Small Business Administration Research Articles

Can Bank Density Provide Insights on the Appropriation of Disaster Relief Funds? Evidence from the Paycheck Protection Program

In response to the COVID-19 pandemic, Congress in conjunction with the Small Business Administration (SBA) created the Paycheck Protection Program (PPP) to help small business owners retain employees. We improve on previous literature exploring the relationship between bank density and PPP loan dispersion by implementing precisely geocoded data to map loans to commuting zones. Revisiting results in Deming and Weiler (2023), we confirm the statistically significant relationship between bank density and PPP loans per small business. We consider three distinct periods of lending to analyze how changes in the PPP loan program rules might have impacted dispersion. The coefficient in the regression of loans per small business on bank density is largest in the final wave of lending, and in commuting zones with high loan densities. This suggests that having additional bank branches in the commuting zone may have helped facilitate rapid dispersion of relief funds to high demand areas during the program.

The Financial Consequences of Online Review Aggregators: Evidence from Yelp Ratings and SBA Loans

This paper demonstrates the financial and real consequences of online review aggregators. Exploiting a regression discontinuity design that overcomes the endogenous relationship between Yelp reviews and Small Business Administration loan outcomes, I show that higher coarse Yelp ratings lead to improved loan terms and performance. Specifically, a one-half-star increase in Yelp ratings corresponds to a 25-basis-point decrease in loan spread and 6% lower collateral requirements. The effects are more pronounced when banks have less borrower information. Higher Yelp ratings also contribute to increased consumer demand and the likelihood of future business openings. These findings indicate that online review aggregators influence both consumer choices and banks’ financing decisions. This paper was accepted by Gustavo Manso, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.03003 .

Leveraging Machine Learning Algorithm to Enable Access to Credit for Small Businesses in the United States of America

The research centers on the essential function of loans in driving economic expansion and the intrinsic danger of loan defaults. Small businesses play a crucial role in generating employment and fostering economic growth, highlighting the significance of precise loan eligibility evaluations. The Small Business Administration's (SBA) 7(a) loan program seeks to assist small businesses by providing loan guarantees to mitigate risks for financial institutions (Mini et al., 2018). The study utilizes sophisticated machine learning methods, particularly the Random Forest and XGBoost algorithms, to forecast loan defaults and ascertain ideal loan amounts for small businesses. A dataset comprising 27 variables is analyzed, encompassing loan attributes, borrower details, and loan outcomes. The results highlight the effectiveness of both Random Forest and XGBoost in generating loan default predictions, with a slight edge for XGBoost. Additionally, Linear Regression is used to estimate loan amounts for qualified borrowers. The analysis identifies key factors contributing to loan defaults, with variables such as Term, Disbursement Gross, SBA Approval, and Gross Approval playing a significant role in Random Forest predictions. The study reveals intriguing patterns in loan defaults across various industrial sectors, emphasizing the complex nature of assessing loan performance within these industries (Zhou et al., 2023). This research aims to improve lending practices, benefiting both lenders and borrowers while enhancing our understanding of risk management in the context of small businesses.

Disaster Lending: “Fair” Prices but “Unfair” Access

We find the Small Business Administration’s disaster-relief home loan program denies significantly more loans in areas with larger shares of minorities, subprime borrowers, and higher income inequality. We find that risk-insensitive loan pricing, a feature present in many regulated and government-run lending programs, is an important driver of these disparities in access to credit. The differences in denial rates are disproportionately high compared with private-market lending and government-insured risk-sensitive loan pricing programs. Thus, despite ensuring “fair” prices, the use of risk-insensitive pricing may lead to “unfair” access to credit. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03199 .

THE ROLE OF GOVERNMENT POLICY IN FOSTERING ENTREPRENEURSHIP: A USA AND AFRICA REVIEW

This Review explores the crucial role of government policies in fostering entrepreneurship by examining the experiences of the United States (USA) and various African nations. Entrepreneurship is a driving force for economic growth, job creation, and innovation, and government policies play a pivotal role in shaping the entrepreneurial landscape. In the USA, a robust history of entrepreneurial success is attributed in part to supportive government policies. The Review delves into specific policies that have contributed to fostering entrepreneurship, including tax incentives, access to funding through Small Business Administration (SBA) programs, and initiatives promoting innovation and research. The analysis extends to the regulatory environment, emphasizing the importance of flexible regulations that encourage innovation while ensuring consumer protection. Turning attention to Africa, the Review explores the diversity of government approaches across the continent. It considers initiatives implemented by African nations to promote entrepreneurship, such as the establishment of entrepreneurial ecosystems, funding mechanisms for startups, and efforts to streamline regulatory frameworks. The review acknowledges the unique challenges faced by African entrepreneurs, including limited access to capital and infrastructure constraints, and evaluates how government policies aim to address these issues. The comparative analysis highlights lessons that African nations can draw from the USA's successful policies, such as the importance of fostering a culture of innovation, providing targeted financial support, and creating an enabling regulatory environment. Moreover, it emphasizes the need for context-specific policies that consider the unique socio-economic conditions of each African nation. In conclusion, the Review underscores the critical role of government policy in nurturing entrepreneurship, drawing insights from the USA's experience and the varied approaches across African nations. It calls for a nuanced understanding of the diverse African entrepreneurial landscape and encourages policymakers to tailor initiatives that catalyze entrepreneurship, driving economic development across the continent.
 Keywords: Role, Government, Policy, Fostering, Entrepreneurship.

Modeling post-disaster recovery: Accounting for rental and multi-family housing

Post-disaster housing recovery models increase our understanding of recovery dynamics, vulnerable populations, and how people are affected by the direct losses that disasters create. Past recovery models have focused on single-family owner-occupied housing, while empirical evidence shows that rental units and multi-family housing are disadvantaged in post-disaster recovery. To fill this gap, this article presents an agent-based housing recovery model that includes the four common type–tenure combinations of single- and multi-family owner- and renter-occupied housing. The proposed model accounts for the different recovery processes, emphasizing funding sources available to each type–tenure. The outputs of our model include the timing of financing and recovery at building resolution across a community. We demonstrate the model with a case study of Alameda, California, recovering from a simulated M7.0 earthquake on the Hayward fault. The processes in the model replicate higher non-recovery of multi-family housing than single-family housing, as observed in past disasters, and a heavy reliance of single-family renter-occupied units on Small Business Administration funding, which is expected due to low earthquake insurance penetration. The simulation results indicate that multi-family housing would have the highest portion of unmet need remaining; however, some buildings with unmet needs are anticipated to be able to obtain a large portion of their funding. The remaining portion may be filled using personal financing or may be overcome with downsizing or downgrades. Multi-family housing would also benefit the most from Community Development Block Grants for Disaster Recovery (CDBG-DR). This benefit is a result of modeling the financing sources, that CDBG-DR is available, and that many multi-family buildings do not qualify for other sources. Communities’ allocation of public funding is important for housing recovery. Our model can help inform and compare potential financing policies to allocate public funds.

Unravelling the impact of the Community Reinvestment Act on SBA lending: Insights from a regression discontinuity design analysis

ABSTRACT This study investigates the influence of the Community Reinvestment Act (CRA) on Small Business Administration (SBA) lending in various U.S. urban areas. Focusing on central cities, inner-ring, and outer-ring suburbs across seven major metropolitan statistical areas, it employs a regression discontinuity design to analyze CRA’s effect on neighborhoods with median family incomes at or below 80% of the area’s median. Results show that CRA significantly increases SBA lending in inner-ring suburbs but not in central cities or outer-ring suburbs, indicating a geographically varied impact. This research underscores the spatial differences in CRA’s effectiveness, suggesting a need for more tailored strategies to improve SBA lending in diverse urban settings.

How access to federal recovery credit assistance influences future wealth trajectories after a natural disaster: The case of Hurricane Harvey

Hurricane Harvey made landfall in Texas in August 2017. The Small Business Administration (SBA) provided direct-to-household loan aid for over $2.9 billion to assist recovery, twice as large as the Federal Emergency Management Agency's direct-to-household grant program to individuals impacted by the storm. The author examined the role of federal disaster home loan availability and wealth creation through changes in home values after Hurricane Harvey. The study linked SBA data to seasonally adjusted private market housing data. The author used a fixed-effect difference-in-difference regression with propensity score matching study design to estimate the effect of federal credit recovery assistance on log median home values over time. Communities receiving federal credit assistance saw a statistically significant 6.4 % faster increase in home values than communities receiving no federal credit assistance post-Harvey. In addition, communities with shorter (two weeks or less) wait times to receive SBA loans saw a 2 % faster rise in home value prices post-Harvey than communities with longer median wait times. While disaster damage influenced the receipt of assistance, pre-disaster wealth influenced the speed of the assistance received. Understanding at what points barriers to access arise within the delivery of aid, and the wealth outcomes of such inequities, point to clear ways policy changes may promote equitable recovery for all.

Assessing the Loss Given Default of Bank Loans Using the Hybrid Algorithms Multi-Stage Model

The loss given default (LGD) is an important credit risk parameter in the regulatory system for financial institutions. Due to the complex structure of the LGD distribution, we propose a new approach, called the hybrid algorithms multi-stage (HMS) model, to construct a multi-stage LGD prediction model and test it on the US Small Business Administration (SBA)’s small business credit dataset. We then compare the model’s performance under four routes by different evaluation metrics. Finally, pertinent business information and macroeconomic features datasets are added for robustness validation. The results show that HMS performs well and stably for predicting LGD, confirming the superiority of the proposed hybrid unsupervised and supervised machine learning algorithm. Financial institutions can apply the approach to make default predictions based on other credit datasets.

Requisite variety theory and third‐party guarantee loans: How local bank branches can reduce loan defaults

AbstractRequisite variety theory posits that internal variety is essential to develop responses to external variety. Facing increased local competition local bank branches could lower the odds of Small Business Administration (SBA) loan defaults by increasing knowledge variety through variations in geographic distances and industries among SBA loans. Our analysis shows that although local competition increases the odds of default, variations in industries represented in the SBA portfolio, the variation in geographic distances among loans in the portfolio lowers the odds of default. Our study emphasizes the importance of requisite variety at the branch level and presents important implications for policymakers and practitioners in the third‐party loan guarantee milieu.

A methodology to estimate postdisaster unmet housing needs using limited data: Application to the 2017 California wildfires.

In the United States, assistance from the Department of Housing and Urban Development (HUD) plays an essential role in supporting the postdisaster recovery of states with unmet housing needs. HUD requires data on unmet needs to appropriate recovery funds. Ground truth data are not available for months after a disaster, however, so HUD uses a simplified approach to estimate unmet housing needs. State authorities argue that HUD's simplified approach underestimates the state's needs. This article presents a methodology to estimate postdisaster unmet housing needs that is accurate and relies only on data obtained shortly after a disaster. Data on the number of damaged buildings are combined with models for expected repair costs. Statistical models for aid distributed by the Federal Emergency Management Agency (FEMA) and the Small Business Administration (SBA) are then developed and used to forecast funding provided by those agencies. With these forecasts, the unmet need to be funded by HUD is estimated. The approach can be used for multiple states and hazard types. As validation, the proposed methodology is used to estimate the unmet housing needs following disasters that struck California in 2017. California authorities suggest that HUD's methodology underestimated the state's needs by a factor of 20. Conversely, the proposed methodology can replicate the estimates by the state authorities and provide accounts of losses, the amount of funding from FEMA and SBA, and the total unmet housing needs without requiring data unavailable shortly after a disaster. Thus, the proposed methodology can help improve HUD's funding appropriation withoutdelays.

Association of Receipt of Paycheck Protection Program Loans With Staffing Patterns Among US Nursing Homes

Staffing shortages in nursing homes (NHs) threaten the quality of resident care, and the COVID-19 pandemic magnified critical staffing shortages within NHs. During the pandemic, the US Congress enacted the Paycheck Protection Program (PPP), a forgivable loan program that required eligible recipients to appropriate 60% to 75% of the loan toward staffing to qualify for loan forgiveness. To evaluate characteristics of PPP loan recipient NHs vs nonloan recipient NHs and whether there were changes in staffing hours at NHs that received a loan compared with those that did not. This economic evaluation used national data on US nursing homes that were aggregated from the Small Business Administration, Nursing Home Compare, LTCFocus, the Centers for Medicare & Medicaid Services Payroll Based Journal, the Minimum Data Set, the Area Deprivation Index, the Healthcare Cost Report Information System, and the US Department of Agriculture Rural-Urban Continuum Codes from January 1 to December 23, 2020. Paycheck Protection Program loan receipt status. Staffing variables included registered nurse, licensed practical nurse (LPN), and certified nursing assistant (CNA) total hours per week. Staffing hours were examined on a weekly basis before and after loan receipt during the study period. An event-study approach was used to estimate the staffing total weekly hours at NHs that received PPP loans compared with NHs that did not receive a PPP loan. Among 6008 US NHs, 1807 (30.1%) received a PPP loan and 4201 (69.9%) did not. The median loan amount was $664 349 (IQR, $407 000-$1 058 300). Loan recipients were less likely to be part of a chain (733 [40.6%] vs 2592 [61.7%]) and more likely to be for profit (1342 [74.3%] vs 2877 [68.5%]), be located in nonurban settings (159 [8.8%] vs 183 [4.4%]), have a greater proportion of Medicaid-funded residents (mean [SD], 60.92% [21.58%] vs 56.78% [25.57%]), and have lower staffing quality ratings (mean [SD], 2.88 [1.20] vs 3.03 [1.22]) and overall quality star ratings (mean [SD], 3.08 [1.44] vs 3.22 [1.44]) (P < .001 for all). Twelve weeks after PPP loan receipt, NHs that received a PPP loan experienced a mean difference of 26.19 more CNA hours per week (95% CI, 14.50-37.87 hours per week) and a mean difference of 6.67 more LPN hours per week (95% CI, 1.21-12.12 hours per week) compared with nursing homes that did not receive a PPP loan. No associations were found between PPP loan receipt and weekly RN staffing hours (12 weeks: mean difference, 1.99 hours per week; 95% CI, -2.38 to 6.36 hours per week). In this economic evaluation, a forgivable loan program that required funding to be appropriated toward staffing was associated with a significant increase in CNA and LPN staffing hours among NH PPP loan recipients. Because the PPP loans are temporary, federal and state entities may need to institute sufficient and sustainable support to mitigate NH staffing shortages.

Comparison of Gini indices using sequential approach: Application to the U.S. Small Business Administration data

The comparison of Gini inequality indices is an important study related to regional imbalance in equality. In the design of such a study, both the cost constraints and variability of the difference of inequality indices play an active role. In this article, we compare the Gini inequality indices for two regions under cost constraints leveraging on the concept of sequential analysis. Without prior knowledge of the statistical distributions of the data in the two regions of interest, the optimal sample sizes that balance the cost constraint and the accuracy of the comparison cannot be calculated under a fixed-sample-size methodology. Therefore, in this article, we develop a sequential procedure for comparing Gini indices for two regions under a budget constraint. With no specific assumption about the population distribution of the data, we examine and prove the large-sample properties offered by the proposed purely sequential procedure. Further, we use extensive simulations to empirically examine the characteristics of this procedure and illustrate its application using data on the Paycheck Protection Program loan from the U.S. Small Business Administration for the states of Connecticut and Rhode Island.

Small business administration loans, economic development, and state‐level employment

AbstractThis study analyzes the relationship between the US Small Business Administration (SBA) loan programs and state‐level employment by utilizing a dynamic first‐difference generalized method of moments fixed effects statistical approach using annual data from 1996 to 2013. The US state‐level panel data show a positive relationship between growth rates in SBA lending per capita and states' civilian employment levels. This relationship appears to be stronger in states with below‐average incomes than in states with higher incomes. This study contributes to the understanding of how SBA lending programs can best help foster state‐level economic development and employment.

기업의 지속가능경영전략과 성과에 조직요인이 미치는 영향에 대한 실증분석

The purpose of this paper is to analyze the impact of sustainable management strategies on non-financial performance as a sustainable competitive advantage, and to examine the moderating effects of organizational factors on sustainable management strategies and performance. To collect appropriate data for the study, we selected companies to survey based on panel data from small and medium-sized enterprises nationwide collected by the Small and Medium Business Administration in 2022. The survey was conducted on manufacturing small and medium-sized enterprises with 100 or more employees and total sales of 5 billion or more. Empirical analysis shows that organizational factors (innovative organizational culture, SMCS system) have a significant moderating effect on sustainable management strategies and performance.

Industry Specialization and Small Business Lending

This paper examines the rise and competitive impact of industry-specialized small business lenders. Using loan-level data with detailed industry codes from the Small Business Administration (SBA), we document a recent increase in lenders that originate loans nationally but to a limited number of industries. We then examine the impact of these industry-specialized lenders on credit availability and banking competition. Exploiting the staggered entry of a large, specialized lender, we find significant increases in total SBA-backed lending with no evidence of substitution from other lenders. We then explore potential mechanisms behind the increase in lending.

Limits to capital: Assessing the role of race on the Paycheck Protection Program for African American farmers in America

AbstractWe examine Paycheck Protection Program (PPP) lending among African American and white farmers. Using data from the U.S. Small Business Administration we run fixed effect estimations with a Mills inverse ratio. Our findings suggest discrimination due to limited capital access. We find statistically significant differences between approved loan amounts among African American farmers due to income level and location of their farms. These had minimal effects on white farmers. Our results call for policymakers to carefully monitor the PPP distribution in areas more vulnerable to financial difficulties. We provide policy recommendations.

The Paycheck Protection Program: Minority vs. Non-Minority Bank Response

We are the first to empirically analyze whether the "Guidance on Accessing Capital for Minority, Underserved, Veteran and Women-Owned Business Concerns" (Guidance) program was successful. The program was implemented to promote the Paycheck Protection Program (PPP) loans provided by Minority Depository Institutions (MDIs) in the second round compared to the first round of PPP. Using both loan-level data from the Small Business Administration (SBA) and balance-sheet-level data from the Federal Deposit Insurance Corporation (FDIC), we obtain compelling empirical evidence indicating that MDIs issued relatively more PPP loans (scaled by institution assets) than non-MDIs in the second round. This finding is evidence that the goal of dedicated access for MDIs under the Guidance program was successful. It also enhances the understanding of the PPP lending program and the impact of the Federal Reserve PPP Liquidity Facility (PPPLF) in terms of the role played by MDIs.

Small business lending under the PPP and PPPLF programs

We use Call Report data to examine the effects of the Paycheck Protection Program (PPP) and the PPP Liquidity Facility (PPPLF) on small business and farm lending by individual commercial banks. As program participation was associated with small business lending, we adopt an instrumental variables approach to identify causal implications based on historical bank relationships with the Small Business Administration and the Federal Reserve’s discount window. Our results indicate that both programs encouraged lending growth over the first half of 2020. However, while the PPP encouraged greater lending across all banks, only small and medium-sized bank lending growth was significantly related to participation in the PPPLF.

Strategic imperatives for privately owned fashion boutiques in the southeast united states

Consumers have greater purchase choice options to fulfill their wants and desires in a networked world. In the United States fashion business, privately owned brick-and-mortar fashion boutiques face the unique challenge of strategically competing with online retailers who usually have low overheads and inventories. Low adaptability,limited innovation, and inadequate attention to a changing marketing landscape could be responsible for the decline in the survival rate of several traditional, or pure brick-and-mortar fashion boutiques. Newer approaches may be essential for traditional and online entrepreneurs, to increase adaptability and business sustainability and in increasing customer marketing outreach. Grounded in the general systems theory, the research conducted in this qualitative study was with the invocation of a multiple case design and entailed gaining insight into the exploration of strategies used by successful brick-and-mortar fashion boutique owners in contending with the purely online retailers. Semi-structured interviews constituted the means for data collection and involved 5 brickand-mortar fashion boutique owner participants, who operated retail fashion businesses in the southeast region of the United States. The successful existence of the business for more than 5 years was an important prerequisite for participation criteria in the study, as also indicated merit in studying the critical success factors in traditional fashion retailing. The data from interviews were analyzed and triangulated with credible secondary sources such as information and reports from the U. S. Small Business Administration, U. S. Census Bureau, and the fashion industry. From a thematic analysis of the interview data, the major emergent themes were the importance of the business success of marketing strategies, cognizance of fashion trends and the role of customer preference in economic success resepectively. The ensuing knowledge from the utilization of the study may serve to increase the success rates of traditional and online fashion startups and possibly provide knowledge on the strategic imperatives for these businesses to compete in an online business world.