PERSPECTIVE: Life in the Slow Lane? Drive Through Data

by Patrick Flaherty The Connecticut State Data Center at the University of Connecticut recently released population projections for Connecticut and its towns through 2040¹. The projections suggest a slowing of population growth but do not show an exodus of young people from Connecticut. Declines in the younger population groups are driven by a low birth rate while migration out of state is concentrated in older age groups.

Nevertheless, the number of senior citizens will increase while the school-aged population will decline. Growth with be uneven across cities and towns with some (particularly the largest cities) gaining significant population while others decline. Some of the smallest towns are projected to reverse part of the strong growth they have experienced in recent decades.

Statewide Overview: Connecticut's population increased by over 255,000 from 1970 to 1990 and added an additional 300,000 from 1990 to 2015, a 9.3% increase (Chart 1). Population growth is projected to grow just 1.7% in the 25 years from 2015 to 2040, less than 20% of the growth rate of the previous 25 years.

Focusing on the most recent 15 year period and comparing it to the next shows a similar pattern. Population grew 5.5% from 2000 to 2015 but is projected to grow just 1.1% from 2015 to 2030. While these projections are not predictions of what will happen (unforeseen events such as changes in the economy could affect these projections), they are carefully calculated projections based on fertility rates, survival rates, domestic migration, international migration, and college migration.

Age profile: The age profile of Connecticut’s population will change during the projections period. As shown in Chart 2, compared to 2015, in 2040 Connecticut is projected to have more children under age 10, people aged 25 to 44, and age 70 and over. On the other hand, there will be fewer aged 10 to 24 and 45 to 69.

Focus on 2015-2030: While the longer-term trends are of interest, many planning horizons are of shorter duration². The rest of this article will compare the 15 years from 2000 to 2015 with the projections for 2015 to 2030. The age distribution of the population changed from 2000 to 2015 as the largest cohort aged into its 50s and beyond.

There will be more changes by 2030 (Chart 3) as the number of school and college-aged (age 5 to 24) is expected to decline and the number of those mid-twenties to mid-forties is projected to increase as the “millennial” generation ages. The number of people in their mid-forties through late-fifties will decline as the last of the baby-boomers moves past age 60. Chart 4 compares the 2015 and projected 2030 populations but also includes an “Aged 2015” population – that is, a representation of what the 2030 population would look like if everyone in Connecticut in 2015 were still here in 2030 and no one died or moved in or out.

Compared to the “Aged 2015” population, the 2030 projected population shows more people from age 40 to 54, but fewer people aged 55 and above. While some of this is due to natural decrease (death) the majority of the decline is due to migration to other states. For example, in 2015 the largest five-year age cohort were those aged 50 to 54. By 2030 there are projected to be more than 90,000 fewer people aged 65 to 69 than there were people aged 50 to 54 in 2015. Three-quarters of this decline is due to domestic net migration (people leaving Connecticut for other states).

Statewide Overview: In addition to statewide projections, the Connecticut State Data Center provides population projections by age for every town in Connecticut.

From 1970 to 2000, Connecticut largest cities lost population. Hartford had the largest decline (down 36,439), but Bridgeport (down 17,013), New Haven (down 14,081) and New Britain (down 11,903) all lost significant population. On the other hand, Danbury and suburban towns such as New Milford, Glastonbury, Shelton, and Southbury all gained more than 10,000 residents each with other suburban towns such as Cheshire, Guilford, Farmington, South Windsor and Southington not far behind. Since 2000 some of this trend has reversed.

From 2000 to 2015 New Haven gained the most population of any city or town in Connecticut (+8,245) followed by Danbury, Stamford, Norwich, and Bridgeport (+6,313). Hartford gained more than 3,000 residents and New Britain more than 2,000. Towns that lost the most population from 2000 to 2015 were Branford, Enfield and Greenwich.

When considering the towns that are projected to lose population, the Connecticut State Data Center (CSDC) emphasizes that the projections are for resident population. As noted on the CSDC website, “Resident population is defined as those persons who usually reside within a town in the state of Connecticut (where they live and sleep majority of the time). Individuals who reside in another state but either own property or work remotely in a town within the state of Connecticut are not included in these population projections.”

Looking ahead through 2030, towns expected to gain the most population are New Haven, West Haven, Manchester, Bridgeport, Norwich, and Danbury. Greenwich, Westport, Monroe, New Fairfield and Wilton will have the largest losses.

The five largest cities in 1970 -- Hartford, Bridgeport, New Haven, Stamford and Waterbury -- had 60,000 fewer residents by 2000, but they have been increasing since and are projected to top their 1970 population by 2030. On the other hand, the 10 smallest towns in 1970 gained nearly 60% by 2015 but are projected to decline through 2040.

School-Aged population: Connecticut’s population aged 5 to 19 fell by just over 1,000 from 2000 to 2015 and is projected to decline nearly 40,000 by 2030. However, some towns will see an expanding school-aged population with three towns (Manchester, Stamford, and West Haven) increasing by more than 2,000 school-aged children each³.

While the upper end of the 5 to 19 age group may include those no longer in school, for towns losing school-aged population the largest declines are all in the age 10 to 14 cohort. Similarly, towns gaining school-aged population, the largest increases are in the age 10 to 14 group. As noted, these are population projections, not projections of school enrollment. Nevertheless, these projections suggest there will be towns with significant increases in school-aged population even as the statewide number of people of school-age will be declining.

Senior population: Connecticut is projected to see an increase of more than 84,000 in the population aged 70 and over from 2015 to 2030. Nearly every town will see a population increase for this age group. For example, as shown in Chart 6, Oxford, Newtown, Wallingford, and Southington are projected to see the largest increases in the population aged 70 and above.

The enormous increase in Oxford is a good illustration of the difference between a projection and a forecast and shows the limitations of the projections. Oxford has seen a significant number of seniors moving into town over recent decades.

The models used to create the projections assume this trend will continue. A forecast (which tried to predict exactly how many seniors would be living in Oxford in 2030) would need to consider other factors such as the availability of housing for seniors and not just past trends. Nevertheless, the projections are a useful indication of where things are headed, even though other factors – from economic events to policy changes – will affect the course of population growth in Connecticut.

Implications: As the millennial generation ages into its 40s, Connecticut may have an opportunity to attract even more of this large generation than the projections suggest. The projections may also understate the aging of the population – the 85+ age group is the most difficult to project and the groups just under that may not leave Connecticut at the pace suggested by the projections. On the other hand, the declines in the school-aged population have already begun and are likely to continue even as some towns and school districts are facing an influx of new students.

___________________

Patrick Flaherty is Assistant Director of Research for the Connecticut Department of Labor.  This article first appeared in the December 2017 issue of The Connecticut Economic Digest, published by the Department. 

1 Details about the projections including on-line data visualizations are available at http://ctsdc.uconn.edu/. Questions about the methodology for producing the projections should be directed to the Connecticut State Data Center through the above-referenced website.

2 For example, the Department of Labor’s long term industry and occupational projections look out 10 years.

Climate Change, Children and Pollutants: Recipe for Health Concerns

The environmental damage caused by continuing to burn fossil fuels affects children most, with one study indicating that an estimated that about 88 percent of the disease from climate change afflicts children. In an article this month in the web-based science publication Massive, Renee Salas, an academic emergency medicine physician at Massachusetts General Hospital and Harvard University Medical School, says that while studies on climate change are still emerging, there has been enough research to result in a broad scientific agreement that climate change is negatively affecting children’s health.

The article points out that Frederica P. Perera, a professor of environmental health sciences and director of the Columbia Center for Children’s Environmental Health, recently released a review article “showing yet again how air pollution and climate change interact to multiply the negative health effects children face.”  The combination of air pollutants and warmer temperatures creates a perfect storm where chemicals emitted into the atmosphere interact to multiply the effects that each would have alone, the article states.

“People of all ages are exposed to this myriad of air pollutants in the changing climate, but children are more at risk of a wide spectrum of negative health effects because their developing bodies can suffer permanent damage from interference with their growth, Salas explains.

Investigators at the Yale Center for Perinatal, Pediatric and Environmental Epidemiology (CPPEE) at the Yale School of Public Health are engaged in a number of population-based studies in the U.S. and China intended to give us a better understanding of the health risks associated with exposure to relatively low and high levels of air pollution in childhood and during pregnancy.

The Center’s website points out that environmental factors are estimated to account for 24 percent of global diseases (WHO – Preventing Disease through Healthy Environments). In terms of the environmental contribution to disease, respiratory infections are ranked second, perinatal conditions seventh, and asthma fifteenth.  Air pollution is a major environmental risk factor in all three diseases.

Asthma is a major chronic disease in the US, accounting for more than two million emergency room visits and $14 billion in health care costs and lost productivity per year, the website indicates. Asthma is the most common chronic illness of childhood, accounting for more absenteeism (14 million missed school days per year) than any other chronic disease.  Absenteeism impacts academic performance, participation in extracurricular activities, and peer acceptance.

The Yale School of Public Health also points out that “underserved populations are especially affected by asthma.” In Connecticut, for example, asthma prevalence of 9.9 percent is among the highest in the U.S., they report. The rate among children enrolled in Connecticut’s HUSKY program (health insurance program for uninsured children) is 19.5 %. Increases in asthma and allergy are likely due to a combination of factors--genetic, environmental, socioeconomic, lack of access to care, and differential treatment.

The Massive article goes on explain that the potential harm starts early.  Once a child is born, the brain, lungs, and immune system aren’t fully formed until the age of six, the article states. “Even their air and food exposure in proportion to their size is much higher than adults – the amount they eat in relation to their body weight is three to four times greater than that of adults.”

She goes on to state the “Children also have an increased risk for being developmentally delayed, having lower intelligence scores, and less of a certain part of the brain called white matter, the stuff that helps you walk and talk. Their mental health is also at risk as children exposed to air pollution have higher rates of anxiety, depression, and difficulty paying attention.”

Salas notes that in addition to caring for patients who have negative health impacts from climate change, she uses her masters in Clinical Research and masters in Public Health in Environmental Health for research, education, and advocacy in this field. Says Salas, “I believe that climate change is the biggest public health issue facing our globe and am dedicating my career to making any positive difference I can.”

Connecticut, Massachusetts Economies on Divergent Paths

Connecticut and Massachusetts share a border but diverge dramatically in economic standing.  The stark contrast was evident this week in local updates provided by the Boston Globe and Connecticut Business and Industry Association newsfeeds. First, Connecticut:

Connecticut lost 3,500 jobs in November, extending a five-month slide that now marks a crisis point for the state's struggling economy.  The state has lost 15,300 jobs since reaching a post-recession employment high in June—a trend that stands in stark contrast to what's happening in the region and the country.

Connecticut has lost 15,300 jobs since hitting a post-recession employment high in June.

CBIA economist Pete Gioia noted that after an encouraging start to 2017, Connecticut's year-over-year job growth is now flat.

The New England states average 1.2% growth over the last 12 months, while U.S. growth is at 1.4%.

"You can't deny the fact that we now have a full-blown crisis in jobs," Gioia said.  "It's difficult to define the glass as half full when we see continued job losses like this."

Next, Massachusetts:

The Massachusetts unemployment rate dropped to 3.6 percent in November, from 3.7 percent in October, the fourth consecutive monthly decline – the Executive Office of Labor and Workforce Development reported.  The state jobless rate remained one-half percentage point below the national average of 4.1 percent, according to the Massachusetts Department of Unemployment Assistance.

An estimated 6,700 jobs were added to payrolls statewide.  In the private sector, most of the gains occurred in areas that included leisure and hospitality education and health services, construction and manufacturing.  The state labor force dropped by 8,200 from October and is now at more than 3.6 million.

The U.S. Bureau of Labor Statistics estimates that Massachusetts has added 65,200 jobs since last November.

Where Are America’s Big Spenders? Connecticut Ranks Number 6

Consumer spending is the engine that powers the American economy, accounting for about 70 percent of all activity. When the U.S. Bureau of Economic Analysis— part of the U.S. Department of Commerce—published new numbers in October tabulating personal consumption, the website howmuch.net reviewed the data and published the state-by-state breakdown. The data includes areas such as housing and utilities, health care expenses, and eating at restaurants. Washington D.C. topped the personal consumption per capita list, and Connecticut reached the top ten, landing at number six.  The data, the website suggests, “reveals an interesting snapshot about the economy.”  The top ten:

1. Washington, DC: $56,843
2. Massachusetts: $51,981
3. Alaska: $49,547
4. New Jersey: $48,972
5. New Hampshire: $48,810
6. Connecticut: $48,497
7. North Dakota: $48,225
8. Vermont: $47,648
9. New York: $46,906
10. Hawaii: $45,123

The map of the data illustrates a number of trends including that the Northeast has a cluster of heavy consumer spending states.  Six of the top ten most expensive places are in the Northeast, including three of the New England states, led by Massachusetts.

There is also a collection of lower consumer spending states across the Deep South to the Southwest, stretching all the way from North Carolina ($33,779) to Nevada ($36,177) and even up to Oregon ($39,742). At the bottom of the list is Mississippi, “where it costs only $30,200 to pay for life’s most common expenses,” the website points out.

PERSPECTIVE: Is Algorithmic Transparency the Next Regulatory Frontier in Data Privacy?

by William J. Roberts, Catherine F. Intravia and Benjamin FrazziniKendrick  The U.S. House of Representatives Energy and Commerce subcommittee on Digital Commerce and Consumer Protection held a hearing last month on the use of computer algorithms and their impact on consumers.[1]  This was the latest in a series of recent efforts by a variety of organizations to explore and understand the ways in which computer algorithms are driving businesses’ and public agencies’ decision-making, and shaping the digital content we see online.[2]

In its simplest form, an algorithm is a mathematical formula, a series of steps for performing mathematical equations. The witness testimony and questions from the members of the Subcommittee highlighted a number of issues that businesses and government regulators are facing.

Bias and Discrimination

A variety of businesses use algorithms to make decisions, such as social media platforms determining what content to show users, and credit card companies deciding what interest rates to charge consumers. However, the algorithms may treat otherwise similarly-situated consumers differently based upon irrelevant or inappropriate criteria.[3] Examples of bias in these algorithms abound.

For example, research shows that credit card algorithms drive interest rates up for individuals who have entered marriage counseling. Advertisement algorithms have shown job advertisements in engineering to men more frequently than women.

Exploitation of Consumer Data – Hidden Databases and Machine Learning

One way in which businesses and other entities can exploit consumer information is by creating databases of consumers who exhibit certain online behaviors. For example, they can identify users who search for terms such as “sick” or “crying” as possibly being depressed and drive medication ads to them. Companies have been able to develop databases of impulse buyers or people susceptible to “vulnerability-based marketing” based on their online behavior.[5]

Further, the past few years have seen a huge growth in the use of “machine learning” algorithms.[6] The cutting edge of machine learning is the use of artificial neural networks, which are powering emerging technologies like self-driving cars and translation software. These algorithms, once set up, can function automatically. To work properly, however, they depend on the input of massive amounts of data, typically mined from consumers to “train” the algorithms.[7]

These algorithms allow companies to “draw predictions and inferences about our personal lives” from consumer data far beyond the face value of such data.[8] For example, a machine learning algorithm successfully identified the romantic partners of 55% of a group of social media users.[9] Others have successfully identified consumers’ political beliefs using data on their social media, search history, and online shopping activity.[10]  In other words, online users supply the data that allows machine learning algorithms to function, and businesses can use those same algorithms to gain disturbingly accurate insights into individuals’ private lives and drive content to users “to generate (or incite) certain emotional responses.”[11] Additionally, companies like Amazon use machine learning algorithms “to push customers to higher-priced products that come from preferred partners.”[12]

Concerns in Education

In the education context, the use of algorithms to drive decision-making about students raises concerns.[13] How the algorithms will affect and drive student learning is an open question. For example, will algorithms used to identify struggling pre-med students be used to develop interventions to assist those students, or used as a tool to divert students into other programs so that educational institutions can enhance statistical averages of applicants who are accepted to medical school?

Additionally, how will a teacher’s perception of a student’s ability to succeed be affected by algorithms that can identify students as being “at-risk” before the student even sets foot in class?[14]  The bias in algorithms could also affect the ability of students to access a wide variety of learning material. For example, university librarians have noted that algorithms they use to assist students with research suffer from inherent bias where searches for topics such as the LGBTQ community and Islam return results about mental illness.[15]

Transparency is also at issue. Should students and families be aware that educational institutions are basing decisions about students’ education and academic futures on algorithmic predictions? And, if students have a right to know about the use of algorithms, should they also be privy to how the specific institution’s algorithmic models work?

Finally, concern has grown over the extent to which algorithms, owned and operated by for-profit entities, may drive educational decisions better left to actual teachers.[16] Presumably, teachers are making decisions based on the students’ best interests, where algorithms owned by corporations may be making decisions to enhance the company profit. 

Future Issues for Consideration

Regulation in this area may be forthcoming. Already, the European Union’s General Data Protection Regulation (GDPR), for example, gives EU residents the ability to challenge decisions made by algorithms, such as a decision by an institution as to whether to deny a credit application.[17] New York City is considering a measure to require public agencies to publish the algorithms they use to allocate public resources, such as determining how many police officers should be stationed in each of the City’s departments.[18]

In the meantime, educational institutions in particular should carefully consider issues such as:

• Are companies using software to collect student data and build databases of their information?
• Which educational software or mobile applications in use by an institution are using machine learning algorithms to decide which content to show students?
• Should institutions obtain assurances from software vendors that their applications will not discriminate against students based on students’ inclusion in a protected class, such as race or gender?
• How will the educational institution address a bias or discrimination claim based on the use of a piece of educational software or mobile application?
• Is technology usurping or improperly influencing decision-making functions better left to teachers or other staff?

While no regulatory framework currently exists, educational institutions may find they are best able to proactively address algorithmic transparency while negotiating contracts for the use of educational technology.

In negotiating contracts with educational technology vendors, for example, education institutions may want to determine what algorithms the technology is using and whether student data the vendor is gathering from students will be used to train other machine learning models. Further, educational institutions may want to consider issues of bias in the algorithms and negotiate protections against future discrimination lawsuits if the algorithms consistently treat similarly situated students differently.

Ultimately, educational institutions will need to evaluate each piece of educational technology to understand how its built-in algorithms are influencing the data it collects and the information it presents to users.

_________________________________

William Roberts is a partner in Shipman & Goodwin LLP’s Health Law Practice Group and is the Chair of the firm’s Privacy and Data Protection team.  Catherine Intravia focuses her practice at the firm on intellectual property, technology and information governance matters. Benjamin FrazziniKendrick is an associate in the firm’s School Law Practice Group, providing legal advice to public schools and other institutions in civil litigation, special education, and civil rights compliance.

Notes

[1] Algorithms: How Companies’ Decisions About Data and Content Impact Consumers: Hearing Before the H. Committee on Energy and Commerce, Subcommittee on Commc’n and Tech. and Subcommittee on Digital Commerce and Consumer Prot., 115th Cong. (2017) (hereinafter Algorithm Hearing), video and written testimony available at https://energycommerce.house.gov/hearings/algorithms-companies-decisions-data-content-impact-consumers/

[2] INT 1696-2017, 2017 Leg. (N.Y.C. Council 2017), available at http://legistar.council.nyc.gov/LegislationDetail.aspx?ID=3137815&GUID=437A6A6D-62E1-47E2-9C42-461253F9C6D0; see also Dan Rosenblum, The Fight to Make New York City’s Complex Algorithmic Math Public, City and State New York (Nov. 27, 2017), http://cityandstateny.com/articles/politics/new-york-city/making-new-york-city-algorithms-public.html#.WiKktbQ-ccg.

[3] Algorithm Hearing, supra note 1, written statement of Dr. Catherine Tucker, Sloane Distinguished Professor of Management Science and Professor of Marketing, MIT Sloane School of Management at 3-4, available at http://docs.house.gov/meetings/IF/IF17/20171129/106659/HHRG-115-IF17-Wstate-TuckerC-20171129.pdf.

[5] Algorithm Hearing, supra note 1, written statement of Frank Pasquale, Professor of Law, University of Maryland at 10 (hereinafter Statement of Pasquale) (citing Latanya Sweeney, “Discrimination in Online Ad Delivery,” Communications of the ACM 56 (2013): 44, abstract available at https://cacm.acm.org/magazines/2013/5/163753-discrimination-in-online-ad-delivery/abstract).

[6] See generally Bernard Marr, A Short History of Machine Learning — Every Manager Should Read, Forbes (Feb. 19, 2016, 2:31 am), https://www.forbes.com/sites/bernardmarr/2016/02/19/a-short-history-of-machine-learning-every-manager-should-read/2/#6ed1622d6b1b; Erick Brynjolfsson and Andrew McAfee, What’s Driving the Machine Learning Explosion, Harvard Business Review (July. 18, 2017), https://hbr.org/2017/07/whats-driving-the-machine-learning-explosion.

[7] Algorithm Hearing, written statement of Michael Kearns Professor and National Center Chair, Department of Computer and Information Science, University of Pennsylvania at 1-2 (hereinafter Statement of Kearns), available at http://docs.house.gov/meetings/IF/IF17/20171129/106659/HHRG-115-IF17-Wstate-KearnsM-20171129.pdf

[8] Id. at 1.

[9] Id.

[10] Id. at 1-2

[11] Statement of Kearns, supra note 7, at 3-4.

[12] Statement of Pasquale, supra note 4, at 16.

[13] Learning From Algorithms: Who Controls AI in Higher Ed, and Why it Matters, EdSurge On Air, transcript and audio download available at https://www.edsurge.com/news/2017-11-14-learning-from-algorithms-who-controls-ai-in-higher-ed-and-why-it-matters-part-2.

[14] Statement of Pasquale, supra note 4, at 15.

[15] Id. at 16 (citing Matthew Reidsma, Algorithmic Bias in Library Discovery Systems, Matthew.Reidsrow.com (Mar. 11, 2016), https://matthew.reidsrow.com/articles/173).

[16]  Statement of Pasaqule, supra note 4, at 16 (citing Elana Zeide, The Structural Consequences of Big Data-Driven Education, 5 Big Data 164-172 (2017), abstract available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2991794)

[17]  Is Your Institution Ready for GDPR?

[18] Dan Rosenblum, The Fight to Make New York City’s Complex Algorithmic Math Public, City and State New York (Nov. 27, 2017).

New England Colleges Prepare Report on Employability of Students; Draft Recommendations Outlined

December 22 is the deadline for those seeking to comment on the draft report and recommendations of the Commission on Higher Education & Employability, established earlier this year by the New England Board of Higher Education (NEBHE).  The Commission, which includes nine representatives of institutions and organizations in Connecticut, released its preliminary findings at a day-long Summit in Boston. “Despite the region’s strength in postsecondary institutions, employers remain concerned about a lack of qualified, skilled workers, particularly in technology-intensive and growth-oriented industries,” the draft report notes. “The Commission has proposed a draft action agenda, policy recommendations, strategies and next steps to align institutions, policymakers and industry behind increasing the career readiness of graduates of New England colleges and universities—and facilitate their transitions to work and sustained contributions to the well-being and competitiveness of the region.”

In addition to five strategic priorities,  the draft report includes specific recommendations are being considered in five areas:  Labor Market Data & Intelligence; Planning, Advising & Career Services; Higher Education-Industry Partnerships; Work-Integrated Learning; Digital Skills; and Emerging Credentials.

Among the recommendations being considered are a call for higher education institutions to incorporate employability into their strategic plans/priorities; determine their effectiveness in embedding and measuring employability across the institution; and develop a regional partnership for shared purchasing and contracting of labor market data, information and intelligence services.

The proposed recommendations also call on the New England states to “collaborate to launch multistate, industry-specific partnerships beginning with three of the top growth-oriented sectors, including: healthcare, life and biosciences and financial services.” It further urges the states to explore “implementing policies (public and institutional) that incentivize businesses (through tax credits or other means) to expand paid internships.”  The draft report also calls for the establishment of a New England Planning, Advising and Career Service Network.

The draft report calls on the states to “confront notable college-attainment gaps and the related personal and societal costs,” and “consider specific employability strategies to target and benefit students who are at risk of not completing postsecondary credentials, including underrepresented populations.”

Eastern Connecticut State University President Elsa Núñez led a session at the Summit about the Commission's “Equity Imperative.” Officials indicate that Commission's workforce vision serves all New Englanders ... “as a matter of social justice, but also as a matter of sound economics in the slow-growing region.”  Núñez highlighted her internship work with students who may not have cars or other resources to capitalize on off-campus work-integrated learning.

In addition to Núñez, the nine members of the Commission from Connecticut are:

• Andrea Comer, Vice President, Workforce Strategies, Connecticut Business & Industry Association Education and Workforce Partnership
• Freddy Cruz, Student, Eastern Connecticut State University
• Maura Dunn, Vice President of Human Resources & Administration, General Dynamics Electric Boat
• Mae Flexer, State Senator
• Tyler Mack, Student Government Association President, Eastern Connecticut State University
• Mark Ojakian, President, Connecticut State Colleges & Universities
• Jen Widness, President, Connecticut Conference of Independent Colleges
• Jeffrey Wihbey, Interim Superintendent, Connecticut Technical High School System

The commission also includes six members from Vermont, seven members from New Hampshire and Maine, 11 from Massachusetts, 12 from Rhode Island, as well as two regional members and six representatives of NEBHE. The Commission's Chair is Rhode Island Governor Gina Raimondo.  The proposed recommendations, developed during the past six months, have broad implications, according to officials, “critical to building a foundation for moving forward the Commission's efforts toward strengthening the employability of New England's graduates.”

At Eastern Connecticut State University—which is about 30% students of color—lower-income, minority and first-generation students often had no cars, so had difficulty traveling off campus to internships. White students got most of the internships, President Elsa Núñez told the NEHBE Journal earlier this year.

The Journal reported that Eastern’s Work Hub eliminates that need, allowing students to develop practical skills doing real-time work assignments without having to travel off campus, and providing the insurance company Cigna with a computer network and facility where its staff could provide on-site guidance and support to Eastern student interns.

The draft report’s strategic priority recommendations include:

• New England state higher education systems, governing and coordinating boards, together with New England’s employers, should make increased employability of graduates a strategic priority—linked to the strategic plans, key outcomes, performance indicators and accountability measures for the higher education institutions under their stewardship.
• New England higher education institutions should incorporate employability into their strategic plans/ priorities supported by efforts to define, prioritize and embed employability across the institution and in multiple dimensions of learning and the student experience—both curricular and extracurricular.
• New England should make strategic efforts and investments—at the state, system and institution level— to expand research, data gathering, assessment capacity and longitudinal data systems to enable more effective understanding and documentation of key employability-related measures and outcomes.
• New England higher education institutions should undertake formal employability audits to review the strategic, operational and assessment-oriented activities related to employability–and their effectiveness in embedding and measuring employability across the institution.
• To confront notable college-attainment gaps and the related personal and societal costs, states must consider specific employability strategies to target and benefit students who are at risk of not completing postsecondary credentials, including underrepresented populations.

The Boston-based New England Board of Higher Education promotes greater educational opportunities and services for the residents of New England. Comments on the recommendations are accepted on-line through Dec. 22.

Philanthropy 101? CCSU to Offer Course, Capstone Project Aimed at Boosting Philanthropy

Throughout Connecticut, philanthropic organizations distribute more than a billion dollars every year and individuals donate nearly $4 billion more. Responding to both the State’s philanthropic needs and need for skilled philanthropy professionals, Central Connecticut State University is offering an innovative course in the practice of philanthropy beginning next semester. The ambitious 16-week course is to include 25 presenters from local nonprofit and philanthropic organizations, many based in New Britain, as is the CCSU campus.   As part of the course, students will study local needs, create a case study, and write a proposal following the American Savings Foundation grant-making guidelines.  Officials say that at the conclusion of the course, up to two of these projects may each be funded with a $5,000 grant from the American Savings Foundation.

Connecticut ranked 47th among the 50 states in philanthropic giving as a percentage of income, with a “giving ratio” of 2.4 percent, 25 percent lower than the national average, according to an analysis earlier this year by the Chronicle of Philanthropy. The giving ratio is the total of a locality’s charitable contributions as a share of its total adjusted gross income.

“They will learn more than just philosophy,” points out Professor Carol Shaw Austad, who will co-teach with former CCSU President Richard Judd. “This class is hands-on. Students will work in teams to design a philanthropic strategy. They will meet with New Britain nonprofits and evaluate impact, just like any foundation. We expect them to make a strong case statement for an organization or project.”

“There is so much to like about this course,” notes Maria Falvo, president of the American Savings Foundation.  “For one, many of these young people may go on to work at or volunteer with nonprofits. They may be fortunate enough to become donors themselves. This essential training lays the groundwork for a future in philanthropy,” Falvo says.

The giving percentage varied across the state, according to the Chronicle study:  the Fairfield County giving ratio was 2.8%, New Haven County and Litchfield counties 2.1%, Hartford County 1.9%, New London County 1.8%, Middlesex and Tolland Counties 1.7%, and Windham County 1.6%.

Donations from households earning $200,000 or more now total 52 percent of all itemized contributions. In the early 2000s, that number was consistently in the 30s, the Chronicle reported.  The report raises questions about the traditional habit of charitable donations among middle and low income individuals lessening, perhaps as a lingering after-effect of the recession. The Chronicle’s conclusion: “The number of households making room in their budgets for charitable giving is shrinking.”

CCSU President Dr. Zulma R. Toro said “I am especially pleased that this course engages our hometown of New Britain. This is a perfect example of what we mean by “CCSUConnected.” We are connecting to our communities in a mutually beneficial way by engaging our students academically in the life of our community. Our students learn skills and experiences that can prepare them for rewarding careers, and our communities can benefit from our students’ work focused on New Britain’s needs.”

“These community leaders are such a diverse and thoughtful group,” said Dr. Toro.  “Pastor Thomas Mills of Grace Church shares a session with New Britain Mayor (and CCSU alumna) Erin Stewart.  Dr. Ali Antar of the Berlin Mosque is on the agenda as is Dr. Claudia Thesing, formerly director of development at the New Britain Museum of American Art.  The speakers represent a real cross-section of the community.”

The course will run from January 17 to May 9, and is open to undergraduate students at CCSU.

Senior Citizens Less Diverse, Growing in Percentage of State’s Population

Over 575,000 Connecticut residents are age 65 and older, making up an estimated 16 percent of the state’s total population of 3.6 million, according to U.S. Census data updated through 2016.  Those numbers are expected to grow – steadily and rapidly – during the next two decades, experts anticipate. Among Connecticut’s eight counties, the largest percentage of seniors is in Litchfield County, 19.7 percent, followed by Middlesex County, 18.8 percent, and New London County, 17.1 percent.  New Haven (16.3%) and Hartford (16.2%) counties are next, followed by Fairfield and Tolland Counties, both at 14.8 percent.

The data, highlighted by the Connecticut Office of Legislative Research (OLR)  in a recent report, also shows that “Connecticut’s senior population is less ethnically and racially diverse than the state as a whole.”

Just over 89 percent of the state’s seniors (age 65+) are white, compared with 77 percent of the state’s population as a whole.  While 10 percent of the state’s population are Black or African American, that is true of only 6.4 percent of seniors.  The state’s Asian population is 4.2 percent of the total; among seniors, less than half that, only 2 percent, are of Asian heritage.

While the total state population is almost evenly split between male (49%) and female (51%) residents, the senior population has a larger percentage of females (57%) compared to males (43%), the analysis found.  Connecticut seniors are more likely to be veterans (20% vs. 7% of all residents) and more likely to have a disability (32% vs. 11% of residents).

According to a recent report by the state’s Commission on Women, Children and Seniors, Connecticut is the 7th oldest state in the nation.  Roughly one-third of the state’s population are baby boomers, born between 1946 and 1964.  The state also has nearly 1,000 people over the age of 100.  As has been previously projected, the number of Connecticut towns with at least 20 percent of residents age 65 or older will dramatically increase between 2010 and 2020 (see maps below).  The 65 and older population is expected to grow by 56 percent in Connecticut between 2010 and 2040, compared with  1.5 percent growth in the population between ages 20 and 64.

Approximately 7 percent of Connecticut seniors had incomes which fell below the census poverty level, with an additional 8 percent of seniors having incomes between 100 percent to 149 percent of the threshold, the OLR report indicated. The most common source of income for Connecticut seniors is Social Security, with an average benefit of $20,591 per year, as of 2015. An estimated 90 percent of senior homeowners and renters receive Social Security benefits. The second most common source (50.7%) is personal retirement income, averaging $27,240 per year in 2015.

Of the more than 330,000 senior households, an estimated 76 percent are homeowners and 24 percent are renters. This represents higher home ownership rates than the state as a whole (67% of 1.35 million households).

The most common source of income for Connecticut seniors, the report indicated, is Social Security, with an average benefit of $20,591 per year in 2015. An estimated 90 percent of senior homeowners and renters receive Social Security benefits. The second most common source (50.7%) is personal retirement income, averaging $27,240 per year in 2015.

The demographic characteristics of Connecticut’s senior population (e.g. residents age 65 years and older) used by OLR were largely based on the 2011-2015 American Community Survey 5-Year estimates from the U.S. Census Bureau.

Better Outcomes from Female Surgeons, Study Finds; Local Hospital Highlights Their Own

In a study that has gained international attention and peaked interest locally, the patients of female surgeons tended to have lower death rates, fewer complications and lower readmissions to the hospital a month after their procedure, compared to the patients of male surgeons. The study, published in the BMJ (British Medical Journal), and highlighted in TIME magazine, was conducted in Ontario, Canada, and included all of the people in the province who had operations between 2007 and 2015.  The results are bringing some attention to female surgeons, and Connecticut Children’s Medical Center is shining a spotlight on their surgical staff in the aftermath of the study’s publication.

Connecticut Children’s which has nine female surgeons, including the surgeon-in-chief, is stressing not only that they are “leaders in this field,” but they are also “moms at home.”  They’re using the two roles to launch a social media campaign called #momsurgeons, and will be profiling each of the surgeons on social media, website and billboards in greater Hartford this week.

“We wanted to bring attention to the fact that we are moms too. We truly understand what our patient families are experiencing when their child is heading into surgery,” said Christine Finck, Surgeon-In-Chief at Connecticut Children’s. “We also understand the daily struggles many moms face trying to find that work-life balance.  It’s hard.  We get it.”

Finck, appointed surgeon-in-chief in 2016, previously served as Chief of the Division of Pediatric Surgery since 2007 and is an associate professor of pediatrics and surgery at UConn Health.  In announcing her appointment, Connecticut Children’s pointed out that through her research, Finck “revolutionized outcomes of pediatric and neonatal diseases, most specifically leading efforts focused on identifying and treating those that affect the lungs, esophagus and brain.” She was honored by The Group on Women in Medicine and Science, who awarded her the Outstanding Clinical Scientist Woman Faculty Award, last year.

After accounting for patient, surgeon, and hospital characteristics, the study concluded that “patients treated by female surgeons had a small but statistically significant” decrease in 30 day mortality and similar surgical outcomes (length of stay, complications, and readmission), compared with those treated by male surgeons.

The study’s authors noted that the findings “support the need for further examination of the surgical outcomes and mechanisms related to physicians and the underlying processes and patterns of care to improve mortality, complications, and readmissions for all patients.”

By drawing attention to this profession, officials said, “our #momsurgeons hope they can serve as role models for aspiring young ladies who also hope to one day enter the field.”

“Every time I operate, I stop and think about how I would want the operation to go if it my own child was in front of me,” said Meghna Misra, pediatric surgeon at Connecticut Children’s.

Surgery has long been a male-dominated occupation, TIME reported, “first because few women enrolled in medical school, and then because they weren’t perceived (by male surgeons, no less) to have the temperament needed to make the life-and-death decisions required in an OR.”

In the study, 104,630 patients were treated by 3,314 surgeons, 774 female and 2,540 male. Dr. Raj Satkunasivam, assistant professor of urology at Houston Methodist Hospital was leader of the study.

Connecticut Children’s Medical Center is the only hospital in Connecticut dedicated exclusively to the care of children and is ranked by U.S. News & World Report as one of the best children’s hospitals in the nation, with a medical staff of more than 1,000.

Population Density in Three CT Cities Reaches Top 100 in USA, Data Shows

Bridgeport’s population density, 9,138 people per square mile, is among the top 60 nationally, according to data compiled by Governing magazine for jurisdictions with populations of at least 50,000.  Bridgeport, the state’s largest city, had a population of just over 145,000 living in 16 square miles, the data indicated, ranking at number 58.  It is one of three Connecticut cities in the top 100. The “dense” top ten:  Union City, New Jersey; West New York, New Jersey; Hoboken, New Jersey; New York, New York; Passaic, New Jersey; Somerville, Mass.; Huntington Park, CA.; San Francisco; Jersey City; Paterson, New Jersey and Cambridge, MA.   Boston ranks at #19; Providence is #54. 

Lower on the list of America’s most dense population centers is Hartford, 17 square miles and a population of 123,000, with a population density of 7,091 people per square mile; New Haven, just three notches below Hartford at 6,956, in a city of 130,000 covering 19 square miles.  Both were in the 100 most dense cities; Hartford at #97, New Haven at #100.

They are followed later by New Britain with a land area of 13 square miles at 5,419; West Haven, at 5,071 population density over 11 square miles, and Norwalk, with a population density of 3,869 in an area covering 23 square miles.  Waterbury, at 29 square miles, has a population density of 3,796; Stamford’s population density is 3,430 in a city of 38 square miles.

The data is based on the U.S. Census Bureau, Population Division, estimates current through July 1, 2016.  Governing notes that “jurisdictions with the highest population densities tend to be concentrated in northern regions, particularly the New York metropolitan area.”

According to the 2010 Census, Connecticut overall ranked sixth in the nation in population density, with a population of 3,574,097 and 738 people per square mile.  The state’s population has dropped since that Census, and is now estimated at 3,568,174.  The nation’s densest populations, as of 2010, were in the District of Columbia, New Jersey, Puerto Rico, Rhode Island, and Massachusetts.