The impact of corporate governance code on

Journal of Accounting in Emerging Economies The impact of corporate governance code on earnings management in listed nonfinancial firms: Evidence from Kenya Erick Rading Outa, Paul Eisenberg, Peterson K. Ozili,

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The impact of corporate governance code on earnings management in listed non-financial firms Evidence from Kenya Erick Rading Outa Business School, Strathmore University, Nairobi, Kenya

Paul Eisenberg Downloaded by Mr Paul Eisenberg At 10:21 02 June 2018 (PT)

Business School, University of Portsmouth, Portsmouth, UK, and

Peterson K. Ozili Essex Business School, University of Essex, Colchester, UK Abstract Purpose – The purpose of this paper is to examine whether voluntary corporate governance (CG) code issued in 2002 constrain earnings management (EM) among listed non-finance companies in Kenya. Design/methodology/approach – Using a panel data of 338-firm year’s observations between 2005 and 2014, the authors test the hypothesis that CG constrains EM in non-finance firms listed in Kenya. The authors regress discretionary accruals (DA) against a developed Corporate Governance Index (CGI). Findings – The overall results show that DA is not significantly related to CG suggesting the voluntary CG code does not deter EM in non-finance companies in Kenya. Practical implications – Evidence of income decreasing\increasing accruals implies EM still exists among the listed firms. This suggests that policymakers may need to consider radical actions including alternative or new CG approaches and new institutions to improve the effectiveness of CG. Originality/value – This study extends existing studies by including composite CG as possible explanatory variable for constraining EM. The authors contribute to the debate by demonstrating that the voluntary CG code in Kenya is not effective in constraining DA and therefore the current initiatives by the regulator to change the current CG code are appropriately directed. Keywords Discretionary accruals, Corporate governance, Kenya, Earnings management Paper type Research paper

1. Introduction We investigate whether corporate governance (CG) guidelines issued by Capital Markets Authority (CMA) limit earnings management (EM) among listed non-financial firms in Kenya. This topic is important as it investigates the growing importance of the effectiveness of CG on discouraging EM. On the other hand EM is regarded as problematic with disperse evidence that it can be constrained by good CG practices. CG and EM are a well-researched area because many countries have adopted CG codes and adopted International Financial Reporting Standards (IFRS) aimed at controlling ethical lapses and improving reliability of financial information provided to markets. However, the existing empirical findings and conclusions as to whether the CG codes have Journal of Accounting in Emerging Economies Vol. 7 No. 4, 2017 pp. 428-444 © Emerald Publishing Limited 2042-1168 DOI 10.1108/JAEE-09-2016-0081

Thanks to anonymous reviewers and participants at the Italian Academy of Management (Accademia Italiana di Economia-AIDEA) Conference in Lecce, Italy in September 2013 and the interaction with participants at IFC Corporate Governance Forum at the Indian Business School in Hyderabad, India in Aug 2014 also helped in improving this paper. The authors also appreciate insightful comments and suggestions from two anonymous reviewers and the editors of JAEE.


achieved the objective are mixed, inconsistent and difficult to generalize (Garcia-Meca and Sánchez‐Ballesta, 2009; Kaaya, 2015). Akyeampon et al. (2013) examine the link between CG and EM of firms in some selected countries in Sub-Saharan Africa. They find varying results and explain that different CG mechanisms affect EM across the various countries. Okougbo and Okike (2012) find a positive significant relationship between the size of the board, return on assets (ROA) and EM but did not test overall CG indicators for non- finance firms listed in Nigeria. There is scanty research in Africa on CG and EM. Ali et al. (2009) instead, find CG increases EM in Pakistan. Rwegasira (2000) argues that the concept of CG is not necessarily the best solution for developing economies given some have unstable political regimes, low per capita incomes and diseases. Mak and Kusnadi (2005) argue that developing countries are often faced with a multitude of problems that include uncertain economies, weak legal controls, protection of investors and frequent government intervention that make it even more necessary to adopt effective CG structures. However, Agbonifoh (2010) argue that CG in Africa requires more elaborate solutions than simply adopting CG practices. In addition to the debates above, the current study is motivated by factors attributed to Kenya context. Kenya has adopted most of the international codes including CG and IFRS and undertaken many financial sector reforms. The 2002 code enacted in Kenya was developed with the assistance of the Commonwealth Association for Corporate Governance and the organization for economic corporation and development who follow the Anglo American models of CG. These models have been questioned by Singh and Zammit (2006) as to their appropriateness in developing countries. The questions arise from the notion that success of the codes depends on a number of institutional settings such as well-developed capital markets, established accounting bodies, democratic institutions and various autonomous bodies whose effectiveness in Africa are debatable. Some studies show that IFRS may not result in lower levels of EM because IFRS are fair value standards and might afford greater opportunities for firms to manage earnings (Lin et al., 2012). We are curious to understand how CG code enacted in 2002 may have affected EM given that Kenya adopted IFRS in 1999 and so its financial reporting should be of high quality. Kenya CMA recently issued revised CG code where it clearly admitted that there are several governance issues with listed firms and went further to suggest mandatory disclosures contrary to the spirit of voluntary “comply and explain” code in operation. Moreover, the World Economic Forum (2012) and Ernst & Young (2013) survey raised issues on the quality of profitability in Kenyan listed companies suggesting EM may exist in Kenya. Kaboyo and Wamwea (2014) in a Kenyan exploratory survey, reports that 69 percent of companies practice EM and suggests future research to measure EM. Iraya et al. (2015) reported EM is negatively related to ownership concentration, board size and board independence while Waweru and Riro (2013) find ownership structure and board independence as the main CG variables influencing EM in Kenya. Both studies like many existing studies focus on components of CG when the CG code issued by the regulators contains many requirements. It is unclear from these studies how single component(s) influences EM to the exclusion of others. Furthermore, the current study is different as it excludes finance firms (Klein, 2002; Gonzalez and Garcia-Meca, 2014, Outa and Waweru, 2016) because finance firms are highly regulated, the behavior of their accruals differs from other industries (Saleh et al., 2005; Ali et al., 2009) and their accounting practices are peculiar. Our study also applies a composite CG measure developed from CG (2002) that takes into account the overall effect of CG. Brown et al. (2011) and Chang and Sun suggest composite measures are increasingly employed in the literature contrary to the early studies that use single measures such as duality and others. They propose condensed information containing a large number of individual governance items into a single informative measure. Consequently we employ an index of 46 items derived from the CG guidelines issued by

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CMA (2002). Furthermore, our study investigates CG and EM over the 2005-2014 periods which are more recent and extensive when compared with the periods examined by existing studies. To realize our research objective, we examine three related research questions: RQ1. To what extent do Kenyan non-financial listed firms comply with CG codes issued by CMA? RQ2. To what extent do Kenyan non-financial listed firms manage their earnings?


RQ3. To what extent does the Kenyan CG code constrain EM in non-financial listed firms? We regress DA on Corporate Governance Index (CGI). Our results suggest that the medium level CG scores does not discourage EM. We contribute to EM literature in several ways. First, prior research (e.g. Dechow et al., 1995) suggests that managers use DA to manage performance targets such as the level of earnings. This study demonstrates how CG intervention influences firms in the management of earnings targets by including composite CG as a possible explanatory variable for EM. Second, Kenya CG code was designed to guide and control CG so as to improve outcomes for investors (principals). We provide evidence suggesting that the CG is not significantly associated with a decrease in DA and therefore the agency problem has not been effectively addressed given the increased levels of DA. Finally, for policy implications we show that the current CG code is not effective in constraining EM and so regulators and boards of companies need to take radical actions such as alternative or new CG approaches and new institutions to improve the effectiveness of CG. The rest of the paper is organized as follows. Section 2 reviews the theoretical framework, Kenyan context of the study and the literature review and hypothesis development. Section 3 discusses the methodology inclusive of the population and sampling, index development and the design while Section 4 discusses the findings. Section 5 concludes the paper, discusses the limitations and suggestions for further research. 2. CG and EM 2.1 Theoretical framework We apply Cadbury (1992) definition of CG as the system by which a company is directed and controlled. We offer an explanation for the link between CG and the agency problem which occurs because of the separation between managers and owners (Dechow et al., 2012, Davidson et al., 2005). EM on the other hand is the discretionary manipulation of earnings by managers in particular when managers choose accounting options within the boundaries of accounting standards and the law (Tucker and Zarowin, 2006). Benkel et al. (2006), Davidson et al. (2005) and Koh (2003) contend that the agency theory considers monitoring managers as a necessity. Therefore, CG is applied to improve internal and external control of managers by monitoring the board of directors with a view to decreasing opportunities for EM and improve credibility of financial information (Rogers, 2006). In other words, EM is a consequence of agency problem that can be mitigated by good CG practices such as those enunciated through CG codes. CG addresses agency problem through two opposite effects: by increasing monitoring and bonding costs or decreasing the residual values (Safari et al., 2015). 2.2 CG in Kenya Kenya is a low middle-income common law developing country in East Africa and the ninth largest economy in Africa ACGN (2016). It has a large private sector that accounts for a


majority of the country’s GDP. It has undergone many reforms including the enactment of voluntary CG codes in 2002 with a revision in 2016 and the national CG systems which includes a new Company’s Act in 2015 and a new constitution in 2010. The Company’s Act deals with director’s duties and shareholders protection and cover issues to be reported in the financial statements prepared for the annual general meeting. Listed firms are supposed to comply with the CG code requirements they have complied with otherwise they should explain reasons for non-compliance. The Kenyan CG code is categorized into principles of CG practices and recommended best practices. A CG Steering Committee set up in 2014 indicated existence of scandals in several companies leading to questions as to how effective the existing CG framework was. These scandals resulted in poor profit and market performance among the affected firms according to CMA (2014). The recent capital market reforms from 2015 introduce best practice in the areas of board operations and control, shareholder rights and minority shareholder protection and transparency and disclosure (including improving company interaction with shareholders, investors and other stakeholders). According to the blue print, the country is ranked low on governance and accountability, competitiveness, and investor protection. 2.3 Empirical literature and hypothesis Informed by insights from existing empirical studies on non-financial listed firms (e.g. Abbadi et al., 2016; Akyeampon et al., 2013; Safari et al., 2015; Kaboyo and Wamwea, 2014; Gonzalez and Garcia-Meca, 2014; Okougbo and Okike, 2012; Brown et al., 2011; Chang and Sun, 2009 and others), we identify drivers for EM. In particular we examine how CG guidelines issued by CMA (2002) can limit EM. There are a few studies that investigate overall compliance levels with CG codes, e.g. Outa and Waweru (2016), Waweru (2014) and Ntim et al. (2012) but they do not examine the link between the aggregate levels of compliance with EM. Chang and Sun (2009) examine CG scores from five CG attributes from Sarbanes-Oxley Act (2002) to investigate the association between overall CG and EM for a sample of companies in the USA. They contend that an optimal CG structure takes into account various CG dimensions consistent with Brown et al. (2011). Abbadi et al. (2016) apply four categories and ten standards for CGI in Jordan. They find that EM is deterred even though it still exists. Safari et al. (2015) examines the aggregate influence of 27 recommendations introduced under the eight CG principles in non-financial sector in Australia. The results demonstrate a significant negative relationship indicating that companies with higher levels of compliance engage in lower levels of EM via DA. Following Safari et al. (2015) and Abbadi et al. (2016) we expect that CG codes compliance in Kenya should discourage EM among firms. Therefore, we hypothesize that: H1. CG compliance level is negatively associated with the level of EM among listed non-finance firms in Kenya. 3. Methodology We apply quantitative methods to examine the relationships between the independent variable CGI and the dependent variable EM and the control variables (Size, DE, Growth, ROA) and other temporal variables (Loss, Big4, Majority independent director). This design is chosen because the population is small and the use of panel data increases the number of observations, thus allowing meaningful statistical analysis. The rationale for using a single case study is to enable us to comprehensively consider and apply a substantial number of contextual factors and causal explanations in a relatively brief space (George and Benet, 2005).


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3.1 Population, sample selection and firm characteristics Out of a population of 60 companies listed in Kenya through the Nairobi Securities Exchange, 16 were removed as they are banks and insurance companies while 6 were missing data leaving a sample of 38 companies as shown in Table I. The 38 companies are therefore a good representative sample as they constitute 86.3 percent of the listed non-financial companies. The companies are from various sectors with manufacturing, construction and telecoms leading with market capitalization. Lai (2011) states that at least 20 firms in any industry in a year can be adequate in order to provide sufficient observations for estimation. At 95 percent confidence level, the z-score is 1.96 and with a margin of error and standard deviation of 0.2, the minimum observations should be ((1.96)2 × 0.2(0.8))/(0.05)2 ¼ 245, that is 24 firms. Therefore, the current 38 firms with 338-firm years are sufficient for statistical analysis. We analyze firms into profit or loss making to enrich our understanding of the EM behavior in Kenya. Barth et al. (2008) argue that there is no incentive for EM when the firm is doing well. This precedes Moreira and Pope (2007) who analyze firm’s EM behavior to avoid losses conditional on the incentive underlying market (positive/negative) returns and finds evidence to support the intuition that firms with negative returns in the period face a higher incentive to undertake EM. They conclude that bad news firms show higher EM pervasiveness. Brown and Caylor (2005) find that EM thresholds evidence is the avoidance of negative earnings surprises. Earlier, Burstahler and Dichev (1997) find strong evidence that firms manage earnings to avoid income decreases and losses. They show that 8-12 percent of firms manage earnings to avoid earnings decreases and 30-44 percent of firms manage earnings to avoid small losses and show positive earnings. We use these findings to argue that the behavior of loss firms is different from profit making firms. 3.2 Development of CG index To answer our RQ1, we follow Brown et al. (2011) methodology and compute a single but informative measure that represents the overall impact of CG. We develop CG scores based on the CG code issued in 2002. Following Ntim et al. (2012) and Outa and Waweru (2016) we develop a CGI that captures most of the 46 items recommended by the code as shown in Table III. Each item complied with is scored 1 while any partial compliance is scored proportionately. The scores are then expressed out of 46 and then as a percentage and the result provide the compliance score for each firm for the mandated CG. This method is consistent with Ntim et al. (2012) as justified by Gompers et al. (2003) where the simplicity was justified on the grounds that there is lack of rigorously developed weights which can be assigned to the various CG disclosure practices. Furthermore, Beiner et al. (2006) indicate that an unweighted index is easy and transparent to replicate Sectors

Table I. Sample distribution by industry

1. Construction 2. Manufacturing 3. Automobiles 4. Telecoms 5. Commercial 6. Agriculture 7. Energy 8. Investment Sub total 9. Financial firms Total

Population

Missing data

Firms

Firm years

5 7 5 2 8 9 4 4 44 16 60

0 0 1 0 1 2 0 2 6 0 6

5 7 4 2 7 7 4 2 38 16 54

50 70 40 20 70 70 40 20 380 160 540


and finally prior studies suggest that the use of weighted and unweighted indices tend to provide similar results. Our method of computing the CG index is different from existing literature in particular Waweru (2014) who apply data provided by the Institute of Shareholder Services (ISS) whose index comprises of 51-firm-specific provisions. A value of 1 is scored if a factor is present, 0 otherwise and the index is modified for factors that take into account the South African 2009 King Report all “inclusive” approach factor. Waweru (2014) may have measured factors not required by the Kenyan code even though they could be good for CG. Moreover, their code applied a binary system of either 1 or 0, when our scores are unweighted including partial scores when a requirement is not fully met. 3.3 Research design To answer our RQ2, we apply the modified Jones (1991) model as it is regarded as more accurate because of the idea of additional variables including change in sales and change in receivables (Hassan et al., 2014). Dechow et al. (1995) compare Jones, the modified Jones and the industry models and conclude that modified Jones (1991) model is the most powerful model for measuring DA consequently it is applied in the study. Consistent with Klein (2002), the model is estimated for all firms and we control for firm-specific factors that affect EM mainly firm size, profitability and capital structure. Bartov et al. (2000) show that failure to control for confounding factors may result in falsely rejecting the null hypothesis of no abnormal accruals when in fact the null is true. The method is also justified on the grounds that Kenyan firms apply IFRS and are not necessarily conservative as they have elements of accruals such as provisions, reversals, choice of depreciations method and useful life of fixed assets. To estimate non-discretional accruals amounts, firm-specific amounts for each independent variable are used for each year over the ten-year period: NDAit =TAt1 ¼ b0 þb1 1=TA t1 þb2 ðDSalesit DRecit Þ=TAt1 þb3 GPPEit =TAt1 þb4 ROAit ; t 1 þ e it :

(1)

where NDA is the non-discretionary accruals for firm i in year t; Β0 the intercept; TAt−1 the lagged total assets for firm i in year t; ΔSalesit the change in sales for firm i in year t; ΔRecit the change in receivables for firm i in year t; GPPEit the gross property plant and equipment for firm i in year t; ROIit the Rate of return on lagged assets for firm i in year t; éit the error term for firm i in year t. The coefficients βo, β1, β2, β3, and β4 were determined by running the regression with the total accrual as the dependent variable and the resulting cross-sectional coefficients together with each firm data were processed to generate non-discretional accruals for the year. The discretionary accrual (DA) is determined by: DA=TAt1 ¼ TA=TAt1 NDA=TAt1 where DA is the discretionary accruals (deflated by lagged total assets); TA the total accruals (net income –cash flow) from operations scaled by lagged total assets; NDA the non-discretionary accruals determined above. To compute the absolute DA (AbsDA) we convert the negative observations to positive values (absolute numbers have only + signs). We then take the mean to show the average level of EM consistent with Alves (2012) and Lai (2011). The use of absolute value of DA is consistent with previous studies on EM which point out that the study of the quality of results does not impose any direction or sign on the expectations of EM (Chen et al., 2007).


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To answer our RQ3, on the influence of CG on EM, we regress the AbsDA on CGI, controls and temporal effect variables as shown in the following equation: AbsDAit ¼ b0 0 þb1 CGIit þb2 DEit þb3 Sizeit þ b4 ROAit þb5 GROWTHit þb6 LOSSit þb7 IDit þb8 BIG4it þnit þyit þeit


434

(2)

The variables are defined in Table II. The unobserved heterogeneity is controlled by the individual effects of companies (nit). We include dummy variables to control for temporal effects ( yit) and the error term (εit). The coefficient of interest in Equation (2) is β1 (the coefficient of CGI). A negative coefficient would indicate that firms with high CGI are able to deter EM consistent with our prediction that CG compliance level is negatively associated with the level of EM. Size is computed as the natural logarithm of the assets, regarded as control variable as some research shows a negative relationship between firm size and EM. Big size firms are expected to have better controls and are subject to increased monitoring hence less chance of accounting manipulation (Prior et al., 2008). Leverage computed as the ratio of debt to equity is associated with the risk of debt which might motivate a firm to conceal inconvenient information (Balsam et al., 2003; Dechow et al., 1995). We control for firm performance following Francis and Wang (2004) by including growth and ROA. Growth is measured as the percent change in sales over the previous year. McNichols (2000) contend that high growth firms are more likely to use DA to manage earnings. ROA is the ratio of the earnings before extraordinary items, interest and taxes to total net assets at the beginning of the year. Kothari et al. (2005) argue that managers can manipulate results by increasing profits with the intention of making the company more attractive. Nurul et al. (2010) argue that poor financial condition of the company could increase agency costs and encourage management to manipulate the accounting numbers. We therefore include a variable for loss that takes the value of 1 if the company had losses in the last 2 years and 0 otherwise. We also control for temporal effects. With regard to auditor Variable(s)

Definition and operationalization

Discretionary accrual (DA) and Discretionary accrual and Absolute discretionary accruals (proxy for absolute discretional accrual (AbsDA) EM) are computed using the modified Jones (1991) model Corporate governance Corporate Governance Index (CGI) Control variables Cash flow (CF) Firm size (Size) Leverage (DE) Return on assets (ROA) GROWTH Dichotomous variables Majority independent directors (ID) Table II. Definition and operationalization of variables

Big4 LOSS

An average score of the 46 items extracted from the CG guidelines issued by Kenya CMA (2002) Cash flow from operations divided by total assets The natural log of total assets at the financial year end Debt to equity ratio is the ratio total debt at the end of the financial year scaled by the total book value of shareholder’s equity at that date Ratio of earnings before interest, tax and extraordinary items to total net assets at the beginning of the year % change in sales from the previous period Takes the value of 1 if boards has a majority independent directors , 0 otherwise Takes the value of 1 if the firm is audited by one of the Big4, 0 otherwise Takes the value of 1 if the companies have incurred losses in the last 2 years, 0 otherwise


quality, Lennox (1999) argue that accounting quality information is associated with prestige and quality of external auditor because of the auditor’s perceived ability to limit EM. We include a dummy variable of 1 if the company is audited by the Big4 and 0 otherwise. We also include a variable that takes the value of 1 if boards have a majority of independent directors and 0 otherwise because independent directors can enhance managerial monitoring.

Impact of corporate governance code

3.4 Data analysis Data were analyzed using STATA 12 among many choices of statistical packages due to its ease and wide use in statistical analysis. Regression models are estimated through panel data analysis and the relationships studied can be characterized by joint heterogeneity among the test variables. This means that most explanatory variables in the model are either simultaneously determined by the dependent variable or have a two-way causal relationship with it. We also recognize that firm-related effects may occur that may result in non-consistent estimates if ignored. Previous studies show that OLS is not consistent because the explanatory variables are not strictly exogenous. Panel data analysis is applied because it is able to deal with heteroscedasticity and autocorrelation commonly associated with time series analysis while preserving heterogeneity among the variables (Baltagi, 2005). The current data for all CGI and DA are a balanced panel but subsequent analysis of loss and profit making firms included unbalanced panel consistent with Baltagi and Chang (1994) and Gonzalez and Garcia-Meca (2014) who discourage discarding some observations to balance panels as this may occasion conditional survival bias. Because of the panel data used in the study, Hausman test was applied to decide between random-effects GLS and fixed effects model. According to the results, the random effects GLS and the fixed effects model are not significantly different as to the regressors and unobserved heterogeneity leading to non-rejection of the hypothesis. We opt for the random effects GLS (default in STATA, assuming heteroskedasticity is not violated) because it is unbiased, efficient and consistent as an estimator when compared to fixed effects.

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3.5 Endogeneity and multicollinearity To test for endogeneity, after completing the regression, the error term was extracted and correlated with all the independent variables. The results (not presented here) indicate that there are no significant correlations between the error term and the independent variable suggesting that endogeniety is not a problem. In addition to the regression analysis, the independent and dependent variables are run for correlation to determine how each variable is related to each other. Even though the correlation tests can be used to detect multicollinearity problems, non-existence of high correlation does not mean there is no multicollinearity. Consequently, we test the variance inflation factor (VIF) values for independent variables. The values of the VIF greater than 10 will indicate existence of multicollinearity (Rogerson, 2001). 4. Findings 4.1 Extent of compliance with CG codes Table III includes the compliance levels for the CG codes. Overall compliance stands at 54 percent and firm’s statement on whether they are complying with CG codes scores 29 percent while indicating reasons for non-compliance scores 18 percent. Disclosures on compensation policies, share options and remuneration for executive and non-executive directors also record low scores. Shareholders associations and representations of minority shareholders are also low. Director’s resignation and reasons for their resignations as well as review of board effectiveness also record low scores.

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No. 1 2 3 4 5 6 7 8 9 10

Principles of good corporate governance practices Statement on compliance with CMA CG guidelines The reasons for non-compliance stated Audit and nomination committee Directors remuneration approval by shareholders Re-election of directors at regular intervals Separation of chair from CEO Approval of major decisions by shareholders Board given relevant accurate timely information Related companies transactions disclosed List of ten major shareholders of the company

% 29 18 84 79 92 92 49 95 92 92

No. 24 25 26 27 28 29 30 31 32 33

11 12 13 14 15

Share options and other forms of compensation Aggregate directors’ loans Directors approaching their 70th birthday Director resignation with reasons Date, location and agenda of the AGM Subtotal average Recommended best practice Approved strategy and budgets Internal controls +compliance with statutes Independent directors constitute one third

3 39 46 30 93 63

34 35 36 37 38

% 46 46 37 0 76 0 11 11 11 11 9 3 95 3 90

Operations highlights of the company 92 Investor briefings 79 Shareholders association 1 Establishment of independent audit 79 committee 19 Board membership reflects shareholding 76 43 Independent directors in the audit 76 structure committee 20 Representation of minority shareholders 5 44 Mandate of audit committee 92 21 Substantial shareholder (o15% of the shares) 92 45 CFO to be a member of ICPAK 32 22 Size of the board (min ¼ 3 ) 95 46 Company secretary be member ICPS 47 23 Conflict of interest 95 Subtotal average 49 Total average 54 Notes: Each item fully complied with is scored 1 while any partial compliance is scored proportionately. The scores are then expressed out of 46 (unweighted) and then as a percentage and the result provide the compliance score for each mandated CG

16 17 18

Table III. Corporate governance guidelines by public listed companies in Kenya

Review of board effectiveness Orientation of new board members Gender representation Directors in more than 5 listed companies Remuneration committee mandate Remuneration policies Consolidated remuneration for ED Consolidated for ED emoluments Remuneration for non ED directors’ fees Remuneration for non ED directors’ emoluments Share options for executives Executive directors with 5 year terms Chair held by independent director Succession plan for CEO and chair Performance reports to shareholders

39 74 40 39 41 92 42

4.2 Descriptive statistics Table IV Panel A, B and C reports the descriptive statistics and standard deviations of the variables included in the regression analysis. Table IV shows that AbsDA ranges from a minimum of 0.2 percent to a maximum of 80.3 percent with a mean of 21.5 percent. Furthermore, DA ranges from a minimum of −64.9 percent to a maximum of 80.3 percent with a mean of 13.7 percent. The CGI has a minimum of 0 percent a maximum of 80.4 percent and a mean of 53.9 percent consistent with Waweru (2014) who reported a compliance level of 52 percent in Kenya. Loss firms grew at −5.9 percent compared to profit firms that grew at 19 percent which may imply some use of DA to manage earnings consistent with McNichols (2000). Loss making firms are smaller, have high debt and have negative returns which may be conducive for EM. The overall DE ratio of 1.047 is fairly low which may suggest a low likelihood of EM due to increased monitoring by debt holders. Loss making firms CGI of 43.8 percent is low compared to profit firms at 55.2 percent implying high chances of DA among loss makers. There are few majority independent directors in loss makers which can induce EM. Firms are considered to have engaged in income increasing (decreasing) DA if they have positive (negative) estimated DA. Therefore, our findings suggest that listed non-finance firms in Kenya engage in some EM.

Mean 0.160 15.538 1.075 0.095 0.539 0.210 0.137 0.119 0.733 0.915 338

All firms SD 0.472 1.649 1.338 0.124 0.196 0.156 0.223 0.324 0.443 0.279 Min. −0.633 10.789 −8.945 −0.405 0.000 0.002 −0.649 0.000 0.000 0.000

Max. 5.974 19.338 9.588 0.883 0.804 0.803 0.803 1.000 1.000 1.000

Profit firms SD Min. 0.489 −0.633 1.631 10.789 1.248 −8.945 0.109 0.001 0.186 0.000 0.156 0.002 0.216 −0.649 0.000 0.000 0.431 0.000 0.249 0.000

Profit firms DA CGI% 25.5 54.3 10.0 54.8 12.6 55.6 16.4 54.7 13.2 55.5 18.4 53.7 15.2 54.9 16.4 54.3 11.5 54.3

Mean 0.190 15.622 1.047 0.118 0.552 0.215 0.154 0.000 0.754 0.934 296

Max. 5.974 19.338 9.588 0.883 0.804 0.803 0.803 0.000 1.000 1.000

Mean −0.059 14.912 1.288 −0.077 0.438 0.171 0.012 1.000 0.575 0.775 40

Loss firms SD Min. 0.227 −0.567 1.672 12.289 1.886 −3.114 0.089 −0.405 0.237 0.000 0.159 0.012 0.235 0.412 0.000 1.000 0.501 0.000 0.423 0.000 Max. 0.438 18.817 7.503 −0.001 0.663 0.642 0.642 1.000 1.000 1.000

Panel C –Descriptive statistics for DA and CGI by sector All firms Loss firms Profit firms Sectors DA CGI% DA CGI% DA CGI% 1 Construction −2.0 61.5 14.8 55.4 3.2 61.9 2 Manufacturing 7.2 60.3 20.5 60.0 6.3 60.3 3 Automobiles −4.0 41.5 22.8 20.1 −13.0 48.6 4 Telecoms 17.6 53.9 51.8 52.0 −28.0 54.0 5 Commercial 7.1 62.4 28.9 58.6 3.5 63.0 6 Agriculture −6.2 36.6 18.1 38.7 −11.9 36.1 7 Energy 7.2 68.6 17.5 59.3 6.7 69.1 8 Investment −25.4 29.3 – – −25.4 29.3 Notes: Table IV Panel A is the tabulated descriptive statistics of the variables applied in the study with the variables as defined in Table II; Table IV Panel B is the average yearly DA and CGI; Table IV Panel C is the average DA and CGI by sector

Panel B: Descriptive statistics per year for DA and CGI DA and CGI All firms Loss firms Years DA CGI% DA CGI% 2006 18.7 53.99 −0.8 51.73 2007 9.0 53.99 0.35 46.95 2008 13.2 53.93 16.6 42.80 2009 16.1 53.81 12.7 43.47 2010 11.8 53.96 −4.9 35.80 2011 22.6 53.93 −20.3 55.60 2012 12.7 53.81 −8.0 44.35 2013 15.2 52.44 3.7 36.68 2014 9.9 50.87 4.4 39.72

Variables Growth SIZE DE ROA CGI AbsDA DA LOSS ID BIG4 N

Panel A: Descriptive statistics



Table IV. Descriptive statistics

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4.3 Correlations The Pearson pair wise correlation CGI variables, AbsDA and DA are shown in Table V. Apart from the loss variable which is negative and significant, the rest of the variables are low and not significant. The results suggest that the dependent and independent variables are not highly correlated. The results show the highest correlation as 0.7 and according to Field (2005) multicollineraity exists when correlation coefficient is more than 0.8 or 0.9 indicating that our analysis is not distorted by correlated variables. 4.4 Random effects GLS regression results Table VI shows the regression model results. The findings show χ2 of 0.000 implying that the model is a good fit and can be relied on to measure the relationship while the R2 show that 18.9 percent of the change can be explained by the CG codes requirement. The results


Variables

AbsDA

CGI

GROWTH

SIZE

DE

ROA

LOSS

ID

BIG4

AbsDA CGI

Table V. Pearson correlations

1 Coeff. 0.064 1.000 p 0.243 GROWTH Coeff. 0.044 −0.008 1.000 p 0.426 0.886 SIZE Coeff. −0.039 0.683 −0.038 1.000 p 0.474 0.000 0.484 DE Coeff. −0.022 −0.040 −0.047 0.170 1.000 p 0.694 0.461 0.395 0.002 ROA Coeff. 0.408 0.079 0.203 0.009 −0.117 1.000 p 0.000 0.147 0.000 0.866 0.033 LOSS Coeff. −0.090 −0.195 −0.171 −0.145 0.069 −0.510 1.000 p 0.099 0.000 0.002 0.008 0.207 0.000 ID Coeff. 0.009 0.576 −0.089 0.565 0.044 −0.029 −0.140 1.000 p 0.872 0.000 0.105 0.000 0.422 0.602 0.010 BIG4 Coeff. 0.095 0.297 0.092 0.234 −0.127 0.259 −0.182 0.173 1 p 0.082 0.000 0.090 0.000 0.019 0.000 0.001 0.001 Notes: Table V represents pairwise Pearson correlation coefficient and the variables are explained in Table II

AbsDAit ¼ b0 0 þb1 CGIit þb2 DEit þ b3 Sizeit þ b4 ROAit þb5 GROWTHit þ b6 LOSSit þb7 IDit þb8 BIG4it þ nit þ yit þ eit AbsDA

Table VI. Random effects GLS regression results

All data CGI Coefficient Sig P W|t

CGI 0.154 0.053 Size −0.019 0.036 DE 0.010 0.146 ROA 0.459 0.000 GROWTH 0.017 0.959 ID 0.000 0.000 BIG4 0.025 0.424 LOSS 0.007 0.009 N 336 18.9 R2% 0.000 χ2 Notes: Table V represents random variables are explained in Table II

VIF 2.302 1.064 2.297 1.172 1.466 1.425 1.645 1.205

Loss making firms Coefficient Sig P W|t VIF 0.325 −0.041 0.015 0.242 0.015 0.004 −0.104

0.193 0.173 0.330 0.456 0.883 0.960 0.269

2.930 1.296 2.756 1.814 1.451 2.729 2.159

Profit making firms Coefficient Sig P W|t VIF 0.119 −0.012 0.003 0.515 0.001 0.005 0.091

0.151 0.179 0.635 0.000 0.009 0.830 0.009

2.126 1.036 2.114 1.115 1.076 1.531 1.082

40 296 25.3 23.5 0.360 0.000 effects GLS regression results for AbsDA, CGI and controls and the


show that CGI coefficient is positive and not significantly related to DA implying CG is not a deterrent of DA. Additionally, we observe the same pattern when we run the same test with data broken into profit or loss making firms. Since our VIF results are below 5, there is low chance the results are affected by multicollinearity. For the control variables, debt and loss are significantly related to DA but not ROA. On the other hand, we find that growth, majority independent directors and Big4 do not have a significant impact on DA while firm size does. Consequently, we reject the hypothesis that the levels of CG compliance negatively affect the levels of EM in non-financial firms listed in Kenya. 4.5 Discussion of results Our findings are able to answer our three research questions by showing the level of CGI of 53.9 percent with R2 of 18.9 percent does not discourage DA whose level stand at AbsDA 21.0 percent. This finding can be understood when compared to Safari et al. (2015) who find CGI of 85.3 percent, AbsDA of 3.9 percent and R2 of 27.6 percent implying higher CG levels are able to limit DA. Our findings are astonishing in the sense they are opposite to expectation that CG should discourage EM, but this finding is not unusual. Garcia-Meca and Sánchez‐Ballesta (2009) in a meta-analysis of CG and EM studies show the effects of compliance with best practices affect EM in some countries but not all countries especially emerging countries characterized by a weak tradition of board monitoring role. Uddin and Choudhury (2008) find key CG measures, in Bangladesh are being ignored by many companies and rules remain ineffective, a situation which could possibly apply to other developing countries such as the ones in Africa where the settings for success of codes are not fully developed. Furthermore, Aguilera and Cuervo-Cazura (2016) contend that codes of good governance are developed to enhance the effectiveness of national CG systems so if both or one is weak, it is unlikely a country will enjoy the intended impact of the codes as that is a common perception in Africa. The fact that the CG Blue Print (CMA, 2014) indicates low ranking for CG in Kenya coupled with weak shareholder protection, it is unlikely the code could have performed its role as expected. On the same breath, Halaoua et al. (2017) from a panel data of 1,771 French and 2,057 British firm-year observations during the period 2002-2012, show that all firms considered manage earnings to beat zero and last year’s earnings. We can explain the inability of CG to deter EM as due to the low levels of CG compliance and enforcement. Our findings are inconsistent with Safari et al. (2015) who find that high levels of CG is associated with lower levels of EM. Our results are also consistent with Ali et al. (2009) in Pakistan who reason that CG compliance as reported by the firms may not be actual. The average level of AbsDA of 21.0 percent (DA ¼ 13.6) is considered high when compared to Waweru and Riro (2013) who reported levels of 2-3 percent and Okougbo who obtained DA of 0 percent in Nigeria. In other developing countries, Hassan et al. (2014) reported AbsDA of 35 percent in Bangladesh. In other emerging economies, Gonzalez and Garcia-Meca (2014) reported AbsDA of 24.7 percent (Argentina), 29.3 percent (Brazil), 23.6 percent (Chile) and 16.7 percent (Mexico). Our results also show that automobile, agriculture, investment and construction decrease their profits while the rest are engaged in profit increasing activities. Our results confirm the World Economic Forum (2012), Ernst & Young (2013) survey, Kaboyo and Wamwea (2014) and CMA (2014) that EM exists among non-finance Kenyan listed companies. 4.6 Robustness tests Iraya et al. (2015) and Waweru and Riro (2013) find components of CG to impact EM in particular Ownership concentration, Audit committee and board independence. We include these variables in separate regressions and our results shown in Table VII remain the same though values are different.


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AbsDAit ¼ b0 0 þb1 CGIit þb2 DEit þ b3 Sizeit þ b4 ROAit þb5 GROWTHit þ b6 LOSSit þ b7 IDit þ b8 BIG4it þ nit þyit þeit AbsDA


440

Table VII. Robustness-random effects GLS results

Ownership concentration Coefficient Sig PW|t VIF

Audit committee coefficient Sig P W|t VIF

Board independence coefficient Sig PW |t VIF

OC −0.001 0.340 1.125 AC 0.042 0.200 1.713 BI −0.050 0.483 1.118 GROWTH 0.000 0.978 1.088 0.002 0.891 1.075 −0.001 0.909 1.058 Size −0.009 0.233 1.702 −0.011 0.140 1.681 −0.007 0.295 1.247 DE 0.010 0.170 1.123 0.008 0.221 1.127 −0.006 0.327 1.120 ROA 0.466 0.000 1.488 0.463 0.000 1.489 0.493 0.000 1.476 LOSS 0.070 0.011 1.426 0.731 0.009 1.426 0.072 0.000 1.405 ID 0.011 0.638 1.504 0.005 0.823 1.679 0.016 0.507 1.512 BIG4 0.035 0.278 1.241 0.021 0.511 1.294 0.028 0.369 1.241 N 336 336 336 17.7 18.4 18.9 R2% 0.000 0.000 0.000 χ2 Notes: Table VII represents results of the robustness test comprising selected components of AbsDA, CG variables and controls and the variables are explained in Table II

Existing research has shown that ownership concentration is one of the key factors in EM. It is possible to argue that block owners in Kenya have not applied block power to significantly reduce EM in Kenya. The high ownership concentration in Kenyan firms and the inability of block shareholders to reduce EM may potentially create problems for minority shareholders and this confirms the CG blue print (CMA, 2014) that ranks Kenya low in CG and minority protection. Board independence is another variable known to influence EM by preserving accountability toward shareholders (Eisenberg, 2016) but our results (not presented here) from correlations and regression show it is not related to DA. Ararat and Dallas (2011) explain that one possible interpretation of this finding is that the nominally independent directors are not independent enough or not really independent at all. The independent directors may also be in such a minority on many boards that they are ineffective in the face of non-independent or affiliated directors. Overall it is possible that the “box ticking” nature of the “comply or explain” codes may play into these results since it is possible to “tick” a “box” even when there is no compliance. AbsDA of 21.0 percent is relatively high compared to zero and may imply the monitoring function such as the board audit committee and independent directors and the external auditors may have not fully executed their mandate. Our findings confirm the existing situation where several companies in Kenya have run into problems because of CG issues related to independent directors and audits as confirmed by the CG blue Print (CMA, 2014). 5. Conclusions Our aim is to examine whether the CG code issued in 2002 discourage EM in non-financial firms listed in Kenya. DA regarded as a proxy for EM, was regressed against a composite CGI and control variables. Our finding suggests that the medium CGI d scores does not have a significant impact on EM among Kenyan listed non-financial firms. This leads to the conclusion that although CG guideline requirements have been found to reduce EM in other environments, the voluntary nature of the Kenyan CG code still allows for EM to exist. This conclusion provides support to the revised rules adopted by CMA and put into law in 2015, which makes some of the CG requirements mandatory.


Moreover, our study potentially contributes in several ways. First, this study extends existing studies by including a composite CG variable as a possible explanatory factor influencing EM. Second, we contribute to the literature by demonstrating the extent of compliance with voluntary CG codes and how they impact EM in a country that greatly differs from countries considered in the existing literature; especially those from developed markets. We show that CG compliance has a non-significant effect on DA. This finding, though unpredicted, mirrors the arguments that CG codes in Africa are sometimes not complied with or enforced or timeously reviewed in line with business changes leading to their ineffectiveness. This paper attempts to provide valuable input to regulators who may be keen for analytical work on the implications of CG choices especially in Africa, where institutional settings are yet to be regarded as effective. We suggest that policymakers may need to consider radical actions including alternative or new CG approaches and new institutions to work alongside national CG systems to ensure compliance and ultimately deterrence to EM. Because of the dynamic nature of CG, regulators need to regularly review CG codes and their enforcement. These suggestions are important so that Kenya and other developing countries do not lose out on the much needed investment on account of ineffective CG. Although our empirical evidence finds non-significant relation between CG and EM, other potential explanations and several caveats need to be taken into account. First, it is difficult to measure EM in Kenya because of the limitations in the methodologies of EM and CG. As prior literature suggests, Kenyan listed companies may use real EM in addition to DA. It is therefore, possible our measures may not fully reflect the dynamic nature of EM. It is also possible that DA could even be lower than actual level because companies may have shifted from accrual based EM to real activities EM after the adoption of IFRSs. Furthermore, it may be interesting to investigate whether the current level of DA in Kenya could be as a result of a shift to real activities EM. These conclusions should be understood within the context that only annual reports and audited financial statements that were filed with the CMA were used as sources of information. References Abbadi, S., Hijazi, Q. and Rahahleh, A. (2016), “Corporate governance quality and earnings management: evidence from Jordan”, Australasian Accounting, Business and Finance Journal, Vol. 10 No. 2, pp. 54-75. ACGN (2016), “State of corporate governance in Africa”, available at: www.iodkenya.co.ke/assets/ resource/52a55520521edb893dc45a0af3c4197e.pdf (accessed December 28, 2016). Agbonifoh, B. (2010), “Human management and corporate governance: the quest for best practices in Nigeria”, Nigerian Academy of Management, Vol. 4 No. 2, pp. 37-47. Aguilera, R. and Cuervo-Cazura, A. (2016), “Codes of good governance worldwide: what is the trigger?”, Organization Studies, Vol. 25 No. 3, pp. 415-443. Akyeampon, D., Amidu, M. and Abor, J. (2013), Corporate Governance and Earnings Management: Evidence from Sub-Saharan Africa, University of Ghana, Accra, available at: http://balme.ug.edu.gh/index. php?option=com_content&view=article&id=202&Itemid=235 (accessed January 5, 2016). Ali, S., Butt, S. and Hassan, A. (2009), “Corporate governance and earnings management empirical evidence from Pakistani listed companies”, European Journal of Scientific Research, Vol. 26 No. 4, pp. 624-638. Alves, S. (2012), “Ownership structure and earnings management: evidence from Portugal”, Australasian Accounting, Business and Finance Journal, Vol. 6 No. 1, pp. 57-74. Ararat, M. and Dallas, G. (2011), Corporate Governance in Emerging Markets: Why it Matters to Investors – and What they Can do About It, The World Bank, Washington, DC, available at: https://openknowledge (accessed December 14, 2016). Balsam, S., Chen, H. and Sankaraguruswamy, S. (2003), “Earnings management prior to stock option grants”, available at: https://ssrn.com/abstract=378440 (accessed May 6, 2016).


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