The California Women’s Well-Being Index

The California Women’s Well-Being Index (WWBI) is a county-level, composite index that shows how women are faring throughout California. The WWBI consists of 30 indicators of women’s well-being that are grouped into five dimensions: Health, Personal Safety, Employment and Earnings, Economic Security, and Political Empowerment. The methodology used to create the index is outlined in detail below.

Creating the Women’s Well-Being Index
In deciding which measures, or “indicators,” to include in the WWBI, the Budget Center first engaged in a thorough review of research on women and families as well as an evaluation of comparable projects at the national and state level.1 This review resulted in a wide-ranging list of potential indicators. Because this county-level index measures women’s well-being, potential indicators were limited to “outputs” – that is, those that measure attributes of the female population or reflect community characteristics. This standard eliminated “input” indicators, such as those that measure public spending or community resources, for example.

The Budget Center subsequently screened data sources to ensure that data for potential indicators were current, available from a reputable source, and based on sound research methods. Data also were screened to verify that they are updated on a regular basis in order to allow for updates to the WWBI in future years. This initial search resulted in a list of 50 potential indicators.

Next, the Budget Center, in cooperation with the Women’s Foundation of California, surveyed individuals who focus on issues of concern to women across the state. (See the Acknowledgments for a list of survey respondents.) The survey allowed respondents to rank the importance of potential indicators in measuring women’s well-being as well as to suggest alternative indicators. The initial survey was sent via email to approximately 200 individuals. Three follow-up emails were sent to the same individuals, yielding a response rate of approximately 30%. Based primarily on survey results, the initial list of 50 indicators was narrowed down to 30 indicators falling within five dimensions of women’s well-being.

Data Sources and Data Quality
The 30 indicators included in the WWBI are based on data from a variety of state and federal agencies as well as from universities. Data for 17 indicators come from surveys, including the US Census Bureau’s American Community Survey (ACS) and the California Health Interview Survey (CHIS) conducted by University of California, Los Angeles. The remaining data come from administrative sources, with this information collected by various agencies and organizations, such as the California Department of Justice or the California Department of Public Health. Data for most indicators reflect multi-year estimates. (Combining data from multiple years increases the reliability of the data.) These multi-year estimates reflect the average condition in each county during a specific time period.

For survey data, margins of error at the 95% confidence level are included in the WWBI whenever possible and applicable.2 In addition, margins of error at the 95% confidence level are included for some administrative data. While these data are not subject to sampling error – because they reflect a full “universe” of individuals rather than a sample – it is common to provide the margin of error for certain vital statistics because they are subject to random variation during any given period.

In some cases, due to data limitations, data for certain counties for specific indicators were deemed unreliable. The Budget Center used several common benchmarks to determine if a data point was unreliable. This includes an event count of less than 20 in a county for any given time period and/or a coefficient of variation greater than or equal to 30%. For the ACS data, a higher standard was applied: a coefficient of variation greater than or equal to 10%.

When data for one or more counties were determined to be unreliable, county groups were created by aggregating estimates. This was done in order to create a more stable estimate applicable to each county in the group. When creating county groups, several considerations were made. First, contiguous counties with unreliable estimates were often grouped together. However, if possible, county groups were created with the smallest number of counties to avoid unnecessary loss of detail. For example, if the data for four contiguous counties were deemed unreliable, ideally two county groups would be created instead of one large county group. In some cases, data for one or more counties were grouped with a county that had a reliable estimate in order to create a stable estimate.

In addition, indicator values were considered when aggregating counties. For example, if a county with an unreliable estimate needed to be grouped with another county, the indicator values were taken into account in order to avoid aggregating counties with disparate estimates. In some cases, groups do not consist of contiguous counties because the estimates for the contiguous counties were too different to be combined.

For some counties and racial/ethnic groups, the Budget Center chose to display the unreliable estimate. In these cases, the estimate is noted in the tool with an asterisk.

Calculating the Women’s Well-Being Index
The WWBI consists of a wide range of data reported in a variety of ways such as rates, ratios, or percentages. These data have varying ranges and scales. In order to construct a composite index, indicators within each dimension were standardized and aggregated to create a county-level value for each dimension and for the overall index. This section outlines the methodology used in standardizing and aggregating the data in the WWBI.

Data were standardized for each indicator by calculating the z-score. The z-score converts a value into units of measurement based on the standard deviation. The z-score is calculated using the 58-county averages and standard deviations for each indicator. This allows for comparing values across indicators with different formats and with varying ranges of data. The z-score is calculated for each county and indicator using the following formula:

In some instances, a higher z-score indicates greater well-being, such as the percentage of women with a bachelor’s degree. In other cases, a higher z-score indicates lesser well-being, such as the female unemployment rate. In order to ensure that higher scores consistently reflect greater well-being, a number of indicators were reverse-coded. This was done by multiplying the z-score by negative 1.

One disadvantage of using z-scores is that the value of the z-score is hard to interpret. In order to create a value that is easy to understand, the z-score for each indicator was converted into a 100-point scale using the following formula:

The highest scaled z-score for any given indicator has a value of 100, and the lowest scaled z-score has a value of 0, with higher scores indicating greater well-being. However, while a score of 100 reflects the best value across all counties, it is not indicative of maximum well-being. For example, a score of 100 for the voter registration indicator does not mean that all eligible women in that county are registered to vote. Likewise, a score of 0 does not indicate that no women in that county are registered to vote.

To calculate scores for each of the five dimensions, we averaged the scaled z-scores for each county within each dimension. We then calculated overall index scores by averaging each county’s five dimension scores. The indicators and dimensions were not weighted prior to aggregation (i.e., they have equal weights). This reflects the belief that each indicator within a dimension has equal bearing on the well-being of women. Likewise, equally weighting the five dimensions indicates that health, personal safety, employment and earnings, economic security, and political empowerment are all equally important in assessing how women are faring in California.3

Counties are ranked by indicator score, by dimension score, and by their overall index score. In general, the WWBI employs a “modified competition ranking system” to rank the counties. In a modified competition ranking system, ties are ranked with the lowest rank. For example, if three of California’s 58 counties are tied for last, they would have a rank of 58. Using the traditional ranking system, their rank would be 56. The exception to this rule is when two or more counties are tied for first. When this occurs, these counties are ranked first.

Updates Made to the Women’s Well-Being Index

The California Budget & Policy Center updated the Index in October 2020 and again in September 2024. The following changes were made across the versions.

2020
In 2020, eight indicators were altered based on feedback received by stakeholders. The following provides a list of the changes made to these indicators.

Economic Security Dimension

Cost of Housing: The Cost of Housing indicator provides data on housing affordability. The first version of the Women’s Well-Being Index measured housing affordability by using data from the US Housing and Urban Development’s calculation of Fair Market Rents and the US Census Bureau’s calculation of single mothers’ median income. The 2020 Index generalizes this measure of housing affordability by using median gross rent and women’s median annual income data from the US Census Bureau’s American Community Survey (2014-2018).

Employment & Earnings Dimension

Labor Force Participation: The Labor Force Participation indicator shows what share of the population is actively working or looking for work. The first version of the Index used the population ages 16 to 64. The 2020 Index uses the prime-age working population – ages 25 to 64 – in calculating the labor force participation rate. This age group would typically be expected to be working or looking for work.

Low-Wage Workers: The first version of the Index provided data on the percentage of women working in low-wage occupations. The 2020 Index refines this indicator, providing data on the percentage of women paid low wages.

Health Dimension

Health Care Coverage: The Health Care Coverage indicator provides data on the share of women without health insurance. The first version of the Index provided data for women ages 18 to 64. The 2020 Index provides data for women ages 19 to 64. This change is due to a modification made by the Census Bureau in the presentation of these data in their American Community Survey table (B27001).

Life Expectancy: The Life Expectancy indicator provides data on the estimated average lifespan of an individual at birth. This indicator replaces the Obesity indicator from the 2016 Index. Life expectancy is a more common measure of health and wellness.

Safety Dimension

Sexual Assault: Prior to 2014, the Federal Bureau of Investigation collected data on three types of sexual offenses: rape, sodomy, and sexual assault with an object. In 2014, the Federal Bureau of Investigation required reporting agencies in the US to aggregate data for these assaults into one offense: forcible rape. This is a broader definition of sexual assault that encompasses a wider range of offenses. The California Department of Justice implemented this change in the reporting of crime data in 2014, which resulted in an increase in the number of reported rapes.

Domestic Violence: The first version of the Index used the total population when calculating domestic violence rates for the population in each county. The 2020 Index uses the female population for all rates in the Personal Safety Dimension to be more consistent across indicators.

Assault: The first version of the Index the female population age 18 and over when calculating rates of assault for the population in each county. The 2020 Index uses the female population for all rates in the Personal Safety Dimension to be more consistent across indicators.

2024
In 2024, two indicators were altered based on recent economic security research and feedback received by stakeholders. The following provides a list of the changes made to these indicators.

Economic Security Dimension

Commuting Time/Digital Equity: The digital equity indicator replaced the commuting time variable as digital equity was determined to be a more relevant variable. Specifically, a series of literature points to challenges with economic security when adults do not have access to broadband or a computer. The racial digital divide is particularly stark. This indicator shows the number and percentage of adults (18 and over) with cable, modem, fiber optic or DSL service and a desktop, laptop, netbook, or netbook computer in the household. This indicator utilizes the US Census Bureau’s 2018-2022 five-year American Community Survey variables reflecting access to in-home internet and access to in-home computer/laptop.

High School Diploma/Bachelor’s Degree: The bachelor’s degree indicator replaced the high school diploma indicator. This change was made to reflect recent research showing that women without a bachelor’s degree were more likely to lose their jobs during the COVID-19 pandemic. The indicator shows the number and percentage of adults (25 and over) that have a bachelor’s degree. This indicator utilizes the US Census Bureau’s 2018-2022 five-year American Community Survey variables reflecting educational attainment.

Food Insecurity/Food Hardship: While the data source and analysis for this indicator remain the same across versions, the label for the food insecurity indicator changed to food hardship. This change was made given that the United States Department of Agriculture’s definition of food insecurity did not completely align with the measure used for this indicator.

Health Dimension

Life Expectancy: Prior to the 2019 dataset, the life expectancy data from the Institute for Health Metrics and Evaluation did not disaggregate life expectancy at birth by race/ethnicity. The 2019 dataset allowed for this disaggregation and is now included in the indicator.

Safety Dimension

Fatal Accidents: The fatal accidents racial/ethnic disaggregation now disaggregates between Asian and Pacific Islander identities. In previous versions, Asian and Pacific Islander were combined into one category.

Hospital Visits Due to Assault: The hospital visits due to assault now includes a racial/ethnic disaggregation for Multi-Race. Previous versions did not include this disaggregation.

Hospital Visits Due to Assault: The hospital visits due to assault indicator now includes a racial/ethnic disaggregation for Multi-Race. Previous versions did not include this disaggregation.

Suicide: The suicide indicator now includes a racial/ethnic disaggregation for Multi-Race. Previous versions did not include this disaggregation.

Endnotes

1 See for example, Helen Boutrous, et al., The Report on the Status of Women and Girls in California: 2015 (Mount Saint Mary’s University, Los Angeles: 2015); Anna Chu and Charles Posner, The State of Women in America: A 50-State Analysis of How Women Are Faring Across the Nation (Center for American Progress: September 2013); Cynthia Hess, et al., The Status of Women in the States 2015 (Institute for Women’s Policy Research: May 2015); Kristen Lewis and Sarah Burd-Sharps, Women’s Well-Being: Ranking America’s Top 25 Metro Areas (Measure of America: April 2012); University of Minnesota, Center on Women and Public Policy, and Women’s Foundation of Minnesota, Status of Women & Girls in Minnesota: Research Overview (June 2014); Wider Opportunities for Women, The Economic Security Scorecard: Policy and Security in the States (2013).
2 A 95% confidence level means that a researcher is 95% confident that the interval defined by the margins of error contains the true value for the population as a whole.
3 For more details on weighting within composite indexes and other methodological issues, see Organization for Economic Co-Operation and Development, Handbook on Constructing Composite Indicators: Methodology and User Guide (2008).

The California Women’s Well-Being Index is a project of the California Budget & Policy Center. 

Laura Pryor (Research Director) and Hannah Orbach-Mandel (Policy Analyst) prepared the 2024 Women’s Well-Being Index (WWBI) with assistance from Nishita Nair (Research Associate) and other Budget Center staff. GreenInfo Network developed the data visualization, downloadable county fact sheets, and indicator fact sheets.

Kristin Schumacher, a former California Budget & Policy Center analyst, created the WWBI to help advocates and policymakers understand how women are faring in California. Since the WWBI was first published in 2016, the Index has been replicated in several states nationwide. Schumacher is a consultant to the 2024 update of the WWBI and continues to advance this work through her consulting firm, Aster Policy Analytics.

The 2024 Women’s Well-Being Index was updated in collaboration with the California Commission on the Status of Women and Girls, which provided funding, communications, outreach, and engagement support. 

We are thankful to a number of individuals who have contributed to earlier work on the Women’s Well-Being Index.

For the 2024 Women’s Well-Being Index, the Budget Center consulted a number of individuals to help inform the tool’s updates. Additionally, in late 2017 and early 2018 a number of individuals shared their thoughts on policies to boost women’s economic security, employment, and earnings. These individuals helped to shape policy recommendations for women related to work supports, boosting income, building wealth, and improving programs and services that benefit women with low-incomes in California. (Organizational affiliation and title reflects position at time of interview.)

• Dr. Fatima Alleyne, Black Women Organized for Political Action
• Jasmine Amons, Program Associate, National Center for Youth Law, Women’s Policy Institute Fellow
• Graciela Aponte-Diaz, Director of California Policy, Center for Responsible Lending
• Sarah Arce, Senior Policy Director, The Campaign for College Opportunity
• Elizabeth Ayala, Senior Program Associate, The Women’s Foundation of California
• Barbara Baran, Co-Director, California EDGE Campaign
• Alexandra Bastien, Senior Associate, Policy Link
• Sarah Bohn, Research Fellow, Public Policy Institute of California
• Christa Brown, Manager, Financial Justice Project, City and County of San Francisco
• Lewis Brown, Jr., Senior Associate, Policy Link
• Tyrone Buckley, Policy Director, Housing California
• Maggie Cervantes, Executive Director, New Economics for Women
• Elena Chávez Quezada, Senior Program Officer, Walter & Elise Hass Fund
• Judy Darnell, Vice President of Public Policy United Ways of California
• Melany de la Cruz-Viesca, Assistance Director, UCLA Asian American Studies Center
• Rachel Deutsch, Supervising for Worker Justice, Center for Popular Democracy
• Jodi Doane, Grants & Community Relations Manager, JVS SoCal, Women’s Policy Institute Fellow
• Rosalyn Epstein, Financial Empowerment Program Manager, National Coalition for Asian Pacific American Community Development
• Tania Flores, Senior Program Associate, The Women’s Foundation of California
• Michelle Freridge, Executive Director, Asian Youth Center
• Emily Gordon, Director of Strategic Research, Center for Popular Democracy
• Sabrina Hamm, Statewide Managing Director, California Asset Building Coalition
• Katie Hern, Co-Founder, California Acceleration Project
• Dr. Hilary Hoynes, Haas Distinguished Chair in Economic Disparities, Goldman School of Public Policy, University of California Berkeley
• Mary Ignatius, Statewide Organizer, Parent Voices
• Jennifer Ito, Research Director, USC Program for Environmental and Regional Equity (PERE)
• LaNiece Jones, Executive Director, Black Women Organized for Political Action
• Rebecca Kauma, Director of Digital Equity, County of Los Angeles
• Tatiana Larkin, Black Women Organized for Political Action
•  Anya Lawler, Policy Advocate, Western Center on Law & Poverty
•  Vihncent Le, Senior Legal Counsel of Tech Equity, The Greenlining Institute
• Pete Manzo, President & CEO, United Ways of California
• Amy Matsui, Senior Counsel and Director of Income Security, National Women’s Law Center
• Heather McCulloch, Founder and Director, Closing the Women’s Wealth Gap
• Krista Niemczyk, Public Policy Manager, California Partnership to End Domestic Violence
• Marj Plumb, Chief Strategist, Policy Advocacy and Training, The Women’s Foundation of California
• Anne Price, President, Insight Center
• Patti Prunhuber, Senior Policy Attorney, Child Care Law Center
• Aaron Schill, Director of Research and Programs, National Digital Inclusion Alliance
• Katie Selenski, Executive Director, CalSavers (Formerly Secure Choice)
• Donna Sneeringer, Director of Government Relations, Child Care Resource Center
• Jessica Stender, Senior Counsel, Workplace Justice & Public Policy, Equal Rights Advocates
• Adie Tomer, Senior Fellow, Brookings Metro
• Marisabel Torres, Senior Policy Analyst, UnidosUS
• Julie Vogtman, Director of Job Quality and Senior Counsel, National Women’s Law Center
• Lisa Williams, Black Women Organized for Political Action
• In addition, individuals who responded to our survey in summer 2015 on selecting indicators were instrumental in guiding the development of the initial version of the California Women’s Well-Being Index. (Organizational affiliation reflects position at time of survey.)
• Dion Aroner, Partner, Aroner, Jewel and Ellis Partners
• Marisol Aviña, Program Manager, The California Endowment
• Barbara Baran, Co-Director, California EDGE Coalition
• Jessica Bartholow, Legislative Advocate, Western Center on Law & Poverty
• Rebecca Hamburg Cappy, Director, Northern California Office, Alliance for Justice
•  Krystle Contreras, Director of Outreach and Prevention, Central California Family Crisis Center
• Betsy Cotton, Director, Close the Gap CA
• Sarah Crow, Vice President, The Opportunity Institute
• Cynthia Douglas, California Department of Social Services
• Myra Duran, Policy Manager, California Latinas for Reproductive Justice
• Stephanie Fajuri, Supervising Attorney, Disability Rights Legal Center – Cancer Legal Resource Center
• Sequoia Hall, Leadership Council Member, East Oakland Building Health Communities
• Marlene Christine Hurd, Human Resources Manager, Administrator, Healthy Communities Inc. dba Healthy Oakland
•  Jennifer Ito, Project Manager, University of Southern California, Program for Environmental and Regional Equity
• Kate Karpilow, Executive Director, California Center for Research on Women & Families
• Kimberly Kenny, Associate Vice President, Mount Saint Mary’s University, Los Angeles
• Mari Lopez, Policy Director, Vision y Compromiso
• Linda Meric, Executive Director, 9to5, National Association of Working Women
• Ami Nagle, President, Nagle & Associates
• Nikki Newsome, Program Manager, Reading and Beyond
• Vanessa Perez, Civic Engagement Specialist, Time for Change Foundation
• Mary L. Perry, Education Consultant
• Vonya Quarles, Executive Director, Starting Over, Inc.
• Maria Reyes, Bilingual Legal Services Specialist, YWCA Silicon Valley
• Gabriela Sandoval, Director of Research and Chief Economic Security Officer, Insight Center for Community Economic Development
• Diana Scott, Chief Human Resources Officer, Prologis
• Laura Segura, Executive Director, Monarch Services, Santa Cruz County
•  Hillary Selvin, Executive Director, National Council of Jewish Women/Los Angeles
• Cathy Senderling-McDonald, Deputy Executive Director, California Welfare Directors Association
•  Ann Stevens, Director, Center for Poverty Research, University of California, Davis
• Nancy Strohl, Consultant, Office of Senator Holly J. Mitchell
• Anne Stuhldreher, Senior Program Manager, The California Endowment
• Jesse Torrey, Associate Director, RISE San Luis Obispo County
• Francesca Vietor, Program Director, Environment, Public Policy, Civic Engagement, The San Francisco Foundation
• Nancy Volpert, Director of Public Policy, Jewish Family Service of Los Angeles
• Heather Warnken, Legal Policy Associate, Warren Institute on Law & Social Policy, University of California, Berkeley, School of Law
• Deborah Peterson Small, Executive Director, Break the Chains: Communities of Color and the War on Drugs
• Nicole D. Vick, Senior Health Educator, Los Angeles County Department of Public Health
• Pete Woiwode, Director, California Partnership