2019 Silicon Valley Index

2019 SILICON VALLEY INDEX

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E C O N O M Y

S O C IE TY P JOINT VENTURE LA SILICON VALLEY CE INSTITUTE for G REGIONAL STUDIES OV ERNANCE JOINT VENTURE SILICON VALLEY INSTITUTE FOR BOARD OF DIRECTORS REGIONAL STUDIES

OFFICERS ADVISORY BOARD MATT MAHAN – Co-Chair, Brigade GEORGE BLUMENTHAL University of California, Santa Cruz HON. SAM LICCARDO – Co-Chair, City of San Jose – President & CEO, Joint Venture Silicon Valley JUDITH MAXWELL GREIG RUSSELL HANCOCK Notre Dame de Namur University

DENNIS JACOBS DIRECTORS Santa Clara University JOHN AITKEN JEAN McCOWN SENIOR ADVISORY STEPHEN LEVY Mineta San Jose Int’l Airport Stanford University Center for Continuing Study COUNCIL of the California Economy DAVID BINI CURTIS MO MARK BAUHAUS Santa Clara & San Benito Counties DLA Piper Bauhaus Productions Consulting MARY PAPAZIAN Building Trades Council San Jose State University MAIRTINI ERIC BENHAMOU JOHN BOLAND NI DHOMHNAILL Benhamou Global Ventures KQED Countsy CHRISTOPHER DAN BOXWELL DENNIS O'MALLEY DiGIORGIO Prepared by: Accenture Caliva Accenture (Ret.) RACHEL MASSARO SCOTT BRANDT MARY PAPAZIAN BEN FOSTER University of California, Santa Cruz San Jose State University Fosterra Clean Energy Consulting Designed by:

RAHUL CHANDHOK JOSEPH PARISI HARRY KELLOGG, JR. JILL MINNICK JENNINGS San Francisco 49ers Therma Inc. SVB Financial Group

MARK DANAJ HON. DAVE PINE KIM POLESE City of Fremont San Mateo County ClearStreet, Inc. Board of Supervisors DIANE DOOLITTLE JONATHAN STOCK Juniper Networks ROBERT RAFFO United States Geological Survey Hood & Strong LLP DAVID ENTWISTLE Stanford Health Care SHERRI R. SAGER Lucile Packard Children's Hospital JAVIER GONZALEZ Google JUAN SALAZAR Facebook TODD HARRIS TechCU JARED SHAWLEE San Jose Earthquakes ERIC HOUSER Wells Fargo SUSAN SMARR Kaiser Permanente DENNIS JACOBS Santa Clara University JOHN A. SOBRATO The Sobrato Organization HON. RO KHANNA United States Congress JOHN TORTORA Sharks Sports & Entertainment KAROLYN KIRCHGESLER Team San Jose JOHN VARELA Santa Clara Valley Water District IBI KRUKRUBO Ernst & Young DANIEL YOST Orrick, Herrington & Sutcliffe, LLP GREG MATTER Jones Lang LaSalle

2 2019 Silicon Valley Index ABOUT THE 2019 SILICON VALLEY INDEX

Dear Friends:

This year’s Index is something of a Rorschach test.

There’s certainly plenty to cheer about. We’re an economy at full employment, and yet we continue adding jobs. Venture capitalists generated an astounding $50 billion, and there are so many “unicorn” companies out there that the term is losing its meaning. There was vigorous acquisition activity, a surfeit of IPOs generating billions, and much of this activity was happening in those key trending areas heralded by the futurists: artificial intelligence, augmented reality, and next-generation immunotherapies for cancer treatment. All of this happens during a time of market volatility and policy uncertainty, and it is a testament to our region’s remarkable agility.

Not surprisingly, Silicon Valley’s income gains are fairly stunning. Average annual earnings have reached $140,000, a figure more than double the national average. The number of high-income households grew by 35 percent over the past four years. As a society we’re making significant education gains, we’re becoming even more diverse, women are increasing their presence in the tech sector and in the halls of government, we’re increasingly clean and green, and we’re voting in elections like never before.

And yet we worry.

It’s because there’s also plenty in these pages to give the pessimists a new set of talking points. It’s not just that our transportation woes continue to mount or that we have the nation’s highest housing costs. It’s not the yawning income divide and the persistence of real poverty in our region. All of that is deeply troubling, of course, but it’s old news.

What’s newly disquieting are indicators suggesting our fundamentals might be changing. More people are leaving the region than coming into it. Most of our growth in tech is being driven by a handful of large, established companies. These same companies are acquiring smaller ones at a pace we’ve never seen, changing the messy way innovation has typically happened here, perhaps even stifling it. Fewer startups are getting their seed funding. Our high costs (including salaries) are causing innovative companies to look elsewhere. All of it is happening against a backdrop of bad press.

Making sense of Silicon Valley is never easy because it is a complex and multi-faceted place. As we talk about who we are and who we’re becoming, it will be more important than ever to have the actual facts at our disposal. We’re pleased to provide them, and eager to facilitate a broad regional conversation about where we go from here.

Sincerely,

Russell Hancock President & Chief Executive Officer Joint Venture Silicon Valley Institute for Regional Studies

2019 Silicon Valley Index 3 WHAT IS THE INDEX?

The Silicon Valley Index has been telling the Silicon Valley story since 1995. Released early every year, the Index is a comprehensive report based on indicators that measure the strength of our economy and the health of our community—highlighting challenges and providing an analytical foundation for leadership and decision-making.

WHAT IS AN INDICATOR? An Indicator is a quantitative measure of relevance to Silicon 2019 Valley’s economy and community health, that can be examined SILICON VALLEY either over a period of time, or at a given point in time. Good Indicators are bellwethers that reflect the fundamentals INDEX of long-term regional health, and represent the interests of the community. They are measurable, attainable, and outcome-oriented.

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Appendix A provides detail on data sources and methodologies for each E C O N O M Y indicator.

S O C IE TY P JOINT VENTURE LA SILICON VALLEY CE INSTITUTE for G REGIONAL STUDIES OV ERNANCE THE SILICON VALLEY INDEX ONLINE Data and charts from the Silicon Valley Index are available on a dynamic and interactive website that allows users to further explore the Silicon Valley story.

For all this and more, please visit the Silicon Valley Indicators website at www.siliconvalleyindicators.org.

4 2019 Silicon Valley Index TABLE OF CONTENTS

PROFILE OF SILICON VALLEY...... 6

THE REGION’S SHARE OF CALIFORNIA’S ECONOMIC DRIVERS...... 7

2019 INDEX HIGHLIGHTS...... 8

PEOPLE Talent Flows and Diversity...... 10

ECONOMY Employment...... 18 Income...... 26 Innovation & Entrepreneurship...... 36 Commercial Space...... 44

SOCIETY Preparing for Economic Success...... 50 Early Education & Care ...... 54 Arts and Culture ...... 56 Quality of Health ...... 58 Safety ...... 62

PLACE Housing...... 64 Transportation...... 74 Land Use...... 82 Environment...... 86

GOVERNANCE City Finances...... 90 Civic Engagement...... 92 Representation...... 94

APPENDIX A ...... 96

APPENDIX B ...... 100

ACKNOWLEDGMENTS ...... 102

2019 Silicon Valley Index 5 PROFILE OF SILICON VALLEY

ADUL EDUCAIONAL AAINEN

SAN FRANCISCO Area: COUNTY 11% LESS THAN 24% HIGH 1,854 Daly City GRADUATE OR Brisbane SCHOOL Colma PROFESSIONAL 15% DEGREE HIGH SCHOOL SQUARE MILES South San Francisco GRAD San Bruno ALAMEDA Paci ca COUNTY Millbrae Union City Population: Burlingame 27% 23% Hillsborough San BACHELOR’S Foster SOME COLLEGE 3.10 MILLION Mateo City Fremont DEGREE Belmont Newark San Carlos Redwood City East Jobs: Half Atherton Palo Alto Moon Woodside Menlo Bay Park 1,674,255 SAN MATEO Palo Alto Milpitas COUNTY Mountain Los Altos View AE DISRIUION Portola Los Altos Sunnyvale Average Annual Earnings: Valley Hills Santa $139,755 Cupertino Clara San Jose 4% Campbell 80 & Saratoga 16% OVER Monte Los Gatos 24% SANTA CLARA 60 79 UNDER 20 Net Foreign Immigration: Sereno COUNTY +20,562

Morgan Hill 27% Net Domestic Migration: 40 59 29% 20 39 -22,299 SANTA CRUZ COUNTY Scotts Valley Gilroy

ENIC COOSIION 2% 5% MULTIPLE BLACK OR & OTHER The geographical boundaries of AFRICAN AMERICAN Silicon Valley vary. Earlier, the region’s SILICON VALLEY IS DEFINED AS THE core was identified as Santa Clara 34% FOLLOWING CITIES: 25% ASIAN County plus adjacent parts of San HISPANIC & LATINO SANTA CLARA COUNTY (ALL) Mateo, Alameda and Santa Cruz counties. However, since 2009, the 34% Campbell, Cupertino, Gilroy, Los Altos, Los Altos WHITE Hills, Los Gatos, Milpitas, Monte Sereno, Morgan Silicon Valley Index has included Hill, Mountain View, Palo Alto, San Jose, Santa all of San Mateo County in order to Clara, Saratoga, Sunnyvale reflect the geographic expansion of the region’s driving industries and

SAN MATEO COUNTY (ALL) employment. Because San Francisco OREIN ORN AFRICA & Atherton, Belmont, Brisbane, Burlingame, Colma, has emerged in recent years as 3% OCEANIA* Daly City, East Palo Alto, Foster City, Half Moon a vibrant contributor to the tech 8.5% Bay, Hillsborough, Menlo Park, Millbrae, Pacifica, economy, we have included some 9% EUROPE 17% OTHER MEXICO Portola Valley, Redwood City, San Bruno, San San Francisco data in various charts AMERICAS Carlos, San Mateo, South San Francisco, Woodside throughout the Index. 10.5% 17% VIETNAM CHINA ALAMEDA COUNTY 11% PHILIPPINES 14% Fremont, Newark, Union City 11% INDIA OTHER ASIA SANTA CRUZ COUNTY

Scotts Valley *Oceania includes American Samoa, Australia, Cook Islands, Fiji, French Polynesia, Guam, Kiribati, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Northern Mariana Islands, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, Wallis and Futuna.

Note: Area, Population, Jobs, and Average Annual Earnings figures are based on the city-defined Silicon Valley region; whereas Net Foreign Immigration and Domestic Migration, Adult Educational Attainment, Age Distribution, Ethnic Composition, and Foreign Born figures are based on Santa Clara and San Mateo County data only. Percentages may not add up to 100% due to rounding.

6 2019 Silicon Valley Index The Region's Share of California’s Economic Drivers

SILICON SAN VALLEY RANCISCO

JOBS

GDP*

M&A ACTIVITY

PATENT REGISTRATIONS

IPOS

LAND AREA ANGEL INVESTMENT

VENTURE CAPITAL POPULATION

*Silicon Valley Percentage of California GDP includes San Mateo and Santa Clara counties only. Data Sources: Land Area (U.S. Census Bureau, 2010); Population (California Department of Finance, 2018); GDP (Moody’s Economy.com, 2018); Venture Capital (Thomson ONE, 2018); Patent Registrations (U.S. Patent and Trademark Office, 2017); Ini- tial Public Offerings (Renaissance Capital, 2018); Jobs (U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages; EMSI, Q2 2018); Angel Investment (Crunchbase, 2018); Mergers & Acquisitions (Factset Research Systems, Inc., 2018).

2019 Silicon Valley Index 7 2019 INDEX HIGHLIGHTS Silicon Valley's hot economy is bumping up against its confines. Venture capitalists are investing at record levels, but overall employment growth fell for a second straight year. Much of the growth is driven by the large, established companies whose eye-popping land deals are transforming the landscape. These same companies are acquiring smaller ones at a rapid pace, changing the face of innovation as fewer startup companies obtain seed or early-stage funding. The income divide is widening, and wage gains are eroded by cost of living increases and the nation’s highest housing prices. Our transportation challenges are unabated.

Silicon Valley’s economy continues to grow, Meanwhile, the Valley generated record but at a slower rate as the region achieves levels of private capital, patents continued full employment. The growth is constrained apace, and the IPO activity was staggering; by a housing shortage and our ongoing yet fewer startups received funding. transportation woes. Venture capital funding reached $50 billion in 2018 ($19 Silicon Valley added 35,558 new workers between June 2017 billion to Silicon Valley and $31 billion to San Francisco), a level and June 2018. For the second consecutive year the rate of higher than any other on record (including 2000). The funding growth (2.2 percent) was slower than the previous year, but went into a record number of “megadeals” (81) over $100 unemployment levels (2.3 percent) are so low that the region is million each, and one investment alone (JUUL Labs) was $13 effectively at full employment. billion. Employment gains were largely in community infrastructure Silicon Valley generated nearly 20,000 patents in 2017, an and technology, with the region’s major tech companies increase over the previous year, and our share of California accounting for a large share of tech job growth. Those same patents has increased dramatically (from 37 percent to 47 companies also have significant and expanding real estate percent) over the past 20 years. footprints, with five of them alone – Google, Apple, Facebook, The Bay Area’s 28 IPOs generated a combined total of $5.9 LinkedIn, and Amazon – currently leasing approximately 18 billion in 2018, nearly quadruple the amount raised in 2017 by percent of all available office and R&D space. 13 IPOs. Thirteen of these newly-public companies were valued Silicon Valley’s average monthly housing costs ($2,341) and at over $1 billion by the end of the year, and the 28 companies apartment rental rates ($2,911) are the highest in the nation, had an average 37 percent post-IPO return (compared to inducing employers to locate workers elsewhere. Affordability negative two percent for all U.S. IPOs). issues also have a growing impact on transportation as workers In 2018 M&A deals involving Silicon Valley or San Francisco locate further from the employment centers. Commute times companies were worth more than $170 billion in just the 25 have increased 20 percent over the past decade (adding 50 percent of deals that disclosed amounts. There were dozens minutes weekly to each commuter, on average), with traffic of additional acquisitions by Silicon Valley’s largest tech delays resulting in an estimated $2.7 billion yearly loss in companies (including Apple, Google, Facebook, Microsoft, productivity). and Adobe). These acquisitions have taken innovation out of Twenty-four percent of Silicon Valley renters and 15 percent public view, and play a role in the four-year decline in early- of mortgage holders spend more than half their gross income stage funding deals to startups. on housing. Although the number of new residential units permitted kept pace with population growth the past two years, only 15 percent of these units are affordable to residents with low to moderate incomes. 8 2019 Silicon Valley Index Silicon Valley’s income gap widens; women and minorities face additional challenges. The average annual earnings in Silicon Valley reached that could achieve self-sufficiency would be a dual-income $140,000 in 2018, a level significantly higher than the state family with no children. ($81,000) and the nation ($68,000). The poverty rate in Silicon Valley (7 percent) remains The number of high-income households (earning $150,000 low relative to the state and nation, yet 37 percent of our or more) in Silicon Valley and San Francisco rose by 35 percent students receive free or reduced-price meals. Ten percent of over the past four years, while the number of lower-income Silicon Valley residents lack consistent access to food that is households declined. More than a quarter of Silicon Valley nutritionally adequate. households have incomes above $200,000 annually (compared Thirty percent of Silicon Valley households rely on public or to 11 percent in California and 7 percent nationally), and the private, informal assistance in order to get by, and more than 57 top two percent of households claim an estimated 27 percent percent of those headed by a Hispanic or Latino householder of the wealth. are not self-sufficient. Meanwhile the cost of living has increased significantly. Women are gaining some increased presence in tech jobs (25 Median home prices have soared above $1 million. The cost of percent) and in the tech industry (28 percent). Meanwhile, the childcare has risen 52 percent since 2012 to $20,900 per year share of science and engineering degrees conferred to women for infants, on average; the cost of the most basic transportation has yet to exceed 30 percent. needs for a family of four has risen 18 percent since 2014 to Women are also gaining representation in Silicon Valley’s $6,300. local governments, with significant gains in the last election It is impossible to be self-sufficient in Silicon Valley at the cycle. Of the 103 seats up for election in 2018, 54 were won current statewide minimum wage ($12 per hour). Even in cities by newcomers (not incumbents) and 32 of these were women. with higher minimum wage ordinances, the only family type

The region’s population is changing composition as residents depart and foreign- Other trends of interest born workers arrive; Asians now outnumber Electric Vehicles. Silicon Valley claims 18 percent of California’s White residents. electric vehicle rebates, and while the share is high it likely underestimates EV adoption since so many Silicon Valley EV The share of foreign-born residents has increased by nearly drivers earn incomes exceeding the rebate program eligibility three percentage points since 2009, reaching 38 percent in limits. 2017 (compared to 27 percent in California and 14 percent in the US). Correspondingly, the share of residents who speak Health Insurance. Coverage for the working-age population a language other than English at home has increased to 52 has increased significantly since the Affordable Care Act percent. went into effect (especially for unemployed residents, up 24 Only 17 percent of Silicon Valley workers in highly technical percentage points since 2013). occupations come from California. Forty percent come from India or China, and 29 percent are from other countries. Obesity. 58 percent of Silicon Valley adults and nearly one Offsetting the inflow of foreign residents, Silicon Valley third of the region’s students are overweight or obese. counties were among those with California’s largest outflow. Our residents are leaving for the greater Sacramento region, Violent Crime. The rate of violent crime increased 10 percent San Joaquin County, Austin, and Seattle. in 2017; reported rapes increased 25 percent in 2018. For the first time in Silicon Valley’s history, Asian residents represent the largest population share (34 percent). Bicycling. Significantly fewer bicycle collisions resulted in Since women with college degrees tend to have children injuries or fatalities (590) than during the prior year (756). later in life and fewer children in total, Silicon Valley’s increasing educational attainment level is likely a factor driving our Civic Engagement. Eligible voter turnout (53 percent) was declining birth rate (which was lower in 2018 than any other higher than any other midterm election in the past 20 years, year in the past half-century). and turnout of young adults (ages 18-24) was the highest on record; Silicon Valley’s absentee voting rate (83 percent) was also the highest on record.

2019 Silicon Valley Index 9 PEOPLETALENT FLOWS AND DIVERSITY

Silicon Valley’s population growth has of residents having a bachelor’s degree WHY IS THIS IMPORTANT? slowed over the past three years, primarily or higher), an increasing share of foreign- due to the region’s low (and declining) born residents, and a corresponding Silicon Valley’s most important asset birth rate. The influx of foreign immigrants increase in the number of people speaking is its people, who drive the economy into the region is more than fully offset foreign languages at home (more than and shape the region’s quality of life. by the number of Silicon Valley residents half of the population in 2017). Population growth is reported as a function moving to other parts of the state and While academic institutions continue of migration (immigration and emigration) nation; those who choose to stay within to confer more science and engineering and natural population change (the California are heading to regions such as degrees locally, only a small share are difference between the number of births the Sacramento and Stockton/Tracy areas conferred to women and therefore a large and deaths). Delving into the diversity where housing costs are significantly majority of women in technical professions and makeup of the region’s people helps lower. were trained elsewhere. For Black or us understand both our assets and our Of those in Silicon Valley’s technical African American women in particular, challenges. occupations, 26% are from India and 14% those who have come in from outside The number of science and engineering are from China. As people come and go, California are more likely to have an degrees awarded regionally helps to Silicon Valley’s population composition undergraduate degree. Women represent gauge how well Silicon Valley is preparing has changed in terms of race and ethnicity a quarter of all technical roles in Silicon talent. A highly educated local workforce (with Asian residents making up the Valley and 18% of those at the region’s is a valuable resource for generating largest population share for the first time), largest tech companies, and a similarly innovative ideas, products and services. educational attainment (with a rising share small share of leadership positions (19%). The region has benefited significantly from

POPULATION CHANGE Silicon Valley’s population Components of Population Change Santa Clara & San Mateo Counties growth has slowed over the past three years – down to less Natural Change Net Migration Net Change 50,000 than half the rate of growth 40,000 experienced during the 30,000 previous five-year period. 20,000 10,000 Population gains over the past two

People years (between July 2016 and July 0 2018) were entirely due to natural -10,000 growth (births minus deaths), as the net migration totals were negative. -20,000 -30,000 -40,000 '98 '99 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18

Data Source: California Department of Finance | Analysis: Silicon Valley Institute for Regional Studies

The population of Santa Clara and San Mateo Counties Population and Percent Change combined has grown slowly over the past year, as has 2008-2018 2017-2018 that of the state as a whole (+0.5% year-over-year). 2008 2017 2018 % Change % Change Over the past decade, the population in Santa Clara and Santa Clara & San Mateo Counties 2.47 M 2.72 M 2.73 M +10.5% +0.48% San Mateo Counties combined has grown more rapidly (+10.5%) than the state (+8.1%). California 36.86 M 39.61 M 39.83 M +8.1% +0.54%

10 2019 Silicon Valley Index the entrepreneurial spirit of people drawn critical to the success of our businesses to Silicon Valley from around the country and our region itself. These backgrounds and the world. Historically, immigrants have shape the perspective by which we contributed considerably to innovation undertake any task. By creating inclusive and job creation in the region, state and communities and workplaces, we are able nation.1 Maintaining and increasing these to build, succeed, and grow together. flows, combined with efforts to integrate Numerous efforts aim to create and

immigrants into our communities, will maintain equality within our talent pool PEOPLE likely improve the region’s potential for (and in educating our future workforce), global competitiveness. and tracking the progress allows us to Diversity and the coming-together reflect and continue to strive for a better, of people with different backgrounds, more inclusive region. cultures, genders, races and ethnicities is

1. Manuel Pastor, Rhonda Ortiz, Marlene Ramos, and Mirabai Auer. Immigrant Integration: Integrating New Americans and Building Sustainable Communities. University of Southern California Program for Environmental and Regional Equity (PERE) & Center for the Study of Immigrant Integration (CSII) Equity Issue Brief. December, 2012. Silicon Valley's domestic out-migration totals in 2016, 2017, and 2018 were greater than in any other year since 2006.

For the third year in a NET MIGRATION FLOWS Foreign and Domestic Migration row, people are moving Santa Clara & San Mateo Counties out of Silicon Valley nearly as quickly as Net Foreign Immigration Net Domestic Migration Net Migration 50,000 they are moving in. 30,000 Between July 2015 and July 2018 (over a 10,000 -10,000

three-year period), the People region gained 61,977 -30,000 foreign immigrants, but -50,000

lost 64,318 residents to -70,000 '98 '99 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18 other parts of California and the United States. Data Source: California Department of Finance | Analysis: Silicon Valley Institute for Regional Studies

Population growth in Santa Clara and San Mateo Counties has slowed over the past three years from a rate of 1.2 to 1.5% annually between 2011 and 2015 to a 13-year low of 0.48% in 2018; population growth has not been this slow since the years following the dot.com bust, which were marked by a significant net outflow of more than 124,000 residents.

2019 Silicon Valley Index 11 PEOPLETALENT FLOWS AND DIVERSITY

NET MIGRATION FLOWS Silicon Valley counties were California Counties with the Largest Net Domestic In/Out Migration 2017-2018 among those with the greatest domestic out-migration in Riverside Placer the state between July 2017 El Dorado San Joaquin and July 2018, with Riverside Fresno Butte County, San Joaquin County, San Francisco Sonoma and counties in the greater Ventura San Bernardino Sacramento area attracting San Mateo Alameda residents from other parts of San Diego Santa Clara California and the nation. Orange Los Angeles -100,000 -80,000 -60,000 -40,000 -20,000 0 20,000

Data Source: California Department of Finance | Analysis: Silicon Valley Institute for Regional Studies Silicon Valley’s population is aging. The number of residents over age 65 has grown San Francisco has a much larger share (39%) of 25-44-year-olds – the core working age group – than California (28%) or the by 35% over the past decade, while the United States (26%); Silicon Valley’s share of 25-44-year-olds (30%) is only slightly higher than in the state as a whole. overall population has only grown by 10%.

POPULATION BY AGE Population Change, by Age Category Age Distribution Santa Clara & San Mateo Counties Santa Clara and San Mateo Counties, San Francisco, California, and the United States | 2017 2007-2017 2016-2017

under 18 +2.2% -1.1% <18 18-24 25-44 45-64 65+ 18-24 +0.7% -0.7%

Silicon Valley 22% 8% 30% 26% 14% 25-44 +10.4% +1.8%

45-64 +9.9% +0.2% San Francisco 13% 7% 39% 25% 15% 65 and older +35.2% +4.8%

Total +10.3% +0.9% California 23% 10% 28% 25% 14%

Silicon Valley’s population under age 18 has grown United States 23% 9% 26% 26% 16% more slowly than other age groups since 2007, and actually declined by 6,700 children between 2016 and 2017.

Data Source: United States Census Bureau, American Community Survey 1-Year Estimates Analysis: Silicon Valley Institute for Regional Studies

12 2019 Silicon Valley Index Data Source: Finance California | Departmentof Analysis: Silicon Valley InstituteforRegional Studies Data Source: UnitedStates Census Bureau, American Community Survey| Analysis: Silicon Valley InstituteforRegional Studies Santa ClaraSanta Mateo Counties &San Population Share by Race/Ethnicity COMPOSITIONRACIAL ANDETHNIC Santa ClaraSanta Mateo Counties, &San andCalifornia Total ofBirths Number BIRTHS Silicon Valley Births Per Year Asian 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 children later inlife(age30)thanCalifornia 5,000 and 27years old, respectively); theyalsotend 0 woman, compared to2.09inCalifornia and to have fewer children (average of 1.88per to havefewerchildren (average of '80 or theUnitedStates 28 overall (average of 24.9% Silicon Valley womentend tostarthaving '81 White 2.6% '82 Silicon Valley '83 '84 3.7% 2007 '85 40.4% '86 '87 Hispanic orLatino '88 28.4% '89 2.13 throughout thecountry). '90 '91 '92 California '93 '94 '95 '96 '97 '98 Black orAfrican-AmericanBlack '99 25.3% '00 2.4% '01 '02 '03 4.8% '04 2017 33.5% '05 '06 '07 '08 34.0% '09 '10 '11 Multiple &Other '12 Health andHumanServices, CDC WONDER | Analysis: Silicon Valley InstituteforRegional Studies Note: Onlyincludeswomen whogavebirthduringthatparticularye '13 Children Per Woman, by Educational Attainment Level Less Than Degree aBachelor's Bachelor's DegreeBachelor's orHigher '14 '15 Average Age at Time of &Number ofFirst Birth '16 All Women '17 '18 Santa ClaraSanta Mateo Counties &San |2007&2017 400,000 600,000 700,000 100,000 200,000 300,000 800,000 900,000 500,000 0 up from 28%in2007. in Silicon Valley at 34%, largest population share residents represent the For thefirsttime, Asian 17.8 births per1,000people. 17.8 births since 1991whenitlastpeaked at rate hasdeclinedsteadily birth over thelasthalf-century. The was lower thanany otheryear Counties combined Mateo San Clara and 1,000 people)inSanta per rate (11births The 2018birth California Births Per Year 2007 31.5 24.8 28.2 2019 Silicon Valley Index The total number of birthsThe totalnumber of it hasbeensince1980. 19%), andin2018wasthelowest significantly since2008(down Mateo Counties hasdeclined annually inSantaClara andSan Age to havechildren (anaverage of Compared to2007, womenare a bachelor’s degree orhigher). additional years forthosewith having slightlyfewer(average now waitinguntillater inlife 2017 Women withhigherlevelsof 2.1 additionalyears) andare 32.0 26.5 30.3 first child (an average of 5.5first child(anaverage of waiting longertohavetheir of 1.8children perwoman).of ar. |DataSource: U.S. Departmentof educational attainment are 2007 1.66 2.10 1.91 Children 2017 1.64 2.17 1.84 13 PEOPLE PEOPLETALENT FLOWS AND DIVERSITY

EDUCATIONAL ATTAINMENT The share of Silicon Valley Percentage of Adults, by Educational Attainment Santa Clara & San Mateo Counties, San Francisco, California, and the United States | 2017 residents with a bachelor’s degree or higher (51.6%) 100% Graduate or 13% 12% Professional Degree increased by more than 24% 23% 80% 21% 20% seven percentage points Bachelor's Degree over the past decade (from 27% 60% 35% 29% 29% Some College or 44.2% in 2007). Associate's Degree 40% 23% Silicon Valley and San Francisco have much higher 19% levels of educational attainment than California or 21% 27% High School Graduate the United States as a whole, with 51% and 58% 20% 15% 12% of adults, respectively, having a bachelor’s degree or higher. 11% 12% 17% 12% Less Than High School 0% Silicon Valley San Francisco California United States 24% of Silicon Valley adults have a graduate or professional degree. Data Source: United States Census Bureau, American Community Survey | Analysis: Silicon Valley Institute for Regional Studies

The share of Silicon Valley’s Black or African American residents with a The share of Silicon Valley Black or African American women in bachelor’s degree or higher increased dramatically over the past decade, the core working age group (25-44) with a bachelor’s degree reaching 35% in 2017 (up from 27% in 2007 and 31% in 2012). or higher is much larger for those who were born outside of California (53%) than for those born within the state (33%). While educational attainment levels for Silicon Valley’s Hispanic or Latino residents remain low relative to other racial and ethnic groups, they have Among Silicon Valley’s Hispanic or Latino residents ages 25-44, increased over time; 18% of Silicon Valley’s Hispanic or Latino residents had the share with a bachelor’s degree or higher is larger for those a bachelor’s degree or higher in 2017, compared to 13% in 2007. who were born in California (particularly women).

EDUCATIONAL ATTAINMENT Share of Hispanic or Latino & Black or African Percentage of Adults with a Bachelor's Degree or American Residents Ages 25-44 with a Bachelor’s Higher by Race/Ethnicity Degree or Higher, by Gender and Origin Santa Clara & San Mateo Counties, and California Santa Clara & San Mateo Counties | 2017

70% Women Men Silicon Valley 2017 California 2017 60% Silicon Valley 2012 California 2012 From From From From California Elsewhere California Elsewhere Silicon Valley 2007 California 2007 50% Hispanic 33% 17% 20% 14% 40% or Latino

30% Black or African 33% 53% 43% 36% American 20% Note: Black or African American is non-Hispanic or Latino, where race includes Black or African American. 10%

0% Educational attainment Asian White Black or Multiple Hispanic Silicon Valley’s percentage of foreign-born African and Other or Latino varies significantly across American racial and ethnic groups. residents (38.2%) is significantly higher than in California or the United States, and Note: Categories Black or African American, Asian, and White are non-Hispanic or Latino. | Data Source: United States slightly higher than in San Francisco. Census Bureau, American Community Survey | Analysis: Silicon Valley Institute for Regional Studies

14 2019 Silicon Valley Index In 2017, there were 17,809 science and engineering degrees The share of Silicon Valley science and conferred among Silicon Valley’s top academic institutions – only engineering degrees conferred to women marginally more (by 88 graduates) than during the previous year. has remained in the 37-39% range for 18 years and has increased by only 1.5 percentage points over the past decade.

SCIENCE & ENGINEERING DEGREES Total Science and Engineering Degrees Conferred Universities in and near Silicon Valley Share of Science & 20,000 5.0% Engineering Degrees Conferred to Women 18,000 4.5% In and Near Silicon Valley PEOPLE 16,000 4.0% 1987 29.9% 14,000 3.5% 1997 33.3% 12,000 3.0% 10,000 2.5% 2007 37.0%

8,000 2.0% 2017 38.5%

6,000 1.5% States in the United Conferred

4,000 1.0% S&E Degrees Total of Share Valley Silicon Total S&E Degrees Conferred in Silicon Valley in Silicon Conferred S&E Degrees Total 2,000 0.5% Nearly ¾ (74%) of Silicon 0 0.0% '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 Valley’s female tech workers ages 25 to 44 are foreign Data Source: National Center for Educational Statistics, IPEDS | Analysis: Silicon Valley Institute for Regional Studies born. These women are The share of Silicon Valley’s population who disproportionately married are foreign born has increased by nearly with children, and primarily three percentage points since 2009. come from Asian countries.

Silicon Valley’s FOREIGN BORN Foreign Born Share of Employed Residents foreign-born Percentage of the Total Population Over Age 16, by Occupational Category population Who Are Foreign Born Santa Clara & San Mateo Counties, 2017 share (38%) – Santa Clara & San Mateo Counties, San Francisco, California, and the United States | 2017 which is much Ages 25-44 All higher than the 45% Women Men Both state as a whole 40% – increases Computer & 64.9% 77.7% 68.3% 70.5% significantly 35% 38.2% Mathematical 35.6% for employed 30% Architectural & residents (47%), 60.7% 65.1% 62.4% 63.1% employed 25% Engineering 26.9% residents in the 20% Natural Sciences 48.3% 46.8% 58.8% 52.7% core working 15% age group Medical & Health 43.2% 37.0% 40.0% 37.8% (50%), and 10% 13.7% Services specifically for 5% Financial Services 45.1% 66.1% 31.5% 48.4% women ages 25- 0% 44 in Computer Silicon Valley San Francisco California United States Other Occupations 43.7% 45.4% 46.0% 45.8% & Mathematical occupations Total 47.1% 48.8% 51.3% 50.2% Data Source: United States Census Bureau, American Community Survey (78%). Analysis: Silicon Valley Institute for Regional Studies

2019 Silicon Valley Index 15 PEOPLETALENT FLOWS AND DIVERSITY

Silicon Valley’s foreign FOREIGN LANGUAGE language-speakers are Languages Other Than English Spoken at Home for the Population 5-Years and Over more likely to speak Santa Clara & San Mateo Counties, San Francisco, California, and the United States | 2017 languages other than Spanish (64%) than in Other Asian and California (35%) or the 100% Arabic Paci c Island languages United States overall 90% French (38%). 80% Tagalog 70% German Vietnamese 60% Other and 50% unspeci ed Other Indo-European languages languages 40% 30% Korean Chinese 20% Slavic Spanish 10% languages Language Other Than Exclusively English at Home at English Exclusively Than OtherLanguage Share of the Population 5-Years and Over Speaking a Speaking and Over 5-Years of the Population Share 0% Silicon Valley San Francisco California United States

Data Source: United States Census Bureau, American Community Survey | Analysis: Silicon Valley Institute for Regional Studies

Population Share That Speaks a More than half of Silicon Valley’s population Language at Home Other Than Exclusively English over age five speaks a language other than

2007 2012 2017 exclusively English at home.

Silicon Valley 49% 51% 52%

San Francisco 45% 45% 44% Of Silicon Valley workers in the TECH TALENT Female Tech Talent in the Core Working Age Group (25-44) California 43% 44% 44% core working age group (25-44) Santa Clara & San Mateo Counties | 2017 United States 20% 21% 22% with a bachelor’s degree or higher, The share of Silicon Valley residents who 100% women represent speak a foreign language at home has 25% of those in increased over the past decade, from 49% technical roles. 80% in 2007 to 52% in 2017; in contrast, San Francisco’s share of foreign language- Men, 75% speakers has declined by one percentage 60% Non-Technical point over the same time period. Roles, 82%

40% In 2017, 18% of highly-educated Silicon 20% Valley women ages 25 to 44 worked in Women, 25% Technical Roles, 18% technical occupations (compared to 43% of 0% their male counterparts). Technical Roles Women Ages 25-44 Data Source: United States Census Bureau, American Community Survey Analysis: Silicon Valley Institute for Regional Studies

16 2019 Silicon Valley Index and leadership positions. companies, technicalroles andamere 19%of workforce at Silicon Valley’s largest tech Women make the upanestimated 28%of Data Source: UnitedStates Census Bureau, American Community SurveyPUMS| Analysis: Silicon Valley InstituteforRegional Studies diversity reports; Silicon Valley Business Journal | Analysis: Silicon Valley Institute forRegional Studies Note: Analysis onlyincludescompaniesforwhichdiversity datawasreadily available. |DataSource: Individual company 1. in 2017were from California, themlikely from withmanyof theBay Area. 7% and5%, respectively, thenewtechtalentthat movedtoSeattle of and Austin According to data from theLinkedIn 2019),8.1%ofallnewLinkedIn Economic membersinAustin in2018were Graph (January from Francisco theSan Bay Area; theBay Area was thenumberoneplace oforiginfor newSeattle LinkedIn members in2018. Santa ClaraSanta Mateo Counties, &San Francisco, San Top andOther U.S.Tech Centers |2017 Migration of Tech Talent intheCore Working Age Group (25-44) TECH TALENT Largest TechnologyCompanies Share ofFemale Employees at Silicon Valley's 10 TECH TALENT Number of New Workers in Technical Occupations within the Private Sector 18.9% Technical Roles 10,000 15,000 20,000 25,000 30,000 5,000 0 Clara County Santa Santa 70% 30% From California Seattle, 93% 7% Leadership Positions WA 19.4% Francisco 72% 28% San San From Elsewhere County Mateo 75% 25% San San Austin, 95% 28.2% 5% TX Total Atlanta, 98% 2% GA Denver, 96% 4% CO Boston, 98% 2% MA Analysis: Silicon Valley Institutefor Regional Studies Data Source: UnitedStates Census Bureau, American Community Survey Santa ClaraSanta Mateo Counties &San |2017 DegreeBachelor's orHigher, by Place ofOrigin Share ofResidents in Technical Occupations witha TECH TALENT Portland, 94% from India(26%)thanfrom withinCalifornia 6% OR workers intechnicalroles inSilicon Valley Other Asia, 5% Asia, Other Washington, Taiwan, 3% Vietnam, 3% 94% 6% D.C. Latin America, 2% Latin America, (16%) or the rest of thecountry (16%). (16%) ortherest of In 2017, there were more high-skilled Hong Kong, 2% Philippines, 2% Europe, 7% China, 14% China, (8%), Texas (8%), and Washington (6%). India (14%), Illinois(9%), New Jersey California, thelargest shares were from 2017 comingfrom placesoutsideof Silicon Valley’sOf newtechtalentin state and30%were from elsewhere. were from anothercounty withinthe to SantaClara County in2017, 70% private sector technical jobs who moved the25-to44-year-olds workinginOf Rest ofU.S., 16% foreign-born techtalentcomefrom India Korea, 2% 1 2019 Silicon Valley Index Canada, 2% Canada, The largest shares of Silicon The largest sharesValley’s of Africa &Oceania,1% Africa California, 17% India, 26% (26%) andChina(14%). 17 PEOPLE ECONOMYEMPLOYMENT

Silicon Valley created nearly 36,000 new the workforce at higher rates than a decade jobs we have and the composition of the jobs in 2018 at a year-over-year growth ago. Despite slower job growth over the region’s workforce affect the availability of rate (+2.2%) that was slower than any past year, the region’s unemployment rate opportunities and uncover potential skills other year since the start of the economic is at an 18-year low (2.3%) and growth of gaps. Examining employment by wage recovery period between 2010 and 2011. tech jobs has been much larger in terms of and skill level allows for a higher level This compares to a 3.4% growth rate in sheer growth in the greater San Francisco of granularity to help us understand the San Francisco over the same time period. Bay Area than in any of the other major changing composition of jobs within the Eighty-one percent of new Silicon Valley U.S. tech talent centers. region. While employment by industry jobs were created in Santa Clara County; and by wage/skill level provides a broader more than half were in Community picture of the region’s economy as a whole, Infrastructure & Services, and 34% were WHY IS THIS IMPORTANT? observing the unemployment rates of the in the tech industry (compared to 29% the Employment gains and losses are a population residing in the Valley reveals prior year). The largest share of job growth core means of tracking economic health the status of the immediate Silicon Valley- since 2010 has been in Tier 1 (high-skill/ and remain central to national, state, and based workforce. The way the region’s high-wage, mostly tech industry) and Tier regional conversations. Over the course of industry patterns change shows how well 3 (low-skill/low-wage, mostly Community the past few decades, Silicon Valley (like our economy is maintaining its position in Infrastructure & Services) jobs, with a lower many other communities) has experienced the global economy. growth rate of jobs in the middle. Older shifts in the composition of industries that Silicon Valley residents are participating in underlie the local economy. The types of

Job growth since the beginning of the economic recovery period in 2010 has been more rapid in San Francisco (up 36%) The total number of Silicon Valley jobs grew more slowly than in Silicon Valley (+29%), Alameda County (+26%), California (+20%), or the between 2017 and 2018 (+2.2%) than any other year since United States overall (+14%). before the start of the economic recovery period (2010).

Silicon Valley gained 35,558 jobs JOB GROWTH between Q2 2017 and Q2 2018. Total Number of Jobs and Percent Change Over Prior Year Silicon Valley 81% of Silicon Valley’s 2017-2018 employment growth was from jobs in Santa Clara County (28,768 of 2,000,000 them), and a mere 8% from San Mateo County (2,792 jobs). 1,800,000 +2.2% +0.2% +4.3% 1,600,000 +3.4% +3.0% +2.5% +0.8% +3.0% 1,400,000 +0.7% +2.5% +4.1% -8.5% +1.9% +2.8% 1,200,000 -5.3% -0.6% -6.3% -1.3% 1,000,000 800,000 Total Number of Jobs Total 600,000 400,000 200,000

0 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Note: Percent change from 2012 to 2018 is based on unsuppressed numbers. Percent change for prior years is based on QCEW data totals with suppressed industries. Percent change for 2018 was updated using Q2 reported growth. Data Sources: U.S. Bureau of Labor Statistics Quarterly Census of Employment and Wages; EMSI | Analysis: BW Research

18 2019 Silicon Valley Index Note: Relative growth isfrom June to June. |DataSources: U.S. Employmentand LaborStatistics QuarterlyCensusWages; Bureau of EMSI| of Analysis: BW Research Note: Definitions of the major areas of economic activityare includedin themajorareas of AppendixNote: Definitionsof A. |DataSources: BW Research; U.S. Employment Labor Statistics Quarterly BureauCensus of of and Wages; EMSI |BW Research Silicon Valley Francisco andSan |2018 Share of Total Employment, by Areas Major ofEconomic Activity AREASOFECONOMICMAJOR ACTIVITY Silicon Valley, Clara Santa Mateo Counties, &San Francisco, San AlamedaCounty, California, andtheUnited States Relative JobGrowth JOB GROWTH Percent Change in Total Number of Jobs 21% higherthanpre-recession (2007)levels. The total number ofjobsinSilicon Valley is 16.1% 3.5% 26.1% 10% 15% 20% 25% 30% 35% 40% 0% 5% Silicon ValleySilicon 4.6% Silicon Valley 49.7% 2007 -2018 Santa Clara MateoSanta &San Counties 23.2% 13.8% 0.9% San Francisco San 5.4% 10% 15% 20% 25% 30% 35% 40% 0% 5% 56.8% San FranciscoSan 2010 -2018 Alameda County (+1.9%). than thoseinSilicon Valley (+2.2%)or between Q22017and2018(+3.4%) Jobs inSanFrancisco grew more rapidly Alameda County & Services Community Infrastructure Products &Services Innovation andInformation & Services InfrastructureBusiness Manufacturing Other Other 10% 15% 20% 25% 30% 35% 40% 0% 5% California Products &Services. and Information 26% are in Innovation Infrastructure &Services; jobs are in Community allSilicon Valley of Half 2019 Silicon Valley Index 2017 -2018 United States 19 ECONOMY ECONOMYEMPLOYMENT

A larger share of Q2 2017 to Q2 2018 job growth was in the tech industry (34%) Silicon Valley jobs in Innovation and Information Products compared to the prior year (29%). & Services – such as Computer Hardware, Software, Internet & Information Services, and Biotechnology – grew by 2.8% (+14,908) between Q2 2017 and Q2 2018.

MAJOR AREAS OF ECONOMIC ACTIVITY More than half (53%) of all Average Annual Employment, by Major Area of Economic Activity Silicon Valley new Silicon Valley jobs created

Q2 2007 Q2 2008 Q2 2009 Q2 2010 Q2 2011 Q2 2012 between Q2 2017 and Q2 Q2 2013 Q2 2014 Q2 2015 Q2 2016 Q2 2017 Q2 2018 2018 were in Community 900,000 800,000 Infrastructure & Services; 700,000 nearly 8,000 new jobs were 600,000 500,000 created in Healthcare & Social 400,000 Services alone. 300,000 Employment in Community Infrastructure & 200,000 Services has grown steadily since 2010 (up 100,000 by more than 175,000 jobs reaching a total 0 of 832,000 in 2018), whereas employment Community Innovation and Information Business Infrastructure Other Manufacturing Infrastructure & Services Products & Services & Services in Other Manufacturing has only grown by 1,000 jobs since then.

Note: Definitions of the major areas of economic activity are included in Appendix A. | Data Sources: BW Research; U.S. Bureau of Labor Statistics Quarterly Census of Employment and Wages; EMSI | BW Research

Employment Growth by Major Areas of Economic Activity Tech industry jobs have Silicon Valley grown significantly since the 2007 - 2018 2010 - 2018 2017 - 2018 beginning of the economic Community Infrastructure & Services +18.7% +26.7% +2.8% recovery period, with a 40% Innovation and Information increase in the number of jobs Products & Services +38.8% +40.1% +3.5% (up by 125,000 jobs between Business Infrastructure & Services +11.6% +23.0% +1.8% Q2 2010 and Q2 2018). Other Manufacturing -14.6% +1.7% +1.7% Silicon Valley employment has far Total Employment +21.3% +29.3% +2.2% surpassed pre-recession levels across all Note: Percent change is from Q2 to Q2. | Data Sources: BW Research; U.S. Bureau of Labor Statistics Quarterly Census of major areas of economic activity except Employment and Wages; EMSI | BW Research Other Manufacturing.

20 2019 Silicon Valley Index Analysis: BW Research Data Source: BW Research; U.S. Employment and LaborStatistics QuarterlyCensusWages; Bureau of California of EmploymentDevelopment Department;EMSI Note: Definitionsof Tier 1(high-skill/high-wage), Tier 2(mid-skill/mid-wage), and Tier 3(low-skill/low-wage)jobsare includedin Appendix A. included in Appendix A. |DataSources: BW Research; U.S. Employmentand LaborStatistics Quarterly CensusWages; Bureau of EMSI|BW of Research economicactivity, themajorareas of Note: Definitionsof andof Tier 1(high-skill/high-wage), Tier 2(mid-skill/mid-wage), and Tier 3(low-skill/low-wage)jobsare 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 Silicon Valley Total Employment by Tier EMPLOYMENTBY TIER Silicon Valley |2018 Employment Areas inMajor ofEconomic Activity, by Tier AREASOFECONOMICMAJOR ACTIVITY 100,000 200,000 300,000 400,000 500,000 600,000 700,000 0 0 '01 Tier 1 Infrastructure Tier 1 Community '02 & Services '03 Tier 2 '04 Tier 2 '05 Information Products '06 Tier 3 Innovation and & Services '07 Tier 3 '08 '09 '10 Infrastructure '11 & Services Business Business '12 '13 Infrastructure &Services. Infrastructure low-wage jobs)are inCommunity 85% ofSilicon Valley Tier 3(low-skill/ '14 '15 Manufacturing '16 Other Other '17 '18 (high-skill/high-wage). primarily (75%) Tier 1 & Servicesjobsare Information Products contrast, Innovation and jobs are Tier 3;in Infrastructure &Services Community 46% of (mid-skill/mid-wage). allSilicon Valley43% of jobsare Tier 2 Tier 3 Tier Tier 2 Tier Tier 1 Tier Percent Change inEmployment, period haveoccurred across all Tiers, but the beginning of theeconomicrecoverythe beginningof +23% +12% +24% Silicon Valley Silicon Valley employmentgainssince 2019 Silicon Valley Index job gainsin Tiers 1and3(+30% +31%, respectively) havebeenmore 2007 -2018 Francisco by Tier +32% +20% +37% rapid than in Tier 2(+25%). San San +31% +25% +30% Silicon Valley 2010 -2018 Francisco +30% +29% +39% San San 21 ECONOMY ECONOMYEMPLOYMENT

Since 2012, the share of Silicon Valley EMPLOYMENT BY TIER jobs in each tier has remained almost Percent of Total Employment by Tier unchanged. Silicon Valley The long-term trend indicates that the Tier 1 Tier 2 Tier 3 share of Silicon Valley employment in Tier 100% 2 jobs has decreased by 4.8% over the past 90% 17 years, although year-to-year changes 80% have been relatively small. 70% 60% 50% 40% 30% 20% 10% 24.1% 47.4% 28.5% 23.7% 46.7% 29.6% 23.5% 46.1% 30.4% 23.3% 45.8% 30.9% 23.4% 45.5% 31.0% 23.5% 45.4% 31.1% 23.8% 45.0% 31.3% 24.0% 44.6% 31.4% 24.5% 43.8% 31.7% 24.5% 43.7% 31.8% 24.6% 43.6% 31.9% 24.6% 42.9% 32.5% 24.6% 43.0% 32.5% 24.4% 42.9% 32.7% 24.6% 42.9% 32.5% 24.8% 42.9% 32.3% 24.8% 42.8% 32.4% 24.9% 42.6% 32.5% 0% '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18 The unemployment rate in Note: Definitions of Tier 1 (high-skill/high-wage), Tier 2 (mid-skill/mid-wage), and Tier 3 (low-skill/low-wage) jobs are included in Appendix A. Data Source: BW Research; U.S. Bureau of Labor Statistics Quarterly Census of Employment and Wages; California Employment Development Department; EMSI Silicon Valley was 2.3% in Analysis: BW Research November 2018 (compared to 2.2% in San Francisco, 3.9% in California, and 3.5% in the United States overall).

Silicon Valley’s UNEMPLOYMENT Monthly Unemployment Rate unemployment rate Santa Clara & San Mateo Counties, San Francisco, California, and the United States is at an 18-year low. Santa Clara & San Mateo Counties San Francisco California United States 14%

San Mateo County had the lowest 12% unemployment rate of all counties in the state (2.0%) in November 2018, 10% followed closely by San Francisco and 8% Marin Counties (both at 2.2%) and Santa Clara County (2.4%). 6% 3.9% Silicon Valley’s unemployment rate in 4% 3.7% May 2018 (2.15%) was lower any other 2.3% 2% 2.2% month since December 1999 (when it was 1.97%), with fewer than 38,000 0% unemployed workers in the labor force. ‘00 ‘01 ‘02 ‘03 ‘04 ‘05 ‘06 ‘07 ‘08 ‘09 ‘10 ‘11 ‘12 ‘13 ‘14 ‘15 ‘16 ‘17 ‘18 ‘19

Note: County-level and California data for November 2018 are preliminary; Rates are not seasonally adjusted. | Data Source: U.S. Bureau of Labor Statistics, Current Population Survey (CPS) and Local Area Unemployment Statistics (LAUS) | Analysis: Silicon Valley Institute for Regional Studies

22 2019 Silicon Valley Index workers ages16andover. |DataSource: UnitedStates Census Bureau, American Community Survey| Analysis: Silicon Valley InstituteforRegional Studies *Data for Two orMore Races were notavailableforSanMateoCounty in2007. |Note:OtherincludesSomeRace and Two orMore Races. Dataincludes Santa ClaraSanta Mateo Counties &San Unemployed Residents' Share ofthe Working Age Population, by Race &Ethnicity UNEMPLOYMENT 10% 12% 0% 2% 4% 6% 8% African American 2007* Black or Black nearly 12%in2011. nearly percentage pointssince itpeaked to (5% in2017)hasdeclinedby seven SiliconAfrican Valley American residents The unemployment rate for Blackor 2009 Hispanic orLatino 2011 2013 White 2015 recession levels in2017. Silicon Valley were below pre- andethnicgroupsall racial in Unemployment rates across Other 2017 Asian 2019 Silicon Valley Index 23 ECONOMY ECONOMYEMPLOYMENT

Labor force participation rates for workers ages 55+ have increased over the past decade, with older workers remaining in the workforce longer.

LABOR FORCE PARTICIPATION Labor Force Participation Labor Force Participation Rates for Residents Ages 55+ Rates, by Age Group Santa Clara & San Mateo Counties, California, and the United States Santa Clara & San Mateo Counties

2007 2017 2007 2017

44% 16-24 56.9% 56.5% 43% 42% 25-54 83.0% 86.5% 41% 55+ 38.5% 43.2% 40% 39% Total 66.9% 69.1% 38% 37% Silicon Valley labor force participation rates 36% increased by 2.2 percentage points overall since 35% 2007, with increases in the 25-54 and 55+ age Share of the Population in the Workforce in the of the Population Share 34% groups only; the labor force participation rate 33% for workers ages 16-24 remained relatively Silicon Valley California United States steady over the past decade.

Data Source: United States Census Bureau, American Community Survey Analysis: Center for Continuing Study of the California Economy; Silicon Valley Institute for Regional Studies

In 2007, 38.5% of Silicon Valley residents ages 55+ and older were in the workforce; by 2017, the share had risen to 43.2%.

24 2019 Silicon Valley Index Data Source: CBRE2018Scoring Tech Talent | Analysis: CBREResearch Data Source: CBRE2018Scoring Tech Talent | Analysis: CBREResearch

Number of New Tech Jobs (thousands) 2012-2017 of TechGrowth Talentin Top U.S. Tech TalentCenters TECH TALENTCENTERS Number of Local Tech Jobs (thousands) 2017 by tech talent asapercentage jobs oflocal TopU.S. Tech TalentCenters TECH TALENTCENTERS 100 150 200 250 300 350 10 20 30 40 50 60 70 80 90 50 0 0 Francisco Bay Area 9.8% Charlotte, NC +59% San Nashville, TN +43% Seattle, 8.8% Indianapolis, IN +40% WA

Hartford, CT +39% Washington, 8.0% Atlanta, GA +35% D.C. San Francisco Bay Area +31% Austin, 7.0% Long Island +28% TX Durham, Durham,

Orlando, FL +27% Raleigh- 6.6% Kansas City, MO +26% NC

Miami, FL +26% Boston, 6.2% MA Portland, OR +25%

Detroit, MI Denver, +25% 6.2% CO Denver, CO +24% Madison,

Milwaukee, WI +22% 6.1% WI Pittsburgh, PA +21% Baltimore,

Raleigh-Durham, NC +21% 5.3% MD San Diego, CA +20% Atlanta, 5.1%

Austin, TX +20% GA Jacksonville, FL +20% jobs isless thanhalf. but thetotalnumberof totaljobs,by share of Seattle isaclosesecond localjobs; percentage of jobs tech total numberof talent centersbyboth among U.S. toptech The Bay Area ranks #1 was significantlymore. tech jobslocallyoverthat timeperiod Bay Area, new thetotalnumberof and 2017at ahigherrate than the have attracted talentbetween2012 While fivetopU.S. techtalentcenters 1. Tech talent workers comprise 20different occupations, whichare highly across all industry sectors. across allindustry concentrated butare spread industry withinthehigh-tech services

1 aswellthe 2019 Silicon Valley Index 25 ECONOMY ECONOMYINCOME

Silicon Valley continues to be a high- each income category – with the region educational attainment, gender, race/ income, low-poverty region relative to the shifting to a larger share of high-income ethnicity, and occupational groups reveals rest of the state and the nation as a whole. households. While these high-income the complexity of our income gap, and the Income gains have outpaced inflation over households are able to provide for their changing distribution of households by the past several years, per capita income basic needs and more (e.g., going on income category sheds light on income rose above $100,000 for the first time, and vacations, saving for their children’s inequality within the region. Looking at the median household income reached an all- college education, owning more than shares of households by investable assets time high of $118,000. Individual median one car per household), a large share of indicates the amount of income that is income rose by nearly $1,000 annually for the region’s households cannot earn the set aside and available for consumer and residents without a high school diploma, wages they require in order to do so. discretionary spending, higher education, likely as a result of recent minimum wage retirement, philanthropy, and providing increases at both the state and local levels. overall financial security; it also helps The share of Silicon Valley households WHY IS THIS IMPORTANT? to examine the extent to which income with incomes above $200,000 annually Income growth is as important a measure inequality leads to wealth inequality. The has grown to 26%, and the share of of Silicon Valley’s economic vitality as job share of households living under the “millionaire” households (households growth. Considering multiple income federal poverty limit and Self-Sufficiency where total investable assets exceeds $1 measures together provides a clearer Standard, as well as the percentage of million) has reached 9%. However, income picture of regional prosperity and its public school students receiving free or disparities persist between residents of distribution. Real per capita income reduced-price meals (FRPM)1 and the various races and ethnicities, and between rises when a region generates wealth extent of food insecurity, are indicators of men and women at the same levels of faster than its population increases. The family poverty. educational attainment. The income median household income is the income 1. To be eligible for the FRPM program, family income must fall below 130% of the federal poverty guidelines for free meals and below 185% for reduced price meals. The federal gap continues to widen and is reflected value for the household at the middle of poverty limit for California in 2017 (used to set 2017-2018 FRPM eligibility) ranged from $12,060 for a one-person household to $41,320+ for a household with eight or more in the changing share of households in all income values. Examining income by people. The poverty limit for a family of four was $24,600.

Per capita income in Silicon Valley is 1.7

PERSONAL INCOME times higher than in California overall. Per Capita Personal Income Santa Clara & San Mateo Counties, San Francisco, California, and the United States Per capita personal income Silicon Valley San Francisco California United States in Silicon Valley was above $140,000 $119,868 $120,000 $100,000 for the first time

$100,000 in 2017 after rising by nearly $102,410 $80,000 $4,200 annually (or $349 per $59,796 $60,000 person per month) over 2016, $40,000 $51,640 after adjusting for inflation.

Per Capita Income (In ation Adjusted) (In ation Income Capita Per $20,000 Per capita income has been increasing steadily $0 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 in Silicon Valley since 2009 and rose by $27,300 annually over that eight-year period (after inflation-adjustment).

Note: Personal income is defined as the sum of wage and salary disbursements (including stock options), supplements to wages and salaries, proprietors' income, dividends, interest, and rent, and personal current transfer receipts, less contributions for government social insurance. Data Source: United States Department of Commerce, Bureau of Economic Analysis | Analysis: Silicon Valley Institute for Regional Studies

26 2019 Silicon Valley Index United States asawhole. Valley andSanFrancisco thaninCalifornia orthe educational attainment levelsisgreater inSilicon varyingThe incomegapbetweenresidents of between 2016and2017. 4.2); thisgapnarrowed by$1,800(a ratio of than thosewithless thanahighschooldiploma professional degree earn nearly $88,000more Silicon Valley residents withagraduate or 1. likely recent minimumwageincreases aresult at of boththestate andlocallevels. approximatelyequivalent toanhourlypayincrease 70cents). of This annualgrowth was with less thanahighschooldiploma(up$934annually, after adjustingforinflation, Between 2016and2017, Silicon Valley individualmedianincomerose by4%forresidents Bureau, American Community Survey| Analysis: Silicon Valley InstituteforRegional Studies survivor ordisabilitypensions;andallotherincome; White, Asian, Blackor African American, Multiple&Otherare non-Hispanic. |DataSource: UnitedStates Census Races; Personal wageorsalaryincome, incomeisdefinedasthesumof netself-employmentincome, interest, dividends, ornet rental welfare payments, retirement, Note: Multiple&OtherincludesNativeHawaiianPacific Islander Alone, AmericanIndian& Alaska Native Alone, SomeOther Race Aloneand Two orMore Between 2016and2017,thestatewide Between minimumwage increased from $10.00to $10.50perhour;additionally, 11outof39Silicon Valley citieshave theirown enacted minimumwage thoughordinances, many ofwhichincludeaplanto increase itincrementally eachyear. Silicon ValleySilicon San Francisco San California United States Educational Attainment Levels Santa ClaraSanta Mateo Counties &San Per Capita Income by Race &Ethnicity PERSONAL INCOME between andLowest Highest Disparity in Median Income inMedian Disparity Per Capita Income (In ation Adjusted) $10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000 $0

2007 $87,767 $80,447 $62,907 $48,066

White 2009 2017 Gap 2011 2013 2015 2017 Asian Ratio 4.2 4.7 3.8 3.1 Black orAfricanBlack Data Source: UnitedStates Census Bureau, American Community Survey | Analysis: Silicon Valley InstituteforRegional Studies Note: SomeCollege includesLess college;Somecollege, than1year of 1ormore years, nodegree; Associate degree; Professional certification. American Santa ClaraSanta Mateo Counties &San Income, Median Individual by Educational Attainment PERSONAL INCOME Median Income (In ation Adjusted) racial andethnicgroupsracial inSilicon Valley. adjusted percapita income for increased all 2016and2017,inflation- Between $100,000 $120,000 $140,000 $20,000 $40,000 $60,000 $80,000 $0 Multiple & Other

High School High School 2007 Less than Graduate 2009 2011 2013 2015 Hispanic orLatino 2017 equivalency) High School High School Graduate (includes Some CollegeSome or Associate's United States California Francisco San ValleySilicon Degree than those of Multiple&Otherraces. than thoseof White residents earn 3.2timesmore slightly higherinSanFrancisco, where residents; theracial/ethnic disparityis times more thanHispanicorLatino White Silicon Valley residents earn three of Highest toof Highest Lowest Income Ratio ofPerRatio Capita Income $26,001 forHispanicorLatino residents. was $77,209for White residents and ethnic group; In2017, percapitaincome significantly amongvariousracial and Silicon Valley percapitaincomediffers Racial/Ethnic GroupsRacial/Ethnic 2019 Silicon Valley Index Bachelor's Bachelor's Degree 2017 1 or Professional Graduate 2.4 2.7 3.2 3.0 Degree 27 ECONOMY ECONOMYINCOME

WAGES Average wages in Silicon Average Wages Silicon Valley, San Francisco, Alameda County, Rest of Bay Area, and California Valley ($119,000) were 1.8 times higher than Silicon Valley San Francisco Alameda County Rest of Bay Area California in California overall $140,000 $119,209 $120,000 ($66,000) in 2018.

$100,000 $109,035 Average wages across all industries $73,858 in Silicon Valley continued an upward $80,000 trend into 2018, reaching $119,000 $65,815 $60,000 (compared to $109,000 in San Francisco $61,182 and $74,000 in Alameda County). $40,000

Average Wages (In ation-Adjusted) Wages Average $20,000

$0 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18

Note: Rest of Bay Area includes Contra Costa, Marin, Sonoma, Solano, and Napa Counties; 2018 average wages were updated to reflect Q2 reported growth. The U.S. Bureau of Labor Statistics strongly discourages the comparison of wage estimates from year to year due to a variety of reasons including classification and other methodological changes. Caution is advised in using this data to draw conclusions about short-term trends. Data Sources: U.S. Bureau of Labor Statistics Quarterly Census of Employment and Wages; EMSI | Analysis: BW Research

2018 median wages varied significantly by occupational WAGES Median Wages for Various Occupational Categories category, with those in Management, Business, Science Greater Silicon Valley* and Arts Occupations earning 3.4 times more than those in $140,000 Management, Business, Service Occupations. Science and $120,000 Arts Occupations $108,070 While there was an uptick in the number of Service jobs in $100,000 Natural Resources, Construction and Silicon Valley in 2018 (+13% Maintenance Occupations year-over-year), median wage $80,000 gains for Service Occupations $59,395 Sales and Oce Occupations did not keep pace with inflation $60,000 $44,736 (down 2.5% after inflation- Production, Transportation $40,000 adjustment). $38,734 and Material Moving Median Wages (In ation Adjusted) (In ation Wages Median $31,689 Occupations $20,000 Service Occupations $0 '10 '11 '12 '13 '14 '15 '16 '17 '18

*Greater Silicon Valley includes the San Jose-Sunnyvale-Santa Clara Metropolitan Statistical Area (Santa Clara and San Benito Counties) plus the San Francisco-San Mateo-Redwood City MSA (Marin, San Francisco, and San Mateo Counties) through 2015, and the San Francisco-Redwood City-South San Francisco Metropolitan Division (San Francisco and San Mateo Counties) for 2016- 2018. | Data Sources: U.S. Bureau of Labor Statistics Quarterly Census of Employment and Wages; EMSI | Analysis: BW Research

28 2019 Silicon Valley Index The median wage for Silicon Valley Tier 1 (high-wage/high-skill) workers was $114,000 in 2018.

WAGES Median Wages by Tier Tier 1 workers in Silicon Silicon Valley, San Francisco, Alameda County, Bay Area, California, and the United States | 2018 Valley earn four times more than Tier 3 workers (a gap Tier 1 Tier 2 Tier 3 of $86,900 in 2018); this compares to a 3:1 wage ratio $140,000 for Tier 1 to Tier 3 workers in $120,000 the country as a whole.

$100,000 ECONOMY

$80,000

$60,000 Median Wages $40,000

$20,000

$0 Silicon Valley San Francisco Alameda County Bay Area California United States

Note: Definitions of Tier 1 (high-skill/high-wage), Tier 2 (mid-skill/mid-wage), and Tier 3 (low-skill/low-wage) jobs are included in Appendix A. Data Sources: BW Research; U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages; California Employment Development Department; EMSI Analysis: BW Research

Men in Silicon Valley with a WAGES Average Wages for Full-Time Workers, by Gender bachelor’s degree or higher Santa Clara & San Mateo Counties | 2017

earn an average of $145,100 Men Women annually – 43% ($43,400) $200,000 $180,000 more than women with the $160,000 same level of educational $140,000 $120,000 attainment. $100,000 $80,000 $60,000 The gender-income gap in Silicon $40,000 Valley is wider at higher levels of $20,000 educational attainment. $0 All Less than High school Some college or Bachelor's Graduate or high school graduate associate's degree professional graduate (includes equivalency) degree degree

Note: Includes all full-time workers over age 15 with earnings. Some College includes Less than 1 year of college; Some college, 1 or more years, no degree; Associate degree; Professional certification. | Data Source: United States Census Bureau, American Community Survey PUMS | Analysis: Silicon Valley Institute for Regional Studies

2019 Silicon Valley Index 29 ECONOMYINCOME

Gender-Wage Disparity for Full-Time Workers The 2017 gender-income gap Average Dollars Earned by a Female Worker for Every Dollar Earned by a Male Worker | 2017 was wider in Silicon Valley than High school Some Less than graduate college or Bachelor's Graduate or in San Francisco, California, or Total high school (includes associate's degree professional graduate equivalency) degree degree the United States as a whole. Silicon Valley $0.75 $0.78 $0.78 $0.84 $0.68 $0.72

San Francisco $0.86 $0.91 $1.00 $0.77 $0.85 $0.76

California $0.81 $0.74 $0.82 $0.79 $0.73 $0.70

United States $0.81 $0.74 $0.82 $0.79 $0.73 $0.70

Median household income Silicon Valley median household income in Silicon Valley is 1.6 times higher than in California is higher than it has ever been – reaching overall, and nearly twice $118,400 in 2017 – following six years with the national figure. annual gains outpacing inflation.

HOUSEHOLD INCOME Percent Change in Median Household Income Inﬂ ation-Adjusted Median Santa Clara & San Mateo Counties, San Francisco, California, and the United States Household Income: 2016-2017 Silicon Valley +5.1% Silicon Valley San Francisco California United States San Francisco +4.6% $140,000 $118,357 California +3.0% $120,000 United States +2.5% $100,000 $110,816

$80,000 $71,805 Increases in median household income between 2016 and 2017 outpaced inflation $60,000 in Silicon Valley, San Francisco, California, $60,336 and throughout the United States. $40,000

$20,000 Median household income in both Silicon

Median Household Income (In ation Adjusted) (In ation Income Median Household Valley and San Francisco grew by 5% in $0 2017, after adjusting for inflation. '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17

Note: Household income includes wage or salary income; net self-employment income; interest, dividends, or net rental or royalty income from estates and trusts; Social Security or railroad retirement income; Supplemental Security income; public assistance or welfare payments; retirement, survivor, or disability pensions; and all other income; excluding stock options. | Data Source: United States Census Bureau, American Community Survey | Analysis: Silicon Valley Institute for Regional Studies

30 2019 Silicon Valley Index Data Source: UnitedStates Census Bureau, American Community Survey | Analysis: Silicon Valley InstituteforRegional Studies all otherincomeexcluding stockoptions. Social Securityorrailroad retirement income, SupplementalSecurityIncome, publicassistance orwelfare payments, retirement, survivor, ordisabilitypensions, and Note: Householdincomeincludeswageandsalaryincome, net self-employmentincome, interest dividends, netrental orroyalty income from estatesandtrusts, Data Source: UnitedStates Census Bureau, American Community Survey| Analysis: Silicon Valley InstituteforRegional Studies all otherincomeexcluding stockoptions. Social Securityorrailroad retirement income, Supplemental SecurityIncome, publicassistance orwelfare payments, retirement, survivor, ordisabilitypensions, and Note: Householdincomeincludeswageandsalaryincome, netself-employmentincome, interest dividends, netrental orroyalty incomefrom estatesandtrusts, -30% -20% -10% Santa ClaraSanta Mateo Counties, &San Francisco andSan |2013-2017 Percent ChangeofHouseholds intheNumber by Income Range HOUSEHOLD INCOME Santa ClaraSanta Mateo Counties, &San Francisco, San California, andtheUnited States Share ofHouseholds With Income of $200,000orMore Annually HOUSEHOLD INCOME 10% 20% 30% 40% 50% 10% 15% 20% 25% 30% 0% 0% 5% <$10 San FranciscoSan '10 Silicon Valley $10-15 '11 $15-25 Silicon Valley San FranciscoSan '12 than $75,000declinedby 15,500 in2017alone. The number ofSilicon Valley households earningless $25-35 Income Category (thousands) '13 $35-50 California '14 $50-75 $75-100 '15 United States $100-150 '16 $150-200 '17 7% 11% 26% 26% ≥$200 the number of lower- the numberof 35% combined, while San Francisco rose by in Silicon Valley and $150,000 ormore) households (earning high-income of 2017, thenumber between 2013and Over afour-year period $150,000 ormore). households in2017(earning nearly 20,700high-income trend, Silicon Valley gained Continuing afive-year upward whole (7%). (11%) ortheUnitedStates asa annually (26%)thanCalifornia earning $200,000ormore high-incomehouseholdsof Silicon Valley hasalarger share $150,000 ormore. annually; 39%earn $200,000 ormore households earn Silicon Valley26% of declined. income households 2019 Silicon Valley Index 31 ECONOMY More than half of all Silicon ECONOMY Valley households have less than INCOME $100,000 in investable assets.

WEALTH Share of Households, by Investable Assets ≥$10 million Santa Clara & San Mateo Counties $5 - $9.99 million <$100,000 $100,000 - $499,000 $500,000 - $999,000 ≥$1 million $3 - $4.99 million 1,000,000 $1 - $2.99 million 900,000 8.3% 9.2% 3,778 7.6% 8.2% 4.4% 800,000 10,788 12.4% 700,000 23.5% 23.6% 600,000

500,000 15,241 400,000 17.6% 56,861

Number of Households 65.6% 300,000 60.7% 59.1% 200,000 100,000 0 2015 2017

Note: Investable assets include education/custodial accounts, individually-owned retirement accounts, stocks, options, bonds, mutual funds, managed accounts, hedge funds, structured products, ETFs, cash accounts, annuities, and cash value life insurance. | Data Source: Phoenix Global Wealth Monitor | Analysis: Kelly Costa, for Open Impact; Silicon Valley Institute for Regional Studies

Share of Children Living Silicon Valley’s in Poverty The 2017 poverty rate in poverty rate remains 2017 Silicon Valley was lower than low (7%) compared to San Francisco (10%), Santa Clara & San Mateo Counties 7.7% any other year since 2008. California (13%), or the United States as a San Francisco 11.4%

POVERTY STATUS whole (13%). California 18.1% Percentage of the Population Living in Poverty Santa Clara & San Mateo Counties, San Francisco, California, and the United States United States 18.4%

Silicon Valley San Francisco California United States Poverty Status by Race/Ethnicity 18% Santa Clara & San Mateo Counties 16% 13.4% 14% Black or African American 11.3% 13.3% 12% 10.0% Hispanic or Latino 10.8% 10% 7.1% 8% Native Hawaiian and Pacifi c Islander 10.7% 6% 4% Multiple and Other 9.9% 2% Asian 6.4% 0% '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 White 4.5% Data Source: United States Census Bureau, American Community Survey | Analysis: Silicon Valley Institute for Regional Studies

32 2019 Silicon Valley Index White Asian/Pacifi cIslander Other Black Hispanic orLatino by ofHouseholder RaceandEthnicity Share ofHouseholds Living Below theSelf-Suﬃ Standard ciency are more than double that of are moreWhiteresidents. thandoublethat of ethnicity; thepovertyrates forsomeracial/ethnic groups Silicon Valley povertyrates varysignificantlybyrace/ 189,000). who liveinpoverty(45,000outof allSilicon ChildrenValley account for24%of residents Silicon Valley every13)liveinpoverty. children (oneoutof thestate; still,comparison tothat of more than45,000 Silicon Valley’s childhoodpovertyrate isrelatively lowin Silicon Valley. households islocated in every tenCalifornia millionaire furthermore, nearly oneoutof the state overall (6.6%); households (9.2%)thanin “millionaire”share of Silicon Valley hasahigher investable assets). have more than$10millionin households, which 3,800of approximately 86,700 to 9%in2017(representing increased from 8%in2015 $1 millionininvestableassets households withmore than Silicon The shareValley of wealth. allthe 27% of an estimated households hold Silicon Valley The top2%of Santa ClaraSanta Mateo Counties &San |2018 18% 26% 36% 45% 57% Data Source: Center for Women's Welfare, Washington Universityof | Analysis: Silicon Valley InstituteforRegional Studies Islander, Black, White, andOtherare non-HispanicorLatino. Note: The Self-Sufficiencyof incomenecessary tomeetbasicneedswithoutpublicsubsidiesorprivate/informal assistance.Standard definestheamount Asian/Pacific their basicneedswithoutpublicorprivate, informalassistance. Silicon Valley householdsdonotearn enoughmoneytomeet Despite arelatively lowhouseholdpovertyrate, all 30%of Bachelor's Degree orHigher College orAssociate'sSome Degree orGED Diploma High School Diploma Less than aHighSchool Santa ClaraSanta Mateo Counties, &San Francisco, San andCalifornia |2018 Standards Percentage ofHouseholds LivinginPoverty andBelow Self-Su ciency SELF SUFFICIENCY 10% 15% 20% 25% 30% 35% 40% 0% 5% by Educational Attainment Level ofHouseholder Below StandardBelow andAbove Poverty Silicon Valley 29.6% Self-Sufficiency Standard,Self-Sufficiency amounting 80,000households. tonearly Silicon Valley householder households live withaHispanicorLatino below the more than57%ofall variessignificantly by race andethnicity; Self-sufficiency (28.3%), butlower thaninCalifornia asawhole(35.2%). higherinSilicon Valleyslightly Francisco (29.6%)thaninSan The share is ofhouseholds livingbelow Self-Sufficiency 15% 40% 57% 79% Below PovertyBelow San FranciscoSan 28.3% Sufficiency Standard. incomes belowtheSelf- high schoolgraduate have the householderisnot a Valley householdswhere tenSilicon eight outof to educational attainment; Self-sufficiency ishighlytied 2019 Silicon Valley Index 35.2% California 33 ECONOMY Self-Sufficiency wages increase significantly when ECONOMY there are fewer adults (earners) per household, or INCOME younger children that require costlier childcare.

SELF SUFFICIENCY It is impossible Hourly Self Su ciency Wages Needed For Various Family Types Santa Clara & San Mateo Counties, and California | 2018 to be above the

Silicon Valley California County Average Self-Sufficiency

1 Adult + 1 Preschooler + 1 Infant $65 Standard in Silicon $36 1 Adult + 1 Infant $50 $27 Valley at the current 2 Adults + 1 Infant + 1 Preschooler + 1 School-Aged Child $41 $25 statewide minimum 2 Adults + 1 Preschooler + 1 Infant $32 $19 wage ($12 per hour 1 Adult + 1 Teenager $31 $17 in California). 2 Adults + 2 School-Aged Children $21 $16 1 Adult $26 In eight out of the 11 Silicon $13 Valley cities with minimum wage 2 Adults $15 $9 ordinances, the only family type $0 $10 $20 $30 $40 $50 $60 $70 $80 that would be self-sufficient Hourly Wage per Adult earning minimum wage would be a dual-income family with no 1 Note: The Self-Sufficiency Standard defines the amount of income necessary to meet basic needs without public subsidies or private/informal assistance. children. Data Source: Center for Women's Welfare, University of Washington | Analysis: Silicon Valley Institute for Regional Studies 1. Eleven out of 39 Silicon Valley cities have enacted their own minimum wage though ordinances, ranging from $13.00 In 2018, the wages needed in order to meet a family’s most basic needs to $15.65 per hour in January, 2019 (with eight cities at without assistance in Silicon Valley ranged from $15/hour for a two-adult $15+ per hour). household with no children to $21/hour per adult in a family of four (with two adults and two school-aged children), and higher.

HUNGER Nearly 40% of Silicon Valley Percentage of Students Receiving Free or Reduced Price School Meals Santa Clara & San Mateo Counties, California students ages 5-17 receive free or reduced-price school meals. Silicon Valley California 70% The share of students receiving free or reduced- price school meals in Silicon Valley and statewide 60% increased by two percentage points in 2018; this is likely due to the passage of California Senate 50% Bill 138, indicating that there was not necessarily 40% more need, but that the existing need was met to a greater degree.1 30% 1. The increase in enrollment for FRPM in 2018 may be due to the passing of California Senate Bill 138, which went into effect on January 1, 2018. The bill eliminated the application hurdle to enrollment, required universal meal service in high-poverty school districts, and 20% automatically qualified children enrolled in Medi-Cal as eligible for FRPM.

10% 34% 51% 35% 51% 36% 54% 37% 56% 38% 57% 37% 58% 37% 58% 38% 59% 37% 59% 36% 59% 35% 58% 37% 60% 0% '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18

Data Source: California Department of Education | Analysis: Silicon Valley Institute for Regional Studies

34 2019 Silicon Valley Index Data Source: The Hunger Index | Analysis: Santa Clara University, Business Leavey Schoolof mealsNote: Missing neededbythemostvulnerable Meals households andthenumberprovided. refer tothegap between thetotalnumberof Data Source: Feeding America, MaptheMeal Gap| Analysis: Feeding America; Silicon Valley InstituteforRegional Studies Santa ClaraSanta Mateo Counties &San Provided ofMeals Number to Vulnerable Meals Households &Missing HUNGER ClaraSanta Mateo Counties, &San Area, Bay andCalifornia Share ofthePopulation that isFood Insecure HUNGER 10% 12% 14% Millions 0% 2% 4% 6% 8% 100 150 200 250 300 350 400 450 50 0 2015 10.3% Meals Provided to Vulnerable Households 55% 45% 2011 Silicon Valley 2016 9.6% 54% 46% 2012 12.4% 56% 44% 2013 Bay Area 11.5% Missing Meals Missing 56% 44% 2014 for themostvulnerable Silicon Valley residents. There was aneedfor 345millionmealsin2016 12.5% 63% 37% 2015 California 11.7% 33% 67% 2016 needed andnotprovided. million “missing meals” that were 2015), there were stillmore than115 households (67%, upfrom 63%in to themostvulnerable Silicon Valley neededmeals providedin theshare of While 2016data showedanincrease nutritionally adequate. food and/orthat is lacks access, at times, to Silicon Valleyresidents everyten One outof 2019 Silicon Valley Index 35 ECONOMY ECONOMYINNOVATION & ENTREPRENEURSHIP

Silicon Valley inventors continue to investments shot up, funding to startups who create new value and new markets register patents at a remarkable rate, continued a downward trend from the through the commercialization of novel generating nearly half of all patents recent peak in 2015. In 2018, there were and existing technology, products and registered in the state and 13% of those 661 Silicon Valley seed or early-stage services. A region with a thriving innovation across the country. The total number of investment deals (107 of which were habitat supports a vibrant ecosystem to patents registered, though, has remained to startups founded by women). Angel start and grow businesses. relatively steady over the past four years, investment within the region also declined Entrepreneurship, in both new and while the increase in patents granted somewhat during the year, although the established businesses, hinges on per capita in San Francisco has been statewide total did as well. investment and value generated by phenomenal (+140% over a six-year There were more than twice as many employees. Patent registrations track the period). Labor productivity, too, has stalled Silicon Valley IPOs in 2018 than during the generation of new ideas, as well as the over the past several years as increases in prior year – mostly in Healthcare (65%) and ability to disseminate and commercialize regional GDP were matched by continued Technology (25%) – and the total amount these ideas. The activity of mergers and (though slowing) employment growth. raised ($3.2 billion) was nearly quadruple acquisitions (M&As) and initial public The amount of venture capital the amount raised in 2017; yet, the $3.2 offerings (IPOs) indicate that a region investments in Silicon Valley and San billion was miniscule in comparison to the is cultivating successful and potentially Francisco companies shot up to a record amount of private capital infusing Silicon high-value companies. And, growth in high of $50 billion in 2018, with $19 Valley companies. firms without employees indicates that billion of it to Silicon Valley. The largest more people are going into business for share of investments (38%) went to themselves. Internet companies, as well as Mobile & WHY IS THIS IMPORTANT? Finally, tracking both the types of Telecommunications, Healthcare, and Innovation, a driving force behind patents and areas of venture capital (VC) Software companies. The total amount Silicon Valley's economy, is a vital source investment over time provides valuable of funding was highly influenced by the of regional competitive advantage. It insight into the region's longer-term unprecedented number of extremely large transforms novel ideas into products, direction of development. Changing deals (81 over $100 million in Silicon Valley processes, and services that create business and investment patterns could and San Francisco combined), including a and expand business opportunities. point to a new economic structure nearly $13-billion-dollar investment in one Entrepreneurship is an important element supporting innovation in Silicon Valley. San Francisco company (JUUL Labs Inc.). of Silicon Valley’s innovation system. While the total amount of venture capital Entrepreneurs are the creative risk takers Value added per Silicon Valley employee held steady in 2018, with a 2.4% increase in inflation-adjusted GDP balanced by a PRODUCTIVITY Value Added Per Employee similar rate of employment gains. Santa Clara & San Mateo Counties, San Francisco, California, and the United States Percent Change in Inﬂ ation-Adjusted Value Added Per Employee Silicon Valley San Francisco California United States 2001-2018 2017-2018 $220,000 $200,000 Silicon Valley +36.2% -0.4% $180,000 San Francisco +20.9% -0.2% $160,000 $140,000 California +22.1% +1.1% $120,000 $100,000 United States +21.2% +1.1% $80,000 $60,000 $40,000 Labor productivity in Silicon Valley has been $20,000 increasing steadily over the past 17 years, Value Added Per Employee (In ation Adjusted) (In ation Employee Per Added Value $0 reaching $207,000 of regional GDP per employee '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18 in 2018 (equivalent to approximately $100 per hour per employee). Data Source: Moody's Economy.com | Analysis: Silicon Valley Institute for Regional Studies

36 2019 Silicon Valley Index Data Source: UnitedStates Patent and Trademark Office | Analysis:Silicon Valley Institutefor Regional Studies Data Source: UnitedStates Patent and Trademark Office | Analysis: Silicon Valley Institutefor Regional Studies allSilicon tenfold (reaching 5,496 in2017)andtheshareValley of patents increased from 13%to28%. Over thepast20years, Silicon Valley thenumberof patent registrations inCommunications hasincreased Silicon Valley Total ofPatent Number Registrations, by Technology Area PATENT REGISTRATIONS Silicon Valley Francisco andSan Share ofCalifornia andUnited States Patents PATENT REGISTRATIONS 10,000 15,000 20,000 25,000 5,000 10% 20% 30% 40% 50% 60% 0% 0 '97 Silicon Valley Francisco +San Share ofCalifornia Silicon Valley Share ofCalifornia 6.8% 37.4% '97 in 2018thanitwastheyear 2001. (inflation-adjusted) was36%higher Silicon Valley laborproductivity '01 '01 '02 '02 '03 '03 '04 '05 '04 '06 '05 '07 '06 '08 11.9% 48.2% '07 '09 '08 '10 '09 '11 '12 '10 '13 '11 Silicon Valley Francisco +San Share ofU.S. Silicon Valley Share ofU.S. '14 '12 '15 '13 '16 19,539 '14 '17 '15 '16 46.5% 12.9% 53.7% 14.9% '17 Silicon Valley orSanFrancisco inventors. California patents were registered to In 2017, (54%)of more thanhalf Measuring, Testing &Precision Instruments Information Storage Computers, Data Processing & Communications &Heating/CoolingElectricity Health Chemical &Organic Compounds/Materials Manufacturing, Assembling, & Treating MaterialsBuilding Construction & Chemical Processing TechnologiesChemical Other of theincrease occurred inthelateof 1990s. from 7%to 13%, respectively), althoughmost increased dramatically (from 37%to47%, and California andU.S.of patent registrations has Over thepast20years, Silicon Valley’s share San Francisco San ValleySilicon California Patents Granted 100,000People per Patents Per Capita 2019 Silicon Valley Index 2011 144 476 75 Per capitapatent registrations in SanFrancisco haveshotup the prioryear. patents than only 153more represents this number inventors); San Francisco to 3,013 (compared inventors Silicon Valley registered to patents were In 2017, 19,539 2017 345 636 106 by 140%since2011. Percent Change 2011-2017 +139.9% +33.6% +41.4% 37 ECONOMY The region’s share of California VC investments ECONOMY increased to 79% in 2018, and the share of INNOVATION & ENTREPRENEURSHIP U.S. investments rose to 45%.

2018 venture capital PRIVATE EQUITY Venture Capital Investment investments in Silicon Valley Silicon Valley and San Francisco

and San Francisco companies Silicon Valley Silicon Valley + San Francisco Share of California Total totaled nearly as much as the San Francisco Silicon Valley + San Francisco Share of U.S. Total $60 90% 79% prior two years combined, and 80% $50 significantly more than any 70% $40 60% other single year since 2000. 45% 50% $30 40% VC investments to Silicon Valley companies increased by 31% year-over-year (up $4.6 $20 30% billion after inflation-adjustment), while the 20% $10 2018 San Francisco VC investment total was Billions Adjusted) of Dollars (In ation 10% nearly three times higher than the year before. Investments Total and U.S. of California Share $0 0% '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18 2018 Silicon Valley and San Francisco venture capital investments totaled $50 billion ($19 billion in Silicon Valley and nearly $31 billion Data Source: PricewaterhouseCoopers/National Venture Capital Association MoneyTreeTM Report (2000-2015); Thomson ONE (2017-2018) | Analysis: Silicon Valley in San Francisco). Institute for Regional Studies

Internet companies received 38% of all 2018 venture capital funding to Silicon Valley.

The share of VC PRIVATE EQUITY funding to Silicon Venture Capital by Industry Valley healthcare Greater Silicon Valley companies remained relatively high in 2018 Business Products & Services 100% (15%) with a total of 90% Other $5.1 billion. Energy & Utilities 80% Food & Beverages Since 2002, the 70% Consumer Products & Services share of VC funding 60% Industrial to Silicon Valley electronics companies Electronics 50% has declined from 18% Computer Hardware & Services 40% to less than 2%. Automotive & Transportation 30% Software (non-internet/mobile) 20% Healthcare 10% Mobile & Telecommunications Internet 0% '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18*

*Includes Q1-3. | Note: The category Other includes Agriculture, Environmental Services & Equipment, Financial, Leisure, traditional Media, Metals & Mining, non-internet/mobile Retail, and Risk & Security. Industry definitions are provided in Appendix A. | Data Source: PricewaterhouseCoopers/National Venture Capital Association MoneyTreeTM Report, Data: CB Insights | Analysis: Silicon Valley Institute for Regional Studies

38 2019 Silicon Valley Index Data Source: Thomson ONE | Analysis: Silicon Valley InstituteforRegional Studies Data Source: Thomson ONE| Analysis: Silicon Valley InstituteforRegional Studies Top Venture Capital Dealsof2018 Snowﬂake Computing Inc. Inc. Anchorfree Inc. Allogene Therapeutics 23andMe Inc. Grail Inc. Neutron Holdings Inc. Robinhood Markets Inc. Inc. Zume Snowﬂake Computing Inc. Zoox Inc. View Inc. Katerra Inc. Investee Company Name Total Number of Venture Capital Deals Silicon Valley, Francisco, San Rest ofCalifornia Megadeals PRIVATE EQUITY Greater Than $100 Million Each 100 120 20 40 60 80 0 Silicon Valley '13 6 4 1 South San Francisco San South Silicon Valley Mountain View Mountain View Redwood City Menlo Park Menlo Park San MateoSan MateoSan MateoSan Foster City Palo Alto Milpitas '14 19 20 4 City San FranciscoSan '15 23 25 12 Amount (millions) Rest ofCalifornia $300.00 $300.00 $462.39 $800.00 $865.00 $263.50 $295.00 $300.00 $333.10 $363.00 $375.00 $437.28 deal of 2018,deal of byfar, wasthe$12.8billioninfusionfrom Altria Group (PhilipMorrisparent Inc., atechnology-drivenbuildingdesignandconstructioncompany. The largest SanFrancisco The largest Silicon Valley 2018was$0.9billiontoMenloPark-basedVC investmentof Katerra company) into JUUL LabsInc. electronic cigarettes. –amanufacturer of '16 10 7 6 Quarter 1 3 2 3 2 3 2 4 4 3 4 1 '17 23 16 12 Postmates Inc. Getaround Inc. Coinbase Inc. OpenDoor Labs Inc. Labs OpenDoor OpenDoor Labs Inc. Labs OpenDoor Slack TechnologiesSlack Inc. DoorDash Inc. DoorDash LYFT Inc. Instacart Inc. Instacart JUUL Labs Inc. JUUL Labs Uber TechnologiesInc. JUUL Labs Inc. JUUL Labs Investee Company Name '18 44 37 26 San FranciscoSan (up from 11in2013). throughout California in 2018 deals over$100millioneach five years, reaching 107 has beenrisingforthepast large venture capitaldeals extremelyThe numberof megadeals in2018. year. SanFrancisco had37 up from 23theprevious $100 millioneach) in2018, 44 megadeals (more than Silicon Valley hadarecord Amount (millions) $12,800.00 $1,250.00 $300.00 $300.00 $300.00 $325.00 $400.00 $427.00 $535.00 $600.00 $600.00 $650.00 2019 Silicon Valley Index Quarter 3 3 4 2 3 3 1 2 4 3 1 4 39 ECONOMY Angel investments in Silicon Valley and San Francisco declined in 2018 ECONOMY by 50% and 13%, respectively (after inflation-adjustment); California INNOVATION & ENTREPRENEURSHIP as a whole experienced a similar decline (-26% year-over-year).

PRIVATE EQUITY Angel Investment San Francisco companies Silicon Valley, San Francisco, and California received three times more

Silicon Valley San Francisco Silicon Valley + San Francisco Share of California Total Angel investment dollars in $800 100% 2018 ($302 million) than $700 90% Silicon Valley companies $600 80% ($100 million). $500 70% Angel investments in Silicon Valley and San $400 60% Francisco represented 76% of the statewide total $300 50% in 2018.

$200 40% While San Francisco and Santa Clara County Angel $100 30% investments showed small year-over-year declines Millions of Dollars Invested (In ation Adjusted) (In ation Millions of Dollars Invested in 2018 (down 13% and 5%, respectively), San $0 20% '11 '12 '13 '14 '15 '16 '17 '18 Mateo County investments were down by 86% ($92 million) in 2018. Note: Only includes disclosed financing data for all deals that were designated specifically as Angel funding rounds and seed stage investments that included at least one Angel investor. | Data Source: Crunchbase | Analysis: Silicon Valley Institute for Regional Studies

Since 2010, there have been more seed or The number of Silicon Valley seed or The share of Silicon early-stage funding deals in San Francisco than early-stage funding deals declined in all the Silicon Valley cities combined. for the third year in a row. Valley seed or early- stage funding deals to STARTUPS Seed or Early-Stage Funding Deals companies founded by Silicon Valley and San Francisco women has increased Silicon Valley San Francisco significantly over the 1,400 past decade, reaching 1,200 16% in 2018. 1,000

800 Share of Seed and Early-Stage Deals to Companies Founded by Women 600 Silicon Valley 400 2008 7% 200 2018 16% 0 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18

*2018 data as of December 12. | Data Source: Crunchbase | Analysis: Silicon Valley Institute for Regional Studies

40 2019 Silicon Valley Index Data Source: Renaissance Capital | Analysis: Silicon Valley InstituteforRegional Studies 2007-2012, thestateexcept Silicon andallof Valley andSanFrancisco for2013-2018. Note: Location basedoncorporate address provided byIPOETF manager Renaissance thestateexcept Capital; California Silicon Rest includesallof of Valley for Biosciences, whichwaspurchased shortlyafter itwentpublicfor$1.5billion. The second highest2018post-IPOreturn inthecountrywasRedwood City-based ARMO Francisco for2013-2018. |DataSource: Renaissance Capital | Analysis: Silicon Valley Institutefor Regional Studies Note: Location basedoncorporate address provided byIPOETFmanagerRenaissance thestateexcept Capital; California Silicon Rest includesallof of Valley for2007-2012, thestateexcept Silicon andallof Valley andSan 20,healthcare andtechnology (13andfiveoutof respectively). ten different industryareas, Silicon Valley IPOswere predominantly in Whereas 2018IPOpricings onU.S. stockexchanges were spread across 2018 IPO Pricings, Area by Industry INITIAL PUBLICOFFERINGS Silicon Valley, Francisco, San Rest ofCalifornia, Rest ofU.S.,andInternational Companies Total ofU.S. Number IPOPricings INITIAL PUBLICOFFERINGS 100 150 200 250 300 50 0 10% 9% 162 '07 60 27 23 Silicon Valley 4% 4% '08 12 26 1% 3% 3 2 28% 1% All '09 15 43 1% 5 1 San FranciscoSan '10 53 76 14 11 40% '11 27 72 14 12 Rest ofCalifornia '12 13 82 16 17 142 '13 19 20 37 4 151 '14 30 23 66 25% 5 Rest ofU.S. '15 15 16 35 97 5% 6 Silicon Valley 5% '16 65 19 9 9 3 '17 13 98 International 36 9 4 65% '18 93 16 20 54 8 of theyear.of 13 were valuedat more than$1billionbytheend U.S. stockexchanges duringthat year. those20, Of Valley companies, allIPOson representing 15%of In 2018, Silicon there were 20IPOpricingsof for allU.S. IPOs). 37%(compared to-2%average post-IPOreturn of increased by10%. These 28companieshadan U.S.while thetotalnumberof IPOpricingsonly thetwoyears prior,respectively) thanineitherof 20andeight,IPO pricingsin2018(atotalof Silicon Valley andSanFrancisco hadtwiceasmany $54 billionnational total. the – representing 6%of the prioryear (bynineIPOs) quadruple theamountof $3.2billion–nearly total of Silicon Valley IPOsraised a Consumer Staples Materials Real Estate Energy Industrials Financials Utilities Consumer Discretionary Technology Health Care 2019 Silicon Valley Index 41 ECONOMY ECONOMYINNOVATION & ENTREPRENEURSHIP

The slight increase in total Silicon Valley M&A 68% of San Francisco’s 2018 M&A activity deals between 2017 and was Acquirer Only deals (compared to 19% of all 2018 California M&A deals 2018 was due to 34 more 55% in Silicon Valley). acquisitions (18 of which involved at least one Silicon Valley were acquisitions of local companies). company (a total of 658 deals).

MERGERS & ACQUISITIONS MERGERS & ACQUISITIONS Percentage of Merger & Acquisition Deals, by Number of Deals and Share of California Deals Participation Type Silicon Valley and San Francisco Silicon Valley and San Francisco Acquirer Only deals Target & Acquirer deals Target Only deals Silicon Valley Silicon Valley Share of Total California Deals San Francisco San Francisco Share of Total California Deals 100% 100% 90% 90% 900 27% 80% 80% 800 24% 70% 70% 700 21% 60% 60% 600 18% 50% 50% 500 15% 40% 40% 400 12% 30% 30% Number of Deals 300 9% 20%

20% Deals of California Share 200 6% 10% 10% 0% 0% 100 3% '11 '12 '13 '14 '15 '16 '17 '18 '11 '12 '13 '14 '15 '16 '17 '18 0 0% Silicon Valley San Francisco '11 '12 '13 '14 '15 '16 '17 '18

Data Source: FactSet Research Systems, Inc. | Silicon Valley Institute for Regional Studies Note: Deals include Acquirers and Targets. Data Source: FactSet Research Systems, Inc. | Silicon Valley Institute for Regional Studies

Of the 10 largest 2018 M&A deals involving Silicon Valley The total number of or San Francisco companies, half were in the software M&A deals involving industry; the rest were spread across various industries. Silicon Valley or San Francisco companies remained relatively Silicon Valley’s largest M&A deal of 2018 was the acquisition by San steady from 2017 to 2018, as did the share Jose-based Broadcom Inc. of CA Technologies (a company in the of total California IT/Software industry based in New York, with a local office in Santa M&A deals. Clara) for $18.6 billion in cash. The largest San Francisco M&A deal with the Microsoft acquisition of San Francisco-based GitHub (a software development platform) for $7.5 billion in cash and stock.

42 2019 Silicon Valley Index Data Source: UnitedStates Census Bureau, NonemployerStatistics | Analysis: Silicon Valley InstituteforRegional Studies *Other includes Accommodation &Food Services; Mining, QuarryingandOil&GasExtraction; Agriculture, Forestry, Fishing &Hunting;andUtilities. Data Source: UnitedStates Census Bureau, NonemployerStatistics | Analysis: Silicon Valley InstituteforRegional Studies 100% Santa ClaraSanta Mateo Counties, &San Francisco, San AlamedaCounty, California, andtheUnited States |2016 Percentage ofNonemployers by Industry NONEMPLOYER TRENDS Santa ClaraSanta Mateo Counties, &San Francisco, San AlamedaCounty, California, andtheUnited States Relative Growth ofFirms Without Employees NONEMPLOYER TRENDS 10% 20% 30% 40% 50% 60% 70% 80% 90% 0% Indexed to 2008 (100=2008 values) Silicon Valley 100 105 110 115 120 125 130 135 Silicon Valley '08 San FranciscoSan '09 San FranciscoSan Alameda County '10 '11 Alameda County California '12 '13 United States California '14 '15 and recreation entertainment,Arts, services Educational insurance Finance and Other* Information trade Wholesale Manufacturing Construction United States '16

and technical services Professional, scienti c, and warehousing Transportation public administration) (except services Other social assistance Health care and and remediation services and waste management Administrative andsupport Retail trade rental andleasing Real estate and Alameda County. compared to+25%inSanFrancisco and+33%in Valley grew by22%between2008and2016, nonemployerfirmsinSiliconThe numberof United States California Alameda County Francisco San ValleySilicon Employees in2016 2019 Silicon Valley Index Firms Without Technical Services. Professional, Scientific, and nonemployer firmsare in Silicon Valley24% of 24,813,048 3,277,415 143,612 214,994 99,307 43 ECONOMY ECONOMYCOMMERCIAL SPACE

The pace of new development in Silicon space rental rates continued a seven-year the available supply of commercial space Valley has remained brisk – measured by upward trend due to a limited supply suggests strengthening economic activity the total amount of new space completed in relation to demand. Although asking and tightening in the commercial real (3.9 million square feet, most of which was rents in Silicon Valley are relatively high estate market. Increases in vacancy (the due to a small number of notable projects) compared to some other growing tech amount of space that is not physically and the staggering amount currently regions across the nation, the region’s occupied), as well as declines in rents, under construction (10.4 million square major tech companies have continued to reflect slowing demand relative to supply. feet). expand their presence with a growing real Rents and vacancy rates near transit Though office space vacancy rates rose estate footprint. Strong preleasing activity illustrate the value that those prime sharply in 2018, they are not necessarily by local tech firms is supporting continued locations provide to tenants and their indicative of a lack of demand; they reflect development, and the resurgence of local employees. Changes in the real estate occupancy losses in specific industries hotel development is a positive indicator footprint of major tech companies show and some consolidation activity of of the region’s overall economic health whether they are expanding or contracting, dominant tenants, as well as preleased and outlook on the future. which affect regional employment levels. spaces that have yet to be occupied. Tech company preleasing activity is also Furthermore, vacancy rates remained low indicative of overall real estate demand in prime submarkets such as Palo Alto WHY IS THIS IMPORTANT? and affects optimism toward speculative and Mountain View, and areas near public Changes in the supply of commercial development. transit. space, vacancy rates and asking rents Asking rents were relatively stable in provide leading indicators of regional 3.9 million 2018 for office space, though industrial economic activity. A negative change in square feet of The pace of new office development has remained brisk, though commercial space completions in 2018 were slightly lower than the previous two years. was completed in 2018 (3.1 million More new Silicon Valley commercial space has been constructed over the of which was past four years (nearly 24 million square feet) than during the previous office space). 13 years combined (18.6 million total between 2002 and 2014).

Of the new Silicon COMMERCIAL SPACE Valley office space New Commercial Development Completions completed in 2018, Silicon Valley 88% of it was due to the largest 11 O ce Industrial R&D projects alone; these 10 projects were spread 9 throughout Palo Alto, 8 San Jose, Sunnyvale, and Menlo Park, and 7 ranged in size from 6 26,800 square feet 5 (500 University Avenue in Palo Alto, preleased 4 to Accel Partners at the Millions of Square Feet Millions of Square 3 time of completion) to 2 777,200 square feet 1 (the Central and Wolfe Campus in Sunnyvale, 0 '98 '99 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18 preleased to Apple).

Data Source: JLL | Analysis: Silicon Valley Institute for Regional Studies

44 2019 Silicon Valley Index Data Source: JLL | Analysis: Silicon Valley InstituteforRegional Studies square feet) andtheGateway Project inNewark(410,000 square feet). Industrial spaceunderwayincludesthePacific Commons inFremont (1.8million SantaClara200,000-square-foot buildingindowntownSan CountyJose. of newspaceat GoogleinMountain 250,000 square feetof View, andthenew foot renovation), Splunk’s newbuildingat SantanaRow (300,000square feet), occupied byFacebook), Microsoft’s campusinMountain View (450,000square Major projects include1.05millionsquare feetat Moffett Towers (slated tobe Data Source: JLL | Analysis: Silicon Valley InstituteforRegional Studies space was preleased at the time of deliveryorleasedspace waspreleased shortlythereafter. at thetimeof newspacewasconstructed, that 2018; whilealarge amountof muchof Office spaceavailabilitydidnotchangesignificantlybetween2017and

Millions of Square Feet Silicon Valley In-ProgressQuarterly Commercial Space Development COMMERCIAL SPACE Millions of Square Feet Silicon Valley Change inSupplyofO ce Space COMMERCIAL SPACE 10 10 12 14 16 18 -4 -2 0 2 4 6 8 0 2 4 6 8 '98 '98 New Construction Added (Completed) O ce '99 '99 '00 '00 '01 '01 R&D '02 '02 '03 '03 '04 Industrial '04 '05 '05 Net Absorption '06 '06 '07 Total '07 '08 '08 '09 '09 '10 Change inAvailable Commercial Space '10 '11 '11 '12 '12 amount ofindustrialspace). R&D, andanunprecedented office space butalso some million square feet, primarily (10.4 under construction phenomenal amountofspace years,two there isstilla lower in2018thantheprior deliveries were slightly spaceWhile commercial '13 '13 '14 '14 '15 '15 '16 '16 '17 '17 '18 2019 Silicon Valley Index '18 1.2 2.5 6.7 10.4 '19 By theendof into theirspace. physically moving indicating tenants square feetinQ4– to 1.4million space), itrose utilization of more efficient combined with (consolidation and rightsizing consolidation vacated dueto space that was occupied) and be delivered (or that wasyetto preleased space the year dueto of the firsthalf negative during absorption was While net Intuitive Surgical. be occupied)by is owned(andwill Sunnyvale, which Kifer Road in feet at 1050 326,000 square Q1 2019)and be completedin feet expected to 850,000 square (including San Francisco buildings inSouth are lifescience whichlarge partof underway –a square feet is 1.17 million market in2018, delivered to the space was While noR&D and MachineZone. Apple, Amazon, tenants suchas was occupiedby Class Aspace pre-leasedfeet of one million square 2018, more than 45 ECONOMY ECONOMYCOMMERCIAL SPACE

Bay Area office space vacancy rates are much lower at locations near public transit (within a ten- minute walk of a Caltrain, BART, or VTA station).

Average Oﬃ ce Space Vacancy Despite a high regional office space vacancy rate in Rates by Proximity to Transit 2018, vacancy remained low in prime submarkets such Bay Area | Q4 2018 as Palo Alto, Redwood City, Menlo Park, Mountain Near Transit 11.0% View, and Sunnyvale (3-6% direct vacancy rates).

Not Near Transit 17.8%

COMMERCIAL VACANCY Silicon Valley industrial space Annual Rate of Commercial Vacancy Silicon Valley vacancy rates are at an 18-year low, reaching 2.6% in 2018; O ce Industrial R&D 25% the last time industrial vacancy was this low was in Q4 2000. 20% 17.8% While industrial and R&D space vacancy rates showed small declines over the past year, office 15% vacancy rose from 14% in 2017 to nearly 18% in 2018. Much of this increase was due to occupancy losses in the airline and finance industries in 10% 7.4% San Mateo County – including space released by Visa, MasterCard, and Virgin Airlines – as well as 5% 2.6% continued M&A and consolidation activity by some of Silicon Valley’s dominant tenants, particularly 0% networking and telecommunications firms in '98 '99 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18 North San Jose and Milpitas.

Data Source: JLL | Analysis: Silicon Valley Institute for Regional Studies

Office space vacancy rates rose sharply in 2018 – reaching nearly 18%.

46 2019 Silicon Valley Index Data Source: JLL | Analysis: Silicon Valley InstituteforRegional Studies locations notneartwo timestherate transit. of publictransit rents at nearlyminute walkof Bay area office spacelocated withinaten- Not Near TransitNot Near Transit Silicon Valley Annual Average Rents Asking COMMERCIAL RENTS Rates by Proximity to Transit In ation-Adjusted Dollars Per Square Foot Average Oﬃce Space Rental $8.00 $0.00 $2.00 $4.00 $6.00 '98 Bay AreaBay |Q42018 Oce (FSG) Oce '99 '00 '01 '02 '03 $4.10 $8.08 Industrial (NNN) '04 '05 '06 '07 '08 Denver Portland Boston Seattle Los Angeles Austin ValleySilicon New YorkCity '09 R&D (NNN) Average Rents for Asking '10 Oﬃce Space, by Region '11 '12 '13 2018 '14 '15 per Square Footper Square (FSG) Average Rental Rate cents persquare foot inflation-adjustment). after stablerelatively in2018(down 1%year-over-year, six Asking rents for office commercial space remained '16 $2.54 $2.66 $3.17 $3.49 $3.57 $3.64 $4.33 $6.44 '17 '18 $1.21 $2.07 $4.33 Portland andDenver. spaceinplaceslike of 1.5 to2timesthecost and Seattle, andwere country, suchas Austin the other regions of were higherthanmany Silicon Valley office space 2018 rental rates for as part of aseven-year as partof increased againin2018 space rental rates Silicon Valleyindustrial relative todemand. space availability of likely duetothelimited thisincrease is Much of $1.21 persquare foot. upward trend, reaching 2019 Silicon Valley Index 47 ECONOMY ECONOMYCOMMERCIAL SPACE

HOTEL DEVELOPMENT There has been a resurgence in Number of New Hotel Rooms Santa Clara & San Mateo Counties, San Francisco, and California hotel development since 2014, with 23 new Silicon Valley and Santa Clara County San Mateo County San Francisco California 1,600 12,000 San Francisco hotels (totaling 1,400 10,000 3,280 rooms) opened over a 1,200 8,000 four-year period. 1,000 800 6,000 California

San Francisco 600 4,000 400 2,000 200 Santa Clara and San Mateo Counties, and Counties, and SanSanta Mateo Clara 0 0 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 New Hotels in Silicon Valley and 1 1 0 2 0 0 0 0 0 4 7 3 9 San Francisco

Note: Data for 2009-2013 was unavailable (reports were not published due to lack of significant hotel development). Data Source: Atlas Hospitality Group | Analysis: Silicon Valley Institute for Regional Studies

48 2019 Silicon Valley Index Data Source: Colliers InternationalSilicon Valley | Analysis: Colliers InternationalSilicon Valley Fremont,Note: IncludesSantaClara County andtheCityof plusMenloPark (Facebook). and R&DspaceinSilicon Valley. allavailableofficeapproximately 18%of and Amazon combinedhaveleased Google, Apple, Facebook, LinkedIn, Silicon Valley Tenants Amount ofCommercial Space Occupied by Major Tech TECH COMPANY PRESENCE industrial andwarehouse. office andR&Dspace,(primarily) as wellas some square feet space, ofcommercial including executed leases andsales totaling 43.6million Five oftheregion’s largest tech companies have Millions of Square Feet 10 15 20 25 30 35 40 45 0 5 Google 2013 Apple 2014 2015 Facebook 2016 LinkedIn 2017 Amazon.com 2018 office spacepreleasing activity. responsible forthemajorityof Bay Area techcompaniesare Data Source: JLL | Analysis: Silicon Valley InstituteforRegional Studies Bay AreaBay |Q42018 Share to Pre-Leased Tech Firms Commercial O ce Space UnderConstruction and TECH COMPANY PRESENCE Millions of Square Feet preleased, (93%)to primarily tech companies. total,Valley). that 7.6millionsquare Of feet was inQ42018(59%ofwhichwasBay Area inSilicon office throughout space the wasunder construction A total of11.3millionsquare feet ofnewcommercial Not Pre-Leased 0 1 2 3 4 5 6 7 8 Pre-Leased to Tech Firms 2019 Silicon Valley Index Pre-Leased to Non-Tech Firms 93% 7% 49 ECONOMY SOCIETYPREPARING FOR ECONOMIC SUCCESS

Silicon Valley high school graduation low-income households did not have complete high school and meet entrance rates and the share meeting UC/CSU broadband internet access (compared to requirements for the University of requirements has increased since 2012, 8% of households overall). California (UC) or California State University and the dropout rate has declined. Both (CSU). Educational achievement can also graduation rates and the share meeting be measured by proficiency in math, UC/CSU requirements continue to vary WHY IS THIS IMPORTANT? which is correlated with later academic significantly by race and ethnicity, though The future success of Silicon Valley’s success. Breaking down high school the gap narrowed for Silicon Valley knowledge-based economy depends on graduation rates and the share meeting Hispanic or Latino students (with a sharp younger generations’ ability to prepare UC/CSU entrance requirements by race increase in the share meeting UC/CSU for and access higher education; it also and ethnicity sheds light on the inequality requirements, reaching to 39% in 2018). depends on providing all residents with of educational achievement in the region. Eighth-grade math proficiency rose for a fundamental requirement for 21st And, whether the region’s residents have the third year in a row to 56% of students century life – robust, high-speed network access to a computer with broadband meeting or exceeding the standard. connectivity. internet connectivity is indicative of their A greater share of Silicon Valley and High school graduation and dropout ability to engage in the community, look San Francisco households has access to a rates are an important measure of how well for jobs, do homework, manage finances, computer with internet connectivity than our region prepares its youth for future interact with government, access a wide in the state or nation as a whole, though success. Preparation for postsecondary variety of resources, and conduct the connectivity varies by household income education can be measured by the business of everyday life. level. In 2017, 25% of Silicon Valley’s proportion of Silicon Valley youth that

Silicon Valley’s high school dropout rate (8%) in 2018 was nearly two percentage points lower than in the state overall (10%).

Silicon Valley’s high school graduation rate GRADUATION AND DROPOUT RATES increased slightly in 2018 (reaching nearly 87%), Rate of Graduation, Share of Graduates Who Meet UC/CSU Requirements, and Dropout Rate as did the share of students meeting UC/CSU requirements (reaching 59%). Silicon Valley and California % of Graduates Meeting Graduation Rates UC/CSU Requirements Dropout Rates 100% 90% 80% 70% 60% 50% 40% 30% 20% 10%

0% 83% 50% 11% 86% 55% 9% 86% 54% 10% 87% 59% 8% 79% 38% 13% 81% 42% 12% 84% 45% 10% 83% 50% 10% 2012 2014 2016 2018* 2012 2014 2016 2018* Silicon Valley California

*Due to changes in the California Department of Education methodology for 2017 and subsequent years, caution should be used in comparing cohort outcome data to prior years. | Note: Graduation and dropout rates are four-year derived rates. | Data Source: California Department of Education | Analysis: Silicon Valley Institute for Regional Studies

50 2019 Silicon Valley Index Latino are non-Hispanic. |DataSource: Education| CaliforniaAnalysis: Silicon Departmentof Valley InstituteforRegional Studies to prioryears. twoormore races, |Note:Multi/Noneincludesstudents of andthosewhodidnotreport theirrace. All racial/ethnic groups asidefrom Hispanic or Educationmethodologyfor2017andsubsequentyears,*Due tochangesintheCalifornia Departmentof cautionshouldbeused incomparingcohortoutcomedata Regional Studies All racial/ethnic groups asidefrom HispanicorLatinoare non-Hispanic. |DataSource: Education| CaliforniaAnalysis: Silicon Departmentof Valley Institutefor data toprioryears. |Note:Graduation rates are four-year derivedrates. twoormore races, Multi/Noneincludesstudentsof andthosewhodidnotreport theirrace. Educationmethodologyfor2017andsubsequentyears,*Due tochangesintheCalifornia Departmentof cautionshouldbeusedincomparingcohortoutcome Silicon Valley Share ofGraduates Who UC/CSURequirements, Meet by Race andEthnicity COLLEGE PREPARATION Silicon Valley Graduation School High Rates, by Race andEthnicity GRADUATION RATES ANDDROPOUT Percentage of Students With UC/CSU Required Courses Percentage of Students Who Graduated in Four Years 100% 10% 20% 30% 40% 50% 60% 70% 80% 90% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 0% Asian 2012 Asian 2012 Filipino White 2014 2014 Multi/None White 2016 2016 Filipino Multi/ None 2018* 2018* Hispanic or Latino American African- Alaska NativeAlaska American Indian or Islander Paci c Islander Paci c Hispanic or Latino American African- Alaska NativeAlaska American Indian or Valley Total Silicon Silicon Valley Total average. above theregional percentage points students nine ethnicity,with Asian rates varybyrace/ High schoolgraduation methodology). changes inthecohortoutcomecalculation that increase mayhavebeenduetoof in 2012to39%2018(thoughaportion CSU entrance requirements rose from 27% Latino highschoolgraduates meetingUC/ Silicon The shareValley’s of Hispanicor requirements. met theUC/CSUentrance school graduates in2018 Silicon Valley59% of high 2019 Silicon Valley Index 51 SOCIETY SOCIETYPREPARING FOR ECONOMIC SUCCESS

56% of Silicon Valley eighth-graders are proficient in math, compared to only 37% in California overall. Eighth-grade math proficiency rose for the third year in a row in Silicon Valley, MATH PROFICIENCY Share of Eighth-Graders Who Met or Exceeded the Standard in Math San Francisco, and statewide. Santa Clara & San Mateo Counties, San Francisco, and California

Silicon Valley San Francisco California 70%

60%

50%

40%

30%

20%

10%

0% '07 '08 '09 '10 '11 '12 '13 '14* '15 '16 '17 '18

*Math proficiency data is not available for 2014. | Note: Beginning with the 2013–14 school year, the California Assessment of Student Performance and Progress (CAASPP) became the new student assessment system in California, replacing the Standardized Testing and Reporting (STAR) system. Data Source: California Department of Education | Analysis: Silicon Valley Institute for Regional Studies

52 2019 Silicon Valley Index Data Source: UnitedStates Census Bureau, American Community Survey| Analysis: Silicon Valley InstituteforRegional Studies California, ortheUnited States overall. Francisco,internet access thanSan households with computers and broadband Silicon Valley hasagreater share of Santa ClaraSanta Mateo Counties, andSan Francisco, San California, andtheUnited States Share ofHouseholds withaComputer andBroadband Internet Access ACCESSCOMPUTER &INTERNET 100% 20% 40% 60% 80% 0% Silicon Valley

92% 2013 96% Francisco With aComputer San San 88% 2017 93% California 87% 94% United States

84% 2017 (upby four and sixpercentage points, respectively). broadband internet access 2013and between increased The share ofSilicon Valley households withacomputer and 91% Silicon Valley 86% 92% Francisco San San

With Internet 82% 88% California 78% 88% United States 73% 84% States United California Francisco San Valley Silicon Santa ClaraSanta Mateo Counties, &San Francisco, San Share ofHouseholds Without Internet Access At Home, by California, andtheUnited States |2017 broadband internetaccess in 2017;thisshare jumps households (earning less households didnothave than $35,000annually). 2019 Silicon Valley Index to 25%forlow-income 8% of allSilicon Valley8% of Income Range Income Low- 34% 29% 35% 25% Moderate- Income 14% 12% 16% 12% Income High- 5% 4% 3% 3% 53 SOCIETY Silicon Valley and San Francisco preschool enrollment SOCIETY rates (62% and 71%, respectively) are higher than in EARLY EDUCATION & CARE California (50%) or the United States overall (48%).

Preschool enrollment rates in Silicon Valley and San Francisco are higher than PRESCHOOL ENROLLMENT in the state and nation as a whole, and a Percentage of the Population 3 to 4 Years of Age Enrolled in School greater share attend private preschools. Santa Clara & San Mateo Counties, San Francisco, California, and the United States Average preschool costs are significantly higher (and rising sharply), though, as 2007 2009 2011 2013 2015 2017 are costs for infant care (which reached 80% nearly $21,000 per year at licensed care facilities in Silicon Valley in 2018). A 70% larger share of the region’s third-graders 60% is proficient in English Language Arts (60% in Silicon Valley and 53% in San 50% Francisco) than in the state overall (48%), 40% though proficiency varies significantly by race and ethnicity. 30% 20% WHY IS THIS IMPORTANT? 10%

Early education provides the foundation 56% 58% 62% 55% 60% 62% 62% 67% 70% 71% 70% 71% 50% 49% 49% 47% 49% 50% 47% 48% 47% 46% 48% 48% 0% for lifelong accomplishment. Research Silicon Valley San Francisco California United States has shown that quality preschool-age education is vital to a child’s long-term Note: Data includes enrollment in private and public schools. success. Private versus public school Data Source: United States Census Bureau, American Community Survey | Analysis: Silicon Valley Institute for Regional Studies enrollment illustrates the economic structure of our community when compared to California and the United States. Reading and writing abilities function as important indicators for a child’s future, as they are Preschool enrollment rates strongly correlated with continued academic achievement. in Silicon Valley rose by seven Child care costs affect the ability of Silicon Valley parents to send their children to percentage points since 2013. preschool, and to provide quality care for their infants while they work.

PRESCHOOL ENROLLMENT Percentage of the Population 3 to 4 Years of Age, by School Enrollment Santa Clara & San Mateo Counties, San Francisco, California, and the United States | 2017 Forty-two percent of Silicon Not Enrolled Private School Public School Valley 3- and 4-year-olds are 100% enrolled in private preschool 20% (up from 34% in 2007). 90% 27% 28% 28% 80% 70% 20% 60% 42% 22% 50% 43% 40% 30% 50% 52% 20% 38% 29% 10% 0% Silicon Valley San Francisco California United States

Data Source: United States Census Bureau, American Community Survey | Analysis: Silicon Valley Institute for Regional Studies

54 2019 Silicon Valley Index Third grade English language arts proficiency in Silicon Valley varies significantly by race/ethnicity, with Asian students having Silicon Valley has a the highest share (80%) meeting or exceeding the standard. higher share of third-

ENGLISH LANGUAGE ARTS PROFICIENCY Third Grade English Language Arts Pro ciency, by Race/Ethnicity graders meeting or Santa Clara & San Mateo Counties | 2018 exceeding the English language arts standard Percent Meeting or Exceeding Standard Percent Below Standard 100% than San Francisco or SOCIETY 80% the state as a whole.

60% Share of Third-Graders Meeting or Exceeding the Standard in 40% English Language Arts

20% 2018

0% Silicon Valley 60% Asian Two or White Filipino American Black or Native Hawaiian Hispanic More Races Indian or African or Paci c or Latino San Francisco 53% Alaska Native American Islander California 48%

Data Source: California Department of Education, California Assessment of Student Performance and Progress (CAASPP) Analysis: Silicon Valley Institute for Regional Studies

Child care costs in Silicon Valley rose by 30-57% (after inflation-adjustment) between 2012 and 2018, Average child care costs at licensed care facilities in Silicon depending on the type of care facility and age group. Valley were an estimated $20,900 per year for infants and $15,300 per year for preschoolers in 2018; infant care centers in San Francisco charge an estimated $22,800 per year.

CHILD CARE COSTS The cost of care Annual Average Child Care Costs, by Care Center Type & Age Group Santa Clara & San Mateo Counties, San Francisco, and California for children under age five 2012 2018* $25,000 rose significantly

$20,000 since 2012 in

$15,000 Silicon Valley, San Francisco, $10,000 and California. $5,000 The cost of preschool is Average Annual Cost (In ation-Adjusted) Cost Annual Average $0 28% higher at Silicon Valley Child Care Family Child Child Care Family Child Child Care Family Child Child Care Family Child Child Care Family Child Child Care Family Child Center Care Home Center Care Home Center Care Home Center Care Home Center Care Home Center Care Home child care centers than at Silicon Valley San Francisco California Silicon Valley San Francisco California those throughout the state. INFANTS PRESCHOOLERS

*2018 estimate based on 2016 market rate data. | Data Sources: Kidsdata.org; California Department of Education | Analysis: Kidsdata.org; Silicon Valley Institute for Regional Studies

2019 Silicon Valley Index 55 SOCIETYARTS & CULTURE

There are more than 400 nonprofit arts are a reflection of regional diversity and organizations in Santa Clara and San Mateo quality of life. In attracting people to the Counties combined. The region had 9,200 area, generating business throughout employees working in arts and culture the community and contributing to local industries in 2013, representing less than revenues, these unique cultural activities one percent of regional employment have considerable local impact. (compared to 5% in San Francisco). Silicon The number of local arts nonprofits is Valley residents spend more money on indicative of a region's ability to organize arts and culture consumption than those and make arts programs available to the in many other regions across the United community. Local employment is one of States, and 28% of all households donate the most substantial ways that arts and money to public broadcasting or the arts culture affect our community. Spending (compared to 35% in San Francisco). on arts and culture activities reflects the public's interest, as well as the amount of money for which producers of the WHY IS THIS IMPORTANT? arts must compete. And the share of Arts and culture play an integral role in households donating indicates how much Silicon Valley’s economic and civic vibrancy. the community values the arts and is As both creative producers and employers, willing to support it. nonprofit arts and culture organizations

San Francisco has significantly more nonprofit arts organizations per capita (57 per 100,000 residents in 2012) than either Santa Clara or San Mateo Counties (17 and 16 per 100,000, respectively); this amounts to approximately 431 nonprofit arts organizations in Santa Clara and San Mateo Counties combined.

In 2012, there were 5.1 and 6.3 nonprofit ARTS & CULTURE performing arts organizations per 100,000 Nonpro t Arts Organizations residents in San Mateo and Santa Clara Counties, Santa Clara & San Mateo Counties, and San Francisco | 2012 respectively (compared to 19.2 per 100,000 in San Francisco). Visual Arts 70 Arts Education 60 Humanities & Heritage 50 Collections-Based 40 Media Arts 30 Per 100,000 People Per 20 Other Arts

Number of Nonpro t Arts Number of Nonpro t Organizations 10 Field Service Arts

0 Performing Arts Santa Clara County San Mateo County San Francisco

Data Sources: Americans for the Arts; National Center for Charitable Statistics | Analysis: Silicon Valley Institute for Regional Studiess

56 2019 Silicon Valley Index Data Source: Americans forthe Arts | Analysis: Silicon Valley InstituteforRegional Studies all employees)inSanFrancisco. employees), compared to22,900(5%of (representing all less thanonepercent of arts andculture employeesin2013 more than 9,200 a combinedtotalof Santa Clara andSanMateo Counties had Data Source: Americans forthe Arts | Analysis: Silicon Valley InstituteforRegional Studies by Region |2015 Consumer &Culture Expenditures onArts Consumption &CULTUREARTS Total Number of Employees ClaraSanta Mateo Counties, &San Francisco andSan Employees Working &Culture inArts Industries &CULTUREARTS Dollars Spent per Person per Year 10,000 15,000 20,000 25,000 30,000 5,000 $100 $200 $300 $400 $500 $600 $0 0 Santa ClaraSanta County 2009

San Francisco Photographic Equipment Admission Fees

Seattle

2011 Denver

San Mateo County San MateoSan County Recorded Media Santa Clara County 2013

Minneapolis Reading Materials

Raleigh

Pittsburgh Musical InstrumentsMusical San FranciscoSan Atlanta

Phoenix

San Diego

Austin the Arts; Scarborough Research | Analysis: Silicon Valley InstituteforRegional Studies Note: 2011datawere collected in2009-2011, and2014data were collectedin2012-2014. |DataSources: Americans for in SanFrancisco. Santa Clara andSanMateo Counties, and arts declinedbetween2011and2014in householdsdonating tothe The share of Sacramento Share of Households Donating ClaraSanta Mateo Counties, &San Francisco andSan Donations to Public Broadcasting orArts &CULTUREARTS Chicago 10% 20% 30% 40% 0% Houston Santa ClaraSanta County 2011 Philadelphia 29.7% residents. Santa Clara County culture activitiesthan on average, onartsand residents spendmore, San Mateo County theaters, stadiums, andconcerthalls. to entertainmentvenuessuchas musical instruments, andadmissions materials, photographic equipment, including recorded media, reading arts andculture products/activities $500 annually, onaverage, on Silicon Valley residents spendnearly 28.1% 2014 San MateoSan County 2019 Silicon Valley Index 34.1%

27.5%

San FranciscoSan 37.1%

34.9% 57 SOCIETY SOCIETYQUALITY OF HEALTH

The share of residents ages 18-64 remained high following state legislation likely to seek routine medical care and covered by health insurance remained in 2016 that eliminated the exemption preventive health-screenings. high in 2017 following a three-year based on personal or religious beliefs. Being overweight or obese increases upward trend in Silicon Valley, San Pregnant women in the Bay Area the risk of many diseases and health Francisco, California, and across the experience poverty at higher rates than the conditions, including Type 2 diabetes, nation. In particular, the share of Silicon overall population, with 26% falling below hypertension, coronary heart disease, Valley unemployed residents with health the federal poverty guideline. Twelve stroke, and some types of cancers. These insurance coverage has increased by 24% percent experience food insecurity, and conditions decrease residents’ ability since the implementation of the Affordable 15% receive food stamps. Silicon Valley’s to participate in their communities, may Care Act in 2014 and its early expansion infant mortality rate remains lower than increase medical expenses, and have program, the Low Income Health Program in the state overall, though the rate varies significant economic impacts on the (which enrolled over 30,000 Silicon Valley significantly by race and ethnicity; Black or nation’s health care system as well as residents in Medi-Cal by the end of 2013).1 African American women in Silicon Valley the overall economy due to declines in Obesity is becoming more prevalent are nearly three times more likely to have productivity. among Silicon Valley adults and an infant die before his or her first birthday Improving the well-being of mothers, throughout the state. The share of Silicon than White women. infants, and children is an important public Valley adults who are overweight or obese health goal for any region. Maternal and rose to 58% in 2017, and nearly one-third infant health statistics provide information of Silicon Valley’s students are above a WHY IS THIS IMPORTANT? about how well we are preparing the next healthy Body Mass Index. Early and continued access to quality, generation of healthy young residents. Nearly all (97%) of the region’s affordable health care is important to Timely childhood immunizations promote kindergarten students have had all of their ensure that Silicon Valley’s residents are long-term health, save lives, prevent required immunizations, a rate that has thriving. Given the high cost of health care, significant disability, and reduce medical

1. California Department of Health Care Services, Low Income Health Program Enrollment individuals with health insurance are more costs. Data, Quarter 2 of Fiscal Year 2013-2014. In 2016 and 2017, 94% of Silicon Valley’s 58% of Silicon Valley adults are overweight or obese, 18- to 64-year-olds were covered by health insurance. compared to 41% in San Francisco and 60% in California.

OBESITY The share of adults who are overweight or obese Adults Overweight or Obese has increased in Silicon Valley and throughout Santa Clara & San Mateo Counties, San Francisco, and California the state over the past decade, while declining slightly in San Francisco.

Overweight Obese 70% 1 60% Nearly /3 of Silicon

50% 26.4% Valley students are 18.6% 16.1% 22.6% 40% overweight or obese. 11.9% 12.5% 30% A smaller share of Silicon Valley students

Overweight Or Obese 20% (32%) is overweight or obese than in San 35.7% 39.7% 31.0% 33.9% 33.9% Percentage of Adults Who Are Are Who of Adults Percentage 28.0% Francisco (34%) or the state overall (39%). 10% The share of Silicon Valley students who 0% 2007 2017 2007 2017 2007 2017 are overweight or obese has remained Silicon Valley San Francisco California steady (around 32%) since 2014.

Data Sources: California Health Interview Survey | Analysis: Silicon Valley Institute for Regional Studies

58 2019 Silicon Valley Index Analysis: Silicon Valley InstituteforRegional Studies Data Source: UnitedStates Census Bureau, American Community Survey Analysis: Silicon Valley Institutefor Regional Studies Data Source: Education, California Departmentof PhysicalFitness Testing Research Files 1. the AffordableCarethrough Act. 2013, coverage influencedbytheavailabilityof population hasincreased significantlysince Health insurance coverage fortheworkingage Changes intheshare ofthepopulation withhealthinsurance coverage between 2013and2016were highlyinfluenced by the of availability – anearlycoverage expansion program administered priorto implementation oftheACA. 1, 2014for theearliestenrollees. Increases incoverage between 2012and2013were likely related to theLow Income Health Program (LIHP) coverage through the2010Patient Protection andAffordable Care Act (ACA, also knownObamacare), as effective onJanuary whichbecame 100% Santa ClaraSanta Mateo Counties, &San Francisco, San California, andtheUnited States Health Insurance Coverage Share ofthePopulation Ages 18-64with HEALTHCARE Santa ClaraSanta Mateo Counties, &San Francisco, San andCalifornia Students Overweight orObese OBESITY Percentage of Student Population 70% 75% 80% 85% 90% 95% that is Overweight or Obese Silicon Valley '08 10% 15% 20% 25% 30% 35% 40% 45% 0% 5% '09 Silicon Valley 32.4% '10

San FranciscoSan 34.6% 2014 '11

San FranciscoSan 38.3% '12 '13 California 31.5% '14 California 1

'15 33.8% 2018 '16

United States 39.0% 87.7% 89.9% 94.4% 95.4% '17 Not in Labor ForceNot inLabor Employed Unemployed United States California San Francisco San Silicon ValleySilicon Change inthePercentage with ofIndividuals Health Insurance, by Employment Status Percentage withHealth ofIndividuals similar increase (+25%)throughout coverage of Silicon coverageValley of employed Insurance, by Employment Status insurance coverage jumpedtwenty- enrollees, unemployed theshare of Silicon Valley residents withhealth force (+7%and+2%, respectively, workers and thosenotinthelabor Santa ClaraSanta Mateo Counties, &San 2013-2017 the state; there hasalsobeenan increase (thoughsmaller)inthe became effective foritsearliest four percentage points, witha Unemployed Since the AffordableCareSince Act 73% 83% 90% 89% between 2013and2017). States asawhole). States and 73%throughout theUnited Francisco,in San 83%inCalifornia, coverage in2017(compared to 90% workers hadhealthinsurance 89% ofSilicon Valley’s unemployed 2017 Employed 89% 91% 96% 95% 2019 Silicon Valley Index +24% +2% +7% Not In Labor ForceNot InLabor 86% 89% 91% 92% 59 SOCIETY SOCIETYQUALITY OF HEALTH Pregnant women in the Bay Area are more than twice as likely to participate in CalFresh than the overall population, and experience poverty at a much higher rate.

More than a quarter of all pregnant women in the Bay Area had incomes below the federal poverty MATERNAL, INFANT, AND CHILDREN’S HEALTH Poverty and Food Insecurity During Pregnancy guideline in 20151 (compared to 10% of the population as a whole), 12% experienced food San Francisco Bay Area, and California | 2013-2015 insecurity (the same rate as for all residents2), and 15% received CalFresh (compared to 6%3 overall). Bay Area California 60% 1. Data collected between 2013 and 2015. 2. According to 2015 food insecurity estimates from Feeding America, Map the Meal Gap. 3. The figure of 6% of all residents in the Bay Area receiving CalFresh is from the California 50% Department of Social Services, Administration Division, CalFresh Dashboard (updated 01/10/19). 40%

30%

20%

10% 26% 40% 12% 16% 36% 53% 15% 25% 0% Income Below Federal Experienced Participated in WIC Received CalFresh Poverty Guideline Food Insecurity (Food Stamps)

Data Source: California Department of Public Health, MIHA Survey | Analysis: Silicon Valley Institute for Regional Studies

The infant mortality rate in Silicon Valley (3.3 deaths per 1,000 live births) was lower than in the state overall (4.2 per 1,000) in 2016.

MATERNAL, INFANT, AND CHILDREN’S HEALTH Infant Mortality Rate Infant Mortality Rate by Race & Ethnicity Santa Clara & San Mateo Counties, San Francisco, and California Number of Infant Deaths per 1,000 Live Births Santa Clara & San Mateo Counties | 2007-2016

Black or African American 6.9 Silicon Valley San Francisco California 6 Hispanic or Latino 3.6

5 Asian or Pacifi c Islander 2.8

4 White 2.4

Note: Black or African American, Asian or Pacific Islander, and White are 3 Non-Hispanic.

1 Number of Infant Deaths per 1,000 Live Births per 1,000 Live Deaths Number of Infant 0 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16

Data Source: U.S. Department of Health and Human Services, Centers of Disease Control and Prevention (CDC) | Analysis: Silicon Valley Institute for Regional Studies

60 2019 Silicon Valley Index before hisorherfirstbirthday. women tohaveaninfantdie times more likely than White Silicon Valley were nearly three African American womenin Over thepastdecade, Blackor Data Source: Public California Departmentof Health | Analysis: Silicon Valley InstituteforRegional Studies

Percentage of Kindergarten Students ClaraSanta Mateo Counties, &San Francisco, San andCalifornia Immunization RatesKindergarten MATERNAL, INFANT, ANDCHILDREN’S HEALTH with All Required Immunizations 100% 82% 84% 86% 88% 90% 92% 94% 96% 98% '07 Silicon Valley '08 '09 '10 San FranciscoSan '11 '12 '13 California '14 '15 '16 '17 '18 94.9% 96.7% 95.1% 2017-18 school year. 97%inthe nearly since 2014,reaching significantly increased has immunizations with allrequired Valley kindergarteners The share ofSilicon 2019 Silicon Valley Index 61 SOCIETY SOCIETYSAFETY

Silicon Valley has a lower rate of violent WHY IS THIS IMPORTANT? crimes than in the state overall; however, in 2017 there were still more than 8,700 Public safety is an important indicator violent crimes reported locally. The rate of societal health. The occurrence of increased by 10% over the prior year, with crime erodes our sense of community a 25% increase in the number of rapes by creating fear and instability and reported. The felony arrest rate remained poses an economic burden as well. The much lower than prior to the passage of number of Silicon Valley public safety California Proposition 47 (in 2014). Though officers provides a unique window into the juvenile felony arrest rate in Silicon the changing infrastructure of our city Valley is very similar to the state overall, and county governments and affects the the adult felony arrest rate is significantly public’s perception of safety. In 2017, there were 237 more lower. aggravated assaults in Silicon Silicon Valley had more public safety Valley (reaching a total of 4,645 officers in 2018 than any other year over reported) than were reported the past decade, with more than 5,000 during the prior year. sworn full-time and reserve personnel.

There were more than 8,700 85% of 2017 violent crimes in violent crimes reported Silicon Valley were aggravated Silicon Valley has a lower violent crime rate (284 within the region in 2017. assault or robbery. crimes per 100,000 people) compared to the state as a whole (452 crimes per 100,000). Silicon Valley’s violent crime rate The number of reported rapes in Silicon increased by 10% in 2017 (compared Valley increased by 25% (+244) in to 1% increase throughout the state). 2017 over the prior year.

CRIMES CRIMES Violent Crime Rate Breakdown of Violent Crimes, by Type Silicon Valley and California Silicon Valley | 2017 Homicide Silicon Valley California 1%

700 Rape 600 14%

500

400 Aggravated Assault 300 Robbery 53% Rates per 100,000 People Rates 200 32%

100

0 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17

Note: Violent crimes include homicide, rape, robbery, and aggravated assault. | Data Sources: California Department of Data Source: California Department of Justice | Analysis: Silicon Valley Institute for Regional Studies Justice; California Department of Finance | Analysis: Silicon Valley Institute for Regional Studies

62 2019 Silicon Valley Index Data Source: California Commission onPeace Officer Standards and Training | Analysis:Silicon Valley Institutefor Regional Studies personnel throughout theregion (70%inSantaClara County). year overthepastdecade, with5,070swornfull-timeandreserve Silicon Valley hadmore publicsafetyofficers in2018thananyother Data Sources: CaliforniaJustice; UnitedStates Census Departmentof Bureau | Analysis: Silicon Valley InstituteforRegional Studies *The felonyarrest rates for2015andsubsequentyears were Proposition affected bythepassage of 47, socautionisadvisedin comparingtoprevious years. Silicon Valley Total ofPublic Number O cers Safety OFFICERS PUBLIC SAFETY ClaraSanta Mateo Counties, &San andCalifornia Felony O enses ARRESTS 1,000 2,000 3,000 4,000 5,000 6,000 Rates per 100,000 Adults and Juveniles 0 Silicon ValleyJuveniles 1,400 1,600 1,800 1000 1200 200 400 600 800 2008 4,689 0 '07 2009 4,715 '08 2010 felony offenses in2017(up by 20%,or239arrests). There was inSilicon asmallincrease Valley juvenile

4,671 '09 Silicon ValleyAdults 2011

4,541 '10 2012

4,275 '11 2013

4,170 '12 California Juveniles 2014 4,267 '13 2015 4,250 '14 '15* 2016 4,832 California Adults '16 2017 4,961 '17 2018 5,070 and 2017. steady through 2016 remained relatively Proposition 47,of then year) duetothepassage 2015 (-31%year-over- dropped significantlyin throughout thestate in Silicon Valley and Felony arrest rates the County of Santa Clara Sherriff’s Office).the County of agencies (theSan Jose Police Departmentand officers in2018, 102were addedbyjusttwo Silicon Valley’sOf 109additionalpublicsafety 2013-2018 2009-2013 Change inthe Total Number Public Oﬃ Safety cers Between 2013and2018, thenumber increased by900swornfull-timeand of Silicon Valleyof publicsafetyofficers 2019 Silicon Valley Index of Silicon Valleyof reserve personnel. +900 -545 63 SOCIETY PLACEHOUSING

Median home sale prices in Silicon Valley up for the lack of building over the prior traffic congestion. It also restricts the skyrocketed in 2018 (+21%) – reaching decade; and, new residential building is ability of crucial service providers—such nearly $1.2 million – and the share of predominantly for high-income residents, as teachers, registered nurses, and police potential first-time homebuyers that could with only 8% of newly approved residential officers—to live near the communities afford a median-priced home declined. units affordable to those earning less than in which they work. Additionally, high The inventory of homes on the market has 80% of the area median income. Housing housing costs can limit families’ ability remained relatively low, as has the total affordability is a contributor to the rise in to pay for basic needs, such as food, number sold each year. multigenerational households, multifamily health care, transportation, childcare, Average rental rates have remained households, and young adults living with and clothing. They can push residents relatively steady (after adjusting for their parent(s). Affordability, evictions, and to live with one another for economic inflation), though the rental rate per other factors are also leading causes of reasons and can increase homelessness. square foot remains higher than any other homelessness within the region. Being evicted from a rental unit can also U.S. metro area. Median monthly housing cause a rise in multifamily households costs are also the highest among metro and is a leading cause of homelessness areas in the country (at more than $2,000 WHY IS THIS IMPORTANT? in our region. As a region’s attractiveness per month), and the housing burden for The housing market impacts a region’s increases, average home prices and rental Silicon Valley homeowners is higher than economy and quality of life. An inadequate rates tend to increase. Higher levels of in the nation overall. supply of new housing negatively affects new housing and attention to increasing Local housing affordability issues are prospects for job growth. A low for-sale housing affordability are critical to the being exacerbated by inadequate new inventory drives up prices. And a lack economy and quality of life in Silicon residential development. Although a large of affordable housing results in longer Valley. number of units have been built over commutes, diminished productivity, the past two years, they have not made curtailment of family time, and increased

Silicon Valley median home sale prices have increased by $300,000 over the past two years alone, reaching nearly $1.2 million in 2018.

While California home price increases HOME SALES were modest in 2018 (up 3.4% year-over- Median Home Sale Prices year, after inflation adjustment), Silicon Santa Clara & San Mateo Counties, San Francisco, and California Valley home prices rose sharply in 2018 (a 21% year-over-year increase). Silicon Valley San Francisco California $1,400,000 In 2018, the median sale price of a Silicon Valley home was $1.18 million, $1,200,000 compared to $1.31 million in San Francisco, $485,000 statewide, and $1,000,000 $221,000 nationwide. $800,000

$600,000

$400,000

Median Sale Price (In ation-Adjusted) Median Sale Price $200,000

$0 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18*

* Based on data through November (Silicon Valley and San Francisco) and October (California). | Data Source: CoreLogic | Analysis: Silicon Valley Institute for Regional Studies

64 2019 Silicon Valley Index * Basedondatathrough November(Silicon Valley andSanFrancisco) andOctober(California). |DataSource: CoreLogic | Analysis: Silicon Valley InstituteforRegional Studies *Includes datathrough November. |DataSource: ZillowReal EstateResearch | Analysis: Silicon Valley InstituteforRegional Studies Santa ClaraSanta Mateo Counties, &San andCalifornia Average Monthly For-Sale Inventory HOME SALES Silicon Valley Silicon Valley and San Francisco ClaraSanta Mateo Counties, &San Francisco, San andCalifornia ofHomesNumber Sold HOME SALES 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 10,000 20,000 30,000 40,000 50,000 60,000 0 0 '00 '10 Silicon Valley '01 Silicon Valley '02 '11 '03 '12 '04 '05 San FranciscoSan California '13 '06 '07 '14 '08 '09 '15 '10 California homes sold in2018than2004. There were halfasmany Silicon Valley '11 '16 '12 '13 '17 '14 '15 '18* '16 '17 160,000 120,000 100,000 140,000 20,000 40,000 60,000 80,000 0 '18* 0 150,000 300,000 450,000 600,000 750,000 900,000 California

homes permonth. prior year), exceeding 3,000 in 2018(up15%overthe month increased slightly homes listedforsaleeach Silicon TheValley numberof what itwasin2011. of Valley isless thanone-half sale inventoryinSilicon The average monthlyfor- California 29,500 homessoldin2012. from themostrecent peak of 25,000 peryear in2018) trend (reaching anestimated continued asix-year downward annually inSilicon Valley homessoldThe numberof 2019 Silicon Valley Index 65 PLACE PLACEHOUSING

The rate of residential building accelerated in 2017, then fell RESIDENTIAL BUILDING Units Included in Residential Building Permits Issued slightly in 2018 with fewer than 8,400 units permitted. Santa Clara & San Mateo Counties

The share of multi-family units in Single Family Multi-Family Multi-Family % of Total Silicon Valley residential building 12,000 100% permits (72% in 2018) has remained relatively steady over the past few years. 80% 9,000

Silicon Valley had 1,240 fewer 60% residential units permitted in 6,000 2018 than during the prior year, and 3,200 fewer than the recent 40% peak in 2014. Number of Units Total 3,000 20% Multi-Family Percentage of Total Units Total of Percentage Multi-Family

0 0% '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18*

*2018 estimate based on data through November. | Data Source: Construction Industry Research Board and California Homebuilding Foundation Analysis: Center for Continuing Study of the California Economy; Silicon Valley Institute for Regional Studies

85% of Silicon Valley’s residential units permitted thus far in the 2015-2023 RHNA cycle were in the Above Moderate (120%+ of the Area Median Income) category.

RESIDENTIAL BUILDING Share of Residential Units Permitted in 2015-2023 Regional Housing Need Allocation (RHNA) Cycle, by A ordability Level Silicon Valley and Bay Area In the first three years of the 2015-2023 RHNA Cycle, Silicon Valley Bay Area Silicon Valley permitted 100% 31% of the residential units needed to reach its goal. 85% 82% 80% Progress Toward 2015-2023 RHNA 60% Total Number Progress 40% of Units RHNA Toward Permitted RHNA 20% Silicon 26,038 82,893 31% 5% 6% 6% 5% 4% 6% Valley 0% Very Low Income Low Income Moderate Income Above Moderate Income Bay Area 68,466 187,990 36%

Note: Data is for RHNA reporting in 2015-2017, and do not include units permitted in 2014 that are being applied toward the current RHNA cycle. Data Source: Association of Bay Area Governments (ABAG) | Analysis: Silicon Valley Institute for Regional Studies

66 2019 Silicon Valley Index Data Source: City Planning and Housing Departments of Silicon Data Source:Valley CityPlanningandHousingDepartmentsof | Analysis: Silicon Valley InstituteforRegional Studies a ordable housing Silicon Valley A ordable Share ofNewly Approved Residential Units RESIDENTIAL BUILDING 10% 15% 20% 25% 30% 35% units 0% 5% New '00 1,659 32% '01 2,816 30% '02 1,826 28% '03 1,507 24% '04 1,147 13% '05 859 6% '06 781 12% '07 571 10% '08 1,404 5% '09 1,273 11% '10 494 23% 80% ofthearea medianincome. to residents earninglessthan residential unitswere affordable Silicon Valley’s approved newly years, seven to eightpercent of Over thepastthree fiscal '11 260 5% '12 83 2% '13 351 8% '14 1,296 12% '15 1,758 16% '16 1,404 7% '17 699 8% '18 614 8% units approved inFY affordable housing There were only614 median income. thearea than 50%of households earning less were affordable for 2017-18, which 489of 2019 Silicon Valley Index 67 PLACE Median Silicon Valley The median rental rate in Silicon PLACE and San Francisco Valley was $3,728 for an apartment HOUSING rental rates remained and $4,498 for a single-family relatively steady over residence (or condo/coop) in 2018. the past two years, after Rental rates are much higher in Silicon Valley and San adjusting for inflation. Francisco than in California or the United States as a whole. Median Apartment Rental Rates Per HOUSING AFFORDABILITY Square Foot Median Rental Rates San Jose and San Francisco Metro Santa Clara & San Mateo Counties, San Francisco, California, and the United States Areas, Other U.S. Metro Areas, California, and the United States

Silicon Valley San Francisco California United States October 2018

Apartments Single-Family Residences and Condos/Coops San Francisco $3.42 $6,000 $6,000 San Jose $3.20 $5,000 $5,000 $4,498 New York $2.67 $4,000 $3,728 $4,000 $3,736 Los Angeles $2.66 $2,911 $2,719 $3,000 $3,000 Boston $2.56 $2,258 $2,000 $2,000 California $2.56 $1,621 $1,650 $1,000 $1,000 Seattle $2.42 Median Rent List Price (In ation-Adjusted) List Price Median Rent $0 $0 Washington, DC $2.23 '11 '12 '13 '14 '15 '16 '17 '18* '11 '12 '13 '14 '15 '16 '17 '18* Denver $1.87 *Based on data through October. | Note: Apartments include multifamily complexes with five or more units. Data Source: Zillow Real Estate Research | Analysis: Silicon Valley Institute for Regional Studies Portland $1.76

Median Monthly Housing Costs Sacramento $1.71 Top 10 United States Metropolitan Statistical Areas, California, and the United States San Jose United States $1.69 2017 and San Atlanta $1.59 1 San Jose-Sunnyvale-Santa Clara, CA $2,341 Francisco are Austin $1.51 2 San Francisco-Oakland-Hayward, CA $2,059 the two most Pittsburgh $1.21 3 Oxnard-Thousand Oaks-Ventura, CA $1,857 expensive of Las Vegas $1.20 4 Santa Cruz-Watsonville, CA $1,840 the country’s 5 Napa, CA $1,823 major The San Francisco and San Jose 6 Bridgeport-Stamford-Norwalk, CT $1,812 st nd metropolitan metro areas ranked 1 and 2 , 7 Washington-Arlington-Alexandria, DC-VA-MD-WV $1,778 regions, based respectively, for apartment rental 8 Urban Honolulu, HI $1,749 on median rates per square foot; these rates 9 San Diego-Carlsbad, CA $1,735 monthly are twice as much as in the U.S. 10 Santa Rosa, CA $1,717 housing costs overall, and nearly three times California $1,567 in 2017. the cost of living in places such United States $1,048 as Pittsburgh or Las Vegas. Data Source: United States Census Bureau, American Community Survey Analysis: Silicon Valley Institute for Regional Studies

68 2019 Silicon Valley Index *2018 datareflects Q1-3. |DataSource: California Realtors | Association of Analysis:Silicon Valley Institutefor Regional Studies Data Source: UnitedStates Census Bureau, American Community Survey| Analysis: Silicon Valley InstituteforRegional Studies 1. housing costs. 2017 were burdened households whorented in allSilicon Valley of Nearly half According to theU.S. ofHousing andUrbanDevelopment, Department housingcosts greater than30%ofhouseholdincome posemoderate to severe financialburdens. Santa ClaraSanta Mateo Counties, andSan Francisco, San California andOther Regions Purchase aMedian-Priced Home Percentage ofPotential First-Time Homebuyers That Can A ord to HOUSING AFFORDABILITY Santa ClaraSanta Mateo Counties, &San Francisco, San California, andtheUnited States Housing Burden HOUSING AFFORDABILITY 10% 20% 30% 40% 50% 60% 70% 80% 90%

0% Percent of households with housing costs greater than 30% of income '03 10% 20% 30% 40% 50% 60% 0% Sacramento Santa ClaraSanta County '04 Silicon Valley 2007 '05 2012 2017 '06 San FranciscoSan San Diego San '07

1 by San MateoSan County '08 Owners '09 California California '10 '11 United States Valley owners, compared to thecountry). 28%across burden for Silicon Valley owners ishigher(35%ofSilicon asawhole, the ofthenation similartorelatively that While thehousingburden for Silicon Valley renters is than half of theirgross incomeonhousingcosts. of than half homeownerswithamortgage)spentmore (and 15%of In 2017, Silicon Valley 24%of householdswhorented '12 Los Angeles '13 '14 Silicon Valley

'15 2007 San FranciscoSan 2012 '16 2017 '17 San FranciscoSan '18* 22.3% 30.0% Renters California lowest levelsince2007. and SanMateo Counties –reaching the percentage pointsinbothSantaClara 2018–downthreethree quartersof Index continuedtofallinthefirst Silicon ValleyHousing AffordabilityThe Francisco, and47%statewide. Clara County, 22%inSan compares to30%inSanta median-priced home;this Mateo County canafford a time homebuyersinSan potentialfirst- Only 22%of United States 2019 Silicon Valley Index fewer burdened nearly 70,000 (amounting to past decade points overthe 16 percentage has declinedby by housingcosts owners burdened Silicon Valley The share of only 1%. has declinedby renters burdened while theshare of households), 69 PLACE Average Silicon Valley household size rose steadily through 2013 PLACE despite declining birth rates and an increasing share of the population HOUSING in older age groups that typically have smaller households; more recently, average household size has leveled off around 2.95 to 2.97.

OCCUPANCY CHARACTERISTICS Although the number of new Average Household Size & Additional Units Needed to Accommodate Population Growth residential units permitted Santa Clara & San Mateo Counties has kept pace with population 8,000 3.05 growth over the past two 6,000 2.98 years, a large shortage (as 4,000 2.90 much as 108,000 units) has 2,000 2.83 accumulated. 0 2.75 Between 2007 and 2016, Silicon Valley created -2,000 a housing shortage of approximately 38,000 Average Household Size Household Average Additional Units Needed Units Needed Additional -4,000 units that would be needed to accommodate the

to Accommodate Population Growth Population Accommodate to region’s growing population even with increases -6,000 in the number of residents per household, and -8,000 despite the offset in 2014 (with excess units '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18 permitted that year beyond the number needed to accommodate annual population growth). Data Source: California Department of Finance; Construction Industry Research Board and California Homebuilding Foundation Analysis: Center for Continuing Study of the California Economy; Silicon Valley Institute for Regional Studies More than a quarter of all Silicon Valley residents live in multigenerational households (amounting to nearly 17% of all households).

OCCUPANCY CHARACTERISTICS Living in multigenerational Multigenerational Households Santa Clara & San Mateo Counties, San Francisco, California, and the United States households is more common in Silicon Valley compared

Silicon Valley San Francisco California United States to San Francisco, where 28% residents are more likely 26% to live with non-family 24% members (one in five San 22% Francisco residents lives in a 20% multifamily household). 18% Multigenerational Households Multigenerational Share of the Population Living in of the Population Share Silicon Valley has a slightly lower share of 16% residents living in multigenerational households 14% (25%) than in the state as a whole (27%). '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17

Note: Multigenerational households include all households with two or more adult generations, where an adult is defined as age 25 and over. Data Sources: IPUMS-USA, University of Minnesota; Pew Research Center | Analysis: Kyle Neering; Silicon Valley Institute for Regional Studies

70 2019 Silicon Valley Index Data Source: IPUMS-USA, Minnesota| Analysis: Kyle Universityof Neering;Silicon Valley InstituteforRegional Studies (ages 18-34)livewiththeirparent(s). allSilicon Valley36% of youngadults

Share of the Population Ages 18-34 Living With a ClaraSanta Mateo Counties, &San Francisco, San andCalifornia Young Adults LivingwithaParent OCCUPANCY CHARACTERISTICS Parent Who is the Householder 10% 15% 20% 25% 30% 35% 40% 45% 0% 5% '07 Silicon Valley '08 '09 San FranciscoSan '10 '11 '12 California '13 (+50,000 people)since 2010. byincreased four percentage points adults livingwiththeirparent(s) has The share ofSilicon Valley young '14 '15 '16 '17 Kyle Neering;Silicon Valley InstituteforRegional Studies lated families. |DataSource: IPUMS-USA, Minnesota| Analysis: Universityof Note: Multifamilyhouseholdsincludeallwithatleast twounre California Silicon ValleySilicon Share ofthePopulation Living Santa ClaraSanta Mateo Counties, &San andCalifornia in MultifamilyHouseholds state overall. more thaninthe the share hasrisen (9.5% in2007), and than adecadeago households (11%) in multifamily residents lives Silicon Valley of A greater share 2019 Silicon Valley Index 2007 9.3% 9.5% 10.2% 11.0% 2017 71 - PLACE PLACEHOUSING

Three quarters of Santa Clara County’s homeless population is unsheltered.

HOMELESSNESS Homeless Population Share and Percentage Sheltered vs. Unsheltered Bay Area Counties | 2018

Sheltered Unsheltered 0.9% 0.8% 0.7% 0.6% 63% 0.5% 0.4% 64% 0.3% 65% of the Total Population Total of the 75% 70% 0.2% 48% 37% 81% 51% 69%

Homeless Population as a Percentage as a Percentage Population Homeless 36% 0.1% 35% 52% 25% 30% 19% 49% 31% 0.0% San Francisco Sonoma Marin Santa Clara Alameda Solano Napa San Mateo Contra Costa

Data Sources: United States Department of Housing and Urban Development; SPUR | Analysis: SPUR; Silicon Valley Institute for Regional Studies

In 2018, nearly 8,500 people in Santa Clara and San Mateo Counties combined were homeless (approximately 100 people fewer than the previous year) including more than 500 unaccompanied minors and 1,400 individuals in families; San Francisco had a homeless population of approximately 6,850 people in 2018.

72 2019 Silicon Valley Index Analysis: SPUR;Silicon Valley InstituteforRegional Studies court-enforced evictions. theminimumnumberof *Represents |DataSource: anestimateof The Eviction LabatPrinceton University Analysis: SPUR;Silicon Valley InstituteforRegional Studies HousingandUrbanDevelopment;SPUR Data Sources: UnitedStates Departmentof is dueto lostjobsorevictions. County’s Clara Half ofSanta homelessness Santa Clara County Clara Santa Santa ClaraSanta County |2017 CausesIndividual ofHomelessness HOMELESSNESS 869 Breakup Divorce/Separation/ 13% Argument with Family/Friend 12% 15% Eviction San Mateo County San 408 Incarceration Evictions* Evictions* Alcohol or Drug UseDrug 19% 2016 6% 35% San Francisco San Lost Job 593 California 41,178 Santa Clara andSanMateo Counties. renters incourt-enforced evictionsof In 2016, there were more than1,200 2019 Silicon Valley Index 73 PLACE PLACETRANSPORTATION

The average number of miles driven to more drivers on certain commute paths, at or above capacity. Ridership on private by Silicon Valley residents declined for a doubling in the amount of time spent shuttles has increased in recent years on a the third year in a row, reaching 22 miles in traffic, and people living farther from large scale, with annual ridership rivaling per day in 2017. There was a slight uptick their workplaces. In 2017, 6.5% of local that of the region’s existing public transit in gas prices that year, contributing to employees (nearly 95,000 people) spent systems. the rising costs of transportation (which more than three hours commuting to and reached $6,300 annually for a family of from work on a daily basis. four in 2018, covering only the most basic A larger share of commuters is choosing WHY IS THIS IMPORTANT? needs). to ride bicycles instead of driving, likely Adequate highway capacity and Though a smaller share of Silicon Valley influenced by significant increases in the improved transportation options, both residents drives to work alone than a miles of bicycle paths/routes throughout public and private, are important for decade ago (72%, down from 75%), solo- the region. the mobility of people and goods as commuting is still the most common way Public transit use per capita has been on the economy expands. Investments to get to work. Average commute times the decline since 2015, though ridership on in public transportation, walking and have increased by 20% over the past some systems has risen. Caltrain ridership, bicycling infrastructure, along with decade – adding an additional 43 hours of for instance, reached an all-time high in improving automobile fuel efficiency driving time per commuter annually – due 2018, with peak trains regularly operating and shifting from fossil fuels to electric

The average number of vehicle miles traveled annually per Silicon Valley declined for the third year in a row, reaching 8,131 miles per year in 2017.

Silicon Valley residents drove an average of 22 miles per day in 2017 – down from 25 miles per day a decade prior; this compares to 11 miles/day in San Gas prices locally (and statewide) Francisco, 25 miles/day in Alameda County, and 24 miles/day statewide. increased slightly in 2017 to $3.08 per gallon but remained $1.42 per gallon (32%) less than the recent peak in 2012 after adjusting for inflation.

VEHICLE MILES TRAVELED TRANSPORTATION COSTS Vehicle Miles Traveled Per Capita Gas Prices Santa Clara & San Mateo Counties San Francisco and California

10,000 San Francisco California 9,054 $5.00 9,000 $4.50 8,000 8,131 $4.00 7,000 $3.50 6,000 $3.00 5,000 $2.50 4,000 $2.00 3,000 $1.50 2,000 $1.00 1,000 $0.50 $0.00 0 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17

Data Sources: California Department of Transportation; California Department of Finance Note: Gas prices are average annual regular retail gas prices for California, inflation-adjusted to 2017 dollars. Analysis: Silicon Valley Institute for Regional Studies Data Source: California Department of Finance; U.S. Energy Information Administration Analysis: Silicon Valley Institute for Regional Studies

74 2019 Silicon Valley Index Data Source: Center for Women's Welfare, Washington Universityof | Analysis: Silicon Valley Institute forRegional Studies commuting behavioraffect congestion how theycommute, andchangesinoverall life. lifestyles andenhancingqualityof region aswellpromoting healthy helping residents getaround withinthe as bikingandwalking, isimportant for transportation,for activemodesof such goals. Further, creating safe conditions quality andcarbonemissionreduction vehicles, are important formeetingair How muchresidents are drivingtheir cars, California Francisco San ValleySilicon Santa ClaraSanta Mateo Counties, &San Francisco, San andCalifornia Average Cost of Transportation NeedsHousehold, per by Family Type TRANSPORTATIONCOSTS Monthly Cost (In ation-Adjusted) Transportation Needs, Inﬂ ation-Adjusted Percent Change in $100 $200 $300 $400 $500 $600 $700 Average Cost of $0 2014-2018 Single AdultSingle 2014 Silicon ValleySilicon -12% +9% +4% 2018 Family ofFour 12% statewide overthesametimeperiod. transportationdecrease needsof inthecostof adjusting forinflation); thiscompares toa Valley rose by4%overthepastfouryears (after transportation needsinSilicon The costof 2014

San Francisco San with familyandfriends. participating inthecommunity, work, orbeing from away time taking – residents our delays affects theeverydaylivesof traffic and commutes long to due wasted other basicneeds. time And theamountof ability togetaround andstillafford their transportation costsaffect ourresidents’ on the region’s roadways. Changing 2018 2014 California 2018 1. year in2018. fourwas$6,300per family of needs basictransportation The costof Includesonlyonetripfor shoppinganderrands eachweek, drivingto andfrom work and school/daycare. 1 foraSilicon Valley 2019 Silicon Valley Index 75 PLACE PLACE Mean Travel Time to Work TRANSPORTATION Minutes

2007 2012 2017 2007-2017 % Change Commute time increases since 2007 have Santa Clara & San Mateo Counties 24.7 25.7 29.7 +20% added an additional 43 hours of driving time per commuter annually (or 50 minutes San Francisco 29.3 31.6 33.8 +15% weekly, assuming a 5-day workweek). California 27.3 27.5 29.8 +9%

Silicon Valley commute COMMUTING Means of Commute times have increased by Santa Clara & San Mateo Counties 1.7% 1.4% 1.4% 20% over the past decade, 1.2% 1.7% 1.6% 100% 2.5% 1.8% 2.4% reaching an average of 59 Other Means 90% 4.2% 4.9% 5.1% minutes per commuter 5.5% 5.2% 6.8% 80% 10% 10% 11% Biked per day in 2017. 70% Walked 60% The share of commuters using public 50% Worked at Home transportation to get to work has increased 40% 75% 75% 72% 44% over the past decade, amounting to Public Transportation an additional 29,000 commuters utilizing of Workers Percentage 30% public transit in 2017 compared to 2007. 20% Carpooled 10% Nearly three-quarters of Silicon Valley Drove Alone 0% residents drive to work alone (73%, 2007 2012 2017 compared to 74% statewide).

Note: Other Means includes taxicab, motorcycle, and other means not identified separately within the data distribution. Data Source: United States Census Bureau, American Community Survey | Analysis: Silicon Valley Institute for Regional Studies

COMMUTING 6.5% of Silicon Valley Megacommuters Santa Clara & San Mateo Counties, Bay Area, and California employees (nearly 95,000 people) travel more than three Silicon Valley Bay Area California hours each day to/from work. 7%

6% Megacommuting (commuting more than 90 minutes to or from 5% work) rates have been increasing steadily in Silicon Valley, the Bay 4% Area, and California since 2009 3% – more than doubling in Silicon Valley over that time period. 2% Commutes of More than 90 Minutes of More Commutes

Percent of Local Employees With One-Way One-Way With Employees of Local Percent 1%

0% '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17

Data Source: United States Census Bureau, American Community Survey Summary Files | Analysis: Jon Haveman, Marin Economic Consulting

76 2019 Silicon Valley Index Regional Studies Data Source: UnitedStates Census Bureau, American Community SurveyPUMS| Analysis: Jon Haveman, MarinEconomicConsulting; Silicon Valley Institutefor 178% since2007, dependingonthecommutepath). increased significantlyoverthepastdecade(upby4%to Silicon Valley commutersin/outof The has numberof Regional Studies Data Source: UnitedStates Census Bureau, American Community SurveyPUMS| Analysis: Jon Haveman, MarinEconomicConsulting; Silicon Valley Institutefor Origin Alameda San Francisco San Santa Clara Santa San Mateo San 2017 ofResidentsNumber Who Commute to Another County Within theRegion COMMUTING 25,019 28,229 14,301 Change ofCross-County intheNumber Commuters 41,767 65,116 51,303 San FranciscoSan San FranciscoSan San FranciscoSan Destination Santa ClaraSanta Santa ClaraSanta Santa ClaraSanta San MateoSan San MateoSan San MateoSan Alameda Alameda Alameda Number +36,627 +15,428 +16,606 +12,768 +18,067 +9,253 +3,398 +4,477 +8,018 +4,303 +4,061 +558 San Francisco San Santa Clara Santa San Mateo San 2007-2017 Alameda +177.8% Percent +29.4% +53.8% +19.9% +18.5% +20.8% +23.6% +10.8% +34.2% +33.0% +4.2% +4.1% Number +10,092 -10,311 +1,098 +4,767 +4,117 +5,572 -7,182 -1,227 -1,749 -3,030 -752 -767 80,787 51,496 84,889 2016-2017 40,776 26,947 Percent +55.6% +12.1% +2.2% +6.3% +6.7% -1.8% -9.0% -7.8% -7.9% -6.1% -3.0% -6.8% 104,701 Francisco or Alameda County. Silicon Valley residents commutingtoSan On atypicalweekdaythere are 169,000 10,000 workers (+56% year-over-year). into SanFrancisco increased byapproximately people commutingfrom SantaClara County Between 2016and2017, thenumberof (approximately 5,600people). to workinSanMateo County increased by12% commuters traveling from SantaClara County Between 2016and2017, thenumberof nearly 37,000peopleoverthesametimeperiod. from Alameda County intoSanFrancisco rose by commuters travelingcommuters); thenumberof Francisco hasincreased by178%(up18,000 commuting from SantaClara County intoSan Over thepastdecade, people thenumberof Bay Area Alameda County Francisco San Mateo County San County Clara Santa Who Cross County Lines, by County ofResidence Share ofCommuters 2019 Silicon Valley Index County commutetoa living inSanMateo 2017 different county. 42% of workers 42% of 29% 36% 23% 42% 14% 77 PLACE Between 2007 and 2017, the share of Silicon Valley PLACE commuters who bike to work increased from 1.2% TRANSPORTATION to 1.6%, amounting to an additional 8,000 people biking to/from work most weekdays.

Number of Bicycle Commute Trips Per Day BICYCLING Santa Clara & San Mateo Counties Share of Commuters Who Bike to Work Santa Clara & San Mateo Counties % Change 2007 2012 2017 2007-2017

27,766 43,143 43,705 +57% 2.0%

In 2017, Silicon Valley had nearly 44,000 1.8% daily bicycle commute trips utilizing 1.5% 1.6% the region’s roadways and other bicycle facilities (representing a 57% increase 1.2% over the past decade). 1.0%

0.5%

The majority of 0.0% Silicon Valley cities 2007 2012 2017 and counties have a Bicycle Master Plan in Data Source: United States Census Bureau, American Community Survey Analysis: Silicon Valley Institute for Regional Studies place, in the planning stage, or in-progress. Significantly fewer Santa Clara County bicycle collisions resulted in an injury or fatality in 2017 (590) than during the prior year (756 in 2016).

BICYCLING BICYCLING Share of Jurisdictions with a Bicycle or Pedestrian Bicycle Collisions, by Severity Master Plan Santa Clara & San Mateo Counties Silicon Valley | 2016 & 2018 Complete Planned/In-Progress Fatality Severe Injury Visible Injury Complaint of Pain Injury 70% 2% 1,200 60% 17% 1,000 50% 2% 445 40% 15% 800 367 320 30% 61% 600 44% 44% 20% 32% 400 568 517 456 10% 200 0% 2016 2018 2016 2018 61 67 66 0 16 7 6 Bicycle Pedestrian 2015 2016 2017

Data Source: Silicon Valley Cities & Counties | Analysis: Silicon Valley Institute for Regional Studies Data Source: Statewide Integrated Traffic Records System (SWITRS); Transportation Injury Mapping System (TIMS) Analysis: Silicon Valley Institute for Regional Studies

78 2019 Silicon Valley Index 1,090 in2015. 2017 resulting ininjuryordeath –downfrom Silicon Valley had848bicyclecollisionsin in severe injuries. resulted in afatality, andanother66resulted In 2017, sixSilicon Valley bicyclecollisions Analysis: Nelson\Nygaard Consulting Associates; Silicon Valley InstituteforRegional Studies Data Source: Metropolitan Transportation Commission; SantaClara Valley Transportation Authority; GoogleMaps;Nelson\Nygaard Consulting Associates Total County Clara Santa Mateo County San 1,000 1,200 1,400 1,600 Santa ClaraSanta Mateo Counties &San ofBicycleFacilitiesMiles BICYCLING 200 400 600 800 0 significantly in Santa Clara in significantly County. collisionbicycle rate declined 2015and2017,the Between hours wastedduetotraffic 2016 (Shared Use Path) Santa ClaraSanta County has more thandoubled Valley andtheBay Area per 10,000DailyCommutersper The number of vehicle The numberof Class 1 2017 over thepastdecade. Annual Bicycle Collisions congestion inSilicon 2018 2016 440.7 435.0 462.5 2015 (Bikeway) Class 1 2017 San MateoSan County 2018 460.4 474.3 414.7 2016 2016 (Bike Route/ Blvd) Class 3 2017 Data Source: Caltrans PeMS | Analysis: Silicon Valley InstituteforRegional Studies Santa ClaraSanta Mateo Counties, &San Francisco, San Area andtheBay Daily Vehicle Hours ofDelay Due To Congestion TRAFFIC CONGESTION 2018 using the 2017 estimate of regional laborproductivity ($100peremployeehour),using the2017estimate of Silicon Valley 388.1 357.1 484.1 2017 100,000 125,000 150,000 175,000 200,000 225,000 250,000 25,000 50,000 75,000 traffic delayscouldamounttoas muchasa$2.7billionloss inproductivity onanannualbasis. In 2017, Silicon Valley commuterslostmore than75,000hourstotraffic congestioneveryday; 2016 (Protected Bikeway) 0 '06 Class 4 2017 Silicon Valley in 2017 that resulted in some sort of injury.in 2017that resulted insomesort of Silicon Valley, 388 experienced acollision every10,000dailybicyclecommutersin Of '07 2018 2016 '08 San FranciscoSan 2017 Total '09 2018 '10 '11 Bay Area are Class 2(bike lanes). bicycle facilities, which themajority(46%)of Silicon Valley hasmore than1,500milesof safety) inSan Jose. bicyclists' comfortand (the “gold standard” for protected bikewaysof including fourmiles bicycle facilitiesin2018, 94 additionalmilesof Silicon Valleygained '12 '13 2019 Silicon Valley Index '14 '15 '16 '17 79 PLACE Despite an overall decline in public PLACETRANSPORTATION transit use, per capita ridership on ACE in Santa Clara County rose by Public transit use per capita in Silicon Valley is 16% in FY 2017-18. lower than it has been over the past 16+ years.

MASS TRANSIT Change in Per Capita Transit Use, 2010-2018 Transit Use The number of rides per capita on public transit San Mateo & Santa Clara Counties Santa Clara & San Mateo Counties declined for the third year in a row (down -3.5% in FY 2017-18), amounting to nearly two million Transportation System 2010 - 2018 fewer rides than during the prior fiscal year. Percent Change 40 Santa Clara Valley Transportation Authority 35 (VTA)

30 -3.5% All Service -20% 25 Express Bus Service +11% 20 SamTrans -26% 15 Caltrain +45%

Public Transportation Systems Transportation Public 10

Number of Rides per Capita on Regional Number of Rides per Capita 5 Altamont Corridor Express (ACE)* +135% 0 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18 Total -9%

Note: Transit data are in fiscal years. | Data Sources: Altamont Corridor Express, Caltrain, SamTrans, Santa Clara Valley Transportation Authority, California Department *Santa Clara County stations only. of Finance | Analysis: Silicon Valley Institute for Regional Studies Despite declining overall transit ridership since 2010 – the beginning of the economic recovery period – VTA Express Bus Service, Caltrain, and ACE* per capita ridership has increased by 11%, 45%, and 135%, respectively.

Caltrain ridership has been MASS TRANSIT Caltrain Ridership increasing steadily since the beginning of the economic recovery period in 2010, and 70,000 reached an all-time high in 60,000 2018 with peak trains regularly 50,000 operating at or above capacity 40,000

(especially north of Palo Alto). 30,000

Between 2017 and 2018, Caltrain ridership 20,000 Average Daily Riders/Boardings Average increased by nearly 1,000 boardings per day (+1.5% year-over-year). Assuming riders take the 10,000 train two ways, this amounts to an additional 500 0 people utilizing Caltrain each weekday. '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17* '18*

*2017 and 2018 data represent average mid-weekday ridership. | Note: Data are in fiscal years. Data Source: Caltrain 2018 Annual Passenger Counts | Analysis: Silicon Valley Institute for Regional Studies

80 2019 Silicon Valley Index Census) | Analysis: Bay Area Council andMetropolitan Transportation Commission Data Sources: Transit operator reports Bay &MTC StatisticalArea Summaryof Transit Operators (viathe Bay Area Council andMetropolitan Transportation Commission 2016Bay Area Shuttle SamTranswith annualridershipjustbelowthat of andCaltrain. Private shuttles represent theBay Area’s 7 tion Commission 2016Bay Area Shuttle Census | Analysis: Bay Area Council andMetropolitan Transportation Commission shuttles.Note: Lineweightisproportional tothenumberof |DataSource: Bay Area Council andMetropolitan Transporta trips betweenSantaClara and Alameda Counties. between SantaClara County andSanFrancisco, and194 On anaverage weekday, there are 474private shuttle trips Bay AreaBay |FY2014-2015 onPrivateRidership Shuttles andRegional Transit Systems SHUTTLES Operator AreaBay |2012-2014 Weekday Shuttle Trips, by Path SHUTTLES Golden GateGolden Transit +Ferry County Marin Francisco County Connection San San San Mateo San Marin Transit SF Bay Ferry Sonoma County County AC Transit SamTrans Shuttles Tri Delta Caltrain SFMTA BART VTA 0 Contra Costa Santa Clara Santa Santa Cruz Santa Alameda County County County County County Solano Sacramento Sacramento 50 County operate withinasinglecounty. Circles represent shuttlesthat 101-200 shuttles 51-100 shuttles 11-50 shuttles >200 shuttles 6-10 shuttles < 5shuttles proportional to the proportional number ofshuttles traveling between Total Annual Passengers (millions) Line weight is two counties. 100 th largest mass transit system, - 150 Bay Area County Clara Santa Mateo County San Francisco San Total ofShuttle Number Trips on Weekdays Santa ClaraSanta Mateo Counties, &San Francisco, San Area andtheBay 200 2012-2014 average nearly 1,100tripswithin 250 Private shuttles are makingan Silicon Valley onadailybasis. Daily Shuttle TripsShuttle Daily 2019 Silicon Valley Index 1,126 843 767 612 81 PLACE PLACELAND USE

The pace of non-residential development approvals remained WHY IS THIS IMPORTANT? brisk in the 2017-18 fiscal year despite the total amount of space approved falling slightly short of the prior year. Over the past By directing growth to already developed areas, local five years combined, more non-residential development was jurisdictions can reinvest in existing neighborhoods, increase approved (54.3 million square feet) than over the eleven years access to transportation systems, and preserve the character prior. With industrial space vacancy rates low and industrial rents of adjacent rural communities while reducing vehicle miles increasing with higher overall demand, planned development traveled and associated greenhouse gas emissions. Focusing of light industrial has increased (much of it accounted for by new commercial and residential developments near rail stations the Fremont Technology Business Center). There has also been and major bus corridors reinforces the creation of compact, a resurgence in hotel development throughout the region, with walkable, mixed-use communities linked by transit. This helps more than one hundred hotels with over 18,000 rooms in various to reduce traffic congestion on freeways, preserve open space stages of planning. near urbanized areas, and improve energy efficiency. By creating The density of newly approved residential units in Silicon Valley mixed-use communities, Silicon Valley gives workers alternatives fell for the second year in a row. The total number of new units to driving and increases access to workplaces. approved near transit declined year-over-year, as did the share of total approvals near transit (71%).

The average density of newly approved Silicon Valley residential units declined for the second year in a row, dropping to 18 units per acre in FY 2017-18.

RESIDENTIAL DENSITY Average Units per Acre of Newly Approved Residential Development Silicon Valley

10 Average Dwelling Units per Acre Dwelling Average 5 10 13 21 23 21 20 21 16 15 16 20 21 19 24 20 18 0 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18

Note: Beginning in 2008, the Land Use Survey expanded its geographic definition of Silicon Valley to include cities northward along the U.S. 101 corridor (Brisbane, Burlingame, Millbrae, San Bruno and South San Francisco). In 2014, the Survey expanded to include all Silicon Valley cities (adding Colma, Daly City, Half Moon Bay and Pacifica). | Data Source: City Planning and Housing Departments of Silicon Valley | Analysis: Silicon Valley Institute for Regional Studies

82 2019 Silicon Valley Index complex downtownwithaco-located childcare facility. as wellaseven-story, 117-unitlow-income seniorapartment aformerautodealership siteinRedwood City a repurposing of View, a443-unittransit-oriented housingproject inUnionCity, mixed-use developmentnear San Antonio Road inMountain FY 2017-18includednearly 1,200unitsinSan Jose, a 632-unit Pockets high-densityresidential developmentapprovals in of San Francisco). Silicon |DataSource:Valley CityPlanningandHousingDepartmentsof | Analysis: Silicon Valley InstituteforRegional Studies Siliconexpanded itsgeographic definitionof Valley toincludecitiesnorthward alongthe U.S. 101corridor(Brisbane, Burlingame, Millbrae, SanBrunoandSouth * Beginningin2012, transit orienteddevelopmenthasbeenchangedfrom 1/4mileto1/3mile. thedefinitionof |Note:Beginningin2008, theLandUseSurvey

Total Approved Near Transit Silicon Valley Corridors, andShare of Total UnitsApproved New Housing UnitsApproved Within Stations ofRail Bus orMajor 1/3Mile NEAR TRANSIT HOUSING 10,000 12,000 14,000 16,000 18,000 20,000 2,000 4,000 6,000 8,000 0 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Share Near Transit residential units. allnewlyapproved71% of 2017-18, representing units near transit in FY than 4,700newhousing approved slightlymore Silicon Valleycities the second year ina transit declinedfor approved near residential units The number of prior fiscal year. than duringthe 2,400 fewer units) (approximately lower inFY2017-18 row, andwas 33% 2019 Silicon Valley Index 83 PLACE 84 PLACE (54.3 millionsquare feet) thanover was approved over thepastfive years More non-residential development Data Source: City Planning and Housing Departments of Silicon Data Source:Valley CityPlanningandHousingDepartments of | Analysis: Silicon Valley InstituteforRegional Studies San Francisco). In2014, theSurveyexpanded toincludeallSilicon Valley cities(addingColma, DalyCity, MoonBayandPacifica). Half Siliconexpanded itsgeographic definitionof Valley toincludecitiesnorthward alongthe U.S. 101corridor(Brisbane, Burlingame, Millbrae, SanBruno andSouth *Beginning in2012, transit orienteddevelopmenthasbeenchangedfrom 1/4mileto1/3mile. thedefinitionof |Note:Beginningin2008, theLandUseSurvey the previous eleven years combined. LAND USE

Millions of Square Feet Silicon Valley Net Non-Residential Development Approved, by Proximity to Transit NON RESIDENTIAL DEVELOPMENT 10 12 14 2019 Silicon Valley Index 0 2 4 6 8 (down from 43%neartransit duringtheprevious fiscal year). which was ormajorbuscorridors within1/3ofamilerail stations demolition) inFY2017-18totaled 8.7millionsquare feet –20%of Net non-residential development approvals planned (after '03 Near Transit Net Square Feet ofNon-Residential Development '04 '05 '06 '07 '08 '09 '10 '11 Further than 1/3 of a Mile from than1/3ofaMile Further Transit Net Square Feet ofNon-Residential Development '12* '13 '14 '15 '16 '17 '18 The pace of Silicon TheValley paceof new Class A office space). industrial/office buildingsandreplacement with older tilt-upfeet includingsomedemolitionof dormitory), andSunnyvale(558,000square centers, andanewSantaClara University (671,000 square feetcombinedfortwodata lightindustrialdevelopment),of SantaClara industrial use), Gilroy (700,000square feet foot Fremont Technology Business Center for Hall), Fremont (includingthe2.5millionsquare Parksidemuseum spaceinthecurrent location of residential,development of office, hotel, and major projects includinga24-storymixed-use in San Jose (2.7millionsquare feetwith significantdevelopmentplannedpockets of were spread throughout Silicon Valley, with Approved non-residential developmentprojects previous fouryears. the falling slightlyshortof FY 2017-18, despite thetotal approvals remained briskin non-residential development Valley InstituteforRegional Studies planning approvals. |DataSource: Atlas HospitalityGroup | Analysis: Silicon Note: Plannedhotelsare invariousstages, andhavenotnecessarily received to only18%and21%overtheprevious twofiscalyears. Santa Clara, Gilroy, Newark, San Jose, andSantaClara. This compares Fremont Technology Business Center, aswellsmallerprojects in whichwasaccountedforbythe industrial space–themajorityof allnewlyapproved non-residential developmentwaslight of Half and Pacifica). Silicon |DataSource: CityPlanningandHousingDepartmentsof Valley | Analysis:Silicon Valley Institutefor Regional Studies Burlingame, Millbrae, SanBrunoandSouthFrancisco). In2014, theSurveyexpanded toincludeallSilicon Valley cities(addingColma, DalyCity, MoonBay Half Note: Beginningin2008, Silicon theLandUseSurveyexpanded itsgeographic definitionof Valley toincludecitiesnorthward alongthe U.S. 101corridor(Brisbane, FY 2017-18 Share ofNon-Residential DemolitionandDevelopment Approvals, by Type NON RESIDENTIAL DEVELOPMENT Santa ClaraSanta County San MateoSan County Planned Development Hotel San FranciscoSan Share ofPlanned Demolition 27% 22% Institutional 2018 Hotels 22 56 38 9% 42% Rooms Commercial 3,094 9,453 5,930 developed overtheentire pastdecade. represents more thanquadruplewhat hasbeen theseprojects willnecessarily bebuilt,of thetotal Francisco planning; whilenotall in various stagesof 18,000 rooms) throughout Silicon Valley andSan moreThere than are 116hotels(withatotalof Share ofNon-Residential Development Approvals O ce O ce 16% 26% 9% Light Industrial 50% 2019 Silicon Valley Index 85 PLACE PLACEENVIRONMENT

Water consumption by Silicon Valley residents remained of natural resources, and how to protect our environmental relatively low in 2018 at 110 gallons per person per day. Electricity resources. consumption stayed fairly constant year-over-year as well, as did Energy consumption affects the environment through the electricity productivity. emission of greenhouse gases (GHGs) and atmospheric Installed solar capacity shot up to 481 MW (with 58,000 installed pollutants from fossil fuel combustion. Sustainable energy residential systems throughout the region) in 2018, the number policies include increasing energy efficiency and the use of of residents driving electric vehicles rose to more than 48,000 clean renewable energy sources. For example, more widespread drivers, and the number of electric vehicle charging stations use of solar generated power diversifies the region’s electricity exceeded 3,000 (amounting to 18% of the state’s charging portfolio, increases the share of reliable and renewable outlets). Due to the recent California wildfires and other factors, electricity, and reduces GHGs and other harmful emissions. there were more unhealthy air days in Silicon Valley over the past Electricity productivity is a measure of the degree to which the two years than over the entire decade prior. region’s production of economic value is linked to its electricity consumption, where a higher value indicates greater economic output per unit of electricity consumed. Electric vehicle WHY IS THIS IMPORTANT? infrastructure and adoption provide indicators on the extent to Environmental quality directly affects the health and well- which Silicon Valley residents are utilizing a cleaner transportation being of all residents as well as the Silicon Valley ecosystem.1 alternative to fossil fuel combustion. The environment is affected by the choices that residents make Water consumption and the use of recycled water are about how to live, how to get to work, how to purchase goods particularly important indicators given California’s recent drought and services, where to build homes, our level of consumption conditions. Local emissions and other contributing factors, such as wildfires, have an effect on regional air quality which can have 1. Studies have quantified the importance of the ecosystem services provided by the region’s natural capital to the health of the economy including clean air, water quality and supply, healthy food, recreation, storm and flood protection, tourism, science and education. Healthy health implications. Lands & Healthy Economies: Nature’s Value in Santa Clara County (Open Space Authority and Earth Economics, 2014) found that each year, Santa Clara County’s natural and working lands provide a stream of ecosystem services to people and the local economy that range in value from $1.6 billion to $3.9 billion.

Silicon Valley per capita water consumption – lower in 2016 than it had been in 15+ years – remained relatively low in 2018 at 110 gallons per person per day.

The share of recycled WATER RESOURCES water used in Silicon Gross Per Capita Water Consumption & Share from Recycled Water Valley declined in Silicon Valley 2018 for the second Gross Per Capita Consumption Recycled Percentage of Total Water Used year in a row, dropping 180 6% to 4.5%.

150 5%

120 4%

90 3%

60 2%

30 1% Used Water Total of Recycled Percentage

Gross Per Capita Consumption (Gallons Per Capita Per Day) Per Capita Per (Gallons Consumption Capita Per Gross 0 FY FY FY FY FY FY FY FY FY FY FY FY FY FY FY FY FY FY 0% 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18*

* FY 2017-2018 data is preliminary. | Data Source: Bay Area Water Supply & Conservation Agency (BAWSCA), Santa Clara Valley Water District, and Scotts Valley Water District Analysis: Silicon Valley Institute for Regional Studies

86 2019 Silicon Valley Index Data Sources: UnitedStates Environmental Protection Agency, Outdoor Air QualityData | Analysis: Silicon Valley InstituteforRegional Studies capita inSilicon Valley ishigher though ithasdeclinedby11% than inSanFrancisco andthe Santa ClaraSanta Mateo Counties &San ofUnhealthyNumber Air Days AIR QUALITY since thelastpeak in2008. Electricity consumptionper Number of Unhealthy Air Days Per Year remained relatively steady between 2015and2017, consumption percapita Silicon Valleyelectricity 10 15 20 25 30 35 40 0 5 '98 rest of California.rest of Unhealthy for Sensitive Groups '99 '00 '01 '02 '03 Studies Data Sources: Moody'sEconomy.com; California Energy Commission; California, State of Finance | Department of Analysis: Silicon Valley InstituteforRegional '04 Kilowatt-hours per Person ClaraSanta Mateo Counties, &San Francisco, San Rest ofCalifornia Consumption Capita per Electricity USE ELECTRICITY '05 10,000 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 '06 0 Unhealthy '07 '02 '08 Silicon Valley '03 '09 '04 '10 '11 '05 '12 '06 San FranciscoSan '13 '07 '14 '08 '15 '09 '16 '10 '17 Rest ofCalifornia '18 '11 '12 this number of unhealthy this numberof region hasnotexperienced for sensitivegroups); the (and 30unhealthy days air daysduringthoseyears experienced 16unhealthy factors,Silicon Valley 2017 and2018other throughout thestate in Due tothewildfires air dayssince2001. '13 '14 '15 2019 Silicon Valley Index '16 '17 87 PLACE PLACEENVIRONMENT

Silicon Valley electricity productivity has been ELECTRICITY USE rising slowly since 2009 (up 36% over an eight- Electricity Productivity year time period), reaching more than $13,000 Santa Clara & San Mateo Counties, San Francisco, Rest of California per megawatt-hour in 2017.

Silicon Valley San Francisco Rest of California $25,000 $22,500 $20,000 Electricity productivity is $17,500 significantly higher in San $15,000 Francisco than in Silicon $12,500 Valley, with nearly double $10,000 the GDP per megawatt-hour $7,500 of electricity consumed. Consumption of Megawatt-hours Consumption $5,000

In ation Adjusted Dollars of GDP Relative to to Dollars of GDP Relative Adjusted In ation $2,500 $0 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17

Data Sources: Moody's Economy.com; California Energy Commission | Analysis: Silicon Valley Institute for Regional Studies

More than 6,000 new solar photovoltaic (PV) systems were installed in Silicon Valley in 2018, 97% of which were residential systems.

The amount of solar CLEANTECH PV installed in Silicon Cumulative Installed Solar Capacity Valley has more than Silicon Valley doubled over the past four years, reaching a Prior to 2007 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018* cumulative installed 600 solar capacity of 481 megawatts in late 500 481 2018. 427 400 There are 58,000 345 solar PV systems on 300 280 272 residential rooftops 248 throughout Silicon Megawatts 217 208 201 200 179 173 Valley, plus another 155 137 134 1,500 non-residential 111 106 117 92 105 installations. 100 80 75 77 59 59 35 46 41 40 50 20 27 20 24 21 9 15 3 6 12 0 Residential Non-Residential Total

*2018 data are through mid-December for the municipal utility data, and through September for the PG&E data. | Data Sources: Palo Alto Municipal Utilities; Silicon Valley Power; Pacific Gas & Electric Analysis: Silicon Valley Institute for Regional Studies

88 2019 Silicon Valley Index Resources Board Clean Vehicle Rebate Project | Analysis: Silicon Valley InstituteforRegional Studies Note: Onlyincludeselectric vehiclesforwhichtheownerappliedaC Santa ClaraSanta Mateo Counties &San Electric Vehicle Adoption CLEANTECH

Cumulative Count of Silicon Valley Electric Vehicle Rebates nearly $110 million. amounting toatotalof therebate program,owners sincethestartof electric vehiclerebates toSilicon Valley EV California hasissued more than48,000 2. 1. filers). household, and$300,000forjoint single filers, $204,000forheads-of- $150,000forannual incomelimitof for thestate rebate program (gross whose incomemakes themineligible Silicon Valley andBay Area residents vehicle adoptiongiventheshare of likely electric stillanunderestimate of drivers). While thisshare ishigh, itis Valley drivers(36%toBay Area rebates havebeenissued toSilicon allCalifornia electricvehicle18% of For instance, according to theICCT report “California’s continued California Clean Vehicle Rebate Project (www.cleanvehiclerebate.org). residents onlyclaimed130California rebates EV for Teslas. sold to Palo Alto residents that alonein2017.During year, Palo Alto vehicle registration data showed that more than1,000 Teslas were vehicle marketelectric development” (May 2018,www.theicct.org), 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000 5,000 1 0 ,

'10 Since 2014, thenumberof public EV charging outlets in Silicon Valley hasmore '11 nearly 3,300in2018). than tripled(reaching '12 '13 '14 '15 alifornia rebate. |DataSource: California Air Analysis: Silicon Valley InstituteforRegional Studies Note: Dataincludepublicstationsonly. Energy, |DataSource: UnitedStates Departmentof Alternative Fuels DataCenter '16

Number of Public Charging Stations or Outlets ClaraSanta Mateo Counties &San VehicleInfrastructure Electric CLEANTECH 1,500 2,000 2,500 3,000 3,500 1,000 '17 500 0 '18 '14 San MateoSan County 12.5% 17.5% 22.5% 10.0% 15.0% 20.0% 25.0% 2.5% 7.5% 0.0% 5.0% '15 Share of California EV Rebates Stations '16 Resources Board Clean Vehicle Rebate Project | Analysis: Silicon Valley InstituteforRegional Studies Note: Onlyincludeselectric vehiclesforwhichtheownerappliedaC Santa ClaraSanta Mateo Counties &San |2010-2018 Electric Vehicle Rebates, by Make CLEANTECH Santa ClaraSanta County Volkswagen, 6% '17 FIAT, 5% '18 BMW, 3% Toyota, 8% '14 Ford, 8% Honda, 1% outlets are inSilicon Valley. 18% ofCalifornia’s charging EV Share ofCalifornia '15 Tesla, 21% Outlets '16 Other, 4% rebates issued toSilicon Valley drivers. 2019 Silicon Valley Index account for 65% of allelectricvehicleaccount for65%of Chevrolet, 22% alifornia rebate. |DataSource: California Air '17 Nissan, 22% Chevrolets, Nissans, and Teslas '18 12% 15% 18% 21% 0% 3% 6% 9%

Share of California Stations or Outlets 89 PLACE GOVERNANCECITY FINANCES

Silicon Valley city revenue increased by 4% regionally in FY 2016-17 (reaching $6.85 billion total). Total expenses increased slightly as well, but revenues still exceeded expenses by $751 million. Nearly half of all city revenues were from charges for services, while investment earnings continued to provide a very small share (1%).

WHY IS THIS IMPORTANT? Many factors influence local government’s ability to govern effectively, including the availability and management of resources. To maintain service levels and respond to a changing environment, local government revenue must be reliable. Property tax revenue is the most stable source of city government revenue, fluctuating much less over time than other sources, such as sales and other taxes. Since property tax revenue represents less than a quarter of all revenue, other revenue streams are critical in determining the overall volatility of local government funding.

Silicon Valley city revenues totaled $6.85 billion in FY 2016-17, representing a 4% increase year-over-year after Nearly half (48%) of all Silicon Valley city inflation-adjustment. revenue comes from charges for services.

CITY FINANCES Investment earnings in FY 2016-17 Investment Earnings Revenues by Source, and Expenses Sales Tax ($36 million) continued to provide a Silicon Valley Property Tax very small share (1%) of total regional Other Revenues city revenues. They amounted to only Charges for Services one eighth of what they were a decade Expenses ago ($304 million in FY 2006-07, half $8 of which came from six cities alone - 5% Revenues 1% 5% 3% 1% 1% 1% Mountain View, Palo Alto, San Jose, 2% 2% 1% 0% 10% 9% $6 10% 9% 9% 8% 9% 10% 10% 10% 10% 18% 18% Santa Clara, Sunnyvale, and Fremont). 24% 24% 27% 26% 22% 19% 19% 19% 27% 24% $4 23% 23% 23% 21% 26% 26% 25% 27% 21% 22% $2 48% 47% 47% 49% 48% 35% 35% 35% 35% 42% 46% 0

-$2 -$4

Billions Adjusted) of Dollars (In ation -$6 Expenses -$8 FY FY FY FY FY FY FY FY FY FY FY 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17

Note: Percentages may not add up to 100% due to rounding. Data Sources: Silicon Valley Cities, Audited Annual Financial Reports; California State Auditor | Analysis: Silicon Valley Institute for Regional Studies

90 2019 Silicon Valley Index Data Sources: Silicon Valley Cities, Audited Annual Financial Reports; California State Auditor | Analysis: Silicon Valley InstituteforRegional Studies (compared to $240millioninFY2006-07). 2016-17 exceeding expenses by $751million ago,than adecade withrevenues inFY much better onanannualbasis financially Silicon Valley cities, inaggregate, are doing

Silicon Valley Silicon Valley, California Revenues Expenses Minus FINANCES CITY (Millions of Dollars, In ation Adjusted) $-600 $-400 $-200 $200 $400 $600 $800 $0 2006-07 FY Silicon Valley 2007-08 FY 2008-09 FY 2009-10 FY California 2010-11 FY 2011-12 FY 2012-13 FY 2013-14 FY 2014-15 FY 2015-16 FY 2016-17 FY $-60 $-40 $-20 $0 $20 $40 $60 $80

California (Billions of Dollars, In ation Adjusted) positive sinceFY 2012-13. California,for theState of havebeen Silicon Valley’s citiescombined, and Revenues minus expenses forallof 2019 Silicon Valley Index 91 GOVERNANCE GOVERNANCECIVIC ENGAGEMENT

An increasing share of Silicon Valley voters is choosing to register with no political party preference (up to 33%), and a majority participate via absentee ballot. The absentee voting rate for the November 2018 election was the highest it has ever been for a general election, and the eligible voter turnout was 53% – higher than any other midterm election in the recent past. Eligible voter turnout is highest among Silicon Valley’s eldest residents, with much lower turnout rates for residents ages 18- 24; however, young adult eligible voter turnout in November 2018 (36%) was the highest on record for any midterm general election.

WHY IS THIS IMPORTANT? An engaged citizenry shares in the responsibility to advance the common good, is committed to place, and holds a level of trust in community The percentage of institutions. Voter participation is an indicator of civic engagement and registered voters with no reflects community members’ commitment to a democratic system, confidence in political institutions, and optimism about the ability of political party affiliation individuals to affect decision-making. continued to increase 33% of Silicon Valley voters are registered with No in Silicon Valley, while Party Preference (compared to 28% statewide). the share registered as Republicans decreased.

PARTISAN AFFILIATION Percentage of Registered Voters, by Political Party Santa Clara & San Mateo Counties

Democratic Republican American Independent No Party Preference Other 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Nov. 2000 Mar. 2002 Nov. 2002 Oct. 2003 Mar. 2004 Nov. 2004 Nov. 2006 Feb. 2008 Jun. 2008 Nov. 2008 May 2009 Nov. 2010 Jun. 2012 Nov. 2012 June 2014 Nov. 2014 Jun. 2016 Nov. 2016 Jun. 2018 Nov. 2018 Presidential Primary General Statewide Presidential Presidential General Presidential Statewide Presidential Statewide General Presidential General Statewide General Presidential General Statewide General General Election Election Special Primary General Election Primary Direct General Special Election Primary Election Direct Election Primary Election Direct Election Election Election Election Election Election Primary Election Election Election Primary Election Primary Election Election Election

Data Source: California Secretary of State, Elections Division | Analysis: Silicon Valley Institute for Regional Studies

92 2019 Silicon Valley Index Analysis: California CivicEngagementProject attheUSCPrice Public Schoolof Policy; Silicon Valley Institute forRegional Studies Data Sources: California CivicEngagementProject attheUSCPrice Public Schoolof Policy, Data:Statewide Finance Database(SWDB)andCalifornia Departmentof Data Source: California State, Secretary of ElectionsDivision| Analysis: Silicon Valley InstituteforRegional Studies higher thananyothermidtermgeneral electionsince(at least) the1980s. the highesteverforageneral election, andtheeligiblevoterturnout(53%)was Silicon Valley’s absenteevotingrate fortheNovember2018election(81%)was Santa ClaraSanta Mateo Counties, &San Francisco, San andCalifornia |2018Midterm General Election Eligible Voter Turnout, by Age VOTER PARTICIPATION Santa ClaraSanta Mateo Counties, &San andCalifornia Eligible Voter Turnout andAbsentee Voting, by Election VOTER PARTICIPATITON

Share of Eligible Voters Who Cast Ballots 100% 20% 40% 60% 80% 0% 10% 20% 30% 40% 50% 60% 70% 80% 0% Presidential Nov. 2000 General Cast Ballots: Election Silicon Valley

36% Mar. 2002 18-24 Primary Primary Election 39% Nov. 2002 General Election

28% Silicon Valley Statewide Oct. 2003 Oct. Election Special Special

54% Presidential 25-34 Mar. 2004 Primary Primary Election 73% FranciscoSan Presidential Nov. 2004 General 41% Election California Nov. 2006 General Election 52% 35-44 Presidential Feb. 2008 Primary Primary 63% Election California Statewide Jun. 2008 Jun. Primary Primary Election

47% Direct Presidential Nov. 2008 General 56% Election 45-54 Statewide May 2009 Election 61% Special Voted Absentee:

51% Nov. 2010 General Election Presidential Jun. 2012 Jun. Primary Primary 62% Election 55-64

57% Nov. 2012 General Election 59% Silicon Valley June 2014 June Statewide Election Primary Direct Direct Nov. 2014 General 68% Election 65+

60% Presidential Jun. 2016 Jun. Primary Primary Election 50% Nov. 2016 General Election California Statewide June 2018 June Election Primary Direct Direct compared tootheragegroups. (39%), andstatewide (28%) islow 24 inSilicon Valley (36%), SanFrancisco Voter youngadultsages 18 to turnout of across allage groups. than inthestate overall, Silicon Valley ishigher Eligible voterturnoutin Nov. 2018 General Election Silicon ValleySilicon San Francisco San California Young Adults (Ages 18-24) Eligible Voter Eligible Turnoutof Eligible voter turnout of youngadultsinEligible voterturnoutof November 2018 was the highest of anyNovember 2018wasthehighestof 2019 Silicon Valley Index midterm general electiononrecord. primary election. in the June 2018 84% votedabsentee California asawhole); (compared to65%in general election ballots inthe2018 voters castabsentee Silicon Valley81% of 22.2% 19.8% 18.5% 2010 cast ballotsinthe Silicon Valley voters 53% ofeligible November 2014. the 35%whovoted in – much higherthan 2018 general election 11.2% 13.1% 2014 8.2% 36.0% 27.5% 38.6% 2018 93 GOVERNANCE GOVERNANCEREPRESENTATION

While women are still underrepresented The share of elected officials identifying as Valley’s constituency is represented, and in local elected offices, the share of female Hispanic or Latino have increased slightly gain insight on the backgrounds that may officials serving on Silicon Valley’s city since 2017. shape their decisions as representatives and town councils and county boards of of our communities. The composition of supervisors has increased significantly a region’s local elected officials is also over the past two years. Of the 103 seats WHY IS THIS IMPORTANT? critical because it represents the future up for election in 2018, 54 were won by Local government is considered the cohort of state and regional leadership.1 newly-elected officials (not incumbents), closest level of government to the people; If any given constituency is not cultivating and 32 of them were women (59% of all yet, there is little scholarship and reporting at the local level, they are unlikely to gain newly elected officials). Representation on the activities and identities of local increased representations at the State and by elected officials identifying as Asian or elected officials. In Silicon Valley, each local Federal levels.

Pacific Islander is much higher in Silicon elected official represents, on average, 1. For example, in 2015, 58% of California Senators and Assemblymembers had previously served in local government – in the Assembly alone, 67% of members were former local Valley than throughout the state, as are more than 13,000 residents. By examining government officials. This means that broadly, more than half of the California State the shares with professional backgrounds these local representatives, we are able legislature is comprised of former local elected officials. in engineering, technology, and science. to illustrate the extent to which Silicon The majority of the Consistent with State and Federal government Of the 48 women elected in 2018, 32 were newly- representation,2 women are underrepresented in elected (not incumbents) – significantly increasing elected officials serving local elected office in Silicon Valley; however, the the share of women in local elected office. on City and Town share of female local elected officials is quickly approaching proportional representation with a Councils and County gain of nine percentage points since 2017. The share of female local elected officials in Silicon Valley (45%) Boards of Supervisors 2. The Leadership California Institute, Women 2014: The Status of Women in California (www. grassrootslab.com/sites/all/files/Women2014FullReport.pdf). is now much higher than in the in Silicon Valley are state overall (35%). 59% of those newly elected to local Democrats (74%, up office in 2018 were women. from 72% in 2017).

REPRESENTATION REPRESENTATION Share of Local Elected O cials, by Gender Share of Local Elected O cials, by Partisan A liation Silicon Valley Silicon Valley

Men Women Democrat Republican Decline to State, or Other 100% 100% 11% 11%

36% 80% 80% 17% 15% 45%

60% 60%

40% 40% 72% 74% 64% 55% 20% 20%

0% 0% 2017 2019 2017 2019

Note: Includes local elected officials serving on City and Town Councils and County Boards of Supervisors. | Data Source: GrassrootsLab (www.grassrootslab.com) | Analysis: GrassrootsLab

94 2019 Silicon Valley Index 17% of theelectorate. 17% of Republicans, compared to local electedofficials are Silicon Valley’s15% of Data Source: GrassrootsLab (www.grassrootslab.com) | Analysis: GrassrootsLab Note: Includeslocalelectedofficials servingonCityand Town Supervisors. Councils and County Boards of affinity towards careers inEngineering, Government; however, representatives An overwhelming majority of CityandAn overwhelmingmajorityof Silicon Valley O cials,byShare Race andEthnicity ofLocal Elected REPRESENTATION 100% County officials inbothSilicon Valley in Silicon Valley showamuchhigher 20% 40% 60% 80% Technology, andSciencethanthose 0% Caucasian/Other/Unknown and California identifyasworking throughout thestate asawhole. in Business, Law, Education, and 2017 70% 16% 10% 4% Asian andPaci cAsian Islander Data Source: GrassrootsLab (www.grassrootslab.com) | Analysis: GrassrootsLab Note: Includeslocalelected officials servingonCityand Town Supervisors. Councils and County Boards of 100% Silicon Valley andCalifornia |2017 O cials,byShare Professional ofLocal Elected Background REPRESENTATION 20% 40% 60% 80% 0% Hispanic orLatino Silicon Valley 24% 15% 20% 7% 7% 6% 6% 4% 4% 4% 3% 2019 69% 15% 12% 4% Black orAfricanBlack American California 21% 10% 37% 8% 6% 3% 7% 3% 1% identifying asBlackor African American localelectedofficialsThe share of throughout thestate). localelectedofficials of such (compared to5% officials identifyingas localelected 15% of Silicon Valley,with is relatively highin Islander representation Asian andPacific 2019 (upfrom 10%to12%). Hispanic orLatino between2017and increase intheshare identifying as remained at 4%, whilethere wasaslight 2019 Silicon Valley Index 2% 2% Business Law Education Government Aairs Engineering Technology Non-Pro t Public Safety Banking/Finance Science Unidentifed/Other 95 GOVERNANCE APPENDIX A PROFILE OF SILICON VALLEY

Area Adult Educational Attainment Land Area includes Santa Clara and San Mateo counties, Fremont, Newark, Union City, and Scotts Valley. Land Area data (except for Scotts Valley) is from the U.S. Data for adult educational attainment are for Santa Clara and San Mateo counties and are derived from the United States Census Bureau, 2017 American Community Census Bureau: State and County QuickFacts. Land area is based on current information in the TIGER® database, calculated for use with Census 2010. Scotts Valley data Survey, 1-Year Estimates. Data reflects the educational attainment of the population 25 years and over. Percentages may not add up to 100% due to rounding. is from the Scotts Valley Chamber of Commerce. Age Distribution Population Data are for Santa Clara and San Mateo Counties and are derived from the United States Census Bureau, 2017 American Community Survey, 1-year estimates. Data for the Silicon Valley population comes from the E-1: City/County Population Estimates with Annual Percent Change report by the California Department of Percentages may not add up to 100% due to rounding. Finance and are for Silicon Valley cities. Population estimates are for January 2018. Ethnic Composition Jobs Data are for Santa Clara and San Mateo Counties and are derived from the United States Census Bureau, 2017 American Community Survey, 1-year estimates. Multiple The total number of jobs in the city-defined Silicon Valley region for Q2 of 2018 was estimated by BW Research using Q1 2018 United States Bureau of Labor Statistics and Other includes Native Hawaiian and Other Pacific Islander Alone, Some Other Race Alone, American Indian and Alaska Native alone, and Two or More Races. Quarterly Census of Employment and Wages data and Q2 2018 reported growth, modified slightly by EMSI, which removes suppressions and reorganizes public sector Percentages may not add up to 100% due to rounding. White, Asian, and Black or African-American are non-Hispanic. employment. Foreign Born Average Annual Earnings Data are for Santa Clara and San Mateo Counties and are derived from the United States Census Bureau, 2017 American Community Survey 1-Year estimates. The Average Annual Earnings for Silicon Valley was calculated by BW Research using data from the United States Bureau of Labor Statistics Quarterly Census of Employment Foreign Born Population excludes those who were born at sea. Data for China includes Taiwan. Oceania includes American Samoa, Australia, Cook Islands, Fiji, French and Wages and modified slightly by EMSI (which removes suppressions and reorganizes public sector employment). Data for Silicon Valley includes San Mateo and Santa Polynesia, Guam, Kiribati, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Northern Mariana Islands, Palau, Papua New Guinea, Clara Counties, and the Cities of Fremont, Newark, Scotts Valley, and Union City. Earnings include wages and supplements. Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, Wallis and Futuna. Percentages may not add up to 100% due to rounding. Foreign Immigration and Domestic Migration Data are from the California Department of Finance E-2 and E-6 Population Estimates and Components of Change and include San Mateo and Santa Clara Counties. Estimates for 2018 are preliminary. Net migration includes all legal and unauthorized foreign immigrants, residents who left the state to live abroad, and the balance of hundreds of thousands of people moving to and from California from within the United States.

PEOPLE

TALENT FLOWS AND DIVERSITY Science and Engineering Degrees Population Change Data are from the National Center for Education Statistics. Regional data for the Silicon Valley includes the following post-secondary institutions: Menlo College, Data are from the California Department of Finance E-2 and E-6 Population Estimates and Components of Change and include San Mateo and Santa Clara Counties. Cogswell Polytechnic College, University of San Francisco, University of California (Berkeley, Davis, Santa Cruz, San Francisco), Santa Clara University, San Jose Estimates for 2018 are preliminary. State University, San Francisco State University, Stanford University, and Golden Gate University. Beginning with the 2015 data, California State University-East Bay, International Technological University, and Notre Dame de Namur University were added. The academic disciplines include: computer and information sciences, engi- Net Migration Flows neering, engineering-related technologies, biological sciences/life sciences, mathematics, physical sciences and science technologies. Data were analyzed based on first major Data are from the California Department of Finance E-2 and E-6 Population Estimates and Components of Change, and include San Mateo and Santa Clara Counties. and level of degree (bachelor’s, master’s or doctorate). The year listed represents the end of the school year (e.g., 2017 represents the 2016-2017 school year). Estimates for 2018 are preliminary. Net migration includes all legal and unauthorized foreign immigrants, residents who left the state to live abroad, and the balance of hundreds of thousands of people moving to and from California from within the United States. Foreign Born Data for the Percentage of the Total Population Who Area Foreign Born are from the United States Census Bureau, 2017 American Community Survey, 1-Year Estimates. Age Distribution; Population Change, by Age Category Silicon Valley includes Santa Clara and San Mateo Counties. Data for the Foreign Born Share of Employed Residents Over Age 16, by Occupational Category are from Data are from the United States Census Bureau, 2017 American Community Survey, 1-Year Estimates. Silicon Valley data are for Santa Clara and San Mateo Counties. the United States Census Bureau, 2017 American Community Survey Public Use Microdata, and include Santa Clara and San Mateo Counties. Foreign born residents do not include those who were Born Abroad of American Parent(s). Estimates for the foreign born share include employed residents over age 16 who are at work only. Population Share by Race/Ethnicity Data are from the United States Census Bureau, American Community Survey 1-Year Estimates. Silicon Valley data include Santa Clara and San Mateo Counties. Foreign Language Multiple & Other includes American Indian and Alaska Native alone, Native Hawaiian and Other Pacific Islander alone, Some other race alone, and Two or more races. Data for Silicon Valley include Santa Clara and San Mateo Counties, and are from the United States Census Bureau, American Community Survey 1-Year Estimates, for the population five years and over. German includes other West Germanic Languages, French includes Haitian or Cajun, Tagalog includes Filipino, Slavic Languages Total Number of Births include Russian, Polish, and other Slavic Languages, and Chinese includes Mandarin and Cantonese. Data are from the California Department of Finance E-6 Population Estimates and Components of Change by County. Silicon Valley data are for San Mateo and Santa Clara Counties. Estimates for 2018 are preliminary. Migration of Tech Talent in the Core Working Age Group (25-44) Data are from the United States Census Bureau, 2017 American Community Survey 1-Year Estimates. Data are for the counties associated with the cities listed, and Average Age at Time of First Birth & Number of Children Per Woman, by Educational include adults in the core working age group (ages 25-44) with a bachelor’s degree or higher, who are employed full-time (35 or more hours per week) in the private sector, Attainment Level and work in Computer, Mathematical, Architectural and Engineering occupations and moved to that specific county within one year of responding to the survey. Data is from the United States Department of Health and Human Services (US DHHS), Centers for Disease Control and Prevention (CDC), National Center for Health Female Tech Talent in the Core Working Age Group (25-44) Statistics (NCHS), Division of Vital Statistics, Natality public-use data. Silicon Valley includes Santa Clara & San Mateo Counties. Average Age of Mother At Time of Data are from the United States Census Bureau, American Community Survey 1-Year Estimates, and include women ages 25-44 with a bachelor’s degree or higher. First Birth is calculated as the average age of women who gave birth to their first child that year. Women with a bachelor’s degree or higher includes Bachelor’s degree (BA, Technical roles include Computer, Mathematical, Architectural and Engineering occupations. Silicon Valley includes Santa Clara & San Mateo Counties. AB, BS), Master’s degree (MA, MS), Doctorate (PHD, EdD) or Professional Degree (MD, DDS, DVM, LLB, JD). Women with less than a bachelor’s degree includes 8th grade or less, 9th through 12th grade with no diploma, High school graduate or GED completed, Some college credit but not a degree, and Associate degree (AA, AS). Share of Female Employees at Silicon Valley’s 10 Largest Technology Companies The average number of children per women is calculated only for those women who gave birth that year, and those giving birth to their “6th child and over” were counted Analysis includes the largest companies by rank in the Silicon Valley Business Journal Book of Lists, 2017-2018, and only those for which gender diversity data is as having their 6th child for the purposes of creating an average. It includes live births only, and is a snapshot in time; it is not a replacement for a true population-level disclosed. Companies included are Apple, Google, Cisco, Facebook, Intel, Gilead Sciences, Oracle, Lockheed Martin, Nvidia, and VMware. The share of female workers fertility rate. Data by educational attainment level does not include women whose education attainment level was unknown or excluded. is company-wide, not Silicon Valley-specific. While Tesla Motors is one of Silicon Valley’s largest technology companies, it was not included due to lack of available diversity data. Educational Attainment Data for adult educational attainment are for Santa Clara and San Mateo Counties and are from the United States Census Bureau, American Community Survey 1-Year Share of Residents in Technical Occupations with a Bachelor’s Degree or Higher, by Place Estimates. Data reflects the educational attainment of the population 25 years and over. Educational Attainment by Race/Ethnicity reflects adults whose highest degree of Origin received was either a bachelor’s degree or a graduate degree. Multiple and Other includes Two or More Races, Some Other Race, Native Hawaiian and Other Pacific Islander, and American Indian and Alaska Native. Data for Native Hawaiian and Other Pacific Islander was not available for Santa Clara (2007, 2012, and 2017) or San Data includes all civilian employed workers who reside in San Mateo or Santa Clara Counties, with a bachelor’s degree or higher, who work in technical occupations Mateo Counties (2007 and 2017). Data for American Indian and Alaska Native in San Mateo County. (including Computer, Mathematical, Architectural, and Engineering occupations).

ECONOMY

EMPLOYMENT Labor Force Participation Total Number of Jobs and Percent Change over Prior Year Data is from the United States Census Bureau, American Community Survey 1-Year Estimates. Silicon Valley includes Santa Clara and San Mateo Counties. The Data includes average annual employment estimates as of the second quarter for years 2001 through 2018 from the United States Bureau of Labor Statistics Quarterly labor force participation rate is calculated as the number of employed workers plus those who are unemployed but looking for a job, divided by the total working-age Census of Employment and Wages, and includes the entire city-defined Silicon Valley region. Data for Q2 of 2018 was estimated at the industry level by BW Research population. using Q1 2018 QCEW data and updated based on Q2 2018 reported growth and totals, and modified slightly by EMSI, which removes suppressions and reorganizes public sector employment. Top U.S. Tech Talent Centers Data is from the CBRE 2018 Scoring Tech Talent report. Scoring Tech Talent is a comprehensive analysis of labor market conditions, cost and quality in the U.S. and Relative Job Growth Canada for highly skilled tech workers. The top-50 markets were ranked according to their competitive advantages and appeal to tech employers and tech talent using data Data is from the United States Bureau of Labor Statistics, Quarterly Census of Employment and Wages for Q2 2007, Q2 2010, Q2 2017, and Q2 2018. The total from the U.S. Bureau of Labor Statistics and other sources. Tech Talent includes the following occupation categories: software developers and programmers; computer sup- number of jobs for Q2 of 2018 was estimated by BW Research using Q1 2018 United States Bureau of Labor Statistics Quarterly Census of Employment and Wages data port, database and systems; technology and engineering related; and computer and information system managers. Tech talent workers comprise 20 different occupations, and Q2 2018 reported growth, modified slightly by EMSI, which removes suppressions and reorganizes public sector employment. which are highly concentrated within the high-tech services industry but are spread across all industry sectors. Using this definition, a software developer who works for a logistics or financial services company is included in the data. Major Areas of Economic Activity Data includes average annual employment estimates as of the second quarter for years 2007 through 2018 from the United States Bureau of Labor Statistics Quarterly Census of Employment and Wages, and includes the entire city-defined Silicon Valley region. Data for Q2 of 2018 was estimated at the industry level by BW Research INCOME using Q1 2018 QCEW data and updated based on Q2 2018 reported growth and totals, and modified slightly by EMSI, which removes suppressions and reorganizes public sector employment. Community Infrastructure & Services includes Healthcare & Social Services (including state and local government jobs); Retail; Accommodation Per Capita Personal Income & Food Services; Education (including state and local government jobs); Construction; Local Government Administration; Transportation; Banking & Financial Per capita values are calculated using personal income data from the U.S. Department of Commerce, Bureau of Economic Analysis and population figures from the U.S. Services; Arts, Entertainment & Recreation; Personal Services; Federal Government Administration; Nonprofits; Insurance Services; State Government Administration; Census Bureau mid-year population estimates. Silicon Valley data are for Santa Clara and San Mateo Counties. All per capita income values have been inflation-adjusted Warehousing & Storage; and Utilities (including state and local government jobs). Innovation and Information Products & Services includes Computer Hardware Design and are reported in 2017 dollars using the Bay Area consumer price index for all urban consumers from the Bureau of Labor Statistics for Silicon Valley and San Francisco & Manufacturing; Semiconductors & related Equipment Manufacturing; Internet & Information Services; Technical Research & Development (Include Life Sciences); data, the California consumer price index for all urban consumers from the California Department of Finance May Revision Forecast (April 2018) for California data, and Software; Telecommunications Manufacturing & Services; Instrument Manufacturing (Navigation, Measuring & Electromedical); Pharmaceuticals (Life Sciences); Other the U.S. city average consumer price index for all urban consumers from the Bureau of Labor Statistics. The personal per capita income for the United States is derived Media & Broadcasting, including Publishing; Medical Devices (Life Sciences); Biotechnology (Life Sciences); and I.T. Repair Services. Business Infrastructure & Services from state and regional data (as opposed to National Income and Product Accounts data), which include all persons who reside in a state, regardless of the duration of includes Wholesale Trade; Personnel & Accounting Services; Administrative Services; Technical & Management Consulting Services; Facilities; Management Offices; residence, except for foreign nationals employed by their home governments in the United States. State personal income includes the income of resident foreign nationals Design, Architecture & Engineering Services; Goods Movement; Legal; Investment & Employer Insurance Services; and Marketing, Advertising & Public Relations. working in the United States—including migrant workers—regardless of length of residency. It excludes the portion of income earned abroad by U.S. citizens living Other Manufacturing includes Primary & Fabricated Metal Manufacturing; Machinery & Related Equipment Manufacturing; Other Manufacturing; Transportation abroad for less than a year. It also excludes the earnings of federal civilian and military personnel stationed abroad and the property income received by the federal pension Manufacturing including Aerospace & Defense; Food & Beverage Manufacturing; Textiles, Apparel, Wood & Furniture Manufacturing; and Petroleum and Chemical plans of those workers. Manufacturing (Not in Life Sciences). Per Capita Income by Race & Ethnicity Employment by Tier Data for per Capita Income are from the United States Census Bureau American Community Survey 1-Year Estimates. All income values have been inflation-adjusted Employment by Tier data are from the U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages (QCEW) and modified slightly by EMSI to remove and are reported in 2017 dollars using the Bay Area consumer price index for all urban consumers from the Bureau of Labor Statistics for Silicon Valley and San Francisco suppressions and reorganize public sector employment. 2018 data are estimates based on QCEW 2018 Q2 employment at the industry level using 2018 Q1 data, and data, the California consumer price index for all urban consumers from the California Department of Finance May Revision Forecast (April 2018) for California data, and updated based on 2018 Q2 reported growth and totals reported, and modified slightly by EMSI. Occupational segmentation into tiers has been recently adopted by the the U.S. city average consumer price index for all urban consumers from the Bureau of Labor Statistics. Silicon Valley data includes Santa Clara and San Mateo Counties. California Employment Development Department (EDD), and implemented over the last several years by BW Research for regional occupational analysis. Occupational Per capita income is the mean money income received computed for every man, woman, and child in a geographic area. It is derived by dividing the total income of all segmentation allows for the in-depth examination of the quality and quantity of jobs in a given economy. This occupational segmentation technique delineates the people 15 years old and over in a geographic area by the total population in that area. Income is not collected for people under 15 years old even though these people are majority of occupations into one of three tiers. Tier 1 Occupations include managers (Chief Executives, Financial Managers, and Sales Managers), professional positions included in the denominator of per capita income. This measure is rounded to the nearest whole dollar. Money income includes amounts reported separately for wage or (Lawyers, Accountants, and Physicians) and highly-skilled technical occupations, such as Scientists, Computer Programmers, and Engineers, and are typically the high- salary income; net self-employment income; interest, dividends, or net rental or royalty income or income from estates and trusts; Social Security or Railroad Retirement est-paying, highest-skilled occupations in the economy. Tier 2 Occupations include sales positions (Sales Representatives), teachers, and librarians, office and administrative income; Supplemental Security Income (SSI); public assistance or welfare payments; retirement, survivor, or disability pensions; and all other income. Population data positions (Accounting Clerks and Secretaries), and manufacturing, operations, and production positions (Assemblers, Electricians, and Machinists). They have historically used to compute per capita values are from the United States Census Bureau, American Community Survey 1-Year Estimates. Multiple & Other includes Native Hawaiian provided the majority of employment opportunities and may be referred to as middle-wage, middle-skill positions. Tier 3 Occupations include protective services (Security & Other Pacific Islander Alone, American Indian & Alaska Native Alone, Some Other Race Alone and Two or More Races; White, Asian, Black or African American, Guards), food service and retail positions (Waiters, Cooks, and Cashiers), building and grounds cleaning positions (Janitors), and personal care positions (Home Health Multiple & Other are non-Hispanic. Aides and Child Care Workers). Individual Median Income, by Educational Attainment Monthly Unemployment Rate Data for Median Income by Educational Attainment are from the U.S. Census Bureau American Community Survey, 1-Year Estimates, and include the population Monthly unemployment rates are calculated using employment and labor force data from the Bureau of Labor Statistics, Current Population Statistics (CPS) and the Local 25 years and over with earnings. All income values have been inflation-adjusted and are reported in 2017 dollars using the Bay Area consumer price index for all urban Area Unemployment Statistics (LAUS). Rates are not seasonally adjusted. County-level and California data for November 2018 are preliminary. consumers from the Bureau of Labor Statistics for Silicon Valley and San Francisco data, the California consumer price index for all urban consumers from the California Department of Finance May Revision Forecast (April 2018) for California data, and the U.S. city average consumer price index for all urban consumers from the Bureau Unemployed Residents’ Share of the Working Age Population, by Race & Ethnicity of Labor Statistics, annual estimate based on first half data. Silicon Valley data includes Santa Clara and San Mateo Counties. The 2008 value for those with a graduate or Data are from the U.S. Census Bureau, American Community Survey 1-Year Estimates, and include Santa Clara and San Mateo Counties. The data counts the number of professional degree is for San Mateo County only because the Santa Clara County data reported median income in that category as $100,000+. unemployed persons, as well estimates the total population in each racial/ethnic category for residents 16 years of age and older. Other includes the categories Some Other Race and Two or More Races. Data for Two or More Races was not available for San Mateo County for 2007. White is non-Hispanic or Latino. Data are limited to the Average Wages household population and exclude the population living in institutions, college dormitories, and other group quarters. Average wages are from the U.S. Bureau of Labor Statistics, QCEW data modified slightly by EMSI to take into account yearly changes in methodology and occupational classifications. Average wage data for San Mateo County exhibited an abnormally large increase between 2011 and 2012, which may be reflective of methodological changes in data collection. Wages have been inflation-adjusted and are reported in 2018 dollars using the Bay Area consumer price index for all urban consumers from the

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Bureau of Labor Statistics for the Bay Area data, 2018 estimate based on January-August, the California consumer price index for all urban consumers from the California and U.S.) and forecasts updated on 11/06/2018 (U.S. data), 11/13/2018 (California data) and 11/26/2018 (Silicon Valley and San Francisco). All GDP values ...have been Department of Finance May Revision Forecast (April 2018) for California data. inflation-adjusted and are reported in 2018 dollars using the Bay Area consumer price index for all urban consumers from the Bureau of Labor Statistics for Silicon Valley data, 2018 estimate based on January-August, the California consumer price index for all urban consumers from the California Department of Finance May Revision Median Wages for Various Occupational Categories Forecast (April 2018) for California data, and the U.S. city average consumer price index for all urban consumers from the Bureau of Labor Statistics, annual estimate Data are from the California Employment Development Department, Employment and Wages by Occupation, 2010-2018, for the San Jose-Sunnyvale-Santa Clara based on first half data. Silicon Valley data include Santa Clara and San Mateo Counties. Metropolitan Statistical Area (MSA), including Santa Clara and San Benito Counties, and the San Francisco-San Mateo-Redwood City MSA, including Marin, San Francisco, and San Mateo Counties. The San Francisco-Redwood City-South San Francisco Metropolitan Division replaced the San Francisco-San Mateo-Redwood City Patent Registrations MSA in 2017. Wages have been inflation-adjusted and are reported in 2018 dollars using the Bay Area consumer price index for all urban consumers from the Bureau Patent data is provided by the United States Patent and Trademark Office and consists of Utility patents granted by inventor. Geographic designation is given by the of Labor Statistics for the Bay Area data, 2018 estimate based on January-August, the California consumer price index for all urban consumers from the California location of the first inventor named on the patent application. Silicon Valley patents include only those filed by residents of Silicon Valley. Other Includes: Teaching Department of Finance May Revision Forecast (April 2018) for California data. Management, Business, Science and Arts Occupations include Management; Business and & Amusement Devices, Transportation/Vehicles, Motors, Engines and Pumps, Dispensing & Material Handling, Food, Plant & Animal Husbandry, Furniture & Financial Operations; Computer and Mathematical; Architecture and Engineering; Life, Physical, and Social Science; Community and Social Services; Legal; Education, Receptacles, Apparel, Textiles & Fastenings, Body Adornment, Nuclear Technology, Ammunition & Weapons, Earth Working and Agricultural Machinery, Machine Training, and Library; Arts, Design, Entertainment, Sports, and Media; and Healthcare Practitioners and Technical Occupations. Service Occupations include Healthcare Elements or Mechanisms, and Superconducting Technology. The technology area categorization method was slightly modified in 2012, resulting in minor changes to the Support; Protective Services; Food Preparation and Serving-Related; Building and Grounds Cleaning and Maintenance; and Personal Care and Service Occupations. Sales proportion of patents in each technology area relative to previous years. Population estimates used to calculate the number of patents granted per 100,000 people were and Office Occupations include Sales and Related; and Office and Administrative Support Occupations. Natural Resources, Construction and Maintenance Occupations from the California Department of Finance, E-1: City/County Population Estimates with Annual Percent Change. Beginning in 2015, the USPTO stopped classifying include Farming, Fishing and Forestry; Construction and Extraction; and Installation, Maintenance and Repair Occupations. Production, Transportation and Material patents in the United States Patent Classification (USPC) and began using the Cooperative Patent Classification (CPC), so some USPC codes were unavailable. In those Moving Occupations include Production; and Transportation and Material Moving Occupations. cases, unofficial routing classifications were used in place of the missing UPSC classifications. This process may create some minor inconsistencies between the 2015 and previous years’ data sorted by Technology Area. Median Wages by Tier Median Wages by Tier data are based on Occupational Employment Statistics from the U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages Venture Capital Investment; Top Venture Capital Deals; Megadeals (QCEW) and modified slightly by EMSI county-level earnings by industry. 2018 data are estimates based on QCEW 2018 Q1 data. Occupational segmentation into Data for 2000-2016 are from the MoneyTree™ Report from PricewaterhouseCoopers and the National Venture Capital Association, using data from CB Insights tiers has been recently adopted by the California Employment Development Department (EDD), and implemented over the last several years by BW Research for (beginning with Q4 2015) and Thomson Reuters (prior to Q4 2015). 2017 and 2018 data are from Thomson ONE as of January 2, 2019. Silicon Valley includes the regional occupational analysis. Occupational segmentation allows for the in-depth examination of the quality and quantity of jobs in a given economy. This occupational city-defined region. All values have been inflation-adjusted and are reported in 2018 dollars using the Bay Area consumer price index for all urban consumers from the segmentation technique delineates the majority of occupations into one of three tiers. Tier 1 Occupations include managers (Chief Executives, Financial Managers, and Bureau of Labor Statistics for Silicon Valley and San Francisco data, 2018 estimate based on January-August, the California consumer price index for all urban consumers Sales Managers), professional positions (Lawyers, Accountants, and Physicians) and highly-skilled technical occupations, such as Scientists, Computer Programmers, from the California Department of Finance May Revision Forecast (April 2018) for California data, and the U.S. city average consumer price index for all urban consum- and Engineers, and are typically the highest-paying, highest-skilled occupations in the economy. Tier 2 Occupations include sales positions (Sales Representatives), ers from the Bureau of Labor Statistics, annual estimate based on first half data. Megadeals include those over $100 million each. teachers, and librarians, office and administrative positions (Accounting Clerks and Secretaries), and manufacturing, operations, and production positions (Assemblers, Electricians, and Machinists). They have historically provided the majority of employment opportunities and may be referred to as middle-wage, middle-skill positions. Venture Capital by Industry Tier 3 Occupations include protective services (Security Guards), food service and retail positions (Waiters, Cooks, and Cashiers), building and grounds cleaning positions Data are from the MoneyTree™ Report from PricewaterhouseCoopers and the National Venture Capital Association (with data from CB Insights). 2018 data include (Janitors), and personal care positions (Home Health Aides and Child Care Workers). These occupations typically represent lower-skilled service positions with lower Q1-3. Greater Silicon Valley includes Northern California, the Bay Area, and the coastline. Industries included in the Moneytree report are defined as follows: Agriculture wages that require little formal training and/or education. (all aspects of farming, including crop production and health, animal production and wellness, as well as machinery, products, and related activities), Automotive and Transportation (all elements of travel by air, automobile, train, trucking, and other forms of transportation; also addresses manufacturing, parts, and maintenance), Average Wages for Full-Time Workers, by Gender Business Products and Services (All business needs and associated services: advertising, PR, HR, staffing, training records keeping, legal services, consulting, office supplies Data is from the United States Census Bureau, American Community Survey Public Use Microdata (PUMS), and includes all full-time (35 or more hours per week) and furniture, information services, hardware, facilities, and more; also covers associated services like commercial printing, outsourcing, and packaging), Computer workers over age 15 with earnings. Silicon Valley data includes Santa Clara and San Mateo Counties. Hardware & Services (Physical computing devices and related services, though specifically not the software used on those machines; includes personal and business computers, networking equipment, leasing companies, peripherals, handhelds, servers, supercomputers, gaming devices, and IT services), Consumer Products and Services (all Median Household Income goods and services for personal use, not Business or Industrial, including but not limited to: appliances, automotive services, rentals, consumer electronics, clothes, home Data for Median Household Income are from the U.S. Census Bureau American Community Survey 1-Year Estimates. All income values have been inflation-adjusted and furnishings, jewelry, pet products, tobacco, toys and games), Electronics (Concerned mainly with electronic components like chips, semiconductors, switches, motors, are reported in 2018 dollars using the Bay Area consumer price index for all urban consumers from the Bureau of Labor Statistics for Silicon Valley data, 2018 estimate testing equipment, and scientific instruments; also related manufacturing services), Energy and Utilities (energy production, distribution, and storage, including fossil based on January-August, the California consumer price index for all urban consumers from the California Department of Finance May Revision Forecast (April 2018) fuels, renewables, electric power companies, companies focused on energy efficiency, as well as companies researching new energy sources or technologies), Environmental for California data, and the U.S. city average consumer price index for all urban consumers from the Bureau of Labor Statistics, annual estimate based on first half data. Services & Equipment (companies that deal with repairing damage after an environmental event has occurred or aim to help limit the negative ecological impact of an Silicon Valley data include Santa Clara and San Mateo Counties. Median household income for Silicon Valley was estimated using a weighted average based on the county event or company; this includes environmental and energy consulting, hazardous waste services, recycling, cleanup, and solid waste), Financial (companies dealing with population figures from the California Department of Finance E-4 Population Estimates for Cities, Counties, and the State. wealth in any form, including but not limited to: accounting, banking, credit and collections, investments, online payments companies, and lending), Food & Beverages (food and drink of all kinds: retail and wholesale, fresh ingredients, prepared and canned items, and foodservice, but not restaurants - see Leisure; also includes food Percent Change in the Number of Households by Income Range; Share of Households With safety, flavoring and condiments, alcoholic products, and distribution), Healthcare (all aspects of medical care and wellness: diagnosis, drug development and distribution, Income of $200,000 or More Annually medical products and facilities, healthcare plans, and alternative treatments and elective procedures), Industrial (equipment and facilities that are neither commercial nor residential/consumer and all related applications; mainly concerned with materials, facilities, heavy machinery, and construction), Internet (online applications, but neither Data for Distribution of Income and Housing Dynamics are from the U.S. Census Bureau American Community Survey, 1-Year Estimates. Income ranges for 2013-2017 the hardware on which they are run nor the ISPs that make transactions possible; all ecommerce sites are included, as are webhosting services, browser software, online household counts by income category are based on inflation-adjusted 2017 dollars, and 2010-2012 counts are based on inflation-adjusted 2015 dollars. Silicon Valley data advertising, email, online communications platforms of all kinds, online learning, video, and more), Leisure (in-person entertainment like movie theaters, casinos, lodging, includes Santa Clara and San Mateo Counties. Income is the sum of the amounts reported separately for the following eight types of income: Wage or salary income; Net restaurants of all kinds, sporting events, gyms, and recreation facilities), Traditional Media (all forms of non-Internet entertainment that is also not in-person - see Leisure; self-employment income; Interest, dividends, or net rental or royalty income from estates and trusts; Social Security or railroad retirement income; Supplemental Security includes film, video, music, publishing, radio, and television), Metals & Mining (companies involved with extracting raw materials from the earth and their processing; Income; Public assistance or welfare payments; Retirement, survivor, or disability pensions; and All other income. larger categories contained herein include aluminium, coal, copper, diamonds and precious stones, precious metals, and steel; additionally the brokering and distribution of these items), Mobile & Telecommunications (communications companies and associated technologies, from overarching categories like fiber optics, telecom equipment, Share of Households, by Investable Assets infrastructure, towers, and RFID systems to applications like mobile software, mobile commerce, and the telecom companies that facilitate communication over their Data are from the Phoenix Global Wealth Monitor, and include Santa Clara and San Mateo Counties. Investable Assets include education/custodial accounts, individual- networks), Non-Internet/Mobile Retail (brick-and-mortar retail locations of all kinds: clothes, electronics, appliances, physical media, grocery, office supplies, and every ly-owned retirement accounts, stocks, options, bonds, mutual funds, managed accounts, hedge funds, structured products, ETFs, cash accounts, annuities, and cash value other item purchased in person that is not a leisure activity - see Leisure), Risk & Security (Security services and products that operate primarily in the physical world and life insurance. The Phoenix Wealth and Affluent Monitor (W&AM) U.S. Sizing Report is intended to provide estimates of the number of affluent and High Net Worth encompass personal protective equipment, security and surveillance equipment, security guard companies, consultants, and more), and Non-Internet/Mobile Software households in the country. Sizing estimates are provided at the state level as well as by Core-Based Statistical Areas (CBSAs), which is comprised of Metropolitan and (Software not covered under “Mobile” or “Internet”; It can be hosted on a user’s machine or accessed remotely and can be used for any application; in this category, the Micropolitan Statistical Areas (there are currently 933 in the country). The W&AM sizing estimates are developed using a combination of sources including the Survey software itself is the user’s primary concern, not the delivery method as in Internet and Mobile categories). of Consumer Finance, as well as Nielsen-Claritas. National data and closely linked variables are used to obtain estimates at the local level; thus, the county-level data are approximations only. Angel Investment Data is from Crunchbase and includes the entire city-defined Silicon Valley region, San Francisco, and California. The analysis includes disclosed financing data for Angel Poverty Status Deals (may include small VCs or family funds or individuals, or may just be noted as an Angel round by the company itself), and seed stage investments that included at Data for the percentage of the population living in poverty are from the U.S. Census Bureau, American Community Survey 1-year estimates. Silicon Valley data include least one Angel investor. Angel Deals are typically pre-seed and are not necessarily tied to equity. 2017 and 2018 data were extracted in January, 2019. Investment amounts San Mateo and Santa Clara Counties. Data for the share of children living in poverty include the population under age 18. Following the Office of Management and have been inflation-adjusted and are reported in 2018 dollars using the Bay Area consumer price index for all urban consumers from the Bureau of Labor Statistics for Budget’s (OMB’s) Directive 14, the Census Bureau uses a set of money income thresholds that vary by family size and composition to determine who is in poverty. If the Silicon Valley and San Francisco data, 2018 estimate based on January-August, and the California consumer price index for all urban consumers from the California total income for a family or unrelated individual falls below the relevant poverty threshold (e.g., household income of $24,600 for a family of four in 2017 within the 48 Department of Finance May Revision Forecast (April 2018) for California data. contiguous states and the District of Columbia), then the family (and every individual in it) or unrelated individual is considered in poverty. Multiple and Other includes Some Other Race Alone and Two or More Races. White is non-Hispanic or Latino. Startups Data includes all seed or early stage funding deals, with any type of investor. Silicon Valley data include the city-defined region. Share of Seed and Early-Stage Deals to Self-Sufficiency Companies Founded by Women includes companies where at least one founder identified as Female. Data as of January 25, 2019. Data is from the Self-Sufficiency Standard for California, from the Center for Women’s Welfare at the University of Washington School of Social Work. Silicon Valley data includes Santa Clara and San Mateo Counties. Developed by Dr. Diana Pearce, the Self-Sufficiency Standard defines the amount of income necessary to meet basic needs Initial Public Offerings (including taxes) without public subsidies (e.g., public housing, food stamps, Medicaid or child care) and without private/informal assistance (e.g., free babysitting by a Data is from Renaissance Capital. Locations are based on the corporate address provided to Renaissance Capital. Silicon Valley includes the city-defined region. Rest of relative or friend, food provided by churches or local food banks, or shared housing). The family types for which a Standard is calculated range from one adult with no California includes all of the state except Silicon Valley for 2007-2012, and all of the state except Silicon Valley and San Francisco for 2013-2018. children, to one adult with one infant, one adult with one preschooler, and so forth, up to three-adult households with six teenagers. Asian/Pacific Islander, Black, White, and Other are non-Hispanic or Latino. 2018 data was based on the 2016 ACS 1-Year Estimates, with updated cost estimates and earnings inflation-adjusted to 2018. Mergers & Acquisitions Data are from FactSet Research Systems, Inc., and are based on M&A Activity in Joint Venture’s zip code-defined Silicon Valley region. Transactions include full acquisi- Free or Reduced Price School Meals tions, majority stakes, minority stakes, club-deals and spinoffs. Silicon Valley and San Francisco deals include those involving one or more Silicon Valley or San Francisco Data includes students ages 5-17 who have a primary or short-term enrollment in the school on Fall Census Day. Free and Reduced Meal Program (FRMP) information company. 2017 and 2018 data were updated using data downloaded on January 9, 2019. is submitted by schools to the Department of Education in January. The 2016-17 data were from the October 2016 data collection, certified as of January 27, 2017 and updated on May 9, 2017. The 2015-16 data are from the October 2015 data collection, and were certified as of March 24, 2016. Data for 2012-13 were revised on June Nonemployer Trends 30, 2014. Data files include public school enrollment and the number of students eligible for free or reduced price meal programs. Data for Silicon Valley include Santa Data for firms without employees are from the U.S. Census Bureau, which uses the term ‘nonemployers’. The Census defines nonemployers as a business that has no paid Clara and San Mateo Counties. A child’s family income must fall below 130% of the federal poverty guidelines ($31,980 for a family of four in 2017-2018) to qualify employees, has annual business receipts of $1,000 or more ($1 or more in the construction industries), and is subject to federal income taxes. Most nonemployers are for free meals, or below 185% of the federal poverty guidelines ($45,510 for a family of four in 2017-2018) to qualify for reduced-cost meals. Students may be eligible self-employed individuals operating very small unincorporated businesses, which may or may not be the owner’s principal source of income. Silicon Valley data include for free or reduced price meals based on applying for the National School Lunch Program (NSLP), or who are determined to meet the same income eligibility criteria as Santa Clara and San Mateo Counties. the NSLP through their local schools, or their homeless, migrant, or foster status in CALPADS, or those students “directly certified” as participating in California’s food stamp program. Years presented are the final year of a school year (e.g., 2011-2012 is shown as 2012). In school year 2012-2013, the California Department of Education changed its data collection methodology to utilize CALPADS (California Longitudinal Pupil Achievement Data System) student-level data rather than district-provided COMMERCIAL SPACE data. The Non Public Schools (NPS) and adult schools included in the CALPADS data were excluded from the analysis for consistency, because they were not included in past FRPM files. Because the 2012-2013 data had a large number of schools reporting enrollment and percent eligible but not eligible student counts, counts were Commercial Space; Commercial Vacancy; Commercial Rents; Commercial Office Space estimated by multiplying enrollment by the eligibility rate and rounding to the nearest whole number. Under Construction and Share Pre-Leased to Tech Firms Share of the Population that is Food Insecure Data represents the end of each annual period unless otherwise noted. Commercial space includes Office, Industrial, and R&D space. The JLL inventory includes all Data is from Map the Meal Gap 2018: A Report on County and Congressional District Food Insecurity and County Food Cost in the United States in 2016 (Feeding development above 35,000 square feet, with the exception of Downtown Palo Alto and Downtown Mountain View, and all R&D development above 10,000 square feet. America, 2018) and Map the Meal Gap 2017: Food Insecurity and Child Food Insecurity Estimates at the County Level (using 2015 data). Silicon Valley data includes The data included in this report does not include owner/user developments. Silicon Valley data include San Mateo County, Santa Clara County, and the City of Fremont. Santa Clara and San Mateo Counties. Food insecurity refers to USDA’s measure of lack of access, at times, to enough food for an active, healthy life for all household Bay Area data include the entire nine-county region. Average office space asking rents are “Full Service Gross” (FSG), which is the monthly rental rate and includes members and limited or uncertain availability of nutritionally adequate foods. Food-insecure households are not necessarily food insecure all the time. Food insecurity common area maintenance fees, utility fees, and taxes/insurance fees. Industrial and R&D asking rents are quoted “triple net” (NNN), which is the monthly base rental may reflect a household’s need to make trade-offs between important basic needs, such as housing or medical bills, and purchasing nutritionally adequate foods. Feeding rate in which common area maintenance fees, utility fees, and taxes/insurance fees are excluded. The vacancy rate is the amount of unoccupied space, and is calculated by America estimated food insecurity rates at the county level using a state-level relationship between food insecurity and poverty, unemployment, homeownership, and dividing the direct and sublease vacant space by the building base. The vacancy rate does not include occupied spaces presently being offered on the market for sale or lease. other indicators. The Change in Available Commercial Space only includes office space, and is calculated as the change between Q4 and Q4 of the prior year. Net absorption is the change in occupied space during a given time period. Average asking rents have been inflation-adjusted and are reported in 2018 dollars using the Bay Area consumer price index Meals Provided to Vulnerable Households & Missing Meals for all urban consumers from the Bureau of Labor Statistics for Silicon Valley data, 2018 estimate based on January-August. Near transit is defined as located within a The Hunger Index analysis is conducted by the Food and Agribusiness Institute of Santa Clara University’s Leavey School of Business, through a partnership with Second ten-minute walk of a Caltrain, BART, or VTA station. Harvest Food Bank and Bank of America. Released during the Hunger Action Summit every year, the Index measures the gap between the need for food in Santa Clara and San Mateo counties and the ability of the most vulnerable individuals to get food either on their own or with the help of federal food-assistance programs such as Hotel Development CalFresh and local non-profit organizations like Second Harvest. Other food assistance programs include Senior Nutrition, Summer Meals, School Meals (Breakfast, Data is from the Atlas Hospitality Group annual California Hotel Development Surveys. Data for 2009-2013 was unavailable, as reports were not published due to lack of Lunch - Free and Reduced Price), WIC, CACFP and other sources. The number of unmet meals is known as the meal gap. The most vulnerable households are defined as significant hotel development. New Hotels include those that opened within a given year. having an annual income of less than $50,000, although many households with higher incomes are also at significant risk of food insecurity. Amount of Commercial Space Occupied by Major Tech Tenants Data are from Colliers International Silicon Valley, and represent the aggregate amount of space leased by five major tech tenants (Amazon.com, Apple, Facebook, Google, INNOVATION & ENTREPRENEURSHIP and LinkedIn) in Silicon Valley between 2013 and 2018. Not all space is currently occupied (some has been leased but involves redevelopment or was under construction at the time the leases were executed). Silicon Valley includes Santa Clara County plus Fremont. Facebook space includes the Menlo Park campus in San Mateo County. Productivity Value added per employee is calculated as gross domestic product (GDP) divided by the total employment. GDP estimates the market value of all final goods and services. GDP and employment data are from Moody’s Economy.com estimates using historical data through 2016 (Silicon Valley and San Francisco) and through 2017 (California

SOCIETY

GED completions, those in the cohort who are still enrolled, and also due to suppressed data in some counties/districts for certain racial/ethnic groups. Due to the changes PREPARING FOR ECONOMIC SUCCESS in the methodology for calculating the 2016–17 Adjusted Cohort Graduation Rate and subsequent years, the California Department of Education strongly discourages Graduation and Dropout Rates; College Preparation against comparing the 2016–17 and subsequent years’ Adjusted Cohort Graduation Rate with the cohort outcome data from prior years. Students meeting UC/CSU requirements includes all 12th grade graduates completing all courses required for University and/or California State University entrance. Ethnicities were determined by the California Department of Education. Any student ethnicity pools containing 10 or fewer students were excluded in order to protect Math Proficiency student privacy. Multi/None includes both students of two or more races, and those who did not report their race. All races/ethnicities other than Not-Hispanic or Latino Data for 2015-2018 are from the California Department of Education, California Assessment of Student Performance and Progress (CAASPP). Beginning with the are non-Hispanic. Silicon Valley includes all students attending public high school in San Mateo and Santa Clara Counties, as well as those in Scotts Valley Unified School 2013–14 school year, CAASPP became the new student assessment system in California, replacing the Standardized Testing and Reporting (STAR) system. 2018 CAASPP District, New Haven School District, Fremont Unified School District, and Newark Unified School District. Dropout and graduation rates are four-year adjusted rates. Test Results are from tests administered in 2018. The share of eighth-graders meeting or exceeding the standard includes students who have made progress and met or The adjusted rates are derived from the number of cohort members who earned a regular high school diploma (or dropped out) by the end of year 4 in the cohort divided exceeded the grade standard, and who appear to be ready for future coursework. Data for 2006 through 2013 are from the California Department of Education, California by the number of first-time grade 9 students in year 1 (starting cohort) plus students who transfer in, minus students who transfer out, emigrate, or die during school Standards Tests (CST) Research Files for San Mateo and Santa Clara Counties, and California. In 2003, the CST replaced the Stanford Achievement Test, ninth edition years 1, 2, 3, and 4. Years presented are the final year of a school year (e.g., 2011-2012 is shown as 2012). Dropout and graduation rates do not add up to 100% due to (SAT/9). The CSTs in English–language arts, mathematics, science, and history–social science were administered only to students in California public schools. Except

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for a writing component that was administered as part of the grade four and grade seven English–language arts tests, all questions were multiple-choice. These tests were developed specifically to assess students’ knowledge of the California content standards. The State Board of Education adopted these standards, which specify what all Students Overweight or Obese children in California are expected to know and be able to do in each grade or course. Through the 2012-13 school year, the Algebra I CSTs were required for students Data are from the California Department of Education, Physical Fitness Testing Research Files, and include all public school students in 5th, 7th and 9th grades in San who were enrolled in the grade/course at the time of testing or who had completed a course during the school year, including during the previous summer. In order to Mateo and Santa Clara Counties, San Francisco, and California who were tested through the Fitnessgram assessment. In the 2013-14 school year, the performance protect student confidentiality, no scores were reported in the CST research files for any group of ten or fewer students. The following types of scores are reported by grade standards changed for the Body Mass Index (BMI), one of the three body composition test options. The changes were made to better align with the well–established, level and content area for each school, district, county, and the state: % Advanced, % Proficient, % Basic, % Below Basic, and % Far Below Basic, and are rounded to the health-related body fat standards from the Centers for Disease Control and Prevention (CDC). nearest ones place. Poverty and Food Insecurity During Pregnancy Computer & Internet Access Data are from the California Department of Public Health, Regional Snapshots and Geographic Comparisons from the Maternal and Infant Health Assessment (MIHA) Data for Silicon Valley include Santa Clara and San Mateo Counties, and are from the United States Census Bureau, American Community Survey 1-Year Estimates. For Survey. MIHA is an annual population-based survey of California resident women with a live birth. Data from MIHA 2013-2015 were combined, resulting in a statewide the Share of Households Without Internet Access At Home, by Income Range table, low-income includes households with an annual income of less than $35,000, and sample size of 20,762. Percent (%), 95% confidence interval (95% CI) and estimated number of women in the population with the health indicator/characteristic (i.e., high-income households include those with an annual income of $75,000 or more. numerator of the percent rounded to the nearest hundred) are weighted to represent all women with a live birth who resided in California in 2013-2015. San Francisco Bay Area data include the 9-County region. Food Insecurity was calculated from the modified U.S. Department of Agriculture (USDA) Food Security Module Six Item Short Form and categorized as food secure (0-1) or food insecure (2-6). Responses with one or two missing values were imputed. WIC is the Special Supplemental EARLY EDUCATION & CARE Nutrition Program for Women, Infants, and Children, which provides Federal grants to States for supplemental foods, health care referrals, and nutrition education for low-income pregnant, breastfeeding, and non-breastfeeding postpartum women, and to infants and children up to age five who are found to be at nutritional Preschool Enrollment risk. CalFresh (federally known as the Supplemental Nutrition Assistance Program, SNAP), is a program that helps to improve the health and well-being of qualified Data for preschool enrollment are for San Mateo and Santa Clara Counties, California, and the United States. The data are from the United States Census Bureau, households and individuals by providing them a means to meet their nutritional needs. CalFresh issues monthly electronic benefits that can be used to buy most foods at American Community Survey 1-Year Estimates. Percentages were calculated from the number of children ages three and four that are enrolled in either public or private many markets and food stores. Income Below Federal Poverty Guideline was calculated from monthly family income, before taxes from all sources, including jobs, welfare, school, and the number that are not enrolled in school. disability, unemployment, child support, interest, dividends, and support from family members, and the number of people living on that income. English Language Arts Proficiency Infant Mortality Rate Data are from the California Department of Education, California Assessment of Student Performance and Progress (CAASPP). Beginning with the 2013–14 school Data are from the United States Department of Health and Human Services (US DHHS), Centers of Disease Control and Prevention (CDC), National Center for Health year, CAASPP became the new student assessment system in California, replacing the Standardized Testing and Reporting system (STAR). 2018 CAASPP Test Results Statistics (NCHS), Division of Vital Statistics (DVS) Linked Birth/Infant Death Records 2007-2016, as compiled from data provided by the 57 vital statistics jurisdictions are from tests administered in 2018. The share of third-graders meeting or exceeding the standard includes students who have made progress and met or exceeded the through the Vital Statistics Cooperative Program, on CDC WONDER On-line Database. Silicon Valley data include San Mateo and Santa Clara Counties. Infant mortal- grade standard, and who appear to be ready for future coursework. Silicon Valley data for American Indian or Alaska Native students does not include San Mateo County ity is the death of an infant before his or her first birthday. The infant mortality rate is the number of infant deaths per every 1,000 live births. Data by race and ethnicity because data was not available. indicate the maternal race/ethnicity (not the race/ethnicity of the infant). Black or African American, Asian or Pacific Islander, and White are Non-Hispanic. Child Care Costs Kindergarten Immunization Rates Data are from the California Department of Education via Kidsdata.org and the 2016 Regional Market Rate Survey of California Child Care Providers, prepared by Data for kindergarten immunization rates come from the kindergarten assessment, which measures compliance with the school immunization law, conducted in all schools ICF Macro. Child care centers are facilities that provide care for infants, toddlers, preschoolers, and/or school-age children during all or part of the day. Family Child with kindergartens. Immunizations required by law for children entering kindergarten in California or transitional kindergarten include: Five doses of DTP/DTaP or any Care Homes are child care centers located in the home of a licensed provider, and have no more than 14 children in total. Infants include children under 2 years old. combination with DT (diphtheria and tetanus) vaccine (four doses meets the requirement if at least one was given on or after the fourth birthday); Four doses of polio Preschoolers include children ages 2 to 5. Silicon Valley is calculated as the average of Santa Clara and San Mateo County child care costs. 2018 costs have been estimated vaccine (three doses meets the requirement if at least one was given on or after the fourth birthday); Two doses of MMR vaccine (may be given separately or combined, using 2016 market rate data, inflation-adjusted to 2018 dollars using the Bay Area consumer price index for all urban consumers from the Bureau of Labor Statistics for but both doses must be given on or after the first birthday); Three doses of hepatitis B vaccine; and one dose of varicella (chickenpox) vaccine (or physician documented Silicon Valley data, 2018 estimate based on January-August, and the California consumer price index for all urban consumers from the California Department of Finance varicella disease history or immunity). In the fall, every school in California must provide information on the total enrollment, the number of students who have or have May Revision Forecast (April 2018) for California data. not received the immunizations required, and the number of exemptions to the California Department of Health. Smaller schools are excluded to help protect privacy. In the spring, local and state public health personnel visit a sample of licensed schools with kindergarten classes, to collect the same information for comparison. In the 2014-2015 and 2015-2016 school years, entrants were subject to Assembly Bill (AB) 2109, which added requirements for exemptions to required immunizations based ARTS & CULTURE on personal beliefs. Effective July 1, 2016, California Senate Bill (SB) 277 eliminated the exemption for required immunizations based on personal or religious beliefs. The year shown represents the end of the school year (e.g., 2016 represents the 2015-16 school year). Arts & Culture Data are from the Americans for the Arts Local Index. Arts Donation data represents the share of all households that donate to arts and culture organizations, including public broadcasting. 2011 data was collected in 2009-2011, and 2014 data was collected in 2012-2014 by Scarborough Research. Consumer Expenditure data represents SAFETY a per capita estimate of dollars spent in 2015 by county residents on admissions to entertainment venues – theatres, concert halls, clubs, arenas, outdoor amphitheaters, and stadiums – as well as on products such as recorded media, photographic equipment, musical instruments, and reading materials. These estimates combine the most Violent Crimes recent Consumer Expenditure Survey data with an annual modeling of spending patterns. The number of employees in arts and culture industries is calculated using Data is from the California Department of Justice, Office of the Attorney General, Interactive Crime Statistics. Violent Crimes include homicide, forcible rape, robbery total employment data from the Bureau of Labor Statistics Local Area Unemployment Statistics (LAUS) and data from the Americans for the Arts Local Index (which and aggravated assault. Data for Silicon Valley includes the city-defined Silicon Valley region. Population data is from the California Department of Finance’s E-4 uses County Business Patterns data from the Census Bureau). Arts and culture industries included a set of 44 North American Industrial Classification System (NAICS) Population Estimates. codes selected by Americans for the Arts. In 2009, there were data available from the County Business Patterns on employees for 1,080 of the 3,143 American counties. In 2011 there were data available for 997 counties. And in 2013, there were data available for 923 counties. Data for Nonprofit Arts Organizations are from the National Felony Offenses Center for Charitable Statistics (NCCS) at the Urban Institute. Arts Nonprofits are defined by 43 different categories of several major arts-related groups in the National Data is from the California Department of Justice, Office of the Attorney General, Interactive Crime Statistics. Data for Silicon Valley includes San Mateo and Santa Taxonomy of Exempt Entities (NTEE), and only include organizations that filed the IRS Form 990 in 2009. Arts Establishments include businesses and artists serving Clara Counties. Population data is from United States Census Bureau, American Community Survey 1-Year Estimates. Felony offenses include Violent, Property Offenses, the community, and are defined by 44 North American Industrial Classification System (NAICS) codes representative of arts and culture. Data are from the United States Drug Offenses, Sex Offenses, Weapons, Driving Under the Influence, Hit and Run, Escape, Bookmaking, Manslaughter Vehicular, and Other Felonies. Felony arrest data Census Bureau County Business Patterns series. for 2015 and subsequent years may have been affected by the passage of Proposition 47 in November 2014 which reduced some felony offenses to misdemeanors, and so caution is advised in comparing to previous years. QUALITY OF HEALTH Public Safety Officers All data are from the California Commission on Peace Officer Standards and Training. The total number of Public Safety Officers accounts for all sworn full-time and Healthcare reserve personnel, which may include (but is not limited to) Police Chiefs, Deputy Chiefs, Commanders, Corporals, Lieutenants, Sergeants, Police Officers, Detectives, Data for those with health insurance are from the U.S. Census Bureau, American Community Survey, 1-Year Estimates for the civilian non-institutionalized population. Detention Officers/Supervisors, Sheriffs, Undersheriffs, Captains, and Assistant Sheriffs; it does not include Community Service Officers or other non-sworn (civilian) Silicon Valley data includes Santa Clara and San Mateo Counties. police department personnel. All city, county and school district departments in Silicon Valley are included. Data does not include California Highway Patrol officers. 2018 data were as of July 3, 2018. Adults Overweight or Obese Silicon Valley data include Santa Clara and San Mateo Counties. The California Health Interview Survey (CHIS) is conducted via telephone survey of more than 20,000 Californians across 58 counties each year. The data includes adults 18 years of age and older. Calculated using reported height and weight, a Body Mass Index (BMI) value of 25.0 - 29.99 is categorized as Overweight, and a BMI of 30.0 or greater is categorized as Obese. Starting in 2011, CHIS transitioned from a biennial survey model to a continuous survey model, which enables a more frequent (annual) release of data.

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on housing units with a mortgage. According to the U.S. Department of Housing and Urban Development, housing costs greater than 30% of household income pose HOUSING moderate to severe financial burdens. Median Home Sale Prices; Number of Homes Sold Data are from CoreLogic. Silicon Valley includes San Mateo and Santa Clara Counties. Median sale prices have been inflation-adjusted and are reported in 2018 dollars Percentage of Potential First-Time Homebuyers That Can Afford to Purchase a Median- using the Bay Area consumer price index for all urban consumers from the Bureau of Labor Statistics for Silicon Valley and San Francisco data, 2018 estimate based Priced Home on January-August, the California consumer price index for all urban consumers from the California Department of Finance May Revision Forecast (April 2018) for California data, and the U.S. city average consumer price index for all urban consumers from the Bureau of Labor Statistics, annual estimate based on first half data. Based Data are from the California Association of Realtors’ (CAR) First-time Buyer Housing Affordability Index, which measures the percentage of households that can afford to on public property records, for transactions recorded in each period. Data reflect sales of all new and resale single-family detached houses and condos combined. 2018 purchase an entry-level home in California based on the median price of existing single family homes sold from CAR’s monthly existing home sales survey. Beginning in estimates are based on data through November (Silicon Valley and San Francisco) and through October (California and the United States). the first quarter of 2009, the Housing Affordability Index incorporates an effective interest rate that is based on the one-year, adjustable-rate mortgage from Freddie Mac’s Primary Mortgage Market Survey. Average Monthly For-Sale Inventory Data for Silicon Valley include Santa Clara and San Mateo Counties, and are from Zillow Real Estate Research. The Average Monthly For-Sale Inventory for 2018 includes Average Household Size & Additional Units Needed to Accommodate Population Growth January through November. Average Monthly For-Sale Inventory represents an annual average of the monthly averages of median weekly snapshots of for-sale homes. Data for average household size, number of households, and population living in households are from the California Department of Finance, E-5 Population and Housing Estimates (2011-2018) and E-8 (2004-2010). Data for residential units in building permits issued are from the Construction Industry Research Board and California Residential Building Homebuilding Foundation. Silicon Valley data includes Santa Clara & San Mateo Counties. Additional Units Needed to Accommodate Population Growth are calculated Data is from the Construction Industry Research Board and California Homebuilding Foundation, and includes Santa Clara and San Mateo Counties. Data includes the as the Households Needed to Accommodate Growth minus the Number of Residential Units in Building Permits Issued. Households Needed to Accommodate Growth number of single family and multi-family units included in building permits issued. Single-Family housing units include detached, semi-detached, row house and town are calculated as the change in population (living in households) divided by the average household size from that year. The 2018 estimate of residential units in building house units. Multi-family housing includes duplexes, 3-4 unit structures and apartment type structures with five units or more. permits issued is based on data through November. Regional Housing Need Allocation (RHNA) Multigenerational Households Data includes the number of new housing units for which Bay Area jurisdictions issued permits in calendar years 2015 through 2017. It was compiled by staff from the Data are from the United States Census Bureau, American Community Survey 1-Year Estimates, using the University of Minnesota Population Center IPUMS for Silicon Association of Bay Area Governments (ABAG) / Metropolitan Transportation Commission (MTC) based on permit data provided to ABAG/MTC by local jurisdictions. Valley, San Francisco, and California. Data for the United States are from the Pew Research Center report by Fry & Passel (July 2014) for 2007-2012, the Pew Research Although it compares local permit activity to each jurisdiction’s total housing goals for the 2015-2023 Regional Housing Need Allocation (RHNA) as a point of reference, Center report by Cohn & Passel (August 2016) for 2014, unpublished estimates from the Pew Research Center for 2013 and 2015, and an updated Pew Research Center this data does not represent the official tracking of progress in meeting RHNA goals. That information is compiled by the California Department of Housing and report by Cohn & Passel (April 2018) for 2016 data. Silicon Valley data include Santa Clara and San Mateo Counties. The definition of multigenerational households used Community Development (www.hcd.ca.gov). For more details about housing permit activity in the Bay Area, please visit ABAG/MTC’s Housing Data Explorer at hous- for this analysis goes beyond the Census Bureau’s traditional definition, and includes all households with two or more adult generations, where an adult is defined as age ing.abag.ca.gov. Given that the calendar year 2014 is in-between the 2007-14 and the 2015-2023 RHNA cycles, HCD provides Bay Area jurisdictions with the option 25 and over. The definition is modeled after the methodology developed by the Pew Research Center, published in a report entitled “In Post-Recession Era, Young Adults of counting the units they permitted in 2014 towards either the past (2007-2014) or the current (2015-2023) RHNA cycle. The data is for RHNA reporting periods Drive Continuing Rise in Multi-Generational Living” by Richard Fry and Jeffrey Passel, July 2014. In the definition used, a multigenerational household includes those of 2015 -2017, and do not include units permitted in 2014 that are being applied toward the current RHNA cycle. The Regional Housing Need Allocation (RHNA) is with two adult generations (a parent or parent-in-law and adult child/children, where either generation is the head of household), three generations (parent or parent-in- the state-mandated process to identify the total number of housing units (by affordability level) that each jurisdiction must accommodate in its Housing Element. AMI law, adult child/children, grandchildren), skipped generations (grandparents living with grandchildren where no parent is present), and more than three generations. Due stands for Area Median Income. Silicon Valley data include Santa Clara and San Mateo Counties, and the cities of Fremont, Union City, and Newark. Affordability levels to possible slight differences between the methodology used by the Pew Research Center and the Silicon Valley Institute for Regional Studies, caution should be used in indicated on the chart include Very Low Income (0-50% of the Area Median Income, AMI), Low Income (50-80% AMI), Moderate Income (80-120% AMI), and Above comparing the Silicon Valley, San Francisco, and California estimates to those for the United States as a whole. Moderate Income (120%+ AMI). Young Adults Living With a Parent Affordable Share of Newly Approved Residential Units Data are from the United States Census Bureau, American Community Survey 1-Year Estimates, using the University of Minnesota Population Center IPUMS. Silicon Data are from Joint Venture Silicon Valley’s annual land-use survey of all cities within Silicon Valley. The 38 cities/counties included in the FY 2016-17 Building Valley data includes Santa Clara and San Mateo Counties. Young Adults include residents ages 18 to 34, and only those who live with a parent who is the householder (not Affordable Housing analysis included Atherton, Belmont, Brisbane, Burlingame, Colma, Cupertino, Daly City, East Palo Alto, Foster City, Fremont, Gilroy, Half Moon including parents who live with their young adult children, where the child is the householder). Bay, Hillsborough, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Millbrae, Monte Sereno, Morgan Hill, Mountain View, Newark, Pacifica, Palo Alto, Portola Valley, Redwood City, San Bruno, San Carlos, San Jose, San Mateo, County of San Mateo, Santa Clara, County of Santa Clara, Saratoga, South San Francisco, Sunnyvale, and Multifamily Households Union City. Most recent data are for fiscal year 2017 (July 2016-June 2017). Affordable units are those units that are affordable for a four-person family earning up to 80% Data are from the United States Census Bureau, American Community Survey 1-Year Estimates, using the University of Minnesota Population Center IPUMS for Silicon of the median income for a county. Cities use the U.S. Department of Housing and Urban Development’s (HUD) estimates of median income to calculate the number of Valley, San Francisco, and California. Silicon Valley includes Santa Clara and San Mateo Counties. Multifamily households include all households with at least two units affordable to low-income households in their jurisdiction. unrelated families, including roommates and unmarried couples. Median Rental Rates Homelessness Data for Median Rent List Price is from Zillow Real Estate Research (data downloaded December 28, 2018). Median Apartment Rental Rates include multifamily Data on the homeless population by county is from the United States Department of Housing and Urban Development, estimates for 2018. The population share was complexes with five or more units. Some data for specific rental types were not available for the full year of 2011 or 2012. Rental rates for 2018 are based on data through determined using county-level population estimates from the California Department of Finance, E-4 Estimates. Data on the Individual Causes of Homelessness in Santa October. Rental rates have been rounded to the nearest dollar, and have been inflation-adjusted and are reported in 2018 dollars using the Bay Area consumer price index Clara County is from SPUR via The Urbanist, Issue 560 (September 2017), Homelessness in the Bay Area by Molly Turner. for all urban consumers from the Bureau of Labor Statistics for Silicon Valley and San Francisco data, 2018 estimate based on January-August, the California consumer price index for all urban consumers from the California Department of Finance May Revision Forecast (April 2018) for California data, and the U.S. city average Evictions consumer price index for all urban consumers from the Bureau of Labor Statistics, first half of 2018 for U.S. data. Silicon Valley Median Rent was estimated using a Data is from the Eviction Lab at Princeton University. An eviction happens when a landlord expels people from property he or she owns. Evictions are landlord-initiated weighted average of Santa Clara and San Mateo County rental rates, using population data from the California Department of Finance. Median Apartment Rental Rates involuntary moves that happen to renters, whereas foreclosures are involuntary moves that happen to homeowners when a bank or other lending agency repossesses a Per Square Foot are based on list price. home. In the dataset, an eviction is defined as an eviction judgment issued to a renting home. If a renter at the same address has multiple eviction judgments, we only count the last one. The eviction rate is the number of evictions per 100 renter homes in an area. An eviction rate of 5% means that 5 of every 100 renter homes faced Median Monthly Housing Costs eviction in the selected area that year. Information on the number of renter homes in an area comes from the U.S. Census and ESRI Business Analyst demographic esti- Data are from the United States Census Bureau, American Community Survey 1-Year Estimates. Median Monthly Housing Costs are reported in 2017 dollars. mates. Eviction rates may be undercounted due to the many eviction records that are sealed at the end of a case in California. This policy makes cases no longer accessible to the public. In addition, collection in California can be difficult as a whole, owing to restrictions on the number of records one can collect. Therefore, the eviction data Housing Burden represented here should be used as a low-end estimate (of court-enforced evictions only). Data for owners’ and renters’ housing costs are from the United States Census Bureau, American Community Survey 1-Year Estimates. This indicator measures the share of owners and renters spending 30% or more of their monthly household income on housing costs. Renter data are calculated percentages of gross rent to household income in the past 12 months. Owner data are calculated percentages of selected monthly owner costs to household income in the past 12 months. Owners data are solely based

98 2019 Silicon Valley Index APPENDIX A PLACE continued

TRANSPORTATION Shuttles Vehicle Miles Traveled Data are from the Bay Area Council and Metropolitan Transportation Commission 2016 Bay Area Shuttle Census and includes the number of private shuttles traveling Vehicle Miles Traveled (VMT) estimates the number of vehicle miles that motorists traveled on California roadways. Various roadway types are used to calculate VMT. between Bay Area and adjacent counties each day. Data were collected by the Bay Area Council in 2016 (for the period from 2012 to 2014) via a web portal where shuttle Silicon Valley data include travel within Santa Clara and San Mateo Counties. In 2014, the Department of Transportation migrated the Highway Performance Monitoring sponsors and operators self-submitted their information. Data entry was voluntary and anonymized, so only a partial sampling of the 35 participating sponsors and System to a new Linear Referencing System (GIS layer). The California Department of Finance’s E-4 Population Estimates were used to compute per-capita values. operators was included. Shuttle sponsors included Bay Area companies and academic institutions; shuttle operators included companies that operate shuttle services for numerous individual sponsoring organizations. The Shuttle Census focused on commuter and “last mile” services only and did not include airport or charter transporta- Gas Prices tion services. Daily Shuttles on the Road assumes that shuttles operating between San Francisco and Santa Clara County must travel through San Mateo County; likewise, Data are from the U.S. Energy Information Administration, and have been inflation-adjusted to 2017 dollars using the California consumer price index for all urban shuttles operating between Marin and San Mateo County are assumed to pass through San Francisco. Shuttles operating between Marin and Santa Clara County were not consumers from the California Department of Finance May Revision Forecast (April 2018). assumed to travel through San Francisco or San Mateo County, although it is possible that they do. Cost of Transportation Needs Costs of transportation needs are taken from the Self-Sufficiency Standard for California, from the Center for Women’s Welfare at the University of Washington School of LAND USE Social Work. Silicon Valley is an average of Santa Clara and San Mateo Counties. California data is a California county average. Developed by Dr. Diana Pearce, the Self- Sufficiency Standard defines the amount of income necessary to meet basic needs (including taxes) without public subsidies (e.g., public housing, food stamps, Medicaid Residential Density or child care) and without private/informal assistance (e.g., free babysitting by a relative or friend, food provided by churches or local food banks, or shared housing). The Data are from Joint Venture Silicon Valley’s annual land-use survey of all cities within Silicon Valley. The 35 cities/counties included in the FY 2017-18 Residential Density Standard assumes private transportation (a car) in counties where less than 7% of workers commute within county by public transportation. Only three counties have rates analysis are Atherton, Brisbane, Burlingame, Campbell, Colma, Cupertino, East Palo Alto, Foster City, Fremont, Gilroy, Half Moon Bay, Hillsborough, Los Altos, Los of use among commuters that meet the 7% threshold (Alameda, Mono, and San Francisco); only Alameda and San Francisco are calculated using public transportation Altos Hills, Los Gatos, Menlo Park, Milpitas, Monte Sereno, Morgan Hill, Mountain View, Newark, Pacifica, Palo Alto, Portola Valley, Redwood City, San Bruno, San costs in the Standard. The 2014 California Standard assumed public transit for Contra Costa, Marin, and San Mateo counties, but due to recent shifts in commuting Carlos, San Jose, San Mateo, County of Santa Clara, Santa Clara, County of San Mateo, Saratoga, Sunnyvale, and Union City. Most recent data are for fiscal year 2018 patterns, private transportation has been assumed. Private transportation costs are based on the average costs of owning and operating a car. It is understood that the car(s) (July 2017-June 2018). Residential density was calculated as the average residential density of the participating cities. Beginning in 2014, the residential density analysis will be used for commuting five days per week, plus one trip per week for shopping and errands. In addition, one parent in each household with young children is assumed began to exclude secondary units that were approved with the primary unit. to have a slightly longer weekday trip to allow for “linking” trips to a daycare site. Costs are described as transportation “needs” because they do not represent the average amount of money spent on transportation, but rather the cost of basic transportation needs based on family type and county of residence. 2014 costs have been infla- Housing Near Transit tion-adjusted and are reported in 2018 dollars using the Bay Area consumer price index for all urban consumers from the Bureau of Labor Statistics for Silicon Valley and Data are from Joint Venture Silicon Valley’s annual land-use survey of all cities within Silicon Valley. The 27 cities/counties included in the FY 2017-18 Housing Near San Francisco data, 2018 estimate based on January-August, and the California consumer price index for all urban consumers from the California Department of Finance Transit analysis were Atherton, Belmont, Burlingame, Campbell, Colma, Cupertino, East Palo Alto, Fremont, Gilroy, Los Gatos, Menlo Park, Millbrae, Milpitas, Morgan May Revision Forecast (April 2018) for California data. The California CPI is from the California Department of Finance, and is calculated as the weighted average of San Hill, Mountain View, Palo Alto, Redwood City, San Bruno, San Carlos, San Jose, San Mateo, County of San Mateo, Santa Clara, County of Santa Clara, South San Francisco CMSA, Los Angeles CMSA and (from 1965-1986) San Diego indices. Francisco, Sunnyvale, and Union City. Only cities containing rail stations or major bus corridors were included in the analysis for the share of housing near transit. Most recent data are for fiscal year 2018 (July 2017-June 2018). The number of new housing units within one-third mile of transit are reported directly for each of the cities and Means of Commute; Mean Travel Time to Work counties participating in the survey. Places with one-third of a mile of transit are considered “walkable” (i.e., within a 5- to 10-minute walk for the average person). Transit Data on the means of commute to work are from the United States Census Bureau, American Community Surveys, 1-Year Estimates. Data are for workers 16 years old oriented data prior to 2012 is reported within one-quarter mile of transit. and over residing in Santa Clara and San Mateo Counties commuting to the geographic location at which workers carried out their occupational activities during the reference week whether or not the location was inside or outside the county limits. The data on employment status and journey to work relate to the reference week; that Non-Residential Development is, the calendar week preceding the date on which the respondents completed their questionnaires or were interviewed. This week is not the same for all respondents since Data are from Joint Venture Silicon Valley’s annual land-use survey of all cities within Silicon Valley. Most recent data are for fiscal year 2018 (July 2017-June 2018). The the interviewing was conducted over a 12-month period. The occurrence of holidays during the relative reference week could affect the data on actual hours worked during amount of commercial development within one-third of a mile of transit are reported directly for each of the cities and counties participating in the survey. Places with the reference week, but probably had no effect on overall measurement of employment status. People who used different means of transportation on different days of the one-third of a mile of transit are considered “walkable” (i.e., within a 5- to 10-minute walk for the average person). Transit oriented data prior to 2012 is reported within week were asked to specify the one they used most often, that is, the greatest number of days. People who used more than one means of transportation to get to work each one-quarter mile of transit. The 38 cities/counties included in the FY 2017-18 Non-Residential Development Approvals analysis were Atherton, Brisbane, Burlingame, day were asked to report the one used for the longest distance during the work trip. The categories, “Drove Alone” and “Carpool” include workers using a car (including Campbell, Colma, Cupertino, East Palo Alto, Foster City, Fremont, Gilroy , Half Moon Bay, Hillsborough, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Millbrae, company cars but excluding taxicabs), a truck of one-ton capacity or less, or a van. The category “Public Transportation,” includes workers who used a bus or trolley bus, Milpitas, Monte Sereno, Morgan Hill, Mountain View, Newark, Pacifica, Palo Alto, Portola Valley, Redwood City, San Bruno, San Carlos, San Jose, San Mateo, County of streetcar or trolley car, subway or elevated, railroad, or ferryboat, even if each mode is not shown separately in the tabulation. The category “Other Means” includes taxicab, San Mateo, Santa Clara, County of Santa Clara, Saratoga, South San Francisco, Sunnyvale, Union City, and Woodside. motorcycle, and other means that are not identified separately within the data distribution. Percentages may not add up to 100% due to rounding. Planned Hotel Development Megacommuters Data is from the Atlas Hospitality Group annual California Hotel Development Surveys. Planned hotels are in various stages, and have not necessarily received planning Data are from the United States Census Bureau, American Community Survey Summary Files. Silicon Valley data include San Mateo and Santa Clara Counties. The Bay approvals. Area includes Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, and Sonoma Counties. Commute Patterns ENVIRONMENT Data for Commute Patterns are from the United States Census Bureau, American Community Survey, 1-Year Public Use Microdata Samples (PUMS) using the Place of Work PUMA for San Francisco, San Mateo, Santa Clara and Alameda Counties. Workers include civilian residents over age 16 who were employed and at work. Cross- Water Resources county commuters include those who do not work within their county of residence. Data for Santa Clara County was provided by Santa Clara Valley Water District (SCVWD). Scotts Valley Water District (SVWD) provided Scotts Valley data. Bay Area Water Supply & Conservation Agency (BAWSCA) provided data for member agencies servicing San Mateo County and for Alameda County Water District, which ser- Bicycle Commuters vices the Cities of Fremont, Union City and Newark. These agencies include Brisbane/GVMID, Estero, Burlingame, Hillsborough, CWS - Bear Gulch, Menlo Park, CWS Data are from the United States Census Bureau, American Community Survey 1-Year Estimates, and include workers 16 years old and over residing in Santa Clara and - Mid Peninsula, Mid-Peninsula, CWS - South SF, Millbrae, Coastside, North Coast, Redwood City, Daly City, San Bruno, East Palo Alto, and Westborough. Cordilleras San Mateo Counties commuting to the geographic location at which workers carried out their occupational activities during the reference week whether or not the loca- serves residents in San Mateo County, but is not a BAWSCA member and therefore was not included in this analysis. Data for FY 2017-18 is preliminary. Population tion was inside or outside the county limits. The data on employment status and journey to work relate to the reference week; that is, the calendar week preceding the date figures used to calculate per capita values include the population served by each water agency, and are provided by the agencies directly. Total water consumption figures on which the respondents completed their questionnaires or were interviewed. This week is not the same for all respondents since the interviewing was conducted over a used to calculate per capita values and recycled percentage of total water used do not include consumption for agriculture or by private well-owners in the SCVWD data. 12-month period. The occurrence of holidays during the relative reference week could affect the data on actual hours worked during the reference week, but probably had In the BAWSCA data, the small number of agricultural users in the service area are treated as a class of commercial user and so are included in the consumption figures. no effect on overall measurement of employment status. Bicyclists include people who biked to work as their most common means of commute (the greatest number of Scotts Valley Water District does not serve agricultural customers, so total water consumption figures used to compute both the per capita consumption and the recycled days per week) and/or for the longest distance during the work trip (if they used more than one means of transportation to get to work each day). The number of commute percentage of total water used are the same. trips is estimated as the number of commuters multiplied by two (assuming each commuter has one two-way commute). Air Quality Bicycle Collisions Data are from the United States Environmental Protection Agency, Outdoor Air Quality Data, and include Santa Clara and San Mateo Counties. Unhealthy days are Data are from the Statewide Integrated Traffic Records System (SWITRS) via the Transportation Injury Mapping System (TIMS), and only include those collisions in based on Air Quality Index (AQI) of >100 for sensitive groups, and >150 for the general population. The AQI includes Air Quality Index (AQI) for all AQI pollutants which an injury or fatality occurred. including carbon monoxide, ozone, particulate matter, nitrogen dioxide, sulfur dioxide, and lead. The PM2.5 monitoring network was phased in between 1999 and 2001 in most areas, so earlier years do not include PM2.5 (a type of particulate matter). Bicycle Facilities Data are compiled from MTC, VTA, and Google Streets, and include Santa Clara and San Mateo Counties. Bicycle facility classes have been defined by Caltrans and Electricity Use include Class 1 (Shared Use Path), Class II (Bikeway), Class III (Bike Route/Boulevard), and Class IV (Protected Bikeway). The Santa Clara County dataset was updated Electricity Consumption data is from the California Energy Commission. Gross Domestic Product (GDP) data is from Moody’s Economy.com. GDP values have been by VTA in 2017. The new dataset for Class 1 (Shared Use Path) includes pathway networks in parks, as well as parallel measurements for pathways that run along both inflation-adjusted and are reported in 2017 dollars, using the Bay Area consumer price index for all urban consumers from the Bureau of Labor Statistics for the Silicon sides of waterways. The metric does not include unpaved paths in mountainous state park areas that are mostly used for mountain bike recreation. The new dataset for Valley and San Francisco data, and the California consumer price index for all urban consumers from the California Department of Finance May Revision Forecast (April Class 2 (Bikeway) includes parallel lane measurements for bike lanes that occur on roadways with medians that restrict passage from one side of the road to the other, as 2018) for California data. Silicon Valley data includes Santa Clara and San Mateo Counties. Per capita values were computed from the California Department of Finance’s well as roadway that have shoulders that are treated as bike lanes but may not have stenciling. The 2017 data for Class 3 (Bike Route/Boulevard) includes additional bike E-4 Population Estimates. routes that were not included in the 2016 data. Solar Installations Jurisdictions with a Bicycle or Pedestrian Master Plan Data are from Palo Alto Municipal Utilities, Silicon Valley Power, and Pacific Gas & Electric, and include the entire city-defined Silicon Valley region. Years listed Data includes cities within the city-defined Silicon Valley region, and the Counties of Santa Clara and San Mateo. Data include all bicycle and pedestrian master plans that correspond to when the systems were interconnected. The category Non-Residential includes Commercial, Non-Profit, Government, Industrial, Utility, Military, and were less than seven years old (created in 2011 or thereafter) and were approved, planned or in-progress as of December 2018. 2016 data include plans less than five years Educational. Cumulative installed solar capacity does not include installations prior to 1999. All systems included in the analysis are Net Energy Metered and Non-Export old (created in 2011 or thereafter) and were as of December 2016. PV. PG&E data is from the California Solar Statistics, which publishes all IOU solar PV net energy metering (NEM) interconnection data per CPUC Decision (D.)14- 11-001. 2018 data are through December 13 for Palo Alto Utilities, through December 21 for Silicon Valley Power, and through September for PG&E. Daily Vehicle Hours of Delay Due To Congestion Data are from Caltrans PeMS (Performance Monitoring System) that collect and archives traffic data from the Caltrans network of roadway traffic sensors. The reported Electric Vehicle Infrastructure traffic delay data are based on the detector coverage and health at the time that the data was collected by PeMS. Accordingly, actual traffic delays experienced in each Data are from the U.S. Department of Energy, and include public electric vehicle fueling stations and outlets in Santa Clara and San Mateo Counties, and California. county may be higher than those reported. Data includes California State Freeways only (not all state highways). Silicon Valley data include Santa Clara & San Mateo 2018 data are as of November 13; 2017 data are as of December 18, 2017; 2016 data are as of December 6, 2016; 2015 data are as of November 2, 2015; 2014 data are Counties. One vehicle hour of delay reflects one vehicle stuck in traffic for one hour. Delay refers to speeds less than 60 miles per hour. as of November 14, 2014. Transit Use Electric Vehicle Adoption Estimates are the sum of annual ridership on the light rail and bus systems in Santa Clara and San Mateo Counties (from SamTrans and Santa Clara Valley Transportation Data is from the California Air Resources Board Clean Vehicle Rebate Project, Rebate Statistics. Data last updated November 19, 2018. Retrieved January 2, 2019 from Authority), and rides on Caltrain and Altamont Corridor Express (ACE). Data does not include paratransit, such as SamTrans’ Redi-Wheels program. The California https://cleanvehiclerebate.org/rebate-statistics. Silicon Valley data includes Santa Clara & San Mateo Counties. Electric vehicles include Plug-In Hybrid Electric Vehicles Department of Finance E-4 Population Estimates were used to compute per-capita values. Per capita ridership on ACE includes Santa Clara County only, and is calculated (PHEV), All-Battery Electric Vehicles (BEV), Fuel-Cell Electric Vehicles (FCEV), and other non-highway, motorcycle & commercial BEVs. The 2018 data is through using the Santa Clara County population estimates. August 31. The 2010 data begins on 3/18/10. Not all electric vehicles sold/leased in the state are captured in the database, since not every eligible vehicle owner applies to the CVRP, not every clean vehicle is eligible for the rebate, some vehicles were purchased before the rebate was available, and the rebate does not include PHEV retrofits Caltrain Ridership (only new vehicles). Other includes Mercedes-Benz, Kia, Smart, Audi, Chrysler, Hyundai, Zero, Mitsubishi, Volvo, Miles, Cadillac, Th!nk, CODA, Brammo, GEM, Data are from the Caltrain 2018 Annual Passenger Counts report, and include average weekday daily ridership (through FY 2016) and average mid-weekday daily Victory, and Vectrix. ridership (FY 2017 and FY 2018). Years indicate the end of the fiscal year (e.g., 2018 includes data for FY 2017-18).

GOVERNANCE

CITY FINANCES CIVIC ENGAGEMENT City Finances Eligible Voter Turnout and Absentee Voting Data were obtained from 39 Silicon Valley cities’ audited annual financial reports, including Comprehensive Annual Financial Reports, Annual Financial Statements for Data are from the California Secretary of State, Elections Division. The eligible population is determined by the Secretary of State using Census population data provided the Year End, Annual Financial Reports, Basic Financial Statements Reports, and Annual Basic Financial Statements Reports, as well as the State of California annual by the California Department of Finance. Silicon Valley data are for Santa Clara and San Mateo counties. year-end financial report from the California State Auditor. Data for City Finances include both Government and Business-Type Activities (where applicable). Whenever possible, data were obtained from the following year report (e.g., the 2010 report for 2009 figures) because following year reports sometimes reflects revisions/corrections. Eligible Voter Turnout, by Age 2017 data was obtained from the Fiscal Year 2016-2017 reports. All amounts have been inflation-adjusted and are reported in 2017 dollars using the Bay Area consumer Eligible Voter Turnout by Age data are from the California Civic Engagement Project (CCEP) at the USC Price School of Public Policy, using data from the Statewide price index for all urban consumers from the Bureau of Labor Statistics for Silicon Valley data, and the California consumer price index for all urban consumers from Database (the Redistricting Database for the State of California) and California Department of Finance (for voting age population estimates). Silicon Valley includes Santa the California Department of Finance May Revision Forecast (April 2018) for California data. Values are significant to the nearest $1 million due to rounding in the city Clara and San Mateo Counties. Eligible voter turnout is defined as the percentage of adult citizens who voted. and state reports. Revenues Minus Expenses is reported before Transfers or Extraordinary Items. Other Revenues includes any revenue other than Property Tax, Sales Tax, Investment Earnings, or Charges for Services. Other Revenues includes the following (as categorized by the various cities in Silicon Valley): Incremental Property Taxes; Partisan Affiliation Public Safety Sales Tax; Business tax; Municipal Water System Revenue; Waste Water Treatment Revenue; Storm Drain Revenue; Transient occupancy tax Business, Hotel Data are from the California Secretary of State, Elections Division. Silicon Valley data are for Santa Clara and San Mateo counties. Other includes Green, Libertarian, & Other Taxes; Property transfer tax; Property Taxes In-Lieu; Vehicle license in-lieu fees or Motor Vehicle In-Lieu; Licenses & Permits; Utility Users Tax; Development Natural Law, Peace & Freedom/Reform, and Other. impact fees; Franchise fees; Franchise Taxes Franchise & Business Taxes; Rents & Royalties; Net Increase (decrease) in Fair Value of Investments; Equity in Income (losses) of Joint Ventures; Miscellaneous or Other Revenues; Cardroom Taxes; Fines and Forfeitures; Other Taxes; Agency Revenues; Interest Accrued from Advances to Business-Type Activities; Use of Money and Property; Property Transfer Taxes; Documentary Transfer Tax; Unrestricted/Intergovernmental Contributions in Lieu of Taxes; REPRESENTATION Gain (loss) of disposal of assets. Representation Data are from the GrassrootsLab GrassFire Directory (www.grassrootslab.com), a unique and comprehensive database that closely tracks, updates and categorizes local jurisdictions, elected officials and key staff members in California cities, counties and school districts. Silicon Valley includes the city-defined region. Local elected officials include any person elected through a city-wide or county-wide election to represent at either the Municipal, Mayoral or Supervisorial level. Race/ethnicity of elected officials are based on publicly available documentation that those officials self-identify with a particular racial/ethnic group. Other party affiliation includes American Independent, Green, Libertarian, Natural Law, Peace & Freedom/Reform, and Other.

2019 Silicon Valley Index 99 APPENDIX B - Silicon Valley

PERCENT OF TOTAL EMPLOYMENT SILICON VALLEY PERCENT CHANGE Q2 2018 EMPLOYMENT

2007-2018 2010-2018 2017-2018 TOTAL EMPLOYMENT 1,674,255 100.0% 21.3% 29.3% 2.2% COMMUNITY INFRASTRUCTURE & SERVICES 832,450 49.7% 18.7% 26.7% 2.8% HEALTHCARE & SOCIAL SERVICES1 169,908 10.1% 48.2% 36.4% 4.9% RETAIL 135,825 8.1% 2.3% 10.6% 0.3% ACCOMMODATION & FOOD SERVICES 137,606 8.2% 34.2% 38.2% 3.2% EDUCATION1 129,135 7.7% 37.8% 34.7% 4.3% CONSTRUCTION 80,139 4.8% 11.5% 63.0% 3.3% LOCAL GOVT. ADMINISTRATION2 46,616 2.8% -20.0% 6.0% 1.0% TRANSPORTATION 39,966 2.4% 12.2% 24.1% 2.2% BANKING & FINANCIAL SERVICES 19,867 1.2% -3.9% 18.7% 1.1% ARTS, ENTERTAINMENT & RECREATION 19,453 1.2% 7.4% 8.4% 3.2% PERSONAL SERVICES 17,137 1.0% 42.0% 38.0% 3.9% FEDERAL GOVT. ADMINISTRATION 10,798 0.6% -14.8% -34.0% -3.7% NONPROFITS 9,927 0.6% -14.3% -0.9% 0.3% INSURANCE SERVICES 8,631 0.5% -7.3% 12.3% 0.1% STATE GOVT. ADMINISTRATION2 2,774 0.2% -17.5% 5.3% 1.2% WAREHOUSING & STORAGE 2,649 0.2% 22.2% 14.6% 0.2% UTILITIES1 2,018 0.1% -3.1% -25.9% 2.7% INNOVATION AND INFORMATION PRODUCTS & SERVICES 436,827 26.1% 38.8% 40.1% 3.5% COMPUTER HARDWARE DESIGN & MANUFACTURING 176,722 10.6% 62.5% 60.7% 4.0% SEMICONDUCTORS & RELATED EQUIPMENT MANUFACTURING 43,804 2.6% -22.7% -8.1% -3.1% INTERNET & INFORMATION SERVICES 71,376 4.3% 248.6% 188.4% 10.2% TECHNICAL RESEARCH & DEVELOPMENT (INCLUDES LIFE SCIENCES) 38,928 2.3% 46.5% 17.8% 3.7% SOFTWARE 31,775 1.9% 55.0% 44.8% 3.9% TELECOMMUNICATIONS MANUFACTURING & SERVICES 15,817 0.9% -26.1% -18.0% -2.7% INSTRUMENT MANUFACTURING (NAVIGATION, MEASURING & ELECTROMEDICAL) 16,954 1.0% -27.6% -9.4% -2.7% PHARMACEUTICALS (LIFE SCIENCES) 14,342 0.9% 9.7% 12.8% 3.2% OTHER MEDIA & BROADCASTING, INCLUDING PUBLISHING 7,941 0.5% -3.7% -8.9% -0.6% MEDICAL DEVICES (LIFE SCIENCES) 7,055 0.4% -0.3% 11.7% 0.1% BIOTECHNOLOGY (LIFE SCIENCES) 10,743 0.6% 75.1% 78.0% 9.7% I.T. REPAIR SERVICES 1,369 0.1% -42.3% -49.0% -2.9% BUSINESS INFRASTRUCTURE & SERVICES 269,336 16.1% 11.6% 23.0% 1.8% WHOLESALE TRADE 60,880 3.6% -3.0% 6.3% -0.2% PERSONNEL & ACCOUNTING SERVICES 34,412 2.1% -10.0% 0.8% 2.5% ADMINISTRATIVE SERVICES 32,137 1.9% 23.7% 60.6% 2.9% FACILITIES 28,303 1.7% 15.3% 19.9% 2.2% TECHNICAL & MANAGEMENT CONSULTING SERVICES 23,583 1.4% 23.4% 18.1% 2.8% MANAGEMENT OFFICES 27,182 1.6% 67.2% 72.8% 1.7% DESIGN, ARCHITECTURE & ENGINEERING SERVICES 20,839 1.2% 12.2% 25.6% 3.3% GOODS MOVEMENT 13,460 0.8% 12.7% 35.3% 1.0% LEGAL 11,206 0.7% 0.4% 14.7% 1.1% INVESTMENT & EMPLOYER INSURANCE SERVICES 14,004 0.8% 51.8% 48.9% 4.0% MARKETING, ADVERTISING & PUBLIC RELATIONS 3,329 0.2% -7.1% 32.8% 0.7% OTHER MANUFACTURING 59,115 3.5% -14.6% 1.7% 1.7% PRIMARY & FABRICATED METAL MANUFACTURING 14,909 0.9% -7.7% 3.0% 0.3% MACHINERY & RELATED EQUIPMENT MANUFACTURING 13,161 0.8% -5.0% 20.1% 0.5% OTHER MANUFACTURING 10,528 0.6% 8.5% 19.7% 1.0% TRANSPORTATION MANUFACTURING INCLUDING AEROSPACE & DEFENSE 8,615 0.5% -0.6% -25.4% 5.9% FOOD & BEVERAGE MANUFACTURING 8,198 0.5% -48.5% -3.5% 2.5% TEXTILES, APPAREL, WOOD & FURNITURE MANUFACTURING 3,340 0.2% -12.8% 14.9% 2.3% PETROLEUM AND CHEMICAL MANUFACTURING (NOT IN LIFE SCIENCES) 364 0.0% -66.2% -61.8% -0.8% OTHER 76,527 4.6% 42.1% 57.9% -9.7%

1. Includes government jobs (state and local). 2. Excludes government jobs in Healthcare & Social Services, Education, and Utilities. Note: Table includes annual industry employment data for Silicon Valley from the United States Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW) for 2007, 2010, 2017 and 2018, modified slightly by EMSI, which removes suppressions and reorganizes public sector employment. Data for Q2 of 2018 was estimated at the industry level by BW Research using Q1 2018 QCEW data and updated based on Q2 2018 reported growth and totals, and modified slightly by EMSI. Due to rounding, individual industry employment may not sum to industry group or overall job total. Data Sources: U.S. Bureau of Labor Statistics Quarterly Census of Employment and Wages; EMSI Analysis: BW Research

100 2019 Silicon Valley Index APPENDIX B - San Francisco

PERCENT OF TOTAL EMPLOYMENT SAN FRANCISCO PERCENT CHANGE Q2 2018 EMPLOYMENT

2007-2018 2010-2018 2017-2018 TOTAL EMPLOYMENT 741,590 100.0% 33.2% 35.8% 3.4% COMMUNITY INFRASTRUCTURE & SERVICES 420,862 56.8% 25.9% 30.3% 2.6% HEALTHCARE & SOCIAL SERVICES1 87,707 11.8% 84.6% 80.9% 5.0% RETAIL 45,245 6.1% 6.4% 17.9% 0.9% ACCOMMODATION & FOOD SERVICES 84,602 11.4% 28.1% 28.6% 2.8% EDUCATION1 49,041 6.6% 13.2% 8.9% 1.1% CONSTRUCTION 20,493 2.8% 13.3% 52.4% 4.1% LOCAL GOVT. ADMINISTRATION2 27,858 3.8% 14.0% 14.7% 3.9% TRANSPORTATION 16,769 2.3% 79.8% 108.9% 3.5% BANKING & FINANCIAL SERVICES 17,597 2.4% -0.6% 16.8% 3.3% ARTS, ENTERTAINMENT & RECREATION 16,480 2.2% 25.2% 20.6% 2.5% PERSONAL SERVICES 9,924 1.3% 50.7% 50.7% 4.8% FEDERAL GOVT. ADMINISTRATION 9,666 1.3% -10.5% -10.3% -7.0% NONPROFITS 13,614 1.8% 32.9% 26.7% 2.4% INSURANCE SERVICES 9,122 1.2% -31.5% -9.5% -1.6% STATE GOVT. ADMINISTRATION2 7,818 1.1% 13.9% -2.1% 1.9% WAREHOUSING & STORAGE 195 0.0% -66.1% -35.4% -9.6% UTILITIES1 4,732 0.6% 22.8% 6.8% -1.3% INNOVATION AND INFORMATION PRODUCTS & SERVICES 102,082 13.8% 177.7% 160.5% 8.5% COMPUTER HARDWARE DESIGN & MANUFACTURING 52,651 7.1% 285.9% 216.4% 9.4% SEMICONDUCTORS & RELATED EQUIPMENT MANUFACTURING 56 0.0% 8.0% -28.5% 4.2% INTERNET & INFORMATION SERVICES 26,941 3.6% 1020.6% 584.5% 12.6% TECHNICAL RESEARCH & DEVELOPMENT (INCLUDES LIFE SCIENCES) 2,447 0.3% 119.3% 126.0% 7.4% SOFTWARE 4,303 0.6% 131.9% 93.7% 8.6% TELECOMMUNICATIONS MANUFACTURING & SERVICES 3,437 0.5% -25.7% -12.1% -4.7% INSTRUMENT MANUFACTURING (NAVIGATION, MEASURING & ELECTROMEDICAL) 1,745 0.2% 1919.4% 2768.1% 14.3% PHARMACEUTICALS (LIFE SCIENCES) 477 0.1% 1195.7% 109.9% 10.0% OTHER MEDIA & BROADCASTING, INCLUDING PUBLISHING 7,926 1.1% -26.9% -13.0% -1.8% MEDICAL DEVICES (LIFE SCIENCES) 149 0.0% -26.1% 34.5% -1.3% BIOTECHNOLOGY (LIFE SCIENCES) 1,802 0.2% 0.0% 5.0% 0.3% I.T. REPAIR SERVICES 148 0.0% 59.6% 55.4% 6.7% BUSINESS INFRASTRUCTURE & SERVICES 172,051 23.2% 27.2% 36.7% 3.1% WHOLESALE TRADE 15,439 2.1% 38.9% 62.4% 4.1% PERSONNEL & ACCOUNTING SERVICES 19,608 2.6% 18.6% 24.3% 2.9% ADMINISTRATIVE SERVICES 15,413 2.1% 17.0% 26.2% 2.2% FACILITIES 16,051 2.2% 88.0% 41.5% 5.1% TECHNICAL & MANAGEMENT CONSULTING SERVICES 21,788 2.9% 75.4% 79.5% 5.6% MANAGEMENT OFFICES 23,085 3.1% 47.3% 57.6% 3.3% DESIGN, ARCHITECTURE & ENGINEERING SERVICES 14,629 2.0% 1.3% 40.8% 2.3% GOODS MOVEMENT 6,331 0.9% 35.2% 65.0% 3.8% LEGAL 14,182 1.9% -2.6% 4.7% 0.8% INVESTMENT & EMPLOYER INSURANCE SERVICES 16,053 2.2% -9.8% 1.7% 0.3% MARKETING, ADVERTISING & PUBLIC RELATIONS 9,471 1.3% 50.7% 41.7% 2.9% OTHER MANUFACTURING 6,827 0.9% -21.5% 9.3% 0.3% PRIMARY & FABRICATED METAL MANUFACTURING 524 0.1% -3.8% -11.0% 2.2% MACHINERY & RELATED EQUIPMENT MANUFACTURING 224 0.0% 433.8% 308.2% 9.1% OTHER MANUFACTURING 921 0.1% 7.1% 30.1% 3.9% TRANSPORTATION MANUFACTURING INCLUDING AEROSPACE & DEFENSE 330 0.0% -57.0% -43.4% -1.5% FOOD & BEVERAGE MANUFACTURING 3,062 0.4% 58.5% 67.3% 4.0% TEXTILES, APPAREL, WOOD & FURNITURE MANUFACTURING 1,750 0.2% -60.3% -27.1% -8.2% PETROLEUM AND CHEMICAL MANUFACTURING (NOT IN LIFE SCIENCES) 16 0.0% -88.1% -79.2% -10.0% OTHER 39,769 5.4% -4.6% -23.1% 0.6%

2019 Silicon Valley Index 101 ACKNOWLEDGMENTS

This report was prepared by Rachel Massaro, Vice President and Sr. Research Associate at Joint Venture Silicon Valley and the Silicon Valley Institute for Regional Studies. She received invaluable assistance from Stephen Levy of the Center for Continuing Study of the California Economy, who provided ongoing support and served as a senior advisor.

Jill Jennings created the report’s layout and design; Duffy Jennings served as copy editor.

We gratefully acknowledge the following individuals and organizations that contributed data and expertise:

Altamont Corridor Express (ACE) GrassrootsLab

Association of Bay Area Governments (ABAG) JLL

Atlas Hospitality Group Jon Haveman, Marin Economic Consulting

Bay Area Council Kelly Costa and Open Impact

Bay Area Water Supply & Conservation Agency Kidsdata.org

BW Research Kyle Neering

California Civic Engagement Project at the USC LinkedIn Economic Graph Price School of Public Policy Nelson\Nygaard Consulting Associates California Department of Transportation (Caltrans) Palo Alto Municipal Utilities Caltrain Pew Research Center CBRE Research Phoenix Global Wealth Monitor City/County Association of Governments (C/CAG) of San Mateo County Renaissance Capital

Center for Women’s Welfare, University of SamTrans Washington Santa Clara Valley Transportation Authority Chris Benner and the University of California, Santa Santa Clara Valley Water District Cruz Scotts Valley Water District Cities of Silicon Valley SPUR Colliers International Silicon Valley Silicon Valley Clean Cities CoreLogic Silicon Valley Power Drew Starbird and the Santa Clara University, Leavey School of Business United States Patent and Trademark Office

Elaine Kurtz

102 2019 Silicon Valley Index Generous funding for this report was provided by Silicon Valley Community Foundation and the City/County Association of Governments of San Mateo County (C/CAG).

2019 Silicon Valley Index 103 Joint Venture Silicon Valley Established in 1993, Joint Venture Silicon Valley provides analysis and action on issues affecting our region’s economy and quality of life. The organization brings together established and emerging leaders – from business, government, academia, labor and the broader community – to spotlight issues, launch projects, and work toward innovative solutions.

Silicon Valley Institute for Regional Studies Housed within Joint Venture, the Silicon Valley Institute for Regional Studies provides research and analysis on Silicon Valley’s economy and society.

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