The impact of the deferred tax adjustment on the Economic Value Added ( EVA ) measure

Economic Value Added (EVA) is a value-based accounting measure used by companies to measure the amount of value created for shareholders. EVA requires the conversion of accounting values to economic values. This conversion process is known as the EVA adjustment. If accounting values are not converted to economic values, the value of the EVA can be distorted. Previous studies have shown that companies are experiencing difficulties in implementing EVA adjustments. To reduce these difficulties, companies have decided to limit their EVA adjustments to ten or even fewer. The research problem is that if the appropriate adjustments are not made, an inaccurate EVA measure will be calculated. The aim of the research is to measure whether deferred taxes impact EVA. The study is conducted within a quantitative research paradigm. Secondary data analysis was carried out on JSE-listed food producers over a seven-year period, from 2004 to 2010. The unadjusted EVA was compared to the adjusted EVA measure to determine the before and after effects of deferred taxes on EVA. The findings of the study revealed that deferred taxes either understated or overstated the value of the EVA during the period 2004–2010. In addition, the results from the regression analysis revealed an overall significance for all deferred tax predictors. The results from the study showed that deferred tax had a significant impact on the value of EVA. Therefore, the study recommends that companies implement the deferred tax adjustment on EVA. Melissa Naicker (South Africa) BUSINESS PERSPECTIVES LLC “СPС “Business Perspectives” Hryhorii Skovoroda lane, 10, Sumy, 40022, Ukraine www.businessperspectives.org The impact of the deferred tax adjustment on the Economic Value Added (EVA) measure Received on: 29th of April, 2017 Accepted on: 21st of September, 2017


INTRODUCTION
During recent years, there has been an increasing emphasis on the concept of value creation. Many corporations around the world are focusing on making decisions that create value for the company and for the shareholder. Shareholders are considered as one of the most important stakeholders in a company, as the investment in shares is a primary source of capital.
It is, therefore, essential for managers to act in the best interests of shareholders by making decisions that will benefit the shareholder, and, hence, create shareholder value (Collier, 2012). Consequently, the primary goal of management is to increase shareholder wealth by aligning the interests of management with that of shareholders (Lovata & Costigan, 2002). Sharma and Kumar (2010) also agree that companies are focusing more on maximizing shareholder value.
Investment Management and Financial Innovations, Volume 14, Issue 3, 2017 Company. During the late 1980s, Bennett Stewart and Joel Stern pioneered the EVA measure as one of the value-based accounting measures (Stewart, 1991).
EVA is a tool used to measure the amount of value the company has created and, consequently, measures the amount of value created for shareholders. The EVA measure indicates if the company has created value or destroyed value (Latha, 2009). Ray (2001) points out that EVA has been used increasingly and successfully in the corporate world by corporate giants such as Coca-Cola, AT&T, Briggs Stratton, DuPont, Eli Lilly and Quaker Oats. Stern Stewart and company created the following EVA formula: The above formula can be reduced by multiplying TCE by WACC % to yield COC, where NOPAT -Net operating profit after taxes; TCE -Total capital employed; WACC % -Weighted average cost of capital percentage; COC -Cost of capital.
With reference to the EVA formula, NOPAT represents the profits generated by the company from using invested capital (TCE). TCE represents the amount of capital invested in the company, which constitutes shareholder capital and borrowed capital. The COC represents the cost of using the capital (TCE) invested in the company. The aim is for companies to generate a return (NOPAT ) that would exceed the cost of using capital (COC). A positive EVA ( NOPAT COC > ) indicates value creation, whilst a negative EVA ( NOPAT COC < ) represents value destruction (Young, 1997).
The computation of the EVA measure requires the extraction of accounting information from company annual financial statements (AFS). AFS are prepared according to accounting standards and, therefore, reflect accounting values. Burksaitiene (2009) makes an important point by stating that accounting values are distorted due to the application of Generally Accepted Accounting Practices (GAAP).
Furthermore, accounting values need to be adjusted to reflect an economic value for the purposes of calculating EVA. For example, the 'profit for the period', as reflected on the statement of comprehensive income, and 'capital', as reflected on the statement of financial position, are accounting values. These accounting values need to be converted to economic values. The 'profit for the period' is converted to an economic value called net operating profit after taxes (NOPAT ). Likewise, 'capital' is converted to an economic value called total capital employed (TCE). The conversion of accounting values to economic values is referred to as an adjustment (Burksaitiene, 2009).
Converting accounting values to economic values is important for the purposes of calculating EVA. Economic values must be reflected in the EVA measure, hence, the term 'Economic Value Added'. According to Stewart (1991), EVA must also be adjusted for other accounting transactions that take place during the year. The accounting transactions include research and development, operating leases, depreciation and deferred taxes. The EVA must be adjusted for accounting transactions, because these transactions are accounting values that affect the values of NOPAT and TCE. The NOPAT and TCE are components of EVA. Therefore, adjustments to NOPAT and TCE are synonymous to adjusting the EVA measure.
However, Sharma and Kumar (2010) believe that companies are experiencing difficulties in understanding and implementing adjustments. In addition, Young (1997) states that companies have decided to keep their adjustments to ten or fewer in order to prevent the EVA system from becoming complicated. Furthermore, Young (1997) indicates that some companies prefer not to make any adjustments so that the system is easier to administer and comprehend. The research problem is that the accuracy of the EVA measure is affected for companies that are not prepared to implement the appropriate accounting adjustments. This paper seeks to make a contribution towards improving the accuracy of the EVA measure. This will be achieved by investigating the impact of the deferred tax adjustment on the EVA measure.

CONCEPTUAL FRAMEWORK
According to Stewart (2013), there are several accounting adjustments that can be made to EVA. Accounting adjustments constitute the removal of accounting transactions. There are many types of accounting transactions, namely, operating lease transaction, research and development (R&D) transaction and the deferred tax transaction. These accounting transactions are removed because of their distorting impact on EVA. For example, the operating lease transaction distorts EVA. To remove the distortion, an operating lease adjustment is made to EVA. The operating lease adjustment is one type of an accounting adjustment. In the context of this study, the deferred tax transaction distorts EVA. To remove the distorting impact of deferred taxes on EVA, a deferred tax adjustment is implemented for EVA. The deferred tax adjustment is another example of an accounting adjustment.

The unadjusted TCE and the adjusted TCE
The following studies examined the impact of individual accounting adjustments on TCE.
Studies by Damodaran (2009)  The empirical findings from the above studies showed that a decrease in deferred tax expenses resulted in a higher unadjusted NOPAT and a lower adjusted NOPAT. These studies concluded that a decrease in the provision for deferred tax expenses resulted in an overstated NOPAT.
In addition, the following studies investigated whether deferred taxes were useful in detecting the management of NOPAT. Phillips, Pincus and Rego (2002) and Chang, Herbohn, and Tutticci (2009) suggest that an increase in deferred tax expenses resulted in a lower unadjusted NOPAT and a higher adjusted NOPAT. The above studies concluded that an increase in the provision for deferred tax expenses resulted in an understated NOPAT.

The unadjusted TCE, adjusted TCE and deferred tax assets/liabilities
Previous studies by Gee and Mano (2006) and Gallermore (2012) investigated the relationship between deferred tax assets and TCE in the banking sector. The above studies showed that banks were using deferred tax assets to maintain an adequate level of TCE. Gee and Mano (2006) and Gallermore (2012) yielded similar empirical results as both studies found that deferred tax assets resulted in a higher unadjusted TCE and a lower adjusted TCE. This finding revealed that deferred tax assets overstated TCE. Furthermore, Gee and Mano (2006) and Gallermore (2012) suggest that the reporting of a deferred tax liability would result in a lower unadjusted TCE and a higher adjusted TCE. These studies suggest that the deferred tax liabilities would understate TCE.
The above studies provided evidence that deferred tax expenses distorted NOPAT and that deferred tax assets/liabilities distorted TCE.

Empirical evidence on accounting adjustments and EVA
Previous studies provided empirical evidence on the components of EVA (NOPAT and TCE) and on the different types of accounting adjustments (the deferred tax adjustment being one of the accounting adjustments). However, very few studies have been conducted on EVA and accounting adjustments (Latha, 2009 The study conducted by Anderson et al. (2005) investigated the impact of five types of accounting adjustments on EVA. The five accounting adjustments included the R&D adjustment, operating lease adjustment, advertising adjustment, last-in-first-out (LIFO) adjustment and the bad debts adjustment. The study compared the unadjusted EVA (value of the EVA before accounting adjustments) to the adjusted EVA (value of the EVA after accounting adjustments). The results of the study showed that R&D and LIFO were the two out of the five adjustments that accounted for a major change in the value of the EVA. Furthermore, the regression statistics revealed a lack of statistical significance in relation to the accounting adjustments and EVA.
The reason for the lack of statistical significance was due to the selection of the number and type of adjustments. The number and types of adjustments resulted in a lack of commonality. The lack of commonality resulted in the lack of com-parability. As a result, the overall results could not determine a material effect of accounting adjustments on EVA (Anderson et al., 2005).
In order to prevent the difficulties experienced by Anderson et al., the researcher has chosen only one adjustment. Deferred tax has been chosen, as it is an adjustment that occurs every year for each company and is also a common adjustment between companies. This is evident as a review of the sample companies' AFS showed that each company recognized deferred tax. The deferred tax values were reflected on each company's statement of comprehensive income and on the statement of financial position. In addition, deferred taxes were reflected on each company's AFS for each year starting from 2004 to 2010 (sample period). As a result, the deferred tax adjustment facilitates comparability during each sample year for a single company and between each company.
According to Latha (2009), there is much room for studies to be conducted on the importance and significance of accounting adjustments on EVA within a different sector and under different GAAP settings.
To the best of the researcher's knowledge, no research has been done on the impact of deferred tax adjustments on EVA within a South African context. As a result, this paper investigates the impact of the deferred tax adjustment on EVA for the Johannesburg Stock Exchange (JSE)-listed food producers.

RESEARCH METHODOLOGY
The research methodology encompasses the research design used for this study, which is followed by a discussion of the target population, sampling method, data collection, data analysis and formulation of the hypotheses. The section ends with a brief discussion on how validity was achieved.

Quantitative research study
A close examination into sample companies unadjusted EVA and adjusted EVA values was required, to solve the research problem. This study calculated the unadjusted EVA and adjusted EVA val-ues from company's annual financial statements (AFS). This showed that the entire data collection and data analysis constitute numerical data, and is therefore a quantitative research study.

Time horizon
This study calculated and analyzed independent variables (deferred tax expenses & deferred tax assets/deferred tax liabilities) and dependent variables (unadjusted EVA and adjusted EVA) for each year, over a seven-year period starting from 2004 to 2010. For this reason, this study is longitudinal in nature.

Type of investigation
The primary aim of this research was to determine if the deferred tax adjustment causes a major change in the EVA measure. A causal study was selected to establish any causal relationships between variables. In other words, the causal study was undertaken to determine if variable X causes variable Y. Hence, the statistical analysis produced correlational statistics to measure the impact of the independent variable on the dependent variable.

Research strategy
An experimental design was chosen to study causal links between variables and to furthermore examine whether a change in one independent variable produces a change in the dependent variable. The experimental design focused on the pre-measurement and post-measurement of the dependent variable. In particular, the pre-measurement of the dependent variable constituted the EVA measure without the deferred tax adjustment, whereas the post-measurement of the dependent variable constituted the EVA measure with the implementation of the deferred tax adjustment. To elaborate, the experimental design had a control variable and an experimental variable. The control variable (unadjusted EVA) was the EVA without deferred tax adjustment, which is the dependent variable without any intervention. The experimental variable (adjusted EVA) was the EVA measure with the implementation of the deferred tax adjustment, which is the dependent variable with planned intervention. Drury (2011) states that listed companies who trade shares on the stock market are more likely to adopt EVA. Therefore, the target population constituted 50 industrial sectors from the JSE.

Sampling method
This study used a purposive sampling method. The aim of this sampling method was to choose an industrial sector from the JSE that adopted EVA. A study by Alzawahreh and Khasawneh (2011) presented an empirical finding which showed that companies in the food producer sector used a defender strategy. Furthermore, Lovata and Costigan (2002) stated that companies, which used a defender strategy, adopted EVA. For this reason, the food producers sector was selected for the purposes of this study.
The sample population for this study constituted a total of 14 JSE-listed companies from the food producers sector. However, due to missing information on the McGregors BFA database, the final sample constituted 9 JSE-listed food producing companies.

Data collection
The sample companies AFS together with supporting financial information was downloaded from the McGregor's BFA database. Three types of data sets were downloaded from McGregors BFA database include the statement of comprehensive income, the statement of financial position and the weighted average cost of capital (WACC) calculations. In addition, the last data set included taxation rates that was obtained from the South African Revenue Services (SARS) website. The four data sets constituted the secondary data for this study. The company AFS, WACC calculations and taxation rates are viewed as secondary data, as it was prepared by a third party.

Data analysis
The author used the abovementioned data sets to conduct the data analysis. The data analysis for this study was done on an excel spreadsheet and is referred to as the 'EVA and deferred tax analysis'. The EVA and deferred tax analysis constitute cal-culations of the unadjusted EVA, the deferred tax adjustment and the adjusted EVA.
The statement of comprehensive income was used to calculate the unadjusted NOPAT, and was used to locate values of deferred tax expenses to calculate the adjusted NOPAT. To note, the taxation rates was taken into account in the NOPAT calculations. The statement of financial position was used to calculate the unadjusted TCE, and was used to locate the values of the deferred tax liabilities to calculate the adjusted TCE.
The following formulae, as specified by Bennett Stewart and Joel Stern,were used for the EVA and deferred tax analysis: Deferred tax adjustment as specified by Stern Stewart: • Increases in deferred tax expenses -add back to the unadjusted NOPAT • Decreases in deferred tax expenses -subtract from the unadjusted NOPAT • Deferred tax liability -add back to the unadjusted TCE • Deferred tax asset -subtract from the unadjusted TCE EVA formulae as specified by Stern Stewart: With reference to the above formulae, the unadjusted EVA represents EVA without the implementation of the deferred tax adjustment, this means that the values of deferred tax are included in the unadjusted EVA.
The above formulae were used to remove the deferred taxes from EVA. This required removing the components of deferred taxes (deferred tax expenses and deferred tax liabilities) from the components of EVA (NOPAT and TCE). To elaborate, the deferred tax expenses were removed from the unadjusted NOPAT to arrive at the adjusted NOPAT. In addition, the deferred tax liabilities were removed from the unadjusted TCE to arrive at the adjusted TCE.
The implementation of the deferred tax adjustment results in the removal of deferred taxes from the unadjusted EVA to arrive to the adjusted EVA.
The adjusted EVA represents a more accurate EVA, as the distorting impact of deferred taxes are removed. The EVA and deferred tax analysis was computed for all nine food producers (see to Appendix I for the EVA and deferred tax analysis of a sample company).
The EVA and deferred tax analysis served as an input to compute descriptive statistics and inferential statistics These statistics are interpreted to either accept/reject the hypothesis and, hence, answer the research problem.

Research hypotheses
The study formulated the following research hypotheses: • the null hypothesis for this study was: *H0 = The deferred tax adjustment has no significant impact on the EVA measure; • the alternate hypothesis for this study was: *H1 = The deferred tax adjustment has a significant impact on the EVA measure.
The hypotheses were developed to examine if there is a relationship between the independent variable (deferred tax) and the dependent variable (EVA). Consequently, a regression analysis was se-lected for this study. In addition, the regression analysis provided an indicator of the statistical significance of relationships. The statistical significance output from the regression analysis was used to determine the rejection/acceptance of the alternate hypothesis.

Validity
This study was conducted under laboratory experimental conditions. This means that all nuisance variables had been controlled (  revenue. The decrease in deferred tax expenses is depicted by the horizontal bar to the left, indicating that the company is due for a tax refund from the receiver of revenue. Figure 1 shows that five companies experienced an increase in deferred tax expenses, whilst the other four companies experienced a decrease in deferred tax expenses. Figure 2 shows the unadjusted NOPAT (before the deferred tax adjustment) and the adjusted NOPAT (after the deferred tax adjustment) for all nine sample companies. The implementation of the deferred tax adjustment to NOPAT entails the removal of deferred tax expenses from the unadjusted NOPAT to arrive at the adjusted NOPAT.

Descriptive statistics
Therefore, the findings from Figure 1 and Figure  2 are interpreted together. For example, Afgri Limited reported an increase in deferred taxes (taxes owing) of R3 150 000. Also, Afgri Limited had a unadjusted NOPAT (with deferred taxes) of R776 836 000. When deferred tax expenses are removed, the adjusted NOPAT is R779 986 000. The increase in deferred tax expenses (taxes owing) resulted in the unadjusted NOPAT being lower than the adjusted NOPAT. To elaborate, an increase in deferred tax expenses understated the value of the NOPAT. The NOPAT value was understated for five food producers.
The findings for the current study is compared with the literature findings from previous studies. Phillips, Pincus, and Rego (2002) examined the usefulness of deferred tax expenses in detecting the management of NOPAT. The results of the study suggested that an increase in deferred tax expenses resulted in an understated NOPAT value. The previous literature findings concur with the current empirical findings.
In contrast, Figure 1 and Figure 2 also illustrate how a decrease in deferred tax expenses (tax refund) affects NOPAT. AVI Limited reported a decrease in deferred tax expenses of R8 871 000. AVI Limited had a unadjusted NOPAT of R407 943 000. When deferred tax expenses are removed, the value of the adjusted NOPAT is R399 072 000. A comparison of the unadjusted and adjusted NOPAT shows that the unadjusted NOPAT is higher than the adjusted NOPAT. This implies that a decrease in deferred tax expense overstated the value of NOPAT. The NOPAT value was overstated for four food producers.
The current empirical findings can be compared with the literature findings. The study by Noor et al. (2005) investigated the reason for the widening gap between the unadjusted NOPAT and the adjusted NOPAT. The study showed that the widening gap was due to the management of NOPAT through the use of deferred tax expenses. Furthermore, the previous literature findings revealed that a decrease in deferred tax expenses resulted in an overstated NOPAT value. Therefore, the literature findings by Noor et al. are in agreement with the current empirical findings. No deferred tax assets were reported during the sample period. Figure 4 shows the unadjusted TCE (before the deferred tax adjustment) and the adjusted TCE (after the deferred tax adjustment) for all nine sample companies. To elaborate, the implementation of the deferred tax adjustment to TCE entails the removal of deferred tax liabilities from the unadjusted TCE to arrive at the adjusted TCE.
The empirical findings for Figure 3 and Figure 4 are interpreted together, because the value of the deferred tax liability impacts the value of TCE. For example, Afgri Limited reported an unadjusted TCE of R4 132 012 000. The removal of deferred tax liabilities resulted in an adjusted TCE of R4 266 412 000. A comparison of the unadjusted TCE with the adjusted TCE showed that unadjusted TCE is lower than the adjusted TCE. This result confirms that the value of the deferred tax liability understated TCE. The TCE value was understated for all nine food producers.
A study by Gee and Mano (2006) showed that managers were using deferred tax assets to manage the value of TCE. The results from the study indicated that companies were recognising deferred tax assets to produce an overstated TCE. The previous study also mentioned that if companies reported a deferred tax liability, an understated TCE would be produced. Therefore, the previous literature findings agree with the current empirical findings.
To conclude, the descriptive statistics analysed the relationships between deferred taxes and EVA. This was done by examining the relationships between the components of EVA (NOPAT and TCE) with the components of deferred tax (deferred tax expense and deferred tax liability). These descriptive results showed that deferred tax expenses distort NOPAT and that deferred tax liabilities distort TCE. Hence, descriptive findings revealed that deferred taxes distort EVA.

Inferential statistics
The inferential results constitute regression statistics, the ANOVA test for overall significance and the coefficient test for individual significance for both the unadjusted EVA and adjusted EVA. With reference to Table 1, the unadjusted EVA regression model yielded a perfect positive correlation of +1. This implies that the independent variables accurately predict the value of the (unadjusted EVA) dependent variable.
With reference to Table 2, the ANOVA (f-tests) evaluated the overall significance of the independent variables on the dependent variable. The findings revealed that all independent variables, (unadjusted NOPAT, unadjusted TCE and the unadjusted cost of capital) had an overall significance (p-value < 0.05) in predicting the value of the unadjusted EVA.  With reference to Table 4, the adjusted EVA regression model yielded a perfect positive correla-tion value of +1. This implies that the independent variables predict 100% of the dependent variable (adjusted EVA).
With reference to Table 5, the ANOVA (f-test) showed that all independent variables had an overall significance on the dependent variable.
The findings revealed that all five independent variables had an overall significance (p-value < 0.05) on the value of the adjusted EVA. Table 6 shows the coefficient test for individual significance. The results showed that the unadjusted NOPAT, the deferred taxes that impact NOPAT and the adjusted cost of capital variables significantly impacted (p-value < 0.05) the value of the adjusted EVA. However, the deferred taxes that impacted TCE and the unadjusted TCE were   In addition, Gallemore (2012) found deferred taxes that impacted TCE were statistically significant. The finding by Gallemore contrasts with the current empirical findings, because the study by Gallemore reported a deferred tax asset, whilst the current study reported deferred tax liabilities.
The above comparison shows a variation of empirical results for individual significance of independent variables. The current study shows five independent variables, of which three are statistically significant, whilst the remaining two independent variables are not statistically significant. Although there is a lack of statistical significance amongst some of the independent variables, the study shows a high overall statistical significance for the majority of the independent variables. The variation of empirical findings is also due to the nature of the previous studies that only evaluate specific components of EVA, whereas the current study investigated the entire EVA model. To summarize, the current empirical findings are in partial agreement with the previous literature findings.

LIMITATIONS
The multiple regression models were specifically designed for companies in the food producers sector. Therefore, the results for the current study are specific to companies in the food producers sector. The impact of the deferred tax adjustment could vary amongst other industrial sectors. Consequently, the results of the study can only be generalized for the companies in the food producers sector.

SUGGESTIONS FOR FUTURE RESEARCH
Sharma and Kumar (2010) state that companies are experiencing difficulties in implementing EVA adjustments. The current research study assists in bridging the knowledge gap by investigating the impact of deferred taxes on EVA. However, more research should be done on other types of EVA adjustments. Also, future research should focus on external factors that could impact the accuracy of EVA.

CONCLUSION AND RECOMMENDATIONS
The aim of this study was to determine the impact of the deferred tax adjustment on EVA for JSE-listed food producers in South Africa. The descriptive statistics provided two main empirical findings. The first empirical finding that an increase in deferred tax expenses understated NOPAT, whilst a decrease in deferred tax expenses overstated NOPAT. The second empirical finding revealed that deferred tax liabilities understated TCE.
In addition, the regression statistics revealed an overall statistical significance for all deferred tax predictors in relation to EVA. The regression results led to the rejection of the null hypothesis. This research study, therefore, proved the stated hypothesis, that deferred tax has a significant impact on EVA. As a result, the current study concluded that deferred taxes significantly impacted EVA.
The above findings show that deferred taxes distort the value of EVA. Furthermore, the regression statistics show that deferred taxes significantly impact EVA. The implementation of the deferred tax adjust-ment will remove the distorting effects of deferred taxes on EVA. A further motivation for the implementation of the deferred tax adjustment relates to the aspect of cash flows. The EVA measure represents actual cash flows, whilst deferred taxes do not represent actual cash flows, thus providing another reason for the implementation of the deferred tax adjustment (removal of deferred taxes from EVA).
The deferred tax adjustment will improve the accuracy of EVA. An accurate EVA measure will benefit managers and shareholders, who use EVA for decision-making purposes. Shareholders will benefit, as they will know with a reasonable degree of accuracy, the amount of wealth the company has created for their investment in shares. In addition, managers that use the EVA measure will be able to make better and well-informed decisions, which, in turn, impact shareholder wealth. Therefore, the study recommends that companies implement the deferred tax adjustment.
To date, there is no empirical evidence measuring the impact of deferred taxes on EVA. This study is the first to provide empirical evidence on the impact of deferred taxes on EVA, which is reflected in the descriptive and inferential statistics of this study. Consequently, this study has established the behavioral pattern of deferred taxes on EVA, this behavioral pattern will not change as the deferred tax variable and the EVA variable was calculated using EVA formulae (formulae were developed by the pioneers of EVA, Bennett Stewart and Joel Stern). Therefore, the research results from this study remains relevant regardless of the timing concerning the data collection and data analysis.
In addition, the empirical results from this study will serve as historical data to future researchers and practitioners examining deferred taxes and its impact on EVA. Lastly, the data analysis that utilized data from 2004 to 2010 produced regression models that will be useful in predicting future trends for EVA and deferred tax.