# **6. Other supporting evidence**

In this section, we analyze the characteristics of firms that receive earmarked credit and the features of banks that channel these loans. We also study the conditions in which banks issue simultaneously earmarked loans and working capital loans (i.e., loan bundles).

# **6.1. Recipients of Earmarked Loans**

We examine which firms obtain earmarked credit, and which banks are more likely to issue these loans. To study the firm and bank characteristics associated with earmarked lending, we use the universe of firms in the credit registry. There are 871,392 firms that obtain working capital loans in the sample period (a total of 1.9 million loan-level observations) from 137 banks. Among these, around 67,000 receive at least one earmarked loan from a group of 35 banks.

We aggregate the loan-level data at the firm-bank-year level and run OLS regressions using as dependent variable a dummy variable equal to one if a firm-bank pair is observed with a new earmarked loan in a given year, and zero otherwise. We include as covariates firm size, firm age, the length of the bank-firm relationship, and the indicator variable _Riskfy_ , which equals one for firms with loan ratings below the median in year _y_ . To analyze if the probability of issuing earmarked loans to firms of varying credit risk depends on the market power of lenders, we include as covariate the indicator variable _MPfy_ and its interaction with the variable _Riskfy_ . Finally, to capture the persistence of earmarked loans within a firm-bank pair over time, we include the covariate _Earmarkfby_ .<sup>16</sup> This variable is equal to zero if the firm _f_ has not obtained and earmarked loan from bank _b_ prior to year _y_ and is set to one for firm-bank pairs after the first earmarked loan is issued. To control for other bank- and time-specific factors related to the issuance of earmarked loans, such as aggregate changes in the earmarked program or banks’ specialization in borrowers more likely to benefit from earmarked credit, we introduce year and bank fixed effects. To also control for other demand-specific factors influencing the issuance of earmarked loans (e.g., changes in the targeted population of earmarked programs), we introduce fixed effects at the industry and location level, and in some specifications, fixed effects at the industry*location, and industry*location*year level.

The results of this exercise, displayed in Table A4 in the Appendix, highlight four main findings. First, larger firms are more likely to obtain earmarked loans from banks. Second, the credit risk of firms is not statistically related to their likelihood of obtaining earmarked loans. While this finding might seem surprising, as the interest rate ceiling should prevent riskier borrowers from obtaining these loans, it is in line with _cross-product pricing_ strategies. To the extent that banks can distribute the cost of an earmarked loan over multiple products, even riskier borrowers might be able to access the program. Third, we find that the likelihood that riskier firms obtain earmarked credit is higher in regions where the banking sector is more concentrated. This finding is also consistent with cross-product pricing, since banks with market power are precisely the ones that can adjust the price in free-market credit products.

> 16 The summary statistics of the variables used in this exercise are shown in Panel B of Table 1. The definition of these variables is displayed in Table A1 of the Appendix.

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