Frank Muci Lander

This course is available on the Double Master of Public Administration (LSE-Columbia), Double Master of Public Administration (LSE-Sciences Po), Double Master of Public Administration (LSE-University of Toronto), MPA Dual Degree (LSE and Hertie), MPA Dual Degree (LSE and NUS), MPA Dual Degree (LSE and Tokyo), MPA in Data Science for Public Policy, Master of Public Administration and Master of Public Policy. This course is available with permission as an outside option to students on other programmes where regulations permit.

This course has a limited number of places (it is controlled access) and demand is typically very high. Priority is given to students from the School of Public Policy, students from other programmes will be considered if places remain.

Introductory Microeconomics

Introduction to Econometrics (experience in STATA, R or Python)

The course enables students to deploy a variety of analytical tools to process and interpret the data and formulate a coherent diagnostic narrative that can make sense of simultaneous observations about growth and social outcomes within a particular context. It covers the theory and practice of the Economic Complexity and Growth Diagnostics frameworks, drawing on empirical research, case studies, and real world-data to a) map place-specific opportunities for productive diversification, b) identify the most binding constraints preventing them from materializing, and c) formulating data-driven policy strategies to overcome them.

The course covers a range of topics in development economics. It begins with an overview of Malthusian dynamics, the Great Acceleration and modern growth models, emphasizing the role of productivity and technology. The course then explores Hidalgo and Hausmann’s (2009) Economic Complexity framework, which takes stock of place-specific productive capabilities and defines a roadmap to potential diversification opportunities that can be tapped by redeploying them, thereby reducing coordination problems that surround the process of self-discovery and structural transformation. The course also reviews Hausmann, Rodrik and Velasco’s (2008) Growth Diagnostic framework, a methodology for identifying the most binding constraints to an objective function (i.e. growth, diversification, private investment). Taken together, Economic Complexity and Growth Diagnostics form an innovative conceptual framework that allows policymakers and policy practitioners to focus limited resources on the most impactful issues.

Students will learn to use data-driven tools such as the Atlas of Economic Complexity to map potential avenues for productive diversification and deploy the four diagnostic principles of Growth Diagnostics to identify the most significant constraints preventing them from materializing. The principles of differential diagnostics are illustrated with practical examples that showcase their deployment to test for binding constraints across relevant production factors, such as finance, human capital, infrastructure, market failures (coordination and information externalities), government failures (taxation, regulations, property rights, and corruption) and macroeconomic risks.

The course concludes with several lectures on policy formulation and implementation. There will be a session on building the state capability needed to mobilise and implement reforms using Andres, Pritchett and Woolcock’s (2012) Problem-Driven Iterative Adaptation approach. Students are expected to implement class concepts, methodologies and frameworks on a country of their choosing through a series of hands-on problem sets that develop incremental research outputs that are then used for the final Growth Diagnostics country report and presentation.

30 hours of lectures and 10 hours of seminars in the WT.

The course will have two x 90 minute ‘Harvard’ style lectures plus a one-hour seminar per week.

Students will be expected to produce 1 essay in the WT.

Short pre-class assignments

• Galor, Oded, and David N. Weil (1999). From Malthusian Stagnation to Modern Growth. American Economic Review 89, no. 2.
• Pritchett, L. (1997) Divergence, Big Time. The Journal of Economics Perspectives 11, No. 3.
• Hidalgo, C., and Hausmann, R. (2009). The building blocks of economic complexity. Proceedings of the National Academy of Sciences, 106(26), 10570-10575.
• Hausmann, R., Rodrik, D, and Velasco, A. (2008). Growth diagnostics, in Stiglitz, J. and Serra, N. The Washington Consensus Reconsidered: Towards a new global governance. Oxford University Press.
• Hausmann, R., Pietrobelli, C., and Santos, M.A. Place-specific Determinants of Income Gaps: New Sub-National Evidence from Mexico (forthcoming in the Journal of Business Research).
• Hani, F., and Santos, M.A. (2021). Testing for Human Capital as a Binding Constraint (forthcoming in Cambridge University Press)
• Besley, T., and Persson, T. (2011). Pillars of Prosperity: The Political Economics of Development Clusters , The Yrjö Jahnsson Lectures, Princeton University Press 2011.
• Andrews, M., Pritchett. L., Woolcock, M. (2012). Escaping Capability Traps through Problem-Driven Iterative Adaptation (PDIA). Center for Global Development, Working Paper 299.
• Crespi, G., Fernández-Arias, E., Stein, E. (2014). Rethinking Productive Development. Inter-American Development Bank, Washington DC.
• Collier, P. (2018). The future of capitalism: Facing the new anxieties. Harper Collins Publishers, New York. Chapter 7: The geographic divide: Booming metropolis, broken cities.

Group project (45%) in the WT and ST.
Problem sets (20%), problem sets (20%) and class participation (15%) in the WT.

There will be one individual problem set and one group problem set assigned during Winter Term.