DOI: 10.1111/j.1944-8287.2011.01121.x 

• Neffke, F. and Henning, M. (2013). Skill relatedness and firm diversification. Strat. Mgmt. J., 34: 297-316. https://doi.org/10.1002/smj.2014

• Hidalgo, C.A. et al. (2018). The Principle of Relatedness. In: Morales, A., Gershenson, C., Braha, D., Minai, A., Bar-Yam, Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-96661-8_46 

• Albora, G., Pietronero, L., Tacchella, A., & Zaccaria, A. (2023). Product Progression: a machine learning approach to forecasting industrial upgrading. Scientific Reports 13 (1), 1481

• Straccamore, M., Zaccaria, A., & Pietronero, L. (2022). Which will be your firm’s next technology? Comparison between machine learning and network-based algorithms. Journal of Physics: Complexity.

• Tacchella, A., Zaccaria, A., Miccheli, M., & Pietronero, L. (2021). Relatedness in the era of machine learning. arXiv preprint arXiv:2103.06017.

• Diodato, D., Neffke, F., & O’Clery, N. (2018). Why do industries coagglomerate? How Marshallian externalities differ by industry and have evolved over time. Journal of Urban Economics, 106, 1-26

• 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. Oxford Review of Economic Policy, 36(Supplement_1), S94-S137. https://doi.org/10.1093/oxrep/graa033

• Giuliani, E., Pietrobelli, C., & Rabellotti, R. (2005). Upgrading in global value chains: Lessons from Latin American clusters. World development, 33(4), 549-573.

Sustainability

• Sbardella, A., Perruchas, F., Napolitano, L., Barbieri, N., & Consoli, D. (2018). Green technology fitness. Entropy, 20(10), 776.

• de Cunzo, F., Petri, A., Zaccaria, A., & Sbardella, A. (2022). The trickle down from environmental innovation to productive complexity. Scientific Reports, 12(1), 22141.

• Napolitano, L., Sbardella, A., Consoli, D., Barbieri, N., & Perruchas, F. (2022). Green innovation and income inequality: A complex system analysis. Structural Change and Economic Dynamics, 63, 224-240.

• Sbardella, et al. (2022). The regional green potential of the European innovation system. Joint Research Centre (Seville site).

• Barbieri, N., Consoli, D., Napolitano, L., Perruchas, F., Pugliese, E., & Sbardella, A. (2022). Regional technological capabilities and Green opportunities in Europe. The Journal of Technology Transfer, 1-30.

Inequality

• Sbardella, A., Pugliese, E., & Pietronero, L. (2017). Economic development and wage inequality: A complex system analysis. PloS one, 12(9), e0182774.

• Napolitano, L., Sbardella, A., Consoli, D., Barbieri, N., & Perruchas, F. (2020). Green innovation and income inequality: A complex system analysis.

Firms

• Coad, A., Mathew, N., & Pugliese, E. (2021). Positioning firms along the capabilities ladder (No. 2021-031). United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).

• Pugliese, E., Napolitano, L., Zaccaria, A., & Pietronero, L. (2019). Coherent diversification in corporate technological portfolios. PloS one, 14(10), e0223403.

• Straccamore, M., Zaccaria, A., & Pietronero, L. (2022). Which will be your firm’s next technology? Comparison between machine learning and network-based algorithms. Journal of Physics: Complexity.

• Albora G, Zaccaria A . (2022). Machine Learning to Assess Relatedness: The Advantage of Using Firm-Level Data, Complexity, vol. 2022, Article ID 2095048. https://doi.org/10.1155/2022/2095048

• Costa, S., De Santis, S., Dosi, G., Monducci, R., Sbardella, A., & Virgillito, M. E. (2021). From organizational capabilities to corporate performances: at the roots of productivity slowdown (No. 2021/21). LEM Working Paper Series.

• Dosi, G., Nelson, R. R., & Winter, S. G. (Eds.). (2000). The nature and dynamics of organizational capabilities. Oxford university press.

Patents and Technology

• Pugliese, E., Cimini, G., Patelli, A., Zaccaria, A., Pietronero, L., & Gabrielli, A. (2019). Unfolding the innovation system for the development of countries: coevolution of Science, Technology and Production. Scientific reports, 9(1), 1-12.

• Napolitano, L., Evangelou, E., Pugliese, E., Zeppini, P., & Room, G. (2018). Technology networks: the autocatalytic origins of innovation. Royal Society open science, 5(6), 172445

• Sbardella, A., Perruchas, F., Napolitano, L., Barbieri, N., & Consoli, D. (2018). Green technology fitness. Entropy, 20(10), 776.

• Pugliese, E. and Tübke, A. "Economic complexity to address current challenges in innovation systems: a novel empirical strategy for regional development" Industrial R&I – JRC Policy Insights - July 2019 

• Dosi, G., Pavitt, K., & Soete, L. (1990). The economics of technical change and international trade. LEM Book Series.

Science

• Patelli A., Napolitano, L., Cimini, G. & Gabrielli, A. (2023) Geography of science: competitiveness and inequality, Journal of Informetrics, 17(1), 101357, https://doi.org/10.1016/j.joi.2022.101357

• Cimini, G., Gabrielli, A., & Sylos Labini, F. (2014). The scientific competitiveness of nations. PloS one, 9(12), e113470.

• Patelli, A., Napolitano, L., Cimini, G., Pugliese, E., & Gabrielli, A. (2022). The Evolution of Competitiveness across Economic, Innovation and Knowledge production activities. arXiv preprint arXiv:2206.00368.

• Patelli, A., Cimini, G., Pugliese, E., & Gabrielli, A. (2017). The scientific influence of nations on global scientific and technological development. Journal of Informetrics, 11(4), 1229-1237.

• Corsini, A., Pezzoni, M., & Visentin, F. (2022). What makes a productive Ph. D. student?. Research Policy, 51(10), 104561.

• Patsali, S., Pezzoni, M., & Visentin, F. (2021). The impact of research independence on PhD students' careers: Large-scale evidence from France. Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT) https://www.merit.unu.edu/publications/working-papers/abstract/?id=9071 

EU Policy Briefs

• Pugliese, E. and Tübke, A. "Economic complexity to address current challenges in innovation systems: a novel empirical strategy for regional development" Industrial R&I – JRC Policy Insights - July 2019 

• Pugliese, E. and Tacchella, A. "Economic Complexity for competitiveness and innovation: a novel bottom-up strategy linking global and regional capacities" Industrial R&I – JRC Policy Insights – 2020 

• Pugliese, E. and Tacchella, A., Economic Complexity Analytics: Country factsheets, EUR 30711 EN, Publications Office of the European Union, Luxembourg, 2021, ISBN 978-92-76-37869-3 (online), doi:10.2760/368138 (online), JRC124939. 

• Sbardella, A., Barbieri, N., Consoli, D., Napolitano, L., Perruchas, F. and Pugliese, E., The regional green potential of the European innovation system, European Commission, 2022, JRC124696. 

Network Methods, Projections and Null Models 

• Cimini, G., Squartini, T., Saracco, F., Garlaschelli, D., Gabrielli, A., and Caldarelli, G. (2019). The statistical physics of real-world networks. Nature Reviews Physics, 1(1):58–71. doi:10.1038/s42254-018-0002-6

• Saracco, F., Di Clemente, R., Gabrielli, A., and Squartini, T. (2015). Randomizing bipartite networks: the case of the World Trade Web. Scientific Reports, 5:10595. doi:10.1038/srep10595

• Saracco, F. Straka, M. J., Di Clemente, R., Gabrielli, A., Caldarelli, G., and Squartini, T.. (2017). Inferring monopartite projections of bipartite networks: an entropy-based approach.  New Journal of Physics, 19:053022. doi:10.1088/1367-2630/aa6b38

• Vallarano, N., Bruno, M., Marchese, E., Trapani, G., Saracco, F., Cimini, G., Zanon, M., and Squartini, T. (2021). Fast and scalable likelihood maximization for exponential random graph models with local constraints. Scientific Reports, 11(1):15227. doi:10.1038/s41598-021-93830- 4

• Cimini, G., Carra, A., Didomenicantonio, L., and Zaccaria, A. (2022). Meta-validation of bipartite network projections. Communications Physics, 5(1):76. doi:10.1038/s42005-022-00856-9

• Bruno, M., Mazzilli, D., Patelli, A., Squartini, T., & Saracco, F. (2023). Inferring comparative advantage via entropy maximization. arXiv preprint arXiv:2304.12245.

• Garlaschelli, D., and Loffredo, M. I. (2018). Maximum likelihood: Extracting unbiased information from complex networks, Phys. Rev. E 78, 015101(R)

Handbooks

• Dosi, G. (2023). The Foundations of Complex Evolving Economies. Part One: Innovation, Organization and Industrial Dynamics. Oxford University Press