Women’s Empowerment and Corporate Political Corruption in the MENA Region: Moderating Factors and Policy Implications – Dr. Mohammad Khalil Jadallah
Women’s Empowerment and Corporate Political Corruption in the MENA Region:
Moderating Factors and Policy Implications
Dr. Mohammad Khalil Jadallah
Ph.D. in Administrative Sciences, specializing in Business Administration University of Tunis
هذا البحث منشور في مجلة القانون والأعمال الدولية الإصدار رقم 60 الخاص بشهر أكتوبر/ نونبر 2025
رابط تسجيل الاصدار في DOI
https://doi.org/10.63585/EJTM3163
للنشر و الاستعلام
mforki22@gmail.com
الواتساب 00212687407665
Women’s Empowerment and Corporate Political Corruption in the MENA Region:
Moderating Factors and Policy Implications
Dr. Mohammad Khalil Jadallah
Ph.D. in Administrative Sciences, specializing in Business Administration University of Tunis
Abstract
This study investigates how women’s empowerment impacts political corruption in the SMEs of the MENA region, specifically in Morocco, Tunisia, Egypt, Lebanon, and Jordan. The study uses data from the 6th wave of the BEEPS survey and covers the period from 2018-2020. It considers female ownership percentage and female top managers as measures of women’s empowerment, and bribe payment impact to government officials at different scales as measures of political corruption. The study also examines how political instability, corruption perception level, and financial obstacles moderate the relationship between women’s empowerment and political corruption. Findings highlight the positive effect of female ownership on political corruption reduction, while female CEOs could increase political corruption except in Tunisia. The study highlights what are the factors beyond the studied link at national and regional levels and suggests that reducing barriers to women’s participation in the formal economy can help to reduce bribery and corruption, and policies that promote gender equality, such as legal reforms and increased access to finance, can be effective in achieving this goal.
Keywords: bribe payments, female ownership, gender diversity, female CEO, political stability, corruption, financial obstacles.
تمكين المرأة والفساد السياسي المؤسسي في منطقة الشرق الأوسط وشمال إفريقيا: العوامل المعدّلة وانعكاساتها على السياسات العامة
الدكتور : محمد خليل جاد الله
دكتوراه في العلوم الإدارية تخصص إدارة الاعمال جامعة تونس
الملخص
تبحث هذه الدراسة في أثر تمكين المرأة على الفساد السياسي في مؤسسات الأعمال الصغيرة والمتوسطة في منطقة الشرق الأوسط وشمال إفريقيا، مع تركيز خاص على كلٍّ من المغرب، وتونس، ومصر، ولبنان، والأردن. وقد اعتمدت الدراسة على بيانات الموجة السادسة من مسح (BEEPS)، التي تغطي الفترة من 2018 إلى 2020. وتم قياس تمكين المرأة من خلال مؤشري نسبة الملكية النسائية ونسبة تولي النساء المناصب الإدارية العليا، فيما تم قياس الفساد السياسي من خلال أثر دفع الرشوة للمسؤولين الحكوميين بمستويات مختلفة. كما تناولت الدراسة أثر عدم الاستقرار السياسي، ومستوى إدراك الفساد، والمعوقات المالية كعوامل معدّلة للعلاقة بين تمكين المرأة والفساد السياسي.
أظهرت النتائج أن لملكية المرأة تأثيرًا إيجابيًا في الحد من الفساد السياسي، في حين أن وجود مديرات تنفيذيات من النساء قد يسهم في زيادة الفساد السياسي، باستثناء الحالة التونسية. كما أبرزت الدراسة العوامل الأخرى الكامنة وراء هذه العلاقة على المستويين الوطني والإقليمي، واقترحت أن خفض الحواجز أمام مشاركة المرأة في الاقتصاد الرسمي يمكن أن يسهم في الحد من دفع الرشوة والفساد، وأن السياسات الداعمة للمساواة بين الجنسين، مثل الإصلاحات القانونية وزيادة فرص الحصول على التمويل، قد تكون فعّالة في تحقيق هذا الهدف.
الكلمات المفتاحية: مدفوعات الرشوة، الملكية النسائية، التنوع بين الجنسين، المديرة التنفيذية، الاستقرار السياسي، الفساد، المعوقات المالية.
The political stability and corruption level in a country has a significant impact on its economic situation. A stable political environment attracts investors, which have more confidence in the economy, and therefore increases investments, job creation, and economic growth. However, in case of political instability, such as conflict or unrest, businesses are less likely to invest in the country, leading to decreased economic activity and reduced growth (Godefroidtet al., 2017). Hlioui et al. (2021) argued the importance of trust to ensure economic stability. They indicated that business trust is among the economic stability pillars. Yet, corruption erodes this trust. Along the same line, the world economic forum [1](2017) has conducted cross-industry collaborations such as the Partnering Against Corruption Initiative (PACI) during the last decade to rebuild trust between the different economic agents.
Corruption negatively impacts a country’s economy by distorting the allocation of resources, creating barriers to investment, increasing business costs through bribery, harming ethical businesses, and weakening economic competitiveness. Pervasive corruption undermines the rule of law, erodes trust in government, and diverts funds from essential public investments. On the other hand, political stability fosters an environment conducive to economic growth. Therefore, addressing both political instability and corruption is essential for sustainable economic stability and prosperity (Hossain and Kryzanowski, 2021).
Political corruption in the Middle East and North Africa (MENA) region remains a significant challenge despite ongoing efforts since the Arab Spring in 2011 to combat it. Many MENA countries, such as the UAE, Qatar, and Oman, have implemented anti-corruption legislation and established agencies aimed at addressing this issue. The UAE, for instance, has proposed a federal anti-corruption law, and Qatar has created an anti-corruption agency to oversee state institutions and investigate public fund misuse. Similarly, Oman has prosecuted government officials involved in bribery cases (Feghali, 2014; Reuters, 2014[2]). However, corruption persists as a deeply rooted problem, with the EY survey[3] (2018) revealing that corruption risks in the MENA region remain high-62% compared to the OECD average. This widespread corruption, manifesting in forms like nepotism, embezzlement, and bribery, threatens regional stability and undermines trust in political and economic institutions. As a result, it continues to be a barrier to sustainable development across the region.
Given that an individual’s behaviors, institutions included, are determined not only by their attitudes but also by the surrounding’s norms and traditions, which is supported by the behavioral control theory. Firms in corrupt environments might be under pressure and must adapt and learn how to operate within the framework standards (Brada et al., 2019). Fleming et al. (2022) emphasized the role of the social trapping process in normalizing corrupt company conduct. They stated that there is a certain level of corporate corruption required to operate in a corrupt atmosphere.
Alongside the political corruption issue in the MENA region, gender inequality remains one of its most significant challenges. As noted by Danon and Collins (2020), “Women in MENA fare worse than men and worse than women in most other regions in several measurable areas.” The MENA region was ranked last in terms of achieving gender equality in the World Economic Forum’s (2021) Global Gender Gap Report, with governments from MENA accounting for 12 of the 25 worst-performing countries globally, out of a ranking of 156 countries. The region also performed poorly in the 2021 Women, Peace, and Security Index, with Middle Eastern nations ranking among the top ten poorest performers. In their analysis of the “MENA paradox,” Danon and Collins (2020) highlighted the notable gap in business and entrepreneurial opportunities for women in the region. Despite increasing female educational attainment, female labor force participation remains low, a phenomenon often referred to as the “MENA paradox.” According to the World Bank, the low rates of women’s participation in the public sphere are primarily driven by the region’s conservative social gender norms, juridical and institutional barriers, and the lack of motivation and opportunities generated by local economic structures.
Using data from the 6th wave of the BEEPS survey, this study explores the influence of women’s empowerment on political corruption in MENA SMEs, especially in Morocco, Tunisia, Egypt, Lebanon, and Jordan. The research also investigates the moderating impacts of political instability perception, corruption perception level, and financial barriers on the relationship between women’s empowerment and political corruption. Our results highlight that except for Morocco, the data demonstrate a positive and substantial effect of female ownership on political corruption avoidance. Yet, the presence of a female top manager could foster political corruption. We note that the female CEO effect is not persistent. In addition, in Tunisia, women’s presence as top managers could reduce political corruption. The study also discovered that political instability and financial constraints hinder women’s corruption avoidance rather than corruption perception. As a result, removing impediments to women’s participation can aid in the reduction of bribery and corruption. This may be accomplished by gender equality initiatives such as legal reforms, increased access to funding, and targeted company development services.
This study is grounded in behavioral control theory, which posits that individuals’ behaviors are shaped not only by personal attitudes but also by perceived norms and constraints in the environment. In corrupt settings, women’s actions whether resisting or engaging in bribery are influenced by what is deemed necessary for business survival. Additionally, institutional theory helps explain how firms normalize corrupt behaviors as part of the operating environment. These theories support the idea that women’s empowerment can disrupt entrenched corruption patterns, but only if institutional norms permit ethical alternatives.
This study organized as follows: in the next section we present the literature review and assumptions development. Followed by the sample and data. The descriptive statistics are in section 5, then we present our model and methodology. The results and discussion are in the penultimate section and finally, we conclude.
Paying political bribes is a highly controversial and illegal practice that can have severe consequences for companies that engage in it. Political corruption involves the act of offering money, gifts, or other valuable items to government officials in exchange for specific actions, such as securing business contracts, expediting permits, or receiving favorable treatment. Although the use of bribes is widely condemned, it remains a prevalent issue in many regions of the world, especially in countries with weak rule of law, high levels of corruption, or inadequate regulatory enforcement (Faccio, 2006; Olken and Pande, 2012;Wang et al., 2014).Qafisheh (2019) provided a theoretical study that defines political corruption in an Arabian context, which could at some point differ from political corruption general conceptions. There are several reasons why companies might pay political bribes in the absence of strong judgement of law. First, companies could gain access to markets that are otherwise difficult to penetrate. In some countries, obtaining government contracts or licenses requires navigating a complex web of regulations and bureaucratic procedures that can be challenging for companies without insider connections. Paying bribes may provide a shortcut to bypassing these obstacles and secure access to these markets (Monteiro et al., 2018). Besides, another reason why companies may engage in bribery is to expedite processes such as obtaining permits or licenses. This can be especially prevalent in countries with slow or inefficient bureaucracies, where delays can cause significant financial losses or business disruptions. Paying bribes may provide a way to speed up the process and avoid lengthy delays (Wei and Shleifer, 2000; Uroos et al., 2022).
Companies may also pay political bribes to mitigate risk. In some situations, bribes may be seen as a necessary cost of doing business, especially if competitors are engaging in similar behavior. Paying bribes may be viewed to level the playing field or avoid losing business opportunities to more unscrupulous competitors. Baumol et al. (2007) underlined the economic costs of corruption. They indicated that corruption to face competitors leads to the loss of potential innovators that face unfair competition and to a decrease in investments. In some cases, businesses may use bribes to influence the outcome of a regulatory decision or obtain preferential treatment from government officials. This can give them a competitive advantage over other companies in the same industry (Olken and Pande, 2012). Indeed, there may be a culture of corruption within certain industries or countries that makes bribery seem like a normal part of doing business. Companies operating in these environments may feel pressure to engage in bribery to remain competitive (Banerjee, 1997; Djankov et al., 2002; Olken and Pande, 2012, Bosio et al., 2022). Despite the potential benefits of paying political bribes, the risks are significant. Most of the research that investigates corporate corruption indicated that companies that engage in bribery can face legal and reputational consequences, including fines, imprisonment, loss of business licenses, and damage to their brand reputation. Furthermore, bribery is widely viewed as an unethical and immoral practice that undermines good governance, undermines democratic institutions, and fosters a culture of corruption. Based on prior research our first assumption is that
Hypothesis1: Corporate political corruption is affected by the framework corruption perception.
2.1 Political corruption in MENA region
Consequences of political corruption in MENA region are far-reaching and damaging. It undermines economic growth by distorting the allocation of resources and discouraging investment, while also exacerbating income inequality and hindering the development of a robust civil society. Political corruption also undermines the rule of law and undermines trust in the political and legal systems, reducing the legitimacy of the state and contributing to social and political unrest. According to Aloui (2019), MENA is the most attractive region for foreign direct investment in terms of location and resources, particularly those with major natural resources (oil, natural gas). Historically, MENA nations’ risk-adjusted returns have been greater than those of industrialized countries. Yet, economic and political instability is a major deterrent to foreign direct investment in the MENA region. There have been a few initiatives aimed at tackling political corruption in the MENA region in recent years. These include anti-corruption legislation, the establishment of anti-corruption agencies, and greater transparency in government operations. However, progress has been slow, and corruption remains deeply ingrained in many of the countries in the region. This highlights the need for a more comprehensive and arranged approach to tackling political corruption in the MENA region. Al-Tal and Elheddad (2023) indicated that prior studies that focus on political stability and corruption, consider them as proxies of governance quality. In the same perspective and according to Hossain and Kryzanowski (2021), a higher level of corruption leads to higher equity costs and, as a result, less efficiency. Corruption is a fundamental corporate responsibility problem that impacts business productivity, foreign direct investment, and economic growth (Branco and Matos, 2016).
Based on the corruption perceptions index (2022) Egypt has consistently ranked poorly in Transparency International’s Corruption, and its political system is known for being deeply corrupt. In recent years, former President Hosni Mubarak was convicted of corruption, and his successor, President Abdel Fattah el-Sisi, has also been accused of corruption. According to a report by the Carnegie Endowment for International Peace organization[4], corruption in Egypt is widespread and deeply entrenched in the political and economic systems. Along the same line, Iraq has also struggled with political corruption, particularly in the aftermath of the US-led invasion in 2003. According to a report by the Brookings Institution, corruption is pervasive in Iraq’s political and economic systems, and it has contributed to the country’s ongoing instability.
Lebanon also part of the middle east region is known for having one of the most corrupt political systems. According to Transparency International raking, Lebanon ranks 150 out of 180 countries in its Corruption Perceptions Index. The country’s political elite is widely seen as corrupt and out of touch with the needs of ordinary citizens. The effect of political corruption is even worse for Syria and Yemen, which have been wracked by political corruption for decades. We note that this corruption has played a role in the ongoing civil wars in both countries. Overall, political corruption is a major challenge in many countries in the MENA region, and it has contributed to ongoing instability and insecurity.
In conclusion, political corruption remains a major challenge to the stability and development of the MENA region. Addressing this issue will require a multi-faceted approach that recognizes the complexities of corruption and the need for a sustained and coordinated effort. This research study will provide valuable insights into the political corruption in the MENA region and contribute to the ongoing efforts to tackle and face it.
2.2 Women’s empowerment to fight against corruption.
Gender and corruption research first appeared at the beginning of the 21st century with two World Bank studies that found a link between low levels of corruption and women’s presence in government. Dollar et al. (2001). Since then, previous research has highlighted the influence of gender on business behavior. According to Boulouta (2013), feminine stereotypes are associated with responsible human behaviors such as empathy. women are seen to be more responsible. Furthermore, female entrepreneurs are more risk-averse. As a result, they avoid incidents that might jeopardize their reputation. Female entrepreneurs relate to reduced bribery (Jagger and Shively, 2015; Rivas, 2013; Breen et al., 2017; Asomah et al., 2022).
Indeed, when women are empowered and have access to political and economic resources, they are more likely to hold those in power accountable for their actions. This can lead to greater transparency and less corruption (OECD[5], 2021). In addition, women’s presence in political and economic decision-making brings different perspectives and experiences to the table (Farza et al. 2022). This can help to reduce corruption by challenging entrenched power structures and promoting more inclusive governance. Moreover, women are more likely to invest in their families and communities (Boukattaya and Omri, 2021), which can lead to improved social welfare. This can reduce the need for bribery or other forms of corruption as people are more likely to receive the services and support, they need through legitimate channels. Hence, our second assumption
Hypothesis 2: Women avoid politically corrupt behaviors.
There is evidence to suggest that gender equality and women’s empowerment are linked to lower levels of corruption. However, some researchers contend that the contrary is true because women confront higher challenges in reaching corporate success (Watson, 2003). Along the same perspective, Nguyen et al. (2021) found that in transition economies, women are more inclined to employ bribes to escape bureaucratic costs. Wellalage et al. (2019) supported the same view and underlined that bribery is used more effectively by female CEOs compared to their male counterparts.
The United Nations Office on Drugs and Crime (2017) 8th module report [6] indicated that, considering the wide range of differences between people of any gender. The individual personality is important, as are environmental elements such as class, susceptibility, and budgetary levels, which are in line with the behavioral control theory. Indeed, the interplay of various contextual elements might result in impacts that are greater than the sum of their separate effects. The intersectionality analytical framework is used to illustrate how other factors contribute to different gender reactions and interactions, compounding their impacts. Therefore, our third assumption
Hypothesis3: Women’s anti-corruption behaviors are affected by the framework constraints and corruption level.
2.3 Women in empowerment in the MENA region
The term “MENA paradox” refers to the reality that, despite significant improvements in female educational attainment across the MENA region, women’s participation in the labor force remains persistently low. According to the World Bank, this limited engagement in public and economic life is largely attributed to deep-rooted societal gender norms, institutional and legal barriers, and a lack of opportunities and incentives shaped by the region’s economic structures. Some experts suggest that this mismatch is demand-driven, as young women have entered the labor market during a period of stagnant job growth for both genders. Others argue that the paradox can be explained by a trifecta of supply-side constraints, including discriminatory norms, attitudes, and behaviors that continue to restrict women’s full participation in the workforce. Along the same line, the OECD’s Social Institution and Gender Index (SIGI) reported by the OCED report (2019)[7]highlighted that women in Northern Africa have to overcome one of the highest discrimination levels in terms of accessing productive and financial resources. According to Arab Barometer surveys (2019)[8]conducted in 2020/2021, the publics across the area ascribe women’s poor labour force participation to structural rather than cultural constraints, citing a lack of childcare choices, a lack of transportation, and low pay as the top impediments to women working. Merrill (2017) emphasized that gender inequality has been among the most pressing issues in the region and that women are yearning for more political inclusiveness.
We note that despite the growing studies about gender inequality and corruption (Rivas, 2013; Jagger and Shively, 2015; Fuentes, 2018; Nguyen et al., 2021). Studies focusing on the MENA are almost inexistent. Therefore, we provide a first attempt and investigate the different factors that could affect women’s entrepreneurship and their corrupt behavior in a context specified by gender decimation and high constraints.
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- Relationship between Women’s Empowerment and Corporate Political Corruption:
Empowered women are in all probability to engage in decision-making processes that advocate for ethical practices and transparency in business operations. Research indicates that higher levels of female representation in corporate and political settings can lead to lower incidences of corruption due to diverse perspectives and a greater emphasis on accountability. Conversely, in environments marked by significant corporate political corruption, women’s empowerment initiatives may face obstacles, potentially limiting their effectiveness.
Obstacle perception refers to the awareness and acknowledgment of barriers that hinder an individual’s or group’s ability to achieve their goals. In the context of women’s empowerment, these obstacles can include cultural norms, institutional biases, lack of access to resources, and systemic inequalities.
Obstacle perception can avail as a moderating factor in the relationship between women’s empowerment and corporate political corruption in the following ways:
- Impact on Agency: Women who perceive significant obstacles may feel disempowered and less capable of effecting change, even if they possess the skills and knowledge. This perception can dampen their willingness to challenge corrupt practices, leading to a perpetuation of corruption within corporate structures.
- Influence on Collective Action: Awareness of obstacles can either mobilize or deter collective action among women. High levels of perceived obstacles may lead to feelings of hopelessness and resignation, undermining efforts to promote empowerment initiatives that could mitigate corruption.
- Contextual Variability: The perception of obstacles can vary based on contextual factors such as socio-economic conditions, political climate, and cultural attitudes towards women. These variances can influence the extent to which women’s empowerment is translated into effective resistance against corporate political corruption.
Understanding how women perceive obstacles can help shape more effective policies and programs that promote their empowerment. By addressing the specific barriers women face and fostering an environment that reduces these obstacles, stakeholders can create conditions that enable women to actively participate in combating corruption.
Organizations and governments should consider incorporating training and support mechanisms that build resilience and awareness of potential barriers, thereby enhancing women’s agencies and effectiveness in advocating for integrity in corporate governance.
The relationship between women’s empowerment and corporate political corruption appears to be multifaceted and significantly influenced by women’s perceived institutional and cultural obstacles. Recognizing and addressing these obstacles is vital for fostering an environment where empowered women can contribute to reducing corruption in corporate contexts. This theoretical framework sets the stage for further empirical investigation into the dynamics of these relationships and their implications for societal progress.
Our data is provided by the European Bank for Reconstruction and Development (EBRD) and the World Bank (WB). As part of their Business Environment and Enterprise Performance Survey (BEEPS) project, they provide firm-level Survey anonymous data for several Countries including ones of the MENA region.
In this study, we use the 6th survey wave covering a period from 2018-2020. We note that the survey was conducted in the pre-Covid period. This dataset includes financial data, general information about SMEs and direct questions focusing on corruption, informal competition, environmental management and so on.
Since the database includes corruption and illegal practices information, companies are presented anonymously for more reliable information. In addition, a selection based on the answers’ truthfulness could be applied. Non-reliable observations are excluded. Besides, companies with more than 250 employees are excluded from our sample. Our study focuses on five countries in the MENA region Jordon, Lebanon, Egypt, Tunisia and Morocco with a final sample of 2551 companies extracted from a total one of 5764companies in the region.
The sample excludes companies operating in the primary sector such as mining and agriculture as well as companies operating in public services such as education and health care.
To measure the political corruption of a company we selected three direct questions that reflect political corruption at different levels through ordinal variables. In addition, we built a continuous construct based on the companied three questions. The variables’ definitions are presented in the first table. We note that we focus on the political corruption impact on companies’ activities and not the frequency of the act.
Table 1: The variables’ definitions
Table 2 presents the descriptive statistics of our selected variables. By average SMEs in the MENA region consider that their bribe payments to the Parliamentarians to affect votes, to national government officials to affect decrees and to local or regional government officials to affect policies have minor impact with averages respectively 1.014, 1.001 and 1.000. These averages differ among the sampled countries. Figure 1 shows the average impact of the corporate bribe payments for political purposes. Statistics highlight remarkably high political corruption in Morocco and Tunisia followed by Egypt then Lebanon and Jordon. Indeed, in Morocco and Tunisia, the effect of political corruption has a moderating impact on the companies’ activities. We note in Jordon that political corruption at the regional and local level has more impact on the company’s operation than the other corruption types. The second figure results support the first statistics. Only Jordan and Lebanon have a positive constructed political ethical index. Morocco, Tunisia and Egypt have negative averages with respectively -0.798, -0.374 and -0.047. Compared to Transparency International’s Corruption Perceptions Index, Lebanon and Egypt are the most corrupt countries followed by Morocco, Tunisia then Jordon.
Figure1: The companies ‘political bribe payments impact on the companies’ activity by country.
This figure presents the level of bribe payments to influence political decisions by countries. BVOT indicates bribe payments to parliamentarians to affect votes, BDEC indicates bribe payments to national government officials to affect decrees, BPOL is the average level by country of SMEs bribe payments to local or regional government officials to affect policies.
The researcher notes that the measures in our study reflect the impact of the political bribe payment on the company’s activity and not the frequency of the bribe payments as the index consider, which could highlight the difference between the bribe frequency and its effectiveness. For instance, Lebanon’s economic crisis is due to high levels of corporate bribery and political corruption, with the country ranking 150th in Transparency International’s Corruption Perceptions Index in 2022. This has led to widespread mistrust in the government and institutions, with politicians accused of embezzlement and money laundering. Inflation, unemployment, and poverty are high, with the currency losing over 90% of its value, and the banking sector is in crisis. Lebanon’s SMEs are vulnerable to political corruption due to the complex regulatory environment, with demands for bribes and preferential treatment common. Honest entrepreneurs face an unfair playing field, with political connections and bribes providing an advantage. SMEs also face high taxes, limited access to finance, and poor infrastructure. Yet, in terms of the impact of the bribe payment impact Lebanese SMEs consider that their political bribes have a low impact, which might be due to their high frequency.
Figure2: The company’s political bribe payments index by country.
Figure 2 presents the level of political ethical engagement constructed index to influence political decisions and therefore the company’s operations by countries.
Table 2 presents the perception of political instability as an obstacle with an overall average equal to 1.978 signifying that companies perceive political instability as a moderating obstacle. Along the same line, the surrounding corruption perception as an obstacle to corporate operations has an overall average of 1.908, while the financial obstacles perception has an average of 1.532.
Table 2 shows the descriptive statistics for the variables selected in this study
The standard deviation is excluded for the dummy variables.
Figure 3 presents the average by countries of political instability, corruption, and financial obstacles. The graph shows an average of almost 3 for the political instability in Lebanon which is expected given that Lebanon’s political instability is rooted in a complex mix of factors, including historical, sectarian, economic, and geopolitical issues. The country’s political system is based on power-sharing between different religious groups, which can create tensions and competition for resources and influence. Economic challenges have also contributed to social unrest and protests. In addition, regional conflicts, and tensions, such as the Syrian war and the conflict between Israel and Hezbollah, have also had an impact on Lebanon’s stability. Political polarization, corruption, and a lack of trust in the government have further exacerbated these challenges.
Regarding the financial obstacles, Tunisia has the highest average. SMEs play a significant role in the MENA region economies including the Tunisian one, they represent most businesses and contribute to job creation and economic growth. In Tunisia, micro and SMEs represent almost 98% of total enterprises. According to the Confederation of Citizen Enterprises of Tunisia,87% of those companies employ one person, implying an incapacity of job creation and high resource scarcity. In the absence of clear economic development policies, these companies face major financial hurdles like limited access to credit due to high-interest rates, collateral requirements, and a requirement of financial literacy among entrepreneurs. Banks are reluctant to lend to SMEs, as they are perceived as high-risk borrowers. Those results are in line with the Word Bank report (2023)[9] in which they indicated that financial constraints are considered as the major obstacle by 43.9% of SMEs against only 23% in 2013. This limited access to financing can constrain the growth and development of SMEs, especially with a high tax burden and government support reduction. Indeed, the Tunisian government did not implement effective policies to support the development of SMEs basically due to the political instability and the fast change of governments leading to an absence of economic analysis period. In other words, SMEs in Tunisia face a weak institutional environment, which includes a lack of effective legal and regulatory framework, inadequate protection of intellectual property rights, and inefficient public services.
We note that our results are in line with Goedhuys et al. (2016) who investigated the corruption effect on the firm’s growth in Tunisia and Egypt and indicated that Tunisian SMEs perceive corruption as an obstacle more than Egyptian ones.
Figure 3: The SMEs’ perception level of political instability, corruption, and financial obstacles
The figure presents the average level by country of the FINOBS (corporate perception of financial constraints as obstacles) COROBS (corporate perception of corruption as an obstacle), and POLINS (corporate perception of political instability as an obstacle).
Figure 4 presents the frequency of female top managers by country as well as the average of female ownership. The results show that Tunisia has the highest averages for the two variables, while Morocco has the lowest average in terms of women ownership, with only 3% of SMEs by average are owned by women. Lebanon has the lowest average in terms of women occupying top management positions. Despite a high average in the Tunisian context, most businesswomen in Tunisia work in the agriculture or handicrafts sector, which are often informal. According to the International Labor Organization (ILO) (2017),[10]nearly (50%) of the women entrepreneurs operating in the unofficial part run businesses from home due to several obligations such as paying taxes and contributing to the social security system. In addition, these Tunisian women do not fully comprehend the benefits of registering their businesses. To address these issues, Tunisia introduced a draft law in 2017 aimed at creating an auto-entrepreneur status. In 2020, the regime for auto-entrepreneurs was established under Decree Law No. 2020-33 on June 10, 2020, which was subsequently amended in the 2023 finance law.
Figure 4: Women empowerment by country in the MENA region
Figure 4 presents the average female ownership by country in the MENA region (FEM) and the percentage of women that occupy a top manager position (FCEO).
The OECD, ILO and CAWTAR report (2020)[11], in Egypt the ownership structure significantly affects women’s empowerment. The report indicates that the presence of foreign owners fosters women’s leadership and increases the likelihood of having a female top manager.
To check the gender-different perceptions of the political bribe impact on corporate activity, we presented the averages of the bribe payments by CEO gender (see figure 5). Our statistics indicate that female top managers consider that their political bribe payments have less impact on corporate activity compared to their male counterparts. These differences are more pronounced when bribes are addressed to regional government officials.
Figure5: The companies ‘political bribe payments impact the companies’ activity by CEO gender.
Figure 5 presents the level of bribe payments to influence political decisions by countries. The BVOT index refers to bribe payments to parliamentarians to influence votes, the BDEC index refers to bribe payments to national government officials to influence decrees, and the BPOL index represents the average level of bribe payments made by SMEs to local or regional government officials to influence policy.
Before defining our model, we test the multilinearity between our variables, using the variance inflation factor (VIF) and the bivariate correlation. Table 3 presents the correlation matrix and VIF of our selected variables. Based on the VIF results there is no multilinearity issue, which enables us to present the variables within the same estimation equation. We note that between our independent and control variables there is no high correlation. Yet, a coefficient of 0.524 is reported between the perception of corruption as an obstacle and the perception of political stability as an obstacle to corporate operation. Given that these variables will not be integrated into the same model there are no multicollinearity issues.
Table 3: Matrix of correlations and variance inflation factor
To test the effect of women empowerment on corporate political corruption and how this effect is influenced by the framework corruption perception level, political instability level and financial obstacles level. We run ordered probit regressions for our ordered dependent variables and two-Stage least squares (2SLS) estimation for our continuous construct in the robustness. For the instrumental variables, we use the female ownership and female top manager averages by the industry for each region within the country. In addition, we control the industry fixed effect as well as the regional fixed effect for the country-level regressions. Therefore, our equations are as follows:
+
With corporate political corruption of company i in the country j in the industry k, is defined through 4 proxies, the political ethics PETH, the impact of bribe payments for parliamentarians to affect votes BVOT, the impact of bribe payments to national government officials to affect the decrees BDEC, and impact of bribe payments to local and or regional government officials to affect policies BPOL. Women empowerment is measured through two main variables, which are the female ownership percentage FEM and female top manager CFEM. The Obstacles perception is presented through three variables the political instability perception POLINS, the corruption level perception COROBS and the financial obstacles perception FINOBS. Regarding the interaction term, each regression considers one of the women empowerment proxies. Besides, we control the firm-level characteristics by considering the PLAR, FRG, GOV, FAM, CEXP, SIZ, INTC, and BUP and the country-level controls through the INF and RLW.
To investigate how women’s empowerment affects corporate political corruption and how the perception of the company’s framework characteristics influences this link. We focus on three factors that could affect the women empowerment and political corruption link, which are political instability, corruption impact and financial obstacles. Therefore, we run as a first step an ordered probit estimation. Table 4 presents the effect of female ownership and female CEO on the expected impact of paying bribes to parliamentarians to affect the votes results show a negative significant impact of female ownership percentage on the level of the impact of the bribe payment. The increased representation of women in business ownership could potentially reduce the impact of paying bribes to parliamentarians. Since, women-owned businesses may be less likely to engage in corrupt practices in line with Breen et al., 2017 and Asomah et al., 2022, women-owned businesses tend to be more ethical than male-owned businesses. Women are more likely to prioritize social responsibility over unethical profits not only due to their ethical behavior but also their risk aversion, and this may lead them to avoid engaging in corrupt practices like paying bribes.
Considering the positive effect of female CEOs (in most cases non-significant) we can conclude that women continue to face barriers as managers doing business in the MENA region, including cultural attitudes, legal restrictions, and lack of access to finance, which could exacerbate the issue of bribery and corruption. When women encounter barriers to accessing finance, markets, resources, and it can limit their ability to grow their businesses and compete in the market. It may force them in resort to unethical practices, such as paying bribes to gain a competitive advantage. If these barriers were reduced, women-owned businesses could become more competitive, and this could reduce the need to pay bribes to gain a competitive advantage. While owners are investors that require a certain level of returns, the top manager is responsible for providing these returns. Therefore, the behavior of female owners, which is more ethical, is different from the female CEO who manages the company and has targets to achieve as decision-makers. We should point out that women’s business experience could be among the factors that reduce female risk aversion and ethical behavior in a corrupt environment so they can persist and surpass barriers. This justification is in line with the positive significant effect of the CEO experience.
In the MENA region, bribery and corruption are often deeply ingrained in the business culture. CEOs who have spent significant time in the business field may have become accustomed to these practices and may view them as a necessary part of doing business. Especially after building connections and extensive networks, including influential individuals such as politicians and government officials. These connections may make it easier for them to engage in corrupt practices, such as paying bribes to secure contracts or favorable treatments.
Our results highlight a positive significant effect on the company size and the elaboration of formal business strategy on the bribe payment likelihood. In other words, there is a certain normalization of the use of bribes as a tool while elaborating business plans. Besides, those companies may face pressure to meet planned ambitious targets, which can lead to an increased risk of corruption. Employees may feel pressured to engage in corrupt practices, such as paying bribes, to secure contracts or meeting sales targets. Regarding the international certification quality, a negative significant effect on the bribe payments is reported. Companies with internationally recognized certifications are more ethical and fear more repercussions since they work on their depiction.
Given that the framework barriers present an important factor when explaining the effect of women empowerment effect on corporate political corruption, we investigate three factors that could this relationship. We control political instability perception as an obstacle, financial obstacles, and corruption level perception as an obstacle by the company and their moderating effects.
Table 4: Ordered probit regression to test the effect of women empowerment on the impact of corporate bribe payments to parliamentarians to affect votes.
| BVOT | |||||||
| PLAR | 0.091
(0.094) |
0.156*
(0.093) |
0.141
(0.093) |
0.097
(0.094) |
0.162*
(0.093) |
0.154*
(0.092) |
|
| FRG | 0.000
(0.001) |
-0.001
(0.001) |
-0.002
(0.001) |
0.000
(0.001) |
-0.001
(0.001) |
-0.002
(0.001) |
|
| GOV | -0.005
(0.006) |
0.000
(0.005) |
0.000
(0.005) |
-0.005
(0.006) |
0.000
(0.005) |
0.000
(0.005) |
|
| FAM | 0.001
(0.001) |
0.001*
(0.001) |
0.001**
(0.001) |
0.001
(0.001) |
.001*
(0.001) |
0.001**
(0.001) |
|
| CEXP | 0.005**
(0.002) |
0.005**
(0.002) |
0.005**
(0.002) |
0.005**
(0.002) |
.005**
(0.002) |
0.005**
(0.002) |
|
| SIZ | 0.104***
(0.025) |
0.105***
(0.025) |
0.106***
(0.025) |
0.105***
(0.025) |
0.103***
(0.025) |
0.107***
(0.025) |
|
| INTC | -0.362***
(0.071) |
-0.401***
(0.071) |
-0.390***
(0.071) |
-0.358***
(0.071) |
-0.394***
(0.071) |
-0.389***
(0.071) |
|
| BUP | 0.351***
(0.056) |
0.320***
(0.055) |
0.263***
(0.055) |
0.353***
(0.056) |
0.324***
(0.055) |
0.265***
(0.055) |
|
| INF | -0.007***
(0.002) |
-0.008***
(0.002) |
-0.007***
(0.002) |
-0.007***
(0.002) |
-0.008***
(0.002) |
-0.007***
(0.002) |
|
| RLW | 0.300****
(0.095) |
0.510***
(0.095) |
0.619***
(0.095) |
0.294***
(0.095) |
0.513***
(0.095) |
0.616***
(0.096) |
|
| FEM | -0.009***
(0.003) |
-0.005**
(0.002) |
-0.008***
(0.003) |
-0.005***
(0.001) |
-.002*
(0.001) |
-0.004***
(0.001) |
|
| CFEM | 0.213**
(0.108) |
0.074
(0.108) |
0.177
(0.107) |
0.240
(0.166) |
0.321*
(0.176) |
0.028
(0.175) |
|
| FINOBS | 0.404***
(0.022) |
0.416***
(0.021) |
|||||
| COROBS | 0.289***
(0.020) |
0.306***
(0.020) |
|||||
| POLINS | 0.277***
(0.020) |
0.284***
(0.020) |
|||||
| FINOBSXFEM | 0.002*
(0.001) |
||||||
| COROBSXFEM | 0.001
(0.001) |
||||||
| POLINSXFEM | 0.002**
(0.001) |
||||||
| FINOBSXCFEM | -0.023
(0.087) |
||||||
| COROBSXCFEM | -0.123*
(0.071) |
||||||
| POLINSXCFEM | 0.070
(0.071) |
||||||
| Industry | Yes | Yes | Yes | Yes | Yes | Yes | |
| Number of obs. | 2188 | 2172 | 2182 | 2188 | 2172 | 2182 | |
| Pseudo r-squared | 0.100 | 0.073 | 0.071 | 0.100 | 0.073 | 0.071 | |
| Chi-square | 572.067 | 414.500 | 407.930 | 568.621 | 415.338 | 403.484 | |
| Prob > chi2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| Table 4 presents the women empowerment measures on the impact of bribe payments to parliamentarians to affect votes. The value between parentheses presents the standard error and the stars reflect the p-value with *** p<0.01, ** p<0.05, * p<0.10 | |||||||
Our findings emphasize the positive significant influences of the three obstacle types on political corruption proxies at the Parliamentarians (table4) government officials (table 5) and region officials (table 6) levels.
Indeed, Companies may view political instability as an obstacle to doing business in the MENA region, as it can create uncertainty and increase the risk of losses. In some cases, companies may feel that paying bribes is necessary to protect their investments and maintain their operations in the region. This is particularly true if the company perceives that government officials may be involved in corruption or may be able to offer protection in exchange for bribes. In the same line, Companies may view corruption levels as an obstacle to doing business, particularly if they perceive that corruption is widespread and accepted. In other words, companies believe that corruption acts as an efficient grease. In some cases, companies may feel that paying bribes is necessary to remain competitive or to avoid retaliation from government officials or competitors who engage in bribe payments. Those practices are more likely to be adopted when the company faces financial obstacles that hinder the achievement of its goals. limited access to credit and financial sources can make it difficult for companies to compete and can increase the pressure.
We remark that these factors moderate women’s empowerment and corporate political corruption differently. According to table 4, only the political instability moderates the link between female ownership and bribe payments to Parliamentarians at 5% significance level. In other words, the impact of female ownership on bribe payments to affect votes depends on the level of the political instability perception. The negative effect of female ownership is weakened when political instability. This suggests that political instability has a buffering effect on the negative relationship. When political instability is higher, it helps to offset the negative impact of female ownership. For the financial constraints or corruption level on the women empowerment measures, there is a non-significant effect (at 5% significance level). In addition, political instability has a non-moderating effect on female top manager since female CEO does not significantly affect this political corruption level. This result implies that female ownership is not affected by the corruption normalization but by the fear and pressure in an unstable political situation which could increase their tolerance to use a bribe to affect votes.
Table5: Ordered probit regression to test the effect of women empowerment on the impact of corporate bribe payments to government officials to affect decrees.
| BDEC | |||||||
| PLAR | 0.066
(0.094) |
0.093
(0.094) |
0.102
(0.093) |
0.073
(0.094) |
0.099
(0.094) |
0.112
(0.093) |
|
| FRG | -0.001
(0.001) |
-0.002
(0.001) |
-0.003*
(0.001) |
-0.001
(0.001) |
-0.002
(0.001) |
-0.003*
(0.001) |
|
| GOV | -0.004
(0.005) |
0.000
(0.005) |
0.001
(0.005) |
-0.004
(0.005) |
0.000
(0.005) |
0.001
(0.005) |
|
| FAM | 0.000
(0.001) |
0.000
(0.001) |
0.001*
(0.001) |
0.000
(0.001) |
0.000
(0.001) |
0.001*
(0.001) |
|
| CEXP | 0.007***
(0.002) |
0.006***
(0.002) |
0.006***
(0.002) |
0.007***
(0.002) |
0.006***
(0.002) |
0.006***
(0.002) |
|
| SIZ | 0.093***
(0.025) |
0.094***
(0.025) |
0.093***
(0.025) |
0.094***
(0.025) |
0.091***
(0.025) |
0.094***
(0.025) |
|
| INTC | -0.406***
(0.071) |
-0.435***
(0.071) |
-0.421***
(0.071) |
-0.406***
(0.071) |
-0.428***
(0.071) |
-0.42***
(0.071) |
|
| BUP | 0.296***
(0.056) |
0.268***
(0.055) |
0.209***
(0.055) |
0.301***
(0.056) |
0.271***
(0.055) |
0.210***
(0.055) |
|
| INF | -0.008***
(0.002) |
-0.009***
(0.002) |
-0.008***
(0.002) |
-0.008***
(0.002) |
-0.009***
(0.002) |
-0.008***
(0.002) |
|
| RLW | 0.318***
(0.095) |
0.532***
(0.095) |
0.636***
(0.095) |
0.314***
(0.095) |
0.535***
(0.095) |
0.633***
(0.095) |
|
| FEM | -0.006**
(0.002) |
-0.003
(0.002) |
-0.005**
(0.002) |
-0.003***
(0.001) |
-0.001
(0.001) |
-0.002
(0.001) |
|
| CFEM | 0.171
(0.107) |
0.023
(0.107) |
0.135
(0.106) |
0.126*
(0.075) |
0.248
(0.174) |
0.074
(0.172) |
|
| FINOBS | 0.41***
(0.022) |
0.416***
(0.021) |
|||||
| COROBS | 0.309***
(0.020) |
0.325***
(0.020) |
|||||
| POLINS | 0.289***
(0.020) |
0.296***
(0.020) |
|||||
| FINOBSXFEM | 0.001
(0.001) |
||||||
| COROBSXFEM | 0.001
(0.001) |
||||||
| POLINSXFEM | 0.001
(0.001) |
||||||
| FINOBSXCFEM | 0.057***
(0.087) |
||||||
| COROBSXCFEM | -0.111
(0.069) |
||||||
| POLINSXCFEM | 0.027
(0.070) |
||||||
| Industry | Yes | Yes | Yes | Yes | Yes | Yes | |
| Number of obs. | 2186 | 2171 | 2182 | 2186 | 2171 | 2182 | |
| Pseudo r-squared | 0.100 | 0.076 | 0.071 | 0.100 | 0.077 | 0.071 | |
| Chi-square | 571.086 | 432.110 | 405.245 | 569.243 | 432.744 | 402.766 | |
| Prob > chi2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| The table 5 presents the women empowerment measures on the impact of bribe payment to government officials to affect decrees. Value between parentheses present the standard error and the stars reflect the p-value with *** p<0.01, ** p<0.05, * p<0.10 | |||||||
Table6: Ordered probit regression to test the effect of women empowerment on the impact of corporate bribe payments to regional or local officials to affect policies.
| BDEC | |||||||
| PLAR | 0.058
(0.094) |
0.100
(0.093) |
0.098
(0.093) |
0.065
(0.094) |
0.109
(0.093) |
0.110
(0.092) |
|
| FRG | 0.000
(0.001) |
-0.001
(0.001) |
-0.002
(0.001) |
0.000
(0.001) |
-0.001
(0.001) |
-0.002
(0.001) |
|
| GOV | -0.002
(0.005) |
0.003
(0.005) |
0.003
(0.005) |
-0.002
(0.005) |
0.003
(0.005) |
0.003
(0.005) |
|
| FAM | 0.000
(0.001) |
0.000
(0.001) |
0.001
(0.001) |
0.000
(0.001) |
0.000
(0.001) |
0.001
(0.001) |
|
| CEXP | 0.007***
(0.002) |
0.006***
(0.002) |
0.006***
(0.002) |
0.007***
(0.002) |
0.006***
(0.002) |
0.007***
(0.002) |
|
| SIZ | 0.106***
(0.025) |
0.110***
(0.025) |
0.107***
(0.025) |
0.107***
(0.025) |
0.109***
(0.025) |
0.107***
(0.025) |
|
| INTC | -0.35***
(0.071) |
-0.382***
(0.071) |
-0.363***
(0.071) |
-0.349***
(0.072) |
-0.376***
(0.071) |
-0.360***
(0.071) |
|
| BUP | 0.286***
(0.056) |
0.246***
(0.055) |
0.198***
(0.055) |
0.290***
(0.056) |
0.248***
(0.055) |
0.199***
(0.055) |
|
| INF | -0.008***
(0.002) |
-0.009***
(0.002) |
-0.008***
(0.002) |
-0.008***
(0.002) |
-0.009***
(0.002) |
-0.008***
(0.002) |
|
| RLW | 0.318***
(0.095) |
0.499***
(0.096) |
0.610***
(0.096) |
0.313***
(0.095) |
0.501***
(0.096) |
0.603***
(0.096) |
|
| FEM | -0.007***
(0.003) |
-0.005**
(0.002) |
-0.006**
(0.003) |
-0.003**
(0.001) |
-0.001
(0.001) |
-0.002
(0.001) |
|
| CFEM | 0.168
(0.108) |
0.021
(0.108) |
0.128
(0.107) |
0.083
(0.168) |
0.065
(0.179) |
0.079
(0.172) |
|
| FINOBS | 0.411***
(0.022) |
.0418***
(0.022) |
|||||
| COROBS | 0.28***
(0.020) |
0.294***
(0.020) |
|||||
| POLINS | 0.271***
(0.020) |
0.28***
(0.020) |
|||||
| FINOBSXFEM | 0.002
(0.001) |
||||||
| COROBSXFEM | 0.002**
(0.001) |
||||||
| POLINSXFEM | 0.002**
(0.001) |
||||||
| FINOBSXCFEM | 0.055
(0.088) |
||||||
| COROBSXCFEM | -0.021
(0.070) |
||||||
| POLINSXCFEM | 0.019
(0.070) |
||||||
| Industry | Yes | Yes | Yes | Yes | Yes | Yes | |
| Number of obs. | 2183 | 2167 | 2178 | 2183 | 2167 | 2178 | |
| Pseudo r-squared | 0.101 | 0.071 | 0.067 | 0.101 | 0.070 | 0.067 | |
| Chi-square | 571.417 | 394.780 | 379.368 | 569.227 | 389.510 | 375.471 | |
| Prob > chi2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| Table 6 presents the women empowerment measures on the impact of bribe payment to regional and or local officials to affect policies. The value between parentheses presents the standard error and the stars reflect the p-value with *** p<0.01, ** p<0.05, * p<0.10 | |||||||
Table 5 confirms that bribe payments at the government officials’ level to affect decrees are also fostered by political instability, financial obstacles and corruption impact level. Yet only the financial obstacles moderate positively (i.e. intensify) the link between the female CEO, which have a positive effect on the bribe payment and the political corruption at the government officials’ level. This pressure could explain the positive effect (at10% significance level) as the moderating effect of financial obstacles.
Table 6 tests the women empowerment effect on political corruption at the local or regional level. The negative effect of female ownership as well as the positive effects of political instability, corruption perception and financial obstacles are also confirmed at the regional scale. Besides, we report a positive significant moderating effect of political instability and corruption perception on the female ownership and bribe payment to a regional official. In less politically stable environments and with higher corruption obstacle perception female ownership resistance and refusal of corrupt behaviors at a regional scale decrease. Women may be more likely to have role models and networks of support at the regional level than at the governmental or parliamentary scale, since the higher the position the less likely to be occupied by a female. Therefore, considering this network in a corrupt and politically unstable environment, female owners’ refusal of unethical practices could fade, and normalization of the act could affect their judgment.
While the findings suggest that increased female ownership is associated with reduced political corruption, the potential for reverse causality must be acknowledged. Specifically, it is plausible that countries or sectors with lower levels of institutional corruption are more conducive to female economic participation, including ownership. This would imply that the causality runs from institutional quality to gender inclusiveness, rather than the reverse. To address this concern, we employed a two-stage least squares (2SLS) estimation strategy using industry-region-level female participation rates as instruments. Nonetheless, we recognize that instrument validity and the possibility of residual endogeneity require cautious interpretation of the results. Future studies should explore longitudinal data or natural experiments to more precisely identify causal mechanisms.
To check the robustness of our results we built a continuous variable PETH reflecting political corruption avoidance, then we use two-Stage least squares (2SLS) estimations. The Kleibergen-Paap (KP) test is used in instrumental variables (IV) regression to test the validity of the instruments. Its null hypothesis is that the instruments are valid, and the alternative hypothesis is that at least one instrument is weak. If the p-value is less than 5%, then we reject the null hypothesis and conclude that there is evidence of weak instrument bias in the regression model, while the Hansen tests for the presence of overidentifying restrictions, which are additional restrictions placed on the instruments in addition to the exclusion restriction used to identify the causal effect of the endogenous variable on the outcome variable. The null hypothesis of the Hansen test is that the instruments are valid and the overidentifying restrictions are held, while the alternative hypothesis is that at least one of these restrictions is violated.
Table7: Instrumental variables (2SLS) regression testing the effect of women empowerment on political corruption avoidance in Egypt
| PETH | Coef. | ||||||
| FEM | 1.561*
(0.946) |
0.641*
(0.366) |
0.639*
(0.342) |
0.291**
(0.124) |
0.162
(0.109) |
0.085
(0.079) |
|
| CFEM | -2.04*
(1.58) |
-0.064**
(0.030) |
-7.865*
(4.429) |
-9.505**
(4.543) |
-2.053*
(1.487) |
-7.986***
(2.159) |
|
| PLAR | -2.111
(2.203) |
-0.075
(0.140) |
-0.846
(0.862) |
-0.177
(0.405) |
-0.018
(0.459) |
-0.491
(0.388) |
|
| FRG | -0.028
(0.035) |
0.001
(0.002) |
0.000
(0.009) |
-0.004
(0.006) |
-0.005
(0.005) |
-0.006
(0.005) |
|
| GOV | -0.065
(0.052) |
0.008*
(0.005) |
-0.025**
(0.019) |
0.008
(0.010) |
-0.005
(0.012) |
0.002
(0.010) |
|
| FAM | -0.045
(0.033) |
0.003*
(0.001) |
-0.018**
(0.008) |
-0.015**
(0.007) |
-0.009
(0.006) |
-0.004
(0.005) |
|
| CEXP | -0.014
(0.053) |
0.000
(0.004) |
-0.003
(0.018) |
0.000
(0.012) |
0.009
(0.014) |
-0.001
(0.012) |
|
| SIZ | -0.649
(0.541) |
0.056*
(0.031) |
-0.130
(0.158) |
-0.117
(0.143) |
-0.126
(0.130) |
-0.190
(0.122) |
|
| INTC | 0.936
(1.321) |
-0.123
(0.090) |
-0.287
(0.473) |
-0.278
(0.322) |
-0.088
(0.325) |
0.189
(0.333) |
|
| BUP | 4.33
(2.881) |
-0.077
(0.088) |
1.561*
(0.866) |
1.088**
(0.549) |
1.086**
(0.467) |
1.126***
(0.437) |
|
| COROBS | -2.206
(1.367) |
-0.050
(0.080) |
|||||
| POLINS | -0.036
(0.057) |
-0.360
(0.279) |
|||||
| FINOBS | -0.724**
(0.368) |
-0.541***
(0.158) |
|||||
| COROBSXFEM | -0.492
(0.301) |
||||||
| POLINSXFEM | 0.021**
(0.010) |
||||||
| FINOBSXFEM | -0.202*
(0.104) |
||||||
| COROBSXCFEM | 1.994
(1.227) |
||||||
| POLINSXCFEM | 6.455
(4.212) |
||||||
| FINOBSXCFEM | -3.583***
(1.192) |
||||||
| Constant | 0.390
(2.329) |
0.143
(0.191) |
0.208
(0.868) |
0.844
(0.693) |
.893
(0.568) |
1.603**
(0.694) |
|
| Industry | Yes | Yes | Yes | Yes | Yes | Yes | |
| Region | Yes | Yes | Yes | Yes | Yes | Yes | |
| Number of obs | 1290 | 1304 | 1295 | 1290 | 1304 | 1295 | |
| Uncentered R2 | 0.0967 | 0.2104 | 0.095 | 0.0998 | 0.0994 | 0.0962 | |
| Test of under-identification
Kleibergen-Paap test p-value |
2.918
0.7126 |
10.058
0.0736 |
9.927 0.0773 | 10.873
0.0540 |
3.393
0.4943 |
3.621
0.4598 |
|
| Test of overidrest
Hansen J test p-value |
3.844 0.4275 | 3.823
0.4310 |
2.458
0.6522 |
2.976
0.5619 |
1.651
0.7995 |
2.142
0.5436 |
|
| Values in parentheses represent the robust standard error, while stars reflect the probability (p) value where *** p < 0.01, ** p < 0.05, * p < 0.1. The region variable includes Middle and East Delta, North Upper Egypt, South Upper Egypt, Suez region, and West Delta, while the industrial sector distinguishes between basic metals, chemicals, construction, electronics, fabricated metals, food, furniture, clothing, hotels and restaurants, information technology, leather, machinery and equipment, non-metallic mineral products, paper, plastics and rubber, precision instruments, publishing, printing and recorded media, recycling, refined petroleum products, retail trade, motor vehicle services, textiles, tobacco, transportation sector, transportation equipment, wholesale trade, and timber. | |||||||
After controlling the regional and industry effects, the results in table 7 confirm the positive effect of female ownership on corporate political bribe payment avoidance and negative impact of female CEO. Consistent with previous findings, female ownership reduces political corruption while it is the opposite for the female CEO. Besides, we confirm that the financial obstacles negative effect on political corruption avoidance, while the political instability and the corruption level perception as an obstacle by the company have non-significant effects. In other words, the main reason that fosters corporate political corruption is financial more than cultural such as normalizing corruption in the context of politics. Corporate political corruption has financial purposes in Egypt. Moreover, the financial obstacles moderate the link between the female CEO and political corruption avoidance. Indeed, the financial obstacles intensify the negative effect of female CEO. We underline that while the political instability has a non-significant effect on the corruption avoidance proxy, we report a positive significant influence of its joint effect with female ownership implying that the political instability intensifies the positive effect of female ownership. The Siemens Integrity Initiative, the United Nations Office on Drugs and Crime (UNODC)[12] in Egypt launched a new project that confirms the link between corruption and financial inclusion in the country.
For Jordan and Lebanon, female ownership also has a positive important effect on political corruption avoidance meanwhile female CEO increase political corruption only in Jordan (see table 8). This difference could have two possible explanations. The first suggests that Lebanon has the lowest percentage of female CEO, implying that only women with deep contextual knowledge and network could survive in the market. Those female CEOs could respond and face the possible obstacleswithout using bribes thanks to their expertise. The second possible explanation is based on women’s risk aversion. Given that Lebanon’s classification based on the corruption perceptions index is alarming, female top managers could fear more political scandals leading to a non-significant effect after trading off between the faced obstacles and the potential losses due to political corruption, especially, with the higher societal refusal of corrupted acts when women are involved.
Table8: Instrumental variables (2SLS) regression testing the effect of women empowerment on political corruption avoidance in Jordan and Lebanon
| PETH | Jordan | Lebanon | ||||||||||
| FEM | 0.044***
(0.014) |
0.056**
(0.027) |
0.063***
(0.016) |
0.026***
(0.009) |
0.034***
(0.011) |
0.034***
(0.010) |
2.99***
(1.132) |
3.480***
(1.196) |
3.556***
(1.142) |
0.057**
(0.023) |
0.007**
(0.003) |
5.383*
(3.208) |
| CFEM | -2.451**
(1.019) |
-3.326*
(1.875) |
-3.199***
(1.144) |
-2.092***
(0.802) |
-2.985***
(0.964) |
-2.225**
(0.972) |
0.001
(0.012) |
-0.017
(0.029) |
-0.004
(0.014) |
-1.269
(1.573) |
-1.849
(2.709) |
0.001
(0.006) |
| PLAR | 0.656
(0.501) |
0.591
(0.598) |
-0.042
(0.777) |
0.058
(0.596) |
-0.038
(0.689) |
-0.009
(0.720) |
-0.576*
(0.321) |
-0.646*
(0.355) |
-0.648*
(0.330) |
-0.129
(0.346) |
-0.260
(0.268) |
-0.335
(0.243) |
| FRG | 0.002
(0.003) |
0.003
(0.003) |
0.007*
(0.004) |
0.002
(0.003) |
0.002
(0.003) |
0.004
(0.003) |
-0.027**
(0.010) |
-0.030***
(0.011) |
-.03***
(0.011) |
0.006
(0.011) |
-0.030***
(0.010) |
-0.019**
(0.008) |
| GOV | 0.000
(0.000) |
0.000
(0.000) |
0.000
(0.000) |
0.000
(0.000 |
0.000
(0.000) |
0.000
(0.000) |
0.000
(0.000) |
0.000
(0.000) |
0.000
(0.000) |
0.000
(0.000) |
0.000
(0.000) |
0.000
(0.000) |
| FAM | -0.007**
(0.003) |
-0.007*
(0.004) |
-0.007**
(0.003) |
-0.006**
(0.003) |
-0.007**
(0.003) |
-0.007**
(0.003) |
0.000
(0.002) |
0.000
(0.002) |
0.000
(0.002) |
-0.004**
(0.002) |
-0.002
(0.002) |
-0.001
(0.002) |
| CEXP | 0.005
(0.008) |
0.003
(0.009) |
0.000
(0.010) |
0.000
(0.008) |
0.003
(0.008) |
-0.004
(0.008) |
0.010*
(0.006) |
0.012*
(0.006) |
.012**
(0.006) |
0.001
(0.007) |
0.000
(0.005) |
0.006
(0.005) |
| SIZ | 0.033
(0.108) |
0.063
(0.113) |
0.101
(0.122) |
0.089
(0.112) |
0.118
(0.117) |
0.088
(0.114) |
-0.057
(0.056) |
-0.065
(0.059) |
-.076
(0.058) |
-0.041
(0.062) |
-0.030
(0.055) |
-0.078
(0.054) |
| INTC | -0.235
(0.313) |
-0.376
(0.353) |
-0.068
(0.367) |
-0.290
(0.315) |
-0.411
(0.345) |
-0.218
(0.347) |
-0.022
(0.209) |
-0.011
(0.211) |
-.015
(0.231) |
-0.361
(0.252) |
-0.118
(0.161) |
-0.196
(0.221) |
| BUP | 0.016
(0.222) |
0.101
(0.236) |
-0.126
(0.270) |
0.021
(0.211) |
-0.015
(0.239) |
-0.105
(0.231) |
0.048
(0.141) |
0.064
(0.149) |
.089
(0.148) |
-0.093
(0.177) |
-0.052
(0.123) |
0.069
(0.135) |
| COROBS | -0.059
(0.069) |
-0.050
(0.060) |
-0.039
(0.049) |
-0.080
(0.056) |
||||||||
| POLINS | -0.103
(0.082) |
-0.129
(0.079) |
0.013
(0.058) |
-0.077
(0.069) |
||||||||
| FINOBS | -0.351***
(0.086) |
-0.118*
(0.070) |
-.012
(0.050) |
0.067
(0.055) |
||||||||
| COROBSXFEM | -0.014***
(0.005) |
-0.003
(0.005) |
||||||||||
| POLINSXFEM | -0.016*
(0.009) |
0.002
(0.009) |
||||||||||
| FINOBSXFEM | -0.022***
(0.006) |
-.003
(0.005) |
||||||||||
| COROBSXCFEM | -0.154
(0.190) |
-0.182
(0.489) |
||||||||||
| POLINSXCFEM | 0.194
(0.256) |
0.654
(0.765) |
||||||||||
| FINOBSXCFEM | -0.592**
(0.259) |
-1.678
(1.115) |
||||||||||
| Constant | -.914*
(0.543) |
-1.042*
(0.575) |
-1.403**
(0.681) |
-0.821
(0.527) |
-0.802
(0.522) |
-0.884
(0.580) |
-0.150
(0.327) |
-0.336
(0.379) |
-0.264
(0.286) |
0.320
(0.338) |
0.273
(0.330) |
-0.231
(0.270) |
| Industry | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Region | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Number of obs | 225 | 224 | 229 | 225 | 224 | 229 | 289 | 290 | 290 | 289 | 290 | 290 |
| Uncentered R2 | 0.1459 | 0.1573 | 0.1291 | 0.1375 | 0.1415 | 0.1606 | 0.1595 | 0.1710 | 0.1725 | 0.1510 | 0.1667 | 0.1667 |
| Test of under-identification
Kleibergen-Paap test p-value |
18.904
0.6512 |
14.730
0.8736 |
15.782
0.8266 |
16.402
0.7954 |
16.698
0.7798 |
13.117
0.9299 |
20.498
0.5519 |
19.906
0.5889 |
19.875
0.5909 |
6.649
0.9793 |
12.134
0.9545 |
11.143
0.9727 |
| Test of overid rest
Hansen J test p-value |
25.761
0.2157 |
22.711
0.3596 |
21.271
0.4425 |
25.154
0.2405 |
22.708
0.3597 |
21.196
0.4470 |
24.012
0.2925 |
24.218
0.2826 |
24.157
0.2855 |
23.798
0.3029 |
18.228
0.6345 |
23.893
0.2982 |
| Values between parenthesis present the robust standard error and the stars reflect the p-value with*** p<0.01, ** p<0.05, * p<0.1. The region variable in Jordan includes Irbid, North West & Centre West (Ajloun, Balq..), South (Aqaba, Karak, Ma’an, Taf..), Zarqa while in Lebanon Bekaa Valley & North Lebanon, Mount Lebanon, Nabatieh, South Lebanon.The industry is similar to the Egyptian classification (see table 7) | ||||||||||||
For the political instability, corruption perception and financial obstacles we do not report any significant effect in Lebanon. Yet, in Jordan, the financial obstacles significantly reduce political corruption avoidance. We also underline that the financial obstacles and the political instability in Jordan reduces the female ownership positive effect while the financial obstacles intensify the negative effect of the female CEO. We should point out that the political system (a parliamentary monarchy in Jordan and a parliamentary democratic republic in Lebanon) affects significantly the political corrupt behavior.
For the Tunisian context, we register a positive significant effect for both the female ownership and female top managers. Considering that Tunisia has the highest averages in terms of women empowerment compared to the rest of the countries in the study (still, a low average compared to the global one) we could conclude that women face fewer obstacles as managers in Tunisia which could justify the positive effect of female CEOs on the political corruption avoidance. Moreover, we report a negative significant effect for all three obstacle types. Indeed, during the last Years the politically unstable situation has affected considerably the economic context. with higher uncertainty about the newly enacted laws company’s female top manager tolerance towards politically corrupt behavior in Tunisia could increase. In addition, financial obstacles form an important challenge for female entrepreneurs that could lead to weakened political corruption resistance. Quite the contrary, in Morocco we did not report any significant effect of female ownership or female top managers on political corruption.
A surprising finding of this study is the contrast between the effects of female ownership, which tends to reduce corruption, and female CEOs, whose presence is often associated with increased corruption except in Tunisia. This divergence may reflect the realities of tokenism and survival strategies in male-dominated business environments. In many MENA contexts, female CEOs may be appointed as symbolic leaders without real authority, making them vulnerable to institutional corruption they cannot control. Alternatively, these women may adopt corrupt practices as pragmatic tools to navigate exclusive male networks and secure operational continuity. Tunisia’s exception may be explained by stronger institutional frameworks that support genuine female leadership, legal empowerment, and donor-backed gender reforms. Thus, the role and effectiveness of women in leadership should be understood in context, and not assumed as inherently anti-corruption unless backed by institutional and cultural inclusiveness.
Table8: Instrumental variables (2SLS) regression testing the effect of women empowerment on the political corruption avoidance in Tunisia and Morocco
| PETH | Tunisia | Morocco | ||||||||||
| FEM | 0.397**
(0.201) |
0.022
(0.029) |
0.051**
(0.023) |
0.182
(0.154) |
0.015
(0.045) |
0.025
(0.047) |
0.013
(0.022) |
0.021
(0.076) |
0.063
(0.040) |
-0.025
(0.026) |
-0.032
(0.034) |
-0.002
(0.019) |
| CFEM | 0.891
(5.696) |
3.653
(1.293) |
1.702**
(0.358) |
7.480
(4.635) |
2.090*
(1.212) |
6.377
(5.648) |
-0.282
(0.860) |
0.170
(0.700) |
-0.723
(0.794) |
1.907
(1.625) |
1.465*
(0.787) |
0.695
(1.120) |
| PLAR | -0.346
(0.826) |
-0.258
(0.183) |
0.028
(0.145) |
-1.773
(1.498) |
-0.523
(0.407) |
-0.412
(0.402) |
0.197
(0.505) |
0.155
(0.483) |
0.153
(0.442) |
0.184
(0.497) |
-0.129
(0.346) |
0.111
(0.422) |
| FRG | -0.015
(0.023) |
-0.003
(0.003) |
-0.002
(0.002) |
0.009
(0.014) |
0.001
(0.003) |
0.001
(0.003) |
0.005
(0.003) |
0.005
(0.003) |
0.005
(0.003) |
0.006
(0.004) |
0.006
(0.011) |
0.006
(0.004) |
| GOV | -0.046
(0.028) |
-0.004
(0.005) |
-0.004
(0.004) |
-0.021
(0.023) |
0.001
(0.009) |
-0.006
(0.007) |
0.021**
(0.008) |
0.021**
(0.010) |
0.019**
(0.008) |
0.020**
(0.009) |
0.0212**
(0.010) |
0.018**
(0.009) |
| FAM | -0.018
(0.011) |
-0.001
(0.002) |
-0.001
(0.001) |
-0.032
(0.019) |
-0.007
(0.005) |
-0.006
(0.007) |
-0.003
(0.004) |
-0.002
(0.004) |
-0.004
(0.004) |
-0.002
(0.004) |
-0.004**
(0.002) |
-0.003
(0.004) |
| CEXP | 0.018
(0.024) |
0.000
(0.007) |
0.005
(0.005) |
0.004
(0.034) |
-0.001
(0.012) |
0.000
(0.008) |
0.006
(0.009) |
0.008
(0.010) |
0.007
(0.009) |
0.005
(0.008) |
0.001
(0.007) |
0.005
(0.009) |
| SIZ | 0.463
(0.373) |
-0.009
(0.073) |
0.031
(0.059) |
0.137
(0.320) |
0.103
(0.097) |
0.036
(0.072) |
-0.149*
(0.085) |
-0.177**
(0.087) |
-0.154*
(0.081) |
-0.147*
(0.083) |
-0.041
(0.062) |
-0.138*
(0.082) |
| INTC | -0.004
(0.894) |
0.299
(0.197) |
0.233
(0.155) |
0.588
(1.205) |
0.292
(0.407) |
0.054
(0.285) |
0.035
(0.218) |
-0.501
(1.096) |
0.805*
(0.466) |
0.289
(0.324) |
-0.361
(0.252) |
0.027
(0.278) |
| BUP | -0.574
(0.919) |
0.075
(0.193) |
-0.042
(0.137) |
-0.848
(0.999 |
-0.650*
(0.336) |
-0.144
(0.245) |
0.146
(0.248) |
0.260
(0.254) |
0.183
(0.217) |
0.290
(0.248) |
-0.093
(0.177) |
0.201
(0.244) |
| COROBS | -1.729**
(0.845) |
-1.201**
(0.581) |
-0.049
(0.093) |
-0.029
(0.087) |
||||||||
| POLINS | -0.031
(0.137) |
-0.566***
(0.192) |
-0.064
(0.122) |
-0.080
(0.056) |
||||||||
| FINOBS | -0.272***
(0.075) |
-0.285
(0.221) |
0.000
(0.097) |
-0.031
(0.099) |
||||||||
| COROBSXFEM | -0.129*
(0.067) |
-0.014
(0.011) |
||||||||||
| POLINSXFEM | -0.004
(0.010) |
-0.018
(0.025) |
||||||||||
| FINOBSXFEM | -0.016**
(0.007) |
-0.049*
(0.027) |
||||||||||
| COROBSXCFEM | -3.452*
(2.469) |
-0.980
(0.844) |
||||||||||
| POLINSXCFEM | -4.575**
(1.939) |
-0.747*
(0.411) |
||||||||||
| FINOBSXCFEM | -2.761
(2.534) |
-0.445
(0.389) |
||||||||||
| Constant | -2.944
(2.353) |
0.131
(0.630) |
-0.766*
(0.394) |
1.642
(1.831) |
0.071
(0.803) |
-0.476
(0.462) |
-0.052
(0.554) |
-0.314
(0.771) |
-0.796
(0.655) |
-1.061*
(0.604) |
-1.037
(0.795) |
-0.821
(0.695) |
| Industry | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Region | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Number of obs | 276 | 277 | 282 | 276 | 277 | 282 | 106 | 101 | 106 | 106 | 101 | 106 |
| Uncentered R2 | 0.1029 | 0.1183 | 0.1213 | 0.1228 | 0.1222 | 0.1066 | 0.1345 | 0.1775 | 0.1352 | 0.1566 | 0.1821 | 0.1256 |
| Test of under-identification
Kleibergen-Paap test p-value |
2.072
0.3548 |
16.439
0.8358 |
25.641
0.4829 |
32.038
0.2307 |
15.859
0.8612 |
25.018
0.4048 |
4.598
0.9997 |
10.751
0.9319 |
4.752
0.9996 |
7.909
0.9876 |
7.665
0.9897 |
7.909
0.9876 |
| Test of overid rest
Hansen J test p-value |
24.053
0.5729 |
18.387
0.6828 |
25.641
0.4829 |
24.861
0.5268 |
18.036
0.7038 |
20.796
0.5936 |
22.3171
0.2182 |
22.6326
0.2051 |
18.2939
0.4365 |
24.5603
0.1375 |
21.6563
0.2476 |
22.4801 0.2114 |
| Values between parenthesis present the robust standard error and the stars reflect the p-value with*** p<0.01, ** p<0.05, * p<0.1. The region variable in Morocco includes Casablanca-Settat, Fès-Meknès, Marrakech-Safi, Oriental, Rabat-Salé-Kénitra, Souss-Massa.For Tunisia, it includesCentre East, North East, North West & Centre West. The industry is similar to the Egyptian classification (see table 7) | ||||||||||||
In this study, we investigate the effect of women empowerment on the SMEs’ political corruption in the MENA region over period from 2018 to 2020. We focus on Morocco, Tunisia, Egypt, Lebanon, and Jordan. Our data is extracted from the 6th wave of the BEEPS survey. We consider the female ownership percentage and the female top managers as proxies to women empowerment, while we consider the level of bribe payment impact to parliamentarians, government officials and local or regional officials as measures of political corruption. In addition, we build a new construct that reflects political corruption avoidance using the jointed correspondence analysis method. We run our regressions using an ordered probit and Two-Stage least squares estimations. To better understand what is beyond the studied effect we select three main factors that could affect the link between women’s empowerment and political corruption. First, we examine the effect of political instability perception on political corruption measures and its moderating effect on the link between women’s empowerment and political corruption. Then, we follow the same process for the corruption perception level and financial obstacles. Those factors reflect three possible explanations of political corruption, which are the political uncertainty that could affect business, the corruption normalization that increases the tolerance towards unethical practices and the financial constraints that create pressure on companies leading to unethical behaviors to achieve goals.
Our results highlight an overall positive significant effect of female ownership on political corruption those findings are confirmed in all the studied countries except for Morocco where we reported a non-significant. For the female top manager, we report a positive but not persistent significant effect on political corruption except for Tunisia where the female CEO presence reduces political corruption. We point out according to our statistics, Tunisia has the highest average in terms of female CEO percentage, implying that women in Tunisia face fewer business barriers, which could explain their prioritization of political corruption avoidance. Besides, generally, political instability and financial obstacles are the main reasons that could weaken women’s corruption avoidance and not the corruption perception. In other words, female ownership is not affected by the corruption normalization but by the fear of unstable political situations and the pressure of financial constraints which could increase their tolerance to use bribes. Yet, at the regional level, female owners may be more likely to have role models and networks of support than at the governmental or parliamentary scale, since the higher the position the less likely to be occupied by a female. Therefore, considering this network in a corrupt and politically unstable environment, female owners’ refusal of unethical practices could fade, and normalization of the act could affect their judgment.
Based on this research results reducing barriers to women’s participation in the formal economy can help to reduce bribery and corruption. It fosters the positive effect of female ownership and helps female CEOs to avoid political corruption. This can be accomplished through policies preferred gender equality, such as reforms to legal frameworks, increasing access to finance, and providing business development services tailored to the needs of women entrepreneurs. By increasing women’s participation in the formal economy, we can reduce the need for unethical practices and create a more level playing field for all businesses.
While this study measures political corruption through firms’ perceptions of bribe-related impacts, it does not explicitly differentiate between distinct forms of corruption such as petty bribery, nepotism, and state capture. This is a limitation, as these corruption types may affect women differently. For example, women entrepreneurs may be disproportionately burdened by petty bribery due to limited access to informal networks or financial capital. In contexts dominated by nepotism or state capture, women are often excluded from elite patronage systems, which undermines the meritocratic value of education and professional experience. Recognizing and measuring these distinctions in future research would enhance understanding of the nuanced barriers that women face and provide more targeted policy insights.
Appendix1: Joint correspondence analysis for political ethics construct
Number of obs = 2,33 Total inertia = 1.2057277Number of axes = 1
| Dimension | Principal inertia | Percent | Cumul percent |
| dim 1 | 0.711 | 58.970 | 58.970 |
| Total | 1.206 | 100.000 |
Appendix2: Statistics for column categories in standard normalization
| Categories | Mass | Quality | %inert | Coord | sqcorr | Contrib |
| BVOT | ||||||
| 0 | 0.169 | 0.912 | 0.084 | 0.878 | 0.912 | 0.130 |
| 1 | 0.055 | 0.052 | 0.035 | -0.238 | 0.052 | 0.003 |
| 2 | 0.055 | 0.436 | 0.045 | -0.782 | 0.436 | 0.034 |
| 3 | 0.042 | 0.757 | 0.081 | -1.565 | 0.757 | 0.104 |
| 4 | 0.011 | 0.435 | 0.081 | -2.288 | 0.435 | 0.060 |
| BDEC | ||||||
| 0 | 0.171 | 0.914 | 0.083 | 0.865 | 0.914 | 0.128 |
| 1 | 0.055 | 0.029 | 0.038 | -0.183 | 0.029 | 0.002 |
| 2 | 0.057 | 0.440 | 0.053 | -0.828 | 0.440 | 0.039 |
| 3 | 0.037 | 0.722 | 0.084 | -1.658 | 0.722 | 0.103 |
| 4 | 0.013 | 0.461 | 0.079 | -2.157 | 0.461 | 0.062 |
| BPOL | ||||||
| 0 | 0.174 | 0.915 | 0.083 | 0.861 | 0.915 | 0.129 |
| 1 | 0.050 | 0.037 | 0.037 | -0.215 | 0.037 | 0.002 |
| 2 | 0.056 | 0.435 | 0.050 | -0.815 | 0.435 | 0.037 |
| 3 | 0.042 | 0.759 | 0.083 | -1.598 | 0.759 | 0.107 |
| 4 | 0.012 | 0.426 | 0.084 | -2.296 | 0.426 | 0.061 |
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