banking market structure and its effect on the paycheck protection program distribution during the 2020 Covid-19 pandemic

1. Soomi Lee
University of La Verne
DPA Research Roundtable
October 30, 2021
Regional Banking Market Structure and Emergency Loans to
Small Businesses: Examining the Paycheck Protection Program
during the Covid-19 Pandemic

2. Overview
• Goals & Motivations
• Theories and Literature on Small Business Lending
• Market concentration
• Community banks & relationship lending
• Small business lending in times of crisis
• Details of the Paycheck Protection Program
• Data, Variables & Empirical Model
• Results
• Conclusion
• Q&As
Lee | DPA Roundtable Fall 2021 1

3. Goal
1. Examine the effect of regional banking market structure on a
federal fiscal policy through the banking sector during an
economic crisis.
• Regions
• Banking market
• Banking market structure
• Small business
• Economic crisis
2. Analyze county-level data for the Paycheck Protection
Program (PPP), “the most ambitious and creative fiscal
policy response to the Pandemic Recession (Hubbard et al.
2020).”
Lee | DPA Roundtable Fall 2021 2

4. Motivation
1. Regional economic resilience and the role of financial
intermediaries (Martin and Sunley 2020)
• Q: What are the characteristics (initial conditions) of regional financial
markets that promote economic resilience?
• Disruption: shocks to regional economies
• Global, endogenous shocks: financial crisis
• Local, exogenous shocks: natural disasters
• Global, exogenous shock: Covid-19 pandemic
2. Paycheck Protection Program: “the most ambitious and
creative fiscal policy response to the Pandemic Recession
(Hubbard et al. 2020)
Lee | DPA Roundtable Fall 2021 3

5. 1. Market concentration in the banking sector
• Decline in # commercial banks: 68% decline between 1986 and 2019
(FDIC)
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6. 1. Market concentration in the banking sector
• Asset concentration: 12 largest banks hold 60 percent of all domestic
assets in 2020 (The Fed 2020)
• Asset size of all non-community banks was 82 times larger than that
of all community banks in 2019 (FDIC 2020)
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7. 1. Market concentration in the banking sector (cont’d)
1. Traditional view
Market concentration ⇡, small businesses lending ⇣
(Berger et al. 2004; Cetorelli and Strahan 2004)
2. “Investment theory”
Market concentration ⇡, small businesses lending ⇡
(Francis, Hasan, and Wang 2008)
3. Recent development
Nonlinear relationship; Considers types and sizes of banks and
borrowers (Strahan 2008)
Lee | DPA Roundtable Fall 2021 6

8. 2. Community banks and relationship lending
• Community banks – Definition
• Small banks, local banks
• FDIC definition: asset size, geographic footprint, specialty, foreign
assets, etc.
• Acquire deposits locally and make loans to local businesses, playing a
vital role in local economies (Rogers 2012).
• Community banks ⇡, small business lending ⇡.
• Mechanism: relationship lending using ”soft information”
• C.f., transaction lending using “hard information”
• In favor of young firms & local/small businesses
Lee | DPA Roundtable Fall 2021 7

9. 3. Market concentration X relationship lending
• Dynamics of competition is determined by types of banks in the
market (Presbutero and Zazzaro 2011; Elsas 2005; Canales and
Nanda 2012)
• Effects of Bank M&As on SME lending depends on players (Peek and
Rosengren 1998; Strahan and Weston 1998; and Avery and Samolyk
2004)
Lee | DPA Roundtable Fall 2021 8

10. 4. SME lending during economic crises
• Economic recessions – opposing views & evidence
• Relationship lenders lend more at a lower interest rate to SMEs
(Bolton et al. 2013); Relationship borrowers better at mitigating
credit restrictions during a recession (Sette and Gobbi 2014)
• Banks with more market power (non-relationship lenders) lend more
to non-relationship small businesses at a higher interest rate
(Cubillas and Suarez 2018).
• Natural disasters
• Geographically close local banks increase loans to local businesses
after natural disasters (Cortés 2014; Cortes et al. 2017; Koetter et
al. 2020 ; Ivanov et al. 2020)
• Prior relationships between lender and borrowers help ease the
lending restrictions after a natural disaster (Berg and Schrader 2012)
Lee | DPA Roundtable Fall 2021 9

11. In sum…
• Clearly, relationship banking and market concentration have significant
impacts on credit access for small businesses.
• PPP uses the banking sector to disburse “loans” which means
heterogeneous banking market characteristics would lead to differing
policy outcomes.
• Yet, we need to understand details of policy design in PPP first.
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12. Economic Impact of Covid-19
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13. Economic Impact of Covid-19
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14. Paycheck Protection Program
• Approved by the Congress on March 27, 2020
• Implemented by the Small Business Administration from April 2020
• For SMEs with less than 500 employees & other small organizations
• Help small businesses with payroll, rent, etc. (operating costs)
• Fixed interest rate at 1%
• No collateral
• Potentially forgiven
• “Loans” through the financial sector
• As of May 2021, 11.8 million loans approved; $800 billion distributed
• (Most of them, $700 billion were disbursed in April – August 2020)
• 5,460 financial institutions participated.
Lee | DPA Roundtable Fall 2021 13

15. 5. PPP literature
• Banks’ characteristics and their relationship with firms have
significant impacts on the distribution of PPP loans (Amiram and
Rabetti 2020; Bartik et al. 2020; James et al. 2020; Granja et al.
2020; Li and Strahan 2020)
• Relationship lenders alleviate firms’ insufficient information about
the program (Humphries et al. 2020).
• Consistent with Bolton et al. (2016)
• Economic Impacts: PPP loans helped small businesses (Bartik et al.
2020; Cororaton 2020; Hubbard et al. 2020) and the regional
economies (Barrios 2020; Li and Strahan 2020; Faulkender et al.
2020; Doniger and Kay 2021; James et al. 2020; Mitchell 2020).
• Existing studies overlooked the structural factor, particularly
market power, at the regional level analyses.
Lee | DPA Roundtable Fall 2021 14

16. Data
• Approved PPP loans from April to August 2020.
• Cross-sectional data with 3,100 U.S. counties
• Compiled data from SBA, FDIC, U.S. Census, Labor Statistics, New
York Times, and other sources
Lee | DPA Roundtable Fall 2021 15

17. Variables – Dependent Variable
• PPP loans per 100 businesses in each county
• Why loan counts instead of loan amounts?
• Steps of variable construction:
1. Aggregated 5.2 million loan-level data by county using county FIPS
codes & Zip code crosswalk file
2. Divided the number of approved PPP loans by the number of small
businesses in each county
• Source: Paycheck Protection Program Loan Level Data (Small
Business Administration); Quarterly Census of Employment and
Wages (U.S. Department of Labor)
Lee | DPA Roundtable Fall 2021 16

18. Variables – Key Explanatory Variables
• Market concentration index (HHI, log)
• 𝐻𝐻𝐼! =
"
"##
∑$%"
&
𝑆$
'
where i=county, j=bank, k=# banks, S=market share of j’s
deposit in i’s total deposit.
• Source: Statement of Deposit as of March 30, 2020, FDIC.
• Higher values indicate greater concentration of market power
• Presence of community bank (CBratio, log)
• Number of community bank branches / total number of bank branches.
• Community Banking Study Reference Data, 2020, FDIC.
• Higher values indicate a greater presence of community banks in the county
• Branch density (log)
• # full-service bank branches per 1000 businesses.
• Statement of Deposit as of March 30, 2020, FDIC.
• Higher values indicate higher density of bank branches
Lee | DPA Roundtable Fall 2021 17

19. Variables – Control Variables
• Per capita income ($), BEA
• Total population, education (% of college degree holders among
people aged 25+), age structure (% of 65+, % of children),
racial/ethnic composition (% non-Hispanic white, non-Hispanic
African American, Hispanic), U.S. Census.
• Covid-19 confirmed cases, the New York Times.
• % jobs in hospitality, goods-producing industries, and trade
(separately), Bureau of Labor Statistics
• Metropolitan indicator, OMB
• State indicators
Lee | DPA Roundtable Fall 2021 18

20. Empirical Model: Quantile Regression
• Using Quantile Regression
• Instead of OLS, the Least Absolute Deviation model was used (baseline, q=0.5).
• Estimates are based on the median conditional function instead of the mean
conditional function.
Lee | DPA Roundtable Fall 2021 19
!! = #! + %"#(''(!) + %$#(*+,-./0!) + %%#(''(!)(*+,-./0!) + 1!
&
2# + ∑ 4'
("
')" + 5!,
𝑦! = Number of PPP loans per 100 businesses in county i
HHI = Market concentration index
Cbratio = community bank branch ratio
𝑎! = intercept, 𝑥! =control variables, ∑ 𝑆=state fixed effects,
𝑒!=error term
Marginal effect of HHI = 𝛽"+ 𝛽( 𝐶𝐵𝑟𝑎𝑡𝑖𝑜
Marginal effect of CBratio = 𝛽' + 𝛽((𝐻𝐻𝐼)

21. 0 20 40 60 80 100
Approved PPP Loans per 100 Business
Urban Rural
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22. 0 .2 .4 .6 .8
Community Bank Branch Ratio
Urban Rural
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23. 2 2.5 3 3.5 4 4.5
HHI, log
Urban Rural
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24. Main Results
Lee | DPA Roundtable Fall 2021 23
OLS Estimates Quantile Regression Estimates
q=.1 q=.25 q=.5, LAD q=.75 q=.9
(1) (2) (3) (4) (5) (6) (7)
HHI
-1.217*** -3.810*** -2.461*** -2.388*** -2.038*** -2.542*** -3.477***
(.287) (.511) (.517) (.501) (.429) (.468) (.686)
CBratio
1.175 -17.751*** -5.953* -7.985*** -8.596*** -13.785*** -21.88***
(.714) (3.174) (3.110) (2.979) (2.690) (3.264) (5.336)
HHI X CBratio .
5.153*** 1.743** 2.410*** 2.749*** 4.175*** 6.248***
(.842) (.883) (.847) (.753) (.915) (1.507)
Bank Branches
per 100 biz.
1.165*** 1.204*** 1.096*** 1.076*** 1.112*** .989*** 1.163***
(.143) (.142) (.239) (.202) (.152) (.217) (.200)
State FE Yes Yes Yes Yes Yes Yes Yes
N 3100 3100 3100 3100 3100 3100 3100
Adj. R2
.547 .552
Notes: Standard errors in parentheses. All models include control variables.
* p<0.1, ** p<0.05, *** p<0.01.

25. Interaction: Concentration & Community Banks
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18
19
20
21
22
23
Linear
Prediction
Number
of
PPP
Loans
per
100
Business
1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6
Market Concentration of Banking Sector, HHI (log)
q=0.25 q=0.5 q=0.75
Community Bank Branch Ratio to All Branches
Weak presence of
community banks
Strong presence of
community banks
Effects of market concentration
deteriorate with greater presence of
community banks.

26. Average Marginal Effects of Market Concentration
Lee | DPA Roundtable Fall 2021 25
0.55
-3
-2
-1
0
1
Effects
of
Banking
Market
Concentration
Linear
Prediction
of
PPP
Loans
0 .2 .4 .6
Community Bank Branch Ratio (log)
No effect of market
concentration
Negative Impact of
Market Concentration
ME = {-2, 0.25}

27. Average Marginal Effects of Community Bank
Lee | DPA Roundtable Fall 2021 26
2.15 3.6
-10
-5
0
5
Effects
of
Community
Bank
Brach
Ratio
2 2.5 3 3.5 4 4.5
Banking Market Concentration (HHI, log)
ME = {-3.5, 4}
68% of the market 32% of the market
0.35%
of the
market

28. • Market concentration reduces PPP loans, but the effect is mitigated
by a greater presence of community banks.
• Community banks significantly increase PPP loans in a highly
concentrated market.
• Wolman et al. (2017) find federal fiscal and monetary policies are
most effective in regional economic resilience while effects of state
and local policies are limited. PPP-style federal policies need to
consider the heterogeneous transmission effects of regional banking
sectors.
• Interpretations are limited to correlations.
Lee | DPA Roundtable Fall 2021 27
Conclusion

29. Thank you!
Questions & Comments Welcome!
Lee | DPA Roundtable Fall 2021 28

30. Main Results – Full Table
Lee | DPA Roundtable Fall 2021 29
OLS Estimates Quantile Regression Estimates
q=.1 q=.25 q=.5, LAD q=.75 q=.9
(1) (2) (3) (4) (5) (6) (7)
Concentration, HHI
-1.217*** -3.810*** -2.461*** -2.388*** -2.038*** -2.542*** -3.477***
(0.287) (0.511) (0.517) (0.501) (0.429) (0.468) (0.686)
Community Bank
Ratio, CB
1.175 -17.751*** -5.953* -7.985*** -8.596*** -13.785*** -21.878***
(0.714) (3.174) (3.110) (2.979) (2.690) (3.264) (5.336)
HHI X CB .
5.153*** 1.743** 2.410*** 2.749*** 4.175*** 6.248***
(0.842) (0.883) (0.847) (0.753) (0.915) (1.507)
Bank Branches per
100 biz.
1.165*** 1.204*** 1.096*** 1.076*** 1.112*** 0.989*** 1.163***
(0.143) (0.142) (0.239) (0.202) (0.152) (0.217) (0.200)
Metropolitan
Indicator
-0.254 -0.332 -0.317 -0.413* -0.202 -0.360 0.427
(0.266) (0.265) (0.243) (0.225) (0.208) (0.259) (0.419)
Population
-1.044*** -1.143*** -0.072 -0.322** -0.583*** -0.973*** -1.376***
(0.144) (0.144) (0.162) (0.153) (0.144) (0.164) (0.210)
Covid-19 Cases Per
Capita
0.030 0.041 0.128 0.122 0.151* 0.140 0.090
(0.089) (0.089) (0.096) (0.091) (0.086) (0.108) (0.148)
Income Per Capita
7.851*** 7.308*** 2.815*** 4.650*** 5.937*** 7.153*** 6.701***
(0.553) (0.557) (0.549) (0.570) (0.632) (0.713) (1.090)
Jobs in Hospitality
-17.412*** -17.068*** 1.467 -2.023 -4.600* -10.247*** -16.732***
(2.531) (2.517) (2.562) (2.422) (2.519) (3.254) (3.692)
Jobs in Goods-
Producing
-4.478*** -3.876*** 0.495 -1.020 -2.041 -4.628** -5.741**
(1.323) (1.319) (1.336) (1.245) (1.360) (1.798) (2.392)
Jobs in Trade
7.114*** 7.194*** 6.300*** 7.689*** 6.445*** 3.655 0.255
(2.448) (2.434) (2.251) (2.307) (2.279) (3.528) (5.184)
State FE Yes Yes Yes Yes Yes Yes Yes
N 3100 3100 3100 3100 3100 3100 3100
Adj. R2
0.547 0.552
Notes: Standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01.

31. Summary Statistics
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Variable N Mean Median S.D. Min Max
Number of PPP loans per 100 business establishments 3100 21.162 19.440 8.163 .162 78.607
Number of PPP loans per 1000 population 3100 16.642 13.771 1.533 .335 85.833
Branch Density 3100 2.221 2.061 .962 0 5.439
HHI (deposit, log) 3100 3.362 3.324 .561 1.857 4.615
HHI (branch, log) 3100 3.342 3.367 .684 1.294 4.615
CBratio (community bank to all banks’ branch ratio (log)) 3100 .492 .561 .209 0 .693
Population (log) 3100 1.309 1.177 1.457 6.035 16.128
Income per capita (log) 3100 1.704 1.679 .240 9.877 12.345
% college degree (log) 3100 .132 .125 .049 .007 .374
% population age 65 and older (log) 3100 .171 .169 .038 .031 .449
% population age under 18 (log) 3100 .200 .200 .028 .070 .349
% non-Hispanic white (log) 3100 .561 .609 .123 .007 .693
% Hispanic (log) 3100 .083 .041 .109 0 .689
% non-Hispanic black (log) 3100 .078 .022 .117 0 .627
Covid-19 cases (log) 3100 1.733 1.950 1.456 0 6.871
Share of jobs in hospitality (log) 3100 .089 .085 .047 0 .492
Share of jobs in goods-producing (log) 3100 .189 .178 .088 0 .592
Share of jobs in trade (log) 3100 .171 .170 .044 0 .536
Metro (1=metro, 0=non-metro) 3100 .367 .000 .482 0 1

32. Lee | DPA Roundtable Fall 2021
0
.2
.4
.6
.8
Presence
of
Community
Banks
(CBratio)
2 2.5 3 3.5 4 4.5
Market concentration (HHI)