metadata: authors: 0: author: Batana,Yele Maweki
1: author: Cockburn,John Murray
count: Africa
docna: 0: docna: Do demographics matter for African child
            poverty ?
docty: Policy Research Working Paper
subtopic: Inequality,Demographics,Poverty Monitoring & Analysis,Poverty Diagnostics,Poverty Lines,Poverty Impact Evaluation,Small Area Estimation Poverty Mapping,Poverty Assessment,Economic Conditions and Volatility
teratopic: Macroeconomics and Economic Growth,Health, Nutrition and Population,Poverty Reduction
abstracts: cdata!: The effect of demographics on poverty
            measurement based on per capita consumption is well known.
            The size and composition of the household can affect the
            well-being of everyone in the household, with respect to
            total consumption within that household. Failure to address
            this issue may often lead to an underestimation or
            overestimation of poverty, especially for children. Many
            studies have tried to address the issue, using the generic
            approach of equivalence scales. However, the choice of scale
            is controversial and may lead to comparability problems
            between countries because of the different demographic
            structures and choice of the pivot household for
            establishing the per capita poverty line. Based on the World
            Bank's African poverty database, this study estimates
            poverty rates for African children using the new
            international poverty line of $1.90 a day defined in terms
            of 2011 purchasing power parity. The equivalence scales
            approach (Food and Agriculture Organization/World Health
            Organization) is used with the adjustment suggested by
            Deaton after the identification of the pivot household,
            which is defined as the household whose per capita
            consumption is around the international poverty line. This
            study shows that taking account of demographics results in
            downward adjustments of child poverty, adult poverty, and
            child-adult poverty gaps. Moreover, breakdowns by country
            show that poverty may vary significantly depending on
            demographics, which may cause some reranking when comparing
            poverty between African countries. Finally, sensitivity
            analyses reveal that child poverty is not sensitive to the
            child discount factor, unlike adult poverty, but, overall,
            taking account of demographics is helpful for better
            identifying poor children.
repnme: repnme: Do demographics matter for African child
            poverty ?
display_title: Do demographics matter for African
            child poverty
listing_relative_url: /research/2018/05/29860905/demographics-matter-african-child-poverty
topicv3: Macroeconomic Vulnerability and Debt
            m1326057 1104,Poverty m1326687 695,Poverty Measurement and Analysis m1326941 707,Demographic Change m1327040 1448,Population, Aging and Health m1327736 640
publishtoextweb_dt: 2018-05-03T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Health, Nutrition and Population,Poverty Reduction
no_of_pages: 23
author_keys: 0: author_key: 1156965
1: author_key: 711927
model_extractions: WPS8426 Policy Research Working Paper 8426 Do Demographics Matter for African Child Poverty? _Yele M. Batana John Cockburn_ Poverty and Equity Global Practice May 2018
Policy Research Working Paper 8426

# **Abstract**

The effect of demographics on poverty measurement based on per capita consumption is well known. The size and composition of the household can affect the well-being of everyone in the household, with respect to total consumption within that household. Failure to address this issue may often lead to an underestimation or overestimation of poverty, especially for children. Many studies have tried to address the issue, using the generic approach of equivalence scales. However, the choice of scale is controversial and may lead to comparability problems between countries because of the different demographic structures and choice of the pivot household for establishing the per capita poverty line. Based on the World Bank’s African poverty database, this study estimates poverty rates for African children using the new international poverty line of $1.90 a day defined in terms of 2011 purchasing power parity. The equivalence scales approach (Food and Agriculture Organization/World Health Organization) is used with the adjustment suggested by Deaton after the identification of the pivot household, which is defined as the household whose per capita consumption is around the international poverty line. This study shows that taking account of demographics results in downward adjustments of child poverty, adult poverty, and child-adult poverty gaps. Moreover, breakdowns by country show that poverty may vary significantly depending on demographics, which may cause some reranking when comparing poverty between African countries. Finally, sensitivity analyses reveal that child poverty is not sensitive to the child discount factor, unlike adult poverty, but, overall, taking account of demographics is helpful for better identifying poor children.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at ybatana@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Do Demographics Matter for African Child Poverty?**<sup>*****</sup>

Yele M. Batana<sup>†</sup> and John Cockburn<sup>‡</sup>

**Keywords** : Africa, poverty measurement, equivalence scale, pivot household, demographics, child.

**Jel Classification** : C10, C13, D31, I30, I32, O55

> * The authors would like to thank Abdelkrim Araar and Aly Sanoh for their review of earlier draft. This work was supported by the Belgium Poverty Reduction Partnership Program (BPRP) III, the Partnership for Economic Policy (PEP), which receives funding from the Department for International Development (DFID) of the United Kingdom (or UK Aid), and the Government of Canada through the International Development Research Centre (IDRC).

> † Ph.D. in Economics, Sr Economist at World Bank, E-mail: _ybatana@worldbank.org_ ‡ Ph.D. in Economics, Scientific advisor at Partnership for Economic Policy (PEP) and Associate Professor at Université Laval, E-mail: _john.cockburn@ecn.ulaval.ca_
# **1 Introduction**

The effect of demographics on poverty measurement based on per capita consumption is well known through the literature (Coulter et al, 1992; Lanjouw and Ravallion, 1995). In fact, the size and the composition of the household can affect the well-being of everyone in the household, with respect to total consumption within that household. Failure to address this issue may often lead to an overestimation of global and regional poverty, especially for children. Many studies have tried to address the issue using the generic approach of equivalence scales (Klasen, 2000; Ray, 2000; Meenakshi and Ray, 2002; White and Masset, 2003; Streak et al, 2009). There has recently been a resurgence in the issue of demographics in global and regional poverty estimation. However, if the use of equivalence approaches is suggested to provide a more accurate measure of poverty, the global monitoring of poverty still mainly uses per capita consumption as the core measure of welfare.

In fact, adjustments by equivalence scales face an additional issue in terms of the actual significance of the international poverty line. This international line is determined by Chen and Ravallion (2010) as the mean of the national poverty lines for the 15 poorest countries in terms of consumption per capita. Most national poverty lines are established based on a reference household with a given size and composition. Even if the poverty line is for a single adult, it is not calibrated to single adults living alone (Ravallion, 2015). Once the basic caloric requirement of a single adult is determined, the food consumption structure that is used to derive the required food expenditure usually comes from an average food basket at the national level. The poverty line is then suitable for a single adult from a household having the same consumption structure as this national average. According to Deaton (2003), Deaton and Zaidi (2002), and Ravallion (2015), the use of this reference household as a pivot should be combined with the equivalence scales for more accurate estimates of poverty. The identification of such a household is much more difficult for the international poverty line, which is based on the average of the national reference households of 15 countries.

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How can we measure global or regional poverty more accurately, especially among children? Are poverty comparisons between age groups and between countries appropriate given the large demographic disparities observed? Most studies on child poverty focus on one or few countries (Ray, 2000; Streak et al, 2009), without addressing the issue from a regional or a global perspective. Using household surveys collected during 1995-2005 from 73 developing countries, Batana et al (2013) found that, when conservative estimates of economies of scales and discount factors are used instead of the per capita approach, global poverty may dramatically fall while child-adult gaps are reduced. Other studies with similar results include Olinto et al (2013) and Evans and Palacios (2016). More recently, Newhouse et al (2017) present new estimates of child poverty across the developing world based on the new international poverty line of $1.90 a day in terms of 2011 purchasing power parity (PPP), and using the World Bank’s Global Micro Database (GMD), which contains micro-data from surveys collected between 2009 and 2014 in 89 developing countries. Various equivalence scales are considered based on the commonly expression used, as introduced by Culter and Katz (1992), which is appropriate to perform the analysis of poverty sensitivity according to two parameters (scale factor and child discount factor). Unlike previous studies, Newhouse et al (2017) adjust the poverty line as suggested by Ravallion (2015). They find that poverty rates for children remain significantly greater than adult poverty rates even when considering several alternative equivalence scales.

Do the Newhouse et al (2017) findings remain robust when considering Africa only, where households tend to be larger and contain a large share of children? That is what this study aims to investigate, using the World Bank's African poverty database including the most recent survey in 35 African countries over the period 2007-2012, which accounts for around 75 percent of the total population of Sub-Saharan Africa. This will be done using the equivalence scales and adjustment method suggested by Deaton (2003), Deaton and Zaidi (2002) and Ravallion (2015). Even if the choice of a pivot household different from a single adult household can mitigate the methodological bias caused by demographics in poverty measurement, there is no consensus rule to guide the choice of a particular equivalence scale method (Coulter et al, 1992; Deaton, 2003; 3
Deaton and Zaidi, 2002). Moreover, even when a method is adopted, the equivalence scales are not easily comparable between countries insofar as there is no certainty that the needs of individual households, in relation with their size and composition, are identical in all countries. Indeed, Lancaster et al. (1999) find that equivalence scales, while varying according to child age groups, also differ according to countries. The same result is obtained by Duclos and Mercader-Prats (1999), who compare Spain and the United Kingdom. In this situation, some authors (De Vos and Zaidi, 1997; Streak et al, 2009) advise considering several definitions of equivalence scales to analyze the sensitivity of poverty measurement. Two equivalence scales are considered, namely the FAO/WHO equivalence scales adopted by Batana et al (2013), and the square root equivalence scales. The former takes account of the size and composition of households while the latter incorporates only size effects. According to Ray (2000) and Meenakshi and Ray (2002), the square root method may be suitable for countries where large families comprise relatively many children (for instance developed countries), since the effects of the size and composition of households tend to be linked. It is different for most developing countries where, with the existence of extended and polynuclear families, a household may include many adults. The study will also allow to analyze to what extent comparisons between countries are affected by the selected approach. Indeed, as pointed out by Coulter et al (1992), choosing different equivalence scales may affect not only the level of poverty of a country or a group of individuals, but also the rankings between groups or countries. The study will finally investigate the sensitivity of poverty measurement to demographics and to several alternative equivalence scales.

The next section describes current demographics in African countries, as well as past and future trends. The third section describes the methodological approach used to adjust poverty estimates to account for demographics. The fourth section presents the main results including comparisons between various poverty estimates and some sensitivity analyses. Section five concludes the paper.

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<!-- Start of picture text --> A - Number of children B - Proportion of children<br>&<br>5 5<br>HS Mean<br>s 4 —+— Standard deviation 4 ~eS&ci<br>=oO c=}c Ss3S 4<br>o ——SB oO<br>oO 3 3 @> c°o<br>2 = «(6S<br>Ssc sooa <4roQ<br>aDo©2255 2ao<br><g®52<br><=<br>11<br>00<br>Q<uUSROLOOFNYK>Ag<SIINWOOLMOAZr abs Ones nugao sna goReoCORBEZOZOCYa2ZR SES 0 Q®fuUtOn>orrodtuadZ2Z2eYr228893596S88 gO SSE arzsagnooz¢<5j<oo0rai 82529958 25398<br>Countries Countries<br><!-- End of picture text -->
Some African countries also seem to be experiencing some demographic transition. As shown in Figure 3, the proportion of individuals under 20 years has increased since the 1960s to reach a peak during the 1980s and 1990s, before experiencing a gradual decline. This decline is mainly due to change in the number of children under 10. For example, it is expected that the proportion of under five will represent only 14 percent in 2030 against about 18 percent in 1960. This decrease was also observed for the age group of 5 to 9 years. In contrast, it is rather observed a very slight increase for older children (10 to 19 years). Conversely, the proportion of elderly is expected to rise by more than one percentage point, from less than 2.5 percent to just over 3.5 percent between 1960 and 2030. These trends coincide with an increase in population growth rate since 1960, followed by a decline from 1980. In total, it is expected that the proportion of working age population (here 20 to 64 age group) will increase from 44 percent to 47 percent, which represents a certain demographic dividend.

<u>Figure 3: Demographic changes in Sub-Saharan Africa</u> <!-- Start of picture text --> A - Various children age groups B - Elderly and population growth<br>3.5<br>18<br>0-4 age group<br>5-9 age group<br>16 10-14 age group<br>3<br>14-19 age group<br>14<br>2.5<br>Proportion of elderly<br>12 Population growth<br>10 2<br>Years Years<br>196019651970197519801985199019952000200520102015202020252030 196019651970197519801985199019952000200520102015202020252030<br>Percentage<br>Proportion (% total population)<br><!-- End of picture text --> <u>Source: World Bank: Health Nutrition and population Statistics.</u> Data to conduct a similar analysis of the evolution of household sizes were not available but, with declining fertility rates, these will be trending downwards, although likely at rates that differ from country to country. Again, this could introduce biases in studies on the evolution of poverty, and cross-country variations therein, depending on the choice of equivalence scale.

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# **3 Methodology**

Let’s consider, for a given household, the adult equivalent (AE) structure as follows: In this equation, _i_ stands for each additional adult in the household, while _j_ represents each child. The parameters  _i_ and  _j_ are then the relative costs respectively for an adult _i_ and for a child _j_ of the household, with  equal one for the first adult. These relative costs generally depend on a set of social and demographic characteristics such as age and gender. The variables<sup>_n_</sup> _a_<sup>and</sup><sup>_n_</sup> _c_<sup>represent respectively the number of adults and the</sup> number of children in the household. The parameter  is the size-elasticity which captures the economies of scale in the household. The per capita approach which is often used for poverty assessment studies in developing countries corresponds to the case where  _i_ ,  _j_ and  are all equal to one. But in assuming that economies of scale exist in the household, many equivalence scales can be derived from this general formula. Two equivalence methods are considered here, namely the FAO/WHO approach and the square root approach. In the first case,  _i_ and  _j_ will be provided by the required calories intake for each adult and each child regarding their age and sex. In the second case,  _i_ and  _j_ are set to one, while  is equal to 0.5. Equation (1) becomes: 0.5 _N eq_   _na_  _nc_  (2) As suggested by Deaton (2003), Deaton and Zaidi (2002) and recently by Ravallion (2015), a straightforward way to adjust consumption by the number of adult equivalents is to select a reference household as "pivot", such that poverty in households with the same demographics remains unaffected by changes in the parameters. This adjustment may be done through two equivalent procedures. The first one is to reflect information on the "pivot" in the equivalence scale such that the expenditure per AE is unchanged for the "pivot" households. The second procedure is to keep the usual expenditure per AE and to use information on the "pivot" rather to alter the poverty line of each household. In that case, the poverty line will be changed for "pivot" households in the same proportion as 8
the expenditure per AE, leaving unchanged the poverty rate for them as compared with the poverty rate from the per capita approach. Unlike Newhouse et al (2017) who opted for adjustment by the poverty line following the approach proposed by Ravallion (2015), this study considers rather the first procedure based on the alteration of the expenditure per AE. Let _Nref_ and _NrefEq_ be respectively the household size and the number of AE for the reference household, then _y_ *, the scaled version of the expenditure per AE for any household, could be expressed as follows: where _y_ is the total household expenditure, while _yAE_ is the usual expenditure per AE. Then, the expenditure per AE will be the same as the expenditure per capita, while poverty will remain unchanged for the "pivot" households. A crucial question is determining the demographics of the reference household. Given the way the international poverty line was derived, there is no dedicated theoretical method to determine the pivot or reference household. In practice, the choice of a specific method is based on value judgments. This led this study to explore two approaches.

The first approach is to define the "pivot" as the household whose average caloric requirements are around 2,100 calories.<sup>1</sup> The analysis of the distribution in terms of demographics of a sample of households whose caloric needs are around this figure allows to characterize the "pivot" as households typically comprising three members, two adults and one under-five child. The modal number of adults and children are chosen among households with average caloric requirements between 2090 and 2110 calories.

The second approach, based on Ravallion’s (2015) suggestion, rather considers households whose per capita consumption is around the international poverty line. This time, the modal numbers of adults and children are determined among households whose per capita consumption is between 1.85 and 1.95. The distribution analysis leads to

> 1 The caloric needs of 2,100 calories per person per day appears to be the most common base to derive the international poverty line (Ravallion, Chen and Sangraula, 2008).

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defining as "pivot" a household of five members, two adults and three children (one under-five years child, one child between five and nine, and one over nine child). This second approach has been retained for poverty estimation.

To perform sensitivity analyses, Equation (1) could be written in accordance with the formulation of Cutler and Katz (1992) as follows: In this case, the effect of the household size is captured by  while the effect of the household composition is captured by a single discount factor  .

The study is based on data from the World Bank’s African Database. Only surveys over the period 2007-2012 are considered, which represents 35 African countries and accounts for around 75 percent of the total population of Sub-Saharan Africa. The main variables include household consumption and other socioeconomic and demographic characteristics of households.

# **4 Results**

# **4.1 Poverty estimates and age group comparisons**

Table 1 shows initial and adjusted poverty rates in Africa by various age groups. Poverty is based on the international poverty line ($1.90 a day, 2011 PPP). With the initial poverty based on the per capita approach, there are some statistically significant differences between various age groups of children, as well as between children and adults. In fact, the poverty incidence appears to be higher for under-five children, followed by the poverty incidence for children aged 5 to 9 years. All together, all under 18 children have a poverty incidence of 50.4 percent, which is higher than that of adults by 11.4 percentage points. When poverty is adjusted using the FAO/WHO equivalence scale, gaps between groups of children disappear, while the difference between children and adults is reduce to 6.7 percentage points. A similar pattern is observed for the squareroot equivalence scale with somewhat lower poverty rates.

10
<u>Table 1: Poverty rates in Africa by age group ($1.90 a day, 2011 PPP)</u>

|<br>**Age groups**|<br>**Per capita**<br>**approach**|<br>**FAO/WHO**<br>**equivalence**<br>**scale**|<br>**Square-root**<br>**equivalence**<br>**scale**|
|---|---|---|---|
|_0-4 _|52.8|46.4|43.8|
|_5-9 _|50.1|46.6|43.0|
|_10-17_|48.8|46.8|40.2|
|**_0-17_**|50.4|46.6|42.1|
|**_+18_**|39.0|39.9|37.0|
|**_All_**|44.5|43.2|39.5|

<u>Source: World Bank's African Poverty Database</u> Tables 2 and 3 show the distribution of poverty among adults and children according to respectively the number of children in the household and the household size. In the per capita approach, poverty increases naturally with the number of children in the household. The same trend may be observed in the case of the FAO/WHO equivalence scale, except that its slope is less marked. Importantly, the results show that proper accounting for economies of scale tends to increase the poverty rate of children in households with fewer than three children, while it reduces it in those with three or more children. As a result, the poverty gap according to the number of children decreases. The adult poverty distribution follows the same pattern. This in turn leads to the reduction of poverty gaps between the different categories of households. It appears also that, with households with more than five children, poverty tends to decrease. The same patterns may be observed when poverty is broken down by household size.

<u>Table 2: Poverty rates for children and adults by number of children in household</u>

|<br>**Number of**|<br>**Per capita**|<br>**approach**|<br>**FAO/WHO e**<br>**scal**|<br>**quivalence**<br>**e**|<br>**Squar**<br>**equivale**|<br>**e-root**<br>**nce scale**|
|---|---|---|---|---|---|---|
|**children**|**_Children_**<br>**_(0-17)_**|**_Adults_**<br>**_(+18)_**|**_Children (0-_**<br>**_17)_**|**_Adults_**<br>**_(+18)_**|**_Children_**<br>**_(0-17)_**|**_Adults_**<br>**_(+18)_**|
|_0 child_|-|16.7|-|26.5|-|30.2|
|_1 child_|24.2|26.4|33.0|34.0|36.8|34.6|
|_2 children_|36.2|37.2|39.6|40.0|40.8|38.4|
|_3 children_|46.0|45.9|45.2|44.9|43.7|41.2|
|_4 children_|52.5|51.3|48.3|47.1|44.9|41.9|
|_5 children_|58.9|57.5|52.0|50.6|45.8|42.6|
|_6 children_|63.2|60.9|51.4|48.6|38.9|34.8|

<u>Source: World Bank's African Poverty Database</u> 11
<u>Table 3: Poverty rates for children and adults by household size</u>

|**Household**<br>|<br>**Per capita**|<br>**approach**|<br>**FAO/WHO e**<br>**scal**|<br>**quivalence**<br>**e**|<br>**Squar**<br>**equivale**|**e-root**<br>**nce scale**|
|---|---|---|---|---|---|---|
|**size**|**_Children_**<br>**_(0-17)_**|**_Adults_**<br>**_(+18)_**|**_Children (0-_**<br>**_17)_**|**_Adults_**<br>**_(+18)_**|**_Children_**<br>**_(0-17)_**|**_Adults_**<br>**_(+18)_**|
|_1 member_|5.4|5|16.7|15.9|36.9|28.5|
|_2 members_|18.8|12.9|29.6|24.7|43|32|
|_3 members_|27.1|23.5|35.4|33.2|42.2|37.3|
|_4 members_|36.9|32.9|40.2|38|43.1|39.1|
|_5 members_|45.1|40.1|44.8|41.5|45.1|40.1|
|_6 members_|51.2|45.8|48.4|45.1|45.7|40.6|
|_7 members_|54.6|48.3|49.5|45.6|44.3|38.7|
|_8 members_|58.2|52.8|51.5|48.3|44.2|39.4|
|_9 members_|59.8|55.8|52.3|50.3|43.1|40.1|
|_10 or more_|60.6|55.3|48.1|44.6|32.4|29.4|

<u>Source: World Bank's African Poverty Database</u> The difference in poverty between children and adults may vary depending on the number of children in the household or the size of the household (see Figure 4). When considering the per capita approach, the gap between children and adults is about 6 percentage points for households with two members. It is reduced to less than 4 for households with 3 members, but gradually increases to a little more than 6 percentage points for households with 7 members. When poverty is adjusted using the FAO/WHO equivalence scale, the distribution of poverty according to the household size keeps the same pattern. However, the gap is reduced, varying only between 2 and 4 percentage points for households with 3 or more members. With the square-root equivalence scale, the children-adults gap follows a trend closer to those of the per capita approach for households with 3 members or more, probably given that both approaches do not take account of the household composition. The breakdown by the number of children shows a growing trend of children-adults differences as the number of children increases, regardless of the approach retained. If, for households with 2 or fewer children, adult poverty appears somewhat higher than that of children according to the FAO/WHO equivalence and per capita approaches, the poverty rates of the two groups become similar for households with exactly 3 children. The gap becomes unfavorable for children for households with more than 3 members.

12
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# **5 Conclusion**

Demographics can matter for estimates of African child poverty for several reasons. First, the international poverty line is a threshold actually based on the needs of an adult from an unspecified reference household. The poverty status of a given household may therefore be biased when its demographic characteristics are different from that of the reference household if an adequate equivalence scale is not adopted. Moreover, comparisons among African countries may not be valid, given their significant demographic disparities.

The accuracy of poverty estimates can be further challenged when considering the welfare of an individual of a specific age group, such as children. Indeed, there is a natural correlation between the number of children and poverty within the household, since children usually contribute less than adults to the income generation process in the household. Thereby, a household consisting mainly of children will tend to have a relatively high level of poverty.

In addition to this correlation, poverty measurement itself may be affected by demographics, as the needs of the household vary according to its size and composition. It is then necessary not only to identify the plausible reference or pivot household, but also to adjust household consumption by taking account of both the relative cost of children and the economies scale. Failing to properly deal with this issue may bias poverty estimates, especially for children in Africa where household size and composition vary significantly from a household to another, as well as from a country to another.

The results of the study show that adjustments by the equivalence scale approach, using a hypothetical pivot household, do not eliminate child-adult poverty gaps at the regional level. However, the gaps may be significantly reduced, especially at the country level. Child-adult poverty gaps narrowed at less than 1 percentage for three countries, while they are reduced by more than half for several countries. Sensitivity analysis shows that 19
the gap declines for low levels of the relative cost of children and high levels of economies of scale.

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