the credit-weighted default rate and interest rate are quite close. If default amounted to no recovery, even without a cost of default, banks would receive less money than they lent in expectation (1.047*(1-0.055) _<_ 1). These values must therefore reflect substantial loan recovery even in the event of default, ensuring a gap between expected credit loss and true losses from default (even after default costs). This feature of the data underscores the importance of accounting for partial recovery and the large gap between the government’s share of at-risk credit and actual government burden.

Quantitatively, we simplify the multi-tiered partial guarantee scheme in which guarantees vary by firm size by collapsing it into a single guarantee of _χ_ = 0 _._ 8. We set _r_ ̄ _b,_ 1 = 0 _._ 035, the statutory maximum interest rate under the program. To capture the overall increased willingness to lend, we lower the cost of intermediated capital by 1.65 percentage points.<sup>85</sup> As described in Section 7, we assess the _ex post_ measures under two scenarios: (i) the typical year’s default rates used for the model calibration (and therefore lending decisions) and (ii) an unanticipated systemic shock to the distribution of productivity after credit is already granted.<sup>86</sup> That is, we consider a combination of first and second moment shocks by lowering the productivity distribution’s log mean by 4.2 log points and increasing the log standard deviation by 40%.<sup>87</sup> These values capture a large but reasonable shock to the economy. Moreover, our combined shock lowers the value added of eligible firms by 4.9%. Such a drop in annual output is not outside of the Chilean experience.<sup>88</sup>

# **D.5 Further Intuition Behind the Credit Program Results**

To build intuition for the results in Table 10, we consider the borrowing and default behavior of two different firms. Specifically, Appendix Figure 6 illustrates the credit variables for an average value of productivity, _z_ = 1. We plot equity on a log basis to show details for low levels of equity.<sup>89</sup> The left panels illustrate these functions for firms with _σε_ = 0 _._ 34, essentially the middle of the distribution. The solid dark line illustrates the results for a firm in the benchmark banking system. Firms with equity above one (just less than a typical

> 85This value leads to an increase in lending of about 9.5% under the policy. Though not a perfect comparison, the total lending in the program equals 9.5% of the stock of firm credit in 2019. The model simulates credit without distinguishing between program and non-program credit. To measure program credit in the model, we use the percentage of program credit relative to the total observed credit of eligible firms in 2020, which amounts to 43.6%.

> 86In an alternative simulation where a systemic shock at default rates is anticipated at the time of lending, credit is dramatically reduced, interest rates are higher, and default is intermediate between the two scenarios on which we focus. However, the impacts of the policy relative to the status quo are similar to those we present for a typical year’s default rate.

> 87Xiao (2022) estimates that the second moment of the productivity distribution increases by 40% during the Great Recession for the United States in a model similar to ours.

> 88In 1954, 1973, 1975, and 1982, the drop in real GDP exceeds 5%. In 2020, it declines by 5.8%.

> 89We show results relative to equity to highlight the financial aspects of the model.

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