Impact of Pension Accounting Rule Change on UK Pension Plan Terminations Paul Klumpes, Imperial...

Impact of Pension Accounting Rule Change on UK Pension

Plan TerminationsPaul Klumpes, Imperial CollegeLiyan Tang, University of Stirling

Mark Whittington, Aberdeen University

ARIA Quebec, Aug 7 2

Motivation

Recent demise of employer-sponsored defined benefit pension arrangements in the UK

FRS17 controversy- real or scapegoat? UK firms’ response to uncertainty over pension

accounting and funding regulatory environment Actuarial switching-does it bear on subsequent

terminations? Pension plans terminations- competing

theoretical explanations?


Objectives Examine determinants of plan terminations in

an economic environment of under-funding, where pension surplus reversion does not apply

Investigate competing hypotheses from finance, insurance and labour economic literature on managerial termination decisions

Examine whether terminations are inter-related with firms’ prior accounting policy choice (actuarial valuation method switch)


Outline

Background Literature review Competing hypotheses for termination Sample and data Empirical results Conclusion and further research


Background- Pension Terminations Legality of pension funds as equitable trusts –

‘property rights’ or trustee responsibility? US evidence:

Early 1980s – excess surplus motivates takeovers UK evidence:

Pre-1998 – firms take contribution ‘holidays’1999- equity crash; many funds in deficitPost FRS 17 – many terminations: why?


Background (cont.)-Recent Demise of UK DB Plans

84 ‘Final Salary’ Schemes were closed in 2002, increased by 50 percent from 2001

Creation of the Pension Protection Board Over 30% of private employer schemes are now

closed to new entrants, compared with 17% in 2001

Employer contributions into replacement ‘money purchase’ schemes are only at half of the level of final salary schemes So not just a risk transfer but a cost reduction exercise

-source: NAPF( National Association of Pension Funds) 2002 Annual Survey


Prior Research (US) Early 1980s (excess surpluses, managerial

discretion over spreading of pension costs): Put option voluntary termination to PBGC (Marcus, 1985) Studies identified association between corporate financial

characteristics and managerial decision to terminate over-funded pension plans (Thomas,1989; Mittelstaedt,1989; Stone,1987;Hamdallah and Ruland, 1986)

Other studies documented managerial actuarial switching decisions could be motivated by corporate financial characteristics (Ghicas, 1990; Godwin et al, 1996)


Prior Research (UK)

Klumpes and Whittington (2003) FRS 17: switch to actuarial fair valuation

method Examine corporate vs pension related

determinants of UK actuarial switching results support pension plan characteristics

driving switching decision hence supporting the separation hypothesis


Competing Ownership Hypotheses on Terminations

Integration hypothesis from corporate finance literature (Sharpe,1977;Treynor,1977) Pension fund entirely belong to sponsoring firm

Separation hypothesis from labour economics literature (Ippolito,1985;Cooper and Ross, 2002) Pension fund separate from sponsoring firm

Risk Management hypothesis Actuarial switching is inter-related with terminations


Research Hypotheses a: Integration Perspective

1) Firms with pension deficits are more likely to terminate 2) Firms with lower debt covenant slack are more likely to

terminate b: Risk Management Perspective

3) ERR switch firms are less likely to terminate than their non-switch firms

b: Separation Perspective (Null Hypothesis) Terminations are determined by pension fund characteristics


Sample and Data

Sample: 80 industry matched pair firms (KW)

Data: Accounting, actuarial data related to sponsoring firm and pension fund Integration hypothesis H1,H2:

LEV, SFUND, PRET, FFUND (RUNI control)Risk Management hypothesis H3:

SWITCH, PUT


Table 2

Mean and Median Attributes of Sub-Samples

Panel A Panel B

Switch Firms (n=40)

Non-Switch Firms (n=40)

Curtailers (n=27)

Maintainers (n=53)

Pension and Financial Variable a

Mean Median Mean Median Mean Median Mean Median

PRET 0.43 0.43 0.37 0.32 0.42 0.43 0.44 0.43 (0.10)b (0.08)c (0.70)d (0.63)e FFUND 0.78 0.52 1.45 0.67 0.65 0.58 0.99 0.77 (0.16) (0.46) (0.06) (0.32) SFUND 1.17 1.10 1.06 1.10 1.09 1.08 1.17 1.14 (0.13) (0.40) (0.02) (0.01) LEV 0.46 0.31 0.38 0.30 0.55 0.33 0.63 0.38 (0.53) (0.73) (0.66) (0.77) RUNI 0.42 0.30 0.35 0.38 0.04 0.02 0.06 0.01 (0.23) (0.43) (0.68) (0.53) EXP 0.04 0.02 0.03 0.02 0.05 0.02 0.03 0.02 (0.24) (0.38) (0.25) (0.36) SPD 2.24 2.50 2.95 3.00 2.61 2.50 2.55 2.50 (0.00) (0.00) (0.78) (0.38) SWITCH 37% 57% (0.05) a Details regarding variable definitions see below. All variables for switch sample are measured three years prior to switching to marked-to-market based ERR assumptions (year -3). All variables for curtailing sample are measured three years prior to curtailment (year -3). bValues reported in parentheses below the means for ERR-updating samples are p-values from two-tailed paired t-tests of the null hypothesis that the mean for that sample equals the mean for the industry-matched control sample. cValues reported in parenthesis below the medians for ERR-updating samples are p-values from two-tailed Wilcoxon sign rank tests of the null hypothesis that the median for that sample equals the median for the industry-matched control sample. d,e Values reported in parenthesis below the mean (median)s for curtailing firms are p-values from one-tailed two sample t-tests (rank sum tests) for the hypothesized directions of mean (median) difference to maintaining firms. For SWITCH the p-value are based on chi-square


Findings

Pension asset / pension liability and leverage ratios. Terminating firms have lower pension funding

and higher leverageFirms that frequently adjust their ERR are less

likely to terminateThe put option is just significant


TABLE 3

Comparison of Curtailers and Maintainers (across switch and non-switch samples)

Switch Sample

(n=40) Non-Switch Sample

(n=40) Financial and

Pension Variablesa

Rel. Yearb

10 Curtailers Mean

30 Maintainers Mean

Mann-Whitney One-tail Test

p-value 17 Curtailers

Mean 23 Maintainers

Mean Mann-Whitney One-tail Test

p-value

PRET -6 0.45 0.42 0.68 0.39 0.35 0.31 -3 0.46 0.46 0.95 0.39 0.40 0.48 0 0.51 0.53 0.80 0.45 0.39 0.50 FFUND -6 0.67 0.49 0.29 1.80 1.19 0.22 -3 0.53 0.58 0.90 1.19 1.20 0.27 0 1.02 0.96 0.73 3.96 2.51 0.63 SFUND -6 1.05 1.10 0.20 0.94 1.15 0.40 -3 1.02 1.14 0.01 1.12 1.15 0.25 0 1.05 1.15 0.02 1.09 1.12 0.64 LEV -6 0.57 0.42 0.20 0.34 0.42 0.63 -3 0.84 0.78 0.60 0.37 0.44 0.58 0 0.44 0.61 0.90 0.42 0.62 0.37 RUNI -6 0.32 0.30 0.53 0.30 0.38 0.49 -3 0.02 0.01 0.05 0.02 0.06 0.27 0 0.07 0.03 0.03 0.05 0.04 0.53 EXP -6 0.06 0.03 0.18 0.02 0.03 0.59 -3 0.09 0.03 0.04 0.02 0.04 0.54 0 0.12 0.03 0.03 0.03 0.06 0.33 SPD -6 2.46 2.16 0.04 3.18 2.78 0.10 -3 2.56 2.35 0.23 2.64 2.81 0.70 0 2.20 2.32 0.98 2.43 2.20 0.21 a Median valued of explanatory variables is reported for each sub-sample. Variable definitions refer to Table 2. b Rel. year is the year relative to year 0, which is the year of curtailments for the curtailing firms. All p-values reported are based on one-tailed rank sum (Mann-Whitney) tests for the hypothesized directions of median differences between curtailers and maintainers.


TABLE 4

Binary logit analysis of pension liability curtailmentsa

Panel A Panel B Panel C

All Sample (n=80)

Switch Sample (n=40)

Non-switch Sample (n=40) Financial

and Pension Variable

Exp. Signs

Coefficient t-statistic p-value Coefficient t-statistic p-value Coefficient t-statistic p-value

SWITCH 1.474 -2.42 0.01

PRET - /- /- 2.460 -1.32 0.18 1.974 -0.43 0.67 4.739 -1.17 0.24

FFUND - /- /- 2.256 -3.14 0.00 3.637 -1.14 0.25 2.291 -2.65 0.00

SFUND - /- /- 6.519 -2.35 0.01 13.616 -2.06 0.03 6.719 -1.84 0.06

LEV + /+ /+ 0.332 0.91 0.36 0.186 0.33 0.74 1.322 0.89 0.37

RUNI + /+ /? 0.742 0.35 0.72 3.858 1.37 0.17 11.331 -1.58 0.11

EXP + /+/ ? 9.140 1.78 0.07 14.986 1.69 0.09 23.949 -1.00 0.31 SPD - /? /- 0.083 -0.24 0.81 0.208 0.39 0.69 0.586 -1.33 0.18 Intercept 9.775 2.59 0.00 15.479 1.51 0.13 13.123 2.05 0.04 Model Chi-square 18.67 0.01 Chi-square 8.89 0.26 Chi-square 7.28 0.40 a The dependent variable is the pension liability curtailment decision (1 for curtailers and 0 for maintainers). All explanatory variables are measured at year -3 relative to year 0 (year of curtailment). Detailed variable definitions see Table 2. All t-statistics are White t-statistics based on heteroskedastic-consistent standard errors.


Sensitivity analysis

Effect of change in rules on reported funding ratio (table 5)

Incorporating effects of firms withdrawing from the stock market (table 6)

Effect of termination on switching behavior of surviving firms (table 7)


Table 5

Sensitivity Tests for Change in Funding Ratio

Panel A: Switch firms (n=40) 10 Curtailers 30 Maintainers Mean Two Sample t-test

p-value Mean Two Sample t-test

p-value SSAP 24 funding ratio 1.052 0.01 1.127 0.01 FRS 17 funding ratio 0.979 0.01 1.052 0.01 Change in funding ratio (SSAP 24 – FRS 17)

0.139 0.01 0.071 0.01

Panel B: Non-switch firms(n=40) 17 Curtailers 23 Maintainers Mean Two Sample t-test


p-value SSAP 24 funding ratio 1.024 n.s. 1.139 n.s. FRS 17 funding ratio 0.941 n.s. 1.064 n.s. Change in funding ratio (SSAP 24 – FRS 17)

0.082 n.s. 0.062 n.s.

Panel C: All sample firms (n=80) 27 Curtailers 53 Maintainers Mean Two Sample t-test


p-value SSAP 24 funding ratio 1.047 0.02 1.132 0.02 FRS 17 funding ratio 0.958 0.02 1.059 0.02 Change in funding ratio (SSAP 24 – FRS 17)

0.069 0.02 0.048 0.02

Notes: Mean SSAP 24 funding ratio is defined as the ratio of the actuarial value of pension assets to the actuarial value of pension liabilities, discounted at an equity-linked discount rate. Mean FRS 17 funding ratio is defined as the ratio of the market value of pension assets to the value of pension liabilities using an AA corporate bond discount rate. Mean change in funding ratio is the difference between the mean SSAP 24 funding ratio and the mean FRS 17 funding ratio. n.s. means ‘not significant’.


TABLE 6 Multivariate Logit Model for Termination firms (1) and Non-termination firms (0)

(Termination firms n=33, Non-termination firms n=47)

Variable Expected Signs

Coef. Asymptotic t-statistics

SWITCH - -1.463 -2.09**

PRET - -0.244 -0.17

FFUND - -0.293 -1.08

SFUND - -4.405 -2.40***

LEV + 0.376 1.30

RUNI - -25.392 -2.11**

PUT + 0.001 1.87*

SWI_RUNI + 24.994 2.07**

Constant + 5.899 2.54

Model Chi-square

24.39 P<0.01

________________ Table Notes: SWITCH = firms update their expected rate of return on pension assets assumptions is coded 1; 0 otherwise PRET = percentage of retired workers participating in the pension FFUND = pension fund contributions / pension fund expenditures SFUND = pension asset/pension liability LEV = long term debts/total tangible assets RUNI = (capital Expenditures+ Acquisitions+ R&D)/total assets PUT = sponsoring firm’s option to default on pension obligations SWI_RUNI = interaction term between switch and runi *** Significant at the 0.01 level of significance (two-tailed) ** Significant at the 0.05 level of significance (two-tailed) * Significant at the 0.10 level of significance (two-tailed)


TABLE 7 Logistic Regression Model of the Surviving Firms (n=49) partitioned into

Switch Group (n=28) and Non-Switch Group (n=21)

Switch firms

Non-switch firms

Expected Signs



PRET ? -3.517 2.105* 0.796 0.201 FFUND - -1.764 4.786** -0.136 -0.954 SFUND - -9.570 6.221*** -6.923 -2.745* LEV ? -0.004 0.001 -0.765 -0.848 RUNI - 2.188 0.384 -3.550 -0.176 PUT + -8.272 1.163 -2.423 0.588 Constant + 13.199 7.034*** 7.220 2.450* Model Chi-square

16.214 p-value>0.10 9.259 p-value<0.1

____________________ Table Notes: PRET = percentage of retired workers participating in the pension FFUND = pension fund contributions / pension fund expenditures SFUND = pension asset/pension liability LEV = long term debts/total tangible assets RUNI = (Capital Expenditures+ Acquisitions+ R&D)/total assets PUT = sponsoring firm’s option to default on pension obligations *** Significant at the 0.01 level of significance (two-tailed) ** Significant at the 0.05 level of significance (two-tailed) * Significant at the 0.10 level of significance (two-tailed)


Conclusion Empirical findings suggest:

The need to curtail pension liabilities appear to be the primary motivation associated with termination decisions (integration hypothesis)

Link accounting policy choices and termination decisions: consistent with risk management hypothesis

At least some UK firms have exploited the PUT Option value to default on their pension promises via terminations

Impact of Pension Accounting Rule Change on UK Pension Plan Terminations Paul Klumpes, Imperial...

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