Right data, right policy? Innovations in data collection and dissemination in Nigeria


Wednesday July 22, 2015 / 09.25am /National Bureau of Statistics

Remarks by Dr. Yemi Kale, Statistician-General of the Federation / Chief Executive Officer National Bureau of Statistics, Abuja, Nigeria. Chatham House, London. July 21, 2015


1.       I am privileged, indeed honoured, to be here today at Chatham House, an institution recognized for facilitating insightful discussions on a wide array of global issues. I have been asked to speak on getting data right for developing the right policies.


This is a subject which I consider important, not only because of my professional responsibility as Statistician-General, but because it is a rare opportunity to support the growing efforts to improve development outcomes in Nigeria in particular and the African continent in general.


Accordingly, I invite you to explore with me very briefly, the significance of today’s event in the context of our national consciousness and our national vision in Nigeria.


The utility of statistics and statistical methods

2.      Distinguished ladies and gentlemen, we live in a global information society where the flow of data and information is constantly evolving. Data is playing a major role in shaping almost every aspect of human life, from administration to astronomy, biology to business, housing to health, engineering to environment, commerce to community, marketing to management, industry to infrastructure, politics to policy, even in sports and strategy.


Data, (statistics) are vital, as they provide us with clear, objective, and numerical evidence on all aspects of our lives and the state of our country, including the growth and characteristics of our population, economic performance, levels of health and wellbeing and the condition of our surrounding environment.


It therefore aids the decision making process by enabling us to establish numerical benchmarks and monitor and evaluate the progress of policies or programmes, thereby ensuring that our policy interventions are well designed, meeting initial aims and identifying any areas which require improvement. Statistics puts information in the right perspective so that it is ready to be used in decision-making. Accordingly, the significance of statistical information for making evidenced-based decisions that guide the implementation of new policy, monitoring of existing policy and evaluation of the effectiveness of policy decisions can therefore not be over-emphasized.


Without these, we cannot make well-informed decisions that will catalyse our socio-economic development and transform the future of generations. It is when we are able to collate, understand and interpret data correctly, as well as identify key areas in our society or our economy that require change, that the policy prescriptions and direction of our governments and businesses are more likely to respond to the real needs of our communities.


3.       Furthermore, statistical methods are needed to identify lurking variables and confounding factors, so that valid conclusions on the cause and effect relationship between variables are possible. In the olden days, the kings and other rulers used statistics based on population census, primarily to procure food for the people and prepare the army for security.


Censuses have been used by governments ever since for various purposes, including planning for socio-economic development: how many hospitals, schools, teachers, new roads etc are needed over the next 10 years? How many policemen or women will be required to safeguard a community?


How many graduates and technical persons will join the labour force and how many jobs need to be produced for them in the next 20 years? Arriving at the answers to all of these questions, and many more require that we get our data right.


4.       The hidden treasure in data is exposed by using appropriate statistical methods to interrogate the data and extract value-adding information that may be exploited. Investing resources and time on data is therefore like scanning our environment to rescue apparently unavailable valuables spread around us.


5.      That being said, the misuse of statistics can also not be ignored. Some misuse statistics because they are neither willing nor prepared to face the reality and truth exposed by statistics.


This is not a problem of statistics, but rather a problem of the ill-motive of the users of statistics. Just as a knife can be utilized to save lives if it is used by a surgeon, it could very well be a weapon in the hands of a criminal.


Thus, statistics are ‘innocent’, and, hence do not deserve any wrong labelling. Blames must go instead, to the ones who abuse or misuse it, particularly those who do so knowingly, selectively or deliberately.


6.      The Nigerian statistical system has a long evolutionary history, but for a while, we tolerated and came to accept a statistical system that was less than optimal, weak, uncoordinated and largely ineffectual in meeting the needs of policymakers, business investors and citizens who needed accurate, reliable and timely data on key socio-economic indicators to make informed decisions.


With time, data users ignored official data and made up their own irrespective of the fact that it was largely unscientific, uncoordinated and emotionally-driven.  Likewise, funding authorities de-emphasised statistical funding as planning agencies developed policies and projects without data, and businesses made uninformed decisions – like dancing a complicated tango in the dark. A few succeeded purely out of luck, while many others failed considerably to maximise their potentials


7.      Distinguished ladies and gentlemen, data is meant to be demand-driven and accordingly, whatever data that is produced and how it is disseminated is likely to be influenced by the interest of whoever is funding it.


I recall that when I assumed office about four years ago, over 90% of data being produced was funded by external; bodies especially international development agencies. Dissemination of whatever was produced was restricted to making it available to the funder who would choose how it was disseminated if at all.


The power of the funder was also evident in determining the choice of methodology as the independence to dictate appropriate and unbiased methodology by the statistical office was diminished. Whoever plays the piper will always dictate the tune.


But why would an agency that is created to reduce poverty, for example, and whose performance is based on reducing poverty be responsible for dictating to a national statistics office how poverty should be calculated and measured? Does this ensure that the right data is produced and used to inform the right policy?


8.     `Even if funders don’t directly attempt to influence the outcome of the survey, their choice of indicator in the first place may just be selected to justify a policy. As we know, the choice of indicator or how you choose to measure it can influence the narrative and policy.


Many African countries often complain that international commitments require us to track certain indicators that are more relevant to advanced countries than to our own economies.


In Nigeria for example, we were advised to track Net enrolment rates as the education policy was to get as many children in school and reduce the number of out of school children. This is fine. A noble objective. But is net enrolment the correct indicator to track to verify success?


Nevertheless, we tracked Net enrolment rates as advised and accordingly government policy was designed to improve this ratio by for example introducing cash payments to encourage parents to send their children to school. In other words if you bring your children to school you get some cash and other benefits. Evidently net enrolment rates sky rocketed as a result of such policies and we patted itself on the back. Success! Job done! Mission accomplished!


After a while we decided to check literacy rates and found they were still dropping. How can literacy rates be dropping when school enrolment rates are skyrocketing? The first reaction is that the data must be wrong. When this assumption was proven wrong and indeed both indicators were adjudged correct, we decided to track net attendance and net completion rates.


We observed that while net enrolment rates were high due to a policy of giving cash to parents for bringing kids to school, net attendance and net completion rates were low. Parents enrolled their children to collect their cheque and other benefits and subsequently withdrew them from school to help at home and on their farms.


This was one of the major issues impeding their willingness to send children to school, but this wasn’t addressed so the problem continued. We have to ask ourselves what the main objective of sending children to school is.


It is to receive an education. The selected indicator to track this was, however, one that just focusses on the number of children being enrolled without considering the quality of education they were receiving nor whether or not they were attending and completing schooling and actually genuinely receiving an education.


This is an example of wrong data, wrong diagnosis and wrong policy. The problem is that in more advanced countries were such indicators are developed and recommended to developing countries like Nigeria, some things are assumed as given. The quality of education is assumed as given as is the likelihood that parents will want their children to complete their education once they begin. Under such circumstances, the challenge is simply one of getting parents to send their children to school and net enrolment rates will be the right indicator to track.


In developing countries, however, this is often not the case. In the Nigerian example, what made the data wrong wasn’t the methodology or the data production process but the wrong selection of indicator to define the problem and monitor its progress.


With the wrong indicator selected the wrong policy was introduced and progress remained elusive. Indeed, as one of your most prominent Prime Ministers, Winston Churchill noted, "True genius resides in the capacity for evaluation of uncertain, hazardous, and conflicting information." He clearly understood how statistics could enable the right decisions and the most ingenious solutions to the challenging problems of our time.


9.      While learning from more successful countries is a good thing, economic history teaches us that theories are built from observation of how a society works. As such, methodology, selection of indicators and policy prescriptions should be based on how each country works.


Why then, should we assume that different countries at different levels of growth, development, awareness, technology and exposure all act the same way? Even within countries, should we assume that different regions or states operate identically?


Could the huge degree of spatial inequality observed in Nigeria for example be explained by the fact that a national policy was introduced across a country which benefitted one region more than it did others, not because it was designed to do so but due to the clear differences between the regions and accordingly their ability to absorb that policy?


10.  This understanding of the relationship between the demand for data, the funding source of data, the type of data produced and disseminated, and the usefulness of such data for policy informed our operational philosophy that the production of data relevant for domestic policymaking must be first owned, funded and driven by demand from within the country and this was our starting point when I arrived at the National Bureau of Statistics.

While we have a lot more work to do, I must say we are nevertheless proud of our successes thus far and are happy to share what we have learnt on this journey with you here today.


11.   Against this backdrop, I will share my understanding of the rising interest in Nigeria’s statistics, how this relates to our development experience, the efforts at data-driven policymaking, and how we at the NBS have endeavoured to meet the challenge of providing the right data for right policies. I will conclude with some of our priorities in the near term.


The rising interest in our economic development statistics


12.  Undoubtedly, one of the most engaging and increasingly important areas of discussion since the dawn of the 21st century has been on statistics and getting data right. Today we talk of open data, big data, and the right data. We hear debates about whether African data is poor or whether there is a statistical tragedy or renaissance in Africa.


Attention to the quality of data has increased globally. Even in more developed countries like the UK, Canada and the USA, there are questions being raised about the quality of data, errors in data, wrong use of data and polarisation of data.



13.  Most recently, the post-2008 financial crisis era has witnessed rising interest and attention in the continent – and by extension its related statistics. This stems from two related but distinct factors, at least for Nigeria. First are the exogenous factors, related to external sector developments as global investors seek new markets and higher returns for their investments. Like other emerging economies in Africa, Nigeria witnessed a steadily increasing influx of investments.


Large retailers and global brands are entering the Nigerian market and expanding across the country, beyond the historical commercial centres of Lagos, Kano, Abuja and Port Harcourt, recognising the opportunities that the Nigerian market presents. As a further indicator of increasing interest in Africa, at last count, the Africa Programme at Chatham House had 48 videos uploaded on Youtube, of which 25% are on Nigeria alone.


I am assuming if this lecture is considered useful, it will be the 49th, fingers crossed. But no investor will put venture into a market without taking a look at the data. This was an important source of pressure to improve the quality and quantity of our economic statistics.


14.  To these, we add internally-driven or endogenous factors, which are factors we considered related to our government’s need for evidence based policy-making especially in view of the Millennium Development Goals (MDGs), the Sustainable Development Goals (SDGs), the wider democratic space which allows for the plurality of political participation, as well as new governance expectations as a reflection of citizens’ increasing interest, knowledge and awareness about their own civic responsibility.


Typically, the first port of call for data is the National Bureau of Statistics. For us, the volume of data requests both online and from walkin visitors testify to the increased attention on Nigeria’s statistics. In fact, in the first one month following the release of the rebased national accounts / GDP series last year, we averaged 3 downloads per minute for the main reports. Last year we had almost 6 million hits on our website and users downloaded our reports over 2 million times compared to less than 100,000 hits and less than 50,000 downloads four years earlier.


NBS also had by far the highest Freedom of Information requests and responses of any agency in Nigeria. We note, however, that the improvement in the Nigerian statistical system in recent times and the modernization and technology-driven approach to data collection, processing, dissemination, as well as the renewed openness and willingness to engage data users and answer their questions on the data has also been a driving force.


All of these broad factors have combined to increase the demand for Nigeria’s data in the last few years.


15.   Despite statistical reforms and strong demand however, the Bureau is still reliant on externally-funded projects for most of its operations which also influences the kind of data that we have focused on: the critical macroeconomic indicators and surveys that are of interest to donors.


Though steadily improving, budget allocation to the national and state statistical offices remains considerably low, and when available, can be irregular and unpredictable thereby weakening the independence of the statistics office in determining the right data and methodology.


16.  I recall one of the first actions I had to take when I resumed office was to assert the independence of the NSO on the choice of indicators and methodology despite a clear and present threat of their pulling their funding. In this way we regained our autonomy in determining methodology as dictated by the Statistics Law. On another occasion, we noticed that the few datasets being produced were not being efficiently disseminated. Limited resources were been used to produce hard copies of heavy documents containing hundreds of pages of data.


Firstly, they were very bulky and expensive to produce so not enough copies were available. Secondly, because they were so heavy, it was difficult and expensive to disseminate across the country. By the time the large document was produced there wasn’t any money left to transport them to users.


This meant people had to come to the headquarters and were forced to part with unauthorized charges to access the data. The first thing I did was to convert the hard copies to electronic form, revamp the website and put it all up for free. That cut off the unauthorized fees, expanded dissemination and freed the resources used to produce the hard copies to produce more data.


17.   Since 2011, we have undertaken several innovative ways to improve the quantity and quality of statistical outputs at the NBS. With better funding, the Bureau would certainly be able to improve both the quantity and quality of its data products to meet the increasing demand.


But we understand that the importance of data does not necessarily lie in the volume of data available and the real goal should not be increasing the volume of data simply for the sake of it. We need to ask, for a given problem, or in applying a given policy: what is the right data and how much of it is needed?


In some cases, this would require increasing the volume of data available but for the majority of the problems, much less data produced more frequently is probably necessary. For most cases, when the right data is selected and the appropriate methodology is adopted in producing that data, a lot less data is required for that policy to be successful.


An evolving statistical system

18.  As the demand for data has been on the rise and as the NBS has continued to innovate towards meeting the demand despite funding challenges, we have faced our own share of criticisms, with some describing Nigeria’s and other African statistical systems as ‘a tragedy’. But they often forget that Nigeria is still a developing country with emerging systems and institutions, trying to shed several decades of military rule, not paying adequate attention to data-oriented policymaking and in the process weakening the statistical system we are now striving to rebuild.


They ignore the fact that statistical systems, like many other systems, evolve with time, improving coverage, resource management, methodology, capabilities, as well as understanding of social, economic and political environments, just as the global statistical system also evolves. They also fail to acknowledge that data from more advanced and wellresourced countries have also been brought into question in the UK, US, China and Australia to mention a few.


For example, The Guardian (UK) reported in March 2011 about calculations of the rate of inflation by the Office for National Statistics (ONS) between 1997 and 2009, which may have resulted in an under-reporting of the UK’s inflation figures.


Similarly, Bloomberg and the Wall Street Journal have also raised questions about China’s GDP statistics as I mentioned earlier, in part because the sub-national entities provide numbers that appeared not to add up to the national figures or because it doesn’t meet their own a-priori expectations. And in Australia, the Financial Review reports how the Australian Bureau of Statistics came under fire in October 2014 regarding the calculation of highly sensitive job numbers.


19.  Statistical offices understand that criticism and scepticism come with the territory, even when these are routine methodology upgrades or statistical adjustments. Like these countries, I believe Nigeria’s and Africa’s statistical systems are also learning from their respective political and economic environs and adapting to better respond to the demands of its citizens.


We may not be where we ought to be, but we are not where we used to be either. To better appreciate the measures we have taken and the reasoning behind them, it is perhaps necessary to briefly lay out the context of the policymaking environment, after which it becomes clearer how our data production activities work within this system.


Statistical operations within the Nigerian context

20. Nigeria’s recent economic story has been marked by high economic growth rates, an almost insignificant decline in poverty, accompanied by widening economic inequality and inadequate employment generation opportunities. Some have gone as far as identifying ‘two nations’ within the country: a small rich elite living side-by-side with a large poor majority.


21.  There are clear regional disparities in the distribution of growth and poverty. But where does the growth come from? Agriculture, wholesale and retail trade and a few others, but mostly sectors that are unable to absorb the 1.8 -2.0 million new entrants to the labour force each year.


To stimulate jobs in many states, the traditional wisdom has been to employ into the civil service, or undertake some form of public works and entrepreneurial programmes, each of which barely scratches the surface of the unemployment problem, or the poverty problem. From our point of view however, the fundamental cause of these problems is that only in a few instances are these programmes driven by insights from available data.


22. Given this context, I have always maintained that the priority should be in getting the right data necessary to make informed decisions to address the problems, whether poverty or unemployment or insecurity.


Let’s take unemployment as an example. our data shows that a little over a million jobs are created every year, but the number of new entrants to the labour force doubles that number. Disaggregate the data further and it shows half of the unemployment is from the youth, i.e age 15-35.


Further disaggregation reveals the problem is more of graduate unemployment and underemployment. Compare this to the million jobs being created every year and it explains the high graduate employment in Nigeria.


Thus, the data shows that, firstly, the total number of jobs being created is not enough to meet the yearly demand for jobs which causes the already high stock of unemployed and underemployed to grow.


Secondly, the jobs being created are largely informal ,blue-collar jobs by micro businesses while graduates are looking for formal white-collar jobs that do not exist in the necessary numbers. I believe it is the duty of the statistics office to show what is actually going on and that of the policy maker to interpret it correctly and design the right policy to address the issues revealed by such data.


23. Official data is a public good, meaning that it is more efficiently provided by the public sector than the private sector. It is therefore the role of the statistics agency to understand the needs of data users and policy makers and provide data that is most relevant.


The challenge, therefore, is to make sure that the data produced is used for this purpose. The practice of simply throwing money at problems, and embarking on just any visibly large capital projects or ‘setting up committees’ has not worked, especially where committees do not start off by looking at the data to understand the problem they are trying to solve.


Of recent, we have seen a greater emphasis on evidence based policy making, and more pressure on policy makers to deliver measured and tangible results. Though this is a slow process, with everyone’s account of ‘the reality on ground’ being quite different, I would say we have received a more positive change in attitude, especially at the Federal level, towards supporting data production efforts in recent years, though more can still be done.


24. Ladies and gentlemen, as much as we insist on remaining politically neutral and professional, the policy and political environment within which we operate continually buffets our ability to deliver on our mandate. Policy makers and others will always reject any data that doesn’t agree with their already set narrative or that suggests they are not doing well but will commend the process that led to data that puts them in good light.


An example that springs to mind is when a state governor was upset about his state’s poverty profile, and invited me over to show me the roads and hospitals he had constructed in the previous two years. He couldn’t understand why despite the nice roads and hospitals he built to open up rural areas to markets and healthcare to rural dwellers we dared to suggest poverty and healthcare had worsened in his state.


Well, as controversial and stubborn as I am perceived be (and by the way I think this is a needed quality to run a statistics office well in a developing country like Nigeria), I respectfully showed him with geospatial data that the roads he built were not where the poor in his state were concentrated.


Furthermore, the nice hospitals were not easily accessible to the rural poor that mostly needed them and even when they were accessible, cultural and religious considerations restricted their willingness to use them, so poverty, inequality and health indicators still worsened and he should have checked the data before acting.

I recall another governor who after the poverty study revealed low poverty in his state went to town with the information extolling his leadership credentials. Soon after there was a suggestion that revenue sharing be amended to reflect poverty in states and more should be given to poorer states. This same governor suddenly changed and claimed we understated poverty in his state so the poverty data he only recently extolled was suddenly incorrect.


Supporting the policy process with the right data

25.  In the last three years, the NBS has been actively participating in the major economic policy decision making organs. This ordinarily should not be remarkable, but it is based on the increasing recognition by key policy makers that policy failures and the phenomenon of abandoned projects will persist if they are not based on accurate and reliable data for policy formulation, project identification, or monitoring and evaluation.


While some have suggested, based largely on ignorance, historical cynicism and distrust of ‘anything government’, that the revision of official statistics were undertaken to make the government of the day look good, in fact it was the culmination of years of effort to improve the quality and reliability of our national statistical system and the resulting statistics that originate from it.


26. Take for instance the recently revised unemployment methodology in Nigeria which attempted to modify the nation’s existing unemployment methodology calculations. The primary goal was to provide more information to domestic policy makers, and secondly to align our methodology closer to ILO guidelines for better international comparability.


Our previous threshold for the definition of unemployment was based on a Nigerian definition of fullemployment; an individual working 40 hours a week. This implied that if a person worked 39 hours a week, the individual was classified as unemployed. Just to highlight why this was such an issue in the UK context, this meant that the entire population of full time workers in the UK working between 35 and 39 hours may have been considered as unemployed in Nigeria, since an individual is considered fully employed in the UK if they work 35 hours or more.


As a result, we lowered the time-based threshold for determining who is unemployed, from 40hrs a week, to 20hrs a week. We also further disaggregated already published unemployment figures, by separating those working 0-20hrs (now termed as ‘unemployed’) from those working 20-40hrs (now termed as ‘underemployed’).


27.  The truth is that if we had simply continued to rely on the previous 40hour threshold, policymakers would be none the wiser about the dynamics of the situation. On the other hand, a strict interpretation of the ILO guidelines would mean unemployment rate of 2.1% at the end of 2014 (which would certainly bring the Bureau’s competence into question).


 Thus, in both instances, what some people may have considered ‘right’ data could very well and easily lead to wrong policies. On the other hand, based on this disaggregation, it became clearer that Nigeria’s unemployment situation was really an issue of ‘underemployment’, with many job seekers trying their hands on low-skill, low-paying, non-full-time jobs, just to make ends meet in the absence of any unemployment benefits or social safety net system.


Based on this information, disaggregated by gender, age groups, educational qualifications, state of residence etc, we believe policymakers can make more effective interventions.


28. Again, some observers perceived this as government efforts to influence official statistics. But as I mentioned earlier in the case of the UK, China and Australia, routine methodology revisions are the prerogative of each country’s statistical authority.


The Guardian in 2013 reported a similar incident here in the UK, when the ONS published results that showed over half a million jobs created but political opponents here suggested this was government fiddling with employment figures. The argument was whether to count those who were on skills-training programmes, or similar government-funded programmes as ‘employed’. But the ONS maintained it had clear procedures for counting jobs created.


29. We do not claim that the revised methodology is the best it can be, but we know it represents an improvement in our understanding of the dynamics of unemployment, even as the methodology improves overtime to give a clearer picture.



Innovations in data collection and dissemination

31. On our part, we continue to address the binding constraints to the production and dissemination of reliable data by leveraging on two important pillars: collaboration and technology.


32. First, through increased collaboration, openness and engagement with critical stakeholders, including other government agencies, business associations, academia and the media, NBS has developed a reputation of being able to undertake nationally representative data collection exercises with higher quality. We have been forced to take this route also because funds are limited.


To avoid duplication of outputs across agencies, we favour the harmonisation of statistical activities across all sectors. Previously, each ministry / sector would want to undertake its own sectoral survey, independent of the statistics office and without consideration for how its data relates to other sectors and would apply whatever method suited them in a complicated and disorganised manner.


But increasingly, the NBS is working with these agencies to jointly undertake such activities and to enforce harmonised processes and methodologies. It saves costs, ensures there are no conflicts in the eventual results and the overall system works in a coherent and coordinated way.


In addition, we are able to deepen sector knowledge and skills for our staff, as well as the staff in the collaborating agencies. It also provides a unique opportunity for advocacy as we often involve representatives from these groups to observe and participate in the field work and data processing aspects.


Our openness to such collaboration also informed our engagement with the Oxford Poverty and Human Development Initiative (OPHI) which included Nigeria in the pilot-testing phase for multidimensional poverty analysis. We want to replicate and scale up such collaborative exercises especially with the sub-national governments in future.


33. By taking advantage of the rapid diffusion of information and communications technology in Nigeria in recent years, we have also been able to improve the quality of our activities at every point of the data production process: from data collection to processing to results dissemination and even advocacy.


For example, we publish on our website at the start of each year a data release calendar to intimate our users of the estimated release dates of our statistical products. This facilitates their own planning and also stimulates demand for the products. To a large extent, we have met and sometimes surpassed the targets set by the calendar (In fact, the World Bank country team measures us on this indicator).


34. In addition we have successfully tested, and now deploy more widely, computer assisted personal interview (CAPI) devices to improve the speed, accuracy and reliability of field data collection. This is a recent initiative which will allow us to save costs considerably over the longer term.


It also enables us to scale-up data collection more quickly and effectively as the need arises. In addition, it has also reduced errors and made truancy difficult. One worrying trend that we noticed early on was that some field enumerators were able to fill in questionnaires without administering them to respondents.


We only realised this during data processing because of inconsistencies such as a ‘male’ respondent who is also reported to be ‘pregnant’. But there was no easy way to identify which specific enumerator was responsible for this.


However, since deploying the CAPI systems which can be uniquely identified and matched with a specific enumerator, we are able to monitor who entered what data, when and where through the GPS features. We can also track how long it took to complete the survey interview. The result has been much better quality field data and faster data processing.


35. With respect to data processing, we have consolidated a number of our surveys to reduce duplication and, over time, will de-emphasise surveys altogether while strengthening the system of administrative statistics. This has been a key strategy to ensure greater data quality and reduce costs of data production. As we all know, surveys and censuses are very expensive.


The revisions done to the GDP and unemployment statistics to mention a few were aimed at improving the quality of our data outputs, ensuring they are reflective of current socio-economic realities while still remaining comparable over time so we can evaluate our progress. This informed our theme of ‘Measuring Better’ during the 2014 GDP rebasing exercise.


36. Data dissemination has seen a number of innovations as well, driven largely by efforts to save costs and maximise the use of technology. All our regular and special publications are provided free, online on our website [nigerianstat.gov.ng], while the data is uploaded on the data portal, and ultimately archived in the National Data Archives (NADA).

In addition to our mailing list, we regularly provide our key reports to members of government, while using the social media (especially youtube, facebook and twitter) to reach a wider, more youthful, techsavvy citizenry. These efforts, in our opinion, foster transparency, make users aware of available data, and enable them utilise such data to make their decisions.


We have also started publishing infographics so that the most interesting aspects of our reports are made easily available to those not able to go through hundreds of pages of tables in our reports. Currently, we are developing a mobile-compatible data App for the user on the move.


37. In fulfilling our statistical advocacy function, we launched twitter accounts to reach out to users, engage in discussion on key topics, clarify the statistics and provide explanations to those who remain sceptical but are willing to listen or learn.


Since January, I would say the experience has been mixed. One of my interesting discoveries has been the limited understanding of the nature of economic and other statistics, its interpretation and utility even among the more educated users.


 This, I observed, is partly as a result of not understanding the methodology or the purpose of a particular indicator. More user-education initiatives will therefore be developed to address this.


38. Another current practice is to invite the press to ask questions about our data and how to correctly interpret it. Furthermore, we invite private individuals to join our data collection exercises as observers to monitor the process in line with our philosophy that the best way to deal with someone that doesn’t trust you is to open up completely to and not hide anything so she can see for herself.


 This was the process we adopted during the GDP rebasing exercise and it was a successful strategy in renewing public confidence in our processes. For us, the opportunity to get feedback from our users is an important culture that we try to feed into the data production process, and a key benefit of the emergence of new technologies.


Indeed, it is the feedback we get from our data users that helps us to plan our data release calendar for the subsequent year. These are some of the most visible innovation efforts at the NBS to provide the necessary data to support right policies in Nigeria.



39. Let me conclude by emphasising that we are still ambitious, and there are challenges that we contend with, although I have touched on some of these already. An innovative means of sustainably financing statistical activities both at the federal and state levels is urgently needed.


Secondly, we need to better coordinate with our development partners and ensure a more effective alignment of our mutual interests with respect to an enduring national statistical system. This is especially important in light of the proposed SDGs with over 100 Global Monitoring and National Indicators.


Thirdly, we would like to improve our coordination efforts with state governments by building staff capacity and undertaking joint activities to strengthen institutional partnerships.


Fourthly, we plan to strengthen the system of administrative statistics and rely less on the conduct of expensive surveys. In all of these, we are inclined towards the use of modern technologies to save costs and deliver our services as efficiently as possible.


We are confident that with the continued operational and institutional support from our government and our partners, NBS can continue to remain a model statistical agency for other African countries.


40. I will end with the closing statement that I believe if we do the right things at the right time and for the right reasons, Nigeria and indeed Africa can be great. One of such right things is getting our data right so we can get our policies right.


Once again, thank you for your attention and for inviting me to share these thoughts with you today.


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