The Changing Face of Atlanta
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From 1970-2015, the percentage of the adult population with a college degree or more increased from 12 percent to 35.8 percent.Quick Fact Education
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The Atlanta MSA grew from 5 counties to 29, from 1970-2015. As of July 2015, there were 5,710,795 people – a more than 300 percent increase since 1970.Quick Fact Geography
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From 1970-2015, the nonwhite population in metro Atlanta nearly doubled, increasing from 22 percent of total population to 44 percent.Quick Fact Race/Ethnicity
General Trends
The Atlanta Metropolitan Statistical Area (MSA) has witnessed dramatic changes over the past 45 years, from 1970-2015.
In 1970, the Atlanta MSA, as defined by the federal government, consisted of five counties (Cobb, Clayton, DeKalb, Fulton and Gwinnett), spanned 1,731 square miles and had a population of 1,387,865.
By 2015, the Atlanta MSA consisted of 29 counties, encompassed 8,376 square miles and had a population of 5,710,795. The population density of the five counties increased from 802 people per square mile in 1970 to 2,048 people per square mile in 2015; the population density of the 29 counties in 2015 was 682.
(We created maps that illustrate the demographic changes over time for various categories. Scroll below to view the combined maps. Select a category or time period to see the changes. You also may download these maps in a PowerPoint presentation.)
Over the 45-year period, the current 29-county Atlanta MSA saw a major transformation.
- The percentage of the population that was nonwhite nearly doubled, increasing from 22.2 percent to 44.2 percent of the total population.
- The percentage of the adult population with less than a high school diploma decreased from 52.6 percent to 11.8 percent, while the percentage with a college degree or more increased from 12.0 percent to 35.8 percent.
- From 1970 to 2015, those 17 years of age and under fell from 34.2 percent to 25.6 percent of the total population, while those 65 years of age and over increased from 7.3 percent to 10.3 percent.
- The percentage of households classified as middle income went from 47.8 percent in 1970 to 52.7 percent in 2000, before falling to 46.2 percent in 2015. (Note that for 1970 income is available only for families and not households).
Figures 1-4 show trends in these four characteristics for the years 1970, 1980, 1990, 2000, and 2015. In a section below, see a discussion of the data used in the report.
Atlanta’s degree of segregation also has changed over the past 45 years. Indices can be constructed that measure the degree of segregation. If the white and nonwhite population were fully integrated, then all subareas (e.g., census tracts) would have the same percentage of whites (and thus the same percentage of nonwhites). The Segregation Index measures the percentage of whites (or nonwhites) that would have to move to another subarea in order for the two groups to be fully integrated.
Figure 5 reports the values of the Segregation Index for each of the five years of interest for three regions: the 10-county Atlanta Regional Commission (ARC) region, the 29-county MSA, and the state of Georgia, which we include for comparison purposes. Figure 5 shows that the level of segregation has decreased over the 45-year period in all three regions. For the ARC region, the Segregation Index decreased from 77.4 percent to 46.9 percent, and in the Atlanta MSA, the index decreased from 68.8 percent to 48.3 percent. Those are very significant decreases. Note that the value of the index depends on the number of subareas, so an index calculated using county-level data will be smaller than an index that uses census tracts. This explains some of the differences in the values of the index for the three regions.
We next produced a series of maps to illustrate the geographic patterns of changes in age, race/ethnicity, income and education across the 29-county Atlanta MSA. For each demographic category, we used data from the Census Bureau to create a map for each of the five years: 1970, 1980, 1990, 2000 and 2015. The maps were drawn at the census tract level, or at the county level for those years when there are no census tracts identified in a county. The number in parentheses next to the category labels is the number of subareas in that category. Over the period the number subareas identified in the maps increased from 278 in 1970 to 951 in 2015.
When there are no subareas within a county, it is not possible to see the variations in the category within a county. Thus, the geographic pattern will seem more similar in, say, 1970 than in 2015. In a section below, we discuss the data and how the maps were constructed.
Each of the four categories has several subcategories. For example, for race/ethnicity, we created maps for non-Hispanic white (which we refer to as whites), non-Hispanic African American (which we refer to as African Americans), Asian and Hispanic. For each subcategory, we created a map for each of the five years. For each year for each subcategory, we show the percentage of the population of an area (either the census tract or county) in that subcategory. Thus, a map will show, for example, the percentage of the census tract’s population that is white. The areas are identified by five colors, ranging from white (areas with the smallest percentage of the population in that subcategory) to dark red (areas with the largest percentage). The percentages associated with a given color are the same each year for any given subcategory, but the percentage categories differ by subcategory.
In total, there are 70 maps. The maps show vividly how the four characteristics changed over time and across the MSA. We illustrate these changes below. Select a category or time period to see the differences. View highlights by scrolling over the maps.
The data were obtained from the U.S. Census Bureau. We identified data for each census tract if available. In the earlier years, the Census Bureau did not identify census tracts for several of the counties; thus, we had to report the data for the entire county. Over the period the number of subareas identified in the maps increased from 278 in 1970 to 951 in 2015. Using county-level data rather than census tracts means that the map cannot show the geographic variations within the county.
For race/ethnicity, we selected four categories: non-Hispanic white (which we refer to as whites), non-Hispanic African Americans (which we refer to as African Americans), Hispanic, and Asian. For each subcategory and each year, we calculated the percentage of the census tract’s population (or county’s population if there were no census tracts) in that subcategory.
The Census Bureau reports educational attainment for individuals 25 years of age and over. We considered three education attainment levels: less than a high school diploma, a high school diploma or GED but no education beyond that, and at least a college degree.
For age, we considered three categories: 17 years of age and under, 18 to 64 years of age, and 65 years of age and over.
For income, we created three subcategories: low-income, middle-income and high-income. We also considered the percentage of the population living in poverty as a fourth income subcategory. The middle-income class was defined in each year by the range of income from 67 percent to 200 percent of the region’s median household income. The low-income class comprised households with income less than 67 percent of the median, and high-income households were those with income more than 200 percent of the median income.
David Sjoquist’s areas of expertise are state and local taxation and urban and regional economics. A specialist in the field of public finance, Sjoquist has an extensive interest in urban economics, especially local economic development, central city poverty, and education policy. He has published extensively on topics such as analysis of public policies, teenage employment, capital maintenance expenditures, local government fiscal conditions and the urban underclass.
Lakshmi Pandey is a senior research associate with the Fiscal Research Center and Center for State and Local Finance. He specializes in working with administrative data and also provides analytical and technical support on research projects, such as welfare to work, the Supplemental Nutrition Assistance Program and unemployment insurance for U.S. Department of Agriculture, census data analysis, geographical information systems, incorporation and cityhood studies, and many others
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