Authors
Abstract
The work examined the comparative effects of historical cost accounting and fair value accounting measurement bases on earnings performance of 10 listed manufacturing firms in Nigeria, between the periods 2009-2020.The choice of the manufacturing sector was pertinent as the manufacturing industries are considered vital to economic growth and development. The study adopted the ex-post facto research design, with data collected from published financial statements of 10 manufacturing firms quoted on the Nigerian Exchange Group Ltd. A cross sectional analysis of the financial reports of the 10 manufacturing firms was carried out between eras 2009 - 2012(HCA) and 2013 – 2020(FVA).The regression results revealed that depreciation, dividend and taxation under fair value accounting (FVA) have more positive and significant effect on ROA (earnings performance) than depreciation, dividend and taxation under historical cost accounting (HCA). The work concludes that fair value measurement choice has superior effect on bottom lines of manufacturing firms than the historical cost measurement choice. The empirical findings imply or provide strong support for the proposition that fair value measurement choice has superior effect on bottom lines of manufacturing firms than the historical cost measurement choice. It was recommended amongst others that: Accounting bodies in Nigeria should organize enlightenment workshops for practicing accountants and managers of firms to create awareness of current cost accounting and the need to deviate from the historical cost accounting method during inflationary period.
Keywords: Earnings Performance, Fair Value Accounting, Historical Cost Accounting, Return on Assets, Global Financial Reporting Standards.
Full Text
1. Introduction
The previous globally practiced measurement basis of firms assets and liabilities: the Historical Cost Accounting (HCA) measurement choice, which is a product of the Generally Accepted Accounting Principle (GAAP) was adjudged grossly inadequate in reporting the performance of firms with the assumption that the HCA in reality results to obsolete fixed assets values, insufficient provision for depreciation, taxation and unrealistic profit and distributions to dividend, and the failure to present a fair value of financial position among other drawbacks. Notably, these shortcomings obviously necessitated the relegation of this valuation basis (HCA) and the subsequent enthronement of FVA basis in the current global financial reporting standards. Both the historical cost and fair value accounting methods have some faults in that entities may use them to manipulate their financial positions and results. For instance, a firm using historical cost accounting method may manipulate its figures on depreciation in order to increase or overestimate the useful life of an asset or its residual value. In that case, the firm will overestimate its income. Entities use this shrewd way of inflating income to attract investors and creditors by deceiving them about the profitability and financial position of the business (Belinna, Yen & Yang, 2008). Using historical cost accounting, the management teams have more liberty to hide bad investment decisions and avoid the consequences of declining levels of equity and assets. Thus, it is unlikely for any entity to disclose its financial failure through historical cost accounting method. Regardless of the accounting measurement base a firm chooses, ethical dilemmas are likely to occur among executive management.
Proponents of fair value claim that fair value information is the only information relevant for financial decision making as fair values provide the most current and complete estimations of the value of assets and obligations as well as information about the timing and riskiness of future cash flows.
However, the FVA has also been criticized for its volatile nature; thus, having the tendency of presenting distorted accounting information. Several issues are directly associated with fair value reporting, including recognition, relevance and measurement. Landsman (2007) addressed the issue of value manipulation, and notes that the requirement of relying on managerial estimates for valuation of assets and liabilities introduces the problem of information asymmetry. Information asymmetry will arise whenever managers have discretion regarding the timing or amount of non-market adjustments to amounts arising from past transactions. Such information asymmetry creates two distinct problems; moral hazard and adverse selection.
Critics of fair value accounting and academics have raised concerns as to whether fair value accounting impacts the ability of earnings to predict future earnings and cash flows. Rather than representing economic events such as earning revenues or incurring expenses, critics argue that the recognition of gains and losses in a fair value system is driven by short-term market movements (Chisnall, 2001).
The Financial Accounting Standards Board (FASB) and the International Accounting Standards Board (IASB) consider fair value as a potential measurement basis in almost every decision they make as they believe that in many cases fair value meets the conceptual framework criteria better than other measurement bases (Barth, 2006, 2008).
Until 2012, Nigerian firms reported their financial statements in line with the historical cost accounting (HCA) measurement basis. However, the Financial Reporting Council of Nigeria (FRCN) mandated that every quoted firm on the floor of the Nigeria Exchange Group Ltd adopt IFRS basis in their financial reporting from January 1, 2012, where they will prepare their financial statements based on fair value accounting (FVA). Elfaki and Hammad (2015) in this regard observed that the quality of information is based on such characteristics as objectivity, relevance, reliability, neutrality, capability of information for comparison, materiality and full disclosure. In essence, the objective of financial reporting is to provide useful information about the reporting entity to existing and potential investors, lenders and other creditors in making capital-allocation decisions, using a more accurate measurement choice that reflects the operations of the reporting entity (IASB, 2011).
There is need to therefore empirically examine the quality of earnings using both HCA and FVA measurement choices.
The broad objective of this study was to evaluate the comparative effects of Historical Cost Accounting and Fair Value Accounting measurement bases on earnings performance of selected quoted manufacturing firms in Nigeria using reported returns on assets (ROA) as a proxy for earnings performance (dependent variable) and using depreciation, taxation and dividend (explanatory variables) as proxies for both FVA and HCA.
The specific objectives are to:
a. evaluate the comparative effects of depreciation on earnings performance (ROA) using historical cost accounting and fair value accounting choice of measurement.
b. investigate the comparative effects of taxation on earnings performance(ROA) using historical cost accounting and fair value accounting choice of measurement.
c. determine the comparative effects of dividend on earnings performance(ROA) using historical cost accounting and fair value accounting choice of measurement.
2.
3. Review of Related Literature/Theoretical Framework
2.1 Conceptual Review
2.1.1 Historical Cost Accounting (HCA)
Amanamah and Owusu (2016) opined that historical cost measures an asset at the cost of acquisition and as such it provides a reliable basis for measurement, however, the problem is that as price changes subsequent to acquisition, the relevance of historical cost declines if the objective of measurement is to reflect the current economic benefit represented by the asset. Bessong and Charles (2012) assert that using this method, profit is ascertained by drawing comparison between sales revenue and the original cost of the asset sold. To determine income in this regard, accountants assumed that a business is better off whenever it recovers more than the original sum of money invested in any given asset.
Jaijairam (2013) observed that under historical cost accounting, the initial price paid by the company during the purchase of the asset or incurrence of the liability is the one that matters. The price reflected on the balance sheet either is the purchase price or at a value reduced by obsolescence, depreciation or depletion. For a financial asset, the price on the balance sheet does not change until the security is liquidated. Selling price is stated at current price while the cost of assets used in generating the sales are stated at historical cost, that is, “acquisition cost”. Depreciation is charged based on the acquisition cost of the assets irrespective of the current replacement cost of such assets. This results in overstated profit leading to overpayment of tax and dividend. The effect of this is overstated profit and understated value of assets which will make replacement difficult.
According to Ene, Chilarez and Dindire (2014), one shortcoming of the historical cost accounting approach is that in times of inflation, especially when price variations are very high, presenting the assets and the liabilities at historical costs, leads to distortions of the information presented in the financial statements, namely: in the balance sheet, assets are under-evaluated, resulting to understatement of the net assets; and in the profit or loss account, there is a distortion of the results due to the cost of stocks; undervaluation of the expenses regarding depreciation as a result of the undervaluation of property; financial overstatement due to the gain on debt, over-evaluation of the result determined by the understated expenses and thus increase the tax on profits.
Egbe (2014) also noted that, “it is readily apparent that financial statements prepared in accordance with the historical cost concept are always defective to the extent that: they fail to reflect the impact of changing price level; assets are disclosed in the balance sheet at unrealistic values; and the profit and loss account does not bear proper charges, particularly for depreciation and cost of materials consumed.
2.1.2 Fair Value Accounting
Fair value, according to the International Financial Reporting Standards (2011) as cited in Amanamah and Owusu (2016) is the amount for which an asset could be exchanged, a liability settled, or an equity instrument granted. It could be exchanged between knowledgeable, willing parties in an arm’s length transaction. Chambers (cited in Ashford, 2011) views fair value as the price that would be received to sell an asset or paid to transfer a liability in an orderly transaction between participants at the measurement dates. Jarolim and Oppinger (2012) define fair value as the amount which could be transferred in a transaction between knowledgeable, willing parties under normal market conditions (arm’s length transaction). Therefore, the fair value constitutes a hypothetical market price under ideal market environment. Thus, fair value accounting revolves around recording changes in market values. This results in the recognition of unrealized gains and losses. Unrealized gains and losses will only have an impact on cash flow if sold on the balance sheet date. Fair value is sometimes referred to as “exit values”, however, when fair value is not available due to lack of an actual transaction, it is logical to use information from an active market. On his part, Kochiyama (2011) observed that fair value accounting is becoming increasingly important in accounting standards, driven by the convergence toward or adoption of International Financial Reporting Standards (IFRS) all over the world; and that regulators suggest that fair values lead to improved financial reporting, because fair value numbers are more timely and reliable, and thus facilitate a decision mechanism.
Liu (2010) posits that there are two alternatives for fair value in an imperfect market environment, including bid price and selling price. The former refers to the amount of money paid for a particular property on a measurement date while the latter refers to the amount of money received by selling assets on a measurement date (Liu, 2010).
2.1.3 Applications of HCA and FVA
i. Application on Statement of Financial Position
Under FAS 159, the choice of accounting treatment for recording certain financial assets, which do not require adherence to specific fair value accounting rules, can result in a dramatic impact on the balance sheet, especially for companies with large investment portfolios such as insurance or bank holding companies. In amortized cost, financial securities held up to maturity and notably debt securities are always carried on the balance sheet at the acquisition price paid by the entity. Thus, from one quarter to another there will be no volatility in the prices of individual securities.
On the other hand, with fair value accounting, the price of debt security is adjusted in accordance to the market price at a given time. Such gyrations noted in fair value accounting would have significant impact on the daily operation of the business. Since a balance sheet is a measure of a company’s financial position, for instance, the law requires financial institutions (banks) and insurance companies to maintain certain level of equity usually portrayed on the balance sheet (Zyla, 2010). Standard accounting defines equity as the difference between assets and liabilities.
Thus, as these two figures vary, equity also varies (increases or decreases). Because banks rely on leverage ratio, a small variation in the value of their assets will have a greater impact on their size of equity. For example, during the 2008-2010 economic meltdown, there were financial crisis that led to the decline of asset values (Zyla, 2010). In turn, as the value of assets declined, the equity of banks declined. The position of many banks as shown on the balance sheets deteriorated. This situation called for financial institutions to raise more equity in order to bring their balance sheet back to position required by government regulations.
In the non-financial sector such as manufacturing, wholesale and retail industries, the balance sheet values are less important compared to financial sector but they still have a real impact.
In summary, fair value accounting will have effects on balance sheets of entities; however, financial institutions are likely to be more affected than non-financial sector.
Fair value accounting and historical cost accounting, as applied to assets, focus on different basic snapshots of valuation. Each is subject to different problems and limitations.
ii. Application on Income Statement
Fair Value Option (FVO) election choice may have a substantial effect on income statement and earnings. Whilst certain changes in values are only reflected on the balance sheet, OTTI (Other-Than-Temporary Impairment) changes that flow through income statement have a direct impact on net income; for instance, the value of available for sale securities. FAS 115, states categorically that trading assets are held with an aim of disposing them in the near future (Laux & Leuz, 2010). Securities like bonds and treasury bills are marketable securities thus they are reported at fair value whereby the changes noted are recognized in the income statement.
iii. Application on Cash Flow Statement
Unlike the balance sheet and income statement, the use of fair value accounting does not have a direct impact on the statement of cash flows of an entity. The entities will eliminate any OTTI (Other-Than-Temporary Impairment) charge that applies under fair value accounting in their income statements as part of operating cash flow. The statement of cash flows is however affected by taxes. Tax rules add a layer of complexity to arriving at the level of OTTI, since the Internal Revenue Service (IRS) does not view all impairments equally. For publicly traded securities, the IRS does not allow an OTTI deduction to be taxable income.
2.1.4 Earnings Performance
According to Neely et al (cited in Al-Matari, Al-Swidi and Fadzil, 2014) organizational performance can be defined as the actual results generated by an organization as measured against the organization’s stated goals and objectives. It can be seen as an indicator to measure the effectiveness of an organization in running its daily operations. This will determine whether organizations are able to survive in the market or not. Niresh and Velnampy (2014) opine that firm performance can be measured in different ways and by applying various methods; and the commonly used method for financial analysis is the use of profitability ratios as key measures of firms’ overall efficiency and performance.
One of the widely used accounting based measures of corporate governance in literature is the Return on Asset (ROA) (Finkelstein and D’Aveni 1994; Weir and Laing 1999). The return on assets (ROA) is a measure which shows the amount of earnings that have been generated from invested capital. It is an indication of the number of kobo earned on each naira worth of assets. It allows users, stakeholders and monitoring agencies to assess how well a firm’s corporate governance mechanism is in securing and motivating efficient management of the firm (Chagbadari, 2011). The ROA is the ratio of annual net income to average total assets of a business during a financial year.
It is measured thus: ROA = Annual Net Income / Average Total Assets.
2.2 Empirical Review
Bessong and Charles (2012) critically examined the effects of fair value accounting and historical cost accounting on the reported profits of quoted manufacturing firms on the Nigerian Stock Exchange. Secondary data collected were presented and analyzed using ordinary least square. Findings from the analysis revealed that both historical cost and fair-value accounting have significant effect on reported profit. It revealed no difference in the effect of tax (as a proxy for FVA and HCA) on firms’ profit during each of the two regimes.
Okafor and Ogiedu (2012) evaluated the perception issues relating to fair value accounting in Nigeria. Questionnaire survey of a sample of financial auditors was employed and data collected was analyzed using the Z-Score. The study found that financial statements prepared under fair value accounting are more relevant than those prepared under historical cost accounting and that auditors’ knowledge about fair value accounting in Nigeria is low. The study also found out that fair value accounting poses greater technical challenges for auditors than historical cost accounting and that fair value accounting is not appropriate in the Nigeria environment.
Ijeoma (2013) assessed the impact of fair value measurement on financial instrument of firms in Nigeria. Data collection was carried out through field survey method involving the use of questionnaire administered to 188 samples. The method of data analysis was the Kruskal-Wallis rank sum test statistic. From the result of the analysis, it was observed that the implementation of Fair Value measurements gives sufficient precision in assessing firm’s financial position and earning potential. The study thus concluded that Fair value is the best reflection of the expected future cash flow as it predicts the ability of the entity to take advantage of opportunities or to react to adverse situations.
Egbe (2014) evaluated the effect of historical cost accounting on the reported profit of manufacturing companies in Nigeria with a key focus on evaluating the current cost accounting as an alternative reporting method. The study adopted an ex post facto research design with a sample of ten (10) out of forty-eight (48) manufacturing companies in Nigeria. The study employed a regression analysis in analyzing the data collected while the Pearson Product Moment Correlation Coefficient and Chi-Square were employed to test the hypotheses of the study at 5% level of significance. The results of the study revealed that there is a positive significant relationship between historical cost method and the reported profits of companies in Nigeria while current cost methods does not significantly affect the overstated profits made by these companies.
Ijeoma (2014) studied the contribution of fair value accounting on corporate financial reporting in Nigeria. The study utilized primary data sourced through field survey method involving the use of questionnaire administered to 562 samples. The method of data analysis was the Kruskal-Wallis rank sum test statistic. From the result of the analysis, the study found that the implementation of fair Value Accounting provides more useful information to investors than historical cost reporting. Also, it was equally found that the full fair value of financial instruments fulfils the aim of performance reporting.
Akwu (2014) carried out an examination of Fair Value Measurement in the determination of profitability of listed manufacturing firms in Nigeria. The study sought majorly to ascertain the influence of depreciation on profitability of the manufacturing firms in Nigeria using fair value measurement and historical cost convention; examine the effect of inventory on reported profit of manufacturing firms in Nigeria under fair value measurement and historical cost convention to determine the relationship between volume of tax and reported profit of manufacturing firms in Nigeria using fair value measurement and historical cost convention. Ex-post facto research design was adopted for this study. The study covered five IFRS compliant companies; simple least square regression technique, correlation coefficient, and t-statistic were used with the aid of Econometric Views (E-Views) statistical software. Findings from the analysis showed that depreciation has positive and significant impact on profitability using fair value measurement and historical cost convention. Inventory had positive and significant effect on profitability under fair value measurement and historical cost convention. A positive and significant relationship exists between taxation and profitability using fair value and historical cost convention. The study thus concluded that depreciation, cost of sales and Taxation have significant and positive effects respectively on what is reported as profit under historical cost convention and under fair value measurement; indicating that fair value measurement can serve as a replacement to historical cost convention. As such, fair value should be encouraged.
2.3 Theoretical Framework
The study theoretical framework was based on the conceptual ideal of decision-usefulness which underpins the fair value pattern projected by IASB. Specifically, agency theory and signaling theory was the central underpinning theories of this study.
Agency theory explains the association that exists where the principal delegates work to the agent to carry out a given assignment. This association is described by Jensen and Meckling (1976) as a treaty where the owners engage managers to run the firms operations efficiently and effectively. Information asymmetry may result between the contracting parties as managers may be in possession of superior information about the present and expected future earnings of the entity than the owners.
Signals basically are pointers to unobservable signals quality at a given point in time (Davila, Foster & Gupta, 2003). Signaling theory is primarily concerned with decreasing information asymmetry between parties (Spence, 1973). Management scholars have also used signaling theory to explain the power of information asymmetry in differing research contexts. A study of corporate governance, by Zhang and Wiersema, (2009) documents how CEOs signal the unobservable value of their entities to potential investors through the observable attributes of their financial statements. The use of signaling theory in management literature has gained acceptance in recent years as scholars have increased the range of probable signals and the contexts in which signaling occurs. Financial instruments’ fair value is a signal of the expected future cash flow and the difference there on signals potential earnings. Signaling theory therefore provides a good explanation of fair value intensity, fair value level available for sales and total comprehensive income ability to predict future earnings.
4.
5. Methodology
3.1 Research Design
The ex-post facto research design was adopted for the study. The research adopted a cross sectional analysis of the financial report of 10 manufacturing companies quoted on the Nigerian Stock Exchange between era 2009 – 2012 (HCA) and 2013 – 2020 (FVA) periods. The choice of the manufacturing sector became pertinent as the manufacturing industries are considered vital to economic growth and development (Sanya, 2011).
3.2 Sources of Data
Quantitative data was collected through financial reports for the period between 2009 -2020. The data collected include Depreciation, Taxation, Dividend and earnings performance (ROA).
3.3 Method of Data Analysis
In analyzing the data the panel OLS and t-statistic was adopted and using reported returns on assets (ROA) as a proxy for earnings performance, depreciation, taxation and dividend as proxies for both FVA and HCA to measure the relationship between the dependent and the independent variables.
3.4 Model Specification and Description of Model Variables
In order to test the hypotheses, the research adopted the model of Tearney (2004) and Kekung, Effiong (2012) with slight modifications stated in their explicit form:
Model 1: ROAHCM = β0 + β1DEP + β2TAX + β3DIV + e
Model 2: ROAFVM = β0 + β1DEP + β2TAX + β3DIV + e
Where:
ROAHCM denotes reported profit at historical cost; ROAFVM denotes reported profit at fair value. DEP denotes depreciation; TAX denotes taxes; DIV denotes dividend; β0, denotes constant; β1 - β3 denotes coefficient of effect of measurement choice proxies on reported ROA.
Specifically, both the F statistic and t-statistic was used to test the various hypotheses. The decision rule for both the F and t statistics is that if the F-calculated > F-critical and if the t-calculated > t-critical, we validate the alternative hypotheses and invalidate the null hypotheses.
4. Results and Discussions
4.1 Data Presentation and Analysis
This section analyses both descriptive statistics and correlation matrix.
Table 1: Descriptive Statistics
|
|
DEP_H |
DEP_F |
DIV_H |
DIV_F |
TAX_H |
TAX_F |
ROA_H |
ROA_F |
|
Mean |
122393.7 |
478765.0 |
114565.1 |
402673.1 |
1268935. |
1421274. |
0.116928 |
0.122548 |
|
Median |
56000.00 |
38269.00 |
52711.00 |
23196.00 |
74643.00 |
22466.00 |
0.093086 |
0.054015 |
|
Maximum |
735109.0 |
6900051. |
75309.0 |
190051. |
22554839 |
22554839 |
0.702466 |
0.792676 |
|
Minimum |
15089.00 |
2405.000 |
0.000000 |
0.000000 |
825.0000 |
88.00000 |
-0.155700 |
-0.111004 |
|
Std. Dev. |
160277.6 |
1497543. |
163567.0 |
1491301. |
4554757. |
4567000. |
0.157200 |
0.208869 |
|
Skewness |
2.512589 |
3.664010 |
2.475868 |
3.855236 |
3.996610 |
3.869176 |
2.131899 |
1.864969 |
|
Kurtosis |
8.935691 |
15.01502 |
8.731751 |
16.04765 |
17.57124 |
16.85620 |
8.902336 |
6.333439 |
|
|
|
|
|
|
|
|
|
|
|
Jarque-Bera |
88.20707 |
288.8384 |
83.66846 |
334.9682 |
402.8102 |
367.3197 |
77.31723 |
36.49369 |
|
Probability |
0.000000 |
0.000000 |
0.000000 |
0.000000 |
0.000000 |
0.000000 |
0.000000 |
0.000000 |
|
|
|
|
|
|
|
|
|
|
Source: Eviews 10
The data above reveals that the mean depreciation under HCA is N122, 393; with maximum and minimum values of N735, 109 and N15, 089 respectively for the period under study. On the other hand, depreciation under the FVA reveals a mean value of N478, 765, with maximum and minimum values of N6, 900,051 and N2, 405 respectively. The results indicate that depreciation is higher in the FVA era than the HCA era, given that the mean and maximum values of depreciation in the FVA are higher than the depreciation value in the HCA choice. The dividend data also revealed that cash dividend paid is higher under FVA than HCA. The mean values of dividend paid are N114, 565 under HCA, and N402, 673 under FVA. The maximum values of dividend also revealed that dividend reported under FVA is higher than that reported under HCA, with maximum values of N75,309 and N190,051 in millions.
Furthermore, the data above reveals that the mean taxation under HCA is N1, 268,935, with a maximum and minimum value of N22, 554,839 and N825 respectively for the period under study. On the other hand, depreciation under the FVA reveals a mean value of N1, 121,274, with maximum and minimum values of N22554839 and N22, 554,839 respectively. The results indicate that taxation is higher in the HCA era than the FVA era. The ROA figure indicates that the mean value of 0.117 and 0.123 for HCA and FVA respectively, with maximum values of 0.702 and 0.793, and minimum values of -0.155 and -0.111 respectively for HCA and FVA. The results indicate that profitability is higher in the Fair Value Measurement choice. Overall, fair value measurement choice increases reported profits and minimizes reported losses. The skewness and kurtosis statistics revealed that all the variables are positively skewed, with their data being leptokurtic, i.e. above K=3. The Jarque-Bera statistics, with p=0.0000 for all variables indicate that the data is normally distributed.
Table 2: Correlation matrix
|
|
DEP_H |
DEP_F |
DIV_H |
DIV_F |
ROA_H |
ROA_F |
TAX_H |
TAX_F |
|
DEP |
1.000 |
|
|
|
|
|
|
|
|
|
----- |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
DEP_F |
0.409 |
1.000 |
|
|
|
|
|
|
|
|
0.0146 |
----- |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
DIV_H |
0.985 |
0.382 |
1.000 |
|
|
|
|
|
|
|
0.0000 |
0.0232 |
----- |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
DIV_F |
0.413 |
0.982 |
0.418 |
1.000 |
|
|
|
|
|
|
0.0135 |
0.0000 |
0.0124 |
----- |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
ROA_H |
-0.771 |
-0.134 |
-0.504 |
-0.105 |
1.000 |
|
|
|
|
|
0.0396 |
0.4437 |
0.0436 |
0.5463 |
----- |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
ROA_F |
-0.175 |
-0.340 |
-0.151 |
-0.113 |
0.154 |
1.000 |
|
|
|
|
0.3148 |
0.0028 |
0.3869 |
0.0196 |
0.3764 |
----- |
|
|
|
|
|
|
|
|
|
|
|
|
|
TAX_H |
-0.007 |
0.115 |
-0.168 |
-0.070 |
-0.170 |
-0.126 |
1.000 |
|
|
|
0.9686 |
0.5088 |
0.3359 |
0.6875 |
0.0354 |
0.4705 |
----- |
|
|
|
|
|
|
|
|
|
|
|
|
TAX_F |
0.021 |
0.050 |
0.033 |
0.047 |
-0.116 |
-0.538 |
-0.074 |
1.000 |
|
|
0.9059 |
0.7774 |
0.8491 |
0.7907 |
0.5071 |
0.0024 |
0.6716 |
----- |
|
|
|
|
|
|
|
|
|
|
Source: Eviews 10
The Pearson Correlation Coefficient is used to establish the inter-correlation between the dependent and independent variables. Saunders, Lewis, Thornhill (2003) noted that there could be a strong positive relationship, a weak positive relationship and no relationship and Pearson’s r ranges from –1.0 to 1.0, where a negative coefficient indicates inverse relations between the variables. The pairwise correlation matrix results are explained here.
Under the HCA, ROA is found to have negative and statistically significant relationship with DEP (r= -0.77, p = 0.0396), DIV (r = -0.54, p = 0.0436) and TAX (r = -0.17, p = 0.0354).
Under the FVA, ROA is also found to be negatively and statistically significant correlated with DEP (r = -0.34, p = 0.0028), DIV (r = -0.11, p = 0.0196) and TAX (r = -0.54, p = 0.0024).
The Pearson’s correlation matrix shows that the degree of correlation between the independent variables is either low or moderate, which suggests the absence of multicollinearity between independent variables. As suggested by Van, Shahnaz, Nurasyikin (2008), the Pearson’s R between each pair of independent variables should not exceed 0.80; otherwise, independent variables with a co- efficient in excess of 0.80 may be suspected of exhibiting multicollinearity. The highest correlation as disclosed in the table is between Taxes (TAX) and Reported Profit at Fair value (RPFVA) showing a value of 0.54, between Depreciation (DEP) and Reported Profit at Historical cost (RPHCM) showing a value of 0.54. This confirms that there is no multicollinearity among the variables using the reported profit at historical cost.
Table 3: Regression results of depreciation, taxation and dividend on ROA under HCA
|
PANEL A: Historical Value Measurement choice on profitability (ROA) |
||||
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
C |
0.6789 |
8.557205 |
1.131089 |
0.0016 |
|
LDEP_H |
-0.0598 |
6.002102 |
-4.126243 |
0.0396 |
|
LTAX_H |
-0.0422 |
0.016353 |
-3.584133 |
0.0453 |
|
LDIV_H |
-0.0617 |
0.525029 |
-5.184199 |
0.0163 |
|
R-squared Adj. R-squared Prob(F-statistic) Durbin-Watson |
0.772 0.694 0.0026 2.35 |
|||
Source: Stata 10 Computation
Table 4: Regression results of depreciation, taxation and dividend on ROA under FCA
|
PANEL B: Fair Value Measurement choice on profitability (ROA) |
||||
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
C |
0.156253 |
0.116550 |
1.340659 |
0.1921 |
|
LDEP_F |
0.19263 |
0.016374 |
6.214689 |
0.0001 |
|
LTAX_F |
0.102181 |
0.007519 |
-7.279856 |
0.0022 |
|
LDIV_F |
-0.013177 |
0.016632 |
-9.191043 |
0.0609 |
|
R-squared Adj. R-squared F-statistic Prob(F-statistic) Durbin-Watson |
0.942 0.8589 11.789 0.0001 2.092 |
|||
Source: Stata 10 Computation
Table 3 panel A shows the regression results of the effect between the dependent variable (reported profit at historical cost) and the independent variables (depreciation, taxes and dividend). Thus, as shown above, the value of adjusted R-squared is 0.77, indicating that the independent variables in the model are explaining 77% variation on the dependent variables while the unexplained variation is just 23%. The unexplained variation of 23% accounts for the error term in the model. The high value of the adjusted R-square is an indication of a good relationship between the dependent and independent variables.
It can be observed that the independent variables give a significant effect on the dependent variable, where f-value (8.489) (p-value=.0026) is greater than the f-tabulated (4.74) at df1=2 and df2=7. The test of autocorrelation using Durbin Watson (DW) test shows that the DW value of 2.35 falls within the no serial correlation region of DW partition curve. Hence, it can clearly be concluded that there exists no degree of autocorrelation in the model.
The independent variables result revealed that DEP, DIV and TAX have negative and significant effects on reported profits under HCA. The t-ratio suggests that the estimated coefficients of the regression parameters are statistically significant at the 0.05 level of significance.
Table 4 panel B shows the regression results of the effect between the dependent variable (reported profit at fair value) and the independent variables (depreciation, taxes and dividend). Thus, as shown above, the value of adjusted R-squared is 0.94, indicating that the independent variables in the model are explaining 94% variation on the dependent variables while the unexplained variation is just 6%. The unexplained variation of 6% accounts for the error term in the model. The high value of the adjusted R-square is an indication of a good relationship between the dependent and independent variables.
It can be observed that the independent variables give a significant effect on the dependent variable, where f-value (11.789) (p-value =.0001) is greater than the f-tabulated (4.74) at df1=2 and df2 =7. The test of autocorrelation using Durbin Watson (DW) test shows that the DW value of 2.09 falls within the no serial correlation region of DW partition curve. Hence, it can clearly be concluded that there exists no degree of autocorrelation in the model.
The independent variables result revealed that DEP and TAX have positive and significant effect on earnings performance. DIV has a negative and insignificant effect on reported profits under HCA. The t-ratio suggests that the estimated coefficients of the regression parameters are statistically significant at the 0.05 level of significance for DEP and TAX and insignificant for DIV.
4.2 Test of hypotheses and discussion of findings
Hypothesis one decision:
The table shows that the effect of depreciation on ROA using FVA (coefficient = 0.19263) is positive and significantly higher than the effect of depreciation on ROA under HCA with a negative and significant effect (coefficient = -0.059824), at the 0.05 level of significance. Thus, H01 null is rejected. The research upholds that there is a significant difference in the earnings performance effects of depreciation using HCA and FVA measurement choices of manufacturing firms in Nigeria
Hypothesis two decision:
The table shows that the effect of taxation on ROA using FVA (with coefficient = 0.102181) is significantly higher than the effect of taxation on ROA under HCA (coefficient = -0.042258), at the 0.05 level of significance. Thus, H02 null is rejected. The research upholds that the effect of taxation on earnings performance significantly differ for HCA and FVA measurement choices of manufacturing firms in Nigeria.
Hypothesis three decision:
The table shows that the impact of dividend on ROA using FVA (coefficient = -0.013177) is significantly higher than the impact of dividend on ROA under HCA (coefficient:-0.061739), at the 0.05 level of significance. Thus, H03 null is rejected. The research upholds that there is a significant magnitude of difference between the implications of HCA dividend and FVA dividends on ROA manufacturing firms in Nigeria.
5. Conclusion and Recommendations
5.1 Conclusion
Using the panel OLS and t-statistic, and using reported returns on assets (ROA) as a proxy for earnings performance, depreciation, taxation and dividend as proxies for both FVA and HCA, the empirical findings provide strong support for the proposition that fair value measurement choice has superior effect on bottom lines of manufacturing firms than the historical cost measurement choice as follows:
1. There is a significant difference in the earnings performance effects of depreciation using HCA and FVA measurement choices of manufacturing firms in Nigeria. Depreciation negatively impacts earnings performance under HCA method. Whilst depreciation positively impacts earnings performance under FVA.
2. The effect of taxation on earnings performance significantly differs for HCA and FVA measurement choices of manufacturing firms in Nigeria. Taxation negatively impacts earnings performance under HCA method, while it positively impacts earnings performance under FVA method.
3. The effect of dividend on earnings performance significantly differ for HCA and FVA measurement choices of quoted manufacturing firms in Nigeria
5.2 Recommendations
Based on the findings of the study, the following recommendations were made:
i. There is need to deviate from the historical cost accounting method during inflationary period since under HCA method, depreciation negatively impacts earnings performance, whilst under FVA depreciation positively impacts earnings performance of the firms.
ii. There is need to deviate from the historical cost accounting method during inflationary period since under HCA method, taxation negatively impacts earnings performance whilst under FCA taxation positively impacts earnings performance of the firms.
iii. There is need to deviate from the historical cost accounting method during inflationary period since under HCA Dividend has a negative and insignificant effect on reported profits whilst under FCA Dividend positively impacts earnings performance of the firms.
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|
APPENDIX: DATA VARIABLES
COMPANY |
YEAR |
EBIT |
TA |
TAX |
DEP |
DIV |
|
CHAMPIONS BREW |
2020 |
418,163 |
11,368,517 |
-259,370 |
1,047,423 |
45,671 |
|
2019 |
206,578 |
10,981,383 |
-38,070 |
912,823 |
23,098 |
|
|
2018 |
-255,433 |
10,487,010 |
-8,374 |
710,705 |
21,332 |
|
|
2017 |
603,173 |
10,088,861 |
-85,611 |
627,820 |
15,673 |
|
|
2016 |
637,300 |
9,961,240 |
-106,911 |
631,312 |
12,986 |
|
|
2015 |
210,179 |
10,329,160 |
-133,039 |
622,428 |
11,673 |
|
|
2014 |
-1,071,765 |
9,592,381 |
317,242 |
848,485 |
15,623.00 |
|
|
2013 |
-1,730,432 |
9,137,716 |
552,407 |
696,737 |
87,290 |
|
|
2012 |
-1,928,865 |
6,799,200 |
592,175 |
782,130 |
64,000 |
|
|
2011 |
-1,771,517 |
7,071,342 |
94,977 |
716,983 |
45,634 |
|
|
2010 |
-858,166 |
2,801,539 |
81,908 |
499,152 |
34,982 |
|
|
2009 |
-456,711 |
329,081 |
45,975 |
220,198 |
21,097 |
|
|
FLOUR MILLS |
2020 |
17,537,685 |
314,267,060 |
-4,955,114 |
2,165,187 |
-4,669,639 |
|
2019 |
18,536,249 |
314,058,187 |
-986,742 |
1,199,489 |
-4,257,175 |
|
|
2018 |
14,153,983 |
322,604,582 |
-4,909,254 |
1,096,412 |
-2,838,587 |
|
|
2017 |
10,979,579 |
343,933,157 |
-1,150,533 |
1,837,244 |
-2,971,314 |
|
|
2016 |
6,248,497 |
233,296,607 |
4,177,289 |
1,758,285 |
-3,660,947 |
|
|
2015 |
910,983 |
231,529,878 |
1,508,560 |
964,758 |
-4,981,928 |
|
|
2014 |
12,457,035 |
220,145,554 |
-1,991,517 |
879,012 |
-4,868,865 |
|
|
2013 |
11,640,693 |
223,889,728 |
-2,895,246 |
765,222 |
-4,058,648 |
|
|
2012 |
11,459,537 |
172,539,746 |
-3,259,079 |
546,223 |
-3,833,421 |
|
|
2011 |
14,264,723 |
116,730,494 |
4,168,971 |
3,040,891 |
3,435,970 |
|
|
2010 |
2,109,111 |
111,098,163 |
2,309,122 |
1,098,126 |
2,312,098 |
|
|
2009 |
3,595,444 |
105,691,585 |
1,125,931 |
2,050,164 |
1,553,067 |
|
|
GUINESS NIG |
2020 |
-17,073,641 |
144,145,581 |
4,494,823 |
10,343,189 |
3,329,382 |
|
2019 |
7,103,630 |
160,792,627 |
1,619,898 |
9,734,548 |
4,030,304 |
|
|
2018 |
9,943,164 |
153,254,968 |
3,225,559 |
8,874,523 |
963,768 |
|
|
2017 |
2,662,081 |
146,038,216 |
738,361 |
8,635,004 |
706,557 |
|
|
2016 |
-2,347,241 |
136,992,444 |
231,355 |
8,651,575 |
2,243,948 |
|
|
2015 |
10,795,102 |
122,246,632 |
3,000,203 |
11,215,213 |
4,754,825 |
|
|
2014 |
11,681,560 |
132,328,273 |
2,108,080 |
1,231,232 |
10,541,217 |
|
|
2013 |
17,008,875 |
121,060,621 |
5,145,149 |
3,332,123 |
11,799,404 |
|
|
2012 |
20,383,158 |
106,009,667 |
6,168,538 |
4,009,812 |
14,749,255 |
|
|
2011 |
26,176,966 |
92,227,824 |
8,249,032 |
4,499,168 |
13,199,123 |
|
|
2010 |
19,988,735 |
78,396,876 |
6,252,376 |
4,053,300 |
11,065,600 |
|
|
2009 |
18,991,762 |
74,868,737 |
13,541,189 |
3,565,316 |
18,883,089 |
|
|
DANGOTE SUGAR |
2020 |
46,038,948 |
259,280,544 |
14,668,289 |
5,198,055 |
13,200,000 |
|
2019 |
34,829,243 |
198,129,122 |
10,726,425 |
4,683,018 |
13,200,000 |
|
|
2018 |
38,455,530 |
178,523,711 |
12,624,589 |
3,519,930 |
15,000,000 |
|
|
2017 |
54,882,983 |
196,064,664 |
17,060,375 |
3,136,692 |
13,200,000 |
|
|
2016 |
20,759,524 |
175,936,048 |
6,560,831 |
3,149,141 |
6,000,000 |
|
|
2015 |
18,144,955 |
106,671,333 |
5,485,100 |
2,749,029 |
4,800,000 |
|
|
2014 |
17,472,841 |
97,287,804 |
5,564,151 |
3,098,245 |
7,200,000 |
|
|
2013 |
20,099,517 |
87,112,182 |
6,561,905 |
1,725,252 |
6,000,000 |
|
|
2012 |
16,331,679 |
83,051,450 |
5,535,263 |
2,309,817 |
3,600,000 |
|
|
2011 |
10,921,229 |
72,814,721 |
3,517,632 |
1,117,845 |
7,200,000 |
|
|
2010 |
16,146,930 |
47,551,443 |
4,864,690 |
1,570,494 |
11,129,011 |
|
|
2009 |
19,586,932 |
60,717,447 |
6,401,333 |
1,567,410 |
12,000,000 |
|
|
DANGOTE CEMENT |
2020 |
430,747,000 |
2,116,060,000 |
78,138,000 |
54,571,000 |
272,648,000 |
|
2019 |
315,420,000 |
1,823,984,000 |
54,071,000 |
53,454,000 |
272,648,000 |
|
|
2018 |
392,223,000 |
1,721,974,000 |
89,233,000 |
51,809,000 |
178,925,000 |
|
|
2017 |
342,153,000 |
1,611,087,000 |
87,523,000 |
43,959,000 |
136,324,000 |
|
|
2016 |
374,396,000 |
1,502,564,000 |
6,191,000 |
47,113,000 |
136,324,000 |
|
|
2015 |
220,567,000 |
1,124,475,000 |
7,396,000 |
43,713,000 |
102,243,000 |
|
|
2014 |
213,039,663 |
963,441,064 |
27,225,540 |
34,202,056 |
119,283,552 |
|
|
2013 |
200,010,823 |
820,477,742 |
10,251,931 |
32,028,158 |
51,121,522 |
|
|
2012 |
138,088,716 |
639,466,109 |
14,836,382 |
27,267,634 |
19,364,214 |
|
|
2011 |
113,779,556 |
524,045,921 |
7,635,957 |
16,089,202 |
34,861,544 |
|
|
2010 |
101,334,468 |
402,040,493 |
5,270,941 |
12,098 |
1,837,244 |
|
|
2009 |
49,510,037 |
307,364,397 |
2,258,711 |
10,764 |
1,758,285 |
|
|
MEYER PLC |
2020 |
1,628,880 |
3,015,080 |
520,374 |
11,458 |
3,456,123 |
|
2019 |
-7,071 |
3,720,214 |
6,422 |
14,184 |
2,345,671 |
|
|
2018 |
182,412 |
1,839,132 |
136,885 |
37,420 |
782,130 |
|
|
2017 |
-264,704 |
1,890,966 |
3,035 |
34,614 |
716,983 |
|
|
2016 |
-211,038 |
2,178,705 |
3,364 |
44,542 |
499,152 |
|
|
2015 |
80,544 |
2,301,121 |
7,314 |
51,498 |
220,198 |
|
|
2014 |
-33,893 |
2,435,368 |
4,121 |
66,481 |
2,165,187 |
|
|
2013 |
-22,028 |
2,597,517 |
1,887 |
65,855 |
1,199,489 |
|
|
2012 |
-25,844 |
1,839,132 |
26,213 |
11,458 |
1,096,412 |
|
|
2011 |
-80,304 |
1,890,966 |
4,439 |
14,184 |
2,390,812 |
|
|
2010 |
-231935 |
2,178,705 |
45,101 |
35,671 |
178,451 |
|
|
2009 |
112,908 |
2,301,121 |
34,209 |
34,614 |
631,312 |
|
|
GRIEF NIG |
2020 |
398,528 |
321,852 |
48,041 |
51,001 |
622,428 |
|
2019 |
-311,537 |
173,542 |
29695 |
41,209 |
848,485 |
|
|
2018 |
-245,229 |
475,731 |
17,360 |
15,529 |
696,737 |
|
|
2017 |
77,554 |
786,663 |
28,130 |
18,814 |
25,584 |
|
|
2016 |
37,597 |
722,490 |
10,491 |
17,949 |
25,584 |
|
|
2015 |
40,149 |
715,714 |
15,525 |
18,418 |
25,584 |
|
|
2014 |
58,029 |
663,773 |
14,586 |
18,838 |
25,584 |
|
|
2013 |
52,469 |
682,415 |
21,843 |
15,290 |
12,166 |
|
|
2012 |
61,011 |
631,567 |
22,064 |
14,449 |
12,792 |
|
|
2011 |
192,269 |
4,207,282 |
19,082 |
12,094 |
97,817 |
|
|
2010 |
434,250 |
3,498,445 |
16,093 |
11,289 |
93,122 |
|
|
2009 |
295,331 |
2,823,929 |
34,567 |
10,652 |
87,957 |
|
|
LAFARGE AFRICA |
2020 |
33,941,453 |
505,332,716 |
5,226,569 |
26,132,270 |
7,512,967 |
|
2019 |
23,640,698 |
500,081,653 |
919,082 |
27,160,431 |
4,220,596 |
|
|
2018 |
-7,408,583 |
577,692,296 |
11,550,347 |
16,369,888 |
11,845,272 |
|
|
2017 |
-7,385,863 |
616,169,940 |
5,837,763 |
16,304,267 |
16,280,825 |
|
|
2016 |
19,888,762 |
537,598,212 |
889,586 |
5,170,285 |
1,444,821 |
|
|
2015 |
30,906,793 |
381,272,953 |
1,249,020 |
5,298,867 |
12,991,527 |
|
|
2014 |
41,198,427 |
305,878,828 |
6,537,761 |
4,290,109 |
14,955,251 |
|
|
2013 |
27,714,998 |
161,081,711 |
552,189 |
3,456,122 |
2,109,872 |
|
|
2012 |
21,264,420 |
151,948,633 |
6,552,744 |
1,998,176 |
3,564,129 |
|
|
2011 |
10,235,000 |
152,577,460 |
1,710,000 |
2,066,957 |
750,400 |
|
|
2010 |
8,464,000 |
118,480,913 |
3,583,000 |
1,814,822 |
300,160 |
|
|
2009 |
9,237,328 |
87,163,066 |
4,181,930 |
12,395,763 |
1,801,789 |
|
|
PORTLAND PAINTS |
2020 |
-335,992 |
1,879,208 |
22,700 |
38,516 |
22,770 |
|
2019 |
127,195 |
2,254,911 |
42,301 |
65,856 |
39,671 |
|
|
2018 |
307,533 |
2,251,468 |
100,840 |
59,366 |
23,098 |
|
|
2017 |
123,868 |
2,035,902 |
65,698 |
64,171 |
20,009 |
|
|
2016 |
7,502 |
1,754,321 |
1,094 |
57,176 |
15,673 |
|
|
2015 |
-258,369 |
1,899,281 |
25,384 |
84,746 |
12,986 |
|
|
2014 |
194,296 |
2,277,558 |
45,654 |
84,111 |
11,673 |
|
|
2013 |
123,591.27 |
2,181,300.00 |
16,118.00 |
61,908 |
15,623.00 |
|
|
2012 |
-199,166 |
2,386,022 |
29,199 |
34,567 |
80,000 |
|
|
2011 |
253,188 |
2,286,067 |
79,336 |
71,098 |
64,000 |
|
|
2010 |
345,192 |
1,908,145 |
34,529 |
56,345 |
45,634 |
|
|
2009 |
233,490 |
1,765,125 |
56,198 |
98,245 |
109,863 |
|
|
CADBURY |
2020 |
408,065 |
33,210,684 |
523762 |
1,503,338 |
178,459 |
|
2019 |
1538877 |
28801938 |
468032 |
1486438 |
437149 |
|
|
2018 |
1222831 |
27528040 |
399746 |
1438091 |
300512 |
|
|
2017 |
350,317 |
28,423,122 |
50,319 |
1,517,193 |
891,095 |
|
|
2016 |
-562,870 |
28392951 |
266,468 |
1,415,488 |
460,525 |
|
|
2015 |
1,577,412 |
28,417,005 |
424,117 |
1,721,452 |
1,270,811 |
|
|
2014 |
1,467,314,000 |
28,820,107 |
45,373,000 |
1,998,176 |
109,863 |
|
|
2013 |
7,421,477,000 |
43,172,624 |
1,398,258 |
2,066,957 |
178,459 |
|
|
2012 |
5,361,692,000 |
40,156,508 |
2,011,579 |
1,814,822 |
437149 |
|
|
2011 |
5,309,043 |
32,642,612 |
1,525,832 |
1,288,628 |
13,962 |
|
|
2010 |
4,528,971 |
12,908,345 |
1,090,197 |
1,209,117 |
34,562 |
|
|
2009 |
-2,192,161 |
34,567,198 |
1,168,462 |
1,045,287 |
37,892 |
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