Survey Design and Data Analysis in Labor Economics

survey-design-bootcamp

This is a one-week training on how household surveys are designed, implemented and used in labor economics research, with hands-on training on data analysis of existing large surveys, namely the Egypt Labor Market Panel Survey (ELMPS) and the Surveys of Young People in Egypt (SYPE). This boot camp is beneficial to young researchers, particularly interested in one of the chosen thematic areas of the training. The first four days focus on providing overall knowledge of the design of a survey, sample and questionnaires and hands-on training in exploring data and running regressions using the Stata statistical package. The last day will consist of a review of related literature, discussions on possible descriptive and analytical analysis and hands-on training using related questionnaire modules and data on each of the thematic topics.

Young researchers, including recent PhD holders and post-doctoral scholars, in the social sciences. Minimum academic requirement: BA degree or in the process of obtaining a BA degree in economics with a minimum requirement of knowledge of statistics and basic econometrics tools.

Rania Roushdy is an associate professor of practice at AUC. Before joining AUC, Roushdy was the senior research manager/associate ii of the Poverty, Gender and Youth Program of the Population Council Egypt Office.

Dina Abdel Fattah is an assistant professor of economics at AUC.

Mina Ayad is an assistant professor of economics at AUC.

May Gadallah is a biostatistician with an interest in the field of gender and youth empowerment. She worked as a statistical consultant for several research agencies, including the World Bank, the Social Contract Center (IDSC, Egyptian Cabinet) and the Population Council.

Ali Rashed is statistics manager at the Economic Research Forum (ERF). Before that, he was a senior data analyst at the Population Council Egypt Office.

Day 1: Survey and sample design

Day 2: Questionnaire design

Day 3: Understanding data through descriptive statistics, tabulations and graphs (Bivariate analysis)

Day 4: Introduction to regression (Multivariate analysis)

Day 5: Topics Session with hands-on applications.

  1. Women’s empowerment (Gender issues and roles)
  2. Wage structure
  3. Inequality
  4. Migration