Syllabus

Title
5765 Specialization: Economic and Social Policy
Instructors
PD Dr. Stefan Angel
Contact details
Type
PI
Weekly hours
3
Language of instruction
Englisch
Registration
02/16/21 to 02/21/21
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Dates
Day Date Time Room
Tuesday 03/02/21 12:30 PM - 06:30 PM Online-Einheit
Tuesday 03/09/21 12:30 PM - 06:30 PM Online-Einheit
Tuesday 03/16/21 12:30 PM - 06:30 PM Online-Einheit
Tuesday 03/23/21 12:30 PM - 06:30 PM Online-Einheit
Tuesday 04/13/21 12:30 PM - 06:30 PM Online-Einheit
Tuesday 04/20/21 12:30 PM - 06:30 PM Online-Einheit
Tuesday 04/20/21 12:30 PM - 06:30 PM Online-Einheit
Tuesday 04/27/21 12:30 PM - 05:00 PM Online-Einheit
Contents

This class deals with methods and theory of program evaluation in health economics and selected social policy fields. It comprises three main parts:

  • Module 1:

Paradigms and typologies of program evaluation, big picture on evidence-based policy-making
Social context of program evaluation: different types of stakeholders, dissemination of results, stakeholder analysis

  • Module 2: Methods for impact evaluation

RCT, Matching, Regression Discontinuity, Diff-in-Diff

 

  • Module 3: Methods for economic evaluation

Cost-benefit, cost-effectiveness, cost-utility analysis

 

Learning outcomes

After this course students ...

  1. aware of the importance of the social context of program evaluation,

  2. familiar with the causality concept in impact evaluation and its prerequisites,

  3. familiar with different econometric approaches to identify program effects,

  4. familiar with different approaches how to relate program benefits to its costs and draw conclusions about efficiency,

  5. generally able to critically reflect on different methods of impact and economic evaluation in terms of their limitations and benefits.

     

Attendance requirements

This being a ‘Course with continuous Assessment (PI)’, the university requires students to attend at least 80% of all classes for completing the course successfully.

Teaching/learning method(s)
  • Lectures
  • Examples
  • Group discussions
  • Discussion of distributed papers and examples
  • Application in statistical software (e.g. R/Stata), exercises & examples

 

Assessment
  • 40% (Individual) Written exam, open questions
  • 20% (Group) Completion of small exercises/assignments
  • 30% (Group) Preparation & moderation of hands-on tutorial (Stat. Software, Excel)
  • 10% (Individual) Active participation in class
Availability of lecturer(s)

After class or by e-mail

Last edited: 2021-01-14



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