Figure 1: Allowance Model Comparison: Good Economy, Good Loan
Assume a three-year loan. Economic forecast projects slow, steady
growth. The loan does not show any signs of deterioration over the three
years.
Two side-by-side bar charts comparing CECL and ILM allowance-to-loans ratios over years 0 through 3, with CECL showing a small and decreasing level of allowances over time, in contrast to zero allowances for ILM in all years. As this is a stylized example, the units on the vertical axis are arbitrary. The key point is that CECL requires recognition of some allowances as soon as the expected loss is non-zero, and not only once a loss becomes probable–which never occurs in this example.
Figure 2: Allowance Model Comparison: Good Economy, Bad Loan
Assume a three-year loan. Economic forecast projects slow, steady
growth. The loan shows signs of deterioration in year two and ultimately
defaults at maturity. The red dashed line indicates the loss at
default.
Two side-by-side bar charts comparing CECL and ILM allowance-to-loans ratios over years 0 through 3, with CECL showing a small level allowances in years 0 and 1, in contrast to zero allowances for ILM. In years 2 and 3, both CECL and ILM show similar and significant increases in allowances due to the hypothetical deterioration of credit quality. As this is a stylized example, the units on the vertical axis are arbitrary. As in Figure 1, CECL recognizes some allowances as soon as the expected loss is non-zero (at origination), and not only once a loss becomes probable.
Figure 3: Difference-in-Differences Estimates
These figures plot the results of running the following regression:
\[\begin{eqnarray}
Y_{p,i,t} &=& \beta^{early}_{p}\,\mbox{OnCECL}_{i,t} +
\beta^{late}_{p}\,\mbox{OnCECL}_{i,t}
+ \Gamma_{p}\,\mbox{BankControls}_{i,t-1} +
\Lambda_{p,t}\,\mbox{Region}_{i,t-1} + \,\delta_{p,t} + \theta_{p,i} +
\varepsilon_{p,i,t}, \nonumber
\end{eqnarray}\] where \(Y\) is
loan growth or allowance coverage, and BankControls\(_{i,t-1}\) include three asset quality
controls, three bank characteristic variables, and macroeconomic
controls. In the top figure, the \(\beta^{early}_{p}\) values are plotted with
the dots for each loan type \(p\). The
bottom figure plots \(\beta^{late}_{p}\). The dashed gray lines
represent statistical 95 percent confidence intervals for these
coefficient estimates. cards = credit card loans, com = commercial and
industrial loans, cre = commercial real estate loans, cnstr =
construction loans, and othcons = other consumer loans.
Source: Call Report, FR Y-9C, and author calculations.
Short Alt Text
“Two scatter plots comparing quarterly loan growth versus allowance coverage for different loan categories, showing early versus late CECL adopters in 2020 and 2023”
Extended Description
This figure contains two scatter plots, analyzing the relationship between quarterly loan growth and allowance coverage around CECL adoption dates.
Top Panel – Early Relative to Late Adopters:
X-axis: Allowance Coverage (pp) ranging from \(-0.5\) to 1.5
Y-axis: Quarterly Loan Growth (pp) ranging from \(-2\) to 1
Plots six loan categories: cards, com, cnstr, cre, othcons, rre
Shows distribution of data points for banks that adopted CECL early (in 2020) compared to late adopters
Bottom Panel – Late Relative to Early Adopters:
Same axis ranges as top panel
Shows the inverse comparison (late adopters relative to early adopters in 2020)
Displays the same six loan categories
Key Finding
The scatter plots allow comparison of how loan growth and allowance coverage changed differently across loan categories between early and late CECL adopters in both the 2020 and 2023 adoption periods. Across loan categories, there is a weak negative association between the changes in allowance coverage and loan growth.
Figure 4: Relationship of Loan Growth and CECL Adoption Effect, by Time to
Adoption
The coefficients \(\beta_{\tau}\)
from the following regression are plotted: \[\begin{eqnarray}
\mbox{LoanGr}_{i,t} &=& \beta_{\tau,\tau\neq
-1}\,\mbox{AdoptionEffect}_{i} + \delta_{t} + \theta_{i} +
\varepsilon_{i,t} \nonumber.
\end{eqnarray}\] Data sample covers the years 2016 through
2023.
Short Alt Text
“Line chart showing loan growth effect relative to year prior to CECL adoption, ranging from -7 to 3 years”
Extended Description
This figure displays a line chart analyzing the loan growth effect of increases in allowances due to CECL adoption relative to the year prior to CECL (Current Expected Credit Losses) adoption. The line is bounded by two curves representing a 95 percent confidence interval.
Chart Details
X-axis: “Years to CECL Adoption” ranging from \(-7\) to \(+3\) years
Y-axis: “Loan Growth Effect – Relative to Year Prior to CECL Adoption” (scale ranges from \(-1\) to 1)
The line is relatively flat (near zero) prior to CECL adoption, then dips at the adoption year and stays negative. The values at year 0 and 1 are statistically significantly negative, and the values at years 2 and 3 are almost statistically significant as well.
Key Finding
This chart suggests that CECL adoption had limited effect on loan growth prior to adoption and that early adopters adjusted lending for at least a few years following adoption.
Figure 5: Capital Distributions from Early and Late CECL
Adopters
This chart plots dividends over assets for early adopters and late adopters and repurchases over assets for early adopters. The data starts in 2017 and ends in 2023. All lines decrease a bit in 2020. Dividends to assets range between approximately 0.3 and 0.5. Repurchases over assets range between approximately 0.1 and 0.3.
Figure A1: Geographical Distribution of Branches
These figures plot differences in each state’s share of bank branches
as of the end of 2019. The top plot provides the share from the
aggregate of banks in our sample minus the aggregate of all banks, while
the bottom plot provides the average difference between the early and
late CECL adopting banks within our sample. Branches include only main
office and full service branches; banks without branches are excluded.
Source: FFIEC branch data and author calculations.
These figures plot differences in each state’s share of bank branches as of the end of 2019. The top plot provides the share from the aggregate of banks in our sample minus the aggregate of all banks, while the bottom plot provides the average difference between the early and late CECL adopting banks within our sample. Branches include only main office and full service branches; banks without branches are excluded. Source: FFIEC branch data and author calculations.
Figure A2: Loan Growth Effects for Early Adopters
This chart shows the coefficients \(\beta_{p,t}\) from the regression equation
below, where each panel represents the time series of \(\beta\) for a different loan portfolio
\(p\) (cards = credit card loans, com =
commercial and industrial loans, cre = commercial real estate loans,
cnstr = construction loans, and othcons = other consumer loans), and the
dependent variable \(Y\) is either loan
growth (this figure) or allowance coverage (Figure A3 below). The parallel trends
assumption is tested by checking whether the \(\beta_{p,t}\) are jointly statistically
significant from each other for those quarters \(t\) occuring prior to early CECL adoption
in 2020. The Wald test p-values are indicated on the plots. Source: Call
Report, FR Y-9C, and author calculations.
\[\begin{eqnarray}
Y_{p,i,t} &=& \beta_{p,t}\,\mbox{EarlyAdopter}_{i} +
\Gamma_{p}\,\mbox{BankControls}_{i,t-1} +
\Lambda_{p,t}\,\mbox{Region}_{i,t-1} + \delta_{p,t} + \theta_{p,i} +
\varepsilon_{p,i,t} \nonumber
\end{eqnarray}\]
Short Alt Text
“Six time series charts showing trends from 2016 to 2024 for different loan categories, with varying p-values for pre-2020 analysis”
Extended Description
This chart shows the coefficients \(\beta_{p,t}\) from the regression equation below, where each panel represents the time series of \(\beta\) for a different loan portfolio \(p\) (cards = credit card loans, com = commercial and industrial loans, cre = commercial real estate loans, cnstr = construction loans, and othcons = other consumer loans), and the dependent variable \(Y\) is either loan growth (this figure) or allowance coverage (Figure A3 below). The parallel trends assumption is tested by checking whether the \(\beta_{p,t}\) are jointly statistically significant from each other for those quarters \(t\) occuring prior to early CECL adoption in 2020. The Wald test p-values are indicated on the plots. Source: Call Report, FR Y-9C, and author calculations.
\[\begin{eqnarray} Y_{p,i,t} &=& \beta_{p,t}\,\mbox{EarlyAdopter}_{i} + \Gamma_{p}\,\mbox{BankControls}_{i,t-1} + \Lambda_{p,t}\,\mbox{Region}_{i,t-1} + \delta_{p,t} + \theta_{p,i} + \varepsilon_{p,i,t} \nonumber \end{eqnarray}\]
Individual Chart Details
Chart 1 (Top Left) – CNSTR (Construction):
p-value for pre-2020: 0.65
Y-axis values range from \(-4\) to 2
Shows trend fluctuations centered around zero
Chart 2 (Top Middle) – CRE (Commercial Real Estate):
p-value for pre-2020: 0.25
Y-axis values range from \(-2\) to 2
Shows trend fluctuations centered around zero
Chart 3 (Top Right) – RRE (Residential Real Estate):
p-value for pre-2020: 0.54
Y-axis values range from \(-2\) to 2
Shows trend fluctuations centered around zero
Chart 4 (Bottom Left) – COM (Commercial):
p-value for pre-2020: 0.21
Y-axis values range from \(-2\) to 2
Shows trend fluctuations centered around zero
Chart 5 (Bottom Middle) – OTHCONS (Other Consumer):
p-value for pre-2020: 0.28
Y-axis values range from \(-1\) to 3
Shows trend fluctuations centered around zero
Chart 6 (Bottom Right) – CARDS (Credit Cards):
p-value for pre-2020: 0.69
Y-axis values range from \(-6\) to 5
Shows trend fluctuations centered around zero
Common Features
All charts span from 2016Q1 to 2024Q2 on the X-axis
Vertical gridlines mark quarterly intervals
Y-axes are consistent across all panels (\(-6\) to 6)
Each chart includes a p-value indicating statistical significance for the pre-2020 period
Figure A3: Allowance Effects for Early Adopters
See Figure A2 for a description.
Short Alt Text
“Six time series charts showing trends from 2016 to 2024 for different loan categories with larger Y-axis scale, varying p-values for pre-2020 analysis”
Extended Description
This figure contains six separate time series line charts arranged in a 3\(\times\)2 grid, each showing trends for a different loan category from 2017Q2 through 2024Q2. This figure uses a different Y-axis scale compared to the previous figure.
Individual Chart Details
Chart 1 (Top Left) – CNSTR (Construction):
p-value for pre-2020: 0.63
Y-axis values range from \(-0.1\) to 0.6
Shows trend fluctuations centered around zero
Chart 2 (Top Middle) – CRE (Commercial Real Estate):
p-value for pre-2020: 0.03
Y-axis values range from \(-0.1\) to 0.5
Shows trend fluctuations centered around zero
Chart 3 (Top Right) – RRE (Residential Real Estate):
p-value for pre-2020: 0.62
Y-axis values range from \(0\) to 0.3
Shows trend fluctuations centered around zero
Chart 4 (Bottom Left) – COM (Commercial):
p-value for pre-2020: 0.39
Y-axis values range from \(-0.1\) to 0.4
Shows trend fluctuations centered around zero
Chart 5 (Bottom Middle) – OTHCONS (Other Consumer):
p-value for pre-2020: 0.13
Y-axis values range from \(-0.3\) to 0.7
Shows trend fluctuations centered around zero
Chart 6 (Bottom Right) – CARDS (Credit Cards):
p-value for pre-2020: 0.81
Y-axis values range from \(-0.5\) to 2.6
Shows trend fluctuations centered around zero
Common Features
All charts span from 2016Q1 to 2024Q2 on the X-axis
Vertical gridlines mark quarterly intervals
Y-axes are consistent across all panels (\(-1\) to 3)
Each chart includes a p-value indicating statistical significance for the pre-2020 period