Présentation de Nadia Belhaj Hassine, International Development Research Center, Egypt, à la Conférence Internationale d'Experts sur la mesure et les approches politiques pour améliorer l'équité pour les nouvelles générations dans la région MENA à Rabat, Maroc du 22 au 23 mai 2012.

1. Econometric approaches to measuring child inequalities in MENA
International Experts Conference, UNICEF
Rabat, Morocco 22-23 May 2012
Nadia Belhaj Hassine
nbelhaj@idrc.org.eg
1

2. Presentation Outlines
Inequality & Equity
 Inequality of outcomes along economic dimensions
 Inequality of outcomes along non-economic
dimensions
Inequality of opportunity: A parametric approach
Inequality of opportunity: A non-parametric approach
2

3. Inequality & Equity
Inequality:
Focus is on how equal is the distribution of some economic and non economic
dimensions of welfare (ex-post realization)
Equity (or Inequality of Opportunity):
Focus is on the ex-ante potential to achieve welfare outcome.
Usual measures of inequality (Gini, Theil etc.) fail to capture deeper layers of
inequality that may account for the sense of unfairness in Arab countries where
the level of inequality is moderate.
Understanding the sources of inequality is important for devising policies that
address its underlying causes, especially the role of unequal opportunities.
3

4. Inequality of Outcomes Along
Economic Dimensions
Child inequalities can be measured along income,
wealth or expenditures of the household:
Define & harmonize the well-being indicator: Inequality
measures are sensitive to the items included in the
expenditure aggregates: apples need to be compared to
apples.
Adjust for HH composition: equivalence of scale
Adjust for spatial and temporal price differences
4

5. Common tools to measuring inequality
Lorenz Curve
Gini Index
General Entropy: GE(0), GE(1), GE(2)
GE indices are decomposable into within group and between group
measures of inequality
k groups in a population (identified by location, education, gender , etc.)
K
k
GE( ) (k) GE(k; ) G E ( )
k 1
within between
ϕ(k) is the proportion of the population in group k
μk is the mean income of group k
GE(k;θ) is the GE index of group k
G E ( ) is the GE index of the population if each member of group k was assigned
income μk
5

6. Inequality Determinants
Standard decomposition techniques identify potential determinants of
inequality …and lay the foundation for deeper analysis.
An important limitation of summary measures of inequality and standard
decomposition techniques is that they provide little information regarding
what happens where in the distribution.
6

7. Inequality Determinants
Use the Recentered Influence Function (RIF) regression by Firpo,
Fortin, Lemieux (2010) to decompose the welfare gaps at
different quantiles of the unconditional distribution into the
part explained by the difference in the distributions of
observed household characteristics (between regions, urban-
rural, over time etc.) and the part that is explained by the
difference in the distributions of returns to these
characteristics.
These components are then further decomposed to identify the
specific characteristics which contribute to widening the welfare
gap. 7

8. Unconditional Quantile Regression Decomposition
8

9. Unconditional Quantile Regression Decomposition
9

10. Expenditures and summary measures of inequality ($PPP Cst 2004)
Food Expenditure Expend. Food & Non Durables Total Expenditure
Mean Median Gini Theil Mean Median Gini Theil Mean Median Gini Theil
Egypt
2000 49.42 42.03 0.26 0.12 93.93 71.87 0.33 0.23 104.69 80.22 0.34 0.24
2005 51.18 44.24 0.26 0.12 94.05 74.8 0.32 0.2 107.71 85.57 0.32 0.2
2009 40.72 35.7 0.26 0.12 85.43 69.28 0.31 0.19 101.23 80.93 0.31 0.2
Iraq
2007 47.06 39.92 0.31 0.17 101.1 80.08 0.36 0.23 148.82 114.58 0.37 0.26
Jordan
2006 62.53 51.89 0.33 0.21 156.42 123.71 0.34 0.21 196.39 151.4 0.36 0.24
2008 66.91 56.27 0.31 0.17 158.19 126.75 0.33 0.19 195.87 153.04 0.34 0.21
Libya
2003 52.08 43.32 0.32 0.19 99.95 84.49 0.31 0.18 136.5 114.43 0.31 0.17
Mauritania
2000 44.12 34.33 0.39 0.28 53.59 40.35 0.41 0.31 55.26 41.38 0.41 0.32
2004 94.77 59.79 0.48 0.46 118.72 80.32 0.45 0.4 121.48 81.32 0.45 0.41
Palestine
1996 43.71 37.88 0.29 0.15 107.3 87.22 0.35 0.22 134.3 106.2 0.35 0.23
2009 43.18 35.88 0.32 0.19 121.5 94.83 0.36 0.24 151.5 114.1 0.38 0.26
Syria
1997 51.79 43.99 0.29 0.15 83.27 68.42 0.32 0.19 83.67 68.72 0.32 0.19
2004 80.55 65.27 0.33 0.19 144.6 108.5 0.38 0.27 165.5 126.6 0.36 0.25
Tunisia
2005 72.72 60.56 0.33 0.21 162.6 120.1 0.41 0.3 210.5 153.4 0.41 0.33
Yemen
10
1998 49.69 41.71 0.33 0.18 90.1 74.51 0.33 0.2 102.3 77.5 0.38 0.28

11. Standard Decomposition by HH attributes
Education Gender Age Emp.stat. Fam. type Marital Region Urban/Rural
Egypt
2000 27.00% 0.10% 0.80% 1.30% 18.30% 0.80% 26.50% 20.50%
2004/5 24.30% 0.60% 2.00% 1.80% 19.40% 1.90% 22.00% 18.00%
2008/9 23.20% 0.40% 1.90% 2.10% 19.80% 0.90% 19.90% 17.70%
Iraq
2007 4.40% 0.10% 0.20% 1.90% 16.20% 0.70% 19.40% 8.50%
Jordan
2002 17.40% 0.60% 3.80% 2.50% 23.60% 2.00% 9.70% 2.50%
2008 15.40% 1.00% 6.90% 6.20% 24.60% 2.10% 11.90% 3.40%
Libya
2003 2.10% 1.00% 4.10% 0.10% 29.70% 2.10% 0.90% 0.30%
Mauritania
2004 4.10% 0.10% 1.20% 1.00% 9.70% 0.20% 0.40% 0.60%
Palestine
1996 8.10% 0.20% 0.60% 2.70% 19.80% 1.30% 7.30% 11.80%
2009 5.80% 0.70% 4.30% 3.60% 18.90% 1.70% 4.50% 0.60%
Syria
1997 3.10% 0.40% 1.50% 1.30% 14.90% 0.90% 0.70% 0.80%
2004 4.70% 3.40% 4.40% 6.00% 26.40% 6.90% 4.40% 6.00%
Tunisia
2005 22.20% 0.10% 0.70% 2.20% 8.80% 11.50%
Yemen
1998 9.40% 0.00% 1.10% 1.50% 11.70% 0.30% 12.60% 11.60%
2006 7.30% 0.30% 0.40% 0.40% 8.50% 0.90% 7.00% 10.60%
11

12. Between-Groups Decomposition
 Education, family type and regional location of the HH are
the most important determinants of overall inequality.
 Slight decline over time of the importance of Head educational
attainment as a determinant of inequality
 Signs of income convergence between urban and rural areas and
across regions in Egypt and Yemen.
 The evaluation of between groups inequality against the
maximum benchmark proposed by Elbers et al. 2007 confirm
the consistency of these results.
12

13. Unconditional Quantile Regression Decomposition
Returns effects and endowment effects by Area for Egypt 2000 Returns effects and endowment effects by Area for Egypt 2009
.6
.6
Difference in log real per capita total expenditures
.4
.4
.2
.2
0
0
-.2
.1 .2 .3 .4 .5 .6 .7 .8 .9 .1 .2 .3 .4 .5 .6 .7 .8 .9
Quantiles Quantiles
Confidence interval / endowment effect Confidence interval /returns effect Confidence interval / endowment effect Confidence interval /returns effect
Endowment effect Returns effect Endowment effect Returns effect
13

14. Unconditional Quantile Regression
Decomposition
• Dominance of endowments effects: welfare gap is
caused primarily by the fact that urban households have
superior characteristics
• Endowment effects and returns effects are both larger
at higher quantiles, resulting in a larger urban–rural gap
at higher quantiles.
• The Gap decreased over time except for the lowest
quantile. The returns effects increased over time while
the endowments effects decreased.
14

15. Returns effects and endowment effects by Region for Egypt 2000
.6
.4
.2
0
.1 .2 .3 .4 .5 .6 .7 .8 .9
Quantiles
Confidence interval / endowment effect Confidence interval /returns effect
Endowment effect Returns effect
Returns effects and endowment effects by Region for Egypt 2009
.5
.4
.3
.2
.1
.1 .2 .3 .4 .5 .6 .7 .8 .9 15
Quantiles

16. Returns effects and endowment effects by Area for Syria 2004
.35
.3
.25
.2
.15
Returns effects and endowment effects by Area for Syria 1997
.1
.15
.1 .2 .3 .4 .5 .6 .7 .8 .9
Difference in log real per capita total expenditures
Quantiles
Confidence interval / endowment effect Confidence interval /returns effect
Endowment effect Returns effect
.1
.05
0
.1 .2 .3 .4 .5 .6 .7 .8 .9
Quantiles
Confidence interval / endowment effect Confidence interval /returns effect
Endowment effect Returns effect
16

17. Returns effects and endowment effects by Region for Iraq 2007
.4
.2
0
-.2
Returns effects and endowment effects by Area for Iraq 2007
-.4
.5
.1 .2 .3 .4 .5 .6 .7 .8 .9
Difference in log real per capita total expenditures
Quantiles
Confidence interval / endowment effect Confidence interval /returns effect
.4
Endowment effect Returns effect
.3
.2
.1
0
.1 .2 .3 .4 .5 .6 .7 .8 .9
Quantiles
Confidence interval / endowment effect Confidence interval /returns effect
Endowment effect Returns effect
17

18. Unconditional Quantile Regression Decomposition
 Differences in characteristics such as hhsize, source of
income and % of child under 14 matter the most
important for lowest quantiles, while differences in
educational attainment and experience matter much
more for those who are well off.
 The gap due to differences in educational attainment is
decreasing over time while the gap due the returns to
education is widening:
 Urban markets are now paying more for educational
and experience attributes than rural markets would.
18

19. Unconditional Quantile Regression Decomposition
Regional differences in HH characteristics
matter more than differences in returns to those
characteristics at the bottom of the distribution
At the higher quantiles the welfare gap is caused
primarily by the differences in returns, to those
characteristics even though Metropolitan HH
have superior characteristics.
Convergence of welfare levels between
Metropolitan and the other regions despite an
increase in the magnitude of the returns effects
(returns to education particularly)
19

20. Non-Economic Welfare
• Inequality measures can be applied to non-economic
outcomes
– Health: Anthropometric measures of child nutrition:
• Weight-for-Height (W/H)
• Height-for-Age (H/A)
• Weight-for-Age (W/A)
– Education:
• Years of schooling
• Test scores
20

21. Standardizing the Measures
• Comparison is with distribution in ref. pop. for
individuals of same sex and age (in months) or
height
• Three ways of comparing to ref. population:
– z-score (std. deviation score): difference between
value of indicator and median of reference population
divided by std. deviation of reference pop.
– Percent of Median: ratio of value of indicator and
median value for ref. pop.
– Percentile rank: rank position of individual on
reference distribution expressed as percent of group
the individual equals or exceeds
• All three standardized measured are calculated in
DHS 21

22. Standardized Indicators
• z-score is preferred:
– Allows for calculation of means and std. dev. Of
populations and sub-population, which cannot be
done using percentiles
– Changes at the extremes will not be necessarily
reflected in changes in percentiles
– Percent of median does not correct for the variability
in the reference population
• Criteria for malnourishment when using z-scores
– z-score of -2 or lower (two standard deviations below
the reference median) is typical cutoff
22

23. Health Inequality Measures
• Mean health indicator by quintile of an
economic welfare measure
– Grouped measure of health disparity
• Concentration Curves
– Captures how the distribution of the health
variable relates to the distribution of a variable
measuring living standards, which ranks
individuals from poorest to richest
• Concentration Indices
23

24. Inequality of Education
• Two main measures of education inequality
– Standard deviation of schooling measures the
absolute deviation
– Education Gini measures relative inequality
• The measure can be used to examine
inequality in attainment (years of schooling),
financing or enrollment.
24

25. Education Gini
• Just like the calculation of any Gini, education
Gini can be calculated as follows if individual data
on educational attainment is available
n n
1
Gini yi yj
2n 2 i 1 j 1
But if only grouped data is available, then
M M
1
Gini pi p j y i yj
2 i 1 j 1
Where pi , pj are the prop. of pop. with level of
schooling i, j.
yi, yj are the years of schooling for levels i and j
25

26. Parametric Approaches to Measuring
Inequality of Opportunity
(Roemer 1998)
Outcome (income, education, status…)
Circumstances Effort
(race, gender, parents background, region of birth..)
Outside the individual control Individual responsible choices
Inequality due to circumstances: Inequality due to effort
Inequality of opportunity
26

27. Methodology
Simulate the reduction in overall inequality that would
be attained if circumstance were equalized. The
difference between the observed and the
counterfactual inequality is interpreted as a measure
of inequality of opportunity.
Bourguignon, Ferreira and Menedez (2007)
27

28. The empirical model
The earnings function can be specified in the following
log-linear form :
ln( yi ) Ci Ei vi
ln( yi ) Ci i
28

29. The empirical model
• The counterfactual distribution is obtained by
replacing yi with its estimated value, from the
reduce form: ~yi exp C ˆ ˆi
~~
I F y IF y
I
I F y
where I(F) is the inequality measures (Gini, Theil, ..)
defined on the outcome distribution.
29

30. Total Partial Effects
Total IOP Opp. share Gender Moth.Edu. Fath.Edu. Bir Reg.
Rural 0.404*** 0.030*** 0.075*** 0.006 0.004 0.003 0.031***
(0.061) (0.004) (0.014) (0.035) (0.004) (0.007) (0.003)
Urban 0.423*** 0.086*** 0.203*** 0.060*** 0.050*** 0.063*** 0.046***
(0.028) (0.008) (0.020) (0.010) (0.010) (0.008) (0.009)
Men 0.412*** 0.067*** 0.162*** 0.027** 0.053*** 0.032***
(0.031) (0.007) (0.016) (0.009) (0.012) (0.008)
Women 0.445*** 0.097*** 0.219*** 0.009 0.006 0.005
(0.069) (0.010) (0.039) (0.009) (0.009) (0.010)
2006
Age 29 0.345*** 0.043*** 0.126*** 0.006 0.031 0.018 0.007
(0.042) (0.012) (0.026) (0.018) (0.028) (0.015) (0.013)
Age 44 0.453*** 0.049* 0.108* 0.053* 0.049** 0.065*** 0.052***
(0.047) (0.020) (0.049) (0.021) (0.018) (0.013) (0.010)
Age 45+ 0.381*** 0.032** 0.083** 0.011 0.010 0.020 0.015
(0.047) (0.010) (0.028) (0.008) (0.011) (0.017) (0.012)
Total 0.423*** 0.064*** 0.151*** 0.010 0.018* 0.034*** 0.024**
(0.030) (0.012) (0.029) (0.012) (0.008) (0.008) (0.008)
30

31. Inequality of opportunity share
.3
.25
.2
.15
.1
1988 1998 2006
parametric CI/parametric
31

32. Table1. IOP Math Score 2007
Math Score 2007
GE(2) GINI
Total Within Between IOP_res Total Within Between IOP_res
Algeria 0.00926*** 0.00861*** 0.000655*** 0.0699*** 0.0769*** 0.0741*** 0.0202*** 0.0358***
Bahrain 0.0189*** 0.0145*** 0.00446*** 0.235*** 0.111*** 0.0963*** 0.0528*** 0.129***
Palestine 0.0320*** 0.0240*** 0.00835*** 0.253*** 0.144*** 0.124*** 0.0717*** 0.139***
Iran 0.0212*** 0.0142*** 0.00638*** 0.333*** 0.116*** 0.0948*** 0.0645*** 0.185***
Jordan 0.0260*** 0.0194*** 0.00871*** 0.254*** 0.130*** 0.112*** 0.0732*** 0.141***
Kuwait 0.0197*** 0.0152*** 0.00479*** 0.230*** 0.112*** 0.0986*** 0.0545*** 0.123***
Lebanon 0.0111*** 0.00700*** 0.00453*** 0.370*** 0.0850*** 0.0667*** 0.0545*** 0.215***
Morocco 0.0184*** 0.0146*** 0.00425*** 0.205*** 0.109*** 0.0970*** 0.0498*** 0.112***
Oman 0.0277*** 0.0202*** 0.00791*** 0.272*** 0.134*** 0.114*** 0.0706*** 0.150***
Qatar 0.0388*** 0.0263*** 0.0125*** 0.323*** 0.156*** 0.128*** 0.0890*** 0.178***
Saudi Arabia 0.0216*** 0.0156*** 0.00588*** 0.280*** 0.118*** 0.0995*** 0.0612*** 0.155***
Syria 0.0175*** 0.0134*** 0.00444*** 0.236*** 0.106*** 0.0921*** 0.0534*** 0.131***
Tunisia 0.0105*** 0.00775*** 0.00275*** 0.262*** 0.0821*** 0.0704*** 0.0417*** 0.143***
Turkey 0.0306*** 0.0187*** 0.0112*** 0.388*** 0.140*** 0.109*** 0.0839*** 0.219***
Egypt 0.0267*** 0.0178*** 0.00880*** 0.333*** 0.132*** 0.107*** 0.0750*** 0.188***
Dubai 0.0216*** 0.0132*** 0.00929*** 0.387*** 0.118*** 0.0917*** 0.0772*** 0.222***
32
32

33. Share of Inequality of Opportunity TIMSS 2007
.6
.4
.2 Math Scores (parametric)
0
Algeria Morocco Kuwait Bahrain Syria Palestine Jordan Tunisia Oman S.Arabia Qatar Iran Egypt Lebanon Dubai Turkey
Total Girl
Boy CI/Total
CI/Girl CI/Boy
33

34. Total Inequality
0.004
Height Inequality
0.003
0.002
0.001
0.000
0.004
Height Inequality
0.003
0.002
0.001
0
Morocco Morocco Morocco
87 92 04
34