Sample and data sources

Sample and data sources: Our study sample is drawn from publicly listed firms in Africa. The final sample became 457 firms and 13 countries which are drowned from the total of 1,287 firms and 33 African countries. The study period covers 10 years (from 2006 to 2015). We obtained the firm-level data from Osiris database which provides the financial and non-financial data of publicly listed financial and non-financial companies. We exclude financial companies considering that their operation is different from the non-financial firms that may lead to unique financial slack resources. The banking sector and stock market development indicators’ data were obtained from the World Bank. We include the banking sector and stock market development with missed value because we could not find the full years’ data for some countries from World Bank. The effect of including the banking sector and the stock market development with missed values is a decrease in the observation of the study as it is presented in table 1.
Performance measures: Consistent with prior studies on slack-performance nexus relationship, (Bradley et al., 2011, Latham and Braun, 2009, Marlin and Geiger, 2015, Picolo et al., 2017, Argilés-Bosch et al., 2016, Tan, 2003), return on assets (ROA) and return on sales (ROS) were used as a measure of performance. We used multiple performance measures because no one measure is capable of capturing multiple performance objectives. For instance, ROA captures firm’s executives’ effectiveness for the maximization of profits from investments in assets. However, it has been argued that ROA fails to capture the firm’s operational performance. Hence, we used return on sales (ROS) as a measure of operational performance of firms. While ROA is measured as the ratio of net income to total assets, ROS is measured as the ratio of net income to sales.
Explanatory variables: In this study, we seek to examine the relationship between available, potential and recoverable slack with firm’s financial performance. We operationalized available slack as (1) current ratio computed as current asset divided by current liability, (2) working capital calculated as current asset minus current liabilities divided by sales. Potential slack was operationalized in this study as, (1) debt divided by equity, (2) debt divided by sales and (3) debt divided by assets. Finally, we operationalized Recoverable slack as (1) selling, general, and administrative expenses divided by sales. The use of these measures of financial slack is motivated by several previous empirical studies (Marlin and Geiger, 2015, Bradley et al., 2011, Vanacker et al., 2013, Wiersma, 2017b, Chen and Miller, 2007, Daniel et al., 2004, Latham and Braun, 2009), among others and guided by the availability of data.
Control Variables: We control the firm size and firm growth. While firm size was operationalized as the natural logarithm of total assets, growth was operationalized as the ratio of sales growth to assets growth. Unlike previous studies which ignore the most important institutional development effect that needs to be taken into consideration in the case of slack-performance nexus, we controlled the banking sector and stock market development. The pecking order theory suggested that firms prefer to use their internal source of finance in financing their investment projects as a result of the existence of asymmetric information in the credit market. We also argued that the preference of firms to use internal sources of finance, for financing investment projects is dependent on the institutional development which can provide external sources of finances. For instance, in countries where the banking sector and stock market are well-developed, firms can easily have external sources of finance in the form of debt and equity and support their investment and can have more financial slack in the form of retained earnings. In Africa in general, banking sector and stock market development has been considered to be immature, however, there exist a relative difference of such institutional developments across African countries that really have an influence on the slack-performance nexus. For instance, Pera (2014) reviewed banking sectors in Sub-Saharan Africa and reported that, as the banking sector continues in a strong growth cycle, the need for formal financial services usually rises in tandem, and so does the ratio of bank assets to GDP. Similarly, Ngare et al. (2014) investigated stock market development and economic growth in Africa and their study found that stock market development has a positive effect on investment, that is the development of stock market is critical in supporting external finance provision for firms and have an indirect effect of accumulation of internal sources of finance in the form of slack.
Model specification: The purpose of this study is to examine the relationship between financial slack and firm performance. Hence, we specify the model of the study as follows.
?Perf?_ijt=?+?_(k=1)??_k ?slack?_ijt+?_(h=1)??_h ?control?_ijt+?_it+?_i+µ_(j…………………………………) (1)
Where ?Perf?_ijt stands for performance as proxied by ROA, and ROS of firm i, in country j and at time t, Slack_ijt is (1) Available slack, (2) Potential slack and (3) Recoverable slack of firm i, at time t and in country j, Control_ijt stands for firm level control variables which are size and growth of firm i, at time t and in country j, and banking sector and stock market development ? is constant, ?_k and ?_hare coefficients of explanatory and control variables respectively and ?_it is error term, ?_i captures industry effect and µ_(j ) captures country effect.


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