Primary Submission Category: Place/Communities
Life Course Exposure to Local Racial Income Inequality and Individual Health Outcomes: Zero Sum, Universal Harm, or Countervailing Forces?
Authors: Anna Shetler,
Presenting Author: Anna Shetler*
At country, state, and metropolitan levels, income inequality predicts population health and health disparities. However, research at smaller geographical scales is limited. I contribute to the literature by testing how life course exposure to Census tract income inequality is related to health outcomes. Previous research at the neighborhood level has focused on colorblind income inequality, finding null results for Blacks. I study racial income inequality between Black and white residents – an estimate of exposure to local structural racism.
Places with high structural racism may benefit whites (“zero sum”), harm whites (“universal harm”), or have no effects among whites. Thus, for white Americans, health in racially-unequal places – places where Black incomes are substantially lower than white incomes – may be (1a) better, (1b) worse, or (1c) similar to white health in racially-equal places. Second, I compare Black and white health to assess disparities. Black health may (2a) decline slower, (2b) decline faster, or (2c) be similar to white health in racially-unequal places. Third, I stratify models by gender.
I use the Panel Study of Income Dynamics (1980-2019) to estimate group-based trajectory models. Preliminary results show that Black Americans live in more racially-equal places over time while white Americans remain in white-biased, racially-unequal neighborhoods. For IAPHS 2025, I will use the trajectory groups to predict distal health outcomes. Physical health outcomes may require chronic exposures to inequality whereas mental health outcomes may be short-term, so the dependent health variables include self-rated health, cardiovascular disease, psychological distress, and mental health diagnoses. Sensitivity analyses will assess racial income inequality at county and state levels. I will also compare findings using the Gini coefficient to evaluate differences between racially-explicit and general income inequality measures.