The Zambian Early Childhood Development Project...little evidence is available on early childhood...

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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


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


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)



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


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


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


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



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


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


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


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


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



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


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



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


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


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


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


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


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



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


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


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


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



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


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


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


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


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


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


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


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.

30

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)



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


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


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


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

31


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



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


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


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


32

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



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


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



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

33

Total 6.512 5.368 0 20 0 14 0.842

Raw Score Z-Score


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


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


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


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


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.

34

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



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


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


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


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


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


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


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


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

36

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


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



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


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


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


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


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



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


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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