1. PEP - PMMA TRAINING - ADISS ABABA June 2006 DASP Distributive Analysis Stata Package By Abdelkrim Araar

2. PEP - PMMA TRAINING - ADISS ABABA June 2006 Main objective of the DASP project Main objective  The main aim of this project is to produce a complete package of STATA modules to analyze the distribution of living standards. This is important for measurement as well as policy purposes. Why use the STATA software?  The STATA software has become in the last 20 years a very popular tool to transform and process data. It comes with a large number of basic data management modules that are highly efficient for transformation of large datasets. The flexibility of STATA also enables programmers to provide specialised “.ado” routines to add to the power of the software.

3. PEP - PMMA TRAINING - ADISS ABABA June 2006 Why DASP is very helpful?  DASP (Distributive Analysis STATA Package) can be used easily by all researchers that are interested to produce results and to perform the distributive analysis.  Most interest, this package should be very helpful for researchers in developed countries that can facilitate computation of the desired distributive indices, as well as, the application of the most recent approaches in this field by maintaining an updated form of this package.

4. PEP - PMMA TRAINING - ADISS ABABA June 2006 What is already available in STATA for distributive analysis?  Some STATA “.ado” files already exist for the computation of some specific distributive indices or for plotting some distributive curves.  Furthermore, the few available modules has the following inconvenient : They do not have an unified syntax In some cases, basic option like to weight the data are not available Standard errors are not furnished or do not take into account the design effect. Programs are not optimised to run quickly and some bug can be encountered.

5. PEP - PMMA TRAINING - ADISS ABABA June 2006 In which way does DASP differ from these available modules? The modules of DASP would be designed to:  Cover the most popular indices and curves in the field of distributive analysis.  Support the analysis by using more than one data base  Perform the most popular decomposition of poverty or inequality indices  Unify the syntax and the provision of parameters. Other softwares and DASP DAD  Free.  Very user friendly for the user  Has a broad coverage of distributive analysis.  A limitation is the maximal number of variables (20)  It is not designed to provide basic data processing tools.  Does not support missing values.

6. PEP - PMMA TRAINING - ADISS ABABA June 2006 In which way does DASP differ from these available modules? The modules of DASP would be designed to:  Cover the most popular indices and curves in the field of distributive analysis.  Support the analysis by using more than one data base  Perform the most popular decomposition of poverty or inequality indices  Unify the syntax and the provision of parameters. Other softwares and DASP DAD  Free.  Very user friendly for the user  Has a broad coverage of distributive analysis.  A limitation is the maximal number of variables (20)  It is not designed to provide basic data processing tools.  Does not support missing values.

7. PEP - PMMA TRAINING - ADISS ABABA June 2006 In which way does DASP differ from these available modules? POVCAL  Covers few tools and indices  Is not particularly user friendly  Is not designed to treat micro data.

8. PEP - PMMA TRAINING - ADISS ABABA June 2006 DASP & PEP Projects Many PEP projects, for the wave 2005/06, have already beneficed from the DASP package. The advantage of using directly these modules with Stata are:  Exploiting all facilities of Stata for the treatment of the data.  Writing do programs to keep all procedures of the treatment of the data, variables transformations, recoding, etc.  Writing the do file for all estimations  This organisation allow to the researchers to improve or correct theirs estimations with few time and energy

9. PEP - PMMA TRAINING - ADISS ABABA June 2006 Recommanded Organisation Project (*.do files) DASP (Required *.ado files) data tables (*.log files) graphs

10. PEP - PMMA TRAINING - ADISS ABABA June 2006 Already realised in DASP Distributive Indices ifgt.ado isgini.ado Distributive curves clorenz.ado clorenz can produces the following distributional curves for a given list of variables: Lorenz curves Generalised Lorenz curves Absolute Lorenz curves Concentration curves Generalised concentartion curves Absolute Concentration curves Deficit share curves (p-L(p)) Diffrence between Lorenz curves

11. PEP - PMMA TRAINING - ADISS ABABA June 2006 Already realised in DASP Distributive curves cfgt.ado can produces the following distributional curves for a given list of variables: FGT curves (Foster, J. E., J. Greer, and E. Thorbecke curves) Normalised FGT curves cquantile.ado can produces the following distributional curves for a given list of variables: Quantile curves Normalised quantile curves Absolute quantile curves cdepriv.ado can produces the following distributional curves for a given list of variables: Deprivation curves Absolute Deprivation curves

12. PEP - PMMA TRAINING - ADISS ABABA June 2006 Already realised in DASP Distributive curves cdensity.ado Basing on the Kernel-Gaussian approach, cdensity produces the density curves for a given list of variables or according to population groups. cnpe.ado produces the non parametric regression curves for a given list of variables or according to population groups according to one of the two following approaches: Nadaraya-Watson approach is used. One can also perform regressions with the Linear locally approach

13. PEP - PMMA TRAINING - ADISS ABABA June 2006 Already realised in DASP Poverty dominance povdom.ado Check the poverty dominance for the first, second and the third order of dominance in poverty. Estimates all possible intersections Inequality dominance ineqdom.ado Check the inequality dominance basing on the comparison between the Lorenz curves. Estimates all possible intersections Consumtion dominance curves ccdom.ado ccdom produces the consumption dominance curves (CD) and normalised CD curves for a given list of variables or according to population groups.

14. PEP - PMMA TRAINING - ADISS ABABA June 2006 Already realised in DASP Polarisation Duclos, Esteban & Ray Index (poder.ado) Decomposition Transient and chronic poverty  Jalan & Ravallion approach (dtcjr.ado)  EDE approach (dtcda.ado)