Hao Zhang, Public Assistance Programs during the COVID-19 Pandemic: An Empirical Analysis of Business Recovery Framework (2025)
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Hao Zhang's dissertation for a Doctor of Philosophy in Public Administration at the University of North Texas, dated May 2025, applies a business recovery framework to SBA datasets for the DFW region. Using survival analysis on business characteristics and the amount and type of SBA assistance received, the abstract reports that any type of assistance played a significant role in keeping businesses operating. It also finds that minority-owned businesses were more likely to keep operating than non-minority-owned businesses, a result the author says contradicts many studies, and that more profitable firms and firms with larger sales and fewer employees fared better.
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PUBLIC ASSISTANCE PROGRAMS DURING THE COVID-19 PANDEMIC: AN
EMPIRICAL ANALYSIS OF BUSINESS RECOVERY FRAMEWORK
Hao Zhang
Dissertation for the Degree of
DOCTOR OF PHILOSOPHY
UNIVERSITY OF NORTH TEXAS
May 2025
APPROVED:
Simon A. Andrew, Major Professor and
Chair of the Department of Public
Administration
Yu (Kelly) Shi, Committee Member
Skip Krueger, Committee Member
Hao-Che (Tristian) Wu, External Committee
Member
Nicole Dash, Dean of College of Health and
Public Service
Victor R. Prybutok, Dean of the Toulouse
Graduate School
Zhang, Hao. Public Assistance Programs during the COVID-19 Pandemic: An Empirical
Analysis of Business Recovery Framework. Doctor of Philosophy (Public Administration), May
2025, 125 pp., 12 tables, 8 figures, references, 231 titles.
While much has been written about government failures, few have examined the
importance of public assistance program for small businesses after disasters. Given the nature of
disaster response and recovery, government assistance program also aims to correct for market
failures, which includes disaster relief programs such as business loans and direct cash
assistance. Public assistance programs can improve the survival rate of small businesses. In some
instance, qualified small businesses owners often need to mortgage their property to receive
assistance. Studies have also shown that minority-owned small businesses face challenges
accessing public assistance programs with limited payback capability. The significance of
studying effect of disaster assistance programs can be identified in two aspects. First, it can
contribute to the theory development of economic recovery that what types of policies are most
effective in facilitating business and local economic recovery. In the case of COVID-19 relief
programs, both loans and direct cash assistance are offered for small business owners to apply,
which provide a great chance to compare the efficiency of two type of assistance in recovery.
Secondly, different than other disruptive event, businesses owners are equally impacted by social
distance restriction, which allow researchers to explore the factors that can contribute to
businesses recovery from marketing perceptive. This study adopts the business recovery
framework from existing literature to explore the contribution factors in businesses recovery.
When the interruption event strikes, business characteristics, and hazard events decide how much
damage has been suffered for a business. Meanwhile, businesses’ preparedness measures (e.g.,
insurance) and assistance policies (e.g., financial assistance) can mitigate economic losses and
accelerate the recovery process. Other than that, business operation capability also affects the
outcome of businesses’ recovery, which consists of two major aspects: production capacity and
market context. This study utilized the SBA datasets in DFW region, which contains businesses
characteristics (ownership, employment size, sales volume, and profitability), amount and type
of assistance received from SBA, businesses operation statues (inactive or not), and close date (if
applied). I employed the Survival Analysis and Septically Analysis to determine factors that
explain small businesses recovery outcome. The result of the survival analysis reveals that any
type of assistance plays significant role in helping business to keep operation. Further, minority
owned businesses are more likely to keep operation than non-minority owned businesses. The
results also show that small businesses with higher profitability are active in running their
businesses after the pandemic. Minority-owned businesses in the DFW area recovered faster
from the pandemic, which contradicts many studies. It supports the argument that even though
minority-owned businesses have relatively small business sizes, special cultural roles played by
minority-owned businesses can still positively help businesses’ recovery. Small businesses with
larger sales volumes and fewer employees significantly positively contribute to helping
businesses to sustain operations after the pandemic, which correspond to the studies that
businesses with higher value-add in operation and lower operation cost are less vulnerable in the
pandemic. The findings provide an understanding on the factors explaining public assistance
program for small businesses (i.e., direct cash assistance) as an indicator of businesses recovery.
The findings also show restricted assistance increased risk of business closure.
Copyright 2025
By
Hao Zhang
ii
ACKNOWLEDGMENTS
First and foremost, I would like to thank my wife, Yufei (Virag) Zhang. Without her
unwavering support and understanding, I would not have been able to fully dedicate myself to
pursuing my Ph.D. at the University of North Texas. Additionally, during this journey of earning
my Ph.D., my family was blessed with a new member, Kaiya Zhang, who brings fun and joy to
this family.
I am also deeply grateful to my dissertation committee: Drs. Simon Andrew, Yu (Kelly)
Shi, Skip Krueger, and Hao-Che (Tristan) Wu. Their patience and generosity in sharing their
guidance have been invaluable in helping me transition from an engineer to a researcher in social
science area. Special thanks go to Dr. Simon A. Andrew, my major professor, for his unwavering
support and mentorship throughout my academic journey. His valuable insights into both academic
and professional matters, as well as his advice on navigating daily challenges, have been truly
inspiring and instrumental in my growth. I am also deeply grateful to Dr. Hao-Che (Tristan) Wu
for his exceptional generosity in sharing his experiences, providing thoughtful guidance, and
dedicating significant time and effort to help me navigate the academic job market. His support
has been pivotal in helping me secure an assistant professor position, and I will always appreciate
his encouragement and mentorship. I would also like to express my gratitude to all the faculty and
staff members of the Department of Public Administration at UNT. Their unwavering support and
kindness have always made me feel like part of a family.
Lastly, I would like to express my heartfelt gratitude to my family in China. My parents,
Ruifeng Zhang and Xiaohua Zhao, as well as my father-in-law and mother-in-law, Sen Zhang and
Aixia Wang, have supported and trusted me throughout this journey. Their encouragement has
been a constant source of strength for me.
iii
TABLE OF CONTENTS
Page
ACKNOWLEDGMENTS ............................................................................................................ iiii
LIST OF TABLES ....................................................................................................................... viii
LIST OF FIGURES ....................................................................................................................... ix
CHAPTER 1. INTRODUCTION ................................................................................................... 1
1.1 Overview ....................................................................................................................... 1
1.2 Problem Statement: The Effectiveness Of Public Assistance Programs In Business
Recovery After Disasters .................................................................................................... 3
1.2.1 Public Assistance Programs ........................................................................... 3
1.2.2 Public Assistance Programs as Distributive Policy ....................................... 4
1.2.3 Public Assistance Program for Small Business Disaster Recovery ............... 6
1.2.4 Research Gaps ................................................................................................ 7
1.3 Research Questions ....................................................................................................... 8
1.3.1 Significance of Research Questions............................................................... 9
1.3.1.1 Contribution to Theory ................................................................... 9
1.3.1.2 Contribution to Practice ................................................................ 10
1.4 Theoretical Framework ................................................................................................11
1.4.1 Business Recovery Framework.....................................................................11
1.5 Business Recovery ...................................................................................................... 12
1.5.1 Defining Business Recovery? ...................................................................... 13
1.5.2 Measuring Business Recovery? ................................................................... 15
1.6 Methodology ............................................................................................................... 16
iv
1.6.1 Research Site: Dallas-Fort Worth-Arlington Metropolitan Area and the
COVID-19 Pandemic ............................................................................................ 16
1.6.2 Available Programs: Paycheck Protection Program and Economic Injury
Disaster Loan ........................................................................................................ 17
1.6.3 Measurement of Business Recovery ............................................................ 18
1.6.4 Data Sources ................................................................................................ 18
1.6.5 Analysis Methods ......................................................................................... 19
1.7 Structure of Dissertation ............................................................................................. 19
CHAPTER 2. BUSINESS RECOVERY AND PUBLIC ASSISTANCE PROGRAMS ............. 22
2.1 Introduction ................................................................................................................. 22
2.2 Business Recovery ...................................................................................................... 23
2.2.1 Factors Studied in the Business Recovery ................................................... 24
2.2.2 Significance of Small Business Post-Disaster Recovery ............................. 27
2.3 Public Assistance Programs in Post-Disaster Recovery.............................................. 28
2.3.1 Why We Need Restrictions? ........................................................................ 28
2.3.2 Why There is Loan and Cash? ..................................................................... 30
2.3.3 Significance of Public Assistance Program in Post-Disaster Recovery....... 31
2.4 Public Assistance Programs for Small Business During the COVID-19 .................... 33
2.4.1 Available Programs ...................................................................................... 33
2.4.2 Research Statues .......................................................................................... 35
CHAPTER 3. BUSINESS DISASTER RECOVERY FRAMEWORK ...................................... 37
3.1 Introduction ................................................................................................................. 37
3.2 Businesses Recovery Framework ............................................................................... 37
v
3.3 Neoclassic Economic Theory ..................................................................................... 40
3.4 Modified Research Framework................................................................................... 41
3.4.1 Public Assistance Programs ......................................................................... 42
3.4.2 Business Characteristics............................................................................... 48
3.4.3 Market Context ............................................................................................ 50
3.4.4 Geographic Location.................................................................................... 51
CHAPTER 4. METHODOLOGY ............................................................................................... 56
4.1 Introduction ................................................................................................................. 56
4.2 The Research Site: Dallas Fort-Worth Metropolitan Area and the COVID-19
pandemic ........................................................................................................................... 57
4.3 Measurements ............................................................................................................. 59
4.3.1 Dependent Variable: Business Recovery ..................................................... 59
4.3.2 Independent Variable: Access to Public Assistance Program ...................... 60
4.3.3 Independent Variable: Business Characteristics .......................................... 61
4.3.4 Independent Variables: Market Context ....................................................... 61
4.4 Data Collection ........................................................................................................... 62
4.4.1 Public Assistance Programs: Small Business Administration ..................... 62
4.4.2 Business Characteristics: Mergent Intellect ................................................. 63
4.4.3 Market Context: Census Bureau .................................................................. 63
4.4.4 Data Preparation Process ............................................................................. 64
4.5 Methods of Analysis.................................................................................................... 65
4.5.1 Logistical Regression ................................................................................... 65
4.5.2 Cox Regression with Time-Varying Effect .................................................. 66
vi
4.5.3 Spatial Autocorrelation ................................................................................ 67
CHAPTER 5. ANALYSIS AND DISCUSSION ......................................................................... 73
5.1 Introduction ................................................................................................................. 73
5.2 Descriptive Analysis ................................................................................................... 73
5.3 The Effectiveness of Public Assistance Programs in Helping Business
Recovery ........................................................................................................................... 76
5.3.1 Logistical Regression ................................................................................... 76
5.3.2 Cox Regression with Time-Varying Effects ................................................ 78
5.3.3 The Relationship Between Business Characteristics and Business
Recovery ............................................................................................................... 80
5.3.4 The Relationship Between Market Context and Business Recovery ........... 82
5.3.5The Relationship Between Geographic Location and Business Recovery ... 82
CHAPTER 6 CONCLUSION...................................................................................................... 87
REFERENCES ............................................................................................................................. 95
vii
LIST OF TABLES
Page
1. Summarize the Definition of Business Recovery ..................................................................... 14
2. Classification of Public Assistance Programs ........................................................................... 35
3. Summarize the Mechanism of Public Assistance Programs in Business Recovery ................. 45
4. Summarize the Hypotheses in This Dissertation. ..................................................................... 54
5. Binary Variables Capture Small Business Participation in Assistance Programs ..................... 60
6. Variable used in Logistic Regression ........................................................................................ 69
7. Variable Used in Cox Regression with time varying effect ...................................................... 70
8. Variable Used in Spatial Autocorrelation .................................................................................. 71
9. Descriptive Statistics at the Individual Business Level ............................................................ 74
10. Factors Explaining Small Business Recovery (Logistical Regression) .................................. 77
11. Factors Explaining Small Business Recovery at Each Stage (Cox-Regression with Time
Varying Effect) .............................................................................................................................. 79
12. Result of Spatial Autocorrelation ............................................................................................ 83
viii
LIST OF FIGURES
Page
1. Business Recovery Framework (source: Chang et al. [2022]) ................................................. 38
2. Modified Businesses Recovery Framework ............................................................................. 42
3. Data Preparation Process ........................................................................................................ 655
4. Percentage of Inactive Business at Census Tract Level ............................................................ 75
5. Percentage of Business Receive Any Assistance at Census Tract Level .................................. 76
6. Percentage of Business Access to Restricted (PPP) Assistance Only and Business Active ..... 84
7. Percentage of Business Access to Unrestricted (EIDL) Assistance Only and Business Active 85
8. Percentage of Business Access to Mixed Assistance (PPP and EIDL) and Business Active ... 86
ix
1 CHAPTER 1.
INTRODUCTION
1.1 Overview
While much has been written about business recovery after disasters (Asgary et al., 2012;
Corey & Deitch, 2011; Dahlhamer & Tierney, 1998; Kellenberg & Mobarak, 2011), few have
examined the importance of public assistance programs on small businesses during a global
pandemic (Wang & Kang, 2023). From a neoclassical perspective, business failure or closure
during disasters reflects the effort of the market force that excludes inefficient businesses from
the market. In this context, no public assistance is necessary (Okuyama & Rose, 2019; Schmidt,
2018). However, scholars have also argued for the significance of public assistance programs
highlighting the notion of market failure in the presence of disasters or extreme shock in the
market. Public intervention is justified on equity and public good reasons (Green & Lavery,
2015; G. Smith, 2015; Xu & Mo, 2013).
The purpose of this study is to assess the effectiveness of public assistance programs in
helping small businesses recover after disasters. The significance of this study can be appreciated
from both theoretical and practical perspectives. First, from a theoretical perspective, this study
contributes to the development of theories on business recovery post-disasters and examines how
redistributive policies can play a role in recovery. Second, from a practical standpoint, addressing
the overall research question can assist policymakers in developing more appropriate policies to
facilitate business recovery after disasters.
There are multiple reasons to examine the effectiveness of public assistance programs in
promoting business recovery. First, redistributive policies are not expected to lead to economic
improvement (Auclert, 2017; Peterson, 1981; Schwartz & Ter-Minassian, 1995). In post-disaster
1
scenarios, achieving a rapid economic recovery often necessitates allocating resources to larger
businesses due to effectiveness concerns, potentially damaging the principle of equality (M. Li,
2021; Watson, 2022). Second, the rationality of mechanisms within public assistance programs
can impede or accelerate the recovery process (Chang et al., 2022). The bureaucratic
mechanisms in public assistance programs can limit businesses' adaptation behaviors or increase
businesses' debt load if it is a loan-based assistance. Public assistance programs linked to certain
type of requirements or restrictions can limit the ability of businesses to adapt to their changed
conditions and even decisions that determine their long-term operations and business survival.
For instance, assistance in a loan-based assistance can increase the number and amount of debt –
debt-load, of businesses making them vulnerable to financial risk (Dahlhamer & Tierney, 1998;
Graham, 2007; Xiao & Zandt, 2012b).
This study utilized the Business Disaster Recovery Framework, adopted from the
theoretical framework proposed by Chang et al., (2022) to examine the role of public assistance
programs in long-term business recovery following large-scale disruptive events such as the
COVID-19 pandemic. Using data from small businesses in the DFW region, this study employs
logistic regression, Cox regression with time-varying effects, and spatial autocorrelation to assess
the effectiveness of public assistance programs in reducing the likelihood of business closures,
not only considering overall COIVD-19 period but also both temporal and spatial variations.
The analysis results indicate that public assistance programs can help reduce the
likelihood of business closures, though their impact varies over time and across geographic
locations. In the meantime, restrictions attached to public assistance programs might let business
face more risks compare with business without any assistance. Additionally, businesses with
more resources are more likely to recover from COVID-19. Furthermore, minority-owned
2
businesses demonstrated greater resilience than non-minority-owned businesses during the
pandemic.
1.2 Problem Statement: The Effectiveness of Public Assistance Programs in Business
Recovery After Disasters
Neoclassical economics argue that business closure is the effort of market forces. The
concept of perfect competition, introduced by neoclassical economics, describes an ideal market
structure in which resources are effectively allocated through interactions among numerous
buyers and sellers and individuals have limited impact on the whole market (Clifton, 1977).
According to neoclassical approach, the failure of businesses signals their poor decision when
allocating resources effectively. No government intervention is necessary.
In the post-disaster context, the reliance on the market force suggested by the neoclassic
economics has limitations in its application. For example, the approach relies on perfect
information, rational actors, and efficient markets, which are frequently absent in post-disaster
situations. Disasters caused major market failures (e.g., information gaps, liquidity shortages,
and coordination challenges) hinder businesses from recovering effectively (Atkinson & Sapat,
2013; Dari-Mattiacci & Faure, 2015). Government intervention can provide immediate financial
relief---such as grants and low-interest loans---that businesses need to rebuild their businesses,
infrastructure, and operations, which is critical for small business recovery after disasters
(Yanuarni et al., 2023).
1.2.1 Public Assistance Programs
Governments have multiple tools to help businesses recover when they face a disruptive
market in a post-disaster context, which include but not least to regulations, subsidies and taxes,
and public goods (Furton & Martin, 2018). The government can enact regulations to restrict
3
behaviors among sellers and buyers to avoid moral hazards (Foldvary & Klein, 2001; Vining,
2003). Subsidies and taxes are employed by the government to raise market incentives from both
internal and external perspectives (Besley, 1992). Government intervention tools do not directly
alter the market's resource allocation pattern; instead, they facilitate normal transactions to assist
businesses in recovering from disasters.
Government interventions also face challenges. First, scholars suggest that regulations
and subsidies can lead to inefficiencies and inequality, which can create new problems instead of
addressing the existing problem, which requires caution in policy design (Fuchs & Skrzypacz,
2015; Le Grand, 1991). Further, free raider problems and moral hazards can mitigate the effort of
government interventions (Holmstrom, 1982; Moore, 1995). Moral hazards refer to a situation
where individuals are more likely to take risks because they would not boundary the cost of
increased risk. The free rider problem is defined as the situation in which individuals benefit
from goods without contributing to the cost. Therefore, government interventions also need very
cautious in designing and implementing to achieve expected outcomes.
1.2.2 Public Assistance Programs as Redistributive Policy
Public assistance programs--a form of redistributive policy-- are government initiatives
designed to provide financial and service-based aid by reallocating resources from higher-income
groups to the needy and unfortunate, to achieve greater social equality and welfare (Peterson,
1981; Sausgruber & Tyran, 2007). These policies are shown in a wide range of formats including
taxation, social welfare programs, public goods, and direct financial assistance to individuals and
businesses (Fuchs & Skrzypacz, 2015). The underlying principle of redistributive policy is to
address economic disparities and social inequities by reallocating resources from more affluent
4
segments of society to those in less fortunate circumstances, thereby promoting social welfare
and economic stability
The dilemmas of applying redistributive policy in post-disaster scenarios sourcing from
two aspects. Firstly, redistributive policy aims to allocate resources to needy and unfortunate
groups, but these groups are less likely to promote the recovery process effectively, which conflicts
with the expected outcome of redistributive policy in the post-disaster stage (Peterson, 1981;
Watson, 2022). For instance, businesses with larger businesses size are likely to facilitate the
transaction more effectively but they are not groups who are most needy to assistance (Gerber et
al., 2018; M. Li, 2021; Watson, 2022). However, allocating resources to less needy groups can lead
needy groups to fall into the poverty trap, which conflicts with the value of redistributive policy
and social equality (Balboni et al., 2020; Kraay & McKenzie, 2014).
Secondly, as a form of public goods, redistributive policy needs to set certain restrictions
to prevent the free rider problem during the implementation, especially in the post-disaster context,
unrestricted cash assistance has a limited impact in promoting recovery (Reddy, 2000; Xu & Mo,
2013). However, restrictions set by redistributive policy might reduce recipients’ adaption
capability (Atteridge & Remling, 2018; McVittie et al., 2017; Ostrom & Ostrom, 2019). For
instance, assistance distributed in a loan format aims to enable recipients to generate capital and
facilities recovery but might increase the debt load of the affected groups (Dahlhamer & Tierney,
1998). On the other hand, restricted cash assistance typically requires business recipients to
maintain their employee count, which limits the businesses' ability to survive by laying off
employees (Bartik, Cullen, et al., 2020).
Public assistance programs play a significant role in promoting social stability and
economic development by mitigating poverty and inequality (Peterson, 1981). They aim to allow
5
people suffered from disasters access to necessities such as food, shelter, and healthcare through
the redistributive process. This redistribution helps to mitigate the increased crime rates and
social unrest resulting from poverty and economic disparities (Devaney et al., 1997; Radelet,
2004). Reduced poverty leads to a broader base of human capital and a healthier workforce,
which are essential for long-term economic prosperity (Simões et al., 2022; Yalley et al., 2021).
For instance, Nelson, (2012) suggests that higher benefit levels lead to less extensive poverty,
thus supporting social stability and economic development.
The implementation of public assistance programs also face challenges in equality,
access, efficiency, and effectiveness (Barnes, 2020; Collins et al., 2016; Schneider & Jacoby,
2003; Williamson, 1974). First, during the redistributive process, historical discrimination can
prevent certain groups equal access to public assistance programs, undermining the value of
social equality (Williamson, 1974). Second, the redistribution process can be an administrative
burden, especially when the potential recipient group is relatively large (Barnes, 2020). When
assistance needs to be distributed to a large group, a targeted approach is not appropriate from a
cost-benefit perspective. The targeting approach itself, however, is resource and time-consuming
if distribution agencies want to reach the expected needy group, further increasing administrative
burdens (Henman, 2004). Third, ensuring that public assistance programs produce the expected
outcomes can be challenging. There is a risk that assistance recipients may become dependent on
aid, discouraging self-sufficiency (Schneider & Jacoby, 2003).
1.2.3 Public Assistance Program for Small Business Disaster Recovery
During a small business post-disaster recovery process, public assistance programs can
promote recovery by providing financial assistance to restore operations (Hiramatsu & Marshall,
2017; Watson, 2022). Disasters not only cause physical damage but also hinder businesses'
6
operational capabilities (Watson, 2021). Compared to larger businesses, small businesses are less
likely to have preparedness plans for disasters and lack alternative assistance sources other than
public assistance programs (Andrew et al., 2022; Marshall & Schrank, 2014). To restore
operational capability, small businesses typically need to access assistance from public assistance
programs (Watson, 2022).
However, the effectiveness of public assistance programs in helping small businesses is
impacted by several factors, which are restrictions attached to the program, the format of those
programs, and geographic locations. Restrictions attached to those programs and loan-based
assistance are critical to ensure assistance recipients continue work in business activities instead
of misuse funding, which aims to promote regional economic recovery (Barbieri, 2021; Gu et al.,
2008; Kelly et al., 1998). The restriction and loan-based assistance can also limit business
profitability by limiting self-adjust behavior and increasing the debt load (Dahlhamer & Tierney,
1998; Graham, 2007).
1.2.4 Research Gaps
Despite extensive research on the effectiveness of public assistance programs in small
business post disaster recovery, significant gaps remain, particularly regarding long-term impact
assessment and the comparative analysis of restrictions or unrestricted assistance. Most studies
focus on the short-term impacts of public assistance programs, such as job preservation, and
financial stability (Haynes et al., 2018; Hiramatsu & Marshall, 2017; Watson, 2021). There is a
critical need for studies to examine how these programs affect businesses' survival over the
whole pandemic. Understanding these long-term effects will help in designing more effective
policies and interventions that promote sustained economic health and community resilience.
7
Additionally, research is needed to evaluate the impact of policy regulations attached to
public assistance programs and compare the effectiveness of restrictions attached to assistance.
This is because restrictions can ensure aid is spent effectively, but they may also limit businesses'
flexibility to adapt to specific circumstances, potentially hindering recovery. Comparing loan-
based programs, which impose a repayment burden, with cash-based assistance, which offers
immediate, flexible funds, will provide insights into their respective benefits and drawbacks.
This analysis will help policymakers optimize assistance programs to support both immediate
recovery and the long-term viability of businesses.
Lastly, the influence of geographic conditions on the effectiveness of public assistance
programs is rarely discussed by scholars. When public assistance is distributed to certain areas, it
not only affects the recipients but also impacts surrounding businesses by stimulating economic
activity through spillover effects. Exploring the impact of geoeconomic conditions can provide
valuable insights for assistance distribution agencies, helping them allocate resources more
effectively to maximize economic recovery.
1.3 Research Questions
Even though redistributive policy has been equally accessed, the effort of redistributive
policy in promoting business recovery can be mitigated by restrictions attached, the format of
assistance, businesses’ characteristics, and the market context from the supply and demand side
(Chang et al., 2022). To explore how redistributive policy contributes to the business recovery
process through the given limitation discussed above, this study proposes the following overall
research question the guide: Do public assistance programs mitigate the likelihood of business
closure after major disasters?
8
1.3.1 Significance of Research Questions
1.3.1.1 Contribution to Theory
This research advances the business disaster recovery framework by investigating the
impacts of public financial support, business characteristics, and market conditions on business
recovery after a pandemic. Public financial support, such as grants, low-interest loans, and
subsidies, helps businesses cope with immediate financial challenges and stabilize their
operations. The businesses characteristics, such as their size, age, industry type, and pre-disaster
financial performance, also affect their recovery. The market conditions, which include factors
like supply chain reliability, customer demand, and competitive environment, influence how
rapidly and effectively businesses can resume normal operations. These elements are key for
developing effective recovery strategies that cater to the specific needs of different businesses in
the wake of a pandemic.
Additionally, this research investigates the notion of equitable resource allocation during
and after the pandemic. Making sure that financial support and other recovery resources are
available to all impacted businesses, no matter their size or location, is vital for a comprehensive
recovery. Equitable resource allocation ensures that vulnerable and underserved businesses get
the support they need to rebuild and prosper. This involves assessing the methods of aid
distribution, the eligibility requirements, and the transparency of the allocation process. By
exploring these aspects, the research offers insights into how to enhance the effectiveness of
disaster recovery programs, making sure that they are inclusive and beneficial to the whole
business community.
9
Furthermore, this study examines the effectiveness of redistributive policies in promoting
post-disaster economic recovery. As mentioned earlier, the primary goal of redistributive policies
is to address economic disparities and social inequities, which often necessitates restrictions to
prevent the free-rider problem. However, the rationale behind these restrictions has rarely been
tested, particularly in the context of post-disaster recovery. While restrictions are intended to
ensure aid is distributed fairly and efficiently, they may also prevent business owners from
participating in economic activities that align with market principles, thereby hindering resource
allocation and slowing recovery. By investigating the role of restrictions attached to post-disaster
assistance programs, this study provides valuable insights into the rationality and effectiveness of
these limitations in the post-disaster context. The findings offer insights into whether these
restrictions align with market efficiency principles or inadvertently hinder recovery, providing a
foundation for refining policy frameworks to balance equity, efficiency, and economic resilience
in post-disaster contexts.
1.3.1.2 Contribution to Practice
This study contributes to practical policy by informing policymakers about the impact of
their disaster relief efforts. By analyzing the effects of public assistance on business recovery, the
research can highlight whether these efforts significantly reduce business closures. If a strong
positive impact is demonstrated, policymakers can use this evidence to strengthen or adjust
existing policies to optimize their effectiveness. Such data-driven adjustments could ensure that
public funds are used efficiently and that relief programs are tailored to maximize benefits for
struggling businesses.
10
Additionally, this study provides insights into improving the distribution of public
assistance following disasters. It emphasizes the importance of ensuring that aid reaches all
affected businesses, particularly those in underprivileged or hard-to-reach areas. By identifying
gaps and inefficiencies in current distribution methods, the research can suggest ways to make
assistance more accessible and equitable. This could involve implementing more targeted
outreach strategies, simplifying application processes, or increasing support for businesses that
face additional barriers to recovery. Ensuring comprehensive and effective aid distribution can
help create a more resilient and inclusive economic recovery.
Lastly, the findings of this study can help small business owners choose the most
appropriate assistance programs at different stages to mitigate the risk of closure. By assessing
the effectiveness of restricted and unrestricted public assistance programs throughout different
phases of the COVID-19 pandemic, this research provides valuable insights into which programs
are most effective in helping businesses maintain operations.
1.4 Theoretical Framework
1.4.1 Business Recovery Framework
Chang et al. (2022) present a Business Recovery Framework that provides a
comprehensive way to understand how businesses recover from disruptive events like the
COVID-19 pandemic. This framework takes into account different factors, such as business
features like size and sector, which affect vulnerability, and event characteristics like length and
spatial scale, which determine severity. The effects and recovery processes depend on business
actions, such as getting insurance, and government policies, like financial aid and lockdowns,
which can either slow down or speed up recovery. Recovery mechanisms include internal
factors, such as damage to physical capital and managerial choices, as well as external factors
11
like supply chain interruptions and shifts in household spending habits. These elements generate
complex and continuous recovery outcomes for businesses (Chang et al., 2022).
On the other hand, neoclassic economics advocates that businesses can return to the pre-
disaster growth path by effectively reallocating resources (Okuyama & Rose, 2019; Radovic
Markovic, 2024). Neoclassic economics suggests that market forces can explain the business's
recovery outcome. According to neoclassic economics theory, perfect competition refers to an
ideal market structure in which resources are effectively allocated through interactions among
numerous buyers and sellers and individuals have limited impact on the whole market (Clifton,
1977).
The effectiveness of public assistance programs is not adequately addressed by both the
Business Recovery framework and neoclassical economic theory, indicating the need for a
modified research framework. This new framework aims to answer the overarching research
question: to what extent do public assistance programs mitigate the likelihood of business
closures throughout the entire disaster period? Additionally, the modified framework will
investigate the impact of various factors—such as restrictions, the format of assistance, and
geographic location—on the effectiveness of public assistance programs in helping small
businesses avoid closure.
1.5 Business Recovery
Disasters not only damage physical buildings but also disrupt business operations.
Business recovery is a critical component of disaster recovery, supporting overall economic
stability, community resilience, and long-term regional recovery (Aghababaei et al., 2020).
Effective business recovery helps maintain employment and economic stability, promoting a
12
quicker and more robust recovery for the affected area (Tierney, 2019). However, there are
multiple definition associate with the business recovery,
1.5.1 Defining Business Recovery?
The concept of business recovery after a disaster lacks a universally accepted definition
because recovery has multiple facets and characteristics. To define business recovery, the
essential first step is to clarify what "recovery" refers to. According to Lindell, (2013), disaster
recovery has three interconnected meanings: a goal, a phase, and a process. The goal is to restore
normal community activities that were disrupted by the disaster, often envisioned as a return to
pre-disaster conditions (Ransikarbum & Mason, 2016). The phase, which starts with stabilizing
disaster conditions, marks the end of emergency response and concludes when the community
resumes its usual routines (Rittinghouse & Ransome, 2006). The process encompasses both
planned activities established before the disaster and improvised actions taken afterward (Chang
& Rose, 2012; Lindell, 2013). This study focuses specifically on how public assistance programs
aid small businesses during the recovery process.
Second, how recovery is characterized is critical to define business recovery as well.
According to Chang (2010) when recovery means a process, it can refer to the situation that: 1)
returning to pre-disaster conditions, 2) achieving a state that could have been reached if the
disaster had not occurred, or 3) attaining a new stable stage. Returning to pre-disaster conditions
means the performance of businesses returns to the pre-disaster level after disruption (Cheng et
al., 2015). Aiming to achieve a hypothetical state that could have been reached if the disaster had
not occurred incorporates a more progressive vision, but such a definition also makes recovery
hard to measure (Hosoya, 2017). Lastly, reaching a new stable stage recognizes that recovery is
13
not just a restoration but an evolution (Freitag et al., 2014). This characterization views recovery
as an adaptive process where businesses reinvent themselves to fit new post-disaster realities.
Table 1 summarizes the conceptual definition proposed by scholars regarding business
recovery. As indicated in Table 1, scholars commonly highlight two elements regarding the
definition of business recovery. First, recovery refers to a process instead of a goal or a phrase.
Second, scholars underscore the importance of restoring the functionality of businesses instead
of performance reaching certain points. Following these two highlighted points, this study
proposed the concept of business recovery.
Table 1. Business Recovery – Definition
Literature Sources Concepts Conceptual Definition
Chang et al. (2022) Business Conceptualizes business recovery as the process by which
Recovery businesses restore their operational, financial, and strategic
capabilities after a disaster.
Dahl Hamer & Business Defines business recovery as a holistic process
Tierney (1998) Recovery encompassing the restoration of both physical assets and
organizational functionality within a disrupted market.
Xiao & Van Zandt Business Views business resilience as a broader capability that
(2012) Resilience includes the ability to anticipate, prepare for, respond to,
and adapt to incremental changes and sudden disruptions.
Hamamatsu & Business Discusses business recovery in terms of long-term
Marshall (2018) Recovery sustainability and resilience, focusing on the capacity of
businesses to adapt to new norms and maintain continuity.
Watson (2021, Business Examines business recovery as the ability of businesses to
2022) Recovery leverage public assistance effectively in order to mitigate
the impacts of disasters and resume normal operations.
Zobel (2014) Business Describes recovery as an adaptive process that involves
Recovery strategic reevaluation and operational adjustments in
response to changing environmental and market
conditions.
This study defines business disaster recovery as the process by which businesses regain
stability and avoid closure after experiencing disruptions due to disasters. As previously noted,
14
defining business recovery requires addressing two key aspects: what recovery refers to and how
it is characterized. This study proposes this definition to address both concerns.
First, recovery can refer to a goal, a phase, and a process that depends on the context
(Lindell, 2013). In the present study, we conceptualize recovery as a process, which is in
alignment with other studies regarding small business post-disaster recovery (Chang et al., 2022;
Dahlhamer & Tierney, 1998; Marshall & Schrank, 2020). Viewing recovery as a process includes
both the planned activities established before a disaster and the improvised actions taken
afterward (Lindell, 2013).
Although there is disagreement about what status can be considered recovery, all
definitions generally emphasize that business functionality has resumed (Chang et al., 2022;
Marshall & Schrank, 2014; Watson, 2021; Xiao & Zandt, 2012b). Functionality, in this context,
relates to the ability of a business to resume operations, generate revenue, and maintain its
workforce. According to Chang and Rose (2012) that point, businesses may not always return to
pre-disaster performance levels, but if functionality reaches a new stable state, it still can be
considered recovery from disasters.
1.5.2 Measuring Business Recovery?
Building on how recovery is characterized, scholars have adopted different measurements
to capture business recovery post-disaster. For instance, Khan and Sayem, (2013) define
recovery as a process that returns businesses to pre-operational levels, assessed by re-
establishment behavior and the speed of business revival. Further, Hosoya (2017) indicates that
the hypothetical state that recovery is understood as achieving a state that would have been
reached if the disaster had not occurred can work as a benchmark to evaluate the success of the
recovery effort.
15
Chang (2010) highlights the drawback of defining recovery that refers to ‘return to “pre-
disaster”’ level and conditions as ‘attaining what is supposed to reach without a disaster
occurring.’ The former definition ignores the complicity of recovery that the ‘pre-disaster’ level
can never be reached. Disasters can cause a significant decrease in the demand side, which
makes businesses’ performance hard to return to the ‘pre-disaster’ level (Josephson & Marshall,
2016). The latter definition requires predicting a hypothetical state, which can introduce errors,
uncertainties, and inconsistencies across scholars (Cheng et al., 2015; Stevenson et al., 2018).
As an alternative, Chang and Rose (2012) propose that recovery refers to reaching a new,
stable stage after a disaster. In this approach, business attributes like customer and employee
retention become indicators of post-disaster adaptation (Aghababaei et al., 2020) Further,
Marshall and Schrank (2014) also suggest that business recovery should be measured over time
and include multiple factors such as business profitability, customer retention, and resource
availability. Moreover, following the definition of reaching a new stable stage, scholars use
subjective indicators to capture business recovery as well. For instance, Quarantelli (1999)
advocates for a subjective measure: when a business perceives itself as recovered, it is
considered recovered.
1.6 Methodology
1.6.1 Research Site: Dallas-Fort Worth-Arlington Metropolitan Area and the COVID-19
Pandemic
This study was conducted in the Dallas-Fort Worth-Arlington (DFW) Metropolitan Area.
The DFW metropolitan area consists of two separate metropolitan statistical divisions: the
Dallas-Plano-Irving metropolitan division and the Fort Worth-Arlington-Grapevine metropolitan
division. The DFW Metropolitan Area includes 11 counties: Collin County, Dallas County,
16
Denton County, Ellis County, Hunt County, Kaufman County, Rockwall County, Johnson
County, Parker County, Tarrant County, and Wise County.
The DFW Metropolitan Area and COVID-19 is an appropriate research site for this study
because of two reasons. First, according to Moody’s Analytics (2019) that the DFW Metropolitan
Area has the second most diverse economic structure among metropolitan areas in the US. The
diverse economic structure provides a sufficient sample size for this study. Second, the DFW
Metropolitan Area is the first metropolitan area that returns job levels to the pre-pandemic level
(Maddox, 2022), which provides a great chance to study the factors contributing to this outcome.
The U.S. economy suffered greatly from the COVID-19 pandemic, social distance
restrictions following the pandemic forced many businesses to shut down and caused GDP losses
of $3.2-$4.8 trillion in two years. Many workers lost their jobs or hours, while health costs and
fears added more pressure. Small businesses saw lower revenues and higher bankruptcies, with
some new businesses forming. According to the statistical report from Yelp (2020) that 163,735
U.S. businesses closed, 60% for permanent, mostly in restaurants and retail. The economy may
lose $14 trillion by 2023, with big effects on air travel, dining, and health services. Government
actions helped some businesses, but the long-term economic damage will last, showing the need
for more resilience and recovery support.
1.6.2 Available Programs: Paycheck Protection Program and Economic Injury Disaster Loan
The Paycheck Protection Program (PPP) and Economic Injury Disaster Loan (EIDL) are
the two main assistance programs administrated by the Small Business Administration (SBA)
during the COVID-19 pandemic. Both programs are available in the format of loans and cash.
Compared with EIDL, PPP loan recipients could use loans to pay for payroll costs, mortgage
17
interest, rent, and utilities. PPP loan recipients are eligible for forgiveness if they spend more
than 60% of the funds on eligible payroll costs.
Compared with PPP, EIDL loan has fewer restrictions on the usage of assistance. EIDL
advances were direct funds that did not require repayment but were in smaller amounts. The PPP
gave $650 billion of aid from April 3, 2020, in two rounds of funding to over 5 million firms until
it stopped on August 8, 2020. The SBA closed EIDL loans or advance applications as of January
2022. EIDL loans of $200 billion went to 3.6 million firms, and EIDL advances of $20 billion went
to 5.8 million firms.
1.6.3 Measurement of Business Recovery
This study operationalizes whether a business is considered active or inactive as an
indicator of a business's recovery outcome. Whether businesses still maintain operations is an
indicator that is well-adopted by scholars in the business recovery field to measure business
recovery outcomes (Aghababaei et al., 2020; Headd, 2003; Stevenson et al., 2018).
The source of the information about the activity status of businesses is Mergent Intellect.
Mergent Intellect offers a searchable database with comprehensive information on over 280
million businesses, covering both active and inactive firms in a chosen region. Mergent Intellect
uses the presence or absence of trading activities to identify if a business is inactive (Leaming,
2016). A business that is inactive, according to Mergent Intellect, is no longer trading or
operating. A business that is active, according to Mergent Intellect, until January 2023 is seen as
recovered by this study; otherwise, it is seen as not recovered.
1.6.4 Data Sources
This dissertation relies on three sources of data. First, this study acquired the list of PPP
and EIDL recipients from SBA websites. Second, Mergent Intellect, a searchable database of
18
business information, provides data about businesses in the DFW region, such as business name,
address, sales volume, employee size, sector, and active status. Third, data from the Census
Bureau are used to capture market context, such as minority population, median household
income, and the number of housing units.
1.6.5 Analysis Methods
This dissertation employs three analytical methods to examine different facets of business
recovery during the COVID-19 pandemic: (1) logistic regression, (2) Cox regression with time-
varying effect, and (3) spatial autocorrelation. The three methods address three dimensions of the
overall research question. The logistic regression evaluates whether public assistance programs
help small businesses avoid closure throughout the pandemic period. The Cox regression with
time-varying effect further explores the impact of public assistance programs at different phases
of the pandemic. Finally, the spatial autocorrelation assesses the effectiveness of public
assistance programs from a geographic perspective.
1.7 Structure of Dissertation
The structure of this dissertation is as follows: Chapter 2 examines the factors that
contribute to business recovery by reviewing relevant literature. It also discusses both internal
and external factors that affect the effectiveness of business recovery. The study of business
recovery and the effectiveness of public assistance programs, especially in relation to COVID-
19, is reviewed. The conditions attached to public assistance programs, the types of public
assistance programs, and the geographic location related to business recovery are explored and
identified.
In Chapter 3, we use the Business Recovery framework and neoclassic economic theory
is discussed to examine the factors that influence the business recovery outcome after a disaster.
19
We also modify the research framework to address its limitation of not identifying the
mechanisms through which public assistance programs aid small businesses in recovering from
disasters. In the modified framework, we consider how the restrictions, format, and geographic
locations of the public assistance program affect its effectiveness, as well as how the business
characteristics and market context impact the business recovery process.
This dissertation's data and methods are described in Chapter 4. The data sources consist
of Small Businesses Administration assistance recipient data, Mergent Intellect businesses
demographic data, and Census Bureau demographic data. Four dataset are merged based on their
geographic location and businesses names. The final dataset contains the information about
business characteristic, access to public assistance programs, market context, and activity. To
answer the research question, do public assistance programs mitigate the likelihood of business
closure after disasters, logistical regression, cox-regression with time-varying effects, and spatial
autocorrelation are performed to investigate the effectiveness of public assistance in small
business recovery.
Chapter 5 provides a descriptive analysis of the data that this dissertation uses. It also
reports the results from logistic regression, Cox regression with time-varying effects, and spatial
autocorrelation in Chapter 5. The results suggest that when businesses have access to a single
type of public assistance, it positively contributes to the recovery process. However, when
businesses access multiple types of assistance simultaneously, the effort can be impeded. Further
the result of spatial autocorrelation suggests that businesses in rural areas are easier to recover
even with relative low access rate to public assistance programs.
20
Chapter 6 reviews the main findings and the implications that the results and discussions
suggest. It also covers the limitations and future research directions in Chapter 6.
21
2 CHAPTER 2.
BUSINESS RECOVERY AND PUBLIC ASSISTANCE PROGRAMS
2.1 Introduction
This chapter investigates factors that explain business recovery after diesters and how
public assistance programs help business recovery. In addition, this chapter reviewed available
public assistance programs for small business owners during the COVID-19 pandemic and
research statutes. This is done based on the literature on business recovery and empirical studies
regarding the effectiveness of public assistance programs. Business recovery is defined as “a
process by which return to a new stable situation” (Chang & Rose, 2012), which is a complicated
process that mixed impacts from business characteristics, government intervention, and
marketing context. Regarding public assistance programs, restrictions attached to assistance
programs, the format of assistance, and geographic locations can influence the efforts of those
programs.
Business characteristics refer to features inherent in business, such as size, sector, and
ownership. Business characteristics are often associated with how much damage businesses
experience from disasters by determining the business's ability to absorb shocks from disasters
(M. A. U. Khan & Sayem, 2013; Marshall et al., 2015; Morrish & Jones, 2020). In this process,
public assistance programs aid business recovery by providing funding to help small businesses
restore operations (Josephson & Marshall, 2016). However, market context also influences
business recovery by determining profitability (Morrish & Jones, 2020).
Restrictions and loan-based assistance are necessary to ensure assistance recipients
behave following certain ways that promote economic activities instead of misusing funding
(Chrisman & McMullan, 1996; Gu et al., 2008). However, irrational restrictions can limit
22
recipients’ self-adaptation measures (Graham, 2007). Loan-based assistance can increase the debt
load of recipients (Dahlhamer & Tierney, 1998).
Additionally, public assistance programs may have different impacts depending on
geographic locations, because some areas may have more favorable conditions for businesses to
access the programs, such as well-developed industrial chain (Andersson & Larsson, 2016; Crass
et al., 2016). However, these advantages in accessing public assistance programs can undermine
the principle of equality because, after a disaster, areas that suffer more damage require more
help to resume economic activities (Domingue & Emrich, 2019a).
This chapter has the following structure: First, it reviews the factors that affect business
recovery and the mechanisms behind them; Second, it explains why public assistance programs
have restrictions and offer loans or cash and how these factors and geographic locations
influence the effectiveness of public assistance programs. The available public assistance
programs for small businesses during the COVID-19 and research statutes are also discussed.
2.2 Business Recovery
Business recovery is defined as “the process by which businesses return to conditions of
stability (Chang & Rose, 2012)”. This definition encompasses two key dimensions: first, the
restoration of a business's operational capability, and second, the achievement of a stable
performance level. Instead of strictly defining recovery as reaching a specific point, this
definition offers a more dynamic definition.
Following this definition, scholars employ objective and subjective indicators to measure
business recovery. Subjective indicators in business recovery after disasters focus on perceptions
and experiences. For example, Dwyer & Horney (2014) measure recovery through qualitative
data from disaster recovery experts and researchers, emphasizing the importance of adaptability,
23
comprehensiveness, and self-evaluation. Bronson et al., (2006) measure business owners'
optimism in recovery to understand the influence following disasters. These subjective measures
offer valuable insights into the personal and communal aspects of recovery, capturing the
subtleties of individual and collective experiences.
Objective indicators use measurable and verifiable data to evaluate business recovery.
Horney et al., (2017) created and tested recovery indicators based on empirical data, covering
metrics such as economic recovery and infrastructure restoration. Chang (2010) proposed a
framework for measuring urban disaster recovery using statistical indicators, recording objective
indicators such as population change and socioeconomic indicators. Aghababaei et al., (2020)
applied objective indicators like business stop operation days, revenue recovery, customer
retention, and employee retention to quantify and forecast business recovery through Bayesian
linear regression. These objective measures offer a solid and systematic method to understand
the scope and pace of recovery efforts.
2.2.1 Factors Studied in the Business Recovery
Recovering from disasters is a complicated process for businesses, as it involves
resuming operations, ensuring financial viability, and reconstructing infrastructure. Disasters can
result in serious physical harm, interrupted supply chains, and large financial losses, making
recovery a difficult and multi-dimensional endeavor (Altay & Ramirez, 2010; Khan & Sayem,
2013; Sydnor et al., 2017). Recovery depends on a mix of government assistance, such as grants
and low-interest loans, business resilience, and market context (Hiramatsu & Marshall, 2017;
Watson, 2021). The degree of damage, access to financial resources, and quality of social and
economic networks all influence how fast and well businesses can bounce back from the effects
of disasters.
24
Public assistance programs promote business recovery by providing liquidity for business
owners to reduce financial stress and restore business operation capability (Josephson &
Marshall, 2016). Disasters can cause physical damage; businesses with physical damage need to
purchase new equipment to restore operation (Sydnor et al., 2017). Even though in the case of
COVID-19, some businesses still need to purchase new equipment to reduce contact for
employee safety. Empirical studies suggest that public assistance programs play a significant
role in helping businesses recover after disasters (Berger et al., 2021; Marshall & Schrank, 2020;
Watson, 2021). However, the effectiveness of public assistance programs is influenced by the
rationality attached to and format of these programs, which will be discussed in the following
part of this chapter (Dahlhamer & Tierney, 1998; Xiao & Zandt, 2012a).
Business characteristics, such as sector and size, determine the extent of impact caused
by disasters (Altay & Ramirez, 2010). Businesses' reliance on infrastructure varies in restoring
functionality, depending on their sector. Businesses operating in sectors that rely on
infrastructure not impacted by disasters will recover faster than others. Further, businesses with
more employee size have more market experience and resources, which help them cope with the
negative effects of crises. They also have stronger networks and savings, which let them restart
their operations faster than smaller or newer businesses (Webb et al., 2002). For instance,
according to Khan & Sayem (2013) retail and larger firms with over four employees have an
edge in bouncing back from disasters. Further, businesses with a larger number of employees
typically receive preferential treatment when applying for public assistance, as distributive
agencies also take unemployment rates into consideration (Hartwell et al., 1996). Small
businesses provide a large proportion of job opportunities, and an increase in unemployment can
lead to other social problems (Ayyagari et al., 2011; Paul & Moser, 2009). Distribution agencies,
25
typically public organizations, are concerned with the unemployment rate due to its impact on
broader social well-being and economic stability (Luechinger et al., 2012).
Market context can influence businesses’ capability to maintain profitability after a
disaster, which is essential for business recovery (Aghababaei et al., 2020). Disasters can lead to
a short-term demand increase in certain sectors, such as the construction industry, but massive
relocation following disasters and economic loss from survivors challenge business profitability
(Corey & Deitch, 2011; Kousky, 2014; Sampson et al., 2018). Empirical studies suggest that
businesses that can rapidly restore their customer base are more likely to survive and prosper
after a disaster (Marshall & Schrank, 2014). Further, businesses with access to customers with
higher consumption power are more likely to recover (Xiao et al., 2022). In this case, appropriate
marketing and communication strategies adopted by business owners can help preserve customer
groups and even bring in new customers during the recovery period (Morrish & Jones, 2020).
Socioeconomic factors often cluster geographically due to localized influences, resource
disparities, and the spatial concentration of social and economic conditions. Clustered
socioeconomic factors can influence the likelihood of local businesses accessing public
assistance programs. According to Gu et., (2008) that small assistance programs often target
businesses in certain areas with specific economic needs or development goals, which can
influence the allocation of public resources. In the post-disaster context, individual business
recovery and regional economic recovery are often associated (Rosenfeld, 1997). For example,
businesses in related sectors often cluster together, forming a robust industrial chain. Allocating
public assistance to these areas can more effectively promote recovery, benefiting not only small
businesses but also the regional economy (Albors-Garrigos & Collado, 2019; Skokan et al.,
2012).
26
2.2.2 Significance of Small Business Post-Disaster Recovery
Researching in small business post-disaster recovery have significant values for
economic recovery and regional recovery by creating new job positions and facilizing
community recovery through local networks (Saliola & Bernt, 2012; Torres et al., 2018).
Although small businesses—typically defined as those with fewer than 500 employees or
less than $2 million in sales—are not economically comparable to large corporations, they remain
a significant source of jobs in the United States (Spremo & Mićić, 2016). According to Bureau of
Labor Statistics, (2024) that 55% of total net job creation are contributed by small business
between 2013 to 2023. Compare with big firms, small business have advantages in flexibility,
which allows them easier to adjust operation strategy and create more jobs (Saliola & Bernt, 2012).
Increased job positions are key to post-disaster recovery because it let local residents maintain
purchase power have capability to participants in economic transaction process, which can lead
broader economic recovery (Greenbaum & Petras, 2009).
Secondly, differ than large business, small business can work as a social unit in local
community instead of a simply economic unit (Xiao et al., 2018). In communities with a limited
number of residents, small businesses often serve as gathering spaces, while business owners act
as community anchors, fostering strong local networks. These connections play a crucial role in
post-disaster recovery and enhancing community resilience. (Aldrich, 2012; Dua et al., 2020).
However, small business recovery faces challenges such as supply chain disruptions, labor
shortages, and a reduced customer base. To overcome these obstacles and resume operations, small
business owners must seek financial assistance (Morrish & Jones, 2020). Studies indicate that
small business owners often face challenges in accessing assistance from traditional lenders due
to their financial condition and the unpredictable market conditions in a post-disaster situation
27
(Graham, 2007). In such cases, public assistance programs become a critical source of support,
enabling small business owners to resume operations and facilitate economic recovery.
2.3 Public Assistance Programs in Post-Disaster Recovery
Public assistance programs are critical for post-disaster recovery by minimizing
disruptions, enhancing community resilience, and ensuring equitable recovery efforts
(Davlasheridze & Miao, 2021; Domingue & Emrich, 2019a; Kamel & Loukaitou-Sideris, 2004;
Olshansky & Johnson, 2014). These programs minimize disruptions after disasters by providing
rapid and organized efforts (Olshansky & Johnson, 2014). According to Davlasheridze & Miao
(2021) disaster aid programs in the form of low-interest loans and investments in infrastructure
can significantly improve community resilience to future risks. Vulnerable groups, compared to
wealthier groups, have limited choices for seeking help to promote recovery other than public
assistance programs (Vickery, 2018). Distributing public assistance to needy groups promotes
equitable recovery.
Inappropriate public assistance programs not only fail to promote recovery but also
increase long-term financial risks for governments. For instance, increased grants from FEMA’s
Public Assistance programs can lower households’ preference for purchasing flood insurance
because it gives home owners a signal that government will cover the damage cause by disasters
and increasing federal financial exposure to natural disasters and climate change (Garrett &
Sobel, 2003).
2.3.1 Why We Need Restrictions?
Restrictions often attached to public assistance programs due to concerns about
effectiveness (Gu et al., 2008; Xu & Mo, 2013). To ensure that public assistance programs help
small businesses recover, recipients are expected to use the assistance for activities related to
28
business operations, which promote the recovery process. Moreover, free-riding is a major
problem with public goods; businesses that do not need help can still access and benefit from
unrestricted public assistance programs (Chrisman & McMullan, 1996; Reddy, 2000).
Restrictions are necessary to ensure that assistance is not misused by recipients (Gu et al., 2008).
Restrictions attached to public assistance programs for small businesses come with
eligibility criteria and performance-based criteria (Gu et al., 2008). Eligibility criteria determine
whether a business is eligible to receive assistance based on size, type, and geographic location.
For instance, some assistance programs require businesses to reopen at the same location before
the disaster to receive funding (Graham, 2007). Performance-based criteria require businesses to
meet certain standards to continue receiving assistance. For example, programs like the Paycheck
Protection Program require businesses to spend a certain amount of received loans to continue
qualifying for forgiveness (Barbieri, 2021).
The rationality of restrictions can influence business self-adaptation, which further
impacts the effectiveness of public assistance programs (Chang et al., 2022; Graham, 2007). A
decrease in demand after disasters requires businesses to adopt self-adaptation strategies to
survive. However, restrictions that require businesses to maintain employee size can limit their
ability to survive by laying off employees (Bartik, Cullen, et al., 2020).
Compared to restricted public assistance programs, unrestricted assistance lacks stringent
requirements or qualifications on a verification process, allowing applicants to receive funds
more quickly (Olshansky & Johnson, 2014). Early access to assistance enables businesses to
resume operations sooner, which is vital for local recovery because business provides essential
goods and service to local communities (Xiao et al., 2022). Furthermore, the reopening of
businesses provides residents with a sense of normalcy and restoration, which is essential for the
29
broader community recovery process. This swift re-establishment of local businesses not only
supports economic resilience but also fosters community morale and a quicker path to recovery
(Watson, 2022).
2.3.2 Why There is Loan and Cash?
Public assistance programs for small businesses are typically available in loan-based and
cash-based formats. Loan-based assistance is typically distributed as long-term, low-interest
loans. The loan-based format plays a significant role in helping businesses recover by providing
liquidity to small businesses and maintaining fiscal responsibility at the same time (Kelly et al.,
1998). The purpose of the public assistance program is to support local economic recovery by
giving financial liquidity to small businesses to resume their operations. Assistance based on
loans can motivate recipients to maintain their business activities instead of just spending the
money by demanding repayment. Empirical studies suggest that loan-based assistance programs
can promote the creation and expansion of small businesses, which further promotes the recovery
process (Bach, 2014; Gu et al., 2008). However, loan-based assistance programs face criticism
for increasing the debt load of businesses, which can impede recovery, especially when
businesses face a decreased demand market after disasters (Dahlhamer & Tierney, 1998;
Sampson et al., 2018).
Compared with loan-based assistance, cash-based assistance with less restrictions is a
more appropriate format for small businesses experiencing significant impacts from disasters
(Chang et al., 2022). Small businesses that experience significant damage cannot have repayment
capability compared with others. Cash-based assistance can immediately reduce financial stress
and promote local economic recovery by allowing those business owners to start money to
restore their businesses (Gallagher et al., 2020; Gazi, 2018).
30
Cash-based assistance programs are often criticized for the potential misuse and long-
term insufficiency (Marshall & Schrank, 2020; Watson, 2021). The purpose of cash-based
assistance is to provide flexibility for businesses with unique needs to restart and participate in
local economic activities, promoting recovery. However, post-disaster business operations can be
risky due to reduced demand, which may lead business owners to spend received funds on non-
recovery-related expenses (Doocy et al., 2008). Compared with loan-based assistance, cash-
based assistance requires oversight measures to ensure assistance is not misused. Otherwise, it
can damage the recovery efforts (Watson, 2021).
Although cash grants provide immediate relief, it is expensive for governments and may
not be sufficient for long-term recovery (Josephson & Marshall, 2014). Unlike loan-based
assistance, cash-based assistance does not require repayment from recipients, making it costly for
the government. The high cost associated with cash-based assistance means that each business
owner receives only a limited amount of funding, which may be sufficient for restarting but not
for sustaining long-term adjustments (Roel A. Serrano, 2022).
2.3.3 Significance of Public Assistance Program in Post-Disaster Recovery
In post disaster stage, only a portion of the population can access to public assistance
programs because traditional targeting approach is not appropriate for distributing public
assistance programs in the context of post-disaster recovery. The targeting approach, well
adopted by public assistance programs in distribution, is a method that is designed to identify and
provide support specifically to individuals or groups who are mostly in need (Bartik, Cullen, et
al., 2020; Devereux, 2016). But applying such a method in post-disaster scenarios faces
challenges in cost and efficiency (Fraser, 2004; Henman, 2004). The targeting approach itself is a
31
resource and time-consuming if distribution agencies want to reach the expected needy group,
which conflicts with the urgency of the post-disaster scenario (Henman, 2004).
On the other hand, in a post-disaster scenario, an increasing number of individuals
become the needy group, and implementation of the target approach can overwhelm the
capability of distribution agencies (Jaspars & Shoham, 1999). Thus, to promote the recovery
process at the post-disaster stage through redistributive policy, policy designers and
implementing agencies need to be cautious in designing access policies and restrictions
associated with assistance.
Broader assistance or universal assistance after disasters, opposite to targeting approach,
distributed to affected business cannot lead an effective recovery as well (Yi & Tu, 2018). First,
universal assistance to small businesses often associated with insufficient funds to meet the
needs of impacted businesses, especially small ones lack of alternative funding potion, which can
lead slower recovery process (Watson, 2022). Secondly, the universal distribution of assistance
can exacerbate procedural inequities in public assistance programs, limiting vulnerable
businesses’ accessibility to public assistance programs and creating inefficiencies that leave gaps
in recovery outcomes (Domingue & Emrich, 2019b).
Even portion of population can access to public assistance, still can lead broader
economic recovery by economic multiplier effect and stabilizing key business (Gallagher et al.,
2020; Haynes et al., 2018). First, when financial aid given to specific sectors, like construction or
small businesses, generates a ripple effects. In the post disaster context, demand of
reconstruction can experience increase, their initial spending can create jobs, boosts local
spending, and stimulates related sectors, which can benefit the broader economic recovery
(Andrew et al., 2022; Janvry et al., 2016; Sina et al., 2019).
32
Second, small businesses are not simply economic units but also play an important social
roles in community function. The distributed assistance to businesses helps maintain local
employment and supply chain making a community more cohesive (Gallagher et al., 2020; Xiao
et al., 2018). Locally owned small businesses are often closely connected to their communities,
frequently serving additional roles, such as providing spaces for community gatherings. These
businesses are vital for fostering community cohesion, which becomes especially important in
the post-disaster stage. The assistance contributes to the recovery process (Bonanno & Goetz,
2012; Townshend et al., 2015). For instance, Xiao et al., (2018) use business surveys conducted
in New York 2012 after Hurricane Sandy to explore the recovery pattern of local small business.
The result suggests that even local small business have dual characteristic, economic and
community cohesion, in local community recovery.
Although public assistance programs may not reach all businesses affected by disasters,
they can still play a positive role in recovery by generating economic multiplier effects and
stabilizing key businesses within the local community.
2.4 Public Assistance Programs for Small Business During the COVID-19
2.4.1 Available Programs
The Paycheck Protection Program (PPP) and the Economic Injury Disaster Loan Program
(EIDL) are two main programs that small businesses could apply for during the COVID-19
pandemic. The PPP, administered by the Small Business Administration (SBA), was the largest
public assistance program for small businesses during COVID-19. The PPP aimed to help small
business owners keep their businesses running and retain employees on the payroll. PPP loans
could be used to pay for payroll costs, mortgage interest, rent, and utilities. Small business
owners could apply for the PPP through local lenders such as banks, credit unions, and other
33
financial institutions. The PPP loan amount was typically based on 2.5 months of average payroll
costs. The SBA also offered forgiveness if recipients used more than 60% of the funds for
eligible payroll costs. The PPP started providing assistance on April 3, 2020, with a total of $650
billion. After two rounds of distribution, more than 5 million firms received support from the
PPP until August 8, 2020.
The EIDL is another public assistance program run by the SBA. The EIDL aimed to
provide either loans or advances for businesses that lost revenue due to COVID-19. The EIDL
loan could be used to cover up to 6 months of essential expenses for maintaining operations.
EIDL advances were direct funds that did not require repayment but were in smaller amounts.
Small business owners could apply directly to the SBA for an EIDL loan or advance. The EIDL
started providing assistance in March 2020. As of January 2022, the SBA stopped taking new
applications for EIDL loans or advances. A total of 3.6 million firms were approved for EIDL
loans with a total of $200 billion, and 5.8 million firms were approved for EIDL advances with a
total of $20 billion.
The PPP has certain performance requirements for recipients to continue receiving
forgiveness, unlike the EIDL. This study classifies the PPP as a restricted public assistance
program and the EIDL as an unrestricted public assistance program. Specifically, the PPP loan is
considered restricted loan-based assistance, PPP forgiveness is considered restricted cash
assistance, the EIDL loan is considered unrestricted loan-based assistance, and the EIDL advance
is considered unrestricted cash assistance. Table 2 summarizes the classification of assistance
programs.
34
Table 2. Classification of Public Assistance Programs
Restricted Assistance Unredirected Assistance
Loan-Based Assistance PPP loan EIDL loan
Cash-Based Assistance PPP forgiveness EIDL advance
2.4.2 Research Statues
In the context of the COVID-19 pandemic, studies found that these assistance programs
played a positive role in financial stability for small businesses, business continuity, and job
preservation (Dore & Mach, 2022; Fairlie & Fossen, 2021; Katare et al., 2021; McCall et al.,
2022; Sanchez-Moyano, 2022). Under the impact of social distancing restrictions, small
businesses faced a decreased market from the demand side. Programs like PPP and EIDL
provided critical financial support to small businesses to maintain financial stability (McCall et
al., 2022; Sanchez-Moyano, 2022). This financial support was crucial for businesses with cash
flow problems to avoid closure (Katare et al., 2021). Additionally, funds provided through
restricted public assistance programs like PPP helped small business owners cover payroll costs,
preventing mass unemployment in vulnerable sectors (Fairlie & Fossen, 2021; McCall et al.,
2022).
The implementation of assistance programs faces criticism regarding inequitable
distribution and short-term focus (Granja et al., 2020; Katare et al., 2021; M. Li, 2021; McCall et
al., 2022). Similar to other public assistance programs after disasters, programs like PPP and
EIDL face criticism that these assistance do not reach needy groups (Fairlie & Fossen, 2021; Ko
et al., 2022). Implementing agencies tend to distribute funding to businesses that show better
repayment capability because of accountability concerns instead of businesses that need the
assistance most (M. Li, 2021; Watson, 2022). While PPP and EIDL provide necessary financial
35
liquidity for businesses, they cannot ensure long-term stability (Berger et al., 2021). PPP and
EIDL were primarily temporary measures, and businesses still face a decreased demand market,
which can impede the effectiveness of these programs (Granja et al., 2020).
36
3 CHAPTER 3.
BUSINESS DISASTER RECOVERY FRAMEWORK
3.1 Introduction
In this chapter, the theoretical framework is developed by exploring the business disaster
recovery framework proposed by Chang et al., (2022) and neoclassic economic theory. Both
frameworks include factors that explain the business recovery outcome, but the modified
farmwork is necessary to understand how public assistance programs help small businesses
recover from disasters. The modified framework explored the impact of public assistance
programs, business characteristics, market context, and geographic factors.
3.2 Businesses Recovery Framework
This Business Recovery Framework proposed by Chang et al. (2022) provides a
comprehensive structure to consider how businesses recover from disruptive events like the
COVID-19 pandemic. Figure 1 synthesizes the factors, moderators, and mechanisms influencing
business recovery outcomes after disruptive events. Factors include business characteristics, such
as size and sector, which affect vulnerability, and event attributes, like duration and spatial
extent, which indicate severity. These impacts and recovery processes are moderated by business
actions, such as obtaining insurance and government policies, including financial assistance and
lockdowns, which can either hinder or help recovery. Mechanisms affecting recovery involve
internal processes, like damage to physical capital and managerial decisions, as well as external
factors, such as supply chain disruptions and changes in household consumption patterns.
Together, these elements create nuanced and ongoing recovery outcomes for businesses (Chang
et al., 2022).
37
Figure 1. Business Recovery Framework (source: Chang et al. [2022])
Business characteristics can significantly influence a business's initial pathway of
recovery. Businesses in certain sectors have a harder recovery than others (Altay & Ramirez,
2010). For instance, businesses that rely on infrastructure to maintain operations can lose their
ability to function if a disaster damages that infrastructure (Ding, 2024). Further, business size
and ownership (e.g: woman- or minority- owned business) often associate with limit profitability,
which limit their access to finance assistance and final recovery outcome (Andrew et al., 2022;
Chang et al., 2022).
Business actions do not always accelerate the business recovery process. A business
owner’s decision can significantly influence this process. For example, businesses that have
preparedness measures, such as purchasing insurance, are more likely to survive after disasters
(Han & Nigg, 2011). Further, successful adaptation strategies made by businesses owners can
help business to restore profitability rapider than business without a rational adaptation strategies
38
(Y. G. Lee et al., 2017). On the other hand, irrational public assistance programs can impede
businesses recovery by increasing business debt load and limit their adaptation behavior (Chang
et al., 2022; Dahlhamer & Tierney, 1998; Xiao & Zandt, 2012b).
Market disruption can still impede the recovery process. Massive relocations following
disasters not only impact business productivity due to labor shortages but also lead to decreased
demand. Furthermore, after disasters, people's purchasing behavior tends to become conservative
because of economic losses, potentially resulting in permanent changes in consumption patterns.
This forces businesses to face a diminished market demand.
However, there are still weaknesses in the research framework proposed by Chang et al.,
(2020). Firstly, the research framework does not capture the dynamic features of the recovery
process (Marshall & Schrank, 2014; Zobel, 2014). The recovery process is not a linear process,
the economic environment may change in response to the impact of a disaster, a process that
takes time to become apparent (Zobel, 2014). For instance, in the context of COVID-19, as the
pandemic spread globally, the initial challenges evolved to include significant disruptions in the
international supply chain (Ardekani et al., 2023).
Although the framework listed public assistance programs as a contributing factor, the
discussion is insufficient considering the complexity of public assistance programs (Kapucu,
2008). To prevent the free rider problem, public assistance programs are normally associated
with restrictions. As illustrated above, the rationality of the assistance restrictions significantly
impacts whether it has a positive or negative effect. However, how to evaluate the rationality of
funding restrictions is rarely discussed by authors in the framework. On the other hand, the
framework also does not fully explain whether loan-based assistance increases the debt load of
39
businesses or supports businesses in maintaining operations at some level to recovery (Marshall
& Schrank, 2014).
3.3 Neoclassic Economic Theory
Neoclassical economics explains business disaster recovery by focusing on market
transactions and effective resource allocation (Okuyama & Rose, 2019). Scholars suggest that
businesses can return to the pre-disaster growth path by reallocating employees and assets
effectively (Okuyama & Rose, 2019; Radovic Markovic, 2024). Neoclassical economics argue
that business closure is the effort of market forces. The concept of perfect competition,
introduced by neoclassical economics, describes an ideal market structure in which resources are
effectively allocated through interactions among numerous buyers and sellers, and the individual
has a limited impact on the whole market (Clifton, 1977). This expected market framework has
three strict assumptions. First, buyers and sellers have perfect knowledge. Second, there are no
barriers to entry or exit the market. And lastly, homogeneous demands for products (Hunt &
Morgan, 1995). Under this perfect competition mechanism, governments are unnecessary to be
involved because the market can adjust itself.
Assumptions of neoclassical economics also lead to critiques. (Keen & Standish, 2006)
argue that the perfect competition suggested by neoclassical economics cannot capture the real-
world complexities. Further, asymmetric information significantly challenges the assumption of
perfect information among buyers and sellers, which also leads to problems like adverse
selection (Chiappori & Salanié, 2000). Besides, empirical studies suggest the diversity of
consumer preferences, which is against the assumption of homogeneous demands (Allenby et al.,
1998). Lastly, individuals’ bounded rationality further challenges the neoclassical model that all
agents are fully rational (Camerer, 1998).
40
3.4 Modified Research Framework
The limitations of both neoclassical economic and business recovery frameworks
proposed by Chang et. al (2022) indicate that a modified framework is needed to explore
business disaster recovery outcomes. The modified framework provides the factors that impact
small businesses' recovery outcome, which include, business characteristics (Andrew et al.,
2022; Han & Nigg, 2011; Marshall et al., 2015), the effectiveness of public assistance
(Dahlhamer & Tierney, 1998), and market context (Asgary et al., 2012; Brown et al., 2015).
Figure 2 summarizes the modified theoretical framework.
The modified business recovery framework is developed based on the Business Recovery
Framework proposed by Chang et al. (2022). Although Chang et al. (2022) included public
assistance programs as one of the moderators in the framework, the factors that impact the
effectiveness of these programs are still unclear. The modified framework suggests that
government actions and public assistance programs help businesses recover by providing funds
to restore their functionality. However, the effectiveness of public assistance programs is
influenced by the restrictions attached, the format of funding, and geographic locations. Business
characteristics, such as size, ownership, and sector, influence recovery by determining a
business’s ability to absorb shocks from disasters. Lastly, market context, such as the availability
of a consumer group, can impact business profitability during the recovery process.
41
Figure 2. Modified Businesses Recovery Framework
3.4.1 Public Assistance Programs
Public assistance programs does not reimburse all the losses do citizens experienced, the
principal of public assistance program is to meet basic need and help citizens get back through
their effort, which decided the public assistance programs can not be universal (Kosar et al.,
2005; Yi & Tu, 2018). Public assistance programs can play significant role in helping small
business recovery after disasters in both short-term relief and long-term replacement through
different strategy. From short-term recovery perceptive, public assistance programs helps small
business recovery by overcome cash flow disruptions and improve adaptive capacity (Carton et
al., 2006; Haynes et al., 2018; Josephson & Marshall, 2014). From long-term recovery
perceptive, public assistance programs can help small business recovery by enhancing
community resilience, promoting infrastructure development, and retaining employees (Graham,
2007; Olshansky & Johnson, 2014; Xiao et al., 2018).
42
Compared to large businesses, small businesses are more prone to cash flow issues due to
limited financial management knowledge, lack of resources, and reliance on external credit
(Cheatham et al., 1989; Isle & Freudenberg, 2015). Revenue losses caused by disasters can
exacerbate the likelihood of cash flow problems (Davlasheridze & Geylani, 2017). Public
assistance programs are effective in the immediate recovery stage by providing financial
assistance to affected businesses and allows small bossiness to resume operations rapidly (Brooy,
2009). For instance, according to Maisel and Roman (2013) small bossiness in the state of
Florida that lock of immediate funds after natural disasters often prevents businesses from
reopening.
Disasters can alter market conditions through mass relocation and shifts in consumption
patterns, which require small business adopt rapidly to survive (Jiang et al., 2016; M. Wagner et
al., 2014). Increasing adaptive capacity often associated with extra investments, such as new
equipment and new employees (Stafford et al., 2013). Public assistance programs can support
this by providing crucial financial stability and allow businesses to develop robust adaptive
strategies. For instance, the Economic Injury Disaster Loan (EIDL), administered by the SBA for
businesses affected by disasters, provides assistance to business needing to adapt to changing
market conditions for post-disaster profitability. Multiple studies show that this support has a
significant effect on business continuity after disasters (Davlasheridze & Geylani, 2017; Fairlie
& Fossen, 2021; Hiramatsu & Marshall, 2017; Watson, 2021).
Public assistance program can support business recovery by stabilizing the community’s
economic and social environment, which is critical for business’ long-term recovery (Olshansky
& Johnson, 2014). A stable economic and community environment can offer small businesses a
consistent customer base and reliable consumption capacity, which allows small business to keep
43
a stable income (Victoria & Victoria, 2018). Other than assistance in the format of fundings,
public assistance programs for community resilience available in the format of partnerships
between university and state government as well. According to G. P. Smith and Nguyen (2021)
Hurricane Matthew Disaster Recovery and Resilience Initiative (HMDRRI) is a disaster recovery
program lead by North Carolina Division of Emergency Management and local universities to
deliver research, teaching, and engagement actives related to disaster recovery to local
communities. HMDRRI was able to offer technical support to rural communities and lead
broader recovery in local areas.
Public assistance programs also assists in rebuilding community-wide infrastructure,
which can lead broader economic recovery and indirectly benefits small businesses’ recovery
(Olshansky & Johnson, 2014). After disasters, the demand of reconstruction can experience
dramatic increase because essential public service need to be restored (Andrew et al., 2022). The
rebuilding process is often associated with government contract opportunities, which can aid
small businesses in disaster recovery by providing a stable income (Atkinson & Sapat, 2013).
Businesses in the construction sector have cross-sectoral links to over 31 economic sectors,
including manufacturing and retail (Migliore, 2019). Assistance in construction sector can
stimulate these areas by increasing demand for materials, equipment, and consumer products and
lead broader recovery.
Public assistance program can effectively stabilizing business environment and local
environment by providing assistance to small business owners to retain employees (Knight,
2020). Retaining employee size are directly associated to small businesses revenues recovery and
continuity because employees helps business to keep productivity (Aghababaei et al., 2020;
Runyan, 2006a). Revenue loss from disasters and the relocation options available to survivors
44
can make it difficult for small businesses to find the labor needed to continue operations (Zhang
et al., 2019). Further retained employee also can feedback broader economic recovery by
spending in local business (Watkins et al., 2015).
Table 3. Summarize the Mechanism of Public Assistance Programs in Business Recovery
Literature Source Recovery Stage Mechanism
Maisel and Roman (2013) Overcome cash flow disruptions
Short-term
Stafford et al., (2013) Improve adaptive capacity
Victoria & Victoria (2018) Enhancing community resilience
Atkinson & Sapat (2013) Long-term Promoting infrastructure development
Knight (2020) Retaining employees
Table 3 summarizes the mechanisms the extent to which public assistance programs can
promote small business recovery after disasters from both short-term and long-term perspectives.
Restrictions associated with public assistance are critical for post-disaster recovery,
addressing two key concerns: social equity and economic stability. In a post-disaster context,
resources available can be limited, restrictions attached to public assistance programs are
necessary because unrestricted aid often leads to inefficient resource relocation and exacerbate
social inequalities (Domingue & Emrich, 2019b; Sovacool et al., 2018). Unrestricted assistance
may benefit business owners who do not need aid but have the social status to apply, making it
45
easier for them to secure assistance (Domínguez-Navarro et al., 2019). In contrast, business
owners in lower social status may fall into a poverty trap after disasters because they does not
obtain sufficient assistance to restart. Restrictions on public assistance programs help ensure that
aid reaches those most in need thought setting application criteria (Kunreuther, 1968; Olshansky
& Johnson, 2014).
Further, attaching restrictions to public assistance programs also plays significant role in
stabling economic after disasters by distributing resources to business in certain sectors (Graham,
2007; Watson, 2022). Distributing restricted assistance to businesses in sectors with high cross-
sector linkages supports economic stability by retaining employment rates and promoting
recovery across related sectors. After disasters, immediate revenue losses may lead business
owners to cut operational costs by laying off employees, and decreased income can prevent these
individuals from participating in economic activities until they find new sources of income. By
setting restrictions on assistance programs to ensure businesses retain their employees, these
programs help sustain local economic activity, which is critical for economic recovery (Haynes
et al., 2011). Second, allocating resources to bossiness in sectors with high cross-sector linkages
can stimulate demand from related sectors, which indirectly benefit other local bossiness and
lead broader economic recovery (Janvry et al., 2016).
Unrestricted public assistance programs also critical for post-disaster recovery because it
allows more rapid resource allocation, which can promote comprehensive community recovery
(Olshansky & Johnson, 2014). Compare with restricted public assistance program, unrestricted
assistance does not have strict eligibility criteria, which allows it can be distributed to applicants
more rapidly. In post disaster context, rapid distributed assistance allows business resume their
46
operation earlier, which can prevent further revenue loss, closures and potential permanent
closure, maintaining local economic stability (Watson, 2022).
Additionally, the local community recovery process benefits from the reopening of small
businesses, which are essential for providing goods and services. Rapid support through
unrestricted assistance programs not only helps businesses reopen but also allows residents to
quickly regain access to these vital goods and services (Xiao et al., 2022). Further, reopening of
local business can signal a return to normalcy, which is critical for local residents to restore a
feeling of stability and reliance and promote comprehensive community recovery (Watson,
2022).
In addition, rationality of restrictions attached to public assistance programs can have a
positive or negative effect on business recovery because restrictions can limit business owners
self-adjustment behavior (Graham, 2007; Xiao & Zandt, 2012b). For instance, businesses may
need to relocate to survive due to disruptions in their original community and market. However,
some assistance programs impose location-based restrictions, limiting businesses’ ability to
relocate and reducing their chances of survival (Graham, 2007; Xiao & Zandt, 2012b).
Additionally, business profitability can be challenged by decreased demand after disasters, as
individuals affected by disasters tend to reduce consumption due to economic uncertainties. In
this context, restrictions requiring businesses to maintain employee levels may increase financial
risk for those businesses (M. A. U. Khan & Sayem, 2013). Based on the counter arguments
illustrated above, this study proposes the following hypothesis:
Hypothesis 1: Small businesses accessing to restricted public assistance are more likely to
recover compared to businesses without public assistance.
47
Hypothesis 2: Small businesses accessing to unrestricted public assistance are more likely
to recover compared to businesses without public assistance.
Hypothesis 3: Small businesses accessing to mixed public assistance are more likely to
recover compared to businesses without public assistance.
3.4.2 Business Characteristics
Business characteristics (e.g., employee size, financial condition, and ownership)
interacted with hazard events and decided the initial pathway of the subsequent recovery process
(Chang et al., 2022). Businesses with larger employee sizes can support businesses with more
available resources that can be used to initiate the recovery process (M. A. U. Khan & Sayem,
2013; Marshall et al., 2015). Businesses with larger employee sizes means businesses have
sufficient labor resources to meet emerging market demands (Morrish & Jones, 2020). This
includes more effectively reallocating resources or to replace damaged infrastructure and supply
chains. For example, the study of business recovery in Bangladesh reveals that larger employee
size allows these businesses to implement recovery strategies more rapidly than smaller firms
(M. A. U. Khan & Sayem, 2013).
Businesses with higher sales volume, which suggests better financial conditions, can
support businesses' recovery after a disaster in two ways (Marshall et al., 2015). First, businesses
with better financial conditions are capable of adopting measures to prepare for disasters, such as
insurance, which significantly relates to business recovery (Chang et al., 2022; Han & Nigg,
2011). Secondly, even though disasters can lead to loss of customers, businesses with higher
sales volumes normally have a reserved cash flow in hand, which can promote some level of
operation during and after the disaster (Marshall & Schrank, 2014).
48
Further, businesses owned by minorities are reported to be harder to recover after
disasters because they have limitations in accessing financial assistance compared with other
businesses (Andrew et al., 2022). Minority-owned businesses normally have small business
sizes, which are deemed indicators of business repayment capability (Atkins et al., 2022; Dua et
al., 2020; Page et al., 1979). Assistance distribution agencies have accountability concerns,
which cause them to allocate resources to businesses that have better repayment capabilities (M.
Li, 2021; Watson, 2022).
On the other hand, Morrish and Jones (2020) suggest that the success of business
recovery may be less about business characteristics and more about capacity for adaptation.
Further scholars argue that businesses with larger employee sizes mean relatively high
operational costs, which make businesses vulnerable when facing a disruptive market after
disasters (S. M. Wagner et al., 2017). Furthermore, minority-owned businesses have shown more
resilience than others in industries such as arts and cultural entrepreneurship because they can
provide locally formed cultural production (Wang et al., 2021; Wang & Richardson, 2021). These
competing arguments regarding business characteristics lead to the following hypothesis:
Hypothesis 4: Small businesses with larger number of employees are likely to recover
after disasters.
Hypothesis 5: Small businesses with higher sales volume are likely to recover after
disasters.
Hypothesis 6: Minority-owned small businesses are likely to recover compared with non-
minority-owned businesses after disasters.
49
3.4.3 Market Context
Businesses face a disruptive market that is more difficult to recover, which involves
changes in customer population and consumption capability (Alesch et al., 2001; Corey &
Deitch, 2011). These two factors describe a business’s potential profitability from the demand
side.
Businesses in certain industries are likely to face a demand decrease caused by a decrease
in customer population (Corey & Deitch, 2011; Runyan, 2006b). Large-scale population
relocation and disruption of the local community network, normally followed by disasters, can
significantly lead to customer decreases for businesses (Corey & Deitch, 2011; Wang et al.,
2021). This reduction in customer base not only impacts immediate revenue sources but also
affects businesses' market position, which leads to business closure.
On the other hand, disasters can change consumption behavior because customers also
face the challenge of decreasing income and other concerns (Chang et al., 2022). For example, in
the case of COVID-19, the unemployment caused by the pandemic can lead to consumption
behaviors that tend to be conservative, and concerns about exposure to the virus positively
contribute to consumption decreasing (Felix & Shampine, 2021). Based on the counter
arguments illustrated above, this study proposes the following hypothesis:
Hypothesis 7: Small Businesses with access to larger consumer base are likely to recover
after disasters.
Hypothesis 8: Small businesses with access to higher consumption capability groups are
likely to recover after disasters.
50
3.4.4 Geographic Location
Further, geographic location can influence the effectiveness of public assistance programs
in helping small businesses recovery but the mechanism is still unclear (Crass et al., 2016;
McCann & Folta, 2011). Studies geographic clustering can significantly increase the likelihood
of businesses receiving public assistance because of the localized advantages, for instance, a
well-developed industrial chain and scale of the labor market (Andersson & Larsson, 2016;
Porter, 1998). These clusters create an environment that attracts and justifies the allocation of
public resources. Areas with more public resources allocated suggest businesses in this area with
more financial support to resort function and anticipate a relatively rapid recovery process (Jia-
shan, 2009).
Restricted assistance focused on job retention plays a significant role in promoting
regional economic recovery by stabilizing local demand and supporting broader recovery
through sustained workforce participation (Bosworth & Gray, 2012; Felsenstein, 1992). Areas
with established economic structure plays significant role in providing job opportunities to local
communities. More restricted assistance to areas with established economic structure can prevent
mass job losses. Stabled local job retention rate positive contributed to ensuring local demands,
which also can furtherly feedback to business recovery in these regions (Bosworth & Gray,
2012).
Additionally, allocating restricted assistance focus on sector stability to businesses in
sectors with high cross-sector linkages can more effectively drive broader economic recovery in
a region (H. Li et al., 2022). Businesses in related sectors tend to cluster geographically to reduce
transportation costs and improve access to shared resources (Joyal et al., 2000). By directing
51
resources to central industries in a region, the economic benefits can diffuse more broadly,
leading to a more efficient recovery (Guillon & Mathonnat, 2020).
However, areas with more public resources allocated can also suggest those areas
experience more damage during disasters because equity is a major concern when allocating
public resources after disasters (Domingue & Emrich, 2019). Areas that experience more damage
need more resources to recover. Compared with the private sector, public resources are required
to be allocated to needy groups to ensure social equality (Peterson, 1981). At the same time,
those areas are often associated with a longer recovery process, which can hinder the
effectiveness of public assistance programs (Marshall & Schrank, 2014).
Further, business located in economic hub aeras not always associated with recovery
because of operation cost and economic structure (Gray et al., 1996; Monga, 2020). Economic
hubs, which rely on dense business clustering (or agglomeration), often face unique recovery
challenges. High business density means a greater concentration of affected entities, creating
bottlenecks that reduce individual adaptability and resilience (Monga, 2020). In contrast,
businesses in rural areas tend to be less reliant on economic hubs and serve a relatively stable
customer base, which supports consistent profitability. In these cases, even a small amount of
assistance can lead to an effective recovery process, as rural businesses are often more resilient
and can quickly bounce back with targeted support if there is not a significant change from
demand side (Ring et al., 2010; Young, 2010).
Flexibility associated with unrestricted public assistance program plays significant role in
promoting regions’ recovery by letting business address their own specific needs and lead
broader recovery (Griego et al., 2020). Disasters do not impact all sectors equally, and a region’s
business demands can vary significantly afterward (Gallagher et al., 2022; Haynes et al., 2011).
52
For example, the retail sector may need to maintain cash flow, the utility sector may require new
equipment, and the construction sector might need to adopt new operational strategies. However,
restricted public assistance programs may not fully capture the diverse needs of affected
businesses or design appropriate conditions for each of them, which requires unrestricted public
assistance to help business with specific needs to recovery. By providing flexibility to businesses
in a region, unrestricted public assistance allows bossiness to address their own critical
challenges and foster recovery in the whole region (Chang et al., 2022). For instance, after the
2024 tsunami, unrestricted cash assistance, distributed by Sri Lanka government, allowed
recipient businesses greater spending flexibility, leading to faster recovery in the retail sector
(Mel et al., 2010).
Further, businesses with a flexible fund assistance are able to have a stronger purchasing
power, which helps local region to establish a self-reinforcing cycle of local purchasing and
economic stability (Bolin & Stanford, 1998). Business recovery needs can vary widely after
disasters, generating diverse demands from a market perspective. This diversity enables
businesses across different sectors to maintain profitability, as demand for various goods and
services stimulates economic activity. By helping businesses regain their purchasing power, these
demands indirectly benefit the entire regional business ecosystem, fostering a more resilient and
interconnected local economy (Roenigk, 1993).
Based on the counter arguments illustrated above, this study proposes the following
hypothesis:
Hypothesis 9: Areas with a higher percentage of businesses receiving restricted assistance
are positively associated with a higher percentage of recovery.
53
Hypothesis 10: Areas with a higher percentage of businesses receiving unrestricted
assistance positively associated with a higher percentage of recovery.
Hypothesis 11: Areas with a higher percentage of businesses receiving mixed assistance
are positively associated with a higher percentage of recovery.
Table 4. Summary of Hypotheses
Research Hypotheses
Small businesses accessing to restricted public assistance are more
H1
likely to recover compared to businesses without public assistance.
Small businesses accessing to unrestricted public assistance are more
H2
likely to recover compared to businesses without public assistance.
Small businesses accessing to mixed public assistance are more likely to
H3
recover compared to businesses without public assistance.
Small businesses with larger number of employees are likely to recover
H4
after disasters.
Small businesses with higher sales volume are likely to recover after
H5
disasters.
Minority-owned small businesses are likely to recover compared with
H6
non-minority-owned businesses after disasters.
Small Businesses with access to larger consumer base are likely to
H7
recover after disasters.
Small businesses with access to higher consumption capability groups
H8
are likely to recover after disasters.
Areas with a higher percentage of businesses receiving restricted
H9 assistance are positively associated with a higher percentage of
recovery.
Areas with a higher percentage of businesses receiving unrestricted
H10
assistance positively associated with a higher percentage of recovery.
Areas with a higher percentage of businesses receiving mixed assistance
H11
are positively associated with a higher percentage of recovery.
54
In the next chapter, the research site, measurement, data source, and analysis methods are
discussed. This study employs data from the Mergent Intellect, US Census Bureau, and SBA to
answer the overall research question. The data from these three sources are merged based on
their businesses name and geographic locations. The logistical regression, Cox regression with
time varying effect, and spatial autocorrelation are performed to analysis the data.
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4 CHAPTER 4.
METHODOLOGY
4.1 Introduction
This dissertation tests the theoretical framework and hypotheses in the Dallas Fort Worth
Metropolitan Area during the COVID-19 pandemic. The DFW metropolitan area is an
appropriate research site because the diverse economic structure can provide a sufficient sample
size from different sectors and recovery outcomes achieved.
This study’s framework proposes that business recovery outcomes are shaped by access
to public assistance programs, business characteristics, market context, and geographic location.
Since business owners can apply for multiple types of assistance, seven binary variables were
used to track access, grouped to show whether a business utilized one, two, or all available forms
of support. Business recovery was measured by two variables: whether the business remained
active until January 2023, and the duration it continued operating before becoming inactive.
Business characteristics were assessed using four variables: minority ownership, sales
volume, employee size, and business sector (based on the first two digits of the 2017 NAICS
code). For market context, three factors were considered: minority population, median household
income, and the number of housing units within a 5-kilometer service range.
Lastly, seven variables were used to capture geographic context, reflecting the percentage
of businesses accessing different combinations of public assistance and the business activity rate
at the census tract level.
This dissertation relies on data mainly from three sources: Small Business Administration
(SBA), Mergent Intellect, and Census Bureau. SBA provides a list of assistance recipients from
different programs on its official website, which includes the recipient's names, addresses, and
56
the amounts of assistance received. This dissertation obtains business records from Mergent
Intellect for the DFW metropolitan area, which include business name, address, employee size,
ownership, and sectors. These two datasets are matched based on company names and addresses.
Finally, this study uses a 5-kilometer service range distance to conduct the spatial analysis to
estimate the potential customer population and purchasing power for each business based on
demographic data from the American Community Survey (2021) by the US Census Bureau.
This dissertation uses three analytical methods to investigate different aspects of business
recovery during the COVID-19 pandemic: (1) logistic regression, (2) Cox regression with a time-
varying effect, and (3) spatial autocorrelation. The three methods answer three dimensions of the
main research question. The logistic regression examines whether public assistance programs
prevent small businesses from closing during the pandemic period. The Cox regression with a
time-varying effect further analyzes the influence of public assistance programs on different
stages of the pandemic. Lastly, the spatial autocorrelation evaluates the effectiveness of public
assistance programs from a geographic viewpoint.
4.2 The Research Site: Dallas Fort-Worth Metropolitan Area and the COVID-19 pandemic
This study chooses the Dallas-Fort Worth (DFW) Metropolitan Area as the research site
because even though the DFW area suffered a significant loss of jobs and economic decrease,
DFW demonstrated a profound resilience among Metropolitan Areas in the United States
(Maddox, 2022).
Before the COVID-19 pandemic, the DFW metropolitan area experience a strong
economic growth, a diverse and expanding workforce, and population growth. The DFW
metroplex was home to 24 Fortune 500 company headquarters, ranking it third in the nation
behind New York and Chicago (Kotkin & Clark, 2021). Between 2012 and 2022, employment in
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the DFW region increase by 29 percentage, which over the average number in Texas(23.5
percent) and the U.S. (13.9 percent) (Texas Comptroller of Public Accounts, 2024).
During the COVID-19 pandemic, the DFW metropolitan area experienced significant
economic loss, which included $23 billion in real gross product loss and over $15 billion in lost
personal income (Bill Hethcock, 2020). All sectors were affected, including the airlines,
automobiles, energy equipment, and service industries, which had particularly significant
impacts(Luka Vidovic, 2022). Decreased demand, supply chain disruption, and market
uncertainties challenge businesses’ capability to maintain operations.
However, DFW shows resilience because of its diverse economic structure and well-
developed infrastructures. First of all, the DFW Metropolitan Area has the second most diverse
economic structure among Metropolitan Area s in the United States (Moody’s Analytics, 2019).
Regions’ diverse economic structure can significantly contribute to regional economic recovery
(Xiao & Drucker, 2013). In the meantime, the diversity in economic structure allows us to
capture recovery patterns for businesses from different industries under the context of the
pandemic.
Further, increasing investment in infrastructure in DFW Metropolitan Area promotes
businesses' recovery after disasters (Maddox, 2022). Well-developed infrastructure provides
basic services for businesses to maintain normal operations (Ding, 2024). For instance,
interruption of water supply has a significant impact on businesses in the agriculture sector.
After the pandemic, the pandemic, the DFW region demonstrated a rapid recovery. In
2022, the Gross Domestic Product (GDP) of the DFW metropolitan area reached $688.9 billion,
with a real GDP growth of 5.7 percent—well above the national average of 2.1 percent for the
same period (Bureau of Economic Analysis, 2023). Further, the DFW region continued to
58
experience strong population growth, with an increase of 152,600 residents from July 2022 to
July 2023.
On the other hand, the DFW Metropolitan Area is the first metropolitan area that returns
job levels to pre-pandemic levels (Maddox, 2022). In addition, according to Glenn Hegar (2022)
federal stimulus funds distributed to Texas, they have a positive impact on mitigating revenue
losses and maintaining economic stability. This provides an excellent opportunity to study the
factors contributing to this remarkable recovery.
4.3 Measurements
4.3.1 Dependent Variable: Business Recovery
This study uses the method utilized by Watson, (2021) to determine whether a business is
considered active or inactive as an indicator of a business’s recovery outcome. Whether
businesses still maintain operations is an indicator that is well-adopted by scholars in the
business recovery field to measure business recovery outcomes (Aghababaei et al., 2020; Headd,
2003; Stevenson et al., 2018). The inactiveness of businesses can caused by both positive and
negative reasons, such as strategic adjustment or lock of profitability (Bates, 2005). However, the
inactiveness of businesses indicates business owners’ lost confidence in future profitability
during the recovery process (H. Khan & Upadhayaya, 2019).
The information about whether businesses are active or not is sourced from Mergent
Intellect. Mergent Intellect provides a fully searchable database with detailed information on
over 280 million businesses, including both active and inactive firms. The database induced a
wide range of information, which includes company profiles, financials, industry profiles, and
other details (Winn & Goodrich, 2018). The database is well adopted by scholars in business
administration fields (Chandrasekhar et al., 2022; Federico & Banks, 2020).
59
Mergent Intellect determines whether a business is inactive by detecting the presence or
absence of trading activities (Leaming, 2016). If a business is identified as inactive by Mergent
Intellect, it is deemed as no longer trading or operating. If a business is considered active by
Mergent Intellect until January 2023, deems the business as recovered; otherwise, it is
considered not recovered. This study also collected how many months the business sustained
operations from Dec 2019 for cox regression analysis.
4.3.2 Independent Variable: Access to Public Assistance Program
During COVID-19, small business owners had the option to apply for both the PPP and
EIDL programs, enabling simultaneous access to both types of assistance. This study uses three
binary variables to capture small business participation in these programs: businesses accessing
only restricted assistance programs (PPP loans and PPP forgiveness), businesses accessing only
unrestricted assistance programs (EIDL loans and EIDL advances), and businesses accessing
both restricted and unrestricted assistance (PPP and EIDL) simultaneously.
Overall, there were about 12,150 businesses only receiving assistance from the PPP
program, comprising 1.68% of the total number of businesses. Additionally, 6,548 businesses
only received from EIDL programs, accounting for 0.91%; and 1,188 businesses received
assistance from PPP and EIDL simultaneously, making up 0.16%.
Table 5. Binary Variables Capture Small Business Participation in Public Assistance Programs
Variables Descriptions
Restricted Assistance Business only access to restricted assistance (PPP loan or PPP
Only forgiveness).
Unrestricted Assistance Business only access to unrestricted assistance (EIDL loan and
Only EIDL Advance).
Mixed Assistance Business access to restricted and unrestricted assistance (PPP and
Received EIDL).
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4.3.3 Independent Variable: Business Characteristics
The variables used to capture business characteristics include employee size, sales
volume, ownership, and sectors.
Employee size is a fundamental indicator for businesses to measure their scale and
capability of operation (O’Boyle et al., 2016). This indicator is well adopted by scholars in the
business recovery field to measure business size (Aghababaei et al., 2020; J. Lee, 2019).
Businesses' employee size is captured by the actual number of employees that are sourced from
Mergent Intellect.
This study uses the method adopted by Morrish & Jones (2020) to use actual average
annual sales volume in the past year as an indicator to measure businesses' financial condition.
Sales volume serves as a direct indicator to measure business performance in gaining interest
from scholars (Aghababaei et al., 2020; Heileman et al., 2016). This study uses the natural log of
businesses’ sales volume to represent the financial condition.
This study also controls for whether a business is owned by minority groups by a binary
variable because empirical studies suggest that minority-owned businesses show different
accessibility to public assistance programs and recovery patterns compared with non-minority-
owned businesses (Andrew et al., 2022). There are 0.46% of businesses are considered minority-
owned businesses by Mergent Intellect. Further the business sector are controlled based on the
first two digits of the 2017 NACIS code.
4.3.4 Independent Variables: Market Context
This study uses 5 5-kilometer service range distance to perform the spatial analysis to
determine the possible customer population and purchasing power for each business. The 5
kilometers, service range distance, is frequently adopted by studies in service range because
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empirical studies suggest that individuals prefer to use services within the 5-kilometer distance
of their homes (Hillsman & Hypothesis oda, 1978; Wardrop et al., 2018).
This study uses the number of housing units and total minority population and housing
units within a 5 km service area as indicators of the potential customer population and demands.
To capture whether minority-owned businesses gain support from minority populations
suggested by Wang et al., (2021) or not, this study uses the total minority population as an
indicator instead of the total population. Further, empirical studies have also frequently adopted
the number of housing units to capture customer demand (Fahrioglu & Alvarado, 2001).
This study employes a similar approach adopted by Oka (2022), i.e., the average median
household income within the 5km service range at the US Census tract level, to capture the
purchasing power of the potential population.
4.4 Data Collection
4.4.1 Public Assistance Programs: Small Business Administration
The United States Small Business Administration (SBA) is an independent government
agency dedicated to supporting entrepreneurs and small businesses. Its mission is “to maintain
and strengthen the nation’s economy by enabling the establishment and viability of small
businesses and assisting in the economic recovery of communities after disasters. During the
COVID-19 pandemic, public assistance programs provided by federal government are
administrated by SBA. Applicants need to process application through SBA to access to federal
assistance. SBA publishes and manages PPP and EIDL recipient databases separately. The
database is public on the SBA website, and it has data on the recipient’s name, address, state,
city, zip, amount of assistance received, and received date.
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4.4.2 Business Characteristics: Mergent Intellect
Mergent Intellect is a web-based business research tool that provides a fully searchable
database of worldwide business information and has been well-adopted by scholars in the area of
business administration (Sen, 2022; Winn & Goodrich, 2018). In 2012, Mergent partnered with
Dun & Bradstreet (D&B) and soon released an expanded online version of D&B’s Million Dollar
Directory (MDDI). This resource was later integrated with other datasets to launch Mergent
Intellect in 2014. Designed as a comprehensive resource for business data and market research,
Mergent Intellect combines datasets and products from D&B/Hoovers with new, complementary
modules. While Hoovers’ company profiles, along with First Research and Key Business Ratios
(KBR), have been long-established, Mergent enhanced the platform with integrated directory,
demographic, consumer, and job modules. This expansion allowed it to compete with major
players like InfoGroup, DatabaseUSA, and Gale. The result is Mergent Intellect—a robust,
global database offering extensive business-to-business (B2B) and business-to-consumer (B2C)
data.
The database includes information on over 100 million private businesses in the US,
Canda, and globally, and over 6,000 public companies in the US. This study obtains a whole list
of business records of DFW MSA along with their basic characteristic information from Mergent
Intellect, which include business name, sales volume, employee size, addresses, state, city, and
zip code.
4.4.3 Market Context: Census Bureau
This study utilized census tract-level demographic data from the American Community
Survey (ACS) 5-Year Data (2022) provided by the U.S. Census Bureau. As part of the U.S.
Department of Commerce, the Census Bureau is the federal government’s primary source of data
63
on the nation’s population and economy. The ACS is an annual demographic survey conducted
by the Census Bureau, covering a wide range of information, including ancestry, U.S. citizenship
status, educational attainment, income, language proficiency, migration, disability, employment,
and housing characteristics. The ACS 5-year estimates represent averages for a geographic area,
derived from data collected over a five-year period. While they are the most reliable and precise
estimates available from the ACS, they are also the least up-to-date.
To capture the market context for each business, which includes various demographic
factors such as population density, income levels, age distribution, educational attainment, and
employment statistics. This study incorporates three variables into this study, which are the total
minority population, total number of housing units, and median housed income.
4.4.4 Data Preparation Process
Figure 2 summarizes the data preparation process for this study. This study performs
three steps to obtain the final dataset. First, this study obtains all the business records within the
DFW Metropolitan Area and filters out businesses that do not meet the Small Business
Administration's criteria for small businesses based on the first two dights of the 2017 North
American Industry Classification System (NACIS) code. Furthermore, this study identifies
whether a small business received loan-based and cash-based assistance from the PPP and EIDL
by identifying common business addresses and names from the SBA recipient list. This is
achieved by matching addresses and business names with the recipient list from the SBA. Lastly,
this study performs spatial analysis for each business to identify the total number of minority
populations, average median household income, and total number of housing units within a 5-
kilometer service range at the census tract level.
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Figure 3. Data Preparation Process
4.5 Methods of Analysis
4.5.1 Logistical Regression
To answer the overall research question, to what extent do public assistance programs
mitigate the likelihood of business closure after disasters, this study employs the logistical
regression as the analysis approach. The logistic regression is used because the dependent
variable in this study is binary, with proportions of 46.70% of businesses remaining active and
53.3% of businesses being detected as inactive. This distribution fits the assumptions of logistic
regression.
The logistic regression model can be expressed as follows:
𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙(𝑃𝑃) = 𝛽𝛽0 + 𝛽𝛽1 𝑋𝑋1 + 𝛽𝛽2 𝑋𝑋2 + … + 𝛽𝛽𝑛𝑛 𝑋𝑋𝑛𝑛 1.
where:
𝑃𝑃 is the probability of successful disaster business recovery;
𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙(𝑃𝑃) is the natural logarithm of the odds of successful disaster business recovery;
𝛽𝛽0 is the intercept term;
65
𝛽𝛽1, 𝛽𝛽2, ..., 𝛽𝛽2 are the regression coefficients for the independent variables;
𝑋𝑋1, 𝑋𝑋2, ..., 𝑋𝑋𝑛𝑛 are the independent variables that influence disaster business recovery.
4.5.2 Cox Regression with Time-Varying Effect
Logistic regression has two key limitations in studies of disaster recovery: it cannot track
survival time and it fails to account for environmental changes within the chosen study timeframe.
Unlike Cox regression, logistic regression does not consider how long businesses sustain
operations, an important indicator for evaluating the effectiveness of public assistance programs
in preventing closures. Additionally, disaster recovery is a dynamic process influenced by
environmental changes, which logistic regression is unable to capture.
To further investigate the efforts of public assistance programs in mitigating the likelihood
of disaster-induced business closure in different stages of the COVID-10 pandemic, this study
employs cox regression with a time-varying effect proposed Crowley and Hu (1977) to analyze
the data. Compared with standard cox regression, cox regression with a time-varying effect has a
distinctive advantage in that it can capture the changing challenges faced by businesses at different
stages of recovery.
In the case of the COVID-19 pandemic, businesses encountered different challenges at
various stages; for instance, at the beginning, businesses may face the issue of decreasing
demand (Bartik, Cullen, et al., 2020); at the middle stage, businesses may face challenges of
shortages in the international supply chain (Ardekani et al., 2023); at the later stage, business
may face challenges of policy uncertainty (Trump & Linkov, 2020). At different stages, the
efforts of public assistance programs in helping businesses recover may be different. Cox
regression with a time-varying effect can help us to capture this. The entire time frame of this
66
study has been divided into three stages, which are Stage 1 from January 2020 to November
2020; Stage 2 from December 2020 to January 2022; and Stage 3 from February 2022 onwards.
The equation of Cox regression with a time-varying effect can be represented as follows:
ℎ 𝑡𝑡 ∣∣ 𝑋𝑋(𝑡𝑡) = ℎ0 (𝑡𝑡)exp (𝛽𝛽1 (𝑋𝑋1 (𝑡𝑡) ∙ 𝑔𝑔(𝑡𝑡)) + ⋯ + 𝛽𝛽𝑛𝑛 (𝑋𝑋𝑛𝑛 (𝑡𝑡) ∙ 𝑔𝑔(𝑡𝑡)) + 𝛾𝛾1 𝑋𝑋1 (𝑡𝑡) ∙ 𝑔𝑔(𝑡𝑡) + ⋯
+ 𝛾𝛾𝑛𝑛 𝑋𝑋𝑛𝑛 (𝑡𝑡) ∙ 𝑔𝑔(𝑡𝑡) + 𝜇𝜇1 𝑋𝑋1 (𝑡𝑡) ∙ 𝑔𝑔(𝑡𝑡) + ⋯ + 𝜇𝜇𝑛𝑛 (𝑋𝑋𝑛𝑛 (𝑡𝑡) ∙ 𝑔𝑔(𝑡𝑡))) 2.
and
𝑔𝑔(𝑡𝑡) = 0, 𝑖𝑖𝑖𝑖 𝑡𝑡 𝑖𝑖𝑖𝑖 𝑛𝑛𝑛𝑛𝑛𝑛 𝑤𝑤𝑤𝑤𝑤𝑤ℎ𝑖𝑖𝑖𝑖 𝑐𝑐ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓;
= 1, 𝑖𝑖𝑖𝑖 𝑡𝑡 𝑖𝑖𝑖𝑖 𝑤𝑤𝑤𝑤𝑤𝑤ℎ𝑖𝑖𝑖𝑖 𝑡𝑡ℎ𝑒𝑒 𝑐𝑐ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓. 3.
Where:
ℎ 𝑡𝑡 ∣∣ 𝑋𝑋(𝑡𝑡) is the hazard function at time 𝑡𝑡 given time-dependent covariates 𝑋𝑋(𝑡𝑡);
ℎ0(𝑡𝑡) is the baseline hazard function, representing the hazard for an individual with the
baseline level of covariates.
𝑋𝑋1 (𝑡𝑡), 𝑋𝑋2 (𝑡𝑡), … , 𝑋𝑋𝑝𝑝 (𝑡𝑡)are the time-dependent covariates;
𝛽𝛽1 , … , 𝛾𝛾1 , … , 𝜇𝜇1, … are the coefficients for the time-dependent covariates at each stage,
representing the log hazard ratio;
g(t) is a function of time.
4.5.3 Spatial Autocorrelation
According to the framework proposed in this study (Fig. 2), geographic location plays a
significant role in business recovery. However, neither logistic regression nor Cox regression with
time-varying effects can fully capture the impact of geographic variations. Socioeconomic
structures are often geographically clustered, and surrounding areas are influenced by spillover
effects, which shape business operations and profitability. For example, businesses in urban centers
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may have better access to resources and infrastructure compared to those in rural areas. When
businesses in these areas receive public assistance to restore purchasing power and re-engage in
economic activities, the resulting spillover effects can benefit surrounding businesses—an effect
that cannot be fully captured by logistic or Cox regression. Additionally, geographic variations
influence key factors such as customer bases, supply chains, and labor availability, all of which
are critical to post-disaster recovery. Given these complexities, spatial analysis is essential to better
understand and quantify the impact of geographic factors on business recovery.
This study uses spatial autocorrelation at the census tract level to analyze the data and
explore whether areas with higher public resource allocated is positive related to business recovery
in all the DFW region. Spatial autocorrelation is employed because socioeconomic environmental
factors may cluster at the spatial level. For example, businesses in the downtown area of Dallas
typically incur relatively higher operational costs compared to surrounding areas, which can
influence the business recovery process.
The equation of Moran’s I, commonly used for spatial autocorrelation, can be represented
as follows:
𝑁𝑁 ∑𝑖𝑖 ∑𝑗𝑗 𝑤𝑤𝑖𝑖𝑖𝑖 (𝑋𝑋𝑖𝑖 − 𝑋𝑋)(𝑌𝑌𝑗𝑗 − 𝑌𝑌)
𝐼𝐼 = ∙ 4.
𝑊𝑊 ∑ (𝑋𝑋 − 𝑋𝑋)2 ∑ 𝑌𝑌 − 𝑌𝑌2
𝑖𝑖 𝑖𝑖 𝑗𝑗 𝑗𝑗
Where:
𝑁𝑁 is the number of spatial units indexed by 𝑖𝑖 and 𝑗𝑗;
𝑋𝑋𝑖𝑖 and 𝑌𝑌𝑗𝑗 are the values of two variables at locations 𝑖𝑖 and 𝑗𝑗;
𝑋𝑋 and 𝑌𝑌 are the means of 𝑋𝑋 and 𝑌𝑌 variables;
𝑤𝑤𝑖𝑖𝑖𝑖 is a spatial weighting between locations 𝑖𝑖 and 𝑗𝑗;
𝑊𝑊 is the sum of all 𝑤𝑤𝑖𝑖𝑖𝑖 , ∑𝑖𝑖 ∑𝑗𝑗 𝑤𝑤𝑖𝑖𝑖𝑖 .
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To examine the effectiveness of public assistance programs and various combinations of
them in supporting small businesses' continued operations from a geographic perspective, we
conducted seven spatial autocorrelation analyses. The combinations included restricted cash
assistance only, unrestricted loan only, unrestricted cash only, restricted cash and unrestricted
loan, restricted cash and unrestricted cash, unrestricted loan and unrestricted cash, and all three
types of assistance.
Tables 6 to 8 summarize the variables, descriptions of variables, and corresponding
analysis used in this study.
Table 6. Variable used in Logistic Regression
Variables Descriptions
Whether a business is identified as inactive by Mergent Intellect, it is
Active
deemed as no longer trading or operating until January 2023.
Restricted Assistance
Business only access to restricted assistance (PPP).
Only
Unrestricted
Business only access to unrestricted assistance (EIDL).
Assistance Only
Mixed Assistance Business access to restricted and unrestricted assistance (PPP and
Received EIDL).
Minority Owned Whether a business is owned by minority group; 0 = No; 1= Yes.
Sales Businesses’ sales volume in the last calendar year.
Sales (logged) Natural log of businesses’ sales volume in the last calendar year.
Employee size (freq.) Business’s employee size.
Total minority
population within Total minority population can be covered by a business within 5KM
5KM service range service range
(unit:1,000)
Average median
household income Average median income of areas that can be covered by a business
within 5KM service within 5KM service range.
range (unit:1,000)
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Total housing units
Total housing units can be covered by a business within 5KM service
within 5KM service
range.
range (unit:1,000)
Business sector that classified based on first two-digit of 2017 NACIS
Sectors
code.
Table 7. Variables Used in Cox Regression with Time Varying Effect
Variables Descriptions
Survival Months How many months does business sustain operation from Dec 2019.
Whether a business is identified as inactive by Mergent Intellect, it
Active
is deemed as no longer trading or operating until January 2023.
Restricted Assistance
Business only access to restricted assistance (PPP).
Only
Unrestricted Assistance
Business only access to unrestricted assistance (EIDL).
Only
Mixed Assistance Business access to restricted and unrestricted assistance (PPP and
Received EIDL).
Minority Owned Whether a business is owned by minority group; 0 = No; 1= Yes.
Sales Businesses’ sales volume in the last calendar year.
Sales (logged) Natural log of businesses’ sales volume in the last calendar year.
Employee size (freq.) Business’s employee size.
Total minority
population within 5KM Total minority population can be covered by a business within
service range 5KM service range
(unit:1,000)
Average median
household income Average median income of areas that can be covered by a business
within 5KM service within 5KM service range.
range (unit:1,000)
Total housing units
Total housing units can be covered by a business within 5KM
within 5KM service
service range.
range (unit:1,000)
Business sector that classified based on first two-digit of 2017
Sectors
NACIS code.
To perform spatial autocorrelation, this study grouped individual business data at the
census tract level and weighted it by the total number of businesses in each corresponding census
70
tract. This approach allowed us to calculate the percentage of businesses receiving different
combinations of assistance and the percentage of businesses still active. The spatial
autocorrelation test examined the relationship between the percentage of businesses receiving
various combinations of assistance and the percentage of businesses that remained active.
Table 8. Variables Used in Spatial Autocorrelation
Variable Descriptions
Active Rate Percentage of active business at the census tract level.
Restricted
Percentage of business access to restricted (PPP) assistance at the census
Assistance Only
tract level.
Rate
Unrestricted
Percentage of business access to unrestricted (EIDL) assistance at the
Assistance Only
census tract level.
Rate
Mixed Assistance Percentage of business access to restricted (PPP) unrestricted (EIDL)
Rate assistance at the census tract level.
In summary, this study selected the DFW Metropolitan Area as the research site due to its
diverse economic structure and notable resilience during the COVID-19 pandemic. Three binary
variables were used to capture business access to public assistance programs, while sales
volume, ownership, and employee size measured business characteristics. Market context was
assessed through potential consumer population and purchasing power. The data primarily came
from three sources: Mergent Intellect, the Census Bureau, and the SBA. To address the research
question, to what extend public assistance programs mitigate the likelihood of business closure,
71
this study employed logistic regression, Cox regression with time-varying effects, and spatial
autocorrelation analysis.
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5 CHAPTER 5.
ANALYSIS AND DISCUSSION
5.1 Introduction
This dissertation conducted logistical regression, cox-regression with time-varying effect,
and spatial autocorrelation to explore the overall research question that to what extend public
assistance programs mitigate the likelihood of business closure, and three dimension of business
recovery during the COIVD-19 pandemic.
5.2 Descriptive Analysis
The analysis covers 722,280 individual business data. Of these, 46.7% of businesses
remain active until Jan 2023. The mean duration of keeping the business running for inactive
businesses is 29.75 months, with a standard deviation of 9.70.
Among 722,280 businesses, 3,355 businesses are minority-owned, which represents
0.46%. Regarding businesses that receive assistance from programs, most of the businesses that
receive assistance, about 12,150 (1.68%), access only restricted assistance (PPP). Next, 6,548
businesses, or 0.91% of the total sample size, only access to unrestricted (EIDL) assistance.
Finally, 1,188 businesses, or 0.16%, access both restricted (PPP) and unrestricted (EIDL)
assistance. Table 9 presents the descriptive statistics at the individual business level.
In terms of business characteristics, the mean business sales volume is $127,276, with a
standard deviation of 500,632.30. After applying the natural log transformation to the variable,
the mean value becomes 7.26, with a standard deviation of 5.63. The mean business employee
size is 6.69, with a standard deviation of 26.89. After applying the natural log transformation of
the variable, the mean value becomes 1.1, with a standard deviation of 0.96.
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Table 9. Descriptive Statistics at the Individual Business Level
Variable Mean Min Max S.D Obs. [%]
Active 337,279 46.7%
Restricted Cash Only 12,150 1.68%
Unrestricted Loan Only 6,548 0.91%
Mixed Assistance Received 1,188 0.16%
Minority Owned 3,355 0.46%
Survival Months 29.75 1 37 9.70
Sales 127,276 0 13,484,630 500,632.30
Sales (logged) 7.26 0 16.42 5.63
Employee size (freq.) 6.69 0 985 26.89
Employee size (logged) 1.1 0 6.89 0.96
Total minority population within
57.26 0.1 173.82 29.85
5KM service range (unit:1,000)
Average median household income
within 5KM service range 80.38 30.79 178.11 23.53
(unit:1,000)
Total housing units within 5KM
65.84 1.72 170 31.62
service range (unit:1,000)
Total N = 722,280
Business Sector: Agriculture, Forestry, Fishing and Hunting (11), Ming, Quarrying, and Oil and Gas Extraction
(21), Utility (22), Construction (23), Manufacturing (31-33), Wholesale Trade (42), Transportation and
Warehousing (48-49), Information (51), Finance and Insurance (52), Real Estate and Rental and Leasing (53),
Professional, Scientific, and Technical Service (54), Management of Companies and Enterprises (55),
Administrative and Support and Waste Management and Remediation Service (56),Education Service (61),
Health Care and Social Assistance (62), Arts, Entertainment, and Reaction (71), Accommodation and Food
Service (72), Other (81).
Regarding the market context factors, the average number of potential minority
population that a business can access with a 5 km service range is 57.26 thousand, with a
standard deviation of 29.85. The average amount of median household income from areas that a
business can cover with a 5 km service range is 80.38 thousand, with a standard deviation of
23.53. The average number of total housing units that a business can cover with a 5 km service
range is 65.84 thousand, with a standard deviation of 31.62.
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Figure 4. Percentage of Inactive Business at Census Tract Level
Figure 4 shows the map of business inactive rate in DFW at the census tract level. Areas
in red from light to dark indicate the percentage of inactive businesses from low to high. As
indicated in Figure 4, dark red areas tend to occur in urban areas, which suggests that compared
with rural areas, census tracts in urban areas tend to have a higher percentage of businesses
inactive since Dec 2019.
Figure 5 shows the map of the percentage of business access to any type of public
assistance program in DFW at the census tract level. Areas in blue from light to dark indicate the
percentage of business access to any type of public assistance program from low to high. As
indicated in Figure 5, dark blue areas tend to occur in relatively rural areas, which suggests that
assistance distribution agencies tend to distribute public assistance programs to relatively rural
areas instead of urban areas.
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Figure 5. Percentage of Business Receive Any Assistance at Census Tract Level
5.3 The Effectiveness of Public Assistance Programs in Helping Business Recovery
5.3.1 Logistical Regression
Table 10 shows the result of logistic regression. As Table 10 indicates, different
combinations of public assistance programs have different effects on business recovery. For
businesses that only receive one type of assistance, either type of assistance can raise the
likelihood of the outcome occurring. Specifically, receiving restricted assistance only (PPP)
assistance increases the odds of staying active by about 16% (Odd Ratio=1.15, p<0.05)
compared to businesses that do not receive any assistance, which supports Hypothesis 1.
Receiving unrestricted assistance only (EIDL) increases the odds of staying active by a factor of
4.91 (Odd Ratio= 4.91, p<0.05) compared to businesses that do not receive any assistance, which
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supports Hypothesis 2. For businesses that receive mixed types of assistance at the same time,
receiving restricted (PPP) and unrestricted (EIDL) assistance increases the odds of staying active
by a factor of 2 (Odd Ratio= 2, p<0.05) compared to businesses that do not receive any
assistance, which supports Hypothesis 3.
Table 10. Factors Explaining Small Business Recovery (Logistical Regression)
Small Business Recovery (0 = Inactive,
1= Active)
Estimate p Odd Ratio
Access to Assistance: Ref. None Assistance Received
***
Restricted Assistance Only 0.15 1.16
***
Unrestricted Assistance Only 1.59 4.91
***
Mixed Assistance Received 0.69 2
***
Minority Owned 3.52 35.46
***
Sales (logged) 0.26 1.3
***
Employee size (logged.) 0.34 1.4
Total minority population within 5KM service range (unit:1,000) 6*10-4 ***
1.00
Average median household income within 5KM service range
(unit:1,000)
5.3*10-3 ***
1.00
Total housing units within 5KM service range (unit:1,000) -1.5*10-3 ***
1.00
***
Constant -4.44 0.01
Also control for business sectors based on first two-digit of 2017 NACIS cod
Pseudo R Square = 0.36
Total N = 722,280
* p<.05; ** p<.01; *** p<.001
Business Sectors: Agriculture, Forestry, Fishing and Hunting (11), Ming, Quarrying, and Oil and Gas Extraction
(21), Utility (22), Construction (23), Manufacturing (31-33), Wholesale Trade (42), Transportation and
Warehousing (48-49), Information (51),Finance and Insurance (52), Real Estate and Rental and Leasing (53),
Professional, Scientific, and Technical Service (54), Management of Companies and Enterprises (55),
Administrative and Support and Waste Management and Remediation Service (56), Education Service (61),
Health Care and Social Assistance (62), Arts, Entertainment, and Reaction (71), Accommodation and Food
Service (72), Other (82).
The results suggest that businesses with access to public assistance programs recover
more effectively than those without, as the assistance provides liquidity to sustain operations.
However, when businesses apply for a mix of restricted and unrestricted assistance
simultaneously, their likelihood of recovery may decrease compared to those relying solely on
unrestricted assistance. This indicates that restrictions attached to programs like PPP, which
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require a certain portion of funds to be spent on payroll costs, can limit the effectiveness of EIDL
by reducing businesses’ flexibility to adapt.
5.3.2 Cox Regression with Time-Varying Effects
This study continues to perform Cox regression with time-varying effects to further
investigate the effectiveness of public assistance programs at different stages. Table 11
summarizes the results of the cox regression with time-varying effects.
As indicated in Table 11, the combination of public assistance programs has different
impacts on business recovery at different stages. Specifically, at Stage 1, receiving restricted
assistance only (PPP) assistance can increase the risk of a business becoming inactive by 78%
(HR=1.78, p<0.05). Furthermore, receiving both restricted assistance (PPP) and unrestricted
assistance (EIDL) simultaneously can increase the risk of business inactivity by 245% (HR=3.45,
p<0.05).
At Stage 2, comparing with business without any assistance received, receiving restricted
(PPP) assistance can decrease the risk of a business becoming inactive by 29% (HR=0.71,
p<0.05); receiving unrestricted (EIDL) assistance can decrease the risk of a business becoming
inactive by 15% (HR=0.85, p<0.05). Additionally, accessing both restricted (PPP) and
unrestricted (EIDL) assistance can reduce the risk of business inactivity by 5% (HR=0.95,
p<0.05).
At Stage 3, comparing with business without any assistance received, only receiving
restricted (PPP) assistance decrease the risk of a business becoming inactive by 6% (HR=0.94,
p<0.05); receiving unrestricted (EIDL) assistance reduces the risk of business inactivity by 69%
(HR=0.31, p<0.05); and receiving restricted (PPP) and unrestricted (EIDL) at the same time
decreases the risk of business inactivity by 23% (HR=0.77, p<0.05).
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These results also show the small business recovery pattern with the aid of public
assistance programs at different stages of the pandemic. At Stage 1 (Jan 2020 – Nov 2020),
business mainly face challenges of decreasing demand following social distance restrictions,
businesses that received restricted cash assistance faced higher risks than businesses that did not
receive any assistance, which suggests that at this stage, maintaining operations with the current
employee size increased the likelihood of inactivity. Business owners should use strategic
adaptation methods to survive, such as reducing employees to lower operational costs.
During Stage 2 (December 2020 – January 2022) and Stage 3 (February 2022 – January
2023), businesses faced challenges from international supply chain shortages. Access to
restricted assistance (PPP), unrestricted assistance (EIDL), or a combination of both helped
reduce the risk of business inactivity, highlighting the importance of liquidity in sustaining
operations as market demand stabilizes. In Stage 2, restricted assistance (PPP) was the most
effective in reducing the risk of closure. However, accessing mixed assistance (PPP and EIDL)
was less effective than relying on a single type of assistance during this period. In contrast,
during Stage 3, mixed assistance became more impactful than restricted assistance alone, though
it remained less effective than unrestricted assistance.
Table 11. Factors Explaining Small Business Recovery (Cox-Regression with Time Varying
Effect)
Small Business Recovery (0 = Active, 1= Inactive)
Stage 1 (Jan 2020 Stage 2 (Dec 2020 – Stage 3 (Feb 2022 –
– Nov 2020) Jan 2022) Jan 2023)
Estimate HR Estimate HR Estimate HR
Access to Assistance: Ref. None Assistance Received
Restricted Assistance Only 0.58*** 1.78 -0.34*** 0.71 -0.06*** 0.94
Unrestricted Assistance Only -0.02 0.98 -0.16* 0.85 -1.18*** 0.31
Mixed Assistance Received 1.24*** 3.45 -4.71*10-2 0.95 -0.26** 0.77
Minority Owned -0.43* 0.65 0.12 1.12 -3.67*** 0.03
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Sales (logged) 1.91*10-4 1 7.66*10-3*** 1.01 -0.14*** 0.87
Employee size (logged) 2.44*10 -2***
1.03 4.29*10 -2***
1.04 -1.76*10 -3***
0.84
Total minority population within 5KM
3.36*10-3*** 1 5.36*10-3*** 1.01 -1.85*10-3*** 1
service range (unit:1,000)
Average median household income within
3.71*10-3*** 1 0.01*** 1 -5.17*10-3*** 1
5KM service range (unit:1,000)
Total housing units within 5KM service
2.2*10-3*** 1 -7.69*10-4*** 1 -1.22*10-3*** 1
range (unit:1,000)
Also control for business sectors based on first two-digit of 2017 NACIS code
Pseudo R Square = 0.78
Wald Chi2 = 515,801***
Total N = 722,280
*
p<.05; ** p<.01; *** p<.001
Business Sectors: Agriculture, Forestry, Fishing and Hunting (11), Ming, Quarrying, and Oil and Gas Extraction
(21), Utility (22), Construction (23), Manufacturing (31-33), Wholesale Trade (42), Transportation and
Warehousing (48-49), Information (51),Finance and Insurance (52), Real Estate and Rental and Leasing (53),
Professional, Scientific, and Technical Service (54), Management of Companies and Enterprises (55),
Administrative and Support and Waste Management and Remediation Service (56), Education Service (61),
Health Care and Social Assistance (62), Arts, Entertainment, and Reaction (71), Accommodation and Food
Service (72), Other (82).
5.3.3 The Relationship Between Business Characteristics and Business Recovery
Table 10 also summarizes the results that explain the relationship between business
characteristics and business recovery. As indicated in Table 10, being a minority-owned business
can increase the odds of staying active by a factor of 35.46 (Odds Ratio=35.46, p<0.05)
compared with non-minority-owned businesses, which supports Hypothesis 6. Further, every unit
increase in the natural log of sales volume can increase the odds of staying active by a factor of
1.3 (Odds Ratio=1.3, p<0.05), which supports Hypothesis 5. Also, every one-unit increase in the
natural log of employee size can increase the odds of staying active by a factor of 1.4 (Odds
Ratio=1.4, p<0.05), which support Hypothesis 4.
Table 11 displays the outcome of Cox regression with time-varying effects. According to
Table 11, controlling for other variables, being a minority-owned business can lower the risk of
being inactive by 35% (HR=0.65, p<0.05) and 97% (HR=0.03, p<0.05) at Stage 1 and Stage 3,
respectively, compared to non-minority owned businesses. Moreover, every one unit increase in
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the natural log of sales volume can decrease the risk by 13% (HR=0.87, p<0.05) at Stage 3.
Additionally, each one-unit increase in the natural log of employee size was associated with a 3%
increase in the risk of business inactivity at Stage 1 (HR=1.03, p<0.05) and a 4% increase at
Stage 2 (HR=1.04, p<0.05). However, at Stage 3, the relationship reversed, with a one-unit
increase reducing the risk of business inactivity by 16% (HR=0.84, p<0.05).
These findings indicate that during the COVID-19 pandemic, businesses with more
employees are more prone to becoming inactive in the first two years, which conflicts with
previous studies (M. A. U. Khan & Sayem, 2013; Marshall et al., 2015). This is due to the fact
that during the COVID-19 pandemic, the labor cost rose, which harmed the profitability of
businesses, especially those in labor-intensive sectors (Dvořák et al., 2020). However, from the
results of both logistic regression and Cox regression analysis, the values of the odds ratio and
hazard ratio are close to or equal to 1, suggesting that the impact of employee size is relatively
small in our sample.
Businesses with higher sales volume have a better chance of recovery, which is consistent
with previous studies (Han & Nigg, 2011; Marshall & Schrank, 2014). Business sales volume
reflects business financial resources that can be utilized to responding disasters; higher sales
volume suggests a better capability to absorb impacts from disasters, which helps business
recovery. However, at Stage 2, the increase of the natural log of sales volume slightly increases
the risk of becoming inactive. This is likely due to the disruption of international supply chains
during this stage, which challenges business profitability, especially for businesses with higher
sales volumes. High sales volume is normally associated with high dependencies on the
functioning of supply chains; this interruption can lead to an increased risk of being inactive
(Craighead et al., 2007).
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Moreover, minority-owned businesses show resilience during the COVID-19 pandemic,
as being a minority-owned business can significantly facilitate recovery, which conflicts with
previous studies (Andrew et al., 2022; Atkins et al., 2022). One possible reason for this is that
minority-owned businesses can receive support from local communities, especially if they offer
products that are locally demanded (Ram, 1998). As more people work remotely after the
COVID-19 pandemic, the demand for household goods rises (Devereux et al., 2020), which can
benefit the businesses that supply local demand goods.
5.3.4 The Relationship Between Market Context and Business Recovery
Even though the three variables used to capture market context achieve statistical
significance in both logistical regression and cox-regression analysis, the value of odds ratio and
hazards ratio are close to 1 or equal to 1, which suggests that these three variables do not have a
significant impact on risk of business inactive, which reject Hypotheses 7 and 8. This conflicts
with previous studies (Alesch et al., 2001; Chang et al., 2022; Corey & Deitch, 2011). One
possible explanation for the low influence of the variables that capture the market context in this
study on small business recovery is that the DFW metropolitan area did not experience a
significant population shift during the COVID-19 pandemic, which can maintain a consistent
demand baseline for business (Chatzinikolaou & Vlados, 2021; Wang & Kang, 2021).
5.3.5 The Relationship Between Geographic Location and Business Recovery
Table 12 summarizes the result of spatial autocorrelation (bivariate Moran’s I).
Specifically, Moran’s I value for receiving restricted (PPP) assistance (Moran’s I = -0.13,
p<0.05), unrestricted (EIDL) assistance (Moran’s I = -0.11, p<0.05) are negative associate with
business active rate, which reject Hypothesis 9 and 10.
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Table 12. Result of Spatial Autocorrelation
Variables Moran’s I Sig. z-value
Restricted Assistance Only Rate -0.13 0.001 -10.23
Unrestricted Assistance Only Rate -0.11 0.001 -9.28
Mixed Assistance Received Rate 0.07 0.001 5.96
As indicated in Table 12, at the census tract level, the percentage of business access to
restricted (PPP) and unrestricted (EIDL) assistances at the same time positively associated with
the percentage of business kept active (Moran’s I =0.07, p<0.05), which supports Hypothesis 11.
These results suggest that combinations of types of assistance significantly help small
businesses in the DFW area recover. This effect can be due to the increased cash flow in the
DFW area, which promotes purchasing behavior and, consequently, overall business recovery in
the region. However, this does not necessarily imply the recovery of individual businesses.
Figure 6 to Figure 12 presents the result of bivariate local Moran's I analysis. Areas
represented in red and blue indicate that, in those areas, the percentage of businesses accessing
public assistance programs is positively associated with the percentage of active businesses.
Areas represented in orange and light blue indicate that, in those areas, the percentage of
businesses assessing public assistance programs is negatively associated with the percentage of
active businesses.
As shown in Figures 6 to 8, at the census tract level, a similar pattern emerges: businesses
in areas that farther from each county center have higher activity rates, which consistent with
current studies (Chrisman et al., 1987; Craig et al., 2008). This pattern may be due to two factors.
First, compared to urban centers, businesses in rural areas often lack alternative funding options
beyond public assistance programs. This limitation creates pressure to make full use of the
assistance they receive, which in turn is more likely to encourage business innovation (Mann et
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al., 2021). Secondly, the financial ecosystem of business in rural area are less likely to relay on
centralized economic hubs, assistance to key business in this area can lead broader economic
recovery (Craig et al., 2008). Small businesses in rural areas are often central to local networks,
with their scale and profitability determined by the local consumer base (Miller & Soyoung,
1999). Unlike businesses in urban centers, rural consumer demand and purchasing power tend to
be more stable (Grube & Storr, 2018). Temporary assistance to help these businesses resume
operations can therefore be highly effective for community recovery.
Figure 6. Percentage of Business Access to Restricted (PPP) Assistance Only and Business
Active
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Figure 7. Percentage of Business Access to Unrestricted (EIDL) Assistance Only and Business
Active
85
Figure 8. Percentage of Business Access to Mixed Assistance (PPP and EIDL) and Business
Active
The following chapter covers the implications and conclusions of this study. Chapter 6
examines how this study contributes to the field and practices. It also outlines the limitations of
this dissertation and the suggestions for future research.
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6 CHAPTER 6
CONCLUSION
The objective of this dissertation is to answer the overarching research question: to what
extent do public assistance programs mitigate the likelihood of business closure after disasters?
This research question is important because business recovery is a critical part of the disaster
recovery process. In addition, as the primary source of assistance available for small businesses,
the long-term effectiveness of public assistance programs in helping businesses recover remains
unclear (Chang et al., 2022). Furthermore, there is a lack of studies investigating how the types
and combinations of public assistance programs impact their effectiveness in aiding business
recovery (Josephson & Marshall, 2016; Marshall & Schrank, 2020; Wang & Kang, 2023;
Watson, 2021). Therefore, studying business recovery with the support of public assistance
programs can help policymakers understand the rationality of these programs and inform small
business owners on how to choose appropriate combinations of public assistance programs when
multiple options are available after disasters.
This study's research questions contribute to academia by investigating the impact of
different combinations of public assistance programs on business recovery post-disaster. By
exploring the mechanisms behind these programs, this research addresses a gap in the literature
concerning how restrictions attached to public assistance programs and the differences between
loan-based and cash-based assistance affect their overall effectiveness.
Theoretically, this research question expand based on the Business Recovery framework
proposed by Chang et al., (2022), which argue that restrictions attached to public assistance
programs, format of assistance are critical to succeed of those programs and can affect business
87
recovery. Answering the overall research question can contribute the development of business
recovery theory by investigating the significance of public assistance programs.
The main research question helps the practice by telling policymakers about the effect of
the mechanism behind those public assistance programs. Moreover, answering main research
question can guide small business owners to choose suitable assistance for them to boost
recovery instead of raising operation risk after disaster. Also, the main research questions show
insights on how public assistance programs allocated geographically, which can propose ways to
make assistance more reachable and equitable.
A literature review was performed to investigate a preliminary question of the main
research question: what factors can account for business recovery after disasters? The main
factors that affect disaster recovery and business continuity were explored through a range of
studies. These factors are, public assistance programs, geographic locations, business
characteristics, and market context.
Public assistance programs help businesses recover by offering them liquidity to ease
their financial pressure and restore their operational capacity (Josephson & Marshall, 2016).
Disasters can result in physical damage, and businesses with physical damage need to buy new
equipment to resume operations (Sydnor et al., 2017). Previous studies suggest that public
assistance programs have a significant role in supporting business recovery after disasters
(Berger et al., 2021; Marshall & Schrank, 2020; Watson, 2021). However, the effectiveness of
public assistance programs depends on the rationality of the restrictions attached and the format
of public assistance programs. Unreasonable restrictions can hinder businesses' ability to adapt,
and loan-based assistance can add to their debt burden, which slows down the recovery process
(Dahlhamer & Tierney, 1998; Graham, 2007).
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Geographic location can influence the effectiveness of public assistance programs in
helping small businesses recovery by influencing distribution patterns (Crass et al., 2016;
McCann & Folta, 2011). Geographic locations can significantly increase the likelihood of
businesses receiving public assistance because of the localized advantages, for instance, well-
developed industrial chain and scale of labor market (Andersson & Larsson, 2016; Porter, 1998).
Allocating public assistance to these areas can more effectively promote recovery, benefiting not
only small businesses but also the regional economy (Albors-Garrigos & Collado, 2019; Skokan
et al., 2012).
The degree of impact that disasters have on businesses depends on their characteristics,
such as their sector and size, which decide the initial pathway of the subsequent recovery process
(Altay & Ramirez, 2010). Businesses in different sectors have different levels of dependence on
infrastructure to restore their functionality. Moreover, businesses with more employees have
more resources and market experience to deal with the negative effects of disasters. They also
have stronger networks and savings, which allow them to resume their operations faster than
smaller or newer businesses (Webb et al., 2002). In addition, businesses with a larger number of
employees tend to get priority when applying for public assistance, as distribution agencies also
consider unemployment rates following disasters (Hartwell et al., 1996). Further, businesses with
higher sales volume can support businesses' recovery after a disaster from by increasing business
resilience (Marshall et al., 2015).
Market context can decide businesses' ability to stay profitable after a disaster, which is
crucial for business recovery (Aghababaei et al., 2020). While some sectors, like construction,
may experience a temporary increase because of reconstruction demand after a disaster, large-
scale population relocation and economic losses among survivors pose challenges to business
89
profitability (Corey & Deitch, 2011; Kousky, 2014; Sampson et al., 2018). Empirical studies
indicate that businesses that can quickly restore their customer base have a higher chance of
surviving and thriving after a disaster (Marshall & Schrank, 2014). Moreover, businesses that
have access to customers with higher purchasing power are more likely to recover (Xiao et al.,
2022).
In summary, business recovery is influence by factors include types of public assistance
programs, geographic factors, business characteristics such as sector, size, and ownership, and
market context such as spending power of customer groups. This dissertation suggest that
restrictions attached to public assistance programs and format of assistance can influence the
effectiveness of those programs.
Why is it necessary to set retroactions to public assistance programs and set the majority
of assistance in a loan-based format? Restrictions often attach to public assistance programs from
concerns of effectiveness (Gu et al., 2008; Xu & Mo, 2013). Restrictions are used to ensure
recipients use the assistance for activities related to business operations instead of misusing the
fund. The loan-based format is critical for business recovery because it not only provides
liquidity but also promotes creation and expansion with appropriate pressure (Bach, 2014; Kelly
et al., 1998).
This dissertation examines how various restrictions and formats of public assistance
programs affect business recovery. This study uses logistic regression and cox regression with
time-varying effects to analyze the data. The reason for using cox-regression as a complement to
logistic regression is that it enables us to measure the varying impacts of public assistance
programs at different stages. Businesses encountered different challenges at different times
during the COVID-19 pandemic, such as reduced demand in the early stage, disrupted
90
international supply chains in the middle stage, and regulatory and policy uncertainties at the end
stage (Ardekani et al., 2023; Bartik, Bertrand, et al., 2020; Trump & Linkov, 2020).
The results of the analysis reveal three findings. First, the effectiveness of public
assistance programs does not have a cumulative effect on business recovery. At the individual
business level, while individual public assistance programs positively impact business recovery
regardless of attached restrictions and funding format, inappropriate combinations of assistance
programs can negate these efforts. When businesses receive restricted (PPP) assistance and
unrestricted (EIDL) assistance, this combination of assistance might require businesses to
maintain their employee size and repay loan interest, which can increase operational costs and
raise the risk of business inactivity. On the other hand, from a geographic perspective, in the
entire DFW area, the more unrestricted cash flows in, the more likely overall business recovery
is to occur.
Second, business size is not always positively associated with business recovery.
Businesses with a large number of employees need to afford relatively high payroll costs.
Considering the increase in labor costs following the pandemic, these increased payroll expenses
can affect business profitability and raise the risk of inactivity (Dvořák et al., 2020). On the other
hand, businesses with larger sales volumes can positively associate with recovery during the
pandemic if they do not face supply chain disruptions.
Third, market context does not influence business recovery if there is a lack of massive
population relocation because stable local residents can ensure a baseline of demand. Businesses
can establish a well-developed operation pattern with customers before the pandemic; even
though the social distance restriction policy limits customers’ purchasing behavior, it does not
decrease the demand, does not (Chatzinikolaou & Vlados, 2021; Wang & Kang, 2021). On the
91
other hand, the increase in household demand due to more people working from home during the
pandemic can promote recovery (Devereux et al., 2020). This also explains why minority-owned
businesses show more resilience than non-minority-owned businesses, as they can better provide
local demand goods (Ram, 1998; Wang et al., 2021).
This study has several limitations. The first limitation is that it merges business records
from Mergent Intellect and the recipient list from the SBA based on business names and
addresses. This merging method can result in mismatches due to duplicate business names, such
as Seven Eleven. Additionally, it does not account for businesses that relocated between the
times the addresses were collected by Mergent Intellect and the SBA.
The second limitation is that this study does not filter out potential fraud cases in both
PPP and EIDL recipient lists. According to Griffin et al., (2021) the adaptation of fintech lenders
during the COVID-19 pandemic to distribute PPP assistance promoted fraud in PPP programs.
Although merging the recipient list with business records from Mergent Intellect can indirectly
filter out some fraud cases, there might still be fraudulent cases in the sample of this study. The
presence of fraud cases can distort the effectiveness analysis of the assistance programs, as
fraudulent recipients do not represent genuine business needs and recovery efforts.
The third limitation is that the conclusions are based on data from the DFW region. While
the same public assistance programs were distributed nationwide by the SBA, local economic
structures, infrastructure status, and geographic factors can limit the generalizability of this study
to other areas. The unique characteristics of the DFW region, such as its economic diversity,
population density, and infrastructure development, might not be representative of other regions.
Therefore, the findings might not fully apply to areas with different economic and infrastructural
contexts.
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The fourth limitation is that this study only includes two major public assistance
programs from the SBA during the COVID-19 pandemic. Other accessible assistance at the state
and local levels is not included. For example, the State of Texas also provided several small
business assistance programs to reboot the economy during the pandemic. This study does not
cover those programs due to a lack of relevant data. Excluding these additional programs means
that the analysis might not capture the full spectrum of available support and its combined
impact on business recovery.
The next step of this dissertation is to conduct further studies to overcome these
limitations and expand the scope of the business recovery research. The modified research
framework proposed in this study will be applied to other metropolitan areas that have
experienced long-term disruptive events similar to the COVID-19 pandemic. By broadening the
geographic scope, the study aims to test the robustness and applicability of the framework across
different contexts. Future studies will also include all available public assistance programs for
small business owners at both the federal and state levels. This comprehensive approach will
allow for a more detailed investigation into the effectiveness of various combinations of public
assistance programs in aiding small business recovery. By analyzing a wider array of assistance
options, the research can provide more nuanced insights into how different types of aid interact
to support business resilience.
In future research, instead of focusing on a single metropolitan area, multiple
metropolitan areas with varying infrastructure characteristics will be used as research sites. This
comparative approach will enable the study to assess the impact of infrastructure development on
the effectiveness of public assistance programs. By selecting metropolitan areas with diverse
infrastructure profiles, the research can identify which aspects of infrastructure development
93
most significantly mitigate the challenges faced by businesses during recovery. This will help to
understand how investments in infrastructure can enhance the effectiveness of public assistance
programs and contribute to more resilient economic systems. The modified theoretical
framework will be rigorously tested using data from multiple metropolitan areas, providing a
broader and more comprehensive understanding of business recovery dynamics.
Lastly, since the COVID-19 pandemic did not cause physical damage to businesses, this
study does not account for business damage profiles. However, when applying the proposed
theoretical framework to future disaster events, it will be essential to include the damage profiles
caused by these events. Future studies will investigate the interaction between the extent of
physical damage caused by disasters and the combinations of public assistance programs. This
will involve analyzing how different levels of damage influence the effectiveness of various aid
packages and identifying the optimal combinations of assistance for different scenarios. By
incorporating damage profiles, the research can offer more tailored recommendations for disaster
recovery planning and policy-making, ensuring that public assistance programs are designed to
address the specific needs and challenges faced by businesses in the wake of physical
destruction.
94
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