1

RETHINKING POVERTY AND INEQUALITY MEASUREMENT IN ARAB COUNTRIES

Khalid Abu-Ismail

Measurement and Policy Approaches to Enhance Equity for the New Generations in the Middle East and North Africa, UNICEF,

Rabat 22 - 23 May

Arab Development Challenges Report 2011Towards the Developmental State in the Arab Region

2

Outline of Presentation

1. Rethinking Money Metric Poverty Measurement

2. The Paradox of Growth and Inequality in Arab countries

3. Other facets of inequality

4. Conclusions

3

1. Money- Metric Poverty Stories

4

Typology of Poverty lines

National Poverty Lines (Household Specific)

International Poverty Lines (Based on 2005 PPP)

Variable (PL varies with income)

Fixed ($1.25, $2.00, etc)

Lower Poverty Line

Upper Poverty Line

Food Poverty Line

Money-Metric Poverty Lines

5

Conventional storyline on Arab poverty based on $1.25 poverty line

Lowest poverty incidence world-wide

Fast poverty reduction since 1990 implying region is on track to halve extreme poverty by 2015.

6

Population living below $1.25 (2005 PPP), Developing

Regions, 1990-2009 (%)

AC

EA

& P

E &

CIS

LA

& C SA

SS

A

DR

0

10

20

30

40

50

60

70

6

38

49

47

59

35

4

17

25

40

50

24

1990-2000 2000-2009

7

Story changes with higher poverty lines

Arab region is most sensitive to choice of PL among developing regions.

8

Highest % change among Developing Regions when

moving from $1.25 to higher PLs (2000-2010)

AC EA & P E & CIS LA & C SA SSA DR0

10

20

30

40

50

60

70

80

90

100

0%

50%

100%

150%

200%

250%

300%

350%

400%

450%

4

17

25

40

50

2419

40

612

74 74

4640

57

1220

8784

60

$1.25 $2 $2.75 % Change from $1.25 to $2.00 % Change from $2.25 to $2.75

Poverty rate% Change in

poverty

9

Poverty rates for Arab Countries and Developing Regions across a range of poverty lines (in 2005 PPP based on most recent surveys)

0.2

0.6

1.0

1.3

1.6

2.0

2.4

2.8

3.2

3.6

4.0

4.4

4.8

5.2

5.6

6.0

6.4

6.8

7.2

7.6

8.0

8.4

8.8

9.2

9.6

10.0

0

10

20

30

40

50

60

70

80

90

100

0

10

20

30

40

50

60

70

80

90

100

World East Asia & PacificEurope & Central Asia Latin America & CaribbeanSouth Asia Sub-Saharan Africa

P0 (%)

10

Problems with international PLs

Problems in equalizing purchasing parity across countries.IPLs are not household specific and don’t take into account family size, location, demographic composition or local prices.

11

Why not resort to National Poverty Lines?Headcount poverty rates (P0) based on NPLs for Developing Regions, 1990-2000 and 2000-2009

AC EAP LAC SAS SSA DR0

5

10

15

20

25

30

35

40

45

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

31

18

13

3740

27

23

10 9

31

36

20

1997 2008 % Change Change

12

Problem: Countries set NPL differently but most apply the LPL

Solution: Back to the stylized facts on ratio of the NPL to PCE

In poorest countries it will be 0.8-1

In richest developing countries it will be less than 0.2-0.4

13

Log PCE and actual ratio of NPL to PCE (A) and PCE and NPL/PCE rearranged in descending order (from richest to poorest) (B)

NPL to PCE and PCE Rearranging countries according to their PCE

0

0.5

1

1.5

2

2.5

3

CHN-U2005

2.60179933643453

2.28393411345664TUN2000

2.65327041584621

2.18785900034096

2.14807797676253

2.61577124957134

2.615213334801362.388545220095962.17978151583701

2.6195107208385

2.69432435211636

1.9670797341445

BRA2009

2.310289623796082.14460533871474

2.58818203678948

2.56474889181302

CHN-R2005

1.54207814633563

2.54512148086483

2.503286663558912.35793484700049

2.59690484088138

2.19777661127139

2.44407603900157

2.27982658179402

2.4428715548212

2.53299629386576

1.93429640681941

2.5050821145051

2.44966349415331

2.43827330349236

2.46125835286184

2.43901672838752

MAR2007

2.453119497764

2.44510580186254

2.4877886228456

2.438162471672972.2290415731734

2.42354076627433

2.43370584475694

2.45733679515806

2.18508864275795

2.41202351304198

2.4227047173768

2.47849434766081

2.1666964704796

2.23319924158482.10050839450196

2.49045012035602

2.42181773136413

2.4208465416012

2.36543182249042

2.34795416989402

1.94987770403689

2.19153484700681

2.63739965910542

2.12600145678768

2.39450421327172

2.400537989391952.24440083380137

2.21386293038682

IDN-U20091.90036712865648

2.31590739966626

2.67629112934826

2.28900891153946

TUR2005

1.68006342748195

2.197638875555572.11320776982274COM2004

2.07565643359791

2.36466354089477

2.565753396565982.35260695413652

2.32234336114868

2.30621050816776

2.3163478287331

2.00650882777529

2.4682439146522

JOR2006

1.7849737099544

2.38815450776888

1.98775561673853

1.91918272904252

2.295105072809812.13155450976124

2.564606892035652.44227590070658

2.41097957589457

2.45304299571478

2.48654381988252

1.99559132425235

2.47677368008574

2.63197109624041

2.18463465658632.093771781498741.85709115467351

1.90568796771187

2.3175828397802

2.39830466290349

2.38153021583627

2.54147939465728

2.47897030267259

1.97891057717558

2.48245909179749

2.40929050064046

1.85199174796214

2.32446765989372.24014971331248

2.47474059847989

1.74958173486556

2.0002604985474

2.3651883681271

1.93696589710787

2.08382499605334

2.47532245076669

2.472522422916882.32647923638096

2.0135113334659

2.468657430019672.29014595464781

2.85493121862284

2.3180424549184

2.438542348786112.346352974450622.19939860235962

1.89042101880091

2.113396228527171.92147838037569

1.96740755659747

2.35403156606429

2.482444791918262.395710709035142.30920417967041

2.24755594692816

2.3937155586015

2.33489586301185

2.25440304639068

2.4079175177032.3092041796

7041SYR2007

1.79483645781455

2.28422763959348

2.27797574622322

2.22899031084073

2.20994373568496

2.23565467695695

2.187830820444

2.49299771986491

2.180584596603

DZA1995

2.43080042462414

2.43389778398306

1.72164576628975

1.75473046902376

2.02069267868203

IND-U2005

2.173943437284382.04995414802207

2.26088195046359

1.61066016308988

2.00479411038871

1.77611979905299

2.344254692556942.17672775764419

2.2903017873057

1.80908813134636

2.36477605727745

IDN-R2009

1.70070371714501

2.02836788369706

2.25561015840774

1.947531745695591.79885773174749

2.33368904170391

2.0511911246857

2.32645875707122

2.37747017358213

2.32097667734282

1.79413935576777

2.51900060333962

2.54129211534237

2.17949434100546

2.23067878114648

1.91897343049295

1.88806711340744

1.99051644402825

2.00380507356502

IRQ2007

2.33609934008508

1.79789048305835

2.03901732199742

2.10748131891127

2.12309995525558

2.38080798645616

2.38458636148552

1.91158370098108

2.54710983688775

1.92142634101527

2.1033247070615

2.02767571590489

2.45606222445493

1.81796180453199

MEX2008

1.56572978783111

1.88252453795488

1.62013605497377

2.43047818793204

2.3503255639967

1.66332393362822

1.99633651809579

EGY2009

1.74507479158205

2.48624623657305

1.93911971764849

2.24743312956304

2.280669407886892.05084358095692

1.73965144370937

1.79699039054567

2.42642992519392

1.889581802149621.69178852440272

2.23299611039215

2.42537116643894

2.13309161025471

2.17762306163138

1.79239168949825

2.06246916581425

2.13624480174614

2.42344249107523

1.72040740080311

1.72238709417712

2.42470200402398

2.39396107133754

2.1271696135999

2.3901222515067

2.38051875807046

IND-R20051.6971421262

7547

2.24019967528916

1.70825088859138

1.73295636957562

2.3183555502257

1.94270236888868

1.35218251811138

1.70663245087329

2.4247999946566

2.40878249440414

2.416890030173

DJI20022.1122362308

6894

2.26688993247105

2.48723738898986

2.42680394551486

1.58342550040651

1.958945932493951.905957699092431.65657729139611

2.388474207630972.2517598545288

1.76230336328777

2.08586117378845

1.73766962735663

2.25674179262526

1.61531865661148

1.68367729881869

2.34275814241928

1.56808433131539

1.6707095952238

2.26183362057575

1.72835378202123

1.39375064034808

1.51956550088051

2.35451179974427

1.68376726142531

2.07166112344176YEM2005

1.94610823043692

2.16560040251742

1.73174988352726

2.30858575428908

2.28391152630375

1.69134676413482

1.70994801651076

1.63195082625923

1.85961857877218

1.99616129336801

1.64107731332537

1.98860354334566

2.22760393569659

1.66773305253327

2.19119941970152

1.53655844257154

1.973358799886411.74873055609849

2.31475177371504

2.0502637226458

1.63457802285391.53300902249548

1.65098709438345

1.63648789635338

1.82516637225655

2.30738905565331

1.90628115577215

2.26948959442453

1.63052957142683

1.68895346263742

1.68735056955805

1.75442478927726

1.99312748510571

1.57840997033125

1.59615708091619

1.89569872695931

1.61310151696691

1.74358815015991.52840243795362

1.76752689940837

2.20213398006082

1.615318656611481.46996920949994

1.61909333062674

2.34953006379626

1.900967623919121.79837437668156

2.23299611039215

1.61762929775782

2.2145789535705

1.65147185219903

1.60281934243269

2.22481783739504

2.317080886073192.07714979471697

1.744292983122681.59494473669508

1.72164576628975

2.245043573930612.13360277051016

1.81204389793023

2.28019124787221

1.63032615480395

1.67348169707334

1.66341821225267

1.70926996097583

1.74981358529293

1.43488812086732

2.00906827619222

2.30373588903991

1.46179855752509

1.46819958607261

1.61193562504012

1.93434692673828

1.56002624891289

1.50009919191572

1.68511446904655

1.66791968531735

1.94056628649009

1.4201208480857

HTI2001

Log pce Linear (Log pce)

0

0.5

1

1.5

2

2.5

32.854931218622

842.69432435211636

2.67629112934826

2.65327041584621

2.63739965910542

2.63197109624041

2.6195107208385

2.61577124957134

2.61521333480136

2.60179933643453

2.59690484088138

2.58818203678948

BRA20092.56575339656598

2.56474889181302

2.56460689203565

2.54710983688775

2.54512148086483

2.54147939465728

2.54129211534237

2.53299629386576

MEX2008

2.51900060333962

2.5050821145051

2.50328666355891

2.49299771986491

2.49045012035602

2.4877886228456

2.48723738898986

2.48654381988252

2.48624623657305

2.48245909179749

2.48244479191826

2.47897030267259

2.47849434766081

2.47677368008574

2.47532245076669

2.47474059847989

2.47252242291688

2.46865743001967

2.4682439146522

2.46125835286184

2.45733679515806

2.456062224454932.453119497764

2.45304299571478

2.44966349415331

2.44510580186254

2.44407603900157

2.4428715548212

2.44227590070658

2.43901672838752

2.43854234878611

2.43827330349236

2.43816247167297

2.43389778398306

2.43370584475694

2.43080042462414

2.43047818793204

2.42680394551486

2.42642992519392

2.42537116643894

2.4247999946566

2.42470200402398

2.42354076627433

2.42344249107523

2.4227047173768

2.42181773136413

2.42084654160122.4168900301732.41202351304198

2.41097957589457

2.40929050064046

2.408782494404142.4079175177032.40053798939195

2.39830466290349

2.39571070903514

2.39450421327172

2.39396107133754

2.3937155586015

2.3901222515067

2.38854522009596

2.38847420763097

2.38815450776888

2.38458636148552

2.38153021583627

2.38080798645616

2.38051875807046

2.37747017358213

2.37032800777951

2.36543182249042

2.3651883681271

2.36477605727745

2.36466354089477

2.35793484700049

2.35451179974427

2.35403156606429

2.35260695413652

2.3503255639967

2.34953006379626

2.34795416989402

2.34635297445062

2.34425469255694

2.34275814241928

2.33609934008508

2.33489586301185

2.33368904170391

2.32647923638096

2.32645875707122

2.3244676598937

2.32234336114868

JOR20062.320976677342

822.318355550225

72.318042454918

42.317582839780

22.317080886073

192.316347828733

12.315907399666

262.314751773715

042.310289623796

082.309204179670

412.309204179670

412.308585754289

082.307389055653

312.306210508167

762.303735889039

912.295105072809

812.290301787305

72.290145954647

812.289008911539

462.284227639593

48

2.283934113456642.28391152630375

2.28066940788689

2.28019124787221

2.27982658179402

2.27797574622322

2.26948959442453

2.26688993247105

2.26183362057575TUN2000

2.26088195046359

2.25674179262526

2.25561015840774

2.25440304639068

2.2517598545288

2.24755594692816

2.24743312956304

2.24504357393061

2.24440083380137

2.240199675289162.24014971331248

2.23565467695695

2.2331992415848

2.23299611039215

2.23299611039215

2.23067878114648

2.2290415731734

2.228990310840732.22760393569659

2.22481783739504

2.2145789535705

2.21386293038682

2.20994373568496

CHN-U2005MAR2007

2.20213398006082

2.19939860235962

2.19777661127139

2.19763887555557

2.19153484700681

2.19119941970152

2.18785900034096

2.1878308204442.18508864275795

2.18463465658632.1805845966032.17978151583701

2.17949434100546

2.17762306163138

2.17672775764419

2.17394343728438

2.1666964704796

2.16560040251742

2.14807797676253

2.14460533871474

2.13624480174614

2.13360277051016

2.13309161025471

2.13155450976124

2.1271696135999

2.12600145678768

2.12309995525558

2.11339622852717

2.11320776982274

2.11223623086894

2.10748131891127

2.1033247070615

2.10050839450196

SYR20072.09377178149874

2.08586117378845

2.08382499605334

EGY2009

2.07961532352694

2.07714979471697

2.07565643359791

2.07166112344176

2.06246916581425

2.05119112468572.05084358095692

2.0502637226458

2.04995414802207

2.03901732199742

2.02836788369706

2.02767571590489

2.02069267868203

2.0135113334659

IRQ20072.009068276192

222.006508827775

292.004794110388

712.003805073565

022.000260498547

41.996336518095

791.996161293368

011.995591324252

351.993127485105

711.990516444028

251.988603543345

661.987755616738

531.978910577175

581.974971994298

071.973358799886

41

DJI2002

1.96740755659747

1.9670797341445

1.95894593249395

1.95588008622538

1.94987770403689

1.94753174569559

MRT2000

1.94270236888868

1.94056628649009

1.93911971764849

1.93696589710787

1.93434692673828

1.93429640681941

1.92438267720197

IDN-U2009

1.92147838037569

1.92142634101527

1.91918272904252

1.91897343049295

1.91158370098108

1.90628115577215

1.90595769909243

1.90568796771187

1.90096762391912

1.90036712865648

1.89569872695931

1.89042101880091

1.88958180214962

1.88806711340744

1.88252453795488

1.85961857877218

1.85709115467351

CHN-R2005

1.85199174796214

1.83486557990005

1.82516637225655

1.81796180453199

1.81204389793023

1.80942502879703

1.80908813134636

1.79885773174749

1.79837437668156

1.79789048305835

1.79699039054567

1.79539333493129

1.79483645781455

1.79413935576777

1.79239168949825

1.7849737099544

1.77611979905299

1.76752689940837

1.76230336328777

1.75473046902376

1.75442478927726

1.74981358529293

1.74958173486556

1.74873055609849

1.74507479158205

1.74429298312268

1.7435881501599

1.73965144370937

1.73766962735663

1.73295636957562

1.73174988352726

1.72835378202123

1.72238709417712

1.72164576628975

1.72164576628975

1.72040740080311

1.70994801651076

1.70926996097583

1.70825088859138

1.70663245087329

1.70070371714501

IND-R20051.697142126275

471.691788524402

721.691346764134

821.688953462637

421.687350569558

051.685114469046

551.683767261425

311.683677298818

691.680063427481

951.673481697073

341.670709595223

81.667919685317

351.667733052533

271.663418212252

671.663323933628

221.656577291396

111.651471852199

031.650987094383

451.641077313325

371.636487896353

381.634578022853

91.631950826259

231.630529571426

831.630326154803

951.620136054973

771.619093330626

741.617629297757

821.615318656611

481.615318656611

481.613101516966

911.611935625040

121.610660163089

881.602819342432

691.596157080916

191.594944736695

081.583425500406

511.578409970331

251.568084331315

391.565729787831

111.560026248912

891.542078146335

631.536558442571

541.533009022495

481.528402437953

621.519565500880

511.500099191915

721.469969209499

941.468199586072

611.461798557525

091.434888120867

321.420120848085

71.393750640348

081.352182518111

38

log PCE PL/PCE

14If majority of countries get their NPLs right then a simple

regression can overcome problems of overshooting (Brazil) and

undershooting (China).Ln GDP versus Poverty Line per capita per month

0 100 200 300 400 500 6000

1

2

3

4

5

6

f(x) = − 1.51769332024027E-05 x² + 0.0106147343903591 x + 3.06742921547923R² = 0.741906664974383

15

National poverty lines and UNDP estimated (RPL) poverty lines (2005 PPP per capita per day) for Developing countries by

expenditure groups, 1990-2000 and 2000-2009

PCE per capita per

month NPL per day RPL per day NPL/PCE RPL/PCE Low Income Countries (average per capita expenditure below 60 dollars per month)

1990-1999 47 0.9 1.13 0.59 0.73 2000-2009 49 1.1 1.15 0.65 0.71

Lower Middle Income Countries (average per capita expenditure from 60 to 100 dollars per month) 1990-1999 77.7 1.1 1.5 0.44 0.58 2000-2009 70.2 0.9 1.4 0.4 0.6

Middle Income Countries (average per capita expenditure from 100 to 150 dollars per month) 1990-1999 114 2.3 2 0.6 0.52 2000-2009 109.6 1.8 1.9 0.49 0.52 Upper Middle Income Countries (average per capita expenditure from 150 to 200 dollars per month)

1990-1999 165.8 3 2.7 0.55 0.5 2000-2009 163.2 0.9 2.7 0.17 0.5

High Income Countries (average per capita expenditure above 200 dollars per month) 1990-1999 239.2 3.8 3.7 0.48 0.47 2000-2009 308.2 4 4.1 0.39 0.41

16

National poverty lines and UNDP estimated regression based poverty lines (RPL) (2005 PPP per capita per day) for Developing

Regions and Arab Sub-regions, 1990-2000 and 2000-2009

PCE per capita per month

NPL per day RPL per day NPL/PCE RPL/PCE

Sub-Saharan Africa (11)

1990-1999 48 1.2 1.1 0.73 0.72 2000-2009 58.8 1.3 1.3 0.65 0.66

South Asia (6) 1990-1999 48.9 1.1 1.2 0.69 0.71 2000-2009 55.2 1.1 1.2 0.59 0.67

East Asia and Pacific (9) 1990-1999 59.6 0.8 1.3 0.4 0.65 2000-2009 102.4 0.8 1.8 0.24 0.54

Arab Countries (8) 1990-1999 117.9 1.9 2 0.49 0.52 2000-2009 130 2.1 2.2 0.5 0.51

Europe and Central Asia (9) 1990-1999 167 3.2 2.7 0.59 0.5 2000-2009 257.2 3.3 3.8 0.39 0.45

Latin America and the Caribbean (16) 1990-1999 254.3 3.9 3.9 0.47 0.46 2000-2009 323.2 4.3 4.2 0.41 0.39

Developing Regions (59) 1990-1999 87.1 1.5 1.6 0.52 0.57 2000-2009 121.1 1.5 2 0.39 0.5

17

National poverty lines and UNDP estimated poverty lines (2005 PPP per capita per day) for Arab countries, 2000-2009

Djibouti (2002)

Mauritania (2000)

Yemen (2005)

Morocco (2007)

Tunisia (2000)

Egypt (2009)

Jordan (2006)

Syria (2007)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0 NPL UNDP RPL

18

Results: Arab Poverty still lower than expected for expenditure per

capitaUNDP Estimated Poverty rates and per capita Expenditure (in 2005 PPP based on most recent surveys)

-50 50 150 250 350 450 550-6

4

14

24

34

44

54

64

74

DJIMRT

YEMMAR TUN

EGYJOR

SYR

KHM

LAO

MNG

PHL

VNMCHN

IDN

AZE

BLR

BGRKAZ

ROMRUS

TJKTUR

UKR

BOL

BRA

CHL

COL

CRI

DOMECU

SLVGUY

HND

JAM MEX

NIC

PANPER

VEN

BGD

NPL

PAKLKA

IND

BFA

BDI

CMRETH

GHA KEN

MDGMWI

MOZUGA

ZMB

AC

EAP

ECA

LAC

SAS

SSA

DR

Eastern Europe and CIS

Latin America & Carib.East Asia & Pa-cific

Arab Countries

South Asia

Sub-Saharan Africa

19

But far more than in the $1.25 and slow poverty reduction since 1990s

Estimates Based on World Bank Poverty Lines UNDP Estimates

$1.25 $2.00 $2.75 NPL RPL Headcount Poverty Rate (%) in 2000-2009 and rank Arab Countries 3.9 2 19 3 40 3 19.1 3 21.5 2 East Asia & Pacific 16.9 4 39.5 4 57.1 4 5.6 1 28.1 3 Europe & Central Asia 1.7 1 5.6 1 11.7 1 14.7 2 20.3 1 Latin America & Caribbean 5.5 3 12.3 2 19.6 2 34.1 5 32.4 4 South Asia 40.3 5 73.9 6 87.5 6 28.4 4 37 5 Sub-Saharan Africa 49.8 6 73.6 5 84.1 5 45.8 6 47.3 6

Developing Regions 23.6 46.4 60.5 19.7 31.8 Poverty Change (%) from 1990-1999 Arab Countries -35.7 4 -24.3 4 -12.4 4 -14.4 5 -8 5 East Asia & Pacific -55.1 1 -40.8 2 -30 3 -49.1 2 -21.8 2 Europe & Central Asia -50.5 2 -59.1 1 -56.1 1 -55.2 1 -11.1 4 Latin America & Caribbean -41.6 3 -39.5 3 -35.1 2 -20.4 4 -22.7 1 South Asia -14.3 6 -7.1 6 -3.8 6 -23.1 3 -6.1 6 Sub-Saharan Africa -16.3 5 -7.7 5 -5.1 5 -13.5 6 -12.5 3

Developing Regions -32.3 -23.4 -17.9 -26.9 -14.4

20

2. The Stable Gini Paradox

21

Inequality in expenditure for Developing Regions (Gini coefficient), 1990-1999 and 2000-2009

0

10

20

30

40

50

60

35.232.5

43.4

55.3

31.2

42.941.6

34.735.8 36.4

52.6

33.2

42.240.5

1990s 2000s

22

Arab countries ranked according to level of income and inequality in expenditure (Gini coefficient), 1990-2009

Middle income/low inequality Low income/high inequality Syria 1997 34.0 Djibouti 1996 36.8 2004 35.8 2002 40.0 2007 32.0 Comoros 2004 64.3 Egypt 1991 32.0 2005 32.1 2009 30.1

Middle income/medium inequality Low income/medium inequality Jordan 1992 43.4 Yemen 1996 33.4 1997 36.4 2006 37.7 2002 38.9 Mauritania 1995 37.3 2006 37.7 2000 39.0 Lebanon 2004 36.0 Algeria 1998 40.0 1995 35.3

Middle income/high inequality High income/medium inequality Tunisia 1995 41.7 UAE 2007 38.8 2000 40.8 Kuwait 1999 36.0 Morocco 1991 39.2 1998 39.5 2007 40.9

Arab Region 1990s 35.2 High income/high inequality 2000s 34.7 Oman 2000 39.9

23

Story thus far: 1.High GDP growth driven by Private Consumption on the demand side and services sector.2. Low poverty reduction and3. Stagnating inequality.

Does this make sense ?

24

HCE 1990sHCE 2000

s

HCE* 1990

s

HCE* 2000s

HCE/HCE* 1990s

HCE/HCE* 2000s

∆ Gini (%)

∆ HCE (%)

∆ HCE* (%)

∆ HCE*/

∆ HCE (%)

Djibouti 150.5 93.5 90.6 119.5 1.66 0.78 0.01 -0.076 0.05 -0.62

Mauritania 78.7 88.3 98.8 97.3 0.80 0.91 0.01 0.029 0.00 -0.13

Yemen 82.6 84.0 128.5 110.1 0.64 0.76 0.02 0.002 -0.02 -8.89

Morocco 155.4 161.4 150.1 181.1 1.04 0.89 0.00 0.002 0.01 4.99

Tunisia 151.3 182.4 213.6 278.0 0.71 0.66 0.00 0.019 0.03 1.41

Egypt 100.9 121.1 219.2 312.8 0.46 0.39 0.00 0.010 0.02 1.96

Jordan 151.6 210.1 231.3 315.2 0.66 0.67 0.00 0.037 0.03 0.95

Syria 129.8 125.5 200.3 212.3 0.65 0.59 -0.01 -0.003 0.01 -1.73

Large and rising gap between Household Consumption Expenditure from household surveys (HCE) and national income accounts (HCE*) (in 2005 PPP), 1990-2000 and 2000-2009

25

  HCE 1990s

HCE 2000s

HCE* 1990s

HCE* 2000s

HCE/HCE* 1990s

HCE/HCE* 2000s

∆ Gini (%)

∆ HCE (%)

∆ HCE* (%)

∆ HCE*/

∆ HCE (%)

AC 117.9 130.0 189.4 247.1 0.62 0.53 -0.01 0.102 0.30 2.99

EA&P 59.5 102.4 88.2 141.8 0.67 0.72 0.10 0.719 0.61 0.84

E&CIS167.0 257.2 280.7 440.6 0.59 0.58 0.00 0.540 0.57 1.05

LA&C 254.3 323.2 415.0 505.2 0.61 0.64 -0.05 0.271 0.22 0.80

SA 48.9 55.2 77.4 110.0 0.63 0.50 0.06 0.130 0.42 3.25

SSA 48.0 58.8 52.4 61.3 0.91 0.96 -0.02 0.226 0.17 0.75

DR 87.1 121.0 137.0 192.7 0.64 0.63 0.00 0.390 0.41 1.04

This large and rising gap may partly explain slow poverty reduction in Arab countries and South Asia

26

Conclusion:

Relative to other regions, inequality in

expenditure is probably more

underestimated in Acs: HIESs did not capture the expenditure of the

very rich

27

3. Other facets of

inequality in Arab

countries

28

Inequality in Human Development

29

Components of Human Development Index

30

Disparities in HDI and HDI Progress 1970-2010Using 1970 as the base year, the region appears to have done well in human development but the rate of progress on human development slowed down noticeably since 1990 and large disparities between countries

HDI improve

mentRank

Country Name

Non- Income

HDI rank

GDP Growth

rank

HDI Improve

mentRank

Non- Income

HDI rank

GDP Growth

rank

1970-2010 1990-20101 Oman 1 19 15 7 405 KSA 3 111 18 2 1087 Tunisia 6 20 14 12 219 Algeria 5 100 30 19 98

10 Morocco 14 42 12 10 4313 Libya 4 132 41 18 11417 Egypt 25 39 21 28 3219 UAE 24 38 103 88 11834 Bahrain 21 104 94 93 6743 Jordan 26 87 51 53 4458 Qatar 73 121 104 104 5867 Sudan 121 72 22 118 968 Kuwait 48 131 61 59 5094 Lebanon 89 92 29 54 8

122 Djibouti 117 133 100 109 130

31

HDI versus GNI per capita• Despite differences in level of income and human

development almost all Arab countries still lag behind other regions in terms of human development

2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.50.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

BahrainKuwait

QatarKSA

UAE

DjiboutiSudan

Algeria

Libya

Morocco

Tunisia

Egypt

Jordan

0.540.72

0.680.69

0.57

0.770.67

0.68

0.70

0.66

0.740.720.75

0.780.780.70

0.75

0.64 0.800.64

0.780.77

0.61

0.75

0.75

0.73

0.63 0.81

0.760.82

0.75

0.780.730.75

0.70

0.66 0.670.67

0.72

0.790.63

0.790.69

0.760.82

0.79

0.37

0.50

0.58

0.50 0.53

0.47

0.65

0.37

0.34

0.50

0.360.36

0.55

0.30

0.46

0.41

0.490.50

0.46

0.37

0.490.45

0.37

0.75

0.380.32

0.480.46

0.45

0.54

0.460.480.43

0.18

ACEAP ECA

LAC

SAS

SSA

DR

f(x) = 0.289440724198728 x − 0.424034191868091R² = 0.881327328330302

Log GNI per capita

Hybrid HDI

32

HDI and IHDI

33

Computation of IHDI• Under perfect equality the IHDI is equal to the HDI, but falls below the HDI when inequality rises.

• In this sense, the IHDI is the actual level of human development (taking into account inequality), while the HDI can be viewed as an index of the potential human development that could be achieved if there is no inequality. 

Relatively high inequality for ACsThe average world loss in HDI due to inequality is about 23%—ranging from 5% (Czech Republic) to 43.5% (Namibia). Arab states suffer the largest losses, following sub-Saharan Africa and South Asia.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

HDI IHDI Overall loss in IHDI (%)

35

IHDI Components for Arab States vs. other regions

Arab States have considerable losses due to unequal distribution in education but

problems with the income/expenditure estimates.

Inequality-adjusted life expectancy index

Inequality-adjusted Education index

Inequality-adjusted income index

ECALAC

EAP ASSAS

SSA

Wor

ld0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

Value Loss(%)

ECAEAP

LACSSA AS

SAS

Wor

ld0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

Value Loss (%)

SASECA AS

EAPSSA

LAC

Wor

ld0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

Value Loss (%)

36

IHDI Components for Arab StatesWithin ACs losses in education are highest in Morocco, Djibouti and

YemenInequality-adjusted life expectancy index

Inequality-adjusted Education index

Inequality-adjusted income index

Syria

Jord

an

Egypt

Yemen

Arab

State

s 0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

0

5

10

15

20

25

30

35

40

Value Loss(%)

Jord

anSyr

ia

Egypt

Djibou

ti

Arab S

tate

s 0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.0

10.0

20.0

30.0

40.0

50.0

60.0

Value Loss (%)

Egypt

Syria

Djibou

ti

Mor

occo

Arab S

tate

s 0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Value Loss (%)

37

Inequality between regions and income/wealth groups

38

High rural-urban disparities in money-metric poverty- Egypt in particular

Urban RuralHeadcount Index (%}

Poverty Gap (%)

Headcount Index (%}

Poverty Gap (%)

Ratio of Rural to Urban Headcount Poverty

Egypt (2000) 9.2 1.7 22.1 3.9 2.4Egypt (2009) 10.1 28.9 2.9Jordan (1997) 19.7 4.8 27.0 7.2 1.4Jordan (2002) 12.9 2.9 18.7 4.7 1.4Syria (1997) 12.6 2.3 16.0 3.5 1.3Syria (2004) 8.7 1.5 14.3 2.6 1.5

Yemen (1998) 32.3 8.7 42.5 13.1 1.3Yemen (2006) 20.7 4.5 40.1 10.6 1.9Algeria (1995) 9.0 19.0 2.1Algeria (2000) 10.3 14.7 1.4Tunisia (1990) 3.3 0.7 14.8 3.2 4.5Tunisia (2000) 1.7 8.3 4.9

Morocco (1990) 7.6 1.5 18.0 3.8 2.4Morocco (2007) 4.8 14.5 3.0

39

Rural-urban divide: HPI and P0 Syria, Lebanon,

Egypt and Yemen

CsR

CnR

TCn NER*

NERTNER

TR

CsU*

CnU* TCs*TCn*

CnR*CsR*

SU*

CnUSR

TU

TS*

TCs

SU

SR*

NEU T

TNER*

NEU*TU*

CsU

TR*

TST*

4

6

8

10

12

14

16

18

20

22

24

4 9 14 19 24 29 34 39 44

HPI

P0

National Average HPI 1997National Average HPI 2007

National Average P0 1997

National Average P0 2007

High P0 and HPIHigh P0 and Low HPI

High HPI and Low P0Low P0 and HPI

R2 = 0.42

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70

Akkar/Minieh-Dennieh

Tripoly City

Koura/Zgharta/Batroun/Bsharre

Keserwan/Jbeil

Maten

Beirut City

Baabda

Shouf/Aley

Jezzine/Saida

Nabatieh City

Sour

Bent Jbeil/Marjaayoun/Hasbayya

West Bekaa/Rashayya

Zahle

Hermel/Baalbek

Nabatieh

Mount Lebanon

North

Bekaa

South

UBN (%)

P0 (%)

National Average

National Average

High UBN and Low P0

Low UBN and Low P0 Low UBN and High P0

High UBN and High P0

0 10 20 30 40 50 60 701520253035404550556065707580

Ibb

AbyanAl-Baida

Taiz

Al-JawfHajjah

Al-Hodeidah

Hadramout

Dhamar

Shabwah

Sa'adah

LahegMareb

Al-Mahweet

Al-Maharah

Amran

Al-Daleh

Reymah

All Yemen

Income Poverty (%)

HPI High P0 and HPI

High P0 and low HPI

Low P0 and high HPI

Low P0 and HPI

National Average

Na

tio

na

l A

ve

rag

e0 10 20 30 40 50 60 70

0

5

10

15

20

25

30

35

CairoAlexandria

Port SaidSuez

Damietta

Dakahlia

Sharkia

Qualiobia

Kafr el Sheikh

Garbeyya

Menoufia

Beheira

Ismailia

Giza

Bani SuefFayoum

Menia

AssiutSohag

Qena

Aswan

luxor

Red SeaNew Valley

Matrouh Nort.

South Sinai

Total

Income poverty

HPILow P0 and high HPI

High P0 and HPI

Low P0 and HPI

High P0 and low HPI

National Av-erage

Nat

ion

al A

ver

-ag

e

40

Region/ quintile

MPI H A

Egypt (2009) Total 0.05 0.11 0.41Rural/Urban 2.6 2.5 1

Q1/Q5 3.9 3.3 1.2Morocco

(2007)Total 0.04 0.1 0.4

Rural/Urban 7.5 7.1 1.02Q1/Q5 12.2 12.2 0.96

Syria (2007) Total 0.03 0.07 0.38Rural/Urban 4.6 4.4 1.1

Q1/Q5 1.7 1.6 1Yemen (2006) Total 0.27 0.54 0.51

Rural/Urban 1.7 1.5 1.1Q1/Q5 1.9 1.7 1.2

MPI for rural and urban regions and highest and lowest expenditure quintiles for :Egypt, Morocco, Syria and Yemen (2006-2009)

41

High inequality in Under Five Mortality Rates, Delivery Assistance by a Skilled Health Personnel and Antenatal Care Visits for Egypt and Yemen in particular

Under five mortality rates

Delivery by a Skilled Health Personnel

Antenatal Care Visits

Syr

ia

OP

T

Jord

an

Su

da

n

Eg

ypt

Ye

me

n

0

20

40

60

80

100

120

140Poorest Richest

Ra

te p

er

1,0

00

live

birt

hs

Jord

an

OP

TA

lge

riaD

jibu

tiT

un

isia

Syr

iaIr

aq

E

gyp

tS

ud

an

Ye

me

n0

20

40

60

80

100

120Poorest Richest

OP

TJo

rda

nT

un

isia

Djib

ou

tiA

lge

riaS

yria

Ira

qE

gyp

tS

ud

an

Ye

me

n

0

20

40

60

80

100

120

Poorest Richest

% o

f AN

C (

1 o

r m

ore

vis

its)

42

Concluding Remarks

43

Measurement issues:• At the international level, current

poverty measurement is very misleading.

• At the national level, influenced by politics. Also there are good reasons why we should not rely on the actual food basket of the poor (Ravallion/WB method).

• Need to better harmonize surveys and improve methodology (even for NPL).

• Women and children not captured in HIES so focus on human deprivation indicators.

• Surveys excluding the rich.

44

Observations from HIEs on Children in Poverty

• Little difference in poverty measures FHH and MHH but Households headed by divorced or separated women and widows with more than three children are over-represented among the poor.

• The risk to illiteracy of children, living in poor female headed households, is the highest.

• Poverty pushes children to work. Girls are kept at home to do domestic work, while boys go to work to help their poor families.

• Child labor in rural Yemen is worst at nearly 10 percent of poor boys (aged 6-14 years).

45

Policy conclusions• Target poor FHHs with children.

FHHs are more vulnerable in Acs since they rely less on wages or own account production. Social protection programs and transfers are thus key to FHH.

• No doubt poverty and inequality need to be addressed within a broader set of influencing policies that aim to establish the Arab Developmental State Model.