Jump to ContentJump to Main Navigation
Measuring Poverty and Wellbeing in Developing Countries$

Channing Arndt and Finn Tarp

Print publication date: 2016

Print ISBN-13: 9780198744801

Published to Oxford Scholarship Online: January 2017

DOI: 10.1093/acprof:oso/9780198744801.001.0001

Show Summary Details
Page of

PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2017. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a monograph in OSO for personal use (for details see http://www.oxfordscholarship.com/page/privacy-policy).date: 22 April 2018

(p.343) Index

(p.343) Index

Measuring Poverty and Wellbeing in Developing Countries
Oxford University Press

Note 1: Tables, figures and boxes are indicated by an italic t, f, or b following the page number.

Note 2: As most chapters pertain to a particular country, the sub-headings for each country have not been double-entered as main entries, so the reader is advised to locate main entries of interest under the country headings.

Note 3: The following abbreviations have been used in sub-headings:

FOD: first-order dominance

PLEASe: Poverty Line Estimation Analytical Software

absolute poverty lines, estimation of10–11, 22, 269–71
challenges of269–70
consistency and specificity12, 16–20, 152–3
consumption bundles12
cost of basic needs (CBN) approach13–16
data shortcomings270
food energy intake (FEI) approach13
revealed preferences19–20, 153
specific utility-consistent poverty lines21–2
steps in144–5
substitution effects16–17 f
unidimensional/multidimensional approaches178–9
Alkire, S179
Alkire-Foster (AF) multidimensional index217–18, 302
Appleton, S143, 149
Arndt, C21, 22, 56, 153, 276
Atkinson, A B25
Bidani, B123, 151, 152
Bourguignon, F25
climate, and energy requirements20
consistency and specificity12, 16–20, 152–3
consumption, and poverty estimation41–2
consumption surveys
avoiding excessive complexity299–300
need for increased frequency of298–9
cost of basic needs (CBN) approach13–16, 144, 280
Daniels, L144
Democratic Republic of Congo, estimating childhood poverty in160–1, 175–6
children not deprived by welfare indicators164–5 t, 166
definitions of child deprivation indicators164
Demographic and Health Survey (DHS)163
Enquête 1-2-3163
first-order dominance (FOD) approach160, 161
first-order dominance (FOD) indicators163–6
indeterminate outcomes in FOD161–2 f, 163
Multiple Indicator Cluster Survey (MICS)163
results using FOD approach166–75
spatial FOD bootstrap comparisons169, 170–3 t
spatial rankings169–74 t, 175 t
survey data163
temporal net FOD comparisons166 t, 167, 168 t
Emwanu, T140
Estimating First-Order Dominance (EFOD) software4, 40, 48, 271, 297
(p.344) implementation50
output and interpretation51
user guide325–40see also first-order dominance (FOD)
Ethiopia, estimating poverty in55–6, 65–72
application of PLEASe software59–61
Central Statistics Agency (CSA)56, 58
cumulative distributions of household per capita consumption61 f
data preparation59–60
data sources58–9
decline in poverty61–2, 72
differences from official estimates62–5
food poverty lines57
household food consumption baskets by spatial domains66–71 t
Household Income, Consumption and Expenditure Surveys (HICES)58
minimum calorie requirements63–5 t
PLEASe code preparation60–1
poverty estimates61–2 t, 63–4 t, 65, 72
Ferreira, F H G37
first-order dominance (FOD)4, 5, 26, 36–7, 271–3
alternative dominance criterion35–6
assessment of approach271–3
bootstrapping approach33–4
checking multidimensional FOD31–2
checking one-dimensional FOD28–9
detecting in practice32–3
faster solution algorithms34–5
limitations26, 33
mitigating limitations of33–4
multidimensional FOD29–33
notations and definitions27–8, 29–31
one-dimensional FOD27–9
food energy intake (FEI) approach13, 144
food poverty lines42–4
Foster, Greer and Thorbecke (FGT) poverty measures14, 45, 57, 76, 123, 145
General Algebraic Modelling Systems (GAMS)40, 41, 48, 50, 183
Ghana, estimating child poverty in191–2
approaches to measuring179–80
bootstrap sampling182, 183
Bristol (headcount) approach to179
children by combination of welfare indicators185, 187 t
children not deprived by welfare indicators185, 186 t
comparison between deprivation, income and consumption expenditure poverty189–90 t, 191
data sources184
definitions of child deprivation indicators182–3
Ghana Demography Health Survey181
Ghana Living Standards Survey (GLSS)180, 184
Ghana Statistical Service184
income-based approach183–4
multidimensional first-order dominance (FOD) approach180, 181–3
Multiple Indicator Cluster Survey (MICS)181
poverty reduction178
spatial FOD comparisons188–9 t
studies of181
temporal FOD comparisons185–8 t
Global Trade Analysis Project (GTAP) (Purdue University)7
Gordon, D164, 179
inequality, measuring in developing countries274–5, 292–3
approaches to measuring poverty280–1
composition consumer price indices by country285–7, 288 t
composition effect274, 276–8, 284–7, 292, 293
consumption aggregates274
consumption bundles276–8
consumption shares by consumption percentiles285, 286 t
data sources275, 281–2 t, 283–4
deflated consumption aggregate280
diversity in inequality across countries283
diversity of country experiences283
food and non-food consumer price indices284 t
Gini coefficients using alternative deflators287–90 t, 291
inequality and poverty287–92
poverty rates using different inequality measures291 t, 292
quantity discounting effect274, 278–80, 287, 289 t, 292, 293
SiMP methodology275, 281
International Food Policy Research Institute (IFPRI)7, 92
Levine, S141, 143
Lokshin, M20, 21, 153
(p.345) Madagascar, estimating poverty in74–5, 85–7
application of PLEASe software77–80
consumption baskets85, 86 t, 87
data preparation77–9
data sources76–7
dealing with extreme values78
differences from INSTAT estimates80–4, 85–7
Enquête Périodique auprès des Ménages (EPM)76
food poverty lines76, 84
Institut National de la Statistique (INSTAT)75, 76
minimum calorie requirements84, 85 t
PLEASe code preparation79–80
poverty estimates80–1 t, 82–5
spatial domains77–8, 81–2 t
Mahrt, K164
Malawi, estimating poverty in88–9, 105–6
adjustments of the PLEASe methodology91 b
adjustment to PLEASe code93 b
baseline estimates91–2
consumption aggregates99–100 t
differences from National Statistical Office estimates88–9
food bundles102–3 t, 104–5
food consumption conversion factors92, 94
food poverty lines94
Integrated Household Survey (IHS)88, 105
methodological choices investigated90 t
methodological consistency with National Statistical Office approach89–90
methodological differences with National Statistical Office approach90–7
National Statistical Office (NSO)88
poverty headcount rates under different methodological choices100–1 t, 102
poverty lines under different methodological choices97–8 t, 99
regional poverty lines94, 98
temporal changes in food basket composition95
temporal changes in non-food consumption95–6 f, 97
using survey prices to update poverty lines94–5, 99
utility consistency94
Malik, S J122, 127–8
Minot, N144
Minujin, A179
Mozambique, estimating poverty in108–9, 118–19
challenges facing first national assessment109
comparison of official and PLEASe estimates111 t, 114
correlations between official and PLEASe estimates115 t
cost of basic needs (CBN) approach110, 115
differences between official and PLEASe approaches115–17
methodological choices in national surveys109–14
national surveys108
PLEASe estimates114–18, 119
regional variations110
revealed preference conditions116
similarity of official and PLEASe approaches115
trends in119
Multidimensional Poverty Index (MPI)180, 217
multidimensional welfare180
comparing approaches to multidimensional analysis218–19
measurement of24–6, 216–19
Multiple Overlapping Deprivation Analysis (MODA)180
Museveni, Y156
Nanivazo, M164
Nigeria, estimating poverty in194–5, 213–14
bootstrap sampling195–6
data sources196
Demographic and Health Survey (DHS)196
first-order dominance (FOD) approach195–6
first-order dominance (FOD) welfare indicators196–8
geographical zones of Nigeria196, 197 f
Multiple Indicator Cluster Survey (MICS)198
National Bureau of Statistics (NBS)194
regional inequalities195, 213
spatial FOD comparisons203–5 t
spatial sensitivity analysis205–6 t, 207
state-level FOD results207, 208–12 f, 213
temporal net FOD comparisons200–1 t, 202 t
temporal sensitivity analysis201–3
welfare indicator results198–9 t, 200
World Bank estimates194
Pakistan, estimating poverty in133–4
calorie requirement calculation130
cost of basic needs (CBN) approach123–4
data preparation126
data sources124–6
evolution of estimates of121–2
food energy intake (FEI) approach123
Household Integrated Economic Survey (HIES)122, 124–6
(p.346) inflation adjustment123
modified PLEASe approach129–31
national poverty headcounts131 f
Pakistan Bureau of Statistics124
problems with using consumer price index (CPI)122, 128
results using food energy intake (FEI) approach128–9 t, 130 t, 132 t, 135–6 t
results using official methodology133 t
results using PLEASe methodology131–2 t, 133 t, 136–7 t
revised estimation methodology122
rural/urban differences131
sample population130
shortcomings in estimates of122
spatial domains126
trends in poverty indicators127 t
using official methodology127–8
Pinkovskiy, M275, 281
poverty analysis, capability-building298
avoiding excessive complexity299–300
coming to grips with price trends300–1
comparability issues300–1
increased frequency of consumption surveys298–9
using variety of methods301–2
Poverty Line Estimation Analytical Software (PLEASe)4, 5, 6, 40, 297
food poverty lines42–4
non-food poverty lines44–5
poverty measurement45–6
user guide305–23
utility consistency46–7
Range, T M35
Ravallion, M10, 11, 12, 20, 21, 123, 151, 152, 153, 183, 302
relative poverty lines11
Roach, J M179
Sala-i-Martin, X275, 281
Sen, A24
Simler, K R21, 22, 56, 153
Tanzania, multidimensional assessment of child welfare
Alkire-Foster (AF) approach217–18
Alkire-Foster (AF) approach results233–4 t, 235 f, 236, 237 t
bootstrap sampling217
comparing approaches to multidimensional analysis218–19, 238–9
comparing FOD and Alkire-Foster results236–8 t
data sources219–20
first-order dominance (FOD) approach216–17
multidimensional poverty measurement216–19
poverty assessment studies215–16
spatial FOD comparisons228, 229–30 t, 231
spatial FOD rankings231, 232–3 t
Tanzania Demographic and Health Surveys (TDHS)216, 219–20
temporal net FOD comparisons225, 226–7 t, 228
trends in deprivation by welfare indicator221–2 f, 223–4 t, 225
welfare indicators220–1 t
World Bank poverty assessment215, 216, 300
Uganda, estimating poverty in140–1, 156–7
accounting for local diets150–1
average calorie requirements by spatial domain149–50 t
constructing welfare indicator145–9
cost of basic needs (CBN) approach149–52
cost per kilo of staple crops150, 152 f
data sources145
Demographic and Health Surveys (DHS)144
density estimates for welfare indicators148 f
diverging views on levels of140–1
diversity in diets150–1 f
estimated vs official poverty lines154–5 t
headcount poverty estimates155–6 t
increased inequality143
official estimates141–2 t, 143–4
poverty lines for each spatial domain153–4 t
poverty reduction140
problems with official estimates141, 143–4
regional variations156 t
revealed preference approach153
spatial domains152
steps in measuring poverty144–5
Uganda Bureau of Statistics (UBOS)141–2, 145
Uganda Census of Agriculture147
Uganda National Household Survey145
Uganda National Panel Survey (UNPS)145
utility consistency152–4
variation in poverty reduction rates142–3
United Nations Children’s Fund (UNICEF)179
Global Study on Child Poverty and Disparity181
(p.347) United Nations Development Programme (UNDP), Multidimensional Poverty Index (MPI)180, 217
United Nations University World Institute for Development Economics Research (UNU-WIDER)
Growth and African Poverty Project (GAPP)4–5, 275, 281
World Income Inequality Database (WIID)283
Verduzco-Gallo, I 92, 104, 105
wellbeing, measurement of24–5, 36–7
Zambia, estimating poverty in242–3, 263–4
agricultural support programmes245
area rankings by probability of net domination254, 255–6 t, 257
bootstrap spatial FOD comparisons (sanitation)261–2 t
Central Statistical Office (CSO)246
consumption poverty headcount rates244 t
data sources246–7
deprivation by sanitation indicator258 t
economic background242, 263
Farmer Input Support Programme (FISP)245
first-order dominance (FOD) approach246
first-order dominance (FOD) welfare indicators247 t, 248
Food Reserve Agency (FRA)245
household stratum comparisons257
indeterminate outcomes in FOD261
levels of deprivation249, 250 t
Living Conditions Monitoring Surveys (LCMS)243, 246–7
National Development Plans243, 247
Poverty Reduction Strategy Paper (PRSP)243
public service delivery245
rural poverty context243–5, 263
sanitation indicators248 t, 257–61
sensitivity of FOD outcomes to indicator definition257–61, 262–4
spatial FOD comparisons251, 252–3 t, 254
spatial FOD comparisons by sanitation indicator259–60 t, 261
temporal net FOD comparisons249–51t
temporal net FOD comparisons by sanitation indicator259 t
urban and rural poverty trends244 f
welfare definition243
Østerdal, L P35