If you are a committed, creative professional and are passionate about making a lasting difference for children, the world's leading children's rights organization would like to hear from you.
For 70 years, UNICEF has been working on the ground in 190 countries and territories to promote children's survival, protection and development. The world's largest provider of vaccines for developing countries, UNICEF supports child health and nutrition, good water and sanitation, quality basic education for all boys and girls, and the protection of children from violence, exploitation, and AIDS. UNICEF is funded entirely by the voluntary contributions of individuals, businesses, foundations and governments.
Purpose of Assignment:
The consultancy implies working with and presenting anthropometric development outcomes – a methodologically well-established area of early childhood analytics centred on nutrition – it involves looking at and presenting these data in ways that are atypical in nutrition research and resemble more the way human performance is analyzed in labour and human resource statistics. The reason for this is an intention to use the results of this investigation for creating analytical links to poverty reduction, inclusive economic growth as well as for creating new ways of looking at and measuring "cognitive capital".
"Cognitive capital" was understood as "the complete set of intellectual as well as non-cognitive skills, primarily nurtured prenatally and in early childhood, that determine human capabilities" at a High Level Asia-Pacific South-South Meeting in Kuala Lumpur in November 2016. It reflects the brain's evolving structure and its core functioning habits i.e. the base upon which human capabilities, assets (intellect, interpersonal skills etc.) are built further when children enter the school system and function in their broader communities. To a large extent it is cognitive capital that determines human wellbeing and success in life – e.g. educational performance, income earning capacity, health status and longevity. To ensure that cognitive capital is developed optimally in the child population it should be assessed as early as possible so that interventions (maternal health, better care and nutrition practices, early childhood education, social assistance and protection) can be mobilized for and directed towards children and their caregivers.
Research has, indeed, shown that suboptimal physical, cognitive and socio-emotional development outcomes during early childhood have lifelong negative implications for individuals' intellectual performance, educational achievement, employability, labour productivity, and income earning capacity as well as health status and propensity to engage in crime. Suboptimal child outcomes are thus associated with an increased risk of being both monetarily and multi-dimensionally poor later in life. Adults living in poverty, in turn, are on average less capable of providing their children with the inputs they need for optimal development. Eliminating gaps in early childhood development outcomes is likely to level the playing field and remove some of the most powerful and unjustifiable causes of poverty and inequity within societies. Early childhood specialists, however, agree that at the moment "we do too much too late" ("Child Development Core Story", Harvard Center of the Developing Child, 2017,). Instead of investing in children during their most important period of development, public policies direct the bulk of our money towards curing the symptoms of these missed investments in adulthood.
At the country level, the following six-step approach could potentially contribute to eliminating gaps in early childhood development outcomes by systematically building evidence on gaps in child development outcomes:
- Understand what data, measures and indicators are available to assess whether children's physical, cognitive and socio-emotional development is on track in the reference period.
- Produce evidence on the continuum of early child physical, cognitive and socio-emotional development results across the child population. Evidence should be presented in a way that allows for comparison of child outcome distributions – similar to the way income distributions are presented.
- Identify optimal development outcomes that can be achieved by children if required inputs are provided timely, i.e. universally valid child outcome "goalposts" – similar to the WHO growth standards established for 0-4 year old children by the Multicentre Growth Reference Study (MGRS).
- Establish ways to measure the performance gap, i.e. how far children's actual performance is removed from these goalposts – similar to how Foster–Greer–Thorbecke indices measure poverty headcount, poverty gap or the squared poverty gap.
- Compare how young children from different socioeconomic status groups and/or countries perform; monitor these results and gaps.
- Identify the main explanatory (causal) factors for differences in performance or changes in the trend.
The approach builds on the assumption that if information to be collected through the above six steps is available, decision-makers and the broader public are better informed on socio-economic status-related factors correlated with gaps in optimal child development. This could help them when mobilizing policy actions, programmes and interventions to close these gaps, in much the same way poverty analytics inform poverty eradication efforts.
The empirical investigation and multi-country analyses foreseen in this consultancy are meant to check the validity and robustness of the above-described approach through initial data analysis related to the above six steps.
The analysis will make use of anthropometric data to identify socioeconomic status-related gaps in child development outcomes (found e.g. from MICS, DHS and National Nutrition/Health surveys). The reason for using anthropometric data is that these are more widely available as opposed to for instance data on cognitive development.
Individual child development results at a specific age expressed in natural units - such as height in centimeters, or arm or head circumference in millimeters - reflect both (a) genetic variance, which is expected to follow a normal distribution, and (b) variance caused by differences in conditions for child development (e.g. insufficient food intake, lack of hygiene or care attention or other socioeconomic status-related variables) which could skew the distribution.
Anthropometric data are commonly used by nutrition analysts to establish "malnutrition" using pre-defined cut-off points, "norms" constructed by WHO, which are based on detailed, age-specific analysis of the standard biological variance in anthropometric development. Using these cut-off points, nutrition analysts tend to ignore "raw" data measured in natural units in the surveys and use instead standardized results. In the case of height, for instance, the indicators nutrition analysts tend to focus on – for instance stunting rates, i.e. the percentage of children whose height for age is below 2 standard deviations from the WHO norm – used to trigger alert for need for intervention. However, these data points tell us little about the mean and the shape of the distribution/variance around the mean. Stunting rates thus resemble poverty headcounts, and like simple poverty headcounts they reveal nothing about the average depth of deprivation (the poverty gap or deprivation intensity when borrowing terms from poverty statistics), variance in this gap (i.e. whether there are some children who are outstandingly short for their age) or how skewed is the distribution. Looking at anthropometric distributions in natural units, on the other hand, can be compared to looking at an income distribution, which retains information about the mean and the shape of the distribution/variance around the mean.
For the current analysis, child anthropometric outcomes - such as height, weight and/or brain circumference - should be presented in a continuum (i.e. in natural units), leaving pre-established cut-off points to establish malnutrition aside so that the shape of the distribution/variance around the mean remains visible and analyzable. This analysis thus deconstructs standard anthropometric investigation methods and reconstructs them in a way that lends itself to a more generally applicable and user-friendly way of looking at children's developmental performance along a continuum and gaps in outcomes.
The analysis should focus on answering the following descriptive and analytical research questions:
- What distribution curves do anthropometric child outcomes show in natural units (e.g. height or brain circumference in cm/mm) across the total (relevant age) child population?
- Can the 2011 WHO child anthropometric goalposts, i.e. the international reference population results, be found in the total population under investigation?
- What is the distance (gap in natural units) between these reconstructed "WHO reference/goalpost population" median outcome and that of the rest of the population in the country/province (survey) in natural unit and %?
- How does the distribution change by considering socioeconomic status (SES) gradients of advantage/disadvantage? Do SES group specific distribution curves follow a normal distribution or a skewed distribution?
- Which socioeconomic status indicators are the most powerful determining (causal) variables of early childhood development outcomes?
Datasets that could potentially be used include
- Cambodia – 2014 National Micronutrient Survey
- Mongolia – 2016 MICS
- Pakistan – 2014 MICS
- Thailand – 2016 MICS (to be released)
- Timor Leste – 2013 Food and Nutrition Survey or 2012 DHS
The final decision of which country/dataset should be selected for the investigation will be made in consultation with UNICEF's advisors. The methods explored here should be developed in a way that can be employed in the future for further empirical investigations on how far children's performance falls from what could otherwise be expected/be the norm and what the reasons for this could be.
Work Assignment, Work Schedule and Expected Deliverables:
Under the supervision of Regional Adviser - Social Policy and Economic Analysis, the consultant will perform the following tasks:
Deliverables and Timeline:
Phase I – Pilot on dataset 1
I.I Inception report and basic descriptive tabulations 1 15 working days
I.II Analytical report 1 10 working days
Phase II – Similar analysis (taking into account lessons learned from pilot analysis) on dataset 2, 3, 4
II.I Basic descriptive tabulations and analytical report 2 15 working days
II.II Basic descriptive tabulations and analytical report 3 15 working days
II.III Basic descriptive tabulations and analytical report 4 15 working days
Estimated Duration of Contract: 70 working days over the period of February – July 2017
Official Travel: Home-based. Travel may be required as deemed necessary.
Qualifications or Specialized Knowledge/Experience Required:
- Advanced university degree in Economics or related area.
- 3 to 5 years of relevant experience in data analysis with Stata or equivalent advanced package.
- Previous experience in preparation of reports and concept papers.
- Excellent interpersonal and collaboration skills.
- Excellent spoken and written English.
Interested candidates are requested to submit CV or P-11, full contact information of minimum 2 references, availability, detailed work plan, and proposed daily professional fee in USD by 21 February 2017.