79217 - Analysis of Income, Poverty and Inequality

Course Unit Page

  • Teacher Maria Ferrante

  • Credits 6

  • SSD SECS-S/03

  • Teaching Mode Traditional lectures

  • Language Italian

  • Campus of Bologna

  • Degree Programme Second cycle degree programme (LM) in Statistics, Economics and Business (cod. 8876)

  • Course Timetable from Nov 09, 2021 to Dec 15, 2021

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

No poverty Good health and well-being Decent work and economic growth Reduced inequalities

Academic Year 2021/2022

Learning outcomes

By the end of the course, the student is aware of the statistic methods for analysing poverty, inequality and income distribution.

The student is able:

  • to estimate parameters in income distribution models
  • to use income poverty and inequality indicators
  • to read official statistical information on this subjects
  • to interpret the trend of poverty and inequality

The student is introduced to statistical software R with particular reference to applications in poverty, inequality and income distribution.

 

 

 

Course contents

  1. Evidences and motivations

    Illustrative examples on poverty, inequality and income distribution in Italy, Europe and in the world. Why is so important to measure income, poverty and inequality? Goals and outline of the course.

  2. Introduction and definitions

    GDP and disposable income. Functional and personal income distribution. The equivalent income.

  3. Sample surveys and administrative sources

    The main sample surveys on income and wealth. Administrative fiscal data on income.

  4. Poverty

    Absolute and relative poverty. Poverty threshold. Laeken indicators. Multidimensional poverty. Deprivation index.

  5. Inequality

    Statistical and axiomatic approaches. Concentration indexes. Lorenz dominance. Entropy measures. The normative approach. Inequality decomposition.

  6. Estimation of poverty and inequality indicators

    Estimators in complex surveys. Variance estimation.

  7. Statistical income distribution

    Usual parametric models (Lognormal, Pareto, Singh-Maddala, Dagum, GB2)

  8. Some further issues

    Causal models, economic growth and inequality, wealth distribution, policies to address poverty and inequality, poverty mapping, measuring well-being.

  9. R packages on Laeken indicators, inequality measures and income distribution

Readings/Bibliography

Baldini M., Toso. S. (2009), Diseguaglianza, povertà e politiche pubbliche, Bologna, Il Mulino.

Wolff E. N. (2009), Poverty and income distribution, Wiley-Blackwell.

Alfons A., Templ M. (2013), Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken, Journal of Statistical Software, 54, 15, 1-25

Graf M., Nedyalkova D. (2014), Modelling of income and indicators of poverty and social exclusion using the Generalized Beta distribution of the second kind, The review of Income and Wealth, 60, 4, 821-842.

For further in depth information:

Atkinson A.B., Bourguignon, F. eds., Handbook of Income Distribution (vol. 2A, 2014 - vol. 2B, 2015), Elsevier, North Holland, Amsterdam.

Extra material will be provided by the Prof.

Teaching methods

Lab based on the open source software R and classroom lectures.

As concerns the teaching methods of this course unit, all students must attend Module 1, 2

Assessment methods

The final examination aims at evaluating the achievement of the following objectives:

  • deep knowledge concerning theoretical topics covered during the lectures;
  • ability to analyze real data;
  • ability to use R software
  • ability to use empirical evidences to interpret the phenomenon.

The final test will consist of a written test. Questions may have closed open-ended format, and may regard methodology, interpretation of the output of the statistical software used in computer sessions, simple exercises.

Students may participate in not-compulsory software tests during the last week lessons to earn maximum 2 bonus points for the final exam. The bonus points are valid in case the student passes the exam by February.

Teaching tools

Teaching material is downloadable from the web page:

https://virtuale.unibo.it/course/view.php?id=26051

Software R, downloadable from http://www.r-project.org/ .

Office hours

See the website of Maria Ferrante