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THE EAGLETARIAN 2012 Copyright 2012

The Journal The Eagletarian, sponsored by Boston College Economics Association, is an academic research journal in which Boston College students from all disciplines can contribute to the advancement of economic research. We serve to: 1) Provide awareness for the importance of economics in the real world. 2) Highlight the use economics in various academic disciplines. We hold the exclusive right to edit, print, and distribute all submissions once accepted. You may submit to The Eagletarian at

The Organization The Boston College Economics Association was created to provide forums for discussion about economic issues among students, faculty, and the greater Boston College community. The Economics Association will facilitate this discussion through 1) luncheons in which we provide presentations through the Career Center and through the Economics Department 2) panel presentations and lectures, which increase contact between students and faculty as well as professions with experience in the application of economics, and 3) The Eagletarian.

The Members Sam Hocking: Co-President Jacklyn Murray: Co-President Bryan Patenaude: Senior Event Coordinator Jenna Grippo: Event Coordinator Eric Parolin: Event Coordinator Christine Taylor: Secretary Stephen Sikora: Treasurer Vinh-Khoi Le: Director of Design and Publication Raissa Horimbere: Editor Jack L. Hill: Editor 2

Table of Contents {01} Balanced Budget Amendment A Viable Solution to the Debt Crisis? Anthony Goodwin

{11} Exploring a Legitimate Cost-Benefit Analysis of the Boston Marathon Dj Adams

{23} An Econometric Analysis of the Relation between Consumer Confidence Indices and Consumer Spending Eric Parolin, Jingni Wei, Margaret Foley

{53} The Nuclear Dilemma: How to Address the Nuclear Waste Concerns in the U.S. Sven Benson


Balanced Budget Amendment: A Viable Solution to the Debt Crisis? Anthony Goodwin

Balanced Budget Amendment A Viable Solution to the Debt Crisis? Anthony Goodwin Introduction This paper will examine the effects of adding a balanced budget amendment to the United States Constitution. In recent decades, the federal debt of the United States has grown to levels only seen during World War II. There have been great public outcries to lower public debt but there is no fiscal plan in place to reduce the public debt. Recently, the Hatch-Lee Balanced Budget Amendment was introduced to Congress as one way of solving the debt crisis. The amendment states that the president must always submit a balanced budget to Congress and two-thirds Congressional majority is needed for expenditure to exceed 18 percent of the gross domestic product in any given year or to pass any bill that would levy a new tax or increase the rate of any tax (Wall Street Journal, “The Balanced-Budget Backfire”). While supporters state that a balanced budget amendment forces Congress into lowering the public debt, others claim that decreased spending will hurt the economy and favor wealthy organizations over poorer interest groups. This paper will weigh the benefits of deficit spending to promote the general welfare against the growing financial problems due to the increasing government debt. Beginning of Government Debt Following the conclusion of the Revolutionary War, Secretary of the Treasury, Alexander Hamilton, proposed a charter for the First Bank of America (Day 6). Britain’s management of national debt heavily influenced Hamilton. In England, the public debt grew from £16.7 million in 1700 to £245 million after the American Revolution (id). Even with growing national debt, England continued to prosper and maintain a global empire. Britain’s success was highly dependent on the structure of the bank of England that allowed the government to modernize revenue collection and successfully monetize debt (id). Using the Bank of England as a model, Hamilton used the First Bank to issue bonds and liquefy debt. The monetization of currency allowed funds to flow smoothly to where they were needed for commerce and industry. By creating long-term public debt, Hamilton was able to honor all war debts, establish strong credit for the United States, and provide the necessary funds to operate the government (id). The New Deal and the Rise of Keynesian Economics In the mid 1930s, a new economic school of thought known as Domesticated Keynesianism gained popularity in America. Keynesianism pro2

moted tax hikes during economic growth and tax cuts and increased spending during downturns (Thorndike 2). The goal of Keynesian economics is to support wage levels, which in turn increases consumption and encourages growth. President Roosevelt used Keynesian theory as the model to take America out of the Great Depression. New Deal spending started with the National Industrial Recovery Act that authorized $3.3 billion in newconstruction spending (id). The Roosevelt administration would continually champion counter-cyclical tax cuts and increased spending. From the 1920s to the 1930s, the average total spending of the government as a percentage of the GDP rose from 11.989 percent during the twenties to 19.206 percent during the thirties (TreasuryDirect). The spending of the thirties spurred a revolution in fiscal policy as the United States government has continued to use increased spending to spur the economy and balanced budgets have become a rare occurrence (id). Analysis In relation to a balanced budget amendment, proponents will argue:  The United States Debt is climbing at an alarming rate.  Interest rates place too much of a burden on future generations.  Certain programs are costing the government far more than their benefits. In contrast, opponents of the Hatch-Lee Balanced Budget Amendment will argue:  The current deficit is largely due to war and will fall during times of peace.  Spending is an investment for the future.  The Balanced Budget Amendment will benefit the wealthy at the expense of the poor. Proponents Primarily, the public debt of the United States is growing at nearly an exponential rate1. As it clearly shows, the public debt in the United States continues to grow every year and future projects do not show any sign of spending cuts. By following the current spending model, the public debt will climb to nearly 25 trillion dollars by 2015. Equally as important as the total debt is the steep slope of the spending curve. If the government does not change the current spending policy, the debt will only increase further and lay the burden of payment on future generations. Additionally, the debt as a percentage of the GDP has grown to over 100%. This means that the government debt is larger than all of the goods and services produced in the United States per year. If the government continues to increase spending even though the (1) Refer to Figure 1 and Figure 2 in the appendix


debt is already higher than the GDP, there will be no possible way to pay off the government debt. In order to ensure that the debt is lowered, Congress must be forced to curb spending. A balance budget amendment prevents the government from running a deficit and therefore will change the slope of the debt curve from positive to negative. A negative slope indicates the economic situation is improving and future generations will hold less public debt. Continuing on the subject of government spending, the interest accrued from outstanding debt will become more of a problem as the debt increases. In 2009, interest payments accounted for 8.5 percent of the total government spending (Repici 6). Within the next few years, the interest payments will grow to over 10 percent with no fiscal plan to reduce the debt (id). Young and future Americans are more adversely affected by deficit spending because the benefits of deficit spending are obtained when the debt is incurred but future taxpayers pay for the expenses. As the debt increases, the interest rates on debt will only rise as well. Instead of paying the interest on debt incurred by previous generations, more funds could be allocated to other spending programs. Through deficit spending, current generations are able to reap benefits but also put the cost onto taxpayers of the future. Moreover, opponents of a balanced budget amendment argue that pending cuts will hurt the United States in the long run because many types of government spending are investments that have long term benefits. However, spending on investments like education does not nearly account for as much spending as do programs that are projected to lose money in the future like Social Security, Medicare, and Medicaid. In the 2011-projected budget, education only accounted for 4 percent of total government spending where Medicare and Medicaid combined for 24 percent and Social Security accounted for 21 percent (Budget of the US Government 2011, Section 13). These percentages are staggering considering that in 1968, Medicare and Medicaid only accounted for 4 percent of government spending, and Social Security accounted for 13 percent of the budget (Repici 6). Furthermore, one of the most effective ways to cut the deficit will be to cut spending to the entitlement programs previously mentioned. The Social Security and Medicare systems are both set to run deficits in the near future. By 2037, retirees will only receive 75 percent of promised benefits from Social Security (id). Medicare’s standing is much worse because Trust Fund assets will run out by 2017 and by 2035, taxes will only pay for 50 percent of Medicare benefits (id). Following this further, the major contributor to the increased spending on Medicare, Medicaid, and Social Security is the aging population (id). To reduce the costs of entitlement programs, reduce the federal debt, and balance the budget, the government could raise the minimum age for Social Security, Medicare, and Medicaid. After significantly decreasing spending on entitlements, the government could allocate more funds to education, infra4

structure, and defense all while cutting down on overall spending. Opponents While supporters of a balanced budget amendment claim there is a growing problem with the federal debt, the current state of the US economy is consistent with the country’s entire economic history. Every spike in the federal debt can be attributed to economic depression or wars, which do not reflect the normal economic cycle of the country. 2 There are noticeable increases in the public debt in 1812, 1861, 1917, 1929, 1941, 1984, and 2001. The dates refer respectively to The War of 1812, the American Civil War, World War I, The Great Depression, World War II, the nuclear arms race during the Cold War3, and the current War on Terror. After each event, (besides the Great Depression, which led into WWII) the government spending falls considerably. The US government’s current deficit is normal for times of war. Following the previous trends of history, the debt will fall once the United States is no longer engaged in military conflicts and the economy is operating at a normal cycle. Additionally, some deficit spending is used for investing rather than consumption. By investing and forgoing savings today, the United States will have more assets in the future. The federal government spends billions of dollars on all levels of education. Included in the budget are programs for the upkeep of school buildings and libraries (Repici 6). Also, the Department of Education provides states with resources to hire more teachers and fund grant and loan processes that allow millions of students to attend college and graduate schools (id). The government can also save through the federal loan program where the government saves $0.02 on every dollar loaned (id). Investing in education benefits the future workforce and taxpayers by providing them with better potential for increased earnings. By implementing deficit spending today, the government can help create a stronger workforce and increase productivity in the economy. When production increases, the government will have more funds and can decrease the public debt in the future. Pursuing this further, infrastructure and improvement spending are other methods for the government to reap long-term benefits. Roads, bridges, parks, playgrounds, energy transmission facilities, and expanded Internet access all fall under the umbrella of infrastructure (id). Creating better roads and bridges allows for better commerce and travel that can be usable for years or decades. In addition, the government also spends a large amount of money on environment programs to improve the water supply and increase the national energy capacity from sources such as solar, coal, wind, oil, and gas. By spending money on developing new energy technology, the (2) Refer to Figure 3 in the appendix for visual reference (3) The arms race was increased in 1984 with the Strategic Defense Initiative (Federation of American Scientists)


United States will become less dependent on foreign oil and will overall spend less on energy. Once the government reduces the spending on energy and increases income from commerce, there will be more funds to pay off previous debts. Moreover, the Hatch-Lee Balanced Budget Amendment will benefit well-organized and wealthy interest groups while unorganized groups would no longer receive the necessary government funding. For example, the companies that have expressed strong support of a balance budget amendment include the National Association of Home Builders, the National Association of Realtors, and the Financial Executives, Inc (Staudt 12). All of these organizations are much more wealthy than the organizations expressing opposition to the amendment such as the Advocates for Youth and the Coalition for Low Income Community Development (id). After undergoing massive expenditure cuts, there will not be enough funds for every organization calling for government aid. The organizations most likely to receive federal funding will be well organized and have the money to support legislators in upcoming elections. Without government support, groups that mostly help lower income people will eventually fail. Passing a balance budget amendment may eliminate federal debt now but it will be at the expense of many American citizens. Conclusion Proceeding into the future, it is unlikely Congress will pass the HatchLee Balanced Budget Amendment. Placing an expenditure cap at 18 percent of the GDP when the government spending for 2009 was 42.40 percent of the GDP would require the government to cut too much spending too quickly (Budget of the US Government 2011, Section 1). Also, with the current trend of increasing deficit spending, it does not appear that Congress would favor large budget cuts. However, failing to pass the Amendment does not mean the federal debt will continue to increase to unmanageable numbers. Throughout American history, the federal debt has peaked during times of war and declined during times of peace. If the government is not funding an expensive war and has a strong fiscal policy, the debt problem can be overcome. In order to decrease federal debt, the government will have to make cuts and eventually balance the budget. Once the War on Terror is over, government spending will decrease significantly. Then during peacetime, the government will also have to work on spending in other areas. Since Social Security, Medicare, and Medicaid contribute to a large percent of government spending and will not remain profitable in the near future, it would be logical to raise the minimum age of entitlement payments. By cutting these programs during peacetime, the government could eliminate a large percentage of the government debt. Reducing the public debt will take many years and require significant budget cuts but can be done without a balanced budget amendment as long as Congress develops a plan for expenditure cuts in the future. 6

Bibliography Day, C. C. (2001). Investor Power & Liquidity: Corporations, Capital Markets and the Industrialization of America. Lewis & Clark College The Journal of Small and Emerging Business Law Repici, L. (2010). Taxation Without Gestation: The Constitutionality of Our $ 13 Trillion National Debt. Charlotte Law Review Staudt, N. C. (1998). Constitutional Politics and Balanced Budgets. University of Illinois Law Review, Thorndike, J. J. (2010). The Fiscal Revolution and Taxation: The Rise of Compensatory Taxation, 1929-1938. Law and Contemporary Problems (2010). Budget of the United States Government, fiscal year 2011. Executive Office of the President. (2010). Historical debt outstanding US Department of the Treasury, Bureau of the Public Debt. The Balanced-Budget Backfire. (2011, November 18). Wall Street Journal, 16.


Appendix Figure 1

(US Department of the Treasury, “Historical Debt Outstanding) Figure 2

(US Department of the Treasury, “Historical Debt Outstanding)


Figure 3

(US Department of the Treasury, “Historical Debt Outstanding)



Exploring a Legitimate CostBenefit Analysis of the Boston Marathon DJ Adams

Exploring a Legitimate Cost-Benefit Analysis of the Boston Marathon DJ Adams Abstract This paper explores the characteristics of marathons that make them unique from a cost-benefit analysis in comparison with other large-scale sporting spectacles. Sporting teams, stadia, and events have lately become popular destinations, either purposefully or indirectly, for investments from the public sector. Generally these subsidies are justified by complex analyses, which maintain notable clout despite the fact that many leading economists have shown these studies to be highly inaccurate ways of measuring economic impact. Supporters of numerous mega-events falsely overstate the true economic impacts they contribute to the particular region in which they are held, particularly their focus on employment. When looking at the Boston Marathon, however, there is strong evidence that certain sporting events actually could merit local governmental support if the correct CBA is created. Though initially the numerical economic impact of these glorified runs may seem falsely enhanced by biased officials, it can be shown through future quality-of-life impacts, the import substitution effect, and remedies of supposed costs that marathons are worthy investments that possess positive externalities and support local public funding. Introduction On Patriot’s Day, April 18 of this past year, the Boston Marathon took place for the 115th time. The classic 26.2-mile route, one that champions grueling sections of hills as well as screaming crowds of fans, has become one of the premier sporting events across the globe. Despite its stiff entry requirements, according to, Boston’s large-scale event, run by the Boston Athletic Association (BAA), still touts the third-largest total of finishers of any marathon in the United States. In 2011, 23,913 runners crossed the yellow line, including citizens of 67 different countries, yet they weren’t the only humans impacted by the immensity of the event (“2011 Boston Marathon Statistics,” 1). An estimated 500,000 spectators span the stretch that begins in rural Hopkinton, Mass. and finishes on Boylston Street in downtown Boston each year to support, volunteer, and participate in the many activities associated with the spectacle (“Boston Marathon Weekend,” 1). The Greater Boston Convention and Visitor’s Bureau (GBCVB) has confirmed that no sporting event in Boston draws a larger or more diverse media corps, as more than 1000 members of the media from 200 separate outlets covered last year’s big run (“Boston Marathon Weekend 2011,” 1). Though the Boston is at the top of the list when it comes to popular12

ity, by no means is the marathon a unique occurrence across the country. Approximately 503,000 finishing times were recorded in 2010 from 483 timed marathons, and this year’s total is expected to be larger (“USA Marathoning: 2010 Overview,” 1). Staging marathons is an increasingly common phenomenon in U.S. cities. The aggregate number of events has risen 77.5 percent in the past decade alone. Between 2009 and 2010, however, while overall participants multiplied, only one new marathon completed its inaugural run. Therefore, widening expanses of people enjoy running marathons for fitness or personal reasons, and are finally finding specific locations for doing so. One’s attachment to the metropolitan area of a particular marathon is now as crucial to the participant as the event itself. Much like how runners flash their extensive training and talents on race day, event organizers in each city holding a marathon hope to demonstrate both to foreign tourists and festive locals the vitality of their respective communities. Marathons have transcended their traditional role, as the mega-events in today’s interconnected society can be a stimulus to a local economy. In fact, the GBCVB estimated that this year’s Boston Marathon accrued an astounding $132.2 million in spending impacts for the greater Boston area (“Boston Marathon Weekend 2011,” 1). In the context of sport, economic impact is defined as the net economic change in a host community that results from spending attributed to a sport event or facility (Turco & Kelsey, 1992). However, many calculations solely utilize input-output modeling in their findings, and attempt to justify an event’s importance on the basis of the economic benefits they will bring without any other externalities or values of community image. According to economist John Crompton, this is often a result of officials commissioning studies in response to increasing political pressures that hold them accountable for the power of tax dollars in a struggling economy (Crompton, 1995). Problems abound with this limited approach. Issues include, but are not limited to, exaggerating visitor numbers and expenditure, failure to properly deduct residents’ expenditures prior to modeling, abuse of multipliers, and the tendency to ignore the various costs associated with special events, like opportunity costs, fees borne especially by locals, or displacement costs (Dwyer & Forth, 2009). Not only does this amount to a worthless estimation of the true economic impact of a given public venture, a terrible precedent emerges when economists frequently revisit government press releases and cite significant issues of inconsistency. Therefore, a project that actually poses a proper cost-benefit might be ignored due to the several misallocated events that failed in the past (Crompton, 1995). The vindication of that worry is the primary goal of this paper. A common remedy asks one to construct a cost-benefit analysis composed of thoughtful consideration. It must identify all the sources of true benefits and costs associated with the event, not just those present under an input-output model, through application of clever qualitative and proxy-quantitative tech13

niques. Perhaps more importantly, though, the CBA should exclude those bogus benefits that have a knack for sneaking their way into various analyses. This can be an incredibly difficult task to perform, especially without extensive knowledge of data construction at one’s disposal. The nature of the study possesses much uncertainty. But all that matters is the expected net benefits of an event in allocating scarce resources to its support. Thus, all detailed assessments of mega-events should require a proper CBA analysis. However, the marathon is a unique sporting event that differs greatly from sporting functions previously investigated. Its structure, possessing many oddities, asks one to stray significantly from many of the common assumptions used in past economic analyses and CBA studies. As a result, a method combining the strengths of the two techniques will lead to the proper conclusion for whether the event is worth having and if it should be supported by government funds. In the absence of concrete data, as extensive details concerning the spending habits and personality profiles of the marathon’s participants were unattainable, this study will outline the benefits specific to a marathon, use statistics from past analyses performed on other popular marathons, and propose those models which would best examine the true value of the event should further information on the Boston become more readily available. Unique benefits When performing an analysis of a mega-event, there are several costs and benefits that must be included in the measure of its worth. The advantages of the marathon can be classified into three distinct groups: resident consumer benefits, surpluses to local businesses, and benefits to resident labor above any opportunity costs. In regards to resident consumer benefits, specifically, the profits from this group can be further subdivided into three sources: consumer surpluses, satellite activities, and quality-of-life impacts, the last of which provides the greatest benefit. First, there are considerable consumer surpluses derived from local spectators. The Boston Marathon in 2011 enticed 500,000 total viewers to line the streets of its route, as well as contribute $10 million, or about 7.56 percent of the marathon’s profit, to the overall economic growth of the Boston community (“Boston Marathon Weekend 2011,” 1). What makes the consumer surplus especially substantial in the marathon’s case, however, is that none of the wealth is due to ticket sales. While there is an entry fee to participate in the race, those who come for the sole purpose of observing are clearly willing to pay more than the non-existent ticket price in order to attend. A simple survey and contingent valuation process could be performed to first evaluate local spectators’ willingness to spend, and then later denote whether this surplus is indeed large (Tresch, 2008). The lack of ticket sales also de14

creases a portion of the BAA’s pre-event planning expenses for ticket distribution and hosting costs of admitting the spectators into a given area upon proof of voucher, fees which economists frequently outline as a noteworthy portion of operating costs (Chen, 2008). Second, benefit can be acquired from satellite activities that serve as supplements to the marathon. The John Hancock Sports & Fitness Expo, for example, is just one of many large conventions held the weekend leading up to the marathon’s completion. Attendees browse among more than 200 exhibitors, and experience what was once voted the “Best Runner's Expo in the Country” by Runner's World Magazine ( Dwyer and Forsyth showed retail galas that promoted the 2005 Grand Prix in Melbourne to produce $1.9 million for the surrounding area, a number one can safely assume is easily surpassed by the Boston considering that the various satellite events in Australia hosted just a few thousand people (Dwyer & Forsyth, 2009). The Hancock, meanwhile, accrues 80,000 yearly attendees. The third and final resident consumer benefit, which is perhaps the most important in analyzing the question of whether the marathon deserves a public subsidy, is the indirect enjoyment, or quality-of-life impacts, that are created by the event. These externalities are difficult to estimate, but as one economist claims, “it’s glorious fun for the fans, with the event giving residents pride that their city could produce such a tournament” (Chen, 2008, 49). This occurs by externalities and public goods. A public good must either be non-rival, meaning one’s consumption of the event does not decrease another’s ability to do so, or non-exclusive, in which case people cannot be billed for consuming that good. Externalities increase a consumer’s welfare by enhancing his life in a way ignored by market transactions (Siegfried, 2006). In the case of the Boston Marathon, both conditions are satisfied. In fact, each person’s enjoyment may enhance the amusement of other consumers in a cooperative effect. The marathon provides communities with a sense of identity and spirit, regardless of social status, that few other goods can replicate. One hundred fifteen years of success allowed the Boston Marathon to become a glorious tradition for the greater Boston area. Methods such as contingent valuation, hedonic price estimation, and demand curve analysis gauge consumer surplus and have been performed to estimate this value before, but most are simply rough sketches of value (Siegfried, 2006). Chen recommends using these tools, but also incorporating measures of media interaction and charitable contributions into the analysis (Chen, 2008). The Boston Marathon’s extensive media coverage has already been cited, with over 1000 members present for the weekend. This presence contributes about $10 million directly to the economy, but also indirectly promotes Boston as the destination for a premier running experience. Through its Official Charity Program, according to the BAA’s website, the marathon also raises more than $10 million annually for 31 separate charities in the greater Boston area, and has accumu15

lated over $122 million in the program’s 24-year existence. These impacts have an immense impact on fueling future growth and good feelings within the Hub and are the most important reasons further public subsidy by the local government can be justified. Very few other mega-events possess similar qualities. Resident consumer benefits aside, surpluses to local businesses may also be measured as proper benefits. The 26.2-mile span of marathons allows those cities that specifically construe their routes through small towns and suburbs to develop significant economic benefits above opportunity costs. While there may be a significant crowding-out effect applied if the event were held solely in downtown Boston, as spending would have taken place elsewhere within the city limits regardless of the marathon’s existence, smaller communities along the route experience large crowds of consumers on Marathon Monday that otherwise would never have frequented the outlier suburbs. A large portion of spectators line the route rather than wait for the race’s finish downtown, which contributes to this benefit for more rural neighborhoods. Lastly, an event may result in additional employment in the subregion. Labor surpluses are generated because some jobs go to unemployed or underemployed workers who can be hired on a more immediate basis to meet excessive demand (Dwyer & Forsyth, 2009). Surplus results when labor is employed at a wage rate higher than what employees would have normally accepted to enter a contract. The Marathon’s primary costs are those related to security and injury prevention. One source estimates fees upwards of $7 million per year (“Boston Marathon’s Money Behind the Race,” 1). Though it is natural to assume full employment in economic analysis, because Patriot’s Day is a local holiday in Massachusetts, the officers must be paid overtime for their additional work and could even be considered unemployed for the duration of the celebration. Therefore, a small source of surplus already exists. When this fact is coupled with the immense volunteer efforts involved, however, the surplus would likely grow to large levels. 8,000 volunteers openly assisted the marathon efforts without pay, and in 2011, 2,000 additional men and women were turned down due to the excess supply of labor (“About Boston Athletic Association,” 3). Granted it could be costly from a medical or policing perspective, if it were possible for average citizens to volunteer for positions of precaution and safety, chances are the Boston community would bear the costs and oblige without pay. This means that the surplus is likely of tremendous value. There are numerous alternative sources of benefits present within the marathon’s structure, but the three specific ones noted above concern rewards that other mega-sporting events cannot necessarily lay claim to. Import Substitution Effect One highly contested issue within analysis of large-scale events such as the marathon is to what degree local spending should be included in economic analysis. Many economists invariably recommend that expenditures by 16

local residents be intentionally removed from all equations, as true impact attributable to an event relates exclusively to the influx of money from outsiders of a given community (Crompton, 1997). The expenditures of Boston locals should remain absent, they argue, because their spending is simply a recycling of money that would have been inevitably spent for some substitute purpose. This drastically alters economic impact for an event like the Boston Marathon, where the benefits outlined above largely exist due to extensive participation in the event by local inhabitants. The supposed influence the marathon has lessens remarkably. However, there exists evidence that combats this concern. Donald Getz, a leading economist at the University of Calgary, states, “There is some evidence to suggest that major events do keep some residents at home who otherwise would leave the area for a trip. And it is also probable that a community with attractive events encourages more local spending for entertainment and merchandise” (Crompton, 1997, 27). Viewing local subsidiaries under this lens has been titled the import substitution effect, and it generally advocates the inclusion of local expenditures on the basis of consideration that new money locally spent is really money retained to the host community that would otherwise have been squandered. Though survey analyses of the Boston Marathon’s participants and their spending habits are not publicly available, Steven Cobb of Xavier University demonstrated the undervaluation that occurs when one ignores import substitution in an analysis of the 2006 Cincinnati Flying Pig Marathon. This study can be used as a framework for how one would proceed in attaining data for the Boston, but also proves the possibility that the Boston Marathon contains even more fiscal value than originally thought. In the 2006 Flying Pig, there were 4,178 runners who finished the race, 60% of them from outside the Cincinnati metropolitan area. Local finishers indicated by survey that 45% of them were “sure” they would have run in another race that weekend had the Flying Pig not taken place, a significant amount (Cobb, 2007, 113). Each local marathon participant estimated that they would have spent about $615 in an out-of-town race. The economic analysis of the Flying Pig that proceeded Cobb’s investigation eliminated local spending from the equation, resulting in an understatement of economic impact of about 25% as it failed to account for the $800,000 that would have gone to another community were the marathon not to take place (Cobb, 2007, 114). In total, 23,913 individuals crossed the finish line in the 2011 Boston Marathon. Of those who finished, 4,505 were residents of Massachusetts, or about 18.9% (“2011 Boston Marathon Statistics,” 1). Considering that most of the 500,000-plus spectators of the event are from the nearby area, a sizeable portion of local interest exists in the marathon. Reiterating that Patriot’s Day is a local holiday, it is safe to assume that many of these dedicated runners (since the Boston Marathon requires impressive qualification times) and their local supporters would have taken the opportunity to vacation elsewhere to 17

compete in another marathon or enjoy the long weekend elsewhere were the Boston not to exist. Therefore, if local residents are excluded from the economic assessment of the Boston Marathon, an inaccurate reading will most likely result. Since Boston has generally higher cost-of-living expenses than Cincinnati, using a $615 spending per person figure would actually be quite conservative, and the overall underestimation of value that would result from excluding local expenditures could potentially be substantial. The biggest reason for including local participants, however, could be their significant volunteer efforts. Cobb’s analysis properly measures the expenditures of a marathon, but his Flying Pig Marathon lacks the philanthropic drive of the Boston Marathon. The civic pride that results from the local community’s active participation in the marathon is an important benefit. Both economically and personally, utility is increased for many Bostonians as a result of volunteer activities. Therefore if asked through contingent evaluation what they would be willing to pay in order to retain the marathon, it is reasonable to assume the locals would offer large sums. These are valuations, though not based on actual expenditures, which should be performed in any useful cost-benefit analysis. Remedying costs and bogus benefits Even with much evidence to prove the Boston Marathon’s extensive value, there are arguments against its worth that cite expensive costs. As previously noted, the Marathon costs about $7 million per year to perform, a number that must be shared by the many cities along the route. The city is shut down for an entire day, which causes numerous costs not easily valued numerically such as time and convenience for those in the immediate area attempting to accomplish other tasks besides viewing the spectacle. The sheer number of tourists might cause locals to take the extra holiday and their disposable income elsewhere. In terms of benefits, many could be largely overstated. A local government’s decision to invest in the marathon would likely be based on the fact that economic growth would result. Increasing funding, whether it be the construction of a new route path or some other investment would increase jobs, but for an event that only occurs once a year. Wouldn’t putting that sum toward a stadium or public project with more daily relevance be a better judgment? It turns out that the marathon answers these dilemmas better than one might think. While the event does take considerable cash and planning to produce, 70% of the contributions come directly from corporate sponsors at this point. The Marathon has locked up deals with Adidas and John Hancock through 2023 and 2024, respectively (“Boston Marathon’s Money Behind the Race”). In addition, the towns of Hopkinton, Ashland, Framingham, Natick, Wellesley, and Brookline, the cities of Newton and Boston, and the Commonwealth of Massachusetts received a combined $753,000 from the BAA for 18

their compliance and policing efforts this past marathon (“About Boston Athletic Association,” 3). This number is expected to increase to over $800,000 next spring. In addition to offsetting costs, these funds provide marginal profit for the towns. The financial support is usually funneled directly into youth development programs and other local initiatives intended to promote future growth in the greater Boston area. Addressing the issues of tourism, there is a cap limit on the number of participants able to enroll in the event. This keeps the overall number of expected visitors to Boston essentially constant from year to year, an oftenoverlooked convenience that allows hotels to better plan the crazy weekend without too much unexpected variation. Patriot’s Day enables any locals truly flustered by the influx of foreigners to pack up and enjoy the added day off, solving issues of vexation, but also subtly entices members of the community to embrace the tradition of Marathon Monday and receive numerous qualityof-life benefits from choosing to do so. It’s that willingness to be a part of the tradition that prevents nuisance costs to the community. So many volunteers exist that individuals are turned down from helping out. The people bear the costs openly, and must get a legitimate benefit from doing so. It seems that private charity, in the case of the marathon, imposes a win-win proposition. Rather than negatively viewing their daily contributions as a necessary evil for proper end-results equity, the altruistic impulses are a portion of the individual utility functions of the volunteers. Under this public choice perspective, the donors receive benefits along with the direct beneficiaries of their efforts. Everyone’s utility grows. The quality-of-life impacts that result from this event cannot be understated. Nuisance to the community, therefore, is not a pressing issue for the Boston Marathon. Though public funding of the Marathon probably wouldn’t provide significant employment results for the Boston area, it wouldn’t necessarily have to. The excess labor supply that already exists in the market for volunteers could be remedied by giving these people work based on what the government chooses to fund. If it were route maintenance or updated facilities, for example, these extra volunteers would do the job willingly without requiring wages. For an event that has lasted over 100 years, there is a constant need for route maintenance and updated facilities. These projects could be financed by the local government each year, and finally provide those who are willing to volunteer but previously were unable to do so a proper purpose. To reiterate, there seems to be a significant quality-of-life benefit associated with volunteering at the Boston Marathon. Therefore, the aggregate social welfare of the local community would increase from the added workers. Conclusion This paper addresses important false assumptions related to accurately estimating the economic impact of sporting events. Though many large19

scale spectacles such as the Super Bowl and the Olympics receive constant flak from economists for a lack of true economic impact, and perhaps rightfully so, it can be shown that there are circumstances where popular sporting events increase the overall well-being of a local community. For 115 years in Boston, that has been the case with the Boston Marathon. Some of these benefits include consumer surpluses and impacts of supplementary events. But there are also unique quality-of-life improvement factors that a marathon possesses, which one must assess. Marathons contain a significant amount of import substitution that when excluded from analysis results in a gross underrepresentation of total economic impact. Using the methods outlined in Cobb’s study of the 2006 Flying Pig Marathon in Cincinnati, the Boston Marathon can be shown to own identical characteristics and similar results. Lastly, bogus benefits and ignoring of costs can undermine the value of any study. Even those most commonly cited arguments against the valuing of megaevents fail to offset the Boston Marathon’s strong societal worth due to extensive volunteer efforts. Though these results and musings are preliminary, they present a valid argument for assessment as to whether the Boston Marathon would be worthy of public subsidies and investments. If the findings above hold true, and the marathon does prove to be an event with positive externalities, then it would be wise to investigate through means of survey analysis or hedonic price estimation what the precise worth of this glorious race is to the average Bostonian.


Bibliography Bibliography "2010 USA Marathon Statistics and Report." - Marathons, Running Directory and Community. Web. 13 Dec. 2011. < Articles/2010RecapOverview.cfm>. "2011 Boston Marathon Statistics." Boston Athletic Association - Web. 3 Dec. 2011. <>. "About Boston Athletic Association." Boston Athletic Association. Web. 3 Dec. 2011. <http://>. "Boston Marathon Weekend 2011 Will Mean $132.2 Million for Greater Boston Economy." GBCVB. Web. 3 Dec. 2011. < press-releases-/503-general/2793-boston-marathon-weekend-2011-will-mean-1322million-for-greater-boston-economy>. Chen, Na. What Economic Effect Do Mega-Events Have on Host Cities and Their Surroundings? Diss. Howard College, 2008. ProQuest. Web. 3 Dec. 2011. Cobb, Steven, and Douglas Olberding. "The Importance of Import Substitution in Marathon Economic Impact Analysis." International Journal of Sport Finance 2 (2007): 108-18. Print. Crompton, John L. "Economic Impact Analysis of Sports Facilities and Events: Eleven Sources of Misapplication." Journal of Sport Management 9 (1995): 14-35. ProQuest. Web. 3 Dec. 2011. Dwyer, Larry, and Peter Forsyth. "Public Sector Support for Special Events." Eastern Economic Journal 94.35 (2009): 481-99. ProQuest. Web. 3 Dec. 2011. "John Hancock Sports & Fitness Expo." Conventures, Inc. - Marketing, Communications, Special Events. Web. 13 Dec. 2011. < -sports-a-fitness-expo.html>. Notte, Jason. "Boston Marathon's Money Behind the Race - TheStreet." Stock Market Today Financial News, Quotes and Analysis - TheStreet. 18 Apr. 2011. Web. 3 Dec. 2011. <>. Siegfried, John, and Andrew Zimbalist. "The Economic Impact of Sports Facilities, Teams and Mega-Events." The Australian Economic Review 39.4 (2006): 420-27. ProQuest. Web. 3 Dec. 2011. Tresch, Richard W. "Example 20.3 Justifying Public Subsidies to Professional Sports Teams with Economic Impact Analyses." Public Sector Economics. Abingdon: Routledge, 2008. Print.



An Econometric Analysis of the Relation between Consumer Confidence Indices and Consumer Spending Eric Parolin, Jingni Wei, Margaret Foley

An Econometric Analysis of the Relation between Consumer Confidence Indices and Consumer Spending Eric Parolin, Jingni Wei, Margaret Foley Introduction Consumer expenditure accounts for over 70% of the U.S economy. Consumer spending has the power to determine the pace at which we recover from the last recession. As a consequence, financial industry experts closely follow consumer confidence in the economy, as it has proven to be a strong indicator of future consumer expenditure. Different measures have been developed to gauge consumerâ&#x20AC;&#x2122;s sentiments about the current state of the economy and their expectations about the future of the economy. Three reliable measures that have been widely used and published are the University of Michigan Index of Consumer Sentiment, the Conference Board Consumer Confidence Index, and the Bloomberg Consumer Comfort Index. Each index uses different means to gather data on consumer sentiment across the United States, and transforms this data to arrive at a number reflecting consumer confidence for that month. The purpose of this paper is to determine which consumer confidence index best reflects and correlates with consumer expenditure. It is important to understand the other explanatory variables for consumer expenditure in order to compare the consumer confidence indices. Aside from consumer confidence, consumer expenditure depends on many factors, including the level of real disposable household income, interest rates and availability of credit, changes in household financial wealth, and changes in employment and unemployment. These variables must be controlled for when looking at the effectiveness of each consumer confidence index. The University of Michigan gathers data to produce three measures that gauge consumer attitudes toward the overall business climate, state of personal finances, and consumer spending. The three figures released each month are Index of Consumer Sentiment (CS), Index of Current Economic Conditions (ICC), and Index of Consumer Expectations (ICE). This paper will focus on the Index of Consumer Sentiment. In order for the monthly change to be considered significant at the 95% level, the Index of Consumer Sentiment must change by at least 4.8 points. The survey is a rotating panel survey based on a nationally representative sample that gives each household in the United States and equal probability of being selected. Interviews are conducted throughout the month by telephone. This data is widely used by a broad range of business firms, financial institutions, and federal agencies. The five questions asked by the University of Michigan survey are: Do you think now is a good or bad time for people to buy major household items? [good time to buy; uncertain/depends; bad time to buy]; Would you say that you (and your family living there) are better off or worse off financially than you were a year ago? [better, same, worse]; Now turning to busi24

ness conditions in the country as a whole—do you think that during the next twelve months, we’ll have good times financially or bad times or what? [good times; uncertain; bad times]; Looking ahead, which would you say is more likely—that in the country as a whole we’ll have continuous good times during the next five years or so or that we’ll have periods of widespread unemployment or depression, or what? [good times; uncertain; bad times]; Now looking ahead—do you think that a year from now, you (and your family living there) will be better off financially, or worse off, or just about the same as now? [better; same; worse]. By comparing this index to other indices as a reflector of consumer expenditures, its validity as a measure of consumer sentiment will be more evident. The Conference Board Consumer Confidence index is a “barometer of the health of the U.S. economy from the perspective of the consumer” (Consumer Confidence Survey 1) The Consumer Confidence Survey uses an address-based mail sample design, deriving the sampling frame from the files created by the U.S. Postal Service. This represents nearly universal coverage of all residential households in the United States, and the files are updated monthly to ensure up-to-date coverage (CCS 2). To develop each month’s random sample from the household universe frame, the Consumer Confidence Survey uses a probability sample design. First, the frame is stratified geographically to provide a proportionate geographic distribution, followed by a systematic sample of household addresses. The sample addresses are then used for the mailing (CCS 2). The calculation of the Index is based on five survey questions, to which the responses can be “positive,” “negative,” or “neutral.” For each question, the positive figure is divided by the sum of the positive and negative to yield a proportion, called the “relative” value (CCS 2). The average relative value for the year 1985 is the benchmark used to create the index value for that question. The Consumer confidence index is the average of all five indices (CCS 2). The five questions are as follows: How would you rate present general business conditions in your area? [good; normal; bad]; What would you say about available jobs in your area right now? [plentiful; not so many; hard to get]; Six months from now, do you think business conditions in your area will be [better; same; worse]? Six months from now, do you think there will be [more; same; fewer] jobs available in your area? How would you guess your total family income to be six months from now? [higher/same/lower] (Bram & Ludvigson 61). By testing this index against other indices, we can decide if this method yields an index that accurately reflects consumer expenditures. The Bloomberg Consumer Comfort Index measures American’s perceptions on three important variables: the state of the economy, personal finances, and whether it is a good time to buy needed goods or services. Unlike the University of Michigan Consumer Confidence Index and the Conference Board’s Consumer Sentiment Index, the Bloomberg Consumer Comfort 25

Index is generated every week. In order to compare it to the other two indices we used the index generated in the last week of each month, because the index is a rolling average of each week in that month. The information to generate the index is gathered by telephone interviews with a random sample of 1,000 consumers aged 18 and over. Each week, 250 respondents are asked for their views on the economy, personal finances, and buying climate; the percentage of negative responses is subtracted from the share of positive views and divided by three. The comfort index can range from 100 (if every participant in the survey had a positive response to all three components) to negative 100 (if all views were negative). Literature Review One piece of literature we found to supplement our analysis is a paper by Jason Bram and Sydney Ludvigson called “Does Consumer Confidence Forecast Household Expenditure?” This report compares the Michigan and Conference Board indices, but not the Bloomberg index. Both Michigan’s and Conference Board’s Consumer Confidence/Sentiment Indices question households based on present and future conditions. The Conference Board’s present conditions component takes a “snapshot” approach, asking respondents to evaluate current business conditions and job availability. Therefore, the Conference Board’s present conditions component takes into account the nation’s unemployment rate, and year-over-year changes in the index are closely correlated with payroll employment growth (Bram 61). Michigan’s questions deal more with the advisability of big-ticket household purchases, asking respondents to assess changes in their own financial situation. Therefore, their present conditions component is less closely tied to labor market conditions; it tends to reflect recent changes in the economy rather than the level of economic activity (Bram 62). Looking at the differences, it appears that Michigan’s index generally peaks in the early stages of economic recovery when growth is high, while the Conference Board’s peaks in the late stages of economic expansion when unemployment is low and the level of economic activity is high. Charting the two indices based on present conditions components shows they are not closely correlated [Figure 1] Conversely, the questions regarding consumers’ expectations for both the Michigan Index and Conference board are comparable. The Michigan index asks about expected business conditions over the next year and five years, and expected changes in the respondent’s financial situation over the next year. The Consumer Confidence Board poses questions on expected changes in business conditions, job availability, and respondents’ income over the next six months. The expectations components of the two surveys are highly correlated (Bram 62). In general, it is difficult to compare the index levels because of differences in survey methodology, index construction, and base year. Therefore, 26

monthly changes must be compared on a standardized basis rather than in absolute terms. Bram and Ludvigson suggest that a one-point move in Michigan’s index is roughly comparable to a two-point move in the Conference Board’s index (63). Returning to the question of how to treat the present and expected components of the indices, these authors believe that for the Conference Board index, it is particularly useful to examine the present conditions and expectations components individually. The level of the present conditions component serves as a good indicator of the level of activity, while the expectations component is more closely correlated with the rate of economic growth (Bram 63). In Michigan’s survey, both components are closely correlated and generally indicate the pace of economic growth (Bram 63). After looking at the elements of the Bloomberg index, it appears it reflects more of the current level of economic activity rather than economic growth. The paper goes into depth about how the two indices are calculated. They use an example “current month” and “prior month” to show the calculation of the indices [Figure 2]. Michigan calculates a diffusion measure by adding the difference between the positive and negative percentages to 100. Thus, the value at the time the paper was written is 112 [100 + 24 – 12] , and the prior month’s level is 120 [100 + 30 – 10] . Next, an index is constructed by dividing the level of the diffusion measure by the base period level of 110, and then multiplying by 100. In this example, this calculation yields a value of 101.8 [112/110 *100] in the current month, down from the prior month’s value of 109.1 [120/110 * 100], a drop of 7.3 points. If the Conference Board were to use the same raw responses, it would calculate the diffusion measures by dividing the positive response percentage by the sum of the positive and negative response percentages. This method gives a value of 66.7 [24/(24+12) * 100] for the current month and 75 [30/(30+10) * 100] for the prior month, a drop of 13.3 points. The paper points out some subtle differences in the construction of the indices. The first difference is that the Conference Board converts each diffusion index to a base-year index and then averages the indices together, while Michigan first averages the diffusion indices into a composite diffusion index and then converts the results to a base-period index. Another difference is the Conference Board’s responses are seasonally adjusted, while Michigan’s are not. This seasonal adjustment has little effect on our results because neither index exhibits changes based on the season. The third difference highlighted in the paper is the Conference Board and Michigan use different base periods (1985 and 1966:Q1, respectively), so the response patterns on which the indices are based may differ. The Index levels of the two surveys are therefore not comparable. After explaining how the indices are calculated, Bram and Ludvigson go into the construction of their model to figure out which index better pre27

dicts consumer expenditure. They begin with a construction of a baseline model without any indices included. The equation looks like this: Δ ln ( Ct ) = α0 + γZt – 1 + εt , where Ct is real consumption spending and Zt – 1 is a vector of control variables. They considered many different indicators to include in Z t – 1 and ended up with the following control variables: four lags of the dependent variable, four lags of the growth in real labor income, four lags of the log first difference in real labor income, four lags of the log first difference in the real stock price index (as measured by S&P’s 500 index) and four lags of the first difference of the three-month Treasury bill rate. They look to earlier work of Carrol, Fuherer, and Wilcox of 1994 and found that their baseline equation placed lagged values of the dependent variable and of labor income growth, which is motivated by a large and growing body of empirical work showing that consumption growth Is related to lagged (predictable) income growth. They use four lags of each variable when including these indicators on the right side of the equation. They found that consumer attitudes are only weakly correlated with variables such as unemployment and industrial production once financial indicators are included. They therefore include the log first difference of the real stock price and the first difference of the threemonth Treasury bill in the Z vector. When they run the regression, the long-run impact of most variables has the expected sign. Consumption growth is positively related to lagged consumption growth for most of the categories, while lagged interest rates have a small negative effect on future consumption. The inclusion of the consumption and interest rate variables reduces the statistical significance of income and stock market variables in forecasting consumption growth. The table in Figure 3 shows the Baseline Forecast of Consumption Growth included in the paper. Note that the table reports the sum of the coefficients on the lags of the variable indicated: the probability that the variable can be excluded from the prediction equation appears in parenthesis. The sample covers the period from the first quarter of 1968 to the third quarter of 1996. The next step is to add Consumer Confidence Indices to the baseline equation in order to determine whether consumer attitudes help forecast future consumer spending. The equation appears in the paper as Δ ln ( Ct ) = α0 +∑ni = 1 βi St – i + γZt – 1 + εt , where S is consumer confidence as measured by either the Michigan or the Conference Board index. When they augment the baseline equation to include each of the attitudinal indicators (consumer indices) they report the increment to the adjusted R 2. The table in Figure 4 reports the increment to the adjust R2 statistic from adding four lags of the confidence measures: p-values for the joint marginal significance of the lags of the confidence measures appears in parentheses. The sample covers the period form the first quarter of 1968 to the third quarter of 1996. The results shown in the table above reveal a gap in the indices’ forecasting power for total personal consumption growth. For the Michigan sur28

vey, the lagged values of consumer sentiment do not increase the adjust R 2 in the regression where total personal consumption growth is the dependent variable. The inclusion of Michigan’s overall index actually weakens the predictive power of the baseline equation. Using the Michigan expectations component, the results are similar. On the other hand, the Conference Board’s overall measure of consumer confidence and its measure of consumer expectations are incrementally informative about the future path of total personal consumer spending growth. Adding the Conference Board index predicts an additional 9% of the variation in the next period’s consumption growth, and an additional 12% of the variation in future consumer spending for the expectations component. Also, it is statistically significant at the 5% level. For spending on services and durable goods, the Michigan index added little/no explanatory power to the consumption growth regressions. Lagged values of the Conference Board’s overall index appear to be of value in predicting spending in durables and services. Adding lags of the Conference Board index for durable goods increases the fraction of regression variance explained by consumer confidence by 15%, and it is highly statistically significant. The Conference Board expectations component appears strongly related to future services expenditures—increases the adjusted R2 by a statistically significant 6%. Thus it appears that the Conference Board index generally serves as a better predictor of spending than the Michigan index. When including both indices in the model, the increment to the adjusted R 2 is 21%, which is much larger than any increase when the equation only incorporates one of the indices. In the words of Bram and Ludvigson, “We find that lagged values of the Conference Board Consumer Confidence Index provide information about the future path of spending that is not captured by lagged values of the Michigan Index of Consumer Sentiment, labor income, stock prices, interest rates, or the spending category itself. These results contrast with those of other researchers, such as Carroll, Fuhrer, and Wilcox, who find that consumer attitudes, as measured by the University of Michigan index, contribute little additional information. The most obvious implication of our empirical results is that the forecasts of total personal consumer spending may be made more accurate by utilizing the Conference Board’s Consumer Confidence Index.” They went into further research regarding the specific questions asked by each index, and found that a surge in consumer confidence may be largely driven by the questions about future job availability, a greater potential for increased consumer spending than a surge in 29

confidence that is driven by other questions. Consumers may spend more when they feel good about future job prospects than they do when they think business conditions are favorable. We can look at our regression results, and questions asked by each index (including Bloomberg in our analysis) to add to this conclusion. Our Model Our baseline model is a loose replication of the model used by Bram and Ludvigson, with some slight changes. We formed a dataset that includes economic indicators from December 1985 (month of first Bloomberg Consumer Comfort Index) through October 2011. The baseline model regressed log of consumer expenditure on several right-hand side variables: Dependent Variable Log of consumer Expenditure (logce): This data is monthly personal consumer expenditures in nominal dollars in billions. Log of Consumer Expenditure on Durable Goods (logcedurable): This is monthly personal consumer expenditure on durable good in nominal dollars in billions. Log of Consumer Expenditure on Non Durable Goods (logcenondurable): This is monthly personal consumer expenditure on non durable good in nominal dollars in billions. Log of Consumer Expenditure on Services (logceservices): This is monthly personal consumer expenditure on services in nominal dollars in billions. Explanatory Variables: Four-quarter lag of unemployment (uelag): This variable is the percentage of unemployment in the economy. This can be an indication of how strong the economy is, and therefore be a barometer of how willing people are willing to spend. All else being equal we would expect a higher unemployment rate have a negative correlation with consumer expenditure meaning higher unemployment, leads to consumer lower consumer spending. The data is lagged to account for a slowness in consumer spending after they have lost their job. Four-quarter lag of average hourly income (averagehourlyincomelag): This variable is the monthly US Average Hourly Earning Private Non Farm Payroll Total Nominal in US Dollar. The hourly income variable is a substitute for a measure of change in labor income over time. As income grows we would expect consumer expenditure to grow as well. Four-quarter lag of monthly treasury bill yield (monthtbillyeildslag): We are using monthly T-bill yields as a measure of interest rate, and in affect measuring overall availability of credit in the 30

economy. All else being equal, we would expect high interest rate to be positively correlated with consumer expenditure. Four-quarter lag of consumer expenditure (celag): We used the four-quarter lag of consumer expenditure, which is our dependent variable, measured in billions. We decided to include the lag of our dependent variable in our model as an explanatory variable due to the idea that consumer expenditure may build on itself, or at least influence itself later on. On a more individual basis, current spending can be influenced by past spending if big-ticket items or just durable goods were purchased. For example, the likelihood that someone would buy two cars within a year is unlikely. We expected lagged consumer expenditure to have a positive coefficient with total expenditure and non-durables but a negative coefficient with durable expenditure. For total and non-durables, however, we anticipated that it would have minimal economic significance. Four-quarter lag of the change in the log of the S&P rating (changlogsplag): The stock market is a major indicator of a number of things, but most importantly it signifies the strength and state of the economy. The stock market is also an alternate source of income for a number of people. To measure this we chose the S&P 500 index to mirror the Bramm and Ludvigson. We decided to use the first log difference in this case to look at the percent change from one day to the next. This way, we could see how the change in the stock market affected expenditure. Discussion of Data We retrieved all of our data from the Bloomberg Terminal at the BC Library. This terminal gave us data on unemployment levels, average hourly income, monthly treasury bill yield, consumer expenditure, and S&P Ratings from 1985 to 2011. We were also able to retrieve the three consumer indices from 1985 to 2011 from this terminal, as well as other variables that we ended up removing from our analysis (disposable income, personal savings, etc.). The averages of all our important variables are as follows: consumer expenditure = 6443202, consumer expenditure on durable goods = 801.49, consumer expenditure on non-durable goods = 1485.59, consumer expenditure on services = 4156.12, Michigan Index of Consumer Sentiment = 87.95, Conference Board Consumer Confidence Index = 94.37, Bloomberg Consumer Comfort Index = -14.76, unemployment rate = 5.97, average hourly income = 13.56, change log S&P = 0.0025, monthly t-bill change = 0.02. The summary table is attached in Figure 5. It is interesting to note the averages of the three consumer indices: Michigan Index of Consumer Sentiment = 87.95, Conference Board Consumer Confidence Index = 94.37, Bloomberg Consumer Comfort Index = -14.76. The Michigan Index of Consumer Sentiment and the Conference Board Consumer 31

Confidence Index are both positive and close in value. Michigan calculates a diffusion measure by adding the difference between the positive and negative percentages to 100. The index is constructed by dividing the level of the diffusion measure by the base period level, and then multiplying by 100. The Conference Board calculates the diffusion measures by dividing the positive response percentage by the sum of the positive and negative response percentages. Bram and Ludvigson point out that the Conference Board converts each diffusion index to a base-year index and then averages the indices together, while Michigan first averages the diffusion indices into a composite diffusion index and then converts the results to a base-period index. Also, the Conference Board and Michigan use different base periods, so the response patterns may differ because of the differing base year they to which they compare the responses. Although they appear similar, the Index levels of the two surveys are therefore not comparable. The Bloomberg Index can range from 100 (if every participant in the survey had a positive response to all three components) to negative 100 (if all views were negative). Therefore, this index is certainly not comparable to the other two. Our Methodology To test whether or not the confidence indices have any explanatory power, we first need to build a baseline model without the indices that attempt to predict consumer expenditure first. To do this we used the variables listed above: four quarter lag of consumer expenditure, four quarter lag of unemployment, four quarter lag of average hourly income, four quarter lag of monthly treasury bill yield, and four quarter lag of the change in the log of the S&P rating. After building this model, which yielded satisfactory results, we then began inserting various consumer confidence indices one by one to test for explanatory power. Upon doing this, we can tell which indices are statistically significant and also compare which have the most explanatory power by looking at the R2 values of the various models. We can then repeat this step for our various dependent variables, log of total consumer expenditure, log of consumer expenditure on durable goods, log of consumer expenditure on non durable goods, and log of consumer expenditure on services. Our Results Regressions for US Consumer Expenditure Model (1) Baseline Regression, Robust [Figure 6]: Looking at our model, our parameters have fairly small magnitudes compared to the large constant term; this is an indication that on the whole consumer expenditure does not experience large fluctuations. We have highly statistically significant T-stat for all our right hand side variables, with exception of the celag which has a t stat of -2.28, which is significant at the 10% level but not at the 5% level. Our R2 is .9864- this indicated our model was 32

able to explain 98% of the variation in consumer expenditure. As we ran this regression using “robust”, we corrected the model for the heteroskedasticity in our data. Comparing this to the same regression without the robust analysis, we see negligible changes to the overall statistical significance. (2) Baseline Model, not corrected for heteroskedasticity [Figure 7]: The regression without robust analysis simply shows that the p-value for four-quarter lag in consumer expenditure increases slightly when correcting for heteroskedasticity. The p-value for “celag” is 0.023 for the robust model, and 0.017 for the non-robust model. This shows that the variance differs across the data rather than being the same for all data points. (3) Regression including Consumer Sentiment [Figure 8] The Michigan Index of Consumer Sentiment is included in this regression. It is statistically significant with a p-value of 0.000 and a t-stat of 6.45. R2 is 0.9881. With addition of the Michigan Index, the four-quarter lag of consumer expenditure became highly insignificant, which may be due to omitted variable bias. All other RHS variables are statistically significant. Adjusted R 2 increased by 0.0017 from our baseline model, which illustrates the addition of a statistically significant explanatory variable, the Michigan Index. (4) Regression including Consumer Confidence Index [Figure 9]: The Conference Board Consumer Confidence Index is included in this regression. It is statistically significant with a p-value of 0.000 and a t-stat of 4.60. R2 is 0.9873. With addition of the Conference Board Index, the four-quarter lag of consumer expenditure became insignificant, which may be due to omitted variable bias. All other RHS variables are statistically significant. Adjusted R 2increased by 0.0009 from our baseline model, which illustrates the addition of a statistically significant explanatory variable, the Conference Board Index. (5) Regression including Consumer Comfort Index [Figure 10]: The Bloomberg Consumer Comfort Index is included in this regression. It is statistically significant with a p-value of 0.000 and a t-stat of 4.72. R2 is 0.9873. With addition of the Bloomberg Index, the four-quarter lag of consumer expenditure became insignificant, which may be due to omitted variable bias. All other RHS variables are statistically significant. Adjusted R 2 increased by 0.001 from our baseline model, which illustrates the addition of a statistically significant explanatory variable, the Bloomberg Index. Analysis for total US Consumer Expenditure Model Looking at these three regressions, we can easily compare them to see which index has the most explanatory power. Since the only variable that changes from model to model is the index, we can compare each regression 33

simply by looking at the R2. The R2 value for the Michigan Sentiment Index is 0.9881, for the Consumer Conference Board 0.9873, and for the Bloomberg Consumer Comfort Index 0.9873. We see that judging by R 2, the Michigan Sentiment Index clearly has the most explanatory power. Looking to the Consumer Conference Board and the Bloomberg Consumer Comfort Index we see that R2 are the same, but looking at their t-statistics we notice that the Consumer Conference Board has a t-stat of 4.60 and that Bloomberg Consumer Comfort Index has a t-stat of 4.72, thus showing that the Bloomberg Consumer Comfort Index has a higher explanatory power than the Consumer Conference Board. Moving to a qualitative analysis, this result may be due to the nature of the questions asked. The Michigan Index focuses primarily on consumers’ perception of their personal economic condition, while the Conference Board focuses on their perception of the economy. Since the Michigan Index has the most explanatory power, we see that perception of personal well-being is more of a factor in consumption than is perception of total economic well being. It also makes sense that the Bloomberg Index is in between these two indices as the questions reflect both personal perception and the economic perception. Regressions of US Consumer Expenditure on Durable Goods (6) Regression for Consumer Expenditure on Durable goods [Figure 11] (7) Regression for Consumer Expenditure on Durable goods with Michigan Index of Consumer Sentiment [Figure 12] (8) Regression for Consumer Expenditure on Durable goods with Conference Board Consumer Confidence Index [Figure 13] (9) Regression for Consumer Expenditure on Durable goods with Bloomberg Consumer Comfort Index [Figure 14] After regressing log of consumer expenditure of durable goods on all the RHS variables and each consumer confidence index, the Bloomberg Index is the best consumer confidence index to explain the expenditure on durable goods. A durable good is one that does not quickly wear out, or more specifically, one that yields utility over time rather than being completely consumed in one use. Examples include cars, household goods (home appliances, consumer electronics, furniture, etc.), sports equipment, and toys. As stated in our Introduction, the Bloomberg Index measures Americans’ perceptions on three important variables: the state of the economy, personal finances, and whether it is a good time to buy needed goods or services. This appears to be a combination of the Michigan and Conference Board indices. Perhaps for this reason, Bloomberg best predicts expenditure of durable goods. Bloomberg may best predict these as a result of its formulation, as it is a mix of consumer’s perception of their own personal standing as well as the economy as a whole.


Regressions of US Consumer Expenditure on Non Durable Goods (10) Regression for Consumer Expenditure on Non Durable goods [Figure 15] (11) Regression for Consumer Expenditure on Non Durable goods with Michigan Index of Consumer Sentiment [Figure 16] (13) Regression for Consumer Expenditure on Non Durable goods with Conference Board Consumer Confidence Index [Figure 17] (14) Regression for Consumer Expenditure on Non Durable goods with Bloomberg Consumer Comfort Index [Figure 18] After regressing log of consumer expenditure of non-durable goods on all the RHS variables and each consumer confidence index, the Michigan Index is the best consumer confidence index to explain the expenditure on non-durable goods. Non-durable goods are defined either as goods that are immediately consumed in one use or goods that have a lifespan of less than 3 years. Examples include fast moving consumer goods such as cosmetics and cleaning products, food, fuel, office supplies, packaging and containers, paper and paper products, personal products, rubber, plastics, textiles, clothing and footwear. As stated in our Introduction, the Michigan Index asks about more personal finances and feelings about one’s own situation, while the other two indices pose more questions about the job market and the economy as a whole. Michigan may best predict the expenditure of non-durable goods because people’s purchases of these goods are frequent and personal. Indices that capture sentiments on the economy as whole would not be great predictors of non-durable expenditure, because feelings about the entire economy are not reflected in such purchases. Regressions of US Consumer Expenditure on Services (15) Regression for Consumer Expenditure on Services [Figure 19] (16) Regression for Consumer Expenditure on Services with Michigan Index of Consumer Sentiment [Figure 20] (17) Regression for Consumer Expenditure on Services with Conference Board Consumer Confidence Index [Figure 21] (18) Regression for Consumer Expenditure on Services with Bloomberg Consumer Comfort Index [Figure 22] After regressing log of consumer expenditure of services on all the RHS variables and each consumer confidence index, the Michigan Index is the best consumer confidence index to explain the expenditure on services. Since the Michigan Index asks about one’s personal economic standing it may best predict the expenditure on services. Indices that capture sentiments on the economy as whole may not be as good predictors of services expenditure as those that capture feelings about personal finances.


Further Analysis While building our baseline we attempted different variations of explanatory variables. In one model we included annual dummy variables, however with this change several explanatory variables lost statistical significance [Figure 23, 24]. Conclusion Upon looking at our entire analysis, we find dissimilar results to those of Bram and Ludvigson in â&#x20AC;&#x153;Does Consumer Confidence Forecast Household Expenditure?â&#x20AC;? Bram and Ludvigson found that the Conference Board Consumer Confidence Index was the best predictor of overall consumer expenditure. They analyzed the questions of both the Michigan and Conference Board indices, and found that lagged values of the Conference Board Consumer Confidence Index provide information about the future path of spending that is not captured by lagged values of the Michigan Index of Consumer Sentiment, labor income, stock prices, interest rates, or the spending category itself. We found that the Michigan Index of Consumer Sentiment was the most accurate predictor of consumer expenditure on the whole. When separating consumer expenditure into durable goods, non-durable goods, and services, the Michigan index prevailed in all but durable goods, for which the Bloomberg Index was the best predictor. Our results differed so greatly from those of Bram and Ludvigson because our data extends 13 years beyond their analysis, which was completed in 1998. In addition, we have different data sets in general. They did not obtain their data from Bloomberg as we did, and did not provide the source of their own data. This results in entirely different regression results. In conclusion, our analysis supports the continued use of the Michigan Index of Consumer Sentiment as an indicator of consumer attitudes about the state of the U.S. economy.


Bibliography Baxter, Annie. "Consumer Spending Accounts for Two-thirds of U.S. Economy | Minnesota Public Radio News." Minnesota Public Radio. Minnesota Public Radio, 8 Oct. 2008. Web. 11 Dec. 2011. < _consumer_spending/>. “Bloomberg US Weekly Consumer Comfort Index.” Bloomberg, 2011. Web. 4 Dec. 2011. Bram, Jason and Ludvigson, Sydney. “Does Consumer Confidence Forecast Household Expenditure? A Sentiment Index Horse Race.” Federal Reserve Bank of New York Economic Policy Review. June 1998: 59-78. Carroll, Christopher D., Jeffrey C. Fuhrer, and David W. Wilcox. 1994. “Does Consumer Sentiment Forecast Household Spending? If So, Why?” American Economic Review. 84, no. 5: 13971408. “The Conference Board Consumer Confidence Index Improves.” The Conference Board: Consumer Confidence Survey. 29 Nov. 2011. 4 Dec. 2011. < consumerconfidence.cfm>. “Consumer Confidence Survey Technical Note.” The Conference Board: Consumer Confidence Survey. February 2011. 4 Dec. 2011. < / TechnicalPDF_4134_1298367128.pdf>. Curtin, Richard. “May 2011 survey results.” Thomson Reuters/University of Michigan Surveys of Consumers. 27 May 2011. 4 Dec. 2011. < ents.php? c=r>. Curtin, Richard. “Record Low Confidence in Economic Policies.” Thomson Reuters/University of Michigan Surveys of Consumers. 28 Oct. 2011. 4 Dec. 2011. < content/financial/pdf/i_and_a/438965/record_low_confidence_in_economic_policies>.


Appendix Figure 1

Figure 2

Percentage of responses Positive Neutral Negative Indicator level Michigan diffusion measure Michigan index

Base Period

Prior Month

Current Month

25 60 15

30 60 10

24 64 12











62.5 Conference board diffusion measure 100.0 Conference board index


Figure 3 Predicted Variable Total

Four lags of Four lags of consumption income

0.83 (0.000) Motor Vehi- 0.47 cles (0.230) Goods, ex0.88 cluding motor vehicles (0.000 Services 0.05 (0.021) Durable 0.80 goods, excluding Motor vehicles (0.0000

Four lags of S&P 500

0.04 (0.263) 0.40

Four lags of treasury bill rate -0.002 (0.006) -0.024

(0.221) 0.04

(0.068) -0.001

(0.012) 0.0

(0.356) 0.50 (0.102) 0.16

(0.094) -0.007 (0.000) -0.006

(0.148) -0.02 (0.276) 0.0




-0.01 (0.056) -0.05

Figure 4


Figure 4 (cont.)

Figure 5


Figure 6

Figure 7


Figure 8

Figure 9


Figure 10

Figure 11


Figure 12

Figure 13


Figure 14

Figure 15


Figure 16

Figure 17


Figure 18

Figure 19


Figure 20

Figure 21


Figure 22


Figure 24 Extent of Multicollinear in our data Variance influence factor of Michigan Index on all other RHS variables

1/(1- 0.5305)=

Variance influence factor of Bloomberg Index on all other RHS variables

1/(1- 0.4722)=

Variance influence factor of Bloomberg Index on all other RHS variables

1/(1- .5558)=





Figure 25 log(ce)



























Board Bloomberg


The Nuclear Dilemma: How to Address Nuclear Waste Concerns in the U.S. Sven Benson

The Nuclear Dilemma: How to Address the Nuclear Waste Concerns in the U.S. Abstract On December 2nd, 1957, the United Statesâ&#x20AC;&#x2122; first nuclear power plant began functional operations in Shippingport, Pennsylvania. At the time of its planning and construction, the burdens of nuclear waste were well understood and, although no official plan or oversight to deal with the waste was in place, the Atomic Energy Act of 1954 made the government responsible for waste generated by commercial nuclear plants (Beaver 2010). However, over thirty years after operations at Shippingport Nuclear Power Station ended, the United States still lacks any major program or direction to deal with nuclear waste. In this paper I will explore how and why the attempts to solve the problem of nuclear waste in United States have failed to produce any viable solution. The paper will focus on three key issues that must be addressed: risk transfer, site funding and support, and innovative technologies. Introduction In many ways the current situation in the United States is dire. Almost 800 individual waste containers are located on 34 different sites across the country that account for 60 thousand metric tons of nuclear waste. (Schaffer 2011, Pickard 2010). According to a Government Accountability Office (GAO) report issued this year, those numbers are set to double by 2055 1. In 2009, the Department of Energy (DOE) drastically exasperated the problem by defunding the Yucca Mountain project. This abandonment of 30 years of work and $32 billion of public and private investment highlights the critical nature of the dilemma. For the United States to choose an attainable solution for nuclear waste disposal, it must avoid the pitfalls of past attempts. Risk Transfer Amendments to The Atomic Energy Act (AEA) passed in 1954 opened the possibility for nuclear power in the United States. It outlined the initial guidelines for both military and civilian nuclear projects. This legislation ultimately saddles the government with the full risks and responsibilities associated with any nuclear waste generated from these projects (Beaver 2010). The government had to assume these risks in order to foster a market for nuclear utilities. Firms probably would have refused to build a plant if they were held responsible for handling the waste since it is a complex and costly task. However, the policy did not outline potential disposal methods that the government would employ, nor did it propose any pertinent compensation (1) GAO-11-731T June 1, 2011


system by which the government could recoup costs associated with the assumed risk. It would be almost 30 years until the government meaningfully addressed these issues. The Nuclear Waste Policy Act (NWPA) in 1982 established the current framework of waste disposal in the United States. The NWPA designated the federal government as responsible for both the construction and longterm operation of waste repository sites in exchange for fees paid by the commercial utilities to the DOE (De Roo 2010). The decision to absolve the utilities of all liability in return for a fee left firms with limited financial responsibility and effectively distanced them from both the problem and the solution. It did little to cultivate innovation and left the government with an unequal burden. The shortfalls of the 1982 NWPA have only recently come to light in the United States. Initially the annual disposal fee was set at 1 mill/kWh in order to assure a full transfer of risk, however “despite inflation, changes in anticipated costs, evolutions in number of repositories, or building schedules, the fee has remained unchanged for 27 years” (De Roo 2010, p.11). This resulted in only partial transfer of long-term risk. Any disposal program must properly address these long-term risks as the utility firms are not likely to remain in business long enough to internalize the full costs of disposal. Failure to do so in the United States has resulted in a breakdown of the entire waste disposal system. The total collapse of the United States nuclear waste program stands in contrast with other countries’ efforts and solutions. Although many countries have experienced resistance and delays with regards to repository siting, the infrastructure of the programs have remained intact (Beaver 2010). By requiring up front payment called “guarantees” from nuclear utilities instead of a flat yearly fee foreign governments have been able to manage their waste disposal programs (De Roo 2010). They are also referred to as assurance bonds, which is a standard practice in other industries where environmental hazard is likely to occur. A firm will set aside a bond fund to hedge against the risk that they go bankrupt and cannot pay for cleanup of their operations. Other countries, such as Sweden, have implemented these types of policies with success because it requires more substantial financial commitment from the utilities themselves. Sweden utilizes this system by requiring one guarantee “that covers the event of premature shutdown, while another covers the possibility of funds shortage after all of the reactors have been shut down” (De Roo 2010, p.8). This transfers two types of risk from the government to the utilities: funding risks and project risks. Funding risks involve scenarios in which the utility is not able to procure the anticipated amount to cover all future costs incurred by the generated waste. These payments would insure against premature shutdowns or bankruptcy. Project risks can include natural disasters and protect against unforeseen costs associated with the project. Allowing 55

the utilities to share these risks is crucial when financing long-term projects with highly variable costs. The United States would surely benefit from exposing the utilities to more of the risk in this manner. Siting and Funding Issues The 1982 amendment also implemented a siting program in order to consider potential locations for a future repository. The Government has to provide this service for two reasons: the market had no incentive to locate a site that multiple firms would benefit from and the long term on site storage was not a viable option as many plants are located close to population centers or bodies of water (Beaver 2010). Unfortunately for the government, the costs of locating and evaluating potential sites escalated well beyond initial estimates (De Roo 2010). These mounting costs lead to a 1987 NWPA amendment that designated Yucca Mountain, Nevada as the sole site for a repository plant. At the time of the amendment the facility was scheduled to open in 1998, a far cry from where we now stand. As work continued on the development of Yucca Mountain, consistent funding became a paramount issue. Originally funds were accumulated in the U.S. Nuclear Waste Fund, separate from the federal treasury, but the Balanced Budget and Emergency Deficit Control Act of 1985 subjected the fund to the congressional sequestering process. This led to lack of stable funding that proved detrimental to the project. In 2009 The GAO noted that lack of stable funding contributed to rising costs and diminished project momentum (De Roo 2010). Because the fund is no longer tied directly to funds receipts, the recession has taken a significant toll on the projects funding. The project has experienced decreased funding every year since 2004. The budget fell from a peak of $557 million in 2004 to $288 million in 2009 (De Roo 2010). The 2011 federal budget eliminated all funding for the development of Yucca Mountain. The governmentâ&#x20AC;&#x2122;s lack of vision and appropriate planning has resulted in mounting problems for both the government and the utilities. Recently lawsuits brought by the utilities have forced the government to pay billions in reparations. In 2000, the U.S. Court of Appeals for the Federal Circuit found the DOE liable for breach of contract damages because of its failure to uphold the 1982 NWPA, which stated that the government would remove waste from individual sites by 1998 (CIS 2002). The government assumed full responsibility for the waste but has yet to remove waste from any individual sites. Unfortunately, financial woes are not the only problems generated by prolonged on site storage. Currently waste is distributed among 34 different locations across the United States. The facilities faced with this burden were not designed to accommodate the incredibly demanding task of securing large amounts of hazardous waste. Storage in this manner presents significant dangers that must be eliminated via a central site. Although claims of terrorist and prolif56

eration risks have been used to argue against central waste repositories and refineries, the most recent studies conclude these risks are minimal compared to meagerly protected on site storage (Schaffer 2011). Environmental disasters such as earthquakes also pose much greater threats to the individual sites. The government must make good on its promise to remove these wastes and store them properly before it is too late. Innovative Technologies In the infancy of the nuclear industry, firms were more willing and able to innovate than they are today. So much so, perhaps, that their enthusiasm may have hampered future firms willingness to do so. The government was also largely involved in attempts to find storage solutions. After the AEA of 1954 the government tested deep-sea dumping, underground storage in salt domes and even disposal in outer space but ultimately all were rejected as suitable long-term storage solutions (Shapiro 228). Failures in depository methods led to early development of reprocessing technologies, and by 1966 the first reprocessing plant opened in New York. At the time the private repository market was developing healthy competition because of the assumption that uranium prices would increase as more power plants increased demand (Beaver 2010). However, uranium never entered short supply and firms soon began to exit the market because it was evident that reprocessing would never be deemed profitable. Consequently, the New York plant closed in 1972 and costs associated with its cleanup were estimated in 2008 at $5 billion with another 40 years of work till full restoration2. In 2005, the same year that Congress demanded the DOE reconsider nuclear waste reprocessing, a study estimated uranium prices would need to increase by 1000% percent to make reprocessing spent fuel economically viable (Kintish 2005). This recent push to review reprocessing as a way to deal with waste marks the first time in 30 years that the government has made any attempt to explore innovate alternatives to storing waste. Two issues have contributed to the lack of sustained innovation in the nuclear industry. Government and market failures early on discouraged private and public investment in new technologies and the 1982 NWPA convinced private firms that they need not invest in R&D if the government assumed all responsibilities for the waste created by current technologies. Consequently, the United States has failed to proactively adopt newer and more efficient technologies as they have become available over the last twenty years. Recently many countries have switched from building open cycle reactors to closed cycle technologies, which offer substantial benefits. Currently all of 103 nuclear power plants operating in the United States are opencycle (Schaffer 2010). There are also many new fuel type options, such as Tris(2) â&#x20AC;&#x153;Final EIS for Decommissioning and/or Long-Term Stewardship at the West Valley Demonstration Project and Western New York Nuclear Service Centerâ&#x20AC;? DOE 2010


tructural-isotropic (TRISO) fuel, that American utilities have failed to implement. Normally a market based incentive, as opposed to command and control options, solution would most effectively reach an economically efficient solution but the social costs of allowing new utilities based on old technologies to enter the market would be too great. Therefore, the government should mandate that all new installations should be TRISO fueled closed-cycle reactors as “they hold the most promise for redressing nuclear power concerns” (Schaffer 2007). TRISO fueled reactors provide waste disposal benefits for three main reasons. The first is that the amount and radioactivity of the waste produced is significantly less compared to open cycle systems. Waste from all other fuel types contain high levels of uranium 235 and 238, both of which have halflives of well over a million years (Schaffer 2010). The second is that the waste would pose no nuclear proliferation or terrorist threat as the levels of plutonium 239 is low and the mixture “does not permit useable explosive device” production (Schaffer 2010). The third advantage comes from the TRISO process itself which seals the waste in canisters impervious to water leaching. This opens new possibilities for underground and underwater storage unavailable to different types of nuclear waste. Therefore the government must mandate these higher technology standards for new utilities. This is the most effective manner of stemming new production of nuclear waste. These solutions do not address the waste already produced. The government must devise a resolution that removes or reduces waste on individual sites as soon as possible. A decentralized storage policy posses increased environmental and terrorism risks. The Obama administration abandoned Yucca Mountain repository plans and instead instituted a Blue Ribbon Commission to determine a path forward. If the commission plans to choose a new repository site it is “unlikely any site will measure up to the scrutiny to which it will be subjected” (Beaver 2010). Therefore any constructive decisions may need to rely on instituting reprocessing technologies such as transmutation and MOX fuel. Although transmutation can effectively lower the radioactive life of nuclear waste to a few hundred years a report issued by the Institute for Energy and Environmental Research discourages it because of “fundamental limitation and associated problems…[and] needless costs” (Zerriffi 2000). Attempts to reinstate nuclear waste reprocessing in the United States have met stiff opposition and delays. One such MOX fuel plant in South Carolina, which was intended to demonstrate the process, now is not set to open until 2017 (Henderson 2009). It is likely that these technologies won’t be practicable in the United States for some time to come. The least the government can do is provide funding for public and private R&D in the reprocessing field in order to speed the process.


Conclusion Nuclear waste will continue to be a definitive issue for years to come. The United States is not alone in its struggles to effectively deal with nuclear waste, but it does have some ground to make up. The United States must institute more substantial risk transfer policies, such as assurance bonds and guarantees. We must secure a central site to securely store the waste that now existed unprotected on multiple locations across the United States. Finally, new technologies must be adopted in order to limit the burden on future generations. Nuclear energy has the potential to be an amazing source of power, but these issues must be attended sooner rather than later.


Bibliography Beaver, William. “The Demise of Yucca Mountain.” The Independent Review; Spring 2010; 14, 4; Alt-Press Watch (APW) 535-547. De Roo, Guillaume. Risk and Responsibility Sharing in Nuclear Spent Fuel Management. Tech. no. 10-007. Cambridge: MIT, 2010. Jeong, Hae-Yong. "A ‘must-go path’ Scenario for Sustainable Development and the Role of Nuclear Energy in the 21st Century." Energy Policy 38 (2010): 1962-8. GAO. 2009. “GAO Cost Estimating and Assessment Guide, Best Practices for Developing and Managing Capital Program Costs”. Henderson, Bruce. 2009. “Duke Drops Pact to Use Bomb Fuel.” McClatchy-Tribune Business News, March 17. Eli Kintisch, "DOE Pushing Spent Fuel Reprocessing," Science, Vol. 310. no. 5753, 2 December 2005. 1406-15 “Final EIS for Decommissioning and/or Long-Term Stewardship at the West Valley Demonstration Project and Western New York Nuclear Service Center.” U.S. Department of Energy, West Valley, NY Jan. 2010 "Nuclear Waste Litigation." LEXIS-NEXIS® Congressional Universe-Document. Congressional Information Service Inc., 31 Jan. 2002. Web. 26 Nov. 2011. <http:// S_ENR_092800.htm>. Pickard, William F. "Finessing the Fuel: Revisiting the Challenge of Radioactive Waste Disposal." Energy Policy 38 (2010): 709-14. Schaffer, Marvin B. "Nuclear Power for a Clean, Safe and Secure Energy Independence." Foresight 9.6 (2007): 47-60. Schaffer, Marvin B. "Toward a Viable Nuclear Waste Disposal Program." Energy Policy 39 (2011): 1382-388. Zerriffi, Hisham. "The Nuclear Alchemy Gamble: An Assessment of Transmutation as a Nuclear Waste Management Strategy." Editorial. IEER Report. IEER, 24 May 2004. Web. 25 Nov. 2011. <>.



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