In 2022, the population of the Cleveland-Elyria metropolitan area in the United States was about 2.06 million people. This is a slight decrease from the previous year, when the population was about 2.07 million people.

Graph and download economic data for Resident Population in Cleveland-Elyria, OH (MSA) (CVLPOP) from 2000 to 2022 about Cleveland, OH, residents, population, and USA.

Historical dataset of population level and growth rate for the Cleveland metro area from 1950 to 2025.

Graph and download economic data for Resident Population in Cleveland, TN (MSA) (CLVPOP) from 2000 to 2024 about Cleveland, TN, residents, population, and USA.

Graph and download economic data for Employed Persons in Cleveland-Elyria, OH (MSA) (LASMT391746000000005) from Jan 1990 to Dec 2024 about Cleveland, OH, household survey, persons, employment, and USA.

Comprehensive demographic dataset for West Side, , OH, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Resident Population in Cleveland, TN (MSA) was 132.12000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, Resident Population in Cleveland, TN (MSA) reached a record high of 132.12000 in January of 2024 and a record low of 104.30700 in January of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for Resident Population in Cleveland, TN (MSA) - last updated from the United States Federal Reserve on November of 2025.

Disclaimer: These data are updated by the author and are not an official product of the Federal Reserve Bank of Cleveland.This project provides two sets of migration estimates for the major US metro areas. The first series measures net migration of people to and from the urban neighborhoods of the metro areas. The second series covers all neighborhoods but breaks down net migration to other regions by four region types: (1) high-cost metros, (2) affordable, large metros, (3) midsized metros, and (4) small metros and rural areas. These series were introduced in a Cleveland Fed District Data Brief entitled “Urban and Regional Migration Estimates: Will Your City Recover from the Pandemic?"The migration estimates in this project are created with data from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP). The CCP is a 5 percent random sample of the credit histories maintained by Equifax. The CCP reports the census block of residence for over 10 million individuals each quarter. Each month, Equifax receives individuals’ addresses, along with reports of debt balances and payments, from creditors (mortgage lenders, credit card issuers, student loan servicers, etc.). An algorithm maintained by Equifax considers all of the addresses reported for an individual and identifies the individual’s most likely current address. Equifax anonymizes the data before they are added to the CCP, removing names, addresses, and Social Security numbers (SSNs). In lieu of mailing addresses, the census block of the address is added to the CCP. Equifax creates a unique, anonymous identifier to enable researchers to build individuals’ panels. The panel nature of the data allows us to observe when someone has migrated and is living in a census block different from the one they lived in at the end of the preceding quarter. For more details about the CCP and its use in measuring migration, see Lee and Van der Klaauw (2010) and DeWaard, Johnson and Whitaker (2019). DefinitionsMetropolitan areaThe metropolitan areas in these data are combined statistical areas. This is the most aggregate definition of metro areas, and it combines Washington DC with Baltimore, San Jose with San Francisco, Akron with Cleveland, etc. Metro areas are combinations of counties that are tightly linked by worker commutes and other economic activity. All counties outside of metropolitan areas are tracked as parts of a rural commuting zone (CZ). CZs are also groups of counties linked by commuting, but CZ definitions cover all counties, both metropolitan and non-metropolitan. High-cost metropolitan areasHigh-cost metro areas are those where the median list price for a house was more than $200 per square foot on average between April 2017 and April 2022. These areas include San Francisco-San Jose, New York, San Diego, Los Angeles, Seattle, Boston, Miami, Sacramento, Denver, Salt Lake City, Portland, and Washington-Baltimore. Other Types of RegionsMetro areas with populations above 2 million and house price averages below $200 per square foot are categorized as affordable, large metros. Metro areas with populations between 500,000 and 2 million are categorized as mid-sized metros, regardless of house prices. All remaining counties are in the small metro and rural category.To obtain a metro area's total net migration, sum the four net migration values for the the four types of regions.Urban neighborhoodCensus tracts are designated as urban if they have a population density above 7,000 people per square mile. High density neighborhoods can support walkable retail districts and high-frequency public transportation. They are more likely to have the “street life” that people associate with living in an urban rather than a suburban area. The threshold of 7,000 people per square mile was selected because it was the average density in the largest US cities in the 1930 census. Before World War II, workplaces, shopping, schools and parks had to be accessible on foot. Tracts are also designated as urban if more than half of their housing units were built before WWII and they have a population density above 2,000 people per square mile. The lower population density threshold for the pre-war neighborhoods recognizes that many urban tracts have lost population since the 1960s. While the street grids usually remain, the area also needs su

Though most people in the US live in metropolitan areas, they’ve been choosing to live farther and farther from the center of those areas since the 1950s. While that trend continues to this day, there are some dramatic changes. The exodus from the center of town is slowing down quite a bit, for one. For another, those residents who now live in the central city are better educated than they used to be.

Graph and download economic data for Civilian Labor Force in Cleveland-Elyria, OH (MSA) (LAUMT391746000000006A) from 1990 to 2023 about Cleveland, OH, civilian, labor force, labor, household survey, and USA.

About this dataset

Context

The dataset presents the median household income across different racial categories in Cleveland. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Cleveland population by race & ethnicity, the population is predominantly Black or African American. This particular racial category constitutes the majority, accounting for 52.17% of the total residents in Cleveland. Notably, the median household income for Black or African American households is $24,555. Interestingly, despite the Black or African American population being the most populous, it is worth noting that Asian households actually reports the highest median household income, with a median income of $115,207. This reveals that, while Black or African Americans may be the most numerous in Cleveland, Asian households experience greater economic prosperity in terms of median household income.

https://i.neilsberg.com/ch/cleveland-ms-median-household-income-by-race.jpeg" alt="Cleveland median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Cleveland.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cleveland median household income by race. You can refer the same here

Graph and download economic data for Unemployed Persons in Cleveland-Elyria, OH (MSA) (LAUMT391746000000004A) from 1990 to 2023 about Cleveland, OH, household survey, persons, unemployment, and USA.

This data was created as part of a study that examined the accessibility to jobs by transit in 46 of the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's largest metropolitan areas. This data was created as part of a study that examined the accessibility to jobs by transit in 46 of the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's largest metropolitan areas. Downloads are available for individual metropolitan regions in CSV or Shapefile format. Combined ZIP files containing the data for all metropolitan regions are also available in CSV and Shapefile format, and are labeled as 'All Metropolitan Regions.'

Using a common reduced-form regional growth model framework, an expanded geographic classification of counties, additional years of data, a trio of income inequality metrics, and multiple empirical specifications, this analysis confirms and builds upon the notion that the nature of the relationship between income inequality and economic growth varies across geography (Fallah and Partridge, 2007). A positive relationship between an income Gini coefficient and per capita income growth is observed only in central metro counties with population densities greater than 915 people per square mile or in about 5 percent of all counties, whereas previous research found a positive relationship in all metropolitan counties (27 percent of counties) and a negative relationship in nonmetropolitan counties. Where inequality is in the distribution is also shown to impact this relationship. Inequality in the top and bottom halves of the income distribution has a positive relationship with growth within this 5 percent of counties. However, in most locations (the other 95 percent of the counties), inequality in the bottom half of the income distribution has either no statistical relationship with growth or a positive relationship, while inequality in the top half of the income distribution tends to have a negative relationship. These patterns are relatively stable over time but tend to not be robust to the inclusion of county fixed effects. These results provide some evidence that the mechanisms explaining how this relationship varies across places are more likely associated with agglomeration and market incentives rather than social cohesion. This analysis also highlights the need for a robust research agenda focused on further refining the growth model along with incorporating new data sources and concepts of income inequality.

Graph and download economic data for Unemployment Rate in Cleveland-Elyria, OH (MSA) (LASMT391746000000003) from Jan 1990 to Dec 2024 about Cleveland, OH, household survey, unemployment, rate, and USA.

Graph and download economic data for Employed Persons in Cleveland, TN (MSA) (LAUMT471742000000005) from Jan 1990 to Aug 2025 about Cleveland, TN, household survey, persons, employment, and USA.

Why has average income grown in some poor neighborhoods over the past 30 years and not in others? We explore that question and find that low-income neighborhoods that experienced large improvements in income over the past three decades tended to be located in large, densely populated metro areas that grew in income and population. Residential sorting—changes in population and demographics within neighborhoods—could help to explain this relationship.

Graph and download economic data for Unemployed Persons in Cleveland, TN (MSA) (LAUMT471742000000004) from Jan 1990 to Aug 2025 about Cleveland, TN, household survey, persons, unemployment, and USA.

Graph and download economic data for Unemployment Rate in Cleveland, TN (MSA) (LAUMT471742000000003A) from 1990 to 2024 about Cleveland, TN, household survey, unemployment, rate, and USA.