The Bay Area's workforce has become more skewed towards high- and low-wage jobs over the years.
In 2017, jobs in middle-wage occupations represented one-fifth of our region's employment, having declined in both relative and absolute terms since 2001. Diminishing middle-wage employment has meant increased job polarization, leaving low-wage workers fewer options for upward mobility. Many of the middle-wage jobs lost have been in occupations specific to manufacturing. Meanwhile, the number of middle-wage jobs in the healthcare sector, such as laboratory technicians and dental assistants, has seen sustained growth. Middle-wage employment in the construction sector has also recovered from its Great Recession downturn.
While high-wage jobs represent the majority of all jobs in the Bay Area, both the absolute number and share of workers employed in low-wage jobs has been on the rise in recent years. From 2016 to 2017, the region added roughly 90,000 low-wage jobs and 9,000 middle-wage jobs, while the number of high-wage jobs decreased by around 12,000. The growth in low-wage jobs has been predominantly in the retail and food service sectors.
Middle-wage jobs made up just 20% of regional employment in 2017
Historical Trend for Share of Jobs by Wage Level
High-wage jobs are increasingly concentrated in Silicon Valley and San Francisco.
The change in jobs across wage levels has not been evenly spread across the Bay Area. A disproportionate share of high-wage jobs were created in Silicon Valley and San Francisco, the epicenters of recent economic growth. To a lesser extent, this trend also has been seen in the East Bay, where the gap between the share of low-wage and high-wage jobs has narrowed since 2001. While each of these subregions has seen gains in high-wage technology jobs, job growth in the East Bay has been more pronounced in education and health service occupations.
Low-wage occupations, such as janitors or retail salespeople, are not as prone to geographic clustering as middle- and high-wage work. As a result, the distribution of low-wage jobs largely has been proportionate to total employment. Yet contrary to the regional trend, the North Bay has seen its share of low-wage jobs grow, powered by growth in occupations like personal care aides and food service workers.
High-wage occupations account for nearly half the jobs in the South Bay
Historical Trend for Share of Jobs by Wage Level by Subregion
Washington, New York and the Bay Area have the most uneven distribution of jobs by wage level.
In 2017, middle-wage jobs represented roughly one-fifth of all jobs in Washington, New York and the Bay Area, making their shares of middle-wage jobs smallest among the large metro areas. An increasingly high median wage for the Bay Area has resulted in traditionally middle-wage occupations like medical assistants and office clerks being classified as low-wage. As a result, the Bay Area's share of low-wage workers is nearly 40 percent, the highest of any of its peer metro areas.
While all peer metro areas have larger shares of low- and high-wage jobs than middle-wage jobs, Miami, Atlanta, Dallas and Houston have more even distributions of jobs by wage level. Miami and Atlanta's large service sector workforce means its median wage is relatively low, and therefore many consumer service occupations are classified as middle-wage. In Texas metros, the oil and gas industry has created jobs in related middle-wage occupations, such as technicians and transportation operators.
Metro Comparison For Share Of Jobs By Wage Level (2017)
Sources & Methodology
Jobs are determined to be low-, middle- or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.
Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.
Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.
Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.
In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime).
Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are 'filled-in' using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.