PERSPECTIVE: An Unexpected Journey on the Path to News Literacy

by Amanda Muntz 2008: I was 10. I looked away from the television, where Fox News was broadcasting the election results. My father shook his head in disbelief.

“Well, that’s it, folks. Barack Obama has just been elected the 44th president of the United States of America.”

My father, who prides himself on being a “constitutionalist,” went on: “Well, he’s got America fooled.” And: “You’re living in a totally different world now, Amanda.”

I was too young to process what was going on, but I trusted my parents and I believed that Obama could only be bad for this country. Back then, I thought of the government as an immoral institution that didn’t have the majority’s best interest in mind.

2017: At 19, I now recognize that I lived in a political bubble. It took a move and a new school to start broadening the views that I was exposed to. And when I began an internship with the News Literacy Project, I realized that if I had been taught at a younger age what I learned this summer, I would have been spared a long and rocky road to reaching an understanding of news literacy. NLP taught me how to properly check citations for credibility and to research facts across different sources. This ability alone has made sifting through large amounts of information much more manageable and efficient.

As a child, I’d hear members of my extended family mutter “socialist devil” and yell “Oh, all you do is lie!” whenever they saw Obama on television. I was never exposed to anything positive about the president and his family until I moved from Austin, Texas, to New York City at age 16.

The students at my new high school were more liberal than my classmates in Texas, and, over time, I saw that although I had been raised as a conservative, I had no idea what I truly thought about politics. My new friends would discuss Obama, and I recognized that I knew nothing about his administration or policies. I had heard at home that nothing he said could be believed, and I knew that most people who were close to me couldn’t stand him. But once I came to the realization that their opinions weren’t necessarily mine, I decided to take a step back.

I stopped talking about the president. I figured I had no business expressing an opinion that I wasn’t even sure was mine. I started to lower the defenses I had been taught to put up when listening to or about Obama.

Instead, I began reading articles from news outlets across the political spectrum. And I entered my senior year of high school with this conclusion: I had absorbed too much vitriol against Obama and his administration to have an unbiased opinion. That possibly wasn’t the right lesson to take away; in hindsight, I see that I wasn’t equipped with the educational tools to know how to sift through the immense amount of information I was reading or how to distinguish news — facts presented impartially — from opinion, which can be fact-based but also include personal views or even advocacy. However, it did lead me to have the confidence to say, “Honestly, I don’t have enough unbiased information on that issue to have an opinion that I’m comfortable sharing right now.”

I didn’t know it then, but I was taking my first steps toward news literacy.

I began to hear people with opposing views, instead of just listening for the sake of arguing against them. I wasn’t afraid to acknowledge when someone made a good point, and I learned to disagree with a degree of curiosity — wanting to hear their response, rather than to pick a fight. I began to tell the difference between news and opinion.

Those skills became increasingly important when it came time for the 2016 presidential election — the first election I could vote in.

I was in my first year at Wesleyan University in Middletown, Connecticut. Between the polarized political atmosphere across the United States and the largely liberal environment on campus, I became increasingly frustrated with people simply parroting what they found on their Facebook feeds or other social media platforms. While I’m glad there are places online for everyone to share their opinion, I wish my peers wouldn’t read every Tumblr rant as if it were a Pulitzer Prize-winning news report. Amid all this chaos, I knew it was up to me to make an informed decision.

So I put two cable news outlets — CNN and Fox News — to the test. I livestreamed the Republican National Convention with friends, so there were no commercial breaks or commentary. For the Democratic National Convention, I decided to go back and forth between Fox and CNN. To avoid leaning left, I tried to watch more of the commentary on Fox. The results were not comforting.

What I found was that while CNN aired most of the speeches and the comments were generally positive, Fox didn’t even show half of the people at the podium. Instead, the Fox reporters and commentators were drowning them out — talking over them about topics that the speakers weren’t even discussing. As the first night of the convention came to an end, and more prominent figures such as Sen. Bernie Sanders, Sen. Elizabeth Warren and Michelle Obama took the stage, Fox finally started to stick with the speakers. I found myself wondering how CNN’s coverage during the Republican convention compared with this.

I didn’t stop there. I enrolled in government and economics classes. I began reading articles from a variety of news outlets, including The New York Times and The Wall Street Journal. I finally started to develop my own political opinions — and am finding that I’m more progressive on social issues and more conservative on fiscal ones.

News literacy is — and should be — an increasingly pressing concern in today’s world of social media and endless platforms for opinions. The lack of awareness of fake news and heavily biased news is what attracted me to accept an internship at the News Literacy Project. Being an intern at NLP has taught me how to properly sift through information and how to truly reach my own conclusion by checking facts and reading across multiple sources. Throughout this summer, I’ve seen what a difference these lessons can make.

I particularly urge high school and college students to try to make the distinction between news and opinion and begin implementing news literacy in their everyday lives. While it’s important to listen to different people and hear their points of view, it is even more important to process this information and formulate your own opinions. The News Literacy Project provides an excellent platform to begin educating yourself and others.

________________________

Wesleyan University student Amanda Muntz is studying international law and globalization at the University of Birmingham in England.  This article first appeared on the website of The News Literacy Project.

Accrediting Organization to Decide Fate of Plan to Merge 12 Community Colleges into One

Plans to merge Connecticut’s 12 community colleges into a single institution, expected to be called the Community College of Connecticut, are now being reviewed by the region’s accrediting body, the New England Association of Schools and Colleges, known best by the acronym, NEASC. Back in August, after first learning about the Connecticut merger proposal in an 18-page outline provided by Connecticut officials, NEASC had questions, and many of them.  In a detailed four-page letter to the leadership of the Connecticut State Colleges & Universities (CSCU), NEASC indicated they had yet to receive “sufficient information to be confident CSCU’s process will result in arrangements that are compliant with the Standards for Accreditation.”  The letter from David Angel, Chair of NEASC's Commission on Institutions of Higher Education, was shared with the leadership of all the colleges and universities in the state's public CSCU system.

NEASC officials met three times with Connecticut officials last year, the Connecticut Post reported recently. Another meeting in Connecticut is planned for this month.

The President/Chief Executive Officer at NEASC, since 2011, is Cameron Staples, a former Connecticut state legislator and former chair of the legislature’s Education Committee and Finance, Revenue and Bonding Committee.  In 2010, he briefly sought the Democratic nomination for Attorney General.  

The letter from NEASC also indicated that “the materials submitted to date have been very clear on the financial reasons for the proposed change but less clear on a rationale tied more directly to the mission of the colleges.”  NEASC noted that the proposal stated plans to retain the “unique mission” and “local community connection” of each of the 12 institutions after the merger, but indicated the need for “further information about how this will be accomplished through the proposed merger.”

The consolidation plan was subsequently approved by the Board of Regents of CSCU in December, with only one member of the Board abstaining and others unanimously supporting the plan, developed to save money across the system by eliminating staff positions, many said to be duplicative, that would not adversely impact students.  Student and faculty groups at the campuses have raised questions about the ultimate effectiveness of the plan, or have opposed it outright.

Following approval by the Regents, a more detailed plan was submitted to NEASC seeking approval from the accrediting organization.  If NEASC accreditation is obtained, Connecticut officials hope to have initial implementation by July 1 of this year and the new structure fully in place by July 1 of next year.  That is predicated on receiving NEASC approval by June; published reports indicate that NEASC officials anticipate consideration at the organization’s board meeting this spring.

NEASC’s Barbara Brittingham, president of the Commission, recently told the CT Post that Connecticut’s timeline was “ambitious,” particularly for a “substantive change” that involved 12 colleges.  The newspaper also reported that several Regents committees are at work looking at 1) integrating new positions and selecting people to fill those jobs, 2) aligning 12 academic course catalogs and 3) fine-tuning the projected savings of the new system.

Since December’s Regents approval, in media interviews and public explanations, details of what’s planned are being highlighted, while the system awaits NEASC approval.  The merger plan was initially proposed last April as an “expedient solution” in reaction to state funding cuts to the colleges and an ongoing “structural deficit” resulting from operational costs outpacing revenue.

The plan calls for 12 college president positions to be eliminated, with a new structure to take its place that would include a creation of a “vice chancellor” position to lead the new 12-campus community college system, along with three new regional president positions that would report to the vice chancellor, each with presumably jurisdiction over four college campuses.  Each of those 12 campuses would be led by a campus vice president.

The Regents plans would consolidate college functions in six areas:  Information Technology, Human Resources, Purchasing, Financial Aid Services, Institutional Research and Assessment, and Facilities Management. 

The plan anticipates saving $28 million a year by eliminating college presidents, a process that has already begun, as well as budget staff and other administrators at each institution and creating a centralized staff to run the public colleges. Another plan aimed at saving an additional $13 million by reorganizing how financial aid, enrollment management and other services are delivered is also part of the proposal.

The proposal would create one of the nation’s largest community colleges with about more than 53,000 students. Among the largest currently are Miami Dade College with 174,000 students; Lone Star College in Houston, with 90,000; Northern Virginia Community College, in Springfield, VA, with 76,000 students; Broward College in Fort Lauderdale, with 67,000 students, and Houston Community College with 63,000 students.

Officials note that Connecticut’s higher education system has changed previously, including when the four regional state universities and 12 community colleges, along with the on-line Charter Oak State College, were brought together under the newly established Board of Regents umbrella six years ago, and when the state’s technical colleges and community colleges merged in the 1990’s.

NEASC is the regional accreditation agency for colleges and universities in the six New England states: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont, recognized by the U.S. Secretary of Education. NEASC accreditation is a system of accountability that is ongoing, voluntary, and comprehensive in scope.  It is based on standards which are developed and regularly reviewed by the members and which define the characteristics of good schools and colleges, according to the organization’s website.

New Requirements for Data and Analysis Due in Economic Development Report on February 1

A new state law is making changes to the annual report of the state Department of Economic and Community Development (DECD), due to be completed by February 1.  The law changes the mix of data and analyses DECD must include in the report, eliminating many types of previously required information but also requiring more data and analyses about the impact of all economic development programs, not just those DECD administers, according to the Office of Legislative Research (OLR). The analysis of each program in the DECD annual report must now include:

1. an analysis of the program’s impact on the state’s economy, including, if available, the number of new jobs it created and its estimated impact on the state’s annual revenues;
2. an assessment of whether the program is meeting its statutory and programmatic goals and, if possible, the obstacles preventing it from meeting those goals;
3. recommendations about whether the program should be continued, modified, or repealed and the reasons for each recommendation;
4. recommendations for additional data that must be collected to improve the evaluation; and
5. a description of the methodologies used and the assumptions made to analyze the program.

DECD must also include how much it cost the state to borrow funds to finance them.

Public Act 17-219 also requires DECD to include:

• an overview of its tourism, arts, and historic preservation activities and
• an economic impact analysis of each state economic development business assistance or incentive program, including those administered by other agencies that had 10 or more recipients or awarded over $1 million in assistance during the prior fiscal year.

Examples of economic development programs administered by other agencies include the Labor Department’s Subsidized Training and Employment Program and Connecticut Innovations’ Angel Investor Tax Credit.

Instead of submitting a separate report about film industry tax credits, as was done previously, DECD must report about them in the annual report. In doing so, the law passed in 2017 requires DECD to summarize its efforts concerning media and motion picture production in Connecticut and indicate the total (1) amount of credits it issued during the reporting period and (2) production costs and expenses credit recipients incurred in Connecticut.

The law also requires DECD to submit the report annually, by February 1 to the governor, the auditors, and the legislative review committees. Under prior law, it had to submit the report to the governor and the entire legislature annually by that date. Beginning March 1, 2018, OLR indicates, the law requires the legislature’s review committees to hold one or more separate or joint annual hearings on DECD’s report, focusing on the analyses of DECD’s community development projects and DECD’s efforts to promote international trade.  The new law also calls for the Appropriations; Commerce; and Finance, Revenue and Bonding committees to hold hearings periodically on the economic impact of state economic development programs.

The law further requires DECD to analyze the First Five Plus program’s net return to the state and include that analysis in its biannual report on the program, which, by law, it must submit to the Commerce and Finance, Revenue and Bonding committees.   It also requires the committees to hold a hearing exclusively on the program, which combines financing and tax incentives under various programs into a comprehensive assistance package for business development projects that meet specified investment and job creation targets.

OLR also notes that among other things, the law approved by the state legislature last year eliminates the requirement that the report include data about specific businesses, municipalities, and projects that received DECD funding and instead requires the report to identify the website where this information can be found.

Electing More Women to Legislature in 2018 Would Reverse Trend in CT

Among the political questions of the new year is whether the events of 2016 and 2017 will lead to more women running for legislative seats in 2018 and to more being elected.  That’s on the mind of political obervers in Connecticut as elsewhere around the nation.  If that were to happen in Connecticut, it would reverse a near decade-long decline in the number of women serving at the State Capitol, which has seen the state fall from 7th to 19th since 2011 in the percentage of women serving in the legislature. When the current legislature was elected, the make-up of Connecticut’s General Assembly was 27.8 percent women.  That ranked Connecticut 19th among the states, slightly above the states average of 24.9 percent, according to data from the National Conference of State Legislatures.

The Connecticut legislature has 187 members, including 151 in the House and 36 in the Senate.  The number of seats in other states varies.  Of the 151 House members, 43 are women as 2018 begins. In the Senate, nine of the 36 members are women.

Higher percentages of women were elected to serve in state legislatures in the New England states of Maine, New Hampshire, Rhode Island and Vermont, as well as Alaska, Arizona, Colorado, Idaho, Illinois, Kansas, Maryland, Minnesota, Montana, Nevada, New Jersey, New Mexico, Oregon, and Washington.

The percentage in Massachusetts was 25.5 and in New York 27.7, just behind Connecticut.  Arizona’s 40 percent, Nevada’s 39.7 percent, Vermont’s 39.4 percent, and Colorado’s 38 percent lead the nation.

Compared with other states, the percentage of women in Connecticut’s legislature has been dropping, in real numbers and as compared with other states.  In 2015, the percentage was 28.3 percent; in 2013 it was 29.4 percent; in 2011 Connecticut’s legislature was 29.9 percent women.  In 2009, Connecticut’s legislature included 31.6 percent women, which was the seventh highest in the nation.

Currently, the highest ranking woman in the legislature is House Minority Leader Rep. Themis Klarides (R-Derby).  During 2017, in  handful of legislative Special Elections to fill vacant seats, the only woman to run, Democrat Dorinda Keenan Borer, was elected to represent West Haven’s 115th Assembly District in February.

In Virginia’s election this past November, pending final certification of results, there will be 28 women in the Virginia House next year. Including the 10 women serving in the Senate, which did not have elections, the 38 women will make up 27 percent of Virginia’s legislators. NCSL reports “this is a significant increase from the pre-election numbers, of 27 women, or 19 percent of the legislature, and the most women ever to serve in Virginia.”  One of the races has yet to be decided, and is currently considered to be a tie.  One of the two candidates is a woman.

The data, compiled at the start of legislative terms, is subject to change during legislative terms due to resignations, appointments and special elections, in Connecticut and other states.

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.