Third grader Mason wants to be an astronaut when he grows up. He’s really   motivated and by 10:00 a.m. he’s finished all three of his math worksheets and Mrs. Sucy resorts to giving him busy work while she finishes coaching his fellow classmates through their work. Third grader Sophia, however, is still stuck on math problem number one. She’s too embarrassed to ask Mrs. Sucy to slow down and repeat the lesson again.

 Mrs. Sucy is a terrific teacher and she recognizes her students each learn in different ways – some by doing, some audibly, some visually. However, there’s just not enough time in a day to fully meet each of her students varying needs. Mrs. Sucy’s 25 students are akin to train cars all on the same track, all forced to go the same speed, run by one engine.  If she as the engine slows the train down, Mason will be altogether bored and break away. If she speeds up to accommodate Mason, she’ll certainly lose students, and most definitely blast Sophia beyond her speed!

Technology is the key to revolutionizing education for all kids around the world.  The term “online learning” embraces this very concept.

(Download the full report here)

What is online learning?

Online learning, often referred to as “anywhere, any time learning,” is an education model whereby a student completes his coursework through internet-based programs. Of course this model can take many different shapes. It is possible for a student to enroll in a full-time online learning program which is comprehensive of the entire subject matter for his grade level. Or, a student may take just one or a handful of courses online while he is enrolled in a traditional brick and mortar school.

Most popular models of online learning

Full Time:

Students enrolled in full time online learning perhaps have the most flexibility in their education. Rather than a traditional brick and mortar school setting, students “log on” to school with the click of a button on their computer from anywhere with internet …  a home desktop, an airplane, a hospital bed, a hotel room, the list is endless.

Examples: K12, Inc. and Connections Academy – Both are widely popular across the U.S. and each were to be online learning providers for the two proposed virtual charter schools in Maine. Last year, the two proposed virtual charter schools were recommended by the Maine Charter School Commission to resubmit their applications in the next reviewing cycle and they did so in January of 2013. The Commission denied both applications and has not yet approved a virtual charter school in Maine.

Although Maine’s charter school law does allow provisions for a full time virtual charter school, use of this full time virtual school model currently exists in Maine only in a home school situation where parents pay for it out of pocket (in addition to their taxes which in part fund the local public school system).

Blended:

Students’ time is divided between online learning and teacher-led, hands on workshops where there is engaging discussion and activities which complement the individual learning taking place through the online programs. Traditional desks are replaced by mini cubicles containing personal computers. Students are with their fellow classmates in a brick and mortar school and are supervised by teachers. Students take the same online courses but may progress at each of their individual paces.

So for example, Mason and Sophia are taking the same third grade math curriculum. Mason is completing his long division unit after watching the virtual lesson on his computer earlier this morning; he looks forward to moving on to fractions next week! Sophia is feeling confident in her division skills as she was able to hit “pause” during the virtual lesson, while she takes some extra notes. She proceeds to start her exercises and realizes she forgets her first step. She goes back to the virtual lesson and clicks “repeat.” Meanwhile, Mrs. Sucy who has the ability to mill around the room and monitor the progress of her students as individuals, has her own computer and receives a red flag notification on her monitor indicating Sophia may need some encouragement or a bit of an explanation.

Examples: Carpe Diem Collegiate High School and Middle School in Yuma, Arizona

Carpe Diem’s expenditures per student are $4,000 less than the national average. ^[1]

This model, in its purest form, does not yet exist in Maine. However, with state surplus funds, the Maine Learning Technology Initiative, launched in 2001 by the Maine Department of Education and Apple, Inc., issued laptops to all middle school students and teachers. Through negotiations with Apple, Inc. in 2009, the MLTI expanded and supplied new laptops to all of Maine’s public high school students.^[2] Given this laptop program and the widespread support of superintendents throughout the state, Maine has a nearly perfect foundation to implement the blended model of online learning.

Supplemental:

A student may enroll in an online class or two, in addition to his traditional education, for various reasons. Some students, especially those who live in rural areas, would not otherwise have opportunities to learn Mandarin Chinese or take an Advanced Placement course in preparation for college. Others need to catch up on a particular subject over the course of the summer; perhaps they were sick for an extended period of time or just simply succeeded in all but one subject.  Those who don’t like to get their hands dirty, can even virtually dissect a frog in an online biology class!

Examples: PLATO Learning, Inc. and Virtual Learning Academy (with either of these providers, students may enroll in one or two courses or full time)

This model does exist in Maine. Founded in January 2012, the Maine Virtual Learning Consortium which was established by the Maine International Center for Digital Learning and RSU 19, offers eight courses including Latin, Anatomy and Physiology, and Art History. Schools which choose to participate are called “Partner Schools;” they pay an annual enrollment fee and must contribute two one-semester online courses to be distributed for use throughout the other Consortium Partner Schools.  ^[3]

Who provides online learning?

Just in the past year, the number of Maine state-approved online learning providers has increased from three to seven. These private providers, approved for use in the public school classrooms, are:  Advanced Academics, Apex Learning, Connections Academy, K12, Inc., Lincoln National Academy, PLATO Learning, Inc., and Virtual Learning Academy.

Elluminate Live! Session

Teachers

Online learning teachers interact with their students through e-mail, electronic real time white boards, instant messaging, blogs, forums, phone, chat rooms, and more! The screenshots below give you an idea of the face to face class time and accountability that can take place even through cyberspace. Students interact and respond to questions through use of the chat box, private messaging, and through a microphone when called upon by their teacher. Teachers can use the whiteboard to type instructions, draw shapes and even graph mathematical equations. Class may meet once a week or multiple times per week. Extra tutoring can take place between the teacher and students as needed on their own time. Homework can be submitted via e-mail or posted on a forum with indications of whether or not students have met the set deadline.

Tutor Trove Lesson

Conclusion

Online learning embodies the greatest qualities of customized learning. Through online learning, classmates like Mason and Sophia can progress in courses at their own pace, according to their strengths and weaknesses in different subjects. Teachers like Mrs. Sucy, who tirelessly strive to meet the needs and interests of each of her students, can devote more time to tracking and encouraging the progress of her students as unique individuals. Online learning empowers her as just one teacher to have several “engines” running, with students each on their own tracks – slowing down, breaking, and accelerating according to their abilities. The bottom line is leveraging technology maximizes results. Given the laptop programs and online learning programs already in place throughout our state, Maine has the potential to revolutionize education to such a degree that every student can realize his full potential.

(Download the study here)

This study looks beyond the regulations to highlight the practical impact PL90 is having for end users, the businesses and consumers who purchase private health insurance in Maine.  This is a measure all too often dismissed by critics yet perhaps the only measure that truly matters.  The following case studies illustrate the effects of specific provisions of PL90.  Names have been altered for confidentiality purposes, but the profiles are of real companies and individuals. All material details presented in the case studies are accurately portrayed.

PL90 is demonstrating who truly benefits when we free our markets to respond to consumer needs, the many individuals and small businesses in Maine who rely on private health insurance—a 56 year old woman with a newly transplanted heart able to afford her anti-rejection medications, a small business lowering their cost rather than accepting a 23 percent rate increase, and another small business able to continue providing health insurance to its employees without having to ask them for a premium contribution.

Case Study 1: Improved Individual Market

Background:

PL90 contained numerous provisions aimed at improving Maine’s individual and small group health insurance markets.  The Maine Guarantee Access Reinsurance Association (MGARA) was created to subsidize high cost claimants in the individual health insurance market.[2]  MGARA assesses $4 per month from policyholders across all insurance markets in Maine raising approximately $25 million.  This fund is supplemented with premiums paid by insurers who have high cost individuals in the reinsurance pool.

Once an individual is designated to the reinsurance pool, MGARA reimburses the insurer after the first $7,500 of claims paid, 90 percent of the next $25,000 of claims paid, and 100 percent of claims paid in excess of $32,500.

MGARA is transparent to policyholders who are unaffected by the program in terms of their coverage and premium cost.  In most cases, policyholders are likely unaware that they are in the reinsurance pool.  Their plan choices are the same as any other policyholder and their premiums are the same as a healthy individual of the same age and gender.

Profile:

Jane Doe, a 56 year old single woman, worked for a small Maine employer who offered a group health insurance plan covering Jane and four additional employees.  In the fall of 2011, Jane suffered a massive heart attack.  The attack resulted in significant irreparable tissue damage and left Jane in and out of consciousness for months in an intensive care unit first in Maine and then a hospital in Boston.

She was eventually released after months of hospitalization with a pump surgically implanted in her chest that circulated her blood while she waited on the heart transplant list.  Jane overcame many odds simply by surviving, but she would not be returning to work.

During this timeframe Jane applied for and was approved for social security disability.   She must now wait two years before she can apply for Medicare.  Her employer generously maintained her group insurance coverage for the maximum timeframe allowed under the group insurer’s eligibility rules.  Because this is a small employer ineligible under the Consolidated Omnibus Budget Reconciliation Act (COBRA), Jane found herself needing to seek coverage in the individual insurance market.

Effect of PL90:

Jane’s biggest challenge in affording individual health insurance was out-of-pocket costs.  She had sufficient savings to cover her monthly premium cost but worried about the plans additional out-of-pocket expenses.  Many plans have deductibles and out of pocket limits in the thousands and occasionally in excess of $10,000 annually.

Jane was able, however, to purchase a new individual product offered by Anthem, compatible with a Health Savings Account (HSA), that limited her annual out-of-pocket exposure to $2,600.[3]  It has been years since Anthem, Maine’s largest individual major medical insurance provider, has introduced new products.  The individual market has been steadily deteriorating, raising alarms the market could collapse entirely.  The Maine Bureau of Insurance issued a white paper detailing this problem in 2000 which was updated in 2001.[4]

MGARA has breathed new life into the individual health insurance market.  A viable market attracts investment as has been demonstrated by Anthem who introduced this new HSA product in addition to a portfolio of new products Anthem refers to as “HealthChoice Plus”[5] with premiums as much as 72% lower than products previously available.[6]  Since MGARA became operational in July of 2012, Anthem saw new products sales increase approximately 60 percent over the same timeframe in 2011.[7]

PL90 created a more stable individual health insurance market.  The result was new investment by a Maine insurer that provided invaluable coverage for an individual in need.

Case Study 2: Substitution Effect

Background:

The small group health insurance market in Maine includes several insurers who compete for market share.  Because of this, it is typical for small companies to shop their insurance coverage each year to make sure they are getting the best price.  As an insurance broker, I can tell you that small companies change insurers and products frequently, sometimes literally on an annual basis.

PL90 not only instituted MGARA and changes to insurance rating rules, it also removed barriers to new products and investment.  One provision lifted a somewhat obscure Maine law that prohibited individual Health Maintenance Organization (HMO) deductibles in excess of $1,000.  As a result, multiple insurers offered new small group HMO products including Maine’s non-profit health insurer, Harvard Pilgrim Health Care.[8]

Profile:

ABC Architecture is a small firm in southern Maine with a dozen employees.  They offer health insurance to their staff of which ten participate.  Their plan renews each year on January 1^st.  This year they faced a 23 percent rate increase from their insurer which translated to a premium increase of over $8,300.

Effect of PL90:

As shown in Table 1, instead of a 23 percent rate increase, ABC Architecture experienced an 11 percent rate decrease saving the company over $4,000 over 2012, and over $12,000 compared to their premium cost had they renewed with their existing health insurance plan. ABC Architecture shopped as they do each year and changed to one of Harvard Pilgrim’s new HMO products.  The coverage was almost identical with only a $50 increase in their employee’s annual out-of-pocket exposure.  This was a Health Savings Account (HSA) compatible plan like their previous plan, but it included an enhanced prescription benefit.

The chart below, prepared by the Maine Bureau of Insurance, shows a growing trend of health insurance rate decreases rising from less than 3 percent before PL90 to 9.4 percent in 2011 and 17.5 percent in 2012.[9]  The percentage of small businesses experiencing increases dropped in every category and the substitution effect tells us that many more companies changed products or insurers to further mitigate costs.The substitution effect of companies changing insurance plans is an important aspect of the small group health insurance market.  It demonstrates that many companies change plans and insurers to avoid or mitigate rate increases.  Given that fact, when you see data regarding rate decreases, the numbers are invariably understated.

Case Study 3: Innovation

Background:

One PL90 provision enabled the formation of a health insurance captive which allows companies to band together to manage their health insurance expenses.  One group in Maine, the Maine Wellness Association, took advantage of this provision and formed a health insurance captive called MaineSense.[10]

MaineSense provides a new option for Maine companies with a number of unique features such as employer ownership in the program.

Profile:

XYZ Builders is a commercial building contractor in central Maine.  The company has provided health insurance to its 20 employees for many years.  They still pay 100 percent of the employee premium though rate increases are threatening their ability to continue doing so.

Effect of PL90:

Concerned about historical health insurance rate increases and interested in the opportunity to participate in what they viewed as an innovative new program, XYZ Builders joined MaineSense in January of 2012.  Doing so the company held their cost level with 2012 while improving their coverage.

PL90 created the opportunity for the Maine Wellness Association to launch a new program.  That new program translated to a choice for XYZ Builders that did not otherwise exist.  Not only did XYZ Builders enjoy participation in an innovative program of which they are now a part owner, they have experienced two years of equal or lower health insurance costs, an uncommon experience for a Maine small business.The employee single plan out-of-pocket limit fell from $3,500 excluding prescription out of pocket costs to $2,550 including prescription out of pocket costs.  January first of 2013, XYZ Builders renewed their plan unchanged with MaineSense at a 1% premium decrease.

Conclusion:

Creating viable, competitive health insurance markets should not be viewed as a “gift to insurance companies,” but instead should be recognized for what they truly are—a gift to the consumers who purchase through those markets.

When we focus on policies that stabilize our insurance markets, we see rates begin to stabilize, investment in new products, heightened competition, and innovative market entrants.  We also see a 56 year old woman with a newly transplanted heart able to afford her anti-rejection medications.  We see a small business lowering their cost rather than accepting a 23 percent rate increase.  We see another small business able to continue providing health insurance to its employees without having to ask them for a premium contribution.

PL90 is demonstrating who truly benefits when we free our markets to respond to consumer needs, the many individuals and small businesses in Maine who rely on private health insurance.

Notes and Sources

This is the first of a three-part series on customized learning in Maine. Download the report here.

By Amanda Clark

MHPC Education Policy Analyst

Customized learning is a student-focused system where kids enroll in the curriculum which best meets their educational needs.  Customized learning is not new and, in fact, is at the heart of Maine’s well-rooted educational history going back to the days of town academies.  Unfortunately, this individualized method of education never fully flourished to its full potential where every Maine child could thrive in a customized learning environment.

More than ever, Maine needs creative solutions for today’s kids.  Maine now faces a “Demographic Winter” where the shrinking number of children threatens the very sustainability of the current population level and economy.  As a consequence, falling student enrollments will mean fewer educational opportunities for today’s children.  Yet, specialized career interests, Gifted and Talented programs, apprenticeship opportunities, foreign language courses and more are all what make individual schools unique—almost as unique as the individual needs of our children.

For the sake of our kids and for the sake of Maine’s future, customized learning is the best way to grow our students and our economy.   Already a few tentative steps have been taken toward building a greater customized learning environment with the recent introduction of charter schools and online learning.  More still needs to be done.

This is the first study of a three-part series examining customized learning in Maine.

The second study will highlight successful examples of customized learning in Maine.  The third study will lay out a policy roadmap to customized learning for all Maine children.

Early Customized Learning: Town Tuitioning

Throughout the late 1700s and the 1800s, many private insitutions of learning, known as Maine’s town academies or independent schools, sprouted with various purposes concerned with the needs of children.  Some schools were founded on religious grounds, some

offered comprehensive boarding programs, and even one, the Carrabassett Valley Academy, originated as a tutoring establishment for those training in the arts of winter sports on Sugarloaf Mountain.

Berwick Academy was Maine’s first academy, founded in 1791, nearly thirty years before Maine became a state.  The people of Berwick, York, Kittery, Rollinsford, Portsmouth and Wells got together and financed the founding of Berwick Academy, to educate the “deplorable youth in this part of the country.” To give you an idea of Berwick Academy’s historical timeline, recall that also in 1791, the United States Bill of Rights was passed, King Louis XVI swore an oath as a “constitutional king” during the French Revolution, and Congress created the United States Mint.

The other 11 academies which continue to serve Maine’s students today are:

• Carrabassett Valley Academy (1982)
• Erskine Academy (1883)
• Foxcroft Academy (1823)
• Fryeburg Academy (1792)
• George Stevens Academy (1803)
• Gould Academy (1835)
• Hebron Academy (1804)
• Lee Academy (1845)
• Lincoln Academy (1801)
• North Yarmouth Academy (1814)
• Thornton Academy (1811)
• Washington Academy (1792)

Following an 1873 law which provided for the receipt of state aid by public schools, Maine’s legislature mandated in 1903 the local towns’ responsibility for the education of their respective residing school-aged kids.  Even with state aid, many of the towns, especially those in rural Maine, could not afford to build a local high school.  The solution in these situations was the ability for the child’s town of residence to send a “tuition” payment with each child to the public or private, religious or non-religious school of his choice.  Of course, many of the private schools at that time happened to be town academies.  Although many of the academies initially did have religious grounding and affiliations, over time, they secularized their missions.  Since 1980, due to a ruling by Maine’s highest court, religious schools may no longer obtain public funds.

Obamacare is, first and foremost, an affront to liberty-loving Americans who cherish their Constitutional right to determine their own healthcare.[i]  Obamacare puts liberty on the back burner in pursuit of a one-size-fits-all healthcare system to lower healthcare costs and, in the end, to boost job-creation.  However, as this first of three case studies will show, Obamacare will actually increase costs on employers which, in the long run, will mean fewer jobs.  As such, Obamacare’s much-touted health and economic benefits are simply not worth crushing American liberties and should be repealed.

Download full case study here (PDF)

Company Profile

The Maine company utilized for this illustration is a concrete company largely serving commercial construction contracts.  The company employs approximately 60 full-time year-round employees consisting of 10 employees working in the office or company headquarters and 50 working on job sites.

In the summer during the construction season, the company adds on average another 25 field positions, which increases the workforce on job sites to 75.  However, given the transient nature of the construction workforce, the company may actually hire up to 50 people in the summer—adding another 25 employees for the summer construction season.  Approximately half of the employees hired for the summer will leave the company within 30 to 90 days (25 employees), while the remainder will stay on for six to eight months (25 employees).

Pre-Obamacare Eligibility:  New employees become eligible for the company health insurance plan if they work, on average, at least 40 hours per week and have satisfied a six-month waiting period.

Total Eligible for Health Plan Pre-Obamacare: 

• There are 60 full-time year-round employees, but 25 of them waive the employer health insurance because they have coverage elsewhere or simply choose not to participate.
• There are 10 seasonal employees who work long enough to satisfy the six-month waiting period and qualify for health insurance coverage for an average of one month.

Table 2 translates the number of employees enrolled in health insurance into the total number of months they are enrolled during the year, based on the numbers outlined above.  Therefore, 35 employees enrolled for the entire year represents 420 (35 times 12) coverage months, while 10 employees enrolled for an average of one month of the year equals 10 (10 times 1) coverage months—for a total of 430 months.  This 430 months of health insurance coverage will be used to analyze the cost impact for the employer when the waiting period for health benefits is reduced to comply with Obamacare.

Post-Obamacare Eligibility:  New employees are considered eligible for the company health insurance plan if they work, on average, at least 30 hours per week and have satisfied a two-month waiting period.  Obamacare also redefines a full-time employee as working, on average, 30 hours per week.  Waiting periods cannot exceed 90 days from the hire date under Obamacare; therefore, the company waiting period must change to the first of the month following 60 days from the hire date—significantly reducing the waiting period in the real world.

The Obamacare employer mandate applies to all companies with 50 or more full-time employees, which qualifies this employer for all 12 months of the year.

Total Eligible for Health Plan Post-Obamacare:

• There are 60 full-time year-round employees, but 25 waive the employer health plan because they have health insurance coverage elsewhere or simply choose not to participate.
• There are 30 seasonal employees who work long enough to satisfy the 90-day waiting period and qualify for health insurance coverage for an average of four months.

Table 3 shows 35 employees enrolled for the entire year represents 420 (35 times 12) coverage months, but now includes 30 employees enrolled for an average of four months of the year equals 120 (30 times 4) coverage months—for a much higher total of 540 months versus  pre-Obamacare.

Health Plan Annual Cost

This employer offers a Health Savings Account (HSA) compatible health plan.  The employer contributes to the cost of the health plan premium.  In addition, the employer deposits money into individual Health Savings Accounts on behalf of employees and funds a portion of the plan out-of-pocket costs via a Health Reimbursement Arrangement (HRA).

To analyze the cost impact of Obamacare, the percentage participation in each coverage level was calculated, as was the employer monthly cost for the health plan, HSA and HRA.  An HRA is a promise by the employer to pay a portion of health care out-of-pocket costs.  Since expenses incurred will differ among employees year to year, the HRA represents a variable cost.  Therefore, the analysis utilizes the industry average HRA utilization of 30% and a monthly administrative cost of $5 per employee per month.

The annual HRA benefit equals $1,500 for employees covered as singles on the health plan and $3,000 for employees covering dependents on the health plan.

Table 4 below shows the four health plan coverage levels followed by the percentage of employees enrolled in each coverage level.  The third column shows the total monthly cost of the health insurance plan; and the fourth column illustrates the employer monthly contribution toward the health plan premium.  The next two columns show the employer monthly contribution to employee Health Savings Accounts and the monthly cost of the HRA.  The employee monthly total column is the sum of the employer premium contribution, HSA contribution and HRA expense.  This represents the total monthly cost per employee.

Table 2 above identifies a total of 430 coverage months.  This is multiplied by the percent of employees enrolled at each coverage level to arrive at the number of employee coverage months in the second to last column.  The final column illustrates the annual employer cost for each level of coverage and totals the employer annual cost below.  (Example: 283.8 employee only coverage months times $204 monthly employer cost = $57,753)

Higher Employer Costs from Obamacare

This case study primarily analyzes the impact of the Obamacare provision that requires employers to offer coverage to new employees no later than 90 days from the hire date.  This provision has a particularly acute impact on companies that currently impose a longer waiting period, which is common in the construction industry where employee turnover is high.

In the case of this particular company, based on the assumption that the health care plan benefits and cost remain constant, the annual cost increase because of Obamacare is almost $30,000.  This represents a 27% increase in the cost of providing health insurance benefits. It is interesting to note that if this employer chose to drop health coverage all together, the tax would fall somewhere between $60,000 and $110,000, which is significantly lower than their post-Obamacare health insurance cost of $137,932. By this accounting, some employers would find themselves faced with the need to drop health insurance altogether to keep from facing higher costs.

This analysis also does not account for the administrative costs associated with managing Obamacare eligibility rules, which in this case would be quite significant.

Conclusion

It is unfortunate that America’s policymakers did not thoroughly vet Obamacare before enacting it to verify that its grandiose economic claims of lower health insurance costs and increased jobs were based in reality.  Instead, Americans are now saddled with Obamacare and its unintended consequences. This case study of a real company finds that Obamacare will actually mean higher health insurance costs to employers and, in the long run, fewer jobs.  Liberty-loving Americans who value control of their own healthcare must demand that policymakers repeal Obamacare as soon as possible.

Methodology

This study makes a number of conservative assumptions:

First, the analysis assumes that the current health plan will meet minimum essential benefit and actuarial value standards as established by Obamacare and that employer contributions are adequate to meet minimum contribution requirements.  If either of these assumptions is false, the cost of the health plan could increase prior to considering the cost impact of covering a larger percentage of the workforce.

Second, the analysis also does not account for the fact that some of the employees currently eligible for, but who waive coverage, may join the company health plan to avoid penalties associated with the individual mandate, further adding to the employers cost.

Finally, the analysis also assumes that the employer in this case is not using the safe harbor provisions for calculating variable hour employees, which could potentially delay eligibility for their seasonal workforce.

Download full case study here (PDF)

An update of The Maine Heritage Policy Center’s “Fix the System” report on Maine’s welfare system shows that while recent reforms by the LePage administration have improved Maine’s system, more must be done to move Maine out of welfare dependency. The report takes advantage of updated numbers from the U.S. Census Bureau, the Department of Health and Human Services and other sources to show exactly where Maine’s welfare system stands.

Download full report here (PDF)

According to the report, Maine is the only state in the country to rank in the top ten of three major areas of welfare. Maine ranks sixth in percent of households receiving food stamps, second in the nation in percent of households receiving cash assistance and third in the country in percent of population enrolled in Medicaid. Only California and Vermont have a higher percentage of their populations enrolled in Medicaid.

This updated report makes it very clear once again that welfare reform should be a major issue for our leaders, said MHPC CEO and co-author of the report Scott Moody. While recent reforms have improved the system, more must be done to fix the system and free Maine families from welfare dependency.

The report highlights reforms that were a priority of the LePage administration and passed by the current Legislature. Many of the reforms had been suggested in the 2010 version of the “Fix the System” report. The reforms that were successfully implemented included a five-year limit on cash assistance, stricter sanctions for violation of program requirements, drug testing for welfare recipients accused of drug crimes, tightened Medicaid eligibility requirements, improvement of fraud detection and legal non-citizens waiting period for welfare benefits.

The report also highlights the many policies of Maine’s welfare system that continue to be out of the mainstream and push Maine to the top of welfare dependency nationally, including welfare eligibility levels that continue to be among the highest in the country. The report advocates for solutions to fixing Maine’s welfare system including increased tightening of eligibility levels, more effective use of diversion programs, further strengthen job search and work requirements, and increasing agency accountability and program management.

Maine has made some great strides in reforming our broken welfare system, but there is more work to be done to give Mainers the kind of system that is built to move our families from welfare to independence,” said Moody. “We must continue reform efforts to ensure that our welfare system focuses aid to the truly needy, encourages independence, rewards hard work and self-sufficiency and ultimately helps get Mainers back on their feet and freed from dependence.

Download full report here (PDF)

Download Full Report Here (PDF)

In June 2006, then-Governor John Baldacci signed legislation to counter the perception that the RPS law lacked environmental benefits. The new goal: Increase the amount of new renewable energy to 10 percent by 2017, with annual increases of one percent beginning in 2008 until the goal is reached.^[ii] Since these “Class I standards” consider only small generation plants reaching service after September 2005, the law will affect the fuel mix of Maine’s power industry.

The Beacon Hill Institute applied its STAMP^® (State Tax Analysis Modeling Program) to estimate the economic effects of these RPS mandates. The U.S. Energy Information Administration (EIA), a division of the Department of Energy, provides optimistic estimates of renewable electricity costs and capacity factors. We base our estimates on EIA projections, but we also provide three estimates of the cost of Maine’s RPS mandates ─ low, average and high ─ using different cost and capacity factor estimates for electricity-generating technologies from the academic literature. Our major findings show:

• The Maine RPS law will raise the cost of electricity by $145 million for the state’s consumers in 2017, within a low-range estimate of $120 million and a high-range estimate of $175 million
• Maine’s electricity prices will rise by 8 percent by 2017, due to the RPS law.

The increased energy prices will hurt Maine’s households and businesses and, in turn, inflict significant harm on the state economy. In 2017, the RPS will:

• Lower employment by an average of 995 jobs, within a range of 820 jobs and 1,165 jobs
• Reduce real disposable income by $85 million, within a range of $70 million and $100 million
• Decrease investment by $11 million, within a range of $9 million and $13 million
• Increase the average household electricity bill by $80 per year; commercial businesses by an average of $615 per year; and industrial businesses by an average of $14,350 per year.

Introduction

Maine has two different sets of Renewable Portfolio Standard (RPS) laws. The first went into effect in 1999 and, in effect, codified the existing 30 percent of retail energy derived from renewable sources. Maine’s abundant natural resources provided ample and cost-effective resources to produce renewable electricity.^[iii] Many small and efficient hydroelectric plants produced low cost energy at the same time electric utilities burned wood waste and other biomass byproducts. The 30 percent mandate had minor, if any, effect on the energy market in Maine.

The second, more recent RPS law, commonly referred to as the Class I standards, does not mandate a share of total production for renewables, like many state RPS laws. Instead, the law mandates that from 2017 onward, at least 10 percent of total retail electricity sales must be generated from new renewable sources.^[iv] The law requires that beginning in 2008 at least one percent of electricity must be from renewable generation plants reaching service after September 2005, increasing one percent each year until 2017.

Another component of the law – the use of Generation Information Systems certificates (GIS) – could help defray costs. GISs are similar to Renewable Energy Credits (REC), which account for production of renewable energy and are equivalent to one kilowatt hour of renewable production. RECs are tradable commodities that are certified to represent a unit of production of renewable energy. The GISs may only be banked for one year, so the actual cost effect will be minimal in subsequent years if electric utilities fail to exceed the mandate for the previous year.

By producing more renewable energy than required by the law, energy suppliers could bank credits to reduce future requirements. However, the Energy Information Administration (EIA) projections made prior to the law show a baseline scenario in which renewable electricity generations will fall below RPS minimums. Therefore, it is unlikely that producers will supply excess renewable energy to trigger significant banking. All renewable energy produced will go toward the requirement that year, not banked for future consumption. For this reason, we assume that the GIS certificates will have no effect on overall price of production.

Additionally, the law implements an Alternative Compliance Payment (ACP) that Utilities can pay instead of producing renewable energy. The ACP rate grows at the speed of inflation, and is currently set at $62.10 per MWh.^[v] Historically the ACP has not played much part in meeting the RPS for any utilities. The amount of money spent on ACPs has declined from $690,000 in 2008 to $20,000, or 0.3 percent of compliance costs, in 2010.^[vi] To calculate the true cost of the RPS law, we assume that the ACP will continue to play an insignificant role.

Since renewable energy generally costs more than conventional energy, many have voiced concerns about higher electric rates. A wide variety of cost estimates exists for renewable electricity sources. The EIA provides estimates for the cost of conventional and renewable electricity generating technologies. However, the EIA’s assumptions are optimistic regarding the cost and capacity of renewable electricity generating sources to produce reliable energy.

A review of the literature shows that in most cases the EIA’s projected costs can be found at the low end of the range of estimates, while the EIA’s capacity factor for wind to be at the high end of the range. The EIA does not take into account the actual experience of existing renewable electricity power plants. Therefore we provide three estimates of the cost of Maine’s RPS mandate: low, average and high, using different cost and capacity factor estimates for electricity-generating technologies from the academic literature.

Governments enact RPS policies because most sources of renewable electricity generation are less efficient and thus more costly than conventional sources of generation. The RPS policy forces utilities to buy electricity from renewable sources and thus guarantees a market for them. These higher costs are passed on to electricity consumers, including residential, commercial and industrial customers.

Increases in electricity costs are known to have a profound negative effect on the economy – not unlike taxes – as prosperity and economic growth are dependent upon access to reliable and affordable energy. Since electricity is an essential commodity, consumers will have limited opportunity to avoid these costs. For the poorest members of society, these energy taxes will compete directly with essential purchases in the household budget, such as food, transportation and shelter.

The Maine Heritage Policy Center and The Beacon Hill Institute at Suffolk University (BHI) estimates the costs of this RPS law and its impact on the state’s economy. To that end, BHI applied its STAMP^® (State Tax Analysis Modeling Program) to estimate the economic effects of the state RPS mandate.^[vii]

Estimates and Results

We estimate of the effects of Maine’s Class I RPS mandate using low, average and high cost scenarios of both renewable and conventional generation technologies. Each estimate represents the change that will take place in the indicated variable against the counterfactual assumption, or baseline, that the Class I mandate would not be in place. The Appendix contains details of our methodology. Table 1 displays the cost estimates and economic impact of the current RPS mandate in 2017, compared to a baseline.

Table 1: The Cost of the RPS Mandate on Maine (2012 $)

The current RPS will impose costs of $145 million in 2017, within a range of $120 million and $175 million. Over the entire period between 2012 and 2017, the RPS will cost Maine $655 million within a range of $535 million and $775 million. As a result, the RPS mandate would increase electricity prices by 1.24 cents per kilowatt hour (kWh) or by 8 percent, within a range of 1.01 cents per kWh, or by 6.6 percent, and 1.46 cents per kWh, or by 9.5 percent.^[viii]

The STAMP model simulation indicates that, upon full implementation, the electricity price increases due to the RPS law will negatively affect the Maine economy. The state’s ratepayers will face higher electricity prices that will increase their costs, which will in turn put downward pressure on household and business income. By 2017 the Maine economy will shed 995 jobs, within a range of estimates of 820 and 1,165 jobs.

The job losses and price increases will reduce real incomes as firms, households and governments spend more of their budgets on electricity and less on other items, such as home goods and services. In 2017, real disposable income will fall by an average of $85 million, between $70 million and $100 million under the low and high cost scenarios respectively. Furthermore, net investment will fall by $11 million, within a range of $9 million and $13 million.

Table 2 shows how the RPS mandate affects the annual electricity bills of households and businesses in Maine. In 2017, the RPS will cost families an average of $85 per year; commercial businesses $615 per year; and industrial businesses $14,350 per year. Between 2012 and 2017, the average residential consumer can expect to pay $365 more for electricity, while a commercial ratepayer would pay $2,715 more and the typical industrial user would pay $63,305 more.

Table 2: Annual Effects of RPS on Electricity Ratepayers (2012 $)

Emissions: Life Cycle Analysis

One could justify the higher electricity costs if the environmental benefits – in terms of reduced

greenhouse gases (GHG) and other emissions – outweighed the costs. In the previous sections we calculated and displayed the costs and economic effects to require more renewable energy in the state of Maine. The following section conducts a Life Cycle Analysis (LCA) of renewable energy and the total effect that the state Class I RPS law is likely to have on Maine’s emissions.

The burning of fossil fuels to generate electricity produces emission of gases as waste, such as carbon dioxide (CO₂), sulfur oxides (SO_x) and nitrogen oxides (NO_x). These gases are found to negatively affect human respiratory health and the environment (SO_x andNO_x) or said to contribute to global warming (NO_x andCO₂).

Many proponents of renewable energy, such as wind power, solar power and municipal solid waste (MSW) justify the higher electricity prices, and the negative economic effects that follow, based on the claim that these sources produce no emissions (see examples below). But this is misleading. The fuel that powers these services — such as the sun and wind – create no emissions. However, the process of construction, operation and decommissioning of renewable power plants does create emissions. This begs the question: Is this renewable energy production as environmentally friendly as some proponents claim?

“Harnessing the wind is one of the cleanest, most sustainable ways to generate electricity. Wind power produces no toxic emissions and none of the heat trapping emissions that contribute to global warming.”^[ix]

“Wind turbines harness air currents and convert them to emissions-free power.”^[x]

~Union of Concerned Scientists

“As far as pollution…Zip, Zilch, Nada… etc. Carbon dioxide pollution isn’t in the vocabulary of solar energy. No emissions, greenhouse gases, etc.”^[xi]

~Let’s Be Grid Free. Solar Energy Facts

The affirmative argument is usually based on the environmental effects of the operational phase of the renewable source (that will produce electricity with no consumption of fossil fuel and no emissions) excluding the whole manufacturing phase (from the extraction to the erection of the turbine or solar panel, including the production processes and all the transportation needs) and the decommission phase. LCA provides a framework to provide a more complete answer the question.

LCA is a “cradle-to-grave” approach for assessing industrial systems. LCA begins with the gathering of raw materials from the earth to create the product and ends at the point when all materials are returned to the earth. By including the impacts throughout the product life cycle, LCA provides a comprehensive view of the environmental aspects of the product or process and a more accurate picture of the true environmental trade-offs in product and process selection. Table 3 displays LCA results for conventional and renewable sources.

Table 3: Emissions by Source of Electricity Generation (Grams/kWh)

Coal and gas produce significantly more emissions of all three gases than all the other technologies. Nuclear and wind produce the least emissions of the nonconventional types, with solar and biomass significantly higher due to construction and decommission for solar and production and operations for biomass. However, the construction and decommission phases of wind and solar produce non trivial levels of emissions, with solar several factors higher than the others. Nevertheless, LCA analysis shows that wind, nuclear, solar and biomass produce significantly less emissions than coal and gas.

However, this LCA analysis is incomplete. The analysis shows that wind and solar technologies derive benefits from their ability to produce electricity with no consumption of fossil fuels and subsequent pollution without adequately addressing the intermittency of these technologies. These intermittent technologies cannot be dispatched at will and, as a result, require reliable back-up generation running – idling – in order to keep the voltage of the electricity grid in equilibrium. For example if the wind ceases, or blows too hard (which trips a shutdown mechanism in commercial windmills), another power source must be ramped up (or cycled) instantaneously. Therefore new wind and solar generation plants do not replace any dispatchable generation sources.

This cycling of coal and (to a much lesser extent) gas plants causes them to run inefficiently and produce more emissions than if the intermittent technologies were not present. As a result – according to a recent study – wind power could actually increase pollution and greenhouse gas emissions in areas that generate a significant portion of their electricity from coal.^[xii] The current LCA literature ignores this important portion of the analysis, which provides a distorted assessment of wind and solar power.

Nevertheless, even incorporating renewable sources does, in and of themselves, produce much less emissions than conventional sources renewable sources, displacing only a small amount of emissions from conventional sources. Indeed this amount is multiplied, due to lower capacity ratings of many green energy sources and required back-up generation.

To better judge the actual total benefit derived from switching from the current energy source portfolio to one that involves more renewable energy – as the RPS dictates in Maine – BHI compared the total emissions impact according to our projections using a life cycle analysis for the various energy sources. Table 4 displays the results.

Table 4: Change in Emissions Due to the Maine RPS Mandates

(‘000 metric tons)

The results are somewhat counterintuitive. The RPS mandates reduce emissions of CO₂ by 163,000 metric tons in 2017, with a total reduction compared to baseline of 728,000 tons between 2012 and 2017. If no back up capacity was required due to the intermittency issues of renewables, then the reduction would be more than three times as much. Surprisingly, SO_x and NO_x emissions show a slight increase compared to a baseline in all years. The reason for this is that biomass and wood waste – two large sources of renewable energy in Maine – emit large amounts of these two types of particulate matter.

Conclusion

Proponents of renewable energy in Maine were disappointed with the outcome of the first RPS laws in Maine. In effect it made legal requirements and consequences for what was already taking place in Maine. Where it was cost efficient, renewable energy was growing in Maine. But that was not enough for renewable energy advocates. In this paper we reviewed the implications of a new RPS law that began in 2008. This version, commonly referred to as Class I requirements, required that 10 percent of energy come from new renewable sources by 2017.

The most recent Maine Public Utilities Commission review of the RPS states:

“Assuming half of the wind generation proposed in the Interconnection Queue for Maine is developed over time (625 MW installed capacity) at a total investment cost of more than $2,000/KW at that and that 35 percent of the capital costs are spent in Maine this could result in approximately $560 million of investment in Maine. This level of investment will result in a roughly ($1.14 billion) increase in GSP and 11,700 jobs created during construction.”^[xiii]

This thinking – that the higher the cost of renewable technologies rise, the more investment and jobs the technologies create – is dangerous. For example, if investment cost rose to $4,000 per KW, then the resulting investment would rise to $1.12 billion and state GSP would rise by some derivative of $2.28 billion and job creation by 23,400. But what would that increase in investment cost mean for the price of wind energy that Maine’s households and business are mandated to purchase? The price would rise and hurt the state’s electricity consumers. Moreover, the investment spending has an opportunity cost in terms of the industries that might have received this investment in the absence of the RPS mandates.

Supporters of the Maine RPS use a hidden tax approach, with the quote above showing they fail to undertake any reasonable cost-benefit analysis backed up by economic reasoning. The Maine RPS puts the state’s robust competitiveness at risk. While the RPS may generate economic benefits, Maine electricity ratepayers will pay higher rates, face fewer employment opportunities, and watch investment flee to other states with more favorable business climates, resulting in net negative effects on the state.

Firms with high electricity usage will likely move their production, and emissions, out of Maine to locations with lower electricity prices. Therefore, the Maine policy will not reduce global emissions, but rather send jobs and capital investment outside the state.

Appendix

Electricity Generation Costs

As noted above, governments enact RPS policies because most sources of renewable electricity generation are less efficient and thus more costly than conventional sources of generation. RPS policies force utilities to buy electricity from renewable sources and thus guarantee a market for the renewable sources. These higher costs are passed to electricity consumers, including residential, commercial and industrial customers.

The EIA estimates the Levelized Energy Cost (LEC), or financial breakeven cost per MWh, to produce new electricity in its Annual Energy Outlook.^[xiv] The EIA provides LEC estimates for conventional and renewable electricity technologies (coal, nuclear geothermal, landfill gas, solar photovoltaic, wind and biomass) assuming the new sources enter service in 2016. The EIA also provides LEC estimates for conventional coal, combined cycle gas, advanced nuclear and onshore wind only, assuming the sources enter service in 2020 and 2035.

While the EIA does not provide LEC for hydroelectric, solar photovoltaic and biomass for 2020 and 2035, it does project overnight capital costs for 2015, 2025 and 2035. We can estimate the LEC for these technologies and years using the percent change in capital costs to inflate the 2016 LECs. In its Annual Energy Outlook, the EIA incorporates many assumptions about the future price of capital, materials, fossil fuels, maintenance and capacity factor into their forecast. Table 5 shows the EIA projects that the LEC for all four electricity sources (coal, gas, nuclear and wind) will fall significantly from 2016 to 2035. The fall in capital costs drives the drop in total system LEC over the period.

Using the EIA change in overnight capital costs for solar and biomass produces reductions in LECs similar to wind from 2016 to 2035. The biomass LEC drops by 38.7 percent and solar by 53.5 percent over the period. These compare to much more modest cost reductions of 5.2 percent for coal, an increase of 14.2 percent for gas, and a drop of 22.1 percent for nuclear over the same period. EIA does provide overnight capital costs for renewable technologies under a “high cost” scenario. However, for each renewable technology the EIA “high cost” scenario projects capital costs to drop between 2015 and 2035.

Table 5 displays capacity factors for each technology. The capacity factors measure the ratio of electrical energy produced by a generating unit over a period of time to the electrical energy that could have been produced at 100 percent operation during the same period. In this case, capacity factor measures the potential productivity of the generating technology. Solar, wind and hydroelectricity have the lowest capacity factors due to the intermittent nature of their power sources. EIA projects a 34.4 percent capacity factor for wind power, which, as we will see below, appears to be at the high end of any range of estimates for the nation.

Estimating a capacity factor for wind power is particularly challenging. Wind is not only intermittent but its variation is unpredictable, making it impossible to dispatch to the grid with any certainty. This unique aspect of wind power argues for a capacity factor rating of close to zero. Nevertheless, wind capacity factors have been estimated to be between 20 percent and 40 percent.^[xv] The other variables that affect the capacity factor of wind are the quality and consistency of the wind and the size and technology of the wind turbines deployed. As the U.S. and other countries add more wind power over time, presumably the wind turbine technology will improve, but the new locations for power plants will likely have less productive wind resources.

The EIA estimates of LEC and capacity factors paint a particularly rosy view of the future cost of renewable electricity generation, particularly wind. Other forecasters and the experience of current renewable energy projects portray a less sanguine outlook.

Today wind and biomass are the largest renewable power sources and are the most likely to satisfy future RPS mandates. The most prominent issues that will affect the future availability and cost of renewable electricity resources are diminishing marginal returns and competition for scarce resources. These issues will affect wind and biomass in different ways as state RPS mandates ratchet up over the next decade.

Both wind and biomass resources face land use issues. Conventional energy plants can be built within a space of several acres, but a wind power plant with the same nameplate capacity (not actual capacity) would require many square miles of land. According to one study, wind power would require 7,579 miles of mountain ridgeline to satisfy current state RPS mandates and a 20 percent federal mandate by 2025.^[xvi] Mountain ridgelines produce the most promising locations for electric wind production in the eastern and far western United States.

After taking into account capacity factors, a wind power plant would need a land mass of 20 by 25 kilometers to produce the same energy as a nuclear power plant that can be situated on 500 square meters.^[xvii]

The need for large areas of land to site wind power plants will require the purchase of vast areas of land by private wind developers, and/or allowing wind production on public lands. In either case land acquisition/rent or public permitting processes will likely increase costs as wind power plants are built. Offshore wind is vastly more expensive than onshore wind power and suffers from the same type of permitting process faced by onshore wind power plants, as seen in the 10-year permitting process for the planned Cape Wind project off the coast of Massachusetts.

The swift expansion of wind power will also suffer from diminishing marginal returns as new wind capacity will be located in areas with lower and less consistent wind speeds. As a result, fewer megawatt hours of power will be produced from newly built wind projects. Moreover the new wind capacity will be developed in increasingly remote areas that will require larger investments in transmission and distribution, which will drive costs even higher.

The EIA estimates of the average capacity factor used for onshore wind power plants, at 34.4 percent, appears to be at the higher end of the estimates for current wind projects. This figure is inconsistent with estimates from other studies.^[xviii] According to the EIA’s own reporting from 137 current wind power plants in 2003, the average capacity factor was 26.9 percent.^[xix] In addition, a recent analysis of wind capacity factors around the world finds an actual average capacity factor of 21 percent.^[xx] Moreover, other estimates find capacity factors in the mid-teens and as low as 13 percent.^[xxi]

Biomass is a more promising renewable power source. Biomass combines low incremental costs relative to other renewable technologies and reliability. Biomass is not intermittent and therefore it is distributable with a capacity factor that is competitive with conventional energy sources. Moreover biomass plants can be located close to urban areas with high electricity demand. But biomass electricity suffers from land use issues even more so than wind.

The expansion of biomass power plants will require huge additional sources of fuel. Wood and wood waste comprise the largest source of biomass energy today. Other sources of biomass include food crops, grassy and woody plants, residues from agriculture or forestry, oil-rich algae, and the organic component of municipal and industrial wastes.^[xxii] Biomass power plants will compete directly with other sectors (construction, paper, furniture) of the economy for wood and food products and arable land.

One study estimates that 66 million acres of land would be required to provide enough fuel to satisfy the current state RPS mandates and a 20 percent federal RPS in 2025.^[xxiii] When the clearing of new farm and forestlands are figured into the GHG production of biomass, it is likely that biomass increases GHG emissions.

The competition for farm and forestry resources would not only cause biomass fuel prices to skyrocket, but also cause the prices of domestically-produced food, lumber, furniture and other products to rise. The recent experience of ethanol and its role in surging corn prices can be casually linked to the recent food riots in Mexico, and also to the struggle facing international aid organizations that address hunger in places such as the Darfur region of Sudan. These two examples serve as reminders of the unintended consequences of government mandates for biofuels. The lesson is clear: biofuels compete with food production and other basic products, and distort the market.

Calculation of the Net Cost of New Renewable Electricity

To calculate the cost of renewable energy under the RPS, BHI used data from the EIA to determine the percent increase in utility costs that Maine residents and businesses would experience. This calculated percent change was then applied to calculated elasticities, as described in the STAMP modeling section.

In our cost analysis we only reviewed the costs for the Class I standards. Class II standards, we assumed, would have little or no cost due to the base line scenario already covering the requirements. To determine that cost of the Class I standards, we used EIA projections to determine the total retail sales into the future. Since the Class I standards require new renewable energy, we assumed that these are generation sources that would not have been created in a baseline scenario. So we multiplied the requirement percentage by the baseline scenario, and the resulting figure was the amount of MWhs that the state needs to add to meet the RPS requirements. This figure also represents the maximum number of MWhs of electricity from conventional sources that are avoided, or not generated, through the RPS mandate. We will revisit this shortly. Table 6, as follows, contains the results.

To estimate the cost of producing the additional extra renewable energy under an RPS against the baseline, we used estimates of the LEC, or financial breakeven cost per MWh, to produce the electricity.^[xxiv] However as outlined in the “electricity generation cost” section above, the EIA numbers provide a rather optimistic picture of the cost and generating capacity of renewable electricity, particularly for wind power. A literature review provided alternative LEC estimates that were generally higher and capacity factors that were lower for renewable generation technologies than the EIA estimates.^[xxv] We used these alternative figures to calculate our “high” LEC estimates and the EIA figures to calculate our “low” cost estimates and the average of the two to calculate our “average” cost estimates. Table 7 below displays the LEC and capacity factors for each generation technology.

We used the 2016 LEC for the years 2010 through 2018 to calculate the cost of the new renewable electricity and avoided conventional electricity, assuming that before 2016 LEC underestimates the actual costs for those years and for 2017 and 2018, the 2016 LEC slightly overestimates the actual costs. We assumed that the differences will, on balance, offset each other. For 2019 and 2020 we used the 2020 LEC. The assumption is that LEC will decline over time due to technological improvements over time.

We used the EIA’s reference case scenario for all technologies. Since capital costs represent the large component of the cost structure for most technologies, we used the percentage change in the capital costs from 2015 to 2025 to adjust the 2016 LECs to 2025. For the technologies that the EIA does not forecast LECs in 2020, we used the average of the 2016 and 2025 LEC calculations, assuming a linear change over the period.

Once we computed new LECs for the years 2020 and 2025 we applied these figures to the renewable energy estimates for the remainder of the period.

For conventional electricity we assumed that the technologies are avoided based on their costs, with the highest cost combustion turbine avoided first. For coal and gas, we assumed they are avoided based on their estimated proportion of total electric sales for each year. Although hydroelectric and nuclear are not the cheapest technology, we assumed no hydroelectric or nuclear sources are displaced since most were built decades ago and offer relatively cheap and clean electricity today.

Table 7: LEC and Capacity Factors for Electricity Generation Technologies

We also adjusted the avoided cost of conventional energy to account for the lower capacity factor of wind relative to conventional energy sources. We multiplied the cost of each conventional energy source by the difference between its capacity factor and the capacity factor for the renewable source and then by the ratio of the new generation of the renewable source to the total new generation of renewable under the RES. With coal, for example, we multiplied the avoided amount generation of electricity from coal (3.41 million MWhs in 2020) by the LEC of coal ($85.21 per MWh) and then by the difference between the capacity factor of coal and the weighted average (using MWs as weights) capacity factor of wind (37.4 percent). This process is repeated for each conventional electricity resource.

These LECs are applied to the amount of electricity supplied from renewable sources under the RES, because this figure represents the amount of conventional electricity generation capacity that presumably will not be needed under the RES. The difference between the cost of the new renewable sources and the costs of the conventional electricity generation Maine represents the net cost of the RPS. Tables 8, 9 and 10 on the following pages display the results of our Average, Low and High Cost calculations for the RPS, respectively.

We converted the aggregate cost of the RPS into a cost per-kWh by dividing the cost by the estimated total number of kWh sold for that year. For example, for 2017 under the average cost scenario above, we divided $147 million into 11,923 million kWhs for a cost of 1.24 cents per kWh.

Table 8: Average Cost Case RPS Mandate from 2012 to 2017

Table 9: Low Cost Case RPS Mandate from 2012 to 2017

Table 10: High Cost Case of a RPS Mandate from 2012 to 2017

Ratepayer Effects

To calculate the effect of the RPS on electricity ratepayers we used EIA data on the average monthly electricity consumption by type of customer: residential, commercial and industrial.^[xxvi] The monthly figures were multiplied by 12 to compute an annual figure. We inflated the 2010 figures for each year using the average annual increase in electricity sales over the entire period.^[xxvii]

We calculated an annual per-kWh increase in electricity cost by dividing the total cost increase – calculated in the section above ─ by the total electricity sales for each year. We multiplied the per-kWh increase in electricity costs by the annual kWh consumption for each type of ratepayer for each year. For example, we expect the average residential ratepayer to consume 6,691 kWhs of electricity in 2017 and we expect the average cost scenario to raise electricity costs by 1.24 cents per kWh in the same year. Therefore we expect residential ratepayers to pay an additional $83 in 2020.

Modeling the RPS using STAMP

We simulated these changes in the STAMP model as a percentage price increase on electricity to measure the dynamic effects on the state economy. The model provides estimates of the proposals’ impact on employment, wages and income. Each estimate represents the change that would take place in the indicated variable against a “baseline” assumption of the value that variable for a specified year in the absence of the RPS policy.

Because the RPS requires Maine households and firms to use more expensive “green” power than they otherwise would have under a baseline scenario, the cost of goods and services will increase under the RES. These costs would typically manifest through higher utility bills for all sectors of the economy. For this reason we selected the sales tax as the most fitting way to assess the impact of the RES. Standard economic theory shows that a price increase of a good or service leads to a decrease in overall consumption, and consequently a decrease in the production of that good or service. As producer output falls, the decrease in production results in a lower demand for capital and labor.

BHI utilized its STAMP (State Tax Analysis Modeling Program) model to identify the economic effects and to understand how they operate through a state’s economy. STAMP is a five-year dynamic CGE (computable general equilibrium) model that has been programmed to simulate changes in taxes, costs (general and sector-specific) and other economic inputs. As such, it provides a mathematical description of the economic relationships among producers, households, governments and the rest of the world. It is general in the sense that it takes all the important markets, such as the capital and labor markets, and flows into account. It is an equilibrium model because it assumes that demand equals supply in every market (goods and services, labor and capital). This equilibrium is achieved by allowing prices to adjust within the model. It is computable because it can be used to generate numeric solutions to concrete policy and tax changes.^[xxviii]

In order to estimate the economic effects of a national RPS we used a compilation of six STAMP models to garner the average effects across various state economies: New York, North Carolina, Washington, Kansas, Indiana and Pennsylvania. These models represent a wide variety in terms of geographic dispersion (northeast, southeast, midwest, the plains and west), economic structure (industrial, high-tech, service and agricultural), and electricity sector makeup.

First we computed the percentage change to electricity prices as a result of three different possible RPS policies. We used data from the EIA from the state electricity profiles, which contains historical data from 1990-2008 for retail sales by sector (residential, commercial, industrial, and transportation) in dollars and MWhs and average prices paid by each sector.^[xxix] We inflated the sales data (dollars and MWhs) though 2020 using the historical growth rates for each sector for each year. We then calculated a price for each sector by dividing the dollar value of the retails sales by kWhs. Then we calculated a weighted average kWh price for all sectors using MWhs of electricity sales for each sector as weights. To calculate the percentage electricity price increase we divided our estimated price increase by the weighted average price for each year. For example, in 2017 for our average cost case we divided our average price of 15.36 cents per kWh by our estimated price increase of 1.24 cents per kWh for a price increase of 8.2 percent.

Table 11: Elasticities for the Economic Variables

Using these three different utility price increases – 1 percent, 4.5 percent and 5.25 percent – we simulated each of the six STAMP models to determine what outcome these utility price increases would have on each of the six states’ economy. We then averaged the percent changes together to determine what the average effect of the three utility increases. Table 11 displays these elasticities, which were then applied to the calculated percent change in electricity costs for the state of Maine discussed above.

We applied the elasticities to percentage increase in electricity price and then applied the result to Maine economic variables to determine the effect of the RPS. These variables were gathered from the Bureau of Economic Analysis Regional and National Economic Accounts as well as the Bureau of Labor Statistics Current Employment Statistics.^[xxx]

Life Cycle Analysis

For our LCA we used various studies to determine what the cradle to grave emissions per MWh was, taking into account construction, decommission, operation and maintenance.

For coal we reviewed three different system types: An ‘average system’ that accounts for emissions from typical coal fired generation in 1995; New Source Performance Standards based on requirements put into effect for all plants built after 1978; and Low Emission Boiler Systems, which are newer, more efficient coal plants.^[xxxi] The LCA calculations account for various inputs including, but not limited to, mining, transportation of minerals, power plant operation as well as decommissions and disposal of a plant. Natural gas plants’ LCAs were based on the LCA for Gas Combined Cycle Power Generation plants, a type of plant that is similar to the majority of the natural gas plants in the United States.^[xxxii]

The LCA for wind power accounted for both onshore and offshore wind power, which has different values for manufacturing, dismantling, operation and transportation for each type.^[xxxiii] Solar photovoltaic estimates were wide ranging, but a Science Direct paper supplied an in-depth, comprehensive review.^[xxxiv] It reviewed three different types of crystalline silicone modules as well as a CdTe thin film version and induced many different costs such as emissions from building the module and frame (for the crystalline silicone version) as well as operation and maintenance emissions. For biomass and wood waste LCA we used a report that looked at the production of energy using wood and biomass byproducts to produce energy.^[xxxv] Different types of delivery systems (lorry, train and barge) for the fuel were identified, as well as construction, operation and decommissioning.

With total emissions per MWh calculated, we were able to use our in-house model to calculate the total emissions that would be added to and removed from the Maine energy system. The first calculation used the amount of renewable energy added per the Class I RPS law, as well as the amount of conventional power that would be removed, after accounting for capacity factor requirements to keep a constant amount of energy produced. Each MWh added was multiplied by its respective LCA emission, and then we subtracted the amount of conventional time LCA emissions. With a basic conversion from grams to metric tons, we had calculated the results seen in Table 5. An identical calculation was done, but not accounting for capacity factors.

Download Full Report Here (PDF)

Download full report (PDF)

The current Medicaid program was designed to cover medical services for particular categories of vulnerable individuals such as pregnant women, children, needy families, the blind, and the disabled.  Obamacare transforms Medicaid to meet the healthcare needs of the entire nonelderly population with income below 133% of the federal poverty level.[2] (See table 1)

Medicaid is a joint federal and State funded program.  The courts have previously ruled that the federal government can condition the funds provided to the States for Medicaid regarding how those funds can be utilized.  However, the Supreme Court found that the Medicaid expansion under Obamacare violated the Constitution by threatening States with the loss of their existing Medicaid funding if they decline to comply with the expansion.  In short, new federal funds for Medicaid can be conditioned on the states expanding eligibility as directed under the ACA, but existing Medicaid funding cannot be pulled should states choose not to comply with the expansion.[3]

Maine now faces a critical choice regarding whether or not to expand Medicaid under Obamacare.

Medicaid is already the single largest line item in most state budgets, representing 23.6 percent of total state spending in fiscal 2011.[4] In Maine, Medicaid or MaineCare represents 17% percent of total general fund spending, which translates to $483,312,486 in 2011 alone[5].  This excludes administrative costs including over $14 million for salary and benefits for MaineCare personnel.  Maine has already recognized the challenge of maintaining our current Medicaid program.

In 2006 the state was sued by a number of Maine Hospitals for its failure to pay for services rendered to MaineCare recipients dating back to 2003.  Even with the debt reduced by $250 million in 2011 from the LePage budget and corresponding federal matching funds, the debt was still near $400 million in January of this year.

Obamacare offers federal funding to cover the Medicaid expansion at 100% for 2014 with gradual decrease in federal funding to 90% by 2020.  Proponents argue that this “free” money is justification for expansion, but there are other considerations that should give us pause.

First and foremost, federal money is never free.  The government must first take by way of taxes and fees before it can give anything.  We are fooling ourselves by pretending that federal money is anything other than our own money.  The federal government absorbing the lion share of the cost of expanding Medicaid does not absolve us from the expense but rather obligates us as taxpaying citizens.

We should also be cautious in accepting the government’s word that federal funding will not drop below 90% for this expansion. Federal spending and mounting deficits are putting enormous pressure on our lawmakers to reduce future spending. Future congresses cannot be bound by today’s promises, nor should we dismiss the idea that today’s leaders will change the terms of the expansion. Even President Obama’s fiscal 2013 budget proposal recommends saving nearly $18 billion over 10 years by pushing more Medicaid costs to states.[6]

Also, federal funding is only for eligibility expansion, not administrative costs. With another Medicaid expansion, Maine would bear the cost of administering a projected 37,000 new enrollees, most of which will translate to new permanent fixed administrative expenses.

Medicaid enrollment has been increasing even prior to the ACA.  Nationally Medicaid enrollment increased 5.1 percent during fiscal 2011 and 3.3 percent in fiscal 2012, and is projected to increase by 3.6 percent in fiscal 2013.[7] Persistent growth in total spending is primarily the result of increased enrollment due to a lackluster economy and increased per capita costs for health care. Both of these root problems are likely to persist.

Increased Medicaid enrollment increases private insurance rates due to cost shifting. It is well documented that Medicaid and Medicare, the two largest governments subsidized insurance programs, under-pay providers for the services they deliver. Medicaid is estimated to reimburse hospitals at a rate of approximately 70 cents for every dollar spent to deliver care. Providers make up for this loss by overcharging private insurers.

If Medicaid enrollment was a direct transfer of the uninsured, you could argue that 70 cents is an improvement over the charity care often delivered to uninsured patients who do not pay their bills, but the facts don’t support this argument. Maine’s own experiment with Medicaid expansion has resulted in little to no reduction in our uninsured rates while private insurance enrollment has decreased (See chart 1 on next page).  When we expand Medicaid, many people who would otherwise purchase private insurance drop coverage for the free, taxpayer-funded coverage offered under Medicaid.[8]

We should also not lose sight of how governments control cost in health care.  The spring 2012 Fiscal Survey of States, a report prepared by the National Governors Association, outlines the Medicaid cost containment measures proposed by Governors for fiscal 2013 budgets.  25 states proposed freezing or reducing provider rates.[9] We have to ask ourselves what incentives this creates for doctors or those who may be considering entering the medical profession and ultimately how this could affect quality of health care in general.

 Chart source[10]

Conclusion

As we look to the long-term future of health care in Maine we must also consider the incentives we are creating for future generations. Medicaid began as a program for particular categories of vulnerable individuals, and now it is being used to assist all citizens, including the able-bodied, below an arbitrary income level.  As a young person entering the workforce, earning $14,856 means free health care.[11]  Increase those earnings by just one dollar, to $14,857, and that same young person not only loses the free coverage, but it becomes an obligation to purchase coverage or else face a penalty.

The culture of dependency created by our current Medicaid system is straining the very fabric of our nation and our state. Our decisions today will impact the future for our children, not only in regards to financial obligations, but also in regards to the culture that will shape their generation.

Sources:

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Of course, this large personal income tax burden comes at an economic price which manifests itself, in part, through the out-migration of people and income to greener pastures. This study examines Maine’s net migration to states without a personal income tax from 1995 (the first year of available data) to 2009 (the latest year of available data) based on data from the Internal Revenue Service.[4] [5]

Analysis of this migration data shows that Maine has lost people and their income to the nine states that have no personal in-come tax for nearly every year in this time-period. Cumulatively, as shown in Table 1, Maine has lost 11,486 people and $661,274,000 in income. The lost income is a conservative estimate because it does not account for the compounding of in-come over time and the multipliers of the income as it ripples through the economy.

Additionally, this lost income has consequences for Maine’s state and local governments through reduced tax receipts. Table 1 shows that Maine’s state and local governments have, conservatively, lost $87,004,000 over this time-period.

The first step in the long road back to economic recovery is to give these out-migrants a reason to stay or return home. This evidence strongly suggests that eliminating the personal income tax would help level-the-playing-field and give Maine a fight-ing chance to convince residents to stay put. It would also help Maine’s small businesses and create new jobs.[6]

Voting with Their Feet 

Chart 1 and Table 1 shows how much income, as measured by taxpayer’s Adjusted Gross Income, that Maine has lost between 1995 to 2009 to states with no personal income tax. On average, every year $44,085,000 in income leaves the state to never re-turn. Chart 1 shows that not only would Maine’s net in-migration of income be higher than it was for nearly every year over this time-period, but that the last two years of net income losses would have remained positive.

Chart 2 and Table 1 shows how many people, as measured by taxpayer’s claimed exemptions, that Maine has lost between 1995 to 2009 to states with no personal income tax. On aver-age, 776 people leave the state to never return. Chart 1 shows that Maine’s net in-migration would have been higher than it was for nearly every year over this time-period.

As a result of this out-migration of people and their income, Maine’s state and local governments have also suffered through reduced tax receipts. Table 1 shows that Maine’s state and local governments have, conservatively, lost $87,004,000 in income taxes, sales taxes, property taxes, etc. over this time-period.

Keep in mind that all of the income and tax loss estimates are based on the net out-flow of income in only the year of migration. However, this income is lost forever which means the compounded losses are significantly larger—$1 lost 15 years ago is worth $15 compounded in every year. Also, these income and tax loss estimates do factor in the lost multipliers this money would have had on Maine’s economy as it ripples through the economy—$1 spent may eventually become $3 or $4 in total economic activity. As such, these are very conservative estimates.

Conclusion 

Opponents of eliminating Maine’s personal income tax will, quite predictably, lament the loss of state tax revenue. However, this analysis clearly shows that the loss revenue is significantly lower than commonly assumed. The economic benefits of stem-ming, or even reversing, the out-flow of people and their income to states with no personal income tax would be a boon to the economy.

On a static basis, eliminating Maine’s personal income tax, in FY 2010, would cost the state approximately $1.3 billion. On a dynamic basis, which would factor in things like reversing out-migration and the creation of new businesses and jobs, the loss in state revenue would be significantly lower. This study only highlights the economic benefits of stemming Maine’s out-migration to states with no personal income tax.

In the near future, a more comprehensive dynamic tax analysis will be performed to get a fuller accounting of the positive bene-fits of eliminating Maine’s personal income tax on taxpayers, the economy, and state and local government. Stay tuned.

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Notes and Sources: 

[1] Moody, J. Scott, “Higher Taxes? Not with Maine’s High Tax Burden,” The Maine Heritage Policy Center, Path to Prosper-ity, February, 23, 2012. http://www.mainepolicy.org/wp-content/uploads/Path-to-Prosperity-Maines-Tax-Burden-022112-2.pdf

[2] State tax data is from the U.S. Department of Commerce: Census Bureau and can be found here: http://www.census.gov/govs/statetax/ 

[3] Keating, Raymond J., “Small Business Survival Index 2011,” Small Business & Entrepreneurship Council, November 2011, pg. 40. http://www.sbecouncil.org/uploads/SBSI2011%5B1%5D.pdf The top rate will fall to 7.95 percent in 2013 which would drop Maine to the 9th highest in the country.

[4] The IRS state migration data by state can be found here: http://www.irs.gov/taxstats/article/0,,id=212683,00.html 

[5] The states without a personal income tax include Alaska, Florida, Nevada, New Hampshire, South Dakota, Tennessee, Texas, Washington, and Wyoming.

[6] Moody, J. Scott, “Who are Maine’s ‘Rich?’,” The Maine Heritage Policy Center, Path to Prosperity, April, 11, 2012. http://www.mainepolicy.org/wp-content/uploads/Path-to-Prosperity-Who-Are-Maines-Taxpayers-031812.pdf 

In these dark economic times, the siren call of “tax the rich” is hurled at policymakers as the solution to Maine’s budget woes. The accusation is that the rich are just sitting on their money and having government take it from them in order to spend it will get the economy moving again.

Of course, this caricature of the rich does not stand up to reality. This study analyzes data from the Internal Revenue Service for 2009 that shows the dramatic differences in the composition of income by various income groups.[1] Simply looking at total income can be misleading if one does not understand the underlying income dynamics because today’s tax code includes a mishmash of personal and business income. This study also provides an illustrative example of how this can happen.

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The “Rich” are Overwhelmingly Married and Entrepreneurial 

Chart 1 shows the various characteristics of Maine’s taxpayers by income group as a percent of tax filers within each income group. The first item to note is how marriage affects the perception of “rich” versus “poor.” For those earning under $50,000, the percent of married filers is 24.6 percent, but for those earning over $200,000 the percent of married filers is 86.6 percent.

This makes perfect sense since in today’s economy where both husband and wife are in the workforce, marriage often results in a doubling of household income. A single person earning $40,000 would fall in the “under $50,000” category whereby a couple earning a combined $80,000 would show up in the “$75,000 to $100,000” category. By this metric, the married couple misleadingly looks better off economically than the single person.

The second item to note is that the percentage of taxpayers that have some kind of business income soars at the income levels over $200,000—93.2 percent of taxpayer have interest income, 71 percent of taxpayers have dividend income and 63.3 percent of taxpayers have capital gains income.

More importantly, the percentage of taxpayers that have partnership/S-corporation income is dramatically higher for those earning over $200,000 (45.2 percent). This is almost three times as high as those earning between $100,000 and $200,000 (15.9 percent). The income from S-corps is particularly problematic because, unlike C-corps, S-corp income is taxed at the personal level. This pass-through income is not necessarily indicative of the taxpayers actual financial condition (see next section).

Along the same lines, the Tax Foundation published a booklet titled “Putting a Face on America’s Tax Returns” which shows that “the vast majority of taxpayers who face the highest marginal tax rates [meaning high-income] tend to be married couples. But aside from being married, they also tend to be dual-income, residents of high-cost urban areas, older, college educated, and engaged in business activities.”[2]

Graduated/Progressive Marginal Income Tax Rates Discriminate Against Entrepreneurs 

For tax year 2011, Maine’s had four tax rates: 2 percent, 4.5 percent, 7 percent and 8.5 percent. These increasing tax rates are called a “graduated” or “progressive” income tax rate system and can lead to all kinds of economic distortions. To see how, let’s compare two hypothetical taxpayers—a single taxpayer with wages of $50,000 and a married couple (with two children) with combined wages of $100,000, S-corp income of $50,000, capital gains income of $20,000 and interest/dividend income of $5,000.

As shown in Table 1, taxes for the single taxpayer are straightforward and amounts to a tax bill of $2,842. As a percent of total and actual income (there is no difference for this taxpayer), this amounts to a tax burden of 5.7 percent.

The married couple’s tax bill is not so simple. The problem stems from the S-corp income which is derived from a family-owned business that is in financial trouble. The business needs to make some necessary investments to stay competitive so, for the next few years, all profits will be retained to fund them. However, the profits must still be distributed to shareholders for taxation. So, this family’s share of profits comes to $50,000 even though they won’t actually receive $50,000—this is often referred to as “phantom income.”

As a result of having to pay taxes on the S-corp income, the family will have to sell some stocks resulting in $20,000 worth of capital gains. They also receive $5,000 in interest and dividends from personal savings for col-lege/retirement. The end result is that they will owe $12,413 in Maine income taxes.

While on paper it looks like this family is better off than the single taxpayer, the single taxpayer is getting a much better tax deal with a tax burden on actual income of 5.7 percent. The family’s income may be 3.5 times higher, but their tax bill is 4.4 times higher thanks to Maine’s increasing marginal tax rates. Adding insult to injury, their overall tax burden is significantly higher, almost double the single taxpayer, at 9.9 percent of actual income—keep in mind that they never received the $50,000 from the S-corp which was kept by the business as retained earnings.

Conclusion 

This simple illustration shows that the interplay between personal and business income within the tax code can lead to misleading conclusions about the actual financial condition of taxpayers. A “high-income” family with children and a business could be one step away from financial disaster while a “low-income” single taxpayer may be more stable and well-off.

As a result, comparing a taxpayers in the “under $50,000” income category with a taxpayer in the “over $200,000” income category is not an apples-to-apples comparison. Only 3.3 percent of taxpayers earning less than $50,000 have any partnership/S-corp income while 45.2 percent of all taxpayers earning more than $200,000 have partnership/S-corp income. It is absurd to compare the income of an individual with that of a doctor’s office, yet that is exactly what is happening when using income tax data to make such comparison between aggregate income groups.

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Notes and Sources: 

[1] The IRS income tax data by state can be found here: http://www.irs.gov/taxstats/article/0,,id=171535,00.html 

[2] Hodge, Scott A., “Putting a Face on America’s Tax Returns,” The Tax Foundation, 2005. http://taxfoundation.org/files/dba37618d9c2d2df02f24766ac4cc39d.pdf 

[3] Nutter, Sarah E., Wilkie, Patrick J. and Young, James C., “Corporate Business Activity Before and After the Tax Reform Act of 1986,” 1996 SOI Winter Bulletin. http://www.irs.gov/pub/irs-soi/86cobusact.pdf 

[4] For more information, see SOI Bulletin Historical Table 21: http://www.irs.gov/taxstats/article/0,,id=175902,00.html 

As Maine’s state government grapples with budget shortfalls in the Department of Health and Human Services budget and a recent lawsuit aimed at rolling-back necessary pension reforms, the calls for higher taxes on Maine’s beleaguered private sector will grow louder. However, policymakers must resist the siren call for higher taxes as Maine’s tax burden is significantly higher than commonly believed.

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The standard methodology for calculating tax burdens is to divide total state and local (S&L) tax collections by total personal income. This method is flawed because personal income includes both private and public sector sources of income. This distinction between the two sectors is important because only the private sector creates new income.

The public sector can only redistribute income through taxes and spending. More specifically, public sector spending consists of personal current transfer receipts (Medicare, Medicaid, Social Security, etc.) and government employee compensation (federal, state and local).

As shown in Chart 1, under standard tax burden calculations, Maine’s S&L tax collections as a percent of personal income was 11.7 percent in Fiscal Year (FY) 2009. Nationally, the tax burden was 10.5 percent.

However, as previous research has shown, Maine’s private sector as a percent of personal income was a dismal 63.9 percent in 2010—the 12th smallest private sector in the country.[1] In the long run, this matters to overall economic well-being since, on average, a 1 percentage point increase in the size of the private sector yields an increase in household income of $2,561.

Therefore, a more appropriate measure of Maine’s tax burden is using S&L tax collections as a percent of private sector personal income. Under this measure, as shown in Chart 1 and Table 1, Maine’s tax burden soars to 18 percent of private sector personal income in FY 2009—the 6th highest in the country and the highest in New England. In fact, Maine’s tax burden as a percent of private sector income is 54 percent higher when measured as a percent of personal income (11.7 percent versus 18 percent).