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Russell Hancock – President & CEO
Joint Venture Silicon Valley
Chris DiGiorgio – Co-Chair,
Accenture, Inc.
John M. Sobrato, Chair
Chief Executive Officer,
The Sobrato Organization
Hon. Chuck Reed – Co-Chair,
City of San José
OFFICERS
J O I N T V E N T U R E B OA R D O F D I R E C TO R S
DIRECTORS
Larry Alder
Google
Hon. Elaine Alquist
California State Senate
Mark Bauhaus
Juniper Networks
George Blumenthal
University of California at Santa Cruz
Steven Bochner
Wilson Sonsini Goodrich & Rosati
Ed Cannizzaro
KPMG LLP
Emmett D. Carson
Silicon Valley Community Foundation
John Ciacchella
Deloitte & Touche LLP
Mary Dent
SVB Financial Group
Ben Foster
Optony
Kevin Gillis
Bank of America
Judith Maxwell Greig
Notre Dame De Namur University
Paul Gustafson
TDA Group
Eric Houser
Wells Fargo
Jim Kelly
Menlo College
W. Keith Kennedy Jr.
Con-way
Dave Knapp
City of Cupertino
Tom Klein
Greenberg Traurig LLP
Hon. Liz Kniss
County of Santa Clara Board of Supervisors
Chris Martin
UPS
Stacy McAfee
University of Phoenix
Tom McCalmont
McCalmont Engineering
Jim McCaughey
Lucile Packard Children’s Hospital
Jean McCown
Stanford University
Curtis Mo
Wilmer Cutler Pickering
Hale and Dorr LLP
Mairtini Ni Dhomhnaill
Accretive Solutions
Joseph Parisi
Therma Inc.
Lisa Portnoy
Ernst & Young llp
Mo Qayoumi
San Jose State University
Bobby Ram
SunPower
Paul Roche
McKinsey & Company
Bill Sherry
Norman Y. Mineta San Jose International
Airport/Team San Jose
Harry Sim
Cypress Envirosystems
Susan Smarr
Kaiser Permanente
John Sobrato, Sr.
Sobrato Development Companies
Neil Struthers
Santa Clara County Building
& Construction Trades Council
Linda Thor
Foothill De-Anza Community
College District
Mark Walker
Applied Materials
Chuck Weis
Santa Clara County Office of Education
Linda Williams
Planned Parenthood Mar Monte
Daniel Yost
Orrick, Herrington & Sutcliffe, LLP
S I L I C O N VA L L E Y C O M M U N I T Y F O U N DAT I O N B OA R D O F D I R E C TO R S
CHAIR
INDEX ADVISORS
Chris Augenstein
Santa Clara Valley Transportation Authority
Bob Brownstein
Working Partnerships USA
Leslie Crowell
Santa Clara County
Jeff Fredericks
Colliers International
Tom Friel
Silicon Valley Community Foundation
Corinne Goodrich
SAMTRANS
Tom Klein
Greenberg Traurig, LLP
James Koch
Center for Science, Technology & Society
at Santa Clara University
Stephen Levy
Center for Continuing Study
of the California Economy
Rocio Luna
Santa Clara County Public Health Dept
Connie Martinez
1st ACT Silicon Valley
Sanjay Narayan
Sierra Club
Dan Peddycord
Santa Clara County Public Health Dept
Jeff Ruster
City of San Jose
AnnaLee Saxenian
University of California Berkeley
Mike Curran
County of San Mateo,
Workforce Investment Board
Susan Smarr
Kaiser Permanente
Santa Clara Medical Center
Kris Stadelman
NOVA
Anandi Sujeer
Santa Clara County Public Health Dept
Lynne Trulio
San Jose State University
Kim Walesh
City of San Jose
E. Chris Wilder
Valley Medical Center Foundation
Linda Williams
Planned Parenthood Mar Monte
Erica Wood
Silicon Valley Community Foundation
Amy Kishimura
Kim Held
Jessie Oettinger
Clare Brown
Marisol Catchings
Liz Brown
SENIOR ADVISORY COUNCIL
John C. Adams
Wells Fargo
Frank Benest
City of Palo Alto (Ret.)
Eric Benhamou
Benhamou Global Ventures
Harry Kellogg Jr.
SVB Financial Group
William F. Miller
Stanford University
DIRECTORS
Jayne Battey
Director of Land and
Environmental Management
for Pacific Gas and Electric Company
Steve Bennett
Chairman of the Board, Symantec
Gloria Rhodes Brown
Outreach Director,
Mills-Peninsula Health Services
Emmett D. Carson, .
Chief Executive Officer,
Silicon Valley Community Foundation
Caretha Coleman
Community Leader
Gregory M. Gallo
Partner, DLA Piper US LLP
Nancy H. Handel
Retired Senior Vice President,
Chief Financial Officer,
Applied Materials, Inc.
John F. Hopkins
Of Counsel, Hopkins & Carley
Susan M. Hyatt
Community Leader
Samuel Johnson, Jr.
Director of Administrative Services,
Notre Dame de Namur University
Robert A. Keller
Managing Director, JPMorgan
Anne F. Macdonald
Partner, Frank, Rimerman & Co., LLP
Catherine A. Molnar
Executive Director,
CHS Management LLC
Ivonne Montes de Oca
The Pinnacle Company
. Park
Former Chairman and CEO, Maxtor Corp.
Eduardo Rallo
Managing Partner,
Pacific Community Ventures LLC
Sanjay Vaswani
Managing Partner,
Center for Corporate Innovations, Inc.
Erika Williams
Managing Director,
The Erika Williams Group
Gordon Yamate
Former Vice President and General Counsel,
Knight Ridder
VICE CHAIR
Thomas J. Friel, Vice Chair
Retired Chairman,
Heidrick & Struggles International, Inc.
Prepared By:
COLLABORATIVE ECONOMICS
Doug Henton
John Melville
Tracey Grose
Tiffany Furrell
Gabrielle Halter
Aris Harutyunyan
Dear Friends:
Silicon Valley is making an impressive recovery—impressive because our region was the last to succumb when
an historic recession gripped our nation, and now it appears to be the first to emerge. The growth is led by a
few key sectors which fueled the overall creation of more than 42,000 jobs over the past year, and this report
chronicles those developments in careful detail. It also shows how our innovation engine—measured by venture
capital, patent registrations, new firm formation, and even IPOs—is clearly revving up again.
Though encouraging, we don’t see the report as cause for celebration. The gains are sector specific and not
widespread; small businesses are clearly not out of the rough; the public sector is still in the throes of a fiscal
crisis; and median household income continues to fall as the gap between those succeeding and those struggling
grows wider and wider. It’s as if we’re becoming two valleys.
When we’re at the top of our game the region will be creating jobs across the board, our workforce will be able
to move up the mobility ladder, and there will be robust growth in the mid-range professions. This requires both
a strong economy and a strong community, with thriving public institutions and a first-class infrastructure.
Unfortunately, even a stunning economic recovery won’t address our fiscal woes. That is because our tax system,
geared to a 19th century economy, doesn’t track with the 21st century economy that is being invented (and re-
invented) in Silicon Valley. We highlighted the fiscal crisis facing our local governments in last year’s Index.
This year’s Special Analysis builds on that report and analyzes a key component of our revenue model, property
taxes and the long-term impact of Proposition 13. The findings are sobering: we can’t count on property taxes
to drive a public sector comeback any time soon.
Our hope is that 2012 is the year when a real conversation about reform takes hold, and that Silicon Valley’s
is an outspoken voice in that conversation. The creativity that we rightly celebrate in our private sector needs
to take hold in our public sector as well. When it does, we will truly have cause to celebrate.
We’re pleased that the Index and Special Analysis can provide the analytical foundations for these important
conversations.
Sincerely,
Russell Hancock, . Emmett D. Carson, .
President & Chief Executive Officer CEO & President
Joint Venture Silicon Valley Silicon Valley Community Foundation
A B O U T T H E 2 0 1 2 S I L I C O N VA L L E Y I N D E X
Por
tola
Vall
ey
Palo A
lto
Woo
dside
Los
Alt
os
Hil
ls
Lo
s A
lto
s
East Palo
Alto
Half Moon Bay
Atherton
Redwood City
San Carlos
Foster City
San Mateo
Su
nn
yv
ale
Cu
pe
rti
no
Santa Clara County (all)
Campbell, Cupertino, Gilroy, Los Altos,
Los Altos Hills, Los Gatos, Milpitas,
Monte Sereno, Morgan Hill,
Mountain View, Palo Alto, San Jose,
Santa Clara, Saratoga, Sunnyvale
Alameda County
Fremont, Newark, Union City
San Mateo County (all)
Atherton, Belmont, Brisbane, Broadmoor,
Burlingame, Colma, Daly City, East Palo Alto,
Foster City, Half Moon Bay, Hillsborough,
Menlo Park, Millbrae, Pacifica, Portola Valley,
Redwood City, San Bruno, San Carlos,
San Mateo, South San Francisco, Woodside
Santa Cruz County
Scotts Valley
Santa Clara
San Jose
Newark
Fremo
nt
Unio
n Ci
ty
Morgan Hill
Sa
ra
to
ga
M
on
te
S
er
en
o
Lo
s
G
at
os
Milpitas
Mo
un
tai
n V
iew
Scotts ValleyGilroy
Hillsborough
Campbell
Broadm
oor
South San Francisco
D
aly C
ity
Pacifica
Menlo Park
San Bruno
Brisbane
C
olm
a
Millbrae
Burlingame
Belmont
Foreign Born: 37%
Origin:
58% Asia
31% Americas
8% Europe
% Africa
1% Oceana
T H E S I L I C O N V A L L E Y R E G I O N
Area: 1,854 square miles
Population: 3 million
Jobs: 1,330,846
Average Annual Earnings: $86,540
Foreign Immigration: +13,888
Domestic Migration: -9,591
Adult educational attainment:
15% Less than High School
17% High School Graduate
25% Some College
25% Bachelor’s Degree
18% Graduate
or Professional Degree
Age distribution:
24% 17 and under
9% 18-24
30% 25-44
26% 45-64
12% 65 and older
Ethnic composition:
37% White, non-Hispanic
30% Asian, non-Hispanic
27% Hispanic
2% Black, non-Hispanic
4% Multiple and Other
The geographical boundaries of Silicon Valley vary. Earlier, the region’s
core was identified as Santa Clara County plus adjacent parts of San
Mateo, Alameda and Santa Cruz counties. However, since 2009, the Silicon
Valley Index has included all of San Mateo County in order to reflect the
geographic expansion of the region’s driving industries and employment.
Silicon Valley is thus defined as the following cities:
2012 INDEX HIGHLIGHTS 4
INDEX AT A GLANCE 6
SPECIAL ANALYSIS – Proposition 13: Implications for Local Government Finance and the Silicon Valley Economy 8
P E O P L E
Silicon Valley’s population is growing in number and diversity, and educational attainment is improving across all ethnic groups.
Talent Flows and Diversity 12
E C O N O M Y
Employment posted solid gains, but the median family income continues to lag.
Employment 16
Innovation 20
Entrepreneurship 24
Commercial Space 28
Income 30
S O C I E T Y
Student achievement is improving yet health outcomes vary.
Preparing for Economic Success 34
Early Education 36
Arts and Culture 38
Quality of Health 40
Safety 42
P L A C E
The region’s residents are adopting habits that will encourage environmental sustainability. The housing market has made
strides towards recovery but land use density and affordable housing is slipping.
Environment 44
Transportation 48
Land Use 50
Housing 52
G O V E R N A N C E
While Silicon Valley’s residents are engaging in the political process at record levels, our cities are facing mounting fiscal challenges.
Civic Engagement 56
Revenue 58
SPECIAL ANALYSIS cont inued 62
APPENDICES 72
ACKNOWLEDGMENTS 77
TA B L E O F C O N T E N T S
4
2012 INDEX
HIGHLIGHTS
The Silicon Valley economy is mounting a solid recovery from the recession, one
that is being fueled by a few high-performing sectors, but the gains are not yet
widely distributed.
The region added more than 42,000 jobs in 2011. Monthly employment increased
percent in the region from December 2010 to December 2011, while the
. posted gains of percent.
• Quarterly employment in the region improved for the first time in three years, growing two percent from Q2 2010 to 2011.
• Unemployment in Silicon Valley fell percent over the previous year to percent in December 2011. This is lower than
California at percent, and on par with the . at percent.
• All major areas of economic activity experienced growth from 2010-2011, except for Other Manufacturing (excluding IT), which
has fallen every year since 2007, driven largely by Space & Defense Manufacturing.
• Demand is up for most types of commercial space as vacancy rates dropped and asking rents held steady from 2010 to 2011.
Silicon Valley’s innovation engine has heated up again.
• Accounting for the largest observable year-to-year gain, Silicon Valley patent registrations leapt by 30 percent over 2009 with
13,311 new patents registered in 2010 and largely in Computers, Data Processing & Information Storage. The region accounted
for 49 percent of total registrations statewide and 12 percent nationally, a one percent drop over the prior year.
• Total venture capital investment rose 17 percent in 2010. Investment continues to grow in Industry/Energy, Biotechnology and
Medical Devices.
• Venture capital investment in clean technology increased 48 percent over the prior year and was strongest in Energy Generation,
Efficiency and Storage. The region represents 49 percent of total California investment in clean technology.
• Silicon Valley’s IPOs increased from 11 to 12 in 2011 while global activity slowed. The region represents 46 percent of IPOs
statewide and twelve percent nationally.
• Small Business Innovation and Technology (SBIR/STTR) funding per million dollars of GDP expanded by one percent from 2009
to 2010, the first year of growth since 2004. Total grants and funding values remained similar to 2009 levels. The region by far
outpaces other innovation hubs in the country.
The region continues to grow a rich talent base.
• Science & Engineering talent expanded by four percent in Silicon Valley and by eight percent in the .
• The region is one of the nation’s most culturally diverse. Half of the population speaks a language other than English in the home.
Asian speakers make up large shares, however speakers of European languages are on the rise.
• Educational attainment has been increasing across all racial/ethnic groups since 2006.
• Although ‘arts-centric’ businesses have declined by 16 percent from 2009 to 2010, Silicon Valley ranks above the national average
in ‘arts-centric’ businesses per one thousand residents.
5
Though the recovery is underway, income growth is mostly limited to high
earners, and is not spread across other segments of the population.
• Silicon Valley’s per capita income in 2011 expanded by four percent to reach $66,000. While per capita income in the region is
consistently higher than statewide or national values, it is also more volatile as high incomes track tech stock values. Per capita
income in California and the nation increased just two percent over 2010.
• In contrast, most residents continued to suffer earnings losses in 2010 as the region’s median income continued to slide for the
second year in a row. Incomes dropped three percent in the region, seven percent statewide and two percent nationally.
• From 2008 to 2010, real per capita income dropped for every racial/ethnic group in the region except for Blacks, whose income
rose by 16 percent.
• Since 2004, the share of households in the low and middle income ranges has declined by four percent each while higher-income
households increased to 43 percent of the region’s total households.
• The percentage of students receiving school meals increased to 31 percent in the region and 49 percent in the state.
The region’s youth are showing educational gains.
• School expulsions due to violence or drugs and gang related homicides both fell in the most recent period in Silicon Valley after
fluctuating in previous years.
• Graduation rates, the percentage of students meeting UC/CSU requirements, and Algebra I scores are improving, and the region’s
overall dropout rate has declined.
The housing picture is mixed.
• Residential foreclosures fell 16 percent from the first half of 2008 to the first half of 2011 in the region and declined by 24 percent
statewide.
• Only five percent of new housing development was classified as affordable, reaching a 14-year low. Total new residential development
expanded by 165 percent in the last year.
• The housing cost burden for renters increased statewide in 2010 and held steady in the region. For Valley homeowners, the cost
burden slipped marginally.
The region’s public sector fiscal crisis persists, making it difficult to finance
essential public services.
• In the fiscal year 2009/2010, city revenues fell by eleven percent from the year prior, marking the second straight year of declining
revenue since 2003/2004.
• Property tax was the fastest growing revenue source for Silicon Valley cities, increasing from ten to 25 percent of total city revenue
since 2000/2001.
• From 2009 to 2010, total debt funding increased by 43 percent with $ billion in 2010. This growth was mostly due to increased
debt funding in Education, Transportation Infrastructure and Housing.
6
San Mateo and
Santa Clara
Counties
+%
0
10,000
20,000
30,000
1996 2011
Net Population Change
Percent Change between
2010 and 2011
Silicon Valley +%
California +%
40,000
99
103
104
Dec 2010
105
.
+%
Dec 2011
10
0=
D
ec
2
01
0
Va
lu
es
102
101
100
$
2008 2009 2010 2011
0%
20%
30%
40%
1990 2010
California
50%
10%
2000
.
-10,000
0
2000
25,000
-25,000
2011
Net Foreign Immigration
Net Domestic Migration
-50,000
Spanish
39%
Chinese
15%
European
15%
Other Asian &
Pacific Island
10%
Vietnamese
9%
Tagalog
9%
Other and
Unspecified
3%
49%
12%
AT A GLANCE
WHAT IS THE INDEX?
The Silicon Valley Index has been telling the Silicon Valley
story since 1995. Released early every year, the Index is
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?
Indicators are measurements that tell us how we are doing:
whether we are going up or down, going forward or backward,
getting better or worse, or staying the same.
Good indicators:
• are bellwethers that reflect fundamentals
of long-term regional health;
• reflect the interests and concerns of the community;
• are statistically measurable on a frequent basis; and
• measure outcomes, rather than inputs.
Appendix A provides detail on data sources for each indicator
THE
2012
INDEX
ECONOMY
Employment, investment and patents
are posting solid gains, yet business
loans and median family income
continue to lag.
PEOPLE
Silicon Valley’s population is growing,
and our diversity increases; educational
attainment is improving across all ethnic
groups.
Net Population Change
Net Migration Flows
Change in Jobs Relative
to December 2010
Second Language Spoken at Home
Population 5 years and older
Venture Capital Investment
in Clean Technology
Silicon Valley - Billions of Dollars Invested
Silicon Valley’s Percentage
of . and California
Patents Registration
Population Change between
2010 and 2011
Net Foreign Immigration +8%
Net Domestic Migration -45%
7
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
S O C I E T Y 34 | 43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
7,000
9,000
8,000
0%
100%
60%
40%
20%
80%
7,000
9,000
8,000
0%
12%
10%
8%
6%
4%
2%
1998
1998
Drove Alone
0%
90%
30%
08-09
Graduation Rates
Percentage of Graduates
who Meet UC/CSU Requirements
W
hit
e
70%
100%
80%
90%
85%
75%
Tw
o
or
M
or
e R
ac
es
As
ian
Af
ric
an
A
me
ric
an
Hi
sp
an
ic
So
me
O
th
er
R
ac
e
95%
2009 2010 SV Average in 2010
60%
09-10 08-09 09-10
Silicon Valley California
2000 2002 2004 2006 2008 2010
700
1,200
1,100
1,000
900
800
C
hi
ld
A
bu
se
p
er
1
,0
00
C
hi
ld
re
n
N
um
be
r
of
S
oc
ia
l S
er
vi
ce
E
m
pl
oy
ee
s
Substantiated Cases of Child Abuse
Number of Social Services Employees
2002 2006 2010
kW
h
pe
r
pe
rs
on
El
ec
tr
ic
ity
P
ro
du
ct
iv
ity
Electricity Consumption per Capita
Electricity Productivity
2010
Pe
rc
en
ta
ge
o
f W
or
ke
rs
Other Means
2008 2010 % Change
Silicon Valley 8,831 6,933 -21%
California 238,396 169,657 -29%
2001-02Pe
rc
en
t
of
C
A
P
ub
lic
S
af
et
y
Ta
x
R
ev
en
ue
2003
2004-05 2007-08 2010-11
California Share of
Public Safety Tax Revenue
Santa Clara & San Mateo Counties
Means of Commute
GOVERNANCE
Local governments are still faced with
grave fiscal challenges.
Silicon Valley – 2011
Percentage of CA
Population %
Percentage of CA Public
Safety Tax Revenue %
PLACE
The region’s residents are adopting
habits that promote environmental
sustainability. The housing market has
made strides towards recovery but
land use density and housing
affordability are slipping.
SOCIETY
Student achievement is improving, while
health outcomes vary.
Electricity Productivity and
Consumption per Capita
High School Graduation
Child Abuse
Silicon Valley
87%
Number of Silicon Valley
Foreclosures
Percent of Population with
Health Insurance
by Ethnicity
49%
88%
50%
79%
35%
81%
36%
78% 75%
Change in City Revenue
Fiscal Year 08/09–09/10
Property Taxes -6%
Sales Taxes -1%
8
Proposition 13
Implications for Local Government Finance
and the Silicon Valley Economy
Proposition 13—the landmark initiative
passed by California voters in 1978 to
limit property taxes—has reemerged
in the public discussion about possible
fiscal Special Analysis
section of the 2012 Index is intended
to aid in that discussion, by describing
the history and implications of
Proposition 13 in the context of
changing economic and fiscal
circumstances.
AN
AL
YS
IS
SP
EC
IA
L
Prepared by Stephen Levy
9
40%
35%
15%
25%
30%
20%
10%
5%
1972 1973 1974 1975 1976 1977 19781971
Source:California Association of Realtors
0%
9%
7%
9% 10%
20%
17%
28%
14%
Figure 1
Increase in California Median Home Price
200%
150%
100%
50%
Median Home Price Consumer Price Index Per Capita Income
Source: California Association of Realtors, California Department of Finance
Analysis: Center for Continuing Study of the California Economy
0%
164%
Figure 2
64%
90%
Median HomePrice, Consumer Price and
Per Income Growth in California 1971-78
It builds on the Special Analysis published in the 2011 Index, as well as two recent other Silicon Valley local workforce and economic
competitiveness studies that confronted the fiscal crisis facing local government and identified the critical link between the region’s
infrastructure, education and other public services and our ability to stay competitive. 1, 2, 3
BACKGROUND
As voters went to the polls in June 1978 California was in the midst of a steady and large increase in the price of single-family homes.
In 1978 the median home price was $70,890, up 164 percent from the $26,880 median in 1971. Residents were experiencing large
property tax increases and feared that more increases were on the way.
In the years from 1971 through 1978 median prices had risen by
between 7 percent and 28 percent each year (Figure 1), far
outpacing the rate of overall inflation and income gains. Even
though income gains were historically large and outpaced the
growth in consumer prices, both measures were overshadowed
by the 164 percent increase in median home prices (Figure 2).
And for households living on fixed incomes, the effects of rising
home prices, assessed values, and property taxes were even
more of a problem for them financially.
Though assessed values were surging, local governments did not
respond by lowering local tax rates.
Primarily as a result of these trends, voters approved Proposition 13
by a 65-35 percent margin.
The proposition included two components that were well known
to voters:
• Lowering the maximum property tax rate
to 1 percent—a nearly 60 percent decrease. The purpose
of this provision was to lower the property taxes that had
recently soared for residents. An additional property tax
rate for locally approved bonds was allowed through a
later amendment. Though there was less public discussion
of the implications, Proposition 13 also lowered property
taxes for businesses.
• Limiting increases in assessed value to a maximum
of 2 percent per year as long as the property did not change
ownership. The purpose of this provision was to limit future
property tax increases and bring a large measure of certainty
to taxpayers about their future property tax liability. The
certainty about future property tax increases was perceived
as a major benefit of Proposition 13.
These two provisions were easily understood by voters and were
very popular.
Special Analysis Proposition 13
Implications for Local Government Finance and the Silicon Valley Economy
1 Index of Silicon Valley 2011, Joint Venture Silicon Valley Network and Silicon Valley Community Foundation, 2011.
2 Silicon Valley in Transition: Economic and Workforce Implications in the Age of iPads, Android Apps and the Social Web, Silicon Valley Workforce Boards: NOVA, work2future, Santa Cruz Co., San Mateo Co., July 2011
3 Silicon Valley CEO Business C limate Survey 2011, Silicon Valley Leadership Group,
10
4 After the Tax Revolt: California’s Proposition 13, edited by Jack Citrin and Isaac William Martin, Institute of Governmental Studies, University of California Berkeley, 2009;
Has Proposition 13 Delivered? The Changing Tax Burden in California, Michael A. Shires, John Elwood and Mary Sprague, Public Policy Institute of California, 1998; The
Demographics of Proposition 13, Dowell Myers, . Population Dynamics Research Group., September 2009; Proposition 13: Some Unintended Consequences, Jeffrey
Special Analysis Proposition 13
Implications for Local Government Finance and the Silicon Valley Economy
The proposition also included three other major provisions whose impacts were not as well analyzed in the public debate about Proposition 13:
• Prohibiting increases in the property tax rate. After the passage of Proposition 13, local governments and school
districts could no longer go to the voters to approve property tax increases to maintain or increase public services.
• Requiring a 2/3 vote of the electorate for future state taxes, local special-purpose taxes, and local bonds.
• Transferring the authority to allocate property taxes among jurisdictions to the state.
Now more than 30 years later, thousands of pages of analysis4 have delineated the major consequences of Proposition 13:
• Assessed value increases limited to a maximum of 2 percent per year have turned out to be approximately half as
large as inflation increases over the first 30 years of Proposition 13.
• The majority of local school revenues are now provided through the state budget instead of by local taxpayers,
severing the connection between local taxes and the quality of services provided.
• Cities (and to a lesser extent, counties) responding to such sharp declines in property tax revenues have introduced
a wide variety of new local taxes and fees. This trend reemerged in recent years as the recession deepened and
as property tax revenues stopped growing.
• Tax measures that would have passed under a simple majority vote requirement have been defeated by failing to
get a supermajority (two-thirds) approval.
• Property owners pay substantially different amounts on similarly valued properties, depending on the date of acquisition.
• The share of property taxes paid by homeowners has increased, while the share paid by owners of non-residential
properties has decreased.
But analyzing the implications of Proposition 13 is complicated for several reasons. For one thing, since the measure’s passing the state
has made a large number of budget decisions that affect local government and education funding apart from Proposition 13. Additionally,
there have been several other changes passed by voters that have transferred local revenue to the state and extended the 2/3 vote
requirement for local taxes. Also, the recent recession has had major implications for local revenues and education funding that, while
not caused by Proposition 13, are affected by the way Proposition 13 is implemented.
PROPOSITION 13 IN THE FIRST 30 YEARS
Three trends lessened the impact of Proposition 13 on local government revenues in the first 30 years after its passage: 1) surging home
prices and assessed value growth, 2) the related increase in assessed value as properties were sold and reassessed, and 3) a wide range
of new taxes and fees imposed or expanded by local governments.
1. After the Initial Rate Cut, Assessed Values and Property Tax Revenue Surged. Proposition 13 reduced property tax revenues
by approximately 60 percent in the year after it was adopted, as the base rate fell from percent to 1 percent.
However, in the years after the initial rate cut housing prices surged, and high construction levels combined with the turnover of properties
led to large increases in assessed valuation and property tax revenues in Silicon Valley (and across California).
For nearly all years from 1980 through 2008, assessed values in San Mateo and Santa Clara counties increased faster than consumer
prices, and often at rates more than double the rate of consumer price increases.
Assessed value in Santa Clara County increased at an average rate of percent between 1980 and 2008, while the growth rate in San
Mateo County averaged percent and both growth rates were more than double the Bay Area consumer price increase of percent
per year. There were three factors driving the growth in assessed value: the increase in housing and commercial property prices, new
construction, and the increase in assessed value that comes when properties change ownership.
A similar result was experienced statewide. The average annual increase in assessed value for all counties combined was percent
between 1980 and 2008, while the average increase in consumer prices was percent (as shown on Figure 3).
11
continued on page 62
%
%
%
%
1980-81 2008-09
%
1982-83 1984-85 1986-87 1988-89 1990-91 1992-93 1994-95 1996-97 1988-89 2000-01 2002-03 2004-05 2006-07
$22
21
17
19
20
18
16
2005-06
Santa Clara and San Mateo County Assessor’s Offices
Analysis: Center for Continuing Study of the California Economy
$B
ill
io
ns
%
o
f A
ss
es
se
d
Va
lu
e
C
ha
ng
e
2006-07 2007-08 2008-09
Assessed Value Increase from
Change in Ownership
% of Total Assessed
Value Change
Source: California Board of Equalization, Santa Clara and San Mateo County Assessor’s Offices,
California Department of Finance
Analysis: Center for Continuing Study of the California Economy
Santa Clara CA CPICaliforniaSan Mateo
Figure 3
15
Figure 5
Impact of Change in Ownership on
Assessed Value Growth in Silicon Valley
10%
9%
5%
7%
8%
6%
4%
3%
1978-2007
CA Median
Price
California Association of Realtors, California Department of Finance
Analysis: Center for Continuing Study of the California Economy
2%
1%
CA CPI 1992-2007
Santa Clara
Median Price
San Mateo
Median
Price
Bay Area CPI
A
ve
ra
ge
A
nn
ua
l G
ro
w
th
R
at
e
0%
%
Figure 4
%
%
%
%
House Prices Surge Through 2007
Growth of Assessed Value and Consumer Prices
2. Large Increases in Home Prices Kept Assessed Value
Growing. Median home prices in California and Silicon Valley
increased far faster than consumer prices until 2008. This did
not affect the tax liability of existing property owners but did
affect the taxes paid on newly built or recently sold properties.
In California, median prices increased by percent per year
from 1978 through 2007, compared to an average increase in
consumer prices of percent (as shown on Figure 4). Available
data on home prices in San Mateo and Santa Clara counties for
1991-2007 show an annual increase in median home prices of
percent in Santa Clara County and percent in San Mateo
County, compared to the Bay Area consumer price gain of
percent per year.
So even though there was a limit of 2 percent annual increases in
assessed value on properties that did not change ownership,
these large gains in home prices not only boosted the assessed
value for newly built homes and commercial space, but also
made a substantial contribution to increases in assessed values
when properties changed ownership.
There was also the impact of changes in ownership to consider.
Increases in assessed valuation from changes in property
ownership contributed between $17 and $21 billion, or close to
60 percent of the growth in assessed value in Santa Clara and
San Mateo counties in the years leading up to the housing crash
(Figure 5). Without these gains, which came largely as a result
of surging home prices, the growth in assessed value would have
been much smaller in these years.
The contrast between this period and the years following the housing
downturn is shown on Figure 12, which illustrates the changing
composition and level of growth in assessed value after 2007.
70%
60%
20%
40%
50%
30%
10%
0%
12
20,000
30,000
50,000
-10,000
10,000
40,000
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
Natural
Change
* Provisional population estimates for 2011
Data Source: California Department of Finance
Analysis: Collaborative Economics
Components of Population Change
Santa Clara & San Mateo Counties
Net
Migration
Net Change
Pe
op
le
20
05
20
06
20
07
20
08
*
20
09
20
11
-20,000
-30,000
-40,000
20
10
Population Change
0
The Silicon Valley
population grew
for the first time
in three years
WHY IS THIS IMPORTANT?
Silicon Valley’s most important asset is its people, who drive the
economy and shape our region’s quality of life. And because the
Valley is a knowledge economy, our success depends on the talent
of our population. The number of science and engineering degrees
awarded regionally helps to gauge how well Silicon Valley is
preparing talent for our driving, export-oriented industries. A
local workforce equipped with strong skills is a valuable resource
for generating new ideas and innovative products and services.
The region has benefited significantly from the entrepreneurial spirit
of people drawn to Silicon Valley from around the country and
around the world. In particular, immigrant entrepreneurs have
contributed considerably to innovation and job creation in the
Traditionally, the region’s universities have served as the
primary port of entry of foreign talent. Examining the continued
flows of foreign graduates from our universities indicates to what
degree our region remains a global magnet for talent. Maintaining
and increasing these flows vastly improves the region’s potential
for closer integration with other innovative regions and thereby
bolsters its global competitiveness.
We report population growth as a function of migration (immigration
and emigration) and natural population change (the difference
between the number of births and number of deaths).
HOW ARE WE DOING?
The population in Silicon Valley continues to grow, picking up speed
in 2011. For the first time since 2008, growth rates increased.
Over the past three years both natural population change (births
minus deaths) and migration have been slowing down. While
natural population continued its slowing, gains in foreign migration
led to a 196 percent increase in total migration, which pushed
the growth rate positive. Migration trends continue to mirror
those of the previous decade and are characterized by domestic
out-flows and foreign in-flows.
The 25 to 44 age group is the largest in Silicon Valley, California and
United States as a whole with 755,000, 10,515,000 and 82,164,000
people respectively. Age distribution across the three different
geographies is similar, with Silicon Valley having a slightly lower
percent of those 18 to 24 years of age when compared to California.
Talent Flows and Diversity
Silicon Valley’s population is growing,
becoming more highly educated and
increasingly diverse.
PEOPLE
Silicon Valley residents are much more likely than . residents overall
to speak a language other than English. The share of residents
speaking another language increased more in Silicon Valley from
2006 to 2010 than in the state and nation. The region’s language
diversity has stronger growth rates in Chinese, Other Asian &
Pacific Islander, and Korean than is the case in California or the
. Spanish speakers make up the largest group with 39 percent.
Chinese follows with 15 percent and Vietnamese and Other
European languages account for ten percent in the region.
Educational attainment is higher across all racial and ethnic groups in
Silicon Valley than California as a whole. The percentage of adults
with Bachelor’s degrees or higher has increased seven percent
from 2006 to 2010 which is similar to the state as a whole. These
educational gains are shared; from 2006 to 2010 the percent of
adults with a four-year degree increased across all ethnic groups.
As an ethnic group, Asians have the highest per capita educational
achievement; 58 percent of Asian adults in Silicon Valley and 38
percent in California hold a four-year degree or higher.
The number of science and engineering (S&E) degrees conferred in
2010 increased one percent in Silicon Valley and seven percent
nationally, achieving a new high in both geographies. Trends in
S&E degrees conferred by universities in the broader region to
foreign students varies significantly between undergraduate and
graduate degrees. Undergraduates have continued to decline,
with foreign students representing percent of S&E degrees.
Graduate degrees conferred to foreign students in S&E disciplines
grew between 2007 and 2009, but fell by percent in 2010 to
percent.
1 AnnaLee Saxenian. 2002. Local and Global Networks of Immigrant Professionals in Silicon Valley. San Francisco: Public Policy
Institute of California. See also, S. Anderson & M. Platzer. 2006. “American Made. The Impact of Immigrant Entrepreneurs
and Professionals on Competitiveness.” National Venture Capital Association.
13
P
E
O
P
L
E
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
Talent Flows and Diversity
12–15
E C O N O M Y 16 | 33
S O C I E T Y 34 | 43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
10,000
20,000
40,000
-10,000
30,000
Net Foreign
Immigration
* Provisional population estimates for 2011
Data Source: California Department of Finance
Analysis: Collaborative Economics
Foreign and Domestic Migration
Santa Clara & San Mateo Counties
Net Domestic
Migration
Net Migration
-30,000
-20,000
-40,000
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
09
20
08
Pe
op
le
*
Data Source: . Census Bureau, 2010 American Community Survey
Analysis: Collaborative Economics
Santa Clara & San Mateo Counties, California, and the .
2010
0%
10
0%10
%
20
%
30
%
40
%
50
%
60
%
70
%
80
%
90
%
Silicon
Valley
California
United
States
17 and under 18-24 25-44 45-65 65 and older
-50,000
-60,000
20
10
20
11
Net Migration Flows
Age Distribution
0
24% 9% 30% 26% 12%
25% 11% 28% 25% 11%
24% 10% 27% 26% 13%
Net migration was
positive for the first
time in three years
Thirty percent
of Silicon Valley’s population
is 25 to 44, the core
working age group
14
Note: Does not include English-only households
Data Source: . Census Bureau, American Community Survey
Analysis: Collaborative Economics
Silicon Valley, California and the .
10%
20%
30%
40%
50%
100%
Silicon Valley
1990
90%
80%
70%
60%
2000 2005 2010 1990 2000 2005 2010 1990 2000 2005 2010
California United States
German
French
Slavic
Korean
Other and Unspecified
Other Asian and Pacific Island
Tagalog
Vietnamese
Other European
Chinese
Spanish
5%
Data Source: . Census Bureau, American Community Survey
Analysis: Collaborative Economics
Santa Clara & San Mateo Counties, California, and .
10%
15%
20%
25%
30%
35%
40%
45%
50%
0%
Language Spoken at Home for the Population 5 Years and Over
Santa Clara & San Mateo Counties, 2010
Languages Spoken at Home Other Than English
Population Share That Speaks Language Other Than Exclusively English
Spanish
39%
Chinese
15%
Tagalog
9%
7% Other Asian
and Pacific Island
Vietnamese
10%
Other
European
10%
3% Other and Unspecified
3% Korean
2% Slavic
1% French
1% German
0%
48%
43%
20%
2006
Silicon Valley California United States
50%
44%
21%
2010
Pe
rc
en
t
of
P
op
ul
at
io
n
T
ha
t
Sp
ea
k
a
La
ng
ua
ge
O
th
er
T
ha
n
En
gl
is
h
Asian speakers make up large
shares but speakers of
European languages are
on the rise
Half of Silicon Valley’s
population speaks
a language other than
English at home
Foreign Language
Speakers of
Asian languages
make up 44 percent
of the population
PEOPLETalent Flows and Diversity
Note: Does not include
English-only households.
Data Source: . Census Bureau,
American Community Survey
Analysis: Collaborative Economics
15
P
E
O
P
L
E
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
Talent Flows and Diversity
12–15
E C O N O M Y 16 | 33
S O C I E T Y 34 | 43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Note: Categories Black, White and Asian are non-Hispanic
Multiple and Other includes American Indian and Alaskan, native
Hawaiian and Pacific Islander, Two or More Races and
Other Races
Data Source: . Census Bureau, American Community Survey
Analysis: Collaborative Economics
Percentage of Adults with a Bachelor’s Degree or Higher by Ethnicity
Santa Clara & San Mateo Counties, and California
10%
20%
30%
40%
50%
60%
0%
Santa Clara & San Mateo Counties California
Asian White Black or
African
American
Multiple
and
Other
Hispanic Asian White Black or
African
American
Multiple
and
Other
Hispanic
16,000
20
01
20
03
20
04
Note: Data are based on first major and include bachelors, masters and doctorate degrees.
Data Source: National Center for Educational Statistics, IPEDS
Analysis: Collaborative Economics
Universities in and Near Silicon Valley and the .
20
05
20
06
20
00
19
98
19
97
19
99
20
02
20
07
20
08
20
09
20
10
400,000
2,000 50,000
4,000 100,000
6,000 150,000
8,000 200,000
10,000 250,000
12,000 300,000
14,000 350,000
19
96
19
95
8%
20
01
20
03
20
04
Note: Data are based on first major and include bachelors, masters and doctorate degrees.
Data Source: National Center for Educational Statistics, IPEDS
Analysis: Collaborative Economics
Science & Engineering Degrees Conferred to Temporary Nonpermanent Residents
Universities in and Near Silicon Valley
20
05
20
06
20
00
19
98
19
97
19
99
20
02
20
07
20
08
20
09
20
10
40%
1% 5%
2% 10%
3% 15%
4% 20%
5% 25%
6% 30%
7% 35%
19
96
19
95
Educational Attainment
20
02
20
06
20
10
0% 0%
0 0
Educational attainment
rising
across all ethnicities
U
nd
er
gr
ad
ua
te
S
&
E
D
eg
re
es
C
on
fe
rr
ed
in
S
ili
co
n
Va
lle
y
G
ra
du
at
e
S&
E
D
eg
re
es
C
on
fe
rr
ed
in
t
he
S
ili
co
n
Va
lle
y
Science and engineering
degrees conferred
to foreign residents
declined
To
ta
l S
&
E
D
eg
re
es
C
on
fe
rr
ed
in
S
ili
co
n
Va
lle
y
To
ta
l S
&
E
D
eg
re
es
C
on
fe
rr
ed
in
t
he
U
ni
te
d
St
at
es
Science and engineering
degrees conferred
continue to rise
in the region and country
Science & Engineering Degrees Conferred
Total Science & Engineering Degrees Conferred
16
Santa Clara & San Mateo Counties, and the United States
20
07
*Data for December 2011 is preliminary
Note: Data is not seasonally adjusted.
Data Source: . Bureau of Labor Statistics, Current Population Survey (CPS) and Local Area Unemployment Statistics (LAUS)
Analysis: Collaborative Economics
20
11
M
on
th
ly
E
m
pl
oy
ed
R
es
id
en
ts
R
el
at
iv
e
to
D
ec
em
be
r
20
07
(1
00
=
D
ec
em
be
r
20
07
V
al
ue
s)
93
98
99
100
101
94
95
96
97
20
10
20
11
M
on
th
ly
E
m
pl
oy
ed
R
es
id
en
ts
R
el
at
iv
e
to
D
ec
em
be
r
20
10
(1
00
=
D
ec
em
be
r
20
10
V
al
ue
s)
99
100
105
104
103
102
101
Change in Residential Employment
.
%
San Mateo
& Santa Clara
Counties
%
.
+%
San Mateo
& Santa Clara
Counties
+%
D
ec
em
be
r
D
ec
em
be
r
D
ec
em
be
r
D
ec
em
be
r
* *
Santa Clara & San Mateo
Counties outpace the
nation in job growth
WHY IS THIS IMPORTANT?
Tracking employment gains and losses is a basic measure of economic
health. Shifts in employment across industries suggest structural
changes in Silicon Valley’s economic composition. Over the course
of the business cycle, employment change across industries can
be cyclical, but the permanent changes reflect how the region’s
industrial mix evolves. While employment by industry provides
the broader picture of the region’s economy, observing the
employment and unemployment rates of the population residing
in the Valley reveals the status of the immediate Silicon Valley-
based workforce. A large number of science and engineering jobs
regionally indicates a local workforce equipped with strong skills
that are valuable for generating new ideas and innovative products
and services. Occupational needs of the region change over time
as technology changes, the region’s mix of industries shifts, and
markets become more specialized. The way in which the region’s
occupational patterns change shows how well our economy is
maintaining its position in the global economy.
HOW ARE WE DOING?
Silicon Valley is rebounding at a faster rate than the nation. From
December 2010 to December 2011, regional employment
expanded by percent, while national employment inched up
by percent. Over the 12-month period, the region added
more than 42,000 jobs, bringing employment levels to million
overall. However, employment has still not recovered to 2007 levels.
The Valley’s employment growth was shared across all major areas of
economic activity, except manufacturing. The strongest employment
gains over the previous year occurred in Information Products
& Services, which expanded by six percent from Q2 2010 to
Q2 2010.
The combined unemployment rate for the region fell percent
over the past year, bringing it down to percent in December
2011. California and the . fell to and percent
unemployment, respectively. However, the employment increases
have not been uniform across ethnic groups. From 2009 to 2010
the unemployment rate slowed for Whites and Other, but
increased for Hispanics (+%), African Americans (+%) and
Asians (+%).
Science and Engineering (S&E) talent represents 17 percent of all
occupations in Silicon Valley, up one percent from 2000. Nationally,
S&E talent accounts for six percent, holding steady at 2000 levels.
Physical Engineers drove much of the growth in S&E talent in the
Valley over the decade, increasing by 19 percent.
Employment
Unemployment is waning as job growth
is taking place across most industries.
ECONO
17
E
C
O
N
O
M
Y
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
Employment
16-19
Innovation
20-23
Entrepreneurship
24-27
Commercial Space
28-29
Income
30-33
S O C I E T Y 34 | 43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Number of Silicon Valley Jobs in Second Quarter with Percent Change over Prior Year
Silicon Valley
20
05
750,000
1,000,000
250,000
500,000
1,250,000
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
0
1,750,000
20
06
20
07
1,500,000
20
11
To
ta
l N
um
be
r
of
Jo
bs
20
08
Total Employed Residents by Month
San Mateo & Santa Clara Counties
20
05
600,000
800,000
1,400,000
200,000
400,000
1,000,000
19
98
19
99
20
00
20
01
20
02
20
03
20
04
*Data for December 2011 is preliminary
Note: Data is not seasonally adjusted.
Data Source: . Bureau of Labor Statistics, Local Area Unemployment Statistics (LAUS)
Analysis: Collaborative Economics
20
06
20
07
To
ta
l N
um
be
r
Em
pl
oy
ed
R
es
id
en
ts
20
09
20
08
1,200,000
20
09
20
11
20
10
20
10
Quarterly Job Growth
Data Source: California Employment Development Department, Labor Market Information Division,
Quarterly Census of Employment and Wages
Analysis: Collaborative Economics
0
*
Employment
+%
+% +%
+% %
%
% % +%
+%
+%
+%
Percent change
over previous year
+%
Q
2
Q
2
Q
2
Q
2
Q
2
Q
2
Q
2
Q
2
Q
2
Q
2
Q
2
Q
2
Q
2
Q
2
%%
Q
2
Quarterly job growth
sees first improvement
in three years
Monthly employment for
Silicon Valley rose in 2011
Silicon Valley Employment in Public Sector
Major Areas of Economic Activity
2007 2011 % Change
Local Government
11,870 11,059 -7%Administration
State Government
79 42 -47%Administration
TOTAL
11,949 11,101 -7%EMPLOYMENT
MY
18
Average Annual Employment
600,000
700,000
800,000
300,000
400,000
500,000
Em
pl
oy
m
en
t
Community
Infrastructure
Information
Products
& Services
Innovation &
Specialized
Services
Other
Manufacturing
Business
Infrastructure
Life
Sciences*
200,000
100,000
U
ne
m
pl
oy
m
en
t
R
at
e
San Mateo & Santa Clara Counties, California and the United States
14%
12%
10%
8%
6%
4%
2%
20
08
20
07
20
06
20
05
20
04
20
03
20
02
20
01
20
00
19
99
United States
California
San Mateo &
Santa Clara Counties
2009 2010 2011 Q1&2
20
09
20
11
20
10
Silicon Valley Major Areas of Economic Activity
*In 2010, employment in Pharmaceuticals was suppresed for confidentiality reasons, causing the significant drop in total
Life Sciences employment.
Data Source: California Employment Development Department, Labor Market Information Division, Quarterly Census of
Employment and Wages
Analysis: Collaborative Economics
*Data for December 2011 is preliminary
Data Source: . Bureau of Labor Statistics, Current Population
Survey (CPS) and Local Area Unemployment Statistics (LAUS)
Analysis: Collaborative Economics
Unemployment Rate
20082007
0% *
0
Silicon Valley Employment Growth
by Major Areas of Economic Activity
Percent Change in Q2
Major Areas of Econ Activity 2009-10 2010-11
Innovation & Specialized Services +2% +2%
Information Products & Services 0% +6%
Community Infrastructure -1% +1%
Other Manufacturing -6% -13%
Business Infrastructure -6% +1%
Life Sciences* -36% +1%
TOTAL EMPLOYMENT -1% +1%
Average annual
employment seeing
gains in most sectors
Regional unemployment
rate declined
percent from December
2010 to December 2011
Employment ECONO
19
E
C
O
N
O
M
Y
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
Employment
16-19
Innovation
20-23
Entrepreneurship
24-27
Commercial Space
28-29
Income
30-33
S O C I E T Y 34 | 43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Santa Clara County
8%
10%
12%
2%
4%
6%
Other African
American
Hispanic Asian White
Santa Clara & San Mateo Counties
2000
200,000
150,000
100,000
50,000
2010S&
E
W
or
ke
rs
in
S
an
ta
C
la
ra
&
S
an
M
at
eo
C
ou
nt
ie
s .
2000
8,000,000
6,000,000
4,000,000
2,000,000
2010
S&
E
W
or
ke
rs
in
t
he
U
.S
.
2007* 2008 2009 2010
Percent Unemployed by Ethnicity
Note: Other includes the category Two or More Races in 2008–2010
*Date for Two or More Races is not available for 2007
Data Source: . Census Bureau
Analysis: Collaborative Economics
Science and Engineering Talent by Category
Data Source: . Census Bureau, 2000 Decennial PUMS, 2010 American Community Survey PUMS
Analysis: Collaborative Economics
0%
0
Biological
Mathematics
Computer
Physical Engineers
Design
Aerospace Engineers
& Scientists
250,000
0
10,000,000
42% 43%
26%
29%
15%
2% AE&S
3% Math
9% Bio
2% AE&S
3% Math
7% Bio
+8%
54%
28%
9%
2% AE&S
2% Math
5% Bio
53%
32%
7%
+4%
1% Math
2% AE&S
5% Bio
19%
Percent Change in
Unemployed by Ethnicity
Santa Clara County 2009-2010
Other %
African American +%
Hispanic +%
Asian +%
White %
In 2010, the unemployment
rate continued to rise
for most but
at a slower rate
Engineers
are driving growth
in region’s S&E talent
MY
20
Value Added per Employee
Santa Clara & San Mateo Counties, California and .
20
05
80,000
100,000
140,000
20,000
40,000
60,000
120,000
19
90
20
00
20
01
20
02
20
03
20
04
Data Source: Moody’s
Analysis: Collaborative Economics
CaliforniaSilicon Valley
20
06
20
07
Va
lu
e-
A
dd
ed
p
er
E
m
pl
oy
ee
(
In
fla
tio
n
A
dj
us
te
d)
20
08
United States
20
10
20
09
19
99
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
20
11
$160,000
0
Percent Change in Value
Added per Employee
2010-2011
Silicon Valley %
California +%
United States +%
Value Added
Value added per employee
flat in 2011
WHY IS THIS IMPORTANT?
Innovation drives the economic success of Silicon Valley. More than
just in technology products, innovation includes advances in
business processes and business models. The ability to generate
new ideas, products and processes is an important source of
regional competitive advantage. To measure innovation, we
examine the investment in innovation, the generation of new
ideas, the value-added across the economy and small business
innovation funding. Additionally, tracking the areas of venture
capital investment over time provides valuable insight into the
region’s longer-term direction of development.
HOW ARE WE DOING?
Productivity is one measure for overall economic health in a region.
Peaking in 2010, value added per employee remained flat in the
region, state and nation as a whole in 2011. However, in the past
decade value added grew in Silicon Valley at a faster rate (+25%)
than California (+20%), and the . overall (+18%).
Silicon Valley accounted for 49 percent of total California patents and
12 percent of total . patents in 2010, with registrations in
Silicon Valley growing 30 percent in the last year. One category
(Computers, Data Processing & Information Storage) claimed 40
percent of the region’s total patents in 2010. Chemical Processing
Technologies was the fastest growing category from 2009 to
2010, logging a 50 percent increase in patents. Registrations in
Communications increased 24 percent during the same period.
Venture capital (VC) investment in the Silicon Valley increased 17
percent in 2011. With a total of $ billion, regional investment
has recovered to 2004 levels following the drop in 2009. The
region accounted for 27 percent of the nationís total VC investment
and 52 percent of the stateís in 2011. By industry, Software
attracts the largest share of total investment but funding flows
are increasing in other areas. Following robust growth over the
last few years, investment in Industry/Energy remains strong.
Funding continues steadily in Biotechnology and Medical Devices
Increasing 48 percent, cleantech venture capital investment in Silicon
Valley rose to $ billion in 2011. The region currently accounts
for 49 percent of total California cleantech investment and 28
percent of total cleantech investment in the nation. Reflecting
growing activity in the region, Silicon Valleyís overall share of
cleantech investment, increased from 41 percent in California and
24 percent nationally over the prior year.
Energy Efficiency accounted for 24 percent of 2011 total cleantech
investment, up from seven percent in 2008. Energy Generation
continues to attract the most investment, though its share of
total venture capital is diminishing.
Venture capital investment is on the rise
and patent registrations are increasing
in key technology areas.
Innovation ECONO
Small businesses in the region were awarded 254 grants in 2010 through
the . Small Business Innovation Research (SBIR) and Small
Business Technology Transfer (STTR) Programs. Garnering $89
million from these highly competitive grants, funding in the region
increased by three percent over 2009 and 22 percent from 1990.
Since 1990, Silicon Valley has reported the highest SBIR/STTR
funding per million dollars of GDP. Tracking SBIR funding relative
to the size of the economy (., the region’s GDP) allows
comparisons with other places. At $510 per million dollars of
GDP, Silicon Valley’s figure was two and a half times that of the
Boston region ($203), which ranked second. Silicon Valley’s
funding per GDP is more than four times greater than the
remaining top regions, San Diego, Research Triangle, and the
Greater Washington . region.
21
E
C
O
N
O
M
Y
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
Employment
16-19
Innovation
20-23
Entrepreneurship
24-27
Commercial Space
28-29
Income
30-33
S O C I E T Y 34 | 43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Silicon Valley’s Percentage of . and California Patents
20
05
19
90
19
95
19
97
20
01
20
03
Percentage of .
20
08
40%
30%
20%
10%
Percentage of California
Data Source: . Patent and Trademark Office
Analysis: Collaborative Economics
20
09
19
99
50%
Pe
rc
en
ta
ge
o
f C
al
ifo
rn
ia
a
nd
U
.S
. P
at
en
t
R
eg
is
tr
at
io
ns
60%
20
07
19
94
19
91
19
92
19
93
19
96
19
98
20
02
20
04
20
00
20
06
20
10
49%
12%
By Technology Area
Silicon Valley
2,000
4,000
6,000
8,000
10,000
14,000
12,000
20
05
19
90
20
00
20
01
20
02
20
03
20
04
20
06
20
07
20
08
20
10
20
09
19
99
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
Billions of Dollars Invested
Silicon Valley
20
05
15
20
$30
5
25
20
00
20
01
20
02
20
03
20
04
Data Source: PricewaterhouseCoopers/National Venture Capital Association MoneyTreeTM Report, Data: Thomsom Reuters
Analysis: Collaborative Economics
20
06
20
07
Bi
lli
on
s
of
D
ol
la
rs
In
ve
st
ed
(
In
fla
tio
n
A
dj
us
te
d)
20
11
20
08
10
20
09
20
10
Patent Registrations
Patent Registrations
Data Source: . Patent and Trademark Office
Analysis: Collaborative Economics
Venture Capital Investment
0%
0
0
Construction & Building Materials
Manufacturing, Assembling, & Treating
Other
Chemical & Organic Compounds/Materials
Health
Measuring, Testing & Precision Instruments
Chemical Processing Technologies
Electricity & Heating/Cooling
Communications
Computers, Data Processing & Information Storage
Silicon Valley represents
large shares of total state
and . patents
Patents in computers, data
processing & information
storage soar in 2010
Silicon Valley
Venture Capital
Investment
%CA %.
2001 59% 24%
2006 61% 29%
2011 52% 27%
Venture capital investment
grew for the
second straight year
MY
22
Percentage of Total VC Investment in Clean Technology
Silicon Valley
80%
60%
40%
20%
Pe
rc
en
ta
ge
o
f T
ot
al
V
C
In
ve
st
m
en
t
in
C
le
an
T
ec
hn
ol
og
y
2006 2008 2011
Venture Capital Investment in Silicon Valley by Industry
40%
50%
10%
20%
30%
60%
70%
80%
90%
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Billions of Dollars Invested
Silicon Valley
20
05
$
19
99
20
00
20
01
20
02
20
03
20
04
Data Source: Cleantech GroupTM, LLC ()
Analysis: Collaborative Economics
20
06
20
07
Bi
lli
on
s
of
D
ol
la
rs
In
ve
st
ed
(
In
fla
tio
n
A
dj
us
te
d)
20
10
20
08
20
09
20
11
VC Investment in Clean Technology by Segment
Data Source: Cleantech GroupTM, LLC ()
Analysis: Collaborative Economics
Venture Capital by Industry
* Note: Other includes Business Products & Services, Financial
Services, Consumer Products & Services,
Retailing/Distribution, Healthcare Services and Other
Data Source: PricewaterhouseCoopers/National Venture
Capital Association MoneyTreeTM Report, Data:
Thomson Reuters
Analysis: Collaborative Economics
Venture Capital Investment in Clean Technology
0%
100%
Green Building
Agriculture
Water & Wastewater
Energy Infrastructure
Air & Environment
Advanced Materials
Clean Transportation
Energy Storage
Energy Efficiency
Energy Generation
Highlighted fields
indicate longer
term areas
of growth
0%
100% Networking & Equip.
Telecommunications
Computers and
Peripherals
Other*
Electronics/
Instrumentation
IT Services
Media and
Entertainment
Semiconductors
Industrial/
Energy
Medical Devices
& Equipment
Biotechnology
Software
Investment in Energy
Storage and Energy
Efficiency is growing
proportionally
Venture capital investment
in software expanded
in 2011
Cleantech VC investment
expanded 48 percent
in 2011
Innovation ECONO
23
E
C
O
N
O
M
Y
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
Employment
16-19
Innovation
20-23
Entrepreneurship
24-27
Commercial Space
28-29
Income
30-33
S O C I E T Y 34 | 43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Total Number and Value of Awards Granted to Small Businesses
Silicon Valley
20
05
200
250
350
50
100
150
300
19
90
20
00
20
01
20
02
20
03
20
04
Note: Small Business Innovation Research (SBIR) Awards and Small Business Technology Transfer (STTR) are included data
Data Source: . Small Business Administration, Office of Technology
Analysis: Collaborative Economics
# of Awards
20
06
20
07
To
ta
l N
um
be
r
of
A
w
ar
ds
20
08
Total Value of Awards
20
10
20
09
19
99
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
400400 $140
120
100
80
60
40
20
To
ta
l V
al
ue
o
f A
w
ar
ds
(
m
ill
io
ns
o
f f
ir
st
h
al
f 2
01
1$
)
20
05
19
90
19
95
19
97
20
01
20
03
Boston
20
08
700
600
500
400
Silicon Valley
Data Source: . Small Business Administration, Office of Technology; Moody’s
Analysis: Collaborative Economics
20
09
19
99
800
SB
IR
&
S
T
T
R
F
un
di
ng
p
er
$
1
M
ill
io
n
G
D
P
(In
fla
tio
n
A
dj
us
te
d)
$900
20
07
19
94
19
91
19
92
19
93
19
96
19
98
20
02
20
04
20
00
20
06
20
10
Funding per $1 Million GDP
Boston, Greater Washington ., Research Triangle, San Diego, and Silicon Valley
300
200
100
San DiegoResearch Triangle
Greater Washington .
0
0
0
Small Business Innovation and Technology Awards
Small Business Innovation and Technology Research
Small business innovation
and technology funding
holding steady
over prior year
Silicon Valley leads
in small business innovation
funding relative to total
economic output
MY
24
IPOs increased, but diminishing access
to business loans contributed to severe
business losses in 2009.
Entrepreneurship ECONO
WHY IS THIS IMPORTANT?
Entrepreneurship is an important element of Silicon Valley’s innovation
system. Entrepreneurs are the creative risk takers who create
new value and new markets through the commercialization of
novel and existing technology, products and services. A region
with a thriving innovation habitat supports a vibrant ecosystem
for businesses to start and to grow.
The activity of mergers and acquisitions and initial public offerings
indicate that a region is cultivating innovative and potentially high-
value companies. Small business financing is vital for startups as
well as established businesses wanting to grow. When hiring
slows, some people go into business for themselves, and structural
change is evident as the growth of companies without employees
(nonemployers) outpaces the growth in payroll employment. The
movement of businesses to and from Silicon Valley provides some
insight into the continued attractiveness of the region.
HOW ARE WE DOING?
Global initial public offerings (IPOs) have dropped 49 percent since
2010, while Silicon Valley’s IPOs increased modestly in 2011 to 12
pricings, representing 46 percent of IPOs statewide and 12 percent
nationally. . IPO pricings fell to 98 in 2011, a three percent
decrease from 2010 levels. In the cleantech sector, Silicon Valley
accounted for four of the 13 California cleantech IPOs in 2011.
Mergers and acquisitions (M&As) decreased 19 percent from 1,044
deals in the third quarter 2010 to 841 in the third quarter 2011.
In 2011, Silicon Valley accounted for 54 percent of all M&A deals
in California, up from 51 percent in 2010. In clean technology,
15 M&As were announced in 2011, up from 13 in 2010. The
region accounted for 39 percent of state deals and seven percent
of . deals in 2011. In clean technology, 15 M&As were announced
in 2011, up from 13 in 2010. The region accounted for 39 percent
of state deals and seven percent of . deals in 2011.
Access to small business loans continues to be tight. Between 1996
and 2010, small business loans in the region increased 41 percent
in total value (from $ billion to $ billion) and by 189 percent
in total number of loans. Silicon Valley outpaced the nation, which
reported growth of 18 percent in total value and 77 percent in
total number of loans over the same time period. Since the peak
in 2007, small business loan activity in the region dropped 52
percent in value ($ billion to $ billion) and by 66 percent
in total number of loans. However, the dropoff in the most recent
year was slower, decreasing by ten percent in value and by seven
percent in total number of loans.
Relative to 2004, Silicon Valley’s non-employer firms increased in
number by five percent between 2004 and 2009. Over the same
period, non-employer firms increased seven percent statewide
and eight percent nationally.
In January 2010, net business establishment growth plummeted for
the first time since 2000 in Silicon Valley. Recent national research
reflects similar declines in business creation, citing a 23 percent
decline since 2007 in new business creation across the
In the last observable period (January 2009-2010), 17,200 new
establishments were created and 46,800 closed in Silicon Valley.
Over 88 percent of the closed establishments had less than five
employees. On average, between 1995 and 2010, every year
Silicon Valley has gained approximately 17,300 establishments due
to businesses opening or moving in and lost an average of 12,800
establishments due to businesses closing or moving out. The
average net change in Silicon Valley establishments, a gain of 6,700,
is the equivalent of percent of total Silicon Valley establishments
in 2010.
The number of businesses leaving Silicon Valley has exceeded the
number moving to the region every year from 1995 to 2010, with
a majority of the movement staying within the state. The share
of businesses moving out of Silicon Valley but remaining in California
increased from 54 percent in 2009 to 77 percent in 2010.
2 Manyika James, S. Lund, B. Auguste, L. Mendonca, T. Welsh, S. Ramaswamy. An economy that works: Job creation and Americaís
future. McKinsey Global Institute, Jun. 2011. pp. ii.
Further findings reported by the Kauffman Foundation reveal that the number of new businesses with employees has dropped
27 percent since 2006. . Reedy and R. Litan. Starting Smaller; Staying Smaller: Americaís Slow Leak in Job Creation. Ewing
Marion Kauffman Foundation. Jul. 2011. pp. 4.
25
E
C
O
N
O
M
Y
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
Employment
16-19
Innovation
20-23
Entrepreneurship
24-27
Commercial Space
28-29
Income
30-33
S O C I E T Y 34 | 43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Total Number of . IPO Pricings
Silicon Valley, California, ., and International Companies
2007
250
200
150
100
N
um
be
r
of
P
ri
ce
d
In
iti
al
P
ub
lic
O
ffe
ri
ng
s
2008 2009 2010
50
Silicon Valley
Rest of California
International
Rest of .
2011
Initial Public Offerings
Note: Location based on corporate address provided by
Data Source: Renaissance Capital’s
Analysis: Collaborative Economics
Data Source: Cleantech GroupTM, LLC ()
Analysis: Collaborative Economics
0
300
162
26 43
76
60
12
15
53
2 Silicon Valley
3 Rest of CA
1 Silicon Valley
5 Rest of CA
11 Silicon Valley
14 Rest of CA
27
23
72
27
12 Silicon Valley
14 Rest of CA
IPO pricings fell nationwide
and globally,
while increasing in
Silicon Valley
IPO Pricings in Clean Technology
2005 2006 2007 2008 2009 2010 2011
Silicon Valley 2 0 0 0 0 2 4
Rest of CA 1 3 3 1 0 4 9
Rest of . 2 14 15 5 5 6 23
Cleantech IPOs
remained stable
in Silicon Valley in 2011
MY
26
Number of Deals
Silicon Valley, California and .
20
05
1,000
1,200
1,600
400
600
800
1,400
20
00
20
01
20
02
20
03
20
04
*Data is through the third quarter of 2011
Note: Deals include Buyers and Sellers
Data Source: Factset Mergerstat LLC
Analysis: Collaborative Economics
Number of Silicon Valley Deals
20
06
20
07
N
um
be
r
of
S
ili
co
n
Va
lle
y
D
ea
ls
20
08
Percentage of Total California Deals
20
10
20
09
19
99
19
92
19
93
19
94
19
95
19
96
19
97
19
98
200
Percentage of Total . Deals
*
20
11
Santa Clara & San Mateo Counties
20
05
19
96
19
97
20
01
20
03
Total Number .
20
08
800
700
600
500
Total Number Silicon Valley
Data Source: Federal Financial Institutions
Examination Council (FFIEC)
Analysis: Collaborative Economics
20
10
19
99
900
G
ro
w
th
R
el
at
iv
e
to
1
99
6
(1
00
=
19
96
v
al
ue
s)
20
07
19
98
20
02
20
04
20
00
20
06
400
300
200
Total Value Value Silicon Valley
20
09
Data Source: Cleantech GroupTM, LLC ()
Analysis: Collaborative Economics
Relative Growth of Small Business Loans
0
50%
60%
80%
20%
30%
40%
70%
0
10%
100
Mergers & Acquisitions
Pe
rc
en
ta
ge
o
f C
al
ifo
rn
ia
a
nd
U
.S
. D
ea
ls
Silicon Valley’s share of
California and . M&As
grew, while the total
number of deals declined
M&As in Clean Technology – Number of Deals, by Date Announced
2005 2006 2007 2008 2009 2010 2011
Silicon Valley 0 3 5 2 7 13 15
Rest of CA 9 15 10 18 43 24 23
Rest of . 30 99 86 105 130 176 170
The number and value
of small business loans
continued to decline
in 2010
Entrepreneurship ECONO
27
E
C
O
N
O
M
Y
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
Employment
16-19
Innovation
20-23
Entrepreneurship
24-27
Commercial Space
28-29
Income
30-33
S O C I E T Y 34 | 43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Silicon Valley, California, and .
20
05
20
04
108
106
104
Data Source: . Census Bureau, Nonemployer Statistics
Analysis: Collaborative Economics
20
09
110
N
on
em
pl
oy
er
F
ir
m
G
ro
w
th
R
el
at
iv
e
to
2
00
4
(1
00
=
20
04
v
al
ue
s)
112
20
07
20
06
20
08
102
30,000
95
-9
6
05
-0
6
06
-0
7
07
-0
8
Data Source: National Estiblishment
Time Series Database (NETS)
Analysis: Collaborative Economics
Firms Moving In
Firm Openings
Es
ta
bl
is
hm
en
ts
Net Establishment Churn
(Gain – Loss)
04
-0
5
96
-9
7
97
-9
8
98
-9
9
99
-0
0
00
-0
1
01
-0
2
02
-0
3
03
-0
4
-50,000
20,000
10,000
-20,000
Firms Closing
Firms Moving Out
-30,000
-40,000
08
-0
9
-10,000
Entering
Silicon
Valley
Leaving
Silicon
Valley
Total
Establishments 11
2,
02
6
11
7,
97
7
11
9,
63
1
12
2,
68
6
12
8,
79
3
14
3,
89
7
15
3,
90
6
15
5,
77
0
16
1,
51
0
16
9,
76
6
17
3,
93
8
18
6,
22
5
20
7,
66
6
17
7,
85
3
12
3,
08
9
09
-1
0
Nonemployer Firm Growth Relative to 2004
+8% .
+7%
California
+5%
Silicon
Valley
Data Source: Cleantech GroupTM, LLC
Analysis: Collaborative Economics
100
0
Nonemployer Firms
in 2009
Silicon Valley 174,900
California 2,674,300
United States 21,090,800
Nonemployer firms
continue to grow
at a slower rate
in the region
Establishment Churn
Santa Clara & San Mateo Counties
Santa Clara and San Mateo Counties – Percent of Total
Establishments Jobs
1995-1996 2009-2010 1995-1996 2009-2010
From Rest of CA 87% 75% 72% 80%
From Rest of . 13% 25% 28% 20%
To Rest of CA 81% 77% 55% 80%
To Rest of . 19% 23% 45% 20%
Firm closures outpaced
firm openings
for the first time since 2000
MY
28
Change in Supply of Commercial Space
Santa Clara County
20
00
20
01
20
02
20
03
20
04
20
05
-15
-10
-5
10
20
5
15
-20
20
08
19
98
20
06
20
07
Annual Rate of Commercial Vacancy
Santa Clara County
19
98
20
00
20
01
20
02
20
03
20
04
20
05
5%
10%
15%
20%
20
06
20
08
20
07
20
11
20
11
25%
19
99
20
09
20
09
19
99
20
10
20
10
25%
20%
15%
10%
5%
‘0
6
‘0
7
‘0
9
‘1
0
‘1
1
‘0
8
All Commercial Space Office R&D Industrial Warehouse
All Commercial Space Office
R&D Industrial/Warehouse
*
Commercial Space
* As of October 2011
Data Source: Colliers International
Analysis: Collaborative Economics
New Construction Added Net Absorption
Net Change in Supply
of Commercial Space
Sp
ac
e
A
dd
ed
/A
bs
or
be
d
(m
ill
io
n
sq
. f
t.)
Commercial Vacancy
* As of October 2011
Data Source: Colliers International
Analysis: Collaborative Economics San Mateo County
0
0%
0%
Available commercial space
in Santa Clara County
fell below 2007 levels
Overall, Santa Clara County
commercial vacancy
continued to fall in 2011.
San Mateo County followed
a similar pattern, reporting
drops in all sectors.
WHY IS THIS IMPORTANT?
Tracking the supply of commercial space, vacancy rates and asking
rents (., the rent listed for new space) provides leading indicators
of regional economic activity. In addition to office space, commercial
space includes R&D, industrial, and warehouse space. The change
in the supply of commercial space, expressed as the absorption
rate, reflects the amount of space rented, becoming available, and
added through new construction. Gross absorption is a measure
for total activity over a period while net absorption is the outcome.
A negative change in the supply of commercial space shows a
tightening in the commercial real estate market. The vacancy rate
measures the amount of space that is not occupied. Increases in
vacancy, as well as declines in rents, reflect slowing demand relative
to supply.
HOW ARE WE DOING?
Demand is rising for commercial space and as new construction has
been slow over the past several years, gross absorption was up
143 percent and new construction down 100 percent in the past
year. The net change in available commercial space decreased by
139 percent from 2010 to October 2011. Gross absorption is
a measure of demand in the rental property market. As of October
2011, no new commercial space construction had been added in
Santa Clara County. Since 2009 the only new developments have
been in the office sector.
In 2011, the vacancy rate decreased by percent across all commercial
space sectors in Santa Clara County. Warehouse space was the
only sector that experienced a small increase ( percent). In
San Mateo County, vacancy rates decreased by percent across
all commercial space sectors, with a decrease seen in every
category. Overall, the average annual asking rents stabilized from
2010 to 2011 in both counties.
As vacancy rates fall and rents stabilize,
recovery in commercial real estate markets
reflect increasing economic activity
in 2011.
Commercial Space ECONO
*
*
29
E
C
O
N
O
M
Y
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
Employment
16-19
Innovation
20-23
Entrepreneurship
24-27
Commercial Space
28-29
Income
30-33
S O C I E T Y 34 | 43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
19
98
20
00
20
01
20
02
20
03
20
04
20
05
20
08
2
4
7
8
$9
20
06
By Sector
Santa Clara County
19
98
20
00
20
01
20
02
20
03
20
04
20
05
2
3
5
6
20
08
20
06
20
07
1
20
07
20
11
20
11
20
09
19
99
6
19
99
20
09
4
5
1
3
20
10
$4
3
2
1
‘0
6
‘0
7
‘0
9
‘1
0
‘1
1
‘0
8
Office R&D Industrial/Warehouse
*
20
10
New Commercial Development
*As of October 2011
Data Source: Colliers International
Analysis: Collaborative Economics
San Mateo County
Annual Average Asking Rents
Santa Clara County
Commercial Rents
D
ol
la
rs
p
er
S
qu
ar
e
Fo
ot
p
er
M
on
th
* As of October 2011
Data Source: Colliers International
Analysis: Collaborative Economics
Office R&D Industrial Warehouse
0
0
M
ill
io
ns
o
f S
qu
ar
e
Fe
et
Office R&D Industrial Warehouse
0
Santa Clara County
commercial rents
declined in both office and
warehouse sectors, while
ticking up in R&D.
In San Mateo County,
office asking rents
ticked up while rents for
other space held steady.
Across all sectors
no new commercial
space construction
has been added
MY
*
*
30
Santa Clara & San Mateo Counties, California and .
40,000
50,000
$80,000
10,000
20,000
30,000
60,000
19
90
20
00
20
02
20
04
Silicon Valley
0
California
20
06
20
10
.
20
08
Pe
r
C
ap
ita
In
co
m
e
(In
fla
tio
n
A
dj
us
te
d) 70,000
19
92
19
94
19
96
19
98
20
11
19
91
20
01
20
03
20
05
20
07
20
09
19
93
19
95
19
97
19
99
Real Per Capita Income
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: Moody’s
Analysis: Collaborative Economics
Per capita income
is increasingly volatile
in the region
WHY IS THIS IMPORTANT?
Earnings growth is as important a measure of Silicon Valley’s economic
vitality as job growth. A variety of income measures presented
together provides an indication of regional prosperity and the
distribution of prosperity. Real per capita income rises when a
region generates wealth faster than its population increases. The
median household income is the income value at the middle of
all income values. Tracking trends in the percentage of student
receiving free meals provides an additional indication of economic
stress in the region.
HOW ARE WE DOING?
Silicon Valley’s real per capita income increased for the second
consecutive year in 2011 to roughly $66,000, a four percent gain
over the previous year. Following a similar pattern, real per capita
income inched higher in the state and the nation in 2011, both
reporting a two percent increase since 2010. Silicon Valley’s real
per capita income is 59 percent higher than that of the nation
and 48 percent higher than that of the state.
From 2008 to 2010, per capita income dropped across all other groups
but increased 16 percent for Blacks. Statewide and nationally,
Blacks witnessed the smallest drops in income. Falling 15 percent,
Hispanics saw the largest losses in income from 2008 to 2010 in
Silicon Valley. Hispanics were also hardest hit both state and
nationwide. Although Whites saw a decrease of six percent, their
incomes remained the highest of all groups at $56,900.
Median household income for Silicon Valley fell three percent from
2009 to 2010. The region trailed the statewide drop of seven
percent while it exceeded the two percent drop nationwide.
Since 2004, the proportion of middle income households (earning
$35,000-$99,999) has shrunk by four percent and now accounts
for 37 percent of Silicon Valley households. The percentage of
households earning more than $100,000 per year continued to
grow and now accounts for 43 percent of all households in Silicon
Valley, up from 35 percent in 2004. Meanwhile, the share of
households earning less than $35,000 has decreased from 24
percent in 2004 to 20 percent in 2010.
The percentage of students receiving free meals has risen consistently
since 2003, in both the region and the state as a whole. Participation
of students ages 5 to 17 in the free meals program increased
from 30 to 31 percent in Silicon Valley and from 47 to 49 percent
in California from 2009 to 2010.
Per capita income continues to rise, while
other income indicators still bear the
effects of the recession.
Income ECONO
31
E
C
O
N
O
M
Y
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
Employment
16-19
Innovation
20-23
Entrepreneurship
24-27
Commercial Space
28-29
Income
30-33
S O C I E T Y 34 | 43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Santa Clara & San Mateo Counties
40,000
50,000
$70,000
10,000
20,000
30,000
60,000
White
M
ed
ia
n
In
co
m
e
(In
fla
tio
n
A
dj
us
te
d)
Asian Black Multiple
and Other
Hispanic
Per Capita Income by Race & Ethnicity
Note: Multiple & Other includes Native Hawaiian & Other Pacific Islander Alone, American Indian & Alaska Native alone, Some
other race alone and Two or more races
Personal income is defined as the sum of 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,
welfare payments, retirement, survivor or disability pensions; and all other income
Data Source: US Census Bureau, American Community Survey
Analysis: Collaborative Economics
20
04
20
06
20
08
0
20
10
Percent Change in Per Capita Income – 2008-2010
Silicon Valley California United States
White -6% -6% -6%
Asian -8% -7% -7%
Black +16% -4% -5%
Multiple & Other -7% -5% -8%
Hispanic -15% -9% -9%
Per capita income
dropped across all groups
except blacks
MY
32
Santa Clara & San Mateo Counties, California and the .
20
05
$100,000
90,000
20
00
20
01
20
02
20
03
20
04
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: . Census Bureau, American Community Survey
Analysis: Collaborative Economics
0
20
06
70,000
80,000
50,000
60,000
In
fla
tio
n
A
dj
us
te
d
D
ol
la
rs
(
Fi
rs
t
H
al
f $
20
11
)
Silicon Valley California
20
10
.
20
07
20
08
40,000
30,000
20,000
10,000
20
09
Santa Clara & San Mateo Counties, California and the .
30%
40%
Note: Income ranges reflect nominal values. Hosehold income includes wage and salary income, net self-employment income,
interest dividends, net rental or royalty income from estates and trusts; Social Security or railroad retirement income;
Supplemental Sescurity Inoome; public assistance or welfare payments; retirement, survivor, or disability pensions; and
all other income; excluding stock options.
Data Source: . Census Bureau, American Community Survey
Analysis: Collaborative Economics
100%
50%
20%
10%
2004
90%
80%
70%
60%
2007 2010 2004 2007 2010 2004 2007 2010
United States California Santa Clara &
San Mateo Counties
$100,000 or more $35,000 - $99,000 Under $35,000
Pe
rc
en
ta
ge
o
f H
ou
se
ho
ld
s
by
In
co
m
e
R
an
ge
Median Household Income
Distribution of Households by Income Ranges
0%
39%
46%
15%
35%
46%
20%
36%
44%
20%
34%
45%
21%
29%
44%
27%
31%
43%
26%
24%
41%
35%
20%
38%
42%
20%
37%
43%
Change in Percent of Households
by Income Ranges
Silcon Valley 2004-2010
$100,000 or More High Income +%
$35,000-$99,999 Middle Income %
Under $35,000 Low Income %
Percent Change in
Median Household
Income
2009-2010
Silicon Valley -3%
California -7%
United States -2%
Median household income
continues to slip
Since 2004, the share of
households in the low and
middle income ranges
has declined
Income ECONO
33
E
C
O
N
O
M
Y
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
Employment
16-19
Innovation
20-23
Entrepreneurship
24-27
Commercial Space
28-29
Income
30-33
S O C I E T Y 34 | 43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Percent of Students Receiving Free Meals
Santa Clara & San Mateo Counties and California
30%
40%
Data Source: California Department of Education
Analysis: Collaborative Economics
60%
50%
20%
10%
Santa Clara & San Mateo Counties
2003 2004 2005 2006 2007 20102008
California
2009
Free School Meals
0%
Participation in free meals
program still rising
Percent of Students
Receiving Free Meals
2009 2010
Silicon Valley 30% 31%
California 47% 49%
MY
34
Rate of Graduation, Share of Graduates Who Meet UC/CSU Requirements
and Dropout Rate
Silicon Valley High Schools
40%
50%
10%
20%
30%
60%
100%
70%
80%
90%
Silicon Valley California
Graduation Rates % of Graduates Who Meet UC/CSU Requirements
By Ethnicity
Silicon Valley High Schools, 2009-2010
40%
50%
10%
20%
30%
60%
100%
Asian White Filipino Pacific
Islander
American
Indian
African
American
Hispanic Silicon
Valley
Total
70%
80%
90%
Pe
rc
en
ta
ge
o
f H
ig
h
Sc
ho
ol
S
tu
de
nt
s
w
ho
G
ra
du
at
ed
in
4
Y
ea
rs
2008-20092007-2008 2008-2009 2007-2008 2009-20102009-2010
Dropout Rate
Pe
rc
en
ta
ge
o
f H
ig
h
Sc
ho
ol
G
ra
du
at
es
w
ho
m
et
U
C
?C
SU
r
eq
ui
re
m
en
ts
High School Graduation Rates
96
%
94
%
93
%
89
%
85
%
83
% 88
%
78
%
High School Graduation and Dropout Rate
0%
Notes: 2006-07 marks the first year in which the CDE derived graduate and dropout counts based upon student level data
Data Source: California Department of Education
Analysis: Collaborative Economics
0%
Data Source: California Department of Education
Analysis: Collaborative Economics
86
%
47
%
10
%
87
%
49
%
14
%
88
%
50
%
11
%
80
%
34
%
15
%
79
%
35
%
17
%
81
%
36
%
13
%
Graduation rates and
the percentage of graduates
with UC/CSU requirements
continues to rise as
dropout rates fall
Asian, White, Filipino
and Pacific Islander
students fall above
the state average
for graduation rates
WHY IS THIS IMPORTANT?
The future success of the region’s young people in a knowledge-based
economy will be determined in part by how well elementary and
secondary education in Silicon Valley prepares its students for
higher levels of education.
How well the region is preparing its youth for postsecondary education
can be observed in graduation rates and the percentage of
graduates completing courses required for entrance to the
University of California (UC) or California State University (CSU).
Likewise, high school dropouts are significantly more likely to be
unemployed and earn less when they are employed than high
school graduates. Indicators in gateway skills such as algebra
proficiency are highly correlated with later academic success.
HOW ARE WE DOING?
The region again experienced a steady increase in the graduation rate
from the previous academic year, a marginal increase in the share
of graduates who met the UC/CSU requirements, and a decline
in the dropout rate. For all three measures, Silicon Valley has an
edge over the state as a whole. Dropout rates in Silicon Valley
and California have trended together over the past three school
years while at a persistently lower rate.
The region’s overall graduation rate for the 2009-2010 school year
was 88 percent - up from percent in the previous year, but
disparities by race and ethnicity persist. Hispanic students trail
other groups with a graduation rate of 78 percent. Drop-out
rates for American Indians (20%), Hispanics (19%) and African
Americans (14%) students exceed the regional average.
The percentage of Silicon Valley students scoring at the Advanced or
Proficient level on the CST Algebra I test in 2011 edged up one
percentage point each while scores at the lower levels remained
flat. This brings the percentage of Silicon Valley students scoring
at or above the proficiency level to a recent high of 57 percent.
Preparing for Economic Success
Student achievement is improving. SOCIETY
35
S
O
C
IE
T
Y
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
Economic Success
34-35
Early Education
36-37
Arts and Culture
38-39
Quality of Health
40-41
Safety
42-43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
by Ethnicity
Silicon Valley High Schools, 2009-2010
10%
20%
American
Indian
Hispanic African
American
Filipino White Asian Silicon
Valley
Total
Pacific
Islander
12%
16%
14%
4%
Pe
rc
en
ta
ge
o
f H
ig
h
Sc
ho
ol
S
tu
de
nt
s
w
ho
D
ro
po
ut
18%
Percentage of Eighth Graders Tested Who Scored at Benchmarks on CST Algebra I Test
Silicon Valley Public Schools
Data Source: California Department of Education
Analysis: Collaborative Economics
Advanced Proficient Basic Below Basic Far Below Basic
Pe
rc
en
ta
ge
o
f E
ig
ht
h
G
ra
de
rs
T
es
te
d
W
ho
S
co
re
d
at
B
en
ch
m
ar
ks
o
n
C
ST
A
lg
eb
ra
I
Te
st
40%
35%
30%
25%
20%
15%
10%
5%
8%
2%
6%
High School Dropout Rates
20
%
19
%
14
%
9%
7%
5%
4%
11
%
Algebra I Scores
0%
Data Source: California Department of Education
Analysis: Collaborative Economics
0%
20
06
26
%
31
%
20
%
18
%
6%
20
07
20
08
20
09
20
10
20
11
High school dropout
rates vary greatly
by ethnicity
Silicon Valley’s share
of eighth graders scoring
at advanced and
proficient benchmarks
increased in 2011
36
Percentage of Population 3 to 5 Years of Age Enrolled in Preschool
Santa Clara & San Mateo Counties, California, and the United States
30%
35%
40%
Note: Data includes enrollment in preschool and nursery school, and population for children three to five years of age.
Data Source: . Census Bureau, 2002-2010 American Community Survey and 2000-2001 Supplementary Survey
Analysis: Collaborative Economics
50%
45%
25%
20%
15%
10%
United States California Silicon Valley
2000 2001 2002 2003 2004 2005 2006 2007 2009
5%
2008
San Mateo & Santa Clara Counties, 2011
40%
50%
70%
10%
20%
30%
60%
80%
90%
100%
Proficient and Advanced
Far Below Basic, Below Basic, and Basic
En
gl
is
h-
La
ng
ua
ge
A
rt
s
Pr
of
ic
ie
nc
y
R
at
e
2010
Preschool Enrollment
Third Grade English-Language Arts Proficiency by Race/Ethnicity
Note: Ethnic groups not included did not have data available
Data Source: California Department of Education
Analysis: Collaborative Economics
0%
0%
Ch
ine
se
As
ian
In
di
an
Ko
re
an
As
ian
O
th
er
A
sia
n
Jap
an
es
e
W
hit
e
Vi
et
na
me
se
Tw
o
or
M
or
e
Ra
ce
s
Af
ric
an
A
me
ric
an
N
at
ive
H
aw
aii
an
o
r P
ac
ifi
c I
sla
nd
er
Hi
sp
an
ic
or
L
at
ino
O
th
er
P
ac
ifi
c I
sla
nd
er
Sa
mo
an
Fil
ipi
no
Percentage of Population
Enrolled in Preschool (Ages 3 to 5)
2010
Silicon Valley 43%
California 37%
United States 40%
The Preschool enrollment
rate held in Silicon Valley
but dropped in the state
and the .
Third grade English-
Language Arts proficiency
varies by race
WHY IS THIS IMPORTANT?
When children are subject to positive early childhood experiences,
including attendance in high-quality preschool programs, those
opportunities enhance their physical, social, and emotional wellbeing
and their academic skills. Children’s academic success is in part
a function of increasing literacy skills. Research shows that children
who read well in the early grades are far more successful in later
years; and those who fall behind often stay Success and
confidence in reading are critical to long-term success in school.
HOW ARE WE DOING?
Silicon Valley has a higher preschool enrollment rate for children
between the ages of three and five than the state and country
as a whole. Holding steady since 2009, 43 percent of this age
group were enrolled in preschool in the region. Enrollment was
40 percent nationally and 37 percent statewide.
Disparities in proficiency exist in English-Language Arts by race and
ethnicity. For the Chinese, Asian Indian, and Korean populations,
more than 83 percent of students tested at the proficient level
or higher, and over 56 percent of Chinese and Asian Indian
students scored at the advanced level. Japanese students had
the largest increase; those scoring at proficient and advanced
jumped from 69 percent in 2010 to 75 percent in 2011. Samoan
and Hispanic or Latino groups had the highest percentage of
students with basic proficiency and below scoring 66 and 65
percent respectively.
Thirty-eight percent of families in Silicon Valley receive childcare from
multiple providers and provider types. ‘Other’ sources of
care–including non-family members, nursery schools, and state-
sponsored programs–have decreased in share by two percent
from 2007 to 2009. The share of families choosing childcare
provided by a grandparent or other family member in Silicon
Valley (15%) has decreased since 2007 and is much lower than
in California (24%).
Early Education
English-Language Arts proficiency has
improved overall, however general
disparities by ethnicity persist.
SOCIETY
37
S
O
C
IE
T
Y
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
Economic Success
34-35
Early Education
36-37
Arts and Culture
38-39
Quality of Health
40-41
Safety
42-43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Type of Care for Children 12 Years Old and Younger
60%
70%
80%
*Other includes Head Start/State Program, Preschool or Nursery School, Non-Family member, and Other Source
Note: Childcare for children receiving 10 or more hours of childcare per week
Data Source: UCLA California Health Interview Survey
Analysis: Collaborative Economics
100%
90%
50%
40%
30%
20%
2001 2003 2005 2007 2009
10%
Silicon Valley
2001 2003 2005 2007 2009
California
More than
one source
Other*Childcare
Center
Grandparent
or family member
Pe
rc
en
ta
ge
o
f C
hi
ld
re
n
Childcare Arrengements
0%
More Silicon Valley
families are choosing
childcare centers
38
2010
350
400
*Silicon Valley includes Santa Clara County
Data Source: Americans for the Arts, Dun & Bradstreet, 2010
Analysis: 1st ACT
500
450
200
50
100
150
250
300
A
rt
s-
ce
nt
ri
c
bu
si
ne
ss
es
p
er
o
ne
h
un
dr
ed
t
ho
us
an
d
re
si
de
nt
s
Santa Clara & San Mateo Counties
6,000
7,000
8,000
Data Source: National Establishment Time-Series (NETS) Database; Americans for the Arts
Analysis: Collaborative Economics
10,000
9,000
5,000
4,000
3,000
2,000
2000 2005
1,000
20101995
Visual Arts/Photography Design and Publising
Film, Radio, and TelevisionSchools and ServicesMuseums and Collections
Performing Arts
Es
ta
bl
is
hm
en
ts
Arts-centric Businesses per One Hundred Thousand Residents
Arts-Centric Businesses
0
Po
rt
lan
d
Au
sti
n
M
inn
ea
po
lis
M
iam
i
Sa
n
Di
eg
o
Re
se
ar
ch
Tr
ian
gle
, N
C
Sil
ico
n V
all
ey
*
Pi
tts
bu
rg
h
Ch
ica
go
N
at
io
na
l A
ve
ra
ge
Sa
cr
am
en
to
Tu
sc
on
De
tro
it
0
The region has a higher
concentration of arts-
centric businesses than
the nation as a whole
Two thirds of arts-centric
businesses are in design
and publishing and
visual arts/photography
SOCIETY
WHY IS THIS IMPORTANT?
Art and culture are integral to Silicon Valley’s economic and civic
future. Participation in arts and cultural activities spurs creativity
and increases exposure to diverse people, ideas and perspectives.
Increasingly, broad-based creativity is understood as fundamental
to our region’s innovative milieu. Arts related businesses and
the creative people they employ stimulate innovation in today’s
global marketplace.
A vital arts community is also a factor in a region’s attraction and
retention of talent. A robust ecosystem of arts education
opportunities for young people is understood as a competitive
advantage in attracting young talent and families that put a high
premium on education offerings
HOW ARE WE DOING?
Nationally, ‘arts-centric’ businesses, as defined by Americans for the
Arts, made up four percent of all business establishments in 2010.
In Santa Clara County the concentration is slightly higher (
percent). These businesses range from non-profit organizations
such as museums and theaters to for-profit architecture, advertising,
film, entertainment, publishing and a multitude of specialized
design services.
In 2010, there were 7,800 ‘arts-centric’ businesses in the region. Over
a third of those businesses were in Design and Publishing and 30
percent were in Visual Arts/Photography. However, these numbers
represent an overall decline in Silicon Valley, and a slightly higher
rate of decline in 2010 (16 percent) than the economy overall
(14 percent). However, since 1995 businesses of this type have
grown 33 percent. Design and Publishing has been the largest
contributor to the long-term growth, adding over 1,000
establishments from 1995 to 2010 (a growth of 56 percent).
Arts education opportunities are a well-established priority for Silicon
Valley families. Repeated surveys over the past ten years have
found more than 90 percent of the Valley’s parents wanting arts
education as mandatory subject matter in our schools.
Nevertheless, Silicon Valley trails many other regions in the
presence of arts education talent.
Arts and Culture
Creativity is fundamental to Silicon Valley’s
competitive edge. Creative business enterprises and
arts organizations together help build an innovative
talent base for the future.
39
S
O
C
IE
T
Y
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
Economic Success
34-35
Early Education
36-37
Arts and Culture
38-39
Quality of Health
40-41
Safety
42-43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
2010
20
25
*Silicon Valley includes Santa Clara County
Data Source: Americans for the Arts, Music Educators National Conference, National Dance Educators Organization, Educational
Theater Organization, National Arts Educators Association, 2010
Analysis: 1st ACT
35
30
5
10
15
A
rt
s
ed
uc
at
or
s
pe
r
on
e
hu
nd
re
d
th
ou
sa
nd
r
es
id
en
ts
Arts Educators per One Hundred Thousand Residents
0
Po
rt
lan
d
Au
sti
n
M
inn
ea
po
lis
M
iam
i
De
tro
it
Re
se
ar
ch
Tr
ian
gle
, N
C
Sil
ico
n V
all
ey
*
Pi
tts
bu
rg
h
Ch
ica
go
N
at
io
na
l A
ve
ra
ge
Tu
sc
on
Sa
cr
am
en
to
Sa
n
Di
eg
o
Silicon Valley has a lower
concentration of arts
educators than the nation
as a whole
40
Percent of Kindergarten Students with All Required Immunizations
Santa Clara & San Mateo Counties, and California
40%
50%
10%
20%
30%
60%
100%
2006-2007
70%
80%
90%
2007-2008 2008-2009 2009-2010
Kindergarten Immunizations
% % % % % %
S
ili
co
n
V
al
le
y
C
al
ifo
rn
ia
% %
0%
Data Source: California Department of Public Health, Kindergarten Immunization Assessment
Analysis: Collaborative Economics
Kindergarten immunization
rates slow very slightly
SOCIETY
WHY IS THIS IMPORTANT?
Poor health outcomes generally correlate with poverty, poor access
to preventative health care, lifestyle choices and education. Early
and continued access to quality, affordable health care is important
to ensure that Silicon Valley’s residents are healthy and prosperous.
For example, timely childhood immunizations promote long-term
health, save lives, prevent significant disability and reduce medical
costs. Health care is expensive, and individuals with health insurance
are more likely to seek routine medical care and to take advantage
of preventative health-screening services.
HOW ARE WE DOING?
The percentage of kindergarten students who have received all required
immunizations in Silicon Valley has exceeded the state as a whole
in every year reported. However, rates have fallen since 2006 in
both geographies. Low levels of immunization affect the susceptibility
of the region to outbreaks of childhood illnesses like pertussis
(whooping cough) or chicken pox.
In 2010, 88 percent of people in Silicon Valley had health insurance,
holding steady from 2009. Health insurance coverage includes
both private coverage including employer or union-based as well
as public coverage including Medicare and Medicaid. Improvement
in coverage was reported across four of six groups. African
Americans with coverage jumped from 85 to 88 percent in 2010.
While the rate of coverage held steady for Asians and the category
Two or More Races over the most recent year, these groups,
along with Whites reflect the highest coverage rates.
From 2008 to 2009, the infant mortality rate increased from to
per 1,000 live births in Silicon Valley and decreased from
to in California. Infant mortality has steadily declined in
California and declined with some volatility in Silicon Valley. Since
1994, the infant mortality rate has dropped by deaths in Silicon
Valley and by deaths statewide.
Quality of Health
While health insurance coverage is improving,
immunization rates are holding, and infant
mortality rates are increasing.
41
S
O
C
IE
T
Y
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
Economic Success
34-35
Early Education
36-37
Arts and Culture
38-39
Quality of Health
40-41
Safety
42-43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
White
By Ethnicity
Santa Clara & San Mateo Counties
50%
60%
70%
Data Source: . Census Bureau, American Community Survey
Analysis: Collaborative Economics
100%
80%
40%
30%
20%
10%
90%
2009 2010
Two or More
Races
Asian African
American
Hispanic Some Other
Race
Number of Deaths per 1,000 Live Births
Santa Clara & San Mateo Counties, California
4
5
6
Data Source: California Department of Public Health, Center for Health Statistics
Analysis: Collaborative Economics
8
7
3
2
1
Santa Clara & San Mateo Counties California
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
N
um
be
r
of
D
ea
th
s
pe
r
1,
00
0
Li
ve
B
ir
th
s
Percentage of Population with Health Insurance
Percent with Health Insurance in
Santa Clara & San Mateo Counties in 2010
Infant Mortality Rate
0%
0
Silicon Valley
Percentage of Individuals
with Health Insurance
2009 2010
White 93% 94%
Two or More Races 92% 91%
Asian 91% 91%
African American 85% 88%
Hispanic 77% 77%
Some Other Race 73% 75%
Health insurance
increased for nearly
all ethnicities
Infant mortality rate
up slightly in Silicon Valley
42
8
10
14
2
4
6
12
19
99
20
00
20
01
20
02
20
03
20
04
Note: The recent decline in cases from 2007 to 2010 can be explained in part by large funding cuts in social services
programs for children.
Data Source: California Department of Social Services, UC Berkeley Center for Social Services Research
Analysis: Collaborative Economics
Su
bs
ta
nt
ia
te
d
C
as
es
o
f C
hi
ld
A
bu
se
, p
er
1
,0
00
C
hi
ld
re
n
Silicon Valley California
Substantiated Cases of Child Abuse per 1,000 Children
Silicon Valley and California
20
05
20
09
19
98
20
06
20
07
20
08
N
um
be
r
of
S
oc
ia
l S
er
vi
ce
E
m
pl
oy
ee
s
0
1000
600
400
200
20
10
800
Number of Social Service
Employees in Silicon Valley
1,200
Child Abuse
0
Both substantiated cases of
child abuse and the number
of social services employees
continue to drop
Child welfare services are faced with
shrinking budgets.
Safety SOCIETY
WHY IS THIS IMPORTANT?
The level of crime is a significant factor affecting the quality of life in
a community. Incidence of crime and gang activities not only
poses an economic burden, but also erodes our sense of community
by creating fear, frustration and instability. Occurrence of child
abuse and or neglect is extremely damaging to the child and
increases the likelihood of drug abuse, poor education performance
and criminality later in life. Research has also linked adverse
childhood experiences, such as child abuse and or neglect, to
poor health outcomes including heart disease, depression, and
liver and sexually transmitted diseases. Safety for the community
starts with safety for children in our homes.
HOW ARE WE DOING?
The rate of substantiated child abuse in Silicon Valley began declining
at a faster rate in 2008. By 2010, the rate dropped 14 percent,
bringing the number of child abuse cases per thousand to a low
of . This drop can be explained in part by the concurrent
decline in social service employees in the region, which has fallen
six percent since 2007, and four percent in the last year. In
California, child abuse cases fell four percent in 2010, bringing the
number of child abuse cases per thousand to a low of . As
public revenues fall (see Governance section), public services
also diminish.
After increasing the previous year, school expulsions due to violence or
drugs fell during the 2010-11 academic year in both the state and
the region. Expulsions dropped by per 1,000 students in Silicon
Valley and by statewide. After peaking the previous academic
year, expulsions fell to two per 1,000 students in the region.
Gang related homicide in both Silicon Valley and the state has fluctuated
since 1997. Since peaking in 2007, gang related homicides in the
region have fallen 31 percent while increasing 19 percent in the
state over the same time period. From 1997 to 2009, gang related
homicides increased by 22 percent in the region and one percent
in the rest of the state.
43
S
O
C
IE
T
Y
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
Economic Success
34-35
Early Education
36-37
Arts and Culture
38-39
Quality of Health
40-41
Safety
42-43
P L A C E 44 | 55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Expulsions Per 1,000 Enrolled K-12 Students
Silicon Valley, California
Data Source: California Department of Education
Analysis: Collaborative Economics
2004-2005 2005-2006 2006-2007 2009-2010
Ex
pu
ls
io
ns
p
er
1
,0
00
E
nr
ol
le
d
St
ud
en
ts
2007-2008 2008-2009
20
09
Growth in Gang Related Homicide Relative to 1997
Santa Clara & San Mateo Counties, and Rest of California
140
160
80
100
120
180
Data Source: California Governer’s Office of Gang and Youth Violence Policy
Analysis: Collaborative Economics
200
19
97
19
99
20
00
20
01
20
02
20
03
20
04
20
05
In
de
xe
d
to
1
99
7
(1
00
=
19
97
v
al
ue
s)
20
06
20
07
20
08
60
40
20
19
98
2010-2011
Public School Expulsions Due to Violence/Drugs
Gang Related Homicide
Santa Clara & San Mateo Counties Rest of California
0
Si
lic
on
V
al
le
y
C
al
ifo
rn
ia
Public school expulsions
are down
Gang Related Homicide
% Change
1997 2008 2009 97-09 08-09
Silicon Valley 9 15 11 +22% -27%
Rest of CA 535 468 541 +1% +16%
Gang related homicide
dropping in the region
44
Environment
Silicon Valley reduced resource
consumption and made progress toward
improved efficiency in electricity
generation and use.
PLACE
WHY IS THIS IMPORTANT?
Environmental quality directly affects the health of all residents as well
as the Silicon Valley ecosystem, which is in turn affected by the
choices that residents make about how to live–how we chose to
access work, other people, goods and services; where we build
our homes; how we use our natural resources; and how we
enforce environmental guidelines.
Water is one of the region’s most precious resources, serving a
multitude of needs, including drinking, recreation, supporting
aquatic life and habitat and agricultural and industrial uses. Water
is also a limited resource because water supply is subject to
changes in climate and state and federal regulations. Sustainability
in the long run requires that households, workplaces and agricultural
operations efficiently use and reuse water.
Energy consumption impacts the environment with the emissions of
greenhouse gases and atmospheric pollutants through the
combustion of fossil fuels. Sustainable energy policies include
increasing energy efficiency and the use of clean renewable energy
sources. For example, more widespread use of solar generated
power diversifies the region’s electricity portfolio, increases the
share of reliable and renewable electricity, and reduces greenhouse
gasses and other harmful emissions. Electricity productivity
illustrates the degree to which the region’s production of economic
value is linked with its electricity consumption.
In recent years, residents and businesses are investing in renewable
energy installations. The length of a municipality’s required
permitting process can pose significant barriers to the widespread
adoption of renewable energy installations and add significantly
to the costs. Streamlining the region’s permitting requirements
will yield environmental and economic gains.
HOW ARE WE DOING?
Silicon Valley residents are making progress toward reducing water
consumption. In the past year alone, water consumption per
capita in the region fell by nine percent. Since 2000, gross per
capita consumption dropped 18 percent. In 2010, percent of
the total water consumed in Silicon Valley was from recycled
sources, the highest recycled consumption level since measurement
began in 1999.
Electricity consumption per capita is a measure of efficiency. Since
1998, per capita consumption has fallen seven percent in Silicon
Valley, compared with two percent in the rest of California. Most
recently, per capita consumption fell in both geographies in the
last year, declining by one percent in the region and by two percent
in the rest of California.
The economic value produced per mega watt hour consumed is a
measure of the region’s electricity productivity. In 2010, Silicon
Valley’s electricity productivity was 19 percent higher than that
of California. In the last year, electricity productivity increased
by three percent in the region and by two percent in the rest of
the state. In the long run, electricity productivity is up ten percent
since 1998 in Silicon Valley and up by 13 percent in the rest of
California.
The region’s new solar capacity increased by 41 percent from 2010
to 2011, but only rose by 21 percent statewide. The residential
sector accounted for 60 percent of the solar capacity added in
Silicon Valley in 2011, but added capacity in this sector declined
20 percent from 2010. The large commercial sector increase of
2,127 percent in 2011 accounted for the overall gain in capacity
in 2011.
Permitting times decreased for all technologies from 2009 to 2011,
with the exception of solar installations. In 2011, electric vehicle
charging station installations had the shortest median permitting
time with 50 percent of Silicon Valley cities reporting permits
could be obtained in one day or less. In that same year, 75 percent
of cities reported that electric vehicle charging station installations
could be permitted in days or less, and the maximum
permitting time was 49 days. In that same year, Solar installations
had the second shortest median permitting time of 5 days,
followed by wind installations (6 days) and geothermal installations
( days).
45
P
L
A
C
E
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
S O C I E T Y 34 | 43
Environment
44-47
Transportation
48-49
Land Use
50-51
Housing
52-55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
99
-0
0
00
-0
1
01
-0
2
02
-0
3
03
-0
4
05
-0
6
G
ro
ss
P
er
C
ap
ita
C
on
su
m
pt
io
n
(G
al
lo
ns
P
er
C
ap
ita
P
er
D
ay
)
R
ec
yc
le
d
Pe
rc
en
ta
ge
o
f T
ot
al
W
at
er
U
se
d
04
-0
5
07
-0
8
06
-0
7
09
-1
0
Gross Per Capita Consumption & Share of Consumption from Recycled Water
Silicon Valley BAWSCA Members
Santa Clara & San Mateo Counties, Rest of California
20
02
20
03
20
04
20
05
20
09
20
06
Silicon Valley Electricity Consumption per Capita Silicon Valley Electricity Productivity
20
07
20
08
19
98
19
99
20
00
20
01
Rest of CA Electricity Consumption per Capita Rest of CA Electricity Productivity
08
-0
9
20
10
140
160
100
120
80
20
40
60
180
Data Source: Bay Area Water Supply & Conservation Agency Annual Survey
Analysis: Collaborative Economics
Gross Per Capita Consumption (GPCPD) Percentage of Total Water Used in
Silicon Valley that is Recycled
Water Resources
Electricity Productivity & Consumption per Capita
El
ec
tr
ic
ity
C
on
su
m
pt
io
n
pe
r
C
ap
ita
(
kW
h
pe
r
pe
rs
on
)
El
ec
tr
ic
ity
P
ro
du
ct
iv
ity
(
In
fla
tio
n
A
dj
us
te
d
D
ol
la
rs
o
f G
D
P
R
el
at
iv
e
to
C
on
su
m
pt
io
n
of
M
eg
aw
at
th
ou
rs
)
0
Data Source: Moody’s , California Energy Commission; State of California, Department of Finance
Analysis: Collaborative Economics
6,000 6,000
4,000 4,000
2,000 2,000
10,000 10,000
8,000 8,000
0 0
1.
3%
1.
8% 2
.1
%
2.
9%
3.
4%
3.
5%
3.
5% 3.
5%
3.
4% 3
.6
%
3.
3%
FY FY FY FY FY FYFY FYFY FY
Per Capita
Water Consumption
% Change, 2009-2010
-9%
FY
Water consumption
dropped for a fourth
year in a row, and recycled
water use increased
Electricity consumption
per capita fell as electricity
productivity increased
%
%
%
%
%
%
46
Capacity (kW) Installed Through the California Solar Initiative
Silicon Valley
3,000
5,000
8,000
4,000
2,000
1,000
GovernmentNon-ProfitCommercialResidential
6,000
7,000
Data Source: California Public Utilities Commission,
California Solar Initiative
Analysis: Collaborative Economics
Solar Installations by Sector
In
st
al
le
d
ki
lo
w
at
ts
0
2007 2008 2009 2010
Growth in Solar Capacity
(kW) added through the
California Solar Initiative
2010-2011
Silicon Valley +41%
Rest of California +21%
2011
Large gains in commercial
capacity drive new solar
capacity in 2011
Environment PLACE
47
P
L
A
C
E
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
S O C I E T Y 34 | 43
Environment
44-47
Transportation
48-49
Land Use
50-51
Housing
52-55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
2009
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
4
2010
4
2011
5
Days Required for Solar Permits
Solar
0 5 15 20 25 30 35 40
Days Required for Wind Permits
Wind
10
0 5 10 15 20 25 30 35 40 45 50 55
Days Required for Geothermal Permits
Geothermal
Days Required for Electric Vehicle Permits
Electric Vehicle Charging Station
0 5 10 15 20 25 30 35 40 45 50 55
*Beginning in 2008, the Land Use Survey expanded its geographic definition of Silicon Valley to include cities
northward along the . 101 corridor (Brisbane, Burlingame, Millbrae, San Bruno and South San Francisco)
Data Source: City Planning and Housing Departments of Silicon Valley
Analysis: Collaborative Economics
2009
2010
2011
2009
2010
2011
2009
2010
2011
14
21
6
1
1
Time Required for Permitting of Renewable Energy Installation
The permitting time required for the installation of renewable energy
systems is dropping in the region. In the charts above, the blue box
represents the range for which the middle 50 percent of the
responses fall. The vertical black line in the blue box represents the
median (middle) value of the data set. The left-hand line represents
the range for the lower 25 percent of the responses, and the right-
hand line represents the range for the upper 25 percent.
Permitting times for
renewable energy
systems are decreasing
48
Santa Clara & San Mateo Counties
5,000
2,000
8,000
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
4,000
1,000
7,000
20
08
19
95
20
10
3,000
6,000
20
09
Vehicle Miles of Travel per Capita and Gas Prices
Note: Gas prices are average annual retail gas prices for California
Data Source: California Department of Transportation; Energy Information Administration, . Department of Energy;
California Department of Finance
Analysis: Collaborative Economics
Ve
hi
cl
e
M
ile
s
of
T
ra
ve
l p
er
C
ap
ita
A
ve
ra
ge
A
nn
ua
l G
as
P
ri
ce
s
(in
fla
tio
n
ad
ju
st
ed
)
0
10,000
9,000
$
Vehicle Miles Traveled
continues to decline as
gas prices are
on the rise
Percent Change 2009-2010
VMT Per Capita –3%
Gas Prices +13%
PLACETransportation
WHY IS THIS IMPORTANT?
The modes of transportation we use, including the type of cars we
drive, affect the quality of our air and the region’s transportation
infrastructure. By utilizing alternative modes of transportation,
such as public transit and walking, as well as choosing vehicles
that are more fuel-efficient or use alternative sources of fuel,
residents can reduce their ecological footprint.
Shifting from carbon-based fuels to renewable energy sources and
reducing consumption together have the potential for wide-
reaching impact on our environmental quality in terms of local
air quality and global climate change.
HOW ARE WE DOING?
For five consecutive years Silicon Valley residents have continued to
drive less, steadily reducing travel (VMT) per capita in the region.
The year 2010 marks the lowest VMT the region has seen since
1995. Meanwhile, gas prices reached an all time high in 2008 and
fell dramatically the following year. However, from 2009 to 2010,
prices began to recover and are now close to 2006 price-levels.
The percentage of Silicon Valley commuters driving alone to work
dropped three percent from 2003 to 2010 (78% to 75%), as
residents are finding alternative means of commute or working
from home. In 2011, transit ridership in Silicon Valley increased
percent, to roughly 27 rides per capita in 2011. This marks
the first positive growth in transit use since 2008.
Silicon Valley residents are finding
alternative means of commute, including
public transit.
49
P
L
A
C
E
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
S O C I E T Y 34 | 43
Environment
44-47
Transportation
48-49
Land Use
50-51
Housing
52-55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Percentage of Workers
Santa Clara & San Mateo Counties
40%
30%
20%
10%
Number of Rides per Capita on Regional Public Transportation Systems
Santa Clara & San Mateo Counties
35
30
25
20
15
10
5
2002 2003 2004 2005 2006 2007 20112008 2009 2010
Note: Other means includes taxicab, motorcycle, bicycle and other means not identified separately within the data distribution.
Taxicabs are included in 2003 Public Transportation data and 2009 Other Means data.
Data Source: . Census Bureau, American Community Survey
Analysis: Collaborative Economics
Means of Commute
Walked
2003 2010
Other Means Worked at Home
Public Transportation Carpooled
90%
80%
70%
60%
50%
Drove Alone
Pe
rc
en
ta
ge
o
f W
or
ke
rs
Transit Use
Note: Date is in fiscal years
Data Source: Altamont Commuter Express, Caltrain, Sam Trans, Valley Transportation Authority, California Department of Finance
Analysis: Collaborative Economics
N
um
be
r
of
R
id
es
p
er
C
ap
ita
0
100%
78% 75%
0
+
4.
9%
Means of Commute
Change in Distribution
2003-2010
Other Means +%
Worked at Home +%
Walked +%
Public Transportation +%
Carpooled +%
Drove Alone %
Drivers are
finding alternatives
to driving alone
Transit Use per Capita
2010-2011
Silicon Valley +%
Transit ridership per capita
increased percent
in 2011
50
Average Units per Acre of Newly Approved Residential Development
Silicon Valley
19
98
20
00
20
01
20
02
20
03
20
04
20
05
20
08
20
06
20
07
20
11
19
99
20
09
20
10
5
10
15
20
25
0
Residential Density
Note: Beginning in 2008, the Land Use Survey expanded its geographic definition of Silicon Valley to include cities northward
along the . 101 corridor (Brisbane, Burlingame, Millbrae, San Bruno and South San Francisco)
Data Source: City Planning and Housing Departments of Silicon Valley
Analysis: Collaborative Economics
A
ve
ra
ge
D
w
el
lin
g
U
ni
ts
p
er
A
cr
e
Residential density
continues to slip
6.
6
10
.3 11
.1
9.
6 1
1.
6
10
.1
12
.9
20
.6
22
.8
21
.1
20
.2 20
.6
16
.2
14
.6
WHY IS THIS IMPORTANT?
By directing growth to already developed areas, local jurisdictions can
reinvest in existing neighborhoods, increase access to transportation
systems, and preserve the character of adjacent rural communities.
Focusing new commercial and residential developments near rail
stations and major bus corridors reinforces the creation of
compact, walkable, mixed-use communities linked by transit. This
helps to reduce traffic congestion on freeways, preserve open
space near urbanized areas and improve energy efficiency. By
creating mixed-use communities, Silicon Valley provides workers
with alternatives to driving and increases access to workplaces.
HOW ARE WE DOING?
Increased residential density is a sign of reduced urban sprawl. In each
year from 2004 to 2009, newly approved units were developed
with 20 units per acre or more. In the last two years residential
density has fallen and reached units/acre in 2011. Net
residential development approved in the region increased by 165
percent from approximately 2,100 units in 2010 to more than
5,600 units in 2011. However, net residential development has
dropped 78 percent since peaking in 2008.
Residential and commercial development near public transit reduces
need for personal vehicles for transportation, decreasing road
congestion and harmful emissions. The share of housing units
approved to be built near mass transit increased from 53 percent
in 2010, to 54 percent in 2011. In each of the past five years,
over 50 percent of approved housing was developed within
walking distance of mass transit.
In 2011, 50 percent of net square feet of non-residential development
was near transit. Total non-residential development increased by
186 percent over 2010.
Land Use PLACEDevelopment near transit increases while
residential density continues to slip.
51
P
L
A
C
E
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
S O C I E T Y 34 | 43
Environment
44-47
Transportation
48-49
Land Use
50-51
Housing
52-55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Share of New Housing Units Approved That Will Be
Within 1/4 Mile of Rail Stations or Major Bus Corridors
Silicon Valley
19
98
20
00
20
01
20
02
20
03
20
04
20
05
10%
20%
30%
40%
50%
60%
80%
20
08
20
06
20
07
20
11
Change in Non-Residential Development Near Transit
Silicon Valley
500,000
1,500,000
2,500,000
3,500,000
5,500,000
7,500,000
4,500,000
6,500,000
(500,000)
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
11
20
07
20
08
19
99
20
09
70%
20
09
20
10
20
10
Housing Near Transit
Note: Beginning in 2008, the Land Use Survey expanded its geographic definition of Silicon Valley to include cities northward
along the . 101 corridor (Brisbane, Burlingame, Millbrae, San Bruno and South San Francisco)
Data Source: City Planning and Housing Departments of Silicon Valley
Analysis: Collaborative Economics
Development Near Transit
Non-residential development further than 1/4 mile from transit
Non-residential development near transit
N
et
S
qu
ar
e
Fe
et
Note: Beginning in 2008, the Land Use Survey expanded its geographic definition of Silicon Valley to include cities northward
along the . 101 corridor (Brisbane, Burlingame, Millbrae, San Bruno and South San Francisco)
Data Source: City Planning and Housing Departments of Silicon Valley
Analysis: Collaborative Economics
0%
Housing near transit
increases
Development near transit
rebounds
29
%
57
%
40
%
64
%
32
%
49
%
36
% 39
%
40
%
55
%
69
%
62
%
53
%
54
%
52
Affordable Units as a Percentage of Total Approved New Residential Units
Silicon Valley
19
98
20
00
20
01
20
02
20
03
20
04
20
05
5%
10%
15%
25%
20%
20
06
20
08
20
07
20
11
30%
35%
20
09
19
99
20
10
Building Affordable Housing
Note: Beginning in 2008, the Land Use Survey expanded its geographic definition of Silicon Valley to include cities northward along
the . 101 corridor (Brisbane, Burlingame, Millbrae, San Bruno and South San Francisco)
Data Source: City Planning and Housing Departments of Silicon Valley
Analysis: Collaborative Economics
0%
New affordable housing
development at
14-year low
15
%
14
%
32
%
30
%
28
%
24
%
13
%
6%
11
%
10
%
5%
11
%
23
%
5%
WHY IS THIS IMPORTANT?
The affordability of housing affects a region’s ability to maintain a viable
economy and high quality of life. Lack of affordable housing in a
region encourages longer commutes, which diminish productivity,
curtail family time and increase traffic congestion. Lack of affordable
housing also restricts the ability of crucial service providers–such
as teachers, registered nurses and police officers–to live in the
communities in which they work. The current financial crisis has
greatly added to housing pressures in the region.
HOW ARE WE DOING?
Falling from the recent high in 2010, five percent of new residential
development in 2011 was classified as affordable. This is the lowest
percent over the last 14 years. While the number of net residential
units approved for development increased 165 percent from 2010
to 2011, the number of net affordable units approved decreased
by 47 percent. A total of 260 net units were approved for
construction in 2011.
In 2011, average monthly rent rose to $1,750 in Silicon Valley. This
change represents an eight percent jump from 2010 and the first
increase since 2008. Meanwhile, median household income fell
for the second year in a row, declining by three percent in 2010.
The percentage of first-time homebuyers that can afford to purchase
a median-priced home increased six percent in 2011 in Silicon Valley.
This reflects trends across the state. Home affordability for first-
time buyers increased five percent statewide. Of other California
metro areas, Silicon Valley continues to be the least affordable.
The housing cost burden for renters and owners in Silicon Valley
decreased in the last year. The percentage of renters spending 35
percent or more of their income on housing dropped in Silicon
Valley by one percent and increased statewide by one percent to
a peak of 45 percent in 2010. The housing cost burden for
homeowners dropped in the region and statewide by one percent
over the same time frame. Over the long term, mortgage payments
have continued to represent a growing percentage of household
incomes both in the region and the state, increasing seven percent
and eleven percent respectively.
Since the peak in 2008, residential foreclosures in Silicon Valley have
subsided. The number of foreclosures fell five percent in 2010 to
6,626 foreclosures. Foreclosures in the state fell eleven percent
during that time frame with 169,657 foreclosures in 2010. The first
half of 2011 represents a 16 percent drop over the first half of the
prior year.
Housing PLACEWhile housing costs eased in 2010, they
began rising again in 2011.
53
P
L
A
C
E
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
S O C I E T Y 34 | 43
Environment
44-47
Transportation
48-49
Land Use
50-51
Housing
52-55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Apartment Rental Rates at Turnover Compared to Median Household Income
Santa Clara & San Mateo Counties
20
02
20
03
20
04
20
05
20
11
*
20
06
Average Rent Median Household Income
20
07
Percentage of Potential First-Time Homebuyers That Can
Afford to Purchase a Median-Priced Home
Silicon Valley and Other California Regions
20
04
20
05
20
09
60%
50%
40%
30%
20%
10%
20
03
20
06
*
20
07
80%
20
08
70%
20
08
20
09
20
11
90%
20
10
20
10
Rental Affordability
$100,000
90,000
80,000
70,000
60,000
50,000
40,000
30,000
20,000
10,000
A
ve
ra
ge
R
en
t
(In
fla
tio
n
A
dj
us
te
d)
M
ed
ia
n
H
ou
se
ho
ld
In
co
m
e
(In
fla
tio
n
A
dj
us
te
d)
Home Affordability
* Estimate based on Quarters 1-2, 2011
Data Source: California Association of Realtors, Home Affordability Index; RAND California Statistics
Analysis: Collaborative Economics
* Estimate based on Quarters 1-2, 2011
Data Source: Real Facts; United States Census Bureau, American Community Survey
Analysis: Collaborative Economics
0
0%
400
800
$2,000
1,200
1,600
0
600
1,000
1,400
1,800
200
Sacramento
California
Silicon Valley
Los Angeles
San Diego
Santa Barbara Area
Average rent increased
for the first time since
2008, but median
household incomes are
still declining
Affordability improves
for potential first-time
homebuyers
54
Percent of Households with Housing Costs Greater than 35% of Income
Renters and Owners
Santa Clara & San Mateo Counties, California
20
02
20
03
20
04
20
05
20
06
Santa Clara and San Mateo County Renters Santa Clara and San Mateo County Owners
20
07
20
08
California Renters California Owners
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
20
10
20
09
Housing Costs
Data Source: . Census Bureau, American Community Survey
Analysis: Collaborative Economics
0
Housing cost burden
eased for Silicon Valley
residents in 2010
Housing PLACE
55
P
L
A
C
E
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
S O C I E T Y 34 | 43
Environment
44-47
Transportation
48-49
Land Use
50-51
Housing
52-55
G O V E R N A N C E 56 | 61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Annual Number of Foreclosures
Silicon Valley
20
02
20
03
20
04
20
05
4,000
5,000
6,000
8,000
7,000
20
06
20
08
20
07
20
11
9,000
10,000
20
09
3,000
2,000
1,000
*
20
10
Residential Foreclosure Activity
*The year 2011 includes data through June 2011
Data Source: RAND California Statistics
Analysis: Collaborative Economics
N
um
be
r
of
F
or
ec
lo
su
re
s
0
Number of Silicon Valley Foreclosures
January – June
2008 2011 % Change
Silicon Valley 3,690 3,117 -16%
California 111,718 84,752 -24%
Annual foreclosures
continue to drop in both
Silicon Valley and California
56
Eligible Voter Participation Rate and Absentee Voting Rate
Santa Clara & San Mateo Counties and California
10%
20%
30%
40%
60%
50%
70%
80%
19
98
M
id
te
rm
El
ec
tio
n
20
00
20
02
M
id
te
rm
El
ec
tio
n
20
04
20
06
M
id
te
rm
El
ec
tio
n
20
08
20
10
M
id
te
rm
El
ec
tio
n
20
09
Sp
ec
ia
l
El
ec
tio
n
Voter Participation
Note: All yearly figures are based upon general election date, excluding 2009 special election
Data Source: California Secretary of State, Elections Division
Analysis: Collaborative Economics
Eligible Voter Participation Rate: Silicon Valley California
Absentee Voting Rate: Silicon Valley California
0%
Pe
rc
en
ta
ge
o
f T
ot
al
V
ot
er
s
Voter participation in
midterm elections
continues to increase
Absentee Voting Rate
Change from
1998 2010 1998-2010
Silicon Valley 24% 64% +39%
California 25% 48% +24%
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 institutions. Voter participation is an indicator
of civic engagement and reflects community members’
commitment to a democratic system, confidence in political
institutions and optimism about the ability of individuals to
affect public decision-making.
HOW ARE WE DOING?
Voter turnout in the November 2010 general election continued the
increasing trend from the 2002 and 2006 midterm-elections. In
2010, 46 percent of Silicon Valley eligible voters and 44 percent
of California’s eligible voters participated. Rates of absentee
voting declined from 2009 election-highs for both Silicon Valley
and California. However, the percentage of absentee voters has
increased dramatically in both Silicon Valley (39%) and California
(24%) since 1998.
Although no bonds were proposed in 2011, Silicon Valley voters
approved all 11 measures proposed in 2010. Each of these 11
bond measures sought financing for school districts. Since 2000,
Silicon Valley voters have approved 90 percent of all local bond
measures, including county, city and school district measures.
During the period from 2000 through 2011 in Silicon Valley,
schools accounted for 80 percent, and cities accounted for 17
percent of all bond measures proposed. This follows statewide
trends in which school districts are responsible for a majority of
bonds on ballots.
Civic Engagement
The voter participation rate increased
in 2010.
GOVERN
57
G
O
V.
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
S O C I E T Y 34 | 43
P L A C E 44 | 55
Civic Engagement
56-57
Revenue
58-61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Number of Local Bond Measures on Ballots
Santa Clara & San Mateo Counties
12
14
20
00
20
02
20
04
20
06
20
11
20
01
20
03
20
05
20
07
20
09
20
08
2
8
10
4
6
20
10
**
Local Bond Measures
*No bond measures were on the ballot in 2009 and 2011
Data Source: California Secretary of State Elections Division, Santa Clara County Registrar of Voters, and San Mateo County
Board of Elections
Analysis: Collaborative Economics
San Mateo Santa Clara
0
Local bond measures
absent in 2011
ANCE
58
WHY IS THIS IMPORTANT?
Governance is defined as the process of decision-making and the
process by which decisions are implemented. Many factors influence
the ability of local government 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. Local revenues are affected by economic
fluctuations and by state takings of locally generated revenue.
Property tax revenue is the most stable source of city government
revenue, fluctuating much less over time than do other sources
of revenue, 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.
Public safety tax revenue is generated by a half-cent sales tax and is
allocated by the State Board of Equalization based on the county’s
share of statewide taxable sales. Revenues can be used for public
services like police and fire. The share of public safety tax revenue
a county receives from the state is reflective of local economic
performance.
Municipalities can issue bonds to finance capital projects. Amassing
excessive amounts of municipal debt obligations can lead to
potential funding shortfalls in the future and also raise the cost
associated with future debt.
HOW ARE WE DOING?
In fiscal year 2009-2010, city revenues fell by eleven percent from the
year before, marking the second straight year of declining revenue.
Sales tax and other revenue sources have not recovered to the
levels of 2000/01, and while property tax revenue has climbed
since 2004, it dropped off by six percent from fiscal year 2008/09
to 2009/10.
Property tax was the largest and fastest growing revenue source for
Silicon Valley cities, increasing from ten to 24 percent of total city
revenue since 2000/01. However, because property tax collections
lag the real estate market, the full effects of the downturn in the
real estate market will become increasingly evident in lower city
property tax revenues. Revenue from sales taxes as a percentage
of total city revenue declined from 18 to ten percent over the
past decade. Intergovernmental transfers from the State have
also decreased for Silicon Valley cities since fiscal year 2003-04.
Revenue
Local governments are still faced with
grave fiscal challenges.
GOVERN
The amount of revenue from the public safety tax received by a county
is an indicator of local economic performance. In fiscal year 2001-
02 the actual amount of public safety tax revenue received far
exceeded the amount budgeted due to the high concentration
of dot-com activity in the region. In subsequent years, both the
budgeted and actual revenues fell as a result of the dot-com burst
and the recent recession. However, in fiscal year 2010-11 the
revenue received by Santa Clara and San Mateo Counties increased
for the first time since fiscal year 2006-07, also marking the first
year since 2004-05 that the actual revenue received exceeded
the amount budgeted. The projected revenue received in fiscal
year 2011-12 is expected to increase by 11 percent over the
prior year. Additionally, the region’s percent of total revenue
from the public safety tax in California increased in the fiscal year
2010-11 by percent.
Total city expenditures have increased 15 percent since fiscal year
2000-01, and to keep up with rising costs related to personnel
and pension services, other categories of spending are being cut
back. Personnel services, which consist of salaries and wages,
health care costs for employees and retired workers, and
compensation insurance charges are the largest category (76%)
in total city expenditures.
Total municipal debt including short term, long term and notes, has
fluctuated over the past 11 years, with peaks in both 2002 ($
billion) and 2006 ($ billion). Lows were observed in 2000 ($
billion) and 2009 ($ billion). Public entities in the region have
issued on average a combined annual municipal debt of $ billion
from 1999 to 2010. As of July 2011, public entities in Silicon Valley
have issued one billion dollars in debt. Between 1999 and 2010,
education has accounted for the largest sums, nearly $880 million
every year on average. From 2009 to 2010, total debt funding
increased by 43 percent reaching $ billion in 2010. This growth
was mostly due to increase debt funding in Education,
Transportation Infrastructure and Housing.
Silicon Valley’s contribution to California tax revenue through personal
income tax held steady at 15 percent in 2009 and 2010.
59
G
O
V.
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
S O C I E T Y 34 | 43
P L A C E 44 | 55
Civic Engagement
56-57
Revenue
58-61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Aggregate Silicon Valley Revenue by Source
Silicon Valley
$
Sales Tax *Other Taxes Property Tax **Other Revenue Sources
19
98
-9
9
FY
19
99
-0
0
FY
20
00
-0
1
FY
20
01
-0
2
FY
20
02
-0
3
FY FY
20
03
-0
4
FY
20
05
-0
6
FY
20
04
-0
5
FY
20
06
-0
7
FY
20
07
-0
8
FY
20
08
-0
9
FY
20
09
-1
0
Silicon Valley
20
00
-0
1
20
01
-0
2
20
02
-0
3
20
03
-0
4
20
05
-0
6
20
04
-0
5
20
06
-0
7
20
07
-0
8
20
08
-0
9
20
09
-1
0
40%
100%
10%
20%
30%
50%
60%
70%
80%
90%
*
*Other Taxes include revenue sources such as transportation taxes, transient lodging taxes, and business license fees.
**Other Revenue include revenue of use of money and property, sale of real and personal property, and intergovernmental transfers.
Data Source: California's State Controller's Office
Analysis: Collaborative Economics
Bi
lli
on
s
of
D
ol
la
rs
(
In
fla
tio
n
A
dj
us
te
d)
*Fiscal year 2009/10 is projected.
Note: Only Silicon Valley cities that provided financial data for all years are included in revenue.
Data Source: Cities of Silicon Valley
Analysis: Collaborative Economics
Pe
rc
en
ta
ge
o
f T
ot
al
R
ev
en
ue Intergovernmental
Total Other Financing Sources (Uses)
Use of Money and Property
Other
Charges for Services
Licenses for Fines
Other Taxes
Sales Taxes
Property Taxes
City Revenue
City Revenue by Source
0%
Property tax is a
growing contributor
to city revenue
City revenue
continues to decline
in the fiscal year 2009/10
ANCE
60
Silicon Valley
20
00
-0
1
20
01
-0
2
20
02
-0
3
20
03
-0
4
20
05
-0
6
20
04
-0
5
20
06
-0
7
20
07
-0
8
20
08
-0
9
20
09
-1
0
40%
100%
10%
20%
30%
50%
60%
70%
80%
90%
*
Santa Clara & San Mateo Counties
300
$330
240
270
180
210
20
02
-0
3
20
03
-0
4
20
05
-0
6
20
04
-0
5
20
06
-0
7
20
07
-0
8
20
08
-0
9
20
09
-1
0
20
10
-1
1
20
11
-1
2
20
01
-0
2
*
Percent of California Public Safety Tax Revenue Received
Santa Clara & San Mateo Counties
10%
12%
6%
8%
2%
4%
20
02
-0
3
20
03
-0
4
20
05
-0
6
20
04
-0
5
20
06
-0
7
20
07
-0
8
20
08
-0
9
20
09
-1
0
20
10
-1
1
20
01
-0
2
*Fiscal year 2009/10 is projected.
Note: Only Silicon Valley cities that provided financial data for all years are included in expenditures.
Data Source: Joint Venture Survey of Silicon Valley Financial Officers
Analysis: Collaborative Economics
Pe
rc
en
ta
ge
o
f T
ot
al
E
xp
en
di
tu
re
s
Debt Services
Capital Improvements
Other
Pension Cost (Annual required contribution)
Supplies & Contractual Services
Personnel Services
Public Safety Tax Revenue
*The amount received for FY 2011-12 is projected.
Data Source: Santa Clara County Office of Budget and Analysis and San Mateo County Manager's Office
Analysis: Collaborative Economics
Amount Budgeted Revenue Received
Bu
dg
et
ed
a
nd
R
ec
ei
ve
d
Pu
bl
ic
S
af
et
y
Ta
x
(m
ill
io
ns
o
f i
nf
la
tio
n
ad
ju
st
ed
d
ol
la
rs
)
Public Safety Tax Revenue
Data Source: California State Controller’s Office
Analysis: Collaborative Economics
Pe
rc
en
t o
f C
al
ifo
rn
ia
P
ub
lic
S
af
et
y T
ax
R
ev
en
ue
City Expenditures by Category
0%
150
0%
Personnel services
represented a growing
percentage of expenditures
Public safety tax revenue
is recovering from the
effects of the recession
Silicon Valley’s share of the
public safety tax revenue
increased over the
previous year
Revenue GOVERN
61
G
O
V.
About the 2012 Index | 01
Map of Silicon Valley 02 |
Table of Contents | 03
Index 2012 Highlights 04 | 05
Index at a Glance 06 | 07
Special Analysis 08 | 11
P E O P L E 12 | 15
E C O N O M Y 16 | 33
S O C I E T Y 34 | 43
P L A C E 44 | 55
Civic Engagement
56-57
Revenue
58-61
Special Analysis continued 62 | 71
Appendices 72 | 76
Acknowledgments | 77
Issued by Category
San Mateo & Santa Clara Counties
20
00
20
02
20
04
20
06
20
08
20
01
20
03
20
05
20
07
19
99
20
10
20
09
$
Contribution to California State Revenues from Personal Income Tax
Santa Clara & San Mateo Counties
20%
25%
19
95
20
00
20
02
20
04
20
06
20
10
19
99
20
01
20
03
20
05
20
07
20
09
20
08
19
96
19
97
19
98
5%
10%
15%
Municipal Debt Obligations
Data Source: California State Treasurer’s Office
Analysis: Collaborative Economics
Bi
lli
on
s
of
D
ol
la
rs
o
f D
eb
t
(In
fla
tio
n
A
dj
us
te
d)
Health Care Infrastructure
Redevelopment
Miscellaneous
Parks & Recreation
Water & Wastewater
Other Public Infrastructure
Housing
Financing
Transportation Infrastructure
Education
Note: As of July 2011
Transportation Infrastructure, Housing and Health Care Infrastructure
did not have any debt obligations issued in the first half of 2011
Regional-State Interface
Data Source: California Franchise Tax Board, Economic and Statistical Research Bureau
Analysis: Collaborative Economics
C
on
tr
ib
ut
io
n
to
C
al
ifo
rn
ia
S
ta
te
R
ev
en
ue
s
fr
om
P
er
so
na
l I
nc
om
e
Ta
x
0%
Municipal Debt Obligations Issues in First Half of 2011
Education $664,714,770
Financing 65,435,000
Other Public Infrastructure 92,013,000
Water & Wastewater 31,990,000
Parks & Recreation 20,500,000
Miscellaneous 138,410,000
Redevelopment 31,411,295
Education debt obligation
rose in 2010
Silicon Valley’s contributions
remained the same
in 2010
ANCE
62
3. New Taxes and Fees. Since the passage of Proposition 13 in 1978, local governments have added or expanded a wide variety of
taxes and fees. These include development fees on new residential and commercial construction, transient occupancy (hotel) taxes,
utility taxes, and taxes on the transfer of property.
Prior to 1986 local governments could increase certain taxes without a vote approval by residents. It is in this period that many new utility,
hotel and business license taxes were introduced or increased.
In more recent years leading up to 2008 some cities passed increases in their sales tax rates and adopted parcel taxes for local government
services—a trend that accelerated with the onset of the recession.
However, increases in property tax rates are prohibited in California under Proposition 13. While communities across the country are
implementing or considering property tax hikes it is not legally permissible in California even if two-thirds of the voting population
are in support.
One question for consideration is whether the taxes and fees that are adopted or increased make for better public policy today than if
the same amount of money could be raised from property taxes.
In summary, after the initial decline in property tax revenues caused by Proposition 13, local governments saw a steady growth in property
tax revenue while at the same time adopting additional taxes and fees to offset the impact of the decline in the property tax rate. But
this came to a halt in 2008.
PROPOSITION 13 IN THE NEW NORMAL
Dramatically (and unexpectedly), the prior 30 years’ experience with Proposition 13 and property tax revenues was turned upside down
in 2008, when California’s economy plunged into recession.
The downturn took California and Silicon Valley from a world where new construction and large increases in assessed value generated
ongoing tax gains (despite Prop 13), to a world where all the positive trends turned negative. Recent events have produced five major
impacts on property tax revenue, all of them negative:
1. Median home prices have fallen.
2. The gain in assessed value and property taxes from new construction has plummeted. Some construction gains
are expected in the coming years, but construction levels are unlikely to return to peak levels any time soon.
3. The gain in assessed value and property taxes from changes in ownership has plummeted.
4. A large share of the assessed value in most California counties is in properties with a recent base year valuation,
including many bought at peak prices. As a result, we will see lower gains from change in ownership, for many
years to come.
5. The number of properties reassessed downward has surged.
continued from page 11
Special Analysis Proposition 13
Implications for Local Government Finance and the Silicon Valley Economy
63
%
%
%
%
%
2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12
$1,000,000
2003 2004 2005 2006 2007 2008 2009 2010 2011
Source: California Board of Equalization, Santa Clara County Assessor’s Office; Analysis: Center for Continuing Study of the California Economy
Source: California Association of Realtors CaliforniaSan MateoSanta Clara
Figure 6
%
%
%
%
%
%
%
% %
% % %
% %
5 California Board of Equalization
Figure 7
200,000
400,000
600,000
800,000
0
Change in Assessed Value in Silicon Valley and California
Silicon Valley California
Growth of Assessed Value and Consumer Prices
1. Median Home Prices Decline. The period of large increases in home prices—and large contributions to property taxes from changes
in ownership and new construction—came to a crashing halt with the arrival of the housing market crash, the recession, and new
financial conditions for lenders and potential homebuyers. There is no doubt about what happened after 2007 but the real question
is whether or not the post-2007 world is the “new normal” or whether a return to the pre-crash days for property tax increases is close
at hand.
Housing prices fell substantially in Silicon Valley with even larger losses in the statewide data. Median resale home prices fell 37 percent
in Santa Clara and 28 percent in San Mateo County between 2007 and 2009 while they declined by 51 percent statewide as shown
on Figure 7. After a brief rebound, median prices were declining again toward the end of 2011.
We discuss each in turn below, but the cumulative result of these changes has been a dramatic slowdown in the rate of growth for assessed
valuation and property taxes. The changes in Silicon Valley and California are shown on Figure 6. Assessed values increased by
more than 8 percent per year between 2005 and 2008 in Silicon Valley (more than 10 percent per year in California) but the 2011-
12 values are below the assessed value for 2008-09 because the increases that had been so steady for most of the preceding three
decades suddenly stopped.
Other counties suffered even larger drops in assessed value, with losses of percent in Riverside County, percent in Sacramento
County, percent in San Bernardino County and percent in San Joaquin County, all locales hard hit by the housing
64
$120
2004 2005 2006 2007 2008 2009 2010 2011
40
60
80
100
0
20 40
100
150
200
0
$250
$B
ill
io
ns
T
ho
us
an
ds
Residential Permits
$25
2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12
$B
ill
io
ns
Source: Construction Industry Research Board Value of Total New Construction
Source: Santa Clara and San Mateo County Assessor’s Offices
6 2011 Annual Report of the Assessor, Los Angeles County Assessor,
Figure 8
Figure 9
5
10
15
20
0
Value of New Construction in California
Increase in Assessed Value from Change in Ownership
Santa Clara and San Mateo County ($Billions)
In Los Angeles County the annual increase in assessed value from changes in ownership went from an average of $56 billion per year
between 2005 and 2009 to an average of $12 billion between 2009 and the 2011-12 tax
4. A Large Share of Assessed Value is in Properties Bought Near the Peak. A large number of properties in California and
Silicon Valley are now valued on the market at less than or close to their original acquisition price. This trend has two implications:
First, some properties are being reassessed at reduced valuations. In the 2010-11 tax year there was a decrease in assessed valuation of
$ billion, equal to percent of total assessed value in Santa Clara and San Mateo counties. In the 2009-10 tax year the decline
was even larger, with a loss of $ billion or percent of total assessed valuation resulting from successful appeals and adjustments
to property assessed values.
Special Analysis Proposition 13
Implications for Local Government Finance and the Silicon Valley Economy
2. Value of New Construction Plummets. At the same time, the volume of new construction fell throughout California as the state
went from 213,000 building permits in 2004 to 36,000 in 2009 (see Figure 8). As a result the value of new residential and non-
residential construction dropped from $100 billion in 2004 to $40 billion in 2009. And construction values have remained near the
lowest levels throughout 2010 and 2011, with no substantial upturn expected in 2012.
3. Assessed Value Growth from Change in Ownership Plummets. Often, the largest component of assessed valuation growth is
the gain from revaluation when properties change ownership. Until very recently these changes often brought large increases in the
assessed valuation for properties where the prior acquisition date was many years ago. In Silicon Valley changes in ownership provided
an average of $ billion per year in increased assessed value for the four years starting in 2005-06--over half of the overall increase
in assessed value. For the most recent three years, changes in ownership averaged just under $6 billion per year (Figure 9).
65
%
%
%
%
pre 1979
%
%
1979-2002 2003-2007 2008-2011
140,000
2005 2006 2007 2008 2009 2010 2011
$30
40,000
60,000
80,000
120,000
20,000
100,000
$B
ill
io
ns
Properties AV Reduction
Source: Santa Clara County Assessor’s Office
Source: Santa Clara County Assessor’s Office
7 2010 Annual Report, Sacramento County Assessorís Office
Figure 10
%
%
%
%
Figure 11
0
10
15
20
25
0
5
Distribution of Assessed Value by Base Year
Santa Clara County
Assessed Value Reductions
Santa Clara County
5. There has been a Surge in Properties Receiving Reduced Assessments. Many properties bought before the recession now
have market values below their purchase price. Under Proposition 8 county assessors have the responsibility to adjust assessed
valuations, and thus lower property taxes, when market value has fallen below the base year purchase price.
While these reductions can be recovered as prices rise, there has been a surge in assessment reductions since the recession and it will
take many years in some cases before prices rise to pre-recession levels.
Figure 11 shows the number of properties with an assessment reduction in Santa Clara County, and the total value of all reductions.
For the 2011-12 tax year, there were 124,148 properties with reduced assessments for a total value of reductions of $ billion as
shown on Figure 11.
Large reductions were noted in other counties as well.
Second, as a result of the slump in housing prices, the number of properties held for a long time and with a low assessed value is steadily
declining as properties turn over. In Santa Clara County only percent of the total assessed value is in properties purchased prior
to Proposition 13 (Figure 10). Another percent are properties purchased when prices were still rising rapidly.
Data for Sacramento County, which may be more typical of counties that experienced the sharpest increase in home prices during the
boom, shows a similar result but with an even higher share of assessed value ( percent) for properties purchased in the 2003-
2007
Moreover, forecasts of home price trends by the California Association of Realtors and other organizations anticipate a very slow increase,
if any, in home prices from current levels for the coming years.
66
$B
ill
io
ns
10
5
15
20
$25
2003-04
0
-10
-15
2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12
-5
40%
20%
60%
80%
100%
Residential
0%
Other Residential Other
Santa Clara San Mateo
Source: Santa Clara County Assessor’s Office
Source: Santa Clara and San Mateo County Assessor’s Offices
Figure 12
Figure 13
50%
67% 68%
50%
33% 32%
59%
71%
76%
41%
29%
24%
19
77
/7
8
20
07
/0
8
20
11
/1
2
Changes in Assessed Value
Santa Clara County
Inflation
New Construction
Ownership Changes
Temporary Value Decline
Residential Share of Assessed Value
Special Analysis Proposition 13
Implications for Local Government Finance and the Silicon Valley Economy
In summary, the level and composition of assessed value growth has changed substantially since 2007. The trends for Santa Clara County,
typical of the changes throughout California, are shown on Figure 12. In recent years the gains from ownership changes, new
construction and the annual inflation increase have ceased, and assessed value remains below peak levels.
The result has been a sharp drop in the annual change in assessed value and related property taxes.
While some of these changes may be temporary, growth in assessed value and property taxes is likely to remain low since large increases
in home prices and new construction are not expected soon, and because gains from reassessment of properties upon sale are likely
to remain at low levels for many years.
DIRECT AND INDIRECT CONSEQUENCES OF PROPOSITION 13
THAT INCREASED OVER TIME
Unlike trends in the growth of property taxes, many consequences of Proposition 13 did not change with the recession and have been
increasing with the passage of time.
1. The Residential Share of Assessed Value Has Increased. One goal of Proposition 13 was to protect homeowners from large
increases in property taxes, and that has been largely achieved. But one unanticipated consequence of Proposition 13 is that the
residential single family and condo share of assessed value has increased since 1978. Figure 13 shows this for Santa Clara and San
Mateo County but the trends are similar for other jurisdictions.
In the 1977-78 tax year, the residential share of assessed value was 50 percent in Santa Clara County and 59 percent in San Mateo County.
By the 2007-2008 tax year, those shares had increased to 67 percent in Santa Clara County and 71 percent in San Mateo County
(Figure 13). The residential shares continued to increase after 2007 despite a sharp drop in home prices. As a result, homeowners
as a group pay a much higher share of total property taxes currently than at any time since Proposition 13 was adopted.
67
8 The Demographics of Proposition 13, Dowell Myers, . Population Dynamics Research Group., September 2009
There are two primary reasons for the increasing share of property taxes paid by single family homeowners. One is that these properties
turn over (are sold) more frequently than commercial properties, and until very recently most sales resulted in large increases in single
family property assessed values as home prices continued to rise significantly until 2007.
The second reason is because there was a larger increase in new residential construction versus non residential uses, particularly in the
early years after 1978 when some agricultural land was converted to homes and other uses.
2. Property Owners Can Pay Substantially Different Property Taxes on Similar Valued Property. This possibility exists for
both residential and non-residential property and depends on the length of ownership. The discussion here uses data on home prices
to illustrate this consequence of Proposition 13.
The savings to homeowners from the 2 percent maximum annual increase in assessed value depends on when they bought their home,
the length of time it has been owned and also on where they live. A new buyer of a median priced home in 2007 would pay more than
four times the property tax of an owner from 1978, but only double the property taxes of owners who bought median priced homes
during much of the 1990s.
The sharp decline in housing prices since the 2007 peak has reduced the difference between property taxes paid by long-time homeowners
and recent buyers. For example, a median priced home in California bought in 1985 would have a maximum AV increase of 64 percent
if the 2 percent increase was applied each year. The median price of a home in California increased by 153 percent between 1985
and 2010 so a median price buyer in 2010 would pay roughly 2 1/2 times the property tax of a 1985 median home price buyer—less
of a differential than the 4x differential that existed in 2007.
But recent buyers in the higher-priced markets are still paying 4 times as much as 1985 buyers of a median priced home. For example,
median prices in the Bay Area increased by 291 percent between 1985 and 2010 compared to the maximum 64 percent increase in
assessed value, with similar increases in Orange and San Diego counties.
Dowell Myers authored a paper exploring the implications of Proposition 13 in the new world of housing price declines after the peak in
2007. His paper highlights some of the generational impacts of Proposition 13. Owners who have lived in the same home since 1978
or even since 1990 are older, on average, than owners who bought more recently since many of them are younger, first-time buyers.
Younger owners who are starting their careers and family, as a result of Proposition 13, pay higher taxes on similarly valued properties
than do older owners. Myers argues that these young, new buyers not only struggle with the affordability of purchasing homes, but
also implicitly subsidize the lower property tax payments of long-time
There is a distinct generational aspect about those who receive the most benefit from the 2 percent limit on assessed value imposed by
Proposition 13. This generational benefit for older homeowners is increased by the provision (added after Proposition 13’s passage)
that owners aged 55 and above can retain the assessed value base on their old home in most cases even if they sell and buy another home.
Rather than providing an incentive for new economic activity, Proposition 13 places a higher tax burden on new investments. The result
of the 2 percent limitation on assessed value increases as long as property does not change hands results in a situation where new
home owners or new business owners are paying much higher property taxes than those who bought homes or business property years
or decades earlier.
68
$500
1970
0
-1,000
-3,500
-500
-1,500
-2,000
-2,500
-3,000
1980 1990 2000 20101975 1985 1995 2005
Source: Santa Clara County Assessor’s Office
9 EdSource, adjusted to use comparable wage index, September 2010
10 A Decade of Disinvestment; California Education Spending Nears the Bottom, California Budget Project, October 2011
11 Data from Scroll down to Local Tax Votes
Figure 14
California’s Per Student Spending Lags the Rest of the .
Special Analysis Proposition 13
Implications for Local Government Finance and the Silicon Valley Economy
3. Per Pupil Spending on K-12 Education Falls Relative to the Nation. Ed Source reports that state spending per pupil ranked
43rd among states in 2007-08 before the recession The California Budget Project estimates that state spending per pupil fell to
46th in the 2010-11 school year and that per pupil spending in California lags the nation by the largest amount ever as shown on
Figure Both Ed Source and the California Budget Project report that California ranks in the bottom two states in teachers per
student, last in librarians per student and next to last in school counselors per student.
This decline in K-12 per pupil spending relative to the nation is the result of multiple factors, and the link to Proposition 13 is indirect.
It is also true that a major culprit in the recent decline in K-12 funding is the state’s continuing budget and fiscal woes, which are
explained by larger dynamics as well as the effect of Proposition 13.
However, there is a link between Proposition 13 and K-12 funding challenges. It has to do with the way Prop 13 shifted primary
responsibility for K-12 funding to Sacramento and away from the localities. Under California’s current system of K-12 funding, the
state provides funds that are needed to meet the K-12 revenue limit in each year after subtracting the amount contributed by local
property taxes.
As a result, if property tax revenues grow faster than the revenue limit as happened in many years before the housing crash, the required
state contribution to K-12 education is lowered. However, when property tax growth slows as occurred in recent years, the state
contribution would normally increase. But that has not been possible to maintain in recent years since the recession led to a decline
in state revenues. The upshot is that in the 2010-11 budget year (and before the recent K-12 spending cuts), California students
received nearly $3,000 less than the national average.
4. The Impact of the 2/3 vote majority. Proposition 13 imposed a two-thirds majority vote requirement for state tax increases and
for local bond or special tax elections. The two-thirds majority was reduced to 55 percent for school bond elections by Proposition
39 (passed in 2000).
A study of recent elections shows the extent to which a lower majority of 50 or 55 percent would have led to the passage of local taxes
and bonds that did not receive a 2/3 majority
69
In the November 2011 election five school parcel tax increases or extensions received more than two-thirds of the votes, while two taxes
failed but both received more than 55 percent of the total vote. Six of eight school bonds passed, but only three would have been
approved if the required majority was two-thirds rather than 55 percent. Nine city, county and special district parcel taxes received
more than two-thirds of the vote but three more failed while receiving more than 55 percent. One other tax got less than 50 percent
of the votes.
In the November 2010 election 10 city, county and special district parcel tax elections received more than two-thirds of the vote while
nine others failed, even though receiving more than 55 percent of votes cast. Six received less than 55 percent. Two school parcel
taxes passed while 11 failed thought receiving more than 55 percent of votes cast. Five received less than 55 percent. Sixteen school
bond elections received more than two-thirds of votes cast while 30 elections received more than 55 percent but less than two-thirds.
17 elections did not receive 55 percent of votes cast.
While local tax and bond elections continue to be adopted by voters even with a two-thirds majority, it is also true that a substantial
number of elections have failed that would have passed with a 55 or 50 percent requirement.
5. Control of the allocation of the 1 percent local property tax among jurisdictions. One of the issues that underlies the shift
in power between local government and the state is the provision in Proposition 13 that requires the state to allocate the property tax
among local governments within each county. This provision was a significant break in the long-standing tradition that the property
tax was to be used as a source for local, as opposed to state services. The authority for local government to control the property tax
was put in the constitution in 1910, when the Progressive movement pushed for greater local control. Proposition 13 reversed that
trend by granting the power to allocate the tax to the state and ended a 68 year tradition connecting the local property tax payer and
local services.
Not only do local governments no longer have power to raise property taxes, they do not have the power to adapt to changing circumstances
and develop local agreements to reallocate the 1 percent among local jurisdictions.
70
Special Analysis Proposition 13
Implications for Local Government Finance and the Silicon Valley Economy
THE BOTTOM LINE
Proposition 13 substantially reduced the ability of local governments to control their property tax revenues. This was achieved by reducing
the maximum tax rate to 1 percent (which was 60 percent below previous levels), by mandating that annual increases in assessed
value on each property be limited to 2 percent and by prohibiting local governments from increasing property tax rates.
All of these changes gave property owners a high measure of certainty about their future property tax liability.
However, from a governance standpoint Proposition 13 reduced the ability of local governments and the state to raise many other tax
rates and transferred the allocation of property taxes among local jurisdictions to the state. In addition, Proposition 13 effectively
made state government the primary source of local education revenues.
After the passage of Proposition 13 there were many legislative and ballot measures that mitigated the impact of Proposition 13 and also
many that made raising local revenues more difficult. Cities responded to Proposition 13 by adopting or increasing other local revenues.
In December 2011 the courts upheld the right of the legislature to end redevelopment agencies as presently structured and this
decision will have implications for the allocation of property tax revenues depending on legislative decisions in 2012.
For some thirty years, many of the adverse impacts were softened by rapidly rising property tax revenues, which
increased faster than consumer prices, faster than the growth of the economy, and faster than most other local
government revenue sources. This happened because home prices surged over this three-decade period; because population
growth and new construction levels increased; and because as properties were sold and reassessed to market value, there were usually
large gains in assessed value for the local jurisdiction.
The era of rapidly rising property tax revenues came to a halt around 2008. All three components of rapid property tax
gains ended with the housing slump that brought a large decline in new construction, a fall in home prices and a sharp drop in the
growth of assessed value as properties were sold and reassessed. These changes were amplified by the recession that followed, which
lowered all state and local government revenues.
71
CONCLUSION
If property tax revenues were soon going to return to pre-recession growth rates, it is possible that talk of reforming Proposition 13 would
fade away. But such a favorable outcome is unlikely. For the next few years and possibly longer, property tax revenues are likely to
grow more slowly than the economy, and more slowly than local government expenditures. It is most likely that the years since 2008
and the recession are more like the “new normal” than the preceding thirty. This is because of three primary factors:
1) Home prices are expected to recover slowly and take several years to reach pre-recession levels.
2) Population growth in California has slowed and it is very unlikely that the demand for additional housing will
ever reach the levels experienced between 1978 and 2008. Between 1978 and 2008, annual population growth
in California was 467,000 or percent per year. During the past three years annual growth has averaged
240,000 or a percent annual gain.
3) The gains in assessed value from changes in ownership are likely to continue at low levels for many years as many
properties now are worth less than when they were purchased. Moreover, the number of residential properties bought
more than 20 years ago now makes up a small proportion of total properties in most jurisdictions.
While these changes are not the result of Proposition 13, the impact on property tax revenues in the future is a direct result of how property
taxes are structured under Proposition 13.
In addition, sales taxes, the other major general revenue source for local governments, have grown more slowly than the economy over
the past three decades as consumers are spending a larger proportion of their income on services instead of goods and these services
are not subject to sales tax.
As the economy recovers there will be a temporary surge in local government revenues compared to recent recession levels, but the long-
term outlook under the existing tax system is for local government revenues to grow relatively slowly. This trend, combined with
continuing population growth and the challenges of funding retirement benefits, will make it more difficult for local government to
fund high quality public services.
Moreover, the slow future growth in property taxes will put pressure on the state budget. While rapid property tax growth during the period
from 1978 through 2008 reduced the state budget allocation for local school funding, the slower growth expected in the future will
translate into larger state budget funding responsibilities given the way that the state budget helps fund local education.
In light of all this, it is clear we face considerable challenges in financing our future. If we don’t raise new revenues, then Silicon Valley
and California must (continue to) make significant cuts. If we’re not willing to cut into education and services any further, then a
serious conversation has to take place about new revenue streams.
That conversation is critical for Silicon Valley’s economic competitiveness.
The 2011 CEO Business Climate Survey conducted by the Silicon Valley Leadership Group reported:
“Increasingly it is difficult for Silicon Valley companies to compete against other centers of innovation and entrepreneurship—both
domestic and abroad. Among the unique challenges are globalization and the international competition for talent. A deteriorating state
infrastructure in areas ranging from public education to public transportation has added to the difficulties of recruiting the best
workforce, finding them available housing, and educating their children to be tomorrow’s world-class workforce.” 3
Raising taxes versus cutting services is normally viewed as a stalemate: the political climate doesn’t allow for one and political dysfunction
prevents the other. But there might be a way out of the stalemate—a third way—and that would be to reform our tax system so that it
addresses the abnormalities we’ve described here and tracks more closely with the 21st century economy.
That discussion should proceed with Proposition 13 squarely on the table along with other options. The year 2012 is the time to begin
that discussion in earnest.
Stephen Levy is Director and Senior Economist of the Center for Continuing Study of the California Economy
72
FRONT PAGE STATISTICS
Area
Data are for Santa Clara and San Mateo Counties, Fremont, Newark, Union City, and Scotts Valley. Land Area data (except for Scotts Valley) is from the . Census Bureau: State and County QuickFacts. Data is derived from Population Estimates, 2000 Census of Population
and Housing, 1990 Census of Population and Housing, Small Area Income and Poverty Estimate, County Business Patterns, 1997 Economic Census, Minority-and Women-Owned Business, Building Permits, Consolidated Federal Funds Report, Census of Governments. Scotts
Valley data is from the Scotts Valley Chamber of Commerce.
Population
Data for the Silicon Valley population come from the E-I: City/County Population Estimates with Annual Percent Change report by the California Department of Finance and are for Silicon Valley cities. Population estimates are for 2011.
Jobs
Silicon Valley employment data are provided by the California Employment Development Department and are from Joint Venture: Silicon Valley Network’s unique data set. The data set counts jobs in the region and uses data from the Quarterly Census of Wages and Employment
program that produces a comprehensive tabulation of employment and wage information for workers covered by State unemployment insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. Employment
data exclude members of the armed forces, the self-employed, proprietors, domestic workers, unpaid family workers, and railroad workers covered by the railroad unemployment insurance system. Covered workers may live outside of the Silicon Valley region. Multiple jobholders
(., individuals who hold more than one job) may be counted more than once. Data for Quarter 2 2011 are preliminary-revised. Data is for Santa Clara and San Mateo Counties, Scotts Valley, Fremont, Newark, and Union City.
Average Annual Earnings
Figures were derived from the EDD/Joint Venture Silicon Valley Network data set and are reported for Fiscal Year 2011 (Q3 & Q4 2010, Q1 & Q2 2011). Wages were adjusted for inflation and are reported in first half of 2011 dollars using the . city average Consumer
Price Index (CPI) of all urban consumers, published by the Bureau of Labor Statistics. Data for Quarter 2 2011 are preliminary-revised. Data is for Santa Clara and San Mateo Counties, Scotts Valley, Fremont, Newark, and Union City.
Foreign Immigration and Domestic Migration
Data are from the E-6: Population Estimates and Components of Change by County - July 1, 2010-2011 reported by the California Department of Finance and are for San Mateo and Santa Clara Counties. Estimates for 2011 are provisional. 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.
Age Distribution, Adult Educational Attainment, Foreign Born, and Ethnic Composition
Data for age distribution for Santa Clara and San Mateo Counties and are derived from the United States Census Bureau, 2010 American Community Survey.
Adult Educational Attainment, Foreign Born, and Ethnic Composition
Data for age distribution, adult educational attainment, and foreign born (front page statistics) are for Santa Clara and San Mateo Counties and are derived from the United States Census Bureau, 2010 American Community Survey. For educational attainment, Some College
includes Some college, less than 1 year of college; Some college, 1 or more years, no degree; Associate's degree.
PEOPLE
Talent Flows and Diversity
Population Change and Net Migration Flows
Data are from the E-6: Population Estimates and Components of Change by County - July 1, 2010-2011, July 1, 2000-2010 and July 1, 1990-2000 reported by the California Department of Finance and are for San Mateo and Santa Clara Counties. Estimates for 2011 are
provisional. Data for the years 2000-2010 are based on revised estimates released in December 2011. 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.
Age Distribution
Data for age distribution for Santa Clara and San Mateo Counties and are derived from the United States Census Bureau, 2010 American Community Survey.
Language Spoken at Home for Population 5 years and older
Data for Languages Spoken at Home for Santa Clara and San Mateo Counties and are derived from the United States Census Bureau, 2010 American Community Survey.
Educational Attainment by Ethnicity
Data for adult educational attainment are for Santa Clara and San Mateo Counties and are derived from the United States Census Bureau, 2010 American Community Survey. Data
reflects the educational attainment of the population 25 years and over whose highest degree received was either a bachelor's degree or a graduate degree. Multiple and Other includes
American Indian and Alaska Native Alone, Native Hawaiian and Other Pacific Islander Alone, Some Other Race Alone, and Two or More Races.
Total Science & Engineering Degrees Conferred to Temporary Nonpermanent Residents
State and regional data for 1995-2010 are from the National Center for Education Statistics. Regional data for the Silicon Valley includes the following post secondary institutions: Menlo
College, Cogswell Polytechnic College, University of San Francisco, University of California (Berkeley, Davis, Santa Cruz, San Francisco), Santa Clara University, San Jose State University,
San Francisco State University, Stanford University, Golden Gate University. The academic disciplines include: computer and information sciences, engineering, engineering-related technologies,
biological sciences/life sciences, mathematics, physical sciences and science technologies. Data were analyzed based on 1st major, citizenship, and level of degree (bachelors, masters or
doctorate). Note that a new classification scheme for degrees awarded was adopted in 2009 and one or more degree categories were eliminated and others consolidated. The current
category of Doctorate Degree- Professional Practice equates to the old First professional Degree. The old Doctorate Degree breakout equates generally to the sum of the other three
doctorate degree categories. However, any re-categorization could result in measurement error when data are compared to previous years.
ECONOMY
Employment
Total Employed Residents by Month
Silicon Valley monthly jobs data are from the Bureau of Labor Statistics, Local Area Unemployment Statistics and is for San Mateo and Santa Clara Counties. Data is not seasonally
adjusted. September data is preliminary. National Employment data is from Bureau of Labor Statistics, Current Population Survey. Data is not seasonally adjusted.
Quarterly Job Growth; and Major Areas of Economic Activity
Silicon Valley employment data are provided by the California Employment Development Department and are from Joint Venture: Silicon Valley Network’s unique data set. The data set
counts jobs in the region and uses data from the Quarterly Census of Wages and Employment program that produces a comprehensive tabulation of employment and wage information
for workers covered by State unemployment insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. Employment
data exclude members of the armed forces, the self-employed, proprietors, domestic workers, unpaid family workers, and railroad workers covered by the railroad unemployment
insurance system. Covered workers may live outside of the Silicon Valley region. Multiple jobholders (., individuals who hold more than one job) may be counted more than once. All
industries are included in the major areas of economic activity. Quarter 2 for 2010 and 2011 are preliminary-revised. Data is for Santa Clara and San Mateo Counties, Scotts Valley,
Fremont, Newark, and Union City.
Unemployment Rate; and by Ethnicity
Unemployment data by ethnicity is from the . Census Bureau, American Community Survey. This data counts the number of unemployed persons in each race, by gender, for age
groups ranging from 16 years of age to 75 years of age and older. Ethnicity breakdowns include Black or African American, Asian, Some Other Race, Two or More Race, White (Not
Hispanic of Latino), and Hispanic or Latino. Data are limited to the household population and exclude the population living in institutions, college dormitories, and other group quarters.
Data is for Santa Clara County in the years 2007 through 2010. Monthly unemployment rate data are from the Bureau of Labor Statistics, Current Population Statistics (CPS) and the
Local Area Unemployment Statistics (LAUS). Data is not seasonally adjusted. Data is for the San Mateo and Santa Clara Counties.
Science & Engineering Talent by Categories
Data for Science & Engineering (S&E) Talent provided by the United States Census Bureau, 2000 Decennial Census and 2010 American Community Survey Public Use Microdata
Samples (PUMS). A list of S&E occupations were divided into six categories: Computer, Physical Engineers, Design, Biological, Mathematics, and Aerospace Engineers & Scientists. Design
includes Designers and Artists & Related Workers. Both were added to the S&E occupations to try to capture the employment in Graphic Designers and Multi-Media Artists & Animators.
According to the . Bureau of Labor Statistics Occupation Employment Statistics (May 2009), both occupations represent almost 60 percent of employment in both Designers and
Artists & Related Workers.
Innovation
Science & Engineering Talent by Categories
Data for Science & Engineering (S&E) Talent provided by the United States Census Bureau, 2000 Decennial Census and 2010 American Community Survey Public Use Microdata
Samples (PUMS). A list of S&E occupations were divided into six categories: Computer, Physical Engineers, Design, Biological, Mathematics, and Aerospace Engineers & Scientists. Design
includes Designers and Artists & Related Workers. Both were added to the S&E occupations to try to capture the employment in Graphic Designers and Multi-Media Artists & Animators.
According to the . Bureau of Labor Statistics Occupation Employment Statistics (May 2009), both occupations represent almost 60 percent of employment in both Designers and
Artists & Related Workers.
Value Added per Employee
Value added per employee is calculated as regional 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 . Employment data does not include farming. All GDP values are inflation-adjusted and reported in first half 2011 dollars, using CPI
for the . City Average from the Bureau of Labor Statistics. Silicon Valley data is for Santa Clara and San Mateo Counties.
Patent Registrations; Patents Registrations by Technology Area
Patent Data is provided by the . Patent and Trademark Office and consists of Utility patents granted by inventor. Geographic designation is given by the location of the first inventor
named on the patent application. Silicon Valley patents include only those patents filed by residents of Silicon Valley cities. Data are based on Joint Venture's city defined region of Silicon Valley.
Venture Capital Investment: Total, Share of ., by industry
Data provided by Cleantech Group™, LLC. For this analysis, venture capital is defined as disclosed clean tech investment deal totals. Data are based on Joint Venture's City-defined region
of Silicon Valley. The Cleantech Group describes cleantech as new technology and process, spanning a range of industries that enhance efficiency, reduce or eliminate negative ecological
impact, and improve the productive and responsible use of natural resources. Cleantech Group has changed its industry groupings in 2011 so analysis was performed to re-organize new
classifications into past industry groupings. Due to this change, current segment groups will not perfectly correlate past reports. All values are inflation-adjusted and reported in first half
2011 dollars, using the CPI for the . City Average from the Bureau of Labor Statistics.
Cleantech Venture Capital: Total & by Segment
Data provided by Cleantech Group™, LLC. For this analysis, venture capital is defined as disclosed clean tech investment deal totals. Data are based on Joint Venture's City-defined region
of Silicon Valley. The Cleantech Group describes cleantech as new technology and process, spanning a range of industries that enhance efficiency, reduce or eliminate negative ecological
impact, and improve the productive and responsible use of natural resources. Cleantech Group has changed its industry groupings in 2011 so analysis was performed to reorganize new
classifications into past industry groupings. Due to this change, current segment groups will not perfectly correlate past reports. All values are inflation-adjusted and reported in first half
2011 dollars, using the CPI for the . City Average from the Bureau of Labor Statistics.
A P P E N D I X A
Cleantech
Industry Segments
E n e r g y G e n e r a t i o n
Wind
Solar
Hydro/Marine
Biofuels
Geothermal
Other
E n e r g y S t o r age
Fuel Cells
Advanced Batteries
Hybrid Systems
E n e r g y I n f r a s t r u c t u re
Management
Transmission
E n e r g y E f f i c i e n c y
Lighting
Buildings
Glass
Other
Tr a n s p o r t a t i o n
Vehicles
Logistics
Structures
Fuels
Wa t e r & Wa s t ew a t e r
Water Treatment
Water Conservation
Wastewater Treatment
A i r & E nv i ro n m e n t
Cleanup/Safety
Emissions Control
Monitoring/Compliance
Trading & Offsets
M a t e r i a l s
Nano
Bio
Chemical
Other
M a n u f a c t u r i n g / I n d u s t r i a l
Advanced Packaging
Monitoring & Control
Smart Production
A g r i c u l t u re
Natural Pesticides
Land Management
Aquaculture
R e c y c l i n g & Wa s t e
Recycling
Waste Treatment
Source: Cleantech Group™, LLC
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Small Business Innovation & Technology Research
Data for Small Business Innovation Research (SBIR) and Small Business Technology Transfer awards come from the . Small Business Administration. Data include Phase 1 and Phase 2 awards for all agencies and branches for the years 1990-2010. Data for GDP are from
Moody's and are inflation adjusted to first half 2011 dollars. Regional comparison GDP data are for the following regions: Greater Washington DC (Washington-Arlington-Alexandria, DC-VA-MD-WV MSA), Research Triangle (Hartnett County, Raleigh-Cary, NC
MSA and Durham-Chapel Hill, NC MSA), Boston (Boston-Cambridge-Quincy, MA-NH MSA), San Diego (San Diego-Carlsbad-San Marcos, CA MSA) and Silicon Valley. SBIR & STTR data for the regional comparisons were determined by the zip codes associated with counties
under each of the region's MSA or county definition.
Entrepreneurship
Initial Public Offerings
Data is from Renaissance Capital's and the location based on corporate address provided by . The data was pulled from the website on November 30, 2011.
Mergers & Acquisitions
Data provided by FactSet Mergerstat LLC. Data are based on Joint Venture's ZIP-code-defined region of Silicon Valley. All merger and acquisition deals do not disclose value. Total values are based on all the deals with values disclosed. All forms of mergers and acquisitions are
included in count except for joint ventures.
Initial Public Offerings and Mergers & Acquisitions in Clean Technology
Data provided by Cleantech Group™, LLC. For this analysis, venture capital is defined as disclosed clean tech investment deal totals. Data are based on Joint Venture’s ZIP-code-defined region of Silicon Valley. The Cleantech Group describes cleantech as new technology and
processes, spanning a range of industries that enhance efficiency, reduce or eliminate negative ecological impact, and improve the productive and responsible use of natural resources. See box for cleantech industry segments. IPO Count is based on IPO pricing each year. M&A
count is based on number of announced merger and acquisition deals each year, by year of deal announced.
Small Business Loans
The data for Small Business Loan Origination comes from Federal Financial Institutions Examination Council (FFIEC), specifically from the Community Reinvestment Act (CRA) data products. Small business loans are defined as those whose original amounts are $1 million or
less and were reported as either loans secured by nonfarm or nonresidential real estate or Commercial and Industrial loans in Part I of the Consolidated Reports of Condition and Income (Schedule RC-C, Part II) or the Thrift Financial Report (Schedule SB).
Nonemployer Firm Growth Relative to 2004
Data for Nonemployers are from the . Census Bureau. Nonemployer statistics summarizes the number of establishments and sales or receipts of businesses without paid employees that are subject to federal income tax. Most nonemployers are self-employed individuals
operating very small unincorporated businesses, which may or may not be the owner’s principal source of income. Silicon Valley represents Santa Clara and San Mateo counties.
Establishment Churn
The National Establishment Time-Series Database (NETS), prepared by Walls & Associates using Dun & Bradstreet establishment data, was sourced for jobs data and establishment counts. Silicon Valley is defined as Santa Clara and San Mateo Counties in this analysis.
Commercial Space
Commercial Space; Vacancy; Rents; and New Commercial Development
Data is from Colliers International. Commercial space includes office, R&D, industrial and warehouse space. The vacancy rate is the amount of unoccupied space and is calculated by dividing the sum of the direct vacant and sublease vacant space by the building base. The
vacancy rate does not include occupied space that is presently being offered on the market for sale or lease. Net absorption is the change in occupied space during a given time period. Average asking rents are inflation-adjusted and reported in first-half 2011 dollars, using the
CPI for the . City Average from the Bureau of Labor Statistics.
Income
Real per Capita Income
Total personal income and population data are from . Income values are inflation-adjusted and reported in first half 2011 dollars, using the CPI for the . City Average from the Bureau of Labor Statistics.
Per Capita Income by Race and Ethnicity
Data for Distribution of Per capita Income are from the 2000-2010 American Community Survey from the . Census Bureau. All income values are inflation-adjusted and reported in first half 2011 dollars, using CPI for the . City Average from the Bureau of Labor Statistics.
Silicon Valley data includes Santa Clara and San Mateo Counties. Per capita income is the mean money income received in 1999 computed for every man, woman, and child in a geographic area. It is derived by dividing the total income of all people 15 years old and over in a
geographic area by the total population in that area. Note -- income is not collected for people under 15 years old even though those people are 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 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 income; Supplemental Security Income (SSI); public assistance or
welfare payments; retirement, survivor, or disability pensions; and all other income.
Median Household Income
Data for Distribution of Income and Median Household Income are from the American Community Survey from the . Census Bureau. All income values are adjusted into 2011 . dollars for the first half of the year, using CPI for the . City Average from the Bureau of
Labor Statistics. Silicon Valley data includes Santa Clara and San Mateo Counties.
Income Distribution
Data for Distribution of Income are from the American Community Survey from the . Census Bureau. Income ranges are in nominal values. Silicon Valley data 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 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.
Percent of Students Receiving Free Meals
Free and Reduced Meal Program (FRMP) information is submitted by schools to the Department of Education in January; however, the data is current as of October 2010. Data files include public school enrollment and the number of students eligible for free or reduced price
meal programs. Data for Silicon Valley is from Santa Clara and San Mateo Counties.
SOCIETY
Preparing for Economic Success
High School Graduation Rate & Share Who Meet UC/CSU Entrance Requirements; High School Graduation Rates by Ethnicity; High School Dropout Rate
Data for the 2009/10 academic year are provided by the California Department of Education. This is the fourth year in which statistics have been derived from student-level records. California Legislature enacted SB1453, which establishes two key components necessary for
a long-term assessment and accountability system: Assignment of a unique, student identifier to each K-12 pupil enrolled in a public school program or in a charter school that will remain with the student throughout his or her academic 'career' in the California public school
system; and Establishment of a longitudinal database of disaggregated student information that will enable state policy-makers to determine the success of its program of educational reform. Historical data are final and are from the California Department of Education. The
methodology used calculates an approximate probability that one will graduate on time by looking at the number of 12th grade graduates and number of 12th, 11th, 10th and 9th grade dropouts over a four year period. 2006/07 marks the first year in which the CDE derived
graduate and dropout counts based upon student level data. However, for comparability, we report SV and CA data following the non-adjusted protocol; using the grade 9-12 year derived dropout rate. Although the more accurate adjusted rate is available for individual
districts and counties and is available at the state level, the data are not reported for the combination of districts and counties making up the Silicon Valley region.
Algebra I Scores
Data are from the California Department of Education, California Standards Tests (CST) Research Files for San Mateo and Santa Clara Counties. In 2003, the California Standards Tests (CST) replaced the Stanford Achievement Test, ninth edition (SAT/9). The CSTs in English–language
arts, mathematics, science, and history–social science are administered only to students in California public schools. Except for a writing component that is administered as part of the grade four and grade seven English–language arts tests, all questions are 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 children in California are expected to know and be able to do in each grade or course. The
2011 Algebra I CSTs were required for students who were enrolled in the grade/course at the time of testing or who had completed a course during the 2010-2011 school year, including 2010 summer school. The following types of scores are reported by grade level and
content area for each school, district, county, and the state: % Advanced, % Proficient, % Basic, % Below Basic and % Far Below Basic is the percentage of students in the group whose scores were at this performance standard. The state target is for every student to score at the
Proficient or Advanced Performance Standard.
Early Education
Preschool Enrollment
Data for preschool enrollment are for San Mateo and Santa Clara Counties, California, and the United States. The data is from the United States Census Bureau, 2002-2010 American Community Surveys and the 2000-2001 Supplementary Surveys. The population of children
is for children age three to five years old. The age of the population in preschool and nursery schools is from three years and older.
Third Grade English-Language Arts Proficiency by Race/Ethnicity
Data is from the California Department of Education, California Standards Tests (CST) Research Files for San Mateo and Santa Counties. The CSTs in English–Language Arts for third graders was administered only to students in California public schools and all questions were
multiple-choice. These tests were developed specifically to assess students' knowledge of the California content standards, set by the State Board of Education. The 2011 English Language Arts CSTs were required for students who were enrolled in the grade/course at the time
of testing or who had completed a course during the 2010–11 school year, including 2010 summer school. The following types of scores are reported by grade level and content area for each school, district, county, and the state: % Advanced, % Proficient, % Basic, % Below
Basic and % Far Below Basic is the percentage of students in the group whose scores were at this performance standard. The state target is for every student to score at the Proficient or Advanced Performance Standard.
Childcare Arrangements
Data provided by the UCLA California Health Interview Survey are for San Mateo and Santa Clara counties. The type of childcare reflects childcare arrangements for 10 or more hours per week. The childcare Topic is asked of all children – with “Type of Childcare” asked of
children with regular childcare for 10 hours or more in a typical week. Even though a child may be in school most of the day, this question is designed to account for before-school and after-school childcare arrangements. By childcare, it is meant any arrangement where
someone other than the parents, legal guardian, or stepparents take care of (CHILD). {This includes preschool and nursery school, but not kindergarten.} Children are aged birth to 12 years of age. Other includes Head Start/State Program, Preschool or Nursery School, Non-
Family member, and Other Source.
Arts & Culture
Arts-centric businesses per 100K Residents
Data is derived from the Dun+Bradstreet business establishments database for 2010. For this measure Silicon Valley is defined as Santa Clara County. All comparison city regions for this indicator are also defined by their primary county. "Arts-centric businesses" are defined by
Americans for the Arts using 643 Standard Industrial Classification Codes.
Art-Centric Businesses
National Establishment Time-Series (NETS) database, prepared by Walls & Associates using Dun & Bradstreet establishment data was sourced for the Art-Centric industry analysis. The definition used for the industries is based on the Creative Industries 8-digit SIC code definition
used by Americans for the Arts organization. Silicon Valley is defined as Santa Clara and San Mateo Counties.
Arts Educators per One Hundred Thousand Residents
The per capita arts educators measure for this indicator is provided by Americans for the Arts as part of their Local Arts Index project to be published in 2012. Data for the calculation of this measure is from the membership roles of Music Educators National Conference,
National Dance Educators Organization, Educational Theatre Organization, and the National Arts Educators Association. For this measure Silicon Valley is defined as Santa Clara County and each comparison city region are also defined by their primary county.
Quality of Health
Percent of Kindergarten Students with All Required Immunizations
Data for kindergarten immunization rates come from the kindergarten assessment, which measures compliance with the school immunization law, conducted in all schools with kindergartens. Immunizations required by law include: All required immunizations include 5 doses
of DTP/DTaP/DT vaccine (4 doses meets the requirement if at least one was given on or after the fourth birthday); 4 doses of polio vaccine (3 doses meets the requirement if at least one was given on or after the fourth birthday); 2 doses of MMR vaccine (may be given
separately or combined, but both doses must be given on or after the first birthday); 3 doses of hepatitis B vaccine; and 1 dose of varicella vaccine (or physician documented varicella disease history or immunity). In the fall, every school with a kindergarten class in California
must provide information on the total enrollment, the number of students who have or have not received the immunizations required, and the number of exemptions. 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.
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A P P E N D I X A
Percentage of Population with Health Insurance Coverage by Ethnicity
Data for those with health insurance is from the . Census Bureau, American Community Survey. Coverage includes private coverage and public coverage, including Medicare. Estimates of urban and rural population, housing units, and characteristics reflect boundaries of
urban areas defined based on Census 2000 data. Boundaries for urban areas have not been updated since Census 2000. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization. Silicon Valley data includes Santa
Clara and San Mateo Counties.
Infant Mortality Rate
Data is provided by the California Department of Health, Center for Health Statistics, 1994-2009. Silicon Valley estimates are for San Mateo and Santa Clara Counties.
Safety
Substantiated Cases of Child Abuse per 1,000 Children and Number of Layoffs in Social Services
Child maltreatment data are from the California Children's Services Archive, CWS/CMS 2011 Quarter 1 Extract. Data are downloaded from the Center for Social Services Research at the University of California at Berkeley. Population Data Source: California Department of
Finance annual population projections (1998-1999 data based on the 1990 . Census, 2000-2010 data based on the 2010 . Census). Child and Family Services staffing data are from Santa Clara County yearly budget reports located on the Santa Clara County Public
Portal.
Public School Expulsions due to Violence/Drugs
Data is obtained from the California Department of Education, DataQuest site. Numbers reflect violence and drug related expulsions across all grades (K-12) and are presented as a percentage of enrollment. Data was collected for Santa Clara County, San Mateo County and
California.
Gang Related Homicide
Gang related homicide data are from the Governor's Office of Gang and Youth Violence Policy provided by the California Department of Justice.
PLACE
Environment
Water Resources
Data for this indicator were provided by the Bay Area Water Supply and Conservation Agency (BAWSCA). Data is compiled annually among BAWSCA agencies to update key information and assist in projecting suburban demand and population. Gross per capita consumption
includes residential, non-residential, recycled and unaccounted for water use among the Santa Clara and San Mateo County BAWSCA agencies.
Electricity Productivity and Electricity Consumption per Capita
Electricity Consumption data is from the California Energy Commission. Gross Domestic Product (GDP) data is from Moody's . GDP values are inflation-adjusted and reported in first half 2011 dollars, using the CPI for the . City Average from the Bureau of
Labor Statistics. Silicon Valley data includes Santa Clara and San Mateo Counties. To compute per-capita values, Revised County Population Estimates, 1990-2010 with 1990, 2000, and 2010 census counts from the California Department of Finance were used.
Solar Installations by Sector
Data on the Solar Installation by Sector is from The California Solar Initiative (CSI) as part of the Go Solar California campaign. The data shows calculated CEC PTC Rating, a measure of alternating current output of photovoltaic system under PVUSA Test Conditions as
calculated by current output of photovoltaic system under PVUSA Test Conditions as calculated by PowerClerk.
Time Required for Permitting of Renewable Energy Installations
Data are from Joint Venture: Silicon Valley Network of Survey Cities. In recent years, residents and cities have begun investing substantially in renewable energy technology to provide electricity for their property and homes. In order to track achievements in this area, this
year’s survey included questions related to the renewable energy portfolios of the surveyed cities and its residents.
Transportation
Vehicle Miles of Travel per Capita & Gas Prices
Vehicle Miles Traveled (VMT) is defined as total distance traveled by all vehicles during selected time period in geographic segment. VMT estimates for 1995 – 2007 are from the California Department of Transportation’s “2009 California Motor Vehicle Stock, Travel, and Fuel
Forecast.” VMT data for 2008-2010 is from the California Department of Transportation’s, Highway Performance Monitoring System’s “California Public Road Data.”Data includes annual statewide total VMT on State highways and non-state highways. In order to calculate VMT,
Caltrans multiplies the road section length (length in miles along the centerline of the roadway) by Average Annual Daily Traffic (AADT). AADT are actual traffic counts that the city, county, or state have taken and reported to the California Department of Transportation. To
compute per-capita values, Revised County Population Estimates, 1990-2010 with 1990, 2000, and 2010 census counts from the California Department of Finance were used. Gas prices are average annual retail gas prices for California, and come from the Weekly Retail
Gasoline and Diesel Prices (Cents per Gallon, Including Taxes) data series reported by the . Department of Energy, Energy Information Administration. Gas prices are All Grades All Formulations Retail Gasoline Prices (including taxes) and have been adjusted into first half
of 2011 dollars using the . city average Consumer Price Index (CPI) of all urban consumers, published by the Bureau of Labor Statistics.
Means of Commute
Data on the means of commute to work are from the United States Census Bureau, 2003 and 2010 American Community Survey. Data are for workers 16 years old 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 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 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 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 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 day were asked to report the one used for the longest distance during the work trip. The category, “Car, truck, or van,” includes workers using a car (including 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, 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, motorcycle, bicycle and other means that are not identified separately within the data distribution.
Transit Use
Estimates are the sum of annual ridership on the light rail and bus systems in Santa Clara and San Mateo Counties, and rides on Caltrain. Data are provided by Sam Trans, Valley Transportation Authority, Altamont Commuter Express, and Caltrain. Revised County Population
Estimates for January 2001 through January 2010 from the California Department of Finance were used to compute per-capita values.
Land Use
Residential Density
Joint Venture: Silicon Valley Network conducted a land-use survey of all cities within Silicon Valley. Collaborative Economics completed the survey compilation and analysis. Participating cities included: Atherton, Belmont, Burlingame, Campbell, Cupertino, East Palo Alto, Foster
City, Fremont, Hillsborough, Los Altos, Los Altos Hills, Los Gatos, Milpitas, Monte Sereno, Morgan Hill, Mountain View, Palo Alto, Portola Valley, Redwood City, San Bruno, San Carlos, San Jose, Santa Clara, South San Francisco, Sunnyvale, and Union City. Santa Clara and San
Mateo Counties are also included. In 2008, the survey was expanded to include more cities along the 101 corridor: Belmont, Brisbane, Burlingame, Millbrae, San Bruno, and South San Francisco. Most recent data are for fiscal year 2010 (July ’10-June’11). The average units per
acre of newly approved residential development are reported directly for each of the cities and counties participating in the survey.
Housing and Development Near Transit
Data are from Joint Venture: Silicon Valley Network of Survey Cities. The number of new housing units and the square feet of commercial development within one-quarter mile of transit are reported directly for each of the cities and counties participating in the survey.
Places with one-quarter mile of transit are considered “walkable” (. within a 5- to 10-minute walk, for the average person).
Housing
Building Affordable Housing
Data are from Joint Venture: Silicon Valley Network of Survey Cities. Affordable units are those units that are affordable for a four-person family earning up to 80 percent of the median income for a county. Cities use the . Department of Housing and Urban Development’s
(HUD) estimates of median income to calculate the number of units affordable to low-income households in their jurisdiction.
Rental Affordability
Data on average rental rates are from RealFacts survey of all apartment complexes in Santa Clara and San Mateo Counties of 50 or more units. Rates are the prices charged to new residents when apartments turn over and have been adjusted into 2011 dollars using the .
city average Consumer Price Index (CPI) of all urban consumers, published by the Bureau of Labor Statistics. Median household income data is from the United States Census Bureau, American Community Survey.
Home Affordability
Data are from the California Association of Realtors' (CAR) Housing Affordability Index. The data for Silicon Valley includes Santa Clara and San Mateo County and is based on the median price of existing single family homes sold from CAR's monthly existing home sales
survey, the national average effective mortgage interest rate as reported by the Federal Housing Finance Board, and the median household income as reported by Claritas/NPDC. Beginning in 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. Quarterly Sales Volume for Existing Single Family Detached Home Sales data were provided by RAND California Statistics sourced by DataQuick News.
Percent of Households with Housing Costs Greater than 35% of Income
Data for owners and renters housing costs are from the United States Census Bureau, American Community Survey. This indicator measures the share of owners and renters spending 35% 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 on housing units with a mortgage. According to the
. Department of Housing and Urban Development, housing costs greater than 30% of household income pose moderate to severe financial burdens.
Residential Foreclosure Activity
Foreclosure and number of home sales data are from RAND California. RAND compiled originating data from the California Realtors Association and DataQuick News. Data reflects total foreclosures and number of home sales for townhomes, condominiums and single
family homes. Foreclosure data for 2010 is through September. Data are based on Joint-Venture’s Zip-code-defined region of Silicon Valley.
GOVERNANCE
Civic Engagement
Voter Participation
Data is from the California Secretary of State, Elections and Voter Information Division and the California State Archives Division. The eligible population is determined by the Secretary of State using Census population data provided by the California Department of Finance.
Silicon Valley data is for Santa Clara and San Mateo Counties.
Local Bond Measures
Data for the most current ballot bond initiatives are obtained from the Santa Clara County Registrar of Voters and San Mateo County Board of Elections. Past local bond voting results are obtained from the California Elections Data Archive (CEDA) - a joint project of the
Center for California Studies and Institute for Social Research of California State University, Sacramento, and the Secretary of State. Following each local election, CEDA collects and compiles results from city, county, school district, and local ballot measure elections. The reports
are completed in July of each year and include local election results from the previous calendar year. Data is presented for years 2000 to 2011
Revenue
City Revenue by Source
Data is from the Joint Venture Survey of Silicon Valley Financial Officers. Only cities that provided general fund data for all years are included. Data for fiscal year 2009/10 is projected. Revenue and expenditures were adjusted for inflation and are reported in first half 2011
dollars using the . city average Consumer Price Index (CPI) of all urban consumers, published by the Bureau of Labor Statistics. Cities included in the chart: Atherton, Belmont, Daly City, East Palo Alto, Half Moon Bay, Menlo Park, Millbrae, Pacifica, Redwood City, San Mateo,
Woodside, Campbell, Cupertino, Milpitas, Morgan Hill, Mountain View, Palo Alto, San Jose, Santa Clara, and Sunnyvale.
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College, University Facility
K-12 School Facility
Other, Multiple Educational Uses
Cash Flow, Interim Financing
Insurance and Pension Funds
Project, Interim Financing
Health Care Facilities
Hospital
Multifamily Housing
Single-family Housing
Convention Center
Equipment
Parking
Prisons, Jails, Correctional Facilities
Other Purpose
Theatre/Arts/Museums
Flood Control, Storm Drainage
Multiple Capital Improvements, Public Works
Other Capital Improvements, Public Works
Power Generation/Transmission
Public Building
Solid Waste Recovery Facilities
Parks, Open Space
Recreation and Sports Facilities
Redevelopment, Multiple Purposes
Airport
Bridges and Highways
Ports, Marinas
Public Transit
Street Construction and Improvements
Wastewater Collection, Treatment
Water Supply, Storage, Distribution
Education
Financing
Health Care
Infrastructure
Housing
Miscellaneous
Other Public
Infrastructure
Parks &
Recreation
Redevelopment
Transportation
Infrastructure
Water &
Wastewater
Municipal Debt Obligations Issued
Category Groupings
City Revenue Trends
Data provided by the California State Controller’s Office, Cities Annual Report. Fiscal year 2008/09 is preliminary. Revenue is adjusted for inflation, and reported in first half of 2011 dollars using the
. city average Consumer Price Index (CPI) of all urban consumers, published by the Bureau of Labor Statistics. Data is for cities in San Mateo and Santa Clara Counties, Fremont, Newark, and Union
City. Other Taxes include revenue sources such as transportation taxes, transient lodging taxes, and business license fees. Other Revenue includes revenue sources such as revenue of use of money and
property, sale of real and personal property, and intergovernmental transfers.
City Expenditures by Category
Data from the Joint Venture Survey of Silicon Valley Financial Officers. Only cities that provided general fund data for all years are included. Data for fiscal year 2009/10 is projected. Expenditures are
adjusted for inflation, and are reported in first half of 2010 dollars using the . city average Consumer Price Index (CPI) of all urban consumers, published by the Bureau of Labor Statistics. Cities
included in the chart Atherton, Belmont, East Palo Alto, Half Moon Bay, Menlo Park, Millbrae, Pacifica, San Mateo, Woodside, Campbell , Cupertino, Milpitas, Morgan Hill, Mountain View, San Jose, Santa
Clara, and Sunnyvale. Pension Cost is the Annual required contribution of cities that responded.
Public Safety
California data for the monthly half-percent sales tax for public safety are from the Division of Accounting and Reporting at the California State Controller's Office Santa Clara County data is from
the County Office of Budget and Analysis and San Mateo County data is from the County Manager's Office. All values are reported in first half of 2011 dollars using the . city average Consumer
Price Index (CPI) of all urban consumers, published by the Bureau of Labor Statistics. Cities within each county collectively receive approximately six percent of the public safety revenue awarded to
each county. This is not included in the Silicon Valley figures.
Municipal Debt Obligations
The California Debt and Investment Advisory Commission Database (CDIAC), as maintained by the California Department of Treasurer, was used to compile the municipal bond data for both Santa
Clara and San Mateo Counties. State law that took effect in 1982 requires all governmental agencies which issue debt to report information on each issuance to CDIAC [Government Code Sections
8855(k) and 8855(i)]. Agencies must provide data to CDIAC 30 days prior to each issuance, and within 45 days after the signing of the bond purchase contract in a negotiated or private financing, or
after the acceptance of a bid in a competitive offering. Data includes both short and long term bonds as well as notes. Debt was grouped chronologically according to the sale date of type of debt
(see table).
Regional-State Interface
The State of California Franchise Tax Board, Economic and Statistical Research Bureau provided tax liability data by county for years 1995-2006. Data for 2007 through 2010 are provided by zip code.
Silicon Valley data includes Santa Clara and San Mateo Counties. All tax liability values are inflation-adjusted and reported in first half 2010 dollars, using CPI for the . City Average from the Bureau
of Labor Statistics.
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A P P E N D I X B
Silicon Valley Major Areas of Economic Activity
Percent of Total Percent Change
Employment Silicon Valley
2011 Q2 Employment 2007 - 2011 2010 - 2011
Total Employment 1,312,095 % % %
Community Infrastructure 753,374 % % %
Health & Social Services 131,544 % % %
Retail 125,070 % % %
Accomodation & Food Services 107,326 % % %
Education 101,399 % % %
Construction 51,938 % % %
Consumer Services 39,985 % % %
Wholesale Trade 33,207 % % %
Transportation 26,305 % % %
Federal Government Administration 24,540 % % %
Arts, Entertainment, & Recreation 24,097 % % %
Consumer Financial Services 20,314 % % %
Other 20,150 % % %
Goods Movement 19,035 % % %
Local Government Administration 11,059 % % %
Nonprofits 10,740 % % %
Utilities 4,579 % % %
Warehousing & Storage 2,044 % % %
State Government Administration 42 % % %
Information Products & Services 289,567 % % %
Software 92,360 % % %
Computer Hardware 42,559 % % %
Semiconductor & Semiconductor Equipment Manufacturing 37,837 % % %
Internet & Information Services 28,702 % % %
Electronic Component Manufacturing 23,961 % % %
Communications Services & Equipment Manufacturing 20,167 % % %
. Wholesale Trade 19,587 % % %
Instrument Manufacturing 17,075 % % %
Other Media & Broadcasting 4,916 % % %
. Repair Services 2,403 % % %
Innovation & Specialized Services 144,070 % % %
Technical & R&D 47,305 % % %
Personnel 28,265 % % %
Management Offices 25,811 % % %
Specialized Financial Services 21,434 % % %
Legal 9,895 % % %
Marketing/Ad/PR 7,942 % % %
Design 3,418 % % %
Business Infrastructure 56,532 % % %
Facilities 37,573 % % %
Administrative Services 18,959 % % %
Other Manufacturing 47,519 % % %
Other Primary & Fabricated Metal Manufacturing 15,056 % % %
Div. Ag & Food Manufacturing 13,791 % % %
Other Machinery & Equipment Manufacturing 8,507 % % %
Other Petrochemical Manufacturing 4,582 % % %
Textile, Wood, & Furniture Manufacturing 2,725 % % %
Other Misc. Manf. & Space & Defense Manufacturing 1,361 % % %
Paper & Packaging Manufacturing 1,286 % % %
Mining 211 % % %
Life Sciences* 21,033 % % %
Medical Devices 11,159 % % %
Biotechnology 9,874 % % %
Pharmaceuticals *** - - -
*In 2010, employment in Pharmaceuticals was suppresed for confidentiality reasons, causing the significant drop in total Life Sciences employment.
Note: Data is for San Mateo and Santa Clara Counties, Scotts Valley, Fremont, Newark, and Union City.
Data Source: California Employment Development Department, Labor Market Information Division, Quarterly Census of Employment and Wages
Analysis: Collaborative Economics
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J O I N T V E N T U R E S I L I C O N VA L L E Y
S I L I C O N VA L L E Y C O M M U N I T Y F O U N D AT I O N
A C K N O W L E D G M E N T S
Special thanks to the following organizations
that contributed data and expertise:
Alcohol & Drug Services Research Institute
Altamont Commuter Express
Americans for the Arts
Bay Area Water Supply & Conservation Agency Annual Survey
California Association of Realtors
California Department of Education
California Department of Finance
California Department of Justice
California Department of Public Health
California Department of Realtors
California Department of Social Services
California Department of Transportation
California Employment Development Department
California Energy Commission
California Franchise Tax Board
California Governor’s Office
California Health Interview Survey
California Public Utilities Commission
California Secretary of State
California State Controller’s Office
California State Treasurer’s Office
Caltrain
Cities of Silicon Valley
City Planning and Housing Departments of Silicon Valley
Cleantech Group, LLC
Colliers International
Educational Theater Organization
Energy Information Administration
Factset Mergerstat, LLC
Federal Financial Institutions Examination Council (FFIEC)
Integrated Postsecondary Education Study Data System
Joint Venture Survey of Silicon Valley Financial Officers
Moody’s
Music Educators National Conference
National Arts Educators Association
National Center for Educational Statistics
National Dance Educators Organization
National Establishment Time Series Database (NETS)
National Science Foundation
Planning and Evaluation
PricewaterhouseCoopers Money Tree TM
RAND California Statistics
Real Facts
Renaissance Capital’s
SamTrans
San Mateo County Board of Elections
San Mateo County Human Services Agency
Santa Clara County Department of Alcohol & Drug Services
Santa Clara County Registrar of Voters
. Bureau of Labor Statistics
. Census Bureau
. Patent and Trade Office
. Small Business Administration
UCLA Center for Health Policy Research
Valley Transportation Authority
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.
As a comprehensive center for philanthropy serving all of San Mateo and Santa Clara Counties, our mission is to strengthen the common
good, improve the quality of life and address the most challenging problems.
PRIVATE SECTOR
Accenture
Accretive Solutions
ACE Train
Adobe Systems
Agilent
Alston & Bird LLP
American Leadership Foundation
Applied Materials
AT&T
Bank of America
Bay Area SMACNA
Berliner Cohen, LLP
Better Place
Bingham McCutchen, LLP
Bloom Energy
Burr, Pilger, Mayer
Cargil
Cisco Systems
Chevron
Clearwire
Cogswell Polytechnical College
Comcast
Comerica Bank
Cooley Godward, LLP
Cypress Envirosystems
Deloitte & Touche
DLA Piper, LLP
EPRI
Ennovationz
Ernst & Young
Extenet Systems
Fairmont Hotel
Frieda C. Fox Family Foundation
Foothill-De Anza Community College
District Foundation
Google
Grant Thornton LLP
Greenberg Traurig, LLP
Greenstein Rogoff Olsen
Half Moon Bay Brewing Company
Hewlett-Packard
Hobnob
Hood & Strong, LLP
Johnson Controls
Jones Lang
Juniper Networks
Kaiser Permanente
KPMG
Koret Foundation
Lucile Packard Childrenís Hospital at Stanford
Leo M. Shortino Family Foundation
M+NLB
McKinsey & Company
Menlo College
Morgan Family Foundation
Mozes
Microsoft
Mitsubishi International Corporation
Netherlands Consulate
New Spectrum Foundation
NextG Networks
Notre Dame de Namur University
OíConnor Hospital
Oakland Athletics
Optony
Oracle
Orrick, Herrington & Sutcliffe LLP
Pacific Gas & Electric Company
Packard Foundation
Pipe Trades Training Center of Santa Clara County
Robert Half International
Samtrans
San Francisco 49ers
San Jose Sharks
San Jose/Silicon Valley Business Journal
San Jose State University Research Foundation
Santa Clara Building & Construction
Trades Council
Santa Clara County Office of Education
Santa Clara University
Santa Clara Valley Water District
Sensiba San Filippo
Silicon Valley Community Foundation
Silicon Valley Power
Skoll Foundation
Sobrato Development Companies
SolutionSet
South Bay Piping
Stanford University
Summerhill Land
Sun Microsystems
SunPower Corporation
SVB Financial Group
Synopsys
TDA Group
Therma
T-Mobile
UPS
University of California, Santa Cruz
University of Phoenix
Varian Medical Systems
Volterra
Weil Gotshal & Manges
Wells Fargo Bank
Wilmer Hale, LLP
Wilson Sonsini Goodrich & Rosati, LLP
VMware
Volkswagen Group of America
PUBLIC SECTOR
City of Belmont
City of Brisbane
City of Burlingame
City of Campbell
City of Colma
City of Cupertino
City of Daly City
City of East Palo Alto
City of Foster City
City of Fremont
City of Gilroy
City of Half Moon Bay
City of Los Altos
City of Menlo Park
City of Milpitas
City of Monte Sereno
City of Morgan Hill
City of Mountain View
City of Newark
City of Pacifica
City of Palo Alto
City of Redwood City
City of San Bruno
City of San Carlos
City of San Jose
City of San Mateo
City of Santa Clara
City of Santa Cruz
City of Saratoga
City of South San Francisco
City of Sunnyvale
City of Union City
City of Watsonville
County of Alameda
County of San Mateo
County of Santa Clara
County of Santa Cruz
Town of Atherton
Town of Portola Valley
Town of Los Altos Hills
Town of Los Gatos
Town of Woodside
Joint Venture Silicon Valley Investors Council
JOINT VENTURE SILICON VALLEY
100 W. San Fernando Street, Suite 310
San Jose, CA 95113
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email: info@
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SILICON VALLEY COMMUNITY FOUNDATION
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Mountain View, California 94040-1498
t: 650 450-5400 f: 650 450-5401
email: info@
