IDPH Population Projections For Chicago By Age And Sex 2010 To 2025

Dataset

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Data: JSON 100 Rows
Data: CSV 100 Rows
Host data.illinois.gov
Id hqm8-38sz
Name IDPH Population Projections For Chicago By Age And Sex 2010 To 2025
Tags idph, estimates, population, projections, age, sex
Created 2015-04-13T16:46:24Z
Publication Date 2015-04-13T16:52:35Z

Description

Introduction This report presents projections of population from 2015 to 2025 by age and sex for Illinois, Chicago and Illinois counties produced for the Certificate of Need (CON) Program. As actual future population trends are unknown, the projected numbers should not be considered a precise prediction of the future population; rather, these projections, calculated under a specific set of assumptions, indicate the levels of population that would result if our assumptions about each population component (births, deaths and net migration) hold true. The assumptions used in this report, and the details presented below, generally assume a continuation of current trends. Methodology These projections were produced using a demographic cohort-component projection model. In this model, each component of population change ? birth, death and net migration ? is projected separately for each five-year birth cohort and sex. The cohort ? component method employs the following basic demographic balancing equation: P1 = P0 + B ? D + NM Where: P1 = Population at the end of the period; P0 = Population at the beginning of the period; B = Resident births during the period; D = Resident deaths during the period; and NM = Net migration (Inmigration ? Outmigration) during the period. The model roughly works as follows: for every five-year projection period, the base population, disaggregated by five-year age groups and sex, is ?survived? to the next five-year period by applying the appropriate survival rates for each age and sex group; next, net migrants by age and sex are added to the survived population. The population under 5 years of age is generated by applying age specific birth rates to the survived females in childbearing age (15 to 49 years). Base Population These projections began with the July 1, 2010 population estimates by age and sex produced by the U.S. Census Bureau. The most recent census population of April 1, 2010 was the base for July 1, 2010 population estimates. Special Populations In 19 counties, the college dormitory population or adult inmates in correctional facilities accounted for 5 percent or more of the total population of the county; these counties were considered as special counties. There were six college dorm counties (Champaign, Coles, DeKalb, Jackson, McDonough and McLean) and 13 correctional facilities counties (Bond, Brown, Crawford, Fayette, Fulton, Jefferson, Johnson, Lawrence, Lee, Logan, Montgomery, Perry and Randolph) that qualified as special counties. When projecting the population, these special populations were first subtracted from the base populations for each special county; then they were added back to the projected population to produce the total population projections by age and sex. The base special population by age and sex from the 2010 population census was used for this purpose with the assumption that this population will remain the same throughout each projection period. Mortality Future deaths were projected by applying age and sex specific survival rates to each age and sex specific base population. The assumptions on survival rates were developed on the basis of trends of mortality rates in the individual life tables constructed for each level of geography for 1989-1991, 1999-2001 and 2009-2011. The application of five-year survival rates provides a projection of the number of persons from the initial population expected to be alive in five years. Resident deaths data by age and sex from 1989 to 2011 were provided by the Illinois Center for Health Statistics (ICHS), Illinois Department of Public Health. Fertility Total fertility rates (TFRs) were first computed for each county. For most counties, the projected 2015 TFRs were computed as the average of the 2000 and 2010 TFRs. 2010 or 2015 rates were retained for 2020 projections, depending on the birth trend of each county. The age-specific birth rates (ASBR) were next computed for each county by multiplying the 2010 ASBR by each projected TFR. Total births were then projected for each county by applying age-specific birth rates to the projected female population of reproductive ages (15 to 49 years). The total births were broken down by sex, using an assumed sex-ratio at birth. These births were survived five years applying assumed survival ratios to get the projected population for the age group 0-4. For the special counties, special populations by age and sex were taken out before computing age-specific birth rates. The resident birth data used to compute age-specific birth rates for 1989-1991, 1999-2001 and 2009-2011 came from ICHS. Births to females younger than 15 years of age were added to those of the 15-19 age group and births to women older than 49 years of age were added to the 45-49 age group. Net Migration Migration is the major component of population change in Illinois, Chicago and Illinois counties. The state is experiencing a significant loss of population through internal (domestic migration within the U.S.) net migration. Unlike data on births and deaths, migration data based on administrative records are not available on a regular basis. Most data on migration are collected through surveys or indirectly from administrative records (IRS individual tax returns). For this report, net migration trends have been reviewed using data from different sources and methods (such as residual method) from the University of Wisconsin, Madison, Illinois Department of Public Health, individual exemptions data from the Internal Revenue Service, and survey data from the U.S. Census Bureau. On the basis of knowledge gained through this review and of levels of net migration from different sources, assumptions have been made that Illinois will have annual net migrants of -40, 000, -35,000 and -30,000 during 2010-2015, 2015-2020 and 2020-2025, respectively. These figures have been distributed among the counties, using age and sex distribution of net migrants during 1995-2000. The 2000 population census was the last decennial census, which included the question ?Where did you live five years ago?? The age and sex distribution of the net migrants was derived, using answers to this question. The net migration for Chicago has been derived independently, using census survival method for 1990-2000 and 2000-2010 under the assumption that the annual net migration for Chicago will be -40,000, -30,000 and -25,000 for 2010-2015, 2015-2020 and 2020-2025, respectively. The age and sex distribution from the 2000-2010 net migration was used to distribute the net migrants for the projection periods. Conclusion These projections were prepared for use by the Certificate of Need (CON) Program; they are produced using evidence-based techniques, reasonable assumptions and the best available input data. However, as assumptions of future demographic trends may contain errors, the resulting projections are unlikely to be free of errors. In general, projections of small areas are less reliable than those for larger areas, and the farther in the future projections are made, the less reliable they may become. When possible, these projections should be regularly reviewed and updated, using more recent birth, death and migration data.

Columns

| Included | Schema Type | Field Name            | Name                  | Data Type | Render Type |
| ======== | =========== | ===================== | ===================== | ========= | =========== |
| Yes      | series tag  | age_group             | Age Group             | text      | text        |
| Yes      | series tag  | male_1st_april_2010   | Male_1st_April_2010   | text      | text        |
| Yes      | series tag  | female_1st_april_2010 | Female_1st_April_2010 | text      | text        |
| Yes      | series tag  | total_1st_april_2010  | Total_1st_April_2010  | text      | text        |
| Yes      | series tag  | males_1st_july_2010   | Males_1st_July_2010   | text      | text        |
| Yes      | series tag  | females_1st_july_2010 | Females_1st_July_2010 | text      | text        |
| Yes      | series tag  | total_1st_july_2010   | Total_1st_July_2010   | text      | text        |
| Yes      | series tag  | males_1st_july_2015   | Males_1st_July_2015   | text      | text        |
| Yes      | series tag  | females_1st_july_2015 | Females_1st_July_2015 | text      | text        |
| Yes      | series tag  | total_1st_july_2015   | Total_1st_July_2015   | text      | text        |
| Yes      | series tag  | males_1st_july_2020   | Males_1st_July_2020   | text      | text        |
| Yes      | series tag  | females_1st_july_2020 | Females_1st_July_2020 | text      | text        |
| Yes      | series tag  | total_1st_july_2020   | Total_1st_July_2020   | text      | text        |
| Yes      | series tag  | males_1st_july_2025   | Males_1st_July_2025   | text      | text        |
| Yes      | series tag  | females_1st_july_2025 | Females_1st_July_2025 | text      | text        |
| Yes      | series tag  | total_1st_july_2025   | Total_1st_July_2025   | text      | text        |

Time Field

Value = 2010
Format & Zone = yyyy

Data Commands

series e:hqm8-38sz d:2010-01-01T00:00:00.000Z t:males_1st_july_2020="* 1,254,161" t:total_1st_july_2010="* 2,698,283" t:total_1st_july_2020="* 2,562,913" t:females_1st_july_2010="* 1,388,908" t:female_1st_april_2010="* 1,387,526" t:females_1st_july_2020="* 1,308,752" t:males_1st_july_2010="* 1,309,375" t:total_1st_april_2010="* 2,695,598" t:females_1st_july_2025="* 1,267,110" t:male_1st_april_2010="* 1,308,072" t:age_group=Total t:total_1st_july_2025="* 2,506,112" t:total_1st_july_2015="* 2,612,827" t:males_1st_july_2025="* 1,239,002" t:females_1st_july_2015="* 1,342,605" t:males_1st_july_2015="* 1,270,222" m:row_number.hqm8-38sz=1

series e:hqm8-38sz d:2010-01-01T00:00:00.000Z t:males_1st_july_2020="* 91,556" t:total_1st_july_2010="* 186,072" t:total_1st_july_2020="* 175,645" t:females_1st_july_2010="* 91,878" t:female_1st_april_2010="* 91,787" t:females_1st_july_2020="* 84,089" t:males_1st_july_2010="* 94,194" t:total_1st_april_2010="* 185,887" t:females_1st_july_2025="* 73,030" t:male_1st_april_2010="* 94,100" t:age_group=0-4 t:total_1st_july_2025="* 154,749" t:total_1st_july_2015="* 177,484" t:males_1st_july_2025="* 81,719" t:females_1st_july_2015="* 85,880" t:males_1st_july_2015="* 91,604" m:row_number.hqm8-38sz=2

series e:hqm8-38sz d:2010-01-01T00:00:00.000Z t:males_1st_july_2020="* 80,469" t:total_1st_july_2010="* 166,242" t:total_1st_july_2020="* 152,784" t:females_1st_july_2010="* 82,037" t:female_1st_april_2010="* 81,955" t:females_1st_july_2020="* 72,315" t:males_1st_july_2010="* 84,206" t:total_1st_april_2010="* 166,077" t:females_1st_july_2025="* 71,749" t:male_1st_april_2010="* 84,122" t:age_group=5-9 t:total_1st_july_2025="* 155,021" t:total_1st_july_2015="* 153,212" t:males_1st_july_2025="* 83,273" t:females_1st_july_2015="* 75,127" t:males_1st_july_2015="* 78,084" m:row_number.hqm8-38sz=3

Meta Commands

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Top Records

| age_group | male_1st_april_2010 | female_1st_april_2010 | total_1st_april_2010 | males_1st_july_2010 | females_1st_july_2010 | total_1st_july_2010 | males_1st_july_2015 | females_1st_july_2015 | total_1st_july_2015 | males_1st_july_2020 | females_1st_july_2020 | total_1st_july_2020 | males_1st_july_2025 | females_1st_july_2025 | total_1st_july_2025 | 
| ========= | =================== | ===================== | ==================== | =================== | ===================== | =================== | =================== | ===================== | =================== | =================== | ===================== | =================== | =================== | ===================== | =================== | 
| Total     | * 1,308,072         | * 1,387,526           | * 2,695,598          | * 1,309,375         | * 1,388,908           | * 2,698,283         | * 1,270,222         | * 1,342,605           | * 2,612,827         | * 1,254,161         | * 1,308,752           | * 2,562,913         | * 1,239,002         | * 1,267,110           | * 2,506,112         | 
| 0-4       | * 94,100            | * 91,787              | * 185,887            | * 94,194            | * 91,878              | * 186,072           | * 91,604            | * 85,880              | * 177,484           | * 91,556            | * 84,089              | * 175,645           | * 81,719            | * 73,030              | * 154,749           | 
| 5-9       | * 84,122            | * 81,955              | * 166,077            | * 84,206            | * 82,037              | * 166,242           | * 78,084            | * 75,127              | * 153,212           | * 80,469            | * 72,315              | * 152,784           | * 83,273            | * 71,749              | * 155,021           | 
| 10-14     | * 83,274            | * 81,192              | * 164,466            | * 83,357            | * 81,273              | * 164,630           | * 72,796            | * 70,171              | * 142,966           | * 70,218            | * 65,527              | * 135,745           | * 74,634            | * 63,587              | * 138,221           | 
| 15-19     | * 91,528            | * 91,405              | * 182,933            | * 91,619            | * 91,496              | * 183,115           | * 74,251            | * 71,908              | * 146,159           | * 66,486            | * 62,590              | * 129,077           | * 65,504            | * 58,634              | * 124,139           | 
| 20-24     | * 108,407           | * 114,620             | * 223,027            | * 108,515           | * 114,734             | * 223,249           | * 94,362            | * 95,001              | * 189,362           | * 76,048            | * 74,745              | * 150,793           | * 67,723            | * 65,174              | * 132,897           | 
| 25-29     | * 134,931           | * 141,208             | * 276,139            | * 135,065           | * 141,349             | * 276,414           | * 122,648           | * 130,138             | * 252,786           | * 103,945           | * 107,456             | * 211,401           | * 83,099            | * 86,086              | * 169,186           | 
| 30-34     | * 119,828           | * 119,584             | * 239,412            | * 119,947           | * 119,703             | * 239,650           | * 135,877           | * 142,649             | * 278,526           | * 123,031           | * 131,147             | * 254,179           | * 104,151           | * 108,403             | * 212,554           | 
| 35-39     | * 100,651           | * 99,857              | * 200,508            | * 100,751           | * 99,956              | * 200,708           | * 103,397           | * 103,013             | * 206,410           | * 124,024           | * 128,947             | * 252,971           | * 114,060           | * 118,685             | * 232,745           | 
| 40-44     | * 89,957            | * 87,674              | * 177,631            | * 90,047            | * 87,761              | * 177,808           | * 84,855            | * 83,969              | * 168,824           | * 92,043            | * 89,929              | * 181,972           | * 115,049           | * 116,809             | * 231,858           |