Aim & Structure:
This course aims to provide a conceptual and technical introduction to current literature and techniques of poverty measurement with a focus on the implementation of the Alkire Foster counting method. This first course will provide an idea of the relevance of multidimensional measurement and the intuition behind a local implementation.
The programme will include a policy-focused sharing session on the international experience, a comprehensive summary of the current state of the art, the methodology and an intuitive explanation of the implementation process. The course also provides participants the opportunity to design, tailor and calculate a multidimensional poverty measure based on the Alkire Foster methodology. Theoretical lessons will be complemented with empirical calculations (in groups).
At the end of the course, participants will:
1. Know arguments for the relevance of multidimensional poverty measures.
2. Understand why and how multidimensional poverty measures add value to unidimensional measures and
3. Be able to compute the Alkire Foster multidimensional poverty measures and adapt parameters to their own requirements and contexts.
The course is practical, rather than theoretical, and focused on ‘learning by doing’. It is aimed at economists and statisticians who actually implement the techniques, and those who hire them, supervise their work, and verify the quality of the measures.
Data analysts are often interested in describing the association(s) between cross-tabulated categorical variables. Log-linear models are commonly used for modelling contingency tables, where the cell counts are treated as the response variable. A classic example is a 2-way social mobility table, where father's social class is cross-classified by son's social class, and we are interested in describing the association (interaction structure). Standard models for square tables discussed include quasi-independence, quasi-symmetry and symmetry as well as newer models which impose interpretable structures on the interactions, such as the UNIDIFF and row-column association models from sociology. For higher-dimensional tables, log-linear models can be used to study whether 2-way, 3-way or higher-way associations exist among the variables and to estimate their strength of association. Course emphasis will be placed on the usefulness and interpretation of the log-linear models presented using ideas such as conditional independence and mathematical graphs which visually represent the conditional independence structure between variables.
The course is suitable for those who are completely new to R, but some practice with R before the course is strongly recommended (a worksheet will be provided). Familiarity with linear regression will be assumed, but some basic knowledge of logistic regression and/or log-linear modelling is desirable as well as familiarity with the concepts of odds and odds ratios.