Course descriptions

PhD Course on (Socio-) Economic Development – 2020

Overview

The course provides a broad overview of (macro-) economic development. The course aims to provide participants with knowledge on three particular dimensions of economic development: the empirical analysis of (country) development experiences; the theory of economic development; and major issues in development economics.

The course will begin with a discussion of: what is meant by economic (and social) development; how we measure economic and social development (including and beyond per capita GDP); and how difficult the process of development is (based upon a discussion of very long-run data and examples of the few countries that have been able to develop in the more recent past). In the introduction we will further discuss convergence and divergence and will discuss the role of the Sustainable Development Goals (SDGs) as a means of framing the discussion of development.

The remainder of the course will be organised around particular determinants of economic development. These include: The deep determinants of development (Geography and Institutions): Technology and innovation; International trade and globalisation; Industry and economic structure; Human Capital; and Social Policy. During the discussion of each of these topics emphasis will be placed on identifying and understanding both relevant development theories and empirical evidence and country experiences. In addition to obtaining an understanding of the major issues of development at the macro-level, the course will therefore also provide a broad overview of different development theories and an understanding of relevant empirical approaches to address the causes of development at the aggregate level. While much of the discussion of these topics will be on economic indicators such as income per capita, time will be spent identifying impacts on broader social indicators of development, most notably inequality and poverty, with the role of policy in influencing these outcomes also introduced.

PhD Course on Programme and Policy Evaluation

Overview

How do we know whether (public) policies or programmes work for development? For example, does job training improve labour market outcomes? Or, do cash transfers affect children’s health outcomes? Which policies are cost-effective? And how do we measure accurate and reliable outcomes? Answers to these issues helps to better design and target new programmes and policies. The main objective of the course is to build problem solving and research skills for evidence-based interventions and policies, with a focus on developing and transition economies.

Students will learn the basic intuition behind (quasi)-experimental designs including RCTs, DID, PSM, and review assumptions and criteria for the validity of each of these approaches. Students will gain experience in deciding which approach(es) are most appropriate for a given research question and context. We will draw on several empirical applications in class and learn about challenges and potential pitfalls in conducting successful impact evaluations in practice. (STATA) tutorials will help students familiarise with analysing quantitative datasets to assess the impact of a policy or programme using (quasi)experimental methods.

PhD Course on Understanding Innovation and Development as Complex Sociotechnical Processes

Overview

Many processes that are important for innovation and development more generally can be seen as involving many agents who interact in a variety of different ways. The outcomes we can identify at the aggregate level, for the better or the worse, are the collective successes or the failures that emerge therefrom. Sometimes the interactions are simple, and aggregate analysis can be successfully done using simple heuristics such as a representative agent model. However, in many situations this is not the case, and some different approach must be found to make sense of complex or complicated (social) processes. This is generally the case for the processes studied at UNU-MERIT, whether innovation, migration, social protection or institutional evolution. Any approach to this problem will essentially be an attempt to take individual actions and from them derive statements about aggregate behaviour. In all of this, understanding interactions among agents, how they affect individual behaviour, and in turn how that drives collective outcomes is key.

Standard economic theory addresses the essential complexity in economic processes largely by assuming that economic agents interact anonymously with all other agents through the market via prices. However, in many contexts economic and social interactions take place between pairs of agents who know each other’s identity (e.g., R&D alliances, innovation networks). The analysis of complex structures formed by a multitude of such pairs is known as “social network analysis” which is the central focus of this course. Social network analysis is not the only way to analyse a complex system, but it is one that is well-developed, and is particularly suited to the types of issues examined in our institute.

Modelling economic activity using social network analysis tools can be very useful in furthering understanding of a wide variety of phenomena. Our interest, of course, will be largely in how network analysis is useful in understanding innovation and knowledge creation and diffusion. As such we see (social) networks as the infrastructure over which knowledge flows and/or grows. We seek to understand how different actors in an innovation system interact, and how those interactions can be analysed with network tools and concepts. We begin with a general introduction to social network analysis, laying out the basic concepts. The bulk of the course uses these concepts to look at various issues of innovation and development. We look at different network structures and how they might be good or bad for encouraging the making and/or the adoption of innovations; we look at models of network formation, starting with the basic building block of links between pairs of actors. The course presents both theoretical and empirical results. While quite a lot of the course looks at innovation, other topics are not absent, and parallels are drawn when appropriate.

PhD Course on the Economics of Technological Change and Innovation (ETCI)

Overview

In this course, we will cover a broad set of readings that deal with the role of technology and innovation in the economy. The central idea in the course is that the economy can be seen as a system, in which individual actors (people, firms, policymakers, …) interact with each other, and in which there is constant innovation associated to technological change. The actors will form various kinds of subsystems (in which interactions between a limited group of actors is particularly strong), which in turn interact with each other, and with the micro-actors, to form a larger system. We look at coevolution as the principle way in which this system organises, i.e., different subsystems and their actors mutually affect each other, giving rise to multi-directional causality.

For example, we can look at a city as a subsystem that interacts with other subsystems (other cities, but also more abstract subsystems such as the labour market, or the system of international trade). The development of each city is both dependent on and determines the development of other cities, as well as other kinds of subsystems.

We will make one limitation on which kind of system we will deal with in the course: we will not look at firms, or public policy organisations (governments and their various bodies) as subsystems, but rather treat them as individual actors. In other words, we will not look at what goes on inside a firm or government, but rather look at them as single actors with a single strategy. The reason for this simplification is that we wish to clearly remain in the realm of economics, instead of also considering organisation studies, political science, or management science.

PhD Course on the Governance of Development as a Complex Social System

Overview

This course offer an introduction to the study of governance of development as a complex social system. The approach of complex social system emphasises the emergence, evaluation and adaptation of constellations created by social structure, agents and interaction dynamics among them. This approach aims at comprehending the heterogeneity of arrangements, relations and causalities shaping developmental trajectories at sub-national, national, transnational and international levels. By adopting this perspective, the course will analyse the evolution of key priorities, tendencies and dilemmas of development in academic debates as well as in policy practice. Therefore, it also introduces governance challenges faces during the processes of steering and coordination of development. Finally, the course analyses how these dynamics inform the governance of public policy areas by analysis governance principles and policy tools. It involved the understanding of policy tools choices in addressing different public policy challenges and tasks.

Following this basic approach, the course encompasses examples form different policy areas, including innovation systems, health, energy and environment, political regimes in the broader contact of development. The course will also illustrate how the basic concepts and analytical perspectives on complexity of development can be captures by different methods and tools of analysis (/e/g system dynamics modelling, network analysis, qualitative comparative analysis or Q-methodology). However, it will not be a training in research methods.


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