Lugano, Switzerland

Applied Panel Data Analysis

Does a constant poverty rate of 10% mean no change? While cross-sectional data would suggest so, panel data might show that while half of the poor remain poor, the other half exits from poverty and half of the newly poor come from the former non-poor. In addition to such descriptive advantages, panel models need fewer assumptions for causal claims compared to cross-sectional models since – by relying on within-variation – they suffer less from selection effects. To understand the within perspective of panel data as opposed to the between perspective of cross-sectional data is the core of this workshop.

Panel data are repeated measures of the same individuals, the same companies or other units. This course introduces different strategies for panel analysis by providing the skills for choosing the appropriate models, data preparation and result interpretation. We focus on understanding the mechanism behind the different approaches. Participants learn to assess the advantages and limitations of different panel models, given their research questions and available data (sample size, number of waves, amount of within and between variance).

Panel regression models include pooled OLS, change score, fixed effects, random effects, difference-in-difference, and first difference models. In applications, difficulties for data analysis arise due to non-response and attrition or changed measurement tools (e.g., change of survey modes). We use real data and discuss the implications of these issues for data analysis. Examples are mainly from the Swiss Household Panel (SHP), which contains data over more than 20 years, covering topics from different social science disciplines and including different kinds of data files (individual, household, one-time collected information, events). We work in addition with two and three-wave panels, as well as small-N panel data.

In the morning sessions, we present the theory and applications using panel data. In the afternoon sessions, participants will work on practical exercises either with data prepared by the instructors from the SHP or with panel data of their own choice. Other types of panel data with different observational units (e.g., schools, companies) are welcome. As two are two instructors, there is time available to discuss and solve individual problems.

Course leader

Oliver Lipps & Ursina Kuhn

Target group

graduate students, doctoral researchers, early career researchers, experienced researchers

Prerequisites

We will use the Stata software and assume some knowledge of Stata with cross-sectional data. For students not sufficiently proficient in Stata we offer a two-day preparatory online course shortly before the workshop introducing Stata. We will also provide syntax for R users for those who wish to work with R, but we will only discuss syntax in Stata.

Credits info

The Summer School cannot grant credits. We only deliver a Certificate of Participation, i.e. we certify your attendance.

If you consider using Summer School workshops to obtain credits (ECTS), you will have to investigate at your home institution (contact the person/institute responsible for your degree) to find out whether they recognise the Summer School, how many credits can be earned from a workshop/course with roughly 35 hours of teaching, no graded work, and no exams.

Make sure to investigate this matter before registering if this is important to you

Fee info

CHF 700: Reduced fee: 700 Swiss Francs per weekly workshop for students (requires proof of student status).
CHF 1100: Normal fee: 1100 Swiss Francs per weekly workshop for all others.