August 31, 2017 edition
The Connecticut State Data Center provides population projections to assist state agencies, non-profit organizations, businesses, governments, and centers/organizations to identify demographic trends and changes within Connecticut’s Towns. These projections are created based upon administrative and survey datasets and while these projections are developed based on the best data sources available, actual population changes may vary from these projections. To assist in planning, analysis, and decision making, the population projections have been developed based on town derived fertility rates.
The Connecticut State Data Center predicts that towns in Connecticut are projected to slowly gain population as a total, according to the 2015 to 2040 population projections for all 169 towns in the state of Connecticut.
The new projections show that multiple towns are approaching a demographic shift due to an aging population, a near net zero overall migration rate, and a relatively low, but stable, birth rate. Windham, East Windsor, Avon, Oxford, Ellington, Sterling, Norwich, West Haven, Rocky Hill, and Manchester are expected to experience the largest percentage of increase in overall population projected from 2015 to 2040
The towns of Sherman, New Fairfield, Bridgewater, Sharon, Monroe, Cornwall, Salisbury, Old Saybrook, Washington, and Weston are projected to experience the largest percentage of decline in the overall population from 2015 to 2040.
The changing demographics by age cohort for towns in Connecticut provides a more complete picture of the overall trends within towns over time. The Connecticut State Data Center has released an interactive data dashboard to accompany the release which enables users to view demographic changes town by town with data from 1970 to 2040. When reviewing the age cohort data, long-term trends in demographics shifts within towns, and more broadly across the state when comparing multiple towns, indicate which towns are experiencing stable or declining births by examining the under 5 age cohort, as well as visually presenting the demographic shift between age cohorts as individuals age 55 to 64 age into the 65+ age cohort.
For a complete summary of the 2015 to 2040 Connecticut Town Population Projections view the press release.
Click on each graphic to view/download the full graphic in high-resolution. These graphics may be used in press releases, research articles, presentations, or other digital and/or print formats with the branding included and a link/reference back to the Connecticut State Data Center.
These projections do not claim to predict the future, as population dynamics can be influenced by economic, policy, individual decisions, and other aspects which are not accounted for in the projections model. These projections serve as a point of reference for state agencies, regional planning organizations, municipalities, researchers, and other organizations but the actual outcome may differ from the projections.
The population projections provide general statistical projections of the population by sex and five year age cohort from 2015 to 2040. The projections are based on birth and mortality data from the Connecticut Department of Public Health, migration data derived from U.S. Census Bureau Decennial Census, and birth and death data from the Connecticut Department of Public Health. Development of the derived migration rates included the grouping of Connecticut towns into 8 groupings based on data from the U.S. Census Bureau’s Decennial Census and the American Community Survey (ACS) pertaining to median age of housing (ACS), median household income (ACS), owner occupied median home value (ACS), population density (Decennial Census), population over 65 years of age (Decennial Census), population under 15 years of age (Decennial Census), percent of population below poverty level (ACS), percent renter occupied housing (Decennial Census), percent seasonal housing (Decennial Census), and total population (Decennial Census).
These projections provide population projections for individuals who are residents, or are projected to become, residents for towns within the state of Connecticut. These projections are intended to guide planning, analysis, and decision making in the state and for towns and are reviewed on an annual basis to compare projections to the latest administrative and survey data available to identify if there are any significant deviations from the projected population to the observed population for towns in the state of Connecticut.
These projections are based on an annual average of the resident population for towns in the state of Connecticut. Resident population is defined as those persons who usually reside within a town in the state of Connecticut (where they live and sleep majority of the time). Individuals who reside in another state but either own property or work remotely in a town within the state of Connecticut are not included in these population projections.
The basic assumption of this strategy is that the recent demographic trends (i.e. trends of birth, death, and migration) will continue in the projection period. The projections are based on statistical models which utilize historical birth, mortality, and migration data to inform the model and the actual population numbers can be influenced by economic, policy, individual decisions, and other aspects which are not accounted for in the model.
The projections are not intended to be used for the following purposes or should be used with caution when considering:
- Individual school enrollment or school district enrollment planning as these projections do not include school enrollment nor include data on school choice or other dynamics related to school enrollment.
- Group quarters population remains proportional constant and thus these projections are not group quarters enrollment/population projections.
To view trends in the population for towns in the state of Connecticut and between towns, the Connecticut State Data Center has published a data story which provides the viewer with context of past trends, projected future trends, and broader trends between towns in the state of Connecticut. For each visualization, users can interact with the data and download an image of each visualization.
The Connecticut State Data Center has published these data tables to the State of Connecticut Open Data portal to allow users to download, visualize, filter, and connect to the data via APIs.
View and Download Data – Connecticut Open Data data.ct.gov
The development of the 2017 edition of the population projections for all 169 towns in the state of Connecticut is based on over 2 years of research testing different approaches, administrative and survey datasets, and models before the final projections were developed. This research involved a number of individuals and organizations and we want to acknowledge the following individuals/organizations for their assistance from providing data, to assisting and informing the methodology and approaches research, to developing the final projections, datasets, and visualizations which are available to the user.
Connecticut Department of Public Health, Connecticut Office of Policy and Management, Cornell Program on Applied Demographics, Eversource, GeoLytics, Qualidigm, State of Rhode Island Planning Information Center, United Illuminating, UMass Donahue Institute, U.S. Census Bureau
Dr. Thomas Cooke (UConn Geography), Xiaojiang Li (CTSDC), Qinglin Hu (CTSDC) Wenjie Wang (CTSDC), Weixing Zhang (CTSDC)
Dataset Feedback and Guidance
Steve Batt (UConn Library), CABE, Dr. Thomas Cooke (UConn Geography), Hartford Data Meetup Group, Michael Howser (UConn Library)
Steve Batt (UConn Library)
Methodology and Research
Karyn Backus (CT DPH), Michael Howser (UConn Library), Qinglin Hu (CTSDC), Xiaojiang Li (CTSDC), Lloyd Mueller (CT DPH), UMASS Donahue Institute, Wenjie Wang (CTSDC), Weixing Zhang (CTSDC)
Funding and Reporting
Martha Bedard (UConn Library), Stephanie Beron (UConn Geography), Ed Chang (UConn Library), Michael Howser (UConn Library), Deborah Ives (UConn Sponsored Programs), Holly Jeffcoat (UConn Library), Tyler Kleykamp (OPM), MiChelle Lopes (UConn Sponsored Programs), Jillian Lopez (UConn Sponsored Programs), Ryan Marsalisi (UConn Library), Lori Mather (UConn Sponsored Programs)