that participate in it.

We merge these data sets using unique tax identifications of workers and firms that are common across sources. To secure the privacy of workers and firms, the Central Bank of Chile mandates that the development, extraction, and publication of the results should not allow the identification, directly or indirectly, of natural or legal persons. Officials of the Central Bank of Chile processed the disaggregated data and merged them with financial records from the Financial Market Commission. The authors implemented all the analysis and neither involved nor compromised the Central Bank of Chile, the Financial Market Commission, or the Chilean tax authority.<sup>23</sup> The merged data set allows us to study the real and financial aspects of both the credit and the employment programs covering the universe of firms. For most of the analysis, we use the 2018–2020 period of these data sets.<sup>24</sup>

# **4 Credit Distribution across Firms**

## **4.1 Measuring Firm Risk**

To assess a firm’s _ex ante_ credit risk, we estimate a default probability model, which we then use in our selection models of government programs. We estimate the following cross-sectional probit model to predict default during 2019, based on attributes during 2018:<sup>25</sup> Pr( _Defaulti_ = 1) = Φ( _βCharacteristicsi,−_ 1 + _αs_ + _αm_ + _ui_ ) _._ (1)

> _Defaulti_ is a dummy equal to one if the firm defaults on a loan during 2019 (i.e., has a loan past due more than 90 days) and equal to zero otherwise. _Characteristicsi,−_ 1 is a vector of _ex ante_ firm-level attributes during 2018 that the literature uses to predict default rates (Glennon and Nigro, 2005; Crawford et al., 2018).<sup>26</sup> This vector contains five real economic variables reported to the tax authorities: net worth, value added per worker (a proxy for productivity), age, wage bill (a proxy for labor intensity), and sales (a proxy for size). It also includes two financial variables collected by the financial supervisory agency: debt outstanding and loan spread. The spread is the difference between the weighted average

> 23This study was developed within the scope of the research agenda conducted by the Central Bank of Chile in the economic and financial affairs of its competence. The Central Bank of Chile has access to anonymized information from various public and private entities through collaboration agreements signed with these institutions. The information contained in the databases of the Chilean tax authority is of a tax nature originating in self-declarations of taxpayers presented to the authority; therefore, the veracity of the data is not its responsibility.

> 24We build different samples of these datasets depending on the analysis. The different samples are described in Appendix A and Appendix Table 1.

> 25In Appendix Table 2, we estimate firm defaults in 2020 using firm characteristics from 2019. The estimated coefficients are very similar to those in our main analysis, confirming the robustness of the probit model estimated using 2019 data. Also, the explanatory power (R-squared) remains virtually unchanged, indicating that the model is similarly informative across years.

> 26The results hold if we use firm-level data during 2016–2019.

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