Theoretical-practical training course on labor markets, poverty, and inequality in Latin America

The targeted audience includes government officials with a technical profile who work in social areas (e.g. ministries of labor, economics, social development, statistical institutes), social analysts in the region, and advanced students and graduates in Economics and other social sciences. Prior experience or knowledge of the Stata software is not required.

The content of the course – carried out in Spanish – is developed over 5 days, with 6 hours of class each day (3 in the morning and 3 in the afternoon). Each day includes a theoretical presentation for each topic followed by the discussion and implementation of empirical examples. In addition, an optative intensive one-day training on the basic use of Stata software is offered – at no additional cost.

Day 1: Analitic Tools

• Welcome and introduction to the course.
• Overview of labor markets, poverty and inequality in Latin America.
• Information sources: censuses, household and labor surveys.
• Household surveys in Latin America: typology and characteristics.
• Working with household surveys: summary measures and graphical tools.
• Variables for the distributive analysis: income and consumption; labor and non-labor income.
• The construction of income and household consumption based on household surveys.
• Practical problems: adjustment by demographic factors, price adjustments, non-response and underreporting.
• Implementation in Stata.

Day 2: Labor Markets – Basic Aspects

• Characterization and measurement of basic labor market indicators: labor force, unemployment, underemployment; youth unemployment; gender approach.
• Occupational categories: salaried and self-employed. Characterization of the precariousness of employment. Involuntary underemployment. Duration of unemployment.  Workers profiles.
• Informal employment: productive and legal definitions. Security and social protection. The models of binary variables (probit and logit). Application for a characterization of unemployed population. Multinomial models.
• Determinants of income. Theory of human capital: returns to education and training differentials. Mincer equations. Estimation of parameters.
• Methodological problems: selection bias, nonobservability of relevant variables, truncated variables.
• Implementation in Stata.

Day 3: Labor Markets and Related Policies

• Social protection systems, incentives and informality. Universality, targeting, contributory benefits and overlaps.
• Characteristics of formal and informal employment: types of non-wage benefits and their incidence by population group.
• Minimum wages in the context of segmented labor markets. Distributive effects, externalities and the “Lighthouse Effect”.
• Empirical evidence for Latin America.
• Implementation in Stata.

Day 4: Poverty

• Dimensions of poverty: monetary, multidimensional and subjective.
• National and international poverty lines.
• Poverty measures: incidence rate, poverty gap, FGT, Sen, Watts.
• Decompositions of poverty level and poverty profiles.
• Relationship between labor market results and poverty levels.
• Empirical evidence for Latin America and the world.
• Implementation in Stata.

Day 5: Inequality

• Conceptions of equity. Equity as equality of results and opportunities. Sen approach.
• Dimensions of equity: inequality, polarization and mobility. Aggregate welfare.
• Inequality and labor markets: Determinants of training differentials. Supply and demand factors. Technological change and trade liberalization.
• Empirical evidence for Latin America and the world.
• Implementation in Stata.

The use of the software Stata will cover, throughout the course, the topics listed below (*).

• Introduction: Interface (windows), and command syntax.
• The database: import, export, open, save, combine, edit, describe, transform, variables (create, replace, rename, sort, delete, functions, logical conditions).
• Descriptive analysis: basic statistics, tabulation of data, correlations, graphs, use of weights or expansion factors.
• Programming 1: use of do and log files, local and global macros, loops and conditions, and scalars and matrices.
• Regression analysis: estimation by ordinary least squares, hypothesis test, and exporting the results.
• Programming 2: program creation, passage of arguments: args and syntax statements, and program flow control (loops and if-then-else conditions).

(*) The first three subjects will be covered, for the most part, during the optional course day on Stata, which is held on the Saturday before the course.