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EQUAL OPPORTUNITY:
The Oklahoma Workforce System
2
Policy, Research & Economic Analysiswww.oklahomaworks.gov
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Table of ContentsKey Findings1Introduction1Disability:2Current Workforce: 18 to 64 years2Future Workforce: Under 18 years5Age and Gender, 15 to64 years8Race and Ethnicity, 15 to 64 years10English Language Learners, 18 to 64 years13Religion15Unemployment, 40 to 64 years18Maps22Percentage of the Population with a Disability, by County, 18 to 64 years22Percentage of the Population with a Disability, by County, Under 18 years23Percentage of Population, 18-64 years, who speak English “Not Well” or “Not at All”24Minority Population, by County, 15 to 64 years25U.S. Census Bureau Oklahoma Public Use Microdata Area map26Appendix A: State of Oklahoma Data27Disability, 18 to 64 years27Disability, Under 18 years28Age and Gender, 15 to64 years30Race and Ethnicity, 15 to 64 years32English Language Learners, 18 to 64 years33Religious Affiliation, 200934Unemployment, 40 to 64 years35Appendix B: Public Use Microdata Area (PUMS) Data37Unemployment, 40 to 64 years37Appendix C: Workforce Development Area Level Data, sorted by Workforce Area61Central Oklahoma Workforce Development Area61Eastern Oklahoma Workforce Development Area69Northeast Oklahoma Workforce Development Area78South Central Oklahoma Workforce Development Area86Southern Oklahoma Workforce Development Area94Tulsa Workforce Development Area102Western Oklahoma Workforce Development Area109Appendix D: Comparison of Workforce Development Area Level Data, sorted by topic117 Disability, 18 to 64 years117Disability, Under 18 years120Age and Gender, 15 to64 years125Race and Ethnicity, 15 to 64 years136English Language Learners, 18 to 64 years148Religious Affiliation, 2009150Appendix E: County Level Data153 Adair153Alfalfa162Atoka171Beaver180Beckham189Blaine198Bryan207Caddo216Canadian225Carter234Cherokee243Choctaw252Cimarron261Cleveland270Coal279Comanche288Cotton298Craig307Creek316Custer325Delaware334Dewey343Ellis352Garfield361Garvin370Grady379Grant388Greer397Harmon406Harper415Haskell424Hughes433Jackson442Jefferson451Johnston460Kay469Kingfisher478Kiowa487Latimer496Le Flore505Lincoln514Logan523Love532McClain541McCurtain550McIntosh559Major568Marshall577Mayes586Murray595Muskogee604Noble613Nowata622Okfuskee631Oklahoma640Okmulgee649Osage658Ottawa667Pawnee676Payne685Pittsburg694Pontotoc703Pottawatomie712Pushmataha721Roger Mills730Rogers739Seminole748Sequoyah757Stephens766Texas775Tillman784Tulsa793Wagoner803Washington812Washita821Woods830Woodward839
Policy, Research & Economic Analysis18www.oklahomaworks.gov
Key Findings
Introduction
The U.S. Equal Employment Opportunity Commission (EEOC) is tasked with enforcing federal laws that make it illegal to discriminate against job applicants and employees who are included in several diverse categories. These protected groups include age, disability, national origin, race/color, religion, and sex, among others (www.eeoc.gov).
The purpose of this report is to bring awareness of cohorts of Oklahoma citizens possibly at risk for discrimination. Data and analyses are presented for five categories specifically associated with Equal Opportunity enforcement. Age ranges and locations are aligned as closely as possible contingent upon data availability. These categories include:
· Disability (18 to 64 years; Under 18 years);
· Age (15 to 64 years)
· Gender (15 to 64 years)
· Race and Ethnicity (15 to 64 years);
· English Language Learners (18 to 64 years);
· Religion (no age parameter); and,
· Unemployment (40 to 64 years; limited to data for the State as a whole, and the 28 Oklahoma Public Use Microdata Sample (PUMs) Areas defined by the Oklahoma Department of Commerce for the U. S. Census Bureau).
Analyses highlights are presented within each topic according to analysis level: statewide, workforce area or PUMs, and county. State maps illustrating Disability Rates, English Language Learner Proficiency, and Minority Population by County immediately follow the analysis summary. An additional graphic produced by the U. S. Census Bureau illustrating the Oklahoma PUMs Areas is also included. Finally, full data tables are available for review in the following appendices:
1. Appendix A: Statewide data;
2. Appendix B: Selected data for PUMs Areas;
3. Appendix C: Local workforce development area (WFDA) data, sorted by Workforce Area;
4. Appendix D: WFDA data, sorted by topic; and,
5. Appendix E: County-level data.
Disability
Source: American Community Survey, 2016, 5-year Estimates
The U.S. Census Bureau collects disability data aggregated by disability type, termed “difficulties.” Six unique difficulty categories are utilized:
· Hearing;
· Vision;
· Cognitive;
· Ambulatory;
· Self-Care; and,
· Independent Living.
The data is self-reported by American Community Survey respondents based upon their perception of the existence of a disability/difficulty. It should be noted that this survey methodology may introduce biases into the statistical results. Some respondents may perceive themselves as experiencing a disability which, under actual medical examination, may not be substantiated. As a result, these individuals may not meet the parameters for possessing a disability for the purpose of, for example, participating in programs aimed at assisting individuals with disabilities. Conversely, when responding to the survey, some individuals may be reluctant to disclose the existence of a disability/difficulty that could result in similar programmatic qualification. This may be due to a number of factors such as the desire for personal confidentiality in response to a mandatory governmental survey, the personal denial of an existing condition, or the rationalization of an existing condition being attributable to, for example, normal aging, and therefore not consequential. As a result, the data reported must be considered solely as the opinion or perception of the survey respondents.
Under the survey methodology, each respondent may select as many as six difficulties. As a result, the total number of disabilities/difficulties reported will exceed the total number of survey respondents. Statistics are provided for two age categories: 18 to 64 years and Under 18 years. While the 18- to 64-year age bracket represents the current workforce, and is therefore the most relevant information for the purposes of this report, the statistics included for the Under 18-years cohort provide insight into the characteristics of the future workforce.
Current Workforce, 18 to 64 years
Statewide Data Findings
· Overall, the state population is estimated at 3,794,815, of which, 2,301,565 (60.7%) report being between the ages of 18 and 64. Of that 2.3M, over 319,900 report a disability in at least one of the categories indicated above resulting in a state-wide disability rate of 13.9% for this age group. Due to the exclusion of the 65-year-and-older age brackets, this disability rate is significantly lower than the overall 15.7% disability rate for the state which includes all Oklahoma citizens.
· The Oklahoma statewide disability rate of 13.9% for the age 18-64 cohort is significantly higher than the national rate of 10.3% for the same age group. Across the nation, Oklahoma citizens, age 18-64, report the 6th highest disability rate. Only Alabama (14.5%), Mississippi (14.8%), Arkansas (15.0%), Kentucky (15.8%), and West Virginia (17.4%) report higher disability rates for this age group.
· Disability rates are comparable for gender – 15.8% for males versus 15.6% for females.
· Disability rates for specific races and ethnicities in this age group vary significantly. Oklahomans who identify themselves as American Indian or Alaska Native report the highest rate of disability at 17.4%. Asians report the lowest incidence of disability at only 4.4%. Individuals of Hispanic ethnicity, regardless of race, experience a disability rate of 8.5%.
· Within the 18-64 age bracket, ambulatory difficulties are reported most frequently. Of the 319,000 individuals reporting a disability, 53% report experiencing ambulatory difficulties, equivalent to an estimated 169,490 residents. This figure translates into 7.4% of the total state population, age 18-64.
Cognitive difficulties rank second in prevalence at 38.9%; equal to 5.4% of the total state population age 18-64. Self-Care is the least frequently reported difficulty among individuals with disabilities at 16.7%. These patterns of prevalence of disability type are also reflected at the workforce development area and county levels.
Workforce Area Data Findings:
· Central Oklahoma WFDA exhibits the largest number of individuals with reported disabilities in the 18-64 age bracket at 103,191; however, this only accounts for 12.4% of the total population in that age range. Tulsa WFDA exhibits a similar disability rate of 12.5% but, due to population size in this age group, this percentage only accounts for 57,688 individuals.
· In a comparison of workforce development areas, the greatest variance in disability rates is found among respondents reporting a visual difficulty. Southern Oklahoma WFDA reports the highest incidence rate at 27.3% while the Eastern Oklahoma WFDA only exhibits a reported rate of 19.3% -- an eight percentage-point different.
· Overall disability rates, including all disability/difficulty types, for the workforce development areas vary from a low of 11.8% in the Western Oklahoma WFDA to a high of 18.5% in the Southern Oklahoma WFDA:
Workforce Development Area
Disability Rate
Central
12.4%
Eastern
17.2%
Northeast
15.8%
South Central
16.4%
Southern
18.5%
Tulsa
12.5%
Western
11.8%
· Workforce Areas reporting the highest incidence of disability type include:
Disability Type
Incidence of Disability Type
Workforce Development Area
Hearing difficulty
26.0%
Northeast
Vision difficulty
27.3%
Southern
Cognitive difficulty
40.7%
Tulsa
Ambulatory difficulty
56.5%
Eastern
Self-Care difficulty
18.9%
Southern
Independent Living difficulty
35.9%
Southern
County Data Findings
· At the county level, overall disability rates vary from a low of 6.3% in Texas County to a high of 28.8% in Marshall County. A map illustrating the disability rates for every Oklahoma county follows this analysis summary.
· Counties reporting the highest incidence of disability type as a percentage of individuals with disabilities include:
Disability Type
Incidence of Disability Type
County
Hearing difficulty
44.4%
Blaine
Vision difficulty
43.1%
Marshall
Cognitive difficulty
55.6%
Beckham
Ambulatory difficulty
78.7%
Greer
Self-Care difficulty
32.1%
Beckham
Independent Living difficulty
64.2%
Love
· Counties reporting the lowest incidence of disability type as a percentage of individuals with disabilities include:
Disability Type
Incidence of Disability Type
County
Hearing difficulty
13.2%
Greer
Vision difficulty
11.5%
Beckham
Cognitive difficulty
13.9%
Texas
Ambulatory difficulty
35.4%
Blaine
Self-Care difficulty
5.5%
Greer
Independent Living difficulty
16.3%
Blaine
Future Workforce, Under 18 years
Statewide Data Findings
· Approximately 25% of the state’s population is under the age of 18; a total of 950,000 residents. Of these, 46,574 reported the existence of a disability, resulting in a disability rate of 4.9%. This rate is significantly lower than the state rate for 18 to 64 year-olds of 13.9%. Multiple factors may contribute to this differential. Many disabilities manifest as the individual grows older. Conversely, impediments are often difficult or impossible to diagnose in very young children. Some of the disabilities reported to the U.S. Census Bureau are inappropriate for certain age ranges, delaying the reporting of those disabilities.
· Disability rates for this cohort are higher in males as compared with females – 5.6% for males versus 4.1% for females.
· Disability rates for specific races vary significantly. Individuals who self-identify as American Indian or Alaskan Native report the greatest rate of disability for this age group at 5.9%.
Asians report the lowest incidence of disability (2.1%). Additional data indicate Asians also tend to exhibit the lowest disability rates at the WFDA area and county levels. Further research is necessary to determine if this trend presents an accurate representation of the disability rates for individuals of Asian descent or if other factors such as privacy concerns, a reluctance to report the existence of disabilities, or cultural beliefs may be skewing the data.
· Within the Under 18 age bracket, cognitive difficulties are reported most frequently. Of the 46,574 individuals reporting a disability, 65.8% report experiencing cognitive difficulties, an estimated 30,662 residents. This figure translates into 3.2% of the total state population Under 18 years. Vision difficulties rank second in prevalence at 22.3%; equal to 1.1% of the cohort population. Due to the age limitations, independent living difficulties are not applicable for this population. These patterns of prevalence of disability type are also reflected at the workforce development area and county levels.
Workforce Area Data Findings:
· Central Oklahoma WFDA exhibits the largest number of individuals with reported disabilities in the Under 18 age bracket at 15,701; however, this only accounts for 4.6% of the total population in that age range. Southern Oklahoma WFDA experiences the highest overall disability rate among the workforce development areas at 6.1%, with a total of 5,979 residents reporting some type of disability.
· As indicated previously, cognitive disabilities are reported most frequently; however, there is a large variance in the rates among different workforce development areas. Northeast Oklahoma WFDA experiences the highest rate of this difficulty at 73.9%. While it is still the most prevalent disability type in Southern Oklahoma WFDA, cognitive issues are only reported by 58.7% among residents with disabilities – a differential of 15.2 percentage points.
· Overall disability rates, including all disability/difficulty types, for the workforce development areas vary from a low of 3.6% in the South Central Oklahoma WFDA to a high of 5.6% in the Southern Oklahoma WFDA:
Workforce Development Area
Disability Rate
Central
4.1%
Eastern
4.4%
Northeast
4.5%
South Central
3.6%
Southern
5.6%
Tulsa
3.7%
Western
3.9%
· Workforce Areas reporting the highest incidence of disability type include:
Disability Type
Incidence of Disability Type
Workforce Development Area
Hearing difficulty
22.5%
Western
Vision difficulty
26.8%
Southern
Cognitive difficulty
73.9%
Northeast
Ambulatory difficulty
14.3%
Eastern
Self-Care difficulty
16.1%
Eastern
Independent Living difficulty
Not Applicable for this Age Cohort
County Data Findings
· At the county level, overall disability rates vary from a low of 1.0% in Roger Mills County to a high of 14.5% in Marshall County. Ellis County also reports a relatively low disability rate of 1.7%. The second highest disability rate is exhibited by Murray County at 8.7%, nearly six percentage points below the top-ranked Marshall County. A map illustrating the disability rates in this age group for every Oklahoma county follows this analysis summary.
· Some counties report 0.0% disability rates for individuals under the age of 18, particularly for ambulatory difficulties. Of those counties reporting disabilities, the highest incidence of disability type among individuals with disabilities include:
Disability Type
Incidence of Disability Type
County
Hearing difficulty
44.4%
Blaine
Vision difficulty
45.3%
Caddo
Cognitive difficulty
100.0%
Roger Mills
Ambulatory difficulty
78.7%
Greer
Self-Care difficulty
32.1%
Beckham
Independent Living difficulty
64.2%
Love
· Not including those counties that report 0.0% disability rates for this age cohort, counties reporting the lowest incidence of disability type, calculated as a percentage of total individuals with disabilities include:
Disability Type
Incidence of Disability Type
County
Hearing difficulty
13.2%
Greer
Vision difficulty
11.5%
Beckham
Cognitive difficulty
13.9%
Texas
Ambulatory difficulty
35.4%
Blaine
Self-Care difficulty
5.5%
Greer
Independent Living difficulty
16.3%
Blaine
· As indicated previously, the prevalence of particular types of disabilities at the county level generally mirror the state and WFDA results, with Cognitive difficulties most frequently reported for the Under 18 age group. Ten counties exhibit exceptions to this trend:
County
Most Prominent Disability
Incidence of Disability Type
Comparative Percentage of Cognitive Difficulty
Caddo
Vision difficulty
45.3%
43.6%
Dewey
Vision difficulty
53.1%
31.3%
Ellis
Hearing difficulty
Vision difficulty
47.1%*
35.3%
Greer
Hearing difficulty
43.0%
40.5%
Harper
Vision difficulty
80.8%*
0.0%
Kingfisher
Hearing difficulty
36.8%
28.6%
Love
Vision difficulty
44.7%
30.9%
Marshall
Vision difficulty
37.6%
30.9%
Texas
Vision difficulty
Cognitive difficulty
52.8%
52.8%
Woodward
Hearing difficulty
47.1%
19.7%
* Small population of in this age group with disabilities.
Age and Gender, 15 to 64 years
Source: EMSI, Version 2018.1
EMSI reports population data base upon 5-year age brackets. In order to best represent the current workforce, ten age brackets were selected ranging from 15 to 19 years of age through 60 to 64 years of age.
Statewide Data Findings
· The Oklahoma workforce is contracting, with fewer workers projected in this age range within the next 10 years. The total 2017 estimated statewide population for the 15- to 64-year-old age group was just over 2.53 million. By 2027, that number is expected to decline by 0.9% to 2.51 million. In 2017, this age group accounted for 64.2% of the total population; by 2027, it will only account for 61.5%.
· The population of the state is aging. The number of residents over the age of 65 is expected to increase by 21.7% in the next 10 years. During that same time frame, the number of youth under the age of 15 will only increase by 3.2%.
· Regarding gender, in 2017, the age group is split relatively equally – 50.2% male and 49.8% female. While the overall population is anticipated to decrease by 2027, that decline disproportionately affects females. The gap between the genders will widen by 0.2 percentage points, 50.4% male compared with 49.6% female.
· Five of the ten age brackets examined will experience a decline in population, ranging from 4.6% to 15.8%. The greatest decline is in the 55 to 59 years of age bracket with the population dropping from 256,000 in 2017 to 216,000 in 2027.
Workforce Area Data Findings
· Six of the seven workforce development areas are anticipated to experience a decline in the population for the 15- to 64-year age bracket. These losses range from a low of 0.2% in Tulsa WFDA, a decline in population of approximately 10,000 residents, to a high of 4.8% in South Central Oklahoma WFDA, a loss of 9,700 residents. Only the Central Oklahoma WFDA is expected to experience an increase in population for this age range, growing by 22,300 individuals (2.4%).
· While overall experiencing population declines, there is growth in particular age ranges in each workforce development area. These are predominantly found in the prime working ages between 30 to 49 years. The areas containing the two major metropolitan regions – Central Oklahoma WFDA and Tulsa WFDA – experience growth in the workforce at young ages, 15 to 24 years. As a result, the workforce in these two areas trends younger.
· Both male and female populations decline in six of the seven workforce areas. As indicated previously, only Central Oklahoma WFDA experiences an overall increase in population for this age group. In that area, males will increase by 3.0% while females only increase by 1.8%.
County Data Findings
· Over 85% of counties in the state will experience a decline in the population age 15-64 by 2027. Only 11 counties (Canadian, McClain, Oklahoma, Bryan, Cleveland, Logan, Payne, Love, Tulsa, Wagoner, and Marshall) will experience increases in population for this age group. These increases range from a low of 0.1% in Marshall County to a high of 8.0% in Canadian County.
Conversely, the greatest loss of population in this age group will occur in Cimarron County, a decline of 21.3%, -245 residents. It should be noted Cimarron County reports the lowest population in the state in 2017 and exhibits a population density of only 2 persons per square mile. For all age brackets, the county is expected to lose 313 residents by 2027, an overall decline of 15%.
· Like the WFDAs, the population in the 30-49 years-of-age brackets is increasing in 87% of Oklahoma counties. In comparison, less than one-half of the counties are projected to increase populations in the 15-24 years-of-age bracket.
· Examining the population changes by gender reveals that women are adversely affected at a higher rate than men. In two-thirds of the counties, the loss of population for females exceeds that of males, with a differential of up to 9.2 percentage points. Cimarron County experiences the greatest gap with a 16.8% loss among males in the 15-64 years-of-age cohort compared with a loss of 26.0% for females. Coal County also experiences a large differential of 7.8 percentage points, anticipating an 8.8% drop for males and a 16.6% drop in the population of females.
This trend is also applicable to many of the counties that are anticipated to increase in population for this age cohort. Of the 11 counties listed previously, six experience a lower rate of growth for females than for males. Wagoner County exhibits a 2.0% growth in the number of males by 2027 while predictions indicate the population of females will decline by -1.1% -- a gap of 3.1 percentage points. Tulsa’s population by gender will grow equally, reflecting an increase of 0.8% for both males and females. On the other end of the spectrum, McClain County is predicted to provide the greatest differential in which females will grow at a higher rate than males – 7.4% versus 4.7% respectively.
Race and Ethnicity, 15 to 64 years
Source: EMSI, Version 2018.1
The racial and ethnic categories utilized in this report are designated by the U.S. Census Bureau. Data are generally self-reported to the Bureau by survey respondents who are instructed to select the racial/ethnic categories with which they most closely identify. Respondents may select multiple races, but only one ethnicity.
Racial categories utilized include:
· American Indian or Alaskan Native;
· Asian;
· Black or African American;
· Native Hawaiian or Pacific Islander
· Two or more races; and,
· White.
Ethnicity categories are limited to:
· Hispanic; or,
· Non-Hispanic.
Statewide Data Findings
· According to EMSI, in 2017 an estimated 2,537,445 Oklahoma citizens are between the ages of 15 and 64, approximately 64.2% of the total population.
· The most prevalent race, regardless of ethnicity, is reported as White, representing 74.3% of this age group. American Indian or Alaskan Native ranks second, accounting for 9.3% of the age 15-64 population. Only 4,855 Oklahomans self-identify as Native Hawaiian or Pacific Islander, less than 1% of the total population.
· When ethnicity is considered in conjunction with race, White, Non-Hispanic describes the largest group with 65.8% representation. American Indian or Alaskan Native, Non-Hispanic ranks second with 8.5%. The racial/ethnic combination of Native Hawaiian or Pacific Islander, Hispanic represents the smallest cohort at less than 1% of the age-group population – a total of only 1,018 individuals.
· A comparison of ethnicity, regardless of race, indicates that Non-Hispanics are approximately nine times more prevalent than Hispanics – 89.8% compared with 10.2%, respectively.
· Projections for 2027 reveal that the overall population in this age bracket will decrease by 0.9% and the racial and ethnic diversity will change significantly:
· Regarding ethnicity, the Non-Hispanic population is anticipated to decrease by 2.8% while the Hispanic population increases by 16.4%.
· The White racial category is predicted to maintain its majority of the population, but decline by 1.4 percentage points to 72.9%. White is the only racial category anticipated to decrease at the statewide level.
· The Native Hawaiian or Pacific Islander and Asian races will grow substantially, by 24.9% and 16.4% respectively. Despite these gains, representation by these two races among this age population will remain small with a combined total of only 3.3% of the population.
· The representation of multiracial Oklahomans is also expected to increase, growing from 5.3% in 2017 to 5.9% by 2027.
Workforce Area Data Findings
· The population of White, Non-Hispanics is projected to decrease in every workforce area. This decline ranges from a low of 2.2% in the Central Oklahoma WFDA to a high of 7.7% across the Western Oklahoma WFDA.
· Four of the seven WFDAs are projected to report a loss in the Black or African American population. These include Eastern Oklahoma WFDA, South Central Oklahoma WFDA, Southern Oklahoma WFDA, and Tulsa WFDA. No other race is expected to decline in that many areas.
· Regarding ethnicity, six of the seven workforce development areas will experience a loss in the number of Non-Hispanic residents. Only Central Oklahoma WFDA is anticipated to report an increase in this demographic of 0.1% or 600 residents. Conversely, the Hispanic population will grow in every WFDA. The Central Oklahoma and Tulsa WFDAs will experience the greatest growth in Hispanics at 19.1%. The lowest Hispanic growth rate of 5.0% is found in South Central Oklahoma WFDA.
County Data Findings
· As discussed previously at the statewide and WFDA levels, the population of the White racial group (regardless of ethnicity) trends downward in most of Oklahoma’s counties. Only six of Oklahoma’s 77 counties are projected to experience increases in this racial cohort including Canadian (+6.5%), McClain (+5.6%), Logan (+0.9%), Oklahoma (+0.8%), Bryan (+0.5%), and Payne (+0.2%). Five of these six counties are home to cities with populations in excess of 20,000 residents, with Bryan County the exception. The largest city in Bryan County is Durant with an estimated population of 16,700.
As a major metropolitan area, the population of Oklahoma County/Oklahoma City MSA, is anticipated to grow in all racial categories except Native Hawaiian or Pacific Islanders, which is projected to decrease by 4.8%. Due to the small overall population in this racial cohort, this loss equates to only -29 residents.
Tulsa County, while home to a major metropolitan area similar to Oklahoma County, is predicted to experience a decline in the White racial category of -2.1%, a loss of 6,523. As a result, Tulsa’s population will become more diverse with significant increases in the Asian (+25.9%), Native Hawaiian or Pacific Islander (+35.8%), and Two or More Races (+13.3%) cohorts. These increases equate to an additional 7,100 Tulsa County residents.
· The Hispanic population (regardless of race) is expected to increase in most Oklahoma counties by 2027. The representation of this ethnic population, in this age bracket, is predicted to grow in 71 counties. That growth ranges from 1.6% in Okfuskee County to 45.6% (+278 Hispanic residents) in Blaine County. Five counties will experience losses in this population including Murray (-34.5%, a loss of 194 Hispanic residents), Grady (-18.4%, -390 residents), Harmon (-7.6%, -37), Washita (-5.5%, -37) and Atoka (-4.9%, -15).
Conversely, most counties are projected to experience a decline in Non-Hispanic populations. Only six counties are expected to report increases in the Non-Hispanic population by 2027: Canadian (+6.7%), McClain (+5.2%), Bryan (+1.6%), Payne (+0.9%), Cleveland (+0.8%), and Logan (+0.7%).
English Language Learners, 18 to 64 years
Source: American Community Survey, 2016, 5-year Estimates
The U.S. Census Bureau collects data regarding English usage and perceived fluency via the American Community Survey. Survey recipients are first requested to identify the primary language spoken in their home. Language choices are limited to “Spanish,” “Other Indo-European Language,” “Asian and Pacific Island Language,” or “Other Language.”
Respondents who indicate they speak a language other than English are then asked to estimate their level of fluency in English (“How well does this person speak English?”). Four responses are available to this question: “Very Well,” “Well,” “Not Well,” or “Not at All.”
Statewide Data Findings
· Over 254,000 Oklahoma residents between the ages of 18 and 64 report speaking a primary language other than English in their home. This represents 10.8% of the population in this age bracket. Conversely, 89.2% of respondents indicated they spoke “English Only” in the home.
· Spanish is the most commonly reported primary language other than English. Over 66% of non-native English speakers report speaking Spanish at home. Asian and Pacific Island Languages rank second in prevalence, but at a much lower level of 16.2%.
· Statewide, approximately 74.9% of non-native English speakers indicated they speak English either “Very Well” or “Well.” An additional 18.5% rate their level of English proficiency at “Not Well,” with 6.6% reporting that they are unable to speak English at all.
· As a whole, native Spanish speakers rate their perceived English proficiency the lowest with 31.1% indicating they speak English either “Not Well” or “Not at All.” In comparison, individuals who report they speak “Other Indo-European Languages” report low English proficiency levels at only 6.9%.
· “Other Language” speakers report a low English proficiency level of 5.8%. While this proficiency level is below that of those who speak “Other Indo-European Languages” reported previously, there is no indication of the types of languages this category includes. It can be assumed that the variety of languages and dialects is very broad. As a result, this data point provides limited insight into identifying Oklahomans who may or may not be at risk of being marginalized due to language preferences.
Workforce Area Data Findings
· Workforce Areas that encompass the state’s two major metropolitan districts exhibit the highest non-native English speaking populations. The Central Oklahoma WFDA, including the Oklahoma City Metropolitan Statistical Area (MSA), and Tulsa WFDA, incorporating a majority of the Tulsa MSA, report non-native English speaking rates of 13.5% and 12.2% respectively. Those individuals in the Tulsa WFDA exhibit a lower level of English proficiency than in any other Area with 29.7% of non-native English speakers indicating they speak English either “Not Well” or “Not at All,” – 8.1% are unable to speak any English.
· The highest rates of English proficiency (“Very Well” or “Well”) among non-native English speakers are reported in the Northeast and South Central Oklahoma WFDAs at 84.9% and 84.7%, respectively.
· In every WFDA, the majority of non-native English speakers speak Spanish in the home. Over 74% of non-native English speakers in the Western Oklahoma WFDA speak Spanish. The lowest percentage of native Spanish speakers (53.0%) reside in Northeast WFDA where a relatively significant percentage of non-English speakers report the use of other Indo-European Languages (14.3%).
· In all WFDAs, native Spanish speakers, and to a much lesser degree, native Asian/Pacific Island language speakers, rate their English proficiency lowest – between 19% and 32% indicate they speak English “Not Well” or “Not at All.” Conversely, residents who speak other Indo-European Languages or Other Languages in general, report a range of between 1% and 10% who speak English at that same level.
County Data Findings
· In 14 of Oklahoma’s 77 counties, fewer than 10% of the non-native English speakers rate themselves as “Not Well” or “Not at All.”
· No Cotton County residents reported speaking English either “Not Well” or “Not at All.” Approximately 2.3% of Cotton County residents report being non-native English speakers, predominantly speaking Spanish in the home.
· Greer County also reports a very low percentage of individuals who feel they speak English either “Not Well” or “Not at All” at 1.7%.
· Harper County reported the highest percentage of non-native English speakers who felt they spoke English either “Not Well” or “Not at All” at 50.2%. Overall, Harper County reported 486 residents – 22.7% of the age 18-64 cohort – who spoke a primary language other than English, most of whom spoke Spanish (94.7%).
· Texas County also experiences a high level of non-native English speakers who indicate their English proficiency is either “Not Well” or “Not at All” at 39.9%. This equates to 2,077 county residents, 15.7% of the 18-64 years of age cohort.
Religious Affiliation, 2009
While religion is a key component of concern for discrimination regarding equal opportunity, data pertaining to religious beliefs and affiliations is limited. Privately-conducted surveys are the predominant source of information available including the Pew Research Center Religious Landscape Study (http://www.pewforum.org/religious-landscape-study/state/oklahoma/), a 2009 Religious Affiliation study conducted by InfoGroup and reported via Social Explorer at the University of Wisconsin Extension (https://fyi.uwex.edu/community-data-tools/2011/12/05/detailed-data-on-religion-by-county/), and a 2010 Gallup Poll that quantifies perceived feelings of religious discrimination. Additional information is referenced in this report from local newspaper articles and the U.S. Equal Employment Opportunities Commission (EEOC). Despite the limitations of these sources, the statistics included in this report can assist in building a framework for the context of Oklahomans’ religious beliefs, and help to identify the potential for increased risk factors of religious discrimination.
The surveys grouped religious families into categories identifying major religious traditions. These included:
1. Evangelical Protestant;
2. Mainline Protestant;
3. Historically Black Protestant;
4. Roman Catholic;
5. Jewish Congregations;
6. Latter-Day Saint (Mormon);
7. Islamic;
8. Hindu;
9. Buddhist;
10. Orthodox Christian;
11. Jehovah’s Witnesses; and,
12. Other (including the non-religious categories of atheist and agnostic).
Statewide Data Findings
· Most Oklahomans identify with the Evangelical Protestant church. Over 4,200 congregations exist with nearly one million members – 56.7% of all survey respondents. Another 18% identify themselves as Mainline Protestant while 8.4% are Roman Catholic. While there are 66 Latter-Day Saint (Mormon) congregations identified in the state, the membership of those congregations constitutes only 0.7%.
· As indicated above, Roman Catholicism only accounted for 8.4% of the total state religious affiliation, but at 804 members per congregation, presents the highest average congregation size. This congregation size is 2.3 times the size of the next largest, Hindu at 350 average members and Mainline Protestants at 346 average members per congregation. In short, while reporting fewer religious institutions, the Roman Catholic faith draws greater average numbers of members to each institution from the surrounding geographic area.
· At the time of the studies, there was minimal representation in Oklahoma of Non-Christian faiths. Less than one percent each of Oklahomans identified their religion as Islam, Hindu, or Buddhist. Together, these faiths only accounted for nine congregations with a total combined membership of less than 2,000. Further research, however, indicates that these faiths may have grown significantly since the surveys were conducted. According to a 2015 article in the Tulsa World (http://www.tulsaworld.com/how-many-mosques-in-oklahoma/image_72e4dd94-4a33-5c7c-8ed4-ae352eb968b8.html), Islam now boasts nine religious centers, predominantly located in Oklahoma City and Tulsa, but with one each in Stillwater and Edmond as well as two in Lawton. Likewise, temples of the Hindu faith appear to have increased from one to three since the survey, those being located in Oklahoma City, Tulsa, and Edmond.
· A 2010 Gallup Poll survey indicated that 48% of Muslim respondents believed they had experienced religious discrimination. Thirty-one percent of Latter Day Saint (Mormon) followers held the same belief. Only 20% of Catholics and 18% of Protestants felt they had experienced some type of bias based upon their religion. (http://news.gallup.com/poll/157082/islamophobia-understanding-anti-muslimsentiment-west.aspx).
· While EEOC data does not appear to be available at the state level, nationally, religion-based discrimination charges filed with the EEOC rose steadily from 1997 to 2016. In 1997, the EEOC received 1,709 filings based upon perceived discrimination due to religion; 20 years later, in 2016, the EEOC received 3,825 filings, an increase of more than 123%. Additionally, the mixture of findings has altered with significant monetary impact. In 1997, 12.1% of charges received merited resolutions with a monetary benefit total of $2.2 million dollars ($3.3M in 2016 dollars). After reaching a peak at 24.1% merit resolutions with monetary benefits of $6.4M 2007 ($7.5M 2016), merit resolutions dropped in 2016 to 14.9%. Despite this 2016 drop in the percentage of claims upheld, awards rose as monetary benefits reached $10.1 million dollars. Clearly, while merit was found in a lesser number of religion-based EEOC claims in 2016, the average damage award/settlement per merited finding increased. (https://www.eeoc.gov/eeoc/statistics/enforcement/religion.cfm).
Workforce Area Data Findings
· Workforce Area trends generally mimic the statewide trends. Evangelical Protestant members account for the majority of congregations as well as congregational membership at this geographical level of examination. Membership rates for this religion vary from a low of 48.2% in Tulsa WFDA, where the diversity of reported religious affiliation is significantly greater, to a high of 68.8% in the South Central WFDA.
· Roman Catholicism represents a greater percentage of members in the Central Oklahoma and Tulsa WFDA at 11.7% and 9.6% respectively. These figures are predominantly based upon a greater concentration of Catholic institutions in the metropolitan areas. Likewise, non-Christian faiths are centered in the metropolitan areas. As a result, the Central Oklahoma and Tulsa WFDAs must be cognizant of a higher risk of religious discrimination against those of Roman Catholic, Islamic, Hindu, and Buddhist faiths.
· As a percentage of overall congregational membership, the Southern Oklahoma WFDA boasts the largest concentration of Jehovah’s Witnesses. In terms of membership counts, at over 5,500 members, this equates to a greater number of congregational members than those identified in all WFDAs except the Central Oklahoma WFDA (6,900 members).
· Members of the Church of Latter-Day Saints (Mormon) are concentrated more heavily in the Northeast WFDA with an overall representation of 1.2%; the Western Oklahoma WFDA ranks second in member percentage for this religion at 1.0%. Between these two WFDAs, the survey reports Mormon religious membership totaling more than 3,000.
County Area Data Findings
· Again, at the county level, most Oklahomans identify with the Evangelical Protestant religion. Across the state, this faith represents 63.6% of all congregations and 56.7% of religious membership. On average, there are 55 Evangelical Protestant congregations in every county, each reporting an average of 232 members.
· Latimer County reports the highest concentration of Evangelical Protestant congregations representing 85.7% of all religious congregations located in the county. Alfalfa County is unique in that not only does it report the lowest percentage of Evangelical Protestant congregations at 46.7%, the second-ranked religion in the county accounts for nearly as many congregations – Mainline Protestant at 43.3%.
· At the county level, most of the religious categories exhibited very low levels of concentration. These included Jewish Congregations, Latter-Day Saint (Mormon), Islamic, Hindu, Buddhist, Orthodox Christian, and Jehovah’s Witnesses. In most counties, congregations for these religions were non-existent. In those counties where congregations were reported, it was in very low numbers, totaling between one and three congregations across all seven religions. The metropolitan areas were the exception with greater diversity of religious preference reported in Oklahoma and Tulsa Counties, and to a lesser extent, in Cleveland County (Norman).
Unemployment – 40 to 64 years
With regard to unemployment figures, data concerning individuals in the protected age category of 40 and over is difficult to extract. Most sources provide data broken into age ranges inconsistent with these protected class parameters. The data provided in this report was mined via the Data Ferret application from the U.S. Census Bureau’s 2016 American Community Survey Public Use Microdata Sample (PUMs). Unfortunately, the data from this Sample is only available at the national, regional, state and the PUMs levels; not at the county level.
As the Bureau’s designated State Data Center, the Oklahoma Department of Commerce designates the extent of each PUMs Area (PUMA). The latest delineation of PUMAs took place in 2010. Currently, 28 PUMAs are defined for the state of Oklahoma including six for the Oklahoma City area and four for the Tulsa area.
Due to population requirements for the designation of a PUMs Area, each PUMA may include several counties or only a single part of a county, but are not defined or limited by county geographical boundaries. As a result, parts of a single county may be allocated to multiple different PUMAs. This methodology eliminates the ability to either 1) aggregate data into a WFDA, or 2) disaggregate data applicable to individual counties. A U.S. Census Bureau graphic of Oklahoma PUMAs is provided following the analysis section of this report.
For the purposes of this report, data is provided and analyzed at the state level and at the PUMA level. At the county level, when a county is split between multiple PUMAs, an attempt is made to determine in which PUMA the greatest population and the largest population centers lie. No analysis is provided at this quasi-county level, however unemployment data for individuals age 40 and over is provided in each county section of Appendix D with the notation that the data is applicable to the entire PUMA in which that county can best be represented.
Statewide Data Findings
· Nearly 1.2 million Oklahoma citizens report being between the ages of 40 and 64; of that cohort, 843,779 are included in the labor force. This equates to a labor force participation rate of 70.4%. This participation rate is significantly lower than the national rate of 74.3% for the same age group.
· In 2016, statewide, 38,871 respondents reported they were unemployed, representing an overall unemployment rate of 4.6%. Nationally, for a comparable period, the unemployment rate was lower than the Oklahoma rate, at 4.1%.
· Females were 8% less likely to be unemployed than males. Of the total male population within this age cohort, 4.8% reported being unemployed. For females, the unemployment rate was 4.4%.
· With regards to race, respondents self-identifying as Native Hawaiian or Pacific Islander reported the highest unemployment rate at 49.4%. It must be noted, however, that this is a very small population in this age cohort, accounting for only 2,200 residents or approximately 1/5th of 1%. Those respondents of Black or African American race reported the second highest unemployment rate at 8.9%. This equated to 4,590 individuals participating in the labor market, but without a job.
· Asians reported the lowest incidence of unemployment at 3.5%. Again, while not as limited a population as those in the Native Hawaiian or Pacific Islander category, respondents self-reporting as Asian only account for 1.8% of the cohort.
· Whites, constituting 77.1% of the 40-64 years of age cohort, experienced an unemployment rate of 3.7%.
· Respondents identifying themselves as of Hispanic ethnicity are more likely to be unemployed than Non-Hispanics, 5.1% and 4.6% respectively. Hispanics represent 7.4% of the 40-64 years’ age bracket.
· Individuals in this age cohort who self-identify as possessing a disability participate in the labor force at only 36.3% and the unemployment rate is 10.4%. In comparison, individuals without a disability exhibit a 79.2% labor force participation rate and a 3.9% unemployment rate.
PUMA Data Findings
· The highest labor force participation rates for the 40-64 years’ bracket are found in the Roger (Central) and Wagoner (West) Counties – Claremore City PUMA at 80.8%. Similar rates are found in two of the six Oklahoma City PUMAs:
· Oklahoma County (Northwest) – Oklahoma City (Northwest Central) and Bethany Cities PUMA at 80.6%; and,
· Oklahoma County (Northeast) – Edmond and Oklahoma City (North Central) Cities PUMA at 80.7%.
The four Tulsa County PUMAs also report relatively high labor force participation rates varying between 72.6% in the north Owasso city area to 79.3% in the southeast Broken Arrow area. Both of these metropolitan areas boast labor force participation rates significantly higher than the state average. This result would be anticipated based upon the increased variety and density of jobs in these large metropolitan areas.
· At 58.8%, the lowest labor force participation rate among the 28 state PUMAs is reported in the PUMA aggregated from Adair, Cherokee, and Sequoyah counties. These three counties, located in the Eastern Oklahoma WFDA are predominantly rural with population densities ranging from 40 to 63 persons per square mile. Only one city in the PUMA boasts a population greater than 10,000 residents – Tahlequah, with an estimated population of 16,300 residents.
· Unemployment rates vary significantly from one PUMA to another, ranging from 1.3% in the Carter, Garvin, Murray, Love and Pontotoc (West) Counties PUMA to 10.4% in the Stephens, Caddo, Comanche (North), Tillman, Jefferson and Cotton Counties PUMA.
Several large employers offer opportunities within commuting distance of the Carter, Garvin, Murray, Love and Pontotoc (West) Counties, including Michelin North America Inc., Mercy Rehabilitation Services and Mercy Hospital, all located in Ardmore in Carter County. The city of Ada, located in Pontotoc County, is the site of the Mercy Hospital (Ada), Chickasaw Nation Medical Center, Legal Shield, and Solo Cup Company.
Employment in the Stephens, Caddo, Comanche (North), Tillman, Jefferson and Cotton Counties PUMA is spearheaded by Halliburton Energy Services Inc., located in Duncan. Due to the oil and gas industry downturn of the last few years, Halliburton was forced to lay off hundreds of employees, contributing to the significant unemployment rate in the PUMA. While the oil and gas industry is rebounding, the area still experiences overall high unemployment rates for all age ranges.
· With regard to gender:
· Males in this age cohort experience the lowest unemployment rates in the Carter, Garvin, Murray, Love and Pontotoc (West) Counties PUMA at 1.2%. As indicated previously, this PUMA boasts the lowest overall unemployment, regardless of gender.
· Conversely, males are least likely to be employed in the Stephens, Caddo, Comanche (North), Tillman, Jefferson, and Cotton Counties PUMA. The unemployment rate for men in this PUMA in 2016 was reported at 14.3%. As discussed previously, the major employer in this area is involved in the Energy industry. Statistically, in Oklahoma, males are 3.7 times more likely to be employed in the Energy Ecosystem than are females. As a result, the downturn in the oil and gas industry would be expected to disproportionately impact men in this PUMA.
· Female unemployment rates in this age bracket range from 0.0% to 13.7%. Extraordinarily, women in the Southwest Oklahoma PUMA – encompassing the 8-county district of Beckham, Custer, Greer, Harmon, Jackson, Kiowa, Roger Mills, and Washita – reported a 0.0% unemployment rate. According to the female survey respondents, they were either not participating in the labor force (40.2%) or they were employed.
· The highest unemployment rate for females in this age cohort (13.7%) was reported in Central Comanche County/City of Lawton where 1,200 females reported participating in the labor force but unable to achieve employment. In this same PUMA, the unemployment rate for males was reported at only 1.7% (160 unemployed individuals) – a disparity of 12 percentage points.
Based upon NAICS codes, business data indicate the presence of numerous establishments in a variety of industries including education, medical, tribal gaming/entertainment, finance, and retail, all employing between 250 and 2,200 individuals in the Lawton MSA. Data for the area, for all age groups, indicate that females represent a majority – and in some cases a dominant – percentage of employment in some of these industries. Over 80% of Healthcare and Social Assistance staff in this area are reported to be female; likewise, 74% of Finance and Insurance, and 65% of Educational Services industry staff are women. Residents employed in retail are equally distributed between the genders. Further research would be required to determine the cause of this significant level of female unemployment.
· As indicated previously, unemployment rates reported based upon race may be disproportionately affected by the small size of a given population. This is a particular issue at the PUMA level where survey responses received may number in the single- or double-digit range. For racial categories of workforce participation greater than 1,000, the highest unemployment rate reported was 34.6%. This occurred in the Oklahoma County (Northeast) – Edmond and Oklahoma City (North Central) Cities PUMA for individuals self-reporting as Black or African American. The second highest rate utilizing these same parameters was 29.3% for American Indian or Alaskan Natives in the Payne, Seminole, Creek (Southwest), Hughes and Okfuskee Counties – Stillwater City PUMA.
· The PUMA reporting the highest level of unemployment among individuals with a disability was Oklahoma County (Northeast) – Edmond and Oklahoma City (North Central) Cities PUMA at 33.4%.
· Three PUMAs reported a 0.0% unemployment rate for individuals with disabilities:
· Cherokee, Sequoyah and Adair Counties PUMA where, it must be noted that the disability labor force participation rate is only 13.9%;
· Southwest Oklahoma PUMA; and,
· Canadian County – Oklahoma City (West) PUMA.
· The highest labor force participation rate among the 28 PUMAs for individuals with disabilities is reported at 57.9% in the Cleveland County – Norman, Oklahoma City (South) and Moore Cities PUMA. The lowest labor force participation rate for the 40- to 64-year-old cohort with disabilities, as noted previously, is 13.9% in the Cherokee, Sequoyah and Adair Counties PUMA.
Appendix A: State of Oklahoma Data
Sources for data are cited for each table. Data is rounded to the nearest whole number or the nearest tenth, as appropriate. As a result, some columns may not sum exactly to the total reported. Negative numbers are cited in red and are bracketed by parentheses.
Disability
18 to 64 Years – Current Workforce Age Bracket
Disability Rate, 18 to 64 years
Total Population, Age 18-64
Reporting no Disability
Percentage Reporting no Disability
Reporting a Disability
Disability Rate
2,301,565
1,981,629
86.1%
319,936
13.9%
Source: American Community Survey,2016, 5-year Estimates
Prevalence of Disability by Type, 18 to 64 years *
Disability Type
Reporting a Disability
Percentage of Total State Population
Percentage of Disabled Population
Hearing difficulty
73,649
3.2%
23.0%
Vision difficulty
68,373
3.0%
21.4%
Cognitive difficulty
124,393
5.4%
38.9%
Ambulatory difficulty
169,490
7.4%
53.0%
Self-Care difficulty
53,538
2.3%
16.7%
Independent Living difficulty
104,526
4.5%
32.7%
* NOTE: Individuals may select more than one disability type. The total number of disabilities reported will exceed the total population. Source: American Community Survey,2016, 5-year Estimates
Prevalence of Disability by Gender, 18 to 64 years
Gender
Population
Reporting a Disability
Disability Rate
Male
1,133,383
159,936
14.1%
Female
1,168,182
160,000
13.7%
TOTAL
2,301,565
319,936
13.9%
Source: American Community Survey,2016, 5-year Estimates
Prevalence of Disability by Race, 18 to 64 years
Race
Population
Reporting a Disability
Disability Rate
American Indian or Alaska Native
168,038
29,196
17.4%
Asian
53,404
2,361
4.4%
Black
169,479
27,272
16.1%
Native Hawaiian or Other Pacific Islander
2,994
269
9.0%
Some Other Race
61,518
5,121
8.3%
Two or More Races
146,972
23,542
16.0%
White
1,699,160
232,175
13.7%
TOTAL
2,301,565
319,936
13.9%
Source: American Community Survey,2016, 5-year Estimates
Disability Rate Comparison, Highest 10: Oklahoma versus Nation and other States
Geographical Area
Population
Reporting a Disability
Disability Rate
United States
195,226,024
20,188,257
10.3%
West Virginia
1,118,913
194,175
17.4%
Kentucky
2,685,216
424,996
15.8%
Arkansas
1,761,350
264,545
15.0%
Mississippi
1,780,669
263,938
14.8%
Alabama
2,935,565
424,918
14.5%
Oklahoma
2,301,565
319,936
13.9%
Maine
821,334
112,122
13.7%
Tennessee
3,997,479
545,573
13.6%
Louisiana
2,817,139
371,289
13.2%
New Mexico
1,234,232
158,777
12.9%
Source: American Community Survey,2016, 5-year Estimates
Under 18 years – Future Workforce Age Bracket
Disability Rate, Under 18 years
Total Population Under 18 years
Reporting no Disability
Percentage Reporting no Disability
Reporting a Disability
Disability Rate
950,318
903,744
95.1%
46,574
4.9%
Source: American Community Survey,2016, 5-year Estimates
Prevalence of Disability by Type, As a Percentage of Individuals with Disabilities, Under 18 years *
Disability Type
Reporting a Disability
Percentage of Total State Population
Percentage of Disabled Population
Hearing difficulty
8,342
0.9%
17.9%
Vision difficulty
10,400
1.1%
22.3%
Cognitive difficulty**
30,662
4.5%
65.8%
Ambulatory difficulty**
5,419
0.8%
11.6%
Self-Care difficulty**
6,368
0.9%
13.7%
Independent Living difficulty
Not Applicable
*Individuals may select more than one disability type. The total number of disabilities will exceed the total population.
**Some disability types are not applicable to all age-ranges.
Source: American Community Survey,2016, 5-year Estimates
Prevalence of Disability by Gender, Under 18 years
Gender
Population
Reporting a Disability
Disability Rate
Male
485,738
27,365
5.6%
Female
464,580
19,209
4.1%
TOTAL
950,318
46,574
4.9%
Source: American Community Survey,2016, 5-year Estimates
Prevalence of Disability by Race, Under 18 years
Race
Population
Reporting a Disability
Disability Rate
American Indian or Alaska Native
87,893
5,191
5.9%
Asian
17,494
359
2.1%
Black
74,316
4,316
5.8%
Native Hawaiian or Other Pacific Islander
1,622
62
3.8%
Some Other Race
34,850
1,418
4.1%
Two or More Races
126,779
7,227
5.7%
White
607,364
(28,001
4.6%
TOTAL
950,318
46,574
4.9%
Source: American Community Survey,2016, 5-year Estimates
Age and Gender, 15 to 64 years
Age Comparison, 15 to 64 years
Age Bracket
2017 Population
Percent of 2017 Population Age 15-64
2027 Projected Population
Percent of 2027 Projected Population Age 15-64
Growth Rate
15 to 19 years
265,288
10.5%
277,279
11.0%
4.5%
20 to 24 years
275,540
10.9%
281,449
11.2%
2.1%
25 to 29 years
276,149
10.9%
255,445
10.2%
(7.5%)
30 to 34 years
273,189
10.8%
260,735
10.4%
(4.6%)
35 to 39 years
254,733
10.0%
274,615
10.9%
7.8%
40 to 44 years
231,018
9.1%
264,453
10.5%
14.5%
45 to 49 years
228,777
9.0%
245,005
9.7%
7.1%
50 to 54 years
241,687
9.5%
219,951
8.7%
(9.0%)
55 to 59 years
256,529
10.1%
215,952
8.6%
(15.8%)
60 to 64 years
234,535
9.2%
220,495
8.8%
(6.0%)
TOTAL
2,537,445
100.0%
2,515,378
100.0%
(0.9%)
Source: EMSI, Version 2018.1
Gender by Age, 15 to 64 years
Gender/Age
2017 Population
Percent of Total 2017 Population Age 15-64
2027 Projected Population
Percent of Total 2027 Projected Population Age 15-64
Growth Rate
MALE
15 to 19 years
136,669
5.4%
143,088
5.7%
4.7%
20 to 24 years
143,090
5.6%
145,780
5.8%
1.9%
25 to 29 years
140,095
5.5%
129,949
5.2%
(7.2%)
30 to 34 years
137,884
5.4%
132,480
5.3%
(3.9%)
35 to 39 years
128,864
5.1%
137,545
5.5%
6.7%
40 to 44 years
116,061
4.6%
132,883
5.3%
14.5%
45 to 49 years
114,637
4.5%
123,744
4.9%
7.9%
50 to 54 years
119,936
4.7%
109,706
4.4%
(8.5%)
55 to 59 years
124,818
4.9%
106,530
4.2%
(14.7%)
60 to 64 years
112,699
4.4%
106,866
4.2%
(5.2%)
MALE SUB-TOTAL
1,274,754
50.2%
1,268,571
50.4%
(0.5%)
FEMALE
15 to 19 years
128,619
5.1%
134,190
5.3%
4.3%
20 to 24 years
132,450
5.2%
135,668
5.4%
2.4%
25 to 29 years
136,054
5.4%
125,497
5.0%
(7.8%)
30 to 34 years
135,305
5.3%
128,255
5.1%
(5.2%)
35 to 39 years
125,869
5.0%
137,069
5.4%
8.9%
40 to 44 years
114,956
4.5%
131,570
5.2%
14.5%
45 to 49 years
114,140
4.5%
121,261
4.8%
6.2%
50 to 54 years
121,751
4.8%
110,245
4.4%
(9.5%)
55 to 59 years
131,711
5.2%
109,422
4.4%
(16.9%)
60 to 64 years
121,836
4.8%
113,629
4.5%
(6.7%)
FEMALE SUB-TOTAL
1,262,691
49.8%
1,246,807
49.6%
(0.0%)
OVERALL TOTAL
2,537,445
100.0%
2,515,378
100.0%
(0.9%)
Race and Ethnicity, Age 15-64
Race/Ethnicity Combinations, 15 to 64 years
Race/Ethnicity
2017 Population
Percent of 2017 Population Age 15-64
2027 Projected Population
Percent of 2027 Projected Population Age 15-64
Growth Rate
American Indian or Alaskan Native, Non-Hispanic
216,650
8.5%
216,468
8.6%
(0.1%)
Asian, Non-Hispanic
64,303
2.5%
74,768
3.0%
16.3%
Black or African American, Non-Hispanic
199,864
7.9%
198,829
7.9%
(0.5%)
Native Hawaiian or Pacific Islander, Non-Hispanic
3,837
0.2%
4,871
0.2%
27.0%
Two or More Races, Non-Hispanic
123,832
4.9%
134,939
5.4%
9.0%
White, Non-Hispanic
1,669,724
65.8%
1,583,852
63.0%
(5.1%)
American Indian or Alaskan Native, Hispanic
19,934
0.8%
25,357
1.0%
27.2%
Asian, Hispanic
1,792
0.1%
2,153
0.1%
20.2%
Black or African American, Hispanic
8,210
0.3%
9,762
0.4%
18.9%
Native Hawaiian or Pacific Islander, Hispanic
1,018
0.0%
1,192
0.0%
17.1%
Two or More Races, Hispanic
11,801
0.5%
14,164
0.6%
20.0%
White, Hispanic
216,481
8.5%
249,023
9.9%
15.0%
TOTAL
2,537,445
100.0%
2,515,378
100.0%
(0.9%)
Source: EMSI, Version 2018.1
Race Only, Regardless of Ethnicity, 15 to 64 years
Race
2017 Population
Percent of 2017 Population Age 15-64
2027 Projected Population
Percent of 2027 Projected Population Age 15-64
Growth Rate
American Indian or Alaskan Native
236,584
9.3%
241,826
9.6%
2.2%
Asian
66,094
2.6%
76,921
3.1%
16.4%
Black or African American
208,074
8.2%
208,591
8.3%
0.2%
Native Hawaiian or Pacific Islander
4,855
0.2%
6,063
0.2%
24.9%
Two or More Races
135,633
5.3%
149,104
5.9%
9.9%
White
1,886,205
74.3%
1,832,874
72.9%
(2.8%)
TOTAL
2,537,445
100.0%
2,515,378
100.0%
(0.9%)
Source: EMSI, Version 2018.1
Ethnicity Only, Regardless of Race, 15 to 64 years
Ethnicity
2017 Population
Percent of Population Age 15-64
2027 Projected Population
Percent of 2027 Projected Population Age 15-64
Growth Rate
Non-Hispanic
2,278,209
89.8%
2,213,727
88.0%
(2.8%)
Hispanic
259,236
10.2%
301,652
12.0%
16.4%
TOTAL
2,537,445
100.0%
2,515,378
100.0%
(0.9%)
Source: EMSI, Version 2018.1
English Language Learners, Age 18-64
Prevalence of Language Spoken at Home, 18 to 64 years
Language Spoken at Home
Percentage
Speak English only
89.2%
Speak a language other than English
10.8%
Of those that speak a language other than English, language spoken:
Spanish
7.2%
Other Indo-European Languages
1.0%
Asian and Pacific Island Languages
1.7%
Other Languages
0.9%
TOTAL
10.8%
Source: American Community Survey, 2016, 5-year Estimates
Perception of Fluency of English Language Learners, 18 to 64 years
Language Spoken at Home
Speaks English:
Very Well
Well
Total of Very Well or Well
Not Well
Not At All
Total of Not Well or Not At All
Spanish
47.4%
21.5%
68.9%
22.3%
8.8%
31.1%
Other Indo-European Languages
78.7%
14.4%
93.1%
5.7%
1.3%
7.0%
Asian and Pacific Island Languages
50.9%
29.1%
80.0%
16.7%
3.3%
20.0%
Other Languages
80.7%
13.5%
94.2%
4.8%
1.0%
5.8%
Source: American Community Survey, 2016, 5-year Estimates
Religious Affiliation, 2009
Religious Affiliation, 2009
Major Religious Category
Congregations
Percentage of Congregations
Member Count
Percentage of Members
Evangelical Protestant
4,223
63.6%
979,280
56.7%
Mainline Protestant
906
13.7%
313,093
18.1%
Historically Black Protestant
168
2.5%
39,577
2.3%
Roman Catholic
181
2.7%
145,581
8.4%
Jewish Congregations
12
0.2%
3,436
0.2%
Latter-Day Saint (Mormon)
66
1.0%
11,484
0.7%
Islamic
3
0.0%
835
0.0%
Hindu
1
0.0%
350
0.0%
Buddhist
5
0.1%
550
0.0%
Orthodox Christian
11
0.2%
2,551
0.1%
Jehovah’s Witnesses
97
1.5%
27,000
1.6%
Other
964
14.5%
203,645
11.8%
TOTAL
6,637
100.0%
1,727,382
100.0%
Source: University of Wisconsin “Social Explorer,” https://fyi.uwex.edu/community-data-tools/2011/12/05/detailed-data-on-religion-by-county/
Unemployment/Labor Force Participation, Age 40-64
Summary of Unemployment/Labor Force Participation, 40 to 64 years
State
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Unemployed
Unemployment Rate
Oklahoma
843,779
355,365
70.4%
804,908
38,871
4.6%
Unemployment/Labor Force Participation by Presence of a Disability, 40 to 64 years
Presence of a Disability
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Unemployed
Unemployment Rate
Disability
89,277
156,882
36.3%
80,028
9,249
10.4%
No Disability
754,502
198,483
79.2%
724,880
29,622
3.9%
TOTAL
843,779
355,365
70.4%
804,908
38,871
4.6%
Source: Public Use Microdata Sample (PUMs), American Community Survey, 2016
Unemployment/Labor Force Participation by Race Only, Regardless of Ethnicity, 40 to 64 years
Race
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Unemployed
Unemployment Rate
American Indian or Alaskan Native
57,712
27,474
67.7%
53,151
4,561
7.9%
Asian
18,205
3,627
83.4%
17,561
644
3.5%
Black or African American
51,606
27,054
65.6%
47,016
4,590
8.9%
Native Hawaiian or Pacific Islander
2,054
151
93.2%
1,039
1,015
49.4%
Two or More Races
38,135
20,804
64.7%
35,964
2,171
5.7%
White
656,415
267,751
71.0%
632,090
24,325
3.7%
Some Other Race
19,652
8,504
69.8%
18,087
1,565
8.0%
TOTAL
843,779
355,365
70.4%
804,908
38,871
4.6%
Source: Public Use Microdata Sample (PUMs), American Community Survey, 2016
Unemployment/Labor Force Participation by Ethnicity, Regardless of Race, 40 to 64 years
Ethnicity
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Unemployed
Unemployment Rate
Hispanic
63,984
24,414
72.4%
60,751
3,233
5.1%
Non-Hispanic
779,795
330,951
70.2%
744,157
35,638
4.6%
TOTAL
843,779
355,365
70.4%
804,908
38,871
4.6%
Source: Public Use Microdata Sample (PUMs), American Community Survey, 2016
Unemployment/Labor Force Participation by Gender, 40 to 64 years
Gender
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Unemployed
Unemployment Rate
Male
450,821
143,674
75.8%
429,209
21,612
4.8%
Female
392,958
211,691
65.0%
375,699
17,259
4.4%
TOTAL
843,779
355,365
70.4%
804,908
38,871
4.6%
Source: Public Use Microdata Sample (PUMs), American Community Survey, 2016
Appendix B: Public Use Microdata Sample Area (PUMA) Data Unemployment, Age 40 to 64 years
Sources for data are cited for each table. Data is rounded to the nearest whole number or the nearest tenth, as appropriate. As a result, some columns may not sum exactly to the total reported. Negative numbers are cited in red and are bracketed by parentheses.
Summary of Unemployment/Labor Force Participation, PUMA, 40 to 64 years
PUM Area
Included Counties
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Un-
employed
Un-employment Rate
Northeast Oklahoma PUMA
Craig
Delaware
Mayes
Nowata
Ottawa
35,291
18,982
65.0%
33,518
1,773
5.0%
Cherokee, Sequoyah and Adair Counties PUMA
Adair
Cherokee
Sequoyah
19,173
13,451
58.8%
17,853
1,320
6.9%
Southeast Oklahoma PUMA
Choctaw
Haskell
Latimer
Le Flore
McCurtain
Pittsburg
Pushmataha
32,729
21,446
60.4%
31,379
1,350
4.1%
Southwest Oklahoma PUMA
Beckham
Custer
Greer
Harmon
Jackson
Kiowa
Roger Mills
Washita
21,861
11,315
65.9%
20,906
955
4.4%
Source: Public Use Microdata Sample (PUMs), American Community Survey, 2016
Summary of Unemployment/Labor Force Participation, PUMA, 40 to 64 years (continued)
PUM Area
Included Counties
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Un-
employed
Un-
employment Rate
Panhandle and Northwest Oklahoma PUMA
Alfalfa
Beaver
Blaine
Cimarron
Dewey
Ellis
Grant
Harper
Kingfisher
Major
Texas
Woods
Woodward
25,888
8,546
75.2%
24,258
1,630
6.3%
Comanche County (Central) – Lawton City PUMA
Comanche (part)
17,951
10,602
62.9%
16,589
1,362
7.6%
Stephens, Caddo, Comanche (North), Tillman, Jefferson, and Cotton Counties PUMA
Caddo
Comanche (part)
Cotton
Jefferson
Stephens
Tillman
24,358
12,277
66.5%
21,817
2,541
10.4%
Carter, Garvin, Murray, Love and Pontotoc (West) Counties PUMA
Carter
Garvin
Love
Murray
19,543
12,061
61.8%
19,295
248
1.3%
Source: Public Use Microdata Sample (PUMs), American Community Survey, 2016
Summary of Unemployment/Labor Force Participation, PUMA, 40 to 64 years (continued)
PUM Area
Included Counties
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Un-
employed
Un-
employment Rate
Bryan, Pontotoc (East) Marshall, Atoka, Johnston and Coal Counties PUMA
Atoka
Bryan
Coal
Johnston
Marshall
Pontotoc
24,357
14,259
63.1%
23,175
1,182
4.9%
Canadian County – Oklahoma City (West) PUMA
Canadian
31,147
12,303
71.7%
29,471
1,676
5.4%
Cleveland County – Norman, Oklahoma City (South) and Moore Cities PUMA
Cleveland
62,554
19,015
76.7%
61,003
1,551
2.5%
Oklahoma County (Southwest) – Oklahoma City (West Central) PUMA
Oklahoma (part)
18,965
10,248
64.9%
18,221
744
3.9%
Oklahoma County (Northwest) – Oklahoma City (Northwest Central) and Bethany Cities PUMA
Oklahoma (part)
39,237
9,463
80.6%
37,489
1,748
4.5%
Oklahoma County (Northeast) – Edmond and Oklahoma City (North Central) Cities PUMA
Oklahoma (part)
35,224
8,416
80.7%
34,046
1,178
3.3%
Oklahoma County (East) – Midwest, Del and Oklahoma City (Northeast) Cities PUMA
Oklahoma (part)
27,248
12,304
68.9%
26,370
878
3.2%
Oklahoma County (Southeast) – Oklahoma City (East Central) PUMA
Oklahoma (part)
21,933
12,753
63.2%
20,602
1,331
6.1%
Oklahoma County (Central) – Oklahoma City (Central) PUMA
Oklahoma (part)
25,694
10,279
71.4%
24,827
867
3.4%
Source: Public Use Microdata Sample (PUMs), American Community Survey, 2016
Summary of Unemployment/Labor Force Participation, PUMA, 40 to 64 years (continued)
PUM Area
Included Counties
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Un-
employed
Un-employment Rate
Grady, McClain and Pottawatomie (South) Counties PUMA
Grady
McClain
26,714
11,477
69.9%
26,071
643
2.4%
Pottawatomie (North), Logan and Lincoln Counties – Shawnee City PUMA
Lincoln
Logan
Pottawatomie
28,167
16,632
62.9%
26,416
1,751
6.2%
Tulsa County (Central) – Tulsa City (Central) PUMA
Tulsa (part)
45,963
14,454
76.1%
43,377
2,586
5.6%
Tulsa County (Southeast) – Tulsa (Southeast) and Broken Arrow (West) Cities PUMA
Tulsa (part)
52,941
13,804
79.3%
50,920
2,021
3.8%
Tulsa County (North) – Tulsa (North) and Owasso Cities PUMA
Tulsa (part)
27,929
10,532
72.6%
26,689
1,240
4.4%
Tulsa (West), Creek (Northeast) and Osage (Southeast) Counties – Tulsa City (West) PUMA
Tulsa (part)
43,589
14,377
75.2%
42,050
1,539
3.5%
Rogers (Central) and Wagoner (West) Counties – Claremore City PUMA
Rogers
Wagoner
33,016
7,850
80.8%
32,213
803
2.4%
Muskogee, Okmulgee, Wagoner (East) and McIntosh Counties PUMA
McIntosh
Muskogee
Okmulgee
29,431
16,016
64.8%
27,281
2,150
7.3%
Garfield, Kay and Noble Counties – Enid City PUMA
Garfield
Kay
Noble
27,799
7,946
77.8%
25,822
1,977
7.1%
Source: Public Use Microdata Sample (PUMs), American Community Survey, 2016
Summary of Unemployment/Labor Force Participation, PUMA, 40 to 64 years (continued)
PUM Area
Included Counties
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Un-
employed
Un-
employment Rate
Payne, Seminole, Creek (Southwest), Hughes, and Okfuskee Counties – Stillwater City PUMA
Creek
Hughes
Okfuskee
Payne
Seminole
25,862
13,748
65.3%
24,836
1,026
4.0%
Washington, Osage (North and West), Pawnee, Creek (Northwest) Counties PUMA
Osage
Pawnee
Washington
19,215
10,809
64.0%
18,414
801
4.2%
Source: Public Use Microdata Sample (PUMs), American Community Survey, 2016
Summary of Unemployment/Labor Force Participation by Race Only, Regardless of Ethnicity, PUMS, 40 to 64 years
PUM Area
Race
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Un-employed
Un-employment Rate
Northeast Oklahoma PUM Area
American Indian or Alaskan Native
6,488
3,093
67.7%
5,895
593
9.1%
Asian
340
137
71.3%
340
0
0.0%
Black or African American
101
145
41.1%
101
0
0.0%
Native Hawaiian or Pacific Islander
156
0
100.0%
156
0
0.0%
Two or More Races
2,267
1,849
55.1%
2,154
113
5.0%
White
25,399
13,568
65.2%
24,470
929
3.7%
Some Other Race
540
190
74.0%
402
138
25.6%
Southeast Oklahoma PUM Area
American Indian or Alaskan Native
2,680
1,481
64.4%
2,551
129
4.8%
Asian
145
63
69.7%
145
0
0.0%
Black or African American
1,400
677
67.4%
1,198
202
14.4%
Native Hawaiian or Pacific Islander
606
0
100.0%
606
0
0.0%
Two or More Races
3,066
1,993
60.6%
2,902
164
5.3%
White
24,601
17,187
58.9%
23,746
855
3.5%
Some Other Race
231
45
83.7%
231
0
0.0%
Source: Public Use Microdata Sample (PUMs), American Community Survey, 2016
Summary of Unemployment/Labor Force Participation by Race Only, Regardless of Ethnicity, PUMS, 40 to 64 years (continued)
PUM Area
Race
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Un-employed
Un-employment Rate
Cherokee, Sequoyah, and Adair Counties PUM Area
American Indian or Alaskan Native
6,734
3,394
66.5%
6,350
384
5.7%
Asian
36
41
46.8%
36
0
0.0%
Black or African American
497
190
72.3%
420
77
15.5%
Native Hawaiian or Pacific Islander
0
0
0.0%
0
0
0.0%
Two or More Races
2,290
1,058
68.4%
1,700
590
25.8%
White
9,616
8,570
52.9%
9,347
269
2.8%
Some Other Race
0
198
0.0%
0
0
0.0%
Southwest Oklahoma PUM Area
American Indian or Alaskan Native
141
267
34.6%
141
0
0.0%
Asian
0
0
0.0%
0
0
0.0%
Black or African American
268
1,664
13.9%
138
130
48.5%
Native Hawaiian or Pacific Islander
0
0
0.0%
0
0
0.0%
Two or More Races
634
1,444
30.5%
634
0
0.0%
White
19,942
6,946
74.2%
19,117
825
4.1%
Some Other Race
876
994
46.8%
876
0
0.0%
Source: Public Use Microdata Sample (PUMs), American Community Survey, 2016
Summary of Unemployment/Labor Force Participation by Race Only, Regardless of Ethnicity, PUMS, 40 to 64 years (continued)
PUM Area
Race
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Un-employed
Un-employment Rate
Panhandle and Northwest PUM Area
American Indian or Alaskan Native
381
163
70.0%
381
0
0.0%
Asian
0
0
0.0%
0
0
0.0%
Black or African American
239
101
70.3%
0
239
100.00%
Native Hawaiian or Pacific Islander
0
0
0.0%
0
0
0.0%
Two or More Races
578
639
47.5%
578
0
0.0%
White
22,529
6,741
77.0%
21,693
836
3.7%
Some Other Race
2,161
902
70.6%
1,606
555
25.7%
Comanche County (Central) – Lawton City PUM Area
American Indian or Alaskan Native
460
1,036
30.7%
460
0
0.0%
Asian
731
0
100.0%
731
0
0.0%
Black or African American
4,284
1,772
70.7%
3,831
453
10.6%
Native Hawaiian or Pacific Islander
72
105
40.7%
72
0
0.0%
Two or More Races
1,626
805
66.9%
1,626
0
0.0%
White
10,385
6,536
61.4%
9,731
654
6.3%
Some Other Race
393
348
53.0%
138
255
64.9%
Source: Public Use Microdata Sample (PUMs), American Community Survey, 2016
Summary of Unemployment/Labor Force Participation by Race Only, Regardless of Ethnicity, PUMS, 40 to 64 years (continued)
PUM Area
Race
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Un-employed
Un-employment Rate
Stephens, Caddo, Comanche (North), Tillman, Jefferson and Cotton Counties PUM Area
American Indian or Alaskan Native
2,959
927
76.1%
2,959
0
0.0%
Asian
0
23
0.0%
0
0
0.0%
Black or African American
262
514
33.8%
262
0
0.0%
Native Hawaiian or Pacific Islander
0
0
0.0%
0
0
0.0%
Two or More Races
740
784
48.6%
740
0
0.0%
White
19,840
9,147
68.4%
17,351
2,489
12.5%
Some Other Race
557
882
38.7%
505
52
9.3%
Carter, Garvin, Murray, Love and Pontotoc (West) Counties PUM Area
American Indian or Alaskan Native
1,014
806
55.7%
1,014
0
0.0%
Asian
0
0
0.0%
0
0
0.0%
Black or African American
547
464
34.7%
247
0
0.0%
Native Hawaiian or Pacific Islander
0
0
0.0%
0
0
0.0%
Two or More Races
1,197
725
62.3%
1,197
0
0.0%
White
16,736
9,946
62.7%
16,488
248
1.5%
Some Other Race
349
120
74.4%
349
0
0.0%
Source: Public Use Microdata Sample (PUMs), American Community Survey, 2016
Summary of Unemployment/Labor Force Participation by Race Only, Regardless of Ethnicity, PUMS, 40 to 64 years (continued)
PUM Area
Race
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Un-employed
Un-employment Rate
Bryan, Pontotoc (East), Marshall, Atoka, Johnston, and Coal Counties – Ada City PUM Area
American Indian or Alaskan Native
2,661
1,574
62.8%
2,624
37
1.4%
Asian
403
189
68.1%
403
0
0.0%
Black or African American
320
530
37.6%
221
99
30.9%
Native Hawaiian or Pacific Islander
0
0
0.0%
0
0
0.0%
Two or More Races
2,097
866
70.8%
2,080
17
0.8%
White
18,703
11,077
62.8%
17,674
1,029
5.5%
Some Other Race
173
23
88.3%
173
0
0.0%
Canadian County – Oklahoma City (West) PUMA
American Indian or Alaskan Native
530
541
49.5%
530
0
0.0%
Asian
939
319
74.6%
939
0
0.0%
Black or African American
409
409
50.0%
286
123
30.1%
Native Hawaiian or Pacific Islander
0
0
0.0%
0
0
0.0%
Two or More Races
267
37
87.8%
267
0
0.0%
White
27,921
9,187
75.2%
26,368
1,553
5.6%
Some Other Race
1,081
1,810
37.4%
1,081
0
0.0%
Source: Public Use Microdata Sample (PUMs), American Community Survey, 2016
Summary of Unemployment/Labor Force Participation by Race Only, Regardless of Ethnicity, PUMS, 40 to 64 years (continued)
PUM Area
Race
In the Labor Force
Not In the Labor Force
Labor Force Participation Rate
Employed
Un-employed
Un-employment Rate
Cleveland County – Norman, Oklahoma City (South) and Moore Cities PUM Area
American Indian or Alaskan Native
2,333
236
90.8%
2,333
0
0.0%
Asian
2,973
966
75.5%
2,973
0
0.0%
Black or African American
1,621
988
62.1%
1,621
0
0.0%
Native Hawaiian or Pacific Islander
29
0
100.0%
29
0
0.0%
Two or More Races
3,268
880
78.8%
2,827
441
13.5%
White
52,124
15,945
76.6%
51,014
1,110
2.1%
Some Other Race
206
0
100.0%
206
0
0.0%
Oklahoma County (Southwest) – Oklahoma City (West Central) PUM Area
American Indian or Alaskan Native
1,403
496
73.9%
384
1,019
27.4%
Asian
1,085
0
100.0%
1,085
0
0.0%
Black or African American
1,075
1,188
47.5%
1,075
0
0.0%
Native Hawaiian or Pacific Islander
0
0
0.0%
0
0
0.0%
Two or More Races
668
287
69.9%
379
289
43.3%
White
13,835
7,894
63.7%
13,764
71
0.5%
Some Other Race
899
383
70.1%
899
0