DAD
A software for
Distributive Analysis / Analyse Distributive
By
Abdelkrim Araar
and
Jean-Yves Duclos
PEP - PMMA TRAINING - ADISS ABABA
June 2006
What is Distributive Analysis?

Distributive analysis is concerned with the distribution and
redistribution of well-being, usually captured by living
standards at the household level.

The distribution of living standards depends dynamically on
a number of factors, such as:
 Average living standards at the level of the population
 Living standards relative to the mean
 The structure of the economy and the distributional
channels of the richness.
 The economic policies in place (redistribution policies)
 Economic shocks
PEP - PMMA TRAINING - ADISS ABABA
June 2006
What is Distributive Analysis?
Main topics linked to distribution and redistribution:






Absolute & relative poverty
Absolute & relative inequality
Polarisation
Vertical & horizontal equity
Redistribution
etc..
PEP - PMMA TRAINING - ADISS ABABA
June 2006
What is Distributive Analysis?

Example of some relatively recent economic shocks in
developing countries:
 Economic transition from planned to market economies
 Application of macro adjustment programs
 Trade liberalisation
 Globalisation

These shocks can have a significant impact on the distribution
of living standards at different levels (regions, countries,
within households).
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Positioning DAD in distributive analysis
The main features of the software can be summarized as follows:
 Free!
 User friendly – no need for programming
 Estimates easily a number distributive indices and curves that
are extensively used in the literature about the distributive
analysis.
 Estimates accurately the sampling distribution of such indices
and curves




by taking into account the sampling design of household surveys
by means of analytical and numerical procedures
Provides tools for testing the robustness of comparisons
Insists on the power of graphs to provide informative pictures
of the distribution of living standards
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Basic descriptive tools:




Estimation of means, quantiles, variances
Non parametric estimation of
 density
 joint density
 non parametric regression between two variables
 regression slopes
Scatter graphs
Important and flexible graphical abilities
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Poverty decomposition

Static decomposition:
 Population subgroups


Income components


FGT index - analytical approach
FGT Index - Shapley approach
Dynamic decomposition:
 Growth and redistribution


Sectoral decomposition


FGT index – analytical & Shapley approaches
Transient and chronic



FGT index – analytical & Shapley approaches
FGT index – analytical approach
EDE index - analytical approach
Absolute transition matrix - analytical approach
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Inequality decomposition

Static decomposition:
 Population subgroups



Income components



S-Gini index - analytical & Shapley approaches
Generalised entropy index - analytical approach
S-Gini Index - analytical & Shapley approaches
Coefficient of variation index – analytical approach
Dynamic decomposition:
 Difference: population subgroups


Difference: income components


S-Gini index- –analytical & Shapley approaches
S-Gini index- –analytical & Shapley approaches
Social welfare

Atkinson index – analytical approach
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Simulations and policy applications






Impacts of income-component growth on Inequality,
poverty and social welfare
Impact of marginal price changes on poverty, social
welfare and inequality
Impact of demographic changes on poverty
Impact of sectoral changes on poverty
Impact of lump-sum targeting on poverty
Impact of inequality-neutral targeting on poverty
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Simulations and policy applications




Gini income-component elasticity
Growth elasticity of poverty
Impact of marginal tax reforms on poverty and
inequality
Impact of reforms to poverty alleviation programmes,
by targeting/allocation effects
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Estimation of curves for descriptive and
normative purposes:









Lorenz & generalized Lorenz curves
Concentration & generalised concentration curves
Quantile and normalised quantile curves
Poverty gap & cumulative poverty gap (CPG) curves
FGT curves
Pro-poor curves
Bi-polarisation curves
Deprivation curves
Consumption dominance (CD) and normalised CD curves
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Checking the robustness of poverty, social
welfare, inequality and equity comparisons

Estimation of stochastic dominance curves for:
 poverty
 social welfare
 inequality (normalised stochastic dominance)
 relative poverty
 indirect tax reforms
 “Efficient” targeting reforms

Estimation of “ critical ” poverty lines for absolute and
relative poverty
 Estimation of crossing points for Lorenz, CPG and
concentration curves
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Estimating sampling distributions

Data from sample surveys usually display four important
characteristics:
 they are stratified;
 they are clustered;
 they come with sampling weights (SW), also called inverse
probability weights;
 sample observations provide aggregate information (such as
household expenditures) on a number of “statistical units”
(such as individuals)
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Simple Random Sampling
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Usual sampling procedures

A country is first divided into geographical or administrative zones
and areas, called strata.

Each zone or area thus represents a strata.

The first random selection takes place within the Primary Sampling
Units (denoted as PSU’s) of each stratum.

Within each stratum, a number of PSU’s are randomly selected. This
random selection of PSU’s provides “clusters” of information.

PSU’s are often provinces, departments, villages, etc. Within each
PSU, there may then be other levels of random selection.
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Usual sampling procedures

For instance, within each province, a number of villages may be
randomly selected, and within every selected village, a number of
households may be randomly selected.

The final sample observations constitute the Last Sampling Units
(LSU’s).

Each sample observation may then provide aggregate information
(such as household expenditures) on all individuals or agents found
within that LSU. These individuals or agents are not selected –
information on all on them appears in the sample.

They therefore do not represent the LSUs in statistical terminology.
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Sampling Design with two levels of random selection
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Sampling design and statistical significance
Strata A
PSU’s
PSU’s
Highest incomes
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Strata B
Lowest incomes
Example:
Figure 1
The SD of piority survey I (1994) of Burkina
EP1: Burkina (1994)
Strata 1
West
Strata 2
South and West South
Strata 3
North Center
Strata 4
South Center
Strata 5
North
Strata 6
Othres Cities
Strata 7
Ouaga Bobo
PSUs
42
PSUs
37
PSUs
98
PSUs
55
PSUs
66
PSUs
39
PSUs
97
LSUs
839
LSUs
737
LSUs
1960
LSUs
1099
LSUs
1288
LSUs
778
LSUs
1938
Total Observations
8639
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Initialising the sampling design

From the main menu one
chooses the item Edit-> Set
Sample Design.

Indicate the variables to set the
sample design and confirm
your choice by clicking on the
button OK.
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Performing statistical inference


Estimating confidence intervals and p-values
 Estimations are included directly for: FGT, SGini and Atkinson indices
 Can be computed via the “Confidence interval”
application in DAD
Testing hypothesis
 Can be performed directly for FGT, S-Gini and
Atkinson indices
 Can be computed via the “Confidence interval”
application in DAD
PEP - PMMA TRAINING - ADISS ABABA
June 2006
DAD and DATA files







Shows two sheets to load simultaneously two data bases
Can read ASCII files safely through a data wizard
Can support copy/paste to and from sheets of the most
common software (Excel, Stata,...)
Offers its own ASCII format for saving data
Can edit variable information and content
Can add or delete observations
Can generate other variables
PEP - PMMA TRAINING - ADISS ABABA
June 2006
DAD & Graphs


Flexible Graph Options
 For example, one can change easily the:
 main title, title of axis and legends
 graph size
 template choice
 color, width and style of curves
Saving DAD’s graphs
 One can save DAD’s graphs in:
 DAD Graph Format *.dgf
 JPEG, GIF, BMP …


One can also save curves’ coordinates in ASCII format
Editing curves’ coordinates in a new data sheet
PEP - PMMA TRAINING - ADISS ABABA
June 2006
How to learn to use DAD?




The book entitled POVERTY AND EQUITY: MEASUREMENT,
POLICY AND ESTIMATION WITH DAD covers most of the
measurement theory implemented in DAD. The book is also a
comprehensive reference for intermediate and advanced
study in distributive analysis.
DAD’s user manual provides tools for fast learning of DAD
and can lead to rapid use of any of DAD’s applications.
Exercises & Technical notes were written to consolidate the
learning of DAD.
Training sessions are regularly organised to teach distributive
analysis and the use of DAD and other software.
PEP - PMMA TRAINING - ADISS ABABA
June 2006
DAD’s data files




With DAD, micro data from household surveys
are typically required.
A database used in DAD is then a matrix (a
number of columns) whose number of lines is
the number of observations
DAD can load simultaneously two databases.
The maximum number of variables for each
DAD file is currently 20.
PEP - PMMA TRAINING - ADISS ABABA
June 2006
DAD’s spreadsheet
PEP - PMMA TRAINING - ADISS ABABA
June 2006
The structure of a data file
I- Sampling design
 Strata Specifies the name of the variable (in an integer
format) that contains the Stratum identifiers.
 PSU Specifies the name of the variable (in an integer
format) that contains the identifiers for the Primary
Sampling Units.
 SAMPLING WEIGHT Specifies the name of the
Sampling Weights variable.
 Finite Correction Gives the Finite Population
Correction variable that is needed when the number
of PSU is small and sampling was one without
replacement.
PEP - PMMA TRAINING - ADISS ABABA
June 2006
The structure of a data file
II- Basic distributive variables
 VARIABLE OF INTEREST. This is the variable that
usually captures living standards. It can be for the
entire household or for individuals (e.g., per capita or
per equivalent adult expenditure).
 SIZE VARIABLE. This refers to the ”ethical” or
physical size of the sampling observation
 GROUP VARIABLE To perform computations at the
group level (integer variable : ex. Rural (1) Urban (2) )
PEP - PMMA TRAINING - ADISS ABABA
June 2006
The structure of a data file
II- Basic distributive variables

GROUP NUMBER tells DAD on which value of the GROUP
VARIABLE to condition the computation of some distributive
statistics. The value for GROUP NUMBER should be an integer.
For example, rural households might be assigned a value of 1 for
some variable denoted as region.

SAMPLING WEIGHT. Sampling weights are the inverse of the
sampling rate.
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Importing data files into DAD
ASCII files




After preparation of the required
variables, one can export an
ASCII file to be read by DAD.
To import safely the data, a
wizard is used in DAD.
One can also use Copy/Paste to
copy data from other software
sheets (that is more risky
however)
A helpful software that can be
used to prepare DAF files is
Stat/Transfer (though a
commercial software)
PEP - PMMA TRAINING - ADISS ABABA
June 2006
Launching DAD’s applications


From the main menu one
can choose the desired
application; applications
are organised by main
themes.
After choosing the
desired application, a
second widows appears
to indicate the number
of distributions or data
files that should be used.
PEP - PMMA TRAINING - ADISS ABABA
June 2006
DAD’s application for the FGT index
PEP - PMMA TRAINING - ADISS ABABA
June 2006
DAD’s window of results
PEP - PMMA TRAINING - ADISS ABABA
June 2006
DAD’s graphs (ex. Lorenz curves)
PEP - PMMA TRAINING - ADISS ABABA
June 2006
DAD’s graphs (ex. difference between Lorenz curves)
PEP - PMMA TRAINING - ADISS ABABA
June 2006
DAD’s graphs (ex. FGT curves (a=0))
PEP - PMMA TRAINING - ADISS ABABA
June 2006
DAD’s graphs (ex. Concentration & Lorenz curves)
PEP - PMMA TRAINING - ADISS ABABA
June 2006
DAD’s graphs (ex. density curves)
PEP - PMMA TRAINING - ADISS ABABA
June 2006
DAD’s graphs (ex. Non parametric regression)
PEP - PMMA TRAINING - ADISS ABABA
June 2006