Making sense of the Simplified Poverty SS…

County in question: Benton,

 Population 16 years and over
     In labor force
       Civilian labor force
         Employed
         Unemployed
     Less than $10,000
     $10,000 to $14,999
     $15,000 to $24,999
     $25,000 to $34,999
     $35,000 to $49,999
     $50,000 to $74,999
     $75,000 to $99,999
     $100,000 to $149,999
     $150,000 to $199,999
     $200,000 or more
     Median household income (dollars)
   Poverty-All families
Poverty- With related children of the householder under 18 years
Poverty-With related children of the householder under 5 years only
185684 0.642 0.641 0.619 0.022 0.045 0.041 0.093 0.096 0.147 0.202 0.124 0.144 0.049 0.058 59016 0.088 0.129 0.133

 

In Benton County there are 185,684 people of at least 16 years of age, of that number .642 are in the labor force, so 185,684(.642)=119,209 people of age to work who have jobs. Civilian Labor force is nearly the same portion. I can use this formula for the remaining decimals or translate it by using percentages, by moving the decimal to understand the portion of the community pertaining to each category. After considering these numbers and how they relate to the county’s population I think it’s best to look at what categories I think are the most interesting. I think the most news worth categories are the final three,

1. Poverty-All families (.088)= 16,340 total families in poverty
2. Poverty- With related children of the householder under 18 years (.129)16,340= 2,107/8 total families with children
3. Poverty-With related children of the householder under 5 years only (.133)2,108= 280 families with young children
Reviewing these numbers on a spread sheet takes away from the fact that these decimals represent real families.
With these numbers I may continue to look into comparative results of the portion of the Benton county population in poverty with young children to how many are making less than $10,000 a year. I could go through each decimal and find the actual population numbers, then relate each total with the Median Income of the county to understand the scheme of the data. I’m not sure if this is what Professor Wells is looking for in this blog, but this is the sense I can make out of the data presented. I could continue to follow these formulas to understand which counties suffer the most from poverty in Arkansas.