Observations and Conclusions

December 21, 2021

PBGC PIMS Peer Review

Data Input Assessment
2

Christopher Teh

FSA, EA, MAAA, FCA, CFA

Director, Wealth Practice

Buck Team

Stuart Schulman

FSA, EA, MAAA, FCA, CFA

Principal, Wealth Practice

Matthew DeWitt

Senior Consultant, 

Wealth Practice

David Harwood

Ronald Thompson

Senior Consultant, 

Wealth Practice

EA, MAAA, FCA

Principal, Wealth Practice

Rebecca Reynolds

ASA, EA, MAAA

Director, Wealth Practice
Table of Contents

3

Executive Summary 

Key Recommendations

Statement of Objectives from Scope of Services

a) Methodology to Select Input Data for SE PIMS

b) Development of Plan Participant Profiles

− Standard Data Input Assessment of SE PIMS

c) Data Input Items from Form 5500 Used to Develop Expected Cash Flows

d) Availability of Form 5500 Data and Adjustments to Create a Uniform Data Set 

e) Plan Status, Benefit Design, and Features to be Valued

f) Plan Sponsor’s Financial Information Used as Input to Determine Future Bankruptcies

g) Economic and Regulatory Inputs

h) PBGC Asset, Liability, and Premium Information

i) Zone Status Availability for ME Plans

j) Standard Data Input Assessment of ME PIMS
Table of Contents (continued)

4

Calibration Review

a) Calibration of Sample Plans/Firms to the SE Universe

b) Calibration of Plan Benefits

c) Calibration of Starting Position Plans

d) Calibration of Cashflows for ME Plans

Appendix

References & Actuarial Certification
5

Executive Summary
6

Executive Summary – What Did Buck Do?

Independent Analysis for Single-Employer (SE) and Multiemployer (ME) Plans

Review SE & ME PIMS Sampling Methods

• Distribution analysis

• Coverage analysis

• Historical claims

Review SE & ME PIMS Data Inputs

• Processes followed

• Data sources

• Estimation techniques

• Assumption modeling

Review SE & ME PIMS Calibration Methods Review ME PIMS Zone Status Data

• Assess current methodologies

• Cause for calibration

• Reasonable approach

• Reasonable alternatives

• Process followed

• Data sources

• Reasonable alternatives

Solid 

sources

Reasonable 

estimates

Good 

data 

process
7

Executive Summary – What Did Buck Do?

The review focused on the processes followed for preparing the input datasets used for participant, plan, and 

plan sponsor data. Buck was also asked to review several calibration estimates used within the models.

Our review covered nearly 100 files, including data and modeling manuals, along with biweekly meetings with 

PRAD to review the modeling process across a period of 7 months.

Calibration Process

• Sample Plans/Firms to the SE Universe

• Plan Benefits

• Cashflows for ME Plans

Data Inputs & Process

• Methodology to Select Input Data for SE Plans

• Development of Plan Participant Profiles

• Data Input Items from Form 5500 

• Availability of Form 5500 Data

• Plan Status, Benefit Design and Features 

• Plan Sponsor’s Financial Information to 

Determine Future Bankruptcies

• Economic and Regulatory Inputs

• PBGC Asset, Liability and Premium 

• Zone Status Availability for ME Plans 

• Standard Data Input Assessment for ME Plans

Develop Recommendations

• Focused on items that may enhance the 

collection of plan, participant, and plan sponsor 

data

• Focused on assumptions and methods that 

may enhance processes and projections results
8

Executive Summary – What Has Buck Observed?

Overall, the PBGC approach for data inputs to SE PIMS and ME PIMS is reasonable and 

should produce reasonable forecasts of the SE and ME systems. We have identified a few 

areas where processes could be refined to improve the projection methodology.

Generally, our findings show that expanding datasets and incorporating additional information 

that is generally available as part of the annual actuarial valuation, particularly plan cash flows, 

will lead to improvements in modeling.
9

Maintain current 

sampling methods 

for SE PIMS but 

consider strategically 

adjusting input data 

to refine modeling, 

such as increasing 

specific industry or 

plan grouping 

representation

Expand ME PIMS with 

assumptions and 

plan specific data 

from applications 

submitted for Special 

Financial Assistance 

and latest Zone 

Status certifications 

provided by the IRS

Monitor ongoing 

behavior of SE and 

ME plan sponsors 

and adjust modeling 

as needed for recent 

legislative and 

industry changes

Electronic 

submission of the 

projected benefit 

cashflows for both 

SE and ME Plans

Executive Summary – Key Recommendations
10

Key

Recommendations
11

Key Recommendations

Plan Specific Cash Flows

Consider electronic 

submission of the 

projected benefit 

cashflows for both SE 

and ME Plans

• Cash flows are now generally available from annual actuarial valuation 

processing and can be provided annually.

• Leading actuarial valuation software is designed to generate cash 

flows, which will minimize any additional burden on plan sponsors.

• This will help:

— Validate the cashflows generated by PIMS

— Adjust the cashflows of the guaranteed benefits in PIMS

— Confirm whether the interest rate sensitivities of the sample plans are 

reasonable (convexity and duration)

— Adjust any future benefits payment patterns in PIMS, as needed

— Reduce number of calibration steps

— By offering transparency of financial implications of holding a Plan, and 

assisting all stakeholders in the understanding of managing pension risks
12

Key Recommendations

SE Modeling Inputs & Methods

Maintain current SE 

sampling methods but 

consider 

strengthening areas

• Analysis indicated the SE sampling methods are well aligned 

relative to the universe of plans measured on a liability basis. 

• Increase areas of coverage as indicated by the analysis to 

enhance overall modeling and refine the results.

— Consider increasing representation of Small Plans (less than $200M 

in Funding Target Liability) and Underfunded Plans (assets < liability)

• Potential bundling approach of Small Plans as a new sample

• Potential adjustment handled and analyzed outside of PIMS

— Consider increasing Normal Cost coverage

— Consider periodic review of industries, specifically to identify 

distressed industries, and potentially include industry weighting

• Sample of 500 plans should be reviewed regularly to ensure 

reasonable representation.
13

Table displays the funding target liability and

coverage ratios stratified by Funding Target 

Range.

• Sample covers 55.6% of universe liability.

• Big Plans (≥$1B) have good coverage.

• Small Plans (<$200M) have low coverage.

— $4B of $242B, or 1.7%

• 12% of universe liability is in Small Plans.

• Historically, Small Plans account for 98% of 

all defaults and 29% of termination 

liability. Consider bundling plans to address 

representation for this group.

• Broader liability coverage may be achieved 

by targeting the $200M and $400M ranges.

FT Range

Universe Funding 

Target Liability

Sample Funding 

Target Liability FT Ratio

FT Ratio / 

Total FT Ratio 

(55.6%)

$0M - $199.99M 241,998,163,719 4,035,146,680 1.7% 3.1%

$200M - $399.99M 123,728,948,862 11,430,753,451 9.2% 16.5%

$400M - $599.99M 103,937,312,041 35,072,495,135 33.7% 60.6%

$600M - $799.99M 69,169,716,550 31,493,193,236 45.5% 81.8%

$800M - $999.99M 59,705,689,093 29,035,510,295 48.6% 87.4%

$1B or more 1,375,229,615,453 986,746,423,349 71.8% 129.1%

Grand Total 1,973,769,445,718 1,097,813,522,146 55.6% N/A

Key Recommendations

SE Modeling Inputs & Methods – Funding Target Liability Coverage

Small Plans are underrepresented in the Sample, which may understate impact of future accruals on PBGC 

liability.
14

Table displays the funding target normal cost 

and coverage ratios stratified by Funding 

Target Range.

• Sample covers 44.9% of universe normal 

cost.

• Big Plans have good coverage. 

• Small Plans have low coverage.

— $73M of $8B, or 0.9%

• Although we believe the PBGC can 

withstand exposure of expected defaults 

for Small Plans, low coverage of normal 

cost may have projection implications.

• Broader normal cost coverage may be 

achieved by targeting the $200M and 

$400M ranges.

FT Range

Universe Funding 

Target NC

Sample Funding 

Target NC NC Ratio

NC Ratio /

Total NC Ratio 

(44.9%)

$0M - $199.99M 8,036,555,458 72,954,741 0.9% 2.0%

$200M - $399.99M 2,544,047,372 185,255,040 7.3% 16.3%

$400M - $599.99M 2,489,976,480 692,602,995 27.8% 61.9%

$600M - $799.99M 1,541,036,474 666,678,648 43.3% 96.4%

$800M - $999.99M 974,420,570 422,379,448 43.3% 96.4%

$1B or more 27,172,139,412 17,165,782,378 63.2% 140.8%

Grand Total 42,758,175,766 19,205,653,250 44.9% N/A

Key Recommendations

SE Modeling Inputs & Methods – Funding Target Normal Cost Coverage

Normal Cost for Small Plans is underrepresented in the Sample.
15

Key Recommendations

SE Modeling Inputs & Methods – Underfunded Plan Coverage

Plans with funded ratios of 50.00%-79.99% are underrepresented in the Sample.

The chart depicts the funded 

status amount for underfunded plans by 

funded status range and plan operating 

status. The gray line is coverage ratio.

• Sample covers 28% of universe 

underfunded/deficit liability.

• In general, we see low 

coverage across the ranges – just one 

coverage ratio above 50%.

• Aggregate Deficit Amount:

– Universe -$15.8B

– Sample -$4.5B
16

Key Recommendations

SE Modeling Inputs & Methods – Industry Coverage

Manufacturing and others are well represented in the Sample, while Health Care and other sectors are 

underrepresented.

The chart depicts the funding target liability 

amounts by industry for the sample vs. universe 

datasets.

Good coverage: Manufacturing (74%), Finance 

(65%), Utilities (82%), Information (91%), 

Transportation (64%), Arts (55%), and 

Management (95%).

Low coverage: Health Care and Social Assistance.

No coverage: Other Services, Construction, 

Accommodation, Agriculture, Public Administration.

Representation can be meaningful as plan 

sponsors of different industries may have 

different propensity for bankruptcy.
17

Key Recommendations

ME Data Availability

Consider expanding 

the sample of ME data 

used in the model

• Consider reflecting Zone Status information from available IRS 

certification submissions by plan actuaries to reflect the latest 

information.

— Review to consider reasonability of projections from ME PIMS.

• Consider utilizing all the data items to enhance ME PIMS projection 

model for approximately 250 plans that will be made available soon to 

the PBGC from the Special Financial Assistance (SFA) applications.

— Utilize the application information to refine current methodologies 

and/or utilize the actual data instead of sample data, if feasible.

— Consider more direct focus on modeling plans that narrowly miss 

eligibility for SFA or are denied approval and may be in trouble.
18

Key Recommendations

Plan Behavior Monitoring – SE PIMS

Consider the ongoing 

legislative 

environment and how 

the behavior of plan 

sponsors will impact 

modeling

• Based on current observations, consider the following updates to 

reflect American Rescue Plan Act (ARP) of 2021 for SE plans:

— May need to incorporate ARP inputs for plans electing to 

implement fresh-start 15-year amortization in 2019-2022

— Contribution patterns in the short-term and long-term may change:

• Plan sponsors who elected ARP to lower minimum required 

contribution (MRC), may only pay the new, lower MRC

• Additional interest rate relief under the Infrastructure Investment 

and Jobs Act may change contribution patterns for an extended 

period

— Plans in distress are the most likely to elect immediate relief and 

represent the greatest risk to the PBGC
19

Key Recommendations

Plan Behavior Monitoring – ME PIMS

Consider the ongoing 

legislative 

environment and how 

the behavior of plan 

sponsors will impact 

modeling

• Based on current observations, consider the following updates to 

reflect ARP and SFA for ME plans:

— Consider creating a probability of electing SFA based on comments 

and feedback on the interim rules published July 2021.

• Commenters indicate possible hesitation for plans that received 

MPRA benefit suspensions to elect SFA relief because of 

potential conflicts in fiduciary responsibility to their active and 

retiree populations.

• We believe it is not certain that plans that applied for MPRA relief 

will elect SFA and recommend allowing for this possible 

behavior. 
20

Methodology to Select 

Input Data for SE PIMS
Methodology to Select Input Data for SE PIMS

Our

Understanding

• Once plan is identified for inclusion to sample plans dataset 

(sample), it generally stays in the group unless access to data is 

impaired.

• PRAD receives funding data, representing the dataset for all plans 

(universe), in the form of a table matrix grouped by funded status 

ranges (excluding Airline & Auto Plans).

• Sample is grouped by funded status ranges in accordance with 

universe.

• Weight factors are developed by computing the funding target 

liability ratio, universe to sample, across the funded status ranges.

• These weight factors are used later in the SE PIMS projections to 

scale up.

21
Methodology to Select Input Data for SE PIMS

Our Hypothesis on 

Effect of Sampling 

Techniques for 

Modeling

At first glance, the current approach appears limited by focusing only on 

one metric, funded status, to group the plans; and one metric, Funding 

Target Liability, to develop weight factors. Using only the funded status 

metric may result in plans with different key features being grouped 

together – specifically, plans with different benefit structures and 

operating status (e.g., frozen versus ongoing). Similarly, focusing only 

on accrued liability could result in suboptimal modeling of the aggregate 

normal cost, which has implications for projections.

We would expect to find comparisons in our analysis when grouping 

and measuring the liability. We will assess the hypothesis by 

substituting different metrics for grouping and measuring.

22
23

Methodology to Select Input Data for SE PIMS

Summary of Methods

To confirm this hypothesis, two analytical techniques are used in this analysis:

• Percent Distribution: Used to assess alignment – weight to column total

• Quantitative Relation: Used to assess coverage – ratio of sample to universe

Additional groupings considered:

• Benefit type: C-Cash balance; F-Flat; S-Salaried

• Plan operating status: Ongoing or Frozen

• Industry

Measures summarized are:

• Schedule SB Funding Target Liability (FT)

• Schedule SB Funding Target Liability Normal Cost (FTNC)

• Participants

• Plans

Source Data: ‘SE Plans Bundle 2018PY_rev’ with minor adjustments as instructed by PRAD
24

Methodology to Select Input Data for SE PIMS

Summary of Methods (continued)

Key Measurements for the Analysis:

“Well Aligned” – two metrics are within 5%

“Good Coverage” – sample to universe ratio 

is greater than or equal to 50%. If coverage is 

less than 25%, results may be skewed due to 

maximum applied weighting factor of “4x” 

under the current methods.

Well Aligned 

& Good 

Coverage

Well Aligned 

& Low 

Coverage

Misaligned 

& Low 

Coverage

Misaligned 

& Good 

Coverage
25

Methodology to Select Input Data for SE PIMS

Summary of Methods (continued)

Funded Status Percentage 

(MVA / FT Liability)

Funding Range Analyzed:

50%-150%+ in 10% increments

Funding Target Liability

Funding Target Liability Range 

Analyzed:

$0-$1B+ in $200M increments

Funding Target Normal Cost

Funding Target Normal Cost 

Range Analyzed:

$0-$1B+ in $200M increments

Small Plans

Funding Target Liability < $200M

Big Plans

Funding Target Liability ≥ $1B
Distribution Analysis
27

Methodology to Select Input Data for SE PIMS

Summary of Analysis

• This section examines the current SE PIMS 

methodology by comparing the distribution of the 

SE sample and universe datasets.

• The measures summarized are:

— Plan counts

— Participant Counts

— Funding Target Liability

• Groupings are by:

— Benefit Type

— Operating Status

— Industry

• Stratified by funded status ranges

Operating Status Plan Count Participant 

Count

Funding Target 

Liability

Ongoing 55.4% 29.9% 30.8%

C – Cash Balance 28.2% 10.7% 10.8%

F – Flat Benefit 2.3% 4.1% 5.0%

S – Salaried Benefit 24.8% 15.2% 15.0%

Frozen 44.6% 70.1% 69.2%

C – Cash Balance 14.8% 27.4% 29.5%

F – Flat Benefit 4.3% 8.0% 7.7%

S – Salaried Benefit 25.5% 34.7% 32.1%

Grand Total 100.0% 100.0% 100.0%

Distribution of the Universe Dataset by 

Operating Status and Benefit Type

Note: Totals may not add due to rounding.
28

Methodology to Select Input Data for SE PIMS

Summary of Analysis (continued)

Operating Status: Ongoing

Percent of Grand Total Universe Sample

Funding Target 31% 30%

Participants 30% 31%

Plan 55% 27%
29

Methodology to Select Input Data for SE PIMS

Summary of Analysis (continued)

Key Features

• Benefit Type: Alignment improves the representative 

projections for several reasons: benefit accrual patterns 

differ; benefit rights and features differ. Different future 

accrual patterns may impact future exposure for PBGC.

— Projection results would be more predictive if future 

accruals are modeled as reasonably as possible.

— Cash balance plans have a higher propensity for 

paying lump sums.

• Operating Status: Key concern is for accruing plans.

• Funded Status: Are the poorly funded plans well 

represented?

• Industry: Analyze distribution across distressed vs. 

healthy industries, which is useful in bankruptcy analysis.
30

Methodology to Select Input Data for SE PIMS

Distribution of Participants and Plans

The tables display counts and 

the percent of total for the 

sample and universe datasets.

– Participants are well aligned across 

operating status and benefit type.

– Plans display misalignment for 

ongoing cash balance and salaried 

plans. Flat plans are well aligned and 

not overrepresented.

By Participants

Operating Status Ongoing Frozen

Benefit Type Cash Balance Flat Salaried N/A Total

Sample (counts) 955,906 536,202 2,005,386 7,785,518 11,283,012

Sample (weights) 8.5% 4.8% 17.8% 69.0% 100.0%

Universe (counts) 2,513,602 958,763 3,601,154 16,573,071 23,646,590

Universe (weights) 10.6% 4.1% 15.2% 70.1% 100.0%

By Plans

Operating Status Ongoing Frozen

Benefit Type Cash Balance Flat Salaried N/A Total

Sample (counts) 42 19 58 318 437

Sample (weights) 9.6% 4.3% 13.3% 72.8% 100.0%

Universe (counts) 6,082 518 5,394 10,277 22,271

Universe (weights) 27.3% 2.3% 24.2% 46.1% 100.0%

Note: Totals may not add due to rounding.
31

Methodology to Select Input Data for SE PIMS

A closer look at the participant distribution reveals a well aligned sample. A general observation is 

that participants in underfunded plans are not well represented.

Funded Status 

Range Sample Participants Universe Participants

50.00% - 59.99% 0 17,080

60.00% - 69.99% 0 11,525

70.00% - 79.99% 0 126,287

80.00% - 89.99% 147,261 634,566

90.00% - 99.99% 609,842 1,902,704

100.00% - 109.99% 1,705,761 3,809,602

110.00% - 119.99% 2,515,728 4,855,741

120.00% - 129.99% 3,287,940 5,892,086

130.00% - 139.99% 1,443,710 3,239,290

140.00% - 149.99% 479,838 1,001,009

150.00% or more 1,092,932 2,156,700

Grand Total 11,283,012 23,646,590
32

Methodology to Select Input Data for SE PIMS

Funding Target Liability Distribution

Sample vs. Universe

The table displays the funding target 

liability distribution.

— The sample and universe 

distributions are well aligned.

— Sample has no representation for 

plans less than 80% funded.

o From a liability perspective, this is 

a small group in the universe.

Funded Status Range Universe Funding 

Target Liability

Sample Funding 

Target Liability

50.00% - 59.99% 0.0% 0.0%

60.00% - 69.99% 0.0% 0.0%

70.00% - 79.99% 0.4% 0.0%

80.00% - 89.99% 2.4% 1.2%

90.00% - 99.99% 8.2% 6.4%

100.00% - 109.99% 17.5% 18.4%

110.00% - 119.99% 20.9% 22.9%

120.00% - 129.99% 25.6% 28.3%

130.00% - 139.99% 13.7% 13.1%

140.00% - 149.99% 3.7% 2.9%

150.00% or more 7.5% 6.8%

Grand Total 100.0% 100.0%

Note: Totals may not add due to rounding.
33

Methodology to Select Input Data for SE PIMS

Funding Target Liability Distribution (continued)

Exhibit 1

• Background:

— On the following slide, Exhibit 1 depicts the distribution of Funding Target 

liability across benefit type and operating status. The top chart 

represents the sample, and the bottom represents the universe.

— Although not shown, the universe has three bands that are not represented 

in sample plans (Less than 40%, 50%-60%, and 60%-70%), which were 

filtered out for comparison purposes. From a liability perspective, this is a 

small group in the universe.

• Observations:

— Sample liability distribution by benefit type generally aligns with universe.

— Flat benefit type is not overrepresented.

— Liability distribution by operating status aligns within tolerance (the ongoing 

cohort aligns better than the frozen cohort).

— The 70%-79.99% range is not represented in sample.
34

Exhibit 1:

C – Cash Balance, F – Flat, S – Salaried Universe $1,974B. Sample $1,098B
35

Methodology to Select Input Data for SE PIMS

Funding Target Liability Distribution (continued)

Operating Status: Ongoing

The table displays the funding target liability 

distribution for ongoing plans.

As illustrated, the sample and universe 

distributions are well aligned.

Funded Status Range Universe Funding

Target Liability

Sample Funding

Target Liability

Ongoing 30.8% 30.2%

50.00% - 59.99% 0.0% 0.0%

60.00% - 69.99% 0.0% 0.0%

70.00% - 79.99% 0.1% 0.0%

80.00% - 89.99% 0.4% 0.1%

90.00% - 99.99% 1.6% 1.7%

100.00% - 109.99% 3.5% 3.7%

110.00% - 119.99% 4.6% 5.6%

120.00% - 129.99% 8.6% 8.9%

130.00% - 139.99% 6.4% 6.2%

140.00% - 149.99% 2.3% 1.9%

150.00% or more 3.3% 2.2%

Note: Totals may not add due to rounding.
36

Methodology to Select Input Data for SE PIMS

Funding Target Liability Distribution (continued)

Operating Status: Frozen

The table displays the funding target liability 

distribution for frozen plans.

As illustrated, the sample and universe 

distributions are aligned although not as aligned 

as ongoing plans.

Funded Status Range Universe Funding

Target Liability

Sample Funding

Target Liability

Frozen 69.2% 69.8%

50.00% - 59.99% 0.0% 0.0%

60.00% - 69.99% 0.0% 0.0%

70.00% - 79.99% 0.3% 0.0%

80.00% - 89.99% 2.0% 1.1%

90.00% - 99.99% 6.7% 4.7%

100.00% - 109.99% 13.9% 14.7%

110.00% - 119.99% 16.3% 17.4%

120.00% - 129.99% 17.0% 19.3%

130.00% - 139.99% 7.3% 6.9%

140.00% - 149.99% 1.4% 1.0%

150.00% or more 4.2% 4.6%

Note: Totals may not add due to rounding.
37

Methodology to Select Input Data for SE PIMS

Funding Target Liability Distribution (continued)

Industry:

The chart depicts the funding target liability 

distribution by industry for the sample 

vs. universe datasets.

The chart shows misalignment across various 

industries, and several industries have no 

representation in sample. 

Representation can be meaningful as plan 

sponsors of different industries may have 

different propensity for bankruptcy.
38

Methodology to Select Input Data for SE PIMS

Funding Target Normal Cost Distribution

Sample vs. Universe

The table displays the funding target normal 

cost distribution by operating status.

— The sample and universe 

distributions are well aligned.

— Sample has no representation for 

plans less than 80% funded.

o Aggregate NC of $606M, of 

$42.8B

Funded Status Range Universe Funding

Target Normal Cost

Sample Funding

Target Normal Cost

Plan Operating Status Accruing Frozen Accruing Frozen

50.00% - 59.99% 0.0% 0.0% 0.0% 0.0%

60.00% - 69.99% 0.0% 0.0% 0.0% 0.0%

70.00% - 79.99% 0.1% 0.1% 0.0% 0.0%

80.00% - 89.99% 0.5% 1.5% 0.1% 0.9%

90.00% - 99.99% 2.3% 6.1% 1.4% 3.5%

100.00% - 109.99% 4.8% 9.6% 4.2% 10.5%

110.00% - 119.99% 7.0% 12.2% 7.0% 14.7%

120.00% - 129.99% 10.9% 11.6% 11.6% 14.5%

130.00% - 139.99% 9.6% 9.2% 8.2% 10.2%

140.00% - 149.99% 3.1% 1.6% 3.5% 0.9%

150.00% or more 4.8% 4.9% 3.5% 5.2%

Subtotals 43.2% 56.8% 39.5% 60.5%

Note: Totals may not add due to rounding.
39

Methodology to Select Input Data for SE PIMS

What have we learned?

Distributions of 

participants are well 

aligned while plans are

not as well aligned.

Participants in 

underfunded plans are not 

well represented.

Better alignment 

measuring funding 

target liability.

When compared by 

benefit type and operating 

status, distributions align 

well within tolerance.

Misalignment across 

various industries. 

Several industries have 

no representation in 

sample.

Distribution Analysis 

Summary

Wrap-up
40

Methodology to Select Input Data for SE PIMS

Distribution Analysis Summary

Benefit Type:

• The distributions are within tolerance when measuring both funding target liability and participants.

• Distributions do not align well on plan count basis. We see no improvement gained by adding benefit type measures to development of the weight 

factors given current system constraints.

Operating Status:

• The normal cost distributions align across operating status, but there was concern about optimization of normal cost for accruing plans.

• Although the alignment is within tolerance (sample 39.5% and universe 43.2% for accruing plans), there can be improvement, which may be achieved 

by strategically adding plans to sample.

Funded Status:

• Underfunded plans (i.e., less than 100%) are not well represented in the sample based on the analysis.

• Plans less than 80% funded are not well represented, and as shown in the Historical Claims section, most defaults are comprised of underfunded 

plans.

• Consider including additional weight factors. Alternatively, since the current model groups by funded status, adding plans may suffice.

Industry:

• There is misalignment across various industries, and several industries have no representation in sample.

• We recommend periodic reviews of industry alignment with a focus on distressed industries. Modeling improvements may be gained by including 

industry in the development of the weight factors. PRAD should consider if adding industries with no coverage is warranted.
Coverage Analysis
42

Methodology to Select Input Data for SE PIMS

Summary of Analysis

• This section examines the current SE PIMS methodology 

by inspecting the coverage of the SE sample dataset to 

universe.

• The measures summarized are:

— Plan counts

— Participant Counts

— Funding Target Liability

• Groupings are by:

— Benefit Type

— Operating Status

— Industry

• Stratified by funding target liability ranges

• Key Features are the same as Distribution Analysis

• Bundling Process: develop distinct scale-weights for a 

targeted cohort, potentially aggregate small plans 

enhance the representation

Distribution of the Universe Dataset by 

Operating Status and Benefit Type

Operating Status Plan Count Participant 

Count

Funding Target 

Liability

Ongoing 11,994 7,073,519 608,319,290,053

C – Cash Balance 6,082 2,513,602 213,471,081,670

F – Flat Benefit 518 958,763 98,476,577,248

S – Salaried Benefit 5,394 3,601,154 296,371,631,135

Frozen 10,277 16,573,071 1,365,450,155,665

C – Cash Balance 3,435 6,475,802 581,484,905,917

F – Flat Benefit 993 1,882,204 151,135,216,907

S – Salaried Benefit 5,849 8,215,065 632,830,032,841

Grand Total 22,271 23,646,590 1,973,769,445,718
43

Methodology to Select Input Data for SE PIMS

Plan Counts

Table displays plan counts and coverage ratios stratified by 

Funding Target Range.

• Sample covers 2.0% of universe plans.

• Big Plans (funding target liability ≥ $1B) have good 

coverage.

• Small Plans (funding target liability < $200M) have low 

coverage.

— 41 of 21,106, or 0.2%

• 95% of universe plans are Small Plans.

— A bundling approach could be used to better 

represent this group, particularly for accruing plans.

• Broader plan coverage may be achieved by targeting 

the $200M and $400M ranges.

FT Range Universe 

Plans

Sample 

Plans

Plan 

Ratio

$0M - $199.99M 21,106 41 0.2%

$200M - $399.99M 439 38 8.7%

$400M - $599.99M 211 71 33.6%

$600M - $799.99M 99 45 45.5%

$800M - $999.99M 67 33 49.3%

$1B or more 349 209 59.9%

Grand Total 22,271 437 2.0%
44

Methodology to Select Input Data for SE PIMS

Participant Counts

Table displays participant counts and coverage ratios 

stratified by Funding Target Range.

• Sample covers 47.7% of universe participants.

• Big Plans have good coverage.

• Small Plan participants have low coverage.

— 76K of 4M, or 1.8%

• 18% of universe participants are in Small Plans.

— A bundling approach could be used to 

better represent this group, particularly for accruing 

plans.

• Broader participant coverage may be 

achieved by targeting the $200M and $400M ranges.

FT Range Universe 

Participants

Sample 

Participants

Participant 

Ratio

$0M - $199.99M 4,213,536 76,374 1.8%

$200M - $399.99M 1,920,828 186,829 9.7%

$400M - $599.99M 1,410,275 532,699 37.8%

$600M - $799.99M 908,997 436,391 48.0%

$800M - $999.99M 750,995 312,355 41.6%

$1B or more 14,441,959 9,738,364 67.4%

Grand Total 23,646,590 11,283,012 47.7%
45

Methodology to Select Input Data for SE PIMS

Funding Target Liability Coverage

Table displays the funding target liability and

coverage ratios stratified by Funding Target 

Range.

• Sample covers 55.6% of universe liability.

• Big Plans have good coverage.

• Small Plans have low coverage.

— $4B of $242B, or 1.7%

• 12% of universe liability is in Small Plans.

• Historically, Small Plans account for 98% 

of all defaults and 29% of termination 

liability. Consider bundling plans to 

address representation for this group.

• Broader liability coverage may be 

achieved by targeting the $200M and 

$400M ranges.

FT Range

Universe Funding 

Target Liability

Sample Funding 

Target Liability FT Ratio

FT Ratio / 

Total FT Ratio 

(55.6%)

$0M - $199.99M 241,998,163,719 4,035,146,680 1.7% 3.1%

$200M - $399.99M 123,728,948,862 11,430,753,451 9.2% 16.5%

$400M - $599.99M 103,937,312,041 35,072,495,135 33.7% 60.6%

$600M - $799.99M 69,169,716,550 31,493,193,236 45.5% 81.8%

$800M - $999.99M 59,705,689,093 29,035,510,295 48.6% 87.4%

$1B or more 1,375,229,615,453 986,746,423,349 71.8% 129.1%

Grand Total 1,973,769,445,718 1,097,813,522,146 55.6% N/A
46

Methodology to Select Input Data for SE PIMS

Funding Target Liability Coverage (continued)

Industry:

The chart depicts the funding target liability 

coverage by industry for the sample 

vs. universe datasets. 

Good coverage: Manufacturing (74%), 

Finance (65%), Utilities (82%),

Information (91%), Transportation (64%), 

Arts (55%), Management (95%).

Low coverage: Health Care and Social 

Assistance.

No coverage: Other Services, 

Construction, Accommodation, Agriculture, 

Public Administration.

Representation can be meaningful as plan 

sponsors of different industries may have 

different propensity for bankruptcy.
47

Methodology to Select Input Data for SE PIMS

Funded Status – Overfunded

The chart depicts the aggregate 

funded status amount for overfunded 

plans by funded status range and plan 

operating status. The gray line is 

coverage ratio.

• Sample covers 56% of 

universe overfunded/surplus liability.

• In general, we see good coverage 

across the ranges – just three 

coverage ratios below 50%.

• Surplus Amount:

– Universe $432.0B 

– Sample $239.9B
48

Methodology to Select Input Data for SE PIMS

Funded Status – Underfunded

The chart depicts the funded 

status amount for underfunded plans 

by funded status range and plan 

operating status. The gray line is 

coverage ratio.

• Sample covers 28% of universe 

underfunded/deficit liability.

• In general, we see low 

coverage across the ranges – just 

one coverage ratio above 50%.

• Aggregate Deficit Amount:

– Universe -$15.8B

– Sample -$4.5B
49

Methodology to Select Input Data for SE PIMS

Funding Target Normal Cost

Table displays the funding target normal 

cost and coverage ratios stratified by 

Funding Target Range.

• Sample covers 44.9% of universe 

normal cost.

• Big Plans have good coverage. 

• Small Plans have low coverage.

— $73M of $8B, or 0.9%

• Although we believe the PBGC can 

withstand exposure of expected defaults 

for Small Plans, low coverage of normal 

cost may have projection implications.

• Broader normal cost coverage may be 

achieved by targeting the $200M and 

$400M ranges.

FT Range

Universe Funding 

Target NC

Sample Funding 

Target NC NC Ratio

NC Ratio /

Total NC Ratio 

(44.9%)

$0M - $199.99M 8,036,555,458 72,954,741 0.9% 2.0%

$200M - $399.99M 2,544,047,372 185,255,040 7.3% 16.3%

$400M - $599.99M 2,489,976,480 692,602,995 27.8% 61.9%

$600M - $799.99M 1,541,036,474 666,678,648 43.3% 96.4%

$800M - $999.99M 974,420,570 422,379,448 43.3% 96.4%

$1B or more 27,172,139,412 17,165,782,378 63.2% 140.8%

Grand Total 42,758,175,766 19,205,653,250 44.9% N/A
50

Methodology to Select Input Data for SE PIMS

Funding Target Normal Cost (continued)

A closer look at the "$1B or more" cohort from the 

previous slide – the group is further stratified by 

normal cost ranges.

• Sample covers 63.2% of universe normal cost 

for Big Plans.

• This view shows the $600M-$799.99M range 

has no coverage and there is only one plan in 

the universe in this range.

• Higher normal cost coverage may be 

achieved by including the one plan in the 

$600M-$799.99M range in the sample.

FTNC Range Universe FTNC Sample FTNC NC Ratio

FT: $1B or more 27,172,139,412 17,165,782,378 63.2%

NC: $0M - $199.99M 14,572,422,735 8,810,653,195 60.5%

NC: $200M - $399.99M 5,911,272,958 3,707,113,603 62.7%

NC: $400M - $599.99M 3,258,672,457 2,829,193,106 86.8%

NC: $600M - $799.99M 738,425,900 0 0.0%

NC: $800M - $999.99M 2,691,345,362 1,818,822,474 67.6%
51

Funding Target Normal Cost (continued)

Methodology to Select Input Data for SE PIMS

The chart depicts normal cost by benefit type (C – Cash 

Balance, F – Flat Benefit, S – Salaried Benefit).

• Gray line represents the coverage ratio.

• Coverage ratios are below 50% for most accruing cohorts, 

net coverage for the cohort is 41%. Moderate coverage.

• Accruing Cash Balance plans have the lowest coverage at 

29%.

• Flat benefit types are not overrepresented.

• Since data source is from the Schedule SB, the NC for the 

frozen cohort is assumed to be the expense load, or the 

plan could be partially frozen but indicated as frozen on the 

Form 5500 filing.

Operating Status Universe Sample Coverage %

Accruing $18.5B $7.6B 41%

Frozen $24.3B $11.6B 48%

Total $42.8B $19.2B 45%
52

Methodology to Select Input Data for SE PIMS

Funding Target Normal Cost (continued)
2% 48% 2%

56% 45% 72%

Funding Target Liability Funding Target Normal Cost Liability Coverage – Big Plans

Liability Coverage – Small Plans Participants Plans

53

Sample Plans Dataset Coverage of the SE Universe
54

Methodology to Select Input Data for SE PIMS

Good coverage of 

funding target liability.

Sample covers 56% of 

universe liability.

Big Plans have good liability 

coverage: 72%.

Moderate coverage of 

normal cost. 

Sample covers 45% of 

universe normal cost.

Medium alignment across 

benefit type.

Expand coverage by adding 

more sample plans from the 

"$400M or less" FT Range.

Coverage Analysis 

Summary

Wrap-up

What have we learned?

Moderate participant 

coverage may impact 

projection estimates that rely 

on participant counts – PBGC 

premiums.

Small Plans and underfunded 

plans have low coverage.
55

Methodology to Select Input Data for SE PIMS

Coverage Analysis Summary

Benefit Type and Operating Status:

• We used normal cost to inspect the coverage for both features. Sample reasonably covers the normal cost. 

• Without modifying the current model, the sample can be improved by strategically adding plans based on observations from this study and 

internal goals.

• Consider targeting ongoing cash balance plans since that group has the lowest NC coverage.

Funded Status: 

• On average, funded status shows good coverage; however overfunded plans have good coverage, while underfunded plans have low 

coverage.

• The magnitude of the funded status deficit amount is relatively small when compared to that of the surplus amount.

• Without modifying the current model, the sample can be improved by strategically adding plans.

Industry:

• We observed good coverage for top 10 industries with largest proportion of liability except for: Health Care, Professional, Scientific, and 

Technical Service, Other Services, Wholesale Trade.

• We recommend periodic reviews of industry alignment with a focus on distressed industries. Modeling improvements may be gained by 

including industry in the development of the weight factors. PRAD should consider if adding industries with no coverage is warranted.
Historical Claims Analysis
57

Methodology to Select Input Data for SE PIMS

Historical PBGC Claims

YEAR OF TERMINATION TERMINATION LIABILITY PARTICIPANTS PLAN COUNT

1970 - 1979 $461,332,272 88,516 629

1980 - 1989 $3,624,991,132 237,387 1,153

1990 - 1999 $7,343,681,939 394,807 1,140

2000 - 2009 $77,909,321,960 1,311,047 1,312

2010 - 2019 $22,986,057,930 399,358 774

2020 or later $4,711,001,305 58,115 33

Grand Total $117,036,386,538 2,489,230 5,041
58

Historical PBGC Claims (continued)

Historical claims data, from 1974 to current, 

were analyzed.

• Dataset included plan names, participant counts, 

liability, and assets as of date of termination.

• The table displays plan and participant counts for 

terminated plans grouped by funded status 

ranges.

• 91% of terminated plans were underfunded (e.g., 

funded status less than 100%).

Funded Status Range Plan Count Participants

Less than 40% 8 1,259

50.00% - 59.99% 2,776 1,330,849

60.00% - 69.99% 621 498,617

70.00% - 79.99% 445 272,449

80.00% - 89.99% 335 179,463

90.00% - 99.99% 380 97,870

100.00% - 109.99% 270 87,532

110.00% - 119.99% 57 5,389

120.00% - 129.99% 45 2,935

130.00% - 139.99% 21 8,051

140.00% - 149.99% 16 967

150.00% or more 67 3,849

Grand Total 5,041 2,489,230

Methodology to Select Input Data for SE PIMS
59

Historical PBGC Claims (continued)

Methodology to Select Input Data for SE PIMS

Historical claims data, from 1974 to current, was 

analyzed. Dataset included plan names, participant 

counts, liability, and assets as of date of termination.

• The table displays plan and participant counts for 

terminated plans grouped by funded status ranges.

• Funded status less than 80% at termination:

— 76% of plans

— 84% of participants

— 87% of termination liability

• Funded status greater than, or equal, 100% at 

termination:

— 9% of plans

— 4% of participants

— 2% of termination liability

• 91% of terminated plans were underfunded.

Funded Status 

Range

Termination 

Liability (TL) Plan Count Participants

Less than 40% $0 8 1,259

50.00% - 59.99% $64,212,059,247 2,776 1,330,849

60.00% - 69.99% $22,324,628,568 621 498,617

70.00% - 79.99% $15,128,038,603 445 272,449

80.00% - 89.99% $10,696,521,954 335 179,463

90.00% - 99.99% $2,529,856,367 380 97,870

100.00% - 109.99% $1,782,492,160 270 87,532

110.00% - 119.99% $48,606,616 57 5,389

120.00% - 129.99% $25,574,623 45 2,935

130.00% - 139.99% $259,892,746 21 8,051

140.00% - 149.99% $6,801,641 16 967

150.00% or more $21,914,013 67 3,849

Grand Total $117,036,386,538 5,041 2,489,230
60

Historical PBGC Claims (continued)

Zoom in on Small Plans and Big Plans 

at termination using the FT Ranges.

• Data is stratified by funded status ranges.

• Small Plans make up,

— 98% of terminated plans

— 54% of participants

— 29% of Termination Liability (TL)

• Big Plans make up,

— 0.5% (23 plans) of terminated plans

— 27% of participants

— 52% of Termination Liability (TL)

Methodology to Select Input Data for SE PIMS

TL & FUNDED STATUS RANGE TL PLAN COUNT PARTICIPANTS

TL: $0M - $199.99M $33,739,339,943 4,964 1,353,554

Less than 40% $0 8 1,259

50.00% - 59.99% $18,504,547,154 2,741 706,010

60.00% - 69.99% $7,561,972,722 602 246,537

70.00% - 79.99% $3,355,761,510 432 142,588

80.00% - 89.99% $2,349,325,934 331 130,888

90.00% - 99.99% $1,355,138,280 377 75,195

100.00% - 109.99% $504,766,013 268 36,875

110.00% - 119.99% $48,606,616 57 5,389

120.00% - 129.99% $25,574,623 45 2,935

130.00% - 139.99% $4,931,437 20 1,062

140.00% - 149.99% $6,801,641 16 967

150.00% or more $21,914,013 67 3,849

TL: $1B or more $60,357,776,098 23 672,820

50.00% - 59.99% $36,749,148,282 13 459,798

60.00% - 69.99% $7,903,061,216 3 112,643

70.00% - 79.99% $7,358,370,580 3 51,804

80.00% - 89.99% $8,347,196,020 4 48,575

Grand Total (All Plans, See Prior Page) $117,036,386,538 5,041 2,489,230
61

Methodology to Select Input Data for SE PIMS

Historical PBGC Claims (continued)

• Chart depicts termination liability 

at date of termination. 

• Orange line is participant counts.

• Grouped by Funded Status 

range within FT Range.

• Small Plans and Big Plans have 

historically seen most of the 

action.
62

Methodology to Select Input Data for SE PIMS

Bringing It All Together:

Our analysis shows that the current sampling method generates good coverage and well aligned distributions compared to the 

universe when measured on a liability basis. This finding meets expectations since the methods center on the liability.

There are some areas that may be considered for improvement: Small Plans, underfunded plans, and normal cost. Small Plans and

underfunded plans are not well represented, and the normal cost is covered at less than 50%. Based on historical claims data, Small 

Plans and underfunded plans make up most defaults and the aggregate termination liability is significant (28%).

In our hypothesis we questioned if using different groupings and measures would show varied results. The analysis shows that when 

the basis is changed to plans, misalignment and lower coverage is observed across benefit type, operating status and industry. The 

analysis also shows that while the current grouping approach does create 'blind spots' by using only liability for weights, the net 

result is generally sufficient and reasonable.

We do not foresee significant improvements to projections from including additional plan distinctions in the development of the weight 

factors.

Additionally, given current system limitations around configuring plan provision details, any improvements gained by rebalancing on 

benefit type, participant, or plan basis, may be limited. Deficiencies shown for operating status and industry can be addressed by 

strategically adding plans and do not warrant direct changes to the weight factors.
63

Methodology to Select Input Data for SE PIMS

Maintain current SE 

sampling methods but 

consider 

strengthening areas

• Analysis indicated the SE sampling methods are well aligned relative 

to the universe of plans measured on a liability basis. 

• Increase areas of coverage as indicated by the analysis to enhance 

overall modeling and refine the results.

— Consider increasing representation of Small Plans (less than $200M in 

Funding Target Liability) and Underfunded Plans (assets < liability)

• Potential bundling approach of Small Plans as a new sample

• Potential adjustment handled and analyzed outside of PIMS

— Consider increasing Normal Cost coverage

— Consider periodic review of industries, specifically to identify distressed 

industries, and potentially include industry weighting

• Sample of 500 plans should be reviewed regularly to ensure 

reasonable representation.

Key Recommendations:
64

Statement of Objectives 

from Scope of Services
65

Development of Plan Participant Profiles

Our Understanding

& 

Observations

• We understand that the PBGC uses the age/service matrix to create 

the grouped active data population.

• PIMS estimates the age/benefit distribution of the inactive population 

by performing a 100-year projection of the current active population 

and then scales the counts to the actual inactive count and then 

scales benefits to match the initial liabilities.

• There are multiple calibration factors used to align the sample plan’s 

Schedule SB initial liabilities by status and normal costs.

— Two sets of calibration factors are used for active liabilities:

• “Retrospective” calibration factor to align the initial funding target

• “Prospective” calibration factor to align the initial target normal cost

— PIMS also uses separate calibration factors for the initial deferred 

vested and retiree liabilities.
66

Development of Plan Participant Profiles

Recommendations

• Consider requesting cash flows, which are now generally available 

from annual actuarial valuation processing and can be provided 

annually.

• Leading actuarial valuation software is designed to generate cash 

flows, which will minimize any additional burden on plan sponsors.

• This will help:

— Validate the cashflows generated by PIMS

— Adjust the cashflows of the guaranteed benefits in PIMS

— Confirm whether the interest rate sensitivities of the sample plans are 

reasonable (convexity and duration)

— Adjust any future benefits payment patterns in PIMS, as needed

— Reduce number of calibration steps

— By offering transparency of financial implications of holding a Plan, and 

assisting all stakeholders in the understanding of managing pension risks
67

Standard Data Input Assessment of SE PIMS

Our Understanding

& 

Observations

• The data used in PIMS comes from the following sources:

— Census file, plan provisions, and valuation assumptions are collected via 

government filings and summarized by a contractor

— Firm data used to estimate bankruptcy probabilities is sourced from 

CompuStat

— Capital market assumptions are collected from Ibbotson Associates

— Summary of Findings (see Appendix A):

Source of Data Fields Percentage

Not Used 48%

Sourced from Filing and Attachments 18%

Sourced from Third Party Providers 10%

Produced by PRAD 9%

Prescribed 3%

Estimated 3%
68

Standard Data Input Assessment of SE PIMS

Recommendations

• Consider deleting data fields that are not used to create efficiencies 

and avoid errors.

• Consider expanding service brackets of the Schedule SB matrix to 

improve PIMS census file:

— Ungroup after an agreed upon service amount

— Ungroup after an agreed upon age

• Consider incorporating firm financial information from PBGC 4010 

filings for privately held companies with large, underfunded plans in 

the sample data.

• Consider updating RP-2014 based mortality tables to Pri-2012 based 

mortality tables to align with most recent tables published by the SOA.
69

Standard Data Input Assessment of SE PIMS

Recommendations 

(continued)

• PIMS only values single life annuity form of payment, assumes annual 

payment, and assumes all participants are male.

— Consider adding male/female percentage assumption to account 

for longer life expectancy of females.

— Consider making joint and survivor annuity assumption to reflect 

beneficiary life expectancy.

• Consider requesting the following attachment in the Schedule SB to 

improve the quality of the inactive file used in PIMS:

— An attachment with the average age and average benefit amounts 

for terminated vested participants, retirees, and beneficiaries.

• This information is readily available in actuarial valuation reports.

• Can assist with calibration of projected inactive cash flows.
70

Data Input Items from Form 5500 Used to Develop 

Expected Cash Flows

Our Understanding

& 

Observations

• Same process is used for grouped (non-seriatim) data for SE and ME.

• PBGC has opted to maintain the Actuarial Services Division’s 

assumptions for SE.

• Data to generate expected cash flows comes from Form 5500 Filings 

via PIMS Data Entry Program, or DEP, (combination of automated and 

manual data entry) as well as actuarial valuation reports (AVRs).

• Plan is currently condensed down to three participants – one active, 

one deferred vested, and one retiree:

— Based on average age of each group from AVRs

— Retiree monthly benefit reported from AVRs

— Active and deferred vested monthly benefits are determined based 

on liability, average age, and assumed retirement age
71

Data Input Items from Form 5500 Used to Develop 

Expected Cash Flows

Recommendations

• To ensure accurate cash flows, consider requesting SE projected cash 

flows be electronically submitted as part of the Schedule SB and/or 

PBGC filing, as mentioned earlier.

• If requesting cash flows is not feasible, consider adding more 

representative participants to allow for cash flows to be less 

concentrated in the mid-term time horizon and achieve improved 

accuracy overall – primarily for deferred vested participants.

— In the current model used to estimate short-term and long-term 

cash flows, distribution of ages is not extensive.

— Consider using three representative deferred vested participants 

(rather than just one) to scale cash flows – average age and 

average age +/- a specified number of years.
72

Availability of Form 5500 Data and Adjustments to 

Create a Uniform Data Set

Our Understanding

& 

Observations

• Form 5500 data items including the Schedule SB are two to three 

years old and compiled by the DEP, which includes automated and 

manual entry by a third-party contractor. 

• For non-seriatim data, PIMS starting liabilities, asset values and 

census information are projected forward from the Form 5500 

snapshot date to the current PIMS projection date using deterministic 

assumptions based on known economic impact.

• We understand that the starting position is treated as follows:

— If plan year begins January 1, Schedule SB data is as of January 1.

— If plan year begins between January 1 and June 30, Schedule SB data is 

treated as January 1 plan year beginning in the current calendar year.

— If plan year begins between July 1 and December 31, Schedule SB data is 

treated as January 1 plan year beginning in following calendar year (but 

using current year regulations).
73

Availability of Form 5500 Data and Adjustments to 

Create a Uniform Data Set

Recommendations

• Consider using the latest PBGC filing information with more recent 

liability, asset values, and census count information to adjust projected 

starting point liabilities, if available. Continue to use timing 

adjustments for non-calendar plans as needed.

• Consider updates to reflect American Rescue Plan (ARP) for FY 2021:

— May need to incorporate ARP inputs for plans electing to implement freshstart 15-year amortization in 2019-2022.

— Contribution patterns in the short-term and long-term may change:

• Plan sponsors who elected ARP to lower minimum required contribution 

(MRC), may only pay the new, lower MRC.

• Additional interest rate relief under the Infrastructure Investment and 

Jobs Act may change contribution patterns for an extended period.

— Plans in distress are the most likely to elect immediate relief and represent 

the greatest risk to the PBGC.
74

Plan Status, Benefit Design, and Features to be Valued

Our Understanding

& 

Observations

• We understand that PIMS currently does not use the following data 

inputs, which may underestimate the stochastic liabilities; however, 

calibration factors are used to scale up to the starting liabilities:

— Estimated male/female split in the SE plan universe. PIMS 

currently uses a 100% male data set.

— Marriage percentage to reflect beneficiaries.

— For participants currently over age 65, there are no late retirement 

adjustments.

— Supplementary and ancillary benefits are not valued (may not be 

guaranteed benefits).
75

Plan Status, Benefit Design, and Features to be Valued

Recommendations

• Consider requesting electronic cashflows in future Schedule SB 

filings, as discussed earlier, to improve the precision of projected 

liabilities.

— Would this change be considered a burden on plan sponsors 

complying with the Paperwork Reduction Act?

— Could enhanced precision of forecast be used to justify additional 

paperwork burden?
76

Plan Sponsor’s Financial Information Used as Input to 

Determine Future Bankruptcies

Our Understanding

& 

Observations

• PIMS bankruptcy probability estimates are weighted heavily on the 

following historical CompuStat data: 

— Leverage, cashflow, firm size/employment, pension plans’ funding 

ratio, and if the plan is in the financial/utility industry

• We understand that the PBGC uses empirical estimates of the 

volatility of bond ratings to estimate bankruptcy probabilities over time.

• Historic bankruptcy probabilities affiliated with bond ratings are used 

to assign bankruptcy estimates in the stochastic model as described in 

the PIMS System Description.

• To ensure that bankruptcy estimates are reasonably related to existing 

default estimates, PBGC compared the predicted bankruptcy 

probabilities against Standard and Poor's subordinated debt ratings for 

those firms that have reported ratings from 1980-1996.
77

Plan Sponsor’s Financial Information Used as Input to 

Determine Future Bankruptcies

Recommendations

• Consider using the following to refine bankruptcy probabilities:

— Additional CompuStat data, such as profitability, liquidity, solvency 

and activity ratios used in the Altman Z score

— Hedge ratios of the sample plans found in the asset allocation and 

duration of the fixed income security sections of the Schedule R

• Hedge Ratio may have small impact on sponsor bankruptcy, but 

may affect likelihood of plan insolvency (and degree of PBGC 

responsibility) in event of bankruptcy

— PBGC 4010 filing firm financial information for plans with large 

unfunded liabilities or PBGC Form 10 – Early Warning System or 

Missed Quarterly Contributions

— Risk Transfer Transactions (Lump sum windows, annuity 

purchases, etc.) reported in the PBGC filings
78

Plan Sponsor’s Financial Information Used as Input to 

Determine Future Bankruptcies

Recommendations 

(continued)

• We partnered with Conning and Company to review the bankruptcy 

modeling aspect of PIMS.

• The findings of that analysis (included as Appendix B) confirm that the 

PBGC approach, including use of Logit distribution, is reasonable.

• Conning recommends considering additional macroeconomic 

variables to be added to the Logit regression model, such as GDP 

growth.

• Consider rerunning regression analysis (perhaps every 5 years) to 

assess whether weights currently assigned to different variables 

remain the most appropriate predictors of future bankruptcy 

probabilities.
79

Economic and Regulatory Inputs

Our Understanding

& 

Observations

• PIMS currently uses a fixed asset allocation data set for all sample 

plans in the projection:

— 48% stock market returns

— 22% long-term 30 Year Treasury bond returns

— 30% long-term 30 Year Treasury bond yields

• This is from an older study that may no longer represent the asset 

allocation of the entire SE universe.

• PIMS currently does not use the asset allocation in the Schedule R to 

project assets of the entire SE universe or any LDI strategies because 

of current system limitations.

• We understand the inflation assumption currently used in PIMS is 

based on the Social Security Intermediate Assumption (currently 2.4% 

in 2020 Trustees Report).
80

Economic and Regulatory Inputs

Recommendations

• Consider using the latest asset allocation of the entire SE universe 

from the Form 5500 Schedule R as a guide to update the current PIMs 

asset allocation, if needed, or to adjust the asset allocation annually 

during the projection process. 

— May improve the ability of PIMS to estimate the future asset 

performance of the SE universe.

• Consider incorporating lump sum and annuity payout assumptions for 

the sample plans and SE universe found in the Schedule SB 

attachments, Part V (Statement of Actuarial Assumptions and 

Methods).

— Cash outs driven by the low interest environment have a negative 

impact on a plan’s funded ratio (though less impact on funded 

status in dollars) if the funding ratio is below 100%.
81

PBGC Asset, Liability, and Premium Information

Our Understanding

& 

Observations

• Frozen/ongoing (FY20: 62.6% / 37.4%) plan breakdown of the SE 

universe is currently used in estimating PBGC premiums in PIMS.

• PBGC liabilities currently use projected PBGC interest rates based on 

30 Year Treasury rate plus 42 bps spread (historical spread over the 

past three years) in latest projection, and PBGC assets are currently 

projected to 12/31.
82

PBGC Asset, Liability, and Premium Information

Recommendations

• Consider reflecting a “Risk Transfer Activity” assumption in PIMS 

because it reduces PBGC premiums over the projection period while 

leaving the plan’s larger liabilities with the plan sponsor. 

• Consider using the 30-year Treasury rates plus the “appropriate 

spread” that results from the stochastic models instead of trying to 

constrain the results using historical spreads over the past three or 

four years to determine a forward looking PBGC interest rate.
83

Zone Status Availability for ME Plans

Our Understanding

& 

Observations

• Plan Zone Status is taken from the most recently available Form 5500 

Filing Schedule MB. 

• This will often not be the most recently available Zone Status 

information, and as a result, information is taken from the annual zone 

certification submissions to supplement the deficiency.

— Only plans issued as Critical and Declining are updated from 

currently available certification status information filed with the 

DOL.
84

Zone Status Availability for ME Plans

Recommendations

• Consider reflecting Zone Status information from latest available 

annual zone status certification to avoid using outdated information 

resulting from a lag in Schedule MB reporting.

— IRS annually receives zone status certifications for each 

multiemployer plan certified by the plan’s actuary. 

• Request IRS tracking information for these submissions in a 

readable format to gain access to more current information for all 

plans in the ME system.

• If possible, request for the PBGC to be included as a required 

recipient for the annual zone certification submission email 

provided to epcu@irs.gov.

— Consider adding current plan year zone status to the list of items 

submitted for “plan information” with the annual PBGC premium 

filing to gain more current information.
85

Zone Status Availability for ME Plans

Recommendations 

(continued)

• Consider reviewing projections for plans that are generally available 

via Funding Improvement Plan and Rehabilitation Plan annual 

monitoring to have more data points for modeling reasonability review 

rather than a snapshot Zone Status, which can be compiled in 

connection with Zone Status research.

— Information may be obtained from the IRS or research in the Form 5500 

Filings or fund websites.

• If projections reviewed per the recommendation above, also consider 

adding a request for Green plans to submit projections annually to 

have more data points for modeling reasonability review and a more 

comprehensive look at projected multiemployer system health.

— Even if a plan is not Endangered or Critical, the actuary still prepares 

annual projections to certify Green

• Information is available as part of the annual actuarial valuation work.
86

Standard Data Input Assessment of ME PIMS

Our Understanding

& 

Observations

• Data inputs and demographic assumptions from Form 5500 filings and 

supplemental information from the Central States Plan are used to 

generate cash flows for each plan in the ME Universe.

• Demographic assumptions include restriction that no employee is 

older than 65.

• Mortality assumptions reflect RP-2014 base table with static projection 

to 2032 using the MP-2019 improvement scale.

• Active counts are collected from the Schedule MB information, with 

subsequent years rolling forward with stochastic population 

growth/(decline) under the population sub-model – population decline 

has a mean of -1.3% and a standard deviation of 8%.

• It is assumed that assets are invested under a single allocation.
87

Standard Data Input Assessment of ME PIMS

Recommendations

• Consider requesting cash flows, which are now generally available 

from annual actuarial valuation processing and can be provided 

annually.

• Leading actuarial valuation software is designed to generate cash 

flows, which will minimize any additional burden on plan sponsors.

• This will help:

— Validate the cashflows generated by PIMS

— Adjust the cashflows of the guaranteed benefits in PIMS

— Confirm whether the interest rate sensitivities are reasonable (convexity 

and duration)

— Adjust any future benefits payment patterns in PIMS, as needed

— Reduce number of calibration steps

— By offering transparency of financial implications of holding a Plan, and 

assisting all stakeholders in the understanding of managing pension risks
88

Standard Data Input Assessment of ME PIMS

Recommendations 

(continued)

• Consider reviewing assumptions and/or data by industry to streamline 

inputs and processing.

• Data currently available to the PBGC for Central States does carry 

considerable weight due to size, however, that plan does not capture 

the full view of the ME universe.

— Reviewing the chart on the following slide demonstrates that while 

Central States is a large fund, it is still a small percentage of the 

entire ME universe.

• The chart summarizes information for the ME funds that have the 10 

highest Current Liability amounts as reported in the data for the 2020 

Projection Report. 

• These funds vary by industry, participants, and zone status (most of 

which are Green).
89

Standard Data Input Assessment of ME PIMS

Plan Zone 

Status

Current 

Liability 

(000's)

% of ME 

Universe

Total 

Participant 

Count

% of ME 

Universe Industry

WESTERN CONFERENCE OF TEAMSTERS PENSION PLAN Green 58,688,471 6.03% 384,948 5.79% Transportation & 

Warehousing

CENTRAL STATES, SOUTHEAST & SOUTHWEST AREAS 

PENSION PLAN

Critical and 

Declining 17,632,202 4.78% 72,098 3.66% Transportation & Warehousing

CENTRAL PENSION FUND OF THE IUOE & PARTICIPATING 

EMPLOYERS Green 74,083,306 2.95% 609,637 1.92% Construction

NATIONAL ELECTRICAL BENEFIT FUND Green 36,267,147 2.81% 202,017 5.28% Construction

1199SEIU HEALTH CARE EMPLOYEES PENSION FUND Green 34,440,301 2.14% 555,962 2.51% Other Industry

IAM NATIONAL PENSION FUND Green 25,322,193 2.06% 280,019 2.66% Manufacturing

BOILERMAKER-BLACKSMITH NATIONAL PENSION TRUST Endangered 26,211,969 1.65% 264,517 0.69% Construction

NEW ENGLAND TEAMSTERS & TRUCKING INDUSTRY 

PENSION

Critical and 

Declining 20,275,441 1.44% 72,703 0.69% Transportation & Warehousing

PLUMBERS AND PIPEFITTERS NATIONAL PENSION FUND Endangered 15,333,674 1.25% 149,498 1.42% Construction

SHEET METAL WORKERS' NATIONAL PENSION FUND Endangered 14,839,784 1.21% 139,772 1.33% Construction
90

Standard Data Input Assessment of ME PIMS

Recommendations 

(continued)

• Consider Special Financial Assistance (SFA) Data

— Focus on Central States data has been mainly due to availability.

— Consider utilizing all the data delivered to the PBGC with the SFA 

application to adjust the ME PIMS projection model.

• Data for approximately 250 plans in the ME system will soon become 

available to PBGC.

• Census files, cash flows, projections, extensive documentation, etc.

— Utilize the application information to refine current methodologies 

and/or review modeling for reasonability.

— Utilize actual data to generate cash flows, if feasible.

— Also review data of denied applications for plans ultimately deemed 

ineligible for SFA but may be in jeopardy.

• Consider more direct focus in modeling on those plans that narrowly 

miss eligibility for SFA.
91

Standard Data Input Assessment of ME PIMS

Recommendations 

(continued)

• Consider Special Financial Assistance (SFA) Adjustments and 

behavior trends.

— ME PIMS has already considered adjustments for plans electing to 

receive SFA.

• Listing of eligible plans for SFA has been predetermined by PBGC

• Adjustments for SFA amounts

• Behavior modeling including adjustments to contributions and investment 

returns

— May consider creating a probability of SFA elections based on 

comments and feedback on the interim rules published July 2021.

• Commenters indicate possible hesitation for plans that received MPRA 

benefit suspensions to elect SFA relief because of potential conflicts in 

fiduciary responsibility for the plan’s active and retiree populations.

• We believe it is not certain that plans that applied for MPRA relief will 

elect SFA and recommend allowing for this possible behavior.
92

Standard Data Input Assessment of ME PIMS

Recommendations 

(continued)

• Consider extending retirement age beyond age 65 for actives. Current 

retirement trends find workers regularly remaining in active service 

beyond age 65 to receive continued pay and benefits.

— Social Security Normal Retirement Age for a majority of the active 

workforce is between 66 and 67 years old and the Minimum 

Required Distribution Age was recently increased to 72 years old.

— If commencement beyond 65 is implemented for deferred vesteds 

(DVs) as well, actuarial increases can be combined with a forfeiture 

assumption.

• Determine average percentage of DV population over 65 from plans 

managed by the PBGC or request a DV scatter or age distribution be 

included in the Schedule MB submission.

— In our experience, extended retirement assumptions for actives 

and DVs have had a material impact on results.
93

Standard Data Input Assessment of ME PIMS

Recommendations 

(continued)

• Consider reviewing mortality assumptions reflecting Pri-2012 and 

projected improvement scale on a generational basis. 

— Review industries and consider potential blue collar, partial or full weighted, 

adjustments. 

• Consider short-term and/or long-term adjustments to the population 

growth/(decline) assumption.

— Review plan workforce increases/(decreases) from Form 5500 data to 

identify industry workforce patterns. Consider adjusting underlying 

stochastic assumptions to reflect industry specific workforce patterns.

— Review of the recently completed AACG report substantiates our 

suggestion that the population growth expectation should be reviewed 

based on other factors since experience is not uniform. 

• Study includes additional data points and a more expansive assumption 

base, including industry trends captured through plan maturity and zone 

certification status as well as economic trends.
94

Standard Data Input Assessment of ME PIMS

Recommendations 

(continued)

• Consider short-term and/or long-term adjustments to the contribution 

assumptions.

— Review contribution inputs from Form 5500 data. 

— For 2020 base year data, many employer contributions were lower 

than usual due to COVID-19 workforce and hours reductions. 

— This trend may continue for a short period of time or may have 

been a one-time decrease depending on the industry. 

— Consider setting an average contribution for the baseline of the 

projection rather than including snapshot data. 

— Also consider reflecting industry workforce growth or decline in 

contribution projections.

• Consider using the asset allocation information from the Schedule R 

of the Form 5500 data to project the assets accordingly.
95

Standard Data Input Assessment of ME PIMS

How to Identify 

Participating 

Employers

• We are not aware of any method of obtaining information for 

all participating employers outside of direct request to the plans.

• Consider updating PBGC multiemployer plan reporting requirements 

to obtain more information about participating employers. 

• Consider reviewing the current Schedule R reporting of employers 

contributing more than 5% of total contributions to the fund to identify 

the largest participating employers of each plan.

— Incorporating this as a standard process may be beneficial and 

provide further information to the PBGC even if recently 

proposed changes to the Schedule R to include the top 10 highest 

contributing employers regardless of whether they contributed 5% 

of total contributions are not accepted.
96

Calibration Review
97

Calibration of Sample Plans/Firms to the SE Universe

See Methodology Deployed to Select Representative Plans as the Input Data for SE PIMS Section 

on Page 20.
98

Calibration of Plan Benefits

Our Understanding

&

Observations

• For plans that are still accruing pension benefits and have multiple 

divisions, we understand that one accrual rate is chosen (due to PIMS 

current system limitations) to represent several accrual rates in a 

sample plan.

— The chosen accrual rate is selected by making a general 

assumption per user discretion or using the accrual rate used by 

the greatest number of active employees.

• In some situations, this method might not produce the closest 

accrual rate reflected in the normal cost
99

Calibration of Plan Benefits

Recommendations

• Consider using a sample plan’s accrual rate that is closest to the 

accrual rate reflected in the normal cost rate (normal cost ÷ total 

payroll) for ongoing plans with multiple divisions accruing under 

different benefit formulas under a single sample plan – results in an 

unbiased selection process.
100

Calibration of Plan Benefits

Recommendations 

(continued)

• Consider using Schedule SB liabilities projected over 10 years as a 

reasonability check:

— Use a single interest rate scenario.

— Compare the Schedule SB projected liabilities to the PIMS liabilities and 

normal costs to make sure the numbers are reasonably aligned and are 

within a tolerance level acceptable to the PBGC.

• Consider incorporating the possibility of a plan freeze for ongoing 

plans in future projections to reflect how the SE universe might 

change over time by:

— Reviewing plans with lower funded ratios through the PBGC 4010 filing

— Reviewing companies having financial difficulties through the PBGC Form 

10 filing

— Reviewing volume of remaining funding balances in the plan through the 

Schedule SB filing
101

Calibration of Plan Benefits

Recommendations 

(continued)

• Consider potential long-term idea, if changes to SE PIMS are 

contemplated that reflect impact of hedging on PBGC’s exposure:

— Consider collecting (at least for frozen plans) data on asset allocations to 

reflect interest rate risk exposure and how plan sponsors are willing to take 

on lower expected returns so plans can eventually transfer risks by:

• Looking at the plan’s funded status based on the accounting liabilities, 

which will need to be estimated.

• Reviewing frozen plans that have glide paths which increases asset 

exposure to fixed income securities as accounting funded ratios 

increase.

— This change could be considered in the context of adjusting variable 

premiums to reflect exposure to interest rate risk, which would presumably 

require legislation.

— Interest rate hedging may have modest impact on sponsor bankruptcy, if 

contribution obligations are driving insolvency. May have real impact on 

degree of PBGC obligation.
102

Calibration of Starting Position Plans 

Out of scope. The review focus is limited to ongoing Single-employer and multiemployer pension 

plans as inputs to SE-PIMS and ME-PIMS models.
103

Calibration of Cashflows for ME Plans

Our Understanding

&

Observations

• Calibrations are run in two separate stages:

— First Stage includes use of a macro to find the optimal age, service and accrual 

adjustments to best match Current Liability and benefit payment information 

from the Schedule MB.

— Second Stage uses Actuarial Services and Technology Department mortality to 

project these determined demographics into future years, allowing the 

calculation of the benefit payment streams.

• The active calibration includes specific demographic statistics and 

assumptions for approximately 350 plans. 

• The inactive calibration uses Central States as a basis for both age 

and service calibration (within a range of +/- 5 years) and to create an 

accrual pattern calibration – baseline, steepened, flattened.

• Calibrations are based on full plan benefits, not guaranteed benefits.

• Active retirement age assumption calibration uses a maximum of 65.
104

Calibration of Cashflows for ME Plans

Recommendations

• Consider a potential move to an industry specific starting point for 

inactive calibration since ME plans are generally comprised of 

employers within the same industry. 

— If other plan data is unavailable, cash flow data for plans by 

industry may be a better resource to help refine the process. See 

recommendations on next slide regarding cash flow collection.

• Consider periodically reviewing the guaranteed benefit streams of 

plans recently taken over by the PBGC to gauge accuracy of cash 

flow calibrations.

• Consider extending retirement age beyond age 65 for active cash flow 

calibrations as previously mentioned since trends find workers 

regularly remaining in active service beyond age 65.
105

Calibration of Cashflows for ME Plans

Recommendations 

(continued)

• Consider annually reviewing the 10-year cash flow projection included 

as an attachment to the Schedule MB Filing in order to review 

calibrated cash flows for reasonability.

— Cash flows are not reported by status but can be used in aggregate 

as a reasonability measure for ME PIMS.

— Requires manual data entry but potentially request a future 

Schedule MB update to include projected cash flows as part of the 

electronic form submission rather than attachments to ease data 

collection efforts. This update could potentially include cash flows 

by status.
106

Appendix
107

Appendix A
108

Standard Data of SE Plan

SE Data 

Structure

Inputs Not Used

Produced 

by PRAD

Sourced 

from 3rd 

Party Prescribed

PIMS ID 

Metadata

Internal 

System 

Calc

Sourced 

from 

Filings

Sourced 

from Filing 

Attachment Assumed Estimated

Used 

Prior 

Year Unknown Total

Economy 186 21 35 3 2 2 249

Firm 72 31 71 12 7 193

IRS 66 8 32 13 8 127

PBGC 97 24 20 10 4 1 1 157

Plan 

Common 84 9 7 1 26 127

Plan Detail 43 16 4 4 43 11 4 3 6 0 134

Observations
109

Standard Data of SE Plan

Observations (continued)

SE Data 

Structure

Inputs Not Used

Produced 

by PRAD

Sourced 

from 3rd 

Party Prescribed

PIMS ID 

Metadata

Internal 

System 

Calc

Sourced 

from 

Filings

Sourced 

from Filing 

Attachment Assumed Estimated

Used 

Prior 

Year Unknown Total

Plan 

Census 

Data

12 2 2 3 3 9 31

Plan Benefit 

Formulas 30 5 12 150 8 205

Plan 

Experience 

Decrements

31 6 2 1 4 2 8 5 59

Plan Benefit 11 1 6 4 5 9 36

Total 632 116 135 46 62 30 70 173 6 37 6 5 1,318

As Percent 48% 9% 10% 3% 5% 2% 5% 13% 1% 3% 1% 0% 100%
110

Funded Status – Overfunded Coverage
111

Funded Status – Underfunded Coverage
112

Funding Target Normal Cost – Coverage
113

Industry – Distribution 
114

Industry – Coverage
115

Historical Claims
116

Appendix B
MHZ/6004047.2

Modeling Methodology 

Review

Conducted by Conning, Inc. on behalf of 

Buck Consulting
CONNBUCKNOV2021 – Page 2

MHZ/6004047.2

Table of Contents

1 Executive Summary ...................................................................................................... 3

2 Introduction and Scope.................................................................................................. 3

3 Sponsor Bankruptcy Modeling ...................................................................................... 4

3.1 Summary of Approach to Bankruptcy Modeling ..................................................... 4

3.2 Strengths and Limitations of Proposed Approaches................................................. 5

3.3 Comments on the Appropriateness of Methodologies.............................................. 6

3.4 Extensions and Other Credit Risk Modeling Approaches ........................................ 7

3.4.1 Extended Logit Model Using Splines............................................................... 7

3.4.2 Hazard Models................................................................................................. 8

3.4.3 Neural Networks.............................................................................................. 8

3.4.4 Structural Credit Models.................................................................................. 9

3.4.5 Reduced Form Credit Models ........................................................................ 11

3.5 References ............................................................................................................ 12
CONNBUCKNOV2021 – Page 3

MHZ/6004047.2

1 Executive Summary

• Review conducted by quantitative finance experts from Conning, Inc.’s Risk Solutions

group.

• Conning, Inc.’s Risk Solutions group conducted a research review of two pension 

sponsor bankruptcy models. The models considered were one based on the Altman Zscore and one based on logistic regression.

• The Altman Z-score model is considered by the authors to be a viable and appropriate 

model but lists some of its limitations.

• The authors note that the number of drivers of default in the Alman Z-score might not 

be sufficient to capture the complexity of corporate defaults.

• The logistic model is considered by the authors to be superior to the Alman Z-score due 

to the higher number of driving factors, the scope to extend the model and the use of 

statistical estimation techniques to estimate the model robustly.

• Because all models have limitations the authors suggest that users of moth modeling 

approaches consider continual monitoring of the model inputs, the use of scenario 

analysis and stress testing of the model and model extensions which might mitigate 

some of the limitations.

• The authors conducted a thorough review of the academic literature and summarize 

their findings in this report. A full set of references is also given.

2 Introduction and Scope

This report details the results of a review undertaken by Conning, Inc.’s Risk Solutions group

on behalf of Buck Consulting. The defined scope of the project is to review aspects of the 

current process implemented by the PBGC as well as changes to the process that Buck 

Consulting might recommend. Conning, Inc.’s Risk Solutions group is particularly expert in 

areas of quantitative finance, financial modeling, statistical analysis, econometrics, and 

stochastic modeling. 

The following area of the PBGC’s processes and methodologies have been identified for 

review, where Conning’s expertise may be of relevance. 

• Sponsor Bankruptcy Modeling: As part of the PBGC’s forecasting model, it has the 

method of predicting plan sponsor bankruptcy that uses some elements of the “Altman 

Z-Score”, that have been tailored to match the data available to the PBGC. Buck 

Consulting is interested in Conning’s view concerning the bankruptcy model as well as 

other approaches to credit risk modeling. Buck Consulting would like to hear Conning’s

views on how the PBGC is doing its modeling, and whether there are elements of what 

Conning would do for credit risk which might be applicable or recommended.

We now consider this area in some detail and comment on our findings.
CONNBUCKNOV2021 – Page 4

MHZ/6004047.2

3 Sponsor Bankruptcy Modeling

The scope of this section is to review two approaches to sponsor bankruptcy modeling that 

have been proposed. These are the Altman Z-Score and the Logit model. Specifically, this 

section will be concerned with:

• Reviewing, understanding, and summarizing the two model approaches.

• Highlighting the strengths and limitations of the model approaches.

• Detailing alternative approaches from the academic literature elements of which could 

be considered in the future.

• Suggesting other possible extensions for future consideration.

• Commenting on the appropriateness of the two modeling approaches to the task at hand.

3.1 Summary of Approach to Bankruptcy Modeling

To date, the PBGC has been using a model based on the Altman Z-score for modeling 

bankruptcy. Briefly one can calculate the Altman Z-score as follows:

Altman Z-Score = 1.2A + 1.4B + 3.3C + 0.6D + 1.0E

Where:

• A = working capital / total assets

• B = retained earnings / total assets

• C = earnings before interest and tax / total assets

• D = market value of equity / total liabilities

• E = sales / total assets

A score below 1.8 means a company is likely headed for bankruptcy, while companies with 

scores above 3 are not likely to go bankrupt.

Proposed as an alternative is a Logit or Logistic regression model where the probability of 

default of a firm i at time t has the form;

......................................................... [1]

where Bit is a binary variable equal to one if the firm enters bankruptcy in year t, Xit is the 

vector of variables affecting the probability of bankruptcy for firm i in year t, β is a vector of 

parameters to be estimated, and Xit.β is the inner product of these two vectors.

The vector of variables consists of financial and economic variables relevant to default such as 

leverage, cash flow, size and change in employment levels. 

The parameters β are estimated from 30,000 firm year observations from 1980 to 1997, of firms 

that recorded a bankruptcy as well as those that didn’t. Either the firms bankrupted (1) or not 

(0). Logistic regression is used to determine the model parameters.
CONNBUCKNOV2021 – Page 5

MHZ/6004047.2

3.2 Strengths and Limitations of Proposed Approaches

Following a review of the information provided to Conning and further research, the following 

section lists some of the strengths and limitations of the two approaches.

Altman Z-Score:

Strengths:

• Simple to implement and understand

• Gives a good short-term view of credit/default risk and how it is changing

• Default probability increased prior to 2007 as credit markets generally deteriorated

Limitations:

• No stochastic component meaning that there is no assessment of potential unexpected 

losses from default probability fluctuations in the future

• Entirely linear relationship between the covariates and the bankruptcy probability. 

These are known to be non-linear [1]

• Number of covariates in the model may be insufficient to capture the complexity of 

corporate bankruptcy

• May have overestimated actual default during the 2008 crisis

• No link to models of external market factors like a sudden credit shock or sharp fall in 

equity prices

• No modeling of correlation between the different sponsors (i.e. if one sponsor is 

bankrupt does the probability that another is bankrupt increase?)

• Coefficients scaling the covariates seem to be largely empirically derived and may not 

be statistically speaking the best values for the model

• Models based on company balance sheet data may be prone to error in rare cases where 

such data have been fraudulently falsified

Logit Model:

Strengths:

• Larger number of variables are incorporated when compared to the Altman Z-score

• Estimation is relatively straight forward from empirical data using logistical regression

• May be more rigorous than the Altmann Z-score with the coefficients being estimated 

from regression rather than empirically/qualitatively

• Large body of research on the approach and extensions to it

Limitations:

• Data window from 1980 to 1997 may miss some important drivers of bankruptcy in the 

recent data
CONNBUCKNOV2021 – Page 6

MHZ/6004047.2

• Entirely linear relationship between the covariates and the bankruptcy probability. 

These are known to be non-linear [1]

• Estimation of the model is tilted to larger firms. Dynamics of smaller firms may be 

different

• No stochastic component meaning that there is no assessment of potential unexpected 

losses from default probability fluctuations in the future

• No link to models of external market factors like a sudden credit shock or sharp fall in 

equity prices

• No modeling of correlation between the different sponsors (i.e. if one sponsor is 

bankrupt does the probability that another is bankrupt increase?)

• Models based on company balance sheet data may be prone to error in rare cases where 

such data have been fraudulently falsified

3.3 Comments on the Appropriateness of Methodologies

Conning find that both the Altman Z-Score and Logit model proposed are appropriate to the 

task of assessing the probability of bankruptcy for pension fund liabilities covered by the 

PBGC. Both models have some limitations, however, this is true of all models. A review of the 

academic literature (using the JSTOR database) suggests the Logit (logistic) model could be 

considered the standard model from the academic literature and has a wide and long-standing

body of research behind it. We would consider then the logistic model methodology to be a 

superior choice over the Altman Z-Score. 

It is important however to understand what the limitations listed mean in terms of how the 

output from any model is interpreted and what other mitigation procedures might be prudent. 

For both models, the following should be considered:

1. How often the model is run. Given that the input data is at “time 0” each time the 

model is run and given that we know that credit quality can deteriorate quickly leading 

up to default it is important to consider how often the model is run. Annually is 

potentially not frequently enough.

2. Monitoring inputs. It might be important to monitor all or a subset of covariates for 

sudden large changes at a higher frequency than normal model run times. Changes 

above a certain threshold or a large number of changes across many sponsors could be 

flagged to an analyst for further review or trigger an extraordinary run of the model.

3. Scenario analysis and stress testing. Given that neither model uses historical data or 

has a link to any stochastic models of market risk factors scenario analysis and stress 

testing of the model should be considered to understand the spread of likely values for 

the probability of bankruptcy in stressed markets. At least the covariates should each 

be individually and jointly stressed to understand what might happen under changing 

market conditions.

4. Model Extensions. The academic literature has many interesting extensions which 

could be considered. Some of these are explored in subsequent sections. In particular,

the inclusion of market and macroeconomic factors, a consideration of correlation 
CONNBUCKNOV2021 – Page 7

MHZ/6004047.2

between sponsors and with financial market factors and the inclusion of stochastic 

projections might be considered.

3.4 Extensions and Other Credit Risk Modeling Approaches

Conning has undertaken a review of the academic literature centered on corporate bankruptcy 

modeling. This section is intended to give a brief review of some of the most promising and 

relevant models, as well as those which contain suggested improvements to the logit model 

class. This section also acts as a short primer on credit risk modeling approaches commonly 

adopted in financial modeling. This might serve to inform future work or aspects of these

modeling approaches which might be incorporated into the PBGC’s approach.

It is not intended that this section serves as a full specification of model implementation but 

rather a guide to the ideas that prevail in the literature.

3.4.1 Extended Logit Model Using Splines

In [1] the author explores ways of extending the specification of the logistical model using 

splines of the covariate financial ratios. The logistic model proposed in this paper also includes 

macro-economic variables as well as firm specific variables which might serve as another 

useful extension to be explored. The primary advantage of the splines approach is that 

nonlinearity in the dependency of the probability of bankruptcy with respect to the input 

covariates can be modeled. The predictive power of the model was also shown to improve by 

70-90%. Independently and using a different mode of analysis improvements in predictive 

power from modeling non-linearity were shown in [3].

In [1] an extension is proposed in which the normal specification of the logistic model is given 

as;

Where Pi,t is the probability of bankruptcy, α is an intercept, xi,t-1 is a vector of firm specific 

covariates with coefficients β and zt-1 is a vector of macroeconomic variables with coefficients 

γ.

This could be extended to include polynomial terms to introduce non-linearity, but this has 

been shown to produce unreasonable global fits and poor behavior near the boundaries [4]. 

Instead, the author suggests the use of a spline model extension. The author explores the 

extension of the model with a set of additional basis functions to model the non-linearity. The 

set of basis functions chosen is the truncated power basis of order S:
CONNBUCKNOV2021 – Page 8

MHZ/6004047.2

Where k1,...,km are the M dividing points of the local polynomials referred to as knots (how 

many and which knots are used is model set up choice), and;

The probability of bankruptcy equation is then modified as follows:

In the case of this paper the order of the basis function S is chosen to equal 2. The parameters 

ηm are a vector of coefficients that must be estimated.

In the paper, the final spline function chosen was a natural spline with a different form from 

the one above. The general motivation for the extension and the form of the probability function 

is however the same.

One limitation of the approach is that it is perhaps harder to understand and implement than 

the standard logistic model.

3.4.2 Hazard Models

In [5] it is argued that hazard models are more appropriate than single period models for 

forecasting bankruptcy. The authors describe a simple technique for estimating a discrete-time 

hazard model of bankruptcy. They find that about half of the accounting ratios that have been 

used in previous models are not statistically significant. Moreover, market size, past stock 

returns, and idiosyncratic returns variability are found to be all strongly related to bankruptcy. 

This class of model may therefore be a good alternative to consider for the purposes of the 

PBGC’s modeling. Indeed in the [5], the author explores the similarities between “static 

models” (such as the logit or Altman Z-score), which use point in time balance sheet data as 

covariates) and hazard models which can be estimated using time series data. This takes 

account of variability in the covariates and an increase in explanatory power.

3.4.3 Neural Networks

A full review of the performance of neural networks to the problem of sponsor bankruptcy is 

beyond the scope of this document. However, we consider the problem at hand to be a good 

candidate for such techniques. This is because the problem is characterized by many inputs 

(e.g. covariates and macroeconomic variables) with a single output (default probability or 

binary status 1=defaulted 0=surviving). Neural networks are also known to capture non-linear 
CONNBUCKNOV2021 – Page 9

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and other complex relationships between input and output data that traditional regression 

techniques do not.

There are many research papers that look at this type of modeling for predicting bankruptcy. 

Some of these, including [6], directly compare the performance of neural networks with logistic 

models of the type explored above. There was a performance enhancement over the logistic 

model in terms of classification accuracy, type I error (identifying a default when there was 

none) and type II error (identifying no default when one occurred).

Another reference on this subject that may be useful is [7].

3.4.4 Structural Credit Models

The structural credit models as pioneered by Merton [2] are based on the well-known Black 

and Scholes theory of option pricing. The basic idea behind Merton’s development of a pricing 

theory for corporate bonds is the interpretation of corporate liabilities as options on the value 

of the firm issuing those bonds.

Extending this simple idea Merton postulated that firstly, the default of a firm is determined by 

its value, and secondly the event of default occurs if the value of the firms assets V falls below 

the outstanding debt B. Valuation of equity is carried out by applying the solution for the 

valuation of a European call option developed by Black and Scholes. Considering the 

connection of equity and liabilities on a firms balance sheet the value of a debt issue can be 

obtained, and by furthermore using continuous compounding and incorporating the yield to 

maturity y(t, T ), a representation for the appropriate credit spread can be derived;

where;

and;

• y(t, T ) is the yield to maturity of the defaultable liability (e.g. a corporate bond)

• r is the risk free rate

• T − t is the time to maturity

• B is the outstanding debt of the issuing firm

• V is the value of the firms assets
CONNBUCKNOV2021 – Page 10

MHZ/6004047.2

• Φ(h) represents the cumulative distribution function of the standard normal distribution 

evaluated at h

This then gives an expression for the spread of the defaultable liability, or the additional yield 

that is required to compensate an investor for the probability of default. The Merton model is 

relatively simple but performs well in some situations with regards to how the results of the 

model compare with empirical observation. Various extensions have also been proposed, 

including the addition of a random jump process which may trigger defaults.

Implicit within this idea is that the distribution of V is normally distributed. This could be used 

as the basis of an alternative method for assessing default probability on the basis of the 

volatility (and perhaps expected growth) of the firm’s assets V. Under the structural models we 

can define the concept of a distance to default DD;

DD+ =

V − B

σV

Where;

σV is the standard deviation of the firm’s assets. This means that when the value of the firm’s

assets drops below the outstanding debt the firm is in default. The probability of default up to 

time t is the area under the curve for V≤B. This can be visualized in the plot below:

Incorporating this or similar might be a way of bringing in an element that differentiates each 

firm by risk (in terms of volatility of its assets). (Prepared by Conning, Inc. Source © 2021 

Conning, Inc.)
CONNBUCKNOV2021 – Page 11

MHZ/6004047.2

3.4.5 Reduced Form Credit Models

Another dominant class of credit model for corporate bonds are the reduced form models. It is 

perhaps more difficult to see how these types of models could be applied to the problem of 

sponsor bankruptcy. 

A very rough but key distinction between the structural and the reduced form models is the 

information that is available to the modeler. In the structural models, we assume to have very 

detailed information about the firm, and its financial position. Instead in the reduced form 

models, we assume to have much less information, such as financial time series data for asset 

returns or credit spreads. Reduced form models are generally applied as security pricing models 

rather than default prediction models. In the case of the PBGC, we have detailed information 

from the firms’ balance sheets, but we are not really interested in pricing a security, and so the 

reduced models are probably not the best choice for this application. However, structural and 

reduced form models are not completely antithetical, see for instance [8].

However, we include a brief description of this class of model here for completeness. 

Reduced form models attempt to model the default of a bond as an unpredictable event. They 

do not rely on the value of the firm as the driver of defaults but use external processes. In a 

reduced form model external credit ratings are one of the main factors distinguishing the issuers 

with respect to credit quality.

One of the earliest reduced form models was proposed by Jerome Fons in 1994 [9]. In this 

approach, the only source of information included in the model is historical default 

probabilities, rating information and an estimate for the recovery rate. The recovery rate, μ, is 

defined as the percentage of the exposure which investors receive in the case of default.

In the reduced form model of Fons, a cumulative probability of default is specified, CR, for 

each rating category R and a time horizon of t years which reflects the probability, that a bond 

defaults up to year t after holding the rating R. The marginal default probability, MR, in year t 

after holding credit rating R is defined to be the difference in cumulative probabilities between 

year t and t-1. Finally, a forward probability of default, FR, is now defined as the probability 

of defaulting in year t after holding the rating, R, given that default has not occurred up to time 

t-1 and can therefore be expressed as a conditional probability of default (conditional on the 

bond surviving to time t-1). We also define a cumulative survival rate, SR(t) representing the 

probability that a bond survives to time t having held the rating R;

The reduced form model of Fons uses these concepts to build a model for corporate bond 

pricing and credit spreads. The original version of the Fons model is constructed for zero 

coupon bonds, (i.e., bonds not paying a periodic coupon). The price of a zero coupon bond with 

spread, s, to the risk free rate r, maturing in T years and assuming continuous compounding, is 

given by the simple pricing formula;
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Whereas before B represents the final liability or payoff of the bond (= 1 for a zero coupon 

bond). From the principles of risk neutral valuation, the price can be expressed as the 

expected value of the payoffs received from the asset. As the only payoff of a zero coupon 

bond takes place at maturity, for every point of time t < T only the case of default with the 

recovered fraction of the face value B has to be incorporated. Together with the notation for 

the different default and survival probabilities as introduced above, the credit spread, s, can 

now be obtained as;

Fons assumed a constant recovery of market value, μ, and calibrated the remaining parameters 

of the model to default and survival probabilities derived from Moody’s data. The model was 

successful in explaining some of the observed features of the average term structure of the 

spreads on defaultable bonds, however generally underestimated the market spreads. This 

underestimation suggests that additional factors other than default and survival rates were 

required to fully describe the corporate bond market. Nevertheless, the model laid the 

foundations of the theoretical underpinnings of more advanced reduced form models, such as 

that of Jarrow, Lando, Turnbull as well the model used within Conning’s GEMS® Economic 

Scenario Generator.

3.5 References

[1] Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial 

Ratios, Paolo Giordani, Tor Jacobson, Erik von Schedvin and Mattias Villani, The Journal of 

Financial and Quantitative Analysis, AUGUST 2014, Vol. 49, No. 4 (AUGUST 2014), pp. 

1071-1099

[2] Merton, R. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates." 

Journal of Finance, 29 (1974), 449-470.

[3] Bharath, S., and T. Shumway. "Forecasting Default with the Merton Distance to Default 

Model." Review of Financial Studies, 21 (2008), 1339-1369.

[4] Hastie, T.; R. Tibshirani; and J. Friedman. The Elements of Statistical Learning, 2nd ed., 

Springer Series in Statistics. New York: Springer (2009).

[5] Forecasting Bankruptcy More Accurately: A Simple Hazard Model, Tyler Shumway, The 

Journal of Business, January 2001, Vol. 74, No. 1 (January 2001), pp. 101124

[6] Neural Networks in Bankruptcy Prediction - A Comparative Study on the Basis of the First 

Hungarian Bankruptcy Model, M. VIRÁG and T. KRISTÓF, Acta Oeconomica, 2005, Vol. 

55, No. 4 (2005), pp. 403-426
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[7] Artificial neural networks in bankruptcy prediction: General framework and crossvalidation analysis, G. Zhang, M. Hu, Daniel C. Indro, Published 1999, Computer Science, 

Eur. J. Oper. Res.

[8] Structural Versus Reduced Form Models: A New Information Based Perspective, Robert 

A. Jarrow, and Philip Protter, 2004, Journal of Investment Management, Vol. 2, No. 2, 

(2004), pp. 1–10

[9] Fons, Jerome S., Using Default Rates to Model the Term Structure of Credit Risk, 

Financial Analysts Journal, Vol. 50, No. 5, (September/October 1994), pp. 25-33
CONNBUCKNOV2021 – Page 14

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About Conning

Conning (www.conning.com) is a leading investment management firm with a long history of

serving the insurance industry. Conning supports institutional investors, including pension 

plans, with investment solutions and asset management offerings, risk modeling software, and 

industry research. Founded in 1912, Conning has investment centers in Asia, Europe and North 

America.

Legal Disclaimer

©2021 Conning, Inc. This document and the software described within are copyrighted with 

all rights reserved by Conning, Inc. (“Conning”). This document is intended only to inform 

readers about general developments of interest and does not constitute investment advice. 

Conning does not make any warranties, express or implied, in this document. In no event shall 

Conning be liable for damages of any kind arising out of the use of this document or the 

information contained within it. This document is not intended to be complete, and Conning

does not guarantee its accuracy. Any opinion expressed in this document is subject to change 

at any time without notice.

The use and distribution of this document is subject to the terms of a separate agreement 

between Conning, Inc. and Buck Consultants (the “Terms”). Except as set forth in the Terms, 

no part of this document may be distributed by any means or used for any purpose except with 

Conning, Inc.’s prior written authorization. Any third parties that are given access to the 

document are subject to the same the terms of this notice. Any distribution of this document, 

whether in whole or in party, must always include this notice. 

ADVISE®, FIRM®, and GEMS®are registered trademarks in the U.S. of Conning, Inc. 

Copyright 1990-2021 Conning, Inc. All rights reserved. ADVISE®, FIRM®, and GEMS®are 

proprietary software published and owned by Conning, Inc.
117

References & 

Actuarial Certification
References

118

Bankruptcy Files

•bankruptcy calibration.msg

•firm_weighting spreadsheet for FY 19 SE PIMS.msg

•firm_weighting_fy19_v1_recheck.xlsx

•FY19 SEPIMS bankruptcy calibration process.docx

•SE_Firm_Data_Prep_(prelimFY20).xlsb

Calibration Files

•ActCalib_Datalog.csv

•ActCalib_Output_1.22.2021.csv

•Calib FY20.06 Active CalibrationVBA_o age shift.xlsb

•Calib FY20.06 Active IPxLV_w_Grids_AGe65.xlsb

•Firm_weighting_fy19_v1 recheck

•Fy_20_flat_prem_dual_scale_factors

•FY20.08 InactiveCalibration VBA no age shift.xlsb

•InactCalib_DataLog.csv

•InactCalib_Output_1.22.2021.csv

•PIMS TechNote Calibration Process v3

•SE Bundle 2018 FY

Contract & Proposal Files

•06_RFQ+-+16PBGC20Q0104+PIMS+Peer+Review_SF+1449_rev+09+24+2020.pdf

•Buck Form 30 (s).pdf

•Buck's Volume 1 Technical Proposal 16 PBGC 20Q0104.docx

•Buck's Volume II Past Performance 16 PBGC 20Q0104.docx

•Buck's Volume III Price Quote PBGC 16 PBGC 20Q0104.docx

•Contagion Final Report.pdf

•fy-2018 projections report.pdf

•LeapFILE Email on 04222021 with First Zip Files. Pdf

Documentation Files

•Interest Rate in SE PIMS for Funding and VRP

•PIMS Data Manual FY 21.docx

•PIMS Inputs and Assumptions (2020.04.14).docx

•PIMS Inputs and Assumptions Meeting Summary_VF.docx

•PIMS Overview 2011.pdf

•PIMS tech Note - Program Flows v5.xls

•PIMS User Guide.pdf

•SE Data Structure_Chart_SE_FY15 (Econ Highlighted).xlsx

•SE System Descriptons_updated.docx

•SE_ME_data_structure.xlsx

•xf_understanding_the data.pdf
References

119

Process Log Files

•Log_2020-06-21_002_SE_yearly reset.html

•Log_2020-06-23_006_SE_Import 500 Data.html

•Log_2020-06-24_004_SE_Populate PIMS tables.html

•Populate PIMS - Error and Warning Research Summary.xlsb

SE PIMS Data Files

•(Old) FY17 tvested retireds 100yr projection template.xlsm

•active.hire.xxxx.xlsx

•Fields to Populate

•PIMS Data Manual FY21.docx

•PIMS Technote - Program Flow v5.xls

•salary.xxxxx.xlsx

•SE Data Structure 08262021

•SE Data Structure 09092021

•SE Data Structure 08202021

•SE Data Structure Chart SE FY15 (Econ Highlighted).xls

•SE Plan Data

•firm_2021_04_09_05_34_40.xlsx

Non-Seriatim Files

•PIMS Starting Point - Non-Seriatim Cash Flow.msg

•RunRpt4310836.dat

•Starting_Position_Sept_30_2019_June_16_2020.xlsx

•Starting_Position_Sept_30_2019_mwb_AB2.xlsb_the data.pdf

Additional SE PIMS Data Files

•SE Plans Bundle 2018PY_rev

•SE Data_Structure 10082021

•Buck SE claims data 11.4.2021

SE PIMS Decrement Table Files

•active.hire.xxxxx.xlsx

•benreduc.xxxx.xlsm

•disable.xxxxx.xlsm

•FY2020 SE Weekly Progress Report Master.xlsb

•retiree.xxxxx.xlsm

•salary.xxxxx.xlsm

•separate.xxxxx.xlsm

•tvesteds_2021_04_09_05_40_17.xlsx
References

120

ME PIMS Files

•09302020_100GF_Individual Cashflow_IPV5510_IPVFB_Report.xlsx

•09302020_100GF_Individual Cashflow_IPV5510_IPVFB_Report.xlsx.dup

•Data Dictionary 20190708.xlsx

•FY20 ASD Data.xlsm

•FY20 ASD Data.xlsm.dup

•FY20 ME Data FY20.12_setPart1.xlsb

•FY20 ME Data_FY20.12.xlsb

•FY20 ME Data_FY20.12.xlsb.dup

•FY20 PR Data.xlsx

•FY20 PR Data.xlsx

•FY20 PR Data.xlsx.dup

•ME data process.xlsx

•ME data process.xlsx.dup

•ME FY20.12 Model documentation

•ME PIMS Overview Document.doc

•Multiemployer Spreadsheet Model Description 20299520.docx

•SE_ME Data Structure.xlsx

•Study performed by Advanced Analytical Consulting Group 

•Boyce Memo

SE PIMS Data Tables Files

•SE_ME Data Structure.xlsx

•acctbal_2021_04_09_03_36_49.xlsx

•actives_2021_04_09_05_30_41.xlsx

•benreduc_2021_04_09_05_31_55.xlsx

•Bfcashbal_2021_04_09_05_32_33.xlsx

•bfcbcombo_2021_04_09_05_32_47.xlsx

•bffcombo_2021_04_09_05_33_13.xlsx

•bfflat_2021_04_09_05_33_01.xlsx

•bfsalary_2021_04_09_05_33_13.xlsx

•cash balance formula.xlsx

•default benreduc rates.xlsx

•default disable rates.xlsx

•default separate rates.xlsx

•disable_2021_04_09_05_33_42.xlsx

•FY20 account balances master.xlsb

•hire_2021_04_09_05_35_08.xlsx

•partic_2021_04_09_05_36_20.xlsx

•plan_2021_04_09_05_36_16.xlsx

•plan_amort_2021_04_09_05_36_29.xlsx

•plnref_2021_04_09_03_30_13.xlsx

•retire_2021_04_09_05_36_46.xlsx

•salaries_2021_04_09_05_37_28.xlsx

•separate_2021_04_09_05_38_12.xlsx

•sponsor_2021_04_09_05_36_01.xlsx
This report was prepared using generally accepted actuarial principles and techniques in accordance with all 

applicable Actuarial Standards of Practice (ASOPs), particularly ASOP No. 23.

Data Used for the Analysis:

Buck performed the SE and ME data input analysis and assessment using approximately 100 files, including 

data and modeling manuals supplied by the PBGC. The files and manuals were reviewed for reasonableness 

but were not audited. The accuracy of our analysis and assessment is dependent on the accuracy of the data 

provided.

This report was prepared under the supervision of Stuart Schulman who is a Fellow of the Society of 

Actuaries, an Enrolled Actuary and a Member of the American Academy of Actuaries and David Harwood Jr. 

who is an Enrolled Actuary and a Member of the American Academy of Actuaries. Both have met the 

Qualification Standards of the American Academy of Actuaries to render the actuarial opinions contained 

herein and are available to answer any questions.

121

Actuarial Certification
© 2021 Buck Global LLC. All rights reserved. Buck is a trademark of Buck Global LLC. and/or its subsidiaries in the United States and/or other countries.