Audit Quality and Financial Performance of Quoted Firms in Nigeria

Abstract: This study examined the impact of Audit Quality and Financial Performance of Quoted Firms in Nigeria. The study spanned from 2000-2017 which is 18 year period. The independent variable is audit quality which is proxy with auditor’s independence, audit size and audit committee while the dependent variable is Financial Performance and proxy with earnings per share and return on asset. Three firms were chosen for this study, which are: Unilever Nigeria Plc., Oando Plc. and C & I Leasing Company. Time series data were used and gotten from annual report and account of the firms under study. The study applied Ordinary Least Square (OLS) estimation technique through E-view 7.0 version. The result revealed that for model 1 Company is below 5% significant level. The study thereby concludes that audit quality does not have significant impact on returns on asset of quoted firms in Nigeria. The study recommended that management of quoted firms in Nigeria can improve the financial performance and audit independence of their firms by increasing the amount of audit fees paid to the audit firm. This might seem like a profit reducing decision in the short run, but the benefits it will bring to the firm far outweighs the cost. Also management of quoted firms should employ the services of one of the Big 4 audit firms because their character and integrity is beyond question.

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Carbon Emission Accounting and Economic Growth in Nigeria


This study investigated the relationship between carbon emission and economic growth in Nigeria for a period ranging from 1981-2018. The proxy for carbon emission is carbon dioxide CO2 and the proxy for economic growth is gross domestic product. The study used causal research design to mobilize data from the World Bank and Fact Fish publications while gross domestic product is sourced from the Central Bank of Nigeria Statistical Bulletin. The methods of descriptive statistics, Phillip-Perron, dynamic ordinary least Square and bivariate granger causality test are employed to analyze the data. The results show evidence of autoregressive effect of previous records of gross domestic product on its future value; there is an inverse relationship between carbon emission and gross domestic product. The bivariate granger causality confirms no existence of causality running between the variables. On the basis of the findings, the study concludes that there is an insignificant and negative relationship between carbon emission and Gross domestic product. It also concludes that CO2 and GDP are causally neutral to each other. It therefore recommends that earnest effort should be made by government to reduce greenhouse gas (GHG) in Nigeria by adhering to all relevant protocol and standards. Emissions not connected to the production of industrial and consumer goods should be taxed and avoided completely except the inevitable domestic emissions by practically applying the necessary laws both national and international. Precisely, focus should be shifted to going green in terms of energy generation, ensuring positive multiplier effect of constant power supply and the economics of clean air on the human health and productivity.

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Survey Research: Panel, Longitudinal and Cross-Sectional Data

Panel Survey

This a type of survey method that involves repeated interviews of a group of people over a specific period of time. It is used to track progress or evolution over time. In other words, when you study Net Profit Margin (NPM) of banks (Say 4 banks) over a period of time (2018 -2020)

You data will look like this:

Banks        TimeNPM
FCMB2019 55%
First Bank202060%
First Bank2019 70%
First Bank201855%
Fidelity Bank202045%
Fidelity Bank2019 50%
Fidelity Bank201860%
Zenith Bank202080%
Zenith Bank2019 70%
Zenith Bank201875%

Longitudinal Survey

It is another word used to describe panel survey. It refers to any study that examines the same group of people over a period of time.

Cross-sectional survey.

This is refers to data collected by observing many subjects such as individuals, firms, countries at the same point in time or without regard to differences in time. Here data is obtained at a particular point in time. The analysis of cross-sectional data usually consists of comparing the differences among the subjects.  For example, if you are studying the net profit margin (NPM) of four banks in 2020 only. You will have a data that look like this:

Banks        TimeNPM
First Bank202060%
Fidelity Bank202045%
Zenith Bank202080%

This is what we called cross sectional data study since you are studying Banks at a particular point in time (2020).