The unemployment rate for African American men and women in the U.S. is roughly double that of white Americans. This isn’t a new problem, and it’s not an easy one to solve. However, it seems to be one that more mayors are intent on addressing within their cities. The word “equity” now comes up frequently in urban conversations. Maybe it’s because we know some populations still haven’t really come out of the recession. Maybe it’s because mayors seem to be coming to the conclusion that their state houses and federal programs might not be in a position to help them. Cynics might say the mayors are hoping voters will feel like efforts are being made, so they can get re-elected. Regardless of motives, it’s a great goal.
The question becomes, how can you measure progress to see what’s working and what isn’t? The progress might be localized and incremental, but it needs to be quantified. It’s important for the programs, for budgeting, and for selling success stories to constituents and stakeholders. If one mayor in one city can demonstrate a path to improvement, you can bet others will hop on board quickly. There’s a lot at stake.
One way is to create your own metrics. Richard Florida and his Martin Prosperity Institute colleagues created a new measure of economic segregation that looks at education, income and occupation. He ranked the largest metro areas based on those results. Presumably one could track this data over time and gauge improvement. Metros are great economic engines, but people don’t live in or govern in metros. They live in and govern cities.
Likewise, I’ve been doing some analysis about inequality focusing on the gap between African American employment and White employment at the city level. This research grew out of a series of conversations with the mayor of a major U.S. city. This mayor is well-versed in the issues facing his city and trying to both affect and measure positive change. I wanted to see if I could find some good competitive benchmarks. Using data from the Census Bureau’s American Community Survey, I compared employment rates for African Americans, Whites and Hispanics over two three-year periods ending in 2013 and 2010.
Some cities, like Abilene, Texas, Pasadena, Calif., Independence Mo., Reno, Nev., and Alameda Calif. have all reduced the disparities between African American and White unemployment. (Note: there weren’t enough cities with enough data on the Hispanic population to really make for a good analysis, so I focused on African American and White populations). These cities don’t all have something obvious in common, at least to me. Not all cities took the same, or even necessarily a good route toward scoring well on this metric. In Abilene’s case, the gap truly narrowed. African American unemployment dropped from nearly 20 percent to just 6 percent. White unemployment actually crept up a percent, but remained around 5 percent. Great. The story is less rosy in West Covina where the gap narrowed not because African American unemployment fell, but because white unemployment doubled, and both are now slightly over 20 percent.
Clearly this metric needs refinement.
Perhaps we should look at the ratio of African American to White unemployment. It’s a perfect 1:1 in Cary, N.C. Cities like Washington, D.C., Rochester, Minn., and San Angelo, Texas, on the other hand, have ratios that are more like 5 to 1. This might be a slightly better way to measure this component of equity. See the map above, or the interactive version here) for a larger-scale look at this metric.
The broader point is this:
The Census doesn’t give us any miracle metrics. We who evaluate cities are left to make our own frankenstats. About the closest we come for these purposes is the Gini index of inequality, which incidentally was created by a leading “fascist theorist and ideologue,” but that only addresses income not other measures of equity. The margin of errors in this data also mean that these calculations can be fraught with uncertainty or worse, misleading. I spoke to a leading demographer who cautioned against using this data at anything less than the state level.
So how on Earth are our leaders supposed to make good decisions and compare their progress to other cities? Our data just isn’t that great to help them. What we have is in peril. A report on the state of the Census by The Center for American Progress concludes, “the Census Bureau’s ability to carry out its mission objectively and efficiently has been compromised by a lack of timely support for planning resources, as well as by use of the appropriations process to advance fundamental changes to census design and scope, outside the normal legislative process.”
In an era where more and more cities and marketers are realising the importance of big data, we’re in the midst of underfunding one of our most important – and most publicly-accessible – sources of information about our changing cities and changing national demographics.
The U.S. hasn’t gone as far as Canada, which tragically killed off its long-form Census entirely. The Toronto Star recently framed the argument like this: “The long-form census was the demographic backbone for government, business, social services and academic research across the country. Data collected from the census, which enjoyed an approximate 98 percent return rate, was used to make key municipal, provincial and federal decisions.”
Better data can lead to better decisions. We need more of it, not less. The examples I gave above are just one area of potential study that is crucial to the economic and social success of cities. There are many more. If we fund the creation of the data and support those leaders who want to use it to make better decisions, we will wind up with more equitable and, therefore, more livable cities for everyone.
This feature originally appeared in Livability.