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Impact of Macroeconomic Policy Interventions

Trade Credit and Bank Credit

The paper develops an empirical model to test the substitution of trade credit for bank credit using the annual financial data of 1,028 Indian manufacturing firms from 2011 to 2019. It further examines the impact of macroeconomic policy interventions on using these two financing sources.

The macroeconomic policy interventions, leading to a credit-rationing situation, force firms to adjust their ­financial policies to respond to the new emerging scenario. The plausible options for firms to maintain adequate firm-level liquidity in such situations inter alia include: (i) ­deferring non-priority capital investments (Michael and Michael 2010), (ii) raising capital to finance the ongoing projects by restructuring (Bokpin 2009), (iii) increasing internal accruals by reducing dividend payout (Bhat et al 2021), and (iv) substituting the available sources of short- and long-term financing. This paper attempts to understand the dynamics of substitution of trade credit and bank credit as short-term sources of firms’ financing during the credit-rationing scenario faced by firms post macroeconomic policy interventions.

Bank credit and trade credit are two primary sources to meet the working capital requirements of firms and constitute a significant part of their total assets and current assets. The average trade credit and bank credit of 1,028 Indian manufacturing firms for the years 2011 to 2019 represent 13% and 20% of the total assets, respectively, and 28% and 48% of current assets, respectively. The bank credit and trade credit together account for one-third of the total assets and three-fourths of the current assets of the manufacturing firms. As seen in Figure 1 (p 51), the aggregate trade credit as a percentage of the total short-term credit of all firms has increased post 2016.

Indian economy’s momentous macroeconomic policy interventions of demonetisation, the insolvency and bankruptcy code (IBC), and the goods and services tax (GST) implemented in the last decade had significant implications for the firm’s short-term financing policies. Demonetisation produced a ­liquidity shock, and firms experienced liquidity constraints (RBI 2017). The introduction of IBC (2016) led to the tightening of the bank lending norms and constrained the availability of credit for firms. Similarly, the introduction of GST in 2017 incre­ased the working capital requirements of firms in significant ways.

It is in this context that this paper analyses the use of trade credit and bank credit post these macroeconomic policy interventions and attempts to answer the following two research questions:

(i) Did firms substitute trade credit for bank credit post macroeconomic policy interventions? In normal times, firms use bank credit and trade credit based on their ease of availability and cost of financing. Under the credit-constrained situation, as bank credit gets restricted, the trade credit being flexible and spontaneous sources of short-term financing may be more accessible.

(ii) Is this substitution of trade credit for bank credit a temporary phenomenon or a permanent shift in response to macro­economic policy interventions?

The findings of this paper provide an understanding of how the firms respond to macroeconomic policy interventions by realigning their “short-term” financing policies, resorting to the substitution of one source for another, and the temporary or permanent nature of this shift. The literature on how firms respond to macroeconomic policy interventions by realigning their “short-term” financing policy is limited in the Indian context.

Moreover, understanding the interdependency of trade and bank credit, post-macroeconomic policy interventions are vital for the following reasons. First, liquidity and financing constraints vary across firms, and they amplify during the credit-rationing situations created by macroeconomic policy interventions. A few firms may have limited access to bank credit, and the extension of trade by creditworthy trading partners’ to these credit-constrained firms would mitigate the credit rationing effect (Petersen and Rajan 1997). Meltzer (1960) expounds this as “substitution hypothesis.” It is crucial to understand whether firms stand in solidarity and whether creditworthy firms extend trade credit to their credit-constrained trading partners post macroeconomic policy interventions.

Second, the substitution of trade credit for bank credit shows the firms’ financial soundness and indicates that firms treat it as a temporary phenomenon. During the credit-rationing period, small- and medium-sized firms are affected the most and they may resort to more trade credit financing (Nilsen 2002; Casey and O’Toole 2014). After macroeconomic policy interventions, creditworthy firms may extend trade credit to small and credit-constrained firms, particularly when bank credit is constrained. A higher amount of trade credit on the balance sheet of credit-constrained firms provides a signal of safety to the financial system, and as the situation improves, banks re-establish credit to these credit-constrained firms (Cook 1999).

Third, the degree of substitution provides a proxy for the ­efficiency of the legal and financial systems of the country. The research also suggests that in addition to macroeconomic policy changes, access to the capital market to raise funds, the efficiency of the legal system, and financial developments affect the relationship between trade credit and bank credit. Kohler et al (2000) finds that firms having access to the capital market provide more trade credit to their trading partner and ­receive less trade credit during difficult times in the United Kingdom (UK). According to the matching hypothesis, firms that receive more trade credit from their suppliers are more likely to grant trade credit to their customers, and hence acc­ess to bank credit and profitability are not significant variables determining trade credit (Fabbri and Klapper 2011). The ­extent of substitution of trade credit for bank credit reduces with the country’s financial development and that makes most of the firm-specific determinants of trade credit irrelevant (Couppey-Soubeyran and Héricourt 2013). The trade-credit and bank credit mix also depend on the country’s institutional factors (Palacín-Sánchez et al 2018).

Fourth, interest rate deregulation makes the bank credit market more efficient. Firms may use less trade credit as the bank credit becomes more liberal. Bank credit is preferred for short-term financing over trade credit (Chen and Kieschnick 2018). But in times of capital rationing situations, the use of trade credit may be inevitable.

The remaining part of this paper focuses on a few issues. At the onset the paper reviews the relevant literature in this area. Then it goes on to present the hypotheses, empirical model, data, and methodology. Next it discusses the empirical results after which the paper concludes.

Literature Review

The literature on the relationship between trade credit and bank credit goes back to the seminal work of Meltzer (1960), describing the substitution hypothesis. Subsequently, many res­earchers across the globe have provided supporting evidence for the substitution hypothesis in different markets (Petersen and Rajan 1997; Cook 1999). During a severe recession (1979–82) in the United States (US), small and large firms substituted trade credit for bank credit to address credit rationing (Nilsen 2002). Casey and O’Toole (2014) find that credit-constrained small and medium enterprises (SMEs) are more likely to substitute trade credit for bank credit. Similarly, using structural break analysis, Lahiri and Tian (2013) find that smaller firms depend more on trade credit than large firms as small firms are constrained for bank credit. Bastos and Pindado (2013) supported the substitution hypothesis and matching hypothesis. Firms substitute trade credit for bank credit during tight monetary policy conditions (Atanasova and Wilson 2004). Stronger financial firms redistribute bank credit to weaker firms during a financial crisis via trade credit (Love et al 2007).

During the post-2008 financial crisis, the relative importance of trade credit increased, especially for financially vulnerable firms in Ireland (McGuinness and Hogan 2014). It has been observed that small and non-dividend-paying firms (a proxy for finance-constrained firms) are more likely to substitute trade credit for bank credit (Burkart and Ellingsen 2004). Trade credit helps the firms to improve their reputation and acts as a “quality signal,” facilitating firms’ borrowing from bank credit (Alphonse et al 2006).

Oliner and Rudebush (1996), Gertler and Gilchrist (1993), Alphonse et al (2006), and Demirgilc-Kunt and Maksimovic (2001) challenged the substitution hypothesis. Oliner and Rudebush (1996) and Gertler and Gilchrist (1993) analysed the US data from 1974 to 1991 and argued that trade credit and bank credit are not substitutes for each other, but instead, there is a complementary relationship between them.

Few studies provide a mix of evidence on the relationships between trade credit and bank credit. Blasio (2003) found that small and non-dividend-paying firms are more likely to substitute trade credit for bank credit, whereas other firms may complement. Trade credit and bank credit can serve as complements for financially unconstrained firms but substitutes for financially constrained firms (Burkart and Ellingsen 2004). The study also finds that trade credit is more prevalent in less developed credit markets. Firm size is an important determinant of the relationship between trade credit and bank credit. Small firms use both trade credit and bank credit, whereas medium-sized firms substitute trade credit for bank credit (Cunningham 2005). Trade credit substitutes bank borrowing during tight monetary periods, while trade credit complements bank credit during the loose monetary period (Yang 2011). Trade credit substitutes bank credit more often during the upmarket time, whereas it complements during market stagnation (Lau and Schaede 2019). Even in the Indian context, large and highly profitable firms depend less on bank credit (Subramanian and Umakrishnan 2004). In the Indian context, Bhat (2004) supported the substitution hypo­thesis during the restrictive monetary period, whereas Ghosh (2015) supported the complementary hypothesis during the crisis period.

The debate on the relationship between trade credit and bank credit is not conclusive. Most of the prior studies have used the external context of credit markets to study the relationship between trade credit and bank credit, particularly during the crisis or restrictive monetary periods. This paper analyses the relationship between these two sources of financing in the context of macroeconomic policy interventions of demonetisation, IBC, and GST.

Empirical Model, Data, and Methodology

The present study develops an empirical model to answer the following research questions: (i) Do firms substitute trade credit for bank credit post macroeconomic policy interventions?
Is this substitution of a trade credit for a bank credit a temporary phenomenon or a permanent shift?

Meltzer (1960) estimated the substitution of trade credit for bank credit using a linear regression model having monetary policy and liquidity policy variables. The monetary policy variable was measured as a product of interest rate and index of tight money, and the liquidity policy variable was a ratio of cash plus government security to current liability. Kashyap et al (1994) argue that the use of market interest rate or money variable fails to capture the effect of change in financial conditions created due to monetary restrictions. They proposed an inventory change model to capture the impact of credit rationing on firms through the inv­entory accumulation phenomenon. The rationale of using inventory as a variable to measure the effect of credit rationing is that inventory behaviour captures the impact of credit rati­oning through the cost of a financing channel. Under credit rationing, as the cost of financing becomes expensive, the firm reduces its inventory holdings.

The present study adopts the idea of Kashyap (2014) using change in inventory as a dependent var0iable to examine
the relationship between trade credit and bank credit. The study proposes the following empirical model as presented in equation (1).


∆ ln INVi,t is the growth of inventory stock in the current period

is the log of inventory to net sales ratio in the previous period


∆ ln Si,t is the growth of net sales in the current period

LIQi,t =1 is liquidity ratio of the previous year measured by ratio of cash, bank, and marketable securities to total assets

TCRi,t–1 is the trade credit ratio of the previous year, measured as the ratio of trade payable to the total value of trade payable and short-term borrowings of the previous year, where short-term borrowings include short-term bank borrowings, commercial paper, and other short-term borrowings

ln TAi,t–1 is a log of total assets in the previous period

MPCis the macroeconomic policy change, a dummy interaction variable for post-macroeconomic intervention years like 2017, 2018, and 2019 k is the number of dummy interaction variables.

The ratio of log of inventory to net sales, growth of net sales, and log of total assets are used to control for non-financial determinants of the inventory. The model used here also augments LIQ and TCR as explanatory variables in equation (1). It assumes that for credit-constrained firms, liquidity and cash reserves of the firm are significant determinants of the growth of the inventory. The coefficients of LIQ and TCR capture the “average” degree of liquidity and trade credit constraints, respectively (Blasio 2003)The present study uses TCR as a relative measure of trade credit to the total value of trade credit and bank credit and considers the bank credit as part of the estimation model. To test the first hypothesis of substitution of trade credit for bank credit after macroeconomic policy interventions, the study adds MPC*TCR and MPC*LIQ as two dummy interaction variables to measure the “added” effect of macroeconomic policy interventions.

Both the previous studies of Kashyap (1994) and Blasio (2003) had used ∆ ln INVi,t as a dependent variable, however, these results are subject to endogeneity bias because ∆ In INVi,t is the difference between ln INVi,t and ln INVi,t–1 and use of the lagged value of inventory may pose a problem of autocorrelation in the error term. To resolve this problem, this paper expands the dependent variable D ln INVi,t as (ln INVi,t–1 – ln INVi,t–1). It takes the second term ln INVi,t–1 to the right side of the equation as an explanatory variable to transform the model as a dynamic panel regression model. The final empirical model estimated in this study is presented in equation (2).



... (2)

To confirm the efficacy of transformation from equation (1) to equation (2), the study tests if the null hypothesis of coefficient of lagged dependent variable is not statistically significantly different from 1, H0: γ1 = 1. If the null hypothesis is not rejected, it indicates that the relationship between the current and lagged value of the inventory still holds statistically as
∆ ln INVi,t = ln INVi,t – ln INVi,t–1.

The paper estimates two versions of the equation (2). In the first version, the study uses a value of dummy interaction variable MPC as 1 for 2017, 2018, and 2019 and 0 otherwise to test the first hypothesis of substitution in the post-2016 period. In the second version, the paper includes three individual dummy interaction variables, MPC1, MPC2, and MPC3, having values 1 for 2017, 2018, and 2019, respectively and 0 otherwise. This formulation helps to test the second hypothesis of whether the degree of substitution is a temporary phenomenon or a permanent shift. The usage of three individual dummy interaction variables for 2017, 2018, and 2019 enables analysing the impact of the macroeconomic policy interventions separately in each of these years. The coefficients of MPC*TCR and MPC*LIQ are interpreted as shown in Table 1.

The study uses the sign and statistical significance of MPC*TCR and MPC*LIQ coefficients to identify the substitution of trade credit for bank credit.

In equation (2), log of inventory is the dependent variable and among other two explanatory variables, (i) trade credit as percent of total short-term credit and (ii) liquidity (cash, bank, and marketable securities) as a percent of total assets. We use two interaction variables to measure the substitution in the post-macroeconomic policy change years of 2017, 2018, and 2019 (MPC*TCR and MPC*LIQ). The interpretation of the coefficients of these variables is as follows:

First, suppose the trade credit (MPC*TCR) variable has a positive and statistically significant coefficient, in that case, it indi­c­ates that trade credit has gone up (as a result, the bank credit has gone down) to finance the inventory growth. If the liquidity (MPC*LIQ) coefficient is negative and statistically significant, this indicates that despite the reduction in liquidity, trade credit has fin­anced the inventory growth. This is a case of strong substitution.

Second, positive coefficients of both MPC*TCR and MPC*LIQ indi­cate a direct relationship of inventory growth with trade credit and firm liquidity. This shows that inventory growth is financed by the liquidity of the firm and trade credit. Hence, it is evidence of the substitution of trade credit for bank credit.

Third, if the coefficient of MPC*TCR is negative, but the coefficient of MPC*LIQ is positive, it indicates that inventory growth has an inverse relationship with trade credit (TCR) but a direct relationship with liquidity (LIQ). This shows that inventory growth is financed by liquidity and not by trade credit. Hence, it is an evidence of credit rationing but not a substitution of trade credit for bank credit.

Lastly, if both MPC*TCR and MPC*LIQ coefficients are negative, it indicates an inverse relationship of inventory growth with TCR and LIQ. This shows that the inventory goes up despite decreasing the firm’s liquidity and trade credit. This indicates that firms are neither dependent on liquidity nor trade credit to finance their working capital req­uirements. Hence, this may be seen as evidence of the absence of credit rationing.

The empirical analysis used a data set of 2,074 Indian manufacturing firms from 2011 to 2019 collected from the Prowess database, published by the Centre for Monitoring Indian Economy (CMIE). The study removed small firms having net sales of less than `1 million and that reduced the data set to 1,921 firms. Then, using a second filter, the study exc­ludes all those firms having missing values for any of the variables mentioned in equation (2). The study also removed the outliers by winsorising all the variables at 1 and 99 percentile values of each of the variables. Hence, the final sample had a balanced panel of 1,028 firms with data for nine years resulting in 9,252 firm-year observations. The panel formulation allows to control for individual firm effect (ϕi), and time effect (θt), providing more observations, higher variability, lesser collinearity among variables, and more efficiency (Baltagi 1995).

The study reports descriptive statistics for all the primary and derived variables used in equation (2). In addition to the aggregate mean and median values, the study also reports year-wise mean and median values and provides a correlation matrix of variables used in equation (2).

The standard panel estimation methods like fixed-effect and random-effect panel regression models (used by previous studies) have a problem of autocorrelation in the error term because of the presence of lagged dependent variable (ln INVi,t –1) as an explanatory variable (Greene 2003). To rectify this problem, the paper estimates equation (2) using the dynamic panel regression technique, generalised method of moments (GMM) estimation as proposed by Arellano and Bover (1995) and later on, fully developed by Blundell and Bond (1998) with instrument variables. The estimation model uses all the right-hand side variables and their one-lagged values as instruments. The methodology mitigates the problem of endogeneity and considers the dynamic nature of the panel data set (Arellano and Bover 1995). The study estimates a dynamic specification model mentioned in equation (2) using the xtabond2 procedure in Stata 16 (Roodman 2009). To decide between the difference GMM and system GMM, the study follows Bond et al (2001) rule of thumb. It suggests that if the value of the estimated coefficient of lagged dependent variable in difference GMM model is smaller than the value of the estimated coefficient of lagged dependent variable in the fixed-effect model, the system GMM is a better choice in this case. Furthermore, Roodman (2009) suggests that for estimating the two-step difference GMM is more efficient and robust than the one-step difference GMM. Hence, the study estimates an empirical model presented in equation (2) using the fixed-effect model, one-step and two-step difference GMM and one-step and two-step system GMM.1 The paper will now discuss the findings of the study.

Empirical Results and Discussion

The paper reports aggregate and year-wise descriptive statistics of variables in Tables 2 and 3, respectively. The mean and median values of LIQ are going down from 2011 to 2019, whereas mean and median values of TCR are going up during the period. This gives the first intuition of substitution of trade credit for bank credit in post-2016 period.

Similarly, pair-wise correlations between variables used in equation (2) are presented in Table 4. All the values of pair-wise correlations indicate that none of these variables is highly correlated, reducing the multicollinearity problem in a ­balanced panel.


The study reports the result of the two-step difference GMM method for two versions as described in Table 5 (p 49). The coefficients of lagged dependent variable of the two-step difference GMM are 1.228 and 1.226 in versions 1 and 2, respectively, which are significant at 1% level of significance, whereas the coefficient of lagged dependent variable of the fixed-effect model is 0.879, which is again significant at 1% level of significance. As the coefficient of lagged dependent variable in the two-step difference GMM is higher than the coefficient of lagged dep­endent variable of fixed effect model, it indicates that the difference GMM is a better model than system GMM. As a robustness check, the paper also estimates the one-step difference GMM and one-step and two-step system GMM. However, the results of two steps estimations are more efficient than one-step estimation. Also, the probability value of Hansen statistics in the two-step system GMM is less than 0.05, indicating that the ins­truments are weak and the estimation result is unreliable and hence not reported in this paper. The paper applies the Arellano and Bond (1991) test for AR (2) for testing the existence of serial correlation in the residuals and the J-test of Hansen (1982) to ensure the validity of the instruments and test the null hypothesis that instruments are orthogonal to the residuals.

The estimated coefficient of the lagged value of inventory is 1.226 with a standard error of 0.122. Hence, the calculated t statistics for H0γ1 = 1 is 1.85 [(1.226-1)/0.122]. As the calculated t statistic is less than a critical value of t statistic (1.96) at a 5% level of significance, the study fails to reject the null hyp­othesis that the coefficient is equal to 1. Hence, the study conclu­des that the relationship between current and lagged inv­en­tory values still holds as ∆ ln INVi,t = ln INVi,t– ln INVi,t–1 and the proposed empirical model is statistically a valid presentation.

The result of both the versions of two-step difference GMM indicates that coefficients of the lagged value of inventory, ­ratio of inventory to sales of the previous year, current year growth of net sales and lagged value of the total assets are significant at 1% significance level. In addition, the coefficient of TCR is significant at 5% level of significance. The probability values of AR(2) and Hansen statistics are greater than 0.05, indicating there is no second-order serial correlation and the set of ­instruments is good, respectively. In addition, the number of instruments is lower than the number of groups, and the F-statistic is significant at 1% level of significance, which indicates that coefficients are jointly significant.

In the result of version 1, the coefficient of MPC1× TCRi,t is positive and significant at 1% level of significance and the
coefficient of MPC1 × LIQi,t is positive and significant at 5% level of significance. The results suggest that Indian firms substituted trade credit for bank credit in response to the credit-rationing situation created by the post-2016 macroeconomic policy interventions. Similarly, in the result of version 2, the coefficient of is MPC1 × TCRi,t positive and significant at 1% level of significance, but the coefficient of MPC1 × LIQi,t is negative, which indicates a strong substitution of trade credit for bank credit in 2017. Going forward, in 2018 and 2019, coefficients of MPC2 × TCRi,tMPC2 × LIQi,tMPC3 × TCRi,t and MPC3 × LIQi,t are positive statistically significant. The result suggests that in 2018 and 2019, the substitution effect is diluted from strong substitution to less-strong substitution of trade credit for bank credit. It also indicates that the substitution effect dilutes but it still persists and this suggests a provisional shift of trade credit for bank credit in response to macro­economic policy interventions. It is expected that once the effect of macroeconomic policy interventions is absorbed, trade credit and bank credit may start following a predictable course as in normal times.

The significance and sign of control variable coefficients are also according to the expectations. The inventory to sales variable, which measures the firm’s efficiency, is negative and significant, indicating that companies that are using their current assets (particularly inventory) end up having lower sticks of inventory. Similarly, the growth in sales has a predictable positive and statistically significant coefficient, indicating higher growth firms have higher levels of inventory.


Trade credit and bank credit are two primary short-term fina­ncing sources of firms. This paper examines the impact of macroeconomic policy interventions on these two financing sources. The study answers the question, how firms responded to macroeconomic policy interventions post 2016 by realigning their short-term financing sources.

The paper develops an empirical model explicitly incorporating trade credit ratio (trade credit to a total value of trade credit and bank credit) to test the substitution hypothesis. The paper uses balanced panel data of 1,028 Indian manufacturing firms from 2011 to 2019, and a more robust dynamic panel estimation method. The study estimates the empirical model ­using the GMM to test the degree of substitution between these two sources of credit.

The findings suggest that (i) firms substituted trade credit for bank credit in response to the credit-rationing situation created by macroeconomic policy interventions, (ii) the degree of substitution dilutes over a period from strong-substitution to less-strong substitution.

The contribution of this paper is that it empirically estimates the impact of “macroeconomic policy interventions” like demonetisation, IBC and GST on trade credit and bank credit. The paper proposes a single-equation empirical model explicitly incorporating the trade credit variable. In terms of methodology, the study uses a robust GMM estimation technique, which mitigates the endogeneity bias present in the prior studies.

The findings of this study have implications for various stakeholders, including investors, researchers, corporates, and policymakers. The substitution of trade credit for bank credit highlights the financial soundness of firms and the economy because the firms are willing to extend credit to their trading partners, particularly during the times of credit rationing scenario post macroeconomic policy interventions. The findings suggest that firms stand in solidarity and support others by extending credit during tough times. Another implication emanating from the study is the criticality of flexibility in the financial system to readjust short-term financing policies during the credit restriction period created by macroeconomic policy interventions. The flexibility is vital for financial systems particularly in times when information asymmetry between trading partners and banks might increase. On the other hand, this study would also help banks understand the implications of macroeconomic ­policy interventions to decide credit policies, while deciding maximum permissible bank finance limits of firms. Lastly, findings of this study suggest that policymakers should be cognisant that though macroeconomic policy interventions may have long-term benefits, at least in the short run, they have implications for firms’ financing constraints. The prevalence of the substitution hypothesis helps us understand the firms’ short-term fina­ncing behaviour post macro­economic policy ­interventions.


1 The results of the fixed effect model, one step difference GMM and one step and two step system GMM model are available on request.


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Updated On : 1st Aug, 2022
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