Since around 1980, fewer Americans than before are doing better than their parents had—that is, more are experiencing downward social and economic mobility in terms of occupational status and income. A mounting body of evidence points to high and rising levels of inequality, not only in income, but also in other outcomes such as wealth, health, and life expectancy.
To address this issue, a better understanding is needed of the factors that affect mobility outcomes, such as income, wealth, education, employment, and occupation, as well as broader factors such as housing markets, family, neighborhoods, and communities. Research has shown that there are large and persistent racial and ethnic gaps in socioeconomic status, as well as in its transmission from one generation to the next. To make progress on these important issues, it is critical to know whether the existing data and research methods are adequate.
On February 14-15, 2022, the Committee on Population (CPOP) and Committee on National Statistics (CNSTAT) at the National Academies of Sciences, Engineering, and Medicine held a virtual workshop to identify key research, data needs, and priorities for future work on economic and social mobility.
Defining and measuring social mobility: Social mobility describes how individuals move up and down the resource ladder during the course of their lives (intragenerational mobility) or in comparison to their parents (intergenerational mobility). Mobility can be measured by comparing an individual’s origin and destination resource levels (absolute mobility) or ranks (relative mobility).
Representing everyone: Useful data infrastructures for studying social and economic mobility must accurately represent the experiences of all population members and must contain multiple resource measures.
To move forward in social mobility research, it is critical to develop an inte¬grated mobility model and a formal theory to support it.
Intergenerational elasticity: Economic mobility research has measured the persistence of advantage across generations (intergenerational elasticity) and is increasingly focused on establishing causal mechanisms.
To learn more, see Chapter 2 in the workshop proceedings.
Causal mediation analysis allows researchers to assess mechanisms through which treatments affect outcomes.
Traditional mobility research relies on single point measures in the parent and offspring generation. However, parents and offspring overlap in their life courses, which means that transitions, turning points, and stages when careers develop may have implications for intergenerational mobility. A linked mobility trajectory framework can facilitate understanding of how mobility across the life course and intergenerationally are associated.
Translating knowledge into policy: Understanding the social processes that give rise to mobility is necessary but not sufficient for understanding how to translate this knowledge into specific programs and policies.
Qualitative research is essential for contextualizing big data as it encourages the scientific questions rather than the available data to drive the approach. It also provides an essential quality check against large datasets.
To learn more, see Chapter 3 in the workshop proceedings.
Why place matters: While existing research establishes that place matters when it comes to economic mobility, a priority for future research is to understand why. This can be done by leveraging big data to measure additional characteristics of neighborhoods, studying the long-term impact of historical place-based policies, and evaluating recent and ongoing place-based policies, using surrogate outcomes to predict long-run impacts.
Focus on reinforcing spatial stratification and ethnography: More policy research should focus on the active interventions that divide space and reinforce spatial stratification (e.g., home mortgage interest deduc¬tion, land use regulations, occupational licensing requirements, local housing decisions). Ethnography also has to be at the center of the effort to under¬stand social and economic mobility.
Interconnectedness of rural and urban America: Rural areas are deserving of research, both on their own and as part of an interconnected system with urban areas. In order to understand mobility in urban America, it is important to understand what is happening in rural America—and vice versa.
To learn more, see Chapter 4 in the workshop proceedings,
Structural and systemic factors—such as structural racism—influence the rigidity of the economic system and thus the ability of an individual to experience upward mobility.
Research is needed to develop and refine ways of measuring structural racism as a factor in mobility.
Studying immigration and mobility is more important than ever because the “new third generation” has arrived; these grandchildren of post-1965 immigrants can provide valuable insight into the status of immigrants and how they are doing compared with their parents and grandparents.
Data equity and disaggregation are critical to both understanding and serving the Asian American population; means and medians mask diverse outcomes, reify tropes, and result in exclusion from research and policy.
To learn more, see Chapter 5 in the workshop proceedings.
Linking administrative data with survey data has the potential to drastically expand understanding of mobility in the United States.
Using educational data and neighborhood data to understand patterns of inequality can push interventions forward and provide intermediate-level outcomes to measure success.
Data on bank accounts and transactions can be used to better understand economic mobility, including how households are affected by shocks; the large sample size facilitates findings on subgroups.
Striking a balance between data access and data privacy is challenging, but essential.
To learn more, see Chapter 6 in the workshop proceedings.
Members of the planning committee identified these key takeaways from the workshop.
To learn more, see Chapter 7 in the workshop proceedings
Sponsor: Bill & Melinda Gates Foundation