Fresh Insights on Income Mobility From 2005 to 2019

MOVS defines individual income as a person’s share of the total income in their household. This share is calculated using a common strategy called an equivalence scale.

Specifically, MOVS adjusts total household income by the square root of family size. Since the income shared in a household changes depending on the number of individuals, MOVS includes information on how family structure changes over time and by demographic group and geography, including rates of divorce and marriage and the average number of adults and children per household.  

MOVS measures income and tracks income mobility through a rich combination of demographic and tax data linked at the individual level.

This unique data mix produced statistics that address income mobility questions like: 

• How income growth differed for people who initially had low or high income.
• How common was it for people to move up or down the income distribution, and which groups were most likely to make such moves.
• How mobility patterns and income volatility varied by race/ethnicity, gender or a combination of the two?
• How mobility patterns varied by geography, such as state or region.
• How differences in the ways families grew or shrank over time interacted with income dynamics.

MOVS data users can explore the mobility measures that interest them through an interactive data tool. They can also download the underlying aggregate data to develop more complex analyses of their own.

To make these data as broadly accessible as possible, MOVS provides scripts to help users read the data in multiple common statistical packages, along with data dictionaries and use cases, so they may perform more complex analyses.

Figure 1 is an example of how users can examine one measure of mobility — average income by group — using the interactive tool. It shows that Black non-Hispanic men (bottom map) experienced considerably lower income growth than White non-Hispanic men (upper map) between 2005 and 2015, even when we focus on groups that start with similar income levels.

The maps also show that both groups experienced higher income growth in the same parts of the country, including states such as North Dakota and Wyoming. 

In another example, we leveraged the underlying data for all race-ethnicity groups and plotted average earnings over time (Figure 2).

A primary takeaway is that Asian and White non-Hispanic men experienced the most positive increases in average income over the period. In addition, while incomes stagnated for other groups during the Great Recession from 2009 to 2011, all groups began to experience positive average income growth beginning in 2012. 

Figure 3 shows the importance of accounting for household structure when comparing income across geography. The map displays the mean number of children per household in 2005, providing evidence of wide differences between states in household composition. 

MOVS considers how these differences in households affect the measurement of individual income, an important factor that is easily overlooked in discussions of income mobility. 

In addition to providing intuitive visuals of income mobility patterns, MOVS incorporates the more technical statistics researchers use to develop aggregate mobility measures, which form the foundation of assessments of well-being and help inform policy.

For example, MOVS provides transition probabilities from the base-year distribution to income quintiles in future years. Data users can use these transition probabilities to calculate common overall mobility measures, such as the transition matrix below that shows how Asian and White non-Hispanic men transition from a quintile position in the initial 2005 income distribution to quintiles of the 2006 income distribution (Table 1).

The diagonals of the two matrices (shown in boldface) indicate that Asian non-Hispanic men below the top quintile (5) were more mobile than White non-Hispanic men. That is, a higher percentage of Asian non-Hispanic men moved from their original quintile position into a higher or lower quintile, and they were more likely to move up than down. 

This initial release is just the beginning. The MOVS team plans to continue releasing statistics with new base years, beginning with 2006.

MOVS will be a valuable tool for better understanding how economic well-being shifted during and after the COVID-19 pandemic. Census data show that some communities — particularly communities of color and lower-income communities — were especially hard hit economically at the start of the pandemic. However, in recent years the racial income gap has begun to narrow as well as the wealth gap. 

MOVS will allow us to explore the details of those emerging changes as we add more data to the tool in the coming years.

Several supplementary files will also be released with statistics focusing on the extremes of the income distribution, young adults (those ages 18–25 in 2005), and those whose incomes are persistently low.

Together, these outputs will allow users to understand income mobility in all its richness — from the big picture to the finer details.