An online, interactive tool, the Women & Girls Data Platform provides access to publicly available data specific to regional, city, and statewide levels and aggregated for gender and race.
Source: ACS 2019 5-year estimates, table B01001
Counting race is generally complicated.
The US Census Bureau distinguishes between race (eg Asian or Black) and Hispanic ethnicity. In this bar chart, for all but the white non-Hispanic group, counts include those women who self-identify as Hispanic or Latina. Hispanic group includes women of all races. Therefore, the total value of 7 bars exceeds the actual population of females in the area.
Source: ACS 2019 5-year estimates, tables B01001B through B01001I
Generally, females aged 18 and over have higher foreign-born rates than those under 18.
Source: ACS 2019 5-year estimates, table B05003
Over half of females in Hartford and New Haven were never married, while the majority of females in Litchfield and Fairfield counties are married.
Source: ACS 2019 5-year estimates, table B12001
Overall in Connecticut the share of females in nursing facilities has declined, from 69% in 2013 down to 64% in 2019. Bridgeport, however, is experiencing the opposite trend.
Those whose gender is unknown were counted as non-female, so the real share of females in long term care may be slightly higher.
The lines show 7-day average number of new cases and deaths, by day. Cases and deaths include both confirmed and probable numbers. Note that the scales for deaths and cases are different.
This data is available for the state level only. The bar charts below show Covid-19 case and death rates (normalized per 100,000 people) for different demographic groups in Connecticut.
Females have had higher case and death rates than males. Death rate for Black individuals is much higher than those of white or Hispanic individuals.
See up-to-date values in CTData's Connecticut Covid-19 Tracking dashboard.
Source: CT Department of Public Health, 2020
According to the American Community Survey, females are more likely to have health insurance coverage than males in Connecticut. Young adults (age group 26-34) have higher rates of uninsured population among all age groups for many geographies in Connecticut.
Source: ACS 2019 5-year estimates, table B27001
The pie chart on the left shows the share of females aged 15-50 who gave birth in the past 12 months and the pie chart on the right shows their marital status. Now married includes women who are separated whose spouses are absent. Unmarried includes women who are divorced, widowed, or have never been married.
Source: ACS 2019 5-year estimates, table B13002 (including subtables B through I)
The bar chart below shows infant mortality rates per 1,000 births. Race and Hispanic ethnicity are not mutually exclusive groups, and women identifying themselves as Hispanic can be of any race and are also counted in the race breakdown as either "white" or "Black". To avoid high degree of variability, rates for less than 5 deaths are not presented.
Source: The Office of Vital Records at the Connecticut Department of Public Health, 2018 (provisional)
Maternal mortality is the number of deaths from any cause related to pregnancy and its management
(excluding accidental or incidental causes) and up to 12 months following the termination of pregnancy,
per 100,000 live births. This is a 2019 5-year estimate.
This data is only available on the state and national level.
Source: CDC WONDER Online Database, Mortality files, accessed via United Health Foundation's America's Health Rankings
In Connecticut, the preterm birth rate among Black women is 42% higher than the rate among all other women. This data is only available on the state level.
Connecticut's preterm birth rate is 9.4%, which is classified as C+ by March of Dimes. Data is not available for Tolland county and Windham county.
Source: 2019 March of Dimes Report Card
This data is only available on the state level.
Source: Human Anti-trafficking Response Team (HART) in Connecticut, December 2019
In Connecticut, 2 in 3 females aged 25+ have at least some college education. In Norwalk, Stamford, as well as Fairfield and Tolland counties, there are more women with Bachelor's degrees than those with high school diploma. At the same time, over 20% of women in Hartford and Bridgeport do not have a high school diploma.
Source: ACS 2019 1-year estimates, tables B15002, and B15002B through B15002I
Median earnings for women increase with educational attainment.
Source: ACS 2019 5-year estimates, table B20004
The number of suspensions are reported for students with at least one in- or out-of-school suspension or expulsion. Male students received twice as many suspensions than female students.
The most recent data are from the 2018-19 academic year. 2017-18 was the first and only academic year in which data for non-binary students was available.
Source: CT State Department of Education (via EdSight)
According to the YRBSS survey, female students are slightly more likely than male students to participate in extracurricular activities, such as school clubs, sports, music, art, drama, and other organized activities after school and on the weekends. White non-Hispanic female students were also more likely to participate than Hispanic/Latina female students (data for other races and ethnicities is unavailable).
The bar chart below shows percent of respondents who participated in at least one extracurricular activity in the week prior to the survey. This data is only available on the state level.
Before the Covid-19 pandemic, more males received unemployment benefits than females. Since mid-March, the number of women receiving unemployment benefits exceeded that of men, revealing the disproportionate impact of the Covid-19 pandemic on women.
This data is only available on the state level. Click legend to add/remove series from the chart. The bars below show initial claims for unemployment benefits, by gender. Note that these may not result in receiving UI benefits if the individual does not qualify. The line charts show the number of continued claims, or the number of individuals being paid benefits in a particular week. Big jump around July 4 includes one-time add of newly eligible PUA claims. October 4 increase is due to new claims for active PUA claimants who are on extensions, which is not expected to continue in future weeks.
Source: CT Department of Labor
The bar chart shows monthly applications for SNAP benefits submitted directly to the Department of Social Services starting from February 2020.
This data is not disaggregated by gender or race of applicant. This data is only available on the state level.
Source: CT Department of Social Services
The pie chart below shows what percentage of women are below the poverty line, according to the American Community Survey.
Source: ACS 2019 5-year estimates, table B17001
Source: ACS 2019 5-year estimates, tables B17001B through B17001I
The bar charts below shows the distribution of earnings of full-time female workers, by race and Hispanic ethnicity. Use the dropdown to view data for for a select race/ethnicity.
Note that data is missing for many racial categories. Percentages may not add up to 100% due to rounding.
Source: ACS 2019 5-year estimates, tables B20005, and B20005B through B20005I
The chart below shoes the median weekly earnings of full-time female workers as a percent of full-time male workers. Female full-time workers in Connecticut have experienced a larger wage gap compared to the United States. As of 2018, the wage gap remains substantial at 19%.
Source: US Department of Labor, 1998–2018 (annual averages)
Labor force includes females aged 16 and over who are available to work. Those in the labor force are generally split into employed and unemployed. Those not in labor force often include students, retirees, and those who take care of children or other family members.
Source: ACS 2019 5-year estimates, table B12006
Family households with a female householder and no spouse present generally receive SNAP benefits in higher numbers if they have children under 18. Over 60% of such households in Hartford, New Britain, and Waterbury receive SNAP benefits, compared to under 30% in Fairfield and Litchfield counties.
Source: ACS 2019 5-year estimates, table B22002
The ALICE Threshold represents the minimum income level necessary based on the Household Survival Budget (estimate of the total cost of household essentials – housing, child care, food, transportation, technology, and health care, plus taxes and a 10 percent contingency). It is calculated for each county.
Values for Connecticut and some towns are not available.
Source: 2017 ACS, IPUMS USA, University of Minnesota, www.ipums.org.
The bar chars below represent the number of women employed in a particular occupation field, and the median earnings of women in that field. Data are not available for all geographies. In general, median earnings of women working in STEM and management fields are highest. While management occupations comprise 13% of all female employment in Connecticut, STEM field accounts for only 4%.
Source: ACS 2019 5-year estimates, table C24020
Source: ACS 2019 5-year estimates, table B24022
As of 2017, according to the 2017 American Express State of Women-Owned Businesses Report, 113,110 women own businesses in Connecticut which employ 95,300 people and contribute $16.4 billion to Connecticut's gross domestic product.  
In Connecticut, women are still less likely than men to own companies that have paid employees. The highest share of women-owners are in education services, health care & social assistance, and accommodation & food services. The situation looks better when including companies that are equally female/male owned. For example, 53% of health care & social assistance firms in Norwalk in 2012 were owned fully or partially by women.
In the bar chart below, the x-axis represents the number of firms, and percentage represents the share of companies in the industry owned or co-owned by women (including equal male/female ownership). Industries with smaller number of businesses are not included.
Source: ACS 2012 Survey of Business Owners
Connecticut Secretary of the State collects business ownership data through an online survey, including whether firms are owned by women, ethnic minorities, veterans, or people with disabilities.
The bar chart shows number of women and non-women-owned firms by industry in selected geography. Note that many business owners choose not to respond to the survey, so the absolute numbers may not be representative.
Registered business addresses were used to determine which town and county the businesses belong. In the survey, if respondents did not flag "woman-owned", the business is considered non-woman-owned.
Source: CT SOTS, aggregated by CTData Collaborative in December 2020
 Connecticut Business and Industry Association. Number of Connecticut Women-Owned Businesses Grows, 2017. Retrieved from https://www.cbia.com/news/economy/women-owned-businesses-grow/.
 Data from the 2017 State of Women-Owned Businesses Report is extrapolated from the US Census 2012 Survey of Business Owners to get a calculated estimate for number of women-owned businesses in Connecticut for 2017.
Data reflects the current representation of Connecticut legislators as of December 2019. It is to be noted that there are three vacant house member seats (districts 48, 132, 151), leaving the total number of House of Representative members to be 148. Legislator gender was determined from pronoun use (she/her, him/his) on their official legislator website, found through https://cga.ct.gov.
County designations for state legislators were determined from the county that is the majority represented in their district. Towns in each legislator district that informed the county designation can be found on each legislator’s official website through https://cga.ct.gov. The majority of legislators have their districts within one county. County designations for congressional legislators were determined using the hometown provided on their official websites; hometown information was not available on the official websites for Rosa DeLauro and Jahana Hayes. The hometown used for the county designation for Jahana Hayes was found in the Hartford Courant (see endnote). No current hometown location could be found online for Rosa DeLauro so her state office location was used instead, found on her official website.
Several state legislators have districts that fall within multiple counties; their county was determined from the county where the majority of the towns they represent are from. These legislators are listed out below (Tables 5 and 7).
Several state legislators also represent an equal number of towns across in each county in their district, such as representing two towns in one county and two towns in another county. Their county was determined from the location of their hometown which can be found on their official website. These legislators are listed out below (Tables 6 and 8).
Counts were used to determine the number of legislators by gender in each county.
According to the BRFSS survey, adult women were more likely to report poor mental health than adult men. Black (non-Hispanic) respondents suffer from poor mental health at a higher rate than respondents whose race is white (non-Hispanic), Hispanic, and Other (non-Hispanic).
This data is only available on the state level. The bar chart below shows percent of respondents who reported 14 or more days of poor mental health during 30 days prior to the survey. Unfortunately, racial breakdown is unavailable by gender.
According to the BRFSS, 11% of females have thought about taking their own life, compared to 14% of males. Of those who thought about suicide, 31% of females and 30% of males attempted suicide.
This data is only available on the state level. The bar chart shows percent of respondents who reported they had ever thought of taking their own life, and those who thought of and then attempted suicide. White, Black, and Other race do not include Hispanic ethnicity. Unfortunately, racial breakdown is unavailable by gender.
Historically, males are much more likely to commit suicides than females. The line chart below shows annual suicides by gender in Connecticut between 1990–2019.
CT Office of the Chief Medical Examiner records all accidental deaths associated with drug overdose in Connecticut. The latest available data is for 2018.
The bar chart below shows the breakdown of drug-related deaths by race and ethnicity in selected geography. Counts are based on places of residence, not places of deaths (big cities with hospitals tend to experience larger numbers of deaths). 0.3% of all deaths do not have gender information and are excluded from this chart.
According to the YRBSS survey, female students are slightly more likely to have used electronic vapor products at least once. However, male students are more likely to use electronic vapor products frequently.
This data is only available on the state level. Electronic vapor products include e-cigarettes, e-cigars, e-pipes, vape pipes, vaping pens, e-hookahs, and hookah pens. Data is not available for most races. White refers to white non-Hispanic.
UCONN Crash Data Repository project collects data on all registered car accidents in the state. The bar chart shows the number of DUI crashes, injuries, and fatalities in selected geography in 2019.
Source: 2019 CT Crash Data Repository, DUI Enforcement Grant Report
Source: Prison Policy Initiative report
Historically, people of color have had lower homeownership rates than white people. This is also the case for Connecticut and most of its subdivisions. The bar chart shows percentage of women who live in homes that are owned, not rented.
Other category includes women who self-identify as American Indian or Alaska Native, other race, and two or more races.
Source: ACS 2018 5-year estimates, accessed via IPUMS USA, University of Minnesota, www.ipums.org.
In , median gross rent is estimated to be
Source: 2019 American Community Survey, 5-year estimates
The Women and Girls Data Platform (WGDP) is a cutting-edge tool to share information and equip non-profits, government, and community members with information for the advancement of women and girls in the 21st century. An online, interactive tool, the WGDP provides access to publicly available data specific to regional, city, and statewide levels and aggregated for gender and race. The data compares trends across regions and the state and allows understanding of local needs. Reliable information on the needs of women and girls is vital to address community needs through effective programming, advocacy, and resource allocation. The WGDP advances data literacy as a vital tool to increase the capacity and impact of community-based non-profits. According to the international Women’s Funding Network, no similar data sharing platform exists nationwide, and Connecticut is a model for the country to share data to increase equity.
The Platform is funded collaboratively by the Aurora Women and Girls Foundation, Fairfield County's Community Foundation's Fund for Women & Girls, Main St. Community Foundation Women & Girls’ Fund, and Community Foundation of Middlesex County Sari A. Rosenbaum Fund for Women & Girls. The Connecticut Collective for Women and Girls (CCWG), a statewide network for organizations serving women and girls is a primary audience for the platform. The Connecticut Women’s Education and Legal Fund (CWEALF) serves as the administrative backbone for the CCWG and as a collaborator on the WGDP.
The Connecticut Data Collaborative developed and built the Platform for the Collective and the funders with the goal to create a valuable and regularly updated tool.
Native American women and girls are not identified as a separate racial/ethnic group by the public data sources we present because of the small sample size. In several charts, Native Hawaiian and Pacific Islander, people of two or more races, and those classified as "Other" by ACS, were excluded for the same reason.
Gender identity in the public data sources is only presented in the binary terms of male and female, and does not recognize a more accurate understanding of non-binary, trans or fluid gender identity. We recognize these limitations and will seek to address them as we continue to add data sources to this site.