• 2019 Alaska's Construction Spending Forecast

      Goldsmith, Scott (Institute of Social and Economic Research, University of Alaska Anchorage, 2/6/2019)
    • A short brief on the regional dimensions of the Alaska recession

      Guettabi, Mouhcine (Institute of Social and Economic Research, University of Alaska Anchorage, 2/7/2018)
      We provide a short update on the Alaska recession by examining its regional dimensions. Specifically, we evaluate the performance of the Alaska boroughs/census areas in each of the last three years and determine which areas have been resilient and which ones continue losing jobs.
    • Economic Impacts of the South Denali Implementation Plan

      Colt, Steve; Szymoniak, Nick; Fay, Ginny (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-02-01)
      This study estimates the economic effects of carrying out the South Denali Implementation Plan. The plan provides for construction of new visitor facilities in the South Denali Region. ISER economists used the IMPLAN input-output modeling system to project the jobs, income, and sales due to 1) initial construction activity; 2) ongoing operation and maintenance expenses; and 3) additional visitation and visitor spending attributable to the new facilities. The model results include the effects at the Mat-Su Borough and statewide Alaska levels. Local area impacts are also estimated. Suggested Citation: Colt, Steve, Fay, Ginny, Szymoniak, Nick. 2008. Economic Impact of the South Denali Implementation Plan. Prepared for the National Park Service, Denali National Park and Preserve and the Matanuska-Susitna Borough Planning and Land Use Department. Anchorage: University of Alaska Anchorage Institute of Social and Economic Research.
    • Alaska Native Graduates of UAA: What Can They Tell Us?

      Erickson, Diane; Hirshberg, Diane (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-03)
      Alaska Natives make up 9% of students at the University of Alaska Anchorage, and the number attending classes on the Anchorage campus is up more than 40% since 2000—from 950 to nearly 1,400. But despite that fast growth, few Alaska Native students go on to graduate. Less than 5% of the students earning bachelor’s degrees at UAA in 2007 were Alaska Native. And as Figure 1 shows, only about one in 10 of the Native students who were freshmen in 2000 had earned bachelor’s degrees six years later, in 2006. Alaska Native students begin leaving at high rates in their second year at UAA. Among those who started in 2005, less than 60% of the Native freshmen but 70% of all freshmen went on to the next year. Still, that was an improvement over 2000, when only about half the Alaska Native freshmen continued on to their second year (Figure 1). The low graduation rates among Native students—not only at UAA but throughout the University of Alaska—are worrisome. Alaska Natives are under-represented in teaching, health care, business, and many other professions—and that won’t change until more Alaska Native students get the educational credentials they need. But what about those Alaska Native students who do succeed in earning bachelor’s and master’s degrees and doctorates? What keeps them going, when so many others don’t make it to graduation?
    • The Value of Evidence-Based Computer Simulation of Oral Health Outcomes for Management Analysis of the Alaska Dental Health Aide Program

      Kiley, Daniel P.; Haley, Sharman; Saylor, Ben; Saylor, Brian L. (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-03)
      Objectives: To create an evidence‐based research tool to inform and guide policy and program managers as they develop and deploy new service delivery models for oral disease prevention and intervention. Methods: A village‐level discrete event simulation was developed to project outcomes associated with different service delivery patterns. Evidence‐ based outcomes were associated with dental health aide activities, and projected indicators (DMFT, F+ST, T‐health, SiC, CPI, ECC) were proxy for oral health outcomes. Model runs representing the planned program implementation, a more intensive staffing scenario, and a more robust prevention scenario, generated 20‐year projections of clinical indicators; graphs and tallies were analyzed for trends and differences. Results: Outcomes associated with alternative patterns of service delivery indicate there is potential for substantial improvement in clinical outcomes with modest program changes. Not all segments of the population derive equal benefit when program variables are altered. Children benefit more from increased prevention, while adults benefit more from intensive staffing. Conclusions: Evidence‐ based simulation is a useful tool to analyze the impact of changing program variables on program outcome measures. This simulation informs dental managers of the clinical outcomes associated with policy and service delivery variables. Simulation tools can assist public health managers in analyzing and understanding the relationship between their policy decisions and long‐term clinical outcomes.
    • UAA Inventory: Greenhouse Gas Emissions From Transportation

      Szymoniak, Nick; Ralph, Kelcie; Colt, Steve (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-03-25)
      As a signatory of the American College and University Presidents Climate Commitment, UAA has agreed to conduct an inventory of its greenhouse gas (GHG) emissions. This inventory serves as a baseline against which to measure the effectiveness of GHG emissions reduction projects. To fulfill the Commitment UAA agreed to conduct an inventory of its Scope 1 and 2 emissions, as well as some Scope 3 emissions. In addition to signing the Presidents Climate Commitment, UAA signed the Talloires Declaration in April 2004. The Talloires Declaration is a statement of principles and practices for using higher education to promote sustainability. Scope 1 emissions are defined as direct GHG emissions occurring from sources that are owned or controlled by the institution. Scope 2 emissions are indirect emissions generated in the production of energy purchased by the institution. Scope 3 emissions are indirect emissions that are the consequence of the activities of the institution, but occur from sources not owned or controlled by the institution. Pursuant to the Commitment, this study estimates the levels of two types of Scope 3 GHG emissions – commuting by students and employees, and university-funded air travel. Scope 1 and Scope 2 GHG emissions are being estimated in a separate study. Two models were developed and used: a UAA commuter model and a UAA air travel model.
    • Alaska Election Security Report, Phase 2, Executive Summary

      Martin, Stephanie; Picard, LuAnn; Ayers, Mark; Hoffman, David B.; Mock, Kenrick (University of Alaska Anchorage, 2008-04)
      A laska’s election system is among the most secure in the country, and it has a number of safeguards other states are now adopting. But the technology Alaska uses to record and count votes could be improved— and the state’s huge size, limited road system, and scattered communities also create special challenges for insuring the integrity of the vote. In this second phase of an ongoing study of Alaska’s election security, we recommend ways of strengthening the system—not only the technology but also the election procedures. The lieutenant governor and the Division of Elections asked the University of Alaska Anchorage to do this evaluation, which began in September 2007.
    • Alaska Fuel Price Projections 2008 - 2030

      Colt, Steve; Saylor, Ben; Szymoniak, Nick (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-04)
      We generated Low, Medium, and High case fuel price projections for the years 2008-2030 for the following fuels: • Incremental natural gas in Southcentral Alaska delivered to a utility-scale customer • Incremental diesel delivered to a PCE community utility tank • Incremental diesel delivered to a home in a PCE community • Incremental home heating oil purchased in Anchorage, Fairbanks, Juneau, Kenai, Ketchikan, Palmer, and Wasilla This memorandum provides documentation of the assumptions and methods that we used. Two companion Excel workbooks contain the detailed projections
    • Comments on the Lieberman-Warner Climate Security Act and Lieberman-Warner proposed legislation

      Colt, Steve (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-04-11)
      The Lieberman-Warner Climate Security Act (hereafter LW or “the Act”) aims to cover 87% of total U.S. greenhouse gas (GHG) emissions.2 It aims to reduce the emissions of those gases by 4% below year 2005 levels in 2012 and by 17% below 2005 levels in 2020. The Act would impose a cap-and-trade mechanism on most energy-using activities. The number of emissions allowances would be limited in order to keep total emissions in each year below the predetermined cap. The interaction of buyers and sellers of emissions allowances would determine a market price per ton of CO2 equivalent. The Act allows emitters to trade, save, and borrow allowances, so that the most cost-effective GHG emissions reductions can be made where and when they are available. The American Council for Capital Formation and the National Association of Manufacturers (ACCF/NAM) recently issued a report3 that projects some of the economic effects of implementing LW. Both effects on the U.S. economy and effects on individual states are projected. The analysis was conducted by Science Applications International Corporation using the National Energy Modeling System (NEMS). NEMS is a set of interlinked computer models that project energy supply and demand and key macroeconomic outcomes such as gross domestic product and employment. Many assumptions are required as inputs into NEMS. The assumptions driving the ACCF/NAM results were provided by ACCF and NAM. They were not chosen by the consultants who ran the model. Two sets of assumptions were used to generate two set of projections: a “Low Cost” scenario and a “High Cost” scenario.
    • Fuel Costs, Migration, and Community Viability

      Martin, Stephanie; Killorin, Mary; Colt, Steve (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-05)
      ISER researchers compiled and reviewed existing studies and data sources relating to the economic and social viability of remote rural Alaska communities. We particularly looked for possible linkages between high fuel costs and migration. Our review indicates the following: (1) migration from smaller places toward larger places is an ongoing phenomenon that is more noticeable when birth rates drop; (2) there is no systematic empirical evidence that fuel prices, by themselves, have been a definitive cause of migration; (3) the pursuit of economic and educational opportunities appears to be a predominant cause of migration; (4) however, currently available survey data are not sufficient to definitively determine other reasons for migration, which could include concerns about public safety and/or alcohol abuse; 5) most of the survey data pre-date the latest rapid increase (2006-2008) in fuel prices. We suggest several ways that better data could be collected on community viability and the reasons for migration.
    • Dollars of Difference: What Affects Fuel Prices Around Alaska?

      Wilson, Meghan; Saylor, Ben; Szymoniak, Nick; Colt, Steve; Fay, Ginny (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-05)
      The spike in oil prices has hit rural Alaskans especially hard, because they rely mostly on fuel oil for heating. But some rural residents are paying much more than others—at times 100% more. The Alaska Energy Authority asked ISER to analyze what determines the prices rural households pay for fuel oil and gasoline. The agency hopes this research can help identify possible ways of holding down fuel prices in the future. In this summary we report only fuel oil prices, but the full report (see back page) also includes gasoline prices. We studied 10 communities that reflect, as much as possible, the forces driving fuel prices. We collected information in November 2007, and fuel prices have gone up a lot since then. Crude oil sold for $120 a barrel in mid-May, up from about $80 in fall 2007.
    • The University of Alaska: How Is It Doing?

      Kassier, Theodore; Hill, Alexandra (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-05)
      Recent reports on higher education in the U.S. say it’s in trouble— that it’s too expensive, doesn’t offer enough need-based aid, isn’t educating people for today’s jobs, doesn’t demand enough of instructors or students, and isn’t sufficiently accountable to policymakers and taxpayers.1 Is the University of Alaska (UA)—the state’s only public university —offering a good, affordable education for Alaskans? This paper looks at that question. It first presents the available data on various measures and then summarizes successes and continuing challenges for UA. It ends with a discussion of how UA and the state are addressing higher-education issues and what other steps they might consider.
    • Estimated Household Costs for Home Energy Use

      Saylor, Ben; Haley, Sharman; Szymoniak, Nick (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-05)
      This memo estimates how much of their income Alaska households spend for home energy uses, after years of rising energy prices.1 We made the estimates at the request of State Senator Lyman Hoffman. We include costs for electricity, heat, and other home energy uses—but do not include costs for transportation fuel. Keep in mind that these are truly estimates. Because of time lags in data collection and reporting, actual consumer price data for 2008 are not available. To estimate consumer energy prices as of May 2008, we used statistical models of the relationship between oil prices and consumer prices. We also used the most recent data on per capita personal income from the Bureau of Economic Analysis to estimate 2007 annual household income. These estimates are likely to overstate actual household expenditures. As energy costs rise, households find ways to consume less. How much less, we don’t know. For these estimates, we used consumption households reported at the time of the 2000 U.S. Census. Also, the estimates in this memo reflect what energy would cost households for a year, at May 2008 prices. Consumers of course haven’t yet seen a full year at these prices, and we don’t know where prices will go from here.2 Therefore, these estimates are really like a cost index—that is, they estimate what it would cost to buy a specific amount of energy, at specific prices. That’s not the same as actual annual household expenditures. Still, these estimates give a good picture of what
    • State of Alaska Election Security Project Phase 2 Report

      Martin, Stephanie; Picard, LuAnn; Ayers, Mark; Hoffman, David B.; Mock, Kenrick (University of Alaska Anchorage, 2008-05)
      A laska’s election system is among the most secure in the country, and it has a number of safeguards other states are now adopting. But the technology Alaska uses to record and count votes could be improved— and the state’s huge size, limited road system, and scattered communities also create special challenges for insuring the integrity of the vote. In this second phase of an ongoing study of Alaska’s election security, we recommend ways of strengthening the system—not only the technology but also the election procedures. The lieutenant governor and the Division of Elections asked the University of Alaska Anchorage to do this evaluation, which began in September 2007.
    • Components of Delivered Fuel Prices in Alaska

      Wilson, Meghan; Saylor, Ben; Szymoniak, Nick; Colt, Steve; Fay, Ginny (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-06)
      This is a systematic analysis of components of delivered fuel prices in Alaska. Data for the analysis include limited publicly available Alaska fuel prices (fall 2007 prices), as well as information the authors gathered from extensive interviews with fuel retailers and transporters, communities, and agencies. We identify the individual components of delivered fuel costs—including world price of crude oil, refining costs, transportation costs, storage and distribution costs, taxes and financing costs—and investigate how these factors influence the final retail prices of home heating fuel and gasoline. Transportation, storage, and distribution costs appear to be the most variable factors driving the large retail fuel price differentials among Alaska communities. Therefore, we investigate how factors such as seasonal icing, the number of fuel transfers enroute to specific communities, local storage and delivery infrastructure, marine and river characteristics, and distance from refineries or fuel hubs influence fuel prices. We did an in-depth analysis of how those factors influence prices in ten case study communities around the state—Allakaket/Alatna, Angoon, Bethel, Chitina, False Pass, Fort Yukon, Lime Village, Mountain Village, Unalakleet, and Yakutat. Together, the quantitative data and information on Alaska fuel logistics provide a comprehensive analysis of Alaska’s fuel prices.
    • Southeast Rural Outreach Programs and Education Business Survey

      Hanna, Virgene; Marbourg, Ann (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-06)
      The Rural Outreach Programs and Education (ROPE) is designed to strengthen community and small business competitiveness. It is a multi-pronged business development effort to support economic stability and capacity-building in Southeast Alaska. The program will bring together different entities across the state in a collaborative effort, so the program recipients will have increased levels of technical assistance, training, and communication. One component in this process was to conduct a phone survey of businesses in Southeast Alaska. The survey was designed to determine the specific training and assistance needs of participating communities in Southeast Alaska. By focusing on 13 specific communities and gathering extensive information on each one, ROPE will offer targeted training and workshops, one-on-one confidential counseling, need-specific consultants and seminars, and business training. In May and June of 2008, 128 structured interviews were completed in the 13 communities. The majority of these interviews—88—were with businesses in the private sector, and the remaining 40 were with non-profit, tribal, or municipal organizations. Businesses were asked detailed questions about employees, customers, business expenses, and start-up costs and experiences. The questionnaire was designed to gather information about where employees were from, where customers were from, and the percentage of sales that were to local versus non-local customers. Both businesses and organizations were asked about training they felt would be beneficial and to offer advice to organizations trying to help businesses in Southeast.
    • Benefit-Cost Assesment of the Port Mackenzie Rail Extension

      Colt, Steve; Szymoniak, Nick (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-06-20)
      Costs We assume that the Port MacKenzie rail extension would cost $275 million to construct.1 This is a conservative estimate based on a range of between $200 million and $300 million for different route options. The time horizon runs 50 years from 2012 to 2061. O&M costs are assumed to be $1.5 million per year, with a net present value of $26.1 million. The net present value of all costs using a 5% real discount rate2 and a base year of 2010 is $301.1 million. Benefits The rail extension would provide two distinct types of benefits: 1) It reduces the cost of rail transportation; and 2) It is likely to stimulate significant new mines and other major development. These benefits come from a diverse mix of potential projects – thus a strength of the rail extension is that its economic viability does not depend on any one project. Reduced transportation costs Relative to Seward, using the extension would save 140.7 miles per one-way trip.3 Assuming an average cost savings of 6 cents per ton-mile and a 5.0% real discount rate, we estimate that using the extension would save $572 million in avoided rail costs, avoided port costs, and avoided railroad and road upgrades. These savings are shown in the table and figure on the following page. In addition to the above, we estimate that about 22,000 train crossings of Pittman Road and other roads would be avoided by the extension, saving motorists up to 64,000 vehicle-hours of travel time delay between now and 2061.
    • How Vulnerable Is Alaska’s Economy to Reduced Federal Spending?

      Goldsmith, Oliver Scott (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-07)
      About a third of all jobs in Alaska can be traced to federal spending here—and over the past decade the rapid increase in federal spending drove much of the economic growth. Federal spending in Alaska more than doubled between 1995 and 2005, and in 2006 it was $9.25 billion. But now federal spending here has stopped growing, and many Alaskans are worried that the economy is vulnerable to spending cuts as the federal budget tightens. This analysis estimates that Alaska could be vulnerable to federal spending cuts in the range of $450 million to $1.25 billion—which could cost the economy anywhere from about 7,000 to 20,000 jobs in the future. We estimate potential vulnerability as a range, because it’s impossible to predict with any precision how federal spending will actually change. The best we can do is estimate the likely magnitude of reductions, given federal budget problems. Any cuts will likely be made gradually, over time, and recent strength in the petroleum and mining sectors will help cushion the effects.
    • Turnover Among Alaska Teachers: Is It Changing?

      Hill, Alexandra; Hirshberg, Diane (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-07)
      Turnover among Alaska’s teachers was roughly the same in 2007 as it had been in 1999, with about 14% leaving their school districts (Figure 1). Turnover also remained twice as high in rural as in urban districts—about 22%, compared with 10%. That lack of broad change comes after years of efforts by Alaska’s state government, universities, and school districts to reduce teacher turnover, especially in rural areas. The Institute of Social and Economic Research has been tracking Alaska’s progress in reducing teacher turnover since 2004, in partnership with the Alaska Teacher Placement program, the Department of Education and Early Development, and university teacher training programs.
    • Replacement Cost for Public Infrastructure in Alaska: An Update

      Goldsmith, Oliver Scott (Institute of Social and Economic Research, University of Alaska Anchorage, 2008-07)
      Replacing Alaska’s public infrastructure would cost nearly $59 billion, in today’s dollars. That includes, as the table shows, the costs of replacing public buildings as well as transportation and utility systems.1 This is an update of an estimate ISER made in 2007—which at that time was the first comprehensive estimate of the cost to replace Alaska’s public infrastructure.2 That 2007 estimate was considerably less—about $39.5 billion—but we emphasized at the time that it was preliminary. It did not take into account that costs to replace infrastructure in remote areas are higher, and it undercounted and undervalued certain types of infrastructure, including power and telephone systems. This revised estimate is based on an analysis of cost differences across the state, additional data on existing infrastructure, and additional consultation with engineers, architects, and cost estimators.