The Economic Effects of the HayWired Scenario Using 

the Association of Bay Area Governments Regional 

Growth Forecast

By Cynthia Kroll, Shijia Bobby Lu, Aksel Olsen, and Anne Wein

Chapter V3 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:

Kroll, C., Lu, S., Olsen, A., and Wein, A., 2020, The economic effects of the HayWired scenario using the Association 

of Bay Area Governments Regional Growth Forecast, chap. V3 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, 58 p., https://doi.org/10.3133/sir20175013v3. 

ISSN 2328-0328 (online)
iii

Contents

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

Introduction.....................................................................................................................................................1

What Past Experience Tells Us about Long-Term Effects of Natural Disasters..................................2

The HayWired Scenario ...............................................................................................................................4

The ABAG Regional Control Forecast and the REMI Model ..................................................................6

Hypothetical Sequence of Effects and Their Incorporation into REMI ................................................8

Assumptions and Simulated Effects within the REMI Model—Part 1..................................................9

Direct Output Loss from Building Damage .......................................................................................9

Transportation Network and Commuting Flows ............................................................................12

Recovery Time Adjustment for Trade and Manufacturing Sectors............................................20

Effects on Population Beyond Employment ...................................................................................21

Post-earthquake Housing Price Changes ............................................................................22

Post-earthquake Senior Outmigration Assumption .............................................................22

Assumptions and Simulated Effects within the REMI Model—Part 2: Rebuilding the Region.......23

Government Response and Recovery Investment........................................................................23

HayWired Scenario Combined Effects on Employment and Population ............................................26

Employment Effects............................................................................................................................28

Population Effects...............................................................................................................................29

Gross Regional Production Effects..................................................................................................29

Summary of REMI Simulation of HayWired Scenario Effects.....................................................30

Caveats and Limitations ...........................................................................................................31

Sensitivity Tests............................................................................................................................................33

Construction Costs..............................................................................................................................33

Displaced Population .........................................................................................................................33

Commuter Resilience .........................................................................................................................34

Insurance .............................................................................................................................................35

Government Spending .......................................................................................................................36

Technology Sector Exodus................................................................................................................36

Policy Implications.......................................................................................................................................38

Future Research with REMI Modeling .....................................................................................................40

Acknowledgments.......................................................................................................................................40

References Cited..........................................................................................................................................40

Appendixes ...................................................................................................................................................43

Appendix 2.....................................................................................................................................................50

Appendix 3 ....................................................................................................................................................52

Appendix 4.....................................................................................................................................................54

Appendix 5.....................................................................................................................................................56

Appendix 6.....................................................................................................................................................58
iv

Figures

1. Map showing the four subregions in the Association of Bay Area Governments REMI 

model version 1.78 of the San Francisco Bay region, California ..........................................7

2. Maps of the San Francisco Bay region, California, showing the multi-hazard exposure 

of roadways and the Bay Area Rapid Transit (BART) system rail lines, including the 

estimated repair times for California Department of Transportation bridges and BART 

facilities in the HayWired scenario ........................................................................................... 13

3. Map of the San Francisco Bay region, California, showing economic subareas and 

census tracts with concentrated damage in the HayWired scenario...............................15

4. Collins diagram showing the HayWired scenario effects of building and road, rail, 

and bridge damage output loss on employment in the San Francisco Bay region, 

California ......................................................................................................................................18

5. Graphs of employment curves in the HayWired scenario for the San Francisco Bay 

region, California, relative to 2017 levels and the Association of Bay Area 

Governments Plan Bay Area 2040 projection ........................................................................26

6. Graphs of population curves in the HayWired scenario for the San Francisco Bay 

region, California, relative to 2017 levels and the Association of Bay Area 

Governments Plan Bay Area 2040 (PBA2040) projection.....................................................27

7. Collins diagram showing San Francisco Bay region, California, net employment 

change by sector in 2018–2040 for the HayWired scenario................................................28

8. Collins diagram showing San Francisco Bay region, California, net population 

difference by subregion for the HayWired scenario compared to the Association of 

Bay Area Governments Regional Control Forecast..............................................................29

9. Collins diagram of San Francisco Bay region, California, net gross regional product 

change by subregion for the HayWired scenario compared to the Association of Bay 

Area Governments Regional Control Forecast......................................................................29

10. Graphs showing the recovery progression from prerecession peak to peak of recovery 

or next upturn for the east bay subregion and San Francisco Bay region, California, 

compared to job projection in the HayWired scenario........................................................31

11. Graphs showing employment level projection and population level projection curves 

for the San Francisco Bay region, California, using the multiple sensitivity tests 

discussed in this section ...........................................................................................................38

Tables

1. Hazus-estimated direct building and contents value effects from the HayWired 

scenario in the San Francisco Bay region, California............................................................5

2. Direct output loss for the HayWired scenario in the San Francisco Bay region, 

California ........................................................................................................................................5

3. California’s San Francisco Bay subregions and the counties that compose them...........6

4. Direct output loss estimates from Hazus as defined in the REMI Association of Bay 

Area Governments model for the HayWired scenario shown by subregion of the San 

Francisco Bay region, California................................................................................................9

5. Estimate of 2018 employment and population effects in the San Francisco Bay region, 

California, relative to Association of Bay Area Governments forecast levels from 

Hazus-estimated output loss related to HayWired scenario building damage................10
v

6. Estimate of 2019 employment and population effects in the San Francisco Bay region, 

California, relative to Association of Bay Area Governments forecast levels from 

Hazus-estimated output loss related to HayWired scenario building damage................11

7. Ten industries with the largest estimated reduction of employment from the HayWired 

scenario owing to building, population, and other output losses identified in linked 

industries in the San Francisco Bay region, California, in 2018, relative to the 

Association of Bay Area Governments forecast...................................................................11

8. Ten industries with the largest estimated total cumulative output loss from the 

HayWired scenario owing to building, population, and other output losses identified 

in linked industries in the San Francisco Bay region, California, in 2018, relative to the 

Association of Bay Area Governments forecast...................................................................12

9. Commuters in less-affected areas for the HayWired scenario in each county of the 

San Francisco Bay region, California......................................................................................16

10. Percentage of weeks lost during the HayWired scenario because of transportation 

network interruption for all industry sectors for less-affected areas in each county of 

the San Francisco Bay region, California ...............................................................................17

11. Share of weeks lost because of transportation network interruption in the HayWired 

scenario by industry group for less-affected areas in each county of the San 

Francisco Bay region, California..............................................................................................17

12. Total employment and population effects in the HayWired scenario from building 

and transportation damage in 2018, San Francisco Bay region, California......................18

13. Ten industries with the largest employment loss from combined building and 

transportation effects in the San Francisco Bay region, California, in 2018.....................19

14. Ten industries with the largest HayWired scenario total cumulative output loss from 

combined building and transportation effects in the San Francisco Bay region, 

California in, 2018........................................................................................................................19

15. Additional output loss as share of first year (2018) estimated output loss for the 

HayWired scenario in the San Francisco Bay region, California.......................................20

16. Total difference in employment numbers from revised output loss effects, relative to 

the Association of Bay Area Governments regional projection.........................................20

17. Total difference in population from revised output loss effects of the HayWired 

scenario, relative to the Association of Bay Area Governments Plan Bay Area 2040 

projection for the San Francisco Bay region, California......................................................21

18. Post-earthquake housing and rental price increases by year after the event, in 

percent, for the HayWired scenario in the San Francisco Bay region, California ..........22

19. Total employment difference from all loss effects in the HayWired scenario, relative 

to the Association of Bay Area Governments Regional Controls Forecast for the San 

Francisco Bay region, California..............................................................................................23

20. Total population difference from all loss effects in the HayWired scenario, relative 

to the Association of Bay Area Governments Regional Controls Forecast for the San 

Francisco Bay region, California..............................................................................................23

21. Total employment gain from recovery spending, relative to the Association of Bay 

Area Governments Plan Bay Area 2040 projection for the San Francisco Bay region, 

California ......................................................................................................................................25

22. Total population gain from recovery spending in the HayWired scenario, relative to 

the Association of Bay Area Governments regional projection for the San Francisco 

Bay region, California.................................................................................................................26

23. Summary of year 1, year 5, year 10, and 2040 employment, population, and gross 

regional product levels in the HayWired scenario relative to Plan Bay Area 2040 

projections for the San Francisco Bay region, California....................................................30
vi

24. Employment, population, and gross regional product effects with higher 

construction costs in the HayWired scenario relative to Plan Bay Area 2040 

projections for the east bay subregion of the San Francisco Bay region, California .....33

25. Employment, population, and gross regional product effects with higher 

construction costs in the HayWired scenario relative to Association of Bay Area 

Governments Plan Bay Area 2040 projections for the San Francisco Bay region, 

California ......................................................................................................................................33

26. Employment, population, and gross regional product effects with higher 

outmigration in the HayWired scenario relative to Association of Bay Area 

Governments Plan Bay Area 2040 projections for the east bay subregion of the San 

Francisco Bay region, California..............................................................................................34

27. Employment, population, and gross regional product effects with higher 

outmigration in the HayWired scenario relative to Association of Bay Area 

Governments Plan Bay Area 2040 projections for the San Francisco Bay region, 

California ......................................................................................................................................34

28. Employment, population, and gross regional product effects with worse commuting 

and telecommuting conditions in the HayWired scenario relative to Association of 

Bay Area Governments Plan Bay Area 2040 projections for the east bay subregion 

of the San Francisco Bay region, California...........................................................................34

29. Employment, population, and gross regional product effects with worse commuting 

and telecommuting conditions in the HayWired scenario relative to Association of 

Bay Area Governments Plan Bay Area 2040 projections for the San Francisco Bay 

region, California .....................................................................................................................35

30. Employment, population, and gross regional product effects with insurance spending 

in the HayWired scenario relative to Association of Bay Area Governments Plan 

Bay Area 2040 projections for the east bay subregion of the San Francisco Bay 

region, California.........................................................................................................................35

31. Employment, population, and gross regional product effects with insurance spending 

in the HayWired scenario relative to Association of Bay Area Governments Plan Bay 

Area 2040 projections for the San Francisco Bay region, California.................................35

32. Employment, population, and gross regional product effects with lower government 

spending in the HayWired scenario relative to Association of Bay Area Governments 

Plan Bay Area 2040 projections for the east bay subregion of the San Francisco Bay 

region, California.........................................................................................................................36

33. Employment, population, and gross regional product effects with lower government 

spending in the HayWired scenario relative to Association of Bay Area Governments 

Plan Bay Area 2040 projections for the San Francisco Bay region, California.................36

34. Employment, population, and gross regional product effects with technology sector 

exodus in the HayWired scenario relative to Association of Bay Area Governments 

Plan Bay Area 2040 projections for the east bay subregion of the San Francisco Bay 

region, California.........................................................................................................................37

35. Employment, population, and gross regional product effects with technology sector 

exodus in the HayWired scenario relative to Association of Bay Area Governments 

Plan Bay Area 2040 projections for the San Francisco Bay region, California................37
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 V3

The Economic Effects of the HayWired Scenario Using 

the Association of Bay Area Governments Regional 

Growth Forecast

By Cynthia Kroll,1 Shijia Bobby Lu,2 Aksel Olsen,3 and Anne Wein4

Abstract

This chapter uses existing modeling techniques that 

underlie the 2015 regional economic forecast of the San 

Francisco Bay region, California, to estimate how a major 

earthquake along the Hayward Fault would change the 

trajectory of that forecast. The Association of Bay Area 

Governments (ABAG) creates long-term regional forecasts of 

employment, population, and households that span a period of 

two to three decades. The forecast released in January 2015 is 

built on the framework of a Regional Economic Models, Inc. 

(REMI) model for the San Francisco Bay region and projects 

growth in the bay region through 2040. This chapter uses the 

simulation tools in the REMI model to apply the HayWired 

building damages from the HayWired scenario capital and 

output losses estimated using the Federal Emergency 

Management Agency’s Hazus model to ABAG’s economic 

and demographic 2015 regional forecast. 

We apply Hazus estimates of direct output losses from a 

moment magnitude (Mw) 7.0 earthquake along the Hayward 

Fault to the REMI model, to examine direct, indirect, and 

induced effects on gross regional product (GRP), employment, 

and population. The analysis shows an economy that would 

experience substantial damages and losses in the short term. 

In the first year, employment would drop by almost half a 

million jobs, whereas GRP would decrease by 8 percent. The 

two counties near the epicenter of the earthquake would be 

greatly affected, with a 15 percent loss in jobs and 13 percent 

loss in GRP. Population losses because of the economic effect 

would be smaller—on the order of 150,000 (or 2 percent) in 

the first year, although previously reported analyses suggest 

that displacement within the region could be much higher, 

and social effects could lead to a larger outmigration than is 

indicated by the model. Much of the economy could recover 

1

Metropolitan Transportation Commission and Association of Bay Area 

Governments, retired.

2

Metropolitan Transportation Commission and Association of Bay Area 

Governments. 

3

Metropolitan Transportation Commission.

4

U.S. Geological Survey.

within a few years, but a full return to the previous trajectory 

could take more than five years for the region and closer to 

a decade for the most severely affected counties. Recovery 

and rebuilding investments will be crucial to repairing the 

economic base of the region and returning it to its projected 

growth trajectory.

Sensitivity analyses using the model identify some of the 

critical factors that would lead to different levels of change. 

For example, a shortage of construction workers could lead to 

a deeper, longer recession as rebuilding is postponed. Should 

major technology employers decide to relocate substantial 

portions of their operations and later expansion outside of 

the region, the recovery period from the earthquake-induced 

recession could stretch to 6 or 7 years and the region’s 

trajectory could be permanently damped relative to the ABAG 

2015 forecast for 2040.

State and local policies, as well as business and personal 

preparedness can reduce the length and severity of effects. The 

model has substantial inbuilt resilience, whereas some of the 

sensitivity tests reduce that resilience and indicate how lack 

of advanced preparation and absence of relief spending and 

insurance after the fact could slow the region’s trajectory in 

the long term.

Introduction

This chapter is a contribution to a broader set of analyses 

on the potential economic and social effects from a major 

earthquake on the Hayward Fault in the east bay subregion 

of the San Francisco Bay region, California. The chapter has 

a narrow purpose—to use existing modeling techniques that 

underlie the regional forecast of the San Francisco Bay region 

economy and population to estimate how a major earthquake 

would alter that forecast. Other chapters in this volume 

address community vulnerability (through application of past 

experience to existing demographic and social conditions; 

Johnson and others, Communities at Risk Overview and 

Population Movements chapters, in prep. [planned to 

be published as part of this volume]), the effects of the 

earthquake on the region’s economic output during the first 
2 The HayWired Earthquake Scenario—Societal Consequences

6 months after the earthquake (a computable general 

equilibrium analysis; Sue Wing and others, in prep. [planned 

to be published as part of this volume]), a detailed assessment 

of characteristics of businesses in vulnerable parts of the 

region (based on the National Establishment Time Series 

dataset; Wein, Haveman, and others, this volume), and an 

analysis of economic vulnerabilities at the neighborhood level 

in severely affected areas and the spread across neighborhoods 

related to commute flows within the most affected areas, 

between those areas, and in less-affected parts of the region 

(Wein, Belzer, and others, this volume).

This study applies the building damages from the 

HayWired scenario capital and output losses that are estimated 

from the Federal Emergency Management Agency’s (FEMA) 

Hazus model to the Association of Bay Area Governments 

(ABAG) Regional Control Forecast using the Regional 

Economic Models, Inc. (REMI) model 1.7.8. The ABAG 

Regional Control Forecast is a forecast for the San Francisco 

Bay region created using the framework of the REMI model 

and adopted by the ABAG Executive Board in January 2015 

as the basis for creating the projections used in Plan Bay Area 

2040 (described by Kroll and others, 2017). The purpose of 

examining how an earthquake would change the REMI ABAG 

regional control outcome is to address several long-term 

questions on the economy that are not fully fleshed out in the 

short-term computable general equilibrium analysis (Sue Wing 

and others, in prep. [planned to be published as a part of this 

volume]), which focuses on immediate effects during the first 

six months. These long-term questions include:

1. What are the overall effects on economic output 

from the earthquake, taking into account the direct 

output losses from damage to buildings and contents 

(defined as furniture, equipment that is not integral 

with the structure, computers, and other supplies)? 

What are the effects on economic output from 

second-order multiplier effects from interrupted 

firms, capturing both upstream and downstream 

losses in output?

2. How do these losses in output affect employment by 

sector?

3. What are the population effects caused by changing 

workforce demand, changing price levels, and 

changing amenities in the region?

4. How does public and insurance spending affect the 

outcome of the earthquake recovery?

5. Would an earthquake of this magnitude be expected 

to change the long-term economic trajectory for the 

region in the future? Given uncertainties of predicting 

such effects, what types, levels, and duration of 

effects could lead to long-term negative or positive 

changes in the region?

The chapter begins with a brief discussion of the most 

relevant research on the long-term effects of natural disasters, 

especially research focused on urbanized areas in developed 

countries. A more in-depth review of disaster research is 

provided by Johnson and others (Long-Term Recovery, in 

prep. [planned to be published as a part of this volume]). 

We then lay the framework by briefly describing the REMI 

model, which is the basis for the San Francisco Bay regional 

forecast, and the Hazus model, which underlies the estimates 

of physical damage and direct output loss. We describe the 

analysis of the earthquake’s economic effects by defining 

sequentially the measures of effect factors applied to the 

model and the results of each factor, followed by the measures 

of mitigation and policy approaches and the effects of those 

measures. We test the sensitivity of the results to the level 

of public spending, insurance coverage, construction costs, 

and potential relocation decisions of major employers. We 

conclude with an overview of findings, a discussion of how 

shifts in assumptions could change the results, and some 

policy observations.

What Past Experience Tells Us about 

Long-Term Effects of Natural Disasters

The past decade has seen an expansion of literature 

that focuses on the long-term effects of disaster. One 

such analysis is Hsiang and Jina (2014) that outlines four 

trajectories described in the literature of how a disaster may 

affect an economy in the long run. These trajectories are: 

“creative destruction,” where the economy is stimulated by 

aid dollars and replacement investment; “build back better,” 

where a short-term downturn is followed by a long-term 

“strengthening” and replacement capital is an improvement 

over older outmoded damaged capital; “recovery to trend,” 

where economies converge back to the predisaster growth 

trend; and “no recovery,” where the long-term growth is 

permanently reduced by diverted investment (Hsiang and Jina, 

2014). 

Research results to date offer mixed evidence on which 

of these trajectories an economy experiencing a natural 

disaster will follow and how effects may vary by geography, 

community, or country. DuPont and Noy (2015) conclude in 

a literature review that, “To summarize, the literature on the 

long-term impact of economic shocks is inconclusive, but the 

weight of the evidence suggests no long-term impact of even 

catastrophic shocks at the national level.” They go on to note 

that, “Several papers, however, do point to some potentially 

long-term impacts at the local/regional level.”

Methods of analysis to understand the long-term 

effects include statistical analyses across countries or within 

countries, across counties or metropolitan areas, case studies, 

and even more disaggregated analysis of the effects across 

individual households, establishments, or across simulation 

exercises of the type we conduct in this chapter. Economic 

effects examined include those on output (gross domestic 

or regional product) or income (total personal income), on 
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 3

employment, and effects on population movements. The 

widely diverse scales of inquiry, depth of detail, and indicators 

analyzed all lead to ambiguity in understanding the long-term 

implications of a natural disaster.

Cavallo and others (2013) constructed what they called 

“counterfactuals” to compare how economies experiencing 

natural disasters would have grown absent the event. They 

found that even economies experiencing large disasters (defined 

by lives lost relative to population size) have not experienced 

long-term lags in output growth relative to the estimations 

based on comparable areas of growth in a scenario without the 

event’s occurrence. Their research covered diverse disasters 

(“hydrometeorological,” geophysical, or biological), whereas 

Hsiang and Jina (2014) studied the effects of cyclones alone 

across countries. They conducted a statistical analysis based 

on number, timing, and size of events and found that, although 

an individual event has a marginally small effect on long-term 

output and income trajectory, because cyclones can occur in a 

single economy with some frequency, “...repeatedly exposing 

the same population to frequent storms results in an accumulation of income losses over time...” relative to what the economy 

would experience without cyclones (Hsiang and Jina, 2014). 

Boustan and others (2017) investigated disaster effects 

on migration, poverty rates, and home prices in a study of all 

types of disasters across U.S. counties over a 90-year period. 

The results of the study suggest a direct relation between the 

level of outmigration and the number of disasters experienced, 

with a “super-severe” disaster doubling the overall average 

effect. Some variation of effects occurred by type of disaster. Poverty rates also increased, suggesting that wealthier 

households were better able to move after a disaster than were 

poorer households. Relief spending did not mitigate this effect 

and may have actually exacerbated outmigration by making it 

easier for households to move, whereas outmigration was particularly high where a severe event occurred in an area prone 

to disaster. Nevertheless, there was a great deal of variation in 

the propensity to migrate after a disaster. For example, higher 

outmigration occurred from rural areas than from productive 

urban areas. Earthquakes were not found to induce migration, 

in contrast to volcanoes, landslides, and fires (although it is not 

clear how landslides and fires induced by earthquakes affected 

the results). Housing prices were not sensitive to the frequency 

of disasters but did drop after a severe disaster.

Cavallo and Noy (2010), in their review of disaster 

research, note a differential level of impact between large and 

small countries. “Therefore, while direct losses may be high 

in large countries because of the wealth exposure, the greater 

capacity to absorb shocks means that indirect losses may be 

lower, and/or that the size of the damage may be lower relative 

to the size of the country” (Cavallo and Noy, 2010 p. 13). 

They also comment on “poverty traps,” which are related to 

restricted ability to recover owing to the lack of resources in 

poorer countries. A similar divide is found between rural and 

urban counties in recent research by Boustan and others (2017).

Retrospective studies of the case of Kobe, Japan, after 

the 1995 earthquake argue that, although the economy 

“recovered” (in the sense that would be applied to bouncing 

back from a recession, eventually exceeding pre-earthquake 

output and population), there were long-term changes in 

the level of growth and the structure of the economy after 

the earthquake. DuPont and Noy (2015) estimated how 

Kobe’s economy and population size would have grown 

had it maintained the same levels of these factors relative 

to other Japanese metropolitan areas in the post-earthquake 

period. Their analysis indicated that although the population 

returned to pre-earthquake trends within five years, gross 

domestic product (GDP) per capita, despite early fiscal 

stimulus spending, lagged by 13 percent of the synthetically 

constructed estimates of GDP for the metropolitan area absent 

an earthquake 15 years later. 

Okuyama (2014) highlighted the variability of each 

disaster event and its effects on the economy. The author 

disaggregated elements of the Kobe economy before and 

after the earthquake and used a shift-share analysis to identify 

structural changes in the distribution of economic activity. 

Results indicated a decline in regional competitiveness, 

especially in final demand, more than ten years after the Kobe 

earthquake, attributed largely to the population decline that 

followed the earthquake. However, at the aggregate inputoutput level, many differences level out, suggesting that 

negative effects in one sector or one geographic area may be 

offset by positive effects elsewhere in the economy or country.

Simulation models are another approach to analyzing 

the potential or actual effects of a disaster and are generally 

applied to a single case study. Common analytic tools in 

these studies include computable general equilibrium (CGE) 

models, input-output (I-O) models, or, in some cases, the 

REMI model, which uses aspects of I-O, CGE, and other 

tools. Many are used for short-term analysis, as by Sue Wing 

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

volume). Xie and others (2014) used a CGE model to examine 

long-term effects of the 2008 Wenchuan earthquake in China. 

Their findings highlight the dependence of the outcome on 

what aspects of the disaster are included. Analyzing costs 

alone, without taking into account recovery investments, 

gives an unrealistic view of the outcome. Including recovery 

investment in the analysis brings long-term growth much 

closer to the expected trajectory absent the earthquake.

Several authors describe redistribution of activity in 

the absence of measurable long-term effects on a national 

or regional economy. The unevenness of effects may be 

geographic, with locational shifts in patterns of development, 

or through unevenness in the experience and recovery of 

different population groups within the region. Peacock and 

others (2014) examined long-term housing recovery after 

hurricanes in 1992 and 2008. They discovered that lowincome areas (and, in one case, areas with high shares of 

minority populations) were more vulnerable to the initial-level 

damage experienced and recovered more slowly. In one case, 

the recovery differential disappeared after two years, whereas, 

in the second case, the differential continued for a longer time 

period. If effects are separated by owner-occupied and rental 
4 The HayWired Earthquake Scenario—Societal Consequences

property, rental values continue to lag in recovery compared 

to homeowner values and compared to the prestorm values for 

at least four years. Tierney (1997) found that after the 1994 

earthquake in Northridge, California, businesses in the most 

heavily damaged areas were less likely to have recovered to 

pre-earthquake levels, even if the business itself experienced 

no building damage, suggesting that there could be long-term 

effects on the affected area beyond any short-term effects of 

damage. 

The historical literature, then, points to four possible 

areas of effect that should be considered in the analysis:

1. How quickly does recovery occur in the regional 

economy and population base?

2. What are the possible long-term outcomes to the 

economy—does growth (or decline) occur at the 

same rate as before the earthquake?

3. Would the internal composition of the economy 

change relative to expected trends?

4. Would the geographic patterns of growth change as a 

result of the earthquake?

The HayWired Scenario 

The HayWired earthquake scenario examines a 

hypothetical moment magnitude (Mw) 7.0 earthquake on 

the Hayward Fault, centered along the urban corridor of the 

eastern San Francisco Bay region (see Seligson and others, 

2018, for a detailed description of the HayWired scenario). 

The mainshock triggers liquefaction and landslides, and is 

followed by fires, further slip of the fault, and a series of 

aftershocks. Seligson and others (2018) calculated building 

damage of multiple hazards of ground shaking, landslide, 

and liquefaction for the mainshock and aftershock sequence 

of the HayWired scenario using the Hazus model, which 

we refer to as the Hazus analysis hereafter when discussing 

results. Scawthorn (2018) estimated the potential addition of 

building damage (outside of the Hazus model) caused by postearthquake fires. Using the integrated building damage data 

from Hazus by Seligson and others (2018) and the fire analysis 

by Scawthorn (2018), Johnson and others (Concentrated 

Damage, in prep. [planned to be published as part of this 

volume]) estimated that the earthquake could result in 

extensive or complete damage to more than 103,000 buildings 

(both residential and nonresidential) in the San Francisco Bay 

region, as well as to a much smaller number of structures in 

other parts of California. The most severe damage would be in 

Alameda County, where 16 percent of all residential buildings 

and 29 percent of nonresidential buildings would experience 

extensive or complete damage, and where approximately 82 

percent of residential and 85 percent nonresidential building 

square footage would sustain some type of damage, ranging 

from slight to complete. In addition to structural, nonstructural, 

and contents damage to buildings, roadways, rail service, 

and power, water, gas, and communications utilities would 

also be damaged, and critical life-support services (health 

and education) housed in the buildings would be impeded 

as well. The time frame for restoration of these facilities and 

services could range from a few days to nearly a year for some 

roadways and as much as three years for the most severely 

damaged parts of the Bay Area Rapid Transit (BART) system. 

These damages and delays, in turn, may have ripple effects on 

the economy through disruption to households, commute flows, 

and operating capacity within businesses.

The Hazus model estimates the level of physical damage 

from multi-hazards caused by an earthquake, identifying 

different levels of damages and loss for distinct types of 

residential and nonresidential buildings, as discussed in 

more detail elsewhere in the report by Seligson and others 

(2018). In financial terms, direct building and contents losses 

resulting from the mainshock, aftershocks, liquefaction, and 

landslides in the Hazus analysis are estimated to be $72 billion 

in 2016 dollars (table 1). Fire-caused building and content 

losses approach an additional $30 billion in 2016 dollars 

(Scawthorn, 2018). Fire damage would likely be covered by 

insurance rather than the damage directly related to seismic 

causes, although there would still be cases of underinsurance. 

Until these capital losses are repaired or replaced, these direct 

effects to buildings and contents mean considerable disruption 

to economic activity. 

Taking into account building cleanup, repair, and 

recovery time, and the probability of recapturing some 

business-related loss after the event, as defined in the Hazus 

technical manual (see FEMA [2003] and Sue Wing and 

others [in prep., planned to be published as a part of this 

volume] for a more detailed discussion), integrated building 

damage data from the mainshock, landslides, liquefaction, 

aftershocks, and fire effects translates into direct output 

loss estimates of $27.0billion (2016 U.S. dollars) for all 

affected counties. Output loss estimated for Alameda County 

amounts to $17.5billion and for the nine-county bay region to 

$26.9 billion, or 8 and 3 percent of total output, respectively. 

Effects from fire following the mainshock alone account for 

$2.2 billion of the loss to the bay region, whereas $500 million 

of the output loss results from aftershocks. Of aftershockrelated output losses, 80 percent ($397 million) is concentrated 

in Santa Clara County (table 2). 

On the basis of estimates of residential building damage 

from ground shaking and liquefaction, Seligson and others 
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 5

Table 1. Hazus-estimated direct building and contents value effects from the HayWired scenario in the San Francisco Bay region, 

California.

[Amounts are in billions of 2016 U.S. dollars for damages from the mainshock, aftershocks, liquefaction, and landslides. Loss data generated from Seligson 

and others (2018). Note that these numbers do not include the additional loss caused by fire]

County Nonresidential buildings Residential buildings Building contents Total direct loss

Alameda 11.3 15.4 7.0 33.7

Contra Costa 2.7 5.1 2.0 9.8

Marin 0.3 0.8 0.3 1.4

Napa 0.03 0.05 0.03 0.11

San Francisco 1.2 1.4 0.7 3.3

San Mateo 1.5 2.8 1.4 5.7

Santa Clara 5.1 7.6 4.3 17

Solano 0.1 0.2 0.1 0.4

Sonoma 0.03 0.18 0.05 0.26

San Francisco Bay 

region total 22.3 33.5 15.8 71.6

Other areas 0.22 0.41 0.21 0.84

Table 2. Direct output loss for the HayWired scenario in the San Francisco Bay region, California.

[Values in millions of 2016 U.S. dollars]

County Mainshock, landslides, 

liquefaction 

Mainshock, landslides, liquefaction, 

and fire after earthquake

Mainshock, landslides, liquefaction, fire 

after earthquake, and aftershocks 

Alameda 16,136 17,447 17,489 

Contra Costa 2,764 2,949 2,951 

Marin 115 220 220 

Merced 1 1 1 

Monterey 6 6 6 

Napa 9 10 10 

Sacramento 2 2 2 

San Benito 7 7 7 

San Francisco 726 743 746 

San Joaquin 47 47 47 

San Mateo 759 854 910 

Santa Clara 3,533 3,994 4,391 

Santa Cruz 18 18 19 

Solano 58 129 129 

Sonoma 8 10 10 

Stanislaus 14 14 14 

Yolo 1 1 1 

Total1 24,206 26,452 26,956 

San Francisco Bay 

region total1 24,110 26,356 26,858 

1

Because of rounding, sum totals may not be exact.
6 The HayWired Earthquake Scenario—Societal Consequences

(2018) estimate that the displacement from the mainshock 

is about 153,000 households, and the population seeking 

short-term shelter would exceed 48,000 people. Building 

damage from aftershock ground shaking adds more than 

2,800 displaced households, and 1,600 people seeking 

shelter. Johnson and others (Population Movements, in prep. 

[planned to be published as part of this volume]) argue that 

the potential population displacement and sheltering needs 

caused by damage from landslide, fire, lifeline infrastructure 

damage, and utility outages or environmental threats are 

not included in these estimates, and that displacement could 

be as high as 268,000 households and 720,000 people. 

According to Johnson and others (Population Displacement, 

in prep. [planned to be published as part of this volume]), 

the methodology used by Seligson and others (2018) for 

calculating short-term shelter requirements recognizes that 

only a portion of those displaced from their homes will seek 

public shelter, and some will seek shelter even though their 

residence may have low levels of damage.

The Hazus calculation for the HayWired scenario is 

based on a 2005–2006 inventory of buildings. Housing stock 

expanded by 9.8 percent in Alameda County and 7.4 percent 

region-wide between 2005 and 2017. New construction is 

more likely to be resistant to shaking and liquefaction and 

possibly less vulnerable to fire as well, potentially reducing 

the share of buildings and contents vulnerable to damage and 

displacement effects. 

The ABAG Regional Control Forecast 

and the REMI Model

Beginning in the fall of 2014, ABAG used a REMI 

model as a framework for producing a regional forecast for 

the nine counties that compose the San Francisco Bay region 

(Alameda, Contra Costa, Marin, Napa, San Francisco, San 

Mateo, Santa Clara, Sonoma, and Solano). The model divides 

the San Francisco Bay region into four subregions, as shown 

in table 3 and figure 1. A final regional forecast adopted by 

the ABAG executive board in February 2016 (Association of 

Bay Area Governments Planning and Research Department, 

2016) serves as the benchmark comparison forecast for the 

HayWired REMI analysis (see Kroll and others, 2017, for an 

explanation of the regional forecast; appendixes 1–5 describe 

the methodology for producing the ABAG Regional Control 

Forecast). Absent a major earthquake or other disruptive 

event, the region is projected to experience a slowdown of 

growth compared to the very rapid post-recession expansion 

of 2010–2015, and to maintain its competitive advantage in 

such sectors as professional and business services and some 

aspects of the information sector. To do so, the region will 

need to add almost 700,000 jobs and almost 2 million people 

Table 3. California’s San Francisco Bay subregions and the 

counties that compose them.

Subregions Counties

North bay Napa, Solano, and Sonoma

East bay Alameda and Contra Costa

South bay Santa Clara

West bay Marin, San Francisco, and San Mateo

between 2015 and 2040. The forecast—9.6 million people 

and 4.7 million jobs in the region by 2040—has a policy or 

visionary aspect because it assumes the region’s housing needs 

are successfully addressed, so that existing and in-migrating 

workers will be able to find housing in the region and incommuting would be kept to a minimum. A major earthquake 

would certainly disrupt the housing expectations implied in the 

ABAG Regional Control Forecast in the short run because the 

model measures relative real estate prices rather than number 

of units. The pace of the housing market and transportation 

network recovery could have long-term effects depending on 

how both businesses and individuals react to this disruption.

Regional Economic Models, Inc. (2015) describes the 

version of the model used in this analysis (p. 3):

PI+ is a structural economic forecasting and 

policy analysis model. It integrates input-output, 

computable general equilibrium, econometric, and 

economic geography methodologies. The model is 

dynamic, with forecasts and simulations generated 

on an annual basis and behavioral responses to 

compensation, price, and other economic factors.

The model consists of thousands of simultaneous 

equations with a structure that is relatively 

straightforward. The exact number of equations 

used varies depending on the extent of industry, 

demographic, demand, and other detail in the 

specific model being used. The overall structure of 

the model can be summarized in five major blocks: 

(1) Output and Demand, (2) Labor and Capital 

Demand, (3) Population and Labor Supply, (4) 

Compensation, Prices, and Costs, and (5) Market 

Shares.

The model captures economically significant relations, 

such as that between productivity in an industry and associated 

demand for labor, while taking into account the available labor 

supply in the region and its cost relative to other regions. The 

population, in turn, grows through natural increase as well as 

through economic migration when earnings rise to compensate 

for the cost-of-living differential. With the basic parameters 

of the economy and its responses estimated, the model allows 

the analyst to test the effects of specific occurrences to see 
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 7

men19-7347_fig01

"

"

"

"

Santa Rosa

123° 122° 121°

38°

37°

Oakland

San Jose

Sacramento

PA C I F I C O C E A N

Universal Transverse Mercator zone 10 north,

North American Datum of 1983

San Francisco Bay

San Pablo

Bay

Sacramento River

San Joaquin River

SONOMA

SANTA CLARA

NAPA

SOLANO

ALAMEDA

MARIN

CONTRA COSTA

SAN

MATEO

SAN FRANCISCO

0 10 20 KILOMETERS

0 10 20 MILES

East bay

North bay

South bay

West bay

EXPLANATION

Bay Area Sub-Regions in 

ABAG REMI Model

Map

area CALIF.

Figure 1. Map showing 

the four subregions in the 

Association of Bay Area 

Governments (ABAG) REMI 

model version 1.78 of the 

San Francisco Bay region, 

California. REMI refers to 

models produced by Regional 

Economic Models, Inc.

how the system responds given the change. Direct, and some 

indirect, effects of an event (such as an earthquake) are added 

in the policy change part of the model to evaluate how the 

event affects different aspects of the economy, including the 

direct and indirect effects on output, employment, population, 

and labor force. The flexible aspects of the model allow 

sensitivity testing on the level of effects, some aspects of 

mitigation and resilience (such as telework), alternative 

government actions (such as imposed outmigration), and 

social effects (such as changing perceptions toward the 

attractiveness of the region). Some of these are included in 

our application of the model. One caveat is that the level 

of the effects result from assumptions about factors such as 

the degree of outmigration or government spending, which 

ultimately rely on judgment calls by the analyst. Furthermore, 

to the degree that the model is built for times of ordinary 

economic relations rather than prolonged crisis, such as 

long-term recovery stresses from a major earthquake, it could 

underestimate or misallocate the level or location of the effect.

The ABAG REMI model includes four subregions: the 

east, north, west, and south bay (table 3) as well as a fifth 

“region,” the rest of California. The strongest effects are 

felt in the east bay, with some effects spreading to the other 

bay subregions. Some neighboring counties to the east also 

experience small effects (capital and output losses) according 

to the Hazus results, but these are difficult to discern in the 

San Francisco Bay region REMI model v. 1.7.8 because of the 

aggregation with the rest of the State. Therefore, discussion 

of the REMI model results will focus on the four subregions 

within the larger San Francisco Bay region. Rather than 

basing the analysis on the standard regional control built into 

the model, employment and population effects are examined 

relative to the ABAG Regional Control Forecast, which 

becomes the baseline forecast absent an earthquake.
8 The HayWired Earthquake Scenario—Societal Consequences

Hypothetical Sequence of Effects and 

Their Incorporation into REMI

We follow the approach adopted by Rose and others

(2009; a study of border closure effects on the U.S. economy), 

where a set of possible drivers of change is identified, the effect 

of each driver is examined separately, and then a combination 

of factors is used to examine cumulative and interactive 

effects. We first describe conceptually the factors causing 

economic change. We then identify factors in the REMI model 

that represent these changes. We follow with independent 

application of the different types of change and finally with 

the combination of some of these factors to look at overall 

effects. For some factors, the absolute measure of change 

is a straightforward application of Hazus results. For other 

factors, the level of change requires judgment informed by past 

experience. We include some analyses at the end of the chapter 

to test the sensitivity of results to some of these assumptions.

A major earthquake will disrupt the economy through 

the initial physical losses, human casualties inflicted on the 

region, and the disruption to business output and employment 

from the damage, which we refer to as direct effects. However, 

the effects quickly become more complex, because indirect 

effects, listed below, arise and may continue even as recovery 

efforts begin. 

• As production halts in damaged space, suppliers to 

affected businesses experience drops in demand, 

whereas those dependent on the output of damaged 

businesses will face shortages or rising costs.

• Damage to utilities, such as power and water, may 

cause business operations to cease temporarily, even in 

undamaged buildings.

• Damage to road and transit networks can disrupt 

supply chains and employee commutes between work 

and home, as well as tourism, business-related travel, 

and shipments (including supply chains) from within 

and beyond the region.

• Seaport and airport damage can disrupt supply and 

customer networks.

• Housing damage may incapacitate employees and 

customers in the short term and can lead to an 

outmigration of workers and customers in the long 

term. 

• Shortages in supplies and available labor may lead to 

higher production costs.

• Coping with loss and disruption may decrease 

productivity of workers.

• “Reputation” effects can spread beyond the immediate 

effects of damage, as visitors or potential new residents 

avoid the region because of expectations of problems.

Yet there are stimulating effects as well:

• The rebuilding period can bring a boom to some 

industries and occupations, and rebuilding can leave 

behind a newer capital stock than what was damaged.

• Insurance payments and government spending can 

cover some of the costs of response, repair, and 

recovery.

• The influx of rescue and recovery workers can fill 

hospitality services that would otherwise have declined 

because of loss of business and leisure travel caused by 

the earthquake and its aftermath.

There are additional responses that may bring either positive 

effects, leading to resilience, or enhance negative effects:

• The high level of technological sophistication in the 

local economy can have both short-term resilience 

effects and long-term depressing effects on growth. 

• Businesses that make heavy use of portable computers, cloud-based files and services, and wireless 

communications may be able to continue operating 

despite displacing effects of local building damage. 

Demand for their services may even increase if they 

can substitute for more location-bound operations.

• However, these operating characteristics may enable 

the business to be footloose, and to move divisions 

to other locations outside the region. Given anticipated temporary cost spikes and attendant operational disadvantages, temporary relocation from the 

region could evolve into permanent relocation of 

operations beyond the bay region.

• Insurance and government payments need to be paid 

for. The effects on the region will vary depending on 

whether the cost is tied directly to the affected area, 

the bay region, or spread more widely to the State or 

Nation. 

• Businesses, government, and nonprofit organizations 

may be able to relocate to space that is still usable 

and also to provide help to one another, mitigating the 

effects of damage and displacement. For example, temporary agreements to share space can keep businesses 

in operation, reduce operating costs from displacement, and provide continuity of services, making the 

economy more resilient.

The next section describes how these effects and response 

measures are included in the REMI model.
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 9

Assumptions and Simulated Effects 

within the REMI Model—Part 1

To estimate how the effects of the HayWired scenario 

would affect the San Francisco Bay region’s economy, we used 

the regional control scenario in the REMI model from the Plan 

Bay Area 2040 regional forecast and then entered measures of 

effects into the model. The model reports the effects of these 

changes over the forecast period. The model forecast goes from 

2010 through 2040 in annual increments, with the HayWired 

scenario effects examined between 2018 and 2040. 

The major earthquake effects applied to the bay region 

REMI model regional forecast consisted of output loss from 

building damage, output loss from transportation disruption, 

changes in housing prices, and migration decisions. After 

applying the effects that create losses in the economy 

(described in detail below), we also added the stimulating 

effects of recovery efforts to the model. Response, repair, and 

reinvestment in capital stock stimulates new economic activity 

to counterbalance some of the losses and to support a return to 

predisaster levels. We looked at government spending in the 

analysis to assess the overall effects of the earthquake on the 

economy after the expenditure of resources for rebuilding and 

recovery. The effects of the earthquake are reported in terms 

of total output change (the direct effects from damage plus 

secondary and tertiary effects from supply-chain disruption 

and business and population relocation), employment, and 

population change. 

Direct Output Loss from Building Damage

Whereas the REMI model includes residential and 

nonresidential capital stock and capital investment variables, 

the stock variables are not directly related to output in the 

model. Thus, entering a loss in capital stock becomes only 

stimulating as the model accounts for rebuilding, without 

accounting for the output loss tied to the capital stock. For 

this reason, we began with entering output losses estimated 

directly from Hazus into the REMI model.

Direct output loss occurs when building damage disrupts 

economic activity. In the Hazus model, this is the product of 

floor area, output realized per square foot, and the expected 

days of loss of function for each damage state. Recapture 

factors, factors discounting the business-related output 

loss from earthquakes owing to the possibility that some 

of the loss could be recouped to some extent by working 

overtime after the event, are also taken into account in the 

Hazus output calculations (see Sue Wing and others, in prep. 

[planned to be published as a part of this volume] for a more 

disaggregated analysis of direct and recaptured effects). Total 

Hazus-estimated output loss (table 2) was disaggregated into 

loss for a 3-year time period to reflect the time needed for 

rebuilding that causes output loss to last longer than 1 year. 

In the HayWired scenario, a series of aftershocks follow the 

mainshock over the course of 2 years. Although the largest 

four aftershocks hypothetically happen in 2018, two of which 

occur 2 weeks after the mainshock and the other two occur 

about 6 months later, direct output loss from all sequences of 

aftershocks in this analysis were added into the second-year 

total. This captures the spread through time of effects of larger 

aftershocks that occur near the end of 2018, so that most of the 

output loss occurs in 2019. 

Overall, the estimated direct output loss from building 

damage (from the mainshock, aftershocks, and fire following 

earthquake effects) for the bay region is around $26 billion in 

the first year, $580 million in the second year, and $22 million 

in the third year (in 2016 dollars) after the earthquake (table4). 

The east bay subregion of the San Francisco Bay region 

accounts for the majority of the loss: $20 billion in the first 

year, and $105 million and $18 million in the following two 

years, respectively. 

Table 4. Direct output loss estimates from Hazus as defined in the REMI Association of Bay Area Governments model for the HayWired 

scenario shown by subregion of the San Francisco Bay region, California.

[Subregions are defined in table 3. REMI refers to the models produced by Regional Economic Models, Inc. Data sourced from the Association of Bay Area 

Governments and Metropolitan Transportation Commission from Hazus results. There is no estimate for total output for the State of California from Hazus. n.a., 

not applicable]

Subregion 1st year, in millions of 

2016 dollars 

2d year, in millions of 

2016 dollars

3d year, in millions of 

2016 dollars

1st year loss of area’s output, in 

percent

East bay 20,317 105 18 7.1

North bay 147 2 1 0.1

West bay 1,809 67 1 0.7

South bay 3,984 405 2 1.8

San Francisco Bay 

region total1

26,257 579 22 3.0

Rest of California 96 2 0 0.0

California total1 26,354 581 22 n.a.

1

Because of rounding, sum totals may not be exact.
10 The HayWired Earthquake Scenario—Societal Consequences

The level of loss by sector was determined by the type 

of building the sector occupies, the degree of damage to that 

occupancy type, and total employment and output levels of 

the industry. We calculated the share of output loss for each 

industry using Hazus output loss results by occupancy class 

through the North American Industry Classification System 

(NAICS) (appendix 1, table 1.1) and the crosswalk between 

Hazus occupancy class and REMI industry (table 1.2) and 

applied those shares to the total output estimates in 2018 

of the REMI regional control scenario in the version of the 

model used for Plan Bay Area 2040. This leads to slightly 

different total output loss estimates, because the baseline in 

the two models are different. We also found that five industry 

sectors—telecommunications; performing arts and spectator 

sports; museum, historical sites, zoos and parks; amusement, 

gambling, and recreation; and food services and drinking 

places—belong to the same occupancy class. However, utility 

providers report shorter estimated recovery times than the 

Hazus default value. Therefore, for the telecommunication 

sector, we revised the loss of function time for damage state 

and recaptured factor for damage state to recalculate a lower 

level of output loss. The results are shown in appendix 1.

Among all 66 private nonfarm industries, the petroleum 

and coal products manufacturing industry experienced the 

largest value of output loss ($2 billion, 4 percent of the 

industry’s total output) in the San Francisco Bay region in the 

first year, followed by food services and drinking places ($1.9 

billion, 9 percent), ambulatory health care services ($1.8billion, 

6 percent), machinery manufacturing ($1.6billion, 12 percent), 

real estate ($1.3billion, 1percent), and professional, scientific, 

and technical services ($1.2billion, 1 percent). Several 

manufacturing sectors sustained the largest share of industry 

output loss, including 22percent of motor vehicles, bodies 

and trailers, and parts manufacturing; 16percent of paper 

manufacturing, and 12percent of machinery manufacturing. 

In the second year, total output loss declined for the bay 

region. The largest amount of output loss was in ambulatory 

health services ($90million), food services and drinking 

places ($77million), and private hospitals ($72 million). In 

the third year, output loss, although very small, only remained 

in the social assistance and nursing and the residential care 

facilities sectors, because they are more dependent upon the 

reconstruction of buildings (table 1.3). Loss by occupancy class 

is translated to loss by REMI industry sector. See appendix 1 

for further discussion regarding translation methodology. 

Jobs, as reported by REMI, are measured by the Bureau 

of Economic Analysis (BEA) and represent an annual total, 

which includes not only wage employment but also selfemployment, rather than a monthly average of wage and 

salaried employment only, as reported by the Bureau of Labor 

Statistics (BLS). The BEA employment total is about 26 

percent higher than the BLS total for the San Francisco Bay 

region. Percentages are reported here to show the relative 

effects, whatever employment base is used. We reduced 

the forecast industry output in the REMI model Plan Bay 

Area 2040 regional control by the industry-specific loss and 

examined the cumulative effects of how such reductions 

reverberated throughout the economy. Direct output loss 

from building damage led to a loss of 232,000 jobs in 2018, 

and a 50,000 person decline in population related to job loss, 

or 4.6percent of total jobs and 0.6 percent of population. 

Population displacement related to damage to residential 

buildings was almost 15 times this level, according to Seligson 

and others (2018) and Johnson and others (Population 

Movements, in prep. [planned to be published as part of 

this volume]). However, displacement from a home does 

not necessarily mean displacement from the region. The 

population analysis is discussed as a separate category below. 

Job losses in the east bay subregion of the San Francisco 

Bay region are estimated to be 160,000, more than two 

thirds of the region’s total loss, and 10.5 percent of total 

jobs in the east bay subregion (table 5). The north bay, west 

bay, and south bay subregions of the San Francisco Bay 

region experience reductions of 8,000, 33,000 and 33,000 

in employment (1.4, 2.0, and 2.5 percent), respectively. The 

direct population effects of job loss for the east bay subregion 

Table 5. Estimate of 2018 employment and population effects in the San Francisco Bay region, California, relative to Association of Bay 

Area Governments forecast levels from Hazus-estimated output loss related to HayWired scenario building damage.

[Subregions are defined in table 3. Data generated using the Association of Bay Area Governments Planning and Research Department (2016) REMI (Regional 

Economic Models, Inc.) model; analysis of the HayWired scenario from 2018 (this study)]

Subregion Employment loss

Employment loss 

as a percent of 

subregion’s job base

Employment loss as 

a percent of 

total loss

Population loss 

attributed to 

job loss 

Population loss 

as a percent of 

area population

Population loss as 

a percent of 

total loss

East bay 158,872 10.5 68 36,243 1.3 72

North bay 7,623 1.3 3 2,856 0.3 6

West bay 32,585 2.0 14 5,527 0.3 11

South bay 33,212 2.5 14 5,495 0.3 11

San Francisco 

Bay region 

total1 232,293 4.6 100 50,121 0.6 100

 1Because of rounding, sum totals may not be exact.
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 11

are a drop of 1.3 percent in 2018, accounting for more than 

70 percent of the region’s direct job-related loss in population 

(table 6). The other three subregions are estimated to lose 

14,000 people in the first year related to job loss, a much 

smaller share of their populations. In the second year, bay 

region job levels are 19,000 less than the projected levels 

without the earthquake, and population is lower by 41,000 

people, which are 0.4percent of employment level projected 

for the region in REMI and 0.5 percent of the projected 

population level.

The industry with the largest employment loss in thefirst 

year is food services and drinking places, with a loss of 

about 34,000 jobs (11 percent). The construction sector loses 

approximately 19,000 jobs (7 percent), which would create 

challenges for the region as construction activities will be 

needed for the recovery of communities. Large losses in 

employment in critical neighborhood-serving retail trade and 

ambulatory health care services sectors would also make the 

recovery work harder for cities and communities (table 7). 

Because of multiplier effects, total output loss of all private 

nonfarm sectors is approximately $46 billion in 2018 (2016 

dollars), compared with the $27 billion direct output loss 

estimated by Hazus. The real estate sector has the largest 

output loss ($4 billion), followed by professional services and 

construction sectors, both of which sustain a loss of more than 

$3 billion in industry output in year 2018 (table 8; appendix 2).

Table 6. Estimate of 2019 employment and population effects in the San Francisco Bay region, California, relative to Association of Bay 

Area Governments forecast levels from Hazus-estimated output loss related to HayWired scenario building damage.

[Subregions are defined in table 3. Data generated using the Association of Bay Area Governments Planning and Research Department (2016) REMI (Regional 

Economic Models, Inc.) model; analysis of the HayWired scenario from 2018 (this study)]

Subregion Employment loss Employment loss, 

in percent

Employment loss as a 

percent of total loss

Population 

loss

Population loss 

as a percent of 

area population

Population loss 

as a percent of 

total loss

East bay 8,850 0.6 47 29,192 1.1 71

North bay 1,195 0.2 6 2,547 0.2 6

West bay 2,982 0.2 16 4,627 0.2 11

South bay 5,500 0.4 30 4,746 0.2 12

San Francisco 

Bay region 18,527 0.4 100 41,112 0.5 100

Table 7. Ten industries with the largest estimated reduction of employment from the HayWired scenario owing to building, population, 

and other output losses identified in linked industries in the San Francisco Bay region, California, in 2018, relative to the Association of 

Bay Area Governments forecast.

[Data generated using the Association of Bay Area Governments Planning and Research Department (2016) REMI (Regional Economic Models, Inc.) model; 

analysis from 2018 (this study)]

Category Job loss Share of job loss, in percent

Food services and drinking places 33,603 10.90

Construction 18,576 7.30

Retail trade 17,555 4.20

Professional, scientific, and technical services 16,643 2.50

Ambulatory health care services 14,464 8.10

Administrative and support services 12,984 4.20

Social assistance 9,775 4.50

Performing arts and spectator sports 8,225 13.60

Real estate 7,564 3.00

Hospitals 7,370 6.80
12 The HayWired Earthquake Scenario—Societal Consequences

Table 8. Ten industries with the largest estimated total 

cumulative output loss from the HayWired scenario owing to 

building, population, and other output losses identified in linked 

industries in the San Francisco Bay region, California, in 2018, 

relative to the Association of Bay Area Governments forecast.

[Data generated using the Association of Bay Area Governments Planning and 

Research Department (2016) REMI (Regional Economic Models, Inc.) model; 

analysis from 2018 (this study)]

Category Output loss Share of output 

loss, in percent

Real estate 4,095.15 2.9

Professional, scientific, and 

technical services 3,304.39 2.3

Construction 3,053.06 7.5

Ambulatory health care 

services 2,625.5 8.5

Petroleum and coal products 

manufacturing 2,485.79 5.2

Food services and drinking 

places 2,315.74 10.4

Retail trade 1,958.74 4.0

Wholesale trade 1,897.34 4.0

Machinery manufacturing 1,830.1 13.9

Monetary authorities and 

other credit institutions 1,694.08 3.8

Transportation Network and Commuting Flows

The transportation network provides important lifeline 

support to a functional economy. After an earthquake, recovery 

of the region’s economy, housing, and communities depends on 

the restoration of the transportation system. Even if buildings 

are relatively intact, as long as the workers cannot get to their 

place of work, business activities will not be able to recover. 

Jones and others (this volume) identify the potential damages 

to major transportation systems in the region, including the 

road network and regional transit systems, such as the BART 

system. Figure 2 shows the range of repair times for shaking 

damage to the California Department of Transportation 

(Caltrans) roadway bridges and BART facilities. 

Roadway damage would occur on major freeways and 

State highways, most of which require 2 to 3 weeks to repair. 

Severe damage is expected to happen along Interstates 880 

and 580 near San Leandro and Hayward, along Interstate 680 

in the Dublin-Pleasanton area, along Highway 24 in northern 

Oakland, and along Highway 37 near Sears Point between 

Novato and Vallejo, for which the repair time ranges from 4 to 

10 months. 

The effects on commuter flow, in addition to worksite 

buildings being damaged, can lead to further adverse effects on 

the regional economy. Wein, Belzer, and others (this volume), 

in an analysis of commuter flows within the region from or to 

areas with concentrated damage (census tracts with 20 percent 

or more extensive or complete damage to overall building 

square footage), found that commuting flows could affect the 

regional economy beyond the immediate damages from the 

earthquake. We examined commuting flow effects for both the 

areas with concentrated damage and those outside of heavily 

affected areas, which we refer to as “less-affected areas” 

hereafter (figs. 2, 3). 

Damage to the transportation infrastructure system 

interrupts worker commute patterns and can increase the 

business output loss. After the 1989 Loma Prieta, Calif., 

earthquake, commuter transportation disruption (caused by a 

one-month closure of the San Francisco-Oakland Bay Bridge, 

in particular) was largely averted by alternative roadway routes 

and switching modes to the BART system and ferries (Deakin, 

1991). Commutes were as much as 15 minutes longer, and 

limited relief was obtained from changes in work schedules 

or alternate workplaces (including home). After the 1994 

Northridge earthquake damaged four major freeways in the 

Los Angeles area, Boarnet (1998) reported that 31percent of 

employees (in surveyed businesses) endured longer commutes 

for 1 or 2 months on average (depending on the level of effect). 

Gordon and others (1998) estimated that increased travel 

time cost commuters at least $33 million. The most common 

employer policy allowed employees to change their work hours 

(Boarnet, 1998). More than twenty years later, San Francisco 

Bay region commuters are able to leverage technological 

advancements and are already responding to everyday traffic 

congestion with work-at-home options. However, the damage 

to the system is likely to be much greater than from the 1989 

Loma Prieta earthquake, and repairs may last much longer. To 

incorporate the effects of transportation network and commute 

flow disruptions in the REMI analysis, we looked at where 

workers work and live by industry at the detailed census-tract 

level and at where roadway and freeway bridge damage would 

heavily affect workers’ commutes. We differentiate the level 

of effect by the type of economic activity, to the extent such 

information is available, recognizing that flexibility to work at 

home varies by sector and occupation.

The U.S. Geological Survey (USGS), using Hazus, 

estimated that extensively or completely damaged buildings 

in commercial and industrial occupancy classes could lose 

functionality from a week to a year (FEMA, 2003). Regional 

roadways and bridges experiencing downtime could be 

repaired within 2 to 40 weeks (on the basis of calculations 

by Werner and others, 2000). Given the uncertainty of repair 

times for extensively or completely damaged buildings and 

roadways, we assume that the direct output loss for the heavily 

affected areas calculated using the Hazus methodology by 

USGS has accounted for the commuting flow effects in 

these areas, since those who work in heavily affected areas 

would not be commuting to buildings and areas that are 

nonfunctional. On the other hand, in less-affected areas, 

building repair times would be shorter. Likewise, if the 

transportation network is still broken, regardless of residence 

locations, commuters would not be able to get to buildings 

in these areas, leading to additional business output losses. 
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 13

"

"

"

"

"

"

"

"

"

MARIN

CONTRA

COSTA

ALAMEDA

SANTA

CLARA

SOLANO

SONOMA

NAPA

580

580

580

680

880

880

280

280

680

680

680

80

80

101

101

101

92

84

237

24

13

San Jose

SAN FRANCISCO

Oakland

Fremont

Hayward

Alameda

Livermore

Foster City

Richmond

Palo Alto

San Francisco Bay

San Pablo Bay

P A C I F I C O C E A N

SAN

MATEO

122°

38°

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10 north projection

North American Datum of 1983

0 3 6 KILOMETERS

0 3 6 MILES Map

area CALIF.

Highway bridge repair time

3 to 6 months

4 to 10 months

5 to 12 weeks

4 to 8 weeks

2.5 to 6 weeks

2 to 3 weeks

None

Highway Secondary

street

Surface

street

Multi-hazard

exposure

Low

Moderate

Moderate-high

High

Very high

EXPLANATION

Transportation network

men19-7347_fig02a

A

Figure 2 (pages 13–14). Maps of the San Francisco Bay 

region, California, showing the multi-hazard exposure 

of roadways (A) and the Bay Area Rapid Transit (BART) 

system rail lines (B), including the estimated repair times 

for California Department of Transportation bridges and 

BART facilities in the HayWired scenario. Multi-hazard 

exposure refers to the combined shaking, landslide, 

liquefaction, slip, and fire hazards that follow the 

hypothetical earthquake of the HayWired scenario. Map 

modified from Jones and others (this volume).
14 The HayWired Earthquake Scenario—Societal Consequences

#

#

#

#

#

#

#

#

#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*# *#

*# *#

*#

*#

*#

*#

*#

*#

*#*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

122°30' 122°20' 122°10' 122°

38°

37°50'

37°40'

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10 north projection

North American Datum of 1983

Map

area CALIF.

BART rail multi-hazard exposure

Very high

High

Low

Moderate-high

Moderate

BART station repair time range

EXPLANATION

#* New station; not analyzed

#* <1 day

#* 1 to 14 days

#* 2 to 36 weeks

#* 9 to 12 months

#* 1 to 3 years

0 2 4 KILOMETERS

0 2 4 MILES

Lake Merritt Embarcadero

Montgomery Street

Powell Street Civic Center Fruitvale

16th St Mission

Coliseum/

Oakland Airport

24th St Mission

Pittsburg/Bay Point

North Concord/Martinez

Concord

Richmond

Pleasant Hill

El Cerrito

Del Norte

Walnut Creek

El Cerrito Plaza

Lafayette

Orinda

North

Berkeley

Downtown Berkeley

Ashby

Rockridge

MacArthur

19th Street/Oakland West Oakland

12th Street/Oakland City Center

Glen Park San

Leandro Balboa Park

Daly City Dublin/

Pleasanton

Bay Fair Castro

Valley

Colma

Hayward

South Hayward

San Bruno

Millbrae Union City

Fremont

Concord Yard Richmond Yard

West Dublin/

Pleasanton

Daly City

Yard

South San

Francisco

Hayward Yard

CONTRA

COSTA

ALAMEDA

SAN

MATEO

SAN

FRANCISCO

S a n F r a n c i s c o B a y

San Pablo Bay

men19-7347_fig02b

B

Figure 2 (pages 13–14).—Continued.
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 15

men19-7347_fig03

San Pablo

Bay

S a n F r a n c i s c o B a y

PA C I F I C O C E A N

Novato

Vallejo

Central

Alameda County

Southern

Alameda County

Dublin-Pleasanton

Western

Contra Costa County

Northern

Alameda County

Novato

Vallejo

Central

Alameda County

Southern

Alameda County

Dublin-Pleasanton

Western

Contra Costa County

Northern

Alameda County

ALAMEDA

SOLANO

MARIN

CONTRA COSTA

SAN MATEO

SONOMA

NAPA

SANTA CLARA

SAN

FRANCISCO

122°30’ 122°

38°

37°30’

Map

area CALIF.

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Park and highway data from OpenStreetMap, 2017

Universal Transverse Mercator 10 north

North American Datum of 1983

0 3 6 KILOMETERS

0 3 6 MILES

EXPLANATION

Hayward Fault rupture

Highway

County boundary

Economic subarea

Tract with concentrated 

building damage

Preserved land

Figure 3. Map of the San Francisco Bay region, California, showing economic subareas and census tracts with 

concentrated damage in the HayWired scenario. Modified from Wein, Belzer, and others (this volume).
16 The HayWired Earthquake Scenario—Societal Consequences

Therefore, in addition to the direct output loss estimated by 

USGS from building damages for less-affected areas, we 

need to account for how commute flow (table 9) and the 

transportation network affects businesses. 

Less-affected areas in Alameda County employ about 

452,000 workers, of which 72 percent commute from other 

parts of the bay region, including the heavily affected areas in 

Alameda, Contra Costa, Marin, and Solano Counties, as well 

as from counties outside the bay region. San Francisco, with a 

job base of about 695,000, is an example of a county with no 

heavily affected areas, but where 59 percent commute from 

areas outside San Francisco. For these types of less-affected 

areas, we added the loss from commute flow interruption to 

the loss from damaged buildings to get a total for output loss. 

To translate commuting flow interruption from roadway 

and highway bridge damage into an output loss estimate (for 

less-affected areas) that is entered into the REMI model, 

we assumed that the percentage of output loss equals the 

percentage of all workers’ working weeks lost in a year 

while commuters wait for repairs of roadways and bridges. 

We considered that commuters using these disrupted routes 

would seek alternative unblocked travel routes or modes (for 

example, ferry to avoid damaged bridges) if they think the 

new commuting time is still reasonable. Recall that in this 

scenario, bridges across San Francisco Bay are not estimated 

to sustain severe structural damages. Such outcomes would 

substantially disrupt commuters and increase business effects. 

We used Google Maps as our reference to select the optimal 

routes—routes that take the shortest time according to Google 

Maps—and to determine (somewhat subjectively) alternative 

commuting routes and the time it takes to use these routes. 

We then compared the new commute time with the shortest 

time suggested by Google Maps to decide whether commuters 

(as a group instead of individuals) would be willing to adopt 

the new routes given the marginal increase in commute time. 

We assumed those unable or unwilling to take alternative 

routes would have to wait for 7 months—midrange value of 

repair time for the most severely damaged roadways based on 

figure2A—before they resume working. This would be time 

lost with regard to producing industry output.

We combined, for the less-affected areas in each county, 

the weeks lost by commuters waiting for roadways and bridges 

to reopen (table 10) and then divided the total lost commuter 

weeks by total commuter weeks (equal to the total number of 

commuters multiplied by 52) to get the share of lost commuter 

weeks. In order to calculate the share of total worker weeks lost 

for our model, we then multiplied the share of lost commuter 

weeks by the share of commuters calculated in table 9.

(1)

For example, in San Francisco County (where the whole 

county is a less-affected area) for all sectors, 12 percent of 

the working weeks are lost because of transportation network 

damage. Therefore, we assume an additional 12 percent 

of total output loss for the county. In Alameda County, 

commuters are 72 percent of workers in less-affected areas, 

so the share of work weeks lost for this part of the county are 

0.24 × 0.72 = 0.17 (or 17 percent; see the fourth column in the 

first row of table 10).

Based on the approach described above, table 10 shows 

that Alameda County is the most highly affected county, in 

regard to percentage of work time lost, with 17 percent of 

total workers’ weeks lost. Although San Francisco and San 

Commute share of output loss

= Lost commuter weeks

Number of commuters × 52

×

Number of commuters

Number of workers

Table 9. Commuters in less-affected areas for the HayWired scenario in each county of the San Francisco Bay region, California.

[Data generated using an Association of Bay Area Governments analysis from 2018 (this study) of U.S. Census Bureau (2015) Longitudinal EmployerHousehold Dynamics (LEHD) data. LEHD job data is based on company payroll data, which might inflate the total number of commuters where company 

payroll data reporting location is different from actual workplace] 

County Total workers employed in 

less-affected areas 

Total commuters to less-affected 

areas

Share of commuters, in 

percent

Alameda 451,877 323,221 72

Contra Costa 333,926 169,275 51

Marin 106,018 66,801 63

Napa 72,803 36,553 50

San Francisco 695,298 412,119 59

San Mateo 385,083 238,095 62

Santa Clara 1,001,412 385,077 38

Solano 139,565 76,004 54

Sonoma 191,411 56,271 29

Other Areas 1,802,175 785,233 44
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 17

Table 10. Percentage of weeks lost during the HayWired scenario because of transportation network interruption for all industry 

sectors for less-affected areas in each county of the San Francisco Bay region, California.

[Data generated using an Association of Bay Area Governments analysis from 2018 (this study) of U.S. Census Bureau (2015) Longitudinal EmployerHousehold Dynamics (LEHD) data and Jones and others (this volume). LEHD job data is based on company payroll data, which might inflate the total number 

of commuters where company payroll data reporting location is different from actual workplace]

County Total commuter weeks Total lost commuter weeks Commuter weeks lost, 

in percent

Total worker weeks lost, 

in percent

Alameda 16,807,492 4,088,725 24 17

Contra Costa 8,802,300 1,638,960 19 9

Marin 3,473,652 339,518 10 6

Napa 1,900,756 93,944 5 2

San Francisco 21,430,188 4,245,934 20 12

San Mateo 12,380,940 2,105,963 17 11

Santa Clara 20,024,004 3,428,082 17 7

Solano 3,952,208 311,476 8 4

Sonoma 2,926,092 131,547 4 1

Other Areas 23,078,536 1,382,194 6 2

Mateo Counties experience less severe building damage than 

Alameda and Contra Costa Counties, commuters in these 

counties lose around 10 percent of their working time as the 

cross-boundary commute flow is interrupted. 

There are some minor variations in vulnerability to 

commute flow interruption by industry type, as shown in table 

11. We calculated the share of total worker weeks lost by three 

industry groupings, defined and discussed by Wein, Belzer 

and others (this volume), who separate out commute activity 

by industry grouping. In general, trade, transportation, and 

utilities jobs tend to have a slightly higher share of workers 

vulnerable to commute interruption than either goodsproducing or services industries. 

These percentages do not translate directly into output 

loss shares. Businesses and workers are very flexible 

and resilient in commuting and working. For example, 

telecommuting and working from home, an option 6 percent 

of workers in the region chose in 2016 (U.S. Census Bureau, 

2016 [American Community Survey, 1 year]), could help keep 

businesses running, even more so for services industries rather 

than the goods producing, trade, transportation, and utilities 

industries. In addition, some commuters facing difficult or 

impossible commutes may choose to stay in hotels, with 

colleagues or friends, or in temporary living quarters near their 

offices during the week, particularly in places where buildings 

serving accommodation functions are not severely damaged. 

Taking into account these resiliency enablers, we applied 

factors to discount business output loss. In this analysis, we 

assumed one-third output loss from the effect of commuting 

in the goods producing, trade, transportation, and utilities 

industries, and one-fourth output loss in the services industry 

would occur. A method did not exist at the time of this study to 

objectively calculate how commute solutions would reduce the 

level of the commute disruption effect. However, fairly small 

shares were used in order to avoid exaggerating the effect.

For each REMI model subregion, we summed up the 

direct output loss from building damage of heavily affected 

areas and the combined output loss from building damage 

and transportation network interruption of less-affected 

areas to use as inputs to the REMI model. Among all 66 

Table 11. Share of weeks lost because of transportation network 

interruption in the HayWired scenario by industry group for lessaffected areas in each county of the San Francisco Bay region, 

California.

[Data generated using an Association of Bay Area Governments analysis from 

2018 (this study) of U.S. Census (2015) longitudinal employer-household 

dynamics (LEHD) data; and Jones and other, this volume. LEHD job data 

is based on company payroll data, which might inflate the total number of 

commuters when company payroll data reporting location is different from 

actual workplace]

County Goods 

producing

Trade, transportation, 

and utilities Services

Share of weeks lost, in percent

Alameda 17 18 17

Contra Costa 8 12 9

Marin 6 8 6

Napa 2 3 2

San Francisco 12 13 12

San Mateo 12 12 10

Santa Clara 7 9 6

Solano 4 6 4

Sonoma 1 2 1

Other areas 2 3 1
18 The HayWired Earthquake Scenario—Societal Consequences

private nonfarm industries, the professional, scientific, and 

technical services industry would experience the largest value 

of output loss in the San Francisco Bay region in the first 

year, approximately $4.4 billion, 3 percent of the industry’s 

total output, followed by real estate ($4.3 billion, 3 percent), 

computer and electronic product manufacturing ($3.7 billion, 

3 percent), food services and drinking places ($3.1 billion, 

14 percent), and ambulatory health services ($2.6 billion, 9 

percent). In the second year, total output loss is much smaller 

for the bay region (appendix 3). We again reduced the ABAG 

Regional Control Forecast output by this modified industryspecific loss in the REMI model. 

Even with these conservative assumptions, the 

cumulative output loss effects from transportation network 

and commute flow interruption calculated in the model are as 

large as the output loss associated with building damage for 

the bay region. Employment loss increases from 232,000 to 

498,000 jobs, and population decline expands from 50,000 

to 105,000 people. As shown in table 12 and figure 4, loss of 

employment in the east bay subregion of the bay region rises 

from 159,000 to 228,000 jobs. The additional job losses in 

the west bay and south bay subregions are greater than the 

estimates from output losses from building damages alone in 

those areas. The west bay subregion loses 33,000 jobs because 

of building and contents damage, but an additional 108,000 

jobs from commuting effects. Employment loss in the south 

bay subregion also increases by more than twofold.

In the San Francisco Bay region, the industry with the 

largest employment loss in the first year is food services and 

drinking places, with a loss of about 59,000 jobs (19 percent), 

followed by professional services (44,000 jobs, 7 percent) and 

retail trade (43,000 job, 10 percent). The construction sector 

loses 41,000 jobs (table 13). Total output loss approaches $106 

billion in 2018 (2016 dollars) and increases by 120 percent from

$48 billion if only output loss from direct building damage is 

considered. The real estate sector has the largest output loss 

($11 billion), followed by professional services ($10 billion) 

and construction ($7 billion) (table 14 and appendix 4).

Table 12. Total employment and population effects in the HayWired scenario from building and transportation damage in 2018, San 

Francisco Bay region, California.

[Subregions are defined in table 3. Data generated using the Association of Bay Area Governments Planning and Research Department (2016) REMI 

(Regional Economic Models, Inc.) model; analysis of the HayWired scenario from 2018 (this study)]

Subregion Employment 

loss

Employment loss 

as a percent of 

subregion jobs

Employment loss 

as a percent of 

total loss

Population 

loss1

Population loss,1

in percent

Population loss1,2 as a 

percent of total loss

East bay 228,106 14.54 46 55,436 2.0 53

North bay 20,467 3.53 4 6,996 0.7 7

West bay 141,229 8.22 28 23,680 1.2 23

South bay 107,776 7.64 22 18,555 0.9 18

San Francisco 

Bay region 497,578 9.8 100 104,668 1.3 100

1

Population loss relates only to the population effects of the output and employment loss, not the losses because of outmigration as a result of residential 

building damage or loss of community services.

2

Because of rounding, percentages may not be exact.

−250,000

−200,000

−150,000

−100,000

−50,000

0

East

bay

North

bay

West

bay

South

bay

Building and contents damage, all hazards

Road, rail, and bridge damage

EXPLANATION

Employment loss, in number of jobs

Figure 4. Collins diagram showing the HayWired scenario effects of building 

(all hazards) and road, rail, and bridge damage output loss on employment in 

the San Francisco Bay region, California. Data comes from the Association of 

Bay Area Governments HayWired analysis, using REMI model version 1.7.8, 

adjusted control NC3RC1. East, west, north, and south bay subregions refer 

to regions defined in table 3. REMI refers to models produced by Regional 

Economic Models, Inc.
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 19

Table 13. Ten industries with the largest employment loss from combined building and transportation effects in the San Francisco Bay 

region, California, in 2018.

[Data generated using the Association of Bay Area Governments Planning and Research Department (2016) REMI (Regional Economic Models, Inc.) model; 

analysis of the HayWired scenario from 2018 (this study)] 

Sector category Job loss Job loss as a percent of sector jobs

Food services and drinking places 58,654 19.0

Professional, scientific, and technical services 44,174 6.6

Retail trade 43,011 10.2

Construction 41,434 16.2

Administrative and support services 31,846 10.3

Ambulatory health care services 25,735 14.3

Social assistance 20,220 9.2

Real estate 18,753 7.5

Wholesale trade 15,096 9.4

Performing arts and spectator sports 14,698 24.3

Table 14. Ten industries with the largest HayWired scenario total cumulative output loss from combined building and transportation 

effects in the San Francisco Bay region, California, in 2018.

[Data generated using the Association of Bay Area Governments Planning and Research Department (2016) REMI (Regional Economic Models, Inc.) model; 

analysis of the HayWired scenario from 2018 (this study)]

Sector category Total output loss, in 

millions of dollars1

Output loss as a percent 

of sector output 

Real estate 10,934.9 7.6

Professional, scientific, and technical services 9,531.9 6.5

Construction 6,772.3 16.5

Retail trade 4,993.2 10.3

Monetary authorities and other credit institutions 4,652.9 10.4

Ambulatory health care services 4,623.2 14.9

Wholesale trade 4,517.2 9.4

Food services and drinking places 4,174.5 18.7

Computer and electronic product manufacturing 4,091.2 3.2

Petroleum and coal products manufacturing 3,562.9 7.4

 12016 U.S. dollars.
20 The HayWired Earthquake Scenario—Societal Consequences

Recovery Time Adjustment for Trade and 

Manufacturing Sectors

Empirical studies on business recovery for a range of 

crisis events, including hurricanes, earthquakes, and floods, 

have suggested that the more vulnerable businesses tend to be 

those that: are small; are reliant on discretionary income, such 

as retailers; are tenants rather than building owners; experience 

more severe damage; and (or) are closed for a longer period of 

time (Powell and Harding, 2007; Wein, Haveman, and others, 

this volume). In the HayWired scenario, manufacturing and 

mining companies are found to be more vulnerable because 

of direct output losses from building damages and effects 

of concentrated damages in their districts or neighborhoods 

(Wein, Belzer and others, this volume). Based on these 

findings we assumed an increase in the recovery time for retail 

trade, wholesale trade, mining, and selected manufacturing 

sectors. These manufacturing sectors consist of: wood product 

manufacturing; nonmetallic mineral product manufacturing; 

primary metal manufacturing; fabricated metal product 

manufacturing; machinery manufacturing; computer and 

electronic product manufacturing; electrical equipment and 

appliance manufacturing; motor vehicles, bodies and trailers, 

and parts manufacturing; other transportation equipment 

manufacturing; furniture and related product manufacturing; 

miscellaneous manufacturing; food manufacturing; beverage 

and tobacco product manufacturing; apparel manufacturing; 

leather and allied product manufacturing; paper 

manufacturing; petroleum and coal products manufacturing; 

chemical manufacturing; and plastics and rubber product 

manufacturing, which are based on data from Jon Haveman 

(Marin Economics Consultant, written commun., 2018). 

Specifically, we added 80 percent of the first-year direct 

output loss to the second-year direct output loss, 60 percent to 

the third year, and 40 percent to the fourth year for the retail 

sector in the east bay subregion. For nonretail sectors in the 

east bay subregion, we added 60 percent of the direct output 

loss estimated for 2018 to the second-year loss, 30 percent 

to the third year, and 10 percent to the fourth year. And for 

all sectors mentioned above in other subregions, we added 

25 percent of the first-year direct output into the second-year 

loss and 5 percent to the third year. Assumptions of additional 

output loss for these sectors are shown in table 15. The 

differences reflect the degree of building damage in each of 

the REMI-defined subregions.

Table 15. Additional output loss as share of first year (2018) 

estimated output loss for the HayWired scenario in the San 

Francisco Bay region, California.

[Industry sectors of output loss considered are listed in parentheses. 

Subregions are defined in table 3] 

Subregion Output loss, in percent

2019 2020 2021

East bay (retail) 80 60 40

East bay (nonretail) 60 30 10

North bay (all) 25 5 0

West bay (all) 25 5 0

South bay (all) 25 5 0

Rest of California (all) 25 5 0

San Francisco Bay region employment loss reaches 

about 480,000 jobs in 2018 when building damage effects, 

transportation and commute flow effects, and the sector 

recovery time adjustments are combined; employment loss 

continues at a slower pace over the next four years of the 

scenario, and employment reaches a net gain of more than 

4,600 jobs in 2022, five years after the earthquake. Ten years 

after the earthquake, the bay region still maintains a net gain of 

6,000 jobs, but by 2040, the region is projected to have 1,300 

fewer jobs than in the ABAG Plan Bay Area 2040 projection 

for the bay region, which is an insignificant difference from the 

projected 4.7 million jobs in 2040 (table16). 

Table 16. Total difference in employment numbers from revised output loss effects, relative to the Association of Bay Area 

Governments (ABAG) regional projection.

[Difference in employment numbers listed as negative where number of jobs are lost and positive where gained. Subregions are defined in table 3; Data 

generated using the Association of Bay Area Governments Planning and Research Department (2016) REMI (Regional Economic Models, Inc.) model; 

analysis of the HayWired scenario from 2018 (this study)] 

Subregion 2018 2019 2020 2021 2022 2027 2040

East bay −220,659 −42,429 −25,153 −10,515 526 2,861 −637

North bay −19,953 −4,818 −2,714 −1,217 −314 275 −121

West bay −136,545 −14,379 −5,565 −715 1,474 1,239 −347

South bay −103,048 −16,626 −2,560 1,558 2,956 1,707 −191

San Francisco Bay region −480,204 −78,252 −35,992 −10,888 4,642 6,084 −1,295

Percent compared to ABAG projection

−9.4 −1.5 −0.7 −0.2 0.1 0.1 0.0
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 21

Population in the bay region does not recover as fast as 

employment. The region loses about 101,000 people in the 

first year related to reductions in employment. By 2022, the 

population remains about 63,000 below projections (although 

300,000 above the population level in 2018). Ten years after 

the earthquake, the region still has a net deficit of 25,000 

people compared to the ABAG Plan Bay Area 2040 projection. 

By 2040, the region’s population would slightly surpass 

the ABAG Plan Bay Area 2040 projection for the year, by 

around 1,000 people—again an insignificant difference in the 

projection of 9.5 million people in 2040 (table 17).

Effects on Population Beyond Employment

Up to this point in the discussion, the model analysis 

of the earthquake effects on population levels has come 

entirely through the changes to output—economic activity 

changes. Yet, as described by Johnson and others (Long-Term 

Recovery, in prep. [planned to be published as part of this 

volume]), the size of the labor supply and the characteristics 

of the labor force (such as education level, labor participation 

rate, age, and gender) of a region are some factors that 

determine the vitality of its economy. Post-disaster population 

movements and compositional changes that lead to regional 

labor force shifts would affect the economy. There is a broad 

base of household and community-scale socioeconomic 

characteristics that affect population displacement risk 

more than just race, income, and age (Esnard and others, 

2011; Johnson and others, (Population Movements, in prep. 

[planned to be published as part of this volume]). After a 

disaster, businesses might relocate because of damages. They 

may choose more distant locations if the damage has left few 

available spaces. This can also disrupt their customer base, 

and population movement may even lead to the establishment 

of new economic centers outside the affected areas. 

Johnson and others (Population Movements and LongTerm Recovery chapters, in prep. [planned to be published 

as part of this volume]) have extensively discussed areas at 

risk of population displacement and long-term community 

recovery challenges, such as assembling the resources for 

recovery; repairing and replacing damaged housing; finding 

interim housing; overcoming other recovery dependencies, 

such as jobs and neighborhood-serving businesses, 

transportation and utilities, and schools and community 

services; and addressing areas that require substantial 

replanning and intervention in order to recover. They 

estimate that about 216,000 to 268,000 households (almost 

800,000 people and perhaps more) might be displaced in 

the short term in the HayWired scenario. However, longterm population movement remains hard to estimate, partly 

because of a lack of data and the complexity of different 

communities. In New Orleans, Louisiana, it took ten years 

for the city to regain 95 percent of its pre-Hurricane Katrina 

population, whereas in Puerto Rico, still recovering from 

Hurricane Maria, almost 90 percent of a sample of 407,000 

people who left had returned home as of February 2018 

(Echenique and Melgar, 2018).

The degree to which population displacement goes 

beyond the region is also complex to estimate. The region’s 

total housing stock is large relative to the damage—

households displaced from heavily damaged areas may find 

temporary or long-term living situations in other parts of the 

region. On the other hand, were the earthquake to happen 

during a period of very tight housing markets and rapidly 

escalating prices (as in 2017 and 2018), the costs could 

drive households who were barely able to afford their living 

situation prior to the disaster out of the region. 

We used two levers in the model to address population 

displacement—housing prices and senior migration, which is a 

noneconomic migration in terms of how the model is structured.

Table 17. Total difference in population from revised output loss effects of the HayWired scenario, relative to the Association of 

Bay Area Governments Plan Bay Area 2040 projection for the San Francisco Bay region, California.

[Subregions are defined in table 3. Data generated using the Association of Bay Area Governments Planning and Research Department (2016) REMI 

(Regional Economic Models, Inc.) model; analysis of the HayWired scenario from 2018 (this study)]

Subregion 2018 2019 2020 2021 2022 2027 2040

East bay −53,310 −48,627 −46,644 −42,134 −36,447 −16,077 134

North bay −6,799 −6,791 −6,554 −5,900 −5,076 −1,782 55

West bay −22,963 −19,388 −17,275 −14,741 −12,326 −4,196 467

South bay −17,731 −15,100 −12,836 −10,692 −8,786 −3,131 291

San Francisco Bay region −100,802 −89,906 −83,309 −73,467 −62,635 −25,186 947

Percent compared to ABAG projection

−1.3 −1.1 −1.1 −0.9 −0.8 −0.3 0.0
22 The HayWired Earthquake Scenario—Societal Consequences

Post-earthquake Housing Price Changes 

After nearly 170,000 properties were damaged in the 

Christchurch earthquake in New Zealand, the subsequent 

shortage in housing has resulted in a sharp increase in 

housing and rental prices. According to the Reserve Bank of 

New Zealand, housing prices in Christchurch are more than 

40percent higher than their pre-earthquake levels, which is 

more than double the increase that has occurred in the rest of 

New Zealand (Wood and others, 2016). Rents had increased by 

almost 50 percent in Christchurch by the start of 2015 (about 

four years after the initial earthquake in September 2010), 

compared with a nationwide increase of about 15percent 

during that time. The increase in rents has been concentrated in 

the relatively unaffected suburbs because people have moved 

away from harder hit areas. This pattern is consistent with 

what has been observed after the 2017 Northern California 

wildfires. Housing prices in Sonoma County have increased 

faster than the whole San Francisco Bay region (North Bay 

Business Journal, 2018). Rents are also rising in the county 

and neighboring areas outside the immediate affected areas, 

although to a much lesser degree in Marin County; an 

observation perhaps attributable to Marin County’s already 

high housing and rental prices (Halstead, 2018).

In the REMI model, we therefore increased the housing 

and rental prices for the four REMI subregions by the 

percentages shown in table 18 for the 7 years following 

the earthquake. We assumed that the price increase would 

remain relatively small in the first year, peak the fifth year, 

and then drop off as more housing and rental properties 

become available. We used “imputed rental of owner-occupied 

nonfarm” in the REMI model as the equivalent of housing 

sales price, and “rental of tenant-occupied nonfarm housing” 

for rents.

Post-earthquake Senior Outmigration 

Assumption

The housing price variable in combination with the 

economic variables captures some of the earthquake induced 

outmigration. Experience with the effects of the 2017 

Northern California wildfires suggests that senior households 

may be particularly vulnerable to displacement, because of 

the costs and time involved in rebuilding. In this analysis, we 

chose to focus on retired migrants. Specifically, we assumed 

that seniors 65 years old and above that live with only a 

spouse, live in nonfamily households, or in group quarters 

in areas with concentrated damage would leave the region 

and not return. In 2016, there were 97,800 (5-year, American 

Community Survey, 2016) seniors in those heavily affected 

areas, more than 50 percent (50,400) of them meet the criteria 

for our assumption in this analysis. As such, we increased the 

outflow of retired migrants by 25,200 people in 2018, and by 

another 25,200 people in 2019 in the model.

We added these population effects into the REMI model 

with the effects on population from output changes described 

earlier. Job loss attributed to output losses from building 

damage, commute flow interruption, housing cost increases, 

and senior population displacement is estimated at around 

515,000 jobs in 2018, around 96,000 five years after, and by 

2040, the region still has a net loss of 10,000 jobs (table 19). 

Additionally, population loss for the region as a whole from 

the same effects, relative to projected levels, is estimated at 

around 150,000 people in 2018, around 264,000 people five 

years after, and by 2040, there remains a net loss of 23,000 

people (table 20).

Table 18. Post-earthquake housing and rental price increases by year after the event, in percent, for the HayWired scenario in the San 

Francisco Bay region, California.

[Subregions are defined in table 3]

Detail Subregion 1st 2d 3d 4th 5th 6th 7th

Imputed rental of owner-occupied nonfarm housing East bay 5 5 10 15 30 5 5

Imputed rental of owner-occupied nonfarm housing North bay 5 10 15 20 30 5 10

Imputed rental of owner-occupied nonfarm housing West bay 5 5 10 10 10 5 5

Imputed rental of owner-occupied nonfarm housing South bay 5 10 15 20 30 5 10

Rental of tenant-occupied nonfarm housing East bay 5 5 15 20 30 5 5

Rental of tenant-occupied nonfarm housing North bay 10 10 15 20 30 10 10

Rental of tenant-occupied nonfarm housing West bay 5 5 10 15 20 5 5

Rental of tenant-occupied nonfarm housing South bay 10 10 15 20 30 10 10
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 23

Table 19. Total employment difference from all loss effects in the HayWired scenario, relative to the Association of Bay Area 

Governments Regional Controls Forecast for the San Francisco Bay region, California.

[Difference in employment numbers is listed as negative where jobs are lost and positive where gained. Subregions are defined in table 3. Data generated 

using the Association of Bay Area Governments Planning and Research Department (2016) REMI (Regional Economic Models, Inc.) model; analysis of 

the HayWired scenario from 2018 (this study)] 

Subregion 2018 2019 2020 2021 2022 2027 2040

East bay −238,518 −74,107 −60,033 −49,803 −48,398 −736 −3,149

North bay −22,844 −10,136 −10,194 −10,440 −12,164 −780 −1,224

West bay −145,844 −25,566 −22,522 −21,334 −25,422 −1,058 −3,619

South bay −107,998 −22,300 −10,550 −8,425 −9,622 1,635 −2,380

San Francisco Bay region1 −515,204 −132,108 −103,299 −90,002 −95,606 −940 −10,373

Percent compared to ABAG projection

−10.1 −2.6 −2.0 −1.8 −1.9 0.0 −0.2

1

Because of rounding, sum totals may not be exact.

Table 20. Total population difference from all loss effects in the HayWired scenario, relative to the Association of Bay Area 

Governments Regional Controls Forecast for the San Francisco Bay region, California.

[Subregions are defined in table 3. Data generated using the Association of Bay Area Governments Planning and Research Department (2016) REMI 

(Regional Economic Models, Inc.) model; analysis of the HayWired scenario from 2018 (this study)]

Subregion 2018 2019 2020 2021 2022 2027 2040

East bay −86,779 −114,681 −119,304 −124,574 −136,189 −77,351 −8,065

North bay −9,555 −13,575 −18,353 −23,220 −29,483 −17,126 −2,743

West bay −30,516 −32,132 −38,904 −46,389 −57,828 −30,583 −5,404

South bay −23,114 −24,154 −27,760 −32,784 −40,533 −24,214 −66,56

San Francisco Bay region1 −149,965 −184,541 −204,321 −226,968 −264,033 −149,274 −22,867

Percent compared to ABAG projection

−1.9 −2.4 −2.6 −2.9 −3.3 −1.8 −0.2

1

Because of rounding, sum totals may not be exact.

Assumptions and Simulated Effects 

within the REMI Model—Part 2: 

Rebuilding the Region

The elements described above consist of losses 

directly caused by building damage, transportation network 

interruption from the earthquake, and potential housing 

price increase, as well as senior outmigration, that reflect the 

displacement risks and recovery challenges in the region. In 

the following section we look at how Federal disaster relief 

funding and State and local government spending affects the 

level and pace of rebuilding.

Government Response and Recovery Investment

Following an earthquake of such scale (Mw 7.0), 

disaster declarations at the State and Federal level would be 

expected. For Federal fiscal years 2005 through 2014, FEMA 

obligated more than $104.5 billion from the Disaster Relief 

Fund for federally declared disasters across the country 

(U.S. Government Accountability Office, 2016), of which 

$25.4 billion was obligated for the Individual Assistance 

programs, $45.7 billion for Public Assistance programs, and 

$5.4 billion for the Hazard Mitigation Assistance program. 

The remaining $28 billion paid for FEMA administrative 

and mission assignment related costs. In addition to financial 
24 The HayWired Earthquake Scenario—Societal Consequences

assistance available from the Disaster Relief Fund, during the 

same 10-year period, at least $173.1 billion across 17 Federal 

departments and agencies was provided for Federal disasterspecific and disaster-applicable programs and activities 

(U.S. Government Accountability Office, 2016), such as 

the Small Business Administration (SBA), which provides 

federally subsidized loans to repair or replace homes, personal 

property, or businesses that sustained damages not covered by 

insurance following a disaster, the Department of Housing and 

Urban Development Community Development Block Grant 

Program, the U.S. Department of Transportation Federal-Aid 

Highway Emergency Relief Program, the U.S. Department of 

Agriculture and Rural Assistance, and the U.S. Army Corps of 

Engineers Emergency Assistance.

There are three principal forms of Federal financial 

assistance from FEMA: 

Public Assistance Program (FEMA, 2010).—

This program provides grants to tribal, State, and local 

governments, and certain private nonprofit organizations to 

conduct debris removal operations (category A); provides 

emergency protective services (category B); and repairs 

or replaces damaged public infrastructure (category C-G). 

Although certain nonprofit organizations may be eligible for 

these grants, for-profit businesses are not.

Individual Assistance Program.—This program provides 

direct aid to affected households, can take the form of 

housing assistance (temporary housing, lodging expenses 

reimbursement, repair, replacement, and permanent or 

semipermanent housing construction), other needs assistance 

(child care, medical and dental expenses, funeral expenses, 

household, personal and work items, fuel, and moving 

expenses), crisis counseling, case management services, 

legal services, and disaster unemployment assistance. Total 

assistance under this program is capped at $33,300 per 

household, though that amount is adjusted annually (see 

https://www.fema.gov/individual-disaster-assistance for details 

on the program).

Hazard Mitigation Assistance Program.—This program 

funds mitigation and resiliency projects and programs 

typically across the entire State. This form of assistance is also 

cost shared. Mitigation projects can include the construction 

of safe rooms, buy-outs of frequently flooded properties, and 

retrofitting of facilities.

For Hurricane Katrina, Federal spending covered 

72percent of its damages, or $115 billion for the loss of 

$160 billion, whereas 80 percent of Hurricane Sandy’s 

damages were covered by Federal spending. On average, the 

Federal Government has contributed funds to cover 62percent 

of estimated damages for hurricanes that happened between 

Hurricanes Katrina and Sandy (Struyk, 2017). After the 2014 

South Napa earthquake, financial assistance from FEMA 

and the California Governor’s Office of Emergency Services 

reached $40.5 million, including $10.4 million in approved 

Housing Assistance Grants, $1.0 million in approved Other 

Needs Assistance Grants, and $5.5 million obligated Public 

Assistance Grants in categories A–B and $22.5 million in 

categories C–G (U.S. Department of Homeland Security 

and Federal Emergency Management Agency, 2014a). An 

additional $30.7 million in home disaster loans was given 

to 971 applicants and $10.7 million of business disaster 

loans to 125 applicants who were approved by the SBA 

(Carol Chastang, Public Affairs Specialist, U.S. SBA, 

written commun., 2018). SBA loans were included in the 

background analysis because they are an essential part of 

the recovery process that would increase spending in the 

economy. However, the potential effect of loan repayments 

was not included as part of the analysis. For the October 2017 

Northern California wildfires, $18.0 million was approved 

under the Individual Assistance Program, and $357.9 million 

was approved under the Public Assistance Program (U.S. 

Department of Homeland Security and Federal Emergency 

Management Agency, 2014b). The SBA provided $151 million 

total in home and business disaster loans (Carol Chastang, 

Public Affairs Specialist, U.S. SBA, written commun., 2018). 

These recent figures were used to estimate the distribution of 

Government financial assistance, as discussed below.

Financial assistance from the Federal and State 

Governments is vital for the recovery and rebuilding of the 

region in the HayWired scenario because only 10 percent 

of residential fire insurance policyholders have earthquake 

insurance, and assistance from nonprofit organizations and 

foundations will be limited. CoreLogic, a real estate data 

company, estimates that insurance will only be able to cover 

17.6 percent of the estimated losses—Federal assistance 

is important to bridge the funding gaps for the region’s 

recovery (CoreLogic, 2018). Our model uses the assumption 

that insurance will only cover 15 percent of the estimated 

losses. Taking into account the timeline for releasing and 

spending approved grants and the cost-share factor between 

Federal, State, and local governments regarding governments’ 

response, we assumed:

• Insured loss is 15 percent of the total estimated 

building-damage-related loss of $85 billion (2016 

value). This consists of building, content loss, 

inventory, relocation cost, income, rent, and wage 

losses only for the nine San Francisco Bay region 

counties as estimated by Seligson and others (2018);

• Government assistance from all levels covers twothirds of the uninsured loss; one-third of the uninsured 

loss is not recovered;

• Two-thirds of government assistance is spent on 

rebuilding, the other one-third covers all administrative 

functions;
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 25

• State and local governments are responsible for 

one-fourth of all government assistance, the rest is 

exogenous Federal spending into the region;

• Spending would be used throughout a 9-year period 

distributed by 10, 10, 20, 20, 15, 10, 5, 5, and 5 percent 

from the first to the ninth year; and 

• Distribution of grants by San Francisco Bay subregion 

is based on the distribution of building damage related 

losses by subregion.

At the same time, spending from government, businesses, 

and individuals with the money received as part of the 

recovery could be seen as reinvestment into the region’s 

economy: rebuilding and restoration of housing units, 

commercial properties, and public facilities will create huge 

demand in the construction sector, consumer spending on 

apparel and household items, and educational and healthcare 

services will also spur the recovery and growth of the 

economy. During 2011, when three magnitude 6 aftershocks 

affected Christchurch, New Zealand in February, June, and 

December, employment in the region kept falling through 

mid-2012 and has since risen by about 16 percent as of early 

2016. The ratio of construction employment in Canterbury, 

New Zealand, to nationwide construction jobs has doubled, 

whereas corresponding ratios in other industries have fallen 

or remained flat (Wood and others, 2016). Retail sales in 

Christchurch held up well relative to nationwide sales in 

the initial period after the earthquakes, but experienced a 

noticeable increase since 2012, which slowed down in mid2015. At the same time, import volumes into Canterbury have 

increased by more than nationwide imports as rebuild-related 

materials and replacement goods have been brought in (Wood 

and others, 2016). Given the observed delay in construction 

starts and retail sales increases, in this analysis, we assume:

• Three-fourths of government nonadministrative 

spending would be spent in the construction sector, and 

the remaining quarter would be spent on 28 selected 

consumer commodities (appendix 6);

• Spending in construction would be spread across a 

9-year period, as recommended by volume coauthor 

Laurie Johnson (Laurie Johnson Consulting, written 

commun., 2018), based on her experience with the 

pace of recovery in other U.S. natural disasters, distributed by 10, 10, 20, 20, 15, 10, 5, 5, and 5 percent from 

the first to ninth year;

• Spending in consumer products would be used 

throughout a 10-year period distributed by 2.5, 7.5, 10, 

20, 20, 15, 10, 7.5, 5, and 2.5 percent from the first to 

tenth year; 

• Distribution of grants by San Francisco Bay subregion 

is based on the distribution of building damage related 

losses (the sum of building, contents, and inventory 

repair and replacement costs, relocation costs, income 

losses, and output losses) by subregion.

Government spending and recovery have a stimulating 

effect on the economy that can counteract some of the job 

and population losses. To understand the effect of government spending alone on employment and population, we first 

increased government spending in the ABAG Regional Control 

Forecast without including the effects of output loss or population outmigration loss. When added to the ABAG Regional 

Control Forecast, government spending creates 19,000 jobs in 

2018, which increases to more than 39,000 in 2020 as the speed 

of recovery picks up. The effects disappear after the grants are 

exhausted. Meanwhile, the economic activity stimulated from 

this spending brings an additional 5,000 people to the San 

Francisco Bay region in 2018, and as many as 27,000 by 2022. 

By 2040, and assuming no further damaging earthquakes, the 

cumulative effects of recovery spending are no longer apparent 

compared to expected levels from the ABAG Regional Control 

Forecast. However, recovery would have been delayed without 

the recovery spending (tables 21, 22).

Table 21. Total employment gain from recovery spending, relative to the Association of Bay Area Governments Plan Bay Area 

2040 projection for the San Francisco Bay region, California.

[Subregions are defined in table 3. Data generated using the Association of Bay Area Governments Planning and Research Department (2016) REMI 

(Regional Economic Models, Inc.) model; analysis of the HayWired scenario from 2018 (this study)]

Subregion 2018 2019 2020 2021 2022 2027 2040

East bay 10,857 12,183 22,142 24,313 19,582 −91 443

North bay 637 804 1,556 1,822 1,529 78 58

West bay 3,418 3,844 6,933 7,594 5,894 −308 114

South bay 4,177 4,539 8,305 8,743 6,650 −847 72

San Francisco Bay region1 19,089 21,370 38,936 42,472 33,654 −1,169 686

1

Because of rounding, sum totals may not be exact.
26 The HayWired Earthquake Scenario—Societal Consequences

Table 22. Total population gain from recovery spending in the HayWired scenario, relative to the Association of Bay Area 

Governments regional projection for the San Francisco Bay region, California.

[Subreginos are defined in table 3. Data generated using the Association of Bay Area Governments Planning and Research Department (2016) REMI 

(Regional Economic Models, Inc.) model; analysis of the HayWired scenario from 2018 (this study)] 

Subregion 2018 2019 2020 2021 2022 2027 2040

East bay 3,190 5,931 11,275 16,099 18,838 14,645 1,484

North bay 239 481 948 1,410 1,693 1,314 86

West bay 520 969 1,794 2,522 2,848 1,710 31

South bay 651 1177 2,199 3,062 3,462 2,115 91

San Francisco Bay region1 4,600 8,558 16,216 23,093 26,841 19,784 1,691

1

Because of rounding, sum totals may not be exact. 

HayWired Scenario Combined Effects 

on Employment and Population

Running the REMI model with all of the assumptions 

included shows a regional economy that could experience 

a deep recession (or depression in the east bay subregion 

of the San Francisco Bay region). It would ultimately 

recover, but some parts of the region do not recover to 

pre-earthquake employment levels until about 2025 (fig. 5A). 

Compared to projected employment and population levels, 

the region returns to within about 1 percent of the projected 

employment track by 2025 (fig. 5B). The absolute population 

in the region recovers more quickly than the employment 

totals (fig. 6A), but compared to the regional projection 

without an earthquake, the population level is reduced for 

more than a decade, especially in the east and north bay 

subregions (fig. 6B).

80

85

90

95

100

105

110

115

120

125

130

A

East bay

North bay

West bay

South bay

Bay region

Rest of California

75

80

85

90

95

100

105

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040

B

EXPLANATION

Year

Percent employment relative to

ABAG projections Percent employment relative to 2017

Figure 5. Graphs of 

employment curves in 

the HayWired scenario 

for the San Francisco 

Bay region, California, 

relative to 2017 levels (A) 

and the Association of 

Bay Area Governments 

(ABAG) Plan Bay Area 

2040 projection (B). 

Data generated using 

the ABAG Planning and 

Research Department 

(2016) REMI model; 

analysis of the HayWired 

scenario from 2018 

(this study). East, west, 

north, and south bay are 

defined in table 3. Bay 

region refers to the San 

Francisco Bay region. 

REMI refers to models 

produced by Regional 

Economic Models, Inc.
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 27

men19-7347_fig06

80

90

100

110

120

130

140

A

93

94

95

96

97

98

99

100

101

B

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040

Year

East bay

North bay

West bay

South bay

Bay region

Rest of California

EXPLANATION

Population change (in percent) relative to

PBA 2040 forecast

Population change (in percent) relative to

2017 levels

Figure 6. Graphs of population curves in the HayWired scenario for the San Francisco Bay region, 

California, relative to 2017 levels (A) and the Association of Bay Area Governments (ABAG) Plan 

Bay Area 2040 (PBA2040) projection (B). Data generated using the ABAG Planning and Research 

Department (2016) REMI model; analysis of the HayWired scenario from 2018 (this study). East, west, 

north, and south bay are defined in table 3. Bay region refers to the San Francisco Bay region. REMI 

refers to models produced by Regional Economic Models, Inc. 
28 The HayWired Earthquake Scenario—Societal Consequences

Employment Effects

All assumptions included, the region’s net employment 

loss in the first year is more than 496,000 jobs, a 10 percent 

decrease compared to the regional control forecast (fig. 5B). 

All sectors experience job losses in the first few years after 

the earthquake. Food service and drinking places has the 

largest employment loss in 2018, almost 59,000 jobs (19 

percent), followed by professional services (44,000 jobs 

or 7 percent), retail trade (43,000 jobs or 10 percent), and 

administrative services sector (32,000 jobs or 10 percent). 

By 2040, the region has a net job loss of 10,000, which 

can be attributed to losses in all sectors except petroleum 

manufacturing and apparel manufacturing (fig. 7). Appendix 

5 shows detailed industry net employment change. 

−600,000

−500,000

−400,000

−300,000

−200,000

−100,000

0

100,000

20182019202020212022202320242025

202620272028202920302031203220332034203520362037203820392040

Farm

Government

Other services, except public administration

Accommodation and food services

Arts, entertainment, and recreation

Health care and social assistance

Educational services

Administrative and waste management services

Management of companies and enterprises

Professional, scientific, and technical services

Real estate and rental and leasing

Finance and insurance

Information

Transportation and warehousing

Retail trade

Wholesale trade

Manufacturing

Construction

Utilities

Mining

Forestry, fishing, and related activities

EXPLANATION

Number of jobs

Figure 7. Collins diagram showing San Francisco Bay region, California, 

net employment change by sector in 2018–2040 for the HayWired scenario. 

Data from the Association of Bay Area Governments Planning and Research 

Department (2016) REMI model version 1.7.8, adjusted control NC3RC1. REMI 

refers to models produced by Regional Economic Models, Inc.
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 29

Population Effects

Combining the loss and stimulus effects, the San 

Francisco Bay region population decreases by about 145,000 

in 2018, of which a decrease of 83,000 people occurs in the 

east bay subregion. Population net loss, especially from senior 

outmigration after the earthquake in the east bay subregion, 

continues to drive the earthquake’s negative effects on the 

region’s population until 2040. Housing price increases in 

other subregions also drive residents outside of the bay region. 

By 2040, the bay region population remains at a net loss of 

about 21,000 people compared to the ABAG Regional Control 

Forecast (fig. 8).

Gross Regional Production Effects

Overall, San Francisco Bay region GRP decreases by 

about $67 billion in 2018, almost 10 percent of GRP, of 

which a $26 billion loss occurs in the east bay subregion, 

$22 billion in the west bay subregion, and another $16 billion 

in the south bay subregion. The north bay subregion loss is 

the smallest, approximately $2 billion. Loss in GRP changes 

as recovery activities enter different stages. By 2040, the bay 

region GRP remains a net loss of about $1 billion relative 

to GRP projections from the Plan Bay Area 2040 regional 

projection, or less than one-tenth of one percent of the plan’s 

projected GRP (fig. 9).

−250,000

−200,000

−150,000

−100,000

−50,000

0

20182019202020212022202320242025202620272028202920302031203220332034

203520362037203820392040

East bay subregion

North bay subregion

West bay subregion

South bay subregion

Net population difference 

EXPLANATION

Year

Figure 8. Collins diagram 

showing San Francisco 

Bay region, California, net 

population difference by 

subregion for the HayWired 

scenario compared to the 

Association of Bay Area 

Governments (ABAG)

Regional Control Forecast. 

Data from ABAG Planning and 

Research Department (2016) 

REMI model version 1.7.8, 

adjusted control NC3RC1. 

Subregions are defined 

in table 3. REMI refers to 

models produced by Regional 

Economic Models, Inc.

−80

−70

−60

−50

−40

−30

−20

−10

0

10

20182019202020212022

20232024202520262027202820292030

20312032203320342035203620372038

20392040

GRP, in billions of 2016 U.S. dollars

Year

East bay subregion

North bay subregion

West bay subregion

South bay subregion

EXPLANATION

Figure 9. Collins diagram 

of San Francisco Bay 

region, California, net 

gross regional product 

(GRP) change by subregion 

for the HayWired 

scenario compared to the 

Association of Bay Area 

Governments (ABAG) 

Regional Control Forecast. 

Data from ABAG Planning 

and Research Department 

(2016) REMI model version 

1.7.8, adjusted control 

NC3RC1. Subregions are 

defined in table 3. REMI 

refers to models produced 

by Regional Economic 

Models, Inc.
30 The HayWired Earthquake Scenario—Societal Consequences

Summary of REMI Simulation of HayWired 

Scenario Effects

This analysis shows an economy that would experience 

substantial damages and losses in the short term from a 

magnitude 7.0 earthquake on the Hayward Fault. Much of 

the economy could recover within a few years, although a 

full return to the previous trajectory could take more than 

five years for the region and closer to a decade for the 

most severely affected counties. Recovery and rebuilding 

investments will be crucial to repairing the economic base of 

the region and returning to its projected growth trajectory.

The most severe economic losses are concentrated in 

the east bay subregion, where in 2018, there are reductions 

in GRP by 13 percent, employment by 15 percent, and 

population by 3 percent (table 23). Other parts of the region, 

Table 23. Summary of year 1, year 5, year 10, and 2040 employment, population, and gross regional product (GRP) levels in the 

HayWired scenario relative to Plan Bay Area 2040 projections for the San Francisco Bay region, California.

[Subregions are defined in table 3. Negative percentage change in parentheses. GRP change is in billions of 2016 U.S. dollars. Data from the Association of Bay 

Area Governments Planning and Research Department (2016) REMI (Regional Economic Models, Inc.) model version 1.7.8, adjusted control NC3RC1]

Subregion 2018 2022 2027 2040

Employment

East bay −227,564 −28,837 −915 −2,678

(0.15) (0.02) (0.00) (0.00)

North bay −22,213 −10,661 −706 −1,164

(0.04) (0.02) (0.00) (0.00)

West bay −142,438 −19,530 −1,389 −3,492

(0.09) (0.01) (0.00) (0.00)

South bay −103,827 −2,959 772 −2,300

(0.08) (0.00) 0.00 (0.00)

San Francisco Bay region −496,042 −61,987 −2,238 −9,634

(0.10) (0.01) (0.00) (0.00)

Population

East bay −82,978 −117,468 −62,738 −6,738

(0.03) (0.04) (0.02) (0.00)

North bay −9,313 −27,874 −15,846 −2,648

(0.01) (0.03) (0.02) (0.00)

West bay −30,011 −55,005 −28,886 −5,349

(0.02) (0.03) (0.01) (0.00)

South bay −22,408 −37,122 −22,120 −6,559

(0.01) (0.02) (0.01) (0.00)

San Francisco Bay regiona −144,710 −237,469 −29,590 −21,294

(0.02) (0.03) (0.02) (0.00)

GRP change

East bay −26.07 −5.15 0.40 −0.07

 (0.13) (0.02) 0.00 (0.00) 

North bay −2.31 −1.44 −0.02 −0.11

 (0.03) (0.02) (0.00) (0.00)

West bay −22.29 −6.34 0.00 −0.42

 (0.08) (0.02) 0.00 (0.00)

South bay −16.21 −1.54 0.42 −0.26

 (0.06) (0.01) 0.00 (0.00) 

San Francisco Bay region −66.88 −14.47 0.80 −0.86

 (0.08) (0.02) 0.00 (0.00)
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 31

as well as neighboring counties, also experience losses and 

disruptions to work caused by building damage. These losses 

are exaggerated by disruptions to the transportation system. In 

2018, the west bay subregion loses 8 percent in GRP, 9 percent 

in employment, and 2 percent in population; the south bay 

subregion loses 6 percent in GRP, 8 percent in employment, 

and 1 percent in population; and the north bay subregion loses 

3 percent in GRP, 4 percent in employment, and 1 percent in 

population. The rest of California is estimated to lose more 

jobs than the west and south bay subregions (171,000 in places 

outside the bay region) and more people than the south bay 

subregion (23,000) in 2018. Although bay region employment, 

population, and GRP fall below the ABAG Regional Control 

Forecast for Plan Bay Area 2040, the negative effects are 

insignificant by 2040.

The HayWired analysis shows employment losses 

experienced in the east bay subregion will be much sharper 

and deeper than during recent recessions but will have 

a faster rate of recovery than from either the “dot-com” 

(2001–2004) or financial crisis (2008–2010) related recessions 

(fig.10A). For the San Francisco Bay region as a whole 

(fig.10B), employment losses are of a magnitude similar to 

that experienced in the “dot-com” bust, but it ultimately took 

the region much longer to recover the jobs lost then than the 

recovery illustrated here. 

Achieving this pace of recovery happens naturally in the 

model but in actuality is not guaranteed, nor is it independent 

of policy actions taken before or after an earthquake. The 

following sections discuss limitations to the analysis, how 

different assumptions could affect the model results, and 

finally how policies before and after the earthquake may also 

determine the outcomes for individuals, communities, and the 

region as a whole.

Caveats and Limitations 

In a number of ways, this economic analysis incorporates 

significant resilience features. The REMI model has built-in 

resilience, with a tendency to return to long-term trends over 

time. Our application of assumptions also includes some 

resilient features, with the expectation that transportation 

disruptions are moderated by worker behavior (finding 

alternative travel routes), employer adjustments (encouraging 

telecommuting), and a range of supports for housing workers 

close to jobs. 

The assumptions provided by major transportation and 

utility providers also suggest expectations of a sequence of 

repairs that would allow the economy to return to normal 

within a year. Water, utilities, and communications are 

expected to be mostly restored within a few weeks to a couple 

of months. Where repairs are expected to take longer (for 

example, the BART line running south of Oakland to Fremont 

on fig. 2B), alternative transportation modes (buses, ferries, 

carpools, and individual drivers) can provide alternative travel 

options, once bridges are repaired. Some water repairs in 

limited parts of Alameda and Contra Costa Counties could 

take as long as six months, but this is incorporated in the 

population relocation and business dislocation assumptions.

If the restoration of services proceeds as assessed by 

the providers, then most of the negative effects occur in 

80

90

100

110

120

130

1 2 3 4 5 6 7 8 9 10 11

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11

Index (Relative to Peak)

Years after Peak

B. San Francisco Bay region

A. East bay

Figure 9. Recovery Progression from Pre-recession Peak to Peak of Recovery or Next Upturn 

Compared to Job Projection in HayWired Scenario

1990–2000

2000–2007

2007–2016

HayWired

EXPLANATION

Figure 10. Graphs showing the recovery progression from prerecession peak to peak of recovery or next upturn for 

the east bay subregion (A) and San Francisco Bay region, California (B), compared to job projection in the HayWired 

scenario. Data from California Employment Development Department (2018) and Association of Bay Area Governments 

Planning and Research Department (2016) REMI model version 1.7.8, adjusted control NC3RC1. REMI refers to models 

produced by Regional Economic Models, Inc. 
32 The HayWired Earthquake Scenario—Societal Consequences

the first year. The job loss returns to below one percent of 

forecasted employment and population levels in the second 

year, assuming continued access to labor and goods from 

outside of the region and assuming substitution between labor 

and capital (that is, the built-in resilience of the economy in 

the model). Government recovery assistance is a key factor 

in this assessment, which significantly reduces the negative 

consequences of the earthquake through time and expedites 

the recovery process. In reality (as opposed to the model), the 

effectiveness of this recovery assistance will depend not only 

on the amount of dollars provided but on the capacity of State 

and local government to effectively allocate the resources and 

to efficiently manage the recovery efforts. 

A number of factors and assumptions could change the 

outcome of this analysis. On the positive side, the Hazus-based 

estimate of building damage in 2018 could be high when 

applied as a percentage, especially for multifamily and office 

buildings. To the extent that the overall stock has a higher 

share of newly constructed buildings than the base used for the 

Hazus assessment, the share of buildings and square footage 

that experience extreme or complete damage may be lower 

than assumed for this analysis. Effective retrofit programs on 

residential units could reduce the number of displaced residents 

and decrease rebuilding costs after the event. These are not 

included in the model because of the timing of the event. Were 

this type of event to happen instead five years hence and were 

effective retrofits in place in a substantial number of buildings, 

the negative effects would be reduced.

Inclusion of insurance payments as a portion of 

rebuilding investment could reduce the amount of government 

spending cutbacks required, softening the adjustments made 

to the overall effects of government spending. We did not 

explicitly separate out insurance payments in the assessment 

because they would not be large relative to the damage. 

The built-in resilience in the model could be interpreted as 

including natural reinvestment that occurs in an area following 

a downturn, which could include the outside insurance 

payments. In contrast, government payments are an additional 

outside stimulus that would not be available to the region’s 

economy without the earthquake and explicit recovery policy 

by Federal, State, and local agencies.

Our application of the REMI model does not model 

the unanticipated future evolution of industry in the San 

Francisco Bay region. For example, just as the “birth” 

of Silicon Valley provided a huge economic stimulus to 

the region, unforeseen new industries that could develop 

before 2040 would affect the amount of investment, the 

characteristics of industries, and, therefore, the speed of 

economic recovery, in the region. The model and analysis also 

do not explicitly include possible new industries that could 

emerge from the earthquake—for example, technology fixes 

to apply to seismically risky areas or industries that flourish as 

low-cost space emerges in heavily affected areas. The built-in 

resiliency feature of the model essentially relies on the ability 

of the region to respond in this way but, it does not assume the 

region recovers in a stronger growth path than would occur 

otherwise. Similarly, the model does not explicitly address 

changes in occupational training and labor productivity that 

would come from a significant structural change in the 

recovering economy. The San Francisco Bay region has a 

large enough economy that even with the devastating effects to 

the east bay subregion, the majority of the basic industries that 

are the major drivers of the economy would likely continue to 

operate, albeit at a reduced or adjusted level for location in the 

short run.

There are other factors that could lead to long-term 

changes in the outcome of the scenario. We have tried to 

account for a number of these effects with the model, but the 

levels of effect used could be too optimistic:

• Rising home prices and rents may move unevenly. 

Sales may be curtailed in the worst hit parts of 

the region, and land prices may drop, leading to 

foreclosures on loans, homelessness, or departure from 

the region for additional households.

• Higher wages paid to construction workers and worker 

shortages may delay rebuilding and repairs.

• Population displacement effects could persist for 

longer, especially in heavily affected parts of the east 

bay subregion. The slower repair of water systems 

in some of the heavily affected areas, not explicitly 

included, could increase the population displacement 

effects. The negative effect on population could be 

further exaggerated if inmigration is dampened for 

several years in the future, extending other effects as 

well for a longer period of time. 

• We may have been too optimistic in our assessment 

of the ability of commuters to make flexible work 

arrangements or their willingness to adapt to alternate 

(and more time-consuming) routes. 

• Branch operations may be closed down permanently as 

large employers reroute operations to other locations in 

the United States or globally. 

• The need for planning for heavily affected areas may 

delay rebuilding even beyond the delays incorporated 

into the model. 

• The earthquake could have a significant effect 

on local revenues and spending, beyond the shift 

in spending towards reconstruction activities. 

Places damaged by natural disasters are generally 
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 33

reassessed, lowering the property tax revenues to 

local jurisdictions. This could cause sharp budgetary 

cuts, with consequent employment and income 

implications for some smaller east bay cities, where 

real-estate-related taxes and fees can account for close 

to half of all revenues, as estimated by the authors 

using California State Controller 2016 city budget 

data (California State Controller’s Office, 2018). A 

back-of-the-envelope analysis shows that the share 

of lost revenues linked directly to building damage 

could be as high as 10 percent in smaller cities near 

the epicenter, and these shares could be substantially 

higher if inaccessible areas (owing to concentrated 

neighborhood damage and cordons) are considered.

Sensitivity Tests

Sensitivity tests were run on how changes in a few postevent factors could affect the results.

Construction Costs

If construction costs are increased for a 5-year period 

relative to their assumed levels in the model (by 20 percent in 

the first year, 40 percent in the second, 60 percent in the third 

and fourth years, and 40 percent in the fifth year) to reflect the 

unusually high demand for workers and construction materials, 

then the result is an additional 124,000 jobs lost in 2018, which 

expands to a loss of more than 401,000 more jobs in 2020 and 

2021 when construction demand is particularly high because 

of recovery spending. By 2040, the San Francisco Bay region 

would lose an additional 22,000 jobs. The east bay and west 

bay subregions would lose an additional 174,000 and 129,000 

jobs, respectively, in 2021. Bay region employment would not 

recover to the pre-earthquake level until after 2024. Population 

loss would increase by 47,000 people in 2018, and population 

levels would be lower by 348,000 in 2022. By 2040, the bay 

region population would be an additional 50,000 people fewer 

than in the primary HayWired scenario analysis described 

above. Table 24 shows the effects on the east bay subregion 

employment, population, and GRP; table 25 summarizes 

effects for the entire bay region.

Displaced Population

We tested the effects of a higher displacement rate. We 

assumed 25 percent of the displaced population (estimated 

to be 720,000 people displaced in addition to the senior 

population migration assumed in previous runs) leave the San 

Table 24. Employment, population, and gross regional product 

(GRP) effects with higher construction costs in the HayWired 

scenario relative to Plan Bay Area 2040 projections for the east 

bay subregion of the San Francisco Bay region, California.

[Negative percentage change in parentheses. East bay subregion refers to 

Alameda and Contra Costa Counties. Data from the Association of Bay Area 

Governments Planning and Research Department (2016) REMI (Regional 

Economic Models, Inc.) model version 1.7.8, adjusted control NC3RC1]

Affected 

categories 2018 2022 2027 2040

Employment −274,559 −184,745 −14,652 −8,699

(0.18) (0.12) (0.01) (0.01)

Population −104,263 −268,555 −165,078 −23,611

(0.04) (0.10) (0.06) (0.01)

GRP, in billions 

of 2016 U.S. 

dollars

−32.13 −27.37 −2.38 −1.19

(0.16) (0.13) (0.01) (0.01)

Table 25. Employment, population, and gross regional product 

(GRP) effects with higher construction costs in the HayWired 

scenario relative to Association of Bay Area Governments Plan 

Bay Area 2040 projections for the San Francisco Bay region, 

California. 

[Negative percentage change in parentheses. Data from the Association of 

Bay Area Governments Planning and Research Department (2016) REMI 

(Regional Economic Models, Inc.) model version 1.7.8, adjusted control 

NC3RC1]

Affected 

categories 2018 2022 2027 2040

Employment −619,322 −474,817 −49,427 −31,567

(0.12) (0.09) (0.01) (0.01)

Population −192,190 −587,373 −367,948 −70,814

(0.03) (0.07) (0.04) (0.01)

GRP, in billions 

of 2016 U.S. 

dollars

−85.68 −83.3 −13.09 −10.71

(0.10) (0.09) (0.01) (0.01)

Francisco Bay region in 2018. This is distributed according 

to each subregion’s share of building and content loss (for 

example, 110,175 people from the east bay, 1,763 people from 

the north bay, 25,869 people from the west bay, and 42,193 

people from the south bay). This displacement could lead to an 

additional 36,000 job loss, 191,000 population loss, and $4.5 

billion in GRP loss in 2018. Because of higher outmigration 

compared to other subregions, total population in the east bay 

subregion would not recover to its pre-earthquake level until 
34 The HayWired Earthquake Scenario—Societal Consequences

Table 26. Employment, population, and gross regional product (GRP) effects with higher outmigration in the HayWired scenario 

relative to Association of Bay Area Governments Plan Bay Area 2040 projections for the east bay subregion of the San Francisco Bay 

region, California.

[Negative percentage change in parentheses. East bay subregion refers to Alameda and Contra Costa Counties. Data from the Association of Bay Area 

Governments Planning and Research Department (2016) REMI (Regional Economic Models Inc.) model version 1.7.8, adjusted control NC3RC1]

Affected categories 2018 2022 2027 2040

Employment −253,103 −36,186 −5,793 −4,340

(0.17) (0.02) (0.00) (0.00)

Population −203,217 −187,147 −99,364 −7,195

(0.07) (0.07) (0.04) (0.00)

GRP, in billions of 2016 U.S. dollars −28.98 −5.84 0.02 −0.06

(0.14) (0.03) 0.00 (0.00)

Table 27. Employment, population, and gross regional product (GRP) effects with higher outmigration in the HayWired scenario 

relative to Association of Bay Area Governments Plan Bay Area 2040 projections for the San Francisco Bay region, California.

[Negative percentage change in parenthesis. Data from the Association of Bay Area Governments Planning and Research Department (2016) REMI (Regional 

Economic Models Inc.) model version 1.7.8, adjusted control NC3RC1] 

Affected categories 2018 2022 2027 2040

Employment −532,202 −73,353 −12,159 −13,665

(0.11) (0.01) (0.00) (0.00)

Population −335,925 −344,202 −185,445 −24552

(0.04) (0.04) (0.02) (0.00)

GRP, in billions of 2016 U.S. dollars −71.4 −15.708 −0.357 −1.071

(0.09) (0.02) (0.00) (0.00)

after 2025. By 2040, projected employment and population 

levels are also lower, with an additional 4,000 jobs lost, and 

3,000 population loss; insignificant figures relative to the 

projections for 2040 (tables 26, 27).

Commuter Resilience

Commuting flexibilities help to mitigate business output 

losses entered into the final version of the scenario during the 

first year after the earthquake. Assuming more limited commuting flexibilities occur than previously considered—that is, 

two-thirds (rather than one-third) of output loss in goods producing, trade and transportation, and utilities industries, and 

one-half (rather than one-fourth) of output loss in services—

total employment loss would increase to around 699,000 jobs 

and total population loss expand to 188,000 people in 2018, 

compared to 496,000 jobs lost and 145,000 population loss 

estimated with the original assumptions. Employment and 

population loss in the east bay subregion would still be the 

highest among the four subregions, but the west bay subregion 

Table 28 . Employment, population, and gross regional product 

(GRP) effects with worse commuting and telecommuting 

conditions in the HayWired scenario relative to Association of Bay 

Area Governments Plan Bay Area 2040 projections for the east 

bay subregion of the San Francisco Bay region, California. 

[Negative percentage change in parentheses. East bay subregion refers to 

Alameda and Contra Costa Counties. Data from the Association of Bay Area 

Governments Planning and Research Department (2016) REMI (Regional 

Economic Models Inc.) model version 1.7.8, adjusted control NC3RC1]

Affected 

categories 2018 2022 2027 2040

Employment −277,700 −28,558 −176 –2,933

(0.18) (0.02) (0.00) (0.00)

Population −97,954 −126,498 −66,640 −6,954

(0.04) (0.05) (0.02) (0.00)

GRP, in billions 

of 2016 U.S. 

dollars

−32.13 −4.76 0.00 0.00

(0.16) (0.02) 0.00 0.00
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 35

would have more additional job and population loss than the 

rest of the region because of worse commuting conditions, 

reaching a total loss of 235,000 jobs (or 14 percent) and 

46,000 people (2 percent). East bay and the total San Francisco Bay region employment would not recover to pre-earthquake levels until after 2022 (tables 28 and 29, respectively). 

In 2040, the bay region employment is 1,000 jobs below the 

primary HayWired scenario analysis, whereas the population 

is above by 200 people.

Table 29. Employment, population, and gross regional product

(GRP) effects with worse commuting and telecommuting 

conditions in the HayWired scenario relative to Association of 

Bay Area Governments Plan Bay Area 2040 projections for the 

San Francisco Bay region, California.

[Negative percentage change in parentheses. Data from the Association of 

Bay Area Governments Planning and Research Department (2016) REMI 

(Regional Economic Models Inc.) model version 1.7.8, adjusted control 

NC3RC1]

Affected 

categories 2018 2022 2027 2040

Employment −699,374 −59,495 159 −10,290

(0.14) (0.01) 0.00 (0.00)

Population −188,491 −261,456 −138,941 −21,088

(0.02) (0.03) (0.02) (0.00)

GRP, in billions 

of 2016 U.S. 

dollars

−71.4 −97.58 −14.28 1.19

(0.12) (0.02) 0.00 (0.00)

Insurance

Rather than assuming insurance is one of the financing 

sources for the overall investment built into the model, we 

added insurance spending (15 percent of $85 billion, or $12.75 

billion in 2016 U.S. dollars) into the economy separately 

over 5 years, distributed by 10, 20, 25, 25, and 20 percent 

from the first to fifth year, respectively. Again, we assumed 

the distribution is based on the share of building damage 

related losses ((the sum of building, contents, and inventory 

repair and replacement costs, relocation costs, income losses, 

and output losses) by subregion, and three-quarters of the 

payment would be spent on construction, with one-quarter 

on the various aforementioned consumer commodities. 

Because of the stimulus effect from the extra spending, total 

employment loss would decrease by 13,000 to around 483,000 

jobs (10 percent) and total population loss would be reduced 

to 141,000 people (2 percent) in 2018. Employment and 

population loss in the east bay subregion would decrease by 

8,000 and 2,000, respectively, in 2018. Insurance spending 

would also help the region recover faster, San Francisco Bay 

region and east bay subregion employment would recover to 

pre-earthquake levels in 2021 (tables 30, 31).

Table 30. Employment, population, and gross regional product 

(GRP) effects with insurance spending in the HayWired scenario 

relative to Association of Bay Area Governments Plan Bay Area 

2040 projections for the east bay subregion of the San Francisco 

Bay region, California.

[Negative percentage change in parentheses. East bay subregion refers to 

Alameda and Contra Costa Counties. Data from the Association of Bay Area 

Governments Planning and Research Department (2016) REMI (Regional 

Economic Models Inc.) model version 1.7.8, adjusted control NC3RC1]

Affected 

categories 2018 2022 2027 2040

Employment −219,870 −13,835 −1,829 −2,465

(0.15) 0.01) 0.00) (0.00)

Population −80,634 −103,476 −56,116 −6,403

(0.03) (0.04) (0.02) (0.00)

GRP, in billions 

of 2016 U.S. 

dollars

−24.99 −3.57 0.00 0.00

(0.13) (0.02) 0.00 0.00

Table 31. Employment, population, and gross regional product 

(GRP) effects with insurance spending in the HayWired scenario 

relative to Association of Bay Area Governments Plan Bay Area 

2040 projections for the San Francisco Bay region, California. 

[Negative percentage change in parenthesis. Data from the Association of 

Bay Area Governments Planning and Research Department (2016) REMI 

(Regional Economic Models Inc.) model version 1.7.8, adjusted control 

NC3RC1]

Affected 

categories 2018 2022 2027 2040

Employment −482,910 −36,851 −4,169 −9,277

(0.10) (0.01) (0.00) (0.00)

Population −141,348 −217,081 −120,764 −20,928

(0.02) (0.03) (0.01) (0.00)

GRP, in billions 

of 2016 U.S. 

dollars

−65.45 −11.9 0 −1.19

(0.08) (0.01) 0.00 (0.00)
36 The HayWired Earthquake Scenario—Societal Consequences

Government Spending

Rather than assuming Federal, State, and local 

government spending would cover two-thirds of the uninsured 

loss, we assumed a worse situation where the Federal 

Government is not responsive, State and local governments 

are not able to gather enough resources for the recovery 

because of financial distress from the earthquake, and 

government spending in the region would be delayed until 

years later. Specifically, we made the following changes to the 

assumptions of the main analysis: 

• Only one-third of the uninsured loss would be 

covered, and

• Government spending and spending in construction 

would be distributed by 10, 10, 10, 15, 15, 10, 10, 10, 

and 10 percent over a 9-year period. 

As a result of lower level government spending, total 

employment loss and total population loss would increase by 

9,000 to about 503,000 jobs (10 percent) and total 147,000 

people (2 percent) in 2018, respectively. Employment and 

population loss in the east bay subregion would increase by 

5,000 and 2,000, respectively, in 2018. Lower government 

spending would delay the recovery process in the bay region. 

Bay region and east bay subregion employment would not 

recover to pre-earthquake levels until after 2024 (tables 32, 33).

Table 32. Employment, population, and gross regional product 

(GRP) effects with lower government spending in the HayWired 

scenario relative to Association of Bay Area Governments Plan 

Bay Area 2040 projections for the east bay subregion of the San 

Francisco Bay region, California.

[Negative percentage change in parentheses. East bay subregion refers to 

Alameda and Contra Costa Counties. Data from the Association of Bay Area 

Governments Planning and Research Department (2016) REMI (Regional 

Economic Models Inc.) model version 1.7.8, adjusted control NC3RC1]

Affected 

categories 2018 2022 2027 2040

Employment −232,259 −39,447 −848 −2,915

 (0.15) (0.03) (0.00) (0.00)

Population −84,666 −128,497 −69,561 −7,288

 (0.03) (0.05) (0.02) (0.00)

GRP, in billions 

of 2016 U.S. 

dollars

−26.11 −5.94 0.00 0.00

 (0.13) (0.03) 0.00 (0.00)

Table 33. Employment, population, and gross regional product 

(GRP) effects with lower government spending in the HayWired 

scenario relative to Association of Bay Area Governments Plan 

Bay Area 2040 projections for the San Francisco Bay region, 

California. 

[Negative percentage change in parentheses. Data from the Association of 

Bay Area Governments Planning and Research Department (2016) REMI 

(Regional Economic Models Inc.) model version 1.7.8, adjusted control 

NC3RC1]

Affected 

categories 2018 2022 2027 2040

Employment −503,920 −80,018 −1,664 −10,006

 (0.10) (0.02) (0.00) (0.00)

Population −146,947 −253,002 −138,565 −21,947

 (0.02) (0.03) (0.02) (0.00)

GRP, in billions 

of 2016 U.S. 

dollars

−67.66 −16.62 1.19 −1.19

 (0.08) (0.02) 0.00 (0.00)

Technology Sector Exodus

For this sensitivity test, we assumed that the San 

Francisco Bay region would lose half of the job gain 

permanently in the internet publishing and broadcasting; 

internet service providers, search portals, and data processing; 

and other information services sectors between 2011 and 

2015. The Plan Bay Area 2040 REMI-model baseline 

estimates that these sectors saw an increase of 39,000 jobs 

in the four subregions (east bay, west bay, north bay, and 

south bay). We reduced the job increase to about 20,000 for 

the whole bay region for the rest of the forecasting years 

(2018–2040). In addition to the job loss assumption in the 

information subsector, we also reduced the number of jobs 

in the professional, scientific, and technical services sector 

proportionally. Based on Quarterly Census of Employment 

and Wages data from 2011 to 2016 (https://www.bls.gov/

cew/#databases) this sector grew by 30 percent in the 

bay region, with technology-associated computer system 

design and related services accounting for 60 percent of 

the growth, whereas growth in other technology-driven 

professional, scientific, and technical services subsectors, 

including advertising, public relations, and related services; 

scientific research and development services; specialized 

design services; and other professional and technical services 

accounted for 21 percent of the total job growth in the fiveyear period. In this analysis, we reduced half of all job growth 
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 37

in the computer system design and related services sector and 

50 percent of the growth in other aforementioned professional, 

scientific, and technical services subsectors; that is, half of 

the 70 percent job gains in the professional, scientific, and 

technical services sector permanently, or 41,000 for the bay 

region using the Association of Bay Area Governments Plan 

Bay Area 2040 REMI-model baseline. In the Plan Bay Area 

2040 REMI model, the north bay subregion experienced 

negative job change in the professional, scientific, and 

technical services sector between 2011 and 2015; we kept the 

north bay subregion unchanged.

The technology sector exodus would remove 229,000 

additional jobs, for a total job loss of 725,000 (14 percent), 

and total population loss would be 201,000 people (3 percent) 

in 2018. The east bay subregion would lose an additional 

39,000 jobs and 12,000 residents in 2018. A technology sector 

exodus would lead to ripple effects in the economy, decreasing 

overall recovery and expansion for the full forecast period. 

San Francisco Bay region and east bay subregion employment 

would not recover to pre-earthquake levels until after 2028 

and would still lag behind the projected employment and 

population totals in 2040 (tables 34, 35).

Table 34. Employment, population, and gross regional product 

(GRP) effects with technology sector exodus in the HayWired 

scenario relative to Association of Bay Area Governments Plan 

Bay Area 2040 projections for the east bay subregion of the San 

Francisco Bay region, California.

[Negative percentage change in parentheses. East bay subregion refers to 

Alameda and Contra Costa Counties. Data from the Association of Bay Area 

Governments Planning and Research Department (2016) REMI (Regional 

Economic Models Inc.) model version 1.7.8, adjusted control NC3RC1]

Categories 

affected 2018 2022 2027 2040

Employment −266,809 −71,327 −38,839 −45,036

(0.18) (0.05) (0.03) (0.03)

Population −95,389 −153,072 −111,624 −68,378

(0.03) (0.05) (0.04) (0.02)

GRP, in billions 

of 2016 U.S. 

dollars

−30.821 −10.591 −4.879 −6.902

(0.15) (0.05) (0.02) (0.02)

Table 35. Employment, population, and gross regional product 

(GRP) effects with technology sector exodus in the HayWired 

scenario relative to Association of Bay Area Governments Plan 

Bay Area 2040 projections for the San Francisco Bay region, 

California.

[Negative percentage change in parentheses. Data from the Association of 

Bay Area Governments Planning and Research Department (2016) REMI 

(Regional Economic Models Inc.) model version 1.7.8, adjusted control 

NC3RC1]

Categories 

affected 2018 2022 2027 2040

Employment −724,939 −300,263 −226,487 −266,531

(0.14) (0.06) (0.04) (0.05)

Population −201,480 −413,822 −384,891 −356,294

(0.03) (0.05) (0.05) (0.04)

GRP, in billions 

of 2016 U.S. 

dollars

−108.171 −59.738 −46.886 −66.759

(0.13) (0.07) (0.04) (0.05)

Figures 11A and 11B compare the results of the 

sensitivity tests for the San Francisco Bay region as a whole. 

Most changes in post-event factors result in relatively 

small changes compared to the primary HayWired scenario 

results. This indicates that many of the effects are captured 

in the primary analysis. There are a few exceptions. Higher 

outmigration slightly lowers employment levels and has a 

somewhat larger effect on population levels for several years 

after the earthquake. Higher construction costs have a much 

larger and long-term slowing effect to the economy and 

population. Because construction costs filter throughout the 

economy, higher costs can slow down not only the rebuilding 

effort but also many types of employment growth and the 

pace of population recovery. A technology sector exodus 

also exacerbates the employment loss, leading to permanent 

employment and population net loss compared to the Plan Bay 

Area 2040 forecast.

The sensitivity tests show some of the uncertainty in 

assessing possible effects over time. Furthermore, the built-in 

resiliency of the model may be particularly problematic for 

understanding the implications for the east bay subregion. 

Although the whole San Francisco Bay region is strong, 

diverse, and has a large enough economy that a return close 
38 The HayWired Earthquake Scenario—Societal Consequences

men19-7347_fig11

4,000

4,500

5,000

5,500

6,000

6,500

201720182019202020212022202320242025202620272028202920302031203220332034203520362037203820392040

A

B

6,000

6,500

7,000

7,500

8,000

8,500

9,000

9,500

10,000

20172018201920202021202220232024202520262027202820292030203120322033

2034203520362037203820392040

Projections

PBA2040

HayWired primary

Sensitivity test factors

Insurance

Lower government spending

Outmigration

Technology sector exodus

Construction costs

Commuters

Year

EXPLANATION

Employment, in thousands Population, in thousands

Figure 11. Graphs showing 

(A) employment level projection 

and (B) population level 

projection curves for the 

San Francisco Bay region, 

California, using the multiple 

sensitivity tests discussed in 

this section. Data is from the 

HayWired scenario Regional 

Economic Models, Inc. 

(REMI), analysis compared to 

the Association of Bay Area 

Governments REMI model 

regional control NC3RC1. 

PBA2040, Plan Bay Area 2040 

projection; HayWired primary, 

HayWired results before 

sensitivity tests are added.

to the previous trend does not seem unreasonable, there could 

be local effects that are more permanent that are not captured 

by the model. If infrastructure repair is a lengthy process, the 

communities in the epicenter of the earthquake are likely to 

recover much more slowly than the model suggests, even as 

the region as a whole rebounds. The local- and regional-serving 

employment base in that area will find it particularly difficult to 

recover, and even companies with a larger customer base may 

become much less competitive and may either move or close 

down. As recovery proceeds, building brand new resources 

in these heavily damaged places could lead to a boom, but 

the analysis suggests nothing about the distributional effects 

of these changes. Without intervention by the public sector 

(or with the wrong type of intervention), small businesses 

and lower income and wealth-poor individuals are likely to 

suffer lifelong negative effects. The effects of displacement 

on vulnerable populations is addressed in depth by Johnson 

and others (Population Movements, in prep. [planned to be 

published as a part of this volume]), whereas neighborhood 

economic effects are addressed by Wein, Belzer, and others 

(this volume).

Policy Implications

Public policy will be a critical aspect of addressing the 

economic effects of a HayWired scenario-like event. In light 

of the results of the HayWired REMI analysis, some public 

policies that focus on the following areas may reduce and (or) 

mitigate the effects of a major earthquake through investments 

or planning before it occurs and through post-event responses. 

• Direct output loss from building damage may be 

reduced through building design and retrofits that 

reduce the amount of damage that occurs to both 

residential and nonresidential space. 
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 39

• The duration and total effects of output loss from 

building damage and inventory loss can be reduced by 

cooperative responses among businesses and neighbors 

and even between heavily and less affected areas. 

Efforts to build networking ties before the event can 

make it easier to develop these cooperative responses 

in a crisis period.

• Output loss and associated effects caused by fire after 

the earthquake can be mitigated by making buildings 

and neighborhoods more fire resistant to any extent. 

This will reduce not only earthquake related hazards 

but those related to wild land and urban wildfires. The 

effects of fire can be further reduced if cities, counties, 

and the State have identified means for fighting fires in 

cases where water delivery has been interrupted.

• The vulnerability of local serving businesses like 

food services, retail, and health care can have longterm effects on neighborhood resilience. Planning 

for neighborhood response and business relocation in 

advance, as well as providing temporary facilities and 

support services for businesses after the event, can help 

mitigate the otherwise widespread losses expected in 

these sectors.

• The increased demand for construction workers 

coincides with direct effects to the sector. Also, there is 

currently an urgency for construction capability in the 

San Francisco Bay region to build more housing and 

for funded and approved transportation infrastructure 

projects. Loss of construction capacity or increases in 

construction costs will slow the ability to recover. Longterm planning for resilience in the construction industry, 

by major employers, trade organizations, educational 

institutions, and local, State and Federal government 

can improve the industry’s ability to respond not only 

to catastrophic events but also to the wide swings 

experienced by the industry in business cycles.

• The role of major employers in all sectors is also of 

key importance in terms of their ability to provide 

support for their larger workforce and their role in 

the regional economy. Inclusion of major employers 

such as those in the technology sector at all stages—in 

advanced planning, initial responses, and recovery—

can contribute to the success of recovery efforts by 

augmenting the resources available when most needed 

and also ensuring that employees, suppliers, and 

other linked sectors are informed about immediate 

conditions and resources as well as choices that may 

have long-term effects during an event.

• The quick recovery in overall employment depends 

heavily on investment for rebuilding homes, 

nonresidential buildings, and infrastructure. The public 

sector plays a critical role in this by not only providing 

a portion of the funding but by also providing the 

means for clearance of debris, safety checks, and 

issuance of new permits. Without the robust level of 

recovery spending assumed in this analysis, the east 

bay subregion would require at least 5 years to reach 

within 1 percent of the projected employment levels. 

• Investment in recovery is not only the responsibility 

of the public sector. In the sensitivity analysis, 

private insurance payments have a relatively small 

effect on the outcome because these payments 

cover such a small share of the total damage. Were 

a larger share of homes and buildings covered by 

insurance, recovery would be better resourced, and 

displacement and outmigration could be less. Judging 

from past experience, close cooperation among 

assistance agencies, such as FEMA, and the California 

Governor’s Office of Emergency Services and 

private insurers may be needed to ensure the timely 

consideration and payment of claims.

• Restoration of the telecommunications sector is also 

a major factor in allowing many economic sectors to 

fully restore their operations. As seen in the sensitivity 

tests, if networking through telecommunications 

cannot be achieved, then the commute effects could be 

much more severe. 

• Temporary housing services will be crucial in 

maintaining the labor force needed to keep ongoing 

businesses in operation and provide the labor force 

needed for reconstruction. Identification of successful 

efforts in previous disasters may make it easier to 

respond effectively after an event. 

• Expedited permitting will be needed to allow 

rebuilding to happen in the time period shown in 

the model, whereas advanced planning for future 

contingencies would better prepare jurisdictions to 

address rebuilding efforts from a comprehensive 

perspective and effective retrofit programs could 

reduce displacement in the first place.

• Advanced planning to better address the effects 

of a major seismic event should address the likely 

issues faced by vulnerable populations. Clearance of 

damaged neighborhoods can also lead to clearance 

of communities and lower income renter households. 

Advanced planning may make it more likely that 

assistance is available to displaced households and 

could also require that the displaced populations be 

given the opportunity to participate in planning for 

rebuilding. This will affect who stays and who leaves 

the region and the level of resilience of existing 

businesses. This type of planning can happen as a 

mitigating pre-event approach, rather than only relying 

on it as an approach to react to the event.
40 The HayWired Earthquake Scenario—Societal Consequences

Future Research with REMI Modeling

Using the REMI model to examine the HayWired 

scenario made it possible to explore how a range of different 

negative effects or different recovery investments could affect 

the San Francisco Bay region economy. With a modeling 

approach like REMI, there is an unlimited number of tests that 

could be run and effects examined. Although the descriptions 

above may seem exhaustive, there are additional areas of 

research with the model that could be informative. We mention 

a few of these here.

First, the analysis relies on Hazus for output estimates 

because the model lacks a complete link between the loss 

of capital stock and economic and population effects. A 

closer link between capital losses and the economic and 

demographic impacts would improve the analysis. A few 

additional directions to explore include, for one, considering 

the unreconstructed capital stock lost as a factor to determine 

the residential and nonresidential investment totals. Second, 

population displacement from damaged buildings was entered 

in an artificial way in this analysis. Further experiments with 

levers in the model to reflect this effect could include the 

use of residential capital stock or investment, more extreme 

price changes, or additional ways to estimate the change in 

the amenity variable. Third, changes in public revenues could 

be explored further by inserting changes in Federal spending, 

incorporating shifts in local government revenues, and by 

including property and sales taxes in the model.

Acknowledgments

This research benefitted from the sage advice of many 

who have come before us in this world of Regional Economic 

Models, Inc. (REMI) analysis and understanding earthquake 

impacts and other disasters. We would like to thank those 

who shared time with us, including: Billy Leung and Peter 

Evangelakis (REMI), Adam Rose and Dan Wei (University 

of Southern California), Ian Sue Wing (Boston University), 

Vanessa Vargas (Sandia National Laboratories), and Joyce 

Jauer (Texas State Government). We would also like to thank 

our reviewers: Stephen Levy (Center for Continuing Study 

of the California Economy), Mary Comerio (University of 

California, Berkeley), Arrietta Chakos (Urban Resilience 

Strategies), Chris Brown (Common Sense Policy Roundtable), 

and Laurie Johnson (Laurie Johnson Consulting). Dena Belzer 

and Carlina Au of Strategic Economics and Jon Haveman of 

Marin Economic Consultants (coauthors of other papers in this 

volume) also contributed helpful data and conceptual ideas.

Staff at USGS worked behind the scenes to pull together 

the data that underlies much of the analysis. In particular, 

Jamie Jones converted the Hazus damage state data to output 

losses across three years, and Phil Frederick skillfully edited 

the chapter.

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Appendixes 1–6
44 The HayWired Earthquake Scenario—Societal Consequences

Appendix 1

Table 1.1 shows the sector description for each Hazus occupancy class and North American Industry Classification System 

(NAICS) codes included in each Hazus sector. Using the corresponding relation between NAICS and Hazus occupancy class 

from this table, we can map Regional Economic Models, Inc., (REMI) sectors into each Hazus occupancy class, shown in table 

1.2, which would then be used to summarize output loss by REMI sector using Hazus output.

Table 1.1. Industry sector with the corresponding Hazus occupancy class and North American Industry Classification System (NAICS) 

code.

Sector description NAICS code HAZUS occupancy class

Agriculture, forestry, fishing and hunting 11 AGR1

Retail trade 44−45 COM1

Wholesale trade, warehousing and storage 42 COM2

Truck transportation 484 COM2

Repair and maintenance 811 COM3

Personal services / private households 812, 814 COM3

Electric power generation, transmission, and distribution 2211 COM4

Natural gas distribution 2212 COM4

Water, sewage, and other systems 2213 COM4

Air transportation 481 COM4

Rail transportation 482 COM4

Water transportation 483 COM4

Transit and ground passengers 485 COM4

Pipeline transportation 486 COM4

Sightseeing transportation 487 and 488 COM4

Couriers and messengers 492 COM4

Internet publishing and broadcasting 51913 COM4

Data processing, hosting, and related services 518 COM4

Other information services 51911−2 COM4

Computer systems design and related services 5415 COM4

Professional scientific and technical services 54 except for 5415 COM4

Other business services 55, 56 COM4

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

Ambulatory health care and hospitals 621, 622 COM6, COM7

Telecommunications 517 COM8

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

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

Government and nonNAICS n.a. GOV1, GOV2

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

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

for334111, 334112,334411, 334412,334413), 335, 

337, 339,511, 516

IND2

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

Petroleum refineries 32411 IND3

Food, drug, and chemicals 311, 312, 324 except for32411, 325 IND3

Oil and gas extraction 211 IND4

Coal mining 2121 IND4
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 45

Table 1.1. Tabulated description of each sector with the corresponding Hazards U.S. (HAZUS) occupancy class and North American 

Industry Classification System (NAICS) code —Continued.

Sector description NAICS code HAZUS occupancy class

Other mining 212−213 except for 2121 IND4

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

Construction IND6

Religious, grantmaking, and similar organizations 813 REL1

Imputed rental value for owner-occupied dwellings n.a. RES1−RES3

Accommodations 721 RES4

Nursing home/social assistance 623, 624 RES6, COM3

Table 1.2. Tabulated description of Regional Economic Models, Inc. (REMI) 70 sectors and corresponding Hazus occupancy class and 

North American Industry Classification System (NAICS) code.

REMI sectors NAICS code HAZUS occupancy class

Forestry and logging; fishing, hunting, and trapping 113-114 AGR1 

Agriculture and forestry support activities 115 AGR1 

Oil and gas extraction 211 IND4 

Mining (except oil and gas) 212 IND4 

Support activities for mining 213 IND4 

Utilities 22 COM4 

Construction 23 IND6 

Wood product manufacturing 321 IND1 

Nonmetallic mineral product manufacturing 327 IND1 

Primary metal manufacturing 331 IND4 

Fabricated metal product manufacturing 332 IND1 

Machinery manufacturing 333 IND1 

Computer and electronic product manufacturing 334 IND2 

Electrical equipment and appliance manufacturing 335 IND2 

Motor vehicles, bodies and trailers, and parts 

manufacturing

3361-3363 IND1 

Other transportation equipment manufacturing 3364-3369 IND1 

Furniture and related product manufacturing 337 IND2 

Miscellaneous manufacturing 339 IND2 

Food manufacturing 311 IND3 

Beverage and tobacco product manufacturing 312 IND3 

Textile mills; textile product mills 313-314 IND1, IND2

Apparel, leather and allied product manufacturing 315-316 IND2 

Paper manufacturing 322 IND1 

Printing and related support activities 323 IND2 

Petroleum and coal products manufacturing 324 IND3 

Chemical manufacturing 325 IND3 
46 The HayWired Earthquake Scenario—Societal Consequences

Table 1.2. Tabulated description of Regional Economic Models, Inc. (REMI) 70 sectors and corresponding Hazus occupancy class and 

North American Industry Classification System (NAICS) code.—Continued

REMI sectors NAICS code HAZUS occupancy class

Plastics and rubber product manufacturing 326 IND2 

Wholesale trade 42 COM2 

Retail trade 44-45 COM1 

Air transportation 481 COM4 

Rail transportation 482 COM4 

Water transportation 483 COM4 

Truck transportation 484 COM2 

Couriers and messengers 492 COM4 

Transit and ground passenger transportation 485 COM4 

Pipeline transportation 486 COM4 

Scenic and sightseeing transportation and support 

activities for transportation

487-488 COM4 

Warehousing and storage 493 COM3 

Publishing industries, except internet 511 IND2 

Motion picture and sound recording industries 512 IND2, COM4 

Internet publishing and broadcasting; ISPs, search portals, 

and data processing; other information services

518-519 COM4 

Broadcasting, except internet 515 IND2, COM4 

Telecommunications 517 COM8 

Monetary authorities and other credit institutions 521,522,525 COM4, COM5 

Securities, commodity contracts, investments 523 COM4, COM5 

Insurance carriers and related activities 524 COM4, COM5 

Real estate 531 COM4, COM5 

Rental and leasing services; lessors of nonfinancial 

intangible assets

532,533 COM4, COM5 

Professional, scientific, and technical services 54 COM4 

Management of companies and enterprises 55 COM4 

Administrative and support services 561 COM4 

Waste management and remediation services 562 COM4 

Educational services; private 61 EDU1, EDU2 

Ambulatory health care services 621 COM6, COM7 

Hospitals; private 622 COM6, COM7 

Nursing and residential care facilities 623 RES6, COM3

Social assistance 624 RES6, COM3

Performing arts and spectator sports 711 COM8 

Museums, historical sites, zoos, and parks 712 COM8 

Amusement, gambling, and recreation 713 COM8 

Accommodation 721 RES4 

Food services and drinking places 722 COM8 

Repair and maintenance 811 COM3 

Personal and laundry services 812 COM3 

Membership associations and organizations 813 REL1 
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 47

Table 1.2. Tabulated description of Regional Economic Models, Inc. (REMI) 70 sectors and corresponding Hazus occupancy class and 

North American Industry Classification System (NAICS) code.—Continued

REMI sectors NAICS code HAZUS occupancy class

Private households 814 COM3 

State and local government NA GOV1, GOV2 

Federal civilian NA GOV1, GOV2 

Federal military NA GOV1, GOV2 

Farm 111-112 AGR1 

For each subregion, we first calculated the share of output 

loss for each Hazus occupancy class in each year of the first 

three years where output loss is estimated. We then determined 

that the share of loss for each REMI sector was equal to the 

share of loss for the occupancy class the sector belongs to. 

For example, both telecommunication (NAICS 517) and 

performing arts and spectator sports (NAICS 711) are in 

Hazus occupancy class COM8. Therefore, shares of output 

loss in these two sectors are the same, even if the activities are 

significantly different. In the case where a sector is mapped 

to two Hazus occupancy classes, the share of output loss for 

this sector equals the combined share of loss as calculated 

for those occupancy classes. Next, we applied the share of 

loss calculated for each REMI sector in each year to REMIprojected 2018 output to determine the dollar loss which was 

then put in as changes in REMI output variables. Table 1.3 

shows the results of translation. 

Table 1.3. San Francisco Bay region, California, industry direct output loss by year in the HayWired scenario using Regional Economic 

Models, Inc., output baseline.

[Sectors are sorted by first year loss amount. Data from Association of Bay Area Governments calculated using Hazus loss by occupancy class results]

Sector

Value of output loss, in millions of 2016 U.S. 

dollars

Loss of 2018 industry output, in percent

1st year 2d year 3d year 1st year 2d year 3d year 

Petroleum and coal products manufacturing 2016.0 2.1 0.0 4 0 0

Food services and drinking places 1900.5 77.3 0.0 9 0 0

Ambulatory health care services 1770.4 89.7 0.0 6 0 0

Machinery manufacturing 1594.2 4.1 0.0 12 0 0

Real estate 1277.6 34.1 0.0 1 0 0

Professional, scientific, and technical 

services

1237.0 49.6 0.0 1 0 0

Computer and electronic product 

manufacturing

1117.1 19.9 0.0 1 0 0

Hospitals; private 1073.7 72.2 0.0 6 0 0

Construction 848.9 5.1 0.0 2 0 0

Wholesale trade 795.0 17.3 0.0 2 0 0

Retail trade 736.8 12.6 0.0 2 0 0

Fabricated metal product manufacturing 710.5 1.7 0.0 11 0 0

Motor vehicles, bodies and trailers, and 

parts manufacturing

635.9 0.6 0.0 22 0 0

Performing arts and spectator sports 544.4 19.7 0.0 9 0 0

Other transportation equipment 

manufacturing

505.6 2.3 0.0 9 0 0

Accommodation 493.8 55.9 0.0 5 1 0
48 The HayWired Earthquake Scenario—Societal Consequences

Table 1.3. San Francisco Bay region, California, industry direct output loss by year in the HayWired scenario using Regional Economic 

Models, Inc., output baseline.—Continued

Sector

Value of output loss, in millions of 2016 U.S. 

dollars

Loss of 2018 industry output, in percent

1st year 2d year 3d year 1st year 2d year 3d year 

Chemical manufacturing 416.0 2.6 0.0 1 0 0

Monetary authorities and other credit 

institutions

385.9 10.5 0.0 1 0 0

Oil and gas extraction 365.1 0.3 0.0 8 0 0

Internet publishing and broadcasting; ISPs, 

search portals, and data processing; other 

information services

358.7 50.6 0.0 0 0 0

Publishing industries, except internet 349.9 4.9 0.0 1 0 0

Amusement, gambling, and recreation 326.1 11.1 0.0 9 0 0

Management of companies and enterprises 316.1 4.5 0.0 1 0 0

Nonmetallic mineral product manufacturing 314.7 0.6 0.0 12 0 0

Food manufacturing 302.2 0.8 0.0 2 0 0

Administrative and support services 276.8 8.9 0.0 1 0 0

Paper manufacturing 222.6 0.4 0.0 16 0 0

Primary metal manufacturing 206.9 0.3 0.0 5 0 0

Social assistance 189.3 17.8 3.8 2 0 0

Beverage and tobacco product 

manufacturing

178.9 0.6 0.0 1 0 0

Insurance carriers and related activities 176.7 2.7 0.0 1 0 0

Miscellaneous manufacturing 176.3 0.8 0.0 2 0 0

Utilities 159.1 2.5 0.0 1 0 0

Nursing and residential care facilities 151.4 13.4 3.1 3 0 0

Repair and maintenance 143.7 7.2 0.0 2 0 0

Securities, commodity contracts, 

investments

138.3 5.2 0.0 0 0 0

Wood product manufacturing 134.1 0.3 0.0 11 0 0

Rental and leasing services; lessors of 

nonfinancial intangible assets

99.4 2.4 0.0 1 0 0

Personal and laundry services 95.0 4.7 0.0 2 0 0

Truck transportation 93.9 1.4 0.0 2 0 0

Museums, historical sites, zoos, and parks 62.5 2.4 0.0 6 0 0

Air transportation 59.6 0.8 0.0 1 0 0

Waste management and remediation 

services

59.1 0.9 0.0 1 0 0

Motion picture and sound recording 

industries

57.6 0.5 0.0 1 0 0

Electrical equipment and appliance 

manufacturing

54.4 0.4 0.0 2 0 0

Couriers and messengers 46.7 0.6 0.0 2 0 0

Printing and related support activities 41.0 0.2 0.0 2 0 0
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 49

Table 1.3. San Francisco Bay region, California, industry direct output loss by year in the HayWired scenario using Regional Economic 

Models, Inc., output baseline.—Continued

Sector

Value of output loss, in millions of 2016 U.S. 

dollars

Loss of 2018 industry output, in percent

1st year 2d year 3d year 1st year 2d year 3d year 

Water transportation 40.0 0.1 0.0 2 0 0

Plastics and rubber product manufacturing 35.9 0.1 0.0 3 0 0

Warehousing and storage 33.8 0.6 0.0 4 0 0

Telecommunications 33.2 0.0 0.0 0 0 0

Educational services; private 31.3 3.9 0.0 0 0 0

Broadcasting, except Internet 30.2 0.5 0.0 1 0 0

Scenic and sightseeing transportation and 

support activities for transportation

30.1 0.4 0.0 1 0 0

Furniture and related product manufacturing 24.9 0.1 0.0 2 0 0

Membership associations and organizations 21.5 2.0 0.0 0 0 0

Private households 19.9 0.9 0.0 2 0 0

Textile mills; textile product mills 17.5 0.1 0.0 6 0 0

Mining (except oil and gas) 16.9 0.0 0.0 3 0 0

Rail transportation 12.8 0.2 0.0 2 0 0

Transit and ground passenger transportation 12.0 0.2 0.0 1 0 0

Pipeline transportation 11.9 0.1 0.0 2 0 0

Apparel, leather and allied product 

manufacturing

5.5 0.0 0.0 2 0 0

Support activities for mining 0.7 0.0 0.0 1 0 0

Agriculture and forestry support activities 0.1 0.0 0.0 0 0 0

Forestry and logging; fishing, hunting, and 

trapping

0.0 0.0 0.0 0 0 0
50 The HayWired Earthquake Scenario—Societal Consequences

Appendix 2

Table 2.1. San Francisco Bay region, California, total output loss by private industry sector in 2018.

[These output losses include the direct output loss calculated in Hazus from building damage as well as additional output loss the model identifies in linked 

industries and because of the initial output-related population losses. Output losses and sorted by 1st year loss. Data generated using the Association of Bay Area 

Governments Planning and Research Department (2016) REMI (Regional Economic Models Inc.) model analysis from 2018 (this study)]

Category Output loss, in millions 

of 2016 U.S. dollars Share of output loss, in percent

Real estate 4095.2 2.8

Professional, scientific, and technical services 3304.4 2.3

Construction 3053.1 7.4

Ambulatory health care services 2625.5 8.4

Petroleum and coal products manufacturing 2485.8 5.2

Food services and drinking places 2315.7 10.4

Retail trade 1958.7 4.0

Wholesale trade 1897.3 4.0

Machinery manufacturing 1830.1 13.9

Monetary authorities and other credit institutions 1694.1 3.8

Hospitals 1349.2 6.9

Computer and electronic product manufacturing 1308.4 1.0

Administrative and support services 1133.2 3.9

Management of companies and enterprises 969.1 4.1

Fabricated metal product manufacturing 951.2 15.4

Securities, commodity contracts, investments 893.9 2.9

Chemical manufacturing 841.8 1.9

Telecommunications 806.7 2.7

Motor vehicles, bodies and trailers, and parts manufacturing 771.2 26.3

Performing arts and spectator sports 770.4 13.3

Accommodation 700.8 6.8

Publishing industries, except internet 674.6 1.5

Internet publishing and broadcasting; ISPs, search portals, and data processing; 

other information services 673.4 0.7

Insurance carriers and related activities 624.5 3.5

Food manufacturing 604.6 4.5

Oil and gas extraction 594.6 13.5

Utilities 562.0 4.2

Other transportation equipment manufacturing 558.9 9.7

Nonmetallic mineral product manufacturing 453.4 16.8

Primary metal manufacturing 445.4 11.7
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 51

Table 2.1. San Francisco Bay region, California, total output loss by private industry sector in 2018.—Continued

Category Output loss, in millions 

of 2016 U.S. dollars Share of output loss, in percent

Amusement, gambling, and recreation 410.2 11.0

Beverage and tobacco product manufacturing 376.0 1.3

Repair and maintenance 363.9 6.1

Rental and leasing services; lessors of nonfinancial intangible assets 358.3 3.1

Social assistance 342.5 4.4

Paper manufacturing 273.8 19.7

Miscellaneous manufacturing 271.0 3.0

Personal and laundry services 251.1 5.1

Nursing and residential care facilities 237.3 4.2

Truck transportation 233.0 5.0

Educational services 223.8 2.0

Membership associations and organizations 221.2 3.3

Wood product manufacturing 206.2 16.4

Waste management and remediation services 171.6 4.3

Air transportation 169.5 2.0

Broadcasting, except internet 120.9 4.3

Couriers and messengers 115.8 4.2

Electrical equipment and appliance manufacturing 100.3 3.0

Motion picture and sound recording industries 96.0 2.4

Printing and related support activities 94.7 4.3

Plastics and rubber product manufacturing 94.6 7.5

Scenic and sightseeing transportation; support activities for transportation 83.9 3.5

Museums, historical sites, zoos, and parks 80.6 7.3

Warehousing and storage 75.2 8.8

Furniture and related product manufacturing 63.8 5.3

Water transportation 63.2 3.8

Private households 47.6 5.1

Transit and ground passenger transportation 47.2 4.2

Mining (except oil and gas) 36.3 7.5

Pipeline transportation 33.2 4.8

Rail transportation 30.3 4.7

Textile mills; textile product mills 25.1 8.3

Apparel manufacturing; leather and allied product manufacturing 6.9 2.3

Support activities for mining 4.6 5.2

Forestry and logging; fishing, hunting, and trapping 4.2 4.1
52 The HayWired Earthquake Scenario—Societal Consequences

Appendix 3

Table 3.1. Revised San Francisco Bay region, California, output loss from building damage and transportation network interruption in 

the HayWired scenario by year using Regional Economic Models, Inc., output baseline.

[Sectors are sorted by first year loss]

Sector Value of output loss Loss of 2018 industry output, in percent

1st year 2d year 3d year 1st year 2d year 3d year

Professional, scientific, and technical 

services

4416.3 49.6 0.0 3 0 0

Real estate 4250.9 34.1 0.0 3 0 0

Computer and electronic product 

manufacturing

3674.9 19.9 0.0 3 0 0

Food services and drinking places 3141.3 77.3 0.0 14 0 0

Ambulatory health care services 2696.0 89.7 0.0 9 0 0

Internet publishing and broadcasting; ISPs, 

search portals, and data processing; other 

information services

2693.8 50.6 0.0 3 0 0

Machinery manufacturing 2490.4 4.1 0.0 19 0 0

Petroleum and coal products manufacturing 2386.6 2.1 0.0 5 0 0

Retail trade 2104.6 12.6 0.0 4 0 0

Wholesale trade 2060.3 17.3 0.0 4 0 0

Construction 1856.2 5.1 0.0 5 0 0

Hospitals; private 1673.1 72.2 0.0 9 0 0

Chemical manufacturing 1656.4 2.6 0.0 4 0 0

Publishing industries, except internet 1354.1 4.9 0.0 3 0 0

Monetary authorities and other credit 

institutions

1329.6 10.5 0.0 3 0 0

Fabricated metal product manufacturing 1094.9 1.7 0.0 18 0 0

Other transportation equipment 

manufacturing

1052.8 2.3 0.0 18 0 0

Accommodation 934.6 55.9 0.0 9 1 0

Securities, commodity contracts, investments907.5 5.2 0.0 3 0 0

Administrative and support services 887.7 8.9 0.0 3 0 0

Performing arts and spectator sports 877.9 19.7 0.0 15 0 0

Management of companies and enterprises 817.0 4.5 0.0 3 0 0

Motor vehicles, bodies and trailers, and 

parts manufacturing

691.6 0.6 0.0 24 0 0

Insurance carriers and related activities 542.8 2.7 0.0 3 0 0

Amusement, gambling, and recreation 525.0 11.1 0.0 14 0 0

Food manufacturing 514.1 0.8 0.0 4 0 0

Telecommunications 485.4 0.0 0.0 2 0 0

Beverage and tobacco product 

manufacturing

467.3 0.6 0.0 2 0 0

Nonmetallic mineral product manufacturing 448.9 0.6 0.0 17 0 0

Utilities 430.6 2.5 0.0 3 0 0
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 53

Table 3.1. Revised San Francisco Bay region, California, output loss from building damage and transportation network interruption in 

the HayWired scenario by year using Regional Economic Models, Inc., output baseline.—Continued

Sector Value of output loss Loss of 2018 industry output, in percent

1st year 2d year 3d year 1st year 2d year 3d year

Oil and gas extraction 417.4 0.3 0.0 9 0 0

Social assistance 358.2 17.8 3.8 5 0 0

Rental and leasing services; lessors of 

nonfinancial intangible assets

340.4 2.4 0.0 3 0 0

Miscellaneous manufacturing 302.4 0.8 0.0 3 0 0

Paper manufacturing 290.0 0.4 0.0 21 0 0

Primary metal manufacturing 284.4 0.3 0.0 7 0 0

Air transportation 268.2 0.8 0.0 3 0 0

Nursing and residential care facilities 264.5 13.4 3.1 5 0 0

Repair and maintenance 263.8 7.2 0.0 4 0 0

Educational services; private 221.6 3.9 0.0 2 0 0

Personal and laundry services 208.9 4.7 0.0 4 0 0

Truck transportation 193.6 1.4 0.0 4 0 0

Wood product manufacturing 191.5 0.3 0.0 15 0 0

Motion picture and sound recording 

industries

143.2 0.5 0.0 4 0 0

Membership associations and organizations 132.8 2.0 0.0 2 0 0

Waste management and remediation 

services

132.8 0.9 0.0 3 0 0

Museums, historical sites, zoos, and parks 126.8 2.4 0.0 11 0 0

Electrical equipment and appliance 

manufacturing

110.6 0.4 0.0 3 0 0

Couriers and messengers 96.8 0.6 0.0 4 0 0

Broadcasting, except internet 89.1 0.5 0.0 3 0 0

Scenic and sightseeing transportation and 

support activities for transportation

80.3 0.4 0.0 3 0 0

Printing and related support activities 76.7 0.2 0.0 3 0 0

Water transportation 69.9 0.1 0.0 4 0 0

Plastics and rubber product manufacturing 47.3 0.1 0.0 4 0 0

Warehousing and storage 47.2 0.6 0.0 6 0 0

Furniture and related product manufacturing 41.0 0.1 0.0 3 0 0

Private households 40.8 0.9 0.0 4 0 0

Transit and ground passenger transportation 34.4 0.2 0.0 3 0 0

Textile mills; textile product mills 29.2 0.1 0.0 10 0 0

Pipeline transportation 26.1 0.1 0.0 4 0 0

Rail transportation 24.6 0.2 0.0 4 0 0

Mining (except oil and gas) 23.1 0.0 0.0 5 0 0

Apparel, leather and allied product 

manufacturing

10.5 0.0 0.0 3 0 0

Agriculture and forestry support activities 2.8 0.0 0.0 1 0 0

Support activities for mining 2.7 0.0 0.0 3 0 0

Forestry and logging; fishing, hunting, and 

trapping

1.9 0.0 0.0 2 0 0
54 The HayWired Earthquake Scenario—Societal Consequences

Appendix 4

Table 4.1. San Francisco Bay region, California, total output loss from combined building and transportation effects in the HayWired 

scenario by private industry sector in 2018.

[Categories are sorted by 1st year loss. Data generated using the Association of Bay Area Governments Planning and Research Department (2016) REMI 

(Regional Economic Models, Inc.) model analysis from 2018 (this study)]

Category Output loss, in millions of 

2016 U.S dollars Share of output loss, in percent

Real estate 10,934.9 7.6

Professional, scientific, and technical services 9,531.9 6.5

Construction 6,772.3 6.5

Retail trade 4,993.2 10.3

Monetary authorities and other credit institutions 4,652.9 10.4

Ambulatory health care services 4,623.2 14.9

Wholesale trade 4,517.2 9.4

Food services and drinking places 4,174.5 18.7

Computer and electronic product manufacturing 4,091.2 3.2

Petroleum and coal products manufacturing 3,562.9 7.4

Internet publishing and broadcasting; ISPs, search portals, and data 

processing; other information services 3,502.2 3.4

Administrative and support services 2,998.8 10.2

Machinery manufacturing 2,954.8 22.4

Securities, commodity contracts, investments 2,853.6 9.2

Chemical manufacturing 2,716.8 6.3

Telecommunications 2,425.2 8.0

Hospitals 2,303.8 11.8

Management of companies and enterprises 2,245.5 9.5

Publishing industries, except Internet 2,192.0 4.9

Insurance carriers and related activities 1,644.6 9.2

Fabricated metal product manufacturing 1,562.5 25.2

Accommodation 1,469.7 14.2

Performing arts and spectator sports 1,389.9 24.0

Utilities 1,278.1 9.6

Other transportation equipment manufacturing 1,173.3 20.3

Food manufacturing 1,125.7 8.3

Rental and leasing services; lessors of nonfinancial intangible assets 959.1 8.4

Beverage and tobacco product manufacturing 903.2 3.1

Motor vehicles, bodies and trailers, and parts manufacturing 897.3 30.6

Oil and gas extraction 753.3 17.1

Repair and maintenance 737.8 12.4

Amusement, gambling, and recreation 731.9 19.6

Nonmetallic mineral product manufacturing 727.1 26.9
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 55

Table 4.1. San Francisco Bay region, California, total output loss from combined building and transportation effects in the HayWired 

scenario by private industry sector in 2018.—Continued

Category Output loss, in millions of 

2016 U.S dollars Share of output loss, in percent

Social assistance 718.8 9.3

Educational services 687.8 6.1

Primary metal manufacturing 655.7 17.3

Membership associations and organizations 610.5 9.0

Personal and laundry services 574.8 11.6

Miscellaneous manufacturing 529.6 5.9

Air transportation 523.6 6.3

Truck transportation 474.8 10.2

Nursing and residential care facilities 457.0 8.2

Paper manufacturing 380.8 27.5

Waste management and remediation services 377.2 9.4

Wood product manufacturing 321.3 25.6

Broadcasting, except Internet 317.7 11.4

Couriers and messengers 260.6 9.5

Motion picture and sound recording industries 235.6 5.9

Scenic and sightseeing transportation; support activities for 

transportation 210.6 8.8

Electrical equipment and appliance manufacturing 208.3 6.3

Printing and related support activities 204.7 9.2

Museums, historical sites, zoos, and parks 166.6 15.1

Plastics and rubber product manufacturing 145.2 11.5

Water transportation 126.1 7.6

Furniture and related product manufacturing 122.6 10.3

Warehousing and storage 121.4 14.3

Transit and ground passenger transportation 117.8 10.3

Private households 105.9 11.3

Pipeline transportation 67.8 9.8

Rail transportation 57.1 8.9

Mining (except oil and gas) 55.9 11.5

Textile mills; textile product mills 45.2 14.8

Apparel manufacturing; leather and allied product manufacturing 14.3 4.7

Support activities for mining 13.1 4.5

Forestry and logging; fishing, hunting, and trapping 9.5 8.9
56 The HayWired Earthquake Scenario—Societal Consequences

Appendix 5

Table 5.1. San Francisco Bay region, California, total employment net change. 

[Year 1 of the scenario is 2018, year 5 is 2022, year 10 is 2027, and year 23 is 2040]

Category 2018 2022 2027 2040

Forestry and logging; Fishing, hunting, and trapping −62 −1 2 −1

Agriculture and forestry support activities −252 −7 −2 −5

Oil and gas extraction −802 45 68 −5

Mining (except oil and gas) −522 9 30 −7

Support activities for mining −283 11 39 −6

Utilities −1,354 −156 −43 −27

Construction −31,641 −1,406 6,416 −294

Wood product manufacturing −1,159 −86 42 −3

Nonmetallic mineral product manufacturing −2,152 −28 71 −11

Primary metal manufacturing −704 8 8 −2

Fabricated metal product manufacturing −5,731 56 78 −18

Machinery manufacturing −5,292 −14 16 −16

Computer and electronic product manufacturing −3,084 821 269 −47

Electrical equipment and appliance manufacturing −492 24 10 −4

Motor vehicles, bodies and trailers, and parts manufacturing −1,056 −13 5 −5

Other transportation equipment manufacturing −2,345 13 2 −6

Furniture and related product manufacturing −613 −10 16 −6

Miscellaneous manufacturing −1,324 68 25 −12

Food manufacturing −2,486 −233 −102 −40

Beverage and tobacco product manufacturing −750 −70 −32 −24

Textile mills; Textile product mills −215 23 8 −1

Apparel manufacturing; Leather and allied product manufacturing −165 242 79 3

Paper manufacturing −724 −1 −1 −2

Printing and related support activities −1,010 −25 0 −9

Petroleum and coal products manufacturing −482 45 46 2

Chemical manufacturing −1,305 29 11 −18

Plastics and rubber product manufacturing −509 4 8 −3

Wholesale trade −15,094 −131 −85 −221

Retail trade −43,420 −750 −633 −1,018

Air transportation −1,328 56 42 −22

Rail transportation −119 3 4 −1

Water transportation −194 5 5 −4

Truck transportation −2,237 14 33 −26

Couriers and messengers −1,761 36 24 −11
Chapter V3. Analysis of the Economic Effects from the HayWired Scenario 57

Table 5.1. San Francisco Bay region, California, total employment net change. —Continued

Category 2018 2022 2027 2040

Transit and ground passenger transportation −1,958 211 65 −56

Pipeline transportation −81 −7 0 −1

Scenic and sightseeing transportation; support activities for transportation −1,762 133 88 −19

Warehousing and storage −1,412 −24 14 −18

Publishing industries, except Internet −1,744 26 15 −34

Motion picture and sound recording industries −742 77 41 −5

Internet publishing and broadcasting; ISPs, search portals, and data processing; 

other information services

−3,064 0 −20 −53

Broadcasting, except Internet −1,063 −37 −8 −21

Telecommunications −2,525 −114 −28 −77

Monetary authorities and other credit institutions −13,005 −2,355 90 −222

Securities, commodity contracts, investments −6,516 −86 94 −72

Insurance carriers and related activities −6,233 −817 −138 −199

Real estate −24,108 −23,162 −796 −892

Rental and leasing services; lessors of nonfinancial intangible assets −1,864 −2 35 −23

Professional, scientific, and technical services −44,184 −2,479 −7 −806

Management of companies and enterprises −7,016 44 68 −53

Administrative and support services −32,247 −3,143 −71 −418

Waste management and remediation services −1,578 −373 −23 −31

Educational services −9,126 −769 −193 −352

Ambulatory health care services −23,390 −713 −696 −972

Hospitals −11,009 −1,170 −778 −451

Nursing and residential care facilities −5,366 −765 −487 −272

Social assistance −19,307 −2,273 −1,373 −780

Performing arts and spectator sports −14,706 −52 58 −90

Museums, historical sites, zoos, and parks −905 16 6 −9

Amusement, gambling, and recreation −12,535 −117 0 −97

Accommodation −10,226 −535 −207 −236

Food services and drinking places −59,287 −3,994 −1,571 −437

Repair and maintenance −6,893 96 125 −114

Personal and laundry services −9,940 −59 9 −193

Membership associations and organizations −6,272 −652 −121 −198

Private households −5,613 −6 −26 −28

Government −19,501 −17,469 −2,866 −534

Farm −199 0 0 0
58 The HayWired Earthquake Scenario—Societal Consequences

Appendix 6

Table 6.1. Consumer commodities and services that would account for one-quarter of spending and reinvestment into the San 

Francisco Bay region, California, economy in the HayWired scenario.

Commodities

New motor vehicles Furniture and furnishings

Net purchases of used motor vehicles Household appliances

Motor vehicle parts and accessories Household supplies

Motor vehicle fuels, lubricants, and fluids Glassware, tableware, and household utensils

Motor vehicle maintenance and repair Men’s and boy’s clothing

Other motor vehicle services Women’s and girl’s clothing

Fuel oil and other fuels Children’s and infants’ clothing

Ground transportation Other clothing materials and footwear

Tools and equipment for house and garden Personal care products

Musical instruments Physician services

Books, educational and recreational Dental services

Telephone and facsimile equipment Paramedical services

Video, audio, photographic, and information processing equipment 

and media Hospitals

Audio-video, photographic, and information processing equipment Pharmaceutical and other medical products