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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This visualization draws on another dataset made available through the U.S. Census Bureau’s American FactFinder data engine, Annual Estimates of the Resident Population for Selected Age Groups by Sex for the United States, States, Counties, and Puerto Rico Commonwealth and Municipos: April 1, 2010 to July 1, 2015.T The Census Bureau’s Population Division releases yearly data  population estimates by age, race, and ethnicity which update the most recent decennial census counts with the latest birth, death, and international and domestic migration data. The estimates’ methodology incorporates a variety of data sources including IRS, Medicare, and American Community Survey datasets, as well as National Center for Health Statistics birth and death data.

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Last month the Census Bureau’s Population Estimate Program released the latest estimates for states, counties, and county subdivisions, including total estimated population for Connecticut towns as of July , 2015.

Since the 2010 Census, it is estimated that 61 towns gained population, while 108 lost population; Fairfield County gained about 3.4%, while towns in Litchfield County together lost an estimated 3.3% of their population since 2010.

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This dashboard highlights another table available through the U.S. Census Bureau’s American FactFinder data portal:  Income and Apportionment of State-Administered Lottery Funds: 2014 from the 2014 State Government Finances data program.

The data – which includes total revenues of lottery systems, expenditures in lottery system administration and prizes, and total lottery revenue made available to fund state government – show a wide range of both public participation and approaches to state lottery administration among the 43 state lottery systems.

Compared with figures from the 2014 American Community Survey, Massachusetts had the highest lottery revenues from its adult population on a per-capita basis – about $900 of revenue for each adult 18 and over. Massachusetts also had the highest rate of payout to winners – returning  72% of gross lottery revenues in the form of prizes.  25.6% of Massachusetts state lottery revenues were made available to the state, compared with the national average of 34.6% – yet the state still ranked 4th in total lottery proceeds made available to finance other state functions – yielding more than $1.2 billion to the state in 2014.

Arkansas had the lowest percentage of lottery revenues returned to state government – 20.4% – and was the 12th most costly state system to run in terms of lottery administrative costs as a percentage of gross lottery revenue. West Virginia lottery system allocated the smallest percentage of gross lottery revenue on prizes – 17.1% – and returned the highest percentage of lottery  revenue back to the state – 77.8% –  to be made available for other government functions.

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According to the U.S. Census Bureau’s American Community Survey estimates, income inequality increased significantly in 142 U.S. counties between the 2008-10 and 2011-13 survey periods. While this is relatively small compared to the number of counties where there was no significant change (1,688), almost 92 million people – nearly 30% of the U.S. population at the time – resided in these counties in 2013. Eleven counties – with a combined population of 565,00 – had significantly reduced income inequality over the 2011-2013 ACS survey period, compared with 2008-2010. Click any county in the map below to see a link to the original ACS data in the American FactFinder data engine.

This visualization uses table B19083 Gini Index of Income Inequality  from the 2010 and 2013 ACS 3-Year Estimates and compares the values for each county, and their margins of error, between the survey periods. Counties with very close Gini Index values from the two surveys (where the confidence intervals overlap) are considered not to have experienced a statistically significant change in income inequality. Counties which have an upper bound of the 2008-10 confidence interval which is smaller than the lower bound of the 2011-13 confidence interval are considered to have had a statistically significant increase in income inequality income between the two survey periods. Conversely, those counties which have a lower bound of the 2008-10 confidence interval which is greater than the upper bound of the 2011-13 confidence interval are considered to have experienced a statistically significant decline in median household income.

The 3-Year Estimates data series (now discontinued) reported data for counties with populations of 20,000 or more, so counties with smaller populations are excluded from the analysis. The counties in the map below had an aggregate total population of 301 million in 2013, compared with the total U.S. population 314 million at the time. The release of the ACS 2011-2015 5-Year data set in December 2016 will allow similar analysis for all U.S. counties, including those with populations under 20,000. Data from this survey will be able to be compared to results from the 2006-2010 5-Year data.

The Gini Index represents the concentration of income in a given state or country, in a range from 0 to 1. A higher Gini index indicates greater inequality – where income is concentrated among a relatively few individuals or households; a lower Gini score represents more even income distribution. The Gini index is a commonly used economic measure, reported by organizations such as the World Bank and CIA, in its World Factbook.

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