Abstract:

This study investigates the effect of ownership on firm performance of state-owned and non-state-owned enterprises in elected G20 countries and across the development spectrum. Annual financial and accounting data from the period 2011-2015 are compiled for 252, state-owned and 6503, non-state-owned enterprises. The empirical results show that state ownership and firm performance are negatively associated with selected G-20countries. This is also confirmed when countries are considered with respect to their level of development except for High-Income countries. The poor performance of state-owned enterprises in less developed countries can be justified by the argument that state-owned enterprises suffer from the intervention of self-oriented politicians, which leads to lower-than-expected performance.

Key Words:

Firm Performance; State Ownership; Development Spectrum; Leverage

Introduction

Debate on the existence of a relationship between ownership structure and firm performance is not new. The effect of ownership structure on firm performance started from principal-agent relationship firstly studied by Smith (1776) and further investigated by and Berle and Means (1932). Later on, Jensen and Mackling (1976) describe potential problems and benefits of ownership structure to the performance of the firm. These studies are followed by a stream of literature providing evidence of a difference in public and private enterprises that can affect the performance (Kang & Kim 2012; Ahuja & Majumdar 1998; Dinc, 2004; Shah et al., 2011; Driffield et al., 2005; Dang and Vu, 2018). Considering ownership of an important characteristic, this study investigates the effect of ownership on the firm performance of state-owned and non-state-owned enterprises in selected G-20 countries with respect to the development level.

Arguments for the existence of firm performance differences between state and non-state-owned enterprises have been developed in the following way. Although, agency conflict and cost between manager and shareholder can be reduced through debt financing at the same time as ownership structure varies, a principal-agent issue arises. Property-right over the enjoyment and disposal of assets are less protected in state-owned enterprises because of the absence of corporate control in the market. Also, the owner’s incentive to monitor managers reduces, because capitalization of future outcome into current share price is inhabited or restricted  (Putterman, 1993) d. Alchian (1977) argued that differences in performance of state-owned and non-state-owned enterprises are not because of differences in objectives but because of the differences in specifying individual rights, that how cost and benefit will be distributed among the shareholder or the participants of the company. These arguments based on property rights and agency theories are just a subset of broader view in literature. Sheshinski & López-Calva (2003) support the idea of poor efficiency and performance of state-owned enterprises in the light of agency theory, under managerial and political perspectives. From the managerial perspective, it is very hard to observe and monitor the management behaviour of state-owned enterprises compared to non-state-owned enterprises because state-owned enterprises are not market players. From a political perspective, state-owned enterprises managers make efforts to achieve their political prestige and political career on the shareholder(s) cost generally called empire-building hypothesis. Hence, state-owned enterprises managers tend to invest in such projects which may be profitable or not leading to poor efficiency and performance. Boycko, Shleifer, & Robert (1996) argued that inefficiencies and poor performance caused by agency issues in state-owned enterprises are related more to politicians then managers. Politicians use state-owned enterprises for their personal interest in order to gain more political benefit.  Therefore, management engages in empire building instead of maximizing the shareholder wealth. Sometimes, citizens in a democracy cannot find it easy, and neither have incentives to control the state-owned enterprise managers.

A number of studies have provided empirical evidence that state ownership has more advantages than private in terms of performance, efficiency and optimality of capital structure (see Dewenter & Malatesta, 2001; Ting & Lean, 2011; Fraser, Zhang, & Derashid, 2006; Dong, Liu, Shen, & Sun, 2010) while other founded that private ownership has more advantages in terms profitability and optimality of capital structure (see Boardman & Vining, 1989; Davis, 1971; Caves & Christensen, 1980; Vining & Broadman, 1992; Majumdar, 1996; Ting, 2001; Vickers & Yarrow, 1998; Zhengwei, 2013).

Most of the studies reviewed above were conducted to investigate the impact of ownership on the profitability and efficiency of state-owned and non-state-owned enterprises in the European Union and other advanced countries. Unlike other studies, this study aims to investigate the differences in firm performance of state and non-state-owned enterprises for selected G-20 countries and across the development spectrum, High-Income countries (HI), Upper Middle-Income countries (UMI) and Lower Middle-Income countries (classification of World Bank). This study also investigates the effect of various firm-specific variables (leverage, tangibility, size and growth) on firm performance.

Rest of the article is as follows: section two analytical framework and data description. Descriptive statistics and analytical outcomes are discussed in section three. While conclusion and policy consequences are discussed in section four.

Analysis Framework and Data Description 

Data

This study includes 12 (Argentina, Brazil, China, France, Germany, Italy, India, Indonesia, Russia, Republic of Korea, Saudi Arabia, and Turkey) countries from G-20 countries. We selected publically state-owned and non-state-owned firms in these countries. Other countries in G-20 (Australia, Canada, Japan, Mexico, South Africa, United Kingdom and the United States) were excluded from the sample because of the unavailability of information on state-owned firms. Annual financial and accounting data of 252, state-owned and 6503, non-state-owned firms are extracted from Orbis. The sample selection period is from 2011 to 2015, based on the latest availability of updated data. This gets us a sample of 1260 observation of state-owned and 32,515 observations of non-state-owned firms.

We further classified the selected G-20 countries according to High-income countries (Germany, Italy and France), upper-middle-income countries (Argentina, Brazil, China, Russia, Saudi Arabia and Turkey) and lower-middle-income countries according to the classification of WB (World Bank) in order to analyse the impact of ownership on firm performance in these three types of economies.

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Empirical Model

In the light of the discussion presented in the previous section it is postulated that Profitability(?ROA?_icsy) is determined by the leverage (L_icsy), tangibility (T_icsy), size (?lnA?_icsy), profitability(?ROA?_icsy) and growth (?TQ?_icsy) of firm i in country c of sector sin year y. Profitability(?ROA?_icsy) is the dependent variable, employed as a proxy for firm performance and is equal to the ratio of earnings before interest and taxes-to-total assets Dewenter and Malatesta (2001). Leverage (L_icsy) is measured by the book value of a firm’s total debt-to-total asset ratios. Tangibility (T_icsy) equals the ratio of net tangible assets-to-total assets, Size (?lnA?_icsy) is equal to the natural logarithm of total assets, growth (?TQ?_icsy) is equal to the market-to-book ratio of equity. This study focuses on highlighting firm performance differences in state and non-state-owned enterprises. Therefore a dummy, ?DO?_ics, equals to unity if a firm is state-owned and zero otherwise is also considered in the analysis. The empirical model is given as follows:

?ROA?_icsy=?_icsy+?_1 ?lnA?_icsy+?_2 ?LEV?_icsy+?_3 ?TQ?_icsy+?_4 T_icsy+?_5 ?DO?_ics+?_c+?_y+?_s+?_icsy                                                                                                                (1)

where?_c,?_s  And?_y   represent the country, sector and year-specific fixed effects, ln represents natural logarithm and?_icsy represents error term assumed to be randomly distributed with mean zero and homoscedastic variance. The parameter?_5 shows the effect of ownership on the performance of a firm. Particularly, it is the intercept shifter of a state-owned firm as compared to the base case of non-state-owned enterprises. Equation-1 is our base model and statistical significance of?_5 answers the question raised in the introduction. Whereas?_1,?_2,?_3  and ?_4  represents the coefficient for leverage, tangibility, size and growth.  In order to investigate the effect of ownership on firm performance across the development spectrum, two steps are carried. First, dummy variables representing different categories of countries classification, including high income, upper middle income and lower-middle-income countries are created. Second, these dummies are then interacted with the variable?DO?_ics as follows to create dummies representing SOEs and Non-SOEs in these economies. 

SHI=?DO?_ics*Hi

SUMI=?DO?_ics*UMI

SLMI=?DO?_ics*LMI

Hence, SHI is a dummy, equals to unity if a firm is a state-owned in high-income economies and zero otherwise. Rest of the variables can also be defined in the same way. Separate model with each of these variables is estimated. The estimated parameters of each of these models are used to investigate the effect of ownership on firm performance in High Income (HI), Upper Middle Income (UMI) and Lower Middle Income (LMI) countries (Amin et al., (2019); Haq (2013); Haq et al., (2013) and Haq et al., (2010)).

Results and Discussion

We have categorized the selected G-20 countries according to World Bank Classification in High Income, Upper Middle Income and Lower Middle-Income countries. Table 1 provide descriptive statistic of High Income, Upper Middle Income and Lower Middle-Income countries and shows averages of firm-specific variables of state-owned and non-state-owned enterprises. On average, from 2011 to 2015 ROA, leverage, tangibility and size of state-owned enterprises in high-income countries are significantly higher than non-state-owned enterprises. In upper-middle-income countries, leverage, tangibility and size of state-owned enterprises are significantly higher than non- state-owned enterprises. Whereas growth of non-state-owned enterprises in upper-middle-income countries is significantly higher than state-owned enterprises. The results of lower-middle-income countries indicate that on average ROA, Leverage and size of state-owned enterprises are significantly higher than non-state-owned enterprises.  These results indicate that differences do exist between the financial performance of state and non-state-owned enterprises.   

Table 1. Descriptive Statistics Across the Development Spectrum High Income

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High Income width="80" nowrap="" valign="bottom">

Variable width="91" nowrap=""> Ownership width="99" nowrap=""> Number of observations width="59" nowrap=""> Mean width="73" nowrap=""> Median width="53" nowrap=""> SD width="80" nowrap=""> T-test

ROA width="91" nowrap=""> SOE width="99" nowrap=""> 105 width="59" nowrap=""> 0.052 width="73" nowrap=""> 0.043 width="53" nowrap=""> 0.128 width="80" nowrap="" rowspan="2"> 2.060**

width="91" nowrap=""> non-SOE width="99" nowrap=""> 11340 width="59" nowrap=""> 0.031 width="73" nowrap=""> 0.038 width="53" nowrap=""> 0.103

Leverage width="91" nowrap=""> SOE width="99" nowrap=""> 105 width="59" nowrap=""> 0.579 width="73" nowrap=""> 0.604 width="53" nowrap=""> 0.274 width="80" nowrap="" rowspan="2"> 4.308***

width="91" nowrap=""> non-SOE width="99" nowrap=""> 11340 width="59" nowrap=""> 0.491 width="73" nowrap=""> 0.507 width="53" nowrap=""> 0.208

Size width="91" nowrap=""> SOE width="99" nowrap=""> 105 width="59" nowrap=""> 14.440 width="73" nowrap=""> 14.339 width="53" nowrap=""> 1.985 width="80" nowrap="" rowspan="2"> 10.713***

width="91" nowrap=""> non-SOE width="99" nowrap=""> 11340 width="59" nowrap=""> 12.399 width="73" nowrap=""> 12.113 width="53" nowrap=""> 1.942

Tangibility width="91" nowrap=""> SOE width="99" nowrap=""> 105 width="59" nowrap=""> 0.948 width="73" nowrap=""> 0.989 width="53" nowrap=""> 0.077 width="80" nowrap="" rowspan="2"> 3.402***

width="91" nowrap=""> non-SOE width="99" nowrap=""> 11340 width="59" nowrap=""> 0.899 width="73" nowrap=""> 0.969 width="53" nowrap=""> 0.147

Growth width="91" nowrap=""> SOE width="99" nowrap=""> 105 width="59" nowrap=""> 0.916 width="73" nowrap=""> 0.574 width="53" nowrap=""> 1.229 width="80" nowrap="" rowspan="2"> 0.334

width="91" nowrap=""> non-SOE width="99" nowrap=""> 11340 width="59" nowrap=""> 0.872 width="73" nowrap=""> 0.546 width="53" nowrap=""> 1.330

Upper Middle Income width="80" nowrap="" valign="bottom">

ROA width="91" nowrap=""> SOE width="99" nowrap=""> 950 width="59" nowrap=""> 0.042 width="73" nowrap=""> 0.039 width="53" nowrap=""> 0.083 width="80" nowrap="" rowspan="2"> 1.273

width="91" nowrap=""> non-SOE width="99" nowrap=""> 9760 width="59" nowrap=""> 0.046 width="73" nowrap=""> 0.040 width="53" nowrap=""> 0.078

Leverage width="91" nowrap=""> SOE width="99" nowrap=""> 950 width="59" nowrap=""> 0.564 width="73" nowrap=""> 0.587 width="53" nowrap=""> 0.217 width="80" nowrap="" rowspan="2"> 12.802***

width="91" nowrap=""> non-SOE width="99" nowrap=""> 9760 width="59" nowrap=""> 0.468 width="73" nowrap=""> 0.475 width="53" nowrap=""> 0.220

Size width="91" nowrap=""> SOE width="99" nowrap=""> 950 width="59" nowrap=""> 14.736 width="73" nowrap=""> 14.590 width="53" nowrap=""> 1.754 width="80" nowrap="" rowspan="2"> 32.283***

width="91" nowrap=""> non-SOE width="99" nowrap=""> 9760 width="59" nowrap=""> 13.140 width="73" nowrap=""> 13.046 width="53" nowrap=""> 1.422

Tangibility width="91" nowrap=""> SOE width="99" nowrap=""> 950 width="59" nowrap=""> 0.950 width="73" nowrap=""> 0.972 width="53" nowrap=""> 0.082 width="80" nowrap="" rowspan="2"> 4.596***

width="91" nowrap=""> non-SOE width="99" nowrap=""> 9760 width="59" nowrap=""> 0.936 width="73" nowrap=""> 0.963 width="53" nowrap=""> 0.091

Growth width="91" nowrap=""> SOE width="99" nowrap=""> 950 width="59" nowrap=""> 0.755 width="73" nowrap=""> 0.416 width="53" nowrap=""> 0.901 width="80" nowrap="" rowspan="2"> -7.276***

width="91" nowrap=""> non-SOE width="99" nowrap=""> 9760 width="59" nowrap=""> 1.437 width="73" nowrap=""> 0.844 width="53" nowrap=""> 2.876

Lower Middle-Income Countries width="80" nowrap="" valign="bottom">

ROA width="91" nowrap=""> SOE width="99" nowrap=""> 205 width="59" nowrap=""> 0.071 width="73" nowrap=""> 0.060 width="53" nowrap=""> 0.093 width="80" nowrap="" rowspan="2"> 3.452***

width="91" nowrap=""> non-SOE width="99" nowrap=""> 11415 width="59" nowrap=""> 0.045 width="73" nowrap=""> 0.032 width="53" nowrap=""> 0.107

Leverage width="91" nowrap=""> SOE width="99" nowrap=""> 205 width="59" nowrap=""> 0.539 width="73" nowrap=""> 0.539 width="53" nowrap=""> 0.212 width="80" nowrap="" rowspan="2"> 2.838**

width="91" nowrap=""> non-SOE width="99" nowrap=""> 11415 width="59" nowrap=""> 0.491 width="73" nowrap=""> 0.518 width="53" nowrap=""> 0.241

Size width="91" nowrap=""> SOE width="99" nowrap=""> 205 width="59" nowrap=""> 14.272 width="73" nowrap=""> 14.174 width="53" nowrap=""> 1.674 width="80" nowrap="" rowspan="2"> 23.580***

width="91" nowrap=""> non-SOE width="99" nowrap=""> 11415 width="59" nowrap=""> 10.694 width="73" nowrap=""> 10.635 width="53" nowrap=""> 2.161

Tangibility width="91" nowrap=""> SOE width="99" nowrap=""> 205 width="59" nowrap=""> 0.968 width="73" nowrap=""> 0.998 width="53" nowrap=""> 0.116 width="80" nowrap="" rowspan="2"> 0.0856

width="91" nowrap=""> non-SOE width="99" nowrap=""> 11415 width="59" nowrap=""> 0.969 width="73" nowrap=""> 1.000 width="53" nowrap=""> 0.094

Growth width="91" nowrap=""> SOE width="99" nowrap=""> 205 width="59" nowrap=""> 1.041 width="73" nowrap=""> 0.619 width="53" nowrap=""> 1.369 width="80" nowrap="" rowspan="2"> 0.6552

width="91" nowrap=""> non-SOE width="99" nowrap=""> 11415 width="59" nowrap=""> 0.937 width="73" nowrap=""> 0.372 width="53" nowrap=""> 2.279

 

** and *** show significance at 95 and 99 percent levels, respectively. ROA is equal to the ratio of earnings before interest and taxes to total assets. Leverage is the book value of the firm’s total debts to total assets. Tangibility is the ratio of net tangible assets to total assets. The natural log of total assets is equal to Size.  Growth is measured as market capitalization to total assets.  A state-owned enterprise is represented by SOE and is equal to one if a state owns 50.01% shares.

Table 2 shows the correlation between firm performance and other firm-specific variables in the selected G-20 countries. The results indicate a significant correlation between profitability and other firm-specific covariates. The correlation between profitability and leverage is negative and statistically significant, while size, tangibility, and growth are significantly positively correlated with leverage. The correlation between the state dummy and firm performance is also tested, which is the main scope of this study. The result indicates that the state dummy is negatively correlated with the firm performance in selected G-20 countries.

Table 2. Correlation Matrix

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Variables width="76" nowrap=""> ROA width="76" nowrap=""> Leverage width="76" nowrap=""> Size width="83" nowrap=""> Tangibility width="69" nowrap=""> Growth width="69" nowrap=""> SOE

ROA width="76" nowrap=""> 1 width="76" nowrap=""> width="76" nowrap=""> width="83" nowrap=""> width="69" nowrap=""> width="69" nowrap="">

Leverage width="76" nowrap=""> -0.230*** width="76" nowrap=""> 1 width="76" nowrap=""> width="83" nowrap=""> width="69" nowrap=""> width="69" nowrap="">

Size width="76" nowrap=""> 0.119*** width="76" nowrap=""> 0.282*** width="76" nowrap=""> 1 width="83" nowrap=""> width="69" nowrap=""> width="69" nowrap="">

Tangibility width="76" nowrap=""> 0.049*** width="76" nowrap=""> -0.037*** width="76" nowrap=""> -0.187*** width="83" nowrap=""> 1 width="69" nowrap=""> width="69" nowrap="">

Growth width="76" nowrap=""> 0.129*** width="76" nowrap=""> -0.192*** width="76" nowrap=""> -0.0658* width="83" nowrap=""> -0.0324* width="69" nowrap=""> 1 width="69" nowrap="">

SOE width="76" nowrap=""> -0.014** width="76" nowrap=""> -0.065*** width="76" nowrap=""> -0.226*** width="83" nowrap=""> -0.029*** width="69" nowrap=""> 0.021* width="69" nowrap=""> 1

 

*, ** and *** show significance at 90, 95 and 99 percent levels, respectively. ROA is equal to the ratio of earnings before interest and taxes to total assets. Leverage is the book value of the firm’s total debts to total assets. Tangibility is the ratio of net tangible assets to total assets. The natural log of total assets is equal to Size.  Growth is measured as market capitalization to total assets. A state-owned enterprise is represented by SOE and is equal to one if a state owns 50.01% shares.

Table 3 shows the correlation matric of High Income, Upper Middle Income and Lower Middle-Income countries. The result shows that size, tangibility and growth are significantly positively correlated with the firm’s profitability, while the correlation between leverage and the firm’s profitability is negative and statistically significant. These results are similar across the development spectrum. The correlation between state dummy and firm profitability is also tested across the development spectrum. The result shows that state dummy is significantly and negatively correlated with firm’s profitability in upper middle income and lower-middle-income countries, whereas no significant correlation can be found between state dummy and firms’ profitability in high-income countries. These results suggest that differences do exist in firm performance across the development spectrum. However, these results are estimated without controlling the fixed effects and development spectrum, a limitation removed in the following section.

Table 3. Correlation Matrix Across the Development Spectrum

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High Income

Variables width="79" nowrap=""> ROA width="79" nowrap=""> Leverage width="79" nowrap=""> Size width="90" nowrap=""> Tangibility width="79" nowrap=""> Growth width="37" nowrap=""> OS

ROA width="79" nowrap=""> 1 width="79" nowrap=""> width="79" nowrap=""> width="90" nowrap=""> width="79" nowrap=""> width="37" nowrap="">

Leverage width="79" nowrap=""> -0.223*** width="79" nowrap=""> 1 width="79" nowrap=""> width="90" nowrap=""> width="79" nowrap=""> width="37" nowrap="">

Size width="79" nowrap=""> 0.159*** width="79" nowrap=""> 0.310*** width="79" nowrap=""> 1 width="90" nowrap=""> width="79" nowrap=""> width="37" nowrap="">

Tangibility width="79" nowrap=""> 0.060*** width="79" nowrap=""> -0.129*** width="79" nowrap=""> -0.138*** width="90" nowrap=""> 1 width="79" nowrap=""> width="37" nowrap="">

Growth width="79" nowrap=""> 0.094*** width="79" nowrap=""> -0.291*** width="79" nowrap=""> -0.163*** width="90" nowrap=""> -0.041*** width="79" nowrap=""> 1 width="37" nowrap="">

SOE width="79" nowrap=""> 0.019 width="79" nowrap=""> -0.040*** width="79" nowrap=""> -0.099*** width="90" nowrap=""> -0.031*** width="79" nowrap=""> -0.0031 width="37" nowrap=""> 1

Upper Middle Income

ROA width="79" nowrap=""> 1 width="79" nowrap=""> width="79" nowrap=""> width="90" nowrap=""> width="79" nowrap=""> width="37" nowrap="">

Leverage width="79" nowrap=""> -0.306*** width="79" nowrap=""> 1 width="79" nowrap=""> width="90" nowrap=""> width="79" nowrap=""> width="37" nowrap="">

Size width="79" nowrap=""> 0.047*** width="79" nowrap=""> 0.384*** width="79" nowrap=""> 1 width="90" nowrap=""> width="79" nowrap=""> width="37" nowrap="">

Tangibility width="79" nowrap=""> 0.010* width="79" nowrap=""> 0.055*** width="79" nowrap=""> -0.066*** width="90" nowrap=""> 1 width="79" nowrap=""> width="37" nowrap="">

Growth width="79" nowrap=""> 0.073*** width="79" nowrap=""> -0.171*** width="79" nowrap=""> -0.237*** width="90" nowrap=""> -0.068*** width="79" nowrap=""> 1 width="37" nowrap="">

SOE width="79" nowrap=""> -0.012* width="79" nowrap=""> -0.122*** width="79" nowrap=""> -0.297*** width="90" nowrap=""> -0.044*** width="79" nowrap=""> 0.070*** width="37" nowrap=""> 1

Lower Middle Income

ROA width="79" nowrap=""> 1 width="79" nowrap=""> width="79" nowrap=""> width="90" nowrap=""> width="79" nowrap=""> width="37" nowrap="">

Leverage width="79" nowrap=""> -0.191*** width="79" nowrap=""> 1 width="79" nowrap=""> width="90" nowrap=""> width="79" nowrap=""> width="37" nowrap="">

Size width="79" nowrap=""> 0.17788* width="79" nowrap=""> 0.330*** width="79" nowrap=""> 1 width="90" nowrap=""> width="79" nowrap=""> width="37" nowrap="">

Tangibility width="79" nowrap=""> 0.029*** width="79" nowrap=""> 0.004 width="79" nowrap=""> -0.183*** width="90" nowrap=""> 1 width="79" nowrap=""> width="37" nowrap="">

Growth width="79" nowrap=""> 0.210*** width="79" nowrap=""> -0.173*** width="79" nowrap=""> -0.006 width="90" nowrap=""> -0.016* width="79" nowrap=""> 1 width="37" nowrap="">

SOE width="79" nowrap=""> -0.032*** width="79" nowrap=""> -0.026** width="79" nowrap=""> -0.213*** width="90" nowrap=""> 0.008 width="79" nowrap=""> -0.006 width="37" nowrap=""> 1

 

*, ** and *** show significance at 90, 95 and 99 percent levels, respectively. ROA is equal to the ratio of earnings before interest and taxes to total assets. Leverage is the book value of the firm’s total debts to total assets. Tangibility is the ratio of net tangible assets to total assets. The natural log of total assets is equal to Size.  Growth is measured as market capitalization to total assets. A state-owned enterprise is represented by SOE and is equal to one if a state owns 50.01% shares.

We use techniques of Ordinary Least Square (OLS) to examine the effect of ownership on firm performance. To examine the effect, we use profitability as our dependent variable in our models. Each column in table three refers to a different set of regression; the table shows that all models are statistically significant with high explanatory power. The value of R-squared shows the proportion of variation of the dependent variable explained by the variation in the independent variables. The base model in table three shows the impact of ownership and other explanatory variable on firm performance in selected G-20 countries. The regression result shows that the estimated coefficient of state dummy is negative and significant, indicating that state-owned enterprises are not better in accounting performance in G-20 countries. These findings are in support of property right theory and are in line with earlier findings (Dewenter and Malatesta, 2001; Sun et al., 2003; Vining and Boardman, 1992). The results of a firm-specific variable in the base model show that tangibility, size and growth have a positive and significant effect on firm performance. Whereas leverage has a negative and significant effect on firm leverage indicating that firm having a higher level of debt are more likely to have lower performance.

To investigate the effect of ownership on firm performance across the development spectrum, selected countries are further classified into High Income, Upper Middle Income and Lower Middle-Income countries. The classification of the countries according to their income level is based on the World Bank definition. The results are presented as model 1 to 3 in table 4. Result of model two and three shows that state-owned enterprises in Upper Middle Income and Lower Middle-Income countries have a significantly negative effect on firm performance. Whereas, the effect of state ownership on firm performance in High-Income countries is statistically insignificant. These results are in line with the earlier findings (Dewenter and Malatesta, 2001). These results indicate that countries which are less developed and have week institutional environments should encourage privatization of state-owned enterprises that have created in less performance and inefficiency. Addressing the property right theory and principal-agent problem associated with state-owned enterprises, it is argued that privatization of state-owned enterprise will improve both firm performance and efficiency. Estimated coefficients of tangibility, size and growth show a positive and significant effect on firm performance for High income, Upper Middle Income and Lower Middle-Income countries, while the coefficient of leverage is significantly negative in all models. These results are in line with earlier findings (Dewenter and Malatesta, 2001; Sun et al., 2003; Vining and Boardman, 1992).

Table 4. Impact of Ownership on Firm Performance in G-20 Countries and Across the Development Spectrum

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Variables width="106" nowrap=""> Base Model width="97" nowrap=""> Model-1 width="97" nowrap=""> Model-2 width="97" nowrap=""> Model-3

Leverage width="106" nowrap=""> -0.131*** width="97" nowrap=""> -0.144*** width="97" nowrap=""> -0.141*** width="97" nowrap=""> -0.116***

width="106" nowrap=""> (0.000) width="97" nowrap=""> (0.000) width="97" nowrap=""> (0.000) width="97" nowrap=""> (0.000)

Tangibility width="106" nowrap=""> 0.079*** width="97" nowrap=""> 0.094*** width="97" nowrap=""> 0.014* width="97" nowrap=""> 0.097***

width="106" nowrap=""> (0.000) width="97" nowrap=""> (0.000) width="97" nowrap=""> (0.062) width="97" nowrap=""> (0.000)

Size width="106" nowrap=""> 0.014*** width="97" nowrap=""> 0.015*** width="97" nowrap=""> 0.014*** width="97" nowrap=""> 0.014***

width="106" nowrap=""> (0.000) width="97" nowrap=""> (0.000) width="97" nowrap=""> (0.000) width="97" nowrap=""> (0.000)

Growth width="106" nowrap=""> 0.004*** width="97" nowrap=""> 0.003** width="97" nowrap=""> 0.002*** width="97" nowrap=""> 0.007***

width="106" nowrap=""> (0.000) width="97" nowrap=""> (0.018) width="97" nowrap=""> (0.000) width="97" nowrap=""> (0.000)

State width="106" nowrap=""> -0.009*** width="97" nowrap=""> -0.008 width="97" nowrap=""> -0.008** width="97" nowrap=""> -0.013**

width="106" nowrap=""> (0.000) width="97" nowrap=""> (0.412) width="97" nowrap=""> (0.001) width="97" nowrap=""> (0.022)

Fixed Effects width="106" nowrap=""> width="97" nowrap=""> width="97" nowrap=""> width="97" nowrap="">

Country width="106" nowrap=""> 105.740*** width="97" nowrap=""> 43.650*** width="97" nowrap=""> 42.040*** width="97" nowrap=""> 0.67

Industry width="106" nowrap=""> 12.420*** width="97" nowrap=""> 10.530*** width="97" nowrap=""> 8.260*** width="97" nowrap=""> 8.040***

Year width="106" nowrap=""> 55.620*** width="97" nowrap=""> 7.340*** width="97" nowrap=""> 64.130*** width="97" nowrap=""> 16.940***

Summary statistics width="97" nowrap="">   width="97" nowrap="">   width="97" nowrap="">

N. of observation width="106" nowrap=""> 33775 width="97" nowrap=""> 11445 width="97" nowrap=""> 10710 width="97" nowrap=""> 11620

F-Statistics width="106" nowrap=""> 83.570*** width="97" nowrap=""> 35.970*** width="97" nowrap=""> 47.440*** width="97" nowrap=""> 49.900***

R-Squared width="106" nowrap=""> 0.151 width="97" nowrap=""> 0.143 width="97" nowrap=""> 0.192 width="97" nowrap=""> 0.155

Adj R-Squared width="106" nowrap=""> 0.15 width="97" nowrap=""> 0.14 width="97" nowrap=""> 0.189 width="97" nowrap=""> 0.153

RMSE width="106" nowrap=""> 0.09 width="97" nowrap=""> 0.096 width="97" nowrap=""> 0.07 width="97" nowrap=""> 0.098

 

*, ** and *** show significance at 90, 95 and 99 percent levels, respectively. All standard errors are robust. ROA is equal to the ratio of earnings before interest and taxes to total assets. Leverage is the book value of the firm’s total debts to total assets. Tangibility is the ratio of net tangible assets to total assets. The natural log of total assets is equal to Size.  Growth is measured as market capitalization to total assets. SOE represents the state-owned enterprises according to the specification of each model.

Conclusion

This study investigates the effect of ownership on firm performance in selected G-20 countries and across the development spectrum. Due to historical and economic development differences among the countries, we categorized the selected countries into High Income, Upper Middle Income and Lower Middle-Income Countries according to World Bank definition. Annual financial and accounting data from the period 2011-2015 are compiled for 252, state-owned and 6503, non-state-owned enterprises. Ordinary Least Square are employed with country, year and industry fixed effect to estimate the effect of ownership on firm performance. Result of the study indicates that on average state ownership and firm performance are negatively associated in selected G-20countries. This is also confirmed when countries are considered with respect to their level of development except for High-Income countries. These results suggest that in less developed countries, state-owned enterprises are less profitable in comparison to non-state-owned enterprises. Poor performance and efficiency of state-owned enterprises in fewer development countries can be justified by the argument that state-owned enterprises suffer from the intervention of self-oriented politicians, which leads to lower than expected performance.

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