Where numbers matter: development and the data gap

World Leaders gathered in New York in late September 2015 to ratify the new set of development goals to replace the Millennium Development Goals (MDGs). The Sustainable Development Goals (SDGs) are a wider set of 17 goals more than the 8 goals that the MDGs had. Atop the list remains the eradication of poverty, which based on the World Banks 2011 estimates, continues to ravage almost 1 billion people. Another important goal is the improvement of access to better health facilities by the world’s most vulnerable, especially women and children through policies to reduce maternal and infant mortality rates. These issues are evident in the developing countries particularly those in subSaharan Africa where in 2011, an estimated 451 million people still live on less that $1.25 a day and maternal and infant mortality rates are still at unacceptable levels.

Two reports, one by the Data Revolution Group (DRG) and the second by the Overseas Development Institute (ODI), highlight one of the major problems that affected the inability of many countries to meet the MDGs and proffer solutions to address this issue. This issue relates to the lack of credible data that can help improve the success of targeted interventions. The DRG’s report ‘A World that Counts’ highlighted two areas where this continues to be a problem. Firstly, the report notes that there remains a lack of high quality data on one hand and secondly that data that are not used are not usable. The report notes that most data misses out on large sections of society, namely indigenous people that are not reached during data surveys. These groups remain invisible and it makes it difficult if not impossible for any targeted interventions to reach them. The effect of this is that so long as these groups are not incorporated into data surveys, the ability of these new set of goals to meet their targets will continue to be undermined.

The ODI report, ‘The Data Revolution; Finding the missing millions’, raises similar concerns regarding the lack of adequate, high quality and reliable data and it elaborates this by examining how this hampered the attainment of the MDGs especially in developing countries. It goes through the 8 goals and identifies how the data problem affected each of the MDG goals. In relation to poverty numbers, the report notes that because of a reliance on household surveys that are carried out infrequently in many developing countries, it is difficult to rely on the figures that are produced. Also, these surveys are unreliable because many of the population continue to be left of such surveys. School enrolment numbers were also usually overstated (see the Nigerian example discussed later) while maternal and infant mortality numbers were uncertain.

The two reports focus on the technical aspects of data collection and how this needs to be addressed to ensure that no one falls through the cracks during interventions to meet the targets of the SDGs. However, there is an aspect of data collection and management in Nigeria that affects development planning in Nigeria. This is the political aspect which Suberu highlighted in his book ‘Federalism and Ethnic Conflict in Nigeria’ where he noted that because population figures help determine the allocation of political and economic benefits, groups try to manipulate figures to their advantage. This has become a problem in Nigeria, where states and ethnic groups challenge the figures where they are seen as being at a disadvantage.

To illustrate this problem further, one needs to look no further than the process of revenue allocation as carried out by the Federation Allocation Accounts Committee (FAAC). The FAAC relies on figures from various agencies to determine who gets what and while some of the agencies are at the centre, making their figures a little more reliable, other figures are provided to the Revenue Mobilisation and Fiscal Commission (RMAFC) from state and local governments. These include local government school enrolment figures and number of hospital beds. Where a local government council sees an absolute increase in these figures on a year-by-year basis against those of other local governments, it will result in an increase in the revenue allocated to it.

For education, the FAAC Act states that: ‘Education as a parameter for allocation to Social Development Factor (SDF) shall be measured in terms of primary school enrolment, which attracts 60 per cent of the allocation to education while the remaining 40 per cent is made using secondary/commercial school enrolment; and allocation on the basis of primary school enrolment is made solely on direct proportion. 50 per cent of the allocation on the basis of secondary/commercial school enrolment is made in direct proportion while the remaining 50 per cent is made in inverse proportion. School enrolment refers to public funded schools only.’

While for health, the Act states that as: ‘a parameter for allocation to social development factor shall be measured in terms of the number of State/ Local Government hospital beds there are and 50 per cent of the allocation to health shall be made in direct proportion to the number of the State hospital beds, while the remaining 50 per cent shall be made in inverse proportion.’

This results in states and local governments doing all they can to ensure that the annual percentage increase in these figures match or exceed those of other local governments so that they can have higher allocations. The effect of this is that we are left with unreliable figures when it comes to planning especially in the areas of health and education. This inevitably has a negative effect on the effectiveness of health and education interventions because government agencies at the federal and state levels assume the situation is better than it is and understate the level of intervention required. On the other hand, it also provides an avenue for abuse where funds are over budgeted to areas such as school feeding where enrolment numbers are less than what is actually being stated.

We must therefore begin a process of disentangling data collection from the politics of revenue allocation that is the case in Nigeria, because this is the first step to ensuring reliable data for development planning. There must also be a consolidated process of data collection where data collection agencies work together to produce reliable and high quality data. Without this, meeting the SDG targets will be as unattainable as meeting the targets of the MDGs were, leaving many of the country’s 170 million and growing population untouched by the interventions and policies that will be introduced to meet the targets. Finally the One Campaign report ‘The Data Report 2015: Putting the Poorest First’ stresses the importance of access to high quality reliable data so that countries can meet these SDGs and insists that this will require ‘significant investment in data so that we can understand the problems we face and track the impact of investments.’ Nigeria must thus grasp the importance of data collection and collation so that the country’s policies are founded on dependable data that will allow us produce sound policies that we can track.

Dr. Liman wrote this piece from Kaduna