This class explores economic growth over the past millennia. For most of its history, humanity did not experience the type and quality of life, as we know it today (e.g., electricity, running water, medicine, telecommunications, and transport). It is only during the second half of the 18thcentury when Europe (and later U.S.) started to see economic growth. This growth started with the Industrial Revolution. In the first part of the course, we will study models that explain why economic growth is such a recent phenomenon. We will rely on models such as those proposed by Malthus to understand why the world did not grow at all for most of 5 millennia. We will also explore models that help us to understand the explosive economic growth experienced by the U.S. in the 19thand 20thcenturies (and other countries more recently). As a by-product, there will be some discussion on the recent slowdown affecting most Western economies (the so-called Secular Stagnation). Importantly, we will talk about the increasingly reliance on automation for production in the so-called 4thIndustrial Revolution. That is, the impact automation may have on employment, welfare, and society. For example, we will explore how automation is contributing to inequality.A crucial part of the course is to understand how research and development (R&D) and innovation contribute to growth. Therefore, the second part of the course will be devoted to study R&D at the aggregate (macro) level but also at the industry level. We will use case studies in, for example, the pharmaceutical sector to study why R&D is such a crucial factor for growth but also difficult to implement and predict its impact on firms and ultimately on the economy. We will study the current development of vaccines and treatments for Covid-19. In addition, we also analyze the role of automation and big data (data mining, and machine learning) in R&D.As will become clear, programming is a vital skill in the 4thIndustrial Revolution. To prepare students for this shifting labor landscape,students will learn Python to manipulate data and solve models. An example of this is to compute productivity using data from national accounts for different countries.
Written reviews 0
No written reviews yet
Numerical ratings and written feedback are separate. Be the first to share what you wish you’d known before taking this course.