Impact of Unfunded Pension Obligations on Credit Quality of State Governments

Impact of Unfunded Pension Obligations onCredit Quality of State Governments

CHRISTINE R. MARTELL, SHARON N. KIOKO, AND TIMA MOLDOGAZIEV

This study reviews the funding status of state-administered pension plans and theirimpact on state credit quality. As the fund ratio (actuarial assets/actuarial accruedliability) of state-administered pension plans decreases, states are more likelyassigned a lower rating. Moreover, rating outlooks are sensitive to the fund ratio,especially for migration between stable and negative outlooks for states with lowerfund ratios. These results are a timely pretest to the 2013/2014 implementation ofGASB Statements No. 67 and 68, serving as a benchmark to assess whether newreporting requirements will yield information to alter the market’s response tounfunded pension liabilities.

INTRODUCTION

The problem of underfunded retirement benefit programs stems from longstanding

underinvestment that occurred prior to the Great Recession.1 Themagnitude of the underfunding

was exacerbated by large investment losses with pension systems reporting losses between 11

percent and 30 percent of plan assets (GAO 2010). Even though financial markets and

government revenues have returned to prerecessionary levels, the results of chronic

Christine R. Martell is Associate Professor of School of Public Affairs at the University of Colorado Denver, 1380

Lawrence St., Ste. 500, Denver, CO 80204. She can be reached at [email protected].

Sharon N. Kioko is Assistant Professor of Maxwell School at the Syracuse University, 426 Eggers Hall, Syracuse,

NY 13244-1020. She can be reached at [email protected].

Tima Moldogaziev is Assistant Professor of Department of Political Science at the University of South Carolina,

Gambrell 337, Columbia, SC 29208. He can be reached at [email protected].

1. Retirement benefit programs include government sponsored pension plans and other post-employment benefit

programs (OPEBs). Studies may focus exclusively on outstanding pension obligations while others focus on the

aggregate of pension and OPEB liabilities. For example the Pew Center for the States 2010 report examines the

gap on pension and OPEB liabilities while the Center for Retirement Research (CRR) at Boston College reports

exclusively on pension obligations. While both benefit programs generate future obligations, governments have a

greater ability to adjust post-employment benefits than they do pensions (Pew 2010). Our examination here is

limited to pension obligations.

© 2013 Public Financial Publications, Inc.

24 Public Budgeting & Finance / Fall 2013

underfunding of retirement benefit programs for governments are greater long-term retirement

obligations, structural budget deficits, and intergenerational inequities (Peng 2004). The

government’s ability to meet these obligations is further hampered as costs related to benefit

programs are also expected to rise with the growing numbers of retirees, increased

postretirement life expectancy, as well as rising health-care related costs (Coggburn and

Kearney 2010; Eaton and Nofsinger 2004; Marlowe 2007; Rauh 2010).

While the challenges related to adequately funding pension funds are substantial, a vast

majority of governments have the capacity to meet these obligations with pension reform

measures. Notwithstanding, underfunding retiree-benefit programs has become a potent political

issue and taxpayer tolerance for public employee benefits is waning (Pew 2010).2 There has also

been a significant increase in media coverage of state and local government finances especially

focused on the burgeoning budget gaps amid the Great Recession, bankruptcy filings by local

governments,3 and the growing pension gap (Gordon, Rose, and Fischer 2012; Pew 2010).4

In general, these unfunded pension obligations are akin to general obligation debt in that they

will be serviced from general revenues (Marks and Raman 1988). Some states provide

constitutional and statutory provisions that protect beneficiaries thereby implicitly subordinating

other long-term debt payments (Brown and Wilcox 2009; Coggburn and Kearney 2010;

Rauh 2011; Raman and Wilson 1990). However, unlike general obligation debt, pension

obligations are less visible, face no constitutional or statutory limitations, and do not require

voter approval (Allen, Sneed, and Sneed 1998; Bifulco et al. 2012). Moreover, once granted,

governments can do little to modify pension contracts (Pew 2010). As a result, unfunded pension

obligations represent substantial reallocation of future cash flows. The magnitude of these

unfunded obligations would likely negatively impact the government’s credit quality.

What is more, a government’s ability tomeet these unfunded obligations is further diminished

by the political and institutional constraints that have traditionally limited its ability to achieve

budgetary balance in the first place. For example, when Standard and Poor’s (S&P) assigned a

lower rating to general obligation bonds issued by the state of Illinois, the rating agency cited the

lack of action on reform measures intended to improve funding levels and diminish cost

pressures associated with annual contributions (S&P 2012). In assigning a lower rating for the

2. For an example, see “Firefighters Deal with Community Backlash” (http://www.npr.org/2012/12/26/

168059128/firefighters-deal-with-community-backlash, accessed January 2013)

3. Municipal bankruptcies following the recession include Jefferson County AL, City of Vallejo CA, City of

Harrisburg PA, and City of Central Falls RI. For a vast majority of municipal bankruptcies, the challenges

centered along the precipitous decline in revenues or ill-advised borrowing and debt instruments. For example,

Central Falls, RI filed for bankruptcy citing its inability to meet its pension and OPEB obligations. Once it filed

for bankruptcy in August, 2011, the city continued to meet principal and interest payments on its general

obligation debt but declined to do the same for its retiree benefit obligations (Moody’s 2012b).

4. Most notable is the 60 Minutes segment “Day of Reckoning” which included an interview with Meredith

Whitney (http://www.cbsnews.com/stories/2010/12/19/60minutes/main7166220.shtml, accessed January 2013).

Other widely cited reports include the “Trillion Dollar Gap” report prepared by the Pew Center on the States, as

well an article by Joshua Rauh that estimated a vast majority of pension funds will deplete their plan assets in the

next two decades (Rauh 2011; Pew 2010).

Martell et al. / Impact of Unfunded Pension Obligations on Credit Quality of StateGovernments 25

http://www.npr.org/2012/12/26/168059128/firefighters-deal-with-community-backlash


http://www.cbsnews.com/stories/2010/12/19/60minutes/main7166220.shtml

state of New Jersey, Moody’s warned that the fixed costs related to principal and interest

payments, pension, and health-care benefits could exceed 30 percent of the state budget by 2019,

crowding out program-related spending (Bary 2012).

Analysts were also critical of the current pension fund reporting standards as promulgated by

the Governmental Accounting Standards Board (GASB).5,6 This criticism is especially salient

because pensionplans operate in a low regulatory environment.Unlike private retirement systems

that are regulated by thePensionBenefitCorporation (EmployeeRetirement IncomeSecurityAct

of 1974; Coggburn and Kearney 2010), state and local governments are not subject to federal

regulation but rather voluntarily comply with GASB standards. Specifically, analysts thought the

existing GASB standards, Statements No. 25 and 27 (GASB 1994a; GASB 1994b), encouraged

poor pension liability recognition whereby governments were criticized for assuming overly

optimistic expected rates of returnonplan assets,whichwould result in pensionplansunderstating

theirunfunded liabilities.For example in2010, thePewCenteron theStates reported that inat least

33 states, the expected rate of return assumedwas 8 percent. If pension plan assets were valued at

the riskless rate, then the plan would only have sufficient assets to meet 50 percent of its pension

obligation and not the currently reported levels of 75 percent (Munnell et al. 2012). The literature

has also found pension plans that are less-well funded, those that experience declines in annual

contributions, and those where the investment board is politically affiliated and have more

optimistic actuarial assumptions, are more likely to obscure underfunding by selecting higher

expected rates of return, and disclose poorer financial information (Asthana 1999;

Chaney, Copley, and Stone 2002; Eaton and Nofsinger 2004; Kido, Petacchi, and Webber

2012; Mitchell and Smith 1994; Stalebrink 2012; Vermeer, Styles, and Patton 2012).

On June 25, 2012 GASB issued new accounting and reporting standards for state and local

governments, Statements No. 67 and 68 (GASB 2012a; GASB 2012b). GASB believes these

new accounting standards would result in robust disclosures of actuarial assumptions. The

standards also require governments to report their net pension liability (NPL) in their Statement

of Net Position, therebymore appropriately disclosing the extent of all the government’s pension

obligations at the end of the fiscal year.

Our study examines the impact of pension fund levels of state-administered plans on credit

quality. We begin with a review of the structure and challenges related to public sector pension

systems. We also review the literature related to determinants of pension underfunding, as well as

impact of pension underfunding on credit quality of governments. While a wealth of literature

addresses the issue of appropriate pension fund levels, limited research to date has examined the

impact of pension fund levels on credit quality. We discuss our empirical methods and results and

provide a discussion and conclusion, including a brief review of the new pension accounting rules.

5. GASB does not have any enforcement authority. However it has been recognized as the official source of

accounting rules for state and local governments. For a vast majority of state and local governments, their ability

to receive a clean audit opinion or a favorable rating (and as such lower borrowing costs) is dependent on its

compliance with existing GASB accounting standards.

6. In the same spirit, Moody’s announced it would require additional pension information from state and local

governments as part of the rating process (Moody’s 2012b)


PUBLIC SECTOR PENSIONS: STRUCTURE AND CHALLENGES

There are two major types of pension benefit plans—defined benefit (DB) plan and defined

contribution (DC) plan. In the public sector, the DB plan is the predominant pension benefit type

in which the employer guarantees pension benefits to the employee upon retirement based on the

employee’s preretirement salary level. In the DC plan, the employer contributes to the employee

pension plan based on the employee’s current salary and a predetermined pension contribution

rate. From the perspective of the employer, the key difference between a DB plan and a DC plan

is that the obligation to meet the retirement benefits in a DC plan is limited to the employer’s

share of the annual contribution. As a result, there is no long-term obligation for the employer if

the employer meets the annually required contribution. In the DB plan, governments are

contractually required to pay a specified annuity to retirees. Benefit amounts are guaranteed

regardless of whether the retiree’s DB plan is underfunded.Moreover, the benefit amounts are an

obligation of the employer irrespective of its fiscal condition or capacity.

The public employee retirement system is made up of a relatively small number of state-

administered retirement plans and a large number of locally administered pension plans. In 2012,

the Census Bureau estimated there were 3,418 state and local government public employee

retirement plans. Of these, 222 plans were state-administered pension plans that provide pension

benefits to approximately 90 percent (17.5 million) of the covered population and account for

approximately 84 percent or $2.5 trillion of all public pension plan assets. A vast majority of

state-administered plans are either agent multiple-employer or cost-sharing multiple-employer

plans. For the agent multiple-employer plans, pension assets are pooled for investment purposes

only, with the employer’s share of plan assets used to meet the pension benefits of its employees.

In contrast, in a cost-sharing multiple-employer plan, pension assets are pooled and may be used

to pay the pension benefits of employees of any participating employer (GASB 1994b). State and

local governments also sponsor single-employer plans in which the plan sponsor provides

pension benefits to its employees (e.g., legislative retirement plans). A vast majority of the 3,196

retirement plans that are administered by local government are single employer plans. In

aggregate these locally administered retirement plans report $480 billion in plan assets and

provided coverage to approximately 2 million active members and retirees.7

For each of the 222 state-administered pension plans, the state’s proportional share of unfunded

actuarial accrued liability (UAAL)perplan isbasedupon theplan type (i.e., singleemployer, agent-

multiple employer, or cost-sharingmultiple employer) and the state’s participation in the plan. In a

limitednumber ofplans, the stateparticipation is limited toadministration andnopart of theUAAL

reported in the plan is attributable to the state. As a result, annual costs for such plans are limited to

administrative expenses. In a number of state-administered plans the state is not a participating

7. Of the 3,196 locally administered plans, 168 are managed by county governments, 2,176 by municipalities,

634 by townships, 206 by special districts, and 12 by school districts. The Commonwealth of Pennsylvania reports

the largest number of active retirement systems (1,425) followed by Illinois (457) and Florida (303). For extensive

data on coverage see the 2011 Annual Survey of Public Pensions: State & Local Data http://www.census.gov/

govs/retire/ (accessed January, 2013).




employer, however, it is required to meet the annually required contributions for the pension plan

based on employee type (e.g., teachers), statutory requirements (e.g., National Guard), and/or

judicial rulings (e.g., pension plan underfunding).8 In plans for which the state is a participating

employer, theextentofparticipation isnotalwaysapparent.9 Notwithstanding, local governments

participating in any state-administered plan retain responsibility for contributing their share of

the annually required contribution. Similar to states, local governments’ shares of the UAAL

are also a function of the plan type. In a few instances, the state is cited as the guarantor of the

retirement system (or pension plan). For example, in Maryland’s Pension and Retirement

System, which includes a wide variety of pension plans, the state is the statutory guarantor.

The state of New Jersey administers seven cost-sharing retirement systems. According to state

law, all obligations of each retirement system will be assumed by the state should any

retirement system be terminated.

Since a vast majority of governments participate in DB plans, the financial condition of these

pension plans is crucial to their long-term health.10 Pension fund assets and their corresponding

liabilities are pro-cyclical in nature. When the financial markets were bullish, plans generally

report large positive returns. During these periods, governments also have a greater ability to

meet their required contributions. In a recessionary period, a vast majority of pension plans

reported investment losses that were exacerbated by their aggressive investment strategies

(Coggburn and Kearney 2010; GAO 2010; Pew 2010). Pension plan health is also affected by

actuarial assumptions including the assumed investment rate of return and how gains or losses

are smoothed into asset values.

Plans reported drastic declines in the funded ratio following the Great Recession - from 89

percent in 2002 down to 75 percent in 2011 with 64 percent of plans reporting a fund ratio below

80 percent in 2011 (Munnell et al. 2012). The growing demands on limited revenues resulted in

contributions that were below the annual required contribution (ARC), as the funding for

employee benefit programs does not have the same immediacy and urgency as other government

programs (Bullock 2009; Eaton andNofsinger 2004; Giertz and Papke 2007; Peng 2004). During

this period, the percent of ARC, represented as the share of actual to required contribution, fell

8. For example—The state of North Carolina is a participating employer in the Teachers and State Employees

Retirement System, Consolidated Judicial Retirement System, and Legislative Retirement System. The state is

obligated to contribute even though it is not a participating employer to the Firemen’s and Rescue Squad Workers

Pension Fund and the North Carolina National Guard. For the Registers of Deed’s Supplemental Pension Fund,

Sherriff’s Supplemental Pension Fund and Local Governmental Employee’s Retirement System, the state costs in

the plan are limited to administrative expenses.

9. In a cost-sharingmultiple-employer plan, the plans CAFRs do not consistently disclose participation by employer

(e.g., number of employees, number of retirees, total number of active and inactivemembers). Since pension funds are

reported as fiduciary funds in the states’ CAFRs, the notes to the financial statements, as well as the fiduciary fund

statements report the assets or liabilities of the pensionplanor retirement systemanddonotdisclose the states’ shares of

assets or liabilities. FollowingGASBStatement No. 67 and 68, we expect the state’s share of the liability in any plan to

be disclosed in the notes to the financial statements as well as in the Statement of Net Position.

10. See Securities Act Release No. 8751 (retrieved from http://www.sec.gov/litigation/admin/2006/33-8751.pdf

January 2013). The SEC argued that the City of San Diego failed to disclose to investors the magnitude of its future

pension and health care obligations and the local governments’ ability to meet these obligations in the future.



dramatically from 95 percent in 2002 to 79 percent in 2011 (Munnell et al. 2012). What is more,

governments also opted to increase employee pension benefits in lieu of annual wage

adjustments prior to the recession when plan assets exceeded actuarial liabilities (Pew 2010).

When governments offer benefits but fail to make the contributions necessary to meet deferred

costs they are in essence borrowing from their future taxpayers. For some governments, their

obligations to employees likely exceed their bonded obligations (Moody’s 2011; Rauh 2011;

Raman and Wilson 1990).

Some governments have responded to pension underfunding with a wave of pension

reforms that sought to limit benefits for current employees and alter the structure of benefit

programs for future employees. 11 Some states and local governments issued pension

obligation bonds (POBs). POBs simply change the form of obligation from an obligation to

plan beneficiaries to an obligation to external creditors. Moreover, the issuance of POBs does

not resolve the underlying funding issue if governments continue to contribute less than the

full ARC. Moreover, the use of POBs imposes several risks that negatively impact credit

quality: balance sheet and default risk, budgetary risk, loss of flexibility, and management

risk (Moody’s 2012b). It has become evident that POBs are not a panacea, especially if true

interest cost exceeds actual returns on principal invested. Moreover, lump sum amortization

of POBs can result in an operating surplus that encourages the moral hazard problem of

decreased contributions (Peng 2004).

UNDERFUNDED RETIREMENT BENEFIT PROGRAMS AND

THE MUNICIPAL BOND MARKET

The financial health of any government is inextricably linked to the funded status of all its DB

sponsored plans and other postemployment benefit (OPEB) programs. This study seeks to test

whether the underfunding in pension funds has an impact on credit quality of state governments.

If financial markets are rational and fully informed, the risk associated with unfunded pension

obligations should be reflected in the credit quality and borrowing costs of issuers in the

municipal bond markets (Marks and Raman 1988).

Marks and Raman (1988) found borrowing costs were sensitive to unfunded accumulated

liabilities, but not to unfunded projected liabilities. Unfunded accumulated liabilities represent

the present value of benefits based on service and earnings to date. Unfunded projected liabilities

on the other hand represent the present value of benefits based on continued wage growth and

employee separation rates based on past experience. As such, at any given expected rate of

return, the projected liabilities would exceed accumulated liabilities. Following Marks and

Raman (1988) and Raman and Wilson (1990) found that unfunded pension obligations and

11. Specific benefit restructuringmeasures include suspending ormodifying the cost of living adjustment, increasing

the age and years of experience required for retirement, reducing the practice of “spiking,” using overtime pay and sick

leave to drive a higher end-year salary, reducing buyout rates for early retirement, providing tiered benefits to new

employees, whereby new employees contribute a higher share to their pension plans, and moving to a defined

contribution or hybrid plan (Coggburn and Kearney 2010; Munnell et al. 2012; Pew 2012; Rauh 2011).


unfunded projected liabilities have a smaller incremental effect on yields than do bonded debt

obligations. Raman and Wilson (1990) note that in models of bond prices, the level of unfunded

liabilities could be an intervening variable better represented by the assigned rating. Allen et al.

(1998) did not find a significant impact of plan funding levels and borrowing costs and municipal

credit ratings. However, their study was limited to a small sample size and a short time period. In

his examination of the impact of OPEB liabilities, Marlowe (2007) finds OPEB liabilities do not

affect local government borrowing costs, either directly or indirectly but rather OPEB liabilities

indirectly affect credit quality via the municipality’s fiscal capacity, measured by general fund

tax effort per capita and general fund current ratio.

Evidence from recent studies suggests that markets are efficient and do respond to

changes in pension funding levels. A related study finds that municipal market borrowing

costs respond to losses in state pension funds, suggesting an impact of pension fund

management on long-term costs (Novy-Marx and Rauh 2012). Novy-Marx and Rauh (2012)

also test the hypothesis that the worse a state’s credit rating, the more pension fund asset

losses will increase bond spreads. Using state-issued general obligation bonds as the unit of

analysis, they analyze data from the last quarter of 2008, when state pension funds

experienced dramatic asset losses. Their main result is that fiscal imbalances are picked up in

the market for lower-quality states. Specifically, states with ratings of AA or below that

experienced a loss of 10 percent in pension funds relative to own source revenues resulted in

a 7–15 percent increase in yield spreads. These results suggest that “if states are planning to

roll over unfunded pension liabilities into bonds when pension funds eventually run dry, they

may find themselves doing so at substantially higher borrowing costs” (Rauh 2010, 587). Of

note, both Marlowe (2007) and Novy-Marx and Rauh (2012) find differential effects of

funding levels for lower and higher rated entities. That is, the impact of underfunded

pensions on bond spreads is greater for states with lower credit ratings.

METHODS

We examine the impact of pension underfunding on the government’s unenhanced ratings and

assigned rating outlooks. We hypothesize that the government’s long-term (i.e., the unenhanced

ratings) as well as short-term (i.e., assigned outlooks) default risk is greater if its pension plan is

underfunded. Using state data from 2002 to 2011, we test the impact of pension underfunding on

credit quality with the following model:

Credit Quality ¼ f n ðpension funding levels; X Þ

where X represents a vector of control variables, plus an error term. The following sections detail

the variables, their construction, and sources of data.

Outcome Variables: Credit Quality

As this study examines the impact of unfunded pension plans on credit quality, we have two

outcome variables of interest (i) the issuer assigned rating from both Moody’s and S&P and (ii)


the issuer assigned rating outlook as reported by S&P.12 For a fee, a rating agency will provide an

assessment of default risk. The agency will also provide surveillance by monitoring the issuer’s

ongoing financial and economic condition, providing investors with periodic updates on the

issuer’s credit standing after the initial bond issue (Johnson, Kioko, and Hildreth 2012).

Governments issuing any general obligation debt are assigned an underlying credit rating as well

as a rating outlook. Governments have been assigned outlooks since the early 1990s. These

outlooks were introduced by all three major rating agencies in an effort to manage the tension

between stability of the issuer’s rating and accuracy of their signals to investors in the financial

markets (Hamilton and Cantor 2004). Together with the assigned rating, the outlooks are

indicators that a rating could change within the next 24–36 months. If the assigned outlook is

negative, then the rating agency is informing all interested investors in the market that the rating

for this government could be downgraded if specific actions or reforms are not made to remedy

deficiencies. If the government is assigned a positive outlook, then the rating agency is signaling

the financial markets that there is a positive trend in the issuer’s financial condition that could

warrant a positive change in the assigned rating. A stable outlook is an indicator that in the

medium term, nothing warrants a change in the assigned rating. Empirical evidence suggests

markets react strongly to these signals (Alsakka and Gwilym 2012). Given the stability of state

credit ratings, additional information is derived from the signals issued by outlooks. We specify

the underlying credit rating (Rating) as a numerical transformation of the unenhanced rating,

with values between 1 and 8, where larger values correspond to a higher default risk (i.e., less

favorable credit rating). We specify the rating outlook variable (Outlook) as an ordered measure,

where values �1, 0, and þ1 are assigned to negative, stable, and positive S&P rating outlooks

respectively.

Test Variable: Fund Ratio

To empirically test the impact of pension plan funding levels on credit quality, we developed a

metric of funding level—Fund Ratio—equal to a ratio of the actuarial value of assets divided by

actuarial accrued liabilities.13 When sufficient assets have been set aside to meet pension

obligations, the plan is “fully funded,” and its fund ratio is equal to 100 percent (Ippolito 1985). A

plan is “underfunded” if its fund ratio is <100 percent.

The Center for Retirement Research Public Plans Database (CRR-PPD) provided us with a

baseline of America’s largest pension plans.14 From the CRR-PPD we identified pension plans

12. Moody’s unenhanced rating history report for the 50 states does not include the historical assigned outlook

information (Moody’s 2012a). We were unable to obtain the relevant historical outlook information from the

rating agency. S&P’s unenhanced rating history report includes the outlook history for all 50 states (S&P 2013).

13. All state government CAFRs have a note to the financial statements related to fiduciary funds in general, and

to retirement plans more specifically. The note on retirement plans reports information consistent with the

retirement plan/system CAFRs.

14. The Center for Retirement Research has focused on the largest pension plans, a vast majority of which are

agent multiple-employer or cost-sharing multiple employer plans. The CRR-PPD data is available at http://crr.bc.

edu/data/public-plans-database/, accessed August, 2012.




for which the state was contributing to, participating in, and/or was the statutory guarantor of the

UAAL. We excluded all pension plans in the CRR-PPD in which the state had no fiduciary

responsibilities, was not contributing to, and was not a participating employer. We used the

Comprehensive Annual Financial Reports (CAFRs) prepared by the pension plans or retirement

systems, as well as the state government CAFRs to identify the plans that were not included in the

CRR-PPD.15 For these additional plans, we recorded the plan type, valuation date, actuarial

value of assets, actuarial accrued liabilities, fund ratio, annually required contribution and

percent of annually required contribution for each plan.We also verified the correctness of any of

the appended CRR-PPD data.

We then used the plan type to identify the state’s pension liability. If the plan was a single-

employer plan for which the state was either a plan sponsor (e.g., legislative and judicial plans) or

the state was required to meet the annually required contribution (e.g., National Guard), we

recorded the full value of assets and liabilities of the plan. If the plan was an agent multiple-

employer plan, we recorded the state’s share of the value of assets and liabilities as disclosed in

the CAFR of the state, the retirement system, or the pension plan. As we discussed earlier,

pension assets in an agent-employer plan are pooled for investment purposes only. The

employer’s share of plan assets can only be used tomeet the pension benefits of its employees. As

a result the fund ratio for each participating employer will vary based on the employer’s

contribution levels and type of beneficiaries. If the plan was a cost-sharing multiple-employer

plan, of which the state was required to meet an annual contribution requirement, was a

participating employer, or was the guarantor of plan liabilities we recorded the full value of

assets and liabilities. Because cost-sharing plans are not required to disclose a participating

employer’s share of assets or liabilities, we found estimating the state’s share of assets, liabilities,

and UAAL practically impossible. Even so, estimating a government’s participation in a cost-

sharing multiple-employer plan would not change the estimate of the plan’s fund ratio as plan

assets may be used to pay the pension benefits of employees of any participating employer in the

plan. Therefore, any employer participating in the plan reports the same fund ratio.16

15. A vast majority of the excluded plans were single-employer plans. These plans are generally smaller and

employee specific for example, judges, legislators. The CRR-PPD reports on the largest retirement systems and

pension plans of state and local governments.

16. To further refine the state’s liability in cost-sharing multiple agent plans for which the state makes

contributions or is an employer, we attempted to identify the level of contribution or participation. GASB 25 and

27 do not require cost-sharing multiple-employer pension plans to report, in the state or pension plan CAFRs,

participation or contribution by type of employees, number of participants, contribution level (in dollars), or

liability by employer (in dollars). In fact, there is wide variation in disclosure across the state’s cost-sharing

multiple employer plans. Unlike state government CAFRs, there are no consistent standards for reporting

statistical information in the pension plan CAFRs. As a proxy for state liability, we attempted to collect data for

state’s share of participating employees in the plan. The data were limited to the more recent years and only

available for some of the plans. Data were particularly absent when plans were administered on a disaggregated

basis (i.e., if the plans were not administered by a single board, but by a number of boards that each prepare

separate CAFRs). Thus, we could not rely on this proxy as a means of estimating a state’s liability. Moreover, our

estimate for the Fund Ratio measure would not have changed in the cost-sharing multiple-employer plans even if

we had identified the state’s share of participants.


Our variable of interest is the state government’s overall fund ratio, which we estimate as the

aggregate of the actuarial assets relative to the aggregate of the actuarial accrued liabilities. Our

measure of fund ratio is estimated as follows:

Fund Ratio ¼Pn

i Actuarial AssetstPni Actuarial Accrued Liabilitiest

:

Appendix A identifies pension plans or retirement systems for each government included in

our sample. Our survey of the pension and state government CAFRs identified 149 plans: 62

single-employer, 9 agent multiple-employer, and 78 cost-sharing multiple-employer plans.

While Fund Ratio does not provide a full context of the burden of government, it estimates the

retirement system’s assets per dollar of liabilities and allows for cross-state comparison. Because

ourdataset includes thecost-sharingplans forwhichstate liabilitiesarecomingledwith liabilitiesof

other participating employers, using the unfunded actuarial accrued liability (UAAL ¼ actuarial

assets � actuarial accrued liability) would overstate the liability for the state government as a

number of other employers participate in, andmake contributions to thepensionplan.We therefore

limit our examination to theFundRatiomeasure.WebelieveFundRatio is an appropriatemeasure

of expected exposure but not true measures of UAAL. We hypothesize a lower default risk if the

government’s pension fund assets are greater than its liabilities, i.e., Fund Ratio > 1.

Control Variables

We estimate the general fund performance and position using information reported in the fund

statements of the state’s (CAFR). We focus on the general fund given the statutory and

constitutional restrictions governing the other funds and the continued focus on the general fund

by rating agencies (GASB 1999; Johnson, Kioko, and Hildreth 2012; Kioko and Johnson 2013;

Marlowe 2010; Pridgen andWilder Forthcoming; Plummer, Hutchison, and Patton 2007). From

the Balance Sheet we estimate the government’s restricted and unrestricted general fund

position. The restricted fund balance represents current resources for which constitutional,

statutory, or contractual restrictions exist. The unrestricted and undesignated fund balance in the

general fund represents current financial resources, though funds may be designated by the

government for specific uses such as budget stabilization (GASB 1999; Hou 2003; Mead 2001).

Given the current financial resources measurement focus of the governmental funds, we expect

governments reporting a positive restricted general fund balance to have a lower default risk in

spite of the restrictions on use. We also expect states with a positive unrestricted general fund

balance to have a lower default risk (Kioko and Johnson 2013). The unrestricted general fund

balance broadly represents uncommitted resources within the general fund.17 From the

Statement of Revenues, Expenditures, and Changes in Fund Balances we estimate the

17. We recognize funds may be committed though not reported in the financial statements. Following GASB

Statement No. 54, governments will be required to report fund balance as restricted, committed, assigned, or

unassigned.


TABLE1

DescriptiveStatistics,RatingsModel

(N¼

356)

Variable

Variable

description

Mean

SD

Standard&

Poor’sRating

S&Pratingvariable

where1¼

AAA,2¼

AAþ,

3¼

AA,4¼

AA�,

5¼

Aþ

,6¼

A,7¼

A�,

Baa

¼8

2.6039

1.2684

Standard&

Poor’sOutlook

S&Pratingsoutlookwhere�1

¼negative,

0¼

stable,1¼

positive

�0.0393

0.2781

Moody’s

Rating

Moody’s

ratingvariable

where1¼

Aaa,2¼

Aa1,3¼

Aa2,4¼

Aa3,

5¼

A1,6¼

A2,7¼

A3,Baa1¼

8

2.6854

1.3030

Changeto

Global

Ratings(M

oody’s)

Indicatorvariable

forchanges

inMoody’s

global

ratings

0.1994

0.4001

FundRatio

Estim

ated

assum

ofallactuarialassets

instate-administeredpension

plansforstateiat

timetdivided

bythesum

ofactuarialliabilitiesin

pensionplansforstateiat

timet

0.8033

0.1516

RestrictedGeneral

FundBalance

(asratioofGeneral

FundRevenues)

Measure

ofrestricted

general

fundbalance,computedas

therestricted

fundbalance

divided

bytotalrevenues

0.0636

0.0468

Unrestricted

General

FundBalance

(as

ratioofGeneral

FundRevenues)

Measure

ofthegeneral

fundbalance,computedas

theunreserved

and

undesignated

fundbalance

divided

bytotalrevenues

0.0259

0.0960

General

FundSurplus/Deficit(asratio

ofGeneral

FundRevenues)

Measure

ofthesurplusordeficitreported

inthestate’sgeneral

fund,

computedas

totalrevenues

minustotalexpenditures,divided

bytotal

revenues

0.0023

0.2504

General

ObligationDebt(asratioof

Personal

Income)

Outstandinggeneral

obligationdebtas

apercentofpersonal

income

0.0223

0.0191

Other

Debt(asratioofPersonal

Income)

Allother

long-term

debtobligationsincludingrevenuedebt,outstanding

notes,leases,etc.,divided

bypersonal

income

0.0192

0.0157

Population(Log)

Logofthestate’spopulation

15.3063

0.9823

%<18years

Percentofthepopulation<18years

ofage

24.2094

1.9716

%�6

5years

Percentofthepopulation65years

andabove

12.9872

1.6253

Tax

RevenueRatio

(asratioof

OperatingRevenues)

Tax

revenues

asreported

inthegovernment-widestatem

entsapercentof

totalrevenues

0.4894

0.0779

Income(per

Capita,

$’000)

Per

capitapersonal

income(reported

in$’000s)

36.2026

6.3812

(Continued)


TABLE1

(Continued

)

Variable

Variable

description

Mean

SD

VoterApproval

Requirem

ent

Indicatorvariable

ifthestatehas

imposedarequirem

entforvoter

approval

forgeneral

obligationdebt,otherwisevoterapproval

0

0.4551

0.4987

BalancedBudget

Requirem

ent

Indicatorvariable

ifthestatehas

imposedabalancedbudget

requirem

ent

forthegeneral

fund,otherwisebalancedbudget

0

0.8287

0.3773

General

FundTEL

Indicatorvariable

ifthestatehas

imposedarevenueorspendinglimiton

general

fundappropriations,otherwisegeneral

fundTEL0

0.6236

0.4852

ProceduralTEL

Indicatorvariableifthestatehas

imposedavoterapprovalrequirem

entor

legislativesuper-m

ajority

requirem

entfornew

orhigher

taxes,

otherwiseProceduralTEL0

0.3146

0.4650

Note:Thepopulationforthe10yearsstudyperiodis500observations.Thefollowingstates

donotissuegeneralobligationdebt:Arizona,Colorado,Iowa,Idaho,Indiana,

Kansas,Kentucky,NorthDakota,Nebraska,South

Dakota,andWyoming(110excluded

observations).Thefollowingstateoryearswereomitted:Alaskaisomittedas

an

outlier(10observations).N

ewYork

(2002–2004)andWashington(2002–2005)—

noactuarialdatawas

available;Tennessee(even

years),Connecticut(oddyears),Montana

(2011)—

noactuarialwas

valuationperform

ed;Connecticut(2002)andMissouri(2002–2004)—

noGOorother

debtdisclosedin

CAFRs;Illinois,New

Mexico,andOhio

(specificallyOhio

PublicEmployees’

Retirem

entSystem

)—noreportfor2011available

atthetimeofdatacollection(24observations).


government’s general fund budget surplus or deficit as a percent of the state’s operating revenues

(i.e. (operating revenues � operating expenditures)/operating revenues). All else equal, a state

running a budget deficit has a greater default risk and should have a lower rating than a state

running a budget surplus (Kioko and Johnson 2013; Marlowe 2010; Pridgen and

Wilder Forthcoming; Plummer, Hutchison, and Patton 2007).

We developed two metrics of debt—general obligation (GO) debt and other debt, which

includes all other outstanding long-term debt obligations of the state. Each metric is estimated as

a percent of income. We used data reported in the required supplementary information (RSI)

section of the CAFRs for which the governments, under GASB reporting standards, are required

to disclose all long-term obligations by security type. We expect states with higher debt burdens

to have a higher default risk all else equal.

The tax ratio variable captures the relative tax burden of the state. We estimate tax ratio as a

ratio of general revenues divided by operating revenues as reported in the government-wide

statements. The ratio captures the relative tax burden of a state. Higher tax burdens may lead to

taxpayer revolt and refusal to fund programs (Johnson and Kriz 2005; Wang, Dennis, and Tu

2007).We therefore expect stateswith a higher tax ratio to have a higher default risk all else equal.

To control for demographic differences across states, we include percent of population above

the age of 65, below the age of 18, as well as the state’s total population (log form). We believe

these to be indicators of current and future wealth (below the age of 18) and costs (above the age

of 65). As a control for the taxable base, we include personal income (per capita). Our

expectation is that wealthier states have a stronger ability to withstand an economic downturn,

and as such, have a lower default risk.

We include indicator variables for various fiscal institutions including the voter approval

requirement for general obligation debt (Denison, Hackbart, and Moody 2006), general fund tax

and expenditure limits (TELs), aswell as procedural TELs (Kioko 2011;Kioko andMartell 2012),

and balanced budget requirements (Poterba and Rueben 2001). While debt limits have long been

argued to be prudent restrictions on the debtmanagement by governments, the literature on ratings

finds state governments with debt limits to have lower ratings. As Johnson and Kriz (2005) note,

these perhaps arbitrary rules limit the government’s flexibility and political discretion.We expect

the coefficient for debt limit to be consistent with this literature. Procedural TELs are limits on the

government’s authority to levy new or higher taxes. For states with procedural TELs either voter

approval or a legislative super-majority vote is required prior to any new or higher taxes

(Knight 2000;Kioko andMartell 2012). It is also our view that procedural TELs limit flexibility in

government, and as such, default risk would be higher all else equal.

Balanced budget requirements and general fund TELs, on the other hand, are viewed as

prudent fiscal principles (Johnson andKriz 2005; Poterba and Rueben 2001).While the literature

has long demonstrated restrictions on growth in spending (general fund TELs) to be ineffective

restraints, we chose to include these variables in our model given existing credit rating criteria

reports that account for (a) the mere presence of constitutional and statutory provisions and (b)

frequency of use of these rules in the budget process (S&P 2011). We expect states with these

prudent fiscal principles to have a lower risk of default. We report all our descriptive statistics in

Table 1.


RESULTS

The results are presented in two sections: the impact of unfunded pension levels on credit ratings

(Standard & Poor’s and Moody’s) and rating outlooks (Standard & Poor’s only). The data and

models were evaluated for parallel and partial parallel regression assumption,18 multi-

collinearity,19 heteroscedasticity,20 and endogeneity.21 Given the nature of our outcome

18. We tested whether ordered logit and probit models fit the assumptions of parallel and partial parallel

regressions, using the generalized ordered logit models for ordered outcome variables (see Long and Freese 2006).

The likelihood ratio tests for the generalized ordered logit constrained and unconstrained models suggest that our

models for credit ratings do not fail the global test and we fail to reject the null that parallel and partial parallel

assumption is not violated. We find that the ordered regression approach assuming proportional odds in the

association of model covariates with credit ratings is appropriate. The same, however, cannot be said about S&P

credit outlooks. We found our measure of Fund Ratio violates both assumptions of parallel and partial parallel

assumptions. The measure also fails the Brant-test for parallel regression and the likelihood ratio test for partial

parallel regression assumption using the generalized ordered constrained and unconstrained method. The ex-post

model fit statistics of these tests show that the null of parallel and partial parallel regression is rejected. Therefore,

we must employ a multinomial logistic method for the covariates of credit outlooks, where a comparison category

for the binary regressions is the “stable rating” outcome.

19. Correlation coefficients show that only percentage of population below 18 and percentage of population

above 65 have a high two-way correlation to alert us to potential problems due to multicollinearity. Except for this

pair, no correlation coefficient is above 0.50. Variance inflated factors (VIF) are in no case greater than the

conventional threshold of 10 (i.e., the inverse of VIF is >0.1 in all models). For complete confidence that

multicollinearity would not affect the estimates, we removed the two population-related measures from our

estimates altogether, as well as list-wise. In both cases, the significance and direction of other covariates remained

unchanged. We then inserted the two population measures back into the models with replacement one after

another. The significance and direction of other variables remained unaffected. Thus, we did not find any evidence

that multicollinearity is a concern in our estimated models.

20. We ran our models with and without state specific robust standard errors. We find that regressions with state

specific robust standard errors produce more favorable model fit measures compared to regressions without such

correction; the absolute values of differences for BIC and AIC scalars is between 4 and 6 in credit rating models.

Due to moderate differences of such magnitude (as suggested in Long 1997), we rely on the ratings models with

state specific robust standard errors.

21. We ran robustness tests for potential endogeneity problems by assessing our models for omitted variables and

lagging the explanatory variables (known issues of model misspecification). The Ramsey’s RESET test, using

fitted values, shows that we do not have omitted variables bias. Furthermore, the results of the lagged regression

estimates remained consistent with the results in our final, nonlagged, models; the likelihood ratio tests show no

improvement with a lagged approach. Therefore, it is not readily apparent that the threat of potential endogeneity

is related to variable omission or simultaneity. However, as often the case with all observational data of economic

phenomena, there are infinite numbers of unobserved variables that could potentially result in a problem of

unobserved heterogeneity. To address this issue, we conducted a Durbin-Wu-Hausman test for endogeneity. We

regressed Fund Ratio on other exogenous variables in the model and predicted model residuals. We then used the

residuals in the model for credit quality. Similar to the results from the omitted variable tests, the results from the

Durbin–Wu–Hausman test support that endogeneity is not present. For example, in the Standard & Poor’s credit

ratings model, the coefficient for the residuals is not significant and we fail to reject the null that endogeneity is

not present. The appropriate F-statistic is 0.13 with p ¼ 0.7174. While we cannot fully discount that endogeneity

may be an issue, we do not find evidence that it unduly affects our estimates.


variables, we estimated ordered logit and probit regressions for the covariates of credit ratings.

Ordered outcome estimation methods are by far the most widely used approaches for the models

of credit ratings (Amato and Furline 2004; Denison, Yan, and Zhao 2007; Grizzle 2010;

Johnson, Kioko, and Hildreth 2012; Loffler 2004; Marlowe 2007; Palumbo and

Zaporowski 2012; Stallmann et al. 2012; Zhao and Guo 2011). We included year specific

identifiers (omitted from tables) in all models.

Credit Ratings

We report ordered probit estimation results for the covariates of state government credit ratings

by adding our pension measure Fund Ratio to the set of variables traditionally used to estimate

the issuers’ unenhanced rating.22 We estimated rating models for Moody’s and Standard &

Poor’s separately as the criteria generally used by the agencies is not weighted equally. Our

ordered probit regression results for the models are reported in Table 2.

In answer to the first research question, the results show that pension funding levels do impact

credit quality. Regarding our test variable, Fund Ratio, we hypothesized that a government

would have a lower default risk if its accumulated assets were sufficient to meet its liabilities.

Our results support our hypothesis and demonstrate a lower default risk as governments maintain

a better funded ratio for both Standard & Poor’s and Moody’s ratings.

Additionally, to provide a better feel for the magnitude of the effect of underfunded pension

ratios on state credit quality, we plotted the predicted levels of credit ratings for both S&P’s and

Moody’s across the entire range of pension fund ratios, all else held constant at mean values. In

Figures 1 and 2, we can readily observe that the predicted levels of the two highest credit

categories—AAA and AAþ for S&P and Aaa and Aa1 for Moody’s—increase exponentially

across the range of pension fund ratios. In the S&P case, the predicted levels of AAA appear to

increase from about zero to over 0.45 and the predicted levels of AAþ appear to increase from

about 0.08–0.3 when pension fund ratios increase from the lowest level to the highest. For

example, the combined probabilities of AAA and AAþ are about 10 percent when the pension

fund ratio is at 0.4; the same combined probability is about 60 percent when the pension fund

ratio is at 1; and the combined probability of the two top rating categories is over 75 percent when

the pension fund ratio is at 1.2. In other words, all held constant at mean values, a state with a

pension fund ratio of 1 is not likely assigned a rating below AAþ by Standard & Poor’s.

Moody’s presents an almost identical trajectory of predicted levels for the two highest rating

categories—AAA and Aa1. In both S&P and Moody’s, the predicted probabilities of AA/Aa2

initially appear to increase but then sharply fall when funding ratios are at the higher levels.

Anything below AA/Aa2 rating categories appears to have decreasing levels of probabilities

across the range of pension fund ratios. We therefore conclude that there is overwhelming

evidence, both statistically and substantively, that credit quality co-varies with pension fund

ratios.

22. The results of ordered logit estimations are omitted for brevity as we find that the logistic coefficients and

standard errors are within statistically assumed parameters.


TABLE2

Ordered

ProbitRatingModels,Standard

&Poor’sandMoody’s

(N¼

356)

Variable

S&Prating(1

¼AAA,2¼

AAþ,

3¼

AA,4¼

AA�,

5¼

Aþ,

6¼

A,7¼

A�,

8¼

BBBþ)

;

Moody’s

rating(1

¼Aaa,2¼

Aa1,3¼

Aa2,4¼

Aa3,5¼

A1,6¼

A2,7¼

A3,8¼

Baa1)

Standard

&Poor’s

Moody’s

Coefficient

Robust

standard

error

Coefficient

Robust

standard

error

FundRatio

�2.9590��

(1.0923)

�3.0992�

(1.2375)

Changeto

Global

Ratings,Moody’s

——

�1.4053��

(0.5147)

RestrictedGeneral

FundBalance

�7.6145��

(2.8789)

�7.7954��

(3.0648)

Unrestricted

General

FundBalance

�2.4177

(1.8005)

�4.7960��

(1.8320)

General

FundSurplus/Deficit

�1.0304�

(0.4746)

�1.7475��

(0.4761)

GO

Debt

19.8405�

(9.1791)

17.2752†

(8.9887)

Other

Debt

8.3960

(11.0208)

4.3645

(12.3759)

Population(Log)

0.0936

(0.2229)

�0.0308

(0.2215)

%<18years

�0.1013

(0.1903)

0.2175

(0.1887)

%�6

5years

0.0571

(0.2468)

0.4338†

(0.2210)

Tax

Ratio

�3.0652

(1.8664)

�1.6766

(1.5518)

Income(per

Capita,

$’000)

�0.0389

(0.0499)

0.0078

(0.0443)

GO—VoterApproval

Requirem

ent

0.4277

(0.5076)

0.7151

(0.4670)

BalancedBudget

Requirem

ent

�0.3856

(0.5304)

�0.3792

(0.4957)

General

FundTEL

0.1519

(0.3852)

0.0797

(0.3464)

ProceduralTEL

0.3555

(0.4619)

0.5224

(0.4248)

YearSpecific

Identifiers(2002–2011)

Omittedforbrevity,available

uponrequest(year2002is

thecomparisoncategory)

McK

elvey

&Zavoina’sR2

0.503

0.602

CountR2

0.528

0.514

Ho:Ratio

¼0(Prob.>

Chi2)

0.0000

0.0000

Log-likelihood

�425.585

�418.667

Note:State

clustered

robuststandarderrors

inparentheses.

†p<

0.10.

� p<

0.05.

��p<

0.01.

�� p

<0.001.


In terms of control variables, we expected that governments with a restricted general fund

balance to have a lower default risk. Our coefficients are negative and significantly different

from zero.We also supposed that governments reporting an unrestricted fund balance will have a

lower default risk. Our expectation is supported only forMoody’s; the coefficient is negative and

significant at conventional levels. Finally, with regard to financial performance of governments,

we anticipated higher default risk for lower levels of financial performance. Our coefficients are

negative and significantly different from zero in both sets of regressions, consistent with our ex-

ante expectations. As anticipated, the coefficient for GO debt is positive and significantly

different from zero in all models. The coefficients for other control measures are insignificant at

conventional levels.

Credit Outlooks

We also assessed whether short-term credit quality measures are sensitive to pension fund ratios.

Our findings demonstrate that rating outlooks are sensitive to pension funding levels, especially

for states with lower levels of pension funding (i.e., a lower value for Fund Ratio). To this end,

we have estimated amodel for the covariates of credit quality outlooks as reported by Standard&

Poor’s. We re-estimated the state rating outlooks model using a multinomial logit model, which

does not depend on the assumption of parallel or partial parallel regressions.

FIGURE 1

Predicted Levels of Standard & Poor’s Credit Quality across the Measure of Pension Fund

Ratio

0.1

.2.3

.4.5

.6

.4 .5 .6 .7 .8 .9 1 1.1Fund Ratio

Pr(<=AAA) Pr(<=AA+)Pr(<=AA) Pr(<=AA-)Pr(<=A+) Pr(<=A)Pr(<=A-) Pr(<=BBB+)

Predicted Probabilities: S&P Credit Ratings.


Our results support the hypothesis that a government will be less likely to experience a

negative change in the rating outlook if its accumulated assets are sufficient to meet its

liabilities. The results of the multinomial logit model for the covariates of rating outlooks,

reported in Table 3, indicate that there is an asymmetric relationship between pension fund

ratios and S&P rating outlooks. For a state with poorly funded plans, a change in the funding

level affects the chances of an outlook migration between stable and negative outlooks.

Greater pension funded ratios in states appear to decrease the likelihood of negative outlooks

compared to stable outlooks, all held constant. A one standard deviation increase (decrease)

in the state’s pension fund ratio (roughly about 0.15 around the mean ratio of 0.80),

decreases (increases) the odds of a negative outlook by a factor of 0.3259, which is almost a

200 percent decrease (increase) in odds of a negative outlook. However, pension fund ratio is

not significant for outlook migrations between stable and positive outcomes. Hence, changes

in pension fund ratio are found to result in significant shifts between negative and stable

outlooks; however, no shifts are detected between the stable and positive outlooks, all held

constant.

To illustrate, we have depicted the predicted levels of credit outlook categories across the

range of pension fund ratios in Figure 3, all else held at means. This graph shows that the

predicted levels of stable outlooks increase from about 0.81 to about 0.99 across the range of

Fund Ratio, though the steepest transition is observed from the ratio levels of 0.4–0.8, after

which the trajectory approaches a probability of 1. Conversely, the predicted levels of

FIGURE 2

Predicted Levels of Moody’s Credit Quality across the Measure of Pension Fund Ratio

0.1

.2.3

.4.5

.6

.4 .5 .6 .7 .8 .9 1 1.1Fund Ratio

Pr(<=Aaa) Pr(<=Aa1)Pr(<=Aa2) Pr(<=Aa3)Pr(<=A1) Pr(<=A2)Pr(<=A3) Pr(<=Baa1)

Predicted Probabilities: Moody's Credit Ratings.


TABLE3

MultinomialLogitRatingOutlooksModel,Standard

&Poor’s(N

¼356)

Variable

Standard

&Poor’sratingoutlooks(�

1¼

Negative,

0¼

Stable,1¼

Positive)

Comparison:

negativevs.stable

outlook

Comparison:

positivevs.stable

outlook

bNjS

Std.err.

expðb

StdX

NjS

ÞbPjS

Std.err.

expðb

StdX

PjS

ÞFundRatio

�7.3946��

(2.5883)

0.3259

4.2188

(9.9864)

1.8957

RestrictedGeneral

Fund

Balance

�22.5198†

(13.2693)

0.3489

�47.4081†

(25.1876)

0.1090

Unrestricted

General

Fund

Balance

�12.1357��

(3.8614)

0.3121

28.4417†

(16.2512)

15.3221

General

FundSurplus/

Deficit

�1.2439

(1.4723)

0.7324

22.5825

(22.8329)

285.5415

GO

Debt

�10.8648

(30.4913)

0.8128

�96.7309

(67.4360)

0.1580

Other

Debt

�5.7790

(23.8261)

0.9131

54.0906

(122.1959)

2.3429

Population(Log)

1.3181�

(0.6086)

3.6504

5.4529�

(2.6357)

211.9817

%<18years

�0.3550

(0.7155)

0.4966

�1.3111

(1.6182)

0.0754

%�6

5years

0.4992

(0.4661)

2.2511

�1.7843

(1.7327)

0.0550

Tax

Ratio

�8.2027

(6.8309)

0.5277

86.2632�

(40.4471)

830.4992

Income(per

Capita,$’000)

�0.0069

(0.1077)

0.9568

�0.3075

(0.4370)

0.1405

GO—VoterApproval

Requirem

ent

�0.0181

(1.0042)

0.9910

�12.7531�

(5.6152)

0.0017

BalancedBudget

Requirem

ent

0.4876

(1.3029)

1.2020

�1.0733

(4.0774)

0.6670

General

FundTEL

1.4942

(0.9921)

2.0646

34.4271

(253.7901)

1.7907

ProceduralTEL

0.1356

(1.1546)

1.0651

�1.3523

(4.0774)

0.5332

(Continued)


TABLE3(Continued

)

Variable

Standard

&Poor’sratingoutlooks(�

1¼

Negative,

0¼

Stable,1¼

Positive)

Comparison:

negativevs.stable

outlook

Comparison:

positivevs.stable

outlook

bNjS

Std.err.

expðb

StdX

NjS

ÞbPjS

Std.err.

expðb

StdX

PjS

ÞYearSpecific

Identifiers

(2002–2011)

Omittedforbrevity,available

uponrequest(year2002is

thecomparisoncategory)

McFadden’s

R2

0.502

Cragg-U

hler’sR2

0.581

Log-likelihood

�56.732

Note:State

clustered

robuststandarderrors

inparentheses.

†p<

0.10.

� p<

0.05.

��p<

0.01.

�� p

<0.001.

Expðb

StdX

NjSÞischangein

oddsforastandarddeviationchangein

explanatory

variable.Outcome“stable

outlook”isthecomparisonchoice.


negative outlooks decrease from about 0.18 to 0, when Fund Ratios move from 0.4 to about

0.8, after which the predicted probabilities are flat at zero. The predicted levels of positive

outlooks do not show any signs of statistically significant trajectories of change at any levels

of the Fund Ratios. In other words, there is almost an 18 percent increase in the probability

of having a stable outlook when you compare a state with a pension fund ratio of 0.4 versus a

state with a pension fund ratio of 1, all other factors held at mean values. On the flip side,

there is roughly an 18 percent decrease in the probability of having a negative outlook when

you compare a state with a pension fund ratio of 0.4 versus a state with a pension fund ratio

of 1. We find that Fund Ratios are important when the choice is between negative versus

stable outlooks, but not factored at all when the decisions are made on positive versus stable

outlooks.

Regarding control measures, restricted general fund information, unrestricted general fund

information, and percentage of population over the age of 65 are found to have significant

associations for the predicted probabilities of negative versus stable outlooks. Greater levels of

restricted and unrestricted general fund balances decrease the odds of negative outlook versus

stable outlook rather significantly. On the other hand, greater shares of over 65 populations

appear to increase the odds of negative outlooks versus stable outlooks. Greater levels in

unrestricted general funds also are found to increase the odds of positive versus stable outlooks,

though this association is only marginally significant. Higher tax revenue ratios also appear to

increase the odds of positive versus negative outlooks, all held constant.

FIGURE 3

Predicted Levels of Standard & Poor’s Credit Outlook Categories across the Measure of

Pension Fund Ratio

0.2

.4.6

.81

.4 .5 .6 .7 .8 .9 1 1.1Fund Ratio

Pr(<=Negative) Pr(<=Stable)Pr(<=Positive)

Predicted Probabilities: S&P Ratings Outlook


DISCUSSION AND CONCLUSIONS

This study yields two important findings. First, credit ratings are sensitive to the state’s aggregate

Fund Ratio. This result is analogous to credit ratings being sensitive to measures of outstanding

debt obligation. To avoid poorer credit ratings, states have a real incentive to adjust their

actuarial assumptions in order to report stronger positions in their pension plans (i.e., a better

Fund Ratio). Related, state general obligation credit ratings are sensitive to information

disclosed in state CAFRs, including the general fund position and the general fund performance.

The study also finds that credit outlooks respond to pension fund ratios, and control variables,

in the following ways. First, the responses of outlooks are asymmetric. Outlook changes are

more sensitive to changes in poorly funded, versus well-funded, pension plans. Moreover, only

changes between stable and negative respond to changes in fund ratios. Second, outlooks also

respond to changes in control variables in the expected directions. The variables that represent

stronger financial health (general fund balance, unrestricted general fund balance, and tax

revenue) are positively associated with higher outlooks, whereas a threat to financial health (aged

population) is negatively associated with higher outlooks.

CHANGES IN PENSION PLAN REPORTING

On June 25, 2012GASB approved new accounting and financial reporting standards for pensions

provided through state and local governments. Specifically, GASB Statements No. 67 and 68

will replace GASB Statements No. 25 and 27, respectively, on June 15, 2013 and June 15, 2014.

Of the changes, we identify a number that are most pertinent to this study. First, the rules require

governments that sponsor DB plans to recognize a NPL in their Statement of Net Position (i.e.,

their government-wide balance sheet).23 Second, the total pension liability will be calculated on

the basis of the entry age normal actuarial cost method. Third, the discount rate for accounting

purposes may include a portion based on the municipal bond rate depending on whether the

projected assets of the plan are sufficient to cover projected benefits. Fourth, the standards

require governments to recognize the pension expense (PE), which reflects change in the

employer’s net pension liability. Also, for cost-sharing multiple-employers, the new standards

require governments to report their proportionate share of the plans’ NPL and PE. Finally, in

addition to reporting the NPL and PE, the new accounting standards require employers to present

extensive note disclosures and RSI, including descriptive information about the types and

benefits provided, how contributions to the pension plans are determined, and assumptions and

methods used to calculate the pension liability.

GASB believes the new reporting standards for pension plans will improve decision-

usefulness of information contained in financial reports and will enhance its value for assessing

accountability and interperiod equity (GASB 1994b). Whether the new information required by

23. NPL is equal to the total pension liability (TPL) minus the plan’s fiduciary net position (PFNP) or simply the

fair market value of the plan assets.


GASB will be important to the markets is the subject of future research. As adherence to GASB

Statements No. 67 and 68 unfolds, researchers will be able to assess the share of liabilities per

employer, their impact on unrestricted net assets and evaluate impacts of pension liabilities

relative to GO and other debt. The more precise data should allow better measures to assess

financial condition of governments as well as assess their impact on credit quality and borrowing

costs, both at the state and local levels.

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APPENDIX

A

PensionPlansbyState

andPlanType

State

Single

employer

plan

Agentmultiple

employer

plan

Cost

sharingmultiple

employer

plan

Alabam

aEmployee

Retirem

entSystem

Teacher’s

Retirem

entSystem

JudicialRetirem

entFund

Arkansas

ArkansasJudicial

ArkansasPublicEmployeesRetirem

entSystem

ArkansasHighway

and

Transportation

ArkansasTeachersRetirem

entSystem

California

Legislators’Retirem

entFund

PublicEmployeesRetirem

ent

Fund

California

State

TeachersRetirem

entSystem

Judges

Retirem

entFund


entFund

Judges

Retirem

entFundII

Connecticut

State

Employees’

Retirem

ent

System

Teachers’

Retirem

entSystem

JudicialRetirem

entSystem

Delaw

are

Delaw

arePublicEmployees

Retirem

entSystem

—New

State

Police

Delaw

arePublicEmployeesRetirem

entSystem

—State

Employees

Delaw

arePublicEmployees

Retirem

entSystem

—Closed

State

Police

Delaw

arePublicEmployeesRetirem

entSystem

—Special

Delaw

arePublicEmployees

Retirem

entSystem

—Judiciary

Florida

FloridaRetirem

entSystem

Georgia

Georgia

MilitaryPensionFund

Employees’

Retirem

entSystem

TeachersRetirem

entSystem

PublicSchoolEmployeesRetirem

entSystem

LegislativeRetirem

entSystem

Georgia

JudicialRetirem

entSystem

(Continued)


APPENDIX

A(Continued

)

State

Single

employer

plan

Agentmultiple

employer

plan

Cost

sharingmultiple

employer

plan

Haw

aii

Haw

aiiEmployee

Retirem

entSystem

Illinois

General

Assem

bly

Retirem

ent

System

TeachersRetirem

entSystem

Judges

Retirem

entSystem

State

Universities

Retirem

entSystem

State

EmployeesRetirem

ent

System

Louisiana

LouisianaState

Employee’s

Retirem

entSystem

TeachersRetirem

entSystem

ofLouisiana

LouisianaState

Police

Retirem

ent

System

Maine

MaineState

Employee

and

Teacher

Program

Maryland

MarylandPensionandRetirem

entSystem

Massachusetts

State

Employee’s

Retirem

ent

System

s

MassachusettsTeachersRetirem

entSystem

Michigan

State

Police

Retirem

entSystem

State

EmployeesRetirem

entSystem

LegislativeRetirem

entSystem

Judges

Retirem

entSystem


entSystem

Minnesota

Correctional

Employees

Retirem

entFund

State

EmployeesRetirem

entFund

ElectiveState

OfficersFund

TeachersRetirem

entFund

Judges

Retirem

entFund

Legislators

Retirem

entFund

State

PatrolRetirem

entFund

Mississippi

MississippiHighway

SafetyPatrol

Retirem

entSystem

PublicEmployees’

Retirem

entPlan

(Continued)


APPENDIX

A(Continued

)

State

Single

employer

plan

Agentmultiple

employer

plan

Cost

sharingmultiple

employer

plan

SupplementalLegislative

Retirem

entPlan

Missouri

MissouriState

Employees’

Pension(administeredby

MissouriState

Employees

Retirem

entSystem

)

MissouriPublicSchoolRetirem

entSystem

JudicialPlan

MissouriDOTandHighway

Patrol

University

ofMissouriRetirem

ent

System

Montana

Judges

Retirem

entSystem

PublicEmployee

Retirem

entSystem

-Defined

Benefit

Retirem

entPlan

Highway

PatrolOfficers’

Retirem

entSystem

MontanaTeachersRetirem

entSystem

Gam

eWardens’

andPeace

Officers’

Retirem

entSystem

Sheriffs

Retirem

entSystem

Volunteer

Firefighters’

CompensationAct

Nevada

Legislators

Retirem

entSystem

JudicialRetirem

entSystem

PublicEmployees’

Retirem

entSystem

New

Ham

pshire

New

Ham

pshireRetirem

entSystem

New

Jersey

JudicialRetirem

entSystem

Consolidated

Police

andFirem

en’s

PensionFund(closed)

PrisonOfficersPensionFund

(closed)

Police

andFirem

en’s

Retirem

entSystem

(state

only)

Police

andFirem

en’s

Retirem

entSystem

(localonly)


entSystem

(localonly)

(Continued)


APPENDIX

A(Continued

)

State

Single

employer

plan

Agentmultiple

employer

plan

Cost

sharingmultiple

employer

plan


entSystem

(state

only)

State

Police

Retirem

entSystem

s

Teachers’

PensionandAnnuityFund

New

Mexico

PublicEmployees’

Retirem

entFund(includes

Legislative

Retirem

entFund)

Educational

Employees’

Retirem

entFund

JudicialRetirem

entFund

MagistrateRetirem

entFund

Volunteer

Firefighters

Retirem

entFund

New

York

State

TeachersRetirem

entSystem

Employee

Retirem

entSystem

Police

andFireRetirem

entSystem

NorthCarolina

Consolidated

JudicialRetirem

ent

System

TeachersandState

EmployeesRetirem

entSystem

LegislativeRetirem

entSystem

Firem

en’s

andRescueSquad

WorkersPensionFund

NorthCarolinaNational

Guard

PensionFund

Ohio

Ohio

State

Highway

Patrol

Retirem

entSystem

Ohio

PublicEmployees’

Retirem

entSystem

State

Teachers’

Retirem

entSystem

ofOhio

SchoolEmployees’

Retirem

entSystem

Ohio

Police

andFirePensionFund

Oklahoma

Wildlife

ConservationRetirem

ent

System

OklahomaPublicEmployeesRetirem

entSystem

Uniform

Retirem

entSystem

for

Justices

andJudges

OklahomaTeachersRetirem

entSystem

OklahomaLaw

Enforcem

ent

Retirem

entSystem

(Continued)


APPENDIX

A(Continued

)

State

Single

employer

plan

Agentmultiple

employer

plan

Cost

sharingmultiple

employer

plan

Oregon

OregonPublicEmployeesRetirem

entSystem

Pennsylvania


entSystem

State

Employees,Teachers,Higher

EducationEmployees,

PensionPlan

RhodeIsland

JudicialRetirem

entBenefitTrust

Employee

Retirem

entSystem

State

Police

Retirem

entBenefit

Trust

South

Carolina

South

CarolinaGeneral

Assem

bly

Retirem

entSystem

South

CarolinaRetirem

entSystem

South

CarolinaJudges

and

Solicitors

Retirem

entSystem

South

CarolinaPolice

OfficersRetirem

entSystem

South

CarolinaNational

Guard

Retirem

entSystem

Tennessee

State

Employees,Teachers,andHigher

Education

Employees’

PensionPlan

Texas

Law

Enforcem

entandCustodial

Officer

SupplementRetirem

ent

Plan

TeachersRetirem

entSystem

JudicialRetirem

entSystem

Employee

Retirem

entSystem

JudicialRetirem

entSystem

Utah

Judges

Retirem

entSystem

UtahNoncontributory

Retirem

entSystem

UtahGovernors’andLegislators

Retirem

entPlan

Contributory

Retirem

entSystem

Firefighters’Retirem

entSystem

Judges

Retirem

entSystem

Vermont

VermontState

Retirem

entSystem

State

Teachers’

Retirem

entSystem

Virginia

Virginia

Police

Officers’

Retirem

entSystem

Virginia

Retirem

entSystem

(mixed

agent—

cost

sharing

multiple

employer)

(Continued)


APPENDIX

A(Continued

)

State

Single

employer

plan

Agentmultiple

employer

plan

Cost

sharingmultiple

employer

plan

Virginia

Law

Officers’

Retirem

ent

System

Virginia

JudicialRetirem

ent

System

Washington

WSPRS—State

PatrolRetirem

ent

System

WashingtonJudges

Retirem

ent

System

PublicEmployees’

Retirem

entSystem

Plan1

WashingtonJR

S(pay-as-you-go

basis)

PublicEmployees’

Retirem

entSystem

Plan2/3

SchoolEmployeesPlan2/3

Teachers’

Retirem

entSystem

Plan1

Teachers’

Retirem

entSystem

Plan2/3

PublicSafetyEmployees’

Retirem

entSystem

Plan2

Law

Enforcem

entOfficers’

andFireFighters’Retirem

ent

System

Plan1

Law

Enforcem

entOfficers’

andFireFighters’Retirem

ent

System

Plan2

WestVirginia

PublicSafetyDeath,Disabilityand

Retirem

entSystem

(closed)

PublicEmployees’

Retirem

entSystem

State

Police

Retirem

entSystem

Teachers’

Retirem

entSystem

Judges’Retirem

entSystem

Wisconsin

WisconsinRetirem

entSystem

Weexcluded

allplansin

whichthestatehad

nofiduciaryresponsibilities,was

notcontributingto,andwas

notparticipatingem

ployer.


Impact of Unfunded Pension Obligations on Credit Quality of State Governments

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