The dissatisfaction with executive remuneration worldwide has increased because it is generally believed to have been instrumental to the 2008 Global Economic crisis. Central to this is the apparent unsatisfactory relationship between business performance and chief executive officer (CEO) remuneration. The primary aim of this study was to compare the pay-for-performance association between CEOs’ remuneration and state-owned entity performance before, during and after the economic crisis. It did so by assessing the chief executive remuneration link with state-owned enterprise performance from the period 2006 to 2014.
Twenty-one schedule 2 state-owned enterprises in South Africa. This quantitative, longitudinal study, obtained secondary data from the annual reports of state-owned enterprises from the period 2006 to 2014. Ordinary least square multiple regression analysis was used as the principal statistical method.
The findings indicate that the link between chief executive remuneration and state-owned entity performance demonstrated different patterns in the pre- and post-crisis periods.
State-owned entity remuneration committees should place more emphasis on the financial efficiency measurements to enhance efficiencies in South African state-owned enterprises. Shareholders and regulators should take cognisance of measures to be used to assess the potential performance of state-owned entities, through executive remuneration, especially during an economic crisis. Findings could furthermore be of importance to other academics investigating this phenomenon.
This research provides additional knowledge to the limited research available on SOEs in South Africa. Further, it reveals that an economic downturn affects the link between CEOs’ remuneration and SOE performance. This addresses a knowledge gap concerning the pay-for-performance link in South African SOEs and in emerging economies in general.
The dissatisfaction with executive remuneration escalated worldwide because of the perceived weak pay-performance link, especially after the 2008 Global Economic crisis (Modau,
Because the 2008/2009 economic crisis forced companies to reassess how their business operations were conducted, it is envisaged that, as part of this reassessment, the attitudes about determining executive remuneration changed as well to be more in line with company performance (Sonenshine, Larson, & Cauvel,
This study focused on (and was limited to) schedule 2 South African SOEs such as Eskom, Transnet, South African Airways and Denel. These commercial SOEs are autonomous entities and wholly owned by the government to realise government’s various socio-economic objectives (Accountant General South Africa,
However, weak governance, maladministration, fraud and corruption claims and the lack of financial sustainability at a number of these SOEs have been in the public eye for the past few years (Accountant General South Africa,
Considering this, the overarching research question is
The study contributes to the literature in two ways: Firstly, the study extends the existing literature regarding remuneration practices of SOEs (e.g. Ngwenya & Khumalo,
The article is organised as follows: The introduction section is followed by the literature review section. The next section discusses the methodology employed, followed by a section on the empirical findings. The last section discusses and concludes the research.
Most of the literature reviewed has predominantly concentrated on the relationship between executive pay and business performance in listed companies, with a limited number of studies conducted on SOEs, especially from a South African perspective.
State-owned-enterprises and private companies seem to have different motivations and objectives. A private company, for this study, can be defined as a profit company, (1) that is not a public, personal liability or state-owned company and (2) its memorandum of incorporation prohibits it from offering any of its securities to the public and restricts the transferability of its securities. State-owned-enterprises, in this study, are enterprises that are registered in terms of the Act as a company and listed as a public entity in schedule 2 of the
Another difference between SOEs and private sector companies lies in their accountability. Chief executive officers in SOEs are accountable to a larger group of people (everyone in the governed area) and under constant public scrutiny (GetSmarter,
A further difference lies in the leadership of SOEs and private sector companies. Within SOEs, political pressures lead to frequent leadership changes, whilst in private organisations, individuals can stay in leadership positions for an unlimited number of years. Within SOEs, the choice of managers may be made on a political basis instead of merit (Filho & Alves,
Even though CEOs’ remuneration packages in SOEs and private companies are based on the same three components, namely salary, bonus and inter alia performance-based incentives, the stakeholder-orientated nature of SOEs makes it much more difficult to define and measure performance (Filho & Alves,
A general observation from previous studies show that different performance measures are used, with most being accounting and traditional performance measures, such as return on assets, return on equity (ROE) or revenue (Maloa & Bussin,
Davies (
Differences across SOEs, as in most cases, their boards determine executive remuneration. This was supported by Kikeri (
The income disparity between executive management and workers on the lower level of the pay scale, which causes a widening wage gap.
There is no centralised authority to manage SOE remuneration, which may result in executives determining their own packages. This could be linked to the managerial power theory, which argues that because of principal agent relationship, agents (executives) are in the natural position to use their discretion to set their own pay (Otten,
Whilst the poor performance of South African SOEs is widely publicised, many do not follow the remuneration guidelines issued by the Department of Public Enterprises (DPEs) that require remuneration to be benchmarked with the private sector (Maloa & Bussin,
In addition, the excessive remuneration packages of CEOs in SOEs, is misaligned with the performance of SOEs in South Africa, as can be seen from the following cases: Denel’s 2009/2010 annual report indicated that its CEO, Talib Sadik, was being paid R5.6m per annum. This despite Denel declaring a loss of R544m during 2009. Armscor’s 2009/2010 annual report revealed that ex-CEO Sipho Thomo received an R3.27m remuneration packages (Panel to established to oversee SoE salaries, 2010), despite declaring a deficit of R15.8m (Armscor,
Before the publication of the new SOE remuneration guide, it was the responsibility of each SOEs remuneration committee (RemCos) to develop remuneration policies and practices that realise the best value for stakeholders. This non-standardised approach led to certain individuals being offered better remuneration packages (Department of Public Enterprises,
Remuneration may not surpass the market median of the recommended benchmark, without prior permission by the Shareholder Minister.
Guaranteed remuneration will be paid based on a total cost to company approach.
The overall annual increase percentages applied to guaranteed remuneration packages may not exceed the annual increase percentage negotiated with bargaining unit employees.
Annual increases should solely be based on individual and relevant SOE performance.
Individual and SOE performance below any agreed threshold should cancel out any annual increase to the CEO of a SOE.
Although numerous empirical studies have been conducted on CEOs’ remuneration and company performance in both developed and developing economies, these results were mixed (Marimuthu & Kwenda,
Shaw (
Modau (
Studies conducted on Chinese SOEs, and where a positive relationship was found between CEOs’ remuneration and SOE performance are those of Mengistae and Xu (
Ngwenya and Khumalo (
It could be expected that the PFMA, the
The study was limited to South African schedule 2 SOEs. Owing to the small population, no sampling methodology was used. In this study, the entire population of 21 SOEs were included. To be included in the study, SOEs had to have 9 years of audited financial statements and 9 years’ disclosed CEOs’ remuneration data. Subsequently, 18 SOEs were included in the study. Secondary data were gathered from annual reports, which had been subjected to a financial audit of SOEs. Data collected was, therefore, regarded as accurate and credible.
This empirical study followed an archival,
This study used two components of CEOs’ remuneration (the dependent variables): fixed pay and total remuneration (fixed pay and variable pay – short-term incentives only).
Summary of independent variables and measures.
Variable | Measures |
---|---|
Turnover (T) | Turnover (or revenue) is the money received by a company through typical business activities during a specified period (Williams, Haka, Bettner, & Carcello, |
Operating profit/loss (OP) | Operating profit or loss is also known as gross profit or loss, profit or loss before tax (Ward & Price, |
Net profit/loss (NP) | Net profit or loss – also labelled net income or profit or loss after tax – is the absolute measure of accounting profit (Graham & Winfield, |
Liquidity ratio (LR) | Liquidity refers to a company’s ability to pay short-term obligations with its current assets (Williams et al., |
Solvency ratio (SR) | The solvency ratio, or debt ratio, considers the ratio between the total assets and total liabilities of the company. This indicates the proportion of the company assets that have been financed by debt, with a higher value indicating higher risk (Graham & Winfield, |
Return on capital employed (ROCE) | Return on capital employed measures the profitability and the efficiency with which SOEs use their capital (European Commission, |
Return on equity (ROE) | The ROE is often used to describe how well a company is performing. This is because of its ratio of net profit or loss and total equity invested by the shareholders (Graham & Winfield, |
Irregular, fruitless and wasteful expenditure (IFWE) | Classified into three categories (South African Qualifications Authority, |
Long-term incentives (LTIs) were excluded from this study. State-owned-enterprises are not registered on the JSE, with a small number of SOEs providing LTI schemes (Bezuidenhout et al.,
Data were analysed by using SPSS (Version 22, for descriptive statistics) and EViews (Version 8). EViews accommodates panel data and provides the required econometric analysis needed for the type of data obtained. Polakow (2015) raised concerns regarding the use of standard statistical techniques in financial analysis that ignore autocorrelation and stationarity. Stationarity was addressed by conducting unit root tests and assessing autocorrelation through the Durbin–Watson (DW). The author did not use an estimator, such as Heckmann’s two-step correlation for selection bias, as it was (1) deemed unnecessary in the light of the repetitiveness of the sample and (2) assumes of bivariate normality and, therefore, require the use of probit, not logic (M. Pohl, personal communication, February 14, 2020). Potential bias has, therefore, been addressed.
The data set consisted of a panel of 162 observations (18 SOEs × 9 years). Chief executive officers’ remuneration and company performance components were tested for normality, stationarity (by Using the Augmented Dickey–Fuller [ADF] test) and autocorrelation (by using the DW test). Results of the assumption testing were considered in the analysis conducted by choosing the appropriate estimation method.
To ensure the variables used in the model were non-stationary, unit root tests were performed. Contradicting results were found for the various individual unit root tests. This could be because of the sample period (20062014) consisting of 9 years and 18 SEOs included in the sample. In an event of a short time (< 12 years), stationarity testing may be a problem. Contradictory results from the various unit root tests are a typical outcome in the event of a short time (less than 12 years) with a fairly large number of observations (Kao,
Furthermore, the tolerance and variance inflation factor (VIF) information in the regression models were used to test for the presence of multicollinearity. Multicollinearity occurs when two explanatory variables are highly correlated (
Inferential and multivariate statistics were carried out to permit the researcher to conclude the data. Also, Pearson’s product-moment correlation tests were conducted to test the direction and strength of the relationship between the variables. Pooled ordinary least square (OLS) regression analysis was conducted, where it is presumed that the independent variables are strictly exogenous to the error terms of the model (Gujarati & Porter,
Median and mean descriptive statistics of variables for the total data set.
Variable | 2006–2010 |
2011–2014 |
||
---|---|---|---|---|
Mean | Median | Mean | Median | |
CEOs’ fixed pay | R2 462 929.54 | R2 223 425.50 | R3 363 687.34 | R3 169 000.00 |
CEOs’ total remuneration | R4 171 166.68 | R3 501 078.00 | R4 936 334.01 | R4 138 150.00 |
Operating profit | R2 164 328 663.31 | R6 865 735 00.00 | R1 857 234 815.71 | R2 953 528 91.00 |
Net profit | R1 508 857 977.69 | R3 361 960 00.00 | R8 104 537 86.10 | R1 804 655 62.00 |
Turnover | R1 021 415 350 9.52 | R3 418 712 500.00 | R1 613 735 486 3.25 | R4 623 119 500.00 |
IFWE | R7 479 124.08 | 0.00 | R1 650 028 4.25 | R1 170 00.00 |
ROCE | 0.15 | 0.09 | 0.10 | 0.04 |
Return on equity | 0.10 | 0.09 | 0.10 | 0.05 |
Liquidity | 2.06 | 1.17 | 2.19 | 1.65 |
Solvency | 2.20 | 1.65 | 2.21 | 1.62 |
CEO, chief executive officer; IFWE, irregular, fruitless and wasteful expenditure; ROCE, return on capital employed.
Even though the researcher did not adjust the numbers for inflation, the descriptive statistics in
The descriptive statistics further suggest that even though SOEs financial performance declined in the years after the economic crisis (except for turnover and liquidity), CEOs’ remuneration increased. It is interesting to note that Carlson and Bussin (
Correlation between chief executive officers’ remuneration and state-owned-enterprise performance: 2006–2010 (
Variables | Fixed pay | Total remuneration | Turnover | Operating profit | Net profit | Liquidity ratio | Solvency ratio | ROCE | ROE | IFWE |
---|---|---|---|---|---|---|---|---|---|---|
Fixed pay | 1 | - | - | - | - | - | - | - | - | - |
Total remuneration | 0.772 |
1 | - | - | - | - | - | - | - | - |
Turnover | 0.607 |
0.572 |
1 | - | - | - | - | - | - | - |
Operating profit | 0.366 | 0.502 |
0.706 |
1 | - | - | - | - | - | - |
Net profit | 0.184 | 0.388 |
0.426 |
1 | - | - | - | - | - | |
Liquidity ratio | −0.213 |
−0.128 | 0.154 | −0.098 | 0.022 | 1 | - | - | - | - |
Solvency ratio | 0.316 | 0.034 | −0.063 | 0.051 | 0.137 | 0.657 |
1 | - | - | - |
ROCE | −0.198 | −0.156 | −0.031 | 0.130 | 0.086 | −0.031 | 0.080 | 1 | - | - |
ROE | −0.056 | 0.019 | 0.062 | 0.212 |
0.365 |
−0.028 | 0.006 | 0.090 | 1 | - |
IFWE | 0.066 | −0.017 | 0.023 | −0.042 | −0.050 | −0.059 | −0.093 | 0.011 | −0.070 | 1 |
CEO, chief executive officer; IFWE, irregular, fruitless and wasteful expenditure; ROCE, return on capital employed; ROE, return on equity.
, Correlation significant at the 0.05 level (2-tailed);
, Correlation significant at the 0.01 level (2-tailed).
Correlation between chief executive officers’ remuneration and state-owned-enterprise performance: 2011–2014 (
Variables | Fixed pay | Total remuneration | Turnover | Operating profit | Net profit | Liquidity ratio | Solvency ratio | ROCE | ROE | IFWE |
---|---|---|---|---|---|---|---|---|---|---|
Fixed pay | 1 | - | - | - | - | - | - | - | - | - |
Total remuneration | 0.852 |
1 | - | - | - | - | - | - | - | - |
Turnover | 0.639 |
0.517 |
1 | - | - | - | - | - | - | - |
Operating profit | 0.441 |
0.444 |
0.760 |
1 | - | - | - | - | - | - |
Net profit | 0.276 |
0.297 |
0.653 |
0.940 |
1 | - | - | - | - | - |
Liquidity ratio | −0.217 | −0.226 | −0.218 | −0.167 | −0.013 | 1 | - | - | - | - |
Solvency ratio | −0.163 | −0.023 | −0.161 | −0.077 | 0.055 | 0.678 |
1 | - | - | - |
ROCE | 0.056 | −0.085 | 0.036 | −0.052 | −0.067 | −0.087 | −0.070 | 1 | - | - |
ROE | −0.093 | −0.091 | −0.039 | 0.017 | 0.057 | −0.036 | −0.054 | −0.070 | 1 | - |
IFWE | −0.018 | 0.064 | 0.070 | 0.110 | 0.093 | −0.062 | −0.004 | −0.034 | −0.003 | 1 |
CEO, chief executive officer; IFWE, irregular, fruitless and wasteful expenditure; ROCE, return on capital employed; ROE, return on equity.
, Correlation significant at the 0.05 level (2-tailed);
, Correlation significant at the 0.01 level (2-tailed).
The results of the Pearson Correlation analysis in
The results of the Pearson Correlation analysis in
The sections that follow detail the results of the correlation and regression analysis performed. It should be noted that inflation was not considered as research investigating the relationship between CEOs’ remuneration and company performance used nominal data and not constant data (e.g. Carlson & Bussin,
Median fixed pay.
The highest increase in fixed pay, 23%, was between 2006 and 2007. This finding reinforces Kuboya’s (
Median total remuneration.
Noticeably, total remuneration varied throughout the period under study. At first glance, this tendency seems to reflect that of a few components of business performance.
Ordinary least square multiple regression was performed to test the link between the CEOs’ remuneration components (fixed pay and total remuneration) and components of SOE performance. In all the models, the panel data analysis was run with the dependent variables (fixed pay and total remuneration components) and the independent variables (SOE performance). The outcome of each regression model is shown in
ε =
AR (1) = Dependent variable at a lag of 1 (the term was introduced to address autocorrelation).
Regression analysis: Fixed pay and state-owned-enterprise performance components (2006–2010).
Models | 1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | |||||||||
Constant | 2478817.00 | 6.88 | 2476152.00 | 7.02 | 2476627.00 | 7.04 | 2490376.00 | 7.31 | 2496220.00 | 7.41 | 2501185.00 | 7.40 | 2416219.00 | 9.38 | 2454341.00 | 8.90 |
AR (1) |
0.54 |
5.07 | 0.54 |
5.13 | 0.54 |
5.24 | 0.54 |
5.30 | 0.54 |
5.32 | 0.54 |
5.40 | 0.55 |
5.54 | 0.58 |
6.11 |
Turnover | 353000.00 |
2.65 | 353000.00 |
2.67 | 353000.00 |
2.70 | 352000.00 |
2.72 | 359000.00 |
2.87 | 318000.00 |
3.48 | 379000.00 |
3.46 | 361000.00 |
3.19 |
ROE | −227824.40 | −0.79 | −226683.70 | −0.79 | −226046.00 | −0.80 | −225606.10 | −0.80 | −243348.00 | −0.81 | −209075.00 | −0.83 | −205925.40 | −0.82 | - | - |
SR | −38462.60 | −0.32 | −29531.79 | −0.40 | −41288.12 | −0.40 | −43042.58 | −0.43 | −44364.28 | −0.44 | −40497.54 | −0.41 | - | - | - | - |
Net profit | −8880000.00 | −0.22 | −9150000.00 | −0.23 | −8700000.00 | −0.22 | −8900000.00 | −0.23 | - | - | - | - | - | - | - | - |
IFWE | 0.00 | 0.18 | 0.00 | 0.18 | 0.00 | 0.17 | - | - | - | - | - | - | - | - | - | - |
ROCE | −30123.22 | −0.10 | −29531.79 | −0.10 | - | - | - | - | - | - | - | - | - | - | - | |
LR | −4755.16.00 | −0.22 | - | - | - | - | - | - | - | - | - | - | - | - | - | |
9.98 | 0.00 | 11.41 | 0.00 | 13.25 | 0.00 | 15.68 | 0.00 | 19.08(0.00) | - | 24.13(0.00) | - | 32.51(0.00) | - | 48.68(0.00) | - | |
DW stat | 2.61 | - | 2.61 | - | 2.62 | - | 2.63 | - | 2.64 | - | 2.62 | - | 2.63 | - | 2.64 | - |
0.59 | - | 0.59 | - | 0.59 | - | 0.59 | - | 0.59 | - | 0.59 | - | 0.59 | - | 0.59 | - | |
Adjusted |
0.53 | - | 0.54 | - | 0.55 | - | 0.55 | - | 0.56 | - | 0.57 | - | 0.57 | - | 0.57 | - |
Inverted AR roots | 0.54 | - | 0.54 | - | 0.54 | - | 0.54 | - | 0.54 | - | 0.54 | - | 0.55 | - | 0.58 | - |
OP, operating profit; NP, net profit; LR, liquidity ratio; SR, solvency ratio; ROCE, return on capital employed; ROE, return on equity; IFWE, irregular, fruitless and wasteful expenditure; DW, Durbin–Watson.
,
, indicates significance at the 5% level;
, at the 1% level.
, AR (1), auto correlation.
Note: Dependent variable: fixed pay.
In the last regression model, Model 8 was considered as the optimal model, as the
Regression analysis: Total remuneration and state-owned-enterprise performance components (2006–2010).
Models | 1 |
2 |
3 |
4 |
5 |
6 |
||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | |||||||
Constant | 4537979.00 | 3.96 | 4357463.00 | 4.59 | 4245834.00 | 4.75 | 4213653.00 | 4.89 | 3954614.00 | 5.60 | 4247169.00 | 6.99 |
AR (1) |
0.73 |
7.63 | 0.73 |
8.27 | 0.73 |
8.22 | 0.72 |
8.04 | 0.69 |
7.90 | 0.68 |
7.71 |
OP | 0.000414 |
2.56 | 0.000409 |
2.61 | 0.000405 |
2.60 | 0.000421 |
2.80 | 0.000383 |
2.73 | 0.000361 |
2.62 |
NP | −0.000205 |
−2.33 | −0.000204 |
−2.55 | −0.000201 |
−2.53 | −0.000204 |
−2.59 | −0.000188 |
−2.47 | −0.000175 |
−2.33 |
LR | 171366.60 | 0.96 | 149619.70 | 0.92 | 149407.10 | 0.93 | 148583.40 | 0.93 | 143712.50 | 0.92 | - | - |
Turnover | −250000.00 | −0.75 | −249000.00 | −0.76 | −226000.00 | −0.71 | −221000.00 | −0.70 | - | - | - | - |
ROCE | 299364.00 | 0.56 | 310308.00 | 0.59 | 273949 | 0.53 | - | - | - | - | - | - |
IFWE | −0.001082 | −0.54 | −0.001953 | −0.52 | - | - | - | - | - | - | - | - |
SR | −96199.69 | −0.35 | - | - | - | - | - | - | - | - | - | - |
ROE | −13464.73 | −0.03 | - | - | - | - | - | - | - | - | - | - |
10.48 | - | 13.87 | - | 16.33 | - | 19.76 | - | 24.80 | - | 32.87 | - | |
DW stat | 2.51 | - | 2.50 | - | 2.46 | - | 2.46 | - | 2.47 | - | 2.46 | - |
0.61 | - | 0.61 | - | 0.61 | - | 0.61 | - | 0.60 | - | 0.60 | - | |
Adjusted |
0.55 | - | 0.57 | - | 0.57 | - | 0.58 | - | 0.58 | - | 0.58 | - |
Inverted AR roots | 0.73 | - | 0.73 | - | 0.73 | - | 0.72 | - | 0.69 | - | 0.68 | - |
OP, Operating profit; NP, net profit; LR, liquidity ratio; SR, solvency ratio; ROCE, return on capital employed; ROE, return on equity; IFWE, irregular, fruitless and wasteful expenditure; DW, Durbin–Watson.
,
, indicates significance at the 5% level;
, at the 1% level.
, AR (1), auto correlation.
Note: Dependent variable: Total remuneration.
Model 6 was considered the optimal model, as the
Regression analysis: Fixed pay and state-owned-enterprise performance components (2011–2014).
Models | 1 |
2 |
3 |
4 |
5 |
6 |
||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | |||||||
Constant | 2539841.00 | 4.53 | 2549470.00 | 4.66 | 2447136.00(5.05) | 5.05 | 2511930.00 | 5.25 | 2520434.00 | 5.32 | 2557187.00 | 5.47 |
AR (1) |
0.64 |
2.98 | 0.64 |
6.06 | 0.65 |
6.26 | 0.66 |
6.69 | 0.67 |
6.85 | 0.67 |
6.85 |
Turnover | 320000.00 |
3.08 | 319000.00 |
3.11 | 323000.00 |
3.16 | 351000.00 |
3.75 | 345000.00 |
3.81 | 346000.00 |
3.85 |
IFWE | −0.000138 | −1.08 | −0.000139 | −1.10 | −0.000147 | −1.21 | −0.000167 | −1.46 | −0.000165 | −1.45 | −0.000171 | −1.51 |
LR | 21575.90 | 1.55 | 215090.90 | 1.56 | 188999.70 | 1.64 | 178166.90 | 1.59 | 176583.90 | 1.60 | 173260.10 | 1.57 |
ROCE | 118507.70 | 0.69 | 117255.30 | 0.69 | 119753.00 | 0.72 | 113021.70 | 0.68 | 112537.30 | 0.69 | - | - |
NP | −0.000123 | −0.72 | −0.000123 | −0.72 | −0.000120 | −0.71 | −1.24 | −0.29 | - | - | - | - |
OP | 981000.00 | 0.68 | 976000.00 | 0.68 | 928000.00 | 0.66 | - | - | - | - | - | - |
SR | −67223.29 | −0.36 | −68709.25 | −0.38 | - | - | - | - | - | - | - | - |
ROE | −18732.71 | −0.12 | - | - | - | - | - | - | - | - | - | - |
12.38 | - | 14.25 | - | 16.59 | - | 19.52 | - | 23.87 | - | 30.06 | - | |
DW stat | 2.91 | - | 2.92 | - | 2.93 | - | 2.99 | - | 3.03 | - | 3.00 | - |
0.72 | - | 0.72 | - | 0.72 | - | 0.72 | - | 0.72 | - | 0.71 | - | |
Adjusted |
0.66 | - | 0.67 | - | 0.68 | - | 0.68 | - | 0.69 | - | 0.69 | - |
OP, Operating profit; NP, net profit; LR, liquidity ratio; SR, solvency ratio; ROCE, return on capital employed; ROE, return on equity; IFWE, irregular, fruitless and wasteful expenditure; DW, Durbin–Watson.
,
, indicates significance the 1% level.
, AR (1), auto correlation.
Note: Dependent variable: Fixed pay.
Because of the rise in the
Regression analysis: Total remuneration and state-owned-enterprise performance components (2011–2014).
Models | 1 |
2 |
3 |
4 |
||||
---|---|---|---|---|---|---|---|---|
Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | Non-standardised beta coefficient | |||||
Constant | 5615339.00 | 3.45 | 5533097.00 | 3.57 | 5366363.00 | 3.82 | 6068994.00 | 5.60 |
AR (1) |
0.76 |
7.52 | 0.76 |
7.58 | 0.76 |
8.00 | 0.77 |
9.04 |
ROCE | −625030.20 | −1.68 | −619048.30 | −1.69 | −614510.10 | −1.71 | −616921.10 | −1.74 |
ROE | 289099.10 | 0.94 | 323082.40 | 1.08 | 316546.90 | 1.07 | 333883.70 | 1.17 |
IFWE | −0.000426 | −1.66 | −0.000449 |
−1.83 | −0.000460 |
−1.93 | −0.000478 |
−2.06 |
Turnover | 112000.00 | 0.40 | 154000.00 | 0.58 | 153000.00 | 0.58 | - | - |
LR | 195518.40 | 0.67 | 180448.90 | 0.64 | 141505.10 | 0.59 | - | - |
SR | −152083.70 | −0.36 | −108479.40 | −0.26 | - | - | - | - |
OP | 490000.00 | 0.15 | - | - | - | - | - | - |
NP | 662000.00 | 0.02 | - | - | - | - | - | - |
9.97 | - | 13.21 | - | 15.71 | - | 24.13 | - | |
DW stat | 3.39 | - | 3.38 | - | 3.34 | - | 3.41 | - |
0.68 | - | 0.67 | - | 0.67 | - | 0.67 | - | |
Adjusted |
0.61 | - | 0.62 | - | 0.63 | - | 0.64 | - |
OP, Operating profit; NP, net profit; LR, liquidity ratio; SR, solvency ratio; ROCE, return on capital employed; ROE, return on equity; IFWE, irregular, fruitless and wasteful expenditure; DW, Durbin–Watson.
,
, indicates significance at the 5% level.
, AR (1), auto correlation.
Note: Dependent variable: Total compensation.
Model 4 was considered as the optimal model, with an increase of the
With the worldwide dissatisfaction with executive remuneration that escalated during the global economic crisis, researchers postulate that executive remuneration has generally been considered as key in leading to the economic crisis. The purpose of the study was to compare the association between CEOs’ remuneration and SOE performance during two different periods, namely 2006 to 2010 (pre- and during the economic crisis) and 2011 to 2014 (post-economic crisis). A possible explanation for the difference in results between fixed pay and total remuneration could be that fixed pay constitutes the fixed part of CEOs remuneration, regardless of performance. Total remuneration on the other hand, includes fixed pay and the value of any benefits received in addition to the salary and variable remuneration.
The findings of the negative relationship between the CEOs’ remuneration components and some of the SOE performance measures do not support the economic theories of efficient remuneration (Kirsten & Du Toit,
Following the findings in this study, it seems that the link between the CEOs’ remuneration components and SOE performance components demonstrated different patterns pre- and post-economic crisis. These findings are similar to findings of Yang et al. (
Furthermore, results from the regression analysis of the post-crisis (2011 to 2014) date indicated that fixed pay had a positive relationship with turnover and liquidity ratio, respectively. Total remuneration had a negative relationship with ROCE and IFWE, respectively, and a positive relationship with ROE. This finding suggests that capital within SOEs is used less efficiently (European Commission,
The results indicate the troubling effect of the economic downturn for SOEs in South Africa. The only component of SOE performance that did not decline during the study period was IFWE. Although it seems as if SOEs did not report on IFWE prior to 2011, this measure increased substantially from 2011 onwards. Interestingly, along with a draft audit report for the financial year ending 31 May 2014 by one of the leading auditing firms in South Africa, the South African Post Office (SAPO) spent R2.1 billion in IFWE during the 2013/2014 financial year; even though SAPO revealed an overdraft of R250m during the same period (BusinessTech,
The findings of this study are similar to prior results of, for example, Otieno (
The findings from this study, firstly demonstrate that economic conditions will most likely affect the PFP relationship and secondly highlighted key performance measures that affect CEOs’ remuneration in South African SOE before, during and after an economic crisis.
Even though the literature covering CEOs’ remuneration is extensive and continues to evolve with the times, the literature regarding the link between remuneration after an economic shock is still in its embryonic phase, especially within an SOE environment. This study, therefore, contributes to the existing literature on executive PFP, especially within an SOE context given the fact that findings vis-à-vis the link between CEOs’ remuneration and SOEs’ performance stay unclear (Reddy & Whang,
Based on the findings, it is suggested that SOE boards need to design innovative remuneration contracts that incorporate the important role that turnover plays in the fixed pay component of CEOs’ remuneration (especially during an economic crisis). The period of this study is unique because it encompasses a steady economic period before the economic crisis, a demanding and insecure period when the economic crisis occurred, followed by the fallout of the economic crisis. The results of the study indicate that an economic downturn affects the link between CEOs’ remuneration and SOE performance. This addresses a knowledge gap concerning the PFP link in South African SOEs and in emerging economies in general. This knowledge could be useful to stakeholders who should evaluate trends regarding CEOs’ remuneration (to appropriately assess their risks and benefits before approving remuneration at the annual meeting).
The first limitation is that the economic crisis was included in the study period – this could have given a biased representation of SOE performance in normal years. The second limitation is that the chosen remuneration and performance variables may not be the actual variables that will reveal the true relationship between them. There is a risk that the variables chosen are incorrect and, therefore, the study may not accurately report the real relationship between CEOs’ remuneration and company performance. For example, turnover is normally used as a proxy for size, but in this study, turnover was used as a proxy for performance.
Findings in this study highlight key performance indicators that affect CEOs’ remuneration in South African SOEs, which can be used by RemCos to determine the relationship between CEOs’ remuneration and SOE performance, within an economic crisis, based on strong, statistically significant empirical research. Considering that South Africa is currently facing yet another economic crisis, this study is timely.
This article is based on a PhD study and the material in this article is similar to parts of the PhD study. This article should be seen as an extension of the PhD study.
The author declares that she has no financial or personal relationships that may have inappropriately influenced her in writing this research article.
M.B. is the sole author of this research article.
This article followed all ethical standards for research without direct contact with human or animal subjects.
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Data sharing is not applicable to this article as no new data were created or analysed in this study.
The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of any affiliated agency of the author.