Niki vonLockette, Ph.D.
Associate Professor, Penn State University
LATEST
PROJECTS
Project | 01
Assessing the Impact of Systemic Inequality on Racial Income Inequality in Metropolitan Labor Markets
This study examines the impact of multiple domains of structural racial inequality--residential segregation, school segregation, wealth, incarceration, persistent joblessness, occupational disparities, educational disparities, policy, political ideology, discrimination--on income disparities between blacks and whites. I use a systems approach to: 1) Determine if examination of system interdependencies via a structural approach helps explain changes in economic inequality between blacks and whites since the end of the civil rights era. 2) Assess changes in the relationships among the dimensions over time, as well as persistence. 3) Examine the confluence of structural correlates of racial economic inequality across multiple spheres of interaction on change in black/white income inequality in the post-civil rights era.
A systems approach may expand our understanding of economic inequality between blacks and whites by more accurately capturing the systemic nature of racial inequality and the complex interdependence of racial structures that operate in tandem to maintain racial inequality. This project develops a unique, longitudinal dataset of both individuals and US metropolitan areas between 1960 and 2017, and employs structural equation modeling to model the interrelationships and pathways among the multiple domains of racial inequality.
Project | 02
Manual for a Brave New World: A Playbook for Breaking Down the Mechanics of Systemic Racial Inequality.
Drawing on my expertise as a researcher and policy advisor, I will develop a short, easily-digestible manual that explains the mechanics of systemic racial inequality and how it perpetuates racial disparities. By building a sophisticated understanding of its innerworkings, we can develop a more effective working plan for dismantling it. The manual helps readers understand racial inequality as a consequence of a system comprised of a set of dynamically interrelated domains. The manual not only describes in detail the major domains of systemic inequality—residential, school, and occupational segregation, wealth disparities, incarceration, and more—but most importantly, shows that their effectiveness lies in their interdependency on one another. I sorely want to impress upon changemakers that we will make greater gains when we truly grasp the limited efficacy of trying to combat systemic racism in silos. An organization that deals with incarceration will be more effective in combatting racial injustice in the justice system when it better understands residential segregation and how it generates concentrated poverty, school inequality and employment disparities. The manual employs powerful data visualization to quickly illustrate how a domain works. The manual describes ways to address these subsystems using coordinated and integrated approaches that work across the domains. Each section of the manual includes a Fix-It section which details policy prescriptions for addressing system racial inequality at three different levels of change—individual and small networks, community and organization-level, and institution-level, with specific examples of ongoing projects at each of these levels that are working and could change the game if scaled up properly.
Project | 03
Opportunity to Innovate
The shortage of STEM workers in high-tech, innovative firms has received wide attention and has generated concern from industry and policymakers. We argue that the current pool of innovative people is limited and not necessarily by lack of available talent in the US workforce, but by inequality. Fewer women and minorities innovate because they are excluded from the innovative process or the opportunity to innovate. There is less work identifying the mechanisms or socioeconomic structures that enable some groups access to opportunities to innovate and limit access for others. Broad inclusion would potentially stimulate the emergence of new ideas, innovation, and economic growth. Knowing the true potential innovation capacity of the population would identify an upper bound of innovation potential in the US workforce and consequently, the US economy.
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We will conduct analysis of survey data to examine social economic factors that comprise the innovative capacity of the respondents’ social contexts: chiefly, the resources and networks s/he has access to including the occupational status of role models and the core social networks. To hone in on why some social groups (men, whites) have access to the opportunity to innovate more than others (women, minorities), we will conduct group-level analyses, that is, outcomes and predictors measured at the group-level: group disparities in rates of entrepreneurship and employment in STEM field, predicted by group structural inequality in local labor markets.
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Finally, we will undertake economic growth predictive modeling in which we take findings and outputs from the previous two sections to model potential growth in innovation and commensurate economic growth in local and national labor markets under hypothetical scenarios in which there is greater access to opportunity to innovate. The analyses will use individual-, spatial-, and population- level characteristics as inputs, coupled with a neural network associative framework, to build predictive and descriptive models of innovation potential (or capacity) and commensurate economic growth potential (or capacity) as outputs.