The Brookings Institute updated their 2014 analysis of poverty to include the new Current Population Survey March Supplement poverty report: An Update On Who Is Poor in the United States.
Daniel W. Belsky writes in today’s NIH OBSSR blog about a study on the developmental and behavioral paths which connect DNA sequences with life outcomes:
We studied a cohort of 1,037 individuals all born in 1972-3 and followed-up at regular intervals through their 38th year of life: The Dunedin Study. We started at the end. We asked whether children born with a higher complement of education-associated genetic variants were better off four decades later as compared to their peers who carried fewer of these genetic variants. They were. At age 38 years, Study members who carried more of the education-associated variants had more prestigious jobs, higher incomes, better credit scores, fewer financial problems, and so on. In fact, even among Study members who completed the same level of education, those who carried more of the genetic variants we studied achieved better socioeconomic outcomes. In other words, the genetics we were studying were not the genetics of education only. Instead, these genetics predicted a broad pattern of socioeconomic success.
Nathan Yau at Flowing Data has an interactive graphic showing the growth of obesity rates by state, year (since 1985) and gender.
According to an article in yesterday’s Washington Post, the Census Bureau has announced that it is retracting the rural poverty findings in it’s recent Income and Poverty report.
The flawed estimates were based on the bureau’s Current Population Survey, one of several surveys conducted regularly by the bureau. The problem resulted from how, as the population grows and Americans move from one part of the country to another, the bureau must adjust the boundaries that define metropolitan areas. These adjustments, carried out every decade, altered the map for the Current Population Survey last year.
The changes in the boundaries moved almost 6 million people into metropolitan areas. These adjustments rendered meaningless the estimated change in rural incomes from one year to the next, according to the statement.
“The U.S. Census Bureau is removing the statistical comparisons between 2014 and 2015,” the statement read.
A post by Sunmoo Yoon in the NIH OBSSR blog looks at the potential of data mining to offer insights into predictors of physical activity in older urban adults:
Only two out of ten older adults meet the national guidelines for physical activity in the United States. Little is known about interrelationships of many socio-ecological factors to improve physical activity behavior among Hispanic older adults. As we move towards a precision medicine approach, we need innovative strategies to discover precisely tailored targets and accurate interventions. Data mining has the potential to offer such insights.
The Chronicle of Education has gathered race and ethnicity information on more than 4,600 postsecondary institutions, including undergraduate, graduate, and professional schools and presented it in a searchable and sortable table. Note that the search function is very basic: searching for “Michigan” turns up only schools which start with Michigan (e.g. Michigan State U.), and searching for “University of Michigan” gives no results since it is listed as U. of Michigan. Results can be filtered by state.
H/T Flowing Data
The NYTimes Upshot reexamines the Census finding on rural median household income in Actually, Income in Rural America is Growing, Too. Recent reports from the Census showed that while income in metropolitan areas grew 6%, income in rural areas fell by 2%. However, according to statistics buried in American FactFinder, rural income grew by 3.4%.