Initial Estimate of the Impacts of Hurricane Katrina
December 2005
By Brian Richard
Director, Economic Development Resource Center
University of Southern Mississippi
Brian.richard@usm.edu
601-266-6122
ABSTRACT
Hurricane Katrina brought massive and obvious damage along the Gulf Coast. News
media reports have extensively documented the destruction. However, quantifying the
destruction in terms of the demographics of the affected population is more difficult. This paper
attempts to do just that. First, a brief review of the literature relating to the economic impacts of
previous storms is conducted. Then, an estimate of the impacts of Katrina on the Mississippi
Gulf Coast is presented. These estimates include both household and business data.
The analysis identifies several groups that were significantly impacted by Katrina. These
groups include the elderly, Asians, renters and wealthy homeowners. Business sectors
represented more extensively in the damaged areas include retail, financial and services.
August 29, 2005 brought what may turn out to be the costliest natural disaster in United
States history. Hurricane Katrina easily surpassed Camille as the hurricane by which all others
are compared in south Mississippi. The visual images of the destruction shown on television,
newspaper pages and the Internet have documented much of the damage. A more difficult
proposition is to systematically measure the household and business damage. What were the
demographics of the destruction? How many and what types of businesses were impacted? This
paper attempts to answer these questions.
IMPACT ESTIMATES OF PREVIOUS EVENTS
The most direct way of learning the impacts of a natural disaster is to directly survey
those that were impacted. Smith and McCarty (1996) did just that following Hurricane Andrew.
Hurricane Andrew was a category 5 hurricane that raked across south Florida in August 1992.
Prior to Katrina, it was the costliest disaster in U.S. history. Smith & McCarty conducted a
massive (over 5,000 respondents) telephone survey in 1994 to determine the extent of housing
damage, along with information about demographic changes in the area. The survey asked
respondents about the extent of damage to their homes, insurance settlements, living
arrangements of those displaced by the storm, and whether displaced households had yet
returned 2 years after the storm. This followed earlier survey work aimed at simply determining
the population of the area after Andrew (Smith, 1996).
The survey revealed that over half (52 percent) of south Dade county residents were
forced to move out of their homes as a result of Andrew (p. 270). Two years later, only about 62
percent of those displaced households had returned to their pre-hurricane residence (p. 271). A
significant finding of their research was that households that moved farther away from their prehurricane residence were less likely to return to that original residence. Only 10 percent of south
Dade County residents who moved out of Florida following the hurricane returned to their
original home. This compares with 72 percent of households that moved to another location
within Dade County who returned to their original home (p. 273).
Other studies have looked at overall economic impacts of disasters. Chang (1984) found
negative overall impacts when analyzing the impacts of Hurricane Frederic on the Alabama Gulf
Coast. The study relied on U.S. Army Corps of Engineers estimates of damages and disaster
recovery expenditures. Further estimates on the influx of dollars into the area were obtained
from various federal sources as well as directly from the private insurers in the area. Chang
found that the net impact of Frederic on the Alabama coast was a net loss of almost $600 million.
The staff of the South Carolina Budget and Control Board (SCBCB, 1991) estimated the
economic losses that resulted from Hurricane Hugo in September 1989. They obtained estimates
of gross losses and insurance payments from the S.C. Insurance Commission. Residence
destruction and damage assessments were obtained from the Red Cross, based on a damage
assessment survey. The Federal Emergency Management Agency (FEMA) provided data on
payments made under various programs following the storm. Other emergency payments, such
as food stamps, unemployment insurance and crop related payments, were obtained from various
State of South Carolina agencies. The study found that damages from Hugo totaled about $6.4
billion, with reimbursements from private and public sources totaling about $3.4 billion, leaving
$3 billion in un-reimbursed losses.
Guimaraes, Hefner and Woodward (1993) measured the impacts of Hurricane Hugo on
the South Carolina economy in the years following the storm. An econometric model was used
to project the quarterly track of the South Carolina economy as if Hugo had never occurred.
They then compared the quarterly estimates with observed data over a 2 ½ year period following
the hurricane. The study found significant positive income effects in several sectors of the
economy, most notably the construction sector. However, overall positive income effects were
significantly lower than the loss of wealth computed by SCBCB and reported again in this study.
West and Lenze (1994) also use an econometric modeling approach in the analysis of
Hurricane Andrew. They used data from Property Claim Services (insurance claims), Red Cross
surveys, utility companies, and government agency reports to estimate total physical damages
resulting from the storm. These data were then used to estimate reconstruction expenditures in
several sectors: structure replacement, repair services, and new purchases. Expenditure
estimates in the various sectors were used as inputs into a Florida econometric model.
As with the results from Hurricane Hugo in South Carolina, the biggest impacts on the
Florida economy occurred in the construction sector. Both income and employment were
estimated to be significantly higher for 2 to 3 years following Andrew. Increased income was
also estimated in the services and trade sectors over this period. The longer term (the study
looked at the period 1992 to 2005) impacts showed a net negative impact of the storm. Job
losses were estimated at about 12,000, mostly as a result of the closing of Homestead Air Force
Base and the permanent relocation of a portion of the South Florida population. The temporary
increases in income due to post-storm rebuilding were not enough to offset the massive loss of
wealth that occurred as a result of storm damage.
HURRICANE KATRINA IMPACTS
One common aspect of all of the analyses presented above is that they were conducted
two to three years after the storms. While this allows for more complete sources of data, there is
certainly value in more contemporary estimates. Here a more indirect method will be used to
provide estimates of damages just weeks after the storm.
One of the important sources of data for many of the studies described above, the Federal
Emergency Management Agency (FEMA), also provides important data for this study. In the
days after the Katrina made landfall, FEMA provided a series of GIS map layers outlining areas
on the Gulf Coast that were impacted at various levels. “Soon after the disaster event, FEMA
managers and staff use GIS to visualize actual damages by analyzing collected aerial
reconnaissance and ground truth data. Using GIS, MAC customers (i.e. Disaster Field Office
(DFO), Emergency Support Team (EST) personnel, etc.) can see the spatial extent of damage,
learn who was affected by the disaster and which resources were affected (FEMA, 2004).”
Map 1 displays the FEMA estimates of various damage levels on the Mississippi Gulf
Coast1. News reports often describe the railroad tracks as a barrier to the storm surge and thus, a
dividing line between devastation and less severe damage. This is reflected in the FEMA data.
The red strip (representing ‘catastrophic’ damage) that runs along the coast is bounded, with few
exceptions, by the railroad tracks to the north. The total area of all five FEMA damage areas is
about 140 square miles. The area of the two most extensive damage areas totals about 28 square
miles.
1
Definition of FEMA damage categories:
Catastrophic Damage: Most solid and all light or mobile structures are destroyed.
Extensive Damage: Some solid structures are destroyed; most sustain exterior and interior damage (e.g., roofs are
missing, interior walls exposed), most mobile homes and light structures are destroyed.
Moderate Damage: Solid structures sustain exterior damage (e.g., missing roofs or roof segments); some mobile
homes and light structures are destroyed, many are damaged or displaced.
Limited Damage: Generally superficial damage to solid structures (e.g., loss of tiles or roof shingles); some mobile
homes and light structures are damaged or displaced.
Flood Damage: Indicates a separate severe damage category related to the specific effects of flooding.
Map 1. FEMA Damage Assessments.
FEMA Damage Categories
CAT AST ROPHIC
EXT ENSIVE
MODERAT E
LIMIT ED
FLOOD
Based on the areas delineated by the FEMA maps, we can estimate the number of
households and businesses that were impacted by Katrina. Data estimates were obtained using a
GIS based data analysis tool called PCensus. Census 2000 and Claritas 2005 demographic and
business data at the Census block level were aggregated based on the areas outlined by each
FEMA damage category.
Household Impacts
Table 1 displays some basic statistics about the households impacted by Hurricane
Katrina. Over 37,000 persons lived in the ‘catastrophic’ damage assessment areas, representing
about 10 percent of the total population of Hancock, Harrison and Jackson counties. About 37
percent of the total Mississippi Gulf Coast population lived somewhere in one of the five FEMA
damage areas.
Table 1. Estimated Population in FEMA Damage Areas.
Population
370,396
Households
140,103
37,336
10.1%
16,676
11.9%
Extensive
6,416
1.7%
2,140
1.5%
Moderate
13,935
3.8%
5,418
3.9%
Limited
74,282
20.1%
27,075
19.3%
Flood
5,719
1.5%
2,439
1.7%
Total
137,688
37.2%
53,748
38.4%
3 County Total
Catastrophic
Percentages represent the portion of each category that falls in the corresponding FEMA category.
More extensive demographic data is presented in Appendix 1. Hurricane Katrina
impacted a higher proportion of older persons than the population as a whole. One in five people
over 85 lived in the catastrophic zone. This compares with one person out of ten in the overall
population in that zone.
Looking at the race statistics, the Asian population was hit significantly harder than the
population as a whole. Almost ¼ of all Asian people on the coast lived in the catastrophic
damage zone. Black persons were less likely to be in the most heavily damaged areas but are
over represented in the limited damage zone.
The housing data reveals that persons either on the low end or the upper end of the
income spectrum were more likely to have been severely impacted by the storm. Housing units
in the catastrophic damage area were twice as likely to have been renter occupied as opposed to
owner occupied. One in five renter occupied housing units were either in the catastrophic or
extensive damage areas.
Lower income households were more likely to be in the seriously damaged areas. This
may reflect a couple of the trends mentioned above. Both elderly persons and renters tend to
have lower annual incomes than the overall population.
Not surprisingly, because of the damage along the beachfront, expensive homes were
hammered by the storm. Over one quarter of all owner occupied homes valued over $400,000 in
the three county area were in the catastrophic damage zone. This is also reflected in the income
data which shows that households with annual incomes over $150,000 were more likely to be in
the catastrophic zone.
Mississippi Governor Haley Barbour has stated that FEMA will provide about 34,000
mobile homes and travel trailers throughout the state (Cogswell, 2005). From Table 1, we see
that over three quarters of that number, about 27,000 households, were located in the
catastrophic, extensive, moderate, and flood damage zones from the FEMA maps. Based on the
data we have, the 27,000 figure may be a good estimate of unlivable housing at this point, less
than three months after the storm.
Business Impacts
Two out of every five businesses on the Gulf Coast were located in one of the FEMA
damage zones (Table 2). Over 15 percent of the businesses were in the most heavily damaged
zone. The firms in the catastrophic zone provided over 20 percent of all jobs in the three
counties.
Table 2. Estimated Business Population in FEMA Damage Areas.
Establishments
14,099
Employment
180,428
Total Sales
(millions)
$16,937
2,152
15.3%
37,853
21.0%
$2,979
17.6%
Extensive
218
1.5%
2,465
1.4%
$225
1.3%
Moderate
682
4.8%
6,825
3.8%
$642
3.8%
2,286
16.2%
22,157
12.3%
$2,087
12.3%
Flood
166
1.2%
1,542
0.9%
$183
1.1%
Total
5,504
39.0%
70,842
39.3%
$6,116
36.1%
3 Coast Counties
Catastrophic
Limited
Percentages represent the portion of each category that falls in the corresponding FEMA category.
Appendix 2 shows the sectoral breakdown of the businesses in the FEMA damage
categories. Over one quarter of all jobs in the retail, financial, and service industries were in the
catastrophic damage area. Over 86 percent of the jobs in the catastrophic zone were in those
three sectors. Manufacturing was largely spared the destruction. Only about 10 percent of total
manufacturing employment in the three coast counties was in any of the FEMA damage zones.
Mississippi State Tax Commission data reflects the destruction in the retail sector. Table
3 shows retail sales tax collections from cities in the three coastal counties (MSTC, 2005). Five
cities, Waveland, Pass Christian, Bay St. Louis, Biloxi, and Long Beach had lower retail sales in
October 2005 relative to 2004. These cities all had significant portions of their retail sectors
located in the catastrophic damage zone, as displayed in Map 1. Gulfport was aided by having a
large portion of its retail sector located farther inland. However, Gulfport still just matched the
statewide retail growth despite having retail sectors in the surrounding cities virtually wiped out.
Cities with more protected retail sectors, notably Gautier, D’Iberville, and Pascagoula saw very
large increases in retail sales. Also, cities in the counties directly north of the coastal counties
saw large increases in retail sales. Wiggins, 30 miles north of Gulfport on Highway 49 saw a 70
percent increase in retail sales over the previous year.
Table 3. Diversion to Cities from Sales Tax Collections.
City
Waveland
Pass Christian
Bay St. Louis
Biloxi
Long Beach
Moss Point
Gulfport
Ocean Springs
Gautier
D'Iberville
Pascagoula
Totals
Oct 2005
$
25,553
$
21,651
$
60,402
$
692,606
$
99,528
$
131,845
$ 1,593,290
$
375,901
$
242,913
$
431,335
$
716,239
$ 4,391,262
Oct 2004
$
175,080
$
96,161
$
100,131
$ 1,005,454
$
118,771
$
120,523
$ 1,385,744
$
291,529
$
166,974
$
281,184
$
440,036
$ 4,181,585
Change
-85%
-77%
-40%
-31%
-16%
9%
15%
29%
45%
53%
63%
5%
Mississippi
$ 31,993,514
$ 28,127,180
14%
SUMMARY
This analysis describes some of the populations impacted by Hurricane Katrina. In many
cases these populations differ significantly from the overall Mississippi Gulf Coast population.
Groups more likely to live in the catastrophic damage zone include Asians, wealthy
homeowners, and persons living in rental housing. In the business sector, retail, finance and
services were damaged significantly.
Going forward, this data will be used as a basis for estimating the value of the destruction
brought by Hurricane Katrina in Mississippi. The data can then be compared to the damage
estimates in previous disasters. An estimate of the value of destruction from Katrina compared
with the experiences from previous disasters will allow us to make predictions regarding
economic activity during the recovery.
REFERENCES
Chang, Semoon. (1984). Do Disaster Areas Benefit from Disasters? Growth and Change, 15: 2431.
Cogswell, Joshua. (2005, November 22). FEMA extends hotel aid 2 weeks. The Clarion-Ledger.
Retrieved November 22, 2005, from www.clarionledger.com.
Federal Emergency Management Agency Mapping and Analysis Center. (2004). How FEMA
Used GIS in Disaster Response. [electronic version]. Retrieved November 14, 2005, from
http://www.gismaps.fema.gov/gis04.shtm.
Federal Emergency Management Agency Mapping and Analysis Center. (2005). Hurricane
Katrina Remote Sensing Data, September 19, 1000. Retrieved September 29, 2005, from
http://www.gismaps.fema.gov/2005pages/rsdrkatrina.shtm .
Guimaraes, Paulo, Frank L. Hefner, and Douglas P. Woodward. (1993). Wealth and Income
Effects of Natural Disasters: and Econometric Analysis of Hurricane Hugo. Review of
Regional Studies. 23: 39-53.
Mississippi State Tax Commission. (2005). Diversions to Cities from Sales Tax Collections,
October 2005. Retrieved November 22, 2005, from
http://www.mstc.state.ms.us/info/stats/divers.htm.
Smith, Stanley K. (1996). Demography of Disaster: Population Estimates after Hurricane
Andrew. Population Research and Policy Review. 15(5-6): 459-477.
Smith, Stanley K. and Christopher McCarty. (1996). Demographic Effects of Natural Disasters:
A Case Study of Hurricane Andrew. Demography. 33(2): 265-275.
South Carolina Budget and Control Board. Division of Research and Statistical Services, Office
of Economic Research. (1991). “Economic Impact of Hurricane Hugo.” Columbia, S.C.
West, Carol T. and David G. Lenze. (1994). Modeling the Regional Impact of Natural Disaster
and Recovery: a General Framework and an Application to Hurricane Andrew.
International Regional Science Review. 17(2): 121-150.
Appendix 1. Estimated Household Data by FEMA Damage Categories.
3 County Total
Total Population
Total Households
Growth 2005-2010
Growth 2000-2005
Growth 1990 - 2000
Daytime Population
2005 Estimated Total Population by Age
Age 16 and over
Age 18 and over
Age 21 and over
Age 65 and over
Age 85 and over
370,396
140,103
1.85%
1.76%
21.71%
181,383
Percentages represent the portion of each category that falls in the corresponding FEMA category
Catastrophic
Extensive
Moderate
Limited
Flood
Total
%
%
%
%
%
%
37,336 10.1%
6,416 1.7% 13,935 3.8% 74,282 20.1%
5,719 1.5% 137,688 37.2%
16,676 11.9%
2,140 1.5%
5,418 3.9% 27,075 19.3%
2,439 1.7% 53,748 38.4%
-2.20%
2.2%
1.8%
1.1%
3.3%
-2.75%
2.9%
1.2%
1.2%
1.8%
12.40%
24.8%
19.0%
18.6%
51.7%
37,991 20.9%
2,489 1.4%
6,914 3.8% 22,554 12.4%
1,597 0.9% 71,545 39.4%
286,271
275,580
258,738
44,441
4,254
30,322
29,541
28,222
6,423
866
35.64
36.46
39.39
40.25
2005 Estimated Population by Race
White Alone
Black or African American Alone
Asian Alone
273,529
76,590
8,758
28,073
5,893
1,976
10.3%
7.7%
22.6%
6,416
5,118 1.9%
979 1.3%
87 1.0%
2005 Tenure of Occupied Housing Units
Owner Occupied
Renter Occupied
140,103
97,830
42,273
8,898
7,778
9.1%
18.4%
2,140
1,495 1.5%
645 1.5%
2005 Estimated Average Household Income
2005 Estimated Median Household Income
2005 Estimated Per Capita Income
$53,276
$42,075
$20,550
$52,170
$38,019
$23,645
$49,737
$35,995
$19,177
$52,103
$39,815
$20,562
$51,865
$40,848
$19,872
$43,961
$34,268
$18,759
2.57
2.2
2.46
2.44
2.57
2.44
2005 Estimated Median Age
2005 Estimated Average Age
2005 Average Household Size
10.6%
10.7%
10.9%
14.5%
20.4%
5,183
5,013
4,554
773
100
1.8%
1.8%
1.8%
1.7%
2.4%
32.67
35.93
10,940
10,524
9,949
1,995
245
3.8%
3.8%
3.8%
4.5%
5.8%
37.43
38.16
20.2%
20.3%
19.7%
19.9%
20.7%
34
35.85
13,935
10,833 4.0%
2,583 3.4%
201 2.3%
5,418
3,693
1,725
57,902
55,815
50,924
8,835
881
3.8%
4.1%
4,606
4,442
4,225
702
37
1.6%
1.6%
1.6%
1.6%
0.9%
108,953
105,335
97,874
18,728
2,129
38.1%
38.2%
37.8%
42.1%
50.0%
39.52
38.79
74,282
49,530 18.1%
20,555 26.8%
1,592 18.2%
5,719
100,352
5,319 1.9% 98,873 36.1%
177 0.2% 30,187 39.4%
43 0.5% 3,899 44.5%
27,075
17,991 18.4%
9,084 21.5%
2,439
37,072
1,983 2.0% 34,060 34.8%
456 1.1% 19,688 46.6%
3 County Total
Owner-Occupied Housing Value (2000 Census)
Less than $20,000
$20,000 to $39,999
$40,000 to $59,999
$60,000 to $79,999
$80,000 to $99,999
$100,000 to $149,999
$150,000 to $199,999
$200,000 to $299,999
$300,000 to $399,999
$400,000 to $499,999
$500,000 to $749,999
$750,000 to $999,999
$1,000,000 or more
93,850
4,462
8,976
15,152
18,833
15,714
17,394
6,940
4,001
1,160
533
304
107
274
Catastrophic
%
9,041
193
4.3%
618
6.9%
1,438
9.5%
1,676
8.9%
1,504
9.6%
1,745 10.0%
866 12.5%
506 12.6%
184 15.9%
144 27.0%
78 25.7%
31 29.0%
58 21.2%
Extensive
%
1,415
124 2.8%
191 2.1%
280 1.8%
180 1.0%
195 1.2%
248 1.4%
86 1.2%
72 1.8%
22 1.9%
9 1.7%
8 2.6%
1 0.9%
0 0.0%
Moderate
%
3,548
138 3.1%
202 2.3%
577 3.8%
723 3.8%
641 4.1%
829 4.8%
228 3.3%
145 3.6%
24 2.1%
30 5.6%
8 2.6%
1 0.9%
3 1.1%
Limited
%
17,345
624 14.0%
1,398 15.6%
2,949 19.5%
3,738 19.8%
3,090 19.7%
3,271 18.8%
1,159 16.7%
717 17.9%
254 21.9%
62 11.6%
44 14.5%
10 9.3%
27 9.9%
Flood
1,905
170
380
341
313
286
269
76
36
24
6
1
1
1
Total
%
%
3.8%
4.2%
2.3%
1.7%
1.8%
1.5%
1.1%
0.9%
2.1%
1.1%
0.3%
0.9%
0.4%
33,254
1,249
2,789
5,585
6,630
5,716
6,362
2,415
1,476
508
251
139
44
89
28.0%
31.1%
36.9%
35.2%
36.4%
36.6%
34.8%
36.9%
43.8%
47.1%
45.7%
41.1%
32.5%
2005 Estimated Households by Household Income
Less than $15,000
$15,000 to $24,999
$25,000 to $34,999
$35,000 to $49,999
$50,000 to $74,999
$75,000 to $99,999
$100,000 to $149,999
$150,000 to $249,999
$250,000 to $499,999
$500,000 or more
140,103
22,161
17,248
18,599
25,535
27,607
14,044
10,592
3,027
1,009
281
16,676
2,825
2,459
2,447
3,012
2,826
1,312
1,148
424
179
42
12.7%
14.3%
13.2%
11.8%
10.2%
9.3%
10.8%
14.0%
17.7%
14.9%
2,140
429
310
306
365
334
168
148
60
15
4
1.9%
1.8%
1.6%
1.4%
1.2%
1.2%
1.4%
2.0%
1.5%
1.4%
5,418
1,031
675
680
1,006
1,039
465
347
89
71
16
4.7%
3.9%
3.7%
3.9%
3.8%
3.3%
3.3%
2.9%
7.0%
5.7%
27,075
4,523
3,447
3,632
4,966
5,161
2,576
1,980
569
167
54
20.4%
20.0%
19.5%
19.4%
18.7%
18.3%
18.7%
18.8%
16.6%
19.2%
2,439
499
369
380
387
453
167
162
14
8
1
2.3%
2.1%
2.0%
1.5%
1.6%
1.2%
1.5%
0.5%
0.8%
0.4%
53,748
9,307
7,260
7,445
9,736
9,813
4,688
3,785
1,156
440
117
42.0%
42.1%
40.0%
38.1%
35.5%
33.4%
35.7%
38.2%
43.6%
41.6%
2005 Est. Population by Educational Attainment
Less than 9th grade
Some High School, no diploma
High School Graduate (or GED)
Some College, no degree
Associate Degree
Bachelor's Degree
Master's Degree
Professional School Degree
Doctorate Degree
237,401
13,961
32,822
70,956
60,089
17,988
26,657
10,124
3,569
1,235
26,270
1,642
3,087
6,993
7,064
1,711
3,590
1,404
597
183
11.8%
9.4%
9.9%
11.8%
9.5%
13.5%
13.9%
16.7%
14.8%
3,855
273
694
1,120
907
223
402
133
84
18
2.0%
2.1%
1.6%
1.5%
1.2%
1.5%
1.3%
2.4%
1.5%
9,164
634
1,374
2,673
2,174
551
1,044
478
191
46
4.5%
4.2%
3.8%
3.6%
3.1%
3.9%
4.7%
5.4%
3.7%
45,851
2,658
6,232
13,132
11,651
3,511
5,496
2,165
649
356
19.0%
19.0%
18.5%
19.4%
19.5%
20.6%
21.4%
18.2%
28.8%
3,930
362
678
1,153
1,059
230
279
121
42
5
2.6%
2.1%
1.6%
1.8%
1.3%
1.0%
1.2%
1.2%
0.4%
89,070
5,569
12,065
25,071
22,855
6,226
10,811
4,301
1,563
608
39.9%
36.8%
35.3%
38.0%
34.6%
40.6%
42.5%
43.8%
49.2%
Appendix 2. Estimated Business Data by FEMA Damage Categories.
Percentages represent the portion of each category that falls in the corresponding FEMA category
Catastrophic
Extensive
Moderate
Limited
Flood
Total
%
%
%
%
%
%
2,152 15.3%
218 1.5%
682 4.8% 2,286 16.2%
166 1.2% 5,504 39.0%
14 5.6%
2 0.8%
9 3.6%
35 14.0%
3 1.2%
63 25.2%
69 6.6%
9 0.9%
38 3.6%
131 12.5%
11 1.1%
258 24.7%
62 11.8%
9 1.7%
17 3.2%
62 11.8%
7 1.3%
157 30.0%
82 13.2%
9 1.5%
25 4.0%
90 14.5%
12 1.9%
218 35.2%
54 10.1%
8 1.5%
18 3.4%
74 13.8%
7 1.3%
161 30.0%
531 16.6%
57 1.8%
185 5.8%
483 15.1%
53 1.7% 1,309 40.9%
264 17.7%
21 1.4%
79 5.3%
256 17.2%
12 0.8%
632 42.5%
904 15.9%
86 1.5%
263 4.6% 1,000 17.6%
53 0.9% 2,306 40.7%
172 22.4%
17 2.2%
48 6.3%
155 20.2%
8 1.0%
400 52.2%
Establishments
Agriculture & Natural Resources
Construction
Manufacturing
Transportation, Utilities
Wholesale Trade
Retail Trade
Finance, Insurance, Real Estate
Services
Government
3 Coast Counties
Totals
14,099
250
1,045
524
619
537
3,197
1,488
5,672
767
Employment
Agriculture & Natural Resources
Construction
Manufacturing
Transportation, Utilities
Wholesale Trade
Retail Trade
Finance, Insurance, Real Estate
Services
Government
180,428
1,153
9,527
20,335
6,951
5,919
42,016
8,750
71,264
14,513
37,853
48
390
910
845
429
10,984
2,221
19,613
2,461
21.0%
4.2%
4.1%
4.5%
12.2%
7.2%
26.1%
25.4%
27.5%
17.0%
2,465
7
118
183
80
63
487
108
1,160
266
1.4%
0.6%
1.2%
0.9%
1.2%
1.1%
1.2%
1.2%
1.6%
1.8%
6,825
39
242
238
317
124
1,620
416
3,138
730
3.8% 22,157 12.3%
3.4%
191 16.6%
2.5%
988 10.4%
1.2%
737 3.6%
4.6% 1,056 15.2%
2.1%
595 10.1%
3.9% 4,000 9.5%
4.8% 1,352 15.5%
4.4% 10,781 15.1%
5.0% 2,648 18.2%
1,542
11
76
30
89
34
706
63
456
88
0.9% 70,842 39.3%
1.0%
296 25.7%
0.8% 1,814 19.0%
0.1% 2,098 10.3%
1.3% 2,387 34.3%
0.6% 1,245 21.0%
1.7% 17,797 42.4%
0.7% 4,160 47.5%
0.6% 35,148 49.3%
0.6% 6,193 42.7%
Total Sales (millions)
Agriculture & Natural Resources
Construction
Manufacturing
Transportation, Utilities
Wholesale Trade
Retail Trade
Finance, Insurance, Real Estate
Services
Government
16,937
$53
$1,721
$1,610
$681
$1,161
$4,488
$1,799
$5,424
$0
$2,979
$2
$73
$83
$86
$77
$827
$469
$1,364
$0
17.6%
3.8%
4.2%
5.2%
12.6%
6.6%
18.4%
26.1%
25.1%
$225
$0
$21
$14
$8
$11
$70
$24
$77
$0
1.3%
0.0%
1.2%
0.9%
1.2%
0.9%
1.6%
1.3%
1.4%
$642
$2
$46
$20
$32
$23
$211
$82
$228
$0
3.8% $2,087 12.3%
3.8%
$8 15.1%
2.7%
$187 10.9%
1.2%
$66 4.1%
4.7%
$106 15.6%
2.0%
$106 9.1%
4.7%
$498 11.1%
4.6%
$279 15.5%
4.2%
$845 15.6%
$0
$183
$1
$14
$3
$8
$6
$109
$11
$32
$0
1.1% $6,116 36.1%
1.9%
$13 24.5%
0.8%
$341 19.8%
0.2%
$186 11.6%
1.2%
$240 35.2%
0.5%
$223 19.2%
2.4% $1,715 38.2%
0.6%
$865 48.1%
0.6% $2,546 46.9%
$0