child vs. household mpis in colombia: do they identify the ... · m. pinilla-roncancio et al....

Child vs. Household MPIs in Colombia: Do theyIdentify the Same Children as MultidimensionallyPoor?

Mónica Pinilla-Roncancio1& Sandra García-Jaramillo2

& Ana Lorena Carrero1&

Catalina González-Uribe1& Amy Ritterbusch3

Accepted: 30 March 2019/# Springer Nature B.V. 2019

AbstractDespite increased recognition of the importance of measuring child poverty from amultidimensional perspective, most countries with National Multidimensional PovertyIndices (MPIs) have used the household as the unit of identification and used the resultsof national MPIs to define the levels of child poverty in a country. This assumes thatresources are shared equally among all household members, and ignores possible intra-household inequalities. Given the lack of knowledge about whether Household MPIsproperly identify children who are multidimensionally poor, this article aims to com-pare the results of a child-specific MPI exercise and a household-specific MPI exerciseand identify the dimensions and individual characteristics that explain this gap. To fulfilthis objective, we computed a Child MPI for Colombia and compared the results withthe Colombian National MPI. In addition, we estimated probit and biprobit models toidentify the determinants of being a multidimensionally poor child under both mea-sures. The results of the analysis reveal three main findings: (1) there is a mismatchbetween the two measures; (2) the deprivation profiles of multidimensionally poorchildren are different depending on which MPI is used to classify them as poor; and (3)children who are multidimensionally poor according to a Child MPI have differentindividual and family characteristics compared with children who are classified as pooraccording to a Household MPI. These three main findings reveal that it is necessary toanalyse child poverty using an MPI that captures individual deprivations.

Keywords Child poverty .Multidimensional poverty . Colombia . Individualmeasures .

Household measures

Child Indicators Researchhttps://doi.org/10.1007/s12187-019-09639-1

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12187-019-09639-1) contains supplementary material, which is available to authorized users.

* Mónica [email protected]

Extended author information available on the last page of the article

http://crossmark.crossref.org/dialog/?doi=10.1007/s12187-019-09639-1&domain=pdf

http://orcid.org/0000-0002-1443-4649

https://doi.org/10.1007/s12187-019-09639-1

https://doi.org/10.1007/s12187-019-09639-1

mailto:[email protected]

1 Introduction

Child poverty is a problem faced by almost 385 million children around the world, withnegative consequences in social and economic development (United Nations children’sFund (UNICEF) 2016). Over the past two decades, there has been an increasedrecognition of the importance of measuring child poverty from a multidimensionalperspective. This recognition dates from the publication of the first global study of childpoverty, by Gordon et al. (2003), inspired by the UN Convention on the Rights of theChild, in which the integral protection of the rights of this group was proclaimed(United Nations Human Rights Office of the High Commissioner 1989). Then, in 2005,UNICEF presented a definition of child poverty that has influenced subsequent workon this topic: ‘deprivation of the material, spiritual and emotional resources needed tosurvive, develop and thrive, leaving them unable to enjoy their rights, achieve their fullpotential or participate as full and equal members of society’ (United Nations Chil-dren’s Fund (UNICEF) 2005). More recently, the Sustainable Development Goals(SDGs) established in Target 1.2. that countries must reduce at least by half theproportion of men, women, and children living in multidimensional poverty (Inter-Agency and Expert Group on Sustainable Development Goal Indicators 2016).

The definition proposed by UNICEF in 2005 was the first initiative that officiallydefined child poverty as a multidimensional issue and in which aspects related to theprotection of child rights, and access to basic services and opportunities, played a majorrole (Minujin et al. 2006; Roelen and Gassmann 2008; Roelen et al. 2009, 2012).Despite the theoretical acknowledgement of child poverty as a multidimensionalproblem and of the need for child-centred measures, the number of official multidi-mensional child-poverty measures is still limited, and most countries around the wordstill use income-poverty measures to identify poor children and target social protectionprogrammes. Moreover, among those countries that go beyond income to measurepoverty from a multidimensional perspective, the vast majority consider the household,rather than children, as the unit of analysis (Alkire et al. forthcoming).

In the past decade the number of countries with National Multidimensional PovertyIndices (National MPIs) has increased, particularly in Latin America. In this region,nine countries currently have National MPIs (Colombia, Chile, Costa Rica, Ecuador, ElSalvador, Dominican Republic, Honduras, Mexico, and Panama). All of them, exceptfor Mexico, have used the household as the unit of identification, assuming thatresources are equally shared, and deprivations equally experienced among householdmembers (Haddad and Kanbur 1990). In other words, deprivations that affect onehousehold member, regardless of his or her characteristics, will affect all other house-hold members. Also, this assumption ignores the fact that resources within householdsare not distributed according to their needs, and that in most cases, children withspecific characteristics (for example, girls, children with disabilities, or non-household members) are more likely to receive an unequal share of the resources oropportunities in question (Klasen and Lahoti 2016; Rodríguez 2016).

Although most Household MPI measures include child-specific deprivations such asschool non-attendance or child undernutrition, other child-specific dimensions areignored, such as direct access to health care or access to a safe and caring environment.In addition, given that most Household MPIs use the household as the unit ofidentification, it is not possible to measure deprivations at the individual level and

M. Pinilla-Roncancio et al.

therefore understand whether all children within a household experience the samelevels of achievement or deprivation. Consequently, it is not possible to identify thelevels of poverty of children with different characteristics (such as sex, age, disabilitystatus, or relationship with the head of the household).

Previous studies have developed multidimensional child-poverty measures by con-sidering the child as the unit of analysis (Alkire et al. 2016; Alkire and Roche 2011;Biggeri et al. 2010; García and Ritterbusch 2015; Leu et al. 2016; Noble et al. 2006;Pinilla-Roncancio and Silva 2017; Qi and Wu 2018; Roelen 2014, 2017, 2018; Roelenand Camfield 2013; Roelen et al. 2010). Moreover, previous studies from low- andmiddle-income countries (LMICs) have examined the overlap between monetary andmultidimensional measures of household poverty (Bader et al. 2016; Ministerio deDesarrollo Social 2015) and of child poverty in particular (Notten and Roelen 2012;Roelen 2017, 2018; Roelen et al. 2012; Trani et al. 2016), showing a mismatch betweenboth measures. However, evidence on the overlap and mismatch among household-level MPI measures of child poverty and child-specific MPI measures is limited.

Regarding the mismatch between household-level and individual-level povertymeasures, studies have shown deep intra-household inequalities which conceal de-prived individuals within non-poor households (Brown et al. 2017). In the context ofmultidimensional poverty measures, evidence analysing how Household MPIs concealintra-household inequalities is growing at a slow rate (Klasen and Lahoti 2016; Vijayaet al. 2014). These studies focus on intra-household gender inequalities and show thatHousehold MPIs hide disparities in poverty levels between men and women. In thecase of children and adolescents, evidence using Household MPIs is scarce. To ourknowledge, there are no empirical studies that analyse the overlap and mismatch ofHousehold MPIs and individual MPIs with a focus on children. Additionally, there isno information regarding what kinds of inequality children and adolescents sufferwithin households, or if, depending on other characteristics, some children have feweropportunities compared with their siblings.

Given the lack of knowledge in this field, the purpose of this paper is to examine themismatch between a household-specificMPI and a child-specificMPI. The paper aims (1)to analyse if both measures produce similar results regarding the degree of child poverty,(2) to examine to what extent the two measures identify the same children, and (3) toexamine the individual characteristics of children identified as poor under both measures.

We use Colombia as an example, given that it is the only country that has included aChildhood and Youth dimension as part of its National Household MPI (Zavaleta2017), and is one of the Latin American countries with a strong tradition of computingand analysing multidimensional poverty. Also, along with Mexico, Colombia is one thefew countries in the region that, in addition to income poverty measures, use amultidimensional poverty index as an official measure of poverty to monitor socialpolicy performance. Also, there has been some attempts to measure child poverty aspart of an effort to make children needs more visible (García and Ritterbusch 2015),however official poverty measures are provided at the household level. Over the last8 years, Colombia has seen a reduction of more than 10 percentage points on the levelsof multidimensional poverty (Departamento Administrativo Nacional de Estadistica(DANE) 2018). Nevertheless, there is no information on the levels of poverty reductionfor children in this same period of time. For Colombia, there is only one study that hasdesigned and computed the levels of multidimensional child poverty in the country

Child vs. Household MPIs in Colombia: Do they Identify the Same...

(García and Ritterbusch 2015). Thus, this study also contributes to the debate of childpoverty measurement in Latin America and in the Colombian context in particular.

We compute a child-specific MPI using the Quality of Life Survey, which is thesame survey used to compute the National Household MPI (‘Household MPI’ fromnow on); then, using the same data set, we compute and compare the profiles ofchildren identified as multidimensionally poor according to both measures. Second,we estimate a probit model to examine the correlation between both measures. Finally,we estimate a biprobit model in order to understand the characteristics that explain themismatch between the Child MPI and the Household MPI.

Since 2011, Colombia has calculated a Household MPI, with the main purpose ofmonitoring households’ multidimensional poverty and deprivation at the national level(Angulo et al. 2015). Colombian Household MPI uses the Alkire-Foster (AF) method andincludes five dimensions and 17 indicators, which are calculated using the household as theunit of identification (Table 1). The dimension on childhood and youth includes threeindicators capturing specific child deprivations: school attendance, child labour, and childcare. Here it is important to note that the Colombian Household MPI uses the household asunit of identification, which means that if one child is deprived, all members in thehousehold are classified as deprived in that dimension. This means that it is not possibleto identify which child faces the deprivation, or to analyse how individual child character-istics may be associated with the levels of multidimensional poverty of children inColombia.

2 Methodology

2.1 Data

We use the 2016 Colombian Quality of life (QoL) Survey. This survey aims to collectinformation regarding the wellbeing of households in Colombia. It gathers informationon access to public or private goods and services, health care, education, and child care(for children younger than 5). The QoL survey uses a probabilistic, stratified, multi-stage sampling design; it is representative at the national level, for the six largestregions of the country, and for rural and urban areas (Departamento AdministrativoNacional de Estadistica (DANE) 2017). The QoL survey collects information from allhousehold members. For people younger than 12 and for household members who arenot present at the time of the interview, another household member older than 18, whois usually the head of the household, provides the information. The final sample ofchildren included in the analysis is 22,893, which were living in 12,538 households.

2.2 Methods

The Child MPI for Colombia1 is calculated using the Alkire-Foster (AF) method. The AFmethod uses a double cut-off approach, which first identifies individuals or households

1 In Colombia only García and Ritterbusch (2015) have calculated a Child MPI using the child as unit ofidentification. This Child MPI included nine dimensions and 17 indicators and was calculated using theQuality of Life survey 2014. Given data limitations, it was not possible to compute this Child MPI.


Table1

Colom

bian

natio

nalMPI

Dim

ension

Indicator

Ahouseholdisdeprived

ifWeight

Household

education

conditions

Educational

achievem

ent

The

averageeducationlevelfor

peopleaged

15andolderlivinginthehouseholdislowerthan

9years,or

ifthereare

nomem

bersolderthan

15years.

1/10

Literacy

Atleastonemem

berof

thehousehold15

yearsor

oldercannot

read

andwrite.

1/10

Childhood

andyouth

conditions

School

attendance

Atleastonemem

beraged

6to

16yearsisnotattendingschool.

1/20

Noschool

lag

Atleastonemem

beraged

7to

17yearshasaschool

lagof

oneyear

(according

tothenationalnorm

).1/20

Accessto

child

-care

services

Atleastonechild

youngerthan

five

yearsdoes

nothavehealthinsurance,spends

mostofhis/hertim

ein

thecareof

his/herparentsatwork,or

aloneatthehouse,or

underthecareof

ahouseholdmem

beryoungerthan

18years,or

attendskindergarten

butdoes

notreceivefree

breakfastor

lunch.

ORs/he

isfive

yearsoldanddoes

nothave

access

tocare

services,o

rs/he

receives

thisservicebutdoes

nothave

health

insurance,or

s/he

does

notattend

school,o

rs/he

attendsschool

butdoes

notreceivefree

mealsatschool.

1/20

Childrennotworking

Atleastonechild

aged

12to

17yearsisworking.

1/20

Employment

Noonein

long-term

unem

ployment

Atleastoneworking-age

mem

berisin

long-term

unem

ployment(m

orethan

12months).

1/10

Form

alem

ployment

Nomem

beraged

18or

olderandcurrently

working

works

intheform

allabour

market(contributes

toapension).

1/10

Health

Health

insurance

Atleastonemem

berof

thehouseholdaged

five

yearsor

olderdoes

nothave

health

insurance.

1/10

Accessto

health

services

Atleastonehouseholdmem

berdoes

nothave

access

tohealth-careservices

incasesof

need.

1/10

Accessto

publicutilitiesand

housingconditions

Accessto

water

source

Urban

households

donothave

apublicwater

system

OR

1/25

ruralh

ouseholdsobtain

theirwatertopreparefood

from

wells,rainw

ater,springsource,w

atertank,w

atercarrieror

othersources.

1/25

Adequatesewage

system

Urban

households

donoth

aveapublicsewer

system

ORruralh

ouseholdsuseatoiletw

ithouta

sewer

connectio

n,or

alatrine,or

simplydo

nothave

asewagesystem

.1/25

Adequatefloors

The

dwellinghasdirtfloors.

1/25

Adequateexternal

walls

The

exterior

wallsof

thedw

ellin

garebuilt

ofuntreatedwood,

boards,p

lanks,guadua

orothervegetatio

n,zinc,

cloth,

cardboard,

orwastematerial;or

noexterior

wallsexist.In

ruralhouseholds,exteriorwallsarebuilt

ofguadua

orothervegetation,

zinc,cloth,cardboard,o

rwastematerials;or

noexterior

wallsexist.

1/25

Nocritical

overcrow

ding

The

numberof

peoplesleeping

perroom

,excluding

thekitchen,bathroom

,and

garage,isthreeor

moreperroom

inurbanareasandmorethan

threeperroom

inruralareas.

1/25

Source:(A

nguloetal.2

015)


deprived in certain indicators, then a weight is assigned to each indicator and weighteddeprivations are added to create a deprivation score. Individuals whose levels of depri-vation are higher than a poverty cut-off (k) are classified as multidimensionally poor.Three indices are generated using the AF method, first the incidence (H) that is thepercentage of multidimensionally poor individuals in a country; second the intensity (A)or the average number of deprivation that multidimensionally poor individuals face; andfinally the Multidimensional Poverty Index (MPI), which is the product of the incidenceby the intensity (MPI =H ∗ A) (Alkire et al. 2015).

The dimensions and indicators included in the Child MPI are calculated using thechild as unit of identification and are presented in Table 2. The inclusion of dimensionsand indicators was the result of an extensive literature review, the thematic analysis of24 semi-structured interviews with a total of 27 experts on multidimensional povertyand child poverty, and data availability in the QoL survey. (For a detailed description ofthe Child MPI construction, see Pinilla-Roncancio et al. (forthcoming) The finalversion of the Child MPI used in our analysis considers children aged 0 to 17 yearsas the unit of identification and analysis. It includes five dimensions and 11 indicators;four of them capture individual deprivations, and seven capture household deprivations.Indicators in education and protection have deprivation cut-offs by age group. Thus,each indicator identifies age-specific deprivations, following a life-cycle approach.

2.3 Dimension, Indicators, Deprivation Cut-Offs, and Weights

The five dimensions included in the Child MPI are education, health, water andsanitation, housing, and protective environments (Table 2). The dimension of educationincludes only one indicator, learning environments, which captures deprivations ofaccess to education services for different age groups. In the case of children youngerthan five years, the indicators capture deprivation of stimulation, which includeswhether their parents do activities such as reading books or playing with them withtoys. For school-aged children, the indicator captures school attendance.

The dimension of health includes two indicators: food security and access to health-care services. The first indicator captures deprivations of food security for all house-hold members, and the second one uses information related to access to preventive andcurative health-care services at the individual level for children aged 0 to 17 years.Although it was desirable to include information on nutritional status, vaccination, orother health outcomes, information on those indicators was not available in the QoLsurvey. Also, it would be desirable to have data on food security at the individual levelin order to capture unequal distribution of resources within the household. However,this information is not available in the data used in this study.

The third dimension is water and sanitation; it includes two indicators using thesame definitions as the Colombian Household MPI (see Table 1 for details). In thedimension of housing, three indicators are included: household materials, overcrowd-ing, and asset ownership. The first indicator follows the same definition used by theColombian Household MPI. In the case of overcrowding, following García andRitterbusch (2015), the deprivation cut-off was established in terms of householdcomposition and children’s age. If a household has children younger than two, ahousehold is deprived if four or more individuals share the same room. If the householddoes not have any children younger than two, the deprivation cut-off is three or more


Table2

Child

MPI

forColom

bia

Dim

ension

Indicator

Achild

isdeprived

if…

Weights

Educatio

nLearning

environm

ents

S/he

isaged

between0and4yearsanddoes

notpractiseanylearning

activ

ities

with

his/hercaregiver(suchas:readingbooks,

telling

stories,doingartandcrafts,d

oing

indoor

games,g

oing

tothepark,p

ractisingsports,p

laying

instruments)ORisnot

attendingearlyschool.O

Rs/he

isaged

between5and17

yearsandisnotattendingschool

AND

hasnotcompleted

high

school.

20%

Health

Accessto

health

services

S/he

isaged

between0and17

yearsandhasno

health

insuranceORdidnoth

avemedicalcheck-upsatleasto

nceayearORin

anem

ergencydidnotgo

toahealth-servicesinstitutionor

consultahealth-careprofessional.

10%

Food

security

S/he

lives

inahouseholdwhere

forlack

ofmoney

somemem

berdidnotconsumeanyof

threemeals(breakfast,lunch,dinner)on

oneor

moredays

oftheprevious

weekORdidnotreceivehealthyfood

inthelast30

days.

10%

Water

and

sanitatio

nDrinkingwater

S/he

lives

inan

urbanarea

andhis/herhouseholdlacksapublicwatersystem

.ORs/he

lives

inaruralareaandinhis/herhousehold

drinking

water

isobtained

from

wells,rainw

ater,springsource,w

ater

tank,w

ater

carrieror

othersources.

10%

Sanitatio

nS/he

lives

inan

urbanarea

andhis/herhouseholdlacksapublicsewer

system

.ORs/he

lives

inaruralareaandhis/herhousehold

uses

atoiletwithoutasewer

connection,

oralatrine,or

does

nothave

asewagesystem

.10%

Shelter

Safe

construction

materials

S/he

lives

inashelterw

ithdirtfloorsORwith

inadequateexternalwalls,i.e.builtof

untreatedwood,boards,planks,guadua

orother

vegetatio

n,zinc,cloth,cardboard,o

rwastematerialORwhere

noexternalwallsexistOR

with

aroof

built

ofpalm

,wickeror

othervegetatio

nor

wastematerial.

6.67%

Overcrowding

S/he

isaged

between0and2yearsandliv

esin

ahouseholdwhere,excluding

thekitchen,

bathroom

,and

garage,fouror

more

peoplesleepin

thesameroom

OR

s/he

isaged

between3and17

yearsandthreeor

morepeoplesleepin

thesameroom

.

6.67%

Assets

S/he

lives

inahouseholdwhich

does

notownatleasttwoof

thefollowingassets:w

ashing

machine,refrigerator,cooker,television,

computer,bicycle,or

motorcycle.Ifthehouseholdhasacaror

house,achild

will

notbe

considered

asdeprived.

6.67%

Protective

Environments

Protectio

nS/he

isaged

between0and4yearsandhis/hercaregiverisless

than

18yearsold,or

thechild

staysathomealoneORs/he

isaged

between5and11

yearsanddoes

atleasto

nehour

ofeconom

icactivity

perweekORs/he

isaged

between12

and14

yearsand

does

14hof

econom

icactiv

ityperweekinanyform

exceptperm

issiblelig

htworkORs/he

isaged

between15

and17

yearsand

works

inadesignated

hazardousindustry

orin

adesignated

hazardousoccupatio

n,or

forlong

hours.

6.67%

Inform

ation

S/he

lives

inahouseholdwhereatleastone

ofthechild

renbetween12

and17

yearsdoes

notuse

radioor

theinternetforeducation

orinform

ationor

does

notusetheinternetandradioatall.

6.67%

Maternaleducation

The

child

’smotherhasfewer

than

nine

yearsof

education.

6.67%


individuals per room. Finally, deprivation in assets ownership defines a child asdeprived if s/he is living in a household without at least two small assets or a big asset(for example, a car or a dwelling).

The last dimension is protective environments, which includes three indicators:protection, mother’s education, and access to information. The first indicator includesaspects related to the provision of care for children younger than five and child labour(as defined in the Colombian Household MPI) for children aged 5 to 17 years. Theindicator of mother’s education aims to capture parenting practices that facilitate aprotective environment. Although the ideal would be to record actual disciplinarypractices at home, as well as incidence of abuse, the available data did not allow usto do so. The last indicator was information. It defined a child as deprived if no one inthe household has access to the internet or to radio with the purpose of obtaining factsthat enable any household member to obtain useful information or to learn.

The Child MPI uses nested weights; thus, all dimensions and indicators had thesame relative weight. Finally, for the purpose of comparing both poverty indicators, thepoverty cut-off for the Child MPI was defined on the basis of the official multidimen-sional poverty cut-off for the Household MPI. Therefore, a child is multidimensionallypoor if s/he is deprived in 33% or more of the weighted sum of indicators.2

2.4 Comparing Child MPI and National MPI

In order to analyse the overlap and mismatch between Household MPI and Child MPI,we first estimate the incidence (H) and intensity (A) and MPI under both measures.Second, we examine if children classified as multidimensionally poor under theHousehold MPI measure are the same as those classified as poor under the ChildMPI measure for different age groups. We compare the percentage of children who aremultidimensionally poor or non-poor under both measures (overlap) as well as theproportion of children who are classified as poor under one measure but not under theother (mismatch). In addition, the direction of the relationship between both measuresis analysed. Third, we analyse the deprivation profiles of four groups: non-poor underany measure; poor under the Child MPI, but non-poor according to the HouseholdMPI; poor under the Household MPI, but not under the Child MPI; and poor underboth measures. Finally, as we describe next, we estimate a probit model and a biprobitmodel to further understand the correlation and mismatch between the two measures.Similar to logistic regression, probit models aim to analyse dichotomous variables, butthese models use a cumulative normal distribution (Wooldridge 2009).

To compare the two multidimensional poverty indices – Child MPI and HouseholdMPI –and following Ballon et al. (2016), we first perform a univariate probit regressionanalysis to estimate the association between the Household MPI and the probability thata child is multidimensionally poor under a Child MPI. The purpose of this first step isto understand if both indices (Child and Household MPI) measure the same phenom-enon, or if each MPI captures different aspects of poverty. In this analysis, the questionof interest is whether the Household MPI is the only significant determinant of the

2 The results of the analysis were also computed using a k equal to 21%; thus, a child was consideredmultidimensionally poor if she was deprived in 21% or more of the weighted sum of deprivation or in morethan one dimension. The results of this last analysis are available upon request.


multidimensional poverty status of a child when a Child MPI is used as a measure ofchild poverty. The probit regression model can be expressed as follow:

y*i ¼ X iβ þ εi with εi∼N 0;σ2� � ð1Þ

Where yi* is the outcome of interest that measures themultidimensional poverty status of achild. The dependent variable Di is observed if the latent variable y∗ is greater than zero:

Di ¼ 1 if y*i > 00 if y*i < 0

�

Hence, the probit model estimated is:

Pr Di ¼ 1ð Þ ¼ f β0 þ β1H−IPMh þ β2X i þ β3Zh þ β4Rþ εið Þ ð2Þ

Where Pr is the probability that a child i is multidimensionally poor under a Child MPI;Xi is a vector of individual characteristics such as age, gender, disability status, andrelationship with the head of the household; Zh is a vector of family characteristics suchas household size and gender of the household head; and R is a vector of geographiccharacteristics, such as region and living in rural/urban neighbourhoods.

The final step of the empirical analysis aims to understand the non-concordancebetween the two measures. To explain the mismatch, we estimate the joint probabilitythat a child lives in a multidimensional poverty household and is multidimensionallypoor under the Child MPI, using a bivariate probit model. The principal purpose of thisstep is to analyse what factors jointly influence being Household MPI poor and ChildMPI poor (Ballon et al. 2016).

This bivariate probit model can be expressed as follows (Greene 2003):

y1* ¼ X 0

iβ1 þ ε1; y1 ¼ 1 if y1* > 0; 0 otherwise ð3Þ

y2* ¼ X 0

iβ2 þ ε2; y2 ¼ 1 if y2* > 0; 0 otherwise

ε1ε2

jX� �

∼N 00

� �;

1 ρρ 1

� �� ð4Þ

Where y1 represents the poverty status using the Child MPI; y2 is the poverty statusaccording to the Household MPI; and ρ is the tetrachoric correlation between y1 and y2.The bivariate probit model estimated is:

Pr y1i ¼ 1; y2i ¼ 1jXð Þ ¼ Φ2 X0iβ1;X

0iβ2; ρ

; ð5Þ

Where Pr is the probability that a child i is classified as poor by both measures at thesame time (Ballon et al. 2016). For both the probit and biprobit models, marginaleffects at the individual mean are calculated, which measure the predictive probabilitiesof a change in y, when x changes, leaving all other variables constant (Long and Freese2001). All the analyses are conducted using Stata 15.


2.5 Analysis of the Existence of Intra-Household Inequalities

Given that the Child MPI uses the individual as unit of identification, we were able toanalyse the existence of intra-household differences in the poverty status of childrenliving in the same household. Thus, the proportion of multidimensionally poor childrenliving in a household where at least one other child was not multidimensionally poorwas estimated. This analysis was complemented by the analysis of the characteristics ofmultidimensionally poor children deprived in terms of certain indicators, living inhouseholds where another child was not multidimensionally poor.

2.6 Robustness Analysis

In order to check the robustness of the results, different analyses were conducted. First,we estimated the Child MPI using different poverty cut-offs (k) and analysed whetherthe ordering of regions changed between measures, using the Kendall Tau-b andSpearman rank correlation coefficient (Alkire et al. 2015). Then we also computedthe Child MPI using different structures, weighting sets and deprivation cut-offs andtested the robustness of the results using pairwise comparisons. Finally, we comparedthe robustness of the results of the different structures with the results of the HouseholdMPI. The results of the analysis of robustness test for different set of weights arepresented in Table A4 in the appendix. In general, the measures are robust to changes inthe structure of the MPI, structure of weights and deprivation cut-offs.

3 Results

3.1 Comparing the Poverty Measures: Child MPI Vs. Household MPI

According to the Household MPI 23.1% of children were multidimensionally poor in2016, with an average number of deprivation equal to 43.1% and a 0.097 as theirHousehold MPI. When the Child MPI was used as measure of multidimensionalpoverty it was found that 22.1% of children in Colombia were multidimensionallypoor, with an intensity of poverty of 45.8% and a Child MPI of 0.101 (detail results ofboth measures are presented in Table A1 to Table A3 in the appendix).

When comparing the status of multidimensional poverty of children under the ChildMPI and the Household MPI, four different groups can be identified. Group 1 includesnon-poor children under both MPIs (66.7%); Group 2 are children who are poor under aChild MPI but not under a Household MPI (10.2%); Group 3 includes children who areclassified as poor under the Household MPI, but not under the Child MPI (11.2%); andGroup 4 includes children who are multidimensionally poor under both measures (11.9%).As can be seen in Table 3, themismatch between bothmeasures is higher than 20% (Group2 plus Group 3), therefore there are multidimensionally poor children living in householdsthat are not multidimensionally poor, and also there are children living inmultidimensionally poor households who according to an individual measure are non-poor.

In order to understand the mismatch, we focus our analysis only on Groups 2 and 3.Table 4 presents the individual and household characteristics of these groups. On theone hand, children in Group 2 (poor under the Child MPI, but not under the Household


MPI) were more likely to be male, or younger than 5 years old, or (teenagers) pregnantor already having a child, compared with children in Group 3. A higher proportion ofthis group were living with both parents and in the Caribbean Region of the country. Onthe other hand, children in Group 3 (children classified as poor under the HouseholdMPI, but not under the Child MPI) were aged 10 to 14 years, more likely to be relatedto the head of the household, living with their parents, in larger households and in urbanareas, compared with children in Group 2.3 Table A5 in the appendix present detailedresults of the levels of deprivation in each indicator for each group.

In addition, to analyse the correlation between Household MPI and Child MPI, weestimated a univariate probit regression. We first computed a regression model using asindependent variable only the child poverty status defined by the Household MPI (seecolumn 1 of Table 5). The results of this model show that there is a positive andstatistically significant association between the two measures. On average, childrenclassified as poor under the Household MPI are 31.8 percentage points (pp) more likelyto be classified as poor under the Child MPI. While this is a large and significant pointestimate, it is substantially smaller than 1, showing that a Household MPI does not fullyexplain the probability of being multidimensionally poor according to a Child MPI. In asecond stage, we estimated a second model including individual and geographic variablessuch as age group, sex, disability status, region and area of residency (see column 2 ofTable 5). As expected, these variables were significant and increased the explanatorypower of the model; thus, each index measures different, but related, phenomena.

Now we compare the deprivation profiles of mismatched groups: Group 2 (poor byChild MPI and non-poor by Household MPI) and Group 3 (poor by Household MPIand non-poor by Child MPI). Figure 1 presents the incidence of deprivation in the set ofindicators included in the Child MPI. For Group 2 (blue line), we found that 70.3% ofchildren were deprived in learning environments and 66.5% in access to health-careservices; both indicators are measured at the individual level, and therefore they captureindividual deprivations. Instead, Group 3, represented by the red line, has the highestlevels of deprivation in terms of mother’s education (38.8%), access to health-careservices (36.1%), and food security (27.7%), most of those indicators measured at thehousehold level.

Figure 2 exhibits the incidence of deprivation in the set of indicators included in theHousehold MPI. We found that the deprivation profile between Groups 2 and 3 was

Table 3 Cross-tabulation of Child MPI and Household MPI

Household MPI k = 33%

No poor Poor Total

Child MPI k = 33% No poor 66.7% 11.2% 77.9%

Poor 10.2% 11.9% 22.1%

Total 76.9% 23.1% 100%

3 Under the Child MPI, children younger than age 5 were identified as poor mainly because of the inclusion ofthe indicator on learning environments. This explains why the proportion of children under 5 is so high inGroup 2. In the case of the Household MPI, children aged 15 to 17 years have higher levels of deprivation andmultidimensional poverty, mainly given because the high contribution of education achievement and schoollag, which explains the higher proportion of this age group in Group 3.


Table4

Characteristicsof

child

renby

povertymeasurement

Characteristics

Populatio

nshare

(1)Non-poor

n=13,422

(2)Po

orby

Child

MPI

andnon-poor

byHousehold

MPI

n=2829

(3)Po

orby

Household

MPI

butn

on-poor

byChild

MPI

n=2927

(4)Po

orby

both

measures

n=3617

Gender

Female

52.20%

48.33%

47.71%

47.21%

45.46%

Male

47.80%

51.67%

52.29%

52.79%

54.54%

Age

groups

0to

4years

25.81%

21.68%

62.41%

11.96%

30.66%

5to

9years

29.34%

32.16%

15.32%

31.92%

23.13%

10to

14years

27.86%

29.40%

12.13%

35.60%

25.45%

15to

17years

16.98%

16.76%

10.14%

20.52%

20.76%

Teenagepregnancy

Pregnant

orhave

achild

3.33%

1.76%

14.37%

2.61%

9.08%

Not

pregnant

96.67%

98.24%

85.63%

97.39%

90.92%

Disability

status

With

disability

2.26%

1.98%

2.16%

3.00%

3.25%

Withoutdisability

97.74%

98.02%

97.84%

97.00%

96.75%

Livingconditions

Livewith

both

parents

56.82%

58.61%

60.86%

44.97%

54.42%

Liveonly

with

mother

31.85%

31.91%

29.87%

34.60%

30.64%

Liveonly

with

father

3.64%

3.67%

2.87%

4.02%

3.81%

Livewith

neither

biologicalparent

7.69%

5.81%

6.39%

16.41%

11.13%

Relationshipwith

thehouseholdhead

(hh-head)

Child

isason/daughter

76.35%

79.61%

78.38%

60.27%

71.41%


Table4

(contin

ued)

Characteristics

Populatio

nshare

(1)Non-poor

n=13,422

(2)Po

orby

Child

MPI

andnon-poor

byHousehold

MPI

n=2829

(3)Po

orby

Household

MPI

butn

on-poor

byChild

MPI

n=2927

(4)Po

orby

both

measures

n=3617

Child

isamem

berof

thefamily

22.20%

19.17%

19.39%

38.22%

26.56%

Child

isnotamem

berof

thefamily

1.11%

1.07%

1.10%

1.26%

1.22%

Child

ishh-heador

apartnerof

thehh-head

0.34%

0.15%

1.13%

0.25%

0.81%

Com

positio

nof

thehousehold

Meanof

householdsize

4.55

4.60

5.95

6.13

Meanof

numberof

child

renbetween

0and4years

0.48

1.03

0.32

0.89

Meanof

numberof

child

renbetween0and17

years

2.12

2.35

2.94

3.45

Geographiccharacteristics

Urban

73.22%

84.66%

49.02%

67.49%

35.20%

Rural

26.78%

15.34%

50.98%

32.51%

64.80%

Caribbean

24.42%

21.42%

23.52%

31.71%

35.20%

East

18.03%

18.20%

21.58%

16.80%

15.19%

Central

11.94%

11.68%

13.27%

12.31%

11.87%

Pacific

8.52%

5.76%

12.86%

10.40%

18.48%

Bogotá

13.99%

18.22%

6.86%

8.03%

1.95%

Antioquia

12.29%

12.62%

13.44%

9.99%

11.63%

ValledelCauca

8.72%

9.85%

6.34%

8.69%

4.46%

SanAndres

0.10%

0.09%

0.21%

0.05%

0.08%

Orinoquia

1.99%

2.15%

1.93%

2.02%

1.13%

Raw

percentage


Table 5 Marginal effect probit model Child MPI

Model 1 Model 2

dx/dy dx/dy

Household MPI 0.318*** 0.266***

(0.005) (0.005)

Child is female −0.012**(0.005)

Child between 5 and 9 years old a −0.286***(0.007)

Child between 10 and 14 years old −0.292***(0.007)

Child between 15 and 17 years old −0.249***(0.008)

Disability 0.114***

(0.020)

Child lives with mother b −0.007(0.007)

Child lives with father −0.008(0.015)

Child does not live with either parent −0.026***(0.009)

Child is not a family member c 0.048*

(0.025)

Child is a household head or partner of the household head 0.293***

(0.048)

Rural 0.197***

(0.006)

Westd −0.033***(0.009)

Central −0.016*(0.009)

Pacific 0.049***

(0.009)

Bogotá −0.105***(0.013)

Antioquia 0.057***

(0.009)

Valle del Cauca −0.010(0.008)

San Andres and Providence 0.160***

(0.020)

Orinoquía and Amazonía −0.017(0.018)

Household size 0.005***


similar for most indicators. We found differences on three indicators, with a large gap inthe education dimension: deprivation in years of schooling is 38 pp. larger in Group 3than Group 2 (91.9% vs 63.6%), and school lag is 44 pp. larger in Group 3 than Group2 (43.2% vs 87.2%); the highest gap is in years of schooling. Both of these indicators(years of schooling and formal employment) are measured for household membersaged 18 years or older meaning that multidimensionally poor children according to aHousehold MPI are poor because of adults´ deprivations (Table A4 present thedeprivation profiles for each group).

In order to analyse the sources of non-concordance between the two measures, weestimated a bivariate probit regression. The results of this estimation corroborate whatwe found in the probit model: a correlation between the Child MPI and the HouseholdMPI (rho = 0.56). Table 6 presents the results of the marginal effects for Groups 2 and

Table 5 (continued)

Model 1 Model 2

(0.001)

Household is female 0.022***

(0.006)

Pseudo R2 0.115 0.250

Observations 22,795 22,795

Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1a omitted Child is between 0 and 4 years oldb omitted Child lives with both parentsc omitted Child is a son of the HH headd omitted Caribbean

.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00Learning environments

Access to heatlh services

Food Security

Drinking water

Sanitation

Safe construction materialsOvercrowding

Assets

Protection

Information

Maternal education

Poor Child MPI/ Non-poor Household MPI Poor Household MPI/ Non-poor Child MPI

Fig. 1 Deprivation profiles for mismatched groups using Child MPI indicators (% of children deprived)


3. On the one hand, children in Group 2 are more likely to be male, and/or living with adisability, and/or younger than 5 years old, and/or identified as the head of thehousehold, and/or living in rural areas. On the other hand, children in Group 3 aremore likely to be aged 10 to 14 years, and/or living in a household without parents, and/or living in larger households and/or in households whose head is female.

Finally, in order to identify whether or not there are intra-household inequalities, weestimated the proportion of children who are multidimensionally poor (under the ChildMPI measure) and live in a household with another child who is not poor (under theChild MPI measure). We found that 27.5% of multidimensionally poor children live ina household where another child is not classified as multidimensionally poor. In total13.8% of households in Colombia have at least one child who is poor and another childwho is non-poor according to the Child MPI. The deprivation profiles ofmultidimensionally poor children living in households where non-poor children alsolive reveal that those children are deprived in terms of access to health-care services andchild protection. In addition, we found that an individual’s characteristics also play arole. Children who were classified as multidimensionally poor and lived in a householdwhere not all children were multidimensionally poor were more likely to be male, and/or younger, and/or living with a disability, and/or pregnant and/or already had a child.As expected, multidimensionally poor children were more likely to have no relation-ship with the head of the household or to be themselves the head of the household.

In the case of individual deprivations, similar results were found. In the case of learningenvironments, boys, younger children, and children with disabilities are more likely to livein a household where another non-poor child lives. Finally, for the indicator of childprotection, male children, older children (10 to 17 years), those living with a disability, andthose who are the head of the household or are not related to the head of the household aremore likely to be multidimensionally poor, deprived in terms of this indicator and living in

.0010.0020.0030.0040.0050.0060.0070.0080.0090.00

100.00Schooling

Literacy

School Attendance

Educational lag

Child care services

Child work

Long-term Unemployment

Formal employmentHealth insurance

Access to health services

Drinking water

Sanitation

Floor materials

Wall materials

Critical overcrowding

Poor Child MPI/ Non-poor Household MPI Poor Household MPI/ Non-poor Child MPI

Fig. 2 Deprivation profiles for groups 2 and 3 using Household MPI indicators (% of children deprived)


a household where another child is non-poor. In this context, individual characteristics playa role in the number and type of children who are classified as multidimensionally poor.

Table 6 Marginal effects of bivariate probit for Groups 2 and 3

Group 2 Group 3

dy/dx dy/dx

Child is female −0.006* 0.001

(0.003) (0.003)

Child between 5 and 9 years old a −0.197*** 0.093***

(0.006) (0.004)

Child between 10 and 14 years old −0.203*** 0.114***

(0.006) (0.004)

Child between 15 and 17 years old −0.187*** 0.115***

(0.006) (0.005)

Disability 0.042*** −0.001(0.013) (0.011)

Child lives with mother b −0.014*** 0.028***

(0.004) (0.004)

Child lives with father −0.015* 0.030***

(0.009) (0.010)

Child does not live with either parent −0.047*** 0.106***

(0.005) (0.008)

Child is not a family member 0.042** −0.049***(0.017) (0.011)

Child is HH head / partner of the HH head 0.200*** −0.087***(0.043) (0.013)

Rural 0.091*** 0.002

(0.004) (0.004)

Oriental d −0.009 −0.027***(0.006) (0.006)

Central −0.003 −0.017***(0.006) (0.006)

Pacific without Valle 0.019*** 0.000

(0.005) (0.006)

Bogotá −0.041*** −0.053***(0.008) (0.009)

Antioquia 0.037*** −0.029***(0.006) (0.006)

Valle del Cauca −0.003 −0.007(0.005) (0.006)

San Andres and Providencia 0.129*** −0.084***(0.017) (0.009)

Orinoquia and Amazonia 0.000 −0.027**(0.012) (0.013)

Household size −0.008*** 0.030***


4 Discussion

The purpose of this study was to analyse whether children identified asmultidimensionally poor under a Household MPI are the same as the ones identifiedby a Child MPI, using the individual as unit of identification. To answer this question,we used Colombia as an example, given that the National Household MPI of thiscountry includes a dimension to measure specific deprivations for children and youngpeople. We computed a Child MPI using the individual as unit of identification andcompared the results between the Household MPI and the Child MPI. The results of theanalysis revealed three main findings.

First, by comparing the Household MPI with the Child MPI, we found evidence ofmisclassification. In fact, there is a percentage of children who are classified asmultidimensionally poor according to a Child MPI, but not under a Household MPI– and vice versa. Therefore, although both measures capture similar issues and shareindicators in the dimensions of water, sanitation, and housing, whether the household orthe individual was selected as the unit of identification played an important role in thenumber of children who were identified as multidimensionally poor. This has importantpolicy implications, because not all children living in multidimensionally poor house-holds were multidimensionally poor, and there were multidimensionally poor childrenliving in households that were not multidimensionally poor. This last group is usuallyignored by social policies aiming to reduce child poverty and deprivation for children.

Second, the deprivation profiles of children classified as multidimensionally poorunder the Household MPI revealed that adult deprivations were affecting children’smultidimensional levels of poverty. Thus, most children identified as poor under theHousehold MPI were children living in households deprived in terms of years ofschooling or formal employment. In this context, children’s multidimensional povertystatus was the result of their parents’ or other adults’ deprivation and was not the resultsof their own set of individual deprivations.

Third, being classified as multidimensionally poor by a household MPI does notentirely explain classification as multidimensionally poor by a Child MPI. Indeed,children identified as poor under each measure have different characteristics. On theone hand, as expected, individual characteristics tend to be more important thanhousehold characteristics when children are identified as multidimensionally poor by

Table 6 (continued)

Group 2 Group 3

(0.001) (0.001)

Household is female 0.006 0.011**

(0.004) (0.004)

Observations 22,795 22,795

Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1a omitted Child is between 0 and 4 years oldb omitted Child lives with both parentsc omitted Child is a son of the HH headd omitted Caribbean


a measure that uses the individual as unit of identification. In fact, the probability ofbeing multidimensionally poor according to a Child MPI is associated with beingyounger than 5 years, living with disability, being pregnant or being a young mother,not being consanguineously related to the head of the household, or being the head ofthe household. On the other hand, a child classified as multidimensionally poor underthe Household MPI is more likely to be older, to live in a household where parents arenot present, or to live in a female-headed household.

The results of this study revealed that using a Child MPI provides importantinformation that might be lost when using a Household MPI. The selection of the childas the unit of identification is vital to the identification of age-specific deprivations,which are not captured by a Household MPI. Even though a Household MPI includes adimension or indicator capturing child-specific deprivations, as in the case of Colom-bia, it does not guarantee that children’s needs are well represented in a nationalmeasure of multidimensional poverty.

One important finding was that under both measures different age groups havehigher risks of multidimensional poverty. Indeed, in the case of children identified asmultidimensionally poor under a Child MPI, we found that children younger than 5 arethe group with the highest risk of being multidimensionally poor, contrary to what isfound under the Household MPI, where children aged 10 to 14 tend to bemultidimensionally poor. For children classified as multidimensionally poor under aChild MPI, the learning environment was the dimension making the largest contribu-tion to the MPI and driving their levels of multidimensional poverty.

In the case of children, the analysis of intra-household distribution of opportunitiesbecomes fundamental, given that it is well known that parents do not distribute thetypes and numbers of opportunities equally between their children (Rodríguez 2016). Inthe Colombian context, it was found that 13% of the households had at least one childwho was multidimensionally poor and another non-poor child, and 27% ofmultidimensionally poor children lived in a household where at least one sibling wasnot poor. This finding reveals that the problem of unequal distribution of opportunitiesbetween children in the same household is an important phenomenon in Colombia.Also, that – despite the use of a Household MPI including a dimension for children andyouth – the fact that the possibility of intra-household inequality is ignored increasesthe vulnerability to poverty of a group of children and their invisibility in nationalpolicies. When the data were analysed in detail, it was found that multidimensionallypoor children living in a household where other children were non-poor had specificcharacteristics which made them more vulnerable to poverty: being younger than5 years, living with disabilities, not being related to the head of the household, beingthemselves heads of the household, or being pregnant or already having a child. Allthese characteristics increase the probability of child poverty, and the fact that ourfindings reveal that such children are more likely to be multidimensionally poor andalso live in a household where other children are not poor reveals their situation ofdisadvantage.

The gap between both measures, the fact that children’s characteristics are different,and the fact that the two measures are classifying different children suggest that aHousehold MPI alone is not enough to understand child poverty. It is necessary todesign and compute a child-poverty measure using the child as the unit of identifica-tion, thus enabling the study of levels of deprivation and comparative poverty between


children, and also the identification of the characteristics that increase children’svulnerability to poverty. In addition, the analysis of intra-household inequalities isextremely relevant in the study of child poverty, and only with a child-centred measureis it possible to analyse the existence of these types of inequality.

4.1 Policy Implications

The results of this paper have several policy implications. First, countries need torecognise that, despite the importance of Household MPIs, it is necessary to comple-ment their results with information provided by child-centred measures such as a ChildMPI in order to identify children who have been left behind, especially those who areliving in non-poor households but are facing high levels of individual deprivation. Inaddition, the analysis of the deprivations that children face should be disaggregated byage group; this information provides important inputs to define priorities and determinehow policies must respond to child-specific needs. Finally, it is important that policiesrecognise that it is not possible to reduce and eradicate multidimensional poverty forchildren if those facing higher levels of deprivation are not identified, if their individualcharacteristics are unknown, or if their profiles of deprivation are not defined. If povertyreduction starts with children, it is vital to clearly define which children aremultidimensionally poor and which are their most important deprivations. Furthermore,we argue that, in addition to the identification of adequate measures that enable policymakers to tailor particular policies to the specific needs of different groups of children,it may also be productive to think through the role of child participation in child-centredpoverty research in general (Ritterbusch et al. forthcoming-a).

In addition, it is important to recognise that although household characteristics playan important role in the definition of child poverty, the measurement of child povertyshould be at the individual level. In this context, indicators capturing adults’ depriva-tions can be included in a Child MPI; however, the structure of the Child MPI shouldensure a balance between individual and household indicators, allowing a more detailedanalysis of how these two types of indicator interact and contribute to the levels of childpoverty in a country. In this context, policies should recognise that child poverty can bethe result of child deprivations plus household deprivations. Programmes to reducechild poverty should therefore include actions to reduce all types of deprivation.

4.2 Limitations

Although the Child MPI designed for this study captures children’s deprivations betterthan the Household MPI, the numbers and types of dimension and indicator do notcapture all the magnitude of child poverty. Important dimensions such as nutrition,recreation, security, and economic harm were not available in the QoL survey. Inaddition, even though child poverty is recognised as an important issue and theSustainable Development Agenda 2030 has called for the analysis of the levels ofmultidimensional poverty, there is still no consensus concerning what the main dimen-sions of child poverty are (Ritterbusch et al. forthcoming-b), or if environmental orhousehold deprivations should be considered, and which is the most precise method-ological approach to analyse this important topic. Therefore, it is important to call for adata revolution on child indicators, requesting the inclusion of a larger number of


questions, especially in dimensions such as health, recreation, and economic hardship.Also, that more detailed information is included for children in all age groups.Currently most surveys capture information on health mainly in children under 5 andon education for children aged 5 to 17. However, there is not enough information tocompute indicators on health for children aged 5 to 14, or to analyse other aspectsimportant for child development.

Also, related to the quality of data used for this study, the QoL data comes fromreports provided by adults. As research has shown, reports on deprivation can deferbetween children and adults, especially in non-material deprivations (Main and Pople2011). This may lead to a significant measurement error in several dimensions wherethe adults do not have information or do not have the incentive to provide accurateinformation. This limitation, again, calls for the need for children-specific data not onlyin the type of information gathered but also that takes into account children’s ownviews and perception of their reality and needs.

Finally, this paper does not examine the overlap (or lack of overlap) between childpoverty measures using income poverty comparted to multidimensional poverty. Thiswill require not only income data at the household level, but also information about theextent to which the child has access to this income. This policy relevant comparisongoes beyond the scope of this paper, but will be important in future research.

5 Conclusions

Analysing whether household MPIs capture the total number of children living inmultidimensional poverty is important for an understanding of child poverty and for theimplementation of policies to reduce and eliminate child multidimensional poverty. Theresults of this study suggest that even Household MPIs that include a dimension tocapture child and youth deprivations do not identify all children who aremultidimensionally poor; indeed, there is a percentage of children who are living inmultidimensionally poor households who are non-poor, and most importantly there is apercentage of children who are living in non-poor households but experience signifi-cant individual deprivations and are multidimensionally poor. In this context, using achild-centred MPI allows a better analysis of individual child deprivations and com-plements the results of national Household MPIs.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps andinstitutional affiliations.

Affiliations

Mónica Pinilla-Roncancio1& Sandra García-Jaramillo2

& Ana Lorena Carrero1&

Catalina González-Uribe1& Amy Ritterbusch3

1 School of Medicine, Universidad de los Andes, Cra 7 N°, 116-05 Bogotá, Colombia

2 School of Government, Universidad de los Andes, Bogotá, Colombia

3 Department of Social Welfare, Luskin School of Public Affairs, University of California, Los Angeles,Los Angeles, CA, USA


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