Rethinking Poverty and Inequality Measurement in Arab Countries
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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.