Short Abstract:

We develop a data-science platform to establish a relationship between basic needs consumption, subjective well-being, and capabilities integrating approaches from social psychologists, engineers, economists, sociologists, and policymakers. Analyzing data on energy, housing, food, and mobility insecurities alongside life satisfaction, we identify correlations that persist across demographics.

Long Abstract:

From Aristotle to Amartya Sen to Dan Kahneman, thinkers throughout history agree that the pursuit of well-being is a fundamental human goal. Philosophers, psychologists, environmentalists, engineers, economists, sociologists, and policymakers all bring unique approaches to the concept of well-being. Within this diversity of frameworks, there is a pressing need to balance environmental sustainability, societal flourishing, and individual well-being. Amidst this background, a fundamental question arises in this context: Who is deprived based on existing indicators of poverty, and what basic needs are reasonable for a good life? For example, are people’s reporting of food insecurity linked to the proximity of grocery stores and more importantly, with individuals’ overall assessment of their lives? Answering such questions will shed new light on the direction of human development, guided by quantitative objective descriptors, needed for flourishing based on indicators of life satisfaction, physical health, and emotional well-being.

In this paper, we develop a novel data-science platform, fusionACS, to establish the relationship between basic needs consumption, subjective well-being, and capabilities through a holistic view of household well-being lens. We use machine-learning-based data fusion techniques to link multiple U.S. household surveys, including the American Community Survey (ACS), Gallup U.S. Daily Poll, Residential Energy Consumption Survey (RECS), National Household Transportation Survey (NHTS), American Housing Survey (AHS), and Consumer Expenditure Survey - Interview (CEI) based on matching characteristics between the surveys as well as spatial indicators. For example, these characteristics include may variables such as gender, age, household income, race, and household size while spatial variables may include heating degree days, cooling degree days, and public transportation density. To ensure the robustness of our findings, we then establish data standards to gauge the reliability of these novel metrics using both internal methods of cross-validation as well as external validation techniques of comparing metrics to unrelated datasets. This novel dataset offers indicators of cumulative burdens at the household level with unprecedented granularity into geographic and demographic disparities. Specifically, we investigate both subjective (self-assessed) and objective indicators of energy, mobility, housing, and food insecurities along with the self-reported overall life satisfaction and the feeling of being capable.

Next, we analyze these basic needs deprivations along dimensions of heterogeneity such as gender, race, income, expenditure, housing tenure, urbanicity, and climate regions using logistic regressions and latent class analysis. We find that basic needs deprivations of energy, housing, food, and mobility show a strong correlation to self-reported life satisfaction and capabilities even after controlling for confounding factors. While increasing household income and expenditure are correlated to increasing life satisfaction, significant differences in subjective well-being scores persist between the deprived and not-deprived even for the highest level of income/spending. We also show that these newly developed indicators complement existing indicators of Federal Poverty Thresholds and Supplementary Poverty Measures by revealing the extent of deprivation that is being captured by these objective metrics. These results inform the direction of human development and social policy to support a high-quality life for all by revealing the extent of deprivations being faced currently by households from a multi-dimensional perspective.