Inequality Matters

This is one of a series of five papers outlining the particular domains and dimensions of inequality where new research may yield a better understanding of responses to this growing issue.The aim of this paper is to describe, in very broad brushstrokes, the state of academic scholarship regarding social inequality, with an eye toward identifying important gaps. The focus is on four key interacting social domains: 1) socioeconomic (financial and human capital)2) health (including physical and psychological) 3) political (access to power and political representation)4) sociocultural (identity, cultural freedoms, and human rights

The Widening Academic Achievement Gap Between the Rich and the Poor: New Evidence and Possible Explanations

Analyzes growth in income inequality and the "income achievement gap" in test scores of children in high- and low-income families over fifty years. Examines parents' education and investment in cognitive development as factors in children's achievement

Effects of the California High School Exit Exam on Student Persistence, Achievement, and Graduation

Analyzes the impact of the exit exam requirement on student persistence, achievement, and graduation by race/ethnicity and gender and the factors behind the differential effects. Considers implications for the fairness and effectiveness of the exams

Estimating Achievement Gaps from Test Scores Reported in Ordinal "Proficiency" Categories

Test scores are commonly reported in a small number of ordered categories. Examples of such reporting include state accountability testing, Advanced Placement tests, and English proficiency tests. This paper introduces and evaluates methods for estimating achievement gaps on a familiar standard-deviation-unit metric using data from these ordered categories alone. These methods hold two practical advantages over alternative achievement gap metrics. First, they require only categorical proficiency data, which are often available where means and standard deviations are not. Second, they result in gap estimates that are invariant to score scale transformations, providing a stronger basis for achievement gap comparisons over time and across jurisdictions. We find three candidate estimation methods that recover full-distribution gap estimates well when only censored data are available

On the Measurement of “Grayness” of Cities

We consider a situation where individuals belonging to multiple groups inhabit a space that can be divided into smaller distinguishable units, a feature characterizing many cities in the world. When data on an economic attribute (in our case, income) is available, we conceptualize a phenomenon that we refer to as “Grayness” - a combination of spatial integration based upon group-identity and income. Grayness is high when cities display a high degree of spatial co-existence in terms of both identity and income. We lay down some desirable properties of a measure of Grayness and develop a simple and intuitive index that satisfies them. We provide an illustration by using data from the Indian city of Hyderabad, and selected American cities

Agent-based Simulation Models of the College Sorting Process

We explore how dynamic processes related to socioeconomic inequality operate to sort students into, and create stratification among, colleges. We use an agent-based model to simulate a stylized version of this sorting processes in order to explore how factors related to family resources might influence college application choices and college enrollment. We include two types of “agents”—students and colleges—to simulate a two-way matching process that iterates through three stages: application, admission, and enrollment. Within this model, we examine how five mechanisms linking students’ socioeconomic background to college sorting might influence socioeconomic stratification between colleges including relationships between student resources and: achievement; the quality of information used in the college selection process; the number of applications students submit; how students value college quality; and the students’ ability to enhance their apparent caliber. We find that the resources-achievement relationship explains much of the student sorting by resources but that other factors also have non-trivial influences

Integrated genomic characterization of pancreatic ductal adenocarcinoma

We performed integrated genomic, transcriptomic, and proteomic profiling of 150 pancreatic ductal adenocarcinoma (PDAC) specimens, including samples with characteristic low neoplastic cellularity. Deep whole-exome sequencing revealed recurrent somatic mutations in KRAS, TP53, CDKN2A, SMAD4, RNF43, ARID1A, TGFβR2, GNAS, RREB1, and PBRM1. KRAS wild-type tumors harbored alterations in other oncogenic drivers, including GNAS, BRAF, CTNNB1, and additional RAS pathway genes. A subset of tumors harbored multiple KRAS mutations, with some showing evidence of biallelic mutations. Protein profiling identified a favorable prognosis subset with low epithelial-mesenchymal transition and high MTOR pathway scores. Associations of non-coding RNAs with tumor-specific mRNA subtypes were also identified. Our integrated multi-platform analysis reveals a complex molecular landscape of PDAC and provides a roadmap for precision medicine