Kids Count Data Center

Definitions: The definition provided is a condensed version. For the complete definition go to American FactFinder: http://factfinder.census.gov/servlet/BasicFactsServlet The data on employment status was referred to as labor force status in previous censuses. The employment status data shown in Census 2000 tabulations were derived from answers to the long-form questionnaire, which were asked of a sample of the population 15 years and over. The employed civilian data relate to all civilians 16 years old and over who were either (1) ‘‘at work’’ — those who did any work at all during the reference week as paid employees, worked in their own business or profession, worked on their own farm, or worked 15 hours or more as unpaid workers on a family farm or in a family business; or (2) were ‘‘with a job but not at work’’ — those who did not work during the reference week, but who had jobs or businesses from which they were temporarily absent because of illness, bad weather, industrial dispute, vacation, or other personal reasons. Excluded from the employed are people whose only activity consisted of work around their own house (painting, repairing, or own home housework) or unpaid volunteer work for religious, charitable, and similar organizations. Also excluded are all institutionalized people and people on active duty in the United States Armed Forces.

Data Source: Females 16 and over employed for 1980 and 1990 are from the Census of Social & Economic Characteristics. Data were compiled by the South Dakota State Data Center (www.usd.edu/sdsdc). 2000 figures are from Census 2000 Summary File 3 (SF 3) - Sample Data Table P45. PRESENCE OF OWN CHILDREN UNDER 18 YEARS BY AGE OF OWN CHILDREN BY EMPLOYMENT STATUS FOR FEMALES 16 YEARS AND OVER [22] - Universe: Females 16 years and over. Data were compiled by the South Dakota State Data Center. Note: The total for women 16+ employed is all women [with and without children].

Note: Maps use the natural break classification method, which reflects patterns in the data by dividing the map into naturally occurring groups. Using statistical tools, this method determines cut-off points for each group by identifying large gaps in data values.