National labour force surveys (LFS) are the main source behind essential headline indicators of the labour market and the world of work. A wide range of economic and social policies, from monetary and fiscal policies to employment, decent work, vocational education and training, poverty reduction and social inclusion policies depend on data derived from labour force surveys for informed decision-making and monitoring. At the international level, LFS are likewise recognized as a primary source to produce a number of SDG indicators under Goals 8 (Decent Work) and 5 (Gender Equality) following internationally agreed concepts and definitions that enable cross-country comparisons.
This applied short course provides an introduction to labour force surveys as a main source of labour force statistics and indicators, based on internationally agreed concepts and definitions. During the first part of the course, students will become familiar with the key methodological and design features of LFS, the topic and indicator coverage, and the current state of LFS practices around the world.
Focusing on selected headline labour market and decent-work related SDG indicators, the second part of the course will further introduce how internationally agreed concepts and definitions are adapted and implemented in a survey questionnaire for measurement. Students will learn about the role of the International Conference of Labour Statisticians (ICLS) in setting standards for key labour force statistics and indicators, and how these concepts are translated into survey questions.
The course combines interactive lectures with group exercises to help reinforce the information presented.
The object of the course is to present the theoretical and practical aspects involved in the use of administrative data for socio-economic research.
During the course the students will learn:
As regards the contents, the course will firstly present which are the current international practices and recommendations about the use of administrative data for statistical purposes, discussing why and how administrative data have come to a prominent role in the production of statistical information about socio-economic systems. The rest of the course will be devoted to present the main steps in the design of socio-economic data bases built starting from administrative information:
The course will the taught using as a leading example a longitudinal data base on health and work histories developed for the Italian Minister of Health.
Analysing Labour Force Surveys (LFS) in developing countries is at the core of labour economics for development.
This applied course on analysing LFS data consists of providing the students with the essential labour economist tool box for working on developing countries, given data scarcity and imperfections in these countries. This includes: understanding how to manage a LFS using the statistical package STATA (including reading and using a LFS questionnaire, understanding the sampling design of the survey), producing and discussing simple labour market statistics (in particular knowing which ones could be the most relevant in developing countries), identifying vulnerable groups of workers using the ILO decent work indicators and, finally, performing basic econometrics, such as manipulating earnings equations, decomposing the gender earnings gaps, and conducting econometric analysis of unemployment. This course finally provides knowledge on how to establish a labour market profile to monitor labour market conditions.
The organization of the course will be as follows: (1) through practical computer-based exercises, establishing a profile of vulnerable groups in the labour market using LFS data on one or several African countries; (2) using a series of applied exercises to look at some of the determinants of labour market outcomes (earnings, employment, unemployment). Special attention will be devoted to producing indicators that are relevant to gender issues and the rural/urban divide. The course will conclude with an overview of related data needs.