The Economy—
Strengths and Consequences

The region has had a strong recovery coming out of the 2007 to 2009 recession, with both job and income growth proceeding at a pace greater than experienced by California or the nation. Job growth has been particularly strong in the region’s dominant industries—information and professional and technical services as well as in sectors of growing importance such as accommodation and food services. Yet, these strengths need to be understood in the perspectives of both long term patterns of regional growth and the distribution of growth within different counties and among different occupation and income groups.

A Closer Look at Recession and Recovery

By spring 2013, the Bay Area had regained all of the jobs lost during the 2007 to 2009 recession, more than a year before a sustainable job recovery had occurred in either California or the United States, as shown in Figure 2.1. While this growth shows resilience in the region’s economic base, the longer term history indicates that the average rate of growth is much less—indeed close to zero if 2000 is taken as the base year. Average annual employment in 2013 was still below the annual average for 2000, the peak of the dot-com boom era. We estimate that only in 2014 did the region approach full recovery of jobs lost in the 2001 to 2003 downturn.

Figure 2.1 Job Levels Relative to 4th Quarter 2007 (Previous Peak) US, California, and Bay Area

Source: ABAG from US Bureau of Labor Statistics data
Note: Data is not seasonally adjusted. December 2007 level is indexed to 100.

This cyclical growth created the illusion of fast growth in different parts of the region at different points in time. A longer term overview shows that these cycles have averaged out to either slow or no growth in most parts of the region for the past decade and a half. Figure 2.2 shows the number of months until job recovery for the two most recent recessions. Bars with lighter shading indicate that the bar indicates the maximum number of months for which data is available, and that jobs were not yet recovered for the county (for that recession) as of June 2014. Among the region’s nine counties, only San Francisco and Napa had regained all jobs lost in both the 2001-2003 and 2007-2009 downturns by mid-2014.3

Figure 2.2 Months to Recovery By County, from 2001 and 2007 Recessions

Source: ABAG analysis from US Bureau of Labor Statistics data
Note: A bar with lighter shading indicates county has not yet regained pre-recession jobs.

San Francisco employment surpassed the fourth quarter 2007 level first at the end of third quarter 2012 and sustainably beginning in first quarter 2013. Napa’s employment surpassed fourth quarter 2007 job levels first in second quarter 2012 but sustainably only beginning second quarter 2013. In contrast, Sonoma County employment was still below both 2001 and 2007 peaks by June 2014. Solano and Contra Costa counties lost relatively small shares of employment in the 2001-2003 recession and were the first to regain those jobs, yet both counties have not yet recovered from job losses since 2007. Alameda, Santa Clara, and San Mateo have each regained jobs lost relative to the 2007 peak, but had job levels remaining below the 2001 peak as recently as June 2014. Figure 2.3 shows actual employment levels in each of the counties from 1990 through 2013, and an estimate for 2014 based on quarterly economic survey data for the first 6 months of the year. The 2014 estimate indicates that by the end of 2014 or sometime in 2015, all counties are likely to be fully recovered from both recessions. The annual percent change in jobs since 2000 range from -0.3 percent in Santa Clara to 1.3 percent in Napa. Taking a longer term perspective, since 1990, the region has added jobs at a rate of 0.8 percent per year, with county rates ranging from between 0.5 percent and 2.5 percent.

Figure 2.3 Average Annual Employment Bay Area Counties (1990–2013 and 2014 Estimate)

Source: ABAG from California Employment Development Department data

With job recovery, the rate of unemployment has dropped throughout the region. The Bay Area unemployment rate has dropped from an average of 10.6 percent in 2010 to an estimated 5.5 percent in 2014. Our estimates of county unemployment rates for 2014 vary from a low of 4.2 percent in Marin to a high of 7 percent in Solano, which is still below the statewide estimated average of 7.5 percent.

Industry Change and Restructuring

Figure 2.4 shows industry trends in both larger (2.4a) and smaller (2.4b) employment sectors. The most recent period from 2010 to 2013 has moderated some of the long term shifts in the economy. Manufacturing employment in 2013 was at about two-thirds of the employment level it reached in 2000, but the downward slide has reversed since 2010 for the region as a whole and for all of the nine counties. Job losses in financial activities also leveled off in 2010 and show small levels of increase by 2013.

Figure 2.4 Bay Area Industries, Wage and Salary Employment (1990-2013)
A: Larger Sectors

Source: ABAG from California Employment Development Department data

B: Smaller Sectors

Source: ABAG from California Employment Development Department data

Many of the jobs lost over the long term have been replaced by employment in health and education and leisure and hospitality, two sectors with the steadiest employment growth over the past two and a half decades. These are sectors with large local serving components, although the region also provides specialized medical care and draws tourists statewide, nationally and internationally. Yet, the Bay Area’s economy is often characterized by its professional and technical components. Two of the most important “tech-related” sectors, professional and business services and information are also among the most volatile. Both sectors have seen a resumption of long term growth trends, although neither had regained their 2000 peak job levels by 2013. Our estimates show that by the end of 2014, employment in professional and business services would have returned to the previous 2000 peak, while information jobs were still slightly below the peak of the dot-com era.

Several other sectors, after bumpy trajectories for most of the previous decade, have experienced employment growth since 2010. This has been the pattern for retail trade, wholesale trade, and construction. Only the government sector has continued the decline in jobs since 2010, although the pace of decline has slowed.

Figures 2.5 and 2.6 show the distribution of employment and employment change by county for selected sectors with significant amounts of growth.4 Employment concentration across counties differs by sector, with sectors primarily serving the region’s population, such as health and social assistance and retail trade, located close to population centers (and therefore more heavily in the counties with large clusters of residents), while sectors that are primarily developing products heavily used by business or exported beyond the region are located in the counties with the primary business centers. Thus professional and technical services are concentrated most heavily in San Francisco, Santa Clara, and San Mateo counties, whereas manufacturing is found most heavily in Santa Clara County, but is also disproportionately represented in the wine-growing counties of Napa and Sonoma.

Figure 2.5 Sector Employment by County (2013)

Source: ABAG from US Bureau of Labor Statistics Quarterly Census of Employment and Wages data

Figure 2.6 Sector Employment Change by County (2010-2013)

Source: ABAG from US Bureau of Labor Statistics Quarterly Survey of Employment and Wages data

Figure 2.6 ranks sectors by jobs added to the region as well as illustrating where growth has been concentrated. Growth from 2010 to 2013 has tended to further concentrate some of the region’s largest sectors. Health and social services accounted for over 30 percent of employment growth between 2010 and 2013 (compared to only 12 percent of employment in 2010). The next largest sector, professional and technical services, accounted for almost one-fifth of employment growth over the period, compared to 12 percent of 2010 jobs. Half of all job growth in the recovery, thus, was in health and social services or professional and business services. Other large shares of job growth were in accommodation and food services (14 percent of growth compared to 10 percent of 2010 jobs) and information services (8.5 percent of growth compared to just over 4 percent of 2010 jobs).

The largest number of net new health and social service and professional, scientific and technical service jobs went to San Francisco between 2010 and 2013. The city-county drew less than its proportional share of jobs in several other sectors. All of the job growth in the information sector went to Santa Clara, San Francisco, San Mateo and Marin counties, with the East Bay and the other three North Bay counties losing information jobs over the three-year period. Alameda saw the largest gains in manufacturing jobs, but Contra Costa’s loss in manufacturing more than canceled out the gains for the East Bay overall. San Mateo County losses in manufacturing counterbalanced a portion of the gains in Santa Clara and San Francisco, while the four North Bay counties combined added over 2,500 manufacturing jobs.

A location quotient analysis shown in Table 2.1 gives a more detailed look at sector concentration within counties and at the direction of change in concentration over the 2010 to 2013 period. The location quotient shown in this table compares the county’s proportion of jobs in the sector with the US proportion of jobs in the sector. Location quotients greater than one (1) show relative concentration of jobs in a sector.5 Table 2.1 identifies all sectors for which data was available by county that have location quotients greater than one (1).

Table 2.1 Sectors with Location Quotients Greater than 1 in 2013 Bay Area and Counties

Alameda Contra Costa Marin Napa San Francisco San Mateo Santa Clara Solano Sonoma Bay Area
Agriculture, forestry, fishing, and hunting 7.670 1.542 3.678
Construction 1.184 1.409 1.158 1.002 1.623 1.185
Manufacturing 1.012 1.784 1.675 1.185
Wholesale trade 1.230
Retail trade 1.066 1.100 1.264 1.108
Professional and technical services 1.503 1.206 1.529 2.607 1.950 2.020 1.767
Management of companies, enterprises 1.983 1.406 1.264 2.039 1.259
Educational services 1.128 1.009 1.604 1.480 1.715 1.272
Health care and social assistance 1.057 1.147 1.033 1.324 1.107
Transportation and warehousing 1.047 ND ND ND ND ND ND
Information 1.245 1.234 2.041 3.065 2.861 1.909
Finance and insurance 1.301 1.079 1.391
Real estate, rental, leasing 1.308 1.344 1.416 1.100 1.051
Arts, entertainment, and recreation 1.137 1.505 1.413 1.762 1.228 1.068
Accommodation and food services 1.165 1.587 1.316 1.022 1.112
Other services, except public administration 1.095 1.086 1.558 1.313 1.183 1.054 1.048

— Location Quotients down 0.1+ since 2005; — Location Quotients up 0.1+ since 2005
Source: ABAG from US Bureau of Labor Statistics data

Although not the largest employer in the information sector, San Mateo County has the highest location quotient with a share of employment in information almost three times higher than the proportion in the nation as a whole. Furthermore, San Mateo, Santa Clara, and San Francisco all gained share in the information sector relative to the nation in the 2010 to 2013 period, as did the region overall.

Six of the nine counties, and the region as a whole, have high concentration of jobs in professional and technical services, and Alameda, San Francisco, and San Mateo gained in concentration of jobs in that sector. The region also has a relatively high concentration of management of companies and enterprises, with Alameda, Contra Costa, and San Francisco gaining in concentration of management jobs between 2010 and 2013.

Several sectors are important areas of concentration for counties within the region, without having a location quotient greater than 1 for the region. The agriculture, forestry, fishing and hunting sector is heavily concentrated in Napa County, with a location quotient greater than 7. Solano and Sonoma counties also have relatively high employment concentration in this sector. Napa also has high location quotients in manufacturing (primarily wine making and related products) and in accommodation and food services. The county gained in concentration in manufacturing and accommodations, but not in the agriculture portion of the job base (although this remains the most concentrated industry for any county in the region). Several other counties have relatively high arts, entertainment and recreation location quotients. This concentration grew in Solano County, although this may reflect the stability of the sector, rather than job growth, in the face of the county’s continued employment weakness overall. Four of the six counties with high concentrations of employment in construction (Contra Costa, Marin, Solano and Sonoma), lost relative shares in that sector, although all continued to have location quotients above 1. Finally, health and social assistance gained concentration in most of the counties where it was already heavily concentrated.

Changing Labor Force and Workforce Opportunities

The San Francisco Bay Area’s resilient economy has affected the region’s labor force growth and occupational mix. Interactions between a highly skilled labor force and industry growth has led to a set of factors—highly educated workforce, experienced workers in skilled occupations, and specialized clusters of support workers—that reinforces growth in the mix of industries described in the preceding section.

The region’s total labor force (shown earlier in Figure 1.2) has expanded in the long term at close to the rate of population growth, but with fluctuations reflecting much more sensitivity to economic conditions. Based on annual American Community Survey (ACS) data, nationally and statewide, there has been an overall downward trend in the proportion of adults aged 16 and over participating in the labor force, although this trend is much less pronounced for the Bay Area than for the country or state as a whole. As seen in Figure 2.7, nationwide 65.9 percent of US adults were in the labor force in 2005 and again in 2008; in California the percentage rose from 64.9 percent in 2005 to 65.5 percent in 2008, yet both saw the share in the labor force drop to below 64 percent by 2013. The San Francisco Bay Area saw a rise in the proportion in the labor force which continued until 2009, and then dropped, yet the share in the labor force has begun rising again, and is now at almost 67 percent for the region.

Figure 2.7 Percent of Adults in the Labor Force US, California and Bay Area (2005-2013)

Source: ABAG from US Bureau of the Census American Community Survey 1-Year Estimates, 2005-2013

The rate of participation in the labor force at the county level sharply distinguishes between those counties with economies driven by the newest social media-based employment boom and those seeing slower recovery in their traditional industries. The proportion in the labor force was highest in San Francisco in 2013—over 70 percent, higher than either of the two previous peaks in 2005 and 2009. The rate of labor force participation also has risen from earlier periods in San Mateo and Santa Clara counties. Alameda, Marin, and Napa each have a higher share in the labor force than in 2010, but lower than in 2005, while Contra Costa, Solano, and Sonoma are each at their lowest participation rates of the three comparison years. (See Figure 2.8)

Figure 2.8 Percent of Adults in the Labor Force Bay Area Counties (2005-2013)

Source: ABAG from US Bureau of the Census American Community Survey 1-Year Estimates, 2005-2013

The industry information from the earlier section highlights the importance of a skilled labor force in the region’s expanding economy. Recent trends indicate that the Bay Area has the resources to continue this educational advantage. The 2013 ACS shows more than 40 percent of the region’s adult population 25 years old or greater with a bachelor’s, graduate, or professional degree, compared to close to 30 percent for the US and California. At the other end of preparedness, 12 percent have no high school diploma, just below the nationwide level of 13 percent and well below California’s 18 percent. (See Figure 2.9)

Figure 2.9 Education Attainment Bay Area Compared to US and California (2013)

Source: ABAG from US Bureau of the Census American Community Survey 1-Year Estimates, 2013

As diversity in the region grows and the population ages, there is some concern that the Bay Area could gradually lose its academic strength as a less educated population is added to the base and a more educated workforce retires. However, if recent trends continue, the labor force should maintain its share of population that is well educated. Figure 2.10 shows the college-educated number and share have grown over time in the region, from 2000 to 2013.

Figure 2.11 shows three important trends. First, for every age group, the number with a bachelor’s degree or higher grew between 2000 and 2013 (although there was a dip in 2005 for the 25 to 34 year old age group), and second, the percent with a bachelor’s degree or higher was greater for 2013 than 2010, 2005, or 2000, for every age group except the 45 to 65 year old group (where the difference is not large). Third, each age group is better educated than the next subsequent older age group, with the exception of the youngest group, which is the most likely to increase share of bachelor’s and graduate degrees as they build their “human capital.” The concern regarding the loss of educated workers is related not so much to the less educated character of younger workers (they appear to be increasingly well educated) but to the broader problem of overall numbers—as baby boomers retire, then the absolute number of people in the labor force may decrease without in-migration, leading to higher labor costs especially in industries that must compete for experienced and well-educated workers.

Figure 2.10 Educational Attainment over Time Bay Area (2000, 2005, 2010 and 2013)

Source: Bureau of the Census: Census 2000 and American Community Survey 1-Year Estimates

Figure 2.11 Bachelor’s Degree or Higher by Age Group Bay Area (2000, 2005, 2010, 2013)
A: Number with Bachelor’s Degree or Higher by Year

Source: Bureau of the Census: Census 2000 and American Community Survey 1-Year Estimates

B: Percent with Bachelor’s Degree or Higher by Year

Source: Bureau of the Census: Census 2000 and American Community Survey 1-Year Estimates

Within the region, the distribution of educational attainment among counties to some extent matches the skill levels required by the key industries of each county. The counties with the highest shares of college graduates, Marin, San Francisco, San Mateo, and Santa Clara (Figure 2.12), are also the counties with the highest shares of employment in technically-oriented industries. Napa, Solano, and Sonoma, with the highest shares without a college degree, also have the strongest employment in agriculture as well as in tourism-oriented sectors, which have a greater share of low wage jobs which generally do not require advanced education.

Figure 2.12 Educational Attainment by County (2013)

Source: ABAG from US Bureau of the Census American Community Survey 1-Year Estimates, 2013

Wage, Personal and Household Income Changes

Total personal income trends for the region are consistent with the strong recovery shown in employment. However, both wage and salary income and median household income show that the rising tide may not yet be raising all boats.

Total personal income levels in the Bay Area show a clearer recovery from recessionary periods than did the employment trends, as shown in Figure 2.13.6 Adjusting for inflation, regionwide total personal income in 2013 was 10 percent above the previous peak in 2007 and 8 percent above the level reached in 2000 from the dot-com boom. As with employment, personal income change has been more volatile in the Bay Area than in the US or California, dropping more precipitously in downturns, but surging with greater strength in upturns, as seen in Figure 2.14.

Figure 2.13 Bay Area Total Personal Income (1990 to 2013, Inflation Adjusted to 2013 base)

Source: ABAG from Bureau of Economic Analysis, adjusted for inflation using Bureau of Labor Statistics Consumer Price Index for All Urban Consumers (CPI-U) for San Francisco-Oakland-San Jose, CA

Figure 2.14 Percent Change in Total Personal Income US, California, and Bay Area (1990 to 2013, Inflation Adjusted to 2013 base)

Source: ABAG from Bureau of Economic Analysis, adjusted for inflation using Bureau of Labor Statistics Consumer Price Index for All Urban Consumers (CPI-U) for San Francisco-Oakland-San Jose, CA

Bay Area households at the median income level had a less robust experience than is indicated by the aggregate personal income data. The real growth that occurred for median household income between 1979 and 1989, and between 1989 and 1999 has evaporated in the last decade and a half. In each of the region’s nine counties, median household income for 2013 still lagged the previous peak level reached in 1999, 2007, or 2008. Six counties had median household incomes at or below 1989 levels, while three counties, Contra Costa, Solano, and Sonoma, still had inflation-adjusted median incomes close to 1979 levels (see Figure 2.15).7

Figure 2.15 Median Household Income by County (Inflation Adjusted to 2013 Base)

Source: ABAG from US Census 1980, Census 1990, Census 2000 and American Community Survey 1-Year Estimates, adjusted for inflation using Bureau of Labor Statistics Consumer Price Index for All Urban Consumers (CPI-U) for San Francisco-Oakland-San Jose, CA

Occupation and wage trends add further nuances to the employment and income picture. This analysis divides occupation categories into low, medium and high, based on whether the occupation-specific wages are 30 percent below the overall average for all occupations (LOW), 30 percent above the overall average (HIGH), or between the two (MIDDLE). Sorting aggregate occupation categories into these three bins and then adding total jobs and calculating average wages shows employment in high wage jobs growing both in the last half of the 2000s decade and since 2010, as shown in Figure 2.16. Middle wage jobs dropped much more sharply than low wage jobs, on a percentage basis, between 2005 and 2010, and, further, recovered more slowly than either high or low wage jobs between 2010 and 2013. Wage growth was stronger before 2010 and the wage decline smaller after 2010 for high wage jobs, while wages in low wage occupations dropped in both of two time periods, with the rate of loss exceeding that of either middle or high wage jobs in 2010 to 2013.

Figure 2.16 Employment and Wage Change by Occupation Categories

Source: ABAG from California Employment Development Department Occupation and Wage data, adjusted for inflation using Bureau of Labor Statistics Consumer Price Index for All Urban Consumers (CPI-U) for San Francisco-Oakland-San Jose, CA

Table 2.2 shows major occupation categories sorted by 2013 wage levels as well as the amount of job change at each occupation group from 2010 and 2013. Six occupation groups grew by more than 20,000 jobs each between 2010 and 2013. Computer and mathematical occupations grew by the greatest amount, over 36,000 jobs, closely followed by food preparation and serving related jobs and sales and related occupations. Three of the six leading categories are high wage, two are middle wage, while one is a low wage category.

Table 2.2 Major Occupation Categories Sorted by Wage Level (2013)

Occupational Title 2013 Mean Annual Wage 2013 Total Employment Employment Change 2010-2013
Management $142,603 239,950 25,950
Legal $131,217 32,540 1,760
Computer and Mathematical $108,803 211,190 36,490
Healthcare Practitioners and Technical $105,987 157,620 7,140
Architecture and Engineering $103,899 107,370 10,220
Life, Physical, and Social Science $90,199 52,280 5,750
Business and Financial Operations $89,563 230,410 22,660
HIGH $111,192 1,031,360 109,970
Arts, Design, Entertainment, Sports, and Media $67,076 56,560 680
Construction and Extraction $61,195 116,940 4,360
Education, Training, and Library $58,758 190,650 8,160
Protective Service 57,918 67,700 800
Installation, Maintenance, and Repair 55,579 93,580 -1,080
Community and Social Services $55,495 47,500 12,990
Sales and Related $49,548 340,480 33,590
Office and Administrative Support $44,585 508,850 21,680
MIDDLE $51,656 1,422,260 81,180
Production $40,587 145,140 -3,630
Transportation and Material Moving $39,398 171,270 5,020
Healthcare Support $38,628 72,580 -4,250
Building and Grounds Cleaning and Maintenance $32,213 108,660 8,090
Personal Care and Service $29,927 85,990 14,170
Farming, Fishing, and Forestry $26,205 9,460 1,220
Food Preparation and Serving-Related $24,703 297,550 34,760
LOW $32,689 890,650 55,380
TOTAL ALL OCCUPATIONS $64,949 3,344,200 237,300

Source: ABAG from California Employment Development Department tables of US Bureau of Labor Statistics Occupational Employment Statistics data

The aggregate level data shown in Table 2.2 does not tell the full story and implications on long term trends should be drawn with caution. There are several ways to measure low, middle and high wage jobs. A more in-depth analysis of the middle wage problem was conducted for the region’s economic prosperity strategy, using median hourly wage rather than average annual wage as the measure of earnings, showing somewhat greater disparities between the three job levels.8

Table 2.3 shows employment level changes and percent change in wages by metropolitan area within the region, each broken down by high, middle and low wage groups. The largest numbers of jobs were added in the three largest metropolitan districts or metropolitan statistical areas,9 within high wage jobs, as well as in middle wage jobs in the San Francisco-San Mateo-Redwood City metropolitan division. Wage losses in these categories were lower than in the low and middle wage categories in the smaller metropolitan divisions.

Table 2.3 Occupation and Wage Change by Metropolitan Divisions within the Region

Metropolitan Division Wage Grouping Employment Change Percent Change in Wages
San Francisco-San Mateo-Redwood City MD HIGH 39,470 -1.30%
San Jose-Sunnyvale-Santa Clara MSA HIGH 39,230 -2.50%
San Francisco-San Mateo-Redwood City MD MIDDLE 39,030 -3.20%
Oakland-Fremont-Hayward MD HIGH 26,130 -0.60%
San Jose-Sunnyvale-Santa Clara MSA LOW 18,400 -5.70%
San Francisco-San Mateo-Redwood City MD LOW 18,240 -3.70%
Oakland-Fremont-Hayward MD LOW 13,770 -4.40%
Oakland-Fremont-Hayward MD MIDDLE 12,810 -4.90%
Vallejo-Fairfield MSA HIGH 4,370 1.00%
Santa Rosa-Petaluma MSA MIDDLE 2,990 -5.90%
Santa Rosa-Petaluma MSA LOW 2,870 -6.00%
Napa MSA LOW 2,360 -7.20%
Napa MSA MIDDLE 1,680 -9.20%
Vallejo-Fairfield MSA MIDDLE 1,030 -5.00%
San Jose-Sunnyvale-Santa Clara MSA MIDDLE 900 -5.90%
Napa MSA HIGH 730 -13.40%
Santa Rosa-Petaluma MSA HIGH 40 -4.70%
Vallejo-Fairfield MSA LOW -260 -3.80%

Source: ABAG from California Employment Development Department tables of US Bureau of Labor Statistics Occupational Employment Statistics data

Income Distribution and Poverty

The overall decline in median household income and in wages in many occupations while total personal income is rising indicates that the strength in total personal income growth may come from two factors—an overall growth in population and growth in asset based income, such as returns on investments and rents. Differential rates of change of asset and wage income (as well as retirement income and transfers) can lead to changing shares of population in poverty and changes in income distribution.

The distribution of income levels by income quintile10 and the ratio between the highest and lowest quintiles each contribute to the understanding of how income distribution has changed during the recession and recovery, giving an indication of the “spread” between incomes at the bottom and top tiers of the economy. Figures 2.17-2.20 compare changes in income quintile categories, first for 2007 to 2010 and then for 2010 to 2013. Each bar represents the percent change from the earlier period to the later in the top income level of the income category. During the recession (2007-2010), median income dropped for all income categories, from the lowest income 20 percent to the highest. The declines were greater in California than in the US as a whole. Within the Bay Area, the lowest income populations were particularly vulnerable to income losses in the four North Bay counties, while income losses over all income categories were much lower in San Francisco, San Mateo, and Santa Clara than in the remaining counties.

Figure 2.17 Household Income Percent Change by Quintile US and California (2007 to 2010)

Source: ABAG from US Bureau of the Census American Community Survey data, inflation adjusted with US CPI

Figure 2.18 Household Income Percent Change by Quintile Bay Area Counties (2007 to 2010)

Source: ABAG from US Bureau of the Census American Community Survey 1-Year Estimates data, adjusted for inflation using Bureau of Labor Statistics Consumer Price Index for All Urban Consumers (CPI-U) for San Francisco-Oakland-San Jose, CA

Figure 2.19 Household Income Percent Change by Quintile US and California (2010 to 2013)

Source: ABAG from US Bureau of the Census American Community Survey 1-Year Estimates data, inflation adjusted with US CPI

In the Bay Area, gains and losses are spread unequally, with households in the bottom four quintiles in Marin County seeing rising incomes, and those in the four quintiles in Solano and Contra Costa counties experiencing income losses. For the great majority of counties, the 20th percentile category has the highest losses or lowest gains in household income. In each of the five largest counties, the 80th percentile category has the highest gains or least losses. Trends are a little more randomly distributed among the North Bay counties, but this may reflect a higher degree of statistical uncertainty as the counties and thus survey samples are smaller.

Figure 2.20 Household Income Percent Change by Quintile Bay Area Counties (2010 to 2013)

Source: ABAG from US Bureau of the Census American Community Survey data, adjusted for inflation using Bureau of Labor Statistics Consumer Price Index for All Urban Consumers (CPI-U) for San Francisco-Oakland-San Jose, CA

These trends appear to be leading to increasing income inequality, as shown in Table 2.4. This table shows the ratio between the top income of the 80th percentile and the top income of the 20th percentile. In almost all cases, US, California, and Bay Area counties, the ratio has been rising since 2007. Napa is the only county where the ratio is lower in 2013 than in 2010 (but higher than in 2007). Even so, this trend may mask very different types of situations. Areas with higher ratios may be more diverse in their population baseā€”for example, San Mateo, with a ratio lower than Alameda, Santa Clara, or San Francisco, may be in this circumstance because of less opportunity for lower income households to live in the area.

Table 2.4 Ratio of Income at the 80th Percentile to Income at the 20th Percentile

2007 2010 2013
US 4.7 4.8 5.0
California 4.7 5.0 5.3
Alameda 5.3 5.4 5.5
Contra Costa 4.5 4.6 5.0
Marin 5.3 5.7 5.7
Napa 4.2 4.7 4.6
San Francisco 6.3 6.6 7.1
San Mateo 4.4 4.4 4.7
Santa Clara 4.7 4.9 5.2
Solano 3.8 4.1 4.3
Sonoma 4.1 4.4 4.6

Source: ABAG from US Bureau of the Census American Community Survey 1-Year Estimates

This becomes even more evident comparing the ratio for cities. San Francisco, Berkeley, and Oakland all have very high 80/20 ratios, but these cities have strong affordable housing programs that serve low income households, allowing lower income households to live within each city. In Oakland and Berkeley, the 20th percentile income in 2013 was about $18,500. In contrast, the cities with the lowest 80/20 ratios, Clayton, San Ramon, and Dublin, had both 80th and 20th percentile incomes well above those found in the three most “unequal” cities. (Tables showing the full ranking by city can be found here).

Poverty shows roughly similar overall trends and countywide differences to the income distribution data, as measured by the percent of families earning less than the official Federal poverty level. Using national statistics, the Bay Area as a whole has a smaller share of families in poverty than does California or the nation, but the official US poverty measure11 does not take into account geographic variation in cost of living, and also does not take into account supplemental cash and noncash income from government programs for low income families. The share of families in poverty in the Bay Area is rising based on the official statistics, although at a slightly slower pace than in the US and California, as shown in Figure 2.21.

Figure 2.21 Poverty Trends in the US, California and Bay Area

Source: ABAG from US Census 2000 and American Community Survey, 2005-2012

Trends at the county level are consistent with the changes seen in inequality among household incomes. Napa and Marin counties have seen significant drops in the share of people in poverty since 2010, and the East Bay has experienced more modest declines in the poverty rate, as measured using the official standard. Rates have risen in San Francisco, San Mateo, Santa Clara, Solano, and Sonoma (Figure 2.22).

Figure 2.22 Dissimilar Poverty Trends in the Region’s Counties

Source: ABAG from US Census 2000 and American Community Survey, 2005-2012

The share in poverty is much greater when cost of living and social programs are both taken into account. An alternative measure developed by the Public Policy Institute of California showed rates at close to double the official levels in 2011 for Napa and San Mateo, and at least 50 percent above the official level for most of the other counties (see Figure 2.23).12

Figure 2.23 California Poverty Rate Measure Compared to US Official Poverty Rate (2011)

Source: ABAG from Public Policy Institute of California, October 2013

The trends in equity measure for the region and counties should be interpreted recognizing that they do not necessarily represent only changing conditions for individuals over time. These changes may also come with shifts in the population base. Aging and retirement may reduce household income for reasons other than inequitable wages. A changing employment base may draw in new migrants, some of whom are both young and highly paid, others in low-paid informal work, shifting overall ratios of rich to poor in a way different from varying levels of income growth by occupation. The section that follows tracks some of the demographic trends that contribute to the changing economic conditions just described.


3 Note that some counties have regained jobs lost from the last recession, but not the one before it.

4 Sectors discussed for the region as a whole are more aggregated than those discussed by county. It is at this level that the most recent data, provided on a monthly basis, can be aggregated across metropolitan areas. While both sets of data come from the Bureau of Labor Statistics, the data by county is provided at a different level of aggregation, and is updated more slowly. Thus, for example, these charts present data for professional, scientific and technical services rather than the more aggregated professional and business services shown elsewhere.

5 The location quotients equation for this table is: LQi,j=[Empi,j/TotalEmpj]/[Empi,US/TotalEmpUS]; a location quotient of 1.18 for an industry in a county implies that the county has an 18 percent greater share of jobs in that industry than would be expected were it to have a share proportional to that found in the US as a whole. A location quotient of 3 for an industry in a county implies three times the “expected” level of employment. These location quotients were directly calculated for the project from BLS data, rather than drawn from the BLS web site. This allowed for estimates where data is withheld for key sectors for some counties.

6 Total personal income is an indicator of total value gained by residents, as compared to gross regional product, which measures output from the region, but not income generated from that output or gained by residents from output produced outside of the region.

7 Comparisons between American Community Survey and decennial census years are not exact because different survey methodologies were used in the two types of surveys. Small differences may not accurately reflect at which point median incomes were higher.

8 The most recent update on the data is reported in Levy, Stephen, “Occupation and Industry Job and Wage Trends: Update to the Analysis in the Regional Prosperity Strategy Report,” Center for the Continuing Study of the California Economy, January 2, 2015.

9 For statistical purposes, the Bay Area has six metropolitan statistical areas (MSA) or districts (MD) by which some economic data is reported, including the occupations data. These metropolitan areas include: Napa MSA (Napa County), Oakland-Fremont-Hayward MD (Alameda and Contra Costa counties), San Francisco-San Mateo-Redwood City MD (Marin, San Francisco and San Mateo counties), San Jose-Sunnyvale-Santa Clara MSA (San Jose and San Benito counties), Santa Rosa-Petaluma MSA (Sonoma County), and Vallejo-Fairfield MSA (Solano County). San Benito County is a small county of 58,000 population and 15,000 jobs outside of the nine county San Francisco Bay Area. Where possible, this is excluded from Bay Area counts, but it is included with the San Jose-Sunnyvale-Santa Clara MSA where county level data is not available.

10 The quintile levels are calculated by sorting households by income level and dividing the households into five income groups in order of income level, identifying the break-points for the 20th, 40th, 60th, 80th and 95th percentiles.

11 The data here is reported for families, consistent with how the Census Bureau measures it. Poverty rates for the population as a whole are also frequently reported, and tend to be higher.

12 See Sarah Bohn, Caroline Danielson, Matt Levin, Marybeth Mattingly, Christopher Wimer October 2013. The California Poverty Measure: A New Look at the Social Safety Net. (Public Policy Institute of California; in collaboration with the Stanford Center on Poverty and Inequality).