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DASP Distributive Analysis Stata Package By Abdelkrim Araar. Main objective of the DASP project. Main objective

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dasp distributive analysis stata package by abdelkrim araar
DASPDistributive Analysis Stata PackageByAbdelkrim Araar

PEP - PMMA TRAINING - ADISS ABABA June 2006

main objective of the dasp project
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.

PEP - PMMA TRAINING - ADISS ABABA June 2006

why dasp is very helpful
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.

PEP - PMMA TRAINING - ADISS ABABA June 2006

what is already available in stata for distributive analysis
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.

PEP - PMMA TRAINING - ADISS ABABA June 2006

in which way does dasp differ from these available modules
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.

PEP - PMMA TRAINING - ADISS ABABA June 2006

in which way does dasp differ from these available modules1
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.

PEP - PMMA TRAINING - ADISS ABABA June 2006

in which way does dasp differ from these available modules2
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.

PEP - PMMA TRAINING - ADISS ABABA June 2006

dasp pep projects
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

PEP - PMMA TRAINING - ADISS ABABA June 2006

recommanded organisation
Recommanded Organisation

PEP - PMMA TRAINING - ADISS ABABA June 2006

already realised in dasp
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

PEP - PMMA TRAINING - ADISS ABABA June 2006

already realised in dasp1
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

PEP - PMMA TRAINING - ADISS ABABA June 2006

already realised in dasp2
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

PEP - PMMA TRAINING - ADISS ABABA June 2006

already realised in dasp3
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.

PEP - PMMA TRAINING - ADISS ABABA June 2006

already realised in dasp4
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)

PEP - PMMA TRAINING - ADISS ABABA June 2006

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