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Outlook 2060: Demographic-Economic Estimates & Projections
The Outlook 2060 estimates and projections are updated annually. Outlook 2060 Estimates & Projections Scope & Development The Outlook 2060 estimates and projections are focused in these sectors: • Demographic: Population and Housing - details • Education: K-20 Participation and Attainment - details • Labor Force: Employment and Earnings - details • Productivity: Output and Trade - details • Fiscal: Government Revenue and Expenditures - details Demographic: Population and Housing Estimates and projections are developed for the resident population and demographic components of change (births, deaths, and migration). The estimates/projections are developed by age, gender, race, and Hispanic origin for each year from July 1, 2000 to July 1, 2060. The projections are based on Census 2000, Census 2010 and annual American Community Survey estimates. The estimates/projections are developed using a cohort-component method and associated demographic-economic cause and effect simultaneous equations. Housing items include total housing units, occupied units (owner-occupied, renter-occupied), vacant units and units in structure (single unit, 2 units, 3-19 units, 20 units or more). The methodology makes use of county level models. Estimates and projections are developed at the county level and aggregated to the state and national and other levels. The county level models follow a generic structure but each county model can be modified to reflect a specification unique to that county. County-specific trends in fertility, mortality and migration are used. The generic county model makes use of the population identity equation: P[i,a,g,r,t] = P[i,a,g,r,t-1] + B[i,a,g,r,t] - D[i,a,g,r,t] + MD[i,a,g,r,t] +MI[i,a,g,r,t] where B[i,a,g,r,t] =0 for a>=1 (births only in age 0 cohort) The total population for any single county, gender, race/origin group, and year is determined by summing over age (a). Equation Terms; Race/Origin Groups (scroll box)
Illustrative Age-Gender Detailed Profile; Annual Population Estimates and Projections: 2000 to 2060 by Age Harris County, TX; Total Population; Scenario 2: Maintained Trend in Post 2010 Migration. Data in this table are for structural illustration only. See usage notes below table. See related ranking tables -- http://proximityone.com/rankingtables.htm. Scope of Table Datasets There are 3,143 tables (each county) having the above structure iterated for each of 15 race/origin groups (47,145 table datasets) plus aggregates of these county datasets for each metro, state and the U.S. Each all-U.S. variation using a different scenario (e.g., migration structure, alternative mortality rates, alternative fertility rates) doubles this scope of data. Developing Insights Aggregating single year of age data in the above table shows that the 5-to-17 year of age K-12 school population Harris County, Texas (Houston area) changes from 703,195 (Census 2000) to 811,251 (Census 2010) to 1,158,403 (in 2060). Using education participation rates, these data provide insights into projected enrollment by grade (by gender by race/origin) to facilitate education resource planning. Table Usage Notes Each row corresponds to an age group (mostly a single year of age) Each three column set corresponds to a year: male+female ... male ... female Years are annual and run 2000 through 2060 Data for Census 2000 and Census 2010 are as of April 1, all other data are as of July 1 Column headers follow the template GYY-N where: - G -- gender: T, M, or F - YY -- year (00 indicates 2000) - N -- column sequence Census 2000 headers appear as: CT00-1 CM00-2 CM00-3 Census 2010 headers appear as: CT10-34 CM10-35 CM10-36 Model Specifications & Time Series A model, in the sense used here, refers to a set of mathematical-statistical equations that together simultaneously determine values for jointly dependent variables. These time series models are designed to reflect cause and effect relationships as they occur in the real world. For each county there are model specifications that differ slightly for three time periods. Intercensal estimates are developed for the period 2000 to 2010 to develop a consistent annual time series from 2000 to 2060. The 2000 to 2010 estimates start with Census 2000 as the base year. The 2000 to 2010 intercensal estimates help serve as a validation with regard to how accurately they predict Census 2010 results without using Census 2010 data. Post Census 2010 estimates and projections use model specifications that differ slightly for the periods 2010 to 2030 and 2031 to 2060. During the period 2010 to 2030 there are supplemental sectors/simultaneous equations that determine migration. Migration, a function of many variables varying from county to county, is determined through use of cause and effect equations in the models. The change in county to county origin-destination patterns over time is reflected in the model. For the period 2031 to 2060, migration is determined based on a set of assumptions and trends determined for the period 2010 to 2030. Following tradition, all estimates and projections are as of July 1 in the corresponding year. The Census 2000 and Census 2010 data are as of April 1 and are also carried in the Outlook 2060 database. Education: K-20 Participation and Attainment Education participation projections are based on projections of the future size and composition of the population, as well as on the trends in education participation rates of different age, gender, race/ethnic groups. Labor Force: Employment and Earnings Labor force projections are based on projections of the future size and composition of the population, as well as on the trends in labor force participation rates of different age, gender, race/ethnic groups. Projections of the resident population are developed using the specifications described above. Additional equations in the model develop the civilian noninstitutional population projections. The size and composition of the population affect not only the labor force projections, but the projected aggregate economy and demand for workers in various industries and occupations. Participation rate projections are developed for each age, gender, race/ethnicity group and multiplied by the corresponding projection of the civilian noninstitutional population to obtain the labor force projection for each group. The groups are then summed to obtain the total civilian labor force. Productivity: Output and Trade Projections of output are based on projections of the future size and composition of the labor force, productivity measures as well as on the trends in productivity. Fiscal: Government Revenue and Expenditures Local governmental units are associated with counties. Projections of revenues and expenditures are based on projections of the future size and composition of the population, as well as on the trends in governmental finances relating to different age, gender, race/ethnic groups. Alternative Scenario Projections One set estimates and projections is developed annually following the most likely set of assumptions. The ProximityOne Modeler software and associated Situation & Outlook database can be used to develop alternative scenario estimates and projections. Access The Outlook 2060 summary reports provide an overview of trends and patterns between 2000 and 2060. Selected summary data from Outlook 2060 estimates and projections are included in an updated interactive ranking table similar to the presently existing Demographics 2020 (extended to include projections to 2030). Subscription, on-demand and datasets are available on a fee basis. Contact us for more information (mention Outlook2060) or call (888)364-7656. Additional Information Proximity develops geographic-demographic-economic data and analytical tools and helps organizations knit together and use diverse data in a decision-making and analytical framework. We develop custom demographic/economic estimates and projections, develop geographic and geocoded address files, and assist with impact and geospatial analyses. Wide-ranging organizations use our tools (software, data, methodologies) to analyze their own data integrated with other data. |
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