I have the senior project term paper and will provide you with that. In addition, I have 9 sources that are needed for this paper. Feel free to include more sources that are relevant.
Michelle_Michy
5b2ac67696d71.pdf
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A Typology of Fiscal Stress Following Disasters Julie Winkler and Skip Krueger* Department of Public Administration, University of North Texas, Denton, USA; *Corresponding author: [email protected]
Julie Winkler is a doctoral student in the Department of Public Administration at the University of North Texas. Her research interests center around the financial aspects of disasters, communication processes during disaster response, and long-term housing recovery. Skip Krueger is an Associate Professor and PhD Program Coordinator in the Department of Public Administration at the University of North Texas. His research interests include the financial aspects of natural disasters, regional cooperation among local governments, and state and local budgeting.
Abstract: In the United States, local governments often face significant fiscal challenges following disasters. For example, damage to homes and businesses can impact various revenue sources; emergency response, debris removal, and infrastructure stabilization can temporarily drive expenditures above normal levels; and long-term repair and replacement of facilities can all place sometimes dramatic stress on the resources of local governments, including cities, counties, townships, and special districts. While federal and state governments may later help ease some of the fiscal stress of a disaster, those resources are usually provided much later, are not available for some categories of public-sector losses, and typically only partially cover local costs. Despite all this, few studies have been conducted on the fiscal impact of disasters on local governments and the recovery process those governments go through in the aftermath of disasters. More importantly, existing studies – while insightful – have not utilized a theoretical framework to systematically understand how disasters affect local fiscal conditions in the short and long run. We address this issue by developing a typology of fiscal stress experienced by U.S. local governments that results from natural disasters. We then apply that typology to four case studies of cities that faced fiscal stress after hurricanes. We evaluate 10 different fiscal indicators over several years before and after a major hurricane in four cities: Orange and Galveston, Texas, before and after Hurricane Ike, and Biloxi and Gulfport, Mississippi, before and after Hurricane Katrina. We assess the degree to which the disaster fiscal stress typology fits the different experiences in these four cities.
Keywords: disaster finance, fiscal stress, disaster recovery, local government
Special thanks to Brittney Huff for help in data collection.
Prepared for the IPPA Workshop on Emergency Management, Disaster, and Policy at the International Workshops in Public Policy, University of Pittsburgh, June 26-28, 2018. Please do not cite without permission of the authors.
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Introduction
Local governments whose communities experience local disasters are caught in a catch 22; damage
to homes and changes in demography as residents choose not to return can degrade the tax base and cut
revenue, while response and recovery is costly, requiring added expenditure to remove debris and fix
public facilities. But not all experiences of disaster are the same; disaster events can add on to already
existing socioeconomic conditions, influencing recovery, and the severity of a disaster can determine
fiscal patterns for several key revenues local governments rely on.
We know that cities are strategic in their response to disaster; changing tax rates (Hildreth 2009) , or
recognizing the cyclic nature of disaster in designing their land management and planning schedules
(Handley 2006), but so far research has been limited to case studies, making it difficult to determine what
predictable patterns cities follow, to manage their fiscal stress following disaster. In this paper, we hope
to develop a framework to classify the type of fiscal stress cities face following disaster, to better
understand the diversity of fiscal challenges of disaster found in the literature.
The impact of a disaster can vary in severity, in terms of the number of people effected, homes
damaged, businesses disrupted, area covered, duration of the hazardous conditions. During a hurricane,
cities who are coastal may experience storm surge and sudden wave conditions, while further low-lying
inland areas may face flooding and longer lasting stagnant water conditions.
Disasters events act within specific socioeconomic conditions, which can influence the speed of
recovery. Communities which have less access to financial markets, are already facing high levels of
unemployment, are experiencing an economic depression, are highly dependent on resources impacted
by the storm, are also likely to have a drawn-out recovery process. Cities with less resources and low
staffing will find it difficult to manage additional grants and projects related to recovery.
Following a disaster, cities often provide the first response, organizing evacuation and return,
clearing debris, repairing roads and vital facilities damaged by a storm, and working with the state and
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county to disburse federal grants for public assistance. While the dominant paradigm may suggest that
cities do little more than await federal assistance, others suggest that cities act strategically. Cities hold
more cash on hand when they are more reliant on volatile revenue streams or are more at risk to
hazardous conditions.
However, the fiscal impact and recovery process of local governments following disasters is not
well understood. Several case studies have shown that disasters can have complex and lasting influence
on city finance, but these disparate cases are not grounded in a framework which allows for comparison.
Do cities with different experiences of disaster face different financial challenges during the recovery
phase, and do they respond using different strategies? Which fiscal patterns following a disaster are more
predictable, and where are they more volatile?
We develop a framework, adapted from Levine (1985), to classify cities’ experiences of disaster
based on the severity of the event and duration of recovery, based on the fiscal impact on several key
indicators in their revenue stream and expenditure patterns. We look at four cities, two in Texas following
hurricane Ike, and two in Mississippi following hurricane Katrina, and measure 10 variables for two years
before the storm, and seven years after the storm, to determine what patterns emerge.
In the following section we will provide a discussion of the literature on disaster finance and the
local and state level. Then we will develop our framework, based on Levine’s (1985) typology of fiscal
stress, before analyzing the four cities using our model.
Literature Review
Following at least some types of disasters, local governments can face significant fiscal stress.
Unanticipated fiscal effects may include having to pay for additional staffing and assets, rebuilding
damaged or destroyed buildings and equipment vital for delivering services, and sometimes severe
revenue shortfalls.
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Sylves (2007) suggests that local governments are often constrained in their spending by the level
of revenue they take in, due to requirements to keep a balanced budget. Local governments are able to
use taxes and fees, intergovernmental funds, special assessments, and bonds to pay for emergency
management efforts. As Settle (1985) describes, disasters often face losses in property taxes, have a
shrinking tax base, business disruptions can cut into sales tax, and new costs can increase the debt ratio.
However, disasters can vary in factors like severity and length, resulting in different financial outcomes
following disaster, making disaster finance generalizations difficult.
Several case studies have considered a single catastrophic event and the resulting fiscal effects.
Ebdon, O’Neil, and Chen (2012) looked at three overlapping jurisdictions, Omaha Airport Authority,
Omaha Public Power District, and the city of Omaha, during the Missouri River flooding in 2011. Because
the flooding event lasted months, this case served to better understand the fiscal strain caused by
continuing emergency management costs, where response, recovery, mitigation and even preparedness
activities may have occurred simultaneously. Furthermore, these overlapping jurisdictions had
interdependencies, and shared decision-making networks, possibly making response more difficult.
In Omaha, no major destruction happened: the airport stayed open; the powerplant was not
severely damaged; major flooding within the city was avoided (Ebdon, O’Neil, and Chen 2012). While
some sewage was released into the river, the sewage plant remained unscathed and the city was able to
continue functions, including holding festival events. Mitigation efforts, like using sandbags to reinforce
the levees, and the timely construction of levees to protect the sewage plant were deemed successful,
and communication between the three jurisdictions was fruitful. However, the reliance between the
jurisdictions may indicate a higher level of risk; if the airport had shut down, the city would have seen
more consequences as business would also be disrupted. While mitigation efforts throughout the
flooding event were costly, the three jurisdictions avoided longer-term costs from any severe damages.
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Analyzing the recovery process following hurricane Katrina in New Orleans, Hildreth (2009) finds
that the city’s budgets and priorities were revised, revenues trended downwards as expenditures went
up, and assets needed repair but intergovernmental revenue and other new revenue streams to use in
response followed complicated new rules and processes which were difficult to navigate. Rising costs of
recovery initially forced New Orleans to lay off 2,400 employees, about half their workforce, in order to
cut spending to match revenues. The result was a decreased scope of services like garbage pickup, while
other emergency staff worked overtime.
Hildreth (2009) also found that in the year after Katrina, property assessment values had
substantially decreased, and in order to compensate the city increased the property tax rate, to cover the
debt supported by the tax. The burden of this tax largely fell on income-producing property, which were
not included in the homestead exemption. Furthermore, operations funded through enterprise funds
faced difficulty in continuing services, and in securing income.
Even though the hurricane had a relatively short duration, federal reimbursements for public
assistance takes years to materialize (Hildreth 2009). This led to difficulty for local officials to properly
forecast revenues, and in turn properly budget expenditures in the following years. At the state level,
Louisiana saw more extreme budget changes than Mississippi. Despite regulations requiring funding to be
fully obligated within 2 years for the public assistance program, FEMA seemed to drag their feet, with a
large increase in projects being obligated a week before being required to report their progress, rather
than steadily disbursed throughout the 2 years. Furthermore, FEMA seemed to have preference for
suburban infrastructure, rather than repairing older systems within the city, and were required to only
rebuild to previous conditions, rather than upgrade or improve facilities.
Focusing on the financial impacts of hurricanes Ivan and Katrina on Alabama, Handley (2006)
finds that in the Mobile region communities are proactive and strategic towards storm preparation. In
Mobile, sales tax revenues two months following hurricane Katrina were 50% higher than the year before,
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which had been 1 month following hurricane Ivan. Unemployment also declined following Katrina and
Ivan, at least in part due to increased construction activities leading to new job opportunities. While
hurricane activities did discourage tourism, revenues from attractions did not see overwhelming changes.
While other states with more damage had negative impacts to their bond ratings, Alabama was not
downgraded following Katrina. Mobile recognizes the cyclic nature of coastal storms and try to
incorporate the likelihood of damage into their decision-making framework for land use and
development.
MacManus and Caruson (2008) surveyed Florida city and county finance officers, and found that
motivations for entering into cost-sharing agreements for emergency management include a stronger
faith in horizontal than vertical agreements, and a perceived inadequacy in federal funding. Local
government officials find that they cannot secure enough preparedness mitigation funds prior to an
event, and following an event, federal reimbursement programs take years to approve and obligate. The
use of interlocal agreements and mutal aid in emergency and fire services is well established in the
literature, (Andrew and Hawkins 2013; Cohen and Werker 2008; Carr and LeRoux 2005; Kwon, Feiock,
and Bae 2014) showing that local governments make use of multilateral agreements in preparation and
response to disasters.
Ismayilov and Andrew (2016) look at sales tax revenues following hurricane Ike, for three cities in
Texas, and find that during a two year period the cities experienced a short-term spike in revenues,
followed by a long-term decline. This suggests a more nuanced management approach is necessary, to
ride through an initial bump in revenue, bracing for a longer decline during the start of the long-term
recovery phase. In a nuanced 50 state panel analysis over 43 years, Miao, Hou, and Abrigo (2018) looks at
how disasters impact state revenue, expenditure, debt issuance and federal state transfers, and find that
often there are fluctuations in sales tax revenues, matching similar findings at a local level.
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Miao, Hou, and Abrigo (2018) also find that following a disaster there is also often an increase in
non-disaster related federal welfare program funding, often because natural disasters may stretch the
resources of those just above aid program requirements into needing assistance, due to property damage
and income insecurity or business disruption.
While the literature has several detailed case studies of the fiscal effects of natural disasters at
the local or state level for many different revenue and expenditure streams, there is a definite absence of
a comprehensive theoretical framework which links these different case studies in a way which allows for
comparison. While some analyses focus on one kind of revenue, like sales tax, over several cities or
disasters, a more complete analysis on a wide area of fiscal measures in both revenue and expenditure is
necessary to determine what strategies cities implement when facing fiscal stress from disaster.
Theory
Previous studies provide important insights on the fiscal effects of disaster on local government.
Missing, however, is a theoretical framework that links the disparate findings in a way that would allow
for accumulation of knowledge about disaster effects and local government responses to those effects.
We suggest that an existing framework for understanding fiscal crises can be adapted to
systematically evaluate the fiscal effects of natural disasters on local governments. Specifically, we
propose to borrow Levine’s (1985) classification of fiscal stress to classify different levels and types of
disaster impacts and their consequent responses by local governments. We suggest that the fiscal effects
of natural disasters on local governments can vary in both intensity and duration in ways very similar to
the more commonly studied effects of economic crises.
Levine’s (1985) framework for understanding the fiscal stress caused by general economic
fluctuations suggests that local governments react strategically to both the severity and the duration of
economic downturns. Shorter or less severe fiscal stresses may require short term solutions like deferred
maintenance and hiring freezes, while more intense fiscal stress that spans multiple years may require
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significant restructuring of the services being provided and revenue streams utilized and may include
such innovative solutions as partnering with nonprofits, outsourcing, or creating multilateral agreements
with other jurisdictions.
Levine (1985) categorizes economic crises into four types, based on their severity and length. Using
police departments as an example, he finds that public organizations use different solutions to respond to
different types of stress. The first category - an economic downturn of low severity and short duration - is
identified as a fiscal crunch and is typically associated with a modest decline in revenues due to moderate
recessions. Levine says that local government responses to crunches should likewise be expected to be
modest, with freezing unfilled positions being the most common response. Levine (1985) defines the
second category – a low severity economic downturn with a long duration -a fiscal squeeze. These are
economic declines which may develop over a period of years. Local governments are likely to aim for
efficiency improving systems, increased use of service fees, and downsizing fleets. The third category, a
steep economic downturn with a short duration, is named a fiscal crisis. A Crisis may be caused sudden
economic events leading to year-end budget deficits and can must be dealt with by shutting down
programs and laying off employees. Finally, the fourth category, named a fiscal crush, is classified as a
steep economic downturn with a long duration. A Crush may be caused by a major employer or military
base closure, leading to degraded tax base. Coping methods may require innovative solutions and
extensive contracting and multilateral partnerships.
As laid out by Levine (1985), cities act strategically to fiscal stress, based on factors of duration
and intensity. We extended this model to analyze the fiscal effects of hurricanes, because we believe that
the fiscal challenges faced following disasters are similar to those faced in cut back management. As we
have seen in the literature, cities face different hazards with different levels of severity, within the
context of different types of economic conditions, and illicit different strategic responses, and different
types of fiscal stress. Adapting from Levine, we propose to categorize cities’ experiences of hurricanes
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into four categories, based on level of storm severity, and duration of economic consequences,
categorized in a similar fashion as Levine (1985) for economic stressors. See Table 1 for a description of
the disaster conditions expected in the four categories.
[Table 1 here]
Fiscal Crunch
In a fiscal crunch, a city experiences fiscal stress that is short term and has low severity after a
natural disaster. In this category, most economic losses are insured in the private marketplace, and public
losses – including revenue declines and extraordinary expenditures are minimal. Such events may have
widespread flooding, and some destruction of private commercial and residential property, as well as
modest effects on public infrastructure, but those effects would be primarily resolved within a year of the
hurricane. A fiscal crunch should have little business disruption, and any evacuees quickly returning
home. Any impacts to finance for local government should be small, with a faster recovery.
Fiscal Squeeze
In a fiscal squeeze, a city experiences fiscal stress that has a long duration, but is low severity,
following a natural disaster. We would expect that areas which already faced financial strain in the years
prior to a hurricane may not have ample resources to deal with a new stressor. In this category, a lack of
ability to manage and obtain grants may hinder recovery speeds. We expect that the city will spend fund
balance reserves to balance the budget. Even with a low severity event, a city with little capacity to deal
with new financial stress will likely take longer to recover, with damage still unrecovered after more than
1 year, leading to decreased revenue income from sales and property tax. Widespread damage may delay
evacuee return, leading to higher levels of business disruption. Slow growth may discourage small
businesses or push them out of business. We may even expect a decline in population, as evacuees find
less hope in a full recovery by returning.
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Fiscal Crisis
A fiscal crisis, or high severity event following a natural disaster, but a short duration, will likely
occur in areas where storm surge causes severe damage to infrastructure and roads. Income producing
public infrastructure, like water treatment facilities may be compromised, leading to a temporary decline
in revenue for those facilities, and the need for capital projects to fix facilitates. A city may take on new
debt in order to pay for public infrastructure, beyond any federal aid or intergovernmental money. We
would expect significant but short-term revenue decline in all categories. Federal assistance from
presidential declarations and new debt are able to cover recovery costs, leading to a quick recovery. A
previously healthy economy is able to restart quickly following the event.
Fiscal Crush
Finally, a fiscal crush is classified as a high severity event with lasting economic consequences.,
following a natural disaster. We would expect that a fiscal crush is best classified as an event like
hurricane Katrina, where massive destruction across the board leads to changes in demographics and
population for the city permanently, changing the tax base, and can alter expected services and citizen
priorities. There is likely a permanent reduction in budget size, and total assessed value of housing stock.
Extreme housing damage may lead to new land use and changes to housing patterns during recovery.
High costs to deal with debris and damages lead to fiscal structural imbalances, as the city struggles to
match revenue and expenditure. There are dramatic and permanent revenue declines, and even layoffs
and cuts in service. As with a fiscal squeeze, limited management capacity leads to a reduced ability to
process grants, leading to slow implementation. The city may see a negative change to their bond rating,
reducing their ability to borrow. There will be permanent business loss, as recovery drags out. The city
may try to increase outsourcing, or partnering with other jurisdictions, nonprofits, or private firms to
reduce costs for service provision, facing long term financial decline in budgets.
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Hypotheses
The framework proposed here generates numerous hypothesis, not all of which can be tested
simultaneously within a single model. The processes described in the framework operate at multiple
levels of analysis and timeframes, requiring multiple data sources. For the purposes of this paper, we
narrowly focus on the fiscal effects measurable within a local government’s Comprehensive Annual
Financial Report (CAFR).
We focus on 10 variables, which we expect to help differentiate between the four categories laid
out in the theory. We look at four measures of revenue: Own source revenue (net of taxes), property tax
revenue, sales tax revenue, and intergovernmental revenue. While we expect intergovernmental
revenue, including donations and grants, to increase following a disaster, we expect all other forms of
revenue to decrease following disaster due to the damage and business disruption caused by the disaster.
We expect own source revenue to drop due to disruption in service and business. We expect property tax
revenue to decline because the damage to housing likely decreases assessed values. Sales tax will likely
see an initial increase due to immediate recovery needs and debris removal, but more permanent
disruption to business and changes in demography as some evacuees do not return may lead to a decline
in sales tax revenue. We anticipate that the level of change and duration in revenue will be a function of
the intensity of the disaster, and the socioeconomic context.
We look at three measures of expenditure: capital outlay, public works, and all other
expenditures. In all three cases, we expect a level of increase to match the level of severity of the storm,
and the speed of recovery for the fiscal effects to match the economic pace of recovery. We expect
capital outlay to increase when there is more rebuilding necessary, due to destruction from the storm.
We expect public works to increase due to debris removal and road repair. Other types of expenditure
are likely to also increase, as costs in transporting evacuees, increased work hours, sheltering costs, and
other disaster related activities may come out of several types of expenditures. We anticipate that the
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level of change in expenditure and the duration will be a function of the intensity of the disaster, and the
socioeconomic context.
Finally, we consider three additional measures, of tax rates, assessed values, and fund balance. In
some prior literature changing the tax rates has been suggested as a tool for increasing revenue when
assessed value declines, so we are using these measures to further analyze recovery of property taxes,
which cities are largely reliant on. Fund balance is another measure of fiscal solvency. For a complete list
of our expectations, see Table 2.
[Table 2 here]
Methodology
In this paper, we focus on four cities; two in Mississippi which were impacted by hurricane
Katrina in 2005, and two in Texas which were impacted by hurricane Ike in 2008. These cities vary in size,
density, and demographic characteristics, allowing for a more nuanced and varied narrative on how cities
cope in response to natural disasters. For descriptive statistics on the four cities, see Table 3. Table 4
shows the amount of federal assistance FEMA approved, for the individual and public assistance
programs.
[Table 3 here]
[Table 4 here]
Hurricane Katrina struck the gulf coast in August 2005, leading to widespread damage along the
coast. While media and research often focus on the levees breaking and the utter destruction in New
Orleans, the Mississippi coast also sustained record 145 MPH winds and so much as 30-foot storm surges,
surpassing protective seawalls, damaging homes and facilities, resulting in 238 deaths in Mississippi, 53 of
which were in Biloxi (Smith 2012).
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In Mississippi we examine the financial condition of Gulfport and Biloxi, which together share the
county seat for Harrison County. Both are coastal and underwent severe damage in the storm. These two
larger cities and their neighboring smaller communities and suburbs also make up the Gulfport–Biloxi
metropolitan statistical area, which extends into two neighboring counties. In the year before the
hurricane in 2004, Biloxi had seen a 3% decrease in population since 2000, while Gulfport had seen an 8%
increase (City of Biloxi Biloxi, Mississippi 2004, 2004; United States Census Bureau 2000).
Hurricane Ike struck Texas on September 13th, 2008, making landfall near Galveston as a category
2 storm. In Texas we focus on Galveston and Orange. Substantial damage occurred in Galveston island
and Galveston bay, where storm surge waves were recorded at up to 20 feet, surpassing seawalls (Morss
and Hayden 2010). Record windfall and winds resulted in additional damage as the storm moved forward.
The city of Orange, had flooding up to 15 feet, and even though the county is 20 miles inland, the deep
waterways channeled the 22-foot storm surge into the county, resulting in the destruction of care
facilities and schools, leaving debris in its wake. Prior to the storm, in 2007 Galveston had a population of
58,329, almost a 2% increase from 2000, while Orange had a smaller population of 18,643, with the same
estimate from seven years earlier.
Pulling data from the Comprehensive Annual Financial Reports (CAFR) for the four cities from
three years prior to the storm and seven years following the storm, we focused on revenue and
expenditure values which have been shown to change following a disaster, to look at the long-term
trends of recovery. In order to better compare the results, we normalized the data using CPI to account
for inflation, and calculated values per capita, using 2000 population values. We then calculated the
change in dollars from the year before the hurricane, to better illustrate whether values returned to pre-
hurricane levels.
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Findings
Own Source Revenue Net of Taxes
We measured own source revenue, excluding property tax, sales tax and also excluding
intergovernmental funds, grants, and donations. We would expect that a disaster, regardless of severity,
would result in a decrease in this type of revenue, because of a disruption of business activities which
would be captured in the form of a loss of revenue from fees to the city. The level of severity and the
socioeconomic context should determine the level of decline.
Following the hurricane, both Galveston and Orange saw a loss of own source revenue following the
hurricane. However, both of the year of hurricane Ike for these cities also corresponds with the peak of
the 2008 recession, and so at least for Galveston, the decrease in own source revenue appears to be
following a larger trend from the recession.
Biloxi and Gulfport both saw an increase in own source revenue following the disaster, and then a
decline following the peak of the 2008 recession, three years after hurricane Katrina. It may be that the
hurricane events in our sample have such short time periods when business is disrupted, that there is no
extensive drop in revenue. Alternatively, any decrease in revenue from business disruption is made up for
in building permits during recovery.
In own source revenue, there was a lack of distinct pattern following all hurricanes, where Mississippi
cities saw an increase in revenue, while Texas cities saw a decline as we had anticipated. This suggests
that own source revenue is largely swayed by the economic recession, but the experience of hurricane
Katrina in the Mississippi coast, while considered high severity by many scholars, did not decrease
revenue. Please see Figure 1 for the full trend of the four cities in terms of own source revenue, net of
taxes.
[Figure 1 here]
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Property Tax Revenue
Because of the extensive damage to homes during a hurricane, we anticipated that property tax
revenue would decline, relative to the severity of the event. However, in three of the four cities we
analyzed there was a slight increase in property tax revenue the year following the event, followed by a
steep decline in the second year after the hurricane. This may be due to the difficulty in assessing the
value of the homes directly following the disaster, leading to a lag in revenue decline.
Biloxi and Gulfport saw a deep decline in the second year, possibly due to the additive effect of the
recession in 2008, which largely impacted the housing market. Biloxi only returned to pre-hurricane levels
four years after the hurricane, and then again saw a fall in revenue. Hurricane Katrina may have also
resulted in more extensive or widespread damage, leading to slower recovery.
Galveston and Orange, who were hit with hurricane Ike during the same year as the peak of the
recession saw less dramatic declines in property tax revenue and never dipped below pre-hurricane
levels. Orange even saw a continuous increase in revenue until five years following the storm, likely due
to unrelated factors. The revenue from property taxes appears to be unaffected by the hurricane for
Orange, Texas. Please see Figure 2 for the full trend of the four cities in terms of property tax revenue.
[Figure 2 here]
Donations, Grants & Intergovernmental Revenue
Federal Aid following disasters comes in the form of reimbursement and is managed as
intergovernmental revenue at the local level. We anticipated that immediately following the disaster
there would be an increase in donations, grants, and intergovernmental revenue, and the level of
increase would correspond to the severity of the event. Differences in the duration of the increase in
revenue before returning to pre-hurricane levels would be influenced by the economic context and socio-
economic factors.
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For Galveston, Biloxi and Gulfport, there was an initial increase in revenue as anticipated; Orange
however, saw little change in revenue until year four and six after the event, which are likely unrelated.
Galveston continued to have elevated levels of intergovernmental revenue during all seven years
following the hurricane, while Biloxi and Gulfport both dropped to near pre-hurricane levels in the third
year. Please see Figure 3 for a graph of the trends in donations, grants, and intergovernmental revenue.
[Figure 3 here]
Sales Tax Revenue
With sales tax, we anticipated an overall trend showing a decrease in revenue. Previous literature
suggests a more nuanced trend, where a bump in sales tax due to rebuilding in the months following the
event and then see a larger decline, resulting in below pre-hurricane levels. Galveston saw a short
decrease, then a short increase for a few months, before seeing a severe larger decline in January 2011.
By 2015 sales appears to be at levels consistently similar to pre-hurricane levels.
Orange on the other hand saw a direct increase following the hurricane, followed by a slower decline
back to pre-hurricane levels by 2012, and in 2015 there is again growth to levels of sales tax revenue
which exceed pre-hurricane levels. For Galveston and Orange, the initial increase occurs during the
recession, which differs from Biloxi and Gulfport, which saw a downward trend during the recession.
Gulfport saw a trend similar to Orange, where a large increase peaked a few months following the
storm, and then declined and stabled out six years after the hurricane. In Gulfport, the new stable
conditions were lower than pre-hurricane levels, and there was no trend for increasing revenues, with
steady revenue from 2011 until 2017.
Biloxi saw a steep decline in sales tax revenue directly following the hurricane, followed by a brief
increase in 2007, before seeing a decrease throughout the recession in 2008. Sales tax revenue saw a low
in 2011, and only small growth until 2017. For Biloxi and Gulfport, sales tax never reached pre-storm
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levels, or even levels from before the recession. Please see Figures 4-9 for the graphs showing moving
average of sales tax for the states of Texas and Mississippi, as well as each of our cities: Orange,
Galveston, Biloxi and Gulfport. In these graphs, the line represents the hurricane event, while the grey
area shows the years of the recession.
[Figures 4-9 here]
Capital Outlay
Hurricanes can cause a lot of damage, which results in high expenditures to rebuild and repair. We
anticipated that following a hurricane, there would be an increase in capital outlay, and the level of
severity of the city’s experience of the storm would determine the level of expenditure, and economic
factors would determine the duration of the increase in expenditures.
Orange saw three distinct bumps in capital outlay, which correspond to years when specific facilities
were rebuilt. It is likely that a lack of capacity and resources to manage all projects capital projects
following the event resulted in the three peaks in the years following the event. Galveston saw a delay in
capital outlay, with an increase in expenditure only occurring two years following hurricane Ike. Capital
outlay levels remained higher than pre-storm levels for the duration of the seven years following the
hurricane.
Gulfport saw a gradual increase in capital outlay, with a larger bump following the peak of the
recession, three years after the hurricane. Biloxi on the other hand saw a steep decline in capital outlay in
the first year following the hurricane, and continued to have low levels of expenditure, almost reaching
pre-hurricane levels five years after the storm, before again dipping down. It is possible that hurricane
damages disrupted existing capital outlay projects, resulting in the steep decline. Please see Figure 10 for
a graph of the trends in capital outlay.
[Figure 10 here]
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Public Works
The process to clean up debris and repave roads following a hurricane falls largely under the prevue
of public works, and we therefore expected a quick increase in public works expenditure in the year
following the hurricane, with variation in the level of increase and the duration dependent on factors like
the incident severity and other economic and sociodemographic factors which influence recovery.
Orange Texas had little change in public works expenditure, but the other three cities had an increase in
public works expenditure in the year following the hurricane, followed by a steep decrease in the second
year. Biloxi and Gulfport saw a steady decrease in expenditure, while Galveston saw a longer recovery,
with elevated levels of public works expenditure in year three after the storm. All four cities had stable
levels of public works expenditure at the same level as before the hurricane starting four years after the
hurricane. Please see Figure 11 for a graph of the trends in public works expenditure.
[Figure 11 here]
Other Expenditure Net Capital Outlay and Public Works
All other expenditures, without capital outlay and public works, we also expect to have an increase,
proportionate to the intensity of the hurricane and the economic context. However, in Orange, Gulfport
and Biloxi only saw a minor increase by year three, with no steep increase directly following the event.
However, Galveston had a large jump in expenditures in year two, which are mostly attributable to
increases in planning and community development expenditures, and debt service. This rise in
expenditures slowly decreased but did not drop down to pre-hurricane levels in the seven years we
considered. Please see Figure 12 for a graph of the trends in all other expenditures, net of capital outlay
and public works.
[Figure 12 here]
19
Property Tax Rates
Tax rates were mostly stable, throughout the time period we looked at, both before and after
hurricanes, for all four cities. Orange saw a small decrease in operating millage prior to the hurricane. In
the second year after the hurricane Galveston saw a small increase in tax rates, which stayed at the
higher rate for the remaining five years afterwards. It is possible that this increase in property tax rates
were used to offset the decrease in assessed value of housing stock, in order to maintain a higher level of
tax revenue, however there was only a small increase, and this level was mostly stable in the following
years and did not dip back down to pre-hurricane levels. It is possible that political factors make it difficult
to gain support for tax rate increases, especially to those who have just suffered loss or damages from the
hurricane. Please see Figure 13 to see the trends in property tax rates.
[Figure 13 here]
Assessed Property Values
We would expect a decrease in assessed values following the hurricane, as the damaged houses
would bring down the property values. Instead, in the year following the hurricane, we see an increase in
Orange and Galveston. However, the hurricane also coincides with the peak of the recession in 2008, and
so the increase in assessed values may stem from changes in the economy, rather than due to the
hurricane. A similar, smaller bump occurs following Biloxi and Gulfport in 2009 following the recession,
four years after they were hit by hurricane Katrina. Orange saw a steady increase in housing values,
throughout the seven years following the storm, but Galveston saw a large decrease in values two years
after the storm, but assessed values continued to stay above pre-hurricane levels. Biloxi also saw a small
dip, two years after the storm, and both Mississippi cities had little change in assessed values throughout
the seven years. Please see Figure 14 to see the trends in assessed property values.
[Figure 14 here]
20
Fund Balance
Because we anticipated an increase in expenditures and decrease in revenues, we also expected that
cities would need to draw from their fund balance, and we would see a decrease in fund balance, with
the level of decrease and duration consistent with factors of the storm intensity, within the context of
socioeconomic factors. Instead, in Biloxi and Gulfport we saw an increase in fund balance, with the values
peaking in the first year following the hurricane for Gulfport, and two years following the hurricane in
Biloxi. Galveston and Orange, which were just coming out of the recession as hurricane Ike hit, did not see
an increase, and mostly stayed steady around no change from pre-hurricane levels. Please see Figure 15
to see the trends in fund balance, and table 5 for a summary of the percent change from pre-hurricane
levels for all ten variables.
[Figure 15 here]
[Table 5 here]
Conclusion
The findings show that cities have different fiscal experiences following hurricanes, resulting in
variations in the level and even direction of revenue and expenditure for years following the event.
Trends developed following the storm may take years to peak before returning to pre-hurricane
conditions. What becomes apparent is that not all cities fit one mold; disasters may exacerbate longer
term economic trends, and other factors about the disaster can determine if revenues and expenditures
go up and down, and how long it takes to recovery to pre-hurricane levels. We find that the four cities
have three different patterns of fiscal stress following the hurricane.
Throughout the ten fiscal measures considered, Orange had the least change following the hurricane.
In some cases, Orange was even able to continue growth in property tax following the hurricane, while
other cities in our model saw a decline in year two after the event. While capital outlay had several
spikes, which correspond with rebuilding facilities damaged with the hurricane, Orange otherwise had
21
little impact, most notably there was no increase in public works following the hurricane, likely because
the damage was confined to areas near waterways, and not widespread., since the city was more inland
and therefore more protected than coastal cities. We would classify the experience for Orange County to
be a crunch, in our model.
Galveston, on the other hand, saw tremendous changes following the hurricane. Own source revenue
declined and stayed low, and there was a large increase in intergovernmental revenue, which stayed
elevated for years following the hurricane. A large decline in sales tax saw a relatively fast recovery, and
capital outlay saw a modest but continual increase following the hurricane. There were increases in public
works expenditure, which continued to have elevated levels a year longer than the Mississippi cities.
Other expenditure was also heavily increased by the second year following the storm, possibly linked to
recovery efforts. Galveston even saw a minor increase in tax rates, possibly to counteract the large dip in
assessed values two years following the hurricane. Because Galveston often saw the most extreme
changes in expenditure and revenue following the hurricane, we classify Galveston as a fiscal crisis. While
there was an extreme decline in sales tax revenue following the hurricane, this was followed by a much
faster recovery than other cities in our model, who had modest growth in the years following a decline.
Furthermore, Galveston saw growth in property values after the second year following the hurricane.
Gulfport and Biloxi, who share a county seat and are geographically close, saw many similar trends in
fiscal recovery following the hurricane. Both saw a dip in property tax in year two, and then saw an
increase following the recession, three years after the hurricane, suggesting that economic conditions
outside of the events of the hurricane had a larger sway on the cities’ fiscal outcomes. Similar to
Galveston they saw an increase in intergovernmental revenue, and both Mississippi cities saw a
continuous decline in sales tax revenue during the recession, however between the hurricane and the
recession, the two cities saw reverse trends in sales tax revenue. Biloxi saw a large decline in capital
outlay, while Gulfport saw a steady increase. Biloxi also saw a decrease in other expenditure in the two
22
years following the hurricane, while Gulfport saw little change. Publics works expenditure for both cities
peaked in year one, and saw a faster return to normalcy, compared with Galveston. They both saw little
change in assessed value in the years before and after the hurricane. Interestingly, they both saw a large
peak and then gradual recovery in fund balance. We extended lack of growth in assessed values, which
affected property tax revenue, and the lower levels of change in all variables, compared with Galveston,
suggests that this was a low intensity longer duration event. Economic factors like the recession extended
fiscal recovery. We classify both cities as a fiscal squeeze.
Using the ten variables we considered, three distinct patterns emerge. Orange has the least fiscal
impact following the disaster and behaves like a fiscal crush in our model. Galveston had the most
extreme changes, with extended high levels of public works even three years after hurricane Ike. Given
the speed of recovery from these extreme changes, especially in sales tax, we classify Galveston as a fiscal
crisis. Biloxi and Gulfport had remarkably similar patterns to one another throughout, and both saw
almost no growth in assessed values throughout the seven years following the storm, even after the peak
of the recession, suggesting a protracted recovery, despite lower levels of change in expenditure and
revenue to recovery from, compared with the extreme changes seen in Galveston. With a slow recovery
and a lower level of fiscal impact, we classify the Mississippi cities of Biloxi and Gulfport and their
experience of Katrina as a fiscal squeeze.
23
Table 1: Typology of Fiscal Stress for Disasters – Adapted from Levine (1985)
Short Duration Long Duration
Low severity I. Fiscal Crunch (a) Economic losses primarily
covered through private insurance.
(b) Most recovery projects completed within first year of disaster.
II. Fiscal Squeeze (a) Relatively modest, but widespread
damage. (b) Weak pre-disaster economic conditions
slow recovery. (c) Slow recovery disrupts local business
activity.
High severity III. Fiscal Crisis (a) Severe disaster, but relatively
quicker recovery. (b) Significant portion of recovery
within one year. (c) Major infrastructure damage. (d) Income producing
infrastructure disrupted.
IV. Fiscal crush (a) Slow or no complete recovery. (b) Permanent changes to demography and
severe damage to housing stock degrade the tax base.
(c) Fiscal structural imbalance. (d) Capital projects overwhelm management
capacity. (e) Ability to borrow reduced, and possible
debt rating reductions.
24
Table 2. Summary of Expectations
Independent Variable Crunch Squeeze Crisis Crush
Own Source Revenue Net of Taxes small decrease / fast recovery
small decrease / slow recovery
large decrease / fast recovery
large decrease / slow recovery
Property Tax Revenue small decrease / fast recovery
small decrease / slow recovery
large decrease / fast recovery
large decrease / slow recovery
Sales Tax small decrease / fast recovery
small decrease / slow recovery
large decrease / fast recovery
large decrease / slow recovery
Donations, Grants & Intergovernmental Revenue
small increase / fast recovery
small increase / slow recovery
large increase / fast recovery
large increase / slow recovery
Capital Outlay small increase / fast recovery
small increase / slow recovery
large increase / fast recovery
large increase / slow recovery
Public Works small increase / fast recovery
small increase / slow recovery
large increase / fast recovery
large increase / slow recovery
Other Expenditure Net Capital Outlay and Public Works
small increase / fast recovery
small increase / slow recovery
large increase / fast recovery
large increase / slow recovery
Tax Rates no change small increase no change large increase
Assessed Values small decrease / fast recovery
small decrease / slow recovery
large decrease / fast recovery
large decrease / slow recovery
Fund Balance no change small decrease / slow recovery
large decrease / fast recovery
large decrease / slow recovery
25
Table 3. Demographics of Cities
Mississippi Texas
Biloxi Gulfport Galveston Orange
Baseline
2000 Population 50,644 (1) 71,127 (1) 57,247 (1) 18,643 (1)
Percent White 71.40% (1) 62.20% (1) 58.70% (1) 60.60% (1)
Square Miles 46.53 (1) 64.23 (1) 208.35 (1) 20.77 (1)
Median Household Income 34,106 (1) 32,779 (1) 28,895 (1) 29,519 (1)
Percent Owner Occupied Housing 49% (1) 59% (1) 44% (1) 60% (1)
2004 Population (Pre-Hurricane Katrina) 48,972 (3) 77,000 (2)
2007 Population (Pre-Hurricane Ike)
58,329 (4) 18,643 (5)
Sources: (1) 2000 Census, (2) 2004 CAFR, City of Gulfport, (3) 2004 CAFR, City of Biloxi, (4) 2007 CAFR, City of Galveston, (5) 2007 CAFR, City of Orange
26
Table 4. Level of Damages
County Disaster
Individual and Households Assistance
(Per Capita)
Public Assistance (Per Capita)
Orange (County) Hurricane Ike (DR-1791)
$805.50 $520.77
Galveston (County) $760.11 $1,655.33
Harrison (County) (Gulfport and Biloxi)
Hurricane Katrina (DR-1604)
$2,613.13 $5,871.08
27
Table 5. Summary of Percent Change from Pre-Hurricane Levels
Change from Pre-Hurricane Levels (Percent)
Biloxi Galveston Gulfport Orange
Year 1 3 5 1 3 5 1 3 5 1 3 5
Assessed Values -1% -16% 3% 29% 19% 21% -4% -12% 7% 36% 46% 45%
Capital Outlay -87% -58% -6% 28% 352% 441% -51% 158% 376% 294% 72% 14%
Donations, Grants & Intergovernmental Revenue 111% -16% 62% 1076% 626% 517% 857% 106% 159% 28% 109% 24%
Fund Balance 139% 58% -26% 16% -31% 20% 101% -39% -74% 4% -22% -21%
Other Expenditure (Net Capital Outlay & Public Works) -10% 3% -1% 9% 84% 63% 10% 30% 10% 14% 13% 7%
Own Source Revenue (Net of Taxes) 42% 406% 88% -27% -26% -34% 35% 21% -13% 7% -5% -7%
Property Tax Revenue 1% -17% 1% 16% 14% 22% 9% -10% 10% 27% 32% 28%
Public Works 573% 52% 22% 1803% 474% 11% 835% 65% 0% -4% -2% -21%
Tax Rates -7% -7% -7% 3% 16% 18% 0% -4% -5% -12% -13% -15%
28
-$150
-$100
-$50
$0
$50
$100
$150
$200
-1 HURRICANE 1 2 3 4 5 6 7
Figure 1. Per Capita Change from Pre-Hurricane Levels: Own Source Revenue (Net of Taxes)
Galveston Orange Biloxi Gulfport
-$150
-$100
-$50
$0
$50
$100
$150
-1 HURRICANE 1 2 3 4 5 6 7
Figure 2. Per Capita Change from Pre-Hurricane Levels: Property Tax Revenue
Galveston Orange Biloxi Gulfport
-$500
$0
$500
$1,000
$1,500
$2,000
-1 HURRICANE 1 2 3 4 5 6 7
Figure 3. Per Capita Change from Pre-Hurricane Levels: Donations, Grants & Intergovernmental Revenue
Galveston Orange Biloxi Gulfport
29
Figures 4-9. Sales Tax Revenue
1700 1800 1900 2000 2100 2200 2300 2400 2500 2600
O c
to b
e r-
05
M a y-
06
D e
ce m
b e
r- 06
Ju ly
-0 7
Fe b
ru a
ry -0
8
Se p
te m
b er
-0 8
A p
ri l-
0 9
N o
ve m
b e
r- 0
9
Ju n e
-1 0
Ja n
u a
ry -1
1
A u
gu st
-1 1
M ar
ch -1
2
O c
to b
e r-
12
M a y-
13
D e
ce m
b e
r- 13
Ju ly
-1 4
Fe b
ru a
ry -1
5
Se p
te m
b er
-1 5
A p
ri l-
1 6
N o
ve m
b e
r- 1
6
Ju n e
-1 7
M ill
io n
s
Texas Moving Average
Grea t Recession Hur ricanes-T X Mov ing Avera ge - T exas
1500
1700
1900
2100
2300
2500
2700
2900
O ct
o be
r- 0
2
Ju ly
-0 3
A p
ri l-
0 4
Ja n
u ar
y- 05
O ct
o be
r- 0
5
Ju ly
-0 6
A p
ri l-
0 7
Ja n
u ar
y- 08
O ct
o be
r- 0
8
Ju ly
-0 9
A p
ri l-
1 0
Ja n
u ar
y- 11
O ct
o be
r- 1
1
Ju ly
-1 2
A p
ri l-
1 3
Ja n
u ar
y- 14
O ct
o be
r- 1
4
Ju ly
-1 5
A p
ri l-
1 6
Ja n
u ar
y- 17
T h
o u
sa n
d s
Gulfport Moving Average
Grea t Recession Hur ricanes-MS Mov ing Avera ge - T exas
900 950
1000 1050 1100 1150 1200 1250 1300 1350 1400
O ct
o b
e r-
0 2
Ju ly
-0 3
A p
ri l-
0 4
Ja n
u ar
y- 05
O ct
o b
e r-
0 5
Ju ly
-0 6
A p
ri l-
0 7
Ja n
u ar
y- 08
O ct
o b
e r-
0 8
Ju ly
-0 9
A p
ri l-
1 0
Ja n
u ar
y- 11
O ct
o b
e r-
1 1
Ju ly
-1 2
A p
ri l-
1 3
Ja n
u ar
y- 14
O ct
o b
e r-
1 4
Ju ly
-1 5
A p
ri l-
1 6
Ja n
u ar
y- 17
T h
o u
sa n
d s
Biloxi Moving Average
Grea t Recession Hur ricanes-MS Mov ing Avera ge - Biloxi
1400 1450
1500 1550
1600
1650 1700
1750 1800
O ct
o b
e r-
0 5
M ay
-0 6
D e
ce m
b e
r- 0
6
Ju ly
-0 7
Fe br
u a
ry -0
8
Se p
te m
b er
-0 8
A p
ri l-
0 9
N o
ve m
b e
r- 0
9
Ju ne
-1 0
Ja n
u ar
y- 11
A u
gu st
-1 1
M ar
ch -1
2
O ct
o b
e r-
1 2
M ay
-1 3
D e
ce m
b e
r- 1
3
Ju ly
-1 4
Fe br
u a
ry -1
5
Se p
te m
b er
-1 5
A p
ri l-
1 6
N o
ve m
b e
r- 1
6
Ju ne
-1 7
T h
o u
sa n
d s
Galveston Moving Average
Grea t Recession Hur ricanes-T X Mov ing Avera ge - Galveston
250 270 290 310 330 350 370 390 410 430 450
O ct
o b
e r-
0 5
M ay
-0 6
D e ce
m b
e r-
0 6
Ju ly
-0 7
Fe br
u a
ry -0
8
Se p te
m b
er -0
8
A p
ri l-
0 9
N o
ve m
b e
r- 0
9
Ju ne
-1 0
Ja n
u a
ry -1
1
A u
gu st
-1 1
M ar
ch -1
2
O ct
o b
e r-
1 2
M ay
-1 3
D e ce
m b
e r-
1 3
Ju ly
-1 4
Fe br
u a
ry -1
5
Se p te
m b
er -1
5
A p
ri l-
1 6
N o
ve m
b e
r- 1
6
Ju ne
-1 7
T h
o u
sa n
d s
Orange Moving Average
Grea t Recession Hur ricanes-T X Mov ing Avera ge - Ora ng e
130
140
150
160
170
180
190
200
210
O c
to be
r- 02
Ju ly
-0 3
A p
ri l-
0 4
Ja n
u ar
y -0
5
O c
to be
r- 05
Ju ly
-0 6
A p
ri l-
0 7
Ja n
u ar
y -0
8
O c
to be
r- 08
Ju ly
-0 9
A p
ri l-
1 0
Ja n
u ar
y -1
1
O c
to be
r- 11
Ju ly
-1 2
A p
ri l-
1 3
Ja n
u ar
y -1
4
O c
to be
r- 14
Ju ly
-1 5
A p
ri l-
1 6
Ja n
u ar
y -1
7
M ill
io n
s
Mississippi Moving Average
Grea t Recession Hur ricanes-MS Mov ing Avera ge - Mississippi
30
-$600
-$400
-$200
$0
$200
$400
$600
$800
-1 HURRICANE 1 2 3 4 5 6 7
Figure 10: Per Capita Change from Pre-Hurricane Levels: Capital Outlay
Galveston Orange Biloxi Gulfport
-$200
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
-1 HURRICANE 1 2 3 4 5 6 7
Figure 11. Per Capita Change from Pre-Hurricane Levels: Public Works
Galveston Orange Biloxi Gulfport
31
-$400
-$200
$0
$200
$400
$600
$800
$1,000
$1,200
-1 HURRICANE 1 2 3 4 5 6 7
Figure 12. Per Capita Change from Pre-Hurricane Levels: Other Expenditure (Net Capital Outlay & Public Works)
Galveston Orange Biloxi Gulfport
-0.15
-0.10
-0.05
0.00
0.05
0.10
-1 HURRICANE 1 2 3 4 5 6 7
Figure 14. Change from Pre-Hurricane Levels: Property Tax Rates
Galveston Orange Biloxi Gulfport
32
-$10,000.00
-$5,000.00
$0.00
$5,000.00
$10,000.00
$15,000.00
$20,000.00
$25,000.00
-1 HURRICANE 1 2 3 4 5 6 7
Figure 14. Per Capita Change from Pre-Hurricane Levels: Assessed Values
Galveston Orange Biloxi Gulfport
-$400.00
-$200.00
$0.00
$200.00
$400.00
$600.00
$800.00
$1,000.00
-1 HURRICANE 1 2 3 4 5 6 7
Figure 15. Per Capita Change from Pre-Hurricane Levels: Fund Balance
Galveston Orange Biloxi Gulfport
33
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