The Current Employment Statistics (CES) program is a Federal-State cooperative effort in which monthly surveys are conducted to provide estimates of employment, hours, and earnings based on payroll records of business establishments. The CES survey is based on approximately 119,000 businesses and government agencies representing approximately 629,000 individual worksites throughout the United States.

CES data reflect the number of nonfarm, payroll jobs. It includes the total number of persons on establishment payrolls, employed full- or part-time, who received pay (whether they worked or not) for any part of the pay period that includes the 12th day of the month. Temporary and intermittent employees are included, as are any employees who are on paid sick leave or on paid holiday. Persons on the payroll of more than one establishment are counted in each establishment. CES data excludes proprietors, self-employed, unpaid family or volunteer workers, farm workers, and household workers. Government employment covers only civilian employees; it excludes uniformed members of the armed services.

The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS.

Graph and download economic data for All Employees: Government: County Government in Los Angeles-Long Beach-Anaheim, CA (MSA) (DISCONTINUED) (SMU06310809093901201) from Jan 1990 to Dec 2015 about local govt, Los Angeles, CA, government, employment, and USA.

VITAL SIGNS INDICATOR Jobs by Industry (EC1)

FULL MEASURE NAME Employment by place of work by industry sector

LAST UPDATED July 2019

DESCRIPTION Jobs by industry refers to both the change in employment levels by industry and the proportional mix of jobs by economic sector. This measure reflects the changing industry trends that affect our region’s workers.

DATA SOURCE Bureau of Labor Statistics: Current Employment Statistics 1990-2017 http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) The California Employment Development Department (EDD) provides estimates of employment by place of work and by industry. Industries are classified by their North American Industry Classification System (NAICS) code. Vital Signs aggregates employment into 11 industry sectors: Farm, Mining, Logging and Construction, Manufacturing, Trade, Transportation and Utilities, Information, Financial Activities, Professional and Business Services, Educational and Health Services, Leisure and Hospitality, Government, and Other. EDD counts all public-sector jobs under Government, including public transportation, public schools, and public hospitals. The Other category includes service jobs such as auto repair and hair salons and organizations such as churches and social advocacy groups. Employment in the technology sector are classified under three categories: Professional and Business Services, Information, and Manufacturing. The latter category includes electronic and computer manufacturing. For further details of typical firms found in each sector, refer to the 2012 NAICS Manual (http://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2012).

The Bureau of Labor Statistics (BLS) provides industry estimates for non-Bay Area metro areas. Their main industry employment estimates, the Current Employment Survey and Quarterly Census of Employment and Wages, do not provide annual estimates of farm employment. To be consistent, the metro comparison evaluates nonfarm employment for all metro areas, including the Bay Area. Industry shares are thus slightly different for the Bay Area between the historical trend and metro comparison sections.

The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of the nation’s employment in that same sector. Because BLS does not provide national farm estimates, note that there is no LQ for regional farm employment. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.

VITAL SIGNS INDICATOR Change in Jobs by Industry (EC2)

FULL MEASURE NAME Employment by place of work by industry sector

LAST UPDATED May 2019

DESCRIPTION Change in jobs by industry is the percent change and absolute difference in the number of people who have jobs within a certain industry type in a given geographical area

DATA SOURCE California Employment Development Department: Current Employment Statistics 1990-2017 http://www.labormarketinfo.edd.ca.gov/

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) The California Employment Development Department (EDD) provides estimates of employment by place of work and by industry. Industries are classified by their North American Industry Classification System (NAICS) code. Vital Signs aggregates employment into 11 industry sectors: Farm, Mining, Logging and Construction, Manufacturing, Trade, Transportation and Utilities, Information, Financial Activities, Professional and Business Services, Educational and Health Services, Leisure and Hospitality, Government, and Other. EDD counts all public-sector jobs under Government, including public transportation, public schools, and public hospitals. The Other category includes service jobs such as auto repair and hair salons and organizations such as churches and social advocacy groups. Employment in the technology sector are classified under three categories: Professional and Business Services, Information, and Manufacturing. The latter category includes electronic and computer manufacturing. For further details of typical firms found in each sector, refer to the 2012 NAICS Manual (http://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2012).

The Bureau of Labor Statistics (BLS) provides industry estimates for non-Bay Area metro areas. Their main industry employment estimates, the Current Employment Survey and Quarterly Census of Employment and Wages, do not provide annual estimates of farm employment. To be consistent, the metro comparison evaluates nonfarm employment for all metro areas, including the Bay Area. Industry shares are thus slightly different for the Bay Area between the historical trend and metro comparison sections.

The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of the nation’s employment in that same sector. Because BLS does not provide national farm estimates, note that there is no LQ for regional farm employment. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.

Graph and download economic data for All Employees: Government: Federal Government in Santa Maria-Santa Barbara, CA (MSA) (SMU06422009091000001) from Jan 1990 to May 2025 about Santa Barbara, federal, CA, government, employment, and USA.

Context

The Uniform Crime Reporting (UCR) Program has been the starting place for law enforcement executives, students of criminal justice, researchers, members of the media, and the public at large seeking information on crime in the nation. The program was conceived in 1929 by the International Association of Chiefs of Police to meet the need for reliable uniform crime statistics for the nation. In 1930, the FBI was tasked with collecting, publishing, and archiving those statistics.

Today, four annual publications, Crime in the United States, National Incident-Based Reporting System, Law Enforcement Officers Killed and Assaulted, and Hate Crime Statistics are produced from data received from over 18,000 city, university/college, county, state, tribal, and federal law enforcement agencies voluntarily participating in the program. The crime data are submitted either through a state UCR Program or directly to the FBI’s UCR Program.

This dataset focuses on the crime rates and law enforcement employment data in the state of California.

Content

Crime and law enforcement employment rates are separated into individual files, focusing on offenses by enforcement agency, college/university campus, county, and city. Categories of crimes reported include violent crime, murder and nonnegligent manslaughter, rape, robbery, aggravated assault, property crime, burglary, larceny-theft, motor vehicle damage, and arson. In the case of rape, data is collected for both revised and legacy definitions. In some cases, a small number of enforcement agencies switched definition collection sometime within the same year.

Acknowledgements

This dataset originates from the FBI UCR project, and the complete dataset for all 2015 crime reports can be found here.

Inspiration

• What are the most common types of crimes in California? Are there certain crimes that are more common in a particular place category, such as a college/university campus, compared to the rest of the state?
• How does the number of law enforcement officers compare to the crime rates of a particular area? Is the ratio similar throughout the state, or do certain campuses, counties, or cities have a differing rate?
• How does the legacy vs. refined definition of rape differ, and how do the rape counts compare? If you pulled the same data from FBI datasets for previous years, can you see a difference in rape rates over time?

Graph and download economic data for All Employees: Federal Government in San Luis Obispo-Paso Robles-Arroyo Grande, CA (MSA) (SMU06420209091000001A) from 1990 to 2024 about San Luis Obispo, federal, CA, government, employment, and USA.

Graph and download economic data for All Employees: Government: Federal Government in San Diego-Chula Vista-Carlsbad, CA (MSA) (SMU06417409091000001SA) from Jan 1990 to Jun 2025 about San Diego, federal, CA, government, employment, and USA.

This dissertation studies the effectiveness of various policies focusing on examining experimental and nonexperimental data using various methodologies. The first chapter presents a novel dataset of the universe of municipal gun ordinances between 2008 and 2019 to estimate the effects of gun ordinances on firearm fatalities. To do so, I isolate plausibly exogenous variation in the passage of municipal firearm ordinances that result from county level weather conditions on the day of the National School Walkout. I find no discernible effect of ordinances on firearm deaths at the county level. These estimates are precisely zero, and I can rule out any effects larger than a 1% change from the mean number of firearm deaths. In further analyses of effects by victim subgroups defined by race, education, and age, I again do not detect any significant impact of ordinances on deaths. The first stage provides evidence that increased turnout for the 2018 National School Walkout led to more firearm ordinances being passed. Counties in states with punitive firearm preemption laws are much less likely to pass firearm ordinances.

The second chapter conducts a randomized controlled trial of one such program. Participants randomly assigned to intensive, holistic, wrap-around services have 10 percentage points higher employment rates after one year compared with a control group offered only help with an immediate need (p=0.03). Most of this effect appears to persist after programming ends (p=0.13). However, we find limited evidence that intensive, holistic services affect areas beyond employment, even when other areas of life are participants' primary goals. We find some evidence that the program works by increasing hopefulness and agency among participants, which may be more useful in supporting labor force participation than in meeting other goals.

The third chapter studies California’s 2017 minimum wage law, which implemented a higher wage floor for firms with at least 26 employees. Using establishment-year data from the Longitudinal Business Database, we estimate the law’s effect on firm size. Applying a standard TWFE difference-in-differences approach, we find that the policy reduced the likelihood that California firms have just over 26 employees. The statistical significance of the estimates is sensitive to the inclusion of county-level controls interacted with year effects. We present suggestive evidence that the law disincentivizes firms from increasing employment.

Despite the diversity of topics, each chapter centers on evaluating how policy interventions shape economic and social outcomes. These papers are unified by their focus on real-world policies and programs—whether enacted by governments or nonprofit organizations. Across chapters, the aim is to understand how institutions can influence behaviors in ways that promote—or fail to promote—desired outcomes.

The policy questions addressed span different domains: gun violence, labor markets, and social services. They also vary across levels of government and institutional actors—from municipal and state governments to community-based nonprofits. Yet they are united by the central concern of whether and how interventions succeed. Together, these findings highlight where policy can be a powerful tool—and where it may fall short—offering evidence to guide smarter policy design.