The impact of Covid-19 on the cash flow of food and beverage industry

Nam Huong Dau; Nguyen Duy Van; Hai Thi Thanh Diem; Nhung Le Hong Nguyen Article The impact of Covid-19 on the cash flow of food and beverage industry Global Business & Finance Review (GBFR) Provided in Cooperation with: People & Global Business Association (P&GBA), Seoul Suggested Citation: Nam Huong Dau; Nguyen Duy Van; Hai Thi Thanh Diem; Nhung Le Hong Nguyen (2024) : The impact of Covid-19 on the cash flow of food and beverage industry, Global Business & Finance Review (GBFR), ISSN 2384-1648, People & Global Business Association (P&GBA), Seoul, Vol. 29, Iss. 3, pp. 161-173, https://doi.org/10.17549/gbfr.2024.29.3.161 This Version is available at: https://hdl.handle.net/10419/305977 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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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-nc/4.0/ Received: Feb. 17, 2024; Revised: Mar. 3, 2024; Accepted: Mar. 15, 2024 † Corresponding author: Duy Van Nguyen E-mail: [email protected] I. Introduction The global impact of the Covid-19 pandemic on GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 3 (APRIL 2024), 161-173 pISSN 1088-6931 / eISSN 2384-1648∣Https://doi.org/10.17549/gbfr.2024.29.3.161 ⓒ 2024 People and Global Business Association GLOBAL BUSINESS & FINANCE REVIEW www.gbfrjournal.org1) for financial sustainability and people-centered global business The Impact of Covid-19 on the Cash Flow of Food and Beverag e Industry Nam Huong Daua, Duy Van Nguyenb†, Hai Thi Thanh Diemc, Nhung Le Hong Nguyend aCenter for International Knowledge Sharing, Ho Chi Minh National Academy of Politics, Hanoi, Vietnam bFaculty of Economics and Business, Phenikaa University, Hanoi 12116, Vietnam cFPT University, Greenwich Vietnam, Hanoi, Vietnam dInternational School. Vietnam National University, Hanoi, Vietnam A B S T R A C T Purpose: The food and beverage industry is one of the industries that has its own specific products in the manufacturing industry, and it is indispensable for a developing economy. Therefore, research on the impact of Covid-19 on the movement of cash flows in food and beverage companies is necessary to better understand the status of manufacturing enterprises. Design/methodology/approach: To examine the impacts of Covid-19 on cash flow of food and beverage companies, the researchers used financial data from the Fiin Pro database of 33 Vietnamese food and beverage companies over 6 years from 2017 to 2022. The study used the regression model analysis data regression analysis with the Feasible generalized least squares (FGLS) method by using the multi-regression model developed by the previous studies. Findings: To examine the impacts of Covid-19 on cash flow of food and beverage companies, the researchers used financial data from the Fiin Pro database of 33 Vietnamese food and beverage companies over 6 years from 2017 to 2022. The study used the regression model analysis data regression analysis with the Feasible generalized least squares (FGLS) method by using the multi-regression model developed by the previous studies. Research limitations/implications: Firstly, the study only utilizes model adjustment through FGLS but does not address the endogeneity phenomenon. Secondly, the study only focuses on developing countries without comparison to developed countries. Therefore, the authors propose some implications for future research. Firstly, studies could continue to expand on examining the endogeneity phenomenon. Secondly, research could broaden its scope to include other developing countries for a deeper comparison. Originality/value: The research will help food and beverage industry enterprises predict cash flow within the company when a crisis like COVID-19 occurs. This will assist enterprises in better preparing for business operations and enhancing competitive capabilities during crisis periods. Keywords: COVID-19, Cash flow, Food and beverage, FGLS ⓒ Copyright: The Author(s). This is an Open Access journal distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution , and reproduction in any medium, provided the original work is properly cited. GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 3 (APRIL 2024), 161-173 162 economies and industries, including the food and beverage sector, has been profound and multi-faceted (Amore et al., 2022; Dananti et al., 2022; Ding et al., 2021; Oh & Yi, 2023). With challenges ranging from supply chain disruptions to decreased consumer demand, government-imposed restrictions, and shifting consumer behaviors, this sector has encountered significant hurdles (Deaton & Deaton, 2020; Moon & Ji, 2023). For food and beverage companies, cash flow is vital — it signifies the flow of money in and out of the business, encompassing revenue from sales, expenses, investments, and financing activities. Effective cash flow management is paramount for sustaining financial well-being and operational continuity (Chowdhury et al., 2022). Throughout the pandemic, the cash flow dynamics of companies in this industry have been severely affected (Goldstein et al. 2021). Restaurants and cafes, for instance, grappled with limitations on dine-in services, resulting in plummeting sales revenues. Disruptions in the supply chain led to challenges in sourcing raw materials and ingredients, impacting production processes and inventory management (Adi & Daryanto, 2021). Moreover, adapting to evolving consumer behaviors and preferences necessitated strategic adjustments in marketing strategies and product offerings, potentially influencing cash flow patterns. Overall, navigating the complexities of cash flow management amid the Covid-19 pandemic has presented formidable obstacles for food and beverage companies, compelling them to innovate and adapt to ensure their resilience and sustainability in a rapidly changing landscape. Cash flow serves as a vital indicator of a company's financial vitality, encompassing the movement of cash within its operations, investments, and financing activities (Chowdhury et al., 2022; Hamid et al., 2023). Its significance lies in its ability to sustain operational activities by facilitating the payment of suppliers, employees, and other obligations (Aggarwal & Padhan, 2017). Moreover, it provides crucial insights for strategic decision-making, guiding the allocation of funds towards growth initiatives and ensuring timely debt repayments and shareholder dividends. Effective cash flow management not only fosters financial resilience but also enhances stakeholder confidence and trust. In times of economic uncertainty or disruptions, maintaining a positive cash flow becomes imperative for ensuring business continuity and stability (Muzata & Marozva, 2023). Hence, understanding and managing cash flow are essential for achieving long-term profitability and sustainability in today's dynamic business environment. This research article delves into the importance of cash flow analysis and management strategies, highlighting its critical role in driving business success and resilience in Covid-19 context. Several studies have assessed the impact of Covid-19 on cash flow in companies (Amore et al., 2022; Ding et al., 2021; Drissi & Lamzaouek, 2022; Vo et al., 2022; Song et al., 2021. Some studies suggest that Covid-19 will decrease cash flow in companies (Drissi & Lamzaouek, 2022). However, other studies indicate a positive impact of Covid-19 on cash flow (Ding et al., 2021; Song et al., 2021). In Vietnam, particularly in the food and beverage industry, there is currently no research on Covid-19 and cash flow. Therefore, this study aims to assess the impact of Covid-19 on operating cash flow, investment cash flow, and cash flow from financing. II. Literature Review A. Definitions and Role of Cash Flows The cash flow, according to (Karas & Reznakova, 2020) is a type of cash index that an enterprise receives and pays out in a certain period. It is considered as a tool which reflects all of company operations clearly. Besides, Wingerard et al (2013) also have the same idea about cash flow with Karas & Reznakoya, the cash flow is the amount of money that a company can get money from customers and amount of cash used in a financial period. Regarding the second group of points, some scientists have explained definitions of cash flow through considering the process of frequent movement of cash flow in Nam Huong Dau, Duy Van Nguyen, Hai Thi Thanh Diem, Nhung Le Hong Nguyen 163 and out. According to (Cooke & Jepson, 1979), cash flow is described as the actual movement of money in and out of a business. Positive cash flow indicates that money is flowing into a business while negative cash flow shows money paid out. And the difference between the positive and negative cash flow is called the net cash flow. As there are different views of cash flow means, cash flow as described by (Cooke & Jepson, 1979) has been conceptualized in this study: "Cash flow of an enterprise is defined as the movement of cash and cash equivalents, inflow as well as outflow of cash from business, it is a clear reflection of how an enterprise receives and pays out through the signs of the cash flows". B. Types of Cash Flow 1. Cash flow from operating Cash flow from operating activities is determined as associated with primary business activities and source of revenue for an enterprise. Operating cash flow is considered as an essential indicator to evaluate the fiscal financial health of a business, which is an important tool for evaluating the ability to make earnings to cover its debt, maintain operating activities, payout dividend and conduct new investments with no needed external financing (Nguyen, 2022). Regarding operating cash flow components, according to Livnat & Zarowin, (1990), and (Weygandt et al., 2019), which are described in more detail as follows. There are two approaches to report operating cash flow, as following (Weygandt et al., 2019). Direct method: Collecting information directly from transactions arising and reporting inflows and outflows in that financial period. Indirect method: This method is converted from balance sheet and income statement, and indirect method is only used for defining cash flow from operating of business. The formular that is used as follow: The indirect method is popular in many countries in the world as when defining operating cash flow under indirect method, enterprises would understand the relationship among financial statements and reduce identified cash flow cost. However, through analyzing operating cash flow, which is reported under direct method, operating cash flow is likely a better criterion to measure the company's net profit than net profit, because enterprise can repay debt while reporting gain or loss net profit. The difference between net profit and operating cash flow is a tool to help enterprises to assess the quality of their profit. Detail in Table 1. 2. Cash flow from investing Cash flow from investing is the type of cash flow associated with activities of acquiring and disposing of investments, property, plant, and equipment, or even lending and collecting loans (Weygandt et al., 2019). Inflows and outflows for investing cash flow can be more information as following in Table 2. 3. Cash flows from financing Cash flow from financing includes receiving cash from issuing debt (short-term and long-term) and repaying the amount borrowed, and raising capital from shareholders, repurchasing shares, and paying Inflows Outflows Cash collections from sales Cash receivables from interest and dividends Other receivables operating cash flows Cash payments to suppliers for inventory Wages to employees Cash payments to tax authority Cash payments to lenders for interest Other payments operating cash flows Table 1. Clarifications of cash flows from operating Inflows Outflows Sales of fixed asset such as property, plant, and equipment Sale of investments in other entities such as debt, or equity securities Purchase property, plant, and equipment Investing by purchasing debt or equity securities of other entities Make loans to other organizations. Table 2. Clarifications of cash flow from investing GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 3 (APRIL 2024), 161-173 164 dividends for shareholders (Weygandt et al., 2019). In general, cash flow from financing is related to activities of restructuring capital. Some activities would be listed in statement of cash flow from financing. This is the way to classify based on each group of business activities to show the relationship among 3 groups of cash flow from operating, investing, and financing. Detail in Table 3. C. Signaling Theory Signaling theory (Spence, 1978) proposes to explain the behavior of two different parties in terms of the ability to receive and communicate information (Connelly et al., 2011). In commercials, signaling indicates that the actions are taken by a seller to affect behavior and decision of a buyer (Mavlanova et al., 2016) Moreover, one party as an information provider (financial report provider) has to choose whether to provide or not provide and how to transmit (or signal) that information (Connelly et al., 2011). On the other hand, the other party, those receiving information from the report can be shareholders, investors, and stakeholders, choose to interpret the signals they receive. In research of Tangngisalu et al., (2022) they argued that the information received by the firm's management and relevant parties is information asymmetrical. Therefore, interested parties need to be provided financial statements by the business's management and sometimes, this signal can become a form of promotion or better information to attract investors (Tangngisalu et al., 2022). The core of signaling theory indicates the reading and analysis of information and understanding in each applied - situation (Spence, 2002). Thus, signal strategy means the actions that signal generators take performance to influence the decisions of investors and stakeholders. However, these disclosures may have a positive or negative impact on users, including shareholders and investors, depending on whether they carry a positive or negative signal (Connelly et al., 2010). Reading comprehension theory and related empirical results also show that reading ability also affects readers' perceptions and decisions (Kintsch & Dijk, (1978); Rennekamp, (2012); Shah & Oppenheimer, (1930). If the information in the report is easily read and understood by the recipient, it can be seen as a useful positive signal for the reader's decision-making (Connelly et al., 2011). Complex information requires a more conscious effort from investors and other stakeholders. This weakens the receiver's understanding and ability to judge a company's prospects based on the information and thus may reduce that company's decision-making ability. III. Method A. Research model To measure and assess the effectiveness of cash flow management of the business, the author uses the cash flows to total assets ratio. From above defined research variables, the authors have built the regression model based on the previous regression model of Aggarwal & Padhan (2017). The variables are presented in Table 4. CFit = β0 + β1COVDEADit + β2LEVit + β3LIDit + β4ROAit + β5ZSit + εit Where: i is the enterprise i, and t is the year t CF: Cash flow, is defined by three alternative proxies for cash flow (CFO to Total Assets ratio, CFI to Total Assets ratio and CFF to Total Assets ratio) COVDEAD: Natural logarithmic of number of confirmed deaths each year (the authors got the number of confirmed deaths due to Covid-19 from Our World in Data Covid-19 dataset. Inflows Outflows Cash receivables from sale of ordinary shares Cash from issuing debt (bonds and notes) Cash receivables from capital contributed by the owner Paying dividends to shareholders Redeem ordinary share (treasury shares) Repaying debt, or bond payments Table 3. Clarifications of cash flow from financing Nam Huong Dau, Duy Van Nguyen, Hai Thi Thanh Diem, Nhung Le Hong Nguyen 165 ZS: Manufacturing Z-Score is calculated using data in its model for the fiscal period. The Z-score is a multivariate formula that measures the financial health of a company and predicts the probability of bankruptcy within two years. The Z-score combines five common business ratios using a weighting system calculated by Altman to determine the likelihood of bankruptcy. The authors got the Z-score data available from Fiin Pro database. Dependent Variable: The dependent variable will be the financial performance of firms in the food and beverage industry, specifically focusing on cash flows. Cash flow measures such as operating cash flow, investing cash flow, and financing cash flow will be considered as dependent variables. In this study, author Independent Variables: The independent variable will be the Covid-19 pandemic, represented by relevant indicators such as the number of Covid-19 death cases, governmentimposed restrictions, and changes in consumer behavior. Other control variables can include profitability, leverage, liquidity, and industry-specific factors. B. Data Collection The data will be collected from multiple sources, including government reports, industry associations, financial databases, and publicly available company reports. The data should cover a specific period before and during the Covid-19 pandemic, ensuring a comprehensive analysis of the impact. The author selects a research sample consisting of companies in the food and beverage industry listed on HOSE and HNX. Selected companies in the simple fully required information for the research. The author chooses a period study from 2017 to year 2022. Thus, the sample includes 33 food and beverage manufacturing enterprises within 6 years, a total of 198 observations. C. Data Analysis The regression models will be estimated using appropriate statistical software STATA 14. The statistical significance and coefficients of the independent variables will be assessed to determine the impact of the Covid-19 pandemic on firm cash flows in the food and beverage industry. Variables Symbols Concepts Prior studies Expected sign Cash flows from Operating CFO Cash flows from Operating activities / Total Assets Shaharuddin et al., (2021) Azhar Farooq & Ahmed Sheikh, (2021) Cash flows from Investing CFI Cash flows from Investing activities / Total Assets Calculated according to CFO Cash flows from Financing CFF Cash flows from Financing activities / Total Assets Calculated according to CFO Covid-19 COV Natural logarithm of deaths recorded Bollyky et al., (2023) - Leverage LEV Debt to Equity ratio Shaharuddin et al., (2021) + Liquidity LID Current Assets / Current Liabilities Dirman, (2020) + Return on Assets ROA Profit after tax/ Total Asset Shaharuddin et al., (2021) + Z-Score ZS Z-Score is defined by using data in its model for the fiscal period. Huang et al., (2022); Almamy et al., (2016) + Source: Author's compilation Table 4. Description of all variables GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 3 (APRIL 2024), 161-173 166 Descriptive statistics will be used to summarize the key variables and provide an overview of the data. Measures such as mean, median, standard deviation, and distribution plots will be utilized to understand the characteristics and trends in the data. For economic research, panel-data have a serious advantage over cross-section and time-series data. The benefits of panel-data are exalting time-variant correlations between independent and dependent variables, which solves multi-collinearity, estimate biases, and heterogeneity issues (Baltagi, 2008). Therefore, before importing the data into STATA 14, the researcher collected it as an Excel file, and transferred it into panel data by using STATA 14. The study sample includes 33 listed food and beverage firms. All steps are performed to assess the data as follows: (1) We used descriptive statistics analysis to understand the data property; (2) we implemented a table of Pearson correlation; and (3) we performed a test run for regression model analysis. By an application of some theoretical tests such as F-test, Breuch-Pagan Lagrange multiplier test and Hausman test to identify the best-suit model among Ordinary least square model (OLS), Fixed effect model (FEM) and Random effect model (REM). Specifically, F-test inspection is applied to determine better model between FEM and OLS, the BreuchPagan LM test which chooses more suitable model between OLS and REM (Pareek et al., 2023) and Hausman (1978) suggested that his Hausman test can be used to select which is better for investigation, comparing FEM and REM. Moreover, after determining the best-fit model, we evaluate the research model flaws, autocorrelation is assessed with Wooldridge test (2010), and heteroscedastic testing by applying White test. Finally, we conducted a test run again for the best model after removing flaws. Sensitivity analyses and robustness checks will be conducted to ensure the robustness of the findings. This can include alternative model specifications, different control variables, or sub-sample analyses to validate the results. IV. Results A. Descriptive Statistic With a representative sample of 198 observations from 33 Vietnamese food and beverage firms in HOSE, HNX over 6 years from 2017 to 2022, from Fiin Pro database the summary of descriptive statistics will be conducted in two stages, including before and during COVID-19. The detail in tables 5, 6. Overview, all three cash flows have both negative and positive value. The table demonstrates the significant changes in value of CFO, before Covid-19 the lowest value was -0.227, and when Covid-19 occurred, the lowest value was reduced to -0.666. The minimum negative value of CFO declines about 3 times, indicating that F&B firms cannot cover their expenses from sales alone during Covid-19. While the highest value of CFO increased by nearly 2 times, indicating that a lot of food and beverage can gain Variable Mean SD Min Max CFO 0.086 0.131 -0.227 0.456 CFI -0.054 0.126 -0.455 0.599 CFF -0.036 0.154 -0.797 0.436 LEV 0.247 0.196 0.000 0.618 LID 0.022 1.337 -2.830 11.649 ROA 0.092 0.087 -0.189 0.321 ZS 3.937 3.233 0.315 16.726 Table 5. Summary of descriptive statistics of v ariables before Covid-19 Variable Mean SD Min Max CFO 0.066 0.176 -0.667 0.815 CFI -0.034 0.106 -0.324 0.667 CFF -0.024 0.146 -0.749 0.498 COV 7.742 3.010 3.555 10.385 LEV 0.229 0.193 0.000 0.733 LID -0.018 1.548 -11.539 8.220 ROA 0.071 0.075 -0.267 0.241 ZS 3.730 2.413 -1.063 12.496 Table 6. Summary of descriptive statistics of all v ariables during Covid-19 Nam Huong Dau, Duy Van Nguyen, Hai Thi Thanh Diem, Nhung Le Hong Nguyen 167 benefits from Covid-19 because of their essential goods. The highest CFI value increased from before Covid-19 to during Covid-19, from 0.599 to 0.66 respectively, showing that they are selling more assets or securities to recoup the big drop in cash inflow in CFO. The CFF does not have too much change. The table shows the all-means value of firm's specific variables declined after occurring Covid-19, in which LID (current ratio) has changed from positive to negative value during Covid-19, demonstrating that Vietnamese food and beverage firms are carrying too much debt, cash balance is running out or not well controlled in account receivables. Regarding Z-score, Vietnamese food and beverage companies are considered in safe-zone because the Z-score value is in the range from 3 to 4. B. Regression for OLS, FEM, REM As mentioned in the previous part, the FEM model is accepted to become the optimal model for results associated with CFO and CFF, while OLS is the best-suit model for CFI regression. However, the authors still compile the results of three approaches for each alternative proxy of Cash flow. Table 7 is the summary of regression results for OLS, FEM and REM associated with Cash flow from Operating of 33 Vietnamese food and beverage enterprises with 198 observations. Results following both three approaches show that there is a negative correlation between Covid-19 and CFO, with significance levels in the range from 10% to 1%. However, the coefficient value of Covid-19 (-0.006) in the FEM model is assessed to be the smallest value compared to other independent variables. Regarding other independent variables, all variables have a strong influence on CFO, except for the variable named Z-scope, although Z-score has a negative effect on CFO, it is not significant. However, the best-fit method which we choose for this regression model related to CFO is FEM. There are only two independent variables, liquidity, and leverage, that have strong negative impacts on CFO with significance level of 1%, while ROA is out of expectation, it has no significant relationship with CFO. The indicator R-squared of the model is 0.235, indicating that the model with all these variables can explain 23.56% for dependent variable-CFO. In conclusion, all independent variables have a negative impact on CFO, however there are only three independent variables, consisting of COVDEAD, LEV and LID, which have statistically significant levels less than 1%. Table 8 is the summary of regression results for OLS, FEM and REM associated with Cash flow from Investing with 198 observations from 33 listed food and beverage companies in Vietnam. Results following both three methods show that there is a positive correlation between Covid-19 and CFI, but not significant and the coefficient value of Covid-19 (0.002) of OLS model, the best suit model for this regression, is assessed to be the smallest value compared to other independent variables. Regarding other independent variables, all variables have a strong influence on CFI, except for the variable named Z-scope. In the results of OLS related to CFI, CFO OLS FEM REM COV -0.004* [-1.86] -0.006*** [-2.90] -0.004* [-1.86] LEV -0.2269*** [-3.82] -0.7093*** [-5.87] -0.226*** [-3.82] LID -0.0201*** [-3.00] -0.02*** [-3.19] -0.020** [-3.00] ROA 0.489*** [-0.02] -0.132 [-0.52] 0.489*** [2.86] ZS -0.0001 [3.59] -0.008 [-0.82] -0.0001 [-0.02] _cons 0.106 0.311 0.106 N 198 198 198 R-sq 0.247 0.235 0.135 Autocorrelation test 0.197 Heteroscedasticity test 0.000 Note(s): The numbers in parentheses are standard errors. *, **, *** indicate significance levels at 10%, 5%, 1% Table 7. Regression results for OLS, FEM, REM model of CFO GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 3 (APRIL 2024), 161-173 168 three independent variables, including leverage, liquidity, and ROA, demonstrated a clear relationship with CFI. In details, both three ones have significance level less than 1%, leverage and ROA negatively affect CFI and liquidity affects positively CFI, The indicator R-squared of the model is 0.2391, indicating that the model with all these variables can explain 23.91% for dependent variable-CFI. In conclusion, for the OLS model, the variable of Covid-19 has a positive relation with CFI but is not significant. However, three variables are LEV, LID, ROA, which have statistically significant levels less than 1% with CFI. Table 9 is the summary of regression results for OLS, FEM and REM associated with Cash flow from Financing of 33 listed food and beverage companies in Vietnam with 198 observations. Three models show a positive correlation between Covid-19 and CFI, especially only the accepted method-FEM model-has the significant level of Covid-19 on CFF at 1%. However, the coefficient of Covid-19 equals 0.0059 and is still evaluated to be much smaller than other independent variables. The remaining variables including liquidity and Z-score have negative and positive effects on CFF, respectively, but are not significant. The indicator R-squared of the model is 0.2637, indicating that the model with all these variables can explain 26.37% for dependent variable-CFF. In conclusion, for the FEM model, the variable of COVD-19 has a positive relation with CFF with a significant level at 1%. However, there are only two other independent variables out of 4 that have 1% statistically significant impact on CFF. C. Regression for FGLS The previous part showed that there are many flaws in OLS, FEM, REM results, consisting of autocorrelation and heteroscedasticity. Therefore, the research applied the feasible generalized least squares (FGLS) approach to overcome the defects of the model. Table 10 demonstrates the regression results for CFO by using the FGLS method. There are few changes in the regression results. In general, Covid-19 is still having negative effects on CFO with the CFF OLS FEM REM COVID-19 0.003 [1.57] 0.005*** [2.78] 0.003 [1.57] LEV 0.296*** [4.93] 0.825*** [6.90] 0.296*** [4.93] LID -0.008 [-1.26] -0.009 [-1.39] -0.008 [-1.26] ROA 0.135 [0.78] 0.747*** [2.99] 0.135 [0.433] ZS -0.002 [-0.54] 0.013 [1.34] -0.002 [0.587] _Cons -0.113 -0.360 -0.113 N 198 198 198 R-sq 0.1693 0.2673 0.1833 Autocorrelation test 0.0097 Heteroscedasticity test 0.000 Note(s): The numbers in parentheses are standard errors. *, **, *** indicate significance levels at 10%, 5%, 1% Table 9. Regression results for OLS, FEM, REM model of CFF CFI OLS FEM REM COV 0.002 [1.20] 0.001 [1.00] 0.002 [1.20] LEV -0.100*** [-2.24] -0.169* [-1.72] -0.10*** [-2.24] LID 0.030*** [5.90] 0.030*** [5.51] 0.030*** [5.90] ROA -0.479*** [-3.71] -0.516*** [-2.52] -0.48*** [-3.71] ZS 0.002 [0.63] -0.001 [-0.14] 0.002 [0.63] _Cons 0.001 0.035 0.001 N 198 198 198 R-sq 0.239 0.224 0.221 Autocorrelation test 0.7717 Heteroscedasticity test 0.000 Note(s): The numbers in parentheses are standard errors. *, **, *** indicate significance levels at 10%, 5%, 1% Table 8. Regression results for OLS, FEM, REM model of CFI