To analyse the effects of economic and social policies on individuals and firms, one needs to use very often micro data that provide information on numerous such individuals or firms at a given point in time, or at successive ones. The primary goal of the analysis of micro data is to estimate the causal effect of a policy (or a characteristic) on an outcome of interest while using the as few and as weak assumptions as possible in order to enhance the credibility of the results. It is thus important to determine which assumptions are the ones that drive the results in any given setting, and to be able to use theory, intuition and judgment to decide whether such assumptions are justified.

Microeconometrics provides us with a rich menu of techniques that can evaluate both assumptions and results and provide statistical inference on the effects of interest. The course presents both the theoretical foundations and the empirical content of these techniques, using empirically relevant examples from economics and other social sciences. It includes 10 computer lab sessions in order to provide hands-on experience with empirical work using various micro datasets.

One two-hour lecture per week for 10 weeks.

One two-hour computer lab per week for 10 weeks.

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Assessment

The main aim of this course is to provide students with tools that are useful for analysing micro data, with an emphasis on the estimation of causal effects. The lectures present advanced linear and nonlinear statistical models that can be used for analysing micro data and integrate them with theoretical concepts when appropriate. The labs focus on implementing advanced statistical models using the Stata software, and at the same time they allow students to become familiar with various micro data sets on individuals and households.

By the end of this course students will be able to:

1. Evaluate the scope of estimating causal effects in micro data settings using theory, intuition and judgment.

2. Assess the role of the assumptions used to estimate empirical models, and the associated problems that such assumptions create for the credibility of the results.

3. Build empirical models in order to analyse micro data.

4. Programme in Stata in order to estimate causal effects and test their underlying statistical assumptions.

5. Collaborate effectively within a group work environment.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.