This paper uses March CPS data to decompose the Gini coefficient by source of income. The sources of income, divided by labor income, capital income, and public transfer income, include earnings; interest, dividends, and net rentals; public assistance and welfare; retirement funds; self-employment; farm or non incorporated self-employment; nonfarm self-employment; Social Security or railroad retirement; supplemental security; wages and salaries; and unearned sources. The decomposition yields the share of a source in total income, the source Gini corresponding to the distribution of income from a source, the Gini correlation of income from a source with the distribution of total income, and the impact of a marginal change in a source on overall income inequality. Labor income had the largest negative impact on income inequality (resulting from wages and salaries mostly), while capital income did worsen it but on a much smaller scale. Public transfers that favor bottom income groups helped to alleviate income inequality for both individuals and households.

Countries such as Haiti—where corruption, violence, and extreme poverty hinder economic growth and recovery— require multifaceted, interdisciplinary solutions. The United States, along with much of the western world, has a flawed understanding of foreign aid. Though current humanitarian efforts have effectively improved the lives of millions of Haitians, they fail to address the systemic roots of Haiti’s issues. Likewise, some efforts have been counterproductive or even harmful. If the US wishes to assist Haiti (and similar developing countries for that matter) in its journey to restoring the rule of law, reducing corruption, and empowering its citizens, it must collaborate with Haitian leaders and prioritize socioeconomic policy programs.

Using a dataset of ASU students from the 2016-2017 cohort, we interact gender and parent education level to observe gaps in academic achievement. We see a statistically insignificant achievement gap for males across parent education level, but a statistically significant achievement gap for females across parent education level. We also observe dropout gaps among these interaction groups. We see the widest dropout gap being between males across parent education level, with the smallest dropout gap being between females across parent education level. So with males we see an insignificant achievement gap but the widest dropout gap across parent education level, and with females we see a significant achievement gap but the smallest dropout gap across parent education level. What is driving these gaps and causing more similarly performing students to drop out at wider rates? At the aggregate level, we see larger gaps in grade- associated dropout probability across parent education level for males which may be able to explain the larger difference in overall proportions of dropouts between males. However, when predicting dropout probability of the semester with the most first generation and non-first generation dropouts, we see that females have the largest differences across parent education level in grade-associated dropout probability. This suggests that our model may be best suited in using college achievement data to predict overall dropout probabilities, not next-semester dropout probabilities using current semester data. Our findings also suggest that first generation students’ dropout probability is more sensitive to the grades they receive than non-first generation students.