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"Disaggregate to Manage"

Our late friend and colleague Harry Hatry long insisted that data is far more valuable when it’s disaggregated. In a January 2022 paper for the Urban Institute, which we were honored to co-author with him, he maintained that performance data is particularly useful when you “compare the outcome values broken out (disaggregated) by demographic characteristics (e.g., by age group, race/ethnicity, gender, education level, and location—such as neighborhood, state, or other geographical location).”

Over the course of years, we have grown to believe that Hatry's point is not just sensible, it's absolutely critical to manage a vastly complex nation. We've grown increasingly frustrated, for example, at the broad descriptions of states as red or blue.

Texas is almost always described as a red state. But according to NBC Affiliate, KXAN, "Four . . . counties have given Democrats an average margin of victory of more than 30 percentage points: Travis, Presidio, El Paso and Webb. Of counties with more than 100,000 registered voters, Travis County, home to Austin, gives Democrats the most support, with an average margin of victory of 41.71 percentage points."

The need to politically disaggregate is only the most obvious example. The attraction of any one-size-fits all managerial solution is headed in the wrong direction (at least for some places). Dealing with the housing crisis in big cities is an entirely different matter than doing the same thing in rural America for example. As a result, states that try to come up with solutions that will work equally well in both kinds of localities are likely going to fail one or the other (or maybe even both).

Michael Jacobson, director of Performance and Strategy at the King County Office of Performance Strategy and Budgets put the matter eloquently when he pointed us some time ago to adage he had once heard: "Aggregate to communicate and disaggregate to manage."

These lessons are brought into stark relief by a report by the Census Bureau that stops us from ever thinking that the median population of states is a truly meaningful figure. The title of the report gets right to the point: "New Census Bureau Visualization Shows Broad Variations in Age."

Consider Florida. Its median age in 2021 was 42.7, somewhat higher than next-door neighbor Georgia with a median age of 37.5. But does this mean that all of Florida is a place where retirees tend to go for low taxes and sunny weather? Not really, if you visit Leon County, with a population approaching 300,000. That county, home to the state capital, appropriately named for explorer Ponce de Leon, who is said to have sought the Fountain of Youth, has a median age of 32.1.

By wild contrast, Sumter County, Florida, has a median age of 68.3, the highest of any county in the country. No surprise there. Sumter County is effectively little more than the home of The Villages, a master-planned age-restricted community with 130,000 people and virtually no children.

Florida is not unusual, as the Census Bureau points out. Median ages ranged from county to county in practically every state. South Dakota, for example, had an extremely low median age of 23.0 in tiny Todd County, primarily home to Native Americans compared to 56.3 in Custer County.

These age ranges are of consequence for a number of reasons. For example, when states distribute finances to counties based on their total populations, it might be wise for them to take the individual counties’ median ages into account. Consider the funding that went out to counties to help them deal with Covid vaccinations, particularly in the early days of the pandemic. Given the greater likelihood of hospitalizations and deaths among older people, it would have made simple sense to look at these disaggregated figures and spend more in the counties with higher median ages.

Lumping people together into any monolith is often misleading, and age is only one example. Consider the words of Laura Zhang Choi, a Warren County school-board member who testified to New Jersey legislators that the state would be well served by breaking down the component parts of New Jersey’s Asian American residents, according to an article in Verve Times.

She pointed the legislators to New York City as a powerful example, and according to the article she told the legislators that, “about 11% of city residents suffer from diabetes, and the rate among Asian Americans is roughly the same at 12%. But a deeper look showed an alarming figure for Indian Americans, nearly double the city average at 21%. That information disappeared when all Asian ethnicities were lumped together.”

The importance of disaggregation – for many other factors – was clearly spelled out in a podcast featuring Amy O’Hara, Research Professor in the Massive Data Institute at Georgetown’s McCourt School of Public Policy.

As she explained, “When we think about the way that our communities are reflected in data, the biggest regular data collection is a decennial census. Every 10 years information is pulled together about every single resident in the United States. And for that information, in order to do apportionment, you say, how many humans are there in the U.S. and that’s adequate for that purpose.

“But then, you really want to start breaking it down. What are the characteristics of these people? How many are male? How many are female? How many are old? How many are young? And you get these disaggregations of the data that were collected. The aggregate information is useful, but depending on what your policy question is, it’s not going to be useful enough.”


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