Elizabeth Alpert et al., Paycheck Protection Program (PPP) COVID-19 relief funding for veteran-, minority-, and women- owned dental businesses: A cross-sectional study (2022)
Summary
Brief research report in Frontiers in Oral Health, published 15 December 2022, by Elizabeth Alpert, Sean G. Boynes, Amber Shaver and Annaliese Cothron, on Paycheck Protection Program funding for dental businesses. Using publicly available PPP data, the authors ran unadjusted and adjusted linear regressions linking loan approval and forgiveness amounts to veteran status and other owner characteristics. Minority racial and ethnic groups and women received less PPP funding and less loan forgiveness, on average, than non-minority groups. In the adjusted model, veterans received more funding and forgiveness than non-veterans. The authors call it the first comprehensive analysis of all dental PPP recipients nationwide and conclude that traditionally underserved dental businesses did not receive increased funding to support employment.
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TYPE Brief Research Report
PUBLISHED 15 December 2022
DOI 10.3389/froh.2022.1041415
Paycheck Protection Program
(PPP) COVID-19 relief funding
EDITED BY
Mohammad Zakaria Nassani,
Dar Al Uloom University, Saudi Arabia
for veteran-, minority-, and
REVIEWED BY
Shariq Najeeb,
women- owned dental
Evidentia Dental Outcomes Research, Canada
Piyada Gaewkhiew, businesses: A cross-sectional
Mahidol University, Thailand
*CORRESPONDENCE study
Annaliese Cothron
cothron@aidph.org
†
Elizabeth Alpert1, Sean G. Boynes2†, Amber Shaver3,4
PRESENT ADDRESS
Sean G. Boynes, and Annaliese Cothron3*
Government Growth and Innovation, Avesis, 1
Department of Restorative Dentistry and Biomaterials Sciences, Harvard School of Dental Medicine,
LLC, Oakdale, PA, United States Boston, MA, United States, 2Dental Medicine Consulting, Oakdale, PA, United States, 3American
SPECIALTY SECTION
Institute of Dental Public Health, San Antonio, TX, United States, 4Department of Psychology,
California State University, Fresno, CA, United States
This article was submitted to Oral
Epidemiology, a section of the journal Frontiers
in Oral Health Background: The Covid-19 pandemic exacerbated dental staffing shortages, which
RECEIVED 12 September 2022 impact care delivery and ultimately oral health equity. Federal funding efforts like the
ACCEPTED 22 November 2022
Paycheck Protection Program (PPP) sought to aid traditionally underserved
PUBLISHED 15 December 2022
businesses including those owned by veterans, minority racial and ethnic groups,
CITATION
and women.
Alpert E, Boynes SG, Shaver A and Cothron A
Objectives: (1) To examine differences in PPP funding between veteran- and
(2022) Paycheck Protection Program (PPP)
COVID-19 relief funding for veteran-, minority-, nonveteran-owned dental care delivery businesses and organizations and (2) to
and women- owned dental businesses: A analyze other relevant factors associated with variation in PPP funding levels for
cross-sectional study. dental businesses.
Front. Oral. Health 3:1041415. Methods: Using publicly available PPP data, we ran unadjusted bivariable and adjusted
doi: 10.3389/froh.2022.1041415
multivariable linear regression models to estimate associations between loan
COPYRIGHT
approval amount and forgiveness amount, veteran status, and relevant covariates.
© 2022 Alpert, Boynes, Shaver and Cothron.
This is an open-access article distributed under Results: Minority racial and ethnic groups and women received less PPP funding and
the terms of the Creative Commons Attribution less loan forgiveness, on average, compared with non-minority groups. In the
License (CC BY). The use, distribution or adjusted model with no missing self-reported demographic observations at p <
reproduction in other forums is permitted,
provided the original author(s) and the 0.10, veterans received more PPP funding and loan forgiveness, on average,
copyright owner(s) are credited and that the compared to non-veterans.
original publication in this journal is cited, in Conclusion: To our knowledge, this is the first comprehensive analysis of all dental
accordance with accepted academic practice.
No use, distribution or reproduction is recipients of PPP funding throughout the United States. Despite PPP program
permitted which does not comply with these intentions and strategies, traditionally underserved dental businesses did not
terms. receive increased funding to support employment.
KEYWORDS
COVID-19, oral health, dental care, healthcare finance, underserved populations, Small
Business Administration, Paycheck Protection Program, CARES act
Abbreviations
ARPA, American Rescue Plan Act; CARES, Coronavirus Aid, Relief, and Economic Security Act; COVID/
COVID 19, Coronavirus 2019 and SARS-CoV-2; EIDL: Economic Injury Disaster Loan; NAICS, North
American Industry Classification System; PPP, Payroll Protection Program; SBA, U.S. Small Business
Administration; STROBE, Strengthening the Reporting of Observational Studies in Epidemiology guidelines
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Alpert et al. 10.3389/froh.2022.1041415
Introduction Methods
Background/rationale Study design and setting
The Covid-19 pandemic impacted the dental care delivery We designed and implemented a cross-sectional study using
system in multiple domains including education, financing, publicly available PPP loan-level data (7) released by the U.S.
regulation, and care delivery (1). By April 2020, employment Small Business Administration. The data contained all loan
in dental offices plummeted to 44% of pre-pandemic levels. approvals over the program duration, from April 3, 2020 (8) to
Despite subsequent recovery, significant staffing shortages program end on May 31, 2021 (9) and included all U.S. states
persist (2). The dental workforce is essential to facilitating and territories. The STROBE (Strengthening the Reporting of
oral health access, quality, and cost (3) and therefore to Observational Studies in Epidemiology) guidelines for reporting
promoting oral health equity. observational studies were followed (10).
Congressional action provided multiple mechanisms of
addressing dental workforce challenges through federal
Covid relief funding: the Economic Injury Disaster Loan Participants and study size
(EIDL) small business loan program, American Rescue Plan
Act (ARPA) funding, and the Paycheck Protection We used a two-step process to select our study sample. First,
Program (PPP) (3). we identified all possible dental care delivery businesses and
Approximately 9 out of 10 dental private practices utilized organizations in the PPP data through Google search engine
the PPP (2), which was established by the Coronavirus Aid, optimization keywords for dental businesses (3). We then
Relief, and Economic Security (CARES) Act and implemented implemented a filtering approach to eliminate non-dental
by the U.S. Small Business Administration (SBA). The PPP (4) businesses from the study sample by only keeping
provided small businesses with funds to pay up to 8 weeks of observations with North American Industry Classification
payroll costs including benefits, as well as interest on System (NAICS) codes relevant to dental businesses: (621210
mortgages, rent, and utilities. It included provisions for full Offices of Dentists; 621111 Offices of Physicians; 621399
loan forgiveness (3). After initial program implementation, Offices of All Other Miscellaneous Health Practitioners;
Congress and the SBA initiated changes to help traditionally 423450 Medical, Dental, and Hospital Equipment and
underserved businesses receive PPP loans, including those Supplies Merchant Wholesalers; 339116 Dental Laboratories;
owned by veterans, minority racial and ethnic groups, and and 339114 Dental Equipment and Supplies Manufacturing).
women (5). There were no exclusion criteria. After applying the above
While there is increased awareness of oral health disparities inclusion criteria, the initial study size was 132,207.
among veterans (6), current literature does not provide
information regarding veteran-owned dental businesses. To
promote oral health equity, it is critical to understand dental Variables
workforce challenges and impact of federal Covid relief funding
among traditionally underserved dental care delivery businesses Primary outcomes
and organizations. • Loan approval amount in dollars (current)
• Forgiveness amount in dollars
Objectives Primary exposure
• Veteran status: Veteran, Non-Veteran, Unanswered
The first objective of this study was to examine
differences in PPP funding between veteran- and
nonveteran-owned dental care delivery businesses and Covariates
organizations. The second objective was to analyze other • Race: White, Asian, Black or African American, American
relevant factors associated with variation in PPP funding Indian or Alaska Native, Other (Eskimo & Aleut, Native
for dental businesses besides veteran status. We Hawaiian or Other Pacific Islander, Puerto Rican, Multi
hypothesized that dental businesses owned by veterans, Group), Unanswered
minorities, and women would receive higher levels of • Ethnicity: Not Hispanic or Latino, Hispanic or Latino,
funding and subsequent loan forgiveness, since the SBA Unanswered
implemented program measures to facilitate PPP loans for • Gender: Male, Female, Unanswered
these groups (5). • Rurality: Urban, Rural
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Alpert et al. 10.3389/froh.2022.1041415
• Low- and moderate-income indicator: No, Yes TABLE 1 Overview of Payroll Protection Program (PPP) approved
lending for dental businesses and loan size distribution, based on
• Number of employees veteran status.
• Business type: Corporation, Partnership, Sole Proprietorship, All Dental Businesses Self-Identified
Subchapter S Corporation, Limited Liability Partnership, Veteran-Owned
Limited Liability Company, Professional Association, Other Dental Businesses
• Loan delivery method: first draw, second draw N = 132,207 N = 3,495
• Lender type: Top 5 Bank, Top 5 Alternative Lender, Other
Loan count 132,207 3,495
Net dollars $11,472,407,083 $312,356,497
Data sources/measurement Average loan $86,776.09 $89,372.39
size
All data for analysis was obtained from the publicly All Dental Businesses Self-Identified Veteran-
Owned Dental Businesses
available PPP loan data and was self-certified by applicants Net Dollars Loan Count Net Dollars Loan Count
for accuracy (11). Regarding lender type, we used categories $50K and $1,427,125,912 51,145 $35,503,719 1,191
previously established in the literature. Top 5 Banks referred under (38.7%) (34.0%)
to Bank of America, JP Morgan Chase, Wells Fargo, U.S. >$50K–$100K $3,382,675,018 46,671 $97,522,561 1,347
Bank, or Citibank. Top 5 Alternative Lenders—either fintech (35.3%) (38.5%)
banks or lenders supporting major fintech lenders and not >$100K–$150K $2,316,323,611 19,052 $63,786,695 520 (14.9%)
(14.4%)
traditional depository institutions—included Cross River Bank,
Kabbage, Celtic Bank, WebBank, or Customer’s Bank (12). >$150K–$350K $2,701,130,620 12,938 (9.8%) $76,340,030 371 (10.6%)
>$350K–$1M $1,078,493,970 2,106 (1.6%) $31,325,576 60 (1.7%)
>$1M–$2M $316,532,695 224 (0.2%) $7,877,914 6 (0.2%)
Statistical methods >$2M–$5M $175,981,398 60 (0.05%) $0 0
>$5M $74,143,860 11 (0.008%) $0 0
Descriptive statistics were used to examine a breakdown of Due to rounding, all percentages may not add up to 100.
all PPP approved lending for all dental businesses and self-
identified veteran-owned dental businesses, along with
covariates of interest for each group.
We first looked at the outcome of loan approval amount. characteristics, and loan characteristics between the entire
Unadjusted bivariable and adjusted multivariable linear study sample (N = 132,207, all dental businesses meeting the
regression models were used to estimate the association specified inclusion criteria) and the study sample limited to
between loan approval amount and veteran status. Models self-identified veteran-owned dental businesses (N = 3,495;
were adjusted for all covariates. Model 1.A (unadjusted) and Tables 1, 2).
Model 2.A (adjusted) analyzed the complete study sample of There were many unanswered responses to the borrower
all dental businesses meeting the specified inclusion criteria. demographic questions (Table 2). Among all dental
Model 3.A (unadjusted) and Model 4.A (adjusted) contains businesses (N = 132,207), 64.4% did not identify their
only dental businesses meeting the specified inclusion criteria veteran status (N = 85,245). 76.8% did not answer race (N =
and with no missing observations. 101,494), 69.6% did not identify their ethnicity (N =
We then applied the same approach in evaluating the 92,042), and 57.6% did not specify their gender (N =
outcome of forgiveness amount. Model 1.B (unadjusted) and 76,133). To address this, we ran unadjusted bivariable and
Model 2.B (adjusted) analyzed the complete study sample of adjusted multivariable linear regression models to estimate
all dental businesses meeting the specified inclusion criteria. the associations between veteran status and loan approval
Model 3.B (unadjusted) and Model 4.B (adjusted) contain amount and veteran status and forgiveness amount with
only dental businesses meeting the specified inclusion criteria both the entire study sample (N = 132,207) and with the
and with no missing observations. study sample restricted to dental businesses with no missing
The statistical significance level was set at 10%, and R demographic observations (N = 23,355; Tables 3, 4).
software version 1.3.1073 was used for analysis. Generally, findings were consistent between the models with
and without the missing data.
Additionally, the publicly released PPP data was limited to
Bias loan approvals and did not include rejected or canceled loans.
These applicants’ demographic, business, and loan
There were similar trends observed in loan size characteristics may exhibit differences compared with those of
distribution, borrower demographics, business approved applicants.
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Alpert et al. 10.3389/froh.2022.1041415
TABLE 2 Characteristics of study sample and PPP loans, based on veteran status.
All Dental Businesses Self-Identified Veteran-Owned Dental Businesses
N = 132,207 N = 3,495
BORROWER DEMOGRAPHICS
Loan Net dollars Average Average loan size Loan Net dollars Average Average loan size
count loan size per # of count loan size per # of
employees employees
Race
White 20,772 $1,841,034,166 $88,630.57 $9,484.49 1,604 $141,122,194.40 $87,981.42 $9,832.93
Asian 7,549 $520,122,204 $68,899.48 $8,363.84 332 $22,873,507.60 $68,896.11 $8,088.23
Black or African 1,291 $87,972,791 $68,143.14 $8,768.34 124 $7,983,254.10 $64,381.08 $8,333.25
American
American Indian 914 $77,356,577 $84,635.20 $9,456.79 64 $6,914,603.40 $108,040.68 $11,680.07
or Alaskan Native
Other* 187 $12,035,065 $64,358.64 $7,438.24 9 $658,487.50 $73,165.28 $10,620.77
Unanswered 101,494 $8,933,886,279 $88,023.79 $9,676.75 1,362 $132,804,449.50 $97,506.94 $9,646.58
Ethnicity
Not Hispanic or 36,949 $3,292,145,925 $89,099.73 $9,395.93 2,385 $214,489,628 $89,932.76 $9,716.40
Latino
Hispanic or Latino 3,216 $217,537,421 $67,642.23 $8,183.94 171 $12,139,651 $70,992.11 $9,127.56
Unanswered 92,042 $7,962,723,736 $86,511.85 $9,682.31 939 $85,727,218 $91,296.29 $9,365.00
Gender
Male 41,309 $3,870,433,085 $93,694.67 $9,468.07 2,874 $266,946,465 $92,883.25 $9,629.76
Female 14,765 $1,087,398,752 $73,647.05 $9,269.92 543 $39,390,397 $72,542.17 $9,356.39
Unanswered 76,133 $6,514,575,246 $85,568.35 $9,676.05 78 $6,019,634 $77,174.80 $9,585.41
BUSINESS CHARACTERISTICS
Loan Net dollars Average Average loan size Loan Net Average Average loan size
count loan size per # of count dollars loan size per # of
employees employees
Rurality
Urban 113,408 $9,823,783,143 $86,623.37 $9,562.54 2,874 $255,587,437 $88,930.91 $9,526.54
Rural 18,799 $1,648,623,940 $87,697.43 $9,582.79 621 $56,769,060 $91,415.56 $9,907.34
Low- and moderate- income (LMI)
No 104,343 $9,095,314,907 $87,167.47 $9,642.56 2,774 $242,738,653 $87,504.92 $9,641.29
Yes 27,863 $2,377,067,210 $85,312.68 $9,281.43 721 $69,617,844 $96,557.34 $9,430.76
Number of employees
0 to 4 36,686 $1,016,189,055 $27,699.64 $10,791.24 841 $24,960,856 $29,679.97 $10,735.85
5 to 9 56,431 $3,745,369,267 $66,370.78 $9,782.56 1,559 $104,649,255 $67,125.88 $9,778.48
10 to 19 30,781 $3,831,451,904 $124,474.58 $9,747.11 863 $108,229,098 $125,410.31 $9,910.18
20 to 49 6,997 $1,880,870,952 $268,811.06 $9,740.25 198 $53,489,903 $270,151.03 $10,176.92
50 to 99 892 $475,144,908 $532,673.66 $7,993.29 29 $18,348,863 $632,719.43 $8,834.31
100 to 499 405 $481,519,342 $1,188,936.64 $6,958.57 1 $68,602 $68,602.00 $137.20
500+ 15 $41,861,657 $2,790,777.13 $5,581.55 4 $2,609,919 $652,479.75 $3,354.65
Business type
Corporation 54,799 $4,856,578,694 $88,625.32 $9,590.08 1,485 $136,987,019 $92,247.15 $9,317.58
Partnership 2,309 $321,153,500 $139,087.70 $9,590.08 52 $7,241,897 $139,267.24 $10,044.24
Sole Proprietorship 11,020 $552,818,607 $50,165.03 $9,357.59 322 $18,583,073 $57,711.41 $9,932.16
Subchapter 28,338 $2,547,570,096 $89,899.43 $9,831.70 696 $63,070,280 $90,618.22 $10,000.04
S Corporation
(continued)
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TABLE 2 Continued
BUSINESS CHARACTERISTICS
Loan Net dollars Average Average loan size Loan Net Average Average loan size
count loan size per # of count dollars loan size per # of
employees employees
Limited Liability 1,091 $137,442,989 $125,978.91 $8,753.77 31 $5,795,220 $186,942.58 $10,517.64
Partnership (LLP)
Limited Liability 30,920 $2,721,594,638 $88,020.53 $9,222.68 803 $69,296,265 $86,296.72 $9,541.00
Company (LLC)
Professional 2,403 $242,013,547 $100,713.09 $10,409.19 86 $8,666,152 $100,769.21 $10,018.67
Association
Other 1,327 $93,235,012 $70,259.99 $10,452.36 20 $2,716,591 $135,829.54 $9,736.88
LOAN CHARACTERISTICS
Loan Net dollars Average Average loan size Loan Net Average Average loan size
count loan size per number of count dollars loan size per number of
employees employees
Loan delivery method
First draw 82,512 $7,009,869,400 $84,955.76 $9,626.92 1,998 $169,907,915 $85,039.00 $9,537.89
Second draw 49,695 $4,462,537,683 $89,798.52 $9,470.44 1,497 $142,448,582 $95,156.03 $9,660.81
Lender type
Top 5 Bank 29,092 $1,945,978,909 $66,890.52 $8,164.65 602 $45,083,945 $74,890.27 $7,927.54
Top 5 7,745 $425,881,998 $54,987.99 $9,042.47 134 $8,507,660 $63,490.00 $9,108.84
Alternative
Lender
Other 95,370 $9,100,546,175 $95,423.57 $9,957.71 2,759 $258,764,892 $93,789.38 $9,976.29
*“Other” includes the following: Eskimo & Aleut, Native Hawaiian or Other Pacific Islander, Puerto Rican, Multi Group.
Results Looking at demographic characteristics among veteran-
owned businesses, average loan size per number of employers
Participants was $9,833 among white borrowers and $8,333 among Black
borrowers. Loan size was $9,716 among non-Hispanic or
The complete study sample consisted of 132,207 loans Latino borrowers, compared with $9,128 for Hispanic or
distributed to dental businesses. 3,495 of those had owners Latino borrowers. It was higher for males ($9,630) than
who self-identified as veterans. There were 23,355 businesses females ($9,356) (Table 2).
that provided complete demographic information (veteran
status, race, ethnicity, and gender).
Main results
Among the unadjusted models, neither Model 1.A (with all
Outcome data observations) nor Model 3.A (with no missing demographic
observations) demonstrated significant associations between
Average loan size was $86,776 among all dental veteran status and loan amount. Adjusted Model 2.A (with all
businesses and $89,372 among self-identified veteran- observations) showed significant associations between loan
owned dental businesses. Nearly three-quarters of loans amount and race, ethnicity, gender, rurality, low- and
were $100,000 and under (74% among all dental moderate-income-indicator, number of employees, business
businesses and 72.5% among veteran-owned dental type, loan delivery method, and lender type. Adjusted Model
businesses). Almost 90% of loans were $150,000 and under 4.A (with all observations) showed significant associations
(88.4% for all businesses and 87.4% among veteran- between loan amount and race, ethnicity, gender, rurality,
owned). For loans above $1,000,000, there were 295 loans number of employees, and business type (Table 3).
(0.2%) among all businesses and 6 loans (0.2%) among There were similar findings with the outcome of forgiveness
veteran-owned (Table 1). amount. Neither of the unadjusted models – Model 1.B (with all
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TABLE 3 Associations between payroll protection program (PPP) loan approval amount and veteran status and other covariates.
Model 1.A Model 2.A Model 3.A Model 4.A
N = 132,207 N = 132,207 N = 23,355 N = 23,355
Unadjusted Adjusted (95% CI) Unadjusted Adjusted (95% CI)
(95% CI) (95% CI)
Veteran status
Non-Veteran REF REF REF REF
Veteran 1,907 (−2,665, 6,479) 1,280 (−4,173, 6,733) 3,416 (−1,246, 8,078) 5,032. (−405, 10,469)
Unanswered −1,147 (−2,680, 385) 1,223 (−2,863, 5,309)
Race
White REF REF
Asian −9,370 *** (−12,516, −6,225) −11,527 *** (−14,387,
−8,668)
Black or African American −6,712 * (−13,117, −307) −8,176 ** (−13,927, −2,424)
American Indian or Alaskan Native −7,738 (−24,104, 8,627) −7,611 (−24,651, 9,428)
Other −15,299. (−30,705, 107) −12,348. (−26,388, 1,692)
Unanswered 6 (−3,148, 3,161)
Ethnicity
Not Hispanic or Latino REF REF
Hispanic or Latino −12,293 *** (−16,698, −13,506 *** (−18,035,
−7,889) −8,976)
Unanswered 1,240 (−2,022, 4,502)
Gender
Male REF REF
Female −6,836 *** (−9,500, −4,172) −7,454 *** (−10,179,
−4,729)
Unanswered −10,139 *** (−14,334,
−5,943)
Rurality
Urban REF REF
Rural 14,109 *** (9,587, 18,630) 17,316 *** (9,166, 25,465)
Low- and moderate- income (LMI)
No REF REF
Yes −1,571. (−3,357, 215) 944 (−2,108, 3,996)
Number of employees 3,050 *** (2,999, 3,101) 1,967 *** (1,872, 2,062)
Business type
Limited Liability Corporation (LLC) REF REF
Corporation 1,702 (−326, 3,730) 3,054. (−404, 6,513)
Limited Liability Partnership (LLP) 33,631 *** (23,599, 43,663) 14,249 (−5,959, 34,457)
Partnership 17,950 *** (10,846, 25,054) 44,110 *** (29,651, 58,578)
Professional association 838 (−4,561, 6,237) 2,332 (−5,449, 10,113)
Sole proprietorship −16,407 *** (−19,694, −14,974 *** (−21,287,
−13,120) −8,660)
S corp 2,750 * (277, 5,224) −86 (−4,619, 4,448)
Loan delivery method
Payroll Protection Program (PPP) for first REF REF
draw
Positive Payment System (PPS) for second 2,892 *** (1,302, 4,482) −711 (−3,329, 1,906)
draw
(continued)
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TABLE 3 Continued
Model 1.A Model 2.A Model 3.A Model 4.A
N = 132,207 N = 132,207 N = 23,355 N = 23,355
Unadjusted Adjusted (95% CI) Unadjusted Adjusted (95% CI)
(95% CI) (95% CI)
Lender type
Top 5 bank REF REF
Top 5 alternative lender −4,626 *** (−6,528, −2,725) −2,907 (−6,735, 921)
Signif. codes: 0 “***” 0.001 “**” 0.01 “*” 0.05 “.” 0.1 “ ” 1.
Linear regression:
Model 1.A contains all dental businesses, unadjusted.
Model 2.A contains all dental businesses, adjusted for all significant covariates from bivariate analysis plus veteran status and rurality.
Model 3.A contains only dental businesses with no missing demographic observations, unadjusted.
Model 4.A contains only dental businesses with no missing demographic observations, adjusted for all significant covariates from bivariate analysis plus veteran status
and rurality.
observations) and Model 3.B (with no missing observations) – PPP funding and had smaller amounts forgiven. This echoes
showed a significant relationship between veteran status and other literature that has already demonstrated evidence of
loan forgiveness amount. However, adjusted Model 2.B (with disparate lending (12). Hispanic and Latino-owned businesses
all observations) demonstrated significant associations between were also given smaller amounts and had smaller amounts of
forgiveness amount and race, ethnicity, gender, rurality, low- loans forgiven, as were female-owned businesses. These
and moderate-income indicator, number of employees, loan findings question the effectiveness of SBA-implemented
delivery method, business type, and lender type. Adjusted strategies to fund traditionally underserved businesses, which
Model 4.B (with no missing demographic observations) had included more diversity in lender type and efforts to target
significant relationships between forgiveness amount and funding to these groups (5). Among the entire sample, those
veteran status, race, ethnicity, gender, rurality, number of with a top 5 alternative lender had $4,626 less in PPP loan
employees, and business type (Table 4). amount and had $3,190 less forgiven compared to those from a
top 5 bank. This queries the role of nontraditional lender types,
who may be more accessible to underserved groups.
Discussion The discrepancy in granted loan amounts and loan forgiveness
among racial and ethnic minorities and women may have
Key results and interpretation detrimental implications on business viability. While some
studies indicated limited impact on long term outcomes, more
Our initial hypothesis that dental businesses owned by evidence reinforces negative impacts within the pattern of
veterans, minorities, and women would receive the most PPP lending discrepancy (13–15). Even when minority and women
funding was weakly supported. Among the study sample business owners receive loans, they often come with more
without missing demographic information (N = 23,355), restrictive terms and higher rates compared to their white male
veteran-owned dental businesses received $5,032 (p = 0.07) counterparts (14, 16–18). Small business who are refused loans,
more, on average, and had an additional $5,251 forgiven (p = are only allowed limited funding, or have smaller amounts of
0.06) compared with non-veteran-owned businesses. While loan forgiveness may experience financial uncertainty as a result.
this was not significant at the p < 0.05 level, it does provide Findings of this study indicating smaller amounts of loan
some evidence for the PPP intention to assist veteran-owned forgiveness convey alarming implications as minority and
businesses. However, opposite conclusions were made women business owners will experience higher burdens of loan
regarding minority- and women-owned businesses. debt, reducing profit margins and jeopardizing sustainability.
After adjusting for relevant covariates in both the entire Evidence suggests that minority small business owners, especially
study sample (N = 132,207) and study sample with complete Black business owners, already distrust lending systems and may
demographic information (N = 23,355), there were significant not seek loans due to perceived discrimination (16). The results
associations between loan amount and race, ethnicity, gender, of this study may validate that attitude increasing the negative
rurality, number of employees, and business type. perception of banking institutions. Ultimately, these data cannot
Compared with white-owned businesses, dental businesses derive causation into the disparate lending and forgiveness
owned by Asian, Black or African American, and Other amounts which requires additional exploration.
(Eskimo & Aleut, Native Hawaiian or Other Pacific Islander, Rural businesses secured more funding and increased
Puerto Rican, Multi Group) racial groups both received less forgiven amounts, on average, compared with urban
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Alpert et al. 10.3389/froh.2022.1041415
TABLE 4 Associations between paycheck protection program (PPP) loan forgiveness amount and veteran status and other covariates.
Model 1.B Model 2.B Model 3.B Model 4.B
N = 132,207 N = 132,207 N = 23,355 N = 23,355
Unadjusted (95% CI) Adjusted (95% CI) Unadjusted (95% CI) Adjusted (95% CI)
Veteran status
Non-Veteran REF REF REF REF
Veteran 1,300 (−3,192, 5,793) 2,246 (−3,121, 7,614) 3,925 (−780, 8,630) 5,251. (−160, 10,663)
Unanswered −1,068 (−2,577, 442) 1,240 (−2,829, 5,309)
Race
White REF REF
Asian −9,139 *** (−12,232, −6,046) −11,380 *** (−14,217, −8,543)
Black or African American −6,473 * (−12,923, −24) −8,114 ** (−13,932, −2,295)
American Indian or Alaskan Native −6,932 (−23,363, 9,498) −7,656 (−24,905, 9,592)
Other −13,176. (−28,852, 2,500) −10,582 (−24,919, 3,755)
Unanswered 38 (−3,069, 3,146)
Ethnicity
Not Hispanic or Latino REF REF
Hispanic or Latino −13,357 *** (−17,702, −9,011) −14,655 *** (−19,170, −10,139)
Unanswered 406 (−2,818, 3,631)
Gender
Male REF REF
Female −7,597 *** (−10,218, −4,976) −7,964 *** (−10,666, −5,262)
Unanswered −9,917 *** (−14,096, −5,737)
Rurality
Urban REF REF
Rural 13,922 *** (9,429, 18,414) 16,739 *** (8,620, 24,857)
Low- and moderate- income (LMI)
No REF REF
Yes −1,733. (−3,498, 31) −240 (−3,279, 2,800)
Number of employees 2,936 *** (2,887, 2,986) 1,991 *** (1,897, 2,086)
Business type
Limited Liability Corporation (LLC) REF REF
Corporation 1,747. (−251, 3,745) 3,072. (−352, 6,497)
Limited Liability Partnership (LLP) 36,049 *** (26,225, 45,874) 16,318 (−3,544, 36,179)
Partnership 19,768 *** (12,806, 26,730) 46,949 *** (32,520, 61,379)
Professional association 989 (−4,292, 6,269) 3,130 (−4,502, 10,762)
Sole proprietorship −16,129 *** (−19,365, −12,893) −14,509 *** (−20,767, −8,262)
S corporation 2,756 * (316, 5,196) 255 (−4,259, 4,768)
Loan delivery method
First draw REF REF
Second draw 3,235 *** (1,651, 4,820) 507 (−2,103, 3,118)
Lender type
Top 5 bank REF REF
Top 5 alternative lender −3,190 ** (−5,096, −1,284) −746 (−4,645, 3,153)
Signif. codes: 0 “***” 0.001 “**” 0.01 “*” 0.05 “.” 0.1 “ ” 1.
Linear regression:
Model 1.B contains all dental businesses, unadjusted.
Model 2.B contains all dental businesses, adjusted for all significant covariates from bivariate analysis plus veteran status and rurality.
Model 3.B contains only dental businesses with no missing demographic observations, unadjusted.
Model 4.B contains only dental businesses with no missing demographic observations, adjusted for all significant covariates from bivariate analysis plus veteran status
and rurality.
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Alpert et al. 10.3389/froh.2022.1041415
businesses. These implications are important for the dental particularly with respect to veteran, minority, women, and
workforce as rural areas were most impacted by Covid-related rural business owners.
staffing and revenue reductions (1, 3). While the PPP
appeared to sustain many dental rural practices during the
early period of the pandemic, rural areas and Medicaid Generalizability
provider networks experienced the most significant attrition of
workforce and active employment since 2020 (3, 19). Previous To our knowledge, this is the first comprehensive analysis of
research determined that the PPP had a limited effect on all dental recipients of PPP funding throughout the United
employment (20). Rural communities have an older dentist States. These findings contribute to the limited body of
population and an increased number of dentists retiring. literature on veteran-owned dental businesses and adds
Rural dental healthcare workers also report lower levels of evidence on disparate lending in the PPP.
satisfaction with their job or career choice which may also
contribute to staffing shortages and limiting the effect of the
PPP for rural dental practices (3, 21, 22). Further research is
Data availability statement
warranted to better understand the continued attrition of
Publicly available datasets were analyzed in this study. This
dental care teams in rural communities.
data can be found here: https://data.sba.gov/dataset/ppp-foia.
Limitations
Ethics statements
As discussed, there were many missing self-reported
demographic characteristics, although information was Ethical review and approval was not required for this study
provided regarding business and loan characteristics. Within in accordance with the local legislation and institutional
the initial loan application form, the SBA did not ask for requirements.
any demographic data from PPP applicants (23). This is
not traditional SBA practice, and it leaves the collection of
this vital data up to the individual lender, which resulted in Author contributions
this information not being collected routinely (24).
Additionally, the racial categories do not allow for the Study conception and design: SB and EA. Data analysis and
disaggregation into specific and critical sub-groups (23, 24). interpretation of results: EA, AC, SB, and AS. Draft manuscript
Selection bias may have impacted reports of veteran status, preparation: EA, SB, and AC. All authors contributed to the
race, ethnicity, and gender. Individuals with all reported article and approved the submitted version.
demographics may not be reflective of the entire study
population. However, similar trends in loan approval
amounts and loan forgiveness amounts were observed
Funding
between the entire group and those without any missing
observations (Tables 3, 4).
This research was financially supported by the American
Additionally, PPP data did not include canceled or rejected
Institute of Dental Public Health (AIDPH).
loans, so it was not possible to evaluate the role of veteran
status, race, ethnicity, gender, business characteristics, or
loan characteristics, in that context.
For a more comprehensive assessment of dental Acknowledgments
recipients of pandemic relief funding, future analyses
should examine additional federal funding mechanisms, American Institute of Dental Public Health (AIDPH); Kelly
Braun, RDH, MSDH.
including the Economic Injury Disaster Loan (EIDL)
small business loan program, and explore outcomes on
the region, state, and county levels. Combining PPP data
with other data sources would allow comparisons of PPP Conflict of interest
funding outcomes like oral health utilization, emergency
department use for non-traumatic dental problems, and Author SB is Vice President at Avesis LLC. The remaining
other measures of oral health equity. Future research authors declare that the research was conducted in the
should also explore the long-term financial sustainability absence of any commercial or financial relationships that
among dental care delivery sites that received PPP funds, could be construed as a potential conflict of interest.
Frontiers in Oral Health 09 frontiersin.org
Alpert et al. 10.3389/froh.2022.1041415
Publisher’s note affiliated organizations, or those of the publisher, the editors
and the reviewers. Any product that may be evaluated in this
All claims expressed in this article are solely those of the article, or claim that may be made by its manufacturer, is not
authors and do not necessarily represent those of their guaranteed or endorsed by the publisher.
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