Firm self-financing, corruption, and the quality of tax administration in Africa

Moumbark, Toure; Koudalo, Yawovi M. A. Article Firm self-financing, corruption, and the quality of tax administration in Africa Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Moumbark, Toure; Koudalo, Yawovi M. A. (2023) : Firm self-financing, corruption, and the quality of tax administration in Africa, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 11, Iss. 2, pp. 1-28, https://doi.org/10.1080/23322039.2023.2266241 This Version is available at: https://hdl.handle.net/10419/304224 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Firm self-financing, corruption, and the quality of tax administration in Africa Toure Moumbark & Yawovi M. A. Koudalo To cite this article: Toure Moumbark & Yawovi M. A. Koudalo (2023) Firm self-financing, corruption, and the quality of tax administration in Africa, Cogent Economics & Finance, 11:2, 2266241, DOI: 10.1080/23322039.2023.2266241 To link to this article: https://doi.org/10.1080/23322039.2023.2266241 © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 11 Oct 2023. Submit your article to this journal Article views: 883 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Firm self-financing, corruption, and the quality of tax administration in Africa Toure Moumbark 1 * and Yawovi M. A. Koudalo 1 Abstract: This study aims to determine the impact of tax administration and corruption on firm self-financing in Africa. The paper also explores the level at which tax administration and corruption are firm financing obstacles and whether these effects on firms differ regarding their size. The article uses data from the World Bank Enterprise Survey, which covers 45,048 firms in 48 countries across Africa. Using the Tobit, IV Tobit, and Multinomial Probit models, the results are robust as we controlled for country, firm diversity, and survey year. The study reveals that corruption reduces a firm’s self-financing by negatively affecting its internal funds or retained earnings. In addition, weak tax administration reduces firm self-financing. The results also reveal that corruption and poor tax administration are severe obstacles to a firm’s self-financed. Furthermore, while weak tax administration generally harms firm financing, the negative impact on larger firms surpasses the adverse effects on smaller and medium firms. The corruption issue is more critical in the case of small and medium firms than big firms as they spend a large portion of their profit to government officials as gifts or informal payments to reduce the burden of regulations and circumvent taxes. Subjects: Industrial Economics; Public Finance; Corporate Finance Keywords: Tax administration; Corruption; Firm self-financing; Africa JEL Classification: D73; E62; H30 1. Introduction Globally, firms are a major contributor to the economy in advanced, emerging, and developing economies (OECD, P. 2017). In Africa, SMEs contribute more than half of the GDP and employment (Africapractice, 2019). Well-functioning small and medium-sized enterprises (SMEs) are a significant part of the economic framework in developing countries. They contribute to GDP growth, reduce unemployment, and encourage innovation and growth. Governments, therefore, concentrate on improving the SME sector to stimulate economic development. These SMEs operate mainly in services, especially trade, and in manufacturing and agro-industries, thus reflecting the structure of these countries GDP. Although productive SMEs are in the minority ahead of those in the tertiary sector, they play a dominant role in these countries’ industrial sectors. For example, from Kauffmann (2005), SMEs represent around 96% of manufacturing activity and 70% of industrial employment in Nigeria, the leading country in sub-Saharan Africa. Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 1 of 28 Received: 23 November 2022 Accepted: 28 September 2023 *Corresponding author: Toure Moumbark, Research Institute of Economics and Management, Southwestern University of Finance and Economics, Chengdu, China E-mail: [email protected]. edu.cn Reviewing editor: Muneer Maher Alshater, Finance & banking, Philadelphia University, Jordan Additional information is available at the end of the article © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. Despite their weight in local economies and their driving role in economic development, SMEs have limited access to financing, particularly in Africa. On the one hand, the bank penetration rate in Africa is meager, total bank assets amount to only 32% of GDP on average, and loans to the private sector constitute less than half of these assets (Kauffmann, 2005). On the other hand, it is mainly large companies, often foreign ones, which benefit from most financing, according to several studies (Aryeetey & Aryeetey, 1998). The problems of corruption, poor infrastructure, or abusive taxation aggravated these financing problems. Deprived of access to the financing market, SMEs often cover all their needs with personal resources (Africapractice, 2005). African small and medium-sized enterprises have limited access to capital, hampering their development and emergence. Their primary capital sources are their earnings and informal investments, self-financing, and credit partnership such as tontines, which, due to their geographic or sectorial orientation, are volatile, not very stable, and lack risk-sharing. Figure 1 indicates in the green color bar that, on average, 19% of small firms, 15% of mediumsized firms, and 14% of large firms have declared access to finance to be an obstacle. Figure 1 confirms that SMEs are severely constrained in self-financing compared to large firms. Large-size firms do not have as many obstacles as depicted. Firms that receive higher government help are more likely to get bank loans (Ruan et al., 2018). Therefore, enterprises strive to find efficient methods to get funds from financial institutions and mitigate financing constraints, such as getting government guarantees and corrupting officials. Because of certain government guarantees, even if firms with state ownership have lower control and achievement, they have higher political status. They are usually viewed as more protected than non-SOEs. State ownership can guarantee firms’ debt; hence, firms with state ownership have a lower possibility of bankruptcy than non-SOEs (Borisova et al., 2015). Government quality is necessary for reputation assets and financing support for firms. Banking institutions’ operations are ordinarily based on state policy in emerging countries. Therefore, firms with good government relations can share governments’ network resources to receive more bank assistance, such as lowcost loans. The government dramatically influences banks’ lending choices in government-led economies and extends a helping hand to relieve financial institutions’ matters concerning corporate moral hazards (Faccio et al., 2006). Government interference in capital allocation modifies the 0 5 10 1520 small(<20) medium(20-99) large(100 and over) mean of corruption mean of Tax administration mean of firm financing Figure 1. Percent of firms identifying access to finance, corruption, and financing as a constraint. Source: Author computation Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 2 of 28 financial market’s allocative function and exacerbates the private sector’s financial climate (Guariglia & Poncet, 2008). Besides government intervention as a helper, tax administration and corruption could be a constraint for firm performance that significantly impact firm self-financing. According to Auriol and Warlters (2005), the ease with which collecting taxes from a relatively small number of large companies may lead the tax administration to concentrate more on SMEs by making their activity more difficult. High tax rates and administration complexity are significant constraints for SMEs and can steer them into the informal sector if the tax burden becomes excessive. However, a large informal economy can reduce government revenues and increase the tax burden on formal sector businesses, which increases the attraction of informal activities (OECD, 2008). Besides that, ineffective tax administration raises compliance costs, which considerably raises companies’ fiscal burden (Dabla-Norris et al., 2017), and can harm their performance. Due to the significant compliance expenses caused by poor tax administration, some firms may decide to operate informally (Moore, 2023). Informality makes it difficult for enterprises to access financing, international exposure, assistance, and training programs. That limits their ability to increase performance (Capasso et al., 2022). From the standpoint of generating public revenue, if tax administration is detrimental to enterprises’ activities, it is likewise detrimental to generating public revenue in three different ways. Firstly, a firm’s capacity to pay tax is based on its development, and if tax administration affects profit and development, government income will decline. Secondly, consumption tax (for example, VAT) will be reduced since firm performance has decreased due to bad tax administration. Lastly, employees’ income taxes will be lessened because of decreased wages/salaries from reduced firm output and earnings. Hence, understanding how tax administration affects firm performance will provide the basis for reforming the tax policy to minimize administrative costs. The above Figure 1 also shows in the maroon color that tax administration as a constraint impacts more firms depending on their size in Africa. The bigger the firm, the more it impacts the tax administration. About 3% of large-size firms are impacted compared to 3% and 2% for medium and small-size firms, respectively. Corruption is an omnipresent and tenacious obstacle to various emerging economies (Martins et al., 2020). It is affirmed by the companies that corruption is one of the biggest barriers to business growth and performance (Gaviria, 2002). The rent-seeking concept of public policy notes that corruption is not helpful for development (Krueger, 1974). Because of information asymmetry in the credit rationing process, financial institutions usually have credit decision-making powers, like loan interest rates, loan length, and collateral forms (Barth et al., 2009). As a result, the rights of bank officers could cause companies to bribe them. Bribery gives corrupt administrators a higher impulse to formulate further complex loan terms, which drives firms to raise their gifts to evade these new terms (Guriev, 2004). Furthermore, a significant level of corruption leads to opportunistic behaviors in ineffective systems. It raises the non-performing loans created by bank institutions due to loan defaults. Corruption practices bring up the uncertainty of banks’ avoidance of default and raise the risk of borrowers’ default. That reduces banks’ readiness to lend to firms and increases the obstacles to firm financing (Qi & Ongena, 2019; Wellalage & Fernandez, 2019). Firms and individuals with low incomes are more likely to join corrupt activities (Haque & Sahay, 1996). Unfair income distribution in countries (Swamy et al., 2001) and changes in regulations such as tax systems, tariffs, and state policies (Gupta et al., 2005) are additional determinants influencing corruption. This problem is more critical in small firms as they spend a considerable portion of their income on the government as a gift or informal payment. Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 3 of 28 For this reason, big businesses are habitually more protected in emerging countries. Further, they are constrained to heavier controls and taxation such as labor rules, company implantation, tax office, and tax rates. Corruption encourages firms to gather resources (Tollison, 2012), but it reduces the financial intermediaries’ performance and entrepreneurship (Djankov et al., 2007). Therefore, corruption due to government interference constrains financial development. Besides, corruption can be a springboard to access to finance. Financial institutions give funds in a challenging and restraining system, which guides the exclusion of firms that do not satisfy financial institutions’ requirements to issue loans. Accordingly, keeping a great connection with banks has become tricky for enterprises to get bank loans. Gift exchange creates significant social capital, allowing firms to keep relationships with administrators (Cai et al., 2011). The lubricant hypothesis implies that bribery reduces the system’s rigidity, which can cause corrupt actions advantageous to economic development (Dreher & Gassebner, 2013). Bribery can relax the rigid credit system, simplify cumbersome loan procedures, decrease loan approval waiting time, and improve investment efficiency (Levin & Satarov, 2000). The bribery of administrators by enterprises decreases the unfavorable influences of red tape and encourages them to get bank loans, although this results in a rise in the short-term bank debt ratio (Chen et al., 2013; Fungáčová et al., 2015). Hence, corruption actions improve the connection among banks and firms and are powerful strategic behaviors for enterprises to get bank credits. From the above figure, the navy color bar shows that 4% of small and 4% of mediumsized firms are experiencing corruption. The figure also indicates that, on average, large firms have to pay 2% as a bribe payment for a contract. This paper will enrich the current literature in several ways. We exclusively focus on the African World Bank Enterprise Survey database. The database gives new insights to apprehend the financing behavior of the firm in Africa. It will help to find creative and innovative ways to achieve strong firm growth that is crucial in alleviating poverty. We diverge from many current studies by designing objective and subjective indicators for firm access to finance and corruption that explain how tax administration and corruption affect corporate self-financing and what drives firms to finance obstacles. Although there is a significant number of previous studies on corruption or poor tax administration, limited studies have been conducted on the effect of poor tax administration and corruption on firm financing, mainly on firm self-financing. To the best of our knowledge, our study is among the first ones to empirically examine the impact of tax administration and corruption on firm self-finance, mainly in Africa. This paper is different from other research on corruption or tax administration in that it studies the combined effect of both corruption and tax administration on firm self-financing using both Tobit and multinomial probit model models. The main finding is that corruption reduces a firm’s self-financing by negatively affecting its internal funds or retained earnings. In addition, we find that weak tax administration reduces firm selffinancing too. Furthermore, we investigate the level at which corruption and tax administration are obstacles to firm financing by employing the Multinomial Probit model. We categorized financing obstacles as minor, moderate, major, and severe. We find that corruption and poor tax administration best predict firm self-finance obstacles. The results reveal that tax administration and corruption increase firm self-finance obstacles (minor, moderate, major, and severe). The findings are also robust after using alternative corruption and tax administration measures. We also find that while weak tax administration harms firm financing in general, the negative impact on larger firms surpasses that on smaller and medium firms. Corruption issue is more critical in the case of small and medium firms than big firms are as they spend a large portion of their profit to the government official as a gift or informal payments to reduce the burden of regulations and circumvent taxes. The results are robust after controlling for firm characteristics, endogeneity issues, year of survey, and country specifications. The continuation of this study is organized as follows. Section 2 examines how the paper compares to the literature. Section 3 sets out the data, hypothesis, and econometric model. Section 4 explains the findings and checks for the robustness of the findings, Section 5 does the extended analysis, and Section 6 concludes. Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 4 of 28 2. Empirical literature review For many decades, the debate on the impact of corruption and taxation on firm growth and financing constraints has been heavily contested. The impact of corruption is also perceived to be more like a tax, primarily because the payment will not end up as government revenue (Johnson et al., 1998). Corruption can be more counterproductive to firm performance than taxes, depriving the government of the revenues needed to provide efficient public goods. More recently, Murphy et al. (1993) claimed that bribes might be much more harmful than taxes due to various higher transaction costs, given the complexity and privacy that inevitably follow bribery payments and the fact that fraudulent contracts are not lawful. Firms that do not satisfy the requirements for financial institutions to grant loans are excluded because financial institutions operate under strict and binding regulation that restricts their ability to give funding. Thus, keeping a positive connection with banks has become a strategic decision for businesses seeking bank loans. Giving and receiving gifts create significant social capital that helps businesses keep their relationships with government officials (Cai et al., 2011). According to the lubricant theory, corruption might be advantageous for economic growth since it reduces the rigor of the system (Dreher & Gassebner, 2013). In particular, corruption can ease the strict loan process, simplify complicated loan processes, shorten the delay for loan approval, and boost investment effectiveness (Loayza et al., 2000). Bribery and other corrupt practices are, in our opinion, the second most successful way to increase a company’s bank loan approval speed. According to the rent-seeking approach of public choice theory, corruption harms growth (Krueger, 1974). Bank executives typically have decision-making authority over credit conditions, including interest rates on loans, loan maturities, and the forms of collateral, due to the knowledge asymmetry in the credit rationing mechanism (Barth et al., 2009). As a result, firms could bribe bank personnel because of their privileges. Bribery encourages dishonest authorities to create more complex loan terms, prompting businesses to pay additional bribes to circumvent the new conditions (Guriev, 2004). Additionally, a high level of corruption encourages opportunistic behavior in poor systems, raising the amount of non-performing loans that banking institutions create due to credit default. Consequently, the question of whether bribery is more detrimental than taxes or harmful is mainly an empirical question. The macro literature has intensively investigated the connection between firm growth and bribery, starting with Mauro (1995). These papers have found a negative link between bribery and growth. Nevertheless, this research body is wholly focused on crosscountry analyses, which often pose significant worries about non-observed heterogeneity through datasets. Also, bribery information is based on perception corruption indices, usually expert perceptions of aggregate corruption in a region, which pose queries about perception biases. Finally, a cross-country analysis of the relationship between bribery and development shows us nothing about the impact of corruption on individual firms. In the empirical review using a firm-level dataset, several authors have argued about the effect of corruption on firms’ access to external financing, including banks and credit institutions. Liu et al. (2020) used Chinese enterprises’ business environment survey by the World Bank and saw an inverted U-shaped association linking corrupt acts and firms’ access to bank credit. They found that a low degree of corruption can encourage firms to have bank funds, but a high level of corruption makes it impossible for companies to secure bank loans. Besides, corruption affects firms’ access to external financing by enabling them to obtain government guarantees, which are positively associated with firm financing. In the same vein, Li et al. (2008), using a nationwide survey of private enterprises in China, find that party membership of private entrepreneurs not only enhances their firm’s performance but also helps them obtain loans from banks. Oppositely, Qi et al. (2018) used a sample of European firms from transition countries from 2008–09 and 2012–14. Their findings reveal that access to bank loans is difficult when a firm is Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 5 of 28 involved too much in corruption practices and that corruption generates this loss of access. The bribery-driven increase in financing obstacles significantly impedes future firm growth. Following the findings, Wellalage and Fernandez (2019) used data on 130,000 firms in 135 countries. He finds that high corruption increases difficulties for financial institutions to control borrower risk and recover loans from a supply side. Unlike large firms, SMEs give bribes to grease the wheel to the informal sector to avoid the attention of tax administration agents. The demand-side considers bribes like tax, increasing credit costs to SMEs. The degree to which private companies obtain bank credit in China is determined by bribery rather than company performance. Bribery encourages financial results by awarding more extensive credits to companies with more significant economic results, and they pay more for bribery (Chen et al., 2013). Many researchers have linked tax administration and firm productivity, and some related tax administration and government intervention in firm financing. However, there is no direct review linking tax administration and firm financing to the extent of our knowledge. In government-led economies, the government considerably influences bank lending choices, extending a helping hand to assuage financial institutions’ worries about firm moral hazards. A comprehensive follow-up research has linked the relationship between tax administration and firm external finance. Fu (2020), using Investment Climate Survey on China’s firms, shows the dilemma of government intervention in a firm’s financing. He argues that government intervention supports a firm’s financial access but impedes the firm’s micro-financial development. So far, Faccio et al. (2006) analyzed the probability of government bailouts of 450 politically related firms in 35 countries from 1997 to 2002. Politically connected firms are significantly more likely to be bailed out than similar non-connected firms. Also, in some countries, political connections determine capital allocation through financial assistance when affiliated companies face economic distress. Government guarantees affect enterprises’ ability to borrow from banks in two directions. Firstly, government guarantees are valuable reputation assets and funding opportunities for firms. Government policy concerns often predicate financial institutions’ behavior in developing countries. Thus, firms with government guarantees can share state network resources to gain additional help from banks, such as low-interest lending. Because of implicit government assurances, firms with state ownership have stronger political prestige and are considered safer than nonSOEs. Secondly, state ownership offers an implicit guarantee for enterprises’ debt, resulting in a decreased likelihood of bankruptcy compared to non-SOEs (Borisova et al., 2015). Private firms that get greater government help are more likely to get bank loans. Thus, firms aim to discover efficient ways to receive cash from banking institutions and reduce financing limitations, such as securing government guarantees and bribing authorities. Government intervention in capital allocation weakens the financial system and affects the financing climate for the private sector (Guariglia & Poncet, 2008). 3. Data, hypothesis, and econometric model This section presents the data and the economic model used to study the relationship between firm self-financing, corruption, and tax administration quality. 3.1. Enterprise surveys dataset This research uses data from the Enterprise surveys, a firm-level data set recently accessible from the World Bank and its collaborators worldwide. The survey was conducted from 2006 to 2020 and represents more than 160,000 firms in 140 countries. Enterprise Surveys concentrate on many aspects that shape the industrial climate and can accommodate or minimize business operations and play a significant role in a country’s success or failure (World Bank, 2020). The survey is done in the non-agricultural economy to a representative group of firms, and a key questionnaire that keeps surveys comparative between various countries and survey years. Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 6 of 28 The essential questionnaire comprising a survey answered by company owners and top managers worldwide offers subjective and objective details regarding firms’ market environment. Subjective analyses demonstrate the severity of the challenges that firms face. The survey asks businesses to rank 16 elements on a range of 1 to 5 (1 is no obstacle, and 5 is a major obstacle). This detail facilitates recognizing the highest obstacles and analyzing the obstacles that businesses deem to be the most relevant. Enterprise Surveys are very useful because they have a range of objective measures, such as how much a firm pays as bribes, total firm sales, and internal funds, or if there are often power outages. These quantitative measures become very useful as we try to overcome the possible limitations of subjective measures. Subjective indicators include the assumption that companies’ business climate impressions represent characteristic variations in the degree of optimism or pessimism of the people’s opinion of the survey. Subjective tests are often deficient when reactions are likely to be heavily affected by the firm’s experience and outcomes (Aterido et al., 2011). This paper investigates the impact of corruption and tax administration quality on firm selffinancing in Africa. We will use subjective and objective indicators of firm financing from the World Bank Enterprise Surveys to reach this aim. We use a sample of 45,048 firms from 47 African nations for our study. Table A1 of Appendix A shows the distribution of firms in the study, and the average sample size is 958 firms, although estimates vary by nation. Some nations are more highly represented than others. Egypt, Kenya, and Nigeria represent more than 5% of each firm sample, with Egypt having the highest total firms in the sample, 17.28% for 7786 firms. The lowest representation goes to Benin, Chad, Togo, Liberia, and Nigeria, representing less than 0.67% of the firm sample. After data cleaning, we remained at 6,798 observations. 3.2. Dependent variable: firm self-financing The self-financing capacity consists of the future capital that is open to the company for selffinancing after the activity. A mixture of financial and non-financial measures will be our appropriate indicator of firm self-financing. The firm’s benefit and profit can express financial measures (Santos & Brito, 2012). They have the bonus that they are objective and easily understandable. They have the limitation that they are not readily accessible and historical, so they just provide lagging information. They may also be vulnerable to manipulation and incompleteness. The downside to non-financial measures is that they are arbitrary (Santos & Brito, 2012). Due to the drawbacks of financial and non-financial measures, using a hybrid solution, including financial and nonfinancial measures, has become the widely accepted norm. Thus, firm self-financing is the dependent variable. Our measure of firm self-financing is an objective indicator that emanated from the question asked in the survey to a firm to know the part of this firm’s working capital financed by Internal funds/Retained earnings. Our subjective is derived from the respondent’s answers in the survey from rating 16 firms’ environmental constraints. In fact, on a range between zero and five (zero for no obstacle, one for minor, two moderate, three major, and four very severe obstacles), respondents rate “access to finance.” Therefore, if access to financing restricts business efficiency, corruption and tax administration will have an increased impact and decrease the effect on its objective measure. The subjective measure is a categorical variable comprising financing as a minor, moderate, major, and severe obstacle. 3.3. Independent variables The first key independent variable is the quality of tax administration. It is based on the question asked to know on a scale of 0–4 to what degree the tax administration poses an obstacle to their operations, with responses as no obstacle (0), minor obstacle (1), moderate obstacle (2), major barrier (3), and severe obstacle (4). The quality of tax administration will be a dummy variable, one if there is a major and severe obstacle and zero otherwise. Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 7 of 28 Table 3. Effect of the effect of corruption and tax administration on firm self-financing: Tobit marginal model IVs Electricity and water Dependent variable: Internal fund/ Retained earning (1) (2) (3) VARIABLES Tobit Tobit IVTobit Tax administration −0.0836*** −0.0720*** −0.0209*** (0.004) (0.011) (0.015) Corruption −0.1124*** −0.1025* −9.3085*** (0.024) (0.055) (1.752) Tax rate −0.0403*** −0.0370*** −0.0211* (0.006) (0.013) (0.015) Female manager −0.0085* −0.0145 −0.0220 (0.005) (0.011) (0.014) Training 0.0048*** 0.0015 0.0814*** (0.007) (0.017) (0.024) Manager experience −0.0204*** −0.0230*** −0.0758*** (0.003) (0.008) (0.013) Employment (log) −0.0508*** −0.0409*** −0.1189*** (0.003) (0.008) (0.017) Production (log) 0.0426*** 0.0281 0.0843** (0.015) (0.040) (0.043) Technology 0.0622*** 0.0250 0.0004 (0.006) (0.015) (0.018) Infrastructure −0.1083*** −0.0534*** 0.0278 (0.006) (0.017) (0.024) Regulation −0.0775*** −0.0773 −0.4025*** (0.017) (0.049) (0.103) Loss due crime/thief −0.0431*** −0.0307*** 0.1997*** (0.004) (0.010) (0.034) Access to land 0.0138*** 0.0072 0.0135 (0.004) (0.010) (0.011) Quality 0.0300*** 0.0149 0.1198*** (0.007) (0.016) (0.027) External audit −0.1240*** −0.1200*** −0.2952*** (0.005) (0.011) (0.036) Firm status −0.0204*** −0.0205*** −0.1043*** (0.003) (0.007) (0.017) Small −0.0946*** −0.0748** −0.2000*** (0.012) (0.030) (0.040) Medium −0.0436*** −0.0301 0.0081 (0.009) (0.024) (0.027) Wald test 210.69 AR test (Continued) Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 14 of 28 4.2. Robustness check To confirm, the results mentioned above of the Tobit model and Multinomial Probit Model, we carry out robustness checks by using different proxies of corruption and the quality of tax administration. In this stage, I use corruption as a dummy variable from the question posed to the firm managers to know whether a gift is requested for an electrical connection. The value of a corruption dummy is 1 if a gift is requested and 0 otherwise. The new proxy of the quality of tax administration that was coded in baseline results as a dummy equal to 1 for major and severe obstacles and 0 otherwise is proxy now as 1 for a response of moderate, major, and severe obstacles and “0” (if otherwise). Table 5 indicates the results of the effect of tax administration and corruption on firm selffinancing using different proxies of corruption and tax administration discussed above. The results related to our key independent variables are similar to those in the baseline results. The formal training and female manager are not statistically significant. The rest of the control variables such as tax rate, manager experience, employment, production, technology, infrastructure, regulation, loss due crime and thief, access to land, firm international quality, external audit and firm status keep the consistent, and the signs of the baseline results. Except for minor differences in the variables coefficient, Table 5 shows the estimates are not statistically different from the baseline findings. Furthermore, the effects of the controlled variables did not significantly differ statistically from those of the baseline. This suggests that the baseline finding is reliable. Table 6 shows with the alternative measures of tax administration and corruption, the effect of tax administration and corruption on firm self-financing constraints does not vary. The results show that tax administration is positive and statistically significant in financing as minor, moderate, major, and severe obstacles. The meaning is that, for poor tax administration, the likelihood of firm all level of obstacles increase. The same results are seen in the second row with corruption. An increase in corruption increases the likelihood of firms at all levels of obstacles from minor to severe obstacles. The result is similar to our baseline results more precisely for our key independent variables. Except for minor differences in the variable coefficients, table 4.6 shows the estimates are not statistically different from the baseline findings by exception regulation that was not significant in became significant as indicates that regulation that is time wasted dealing with official in an obstacle to firm self-finance. Furthermore, the effects of the controlled variables did not significantly differ statistically from those of the baseline. This suggests that the baseline finding is robust. IVs Electricity and water Dependent variable: Internal fund/ Retained earning (1) (2) (3) VARIABLES Tobit Tobit IVTobit Country dummy NO YES YES Firm sector dummy NO YES YES Survey year dummy NO YES YES Observations 22,678 22,678 22,678 Standard errors in parentheses. The standard errors are Huber–White robust standard errors and clustered at firm level in brackets. Significance is denoted by *** p < 0.01, ** p < 0.05, * p < 0.1 Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 15 of 28 Table 4. Effect of tax administration and corruption on firm self-financing constraints: multinomial probit model marginal effects Dependent variables (1) (2) (3) (4) VARIABLES Minor obstacle Moderate obstacle Major obstacle Severe obstacle Tax administration 0.0076*** 0.0085*** 0.0259*** −0.0098*** (0.001) (0.001) (0.002) (0.001) Corruption 0.0107*** 0.0037 0.0434*** 0.0289*** (0.004) (0.004) (0.014) (0.008) Tax rate −0.0023** −0.0006 0.0089*** 0.0060*** (0.001) (0.001) (0.003) (0.002) Female manager 0.0010 0.0017** −0.0073*** 0.0046*** (0.001) (0.001) (0.002) (0.001) Training 0.0009 0.0021** −0.0085*** 0.0055*** (0.001) (0.001) (0.003) (0.002) Manager experience 0.0002 −0.0003 0.0002 −0.0001 (0.000) (0.000) (0.002) (0.001) Employment (log) −0.0014*** −0.0019*** 0.0091*** −0.0057*** (0.000) (0.001) (0.002) (0.001) Production (log) −0.0038** 0.0015 0.0038 −0.0015 (0.002) (0.002) (0.007) (0.005) Technology 0.0035*** 0.0032*** −0.0082*** 0.0014 (0.001) (0.001) (0.003) (0.002) Infrastructure −0.0051*** −0.0036*** 0.0138*** −0.0051*** (0.001) (0.001) (0.003) (0.002) Regulation −0.0009 0.0039 −0.0038 0.0008 (0.002) (0.003) (0.009) (0.005) Loss due crime/thief 0.0046*** 0.0056*** −0.0193*** 0.0090*** (0.001) (0.001) (0.002) (0.001) Access to land 0.0017*** 0.0021*** −0.0110*** 0.0072*** (0.001) (0.001) (0.002) (0.001) Quality 0.0042*** 0.0041*** −0.0106*** 0.0022 (0.001) (0.001) (0.003) (0.002) External audit −0.0033*** −0.0056*** 0.0273*** −0.0184*** (0.001) (0.001) (0.002) (0.002) Firm status −0.0024*** −0.0030*** 0.0115*** −0.0061*** (0.000) (0.000) (0.001) (0.001) Small −0.0082*** −0.0102*** 0.0347*** −0.0162*** (0.002) (0.002) (0.006) (0.004) Medium −0.0014 −0.0038** 0.0084* −0.0032 (0.001) (0.001) (0.005) (0.003) Country dummy YES YES YES YES Firm sector dummy YES YES YES YES Survey year dummy YES YES YES YES Observations 21,227 21,227 21,227 21,227 Standard errors in parentheses. The standard errors are Huber–White robust standard errors and clustered at firm level in brackets. Significance is denoted by *** p < 0.01, ** p < 0.05, * p < 0.1 Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 16 of 28 Table 5. Effect of tax administration and corruption on firm self-financing. Tobit marginal model. Alternative corruption and tax administration measures IVs Electricity and water Dependent variable: Internal fund/Retained earning (1) (2) (3) VARIABLES Tobit Tobit IVTobit Tax administration 2 −0.0812*** −0.0789*** −0.0764*** (0.006) (0.012) (0.006) Corruption dummy −0.0931*** −0.0912*** −0.2649*** (0.009) (0.016) (0.018) Tax rate −0.0463*** −0.0433*** −0.0448*** (0.007) (0.013) (0.007) Female manager 0.0033 −0.0036 −0.0019 (0.005) (0.010) (0.005) Training −0.0115* −0.0086 −0.0095 (0.006) (0.013) (0.006) Manager experience −0.0144*** −0.0134** −0.0144*** (0.003) (0.007) (0.003) Employment (log) −0.0585*** −0.0451*** −0.0468*** (0.003) (0.007) (0.003) Production (log) 0.0461*** 0.0356 0.0354*** (0.012) (0.024) (0.012) Technology 0.0985*** 0.0685*** 0.0650*** (0.006) (0.012) (0.006) Infrastructure −0.1229*** −0.0925*** −0.0863*** (0.005) (0.012) (0.006) Regulation −0.0481*** −0.0469 −0.0398** (0.015) (0.034) (0.015) Loss due crime/thief 0.0607*** 0.0436*** 0.0492*** (0.005) (0.009) (0.005) Access to land 0.0996*** 0.0876*** 0.0925*** (0.005) (0.009) (0.005) Quality 0.0103 0.0061 0.0103 (0.007) (0.013) (0.007) External audit −0.1110*** −0.1027*** −0.1096*** (0.005) (0.009) (0.005) Firm status −0.0188*** −0.0175*** −0.0171*** (0.002) (0.005) (0.002) Small −0.1195*** −0.0996*** −0.1041*** (0.012) (0.024) (0.012) Medium −0.0577*** −0.0491** −0.0505*** (0.010) (0.019) (0.010) Wald test 132.28 AR test Country dummy NO YES YES Firm sector dummy NO YES YES Survey year dummy NO YES YES Observations 26,200 26,200 26,200 Standard errors in parentheses. The standard errors are Huber–White robust standard errors and clustered at firm level in brackets. Significance is denoted by *** p < 0.01, ** p < 0.05, * p < 0.1 Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 17 of 28 Table 6. Effect of tax administration and corruption on firm self-financing constraints: multinomial probit model marginal effects. Alternative corruption and tax administration measures Dependent variables Minor obstacle Moderate obstacle Major obstacle Severe obstacle VARIABLES Tax administration 0.0076*** 0.0085*** 0.0259*** 0.0098*** (0.001) (0.001) (0.002) (0.001) Corruption 0.0107*** 0.0037 0.0434*** 0.0289*** (0.004) (0.004) (0.014) (0.008) Tax rate −0.0023** −0.0006 0.0089*** −0.0060*** (0.001) (0.001) (0.003) (0.002) Female manager 0.0010 0.0017** −0.0073*** 0.0046*** (0.001) (0.001) (0.002) (0.001) Training 0.0009 0.0021** −0.0085*** 0.0055*** (0.001) (0.001) (0.003) (0.002) Manager experience 0.0002 −0.0003 0.0002 −0.0001 (0.000) (0.000) (0.002) (0.001) Employment (log) −0.0014*** −0.0019*** 0.0091*** −0.0057*** (0.000) (0.001) (0.002) (0.001) Production (log) −0.0038** 0.0015 0.0038 −0.0015 (0.002) (0.002) (0.007) (0.005) Technology 0.0035*** 0.0032*** −0.0082*** 0.0014 (0.001) (0.001) (0.003) (0.002) Infrastructure −0.0051*** −0.0036*** 0.0138*** −0.0051*** (0.001) (0.001) (0.003) (0.002) Regulation −0.0009 0.0039 −0.0038 0.0008 (0.002) (0.003) (0.009) (0.005) Loss due crime/ thief 0.0046*** 0.0056*** −0.0193*** 0.0090*** (0.001) (0.001) (0.002) (0.001) Access to land 0.0017*** 0.0021*** −0.0110*** 0.0072*** (0.001) (0.001) (0.002) (0.001) Quality 0.0042*** 0.0041*** −0.0106*** 0.0022 (0.001) (0.001) (0.003) (0.002) External audit −0.0033*** −0.0056*** 0.0273*** −0.0184*** (0.001) (0.001) (0.002) (0.002) Firm status −0.0024*** −0.0030*** 0.0115*** −0.0061*** (0.000) (0.000) (0.001) (0.001) Small −0.0082*** −0.0102*** 0.0347*** −0.0162*** (0.002) (0.002) (0.006) (0.004) Medium −0.0014 −0.0038** 0.0084* −0.0032 (0.001) (0.001) (0.005) (0.003) Country dummy YES YES YES YES Firm sector dummy YES YES YES YES Survey year dummy YES YES YES YES Observations 21,227 21,227 21,227 21,227 Standard errors in parentheses. The standard errors are Huber–White robust standard errors and clustered at firm level in brackets. Significance is denoted by *** p < 0.01, ** p < 0.05, * p < 0.1 Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 18 of 28 5. Extended analysis: effect of tax administration and corruption on firm self-financing by firm size and the interactions with firm size For testing the impact of weak tax administration on firm self-financing according to firm size, we divide our sample into three parts SMALL, MEDIUM, and LARGE size. Table 7 indicates that the effect of poor quality of tax administration and corruption on small firms in column 1 for medium firms in column 2, large firms, and large firms in column 3, respectively. The results show that large firms suffer a lot more than smaller and medium firms do. For example, Column 1 indicates a 0.05 decrease in small firm self-financing for the poor perception of tax administration. However, for medium and larger firms as indicated in Columns 2 and 3, a perception of poor tax administration quality lessens their self-financing by 0.03 and 0.10, respectively. This finding illustrates that, while weak tax administration harms firm financing in general, the negative impact on larger firms surpasses the negative impact on smaller and medium firms. That is, in the face of weak tax administration, small and medium businesses tend to benefit in a way than larger firms in Africa. Small firms can effectively stay informal, and thus escape tax commitments (which can compensate for some of the effects) compared with bigger firms that cannot escape poor taxation administrations, as most of them are they are on display for everyone’s attention. This indicates that firms are not encouraged to work on a large scale. This can account for the presence of several smaller informal firms in Africa and the obstacle posed by officials in the effort to formalize informal firms in Africa. On the other hand, large firms struggle more in the sense that they have to gain social capital and reputation, which might, in turn, contribute to more constraints, causing their incapability to use some means to address any unfavorable pressure put upon them by the poor quality of the fiscal administration (i.e. evading taxes by being informal) (Kamasa et al., 2019). Table 7 also shows that SMEs suffer more from corruption than large enterprises. An increase in corruption decreases the likelihood of small and medium firms to self-finance by, respectively, 0.13 and 0.24, whereas the reduction in large firm is not even significant. Thus, corruption issue is more critical in the case of small and medium firms as they spend a large portion of their profit to the government official as a gift or informal payments to reduce the burden of regulations and circumvent taxes. For this reason, big firms are habitually more protected from corruption but further, they are constrained to heavier controls and taxation such as the judiciary, labor regulations, company authorizing, tax administration, and tax charges (Ezebilo et al., 2019). In Table 8, two interaction terms for tax administration and corruption are included to determine whether the impact of inadequate tax administration on firm self-financing differs for smaller to large enterprises. Table 8 indicates that smaller firms and poor quality tax administration interact negatively (0.0161), and this interaction is significant at 1%. Hence, the overall impact of low tax administration quality on firm self-financing is −0.0659– 0.0161(1) = −0.082. For the large firm, the total effect will be −0.0659–0.0288(1) = −0.0947. In line with the results found in Table 7, this finding demonstrates that although inefficient tax administration reduces self-financing generally, it has a more negative impact on larger firms than on smaller ones. For corruption side, the interaction term for small firm is negative (0.0289) and significant. The overall effect for small firm is −0.0659–0.0289(1) = −0.0948. For large firm, the interation term is −0.0116 but insignificant. The results confirm our findings in Table 7 where we argued that small firms suffer more from corruption than large enterprises. Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 19 of 28 Table 7. Effect of tax administration and corruption on firm self-financing by firm size Firm size Small firm (<20) Medium firm (20–99) Large firm (99+) (1) (2) (3) VARIABLES Tobit Tobit Tobit Tax administration −0.0516*** −0.0317* −0.1023* (0.013) (0.027) (0.053) Corruption −0.1342** −0.2362** −0.0477 (0.065) (0.117) (0.275) Tax rate −0.0219 −0.0407 −0.1044* (0.016) (0.032) (0.060) Female manager 0.0011 −0.0936*** −0.0638 (0.013) (0.027) (0.050) Training 0.0290 −0.0933** 0.0291 (0.025) (0.037) (0.050) Manager experience −0.0155* −0.0178 0.0212 (0.009) (0.018) (0.030) Employment (log) −0.0433*** −0.0344* 0.0521** (0.010) (0.020) (0.026) Production (log) −0.0647 0.0928 0.1213 (0.070) (0.086) (0.084) Technology 0.0441* 0.0592** 0.0115 (0.024) (0.029) (0.049) Infrastructure −0.0486** −0.1787*** −0.2719*** (0.021) (0.039) (0.063) Regulation −0.1561** −0.0640 −0.2125 (0.065) (0.103) (0.153) Loss due crime/thief 0.0426*** −0.0389 −0.0637 (0.012) (0.026) (0.055) Access to land −0.0121 0.0024 0.1143** (0.012) (0.023) (0.044) Quality 0.0523** −0.0128 −0.0452 (0.024) (0.029) (0.046) External audit −0.1300*** −0.0548** −0.0039 (0.013) (0.024) (0.080) Firm status −0.0374*** 0.0255* 0.0137 (0.009) (0.014) (0.026) Country dummy YES YES YES Firm sector dummy YES YES YES Survey year dummy YES YES YES Observations 21,242 1,057 379 Standard errors in parentheses. The standard errors are Huber–White robust standard errors and clustered at firm level in brackets. Significance is denoted by *** p < 0.01, ** p < 0.05, * p < 0.1 Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 20 of 28 Table 8. Interaction between firm size, tax administration, and corruption VARIABLES Tobit Tax administration −0.0659*** (0.020) Corruption −0.0751** (0.032) Tax rate −0.0421*** (0.013) Female manager −0.0077 (0.010) Training −0.0124 (0.013) Manager experience −0.0077 (0.006) Employment (log) −0.0440*** (0.007) Production (log) 0.0271 (0.024) Technology 0.0777*** (0.011) Infrastructure −0.1205*** (0.010) Regulation −0.0648* (0.034) Loss due crime/thief 0.0484*** (0.009) Access to land 0.0931*** (0.009) Quality 0.0089 (0.013) External audit −0.1042*** (0.009) Firm status −0.0166*** (0.005) SMALL −0.0356** (0.022) LARGE −0.0692* (0.036) Tax administration*SMALL −0.0161*** (0.023) Tax administration*LARGE −0.0288* (0.038) Corruption*SMALL −0.0289** (0.036) Corruption*LARGE −0.0116 (Continued) Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 21 of 28 6. Conclusion This study looks into the influence of corruption and tax administration on firm self-financing in Africa. By employing the World Bank Enterprise survey (WBES) data for over 45,048 firms across 48 African countries, the findings using the Tobit model reveal that corruption makes it difficult for a firm to selffinance by securing their internal funds or retained earnings. In addition, we discover that weak tax administration decreases firm self-financing. The results are consistent after taking into account firm characteristics, year and country specifications, and endogeneity issues. The finds are robust by using alternation corruption and tax administration proxies. Furthermore, we investigate the level at which corruption and tax administration are obstacles for firm financing by employing the Multinomial Probit model. We categorized financing obstacles into minor, moderate, major, and severe obstacles. We find that firm self-finance obstacles are best predicted by corruption and poor tax administration. The results show that tax administration and corruption are positive and statistically significant in financing as minor, moderate, major, and severe obstacles. The findings are also robust after using alternative corruption and tax administration measures. In addition, the finding also illustrates that, while weak tax administration harms firm financing in general, the negative impact on larger firms surpasses the smaller and medium firm’s ones. That means for a poor tax administration, small and medium businesses tend to benefit in a way than larger firms in Africa. Small firms can effectively stay informal and thus escape tax commitments compared to A total of 102 large firms that cannot escape from poor taxation administrations, as most of them are on display for all attention. SMEs suffer more from corruption than large enterprises. Also, the corruption issue is more critical in the case of small and medium companies as they spend a large portion of their profit on the government official as a gift to down the burden of impositions and circumvent taxes. For this reason, big firms are habitually more protected from corruption but further, they are constrained to heavier controls and taxation such as the judiciary, labor regulations, company authorizing, tax administration, and tax charges. Funding Authors state no funding was involved. Author details Toure Moumbark 1 E-mail: [email protected] Yawovi M. A. Koudalo 1 1 Research Institute of Economics and Management, Southwestern University of Finance and Economics, Chengdu, China. Disclosure statement No potential conflict of interest was reported by the authors. Authors’ contribution Toure Moumbark (corresponding author) wrote the main manuscript text including the introduction, the literature review, data collection, and the conclusion. Yawovi M. A. Koudalo wrote formal analysis, software and prepared figures and tables. Toure Moumbark and Yawovi M. A. Koudalo did reviewing/editing. Citation information Cite this article as: Firm self-financing, corruption, and the quality of tax administration in Africa, Toure Moumbark & Yawovi M. A. Koudalo, Cogent Economics & Finance (2023), 11: 2266241. Notes 1. The surveys were conducted in 2006 in 8 countries: Angola, Botswana, Burundi, Democratic Republic of Congo, Eswatini, Gambia, Guinea, Guinea-Bissau, Mauritania, Namibia, Rwanda, and Uganda. 2007 for 8 countries: Ghana, Kenya, Nigeria, Mali, Mozambique, Senegal, South Africa, Zambia; 2009 for 17 countries: Benin, Burkina Faso, Cape Verde, Chad, Congo, Cote d’Ivoire, Eritrea, Gabon, Lesotho, Liberia, Madagascar, Malawi, Mauritius, Niger, Sierra Leone, Togo, Cameroon; 2010 for 3 countries: Angola, Botswana, Democratic Republic of Congo; 2011 for 4 countries: CAR, Ethiopia, Rwanda, Zimbabwe. 8 countries for 2014: Burundi, Malawi, Mauritania, Namibia, Nigeria, Senegal, South Sudan, Sudan. 8 countries for 2016: Table 8. (Continued) VARIABLES Tobit (0.070) Observations 26,200 Firm sector FE YES Standard errors in parentheses. The standard errors are Huber–White robust standard errors and clustered at firm level in brackets. Significance is denoted by *** p < 0.01, ** p < 0.05, * p < 0.1 Moumbark & Koudalo, Cogent Economics & Finance (2023), 11: 2266241 https://doi.org/10.1080/23322039.2023.2266241 Page 22 of 28 Cameroon, Egypt, Eswatini, Guinea, Lesotho, Mali, Togo, Zimbabwe. 3 countries for 2017: Liberia, Niger, Sierra Leone. 3 countries for 2018: Chad, Kenya, Mozambique. 3 countries for 2019: Morocco, Rwanda, Zambia. 2 countries for 2020: Egypt, Tunisia. 2. Following Dow and Endersby (2004) who argue that multinomial Logit gives better results even failed to pass IIA assumption, we present the result of multinomial Logit in Table A3 of Appendix A for robustness. The results are similar. References Africapractice. (2005, January). The African Year in 2005. Africapractice. (2019, January). The African Year in 2019. Africa practice. Aryeetey, E., & Aryeetey, E. (1998). Informal finance for private sector development in Africa. African Development Bank Group. Aterido, R., Beck, T., & Iacovone, L. (2011). 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