Federal Reserve Economic Data

The FRED Blog has used county-level data to show where poverty is more prevalent in the US. Today, we use FRED’s recent addition of Census data to discuss the types of families more likely to experience poverty.

The FRED graph above shows the percent of families living below the poverty threshold, sorted into three categories, from lowest poverty to highest poverty: married-couple families (blue line), single-parent families with a male householder (red line), and single-parent families with a female householder (green line).

The data show stark differences in the poverty status of US families. Single-parent households can be between 3 and 6 times more likely to experience poverty than households where both parents are present. This can be explained by the potential ability of married-couple families to combine their two incomes and share childcare responsibilities, which are a handicap to steady participation in the labor market.

The graph also shows clear gender differences among single-parent households living below the poverty threshold. Female single parents are 2 times more likely to experience poverty than male single parents. This can be explained by gender gaps in labor force participation rates, occupations and income, and sharing of childcare responsibilities.

To learn more about this topic, read the US Census report “Poverty in the United States: 2022” by Emily A. Shrider and John Creamer.

How this graph was created: Search FRED for and select “Poverty Status of Families by Type of Family: Married-Couple Families With Children Under 18 Years, Below Poverty Threshold.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Poverty Status of Families by Type of Family: Families With a Male Householder, No Spouse Present, With Children Under 18 Years, Below Poverty Threshold.” Repeat the last step to add ”Poverty Status of Families by Type of Family: Families With a Female Householder, No Spouse Present, With Children Under 18 Years, Below Poverty Threshold.”

Suggested by Diego Mendez-Carbajo.

FRED includes consumer price index data from the US Bureau of Labor Statistics. FRED data tools make it easy to use these price data to calculate the dollar value of figures measured at constant prices—a process known as adjusting for inflation.

The FRED Blog frequently adjusts for inflation when describing prices, whether it’s gasoline prices, stock prices, or foreign exchange (just to name a few data series).

The FRED graph above shows two versions of monthly advance retail sales of retail trade and food services, reported by the US Census. The blue line shows the dollar figures unadjusted for inflation, and the red line shows the dollar figures adjusted for inflation with the aforementioned consumer price index (CPI). The CPI currently uses the years 1982-1984 as the reference period, so you can think of the data shown by the red line as retail sale figures measured in 1982-1984 prices.

FRED makes it easy to customize the reference period to adjust dollar figures for inflation. So, read on!

The second FRED graph above shows the same advance retail sales series as the first graph, but adds two alternatives using customized reference periods: the start of the 2001 recession (the dashed-dotted red line) and the start of the 2020 recession (the dashed red line). The steps for creating that graph are listed below.

A benefit of customizing the reference period, by choosing a more-recent date, is to facilitate the visual comparison between the inflation-adjusted and unadjusted data. And, given that the choice of reference period does not impact the calculation of the rate of growth of inflation-adjusted data, you can be confident using those units to tell the real story behind the numbers.

How the graphs were created: First graph: Search FRED for and select “Advance Real Retail and Food Services Sales.” Use the “Edit Graph” panel to select the “Add Line” tab and search for and add “Advance Retail Sales: Retail Trade and Food Services.” Second graph: Add another line for “Advance Retail Sales: Retail Trade and Food Services” and customize the data by searching for “Consumer Price Index for All Urban Consumers: All Items in U.S. City Average.” Don’t forget to click “Add.” Next, type the formula (a/b)*100 and click “Apply.” Edit the “(b)” series in Line 2 by changing the units to “Index (Scale value to 100 for chosen date)” and selecting a date of your choice. Finally, repeat these last steps with a new line, searching for “Advance Retail Sales: Retail Trade and Food Services” and selecting a different date. You  can also use the “Format” tab to play with the color and style of the lines.

Suggested by Diego Mendez-Carbajo.

The FRED Blog has used data on homeownership from the US Census to discuss regional and racial differences in the fraction of households who own a home. Today, we use data from the US Bureau of Labor Statistics to explore the same topic from a different perspective: We compare the proportion of homeowners to renters across racial groups.

The FRED graph above shows data from the Consumer Expenditure Surveys on the fraction of households who own a home and the fraction of households who rent a home. Households are classified in racial groups as either “White and All Other Races, Not Including Black or African American” (blue line) or “Black or African American” (red line).^* We calculate the proportion of homeowner households to renter households and include a custom horizontal line with a value of “1” (dashed black line) to make the analysis easier.

On average, there are at least two non-Black or African American households that own a home for every non-Black or African American household that rents a home. By contrast, the number of Black or African American households that own a home is consistently smaller than the number of Black or African American households that rent a home.

Why is there a racial gap in homeownership? There are multiple reasons. The US Department of the Treasury’s Office of Economic Policy lists past government policies, discrimination in the private mortgage market, and differences in economic well-being. On this last reason, the Board of Governors of the Federal Reserve System’s Report on the Economic Well-Being of U.S. Households in 2023 emphasizes the role of income in making homeownership accessible. The report also shows recent data on homeownership/renting status according to age and disability status.

Although there are many reasons behind the racial gap in homeownership, income gaps between population groups likely play a key role in it.

^* Data on “Asian” households, by themselves, are shown here because the BLS dataset includes them in the “White and All Other Races, Not Including Black or African American” group.

How this graph was created: Search FRED for the series “Consumer Unit Characteristics: Percent Homeowner by Race: White and All Other Races, Not Including Black or African American.” Click “Edit Graph” and use the “Edit Line” tab to customize the data by adding the data series “Consumer Unit Characteristics: Percent Renter by Race: White and All Other Races, Not Including Black or African American.” Next, type the formula a/b and click “Apply.” Use the “Add Line” tab to search for and add the homeownership data series for “Black or African American” households. Repeat the data customization process described earlier to calculate the ratio of homeowners to renters for “Black or African American” households. Last, use the “Add Line” tab to add a custom line with start and end values equal to “1.”

Suggested by Asise Bhinder and Diego Mendez-Carbajo.