Skip to main content
Log in

Another look at the determinants of the financial condition of state pension plans

  • Published:
Journal of Economics and Finance Aims and scope Submit manuscript


Given the financial troubles facing state pension plans in recent years, we examine determinants of the ratio of assets to liabilities, or the funded ratio, based on data for 153 pension plans from 2001 to 2014. The focus is on the relationship between both the actual investment return on pension assets and the assumed return used to discount pension liabilities, or the funded ratio. Importantly, only when appropriate empirical techniques are employed to address potential econometric problems do we find that these two factors have the expected relationship with the funded ratio. Surprisingly, we also find the actual and assumed returns are negatively correlated, even though the correlation is quite low. Furthermore, the assumed return is on average higher than the actual return and has a much larger marginal effect on the funded ratio. We therefore show how a relatively high value can be assigned to the assumed return to make a pension plan appear to far healthier than actually is the case.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others


  1. The Pension Benefit Guaranty Corporation, which itself is facing financial difficulties, is a federal agency created by the Employee Retirement Income Security Act of 1974 (ERISA) to protect pension benefits in private-sector defined benefit plans. For more information, go to

  2. A few states have hybrid plans that combine features of both defined benefit plans and defined contribution plans. For information on such states, see Barth and Jahera (2015).

  3. See

  4. According to Cebula (2014), quantitative easing has not only reduced cash flows for pensions but also placed them under greater interest rate risk.

  5. Private Pension Plan Bulletin Historical Tables and Graphs, U.S. Department of Labor, 1975–2014.

  6. 2014 Survey of Public Pension: State & Local Data, the U.S. Census Bureau.

  7. Financial Accounts of the United States, Federal Reserve Board, March 12, 2015.

  8. Financial Accounts of the United States, Federal Reserve Board, March 12, 2015.

  9. See American Academy of Actuaries (July 2012). In addition, it is stated “A plan’s actuarial funding method should have a built-in mechanism for moving the plan to the target of 100% funding” (p. 2).

  10. In the published version of their paper, Munnell et al. (2011) do not include ARC, but do include it an earlier working paper. We also include ARC in our empirical model. See Table 2 for the ARC by state for year 2014.

  11. The null hypothesis of the Sargan’s and Hansen’s tests is that specified orthogonality conditions of the instrument set are satisfied. If we reject the null hypothesis of the Sagan’s or Hansen’s tests, we should strongly doubt the validity of the estimates.

  12. A fixed effects model may suffer from a finite sample bias (see Nickell 1981) because we include a lagged endogenous variable in the equation. We therefore used a GMM estimator and conducted the standard diagnosis statistics (e.g., second order autocorrelation test AR (2)), which did not indicate any issue on the validity of the instrumentation at the 5% significant level.


  • Altonji JG, Segal LM (1996) Small-sample bias in GMM estimation of covariance structures. J Bus Econ Stat 14:353–366

    Google Scholar 

  • American Academy of Actuaries (2012) The 80% pension funding standard myth. Issue Brief July

  • Andersen TG, Sørensen BE (1996) GMM estimation of a stochastic volatility model: a Monte Carlo study. J Bus Econ Stat 14:328–352

    Google Scholar 

  • Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58:277–279

    Article  Google Scholar 

  • Arellano M, Bover O (1995) Another look at the instrumental variable estimation of error-components models. J Econ 68:29–51

    Article  Google Scholar 

  • Bagchi S (2016) The effects of political competition on the funding and generosity of public-sector pension plans. Available at SSRN:

  • Barth JR, Jahera JS Jr (2015) Alabama’s public pensions: building a stable financial foundation for the years ahead. Research report for the Alabama policy institute, Birmingham

    Google Scholar 

  • Beck N, Katz JN (1995) What to do (and not to do) with time-series cross-section data. Am Polit Sci Rev 89:634–647

    Article  Google Scholar 

  • Blundell R, Bond S (1998) Initial conditions and moment restrictions in dynamic panel data models. J Econ 87:115–143

    Article  Google Scholar 

  • Bond S, Meghir C (1994) Dynamic investment models and the firm's financial policy. Rev Econ Stud 61:197–222

    Article  Google Scholar 

  • Bound J, Jaeger DA, Baker RM (1995) Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. J Am Stat Assoc 90:443–450

    Google Scholar 

  • Bowsher CG (2002) On testing overidentifying restrictions in dynamic panel data models. Econ Lett 77:211–220

    Article  Google Scholar 

  • Cebula RJ (2014) Preliminary evidence on the impact of budget deficits on the nominal interest rates yield on ten-year U.S. Treasury notes after allowing for quantitative easing. Economia Internazionale 67:181–200

    Google Scholar 

  • Chaney BA, Copley PA, Stone MS (2003) The effect of fiscal stress and balanced budget requirements on the funding and measurement of state pension obligations. J Account Public Policy 21:287–313

    Article  Google Scholar 

  • Eaton TV, Nofsinger JR (2008) Funding levels and gender in public pension plans. Public Budg Finance 28:108–128

    Article  Google Scholar 

  • Elder EM, Wagner GA (2015) Political effects on pension underfunding. Econ Polit 27:1–27

    Article  Google Scholar 

  • Groves C (2014) Public retirement systems: an examination of governance characteristics and their impact on the funded ratio. MPA/MPP capstone projects, Paper 5, University of Kentucky. Available at

  • Hahn J, Kuersteiner G (2002) Asymptotically unbiased inference for a dynamic panel model with fixed effects when both n and T are large. Econometrica 70:1639–1657

    Article  Google Scholar 

  • Hansen LP (1982) Large sample properties of generalized method of moments estimators. Econometrica 50:1029–1054

    Article  Google Scholar 

  • Kelly DG (2014) The political economy of unfunded public pension liabilities. Public Choice 158:21–38

    Article  Google Scholar 

  • Mohan N, Zhang T (2014) An analysis of risk-taking behavior for public defined benefit pension plans. J Bank Financ 40:403–419

    Article  Google Scholar 

  • Munnell AH, Aubry JP, Quinby L (2011) Public pension funding in practice. J Pension Econ Financ 10:247–268

    Article  Google Scholar 

  • Nickell SJ (1981) Biases in dynamic models with fixed effects. Econometrica 49:1417–1426

    Article  Google Scholar 

  • Podestà F (2002) Recent developments in quantitative comparative methodology: the case of pooled time series cross-section analysis. DSS Papers Soc 3:5–44

    Google Scholar 

  • Roodman D (2009) A note on the theme of too many instruments. Oxf Bull Econ Stat 71:135–158

    Article  Google Scholar 

  • Sargan JD (1958) The estimation of economic relationships using instrumental variables. Econometrica 26:393–415

    Article  Google Scholar 

  • Seligman JS (2013) State pension funding practices and the great recession of 2007-2009. J Public Budg, Account Financ Manag 25:346

    Article  Google Scholar 

  • Staiger DO, Stock JH (1997) Instrumental variables regression with weak instruments. Econometrica 65:557–586

    Article  Google Scholar 

  • Wang Q, Peng J (2016) An empirical analysis of state and local public pension plan funded ratio change, 2001-2009. Am Rev Public Adm 46:75–91

    Article  Google Scholar 

  • Yang T, Mitchell OS (2008) Public sector pension governance, funding and performance: a longitudinal appraisal, Chapter 8. In: Evans J, Orszag M, Piggott J (eds) Pension Fund Governance: A Global Perspective on Financial Regulation. Edward Elgar Publishing, Cheltenham

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to James R. Barth.



Table 9 Variance inflation factor (VIF)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barth, J.R., Joo, S. & Lee, K.B. Another look at the determinants of the financial condition of state pension plans. J Econ Finan 42, 421–450 (2018).

Download citation

  • Published:

  • Issue Date:

  • DOI:


JEL Classification