086
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contacted at dnewhouse@worldbank.org.
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‒ Poverty & Equity Global Practice Knowledge Management & Learning Team
This paper is co-published with the World Bank Policy Research Working Papers.
New Estimates of Extreme Poverty for Children 1
David Newhouse
Pablo Suarez-Becerra
Martin C. Evans
World Bank Data for Goals Group
Keywords: Poverty, Children, Scales
JEL code: I3, O1
1
David Newhouse is Senior Economist, Poverty and Equity Practice, World Bank. Pablo Suarez-Becerra is
Economist Consultant, World Bank. Martin Evans is Economic and Social Policy Specialist at UNICEF. The
authors can be contacted at dnewhouse@worldbank.org, psuarezbecerra@worldbank.org, and mcevans@unicef.org.
The Data for Goals Group comprises approximately 20 staff and consultants from the World Bank’s Poverty and
Equity global practice who collectively created the Global Micro Database, and is led by Andrew Dabalen, Joao
Pedro Azevedo, and Nobuo Yoshida. The authors thank Kathleen Beegle, Jose Cuesta, Samuel Freije, Pedro Olinto,
Frank Borge Wietzke and Maika Schmidt for their review of earlier draft and their comments and suggestions; and
thank Antonio Franco Garcia for research assistance and Andrew Dabalen, Ana Revenga, and David Stewart for
their support.
1. Introduction
A wealth of evidence indicates that early childhood investments in human capital are critical for
improving productivity and living standards. Poor families, however, often cannot afford to make these
crucial investments in childhood health, nutrition and education. In part to monitor the extent to which
poverty constrains human capital investment, the Sustainable Development Goals place particular
emphasis on disaggregating poverty and other measures of welfare by age. This paper does so for
children, in the context of the first Sustainable Development Goal, which aims to “By 2030, eradicate
extreme poverty for all people everywhere, currently measured as people living on less than $1.90 a day.”
How many of the extreme poor population are children and what are their characteristics? Unfortunately
global child extreme poverty estimates, unlike global counts, are only calculated and published
sporadically. Batana et al (2013), estimate extreme child poverty rates using household survey data from
73 countries, collected between 1992 and 2005. That study estimates that 38.4 percent of children
younger than 18 lived on less than $1.25 per day in 2000, as compared with 31.4 percent of people
overall. Meanwhile, Olinto et al (2013) use data from 73 countries between 2000 and 2009, to which they
apply the prevailing headcount poverty rates for 2010, measured using the 2005 Purchasing Power Parity
(PPP) exchange rates. Their analysis finds that children under 18 comprise 47 percent of the poor, and
that the poverty rate among children aged 12 and under in the developing world is 32 percent, compared
to 19 percent for persons 13 years and older. Evans and Palacios (2015) apply a relative poverty line to a
sample of 65 developing countries and find that on average children under 15 comprise 36 percent of the
poor. Recent estimates by Watkins and Quattri (2016) were obtained by multiplying, for each country, the
fraction of the population that are children times the number of poor, with an adjustment to account for
the higher fertility rate among the poor. This yielded an estimate of 409.4 million children living in
extreme poverty in 2012, implying that children comprised 45.6 percent of the poor. This figure, which
was subsequently published by UNICEF (2016), could underestimate the extent of child poverty, by not
fully adjusting for children’s higher poverty rates.2 Overall, existing estimates vary considerably in their
source data and age cut-offs, with corresponding differences in results.
This paper makes two main contributions. First, it presents new estimates of child poverty as of 2013, for
a sample of 104 surveys collected since 2009 in 89 developing countries. These estimates define children
as those below the age of 18, in accordance with the Convention of The Rights of The Child.3 These
estimates of child poverty are the first estimates calculated using the Global Micro Database (GMD),
which adds a set of harmonized household characteristics to the same surveys and welfare measures that
are used to produce the poverty estimates published by the World Bank (Castañeda et al 2016). The 89
countries included in the database contain an estimated 84.2 percent of the population in the developing
world, and 82.1 percent of the child population. Second, this analysis tests the robustness of age-group
poverty rates with respect to different equivalence scales, while appropriately adjusting the poverty line to
take into account the composition of families near the poverty line (Ravallion, 2015).
2
The fertility adjustment was made on the basis of separate data from Demographic and Health Surveys. See
Watkins and Quattri (2016), p.34.
3
Convention on the Rights of the Child, Part I, Article 1: “For the purposes of the present Convention, a child
means every human being below the age of eighteen years unless under the law applicable to the child, majority is
attained earlier.”
2
Poverty is defined based on whether per capita household welfare, converted to international dollars using
the revised 2011 PPP conversion factors, falls below the $1.90 per day poverty line (Ferreira et al,
2015).4 We consider all children in poor households to be poor and all children in non-poor households to
be non-poor; therefore, as is standard, child poverty is defined as the percentage of children that live in
poor households. This assumes all household members enjoy the same standard of living, which as
discussed in section four below, may understate the full extent of child poverty.
The face of poverty is young, as children ages 0 and 17 comprise just over half the number of extreme
poor in the sample. Given that 767 million persons are estimated to be extremely poor in 2013, our best
estimate is that 385 million of them are children. That is, 19.5 percent of all children in the sample are
poor, as opposed to 12.5 percent of all persons and 9.2 percent of adults. Poverty rates are 21 percent for 0
to 4 year olds, increase slightly to 21.5 percent among 5 to 9 year olds, and then steadily decline for
successively older age groups. The extreme poverty rate for adults is 9.2 percent. We also examine
moderate poverty, defined as those living between $1.90 and $3.10 per day. The sum of extreme and
moderate poverty rates is substantially higher for children, 44.5 percent live on less than $3.10 per day, as
opposed to 26.6 percent of adults.
These results, based on household welfare per capita, immediately raise the fundamental question of how
child poverty rates depend on adjustments for differences across households in size and composition.5 Per
capita measures give children the same weight as adults when assessing household needs, and do not
allow for larger households to benefit from economies of scale. Relaxing each of these assumptions will
lower poverty rates for children relative to adults; the former by mechanically assuming that children
require less expenditure to meet their minimum needs, and the latter by reducing poverty rates in larger
households, which tend to have more children. Previous research by Batana et al suggested that extreme
poverty rates for children in 2000 would fall drastically, from 38 percent to between 3 and 5 percent if
different equivalence scales were used in place of the per-capita assumption (Batana et al, 2008).
This apparent sensitivity of child poverty rates to the choice of equivalence scale, however, results from a
misleading comparison. The $1.90 per day poverty line is a per capita line derived from the national
poverty lines of 15 poor countries, which in turn are each developed using per capita consumption as the
welfare measure (Ravallion, Chen, and Sangraula, 2009). When examining the sensitivity of child poverty
rates to alternative household equivalence scales, welfare measured in terms of equivalent adults is more
meaningfully compared with a poverty line adjusted using the same equivalence scale, based on the
composition of a typical poor family (Ravallion, 2015). After adjusting the poverty line accordingly,
poverty rates among children are only modestly sensitive to the choice of equivalence scale, and remain
substantially higher than adult poverty rates for all reasonable “two parameter” equivalence scales.6
4
Poverty and extreme poverty are used interchangeably and both refer to welfare, measured in money-metric terms,
falling short of the $1.90 line. While poverty is recognized to be multi-dimensional, the use of a money-metric
poverty line avoids the severe challenges in aggregating different dimensions of wellbeing into a common index
(Thorbecke, 2007).
5
There is an extensive literature on the topic of household equivalence scales, but foundational articles include
Deaton and Muelbauer (1986), and Pollak and Wales (1979), among many others.
6
Two parameter scales are characterized by an alpha that defines the unique scale factor applied to children when
calculating adult equivalence, and a theta that adjusts for household economies of scale.
3
The rest of this paper proceeds as follows. The next section describes the data and the methodology. The
third section presents basic results on extreme poverty rates by age, and how these are divided by region
and country. The following section considers how the estimated poverty rates for adults and children
change when using different household equivalence scales, while the fifth and final section concludes.
2. Data and methodology
The analysis on child poverty presented below is derived from a combined sample of 104 surveys
containing records on 7.7 million individuals from 89 developing countries, taken from the September
2016 vintage of the Global Micro Database (GMD). The GMD is a collection of globally harmonized
household survey data recently developed by the Data for Goals group of the World Bank’s Poverty and
Equity Global Practice. Full details on the background of the GMD are given in Castañeda et al (2016). A
crucial feature of the GMD is that the welfare aggregates are the same as those used to compute the
poverty estimates published by PovcalNet and the World Development Indicators.7 These aggregates are
based on household per capita income or consumption, depending on which concept is used to measure
national poverty in a particular country. The sample of GMD used in this analysis consists of surveys
collected in 2009 or later. The year 2009 was selected to balance the competing goals of increasing
coverage while minimizing error due to extrapolating poverty figures forward when lining up to 2013.
Table 1 shows the sample’s coverage in number of countries, of population and of child population, based
on figures from the UNDESA Population Division. The bottom panel indicates that countries in the
sample have a total population of 5.25 billion persons and 1.69 billion children. This represents around 82
percent of the developing world’s child population in 2013, which was 2.05 billion. 85.4 percent of the
767 million extreme poor reported in PovcalNet are contained in the GMD sample.
Table 1: Coverage of PovcalNet and GMD. Population and Child Population in 2013
World Developing countries GMD sample
Number of countries 214 190 89
Population (millions) 7157.3 6233.7 5249.1
Children (millions) 2239.6 2052.5 1686.1
Extreme poor (millions) 766.7 766.7 655.0
Percent of children that are poor 19.5%
Percent of extreme poor that are children 50.2%
Extremely poor children (millions) 384.9 384.9 328.7
Notes: See Appendix 1. The estimated number of extremely poor children is obtained by multiplying the total number of extreme
poor by the percent of extreme poor in the sample that are children. As in PovcalNet, developed countries are assumed to contain
no extremely poor persons.
7
The term welfare refers to either income or consumption per capita, depending on which measure is used by the
country for their national estimates. For more information on how these welfare aggregates are constructed, see
Ferreira et al (2015)
4
Table 2 shows the number of countries and associated populations in the GMD sample used for the
analysis. In terms of child population, the GMD sample has high regional coverage of developing
countries in South Asia, Latin America, Europe and Central Asia, and East Asia and the Pacific (above 84
percent); partial coverage of Sub-Saharan Africa (74 percent); very low coverage of Middle East and
North Africa (3.9 percent); and no coverage of North America. As a result, the distribution of children by
region is close to that of the world, but the sample heavily underrepresents the Middle East and North
Africa (0.4 vs 6.9 percent) and slightly underrepresents Sub-Saharan Africa (20.7 vs 23 percent). South
Asia is moderately overrepresented in the sample, as it accounts for 35.7 percent of the sample as opposed
to 30.2 percent of the developing world child population. Because of underrepresentation in Middle East
and North Africa we follow the World Bank and do not report that region separately in the results.
However, this region accounts for a very low percentage of the extreme poor and therefore has a minor
impact on the results.8
Table 2: Population and Child Population distribution in Sample of Global Micro Database, by
income group, region and welfare type
Share of
Share of
Share of developing
Number Population sample
Population sample world child
of of children child
(millions) population population
countries (millions) population
(%) represented in
(%)
sample (%)
89 5249.1 100.0 1686.1 100.0 82.1
Income Group
Low Income 21 628.5 12.0 294.5 17.5 76.4
Lower Middle
29 2281.2 43.5 837.6 49.7 86.3
Income
Upper Middle
26 2071.8 39.5 500.2 29.7 82.4
Income
High Income 13 267.5 5.1 53.8 3.2 60.7
Region
East Asia & Pacific 11 1889.3 36.0 458.7 27.2 89.9
Europe and Central
24 424.7 8.1 93.8 5.6 84.5
Asia
Latin America &
18 550.3 10.5 176.9 10.5 89.6
Caribbean
Middle East & North
3 16.3 0.3 5.6 0.3 3.9
Africa
South Asia 7 1667.1 31.8 601.7 35.7 97.1
Sub-Saharan Africa 26 701.4 13.4 349.4 20.7 74.1
Welfare Type
Consumption 59 4497.1 85.7 1452.5 86.1 N/A
Income 30 752.0 14.3 233.6 13.9 N/A
Source: GMD, UNDESA, WDI
8
In 2008, the most recent year MENA estimates are reported, the region accounted for 0.7 percent of the total
number of extreme poor.
5
When classifying countries by income group, the sample has high coverage of low and upper middle
income countries (more than 82 percent), partial coverage of low income countries (76 percent) and low
coverage of high income countries. The two types of countries most underrepresented in the data – those
in the Middle East and North Africa and high income countries – tend to have low poverty rates.
Therefore, the sample overstates developing world poverty rates in the aggregate, though it is less clear
how omitting these countries affects the comparison between child and adult poverty rates; this will be
explored in future work. All subsequent tables and figures in this report are based on the GMD sample
presented in Table 2.
The GMD sample is lined up to represent estimates of extreme poverty in 2013. Lining up the welfare
aggregates in surveys to a common year makes them more comparable across countries, since the
underlying surveys in both PovcalNet and the GMD were conducted in various years. Between 2009 and
2014, population generally increased and poverty generally fell; therefore failing to line up poverty
estimates to a common year would give older surveys less weight overall, but greater weight when
generating profiles of the global poor. Lining up the survey data to 2013 entails adjusting both the
population and the poverty estimates. To adjust the population, we utilize population projections for each
gender and age group provided by the United Nations Population Division (UNDESA), which are
available by gender and five-year age cohort. As a result of the line-up, the sum of the survey weights for
each gender and age group within each country match UNDESA’s reported population of that group in
2013.9 The poverty estimates are constructed to equal those published by PovcalNet for each country. Full
details of this ‘lining up’ procedure are given in Castañeda et al 2016.
Poverty rates among children and adults
How do the prevalence and depth of extreme poverty differ by age in the sample of 89 countries? Table 3
gives the prevalence results, which are striking. More than half of the extreme poor are children (aged
under 18), even though children only comprise a third of the sample population. Among the extremely
poor, children outnumber prime-age adults (aged 18-59) by a factor of 1.14, and the number of elderly
aged 60 or more by a factor of 8.6. The extreme poverty rate for children is 19.5 percent, compared to 9.2
percent for adults, 9.5 percent for prime-aged adults aged 18-59, and 7 percent for people aged 60 or
more. The large number of poor children reflects both the large share of youth in the population (32
percent) as well as high rates of child poverty (19.5 percent).
Poverty rates among children rise slightly for young children, then decline as age group increases.
Children aged 5 to 9 are most likely to be poor, at 21.5 percent, followed by children age 0 to 4 at 21
percent, 18.7 percent of 10 to 14 year olds, and 14.6 percent of the oldest group of children aged 15 to 17.
Lower poverty rates among older children may stem from the lifetime earning cycle of their parents (older
parents with more working experience tend to earn higher income), the labor force participation of
spouses and older children, and fewer dependants, although further investigation would be required to
determine their relative importance.
9
These population estimates are for five-year age groups; we distribute the population uniformly across years of age
within these groups.
6
Table 3: Population by age groups and extreme poverty
Share of Share of
Extreme poor Headcount
extreme poor population
(millions) poverty rate
(%) (%)
GMD Sample of developing countries
Children 0-17 328.7 19.5 50.2 32.1
Children 0-4 104.0 21.0 15.9 9.4
Children 5-9 101.1 21.5 15.4 9.0
Children 10-14 84.4 18.7 12.9 8.6
Children 15-17 39.1 14.6 6.0 5.1
Adults (18 or more) 326.3 9.2 49.8 67.9
Adults 18-59 288.1 9.5 44.0 57.6
Adults 60 or more 38.2 7.0 5.8 10.3
Total 655.0 12.5 100.0 100.0
Source: GMD, UNDESA, WDI, PovcalNet
While these differences in poverty rates by age group are large, how precisely are they estimated? The
sample consists of a roughly 7.7 million observations on individuals, randomly selected under each
survey’s design from a population of about 5.3 billion people living in the 89 countries included in the
sample. Estimating proper standard errors, however, is complicated by the absence of identifiers for the
primary sampling units for many of the surveys in the GMD. Without these identifiers, it is impossible to
properly adjust for survey design when calculating standard errors and to give confidence intervals for the
full sample. Therefore, Table 4 reports confidence intervals around average poverty estimates by age
group, for the 49 countries for which the primary sampling units (PSU) can be identified. Average
poverty rates are significantly higher in this subsample. The size of the confidence interval tends to be
modest, however, ranging from 1 to 1.5 percentage points. A back of the envelope estimate is that the use
of this subsample of 49 countries, instead of the full sample of 89, inflates the estimated size of the
confidence interval by 1.42.10 This implies that the corresponding confidence intervals for the full sample
would range from approximately 0.5 to 1 percentage point. With the exception of the small difference in
poverty rates between the two youngest age groups, we therefore conclude that the declines in aggregate
estimated poverty rates by age group reported in Table 3 are all statistically significant.
The welfare disadvantage faced by children, relative to adults, is also reflected in Figures 1 and 2, which
give the cumulative and probability density functions of welfare by age group. Figure 1 indicates that the
distribution for adults stochastically dominates that of children, meaning that poverty rates for children
are higher at any poverty line. Furthermore, although the differences are smaller, children 10-17 are
slightly better off than younger children throughout most of the distribution. Finally, Figure 2
demonstrates the fat left tail of the child welfare distribution, and the large mass of children that live on
slightly less than the $3.10 per day moderate poverty line.
10
1.42 is approximately equal to the square root of the ratio of the number of observations in the full sample (7.7
million) to the number of observations in the subsample used in Table 9 (3.8 million). The square root of the ratio of
89 countries in the full sample to 49 in the subsample is 1.35. This estimate assumes that the samples in the 49
countries with PSI identifiers are on average as precise as the 40 that are missing this information. In addition, it
slightly overstates the size of the confidence interval by failing to account for the slight gain in precision that would
arise due to the lower estimated poverty rate in the full sample.
7
Table 4: Confidence Intervals for a subsample of 49 countries
Population
Headcount in GMD
Confidence interval
poverty rate Observations subsample
(95%)
(%)) with PSU
(millions)
Children 0-17 26.9 26.4 27.4 1,459,342 1102.7
Children 0-4 29.0 28.3 29.7 389,896 327.1
Children 5-9 29.3 28.7 30.0 426,571 312.4
Children 10-14 25.4 24.8 25.9 409,576 296.9
Children 15-17 20.9 20.3 21.4 233,299 166.2
Adults 15.6 15.3 16.0 2,337,209 1770.2
Adults 18-59 16.1 15.7 16.4 1,985,682 1540.9
Adults 60 or more 12.8 12.3 13.3 351,527 229.3
Total 20.0 19.6 20.4 3,796,551 2872.9
Source: GMD, UNDESA, WDI, PovcalNet
Note: Includes 49 countries for which primary sampling unit is available for all observations. See Appendix 1. For Nicaragua and
Peru we use only the surveys of 2009 and 2012, respectively, not the 2014 survey.
Figure 1: Cumulative density function of household per capita welfare, by age group
Source: GMD, UNDESA, WDI, PovcalNet
Note: Per capita welfare lower than PPP$0.2 or higher than PPP$100 have been excluded from the graph for presentation.
Bangladesh, Cambodia, Jordan and Lao PDR have been excluded from graph since there is no information of PPP for 2011 for
these countries.
8
Figure 2: Probability density function of log-welfare of children (0 to 17) and adults
Source: GMD, UNDESA, WDI, PovcalNet
Note: Per capita welfare higher than PPP$0.2 or lower than PPP$100 have been excluded from the graph for presentation.
Bangladesh, Cambodia, Jordan and Lao PDR have been excluded from graph since there is no information of PPP for 2011 for
these countries.
The analysis so far has only considered headcount poverty rates, which fail to consider how far children
and adults lie below the poverty line relative to adults. An alternative measure, the relative poverty gap, is
equal to the following:
1 EP
1.9 y i
(1) P1
N
i 1 1 .9
where P1 is the relative poverty gap measure, yi is the per capita expenditure of individual i, N is the
number of individuals in the country, and EP is the number of people that are extremely poor. In other
words, poverty gaps measure the distance from individuals’ welfare to the poverty line, positive for the
poor and zero for the non-poor.
The average poverty gap is reported in Table 5 both in absolute terms and, following equation (1), as a
percentage of the poverty line. Over the entire sample, the average poverty gap in PPP terms is 7 cents,
with the child poverty gap at 11 cents and adult poverty gap at 5 cents. The corresponding relative
poverty gaps are 3.7 percent overall, 2.6 percent for adults, and 6 percent for children. When weighing by
population shares, children account for over half (52.4 percent) of the total poverty gap, with infants 0-4
contributing almost 17 percent. Summing across the population, the poverty gap amounts to $212 million
for children, which is about the same as the total gap for adults. In short, the larger headcount rates
observed for children are also reflected in larger poverty gaps.
9
Table 5: Mean welfare and poverty gaps
Adults
Children Infants
All (18 or
(0-17) (0-4)
more)
Poverty Gaps
Mean poverty gap (PPP$ per capita per day) $0.07 $0.05 $0.11 $0.12
Relative poverty gap index (% of poverty line) 3.7 2.6 6.0 6.6
Population weighted relative poverty gap index (%) 3.7 1.8 1.9 0.6
contribution of each group to relative poverty gap index (%) 100.0 47.6 52.4 16.8
Welfare of the poor (PPP$ per capita per day)
Mean welfare of extreme poor $1.34 $1.35 $1.32 $1.31
as percentage of PPP$1.90 poverty line 70.4 71.3 69.5 69.1
Median welfare of extreme poor $1.42 $1.44 $1.40 $1.39
as percentage of PPP$1.90 poverty line 74.9 76.0 73.5 73.0
Aggregate poverty gap (millions PPP$)
Per day 364.0 172.1 191.9 61.8
Per year 132,862 62,821 70,041 22,548
Headcount poverty rate (%) 12.5 9.2 19.5 21.0
Number of poor (millions) 655.0 326.3 328.7 104.0
Total population (millions) 5249.1 3563.0 1686.1 495.3
Source: GMD, UNDESA, WDI, PovcalNet
Finally, we examine the incidence of poverty defined according to a higher poverty line of $3.10 per day
(2011 PPP). Table 6 displays the results, which corroborate Figures 1 and 2 in showing the high rates of
poverty among children at the higher poverty line. Over 44 percent of all children age 0-17 live on less
than $3.10 per day, as opposed to 26.6 percent of all adults. Furthermore, children make up about 44
percent of all extremely or moderately poor, even though they comprise less than a third of the global
population.
Table 6: Extreme or moderate poverty for adults and children
Moderate or Share of
Moderate or
extreme moderate or Share of sample
extreme poor
headcount extreme poor population (%)
(millions)
poverty rate (%) (%)
Children 0-17 749.6 44.5 44.2 32.1
Adults 18 and above 947.2 26.6 55.8 67.9
Total 1696.9 32.3 100.0 100.0
Source: GMD, UNDESA, WDI, PovcalNet
Note Moderate or extreme poverty rate includes all children living in households with welfare per capita lower than $3.10 per
day. The 750 million extreme or moderate poor children shown in the table include the 329 million extremely poor children.
3. Child poverty by country characteristics and region
How are these high rates of child poverty distributed across countries, regions, and country income
groups? As seen in Table 7, the vast majority – 94 percent – of poor children live in low income or lower
middle income countries; India alone accounts for nearly 30 percent of all poor children. Not
10
surprisingly, child poverty rates are highest in low-income countries, where 41.6 percent of children are
extremely poor. The percentage decreases to 22.2 percent for lower middle income countries and 3.9
percent for upper middle income countries.
Table 7: Extreme child poverty by country income status (India and China shown separately)
Extreme poor Children Share of Share of
children headcount extremely poor sample child
(millions) poverty rate (%) children (%) population (%)
Low income 122.6 41.6 37.3 17.5
Lower middle income 186.3 22.2 56.7 49.7
Of which: India 99.7 22.1 30.3 26.8
Upper middle income 19.5 3.9 5.9 29.7
Of which: China 5.8 2.0 1.8 16.7
High income 0.3 0.6 0.1 3.2
Total 328.7 19.5 100.0 100.0
Source: GMD, UNDESA, WDI, PovcalNet
Note: Totals reflect sample of 89 countries.
Table 8 shows similar figures by region. Sub-Saharan Africa (SSA) stands out as the region with the
highest child poverty rate, at 48.7 percent. Over half, 51.7 percent of all extremely poor children in the
sample, live in an SSA country, despite the region only containing 20 percent of the child population.
Poverty rates in South Asia (SAR) are also high, as 19.7 percent of the children in the region are poor.
SSA and SAR, combined, account for over 87 percent of the extremely poor children. While there are 170
million poor African children in the sample, this seriously understates the true number of poor children in
Africa because the sample covers only 26 of the 48 countries, or 74 percent of the children, in the region.
If the African child poverty rate were applied to all African countries, the estimated number of poor
children in SSA would rise to 229 million.
Table 8: Extreme child poverty by country region (India and China shown separately)
Extreme poor Children Share of Share of
children headcount extremely poor sample child
(millions) poverty rate children (%) population (%)
East Asia Pacific 25.6 5.6 7.8 27.2
which includes China 5.8 2.0 1.8 16.7
South Asia 117.2 19.5 35.7 35.7
which includes India 99.7 22.1 30.3 26.8
Sub-Saharan Africa 170.0 48.7 51.7 20.7
Latin America and Caribbean 14.3 8.1 4.4 10.5
Europe and Central Asia 1.3 1.4 0.4 5.6
Total 328.7 19.5 100.0 100.0
Source: GMD, UNDESA, WDI, PovcalNet
11
Countries affected by fragility, conflict and violence are likely to have factors that increase poverty risk,
due to lower and interrupted economic development and problems of governance. These factors also
affect the availability and regularity of household surveys that are used to measure poverty. The GMD
sample does however allow a simple comparison of countries that have ‘fragile’ status and others. Table
9 shows that extreme child poverty in such countries is nearly 58 percent, which is much higher than the
17 percent rate in other, ‘non-fragile’ countries.
Table 9: Extreme Child Poverty and Shares in Fragile States
Share of
Extreme Children Adults Share of
extremely
Number of poor headcount headcount child
poor
countries children poverty poverty population
children
(millions) rate (%) rate (%) (%)
(%)
Not fragile states 78.0 272.1 17.1 8.1 82.8 94.2
Fragile states 11.0 56.5 57.7 46.7 17.2 5.8
Total 89.0 328.7 19.5 9.2 100.0 100.0
Source: GMD, UNDESA, WDI, PovcalNet
Notes: Coverage of fragile states in GMD represents under half of all children living in fragile states (98 of 217 million) and
these estimates should be interpreted accordingly. Fragile states included are: Chad, Dem. Rep. Congo, Guinea-Bissau, Haiti,
Kosovo, Madagascar, Mali, Sierra Leone, Sudan, Togo, West Bank and Gaza. Fragile states without information are:
Afghanistan, Bosnia and Herzegovina, Burundi, Central African Republic, Comoros, Cote d'Ivoire, Eritrea, The Gambia, Iraq,
Kiribati, Lebanon, Liberia, Libya, Marshall Islands, Micronesia, Myanmar, Solomon Islands, Somalia, South Sudan, Syrian Arab
Republic, Timor-Leste, Tuvalu, Yemen, and Zimbabwe.
Taking a global view of the country-level situation for extreme child poverty, Figure 3 displays a map of
the estimated headcount ratio of every country in the sample, and confirms that child poverty rates tend to
be highest in Sub-Saharan Africa, particularly central Africa, with significantly lower rates in much of
South Asia, followed by the rest of the developing world.
Figure 3: Map of extreme child poverty rates by country
Source: GMD, UNDESA, WDI, PovcalNet
12
Looking more closely at child poverty rates by country, Figure 4 presents estimates of extreme and
moderate headcount rates by country, in descending order of most to least poor.
Figure 4: Share of children that are extremely poor, moderately poor, and non-poor, by country
Source: Global Micro Database and Povcalnet
Notes: Extreme poverty is defined as household per capita income or consumption less than $1.90. Near-poverty is defined as
those between $1.90 and $3.10, and non-poor is defined as those living on $3.10 or more per day.
13
The vertical size of the rectangle for each country in Figure 4 is proportional to the size of the population.
Madagascar has the largest estimated extreme poverty rate for children, followed by the Democratic
Republic of Congo, Malawi, Guinea-Bissau, and Zambia. At the bottom are the high income countries
included in the sample for ‘developing countries’. India stands out due to its large population and high
estimated incidence of extreme and moderate poverty rates for 2013 – respectively 22 percent and 39.6
percent – among children.
Poor children tend to live in large households, as two out of three extremely poor children live in
households with six or more members; more than one-third in large households of 8 or more (Table 11).
The poor are overrepresented in the latter group, which contains less than 20 percent of the child
population.
Table 11: Extreme child poverty by household size
Share of Share of
Household size Extreme poor Children Percentage
extremely child
(number of children headcount of children in
poor children population in
members) (millions) poverty rate group
(%) sample (%)
Three or less 13.9 5.5 4.2 14.9 15.5%
Four 33.7 9.0 10.2 22.3 33.8%
Five 53.2 16.2 16.2 19.5 36.7%
Six 59.4 23.5 18.1 15.0 41.2%
Seven 49.1 30.3 15.0 9.6 45.2%
Eight 39.3 36.1 12.0 6.5 47.1%
Nine 26.3 38.6 8.0 4.1 48.2%
Ten or more 53.7 38.8 16.3 8.2 49.1%
Total 328.7 19.5 100.0 100.0 32.1%
Source: GMD, UNDESA, WDI, PovcalNet
The increased risk of extreme child poverty from larger household size is however not clearly associated
with ‘multi-generational’ households in which elderly people co-reside with their adult children and
grandchildren, a far more common practice in developing countries. Table 12 shows that extreme child
poverty rates do not differ greatly according to whether children live solely with prime age adults (often
their parents), or in three-generation households, or live solely with elderly people. However, over three-
quarters of extreme child poverty (78.5 percent) is in two-generation households, suggesting that large
households in this group are a major factor in extreme poverty risk. Only a fifth of extremely poor
children live in three-generation households. Overall, these patterns highlight the importance of the
sensitivity tests of our results to assumptions about economies of scale of households’ expenditure in the
next section.
14
Table 12: Extreme child poverty by Household Age Composition
Extremely Children Adults Share of Share of
poor headcount headcount extremely sample child
children poverty rate poverty rate poor population
(millions) (%) (%) children (%) (%)
2 generation
( people aged 18-59 and
children 0-17) 257.9 19.7 12.5 78.5 77.5
Elderly and children
(people aged >=60 and 0-17) 3.0 21.0 13.4 0.9 0.9
Three generation
(people aged >=60, 18-59, and
0-17) 67.1 18.8 13.0 20.4 21.1
Only children 0.6 7.4 . 0.2 0.5
Total 328.7 19.5 9.2 100.0 100.0
Source: GMD, UNDESA, WDI, PovcalNet
4. Sensitivity to alternative equivalence scales
The estimates of poverty by age group reported are based on per capita consumption, and therefore rest on
the fundamental assumption that per capita consumption reflects individual economic welfare both within
and across households. This assumption, while standard, has been widely questioned on three main
grounds. First, it ignores potential disparities in resource allocation within the household. Since we do not
have access to data from a large number of countries on consumption associated to specific household
members, it is difficult to address this issue. However, failing to account for intra-household inequalities
likely understates child poverty; Dunbar et al (2011) estimate that child poverty rates in Malawi would
increase from 91.3% to 97.4% when unequal allocations within the household are considered. Similarly,
Bargain et al (2013) find that per capita measures of consumption underestimate child poverty estimates
for Côte d’Ivoire, especially for larger households, once both intra-household allocation and economies of
scale are considered. Second, the use of per capita consumption ignores the relative difference in the costs
of satisfying children’s and adults’ needs; specifically, food needs of children, especially younger
children, are less than those of adults, although other needs of children, such as for schooling and
healthcare may be higher. Finally, households with more members are able to achieve greater economies
of scale on household goods such as rent, utilities, some durable goods, and other shared aspects of
household activity and expenditure. Together, the latter two assumptions -- that adults and children have
equal-cost relative needs and that there are no economies of scale -- have been relaxed by employing
‘equivalence scales’ that adjust household size, typically according to their member’s age and the number
of total members.11 These equivalence scales are commonly expressed using two key parameters: α for
the relative cost of children, and θ to control for economies of scale, according to the following formula:
(2) mh nah nch
Where mh is the number of equivalent adults in household h, nah is the number of adults in household h,
and nch is the number of children in household h. The parameters α and θ can take values between zero
and one; lower values of α are used to reflect children’s lower expenditure needs relative to adults; lower
11
Some equivalence scales also adjust for differing needs by gender and other factors such as disability.
15
values of θ express higher economies of scale in households’ expenditure. When α and θ are set equal to
one the number of equivalent adults is equal to the household size. Deaton and Zaidi (2002) emphasize
the importance of checking for sensitivity to equivalence scale assumptions when comparing poverty
estimates for different age-groups, specifically children. They suggest comparing poverty estimates for
every sensible range of α and θ, we follow this approach.
There is a voluminous theoretical and empirical literature on setting and using equivalence scales and we
do not address the ongoing debate on the appropriate choice of equivalence scales. Instead, we turn to the
more recent and most directly relevant literature that has discussed how much equivalence assumptions
affect the prevalence of ‘extreme poverty’ in cross-national profiles, the relative position of children
versus other age groups in such profiles, and on testing alternative equivalence assumptions when
assessing extreme poverty. Children most often live in large households in developing countries (Deaton
and Paxson, 1998) and this makes an assessment of both household size and composition essential for
robustness and sensitivity of results when considering sub-groups of population or international
comparisons (Haughton and Khander 2009).
Accordingly, previous estimates of ‘extreme poverty’ by age have sought to assess how results change
when employing different equivalence scales; Batana et al (2013) use three alternative scales to compare
to the baseline per-capita equivalence assumption and find that child poverty rates fall dramatically in
doing so. The comparisons reported in Batana et al, however, did not adjust the prevailing $1.25 per day
poverty line when utilizing different equivalence scale. This is problematic because the international
poverty line, whether set at $1.25 (2005 PPP) or $1.90 (2011 PPP), are based on the national poverty lines
of 15 poor countries expressed in per capita terms. (Ferreira et al, 2015) The selection of national poverty
lines in each country is based on consumption patterns of households presumed poor by observing their
caloric intakes, and not on the consumption of single-member households.
Ravallion (2015) notes that any comparison of per capita to adult-equivalent expenditure must choose a
“pivot household”, or a reference household for which consumption per adult equivalent remains
unchanged independently of the parameters selected. By construction, a poor pivot household will remain
considered poor regardless of the assumed parameters. In Batana et al (2015), the de facto pivot
household was a single adult household. Ravallion (2015), following Deaton and Zaidi (2002), consider
this assumption inadequate; he proposes “that the poverty comparisons should be anchored to the typical
circumstances of households near the poverty line”. He therefore proposes to use the demographic
characteristics of a typical household in the neighbourhood of the poverty line to set a pivot household
and adjust the international poverty line from per capita to adult-equivalent terms with each set of
parameters.
We follow this approach to test the conclusion that child poverty rates remain higher than adult poverty
rates under alternative equivalence scales. In particular, we examine the sensitivity of the extreme poverty
estimates to both children’s relative cost and household economies of scale (α and θ respectively).
Households’ equivalent size follows equation (2), with alpha and theta lying between zero and one. We
test robustness with respect to six values of α and θ: 1, 0.8, 0.6, 0.4, 0.2, and 0.12 We additionally test the
12
On the extremes α=1 assumes that children require the same consumption as adults to achieve the same utility,
while α=0 is a hypothetical case where children are “free”. On the other side, θ=1 assumes no public goods within
16
case where theta is equal to 0.5 and alpha is equal to 1, which corresponds to the “square root scale”
recently used by the OECD.13 In total, we consider 32 unique values of alpha and theta that are tested,
with results shown in Table 13.
The pivot household contains six people, three adults and three children. This corresponds to the median
number of adults and children in households with welfare between $1.70 and $2.10 per day in the sample.
In each of the 32 scenarios, the poverty line of $1.90 is multiplied by the ratio of household size to the
number of equivalent adults, for the pivot household. For example, the equivalent household size for a
household with three adults and three children is 5.4 when alpha=0.8 and theta=1, as shown in row 2 of
Table 15. Multiplying $1.90 by 6 and dividing by 4.6 gives an effective poverty line for that equivalence
scale of 2.1 which is then used to re-estimate child and adult headcount poverty rates.
The results of this sensitivity test confirm that children have higher poverty rates under every two-
parameter equivalence assumption, apart from those estimated at the extreme assumption that children
have no monetary welfare requirements, equivalent to an α parameter equal to zero. The estimated
poverty rates, however, are always much greater than the very low headcount rates of between 6 and 3
percent reported by Batana et al (2013) who assume a pivot household of one adult. By adjusting poverty
lines and reflecting indicative household composition at the margins of poverty, we find a lower bound
child poverty headcount rate of between 17.2 to 20.9 percent across all values of alpha and theta of 0.2 or
more. This lower bound of 0.2 is far lower than any commonly used equivalence scale and lower than any
of the scales used by Batana et al (2013). Poverty rates using the “square root scale” of theta and alpha
equal to 0.5 and 1, respectively, are 11.6 percent for adults and 18.6 percent for children. We therefore
conclude that higher extreme child poverty rates are robust to any sensible two-parameter equivalence
scale.
5. Conclusion
This paper presents new evidence on child poverty and its sensitivity to alternative adult-equivalence
scales. The analysis is based on a data set of 89 countries taken from the Global Micro Database, a new
database that adds harmonized household characteristics to the same surveys and welfare aggregates that
are used to calculate official World Bank poverty estimates. The results confirm that children are
disproportionately poor. The poverty rate among children in the sample aged 0 to 17 is estimated to be
19.5 percent, which is over twice the 9.2 percent rate for adults 18 and above. The estimated global
poverty rate for 0 to 4 and 5 to 9 year olds is 21 and 21.5 percent, respectively, and declines for each
successively for older age group. Furthermore, an estimated 44.5 percent of children live on less than
$3.10 per day, as opposed to 26.6 percent of adults.
the household (the consumption of one member prevents the consumption of another of the same good), while θ=0
would imply that all goods consumed by the household are public goods.
13
See: http://www.oecd.org/els/soc/CO_2_2_Child_Poverty.pdf
17
Table 13: Children and adults headcount poverty rates considering equivalence of scale
Parameters Equivalence Adjusted Headcount poverty rates (%)
Reference Poverty Difference
Theta Alpha household line Total Adults Children (Child‐
equivalent size (PPP$) Adult)
1.0 1.0 6.0 1.9 12.5 9.2 19.5 10.3
0.8 5.4 2.1 12.8 9.9 19.1 9.2
0.6 4.8 2.4 13.3 10.8 18.5 7.7
0.4 4.2 2.7 14.1 12.3 17.9 5.6
0.2 3.6 3.2 15.4 14.5 17.2 2.8
0.0 3.0 3.8 17.1 17.4 16.4 ‐1.0
0.8 1.0 4.2 2.7 12.9 9.8 19.3 9.5
0.8 3.9 3.0 13.2 10.5 19.0 8.5
0.6 3.5 3.3 13.7 11.4 18.6 7.1
0.4 3.2 3.6 14.4 12.7 18.1 5.4
0.2 2.8 4.1 15.3 14.3 17.3 3.0
0.0 2.4 4.7 16.6 16.7 16.5 ‐0.2
0.6 1.0 2.9 3.9 13.8 11.0 19.5 8.5
0.8 2.8 4.1 14.1 11.7 19.3 7.6
0.6 2.6 4.4 14.5 12.4 19.0 6.5
0.4 2.4 4.8 15.1 13.4 18.6 5.2
0.2 2.2 5.3 15.8 14.7 18.0 3.2
0.0 1.9 5.9 16.6 16.3 17.0 0.7
0.5 1.0 2.4 4.7 14.4 11.9 19.7 7.8
0.4 1.0 2.0 5.6 15.1 12.8 20.1 7.3
0.8 2.0 5.8 15.4 13.3 19.9 6.6
0.6 1.9 6.1 15.8 13.9 19.8 5.9
0.4 1.8 6.4 16.1 14.5 19.4 4.9
0.2 1.7 6.8 16.5 15.4 19.0 3.6
0.0 1.6 7.3 17.0 16.5 18.2 1.7
0.2 1.0 1.4 8.0 16.9 15.0 20.9 5.9
0.8 1.4 8.1 17.1 15.3 20.9 5.6
0.6 1.4 8.3 17.2 15.5 20.8 5.2
0.4 1.3 8.6 17.4 15.8 20.6 4.8
0.2 1.3 8.8 17.6 16.3 20.3 4.1
0.0 1.2 9.2 17.7 16.7 19.7 3.0
0.0 1.0 1.0 11.4 18.8 17.4 21.9 4.6
Notes: Alpha refers to the scale factor applied to children under 18. Theta refers to the assumed household
economies of scale. The reference household is the median number of adults and children of all households with
welfare between $1.70 and $2.10 per day (in 2011 PPP terms). The reference household contains three adults and
three children.
Source: GMD, UNDESA, WDI, PovcalNet
18
Where do poor children live? Child poverty rates, like those for adults, are much high in rural areas.
Nearly one out of three children in rural areas is extremely poor, and over 81 percent of poor children live
in rural areas. Nearly 88 percent of poor children are located in Sub-Saharan Africa or South Asia, and
child poverty rates tend to be much higher in Sub-Saharan Africa than in the rest of the developing world.
This illustrates the magnitude of the challenge of ensuring that children in rural areas, and in Africa and
South Asia, benefit from valuable early childhood investments in human capital.
A fundamental question involves the robustness of these estimates of child poverty with respect to the use
of alternative equivalence scales. The new $1.90 per day poverty line, like the $1.25 line that it is based
on, is a per capita line. It is derived from the national poverty lines of 15 poor countries, which in turn are
each developed using per capita consumption as the welfare measure. Following Ravallion (2015), we
apply alternative equivalence scales to both the consumption aggregate and the poverty line, for a typical
household in the neighbourhood of the poverty line. After making this adjustment, child poverty rates are
quite robust with respect to the choice of two-parameter equivalence scale. Child poverty rates do not dip
below 17 percent until children are given one-fifth of the weight of adults to account for their lower
expenditure needs, which is not realistic. In short, higher child poverty rates are robust to any sensible
two-parameter equivalence scale.
The analysis leaves open three main areas for future work. First, we plan to explore the characteristics of
poor children in greater detail, and analyse the variation across child poverty rates across countries and,
wherever possible, to also consider non-monetary aspects of child poverty. Second, we plan to explore in
detail the relationship between welfare and school attendance. This will help shed light on whether there
is a particular level of welfare at which school attendance rises sharply, and how this varies for boys and
girls at different ages. Finally, a limitation of the current data set is that the lack of temporal coverage
limits the analysis to a single cross-section. Subsequent improvement in historical coverage will allow us
to document trends in child poverty to better understand which countries and types of households have
most rapidly reduced child poverty.
19
Appendix 1: Data and Statistical Appendix
Sample
The study uses a sample of 104 surveys from the September 2016 vintage of the Global Micro Database
(GMD); only surveys between 2009 and 2014 are selected. The sample is composed of 89 developing
countries. The GMD sample is lined up to represent the estimates of extreme poverty in 2013. When a
survey of 2013 is unavailable, two surveys are selected, one before and one after 2013, and their results
are weighted according to the relative distance to 2013 (Castañeda et al, 2016); if only surveys before
2013 are available, we use the latest available. The countries and years selected are showed in Table A1.
Table A1: Developing countries and survey years in GMD sample
Countries (89) Survey years
Armenia, Bulgaria*, Chile, China, Czech Republic, Dominican Republic*, Estonia, 2013 only
Georgia*, Honduras*, Croatia, Hungary, Kazakhstan*, Kosovo, Lithuania, Latvia*,
Moldova, Montenegro*, Pakistan*, Poland*, Romania*, Serbia, Slovak Republic,
Slovenia*, Ukraine (24)
Argentina, Bolivia, Brazil*, Colombia, Costa Rica*, El Salvador, Ecuador, 2012 and 2014
Mexico*, Paraguay*, Peru*, Uruguay, Vietnam (12)
Guatemala*, Indonesia (2) 2011 and 2014
Nicaragua (1) 2009 and 2014
Albania*, Bhutan, Democratic Republic of the Congo*, Djibouti, Guinea*, Haiti, 2012 only
Kyrgyz Republic, Cambodia, Lao PDR, Mongolia, Mauritius*, Panama*,
Philippines, Russian Federation, Sri Lanka*,Thailand, Turkey, Uganda* (18) 7
Chad*, Republic of the Congo*, India*, Niger*, Senegal*, Sierra Leone*, 2011 only
Tanzania*, Togo* (8)
Bangladesh*, Ethiopia*, Guinea-Bissau*, Lesotho*, Madagascar*, Malawi* , 2010 only
Mali*, Nepal*, Nigeria*, Rwanda*, São Tomé and Príncipe*, Tunisia, Vanuatu,
South Africa*, Zambia* (15)
Burkina Faso*, Botswana, Maldives, Nicaragua*, Papua New Guinea, West Bank 2009 only
and Gaza, Sudan*, Swaziland*, Tajikistan, Tonga (9)
*Countries for which GMD has surveys’ PSU.
Lineup
The lineup procedure followed three steps:
1) Calibrate the sample of each survey to represent the national population in 2013 according to
UNDESA. The calibration is made to represent UNDESA’s population divided by 5-year age groups and
gender. The survey weights are calibrated using maximum entropy estimation, and then rescaled to equal
the exact UNDESA figures. 14 The number of children aged 15-17 within the 15-19 age-group is
determined by each survey’s age distribution within the group. For Kosovo we use population estimates
14
Command maxentropy in Stata®14. This technique is used so as, to the extent possible, maintain constant weights
within households. See Wittenberg, Martin. “An Introduction to maximum entropy and miminum cross-entropy
estimation using Stata”. The Stata Journal 10 (2010) for a description of the methodology.
20
from WDI and for Argentina we do not line up the population since the surveys used are not nationally
representative.
2) Line up welfare per capita to reflect growth between the year of the survey and 2013. Depending on the
country we look at growth estimates of household final consumption expenditure or GDP per capita. We
assume that welfare per capita of every household moves at a same growth rate.
3) In order to keep consistency with PovcalNet poverty estimates, we adjust the poverty line in each
country to replicate PovcalNet’s poverty rates.
Child Population
Developing countries in this study include all countries except Australia, Belgium, Cyprus, Finland,
France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, Japan, Netherlands, Norway,
Portugal, Spain, Sweden, Switzerland, United Kingdom and United States. The world’s number of
countries, 214, reflects the number of countries according to WDI.
In Tables 1 and 2 the number of children in the World and in developing countries include three fifths of
UNDESA’s population in the 15 to 19 age bracket (we assume that the population in the age bracket is
uniformly distributed by age). No such assumption is maintained for the child population in the GMD
sample. For 24 countries, UNDESA’s population is unavailable and we use WDI’s population instead.
For these countries, UNDESA population totals for children are estimated using averages across all other
countries in the same region and income group.
China
China is a special case because the World Bank does not have access to individual level records from the
Chinese Household Budget Survey (HBS), which is the source of official Chinese poverty statistics. The
World Bank’s international poverty estimates for China are instead based on an approximate distribution
derived from grouped data, which cannot be used for poverty profiling. This study therefore utilizes
household level data from the 2013 Chinese Household Income Project Survey (CHIPS), made available
to the public by Beijing Normal University. The poverty rate for urban and rural China, derived from the
2013 HBS, is then applied to the CHIPS data to generate profiles of the extreme and moderate poor in
China.
21
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Himelein, K., March 2015
21 No condition is permanent: middle class in Nigeria in the last decade
Corral Rodas, P. A., Molini, V., Oseni, G. O., March 2015
22 An evaluation of the 2014 subsidy reforms in Morocco and a simulation of further reforms
Verme, P., El Massnaoui, K., March 2015
Updated on December 2016 by POV GP KL Team | 2
23 The quest for subsidy reforms in Libya
Araar, A., Choueiri, N., Verme, P., March 2015
24 The (non‐) effect of violence on education: evidence from the "war on drugs" in Mexico
Márquez‐Padilla, F., Pérez‐Arce, F., Rodriguez Castelan, C., April 2015
25 “Missing girls” in the south Caucasus countries: trends, possible causes, and policy options
Das Gupta, M., April 2015
26 Measuring inequality from top to bottom
Diaz Bazan, T. V., April 2015
27 Are we confusing poverty with preferences?
Van Den Boom, B., Halsema, A., Molini, V., April 2015
28 Socioeconomic impact of the crisis in north Mali on displaced people (Available in French)
Etang Ndip, A., Hoogeveen, J. G., Lendorfer, J., June 2015
29 Data deprivation: another deprivation to end
Serajuddin, U., Uematsu, H., Wieser, C., Yoshida, N., Dabalen, A., April 2015
30 The local socioeconomic effects of gold mining: evidence from Ghana
Chuhan-Pole, P., Dabalen, A., Kotsadam, A., Sanoh, A., Tolonen, A.K., April 2015
31 Inequality of outcomes and inequality of opportunity in Tanzania
Belghith, N. B. H., Zeufack, A. G., May 2015
32 How unfair is the inequality of wage earnings in Russia? estimates from panel data
Tiwari, S., Lara Ibarra, G., Narayan, A., June 2015
33 Fertility transition in Turkey—who is most at risk of deciding against child arrival?
Greulich, A., Dasre, A., Inan, C., June 2015
34 The socioeconomic impacts of energy reform in Tunisia: a simulation approach
Cuesta Leiva, J. A., El Lahga, A., Lara Ibarra, G., June 2015
35 Energy subsidies reform in Jordan: welfare implications of different scenarios
Atamanov, A., Jellema, J. R., Serajuddin, U., June 2015
36 How costly are labor gender gaps? estimates for the Balkans and Turkey
Cuberes, D., Teignier, M., June 2015
37 Subjective well‐being across the lifespan in Europe and Central Asia
Bauer, J. M., Munoz Boudet, A. M., Levin, V., Nie, P., Sousa‐Poza, A., July 2015
Updated on December 2016 by POV GP KL Team | 3
38 Lower bounds on inequality of opportunity and measurement error
Balcazar Salazar, C. F., July 2015
39 A decade of declining earnings inequality in the Russian Federation
Posadas, J., Calvo, P. A., Lopez‐Calva, L.‐F., August 2015
40 Gender gap in pay in the Russian Federation: twenty years later, still a concern
Atencio, A., Posadas, J., August 2015
41 Job opportunities along the rural‐urban gradation and female labor force participation in India
Chatterjee, U., Rama, M. G., Murgai, R., September 2015
42 Multidimensional poverty in Ethiopia: changes in overlapping deprivations
Yigezu, B., Ambel, A. A., Mehta, P. A., September 2015
43 Are public libraries improving quality of education? when the provision of public goods is not enough
Rodriguez Lesmes, P. A., Valderrama Gonzalez, D., Trujillo, J. D., September 2015
44 Understanding poverty reduction in Sri Lanka: evidence from 2002 to 2012/13
Inchauste Comboni, M. G., Ceriani, L., Olivieri, S. D., October 2015
45 A global count of the extreme poor in 2012: data issues, methodology and initial results
Ferreira, F.H.G., Chen, S., Dabalen, A. L., Dikhanov, Y. M., Hamadeh, N., Jolliffe, D. M., Narayan, A., Prydz,
E. B., Revenga, A. L., Sangraula, P., Serajuddin, U., Yoshida, N., October 2015
46 Exploring the sources of downward bias in measuring inequality of opportunity
Lara Ibarra, G., Martinez Cruz, A. L., October 2015
47 Women’s police stations and domestic violence: evidence from Brazil
Perova, E., Reynolds, S., November 2015
48 From demographic dividend to demographic burden? regional trends of population aging in Russia
Matytsin, M., Moorty, L. M., Richter, K., November 2015
49 Hub‐periphery development pattern and inclusive growth: case study of Guangdong province
Luo, X., Zhu, N., December 2015
50 Unpacking the MPI: a decomposition approach of changes in multidimensional poverty headcounts
Rodriguez Castelan, C., Trujillo, J. D., Pérez Pérez, J. E., Valderrama, D., December 2015
51 The poverty effects of market concentration
Rodriguez Castelan, C., December 2015
52 Can a small social pension promote labor force participation? evidence from the Colombia Mayor
program
Pfutze, T., Rodriguez Castelan, C., December 2015
Updated on December 2016 by POV GP KL Team | 4
53 Why so gloomy? perceptions of economic mobility in Europe and Central Asia
Davalos, M. E., Cancho, C. A., Sanchez, C., December 2015
54 Tenure security premium in informal housing markets: a spatial hedonic analysis
Nakamura, S., December 2015
55 Earnings premiums and penalties for self‐employment and informal employees around the world
Newhouse, D. L., Mossaad, N., Gindling, T. H., January 2016
56 How equitable is access to finance in turkey? evidence from the latest global FINDEX
Yang, J., Azevedo, J. P. W. D., Inan, O. K., January 2016
57 What are the impacts of Syrian refugees on host community welfare in Turkey? a subnational poverty
analysis
Yang, J., Azevedo, J. P. W. D., Inan, O. K., January 2016
58 Declining wages for college‐educated workers in Mexico: are younger or older cohorts hurt the most?
Lustig, N., Campos‐Vazquez, R. M., Lopez‐Calva, L.‐F., January 2016
59 Sifting through the Data: labor markets in Haiti through a turbulent decade (2001‐2012)
Rodella, A.‐S., Scot, T., February 2016
60 Drought and retribution: evidence from a large‐scale rainfall‐indexed insurance program in Mexico
Fuchs Tarlovsky, Alan., Wolff, H., February 2016
61 Prices and welfare
Verme, P., Araar, A., February 2016
62 Losing the gains of the past: the welfare and distributional impacts of the twin crises in Iraq 2014
Olivieri, S. D., Krishnan, N., February 2016
63 Growth, urbanization, and poverty reduction in India
Ravallion, M., Murgai, R., Datt, G., February 2016
64 Why did poverty decline in India? a nonparametric decomposition exercise
Murgai, R., Balcazar Salazar, C. F., Narayan, A., Desai, S., March 2016
65 Robustness of shared prosperity estimates: how different methodological choices matter
Uematsu, H., Atamanov, A., Dewina, R., Nguyen, M. C., Azevedo, J. P. W. D., Wieser, C., Yoshida, N., March
2016
66 Is random forest a superior methodology for predicting poverty? an empirical assessment
Stender, N., Pave Sohnesen, T., March 2016
67 When do gender wage differences emerge? a study of Azerbaijan's labor market
Tiongson, E. H. R., Pastore, F., Sattar, S., March 2016
Updated on December 2016 by POV GP KL Team | 5
68 Second‐stage sampling for conflict areas: methods and implications
Eckman, S., Murray, S., Himelein, K., Bauer, J., March 2016
69 Measuring poverty in Latin America and the Caribbean: methodological considerations when estimating
an empirical regional poverty line
Gasparini, L. C., April 2016
70 Looking back on two decades of poverty and well‐being in India
Murgai, R., Narayan, A., April 2016
71 Is living in African cities expensive?
Yamanaka, M., Dikhanov, Y. M., Rissanen, M. O., Harati, R., Nakamura, S., Lall, S. V., Hamadeh, N., Vigil
Oliver, W., April 2016
72 Ageing and family solidarity in Europe: patterns and driving factors of intergenerational support
Albertini, M., Sinha, N., May 2016
73 Crime and persistent punishment: a long‐run perspective on the links between violence and chronic
poverty in Mexico
Rodriguez Castelan, C., Martinez‐Cruz, A. L., Lucchetti, L. R., Valderrama Gonzalez, D., Castaneda Aguilar,
R. A., Garriga, S., June 2016
74 Should I stay or should I go? internal migration and household welfare in Ghana
Molini, V., Pavelesku, D., Ranzani, M., July 2016
75 Subsidy reforms in the Middle East and North Africa Region: a review
Verme, P., July 2016
76 A comparative analysis of subsidy reforms in the Middle East and North Africa Region
Verme, P., Araar, A., July 2016
77 All that glitters is not gold: polarization amid poverty reduction in Ghana
Clementi, F., Molini, V., Schettino, F., July 2016
78 Vulnerability to Poverty in rural Malawi
Mccarthy, N., Brubaker, J., De La Fuente, A., July 2016
79 The distributional impact of taxes and transfers in Poland
Goraus Tanska, K. M., Inchauste Comboni, M. G., August 2016
80 Estimating poverty rates in target populations: an assessment of the simple poverty scorecard and
alternative approaches
Vinha, K., Rebolledo Dellepiane, M. A., Skoufias, E., Diamond, A., Gill, M., Xu, Y., August 2016
Updated on December 2016 by POV GP KL Team | 6
81 Synergies in child nutrition: interactions of food security, health and environment, and child care
Skoufias, E., August 2016
82 Understanding the dynamics of labor income inequality in Latin America
Rodriguez Castelan, C., Lustig, N., Valderrama, D., Lopez‐Calva, L.‐F., August 2016
83 Mobility and pathways to the middle class in Nepal
Tiwari, S., Balcazar Salazar, C. F., Shidiq, A. R., September 2016
84 Constructing robust poverty trends in the Islamic Republic of Iran: 2008‐14
Salehi Isfahani, D., Atamanov, A., Mostafavi, M.‐H., Vishwanath, T., September 2016
85 Who are the poor in the developing world?
Newhouse, D. L., Uematsu, H., Doan, D. T. T., Nguyen, M. C., Azevedo, J. P. W. D., Castaneda Aguilar, R. A.,
October 2016
86 New estimates of extreme poverty for children
Newhouse, D. L., Suarez Becerra, P., Evans, M. C., October 2016
87 Shedding light: understanding energy efficiency and electricity reliability
Carranza, E., Meeks, R., November 2016
88 Heterogeneous returns to income diversification: evidence from Nigeria
Siwatu, G. O., Corral Rodas, P. A., Bertoni, E., Molini, V., November 2016
89 How liberal is Nepal's liberal grade promotion policy?
Sharma, D., November 2016
90 CPI bias and its implications for poverty reduction in Africa
Dabalen, A. L., Gaddis, I., Nguyen, N. T. V., December 2016
91 Pro-growth equity: a policy framework for the twin goals
Lopez-Calva, L. F., Rodriguez Castelan, C., November 2016
92 Building an ex ante simulation model for estimating the capacity impact, benefit incidence, and cost
effectiveness of child care subsidies: an application using provider‐level data from Turkey
Aran, M. A., Munoz Boudet, A., Aktakke, N., December 2016
93 Vulnerability to drought and food price shocks: evidence from Ethiopia
Porter, C., Hill, R., December 2016
94 Job quality and poverty in Latin America
Rodriguez Castelan, C., Mann, C. R., Brummund, P., December 2016
Updated on December 2016 by POV GP KL Team | 7
For the latest and sortable directory,
available on the Poverty & Equity GP intranet site. http://POVERTY
WWW.WORLDBANK.ORG/POVERTY
Updated on December 2016 by POV GP KL Team | 8