Report: Census Shows Poverty Distribution In Cleveland Largely Unchanged

Data from the U.S. Census Bureau’s American Community Survey shows a divide in digital access, race and household income across Cleveland’s neighborhoods. That’s according to neighborhood fact sheets released by the Center for Community Solutions.

The data is from 2019 and too early to account for COVID’s impact, said Associate Director Emily Campbell. But it helps to establish a baseline of where things were before the pandemic started, she said.

“As we start to get new information from the Census Bureau and from other places, we can see how Cleveland, its neighborhoods and its residents were doing before the pandemic, to get a sense of how things might have changed,” Campbell said.

The data shows more than 45 percent of the population in the Central, University, Kinsman, Buckeye-Woodhill and St. Clair-Superior neighborhoods are living in poverty. Neighborhoods with the biggest decrease in poverty rates from 2016 to 2019 were Ohio City, Tremont, Lee-Seville, Buckeye-Shaker Square and Mount Pleasant, according to the Center's reports. Overall, the distribution of impoverished residents throughout the city remained largely unchanged.

The fact sheets cover factors such as demographics, population levels, employment, income and health, and they show the ways in which those issues are all interconnected, Campbell said, as well as how they relate to issues like internet connection and computer access.

“The neighborhoods that have higher poverty are also the neighborhoods that have less connectivity,” Campbell said. “In order to propel progress, we’re going to have to work at several different things at once.”

In some areas of Cleveland, about 50 percent of residents were without computer or internet access when the data was collected, Campbell said. That includes Glenville, Goodrich-Kirtland Park, Buckeye-Woodhill, Fairfax, Kinsman and Hough. Those numbers may have changed during the pandemic, Campbell said, thanks to efforts to increase connectivity for remote work and, in particular, remote schooling.

Census data has a large margin of error, though, Campbell said, which makes in-depth analysis difficult.

“You have to be cautious when interpreting differences between places, or year over year,” Campbell said. “But they give us a sense of the direction that places may be heading, and give us a sense of what places might be struggling or might be doing a little bit better.”

Those working in health and social services already know most of the trends displayed in the data, Campbell said. There aren’t many surprises in terms of which neighborhoods need more aid. The goal, she said, is to provide information on where and how assistance can have the biggest impact.

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