Strengthening Systems Accountability for Enterprise Performance and Development Planning Edited by Gbadebo O. A. Odularu Gbadebo O. A. Odularu Editor Strengthening Systems Accountability for Enterprise Performance and Development Planning Contents 1 Introduction: Strengthening Systems’ Accountability for Enterprise Performance and Development Planning 1 Gbadebo O. A. Odularu 2 Accountability and Public Sector Financing in Nigeria 13 Oladimeji Bukola Olaniyi 3 Enterprise Performance, Accountability, and Rurality in South Africa 33 Ishmael Iwara, Gbadebo O. A. Odularu, and Simon Michael Taylor 4 Understanding Health Systems Within a Decentralized and Accountable Development Framework: Trust in the Balance 53 Gbadebo O. A. Odularu, Chuka Onyekwena, and Adeniran Adedeji 5 “Political Will” as an Impediment to Accountability of Law Enforcement in Nigeria 71 Barr Obisanya Tope Ayo and Ishmael Iwara v CHAPTER 6 Probability of Default, Accountability, Bankruptcy, and Digitization Amid COVID-19 Pandemic Olumide Adesina, Gbadebo O. A. Odularu, and Adeniyi Samson Onanaye 6.1 Introduction COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which first case was recorded in China in 2019 has spread globally from 2019 to the present (2021), though the curve is getting flattened in some countries across the world, particularly in Africa. COVID-19 pandemic is a threat to human lives, and the impact is being felt in various industries. The COVID-19 pandemic forced economic O. Adesina (B) · A. S. Onanaye Department of Mathematical Sciences, Redeemer’s University, Ede, Osun State, Nigeria e-mail: adesinas@run.edu.ng O. Adesina Data Science and Business Analytics, University of London, London, UK © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 G. O. A. Odularu (ed.), Strengthening Systems Accountability for Enterprise Performance and Development Planning, https://doi.org/10.1007/978-3-031-11779-4_6 103 104 O. ADESINA ET AL. activities in many cities to shut down globally to curtail the spread of the virus. Inactivity resulting in compulsory lockdown has resulted in the loss of money to organizations and individuals alike. The first confirmed case of COVID-19 in Nigeria was on 9 March 2020 and reported by the Nigeria Centre for Disease Control (NCDC) and the New York Times (Maclean and Dahir 2020). Total cases recorded as of June 4, 2021, were 166,682, and 2117 deaths. Governments around the world are losing money paying workers while little or no activities were going on. Privately owned institutions are not generating revenue and the majority of the school owners are not able to pay their staff. However, institutions globally have resorted to remote classes, also conducting assessments virtually as a way of accepting the new normal resulting from COVID-19. As beneficial as virtually classes could be, inadequate funding, and low coverage of ICT, among others posit challenges to online teaching and learning, particularly in the African context (ADEA 2020). This implies that the COVID-19 pandemic met educational sectors unprepared. During the total COVID-19 pandemic lockdown, commercial activ- ities such as international trade and Air travel have been badly affected since most countries have shut down their borders. Scheduled flights have been canceled till future dates and persons find it difficult to resume work, and unite with families as planned. The hospitality and recreation sectors have been equally badly affected because hotels and recreation centers have been shut down. The study by Maliszewska et al. (2020) showed that Airlines worldwide are projected to lose $113 billion in revenues in 2020. McKibbin and Fernando (2020) mentioned that the world GDP is anticipated to fall between 0.1 and 1.5%, and global trade is expected to fall between 0.2 and 3.75%. The world economic giants such as China, the United States of America, and Japan are expected to experience a decline in their GDPs by 6, 8, and 10%, respectively. Some organizations recorded gains during the period of the COVID-19 pandemic because they have been able to come up with innovations thereby turning the pandemic into gold (Nairametrics 2020); on the other hand, others have recorded huge losses which may result in financial and economic distress. The study G. O. A. Odularu Department of Economics and Finance, Bay Atlantic University, Washington, DC, USA 6 PROBABILITY OF DEFAULT, ACCOUNTABILITY, BANKRUPTCY … 105 conducted by Aifuwa et al. (2020) showed that COVID-19 Pandemic had impacted negatively on both the financial and non-financial performance of private businesses in Nigeria, and McKinsey (2020) mentioned that there is a likelihood for Africa to experience economic contraction with GDP growth between percent −0.4 to −3.9 in 2020. An organization that is in a state of distress may be insolvent. This implies that such an organization may not be able to meet the financial obligations to her creditors which may result in filing for bankruptcy, and bankruptcy posits a legal status of an insolvent organization that cannot pay back debts to the creditors. The study by Senbet and Wang (2010) showed that bankruptcy does not necessarily lead to economic distress or poor economic performance. If an organization is in finan- cial distress, the creditor’s expectations of the firm to pay the debt owed are broken. In addition, if an organization has trouble because of oper- ational inefficiencies, such an organization is said to be economically distressed. The reputations and social status of directors diminish when a firm is in a state of bankruptcy, hence, only reputable, and credible directors are retained in such organizations (Mullens 2014). Relating that to the current happenings, the World Bank Group (2020) stated that the COVID-19 pandemic has resulted in a reduction in demand for the supply of goods and services leading to difficulty in the provision of credit. Therefore, employers are forced to lay off workers because of the inability to pay the salaries of workers. Filings for bankruptcy are on the increase because of financial shocks because non-performing loans are increasing (The World Bank Group 2020). So, COVID-19 Pandemic has poten- tially increased the rate at which firms experience insolvency, and there is a need to measure it to mitigate against it. It is pertinent to note that digital technologies represent innovative pathways and effective tools for overcoming these insolvencies and bringing business back better more dynamically in the post-COVID-19 era as well as minimizing the inherent fragilities which are embedded in the African Continental Free Trade Area (AfCFTA) (Odularu 2020b, c). At the continental level, there are chal- lenges with accessing payment services, savings, credit, and other financial services (Odularu 2020a). Furthermore, innovation in payments, digital payments awareness creation, and the introduction of innovative prod- ucts should be one component of the industry’s response to COVID-19 (Odularu 2020b, c). 106 O. ADESINA ET AL. 6.1.1 Bankruptcy Models Bankruptcy models known as scoring techniques are used to predict insol- vency for companies and may as well be used to monitor a company’s liquidity. The multiple discriminant analysis techniques used in scoring functions identified in the literature include the method introduced by Beaver (1966), followed by Altman (1968), logit and probit by (Ohlson 1980), Revised Z-score (Altman 1983), Taffler (1983). The discriminant analysis technique is applicable in testing equality between the means of two or more groups of items. The Altman Z-score (Altman 1968, 1983) is being used by researchers and practitioners till the present day and can be found in a recent study by Boďa and Úradníček (2016), Ali and Özari (2018), Al-Manaseer and Al-OShaibat (2018), Özyeşil (2020), and Heaton (2020). Following bankruptcy model (Altman 1968) and revised Z-score (Altman 1983), respectively. The Altman Z-score is as follows Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 0.999X5 (6.1) While the revised Z-score defined as Z ' = 0.717X1 + 0.847X2 + 3.107X3 + 0.420 X ' 4 + 0.998X5 (6.2) X1 represent the working capital/total assets (current assets − current liabilities), X2 is retained earnings/total assets, X3 is earnings before interest and taxes/total assets, X4 is market value equity/book value equity, X5 is sales/total assets, and Z is weighted average of five sepa- rate ratios. In the revised Z-score following Altman (1983), X ' 4 market value equity/Book Value of debt. For the revised Z-score (Z '-score) the following apply; when Altman Z-Score < 1.81, the organization is in Distress Zones, when Altman Z-Score is between 1.81 and 2.99, it is in Grey Zones, and when Altman Z-Score > 2.99, it is in Safe Zones. By implication from Altman Z-score model, if the Z-score is less than 1.81, the company is going to experience bankruptcy within a year or two. Organizations are now adjusting and learning ways to live with the COVID-19 pandemic to operate at an optimum level. This study aims to present a practical way of estimating an organization’s insolvency using the Z-score technique with the functions embedded in the “tidy verse” package in R. Also, to determine the probability of an organiza- tion defaulting in meeting their financial obligations to creditors which 6 PROBABILITY OF DEFAULT, ACCOUNTABILITY, BANKRUPTCY … 107 can also be adopted by practitioners as COVID-19 has impacted business activities adversely in recent times. Past studies computed Z-scores based on annual data while in the current study we obtained Z-scores using quarterly records. The Z-scores were computed using simulated data, followed by real-life data of Lafarge Africa. The logit model was used to estimate the probability of default. The remaining part of this paper is arranged as follows; material and methods in Sect. 6.2, results in Sect. 6.3, and finally discussion and conclusion in Sect. 6.4. 6.2 Material and Methods In computing the probability of default, we considered a binary response in two categories, defaults coded as one (1), and non-defaults coded as zero (0). Since we have two categories, the discriminant analysis technique is suitable, and, if the classification is more than two groups’ techniques such as multinomial logistic regression can be employed by Smarandaa (2014). Testing the hypothesis follows that the discriminant analysis will multiply each independent variable by the corresponding weight and adds up these products obtained in the discriminant score calculated for each item in the selected sample. Following this process, the group mean called centroid would be obtained; this implies that if there are two groups, we would obtain two centroids. The statistical significance of the discriminant function generalizes the distance between the groups’ centroids. Since we have a binary function (0, 1), the method of logistic regression would be considered and can be obtained as follows. Let Y be response variable, which is also binary, and y∗ i be an unobserv- able (latent variable). The introduction of the latent variable is to avoid restrictions, y∗ i is defined as follows y∗ i = b0 + kΣ j=1 b j xi j + εi We define yi as yi = ( 1, i f y∗ i > 0 0, i f y∗ i ≤ 0 σ 2 εi=1 108 O. ADESINA ET AL. The probability can be computed as Pi = prob(yi = 1) =prob ⎛ ⎝b0 + kΣ j=1 b j xi j + εi 0 ⎞ ⎠ =prob ⎡ ⎣εi − ⎛ ⎝b0 + kΣ j=1 b j xi j ⎞ ⎠ ⎤ ⎦ = 1 − prob ⎡ ⎣εi ≤ − ⎛ ⎝b0 + kΣ j=1 b j xi j ⎞ ⎠ = 1 − F ⎛ ⎝− ⎛ ⎝b0 + kΣ j=1 b j xi j ⎞ ⎠ ⎞ ⎠ ⎤ ⎦ where F is the cumulative distribution function for ε. However, if ε’s distribution is symmetric, it can be represented as Pi = prob(yi = 1) = 1 − F ⎛ ⎝− ⎛ ⎝b0 + kΣ j=1 b j xi j ⎞ ⎠ ⎞ ⎠ = F ⎛ ⎝b0 + kΣ j=1 b j xi j ⎞ ⎠ (6.3) The maximum likelihood function is: L = Π yi=1 Pi Π yi=0 (1 − Pi ) (6.4) The distribution function F and model type is obtained from the distri- bution of the error term, ε. If the cumulative distribution function of ε is logistic, the logit model is represented as follows From (1), let Pi = F(q), then F(q) = eq 1 + eq Transforming, we obtain q = log ( F(q) 1 − F(q) ) 6 PROBABILITY OF DEFAULT, ACCOUNTABILITY, BANKRUPTCY … 109 It follows that log ( Pi 1 − Pi ) = b0 + kΣ j=1 b j xi j (6.5) The cumulative distribution function of ε is standard normal ε ∼ (0, 1), giving the probit Model A symmetric normal distribution is given as Φ ( X 'b ( Pr (yi = 1|xi ) = Φ ) x ' i b ) Pr (yi = 0|xi ) = 1 − Φ ) x ' i b ) where xi i s a vector o f k × 1, b is also a k × 1 vector o f coe f f i cients The likelihood is given as L(b, xi , yi ) = ( Φ ) x ' i b )yi [ 1 − Φ ) x ' i b )(1−yi ) ]) i f yi = 1, then L(b, xi , yi ) = Φ ) x ' i b ) i f yi = 0, then L(b, xi , yi ) = 1 − Φ ) x ' i b ) For an independent and identically distributed (i.i.d), the joint likelihood will be equal to the product of the likelihoods of the single observations: L(b, xi , yi ) = nΠ i=1 ( Φ ) x ' i b )yi [ 1 − Φ ) x ' i b )(1−yi ) ]) The joint log-likelihood function is e = L(b, xi , yi ) = nΣ i=1 ) yi I nΦ ) x ' i b ) + (1 − yi )In ) 1 − Φ(x ' i b )) (6.6) Pi = Φ ⎛ ⎝b0 + kΣ j=1 b j xi j ⎞ ⎠ 110 O. ADESINA ET AL. Maximizing (4) to obtain the estimations for the coefficient vector, b. l can be written as e = Π i { yi I nF ( b'x ( + (1 − yi )I n [ 1 − F ( b'x (]} ∂e ∂b = Σ i [ yi fi F(b'x) + (1 − yi ) − fi 1 − F(b'x) xi ] = 0 (6.7) The residual of the logit model are given by: ûi = yi − eti 1 + eti , and ûi = b̂0 + kΣ j=1 b̂ j xi j (6.8) The residuals are the difference between the observed variable, and the value estimated in the model, details can on logistic models can be found in James et al. (2013, pp. 113–137) and (Tabachnick and Fidell 2014, pp. 481–493). The probability of defaults relies on the logit model in Eqs. 6.1–6.8. 6.2.1 Simulation Study To predict whether a firm will default or not, we used discriminant anal- ysis. The variables involved are dependent (GROUP) which is binary (1, 0), and predictors are X1 = WCTA , X2 = RETA, X3 = EBITTA, X4 = MVBV, and X5 = SATA. GROUP is a categorical variable representing the actual default of a firm which should normally be collected from historical data. In the variable GROUP, 1 = Default, 0 = non-default. Five hundred (500) random values generated were generated uniformly, WCTA (2.0, 4.0), RETA (1.5, 5.0), EBITTA (2.3, 4.5), MVBV (2.0, 5.0), SATA (1.2, 6.0). Simulation specification is summarized in Table 6.1 and the values obtained represent the ratios for 500 different firms, and the probability of default and non-default were assumed to be 0.45 and 0.55, respectively. The response variable of the simulated data was grouped as one’s for defaults and zeros for non-defaults from which the percentage of defaults and non-defaults was computed. The cut-off limit which divided the firms into default and non-default was also computed. Software by the R Core 6 PROBABILITY OF DEFAULT, ACCOUNTABILITY, BANKRUPTCY … 111 Table 6.1 Parameters for simulation Minimum Maximum WCTA 2.0 4.0 RETA 1.5 5.0 EBITTA 2.3 4.5 MVBV 2.0 5.0 SATA 1.2 6.0 Source Author’s computation team (2020) was used to implement the analysis, and functions in the “tidy verse” package in R by Wickham et al. (2019) were adopted for the simulation study as contained in Kulkarni (2018). 6.2.2 Real-Life Data The real-life data used in this study were obtained from Lafarge Africa (LafargeHolcim) balance sheet. Lafarge Africa plc was formerly trading under the name Lafarge Wapco Plc, it is majorly controlled by Lafarge- Holcim because of the Lafarge merging with Holcim. Affiliated compa- nies of Lafarge Africa are distributors of Elephant cement, UniCem, Lafarge South Africa Pty, Atlas Cement, Lafarge Ready-Mix, Ashaka Cement, WAPCO cement. The data for the case study was obtained from the LafargeHolcim balance sheet https://www.lafarge.com.ng/fin ancial-reports, https://www.marketscreener.com/LAFARGE-AFRICA- PLC-6500000/financials/and presented in Table 6.2. The data in Table 6.2 was used to compute quarterly Z-score and Z '- score, respectively. 6.3 Results 6.3.1 Simulation Based on the simulation carried out, the summary statistics is presented in Table 6.3. Linear regression of GROUP = WCTA ∗X1 + RETA ∗X2 + EBITTA ∗X3 + MVBV ∗X5 + SATA ∗X5, and the result of the linear regression is shown in Table 6.4 as follows. Logit model was used to estimate the number of non-default and default in Table 6.4. 112 O. ADESINA ET AL. Table 6.2 Figures extracted from LafargeHolcim balance sheet Three months ending December 31, 2019 (₦'000) Three months ending March 2020 (₦'000) Total asset 497,152,208 491,813,770 Total liabilities 152,238,207 138,832,880 Total revenue 213,000,000 63,695,766 Retained earning 155,801,325 163,868,214 Current asset 75,045,721 70,964,377 Current liabilities 84,411,770 77,404,263 Market capitalization 246,449,000 169,131,852 EBIT 34,910,000 169,000,000 Total equity 344,914,001 352,980,890 Book value of debt 266,207,059 60,064,499 Source LafargeHolcim Table 6.3 Summary statistics for simulated data Min 1st Qu Median Mean 3rd Qu Max WCTA 2.002 2.498 3.005 3.007 3.529 3.998 RETA 1.501 2.381 3.317 3.272 4.171 4.999 EBITTA 2.304 2.839 3.346 3.370 3.901 4.477 MVBV 2.005 2.726 3.456 3.473 4.224 4.999 SATA 1.202 2.453 3.817 3.719 5.015 5.991 GROUP 0.000 0.000 0.000 0.4640 1.000 1.000 Source Author’s computation Table 6.4 Percentage of defaults and non-defaults Group n % Non-default 268 0.536 Default 232 0.464 Total 500 1.000 Source Author’s computation The Cut-off point that was used to divide firms into default and non- default following discriminant analysis was 0.458. A given firm will default if the Z-score is more than the cut-off, else such a firm will not default. The value obtained from the Z-score was 0.35733 which implies that 6 PROBABILITY OF DEFAULT, ACCOUNTABILITY, BANKRUPTCY … 113 Table 6.5 Classification table based on the Altman Z-score Z-Score Z’-Score Distress 333 340 Grey 63 77 Safe 104 83 Total 500 500 Source Author’s computation Table 6.6 LafargeHolcim quarterly data 3 months ending December 2019 3 months ending March 2020 Z-score 2.05591 (Grey zone) 2.44515 (Grey zone) Z’-score (modified) 1.2914 (Distress zone) 3.61273 (safe zone) Source Author’s computation such a company will default on its financial obligations. Table 6.5 shows a comparative application of the Z-Score and the Revised Z-Score to the simulated data. Results in Table 6.5 show that majority of the simulated firms are in distress zone using both models. 6.3.2 Real-Life Data Table 6.6 shows the Altman Z-Score computed from the data presented in Table 6.2. The computations can be found in the appendix. 6.4 Conclusion: Policy Implications and Areas of Future Research Relative to studies by (Boďa and Úradníček 2016; Al-Manaseer and Al- OShaibat 2018; Ali and Özari 2018; Özyeşil 2020) that used annual data, quarterly data of Lafarge Africa was used in this study. Three months ending December 31, 2019, and three months ending March 30, 2020, was used to compute Altman Z-scores. The result shows that the company was in the Grey zone using Altman Z-score and in Distress zone using the revised Z-score in the three months ending December 2019. For three months ending March 2020, the company was also in the Grey zone 114 O. ADESINA ET AL. using Altman Z-score and in a safe zone using the revised Z-score. Z- score can also help investors to monitor the security of their investments. If it is observed that the Z-score of an organization is declining, in-depth analysis can be conducted to determine the root cause of the decline. From the policy implications perspective, there is no doubt the fact that one of the apparent repercussions of the COVID-19 pandemic includes disruptions in national, regional, and global business systems. However, evidence has revealed that one of the resilient roads to socioeconomic shocks recoveries is strengthening digital business and e-commerce capac- ities as well as supply chain networks from the effects of the COVID-19 pandemic. Of great relevance is the need to understand how digital communication technology, digital infrastructure, and information remain critical components of sustainable policies toward enhancing business trajectories in Africa. COVID-19 and its impact on organizations potentially lead to their inability to meet up to the demands of creditors, hence default in paying monies owed has been considered in this study. This study provided a mathematical technique for estimating organizational finan- cial distress, which potentially leads to bankruptcy amid the COVID-19 pandemic situation. Simulated data were used to compute the Z-scores and probability of default using the logistic model. One of the areas of future research is the need for a critical re-analysis of the COVID-19 outbreak-related challenges and dynamics being faced by African-based large businesses with a specific focus on digitization- related connectivity infrastructure. Furthermore, African governments need to systematically integrate digitalization in their intra- and inter- continental business relations as well as adopt more proactive digital tools toward boosting post-COVID-19 trade, business, and investment volumes, direction, and composition in this digital decade. 6.5 Compliance with Ethical Standards 6.5.1 Conflict of Interest No conflict of interest whatsoever. Dr. Olumide Sunday Adesina declares that he has no conflict of interest. Dr. Gbadebo Odularu declares that he has no conflict of interest. Dr. Adeniyi Samson Onanaye declares that he has no conflict of interest. 6 PROBABILITY OF DEFAULT, ACCOUNTABILITY, BANKRUPTCY … 115 6.5.2 Funding No funding was received for the study. Dr. Olumide Sunday Adesina declares that he has no funding for this study. Dr. Gbadebo Odularu declares that he has no funding for this study. Dr. Adeniyi Samson Onanaye declares that he has no funding for this study. 6.5.3 Ethical Approval This article does not require ethical approval; data were sourced from published annual reports. Appendix A1. Three Months, 2019 Ended December 31 Computed with Naira Value (‘000) X1 = W orking Capital/T otal Assets = (T otal Current Assets − T otal Current Liabili ties)/T otal Assets = (75045721 − 84411770)/497152208 = −0.011884 X2 = Retained Earnings/T otal Assets = 155801325/497152208 = 0.31338 X3 = Earnings Be f ore I nterest and T axes/T otal Assets = EB I T /T otal Assets = 34910000/497152208 = 0.070219944 X4 = Market V alue Equity/Book V alue o f T otal Liabili ties = Market Cap/Total Liabili ties = 246449000/152238207 = 1.6188380 X4' = Market V alue Equity/Book V alue o f debt 116 O. ADESINA ET AL. X4' = 246449000/266,207,059 = 0.925779357 X5 = Revenue/T otal Assets = 213000000/497152208 = 0.428440217 Z = 1.2 ∗ −0.01188 + 1.4 ∗ 0.31338 + 3.3 ∗ 0.07021 + 0.6 ∗ 1.618838036 + 1.0 ∗ 0.42844 = 2.05591 Z ' = 0.717 ∗ −0.01188 + 0.847 ∗ 0.31338 + 3.107 ∗ 0.07021 + 0.420 ∗ 0.92580 + 0.998 ∗ 0.42844 = 1.2914764 A2. Three Months, 2020 Ended March 30 Computed with Naira Value (‘000) X1 = W orking Capital/T otal Assets = (T otal Current Assets − T otal Current Liabili ties)/T otal Assets = (70,964,377 − 77,404,263)/491,813,770 = − 0.013094156 X2 = 163,868,214/491,813,770 = 0.333191594 X3 = Earnings Be f ore I nterest and T axes/T otal Assets = EB I T /T otal Assets = 169000000/491,813,770 = 0.343626003 6 PROBABILITY OF DEFAULT, ACCOUNTABILITY, BANKRUPTCY … 117 X4 = Market V alue Equity/Book V alue o f T otal Liabili ties = Market Cap/Total Liabili ties = 169,131,852/138,832,880 = 1.218240607 X4' = Market V alue Equity/Book V alue o f debt X4' = 246449000/60064499 = 4.103072 X5 = Revenue/T otal Assets = 63,695,766/491,813,770 = 0.129511961 Z = 1.2 ∗ (−0.013094) + 1.4 ∗ (0.33319) + 3.3 ∗ (0.34362) + 0.6(1.21824) + 1.0 ∗ (0.12951) = 2.44515 Z ' = 0.717 ∗ −0.013094 + 0.847 ∗ 0.33319 + 3.107 ∗ 0.34362 + 0.420 ∗ 5.103072 + 0.996 ∗ 0.12951 = 3.612733072 References Aifuwa, H.O., S. Musa, S.A. Aifuwa.: Coronavirus pandemic outbreak and firms performance in Nigeria. Management and Human Resource Research Journal, 9(4), April 2020, 15–25 (2020). Ali, I., Özari, Ç.: Estimating the probability of bankruptcy using Z-score and distance to default model: An application on Istanbul Stock Exchange. International Review of Management and Business Research, 7(1), 491–503 (2018). Al-Manaseer S.R., Al-OShaibat, S.: Validity of Z-score model to predict finan- cial failure: Evidence from Jordan. International Journal of Economics and Finance, 10(8), 181–189 (2018). ISSN 1916-971X E-ISSN 1916-9728. Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corpo- ration bankruptcy. The Journal of Finance, 23, 589–609 (1968). 118 O. ADESINA ET AL. Altman, E.I.: Corporate financial Distress: A complete guide to predicting, avoiding, and dealing with bankruptcy, 1st ed. New York: Wiley (1983). Association for the Development of Education in Africa (ADEA): Delivering education at home in African member states amid the Covid-19 pandemic: Country status report. http://www.adeanet.org/sites/default/files/report_ education_at_home_covid-19.pdf (2020). Beaver, E.H.: Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111 (1966). Boďa, M., Úradníček, V.: The portability of Altman’s Z-score model to predicting corporate financial distress of Slovak companies. Technological and Economic Development of Economy, 22(4), 532–553 (2016). https://doi.org/10.3846/ 20294913.2016.1197165. Heaton, J.B.: The Altman Z score does not predict bankruptcy. Available at SSRN: https://ssrn.com/abstract=3570149 or http://dx.doi.org/10.2139/ ssrn.3570149 (2020). James, G., Witten, D., Hastie, T., Tibshirani, R.: An introduction to statistical learning with applications in R. Springer (2013). Kulkarni, V.: Altman Z-Score. https://www.rpubs.com/vijetk/azscore (2018). Maclean, R., Dahir, A.L.: Nigeria responds to first coronavirus case in Sub-Saharan Africa. The New York Times (2020). Retrieved 10 March 2020. https://www.nytimes.com/2020/02/28/world/africa/nigeria-corona virus.html. Maliszewska, M., Mattoo, A., Mensbrugghe, D.: The potential impact of COVID-19 on GDP and Trade A preliminary assessment. http://docume nts1.worldbank.org/curated/en/295991586526445673/pdf/The-Potent ial-Impact-of-COVID-19-on-GDP-and-Trade-A-Preliminary-Assessment.pdf (2020). McKibbin, W., Fernando, R.: The global macroeconomic impacts of COVID-19. Brookings Institute, no. March: 1–43. https://www.brookings.edu/wpcont ent/uploads/2020/03/20200302_COVID19.pdf, https://www.worldbank. org/en/topic/edutech/brief/how-countries-are-using-edtech-to-support- remote-learning-during-the-covid-19-pandemic (2020). McKinsey: offers four scenarios of Covid-19’s economic impact on Africa (April 3, 2020). https://www.howwemadeitinafrica.com/mckinsey-offers-four-sce narios-of-Covid-19s-economic-impact-onafrica/64426/ (2020). Mullens, D.: Organizational bankruptcy: The consequences of failure on director human and social capital. American Journal of Business and Management, 3(1), 52–59 (2014). Nairametrics: COVID-19: Nigerian companies have records of innovation to turn pandemic challenge to gold. https://nairametrics.com/2020/05/10/ covid-19-nigerian-companies-have-records-of-innovation-to-turn-pandemic- challenge-to-gold/ (2020). 6 PROBABILITY OF DEFAULT, ACCOUNTABILITY, BANKRUPTCY … 119 Odularu, G.: The primer: Bracing Nigerian trading ecosystem for the future. In: Odularu, G. (eds.) Strategic policy options for bracing Nigeria for the future of trade. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-345 52-5_1 (2020a). Odularu, G.: Conclusion and policy recommendations. In: Odularu, G. (eds.) Strategic policy options for bracing Nigeria for the future of trade. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-345 52-5_8 (2020b). Odularu, G.: Digital pathways for fostering post-COVID-19. Available online at: https://www.afronomicslaw.org/2020c/07/18/digital-pathways-for-fos tering-post-covid-19-trade-outcomes/?fbclid=IwAR2FOS9d9U6epp8ItvrqhR lJkfmevHPbITuPmdaXRqt0ed9X12oYEH6U5Fk (2020c). Ohlson, J.A.: Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131 (1980). Özyeşil M.: A relationship between Altman’s Z scores and stock price perfor- mance: A review on listed companies in Bist-30 Index. SSRG International Journal of Economics and Management Studies, 7(2), 179–186. http://www. internationaljournalssrg.org/IJEMS/paper-details?Id=547 (2020). R Core Team.: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-pro ject.org/ (2020). Senbet, L.W., Wang, T.Y.: Corporate financial distress and bankruptcy: A survey, forthcoming. Foundations and Trends in Finance, 5(4). https://ssrn.com/ abstract=2268540 (2010). Smarandaa, C.: Scoring functions and bankruptcy prediction models—Case study for Romanian companies. Procedia Economics and Finance, 10, 217–226 (2014). Tabachnick, Barbara G., Fidell, L.S.: Using multivariate statistics, 6th ed. Pearson New International Edition. ISBN 10: 1–292-02131-4 (2014). Taffler, R. J. (1983). The assessment of company solvency and performance using a statistical model. Accounting and Business Research, 15(52), 295–308. Wickham H., and many others: Welcome to the tidy verse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686 (2019). World Bank Group. Protecting people and economies: Integrated policy responses to Covid-19, https://documents1.worldbank.org/curated/en/ 879461587402282989/pdf/Protecting-People-and-Economies-Integrated- Policy-Responses-to-COVID-19.pdf (2020).