Impact of Pension Accounting
Rule Change on UK Pension
Plan Terminations
Paul Klumpes, Imperial College
Liyan Tang, University of Stirling
Mark Whittington, Aberdeen University
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?

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Objectives
Examine determinants of plan terminations
in an economic environment of underfunding, 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 interrelated with firms’ prior accounting policy
choice (actuarial valuation method switch)

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Outline
Background
 Literature review
 Competing hypotheses for termination
 Sample and data
 Empirical results
 Conclusion and further research

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Background- Pension
Terminations
Legality of pension funds as equitable trusts
– ‘property rights’ or trustee responsibility?
 US evidence:

1980s – excess surplus motivates
takeovers
 Early

UK evidence:
– firms take contribution ‘holidays’
 1999- equity crash; many funds in deficit
 Post FRS 17 – many terminations: why?
 Pre-1998
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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
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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,
7
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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
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Competing Ownership Hypotheses
on Terminations

Integration hypothesis from corporate finance
literature (Sharpe,1977;Treynor,1977)
 Pension

Separation hypothesis from labour economics
literature (Ippolito,1985;Cooper and Ross, 2002)
 Pension

fund entirely belong to sponsoring firm
fund separate from sponsoring firm
Risk Management hypothesis
 Actuarial
switching is inter-related with terminations
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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 nonswitch firms
 b: Separation Perspective (Null Hypothesis)
 Terminations are determined by pension fund characteristics
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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
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Table 2
Mean and Median Attributes of Sub-Samples
Panel A
Pension and
Financial
Variable a
PRET
FFUND
SFUND
LEV
RUNI
EXP
SPD
Switch Firms
(n=40)
Panel B
Non-Switch Firms
(n=40)
Curtailers
(n=27)
Maintainers
(n=53)
Mean
Median
Mean
Median
Mean
Median
Mean
Median
0.43
(0.10)b
0.78
(0.16)
1.17
(0.13)
0.46
(0.53)
0.42
(0.23)
0.04
(0.24)
2.24
(0.00)
0.43
(0.08)c
0.52
(0.46)
1.10
(0.40)
0.31
(0.73)
0.30
(0.43)
0.02
(0.38)
2.50
(0.00)
0.37
0.32
0.43
0.67
0.99
0.77
1.06
1.10
1.17
1.14
0.38
0.30
0.63
0.38
0.35
0.38
0.06
0.01
0.03
0.02
0.03
0.02
2.95
3.00
0.43
(0.63)e
0.58
(0.32)
1.08
(0.01)
0.33
(0.77)
0.02
(0.53)
0.02
(0.36)
2.50
(0.38)
37%
(0.05)
0.44
1.45
0.42
(0.70)d
0.65
(0.06)
1.09
(0.02)
0.55
(0.66)
0.04
(0.68)
0.05
(0.25)
2.61
(0.78)
2.55
2.50
SWITCH
57%
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 onetailed 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
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Findings

Pension asset / pension liability and
leverage ratios.
 Terminating
firms have lower pension funding
and higher leverage
 Firms that frequently adjust their ERR are less
likely to terminate
 The put option is just significant
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TABLE 3
Comparison of Curtailers and Maintainers (across switch and non-switch samples)
Financial
and
Pension
Variablesa
PRET
FFUND
SFUND
LEV
RUNI
EXP
SPD
Switch Sample
(n=40)
Rel.
Yearb
-6
-3
0
-6
-3
0
-6
-3
0
-6
-3
0
-6
-3
0
-6
-3
0
-6
-3
0
10 Curtailers
Mean
0.45
0.46
0.51
0.67
0.53
1.02
1.05
1.02
1.05
0.57
0.84
0.44
0.32
0.02
0.07
0.06
0.09
0.12
2.46
2.56
2.20
30 Maintainers
Mean
0.42
0.46
0.53
0.49
0.58
0.96
1.10
1.14
1.15
0.42
0.78
0.61
0.30
0.01
0.03
0.03
0.03
0.03
2.16
2.35
2.32
Non-Switch Sample
(n=40)
Mann-Whitney
One-tail Test
p-value
17 Curtailers
Mean
0.68
0.95
0.80
0.29
0.90
0.73
0.20
0.01
0.02
0.20
0.60
0.90
0.53
0.05
0.03
0.18
0.04
0.03
0.04
0.23
0.98
0.39
0.39
0.45
1.80
1.19
3.96
0.94
1.12
1.09
0.34
0.37
0.42
0.30
0.02
0.05
0.02
0.02
0.03
3.18
2.64
2.43
23 Maintainers
Mean
0.35
0.40
0.39
1.19
1.20
2.51
1.15
1.15
1.12
0.42
0.44
0.62
0.38
0.06
0.04
0.03
0.04
0.06
2.78
2.81
2.20
Mann-Whitney
One-tail Test
p-value
0.31
0.48
0.50
0.22
0.27
0.63
0.40
0.25
0.64
0.63
0.58
0.37
0.49
0.27
0.53
0.59
0.54
0.33
0.10
0.70
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.
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TABLE 4
Binary logit analysis of pension liability curtailmentsa
Financial
and
Pension
Variable
Exp.
Signs
SWITCH
Panel A
Panel B
Panel C
All Sample
(n=80)
Switch Sample
(n=40)
Non-switch Sample
(n=40)
Coefficient
t-statistic
p-value
1.474
-2.42
0.01
Coefficient
t-statistic
p-value
Coefficient
t-statistic
p-value
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
+ /+/ ?
SPD
- /? /-
9.140
0.083
9.775
Chi-square
1.78
-0.24
2.59
18.67
0.07
0.81
0.00
0.01
1.69
0.39
1.51
8.89
0.09
0.69
0.13
0.26
-1.00
-1.33
2.05
7.28
0.31
0.18
0.04
0.40
Intercept
14.986
0.208
15.479
Chi-square
23.949
0.586
13.123
Chi-square
Model
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.
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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)

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Table 5
Sensitivity Tests for Change in Funding Ratio
Panel A: Switch firms (n=40)
Mean
SSAP 24 funding ratio
FRS 17 funding ratio
Change in funding ratio
(SSAP 24 – FRS 17)
1.052
0.979
0.139
10 Curtailers
Two Sample t-test
p-value
0.01
0.01
0.01
Mean
1.127
1.052
0.071
30 Maintainers
Two Sample t-test
p-value
0.01
0.01
0.01
Panel B: Non-switch firms(n=40)
Mean
SSAP 24 funding ratio
FRS 17 funding ratio
Change in funding ratio
(SSAP 24 – FRS 17)
1.024
0.941
0.082
17 Curtailers
Two Sample t-test
p-value
n.s.
n.s.
n.s.
Mean
1.139
1.064
0.062
23 Maintainers
Two Sample t-test
p-value
n.s.
n.s.
n.s.
Panel C: All sample firms (n=80)
Mean
27 Curtailers
Two Sample t-test
p-value
0.02
0.02
0.02
Mean
53 Maintainers
Two Sample t-test
p-value
0.02
0.02
0.02
SSAP 24 funding ratio
1.047
1.132
FRS 17 funding ratio
0.958
1.059
Change in funding ratio
0.069
0.048
(SSAP 24 – FRS 17)
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’.
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TABLE 6
Multivariate Logit Model for Termination firms (1) and Non-termination firms (0)
(Termination firms n=33, Non-termination firms n=47)
Variable
SWITCH
PRET
FFUND
SFUND
LEV
RUNI
PUT
SWI_RUNI
Constant
Model
Chi-square
Expected
Signs
+
+
+
+
Coef.
-1.463
-0.244
-0.293
-4.405
0.376
-25.392
0.001
24.994
5.899
24.39
Asymptotic
t-statistics
-2.09**
-0.17
-1.08
-2.40***
1.30
-2.11**
1.87*
2.07**
2.54
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)
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TABLE 7
Logistic Regression Model of the Surviving Firms (n=49) partitioned into
Switch Group (n=28) and Non-Switch Group (n=21)
Non-switch firms
Switch firms
PRET
FFUND
SFUND
LEV
RUNI
PUT
Constant
Expected
Signs
Coef.
?
?
+
+
-3.517
-1.764
-9.570
-0.004
2.188
-8.272
13.199
Asymptotic
t-statistics
2.105*
4.786**
6.221***
0.001
0.384
1.163
7.034***
Coef.
0.796
-0.136
-6.923
-0.765
-3.550
-2.423
7.220
Asymptotic
t-statistics
0.201
-0.954
-2.745*
-0.848
-0.176
0.588
2.450*
Model
9.259 p-value<0.1
p-value>0.10
16.214
Chisquare
____________________
Table Notes:
PRET = percentage of retired workers participating in the pension
= pension fund contributions / pension fund expenditures
FFUND
= pension asset/pension liability
SFUND
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)
*
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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
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