Principles of Collective Learning (Harvard ES298BR SES5411)

Principles of Collective Learning

SYNOPSIS:

How do teams, cities, and nations learn? How do they acquire the knowledge they need to improve their capacities or enter new activities? This course will equip students with a basic understanding of the mechanisms that govern the creation and diffusion of knowledge and the tools needed to study these mechanisms. The course will be divided into three parts. The first part describes the accumulation of knowledge in time (e.g. learning curves). The second part focuses on the diffusion of knowledge across geographies, social networks, and activities. The third part explores the consequences of knowledge accumulation, for the spatial concentration of wealth, as well as for the distribution of income and greenhouse gas emissions. These lessons will be complemented with lectures and discussions on the policy implications of this knowledge-based view of economic geography and with mathematical models describing the accumulation and recombination of knowledge. The course’s learning goals will be supported with hands-on exercises in which students will use data on international trade, employment, and patents, to reproduce classical studies.

INSTRUCTOR:

César A. Hidalgo PhD

SCHEDULE:

Mondays 9:45am-11:45am

FIRST DAY OF CLASS:

September 9, 2019

ROOM:

Pierce 100F

COURSE NUMBER:

Engineering: ES298BR

GSD: SES5411

EVALUATION:

Students will be evaluated based on three problem sets and on class participation (class participation is mandatory).

Problem Set 1: 30%
Problem Set 2: 30% (Problem Set , Script , Aggregate Industry Data)
Problem Set 4: 30%
Class Participation 10%

LEARNING OUTCOMES:

At the end of the course students will be able to conduct data driven analyses of the capabilities and diversification opportunities of regional and national economies. The students will also obtain a working understanding of modern theories in economic geography, innovation studies, and economic complexity, which will serve as a segue to academic work or consulting work on these topics.

READINGS & CLASS SCHEDULE:

(Readings are mandatory)

Week 1 (Sep 9):
Introduction. This class will introduce the course’s learning goals and structure. The remainder of the class will be devoted to explaining basic properties of knowledge, such as its non-rival nature and its tacit or explicit nature.

Week 2 (Sep 16):
Learning Curves & Social Learning
(Abernathy & Wayne 1974; Argote & Epple 1990; Henrich 2015 (chapters 1-4))

Week 3 (Sep 23):
The Geography of Knowledge
(Jaffe, Trajtenberg & Henderson 1993; Audretsch & Feldman 1996; Audretsch & Feldman 2004; Ronen et al. 2014; Jara-Figueroa, Yu & Hidalgo 2019)

Week 4 (Sep 30):
The Flow of Knowledge
(Boschma 2005; Breschi & Lissoni 2005; Rapoport 2016; Jara-Figueroa et al. 2018)

Week 5 (Oct 07):
Relatedness
(Hidalgo et al. 2007; Boschma, Balland & Kogler 2015; Guevara et al. 2016; Hidalgo et al. 2018)

Week 6 (Oct 14):
No Class: Indigenous People Day

Week 8 (Oct 21):
Complexity
(Fleming & Sorenson 2001; Hidalgo & Hausmann 2009; Balland & Rigby 2017)

Week 9 (Oct 28):
Implications of Knowledge Relatedness and Complexity
(Hausmann et al. 2014; Hartmann et al. 2017; P. A. Balland et al. 2018)

Week 7 (Nov 4):
Policies and Strategies for Knowledge Relatedness & Complexity
(Lee & Malerba 2017; Zhu, He & Zhou 2017; Alshamsi, Pinheiro & Hidalgo 2018; P. A. Balland et al. 2018; Lee, Szapiro & Mao 2018)

Week 10 (Nov 11):
No-Class: Veterans Day

Week 11 (Nov 18):
Modeling the Complexity of Economies
(Hausmann & Hidalgo 2011; Fink et al. 2017; Hidalgo 2018; Fink & Reeves 2019)

Week 12 (Nov 25):
Hot Buttons
(Zheng et al. 2017; Rodríguez-Pose 2018; Florida 2019a; Florida 2019b)

REFERENCES
(Most of these paper are available in Google Scholar or in your University Library):

Abernathy, W.J. & Wayne, K., 1974, Limits of the Learning Curve, Harvard Business Review, (September 1974).

Alshamsi, A., Pinheiro, F.L. & Hidalgo, C.A., 2018, ‘Optimal diversification strategies in the networks of related products and of related research areas’, Nature Communications, 9(1), 1328.

Argote, L. & Epple, D., 1990, ‘Learning Curves in Manufacturing’, Science, 247(4945), 920–924.

Audretsch, D.B. & Feldman, M.P., 1996, ‘R&D Spillovers and the Geography of Innovation and Production’, The American Economic Review, 86(3), 630–640.

Audretsch, D.B. & Feldman, M.P., 2004, ‘Chapter 61 - Knowledge Spillovers and the Geography of Innovation’, in J.V. Henderson & J.-F. Thisse (eds.), Handbook of Regional and Urban Economics, Cities and Geography., vol. 4, pp. 2713–2739, Elsevier.

Balland, P.A., Boschma, R., Crespo, J. & Rigby, D.L., 2018, ‘Smart specialization policy in the European Union: relatedness, knowledge complexity and regional diversification’, Regional Studies, 1–17.

Balland, P.A., Jara-Figueroa, C., Petralia, S., Steijn, M., Rigby, D.L. & Hidalgo, C., 2018, Complex Economic Activities Concentrate in Large Cities, Social Science Research Network, Rochester, NY.

Balland, P.-A. & Rigby, D., 2017, ‘The Geography of Complex Knowledge’, Economic Geography, 93(1), 1–23.

Boschma, R., 2005, ‘Proximity and Innovation: A Critical Assessment’, Regional Studies, 39(1), 61–74.

Boschma, R., Balland, P.-A. & Kogler, D.F., 2015, ‘Relatedness and technological change in cities: the rise and fall of technological knowledge in US metropolitan areas from 1981 to 2010’, Industrial and Corporate Change, 24(1), 223–250.

Breschi, S. & Lissoni, F., 2005, ‘Knowledge Networks from Patent Data’, in H.F. Moed, W. Glänzel & U. Schmoch (eds.), Handbook of Quantitative Science and Technology Research: The Use of Publication and Patent Statistics in Studies of S&T Systems, pp. 613–643, Springer Netherlands, Dordrecht.

Fink, T.M.A. & Reeves, M., 2019, ‘How much can we influence the rate of innovation?’, Science Advances, 5(1), eaat6107.

Fink, T.M.A., Reeves, M., Palma, R. & Farr, R.S., 2017, ‘Serendipity and strategy in rapid innovation’, Nature Communications, 8(1), 2002.

Fleming, L. & Sorenson, O., 2001, ‘Technology as a complex adaptive system: evidence from patent data’, Research Policy, 30(7), 1019–1039.

Florida, R., 2019a, How Housing Supply Became the Most Controversial Issue in Urbanism, CityLab.

Florida, R., 2019b, Blue-Collar and Service Workers Fare Better Outside Superstar Cities, CityLab.

Guevara, M.R., Hartmann, D., Aristarán, M., Mendoza, M. & Hidalgo, C.A., 2016, ‘The research space: using career paths to predict the evolution of the research output of individuals, institutions, and nations’, Scientometrics, 109(3), 1695–1709.

Hartmann, D., Guevara, M.R., Jara-Figueroa, C., Aristarán, M. & Hidalgo, C.A., 2017, ‘Linking Economic Complexity, Institutions, and Income Inequality’, World Development, 93, 75–93.

Hausmann, R. & Hidalgo, C.A., 2011, ‘The network structure of economic output’, Journal of Economic Growth, 1–34.

Hausmann, R., Hidalgo, C.A., Bustos, S., Coscia, M., Simoes, A. & Yildirim, M.A., 2014, The atlas of economic complexity: Mapping paths to prosperity, MIT Press.

Henrich, J., 2015, The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter, Princeton University Press, Princeton.

Hidalgo, C.A., 2018, ‘Economic complexity: From useless to keystone’, Nature Physics, 14(1), 9–10.

Hidalgo, C.A., Balland, P.-A., Boschma, R., Delgado, M., Feldman, M., Frenken, K., Glaeser, E., He, C., Kogler, D.F., Morrison, A., Neffke, F., Rigby, D., Stern, S., Zheng, S. & Zhu, S., 2018, The Principle of Relatedness, in A.J. Morales, C. Gershenson, D. Braha, A.A. Minai & Y. Bar-Yam (eds.), Unifying Themes in Complex Systems IX, Springer Proceedings in Complexity., 451–457, Springer International Publishing.

Hidalgo, C.A. & Hausmann, R., 2009, ‘The building blocks of economic complexity’, Proceedings of the National Academy of Sciences, 106(26), 10570–10575.

Hidalgo, C.A., Klinger, B., Barabási, A.-L. & Hausmann, R., 2007, ‘The Product Space Conditions the Development of Nations’, Science, 317(5837), 482–487.

Jaffe, A.B., Trajtenberg, M. & Henderson, R., 1993, ‘Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations’, The Quarterly Journal of Economics, 108(3), 577–598.

Jara-Figueroa, C., Jun, B., Glaeser, E.L. & Hidalgo, C.A., 2018, ‘The role of industry-specific, occupation-specific, and location-specific knowledge in the growth and survival of new firms’, Proceedings of the National Academy of Sciences, 115(50), 12646–12653.

Jara-Figueroa, C., Yu, A.Z. & Hidalgo, C.A., 2019, ‘How the medium shapes the message: Printing and the rise of the arts and sciences’, PLOS ONE, 14(2), e0205771.

Lee, K. & Malerba, F., 2017, ‘Catch-up cycles and changes in industrial leadership:Windows of opportunity and responses of firms and countries in the evolution of sectoral systems’, Research Policy, 46(2), 338–351.

Lee, K., Szapiro, M. & Mao, Z., 2018, ‘From Global Value Chains (GVC) to Innovation Systems for Local Value Chains and Knowledge Creation’, The European Journal of Development Research, 30(3), 424–441.

Rapoport, H., 2016, ‘Migration and globalization: what’s in it for developing countries?’, International Journal of Manpower, 37(7), 1209–1226.

Rodríguez-Pose, A., 2018, The revenge of the places that don’t matter, VoxEU.org.

Ronen, S., Gonçalves, B., Hu, K.Z., Vespignani, A., Pinker, S. & Hidalgo, C.A., 2014, ‘Links that speak: The global language network and its association with global fame’, Proceedings of the National Academy of Sciences, 111(52), E5616–E5622.

Zheng, S., Sun, W., Wu, J. & Kahn, M.E., 2017, ‘The birth of edge cities in China: Measuring the effects of industrial parks policy’, Journal of Urban Economics, 100, 80–103.

Zhu, S., He, C. & Zhou, Y., 2017, ‘How to jump further and catch up? Path-breaking in an uneven industry space’, Journal of Economic Geography, 17(3), 521–545.