Economics

This course focuses on public policy and public finance, especially on their basic system and historical and theoretical backgrounds. Through cases of various nation states and international organizations, the course discusses the main issues of public policy and public finance, especially regarding their actors, leadership, resources and strategies.  The course pays special attention to the relationship between nation states and international organizations in terms of public policy coordination and fiscal adjustments. Recent topics such as performance measurement, policy evaluation and performance-based project budgeting are also discussed in the course.

This course gives students an understanding of the power and limitations of the theoretical constructs used to interpret human behavior in economics, as brought to bear on observed consumption behavior; to enable them to critically evaluate policies targeted at individuals, both in terms of their theoretical basis and of their practical importance, and to recognize the importance of measurement in the design and evaluation of policy and the challenges it poses. Students build upon the core material learned in the first two years of the BSc (Econ) Economics degree program to further study problems pertinent to our understanding of individual choices, to their measurement, and to the design and evaluation of policy.

This course provides an introduction to programming in order to numerically solve simple economic models and perform basic data analysis. The first part of the course introduces programming using the general-purpose Python language. It teaches how to write conditional statements, loops, functions, and classes; print results; and produce static and interactive plots. It provides an opportunity to solve simple numerical optimization problems; draw random numbers; run simulations; test, debug, and document code; and use online communities proactively when writing code. The second part of the course instructs how to import data from offline and online sources, structure it, produce central descriptive statistics, and estimate simple statistical models on the data. The third part of the course introduces the concept of a numerical algorithm to write simple searching, sorting, and optimization algorithms, solve linear algebra problems, solve non-linear equations numerically and symbolically, find fixed points, and solve complicated numerical optimization problems relying on function approximation. The course provides hands-on experience with applying the above techniques to solve well-known microeconomic and macroeconomic problems through both a small data analysis project and a larger model analysis project based on a well-known economic model.

The course reviews agglomeration and clustering of economic activities from both geographical and urban economics disciplinary perspectives, in relation to cluster and urban economic policy. The course explains the current innovative and knowledge economy of firms and policymaking in relation to urban competitiveness. The geographical discipline focuses on clusters, network formation and industry evolution using institutional theories in which the actor-approach of firms and governments is central. Urban economics traditionally focuses on the role of externalities and urban contexts as attractions for firm and population location decisions and the growth and innovation potentials of firms in a more quantitative sense. Both disciplines heavily lean on empirical research, using complementary research methods like case-study research, surveys, spatial econometrics and general equilibrium modelling. Those methods are explained in the course in relation to current issues and empirical research on urban development. Much attention is given to regional and urban economic policy issues. Participants apply the theoretical and empirical insights from the lectures in an actual case study of urban policy in Utrecht, the Netherlands.