Modulhandbuch (Module manual)

M.184.5481 Using Big Data to Solve Economic and Social Problems
(Using Big Data to Solve Problems and Social Problems)
Koordinator (coordinator): Martin Kesternich
Ansprechpartner (contact): Martin Kesternich (martin.kesternich[at] )
Credits: 5 ECTS
Workload: 150 Std (h)
Semesterturnus (semester cycle): WS
Studiensemester (study semester): 1-4
Dauer in Semestern (duration in semesters): 1
Lehrveranstaltungen (courses):
Nummer / Name
(number / title)
(contact time)
Status (P/WP)
(group size)
a) K.184.54811 / Using Big Data to Solve Economic and Social Problems Vorlesung / Übung 60 Std (h) 90 Std (h) P 50 TN (PART)
Wahlmöglichkeiten innerhalb des Moduls (Options within the module):
Empfohlene Voraussetzungen (prerequisites):

​A basic knowledge of econometrics (the linear regression model as e.g. covered in W2474 Introduction to Econometrics (Bachelor) or W4479 Econometrics (Master) is assumed. ​

Inhalte (short description):

​"Big data" is one of the buzzwords of our time. The availability of huge amounts of data is becoming increasingly self-evident, combined with the hope of being able to better answer economically relevant questions with the help of data. The big technology companies such as Google, Amazon and Facebook, which specialize in addressing economic questions from private companies with large amounts of data and machine learning methods (for example: What is the best way to place personalized advertising?), are particularly prominent in this area. However, large amounts of data and the latest methods for evaluating them are also suitable for addressing important social problems such as: How to reduce social inequality? How to improve access to good education and health care? What are sensible ways to combat environmental pollution and climate change? And many more.


This course will address important social and societal problems from the perspective of empirical economics. Current empirical studies are discussed and methods of econometric causal analysis and a brief snapshot of machine learning will be introduced.

Lernergebnisse (learning outcomes):
Fachkompetenz Wissen (professional expertise):

  • are familiar with methods of econometric causal analysis of big data and get a first idea of machine learning techniques for economic questions.
  • know how to use the statistical software Stata.
  • ​can apply econometric estimation techniques to own empirical projects (term papers or master thesis).

  • Fachkompetenz Fertigkeit (practical professional and academic skills):

  • can understand and critically evaluate empirical articles as well as scientific results reported on in the daily press.
  • acquire new strategies of knowledge acquisition through a combination of video lectures, solving exercises together with the lecturer, preparation and follow-on lecture material. ….

  • Personale Kompetenz / Sozial (individual competences / social skills):

  • ​​present their solutions in a team during the joint work in the lecture hall. ​

  • Personale Kompetenz / Selbstständigkeit (individual competences / ability to perform autonomously):
  • ​improve their competency in solving problems and their time management by means of the design of the learning process (video lecture, inverted classroom).​

  • Prüfungsleistungen (examinations)
    Art der Modulprüfung (type of modul examination): Modulabschlussprüfung
    Art der Prüfung
    (type of examination)
    a) Klausur 90 minutes 100.00 %
    Studienleistung / qualifizierte Teilnahme (module participation requirements)
    Voraussetzungen für die Teilnahme an Prüfungen (formal requirements for participating in examinations)
    Voraussetzungen für die Vergabe von Credits (formal requirements for granting credit points)
    Die Vergabe der Credits erfolgt, wenn die Modulnote mindestens „ausreichend“ ist
    Gewichtung für Gesamtnote (calculation of overall grade)
    Das Modul wird mit der Anzahl seiner Credits gewichtet (Faktor: 1)
    Verwendung des Moduls in den Studiengängen (The module can be selected in the following degree programmes)
    M.Sc. IBS, M.Sc. BWL, M.Sc. International Economics and Management, M.Sc. Management, M.Sc. Management Information Systems, M.Sc. Taxation, Accountingand Finance, M.Sc. Winfo, M.Sc. Wirtschaftspädagogik, M.Ed. Wirtschaftspädagogik
    Umfang QT (participation requirements):
    Lernmaterialien, Literaturangaben (learning material, literature):
    Teilnehmerbegrenzung (participant limit):
    Sonstige Hinweise (additional information):

    ​This is a course developed by Raj Chetty (Harvard University) so that it can be adapted and used by other universities. The teaching materials created by Chetty and his team (complete lecture videos and lecture slides) are available and can be used at


    methodological approach:

    Students prepare each class by watching a video of Raj Chetty's lecture, which lasts about an hour. In our weekly session in present (90 minutes) we then discuss exercises to better understand the lecture materials. We also have enough time to do our own empirical applications on the topics mentioned above. For this we will work with the statistical software Stata.


    preliminary content:

    We will cover less material than the original course. Here is the content we will probably cover:

    1.            The Geography of Upward Mobility in America

    2.            Causal Effects of Neighborhoods

    3.            Moving to Opportunity vs. Place-Based Approaches

    4.            Higher Education and Upward Mobility

    5.            The Causal Effect of Colleges

    6.            Primary Education

    7.            Teachers and Charter Schools

    8.            Improving Health Outcomes

    9.            The Economics of Health Care and Insurance

    10.          Effects of Air and Water Pollution

    11.          Policies to Mitigate Climate Change


    teaching language: English

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