Kingmaker’s The Child Report 2016 – How They Ranked

The overall State of States ranking from Kingmakers shows how each of the 36 Nigerian states, plus the Federal Capital Territory Abuja, ranked in 23 indicators across 5 categories.

In calculating the rankings, each of the seven categories were assigned equal weightings:

Child Friendly Practices: 20 percent
Child Poverty: 20 percent
Education: 20 percent
Healthcare: 20 percent
Orphans And Vulnerable Children: 20 percent

Note: Weights don’t add to 100 due to rounding.

The elements of the categories and the weightings assigned to each were:

Child Friendly Practices

Disposal of Child Stool: 20 percent
Knowledge of Contraceptives: 20 percent
Knowledge of ORS: 20 percent
Recorded Birth Weights: 20 percent
Use of Mosquito Nets: 20 percent

Child Poverty

Child Poverty: 100 percent

Education

Education Quality: 33 percent
Female Educational Attainment: 33 percent
School Attendance: 33 percent

Healthcare

Assited Delivery: 20 percent
Birth Weights: 20 percent
Child Mortality: 20 percent
Child Nutrition: 20 percent
Immunizations: 20 percent

Orphans and Vulnerable Children

Prevalance: 33 percent
Socioeconomic Status: 33 percent
Support: 33 percent

Kingmakers created an index score for each metric for each state. In each metric, the top state based on the raw data was given 1 point and the bottom state was given 37 points. States between these were indexed proportionally. For the overall rankings, Kingmakers created an average of the seven category rankings, and then ranked the outcome. We chose this method for the overall ranking so that it would not be skewed by large differences in scores at a metric level.

The Data

In order to select metrics for the project, we consulted experts in each of the categories for guidance. These are the criteria we used in choosing metrics:

Metrics that measure citizen outcomes in a state were favoured over inputs or outputs. For example, we selected the percentage of state residents who had finished primary school education or higher instead of the amount spent on education per capita or the number of students enrolled at state schools.
The set of metrics within each category should work together to provide an overview of that category.
Standardized data needed to be available across all or most states.

The data behind the rankings were sourced from

Kingmakers.com Calculations

Population

The data for the population for the states was acquired from the National Population Commission and National Bureau of Statistics. We got the total population for each of the states of the years 2006, 2008, 2010 and 2011. We then calculated the compound average growth rate (CAGR) from 2006 to 2011 and used that to extrapolate the population count for the years 2012 to 2015.

V(t₀): start value, V(t_n): finish value, t_n – t₀: number of years.

The same method was used for calculating for the values for the male and female population as well as the distribution across age groups.

There are a few limitations to using CAGR to forecast metrics and they include:

CAGR calculates the smooth average of growth over a period, it ignores volatility and implies that the growth during that time was steady. Yet, this is never actually the case. As such, we cannot take CAGR at face value.
CAGR is mainly a historic measurement and , no matter how steady the growth of a metric has been over a period of time we cannot safely assume that the growth will continue at the same rate during the following year or years, as other factors may come into play and affect that rate of growth.
Lastly, CAGR has a problem with representation. Say for instance a metric’s CAGR could be an impressive 50% over the past three years. However, due to poor or negative growth in the two years preceding those three years, the CAGR over the past five years could come down to more modest 2.73%.

However, still we adopt a culture of collecting and collating data more periodically, CAGR is one of the tools are our disposal to make calculated guesses to fill in the void for the years no data was collected.

Assisted Delivery

The data used for assisted delivery rate was got from the Demographic and Health Surveys (DHS) Program. There were surveys were carried out in the following years: 1999, 2003, 2008 and 2013.

The results from 1999 were excluded from our calculations as we considered the data was too old. The 2003 and 2008 surveys only contained results for the six geopolitical zones in which the states were located, while only the 2013 survey contained results for both the geopolitical zones and the individual states.

We calculated the CAGR from each of the geopolitical zones from 2008 to 2013 and 2003 to 2013.

Since we had only data at state level from 2013, we used the CAGR from 2008 to 2013 to retrogressively get the values for 2012, 2011 and 2010.

V₀ = V₁ x (100%-CAGR(_{2008, 2013}))

V₀: Retrogressive value to be calculated, V₁: value from the succeeding year, CAGR(_{2008, 2013}): compound annual growth between 2008 and 2013 for the geopolitical zone in which the state is located.

For example:

Abuja FCT
2013 Assisted Delivery Rate (V₁) = 84.6%
CAGR(_{2008, 2013}) for North Central Zone = 2.1%

2012 Assisted Delivery Rate (V₀) = 84.6 x (100% - 2.1%) = 82.8

To get the values for the years succeeding years from 2013 to 2015, we got the value for the by multiplying the value of the preceding year with the following formula

V₀ = V₁ + (V₁ x CAGR(_{2003, 2013}))

V₀: Progressive value to be calculated, V₁: value from the preceding year, CAGR(_{2003, 2013}): compound annual growth between 2003 and 2013 for the geopolitical zone in which the state is located.

By using the longer period of 2003 to 2013 for the CAGR, we were looking to have a much better representation of forecasting what the value might be as we do not have any data for the years succeeding 2013.

Same method used for all ther other paramters except Education Quality & Child Poverty

Education Quality

WAEC Results were used to judge the education quality of each state. For the 2015 WAEC Results, while we got the rankings for all the states, we were unable to get the pass rate for two thirds of the states. The states were Abia (1) - 63.94%, Anambra (2) - 61.18%, Edo (3) - 61.05%, Lagos (6) - 48.02%, Ekiti (11) - 41.97%, Ogun (19) - 32.91%, Kano (24) - 25.44%, Borno (25) - 24.65%, Oyo (26) - 21.03%, Niger (27) - 19.66%, Adamawa (28) - 18.08%, Osun (29) - 18.03% and Yobe (37) - 4.37%

We used linear regression equations, similar to the one below, to get an approximate value for the missing pass rates based on their ranks after listing the states according to their ranks.

y = a + bx

y: the projected pass rate for state, a: the y-intercept, b: the slope and x: the state rank.

The y-intercept and slope were recalculated using the reported pass rates before the missing information, and those just after.

Improved Drinking Water

We got the data for the access to improved drinking by households in Nigeria from the Nigerian Bureau of Statistics which had surveys from 2007 and 2011. The CAGR for each individual states was calculated and then used to forecast possible values for 2010 and 2012 – 2015. The drawbacks of using CAGR as a method as forecasting has already been discussed and its limitation in the area of proper representation was particularly pronounced as in some case, the metric measurement forecasted presented as a percentage of the household using solid fuels for cooking was more than 100 percent. These values had to be adjusted to the maximum possible value.

We used this technique for the data for access to access to sanitary facilities. Both parameters were not directly used to rank the states but weree factored in the calcultion of child porverty rates in Nigeria.

Poverty Rate

To come up with the rankings for our poverty we decided to use the multidimensional approach which takes into account several factors that constitute poor people’s experience of deprivation – such as poor health, lack of education, inadequate living standard, lack of income (as one of several factors considered), disempowerment, poor quality of work and threat from violence.

A multidimensional measure can incorporate a range of indicators to capture the complexity of poverty and better inform policies to relieve it. Different indicators can be chosen appropriate to the society and situation.

For the purpose of the 2016 rankings we used the indicators as recommended by UNICEF and the weights as stated below. The indicators were chosen to reflect lack of education, poor health and inadequate living standards which are all directly under the purview of the state governments.

Underweight Children: 16%
Primary School Enrollment:16%
Child Mortality Rate (Under 5 years): 16%
Skill Attendant at Birth (Assisted Delivery): 16%
Access to Drinking Water: 16%
Access to Basic Sanitation: 16%

The sum of the indicators gave us the proportion of the child population that were likely to be facing multidimensional poverty in each of the states.

Dr. Obi Igbokwe
Co-Founder
Datalogical