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The Zambian Early Childhood
Development Project
2010 Assessment
Final Report
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The Zambian Early Childhood Development Project
2010 Assessment Final Report
Günther Fink, Ph.D.
Harvard School of Public Health
Beatrice Matafwali, Ph.D.
University of Zambia
Corrina Moucheraud Harvard School of Public Health
Stephanie Simmons Zuilkowski
Harvard Graduate School of Education
February 2012
Acknowledgements
The authors would like to express their gratitude to Michael Banda, Jacqueline Jere Folotiya, Tamara Chansa Kabali, Kalima Kalima, Joe Kanyika, John Miller, Teza Nakazwe, Robert Serpell as well as the members of the Center on the Developing Child Global Initiative for their invaluable input and support during the various stages of this project. We are also grateful for the financial support for this projected provided by UNICEF Zambia as well as the Özyegin Family – AÇEV Global Early Childhood Research Fund through the Center on the Developing Child at Harvard University.
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1. Project Background, Goals and Objectives
While a large number of studies have investigated the impact of early childhood experiences on
children’s developmental, health and educational outcomes in developed countries, relatively
little evidence is available on early childhood development in sub-Saharan Africa. In an effort to
address this knowledge gap, the Zambian Early Childhood Development Project (ZECDP) was
launched as a collaborative effort by the Zambian Ministry of Education, the Examination
Council of Zambia, UNICEF, the University of Zambia and the Harvard University Center on
the Developing Child in late 2009. With an explicit goal of capacity-building, the project has
involved over 100 people in Zambia, including University of Zambia students and faculty,
government officials, community-based organization staff, and teachers.
The main objective and stated goal of the ZECDP is to determine the effect of early childhood
environment, health and education on children’s development before and throughout their
schooling careers. As a first step towards achieving this goal, a child development instrument
tailored towards Zambian children of pre-school age was developed from January to May 2010.
As described in further detail in this report, the Zambian Child Assessment Test (ZamCAT),
combines a set of existing as well as newly-developed child development measures in order to
provide a broad, multiple-domain based assessment of children of pre-school age in the Zambian
context. After a careful calibration of the new survey tool through two rounds of piloting, a first
cohort of 1686 children born in 2004 was assessed between July and December 2010.
In addition to presenting the main findings regarding the cohort of children assessed in the 2010
survey, this report provides a detailed description of the survey development process, with a
particular focus on the rationale for the inclusion of each section in the final survey instrument.
In order to introduce the reader to the broader context of the study, we provide some basic
background information on health and education in Zambia in Section 2 of this report. In Section
3, we describe the ZamCAT instrument, as well as its development stages. In Section 4, we
describe the rollout and sample population for the 2010 assessment. Finally, in Section 5, we
show detailed results for the 1686 children assessed in 2010. We show descriptive statistics for
all domains measured as well as results stratified by gender, residence, language group,
geographical region and wealth quintile. We conclude the report with a short summary and
discussion in Section 6.
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2. Country Background
Despite significant recent progress, the Republic of Zambia remains among the poorest countries
in the world. Zambia’s current population is estimated at 13 million people, with an average
annual per capita income of US$ 1,500 in 2008 (World Bank 2010). With an under-5 mortality
rate of 120 per 1000, and an HIV prevalence rate of over 15% among adults, life expectancy at
birth continues to be below 50 years (UNESCO 2010; World Bank 2010).
Zambia’s public education system faces a number of challenges, including funding constraints, a
multilingual student body, and isolation of rural schools. Most children do enroll in school, and
in fact gross intake rates and gross enrollment ratios for lower levels of primary school have been
above 100% in recent years.1 However, 25% of students drop out before completing seven years
of primary education, and in 2007 the GER for secondary school was only 43% (UNESCO
2010). Early childhood care and education (ECCE) remains underdeveloped, with only 17% of
new first-graders benefitting having benefitted from an ECCE experience (UNESCO 2010).
Zambian children today continue to be threatened by a high burden of ill health in general, and
infectious diseases in particular. According to the national Health Management Information
System (HMIS), malaria continues to be the most salient health issue for children under the age
of five in Zambia, with 32% of all under-5 deaths attributed to malaria in 2005 (HMIS, 2009).
Since 2005, Zambia has made significant progress with respect to child health, and in particular
with respect to malaria. Under the direction of the Ministry of Health, the National Malaria
Control Center (NMCC) has been coordinating the efforts of more than twenty-five national and
international partners (Zambia Ministry of Health 2008). Following WHO guidelines, the
National Malaria Control Programme has four main components: distribution of preventive
malaria drugs among pregnant women, indoor residual spraying of households (IRS), supply of
front-line therapy drugs to all health facilities, and distribution of insecticide treated nets (ITNs)
to households. Due to initial capacity constraints, this program was phased in over time. In 2005,
full ITN coverage was achieved in only 2 of Zambia’s 72 districts; in 2006, the goal was reached
in 12 districts; and in 2007, about two-thirds of all districts had reached target net coverage.
Similarly, IRS was initially limited to 15 districts, and gradually scaled up to a majority of urban
areas over time. While the exact magnitude of the program’s health effects cannot yet be fully
estimated, preliminary evidence from both the HMIS and two waves of the Demographic and
Health Surveys (DHS) in 2002 and 2008 suggests that improvements in child health have been
1 Gross enrollment ratios are defined as the number of individuals enrolled in a specific grade divided by the population of children who should theoretically be in that grade. Since many students enter school late, the number of children in grade 1 often exceeds the number of children of age 7 (who should be in grade 1).
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large, with full net coverage lowering child mortality by about 20%, and the likelihood of child
fever by up to 50% (Ashraf, Fink et al. 2010).
3. Development of the ZamCAT Instrument
In order to allow for a comprehensive and context-specific assessment of child development, the
first step for the larger ZECDP was to develop a tool that could i) yield internationally
comparable, multi-domain measures of child development; ii) be sensitive to local culture and
linguistic differences; and iii) be adapted to other developing countries. To achieve these
objectives, we took a broad approach to the measurement of child development. Among the
domains measured are: nonverbal cognition, receptive and expressive language, fine motor skills,
information processing, and executive function—all of which are critical for children’s success
in school. We did not attempt to create entirely new subtests for all measured domains, but rather
followed a mixed approach, using existing assessments where appropriate and developing new
ones where necessary. This mixed approach allowed the expression of local strengths while also
ensuring broad understanding of the instrument among researchers and policymakers.
History of Test Development in Zambia
Research on the assessment of cognitive development has been taking place in Zambia for over
30 years, largely by, or under the direction of, Dr. Robert Serpell through the Psychology
Department at the University of Zambia, Lusaka. An important part of Serpell’s work has
focused on measurement of cognitive skills appropriate for diverse cultural and societal contexts.
As a part of these efforts, the Panga Munthu (“make a person”) test was developed in the 1970s;
the test has since been applied in a variety of settings and has been further refined.2
Two other projects had a strong influence on the development of the ZamCAT: the Development
Assessment in Zambia (CDAZ) and the Zambian Achievement Test (ZAT). The CDAZ (Ettling,
Phiri et al. 2006) is a comprehensive study of child development for children aged 0-72 months,
commissioned by the Ministry of Education in collaboration with UNICEF. While the CDAZ
was not specifically designed to measure children of pre-school age, several items (particularly
for the measurement of fine motor skills) were directly adopted for the ZamCAT tool. The
ZATis the result of an NIH-funded joint effort by U.S.- and Zambia-based researchers to identify
children with academic difficulties in grades one to seven (Stemler, Chamvu et al. 2009). Since
2 The first version of the PMT was a modeling task scored on a 10-point scale: a crude model of a person was presented for about 30 seconds, and the child was asked to copy the model.
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the ZAT was designed for children already in school, many of its tasks were inappropriate for
our preschool target population. Nevertheless, the experiences of ZAT developers (some of
whom worked on this project) and the results of their pilot testing in Lusaka and Eastern
provinces proved useful in our development and planning stages.
Test Development Process
After an initial review of the existing literature, a technical advisory team was formed in Lusaka,
comprised of members from the University of Zambia, UNICEF Zambia, and the Examination
Council of Zambia (ECZ) as well as the Harvard Center on the Developing Child. Based on the
existing literature, seven fundamental domains of child development were identified for
measurement: fine motor skills, language (expressive and receptive), non-verbal reasoning,
information processing, executive functioning, socio-emotional development and task
orientation. After an initial review by the technical advisory team, a first instrument was
developed and pre-tested in April 2010. Upon review of the pre-testing results by the advisory
team, the survey tool was further revised and was re-tested in May 2010. Based on the results
from the second round of testing, several further adjustments were made as described in detail
for each domain below.
Fine Motor Skills
While gross motor skill development is generally completed by the age of 6, children of that age
often continue to struggle with fine motor skills, which becomes of critical importance upon
entering school. If children are not able to properly hold a pencil or chalk, they will have
difficulties learning to write. Beyond school-specific issues, fine motor challenges may also
indicate neurological problems (Fernald, Kariger et al. 2009). More generally, fine motor skills
are a means by which children learn about their environment and further develop abilities in
other domains. As Bushnell and Boudreau argue, “the emergence of particular motor abilities
may actually determine some aspects of perceptual and cognitive development, rather than the
other way around” (1993, p. 1006). As two examples the authors discuss visual depth perception
and haptic perception—the use of the hands to gain information about objects.
Since fine-motor skills had been tested as part of CDAZ, the items on the CDAZ were a natural
starting point for this section of the ZamCAT instrument. Unfortunately, the CDAZ covered
children of a wide age range, and thus provided only a few tasks suitable for children of pre-
school age. Several tasks in the CDAZ survey required pencil skills. While measuring pencil
skills may disadvantage children from poor or rural areas), they are an important indicator of
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school readiness and likely also of early schooling outcomes. As part of the ZamCAT
instrument, we thus decided to ask children to copy letters, numbers, and also a triangle on a
sheet of paper.
In addition to the pencil-based items, we also included a series of tasks more closely reflecting
activities familiar to Zambian children. These tasks included stringing beads onto a shoelace,
putting beans into a cup, unbuttoning and buttoning a shirt and playing a variation on nsolo (a
traditional game).
During pre-testing, assessors reported that the children particularly enjoyed this section of the
assessment. The pencil tasks were difficult for children, particularly those who had not
experienced any type of ECCE. The newly-developed tasks, to the contrary, were very easy for
most children. We therefore decided to convert these items to timed tasks, and set the pass time
to the mean time among pilot round respondents. This offered increased variation in scale scores
across the national sample.
Language Development
Language development and usage is one of the most important experiences of early childhood.
The acquisition of language depends on a child’s ability to express him or herself verbally, as
well as understanding others. The development of language passes through distinct stages. By
the age of six months, the child has mastered the skill of associating a parental voice with its
owner (Spelke and Owsley 1979). At ten months, the child will probably know one word; at
twelve months, about three words. At a year and a half, his or her vocabulary may be 20 words,
and by two years, it may contain as many as 250 words. By the age of three, the child begins to
talk about objects and events that are not present in the immediate context (Snow, Tabors et al.
2001). This remarkable achievement appears to require little conscious effort, and it occurs in a
wide variety of contexts (Gallaway and Richards 1994). By the age of five years, most children
have acquired a relatively sophisticated command of language. Absence of language, or
underdeveloped skills in this domain, may indicate broader cognitive problems. Cognitive
skills—the ability to conceptualize, to distinguish between objects, to categorize—are a base for
emergent language (Clark 2004). As children develop more complex language, this in turn can
influence cognitive development, giving children new labels and categories that allow for more
advanced thinking. Two critical aspects of language ability that we chose to include in our
assessment are receptive and expressive language.
Receptive language: Receptive language skills refer to an individual’s ability to understand
words. The Peabody Picture Vocabulary Test (PPVT) is a widely-used assessment of verbal
skills created to measure receptive vocabulary (Dunn and Dunn 1997). It can be used for a range
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of ages. The main idea of the task is to present the child with a series of spoken words in
increasing difficulty, and show the child four pictures, one of which is an illustration of the
spoken word. The child is then asked which of four displayed pictures best represents the
meaning of each word. A child’s score is directly determined by the number of words whose
meanings are correctly identified. The PPVT has been utilized by many researchers because it is
fast, easy to apply, and has been adapted for use in different languages. It has been used in
Canada (Sénéchal 2006), Ecuador (Paxson 2007), Kenya (Sigman, Neumann et al. 1989),
Jamaica (Walker, Chang et al. 2005), and Ethiopia, Peru, and Vietnam (Sanchez 2009). The
PPVT had previously been used in Zambia by Matafwali (2010), who found PPVT scores to be a
significant predictor of literacy outcomes (β= 0.37, p<0.01) at the end of grade two.
Given the strengths of the PPVT and its availability at the University of Zambia, we used it as a
base for our receptive language assessment. However, we faced several challenges. First, the
PPVT stimulus book contained many pictures that were inappropriate for the Zambian context--
such as ocean liners, children in Halloween costumes and chemistry sets, which were generally
not recognized by children. Second, there are seven official curriculum languages in Zambia, and
our goal was to select vocabulary words that could not only be translated into all seven languages
but would also yield similarly familiar words in each.
We began with a set of 60 PPVT pictures that had been used previously by Matafwali with first-
graders in the Lusaka area. We first excluded items that had been either very difficult for
children in her sample (fewer than 10% of children answering correctly) or very easy (90% or
more answering correctly). We also discarded items for which initial analysis indicated a
problem with the translation, for example a difference between the Nyanja used by children
during play versus the Nyanja used in the classroom and by official Ministry of Education
translators. We then reviewed the selected pictures with Dr. Serpell, who suggested some
changes from his extensive experience working with Zambian children. After a first round of
piloting, it became clear that several of the selected words were too simple for six-year-olds,
such as bottle, lock, running, umbrella, and shoes. We replaced these items with more difficult
words for the second round of piloting (bathing, empty, lightning, pair, and greeting). Item
analysis also revealed words for which there were multiple translations across dialects of a
language. For example, “fruit” was translated formally as “zipatso” for our assessment, rather
than the “town Nyanja” translation of “mafruti,” and consequently fewer than half of children
correctly matched the picture and word.
Before finalizing the instrument, we asked native speakers of each language to review the
translations, keeping in mind differences in local dialects as well as the level of language a six-
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year-old would speak and understand. We made adjustments accordingly, dropping five poorly-
performing words such as uniform (the pictures were not the type of uniforms Zambian children
were familiar with) and serving (which proved difficult to translate), and replacing them with
words that were both challenging for children and more amenable to translation, including
injection, cultivating, and root. The words were sequenced from easiest to hardest according to
the results from the pilot data.
It is important to stress here that the original PPVT instrument was heavily adapted for the
ZamCAT instrument. The PPVT has a list of age-normed and difficulty-ordered vocabulary
words that correspond to sets of four pictures in the stimulus book. However, as described above,
given the context-inappropriateness of some pictures and words, we were unable to use all items
as suggested. To develop new items, we selected pages where all four picture tiles were
appropriate, then chose a word represented by one of those tiles. While these adaptations were
clearly necessary in order to obtain culturally-appropriate pictures of equal difficulty, the
adaptations mean that the PPVT scores of children assessed with the ZamCAT tool cannot be
directly compared to scores based on the original PPVT module.
Expressive language: Expressive language refers to an individual’s ability to produce words and
express his or her thoughts. To measure expressive language we used a task previously piloted
by Matafwali (2010) which asked children to respond to two questions:
1) Can you tell me about something exciting that happened to you?
2) Can you tell me about the people you live with at home?
Assessors rated children’s responses on a zero to five scale, with a child scoring zero being
completely non-responsive and a child scoring five giving a full, multiple-sentence answer using
correct grammar. These questions were added during our second round of pre-testing. We found
that the task performed well overall in all languages; variations in mean scores by tester
highlighted the importance of extensive training with assessors and clear communication of
scoring rules.
Nonverbal Reasoning
Nonverbal cognitive skills are a pillar of early childhood development assessments. While
language deficits may impede children from showing their full potential on assessments that
require them to speak, read, or process language, nonverbal assessments are often designed to
measure intelligence or potential rather than achievement.
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As a first measure of nonverbal cognitive skills, we decided to include the Pattern Reasoning
subscale of the Kaufman Assessment Battery for Children (K-ABC). The K-ABC had been used
recently in Kenya (Holding, Taylor et al. 2004) and in Uganda (Bangirana, John et al. 2009), as
well as in Zambia (Matafwali 2010). In our first pre-pilot round, however, the results for the sub-
scale were disappointing: Lusaka-area children performed poorly, with zero as the most common
sum score for the first five items. One of the main concerns raised by the advisory board was that
the paper-based pattern tasks were not suitable for Zambian children, who are not frequently
exposed to things drawn on paper.
To address this issue, we developed an object-based version of the reasoning test, which we
called the Tactile Pattern Reasoning (TPR) scale. Conceptually, the TPR items follow the same
logic as the Kaufman items, but the patterns are displayed through objects rather than printed on
paper. For example, the first K-ABC item shows a row of five green circles, and asks children to
choose (from a set of four options) which object would complete the sequence (the correct
answer being another green circle). We adapted this to a tactile task by using five beads of the
same color on a paper grid to create the pattern, and offering a bead of the same color, a bead of
a different color, a stone, and a bean as possible choices. The second through fifth Kaufman
items are all ABABAB patterns, and we mirrored those patterns using the items above. The
results from the pilot looked promising: the modal sum score for the first five Kaufman items
was again zero, while the modal sum score for the first five Tactile Pattern Reasoning items was
five. These children were therefore adept at seeing patterns presented in three-dimensional
format, but struggled to see the same patterns in a two-dimensional format.
Given these findings, we decided to expand the TPR scale to ten tasks for the final study
instrument. We added three items (TP6, TP7, TP8) using additional common items, including
wooden blocks and bottle caps. Items on the Kaufman increase in difficulty and complexity; in
an attempt to mirror this, we used two items (TP9, TP10) where the corresponding two-
dimensional designs in the K-ABC sequence were painted onto cardboard squares.
In addition to these two reasoning tasks, we also decided to implement the NEPSY Block Test,
an established measure of nonverbal reasoning (Korkman, Kirk et al. 1998). The NEPSY Block
Test measures children's ability to capture, analyze and replicate abstract forms. Children are
given a set of blocks and were asked to assemble them in reproduction of a pictured design.
Since children have to simultaneously process a two-dimensional stimulus picture and recreate a
three-dimensional representation of the drawing using blocks, this task can be viewed as hybrid
between the two-dimensional Kaufman task and the three-dimensional Tactile Pattern Reasoning
task.
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Information Processing
Information processing is the means by which children take in new information, integrate it with
their existing knowledge, and report it back to others when prompted. In order to learn, children
must absorb knowledge from stimuli (such as a book, a teacher, an object) and rapidly retrieve
previously-learned knowledge.
While the general lack of literacy in the study population precluded reading-based tasks, we
included a Rapid Automatized Naming (RAN) task (Denckla and Rudel 1976) as an indicator
information processing skills. The RAN task asks children to look at a series of stimuli that may
include pictures, colors, letters, or numbers, and to name them as quickly as possible. A strong
body of literature, primarily from developed countries, has found that children’s scores on RAN
tasks are linked to reading achievement both at the time of the test and in the future (Ackerman
and Dykman 1993; Bowers 1995; Manis, Seidenberg et al. 1999; Kirby, Parrila et al. 2003;
Cardoso-Martins and Pennington 2004; Schatschneider, Fletcher et al. 2004; Katzir, Kim et al.
2006). Associations with performance outcomes have been found even after controlling for
socioeconomic status (Swanson, Trainin et al. 2003), IQ (Badian 1993; Hulslander, Talcott et al.
2004), and phonological awareness (Bowers 1995; Manis, Doi et al. 2000; Kirby, Parrila et al.
2003). The RAN task had also been used previously in Zambia (Matafwali 2010). Based on the
recommendations of the authors of the latter study, only the pictures subtest of RAN was
selected for the final questionnaire. The items shown on the stimulus sheet are: chair, tree,
bicycle, duck, scissors. The tasks generally went well during piloting, so no major adjustments
were made.
Executive Functioning
Executive functioning has received increased attention in the education, psychology and
economics literature in recent years, as basic executive functioning processes appear to be robust
predictors of later-life schooling and more general wellbeing outcomes. Technically, “executive
function processes include impulse control, ability to initiate action, ability to sustain attention,
and persistence” (Fernald, Kariger et al. 2009, p. 17). Children’s performance of tasks requiring
these abilities improves with age, as the frontal lobe of the brain develops; this area of the brain
is not fully developed until adolescence (Anderson 1998).
The first domain of executive functioning we decided to measure with the ZamCAT tool is
attention. Children’s ability to focus and sustain their attention is critical to their ability to learn
in a variety of contexts. Duncan and colleagues (2007) found in a meta-analysis of six large data
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sets in the U.S. and U.K. that attention at ages 5-6 was associated with achievement outcomes in
primary school. For children who go directly from the home environment to primary school, it is
a difficult transition to sit and listen to a teacher for many hours each day. Children with
attention deficits may develop disruptive behaviors in the classroom that will impact their and
their classmates’ ability to learn.
While a plethora of attention tests has been used around the world, many are unsuitable for use
with preschool children in developing countries. Some utilize equipment like computers or tape
recorders, while others require counting skills. We opted to use a Pencil Tapping Test recently
developed for first-graders in Kenya (Brooker, Okello et al. 2010). The Pencil Tapping Test is a
simple and child-friendly assessment that takes the form of a game played between the child and
the assessor. The assessor explains the “rules” of the game (i.e., when the child has to tap), and
the child must remember and apply the rules as instructed. The task therefore assesses attention
and memory. The test is made more difficult by also giving the child another small task to divide
his or her attention. During our first pre-pilot round, it became clear that assessors were not
implementing the test correctly because the rules were unnecessarily complicated. So we
simplified the instructions, shifted task scoring to the data analysis phase, and spent more time
on this task during subsequent trainings (no changes were made to the task itself). These steps
led to an improvement of the distribution of scores during the subsequent pre-pilot round.
The second key area of executive functioning assessed in the final survey tool is delayed
gratification. Children who are about to enter school need to be able to control impulses—they
must pay attention in class, do their homework, and avoid disruptive behavior. Delayed
gratification has been linked to current and future socio-emotional and cognitive development
(Mischel, Shoda et al. 1989; Rodriguez, Mischel et al. 1989; Shoda, Mischel et al. 1990). The
ability to defer what one wants in favor of achieving a greater long-term goal has been shown to
be related to positive life outcomes. Researchers frequently use either candy or a wrapped gift in
experiments measuring children’s ability to delay gratification (Evans and English 2002; Li-
Grining 2007).
We chose to use candy as it seemed more practical and more culturally suitable. For the
ZamCAT delayed gratification task, the assessor offers the child a piece of candy and promises
that, if the child waits to eat it until the assessor finishes speaking with the parent (typically 20-
30 minutes), then the child will get a second candy. The children are told that they can eat the
candy right away, but if they decide to do so, they will not get a second piece of candy. Even
though a few urban parents in the first pre-pilot round refused to allow the assessors to give their
children anything to eat, most parents allowed their children to accept the candy; and there were
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no reported problems with parental refusal during the second pre-pilot round. There were some
practical difficulties, however. Tested children sometimes lost their candy to older siblings; in
other cases assessors were pressured into giving candy to all children in the family, which may
have changed subjects’ valuation of the item. More generally, some children appeared to be
reluctant to accept candy from strangers, so differential responses for rural children (particularly
those living in remote villages) were anticipated for this task.
Socio-emotional Development
The early years of life constitute a period of rapid growth but also of great emotional and socio-
emotional vulnerability. Studies have found that negative early childhood experiences can impair
a child’s mental health as well as affect their cognitive, behavioral and social-emotional
development (Cooper, Masi et al. 2009), and children's emotional and social skills appear to be
strongly linked to their early academic standing (Wentzel and Asher 1995). Children who have
difficulty following directions, getting along with others, or controlling negative emotions of
anger and distress do not perform as well in school (Arnold, Ortiz et al. 1999; McClelland,
Morrison et al. 2000). For many children, academic achievement in the first few years of
schooling appears to be built on a firm foundation of emotional and social skills (Ladd,
Kochenderfer et al. 1997).
Thus, children who are emotionally well-adjusted have a significantly greater chance of adapting
to school and of performing well, while children who experience serious emotional difficulties
face grave risks of early school problems. In this respect, social-emotional health may be viewed
as a young child’s growing ability to form close relationships with other people, especially
parents and other familiar caregivers, or as an early measure of “social skills”. A child’s socio-
emotional development affects their ability to interact with others, to trust others to offer
protection, to seek and respond to attention from others, and to make and keep friends.
Children’s socio-emotional skills also include expressing feelings verbally and self-soothing
when upset.
Several instruments have been used previously in the Zambian context: the ESMI checklist, the
Vineland Adaptive Behavior Scales, and the Child Behavior Check List (CBCL). Given the
relatively short time assessors spend with children during the assessment, we designed the socio-
emotional scale to be parent-reported rather than observed by the assessor. The first pre-pilot
results indicated that parents became bored and distracted after a few questions, and therefore
gave repetitive answers. In order to address these concerns, we made three changes for the final
questionnaire. First, we shortened the list of response options to “never -sometimes - usually-
always.” Second, we added in three sub-questions to ask the parent for examples of how the
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child did (or did not) exhibit the relevant behavior. Last, we reduced the number of items to 20
from the 26 originally tested.
Task Orientation
During the hour-plus spent with each child, assessors developed perceptions of the child’s
behavior and ability to pay attention, focus on the given tasks rather than on environmental
disturbances, and follow instructions. The task orientation questionnaire is designed to measure
executive function, compliance and attention as rated by the child evaluator.
The scale has been shown to be predictive of both cognitive and socio-emotional outcomes as
well as executive function measures, and has recently been validated in the US (Smith-Donald,
Raver et al. 2007). The scale performed well throughout the early pilot phases, with Cronbach’s
alphas consistently above 0.85.
4. Study Population and Sample Characteristics
The sampling of the 2010 survey closely followed the two-stage cluster sampling procedure used
for the 2006 Zambia Malaria Indicator Survey (MIS). The MIS randomly selected 120 census
enumeration areas (EAs) from all EAs listed in the 2000 national census, with an explicit
oversampling of urban areas as well as areas targeted by the early stages of the malaria program
(NMCC 2007). For the MIS, all households in the selected EAs were listed, and approximately
every tenth household was randomly selected for the MIS survey. For the purpose of the child
assessment, we followed a similar process.
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Figure 1: ZamCAT 2010 Survey Sample Clusters
In order to guarantee translational accuracy, we restricted the project to the six Zambian
provinces where Nyanja, Bemba, Lozi and Tonga are the dominant local languages (Copperbelt,
Eastern, Luapula, Lusaka, Southern and Western), which results in the spatial distribution
depicted in Figure 1.3 For each cluster, assessors used detailed census maps (provided by the
Zambian Central Statistical Office) to visit households and list all children born in 2004. If the
total number of eligible children was less than or equal to 25, all children were assessed; if more
than 25 children lived in that cluster, a randomization process selected the 25 children for
assessment.
3 Although all 81 EAs originally surveyed by MIS in these six provinces were selected for the ZamCAT survey, fieldwork was completed in only 75 clusters due to logistical challenges and linguistic barriers
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Table 1 shows the sample distribution by residence and province. 50.7% of clusters (37) were
classified as urban, reflecting the intentional oversampling from the original MIS sample. Almost
half of the total sample lived in Lusaka and Copperbelt provinces, while 10-15% of children
were sampled from each of the other four regions.
Table 1: Sample Allocation by residence and province
Clusters Females Males
N % N % N % Total
All 73 100.0% 845 100.0% 841 100.0% 1,686
Residence
Rural 36 49.3% 431 51.0% 423 50.3% 854
Urban 37 50.7% 410 48.5% 422 50.2% 832
Province
Copperbelt 19 26.0% 211 25.0% 222 26.4% 433
Eastern 9 12.3% 104 12.3% 104 12.4% 208
Luapula 9 12.3% 117 13.8% 108 12.8% 225
Lusaka 17 23.3% 186 22.0% 187 22.2% 373
Southern 11 15.1% 139 16.4% 114 13.6% 253
Western 8 11.0% 88 10.4% 106 12.6% 194
Household Composition and Asset Holdings
The average household size in our sample was 5.4. Half of all household members were
children—reflecting national fertility rates that continue to exceed 6 children per woman (DHS
2007). Households are smallest in the Copperbelt, Lusaka and Western regions, and largest in
Eastern region--which, together with Luapula, represents the poorest area in our sample.
Table 2: Household composition by residence, language and province
Children Adults Seniors All ages
Overall 2.858 2.566 0.051 5.433
Residence Children Adults Seniors All ages
Rural 2.993 2.550 0.042 5.550
Urban 2.720 2.583 0.060 5.313
17
Language Children Adults Seniors All ages
Nyanja 2.895 2.546 0.048 5.480
Bemba 2.946 2.570 0.060 5.498
Tonga 2.908 2.665 0.054 5.586
Lozi 2.421 2.525 0.027 4.951
Other 2.286 2.238 0.000 4.524
Province Children Adults Seniors All ages
Copperbelt 2.838 2.570 0.083 5.441
Eastern 3.529 2.668 0.024 6.207
Luapula 3.080 2.569 0.022 5.551
Lusaka 2.584 2.461 0.059 5.088
Southern 2.980 2.700 0.036 5.672
Western 2.294 2.474 0.046 4.799
Asset Quintile Children Adults Seniors All ages
Poorest quintile 2.959 2.500 0.052 5.471
Second quintile 2.871 2.447 0.040 5.330
Third quintile 3.146 2.661 0.050 5.798
Fourth quintile 2.837 2.589 0.080 5.456
Richest quintile 2.483 2.646 0.033 5.126
Regional differences in average wealth are documented in Table 3, which shows average asset
holdings in the households hosting interviewed children. On average, nearly two-thirds of
households own a radio and a cell phone, 42% own a bike, and 27% own a stove. Many sampled
households have access to private sanitation, and access to piped water is high in urban areas.
Table 3: Average asset holdings by residence, language, province and asset quintile
Radio
Cell
Phone Bike Stove Car
Piped
Water
Shoes
for child
Bed for
child
Overall 0.66 0.65 0.42 0.27 0.04 0.42 0.63 0.59
Residence Radio
Cell Phone
Bike Stove Car Piped Water
Shoes for child
Bed for child
Rural 0.67 0.49 0.58 0.10 0.02 0.12 0.52 0.56
Urban 0.65 0.81 0.25 0.43 0.05 0.74 0.74 0.62
18
Language Radio
Cell Phone
Bike Stove Car Piped water
Shoes for child
Bed for child
Nyanja 0.62 0.66 0.34 0.28 0.03 0.55 0.77 0.74
Bemba 0.67 0.67 0.44 0.30 0.04 0.43 0.56 0.47
Tonga 0.68 0.61 0.53 0.12 0.02 0.26 0.55 0.56
Lozi 0.73 0.57 0.42 0.31 0.04 0.18 0.55 0.63
Province Radio
Cell Phone
Bike Stove Car Piped water
Shoes for child
Bed for child
Copperbelt 0.71 0.79 0.34 0.44 0.06 0.62 0.64 0.50
Eastern 0.64 0.37 0.65 0.01 0.02 0.24 0.61 0.78
Luapula 0.60 0.47 0.63 0.04 0.01 0.07 0.42 0.36
Lusaka 0.60 0.81 0.16 0.42 0.04 0.71 0.86 0.73
Southern 0.67 0.58 0.51 0.17 0.02 0.28 0.48 0.63
Western 0.72 0.59 0.46 0.23 0.06 0.23 0.61 0.52
Asset Quintile Radio
Cell Phone
Bike Stove Car Piped water
Shoes for child
Bed for child
Poorest quintile 0.37 0.20 0.45 0.00 0.00 0.08 0.11 0.17
Second quintile 0.58 0.53 0.50 0.00 0.00 0.29 0.62 0.51
Third quintile 0.67 0.80 0.46 0.02 0.00 0.52 0.80 0.71
Fourth quintile 0.82 0.86 0.33 0.52 0.03 0.56 0.78 0.72
Richest quintile 0.87 0.89 0.34 0.84 0.17 0.71 0.85 0.87
There are clear regional differences in assets, particularly for households in Lusaka and
Copperbelt provinces compared to others. On average, 80% of households in Copperbelt and
Lusaka own a cell phone and over 40% have a stove—fractions nearly twice as large as those in
other provinces. Overall, Lusaka households appear best-off with respect to assets, while
children in Luapula, Southern and Eastern Provinces are worst off.
Early Childhood Health
As part of the interview conducted at the child’s home, parents or caregivers were asked an
extensive sequence of questions regarding the mother’s health and health care during pregnancy,
and about the child’s health during the first few years of life. Table 4 shows selected variables
from this part of the questionnaire, and highlights the high burden of morbidity faced by children
in this sample. On average, 76% of children are reported to have suffered from malaria during
the first year of life, and 73% of children to have experienced diarrhea. 26% of respondents
recalled that their child had been hospitalized since birth, and 13% of respondents indicate that
19
the child had experienced at least one traumatic event (most typically the loss of a parent or
family member).
On average, the reported burden of disease appears highest in Eastern province and in Luapula,
where over 85% of respondents recall an episode of malaria during the first year of life, and 31%
and 41% of children respectively were hospitalized since birth. Given the differences with
respect to overall living conditions documented in Table 3, the observed disparities in early
childhood experiences are not surprising. Regional differentials reverse for the fraction of
children having lost a parent, which is highest in the Copperbelt (15%) and Lusaka (13%)
respectively; this may be driven by the generalized HIV epidemic that has taken the greatest toll
in urban areas, with prevalence rates over 20% in these two regions versus the 10-15% range in
the rest of the country (Macro International 2007).
Table 4: Early childhood health and adversity (% of children)
Mother recalls
infancy malaria
Mother recalls
infancy
diarrhea
Ever
hospitalized
Ever
experience
trauma
Lost parent
Overall 77.1 74.8 25.8 12.2 11.6
Residence Rural 82.7 72.2 26.2 10.5 8.6
Urban 71.5 77.6 25.5 13.9 14.7
Language Nyanja 82.8 64.5 21.0 12.0 9.8
Bemba 76.1 83.0 32.3 15.0 14.5
Tonga 68.9 71.8 23.0 4.1 8.0
Lozi 73.0 72.1 20.9 12.1 10.7
Other 88.9 87.5 19.0 14.3 14.3
Province Copperbelt 70.3 87.0 26.1 13.1 16.1
Eastern 92.0 54.1 27.9 15.9 2.9
Luapula 85.5 82.4 44.8 19.4 12.7
Lusaka 77.1 68.2 16.6 9.3 12.8
Southern 79.9 81.9 25.3 3.0 7.6
20
Western 64.5 64.4 19.9 14.8 12.7
Asset Quintile Poorest quintile 79.1 76.3 30.5 11.6 10.8
Second quintile 77.5 69.0 27.1 13.1 10.5
Third quintile 81.8 73.5 26.5 14.1 9.4
Fourth quintile 77.0 79.0 22.0 10.8 9.2
Richest quintile 70.2 76.8 23.0 11.3 18.4
Early Childhood Education
One of the key policy questions surrounding early childhood education is the role of pre-schools,
and the degree to which different kinds of early childhood programs can increase child
development. As Table 4 shows, 63% of urban and 78% of rural children had never attended an
early childhood program at the time of the assessment. Early childhood attendance is by far
highest in more urban areas (Lusaka and the Copperbelt), where more than 40% of children have
attended an early childhood programs; the same is true for fewer than 20% in Eastern, Western
and Luapula provinces. Early childhood education also displays a rather strong association with
household wealth: while less than 20% of children living in households from the poorest two
wealth quintiles have attended an early childhood program, the same is true for more than 50%
of children from the wealthiest quintile.
Table 5: Attendance of Early Childhood Programs (%)
Age first attended early childhood program
<=2 3 4 5 or 6 Don't know Never
Females 1.9 3.5 4.6 14.6 5.4 70.1
Males 1.9 4.5 5.6 13.0 4.9 70.1
Total 1.8 4.4 5.0 13.5 5.2 70.0
Residence
Rural 1.2 1.8 1.9 14.5 3.6 77.0
Urban 2.5 7.1 8.3 12.5 6.9 62.7
Language
Nyanja 3.2 4.3 5.5 14.4 6.0 66.7
Bemba 1.6 6.5 6.8 14.1 5.2 65.8
21
Tonga 0.4 1.3 1.7 12.6 2.9 81.2
Lozi 0.5 1.1 1.6 11.5 5.5 79.8
Province
Copperbelt 2.3 9.5 8.3 17.6 6.5 55.9
Eastern 0.0 1.0 1.0 6.7 5.3 86.1
Luapula 0.0 0.4 4.0 8.4 4.4 82.7
Lusaka 5.1 6.4 8.3 17.4 5.9 56.8
Southern 0.4 1.6 1.6 16.6 2.8 77.1
Western 0.5 1.0 1.5 6.2 5.2 85.6
Asset Quintile
Poorest quintile 0.3 0.3 2.0 14.0 3.2 80.2
Second quintile 0.0 0.6 2.0 8.0 1.1 88.3
Third quintile 1.2 2.2 3.7 14.6 5.3 73.0
Fourth quintile 1.2 6.2 5.9 19.8 6.2 60.7
Richest quintile 6.6 12.9 11.7 11.4 10.5 46.8
5. Child Development Results
Fine Motor Skills
Given the 10 items tested in the fine motor skills section, raw scores ranged from 0 to 10. The
mean score was 6.5, with a standard deviation of 2.7 points. Despite the relatively diverse set of
items used in this section, a Cronbach’s alpha of 0.789 suggests a rather high rate of internal
consistency for scale overall. In order to allow an easier comparison across the various scales,
raw scores were normalized into z-scores.
As Table 6 shows, only minor gender differences in scores were observed. Slight differences
were observed across residential areas, with rural children scoring on average 0.15 standard
deviations lower than urban children. The mean scores also differed slightly across language
groups, with Lozi-speaking children on average performing best, and Tonga-speaking children
on average performing worst on this task. Overall, household wealth appears to be the most
robust predictor of children’s performance on this task, with children from the wealthiest quintile
on average scoring more than half a standard deviation higher than children from the poorest
quintile.
Table 6: Fine Motor Skills
22
Overall Summary Statistics Cronbach
Alpha Mean St.dev Min Max 10th
pctle 90th
pctle
Females 6.464 2.701 0 10 3 10 0.791
Males 6.477 2.679 0 10 3 10 0.784
Total 6.483 2.694 0 10 3 10 0.789
Raw Score Z-Score
Residence Males Females All Males Females All N
Rural 6.136 6.059 6.085 -0.138 -0.167 -0.157 854
Urban 6.841 6.867 6.892 0.124 0.134 0.143 832
Raw Score Z-Score
Language Males Females All Males Females All N
Nyanja 6.336 6.278 6.307 -0.064 -0.086 -0.075 564
Bemba 6.492 6.581 6.579 -0.006 0.027 0.026 679
Tonga 6.018 6.462 6.205 -0.182 -0.017 -0.113 239
Lozi 7.411 6.678 7.038 0.336 0.063 0.197 183
Raw Score Z-Score
Province Males Females All Males Females All N
Copperbelt 6.689 7.151 6.965 0.067 0.240 0.170 433
Eastern 5.020 5.755 5.389 -0.554 -0.281 -0.417 208
Luapula 6.194 5.573 5.871 -0.117 -0.348 -0.237 225
Lusaka 6.973 6.516 6.745 0.173 0.003 0.088 373
Southern 5.963 6.626 6.292 -0.203 0.044 -0.080 253
Western 7.446 6.524 7.036 0.349 0.006 0.197 194
Raw Score Z-Score
Asset Quintile Males Females All Males Females All N
Poorest quintile 5.71508 5.21154 5.47965 -0.295 -0.483 -0.383 344
Second quintile 5.70186 6.12222 5.91404 -0.300 -0.144 -0.221 349
Third quintile 6.41096 6.58824 6.48447 -0.036 0.030 -0.009 322
Fourth quintile 7.06369 7.1506 7.09763 0.207 0.239 0.220 338
Richest quintile 7.55422 7.27152 7.49249 0.390 0.284 0.367 333
Receptive Language
Thirty items from the Peabody Picture Vocabulary Test-R were used for this scale. A Cronbach’s
alpha statistic of 0.83 suggests a high degree of internal consistency within this scale.
Performance was strong across groups, with an overall mean of 21 items correct. No large gaps
were observed between males and females, or between rural and urban children.
23
Larger gaps, approaching one standard deviation, were observed across provinces and asset
quintiles, with the top-performing group on average outperforming the lowest group by about
one standard deviation. Some systematic variation was also detected with respect to language,
with Lozi speakers on average scoring highest and Tonga-speaking children on average
achieving the lowest scores.
Table 7: Peabody Picture Vocabulary Test (PPVT)
Overall Summary Statistics Cronbach
Alpha Mean St.dev Min Max 10th
pctle 90th
pctle
Females 21.502 5.163 0 30 15 29 0.814
Males 21.229 5.566 0 30 14 28 0.838
Total 21.415 5.343 0 30 14 28 0.826
Raw Score Z-Score
Residence Males Females All Males Females All N
Rural 20.876 21.763 21.362 -0.105 0.063 -0.013 854
Urban 21.606 21.242 21.470 0.033 -0.036 0.007 832
Raw Score Z-Score
Language Males Females All Males Females All N
Nyanja 21.000 20.989 21.027 -0.081 -0.083 -0.076 564
Bemba 21.594 21.778 21.713 0.031 0.065 0.053 679
Tonga 19.358 20.949 20.268 -0.390 -0.091 -0.219 239
Lozi 22.800 22.851 22.962 0.258 0.267 0.288 183
Raw Score Z-Score
Province Males Females All Males Females All N
Copperbelt 21.214 21.580 21.450 -0.041 0.028 0.004 433
Eastern 18.960 19.990 19.558 -0.465 -0.271 -0.353 208
Luapula 21.889 21.675 21.778 0.086 0.046 0.065 225
Lusaka 22.330 21.848 22.097 0.169 0.078 0.125 373
Southern 18.741 20.321 19.731 -0.507 -0.209 -0.320 253
Western 23.465 23.988 23.794 0.383 0.481 0.445 194
Raw Score Z-Score
Asset Quintile Males Females All Males Females All N
Poorest quintile 19.592 20.301 20.015 -0.346 -0.213 -0.267 344
Second quintile 19.851 21.044 20.467 -0.297 -0.073 -0.181 349
24
Third quintile 21.062 20.776 20.929 -0.070 -0.123 -0.095 322
Fourth quintile 22.898 23.325 23.133 0.276 0.357 0.320 338
Richest quintile 22.898 22.099 22.583 0.276 0.126 0.217 333
Expressive Language
As described in Section 3 of this report, assessors scored children on expressive language based
on their overall perception of children’s answers to test questions. Assessor-assigned scores on
this task ranged from 0, indicating non-response, to 5, indicating a complete, grammatically-
correct, multi-sentence answer to the prompt. In order to make sure children were as comfortable
as possible (and would not refuse to answer due to shyness), the assessors, who were largely
local schoolteachers, were told to carefully encourage the child for this task. Table 8 summarizes
the main results for this task. Similar to the receptive language scores, only very small
differences were found between males and females, as well as between rural and urban children.
We observed larger differences across language groups and provinces. Quite remarkably, the
general language patterns appear reversed here. While Lozi-speaking children performed on
average best in the receptive language task and Tonga children performed on average worst, the
opposite was true for the expressive language section, with Tonga children performing best, and
Lozi children performing worst. While some of this may be explained by relative differences in
the receptive language tasks as well as potentially different scoring standards by assessors,, it
appears likely that some variation is also generated by differences in cultural norms with respect
to children’s communication.
Table 8: Expressive Language Scores
Overall Summary Statistics
Mean St.dev Min Max 10th
pctle 90th
pctle
Females 2.917 1.514 0 5 1 5
Males 2.878 1.452 0 5 1 5
Total 2.908 1.477 0 5 1 5
Raw Score Z-Score
Residence Males Females All Males Females All N
Rural 2.780 2.767 2.785 -0.085 -0.094 -0.081 854
Urban 2.986 3.060 3.033 0.056 0.107 0.088 832
Raw Score Z-Score
Language Males Females All Males Females All N
25
Nyanja 2.788 2.887 2.848 -0.079 -0.012 -0.039 564
Bemba 3.120 2.971 3.051 0.147 0.046 0.101 679
Tonga 3.159 3.318 3.223 0.174 0.283 0.218 239
Lozi 1.795 2.167 2.000 -0.758 -0.504 -0.618 183
Raw Score Z-Score
Province Males Females All Males Females All N
Copperbelt 3.089 3.149 3.124 0.126 0.167 0.151 433
Eastern 2.719 3.062 2.909 -0.127 0.108 0.003 208
Luapula 3.082 2.446 2.759 0.121 -0.313 -0.099 225
Lusaka 2.904 2.894 2.900 0.000 -0.007 -0.003 373
Southern 3.365 3.336 3.333 0.315 0.295 0.293 253
Western 1.744 2.044 1.933 -0.792 -0.587 -0.663 194
Raw Score Z-Score
Asset Quintile Males Females All Males Females All N
Poorest quintile 2.704 2.328 2.555 -0.137 -0.393 -0.239 344
Second quintile 2.745 2.901 2.814 -0.109 -0.002 -0.061 349
Third quintile 2.986 2.861 2.917 0.056 -0.030 0.009 322
Fourth quintile 2.880 3.250 3.082 -0.016 0.236 0.122 338
Richest quintile 3.106 3.199 3.173 0.138 0.201 0.184 333
Nonverbal Reasoning: Kaufman Pattern Reasoning
Even though the Kaufman Pattern Reasoning task displayed very high internal consistency
(Cronbach’s alpha 0.89), on average children performed poorly on this task. Thirty-four percent
of children got either zero or only one answer right, and only 16% of children scored more than 5
out of 18 possible points. As Table 9 shows, similar to most other tasks, only minor gender
differentials emerged. More surprising was the rural versus urban comparison, which indicates
that rural children on average scored about 0.28 standard deviations higher than urban children
This pattern appears consistent with the findings on wealth, where children from the poorest
quintiles perform nearly as well as children from the top two wealth quintiles.
Table 9: Kaufman Pattern Reasoning
Overall Summary Statistics Cronbach
Alpha Mean St.dev Min Max 10th
pctle 90th
pctle
Females 3.219 3.342 0 18 1 7 0.876
Males 3.576 3.800 0 18 1 9 0.899
Total 3.380 3.552 0 18 1 8 0.887
26
Raw Score Z-Score
Residence Males Females All Males Females All N
Rural 4.055 3.759 3.871 0.210 0.126 0.158 854
Urban 3.064 2.683 2.876 -0.072 -0.181 -0.126 832
Raw Score Z-Score
Language Males Females All Males Females All N
Nyanja 3.776 3.238 3.482 0.131 -0.023 0.047 564
Bemba 3.390 3.368 3.368 0.021 0.014 0.014 679
Tonga 2.789 2.752 2.778 -0.151 -0.161 -0.154 239
Lozi 4.600 3.379 3.945 0.366 0.018 0.179 183
Raw Score Z-Score
Province Males Females All Males Females All N
Copperbelt 3.549 3.493 3.497 0.066 0.050 0.051 433
Eastern 3.337 3.343 3.298 0.005 0.007 -0.006 208
Luapula 3.269 3.128 3.196 -0.014 -0.054 -0.035 225
Lusaka 3.951 3.190 3.568 0.181 -0.036 0.072 373
Southern 2.519 2.573 2.557 -0.228 -0.213 -0.217 253
Western 4.644 3.595 4.134 0.378 0.079 0.233 194
Raw Score Z-Score
Asset Quintile Males Females All Males Females All N
Poorest quintile 3.682 3.288 3.483 0.104 -0.008 0.047 344
Second quintile 2.925 2.739 2.822 -0.112 -0.165 -0.141 349
Third quintile 2.740 3.100 2.935 -0.165 -0.062 -0.109 322
Fourth quintile 3.834 3.506 3.624 0.147 0.054 0.087 338
Richest quintile 4.584 3.536 4.042 0.361 0.062 0.207 333
27
Nonverbal Reasoning: Tactile Pattern Reasoning
As discussed in Section 3, the weak performance of children in the Kaufman Pattern Reasoning
task during the piloting stage of the project led to the development of a new three-dimensional
Tactile Pattern Reasoning task.
As Table 10 shows, children generally did better on this task, with the average child completing
close to 50% of items in this section. The overall distribution of scores on the new assessment
was approximately normal; the correlation between children’s total scores on the Kaufman
Pattern Reasoning task and the Tactile Pattern Reasoning task was 0.43.
Relative to the Kaufman Pattern Reasoning task, the Tactile Pattern Reasoning task scored
slightly lower with respect to internal consistency (Cronbach’s alpha 0.75), which appears to be
mostly driven by the last two items showing highly mixed results. Similar to the Kaufman
Pattern Reasoning task, only very small differences were found with respect to gender, while
rural children on average outperformed urban children in this task.
The overall patterns look fairly similar across both tasks, with children from the top wealth
quintile performing best, and substantial variations across regions. Quite interestingly, children
from the Western region performed best on both of these nonverbal reasoning tasks. While
children from Southern Province did worst in the Kaufman Pattern Reasoning task, children from
Eastern Province had on average the lowest scores in the Tactile Pattern Reasoning task.
Table 10: Tactile Pattern Reasoning
Overall Summary Statistics Cronbach
Alpha Mean St.dev Min Max 10th
pctle 90th
pctle
Females 4.396 2.501 0 10 1 8 0.737
Males 4.566 2.579 0 10 1 8 0.755
Total 4.477 2.538 0 10 1 8 0.746
Raw Score Z-Score
Residence Males Females All Males Females All N
Rural 4.730 4.463 4.567 0.115 0.009 0.050 854
Urban 4.391 4.329 4.386 -0.019 -0.044 -0.022 832
Raw Score Z-Score
28
Language Males Females All Males Females All N
Nyanja 4.134 3.960 4.051 -0.122 -0.190 -0.154 564
Bemba 4.498 4.452 4.476 0.023 0.005 0.014 679
Tonga 4.633 4.915 4.732 0.077 0.188 0.116 239
Lozi 5.789 4.793 5.322 0.536 0.140 0.350 183
Raw Score Z-Score
Province Males Females All Males Females All N
Copperbelt 4.782 4.776 4.764 0.136 0.133 0.129 433
Eastern 3.851 3.578 3.697 -0.234 -0.342 -0.295 208
Luapula 4.139 4.060 4.098 -0.120 -0.151 -0.136 225
Lusaka 4.254 4.071 4.190 -0.074 -0.147 -0.099 373
Southern 4.500 4.656 4.538 0.024 0.086 0.039 253
Western 5.941 5.238 5.588 0.596 0.317 0.456 194
Raw Score Z-Score
Asset Quintile Males Females All Males Females All N
Poorest quintile 4.352 4.256 4.288 -0.035 -0.073 -0.060 344
Second quintile 4.025 3.961 4.000 -0.165 -0.190 -0.175 349
Third quintile 4.178 4.100 4.112 -0.104 -0.135 -0.130 322
Fourth quintile 4.745 4.542 4.630 0.121 0.041 0.076 338
Richest quintile 5.494 5.232 5.372 0.418 0.314 0.370 333
Nonverbal Reasoning: NEPSY
The third measure of nonverbal reasoning included in the main survey was the NEPSY block
test. After substantial difficulties in the two pilot rounds, a set of easier questions was included,
which led to a slight increase in the average scores.
Table 11: NEPSY Block Test
Overall Summary Statistics Cronbach’s
Alpha Mean St.dev Min Max 10th
pctle 90th
pctle
Females 3.328 2.370 0 11 0 7 0.799
Males 3.509 2.442 0 11 0 7 0.799
Total 3.419 2.393 0 11 0 7 0.797
Raw Score Z-Score
Residence Males Females All Males Females All N
Rural 3.292 3.180 3.217 -0.043 -0.090 -0.075 854
Urban 3.742 3.475 3.626 0.147 0.034 0.099 832
29
Raw Score Z-Score
Language Males Females All Males Females All N
Nyanja 3.419 3.202 3.293 0.011 -0.081 -0.043 564
Bemba 4.034 3.671 3.887 0.271 0.117 0.209 679
Tonga 2.486 3.017 2.753 -0.384 -0.159 -0.271 239
Lozi 3.100 2.816 2.913 -0.124 -0.244 -0.203 183
Raw Score Z-Score
Province Males Females All Males Females All N
Copperbelt 4.150 3.971 4.109 0.320 0.244 0.302 433
Eastern 3.030 2.853 2.942 -0.154 -0.228 -0.191 208
Luapula 4.065 3.419 3.729 0.284 0.011 0.142 225
Lusaka 3.508 3.277 3.373 0.049 -0.049 -0.009 373
Southern 2.667 3.198 2.937 -0.307 -0.082 -0.193 253
Western 2.990 2.524 2.747 -0.170 -0.368 -0.273 194
Raw Score Z-Score
Asset Quintile Males Females All Males Females All N
Poorest quintile 3.425 3.051 3.244 0.013 -0.145 -0.063 344
Second quintile 3.248 3.300 3.246 -0.061 -0.039 -0.062 349
Third quintile 3.295 2.971 3.109 -0.042 -0.179 -0.120 322
Fourth quintile 3.694 3.687 3.666 0.127 0.124 0.115 338
Richest quintile 3.867 3.656 3.829 0.201 0.111 0.184 333
The maximum possible score in this task was 11; with an average score of 3.4, only 6% of
children got more than 70% of answers correct. As Table 11 shows, again little difference
emerges between males and females. However, unlike the two previous tasks, no rural advantage
was seen in the NEPSY section. On average, Bemba-speaking children performed best on this
task, while children from Southern province performed most poorly, which is consistent with the
Kaufman Pattern Reasoning results. Relative to the two previous tasks, the wealth gradient
observed for NEPSY appears slightly more pronounced; on average however, the differences do
appear rather small.
Information Processing: RAN
As described in Section 3, the Rapid Automatized Naming task asked children to provide the
name of a sequence of objects as fast as possible. In total, children were given 480 seconds (6
minutes) for the task, with each utilized second (as well as each skipped or misidentified symbol)
lowering the score by one point. The highest overall score was 445; the best-performing child
completed the task in 35 seconds without any errors.
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As Table 12 shows, urban children, and in particular urban females, performed better on this task
than male children. Average scores were fairly similar across all provinces except Western,
where children appear to have scored substantially lower; this is also apparent in the substantially
lower scores for the Lozi group. Compared with other tasks, the most striking difference is the
inverse wealth gradient for this section, with children from the lowest two wealth quintiles doing
better than the rest.
Table 12: Rapid Automatized Naming (RAN)
Overall Summary Statistics
Mean St.dev Min Max 10th
pctle 90th
pctle
Females 351.2 66.2 0 445.0 279.0 413.0
Males 348.0 65.3 0 443.0 281.5 412.0
Total 350.2 65.6 0 445.0 280.0 414.0
Raw Score Z-Score
Residence Males Females All Males Females All N
Rural 346.5 343.3 344.8 -0.084 -0.134 -0.111 854
Urban 349.5 358.8 355.6 -0.037 0.107 0.057 832
Raw Score Z-Score
Language Males Females All Males Females All N
Nyanja 351.9 352.6 352.0 -0.001 0.010 0.002 564
Bemba 355.1 357.2 357.6 0.049 0.082 0.089 679
Tonga 348.9 355.4 352.1 -0.046 0.054 0.003 239
Lozi 307.4 318.1 313.4 -0.689 -0.524 -0.596 183
Raw Score Z-Score
Province Males Females All Males Females All N
Copperbelt 348.4 360.0 356.6 -0.055 0.126 0.073 433
Eastern 356.4 349.9 352.1 0.070 -0.030 0.003 208
Luapula 367.2 353.8 360.2 0.237 0.030 0.129 225
Lusaka 350.4 353.8 352.3 -0.024 0.030 0.006 373
Southern 364.3 365.8 364.4 0.192 0.216 0.193 253
Western 292.8 297.7 297.1 -0.917 -0.840 -0.850 194
Raw Score Z-Score
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Asset Quintile Males Females All Males Females All N
Poorest quintile 355.3 351.8 353.5 0.052 -0.001 0.024 344
Second quintile 350.0 344.1 345.9 -0.029 -0.121 -0.093 349
Third quintile 343.9 345.1 344.9 -0.124 -0.105 -0.109 322
Fourth quintile 343.1 357.4 351.7 -0.137 0.085 -0.004 338
Richest quintile 346.7 358.7 355.0 -0.081 0.106 0.048 333
Letter Naming
In order to assess children’s preparedness for early literacy, we asked them to name letters shown
in random order on a piece of paper. Children were given two minutes for this task, and correctly
named on average 3 letters. As a standard deviation of 5.2 suggests (Table 13), a large degree of
variation was observed with respect to children’s ability to actively name letters. While 44% of
children were not able to name any letter, 10% of children could name 10 or more letters, and
5% of children could name 20 letters or more.
Table 13: Early Literacy - Letter Naming
Overall Summary Statistics
Mean St.dev Min Max 10th
pctle 90th
pctle
Females 3.270 5.087 0 24 0 10
Males 3.431 5.451 0 24 0 12
Total 3.323 5.213 0 24 0 11
Raw Score Z-Score
Residence Males Females All Males Females All N
Rural 3.049 3.056 3.037 -0.062 -0.061 -0.065 854
Urban 3.830 3.477 3.608 0.087 0.019 0.045 832
Raw Score Z-Score
Language Males Females All Males Females All N
Nyanja 3.838 3.245 3.515 0.088 -0.025 0.027 564
Bemba 3.684 3.360 3.493 0.059 -0.003 0.022 679
Tonga 1.778 2.675 2.255 -0.305 -0.134 -0.214 239
Lozi 2.920 3.488 3.184 -0.087 0.022 -0.036 183
Raw Score Z-Score
Province Males Females All Males Females All N
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Copperbelt 4.206 3.784 3.930 0.159 0.078 0.106 433
Eastern 1.674 1.589 1.655 -0.325 -0.341 -0.329 208
Luapula 2.869 2.517 2.686 -0.097 -0.164 -0.132 225
Lusaka 5.065 4.308 4.655 0.323 0.178 0.245 373
Southern 1.514 2.386 2.008 -0.356 -0.189 -0.261 253
Western 3.143 4.060 3.516 -0.044 0.131 0.027 194
Raw Score Z-Score
Asset Quintile Males Females All Males Females All N
Poorest quintile 2.605 2.298 2.484 -0.147 -0.206 -0.170 344
Second quintile 2.553 2.034 2.278 -0.157 -0.256 -0.210 349
Third quintile 1.958 2.208 2.107 -0.271 -0.223 -0.242 322
Fourth quintile 3.148 4.221 3.556 -0.043 0.162 0.035 338
Richest quintile 6.765 5.872 6.230 0.648 0.477 0.546 333
On average, male children performed slightly better on this task, with pronounced variations
across residential groups as well as provinces. Children from the top wealth quintile and children
living in Lusaka or the Copperbelt did best on average on this task, while children from Southern
and Eastern provinces got the lowest average scores.
Executive Functioning: Pencil Tapping Test
As described in Section 3, the objective of the Pencil Tapping Test is to measure children’s
ability to sustain focused attention. Overall, the items on the scale appear well-connected, as
suggested by a Cronbach’s alpha of 0.84 for the 20-item scale (Table 14). The distribution of the
scores was slightly skewed toward zero, with 22% of children getting a score of zero and only
10% of children with a score of 15 or higher.
The average score on this task was 6.5, with virtually no gender differences seen. Overall,
children from Luapula as well as children from the poorest wealth quintile performed best, which
is very different from the patterns across most other scales; this suggests the test may measure
behavioral aspects not directly linked to other cognitive tasks.
Table 14: Pencil Tapping Test
Overall Summary Statistics Cronbach
Alpha Mean St.dev Min Max 10th
pctle 90th
pctle
Females 6.428 5.376 0 20 0 14 0.856
Males 6.483 5.335 0 20 0 14 0.836
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Total 6.512 5.368 0 20 0 14 0.842
Raw Score Z-Score
Residence Males Females All Males Females All N
Rural 6.923 6.885 6.935 0.093 0.086 0.096 854
Urban 6.015 5.976 6.079 -0.075 -0.083 -0.064 832
Raw Score Z-Score
Language Males Females All Males Females All N
Nyanja 6.033 5.888 5.972 -0.072 -0.099 -0.084 564
Bemba 6.984 6.997 7.049 0.105 0.107 0.117 679
Tonga 6.352 5.923 6.269 -0.013 -0.093 -0.028 239
Lozi 6.111 6.558 6.341 -0.058 0.026 -0.015 183
Raw Score Z-Score
Province Males Females All Males Females All N
Copperbelt 6.073 6.512 6.419 -0.065 0.017 0.000 433
Eastern 5.840 5.922 5.865 -0.108 -0.093 -0.103 208
Luapula 8.565 7.650 8.089 0.399 0.229 0.310 225
Lusaka 6.217 5.951 6.105 -0.038 -0.087 -0.059 373
Southern 7.458 6.954 7.262 0.193 0.099 0.156 253
Western 5.178 5.349 5.383 -0.231 -0.199 -0.193 194
Raw Score Z-Score
Asset Quintile Males Females All Males Females All N
Poorest quintile 7.034 6.750 6.930 0.114 0.061 0.095 344
Second quintile 6.346 5.694 6.043 -0.014 -0.135 -0.070 349
Third quintile 6.151 6.418 6.304 -0.050 -0.001 -0.022 322
Fourth quintile 5.809 6.061 5.908 -0.114 -0.067 -0.095 338
Richest quintile 6.958 7.384 7.384 0.100 0.179 0.179 333
Executive Functioning: Delayed Gratification
As discussed in Section 3, the child assessment concluded with a delayed gratification task that
rewarded children if they postponed eating a piece of candy. Approximately 70% of children
waited to eat their candy and received the reward of a second piece of candy.
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As Table 15 shows, female and rural children did slightly better on this task on average. Similar
to the Pencil Tapping Test, the wealth gradient observed here was negative, with children from
the poorest households displaying on average the most “patient” behavior.
The high rates of successful task completion in rural areas of Eastern and Southern provinces
suggest that performance on this task may partially reflect the degree to which children are at
ease with strangers.
Table 15: Delayed Gratification
Overall Summary Statistics
Mean St.dev Min Max 10th
pctle 90th
pctle
Females 0.702 0.458 0 1 0 1
Males 0.688 0.464 0 1 0 1
Total 0.695 0.461 0 1 0 1
Raw Score Z-Score
Residence Males Females All Males Females All N
Rural 0.732 0.714 0.725 0.081 0.041 0.066 854
Urban 0.641 0.689 0.663 -0.118 -0.012 -0.069 832
Raw Score Z-Score
Language Males Females All Males Females All N
Nyanja 0.653 0.721 0.687 -0.090 0.057 -0.017 564
Bemba 0.699 0.689 0.690 0.010 -0.013 -0.010 679
Tonga 0.931 0.788 0.859 0.513 0.201 0.356 239
Lozi 0.459 0.542 0.497 -0.512 -0.332 -0.429 183
Raw Score Z-Score
Province Males Females All Males Females All N
Copperbelt 0.677 0.675 0.672 -0.039 -0.043 -0.051 433
Eastern 0.782 0.816 0.794 0.190 0.264 0.215 208
Luapula 0.728 0.679 0.703 0.072 -0.035 0.017 225
Lusaka 0.590 0.694 0.643 -0.227 -0.001 -0.113 373
Southern 0.911 0.797 0.851 0.469 0.221 0.339 253
Western 0.521 0.519 0.525 -0.378 -0.382 -0.370 194
Raw Score Z-Score
Asset Quintile Males Females All Males Females All N
35
Poorest quintile 0.765 0.738 0.747 0.152 0.094 0.113 344
Second quintile 0.732 0.673 0.707 0.082 -0.049 0.027 349
Third quintile 0.690 0.750 0.724 -0.010 0.120 0.064 322
Fourth quintile 0.601 0.650 0.625 -0.203 -0.097 -0.151 338
Richest quintile 0.643 0.701 0.671 -0.113 0.014 -0.052 333
Socio-emotional Development
In order to also capture parents’ overall perceptions of development, they were asked 20
questions describing the overall behavior of their children. For each question, the parent or
caretaker was asked to indicate whether the child displayed the behavior “never”, “sometimes”,
“usually” or “always”. In order to generate a score, we applied a linear scale, assigning 0-3
points depending on the parental answer category.
As Table 16 shows, the mean score across the 20 items was 1.6, with marginally higher scores
for female children. On average, only very small differences were found across regions and
across wealth quintiles. This suggests that parents’ perceptions of appropriate socio-emotional
behavior for 6-year-olds and may differ more by geographic area and ethnicity than by
socioeconomic group.
Table 16: Socio-emotional Development
Overall Summary Statistics Cronbach’s
Alpha Mean St.dev Min Max 10th
pctle 90th
pctle
Females 1.692 0.450 0.5 3 1.111 2.300 0.856
Males 1.609 0.450 0.44 3 1.050 2.200 0.861
Total 1.645 0.449 0.45 3 1.063 2.250 0.859
Raw Score Z-Score
Residence Males Females All Males Females All N
Rural 1.574 1.675 1.619 -0.174 0.051 -0.073 854
Urban 1.646 1.709 1.671 -0.013 0.128 0.043 832
Raw Score Z-Score
Language Males Females All Males Females All N
Nyanja 1.686 1.774 1.726 0.076 0.272 0.166 564
Bemba 1.620 1.630 1.617 -0.071 -0.050 -0.078 679
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Tonga 1.667 1.813 1.727 0.033 0.359 0.168 239
Lozi 1.256 1.502 1.389 -0.884 -0.334 -0.588 183
Raw Score Z-Score
Province Males Females All Males Females All N
Copperbelt 1.615 1.634 1.612 -0.082 -0.041 -0.090 433
Eastern 1.627 1.708 1.666 -0.056 0.124 0.030 208
Luapula 1.626 1.648 1.638 -0.057 -0.008 -0.032 225
Lusaka 1.719 1.795 1.755 0.149 0.320 0.229 373
Southern 1.718 1.855 1.773 0.147 0.452 0.270 253
Western 1.228 1.390 1.318 -0.946 -0.584 -0.745 194
Raw Score Z-Score
Asset Quintile Males Females All Males Females All N
Poorest quintile 1.541 1.519 1.527 -0.249 -0.296 -0.280 344
Second quintile 1.648 1.710 1.678 -0.009 0.130 0.059 349
Third quintile 1.614 1.810 1.720 -0.084 0.352 0.151 322
Fourth quintile 1.632 1.717 1.666 -0.044 0.145 0.031 338
Richest quintile 1.618 1.688 1.638 -0.076 0.081 -0.030 333
Task Orientation
After survey completion, assessors rated children on their attitude and performance during the
child assessment tasks. The scores below represent the mean score on each item; items were
scored on a scale from 1 to 4, with 4 indicating a better performance on the question of interest.
Overall, responses to the Task Orientation questions were highly consistent, as reflected in a high
Cronbach’s alpha of 0.91 across the 13 items included in the survey.
While there was little difference between males and females, there was a gap of more than a third
of a standard deviation between rural and urban children. Tonga speakers scored, on average,
more than half of a standard deviation below Bemba speakers; similarly, children in Lusaka and
the Copperbelt scored close to half a standard deviation below children in Southern province.
Rather pronounced differences are also apparent with respect to household assets, with children
from the top wealth quintile scoring on average more than half a standard deviation higher than
children from the poorest wealth quintile.
Table 15: Task Orientation
37
Overall Summary Statistics Cronbach's
Alpha Mean St.dev Min Max 10th
pctle 90th
pctle
Females 3.225 0.590 1.308 4 2.385 3.846 0.900
Males 3.194 0.649 1.23077 4 2.231 3.923 0.920
Total 3.213 0.619 1.23077 4 2.308 3.923 0.911
Raw Score Z-Score
Residence Males Females All Males Females All N
Rural 3.050 3.154 3.105 -0.251 -0.084 -0.164 854
Urban 3.345 3.295 3.324 0.220 0.141 0.187 832
Raw Score Z-Score
Language Males Females All Males Females All N
Nyanja 3.163 3.275 3.222 -0.070 0.108 0.024 564
Bemba 3.296 3.263 3.281 0.142 0.090 0.118 679
Tonga 2.915 3.050 2.997 -0.467 -0.252 -0.337 239
Lozi 3.235 3.132 3.190 0.045 -0.120 -0.027 183
Raw Score Z-Score
Province Males Females All Males Females All N
Copperbelt 3.269 3.244 3.261 0.099 0.059 0.085 433
Eastern 3.064 3.305 3.185 -0.229 0.156 -0.036 208
Luapula 3.316 3.243 3.278 0.173 0.056 0.113 225
Lusaka 3.258 3.308 3.287 0.081 0.161 0.128 373
Southern 2.939 3.006 2.988 -0.429 -0.321 -0.350 253
Western 3.191 3.219 3.212 -0.025 0.018 0.008 194
Raw Score Z-Score
Asset Quintile Males Females All Males Females All N
Poorest quintile 2.967 3.004 2.990 -0.384 -0.324 -0.347 344
Second quintile 3.026 3.183 3.106 -0.290 -0.038 -0.162 349
Third quintile 3.298 3.252 3.280 0.146 0.071 0.116 322
Fourth quintile 3.392 3.322 3.343 0.296 0.183 0.218 338
Richest quintile 3.322 3.368 3.361 0.184 0.257 0.246 333
Summary: Child Development Assessment
These results highlight rather pronounced differences in child assessment outcomes, depending
not only on the exact domain analyzed, but also on the specific items used. In order to provide an
38
overview the main patterns emerging from the assessment, we show the correlation of the scores
obtained in each section in Table 16 below.
Table 16: Correlation of Child Assessment Tasks
PPV EL TP KP NP FM LN RN SE TO AT DG
Receptive language (PPV) 1.00
Expressive language 0.18 1.00
Tactile patterns 0.25 0.18 1.00
Kaufman 0.24 0.08 0.43 1.00
NEPSY block test 0.14 0.17 0.32 0.31 1.00
Fine motor skills 0.36 0.26 0.34 0.21 0.27 1.00
Letter naming 0.29 0.17 0.37 0.36 0.24 0.29 1.00
Rapid naming -0.07 0.17 0.06 0.01 0.16 0.11 0.09 1.00
Socio-emotional -0.04 0.19 -0.06 -0.06 0.06 -0.01 0.06 0.05 1.00
Task orientation 0.26 0.30 0.23 0.17 0.21 0.33 0.20 0.14 0.11 1.00
Attention 0.13 0.26 0.23 0.18 0.25 0.26 0.21 0.20 0.08 0.20 1.00
Delayed gratification -0.12 0.11 0.02 -0.02 0.01 -0.03 -0.02 0.16 0.17 0.12 0.12 1.00
As Table 16 shows, the highest correlation between any two scores is observed for the Tactile
Pattern Reasoning task and the Kaufman Pattern Reasoning (0.43). Given that these tests are
designed to measure the same domain of child development, this is not surprising. More
interesting is the correlation of scores obtained from these two tasks with other sections.
Conceptually, the most similar construct to the first two tasks is the NEPSY block test, which
shows correlations of 0.21 and 0.32 with the two pattern tests, respectively. A slightly higher
correlation was found for early literacy (0.36 and 0.37), which may be interpreted as evidence of
early exposure to learning materials positively affecting both domains of child development. The
fine motor skills scale also appears to be relatively highly correlated with this group of measures,
even though the strongest individual correlation for fine motor skills (0.36) was found with the
receptive language (PPV). We also found the task orientation as well as the attention scores to
show consistent positive correlations with all measures of nonverbal reasoning described above.
Three items were not correlated as strongly with other aspects of the assessment as we had
anticipated. The Rapid Automatized Naming score shows statistically meaningful associations
only with the expressive language score, which suggests that children’s reluctance to speak in the
presence of strangers may have hindered performance on both tasks. A similar argument can
likely be made for the delayed gratification task, which could have reflected discipline in the
household or fear of strangers rather than a child’s executive functioning. The last item that
showed only minor correlations with other scales was the socio-emotional scale. Given that
39
parental assessments are by definition subjective, this weaker correlation with other scores may
not be all that surprising. This should not be taken as evidence for parental assessments being
irrelevant–even if child characteristics identified by parents are not generally correlated with
objective measures of specific skills, they may well be important predictors of subsequent child
outcomes.
6. Summary and Conclusion
The Zambian Early Childhood Development Project was launched in 2009 with the objective to
not only identify the key determinants for child development in the medium- to long-run in a
sub-Saharan African context, but also to generate the first comprehensive assessment of child
development in Zambia today.
As has been pointed out by many researchers in this field, measuring child development is a
complicated task: child development is a multi-faceted construct, and the multitude of
developmental domains explored in the literature makes it difficult to identify the right tool for
child assessment. The task is challenging in even the most tightly-controlled and technologically
advanced testing environments in developed countries; it is even more complicated for field
researchers within the developing world, where societal norms differ substantially across regions
and change rapidly over time. In this project, we have made a major effort to combine as many
measures and aspects of child development as possible in a single survey tool, while ensuring
that the tool is appropriate for, and respectful of, local culture. The results of the first such
comprehensive assessment are presented in this report.
While many technical issues remain to be resolved at this stage, the promising news from this
project is that comprehensive child assessments are clearly feasible within standard population-
based household surveys. Despite the increased interest in child development at the international
level, representative data on child development is still remarkably scarce; given this, we hope
that the results of this study will both encourage and facilitate future child development
assessment efforts.
From a policy and child developmental perspective, the results of this study highlight the large
differences among Zambian pre-school children both within and across regions. We hope that the
data collected as part of this project as well as future work in this area will not only improve our
understanding of child development in this context, but also help identify key interventions
towards improved outcomes in a rapidly changing developing world.
40
41
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