This is the latest entry in NPP’s weekly Data Wednesday series, a getting-started guide to NPP’s Federal Priorities Database. All previous posts are archived here.
Back in February, we introduced data normalization. The bottom line: it’s hard to compare data across time and geographies accurately unless that data is normalized. In general, normalizing involves dividing “raw” data by a meaningful denominator. The unemployment rate, for example, is calculated by taking the total number of unemployed and dividing it by the number of people in the labor force.
The Federal Priorities Database presents normalized data whenever possible. A few examples:
|Monthly Food Stamps Per Person||average monthly benefit received per person enrolled in the Supplemental Nutrition Assistance Program||monthly Supplemental Nutrition Assistance Program (i.e., food stamp) benefits ÷ total SNAP enrollees|
|People in Poverty||percentage of the population estimated to be living in poverty||number of people living in poverty ÷ total population|
|Unemployment||percentage labor force who are laid off and/or actively seeking work||number of people laid off or actively seeking work ÷ number of people in labor force|
|Pupil Teacher Ratio||number of students for every public school teacher||number of students ÷ number of public school teachers|
|Food Insecurity||percentage of households with difficulty at some time during the year providing enough food for all members||number of food-insecure households ÷ total households|
|Federal Expenditures||all datasets related to federal expenditures are available in per capita format||total spending amount ÷ total population|
Notice a few key phrases in these names and descriptions? Per person, percentage, for every, per capita? Those phrases are your clues that the data is normalized.
If you have any questions, feel free to e-mail the Federal Priorities Database team.