53 Abstract State and local governments across the United States of America (USA) were hit hard by the recent recession. The fi scal stress alerted the public to the increasing amount of public pension debt for which, despite snowballing levels of pen- sion debt, the causes are unclear. This article ex- amines the factors contributing to annual chang- es in unfunded public pension ratios, focusing in particular on public pension management (in- cluding investment performance, investment as- sumptions, and accounting practices). The data on pension debt for state defi ned benefi t plans comes from the Pew Charitable Trusts for the pe- riod 2005 to 2015. Two methods (random-effects and general estimating equation) were used to verify the consistency of the results. These re- sults showed that investment return decreases the annual change in the public pension debt while using a project unit credit method, and the implementation of the Government Accounting Standards Board (GASB) Statement 67 increase the annual change in the public pension debt. These fi ndings illustrate the importance of public pension management in explaining public pen- sion debt. Keywords: annual change in unfunded pen- sion ratios, investment return, project unit credit, GASB 67. THE DETERMINANTS OF PUBLIC PENSION DEBT IN U.S. STATES*1 Jae Young LIM Jae Young LIM Senior Researcher, Community Wellbeing Research Center, Seoul National University, Seoul, Korea Tel.: 82-2-880-8530 E-mail: jaeyounglim@yahoo.com  Acknowledgments: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2016S1A3A2924563). DOI:10.24193/tras.60E.4 Published First Online: 2020/06/22 Transylvanian Review of Administrative Sciences, No. 60 E/2020, pp. 53-71 54 1. Introduction When the housing bubble burst in the late 2000s, it devastated state and local gov- ernments across the US.  e nationwide fi scal stress this caused drew a ention to the problem of public pension liabilities1. Some scholars estimate that state and local pension liabilities are as high as $4.43 trillion (Novy-Marx and Rauh, 2011).  e gravity of the pension problems aff ecting state and local governments has led to an increase in research on public pensions. However, few studies have directly as- sessed the factors contributing to the annual change in unfunded public pension ratios at the aggregate state level.  is study therefore examines the factors that might infl u- ence unfunded public pension liabilities, focusing primarily on dimensions of public pension management. Data on pension debt for state defi ned benefi t plans from 2005 to 2015 were ob- tained and two statistical methods were used to verify the consistency of the results (random-eff ects and general estimating equation).  e results show that public pen- sion management plays a sizable role in infl uencing public pension debt across U.S. states. For instance, investment return reduces the annual change in public pension debt while using a project unit credit method to calculate pension costs along with the implementation of the GASB 67 increases the annual change in public pension debt.  is underlines the importance of public pension management, such as investment performance and accounting practices, in explaining public pension debt.  e presentation of the research is organized as follows: fi rst, I discuss factors that might contribute to public pension debt, focusing on public pension management and, secondly, I discuss the data and specifi c variables used in the model. Finally, I present the empirical results along with a discussion of their implications, followed by several policy recommendations for public offi cials involved in public pension management. 2.  eory and hypotheses In recent years, an increasing number of studies have explored public pension is- sues; this refl ects the growing importance of such pensions in the public arena. From these studies, several themes have emerged. One such theme is the relationship be- tween state politics and public pensions (Coggburn and Kearney, 2010; Kiewiet, 2010;  om, 2013a and 2013b; Anzia and Moe, 2017). Public unions have also been subject to scholarly inquiry (Mitchell and Smith, 1994; Munnell et al., 2011a). A third, fl our- ishing arena of research is the management of public pension boards concerned with public pension performance and state bond ratings (Hess, 2005; Andonov et al., 2018; Dove et al., 2018).  e estimation of public pension liabilities with respect to discount 1 Unfunded (public) pension liabilities refer to unfunded actuarial accrued liabilities for state defi ned benefi t plans. To avoid using technical jargon, the article refers to unfunded pension liabilities rather than the more technical term. In addition, public pension debt is used interchangeably with unfunded pension liabilities.  is la er term is limited to U.S. states; it is not applicable to local governments. 55 rates and their sustainability have also garnered scholarly a ention (Brown and Wil- cox, 2009; Novy-Marx and Rauh, 2009, 2011; Waring, 2012; Chen and Matkin, 2017) as has an ideal funding level for public pensions (Bohn, 2011; Munnell et al., 2011a).  e investment pa erns of public pension assets have also raised concerns (Lucas and Zeldes, 2009; Pennacchi and Rastad, 2011). In addition, economists have examined the impact of public pensions on labor markets and practices (Munnell et al., 2007; Schieber, 2011; Goldhaber et al., 2017) along with the political and economic aspects of public pension liabilities (Schneider and Damnanpour, 2002; Glaeser and Ponze o, 2014; Kelley, 2014). Scholars have also studied the impact of fi scal institutions such as budget stabilization funds and fi scal conditions on public pensions (Clair, 2012; Chen, 2018). Several scholars have probed the legal structures of public pensions and the possibilities for pension reform (Monahan, 2010, 2012, 2015, 2017; Fitzpatrick and Monahan, 2015; Aubrey and Crawford, 2017). Despite a growing number of studies researching public pensions, few have ex- amined the factors contributing to public pension debt using the ‘annual change in unfunded public pension ratios’ measure.  is study does so with a specifi c focus on the dimensions of public pension management. 2.1. Public pension management: Investment return and assumption Monahan (2015) argued that it is not easy to compare the costs of pension plans due to diff erences in underlying investment assumptions. Despite being ambiguous and o en mystifying, the investment assumption rate – o en used interchangeably with ‘discount rate’ – plays a vital role in determining pension costs. For the same pension plan, a stronger discount rate can make future liabilities seem much lower than they actually are (Munnell, 2012). A majority of states and local governments have anticipated the expected return on pension assets to be approximately 7.5%.  is overly optimistic assumption enables states and local governments to put less than their required contribution into the pension pot. Unlike public pension plans, private companies have, in recent years, discounted their pension liabilities at an average rate of 4.7% to account for real-life circumstances ( e Economist, 2013). Novy-Marx and Rauh (2009, 2011) have argued that the serious shortfall in pension funds facing states and local governments stems in part from optimistic actuarial as- sumptions over the years. Although these assumptions might have been acceptable in the robust economy of the 1990s, such optimism could lead to a calamity in leaner economic times. With the application of a realistic discount rate, pension liabilities of around $900 billion can jump to $3.2 trillion or even higher to $4.43 trillion (Novy-Marx and Rauh, 2011).  e former assumes that pension liabilities are equivalent to states’ general obligation debt; whereas, in the la er, a discount rate is considered a zero-cou- pon Treasury yield. Regardless of whether states should use a ‘risk-free’ rate actuar- ial – because a pension payment is bound to be made in the future – (Novy-Marx and Rauh, 2011; Waring, 2012), an optimistic rate is equated with a decline in pension liabilities. 56  e close relationship between an assumed rate and pension debt also applies to the relationship between an investment return and pension debt; in fact, a discount rate is a hypothesized rate while an investment return is a realized rate. As such, it is also critical in understanding the dynamics of pension debt. For instance, an im- provement in investment yields on pension assets can signifi cantly shrink the size of the pension debt. A healthy economy and accompanying investment returns can hide the size of the public pension debt whereas a poor economy can quickly magnify the problem (Monahan, 2017). Based on reasonable expectations regarding the relation- ship between investment return, investment assumption rate and public pension debt, the following hypotheses were constructed for empirical testing: • Hypothesis 1: An increase in investment return will be associated with a decrease in the annual change in unfunded pension ratios, and • Hypothesis 2: An increase in investment assumptions will be associated with a decrease in the annual change in unfunded pension ratios. 2.2. Public pension management: accounting practices Accounting practices can signifi cantly infl uence the level of public pension debt. Regarding the accounting method, state plans primarily use the entry age normal while approximately 13% of plans employ the project unit credit method. For the entry age normal, employers ‘frontload’ future benefi ts; whereas in the project unit credit employers are backloaded with pension obligations as a retirement horizon approach- es (Munnell, 2012, p. 52). Assuming that employers fully fund their pension obliga- tions, they would have to set aside fewer pension assets by using the project unit credit.  us, the project unit credit is a less stringent method of funding than using the entry age normal (Munnell, 2012). However, having fewer assets to work with will dampen investment opportunities and eventually lead to larger public pension debt. It is also important to note any changes to accounting methods that infl uenced the way pensions were calculated during the period from 2005 to 2015.  e Government Accounting Standards Board announced the GASB 67 in 2012 and it took eff ect in 2014 (Farmer and Maciag, 2015).  e GASB 67 requires states to adopt a realistic discount rate.  e change adopted the blend rate whereby states that regularly make their full annual required contributions can use an assumed investment return, whereas those not doing so are forced to use a market rate. An optimistic discount rate with its overly positive assumptions about future investment returns conceals the true state of public pension funding.  e change exposed several states to the grim reality of worsening pension debt. For example, the state of Kentucky had to lower its investment return assumption by 2%, which meant that it suddenly experienced a 6% increase in pension debt from 2013 to 2014.  e change in method could help explain the variations in un- funded ratios across states that occurred in 2014 and 2015 (Farmer and Maciag, 2015). As such, we arrived at: • Hypothesis 3: States using a project unit credit method will experience an in- crease in the annual change in unfunded pension ratios, and 57 • Hypothesis 4: Implementing the GASB 67 will be associated with an increase in the annual change in unfunded pension ratios. 3. Variables and measurement 3.1. Dependent variable To test the hypotheses, the model, including the following dependent and explan- atory variables, was estimated.  e dependent variable in the model was the annual change in unfunded pension ratios.  e unfunded ratio itself is equal to:   1 100 1 Actuarial Value of Assets Actuarial Accrued Liability           Because the explanatory variables were annual measures, it was appropriate to use ‘the annual change’ in unfunded pension ratios to examine annularity rather than un- funded pension ratios because the la er is a cumulative measure.  e data came from the Pew Charitable Trusts (2018), and covered the period 2005 to 2015.  ese variables accounted for all the defi ned benefi t plans (238 plans) to which the state contributes and/or is legally liable for benefi ts ( e Pew Charitable Trusts, 2015, 2018). Figures 1-3 show the magnitude of public pension debt where Figure 3 in particular illustrates the trajectory of the dependent variable from 2005 to 2015. 1949194919491949194919491949194919491949194919491949194919491949194919491949194919491949194919491949194919491949194919491949194919491949194919491949194919491949194919491949194919491949194919491949 2080208020802080208020802080208020802080208020802080208020802080208020802080208020802080208020802080208020802080208020802080208020802080208020802080208020802080208020802080208020802080208020802080 2260226022602260226022602260226022602260226022602260226022602260226022602260226022602260226022602260226022602260226022602260226022602260226022602260226022602260226022602260226022602260226022602260 2306230623062306230623062306230623062306230623062306230623062306230623062306230623062306230623062306230623062306230623062306230623062306230623062306230623062306230623062306230623062306230623062306 2271227122712271227122712271227122712271227122712271227122712271227122712271227122712271227122712271227122712271227122712271227122712271227122712271227122712271227122712271227122712271227122712271 2300230023002300230023002300230023002300230023002300230023002300230023002300230023002300230023002300230023002300230023002300230023002300230023002300230023002300230023002300230023002300230023002300 2345234523452345234523452345234523452345234523452345234523452345234523452345234523452345234523452345234523452345234523452345234523452345234523452345234523452345234523452345234523452345234523452345 2375237523752375237523752375237523752375237523752375237523752375237523752375237523752375237523752375237523752375237523752375237523752375237523752375237523752375237523752375237523752375237523752375 2456245624562456245624562456245624562456245624562456245624562456245624562456245624562456245624562456245624562456245624562456245624562456245624562456245624562456245624562456245624562456245624562456 2757275727572757275727572757275727572757275727572757275727572757275727572757275727572757275727572757275727572757275727572757275727572757275727572757275727572757275727572757275727572757275727572757 2746274627462746274627462746274627462746274627462746274627462746274627462746274627462746274627462746274627462746274627462746274627462746274627462746274627462746274627462746274627462746274627462746 2288228822882288228822882288228822882288228822882288228822882288228822882288228822882288228822882288228822882288228822882288228822882288228822882288228822882288228822882288228822882288228822882288 2441244124412441244124412441244124412441244124412441244124412441244124412441244124412441244124412441244124412441244124412441244124412441244124412441244124412441244124412441244124412441244124412441 2621262126212621262126212621262126212621262126212621262126212621262126212621262126212621262126212621262126212621262126212621262126212621262126212621262126212621262126212621262126212621262126212621 2775277527752775277527752775277527752775277527752775277527752775277527752775277527752775277527752775277527752775277527752775277527752775277527752775277527752775277527752775277527752775277527752775 2929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929292929 3055305530553055305530553055305530553055305530553055305530553055305530553055305530553055305530553055305530553055305530553055305530553055305530553055305530553055305530553055305530553055305530553055 3176317631763176317631763176317631763176317631763176317631763176317631763176317631763176317631763176317631763176317631763176317631763176317631763176317631763176317631763176317631763176317631763176 3287328732873287328732873287328732873287328732873287328732873287328732873287328732873287328732873287328732873287328732873287328732873287328732873287328732873287328732873287328732873287328732873287 3422342234223422342234223422342234223422342234223422342234223422342234223422342234223422342234223422342234223422342234223422342234223422342234223422342234223422342234223422342234223422342234223422 3689368936893689368936893689368936893689368936893689368936893689368936893689368936893689368936893689368936893689368936893689368936893689368936893689368936893689368936893689368936893689368936893689 3837383738373837383738373837383738373837383738373837383738373837383738373837383738373837383738373837383738373837383738373837383738373837383738373837383738373837383738373837383738373837383738373837 339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339339 360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360360 362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362362 469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469469 658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658658 755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755755 831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831831 912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912912 966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966966 933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933933 1091109110911091109110911091109110911091109110911091109110911091109110911091109110911091109110911091109110911091109110911091109110911091109110911091109110911091109110911091109110911091109110911091 0 10 00 20 00 30 00 40 00 50 00 B ill io n 2005 2010 2015 Year Pension Assets Actuarially Accrued Pension Liabilities Unfuned Pension Liabilities Figure 1: Change in pension assets, actuarial accrued pension liabilities, and unfunded pension liabilities, 2005-2015 58 18.23 17.94 16.65 19.48 24.87 26.95 27.64 29.49 28.98 24.11 27.06 0 10 20 30 A ve ra ge U nf un de d P en si on R at io s 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Figure 2: Average unfunded public pension ratios, 2005-2015 -6 -4 -2 0 2 4 6 A nn ua l C ha ng e (% ) 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Figure 3: Annual change in unfunded pension ratios, 2005-2015 59 3.2. Explanatory variables Five variables were employed to explore public pension management: investment return, investment assumption, project unit credit, GASB 67, and active plan mem- bers. Because the dependent variable was an aggregate measure, it would have been ideal to have state-level aggregate data available. Unfortunately, however, most states manage multiple pension plans, and it is diffi cult to derive aggregate state-level mea- sures.  us, for each state, I used the largest representative defi ned benefi t plan (in terms of total pension assets) for the variables noted above.  e investment return is a fi ve-year return and a ratio; the investment return as- sumption is also a ratio.  e project unit credit was a dummy variable coded as 1 if the representative plan in each state employed the project unit credit method to estimate actuarial pension costs and 0 otherwise. Active plan members were logged to correct for the skewness resulting from large numbers.  e fi nal public pension management variable was the GASB 67, which was a dummy variable coded as 1 if implemented in a given year and 0 otherwise. Table 1 shows the 50 representative plans that were selected to identify plan-spe- cifi c pension management characteristics (Public Plans Data, 2001-2016). Table 1: Representative largest defi ned benefi t plans in each state States Plans Alabama Teachers Retirement System Alaska Public Employees’ Retirement System Arizona State Retirement System Arkansas Teachers Retirement System California Public Employees Retirement System Colorado School Division Connecticut Teachers’ Retirement System Delaware State Employees’ Pension Plan Florida Retirement System Georgia Teachers’ Retirement System Hawaii Employees’ Retirement System Idaho Public Employee Retirement Fund – Base Plan Illinois Teachers’ Retirement System Indiana State Teachers’ Retirement Fund Iowa Public Employees’ Retirement System Kansas Public Employees’ Retirement System Kentucky Teachers’ Retirement System Louisiana Teachers Retirement System of Louisiana Maine Public Employees Retirement System Maryland Teachers’ Retirement and Pension System 60 States Plans Massachusetts Teachers’ Retirement System Michigan Public School Employees’ Retirement System Minnesota Teachers Retirement Fund Mississippi Public Employees’ Retirement System Missouri Public School Retirement System Montana Public Employees’ Retirement System – Defi ned Benefi t Retirement Plan Nebraska Public Employees Retirement System-Schools Nevada Public Employees’ Retirement System New Hampshire Employees Group New Jersey Teachers’ Pension and Annuity Fund New Mexico Public Employee’s Retirement System New York New York State and Local Retirement System North Carolina Teachers’ and State Employees’ Retirement System North Dakota Teachers’ Fund for Retirement Ohio Public Employees Retirement System Oklahoma Teachers’ Retirement System Oregon Public Employees’ Retirement System Pennsylvania Public School Employees’ Retirement System Rhode Island Employees’ Retirement System – Teachers South Carolina South Carolina Retirement System South Dakota South Dakota Retirement System Tennessee State Employees, Teachers, and Higher Education Employees’ Pension Plan Texas Teacher Retirement System Utah Public Employees Noncontributory Retirement System Vermont Teachers’ Retirement System Virginia Virginia Retirement Systems Washington Public Employees’ Retirement System West Virginia Teachers’ Retirement System Wisconsin Wisconsin Retirement System Wyoming Public Employees’ Pension Plan For control variables, I relied on the state’s fi scal constraints, state politics, and state fi scal conditions. Scholars argue that fi scal constraints such as tax and expen- diture limitations (TELs) play a substantial role in explaining a state’s fi scal behavior (Poterba, 1994; Alt and Lowry, 1994; Matsusaka, 1995; Alesina and Bayoumi, 1996; Mullins and Joyce, 1996; Shadbegian, 1999; Pollack, 2003; Rose, 2006; Brunori, 2007, 2011; Martin, 2008; Mathews and Paul, 2010). To capture institutional fi scal constraints for each state I used the tax and expenditure limitations (TELs) index developed by 61 Kallen (2017).  e data ranged from 0 (states such as Alabama and North Dakota) to 28 (Missouri), and the total score was built on the following six categories: ‘Type of TEL,’ ‘Statutory/Constitutional’, ‘Growth Restriction’, ‘Method of Approval’, ‘Override Pro- visions’, and ‘Exemptions’.  is measure of TELs is superior to the simple dummy variable used in many other studies (e.g., Mason, 2005; McGuire and Rueben, 2006) as it refl ects the diversity of TELs measures across states. State politics and public unions were also accounted for in the model. Scholars have found that states controlled by the Democratic Party tend to spend and tax more, and are more likely to support employee-friendly policies and public unions than the Republican Party (Blais et al., 1993; Poterba, 1996; Shadbegian, 1999; Marschall and Ruhil, 2005). Popular media outlets continue to blame public unions for their out- sized pension benefi ts and debt (Lowenstein, 2008; Greenhut, 2009; Malanga, 2010; Erie et al., 2011), although empirical fi ndings on the eff ects of public unions on public pension debt and benefi ts are inconclusive (Mitchell and Smith, 1994; Munnell et al., 2011b).  us, public unions may cut both ways and therefore it was appropriate to see what the data would reveal. State political party variables were lagged at t-1, as deci- sions regarding current budget outcomes are made in the previous year (Budge and Hoff erbert, 1990).  ese variables included Democratic membership in the state house at t-1 (%), Democratic membership in the state senate at t-1 (%), Democratic governor at t-1 (dummy), unifi ed Democratic control at t-1 (dummy), and unifi ed Republican control at t-1 (dummy).  e public union variable consisted of public union mem- bership (%) (Hirsch and MacPherson, 2003; updated). An interaction term between unifi ed Republican control at t-1 and public union membership was also included in the model to account for potential relationships between politics and public unions. Finally, the model accounted for fi scal conditions that may infl uence public pen- sion debt. Unlike federal governments, states and local governments are constrained by having to balance their budgets, even in the midst of a fi scal crisis, and o en cut spending or raise revenue during economic downturns. As such, it is critical to ac- count for the fi scal conditions of states when accounting for fi scal outcomes such as public pension debt. Well-known variables for identifying fi scal stress include unem- ployment rates (Mitchell and Smith, 1994), year-end general fund balance (Wilson, 1983; Wilson and Howard, 1984; National Council of State Legislature, 1987; Raman and Wilson, 1990; Chaney et al., 2002), and long-term debt (Wilson, 1983; Denison et al., 2006). Both long-term debt and year-end general fund balance were expressed as a share of total state revenue to avoid severe right skewness due to the large numbers involved. Table 2 shows the measurement and data sources for the variables while Table 3 presents the descriptive statistics. 62 Table 2: Measurement of dependent and explanatory variables Variable Measurement Sources and Remarks Annual change in unfunded pension ratios Percentage The Pew Charitable Trusts (2018) Unfunded Ratio: 1 100 ) Actuarial Value of Assets Actuarial Accrued Liability           5-year investment return Percentage Public Plans Data. 2001-2016. Center for Retire- ment Research at Boston College, Center for State and Local Government Excellence, and National Association of State Retirement Administrators. Investment assumption Percentage Public Plans Data. 2001-2016. Center for Retire- ment Research at Boston College, Center for State and Local Government Excellence, and National Association of State Retirement Administrators. Project unit credit Indicator Public Plans Data. 2001-2016. Center for Retire- ment Research at Boston College, Center for State and Local Government Excellence, and National Association of State Retirement Administrators. GASB 67 Indicator GASB (2012) Logged active plan members Values ranging from 9.17 to 13.67 Public Plans Data. 2001-2016. Center for Retire- ment Research at Boston College, Center for State and Local Government Excellence, and National Association of State Retirement Administrators. Tax and expenditure limitation index Values ranging from 0 to 30 Kallen (2017) Democratic membership in the state house at t-1 Percentage National Council of State Legislatures (2015) Democratic membership in the state senate at t-1 Percentage National Council of State Legislatures (2015) Democratic governor at t-1 Indicator National Council of State Legislatures (2015) Unifi ed Democratic State at t-1 Indicator National Council of State Legislatures (2015) Unifi ed Republican State at t-1 Indicator National Council of State Legislatures (2015) Public Union Membership Percentage - Percentage of public employed workers who are union members - Hirsch and Macpherson (2003; updated) Unifi ed Republican state at t-1 * public union membership Values ranging from 0 to 70 Interaction term Unemployment Percentage Local Area Unemployment Statistics (2017) Long-term debt as share of total state revenue Percentage State and Local Government Finances by U.S. Cen- sus Bureau (2016); Fiscal Survey of States (2016) Year-end general fund balance as share of total state revenue Percentage Fiscal Survey of States (2016) 63 Table 3: Descriptive statistics for all variables Variable N Mean SD Min Max Annual change in unfunded pension ratios 490 0.88 4.94 -14.69 32.00 5-year investment return 490 6.79 4.18 -1.23 16.20 Investment assumption 490 7.81 0.36 6.70 8.50 Project unit credit 490 0.10 0.30 0.00 1.00 GASB 67 490 0.20 0.40 0.00 1.00 Logged active plan members 490 11.54 0.99 9.17 13.67 Tax and expenditure limitation index 490 9.12 8.36 0.00 28.00 Democratic membership in the state House at t-1 490 51.25 16.32 13.33 92.00 Democratic membership in the state Senate at t-1 490 49.80 17.89 13.33 96.00 Democratic governor at t-1 490 0.48 0.50 0.00 1.00 Unifi ed Democratic state at t-1 490 0.34 0.47 0.00 1.00 Unifi ed Republican state at t-1 490 0.37 0.48 0.00 1.00 Public union membership (%) 490 33.11 18.35 2.70 72.40 United Republican state at t-1 * public union membership 490 8.35 14.08 0.00 70.00 Unemployment 490 6.56 2.16 2.40 13.90 Long-term debt as share of total state revenue 490 170.84 89.73 34.01 621.60 Year-end general fund balance as share of total state revenue 490 4.97 9.96 -118.70 65.08 * Note: Nebraska is not included in the model due to its unicameral legislature. 4. Findings  e model was estimated using two methods: random-eff ects (RE) and general es- timating equation (GEE).  e two models also controlled for year dummies, whose results are not reported in the table.  e results from the two models were consistent and only a few diff erences were evident in terms of coeffi cients and standard errors. Although the fi xed eff ects model is ideal for eliminating potential sources of omi ed variable bias, a Hausman test result (probabilities= 0.72) indicated that the RE was appropriate in this case.  us, the RE model was used fi rst, rather than a fi xed-eff ects model, with standard errors clustered around states.  e results from the GEE equation are also shown here as they provide signifi cant advantages for the panel data.  e GEE equation is an extension of a generalized lin- ear model (Liang and Zeger, 1986; Zeger and Liang, 1986) and was also appropriate, especially given that cross-sectional panels are fraught with correlated data.  is of- ten leads to the violation of homoskedasticity and, consequently, introduces correlat- ed error terms (Kmenta, 1986; Liang and Zeger, 1986; Zeger and Liang, 1986). Because variance within each panel was likely to be heteroskedastic, Huber-White standard errors were employed to produce a robust estimate as well as correlation- al error structures of AR(1), assuming that data in the previous year infl uence the 64 current year’s data and lead clustered errors (Zorn, 2001). Table 4 shows the results for the two models.  e χ2 statistic was 503.84 for the RE model and 594.20 for the GEE model; both numbers indicate that the two models fi t the data well. Table 4: Factors contributing to the annual change in unfunded pension ratios (RE and GEE) Explanatory Variable Annual Change in Unfunded Pension Ratios (RE) Annual Change in Unfunded Pension Ratios (GEE) 5-year investment return -0.493 [0.129]*** -0.486 [0.123]*** Investment assumption -0.228 [0.401] -0.177 [0.381] Projected unit credit 1.208 [0.355]*** 1.302 [0.314]*** GASB 67 5.042 [0.648]*** 5.045 [0.627]*** Logged active plan members 0.246 [0.182] 0.240 [0.173] Tax and expenditure limitation index -0.027 [0.020] -0.029 [0.019] Democratic membership in the state House at t-1 0.002 [0.027] 0.003 [0.026] Democratic membership in the state Senate at t-1 -0.006 [0.020] -0.007 [0.019] Democratic governor at t-1 -0.760 [0.684] -0.845 [0.659] Unifi ed Democratic state at t-1 0.364 [0.415] 0.460 [0.399] Unifi ed Republican state at t-1 0.535 [0.747] 0.582 [0.752] Public union membership (%) 0.020 [0.012]* 0.019 [0.011]* Unifi ed Republican state at t-1 * public union membership -0.017 [0.018] -0.014 [0.018] Unemployment -0.072 [0.129] -0.057 [0.122] Long-term debt as share of total state revenue -0.002 [0.002] -0.001 [0.002] Year-end general fund balance as share of total state revenue 0.017 [0.021] 0.018 [0.020] Constant 2.592 [4.138] 2.281 [3.958] Wald χ2 503.840*** 594.200*** Observations 490 490 States 49 49 – Standard errors clustered around states in parentheses. – Year fi xed effects are included in the model but not shown in the table. – Nebraska is not included in the model due to its unicameral legislature. – * p ≤ 0.10, ** p ≤ 0.05, *** p ≤ 0.01.  e two models show that the four variables signifi cantly infl uenced the annu- al change in unfunded pension ratios. In terms of the main explanatory variables, the investment return was negatively associated with the annual change in unfunded pension ratios.  is means that as an investment return increases, it helps slow the annual increase in public pension debt.  is has implications for offi cials responsible 65 for managing public pension assets in that it implies they should derive strategies or mechanisms to improve investment performance (discussed further in the fi nal section).  e two variables were positively associated with the annual change in un- funded pension ratios: project unit credit and the GASB 67. As hypothesized, using the project unit credit method infl ates the unfunded liabilities vis-à-vis the entry age normal, as states put aside fewer assets with which to gain sizable investment returns.  e GASB 67, the implementation of the accounting change that began in 2014, ap- pears to infl uence the way states estimate their unfunded liabilities.  is contributes to an increase in the annual change in unfunded ratios, worsening the level of pension debt.  e negative impact of project unit credit and the GASB 67 raise two key points for offi cials in public pension management. First, they need to avoid using the project unit credit and instead adopt the entry age normal, which is a more robust accounting method for calculating public pension costs at the beginning of public employment. Second, public offi cials need to apply more a realistic investment assumption rate to their pension assets.  e fact that using the GASB 67, which is more honest than the previous unconstrained investment assumption rate used by states, revealed a darker picture of public pension debt and should inform public offi cials of the need to adopt honest accounting methods and practices. Finally, public union membership was also positively associated with the change in unfunded ratios, albeit at a weak, ten percent level.  is indicates that increased public union membership increases the annual change in public pension debt.  is highlights the need for public offi cials to work with public unions to fi nd ways of dealing with public pension debt.  e results imply that the annual change in unfunded ratios is heavily infl uenced by dimensions of public pension management such as investment returns and ac- counting practices. Except for public unions (via public union membership), the lack of signifi cance of other variables shows that fi scal constraints, state politics (except for public unions), and fi scal conditions do not play an important role in controlling the public pension debt aff ecting U.S. states. Although media outlets have decried political meddling with public pension management in the current pension crises, these political factors did not have any impact when combined with public pension management variables in the model. 5. Implications for public offi cials  is study sheds some light on factors aff ecting the annual change in unfunded public pension liabilities.  e results show that dimensions of public pension man- agement – such as investment returns, accounting methods (project unit credit), and accounting changes (GASB 67) – heavily infl uence public pension debt.  e results have important implications for the public offi cials responsible for managing public pension plans, who therefore need to adopt sound policy options. First, the results suggest that public offi cials need to work hard to boost their invest- ment returns by scrutinizing their investment portfolios. One way to accomplish this 66 is to diversify their pension asset investment strategies. In recent years many plans have relied too heavily on alternative investment methods, which entail paying heavy fees to investment fi rms. Moreover, this information is o en hidden from the watch- ful eyes of the public ( e Pew Charitable Trusts, 2015), eroding the accountability of public pension plans, especially in the eyes of current and retired public employees. If states invest too heavily in risky projects that produce few investment returns, they need to shi their assets to more stable choices such as index funds that correspond to stock market performance and those producing a stable, fi xed income such as a trea- sury bond. Similarly, public offi cials need to adopt ways to regularly monitor public pension asset management. If investment returns do not achieve the yields expected in terms of previously-es- tablished standards, stakeholders in public pension management need to determine what did not work and devise alternative strategies to improve pension performance. Naturally, there has to be a formal rule specifying this type of activity. Comparing the investment performance of pension plans with that of other states will also be benefi cial for public offi cials aiming to improve pension asset performance. Enacting legislation that covers all these aspects will help bind public offi cials to be er forms of public pension management. Second, as Monahan (2015) argued, public offi cials need to adopt several enforce- ment mechanisms to ensure sound public pension management.  is means that major decision makers in the public sector need to adopt state-of-the-art accounting management standards and practices.  ere should also be enforcement mechanisms to prevent public offi cials from utilizing accounting gimmicks for their own political gain. For instance, a practice such as the entry age normal method would ensure that states face larger pension liabilities at the beginning of public employment than would be the case for the project unit method.  is would help states deal with such liabili- ties and enable them to build large pension assets for the investment of assets. More importantly, by clarifying the true state of their unfunded pension liabilities, sound accounting methods and practices will help states confront where they actually are in terms of numbers and what needs to be done. Adopting an accounting change such as the GASB 67 is also benefi cial for public offi cials; although it generates worse numbers than when states were free to adopt their investment assumption rate, confronting this darker picture will eventually help public offi cials to prepare necessary strategies for dealing with public pension debt. By forcing public offi cials to use honest num- bers, optimal accounting methods and practices will provide opportunities for public offi cials to alleviate public pension debt. Using a market rate for investment returns rather than the blended rate suggested by the GASB 67 is one option that can be used to reveal the true condition of the public pension debt and uncover the harsh reality for each state. Legislating enforcement mechanisms that force states to adopt best accounting methods and practices will go a long way in enabling states to identify problems and formulate solutions. 67 Although this study has several merits, it also has certain limitations. For in- stance, the model includes a mixture of state-level and individual-level variables. Second, aggregation of the dependent variable at state level means the model might not have explored or accounted for some potentially crucial individual-level vari- ables. Nevertheless, the study minimizes the potential threat of omi ed variable bias by accounting for a comprehensive array of variables. Furthermore, examining the impact of factors on pension debt at the aggregate state level yields valuable mac- ro-level insights that can inform both policymakers and the public regarding the steps that need to be taken. References: 1. Alesina, A. and Bayoumi, T., ‘ e Costs and Benefi ts of Fiscal Rules: Evidence from U.S. States’, NBER Working Paper 5614, June 1996, [Online] available at h ps://www.nber.org/ papers/w5614.pdf, accessed on August 15, 2018. 2. Alt, J.E. and Lowry, R.C., ‘Divided Government, Fiscal Institutions, and Budget Defi cits: Evidence from the States’, 1994, American Political Science Review, vol. 88, no. 4, pp. 811-828. 3. Andonov, A., Hochberg, Y.V. and Rauh, J.D., ‘Political Representation and Governance: Evidence from the Investment Decisions of Public Pension Funds’, 2018,  e Journal of Finance, vol. 73, no. 3, pp. 2041-2086. 4. Anzia, S.F. and Moe, T.M., ‘Polarization and Policy:  e Politics of Public-sector Pensions’, 2017, Legislative Studies  arterly, vol. 42, no. 1, pp. 33-62. 5. Aubrey, J. and Crawford, C.V., ‘State and Local Pension Reform since the Financial Crisis. State and Local Pension Plans’, Center for Retirement Research at Boston College, no. 54, January 2017, [Online] available at h p://crr.bc.edu/wp-content/uploads/2016/12/slp_54. pdf, accessed on August 14, 2019. 6. Blais, A., Blake, D. and Dion, S., ‘Do Parties Make a Diff erence? Parties and the Size of Government in Liberal Democracies’, 1993, American Journal of Political Science, vol. 37, no. 1, pp. 40-62. 7. Bohn, H., ‘Should Public Retirement Plans Be Fully Funded?’, 2011, Journal of Pension Eco- nomics and Finance, vol. 10, no. 2, pp. 195-219. 8. Brown, J.R. and Wilcox, D.W., ‘Discounting State and Local Pension Liabilities’, 2009, American Economic Review, vol. 99, no. 2, pp. 538-542. 9. Brunori, D., Local Tax Policy: A Federalist Perspective, 2nd edition, Washington, D.C.: Urban Institute Press, 2007. 10. Brunori, D., State Tax Policy: A Political Perspective, 3rd edition, Washington, D.C.: Urban Institute Press, 2011. 11. Budge, I. and Hoff erbert, R.I., ‘Mandates and Policy Outputs: U.S. Party Platforms and Fed- eral Expenditures’, 1990, American Political Science Review, vol. 84, no. 1, pp. 111-133. 12. Bureau of Labor Statistics, ‘Local Area Unemployment Statistics’, 2017, [Online] available at h p://www.bls.gov/lau/, accessed on August 25, 2018. 13. Chaney, B.A., Copley, P.A. and Stone, M.S., ‘ e Eff ect of Fiscal Stress and Balanced Budget Requirements on the Funding and Measurement of State Pension Obligations’, 2002, Jour- nal of Accounting and Public Policy, vol. 21, no. 4-5, pp. 287-313. 68 14. Chen, G. and Matkin, D.S., ‘Actuarial Inputs and the Valuation of Public Pension Liabil- ities and Contribution Requirements: A Simulation Approach’, 2017, Public Budgeting & Finance, vol. 37, no. 1, pp. 68-87. 15. Chen, G., ‘Understanding Decisions in State Pension Systems: A System Framework’, 2018, American Review of Public Administration, vol. 48, no. 3, pp. 260-273. 16. Clair, T., ‘ e Eff ect of Tax and Expenditure Limitations on Revenue Volatility: Evidence from Colorado’, 2012, Public Budgeting and Finance, vol. 32, no. 3, pp. 61-78. 17. Coggburn, J.D. and Kearney, R.C., ‘Trouble Keeping Promises? An Analysis of Underfund- ing in State Retiree Benefi ts’, 2010, Public Administration Review, vol. 70, no. 1, pp. 97-108. 18. Denison, D., Hackbart, M. and Moody, M., ‘State Debt Limits: How Many Are Enough?’, 2006, Public Budgeting and Finance, vol. 26, no. 4, pp. 22-39. 19. Dove, J.A., Collins, C.A. and Smith, D.J., ‘ e Impact of Public Pension Board of Trustee Composition on State Bond Ratings’, 2018, Economics of Governance, vol. 19, no. 1, pp. 51-73. 20. Erie, S.P., Kogan, V. and MacKenzie, S.A., Paradise Plundered: Fiscal Crisis and Governance Failures in San Diego, Stanford, CA: Stanford University Press, 2011. 21. Farmer, L. and Maciag, M., ‘Why Some Public Pensions Could Soon Look Much Worse’, Governing, March 17, 2015, [Online] available at h p://www.governing.com/topics/mgmt/ gov-gasb-pension-plans-may-look-worse-soon.html, accessed on September 11, 2015. 22. Fiscal Survey of States, National Association of State Budget Offi cers, 2016, [Online] avail- able at h p://www.nasbo.org/publications-data/fi scal-survey-of-the-states/archives, ac- cessed on September 4, 2018. 23. Fitzpatrick, T.J. and Monahan, A.B., ‘Who’s Afraid of Good Governance? State Fiscal Cri- ses, Public Pension Underfunding, and the Resistance to Governance Reform’, 2015, Florida Law Review, vol. 66, no. 3, pp. 1317-1371. 24. Glaeser, E.L. and Ponze o, G.A.M., ‘Shrouded Costs of Government:  e Political Economy of State and Local Public Pensions’, 2014, Journal of Public Economics, vol. 116, pp. 89-105. 25. Goldhaber, D., Grout, C. and Holden, K.L., ‘Pension Structure and Employee Turnover’, 2017, ILR Review, vol. 70, no. 4, pp. 976-1007. 26. Greenhut, S., Plunder!: How Public Employee Unions Are Raiding Treasuries Controlling Our Lives and Bankrupting the Nation, Santa Ana, CA: Forum Press, 2009. 27. Hess, D., ‘Protecting and Politicizing Public Pension Fund Assets: Empirical Evidence on the Eff ects of Governance Structures and Practices’, 2005, U.C. Davis Law Review, vol. 39, pp. 187-227. 28. Hirsch, B.T. and MacPherson, D.A., ‘Union Membership and Coverage Database from the Current Population Survey: Note’, 2003, ILR Review, vol. 56, no. 2, pp. 349-354. 29. Kallen, C., ‘State Tax and Expenditure Limitation and Supermajority Requirement: New and Updated Data’, AEI Economics Working Paper 2017-19, 2017, [Online] available at h p://www.aei.org/publication/state-tax-and-expenditure-limitations-and-supermajori ty-requirements-new-and-updated-data/, accessed on July 31, 2018. 30. Kelley, D.G., ‘ e Political Economy of Unfunded Public Pension Liabilities’, 2014, Public Choice, vol. 158, no. 1/2, pp. 21-38. 31. Kiewiet, D.R., ‘ e Day a er Tomorrow:  e Politics of Public Employee Benefi ts’, 2010, California Journal of Politics and Policy, vol. 2, no. 3, pp. 1-30. 32. Kmenta, J., Elements of Econometrics, 2nd edition, New York: Macmillan, 1986. 69 33. Liang, K.Y. and Zeger, S.L., ‘Longitudinal Data Analysis Using Generalized Linear Models’, 1986, Biometrika, vol. 73, no. 1, pp. 13-22. 34. Lowenstein, R., While America Aged: How Pension Debts Ruined General Motors, Stopped the NYC Subways, Bankrupted San Diego, and Loom as the Next Financial Crisis, New York:  e Penguin Press, 2008. 35. Lucas, D.J. and Zeldes, S.P., ‘How Should Public Pension Plans Invest’, 2009,  e American Economic Review, vol. 99, no. 2, pp. 527-532. 36. Malanga, S., ‘ e Beholden State: How Public-sector Unions Broke California’, City Jour- nal. Spring 2010, [Online] available at h p://city-journal.org/2010/20_2_california-unions. html, accessed on March 21, 2014. 37. Marschall, M.J. and Ruhil, A.V.S., ‘Fiscal Eff ects of the Voter Initiative Reconsidered: Ad- dressing Endogeneity’, 2005, State Politics and Policy  arterly, vol. 5, no. 4, pp. 327-355. 38. Martin, I.W.,  e Permanent Tax Revolt: How the Property Tax Transformed American Poli- tics, Stanford, CA: Stanford University Press, 2008. 39. Mason, K.C., ‘Panel’s Report: Tax Limitations Jeopardize State’s Economic Future’, 2005, State Tax Notes, vol. 37, pp. 487-488. 40. Mathews, J. and Paul, M., California Crackup: How Reform Broke the Golden State and How We Can Fix It, Berkeley, CA: University of California Press, 2010. 41. Matsusaka, J.G., ‘Fiscal Eff ects of the Voter Initiative: Evidence from the Last 30 Years’, 1995,  e Journal of Political Economy, vol. 103, no. 3, pp. 587-623. 42. McGuire, T.J. and Rueben, K.S., ‘ e Colorado Revenue Limit:  e Economic Eff ects of TA- BOR’, 2006, State Tax Notes, May 8, p. 459. 43. Mitchell, O.S. and Smith, R.S., ‘Pension Funding in the Public Sector’, 1994, Review of Eco- nomics and Statistics, vol. 76, no. 2, pp. 278-290. 44. Monahan, A.B., ‘Public Pension Plan Reform:  e Legal Framework’, 2010, Education Fi- nance and Policy, vol. 5, no. 4, pp. 617-646. 45. Monahan, A.B., ‘State Fiscal Constitutions and the Law and Politics of Public Pensions’, 2015, University of Illinois Law Review, vol. 4, pp. 117-176. 46. Monahan, A.B., ‘Statutes as Contracts?  e ‘California Rule’ and Its Impact on Public Pen- sion Reform’, 2012, Iowa Law Review, vol. 97, no. 4, pp. 1029-1083. 47. Monahan, A.B., ‘When a Promise Is Not a Promise: Chicago-style Pensions’, 2017, UCLA Law Review, vol. 64, pp. 356-413. 48. Mullins, D.R. and Joyce, P.G., ‘Tax and Expenditure Limitations and State and Local Fiscal Structure: An Empirical Assessment’, 1996, Public Budgeting and Finance, vol. 16, no. 1, pp. 75-101. 49. Munnell A.H, Aubry, J.P., Hurwitz, J. and  inby, L., ‘Union and Public Pension Bene- fi ts’, State and Local Pension Plans, Center for Retirement Research at Boston College, July 2011b, [Online] available at h p://crr.bc.edu/wp-content/uploads/2011/07/slp_19-508.pdf, accessed on September 5, 2013. 50. Munnell, A.H., Aubry, J.P. and  inby, L., ‘Public Pension Funding in Practice’, 2011a, Journal of Pension Economics and Finance, vol. 10, no. 2, pp. 247-268. 51. Munnell, A.H., Haverstick, K. and Mauricio, S., ‘Why Have Defi ned Benefi t Plans Survived in the Public Sector?’, Center for Retirement Research at Boston College, December 2007, [Online] available at h p://crr.bc.edu/briefs/why-have-defi ned-benefi t-plans-survived-in- the-public-sector/, accessed on May 5, 2015. 70 52. Munnell, A.H., State and Local Pensions: What Now?, Washington, D.C.: Brookings Institu- tion Press, 2012. 53. National Council of State Legislature, ‘State Partisan Composition’, 2015, [Online] avail- able at h p://www.ncsl.org/research/about-state-legislatures/partisan-composition.aspx, accessed on August 13, 2018. 54. Novy-Marx, R. and Rauh, J., ‘Public Pension Promises: How Big Are  ey and What Are  ey Worth?’, 2011, Journal of Finance, vol. 66, no. 4, pp. 1211-1249. 55. Novy-Marx, R. and Rauh, J.D., ‘ e Liabilities and Risks of State-sponsored Pension Plans’, 2009, Journal of Economic Perspectives, vol. 23, no. 4, pp. 191-210. 56. Pennacchi, G. and Rastad, M., ‘Portfolio Allocation for Public Pension Funds’, 2011, Journal of Public Economics and Finance, vol. 10, no. 2, pp. 221-245. 57. Pollack, S.D., Refi nancing America:  e Republican Antitax Agenda, Albany, N.Y.: State Uni- versity of New York Press, 2003. 58. Poterba, J.M., ‘Budget Institutions and Fiscal Policy in the U.S. States’, 1996, American Eco- nomic Review, vol. 86, no. 2, pp. 395-400. 59. Poterba, J.M., ‘State Responses to Fiscal Crises:  e Eff ects of Budgetary Institutions and Politics’, 1994, Journal of Political Economy, vol. 102, no. 4, pp. 799-821. 60. Public Plans Data, 2001-2016, Center for Retirement Research at Boston College, Center for State and Local Government Excellence, and National Association of State Retirement Ad- ministrators, [Online] available at h ps://crr.bc.edu/data/public-plans-database/, accessed on September 10, 2018. 61. Raman, K.K. and Wilson, E.R., ‘ e Debt Equivalence of Unfunded Government Pension Obligations’, 1990, Journal of Accounting and Public Policy, vol. 9, no. 1, pp. 37-56. 62. Rose, S., ‘Do Fiscal Rules Dampen the Political Business Cycle?’, 2006, Public Choice, vol. 128, no. 3-4, pp. 407-431. 63. Schieber, S.J., ‘Political Economy of Public Sector Retirement Plans’, 2011, Journal of Pen- sion Economics and Finance, vol. 10, no. 2, pp. 269-290. 64. Schneider, M. and Damanpour, F., ‘Public Choice Economics and Public Pension Plan Funding’, 2002, Administration and Society, vol. 34, no. 1, pp. 57-86. 65. Shadbegian, R.J., ‘ e Eff ect of Tax and Expenditure Limitations on the Revenue Structure of Local Government, 1962-87’, 1999, National Tax Journal, vol. 52, no. 2, pp. 221-238. 66.  e Economist, ‘State Pensions in America: Ruinous Promises’, June 15, 2013, [Online] available at h p://www.economist.com/news/leaders/21579463-states-cannot-pretend-be- good-fi nancial-health-unless-they-tackle-pensions-ruinous-promises, accessed on June 17, 2013. 67.  e Pew Charitable Trusts, ‘ e State Pension Funding Gap: 2016’, April 2018, [Online] available at h ps://www.pewtrusts.org/en/research-and-analysis/issue-briefs/2018/04/the- state-pension-funding-gap-2016, accessed on August 3, 2018. 68.  e Pew Charitable Trusts, ‘ e State Pensions Funding Gap: Challenges Persist’, July 2015, [Online] available at h p://www.pewtrusts.org/en/research-and-analysis/issue-bri efs/2015/07/the-state-pensions-funding-gap-challenges-persist, accessed on November 3, 2016. 69.  om, M., ‘All of the Above: How Fiscal, Political, and Workforce Traits Aff ect Pension Funding’, 2013b, State and Local Government Review, vol. 45, no. 3, pp. 163-171. 71 70.  om, M., ‘Politics, Fiscal Necessity, or Both? Factors Driving the Enactment of Defi ned Contribution Accounts for Public Employees’, 2013a, Public Administration Review, vol. 73, no. 3, pp. 480-489. 71. U.S. Census Bureau, ‘2015 Census of Governments. Surveys of State and Local Govern- ment Finances’, 2016, [Online] available at h p://www.census.gov//govs/local/, accessed on August 15, 2018. 72. Waring, M.B., Pension Finance: Pu ing the Risks and Costs of Defi ned Benefi t Plans Back un- der Your Control, Hoboken, N.J.: Wiley & Sons, 2012. 73. Wilson, E.R. and Howard, T.P., ‘ e Association between Municipal Market Measures and Selected Financial Reporting Practices: Additional Evidence’, 1984, Journal of Accounting Research, vol. 22, no. 1, pp. 207-224. 74. Wilson, E.R., ‘Fiscal Performance and Municipal Bond Borrowing Costs’, 1983, Public Bud- geting and Finance, vol. 3, no. 4, pp. 28-41. 75. Zeger, S.L. and Liang, K.Y., ‘Longitudinal Data Analysis for Discrete and Continuous Out- comes’, 1986, Biometric Society, vol. 42, no. 1, pp. 121-130. 76. Zorn, C., ‘Generalized Estimating Equation Models for Correlated Data: A Review with Applications’, 2001, American Journal of Political Science, vol. 45, no. 2, pp. 470-490.