Characteristics of Businesses Disrupted 

by Building Damages from the HayWired 

Scenario Mainshock

By Anne M. Wein, Jon Haveman, Cynthia Kroll, and Jeff Peters

Chapter V4 of

The HayWired Earthquake Scenario—Societal Consequences

Edited by Shane T. Detweiler and Anne M. Wein

Scientific Investigations Report 2017–5013–R–W

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DAVID BERNHARDT, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2020

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Suggested citation:

Wein, A.M., Haveman, J., Kroll, C., and Peters, J., 2020, Characteristics of businesses disrupted by building damages 

from the HayWired scenario mainshock, chap. V4 of Detweiler, S.T., and Wein, A.M., eds., The HayWired Earthquake 

Scenario—Societal Consequences: U.S. Geological Survey Scientific Investigations Report 2017–5013–R–W, 68 p., 

https://doi.org/10.3133/sir20175013v3.

ISSN 2328-0328 (online)
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................2

Literature on Business and Owner Characteristics.................................................................................3

Business Owner Characteristics .......................................................................................................3

Business Characteristics.....................................................................................................................4

Business Location........................................................................................................................4

Business Sector...........................................................................................................................4

Business Establishment Type ....................................................................................................5

Business Premises ......................................................................................................................5

Business Size................................................................................................................................6

Business Age................................................................................................................................6

Business Experience with Disruption ......................................................................................6

Pre-existing Business Conditions .............................................................................................6

Methods...........................................................................................................................................................6

Data Sources.........................................................................................................................................7

Algorithm................................................................................................................................................8

Results.....................................................................................................................................................................8

Business Characteristics in the San Francisco Bay Region .........................................................8

Distribution of Employment at Risk across Counties ......................................................................9

Disruptive Building Damage Risk by Business Characteristics..................................................10

Study Limitations..........................................................................................................................................13

Policy Discussion and Conclusion............................................................................................................14

Risk from Business Location.............................................................................................................14

Sector Risk and Vulnerability............................................................................................................14

Small Business Vulnerability.............................................................................................................15

Business Owner Vulnerability...........................................................................................................16

Other Vulnerable Business Characteristics ...................................................................................16

Turning Risk into Opportunity............................................................................................................16

Acknowledgments.......................................................................................................................................17

References Cited..........................................................................................................................................17

Appendix 1. Crosswalk between Hazus Occupancy Classes and NAICS Categories .....................22

Appendix 2. Building Damage Risk Results for the HayWired Scenario, San Francisco Bay Region....24

Appendix 3. Building Damage Risk Results for the HayWired Scenario by Business Owner 

Demographics and San Francisco Bay County.........................................................................61

Appendix 4. Building Damage Risk Results for the HayWired Scenario by San Francisco Bay 

Subarea............................................................................................................................................63
iv

Figures

1. Plot of the total and at-risk employment shares by county for the HayWired scenario 

mainshock earthquake in the San Francisco Bay region, California...................................9

2. Plot of at-risk employment share by industry sector for the HayWired mainshock 

earthquake scenario in the San Francisco Bay region, California.....................................10

3. Plot of at-risk employment share by establishment size for the HayWired mainshock 

earthquake scenario in the San Francisco Bay region, California.....................................11

4. Plot of at-risk employment share by establishment age for the HayWired mainshock 

earthquake scenario in the San Francisco Bay region, California.....................................11

5. Plot of at-risk employment share by establishment revenue for the HayWired 

mainshock earthquake scenario in the San Francisco Bay region, California................11

6. Plot of at-risk employment share by business-owner demographics for the HayWired 

mainshock earthquake scenario in the San Francisco Bay region, California................12

7. Plot of at-risk employment share by establishment type for the HayWired mainshock 

earthquake scenario in the San Francisco Bay region, California.....................................12

8. Plot of disruptive building damage risk to the most affected business types, sectors, 

and counties for the HayWired mainshock earthquake scenario in the San Francisco 

Bay region, California.................................................................................................................13
The HayWired Earthquake Scenario—Societal Consequences

Edited by Shane T. Detweiler and Anne M. Wein

Scientific Investigations Report 2017–5013–R–W

[Also see https://doi.org/10.3133/sir20175013v3 ]

Chapter V4

Characteristics of Businesses Disrupted by Building 

Damages from the HayWired Scenario Mainshock

By Anne M. Wein, Jon Haveman, Cynthia Kroll, and Jeff Peters

Abstract

A review of business characteristics associated with 

business vulnerability after a disaster (for example, type of 

business, location, sector, size, age, and owner demographics) 

accompanies a spatial analysis of business characteristics 

exposed to disruptive building damages in the HayWired 

scenario, San Francisco Bay region, California. The National 

Establishment Time-Series Database contains business and 

owner characteristics. Extensive and complete building 

damages from the HayWired scenario mainshock earthquake 

and fire hazards are used to estimate the percentage (or risk) 

of disruptive building damage by occupancy in census tracts. 

The spatial analysis of businesses in nine counties within the 

San Francisco Bay region with regard to building occupancy 

damage estimations provides a more nuanced assessment of 

business establishment and employment vulnerability than can 

be gleaned from building occupancy damage results alone. The 

analysis and literature are used to inform policies, mitigation 

approaches, and programs and implementations that could be 

helpful in the San Francisco Bay region after a large earthquake.

We estimate a 7–8 percent risk of disruptive building 

damage to business establishments and employment in the 

San Francisco Bay region; at least 40,000 establishments and 

240,000 employees could be affected by extensive or complete 

damage at their workplaces. The risks vary by sector and range 

from 5–6 percent (for businesses in utility, other, finance and 

insurance, and management of companies sectors) to 10–11 

percent (for establishments in the construction, education, and 

warehouse and transportation sectors and employment in the 

manufacturing sector). Business location strongly differentiates 

disruptive building damage risk for all types of businesses. 

About 25 percent of Alameda County businesses and employees 

could be affected by limited or restricted use of their buildings, 

and the risk increases to 35 and 40 percent in central Alameda 

and western Contra Costa Counties, respectively, because of 

their close proximity to the HayWired scenario epicenter. In 

Alameda County, risk of disruptive building damage by sector 

range from 15–19 percent (agricultural and utility businesses) 

to about 33–35 percent (construction and education businesses) 

and in central Alameda County, sectoral risks can be as large as 

57 percent for public administration employment and 50 percent 

for construction business establishments. All Alameda County 

establishment and employment risks exceed risks in other 

counties; disruptive building risk in Contra Costa County is 

12–13 percent, followed by 3–4 percent in Santa Clara County. 

Overall, the shares of business characteristics in Alameda 

County largely drives the result for the region. For example, the 

slightly larger regional risk to minority-owned businesses and 

branch-type establishments reflect a larger representation of 

these business characteristics in Alameda County.

The disaster research literature identifies the prevalent 

and vulnerable business characteristics of small size (measured 

either by number of employees or revenue). Small businesses 

(less than 50 employees) represent 98 percent of establishments 

and 60 percent of employment in the San Francisco Bay 

region. About 33 percent of establishments and 10 percent of 

employment have annual business revenues under $100,000. 

Recent studies find that small businesses are underserved by 

disaster recovery resources and seek more innovative solutions. 

Larger businesses have more resources for disaster mitigation, 

preparedness, and recovery, and based on location in the 

HayWired scenario, they show slightly less disruption from 

building damage than medium- and small-sized businesses in 

the region, but not in each county. In Santa Clara County, large 

businesses face a greater risk of building damage, suggesting 

they are located in more hazardous places in the HayWired 

scenario or in more vulnerable buildings than small- and 

medium-sized businesses in the county.

Businesses in the retail sector have also been vulnerable 

after disasters, although retail sales may be maintained as 

customers switch to other retailers. Programs to ensure continued 

operations have assisted retail businesses impacted by disasters. 

Such programs will be important for the retail sector in the San 

Francisco Bay region, which has an above-average disruptive 

building damage risk and a larger composition of small 

businesses. Business establishments in the construction sector 

face larger disruptive building damage and are more likely to 

be small businesses—this may impede their ability to reap the 

benefits of reconstruction in the absence of programs ensuring 

their participation. Manufacturing businesses also face aboveaverage disruptive building damage risk, and combined with the 
2 The HayWired Earthquake Scenario—Societal Consequences

difficulty of relocating fixed assets, may result in changes to the 

business base in Alameda County, requiring coordinated economic 

development. Transportation and warehouse establishments are 

disproportionately located in Alameda County and, therefore, 

the sector is at greater risk of disruptive building damage, which 

could create a need for public and private coordination around 

emergency and production supply networks. The risk to education 

establishments and employees is potentially exacerbated by fire 

following the HayWired scenario mainshock and reinforces the 

importance of fire prevention and safety management in schools 

with laboratories. Slightly larger disruptive building damage risk 

for minority-owned businesses is a reminder to follow up on 

programs for minority business owners. Male-minority-owned 

businesses are exposed to more risk in the HayWired scenario and 

female-owned businesses have been found to be more vulnerable 

after previous disasters.

After the direct benefit of constructing stronger buildings 

to reduce disruptive building risk, opportunities to improve 

business preparedness and recovery reside in interorganizational 

arrangements and programs.

Introduction

Macroeconomic impacts, measured as national and 

regional Gross Domestic Product (GDP) losses, can mask 

business interruption at the microeconomic level (Dahlhamer 

and Tierney, 1998; duPont and Noy, 2015). Individual 

businesses may experience impacts of disasters in widely 

varying ways (Dahlhamer and Tierney, 1998). A disaster may 

interrupt businesses directly through

• Damages to buildings, contents, and inventory 

(Marquis and others, 2015), and

• Transportation and utility (electricity, water, gas, voice 

and data) service outages (for example, Webb and 

others, 2002; Scarinci, 2016), 

and indirectly through

• Disruptions to downstream businesses in supply chains 

(Altnay and Ramirez, 2010),

• Damages to neighborhoods and safety perceptions that 

deter customers (Dahlhamer and Tierney, 1998; Chang 

and Falit-Baiamonte, 2002; [New Zealand] Department 

of Labour, 2011),

• Socioeconomic status of customers (Lam and others, 

2012), and 

• Staff issues1 (Corey and Deitch, 2011; Seville and 

others, 2014).

If a business is closed for any reason, it is vulnerable to losing 

market share (Alesch and others, 2001).

In this study of businesses in the San Francisco Bay region, 

we focus on building damages as the source of business disruption 

from the HayWired scenario earthquake. Of the businesses that 

reported physical damage after the 1989 Loma Prieta earthquake, 

55 percent of them described it as disruptive or very disruptive 

(Wasileski and others, 2011). Building damage and relocation 

was a top issue for the Christchurch, New Zealand, business 

community for at least eight months after the magnitude (M) 6.3 

Christchurch earthquake (Recover Canterbury, n.d.). The effects of 

other sources of business disruption for the HayWired scenario are 

examined in other chapters of this volume: neighborhood impacts 

by Wein, Belzer, and others (in prep., planned to be published as 

part of this volume); supply chain ripple effects by Sue Wing and 

others (in prep., planned to be published as part of this volume); 

and labor and transportation effects by Kroll and others (in prep., 

planned to be published as part of this volume).

Researchers have sought to identify pre-existing business and 

owner characteristics that make businesses vulnerable to failure 

or slow to recover after a disaster (for example, Webb and others, 

2002; Marshall and others, 2015). The goals of such research 

are to inform policies and programs to increase the resilience of 

businesses before, during, and after disasters (for example, Alesch 

and others, 2001; Zhang and others, 2009; Hatton and others, 

2012; Marshall and others, 2015). In this chapter, we draw upon 

this body of research and a detailed inventory of businesses to shed 

light on potential business vulnerability to building damages in the 

San Francisco Bay region after a hypothetical large earthquake on 

the Hayward Fault.

In the HayWired scenario, the Federal Emergency 

Management Agency’s Hazus model is used to estimate building 

damages for building occupancy classes, which broadly defines 

the industrial sector and their building types (Seligson and others, 

2018). Fire following earthquake damages (Scawthorn, 2018) are 

integrated as additional complete building damages (Johnson and 

others, in prep.; planned to be published as part of this volume). 

We assume extensive and complete building damage states are 

particularly disruptive because they approximate conditions of 

building use (that is, restricted use of yellow-tagged buildings 

and prohibited use of red-tagged buildings, respectively). We 

define the risk of disruptive building damage for businesses as 

the percentage of building square footage that is extensively or 

completely damaged in their occupancy class in their census 

tract. Using the National Establishment Time-Series (NETS) 

Database, a more detailed accounting of the businesses and owner 

characteristics with respect to building damage states in census 

tracts allows application of findings from earlier disasters to 

discuss business vulnerability factors for the HayWired scenario.

Therefore, this report combines a review of past disaster 

research on business vulnerability factors with a spatial analysis of 

San Francisco Bay region businesses to provide a more nuanced 

assessment of business vulnerability than is conveyed in Hazus 

occupancy class building damage results. The literature review 

summarizes business and owner characteristics in relation to 

business outcomes (for example, temporary or permanent closure 

1

Staff issues include (1) difficulties finding qualified or suitable staff; (2) the 

additional burden of managing and supporting staff; and (3) reduced productivity 

caused by staff being stressed, emotionally drained, and burned out (Seville and 

others, 2014).
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 3

or partial recovery) after a natural disaster. The method section 

describes the approach to perform a spatial analysis, combining 

disruptive building damage (derived from Hazus results) with the 

detailed business establishment data available from NETS—that 

is, disruptive building damage risk for businesses and employees 

within a specific sector, geographical area, business size, business 

age, establishment type, and owner demographic. The results 

section tallies business characteristics regionally and identifies 

those at high risk of disruptive building damage from HayWired 

mainshock earthquake hazards with and without fire following the 

earthquake.1 We relate these results to findings from the business 

demise and recovery literature to illuminate potential business 

vulnerability in the region after such an event. In the concluding 

sections, we acknowledge the gaps in the data and limitations 

of our method and refer to policy insights from prior studies 

to discuss opportunities to increase business resilience to an 

earthquake in the San Francisco Bay region.

Literature on Business and Owner 

Characteristics 

Drawing upon theoretical frameworks of business resilience, 

studies have considered the influences of business owner 

demographics (for example, Alesch and others, 2001; Marshall 

and others, 2015), business characteristics (for example, Zhang 

and others, 2009), and organizational attributes (for example, 

Lengnick-Hall and others, 2011; Doerfel and others, 2013). 

“Owner characteristics” include the education and business 

experience, gender, minority/majority designation, age, household 

resources, personal resilience, and industry and disaster experience 

of the business owner. “Business characteristics” include the 

business location, sector to which it belongs, establishment type, 

operation in an owned or leased building, size, market reach, labor 

source, business condition and growth, prior business experience 

in the industry, prior business experience with disasters, prior 

short-term disaster business closure, and prior cash flow issues. 

“Organizational resilience” is defined by Lengnick-Hall and others 

(2011) in terms of cognitive, behavioral, and contextual attributes 

of organizations and framed as the corporate cultural dimension 

of resilience by Sheffi (2005). Doerfel and others (2013) find 

resilience in interorganizational relations that (pre-disaster) form 

communities of practice and involve multiple ties with each type 

of supporting organization. Our literature review focuses on 

business and owner characteristics because only these types of 

data are tracked in the NETS data.

The literature spans different approaches and contexts that 

complicate conclusions about the characteristics influencing 

business demise or recovery after a disaster. These study 

differences include:

• Different definitions and measurements of business 

impacts—for example, the condition and experience of 

businesses (Wasileski and others, 2011), business demise 

(Marshall and others, 2015), or recovery relative to impact 

(Brown and others, 2014). See Stevenson and others 

(2018) for a discussion of a business recovery framework.

• Different time periods of measuring business status—

for example, 8 months (Webb and others, 2002; [New 

Zealand] Department of Labour, 2011) or 8 years 

(Marshall and others, 2015) after an event, such that 

businesses are subjected to different influences on 

reopening within different time periods (Lam and 

others, 2012).

• Extent to which business closure is differentiated from 

temporary arrangements (for example, owner/employees 

working for another business), consolidation of the 

business at another location, or reopening under a different 

name (Hatton, 2015).

• Different geographical settings, such as rural versus urban 

(for example, Stevenson and others, 2011).

• Different types and sizes of disasters—earthquakes, 

floods, and cyclones (for example, Webb and others, 

2002; Wasileski and others, 2011; Hsiang and Jina, 2014).

• Different ways of identifying survey participants, including 

random sampling (for example, Marshall and others, 

2015) and drawing from recipients of disaster loans (for 

example, Scarinci, 2016) or members of a business alliance 

organization (for example, Recover Canterbury, n.d.).

• Treatment of background noise of business failure or other 

market trends in the absence of a disaster.

• Recognition of pre-existing economic conditions including 

regional economic structure, commercial and residential 

space vacancy rates, and insurance penetration.

• Documentation of support programs provided to 

businesses after each disaster. Examples include a 

temporary space to congregate businesses (Kroll and 

others, 1990), a subsidy to pay employees (Brown and 

others, 2014) versus a loan (Alesch and others, 2001; 

Webb and others, 2002), and other programs in response to 

monitored business issues (Recover Canterbury, n.d).

Business Owner Characteristics

Marshall and others (2015) provide a state-of-the-art 

literature review of small business owner demographics and their 

cognitive attributes. Based on research and intuition, they make 

a case to investigate a slate of owner characteristics: education 

and disaster experience, gender, race/ethnicity, veteran status, 

household size and disaster experience, and personal resilience 

with respect to business states (reopened, closed and reopened, 

2

This analysis addresses only building damages, rather than the impacts of roadway or utility damages on business activity. These additional impacts are discussed in 

more detail in other chapters of the HayWired scenario analysis, including by Kroll 

and others (in prep., planned to be published as part of this volume) and Johnson and 

others (in prep., planned to be published as part of this volume).
4 The HayWired Earthquake Scenario—Societal Consequences

and closed) eight years after Hurricane Katrina. They found 

the likelihood of business closure (with and without reopening) 

increased for female owners. They note consistency with 

Fothergill (1996) and Council of Economic Advisers (2015), 

who explain that women, in general, have a greater exposure to 

risk because of their social roles as caregivers, lack of mobility, 

and access to capital (for example, aid, savings, insurance, and 

wages) compared to their male counterparts.

The experience of prior damage to business owners’ homes 

did not appear to predispose their business for recovery; rather, 

prior home damage was associated with an increased likelihood 

of immediate or eventual business closure (Marshall and others, 

2015). Recovering from damage to a home may limit household 

resources for business recovery investments.

Marshall and others (2015) found the likelihood of 

business closure (without reopening) was increased for minority 

or veteran owners. They point out that whereas the effects of 

ethnicity and race on business survival and preparedness are 

inconclusive, African-American owned businesses (in the 

retail and service sectors, in particular) have been found to fail 

more often in general, a trend that could be exacerbated in a 

disaster. Compared to non-veteran owners, veteran owners had 

lower perceived business success (which was associated with 

increased likelihood of closure) and owned fewer businesses in 

the service sector (that had a lower likelihood of demise).

The business owner’s personal resilience is thought to be 

an important factor in the owner’s ability to cope with stress 

resulting from a natural disaster (Marshall and others, 2015). 

Marshall and others (2015) found that owner resilience was 

associated with increased likelihood of reopening then closing. 

This corresponds to Alesch and others’ (2001) observations 

of extraordinary psycho-social resiliency that commonly gave 

in to post-disaster stress and pressures, eventually resulting in 

business failure.

Marshall and others (2015) surmise that the business owner’s 

household may be both a source of resources that the business 

can draw upon or a source of competition for those resources. 

They found opposing relationships between business status and 

the owner’s household size. Owners with larger households were 

more likely to close and remain closed (suggesting resource 

competition between a larger household and business) and less 

likely to reopen and close (suggesting that resources from the 

larger household were available for the business).

Furthermore, Marshall and others (2015) found that 

businesses with partnerships were less likely to close. 

However, if businesses with partnerships closed for a time and 

then reopened, they were more likely to close again (perhaps 

caused by a perception of having more resources initially in 

the partnership).

Business Characteristics

Numerous business characteristics have been examined by 

researchers after a disaster. The vulnerabilities summarized here 

pertain to business location; sector; establishment type; size; 

premise type (either owned or leased and [or] home based); 

age; experience with disruption; and pre-existing business 

conditions.

Business Location

Business location differentiates impacts from hazard 

exposure, property and neighbor damages, and utility 

disruptions to businesses (Kroll and others, 1991; Tierney and 

Nigg, 1995; Tierney, 1997; Webb and others, 2002; Wasileski 

and others, 2011; Brown and others, 2014; Marshall and others, 

2015). Location is also relevant where businesses are dependent 

on local customers ([New Zealand] Department of Labour, 

2011; Whitman and others, 2013). In contrast, businesses with 

international markets have recovered more quickly (Webb and 

others, 2000), although there is evidence that Christchurch 

businesses needed to assure national and international customers 

that they were reliable sources despite the ongoing earthquakes 

(observed by A.M. Wein during a 2011 visit to Christchurch; 

Resilient Organizations, 2018).

Business Sector

Business sector has been related to business demise and 

recovery status after a disaster. Businesses in the retail and 

wholesale sectors have fairly consistently been found to be 

more vulnerable to disaster damages and disruption, including 

that from earthquakes: Kroll and others (1990) and Wasileski 

and others (2011) discuss the 1989 Loma Prieta earthquake,3

Webb and others (2002) discuss the 1997 Northridge 

earthquake, and Chang and Falit-Baiamonte (2002) discuss the 

2001 Nisqually earthquake. The [New Zealand] Department 

of Labour (2011) and Statistics New Zealand (2012) showed 

employment declines in the wholesale and combined retail 

and accommodation/food sectors in the Canterbury region for 

more than a year after the most damaging 2011 M6.3 aftershock 

in Christchurch, New Zealand. Retail and wholesale sectors 

were not highlighted in Canterbury earthquake surveys of 

business impact and recovery (Brown and others, 2014), but 

the surveyed businesses in these sectors did not appear among 

the most impacted. The vulnerability of retail and wholesale 

has been explained by the competitive nature of the business 

and tendency for small retail businesses to depend on local 

customers and to occupy older buildings (Alesch and others, 

2001; Chang and Falit-Baiamonte, 2002).

After Hurricane Katrina, Marshall and others (2015) found 

service businesses survived more often than goods-producing 

businesses that rely more on fixed assets. Similarly, Zhang and 

others (2009) classify professional, scientific, and technical 

services as low-vulnerability businesses and manufacturing and 

construction as moderate-vulnerability businesses. Businesses 

in manufacturing and construction sectors were noted to recover 

better than expected after the 1989 Loma Prieta earthquake 

3

However, business assistance muted the effect for retail.
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 5

(Wasileski and others, 2011). The construction sector has shown 

initial decline that can be followed by a building boom (Kroll and 

others, 1990; Statistics New Zealand, 2012) to place it among the 

most recovered sectors (Brown and others, 2015).4 Reconstruction 

brings in construction workers that are new customers for food 

and accommodation sectors that suffer from decreases in tourism 

after a disaster (Brown and others, 2015). The number of tourists 

declined in Christchurch when hotel capacity was impacted and 

tourists were encouraged to visit other parts of New Zealand 

because of ongoing aftershocks (Orchiston and others, 2013). In 

central Italy, there was a drop in tourism that lasted nine months 

after the 1997 earthquake (Mazzocchi and Montini, 2001). After 

the 2014 South Napa earthquake, visitors to restaurants declined 

in the City of Napa for as long as six months (observed by A.M. 

Wein in 2016). Amongst business sectors that typically benefit 

from an influx of potential customers, only the arts, entertainment, 

and recreation sector did not gain from new construction worker 

customers in Christchurch—these businesses were the most 

impacted and the least recovered relative to impact (Brown and 

others, 2015).

Zhang and others (2009) classify finance, insurance, and real 

estate businesses as moderately vulnerable. These businesses were 

considered to be better prepared, but not immune to earthquake 

impacts, surrounding damage, and transportation disruption after 

the Loma Prieta earthquake (Kroll and others, 1990). Brown and 

others (2014) rank finance and insurance businesses among the 

most recovered businesses relative to operations preceding the 

earthquakes and rank rental, hiring, and real estate businesses 

among the most recovered relative to impact (taking into account 

the level of impact as well as strength of business operations in the 

recovery period).

Business-oriented studies have had less to say about public 

and educational entities. The public administration sector was 

ranked among the least recovered entities in Christchurch, but 

one of the most recovered relative to size of impact (Brown and 

others, 2015). In Christchurch, the closure of hundreds of public 

schools affected 70,000 students and reopening of schools was 

delayed by water and wastewater services. Most schools reopened 

a month after the 2011 earthquake and school sites were shared 

to accommodate students from schools that remained closed 

(Earthquake Engineering Research Institute, 2011).

Continued operation of universities has depended on the 

restoration of other support services, including public schools. 

Tulane University was closed for a semester after Hurricane 

Katrina and reopening the university depended on the university’s 

role in rebuilding support services, such as public schools, health 

services, and arranging for housing, as well as on the physical 

repairs (Cowen, 2007). During the hiatus, Tulane University 

helped students find education alternatives. In Christchurch, the 

University of Canterbury progressively reopened after 3–4 weeks 

using temporary (tent) structures onsite, off-site facilities, and 

online instruction. The university suffered from loss of fee-paying 

international students. In the San Francisco Bay region, Comerio 

(2006) reports that the University of California, Berkeley, plans for 

a maximum closure of one month and Stanford University plans to 

reopen within one week after an earthquake.

Business Establishment Type

Establishment type, as in headquarters, branch/affiliates, 

or stand-alone operations, indicate the range of resources and 

informed support and advice that may be available for managing 

business interruption. Headquarters typically have resources 

for emergency planning. Larger companies that have multiple 

sites in the region and nationally are able to shift operations 

(Comerio, 2006). The value of external advice from affiliates 

and headquarters was evident in Bowden’s (2011) survey of four 

national companies when managers with “quake brain” had to 

make relocation decisions. In contrast, stand-alone businesses 

lack the benefit of preparedness guidance from headquarters, 

alternative sites, and (or) advice from others that are familiar with 

their business.

Business Premises

Researchers have fairly consistently found that businesses 

that own their buildings fared better than renters (for example, 

Dahlhamer and Tierney, 1998; Chang and Falit-Baiamonte, 2002; 

Wasileski and others, 2011). Building ownership is an indicator 

of wealth for the business (Wasileski and others, 2011), whereas, 

as Comerio (2006) points out, resolving rental lease agreements 

can be a lengthy process. In contrast, Brown and others (2014) 

found that, in the Canterbury earthquakes, businesses that 

owned buildings fared worse than those that rented. Marquis 

and others (2015) shed light on the potential disadvantage of 

building ownership in Christchurch in the aftermath of the 

earthquake sequence, revealing unique issues and complexities for 

commercial building owners in relation to unexpected technical, 

legal, and financial challenges when making decisions about the 

future of their buildings and a paradoxical effect of insurance5 on 

the recovery of Christchurch. In Brown and others’ (2014) study 

on the Christchurch earthquakes, businesses that both owned and 

rented buildings recovered better, suggesting an advantage of 

having more options and more flexibility with multiple locations 

(Tierney, 1997).

Marshall and others (2015) found that home-based 

businesses had an advantage, perhaps because of better 

preparedness (for dual purposes) and because homes are built to 

higher codes than commercial spaces. Home-based businesses 

may be less likely to be located in built-up areas that are subject 

to concentrated neighborhood damage (see Wein, Belzer, and 

others, in prep.; planned to be published as part of this volume). 

4

Brown and others (2015) distinguish between recovery relative to 

operations preceding the earthquake and recovery relative to the size of 

impact. A sector that has not fully recovered to previous levels may still have 

recovered strongly relative to the level of impact experienced.

5

The paradoxical effect refers to the expectation that insurance aids during 

recovery from a disaster, yet various insurance issues and challenges in 

Christchurch caused delays in repairs and rebuilding.
6 The HayWired Earthquake Scenario—Societal Consequences

It is noteworthy that the number of people working from home 

is increasing but is more prevalent in some sectors than others. 

Working from home has been most prevalent in management, 

business, and financial occupations and has been increasing 

most rapidly for computer, engineering, and science occupations 

(Mateyka and others, 2012). In 2015, the U.S. Bureau of Labor 

Statistics (2016) measured 24 percent of workers are working 

from home in some capacity, including 30–40 percent of the 

management, business, and financial and professional service 

occupations, but fewer than 10 percent of production and 

transportation workers work from home.

Business Size

Business size, measured by number of employees, wages, 

or revenues, is consistently found to be a factor in business 

recovery—small businesses are identified as more vulnerable 

to disasters than larger businesses (Dahlhamer and Tierney, 

1998; Chang and Falit-Baiamonte, 2002). For example, small 

businesses had a lower probability of reopening in New 

Orleans after Hurricane Katrina (Lam and others, 2012). Small 

business size corresponds to less resources to prepare, fewer 

cash reserves, lower uptake of insurance, and a lack of access 

to government programs for recovery (Kroll and others, 1990; 

Webb and others, 2000; Alesch and others, 2001; Dunaway and 

Shaw, 2010; Collier and others, 2016). Wasileski and others 

(2011) caution that small business use of external aid may 

exacerbate the problem of debt.

Brown and others (2014) did not detect a small business 

size effect on business recovery, but small businesses were 

underrepresented in their sample. Although the five businesses 

in their survey that ceased operation had less than 50 employees 

(Brown and others, 2014), four of these businesses reopened 

under a new name (Tracy Hatton, Resilient Organizations, 

written communication, 2018). Also, the government earthquake 

support subsidy to compensate employees in Canterbury after 

the Christchurch earthquake was reportedly very helpful for 

keeping businesses going ([New Zealand] Department of 

Labour, 2011) and was taken up by relatively more small- and 

medium-sized businesses (Brown and others, 2015).

Business Age

Some studies find evidence of the vulnerability of young 

business age caused by limited resources (including insurance), 

less experience, and smaller size (Dahlhamer and Tierney, 1998; 

Webb and others, 2002; Collier and others, 2016). However, 

results vary. No age effect was detected with respect to business 

recovery in Christchurch, but older businesses prevailed (Brown 

and others, 2014). Conversely, old businesses struggled more 

in Miami-Dade County, Florida, in the aftermath of Hurricane 

Andrew (Webb and others, 2002) and Scarinci (2016) gathered 

evidence that some smaller and newer businesses could relocate 

more easily than larger and more established businesses. Business 

age might align with experience, but younger businesses could 

benefit from having an experienced owner, whereas older 

businesses headed by older proprietors may be less likely to 

survive (Brüderl and Ziegler, 1992). This could especially be the 

case where the level of damage requires extensive rebuilding and 

investment on the part of the owner, who may prefer to retire.

Business Experience with Disruption

Marshall and others (2015) found positive effects from 

industry experience and business experience with disruption 

(including cash flow issues) on reopening, although, industry 

experience did not necessarily keep businesses from eventually 

closing. Scarinci’s (2016) post-disaster preparedness investigation 

reinforced that small businesses can be inhibited by a lack of 

reflection caused by intense focus on recovery and even “amnesia” 

to wipe the horrible experience from business owners’ minds.

Pre-existing Business Conditions

Past studies have shown that disasters tend to intensify 

pre-existing business conditions (Marshall and others, 2015). 

Pre-disaster growth and financial condition have been identified 

as indicators of business survival and recovery (Dahlhamer 

and Tierney, 1998; Alesch and others, 2001; Webb and others, 

2002). Positive owner perceptions of business success prior 

to Hurricane Katrina were associated with lower likelihood of 

demise (Marshall and others, 2015). Financial condition was the 

second strongest predictor of long-term recovery in Santa Cruz, 

Calif., after the Loma Prieta earthquake, but counterintuitively 

was found to have a negative effect (Webb and others, 2002).

Methods

Past research, then, shows that a broad range of longterm effects are possible after a disaster relative to the severity 

of the event, and vary with the types of physical damage, 

particular circumstances of the economy, assistance provided, 

industry sector and mix, business size and age, establishment 

type, and other characteristics of business ownership. In the 

description of business characteristics in the San Francisco 

Bay region that follows this section, we highlight a few key 

factors that may contribute to business vulnerability, including 

location with respect to disruptive building damage, industry 

sector, business size, age, establishment type, and ethnic and 

gender characteristics of the business owner. None of these 

factors in themselves is a predictor of business outcomes, and 

indeed two different factors may interact to mitigate the effect 

a single characteristic might have (for example, a large retail 

business would have greater survival chances than a smaller 

one, but if heavily damaged relative to the smaller one, the 

survival likelihood of each could be reversed). Additionally, 

businesses facing compounding challenges are less likely to 

recover (Dahlhamer and Tierney, 1998). We analyze business 

characteristics for disruptive building risk in the HayWired 
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 7

scenario to investigate whether an analysis of business and 

owner characteristics may illuminate the types of policies, 

mitigation approaches, or responses that could be helpful in 

the San Francisco Bay region. Next, we describe our methods 

for relating business characteristics to building damage 

estimates for the HayWired mainshock hazards of shaking, 

liquefaction, landslides, and fire.

Data Sources

The NETS database is an annual collection of data pertaining 

to all of the business establishments in the United States between 

1989 and 2013 sourced from Dunn and Bradstreet (Walls and 

Associates, 2011; Feldman, 2018). “Business” is broadly defined 

to include private for-profit and nonprofit organizations as well as 

government agencies (Barnatchez and others, 2017). In NETS, an 

establishment is a specific line of business at a specific location, 

and employment includes all workers at an establishment, 

potentially including proprietors, independent contractors, and 

temporary workers supplied by outside organizations (Barnatchez 

and others, 2017).6

Barnatchez and others (2017) review studies of 

comparable business datasets and conduct their own 

comparison of NETS data with that from more robust 

official data collection activities of U.S. statistical agencies. 

Using National NETS data for 1992–2014, they find that the 

correlation of NETS employment counts and U.S. Census 

Bureau County Business Patterns employment counts across 

U.S. counties can exceed 0.99; correlations across stateindustry-size class cells are above 0.9 on restricted samples; 

and that zip-code-level correlations are also remarkably 

high. On the other hand, they find that NETS overrepresents 

establishments that have fewer than 10 employees, especially 

business sizes of 1.7 A smaller, but significant, discrepancy 

is found for the largest establishments, but explained by 

large, public educational establishments that are difficult to 

identify as government owned, as well as establishments that 

mistakenly report firm employment numbers in NETS. Also 

relevant is the finding that post-2000 developments in U.S. 

mining, construction, and manufacturing employment do not 

appear to be well captured in NETS, possibly caused in part 

by industry labeling differences. When Barnatchez and others 

(2017) omit very small establishments (especially businesses 

of size 1), NETS agrees reasonably well with official datasets. 

The advantage of using NETS is that it does not require 

extensive proposal and security clearance processes and can be 

used outside of secure government facilities (Barnatchez and 

others, 2017).

The data used for this analysis come from the 2014 version of 

the NETS dataset, and includes all of the business establishments 

in operation in the San Francisco Bay region as of January 2013. 

The San Francisco Bay region is defined as the following counties: 

Marin, Sonoma, Napa, Solano, Contra Costa, Alameda, Santa 

Clara, San Mateo, and San Francisco.

Business characteristic data in NETS used in this analysis 

include: 

• Location,

• Size (number of employees, revenues),8 

• Age,

• Establishment type, and

• Owner characteristics pertaining to gender and minority9/

majority demographics.

Based on the discrepancies between datasets on business size, 

we also examined the distribution of businesses of size 1 (which 

may have 0 employees) across characteristics. Some additional 

characteristics were excluded because of many missing data 

entries. Even so, some location data had to be recalibrated, and 

some establishments were dropped from the dataset because 

location reporting concerns could not be resolved.10 In all, the 

starting total of 627,856 establishments in the database was 

reduced to 570,659 when those with missing or unresolvable 

data were excluded.

The building damage data were sourced from the Seligson 

and others (2018) estimate of earthquake hazards and the 

Scawthorn (2018) estimate of fire hazards. The Hazus building 

damage states in a census tract are separated into: none, slight, 

moderate, extensive, and complete damage states. This analysis 

uses the percentage of building square footage that suffers the 

disruptive (extensive or complete) damage in each census tract 

for each occupancy class. Building damages are reported in 

Hazus by occupancy classes, which in turn are tied through a 

crosswalk to North American Industry Classification System 

(NAICS) codes, as shown in appendix 1. Fire damages were 

classified as complete damage, distributed to occupancy classes, 

6

We do not know the extent to which the shadow economy (including nannies, 

gardeners, house cleaners, or day labor) is represented, but a Red Cross network of 

voluntary organizations estimated that about 450 nonprofit agencies provided some 

type of disaster service to 160,000 victims seeking help outside the regular channels 

compared to 130,000 families that received temporary housing assistance after the 

1997 Northridge earthquake (Comerio [1998]).

7

NETS imputes values for two-thirds of businesses that have less than 5 employees.

8

Four establishments were dropped because they contained implausible data on 

either employment or revenues, most likely because they are headquarters with data 

that reflects activities at various establishments in other locations. The owner of 

the dataset did not respond to inquiries about these establishments. A much larger 

number of establishments were excluded because of missing records for revenue 

(mostly government offices) and age.

9

Proprietor of non-European ancestry.

10The NETS data provided detailed geographic information on each 

establishment, including latitude and longitude, address, and level code. A level 

code indicates how the establishment was located by street, block, census tract, 

or zip code. Some establishments were not located in the level code indicated. 

Where possible, the establishment location was corrected and latitude and 

longitude were recalculated. For example, if an establishment was verified 

at the zip code level, but was not actually found to be located in the zip code 

indicated, the location for that establishment was moved to the indicated zip 

code centroid. 732 establishments with locations outside of the study area were 

relocated in the process of using zip code level codes. Relocating the remaining 

35 establishments with insufficient data on their location was not attempted 

because it is a time-consuming manual process.
8 The HayWired Earthquake Scenario—Societal Consequences

and integrated with the Hazus earthquake damages (appendix in 

Johnson and others, in prep.; planned to be published as part of 

this volume). The combination of earthquake hazard damages 

(Hazus) and fire following earthquake damages is referred to as 

“all hazards.”

Algorithm

For counties in the San Francisco Bay region, we used an 

algorithm that counts business characteristics in each census 

tract by establishment and by numbers of employees. The counts 

are summed using the weight of the percentage of extensive 

and complete damage in the establishment’s census tract for the 

establishment’s occupancy class to calculate disruptive building 

damage risk to establishments and employees. Essentially, the 

risk is the percentage of establishments or employees that are in 

buildings that are extensively or completely damaged.

The steps are as follows:

1. Correct establishment location data in the 2013 NETS 

database for the San Francisco Bay region and assign 

it to a census tract using a geographical information 

system. Each business record contains the census 

tract, sector (6-digit NAICS code), employment, 2013 

revenues, ownership (gender/race), and establishment type 

(headquarters/branch/stand alone).

2. Add further sector classifications to the business records. 

In this case, the 6-digit NAICS code is aligned with the 

Hazus occupancy classes and collapsed to the NAICS 

2-digit industry code. Note that where there is no 

industry in the NAICS data field, the industry is classified 

as “other” and is subject to the average risk for all 

occupancies in the census tract.

3. Assign the sector risk measure to each record according to 

census tracts for both cases of earthquake hazards and all 

hazards to each establishment.

4. Use establishment-level risks assigned by census tract and 

occupancy class to aggregate into a measure of overall risk 

for each business characteristic.

 A. The risk aggregation for business characteristics is 

performed for each sector (2-digit NAICS code) and 

all establishments (total) and aggregations are made 

across establishments using a weighted average of 

risk.

 I. Weights for aggregating risk across characteristics 

are:

a. One for the risk aggregation by establishment.

b. Establishment employment for risk aggregated 

by employment.

 B. The referenced business characteristics are the 

following:

 I. County (or subarea) in which the business is 
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 9

3. Business sizes and sectors.—Large establishments (>500 

employees) are primarily in the public administration, 

utility, manufacturing, or information sectors. Sectors 

with the fewest employees in large establishments 

(>500 employees) include accommodation/food, retail, 

and construction. Sectors that have the largest share of 

employees in small businesses are administrative support 

and waste management; real estate, rental, and leasing; 

and management of companies. Businesses of size 1 are 

most prevalent in professional, scientific, and technical 

services (the share of businesses of size 1 in this sector 

is 163 percent of the regional share of establishments 

in this sector), followed by arts, entertainment, and 

recreation (144 percent of the regional share) and 

construction (133 percent of the regional share).

4. Establishment revenues.—One-third of businesses 

make less than $100,000 (containing 10 percent of 

employees), and a total of 77 percent of establishments 

make less than $250,000 (containing 28 percent of 

employees). Ninety-two percent of businesses of size 1 

report revenues less than $100,000. The establishments 

in the largest revenue category ($2.5 million and above) 

contain 42 percent of all employees.

5. Ages of business establishments.—Most employees work 

for young or old companies: 25 percent of employees 

are in business establishments younger than 6 years (45 

percent of establishments), and 34 percent of employees 

are in business establishments older than 26 years (13 

percent of establishments).

6. Establishment type.—Most employees (77 percent) are in 

stand-alone companies (97.5 percent of establishments), 

followed by 20 percent of employees in headquarters 

(2 percent of establishments), and the remainder in 

branches (3 percent of employees; 0.5 percent of 

establishments).

7. Owner characteristics.—Most employees (90 percent) 

are in businesses that do not qualify as minority- or 

female-owned businesses (91 percent of establishments), 

5 percent of employees work for establishments that 

are non-minority female-owned businesses (6 percent 

of establishments), and another 5 percent of employees 

work for minority male- or female-owned businesses 

(3 percent of establishments). Businesses of size 1 are 

disproportionately non-minority female owned and to a 

lesser degree, minority female owned.

Distribution of Employment at Risk across Counties

Because the building risks are shaped by the geography 

of the region and the earthquake event, we begin with 

a summary of the geographic characteristic. The results 

described below reflect the geographic concentration of the 

shaking and fire damage of the HayWired scenario near the 

cities of Hayward and San Leandro in Alameda County. As 

illustrated in figure 1, Alameda County has 19 percent of the 

region’s employment (as measured in the NETS dataset), 

but 62 percent of the employment that is at risk of disruptive 

building damage from the HayWired mainshock scenario. 

Contra Costa County has 10 percent of the region’s jobs, but 

16 percent of employment that is at risk of disruptive building 

damage. Santa Clara County, with the largest concentration 

of the region’s employment (28 percent) has 13 percent of 

the region’s employment that is at risk of disruptive building 

damage, whereas San Francisco and San Mateo Counties, 

with 17 and 11 percent of the region’s employment, have 3 

and 4 percent that are at risk of disruptive building damage, 

respectively.

The characteristics of business establishments at risk in 

each county differ by the sectoral mix of their businesses and 

the types of buildings that they occupy, as well as any other 

business characteristics that distinguish the mix of business 

establishments in each county. Note that, as shown in other 

economic analyses in this volume, business impacts increase 

and shift in their distributions when additional sources of 

disruption from the transportation, communication, water, and 

power systems, neighborhoods, and supply and customer chains 

(not covered in this chapter) are added to the analysis. This 

assessment differs from Hazus building losses and business 

output losses used in the macroeconomic computable general 

equilibrium and Regional Economic Models, Inc., analyses 

(Kroll and others, in prep. [planned to be published as part of 

this volume]; Sue Wing and others, in prep. [planned to be 

published as part of this volume]), but the overall results are 

consistent across analyses.

County

0 10 20 30 40 50 60 70

Napa

Sonoma

Solano

Marin

San Francisco

San Mateo

Santa Clara

Contra Costa

Alameda

Employment share, in percent

At risk

Total

EXPLANATION

Figure 1. Plot of the total and at-risk employment shares by 

county for the HayWired scenario mainshock earthquake in the 

San Francisco Bay region, California. Risk pertains to disruptive 

building damage from earthquake hazards and fire but does not 

include utility and transportation system impacts.
10 The HayWired Earthquake Scenario—Societal Consequences

Disruptive Building Damage Risk by Business 

Characteristics

Both business establishment and employment at risk 

portray the following results for business characteristics affected 

by disruptive building damage from all hazards. 

1. The risk of disruptive building damage from earthquake

hazards could be greatest for businesses in the construction and 

manufacturing sectors. When fire hazards are included, mining 

and education could slightly surpass these sectors for the 

greatest risk of disruptive building damage (fig. 2). Businesses 

belonging to the agriculture/forestry/fishing/hunting and 

utility sectors show the lowest risk of disruptive building 

damage (relative to the number and size of establishments in 

those sectors) because of their more distant locations from the 

epicenter of the earthquake (for example, almost half of utility 

sector employees are employed in San Francisco County). 

However, when separated out by county, even these relatively 

low-risk sectors in Alameda County are at substantially greater 

risk of disruptive building damages than even the largest risk 

sectors for the San Francisco Bay region as a whole. The risk 

is accentuated further in subareas (appendix 4); as much as 

57 percent of employment (in public administration) and 50 

percent of establishments (in construction) could be at risk of 

disruptive building damage in central Alameda County.

2. For the most part, businesses in different size categories 

have similar risks of disruptive building damage. However, 

the largest businesses (500 or more employees) show a 

slightly lower level of risk, as shown in figure 3, because 

San Francisco and Santa Clara Counties have larger 

shares of large businesses and lower building damage risk. 

Overall, this means that smaller sized businesses may not 

be disproportionately damaged but are susceptible to the 

vulnerability of their size, as seen after prior disasters. 

3. The disruptive building damage risk is distributed fairly evenly

by establishment age (fig. 4) and the results reflect the greater 

risks in Alameda County, However, this does not suggest that 

risks and problems are evenly spread among firms of all ages. 

Firms may experience differential responses to disaster, as 

found by previous researchers (see discussion above).

4. The lowest revenue businesses (less than $100,000) are

slightly more likely to be at risk of disruptive building damage, 

relative to all other revenue categories, but the differences 

are quite small (see fig. 5). This implies that riskier buildings 

may house a disproportionate share of businesses that have a 

lower revenue base overall to cope with the disruptive event. 

Contrary to the regional result, the percentage of employment 

shares of high-revenue businesses affected by disruptive 

building damage is larger in Santa Clara County when 

compared to other counties in the region. This is because Santa 

Clara County hosts more businesses in the largest revenue 

category compared to the other counties.

men19-7348_fig 02

0 5 10 15 20 25 30 35

Agriculture, forestry,

fishing, and hunting

Utilities

Finance and insurance

Management of companies

Information

Other

Professional, scientific,

and technical services

Real estate, rental,

and leasing

Administration, support,

and waste

Arts, entertainment,

and recreation

Accommodation and

food services

Health care and

social assistance

All sectors

Transportation and

warehouses

Other services

Retail trade

Wholesale trade

Public Administration

Manufacturing

Construction

Education services

Mining

Percentage

Santa Clara County

Contra Costa County

Alameda County

Nine counties

EXPLANATION

Figure 2. Plot of at-risk employment share by industry sector 

for the HayWired mainshock earthquake scenario in the San 

Francisco Bay region, California. Data are shown for the three 

riskiest counties (Santa Clara, Contra Costa, and Alameda 

Counties) and the total sector employment in the nine-county 

region. Data are ordered by decreasing risk to all counties 

(from top to bottom). The impacts of fire in the HayWired 

scenario are included, but utility and transportation system 

impacts are not.
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 11

men19-7348_fig03

0

1

2

3

4

5

6

7

8

9

All

establishments

<5 5 to 9 10 to 19 20 to 49 50 to 99 100 to 499 >500

Percentage, weighted by employment

Establishment size

HayWired mainshock

EXPLANATION

With fire

Figure 3. Plot of at-risk employment share by establishment size

(number of employees) for the HayWired mainshock earthquake 

scenario in the San Francisco Bay region, California. Data are 

shown for scenarios with and without fire following the earthquake. 

The analysis is relative to the total for the nine-county region, 

excluding damage beyond the bay region. Utility and transportation 

system impacts are not included. 

0

1

2

3

4

5

6

7

8

9

All

establishments

<6 6 to 10 11 to 15 16 to 20 21 to 25 >26

Establishment age, in years

Percentage, weighted by employment

HayWired mainshock

EXPLANATION

With fire

Figure 4. Plot of at-risk employment share by establishment 

age for the HayWired mainshock earthquake scenario in the 

San Francisco Bay region, California. Data are shown for 

scenarios with and without fire following the earthquake. 

The analysis is relative to the total for the nine-county 

region, excluding damage beyond the bay region. Utility and 

transportation system impacts are not included.

0

1

2

3

4

5

6

7

8

9

All

establishments

<$100 $101 to

$250

$251 to

$500

$501 to

$1,000

$1,001 to

$2,500

>$2,501

Percentage, weighted by employment

Revenues, in thousands of U.S. dollars

HayWired mainshock

EXPLANATION

With fire

Figure 5. Plot of at-risk employment share by establishment 

revenue for the HayWired mainshock earthquake scenario 

in the San Francisco Bay region, California. Data are shown 

for scenarios with and without fire following the earthquake. 

The analysis is relative to the total for the nine-county 

region, excluding damage beyond the bay region. Utility and 

transportation systems impacts are not included.
12 The HayWired Earthquake Scenario—Societal Consequences

5. Disruptive building damage risk by business ownership

characteristics differ marginally from the overall 

ownership profile of establishments in the region. 

Minority-owned businesses are at a slightly greater risk 

of disruptive building damage—over 9 percent, compared 

to 6 to 8 percent of all establishments and non-minorityowned establishments (fig. 6). This is because relatively 

more minority-owned establishments are located in the 

most heavily impacted county, Alameda County (4.6 

percent of establishments and 7 percent of employment), 

and the sectors to which these businesses belong 

experience larger risk of disruptive building damage. We 

see relatively more minority-owned businesses in the 

manufacturing, wholesale trade, and transportation and 

warehouse sectors, which sustain more disruptive building 

damage risk. Whereas some research has shown minorityowned establishments to be more vulnerable than other 

businesses, we should not assume this is necessarily the 

case in the San Francisco Bay region, as many successful 

start-ups in the region are minority owned.

6. There are relatively more employees in branches 

in Alameda and Contra Costa Counties and branch 

businesses in the region are at a slightly greater risk of 

disruptive building damage based on their location and 

building type (fig. 7). These businesses may be resilient 

in the short term as relates to company survival because 

they may have other options for production outside of 

the area or be able to reestablish supply networks more 

quickly. Contrary to regional results, Santa Clara County 

has a larger share of headquarters that have a slightly 

greater risk among all establishment types. 

The regional average disruptive building damage risk 

for all business and owner characteristic categories that 

have above-average risk is shown in figure 8. Note that the 

risks for earthquake hazards and all hazards are averaged 

in two ways: the average across business establishments 

and a weighted (by employment) average of business 

establishments. Overwhelmingly, the risk of disruptive 

building damage from HayWired earthquake hazards and fire 

befalls at least 25 percent of the business establishments in 

Alameda County and 12 percent of establishments in Contra 

Costa County. Within subareas, the average building risk rises 

to about 40 and 35 percent in central Alameda County and 

western Contra Costa County, respectively (appendix 4). For 

Alameda and Contra Costa Counties and most of the other 

characteristics in figure8, the risk is larger for establishments 

than employment—meaning that relatively fewer employees 

are in the riskier buildings in this earthquake scenario. An 

exception is in the mining sector (albeit represents a very 

small share, 0.08 percent, of the region’s employment), where 

employment-related risk (about 10 percent) is noticeably 

larger than establishment risk (about 7.5 percent).

men19-7348_fig06

0 2 4 6 8 10 12

All establishments

Male

Female

Non-minority male

Non-minority female

Minority male

Minority female

Percentage, weighted by employment

HayWired mainshock

EXPLANATION

With fire

Figure 6. Plot of at-risk employment share by businessowner demographics for the HayWired mainshock earthquake 

scenario in the San Francisco Bay region, California. Data 

are shown for scenarios with and without fire following the 

earthquake. The analysis is relative to the total for the ninecounty region, excluding damage beyond the bay region. Utility 

and transportation system impacts are not included.

0

1

2

3

4

5

6

7

8

9

10

All establishments Stand alone Headquarters Branch

Percentage, weighted by employment

HayWired mainshock

EXPLANATION

With fire

Figure 7. Plot of at-risk employment share by establishment 

type for the HayWired mainshock earthquake scenario in the San 

Francisco Bay region, California. Data are shown for scenarios 

with and without fire following the earthquake. The analysis is 

relative to the total for the nine-county region, excluding damage 

beyond the bay region. Utility and transportation system impacts 

are not included.
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 13

men19-7348_fig08

0 5 10 15 20 25 30

Alameda County

Contra Costa County

Education sector

Mining sector

Male-minority-owned

businesses

Construction sector

Manufacturing sector

Transportation and

warehouse sector

Female-minority-owned

businesses

Branch ownership

structure

Wholesale trade

sector

Public administration

sector

Businesses that have

<$100,000 in revenue

Retail sector

Businesses that have

100–500 employees

Businesses older

than 26 years

Businesses

6–10 years old

All businesses

Percentage

Business/owner characteristics

HayWired mainshock hazards

Establishments

Employment, weighted by number of employees

HayWired mainshock plus fire hazards

Establishments

Employment, weighted by number of employees

EXPLANATION

Figure 8. Plot of the business characteristics with the largest 

disruptive building damage risk in the HayWired scenario in the 

San Francisco Bay region, California. Risk is calculated both as 

percentage of establishments and percentage of employment 

with disruptive building damage. Data are shown for hazard 

scenarios with and without fire following the earthquake. Utility 

and transportation system impacts are not included.

After location, most of the business characteristic categories 

that have larger disruptive building damage risk in the region are 

sectors reflecting the performance of the building types they occupy 

and the geographical distribution of employment in those sectors. 

Integrating location and sector, the largest disruptive building 

damage risk for the HayWired mainshock earthquake all-hazard 

case are for establishments and employees working in construction 

in Alameda County (affecting about 32 percent of those employees 

and 35 percent of those establishments from earthquake hazards 

alone) (tables 2.25 and 2.31 of appendix 2). Mining is the industry 

at most risk of disruptive building damage in Contra Costa 

County, showing 16–19 percent risk to mining establishments and 

employment (fig. 2), although this industry is very small in the bay 

region. The disproportionate effect of fire damage in Alameda and 

Contra Costa Counties on the education sector is evident in the 

increased establishment risk from 7 to over 10 percent.

Minority-owned businesses, particularly male-minorityowned businesses, have larger disruptive building damage risk 

compared to businesses with different owner demographics. 

Other business characteristics featured in figure 8 pertain to size 

(smallest revenue or mid-sized businesses) and age (businesses 

6–10 years old), but the differences in these average characteristic 

risks in the region are negligible.

Study Limitations

Our analysis is limited to data tracked by NETS and 

to building damages as the only source of disruption. Some 

discrepancies in the NETS database were identified in the method 

section. The NETS data do not measure some characteristics 

that have been found to differentiate business vulnerability, 

such as owner experience in the industry, business experience 

with disruption and disasters, owned versus leased premises, 

market reach, or home-based businesses. The data in NETS on 

establishment pre-disaster performance were also not adequate for 

this analysis.11 Age of business, which is analyzed in the data, may 

act as a partial proxy for some of these or related characteristics 

(such as experience in the industry and long-term performance).

The resolution of Hazus building data at the census-tract 

scale does not match the available business data by establishment. 

Future work could more tightly associate building damages to 

business establishment data and results could be sensitive to 

increasing the resolution of the damage estimation. Building 

data is captured by UrbanSim (http://www.urbansim.com), but 

crosswalks between Hazus damage estimation to the buildings 

11We considered two other variables in the NETS data that we thought 

might indicate the condition of the business prior to disaster. We found the 

establishment-based payment performance (PayDex)—how quickly bills are 

paid—to be a useful statistic. The SalesGrowthPeer data could have been 

useful, but are considered unreliable. In both cases, there were too many 

missing entries in the database.
14 The HayWired Earthquake Scenario—Societal Consequences

would be needed. Current efforts to develop tools that increase the 

resolution of damage estimates to buildings within census tracts 

(for example, those by SimCenter, available at https://simcenter.

designsafe-ci.org/research-tools/pbe-application) would avoid 

introducing errors from the crosswalks.

The building damage state results are estimated using Hazus 

(Seligson and others, 2018) and a distribution of burned building 

square footage (Scawthorn, 2018) to occupancy classes using 1994 

Northridge earthquake fire data (see appendix of Johnson and 

others, in prep.; planned to be published as part of this volume). 

Hazus representation of construction offices as manufactured 

buildings and fires in school laboratories after the Northridge 

earthquake are influencing the results for the HayWired scenario. 

By only considering disruptive building damage, we 

underestimate the risk of business disruption from concentrations 

of damage in neighborhoods. Neighborhood damage and hometo-workplace relation effects are examined by Wein, Belzer, and 

others (in prep.; planned to be published as part of this volume). 

Their study uses subareas, defined by contiguous city boundaries, 

containing census tracts with concentrated building damages. We 

also calculate disruptive building damage risk for subareas using 

the Hazus building damage and NETS data; results are available in 

appendix 4. 

Utility outage geographies are not analyzed here. Dahlhamer 

and Tierney (1998) called attention to effects of utility disruptions 

and Kachali and others (2015) also identified them as a significant 

factor impacting organizations in Canterbury, New Zealand, 

after the 2011 earthquakes. Across utilities and transportation, 

the duration of electric power outages has been found to most 

adversely affect business productivity (Tierney and Nigg, 1995; 

Wasileski and others, 2011), and for manufacturing businesses 

in particular (Kajitani and Tatano, 2009). Brown and others 

(2014) noted that 30 percent of businesses were able to operate 

without power, water, and (or) wastewater services at their site 

for short durations or by relocation, such that businesses tended 

to underestimate their ability to operate without these services. 

Various utility restoration curves are available by county for the 

HayWired scenario (see Jones and others, this volume). Utility 

and transportation disruptions include initial widespread electrical 

power outages, days to weeks of disrupted voice and data services, 

months of water supply outages in Alameda, Contra Costa, and 

San Mateo Counties, months of highway bridge repairs, and years 

of rapid transit service disruptions along the east bay corridor.

Policy Discussion and Conclusion

In conclusion, we consider the implications of our results 

in conjunction with what is known about business vulnerability 

after a disaster, as well as policy and program ideas and 

implementations that can influence business outcomes. Here, we 

consider findings for business location and various sectors that 

show the largest disruptive building damage risks. We also discuss 

characteristics that were found to be associated with vulnerable 

establishments in previous disasters, such as small businesses and 

various business-owner characteristics.

Risk from Business Location

First and foremost, location (primarily Alameda County and 

secondarily Contra Costa County) most strongly differentiates 

disruptive building damage risk for all types of businesses. Many 

of the programs and resources developed to address post-event 

impacts of the earthquake and related issues in the San Francisco 

Bay region would need to be centered in the areas of greatest 

impact, but also cannot disregard the range of impacts to and 

needs of businesses outside the high-damage zones. Preparatory 

actions, however, should be widespread, because the HayWired 

scenario is only one of many possible earthquakes that could 

affect the bay region.

Sector Risk and Vulnerability 

Large disruptive building damage risk reflects lowperforming building types that certain industries tend to 

occupy and the concentration of certain industries in heavily 

impacted areas. In the HayWired scenario, these industries are 

manufacturing, transportation and warehousing, wholesale 

and retail, construction, and education (caused by fire only). 

Although employment-weighted risk in the mining sector is 

relatively large, we do not draw any conclusions for this small 

sector of a few thousand employees.

Marshall and others (2015) propose targeting businesses 

in the goods sectors (that have fixed assets, such as equipment 

and inventory) with pre-disaster assistance. Post-disaster, 

manufacturing businesses with disruptive building damage may 

be more difficult to relocate in the region. There could be larger 

risk of long-term displacement from property owners that choose 

to rebuild to a higher value use (see Wein, Belzer, and others, in 

prep.; planned to be published as part of this volume). Although 

manufacturing has tended to recover in the wake of reconstruction, 

establishments may not remain in devastated areas. The region’s 

proposed Priority Production Area program (see Kroll and 

Jaramillo, 2017) could include pre-disaster planning for preserving 

space for critical industries. Coordinated economic development in 

the most heavily impacted areas also may be needed to provide a 

viable business base after the event.

The large presence of the transportation and warehouse 

sector in Alameda County leads to a larger risk of disruptive 

building damage for the sector in the region, particularly for 

establishments (as opposed to where the employees work). The 

establishments are important to supply networks. The public 

sector will need to work cooperatively with private logistics 

firms to ensure the delivery of necessary emergency supplies 

at the outset, to ease supply bottlenecks for local producers 

during recovery, and to maintain pathways for distributing 

final products. Growing automation in the sector and the use 

of drones and autonomous delivery equipment (for example, 

electric carts) may provide an opportunity for supporting supply 

networks to residents and businesses in the region. Advanced 

planning would be needed to ensure this type of equipment is 

licensed to operate in a post-earthquake setting and to establish 

enough redundancy in logistics operations to adapt to areas 
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 15

with downtime. However, even if this approach addresses the 

delivery problem around damaged roadways, stored goods 

will be lost on damaged sites and logistics functions may be 

disrupted or require relocation. 

Wholesale and retail businesses fairly consistently 

show vulnerability after disasters. In the HayWired scenario, 

retail has both larger disruptive building damage risk and a 

contingent of small businesses (see below). Retail businesses 

have not emerged as vulnerable when there has been local 

government intervention, for example, to provide a space for 

businesses to congregate near their customers (for example, 

Kroll and others, 1990) or (government-funded) employee 

subsidies after the 2011 Christchurch earthquake. Assistance 

of this type may be particularly critical where damage is 

extensive and widespread.

The presence of small businesses in the construction 

sector (33 percent of the construction sector’s employees are in 

businesses that have less than 5 employees) potentially compounds 

the vulnerability of these types of businesses with disruptive 

building damage. Although, construction businesses could benefit 

from rebuilding, Zhang and others (2009) caution that many of the 

smaller, local contractors miss out on reconstruction when they 

are still recovering themselves, are unable to take on large jobs, 

or both. Pre-registration of construction firms and coordination of 

programs to help in business recovery and to encourage the use of 

local firms in reconstruction could help local construction firms 

survive the challenges posed by the damage.

Fire increases the risk for all sectors, but disproportionately 

affects education. The effect on the education sector is an 

artifact of the 1994 Northridge fire-following-earthquake data 

that was used to distribute burned buildings for the HayWired 

scenario. Reportedly, school laboratories were the source of 

fires in schools. Safety management of hazardous materials 

can address how materials are stored and how to best separate 

materials to prevent potentially volatile mixtures in earthquakeprone areas.

Small Business Vulnerability

Small businesses are a significant source of local 

employment and add to the economic vitality of a community 

(Zhang and others, 2009). Our business type analysis showed 

that the largest businesses (500 or more employees) are 

somewhat less likely than businesses with 500 or fewer 

employees to be located in the riskiest locations or building 

types in the HayWired scenario. The literature (summarized in 

the “Literature on Business and Owner Characteristics” section) 

suggests that among businesses where damage occurs, those 

with less than 50 employees are more likely to close, perhaps 

because they are less diversified in locations and lack assets 

and financial cushions to allow for recovery. However, small 

businesses also may be more flexible in terms of response 

alternatives, providing opportunities to maintain or grow the 

business after a disaster.

 In the NETS database, businesses that have fewer than 

50 employees make up 98 percent of establishments and 

have 60 percent of the employees in the San Francisco Bay 

region; half of those employees work in establishments that 

have less than 5 employees. Another measure of business size 

is revenue: 33 percent of establishments and 10 percent of 

employment are in the lowest revenue category (<$100,000).

In the bay region, small businesses account for a large 

share of administration, support and waste, and other services 

sectors (99.6 percent of sector establishments have less than 

50 employees). The largest numbers of small businesses in 

the bay region are in these two sectors plus the professional, 

technical, and scientific services sector. The businesses in 

professional, technical, and scientific services show a slight 

advantage of lower disruptive building damage risk from the 

HayWired scenario because the industry is more concentrated in 

office buildings on the San Francisco Peninsula (as opposed to 

the east bay, which is closer to the Hayward Fault). They could 

benefit from increased demand for engineering, accounting, 

and legal services (for example, Recover Canterbury, n.d.), and 

they could have more mobility and flexibility for working from 

home. Lister (2017) reports that the professional, scientific, 

and technical service sector has the largest percentage of 

telecommuters, corroborating the mobility of the assets in this 

service sector.

Small business revenue vulnerability would be 

compounded by disruptive building damage in Alameda and 

Contra Costa Counties. For the region, businesses in the lowest 

revenue category are among those with slightly larger disruptive 

building damage risk. Small Business Administration loans 

that increase debt are not likely to be attractive or obtainable 

(because of restrictive criteria) by low-revenue businesses 

(Collier and others, 2016). Lam and others (2012) conclude 

that small businesses need assistance within 6–9 months of 

disruption. Business recovery funding gaps (for small and 

young businesses, in particular) require small, rapidly deployed 

loans and grants to provide a bridge for small businesses 

between the time when a disaster strikes and when State and 

Federal funds first become available, as well as after they are 

no longer available (Collier and others, 2016; Federal Reserve 

Banks, 2017).

Research has found that small business owners may not 

know what can be done to prepare for earthquakes. Alesch and 

others (2001) identify business management practices to reduce 

business exposure and vulnerability to disaster. These include 

diversifying the organization’s customer base, diversifying 

the location of the organization’s inventory, protecting 

organizational electronic and hard-copy data, and having 

multiple business outlets (for example, multiple locations and 

[or] e-commerce). More recently, the effects of Hurricane Sandy 

underscored that computer system and document/file recovery 

were most costly and time consuming for small businesses 

(Scarinci, 2016). Regarding building and content risk, insurance 

helps to protect equity and lease arrangements can be checked 

for repair responsibility and timing. Other considerations 

include coping with psychological stress, entrepreneurial skill to 

adapt to changes in the business environment, and support from 

family, friends, and employees.
16 The HayWired Earthquake Scenario—Societal Consequences

Preparedness planning and testing is seen to decrease with 

business size (Scarinci, 2016). Small business preparedness 

actions tend to be limited to safety (for example, acquiring first 

aid kits) and fall short for business continuity (for example, 

relocation plans, generators for emergency power, training and 

drills, and structural assessment) (Webb and others, 2000). The 

actions that small businesses take are less complicated, less 

expensive, and protect against a range of emergencies rather than 

actions that are technically difficult, more expensive, and time 

consuming (Webb and others, 2000). Lindell and others (2006) 

promote the need for policies, such as incentives, that encourage 

small businesses to engage in emergency planning. Marshall and 

others (2015) add that policies intended to reduce small business 

demise should concurrently consider the interrelatedness of 

family needs, demands, and resources with those of the affected 

business. Overall, Marshall and others (2015) propose that small 

business vulnerability be mitigated with policies that target the 

characteristics of small businesses that make them vulnerable.

Business Owner Vulnerability

Minority-owned businesses are associated with slightly 

larger disruptive building damage risk in the HayWired scenario. 

The risk for minority-owned businesses is a reflection of 

the concentration of minority-owned businesses in Alameda 

County relative to the rest of the bay region and relatively larger 

representation in manufacturing and wholesale trade sectors. This 

illuminates the proposal of Marshall and others (2015) for policy 

support and educational assistance for disaster preparedness that 

targets minority-, female-, and veteran-owned business. They note 

that, even though the Small Business Administration has targeted 

disaster programs for these groups, more awareness and education 

may be necessary. Given limited resources, one approach may be 

to intensify outreach to small businesses in sectors with greater 

building occupancy type vulnerabilities.

Other Vulnerable Business Characteristics

The risk of disruptive building damage is strongly 

differentiated by location relative to the earthquake, and 

secondarily by sector according to building performance. 

We found slight differences in the risk for other business 

characteristics, but the vulnerability still remains for those 

businesses located in riskiest locations and building types. Just 

because our methodolgy could not support analyses of other 

potentially vulnerable business characteristics does not mean 

they are unimportant. Other considerations from the literature 

pertaining to disruptive building damages include:

• Leased business spaces. The business owner is subjected 

to the building owner’s decisions about rebuilding, 

affecting business relocation planning, especially for 

single-location businesses. Education of commercial 

building owners on the likely needs and possible 

services to offer tenants after an event could reduce 

tenants’ vulnerability.

• Owner resilience and prior disaster experience. Businesses 

whose owners lack this kind of experience may be more 

vulnerable. Targeted education and shared experiences 

from those with prior disaster experience could assist 

inexperienced business owners to become more informed 

and better prepared.

• Businesses in decline can have that trend exacerbated by 

a disaster and may need additional business and financial 

planning assistance to make decisions about whether to 

continue the business after the event.

• Business culture may affect business vulnerability—this 

topic has not been explored in this chapter.

Finally, recall that direct building damage is only one 

source of business disruption. Vulnerable business 

characteristics are relevant to other sources of disruption, 

such as utility outages. Wein, Belzer, and others (in prep.; 

planned to be published as part of this volume) consider how 

businesses are impacted by damages in their neighborhoods. 

Business establishments without strong support systems 

(for example, sole proprietorships and [or] dependence on 

a displaced customer base) will be particularly vulnerable 

regardless of the sector, business age, business size, or 

business-owner characteristics. Policy development can 

consider how to coordinate programs to address geographical, 

sectoral, and other vulnerable characteristics of establishments 

and firms.

Turning Risk into Opportunity

Stronger buildings and codes directly reduce disruptive 

building risk. Interorganizational arrangements and programs 

present opportunities to improve business preparedness and 

recovery. With the widespread distribution of risk across firms 

of small, medium, and large sizes, preparedness and resources 

will vary in the region. Programs like the California Stateled Outsmart Disaster Business Resilience Challenge (visit 

https://outsmartdisaster.com/be-resilient/at-work/resilientbusiness-challenge for more information), which grew out of 

the HayWired scenario, encourages businesses to take the next 

challenge to “outsmart disaster”—whether it be at a business 

level, collaboration with their community, or demonstration 

of innovative practices for businesses in the State. Outsmart 

Disaster encourages larger businesses to look beyond 

themselves and provide preparedness assistance to the smaller 

neighborhood businesses on which they depend. In support 

of this incentive, Dunaway and Shaw (2010) found that 

participation in a collaborative partnership was correlated with 

motivation to prepare. Post-disaster, Aldrich (2012) makes the 

case for the role of social capital. After the 2014 South Napa 

earthquake, the tight-knit small business community in the 

City of Napa lent support to each other, and the community 

as a whole rallied to provide financial support to businesses 

in need (Almufti and others, 2016). Stevenson and others 

(2014) document how organizations managed and utilized 
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 17

networks to reduce the impacts of the Canterbury earthquake 

sequence (2010–14) and to adapt to altered post-earthquake 

environments. For example, similar and competitive 

businesses assisted each other by sharing functional space.

Zhang and others (2009) promote coordination between 

business and local government regarding exchanges of 

information about hazards, business buildings and structures, 

employees, business linkages, and response and recovery 

needs. For example, the 2018 California Assembly Bill 

AB–2681 sought to require local communities to inventory 

potentially vulnerable buildings. Although the bill was 

vetoed by the governor, some cities in the bay region are 

already creating such inventories. For those communities 

that have inventoried vulnerable buildings, it might help 

to also inventory the uses of those buildings (for example, 

commercial versus residential) and to understand if specific 

business types or sectors are concentrated in vulnerable 

buildings and to plan for potentially disruptive building risk. 

Local government can plan for temporary business locations, 

restoring transportation and utility systems, expediting 

building inspection and permitting, and other support for 

local businesses facing external competition (Zhang and 

others, 2009).

Recovery funding for small businesses is an innovation 

opportunity in community development (Collier and others, 

2016; Federal Reserve Banks, 2017). Beyond suggestions 

to make changes to the Small Business Administration loan 

policy (Collier and others, 2016), cross-sector and cross-agency 

collaboration can support and guide small businesses by 

providing individualized small business risk assessments and a 

small grant to help implement recommendations (for example, 

adequate insurance coverage, digitizing records, or adding a 

generator) (Federal Reserve Banks, 2017). Small businesses 

could be incentivized to become eligible for post-disaster 

funding that fits their needs during the critical time periods by 

implementing preparedness actions (Collier and others, 2016).

Doerfel and others (2013) find, more generally, that 

resource-vulnerable businessesexhibit diverse interorganizational relations that are associated with low resilience after a 

disaster because the relations lack redundancy. Post-disaster, 

Doerfel and others (2013) suggest that businesses can increase 

resilience (access to resources and information) by building 

pre-disaster interorganizational relations. Examples include 

building communities of practice (for example, professional 

associations, similar types of business functions), establishing relations with cross-community entities (that is, suppliers, 

clients, different types of businesses, banks, endowment or 

funding sources, nonprofits, and city government), and having 

multiple ties with each type of these supporting organizations. 

Scarinci (2016) reports that new small business alliances 

developed after Hurricane Sandy helped businesses by facilitating the sharing of information and resources and assisting with 

loan applications. Based on the finding of inhibited learning 

after a disaster, implementing programs for small business 

preparedness with recovery resources and assistance could 

improve business resilience from its pre-disaster state.

Acknowledgments

Reviewers provided useful feedback to improve all aspects 

of this report. Special thanks to Charlotte Brown (Resilient 

Organizations, Ltd.), Stephen Levy (Center for Continuing Studies 

of the California Economy), Mary Comerio (retired, University 

of California, Berkeley), and Jeff Bellisario (Bay Area Council 

Economic Institute). We also appreciated complementary literature 

shared by Monika Stoeffl (California Resiliency Alliance).

References Cited

Aldrich, D.P., 2012, Building Resilience—Social Capital in PostDisaster Recovery: University of Chicago Press, 232 p.

Alesch, D.J., Holly, J.N., Mittler, E., and Nagy, R., 2001, What 

happens when small businesses and not-for-profits encounter 

natural disasters: Fairfax, Va, Pacific Entity Risk Institute, 116 p.

Almufti, I., Mieler, M., Kroll, C., Wein, A., Tremayne, H., 

Fatima, S., and Xiao, Y., 2016, Quantifying business 

interruption, downtime and recovery following the 2014 

South Napa earthquake and identifying the causes in

Structural Engineers Association of California Convention 

Proceedings, Oct. 12–15, 2016, Kaʻanapali, Maui: Structural 

Engineers Association of California.

Altay, N., and Ramirez, A., 2010, Impact of disasters on firms in 

different sectors—Implications for supply chains: Journal of 

Supply Chain Management, v. 46, no. 4, p. 59–80.

Barnatchez, K.L., Crane, D., and Decker, R.A., 2017, An 

Assessment of the National Establishment Time Series 

(NETS) Database in Finance and Economics Discussion 

Series 2017-110: Washington D.C., Board of Governors of 

the Federal Reserve System, 51 p., https://doi.org/10.17016/

FEDS.2017.110.

Bowden, S., 2011, Aftershock—Business relocation decisions in 

the wake of the February 2011 Christchurch earthquake: Journal 

of Management and Organization, vol. 17, no. 6, p. 857–863, 

https://doi.org/10.1017/S183336720000122X.

Brown, C., Seville, E., Stevenson, J.R., Giovinazzi, S., and Vargo, 

J., 2015, Developing the Business Behaviours Module within 

MERIT: ERI Research Report 2015/02, 164 p. 

Brown, C., Stevenson, J., Giovinazzi, S., Seville, E., and Vargo, 

J., 2014, Factors influencing impacts on and recovery trends 

of organisations—Evidence from the 2010/2011 Canterbury 

earthquakes: International Journal of Disaster Risk Reduction, 

v. 14, no. 1, p. 56–72, https://doi.org/10.1016/j.ijdrr.2014.11.009.

Brüderl, P.P., and Ziegler, R., 1992, Survival chances of newly 

founded business organizations: American Sociological 

Review, v. 57, no. 2, p. 227–242.
18 The HayWired Earthquake Scenario—Societal Consequences

Chang, S.E., Falit-Baiamonte, A., 2002, Disaster vulnerability 

of businesses in the 2001 Nisqually earthquake: Global 

Environmental Change Part B; Environmental Hazards, v. 4, 

p. 59–71.

Collier, B.L., Haughwout, A.F., Kunreuther, H.C., Michel-Kerjan, 

E.O., and Stewart, M.A., 2016, Firm age and size and the 

financial management of infrequent shocks: National Bureau of 

Economic Research Technical Report, 55 p.

Comerio, M.C., 1998, The impact of housing losses in the 

Northridge Earthquake in Proceedings of the NEHRP 

Conference and Workshop on Research on the Northridge, 

California Earthquake of January 17, 1994: Richmond, Calif., 

California Universities for Research in Earthquake Engineering, 

v. IV, p. 85–93, accessed on January 3, 2020, at https://curee.

org/conferences/Northridge/Proceedings/Volume_IV_Social_

Services_and_Emergency_Management.pdf.

Comerio, M., 2006, Estimating downtime in loss modeling: 

Earthquake Spectra, v. 22, no. 2, p. 349–365.

Corey, C.M., and Deitch, E.A., 2011, Factors affecting business 

recovery immediately after Hurricane Katrina: Journal of 

Contingencies and Crisis Management, v. 19, p. 169–181, 

https://doi.org/10.1111/j.1468-5973.2011.00642.x.

Council of Economic Advisers, 2015, Gender pay gap—Recent 

trends and explanations: Council of Economic Advisors Issue 

Brief, 8 p., accessed July 16, 2018, at https://obamawhitehouse.

archives.gov/sites/default/files/docs/equal_pay_issue_brief_

final.pdf. 

Cowen, S., 2007, Tulane University—From Recovery to Renewal: 

Liberal Education, v. 93, no. 3, accessed December 26, 2019, at 

https://www.aacu.org/publications-research/periodicals/tulaneuniversity-recovery-renewal. 

Dahlhamer, J.M., and Tierney, K.J., 1998, Rebounding from 

disruptive events—Business recovery following the Northridge 

earthquake: Sociological Spectrum, v. 18, no. 2, p. 121–141.

Doerfel, M.L., Chewning, LV., and Lai, C.-H., 2013, The 

evolution of networks and the resilience of interorganizational 

relationships after disaster: Communication Monographs, v. 80, 

no. 4, p. 533–559, https://doi.org/10.1080/03637751.2013.828

157.

Dunaway, W.M., and Shaw, G.L., 2010, The influence of 

collaborative partnerships on private sector preparedness 

and continuity planning: Journal of Homeland Security 

and Emergency Management, v. 7, no. 1, https://doi.

org/10.2202/1547-7355.1690.

duPont, W., and Noy, I., 2015, What happened to Kobe? A 

reassessment of the impact of the 1995 earthquake in Japan: 

Economic Development and Cultural Change, v. 63, no. 4, 

p. 777–812.

Earthquake Engineering Research Institute, 2011, Learning 

from earthquakes—The M 6.3 Christchurch, New Zealand, 

earthquake of February 22, 2011: Earthquake Engineering 

Research Institute Special Earthquake Report, 16 p., 

accessed June 18, 2018, at https://www.eeri.org/site/images/

eeri_newsletter/2011_pdf/EERI_NewZealand_EQRpt_web.

pdf. 

Federal Reserve Banks, 2017, Small Business Credit Survey 

Report on Disaster-Affected Firms: U.S. Federal Reserve 

System, 17 p., accessed March 2, 2019, at https://www.

newyorkfed.org/medialibrary/media/smallbusiness/2017/

SBCS-Report-on-Disaster-Affected-Firms.pdf. 

Feldman, M., 2018, National establishment time-series (NETS): 

The University of North Carolina at Chapel Hill web page, 

accessed April 12, 2018, at http://maryannfeldman.web.unc.

edu/data-sources/longitudinal-databases/national-establishmenttime-series-nets.

Federal Emergency Management Agency, 2012, Hazus multihazard loss estimation methodology, earthquake model, 

Hazus®-MH 2.1 technical manual: Federal Emergency 

Management Agency, Mitigation Division, 718 p., accessed 

July 18, 2017, at http://www.fema.gov/media-librarydata/20130726-1820-25045-6286/hzmh2_1_eq_tm.pdf.

Fothergill, A., 1996, Gender, risk, and disaster: International 

Journal of Mass Emergencies and Disasters, v. 14, p. 33–56.

Hatton, T., 2015, Collaborative approaches to the post-disaster 

recovery of organisations: Christchurch, New Zealand, 

University of Canterbury, Ph.D. dissertation, 347 p.

Hatton, T., Seville, E., Vargo, J., 2012, Improving the 

resilience of SMEs—Policy and practice in New Zealand: 

Resilient Organisations Research Report 2012/012, 

accessed March 3, 2019, at https://ir.canterbury.ac.nz/

bitstream/handle/10092/11241/12651093_apec%20

sme%20resilience%20policy%20final%20report.

pdf?sequence=1&isAllowed=y. 

Hsiang, S.M., and Jina, A.S., 2014, The causal effect of 

environmental catastrophe on long-run economic growth—

Evidence from 6,700 cyclones: Cambridge, Mass., National 

Bureau of Economic Research working paper no. 20352, 68 p., 

https://doi.org/10.3386/w20352.

Kachali, H., Whitman, Z.R., Stevenson, J.R., Vargo, J., Seville, 

E., and Wilson, T., 2015, Industry sector recovery following the 

Canterbury earthquakes: International Journal of Disaster Risk 

Reduction, v. 12, p. 42–52.

Kajitani, Y., and Tatano, H., 2009, Estimation of lifeline resilience 

factors based on surveys of Japanese industries: Earthquake 

Spectra, v. 25, no. 4, p. 755–776.
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 19

Kroll, C., and Jaramillo, J., 2017, Priority Production Area 

Program: Memo to the Association of Bay Area Governments 

Executive Board, January 12, accessed January 3, 2020, 

at https://abag.ca.gov/sites/default/files/meetings/packets/

e011917a-agenda_packet.pdf.

Kroll, C.A., Landis, J.D., Shen, Q., and Stryker, S., 1990, 

Economic impacts of the Loma Prieta earthquake—A focus on 

small business: Berkeley Planning Journal, vol. 5, no. 1, 

p. 39–58, https://escholarship.org/uc/item/6s67g8mh.

Lam, N.S.N., Arenas, H., Pace, K., LeSage, J., and Campanella, 

R., 2012, Predictors of business return in New Orleans after 

Hurricane Katrina: PLoS ONE, v. 7, no. 10, https://doi.

org/10.1371/journal.pone.0047935.

Lengnick-Hall, C.A., Beck, T.E., and Lengnick-Hall, M.A., 2011, 

Developing a capacity for organizational resilience through 

strategic human resource management: Human Resource 

Management Review, v. 21, p. 243–255.

Lindell, M.K., Prater, C.S., and Perry, R.W., 2006, Fundamentals 

of Emergency Management: Emmitsburg, Md., Federal 

Emergency Management Agency Emergency Management 

Institute, accessed December 26, 2019, at https://training.fema.

gov/hiedu/aemrc/booksdownload/fem.

Lister, K., 2017, State of telecommuting in the U.S. employee 

workforce: Global Workplace Analytics and Flexjobs, 27 p., 

accessed January 3, 2020, at https://globalworkplaceanalytics.

com/whitepapers.

Marquis, F., Kim, J.J., Elwood, K.J., and Chang, S., 2015, 

Understanding post-earthquake decisions on multi-storey 

concrete buildings in Christchurch, New Zealand: Bulletin 

of Earthquake Engineering, v. 15, no. 2, 731–758, https://doi.

org/10.1007/s10518-015-9772-8.

Marshall, M.I., Niehm, L.S., Sydnor, S.B., and Schrank, H.L., 

2015, Predicting small business demise after a natural disaster; 

an analysis of pre-existing conditions: Natural Hazards, v. 79, 

no. 1, p. 331–354, https://doi.org/10.1007/s11069-015-1845-0.

Mateyka, P.J., Rapino, M.A., and Landivar, L.C., 2012, Homebased workers in the United States—2010; Household 

Economic Studies—Current Population Reports: U.S. Census 

Bureau, 31 p., accessed June 14, 2018, at https://www.census.

gov/prod/2012pubs/p70-132.pdf. 

Mazzocchi, M., and Montini, A., 2001, Earthquake effects on 

tourism in central Italy: Annals of Tourism Research, v. 28, 

no. 4, p. 1031–1046.

[New Zealand] Department of Labour, 2011, A changing 

landscape—The impacts of the earthquakes on Christchurch 

workplaces: New Zealand Department of Labour, 28 p., 

accessed June 19, 2018, at http://www.rebuildchristchurch.

co.nz/i/c574ea00a4a9d75e.pdf.

Orchiston C., Vargo, J., and Seville, E., 2013, Insurance in the 

tourism sector after the Christchurch earthquakes: Australia 

New Zealand Institute of Insurance and Finance, v. 36, no. 4, 

p. 20–23.

Recover Canterbury, n.d., The city rebuilds—The recover 

Canterbury story: Recover Canterbury, 130 p., accessed 

December 26, 2019, at http://www.eqrecoverylearning.org/

assets/downloads/A-City-Rebuilds-The-Recover-CanterburyStory.pdf.

Resilient Organisations, 2018, Leadership, good networks 

and determination—The key to business recovery for EPL: 

Resilient Organisations webpage, accessed November 26, 

2018, at https://www.resorgs.org.nz/resources/case-studies/

epl-case-study. 

Scarinci, C., 2016, A post-Superstorm Sandy study of small 

business disaster preparedness and perspective on 

planning for future incidents: Journal of International and 

Interdisciplinary Business Research: v. 3, p. 61–74, accessed 

March 12, 2019, at http://scholars.fhsu.edu/jiibr/vol3/iss1/7. 

Scawthorn, C., 2018, Fire following the HayWired scenario 

mainshock, chap. P of Detweiler, S.T., and Wein, A.M., 

eds., The HayWired earthquake scenario—Engineering 

implications: U.S. Geological Survey Scientific 

Investigations Report 2017–5013–I–Q, 429 p., https://doi.

org/10.3133/sir20175013v2.

Seligson, H.A., Wein, A.M., and Jones, J.L., 2018, HayWired 

scenario—Hazus analyses of the mainshock and aftershocks, 

chap. J of Detweiler, S.T., and Wein, A.M., eds., The 

HayWired earthquake scenario—Engineering implications: 

U.S. Geological Survey Scientific Investigations Report 

2017–5013–I–Q, 429 p., https://doi.org/10.3133/

sir20175013v2.

Seville, E., Stevenson, J., Brown, C., Giovinazzi, S., and Vargo, 

J., 2014, Disruption and resilience: How organisations coped 

with the Canterbury earthquakes: Economics of Resilient 

Infrastructure Research Report 2014/002, 44 p., accessed 

December 26, 2019, at https://resorgs.org.nz/wp-content/

uploads/2017/07/disruption_and_resilience.pdf.

Sheffi, Y., 2005, Building a resilient supply chain: Harvard 

Business Review Supply Chain Strategy, v. 2, n. 8. 

Statistics New Zealand, 2012, Household Labour Force 

Survey—June 2012 quarter: Statistics New Zealand, 

accessed on January 2, 2020, at http://archive.stats.govt.

nz/browse_for_stats/income-and-work/employment_

and_unemployment/HouseholdLabourForceSurvey_

HOTPJun12qtr.aspx under “downloads.”

Stevenson, J.R., Brown, C., Seville, E. and Vargo, J., 2018, 

Business recovery—An assessment framework: Disasters, 

v. 42, p. 519–540, https://doi.org/10.1111/disa.12261.
20 The HayWired Earthquake Scenario—Societal Consequences

Stevenson, J.R., Chang-Richards, Y., Conradson, D., 

Wilkinson, S., Vargo, J., Seville, E., and Brunsdon, D., 2014, 

Organizational networks and recovery following the Canterbury 

earthquakes: Earthquake Spectra, v. 30, no. 1, p. 555–575.

Stevenson, J., Vargo, J., Seville, E., Kachali, H., McNaughton, 

A., and Powell, F., 2011, The recovery of Canterbury’s 

organisations—A comparative anlaysis of the 4 September 

2010, 22 February and 13 June 2011 earthquakes: Resilient 

Organisations Research Report 2011/04, 54 p., accessed 

November 30, 2018, at http://hdl.handle.net/10092/9819. 

Tierney, K.J., 1997, Business impacts of the Northridge earthquake: 

Journal of Contingencies and Crisis Management, v. 5, no. 2, 

p. 87–97, http://dx.doi.org/10.1111/1468-5973.00040.

Tierney, K.J., and Nigg, J.M., 1995, Business vulnerability to 

disaster-related lifeline disruption in Proceedings of the Fourth 

U.S. Conference of Lifeline Earthquake Engineering: American 

Society of Civil Engineers Technical Council of Lifeline 

Earthquake Engineering Monograph, no. 6, p. 72–79. 

U.S. Bureau of Labor Statistics, 2016, 24 percent of employed 

people did some or all of their work at home in 2015 on the 

Internet: The Economics Daily, U.S. Bureau of Labor Statistics, 

accessed March 25, 2019, at https://www.bls.gov/opub/

ted/2016/24-percent-of-employed-people-did-some-or-all-oftheir-work-at-home-in-2015.htm.

Walls and Associates, 2011, National Establishment TimeSeries (NETS) Database—2010 database description: 

Oakland, Calif., Walls and Associates, 10 p.

Wasileski, G., Rodríguez, H., and Diaz, W., 2011, Business 

closure and relocation—A comparative analysis of the 

Loma Prieta earthquake and Hurricane Andrew: Disasters, 

v. 35, no. 1, p. 102–129, http://dx.doi.org/10.1111/j.1467-

7717.2010.01195.x.

Webb, G.R., Tierney K.J., and Dahlhamer J.M., 2000, 

Business and disaster—Empirical patterns and unanswered 

questions: Natural Hazards Review, v. 1, p. 83–90. 

Webb, G.R., Tierney K.J., and Dahlhamer, J.M., 2002, 

Predicting long-term business recovery from disaster—A 

comparison of the Loma Prieta earthquake and Hurricane 

Andrew: Global Environmental Change Part B—

Environmental Hazards, v. 4, p. 45–58.

Whitman, Z., Stevenson, J., Kachali, H., Seville, E., Vargo, J., 

and Wilson, T., 2013, Organisational resilience following 

the Darfield earthquake of 2010: Disasters, v. 38, p. 148–

177, http://dx.doi.org/10.1111/disa.12036.

Zhang, Y., Lindell, M.K., and Prater, C.S., 2009, Vulnerability 

of community businesses to environmental disasters: 

Disasters, v. 33, p. 38–57.
Appendixes 1–4
22 The HayWired Earthquake Scenario—Societal Consequences

In order to evaluate risk to San Francisco Bay region businesses, 

we needed to first convert results for Hazus occupancy classes to 

results for North American Industry Classification System (NAICS) 

codes. Risk values have been estimated using Hazus for occupancy 

classes. The following table provides a mapping to translate those 

risks to NAICS codes. The table lists the NAICS code and the 

equivalent Hazus occupancy class (Federal Emergency Management 

Agency, 2002). 

Appendix 1. Crosswalk between Hazus Occupancy Classes and NAICS Categories

A single NAICS code may map to several Hazus occupancy 

classes. In this case, a new Hazus occupancy class is created that 

combines the several classes in the mapping. The new combined 

occupancy class is created simply by adding together the total 

and extensive and complete damaged square footage in each 

class for the geographic unit. The sum of the extensive complete 

damage square footage is then divided by the sum of the total 

square footage.
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 23

Table 1.1. Mapping of NAICS codes to Hazus occupancy classes.

[Table derived from Sue Wing and others (in prep.; planned to be published as part of this volume). NAICS, North American Industry Classification System]

Sector description NAICS code1 Hazus occupancy 

class

Agriculture, forestry, fishing and hunting 11 AGR1

Oil and gas extraction 211 IND4

Coal mining 2121 IND4

Other mining 212–213 except for 2121 IND4

Electric power generation, transmission, and distribution 2211 COM4

Natural gas distribution 2212 COM4

Water, sewage, and other systems 2213 COM4

Construction IND6

Heavy industry 313, 321, 322, 327, 332, 333, 336 IND1

Light industry 314, 315, 316, 323, 326, part of 334 (except for 334111, 

334112, 334411, 334412, 334413), 335, 337, 339, 

511, 516

IND2

Petroleum refineries 32411 IND3

Food, drug, and chemicals 311, 312, 324 except for 32411, 325 IND3

Primary metal manufacturing 331 IND4

Electronic computer manufacturing 334111 IND5

Computer storage device manufacturing 334112 IND5

Semiconductor and related device manufacturing 334413 IND5

Other high-technology-related manufacturing 334411, 334412 IND5

Wholesale trade, warehousing, and storage 42 COM2

Retail trade 44–45 COM1

Air transportation 481 COM4

Rail transportation 482 COM4

Water transportation 483 COM4

Truck transportation 484 COM2

Transit and ground passengers 485 COM4

Pipeline transportation 486 COM4

Sightseeing transportation 487 and 488 COM4

Couriers and messengers 492 COM4

Publishing, motion picture, and broadcasting 511, 512, 515 IND2, COM4

Internet publishing and broadcasting 51913 COM4

Telecommunications 517 COM8

Data processing, hosting, and related services 518 COM4

Other information services 51911–2 COM4

Finance, insurance, real estate, and leasing 52, 53 COM4, COM5

Computer systems design and related services 5415 COM4

Other professional scientific and technical services 54 except for 5415 COM4

Other business services 55, 56 COM4

Educational services 6111, 6112–3, 6114–7 EDU1, EDU2

Ambulatory health care and hospitals 621, 622 COM6, COM7

Nursing home/social assistance 623, 624 RES6, COM3

Arts, entertainment, and recreation 711–713, 722 COM8

Accommodations 721 RES4

Repair and maintenance 811 COM3

Personal services/private households 812, 814 COM3

Religious, grantmaking, and similar organizations 813 REL1

Government and non-NAICS 92 GOV1, GOV2

1

See the NAICS website (available at https://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2017) for examples of establishments in the various NAICS sectors.
24 The HayWired Earthquake Scenario—Societal Consequences

Appendix 2. Building Damage Risk Results for the HayWired Scenario, 

San Francisco Bay Region

This appendix contains four sets of tables of HayWired 

building damage risk for business characteristics in the 

San Francisco Bay region with respect to the sector to 

which the businesses belong. The four sets pertain to two 

different ways of calculating the risk: (1) average across 

establishments and (2) employment weighted average; 

risk is also calculated for two different sets of extensive 

and complete (disruptive) building damages: (1) from 

earthquake hazards only (no fire) and (2) from earthquake 

plus fire hazards. Within each set, business characteristics 

are summarized by county, business size, revenue, ownership 

structure, and owner demographics. There are two tables for 

each characteristic; the first table presents risk calculations 

and the second table shows the distribution of establishments 

or employees in the categories of interest. The business 

distribution table is not repeated for fire hazard risk (third 

and fourth sets) because the distribution of business 

characteristics stays the same. 
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 25

Disruptive Building Damage Risk from Earthquake Hazards for Establishments in the HayWired Scenario

Table 2.1. Establishment risk (in percent) from earthquake hazards in the HayWired scenario by county and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector

Average 

risk across 

establishments 

in 9 counties

County

Alameda Contra 

Costa Marin Napa San Francisco

San 

Mateo

Santa 

Clara Solano Sonoma

Agriculture, forestry, fishing, 

and hunting

3.2 17.8 9.8 0.7 0.0 1.5 1.4 2.1 0.0 0.0

Mining 7.0 27.7 16.3 1.0 0.1 1.3 4.5 3.0 0.2 0.0

Utilities 4.5 19.1 10.0 0.9 0.0 1.6 1.8 2.7 0.1 0.0

Construction 9.2 33.3 14.2 1.0 0.0 1.3 2.2 3.8 0.2 0.0

Manufacturing 8.5 27.1 13.3 1.1 0.0 1.9 2.9 3.8 0.3 0.0

Wholesale trade 8.0 25.5 11.4 1.0 0.0 1.8 2.5 3.0 0.2 0.0

Retail trade 7.7 25.2 12.7 0.9 0.0 1.4 2.2 2.7 0.3 0.0

Transportation and 

warehouses

9.4 25.8 11.9 1.1 0.0 1.6 2.2 3.0 0.2 0.0

Information 6.7 23.9 13.8 0.9 0.0 1.5 2.4 2.4 0.2 0.0

Finance and insurance 6.2 24.0 11.1 0.9 0.0 1.3 2.3 2.2 0.2 0.0

Real estate, rental, leasing 7.2 25.2 11.5 1.0 0.0 1.3 2.2 2.3 0.2 0.0

Professional, scientific, and 

technical services

6.7 23.0 12.1 0.9 0.0 1.4 2.2 2.4 0.2 0.0

Management of companies 6.1 21.8 12.7 0.9 0.0 1.3 2.1 2.2 0.3 0.0

Administration, support, and 

waste

6.6 24.2 11.7 0.8 0.0 1.1 1.9 2.2 0.1 0.0

Education services 7.1 22.2 11.5 0.9 0.0 1.2 2.2 2.1 0.4 0.0

Health care and social 

assistance

7.1 23.8 11.8 0.9 0.0 1.0 1.8 2.1 0.3 0.0

Arts, entertainment, and 

recreation

7.5 25.3 13.1 0.9 0.0 1.3 2.1 2.3 0.2 0.0

Accommodation and food 

services

7.3 24.8 13.0 0.8 0.0 1.3 1.9 2.7 0.4 0.0

Other services 7.6 24.8 11.5 1.0 0.0 1.3 2.2 2.3 0.4 0.0

Public administration 7.0 20.7 12.7 0.7 0.0 1.7 2.1 3.8 0.5 0.0

Other 5.6 21.0 7.9 0.7 0.0 1.3 1.8 2.3 0.0 0.0

Total 7.3 25.0 12.2 0.9 0.0 1.3 2.1 2.5 0.2 0.0
26 The HayWired Earthquake Scenario—Societal Consequences

Table 2.2. Distribution of establishments (in percent) in the San Francisco Bay region, California, by county and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Number of 

establishments

County

Alameda Contra 

Costa Marin Napa San Francisco

San 

Mateo

Santa 

Clara Solano Sonoma

Agriculture, forestry, fishing, 

and hunting

5,002 9.7 10.2 5.2 13.8 5.2 7.1 14.1 7.7 27.1

Mining 240 12.1 14.2 7.9 0.8 13.8 11.7 21.3 7.5 10.8

Utilities 596 11.6 11.2 6.4 2.0 18.8 13.4 22.5 2.9 11.2

Construction 45,328 17.1 15.6 5.8 2.5 9.9 11.5 22.0 5.1 10.4

Manufacturing 20,899 20.9 9.0 4.3 3.7 10.6 10.1 29.7 3.1 8.7

Wholesale trade 23,316 21.4 11.4 4.8 3.7 13.1 10.5 23.0 3.7 8.3

Retail trade 43,904 20.1 12.3 5.1 2.5 15.0 9.7 22.1 5.0 8.2

Transportation and warehouses 9,938 25.7 13.6 3.4 2.1 9.8 14.6 18.8 5.8 6.1

Information 15,155 17.5 9.4 7.1 1.2 20.0 10.5 26.2 2.7 5.4

Finance and insurance 25,675 14.8 13.8 6.4 1.4 16.9 12.3 26.0 3.0 5.4

Real estate, rental, leasing 25,765 17.9 14.1 5.7 1.8 14.2 11.1 24.1 4.4 6.7

Professional, scientific, and 

technical services

92,391 17.7 12.6 6.7 1.6 16.9 10.2 25.0 3.0 6.1

Management of companies 1,701 15.3 12.7 5.4 1.2 22.9 12.6 22.6 2.9 4.3

Administration, support, and 

waste

113,786 16.6 13.1 5.8 1.8 12.5 10.9 27.8 4.5 6.8

Education services 9,802 21.4 12.0 6.1 1.8 13.8 9.7 24.8 3.7 6.8

Health care and social 

assistance

47,398 19.9 12.9 5.9 1.8 15.2 9.8 23.7 4.0 6.9

Arts, entertainment, and 

recreation

11,472 19.4 12.3 7.1 2.3 17.5 8.9 20.0 4.5 7.8

Accommodation and food 

services

21,452 19.3 10.8 4.3 2.3 21.5 10.1 20.8 4.0 6.8

Other services 55,809 20.5 13.7 4.7 1.9 14.1 10.2 22.8 5.1 7.2

Public administration 587 19.4 13.6 6.1 2.4 15.7 12.1 14.7 6.5 9.5

Other 443 16.3 14.4 4.7 1.6 18.7 9.3 26.0 3.6 5.4

Total 570,659 18.4 12.8 5.7 2.2 14.4 10.5 24.4 4.2 7.4
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 27

Table 2.3. Establishment risk (in percent) from earthquake hazards in the HayWired scenario by number of employees and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Average risk across 

establishments <5 5–9 10–19 20–49 50–99 100–499 >500

Agriculture, forestry, fishing, and 

hunting

3.2 3.4 2.6 1.8 3.1 0.9 1.0

Mining 7.0 7.1 8.1 4.8 4.0 5.2 13.0

Utilities 4.5 4.1 3.4 6.0 7.1 8.6 5.5 6.3

Construction 9.2 9.2 9.0 9.0 9.4 9.8 9.8 3.6

Manufacturing 8.5 8.4 8.5 8.2 8.7 9.4 10.1 7.3

Wholesale trade 8.0 7.9 8.3 8.5 8.4 9.4 10.2 5.4

Retail trade 7.7 7.8 7.2 7.2 6.9 7.7 8.1 8.5

Transportation and warehouses 9.4 9.6 8.7 7.9 7.6 6.8 8.1 1.6

Information 6.7 7.0 5.5 6.8 5.1 4.6 6.1 4.7

Finance and insurance 6.2 6.3 5.8 5.5 5.2 5.0 5.2 3.5

Real estate, rental, leasing 7.2 7.2 7.1 7.1 6.8 7.8 5.5 2.4

Professional, scientific, and 

technical services

6.7 6.8 6.2 6.3 5.6 5.4 4.7 4.1

Management of companies 6.1 6.1 5.6 6.9 4.3 4.8 1.2 4.2

Administration, support, and waste 6.6 6.6 5.6 6.9 7.3 7.1 8.1 6.4

Education services 7.1 7.0 7.3 7.4 7.5 6.3 8.6 5.7

Health care and social assistance 7.1 7.1 6.9 7.5 7.9 8.3 7.2 6.9

Arts, entertainment, and recreation 7.5 7.6 6.9 7.4 5.8 6.5 6.8 6.8

Accommodation and food services 7.3 7.5 7.3 7.2 6.6 6.0 4.2 6.0

Other services 7.6 7.7 7.3 7.3 7.0 5.3 4.5 9.7

Public administration 7.0 6.9 10.4 2.8 4.3 10.7 8.6 4.4

Other 5.6 5.7 5.1 3.3 7.5 1.5

Total 7.3 7.3 7.1 7.3 7.1 7.2 7.5 5.9
28 The HayWired Earthquake Scenario—Societal Consequences

Table 2.4. Distribution of establishments (in percent) in the San Francisco Bay region, California, by number of employees and NAICS 

2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Number of 

establishments <5 5–9 10–19 20–49 50–99 100–499 >500

Agriculture, forestry, fishing, and hunting 5,002 84.8 8.0 3.9 2.3 0.7 0.4

Mining 240 68.3 17.1 5.8 5.4 1.7 1.7

Utilities 596 64.8 13.4 10.7 5.9 2.5 2.2 0.5

Construction 45,328 84.4 7.8 3.9 2.7 0.7 0.4 0.0

Manufacturing 20,899 63.0 15.3 8.9 7.2 2.8 2.4 0.4

Wholesale trade 23,316 76.5 12.2 6.1 3.5 1.1 0.6 0.0

Retail trade 43,904 83.0 10.1 3.9 1.9 0.7 0.3 0.0

Transportation and warehouses 9,938 83.1 8.2 4.2 3.1 0.8 0.5 0.0

Information 15,155 79.7 9.3 4.7 3.4 1.5 1.2 0.2

Finance and insurance 25,675 87.5 6.7 2.9 1.9 0.6 0.4 0.1

Real estate, rental, leasing 25,765 86.9 7.4 3.0 1.8 0.7 0.2 0.0

Professional, scientific, and technical 

services

92,391 85.4 7.4 3.7 2.3 0.8 0.4 0.0

Management of companies 1,701 94.1 2.9 1.1 1.1 0.4 0.2 0.1

Administration, support, and waste 113,786 95.1 2.8 1.0 0.7 0.3 0.1 0.0

Education services 9,802 57.3 24.0 8.3 6.4 2.7 1.1 0.2

Health care and social assistance 47,398 71.5 20.3 4.6 2.3 0.8 0.4 0.1

Arts, entertainment, and recreation 11,472 79.2 11.8 4.3 2.8 1.4 0.5 0.1

Accommodation and food services 21,452 33.7 37.0 18.5 8.3 1.8 0.7 0.0

Other services 55,809 86.8 8.4 2.9 1.5 0.3 0.1 0.0

Public administration 587 39.0 11.6 9.0 14.8 11.8 11.1 2.7

Other 443 84.2 7.7 4.7 2.7 0.7

Total 570,659 82.2 9.9 4.1 2.4 0.8 0.4 0.1
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 29

Table 2.5. Establishment risk (in percent) from earthquake hazards in the HayWired scenario by establishment age (in years) and 

NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector

Average risk 

across all 

establishments

<6 6–10 11–15 16–20 21–25 >26

Agriculture, forestry, fishing, and hunting 3.2 4.0 3.6 3.0 2.4 2.2 1.9

Mining 7.0 9.7 5.7 5.1 5.2 7.5 6.6

Utilities 4.5 4.5 5.8 2.4 3.5 3.5 5.9

Construction 9.2 9.5 9.2 9.1 8.4 8.6 8.9

Manufacturing 8.5 8.2 8.3 7.8 8.1 7.9 9.7

Wholesale trade 8.0 7.8 8.1 8.1 8.4 8.0 8.3

Retail trade 7.7 7.7 7.7 7.8 7.1 7.4 8.0

Transportation and warehouses 9.4 10.2 9.5 8.6 7.4 7.1 7.9

Information 6.7 6.7 6.9 6.4 6.9 6.7 7.2

Finance and insurance 6.2 6.1 6.6 6.1 5.9 5.9 6.7

Real estate, rental, leasing 7.2 6.8 7.7 7.5 7.4 7.6 7.0

Professional, scientific, and technical services 6.7 6.7 6.8 6.6 6.7 6.8 6.9

Management of companies 6.1 6.1 6.4 4.6 7.0 7.1 5.7

Administration, support, and waste 6.6 6.6 6.7 6.1 6.5 6.9 7.1

Education services 7.1 7.1 7.2 7.3 7.2 6.7 7.2

Health care and social assistance 7.1 6.9 7.3 7.2 6.9 7.1 7.5

Arts, entertainment, and recreation 7.5 7.7 7.6 6.8 6.8 7.6 7.3

Accommodation and food services 7.3 7.2 7.6 7.3 7.1 6.9 7.2

Other services 7.6 7.3 7.5 7.9 8.1 8.0 8.0

Public administration 7.0 8.1 3.6 8.6 3.7 12.0 6.1

Other 5.6 5.2 4.1 6.6 6.8 7.3 5.7

Total 7.3 7.2 7.3 7.2 7.2 7.3 7.7
30 The HayWired Earthquake Scenario—Societal Consequences

Table 2.6. Distribution of establishments (in percent) in the San Francisco Bay region, California, by establishment age (in years) and 

NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Number of 

establishments <6 6–10 11–15 16–20 21–25 >26

Agriculture, forestry, fishing, and hunting 5,002 35.8 20.4 13.7 6.7 2.4 21.0

Mining 240 30.8 22.5 13.8 8.8 2.5 21.7

Utilities 596 45.6 17.1 16.1 3.5 1.3 16.3

Construction 45,328 45.3 19.4 9.7 5.4 4.0 16.2

Manufacturing 20,899 28.7 19.8 11.7 9.1 5.3 25.3

Wholesale trade 23,316 41.4 19.2 9.9 7.0 4.6 17.9

Retail trade 43,904 37.1 19.9 11.7 8.3 4.3 18.7

Transportation and warehouses 9,938 47.4 21.2 10.7 5.5 3.2 12.0

Information 15,155 48.6 20.8 13.1 6.9 2.8 7.8

Finance and insurance 25,675 48.8 21.1 12.2 4.8 2.9 10.2

Real estate, rental, leasing 25,765 41.8 19.9 11.5 5.8 3.9 17.2

Professional, scientific, and technical services 92,391 44.8 19.9 11.5 7.5 4.3 11.9

Management of companies 1,701 65.1 23.5 6.4 1.7 0.9 2.4

Administration, support, and waste 113,786 59.2 26.6 8.4 1.9 1.0 2.9

Education services 9,802 45.2 18.9 11.4 6.5 3.5 14.5

Health care and social assistance 47,398 39.3 19.3 11.7 7.6 5.3 16.7

Arts, entertainment, and recreation 11,472 44.5 20.2 12.1 6.8 3.4 13.1

Accommodation and food services 21,452 35.6 20.5 11.4 8.9 5.0 18.5

Other services 55,809 40.4 18.1 12.0 7.1 4.2 18.2

Public administration 587 43.8 6.6 6.8 3.7 2.2 36.8

Other 443 43.8 14.9 36.8 2.3 1.1 1.1

Total 570,659 45.3 21.1 10.9 6.0 3.6 13.2
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 31

Table 2.7. Establishment risk (in percent) from earthquake hazards in the HayWired scenario by establishment revenue (in thousands 

of U.S. dollars) and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Average risk across 

establishments <$100 $101–250 $251–500 $501–1,000 $1,001–2,500 >$2,501

Agriculture, forestry, fishing, and hunting 3.2 4.0 2.3 2.1 2.2 2.5 1.4

Mining 7.0 6.5 6.8 9.4 7.0 4.9 5.9

Utilities 4.5 4.2 3.8 2.6 4.4 7.1 6.6

Construction 9.2 9.6 9.0 8.9 9.0 9.0 9.3

Manufacturing 8.5 8.7 8.5 7.9 8.3 8.7 8.9

Wholesale trade 8.0 8.4 8.0 7.4 8.2 8.4 9.1

Retail trade 7.7 7.7 7.9 7.5 7.6 7.5 7.5

Transportation and warehouses 9.4 9.7 9.6 7.9 8.9 8.3 8.7

Information 6.7 7.6 6.5 6.0 5.6 5.7 5.0

Finance and insurance 6.2 6.8 6.3 5.9 6.4 5.1 5.7

Real estate, rental, leasing 7.2 7.3 7.1 7.3 7.5 6.9 6.6

Professional, scientific, and technical services 6.7 7.3 6.4 6.3 5.9 6.0 5.3

Management of companies 6.1 4.5 6.2 5.6 6.1 7.3 3.0

Administration, support, and waste 6.6 7.7 6.0 6.2 6.6 7.3 6.7

Education services 7.1 7.0 7.2 7.1 7.7 6.8 7.1

Health care and social assistance 7.1 8.0 6.8 6.8 6.8 7.7 7.9

Arts, entertainment, and recreation 7.5 7.9 7.1 6.4 7.0 5.4 6.5

Accommodation and food services 7.3 7.8 7.4 7.3 6.7 5.6 5.4

Other services 7.6 7.7 7.5 7.6 8.0 7.1 6.1

Public administration 7.0 7.1 4.7

Other 5.6 5.7 3.4 1.1 1.4 3.3 0.7

Total 7.3 7.7 6.9 7.1 7.3 7.2 7.4
32 The HayWired Earthquake Scenario—Societal Consequences

Table 2.8. Distribution of establishments (in percent) in the San Francisco Bay region, California, by establishment revenue (in thousands 

of U.S. dollars) and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Number of 

establishments <$100 $101–250 $251–500 $501–1,000 $1,001–2,500 >$2,501

Agriculture, forestry, fishing, and hunting 5,002 56.5 28.3 6.9 4.5 2.3 1.5

Mining 240 14.6 30.4 20.0 16.3 10.0 8.8

Utilities 596 30.2 24.3 10.1 12.6 4.4 18.5

Construction 45,328 30.3 43.7 11.8 6.7 4.1 3.4

Manufacturing 20,899 23.6 30.0 14.4 11.7 8.6 11.8

Wholesale trade 23,316 10.9 32.3 26.9 13.1 8.4 8.3

Retail trade 43,904 30.9 38.5 14.5 8.5 4.5 3.2

Transportation and warehouses 9,938 31.6 46.1 10.3 5.2 3.8 2.9

Information 15,155 41.4 35.8 8.2 5.2 4.3 5.0

Finance and insurance 25,675 7.4 64.6 16.5 5.0 3.5 3.0

Real estate, rental, leasing 25,765 12.9 66.7 11.7 4.8 2.3 1.5

Professional, scientific, and technical services 92,391 43.1 36.9 9.0 5.0 3.4 2.6

Management of companies 1,701 3.4 87.2 4.1 1.8 1.6 1.9

Administration, support, and waste 113,786 31.8 61.7 3.2 2.0 0.8 0.6

Education services 9,802 44.6 18.6 17.2 7.9 5.8 5.8

Health care and social assistance 47,398 23.3 43.4 23.4 5.5 2.7 1.7

Arts, entertainment, and recreation 11,472 55.8 30.5 6.1 3.4 2.1 2.0

Accommodation and food services 21,452 15.3 31.4 38.5 9.2 4.3 1.4

Other services 55,809 63.5 23.3 6.8 3.3 1.9 1.0

Public administration 587 99.1 0.3 0.2 0.3

Other 443 96.8 1.1 0.5 0.9 0.2 0.5

Total 570,659 33.3 43.4 12.0 5.4 3.2 2.7
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 33

Table 2.9. Establishment risk (in percent) from earthquake hazards in the HayWired scenario by type of establishment and NAICS 

2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Average risk across 

establishments Stand alone Headquarters Branch

Agriculture, forestry, fishing, and hunting 3.2 3.2 4.6 3.6

Mining 7.0 7.0 7.9 0.4

Utilities 4.5 4.3 8.5 5.2

Construction 9.2 9.2 11.4 13.2

Manufacturing 8.5 8.5 8.5 8.9

Wholesale trade 8.0 8.0 9.4 8.0

Retail trade 7.7 7.7 7.0 7.7

Transportation and warehouses 9.4 9.4 9.2 9.3

Information 6.7 6.8 5.3 5.1

Finance and insurance 6.2 6.3 4.8 5.1

Real estate, rental, leasing 7.2 7.2 6.2 7.5

Professional, scientific, and technical services 6.7 6.7 5.8 6.5

Management of companies 6.1 6.1 4.3 12.1

Administration, support, and waste 6.6 6.6 7.0 8.5

Education services 7.1 7.1 6.9 7.1

Health care and social assistance 7.1 7.1 7.7 6.7

Arts, entertainment, and recreation 7.5 7.5 5.3 5.9

Accommodation and food services 7.3 7.3 7.5 7.4

Other services 7.6 7.6 7.5 5.7

Public administration 7.0 6.9 6.8 8.3

Other 5.6 5.6 4.6

Total 7.3 7.3 7.3 7.3
34 The HayWired Earthquake Scenario—Societal Consequences

Table 2.10. Distribution of establishments (in percent) in the San Francisco Bay region, California, by type of establishment and NAICS 

2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Number of 

establishments Stand alone Headquarters Branch

Agriculture, forestry, fishing, and hunting 5,002 98.7 1.2 0.2

Mining 240 89.2 9.6 1.3

Utilities 596 91.9 6.4 1.7

Construction 45,328 98.8 1.1 0.2

Manufacturing 20,899 93.1 6.2 0.6

Wholesale trade 23,316 94.8 4.4 0.8

Retail trade 43,904 96.1 2.6 1.3

Transportation and warehouses 9,938 96.9 2.4 0.7

Information 15,155 96.2 3.3 0.5

Finance and insurance 25,675 96.6 2.6 0.8

Real estate, rental, leasing 25,765 97.1 2.2 0.7

Professional, scientific, and technical services 92,391 97.6 2.0 0.4

Management of companies 1,701 96.1 3.6 0.2

Administration, support, and waste 113,786 99.4 0.4 0.1

Education services 9,802 94.8 3.6 1.5

Health care and social assistance 47,398 97.6 1.8 0.6

Arts, entertainment, and recreation 11,472 98.2 1.2 0.6

Accommodation and food services 21,452 97.0 2.7 0.3

Other services 55,809 98.6 1.1 0.4

Public administration 587 59.8 24.7 15.5

Other 443 99.1 0.9

Total 570,659 97.6 1.9 0.5
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 35

Table 2.11. Establishment risk (in percent) from earthquake hazards in the HayWired scenario by business owner demographics and 

NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Average risk across 

establishments Male Female Non-minority Minority

Male Female Male Female

Agriculture, forestry, fishing, and hunting 3.2 3.2 3.9 3.2 3.9 3.6 3.9

Mining 7.0 7.0 7.0 7.0 6.4 7.1 7.5

Utilities 4.5 4.4 9.3 4.1 5.4 18.4 23.0

Construction 9.2 9.2 10.1 9.1 9.5 12.5 11.3

Manufacturing 8.5 8.4 9.4 8.3 9.1 10.7 10.3

Wholesale trade 8.0 8.0 8.4 7.9 7.9 10.9 9.7

Retail trade 7.7 7.8 7.4 7.7 7.1 9.3 8.9

Transportation and warehouses 9.4 9.3 10.0 9.3 9.9 10.6 10.1

Information 6.7 6.7 7.7 6.6 7.5 8.4 8.5

Finance and insurance 6.2 6.2 7.4 6.2 7.1 9.3 8.3

Real estate, rental, leasing 7.2 7.2 7.3 7.2 6.6 8.8 10.3

Professional, scientific, and technical services 6.7 6.7 7.0 6.6 6.7 8.6 8.0

Management of companies 6.1 6.1 4.4 6.1 5.0 6.1 2.4

Administration, support, and waste 6.6 6.5 7.9 6.5 7.5 9.6 8.9

Education services 7.1 7.1 7.2 7.1 7.0 9.1 8.4

Health care and social assistance 7.1 7.1 7.7 7.1 7.6 8.6 8.2

Arts, entertainment, and recreation 7.5 7.6 6.3 7.6 6.1 9.4 7.9

Accommodation and food services 7.3 7.2 7.6 7.2 7.3 9.3 8.4

Other services 7.6 7.6 8.0 7.6 7.8 10.1 8.8

Public administration 7.0 7.1 0.0 7.1 0.0

Other 5.6 5.5 6.3 5.5 7.0 4.8 4.1

Total 7.3 7.2 7.7 7.2 7.3 9.8 8.9
36 The HayWired Earthquake Scenario—Societal Consequences

Table 2.12. Distribution of establishments (in percent) in the San Francisco Bay region, California, by business owner demographics 

and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Number of 

establishments Male Female Non-minority Minority

Male Female Male Female

Agriculture, forestry, fishing, and hunting 5,002 93.0 7.0 92.2 6.4 0.8 0.6

Mining 240 95.8 4.2 94.2 2.1 1.7 2.1

Utilities 596 97.0 3.0 95.0 2.3 2.0 0.7

Construction 45,328 97.0 3.0 94.6 2.1 2.4 0.9

Manufacturing 20,899 89.0 11.0 84.9 7.9 4.1 3.1

Wholesale trade 23,316 90.8 9.2 87.8 6.7 3.0 2.5

Retail trade 43,904 88.4 11.6 86.2 9.2 2.2 2.4

Transportation and warehouses 9,938 94.8 5.2 91.7 3.6 3.1 1.6

Information 15,155 93.8 6.2 92.1 4.9 1.7 1.3

Finance and insurance 25,675 95.8 4.2 94.8 3.3 1.0 0.9

Real estate, rental, leasing 25,765 94.0 6.0 93.0 4.9 1.1 1.1

Professional, scientific, and technical services 92,391 89.3 10.7 86.9 8.5 2.4 2.2

Management of companies 1,701 99.2 0.8 98.8 0.6 0.4 0.2

Administration, support, and waste 113,786 97.3 2.7 96.6 2.0 0.7 0.7

Education services 9,802 90.3 9.7 89.4 8.3 0.9 1.4

Health care and social assistance 47,398 91.5 8.5 90.5 7.0 1.0 1.6

Arts, entertainment, and recreation 11,472 91.2 8.8 90.3 7.8 0.9 1.0

Accommodation and food services 21,452 91.9 8.1 88.6 5.7 3.3 2.4

Other services 55,809 91.0 9.0 89.4 7.0 1.6 2.0

Public administration 587 99.8 0.2 99.8 0.2

Other 443 91.9 8.1 88.3 6.3 3.6 1.8

Total 570,659 92.8 7.2 91.0 5.6 1.7 1.6
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 37

Disruptive Building Damage Risk from Earthquake Hazards for Employment

Table 2.13. Employment risk (in percent) from earthquake hazards in the HayWired scenario by county and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector

Average 

risk across 

employees in 

9 counties

County

Alameda Contra 

Costa Marin Napa San Francisco

San 

Mateo

Santa 

Clara Solano Sonoma

Agriculture, forestry, fishing, 

and hunting

2.4 16.0 10.1 0.6 0.0 1.5 1.8 1.8 0.0 0.0

Mining 9.7 21.7 17.4 1.8 0.1 1.8 4.1 3.3 0.1 0.0

Utilities 4.6 18.0 7.5 0.9 0.0 0.7 3.5 3.4 0.0 0.0

Construction 9.0 29.7 12.2 1.4 0.1 2.0 3.2 4.5 0.3 0.0

Manufacturing 9.1 22.5 12.2 1.4 0.0 2.6 3.4 4.5 0.2 0.0

Wholesale trade 8.4 22.9 10.9 1.2 0.1 2.3 3.2 3.6 0.2 0.0

Retail trade 7.7 23.8 12.4 1.0 0.0 1.5 2.3 2.7 0.4 0.0

Transportation and 

warehouses

7.2 23.7 9.6 1.5 0.1 2.4 1.8 3.1 0.3 0.0

Information 5.2 18.4 12.5 1.1 0.1 1.8 3.3 3.0 0.1 0.0

Finance and insurance 5.1 21.2 11.0 1.1 0.1 1.2 3.6 2.3 0.2 0.0

Real estate, rental, leasing 6.6 24.4 12.5 1.0 0.0 1.3 2.6 2.4 0.2 0.0

Professional, scientific, and 

technical services

5.6 19.4 11.7 0.9 0.0 1.5 3.0 2.7 0.2 0.0

Management of companies 5.2 20.8 13.4 0.8 0.0 1.1 2.7 4.8 0.3 0.0

Administration, support, and 

waste

6.6 23.2 11.3 0.9 0.0 1.2 2.1 2.5 0.1 0.0

Education services 6.9 23.9 11.2 0.9 0.0 1.0 2.1 2.2 0.3 0.0

Health care and social 

assistance

7.1 23.1 10.2 1.0 0.0 1.2 1.7 2.0 0.3 0.0

Arts, entertainment, and 

recreation

6.7 23.1 12.2 1.2 0.0 1.5 2.0 2.6 0.2 0.0

Accommodation and food 

services

6.7 23.8 12.8 0.9 0.0 1.4 2.0 2.5 0.3 0.0

Other services 7.3 23.7 11.1 1.1 0.0 1.3 2.4 2.3 0.3 0.0

Public administration 7.0 21.4 11.7 0.5 0.1 1.2 1.6 4.8 0.2 0.0

Other 4.9 21.5 6.7 0.5 0.0 1.1 1.6 2.4 0.0 0.0

Total 7.0 22.9 11.6 1.0 0.0 1.5 2.6 3.1 0.2 0.0
38 The HayWired Earthquake Scenario—Societal Consequences

Table 2.14. Distribution of employment (in percent) in the San Francisco Bay region, California, by county and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Number of 

employees

County

Alameda Contra 

Costa Marin Napa San Francisco

San 

Mateo

Santa 

Clara Solano Sonoma

Agriculture, forestry, fishing, and 

hunting

20,740 7.4 7.5 3.3 19.6 4.1 6.2 15.4 9.3 27.2

Mining 2,451 7.8 41.5 5.9 0.2 7.0 5.2 11.9 12.9 7.6

Utilities 11,069 16.4 8.8 5.0 0.9 45.8 5.0 12.7 1.5 4.0

Construction 207,224 18.8 13.7 4.7 2.5 9.9 11.8 24.2 5.9 8.5

Manufacturing 370,952 27.1 5.4 1.3 2.2 5.0 7.9 43.8 1.7 5.4

Wholesale trade 146,761 25.6 8.0 3.1 3.0 11.1 10.9 28.0 3.2 7.0

Retail trade 201,716 21.9 11.6 4.4 2.2 15.8 10.3 21.3 4.4 8.1

Transportation and warehouses 60,505 21.2 9.3 2.9 2.2 9.7 30.1 15.4 4.4 4.9

Information 153,971 14.5 4.2 3.7 0.6 19.8 15.9 37.2 0.9 3.2

Finance and insurance 145,999 11.2 12.3 6.8 1.2 31.2 15.4 16.1 2.3 3.5

Real estate, rental, leasing 113,725 15.9 12.3 4.8 1.7 24.4 9.8 22.3 3.3 5.6

Professional, scientific, and 

technical services

446,889 15.8 8.7 4.1 1.0 21.0 9.7 34.3 1.6 3.8

Management of companies 7,595 8.6 7.2 3.6 0.7 24.3 9.3 39.0 4.7 2.7

Administration, support, and 

waste

341,069 17.7 11.8 4.6 1.4 17.6 10.2 26.7 3.8 6.2

Education services 112,573 20.0 9.7 4.8 2.0 19.1 8.3 22.9 3.8 9.4

Health care and social assistance 311,315 21.1 12.9 5.1 2.0 14.7 10.7 22.9 3.7 6.8

Arts, entertainment, and 

recreation

68,028 17.4 12.7 6.0 3.2 16.7 9.3 23.6 3.2 7.8

Accommodation and food 

services

235,814 17.5 11.1 4.4 3.1 23.7 9.5 20.3 3.8 6.5

Other services 195,238 20.8 12.3 4.3 1.8 16.7 10.1 22.5 4.8 6.7

Public administration 37,625 19.6 9.5 7.1 1.3 16.4 11.2 25.2 6.3 3.4

Other 1,644 15.0 9.3 4.7 0.6 23.1 11.9 26.6 3.2 5.5

Total 3,192,903 19.3 10.2 4.2 2.0 16.7 10.7 27.6 3.3 6.1
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 39

Table 2.15. Employment risk (in percent) from earthquake hazards in the HayWired scenario by establishment size (number of 

employees) and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector

Average 

risk across 

employees

<5 5–9 10–19 20–49 50–99 100–499 >500

Agriculture, forestry, fishing, and hunting 2.4 3.2 2.5 1.7 3.2 0.9 1.1

Mining 9.7 7.1 8.6 3.9 3.6 5.2 14.7

Utilities 4.6 3.8 3.2 6.2 6.8 9.9 4.3 2.9

Construction 9.0 9.1 9.0 9.0 9.5 9.9 9.0 4.7

Manufacturing 9.1 8.3 8.5 8.2 8.8 9.4 10.1 8.9

Wholesale trade 8.4 7.8 8.5 8.6 8.5 9.5 9.6 4.9

Retail trade 7.7 7.8 7.2 7.1 6.9 7.9 8.2 10.5

Transportation and warehouses 7.2 9.4 8.8 7.8 7.8 6.9 8.1 1.3

Information 5.2 6.9 5.6 6.7 5.0 4.5 6.1 4.0

Finance and insurance 5.1 6.3 5.8 5.3 5.1 4.9 5.5 3.2

Real estate, rental, leasing 6.6 7.1 7.1 7.1 7.0 8.2 5.6 1.3

Professional, scientific, and technical services 5.6 6.7 6.1 6.2 5.5 5.4 4.4 4.1

Management of companies 5.2 6.1 5.6 6.6 3.5 4.6 1.4 5.6

Administration, support, and waste 6.6 6.3 5.7 6.9 7.0 6.9 8.1 7.0

Education services 6.9 7.0 7.3 7.4 7.3 6.4 8.1 5.2

Health care and social assistance 7.1 7.0 6.9 7.5 8.0 8.6 7.2 5.9

Arts, entertainment, and recreation 6.7 7.4 6.9 7.4 5.7 6.8 6.0 6.5

Accommodation and food services 6.7 7.5 7.3 7.2 6.6 5.9 3.9 9.0

Other services 7.3 7.6 7.3 7.3 6.8 5.3 4.1 12.7

Public administration 7.0 7.0 9.7 2.8 4.4 9.7 9.2 5.3

Other 4.9 5.7 6.2 3.0 6.6 1.5

Total 7.0 7.1 7.1 7.2 7.1 7.2 7.4 6.1
40 The HayWired Earthquake Scenario—Societal Consequences

Table 2.16. Distribution of employment (in percent) in the San Francisco Bay region, California, by establishment size (number of 

employees) and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Number of 

employees <5 5–9 10–19 20–49 50–99 100–499 >500

Agriculture, forestry, fishing, and hunting 20,740 36.6 11.7 11.6 15.4 11.5 13.3

Mining 2,451 14.5 11.0 8.0 14.3 9.5 42.7

Utilities 11,069 7.1 4.5 7.3 9.2 9.9 19.9 42.1

Construction 207,224 32.6 10.7 10.8 16.6 9.8 14.5 5.0

Manufacturing 370,952 7.7 5.5 6.4 11.9 10.6 25.5 32.4

Wholesale trade 146,761 25.5 11.9 12.3 16.1 11.4 16.5 6.4

Retail trade 201,716 36.4 13.5 10.7 11.9 10.3 11.5 5.7

Transportation and warehouses 60,505 28.0 8.6 8.6 14.3 8.9 14.5 17.1

Information 153,971 14.8 5.6 5.8 9.7 10.0 21.1 32.9

Finance and insurance 145,999 32.5 7.2 6.3 9.2 7.1 11.9 25.7

Real estate, rental, leasing 113,725 43.1 9.9 8.6 11.5 10.5 7.3 9.1

Professional, scientific, and technical services 446,889 30.5 9.5 9.7 13.3 10.3 13.6 13.1

Management of companies 7,595 43.8 3.7 2.9 7.4 5.8 10.5 25.9

Administration, support, and waste 341,069 59.3 5.3 4.2 6.3 5.4 7.1 12.4

Education services 112,573 8.9 13.4 9.1 16.9 15.6 16.9 19.2

Health care and social assistance 311,315 27.2 18.1 8.7 9.9 8.3 10.6 17.3

Arts, entertainment, and recreation 68,028 24.4 12.3 9.3 13.4 15.2 15.8 9.5

Accommodation and food services 235,814 7.6 24.6 21.2 21.5 10.6 10.6 3.8

Other services 195,238 49.5 14.7 10.5 11.7 5.7 4.7 3.2

Public administration 37,625 1.7 1.0 1.9 7.2 13.1 32.1 42.9

Other 1,644 40.5 13.0 15.6 17.8 13.1

Total 3,192,903 28.8 11.1 9.2 12.5 9.5 13.8 15.1
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 41

Table 2.17. Employment risk (in percent) from earthquake hazards in the HayWired scenario by establishment age (in years) and 

NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Average risk 

across employees <6 6–10 11–15 16–20 21–25 >26

Agriculture, forestry, fishing, and hunting 2.4 4.0 3.7 2.8 1.2 1.7 1.5

Mining 9.7 10.4 6.0 5.1 2.9 5.4 11.7

Utilities 4.6 5.0 7.4 3.0 5.5 2.7 4.3

Construction 9.0 9.4 8.8 9.3 6.8 9.6 9.1

Manufacturing 9.1 8.2 13.9 6.1 7.5 7.9 9.3

Wholesale trade 8.4 7.5 7.3 8.6 9.7 7.8 8.9

Retail trade 7.7 8.1 8.7 7.9 6.8 7.8 7.3

Transportation and warehouses 7.2 9.5 8.1 7.4 7.3 6.6 5.8

Information 5.2 5.4 4.9 5.4 4.0 5.5 6.4

Finance and insurance 5.1 5.7 5.7 5.9 5.2 5.8 4.3

Real estate, rental, leasing 6.6 6.5 5.4 8.1 7.6 6.3 6.9

Professional, scientific, and technical services 5.6 5.1 5.9 6.1 6.4 5.6 5.5

Management of companies 5.2 5.4 5.0 3.4 5.8 7.4 2.8

Administration, support, and waste 6.6 6.4 6.1 5.5 6.4 7.2 7.9

Education services 6.9 7.0 7.8 7.9 6.4 6.4 6.6

Health care and social assistance 7.1 6.8 7.4 7.6 7.4 6.1 7.0

Arts, entertainment, and recreation 6.7 7.3 5.9 5.3 6.9 5.5 7.2

Accommodation and food services 6.7 6.9 7.4 6.3 6.8 6.6 6.1

Other services 7.3 6.7 7.1 7.4 7.0 7.9 7.8

Public administration 7.0 8.8 2.6 10.2 7.0 6.1 6.9

Other 4.9 5.4 3.0 5.3 6.8 9.0 5.2

Total 7.0 6.7 7.4 6.7 6.6 7.0 7.3
42 The HayWired Earthquake Scenario—Societal Consequences

Table 2.18. Distribution of employment (in percent) in the San Francisco Bay region, California, by establishment age (in years) and 

NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Number of employees <6 6–10 11–15 16–20 21–25 >26

Agriculture, forestry, fishing, and hunting 20,740 18.9 13.3 10.1 8.4 3.3 46.0

Mining 2,451 10.4 9.5 5.5 8.2 5.1 61.2

Utilities 11,069 16.1 6.5 5.1 2.7 0.7 69.0

Construction 207,224 22.5 13.5 8.9 7.8 6.2 41.0

Manufacturing 370,952 8.5 12.3 11.4 10.8 6.9 50.1

Wholesale trade 146,761 18.3 14.4 10.9 9.1 7.2 40.1

Retail trade 201,716 23.6 16.6 10.6 9.2 4.7 35.4

Transportation and warehouses 60,505 20.4 15.7 8.6 5.9 4.5 44.8

Information 153,971 22.2 13.4 14.8 22.9 3.3 23.4

Finance and insurance 145,999 23.6 14.3 8.4 8.8 6.0 38.9

Real estate, rental, leasing 113,725 27.2 22.5 10.5 6.1 6.1 27.6

Professional, scientific, and technical services 446,889 30.9 16.2 14.7 11.1 6.3 20.8

Management of companies 7,595 39.9 16.8 10.1 1.6 19.1 12.5

Administration, support, and waste 341,069 40.5 21.9 8.8 5.4 2.5 20.7

Education services 112,573 24.2 10.2 6.5 4.5 2.5 52.1

Health care and social assistance 311,315 24.6 13.8 9.4 7.8 4.6 39.7

Arts, entertainment, and recreation 68,028 23.5 15.0 11.7 9.6 4.8 35.3

Accommodation and food services 235,814 29.3 19.2 11.9 11.2 5.0 23.4

Other services 195,238 28.7 14.0 10.9 7.9 4.4 34.0

Public administration 37,625 9.7 3.5 3.2 8.4 2.3 72.9

Other 1,644 51.4 19.9 25.5 0.6 0.5 2.1

Total 3,192,903 25.0 15.5 10.8 9.3 5.1 34.2
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 43

Table 2.19. Employment risk (in percent) from earthquake hazards in the HayWired scenario by establishment revenue (in thousands of 

U.S. dollars) and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector

Average 

risk across 

employees

<$100 $101–250 $251–500 $501–1,000 $1,001–2,500 >$2,501

Agriculture, forestry, fishing, and hunting 2.4 4.1 2.3 2.1 2.2 2.5 1.1

Mining 9.7 10.0 6.4 10.4 6.4 4.3 11.5

Utilities 4.6 4.0 3.1 2.5 3.6 10.5 4.5

Construction 9.0 9.6 9.0 8.8 9.2 9.0 8.9

Manufacturing 9.1 8.4 8.7 8.2 8.0 8.7 9.4

Wholesale trade 8.4 8.3 8.1 7.3 8.3 8.1 8.7

Retail trade 7.7 7.7 7.8 7.2 7.4 7.3 8.1

Transportation and warehouses 7.2 9.4 9.3 7.4 8.1 7.8 5.6

Information 5.2 7.5 6.6 6.5 5.7 6.1 4.7

Finance and insurance 5.1 6.9 6.3 5.9 6.1 4.9 4.3

Real estate, rental, leasing 6.6 7.1 7.0 7.2 5.6 7.4 5.8

Professional, scientific, and technical services 5.6 7.3 6.5 6.4 5.8 6.0 4.7

Management of companies 5.2 4.6 6.1 5.4 5.2 6.5 4.2

Administration, support, and waste 6.6 7.5 5.9 5.9 6.8 7.5 7.1

Education services 6.9 7.0 7.4 7.2 7.5 7.0 6.6

Health care and social assistance 7.1 7.9 6.9 6.9 7.1 7.6 7.0

Arts, entertainment, and recreation 6.7 7.9 7.3 5.9 7.1 5.2 6.5

Accommodation and food services 6.7 7.6 7.4 7.4 6.8 5.7 5.5

Other services 7.3 7.6 7.2 7.3 7.9 6.8 6.9

Public administration 7.0 7.0 4.1

Other 4.9 5.2 3.3 0.8 1.3 3.3 0.7

Total 7.0 7.5 6.9 7.0 7.1 7.0 6.9
44 The HayWired Earthquake Scenario—Societal Consequences

Table 2.20. Distribution of employment (in percent) in the San Francisco Bay region, California, by establishment revenue (in thousands 

of U.S. dollars) and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Number of 

employees <$100 $101–250 $251–500 $501–1,000 $1,001–2,500 >$2,501

Agriculture, forestry, fishing, and hunting 20,740 21.2 17.3 10.3 12.3 13.9 25.1

Mining 2,451 2.4 6.5 5.8 9.5 12.3 63.5

Utilities 11,069 2.1 3.6 2.0 4.2 3.2 84.9

Construction 207,224 7.2 20.0 8.6 10.0 12.1 42.2

Manufacturing 370,952 2.0 4.2 3.6 5.0 7.1 78.0

Wholesale trade 146,761 2.1 9.6 11.3 10.5 12.1 54.4

Retail trade 201,716 8.9 18.7 11.6 11.1 11.7 38.0

Transportation and warehouses 60,505 8.0 18.1 8.4 8.3 11.2 46.0

Information 153,971 6.2 8.5 4.2 4.6 7.1 69.4

Finance and insurance 145,999 1.6 23.0 10.0 5.8 7.5 52.0

Real estate, rental, leasing 113,725 3.8 34.8 12.3 13.7 11.4 24.0

Professional, scientific, and technical services 446,889 10.3 17.1 8.2 8.4 11.1 44.9

Management of companies 7,595 1.0 40.7 3.5 2.3 5.6 46.8

Administration, support, and waste 341,069 13.9 44.8 5.2 5.5 5.7 25.0

Education services 112,573 6.3 6.8 10.4 8.8 12.4 55.3

Health care and social assistance 311,315 5.9 20.9 18.3 9.2 9.5 36.3

Arts, entertainment, and recreation 68,028 15.7 18.9 8.6 8.9 11.1 36.7

Accommodation and food services 235,814 2.3 13.0 32.9 17.1 16.1 18.5

Other services 195,238 32.9 21.4 11.5 9.8 10.0 14.3

Public administration 37,625 99.8 0.0 0.0 0.1

Other 1,644 92.4 0.9 0.5 0.9 1.5 3.8

Total 3,192,903 9.6 18.8 10.7 8.7 9.9 42.2
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 45

Table 2.21. Employment risk (in percent) from earthquake hazards in the HayWired scenario by type of establishment and NAICS 

2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Average risk 

across employees Stand alone Headquarters Branch

Agriculture, forestry, fishing, and hunting 2.4 2.4 2.8 10.1

Mining 9.7 8.1 12.3 0.3

Utilities 4.6 4.1 4.7 9.2

Construction 9.0 9.0 8.7 12.2

Manufacturing 9.1 10.5 7.3 10.7

Wholesale trade 8.4 8.0 9.4 10.4

Retail trade 7.7 7.8 6.9 9.2

Transportation and warehouses 7.2 7.0 7.9 8.6

Information 5.2 6.1 4.1 5.0

Finance and insurance 5.1 5.5 4.1 5.4

Real estate, rental, leasing 6.6 6.7 6.0 6.1

Professional, scientific, and technical services 5.6 5.8 4.6 9.4

Management of companies 5.2 5.0 5.5 9.9

Administration, support, and waste 6.6 6.5 6.6 11.7

Education services 6.9 6.9 6.6 7.8

Health care and social assistance 7.1 7.6 5.4 4.9

Arts, entertainment, and recreation 6.7 7.0 4.4 4.1

Accommodation and food services 6.7 6.6 8.1 6.2

Other services 7.3 7.4 6.3 6.4

Public administration 7.0 7.3 7.1 6.6

Other 4.9 4.9 4.0

Total 7.0 7.2 6.3 7.9
46 The HayWired Earthquake Scenario—Societal Consequences

Table 2.22. Distribution of employment (in percent) in the San Francisco Bay region, California, by type of establishment and NAICS 

2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Number of employees Stand alone Headquarters Branch

Agriculture, forestry, fishing, and hunting 20,740 92.3 7.5 0.2

Mining 2,451 60.7 38.1 1.3

Utilities 11,069 43.2 53.2 3.6

Construction 207,224 87.6 11.5 0.9

Manufacturing 370,952 54.6 43.2 2.2

Wholesale trade 146,761 75.5 22.9 1.6

Retail trade 201,716 80.6 14.0 5.4

Transportation and warehouses 60,505 83.2 15.3 1.5

Information 153,971 58.0 41.0 1.0

Finance and insurance 145,999 67.9 29.7 2.4

Real estate, rental, leasing 113,725 85.6 11.5 2.8

Professional, scientific, and technical services 446,889 80.2 17.5 2.3

Management of companies 7,595 68.5 30.9 0.6

Administration, support, and waste 341,069 92.0 7.3 0.7

Education services 112,573 61.4 33.4 5.2

Health care and social assistance 311,315 78.2 18.4 3.4

Arts, entertainment, and recreation 68,028 88.5 8.0 3.5

Accommodation and food services 235,814 91.9 7.3 0.8

Other services 195,238 92.9 6.0 1.1

Public administration 37,625 15.7 60.3 24.1

Other 1,644 99.6 0.4

Total 3,192,903 77.5 20.0 2.4
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 47

Table 2.23. Employment risk (in percent) from earthquake hazards in the HayWired scenario by business owner demographics and 

NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Average risk across

employees Male Female Non-minority Minority

Male Female Male Female

Agriculture, forestry, fishing, and hunting 2.4 2.3 4.0 2.3 3.5 1.1 5.9

Mining 9.7 10.6 0.8 10.6 0.2 10.1 3.0

Utilities 4.6 4.6 3.0 4.5 2.2 26.5 6.7

Construction 9.0 8.9 11.4 8.8 10.7 9.7 12.7

Manufacturing 9.1 9.1 10.0 9.0 9.9 11.3 10.3

Wholesale trade 8.4 8.3 9.2 8.1 9.3 12.9 8.8

Retail trade 7.7 7.7 7.9 7.7 7.1 8.8 10.9

Transportation and warehouses 7.2 7.2 7.7 7.0 7.5 12.0 8.0

Information 5.2 5.2 6.3 5.2 5.8 6.8 8.0

Finance and insurance 5.1 5.1 7.1 5.0 7.0 9.4 7.3

Real estate, rental, leasing 6.6 6.6 6.7 6.6 6.0 6.4 9.4

Professional, scientific, and technical services 5.6 5.7 5.3 5.6 4.6 7.5 7.2

Management of companies 5.2 5.2 4.4 5.2 4.9 4.6 2.4

Administration, support, and waste 6.6 6.5 7.1 6.5 6.9 8.7 7.4

Education services 6.9 6.9 6.2 6.9 6.3 8.5 5.9

Health care and social assistance 7.1 7.0 7.4 7.0 7.3 11.1 8.0

Arts, entertainment, and recreation 6.7 6.7 7.6 6.7 7.9 6.7 5.8

Accommodation and food services 6.7 6.7 6.9 6.7 6.1 7.1 8.5

Other services 7.3 7.3 7.6 7.2 7.3 9.1 8.5

Public administration 7.0 7.0 0.0 7.0 0.0

Other 4.9 4.8 7.5 4.9 9.1 2.5 4.6

Total 7.0 7.0 7.2 6.9 6.7 9.2 8.5
48 The HayWired Earthquake Scenario—Societal Consequences

Table 2.24. Distribution of employment (in percent) in the San Francisco Bay region, California, by business owner demographics and 

NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Number of 

employees Male Female Minority Non-minority

Male Female Male Female

Agriculture, forestry, fishing, and hunting 20,740 93.3 6.7 91.6 5.2 1.7 1.5

Mining 2,451 91.1 8.9 90.6 7.1 0.4 1.8

Utilities 11,069 97.0 3.0 96.4 2.5 0.6 0.5

Construction 207,224 94.6 5.4 90.4 3.6 4.2 1.8

Manufacturing 370,952 94.2 5.8 90.6 3.8 3.6 2.0

Wholesale trade 146,761 91.0 9.0 86.3 5.7 4.7 3.3

Retail trade 201,716 91.0 9.0 88.3 7.0 2.7 2.0

Transportation and warehouses 60,505 93.9 6.1 90.2 3.8 3.7 2.3

Information 153,971 96.4 3.6 94.5 2.7 2.0 0.9

Finance and insurance 145,999 97.1 2.9 96.2 2.2 0.9 0.7

Real estate, rental, leasing 113,725 94.3 5.7 93.0 4.5 1.3 1.1

Professional, scientific, and technical services 446,889 85.2 14.8 80.8 10.9 4.4 3.9

Management of companies 7,595 99.6 0.4 98.9 0.3 0.8 0.1

Administration, support, and waste 341,069 92.3 7.7 89.4 4.7 2.9 3.0

Education services 112,573 96.8 3.2 96.4 2.7 0.4 0.5

Health care and social assistance 311,315 94.0 6.0 93.3 4.9 0.8 1.1

Arts, entertainment, and recreation 68,028 95.2 4.8 94.3 4.2 0.8 0.6

Accommodation and food services 235,814 94.1 5.9 91.0 4.0 3.1 1.9

Other services 195,238 92.6 7.4 90.7 5.4 1.9 2.0

Public administration 37,625 100.0 0.0 100.0 0.0

Other 1,644 95.0 5.0 92.8 3.3 2.3 1.7

Total 3,192,903 92.7 7.3 90.0 5.2 2.7 2.0
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 49

Disruptive Building Damage Risk from Earthquake and Fire Hazards for Establishments

See section on “Disruptive Building Damage Risk from Earthquake Hazards for Establishments” for distribution tables.

Table 2.25. Establishment risk (in percent) from earthquake and fire hazards in the HayWired scenario by county and NAICS 2-digit sector.

[See table 2.2 for the distribution of establishments by county. NAICS, North American Industry Classification System]

NAICS sector

Average 

risk across 

establishments 

in 9 counties

County

Alameda Contra 

Costa Marin Napa San Francisco

San 

Mateo

Santa 

Clara Solano Sonoma

Agriculture, forestry, fishing, and hunting 3.3 18.6 9.8 0.7 0.0 1.5 1.5 2.1 0.3 0.0

Mining 7.5 28.8 17.1 2.0 0.1 1.3 4.7 3.3 0.6 0.0

Utilities 4.7 19.9 10.3 1.0 0.0 1.7 1.8 2.9 0.2 0.0

Construction 10.0 35.5 14.8 2.3 0.1 1.5 2.6 4.5 1.3 0.0

Manufacturing 9.0 28.3 13.7 2.0 0.0 2.0 3.2 4.2 1.2 0.0

Wholesale trade 8.2 25.9 11.6 1.3 0.0 1.9 2.6 3.1 0.4 0.0

Retail trade 8.1 26.3 13.1 1.4 0.0 1.4 2.4 3.0 1.0 0.0

Transportation and warehouses 9.7 26.7 12.2 1.4 0.1 1.7 2.4 3.3 0.6 0.0

Information 7.1 24.8 14.2 1.3 0.0 1.5 2.6 2.7 0.7 0.0

Finance and insurance 6.5 24.8 11.5 1.3 0.0 1.3 2.5 2.4 0.7 0.0

Real estate, rental, leasing 7.5 25.9 11.8 1.3 0.0 1.3 2.3 2.5 0.6 0.0

Professional, scientific, and technical services 7.0 23.7 12.4 1.2 0.0 1.4 2.4 2.6 0.6 0.0

Management of companies 6.3 22.5 13.1 1.0 0.0 1.3 2.2 2.4 0.5 0.0

Administration, support, and waste 6.8 25.0 11.9 1.1 0.0 1.2 2.0 2.4 0.4 0.0

Education services 11.1 32.4 15.3 5.2 0.1 1.5 4.0 4.8 3.6 0.1

Health care and social assistance 7.6 25.0 12.3 1.3 0.0 1.1 2.0 2.4 0.8 0.0

Arts, entertainment, and recreation 7.8 26.2 13.5 1.3 0.0 1.3 2.2 2.6 0.8 0.0

Accommodation and food services 7.7 26.0 13.5 1.4 0.0 1.3 2.2 3.0 1.0 0.0

Other services 8.3 26.3 12.2 1.7 0.0 1.3 2.5 2.8 1.6 0.0

Public administration 8.5 23.4 14.4 1.1 0.1 1.7 2.7 5.0 7.1 0.0

Other 6.4 23.9 8.5 2.1 0.0 1.4 2.2 2.9 0.8 0.0

Total 7.7 26.2 12.6 1.5 0.0 1.4 2.4 2.9 0.9 0.0
50 The HayWired Earthquake Scenario—Societal Consequences

Table 2.26. Establishment risk (in percent) from earthquakes and fire in the HayWired scenario by number of employees and NAICS 

2-digit sector.

[See table 2.4 for the distribution of establishments by number of employees. NAICS, North American Industry Classification System]

NAICS sector Average risk across 

establishments <5 5–9 10–19 20–49 50–99 100–499 >500

Agriculture, forestry, fishing, and hunting 3.3 3.5 2.7 1.8 3.3 1.0 1.2

Mining 7.5 7.4 8.7 6.2 4.2 5.9 14.4

Utilities 4.7 4.3 3.6 6.3 7.4 8.9 5.9 6.4

Construction 10.0 10.0 9.8 10.1 10.8 11.2 11.3 5.5

Manufacturing 9.0 8.9 9.1 8.8 9.3 10.1 10.7 7.9

Wholesale trade 8.2 8.0 8.5 8.7 8.6 9.6 10.5 5.5

Retail trade 8.1 8.2 7.7 7.6 7.3 8.1 8.6 9.1

Transportation and warehouses 9.7 10.0 9.2 8.2 7.9 7.3 8.6 1.9

Information 7.1 7.3 5.8 7.2 5.5 5.0 6.4 5.1

Finance and insurance 6.5 6.6 6.1 5.7 5.5 5.3 5.5 3.6

Real estate, rental, leasing 7.5 7.5 7.4 7.4 7.1 8.0 5.9 2.5

Professional, scientific, and technical services 7.0 7.1 6.5 6.6 6.0 5.8 5.0 4.4

Management of companies 6.3 6.3 5.9 7.2 4.5 4.9 1.3 4.3

Administration, support, and waste 6.8 6.8 5.8 7.2 7.7 7.5 8.5 6.8

Education services 11.1 10.8 11.3 11.6 11.6 11.0 12.1 7.7

Health care and social assistance 7.6 7.6 7.3 7.9 8.4 8.7 7.5 7.4

Arts, entertainment, and recreation 7.8 8.0 7.2 7.8 6.2 6.9 7.2 7.3

Accommodation and food services 7.7 8.0 7.7 7.6 7.0 6.5 4.7 7.2

Other services 8.3 8.3 7.9 8.0 7.7 6.1 5.1 10.2

Public administration 8.5 8.6 12.0 4.4 4.8 12.0 10.3 5.8

Other 6.4 6.6 5.9 3.9 7.9 1.9

Total 7.7 7.7 7.7 7.9 7.8 8.0 8.2 6.4
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 51

Table 2.27. Establishment risk (in percent) from earthquakes and fire hazards in the HayWired scenario by establishment age (in years) 

and NAICS 2-digit sector.

[See table 2.6 for the distribution of establishments by establishment age. NAICS, North American Industry Classification System]

NAICS sector Average risk across 

establishments <6 6–10 11–15 16–20 21–25 >26

Agriculture, forestry, fishing, and hunting 3.3 4.2 3.7 3.1 2.5 2.3 2.0

Mining 7.5 10.1 6.1 5.3 6.1 7.9 7.1

Utilities 4.7 4.7 6.1 2.5 3.8 3.7 6.1

Construction 10.0 10.3 10.0 10.0 9.2 9.4 9.7

Manufacturing 9.0 8.7 8.9 8.2 8.6 8.4 10.2

Wholesale trade 8.2 8.0 8.2 8.3 8.6 8.2 8.5

Retail trade 8.1 8.2 8.2 8.2 7.5 7.8 8.4

Transportation and warehouses 9.7 10.6 9.9 9.0 7.7 7.4 8.2

Information 7.1 7.0 7.2 6.7 7.2 7.1 7.6

Finance and insurance 6.5 6.4 6.9 6.3 6.1 6.1 7.0

Real estate, rental, leasing 7.5 7.1 8.0 7.8 7.7 7.8 7.2

Professional, scientific, and technical services 7.0 7.0 7.1 6.9 6.9 7.1 7.1

Management of companies 6.3 6.3 6.6 4.8 7.2 7.5 5.9

Administration, support, and waste 6.8 6.8 6.9 6.4 6.8 7.2 7.4

Education services 11.1 11.0 11.5 11.3 11.1 10.4 10.6

Health care and social assistance 7.6 7.4 7.7 7.6 7.3 7.6 7.9

Arts, entertainment, and recreation 7.8 8.1 8.0 7.2 7.1 7.9 7.6

Accommodation and food services 7.7 7.6 8.0 7.8 7.5 7.4 7.6

Other services 8.3 8.0 8.1 8.5 8.6 8.4 8.6

Public administration 8.5 10.1 4.8 9.4 4.6 15.4 6.9

Other 6.4 6.0 4.8 7.5 7.6 8.1 6.1

Total 7.7 7.6 7.7 7.6 7.6 7.8 8.2
52 The HayWired Earthquake Scenario—Societal Consequences

Table 2.28. Establishment risk (in percent) from earthquake and fire hazards in the HayWired scenario by establishment revenue (in 

thousands of U.S. dollars) and NAICS 2-digit sector.

[See table 2.8 for the distribution of establishments by revenue. NAICS, North American Industry Classification System]

NAICS sector Average risk across 

establishments <$100 $101–250 $251–500 $501–1,000 $1,001–2,500 >$2,501

Agriculture, forestry, fishing, and hunting 3.3 4.2 2.3 2.1 2.2 2.6 1.4

Mining 7.5 6.8 7.1 9.9 7.3 5.4 7.2

Utilities 4.7 4.4 4.0 2.7 4.5 7.3 6.9

Construction 10.0 10.4 9.8 9.7 9.9 10.1 10.6

Manufacturing 9.0 9.2 9.0 8.4 8.9 9.2 9.6

Wholesale trade 8.2 8.6 8.1 7.6 8.3 8.6 9.4

Retail trade 8.1 8.1 8.3 7.9 8.1 7.9 8.0

Transportation and warehouses 9.7 10.0 10.0 8.3 9.3 8.7 9.1

Information 7.1 7.9 6.8 6.3 6.0 6.1 5.3

Finance and insurance 6.5 7.1 6.6 6.2 6.6 5.4 6.0

Real estate, rental, leasing 7.5 7.5 7.4 7.6 7.8 7.2 6.9

Professional, scientific, and technical services 7.0 7.6 6.7 6.6 6.2 6.3 5.6

Management of companies 6.3 4.7 6.4 5.8 6.4 7.7 3.1

Administration, support, and waste 6.8 8.0 6.3 6.5 6.8 7.6 7.1

Education services 11.1 10.9 11.2 11.0 11.4 11.4 11.0

Health care and social assistance 7.6 8.4 7.3 7.3 7.2 8.1 8.3

Arts, entertainment, and recreation 7.8 8.3 7.4 6.7 7.4 5.8 6.9

Accommodation and food services 7.7 8.3 7.9 7.7 7.2 6.1 6.0

Other services 8.3 8.3 8.2 8.3 8.6 7.8 6.8

Public administration 8.5 8.5 5.7

Other 6.4 6.6 3.9 1.3 1.4 3.3 1.1

Total 7.7 8.2 7.3 7.6 7.8 7.8 8.0
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 53

Table 2.29. Establishment risk (in percent) from earthquake and fire hazards in the HayWired scenario by type of establishment and 

NAICS 2-digit sector.

[See table 2.10 for the distribution of establishments by type. NAICS, North American Industry Classification System]

NAICS sector Average risk across 

establishments Stand alone Headquarters Branch

Agriculture, forestry, fishing, and hunting 3.3 3.3 4.6 3.6

Mining 7.5 7.5 8.5 0.4

Utilities 4.7 4.4 8.8 5.6

Construction 10.0 10.0 13.0 14.2

Manufacturing 9.0 9.0 9.1 9.4

Wholesale trade 8.2 8.1 9.6 8.2

Retail trade 8.1 8.2 7.4 8.2

Transportation and warehouses 9.7 9.7 9.6 9.7

Information 7.1 7.1 5.6 5.3

Finance and insurance 6.5 6.6 5.1 5.4

Real estate, rental, leasing 7.5 7.5 6.5 7.9

Professional, scientific, and technical services 7.0 7.0 6.2 6.9

Management of companies 6.3 6.4 4.4 13.2

Administration, support, and waste 6.8 6.8 7.3 8.9

Education services 11.1 11.1 10.8 10.7

Health care and social assistance 7.6 7.6 8.1 7.3

Arts, entertainment, and recreation 7.8 7.9 5.7 6.3

Accommodation and food services 7.7 7.7 7.9 8.0

Other services 8.3 8.3 8.1 6.2

Public administration 8.5 8.5 7.9 9.8

Other 6.4 6.5 5.4

Total 7.7 7.7 7.8 7.9
54 The HayWired Earthquake Scenario—Societal Consequences

Table 2.30. Establishment risk (in percent) from earthquake and fire hazards in the HayWired scenario by business owner 

demographics and NAICS 2-digit sector.

[See table 2.12 for the distribution of establishments by business owner demographics. NAICS, North American Industry Classification System]

NAICS sector Average risk across 

all establishments Male Female Non-Minority Minority

Male Female Male Female

Agriculture, forestry, fishing, and hunting 3.3 3.3 4.1 3.3 4.1 3.6 4.2

Mining 7.5 7.5 7.2 7.5 6.6 7.3 7.8

Utilities 4.7 4.6 9.5 4.3 5.5 18.8 23.3

Construction 10.0 10.0 11.1 9.9 10.4 13.6 12.7

Manufacturing 9.0 8.9 9.9 8.8 9.6 11.3 10.9

Wholesale trade 8.2 8.2 8.6 8.1 8.1 11.2 10.0

Retail trade 8.1 8.2 7.8 8.1 7.4 9.8 9.4

Transportation and warehouses 9.7 9.7 10.3 9.7 10.2 11.1 10.5

Information 7.1 7.0 8.0 7.0 7.8 8.9 8.9

Finance and insurance 6.5 6.5 7.7 6.4 7.4 9.6 8.8

Real estate, rental, leasing 7.5 7.5 7.6 7.4 6.9 9.3 10.6

Professional, scientific, and technical services 7.0 7.0 7.3 6.9 7.0 9.0 8.4

Management of companies 6.3 6.3 4.6 6.3 5.2 6.3 2.7

Administration, support, and waste 6.8 6.8 8.2 6.8 7.8 9.9 9.3

Education services 11.1 11.0 11.2 11.0 10.9 13.1 12.9

Health care and social assistance 7.6 7.5 8.1 7.5 8.0 9.2 8.7

Arts, entertainment, and recreation 7.8 7.9 6.6 7.9 6.4 9.9 8.2

Accommodation and food services 7.7 7.7 8.0 7.6 7.7 9.8 8.9

Other services 8.3 8.3 8.3 8.2 8.1 10.5 9.2

Public administration 8.5 8.5 0.0 8.5 0.0

Other 6.4 6.4 7.4 6.4 8.2 5.8 4.6

Total 7.7 7.7 8.1 7.6 7.8 10.3 9.4
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 55

Disruptive Building Damage Risk from Earthquake and Fire Hazards for Employment

See section on “Disruptive Building Damage Risk from Earthquakes for Establishments” for distribution tables.

Table 2.31. Employment risk (in percent) from earthquake and fire hazards in the HayWired scenario by county and NAICS 2-digit sector.

[See table 2.14 for the distribution of employment by county. NAICS, North American Industry Classification System]

NAICS sector

Average 

risk across 

employees in 

9 counties

County

Alameda Contra

Costa Marin Napa SanFrancisco

San

Mateo

Santa

Clara Solano Sonoma

Agriculture, forestry, fishing, and hunting 2.5 17.1 10.1 0.6 0.0 1.5 1.9 1.8 0.1 0.0

Mining 10.7 22.7 19.1 3.8 0.1 1.8 4.3 3.8 0.2 0.0

Utilities 4.8 18.6 7.8 1.0 0.0 0.7 3.7 3.6 0.0 0.0

Construction 10.1 32.1 12.9 4.0 0.1 2.2 3.7 5.6 2.6 0.0

Manufacturing 9.8 23.9 12.7 2.9 0.0 2.6 3.8 4.9 0.7 0.0

Wholesale trade 8.6 23.4 11.2 1.5 0.1 2.3 3.3 3.8 0.3 0.0

Retail trade 8.2 24.8 13.0 1.4 0.0 1.5 2.5 3.0 0.8 0.0

Transportation and warehouses 7.6 24.5 10.0 2.2 0.1 2.4 2.0 3.4 0.6 0.0

Information 5.6 19.3 12.9 1.9 0.1 1.8 3.5 3.3 0.7 0.0

Finance and insurance 5.3 22.0 11.4 1.4 0.1 1.2 3.7 2.6 0.5 0.0

Real estate, rental, leasing 6.8 25.1 12.9 1.3 0.0 1.4 2.8 2.6 0.5 0.0

Professional, scientific, and technical services 6.0 20.2 12.1 1.5 0.0 1.5 3.2 3.0 0.7 0.0

Management of companies 5.4 21.5 13.8 1.0 0.0 1.2 2.8 5.0 0.3 0.0

Administration, support, and waste 6.9 24.0 11.6 1.3 0.0 1.3 2.3 2.8 0.5 0.0

Education services 10.5 33.2 15.5 4.9 0.1 1.9 3.8 5.0 4.9 0.1

Health care and social assistance 7.5 24.3 10.7 1.4 0.0 1.2 1.9 2.3 0.8 0.0

Arts, entertainment, and recreation 7.1 24.1 12.7 1.8 0.1 1.5 2.2 2.9 1.3 0.0

Accommodation and food services 7.1 25.1 13.3 1.3 0.0 1.5 2.3 3.0 0.7 0.0

Other services 8.0 25.3 11.8 2.0 0.0 1.3 2.7 2.8 1.7 0.0

Public administration 8.6 25.3 12.9 0.6 0.1 1.2 2.1 6.3 5.0 0.0

Other 5.6 23.8 7.2 1.0 0.0 1.1 2.2 2.9 0.5 0.0

Total 7.6 24.4 12.2 1.8 0.0 1.5 2.9 3.5 1.2 0.0
56 The HayWired Earthquake Scenario—Societal Consequences

Table 2.32. Employment risk (in percent) from earthquake and fire hazards in the HayWired scenario by number of employees and 

NAICS 2-digit sector.

[See table 2.16 for the distribution of employment by number of employees. NAICS, North American Industry Classification System]

NAICS sector Average risk across 

employees <5 5–9 10–19 20–49 50–99 100–499 >500

Agriculture, forestry, fishing, and hunting 2.5 3.3 2.7 1.8 3.3 1.0 1.3

Mining 10.7 7.4 9.2 5.5 3.8 5.8 16.2

Utilities 4.8 4.0 3.4 6.4 7.0 10.2 4.7 3.0

Construction 10.1 9.9 9.8 10.1 10.8 11.2 10.5 6.7

Manufacturing 9.8 8.8 9.1 8.8 9.4 10.1 10.8 9.6

Wholesale trade 8.6 8.0 8.7 8.8 8.7 9.7 9.9 5.1

Retail trade 8.2 8.2 7.6 7.6 7.4 8.3 8.7 10.9

Transportation and warehouses 7.6 9.7 9.2 8.2 8.1 7.4 8.6 1.6

Information 5.6 7.2 5.9 7.1 5.4 4.8 6.4 4.3

Finance and insurance 5.3 6.6 6.1 5.6 5.3 5.1 5.8 3.3

Real estate, rental, leasing 6.8 7.4 7.5 7.4 7.3 8.4 5.8 1.4

Professional, scientific, and technical services 6.0 6.9 6.4 6.5 5.9 5.8 4.8 4.3

Management of companies 5.4 6.3 5.8 6.9 3.7 4.8 1.7 5.7

Administration, support, and waste 6.9 6.6 5.9 7.3 7.4 7.3 8.5 7.3

Education services 10.5 10.8 11.2 11.6 11.4 11.2 11.3 7.3

Health care and social assistance 7.5 7.4 7.3 7.9 8.4 9.0 7.6 6.4

Arts, entertainment, and recreation 7.1 7.7 7.2 7.7 6.1 7.2 6.5 7.1

Accommodation and food services 7.1 8.0 7.7 7.5 7.0 6.4 4.3 10.0

Other services 8.0 8.3 7.9 8.1 7.5 6.2 4.7 13.2

Public administration 8.6 8.7 11.1 4.6 4.9 11.1 10.8 7.1

Other 5.6 6.5 7.1 3.5 7.0 1.8

Total 7.6 7.6 7.7 7.8 7.8 8.0 8.1 6.6
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 57

Table 2.33. Employment risk (in percent) from earthquake and fire hazards in the HayWired scenario by establishment age (in years) 

and NAICS 2-digit sector.

[See table 2.18 for the distribution of employment by establishment age. NAICS, North American Industry Classification System]

NAICS sector Average risk across 

employees <6 6–10 11–15 16–20 21–25 >26

Agriculture, forestry, fishing, and hunting 2.5 4.1 3.9 3.1 1.3 1.7 1.6

Mining 10.7 10.8 6.4 5.3 4.3 6.0 12.9

Utilities 4.8 5.1 7.7 3.3 6.0 2.9 4.5

Construction 10.1 10.2 10.0 10.4 8.0 10.6 10.4

Manufacturing 9.8 8.8 14.7 6.7 8.2 8.5 10.0

Wholesale trade 8.6 7.7 7.4 8.8 9.9 8.0 9.2

Retail trade 8.2 8.5 9.1 8.3 7.3 8.2 7.7

Transportation and warehouses 7.6 9.9 8.5 7.8 7.6 6.9 6.2

Information 5.6 5.8 5.2 5.8 4.2 5.8 6.8

Finance and insurance 5.3 5.9 6.0 6.1 5.3 6.2 4.5

Real estate, rental, leasing 6.8 6.8 5.6 8.4 7.9 6.5 7.1

Professional, scientific, and technical services 6.0 5.4 6.2 6.5 6.7 5.9 5.8

Management of companies 5.4 5.7 5.1 3.5 6.0 7.6 2.9

Administration, support, and waste 6.9 6.6 6.4 5.7 6.8 7.6 8.3

Education services 10.5 10.9 13.4 11.4 10.0 10.1 9.7

Health care and social assistance 7.5 7.3 7.8 8.0 7.9 6.5 7.5

Arts, entertainment, and recreation 7.1 7.6 6.2 5.7 7.2 5.8 7.7

Accommodation and food services 7.1 7.2 7.9 6.7 7.2 7.0 6.5

Other services 8.0 7.5 7.7 8.0 7.5 8.4 8.5

Public administration 8.6 11.2 3.5 11.3 11.5 8.4 8.1

Other 5.6 6.1 3.5 6.0 7.6 10.1 5.4

Total 7.6 7.2 8.0 7.2 7.1 7.4 8.0
58 The HayWired Earthquake Scenario—Societal Consequences

Table 2.34. Employment risk (in percent) from earthquake and fire hazards in the HayWired scenario by establishment revenue (in 

thousands of U.S. dollars) and NAICS 2-digit sector.

[See table 2.20 for the distribution of employment by establishment revenue. NAICS, North American Industry Classification System]

NAICS sector Average risk across 

employees <$100 $101–250 $251–500 $501–1,000 $1,001–2,500 >$2,501

Agriculture, forestry, fishing, and hunting 2.5 4.3 2.4 2.2 2.3 2.6 1.2

Mining 10.7 10.4 6.8 11.0 6.7 4.6 12.8

Utilities 4.8 4.1 3.3 2.6 3.7 10.7 4.7

Construction 10.1 10.4 9.8 9.5 10.1 10.2 10.3

Manufacturing 9.8 8.9 9.2 8.7 8.5 9.2 10.0

Wholesale trade 8.6 8.4 8.2 7.4 8.5 8.3 9.0

Retail trade 8.2 8.1 8.3 7.6 7.9 7.8 8.5

Transportation and warehouses 7.6 9.8 9.7 7.7 8.5 8.1 6.0

Information 5.6 7.8 7.0 6.8 6.0 6.5 5.0

Finance and insurance 5.3 7.1 6.6 6.2 6.4 5.1 4.5

Real estate, rental, leasing 6.8 7.4 7.2 7.5 5.9 7.6 6.0

Professional, scientific, and technical services 6.0 7.6 6.7 6.7 6.1 6.4 5.0

Management of companies 5.4 4.8 6.4 5.6 5.4 6.8 4.3

Administration, support, and waste 6.9 7.8 6.2 6.2 7.1 7.8 7.5

Education services 10.5 10.8 11.5 11.2 11.0 12.0 9.8

Health care and social assistance 7.5 8.3 7.3 7.4 7.5 8.1 7.4

Arts, entertainment, and recreation 7.1 8.2 7.6 6.2 7.5 5.5 6.9

Accommodation and food services 7.1 8.1 7.9 7.8 7.2 6.1 6.1

Other services 8.0 8.2 7.9 7.9 8.6 7.5 7.6

Public administration 8.6 8.6 5.1

Other 5.6 5.9 3.8 0.9 1.4 3.3 1.1

Total 7.6 8.2 7.3 7.6 7.6 7.7 7.6
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 59

Table 2.35. Employment risk (in percent) from earthquake and fire hazards in the HayWired scenario by type of establishment and 

NAICS 2-digit sector.

[See table 2.22 for the distribution of employment by establishment type. NAICS, North American Industry Classification System]

NAICS sector Average risk across 

employees Stand alone Headquarters Branch

Agriculture, forestry, fishing, and hunting 2.5 2.5 2.8 10.1

Mining 10.7 8.8 13.6 0.3

Utilities 4.8 4.2 4.9 9.4

Construction 10.1 10.0 10.7 13.6

Manufacturing 9.8 11.2 7.9 11.2

Wholesale trade 8.6 8.2 9.6 10.7

Retail trade 8.2 8.2 7.2 9.7

Transportation and warehouses 7.6 7.4 8.3 9.0

Information 5.6 6.4 4.3 5.2

Finance and insurance 5.3 5.8 4.3 5.6

Real estate, rental, leasing 6.8 6.9 6.2 6.3

Professional, scientific, and technical services 6.0 6.1 4.9 9.8

Management of companies 5.4 5.2 5.7 11.0

Administration, support, and waste 6.9 6.8 7.0 12.1

Education services 10.5 10.8 9.7 12.2

Health care and social assistance 7.5 8.0 5.8 5.2

Arts, entertainment, and recreation 7.1 7.4 4.8 4.4

Accommodation and food services 7.1 7.0 8.6 7.1

Other services 8.0 8.0 7.0 7.1

Public administration 8.6 10.7 8.5 7.6

Other 5.6 5.6 5.2

Total 7.6 7.7 6.9 8.7
60 The HayWired Earthquake Scenario—Societal Consequences

Table 2.36. Employment risk (in percent) from earthquake and fire hazards in the HayWired scenario by business owner demographics 

and NAICS 2-digit sector.

[See table 2.24 for the distribution of employment by business owner demographics. NAICS, North American Industry Classification System]

NAICS sector Average risk across 

employees Male Female Non-minority Minority

Male Female Male Female

Agriculture, forestry, fishing, and hunting 2.5 2.4 4.4 2.4 3.6 1.2 6.8

Mining 10.7 11.6 0.8 11.7 0.2 10.4 3.2

Utilities 4.8 4.8 3.1 4.7 2.3 26.8 6.8

Construction 10.1 10.0 12.6 9.9 11.6 10.8 14.7

Manufacturing 9.8 9.7 10.6 9.6 10.4 12.2 10.9

Wholesale trade 8.6 8.5 9.4 8.3 9.5 13.3 9.1

Retail trade 8.2 8.2 8.3 8.1 7.4 9.3 11.5

Transportation and warehouses 7.6 7.5 8.1 7.3 7.9 12.5 8.4

Information 5.6 5.5 6.8 5.5 6.3 7.4 8.4

Finance and insurance 5.3 5.3 7.3 5.2 7.2 9.7 7.6

Real estate, rental, leasing 6.8 6.8 6.9 6.8 6.3 6.8 9.7

Professional, scientific, and technical services 6.0 6.0 5.5 5.9 4.8 8.1 7.6

Management of companies 5.4 5.4 4.6 5.4 5.1 4.7 2.7

Administration, support, and waste 6.9 6.8 7.4 6.8 7.2 9.1 7.7

Education services 10.5 10.5 10.5 10.5 10.2 13.5 12.6

Health care and social assistance 7.5 7.5 7.8 7.4 7.7 11.5 8.5

Arts, entertainment, and recreation 7.1 7.1 8.0 7.1 8.3 7.2 6.2

Accommodation and food services 7.1 7.1 7.3 7.1 6.5 7.5 9.1

Other services 8.0 8.0 8.0 7.9 7.6 9.5 8.9

Public administration 8.6 8.6 0.0 8.6 0.0

Other 5.6 5.4 8.6 5.5 10.6 3.2 4.9

Total 7.6 7.6 7.7 7.5 7.1 9.8 9.1
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 61

Appendix 3. Building Damage Risk Results for the HayWired Scenario by 

Business Owner Demographics and San Francisco Bay County

These tables explain the large disruptive building damage 

risk for minority business owners found in this study that is caused 

by larger concentrations of minority business owners in Alameda 

County—the most heavily impacted county in the region.

Table 3.1. Establishment risk (in percent) from earthquake hazards in the HayWired scenario by business owner demographics and county.

County Average risk across 

employees Male Female Non-minority Minority

Male Female Male Female

Alameda 25.0 24.9 25.7 24.9 25.9 26.1 25.0

Contra Costa 12.2 12.1 12.9 12.1 12.8 14.7 13.2

Marin 0.9 0.9 0.8 0.9 0.8 1.0 1.0

Napa 0.0 0.0 0.0 0.0 0.0 0.0 0.0

San Francisco 1.3 1.3 1.3 1.3 1.3 1.4 1.4

San Mateo 2.1 2.1 2.1 2.1 2.0 2.5 2.5

Santa Clara 2.5 2.5 2.6 2.5 2.4 3.3 3.0

Solano 0.2 0.2 0.3 0.2 0.2 0.4 0.3

Sonoma 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Total 7.3 7.2 7.7 7.2 7.3 9.8 8.9

Table 3.2. Employment risk (in percent) from earthquake hazards in the HayWired scenario by business owner demographics and county.

County Average risk across 

establishments Male Female Non-minority Minority

Male Female Male Female

Alameda 24.4 24.3 25.3 24.4 26.2 22.1 23.8

Contra Costa 12.2 12.2 12.4 12.1 12.6 13.4 11.7

Marin 1.8 1.9 1.6 1.9 1.4 2.4 2.9

Napa 0.0 0.0 0.0 0.0 0.0 0.0 0.1

San Francisco 1.5 1.5 1.8 1.5 1.7 1.9 1.9

San Mateo 2.9 2.9 3.4 2.8 2.5 3.4 4.8

Santa Clara 3.5 3.6 3.2 3.5 2.9 4.0 3.9

Solano 1.2 1.3 0.7 1.3 0.6 0.9 0.9

Sonoma 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Total 7.6 7.6 7.7 7.5 7.1 9.8 9.1

Table 3.3. Establishment risk (in percent) from earthquake and fire hazards in the HayWired scenario by business owner demographics and county.

County Average risk across 

establishments Male Female Non-minority Minority

Male Female Male Female

Alameda 26.2 26.2 26.9 26.1 27.1 27.2 26.2

Contra Costa 12.6 12.6 13.3 12.5 13.2 15.1 13.6

Marin 1.5 1.5 1.3 1.5 1.3 1.8 1.7

Napa 0.0 0.0 0.0 0.0 0.0 0.1 0.1

San Francisco 1.4 1.4 1.3 1.3 1.3 1.5 1.4

San Mateo 2.4 2.4 2.3 2.3 2.2 2.7 2.7

Santa Clara 2.9 2.9 2.9 2.8 2.7 3.7 3.4

Solano 0.9 0.9 0.8 0.9 0.8 1.1 0.9

Sonoma 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Total 7.7 7.7 8.1 7.6 7.8 10.3 9.4
62 The HayWired Earthquake Scenario—Societal Consequences

Table 3.4. Employment risk (in percent) from earthquake and fire hazards in the HayWired scenario by business owner demographics and county.

County Average risk across 

establishments Male Female Non-minority Minority

Male Female Male Female

Alameda 24.4 24.3 25.3 24.4 26.2 22.1 23.8

Contra Costa 12.2 12.2 12.4 12.1 12.6 13.4 11.7

Marin 1.8 1.9 1.6 1.9 1.4 2.4 2.9

Napa 0.0 0.0 0.0 0.0 0.0 0.0 0.1

San Francisco 1.5 1.5 1.8 1.5 1.7 1.9 1.9

San Mateo 2.9 2.9 3.4 2.8 2.5 3.4 4.8

Santa Clara 3.5 3.6 3.2 3.5 2.9 4.0 3.9

Solano 1.2 1.3 0.7 1.3 0.6 0.9 0.9

Sonoma 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Total 7.6 7.6 7.7 7.5 7.1 9.8 9.1

Table 3.5. Distribution of establishments (in percent) in the San Francisco Bay region, California, by business owner demographics and county.

County Number of establishments Male Female Non-minority Minority

Male Female Male Female

Alameda 105,186 92.3 7.7 89.8 5.6 2.5 2.1

Contra Costa 73,127 93.1 6.9 91.7 5.5 1.4 1.4

Marin 32,458 91.7 8.3 91.0 7.5 0.7 0.8

Napa 12,334 92.5 7.5 91.8 6.8 0.6 0.8

San Francisco 82,230 92.6 7.4 90.3 5.6 2.2 1.8

San Mateo 60,163 92.9 7.1 91.2 5.6 1.7 1.5

Santa Clara 139,293 93.4 6.6 91.6 4.8 1.8 1.7

Solano 23,870 92.1 7.9 90.7 6.2 1.4 1.7

Sonoma 41,998 92.9 7.1 92.2 6.6 0.6 0.6

Total 570,659 92.8 7.2 91.0 5.6 1.7 1.6

Table 3.6. Distribution of employment (in percent) in the San Francisco Bay region, California, by business owner demographics and county.

County Number of employees Male Female Non-minority Minority

Male Female Male Female

Alameda 615,336 92.9 7.1 88.5 4.5 4.4 2.6

Contra Costa 324,598 93.2 6.8 90.9 5.0 2.3 1.8

Marin 133,599 92.5 7.5 91.8 6.5 0.7 0.9

Napa 63,453 94.3 5.7 93.2 4.9 1.1 0.8

San Francisco 532,713 94.4 5.6 92.1 4.0 2.4 1.6

San Mateo 343,010 93.0 7.0 90.7 4.4 2.4 2.6

Santa Clara 879,667 91.3 8.7 88.4 6.3 2.9 2.4

Solano 104,978 92.8 7.2 90.6 5.2 2.1 2.0

Sonoma 195,549 92.5 7.5 91.3 6.8 1.2 0.7

Total 3,192,903 92.7 7.3 90.0 5.2 2.7 2.0
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 63

Appendix 4. Building Damage Risk Results for the HayWired Scenario by 

San Francisco Bay Subarea

This appendix contains four tables of building damage risk 

to businesses separated by subarea (seven San Francisco Bay 

region subareas that contain concentrations of extensive and 

complete damages in the HayWired scenario) with respect to the 

industry sector to which the businesses belong. The four tables 

pertain to two different ways of calculating risk: (1) average across 

establishments and (2) employment-weighted average; risk is 

also calculated for two different sets of extensive and completed 

(disruptive) building damages: (1) from earthquake hazards only 

(no fire) and (2) from earthquake plus fire hazards. Also included 

in this appendix are two tables showing the distribution of 

establishments and employment in each subarea.

Table 4.1. Establishment risk (in percent) from earthquake hazards in the HayWired scenario by subarea and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector

Average 

risk across 

establishments 

in 9 counties

Central 

Alameda 

County

DublinPleasanton

North 

Alameda 

County

Novato

South 

Alameda 

County

Vallejo

West 

Contra 

Costa 

County

Agriculture, forestry, fishing, 

and hunting

21.1 32.4 19.4 22.4 0.6 11.2 0.5 35.8

Mining 26.6 45.6 27.3 28.0 1.2 15.4 1.1 52.3

Utilities 20.5 36.2 22.6 22.8 0.7 6.7 0.2 36.7

Construction 32.5 47.7 31.2 28.6 0.7 18.0 0.7 47.8

Manufacturing 27.3 41.4 26.4 26.0 1.2 11.4 1.0 36.8

Wholesale trade 25.6 38.8 22.7 23.3 1.0 10.7 0.8 33.0

Retail trade 24.9 39.3 22.9 19.8 0.7 12.8 1.1 36.1

Transportation and warehouses 26.0 34.9 21.0 21.1 0.8 12.5 0.6 37.9

Information 23.9 39.9 21.4 21.8 0.9 11.5 0.4 42.5

Finance and insurance 23.2 40.2 19.5 22.6 0.8 11.0 0.7 34.9

Real estate, rental, leasing 24.5 38.9 20.3 21.9 0.7 11.9 0.7 36.4

Professional, scientific, and 

technical services

22.9 38.3 19.9 22.0 0.8 10.9 0.6 38.2

Management of companies 22.4 37.0 18.3 23.2 0.4 9.4 1.1 33.9

Administration, support, and 

waste

23.7 38.3 19.4 23.2 0.5 12.0 0.4 39.2

Education services 22.7 37.3 21.0 20.3 0.7 11.0 2.0 32.9

Health care and social 

assistance

23.3 41.4 20.4 19.5 0.7 10.7 0.9 36.3

Arts, entertainment, and 

recreation

25.2 42.6 24.4 21.3 0.8 11.7 0.6 39.0

Accommodation and food 

services

24.9 42.7 23.2 18.2 0.7 12.6 1.2 38.4

Other services 24.1 38.6 21.5 18.5 0.7 11.9 1.1 34.2

Public administration 21.9 37.9 20.3 20.0 0.3 10.3 1.4 33.9

Other 19.5 31.5 13.0 18.5 1.1 8.4 0.2 18.6

Total 24.7 39.9 21.6 21.6 0.7 12.0 0.8 37.9
64 The HayWired Earthquake Scenario—Societal Consequences

Table 4.2. Employment-weighted risk (in percent) from earthquake hazards in the HayWired scenario by subarea and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector

Average 

risk across 

employees

Central 

Alameda 

County

DublinPleasanton

North 

Alameda 

County

Novato

South 

Alameda 

County

Vallejo

West 

Contra 

Costa 

County

Agriculture, forestry, fishing, 

and hunting

19.9 28.5 19.0 17.2 0.5 11.2 0.5 33.7

Mining 25.1 37.3 27.4 28.2 3.0 16.1 1.2 48.3

Utilities 19.3 22.2 26.6 21.3 0.6 5.7 0.1 52.7

Construction 29.2 44.8 31.8 27.0 1.1 14.8 0.8 38.4

Manufacturing 23.5 37.5 25.0 22.0 1.7 8.6 1.1 26.5

Wholesale trade 23.4 36.2 21.7 23.5 1.2 8.5 1.0 27.0

Retail trade 24.2 39.2 21.7 19.0 0.7 11.5 1.4 36.0

Transportation and warehouses 23.8 32.6 23.0 20.6 1.3 10.0 1.4 30.0

Information 18.2 44.9 21.8 19.1 0.9 6.6 0.3 44.2

Finance and insurance 18.1 39.7 18.2 20.2 0.5 10.3 0.6 32.9

Real estate, rental, leasing 24.5 40.4 20.5 21.0 0.6 11.1 0.7 37.0

Professional, scientific, and 

technical services

19.4 35.9 19.1 21.1 1.1 8.4 0.7 34.7

Management of companies 21.9 37.5 19.4 23.6 0.4 9.8 1.5 31.6

Administration, support, and 

waste

23.3 37.2 18.2 23.3 0.7 10.7 0.4 36.7

Education services 24.8 40.1 22.5 20.7 0.9 9.0 1.9 33.5

Health care and social assistance 22.7 40.4 21.2 17.6 0.7 9.8 1.0 33.6

Arts, entertainment, and recreation23.7 42.5 22.5 21.2 0.9 11.3 0.5 36.0

Accommodation and food 

services

23.8 43.1 21.8 17.7 0.7 11.8 0.8 38.7

Other services 23.2 38.2 25.7 17.5 0.9 11.0 1.0 33.7

Public administration 20.8 52.8 24.2 18.8 0.4 9.6 0.7 37.0

Other 19.5 32.7 13.2 18.8 0.3 8.5 0.2 18.5

Total 23.0 39.1 22.2 20.4 0.8 9.6 0.9 34.5
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 65

Table 4.3. Establishment risk (in percent) from earthquake and fire hazards in the HayWired scenario by subarea and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector

Average 

risk across 

establishments

Central 

Alameda 

County

DublinPleasanton

North 

Alameda 

County

Novato

South 

Alameda 

County

Vallejo

West 

Contra 

Costa 

County

Agriculture, forestry, fishing, 

and hunting

22.1 33.3 20.2 23.8 0.6 11.6 5.0 35.9

Mining 28.5 47.6 29.0 28.6 6.1 16.6 4.6 55.1

Utilities 21.2 37.1 23.7 23.3 0.7 7.8 0.8 37.2

Construction 35.0 50.2 34.1 30.4 4.3 21.1 5.9 48.9

Manufacturing 28.7 43.0 27.5 26.9 4.1 12.7 5.3 37.8

Wholesale trade 26.1 39.4 23.1 23.6 2.0 11.2 1.9 33.4

Retail trade 26.2 40.7 24.1 20.6 2.3 14.2 3.5 37.0

Transportation and warehouses 27.0 36.0 22.0 21.5 1.8 13.5 2.1 38.7

Information 24.9 40.9 22.2 22.6 2.9 12.8 2.5 43.1

Finance and insurance 24.1 41.1 20.4 23.2 2.1 12.1 2.5 35.6

Real estate, rental, leasing 25.3 39.8 21.1 22.5 1.7 12.9 2.3 37.3

Professional, scientific, and 

technical services

23.7 39.2 20.7 22.6 2.2 11.9 2.3 38.8

Management of companies 23.2 37.9 19.1 23.8 0.7 10.3 1.9 34.6

Administration, support, and 

waste

24.5 39.2 20.2 23.9 1.2 12.9 1.7 39.7

Education services 33.5 48.6 34.6 29.5 16.5 23.1 18.2 39.9

Health care and social assistance 24.5 42.5 21.6 20.6 1.9 12.6 2.7 37.3

Arts, entertainment, and recreation26.3 43.6 25.4 22.1 2.8 13.0 3.3 39.7

Accommodation and food 

services

26.2 44.1 24.4 19.2 2.6 14.1 3.7 39.3

Other services 25.9 40.3 23.3 19.9 3.0 13.8 5.1 35.8

Public administration 26.3 41.6 23.1 22.3 0.3 16.5 26.3 36.3

Other 22.4 35.3 15.6 21.1 6.2 11.0 4.2 19.2

Total 26.1 41.4 23.0 22.7 2.5 13.5 3.5 38.9
66 The HayWired Earthquake Scenario—Societal Consequences

Table 4.4. Employment risk (in percent) from earthquake and fire hazards in the HayWired scenario by subarea and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector

Average 

risk across 

employees

Central 

Alameda 

County

DublinPleasanton

North 

Alameda 

County

Novato

South 

Alameda 

County

Vallejo

West 

Contra 

Costa 

County

Agriculture, forestry, fishing, 

and hunting

21.2 30.1 21.5 18.7 0.5 11.6 3.5 33.8

Mining 28.2 38.7 29.2 28.7 16.0 17.2 5.0 52.5

Utilities 19.9 23.0 28.3 21.7 0.6 6.9 0.5 53.6

Construction 32.5 47.9 35.0 28.7 8.2 17.9 9.8 39.7

Manufacturing 24.9 39.3 25.9 23.1 6.9 9.8 5.7 27.3

Wholesale trade 24.0 36.9 22.1 23.8 2.4 9.0 2.1 27.6

Retail trade 25.3 40.4 22.5 19.9 2.2 12.9 3.3 37.5

Transportation and warehouses 24.7 33.6 24.2 21.1 3.7 11.0 2.7 31.0

Information 19.2 46.0 22.4 19.8 3.5 7.8 3.2 44.8

Finance and insurance 18.9 40.7 19.1 20.8 0.9 11.5 2.6 33.7

Real estate, rental, leasing 25.4 41.3 21.3 21.6 1.6 12.1 2.3 38.3

Professional, scientific, and 

technical services

20.4 36.8 19.9 21.7 3.3 9.6 2.9 35.7

Management of companies 22.8 38.4 20.1 24.3 0.7 10.8 2.0 32.9

Administration, support, and 

waste

24.2 38.2 19.0 24.1 1.8 11.7 1.9 37.5

Education services 35.4 50.7 36.9 29.5 22.9 18.2 30.2 45.5

Health care and social assistance 23.9 41.6 22.4 18.7 1.6 12.1 2.7 34.5

Arts, entertainment, and 

recreation

25.0 43.5 23.3 22.3 3.4 12.7 4.3 36.9

Accommodation and food 

services

25.1 44.4 23.4 19.0 2.1 13.4 2.6 39.6

Other services 25.1 39.9 27.4 19.0 4.7 13.0 5.7 35.4

Public administration 25.8 57.3 25.9 21.1 0.4 16.3 24.7 38.0

Other 21.6 35.2 15.9 21.0 0.9 10.7 5.9 19.1

Total 24.7 40.9 23.5 21.8 3.4 11.2 5.5 36.0
Chapter V4. Characteristics of Businesses Disrupted by Building Damages 67

Table 4.5. Distribution of establishments (in percent) in the San Francisco Bay region, California, by subarea and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector

Number of 

establishments 

in 9 counties

Central 

Alameda 

County

DublinPleasanton

North 

Alameda 

County

Novato

South 

Alameda 

County

Vallejo

West 

Contra 

Costa 

County

Agriculture, forestry, fishing, 

and hunting

412 20.9 12.9 30.1 8.0 12.6 5.3 10.2

Mining 34 17.6 11.8 26.5 8.8 20.6 5.9 8.8

Utilities 73 17.8 9.6 32.9 8.2 19.2 4.1 8.2

Construction 8,917 35.1 7.6 21.3 7.9 12.0 5.5 10.5

Manufacturing 4,640 34.3 6.8 25.0 4.5 19.6 3.0 6.9

Wholesale trade 5,352 35.9 7.2 23.5 4.8 17.9 3.0 7.7

Retail trade 10,168 30.9 6.8 28.5 4.0 13.8 5.7 10.3

Transportation and warehouses 2,891 43.9 5.4 19.7 2.6 13.5 5.3 9.7

Information 3,013 21.4 7.4 40.8 6.3 13.1 3.3 7.7

Finance and insurance 4,242 25.2 12.1 28.6 6.6 15.7 4.7 7.1

Real estate, rental, leasing 5,219 30.4 8.4 29.0 6.0 12.9 5.2 8.2

Professional, scientific, and 

technical services

18,242 20.8 9.7 38.7 5.8 14.0 3.5 7.5

Management of companies 266 23.7 14.3 31.2 4.1 15.4 4.5 6.8

Administration, support, and 

waste

22,043 26.5 8.4 26.8 7.1 16.2 5.5 9.4

Education services 2,395 23.0 8.0 38.5 4.5 12.9 3.7 9.3

Health care and social assistance 10,782 23.4 8.1 38.3 4.0 12.8 5.1 8.2

Arts, entertainment, and 

recreation

2,498 24.1 6.4 39.5 4.6 11.4 4.9 9.1

Accommodation and food 

services

4,712 26.9 7.2 36.1 3.6 12.1 4.7 9.3

Other services 13,395 31.7 7.2 28.3 3.9 12.3 6.5 10.1

Public administration 127 18.9 7.1 48.0 3.1 5.5 6.3 11.0

Other 83 28.9 8.4 37.3 4.8 8.4 3.6 8.4

Total 119,504 27.9 8.1 30.6 5.4 14.1 4.9 8.9
68 The HayWired Earthquake Scenario—Societal Consequences

Table 4.6. Distribution of employment (in percent) in the San Francisco Bay region, California, by subarea and NAICS 2-digit sector.

[NAICS, North American Industry Classification System]

NAICS sector Number of 

employees

Central 

Alameda 

County

DublinPleasanton

North 

Alameda 

County

Novato

South 

Alameda 

County

Vallejo

West 

Contra 

Costa 

County

Agriculture, forestry, fishing, 

and hunting

1,426 19.0 9.4 37.3 4.8 11.6 3.1 14.8

Mining 144 20.1 19.4 18.1 12.5 14.6 6.2 9.0

Utilities 1,823 12.5 8.6 56.1 5.3 14.5 0.4 2.7

Construction 42,059 33.5 10.5 20.2 6.4 13.6 7.4 8.4

Manufacturing 99,457 36.1 8.3 19.4 1.2 30.7 0.8 3.5

Wholesale trade 39,038 34.2 10.1 21.1 2.9 24.3 1.0 6.4

Retail trade 48,312 27.3 18.5 25.6 3.1 12.7 4.3 8.5

Transportation and warehouses 13,834 40.9 5.2 27.7 1.9 13.1 4.1 7.1

Information 24,039 11.7 15.2 32.5 5.8 30.5 1.2 3.1

Finance and insurance 21,133 15.6 18.1 25.9 19.6 11.5 3.0 6.3

Real estate, rental, leasing 20,124 28.2 8.5 29.7 5.2 15.6 3.8 9.1

Professional, scientific, and 

technical services

75,728 14.0 12.8 39.4 5.1 21.6 2.0 5.0

Management of companies 631 21.1 15.1 27.7 4.0 19.8 4.3 8.1

Administration, support, and 

waste

66,000 25.4 9.4 31.8 6.1 14.3 4.4 8.5

Education services 24,650 28.7 5.3 41.4 2.4 11.9 3.6 6.8

Health care and social assistance 72,692 26.5 5.6 40.9 3.9 11.4 4.6 7.1

Arts, entertainment, and 

recreation

12,861 19.1 9.8 39.8 5.5 10.6 4.9 10.2

Accommodation and food 

services

46,070 24.4 9.8 38.3 3.7 11.0 5.1 7.7

Other services 46,061 27.5 11.3 31.8 3.1 11.8 6.2 8.3

Public administration 8,492 15.2 0.9 42.7 3.3 25.3 5.4 7.3

Other 278 32.7 11.5 30.6 10.8 9.4 1.4 3.6

Total 664,852 26.5 10.3 30.9 4.4 17.8 3.5 6.7