Governance and State-Owned Enterprises: How Costly is Corruption?
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 2 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 3 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 4 https://isni.org/isni/0000000404811396, International Monetary Fund

State-owned enterprises (SOEs) are present in key sectors of the economies around the world. While they can provide an important public service, there is widespread concern that their activities are negatively affected by corruption. However, there is limited cross-country analysis on the costs of corruption for SOEs. We present new evidence on how corruption affects the performance of SOEs using firm level data across a large number of countries. One striking result is that SOEs perform as well as private firms in core sectors when corruption is low. Taking advantage of a novel database reforms, we also show that SOE governance reforms can generate significant performance gains.

Abstract

State-owned enterprises (SOEs) are present in key sectors of the economies around the world. While they can provide an important public service, there is widespread concern that their activities are negatively affected by corruption. However, there is limited cross-country analysis on the costs of corruption for SOEs. We present new evidence on how corruption affects the performance of SOEs using firm level data across a large number of countries. One striking result is that SOEs perform as well as private firms in core sectors when corruption is low. Taking advantage of a novel database reforms, we also show that SOE governance reforms can generate significant performance gains.

I. Introduction

State-owned enterprises (SOEs)2 have a strong presence in the global economy and, in many advanced and developing economies, play a significant role in implementing public policy. SOEs are seen as a way to address market failures, such as natural monopolies, exert better control of natural resources, or promote other policy goals. In practice, public ownership continues to be important in many sectors, especially transportation, utilities (water, gas and electricity), and exploration of natural resources (oil and mining).

Concerns with poor governance, however, have fueled doubts about whether SOEs can achieve the desired goals or are the best option to address market failures. In particular, corruption, the abuse of public power for private gain, can negatively affect how firms operate. Firms may dedicate efforts and resources to rent-seeking activities, instead of focusing on using resources in the most efficient way.3 This may be particularly the case when these firms manage large natural resources and when there is weak transparency and scrutiny on the activities of these firms.

There are also reasons that could make corruption more prevalent in SOEs compared to private firms. It is easier for corrupt politicians to intervene in publicly-owned firms— especially when transparency and accountability are weak—and they have an incentive to do so, as they will benefit from the rents without bearing the cost (Boycko et al., 1996). However, empirical studies that attempt to assess the effect of weak governance on the performance of SOEs are limited, and most rely on specific country examples and do not always differentiate between private firms and SOEs (Fisman and Svensson, 2007, and Nguyen and Dijk, 2012, Kong et al., 2017, Richmond et al., 2019).4

In this paper, we provide new evidence that corruption impacts the financial performance of SOEs negatively for a large sample of firms across 88 countries. Our focus is on the sectors where SOEs are more prevalent, including utilities, natural resources, and transportation. These sectors are also where corruption tends to be more prevalent (OECD, 2018). We show that in more corrupt countries, the performance of SOEs, both in terms of profitability and productivity, is significantly worse relative to SOEs in other countries. The results are similar when we study the impact of fiscal transparency in the public sector. That is, more transparency allows for greater accountability and contributes to better performance by state-owned enterprises.

Our analysis also contributes to the literature on how ownership affects performance. The majority of the empirical literature tends to find that private firms perform better (Dewenter and Malatesta, 2001, Shirley and Walsh, 2000, and Grünfeld et al., 2005).5 However, there is also some evidence that this is not always the case (UNDP, 2015), including on the area of health and sectors that are highly regulated or with monopolies (e.g. electricity). Our results suggest that, in general, private firms have better performance in such sectors, but this depends on the level of corruption in the country. One striking result is that SOEs perform as well as private firms in core sectors (mining, electricity and gas, water, and transport) when corruption is low.

In addition to studying the impact of the degree of corruption in the country, we also analyze the impact of specific governance reforms on the performance of SOEs, taking advantage of a novel database based on IMF programs.6 Close to 90 percent of IMF programs included structural conditionality on SOEs in the period 2002-17. Taking advantage of data from these programs, we analyze the impact of specific governance reforms at the sector level on SOE performance for 31 countries. The results show that these reforms have affected the performance of non-financial SOEs positively, especially on productivity.

The paper is structured as follows. Section II outlines the main channels through which weak governance can affect SOE performance. It then discusses the prevalence of corruption and other governance weaknesses in SOEs. Section III develops the empirical methodology and describes the data. Section IV analyses how corruption affects the performance of SOEs and compares the impact with private firms. Section V uses information from IMF programs to study the direct impact of SOE governance reforms on SOE performance. Section VI concludes.

II. Corruption and Mismanagement in State Owned Enterprises

Corruption can affect economic growth by distorting the behavior of firms. Instead of focusing on being the most efficient, firms may put their efforts, including by paying bribes, to get privileged access to public contracts, public services or infrastructure (e.g. to obtain licenses), relaxing regulatory oversight, and avoid paying taxes (Svensson 2003, Fisman and Svensson 2007). The concerns with these questions have led to a literature on politically connected firms and showing that these are significant and more prevalent in countries where corruption is higher (for example. Faccio, 2006, and Fisman, 2001).7 However, there is limited discussion on assessing the impact of corruption on the performance of private versus state-owned enterprises.

Corruption is likely to have a deeper impact on how state-owned enterprises operate given the close relationship between the state (bureaucrats, politicians) and the company. State-owned enterprises are oftentimes created to help address market failures and achieve economic and social policies at reasonable costs. However, there are many examples where SOEs prove to be inefficient, a considerable burden to taxpayers, or fail to achieve its objectives. These problems are likely exacerbated in an environment of weak local or national governance and when there is undue political influence (Shleifer and Vishny, 1994, Transparency International, 2018). For example, corrupt politicians or civil servants can use political influence and favoritism to influence the choice of management and hiring policies. Lack of effective monitoring by the government and weak reporting by the SOE can also undermine accountability.

The corruption risks associated with SOEs are also heightened as many of them operate in sectors with large economic rents or have monopoly power. SOEs tend to operate in sectors such as electricity and gas, water, areas of transportation, as well as management of natural resources (e.g. oil exploration) that countries perceive as being of national importance or that private entities might be less willing to undertake. The potential large rents, especially in an environment of weak transparency and regulatory oversight, makes these companies particularly exposed to corrupt public officials.

Institutional weakness and corruption

Vulnerabilities to corruption are usually associated with institutional weaknesses (IMF 2019). In the case of SOEs, some of the key weaknesses include lack of independent and professional boards and management, weak procurement processes, and lack of transparency.

The lack of independent and professional SOE board members weakens the ability for oversight of the companies’ operations and management, facilitating bribe-taking and political or third-party influence over SOE resources. Inadequate scrutiny in the nomination process, oversight and unclear objectives make assessing managerial performance difficult. They also make it easier for government officials to interfere in company affairs for political gain. According to one OECD (2018) study of over 300 firms, nearly forty percent of SOEs in which corruption or irregular practices were observed involved a board member, public official, or shareholders.

Some of the main vulnerabilities to corruption arise from:

  • Conflict of interests. The appointment of board members by public officials might be driven by political motives, either financial or otherwise. For instance, the Petrobras scandal in Brazil (discussed below) was in part used as a financing vehicle for political activity. In the Philippines, cabinet secretaries are presidential appointees to SOE boards, and are expected to execute the orders of the President (OECD, 2015).

  • Weakness in developing effective internal controls and audits. Many SOEs have weak internal controls and processes, inadequate accounting and auditing methods, and weak compliance and disclosure practices. Not only does such an environment undermine financial and nonfinancial competitiveness, it is also conducive to corruption. Further, some SOEs, such as the national oil company of the Philippines, have regulatory power (OECD, 2015).

  • Lack of a culture of integrity and accountability. Board members are often in charge of integrity functions such as compliance, audit, and legal counsel. Nevertheless, the OECD (2018) reported that around 70 percent of bribes paid to public and non-public SOE officials were paid or authorized by management in all foreign bribery cases concluded between 1999 and 2014. Management might be less likely to report corrupt practices in order to maintain their positions, which helps to normalize such practices. In short, the tone at the top matters.

Procurement tends to be an area of high risk for corruption throughout the public sector, including SOEs. Public contracting is susceptible to political interference by government officials and employees. The sourcing, evaluation, awarding, and monitoring of contracts might be affected through bribes, kickbacks, patronage, bidding collusion by third parties, related-party trading, use of suppliers owned by public officials, or providing illegal insider information (Transparency International 2018).8

In some cases, SOEs have special procurement rules to grant more flexibility to make decisions, which can weaken controls. For instance, in Thailand, SOEs may accept or reject any or all bids and may modify the technical requirements during the bidding process if corruption is suspected. This policy grants leeway to the SOE while also denying bidders recourse to challenge procedures (US State Department, 2012). Similarly, when SOEs engage in projects such as public-private partnerships (PPPs), weaknesses in the PPP governance framework could lead to opaque handling of sometimes large and complex projects, increasing the risks for corruption.

Lack of transparency leads to lower accountability and greater opportunities for corruption. For example, the lack of audited information on SOE operations (in line with international standards) will prevent the uncovering of financial and strategic weaknesses. This in turn will make it harder to ensure that the SOE is working in accordance with the public’s best interest. Transparency regarding SOE financials is often weak, and many SOEs are not subject to reporting of ultimate ownership. A state’s auditing apparatus can similarly be compromised. For instance, reportedly the State Commission on Audit in the Philippines took bribes and colluded with board officials to conceal corruption (De Ocampo Bantug 2011). Similarly, a lack of transparency regarding financial assistance or other transactions between the SOE and the government—including guarantees, arrears, contractual commitments and liabilities arising from PPPs—could result in large hidden costs associated with corruption.9

Weaknesses in SOEs governance remain a widespread challenge

Many countries continue to struggle with corruption in SOEs. In an OECD survey, 42 percent of SOE respondents reported that corrupt acts or other irregular practices occurred in their company during the past three years (OECD 2018). Other examples include:

  • Andres et al. (2011) conclude through a study of forty-four water and electricity SOEs covering twenty-six countries in Latin America that those industries are particularly prone to performance losses through weaknesses in selection and composition of Board of Directors and inadequate performance-orientation.

  • Construction firms in Eastern Europe believe that “a typical payoff made for securing a government contract in their industry is around seven per cent of the contract value” (Kenny 2010). Further, Kenny (2010) finds that costs paid per square meter of highway rehabilitation is 53 percent higher for countries in which the reported bribe payment for government contracts was above 2 percent of the total budget, as compared to the cost per square meter of those countries with less than 2 percent of the total budget reported as bribe payments.

  • The 2012 anticorruption campaign announced in China created a “natural experiment” to assess the impact of corruption on SOEs. Lin et al. (2018) found that shares of Chinese SOEs noticeably rose relative to other listings in three- and five day windows around the announcement of the campaign. Kong et al. (2017) also found evidence that the anti-corruption campaign contributed to an improvement in SOEs financial indicators, return on equity and return on sales, since 2013.

In the extractive industry, a sector particularly prone to corruption due to large economic rents, most SOEs have relatively weak governance. Specifically, only nine of eighty-one SOEs assessed in the 2017 Resource Governance Index (RGI) achieved a good standard (scoring about 75 out of 100) on transparency and accountable governance (Figure 1). A well-publicized case has been the scandal surrounding the oil company—and largest company in Brazil—Petrobras, of which the Brazilian government owns a controlling interest. In 2014, public prosecutors and the Brazilian Federal Police began an investigation (“Car Wash”) that would reveal a major corruption scheme centered on Petrobras. It involved billions of dollars in kickbacks from large contracts paid by suppliers to executives of the oil company and politicians, a cartel of contractors that overcharged Petrobras, and Swiss bank accounts (Lima-de-Oliveira, 2019).

Figure 1.
Figure 1.

Distribution of Extractive SOE Scores on Resource Governance Index

Citation: IMF Working Papers 2019, 253; 10.5089/9781513519296.001.A001

Source: Resource Governance Index 2017 from the Natural Resource Governance Institute.

SOEs are also heavily involved in cross-border corruption. In fact, SOE officials are the main beneficiaries of foreign bribes. The 2014 Foreign Bribery Report from the OECD indicated that of the observed instances of bribery promised, offered, or given, 81 percent involved SOE officials.10 In addition, in some cases, SOEs are the ones bribing foreign officials. For example, Telia, a Sweden-based telecommunication company owned in part by the Swedish and Finnish governments, obtained contracts in Uzbekistan that generated over US$2.5 billion through bribery from at least 2007 to 2010. Prosecuting the case involved complex international litigation. Ultimately the case was tried in a New York court and a settlement under the FCPA led to the fine of US$1 billion.11

The importance of SOEs, and related governance weaknesses, is also reflected in IMF programs—almost 90 percent of all programs between 2002 and 2017 included conditionality on SOEs.12 Specifically, structural benchmarks (SBs) related to SOEs were set in 206 out of 240 programs, covering 91 out of 97 program countries. For example, in Ukraine from 2008 to 2017, the IMF set more than thirty structural benchmarks addressing SOEs governance in multiple sectors, notably gas and other utilities. These included audits of specific SOEs as well as the passing of legislature concerning regulation and transparency of SOE management. Similarly, 11 SOE benchmarks were set in the 2011-14 IMF program in Portugal alone, of which 10 were on non-financial SOEs and 5 targeted specific governance reforms.

III. Empirical strategy and Data

A. Data

In this paper we study whether corruption affects the performance of SOEs and if the impact of corruption on private firms differs from that on SOE. Our main source of information comes from firm-level income statements and balance sheets from the ORBIS database, which we complement with additional information for some countries. SOEs in ORBIS are identified through ownership as “organizations ultimately owned or de facto controlled by public sector entities”. Our analysis focuses on a few key performance indicators: profitability (return on equity and operating profit per sales), productivity and efficiency (sales per worker and labor costs).

The ORBIS database, while very rich, also requires treatment to correct for some data issues or adjust for the objective of this study. In particular,

  • Our focus is on domestically owned SOEs. As such, we drop firms whose Global Ultimate Owner (GUO) and Immediate Shareholders (ISH) have a different country of origin with respect to the location of the firms. In most of the analysis, we focus on four sectors that have a high incidence of SOE presence: mining (including oil) and quarrying, electricity and gas, water and sewerage, and transport.13

  • We follow the cleaning procedure suggested by Kalemli-Ozcan et al. (2015). We drop (SOEs or private firms): duplicates in terms of identifier and year; observations with missing years; company-years with missing information on total assets, operating revenue, sales and employment (simultaneously). We also exclude a company (all years) if total assets are negative in any year; employment (in persons) is negative in any year; and if labor cost is negative or missing. Finally, we retain only firms whose status is “Active”.

  • We do additional adjustments to address outliers. While the majority of ROE observations lies within plus and minus 20 percent, we find a significant amount of observations with very high values (positive and negative), which might either be indicative of misreporting, or of SOE equity close to zero. We therefore exclude companies if the ROE is above/below 50 percent.14 We also exclude firms that have zero sales and sales above US$1.5 million per employee,15 and/or zero labor costs per operating revenue.

The analysis includes 88 countries (Table.1) with SOEs data between 2000 and 2016 and 94 countries with private firm data between 2007 and 2016.16

To assess the degree of corruption in a country, we use the control of corruption (CC) indicator from the Worldwide Governance Index (WGI). The CC is mainly based on surveys of perception of corruption (see Kaufmann et al. 2007 and 2010), and available since 1996.17 In addition to corruption, we also test whether the degree of fiscal transparency has an impact on the performance of firms using an index based on IMF (2019). See also Appendix 1.

A second objective of this paper is to analyze if and how governance reforms improve SOE performance, given that corruption itself is difficult to target directly. For that, we built a novel dataset on SOE governance reforms based on IMF programs from 2002. These programs include conditionality, structural benchmarks (SBs), on targeted reforms of SOEs. It allows us to identify different types of SOE conditionality, split by sector, type of reform, and whether the reforms have been implemented or not. For example, we can study the impact of being in IMF programs with any SOE conditionality, the impact of specific SOE reforms. The dataset is described in more detail in Section V.

For the analysis of the reforms, we include additional SOEs from two sectors that were targeted by IMF structural benchmarks for robustness - agriculture and construction. We continue to use the above baseline cleaning procedure for SOE data in ORBIS.

Table 2 presents descriptive statistics for SOE and POE performance series in the 4 main sectors after cleaning the data. The distribution does not change significantly if agriculture and construction are included. The number of SOEs and POEs across sectors is shown in Table 3.

Table 2

Descriptive statistics for SOEs and POEs performance criteria

article image
Notes: Sectors included are electricity and gas, mining, water and sewage, and transport. The time dimension ranges from 2000 to 2016 for SOEs and 2007 to 2016 for POEs.
Table 3

Number of firms by sector

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Notes: Number of firms after outlier cleaning. The time dimension ranges from 2000 to 2016 for SOEs and 2007 to 2016 for POEs.

B. Empirical specification

Measuring the impact of corruption on firms’ performance

Our main objective is to estimate the impact of corruption on the performance of firms using panel data. In particular, we investigate the following relationship:

PERi,t=α0+α3Gk,t+α4Xi,t+α5Yk,t+μi+μt+εi,t(1)

where PERit represents a specific performance indicator of firm i at time t. Four variables are used to assess the financial performance of firms: returns on equity (ROE),18 operating profit per sales, cost of employees normalized by operating revenue, and sales per employee. ROE and operating allow to gauge the profitability of a firm, while the cost of employees and sales per employee provide an indication of productivity or efficiency.19

Gkt is a measure of corruption in country k. Xit represents a set of time-varying firm-level characteristics. The regressors in Xit include total assets, sales and other firm-level characteristics suggested in the literature, including liquidity (Guariglia et al., 2011) and leverage (Baker, 1973 and Pattitoni et al., 2014), proxied as the ratio of non-current liabilities to total assets. Ykt represents some non-firm level controls such as real GDP growth, GDP per capita (PPP), natural resource endowment and quality of the business environment. We control for GDP per capita as the performance of firm may be correlated with the level of development. We control for natural resource endowment—proxied as the share of oil exports as a share of total exports—because it can affect both the performance of firms and corruption. Indeed, the literature (see e.g. Brollo et al., 2013) suggests that windfalls associated with natural resources may exacerbate corruption, while at the same time raising the profitability of firms, particularly in the extractive sectors. Finally, we also control for the quality of the business environment—proxied by the ease of starting a business20 from the doing business indicators of the World Bank—because it can also affect the performance of firms. μi and μt are firms and time fixed effects, respectively.

However, because our measure of corruption is highly persistent (time invariant), we cannot use fixed effects regressions to estimate the impact, since the FE transformation eliminates all time-invariant regressors.21 To tackle this issue we follow a two-step estimation, proposed by Hsiao (2003).22 In the first step, equation (1) is estimated by using the within estimator (fixed effects) and including only time-varying regressors. Given heterogeneity and potential autocorrelation issues, the standard errors are clustered at the country level. We also include unit-specific effects that can be used to identify the effects of characteristics that are time invariant.

In the second step, the estimated unit effects of the first step are regressed on a constant and slowly-moving variables (equation 2), with a between regression estimator.23 The combination of a between estimator and an unbalanced panel implies that the requirement of a constant variance of model errors might not hold. Therefore, weighted least squares are used to correct for heteroskedasticity.24

μi=β0+β1GDPper captia +β2Governance variable +β3Bussiness environment+ξi(2)

To analyze the different impact of corruption on SOEs and private firms (POEs), we expand equation (1) by building on a similar approach as Dewenter and Malatesta (2001), who focus on the effect of ownership on firm performance:

PERi,t=α0+α1Ownershipi+α2OwnershipiGk+α3Gk,t+α4Xi,t+α5Yk,t+μi+μt+εi,t(3)

Ownershipi is a dummy variable taking the value of 1 if the firm is privately owned and 0 if it is a SOE. If ownership is included, we also add an interaction term between ownership and corruption. As explained below, we will use a two steps approach to estimate the impact of corruption.

Measuring the impact of governance reforms

We also study the impact of governance reforms in SOEs. This is an important complement to the analysis of corruption, as it allows us to study the impact of SOE-specific governance reforms on their performance—while the previous section looked at an indicator of corruption of the public sector as a whole.25 The challenge is that we cannot precisely identify the timing of the reforms, as the conditionality is met at some point during the IMF program. In addition, firms and governments may begin to work towards reforms during the program negotiation and reform preparation stage. These constraints, together with data limitations, make a specific year-on-year impact analysis difficult. The alternative is to study the average impact of governance reforms on performance over the sample period.

We follow a similar two-steps approach as for the analysis of the impact of corruption. In the first step, we regress the change in performance (first difference) on changes in time-varying factors that drive changes in the performance of firms (as in equation 1).26 We also include a unit-specific effect. This will allow us to capture the average change in performance that reflects changes in performance due to governance reforms (that would reduce corruption).

In the second step, we regress the unit effects on the adoption of reforms using the between estimator. The hypothesis we want to test is if in the sample period, firms that have on average more governance reforms have a higher unit effect. In particular, unit effects are likely smaller for cases without governance reforms than in countries with reforms, especially as the quality of institutions and governance is likely to be slow moving. The between estimator answers the question “what is the expected difference in performance improvements between SOEs X and Y if they differ in the number of reforms (structural benchmarks) by 1 during period t?” Or: did SOEs in countries with more governance reforms have a stronger improvement? The between estimation is done over the entire history of the SOE. The estimation can thus be interpreted as the effect of governance reforms on the improvement in SOE performance over time.

IV. The Impact of Corruption on SOEs Performance

To compare the impact of corruption on SOE, and how it differs relative to private firms, we focus on four strategic sectors—mining (including oil) and quarrying, electricity and gas, transport, and water and sewerage.27 They are the sectors where state ownership is highly prevalent in many economies (OECD, 2017) and where there are overlaps between the public and the private firms allowing a comparison between the two.

A. Stylized facts

We start with a simple descriptive analysis of the relationship between corruption and the financial performance of firms. Countries are divided in three groups across the control of corruption indicator: weak, medium and strong. Figure 2. shows the financial performance of POEs and SOEs for the four sectors relative to control of corruption. The evidence suggests that private firms are, on average, more profitable than SOEs. This is not surprising as SOEs may have policy goals beyond profitability. However, it may also reflect other factors, including a differentiated impact of corruption. Indeed, the data suggests that profitability tends to be higher in countries with higher control of corruption and the impact appears more pronounced for SOEs.

Figure 2.
Figure 2.

Corruption and Profitability

Citation: IMF Working Papers 2019, 253; 10.5089/9781513519296.001.A001

Sources: IMF staff calculations using ORBIS and Worldwide Governance Indicators (WGI).Note: The figure shows performance indicators for state-owned enterprises and the private-owned enterprises in the electricity and gas, mining, transport, and water and sewerage. The boxes show the median and the 25th and 75th percentiles. Countries are divided into high, medium, and low corruption, based on the Control of Corruption Index. The data ranges from 2007 to 2016.

The level of productivity of firms is also positively correlated with the degree of control of corruption (Figure 3). An interesting finding is that while private firms tend to be more productive that SOEs, this is not the case when control of corruption is high. This suggests that corruption may be a key driver in explaining differences in productivity. The correlation of the degree of corruption and labor costs is especially strong for SOEs. As Figure 4 shows, there is evidence that countries with greater control of corruption have lower labor costs. However, the differences are less pronounced for private firms. As for productivity measures, labor costs are usually higher among for SOEs, except when corruption is low.

Figure 3.
Figure 3.

Corruption and Labor Productivity

Citation: IMF Working Papers 2019, 253; 10.5089/9781513519296.001.A001

Figure 4.
Figure 4.

Corruption and Labor Costs

Citation: IMF Working Papers 2019, 253; 10.5089/9781513519296.001.A001

Sources: IMF staff calculations using ORBIS and WGI.Note: The figure shows performance indicators for state-owned enterprises and the private-owned enterprises in the electricity and gas, mining, transport, water and sewerage sectors. The boxes show the median and the 25th and 75th percentiles. Countries are divided into high, medium, and low corruption, based on the Control of Corruption Index. The data range from 2007 to 2016.

B. Results

We now turn to a more formal analysis of the relationship between corruption and SOE performance based on the methodology discussed in section III.B. Table 4. shows the results for the different measures of performance. The first step shows the estimated impact for traditional determinants of firm performance that we use as controls. These include firm-level indicators (for example, sales, size of assets) and economy-wide indicators (e.g. economic growth and terms of trade). The second step shows the results that concern us in this paper, the link between corruption and performance (after controlling for other factors that may influence performance). The estimated coefficient for the degree of control of corruption (CC) always has the expected signal and is statistically significant. That is, the higher the CC (lower corruption), the higher are profits (returns on equity or operating profits as share of sales), the higher is labor productivity and the lower are labor costs.

Table 4

Corruption and SOEs’ performance

article image
Note: Standard-errors in parentheses. The regression in the first step includes firms and year dummies. The estimations of these effects are not reported. Residual in the first step at clustered at the country level. The second step coefficients and standard errors are estimated on fixed-effect-averages. Weighted least squares are used in the second step to correct for heteroskedasticity.
***

p< 0.01,

**

p<0.05,

*

p<0.1.

The time dimension ranges from 2000 to 2016.

How large is the impact of reducing corruption? Table 5 provides a simple simulation to illustrate the quantitative of the impact by using the coefficients estimated. If the median country in the weakest governance group improves its control of corruption to the level of the median country in the group with medium corruption, it could, ceteris paribus, improve the average profitability from 0.4 percent to 1.6 percent and decrease their average cost of employee per operating revenue from 37.6 percent to 32.7 percent. Further, productivity would grow by about 10 times. These results show that corruption can have large effects.

Table 5

Performance gains by improving governance

article image
Sources: IMF staff estimates using ORBIS and WGI.

We now turn to a comparison with private firms. Columns 1-4 in Table 6 shows the results for two alternative measures of profitability (ROE and operating profits). The control variables in the first step regressions are in columns 1 and 3. The effect of the size of total assets on performance tends to be positive and statistically significant in both specifications.28 The coefficient of leverage is negative and significant. The coefficients associated to sales are both positive and strongly significant.

The estimated effect of the quality of business environment—proxied by the ease of starting a business—are positive and significant for profitability (ROE and operating profits) and productivity while the coefficient associated to labor costs per operating revenue is negative and significant (Table 6).

Table 6

Corruption and Firms performance - SOEs and POEs

article image
Note: Standard-errors in parentheses. The regression in the first step includes firms and year dummies. The estimations of these effects are not reported. Residual in the first step at clustered at the country level. The second step coefficients and standard errors are estimated on fixed-effect-averages. Weighted least squares are used in the second step to correct for heteroskedasticity.The sample ranges from 2007 to 2016.
***

p< 0.01,

**

p<0.05,

*

p<0.1.

How does the impact of corruption differ between private firms and SOEs? First, the results suggest that private firms have better performance on average (dummy on ownership, Table 6.) broadly in line with the literature (e.g. Dewenter and Malatesta, 2001). Second, while the control of corruption has the expected effect on performance (as in Table 4.), it is not statistically significant for ROE, only for operating profits. The interaction term between the type of ownership and corruption helps assess if there is a difference in the impact on private firms. The results suggest that corruption may have a larger impact on the profitability of SOEs (interaction term is negative, meaning the control of corruption has less of an impact on private firms). As such, there is only partial evidence that the difference in profitability between private firms and SOEs depends on the severity of corruption.

The evidence in favor of a differentiated impact is stronger for productivity. Columns 5-6 in Table 6 show the effect of control of corruption on productivity. The direct effect of better control of corruption is positive and strongly significant for both private firms and SOEs, while ownership (column 6) shows a similar impact on profitability—private firms perform better than SOEs. For productivity, however, the interaction term is negative and strongly significant. This means that the difference in productivity between POEs and SOEs narrows as the control of corruption improves (or corruption declines). Columns 7-8 display the effect of control of corruption on total labor cost per operating revenue. The results are similar to productivity.

The results suggest that the difference in productivity between SOEs and private firms is largely driven by corruption. SOEs operating in countries with low levels of corruption (high CC), on average, have similar levels of productivity and labor costs. This can be seen by calculating the estimated level of productivity (or labor cost) for SOEs and POEs when control of corruption improves (Figure 5). For example, when the control of corruption is high, SOE performance tends to be even higher than in private firms.29

Figure 5.
Figure 5.

Corruption and Productivity

Citation: IMF Working Papers 2019, 253; 10.5089/9781513519296.001.A001

Sources: IMF staff calculations using ORBIS and WGI.Note: The figure shows the performance of SOEs and POEs when control of corruption improves. The graph is based on a between effect estimation in which the means (across units) of the time-dependent variables are added as control variables. The data range from 2007 to 2016.

Transparency and sectoral impacts

So far, we have focused on the control of corruption indicator as a proxy for a country’s governance. However, governance includes multiple dimensions, such as transparency, quality of institutions, and rule of law. We now add a second indicator: fiscal transparency. Fiscal transparency is about providing a comprehensive, relevant, timely, and reliable overview of the government’s financial position and performance, thereby limiting the options for illicit activities between the government and SOEs and increasing public scrutiny of SOE performance. We use a fiscal transparency index developed in the recent Fiscal Monitor (IMF 2019). Table 7 shows the results when the fiscal transparency index (FTI) is used instead of control of corruption. The main conclusions remain valid: weak fiscal transparency is associated with lower performance of firms, particularly when the firm is a SOE.

Table 7

Fiscal Transparency and firms’ performance - SOEs and POEs

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Note: Standard-errors in parentheses. The regression in the first step includes firms and year dummies. The estimations of these effects are not reported. Residual in the first step at clustered at the country level. The second step coefficients and standard errors are estimated on fixed-effect-averages. Weighted least squares are used in the second step to correct for heteroskedasticity.
***

p< 0.01,

**

p<0.05,

*

p<0.1.

The sample ranges from 2007 to 2016.

A second robustness test is done to reduce potential sample bias. If firms in the private sector primarily belong to countries with stronger governance than those in the public sector, we would bias the results towards the conclusion that weak governance hinders SOEs more than POEs. Therefore, we restrict countries in the sample of POEs to be the same as those in the sample of SOEs. The results are very close to those of the baseline regressions both qualitatively and quantitatively.30

We also analyze whether some sectors are more prone to corruption than others (Table 8). For this, the control of corruption is interacted with sector dummies in the second step. The results show that the impact of corruption is robust across all sectors for productivity and is especially large for electricity and mining. Electricity and water are the sectors where the relationship between corruption and performance is robust across all indicators of performance. The mining sector is where the evidence is weaker, except for productivity.

Table 8

Sectoral Split - Interaction between Control of Corruption and Sectors

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Note: Standard-errors in parentheses. The regression in the first step includes firms and year dummies. Residual in the first step at clustered at the country level. The estimation of the first-step is not shown. The second step coefficients and standard errors are estimated on fixed-effect-averages. Weighted least squares are used in the second step to correct for heteroskedasticity.
***

p< 0.01,

*

p<0.1.

** p<0.05,Sample ranges from 2000 to 2016.

V. The Impact of Governance Reforms

We now turn to the question of whether governance reforms in SOEs, or improvements in government oversight, help improve performance. As mentioned, we construct a new dataset on SOE governance reforms based on IMF programs. These programs include structural benchmarks on reforms of SOEs—that is, IMF support is conditional on implementing the reforms. The data can be split into different categories, for example a dummy variable for IMF programs that had any SOE conditionality, or series that count the numbers of reforms, for example on specific sectors, or on specific types of reforms (governance reforms versus others).

A. Data on IMF conditionality

IMF programs (2002-17) included 1015 structural benchmarks (including prior actions) for financial and non-financial SOEs, found in 206 out of 240 programs during this time. This is consistent with 4.3 SOE-related structural benchmarks per IMF program on average. 753 of those benchmarks covered non-financial SOEs and, of those, 428 were related to SOE governance.31 In total, 50 percent of structural benchmarks were found in low-income countries, 45 percent in emerging markets, and 5 percent in advanced economies, which broadly mirrors the distribution of IMF programs across these groups. Therefore, SOE program conditionality is as likely in advanced economies as it is in developing economies, once a program is in place.

The majority of structural benchmarks was set for public utilities and financial SOEs (Figure 6). Electricity, oil and gas, and water SOEs, as well as general utilities (which usually include a combination of oil, gas and electricity SOEs) amount to 417 structural benchmarks, led by those on electricity SOEs. “Structural” SBs cover general governance, fiscal relations between the SOE sector and other entities, and are not sector-specific.32 They are the third most common SB type, indicating frequent governance and risk assessment weaknesses across the SOE sector. The distribution of SBs across sectors has changed little since 2002, with a noticeable decline only for SBs set for financial SOEs.

Figure 6.
Figure 6.

Structural Benchmarks by Sector

Citation: IMF Working Papers 2019, 253; 10.5089/9781513519296.001.A001

Notes: “Structural” includes all benchmarks that concern changes to SOE governance and other reforms that were not sector specific. “Utilities” includes SB combinations of electricity, oil/gas, and water, either when one SOE is responsible for water and electricity, electricity and oil/gas generation simultaneously, or two or more sectors were covered at once. “Multiple sectors” includes a combination of sectors, except for those exclusively related to utilities. “Other” covers SOEs operating in the industries of coffee, insurance, mail services, rail, agriculture, lumber, tourism, steel, fishing, cement, health, chemicals, and construction.a\ 2017 is excluded given not all 2017 SBs were reported at the time this document was produced.

As shown in Figure 7, more than half of all structural benchmarks on non-financial SOEs were related to SOE governance. Governance SBs include monitoring and transparency, particularly auditing, management, arrears clearance, relations between the state and the SOE, structural reforms to a sector as a whole (if they are governance-related), and various other governance elements.33 These reforms are used here as a proxy for SOE governance reforms. The other two broad categories are pricing (such as electricity tariff increases) and privatization.

Figure 7.
Figure 7.

Type of SOE Reform by Sector

(number of observations, 2002-2017)

Citation: IMF Working Papers 2019, 253; 10.5089/9781513519296.001.A001

Source: IMF data and authors’ estimates. “Governance” SBs include monitoring, particularly auditing, management, arrears clearance, relations between the state and the SOE, structural reforms to a sector as a whole (if they are governance-related), and various other governance elements. As for sectoral classification, “structural” includes all benchmarks that concern changes to SOE governance and other reforms that were not sector specific; “other” covers SOEs operating in the industries of coffee, insurance, mail services, rail, agriculture, lumber, tourism, steel, fishing, cement, health, chemicals, and construction.

The dataset also includes the outcomes of planned reforms. Specifically, which structural benchmarks were met or not met. The success of structural benchmarks was mixed, with about half fully met without delay (54 percent), including prior actions (Figure 8). Many structural benchmarks have been set as prior actions (218 total), either before programs were approved, or during programs to trigger the next disbursement or as a re-enforcement of SBs that were delayed. Prior actions are set most frequently on the oil and gas sector, for utilities and electricity SOEs (Figure 9). Most frequently, these prior actions are related to tariff and price setting, which can be implemented most rapidly. The share of SBs that were not met are largest in the mining, telecom and financial sectors. However, more than 20 percent of structural benchmarks were not met in all major sectors, which increases to more than 30 (and in the majority of sectors to more than 40) percent if partially met SBs, and those met with delays are included in the ‘not met’ category.

Figure 8.
Figure 8.

Implementation of Structural Benchmarks

(number of observations, 2002-17)

Citation: IMF Working Papers 2019, 253; 10.5089/9781513519296.001.A001

Notes: PM - Partially met; MD - Met with delay. Structural benchmarks without a final implementation outcome have been excluded. 2 prior actions were not met, and the program was approved. “Structural” includes all benchmarks that concern changes to SOE governance and other reforms that were not sector specific. “Other” covers SOEs operating in the industries of coffee, insurance, mail services, rail, agriculture, lumber, tourism, steel, fishing, cement, health, chemicals, and construction.
Figure 9.
Figure 9.

Implementation by Sector

(number of observations, 2002-17)

Citation: IMF Working Papers 2019, 253; 10.5089/9781513519296.001.A001

In order to study the impact of governance reforms in detail we construct four measures to capture the type of conditionality and whether it was met (the reform was fully implemented):

  • The first is a dummy for IMF programs with any SOE conditionality (zero for no conditionality, 1 for any conditionality related to SOEs), including also financial SOEs, and beyond-governance related structural benchmarks. This allows us to capture the impact when a country has a plan to reform SOEs in general. The hypothesis is that the measures taken may go beyond the specific conditionality, which only captures part of the program efforts. It also allows for potential spillover effects from reforms in the financial sector to non-financial SOEs.

  • The other three indicators refer to the number of structural benchmarks during a program. The first includes non-financial governance SBs only, and the second and third count those governance SBs that were met or not-met, respectively.34 Given timing issues of when benchmarks are first set until when they are met (which could cover years), the number of structural benchmarks is split evenly across the years of a respective IMF program (which usually last between one and three years). For example, if there were 12 SBs total in a three-year program, each year lists 4 benchmarks.

Based the SOE data and coverage across sectors, we find IMF programs with any SOE conditionality in 31 countries, and non-financial SOE governance conditionality in 26 countries. Table 9 presents the numbers of firm observations that had overlaps with IMF program conditionality across the four specifications. Given that most firm-years overlapping with IMF program conditionality are found in Ukraine, Serbia, and Romania, the analysis is repeated without them for robustness.

Table 9

Overlaps of firm years with IMF program conditionality

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Notes: Observation period 2002-2016. Counted are numbers of overlaps between IMF programs with any SOE conditionality or with governance conditionality and firms across all years. The table does not count the number of benchmarks during programs. Benchmarks that were met with delay are classified as “met”. Partially met benchmarks are classified as “not met”. Financial benchmarks are included in IMF Program observations but excluded from non-financial governance SBs.

B. Results

The baseline results show that SOE reforms improve firms’ financial performance significantly (Table 10). All our indicators of reforms—being in an IMF program with any type of SOEs conditionality or having conditionality on SOE governance reforms—have a strong impact across the four measures of SOE performance. For example, each IMF program-year with SOE-related conditionality increases the rate of growth in ROE by more than 1.1 percentage points on average. One needs to be careful in interpreting this impact as it may reflect other factors of being in a program and other reforms that are not governance related (e.g. changes in electricity tariffs may be higher during program years). Nevertheless, when looking only at governance-related conditionality, there still is a robust impact. Each governance reform (additional structural benchmark) specifically increases ROE by about 0.5 percent, a sizeable improvement given the average annual change in ROE in our sample is about zero (see Table 2). Similarly, IMF programs with any SOE conditionality (a proxy for countries having reform plans) improve the operating profit margin by 1.4 percentage points, lead to larger reduction in costs (about 5 percent), and increase productivity growth by about USD 30,000 per employee on average. Results for each individual SOE governance reform are similar, albeit (expectedly) smaller.

Table 10

The Impact of IMF programs and SOE conditionality on SOE performance

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Notes: Observation period 2002-2016. Clustered at the country level and time dummies are included in first stage regressions. Sector fixed effects included in second stage regression. Robust standard errors in parentheses.
***

p<0.01,

**

p<0.05,

*

p<0.1.

Benchmarks that were met with delay are classified as “met”. Partially met benchmarks are classified as “not met”. Financial benchmarks are excluded from non-financial governance SBs.