Income Inequality: How Do Racial and Gender Differences Influence the Incomes in US

Liu, Ting; Yang, Enhao; Wang, Zhiliang

doi:10.4236/jhrss.2019.72014

Cited by 2 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, Race has been considered carrying a stronger effect on annual income increment than Sex. Liu et al ( 2019 ) indicated that as the number of work years increases, the gap of income increment attributable by Race was significantly larger than that by Sex.…”

Section: Discussionmentioning

confidence: 99%

“…Our study has also observed that the income gap is more associated with Race than Sex. We have proposed that the stronger effect of Race on the Income gap as compared to Sex, might be attributable to its stronger effect on recruitment procedure and annual income increment (Baert, 2018 ; Liu et al, 2019 ; McCarthy and Cheng, 2018 ; Skaggs and Bridges, 2013 ). Policymakers are encouraged to pay more attention to any inherent socio-economic obstacles to achieving greater urban sustainability, including sex and racial-driven income inequality.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

What dictates income in New York City? SHAP analysis of income estimation based on Socio-economic and Spatial Information Gaussian Processes (SSIG)

Bai

Lam

2023

Humanit Soc Sci Commun

View full text Add to dashboard Cite

Income inequality presents a key challenge to urban sustainability across the developed economies. Traditionally, accurate high granularity income data are generally obtained from field surveys. However, due to privacy considerations, field subjects are hesitant to provide accurate personal income data. A Socio-economic & Spatial-Information-GP (SSIG) model is thereby developed to estimate district-based high granularity income for New York City (NYC). As compared to the state-of-the-art Gaussian Processes (GP) income estimation model based entirely on spatial information, SSIG incorporates socio-economic domain-specific knowledge into a GP model. For SSIG to be explainable, SHapley Additive exPlanations (SHAP) analysis is undertaken to evaluate the relative contribution of various key individual socio-economic variables to district-based per-capita and median household income in NYC. Differentiating from traditional income inequality studies based predominantly on linear or log-linear regression model, SSIG presents a novel income-based model architecture, capable of modelling complex non-linear relationships. In parallel, SHAP analysis serves an effective analytical tool for identifying the key attributes to income inequality. Results have shown that SSIG surpasses other state-of-the-art baselines in estimation accuracy, as far as per-capita and median household income estimation at the Tract-level and the ZIP-level in NYC are concerned. SHAP results have indicated that having a bachelor or a postgraduate degree can accurately predict income in NYC, despite that between-district income inequality due to Sex/Race remains prevalent. SHAP has further confirmed that between-district income gap is more associated with Race than Sex. Furthermore, ablation study shows that socio-economic information is more predictive of income at the ZIP-level, relative to the spatial information. This study carries significant implications for policy-making in a developed context. To promote urban economic sustainability in NYC, policymakers can attend to the growing income disparity (income inequality) contributed by Sex and Race, while giving more higher education opportunities to residents in the lower-income districts, as the estimated per-capita income is more sensitive to the proportion of adults ≥25 holding a bachelor’s degree. Finally, interpretative SHAP analysis is useful for investigating the relative contribution of socio-economic inputs to any predicted outputs in future machine-learning-driven socio-economic analyses.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

What dictates income in New York City? SHAP analysis of income estimation based on Socio-economic and Spatial Information Gaussian Processes (SSIG)

Bai

Lam

2023

Humanit Soc Sci Commun

View full text Add to dashboard Cite

show abstract

“…In Interaction Effect result, it can be seen that women earn more than men regardless of the type of insurance they invest in. This may be because women receive more from their investment in education than men (Liu, Yang, & Wang, 2019). And the increase in years of education has led to higher earnings for women (Table 3).…”

Section: Interaction Effect Modelmentioning

confidence: 99%

The Effect of Investment in Health Services on Income across Gender: A Panel Dataset

Xue¹

2022

JHRSS

View full text Add to dashboard Cite

In this essay, we study the effect of investment in health services on income across gender. People generally do not consider factors like insurance when researching income, instead, they consider health status or other factors. In many low and middle-income countries, self-pay costs have dominated health care financing. This direct payment approach is inefficient and inequitable. Many families will face financial disaster or bankruptcy because they cannot afford to pay for medical care. About 44 million families in the world are facing catastrophic expenses, and about 2500 families are struggling because of direct medical payments. This means that it is important to access health care with quality assurance, which keeps people from being financially at risk due to costly medical services. This essay will examine the influence of investment in wellness insurance on income, in addition, impact differs between genders.

show abstract

Income Inequality: How Do Racial and Gender Differences Influence the Incomes in US

Cited by 2 publications

References 15 publications

What dictates income in New York City? SHAP analysis of income estimation based on Socio-economic and Spatial Information Gaussian Processes (SSIG)

What dictates income in New York City? SHAP analysis of income estimation based on Socio-economic and Spatial Information Gaussian Processes (SSIG)

The Effect of Investment in Health Services on Income across Gender: A Panel Dataset

Contact Info

Product

Resources

About