# TRADE CREDIT AND CREDIT CRUNCHES: EVIDENCE FOR SPANISH FIRMS FROM THE GLOBAL BANKING CRISIS

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6 Purchases (AP), will be our dependent variables. Our two baseline equations will be as follows: where In the first equation, 0 is the intercept; 1 is a vector of coefficients associated with the vector of control variables X it, vector which includes, among other variables, industry and size dummies (unless the equation is estimated with fixed effects). The variable y st is a year dummy that takes value one for the year we are analysing (t = s). The year dummies coefficients are AR s and coefficients of the interaction terms between the crisis years and our financial slack variable for AR, FS AR it, are AR s. Similarly for the second regression, these terms are 0, which is the intercept, 1, the vector of coefficients associated with the vector of control variables Z it, AP s, the crisis dummy year coefficients and AP s, the coefficients of the interaction terms between the crisis years and our financial slack variable for AP, FS AP it. The financial slack variable is a proxy for how tied the financial constraint for each firm and it is interacted with the crisis years (2008, 2009 and 2010). This interaction allows for the possibility that firms might react in a different manner when granting or receiving trade credit when the crisis arrives, presumably in 2008, depending on the financial position of the firm in the year before. Several variables will be considered for both equations, but we have one good variables will be considered for both equations, but we have one preferred candidate for each equation: Liquidity for the AR regression and External Finance Dependence (EFD) for the AP regression. Regarding the first equation, the hypothesis is that firms with higher liquidity might react differently than firms with lower liquidity and thus the trade credit extended by these two types of firms might be different when the crisis arrives. If trade credit is a substitute for bank finance, we expect that the increase in trade credit extended for firms with more levels of liquidity just before the crisis is higher than this increase for firms with less liquidity. For the second equation, we consider the variable EFD. This variable measures the shortage of internally generated funds in financing new investments. Firms with more EFD might react differently when taking trade credit when the crisis arrives. As before, if trade credit is a substitute for bank finance, we expect that the increase of trade credit taken is higher for firms with more needs of external finance than this increase for firms with less need of external finance in the crisis years. Finally, we will estimate both regressions with two different estimation methods: Ordinary Least Squares (OLS) and Fixed Effects (FE). Even though we perform and report the results under both methods of estimation, we will comment on the results concerning only FE. We will assume unobserved heterogeneity among firms, since there could be some factors that vary for each firm and we do not observe, like for instance how good or bad the firm is being managed, but could be important and influence some of our results. We will not use regressors with no or very little variation over time (size and industry sector dummies). All the controls will be lagged one period in order to reduce multicollineality and reverse causality problems. BANCO DE ESPAÑA 60 ESTABILIDAD FINANCIERA, NÚM. 23

7 4 Data The data we will use is from the Central de Balances, a data base elaborated by the Bank of Spain and that contains the accounting statements (balance sheet and income statement) voluntarily provided every year by Spanish non financial firms. We have data from 1993 to Our initial sample consists of 163,481 observations from 33,321 firms for the period The panel data is incomplete so firms that only appear in the data base before the crisis period but do not appear in the years of the crisis are kept in the data base. We perform several filters to clean the data from inconsistencies and outliers. We exclude public utilities, i.e., those firms in Water Transportation, Water Services, Gas Services and Electric Services industries. This is done because these types of firms are usually heavily regulated. Additionally, we eliminate observations of firms with negative or zero Assets, negative or zero Operating Revenues, negative or zero Cash and Short Term Financial Assets, and observations where the sum of Short Term Financial Assets and Cash is higher than the Assets. Then we eliminate observations where the growth rate of Assets is more than one, since these are most likely to correspond to firms that have merged or experienced other significant restructuring. We also eliminate observations where the growth rate of total Assets or the growth rate of Sales is less than 0.8 since these are most likely to be firms in the process of liquidation or division of Assets. From this point, we will work with two different samples: one for AR and one for AP. For the first sample, we eliminate observations where AR is negative or more than one. When applying the last filter, we drop about 1% of the observations that we had in the sample at that time. For the second sample, we eliminate observations where AP is negative or more than five, filter which eliminates also the 1% of the remaining observations when that filter was applied. Next, for each sample we also eliminate observations for years in which we directly know that firms have experienced a merge, split or cession. We also drop observations where the ratio between Equity and Assets is less than 1 because we want to eliminate firms that are about to become bankrupt. When we apply this filter for the both samples, we eliminate 0.3% of the observations. If we had applied the filter using 0.5 instead of 1, the percentage of deleted observations would have been 0.6%. 1 We eliminate firms with missing information on their industrial classification and firmobservations for which year t 1 data are not available (since the explanatory variables are lagged on period the value of the explanatory variable would not be available for these firms). Finally, we eliminate firms with less than three valid observations over the whole period of analysis. Our final sample for AR consist of 93,091 observations from 11,483 firms for the period , whereas for AP consist of 82,393 observations from 10,061 firms for the same period. 1 One could argue that the filter should be 0, instead of 1 or 0.5, but applying this filter would have eliminated almost 3% of the observations. First, deleted observations would have been too many for this filter, and most importantly, we would have dropped observations where we would not have been taking into account the future value of the firm. There could be firms whose future Equity value is positive and thus are worth being kept in our samples. Although this filter can be easily criticised, the important aspect that we should look at is the fact that results do not change substantially when we perform this filter using 1 or 0. BANCO DE ESPAÑA 61 ESTABILIDAD FINANCIERA, NÚM. 23

8 5 Accounts receivable In this section, we will describe all the results concerning the first regression, where AR is the dependent variable. The equation in section 3 constitutes our baseline regression. After this, we will expand this equation and will include more explanatory variables. Specifically, we will include the interaction between our financial slack variable (liquidity), the crisis years and a size dummy (that takes value one for large firms and zero for small firms) and we will interact these three terms first two by two and then the three of them simultaneously. We define small and medium firms as firms with less than 250 employees and large firms as firms with 250 or more employees. Finally in this section, we will split the sample in two, according to size: one sample will consist of small and medium firms and the other of large firms. The list of variables used in the analysis, together with the definition of each of them, appears in Table BASELINE RESULTS Columns 1 and 2 of Table 2 describe our baseline results. In column 1 we use an OLS estimation method, whereas in column 2 we use a Fixed Effect estimation method. The explanatory variables in the FE estimation exclude all the variables without time variability, such as the size dummy variables. As explained above, we will only comment the FE results. Chart 1 shows the pattern of AR according to size, with the 95% confidence intervals. We can see that small firms give more trade credit than large firms. The coefficients we see in Chart 1 are the dummy year coefficients obtained in our first regression using a FE estimation. Trade credit extended by firms increases with size up to firms that have between 50 and 99 employees. From this point, trade credit decreases with size. Thus, it is clear that large firms extend less trade credit than small firms and that medium size firms are the ones that extend the most trade credit of all. Now let us look at the estimated coefficients of the year dummies. Chart 2, which has the estimated coefficients of the year dummies using a FE estimation, shows that the evolution of these coefficients is stable over time and they increase only from year 2008 indicating that the crisis starting in 2008 has had some effect on the provision of trade credit given by firms. VARIABLES AND DEFINITIONS TABLE 1 Variable AR AP LIQ Age Net pro t margin Sales growth Assets growth Long term liabilities over assets Equity over assets EFD Short term bank borrowings Long term bank borrowings Financial Expenses De nition Accounts receivable / Sales Accounts payable / Purchases (Cash + Short term nancial assets ) / Total assets Actual year First year of the rm EBIDTA / Total operating revenues Growth rate of sales Growth rate of total assets Long term liabilities / assets Equity / Total assets [(Capital assets at t Capital assets at t 1) EBIDTA] / Capital assets at t Short term bank debt / Total assets Long term bank debt / Total assets Financial expenses (incl. Interest payments) / Total assets BANCO DE ESPAÑA 62 ESTABILIDAD FINANCIERA, NÚM. 23

10 extended is higher for firms with more liquidity. For example, the interaction coefficient for 2008 is This means that if firm A has 10% more liquidity than firm B, the former will increase (decrease) their trade credit extended by 0.16% more (less) than the latter will do in This finding is consistent with the hypothesis that firms with high levels of precrisis liquidity act as substitutes for banks when the crisis arrives. But is this positive effect of liquidity on AR when the crisis arrives, in 2008, high enough to offset the total negative effect? To see the answer, we have to look at the marginal effect of liquidity on AR during In the years previous to the start of the crisis, where we do not have any interaction terms, this effect is When the crisis arrives in 2008, this effect becomes less negative, Hence, liquidity still has a negative impact on AR, even in But this negative effect is less intense during this year. This negative effect decreases by 36% in However, the same does not happen during the next two years of the crisis, 2009 and One reason could be that suppliers are willing to extend more trade credit at the beginning of the crisis, thinking that this crisis is going to be temporary, but when they see how deep the crisis is, they possibly decide to stop behaving like banks and thus they go back to act like they were acting before the crisis. So there is no effect of the years 2009 and 2010 in the provision of trade credit. Now we move to column 4 of Table 2, which show the results of the regression when we interact liquidity, which is our financial slack variable, the crisis years and a size dummy, which takes value 1 for large firms. As explained above, we have interacted liquidity with TRADE CREDIT EXTENDED BY FIRMS TABLE Constant *** (14.86) *** (24.55) *** (17.05) *** (57.62) Age *** (7.26) (-0.44) *** (7.03) (-0.51) Sales growth *** (-7.67) *** (-4.51) *** (-8.08) *** (-4.58) Net pro t margin *** (3.28) (0.85) *** (3.23) (0.80) Long term liabilities / Assets *** (-15.43) (0.24) *** (-16.64) (0.47) Equity / Assets *** (-5.00) (1.62) *** (-5.31) (2.16) Liquidity *** (-18.54) *** (-7.05) *** (-16.73) *** (-5.49) Large rm (size dummy) *** (-2.81) *** (-3.76) (0.33) Liquidity * large rm ** (-2.39) ** (-2.06) Liquidity * year *** (4.37) * (1.62) *** (4.50) * (1.80) Liquidity * year *** (3.19) (-0.66) *** (2.99) (-0.76) Liquidity * year *** (3.37) (-0.49) *** (3.11) (-0.33) Year 2008 * large *** (3.38) *** (4.40) Year 2009 * large (1.05) (1.57) Year 2010 * large (0.23) (1.86) Liquidity * year 2008 * large (-0.42) (-0.13) Liquidity * year 2009 * large (0.65) (1.16) Liquidity * year 2010 * large (0.56) (0.30) Estimation method OLS FE OLS FE R-squared Observations Firms ,125 11, ,125 11, ,125 11, ,125 11,483 NOTE: This table presents estimates from an unbalanced panel regressions explaining rm-level annual trade credit provided for the period The dependent variable is Accounts Receivable over Sales. The rst and third columns show the estimates using OLS and the second and fourth ones using FE. Liquidity is calculated as the sum of Cash and Short term nancial assets scaled by Total assets. All controls are lagged one period. Standard errors in parenthesis, next to the coef cients. ***, ** and * indicate signi cance at 1%, 5% and 10% respectively. BANCO DE ESPAÑA 64 ESTABILIDAD FINANCIERA, NÚM. 23

11 the crisis years because more liquid firms might react differently from less liquid firms, extending more or less trade credit, when the crisis arrives. Using the same argument, we can think of this size dummy as a way to capture the fact that large firms might react differently depending on liquidity when the crisis arrives. It is claimed that large firms are likely to have access to more sources of finance than small firms. Specifically we will find out whether large liquid firms extend more or less trade credit than small liquid firms and how this varies when the crisis arrives. Regarding the explanatory variables, in column 4 we can see that again results do not change compared to column 2. The estimated coefficients of Liquidity and Sales Growth are the only ones that are statistically significant and have the same sign and magnitude as the baseline results: and respectively. However, the estimated coefficient of the size dummy for large firms, which was statistically significant in the previous specification, is no longer statistically significant. Interpretations of these two statistically significant regressors are the same ones that we gave above. Now we turn to the interaction terms between our slack variable and the crisis years: only the estimated coefficient of liquidity and the year 2008 is statistically significant, as in the baseline results. This coefficient, however, is higher: , meaning that in this specification the negative effect of liquidity on AR will be more reduced since we have higher interaction coefficient for the year Now we will analyse the new interaction terms. Out of them, only two are statistically significant: the estimated coefficient of the interaction between liquidity and size and the estimated coefficient of the interaction between the crisis year 2008 and size. How do we interpret these coefficients? First, the estimated coefficient of the interaction term between liquidity and size is negative. We know that liquidity has a negative effect on AR. How does this effect vary when the firm is large? As this term is negative, large firms have a more negative effect of liquidity on trade credit than small firms. This is consistent with the fact that both liquidity and large size have a negative effect on AR and that in Chart 1 we saw that large firms extend less trade credit. Second, the estimated coefficient of the interaction term between the crisis year and the size large dummy is positive. How do we interpret this effect? We know that the year start of the crisis, 2008, has a positive effect on AR. This effect is reinforced if the firm is large. That is, the crisis in 2008 has a more positive effect on the extension of trade credit for large firms than for small firms. 5.2 RESULTS BY SIZE Now we split the sample in two subsamples: the first has firms with less than 250 employees, which constitutes 87% of the original sample, while the second subsample has firms with 250 or more employees. Table 3 shows us the results for both subsamples using the OLS and the FE methods. As mentioned above, the idea of splitting the sample by size is that large liquid firms might react differently from small liquid firms when the crisis arrives. Also, this distinction is important because in general large firms have less financial constraints than small firms since the former can rely on sources of external finance that the latter cannot. Regarding the explanatory variables, we see that Sales growth and Liquidity remain statistically significant for both subsamples. The negative effect of these two variables is slightly stronger for large firms (coefficients are more negative or higher in absolute value). Thus, the difference of trade credit extended between large firms whose sales are growing (large firms with high liquidity) and large firms that have low sales growth (low liquidity) is higher that this same difference in small firms. Additionally, we have a new explanatory BANCO DE ESPAÑA 65 ESTABILIDAD FINANCIERA, NÚM. 23

12 TRADE CREDIT EXTENDED BY FIRMS (according to size) TABLE 3 Small rms Large rms Constant *** (14.19) *** (52.56) *** (4.13) *** (28.34) Age *** (6.25) (-0.60) *** (4.39) ** (2.09) Sales growth *** (-7.02) *** (-3.75) *** (-3.09) *** (-3.82) Net pro t margin *** (2.98) (0.74) (-0.71) (-1.53) Long term liabilities / Assets *** (-14.25) (0.89) *** (-2.95) (-1.16) Equity / Assets *** (-3.37) *** (-2.95) ** (-2.37) ** (-2.22) Liquidity *** (-17.51) *** (-6.35) *** (-8.65) ** (-2.55) Liquidity * year *** (4.54) * (1.92) (-0.07) (-0.97) Liquidity * year *** (2.98) (-0.95) (1.40) (0.96) Liquidity * year *** (2.83) (-0.61) ** (2.03) (0.67) Estimation method R-squared Observations Firms OLS ,000 10,325 FE ,000 10,325 OLS ,461 1,531 FE ,461 1,531 NOTE: This table presents estimates from an unbalanced panel regressions explaining rm-level annual trade credit provided for the period according to size. Small rms are those with less than 250 employees and large rms are those with 250 or more employees. The dependent variable is Accounts Receivable over Sales. The rst and third columns show the estimates using OLS, for small and large rms respectively, and the second and fourth ones using FE. Liquidity is calculated as the sum of Cash and Short term nancial assets scaled by Total assets. All controls are lagged one period. Standard errors in parenthesis, next to the coef cients. ***, ** and * indicate signi cance at 1%, 5% and 10% respectively. variable whose estimated coefficient becomes statistically significant under this specification: Equity, whose estimated coefficient is negative. This implies that firms with high equity extend less trade credit. When we look at the interaction terms in both samples, we see that results found in the previous part only hold for the small subsample, since the coefficients are quite similar and have the same significance. This does not happen for the large subsample. Why could the baseline results be only applied to small firms and not to large ones? One possible interpretation could be this one: let us consider the case of a large firm that is a supplier. If this firm has a small firm as a client, the supplier will have more bargaining power, since it is large and it is less dependent on its client than its client is on the supplier. On the other hand, the small firm has little bargaining power and does not have too many options and thus it has to accept the conditions of the large firm. Now, if the large supplier firm has another large firm as a client, what would happen when the supplier does not want to provide trade credit but the client wants to take it? It will depend on the bargaining power of the firms, but assuming that the provider does not want to extend trade credit, as the client is a large firm, it can have access to other sources of external finance. However, this situation does not happen when we consider a small or medium firm as a supplier. As they do not have many clients, they could be more dependent on their clients than large firms are. Thus, when the client is a large firm, the supplier has to extend more trade credit even it does not want to. 6 Accounts payable Now we will analyse trade credit taken by firms. In this case, our dependent variable is AP and we will regress it on some controls, year dummies, our financial slack variable and the interaction between this slack variable and the crisis years. All controls, as always, are lagged one period to reduce a possible multicollineality problem. Our financial slack variable, EFD, is defined as the difference of capital expenditures and cash flow scaled by capital assets. That is, the proportion of new investments that are not covered by the cash BANCO DE ESPAÑA 66 ESTABILIDAD FINANCIERA, NÚM. 23

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