1
Poor children living in rich households: A Blurred Picture or Hidden Realities?
Keetie Roelen
Institute of Development Studies (IDS), Brighton, UK
July 2016
DRAFT: do not cite without author’s permission
Abstract
An expanding evidence base suggests that child poverty is a diverse experience, challenging the
notion that monetary poverty measures can serve as a proxy for multidimensional poverty and vice
versa. This holds particularly true for children given their dependence on others for the provision of
their basic needs. Yet few studies have investigated explanations for differential child poverty
outcomes and their diverse experiences in a comprehensive manner. This study breaks new ground
by providing a unique mixed methods investigation of drivers of differential child poverty outcomes
in Burundi, Ethiopia and Vietnam. It considers the role of measurement error and factors in the
public and private spheres in explaining why some children experience monetary poverty but not
multidimensional poverty and vice versa. It does so by capitalising on secondary large-scale
quantitative panel data and combining this with purposively collected primary qualitative and
participatory data in all three countries.
This study finds that measurement error only provides a partial explanation for differential
outcomes in monetary and multidimensional poverty for individual children. Educational attainment,
occupation and marital status of the heads of household play a significant but highly context-specific
role in explaining diverse experiences. Parental awareness of and attitudes towards investments in
child wellbeing is found to be crucial for promoting children’s outcomes despite limited monetary
resources in all contexts. These factors may also play into a trade-off between household wealth and
child wellbeing with short-term gains in wealth being prioritised over long-term gains in child
development. Cross-contextual findings also indicate that the availability of infrastructure, services
and social protection policies present important enabling factors for or barriers to securing child
wellbeing in situations with limited or ample monetary resources. Finally, aspirations crucially inform
children’s own decisions towards improvements in short-term versus long-term outcomes.
This study exemplifies the need for comprehensive child poverty measurement and mixed methods
analysis of its context-specific underlying drivers and diverse experiences. Most crucially it highlights
the importance of a nuanced policy response that responds to differential outcomes and
experiences in aiming to reduce all forms of child poverty.
Acknowledgements
The author would like to acknowledge the invaluable support of Tsegazeab Kidanemariam Beyene
and Hayalu Miruts in Mekelle, Ethiopia; the Southern Institute of Social Studies in Ho Chi Minh City,
Vietnam; Francisco Cabrero Hernandez; Helen Karki Chettri and Kimberly Wied in the process of data
collection and analysis. This research was funded by ESRC grant ES-K001833-1.
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Introduction
The dichotomy between monetary and multidimensional approaches is a crude but persistent
bifurcation within poverty measurement. It is grounded in normative, conceptual and empirical
notions about the extent to which monetary resources can be translated into non-monetary
outcomes (Alkire et al., 2015; Ruggeri Laderchi, Saith, & Stewart, 2003; Thorbecke, 2008).
Conceptually, monetary measures are predicated on the assumption that if individuals have a
certain degree of purchasing power they will be able to fulfil their basic needs (Thorbecke, 2008;
Tsui, 2002) while multidimensional approaches incorporate a broad base of attributes in their
measures, thereby aiming to direct reflect the many manifestations of poverty (Hulme, 2015). This
distinction is particularly pertinent for children given their inherent limited agency in translating
household resources into improved individual outcomes (Roelen & Sabates-Wheeler, 2012; Vijaya,
Lahoti, & Swaminathan, 2014). Monetary indicators are considered more likely to fluctuate in the
short term than non-monetary indicators (Clark & Hulme, 2005; Hulme & Shepherd, 2003),
suggesting that multidimensional poverty is more representative of a permanent situation (Ayala,
Jurado, & Pérez-Mayo, 2011) or structural condition of poverty (Battiston, Cruces, Lopez-Calva, Lugo,
& Santos, 2013).
Expanding empirical evidence from both developed and developing country contexts suggests that
outcomes based on monetary and multidimensional approaches are often loosely associated and
that one measure cannot serve as a proxy for another (Baulch & Masset, 2003; Bernard, Dercon,
Orkin, & Taffesse, 2014; Bradshaw & Finch, 2003; Gaihre, 2012; Klasen, 2000; Kumar, 2012; Levine,
2012; Nilsson, 2010; Perry, 2002; Ruggeri Laderchi et al., 2003; Sahn & Stifel, 2003; Santos, 2012;
Wagle, 2009). Evidence with respect to child poverty is less extensive but points towards similar
mismatch patterns (Notten, 2012; Roelen, Gassmann, & de Neubourg, 2012; Roelen & Notten, 2013)
and indicates that monetary and multidimensional child poverty are different phenomena (Roelen,
forthcoming).
Despite the expanding evidence base on differential poverty outcomes, little research has been
undertaken to explore drivers underlying this dissonance. While the approaches’ conceptual
underpinnings may offer theoretical explanations, we are not aware of comprehensive empirical
investigations into underlying drivers. Such an investigation would not only serve academic curiosity
but is also crucial for policy purposes as “results and the related analyses may reveal the need for
different policy responses depending on which form(s) of poverty different groups of people
experience” (De Neubourg et al 2004, p. 16). A case for prioritising children is easily made: children
have different basic needs than adults do and a denial of those needs has long-term adverse and
often irreversible consequences, both for children and society at large (Roelen & Sabates-Wheeler,
2012).
This article seeks to advance research and contribute to child poverty reduction efforts by exploring
potential explanations for differential child poverty outcomes in Ethiopia and Vietnam. More
specifically it considers the role of measurement error and explanatory factors in the private and
public spheres. It does so using a unique combination of data and methods by drawing on literature
from high, medium and low income country contexts and by capitalising on secondary quantitative
panel data that has been made available to third party users and complementing this with analysis
of purposively collected qualitative data in both countries.
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This article commences with a short review of evidence regarding the association between monetary
and multidimensional child poverty outcomes and potential explanations for differential outcomes.
This is followed by a section describing data, methods and analytical strategy. Next, the article
provides a brief overview of child poverty overlap and mismatch in Ethiopia and Vietnam, followed
by a discussion of results based on the empirical investigation. The article concludes with an
overview of lessons learned and implications for policy and future research.
Association and mismatch of monetary and multidimensional child poverty
A review of available evidence reveals that outcomes based on monetary and multidimensional
measures are weakly correlated and identify different groups of children as being poor. In Indonesia,
for example, the majority of children living in calorie deficient households were found to be above
the poverty line (Hadiwidjaja, Paladines, & Wai-Poi, 2013). In Congo Brazzaville, indicators for
monetary poverty appeared to be a crude tool for identifying children at risk of deprivation in terms
of their physical environment (Notten, 2009). With respect to child poverty in Darfur, Trani and
Cannings (2013) conclude that income deprivation does not adequately reflect the reality and
complexity of child poverty, which is a finding considered of particular pertinence in emergency
contexts as time, resource and logistical challenges often lead to singular and over-simplified
interventions. Similar results can be observed in developed country contexts. A cross-country study
in four countries in the European Union finds that children in the EU living in monetary poor
households are not necessarily those suffering from deprivation in non-monetary dimensions, and
vice versa, (Roelen & Notten, 2013). Similar observations were made for children in Portugal (Bastos,
Fernandes, & Passos, 2004). And in their study on child poverty in the UK, Brewer et al (2009) point
towards a ‘hump-shaped’ profile; as household income rises, children’s levels of deprivation first rise
and then fall. In sum, monetary and non-monetary measures can firmly be considered to provide
different pictures of child poverty.
Drivers of differential child poverty outcomes
Evidence about drivers of incongruent outcomes is fragmented at best, and fails to provide a
coherent insight into what may underpin differential pictures of child poverty. A first explanation is
grounded in the conceptual notion that monetary resources can be translated into non-monetary
outcomes but that issues with reliability and validity of underlying measures lead to differential
outcomes (Bradshaw & Finch, 2003). Explanations predicated on the view that monetary and
multidimensional poverty are two distinct concepts can be divided into factors pertaining to the
private and public spheres, such as household characteristics, attitudes and awareness, public policy
and cultural norms and values. This section explores potential drivers of mismatch in more detail,
tapping into studies of child poverty and poverty at large in low, medium and high-income country
contexts.
Measurement error
Various studies have considered the role of measurement error in poverty mismatch, particularly in
developed country contexts. It is based on the premise that any poverty measurement is subject to
error and therefore does not represent an accurate reflection of reality (Bradshaw & Finch, 2003). It
follows that attempts to combine or contrast outcomes based on flawed measures will result in
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compounded errors and inevitably lead to different groups being identified as being poor when
using different measures.
In seeking to explain differential outcomes for monetary and non-monetary indicators of child
poverty in the UK, Brewer, O'Dea, Paull, and Sibieta (2009) question the reliability of the income
measure with respect to its equivalence scale and indicator for disposable income as well as the
issue of underreporting of income. At the same time they consider measures of living standards
potentially practically or conceptually flawed. Similarly, Berthoud and Bryan (2011) suggest that
inconsistencies between income estimates and findings based on a deprivation index (including
items related to daily living, financial strain and durables) at the lower end of the income distribution
can be explained by under-reporting of incomes by those with very low incomes. In their cross-
country analysis of poverty in the EU, Ayala et al. (2011) also point towards measurement errors
resulting from misreporting of income with respect to income-based measures and the potential
impact this has on the significance of the relationship between measures of income-based and
multidimensional poverty.
Differential use of units of analysis may also lead to or compound measurement error. Monetary
measures are predicated on household-level aggregates of income, consumption or expenditures
and inferences about individual-level poverty are made based on assumptions about intra-
household distribution. Multidimensional measures generally aim to include more individual-level
indicators, often as a direct consequence of the criticism that monetary measures do not adequately
capture individuals’ living conditions. Indeed, Ayala et al. (2011) postulate that the focus on different
types of individual wellbeing components forms one argument in explaining the lack of a statistically
significant relationship between income poverty and multidimensional poverty. Yet the current most
widely used measure of multidimensional poverty - the Multidimensional Poverty Index (MPI) - is
exclusively based on household-level indicators (Alkire et al., 2015) and studies investigating poverty
using this method in conjunction with household-level monetary measures find considerable
mismatch (Santos, 2012; Tran, Alkire, & Klasen, 2015).
Notwithstanding the existence of measurement error, both empirical and conceptual reasons
suggest that it can fully explain mismatch patterns. Ayala et al. (2011), for example, do not consider
the presence of measurement error sufficient to refute the weak correlation between income-based
and multidimensional poverty as they observed only a small proportion of those exiting income-
based poverty also to fare better with respect to multidimensional aspects of poverty. Despite
considering measurement error as an explanation for different outcomes of child poverty in the UK,
Brewer et al (2009) challenge the role of such error for conceptual reasons, stating: “It should not be
surprising that income and the other measures of living standards often give differing impressions of
the relative position of a particular household as ‘disposable income’ and ‘material living standards’
are fundamentally different concepts, so households with low disposable incomes need not be the
same as those households with low material living standards, even if both were measured perfectly.”
Structural factors
Research within childhood and child development traditions has long recognised that multiple risk
factors play a role in determining children’s outcomes. Parental health and education, child-parent
relationships and neighbourhood conditions, for example, have all been found to influence various
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aspects of child wellbeing in different ways (Ciula & Skinner, 2015). Studies of intergenerational
transmissions of poverty distinguish between factors operating at different levels, such as individual-
and community-level factors (Engle, 2012), household level and extra-household level factors (Bird,
2007) or private and public transmissions (Harper, Marcus, & Moore, 2003). For the purposes of the
analysis in this paper, we explore factors in the ‘private’ and ‘public’ spheres in explaining overlap or
mismatch of child poverty outcomes, while recognising that in practice these factors are often so
interlinked that they do not form distinct processes per se (Engle, 2012; Harper et al., 2003). The
issue of aspirations is considered separately as it sits on the interface between the private and public
sphere.
Private sphere
As a result of widespread availability of household surveys, the correlation between individual and
household characteristics and poverty has been widely studied (Dercon, 2012). It is also our first
point of call for investigating drivers of differential child poverty findings, and examples of studies
considering the association between characteristics and various outcomes abound. Halleröd,
Rothstein, Daoud, and Nandy (2013) point towards the importance of educational attainment of
household heads in explaining lower levels of deprivation in a range of different non-monetary
dimensions of child wellbeing in low and middle-income countries. This positive effect can be
attributed to the interaction between education and productivity, but might also contribute to
practices that are beneficial to child wellbeing. A study on child care and nutrition in Ghana found
that the level of mother’s education played an important factor in childcare, including optimal
feeding practices (Ruel et al. 2001). In Zimbabwe, children living in a household with a disabled adult
were found to be more vulnerable to disease or chronic health issues regardless of household asset
ownership. The lack of a significant relationship between assets and health issues is attributed to the
fact that disabled adults lack the ability to seek medical care for their children regardless of
household wealth (Crea et al., 2013).
Beyond simple observable characteristics, parental awareness and attitudes appear a vital factor
with studies from especially high but increasingly also low-income country contexts establishing
linkages between parental engagement and children’s outcomes. Such engagement can reinforce or
override the monetary situation within a household. While parental attitudes and behaviours are
often correlated with household income (Goodman and Gregg 2010), it can also work to counteract
income effects. A systematic review of studies on parenting and child maltreatment in low-income
contexts indicates that parental awareness and parenting practices can reduce violence against
children and promote safe and nurturing environments (Knerr et al 2011). A global review shows
that parents’ roles in making connection, controlling behaviour, respecting individuality, modelling
appropriate behaviour and offering protection are crucial in preventing health risk behaviours and
improving health outcomes for adolescents (WHO 2007).
A related issue that is crucial for explaining different outcomes between monetary and
multidimensional child poverty refers to the potential trade-off between child wellbeing and
household wealth with respect to children’s role in work and supporting household (re)production.
Economic models of children’s time use assume that households make decisions about children’s
time allocation so that it maximises household utility, thereby balancing short-term income against
returns to investments in children’s long-term development (Orkin, 2012). It follows that if
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households derive greater utility from short-term gains in income, children’s immediate wellbeing
may be compromised, either directly through children engaging in productive activities or indirectly
through children substituting for adult contributions to unpaid care work. The extent to which short-
term gains are prioritised over long-term gains will interact with parents’ as well as children’s
attitudes and aspirations (discussed in greater detail below).
It has to be noted that children’s engagement in work and domestic chores has also been found to
equip them with essential skills and can foster self-esteem (Woodhead, 2004), thereby challenging
binary notions about child work as being either good or bad. This holds particularly true for
adolescents as a strict dichotomy between childhood and adulthood “obscures our understanding of
what happens in the lives of children” (Bourdillon, 2006). Hence, the extent to which children’s
contributions to household wealth - either directly or indirectly – constitute a differential poverty
outcome should be considered with caution.
Public sphere
The public sphere pertains to the wider enabling environment that can have implications for either
monetary or multidimensional child poverty or both. The access and quality of services is a first
important aspect that may explain differential poverty outcomes. Bhutan’s low level of development
with weak infrastructure, incipient markets and poor access to services was found to be a crucial
factor in explaining why a large group of households experienced multidimensionally poverty but
was not considered monetary poor (Santos, 2012). The existence of user fees and lack of health
insurance however may compound the inability to access services (Halleröd et al., 2013), particularly
for children living in income-poor households and thereby leading to overlap of both types of
poverty. This ties into the issue of wider socioeconomic structures; a functioning labour market and
availability of employment opportunities are crucial for supporting children’s outcomes, both from a
monetary and non-monetary perspective (Harper et al., 2003).
Although the role of social relations and belief systems are less widely studied as they are less
amenable to being captured in quantitative methods (Harper et al., 2003), they play a crucial role in
determining children’s outcomes. At the same time, some of the greatest risks to child wellbeing
may originate from traditional cultures that are strongly grounded in patriarchal values and promote
practices such as early marriage and child labour (Boyden 2012). Broader conceptions about
parental sacrifice for children’s development, children’s contributions to household production and
welfare policies acting as a safety net for the most vulnerable will also play an important role in
outcomes for children regardless of their families’ financial status.
Aspirations
An explanatory factor that straddles the private and public spheres is that of aspirations. Aspirations
operate at the individual level but is highly influenced by external factors. They form an important
part of ‘mental models’ that influence decision-making processes and can override or bind
rationality (Bernard, Dercon, Orkin, & Tafesse, 2014). They are both consequence and cause of living
in deprivation but it can be argued that it is the ‘aspirations gap’ - the difference between someone’s
actual and desired standard of living - is what affects behaviour (Ray, 2003). This gap is bounded
though; if it is too small, there will be no desire to change current living conditions but if it is too
large, it will fail to provide positive encouragement (ibid). This non-linear relationship is
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corroborated by findings from Andra Pradesh in India regarding the effect of mother’s aspirations on
children’s educational outcomes, indicating that medium aspirations had a larger effect than low or
high aspirations (Serneels & Dercon, 2014). Recent experimental research in rural Ethiopia suggests
that aspirations have a positive impact on long-term investments, which include investments in child
development through school enrolment and increased spending on education (Bernard, Dercon,
Orkin, & Tafesse, 2014). Further empirical evidence about the role of aspirations in determining
poverty and child wellbeing is limited (Gorard, Huat See, & Davies, 2012), particularly in reference to
the role of adults’ versus children’s aspirations.
Data and methods
The investigation of explanations underpinning differential child poverty outcomes takes a unique
mixed methods approach combining secondary quantitative and primary qualitative data. Mixed
methods approaches are widely acknowledged to offer breadth and specificity that quantitative and
qualitative measures in isolation fail to achieve (Shaffer, 2013). Approaches can vary in their degree
of integration (Carvalho & White, 1997) ranging from the use of participatory methods as a
complement to quantitative data for incorporating issues that are often overlooked or ignored
(Camfield, Crivello, & Woodhead, 2009) to a tightly integrated and iterative study aiming to duly
acknowledge and unpick poverty’s complexities (Roelen & Camfield, 2015). This study seeks to find a
middle ground by combining secondary quantitative panel data with primary qualitative data in an
iterative process.
Data
Sources of secondary quantitative data included in this study are the Ethiopian Rural Household
Survey (ERHS) waves from 1999, 2004 and 2009i and the Vietnam Household Living Standards Survey
(VHLSS) waves from 2004, 2006 and 2008ii.
The ERHS is a panel survey data set focusing on rural livelihoods with rounds in 1994, 1995, 1997,
1999, 2004 and 2009. Despite its relatively small size - it included 15 villages and a sample of 1,477
households in the first full round in 1994, it is representative of the main agricultural systems in
Ethiopia. Sample attrition between 1994 and 2009 is low, with a loss of only 16.1 percent (or 1.1
percent per year) and most of the attrition occurs in the early years of the study; attrition between
2004 and 2009 is less than 0.6 percent per year (Dercon, Hoddinott, & Woldehanna, 2012; Dercon &
Porter, 2011). This study uses data from the last three waves.
The VHLSS is a nationally representative data set and is based on the former Vietnam Living
Standards Survey (VLSS), which was conducted in 1993 and 1998. The VHLSS and has since been
undertaken every second year since 2002 by the Government Statistical Office (GSO), following the
World Bank’s Living Standards Measurement Survey (LSMS) methodology. Survey samples from
2002 to 2010 were drawn from a master sample, which is a random sample of the 1999 Population
Census enumeration areas and includes a rolling sample. It provides micro-data at the level of both
the household and its individual members on a range of issues related to children’s well-being and
poverty as well as social protection. Previous studies using the VHLSS data did not find attrition bias
(Baulch & Masset, 2003) and assumed an unbiased sample (Günther & Klasen, 2009).
Sample sizes per cross-sectional wave and for the full panel data sets are presented in Table 1.
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Table 1 Sample statistics - quantitative data
1999 2004 2009 panel
Ethiopia (ERHS) 5054 3709 4937 1497
2004 2006 2008 panel
Vietnam (VHLSS) 12154 10696 9960 1068
Qualitative data collection took place in four sites in each country from August to December 2013.
Site selection was informed by analysis of secondary data, including quantitative data and other
reports, and pragmatic considerations. In Ethiopia, qualitative fieldwork took place in the northern
region of Tigray in Harresaw and Limat kushets, Harresaw tabia in Atsbi woreda and Kaslen and
Wela-Alabur kushets, Geblen tabia in Subhasaesie woreda. Tigray region was selected given its
relatively high poverty figures; research sites were chosen to mirror those included in the ERHS data
set. In Vietnam, qualitative data collection was undertaken in southern Mekong River Delta region in
Xã Mỹ Hòa and Xã Long Hậu communes in Dong Thap province and Xã Mỹ Hòa and Thị trấn Óc Eo in
An Giang province. These sites were selected as analysis of survey data indicated mismatch of
poverty outcomes was most prominent in these four sites. These sites were selected following
analysis of VHLSS data finding that mismatch of poverty outcomes was most outspoken in Mekong
River Delta region and the selected four sites within that region. Sample sizes per country are
presented in Table 2.
Table 2 Sample statistics - qualitative data
adults children Total
Ethiopia 88 61 159
Vietnam 145 78 223
Qualitative fieldwork engaged both adults and children and consisted of focus group discussions, key
informant interviews, household case studies and both individual- and group-based participatory
exercises. They aimed to elicit views and experiences regarding manifestations and causes of child
poverty. Given the technical nature of and negative connotation with the terms monetary poverty
and multidimensional poverty, questions for adults and children were framed around the positive
concepts of household wealth and child wellbeing as applicable in local languages. Adults and
children were asked about manifestations of child wellbeing and household wealth, the extent to
which they overlapped or not and explanations for differential outcomes. Community members in all
four sites formulated criteria for household wealth and child wellbeing and subsequently discussed
households’ situations with respect to these criteria.
Analysis of qualitative data involved a process of reading and re-reading, followed by a
categorisation and coding of responses. The standardised coding scheme was grounded in Maslow’s
hierarchy of needs theory (Maslow, 1954), Bronfenbrenner’s ecological model of human
development (Bronfenbrenner, 1979) and Minkkinen’s structural model of child wellbeing
(Minkkinen, 2013) with codes reflecting recurrent themes in both countries, ensuring consistency of
analysis.
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Analytical strategy
A multitude of methods is used to investigate explanations for differential poverty outcomes,
including parametric and non-parametric analysis of quantitative data and analysis of interview,
group discussion exercise and case study data. An overview of the analytical strategy is provided in
Table 3.
Table 3 Analytical strategy
Analytical categories Method
Measurement error
Descriptive statistics
Community wealth and wellbeing ranking
Focus group discussions
Structural factors
Private sphere Multinomial regression analysis
Community wealth and wellbeing ranking
Focus group discussions
Case studies
Public sphere
Aspirations
The use of descriptive statistics includes simple poverty profiles as well as transition matrices
considering movements between poverty groups across waves. Multinomial regression models
estimate associations between individual, household and community level factors and child poverty
outcomes. Analysis is undertaken for each wave based on the panel sample, allowing for the
inclusion of previous time periods to control for poverty status in previous periods.
The dependent variable refers to ‘poverty group status’ with children belonging to either one of four
groups: (1) poverty overlap: children that are both monetary poor and multidimensionally poor (AB);
(2) positive mismatch: children that are monetary poor but are not multidimensionally poor (B); (3)
negative mismatch: children that are multidimensionally poor but are not monetary poor (A); and (4)
no poverty overlap: children that are not multidimensionally poor and are not monetary poor (C).
Independent variables at individual level include gender and age of the child. Household factors
include gender, age, marital status, educational attainment and occupational status of the
household head, household size and location. The Ethiopia models include an indicator for the
presence of household members in bad health or being immobile while the Vietnam models include
indicators for proportions of children in the household. Community indicators for Ethiopia include
distance to town, availability of electricity, piped water, schools and government hospital (see also
Dercon et al 2012, Dercon and Porter 2011). Community indicators in Vietnam include living in an
area where a disaster happened in 2004, living in an area with limited opportunities and living in an
area with an ECD centre. As the availability of community indicators in Ethiopia and in Vietnam are
limited to rural areas (Baulch 2011), we estimate an overall model and rural modeliii.
Given the volume of data incorporated in this mixed methods analysis, the results section will only
include selected findings but will otherwise refer to overall results rather than report detailed
outcomes. Readers are referred to the respective annexes for detailed information.
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Mismatch monetary and multidimensional child poverty in Ethiopia and Vietnam
Before preceding to analyse explanations of different poverty outcomes, we report outcomes for
monetary and multidimensional child poverty in Ethiopia and Vietnam and their degree of mismatch
as investigated in earlier work (Roelen mimeo). Measures of monetary child poverty are based on
real per capita consumption in Ethiopia and real per capita expenditures in Vietnam while measures
of multidimensional child poverty include country-specific sets of indicators and employ the
‘counting approach’ for aggregation (Atkinson, 2003), mirroring methodologies as applied by OPHI’s
Multidimensional Poverty Index (MPI) (Alkire et al., 2015) and UNICEF’s Multiple Overlapping
Deprivation Analysis (de Neubourg, de Milliano, & Plavgo, 2014)iv.
Findings indicate that substantial groups of children are either multidimensionally poor (negative
mismatch) or only monetary poor (positive mismatch). Proportions of poverty mismatch are largest
in Ethiopia, with limited correlation between monetary and non-monetary indicators. Despite
greater correlation between monetary and non-monetary outcomes in Vietnam, children living in
multidimensional poverty are not necessarily monetary poor and vice versa. Sensitivity analysis
shows that these levels of mismatch persist across the income distribution. An overview of poverty
group proportions is presented in Table 4.
Table 4 Poverty overlap and mismatch in Ethiopia and Vietnam
Ethiopia
N
(# children)
monetary poor and
multidimensionally
poor (%)
multidimensionally
poor but not
monetary poor (%)
monetary poor but not
multidimensionally
poor (%)
non-
poor
(%)
Total
(%)
1999 2,893 19.2 23.9 25.6 31.3 100
2004 2,726 24.7 25.4 26.8 23.1 100
2009 3,230 13.5 23.2 26.0 37.3 100
Vietnam
N
(# children)
monetary poor and
multidimensionally
poor (%)
multidimensionally
poor but not
monetary poor (%)
monetary poor but not
multidimensionally
poor (%)
non-
poor
(%)
Total
(%)
2004 12,154 22 16 16 45 100
2006 10,696 15 13 16 56 100
2008 9,960 14 13 13 60 100
Source: Roelen, mimeo
Analysis of poverty dynamics points to many transitions between poverty groups over time in both
Ethiopia and Vietnam with large proportions of children changing poverty group from one period to
the next, including moves out of poverty but also falls into poverty. It should be noted that while the
empirical investigation in this article does include longitudinal analysis, the analysis focuses on
explanations for poverty group membership in a given wave as opposed to transitions between
poverty groups over time.
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Explaining poverty mismatch
This section explores explanations of child poverty overlap and mismatch based on the combined
analysis of quantitative and qualitative data, focusing on measurement error, factors in the private
and public spheres, and aspirations.
Measurement error
In assessing the role of measurement error, we firstly consider the extent to which mismatch is
sensitive to the poverty lines used for both poverty measures. A clustering of mismatch around the
poverty lines would suggest that differential outcomes are a result of the ambiguous establishment
of such lines rather than of monetary and multidimensional poverty being distinct experiences.
Analysis of multidimensional child poverty across the income distribution in both Ethiopia and
Vietnam do not provide evidence for such clustering (see Table 5).
Table 5 Multidimensional poverty across income distribution (as denoted by consumption and expenditures)
Ethiopia Vietnam
1999 2004 2009 2004 2006 2008
multidimensionally
poor (%)
multidimensionally
poor (%)
multidimensionally
poor (%)
multidimensionally
poor (%)
multidimensionally
poor (%)
multidimensionally
poor (%)
deciles real per capita consumption/ expenditures
1 43.6 51.2 34.9 78.5 65.6 66.3
2 41.6 46.8 32.9 58.6 53.6 49.4
3 40.7 45.2 33.0 52.6 42.8 39.7
4 45.1 49.1 35.4 46.3 37.0 29.1
5 45.1 47.9 39.9 41.4 33.1 27.1
6 46.3 56.9 36.0 37.1 29.1 25.1
7 46.9 47.6 40.8 32.7 23.4 18.6
8 39.5 48.9 38.2 30.1 19.9 14.3
9 40.9 59.7 33.2 16.9 13.7 10.1
10 40.7 49.2 43.8 7.5 5.3 6.7
Source: Authors’ own calculations based on EHRS 1999, 2004, 2009 and VHLSS 2004, 2006 and 2008
In Ethiopia, multidimensional poverty rates fluctuate across deciles with multidimensional poverty
decreasing across the first three deciles but then increasing with greater per capita consumption,
suggesting the occurrence of positive mismatch (children being monetary but not
multidimensionally poor) at the bottom of the distribution and negative mismatch (children being
multidimensionally but not monetary poor) at the top of the distribution. In Vietnam,
multidimensional poverty rates decline as per capita expenditures increase. Nevertheless, one in 12
children in the highest decile also experience multidimensional poverty.
Qualitative findings shed further light on the extent to which measurement error may form an
explanation. We consider the extent to which indicators for household wealth and child wellbeing as
identified by community members in community wealth and wellbeing exercises match indicators
employed for the quantitative analysis. In Ethiopia, indicators defined by community members did
not directly mirror those available in the quantitative data, particularly with respect to household
wealth. The availability of livestock, land and labour was identified over access to monetary
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resources. Indicators for child wellbeing were more similar with a strong focus on going to school
and working in or outside the home. In Vietnam, the identification of criteria for household wealth
and child wellbeing by community members was more strongly in line with quantitative indicators
available, including income and employment to denote household wealth and education, sanitation
and shelter for child wellbeingv.
While the discrepancies between indicators identified in qualitative exercises and those used in the
quantitative analysis may lead to measurement error, it does not suffice to explain poverty
mismatch. Community members across all sites in both countries indicated household wealth and
material child wellbeing to be distinct concepts with their own criteria. All communities also
identified households with inconsistent situations regarding household wealth and child wellbeing.
While there is no doubt that measurement of both monetary poverty and multidimensional poverty
is subject to error, both quantitative and qualitative findings suggest that child poverty mismatch
cannot be fully attributed to such error.
Structural factors – private sphere
Individual and household characteristics
The role of individual household characteristics was investigated using multinomial regression
models applied to three waves of data in both countries (see Tables A1 to A6 in the Annex). A range
of factors at the individual and household level were found to be important in determining poverty
overlap or mismatch but that the role they play is highly context-specific.
In Ethiopia, estimates indicate that living in a larger household increases the likelihood to
experiencing poverty overlap and positive mismatch but decreases the odds of negative mismatch.
Greater household size may lessen the need for children to withdraw from school or work many
hours in household production, which is an important component of the measure of
multidimensional poverty in Ethiopia. Education of the household head is also important, and this
importance intensified overtime. In 2009, living with a household head without any education
considerably increased the odds of poverty overlap and negative mismatch. In 2004 and 2009, living
with a household head who had completed primary education or more considerably increased
chances of negative mismatch, suggesting that while education may lead to improved economic
outcomes it does not necessarily go hand in hand with greater child wellbeing in terms of school
attendance and work in and outside of the house. Children’s individual characteristics of age and
gender were not significant.
In Vietnam, estimates suggest that living with a household head that is single, divorced or separated
increases the odds for belonging to any poverty group (poverty overlap, positive mismatch or
negative mismatch). Living with a household head that is widowed, however, decreases the odds for
poverty overlap and positive mismatch. Education of the household head appears to play an
important role in terms of poverty overlap: living with a household head having no education
increases the odds for experiencing poverty overlap while living a household head having secondary
education reduces those odds. Living with an unemployed household head is associated with higher
odds to experiencing poverty overlap and negative mismatch while living with a household head that
is a skilled professional is associated with lower odds for belonging to those poverty groups.
13
Ethnicity is strongly associated with poverty overlap; being of ethnic minority increases the odds for
being simultaneously poor across all years and regression models. Similarly, living in North West and
North Central Coast regions is associated with higher odds to being simultaneously poor. Living in
Mekong River Delta, however, is associated with higher odds for poverty overlap and negative
mismatch but decreases the odds for positive mismatch. Again, children’s individual characteristics
were not found to be significant factors in any of the multinomial models.
Trade-off household wealth and child wellbeing
Findings in both countries provide evidence for households’ delicate balancing act between
household wealth and child wellbeing in explaining both positive and negative poverty mismatch.
In Ethiopia, descriptive statistics show that children in higher income quintiles engage in a greater
number of hours worked in household production (see Figure 1).
Figure 1 Livestock ownership and family work across consumption
deciles for children aged 10-15 in rural Ethiopia
810
12
14
16
ave
rage
ho
urs
fam
ily w
ork
(p
er
we
ek)
23
45
6
ave
rage
liv
esto
ck o
wn
ers
hip
(T
LU
)
0 2 4 6 8 10
real per capita consumption (deciles)
livestock (TLU) hours family work (per week)
Source: Roelen (2015)
This finding is corroborated by qualitative data in which adults and children indicate that children in
wealthier households work are usually more involved in herding livestock, contributing to family
production or doing domestic chores. This may go at the expense of studying at home or going to
school: “Sometimes children in rich households are obliged to work in farm activities rather than
going to school” [female caregiver, Geblen, Ethiopia].
A gendered effect appears at work with qualitative data suggesting that children living in male-
headed households more likely to work and experience negative mismatch. Cockburn and Dostie
(2007) find similar results, suggesting that female heads might give greater priority to schooling or
that there are fewer possibilities for children to engage in productive work in female-headed
households. A gender effect is also at play on behalf of children: girls were more likely to undertake
domestic chores and boys to work on the family farm and herding livestock.
14
Qualitative data from Vietnam does not provide strong evidence for direct contributions of children
to productive activities but does point to the existence of substitution effects. Many parents in the
communities included in qualitative fieldwork were observed to be working far away from home,
often leaving their children to care for elderly and disabled household members. While Cuong and
Linh (2013) find effects of parental migration on children’s time use to be negligible, qualitative
findings in this study suggest that time allocation was considerably impacted through reductions in
time spent on studying or leisure: “I stopped my study 2 years ago at grade 5. I help my sister to take
of her children at home” [girl child, An Giang, Vietnam].
Awareness and attitudes
Perspectives from adults and children in the qualitative data indicate that awareness and attitudes
of parents are an important factor in explaining poverty mismatch; they can secure child wellbeing
even when the household has few monetary resources but also contribute to poor child wellbeing
despite the availability of monetary resources.
Adults’ and children’s responses across the board highlight that parents have an important influence
on children’s outcomes beyond the availability of monetary resources. Some respondents suggested
that wealth and personal attention to children may be inversely related, with households
experiencing monetary poverty placing greater emphasis on children’s education and future
opportunities as well as mitigating the effects of limited economic resources: “We are poor but we
try to let our children study properly because we do not want our children to feel disadvantaged
compared to other children” [female caregiver, Dong Thap, Vietnam].
Qualitative findings indicate that general awareness and attitudes regarding child wellbeing have
greatly improved in recent years, particularly in Ethiopia. Parents and social workers indicate how
government campaigns and extension services has instilled the importance of education,
immunisation, pre- and antenatal care and family planning, as indicated by a woman from Limeat:
“People’s general attitudes towards raising and caring for children have significantly changed over
time. For example, most mothers follow up pre and anti-natal care, follow vaccinations, most parents
send their kids to school on time, reduced underage marriages and love and attention for children
increased” [woman, Limeat, Ethiopia]. These findings were corroborated in reference to the balance
between schooling and work with respondents attaching great value to education and prioritised
school over work as education is considered crucial for securing future livelihoods.
Notwithstanding these positive effects, researcher observations and discussions with children
revealed that such expressed perspectives were not necessarily in line with reality as children’s
education was discontinued or interrupted when reaching secondary school. This discrepancy
appears subject to a gender effect with girls’ education receiving less priority than boys. A gender
effect also extends to the household head; children that were identified in the qualitative data as
experiencing good wellbeing despite living in a poor household were more likely to be part a female-
headed household, while children experiencing poor wellbeing despite living in a relatively affluent
household were more likely to be part of a male-headed household.
15
Structural factors – public sphere
Access to services
An important component of the public sphere in explaining poverty overlap and mismatch is the
access to services. In Ethiopia, school attendance rates in the quantitative sample, for example, rose
from 35 percent in 1999 to 65 percent in 2009 and qualitative findings suggest that the availability of
primary education played an important role in this increase. More generally, qualitative findings
indicate that availability of services such access to schools, health posts and safe drinking water is
important in driving positive mismatch - i.e. ensuring children’s wellbeing even if children live in
monetary poor households. By the same token, the absence of such infrastructure can lead to
negative mismatch - multidimensional child poverty even if a child is living in a household with
greater wealth, as illustrated by a social worker from Harresaw: “The wellbeing situation of children
in this community has generally improved over time because infrastructure like health posts, and
primary education are established near to our community. Nevertheless, there are still some critical
problems affecting children like long distance to get to school above grade 4 and lack of potable
water” [social worker, Harresaw, Harresaw].
In Vietnam, regression estimates and qualitative discussions do not point towards a significant role
for services and infrastructure in influencing poverty status in explaining poverty mismatch, largely
due to widespread availability of services and therefore little variance in the data (Baulch & Dat,
2011). Qualitative findings strongly indicate that government social protection programmes play a
positive role in securing children’s needs despite household poverty and therefore driving poverty
mismatch. The most frequently mentioned policy was the ‘poverty certificate’ or ‘poverty book’
policy, which applies to monetary poor households and gives access to support such as tuition fee
waivers, health insurance and commune support: “My child saw other children having poor
household certificate and he asked me why we did not have one. People with such a certificate
receive a great amount of support whereas we don’t receive any” [female caregiver, Dong Thap,
Vietnam].
At the same time, experiences with government involvement were not altogether positive and
qualitative findings suggest that it could also lead to negative mismatch or poverty overlap.
Respondents pointed to the importance of having legal documentation for accessing services
regardless of income status and how the access of such documentation can lead to negative
mismatch: “I have never gone to school because my family lives in a rental house that means we are
temporary residents, so I cannot have legal documents, like birth certificate for school application"
[child, An Giang, Vietnam]. A number of respondents also indicated how they were no experiencing
poverty overlap due to having been moved from the area that securing their main livelihoods: “My
family is in poverty. The hamlet’s officers have had us move here and now we are in a difficult
condition and there is no foundation to work anymore” [female adult, Dong Thap, Vietnam].
Socioeconomic context
Wider socioeconomic conditions were found to be an important driver for explaining poverty
overlap and mismatch. In Vietnam, the absence of stable jobs was considered an important barrier
to securing a stable situation for children, both in terms of income and other areas of wellbeing. It
creates a difficult reality for parents having to work long hours away from home, sometimes leaving
16
children in the care of others with potential adverse effects on child wellbeing: “Household poverty
means that we do not have stable job, which results in unstable income” [female adult, Dong Thap,
Vietnam]. In Ethiopia, lack of economic opportunities beyond agricultural activities was mentioned
as posing barriers to both adults and children in their attempts to improve monetary and non-
monetary outcomes.
Cultural norms and values
Although the role of cultural norms and practices was not explicitly incorporated in fieldwork scripts,
their role inevitable emerged in discussions around what constitutes child wellbeing and what
contributes to child wellbeing. In both countries, looking clean and well-clothed was deemed
important by both adults and children for gaining respect from family and community members.
Although the purchase of clothing and soap was considered to be linked to the availability of
monetary resources, hygienic practices were linked to how strongly this aspect was valued by adult
caregivers.
In Vietnam, living up to societal norms and standards was deemed particularly important. Adult
respondents referred to the importance of obeying parents and teachers, of studying hard and not
being lazy and dressing appropriately. Various respondents pointed towards a direct mismatch
between the emphasis on this component of child wellbeing and availability of monetary resources
with wealthier parents being unable to spend adequate time with children to instil those values: “A
well-off family can have a lot money for children but if parents just pay attention to their business
and have less time to take care of their children, those children surely do not feel happy and in many
cases, those children will be easily deprived” [teacher, An Giang, Vietnam].
Another recurrent element in Vietnam referred to children’s responsibilities towards caring for
elderly and disabled adults in the households, particularly when parents work in areas far from
home: “Parents advise me that I should not go out too much and help my paternal grandparents”
[child, An Giang, Vietnam]. While children appeared to take pride in care responsibilities, there were
also signs that they undermined the opportunity to take part in school, study or leisure activities, as
discussed above.
In Ethiopia, findings indicate that engaging in domestic chores or working on the household farm is a
positive attribute for children: “I don’t send my children to work for other households but I believe
children should work at home in household production” [Male caregiver, Harresaw, Ethiopia] While
the role of work in child wellbeing has to be considered with caution (as discussed above), children’s
responses in this study suggest that the balance often tips in such a way that child wellbeing may
undermined: “I can say my wellbeing is good and bad. It is good because I am in school.
My wellbeing is bad because I am working at home when I return from school” [girl child, Harresaw,
Ethiopia]. This corroborates other research in rural Ethiopia on patterns of children’s work (Abebe,
2007).
Aspirations
An explanatory factor that straddles the private and public spheres is that of aspirations. Children’s
aspirations are important in their own considerations of what constitutes child well-being and how
to achieve it. Qualitative findings in Ethiopia in particular indicate that lack of local economic
17
opportunities and role models within the community shape children’s aspirations and ambitions for
the future, inspiring choices regarding school and work and playing into differential outcomes with
respect to income and non-income based child poverty.
In Ethiopia, children’s aspirations were also considered an important factor in determining whether
a child was going to school or not regardless of monetary resources. Seeking low-skilled work in
Saudi-Arabia was frequently mentioned as a more desirable opportunity than continuing education
in pursuit of a skilled job in the local area. Limited economic opportunities and lack of role models
appeared to feed into these aspirations. In other cases children’s aspirations and parents’ attitudes
appeared in conflict with each other: “If I pass the national examination, I want to continue my
education in the town of Atsbi. I want to be an engineer in order construct roads to my community in
particular and my country in general. But my father wants me to join the Dera high school in order to
support him” [girl child, Harresaw, Ethiopia].
Conclusion
While the empirical evidence on differential outcomes of monetary and multidimensional child
poverty is steadily expanding, few studies have considered underlying drivers. This article aimed to
provide a unique and rich empirical contribution by exploring drivers that differential child poverty
outcomes in Ethiopia and Vietnam. It applies a mixed methods approach using secondary repeated
cross-sectional and longitudinal data and primary qualitative data of adults’ and children perceptions
that allows for a strong level of breadth of specificity.
Findings suggest that measurement error only provides a partial explanation for differential findings
but cannot account for the full extent of mismatch. Structural factors at individual, household,
community and government level are strong in securing child wellbeing despite monetary poverty or
vice versa. Educational attainment, occupation and marital status of heads of household can explain
differential poverty outcomes for children but the strength and direction of the association is highly
context-specific. Parental awareness of and attitudes towards the raising and educating children
plays an important role in translating high or low levels of household resources in improved child
wellbeing. Qualitative findings in Ethiopia point towards a gender dimension with ‘positive’
mismatch (low household resources but good child wellbeing) occurring more frequently in female-
headed households and ‘negative’ mismatch (higher household resources but low child wellbeing)
being more prevalent among male-headed households. Greater emphasis on short-term gains in
household production over long-term investments in child development may lead to a trade-off
between household wealth and chid wellbeing, particularly in terms of the balance between school,
work and leisure. Findings in Vietnam point to the important role of government services and stable
local employment opportunities in preventing both monetary and multidimensional poverty for
children. Lack of such local economic opportunities as well as lack of role models in Ethiopia were
found to shape children’s aspirations and their own choices with respect to the balance between
monetary and non-monetary outcomes.
In conclusion it should be noted that explanations for differential poverty outcomes are not mutually
exclusive and that factors fungible. Perceptions about the appropriate balance between the creation
of household wealth and child wellbeing, for example, are influenced by children’s and adults’
attitudes and aspirations in light of the situation of the household as a whole and grounded in wider
18
structures of service provision, socioeconomic contexts and cultural norms and practices. In other
words, while explanations can be explored and tested empirically - as undertaken in this paper - the
weight that is attached to each of these explanations will inevitably be informed by the researcher’s
conceptual, ideological, disciplinary and methodological underpinnings.
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intervention. The International Journal of Children’s Rights, 12(4), 321-377.
22
Table A1 Multinomial regression Ethiopia 1999
1999
Multinomial model Multinomial model rural (with inclusion of community factors)
AB A B AB A B
b/se b/se b/se b/se b/se b/se
Child is female 1.237 0.665 1.030 1.072 0.707 0.924
(0.334) (0.188) (0.265) (0.328) (0.223) (0.262)
Age of child 1.000 1.013 0.975 0.955 0.991 0.951
(0.059) (0.066) (0.055) (0.069) (0.076) (0.063)
Household is female 2.154 0.351 1.225 2.192 0.359 0.771
(1.463) (0.286) (0.957) (1.720) (0.319) (0.729)
Age of household head 1.021 1.007 1.008 1.024 1.010 1.009
(0.012) (0.012) (0.012) (0.014) (0.014) (0.014)
Household size 1.015 0.872 1.085 1.112 0.933 1.140
(0.072) (0.065) (0.075) (0.093) (0.080) (0.090)
Household head is single 1.106 0.513 0.855 0.880 0.561 0.742
(0.373) (0.176) (0.282) (0.360) (0.232) (0.293)
Household head is single/divorced/widowed 0.870 0.874 0.795 0.470 0.799 0.520
(0.487) (0.661) (0.420) (0.316) (0.636) (0.314)
Household head is polygamous 0.697 1.118 0.719 0.447* 0.926 0.465*
(0.238) (0.379) (0.235) (0.176) (0.358) (0.173)
23
Household head has primary education or more 0.399* 0.294* 0.401* 0.317* 0.270 0.342*
(0.165) (0.164) (0.151) (0.162) (0.182) (0.150)
Household head does domestic work 0.334 1.655 0.304 0.347 1.235 0.512
(0.251) (1.419) (0.281) (0.296) (1.136) (0.549)
Household head does manual work 0.000 0.000 0.577 0.000 0.000 0.014*
(0.000) (0.000) (0.840) (0.000) (0.000) (0.023)
Household head does non-manual work 0.094* 0.583 0.071* 0.008*** 0.359 0.007***
(0.103) (0.522) (0.077) (0.011) (0.334) (0.009)
Household head is not in labour force 0.000 0.621 0.000 0.591 1.97e+15 1.416
(0.000) (0.769) (0.000) (5.80e+07) (1.08e+23) (1.30e+08)
Number of members in bad health/immobile 1.108 1.067 0.971 1.004 0.951 0.904
(0.099) (0.106) (0.085) (0.103) (0.105) (0.091)
Child lives in Tigray 6.540*** 3.648** 2.902 96.144*** 6.715 34.206**
(3.600) (1.803) (1.726) (103.480) (6.899) (37.419)
Child lives in Oromya 1.495 0.508 1.068 29.843*** 1.211 14.770***
(0.649) (0.196) (0.430) (25.052) (0.734) (11.705)
Child lives in SNNPR 8.206*** 0.500 7.270*** 0.510 0.152* 0.804
(3.312) (0.210) (2.639) (0.368) (0.114) (0.526)
Distance to town in kilometres
0.595*** 0.824 0.672***
(0.058) (0.083) (0.062)
24
Community has electricity
243.441*** 8.119 60.855**
(352.421) (11.835) (85.066)
Community has piped water
0.000*** 0.000 0.003**
(0.001) (0.000) (0.007)
Community has primary school
0.070*** 0.397 0.048***
(0.050) (0.244) (0.033)
Community has junior school
26.957* 6.369 9.000
(37.006) (9.528) (11.937)
Community has high school
22.031 5.36e+09*** 9.484
(38.205) (9.37e+09) (13.959)
Community has government hospital
8.974** 2.484 5.630**
(6.095) (1.459) (3.720)
Number of observations 511.000
468.000
P-value 0.000
0.000
Pseudo R-Square 0.139
0.215
BIC 1535.214 . . 1455.502
Note: omitted categories are: child is male; household head is male, household head is married; household head has less than primary education; household head is farmer
or does family work; child lives in Tigray.
Table A2 Multinomial regression Ethiopia 2004
2004
25
Multinomial model Multinomial model rural (with inclusion of community factors)
AB A B AB A B
b/se b/se b/se b/se b/se b/se
Child is female 1.166 0.940 0.785 1.092 0.803 0.809
(0.210) (0.169) (0.135) (0.230) (0.169) (0.160)
Age of child 1.033 1.031 1.022 1.110** 1.091* 1.059
(0.033) (0.033) (0.031) (0.042) (0.041) (0.037)
Household is female 1.164 0.912 0.792 0.986 0.539 0.739
(0.496) (0.407) (0.346) (0.518) (0.296) (0.399)
Age of household head 1.006 1.004 1.009 0.985 0.985 0.986
(0.009) (0.009) (0.009) (0.011) (0.011) (0.011)
Household size 0.958 1.062 0.932 0.952 1.076 0.963
(0.038) (0.043) (0.037) (0.048) (0.054) (0.045)
Household head is single 0.893 1.277 0.737 1.016 1.695 1.008
(0.353) (0.527) (0.297) (0.474) (0.764) (0.474)
Household head is single/divorced/widowed 4.661** 1.181 2.414 2.408 0.783 1.419
(2.430) (0.744) (1.326) (1.647) (0.640) (0.946)
Household head is polygamous 1.311 1.516 1.134 1.323 1.449 1.059
(0.300) (0.371) (0.247) (0.356) (0.413) (0.271)
Household head has primary education or more 0.716 1.783* 0.950 0.621 1.727 1.140
(0.190) (0.461) (0.230) (0.207) (0.532) (0.321)
26
Household head does domestic work 3.136* 0.688 5.339*** 4.889* 0.813 10.622***
(1.538) (0.392) (2.676) (3.093) (0.637) (6.867)
Household head does manual work 0.000 2.90e+10*** 6.07e+09 0.000 4.27e+10*** 1.50e+10
(1502.476) (3.65e+10) . (3171.484) (5.63e+10) .
Household head does non-manual work 0.397* 0.153** 0.286** 0.497 0.155** 0.193**
(0.181) (0.099) (0.131) (0.255) (0.105) (0.108)
Household head is not in labour force 0.140 0.344 1.785 0.353 0.000 2.253
(0.162) (0.305) (1.209) (0.461) (0.000) (2.070)
Number of members in bad health/immobile 0.977 0.905* 1.093 1.146* 0.980 1.219**
(0.047) (0.042) (0.051) (0.078) (0.064) (0.077)
Child lives in Tigray 3.808*** 1.820 2.307* 1.442 0.948 4.013
(1.403) (0.632) (0.855) (1.206) (0.797) (3.172)
Child lives in Oromya 3.616*** 0.589* 2.199** 0.572 0.083*** 0.749
(0.992) (0.158) (0.581) (0.271) (0.048) (0.371)
Child lives in SNNPR 4.875*** 0.888 5.028*** 3.249 0.138 7.873**
(1.229) (0.217) (1.156) (2.880) (0.139) (6.192)
Child was monetary poor in 1999 2.225*** 1.066 2.029*** 2.159** 0.888 2.535***
(0.425) (0.206) (0.367) (0.525) (0.221) (0.572)
Distance to town in kilometres
1.136*** 1.082* 1.108**
(0.041) (0.039) (0.039)
27
Community has electricity
0.247 0.192 4.233
(0.382) (0.332) (5.389)
Community has piped water
1.035 2.278 0.300*
(0.645) (1.690) (0.164)
Community has primary school
3.875 0.633 17.442***
(3.535) (0.540) (11.622)
Community has junior school
0.405 0.941 0.622
(0.483) (1.160) (0.548)
Community has high school
1.898 5.681 3.046
(1.469) (5.642) (2.137)
Community has government hospital
0.426 0.311* 0.420
(0.291) (0.149) (0.204)
Number of observations 1146.000
968.000
P-value 0.000
0.000
Pseudo R-Square 0.103
0.182
BIC 3285.307 . . 2744.320
Note: omitted categories are: child is male; household head is male, household head is married; household head has less than primary education; household head is farmer
or does family work; child lives in Tigray. Only monetary poverty status in previous waves is taken into account; the inclusion of multidimensional poverty status did not
improve the fit of the model.
Table A3 Multinomial regression Ethiopia 2009
2009
28
Multinomial model Multinomial model rural (with inclusion of community factors)
AB A B AB A B
b/se b/se b/se b/se b/se b/se
Child is female 0.877 0.914 0.692* 0.941 0.971 0.746
(0.180) (0.147) (0.121) (0.203) (0.172) (0.134)
Age of child 1.038 1.020 0.977 1.046 1.030 0.972
(0.037) (0.029) (0.030) (0.039) (0.033) (0.030)
Household is female 1.852 2.008 2.713 1.302 0.950 2.408
(1.127) (0.923) (1.451) (0.868) (0.474) (1.393)
Age of household head 0.982 1.000 1.004 0.978* 0.991 0.999
(0.010) (0.008) (0.009) (0.011) (0.009) (0.009)
Household size 1.155** 0.937 1.273*** 1.222*** 0.952 1.317***
(0.062) (0.040) (0.058) (0.071) (0.045) (0.062)
Household head is single 0.442 0.835 0.537 0.527 1.218 0.535
(0.247) (0.342) (0.259) (0.320) (0.536) (0.277)
Household head is single/divorced/widowed 1.501 1.148 1.417 1.621 0.609 1.521
(0.884) (0.508) (0.784) (1.019) (0.305) (0.896)
Household head is polygamous 3.819*** 2.893*** 2.313*** 4.393*** 2.725*** 2.678***
(1.068) (0.687) (0.560) (1.312) (0.733) (0.687)
Household head has primary education or more 1.089 1.510* 1.229 1.020 1.538 1.156
(0.316) (0.312) (0.281) (0.318) (0.353) (0.276)
29
Household head does domestic work 0.853 0.272** 0.933 2.409 0.505 1.706
(0.556) (0.136) (0.545) (1.733) (0.286) (1.093)
Household head does manual work 1.741 1.124 1.032 2.364 1.528 1.066
(1.606) (0.716) (1.039) (2.284) (1.142) (1.101)
Household head does non-manual work 0.405 1.253 0.505 0.358 1.243 0.466
(0.250) (0.499) (0.218) (0.224) (0.529) (0.203)
Household head is not in labour force 1.108 1.176 0.447 1.668 1.666 0.627
(0.730) (0.588) (0.313) (1.217) (0.954) (0.459)
Number of members in bad health/immobile 1.161 1.008 0.922 1.156 1.101 0.873
(0.162) (0.110) (0.121) (0.165) (0.132) (0.122)
Child lives in Tigray 9.177*** 0.492 15.633*** 169.314*** 1.275 13.172**
(4.242) (0.254) (6.464) (252.085) (0.866) (10.417)
Child lives in Oromya 0.536 1.120 0.324** 46.788*** 1.735 0.548
(0.199) (0.226) (0.115) (53.946) (0.677) (0.384)
Child lives in SNNPR 2.570*** 0.521** 3.744*** 2013.148*** 0.387 11.330**
(0.732) (0.123) (0.898) (3470.081) (0.207) (9.614)
Child was monetary poor in 1999 1.652* 0.998 2.669*** 1.174 1.242 1.959**
(0.358) (0.174) (0.497) (0.286) (0.245) (0.403)
Child was monetary poor in 2004 2.356*** 1.165 1.966*** 2.157** 0.873 2.018***
(0.510) (0.208) (0.359) (0.513) (0.181) (0.398)
30
Distance to town in kilometres
1.354*** 0.971 1.076
(0.092) (0.030) (0.046)
Community has electricity
0.142*** 0.743 0.607
(0.074) (0.222) (0.210)
Community has piped water
62.932*** 0.550* 3.090*
(52.054) (0.167) (1.438)
Community has primary school
0.282** 0.133*** 0.959
(0.125) (0.056) (0.313)
Community has junior school
0.202 0.296** 1.063
(0.204) (0.117) (0.645)
Community has government hospital
16.972** 2.458** 2.637
(16.014) (0.690) (1.484)
Number of observations 1182.000
1113.000
P-value 0.000
0.000
Pseudo R-Square 0.177
0.216
BIC 3053.348 . . 2920.843
Note: omitted categories are: child is male; household head is male, household head is married; household head has less than primary education; household head is farmer
or does family work; child lives in Tigray. Only monetary poverty status in previous waves is taken into account; the inclusion of multidimensional poverty status did not
improve the fit of the model.
31
Table A4 Multinomial regression Vietnam 2004
2004
Multinomial model
Multinomial model rural
(with inclusion of community factors)
AB A B AB A B
b/se b/se b/se b/se b/se b/se
Child is female 1.068 0.941 0.942 1.026 0.918 0.830
(0.234) (0.194) (0.183) (0.246) (0.215) (0.174)
Age of child 0.975 1.004 0.956 0.965 0.988 0.959
(0.034) (0.035) (0.030) (0.038) (0.039) (0.032)
Household is female 0.477 0.711 1.538 0.756 0.552 1.946
(0.205) (0.260) (0.473) (0.386) (0.266) (0.707)
Age of household head 0.982 0.979 1.012 0.986 0.981 1.023
(0.014) (0.013) (0.013) (0.015) (0.015) (0.014)
Household head is single 30.959** 0.000 3.573 33.710** 0.000 3.716
(36.096) (0.000) (4.415) (44.819) (0.000) (5.196)
Household head is widowed 1.530 1.944 0.464 1.498 1.824 0.378
(0.927) (1.024) (0.252) (1.045) (1.192) (0.230)
Household head is divorced 22.851* 16.148* 2.505 1.68e+10*** 2.26e+10 1.51e+09***
(31.546) (21.585) (3.464) (2.30e+10) . (2.30e+09)
Household head is separated 4.200 0.000 2.454 7.419 0.000 3.217
32
(5.544) (0.000) (2.746) (10.117) (0.000) (3.619)
Household head has no education 1.969* 1.116 1.599 2.066* 1.128 1.573
(0.608) (0.359) (0.528) (0.717) (0.418) (0.575)
Household head has secondary education 0.210*** 0.806 0.491** 0.254*** 0.976 0.646
(0.061) (0.209) (0.115) (0.081) (0.285) (0.167)
Household head has post secondary
education 0.238* 0.366* 0.328* 0.353 0.652 0.586
(0.145) (0.174) (0.147) (0.243) (0.353) (0.292)
Household head is unemployed 2.321 6.039*** 0.645 2.424 10.376*** 0.789
(1.225) (2.707) (0.367) (1.641) (6.468) (0.538)
Household head is government staff 0.039** 1.046 0.000 0.026** 0.474 0.000
(0.042) (0.720) (0.000) (0.036) (0.450) (0.000)
Household head is a skilled professional 0.379** 1.185 0.751 0.660 1.165 0.917
(0.142) (0.304) (0.188) (0.257) (0.337) (0.248)
Child has other ethnicity 16.964*** 2.129 3.945*** 18.727*** 1.887 4.622***
(6.181) (0.918) (1.556) (7.645) (0.951) (1.963)
Children aged 5-11 present in household 0.612 0.595 1.140 0.562 0.539 1.181
(0.305) (0.254) (0.523) (0.320) (0.281) (0.612)
Children aged >11 present in household 0.949 0.474 1.440 0.791 0.467 1.282
(0.512) (0.229) (0.718) (0.488) (0.272) (0.715)
33
<25% of household members are children 0.146 0.496 0.133 0.026* 0.247 0.093*
(0.157) (0.311) (0.149) (0.041) (0.188) (0.111)
40-49% of household members are
children 2.551* 0.839 1.885 1.761 0.543 1.511
(1.050) (0.337) (0.719) (0.801) (0.256) (0.632)
>49% of household members are children 1.708 1.071 1.577 1.355 0.850 1.468
(0.637) (0.343) (0.538) (0.549) (0.301) (0.541)
Child lives in a rural area 3.026** 3.658*** 4.364***
(1.096) (1.147) (1.457)
Child lives in Red River Delta 0.381 0.801 1.075 0.328 0.650 1.068
(0.204) (0.347) (0.368) (0.190) (0.300) (0.401)
Child lives in North East 1.351 1.400 1.359 0.893 0.655 1.112
(0.668) (0.690) (0.554) (0.473) (0.363) (0.495)
Child lives in North West 15.038** 0.976 2.201 33.317* 2.622 7.948
(13.280) (1.266) (2.068) (53.991) (4.536) (13.326)
Child lives in North Central Coast 2.778* 0.671 1.744 2.583 0.416 1.788
(1.233) (0.344) (0.625) (1.259) (0.237) (0.717)
Child lives in Central Highlands 1.266 1.823 0.264* 1.171 1.525 0.300*
(0.643) (0.909) (0.148) (0.634) (0.800) (0.175)
34
Child lives in South East 0.314* 1.152 0.156*** 0.240** 0.920 0.198***
(0.152) (0.502) (0.073) (0.129) (0.435) (0.097)
Child lives in Mekong River Delta 1.695 4.403*** 0.384* 2.206 4.099** 0.377
(0.755) (1.819) (0.172) (1.057) (1.829) (0.193)
Child lives in area where a disaster
happened in 2004
0.780 0.759 1.132
(0.214) (0.203) (0.261)
Child lives in area where there are non-
farm employment opportunities
0.600 0.614 0.993
(0.158) (0.157) (0.236)
Number of observations 1068.000 858.000
P-value 0.000
0.000
Pseudo R-Square 0.293
0.287
BIC 2571.035 . . 2231.498
Note: Reference values are: child is male; household head is male; household head is married; household head has primary education; household head is unskilled worker;
child is of Kinh/Chinese ethnicity; children aged <5 are present in the household; % of children in household is 25-39%; child lives in South Central Coast; child is non-poor
in 2004; child is non-poor in 2006
35
Table A5 Multinomial regression Vietnam 2006
2006
Multinomial model
Multinomial model rural
(with inclusion of community factors)
AB A B AB A B
b/se b/se b/se b/se b/se b/se
Child is female 1.561 1.657* 0.994 1.630 1.727* 1.064
(0.425) (0.370) (0.237) (0.480) (0.433) (0.279)
Age of child 0.931 0.944 0.918* 0.954 0.953 0.928
(0.042) (0.037) (0.037) (0.047) (0.042) (0.041)
Household is female 0.940 0.748 1.581 1.417 1.151 2.299
(0.551) (0.328) (0.661) (0.972) (0.653) (1.173)
Age of household head 1.007 0.988 1.021 1.008 0.984 1.029
(0.017) (0.014) (0.015) (0.019) (0.017) (0.018)
Household head is single 1.06e+11*** 1.27e+10*** 4.00e+09 1.40e+11*** 5.41e+09*** 4.30e+09
(1.37e+11) (1.77e+10) . (1.98e+11) (9.25e+09) .
Household head is widowed 0.179* 0.742 0.179** 0.261 0.424 0.119**
(0.146) (0.453) (0.119) (0.247) (0.350) (0.096)
Household head is divorced 1.378 0.000 0.000 2.090 0.000 0.000
(1.927) (0.000) (0.000) (3.311) (0.000) (0.000)
Household head is separated 0.000 0.000 17.984 0.000 0.000 35.530
(0.000) (0.000) (30.467) (0.000) (0.000) (83.556)
36
Household head has no education 0.967 1.242 1.048 0.666 1.141 0.574
(0.356) (0.399) (0.365) (0.275) (0.417) (0.227)
Household head has secondary education 0.481* 0.989 0.624 0.487 0.705 0.664
(0.177) (0.290) (0.183) (0.192) (0.232) (0.218)
Household head has post secondary
education 0.237 0.429 0.000 0.152 0.168* 0.000
(0.289) (0.250) (0.000) (0.200) (0.132) (0.000)
Household head is unemployed 8.319** 4.404** 1.692 22.945*** 11.038*** 4.113
(5.846) (2.159) (1.054) (19.600) (7.405) (3.439)
Household head is government staff 2.495 0.000 0.680 1.622 0.000 0.378
(3.522) (0.000) (0.894) (2.469) (0.000) (0.525)
Household head is a skilled professional 0.312* 1.117 0.645 0.281* 1.507 0.489*
(0.168) (0.311) (0.209) (0.172) (0.468) (0.178)
Child has other ethnicity 24.195*** 4.434*** 3.912** 47.880*** 4.586** 5.505***
(10.942) (1.923) (1.728) (25.852) (2.350) (2.706)
Children aged 5-11 present in household 1.314 0.543 0.668 1.138 0.312 0.668
(1.129) (0.295) (0.468) (1.129) (0.204) (0.559)
Children aged >11 present in household 1.331 0.587 0.577 0.957 0.443 0.493
(1.204) (0.353) (0.431) (0.992) (0.317) (0.436)
37
<25% of household members are children 0.482 0.907 0.919 0.427 0.686 0.931
(0.500) (0.565) (0.706) (0.483) (0.545) (0.822)
40-49% of household members are
children 2.579 1.079 2.002 2.548 0.997 1.392
(1.355) (0.454) (0.930) (1.445) (0.496) (0.741)
>49% of household members are children 1.718 0.886 1.545 2.045 1.051 1.814
(0.768) (0.294) (0.622) (0.978) (0.393) (0.791)
Child lives in a rural area 2.141 1.525 1.127
(1.199) (0.507) (0.441)
Child lives in Red River Delta 1.904 0.691 0.762 2.457 0.878 0.881
(1.532) (0.377) (0.354) (2.172) (0.548) (0.466)
Child lives in North East 1.167 0.626 0.596 1.123 0.766 0.522
(0.825) (0.368) (0.309) (0.876) (0.521) (0.311)
Child lives in North West 3.332 1.851 1.248 4.520 1.960 1.345
(3.019) (1.603) (1.020) (4.879) (2.096) (1.346)
Child lives in North Central Coast 5.717* 1.545 2.320 8.446** 1.707 2.137
(4.019) (0.834) (1.040) (6.550) (1.077) (1.090)
Child lives in Central Highlands 7.770** 1.952 4.430** 7.956* 1.411 3.831*
(5.839) (1.168) (2.492) (6.477) (0.992) (2.337)
Child lives in South East 6.011* 1.881 1.445 3.459 2.508 1.757
38
(4.425) (0.979) (0.743) (2.848) (1.478) (0.997)
Child lives in Mekong River Delta 7.010** 4.076** 0.307 11.770** 5.758** 0.070*
(4.850) (1.929) (0.190) (8.859) (3.141) (0.080)
Child is multidimensionally + monetary 368.042*** 10.195*** 29.214*** 277.592*** 12.370*** 26.417***
poor in 2004 (304.626) (3.781) (11.537) (230.377) (5.104) (11.703)
Child is only multidimensionally poor in 28.920*** 4.663*** 0.922 19.140*** 4.477*** 0.817
2004 (24.216) (1.334) (0.543) (16.067) (1.448) (0.506)
Child is monetary poor in 2004 83.886*** 2.912** 24.466*** 66.823*** 2.629* 20.378***
(70.795) (1.105) (8.473) (55.829) (1.100) (7.516)
Child lives in area where a disaster happened in 2004
1.703 1.265 2.399**
(0.573) (0.353) (0.727)
Child lives in area where there are non-farm employment opportunities
1.027 0.708 0.681
(0.362) (0.209) (0.198)
Child lives in area with ECD centre
0.784 1.513 1.066
(0.252) (0.414) (0.306)
Number of observations 1068.000 879.000
P-value 0.000
0.000
Pseudo R-Square 0.405
0.417
BIC 2169.349 . . 1955.165
Note: Reference values are: child is male; household head is male; household head is married; household head has primary education; household head is unskilled worker;
child is of Kinh/Chinese ethnicity; children aged <5 are present in the household; % of children in household is 25-39%; child lives in South Central Coast; child is non-poor in
39
2004; child is non-poor in 2006
Table A6 Multinomial regression Vietnam 2008
2008
Multinomial model
Multinomial model rural
(with inclusion of community factors)
AB A B AB A B
b/se b/se b/se b/se b/se b/se
Child is female 1.116 1.112 1.607* 1.185 1.150 1.609
(0.319) (0.272) (0.388) (0.377) (0.320) (0.424)
Age of child 1.028 1.153** 0.994 1.043 1.214*** 1.009
(0.048) (0.053) (0.041) (0.055) (0.066) (0.046)
Household is female 0.999 0.897 1.342 0.908 0.889 1.474
(0.694) (0.394) (0.587) (0.777) (0.495) (0.696)
Age of household head 0.970 0.976 1.005 0.949* 0.980 1.003
(0.019) (0.016) (0.016) (0.020) (0.018) (0.018)
Household head is single 5.866 0.000 0.000 58.435* 0.000 0.000
(8.889) (0.000) (0.000) (110.464) (0.000) (0.000)
Household head is widowed 0.180 0.432 0.674 0.260 0.399 0.666
(0.170) (0.284) (0.443) (0.299) (0.336) (0.485)
Household head is divorced 135.182 0.000 0.000 6.10e+17 6.757 0.493
(388.833) (0.000) (0.000) (3.60e+25) (8.37e+08) (6.45e+07)
40
Household head is separated 14.201 9.462* 2.824 84.979* 13.918* 1.656
(21.590) (9.531) (3.407) (149.166) (17.190) (2.412)
Household head has no education 2.430* 1.739 2.135* 3.358** 1.962 2.797**
(0.877) (0.607) (0.758) (1.383) (0.773) (1.088)
Household head has secondary education 0.230*** 1.054 0.851 0.274** 1.257 0.977
(0.100) (0.337) (0.252) (0.127) (0.448) (0.310)
Household head has post secondary education 0.000 1.273 0.433 0.000 1.135 0.633
(0.000) (0.673) (0.299) (0.000) (0.697) (0.452)
Household head is unemployed 2.990 5.687*** 0.426 8.664** 12.540*** 0.211
(2.015) (2.830) (0.259) (7.011) (8.019) (0.171)
Household head is government staff 37.156** 0.000 0.743 68.094* 0.000 0.710
(50.794) (0.000) (0.804) (113.679) (0.000) (0.874)
Household head is a skilled professional 0.372 0.430* 0.373* 0.301* 0.451* 0.554
(0.198) (0.154) (0.147) (0.182) (0.176) (0.226)
Child has other ethnicity 4.454*** 2.892** 2.687* 6.727*** 3.237** 2.234
(1.929) (1.121) (1.080) (3.392) (1.460) (0.987)
Children aged 5-11 present in household 1.186 0.840 0.793 2.932 0.902 0.462
(1.359) (1.076) (0.687) (3.752) (1.222) (0.418)
Children aged >11 present in household 2.178 0.582 0.924 5.220 0.421 0.493
(2.589) (0.771) (0.829) (6.905) (0.594) (0.461)
41
<25% of household members are children 0.684 1.630 1.663 0.801 1.933 1.610
(0.543) (0.849) (0.887) (0.689) (1.125) (0.946)
40-49% of household members are children 0.965 1.759 1.568 1.036 2.250 2.190
(0.516) (0.779) (0.702) (0.611) (1.157) (1.061)
>49% of household members are children 1.130 1.928 2.215* 1.266 2.421* 2.566*
(0.479) (0.698) (0.797) (0.604) (1.013) (1.011)
Child lives in a rural area 1.628 1.862 1.345
(0.863) (0.680) (0.573)
Child lives in Red River Delta 1.064 0.961 1.251 0.207 0.640 0.593
(1.078) (0.743) (0.590) (0.338) (0.542) (0.313)
Child lives in North East 2.552 2.290 0.807 3.162 1.546 0.409
(1.955) (1.650) (0.416) (2.672) (1.217) (0.232)
Child lives in North West 6.173* 0.569 0.965 9.337* 0.614 0.504
(5.459) (0.623) (0.685) (9.126) (0.714) (0.395)
Child lives in North Central Coast 7.052* 2.245 1.942 10.364** 1.657 0.835
(5.398) (1.628) (0.878) (8.809) (1.289) (0.419)
Child lives in Central Highlands 4.412 4.323* 1.324 6.488* 4.050 0.661
(3.612) (3.176) (0.752) (5.861) (3.135) (0.417)
Child lives in South East 3.122 3.302 0.670 2.214 2.753 0.726
42
(2.515) (2.274) (0.367) (2.040) (1.991) (0.423)
Child lives in Mekong River Delta 8.897** 7.364** 0.236* 28.161*** 8.663** 0.374
(6.922) (4.929) (0.174) (25.196) (5.976) (0.289)
Child is multidimensionally + monetary poor in 2004 20.097*** 3.891*** 6.808*** 16.488*** 3.088* 6.998***
(13.112) (1.583) (2.906) (11.918) (1.438) (3.329)
Child is only multidimensionally poor in 2004 4.496* 2.722** 1.801 2.552 1.973 1.232
(3.115) (0.886) (0.978) (2.029) (0.731) (0.765)
Child is only monetary poor in 2004 7.325** 0.886 6.150*** 7.392** 0.655 6.555***
(4.979) (0.440) (2.306) (5.515) (0.360) (2.675)
Child is multidimensionally + monetary poor in 2006 15.627*** 3.640** 9.380*** 15.395*** 4.018** 11.306***
(7.970) (1.708) (4.155) (8.896) (2.095) (5.467)
Child is only multidimensionally poor in 2006 2.987* 4.911*** 1.413 2.770 5.677*** 1.283
(1.543) (1.522) (0.667) (1.637) (2.003) (0.664)
Child is only monetary poor in 2006 2.554 0.971 3.885*** 2.964 0.913 4.682***
(1.265) (0.480) (1.264) (1.677) (0.518) (1.697)
Child lives in area where a disaster happened in 2004
2.843** 1.461 0.849
(1.051) (0.456) (0.241)
Child lives in area where there are non-farm employment
opportunities
1.261 1.181 0.548*
(0.471) (0.375) (0.157)
43
Child lives in area with ECD centre
0.791 0.915 2.168**
(0.273) (0.267) (0.647)
Number of observations 1068.000 874.000
P-value 0.000
0.000
Pseudo R-Square 0.430
0.454
BIC 2089.016 . . 1879.155 . .
Note: Reference values are: child is male; household head is male; household head is married; household head has primary education; household head is unskilled worker;
child is of Kinh/Chinese ethnicity; children aged <5 are present in the household; % of children in household is 25-39%; child lives in South Central Coast; child is non-poor in
2004; child is non-poor in 2006
i These data have been made available by the Economics Department, Addis Ababa University, the Centre for the Study of African Economies, University of Oxford and the
International Food Policy Research Institute. Funding for data collection was provided by the Economic and Social Research Council (ESRC), the Swedish International
Development Agency (SIDA) and the United States Agency for International Development (USAID); the preparation of the public release version of these data was
supported, in part, by the World Bank. AAU, CSAE, IFPRI, ESRC, SIDA, USAID and the World Bank are not responsible for any errors in these data or for their use or
interpretation.
ii Data has been made available by the Government Statistical Office (GSO) in Hanoi, Vietnam with support from UNICEF Vietnam. iii The inclusion of community indicators in the rural model has little explanatory power due to lack of variation; primary schools are available in all areas and the inclusion
of road accessible to auto and secondary school does not improve fit of the model. iv A more elaborate discussion of the measures for monetary and multidimensional child poverty and empirical findings can be found in Roelen (mimeo). v A more elaborate discussion of the comparison between quantitative and qualitative indicators used for reflecting multidimensional child poverty and child wellbeing
respectively can be found in Roelen (forthcoming).
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