• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Econometric approaches to measuring child inequalities in MENA
 

Econometric approaches to measuring child inequalities in MENA

on

  • 569 views

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 ...

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.

Statistics

Views

Total Views
569
Views on SlideShare
464
Embed Views
105

Actions

Likes
0
Downloads
4
Comments
0

2 Embeds 105

http://www.unicef.org 70
http://webcms.unicef.org 35

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Econometric approaches to measuring child inequalities in MENA Econometric approaches to measuring child inequalities in MENA Presentation Transcript

    • 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
    • Presentation OutlinesInequality & Equity Inequality of outcomes along economic dimensions Inequality of outcomes along non-economicdimensionsInequality of opportunity: A parametric approachInequality of opportunity: A non-parametric approach 2
    • Inequality & EquityInequality:Focus is on how equal is the distribution of some economic and non economicdimensions 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 ofinequality that may account for the sense of unfairness in Arab countries wherethe level of inequality is moderate.Understanding the sources of inequality is important for devising policies thataddress its underlying causes, especially the role of unequal opportunities. 3
    • Inequality of Outcomes Along Economic DimensionsChild inequalities can be measured along income,wealth or expenditures of the household:Define & harmonize the well-being indicator: Inequalitymeasures are sensitive to the items included in theexpenditure aggregates: apples need to be compared toapples.Adjust for HH composition: equivalence of scaleAdjust for spatial and temporal price differences 4
    • 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 groupmeasures of inequalityk groups in a population (identified by location, education, gender , etc.) K k GE( ) (k) GE(k; ) G E ( ) k 1 within betweenϕ(k) is the proportion of the population in group kμk is the mean income of group kGE(k;θ) is the GE index of group kG E ( ) is the GE index of the population if each member of group k was assignedincome μk 5
    • Inequality DeterminantsStandard decomposition techniques identify potential determinants of inequality …and lay the foundation for deeper analysis.An important limitation of summary measures of inequality and standarddecomposition techniques is that they provide little information regardingwhat happens where in the distribution. 6
    • Inequality DeterminantsUse 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 thespecific characteristics which contribute to widening the welfaregap. 7
    • Unconditional Quantile Regression Decomposition 8
    • Unconditional Quantile Regression Decomposition 9
    • 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 TheilEgypt 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 0.2Iraq 2007 47.06 39.92 0.31 0.17 101.1 80.08 0.36 0.23 148.82 114.58 0.37 0.26Jordan 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 0.21Libya 2003 52.08 43.32 0.32 0.19 99.95 84.49 0.31 0.18 136.5 114.43 0.31 0.17Mauritania 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 0.41Palestine 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 0.26Syria 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 0.25Tunisia 2005 72.72 60.56 0.33 0.21 162.6 120.1 0.41 0.3 210.5 153.4 0.41 0.33Yemen 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
    • Standard Decomposition by HH attributes Education Gender Age Emp.stat. Fam. type Marital Region Urban/RuralEgypt 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
    • 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
    • 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
    • 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
    • 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
    • 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 .9Difference 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
    • 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 .9Difference 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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 1Gini yi yj 2n 2 i 1 j 1But if only grouped data is available, then M M 1Gini pi p j y i yj 2 i 1 j 1Where 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
    • 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
    • MethodologySimulate 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
    • The empirical modelThe earnings function can be specified in the followinglog-linear form : ln( yi ) Ci Ei vi ln( yi ) Ci i 28
    • 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 ywhere I(F) is the inequality measures (Gini, Theil, ..)defined on the outcome distribution. 29
    • 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
    • Inequality of opportunity share .3.25 .2.15 .1 1988 1998 2006 parametric CI/parametric 31
    • Table1. IOP Math Score 2007 Math Score 2007 GE(2) GINI Total Within Between IOP_res Total Within Between IOP_resAlgeria 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
    • 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
    • Total Inequality 0.004Height 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