Archive for the 'Areas (Subject)' Category
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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.
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 Census Bureau gathered data on fertility by asking a “children ever born” question from 1940 to 1990 in the decennial census. The 2000 Census did not ask a fertility question at all. With the advent of the American Community Survey, fertility was covered but with a different question. It asked if a woman had given birth to a child in the past year. This allows researchers to compute a total fertility rate. It performs reasonably well against the measure produced from the vital statistics system. And, given that geography is not readily available with the natality detail files anymore, this is a welcome solution. The main drawback to the ACS question is that the reference year will not span the calendar year that the vital statistics system is based on. Only the December respondents are referencing a January to December calendar year. See the Background section below for a further discussion of this.
However, recently, the Census Bureau noticed some anomalies in the data for selected areas and determined that some interviewers had been sloppy and asked “Have you given birth” rather than “Have you given birth in the last year.” Many more women will answer yes to the former and inflate the numerator. This is a good illustration of how much effort the Census Bureau goes to for producing accurate and robust statistics.
Addressing Data Collection Errors in the Fertility Question in the American Community Survey
Tavia Simmons | Census Bureau
In recent years, a few geographic areas in the American Community Survey (ACS) data had unusually high percentages of women reported as giving birth in the past year, quite unlike what was seen in previous years for those areas. This paper describes the issue that was discovered, and the measures taken to address it.
Indicators of Marriage and Fertility in the United States from the American Community Survey: 2000 to 2004
T. Johnson and J. Dye | Census Bureau
Slides 23 to 26 discuss and illustrate how the ACS and Vital Statistics estimates diverge from each other.
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%.
Andrew Flowers of FiveThirtyEight examines the crisis in affordable housing in Why So Many Poor Americans Don’t Get Help Paying for Housing. The problem is two-fold: affordability and the inability of government programs to keep up with need.
This is a nice tool for getting net migration reports based on IRS tax return data. Note that because these data are based on tax returns, one can also tell whether, on average, a state is losing/gaining wealthier residents. One can generate reports for counties by state or for states. The former is really tedious because one has to generate the county reports one by one.
And here’s the link to raw data for those who find widgets tedious. Note that the site has nice explanations for the methodology, including changes over time in how these files are created: SOI Tax Stats – Migration Data