Bad Data Makes Social Welfare Advocacy Harder Than it Should Be

Last week The Atlantic published a piece about an oft-cited portion of census data.

In 2007, for instance, 6.4 percent of adults who lived under the poverty line and didn’t work in the past year said it was because they couldn’t find a job. As of 2012, it had more than doubled, leaving it at a still-small 13.5 percent. By comparison, more than a quarter said they stayed home for family reasons and more than 30 percent cited a disability [Jordan Weissmann, The Atlantic]

As Weissmann goes on to lay out, conservatives regularly use this data point as a rationale for limiting welfare, and for including short-sited work requirements in recent reforms.

This brings me back to an argument that I find myself making all of the time: bad data makes everything [including social welfare advocacy] a lot harder than it needs to be. Especially because data that is accurate when it comes to folks living below the poverty line is few and far between.

In my mind, there are two major problems that prevent the Census from adequately assessing why folks in low-income communities are unemployed.

(1) Response rates for the Census is consistently low

I tried to get the actual numbers from the Census website and was met with the following (thank you GOP).

However, generally around 64% of households mail back their census forms. In communities of color and in low-income communities, those percentages are consistently lower. These are also the communities that Census workers are most hesitant to enter for their door to door efforts, and as a result rarely are captured in an accurate way. If you add to this the Census’ systematic neglect of homeless populations, I think it’s pretty easy to understand why the Census doesn’t know much about poor people in this country.

(2) The question is a bad one

The Census question causing so much controversy is the following:

“Why have you been out of work?” it then allows the respondent to cite inability to find a job, home/family, disability, school or other, or retired.

The issue with these kind of survey question is that they fail to assess what prevents someone from being employed and/or what their motivations might be for not looking for employment, the reasons for which, are numerous.

• Many folks do not have the educational levels to get a job that would pay above minimum wage. In the United States a family cannot subsist on that kind of income. However in many states, taking that kind of job would render them no longer eligible for the types of social support that would allow them to feed their family. As a result, many folks make the obvious choice. They choose not to take the job at McDonalds, so that they continue to feed their families.
• In my research, many of the women I interviewed encountered years of racism when attempting to apply for jobs. Even the individuals who had BA’s or MA’s were consistently looked over because of their names, addresses, appearance or mode of speaking. After years of being passed over for employment by less qualified candidates, many women simply stopped trying. The entrenched racism/classism/sexism that they encountered on a regular basis felt like too much to continue fighting.
• Many of the women I interviewed felt like they needed to stay home to protect their families. Folks have been separated from their families due to the demolition of housing projects all over the country. This means that there is no one that can walk their children home from school, and make sure that they are safe from the violence they are surrounded by.

At the end of the day, the issue with the Census data is both simple and complex. It fails to adequately assess the impact of institutionalized oppression on the lives of marginalized communities. As a result, the numbers tell a story that is divergent from the realities of their every day lives. I believe, that without side-by-side interview data that attempts to assess the daily-lived experience of the communities in question, we cannot make decisions that affect their livelihood based on these skewed snapshots.