Descriptive Title:

Geographical, ethnic, and annual variations in employment rates

Geographic Unit of Analysis:

Census tract and county

Percent of persons 16 and older in the civilian labor force who are employed (2005-2009)
Neighborhood% employed90% MOE*
Bayview/Hunter's Point 85.8% 5.3%
Bernal Heights 94.3% 2.3%
Castro/Upper Market 95.1% 2.1%
Chinatown 85.3% 8.0%
Excelsior 91.0% 2.6%
Financial District/South Beach 94.3% 12.6%
Glen Park
Golden Gate Park NA NA
Haight Ashbury 95.6% 2.0%
Hayes Valley
Inner Richmond 93.6% 1.8%
Inner Sunset 96.3% 0.6%
Japantown
Lakeshore 92.3% 3.8%
Lincoln Park
Lone Mountain/USF
Marina 95.2% 2.1%
McLaren Park
Mission 95.0% 1.7%
Mission Bay
Nob Hill 95.9% 2.6%
Noe Valley 94.5% 2.5%
North Beach 95.5% 3.3%
Oceanview/Merced/Ingleside
Outer Mission 95.0% 1.5%
Outer Richmond 93.1% 2.1%
Pacific Heights 96.1% 1.7%
Portola
Potrero Hill 90.7% 4.7%
Presidio 96.8% 2.5%
Presidio Heights 96.3% 3.0%
Russian Hill 91.9% 3.3%
San Francisco 93.4% 0.5%
Seacliff 96.0% 13.2%
South of Market 94.2% 2.6%
Sunset/Parkside
Tenderloin
Treasure Island 84.1% 15.6%
Twin Peaks 92.4% 19.1%
Visitacion Valley 89.2% 5.8%
West of Twin Peaks 95.3% 2.1%
Western Addition 93.5% 2.0%

Why Is This An Indicator Of Health and Sustainability?

Unemployment has been consistently linked to poor health,a and has been associated with higher mortality rates, especially from heart disease and suicide.b Women who are unemployed have higher rates of anxiety and depression and lower self rated health status.a A French study showed that unemployed men had higher rates of smoking, alcohol consumption, psychoactive drug use and depression than their employed counterparts.c Finally, in a large scale study involving over 600,000 residents in Sweden, the neighborhood unemployment rate predicted coronary heart disease risk for the neighborhood’s residents, even after controlling for individual demographic and socioeconomic measures.d

Interpretation and Geographic Equity Analysis

This indicator is intended to describe unemployment in San Francisco at the neighborhood level, by ethnicity and over time.  As described in the methods section below, unemployment rates at the census tract and neighborhood level were statistically unreliable, so employment rates are provided instead.  Charts are also presented on unemployment rates over the past decade and by ethnicity. 

The map and table illustrate the rates of employment in San Francisco between 2005 and 2009.  Based on the five year sample from the American Community Survey, the average rate of employment was 93.4% citywide, so the average rate of unemployment can be assumed to be 6.6% during this time.  The dark blue and teal census tracts in the map are the census tracts with the lowest rates of employment and highest rates of unemployment in the City.  Specifically the neighborhoods with the highest average rates of unemployment (10% or higher) are Bayview, Chinatown, Treasure Island, and Visitacion Valley.   Specific census tracts within Downtown/Civic Center, Excelsior, and Lakeshore also have high rates of unemployment (15% or higher) but the neighborhood average is less than 10% unemployment.  The neighborhoods with the lowest rates of unemployment include Haight Ashbury, Inner Sunset, Pacific Heights, the Presidio, Presidio Heights, and Seacliff.

The charts above illustrate the unemployment rate in San Francisco over time in comparison to California’s unemployment rate and by ethnicity and gender.  Between 2001 and 2011, unemployment rates in San Francisco doubled from 4% to 8%.  During this time, unemployment rates tripled in California.  Although unemployment rates appeared to have peaked in 2009/2010 in San Francisco, the rates appear to continue rising for California as a whole.  

Importantly, unemployment rates vary by population.  As illustrated in the charts above, people of color have experienced greater unemployment than non-Hispanic whites.  Specifically, for both males and females, African Americans had almost triple the rate of unemployment as non-Hispanic whites.  Latina/Hispanic females and Asian males had roughly double the rates of unemployment as non-Hispanic white females and males respectively.  Latina/Hispanic females had higher rates of unemployment than Latina/Hispanic males, whereas Asian females had lower rates than Asian males.  Given the small sample size of African Americans in San Francisco, the unemployment rates for African Americans has a larger margin of error (as illustrated by the black bar) compared to other ethnicities.  

Methods

Employment rate

For this indicator, the employment rate, rather than the unemployment rate, was calculated because figures for unemployment were statistically unreliable. This is because a relatively small number of people are unemployed versus employed, and it is generally difficult to generate reliable estimates for small populations from sample surveys like the American Community Survey (ACS).

According to the ACS, civilians 16 years old and over are classified as employed if they are either (1) "at work," that is, they did any work at all during the reference week as paid employees, worked in their own business or profession, worked on their own farm, or worked 15 hours or more as an unpaid worker on a family farm or in a family business; or (2) were "with a job but not at work", that is, they did not work during the reference week but had jobs or businesses from which they were temporarily absent due to illness, bad weather, industrial dispute, vacation, or other personal reasons. Excluded from the employed are people whose only activity consisted of work around the house or unpaid volunteer work for religious, charitable, and similar organizations; also excluded are all institutionalized people and people on active duty in the United States Armed Forces.

The equation used to calculate the employment rate is therefore: (persons 16+ years old in the civilian labor force and employed) / (persons 16+ years old in the civilian labor force).

Because data for the ACS are collected continuously over a five year period, this indicator does not describe the employment rate at a single point in time. The current economic crisis began in late 2008 and thus, ACS data spanning 2005-2009 likely underestimate the true impact of unemployment in San Francisco. November of 2005-2007, unemployment rates in San Francisco were below 5%, while in 2008-2009 they were 6% and 9.2%.  As of November 2011, San Francisco's unemployment rate was 7.8%.  The state unemployment rate in November of 2011 was listed as 10.9%, which is also lower than the previous two years (http://www.bls.gov/lau/).

The ACS is a sample survey, and thus, data are estimates rather than counts. Estimates have accompanying margins of error that indicate the span of values that the true value could fall within. Margins of error should be subtracted from and added to the value to determine the range of possible values. If the margin of error is too big relative to the value, data are not shown because they are statistically unstable. A coefficient of variation of 30% was used to determine statistical instability.

Unemployment rate trends over time 

To develop the unemployment over time chart, we downloaded 2001-2011 data from the Local Area Unemployment Statistics from the US Census Bureau.  Specifically the following series ID were downloaded LAUPS06090003 (San Francisco county), and LAUST06000003, LAUST06000004, LAUST06000005, LAUST06000006 (California statewide).  Data was transposed onto an excel spreadsheet to linearly order the monthly data points over time and allow comparison of San Francisco and California estimates.  Excel was then used to chart the unemployment estimates. 

Unemployment by race/ethnicity

To develop the unemployment rates by ethnicity chart, we downloaded the five year sample American Community Survey data from 2006-2010 on employment status by ethnicity.  Data presented does not include mixed race and other.  Excel was used to chart the unemployment estimates and include error bars to illustrate the margin of error.

Limitations

Unemployment rates may not provide an accurate reflection of those unemployed. Unemployment figures indicate how many are not working for pay but seeking employment for pay. Therefore, critics believe that current methods of measuring unemployment may be underestimates. Examples of classes of people who are excluded include the following:

  • The 2% of the available working population incarcerated in U.S. prisons (who may or may not be working while incarcerated).
  • Those who have lost their jobs and have become discouraged over time from actively looking for work.
  • Those who are self-employed or wish to become self-employed, such as tradesmen or building contractors or IT consultants.
  • Those who have retired before the official retirement age but would still like to work (involuntary early retirees).
  • Those on disability pensions who, while not possessing full health, still wish to work in occupations suitable for their medical conditions.
  • Those who work for payment for as little as one hour per week but would like to work full-time. These people are "involuntary part-time" workers.
  • Those who are underemployed, e.g., a computer programmer who is working in a retail store until he can find a permanent job.

It is important to note that the Hispanic / Latino category is not a mutually exclusive race category. In the American Community Survey, race and Hispanic origin are treated as separate concepts with a separate question asking about Hispanic origin. Hispanics or Latinos are people who classified themselves in at least one of the specific Spanish, Hispanic, or Latino census categories. People of Hispanic origin may also be of any race, and are asked to answer a race question by marking one or more race categories, including: White, Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, and Some Other Race.

The data by neighborhood and by ethnicity provides an average of a five year time-span (2005-2009 and 2006-2010) during which a major economic recession began.  The impacts of the recession upon household incomes today may not be accurately reflected in the data presented above.   More recent data is available at the CA Employment Development Department (EDD) for San Francisco: http://www.labormarketinfo.edd.ca.gov/Content.asp?pageid=164

Certain populations and industries were more impacted by the recession and job layoffs than other populations, which would further impact the unemployment rates described above.   Employment status is impacted by many different factors including educational attainment, race/ethnicity and related racial discrimination, class and inter-generational wealth or poverty, languages spoken, access to social networks, job training, and various other social and economic factors. 

Data Source

Employment rate map and table: American Community Survey (ACS), 5-year Estimates, 2005-2009.

Unemployment by race/ethnicity: American Community Survey (ACS), 5-year Estimates, 2006-2010.

Unemployment over time: 2001-2011 data from the Local Area Unemployment Statistics from the US Census Bureau. 

Map and table created by San Francisco Department of Public Health, Environmental Health Section using ArcGIS software. 

Map data is presented at the level of the census tract. The map also includes planning neighborhood names, in the vicinity of their corresponding census tracts. 

Table data is presented by planning neighborhood. Planning neighborhoods are larger geographic areas than census tracts. SFDPH chose to use the San Francisco Planning Department's census tract neighborhood assignments to calculate neighborhood values. This assignment method relies on a 'centroids within' methodology to convert census tracts to geographic mean center points. Census tracts are assigned to planning neighborhoods based on the spatial location of those geographic mean center points and neighborhood totals are calculated for the table. In a few case, certain census tracts were redesignated to different neighborhoods based on knowledge of the population dispersion in the tract. 

Detailed information regarding census data, geographic units of analysis, their definitions, and their boundaries can be found at the following links:

Interactive boundaries map

http://sfindicatorproject.org/resources/data_map_method

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  2. Canadian Public Health Association. Board of Directors Discussion Paper. Health impacts of social and economic conditions: Implications for public policy. 2001. Ottawa, Canadian Public Health Association.
  3. Khlat, M, Sermet, C, Le Pape, A. Increased prevalence of depression, smoking, heavy drinking and use of psycho-active drugs among unemployed men in France. European Journal of Epidemiology 2004;19:445-451.
  4. Sundquist K, Theobald H, Yang M, et al. Neighborhood violent crime and unemployment increase the risk of coronary heart disease: a multilevel study in an urban setting. Soc Sci Med. 2006;62(8):2061-2071.