adsr brochure

ADSR … facilitating quality research

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BROCHURE

(2013 Edition)

ANALYSTS’ DATA SERVICES & RESOURCES LIMITED

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I. WHO WE ARE

Analysts’ Data Services and Resources (ADSR) Limited was established in 2004 as de analysts dATA services in University of Ibadan, Nigeria. Over the years, the company has been involved in research and training sponsored by various private, government and international organizations. Our vision is to become the first company that comes to mind when issues relating to research support services are mentioned in Africa. Therefore, we have earnestly pursued our mission to acquire and utilise appropriate analytical tools to aid the doing, teaching, learning, and application of research in the continent.

Our Strengths

A team of experienced research fellows that we have been able to build over time; this has made our research outcomes theory-based without sacrificing their policy relevance. This team comprises academics and professionals in the areas of economic, financial, marketing, energy, social and health research.

Our location in and around the Nigeria’s premier university (University of Ibadan) has been exploited to establish affiliations with research centres with which rigorous research are implemented.

The use of latest and proven analytical techniques and software.

Meet Our Esteemed Clients

We have been involved in research works funded by several local and international organizations such as CBN1, NDIC, ECOWAS, AERC, AfDB, UNECA, World Bank, IMF, GIZ, DFID, USAID, WTO, among several others2.

II. HOW WE CAN BE OF SERVICE TO YOU

We offer a wide range of research support services comprising advisory, training and implementation in the following areas:

Research and Study Services

ADSR offers research and study support services in sectors such as:

i Agriculture (Crop Production, Livestock, Forestry, Fishing); ii Industry (Crude Petroleum and Natural Gas, Solid Minerals, Manufacturing);

iii Building and Construction; iv Wholesale and Retail Trade; and v Services (Transport, Communication, Utilities, Hotel & Restaurant, Finance & Insurance,

Real Estate & Business, Government services, Education, Health and Social services).

1 See appendix C for the full list and definitions of acronyms used in this handbook.

2 The detailed profiles of our research fellows that are relevant for any specific project will be sent on demand.

Evidence of our affiliations and clients are equally available


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Our research support services take the forms of proposal writing; literature sourcing and review; implementation of theoretical, methodological and empirical research for professionals, academics, research students, local and international organisations. We offer advisory services and training on how these researches can be conducted and evaluated successfully. ADSR may also be appointed to conduct research on a client’s behalf3. Equally, academics and other researchers can benefit from our support services in their paper publication pursuits.

With our study support services, we can also help with your marketing research and feasibility studies. Also, ADSR can give you qualitative advice and training on how to conduct marketing research and feasibility studies. If your interest is to commit the process of conducting these studies into our hands, we can assure you that we shall duly meet your demands. We pride ourselves over the manner in which we ensure that our clients understand the work we have done for them as though they did it themselves.

Data Collection Services

With ADSR, you can confidently surmount the challenges of data collection and processing. Our data collection services are classified into documentation, cross-sectional and time series/panel data. We offer advice and guides on how to do each of these as well as organise trainings on them.

Do you want ADSR to collect data for you? We will be glad to! We take pride in breaking the barriers to get you all the required data. If you critically consider it, you may find it more effective to spend your precious time on thinking, reading and making investment decisions than to be gathering and verifying data yourself. This is further discussed below.

a. Documentations

ADSR has developed the capacity to extract useful information from documents; hence, you can contact us if you require assistance in this area. Examples of this include; literature sourcing and review on any economic, financial, marketing, energy, social and health issue; and extraction and documentation of data from archives and publications. We can also give you access to past reports (hard copies) of companies that are listed on the Nigerian Stock Exchange.

b. Cross-sectional

We collect both quantitative and qualitative data for you. Target groups include households, business firms and governments (both local and international). Our data collection approaches include survey (questionnaires), in-depth interviews, focus group discussions, key information interviews, observation, content analysis, case studies, life history, and ethnographic studies; among others. We also give advice and training on how to implement any of these approaches.

3 Clients that are research students can benefit from many of our support services at highly subsidised rates.

However, our services to this category of clients are limited to providing guidelines, data/data processing and other related resources. These should not be seen as an alternative for a student client to fully understand, implement and write his/her thesis and dissertation on his/her own.


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Do you know that some international organisations that collect micro-data on various countries also make these data available? For instance, the World Bank has supported the collections of some micro-data in Nigeria; these include: Global Financial Inclusion (Global Findex) Database 2011, General Household Survey, Panel 2010, Migration Household Survey 2009, Enterprise Survey 2007, PETS - QSDS in Health 2002 and Public Delivery of Primary Health Care Services 2002. Similar data have also been collected in many countries of the world. ADSR can assist you in obtaining these data and present them in the manner that you want. We can also offer guides on how you can achieve your aim on any of these databases.

Also, the National Statistical Offices (NSOs) of different countries are involved in the collection of many surveys. For instance, the National Bureau of Statistics (NBS) in Nigeria collects data such as the General Household Survey (GHS) – Cross-sectional, General Household Survey (GHS) – Panel, National Integrated Survey of Households (NISH), National Integrated Survey of Establishments (NISE), Multiple Indicator Cluster Survey (MICS), National Living Standards Survey (NLSS), Harmonized National Living Standards Survey (HNLSS), Labour Force Survey (LFS), Core Welfare Indicator Questionnaire (CWIQ) Survey, National Agricultural Sample Survey (NASS) and National Agricultural Sample Census (NASC). We have all these surveys at our fingertips and we also have the capabilities to work with you on their analysis.

c. Time series/panel data

Time series and panel data can be obtained for empirical analysis at the level of countries, say, GDP, Interest rate and Broad money supply of ECOWAS countries between 1990 and 2000; or at the level of firms, say, EPS, Profit, Tobin’s q and Board size of listed companies between 1990 and 2000. We render services at both levels, as shown below.

i. Country-level time series or panel data

There are many sources that provide time series and panel data on key variables that are used in empirical analysis. The table below shows some few data sources and examples of their databases.

Examples of data sources and databases4 Data source Examples of databases

United Nations (UN) and member organisations

Commodity trade statistics database (comtrade), millennium development indicators, UNODA homicide statistics, Energy statistics database, environment statistics database, Greenhouse gas inventory database (UNFCCC), Gender info, WHO data, UNAIDS data, Human development indices, Labourstat, Global Indicator Database, Industry Statistics (Indstat), UNESCO institute statistics, UNAIDSTAT, United nation high commission for refuge database, UNDS stat,

World Bank (WB) World development indicators, international debt statistics, Africa development indicators, Africa cities diagnostics, climate change data, corporate scorecard indicators, education statistics, exporter dynamics data, gender statistics, global bilateral migration database, global economic monitor (GEM), global financial development, global financial inclusion, millennium development goals tables, health nutrition and population statistics, the atlas of social protection: indicators of resilience and equity, the changing wealth of nations, trade costs dataset, world governance indicators.

International Monetary Fund (IMF)

World Economic Outlook Databases (WEO), International Financial Statistics (IFS), Principal Global Indicators (PGI), Public Sector Debt Statistics Online Centralized Database, Quarterly External Debt Statistics (QEDS), Coordinated Portfolio Investment Survey (CPIS), Coordinated Direct Investment Survey (CDIS), Financial Access Survey (FAS), Financial Soundness Indicators (FSIs), Direction of Trade Statistics (DOTS), Primary Commodity Prices

4 ADSR is also compiling a special handbook that describes fully each of these data sources, their data and usefulness as well as

the countries covered.


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In case you are having any difficulty in obtaining relevant data from these sources, you can contact ADSR. It is also possible that when you have already obtained these data, they are not in the formats that are readily usable for the current analysis. Therefore, we can be contacted to help obtain, arrange and process the data to suit your specific needs.

ii. Industry-/firm-level time series or panel data

We exploit our knowledge of and location in Nigeria to collect and manage a comprehensive database comprising variables of firms that are listed on the Nigerian Stock Exchange (NSE). This database is called: “Financial and Governance (FINGOV) Database”. Our database is very useful if you are an academic, a researcher, capital market operator, government and international institution, NGO and research student (postgraduate and undergraduate). Our services in this area become very helpful in case you encounter difficulties in obtaining such information; or you can obtain them but are constrained by resources such as capital, time and accessibility; or the current forms in which the data are available are not suitable for your purpose; or you already have the information but seek confirmation or updates.

Our experience has shown that a lot of resources are often expended when you attempt to extract relevant information from hard copies of companies’ annual reports. Equally, many stock market indicators are not available in soft copies; and when they do, the time covered may be short or they may not be available in the format you desire. Consequently, you may be discouraged from working with this rich information; or at best, settle for a small sample size. These challenges have been observed to lead to the dearth of rigorous research on firm and stock market behaviours in Nigeria. Thus, ADSR steps in to take the pressure off you.

FINGOV database comprises fundamental and stock market data organised into the following 15 datasets5: Company Profiles, Board structure/Corporate governance I, Board structure/Corporate governance II, Corporate social responsibilities, Shareholders information, International trade/finance, Employee/labour distribution and welfare, Value added, Profit and loss account, Balance sheet, Statement of cash flows, Financial ratios, Specifics to insurance sector, Specifics to banking sector and Equity market indicators.

Sources of information

The primary sources of information for this data are public information published in the annual reports of Nigerian companies and various documents and data released by Nigerian capital market regulators and operators.

Sector covered

Our comprehensive database contains data across all sectors of the Nigerian economy. In the old NSE sector classification system, these sectors are: agriculture, airline services, automobile and tyre, banking, breweries, building material, chemical and paints, commercial services, computer & office equip., conglomerates, construction, engineering technology, food beverages & tobacco, footwear, foreign listing, healthcare, hotel and tourism,

5 The organisation into different datasets is to ease identification and searching; typically however, you will need

variables from more than one dataset. See appendix A for the components of each of these datasets.


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industrial/domestic, information communication & technology, insurance, leasing, machinery, manage funds, maritime, media, memorandum quotations, mortgage companies, packaging, petroleum marketing, printing and publishing, real estate, real estate investment trust (REITs), road transportation, second tier/emerging market and textiles.

NSE adopted a new sector classification system in 2011 and we have ensured that all our datasets bear both the old and new classifications. The new classifications6 are: agriculture, alternative securities market (ASeM), conglomerates, construction/real estate, consumer goods, financial services, healthcare, industrial goods, information & communication technology, memorandum quotations, natural resources, oil & gas and services.

Period covered and mode of presentation

Efforts have been made to ensure that all datasets are available for the period of 1990 till date. All data are arranged in the panel format into the Ms. Excel software. This involves stacking time series data by companies’ and sectors’ names. Therefore, data are already set for use with little or no extra effort. Information from annual reports are updated yearly as they are published and made public while those from the stock market are updated daily. Most of the indices computed in the Equity Market Indicators datasets are float-adjusted. This implies that the share counts used in their calculations are only the ones available to investors rather than all of a company’s outstanding shares.

Samples of data

Samples of the above data can be downloaded free from our website. In order to gain full access to this database, contact us through our telephone lines and emails addresses.

The full description of the FINGOV database is given in appendix A

Data Entry, Management and Storage Services

We are also into data entry, management and storage. This service is to aid researchers and research organisations in the following areas:

a. Data dictionary: We help develop and define data dictionaries for individuals and organisations who may require such service for the purpose of training, internal entry, proposals, publications etc. This can be done in packages such as CSPro, Epi-data, Epi-info, Ms Access, Ms Excel, SPSS, STATA, etc.

b. Data entry for researchers: We enter data for researchers who may require this service perhaps because they have large dataset or they seek to save time and other costs.

c. Data entry for research organisations: Research organisations often have large data to enter and their internal capacity or entry points may be inadequate to execute this project within the allotted time frame. They can outsource this service to us at ADSR. Our well-trained data keyers and several data entry points can be of use to such organisations.

d. Merging of database with similar or slightly dissimilar fields: Sometimes datasets collected with the same instruments but at different time periods or location may need to be

6 The new sector classification is further categorised into many sub-sectors.


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merged into a single datasets. In some situations however, the instrument might have slightly changed and therefore content analysis may be required before such datasets are merged. We render this service to our clients.

e. Data cleaning and preparations: One major challenge in primary data is the occurrence of missing, badly-filled or inconsistent responses. ADSR also helps researchers and organisations clean and make their data ready for further analysis.

f. Database construction and storage: Usually, a lot of useful information which are embedded in hard copy documents need to be coded and entered into the computer for easy search and use. For instance, thousands of reports containing data submitted to a commission or regulatory agency is of little or no use to researchers if they are not keyed and arranged into, say, Ms. Excel. Therefore, one of our services is to aid private and government organisations in building database from their archives which they can then update subsequently.

Data Processing and Analysis Services

We are experts in data processing and analysis with special abilities in handling packages such as, Anthro, AMOS, CSPro, DAD, DEAP, Epi-info, E-Views, GAMS, Gretl, Limdep, Lisrel, Ms. Excel, Ms. Access, PAST, SPSS, STATA, Tora, TSP, R, etc. Some of the tools of analysis at our disposal are included in the table below:

Techniques of data analysis Descriptives Simple inferential Advanced inferential statistics Mathematical, Indices, ratios, etc

Various charts and graphs

Measures of central tendency (Mean, median, mode)

Measures of dispersion (Variance, standard deviation, standard error, range)

Distribution of data Test of normality (Skewness, Kurtosis, Kolmogorov, Jarque-Berra tests)

Analysis of differences (t-tests, one-way ANOVA, etc)

Analysis of relationships and associations (correlation analyses, scatterplots, simple OLS regression, etc)

Tests of reliability and validity of scales (e.g. cronbach alpha, test-retest, etc)

Analysis of differences (Two-way ANOVA, post-hoc tests, ANCOVA, Factorial designs, experimental designs, etc)

Analysis of prediction (Multiple OLS regression; categorical independent variable models-dummy; limited/categorical dependent variable models-logit, probit, tobit, lpm)

Tests (multicollinearity, autocorrelation, heteroscedasticity, Data reduction and classification (Factor analysis, Hierarchical cluster, Discriminant analysis, etc)

Structural equation modelling (Confirmatory factor analysis) Time series analysis (Unit root/stationarity tests-ADF, Phillips Perron etc; Cointegration analysis-Engle Granger, Johansen etc; Error correction models; Vector Autoregressions-VAR; Granger causality test; etc)

Panel regression analysis (Pooled, fixed-, random-effects, Hausman test; Dynamic panel analysis; etc)

Other Estimators and estimation techniques (OLS, GLM, GMM, MLE, 2SLS, 3SLS, WLS, PLS, SURE, ARIMA, ARCH/GARCH, MANOVA, Canonical correlation, Nested logit, Stochastic frontier models, Gravity models, Almost ideal demand system-AIDS; etc)

Accounting ratios Financial modelling (NPV, IRR, WACC, Beta-risk, efficient portfolio, Value at risk, etc)

Linear programming Inventory analysis Indexing model calibration Coefficient standardisation and odd ratios

Contingent valuation Anthropometric measures Poverty and inequality measures (FGT, Lorenz, Atkinson, Gini, Entropy, Dominance, etc)

Computable General Equilibrium (CGE)

Sensory evaluation Dietary recall

First, you may contact us for advisory services in these areas. Under this arrangement; we discuss the assumptions and appropriateness of the available techniques in addressing your objectives and show you how to apply the chosen techniques and interpret the results. Second, you can also contact us to carry out this analysis for you. In this arrangement; you will be involved in a detailed discussion with us before arriving at the appropriate techniques. Subsequently, we go ahead to process, analyze and interpret your results while ensuring that you understand what we have done.


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Training Services

ADSR offers training services to equip you with the pivotal skills and abilities to excel in the dynamic and competitive business and academic environments. Courses are available in data processing, statistical and econometric packages, research methodology and financial modelling. In these courses, attentions are paid to the identification of appropriate techniques, understanding underlying assumptions of the techniques, do-it-yourself ability and sound interpretations. Case studies are drawn from research works with a view to mastering and evaluating analytical techniques adopted in them.

Our team of tutors is made up of academics and professionals with practical knowledge in their various fields. Our classrooms are well-equipped with computers, projectors, internet access and other facilities to enhance adequate learning and teaching. Participants are assessed and graded at the completion of the training and certificates are awarded.

We also offer in-house training to private and government institutions at their chosen locations. Under this arrangement, our course contents and illustrations can be adapted to suit some specific needs of the inviting institutions. In addition, we organise seminars and workshops in the areas of international trade, proposal writing, financial analysis/modelling and other specific research issues. The venues of these seminars and workshops can be in any part of the world. Online training and web conference seminars are equally available for all our services.

The full descriptions of these courses are contained in appendix B

III. CONTACTS OF THE ORGANISATION

Office adddress: 38, Oyo road (Sango-UI road), Old Airport B/Stop, Samonda, Ibadan, Oyo State, Nigeria.

Telephone: 234 (0) 7037470047; 234 (0) 8098764491

Web: www.adsr.com.ng

E-mail: [email protected]

Alternative e-mail: [email protected]

Afolabi E. Olowookere, Ph.D.

http://www.adsr.com.ng/




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APPENDIX A: DESCRIPTION OF THE COMPANIES’ FINANCIAL AND

GOVERNANCE (FINGOV) DATABASE OF NIGERIAN LISTED FIRMS

The database is categorised into 15 datasets. These are; Company Profiles, Board structure/Corporate governance I, Board structure/Corporate governance II, Corporate social responsibilities, Shareholders information, International trade/finance, Employee/labour distribution and welfare, Value added statement, Profit and loss account items, Balance sheet items, Statement of cash flows, Ratios, Specifics to the insurance sub-sector, Specifics to the banking sector and Equity market indicators. The datasets are described in the table below in terms of their names, availability, variables, objectives and likely uses.

S/N Dataset name Dataset code

Description of data variables Objectives and likely uses of data

1 Company Profiles7

COP This dataset contains information such as: Names of companies according to the NSE old sector classification Names of companies according to the NSE new sector and sub-sector

classifications Date of incorporation of each company Date of listing of each company Former names (if there had been a change of name) Nature of business in the mid-1980s, mid-1990s, mid-2000s and mid-

2010s Date delisted (if already delisted) Reasons for delisting (voluntary or regulatory) Merger and acquisition and other related history Indication on whether a sector is newly-created indication on whether a company’s sector is re-classified

This information is available for over 300 firms that have been listed on the Nigerian Stock Exchange. Any study that ignores this type of information is likely to be unable to explain some behaviours underlying the obtained data

This is a must-have for anybody who is to carry out research on Nigerian firms. Often, firms are listed, delisted, acquired, merged with, renamed, re-classified and involved in changes of nature of businesses. This dataset is to aid the researcher in addressing some of these challenges. The following examples are illustrative: Dunlop Nigeria PLC was incorporated and listed in 1961; In 1994, the company bought Hagemeyer and changed the name to DN MEYER PLC; in 2003, it divested its controlling shares from DN MEYER PLC to ACIMS Limited; in 2009-it redefined its relationship with Appollo Tyres South Africa Limited, the franchise owner of Dunlop in Africa. In the same year, it changed its name to DN Tyre and Rubber PLC following its transition from a tyre manufacturer to a trading concern that imports and sells tyres.

Intercontinental Bank PLC was incorporated in 1989 and listed in 2003; in 1995, as Nigerian Intercontinental Merchant Bank Limited (later, Intercontinental Bank PLC), it acquired 70 per cent of Meridien Equity Bank of Nigeria Limited (later, Equity Bank of Nigeria Limited); in 2005, Global bank PLC, Equity bank of Nig. Ltd and Gateway bank of Nig. PLC merged with Intercontinental bank; and in 2011, the Bank was acquired by Access Bank Plc when it was delisted from the Nigerian Stock Exchange Daily Official List.

2 Board structure/Corporate governance I

BCG1 Names and addresses of solicitors, registrars and head offices Names of company secretary Number of directors Number of foreign directors Number of women directors Number of directors with shareholding Total number of directors shares Total number of issued/paid up shares Total directors fees/emoluments Chairman's fees/emoluments Emoluments of highest paid director Name and status of external auditor

An ideal dataset for a researcher interested in general corporate governance, ownership structure and board quality issues of Nigerian firms. For instance, the proportion of foreign directors may indicate the extent to which a company may adopt best international practices. Women on board and status of the auditors may suggest the extent of accountability. Also, larger directors’ stakes may be indicative of lower agency problem. All these may have significant implications on firm performance

7 Many sources of information are used to compile the ‘Company Profile’ datasets. The arrangement of the dataset is cross-

sectional as firms are arranged by sectors. Data is also supplied in Ms Excel for easy identification and usage and years of events are cited in adjacent cells. The company profile dataset is updated monthly as new information become available.


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Auditors fees Number of internal auditors Number of non-director internal auditor Total number and percent of substantial shareholding Names of substantial shareholder Total number (percent) of substantial holding Account month Number of pages of annual report

3 Board structure/Corporate governance II

BCG2 On each director, the following information is documented Name Date appointed Date retired Date resigned (if resigned) If eligible for re-election If re-election is sought If re-elected Direct interest/shares Indirect interest/shares Total shares Status (Chairman, CEO/MD, Chairman/CEO, Secretary, Executive Non-

executive, etc) Degree qualification (Diploma, 1st,2nd,3rd degree) Field of degree qualification (Biz/Acct/Econs, Arts/Law, Sc/Tech) Has professional qualification Field of professional qualification (Biz/Acct/Econs, Arts/Law, Sc/Tech) Has foreign qualification Years of affiliation to the company Board members of another company/organisation Has international job experience Negro or Caucasian Gender Number of meetings attended

This dataset will be relevant for a researcher who wishes to perform a micro-based analysis on the composition, quality and dynamics of the persons who constitute the board of Nigerian companies.

4 Corporate social responsibilities

CSR Total CSR expenditure Lists of all CSR items/activities and their monetary values Is the CSR community/rural based CSR expenditure by category (facilities in primary schools, secondary

schools, tertiary schools, scholarships, conferences/seminars, health/diseases, environmental/conservation, agriculture, electricity, roads, security/safety, employees/staff, international organisations, religious organisations, festivals/ceremonies, youths, sports, government & its agencies, entertainment/arts/culture, NGOs/foundations, women & their organisations, accident/victims, children homes/children, disability/handicap/old, clubs, professional organisations, information technology and compensations).

Issues of CSR are gaining importance and researchers in the area are often interested in this type of information. This dataset shows how much business firms in Nigeria care about the larger society and the resources they commit to perform this role. These can also be augmented with our data on employee welfare (EDW) and value added (VAS).

5 Shareholders information

SHI Total number of shareholders Distribution of shareholders and their shareholdings Share units and percentage of Nigerian individual shareholders Share units and percentage of Nigerian government shareholders Share units and percentage of Nigerian institutions/companies Share units and percentage of foreign shareholders Shareholding concentration index

This also shows the ownership structure and how dispersed or the extent of blockholding or ownership concentration of Nigerian companies.

6 International trade/finance

ITF Foreign currency deposits/amount awaiting remittances to overseas (Balance sheet)

Foreign exchange difference reserve (balance sheet) Foreign currency purchased for imports (statement of cash flows) Net exchange rate effect (statement of cash flows) Value and percent of raw materials that is bought locally (statement of

value added) Value and percent of raw materials that is imported (statement of

value added) Exports/sales abroad (notes to financial statement) Exchange rate gain or loss (notes to financial statement) FDI as measured by share units and percentage of foreign shareholders

(shareholders information) Foreign/exchange rate income (Banking)

This type of information helps researchers determine the effect and responses to foreign exchange risk, the extent to which Nigerian firms source their inputs locally, their export orientation and the degree of internationalization and Foreign Direct Investments among Nigerian firms. The dataset is also useful for studies on the activities of multinational (MNCs) firms in Nigeria.


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7 Employee/ labour distribution and welfare

EDW Total employees Distribution of employees according to salary categories Distribution of employees according to hierarchy (Executive,

managerial, senior, junior) Distribution of employees according to functions (operations,

production, sales and marketing, corporate and human resources) Total labour/staff cost Distribution of labour cost into welfare, training, pension fund

contribution, wages, bonus and awards and medical Wages diversification index (inequality)

This dataset shows the levels and growth in employment structure of Nigerian firms. It can also be used to analyze patterns and changes in the numbers and proportions of employees in different cadres and departments of Nigerian firms. Researchers with interests in the area of labour welfare can combine this dataset with the share of value added that goes to labour to examine the dynamics of the labour market in Nigeria.

8 Value added statement

VAS Total value added Share of value added that goes to labour Share of value added that goes to government Share of value added that goes to capital providers Share of value added that goes to owners (shareholders) Share of value added that goes for depreciation Share of value added that is retained for company growth

This dataset shows the distribution of Nigeria firms’ value added among the different stakeholders. A researcher may be interested in who gets more and what happens to the pattern of distribution over time. The share that goes to labour may also be indicative of labour welfare in some selected companies.

9 Profit and loss account items

PLA Sales/Turnover Cost of sales Gross profit Net operating expenses Advertising and Promotion expenses Interest income/expenses Investment income Income from subsidiaries Profit before tax Taxation Profit attributable to shareholders Dividend

This dataset can be used to evaluate income generating ability of firms as it measures profitability/earnings.

10 Balance sheet items

BAS Fixed assets Investments Deferred tax assets Work in progress Goodwill and trademarks Total non-current assets Stocks/Inventories Bank deposits and cash balances Short term investments Net current assets Total current assets Creditors and accruals Short-term debts Proposed dividend Unclaimed dividend Total current liabilities Long term debt Total long-term liabilities Share capital Share premium Shareholders fund Contingency reserve Research and development reserve Total capital employed

This dataset can be used to perform various forms of financial/performance analysis of Nigerian firms.

11 Statement of cash flows

SCF Net cash flow from operating activities net cash flow from financing activities net cash flow from investing activities net increase in cash and cash equivalents Cash and bank balances Short term deposits

This dataset can be used to evaluate liquidity and other related issues about Nigerian firms.

12 Ratios

RAT Earnings per share Dividend per share Other ratios that can be computed, e.g. return on asset-ROA, return on

equity-ROE, Tobin’s q, return on capital employed-ROCE etc.

This is used for financial analysis in various forms to evaluate performance as it relates to profitability, efficiency, growth, liquidity and others over time and among firms in the same industry and comparison of one sector performance to another sector.


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13 Specifics to the insurance sub-sector

SIS In addition to the above datasets, variables that are specific to the insurance sector are included. These include: Gross earnings Claims incurred underwriting expenses/profit re-insurance cost outstanding claims Insurance fund Classification of insurance fund according to life, fire, accident, motor

vehicles, marine , aviation, oil & energy, burglary etc

Useful for evaluation of performance and issues relating to the insurance sub-sector.

14 Specifics to the banking sector

SBS In addition to the above datasets, variables that are specific to the banking sector are included. These include: Gross earnings Provision for bad and doubtful debt Transfer to statutory account Cash and balance with Central Bank Treasury bills discounted Due from/to other banks Customers deposits CBN accounts Total loans and advances to customers Classification of loans and advances by type (Overdrafts, commercial

papers, term loans, staff loans, etc) Classification of loans and advances by maturity (under 1mth, 1-3mths,

3-6mths, 6-12mths, above 12mths) Classification of loans and advances by security (secured against real

estate, secured by shares of quoted companies, otherwise secured, unsecured)

Classification of loans and advances by performance (performing, non-performing/sub-standard, non-performing/doubtful and non-performing/lost)

Total deposits from customers Classification of deposits by type (demand, current, savings, time,

domiciliary) Classification of deposits by location (Nigeria, abroad) Classification of deposits by maturity (under 1mth, 1-3mths, 3-6mths,

6-12mths, above 12mths)

Useful for evaluation of performance and issues relating to the banking sub-sector.

15 Equity market indicators

EMI The following data are available on daily frequency Price &returns of each stock (growth rates& continuously compounded) Volume and value traded of each stock Days (Mon, ...,Fri) and dates of trading Opening and closing prices of each stock Market capitalisation of each stock/company Market capitalisation of each industry/sector (old and new) Industrial share indexes and returns All share index for the market and returns

The following data are available on weekly/monthly/yearly frequencies Average period price and returns of each stock Period-end price and returns of each stock Number of days each stock is traded in the period Average period volume and value traded of each stock Total period volume and value traded of each stock Period-end volume and value traded of each stock Highest and lowest prices in each period Market capitalisation of each stock using last trading day prices Market capitalisation of each stock using average period prices Market capitalisation of each industry using last trading day prices Market capitalisation of each industry using average period prices Industrial share indexes and returns All share index for the market and returns Foreign Portfolio Investment Beta estimates (exposures to oil prices, local share index, exchange rate

risk and to global, emerging and frontier markets indices like the Morgan Stanley Capital International-MSCI and Standard and Poor’s-S&P)

This dataset can perform several analysis which may include; risk analysis, portfolio construction, selection and efficient set, security/asset pricing, tests of market efficiency, event studies (calendar effects, abnormal returns) excess volatility, liquidity, thin trading etc.

Beta estimates not available at weekly frequency due to small sample size. Most indices are float-adjusted


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Data on delisted firms

We address the problem of survivor-bias in our database. Delisted firms may still carry important information with them and their exclusion may lead to the problem of survivor-bias. For instance, their existence at some periods would have affected others that were and are still listed. Equally, some of them are delisted because they have merged, thereby increasing the size of the currently-listed. In addition, a researcher’s interest may be to study the performance and features of delisted firms. Therefore, for datasets 1 – 14, we maintain a database of all companies whether still listed or delisted, provided such companies have annual reports for a reasonable and consistent number of years. However, data on all companies are maintained in the Equity Market Indicators datasets.

Reasons why our data is the best in and for Nigeria:

Longer history

No survivor-bias

Float-adjusted indices

24-hours delivery scheme

Data samples downloadable

Control for change of names and merger

Companies’ old and new sectors indicated

Data are already stacked on time and companies

Plans to extend services to other African countries

Days of trading are specified to enable event studies

Affordable prices to support researchers and students

Data captured by double-entry and intelligent systems

Membership/subscription

One major objective of building this database is to facilitate research into Nigerian companies and

businesses; therefore, we operate different membership and subscription plans that will help achieve

this purpose. These are explained below:

i. Non-membership plan: This plan is divided into 2 categories, namely;

a. Standard plan: Our standard clients are Nigerian and foreign academic, researchers,

professionals, etc. This group are exposed to our normal rates.

b. Student plan: Nigerian students with evidence of studentship enjoy up to 50% discounts on

our data services

ii. Membership plan: This plan is further divided into 4 categories, namely;

a. Bronze members: Members of this group pay a stipulated monthly/annual subscription fees

and they are entitled to 15% discount on their orders.

b. Silver members: Members of this group pay a stipulated monthly/annual subscription fees

(higher than that of bronze) and are entitled to 30% discount on their orders


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c. Gold members: Members of this group pay no subscription fees, but they are entitled to a

stipulated amount of data free of charge. A bronze or silver client automatically qualifies for

this group after consistent order and having made purchases up to a certain amount.

d. Platinum members: This is a reward for excellence. Members have unlimited free access to

our entire database. Few academics/researchers who have significantly contributed to

knowledge on Nigerian companies and businesses will be considered for this group.

iii. Institutional subscription plan: Institutions like Universities, Business Schools, Research

Institutes, Regulators, Capital Market Operators, etc, may contact us for special arrangements

under which our database will be made available to their staff, students and other members.

iv. Free access plan: The ultimate goal is to grant free access to our entire database. We believe

this will engender rigorous research into Nigerian companies and businesses; and also help in

effective policymaking. We therefore welcome sponsors and partners who share our

developmental idea.

APPENDIX B: CONTENTS OF THE CURRENT TRAINING PROGRAMMES

I. Basic Data Processing Using SPSS II. Advanced Data Processing Using SPSS & STATA

Introduction to data processing Procedure for hypothesis testing Checklist on choosing appropriate techniques Preparation of codebook & data dictionary Introduction to SPSS Data entry and cleaning Data transformation procedures Construction of various charts Descriptive analysis (Frequencies, Measures of central tendency and dispersion and Crosstabs)

Inferential analysis of association/relationship (Chi-square, Correlation)

Inferential analysis of differences (T-test, ANOVA) Introduction to linear regression analysis Basic result interpretations Group assignments and case studies

Data processing in the research process Introduction to SPSS/STATA File management procedures Data transformation procedures Cases management procedures Descriptive analysis (Frequencies, Measures of central tendency and dispersion and Crosstabs) Basic Inferential analysis Tests of association and relationship (chi-square, correlation) Tests of differences (T-tests, ANOVA, Post-hoc and ANCOVA) Regression analysis (OLS, logistic, probit and odd ratios) Advanced inferential analysis Exploratory factor analysis Scale reliability analysis Regression analysis (2 stage least squares and multinomial logit) Introduction to SPSS syntax/STATA command and do-file Group assignments and case studies Discussions of and solutions to participants personal research agenda

: Visit our website for further information on these and our other training programmes that are often available


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APPENDIX C: LIST AND DEFINITIONS OF ACRONYMS

2SLS 2 Stage Least Squares ITF International Trade/ Finance

3SLS 3 Stage Least Squares LFS Labour Force Survey

ADF Augmented Dickey-Fuller Limdep Limited Dependent

ADSR Analysts’ Data Services and Resources Lisrel Linear Structural Relationship

AERC African Economic Research Consortium LPM Linear Probability Models

AfDB African Development Bank MANOVA Multivariate analysis of variance and covariance

AMOS Analysis of Moment Structures MD Managing Director

ANCOVA Analysis of Covariance MICS Multiple Indicator Cluster Survey

ANOVA Analysis of Variance MLE Maximum Likelihood Estimation

ARCH Autoregressive Conditional Heteroscedasticity Ms Access Microsoft Access

ARIMA Autoregressive Integrated Moving Average Ms Excel Microsoft Excel

ASeM Alternative Securities Market NASC National Agricultural Sample Census

BAS Balance Sheet Items NASS National Agricultural Sample Survey

BCG Board Structure/Corporate Governance NBS National Bureau of Statistics

CBN Central Bank of Nigeria NDIC Nigeria Deposit and Insurance Corporation

CDIS Coordinated Direct Investment Survey NGO Non Governmental Organizations

CEO Chief Executive Officer NISE National Integrated Survey of Establishments

CGE Computable General Equilibrium NISH National Integrated Survey of Households

COFINGOV Companies’ Financial and Governance NPV Net Present Value

COMTRADE Commodity Trade Statistics Database NSE Nigerian Stock Exchange

COP Company Profile NSOs National Statistical Offices

CPIS Coordinated Portfolio Investment Survey OLS Ordinary Least Squares

CSPro Census and Survey Processing PAST Paleontological Statistics Software

CSR Corporate Social Responsibilities PGI Principal Global; Indicator

CWIQ Core Welfare Indicator Questionnaire PLA Profit and Loss Account Items

DAD Distributive Analysis/Analysis Distributive PLC Public Limited Companies

DEAP Data Envelopment Analysis Program PLS Partial Least Squares

DFID Department for International Development QEDS Quarterly External Debt Statistics

DOTS Direction of Trade Statistics RAT Ratios

ECOWAS Economic Community of West African States REITs Real Estate Investment Trusts

EDW Employee/ Labour Distribution and Welfare SBS Specifics to the Banking Sector

EMI Equity Market Indicators SCF Statement of Cash Flows

EPS Earnings Per Share SHI Shareholders Information

E-Views Econometric Views SIS Specifics to the Insurance Sub-sector

FAS Financial Access Survey SPSS Statistical Package for the Social Sciences

FGT Foster Greer Thorbecke SURE Seemingly Unrelated Regression

FSIs Financial Soundness Indicators TSP Time Series Processor

GAMS General Algebraic Modeling System UI University of Ibadan

GARCH Generalized Autoregressive Conditional Heteroskedasticity UN United Nations

GDP Gross Domestic Product UNAIDS United Nations AIDS

GEM Global Economic Monitor UNECA UN Economic Commission for Africa

GHS General Household Survey UNESCO United Nations Educational, Scientific and Cultural Organization

GIZ Deutsche Gesellschaft fur Internationale UNFCCC United Nations Framework Convention on Climate Change

GLM Generalized Linear Model USAID United States for International Development

GMM Generalized Method of Moment VAR Vector Autoregressions

Gretl Gnu Regression , Econometrics and Time Series Library VAS Value Added Statement

IFS International Financial Statistics WACC Weighted Average Cost of Capital

IMF International Monetary Fund WEO World Economic Outlook

INDSTAT Industry Statistics WLS Weighted Least Squares

IRR Internal Rate of Return WTO World Trade Organization


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