establishing an electronic government enterprise statistical...

[Establishing A Computer- Aided Government Enterprise Statistical Information System (GESIS) On A Data Warehousing Platform: The Example From Nigeria

Establishing an Electronic Government Enterprise Statistical Information System

(GESIS) on a Data Warehousing Platform: The Example from Nigeria

Vincent O. Akinyosoye, Ph.D.Former Statistician-General of the Federation

Associate Professor of Applied Economics & Data Management

June, 2011

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Establishing an Electronic Government Enterprise Statistical Information System (GESIS) On A Data Warehousing Platform: The Example From Nigeria

Contents

Figures........................................................................................................................................... iv

Tables............................................................................................................................................ iv

Foreword....................................................................................................................................... v

Acknowledgements.......................................................................................................................vi

Acronyms..................................................................................................................................... vii

1. Introduction..............................................................................................................................1

2. Challenges of Data Production and Management in Africa’s Statistical Systems......................3

3. Electronic Government Enterprise Statistical Information System (GESIS)...............................8

3.1. Elements of GESIS............................................................................................................8

4. The Virtual Cloud and the Nigerian Data Nervous System (NDNS)...........................................9

5. The National Data Centre (NDC).............................................................................................10

5.1. Communication Apartment...........................................................................................11

5.2. Server Apartment..........................................................................................................11

5.3. Power System and Fire Protection Apartment..............................................................11

6. Physical Inter-Connectivity of Nigeria’s Data Centres.............................................................12

6.1. Connectivity within National Bureau of Statistics, Headquarters..................................13

6.2. Connectivity between NBS HQ, Zonal Offices, DRS in Lagos and Training Schools........13

6.3. Connectivity between NBS HQ, and Federal MDAs.......................................................15

6.4. Connectivity between NBS HQ, the 36 States & FCT, SSAs, State MDAs & LGSUs.........18

6.5. Connectivity between LGSUs and Political Wards.........................................................19

7. Content Development and Database Management Systems..................................................22

7.1. Sector Statistics and DBMS for Federal MDAs...............................................................24

7.2. State Statistical Yearbook and DBMS for States and LGAs.............................................27

7.3. Political Ward Statistics and DBMS for Managing Data.................................................28

7.4. Special Databases..........................................................................................................32

7.4.1. Market Outlet Database (MODB)........................................................................32

7.4.2. Directory of Educational Establishments Database (DEEDB)..............................33

7.4.3. Directory of Health Establishments Database (DHEDB)......................................34

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7.4.4. Establishments Database (EDB)..........................................................................35

8. Organising for User Needs under GESIS..................................................................................35

9. Organising Effective Data Supply under GESIS........................................................................41

10. Concluding Remarks...............................................................................................................46

References................................................................................................................................... 49

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Figures 1. Traditional Government Enterprise Statistical Information System2. Electronic Government Enterprise Statistical Information System 3. The VPN Cloud of the Nigerian Data Nervous Systems (NDNS) 4. NBS VPN Design for the Zonal Offices and Schools of Statistics 5. Schematic Diagram of the Connections between NBS HQ, Zonal Offices and DRS in

Lagos6. Virtual Presentation of the connectivity between NBS Data Centre and Federal MDAs7. Connectivity between FMF and its Parastatals8. NBS & 36 SSAs and FCT Virtual Private Network (VPN)9. Sample Design of the Connectivity between State Data Centre, State MDAs & Local

Government Areas10. Map of Ondo State by Local Government Areas11. Map of Ondo West Local Government Area by Wards12. Connection between Ondo West LGA and its Wards13. An Enumerator Station Overview Map14. Sample of Digital Scannable Questionnaire

Tables

1. Socio-Economic Characteristics of Political Wards in Ondo West LGA2. List of Regular Surveys in Nigeria’s NSO and their Periodicity

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Foreword

This presentation is about the recent reforms in the National Statistical System (NSS) of Nigeria exemplified by the observable improvements in the delivery of statistical products in the National Statistical Office (NSO) of the country since 2005. The reform is predicated on the Statistical Master Plan (SMP), designed in 2004 under the sponsorship of the World Bank and Federal Government of Nigeria and implemented between 2005 and 2009. One of the principal outcomes of the reform was the creation of a modern statistical environment in the headquarters of the National Statistical Office in Abuja, the nation’s capital. The headquarters is fully-equipped with the latest Information and Communication Technology (ICT) infrastructure allowing for a work space conducive for statistical duties as well as an office run by well - trained and motivated workforce.

The institutionalization of the reform was formalised by the enactment of the Statistical Act of 2007 that legally made National Bureau of Statistics (NBS) the apex statistical agency in the country with the powers to coordinate the production of official statistics in all the Federal Ministries, Departments and Agencies (MDAs), State Statistical Agencies (SSAs), State MDAs and the Local Government Statistical Units (LGSUs). The enactment of the Act also led to the institution of new and enhanced conditions of service, scheme of service and salary structure for NBS staff. The Act moved the staff of the office from the core civil service terms of employment closer to the Universities and Research Institutes, thereby boosting the morale of the workers.

Between 2005 and 2010, NBS was fully transformed and the chief agent of the process was the massive deployment of ICT tools in all areas of statistical operations and elaborately expressed in the main body of this presentation. From survey planning to data collection, processing, management and dissemination, ICT tools have been major factors in data production in NBS since 2005. The entire statistical environment has also been strengthened for the seamless exchange and warehousing of official statistics in the country; creating electronic links between data producers, suppliers and users. The Government Enterprise Statistical Information System (GESIS) described here for Nigeria should be a fore - runner of statistical development in Africa and other developing countries and prepare them for the era of *Cloud Computing* which will soon envelope the information exchange in the world.

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Acknowledgements

I thank Biyi Fafunmi, the Head of the Research and Policy Analysis Unit (RPAU) of NBS and Alfa Mamza, the Manager of the National Data Centre (NDC) for their assistance with this work. I am particularly grateful to them for providing relevant materials and detailed comments on the earlier drafts. Other persons also responded generously to my request for materials.

I wish to express my profound gratitude to the following persons for contributing information: Chuba Moneke, Tunde Adebisi, Temitayo Adebiyi and Louis Gambo. If I have missed out any name, my apologies, please.

Last, but not least, I thank Titi Kadiri for her careful and thorough research assistance, preparation of the document and design of some of the graphics. Any errors in the publication remain those of the author.

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Acronyms

AfDB African Development Bank BISS Business Intelligence Support System BoP Balance of Payments CAPI Computer Aided Personal Interview CBN Central Bank of Nigeria CDB Court Databases CEF Central E-Mailing FacilityCMD Centre for Management Development CPI Consumer Price Index CWIQ Core Welfare Indicators Questionnaire SurveyDBS Data Back-Up System DBMS Database Management System DC Data Centre DEEDB Directory of Educational Establishments DatabaseDFID Department for International Development DHEDB Directory of Health Establishments DatabaseDHRDB Directory of Hotels and Restaurants Database DID Divisions, Items and Details DIDSS Data Information Decision Support System DMO Debt Management OfficeDRSs Data Recovery Sites DSSs Decision Support SystemsEA Enumeration Area EDB Establishments Database EDNs Electricity Distribution Networks ERGP Economic Reform and Governance ProgrammeEMCAP Economic Management and Capacity Assistance Programme EU European Union FAO Food and Agriculture Organisation FCT Federal Capital Territory FIRS Federal Inland Revenue Service FMF Federal Ministry of FinanceFOS Federal Office of Statistics GB Giga Bytes GDP Gross Domestic ProductGESIS Government Enterprise Statistical Information SystemGHS General Household SurveyGIS Geographic Information SystemGPS Global Positioning System

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HHs Households HQ HeadquartersHUs Housing Units ICT Information and Communication TechnologyICTD Information and Communication Technology Department ILO International Labour OrganisationIMF International Monetary FundIPS Intruder Prevention Sensor ISA Internet Security AcceleratorISIC International Standards of Industrial ClassificationIST Investment and Security Tribunal IT Information Technology LAN Local Area Network LFS Labour Force Survey LGAs Local Government AreasLGCs Local Government Councils LGSUs Local Government Statistical UnitsLSEs Large - Scale Enterprises MAN Municipal Area Network MAN Manufacturers’ Association of Nigeria MDAs Ministries, Departments and Agencies MICS Multiple Indicators Cluster Survey MODB Market Outlet Database MUD Master User Database NA National Assembly NACCIMA Nigerian Association of Chambers of Commerce, Industry, Mines and Agriculture NADA National Data Archive CentreNASC National Agricultural Sample Census NASS National Agricultural Sample Survey NBS National Bureau of Statistics NCS Nigerian Customs ServiceNDB National Data Bank NDC National Data CentreNDIC Nigerian Deposit Insurance Corporation NDNS Nigerian Data Nervous System NEG National Electricity GridNEXIM Nigerian Export and Import Bank NGOs Non-Governmental OrganizationsNICON National Insurance Corporation of NigeriaNISE National Integrated Survey of Establishments NISER Nigerian Institute of Social and Economic Research NISH National Integrated Survey of Households NITDA Nigerian Information Technology Development Agency NLSS National Living Standards Survey

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NNPC Nigerian National Petroleum Corporation NPC National Planning CommissionNpopC National Population Commission NPTLs National Power Transmission Lines NSO National Statistical Office NSS National Statistical SystemOCR Optical Character Recognition OAGF Office of the Accountant-General of the FederationPPP Public-Private PartnershipPSDB Petrol Stations Database PSPDB Police Station and Post Database PSRP Public Service Reform Programme PWDB Political Ward Database PWSs Public Water Systems SAN Storage Area Network SAS System of Administration Statistics SDC State Data Centre SEC Securities and Exchange Commission SFP Small Form-Factor Pluggable SMEs Small and Medium Enterprises SMP Statistical Master PlanSSAs State Statistical Agencies SSYB State Statistical Year BookSQL Structured Query Language TB Tera BytesUNDP United Nations Development ProgrammeUNIDO United Nations Industrial Development OrganisationUTP Unshielded Twisted PairVPN Virtual Private NetworkWAN Wide Area Network WB World Bank WBSs Water Booster Stations WDIC Ward Data and Information Centre WSDs Water Storage Dams

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[Establishing A Computer- Aided Government Enterprise Statistical Information System (GESIS) On A Data Warehousing Platform: The Example From Nigeria

1. Introduction

Government’s understanding of the development process in most African countries has always been based on orthodox development theories which tend to support massive public expenditures and investments in all areas of human endeavour: from food production to the provision of all forms of economic, social and institutional infrastructure in addition to performing the basic functions of administering and governing the nation. This approach may be necessary at the incipient stage of the development process but as the economy matures, this model needs to be replaced. The fundamentals of the emerging economy will need to change and the role of Government curtailed to governance and security while creating an enabling environment for the private sector to play leading roles in the provision of public and private goods and services within the guidelines provided by Government. This is the bedrock of a modern economy.

In such a modern economy, Government’s focus is concentrated on enacting laws and setting rules and regulations to control and coordinate the activities of economic and social agents in the system for the good of the society. Public institutions through which Governments perform their roles are managed by public servants (civil servants in line ministries, all categories of staff in other government Departments and Agencies as well as all shades of political office holders at the national and sub-national levels). This explains why the performance of these public servants in the discharge of their duties is significantly correlated with the performance of an economy. As a matter of fact, one major difference between the economies of the rich developed nations and poor, under-developed ones is that while public servants in the former are efficient managers of human and material resources, their counterparts in under-developed societies are wasteful and inefficient in the management of available resources. The low productivity levels in public administration and ineffectiveness of governance in the under-developed nations are attributable to many factors, but the one that is germane to the message of this presentation is their weak data and information management systems such that the right policies are not initiated and those formulated are not based on sound diagnosis of societal

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problems and the analyses of policy options. Programmes and projects are poorly conceived and executed due to archaic data and information management systems.

The consequences of this malaise are enormous and explain such societal problems as wide -spread poverty; the huge income gaps between the rich and poor; large-scale unemployment especially amongst young educated persons; inadequate security contributing to high crime rate; lack of or limited access to clean, portable drinking water; absence of well-functioning educational and health systems; poor transport system exemplified by bad road networks and poor railway systems; and non-functioning or at best epileptic public electricity supply system. The cumulative effect of all these societal problems is that most citizens and private sector operators in poor under-developed countries are perpetually frustrated with the system and have little trust in their Governments. These adverse development results lead to agitations for change and in some cases peoples’ revolutions.

The way out of this mess is to develop systems that will grant public servants of all categories access to data and information about all aspects of the socio-economic conditions of citizens to enable them understand the extent of afore-mentioned societal problems. In these modern times, the only way to do this is to equip public servants with various Decision Support Systems (DSSs), using Information and Communication Technology (ICT) tools to manage the public sector and enhance design and implementation of better policies as well as strengthen accountability and transparency in the conduct of government business. They can therefore alleviate, if not eliminate, the severity of the societal problems plaguing their economies. The use of ICT tools in creating a knowledge-based enabling environment for understanding societal problems is the bedrock of electronic governance (or e-governance), a concept elaborated upon in this publication. The National Statistical System (NSS) of any country has a significant role to play on the pursuance of the goals of e-governance projects because the National Statistical Office (NSO) and other producers of Statistics at the national and sub-national levels have the wherewithal to provide the content (that is, statistical information) for e-governance to have effect. These globally accepted roles for NSOs form the basis for this publication which endeavours to explain the object of thought and modalities for establishing a Government Enterprise Statistical Information System (GESIS) for a country like Nigeria.

GESIS in Nigeria is being championed by the National Bureau of Statistics (NBS) which is the apex data producing agency in the country. By law, NBS coordinates the National Statistical System of Nigeria which includes all Federal Ministries, Departments and Agencies (MDAs), all State Statistical Agencies (SSAs), State MDAs and all Local Government Statistical Units (LGSUs). These bodies represent the production side of the National Statistical System (NSS). On the demand side are the major users of statistical information represented by public servants of all categories from the Head of State to those in Federal, State and Local Government MDAs,

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academicians in the Universities and Research Institutes, operators in the Private Sector Organizations, staff of International Agencies, locally and abroad, as well as the data and information users in Non-Governmental Organizations (NGOs), the media including freelance analysts and the general public. The other important component of the NSS which complements the production side is the group of institutions that supply data into the system. These include the myriads of households that house the human population that supply personal and household base data on consumption, labour force, savings, assets and other accessory individual characteristics. The other group of institutions that supply data into the statistical system are establishments that are engaged in the legal production of goods and services that make up the GDP. These are broadly agricultural and non-agricultural establishments that operate in the formal and informal segments of the economy. And, they could be large, medium, small or micro (family) in scale of operation. These data supplies provide the raw data with which national or sub-national aggregates are compiled by NSOs.

2. Challenges of Data Production and Management in Africa’s Statistical Systems

The management of the economies of African countries are presently operating below par because policies and programmes are not well-designed and implemented with the support of sufficient statistical information; and when such information are available, they are not adequate, timely and credible (Scott, 2005). Hence, the absence of strong NSSs poses a major challenge to the Continent. The consequence is that many African countries either grow at a very slow pace and not as fast as the population growth, or remain stagnant and held down in a trap. In some cases, the economies descend into an abyss of hopelessness (Akinyosoye, 2008).

In most cases, production of goods and services in Africa do not meet the demands of the people. For example, food is generally in short supply and the same is true for energy and communication facilities. The transportation system (roads, railways, waterways and airways) is in very bad state. In the area of social infrastructure, existing primary, secondary and tertiary educational and health facilities are far from being sufficient. Water supplies are grossly inadequate, contributing significantly to incidences of water-borne diseases. Poverty is a common phenomenon, particularly in the rural areas, and absolute reliance on rain-fed agriculture contributes to food shortages and seasonal food price gyrations. The institutional infrastructure of African countries makes life difficult for the average citizen. The public institutions such as the Ministries, Departments and Agencies (MDAs) are inefficiently run and plagued by excessive bureaucracy, over-bloated work force, poorly skilled and educated personnel and corruption. There is also little evidence of accountability and transparency in

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governance. Furthermore, the security situation is precarious and manifests in high crime rates, limited crime detection ability, weak policing and poor dispensation of justice. Unfortunately, statistics on these undesirable phenomena are not captured for planning in these countries.

The afore-mentioned challenges in African countries are difficult to handle because of the existing data production and management systems in use. The existing System of Administrative Statistics (SAS) in the MDAs of African countries is very poorly managed. Technical competence in primary data collection, processing, management and analysis, which is expected to complement administrative statistics, is also very weak. Furthermore, the dissemination of the right quality, quantity and type of statistics on a timely basis is completely absent. Data produced in Government offices which are managed manually through files, are susceptible to misplacement, outright loss, manipulation and theft. In many institutions, there are no functional statistical units to manage data emanating from regular operations. Modern computer-based information management systems with full complements of operational data bases, local area networks, wide area networks,[or Virtual Private Networks (VPN)] Intranet and Internet connections hardly exist. The complete absence of an effective data and information management system in the Governments of African countries, therefore, makes the management of these countries problematic.

Public sector managers in African countries find it difficult to recognize societal problems ahead of observed disequilibrium situations because of lack of data. They do not use statistics to inform policy designs and identify policy choices. They cannot forecast the future demand and supply situations and, therefore, cannot plan for change. And, very importantly, they do not generate and manage the data and information required to monitor policy implementation and evaluate policy outcomes and impacts. Owing to the pervading control of Governments over the economies of African countries, inadequate official statistics has often contributed to poor resource allocation and expenditure programmes with dire consequences on the general welfare of the people. In addition, the perpetual shortage of data required for monitoring and evaluating programme implementation creates avenues for poor accountability and corrupt practices. The poor data management culture in the public administration of African countries makes public sector operations very slow and service delivery prone to under-the-table deals as demanders for services are inclined to circumvent bureaucracy in order to get quick and favourable attention.

At the data-supplier side, where households and agricultural and non-agricultural establishments operate, surveys and censuses are the normal tools to obtain statistical information required by data users. Governments at all levels need household and establishment data to understand their problems and plan to alleviate them. They need these micro-data to monitor and evaluate the impact of public actions. The organized private

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operators also need demographic characteristics of households, family size, spatial distribution of population, age composition of households and their income and expenditure patterns in order to understand how to meet demands for the goods and services they produce. Similarly, researchers and academicians need household and establishment data to conduct micro-level studies. In the same vein, development partners need micro-level data on poverty and welfare status, prevalence of diseases, access to infrastructure, gender problems and so on, to design interventions for African countries. Unfortunately, in many of the African countries, household and establishment statistics are not available as and when required, and many reasons account for this.

Most National Statistics Offices (NSOs) in African countries are not financially and materially equipped to conduct the relevant surveys and censuses needed to obtain the required micro-level data for all shades of users, and to monitor and track Poverty Reduction Strategies (PRS) towards the attainment of the Millennium Development Goals (MDGs). Funding from home Governments for NSOs have always been in short supply, not only for the regular surveys, but also for Population and Housing Censuses. Occasionally, donor agencies collaborate with NSOs and other agencies of government to conduct surveys. The donor agencies also sometimes conduct independent surveys and even contribute substantially to the conduct of national population and housing censuses of many African countries. In many cases, the donor-supported surveys are tailor-made to meet the narrow data requirements of the sponsors. Even when the NSOs manage to conduct surveys and censuses, the data production process is generally manual, slow and prone to errors. Reports are generally not produced early and when produced, are released years after completion. In many cases the data are not disseminated as required. Another common feature of the operations of the NSOs of African countries is the dearth of the use of information and communication technology (ICT) tools in data collection, processing, management, analysis and dissemination. One consequence of the afore-mentioned poor micro-level data production process is that different government agencies and donors conduct surveys on the same subject, using different and often questionable methodologies, thereby producing conflicting results to the embarrassment of the Governments.

In addition to the foregoing problems with data production in the statistical systems of African countries, obtaining data from data suppliers pose enormous challenges to African NSOs. Most household-based data suppliers in Africa are suspicious of the need and uses of the statistical information supplied to data collectors from Government Agencies. They therefore either do not respond or give wrong information on data details as age, household size, number of children, employment status, income, and so on. Establishments in the formal segment of the economy hardly respond to requests for data on production, revenue, salaries, number of employees because of fear of public tax administrators. The informal sector of African economy market is relatively large but obtaining relevant data in this segment of the economy is very

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difficult because the market is unorganised and operators are largely illiterates who do not keep records of their transactions.

Most users of official statistics in African countries fail to appreciate the relevance of the data produced by government statistical agencies. This is particularly disturbing when public servants, politicians, media persons and the general public misuse or misinterpret official statistics. The poor usage of statistics draws principally from the facts that these users are completely oblivious of the methodologies used in collecting and compiling of official statistics such as the National Accounts (GDP) figures, Commodity Prices and Inflation rates, Unemployment rates, morbidity and mortality rates and other micro and macro levels data values. They are ignorant of the principles and mathematics of survey or census taking and the process of extrapolating population parameters from survey outcomes. Furthermore, most data users lack the understanding of the paradigm of Data-Information-Decision (DID) making process and can therefore not able to transform data into information through various analytical methods.

Finally, and very importantly, there is no sufficient coordination of the different agents in National Statistical Systems (NSS) of African countries. There is no systematic and orderly way of bringing data producers, suppliers and users together. In fact, there is no institutional linkage amongst data producers on one hand and data producers, users and suppliers on the other hand in NSSs of African countries. This is the gap in most African NSSs and this is the challenge being explored in this paper with example from Nigeria which has massively deployed ICT tools in the process of integrating different segments of her National Statistical System (NSS). One aspect of this is the establishment of a statistical information system built on a data warehouse platform for the production, storage and seamless flow of data within the NSS and with the outside world. This is the object of thought behind the content of this presentation. The basic idea is the transformation of an erstwhile “confused” traditional government enterprise statistical information system into a modern Electronic Government Enterprise Statistical Information System (GESIS). Figure 1 shows the traditional (largely manual) system while Figure 2 reflects the modern electronic system. The traditional system relies on physical human contact (including plenty of travels in search of data), and the modern system relies mainly on computers and accompanying digital devices and software applications. The traditional system is slow, inefficient and prone to human errors while the modern system is fast, devoid of human errors and cost-effective in the long-run.

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Figure 2: Electronic Government Enterprise Statistical Information System

Figure 1: Traditional Government Enterprise Statistical Information System


3. Electronic Government Enterprise Statistical Information System (GESIS); with Particular Reference to Nigeria

The data production and management model being espoused to create the seamless flow of statistical information within the National Statistical System of a country like Nigeria is the focus of the rest of this treatise. It is known as an “Electronic Government Enterprise Statistical Information System (GESIS)”. It is built on a data warehouse platform, and expected to make data and information available to the entire Nigerian public sector and within the reach of all public servants, including top data users in the public service of the nation and users abroad. It will also facilitate the movement of data to data users in the Private Sector, Universities, Research Organizations, Development Partners, the Media and General Public.

3.1. Elements of GESIS

There are at least nine (9) major elements of GESIS, as follows:

1. Creating a “virtual cloud” to serve as an umbrella for all data suppliers, producers and users within Nigeria with connections to the outside world through the web. This will be analogous to the Nigerian Data Nervous System (NDNS).

2. Development of a National Data Centre (NDC) in NBS to serve as the hub (platform) for GESIS with complementary Data Centres in other data producing Agencies; that is, all Federal MDAs, State Statistical Agencies (SSA), State MDAs and Local Government Statistical Units (LGSUs) which will later include data centres that may be created at Nigerian Political Ward levels.

3. Physical and logical connections of all the data centres through available technologies namely Satellites, Asymmetric Digital Subscriber lines (Broad band), Fibre Optic, Wireless (Radio) and Internet.

4. Establishment of on-site and remote Data Recovery Sites (DRSs) for the National Data Centre and other data centres as a Data Back-up System (DBS).

5. Establishment of a system for content development that will entail the identification of the operational micro and macro statistical and non-statistical information that will be collected through censuses, surveys and routine administrative methods, transformed and integrated into a data pool in the data warehouses (data centres).

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6. Configuration of the necessary Database Management Systems for managing all the micro and macro statistical and non-statistical information that will be stored or warehoused in the data centres.

7. Construction of user Decision Support Systems (DSS) applications to generate information from the data pool; that is, a Business Intelligence Support System (BISS) for data and information dissemination and decision making.

8. Construction of communication systems to move or transmit data and information within each organization in NSS [Local Area Network (LAN)]; or across organizations within the same location or town [Municipal Area Network (MAN)]; in different locations or towns [Wide Area Network (WAN)], or globally through the Internet.

9. Initiation into “cloud computing” in each NSS in Africa.

The foregoing paints a general picture of the elements of GESIS within a typical National Statistical System. However, in Nigeria, there is a component that deals with the connection of sub-offices within the NSO itself; that is, the Local Area Network (LAN) within NBS Headquarters, the six (6) Zonal Offices in Kaduna, Jos, Maiduguri, Ibadan, Enugu and Calabar; the thirty-six (36) State capitals and Federal Capital Territory (FCT), the three (3) schools of Statistics in Ibadan, Kaduna and Enugu and a Disaster Recovery Site (DRS) in Lagos. In future, the DRSs will be extended to six locations in line with the six geo-political regions of the country.

4. The Virtual Cloud and the Nigerian Data Nervous System (NDNS)

The creation of a virtual cloud is to bring all data suppliers, producers and users under one umbrella and with connections to the global network; the Internet, as shown in Figure 3. The object of thought is to create a well-connected “family” of all major “statistical actors” in the National Statistical System of Nigeria. Technically, the idea is to create a National Data Nervous System (NDNS) similar to the nervous system of the human body where all organs are connected through the nervous or blood system. The objective is to create a situation where anybody in any organization at the Federal, State or Local Government level or even at the Political Ward level in Nigeria can obtain any data or information on his or her desktop or laptop or smart mobile phone particularly a blackberry or other smart devices such as an iPad or iPhone. The person may be a public servant, private sector worker, press man, student, researcher, lecturer, consultant or regular person interested in Nigerian data and residing in Nigeria or abroad. The person could also be a foreigner residing in Nigeria or residing in any other part of the world.

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5. The National Data Centre (NDC)

The National Data Centre in Nigeria is located in the Headquarters of the National Bureau of Statistics (NBS) in the Federal Capital Territory, Abuja. It serves as the hub or “brain” of GESIS and it is expected to receive data and information from all data producers in the country and to be the source from which all data users here and abroad can receive data electronically.

The National Data Centre in NBS has three (3) apartments, namely:

1. Communication Apartment

2. Server Apartment and

3. Power System and Fire Protection Apartment.

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Figure 3: The VPN Cloud of the Nigerian Data Nervous System (NDNS)


5.1. Communication Apartment

The Communication Apartment houses all the electronic gadgets that connect NBS with other organizations and individuals that exchange data and information with the Agency. The Apartment has two (2) key components: one housing the connectivity within the NBS headquarters itself and the other housing the connectivity between NBS and other organizations within and outside Nigeria. Technically, the first component manages the Local Area Network (LAN) within NBS headquarters, while the second manages the Municipal Area Network (MAN) that connects all organizations within the Federal Capital Territory (FCT) to NBS. It also manages NBS’s Wide Area Network (WAN) that connects NBS headquarters with other organizations outside the FCT; that is, in the States, Local Government Areas (LGAs) and all NBS Zonal and State offices. Furthermore, this second component manages NBS’s connection to the outside world through the web server.

The communication gadgets germane to the function of the Data Centre are Routers, Switches, the Firewall and the Intruder Prevention Sensor (IPS). These are all the active components of the Communication Apartment. The passive components include the Cables, Snapping Jack Assembly, Fibre, Fibre Terminators, Small Form-Factor Pluggable (SFP) or Optical Trans-Receiver Module, Universal Rack and Accessories, Patch Panel [Fibre and /Unshielded Twisted Pair Cables Minimum Cats (UTP)], Cable Tiers and Cable Rays, Trunkings and PVC Pipes.

5.2. Server Apartment

The Server Apartment contains high level computer systems with programmes and applications that enable computers hold large bodies of data and information and the ability to communicate with themselves within a Local Area Network (LAN), a Municipal Area Network (MAN), a Wide Area Network (WAN) and the Internet. Currently at the National Data Centre (NDC) at NBS, there are many kinds of Servers, namely: Application Server, Storage Server, SQL Server, Print Server, Security Server, Media Centre Server, Virtual Centre Server, ESX Server, Exchange Server, Domain Controller and Domain Controller Back-up and Web Server.

5.3. Power System and Fire Protection Apartment

The National Data Centre (NDC) at NBS has a power system that receives power from the main supply source, processes the power before distributing it to the peripheral devices and computers. The power originates from the public electricity supply system and NBS generators. The processing of power in the data centre is through the following sets of equipment:

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120 KVA stabilizer with 100 per cent expansion tolerance. This is directly connected to the main power supply and supplies clean power to all devices and computers on the network.

Two 80 KVA UPSs.

Power Distribution Panel.

UPS Batteries (40 for each UPS) with capacity for 8 hours of power back-up.

The other set of equipment in this apartment constitute the Fire Protection System which is made up of:

Three (3) 188 kg F-200 Fire Extinguishers.

Smoke Detectors with back-up batteries that can last for two weeks.

The Fire Protection System is controlled from a point in the Data Centre. In addition, all top officers of NBS in the Headquarters’ building have the fire alarm switches in their offices to activate the fire extinguishers in case of a fire disaster originating from their offices.

The design of the National Data Centre (NDC) in the Headquarters of NBS takes into consideration the unstable and problematic power situation in Nigeria. This explains why the National Data Centre at NBS has a power backup system and fire protection apartment to guarantee clean power to the devices and computers in the office, and protect the building against fire outbreak.

This design is recommended for all Data Centres at all levels of governance in the country. The configuration of the various types of equipment in the Data Centres has to be determined by the size of data each organization is expected to manage. It is, therefore, expected that the data centres at the State levels will have a lower configuration than the one in NBS. Similarly, the Data Centre in a typical MDA at the Federal level will be higher than the one at a typical MDA at the State level. There is also a 110 KVA generator dedicated to the National Data Centre (NDC) as a power back-up system.

6. Physical Inter-Connectivity of Nigeria’s Data Centres

The workings of the virtual cloud that covers data producers, suppliers and users within the Nigerian Data Nervous System (NDNS) has been explained along with the workings of the National Data Centre (NDC) which serves as the hub of the data nervous system. In this section, the features of the connectivity; that is, the physical connection between work stations or

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laptops or between one Data Centre and another are discussed in some details. The connectivity comes in different modules as itemized below:

Connectivity within NBS Headquarters in Abuja

Connectivity between NBS Headquarters in Abuja and the six (6) NBS Zonal Offices in Kaduna, Jos, Maiduguri, Enugu, Calabar and Ibadan, the Disaster Recovery Site in Lagos and Training Schools.

Connectivity between NBS Headquarters and the Federal Ministries, Departments and Agencies (MDAs) in Abuja.

Connectivity within each Federal MDAs and their Parastatals.

Connectivity between NBS Headquarters and the thirty-six (36) plus FCT, Abuja, State Statistical Agencies (SSAs), State MDAs and Local Government Statistical Units (LGSUs).

Connectivity between LGSUs and the Political Wards.

6.1. Connectivity within National Bureau of Statistics, Headquarters

Creating a network of connectivity between computer systems and peripheral devices within an office and between other offices in the same locality (town) and between locations and the global community through the internet is the hallmark of a modern National Statistical Office (NSO). In Nigeria, this feature exists in the National Bureau of Statistics (NBS) Headquarters in Abuja, the national capital. The Local Area Network (LAN) in the Headquarters of NBS is the starting point of GESIS in Nigeria. The LAN connects the computers and devices to the National Data Centre (NDC). The LAN effectively connects workers in all Departments and through the Data Centre to each other. The LAN in NBS Headquarters has the capacity for 500 users. The Data Centre has also been configured to connect through a Virtual Private Network (VPN) and the internet to all other agencies within the Nigerian Statistical System and beyond. The first stage of the connectivity outside NBS headquarters is the VPN link between the headquarters in Abuja and NBS Zonal Offices, the Disaster Recovery Site in Lagos, some 850 kilometres away and the three Statistics Training Schools in Kaduna; serving the northern region of Nigeria, Ibadan for the Western Region and Enugu for the Eastern zone. This connectivity is explained further in section 6.2.

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6.2. Connectivity between NBS HQ, Zonal Offices, Disaster Recovery Site in Lagos and Training Schools

NBS operates a Zonal Office structure. The Zonal Offices are in Kaduna for the co-ordination of the North-West geo-political zone of Kebbi, Sokoto, Zamfara, Katsina, Kano, Kaduna and Jigawa States; Jos for the coordination of the North-Cenral geo-political zone of Kwara, Kogi, Nassarawa, Benue and Plateau States and the Federal Capital Territory (FCT), Abuja; Maiduguri for the North-East geo-political zone of Yobe, Adamawa, Borno, Taraba, Bauchi and Gombe States; Calabar for the South-South geo-political zone of Rivers, Cross River, Akwa Ibom, Bayelsa, Edo and Delta States; Enugu for the coordination of the South-East geo-political zone of Enugu, Imo, Anambra, Ebonyi and Abia States; and Ibadan for the coordination of the South-West zone of Oyo, Osun, Ogun, Lagos, Ondo and Ekiti States. Electronic data processing of surveys and censuses outcome takes place at the Zonal Headquarters with full complements of data processing equipment. Data processing for all national surveys for Household and Establishment/Business level surveys, are carried out at the 6 Zonal Offices. Questionnaires are routinely retrieved from the State Offices of NBS to the Zonal Offices during surveys. Similarly, data from Agricultural and Businesses Censuses are also processed at the Zonal Offices.

A Private Virtual Network (VPN) currently connects the 6 Zonal Offices, the Disaster Recovery Site in Lagos and the 3 statistical schools in Kaduna, Ibadan and Enugu to NBS Data Centre in Abuja as shown in Figures 4 and 5. The connection to the schools is to enable the students have access to practical use of ICT tools. There is also the connectivity between NBS headquarters and the Federal Ministries, Departments and Agencies (MDAs) in Abuja. This is discussed in section 6.3.

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Figure 4: NBS VPN Design for the Zonal Offices and Schools of Statistics


6.3. Connectivity between NBS Headquarters and Federal MDAs

Virtually all Federal Ministries, Extra-Ministerial Departments and Agencies are located in Abuja, and they all generate statistics from their regular operations as well as use same for their activities. Such administrative data are very important sources of statistical information for managing the economy. The management and seamless exchange of such data is critical to the work of NBS as the custodian of official statistics in Nigeria. When such Federal administrative data are combined with the State-based data as well as data from national surveys and censuses conducted regularly by NBS and other data producers, the National Statistical System of Nigeria will become data-rich and facilitate Governance at the Federal, State and Local Government levels.

NBS has designed a Virtual Private Network (VPN) to connect all Federal MDAs to its National Data Centre, designed a template to capture Sector Statistics, warehouse the sector statistics in

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Figure 5: Schematic Diagram of the Connections between NBS HQ, Zonal Offices and DRS in Lagos


the MDAs’ Data Centres and move them electronically to NBS Data Centre. A virtual presentation of the connectivity between NBS Data Centre and the Data Centres at the Federal MDAs is shown in Figure 6. In what follows the connectivity between a typical Federal Ministry, the Federal Ministry of Finance and its Extra-Ministerial Departments and Agencies (Parastatals) and NBS is discussed in some detail.

The Federal Ministry of Finance (FMF) manages the fiscal side of the nation’s public finances. It complements the Central Bank of Nigeria (CBN) that manages the monetary side. The idea here is to design a data exchange system that will allow financial data to flow seamlessly from all parastatals of FMF to its Data Centre and from there to the National Data Centre at NBS and then to all users of Nigeria’s financial data on request through direct computer-to-computer connection or through the VPN connections or Internet. A simple design of the connectivity between the Federal Ministry of Finance and its parastatals is shown in Figure 7.

Currently, the FMF has 10 agencies under its supervision, that is, Nigeria Customs Service (NCS), Investment and Securities Tribunal (IST), National Insurance Corporation of Nigeria (NICON), Securities and Exchange Commission (SEC), Nigerian Export and Import Bank (NEXIM), Budget

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Figure 7: Connectivity between FMF and its Parastatals

Figure 6: Virtual Presentation of the Connectivity between NBS Data Centre and FederalMDAs


Office of the Federation, Office of the Accountant-General of the Federation (OAGF), Federal Inland Revenue Service (FIRS), Debt Management Office (DMO) and Nigerian Deposit Insurance Corporation (NDIC). The expectation is that the Data Centre in FMF will be connected to the Data Centre in each of its agencies.

The starting point for implementing GESIS in FMF is the establishment of a Local Area Network (LAN) that will connect the computers and other devices within the Ministry so that everyone (including the Honourable Minister, Permanent Secretary and other top officials) can link up and exchange data and information. There is also supposed to be a connection between FMF and its agencies through the virtual storage linkage between the Data Centre in FMF and the servers in the agencies. As a major producer of government financial statistics, it is prudent for the Ministry to have its own Disaster Recovery Site (DRS) somewhere outside the ministry’s building. Currently, NBS connects directly to public organisations that are heavy producers of statistics and have the resources to be part of GESIS. Notable amongst them are the Central Bank of Nigeria (CBN), Nigeria Customs Service (NCS) and the Ministry of Petroleum Resources.

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Figure 7: Connectivity between FMF and its Parastatals


6.4. Connectivity between NBS Headquarters, the 36 States & FCT, State Statistical Agencies (SSAs), State MDAs & Local Government Statistical Units (LGSUs)

Each of the thirty-six (36) States and the Federal Capital Territory, Abuja has a State Statistical Agency (SSA) and by the design of GESIS, the SSAs are expected to have State Data Centres (SDCs) connected to the National Data Centre (NDC) in National Bureau of Statistics (NBS). Seventeen (17) of such States had been connected to NBS as at end of 2010 with the hope that by 2014 when the National Strategy for the Development of Statistics (NSDS) in Nigeria must have been fully implemented, all other States and FCT will have been connected for the seamless exchange of data across the country. A schematic diagram of the connectivity between NBS Headquarters and the 36 States plus FCT is shown in Figure 8. The active states are Abia, Adamawa, Akwa Ibom, Bauchi, Cross River, Enugu, Gombe, Kano, Kogi, Kwara, Lagos, Niger, Ogun, Ondo, Rivers, Sokoto and Zamfara.

In the design of GESIS, State Statistical Agencies are expected to establish their own Data Centres with technical support from NBS. The Data Centres are to be connected to State MDAs and Local Government Statistical Units as indicated in Figure 9. A State Data Centre (SDC) will have a design similar to the National Data Centre in NBS at Abuja but smaller in scope and configuration.

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Figure 8: NBS & 36 SSAs and FCT Virtual Private Network (VPN)


6.5. Connectivity between Local Government Statistical Units (LGSUs) and Political Wards

The entire GESIS project of Nigeria will cover all data producers, suppliers and users when the virtual cloud covers all Political Wards as earlier indicated. Nigeria has about 8812 Political Wards and they represent the smallest administrative units in the country. Currently, each of these wards is politically managed by a Councillor who is expected to identify with the societal problems at this lowest level of governance. Households as well as private and public sector organisations of all sizes operate at the Ward level and data can then be collected to produce aggregate data on commodity prices, agricultural and non-agricultural production, and employment and so on for policy making. Capturing data at this level ensures that the Government will better understand societal problems to find solutions to. It will also allow the political class; that is, the President, Governors, Senators, House of Representative members, State House of Assembly members, Local Government Chairmen and Councillors to speak with one voice on matters relating to the people they represent and govern.

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Figure 9: Sample Design of the Connectivity between State Data Centre, State MDAs & Local Government Areas


It is expected that when Nigeria’s GESIS is fully developed, there will be a Data Centre in each of the Wards from which data on all aspects of the Nigerian socio-economic life can be assembled and viewed. These data centres will be connected to the ones in the LGAs from where the States and NBS will obtain same set of data through all the connections previously discussed. This is the spirit of GESIS.

Using Ondo State in Nigeria, as an example, all the Local Government Areas (LGAs) are shown in Figure 10. Picking a typical LGA, Ondo West, the Political Wards are shown in Figure 11. With this facility, the level of development in each Political Ward can be exposed to the Governor of the State, as well as the Senator, member of House of Representative, member of State House of Assembly, the Chairman and Councillors representing each of the Wards in the LGA through GESIS. The Physical connections in Ondo West LGA to its 12 Wards under GESIS are shown in Figure 12.

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Figure 10: Map of Ondo State by Local Government Areas

Owo

Ose

Ondo West

Ondo East

Okitipupa

Odigbo

Irele

Ile Oluji/Okeigbo

Ilaje

Ifedore

Idanre

Ese Odo

Akure South

Akure North

Akoko South West

Akoko South East

Akoko North West

Akoko North East

LGA


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Figure 11: Map of Ondo West Local Government Area by Wards

Figure 12: Connection between Ondo West LGA and its Wards


The foregoing explains the linkages within the National Data Nervous System (NDNS) of Nigeria that is expected to connect all data producers in the public sector of the country. All public sector organizations at the Federal, State and Local Government levels are connected. The entire system is analogous to the National Electricity Grid (NEG) that connects the various power stations (coal, thermal, hydro, etc.) through the National Power Transmission Lines (NPTLs) and the Electricity Distribution Networks (EDNs), that is, the supply side of the Nigerian power sector to households and public and private establishments that make up the demand side. It is also similar to Public Water Systems (PWSs) in which Water Storage Dams (WSDs) are built to hold water (like Data Centres holding statistical and non-statistical information) which is transmitted through a network of Water Booster Stations (WBSs) and pipes to users in households, public and private establishments as well as the general public. In GESIS, the counterparts of the power transmitted through the national electricity grid and water through the public water system are statistical and non-statistical information produced by NBS, Federal, State MDAs and Local Government Statistical Units (LGSUs). This statistical and non-statistical information constitutes the contents that are being developed, in most cases, in Databases for the operations of GESIS.

7. Content Development and Database Management Systems

Content development and database constructions form the major activities for archiving, exchanging and disseminating statistical and non-statistical information under GESIS. These activities engage professionals of all categories in NBS and other agencies within the Nigerian Statistical System. And, Information Technology (IT) tools play vital roles in these activities.

In NBS, there are presently four (4) categories of content development and database application development that takes place as displayed below with the computer screen shot.

Sector Statistics and Database Management System (DBMS) for managing data in Federal MDAs

State Statistical Year Book and DBMS for managing data for States and Local Government Areas

Political Ward Statistics and DBMS for managing data from the Wards

Special DBMS for specific datasets

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The process of developing content and DBMSs for running GESIS starts with NBS, (as the apex data producing agency in the country) studying the sources of official statistics (numerical and non-numerical) methods used in their collection, and their real and potential data users in Nigeria and abroad. Specifically, this involved exploring all sources of data in data producing agencies such as the NBS, National Population Commission (NpopC), Central Bank of Nigeria (CBN), Nigerian National Petroleum Corporation (NNPC), Nigerian Custom Service (NCS), Federal Ministries, Extra-Ministerial Departments and Agencies (MDAs), State MDAs and Local Government Statistical Units (LGSUs).

Data and information users in the private and public sectors as well as the UN system, particularly, UNDP, FAO, UNIDO, ILO and other Development Partners like the World Bank, IMF, DFID, AfDB were also investigated as to their data and information requirements. The outcome of this initial effort was the development of four (4) contents and database management systems alluded to earlier and discussed in some details next.

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NBS Applications


7.1. Sector Statistics and DBMS for Federal MDAs

Each MDA at the Federal level administratively assembles statistics peculiar to the sector it manages. Therefore, sector statistics are generally referred to as administrative statistics. As an illustration, while the Federal Ministry of Agriculture assembles and manages agricultural statistics, the Federal Ministry of Education does the same for education statistics. Similarly, while the Nigeria Customs Service assembles and manages the nation’s international trade statistics, the Central Bank of Nigeria caters to all money, banking and Balance of Payments (BoP) statistics in the country.

In managing Nigeria’s Sector Statistics, NBS (after studying the sources and types of data assembled by Federal MDAs), grouped all the socio-economic data from the various sources into Divisions, Items and Details (DID) following the International Standards of Industrial Classification (ISIC) codes as well as the codes of other UN Classification systems. The exercise generated thirty (30) sectoral data divisions in the first instance. Based on this,, NBS, working with subject matter specialists, isolated about 54,000 data details (Variables) on which sector statistics are expected to be assembled. The study on sector statistics also produced a publication “Compendium of Terms, Concepts, Definitions and Methodologies for Statistical Production and Management in Nigeria” published by the National Bureau of Statistics in 2007. The compendium also contains comprehensive meta-data that explain sources and procedures of collecting the sector statistics in Federal MDAs. The content of the compendium was then used to develop a Time-Series Socio-Economic Database of macro-level data that are being used to warehouse macro-data from 1914 (the year the country came into existence) to date.

The 30 data groups (divisions) obtained from the study are listed below:

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1. Agriculture 16. Public Finance 2. Water Resources 17. Prices & Price Indices3. Petroleum 18. National Accounts4. Mining & Quarrying 19. Public Order, Safety & Crime5. Manufacturing 20. Education6. Electricity Supply & Demand 21. Population Vital Statistics7. Water Supply 22. Health & Human Services8. Housing, Building & Construction 23. Employment & Labour9. Distributive Trade Services 24. Environmental Statistics10. International Trade & BOP 25. Membership Organisations11. Hotels, Restaurants & Tourism 26. Recreation & Sporting

Activities12. Transport 27. Religion & Related Activities13. Communications 28. Public Administration & Security14. Money & Banking 29. Meteorological Statistics


Using the Education sector as an example, the Education Statistics Division has eighty (80) as its Division code by the UN Classification and this is broken further down to various Item Codes for Nigeria as indicated below. The Items Codes run from 01 for Primary Education Statistics to 8028-8034 for Statistics of Polytechnics in Nigeria by Institution, Programme and Staff. From the 2-digit Divisional Codes and 2-digit Item Codes, the first data item code name is Primary Education Statistics which has an Item code number that runs from 8001 to 8034.

01 Primary Education Statistics (01-53)02 Secondary Education Statistics (01-53)03 Statistics of Teacher Training, Technical and Vocational Educational Institutions

(01-22)04-06 Applications to JAMB for first Degree Courses by Universities (01-31), Major

Discipline (01-14) and Sex (01-03)07-09 Placements by JAMB for first Degree Courses by Universities (01-31), Major

Discipline (01-14) and Sex (01-03)10 Student Enrolment by Major Disciplines in Nigerian Universities (01-14)11-13 Diploma and Certificate Awards, First Degree Awards and Postgraduate by Major

Disciplines in Nigerian Universities (01-14)14 Number of University Teachers by Major Disciplines (01-14)15 Student - Teacher Ratios in Nigerian Universities by Major Disciplines (01-14)16-17 Distribution of National Youth Corpers by Sex (01-03) and Course of Study (01-09)18-27 Statistics of Colleges of Education in Nigeria by Institution (01-61), Sex (01-03),

Programme (01-12), School (01-05), Staff Qualification (01-09), Grade (01-12), State of Origin (01-32)

28-34 Statistics of Polytechnics in Nigeria by Institution (01-37), Programme (01-12), Sex (01-03), Nationality of Staff (01-04)

The figures in parenthesis are the numbers of Statistical Details/Variable Names under each Item code. The ones for Primary Education Statistics Item code name runs from 800101 to 800153 as indicated below. It is based on these details that data are assembled and stored in the data warehouse.

8001 Primary Education Statistics in Nigeria: Number Observed on Education Day for the Year by State

800101 Total number of primary schools800102 Total number of male pupils in primary 1800103 Total number of female pupils in primary 1800104 Total number of pupils in primary 1 (02+03)800105 Total number of male pupils in primary 2800106 Total number of female pupils in primary 2800107 Total number of pupils in primary 2 (05+06)800108 Total number of male pupils in primary 3

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800109 Total number of female pupils in primary 3800110 Total number of pupils in primary 3 (08+09)800111 Total number of male pupils in primary 4800112 Total number of female pupils in primary 4800113 Total number of pupils in primary 4 (11+12)800114 Total number of male pupils in primary 5800115 Total number of female pupils in primary 5800116 Total number of pupils in primary 5 (14+15)800117 Total number of male pupils in primary 6800118 Total number of female pupils in primary 6800119 Total number of pupils in primary 6 (17+18)800120 Total number of classes in primary schools800121 Total number of male enrolment (primary) (02+05+08+11+14+17)800122 Total number of female enrolment (primary) (03+ 09+12+15+18)800123 Total number of enrolment (primary school) (21+22)800124 Total number of Male Graduate Qualified Primary School Teachers800125 Total number of Female Graduate Qualified Primary School Teachers800126 Total number of Qualified Graduate Primary School Teachers (24+25)800127 Total number of Male Graduate Unqualified Primary School Teachers800128 Total number of Female Graduate Unqualified Primary School Teachers800129 Total number of Graduate Unqualified Primary School Teachers (27+28)800130 Total number of NCE Male Primary Teachers800131 Total number of NCE Female Primary Teachers800132 Total number of NCE Primary Teachers (30+31)800133 Total number of Male Grade I Primary Teachers800134 Total number of Female Grade I Primary Teachers800135 Total number of Grade I Primary Teachers (33+34)800136 Total number of Male Grade II Primary Teachers800137 Total number of Female Grade II Primary Teachers800138 Total number of Grade II Primary Teachers (36+37)800139 Total number of Male HSC Primary Teachers800140 Total number of Female HSC Primary Teachers800141 Total number of HSC Primary Teachers (39+40)800142 Total number of Male WASC Primary Teachers800143 Total number of Female WASC Primary Teachers800144 Total number of WASC Primary Teachers (42+43)800145 Total number of Male Special Education Primary Teachers800146 Total number of Female Special Education Primary Teachers800147 Total number of Special Education Primary Teachers (45+46)800148 Total number of Male Primary Teachers not specified elsewhere800149 Total number of Female Primary Teachers not specified elsewhere800150 Total number of Primary Teachers not specified elsewhere (48+49)800151 Total number of Male Primary Teachers (24+27 +30+33+36+39+42+45+48)800152 Total number of Female Primary Teachers (25+28+31+34+37+40+43+46+ 49)

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800153 Total number of Primary Teachers (51+52)

The same coding procedure is used in the management of each of the 30 data groups that NBS has worked on and are contained in the compendium earlier referred to (NBS, 2007). The process of developing the data codes is continuous as new data divisions, items and details are discovered. This explains why NBS, as the apex data producing agency in the country and by law coordinates the production of statistics at the MDAs, manages and updates the compendium in collaboration with MDAs, who are the primary owners of the sector statistics. Data about each sector are expected to be assembled in each MDA and through the electronic grid, described in the proceeding section of this publication, are sent to National Data Centre (NDC) in NBS where the Time-Series Socio-Economic Databases are housed.

To electronically facilitate and manage this process, NBS has developed a Template for data entry for all the 30 data groups. The Client Server version of the template for each group is expected to be installed on the server in the data centre of each MDA and when data are entered, they will automatically be sent to NBS Data Centre through the VPN connection explained earlier. This way all Nigerian administrative data have replicates in NBS and with NBS back-up system the data are secured permanently. Any change is carried out by the administrators in NBS and the relevant MDA. This way the security and integrity of official statistics are ensured as users can obtain the same set of data on the same subject either from NBS or the original source; that is, the MDA managing the particular dataset.

7.2. State Statistical Yearbook and DBMS for States and LGAs

Similar to what obtains at the Federal MDAs where statistical information is managed by NBS in collaboration, with the MDAs, State and Local Government official statistics are also managed by the State Statistical Agencies (SSAs) and NBS. In this regard, NBS in collaboration with all States developed a State Statistical Yearbook (SSYB) to manage data at the sub-national level. The data covers the 36 States of the Federation, Federal Capital Territory, Abuja and 774 Local Government Areas (LGAs) of the country. The SSYB data coverage ranges from Weather & Climate Statistics as well as Population & Vital Statistics to State sector statistics on Agriculture, Education, Health and Transportation, etc. NBS has designed the database for SSYB for all the States and FCT. The Template for capturing the SSYB data has also been developed to provide the link between the National Data Centre (NDC) in NBS and the State Data Centres (SDCs).

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7.3. Political Ward Statistics and DBMS for Managing Data

At the time of the reform of the National Statistical System in 2005, Nigeria had about 8812 Political Wards which were supposed to represent the lowest political and administration unit in the country. Every member of the political class from the Chairmen of the 774 Local Government Councils up to the President as the Head of the Federal Government belongs to a Political Ward. Each Political Ward is represented in the Local Government Council by a Councillor and that level of political and administrative unit is the closest to the households where people live and go to work. Therefore, the level of socio-economic development or the welfare of people at the ward level can be measured or assessed by the availability or non-availability of socio-economic amenities in each Political Ward. Similarly, this piece of information can be used to assess or measure governance when the information is collected periodically, say annually or every three or five years.

In NBS, a process of collecting data on the availability or non-availability of socio-economic amenities has been put in place. In addition, the database for managing the statistical information from the exercise has been developed. The database can be updated periodically to assess the well-being of people in each of the Wards in Nigeria as well as assess the impact of Governments (Federal, State and Local) on the people of Nigeria. So far, twenty-four (24) socio-economic data details (variables) are being managed with the database. The database can be enlarged to accommodate more Wards or more data details. The detail names in the current Political Ward Database (PWDB) are as listed below:

1. Safe Water (Pipe-borne or Treated Borehole Water)2. Postal Service3. Phone Service4. TV Reception5. Electricity Supply from the National Grid6. Electricity Supply from Rural Electricity Scheme7. Tarred Roads within Ward8. Tarred Roads linking Neighbouring Wards9. Commuter Services (Vehicles)10. Commuter Services (Motor Cycles)11. Boat/Ferry Services12. Market Outlets13. Public Toilets/VIP Latrines14. Incinerators/Dump Sites15. Health “Facilities (Hospitals/Clinics)16. Police Stations/Posts17. Hotels/Guest Houses18. Primary Schools

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19. Public Recreation Centres20. Banks 21. Industrial Establishments within Ward22. Crop Farming Activities23. Livestock Farming Activities24. Fishing Activities

In the collection of the statistical information on the aforementioned, data details, a “yes” or “no” option was adopted. When a particular amenity or activity is available in a particular Ward the statistic is “1” when not available it is “0”. The database was designed in such a way that the following reports can be generated:

1. Ratio of Political Wards with access to amenities

2. Percentage of Political Wards with access to amenities by State

3. Number of Political Wards with access to amenities by State

4. Percentage of Political Wards with access to amenities by Local Government Area

5. Number of Political Wards with access to amenities by Local Government Area

6. Summaries of Numbers and Percentages of Political Wards with access to amenities by Local Government Area

7. Political Wards in Local Government Area

8. Summary Report of Political Wards at National and State levels

As the database can be amended to accommodate more variable names, it can also be queried for more reports. A typical report on the socio-economic characteristics of Political Wards in Ondo West Local Government Area (LGA), one of the eighteen LGAs in Ondo State (one of the thirty-six States in Nigeria), in South-West geographical zone (out of the 6 zones) is shown on Table 1. Ondo West LGA has twelve (12) Political Wards, and the Table, for example, indicates that only 25 per cent of the Wards have facilities for producing clean water from either public water system or treated borehole. Also, while 100 per cent of the Wards have access to TV reception, only 8 per cent have access to public recreational centres, amongst other amenities. Such an analysis can be done for all the 774 LGAs in the country and the results can be used to assess the level of governance in Nigeria. The results can also help measure the effectiveness of Governments at the National, State and Local Government levels as well as assess the performances of Ward Councillors, Chairmen of LGAs, members of State Houses of Assembly and Houses of Representatives, Senators, Governors and the President.

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Table 1: Socio-Economic Characteristics of Political Wards in Ondo West LGA

The information generated from such a database can set in motion a “Grassroots Development Plan” that can revolutionise socio-economic development in Nigeria. Statistical data on individual Wards can also (with the aid of a Spatial Software Application) be used to map results for easy visualization for policy makers as shown in the screen shot below:

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S/N

Ward's Safe

Wat

er(P

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born

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d Tr

eate

d Bo

reho

le)

Post

al S

ervi

ces

Phon

e Se

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TV R

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tion

Natio

nal G

rid

Rura

l Ele

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icat

ion

Tarr

red

Road

s, W

ithin

War

d

Tarr

ed R

oads

Lin

king

Ne

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ard

only

Com

mut

er S

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ces

(Veh

icle

)

Com

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(Mot

or

Cycl

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Boat

/Fer

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Mar

ket o

utle

ts

Publ

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Toile

ts/V

IP L

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Inci

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omes

Prim

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Scho

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Publ

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Indu

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1 BAGBE/ODOWO/IGUNSAOI 0 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 0 1 0 0 1 1 1 0

2 ENUOWA/OBALALU 0 1 1 1 0 1 0 0 0 1 0 1 0 0 1 0 1 1 1 0 1 1 1 0

3 GBAGHENGA/OGBONGBO/AJAGBA-ALAAFIA 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 0

4 IFORE/ODOSIDA/LORO 0 1 1 1 0 1 1 1 0 1 0 1 1 0 1 1 1 1 0 1 1 0 1 0

5 ILUNLA/BAGBE/ODOWO II 0 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 0 1 0 0 1 1 1 0

6 LITAYE/OBUNKEKERE/IGBINDO 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 1 1 0

7 LODASA/IPARUKU/LIJOKA 1 0 1 1 1 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 1 1 1 0

8 ODOJOMU/ERINKETA/LEGIRI 0 0 1 1 1 1 0 0 1 1 0 0 0 0 1 0 1 1 0 0 1 0 1 0

9 OKEAGUNLA/OKERO/OKEKUTA 0 1 1 1 0 1 0 0 1 1 0 1 0 0 1 0 0 1 0 0 1 0 1 0

10 OKELISA OKEDOKO/OGBODU 1 0 1 1 1 0 0 1 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0

11 OKEOTUNBA/OKEDIBO/SOKOTI 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 0 0 1 0 1 0

12 ORISUNMIBARE/ARAROMI 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 1 0

3 4 7 12 4 7 3 4 7 12 0 10 2 0 11 3 6 12 1 3 11 7 12 0

25.0 33.3 58.3 100.0 33.3 58.3 25.0 33.3 58.3 100.0 0.0 83.3 16.7 0.0 91.7 25.0 50.0 100.0 8.3 25.0 91.7 58.3 100.0 0.0

TOTAL

PERCENTAGE

Available = 1, Not Available = 0


The concept of the Political Ward Database can be broadened and institutionalised to collect demographics (births, deaths, marriages) as well as other socio-economic data about individuals such as residency status, employment status, etc. and all establishments in each Ward. This can then be aggregated at Local Governments, State and Federal levels. This exercise will bring the National Bureau of Statistics, the National Population Commission and other relevant MDAs to jointly collect data routinely at each Ward. At the Ward, there will be a Ward Data and Information Centre (WDIC) which will warehouse data at that level. WDIC will be linked to the Data Centre at each LGA which will be further linked to the one at the State and then with the National Data Centre at NBS Headquarters.

The development and sustenance of the GESIS project in Nigeria will make statistical information available on a continuous basis, from the Political Ward level and thus render governance more effective through proper isolation of societal problems and planning and implementation of development programmes from the grassroots.

Altogether, Nigeria can be assured of the bottom-up development model advocated globally by development economists which would make households and the people enjoy the dividends of democracy as depicted in a concentric form below.

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7.4. Special Databases

The National Bureau of Statistics has developed the capacity to establish special databases to further enhance data production in the country. Four of such special databases are:

7.4.1. Market Outlet Database (MODB)

This is used to facilitate and manage price data for the estimation of Consumer Price Indices (CPI) which enables the calculation of the monthly Rates of Inflation. This database houses information on over 50,000 vendors in more than 500 market outlets selling about 650 commodities across Nigeria. The database has a back-end resident in the National Data Centre in NBS Headquarters and a front-end installed on the mobile hand-held device used to capture the data. A screen shot of the template showing the profile of a typical vendor in Osun State of Nigeria; Mrs. Jimoh Mistura operating in a typical market centre, Araromi Iragbiji, and the list of some commodities she sells are presented below:

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7.4.2. Directory of Educational Establishments Database (DEEDB)

DEEDB contains the list of all educational institutions in Nigeria by location, from Primary to Tertiary levels. The directory can be queried by State and Local Government and contains a frame (Population) of all schools in Nigeria. It is useful for any survey on the Nigerian School System. A screen shot showing some of the schools in two typical LGAs, Suleja and Tafa in Niger State of Nigeria, is shown below:

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7.4.3. Directory of Health Establishments Database (DHEDB)

This database holds information on all health institutions in all States and Local Governments of Nigeria. Apart from information on the different types of health establishments, the database can be queried to categorise health establishments by ownership (private, public or religion) and location. A screen shot of a typical query from this database is shown below:

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7.4.4. Establishments Database (EDB)

This is a large database of all private and public establishments in Nigeria by industry and location (State and LGA). It is used to capture general information on establishments and serves as a frame for establishment surveys. All economic and social sector establishments that have the employment size of ten persons and above are covered in this database.

In addition to these special databases, NBS is also working on four others, namely, Directory of Hotels and Restaurants Database (DHRDB), Courts Databases (CDB), Police Stations and Posts Database (PSPDB) and Petrol Stations Database (PSDB). The development of databases is a continuous exercise in NBS and is designed as a major platform to manage statistical and non-statistical information in future.

The ideas espoused so far in this publication have concentrated on the use of ICT tools in strengthening the production of official statistics in Nigeria under the GESIS framework. This is necessary for building a robust National Statistical System (NSS) for the country. In order to fully develop the NSS, the data needs of users have to be taken into consideration as well. This will cater to the demand side of the data “market”. In what follows, NBS’s efforts at organizing user-needs under GESIS is discussed in some details, including the development of Decision Support Systems (DSSs) that will enable policy makers and other senior public sector managers and top-end data users visualise statistical information in a friendly manner.

8. Organising for User Needs under GESIS

Information generated within the National Statistical System (NSS) of Nigeria serve as inputs in the decision-making process of users in both the public and private sectors of the economy. Similarly, the data and information assist international organisations in planning their various intervention programmes. The Nigerian University and Research systems also use statistical information in their various research works. The general public, including the media rely on statistical information to understand how the country is governed and also use such information to assess and grade governance at the national and sub-national levels.

Under GESIS, it is expected that user-agencies can be connected to the National Data Centre at NBS the same way NBS connects to data producers at the Federal, State, Local Government Council and Political ward levels. The same technology of connectivity can also be deployed at

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the data user-agencies in Government and the private sector. The major Government user-agencies include the Central Bank of Nigeria (CBN), National Planning Commission (NPC), the Nigerian Institute of Social and Economic Research (NISER), the Centre for Management Development (CMD), National Assembly and the Presidency. In the private sector, three umbrella organisations are good potential demanders of Nigeria’s official statistics. They are the Manufacturers’ Association of Nigeria (MAN), Nigerian Association of Chambers of Commerce, Industries, Mines and Agriculture (NACCIMA), and the Securities and Exchange Commission (SEC).

These are all expected heavy users of statistical information in the country. All other public sector organisations such as the Federal MDAs and State MDAs which are connected to NBS will not only supply official statistics into the system but with official authorization from NBS, also have access to data from NBS. As an example, if a particular ministry such as the Ministry of Health wants demographic data to complement its own data on a particular disease, it can, through GESIS, obtain such information from NBS seamlessly. Similarly, if a particular State Statistical Agency, say in Anambra State in the South-East zone of Nigeria, requires a particular statistical information on another State, say Sokoto in the North-West zone of Nigeria, it can through GESIS obtain the information with ease from NBS. The connectivity of NBS to major data user-agencies is depicted below:

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The National Planning Commission (NPC) is the apex planning office in Nigeria, coordinating all socio-economic planning efforts in all Federal MDAs, 36 States of the Federation and Federal Capital Territory (FCT), Abuja. By law, it has oversight functions over the National Bureau of Statistics (NBS). This explains why it is instructive to have a one-to-one VPN link between NBS and NPC for seamless flow of statistical information needed by NPC for its work.

The NPC is expected to be a major data user-agency of Government in Nigeria and being the office that supervises the operations of NBS for the Nigerian Government, there will be a connection between NPC and the National Data Centre in NBS and between NPC Data Centre and its individual departments. This way, when a specific data user in NPC needs a particular data about the economy, he or she can obtain them directly from NBS through GESIS.

Apart from linking data users and NBS, the Bureau is also forging ahead to educate data users on how to develop simple analytical capabilities useful for generating information as decision support tools. This is apart from the conventional mathematical, statistical and econometric analytical tools available to users. In what follows, simple graphical analytical tools are shown to visualise statistical information as an example of a Data-Information-Decision Support System (DIDSS).

Using NBS data on Health, the following Dashboard (shown as a screen shot) were created to provide salient information on some health indicators which can influence policy-making in the sector:

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Sources of official Statistics in Nigeria

Data users obtain statistical information in different ways. However, the conventional sources are the official publications of Government and contact with statistical offices of the data agencies. These are the traditional and the main methods of obtaining data from the National Bureau of Statistics (NBS) in Nigeria as well as other data producing agencies such as the Central Bank of Nigeria (CBN), National Population Commission (NpopC), the Nigerian National Petroleum Corporation (NNPC), Federal Ministries, Departments and Agencies (MDAs), State Statistical Agencies (SSAs) and State MDAs. NBS on its own has a long list of publications, some of which are listed below.

1. Annual Abstract of Statistics 2. Digest of Statistics 3. Nigerian Foreign Trade Summary 4. National Accounts of Nigeria5. Price Statistics News6. Statistical Fact Sheets7. Trade Statistics News8. Labour Force Statistics News9. Review of External Trade10. Economic Performance Review11. Poverty Assessment Report12. Consumption Patterns in Nigeria13. Facts and Figures of Women and Men in Nigeria (Volumes 1&2)14. Nigeria in Numerical Figures15. Directory of Health Establishments in Nigeria16. Directory of Nursery Schools in Nigeria17. Directory of Primary Schools in Nigeria 18. Directory of Secondary Schools in Nigeria19. Directory of Tertiary Institutions in Nigeria20. Directory of Building and Construction Materials

Official publications as a source of data are fraught with several problems. They are generally outdated when needed because the typical data producer is not financially strong to print hard copies shortly after data production. Searching for data, particularly when it involves travelling has a high transaction cost. In addition, the process of collating and preparation of statistical tables are mainly manual, which makes the publications prone to human errors. Furthermore, publications are expensive in space because of their bulky nature and also prone to damage from age and natural and man-made disasters like rain, flood, fire and theft. For publications that attract a price, users have no willingness to pay as they consider official statistics public goods which should be free. The flat filing system of warehousing data in publications makes

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referencing tedious and difficult unlike the database system that is amendable to searching for specific data. For example, if a user wants a specific data item, say, quantity of baby milk imported into Nigeria in the month of June 1995, the user will have to look for the Trade Statistics publication for 1995, find the imports for the month of June, then look for the page for baby milk. This process is tedious in a modern environment. This enquiry can be done by just querying a database for the particular data.

The foregoing explains why NBS in the spirit of GESIS has developed a process of reaching out to data users electronically. One process is to provide users with soft copies of publications in mobile disks and other media formats which are inexpensive to produce and disseminate. They are also optimal in the use of space as well as less prone to damage from natural disasters. Secondly, NBS now relies strongly on the Internet to reach users of Nigeria’s official statistics locally and internationally. The agency has a very active web portal, www.nigerianstat.gov.ng that serves as the window to the outside world. Any person, anywhere in the world, who has access to the Internet can visit this web portal and obtain current official statistical information about Nigeria. It is interactive as persons on the portal can search for specific data. It has links with all National Statistical offices of other countries and most NBS publications can be reached on the web portal. It also has links with active web portals of Federal MDAs and State Statistical Agencies (SSAs).

A special feature of NBS web portal is the hosting of a web site within the portal known as the National Data Archive Centre (NADA). NADA houses micro-data collected during households and establishments surveys. This web site exposes users to results of surveys and censuses which can be further subjected to rigorous statistical analyses to generate information for policy making. The web site is usually visited by researchers who need micro-level data for their work.

To complement the availability of data from NBS web portal, the agency anticipates establishing a Central E-mailing Facility (CEF) to link the office with registered users of data in Government. These users may include Mr. President, who will continually be in touch with NBS via or through any media which may include smart mobile phones, tablets or laptops. The configurations of some of the selected public service e-mail addresses that can be registered for such dedicated Internet connections are:

Office of the President [email protected] Office of the Vice-President [email protected] Office of the Head of Service [email protected] Office of the Secretary to the Govt. [email protected] Office of the Hon. Minister of National Planning Commission [email protected] Central Bank of Nigeria [email protected]

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mailto:[email protected]






http://www.nigerianstat.gov.ng/


Each user will have a registered password to link up with NBS and get regular updates on official statistics from the agency.

9. Organising Effective Data Supply under GESIS

There are three (3) main groups of data suppliers within any National Statistical System (NSS). The first is made up of the households in which all persons belong at any particular time in a specific Enumeration Area (EA) in any country. In Nigeria, the EAs are usually demarcated by the National Population Commission (NpopC), the agency with constitutional responsibility to conduct population census and vital (demographic) registration in the country. Each EA is expected to have about 500 persons or 100 households (HHs) as the average HH size in Nigeria is 5. Each HH may have 1 or several persons. The head of each HH may also be a male or female. The members of the HH must eat from the same “pot”. With a population of 150 million persons, there are about 30 million HHs in Nigeria and these are located in the urban and rural areas of Nigeria. In NBS, household-based surveys are the usual means of collecting data on personal and family characteristics, employment status, income, assets and micro-or family-based business enterprises (including small-scale farm holders). The HHs provide the frame for all household-based surveys in NBS.

The second group of data suppliers to NBS is the private establishments made of Small and Medium Enterprises (SMEs) as well as Large Scale Enterprises (LSEs). While micro family enterprises are surveyed along with HHs, data are collected about SMEs and LSEs through establishment surveys. These are business surveys and cover all activities that take place in the process of producing goods and services. These include quantities of all items used in the production process and their costs as well as the outputs and revenues accruing from the production process. It also covers the business environment; that is, factors such as policy measures, enhancing production and profitability or those militating against healthy business environment.

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The third group is the public sector organisations that also use conventional factors of production, land, labour, capital and management in producing public services as captured in the estimation of national accounts. These are the Federal, State and Local Government MDAs as previously discussed. They supply data into the system like their business counterparts but in most cases, administratively, as outcomes of routine activities; but sometimes survey instruments (questionnaires) are used.

There are several options for collecting data but for NBS in Nigeria, there are three survey modules for managing the collection of primary data. These are the National Integrated Survey of Households (NISH), National Integrated Survey of Establishments (NISE) and System of Administration Statistics (SAS). These three modules form the basis for reaching data suppliers in the National Statistical System (NSS) of Nigeria and also the avenue for collecting data which are periodically produced for users in Government and other establishments. Some of the regular surveys of the National Statistical Office (NSO) of Nigeria are listed, with their periodicity, in Table 2.

Table 2: List of Regular Surveys in Nigeria’s NSO and their periodicity

Survey PeriodicityMarket Price Survey Weekly

General Households Survey (GHS) Quarterly

Labour Force Survey (LFS) Quarterly

Establishments Survey Quarterly/Annually

Core Welfare Indicators Questionnaire (CWIQ) Survey 3 - yearly

Multiple Indicators Cluster Survey (MICS) 3 - yearly

National Living Standards Survey (NLSS) 5 - yearly

National Agricultural Sample Survey (NASS) Annually

National Agricultural Sample Census (NASC) 10 - yearly

In the past, NBS had relied on the manual traditional methods of collecting and processing survey data. Planning for data collection normally starts with the random selection of EAs from the frame of EAs obtained from the National Population Commission. Then the sketch maps of the selected EAs are assembled and shared to field enumerators who will list the Housing Units (HUs) from where households will be selected for door-to-door data collection. The same approach is used for establishment surveys in which selected establishments are obtained from

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the business registers (frame) for door-to-door enumeration. After enumeration, data processing commences manually using key to disc data entry method to generate the required statistical outputs. These largely traditional manual methods take time; use a lot of personnel, expensive and prone to human errors. In the last 5 years or so, NBS has initiated, in the spirit of GESIS, a number of ICT-based approaches to modernising the data production process. Some of these initiatives are taken up next.

Survey Planning

In survey planning, the availability of EA maps is very crucial to the successful conduct of any survey. The maps are obtained from NpopC in hard copies, and photocopied for distribution to enumerators. Since the EA maps are continually used over time, they age and become difficult to store since NpopC produces them every ten years. Now, the EA maps used in NBS are digitalized, stored as soft copies and,

In agricultural surveys, two sets of farmers supply data into the statistical system of Nigeria. One is the group of small-holders who are household-based while the other is the large-scale corporate farmers. In planning agricultural surveys for the small-holder farmers, the EAs form the basis for selecting respondents. The selection of large-scale farmers is in the same way as in establishment surveys. Selection is from business farm registers. The random selection of small-holder farmers poses a problem when the traditional method is used. This is because selection of EA is random and when a predominantly non-agricultural EA is selected, it greatly underestimates agricultural production, particularly for small-holder crop farming.

In NBS, at present, planning for agricultural surveys starts with a frame construction using GIS and Google Technologies to separate farming areas from non-farming areas based on satellite imagery. This method helps to indicate particular farming areas to select farmers from. Figure 13 shows details of an EA with identifiable farms, their locations and name of the enumerator. The enumerator can be monitored since his location can be geo-referenced using the GIS/GPS technology.

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Figure 13: An Enumerator Station Overview Map


Size of Farms using hand-held device

Traditionally, farm sizes in agricultural surveys are obtained by using theodolite and chain tapes as done by land surveyors. This method is costly in the use of time and personnel, and for the fact that the approach comes with approximations and does not give accurate farm sizes. In the recent times, farm sizes are obtained in NBS by using Garmin eTrex GPS hand-held device. With this tool, an enumerator needs only to walk round a farm to get the farm size. The enumerator can also be monitored because of the embedded GIS /GPS technology embedded in the hand-held device.

Market Price using Hand-held Device

Manual administration of price questionnaires had been the method of collecting market price data for the estimation of Consumer Price Indices (CPI) and inflation rates in NBS. In the last three years or so, hand-held devices have replaced the use of paper questionnaires. This way, price data are collected quickly in all the selected market outlets, in rural and urban areas, and are sent to zonal collation centres and electronically moved to NBS headquarters for final compilation and computation. Information about each market outlet from which NBS collects price statistics in the country has been pre-installed in this hand-held device so that a particular device cannot be used by enumerators outside designated market outlets. This ensures credibility in the entire process of price data collection in NBS.

Computer Assisted Personal Interview (CAPI)

Traditionally, during household and establishment surveys respondents’ supply data which are captured by enumerators on paper questionnaires. Presently, in NBS, a new system of collecting data in these surveys is being experimented. This is the Computer-Aided Personal Interview (CAPI) method. It uses GPS-enabled hand-held mini-computers to collect data. Questionnaires are embedded in the device. The added advantage is that it will eliminate the manual data entry and processing stages of data production since all editing and collation will be done on the field and sent in batches to the Headquarters.

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This quickens the process of data production and minimizes human error. In addition, it is inexpensive because it eliminates the use of paper questionnaires and data processing personnel.

Scannable Questionnaires

NBS is presently using scannable (digital) questionnaires to collect data in small-scale household surveys. This will be the way until the CAPI method is fully adopted. This approach quickens data processing because once the traditional questionnaires are converted to digital forms shown in Figure 14, the image scanning devise with Optical Character Recognition (OCR) software will produce final results without any further manual data entry. The use of scannable questionnaires also reduces human errors and minimises the use of personnel and materials.

The Use of E-Forms on the Internet

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Figure 14: Sample of Digital Scannable Questionnaire


NBS is looking into the future to use electronic web forms (E-Forms) through the Internet to collect data directly from respondents. This will be useful in collecting establishments-based data from medium-to-large corporations. NBS experimented with E-Forms to collect data on Nigerians in Diaspora during the 2009 National Manpower Stock and Employment Survey and the response was impressive.

10. Concluding Remarks

The central theme of this presentation, on recent developments in the National Statistical System (NSS) of Nigeria, has demonstrated that the availability and use of official statistics in any country can be greatly enhanced by the application of various Information and Communication Technology (ICT) tools in data production, management, exchange and dissemination. In order to make this happen, the entire process in statistical delivery has to be integrated by bringing all data producers, users and suppliers together in a network of inter-connections. The starting point of this modernization process is the re-engineering of the organisation structure of conventional National Statistical Offices (NSOs) to include a strong Information and Communication Technology Department (ICTD). Such a department will be made to facilitate the work of the conventional statistics departments responsible for data production (Censuses, Surveys and Administrative Statistics), field operations and agency coordination as well as the Finance and Personnel departments. The ICT department must be well-staffed with personnel knowledgeable in software engineering, application development, hardware engineering and maintenance, data warehousing, data communication and web portal management, amongst other ICT-based specialisations.

Another key requirement for the success of this modernization drive in the National Statistical System (NSS) of a country is the establishment of a Data Centre at the National Statistical Office (NSO) as the apex data producing and data coordinating agency and complementary Data Centres in all other data producing agencies at the national and sub-national levels of governance. Data Centres are also expected to be established at major data users’ agencies and key data supply organisations. The network of Data Centres will then be linked by various communication technologies, depending on distance between Data Centres and users on the networks and the volume of data to be transmitted. The management of these networks may be ceded to private sector providers under a Public-Private Partnership (PPP) arrangement or public sector organisations established principally for this purpose. The foregoing are the core elements of GESIS. Drawing from the experience of Nigeria in the establishment of this GESIS

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framework, the modernization process has many challenges, some of which are highlighted below:

Traditional NSOs all over the world have organizational structures built round data production activities driven by manual technologies. Over time, the workers, including their senior managers, have lukewarm attitudes towards the introduction of ICT tools into statistical production and other complementary activities. In the case of the top managers, many of them have their training and work experiences devoid of ICT skills and are at the autumn of their stay in the office. This explains why most of them feel reluctant to accommodate and imbibe the new “tricks” these new technologies bring into their work. For the younger workers, most of them feel that the new technologies will render them redundant and make them lose their relevance in the organisation which might lead to their eventual retrenchment. Thus, there is always the lack of cooperation and very limited collaboration between the workers in the ICT department or unit and those of other departments in most NSOs. There is the lack of a team-work environment when ICT is introduced in traditional NSOs.

In many cases, the introduction of ICT tools into the operations of NSOs takes place without the involvement of the top management. Specifically, this is either imposed by Government or Donor Agencies without the involvement of top management, including the Chief Executive of the organisation in the planning and execution. This normally leads to the non-existence of high level management commitments to the use of ICT tools in the organisations, the absence of the development of the strategies for the promotion of their use and the design of any ICT investment programme for the future.

In many NSOs, particularly those in Africa, there is either the absence of or the existence of very low level ICT infrastructure in place. There may be a few stand-alone desktop computers seldom used or restricted to very few users. And, in many cases, they are not used for data management activities but for word processing. There is hardly any Local Area Network (LAN) infrastructure in a typical NSO, not to talk of a Municipal Area Network (MAN) to connect computers in different organisations within its neighbourhood or a Wide Area Network (WAN) to connect computers across different locations in different towns. Many NSOs have no official access to the Internet and several lack a no functional web portal or official e-mail address system.

NSOs as public organisations do not attract the desired attention from Governments, particularly those of the developing world. The poor attention from Governments leads to insufficient budgetary allocations. ICT use, being a novel phenomenon in developing countries, therefore attracts very low government funding. This problem is strongly tied

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to the fact that ICT projects could be complex and expensive to implement at the initial stages.

ICT projects require careful advance planning to effectively achieve the organizational goals, and in environments where planning skills are lacking, ICT projects tend to fail. This may also be due to insufficient sensitisation of management and staff of NSOs as to the benefits of ICT to their work. The lack of understanding, more aptly, the high level of ignorance amongst workers in NSOs regarding the benefits of ICT tools, contributes to the slow pace of their use in many of these statistical offices.

In developing economies, there is a general dearth of highly skilled ICT workers such that NSOs find it difficult to recruit them. In most cases, those employed are difficult to retain because of the poor work environment, conditions of service and salary structure in the public services of these countries. At times, when staff of NSOs are trained at Government expense to acquire high level ICT skills, NSOs lose them to the private sector.

Implementing ICT projects usually involves private sector vendors. And, due to poor project planning and high-level ignorance of top public offices in general, ICT projects attract various types of vendors supplying different systems and software packages that, in the long run, create confusion in NSOs. In some cases, the hardware supplied come with hard drives and memory chips that are too small in size to run the software application to be installed. In other cases, software packages supplied soon become unusable because the licences required were not budgeted or paid for. In yet other instances, some software packages are acquired but not used because the workers on ground do not have the requisite skills to use them. These are a few of the problems emanating from the lack of coordination in ICT procurement in Government offices of developing countries, in general, and in their NSOs in particular.

ICT projects in general face threats from a number of other sources. Viruses can be a serious threat because of the use of external drives and connection to the Internet. Hackers can create problems of their own. Hardware crashes can occur anytime. Software packages can suffer from bugs or design flaws. Workers can mishandle items during use. In a number of cases, access to applications can be problematic due to poor password administration. In developing countries, ICT projects do suffer from erratic and unreliable public power supply. And, theft of data and pieces of equipment can constitute a major challenge.

On balance, these challenges were factored in the design, implementation and management of the GESIS project in Nigeria. The project is expected to ensure good governance, instil

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transparency and accountability in government and promote socio-economic development in the country in the immediate future. This will serve as a model for other developing nations by creating a conducive environment for foreign investment as well as moving the people of Nigeria towards a brighter future. In addition, GESIS will usher in an era of *Cloud Computing* which will soon envelope the ICT world.

References

1. Akinyosoye, V.O. (1995): Operationalizing the Data Banking Concept within a National Statistical and Information System: Problem and Prospects in Nigeria, International Transactions in Operations Research Vol 2, (No 4,): 375-384 (Great Britain) Now in Volume 16 Issue 4 Contribution 100%.

2. Akinyosoye, V.O. (2008): Repositioning the Nigerian Statistical System of African Countries within the Framework of International Best Practices: The Case of Nigeria. The African Statistical Journal, Vol. 6: 191-220 (Addis Ababa) Contribution 100%.

3. Kiregyera Ben (2009): Fresh air from Nigeria E-Mail Message sent from [email protected] to [email protected] on November 23, 2009.

4. National Bureau of Statistics (2007): Compendium of Statistical Terms, Concepts, Definitions and Methodology for Statistical Production and Management in Nigeria, NBS, Abuja.

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