Many want to get rid of the American Community Survey, a Census program which releases region-specific data annually. University of Michigan professor William Frey explains why cutting the survey would be a mistake.
I particularly like the quote from Congressman Webster who is sponsoring the bill to get rid of it “since in the end this is not a scientific survey. It’s a random survey.” There’s a cost to our society of citizens not having basic statistical literacy. http://nyti.ms/LtYsXu
And, another quote from the same congressman, “What really promotes business in this country is liberty,” he said, “not demand for information.” A thoughtful person might be able to argue the privacy angle of this (though I know what side I come down on), but if he thinks in this day and age business doesn’t run on information, he’s living in a time warp.
A high price for the rest of us to pay for his ignorance and cluelessness.
If business wants the information, it can bloody well pay for it out of its own pocket. There is absolutely no reason the tax payer should fork up for market research, especially conducted with traditional government efficiency.
Yep, the government can’t possibly become ever more intrusive without data from surveys like this.
Since the census long form is already gone, losing the ACS would be a disaster for all human services community organizations, public policy makers, etc. That data is so important to see shifting trends in need, demographics, etc. We rely on ACS data for conducting needs assessments and setting multi-year service and policy plans – and explaining and justifying them to community/government/funders. And for seeing the impacts of large-scale service or policy efforts, of economic and cultural trends. The loss of the ACS data will end up huring low-income communities disproportionately.
Become a member.
Learn to visualize your data.
From beginner to advanced.
What you get
I simulated a day for employed Americans to see when and where they work.
See what we ate on an average day, for the past several decades.
Many charts don’t tell the truth. This is a simple guide to spotting them.
“Let the data speak” they say. But what happens when the data rambles on and on?