Board of Governors of the Federal Reserve System

December 03, 2021

New Accounting Framework Faces Its First Test: CECL During the Pandemic

Bert Loudis, Sasha Pechenik, Ben Ranish, Cindy M. Vojtech, and Helen Xu¹

Introduction

On January 1, 2020, most large and mid-sized U.S. banks adopted Current Expected Credit Losses (CECL), a new accounting standard for estimating allowances.² Allowance for credit losses is an estimate of the amount that a bank is unlikely to recover from a financial asset. CECL was issued to replace the incurred loss methodology (ILM) as policy makers determined that the increase in allowances occurred too late in the business cycle under ILM. By changing the timing and level of allowances, CECL also impacts the timing and level of capital, which could impact lending.

Generally, loan losses (also known as net charge-offs) build when unemployment increases and GDP slows.³ This relationship is clear during the 2007–09 financial crisis shown in Figure 1. Just a few months after CECL was adopted, the economy fell into a sharp decline as the COVID-19 pandemic resulted in widespread disruption to business activities (Figure 1, March 2020). This coincidence was an immediate test of the CECL methodology during an economic downturn and recovery, albeit an unusual one.

Figure 1. Banking System Quarterly Net Charge-off Rate with Economic Indicators

When a bank builds allowances through provision expense, net income is reduced and that reduces capital. Consequently, as capital is generally considered a costly funding source, loans that carry larger allowances under CECL (as compared to ILM) become more expensive to underwrite.⁴ In response, banks may change their lending practices. This note estimates how CECL changed allowances and capital, and further explores the relationship between CECL and lending growth. Because only part of the banking system was initially subject to CECL, the analysis compares banks that adopted CECL on January 1, 2020 (CECL adopters) and banks that remained in ILM as of June 30, 2021 (non-adopters) to estimate these effects.⁵

Our analysis shows that CECL adopters' allowances responded more quickly than non-adopters to changes in the economic outlook. In addition, we show that while CECL increased aggregate allowances, the aggregate capital impact has been largely neutralized by the regulatory capital transition rule.⁶ We show that CECL has had a wide range of impact on allowances for different loan types. However, we find limited evidence that the impact of CECL on allowances is associated with decreased lending in the pandemic.

Background

ILM accounting standard limits banks' ability to provision for losses that are not yet probable. The financial crisis of 2007-2009 demonstrated significant problems with this limitation. In particular, banks had low levels of allowances to absorb credit losses in the lead up to the financial crisis. As the crisis unfolded, banks increased provisions for probable loan losses. This put additional pressure on bank capital, which was already being depleted due to other losses during the crisis.

Figure 2 shows this "too little, too late" relationship. Provisions (solid positive bars) sharply increase only after the recession has begun and net charge-offs increase (hollow negative bars). Provisions outpace net charge-offs to build allowances (black line), with allowances peaking around 2010, after the recession has ended.

Figure 2. Provision and Net Charge-off Rates around the 2007-09 Financial Crisis

To address this issue, various stakeholders requested that the accounting standard-setters, the FASB and International Accounting Standards Board, work to incorporate more forward-looking information in standards on loan loss provisioning. In response, the FASB issued a new accounting standard in June 2016 that introduced the CECL methodology for estimating allowances for credit losses.

In comparison to ILM, CECL requires banks to consider forward-looking information and record a provision for expected losses over the entire life of the loan when the loan is originated. CECL does not change the total amount of credit losses that will be ultimately recorded in financial statements, but it affects how early the bank provisions for credit losses. Because CECL allowances represent an estimate of lifetime losses—as opposed to losses over a typically shorter "loss emergence period" under ILM—CECL generally results in a higher level of allowances at each reporting period.

CECL's Impact on Allowances in the Pandemic

Due to the COVID-19 pandemic, many households and businesses faced a sudden loss of income. At the same time, extraordinary fiscal and monetary policy support blunted the financial stress. This section examines the differences in credit loss provisioning for CECL adopters and non-adopters during this unusual and highly uncertain environment.

Figure 3 compares the evolution of allowances for CECL adopters and non-adopters. Allowances of both sets of banks were stable in 2019, reflecting low and stable net charge-off rates. The adoption of CECL resulted in an immediate 37 percent increase in adopters' allowances on January 1, 2020. This impact represents an overall average difference in allowances between CECL and ILM under the generally benign economic outlook that existed on January 1, 2020. From that point onwards, the adoption of CECL and the onset of the pandemic both affected provisioning.

As the pandemic stressed the economy, CECL adopters rapidly increased loss provisions. Not counting the adoption impact, allowances increased by 76 percent in the first half of 2020 relative to 2019:Q4 levels. In comparison, non-adopters' allowances increased by only 32 percent over the same period. After peaking in 2020:Q2, CECL adopters' allowances begin to decline with improvement in the economic outlook. Non-adopters' allowances peaked in 2020:Q4, and have only declined slightly since then. As of 2021:Q2, the difference in allowances between CECL adopters and non-adopters is back to where it was immediately after CECL adoption.

Figure 3. Allowances of CECL Adopters and Non-Adopters

Table 1 shows that the impact of CECL on allowances varies significantly across loan types, presented in descending order of their share of total allowances in 2019:Q4.⁷ Column (1) provides the January 1, 2020 adoption impact.⁸ Consumer loan types that tend to have higher credit losses (e.g., credit cards) or longer maturities (e.g., student loans and auto) had the largest increases in allowances at CECL adoption. Columns (2) and (3) show allowance changes in the first half of 2020, as the economic outlook worsened. Allowances of all loan types increased materially during this period, with CECL adopters' allowances increasing more rapidly than non-adopters in each respective portfolio. Columns (4) and (5) show changes in allowances between June 2020 and June 2021 when the economic outlook steadily improved. During this period, CECL adopters substantially decreased their allowances across portfolios whereas non-adopters' total allowances stayed relatively flat in aggregate. At both adopter and non-adopter banks, decreases in allowances during this period were larger for consumer than wholesale loans. Allowances only continued to increase for the commercial real estate portfolio, which may have been adversely affected by the lingering effects of the pandemic on office occupancy rates.

While there are significant differences between the typical CECL adopter and non-adopter that could account for some of the divergence described above, the pandemic experience suggests that CECL generally increases the responsiveness of provisioning for credit losses to changes in economic outlook.

Table 1: Change in Allowances, by Loan Type

Estimates of CECL's Capital Impact in the Pandemic

Figure 4 shows the evolution of aggregate common equity tier 1 (CET1) capital ratio for CECL adopters—both including (solid black line) and excluding (dashed blue line) the impact of the transition rule.⁹ With most firms using the transition rule, the aggregate capital ratio declined by about 50 basis points to 11.3 percent in 2020:Q1, and climbed thereafter, reaching 12.5 percent in 2021:Q2. The transition rule increased the ratio by about 30 to 40 basis points, depending on the quarter.

To assess the impact of CECL, we first estimate what would have happened to CECL adopters' allowances—and therefore their capital—if they had remained on ILM. We use data on non-adopter banks to estimate a linear regression relating allowance-to-loan ratios in each quarter to bank and loan characteristics. Then, we use this model to predict what the allowances of CECL adopters would have been if they had remained on ILM.¹⁰ Each dollar change in allowances is converted into a negative 79 cent change in capital under the assumption of a 21 percent corporate tax rate. In our estimate, we also assume that CECL had no effect on the dollar amount of net capital distributions (e.g., dividends and share repurchases).

The black dashed lines in the figure represent a range of estimates for what CECL adopters' aggregate capital ratio would have been under ILM. This range is the middle 95 percent of aggregate capital ratios that result from running this regression model on many random samples of the non-adopters, with the size of the range reflecting uncertainty in how the evolution of allowances relates to banks' characteristics.

Our estimates suggest that the transition rule approximately neutralized the aggregate capital impact of CECL so far, with the aggregate capital of CECL adopters falling within or just below the range of estimated outcomes under ILM. However, as suggested by Table 1, the transition rule may not have neutralized the capital impact of CECL across loan types.

Figure 4. Capital (CET1) Ratios of Jan. 1 CECL Adopters, Actual versus Estimates under ILM

CECL Impact on Lending in the Pandemic

While the pandemic provides evidence that CECL presents less of a "too little, too late" concern, commenters on CECL have suggested that the standard may result in tighter bank credit supply when it is in greatest need.¹¹ The generally larger allowance under CECL reduces capital, causing banks to raise or retain more capital in order to preserve their capital buffers. As capital is considered a more expensive funding source, this potentially increases loan funding costs. Consequently, we might expect CECL adopters to reduce the type of lending with increases in allowances under CECL. However, only the diverse "other consumer" loan type experienced a statistically significant decrease in lending at CECL adopters relative to non-adopters. Moreover, as we discuss further, this difference in growth is difficult to attribute to CECL adoption given the pre-existing difference in other consumer loan growth trends across the two types of banks.

To formally investigate the relationship between CECL's impacts on allowances and loan growth, we follow the well-established difference-in-differences methodology. In this method, the outcomes of treated (CECL adopters indicated by $$CECL_i = 1$$) banks are compared to the outcomes of untreated banks (non-adopters), before and after the treatment occurs (post-CECL adoption indicated by $$Post_t = 1$$). This comparison is made separately for each loan type $$p$$ for which granular data on allowances are available, using banks $$i$$ over quarters $$t$$ (2016:Q1-2021:Q2) using the specification below:

$$$$ {Loan\ growth}_{p,i,t} = \beta_p Post_t * CECL_i + \sum_{n=1}^3 \gamma_{p,n} Delinq_{p,n,i,t} + \delta_{p,t} + \theta_{p,i} + \varepsilon_{p,i,t} $$$$

$$$$ Allowance/Loans_{p,i,t} = \beta_p Post_t * CECL_i + \sum_{n=1}^3 \gamma_{p,n} Delinq_{p,n,i,t} + \delta_{p,t} + \theta_{p,i} + \varepsilon_{p,i,t} $$$$

Note the regression specifications includes additional controls: three delinquency rates ($$Delinq_{p,n,i,t}$$), bank-specific fixed effects $$\theta_{p,i}$$, and quarterly fixed effects $$\delta_{p,t}$$.¹² Under the assumption that CECL adoption is the only remaining reason for outcomes (allowances or loan growth) to change differently for adopters and non-adopters after Jan 1, 2020, the coefficient $$\beta_p$$ equals the average effect of CECL on loan growth or additional allowances for loan type $$p$$.

This difference-in-differences assumption—and therefore our estimates of CECL impact—is more likely to be valid when the adopter and non-adopter banks are similar. Thus, we remove banks with atypical business models, as well as the largest (mostly CECL adopters) and smallest (mostly non-adopters) from our regression sample.¹³ We also apply filters to remove immaterial business lines and limit the role of outliers.¹⁴ As very few banks in our sample have material card portfolios, we exclude cards from the analysis.¹⁵ Finally, we assess the plausibility of the difference-in-differences assumption by testing whether CECL adopters and non-adopters faced similar loan growth trends prior to CECL adoption. The test rejects the hypothesis that CECL adopters and non-adopters' "other consumer" loan growth followed the same trend prior to CECL adoption, which casts doubt on the validity of the comparison of these loans across the two sets of banks.¹⁶

The results of our regressions are summarized in Figure 5, with the corresponding regression tables in the appendix (Table A.1 Panel A and B). The values of $$\beta_p$$—the average estimated effects of CECL on allowances and loan growth—are represented by the coordinates of the plotted circles, with each circle representing a specific loan type. The y-axis value is the effect on loan growth, and the x-axis value is the effect on allowances. The dashed crosshairs represent statistical 95 percent confidence intervals for these estimates.

Figure 5. Estimated Average CECL Impact ($$\beta_p$$) on Allowances and Loan Growth, by Loan Type

All five circles are located far enough to the right of the vertical axis that their crosshairs do not cross it—indicating that the average impact of CECL on allowances of each loan type was positive and statistically significant. If CECL's impact on allowances negatively affected loan growth, we should expect the circles to fall below the horizontal axis of Figure 5. Only the "other consumer" loan type shows significantly lower loan growth at CECL adopters following CECL adoption. However, as discussed previously, conclusions based on comparisons of other consumer loans between the two types of banks should be interpreted with caution.

Overall, this analysis does not provide substantial evidence that CECL affected loan growth despite the impact on allowances. One possibility, is that the transition rule sufficiently offset CECL's impact on allowances for each type of loan, suggested by supplemental analysis (Appendix Table A.1 Panel C), and banks factored this benefit into their operational decisions during the unprecedented time of stress.

Conclusion

CECL adopters' loan loss provisioning was noticeably more responsive to the dramatic changes in economic outlook that occurred in the COVID-19 pandemic. In addition, the transition rule performed well in approximately neutralizing the aggregate capital impact of CECL on adopters.

We further explored the impact of CECL on lending. On this point, we interpret the limited evidence with caution. CECL has the potential to reduce lending cyclicality when it acts as a precautionary measure, resulting in banks building allowances as credit risks gradually build in the late stages of the credit cycle. In contrast, the sharp change in the economic outlook during the COVID-19 experience was unlike any other credit cycle in modern times—occurring without and independent of any buildup of credit risk. As such, it is difficult to determine the cyclicality characteristics of CECL. In addition, the sizeable fiscal and monetary policy responses during the pandemic also make it hard to generalize these results to a more standard credit cycle. Regardless, our analysis provides little evidence of an association between CECL's impact on allowances and loan growth across different loan types.

Appendix

Table A.1 presents the results of the regressions corresponding to the equations in the "CECL Impact on Lending" section. Panel A covers the regressions used to estimate CECL's impact on allowances, and Panel B covers regressions of CECL's impact on lending. The values of $$\beta_p$$ are reported in the first row. Each column corresponds to the results obtained for a specific loan type. In addition to the results discussed above, the controls for delinquency rates show that higher delinquency rates are generally associated with lower lending growth and higher allowances.

In Panel C, we test whether CECL has had an impact on allowances after accounting for the impact of an offset to the capital impact of the allowances. Specifically, as the transition rule adds a capital offset to neutralize 25 percent of (post-adoption) allowance increases while using CECL, we adjust allowances of transition rule users by multiplying the allowance-to-loan ratio after CECL adoption by 0.75. Panel C shows that none of the estimates of impact to adjusted allowances are statistically significant at a ten percent level.

Table A.1 - Regression Estimates of the Impact of CECL on Allowances and Loan Growth, by Loan Type

Panel A: Loan growth

Panel B: Allowance/Loan Ratio (pp)

Panel C: Allowance/Loan Ratio – Adjusted for Transition Rule (pp)