Trouble Keeping Promises? An Analysis of Underfunding in ...faculty.cbpp.uaa.alaska.edu/afgjp/PADM601 Fall 2010... · primarily of retiree health care. A brief overview of the economic,

Current Trends in Public Personnel Adminstration

An Analysis of Underfunding in State Retiree Benefi ts 97

Jerrell D. CoggburnRichard C. Kearney

North Carolina State University

Trouble Keeping Promises? An Analysis of Underfunding in State Retiree Benefi ts

Jerrell D. Coggburn is an associate

professor and chair of public administration

in the School of Public and International

Affairs at North Carolina State University.

His primary research interests include public

human resource management, public man-

agement, and administrative reform.

E-mail: [email protected]

Richard C. Kearney is a professor

and director of the School of Public and

International Affairs at North Carolina State

University. He has published extensively in

the fi elds of human resource management,

labor relations, and state and local politics

and policy. His most recent book is Labor

Relations in the Public Sector (4th ed.,

2009).

E-mail: [email protected]

Th is article examines the funding of two key components of state government total compensation: pensions and other postemployment benefi ts (OPEB), the latter consisting primarily of retiree health care. A brief overview of the economic, political, and legal environments of state pensions and OPEB is followed by an analysis of the unfunded liabilities for these respective benefi ts. Regression results suggest the importance of state management capacity, per capita income, and public employee density in understanding diff erences in the states’ pension and OPEB funding performance. Additionally, employers’ level of pension contributions, legislative professionalism, and fi scal constraint are signifi cantly related to pension funding, while political ideology and levels of state pension funding are signifi cantly related to OPEB funding. Th e article concludes by discussing the tensions that states face in attempting to balance the fi scal imperative of funding retiree benefi ts liabilities with the human capital challenge of attracting and retaining a professional workforce. Failure on either could be costly to state government.

With its Statement 27, the Governmental Accounting Standards Board (GASB) issued standards for the fi nancial reporting of

state and local pension benefi ts (GASB 1994). GASB Statements 43 and 45 followed in 2004 (GASB 2004a, 2004b), providing similar guidance for other postem-ployment benefi ts (OPEB), which primarily comprise retiree health care. As a result of these new standards, jurisdictions must now submit extensive fi nancial infor-mation to decision makers and the public, including the amount of unfunded pension and health care liabil-ities. Initial estimates of collective state pension ($361 billion) and OPEB ($381 billion) unfunded liabilities

were shocking (Pew Center on the States 2007). As enormous unfunded pension and health care liabilities

have been identifi ed and released by some states, in the context of what has been proclaimed as the worst state fi scal distress since the Great Depression, outcries have arisen of a fi nancial “crisis” that threatens to produce higher taxes; less money for education, Medicaid, and other important funding needs; and an inequi-table fi nancial burden on future generations.

In theory (and as various corpo-rations have shown in practice), the simple solution is for states to reduce or even eliminate pensions and retiree health care benefi ts. Yet there is a grow-

ing recognition that state public services confront a looming human capital crisis. In addition to the more intense competition with the private and nonprofi t sectors for hiring talented employees, states face a graying of the workforce. Th e leading edge of the baby boom generation reached the age of 62 in January 2008. Impending retirements of up to 20 percent of state employees in the next few years are expected (Barrett and Greene 2005). Th ese experienced work-ers will leave, along with their knowledge, skills, and institutional memories.

Benefi ts are important to all employees, infl uencing their initial attraction to an employer, as well as their motivation, job satisfaction, and tenure with the organi-zation (Bergmann, Bergmann, and Grahn 1994; Center for State and Local Government Excellence 2007; Ellickson and Logsdon 2001; Roberts 2001, 2004). As this article will show, they also have weighty fi nancial implications for the states. Th e two most important (and substantial) public sector benefi ts are pensions and health care, which are the focus of this research.

As enormous unfunded pension and health care liabilities have been identifi ed and released by

some states, [and] in the context of what has been proclaimed

as the worst state fi scal distress since the Great Depression,

outcries have arisen of a fi nancial “crisis” that threatens to produce

higher taxes; less money for education, Medicaid, and other important funding needs; and an inequitable fi nancial burden

on future generations.

98 Public Administration Review • January | February 2010

Table 1 displays the 2006–2007 data on the states’ pension and OPEB liabilities. In the case of pensions, the ratio of unfunded liabilities to total actuarial liabilities (not shown in the table) ranges from 55 percent in West Virginia to 110 percent in Oregon (see Pew Center on the States 2007, 61–62). For OPEB, fi gures appearing in both the total and unfunded liabilities columns are generally the same—that is, total OPEB liabilities equal unfunded OPEB liabilities—because very few states have prefunded OPEB liabilities (GAO 2008). Th ese large, unfunded pension and OPEB liabilities are believed to be the result of various causes. In many instances, unfunded pension liabilities have been accrued as a result of poor investment choices, faulty actuarial assumptions, deferred employer contribu-tions, or diverted or borrowed pension fund assets. Unfunded OPEB liabilities are largely a product of states funding annual health care expenses for employees, retirees, and depen-dents on a pay-as-you-go basis while the rate of infl ation of prescription drugs and health care escalated. Th e present value of future accrued liabilities went unrecognized.

Th e soundness of state pension and retiree health care plans is important to current participants, annuitants and their depen-dants, and, ultimately, to legislators, governors, taxpayers, and the quality of state and local government employment. But why have some states largely avoided the problem of high levels of unfunded liabilities, while others are burdened with them? What are the determinants of state fi scal decisions for pensions and OPEB? Aside from basic description and categorization, we know very little about the factors that have contributed to state pension and OPEB outcomes.

Th e research question that we are addressing is, what factors are associated with levels of unfunded state pension and health care liabilities? Regression models are developed and tested with the objective of specifying determinants of plan soundness in state government. Our primary focus here is on state-level infl uences as opposed to plan-specifi c characteristics (e.g., asset allocation). Following a brief overview of the economic, political, and legal environments of state pension and retiree health care benefi ts, the analysis turns to unfunded liabilities. Specifi cally, we are interested in what factors are related to the size of unfunded liabilities. Sec-ondarily, we attempt a fi rst step toward understanding the “so what” question: what diff erence such unfunded liabilities make.

State Pensions: Development and ContextCorporate bankruptcies, reorganizations, and subsequent pension defaults prodded Congress to pass the Employee Retirement Income Security Act of 1974 (ERISA) to regulate private—but not state and local government—retirement systems through the Pension Benefi t Guarantee Corporation. All told, about 61 percent of private sector employees (compared to 89 percent of state and local workers) were covered by paid company retirement plans in September 2007 (BLS 2008). Th e pronounced trend is for fi rms to move from defi ned benefi t plans to defi ned contribution plans, with the chief objectives of achieving corporate effi ciency and transferring investment risk to individuals (Mitchell and Smetters 2003).

Th e Old-Age, Survivors, and Disability Insurance system, part of the Social Security Act of 1935, funded by workers and their employing organizations, provided a national pension system for employees in all sectors of the economy (hereafter referred to as Social Security). At fi rst, participation for state workers was excluded, but an amend-ment to the act in the early 1950s made participation voluntary. All but seven states eventually decided to participate.1 Additional federal tax changes in the 1970s permitted state employees to contribute tax-free salary dollars to defi ned contribution plans.2 All states had adopted voluntary defi ned contribution plans by 1988 (GAO 1999, 3).

State retirement systems vary greatly in design and coverage. Most state retirement systems have more than one retirement plan, such as special plans for teachers, local government workers, higher education faculty, judges, or state legislators. Some states, however, have one comprehensive retirement program for all state and local employees. Th ere is variance in employee and employer contribution percent-ages, eligibility requirements, and paid-out benefi ts, among other factors. Some states have closed established pension plans to new employees and implemented a second or even

third “tier” for them. Modifi cation of state pension plans for current workers is severely constrained by provisions in state constitutions, statutes, and regulations. Changes can be imposed more easily on newly hired employees.

Th ere are three basic state retirement system designs. Th e traditional and still predominant pension design is the defi ned benefi t plan. Based on the application of a multiplier to a formula that takes into account age, years of service, and salary level, the defi ned benefi t plan pays a guaranteed lifetime pension, adjusted for changes in the cost of living. Th e cost of living adjustment is provided annually in some states and periodically, as determined by the state legislature, in others.

A defi ned contribution plan combines monies contributed to a retirement account by the employer and employee. In the defi ned contribution approach, the employee decides (within parameters) how the money is invested, which, in turn, determines the amount of money ultimately available to be paid out in the pension. Th e 401(k) and 403(b) are the most common defi ned contribution plans. Despite much debate and consideration, only two states, Alaska and Michigan, presently mandate defi ned contribution pension coverage for all new state employees. Two others, Indiana and Oregon, off er plans with components of both defi ned contribu-tion and defi ned benefi t plans. Another eight states off er a defi ned contribution plan as an option, but the take-up rate is low. Because these plans were adopted recently and do not yet contain large num-bers of participants, they are not incorporated as such in this article.3 Th e vast majority of state workers participate in a defi ned benefi t plan, and even when a defi ned contribution plan is adopted for future employees, present funding liabilities remain unchanged. Th is research, therefore, is limited to traditional defi ned benefi t plans.

Th e third option is a hybrid plan. Often referred to as cash bal-ance accounts, these typically credit participants’ accounts with an

State retirement systems vary greatly in design and coverage. Most state retirement systems

have more than one retirement plan, such as special plans for

teachers, local government workers, higher education

faculty, judges, or state legislators.


employer contribution, usually calculated as a percentage of pay, which may be withdrawn upon retirement as an annuity or in lump sum. As in the case of defi ned contribution plans, hybrids have won little support in state government so far, although Nebraska has adopted such a plan.

Traditional defi ned benefi t retirement plan resources are allocated and invested based on actuarial assumptions that seek to balance assets on hand and estimates of future contributions with pres-ent and future liabilities (ideally, thereby making the pension plan “fully funded”). Certain fund performance assumptions are made to project the present value of total estimated retirement benefi ts for past and present employees (see Coronado, Engen, and Knight 2003). Th e average assumed rate of return for state pension assets is approximately 8 percent, which includes an infl ation assumption of 3.75 percent. As a whole, state pension plans were fully funded in fi scal year 2001. By fi scal year 2007, however, the Public Fund Sur-vey (Brainard 2008) found that they were only 86 percent funded, as some plans were still climbing out of the defi cit hole dug by the precipitous decline in the stock market that greeted the millennium (Brainard 2008; Mitchell and Hustead 2001).

Pension funds control and expend an impressive amount of money. According to the U.S. Government Accountability Offi ce (2008, 4), state and local pension systems paid out $151.7 billion in benefi ts to 7.3 million annuitants in 2006. Typically, plans are funded through employer and employee contributions. Assets are invested in equi-ties, fi xed-income instruments, real estate, and other investment vehicles (Brainard 2008). Over the past decade, most states have moved aggressively into equities,4 although a price was paid in the stock market turmoil of 2000–2002 and again in the stock market meltdown that began in 2008. In fact, recent estimates suggest that the 2008–2009 market declines cost public pension plans half a tril-lion dollars, leaving funding ratios hovering around 65 percent (see Karmin 2009; Miller 2009; Whoriskey 2008). Since the mid-1980s, when Congress threatened to intervene in troubled state pension systems and impose ERISA-like regulations on them, the states have, in general, eff ectively managed and adequately funded their retirement plans (Anderson and Brainard 2004; Schneider 2005).5 Some states regulate more strictly than even ERISA requires, and some less so (Eaton and Nofsinger 2004).

Retiree Health Care: Development and ContextGovernments are required by GASB 43 and 45 to report the status of their plans using three measures: the funded ratio (the percent-age of the plan’s liabilities covered by assets), unfunded liabilities (the excess of liabilities over assets), and annual contributions to health care benefi ts.6 Most states have reported substantial unfunded liabilities because, unlike pension plans, their health care plans have not set aside money for future obligations (GAO 2008, 3). Th ese liabilities, owed to current workers who are expected to collect them in the future, place a fi nancial burden on future generations of employees and taxpayers.

Th ere is quite a lot of variance in retiree health care plans among the states. Most states off er group health insurance to retirees under the Medicare-eligible age of 65. Dependants may or may not have access to the plan. Retirees’ share of the insurance premiums range from zero to 100 percent. Once a retiree turns 65, he or she is

shifted to Medicare for primary coverage, with most states off ering supplemental coverage for prescription drugs (GAO 2008, 4–5). Further variance is found in employee deductibles and co-payments, and in state projections of future health care costs. As noted earlier, compared to pension obligations, which are typically embodied in statutes, constitutions, or collective bargaining contracts, states have much more discretion in altering their health care plans (Norfus 2008) and have been doing so freely during the past several years.

Unfunded retiree health care liabilities have been climbing in abso-lute dollar fi gures, as a percentage of salaries, and as a proportion of state operating budgets. To maintain the current levels of benefi ts is increasingly diffi cult in the context of GASB 43 and 45 and the continuing escalation of health care and prescription drug costs. Fearing reductions in bond ratings and eventual voter and taxpayer concern, states today are actively exploring and adopting health care plan modifi cations to rein in current costs and unfunded future liabilities.

Explaining Differences in State Retiree Benefi ts FundingBecause the states have only recently been required to calculate and report their OPEB liabilities, previous research on state retiree ben-efi ts funding has focused almost exclusively on pension plans. Using a variety of fund fl ow (i.e., actual contributions relative to required contributions) and stock (i.e., plan assets relative to liabilities) mea-sures as dependent variables, researchers have examined the eff ects of various managerial, political, and fi scal factors on retiree benefi ts funding. In summing up what we know from this research about retirement plans, Schneider aptly describes them as “highly complex socioeconomic institutions” (2005, 122).

Managerially, researchers have examined facets of plan governance (e.g., the presence, size, and independence of boards), actuarial assumptions (e.g., expected rates of return on plan investments, infl ation rates, expected participant turnover and mortality), invest-ment practices (e.g., asset allocation, investment policies), and broader managerial considerations (e.g., annual plan performance reporting requirements, time perspective, transparency). Th e fi nd-ings have been inconsistent, with some indicating direct eff ects (Romano 1993; Schneider and Damanpour 2002), others indirect eff ects (Useem and Mitchell 2000), and still others little or no eff ects (Eaton and Nofsinger 2001, 2008) between these aspects of plan management and levels of benefi t funding.

Researchers have also studied the eff ects of political pressure on pension practices and performance. Specifi c attention has been paid to politically motivated investment policies (e.g., social and/or economically targeted investment policies) and their eff ects on investment returns, with fi ndings generally suggesting that such policies tend to hurt investment and plan performance (Eaton and Nofsinger 2004; Romano 1993; but see Schneider and Daman-pour 2001). Others have focused on broader political infl uences such as the partisan composition of state legislatures or congres-sional delegations. Th ese fi ndings have also been mixed, with some suggesting that Democratic control leads to greater plan under-funding (Johnson 1997), and others reporting no direct eff ects (Mitchell and Smith 1994). Finally, the political infl uence of public employees and public employee unions has received attention, with fi ndings showing that unfunded retiree benefi t levels are higher in


unionized states (Chaney, Copley, and Stone 2002; Johnson 1997; Mitchell and Smith 1994), whereas the size of the state workforce has no direct eff ects on funding levels (Chaney, Copley, and Stone 2002).

Finally, researchers have examined the eff ects of fi scal measures on retirement plan funding. Owing to the notion that retirement ben-efi ts funding is aff ected by a state’s fi scal condition, researchers have used a variety of measures to operationalize this construct, including the general fund balance, tax capacity, and balanced budget require-ments (Chaney, Copley, and Stone 2002); fi scal constraint fl ow (interest/revenue) and level (total public debt/revenue) variables (Eaton and Nofsinger 2004); state indebtedness (Schneider and Damanpour 2002); and state unemployment (Mitchell and Smith 1994). Regardless of the measure, the fi ndings generally show that a state’s fi scal condition aff ects its benefi t funding, with greater fi scal pressure leading to greater benefi t underfunding.

Against this backdrop, we now turn to our analysis of funding levels for state pensions and OPEB, the two main aspects of state retiree benefi ts. First, we present our dependent variables, which measure the states’ levels of unfunded liability for pension and OPEB, respectively. Th en, we develop two models to explore factors associated with levels of pen-sion and OPEB unfunded liabilities. In line with the preceding characterization of the lit-erature, we model state retiree benefi ts funding as a function of managerial, political, and fi scal infl uences. In so doing, the research seeks to advance the retirement benefi ts literature by expanding focus to include not only pension funding, but also the previously unexamined OPEB funding.

Dependent VariablesOur analysis uses two straightforward, population-adjusted mea-sures of plan funding: unfunded pension liability and unfunded OPEB liability. First, for unfunded pension liability, data are drawn from the Pew Center on the States’ widely cited report Promises with a Price (2007).7 Th e Pew data are derived from the states’ com-prehensive annual fi nancial reports. To account for state size, we divide the Pew data by state population, with the resulting measure being unfunded pension liability per capita (see table 1). Second, for unfunded OPEB liability (which includes all nonpension retiree benefi ts, such as dental, vision, and life insurance, but consists nearly entirely of health care), data were obtained directly from the states’ OPEB valuation reports, thus providing the most accurate and current estimates of these unfunded liabilities.8 As was the case with the pension data, the OPEB data were divided by state popula-tion to create a per capita measure (see table 1).

Explanatory VariablesManagerial infl uence. Research reported earlier suggests the importance of managerial factors to retiree benefi ts funding. Given our interest in retirement plans from both fi nancial and human resource management perspectives and growing empirical evidence that “management matters” to the performance of a variety of public programs, we include most recent “Money” and “People” measures from the Government Performance Project (GPP) (Barrett and Green 2008) to examine the relationship between public manage-

ment and retiree benefi t funding. Both GPP grades are converted to numerical scores (e.g., A+ = 4.33, A = 4.0, A– = 3.67, etc.).

GPP Money is a broad measure of a state’s fi nancial management capacity that takes into account a state’s fi scal time perspective (long or short term), budgetary timeliness and transparency, revenue and expenditure balance, and contracting, purchasing, fi nancial control and reporting eff ectiveness (Barrett and Greene 2008). It is antici-pated that strong GPP Money scores will be associated with lower unfunded pension and OPEB liabilities.

GPP People measures a state’s human resource management (HRM) capacity, focusing on how well a state attracts, retains, and rewards employees (Barrett and Greene 2008). Because a primary HRM goal is attracting and retaining human capital and because a key strategy in pursuit of that goal is off ering an attractive benefi ts pack-age, higher GPP People scores ought to be associated with larger unfunded pension and OPEB liabilities. Th is would suggest the suc-cess of state HRM offi cials in arranging attractive benefi ts packages, but at the potential cost of creating large long-term liabilities.

Political infl uence. To assess the eff ects of political infl uences on retiree benefi ts funding, we include four variables: government ideology, public employee density, public union density, and legislative professionalism. Research sug-gests that liberal-leaning political orientations are associated with more employee-friendly policies (e.g., Blais, Blake, and Dion 1997;

West and Durant 2000). It stands to reason, then, that liberal states would have more generous benefi ts for their employees than conser-vative states (Johnson 1997). Because it is political and government elites who make decisions on pensions and health care benefi ts, we employ Berry et al.’s (1998, 2007) revised measure of government ideology for 2006. Th is variable incorporates ideological scores for the governor and political party delegations for the upper and lower houses of the legislature. Higher values on the measure refl ect more liberal government ideologies; thus, we expect positive relationships between government ideology and our measures of unfunded retiree benefi t liabilities.

State employees have a vested interest in the design and funding of pension and OPEB benefi ts. Th eir ability to infl uence state policy and programs is well established (Elling 2004). Indeed, the political and electoral clout of state employees and unions has been identi-fi ed as an important infl uence on governments’ decisions to enhance retiree benefi ts (McKethan et al. 2006). State employees enjoy col-lective bargaining rights in 32 states (Kearney 2009) and employee associations wield legislative infl uence in others. To capture these eff ects, we follow Chaney, Copley, and Stone (2002) and include multiple measures: public employee density, defi ned as the number of state government employees per 10,000 population (Governing Sourcebook 2007), and public union density, defi ned as the percent-age of state employees belonging to unions (Hirsch and Macpherson 2006). Using these two measures allows us to assess whether it is the size of a state’s workforce or its unionized status (or, perhaps, both) that aff ects retiree benefi t funding. State workers and unions can be expected to pursue more generous benefi ts, so positive relation-ships between public employee density and public union density,

…we develop two models to explore factors associated with levels of pension and

OPEB [other postemployment benefi ts] unfunded liabilities.


Table 1 Total and Per Capita Unfunded Pension and OPEB Liabilities

State Total Pension Liabil-ity (in billions)a

Unfunded Pension Liability (in billions)a

Unfunded Pension Liability per Capitab

Total OPEB Liability (in billions)

Unfunded OPEB Li-ability (in billions)

Unfunded OPEB Li-ability per Capitab

Alabama $33.961 $5.522 $1,216.47 $3.104 $3.104 $683.76

Alaska 13.091 3.370 5,033.92 6.301 3.139 4689.20

Arizona 34.354 5.274 886.10 1.605 0.438 73.59

Arkansas 19.114 3.409 1,229.84 1.465 1.465 528.47

California 355.483 46.674 1,296.84 47.878 47.878 1330.30

Colorado 49.491 12.804 2,739.48 1.248 1.033 221.02

Connecticut 34.190 14.915 4,277.83 21.681 21.681 6218.58

Delaware 6.416 0.208 247.02 3.132 3.106 3688.03

Florida 110.978 (6.182) (348.54) 3.082 3.082 173.77

Georgia 65.994 2.504 274.90 15.035 15.035 1650.80

Hawaii 14.661 5.132 4,048.68 9.679 9.679 7635.80

Idaho 9.951 0.525 368.33 0.362 0.362 253.88

Illinois 103.073 40.732 3,202.32 24.21 24.21 1903.37

Indiana 28.954 10.566 1,688.62 0.442 0.442 70.64

Iowa 21.651 2.507 848.25 0.220 0.220 74.44

Kansas 17.552 5.364 1,956.48 0.293 0.293 106.87

Kentucky 30.659 9.304 2,230.59 5.706 4.833 1158.71

Louisiana 33.358 10.979 2,442.06 19.609 19.609 4361.75

Maine 12.357 2.827 2,154.22 4.756 4.756 3624.39

Maryland 43.538 7.634 1,369.79 14.543 14.543 2609.47

Massachusetts 50.432 14.055 2,186.17 13.287 13.287 2066.69

Michigan 63.268 12.155 1,202.52 13.925 13.925 1377.63

Minnesota 30.787 2.11 412.82 0.565 0.565 110.48

Mississippi 25.681 6.865 2,366.90 0.570 0.570 196.52

Missouri 43.857 8.427 1,455.96 2.186 2.186 377.69

Montana 8.858 1.676 1,790.75 0.449 0.449 479.81

Nebraskac 7.396 0.832 474.78 0 0 0

Nevada 25.795 6.482 2,690.98 2.295 2.295 952.70

New Hampshire 6.403 2.474 1,899.00 2.859 2.859 2193.98

New Jersey 109.611 23.141 2,673.03 68.834 68.834 7950.85

New Mexico 22.545 4.076 2,127.18 4.264 4.110 2144.72

New York 140.150 0.00 0.00 49.663 49.663 2578.22

North Carolina 61.828 (2.954) (340.41) 23.925 23.786 2740.61

North Dakota 3.674 0.007 1,071.80 0.031 0.031 48.75

Ohio 139.251 26.201 2,286.31 30.748 18.723 1633.80

Oklahoma 27.840 11.468 3,243.30 0.814 0.814 230.21

Oregon 51.254 (5.362) (1,477.15) 0.523 0.264 72.73

Pennsylvania 91.494 12.223 988.36 8.659 8.659 700.15

Rhode Island 9.822 4.329 4,058.33 0.480 0.480 449.98

South Carolina 33.712 9.135 2,146.87 10.048 10.048 2361.46

South Dakota 5.904 0.198 253.59 0.076 0.076 97.43

Tennessee 28.117 0.366 61.13 2.146 2.146 358.31

Texas 132.088 15.140 662.77 17.675 17.675 773.73

Utah 18.783 0.690 275.43 0.569 0.569 227.14

Vermont 3.195 0.256 413.66 1.419 1.419 2289.69

Virginia 51.683 9.934 1,314.44 1.813 1.616 213.82

Washington 29.075 5.984 949.52 7.495 7.495 1195.22

West Virginia 11.775 5.331 2,952.24 7.761 7.761 4298.23

Wisconsin 73.736 0.321 57.85 1.473 1.473 265.86

Wyoming 6.216 0.316 624.17 0.072 0.072 142.14

a. Pew Center on the States (2007). Figures are rounded; fi gures in parentheses represent pension funding surpluses. b. Unfunded pension liability per capita is calculated by dividing the unfunded pension liability total (in the fi rst column) by the estimated state population in 2005.

Unfunded OPEB liability per capita is calculated in the same fashion. Population estimates are from the U.S. Census population estimator, available at http://www.census.gov/popest/states/tables/NST-EST2007-01.xls.

c. Nebraska has accrued negligible (effectively $0) OPEB liabilities and, as such, is not producing an OPEB valuation report


respectively, and our measures of unfunded retiree benefi ts liability are expected.

Finally, the most recent version of Squire’s (1992, 2007) legisla-tive professionalism measure is included to account for the eff ects of a state’s legislative capacity. Previous research has not considered the eff ects of this political institution variable, but because profes-sional legislatures have the resources (e.g., temporal, staff ) to more fully study and address the long-term implications of their fi nancial decisions, it is reasonable to assume that they would be reluctant to enact benefi ts enhancements with dire long-term budgetary conse-quences (Hansen, Dothan, and Th ompson 2007). Th us, negative relationships are expected between legislative professionalism and the two dependent variables.

Fiscal infl uence. We include two variables that assess the eff ects of a state’s economic and fi scal condition on retiree benefi t funding. Our fi rst variable, fi scal constraint, is a fi scal fl ow variable devel-oped by Eaton and Nofsinger (2004). It is calculated as each state’s annual interest payments divided by its total revenue. Data for this variable were obtained from the U.S. Census Bureau’s State and Local Government Finances: 2005–2006 (2008). Consistent with previous research (Eaton and Nofsinger 2008; Mitchell and Smith 1994; Schneider and Damanpour 2002), we expect states with higher fi scal constraint to report greater levels of retiree benefi ts underfunding.

Second, we include per capita income as a surrogate for state cost of living (Governing Sourcebook 2006). High-cost states can be expected to have demands for generous pension and health care benefi ts. Th erefore, a positive relationship is expected: higher cost of living should be associated with higher unfunded pension and OPEB liabilities.

Other infl uences. In addition to the preceding variables, which are common to both the pension and OPEB models, the unfunded pension liability model includes measures for the magnitude of the employer’s contribution to the state pension plan and for the coverage of state employees under the Social Security system. Some research indicates that pension plan underfunding is associated with higher required employer contributions, refl ecting the eff ects of the plan’s generosity (Dulebohn 1995; Schneider and Damanpour 2002). Other research attributes this relationship to chronic plan underfunding, which, in turn, requires larger (actuarially deter-mined) annual employer contributions (Eaton and Nofsinger 2004). Setting aside for the moment these rival interpretations, we antici-pate that states making larger, more generous contributions to the pension plan will have higher unfunded liabilities than more penuri-ous states. Here, the employer contribution variable is calculated as a percentage of the total annual portion of salary dedicated to the pension plan paid by the state government (Wisconsin Legislative Council 2006).

A dummy variable for Social Security coverage is included to account for the fact that not all state governments participate in the Social Security system. For those states whose employees are not covered under Social Security (see note 1), there is pressure to provide employees with a compensatory diff erential in the form of a more generous pension benefi t. Th us, the expectation is that states with-

out Social Security coverage (coded 1) will have larger unfunded pension liabilities than those that are covered (coded 0). Given this coding, a positive relationship is expected between this variable and the two dependent variables.

Finally, the OPEB model includes our unfunded pension liability measure as an independent variable. It is likely that a state’s perfor-mance in meeting its pension obligations—obligations that have been known and reported for decades—is a good indicator of how it has handled OPEB obligations. Th e expectation is that states’ performance in meeting their retiree benefi t obligations will be con-sistent: states with large unfunded pension liabilities are also likely to have large unfunded OPEB liabilities.

ResultsTh e relationships between our independent variables and the states’ unfunded pension and OPEB liabilities are estimated using multiple regression.9 Th e results are reported in tables 2 and 3, respectively. Th e fi rst column of each table reports the regression coeffi cients for each variable and their robust standard errors; the second column

Table 2 Infl uences on Unfunded Pension Liability

Independent Variables Regression Coeffi cients(b)

Standardized Coeffi cients (beta)

Fiscal Infl uence

Per capita income .062* .232

(.048)

Fiscal constraint 332.35** .303

(141.07)

Managerial Infl uence

GPP Money –743.89*** –.361

(288.25)

GPP People 193.36 .097

(236.97)

Political Infl uence

Legislative professionalism –2375.71* –.206

(1539.15)

Government ideology -4.98 –.098

(6.07)

Public employee density 2.43** .148

(1.14)

Public union density –1.721 –.023

(11.91)

Other Infl uence

Social Security coverage 455.99 .120

(382.57)

Employer contribution 123.84*** .375

(35.15)

R2/Adj. R2 .56/.44

F-ratio 7.74***

N = 50

Note: Figures in the fi rst column are multiple regression coeffi cients estimated with robust standard errors; the second column contains standardized regression coef-fi cients. Tests for signifi cance are one-tailed: * p ≤ .10, ** p ≤ .05, *** p ≤ .01.


reports the standardized coeffi cients, which allow for comparison of the variables’ relative eff ects. As indicated by the adjusted R2 values, the models fi t the data reasonably well, accounting for 44 percent of the variance in the unfunded pension liability model and 49 percent in the unfunded OPEB liability model. More importantly, the signs of the estimated coeffi cients generally conform to expectations and a number of signifi cant relationships are evident.

Turning fi rst to table 2, the variables that stand out in the unfunded pension liability model are GPP Money, employer contribution, and fi scal constraint. Th e statistically signifi cant coeffi cient estimate for the GPP Money variable (b = –743.89) indicates a direct inverse relationship between the quality of state government fi nancial management and unfunded pension liability. Specifi cally, strong fi nancial management in a state is refl ected in greater eff ort to fully fund the plan and, as a consequence, signifi cantly lower unfunded pension obligations. Th e standardized coeffi cient for this variable (beta = –.361) is the second largest reported, indicating that fi nan-cial management is among the strongest infl uences on the levels of pension funding. Indeed, when it comes to the adequacy of pension funding, this aspect of management appears to matter a great deal.

Employer contribution shows a strong (beta = .375) and signifi cant relationship with unfunded pension liability. Th e coeffi cient esti-mate (b = 123.84) indicates that a one percent increase in employer contribution corresponds to an increase of about $124 in unfunded pension liability per capita. Th e positive relationship means that states that have relatively generous (larger) employer contribution requirements are likely to accrue larger unfunded liabilities than less munifi cent states. As mentioned previously, this result can also be explained from an actuarial standpoint: because annual employer contribution requirements are actuarially determined, a higher contribution rate also refl ects a larger gap between plan assets and liabilities or, in other words, larger unfunded liabilities (Peng 2004; Eaton and Nofsinger 2004). Th erefore, employers facing large unfunded pension liabilities must make regular payments for their new pension liabilities and an additional amount to catch up on the underfunding.

Th e results for both of our fi scal infl uence variables are signifi cant. Th e estimated coeffi cient (b = 332.35) for fi scal constraint indicates a signifi cant positive relationship between a state’s level of fi scal constraint and unfunded pension liability. Th ose states with higher interest payments relative to revenue tend to have higher levels of unfunded liability: a 1 percentage point increase in fi scal constraint is associated with an additional $332 of unfunded liability per capita. Fiscally strained states encounter competing expenditure demands that appear to impact pension funding. Th e estimate for per capita income is also signifi cant (b = .062). Th is result, as anticipated, suggests that higher cost of living states tend to have larger unfunded pension liabilities. Such states tend to have higher salaries that are calculated into pension payments and environ-ments that breed expectations of higher pension payments and cost of living adjustments, all of which has bearing on pension funding status.

Another variable that stands out is public employee density (b = 2.43). As expected, as the number of state government employees (per 10,000 state population) increases, so too does the size of state governments’ unfunded pension liabilities (by about $2.40 per employee). In contrast, the coeffi cient estimate for public union density (b = –1.72) is in the opposite of the hypothesized direction but fails to attain statistical signifi cance. Apparently, and in contrast to previous research (Chaney, Copley, and Stone 2002; Johnson 1997; Mitchell and Smith 1994), these fi ndings suggest that the relative size of a state government’s workforce is more important to pension funding than its unionized status (Kearney 2003). Interest-ingly, these fi ndings seem to support suggestions that unions have shifted some focus from securing tangible results (i.e., higher wages and salaries) for members (Johnson 1997; Mitchell and Smith 1994), to ensuring the sustainability of future benefi ts already promised (see Miller 2009).

Legislative professionalism attains statistical signifi cance, albeit at the .10 level (b = –2375.71). Th e direction of the relationship is negative, as expected, indicating that more professional legislatures are associated with lower unfunded pension liabilities. Th is pro-vides some indication that institutional capacity is important to states’ pension funding behavior. Th ose state legislatures possessing resources for informed decision making appear to have acted in ways that limit the long-term budgetary costs of pensions.

Table 3 Infl uences on Unfunded OPEB Liability

Independent Variables Regression Coeffi cients (b)

Standardized Coeffi -cients (beta)

Fiscal Infl uence

Per capita income .112* .290

(.079)

Fiscal constraint –58.15 –.036

(211.72)

Managerial Infl uence

GPP Money –527.96 –.176

(585.94)

GPP People 772.84* .266

(479.51)

Political Infl uence

Legislative professionalism 739.84 .044

(2337.31)

Government ideology 14.65** .199

(8.85)

Public employee density 9.03*** .377

(2.28)

Public union density 5.67 .052

(18.73)

Other Infl uence

Unfunded pension liability .496*** .340

(.195)

R2/Adj. R2 .59/.49

F-ratio 9.80***

N = 50

Note: Figures in the fi rst column are multiple regression coeffi cients estimated with robust standard errors; the second column contains standardized regression coeffi cients. Tests for signifi cance are one-tailed: * p ≤ .10, ** p ≤ .05, *** p ≤ .01.


Th e three remaining variables in the model—social security coverage, GPP People, and government ideology—fail to achieve statistical signifi cance. Th e coeffi cient for Social Security coverage (b = 455.99) is in the hypothesized direction, but just fails to attain statistical signifi cance at the (lowest) conventional .10 level (p < .12). States not participating in Social Security eff ectively remove one leg of the traditional three-legged stool (i.e., pension benefi ts, Social Security payments, and personal savings) of retirement income. Th us, it would be understandable if these states off ered relatively more generous pensions—with the associated larger unfunded liabilities—to off set this diff erence for their workers. Th at their unfunded pension liabilities do not diff er signifi cantly from states participating in the Social Security system suggests that either they do not off er more generous benefi ts or, alternatively, they are more diligent in meeting their pension funding obligations. Th e sign for the GPP People coeffi cient (b = 193.36) is in the expected direction, but it shows no direct relationship with unfunded pension liability. Th e sign for the government ideology coeffi cient (b = –4.98) is in the opposite direction of what was expected, but its failure to attain statistical signifi cance suggests that state decision makers’ rela-tive liberalism and conservatism is unimportant to understanding unfunded pension liabilities.

Turning now to the OPEB liability model (see table 3), a number of the results conform to expectations. Th e level of explained variance slightly exceeds that attained in the pension funding model, and fi ve of the independent variables achieve statistical signifi cance. Th e vari-ables with the strongest relative eff ects are public employee density (beta = .377), and unfunded pension liability (beta = .340).

Similar to the pension funding model, public employee density (b = 9.03) has a strong, statistically signifi cant association with unfunded OPEB liability. As mentioned earlier, state employees can be a powerful force for competitive retirement benefi ts. Th eir success in securing generous retiree health care benefi ts is associated with larger unfunded OPEB liabilities to the tune of an additional $9 per capita for each additional state employee (per 10,000 population). As was the case with the pension liability model, however, the public unionization variable shows no direct relationship with unfunded OPEB liability. Again, it appears that it is the size of a state govern-ment’s workforce—not its unionized status—that is important.

Th e unfunded pension liability variable (b = .496), intended to capture consistency in addressing—or failing to address—future liabilities for retiree pensions and OPEB, is signifi cant at the .01 level and in the expected direction. States characterized by higher levels of unfunded pension liabilities also tend to have higher levels of unfunded OPEB liabilities. Specifi cally, each additional dollar of unfunded pension liability per capita is associated with about 50 cents of unfunded OPEB liability per capita. Th is leads to the tentative conclusion that states are indeed consistent in their fi scal behavior regarding retiree benefi ts funding, for better or worse.

Th e next strongest estimated relationships are between per capita income (beta = .290) and GPP People (beta = .266) and unfunded OPEB liability. Per capita income is signifi cantly related to unfunded OPEB liability (b = .112). In the same fashion as the pen-sion funding model, higher cost of living states tend to have accrued larger unfunded OPEB liabilities than relatively poorer states: an

additional $1,000 of income is associated on average with an addi-tional $112 of unfunded OPEB liability per capita.

As for GPP People, the coeffi cient estimate (b = 772.84) indicates a signifi cant relationship between the strength of states’ HRM capacity and the size of their unfunded OPEB liabilities. Th is makes sense if one assumes that top HRM professionals pursue attractive benefi ts packages for employees, benefi ts packages that have long provided government with a measure of competitive advantage in the human capital marketplace. Th at their success in securing these benefi ts from legislative and administrative bodies is associated with larger unfunded OPEB liabilities is not surprising. Because OPEB liabilities have only recently been reported and gained the attention of those (e.g., legislatures, fi nancial managers) concerned with fi scal health, it is understandable that the eff ects of GPP People would be pronounced here but not in the unfunded pension liability model.

In contrast to the pension model, the coeffi cient estimated for government ideology (b = 14.65) is statistically signifi cant in the unfunded OPEB liability model. A straightforward interpretation is that more liberal (hence employee-friendly) state governments have devised more generous retiree health care plans, translating into larger unfunded OPEB liabilities. In conservative states, a more prudent funding approach may prevail.

Th e estimated coeffi cients for the three remaining variables—GPP Money, fi scal constraint, and legislative professionalism—failed to achieve statistical signifi cance. Th e sign for the GPP Money variable was in the expected direction but, in contrast to the pension model, this aspect of a state’s public management capacity does not appear to directly aff ect state OPEB liability. It is reasonable to assume that states with stronger fi nancial management systems would have been more aware of—and would have taken steps to address—their looming unfunded OPEB liabilities. Th at this relationship was not more strongly evidenced here seems to underscore how obscure, in the pre-GASB 45 era, these mounting liabilities were even in the best fi nancially managed states.

Th e result for legislative professionalism suggests that states’ legisla-tive capacity is not related to how responsibly they have managed retiree health care liabilities. Similar to fi nancial management, this result could stem from the fact that—unlike pensions—the long-term costs of these benefi t promises were off the radar screen in most states prior to GASB 45, even in those states whose insti-tutional capacity would suggest otherwise. It remains to be seen how legislative professionalism will aff ect state eff orts to address unfunded OPEB liabilities now that their magnitude is more fully known.

Finally, the fi scal constraint variable showed no direct relationship with unfunded OPEB liability, suggesting that those states facing fi scal constraints (i.e., interest payments relative to revenue) were no diff erent from better-off states in addressing OPEB funding. Th is is likely indicative of the fact that, until very recently, only a handful of states had prefunding mechanisms (i.e., dedicated trust funds) for OPEB liabilities, with the vast majority of them handling OPEB payments on a pay-as-you-go basis. Th us, states could not opt out of annual required contributions to prefund accrued OPEB obligations (nor were the unfunded liabilities readily known). With


pensions, in contrast, states facing fi scal constraint might opt (and have opted) to withhold annual required contributions, resulting in larger unfunded pension liabilities. If this is true, then one might expect to see more pronounced eff ects of fi scal constraint on OPEB funding in coming years as states set up dedicated funds paralleling those established for pensions.

Discussion and ConclusionTh e reported results indicate a reasonably good fi t for the two models, point to some generally consistent associations across the models, and raise a number of interesting questions. In both mod-els, measures of fi scal, managerial, and politi-cal infl uence prove important to explaining variance in unfunded liabilities for state retiree benefi ts. Generally, then, our fi ndings cor-roborate Schneider’s (2005) previously cited observation about the complexity of retire-ment plans as socioeconomic institutions.

Fiscally, per capita income is consistently important, as relatively high cost of living states confront higher unfunded pension and retiree health care liabilities. As noted, we suspect that measures of fi scal constraint will eventually point to consistent eff ects, as states opt to replicate pension plan underfunding in their OPEB plans as a strategy for dealing with short-term budget shortfalls.

Politically, the results for public employee density underscore the powerful eff ects that state government employees can have on state policy and programs, while the results for our unionization variable raise questions about the conventional wisdom on unions’ eff ects on retiree benefi ts. Findings for legislative professionalism were mixed. We expected that the enhanced decision-making capacity of professional state legislatures would reveal itself in lower unfunded pension and OPEB liability. In the case of pensions the expected relationship obtained, but not for OPEB. Now that OPEB liabilities are being publicly reported, we expect that states with professional legislative bodies will be at the forefront of eff orts to balance the need to attract and retain talented employees through attractive ben-efi ts with the need to ensure the fi scal health of their states. Finally, government ideology was a poor indicator of unfunded liability in the pension model but a signifi cant one in the OPEB model. As we mentioned earlier, pension provisions are typically embedded in statutes, constitutions, or collective bargaining contracts. Th ey are incorporated in law, and therefore are diffi cult to change or amend. And most of these pension provisions and rules are long-stand-ing, a product of decisions made by earlier government elites who, collectively, may have been more or less liberal than those today. OPEB benefi ts are much simpler for a legislative body, governor, or administration to alter. It does not come as a surprise, then, that government ideology exerts greater impact on retiree health care benefi ts than on pension benefi ts.

Managerially, results from both models suggest that management matters, but in some rather intriguing ways. Our measure of a state’s fi nancial management capacity, GPP Money, incorporates an indica-tor of fi scal time perspective and other aspects of capability. It is an important indicator of funding adequacy in the pension model: stronger fi nancial management is associated with lower unfunded

liability. Our measure of state HRM capacity, GPP People, is signifi -cant in the OPEB model: stronger HRM capacity is associated with larger unfunded liability.

Th ese diff erences between GPP People and GPP Money seem to suggest a clash of cultures between budget and fi nance offi ces on the one hand, and HRM offi ces on the other. Apparently, those who rise to the highest decision-making ranks of state budget offi ces assume the role of watching over the public purse, and specifi cally the pension and—to an increasing degree, we presume—health care benefi ts promised to retirees. Human resource managers have pos-

sibly been successful advocates for generous health care benefi ts, but less persuasive for pensions which may be relatively immune to their pressure. Also, because defi ned benefi t plans have certain advantages for employees and retirees (e.g., the employer assumes the risk for fund performance, pension benefi ts are known and fi xed, cost of living allowances are available, etc.), and because they remain the predominant state government pension plan, it may be that this battle is “won” from

the perspective of HRM professionals. Th ese diff erences are indica-tive of the inherent tension between controlling costs and awarding attractive benefi ts. Ultimately, we believe it is the state legislatures that will serve as the mediators in this clash of people and money cultures and, as argued above, those legislative bodies possessing the greatest institutional capacity can be expected to lead the way toward arriving at an appropriate balance.

More broadly, what diff erence does it make if states accrue large unfunded pension and/or OPEB liabilities, beyond the obvious implications for taxes and possible diversion of appropriations for other pressing state needs to addressing the liability problem? For one thing, states’ unfunded liabilities for retiree benefi ts could have an eff ect on state credit ratings (Keating and Berman 2007; Wisniewski 2005). Lower bond ratings would, in turn, increase the interest rate costs of any new debt issued and increase bond insur-ance premiums. Th ough major rating agencies anticipate that states will be able to fund their OPEB liabilities (Fitch 2005; Standard & Poor’s 2006), they are also taking a wait-and-see approach regard-ing how states report and respond to these liabilities. In the case of pensions, chronic underfunding of these hard liabilities is known to negatively impact bond ratings.

To explore the potential credit implications of pension and OPEB liabilities, the states’ average bond ratings were regressed on our unfunded pension and OPEB liabilities variables (rescaled to per capita amounts in thousands of dollars).10 As shown in table 4, the results include a statistically signifi cant inverse relationship between the states’ unfunded pension liabilities and average bond ratings: larger unfunded pension liabilities are associated with lower aver-age bond ratings. Th e result for the OPEB variable also shows an inverse relationship, but it is not statistically signifi cant. Th is model is not introduced to fully explain variance in state bond ratings, but rather to support the argument that, if pensions are any guide, states should be concerned that large unfunded OPEB liabilities may negatively impact their bond ratings. Th is, in turn, would increase their costs for accessing capital through the bond markets.

In . . . both [of our] models, measures of fi scal, managerial, and political infl uence prove

important to explaining variance in unfunded liabilities

for state retiree benefi ts.


Indeed, at least one knowledgeable observer has asserted that the “kid gloves” treatment that rating agencies have given state and local governments needs to change, with raters instead drawing a “line in the sand” (i.e., with their ratings) for those governments that are not adequately addressing OPEB liabilities (Miller 2008b).

Aside from credit considerations, how the states address their unfunded liabilities for retiree benefi ts is important from a HRM perspective. According to the Center for State and Local Govern-ment Excellence (2007) survey, health care and pension benefi ts are among the most important characteristics that potential employees look for in a job, regardless of sector. Public employers have tradi-tionally off ered relatively generous benefi ts packages, something that has aff orded a measure of competitive advantage in the market for human capital (Brady 2007; Fleet 2007). Very few public employees are eligible for bonuses, profi t sharing, or any other extraordinary compensation that many private sector employees enjoy. Strong ben-efi ts help off set this compensation limitation for public employers. Governments have been able to off er generous benefi ts packages and enhancements because their costs are less immediate and visible rela-tive to salary increases, thus making them more politically palatable (Kearney 2003; Moore 2001; Reilly, Schoener, and Bolin 2007).

However, the opacity of retiree benefi t costs has been rendered more transparent by GASB 45 requirements, which have revealed the heretofore unknown costs of OPEB promises. With this transpar-ency and, for some states, shockingly high liabilities has come increased concern over the sustainability of many government ben-efi t plans. As the impressive equity market gains of the 1990s have given way this century to downturns associated with the dot-com bubble, post-9/11 jitters, the housing bubble, credit and banking crises, and spiraling energy costs, pension funding levels have fallen. As Miller (2008c) notes, the rising costs of health care, longer life spans, and growing numbers of retirees make many current benefi ts plans unsustainable.

States have begun to realize the magnitude of their unfunded liabilities and are exploring ways to address the shortfalls. Th ough it is unlikely that states will abandon retiree benefi ts altogether (Wisniewski 2005), they are searching for cost-cutting strategies. Th is might include creating tiered benefi ts, whereby newly hired employees receive considerably reduced benefi ts. For example, new employees may be required to pay more for health care benefi ts, contribute larger portions of their salaries to their pensions, and/or

work more years to qualify for these benefi ts. More fundamental change might entail shifting new employees to defi ned contribu-tion plans for both pension and retiree health care. Importantly, a number of states have already adopted the tiered approach, leading to the suggestion that new employees in these states are, in eff ect, “second class citizens” (Miller 2008a). Incumbent employees and unions are likely to go along with such strategies (i.e., “give up their unborn”) if it means preserving their own more generous benefi ts (Miller 2008a). But, the more important question concerns the eff ect this will have on governments’ ability to attract new employ-ees (Keating and Berman 2007; Marlowe 2007) and to retain and motivate existing employees who may be employed at lower benefi t tiers. Will qualifi ed people choose public service if benefi ts are substantially reduced? What are the motivation and performance implications for government workforces composed of relative haves and have-nots? Decisions on the design and funding of retiree benefi ts have important implications for the costs and performance of government.

Th e current research off ers insights into the factors associated with the states’ unfunded liabilities for retiree benefi ts. Th e main limitation of this research is its cross-sectional design. Because the states are now required to regularly report their OPEB liabilities, future research will be able to adopt a longitudinal design. A second limitation is that the models tested, like most retiree benefi ts–related research (Dulebohn 1995), do not attempt to standardize the vari-ous actuarial assumptions (demographic, economic, etc.) included in the states’ estimates of pension and OPEB liabilities, nor are certain aspects of pension fund management (e.g., asset allocation) considered. Some assurance that this is not a serious omission is provided by recent analyses of pensions, which found no signifi cant diff erences in asset allocations or actuarial assumptions (Eaton and Nofsinger 2008) and that almost all plans’ actuarial assumptions adhere to GASB guidelines (Dulebohn 1995).

To many public administration scholars, GASB and pension and OPEB liabilities may seem to be esoteric or tedious topics best left to analysis by fi nance and budgeting scholars, economists, and actuaries. In our judgment, this should not be the case. High levels of unfunded liabilities threaten both the structural viability of strug-gling state economies as well as the quality of the future state gov-ernment labor force. More research on these critical issues is needed.

AcknowledgmentsTh e authors gratefully acknowledge the Center for State and Local Government Excellence for its support of this research as well as the insights of Robert L. Clark. An early version of this article was presented at the Annual Meeting of the American Political Science Association, August 28–31, 2008, Boston.

Notes 1. Th e non–Social Security states are Alaska, Colorado, Maine, Nevada, Mas-

sachusetts, Louisiana, and Ohio (see Munnell 2000). Th ey account for about 25 percent of total state government employment. Alaska discontinued Social Security in 1980 (GAO 1999, 3). In several other states, teacher retirement plans do not participate in Social Security.

2. Th ese deferred income defi ned contribution plans are authorized in all states under Internal Revenue Code sections that serve as shorthand for identifi cation purposes: 401(k), 403(b), and 457(b).

Table 4 Effects of Unfunded Pension and OPEB Liability (in billions) on State Bond Ratings

Independent Variables Regression Coeffi cients (b) Standardized Coeffi cients (beta)

Pension liability –.244** –.291

(.123)

OPEB liability –.029 –.051

(.083)

R2/Adj. R2 .10/.06

F-ratio 3.35**

N = 47

Note: Figures in the fi rst column are multiple regression coeffi cients estimated with robust standard errors; the second column contains standardized regression coef-fi cients. Tests for signifi cance are one-tailed: * p ≤ .10, ** p ≤ .05, *** p ≤ .01.


3. For a discussion on the relative advantages of each to employers and employees, see Anderson and Brainard (2004).

4. South Carolina, West Virginia, and Indiana became the last states to remove prohibitions against investing in equities in the mid-1990s.

5. Th ere is debate among economists as to whether public pension funds are less effi cient that private pension plans. See, for example, Coronado, Engen, and Knight (2003); Hsin and Mitchell (1997); Schneider (2005, 120–22).

6. Th ese requirements were phased in from December 15, 2005 to December 15, 2008.

7. Figures include plans aggregated into the state pension system, including, in some cases, teachers and municipal employees. Th e total liability, however, rests with the state.

8. Other estimates of OPEB liabilities are available, including the Pew Center on the States (2007), Credit Suisse (2007), and Standard & Poor’s (2007). In each case, state estimates are derived in multiple ways, casting some doubt on the ac-curacy and consistency of the reported measures. Relying on the state-generated and state-reported OPEB liability fi gures, as we do in the analysis, represents a preferable approach.

9. Th e article reports regressions with robust standard errors, which produce identical coeffi cient estimates to ordinary least squares (OLS) while account-ing for minor violations of normality or homoscedasticity assumptions or for observations with large residuals or infl uence. Regression diagnostics (e.g., normal-quantile plots, histograms of residuals, variance infl ation factor measures, standardized predicted values versus standardized residual plots, leverages) for models estimated with OLS indicated no issues with multicollinearity or linearity assumptions but indicated a potential outlier (New Jersey, with its large value on unfunded OPEB liability, had a studentized residual of 2.98 in the OPEB model) and potentially infl uential cases (Hawaii, with a large value on state employee density; Massachusetts, with a large value on fi scal constraint; and California, with a large value on legislative professionalism, each had large leverage values in both models). Models were rerun, fi rst, with these cases deleted and, second, with these cases included but with robust standard errors. In each instance, the substantive results were nearly identical, diff ering only in terms of levels of signifi cance. Because we are reluctant to discard good but unusual data, we opt to include all cases and attempt to account for the unusual ones by estimating robust standard errors.

10. Average state bond ratings were calculated by converting the Standard & Poor’s, Fitch, and Moody’s ratings to a 10-point scale (e.g., 10 points for Standard & Poor’s AAA, Fitch’s AAA, and Moody’s Aaa; 9 points for Standard & Poor’s AA+, Fitch’s AA+, and Moody’s Aa1), then dividing the total by the number of ratings for each state. Bond ratings were drawn from Standard and Pour’s Direct State Review of Virginia (December 5, 2007), as reported by the Virginia Joint Legislative Audit and Review Commission (2008). Complete details and scores are available from the authors upon request.

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