The Effects of Disasters on State Population

Francis SanzariCollege of the Holy Cross

Advisor: Professor Baumann

AbstractDisasters 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.

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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

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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 Weber-

Burdin (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

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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

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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.

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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

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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

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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.

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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

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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.

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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.

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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 inter-

state 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 one-

month 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.

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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.

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Table 1. Descriptive Statistics for Annual-State Data

VariableObs Mean Std. Dev. Min Max

Net domestic migration 714 0 57,924 -433,991 265,932Percent domestic migration 714 .001 .008 -.067 .042Unemployment rate 714 4.95 1.28 2.3 10.5Per capita personal income 714 280,512 6,559 15,426 60,229Median house price 714 160.12 65.55 66 548Total population 714 5,435,632 6,043,722 469,033 35,800,000Percent of population 0-17 714 25.6 2.1 18.4 35.2Percent of population 18-24 714 9.9 1.0 7.8 14.5Percent of population 25-44 714 29.5 2.4 23.1 36.6Percent of population 45-64 714 22.4 2.4 15.1 29.1Percent of population 65+ 714 12.7 1.9 4.5 18.5Percent with high school degree 714 84.47 4.59 68.5 93Percent with college degree 714 24.85 5.40 11.4 49.1Violent Crime Per 100k 705 454.04 313.10 0 2,930.12Property Crime Per 100k 714 3,581.59 1,261.84 0 9,559.15Percent Hisp 714 0.076 0.087 0.004 0.441Percent White 714 0.830 0.141 0.240 0.985Percent Black 714 0.114 0.118 0.003 0.657Number of disasters 714 1.98 3.65 0 55

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Table 2. Results from Annual-State Fixed Effect Regression—Net Domestic Migration

VariableCoefficient

(Robust SE) t P>|t|

Unemployment rate-8,207(3577) -2.29 0.026

Per capita personal income0.422

(1.180) 0.36 0.722

Median house price-153.88(122.73) -1.25 0.216

Total population0.0238

(0.0087) 2.76 0.008

Percent of population 18-24-1,895.84(2,626.93) -0.72 0.474

Percent of population 25-44-6,120.882,962.39 -2.07 0.044

Percent of population 45-64-1,671.713,015.14 -0.55 0.582

Percent of population 65+-2,326.644,905.45 -0.47 0.637

Percent with high school degree633.23

(816.42) 0.78 0.442

Percent with college degree83.61

(536.59) 0.16 0.877

Violent Crime Per 100k-2.569

(10.091) -0.25 0.8

Property Crime Per 100k-4.185(3.017) -1.39 0.171

Percent Hisp-64,178

(123792) -0.52 0.606

Percent White-51,125

(461588) -0.11 0.912

Percent Black142,047(515767) 0.28 0.784

Number of disasters183.41

(525.83) 0.35 0.729Constant 165,565.6 0.39 0.695

N=705R2=0.412

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Table 3. Results from Annual-State Fixed Effect Regression: Percent Domestic Migration

VariableCoefficient

(Robust SE) t P>|t|

Unemployment rate-0.00182(0.00059) -3.06 0.004

Per capita personal income0.000000307(000000239) 1.29 0.204

Median house price-0.00002

(0.00000975) -2.08 0.043

Total population

.00000000115(0.000000000768

) 1.5 0.14

Percent of population 18-24-0.001890(0.000629) -3.01 0.004

Percent of population 25-44-0.002323(0.000673) -3.45 0.001

Percent of population 45-64-0.001920(0.000712) -2.7 0.009

Percent of population 65+-0.001353(0.001048) -1.29 0.203

Percent with high school degree.00000830(0.00015) 0.05 0.956

Percent with college degree-.00000567(0.00011) -0.05 0.959

Violent Crime Per 100k-0.00000335(0.00000479) -0.7 0.487

Property Crime Per 100k-0.000000550(0.000000425) -1.29 0.202

Percent Hisp-0.1188(0.0432) -2.75 0.008

Percent White0.0151

(0.0786) 0.19 0.848

Percent Black-0.0297(0.0925) -0.32 0.75

Number of disasters0.000043

(0.000038) 1.11 0.27

Constant0.1488

(0.0767) 1.94 0.058N=705R2=0.455

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Table 4. Descriptive Statistics for Monthly-State DataVariable Obs Mean Std. Dev. Min Max

Civilian Population21,68

4 3,894,412 4,229,797 232,000 28,400,000

Unemployment Rate21,68

4 6.02 2.12 2.1 18.1

Table 5. Results from Louisiana Monthly-State Autoregressive Model: Single-Month Hurricane KatrinaVariable Coefficient

(Standard Error)Z P-Value

Differenced Unemployment Rate -144.28(390.35)

-0.37 0.712

Hurricane Katrina (One-Month Dummy) -191,565(2535)

-75.55 0.000

Constant 2,463(415)

5.94 0.000

N=382

Table 6. Results from Louisiana Monthly-State Autoregressive Model: Four-Month Hurricane KatrinaVariable Coefficient

(Standard Error)Z P-Value

Differenced Unemployment Rate -16,909(102)

-165.59 0.000

Hurricane Katrina (Four-Month Dummy) -57,499(920)

-62.47 0.000

Constant 2,751(831)

3.31 0.001

N=382

Table 7. Results from Florida Monthly-State Autoregressive Model: FL Hurricane Season 2004, 2005 & Selected Hurricanes

Variable Coefficient(Standard Error)

Z P-Value

Differenced Unemployment Rate -1,565(1,648)

-0.95 0.342

Florida Hurricane Season 2004 2,662(2,817)

0.94 0.345

Florida Hurricane Season 2005 -2,920(2,512)

-1.16 0.245

Hurricane Andrew -605(51,793)

-0.01 0.991

Hurricane Opal -103(6,097)

-0.02 0.986

Hurricane Georges -819(39,107)

-0.02 0.983

Constant 19,110(853)

22.41 0.000

N=382

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Table 8. Results from NYC Monthly-State Autoregressive Model: Terrorist Attacks of September 11, 2001 Variable Coefficient

(Standard Error)Z P-Value

Unemployment Rate (D1.) -1,585(929)

-1.71 0.088

9/11/2001 89.32(5,826)

0.02 0.988

Constant 2,124.64(424.74)

5.00 0.000

N=382

19

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