News that Cleveland’s poverty rate is the worst in the nation—and rising—has elevated the community’s concern about conditions in the city. But a closer look at the way poverty rates are calculated suggests that all the possible causes of Cleveland’s ranking have not been fully understood.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Cuyahoga County, OH (S1701ACS039035) from 2012 to 2023 about Cuyahoga County, OH; Cleveland; OH; poverty; percent; 5-year; population; and USA.

Graph and download economic data for Estimate of People Age 0-17 in Poverty in Cuyahoga County, OH (PEU18OH39035A647NCEN) from 1989 to 2023 about Cuyahoga County, OH; Cleveland; under 18 years; OH; child; poverty; persons; and USA.

Percent of Population Below the Poverty Level (5-year estimate) in Cleveland County, NC was 17.20% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Cleveland County, NC reached a record high of 20.80 in January of 2016 and a record low of 17.20 in January of 2023. Trading Economics provides the current actual value, an historical data chart and related indicators for Percent of Population Below the Poverty Level (5-year estimate) in Cleveland County, NC - last updated from the United States Federal Reserve on November of 2025.

Percent of Population Below the Poverty Level (5-year estimate) in Cleveland County, OK was 12.80% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Cleveland County, OK reached a record high of 13.30 in January of 2014 and a record low of 12.00 in January of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Percent of Population Below the Poverty Level (5-year estimate) in Cleveland County, OK - last updated from the United States Federal Reserve on November of 2025.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Cleveland County, NC (S1701ACS037045) from 2012 to 2023 about Cleveland County, NC; NC; poverty; percent; 5-year; population; and USA.

Percent of Population Below the Poverty Level (5-year estimate) in Cleveland County, AR was 15.60% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Cleveland County, AR reached a record high of 20.80 in January of 2015 and a record low of 12.40 in January of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Percent of Population Below the Poverty Level (5-year estimate) in Cleveland County, AR - last updated from the United States Federal Reserve on November of 2025.

Not only has poverty recently increased in the United States, it has also become more concentrated. This Commentary documents changes in the concentration of poverty in metropolitan areas over the last decade. The analysis shows that the concentration of poverty tends to be highest in northern cities, and that wherever overall poverty or unemployment rates went up the most over the course of the decade, the concentration of poverty tended to increase there as well.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Lake County, OH (S1701ACS039085) from 2012 to 2023 about Lake County, OH; Cleveland; OH; poverty; percent; 5-year; population; and USA.

Although the U.S. poverty rate was the same in 2000 as it was in 1970, the geographic distribution of the poor has become more concentrated. A higher concentration of poor in poor neighborhoods is a concern because it may mean the poor are exposed to fewer opportunities that affect their outcomes in life, like employment and income. We show where and how poverty has become more concentrated in the United States, and who is most likely to be affected.

U.S. Census Bureau QuickFacts statistics for Cleveland city, Ohio. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

DescriptionThis dataset includes a multimodal assessment of the Cleveland Transportation Network, conducted as part of the Cleveland Moves initiative. It evaluates need and comfort levels to improve safety and mobility on Cleveland streets.The Pedestrian Crossing Level of Stress layer was created by Toole Design and uses attributes such as number of lanes, speed limit, and presence of pedestrian islands to assess crossing stress. Data sources include Ohio and City of Cleveland street and intersection data (2024).The Bicycle Level of Traffic Stress layer, also developed by Toole Design, evaluates stress for cyclists based on lane count, speed limit, bikeway type, and other factors. This data was also generated in 2024.The ODOT Active Transportation Need layer was developed by the Ohio Department of Transportation. It incorporates factors such as vehicle access and poverty rates to determine transportation need.Update FrequencyThis dataset will be updated with additional analysis from the Cleveland Moves planning process by early 2025. After that, updates will occur annually to reflect changes aimed at improving safety and mobility.Related ApplicationsA summary of this dataset is available in the Cleveland Moves Network Assessment Dashboard.The ODOT Active Transportation Need dataset was developed by the Ohio Department of Transportation. More information is available on their website: ODOT GlossaryContactsSarah Davis, Active Transportation Senior Plannersdavis2@clevelandohio.gov

A number of studies have explored the relationship between public housing policy, poverty, and crime. This Commentary discusses the results of a recent study, which investigated the effects of closing large public housing developments on crime. To see if the demolitions—and the associated deconcentration of poverty—reduced crime or merely displaced it, researchers examined the case of Chicago. They found that closing large public housing developments and dispersing former residents throughout a wider portion of the city was associated with net reductions in violent crime, at the city level.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Bradley County, TN (S1701ACS047011) from 2012 to 2023 about Bradley County, TN; Cleveland; TN; poverty; percent; 5-year; population; and USA.

The Urban Institute undertook a comprehensive assessment of communities approaching decay to provide public officials with strategies for identifying communities in the early stages of decay and intervening effectively to prevent continued deterioration and crime. Although community decline is a dynamic spiral downward in which the physical condition of the neighborhood, adherence to laws and conventional behavioral norms, and economic resources worsen, the question of whether decay fosters or signals increasing risk of crime, or crime fosters decay (as investors and residents flee as reactions to crime), or both, is not easily answered. Using specific indicators to identify future trends, predictor models for Washington, DC, and Cleveland were prepared, based on data available for each city. The models were designed to predict whether a census tract should be identified as at risk for very high crime and were tested using logistic regression. The classification of a tract as a "very high crime" tract was based on its crime rate compared to crime rates for other tracts in the same city. To control for differences in population and to facilitate cross-tract comparisons, counts of crime incidents and other events were converted to rates per 1,000 residents. Tracts with less than 100 residents were considered nonresidential or institutional and were deleted from the analysis. Washington, DC, variables include rates for arson and drug sales or possession, percentage of lots zoned for commercial use, percentage of housing occupied by owners, scale of family poverty, presence of public housing units for 1980, 1983, and 1988, and rates for aggravated assaults, auto thefts, burglaries, homicides, rapes, and robberies for 1980, 1983, 1988, and 1990. Cleveland variables include rates for auto thefts, burglaries, homicides, rapes, robberies, drug sales or possession, and delinquency filings in juvenile court, and scale of family poverty for 1980 through 1989. Rates for aggravated assaults are provided for 1986 through 1989 and rates for arson are provided for 1983 through 1988.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Lorain County, OH (S1701ACS039093) from 2012 to 2023 about Lorain County, OH; Cleveland; OH; poverty; percent; 5-year; population; and USA.

U.S. Census Bureau QuickFacts statistics for Cleveland County, North Carolina. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

We characterize Housing Choice Voucher (HCV) use in Low-Income Housing Tax Credit (LIHTC) units with the intent to explore whether the subsidy overlap responds to needs unmet by the HCV program alone. Lacking the data to contrast HCV use in and out of LIHTC units, we turn to a comparison of HCV users in LIHTC units relative to the overall HCV population. Our analysis of 2011 tenant-level LIHTC data from Ohio and HCV data from HUD suggests place-based vouchers, which must be redeemed in an LIHTC unit, are more likely allocated to extremely low-income or special-needs households. On the other hand, HCV users who freely choose to redeem their voucher in an LIHTC unit are similar to the overall HCV population in terms of incomes and ethnicity, although they tend to be older. There is little evidence that using both programs in concert enables access to better neighborhoods for HCV users: households across both programs live in neighborhoods that tend to have poverty rates above 20 percent, with HCV-LIHTC users actually living in higher-poverty neighborhoods in most urban Ohio counties when compared to the HCV population as a whole.

Abstract (en): This study investigated changes in the geographic concentration of drug crimes in Cleveland from 1990 to 2001. The study looked at both the locations of drug incidents and where drug offenders lived in order to explore factors that bring residents from one neighborhood into other neighborhoods to engage in drug-related activities. This study was based on data collected for the 224 census tracts in Cleveland, Ohio, in the 1990 decennial Census for the years 1990 to 1997 and 1999 to 2001. Data on drug crimes for 1990 to 1997 and 1999 to 2001 were obtained from Cleveland Police Department (CPD) arrest records and used to produce counts of the number of drug offenses that occurred in each tract in each year and the number of arrestees for drug offenses who lived in each tract. Other variables include counts and rates of other crimes committed in each census tract in each year, the social characteristics and housing conditions of each census tract, and net migration for each census tract. This study investigated changes in the geographic concentration of drug crimes in Cleveland from 1990 to 2001. The main objectives of the study were: (1) to identify neighborhoods in which drug crimes were concentrated and neighborhoods where persons arrested for drug crimes resided, (2) to describe changes in concentrations of drug offending over time, and (3) to explain changes in patterns of drug offending in relation to changes in the social and physical structure of neighborhoods. The study looked at both the locations of drug incidents and where drug offenders lived in order to explore factors that bring residents from one neighborhood into other neighborhoods to engage in drug-related activities. This study used data collected for the 224 census tracts in Cleveland, Ohio, in the 1990 decennial census for the years 1990 to 1997 and 1999 to 2001. All of the data other than the United States Census data and the drug crime data are available on-line from the Center on Urban Poverty and Social Change's community database, Cleveland Area Network for Data and Organizing (CAN DO). Data on drug crimes for 1990 to 1997 and 1999 to 2001 were obtained from Cleveland Police Department (CPD) arrest records. These records provided the address of the incident and the residential address of the person arrested. These addresses were geocoded into their 1990 census tracts, with a match rate of over 95 percent, to produce counts of the number of drug trafficking and possession incidents occurring within each tract in each year and the number of arrestees for drug trafficking and possession living in each tract. (Users should note that no geocoded data are included in this dataset.) In 1998 the CPD changed the way that drug crimes were recorded, and the accuracy with which types of drug crimes were reported was significantly reduced. As a result, while data on the total number of drug incidents in census tracts were available for the entire length of the study, data on whether these incidents involved drug trafficking or possession were only available for 1990 to 1997. CPD arrest records for non-drug crimes and Cuyahoga County Juvenile Court data were used to produce count and rate data on non-drug crimes for each census tract. Data on the social characteristics and housing conditions of each census tract were gathered from the 1990 and 2000 Censuses. Migration into and out of each tract between 1990 and 2000 was estimated using 1990 and 2000 Census population counts and Ohio Department of Health vital statistics data on births and deaths from 1990 to 2000. Data on the number of schools in each census tract were obtained from the Cleveland Municipal School District. Several sources of data were used to develop measures of the physical characteristics of areas. These included the Cuyahoga County Auditor's parcel-level data (from 1990 to 2000) on land-use patterns, characteristics of dwellings, tax delinquencies, and assessed value, and the Home Mortgage Disclosure Act data (for 1992 to 2001) on home purchase loans and home improvement loans. Variables include 1990 census tract number, year, the City of Cleveland Statistical Planning Area that each census tract belonged to, counts and rates of violent crimes, robberies, robberies with firearms, burglaries committed by adults in each census tract in each year, robberies and violent crimes committed by juveniles in each census tract in each year, number of drug trafficking and possession in...