key: cord-1052731-c3e3h6lo
authors: Al-Hadi, Ahmed; Al-Abri, Almukhtar
title: Firm-Level Trade Credit responses to COVID-19-induced Monetary and Fiscal Policies: International Evidence()
date: 2021-10-29
journal: Res Int Bus Finance
DOI: 10.1016/j.ribaf.2021.101568
sha: 44d5a20e88e05bc2cf60ca0f1f96b868e9b18055
doc_id: 1052731
cord_uid: c3e3h6lo

This paper provides preliminary evidence of the effects of fiscal and monetary policies designed to mitigate and contain the adverse economic impacts of COVID-19 on supplier-customer relationships during the first two quarters of 2020. We compare the impacts of various intervention policies on corporate trade credit for a sample of 14,623 firm-quarter observations, representing 56 countries, after controlling for quarter-, country-, industry-, and firm-fixed effects. We find that, overall, the monetary interventions are associated with lower levels of trade credit, while fiscal interventions increase the use of trade credit. Our results suggest that trade credit is lower in periods of less-restrictive bank credit. This finding has important policy implications for governments as they attempt to help financially constrained businesses survive the pandemic.

Mauro, 2020). As an important source of working capital financing, trade credit plays a critical role in assisting financially constrained businesses to continue operations and reduces the likelihood of severe financial distress (McGuinness et al., 2018; Li et al., 2018) . With continuing to disrupt business operations globally, the role of trade credit should attract more scholarly attention (Goodell, 2020) and, thus, our paper addresses this gap in literature.

Most firms rely on trade credit by borrowing from their suppliers and lending to their customers, both domestically and internationally. For instance, in the United States trade credit is the single most important source of short-term external finance (Petersen and Rajan, 1997) . This is also true for most OECD countries, where trade credit represents more than half of businesses' short-term liabilities and a third of all firms' total liabilities (Boissay and Gropp, 2007) . As volume of trade payables, trade credit represents: one third of non-financial corporations' outstanding bank loans, the size of outstanding corporate bonds, and approximately 20% of world GDP over the past 25 years (Boissay et al. 2020) .

Although trade credit tends to be more expensive than bank credit, the empirical evidence shows that all firms fund their working capital through trade credit (Petersen and Rajan, 1997) .

Several theoretical and empirical studies attempt to explain why firms use trade credit despite its high cost (see Boissay and Gropp, 2007) . From the demand side, trade credit represents the firm's access to capital, especially for the SMEs. Empirically, studies find that firms' demand for trade credit influences their production cycles, optimal ordering quantity, level of inventory, and performance, as well as industry growth (e.g., Fisman and Love, 2003; Chung et al., 2005; Allen et al., 2019) . On the supply side, firms can use trade credit to reduce information asymmetry.

Trade credit can give the supplier the advantage over specialized financial institutions in evaluating the credit risk of buyers. Additionally, trade credit may allow suppliers to price discriminate by using credit when price discrimination is not legally feasible. Trade credit can reduce transaction costs, guarantee product quality, and help maintain long standing business relationships (Petersen and Rajan, 1997) . At the macroeconomic level, empirical evidence suggests a number of factors that determine the demand and supply of trade credit, such as the level of financial market development and the legal and financial structures (Demirgüç-Kunt and Maksimovic, 2001) , national culture (Ghoul and Zheng, 2016) , the cultural background of finance managers (Bedendo et al., 2020) , and level of social trust (Levine et al., 2018) . The literature also studies the relationship between trade credit (at the aggregate level) and economic J o u r n a l P r e -p r o o f activities. Demirgüç-Kunt and Maksimovic (2001) indicate that trade credit is generally procyclical. However, Ghoul and Zheng (2016) and Boissay and Gropp (2007) find a negative correlation between trade credit and GDP.

Prior research also shows the importance of trade credit to corporate finance in economic crises. In general, trade credit supplements bank credit and capital markets during crises and recessions (Bastos and Pindado, 2013; Petersen and Rajan, 1997) . Love et al., 2007 provide evidence that the use of trade credit increased at the peak of the 1997 Asian financial crisis in Indonesia, Malaysia, the Philippines, Mexico, South Korea, and Thailand. During the 2008 global financial crisis, trade credit was the primary source of alternative finance that sustained the global economy (Giannetti et al., 2011) . 1 Although trade credit can mitigate the impact of a macroeconomic credit crunch during economic downturns, this effect can be sustained for a short period only. After that, suppliers also become credit constrained and, thus, cease to extend trade credit (Love et al., 2007) . Thus, on the contrary, trade credit chains can become a channel through which corporate bankruptcies are propagated in an economy. Bastos and Pindado (2013) and Jacobson and von Schedvin (2015) find evidence of trade credit contagion in supply chains during financial crises. Özlü and Yalçın (2012) find that small firms are more likely to rely on trade credit, especially during recessions, while large firms tend to use more bank loans, thus, reducing the contraction of activity of small firms in difficult times.

To help businesses during crises, all countries around the world have deployed numerous measures to cushion the impacts of the COVID-19 pandemic (IMF, 2020). The interventions include policy interest rate reductions, non-conventional monetary measures (e.g., central bank guarantees, changes in reserve requirements, macro-prudential policies, easing lending requirements, foreign exchange operations, etc.), and fiscal measures (e.g., tax deferrals, government loan guarantees, purchase of corporate bonds and corporate relief funds). The monetary and financial policies are devised to ensure the availability of credit in the banking and financial system, which supports households and businesses in the short-term. Fiscal policies are generally directed at stimulating spending and providing urgently needed cash benefits to businesses. Thus, it is interesting to see how those measures have affected the level of trade credit in the short-term.

The purpose of this paper is to investigate the association between fiscal and monetary intervention measures and the extent of corporate trade credit use in the first two quarters of 2020, coinciding with the first wave of Covid-19. Our sample comprises 14,623 firm-quarter observations representing 56 countries, controlling for quarter-, country-, industry-, and firmfixed effects. Although this framework does not provide a quantifiable prediction, it allows us to obtain preliminary evidence of the relationship between firms' trade credit use and monetary and fiscal stimuli. We confine our attention to trade payables, as these indicate the change in trade credit activity in a straightforward way, as well as helping us to understand the trends in business activities and the effects of COVID-19 at the firm level.

Prior research analyzes the channels of transmission from monetary policy to trade credit, particularly during periods of monetary tightening (Mateut, 2005; Altunoka et al. 2020 ). The main channel through which monetary policy affects trade credit, is through bank credit. The relationship between trade credit and bank credit is addressed in the literature through two hypotheses: the substitution hypothesis and the complementary hypothesis. The substitution hypothesis suggests that trade credit is a substitute for bank credit, particularly for firms that are unable to obtain financing from banks (Mateut, 2005; Kestens et al. 2012; Nilsen, 2002) , and/or to enhance transaction efficiency (Altunoka et al. 2020) . Huang et al. (2011) analyze the trade credit and bank-financing relationship and conclude that the substitution effect between trade credit and bank credit is countercyclical. The complementary hypothesis suggests that a decline/rise in bank credit is accompanied by a decrease/increase in trade credit, thereby, exacerbating the impact on financially constrained firms of any financial contraction or expansion (De Blasio, 2003; Mateut, 2005; Jacobson and von Schedvin, 2015) . The transmission of non-conventional monetary policy measures to trade credit can generally be independent of the bank lending channel (Adelino et al. 2020 Recent studies use simulation techniques to estimate the impacts of pandemics on macroeconomic outcomes (Fornaro and Wolf, 2020; , McKibbin and Fernando, 2020) . The firm level study of De Vito and Gómez (2020) uses simulated distress scenarios for 14,245 firms-year observations from 26 countries, based on 2018 data of firms' fundamentals. They examine how COVID-19 fiscal measures (namely tax deferrals and bridge loans) could affect the short-and long-term liquidity risk of listed firms. To the best of our knowledge, our work is the first to provide a microanalysis, using listed firm-level data, of the effect of monetary, non-conventional monetary, and fiscal policies on corporate trade credit. We use a large sample of 14,623 firmquarter observations representing 56 countries during the first half of 2020. Using firm level data allows us to control for possible trade credit determinants that differ across firms in the same industry and that lead firms to respond differently to intervention policies. Such information is lost when using macro data. Second, the paper contributes to the literature on the behavior of corporate credit policy during economic crises. COVID-19 is a unique shock: it is exogenous to all countries and unprecedented in speed and severity. Third, we use a unique sample of countries. The intervention policies across countries vary in timing, substance, and magnitude.

We carefully select the countries that devised similar policy interventions during each of the first two quarters of 2020. We constructed dummies to capture changes in policy variables, to avoid inconsistency across countries in terms of definition and measurements.

Our results suggest that the monetary interventions are associated with lower levels of trade credit. We find that the policy interest rate (IR), Capital Requirements for Market Risk The rest of the paper is organized as follows. Section 2 describes the data and empirical methodology. Section 3 discusses the empirical results. Section 4 concludes. (IMF, 2020) to construct our policy variables. Since the intervention policies across countries vary in their timing, substance and magnitude, we selected countries that devised similar policy interventions during each of the first quarters of 2020, carefully. We have also constructed dummies to capture changes in policy variables, to avoid inconsistency across countries in terms of inclusion and measurement (see Appendix A).

[Insert Table 1 about here]

Following the econometrics literature (e.g., Cameron and Trivedi 2010), we test whether the pooled, random-effect, or fixed-effect regression model is most suitable for estimating the associations. Thus, we conduct the Lagrangian Multiplier (LM) test of the random-effect and pooled OLS (Brusch and Pagan 1980) . We show that the null hypothesis is not rejected, that individual effect ai = 0 for all i. Then, we test the random-and fixed-effect using Hausman's test.

The null hypothesis is that the fixed effect is not correlated with the regressor. We reject the null hypothesis. Therefore, in all of our regressions, we estimate firm-fixed effect regressions, as well as time-fixed effects, and adjust standard errors for heteroskedasticity and within-firm clustering (Petersen, 2009) . Fixed effects generate better estimators and alleviate omitted variable bias. , = 0 + 1−7 Policy , + 7−19 + + + , .

(Eq.1.1).

Where i is firm, t is quarter, c is country.

In line with previous studies, we use two measures of trade credit that were widely used. In the first measure, we follow Aktas et al. (2012) and Ferrando and Mulier (2013) by calculating the ratio of accounts payable scaled by total sales (TC1). In the second measure (TC2), we follow 

We follow Li et al. (2020) to control for firm characteristics. We control for SIZE (natural logarithm of total assets), LEV (total liabilities scaled by total assets), CF (total operating cash flow scaled by total assets, ROA (return on assets), CASH (total cash and cash equivalents scaled by total assets), CAP_INT (total net property, plant and equipment scaled by total assets), INTAN (total intangible assets scaled by total assets), SGA (selling, general and administrative expenses scaled by total assets), and CR (total current assets scaled by total current liability). We also use country level control variables including QGDP (quarterly gross domestic product), QINV (quarterly country investment in infrastructure), COVID19_SINDEX (COVID-19 Stringency Index), M2 (quarterly broad money supply), and UEMP (quarterly unemployment rate). The variable definitions are provided in Appendix A. This figure is very close to that of Zhang (2020) . We find the mean for the ROA is 0.03%, and for CASH 14.6%, which is close to that in Li, Ng, Saffar (2020) . We also present the interaction between all fiscal and monetary policies and firms' mean of trade credit in each country during the pandemic.

[Insert Table 2 As a robustness check, we repeat our analysis, including all monetary, non-conventional monetary policy and fiscal policy variables in one regression with results reported in Table 3 , Panel B. In Model 1 we include all independent variables in one fixed-effect regression with period-fixed effects, and find that, for the first three measures (IR, CRMR, and ELA) our results hold, and all three monetary policy measures have negative and significant associations with trade credit (TC1). In addition, we find BUSPACK, as a fiscal policy measure, to have a positive and significant coefficient with trade credit (TC1). In Model 2, we include COVID19_SINDEX, QGDP, and QINV, as country-level control variables, and find that our results hold, except that CRMR is not significant, while CF becomes positive and significant at p<5%, and both country variables QGDP and QINV negative and significant at p<10%. In Model 3, we add another two country-level economic factors in our regression model (COVID19_SINDEX and UEMP). In addition to our variables in Model 2, the use of firm-fixed effects reduced our sample by half to about 2588 firm-year observations, as data availability for UEMP was poor. We find only the negative coefficients of IR and ELA are significant at p<0.05%. Interestingly, we find the IR and PRUDEN both negative and significant at (p<10% and better), while MARFUN, and BUSPACK both positive and significant at p<1%. In addition, UEMP and COVID19_SINDEX become negative and significant at p<5% and better.

In Model 4 we replace UEMP by M2, which increases our sample size to 5016 firm-year observations, and find our first four measures (IR, CRMR, and ECSR) to be all negative and significant for ELA at p<5%. Both coefficients of COVID19_SINDEX and M2 are negative and significant. Finally, in Model 5, we add all country level measures (COVID19_SINDEX, UEMP and M2) in the regression, which generates 2495 firm-year observations only. We find the coefficients of IR and ELA to be negative and significant at p<1%. This suggests that even after controlling for country aggregate liquidity, country unemployment rate, and country COVID-19 restrictions, we manage to maintain consistent evidence regarding the impact of fiscal and monetary policy on trade credit. It is often the case that firms make financing decisions partly based on current aggregate macroeconomic conditions. However, given the global nature of the COVID-19 pandemic and its similar impact at the macro level across countries, it is no surprise that, after controlling for country aggregate liquidity and the level of economic activities, the results are generally the same.

[Insert Table 3 about Here] 4.2 Robustness check using an alternative measure for trade credit. Table 4 provides regression results that use another measure for trade credit (TC2). See

Appendix A. We repeat our analysis in Table 3 , Panel A, for (Models 1-7), using a firm-fixed effect model. We find our results are consistent for IR in Model 1 at p<0.05%. We also find that the coefficient of PRUDEN in Model 5 is negative and significant at p<0.05% which is consistent with our finding in [Insert Table 4 about Here]

Our previous analysis provides robust results even after controlling for firm characteristics.

However, the monetary and fiscal policies of countries constitute non-random occurrences associated with firm-level factors, suggesting that endogeneity could be an issue. We follow Table 5 . Un-tabulated results show consistent coefficient signs and magnitudes for the IR, PRUDEN, and SUPBRO associations with credit trade (p<0.01). We also find that all the control variable regression coefficients are significant (at p<0.10 or better) across all models.

This supports our main hypothesis that, even when we control for sample self-selection bias, we find results consistent with our main model. 7 measures. These findings can assist national authorities in identifying the policies that deliver adequate support to the economy. However, given the high integration of supply chains worldwide, multilateral collaboration and coordinated interventions among economies is imperative to ensure no disruptions in supply chains, help financially constrained businesses survive the pandemic, and minimize long-run unfavorable consequences on industrial structures.

A coordinated global policy mix may include programs, such as tax deferrals and international guaranteed bank loans to purchase trade receivables (for firms in key industries), and inject cash into supply chains by supporting export credit agencies, working capital financing programs, and new facilities to support exporters and importers. This is especially important as a stress in the corporate sector (or trade credit) could translate into a contagious failure of banks across countries through the supply chain. 

We would like to thank the anonymous reviewers and editor for the valuable contribution. We also thank the Ministry of Higher Education, Scientific Research and Innovation (The Research Council) of the Sultanate of Oman for funding this international research contribution. Assets) + 0.6(Market Value of Equity/Total Liability) + 1.0(Sales/ Total Assets). We use total common stock equity when market value of equity is missing. 

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