According to the latest American Community Survey (ACS) data released today by the Census Bureau, more than half of Americans live in counties that experienced a statistically significant increase in income inequality between the 2007-11 and 2012-16 ACS survey periods. The latest 2012-16 ACS 5-Year Estimates dataset indicates that inequality has increased significantly in almost a quarter of U.S. counties since the 2007-11 ACS 5-Year Estimates survey period. There was a statistically significant increase in inequality in 742 counties, a decrease in inequality in 86 counties, and no statistically significant change in the other 2,312 counties.

American Community Survey 5-Year Estimates datasets include demographic and economic statistics for all geographic entities, including counties with relatively small populations; as the ACS is a sample survey, estimates for these area tend to have higher corresponding margins of error. This visualization utilizes a calculation to take into account the sampling error associated with each estimate of county-level income inequality, to determine if differences in measured inequality between the two survey periods are statistically significant. Click any county in the map below to see a link to the original American Community Survey data for the county, through the American FactFinder data portal.

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The visualization below highlights Census Tracts with median household income less than sixty percent of statewide averages, according to American Community Survey data.

Data source: U.S. Census Bureau American Community Survey 5-Year Estimates, table B19013

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Last week’s release of the 2011-2015 American Community Survey 5-Year Estimates by the U.S. Census Bureau provides new demographic and socioeconomic data for all Connecticut towns, including estimates of  income, poverty, and workforce characteristics. One especially detailed table in this new dataset – made available by the American Community Survey since the program began – is DP03: Selected Economic Characteristics (see links appearing to the left of the table in American FactFinder to view data for previous years). Because the survey periods don’t overlap, the economic estimates published in the  2015 5-Year Estimates dataset can be compared with the 2010 5-Year Estimates data for evidence of change. The graphics below employ a calculation which determines whether town-level economic measures increased or decreased beyond the margin of error of the survey estimates, and illustrate these statistically significant changes between the 2006-10 and 2011-15 survey periods  for several economic indicators.

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For help with locating U.S. Census Bureau data for Connecticut, including American Community Survey data, please contact the Connecticut State Data Center.

This data looks at trends in the maximum level of education attained in Connecticut for residents over the age of 25 from 2010 to 2015. Across the state, the percentage of peoples who have achieved less than the equivalent of a high school education is much lower than those who have. Additionally, the percentage of people who have some college education but no degree or who hold an Associate’s degree is much lower than the percentage of those who hold a Bachelor’s degree. From 2010 to 2015 there is an increase in Graduate or Professional degrees earned and a subsequent decrease in the percentage of people who hold only Bachelor’s degrees. Urban areas such as Hartford, New Haven, and Bridgeport are more likely to have lower rates of degree attainment. Attainment of education beyond a high school diploma or its equivalent is less prevalent in the eastern part of the state while the southwestern part of the state has higher percentages of people who have obtained either their Bachelor’s or a Graduate degree. From 2010 to 2015, many towns saw increases in higher education attainment and decreases in the relative percentages of people who have not attained an education beyond the high school level.

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The following visualizations showcase health insurance trends across New England since 2009 for people aged 18-24. The data is provided by the U.S. Census Bureau through the American Community Survey 1-Year Estimates for 2009 – 2015. 2009 is a great starting point for any analysis concerning health insurance trends given that the Affordable Care Act did not come into effect until after March 2010. For this post, I decided to explore insurance trends for the age group 18-24, due to one of the main aspects of the Affordable Care Act being that an individual can stay on their parents health insurance until age 26.

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Some observations:

1. Massachusetts seems to be in a category of its own with enrollment above 90% for every year between 2009-2015. It is worth considering the effects and legacy of the Massachusetts health care reform of 2006.
2. Rhode Island had only 76.37% of its population aged 18-24 covered with health insurance in 2009. Enrollment would eventually increase by 15.35% between 2009 and 2015, all the way to 91.72% coverage.
3. Rhode Island went from being the New England state with the lowest health insurance enrollment in 2009, to being the state with the third highest enrollment by 2015.
4. Massachusetts and Vermont having the highest health insurance enrollments for age group 18-24 in New England interestingly corresponds with these states being considered the most ‘liberal’ states in the U.S. according to a Gallup poll. A quick comparison across the continental U.S. and Puerto Rico shows that this holds true for not just New England, but also for the entire nation as of 2015.
5. Going on a similar direction as note #3, Maine and New Hampshire are the most conservative New England states according to the same Gallup poll, and they also happen to be the ones with the lowest health insurance enrollments for this particular age group inside New England.
6. Vermont experienced a noteworthy increase in enrollment of 7.17% between 2010 and 2011.
7. How did the 2007-2010 recession impacted Health Insurance enrollment for this particular age group? Although the states of Connecticut, Massachusetts, New Hampshire and Rhode Island experienced drops in health insurance enrollment just by looking at the percentages displayed in the visualizations, none of them had a drop of more than 2%; so effectively all drops in health insurance enrollment between 2009-2010 are covered by the margin of errors for the data corresponding to each state.
8. Between 2009-2015, all of the New England States saw increases in health insurance enrollment for this particular age group.

Minor correction by the author: Observation #6 initially implied that Vermont’s increase in health insurance enrollment for age group 18-24 between 2010 and 2011 could be attributed to a decrease in the estimated population for said group between those two years. That was a data misread. Vermont actually experienced a population increase of 1.4% between 2010 and 2011 – not a decrease. Therefore, Vermont’s increase in enrollment is actually noteworthy.

Data Source:

Health Insurance Coverage Status by Sex by Age: U.S. Census Bureau, 2009-2015 American Community Survey 1-Year Estimates, Table B27001.

The following visualization is a comparison of per capita income of different races of civilians within Connecticut and then taking a look at the occupations that lead to a drop or increase of capita of each race group. This information was obtained from the American Community Survey 2006-2015.

Descriptions of each occupational group:

Management- business and financial operations occupations, management occupations.

Computer Sci- computer and mathematics occupations, architecture and engineering, life physical and social science occupations.

Education- community and social service, legal occupations, education, training and library, arts, entertainment, sports and media.

Healthcare- health diagnostics, technologists, technicians, and health practitioners.

Service- healthcare support, protective service, food preparation and serving occupations. building and grounds maintenance, and personal care.

Sales- sales, office and administrative support.

Maintenance- farming, fishing, forestry, construction and extraction, installation, maintenance and repair.

Production- production, transportation, and material moving.

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This visualization explores changes in the languages spoken at home in Connecticut counties over a five year period from 2011 to 2015.

In the last five years, much of Connecticut has seen a slow trend of decreasing English usage at home and an increase in other languages. Spanish is the most prevalent language spoken in Connecticut after English. Counties that are less populous have more limited lingual variation and have seen less growth in non-English language usage. Litchfield, Tolland, and Middlesex counties are the only counties where Spanish is not the most common non-English language spoken at home and is rivaled by the use of other Indo-European languages. These counties also have a very low percentage of people who speak a language other than English in comparison to the rest of the state. Most counties have seen a general increase in the use of Spanish at home, but other language groups have not displayed the same trends. Asian and Pacific Island languages showed a decrease from 2011 to 2013 and an increase from 2013 to 2015. Conversely, Indo-European languages saw increases from 2011 to 2013 and decreases from 2013 to 2015.

Windham county lacked language data for both 2011 and 2013 and had no data for 2015.

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This visualization converts county-level median household income from the 1980 Census to inflation-adjusted 2014 dollars, and compares these figures with the latest household income data from the 2010-2014 American Community Survey.

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The following post and data visualization is by guest blogger Lisa Bernardo, highlighting her project for Prof. Harmon’s Economics Independent Study class.

This project aimed to analyze diversity of student enrollment versus faculty members at research universities in the United States.  Through our research and analysis, we wanted to determine whether the make up of the student population in terms of race and ethnicity was well represented within that of faculty members.  This was done by calculating race and ethnicity shares of undergraduate students and faculty members from the years 2001 through 2014 using data downloaded from the IPEDS Data Center.

From our analysis, it was concluded that the shares of black and Hispanic faculty members remained significantly below these shares for students consistently over the time period of 2001 to 2014.

We also found that shares of black and Hispanic students as well as faculty members did show significant increases from 2002 to 2014, though the faculty increase lagged the student increase.  The black and Hispanic enrollment share for undergraduate students increased from 4.04 percentage points from 14.87% in 2002 to 18.91% in 2014.  Whereas, this same share for faculty members only increased 3.16 percentage points from 12.65% in 2002 to 15.81% in 2014.

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