The Effects of Disasters
on State Population
Francis Sanzari
College of the Holy Cross
Advisor: Professor Baumann
Abstract
Disasters have long been an object of study and discussion in academic, popular, and political
spheres throughout the United States. A recent rise in the number and intensity of natural
disasters has magnified such discussion. This paper analyzes the relationship between disasters
and state population change. The results of a number of econometric specifications reveal that,
in the time period studied, there is no statistically significant impact of disasters on a state’s
population. Hurricane Katrina alone, the most destructive natural disaster in the time period
studied, is an exception to these findings.
0
Introduction
Disasters have long been an object of study. Concerns about the human and economic
costs of disasters have captured the attention of academics, policymakers, and the public. In
particular, disasters with high human costs tend to draw immense national and even international
attention.
According to the National Oceanic and Atmospheric Administration (NOAA), the
deadliest hurricane to make landfall in the United States hit Galveston, Texas in 1900. The
storm caused an estimated 8,000 to 12,000 deaths, or approximately four times the human costs
of the next deadliest hurricane in U.S. history (NOAA). Officials of the time attributed much of
the high death toll to insufficient structural protection, such as sea walls, for the City of
Galveston (Cline). Clearly other factors, such as a lack of communication, transportation, and
meteorological technology in the time period played a significant role in the immense human
losses of the storm.
Since the Galveston disaster in 1900, enormous strides have been made in disaster
preparedness. The early 20th century brought advances in weather observation and forecasting
technology, methods of mass communication, and professional organization of meteorologists
(NOAA). Such advances have enhanced the ability of government agencies to forecast
destructive disasters, and to communicate such forecasts to the population well in advance of the
disaster’s impact.
The second half of the twentieth century was marked by an increase in coordination
among government agencies responsible for weather prediction and disaster relief. NOAA was
formed in 1970, consolidating and synchronizing a number of federal weather bureaus (NOAA).
In 1979, President Jimmy Carter authorized the formation of the Federal Emergency
1
Management Agency, merging a vast array of federal disaster-response organizations. The
formations of both of these organizations signified increased efforts by the federal government to
predict, prepare for, and respond to disasters on U.S. soil.
To a large extent, technological and organizational developments have been successful in
mitigating the deadly effects of hurricanes and other natural disasters. For example, since 1928,
only one hurricane has caused more than 1,000 deaths (Katrina). The vast majority of modern
disasters on U.S. soil have relatively low human costs, in comparison to the death counts of
disasters from earlier in history.
This is not to say, however, that disasters no longer have an impact. Although deaths
have been reduced, severe disasters continue to cause economic, sociological, and political
problems. I investigate whether disasters have an impact on interstate migration. States that are
prone to disasters, for example gulf states to hurricanes, face higher risks of catastrophe, costs
associated with disaster readiness, and costs of redevelopment after a storm. Negative
population effects could, in turn, trigger adverse economic and political effects, as decreased
populations could hinder economic development, and storm refugees could induce political
instability.
I estimate the impact of disasters on domestic migration patterns and state civilian
population. My results suggest that migration patterns are not affected by the presence or
magnitude of disasters. In the time periods studied, there appears to be no statistically significant
impact of disasters upon migration or population patterns. The only notable exception to these
findings is Hurricane Katrina, which is discussed at greater length below.
2
Literature Review
Although the specific literature regarding the impact of disasters on migration is thin,
there is one article that is similar to my own investigation. Rossi, Wright, Wright and WeberBurdin (1978) explore the demographic impacts of natural disasters, and develop an econometric
model to assess the long-run effects of natural disasters on population change. They use a
variety of independent variables, including total population, median age, lagged median income,
percent of housing over 20 years old, percent non-white, area of location, SMSA population,
SMSA population change, percent unemployed, geographic controls, and dummy variables for
several disaster types. Ultimately, the authors find within their analysis that there were “no
discernable effects of the natural disaster events occurring in [1960-1970] which materially
altered population… trends.” (Rossi et. al., 127)
While Rossi, Wright, Wright and Weber-Burdin provide a useful econometric model,
they also have a number of shortcomings. First, the authors focus on long-term impacts of
natural disasters by using ten-year population changes (1960-1970) in each county. Such a broad
dependent variable may obscure significant effects of disasters on populations that occur in
shorter time periods. Additionally, the dated nature of the research poses significant issues for a
modern application of the authors’ econometric equation. While still broadly relevant, some of
the ideal independent variables may have changed over the 32 years since publication.
Beyond specific analyses of disaster and population, other econometric papers examine
population change. Carlino and Mills (1987) investigate the determinants of county growth
using an empirical approach. The authors include lagged variables of employment density,
population density, percent black, local government taxes per capita, median family income,
median school years attained, and crime rate, as well as leading interstate highway density and
3
regional dummy variables to account for geographic effects. Ultimately, the authors find that
climate and employment prospects have a significant impact on population, while variables
which are dependent on public policy, including taxes and crime rates, are relatively
insignificant.
Other authors focus on the effect of specific variables on population or migration. Treyz,
Rickman, Hunt and Greenwood (1993) find that internal migration is affected by relative
economic opportunities and amenity differentials. In a fairly extensive review of the literature
concerning migration and welfare benefits, Moffitt (1992) writes that some studies current to the
1990’s find positive and significant relationships between the two variables. However Moffitt
qualifies this finding with concerns about methodology and endogeneity between welfare
benefits and cross-sectional variation in residential location, indicating that econometric
estimates may be biased. Beyond welfare benefits, other amenities exist, but many, such as
“topological, climatological, and environmental amenities… may be at least partly reflected in
labor and land markets.” (Greenwood 1985, 527)
Economic indicators are particularly important in determining migration patterns. Many
studies incorporate some measure of employment or unemployment, with the expectation that
employment opportunities (or lack thereof) affect migration to or from an area. For example,
Davies, Greenwood and Li (2001) find that a destination-to-origin unemployment rate ratio is
statistically significant in a conditional logit investigation of migration. In his survey of internal
migration literature, Greenwood (1975) finds that income generally has a positive impact on
migration decisions. In another model of interregional migration, Gabriel, Shack-Marquez, and
Wascher (1992) find that differentials in housing prices between two regions are important
4
determinants of migration. Schachter and Althaus (1989) find that “taxes…have an adverse
effect on migrants.” (Schachter and Althaus, 156)
Demographic characteristics also appear frequently in migration studies. Gallaway
(1969) finds that older individuals generally require a greater level of increased compensation to
migrate than younger individuals. Schwartz (1973) finds a similar result, arguing that the
probability that one will migrate decreases with age until retirement. Schwartz also finds that
education has a strong positive effect on the probability of migration. Other articles focus on the
influence of the family unit on migration decisions. Mincer (1978) finds that married people are
far less mobile than those who are single. In a survey of internal migration literature,
Greenwood (1975) finds that “determinants and consequences of nonwhite migration differ
appreciably from those with white migration.” (Greenwood 1975, 407) According to Davies,
Greenwood and Li (2001), individuals are more likely to move from relatively less-populated
states to states with larger populations.
Method 1—State-Annual
In order to assess the potential relationship between disasters and net domestic migration,
I estimate a model using net domestic migration as the dependent variable and disaster count and
several demographic controls as independent variables. Many of my control variables are
motivated by the independent variables found in the extant literature. I also use state-level and
year-level dummy variables, or fixed-effects, to control for time-invariant factors of domestic
migration.
5
A second model uses net domestic migration as a percentage of the state’s total
population for the dependent variable, and again uses disaster count and demographic controls as
independent variables. I include this specification because more populous states tend to have
larger absolute changes in net domestic migration. State- and year-invariant factors of domestic
migration are again controlled for using dummy variables.
Data
My state-level annual data come from a variety of sources. The dependent variable, net
domestic migration, is from releases of the Population Estimates Program, U.S. Census Bureau.
Disaster counts are derived from FEMA expenditure data, which were provided by FEMA
finance administrators. FEMA disaster expenditures are dependent upon a federal disaster
declaration. Therefore, disaster counts are the product of a disaster declaration request by a
state’s governor, and a subsequent approval of such declaration by the President of the United
States (FEMA). Unemployment figures by state and year are from the Bureau of Labor Statistics
“Local Area Unemployment Statistics” releases. Per capita personal income is taken from the
Bureau of Economic Analysis’ “State Annual Personal Income” statistics. Education statistics
are from the Economics and Statistics Administration of the U.S. Department of Commerce.
Crime statistics are from statistical releases of the Federal Bureau of Investigation’s Uniform
Crime Reports. State and local taxes are also from the U.S. Census Bureau. Finally,
race/ethnicity and age distributions statistics are from the Current Population Survey.
Descriptive statistics for the state-level annual data discussed above are provided in Table
1. With one exception (violent crime) there are a total of 714 observations of each variable, over
the years 1993-2006. Not all states reported violent crime data for every year of my sample
frame. The mean of net domestic migration variable is zero by construction since a net domestic
6
migration gain in one state must come at the expense of another. The relatively high standard
deviations of median house price and violent crimes per 100,000 people indicate that these
controls vary significantly by state, year, or both.
A few variable pairs have notably high correlations. Specifically, median house price
and per capita income (0.8074), property and violent crime levels (0.7138), college education
levels and per capita personal income (0.7509), and college education levels and median house
price (0.6720) are all highly correlated pairs of variables.
Results
The results of the fixed effect regression with net domestic migration as the dependent
variable are presented in Table 2. The standard errors use White’s formula clustered at the state
level to adjust for state-level heteroskedasticity. First, the number of disasters variable is
positive but insignificant. Thus, this estimation suggests that migration patterns are not sensitive
to disasters.
In other important findings, the unemployment rate estimate is negative and statistically
significant. The coefficient of total population is positive and statistically significant, indicating
that larger states attract more domestic migrants than smaller states. Finally, the age distribution
controls suggest that an increase in the percent younger than 18 has a positive effect on net
domestic migration, though not all controls are significant.
Table 3 presents results using percent domestic migration as the dependent variable.
Similar to the net domestic migration estimation, the number of disasters has an insignificant
impact on the dependent variable. The unemployment rate remains negative and significant.
There are a few differences in the estimates between Tables 2 and 3. First, in Table 3 the
coefficients of all the age group variables are negative and significant. Since the age group 0-17
7
is omitted, these results indicate that all other age groups have a weaker positive or more
negative impact on percent domestic migration. The coefficient of total population in Table 3 is
no longer statistically significant, likely due to the incorporation of total population into the
dependent variable by making net domestic migration a percentage of total population. Finally,
in the second regression, percent Hispanic is negative and significant. All other independent
variables are statistically insignificant.
Disaster count yields an insignificant coefficient for either dependent variable. Leading
the dependent variable by one year for both dependent variables in separate fixed effect
regressions yields similar insignificance. Thus, lagging the independent variables by one year
relative to the dependent variable still yields an insignificant disaster count coefficient.
According to these estimations, disasters as declared by FEMA do not affect domestic movement
choices.
There are a number of potential reasons for this result. First, the independent variable
representing disasters could be too broadly defined. Types of disasters included in the count
variable include severe snowstorms, forest fires, droughts, and a number of other types of
disasters that many would consider less fatal than other, more severe natural disasters.
Furthermore, federal expenditures on declared disasters in the studied time period range from $0
for some minor disasters to over $25 billion for Hurricane Katrina in Louisiana. This vast range
exemplifies the stark differences in the magnitude of declared disasters. It could be that severe
natural disasters have a significant impact on domestic migration decisions. If this were the case,
further investigation which limited disasters to only the most severe might yield a significant
impact on population.
8
It is also possible that the concept of moral hazard explains the insignificant effect of
natural disasters on migration. Generally defined, moral hazard is “the tendency of people to
expend less effort protecting those goods that are insured against theft or damage.” (Frank and
Bernanke, 390) Well before the first year considered in these regressions (1993), the federal
government had developed a pattern of intervention in relief efforts for the victims of natural
disasters. Given this social safety net, incentive structures to move from an area might have been
materially altered. Such considerations are substantiated, for example, by the arguments of Baen
and Dermisi (2007), who assert on theoretical grounds that there is a positive relationship
between federal relief programs and redevelopment patterns in a disaster area.
Alternatively, individuals could be moving within their state as a result of disasters rather
than moving to another state. If this were the case, more geographically specific data are
necessary. Unfortunately, the availability of such data, for a number of the control variables, and
more importantly, FEMA disaster data, precludes such an investigation.
Finally, it is possible that my concerns about the length of time in Rossi, Wright, Wright
& Weber-Burdin’s dependent variable may apply to my annual analysis as well. That is, a
natural disaster might have a negative impact on a state’s population, but the state’s population
may recover within a year.
Method 2—State-Monthly
My second method uses monthly data to determine if yearly data are too broad to analyze
disaster effects on population changes. This requires some changes to my variable specification.
First, monthly net domestic migration data are not available, but civilian non-institutional
population data by state and month are. Therefore, total civilian non-institutional population is
the dependent variable within my state-monthly model. Second, of all of the independent control
9
variables included in Method 1, only the unemployment rate is readily available in state-monthly
form. Along with dummy variables to indicate specific disasters or disaster seasons, these data
constitute the independent variables of the state-monthly model.
Autocorrelation is a serious concern in monthly data, particularly for dependent variables
like population change. In my estimations, Durbin-Watson tests suggest the presence of
autocorrelation. I use an autoregressive model, which includes a lagged dependent variable to
account for unexplained changes in civilian population. Furthermore, considering the broad
nature of the “disaster count” variable in my fixed effect model, I study population impacts of
specific severe disasters. I consider the largest disaster of the last 30 years—Hurricane Katrina,
as well as the 10 most expensive hurricanes (in nominal terms) to hit Florida in the time period
studied: Katrina, Andrew, Wilma, Charley, Ivan, Frances, Jeanne, Opal, Dennis, and Georges.
Data
Monthly statewide employment data—including civilian non-institutional population and
the unemployment rate—are from the Bureau of Labor Statistics, Local Area Unemployment
Statistics. Disaster-specific dummy variables were generated based upon the month and year
that the storm was active in a given state.
Descriptive statistics of the state-level monthly data are included below in Table 4. There
are a total of 21,684 monthly observations, for all 50 states plus the District of Columbia and a
subset for New York City. The sample frame is January 1976 until June 2010. Since the dataset
spans several economic expansions and recessions, the wide range of values of the
unemployment rate is to be expected.
10
Results
Tables 5 and 6 investigate the impact of Hurricane Katrina on the civilian population of
Louisiana. Table 5 uses a one-month effect in September 2005. Table 6 uses a four-month
effect from September to December 2005, which reflects the sustained effects of the storm on
Louisiana. The one-month dummy variable for Hurricane Katrina has an estimated coefficient of
-191,565, and is highly statistically significant. Hurricane Katrina also has a negative and
significant coefficient in Table 6, but this extended dummy variable takes on a smaller value of
-57,499, since it spreads the effect of Hurricane Katrina over four months. Notably, in Table 5,
the differenced unemployment rate is insignificant. However, the second estimation yields a
negative and statistically significant coefficient of the differenced unemployment rate.
I also estimate the impact of two of Florida’s most severe hurricane seasons—2004 and
2005—on Florida’s civilian population. In the model, I include the three most expensive
hurricanes (in nominal terms, according to the National Oceanic and Atmospheric
Administration)—Andrew, Opal and Georges—to hit Florida outside of the 2004 and 2005
hurricane seasons. The results of the autoregressive model are presented in Table 7. In the
model, neither the differenced unemployment rate, nor the hurricanes seasons of 2004 or 2005,
nor the dummy variables for Andrew, Opal or Georges have statistically significant effects on
the civilian non-institutional population of Florida.
Finally, I investigate the population impact of one disaster which is distinctly different
from the hurricanes examined above—the terrorist attacks of September 11, 2001 on the World
Trade Center in New York City. The results are presented in Table 8. I find no statistically
significant of the attacks on the civilian non-institutional population of New York City.
11
Conclusions
The results of both methods send a clear message about the relationship between disasters
and population. The results of the first model suggest that there is no statistically significant
relationship between disasters and net domestic migration, nor any statistically significant
relationship between disasters and percent domestic migration. The second model refines the
investigation by studying monthly rather than annual data and by focusing the study on the most
severe disasters, but still yields no significant impact of disasters on civilian population, with the
exception of Hurricane Katrina. These results suggest that natural disasters don’t induce interstate movement. A further study of the impact of a starkly different man-made disaster, the
attacks of September 11, 2001, indicates that even deadly terrorist attacks do not induce
individuals to migrate. Americans, to a large extent, refuse to allow disasters to have an impact
on residency choices.
Within all of my investigations, one specific disaster stands out as an exception to these
findings—Hurricane Katrina. The results of an autoregressive model investigating the onemonth and four-month impacts of Hurricane Katrina on the civilian population of Louisiana
indicate that Katrina had a highly negative, statistically significant effect on state population.
There are many reasons why Hurricane Katrina is the exception. Katrina, according to
the NOAA, is the single most expensive natural disaster that the United States has ever faced,
and so the response of Louisiana’s population could be commensurate with the magnitude of the
disaster. However other disasters, such as Hurricane Andrew in Florida, were not far behind
Katrina in inflation-adjusted costs. It is unclear, then, why Katrina had such a significant impact
in Louisiana, while Andrew had no significant effect on the population of Florida.
12
Mirroring concerns regarding the first method of estimation, migration as a result of
hurricanes within the state of Florida may have been intra-state rather than inter-state. The state
of Florida may, in fact, have suitable relocation sites within state borders, while those evacuating
Louisiana as a result of Hurricane Katrina were driven to sites beyond state lines. According to
the U.S. Census Bureau, as of 1990 there were only three cities in Louisiana with over 100,000
inhabitants, while Florida had nine. The relatively greater availability of in-state relocation
options may reduce the need for Florida residents to flee the state after disaster.
The prolonged effects of Hurricane Katrina on Louisiana’s civilian population are also
likely a product of specific proscriptive policies of the government. Leading up to Katrina,
authorities imposed a mandatory evacuation order on large segments of the population, and such
evacuation orders lasted, in some cases, far beyond the actual date of impact of the storm. Other
considerations, such as the relatively slow rebuilding process, persistent civil unrest, and weak
economic prospects may have also encouraged evacuated individuals to remain out of the state
after evacuation orders were lifted.
Beyond Hurricane Katrina, another reason my investigation indicates that disasters have
no significant effect on population could be the broad interpretation of “disasters,” particularly in
the aggregate fixed effect model. The inclusion of all federal disaster declarations into the
disaster count variable may have diluted the statistical effects that severe disasters have on
domestic migration. However, the autoregressive study of a limited number of severe disasters
yields, with the exception of Hurricane Katrina, no statistical impact of those disasters on civilian
population. Assuming that the results of these focused estimations hold external validity to other
severe natural disasters, then, it is likely that other causes are at play.
13
Considering the federal government’s long-established precedence of disaster relief,
some might argue that moral hazard is at play. The results of my estimations neither confirm nor
deny such an assertion, but it is certainly plausible that incentives to move as a result of disasters
may have been distorted. In order to assess the validity of such claims, additional research
would be required, incorporating data from periods in American history where federal relief was
not expected following a natural disaster.
Despite the observed increase in magnitude and frequency of natural disasters in recent
years, American population patterns are not impacted by these events. This may reflect
improved preparedness, infrastructure, or government aid to disaster-stricken areas. Future
research, ideally, would incorporate more specific geographic areas and a longer time frame.
These additions would help to identify the changes over the past century that may have reduced
the sensitivity of population to disasters.
14
Table 1. Descriptive Statistics for Annual-State Data
Variable
Net domestic migration
Percent domestic migration
Unemployment rate
Per capita personal income
Median house price
Total population
Percent of population 0-17
Percent of population 18-24
Percent of population 25-44
Percent of population 45-64
Percent of population 65+
Percent with high school degree
Percent with college degree
Violent Crime Per 100k
Property Crime Per 100k
Percent Hisp
Percent White
Percent Black
Number of disasters
Obs
714
714
714
714
714
714
714
714
714
714
714
714
714
705
714
714
714
714
714
Mean
0
.001
4.95
280,512
160.12
5,435,632
25.6
9.9
29.5
22.4
12.7
84.47
24.85
454.04
3,581.59
0.076
0.830
0.114
1.98
Std. Dev.
57,924
.008
1.28
6,559
65.55
6,043,722
2.1
1.0
2.4
2.4
1.9
4.59
5.40
313.10
1,261.84
0.087
0.141
0.118
3.65
Min
-433,991
-.067
2.3
15,426
66
469,033
18.4
7.8
23.1
15.1
4.5
68.5
11.4
0
0
0.004
0.240
0.003
0
Max
265,932
.042
10.5
60,229
548
35,800,000
35.2
14.5
36.6
29.1
18.5
93
49.1
2,930.12
9,559.15
0.441
0.985
0.657
55
15
Table 2. Results from Annual-State Fixed Effect Regression—Net Domestic Migration
Coefficient
Variable
(Robust SE)
t
P>|t|
-8,207
Unemployment rate
(3577)
-2.29
0.026
0.422
Per capita personal income
(1.180)
0.36
0.722
-153.88
Median house price
(122.73)
-1.25
0.216
0.0238
Total population
(0.0087)
2.76
0.008
-1,895.84
Percent of population 18-24
(2,626.93)
-0.72
0.474
-6,120.88
Percent of population 25-44
2,962.39
-2.07
0.044
-1,671.71
Percent of population 45-64
3,015.14
-0.55
0.582
-2,326.64
Percent of population 65+
4,905.45
-0.47
0.637
633.23
Percent with high school degree
(816.42)
0.78
0.442
83.61
Percent with college degree
(536.59)
0.16
0.877
-2.569
Violent Crime Per 100k
(10.091)
-0.25
0.8
-4.185
Property Crime Per 100k
(3.017)
-1.39
0.171
-64,178
Percent Hisp
(123792)
-0.52
0.606
-51,125
Percent White
(461588)
-0.11
0.912
142,047
Percent Black
(515767)
0.28
0.784
183.41
Number of disasters
(525.83)
0.35
0.729
Constant
165,565.6
0.39
0.695
N=705
R2=0.412
16
Table 3. Results from Annual-State Fixed Effect Regression: Percent Domestic Migration
Variable
Unemployment rate
Per capita personal income
Median house price
Total population
Percent of population 18-24
Percent of population 25-44
Percent of population 45-64
Percent of population 65+
Percent with high school degree
Percent with college degree
Violent Crime Per 100k
Property Crime Per 100k
Percent Hisp
Percent White
Percent Black
Number of disasters
Constant
Coefficient
(Robust SE)
-0.00182
(0.00059)
0.000000307
(000000239)
-0.00002
(0.00000975)
.00000000115
(0.000000000768)
-0.001890
(0.000629)
-0.002323
(0.000673)
-0.001920
(0.000712)
-0.001353
(0.001048)
.00000830
(0.00015)
-.00000567
(0.00011)
-0.00000335
(0.00000479)
-0.000000550
(0.000000425)
-0.1188
(0.0432)
0.0151
(0.0786)
-0.0297
(0.0925)
0.000043
(0.000038)
0.1488
(0.0767)
t
P>|t|
-3.06
0.004
1.29
0.204
-2.08
0.043
1.5
0.14
-3.01
0.004
-3.45
0.001
-2.7
0.009
-1.29
0.203
0.05
0.956
-0.05
0.959
-0.7
0.487
-1.29
0.202
-2.75
0.008
0.19
0.848
-0.32
0.75
1.11
0.27
1.94
0.058
N=705
R2=0.455
17
Table 4. Descriptive Statistics for Monthly-State Data
Variable
Obs
Mean
Std. Dev.
Civilian Population
21,684 3,894,412
4,229,797
Unemployment Rate
21,684
6.02
2.12
Min
232,000
2.1
Max
28,400,000
18.1
Table 5. Results from Louisiana Monthly-State Autoregressive Model: Single-Month Hurricane Katrina
Variable
Coefficient
Z
P-Value
(Standard Error)
Differenced Unemployment Rate
-144.28
-0.37
0.712
(390.35)
Hurricane Katrina (One-Month Dummy)
-191,565
-75.55
0.000
(2535)
Constant
2,463
5.94
0.000
(415)
N=382
Table 6. Results from Louisiana Monthly-State Autoregressive Model: Four-Month Hurricane Katrina
Variable
Coefficient
Z
P-Value
(Standard Error)
Differenced Unemployment Rate
-16,909
-165.59
0.000
(102)
Hurricane Katrina (Four-Month Dummy)
-57,499
-62.47
0.000
(920)
Constant
2,751
3.31
0.001
(831)
N=382
Table 7. Results from Florida Monthly-State Autoregressive Model: FL Hurricane Season 2004, 2005 & Selected
Hurricanes
Variable
Coefficient
Z
P-Value
(Standard Error)
Differenced Unemployment Rate
-1,565
-0.95
0.342
(1,648)
Florida Hurricane Season 2004
2,662
0.94
0.345
(2,817)
Florida Hurricane Season 2005
-2,920
-1.16
0.245
(2,512)
Hurricane Andrew
-605
-0.01
0.991
(51,793)
Hurricane Opal
-103
-0.02
0.986
(6,097)
Hurricane Georges
-819
-0.02
0.983
(39,107)
Constant
19,110
22.41
0.000
(853)
N=382
18
Table 8. Results from NYC Monthly-State Autoregressive Model: Terrorist Attacks of September 11, 2001
Variable
Coefficient
Z
P-Value
(Standard Error)
Unemployment Rate (D1.)
-1,585
-1.71
0.088
(929)
9/11/2001
89.32
0.02
0.988
(5,826)
Constant
2,124.64
5.00
0.000
(424.74)
N=382
19
References
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and Programs for High Risk Areas.” Journal of Real Estate Literature 15.2 (2007): 229-252.
Bernanke, Ben and Robert Frank. Principles of Microeconomics. Boston: McGraw-Hill
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