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). 4
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. 5
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 7
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
11 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
12 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 13 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 15
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
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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 Cell Piped Shoes Bed for Radio Bike Stove Car Phone Water for child child Overall 0.66 0.65 0.42 0.27 0.04 0.42 0.63 0.59
Cell Piped Shoes for Bed for Radio Bike Stove Car Residence Phone Water child 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
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Cell Piped Shoes for Bed for Radio Bike Stove Car Language Phone water child 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
Cell Piped Shoes for Bed for Radio Bike Stove Car Province Phone water child 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
Cell Piped Shoes for Bed for Radio Bike Stove Car Asset Quintile Phone water child 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
18 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 Ever Mother recalls Ever infancy experience Lost parent infancy malaria hospitalized diarrhea trauma 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
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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
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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
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Overall Summary Statistics Cronbach Mean St.dev Min Max 10 th pctle 90 th pctle Alpha
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. 22
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 Mean St.dev Min Max 10 th pctle 90 th pctle Alpha
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
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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 10 th pctle 90 th 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 24
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 Mean St.dev Min Max 10 th pctle 90 th pctle Alpha
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
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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
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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 Mean St.dev Min Max 10 th pctle 90 th pctle Alpha
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
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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 Mean St.dev Min Max 10 th pctle 90 th pctle Alpha
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
28
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. 29
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 10 th pctle 90 th 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 10 th pctle 90 th 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
31
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 Mean St.dev Min Max 10 th pctle 90 th pctle Alpha
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 10 th pctle 90 th 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
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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 Mean St.dev Min Max 10 th pctle 90 th pctle Alpha
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
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Overall Summary Statistics Cronbach's Mean St.dev Min Max 10 th pctle 90 th pctle Alpha
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
37 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
38 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.
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