Bachelor Economics - Lecture 5th/ 6th semester
Lecturer: Hoang Van Khieu
Tutorial: Hoang Van Khieu
Exam: Take-home exam
Lectures and tutorials will be held in English.
In case of further questions, please contact: Hoang Van Khieu
The lecture consists of four parts contributing to the understanding of wealth distributions.
- Facts about wealth
- A simple approach to modelling wealth distributions
- Modelling the fat right tail of the wealth distribution
In the beginning we will properly motivate the topic of wealth, wealth distributions and inequality. In the course of that students will get to know tools such as density functions, quantiles, top shares, Gini coefficient and a lot more to analyze a distribution. We further take a detailed look at wealth distributions in the US and Germany.
In part 2 we describe a parsimonious yet powerful two-period overlapping generations-model to analyze wealth distributions in the presence of bequests, labor income and stochastic individual abilities. Firstly, we will provide more background on expected utility maximization and the like, such that subsequently, we are able to dissect this model step by step and describe model-inferred distributional properties such as the mean, the variance and the distribution itself of course.
Within part 3 we focus on other approaches to describe wealth distributions as well as inequality given different impacts. In order to give an example, one can think of precautionary saving as a tool to reduce uncertainty and smooth consumption over time. However, this may have an influence on wealth accumulation and, ultimately, the wealth distribution. One can further highlight papers on Pareto distributions at this point. As the latter is what we typically observe for the right tail of a wealth distribution, it marks a powerful connection between theoretical models and empirical data. This will be further explored by covering certain models from well-published papers.
In part 4 students will get to know Python, a widely used programming language that allows to compute numerical solutions. The importance of Python of course goes much beyond behavioural macroeconomics, economics or business administration. It is used in finance, in mathematics and in many other fields. Large banks and insurance companies also use Python (or closely related other numerical software). Students will learn Python from scratch (no previous programming experience is required) and learn how to plot figures, run loops, use if-conditions and solve differential equations. All of this will be taught using the questions on the problem sets of this course. A close link between economic intuition and numerical solutions will always be present.
Slides will be made available in JGU-LMS (Moodle) soon.
We have a total of nine tutorials, which will be held online.
Source: The Guardian
Source: New York Times