CONTAGION OF MASS WITHDRAWALS 

FROM MULTIEMPLOYER PENSION PLANS 

October 1, 2019 

Advanced Analytical Consulting Group, Inc. 

Constantijn W.A. Panis, PhD 

213-784-6400 

stanpanis@aacg.com 

Karthik Padmanabhan, MS, MBA 

312-551-9001 

karpad@aacg.com 
 i 

SUMMARY 

The Pension Benefit Guaranty Corporation (PBGC) insures millions of participants in 

roughly 1,400 multiemployer defined benefit (DB) pension plans against loss of some 

or all benefits in case their plan is unable to pay benefits. Multiemployer plans are 

funded by participating employers, and a particular hazard to a plan’s solvency is the 

potential for all participating employers to collectively withdraw from the plan. While 

such mass withdrawals have been relatively infrequent to date, some stakeholders 

are concerned that a mass withdrawal at one plan may trigger additional mass 

withdrawals at other plans. This report illustrates the potential for contagion by 

mapping out shared ties (and financial risks) among plans that have already 

experienced a mass withdrawal and multiemployer plans in general. We develop a 

theoretical framework that could be used to model contagion effects. 
 ii 

CONTENTS 

Summary ........................................................................................................ i

1. Introduction ............................................................................................. 3

2. Ties among Mass-Withdrawn Plans .......................................................... 4

3. Ties among Multiemployer Plans .............................................................. 8

Common Industry ....................................................................................... 8

Common Contributing Employers ................................................................ 12

4. Interview with a Multiemployer Plan Trustee ......................................... 16

Employer’s Access to and Cost of Funding .................................................... 16

Other Observations ................................................................................... 17

5. A Potential Theoretical Framework for Contagion .................................. 18

Literature ................................................................................................. 18

Individual Withdrawals ............................................................................... 19

Mass Withdrawals ...................................................................................... 20

Contagion of Mass Withdrawals ................................................................... 20

6. Conclusion .............................................................................................. 22

7. References ............................................................................................. 23

Disclaimer ................................................................................................... 24
 3 

1. INTRODUCTION 

In order to monitor the solvency of its insurance operations, the PBGC’s Policy, 

Research and Analysis Department (PRAD) developed a system of connected 

models—the Multiemployer Pension Insurance Modeling System (ME-PIMS)—to 

forecast revenues, expenses, and related metrics related to multiemployer plans. An 

important predictor of multiemployer plan insolvency is the possibility that all 

employers collectively withdraw from the plan. While such mass withdrawals have 

been relatively infrequent to date, some stakeholders are concerned that a mass 

withdrawal at one plan may trigger additional mass withdrawals at other plans. MEPIMS does not currently address that type of contagion of mass withdrawals. The 

PBGC contracted with Advanced Analytical Consulting Group (AACG) to investigate 

potential modeling of contagion of mass withdrawals in ME-PIMS. This document 

reports on our review of specific priority areas. 

Section 2 starts by mapping out the ties among plans that already experienced a 

mass withdrawal. The history on mass-withdrawn plans and their connections is 

sparse, but worthwhile to evaluate for insight and learning. Section 3 expands the 

effort undertaken in Section 2 to map ties among all multiemployer plans to gauge 

the potential for contagion. Plans can be linked by a common industry, common 

contributing employers and, in some cases, multiple sets of common contributing 

employers. Section 4 strives to augment the empirical research in the previous 

sections with first-person, first-hand experience dealing with multiple deteriorating 

multiemployer plans. Section 5 describes a potential theoretical framework that 

combines individual decision making with information-driven contagion forces that 

can be modeled into ME-PIMS. 
 4 

2. TIES AMONG MASS-WITHDRAWN PLANS 

According to a list provided by the PBGC, 40 multiemployer plans experienced a 

mass withdrawal between October 2011 and July 2018. This section documents links 

among those 40 multiemployer plans. 

First consider clustering by industry. The first three columns of Table 1 show the 

distribution of plans over industry sector, separately for plans that did not experience 

a mass withdrawal and for plans that did.1 A Pearson Chi-square test strongly rejects 

the hypothesis that mass withdrawals occurred independently from industry sector 

(p<.001%). The last column calculates the fraction of plans within each industry that 

experienced a mass withdrawal. On average, 40 / 1,472 = 2.7% plans did over 

seven years, but the percentages vary widely and are sensitive to small numbers of 

plans.2

1

 Throughout this report, plans with zero participants at both the beginning and end 

of the reporting period are excluded from the analysis. Table 1 further excludes one 

plan for which industry sector was unknown. The table relates to the most recent 

filing since 2011. Some plans, including two mass-withdrawn plans, stopped filing 

prior to 2017. 

2

 French (2017) reviewed 16 recent mass withdrawals, noting that “plans in 

Construction and Entertainment had by far the lowest incidence of mass withdrawal, 

and those in Manufacturing and Retail had the highest incidence rates.” Our results 

for Construction, Entertainment, and Manufacturing are consistent with his. 

(Entertainment is included in Other Services; we identified 33 Entertainment plans, 

none of which had mass withdrawn.) Unlike French, we found that the mass 

withdrawal rate in Retail Trade (2.3%) was roughly in line with the overall average 

(2.7%). Special withdrawal rules apply for plans in the Construction and 

Entertainment industries (29 U.S. Code §1383), which may imply that all employers 

withdrew from some of those plans without being recorded as a mass withdrawal. 
 5 

Table 1. Distribution of Plans over Industry, by Whether They Experienced a 

Mass Withdrawal (Most Recent Filing) 

Next consider potential contagion related to common contributing employers. Data 

limitations permit only a subset of contributing employers to be identified: Schedule 

R of the Form 5500 generally lists only employers that contributed at least 5% of 

total plan contributions. Our mapping of common contributing employers is therefore 

limited to such “significant contributors.” Schedule R lists the name and Employer 

Identification Number (EIN) of significant contributors; we match them by both, 

where name is normalized so that spelling and legal variations are recognized (e.g., 

ABC, ABC Inc, ABC, Inc., ABC LLP, ABC USA, and ABC Holdings all match). 

The 40 plans that experienced a mass withdrawal consisted of 22 singleton plans, 

i.e., plans whose significant contributors did not contribute significantly to other 

plans. The remaining 18 plans had ties to other plans through one or more common 

significant contributors. Figure 1 depicts the 40 mass-withdrawn plans (inside the 

large circle) and the other plans to which they are connected (outside the large 

circle). Double (triple) connector lines represent two (three) common significant 

contributing employers. The colors of the circles indicate each plan’s financial risk 

Mass withdrawal? Percentage with

No Yes Total mass withdrawal

(A) Ag/Forestry/Fishing 11 0 11 0.0%

(B) Mining 5 0 5 0.0%

(C) Building Construc tion 83 0 83 0.0%

(D) Heavy Construc tion 62 1 63 1.6%

(E) Plumbing Heating A/C 153 1 154 0.6%

(F) Electrical Contrac tors 123 0 123 0.0%

(G) Bldg Finishing Contrs 69 0 69 0.0%

(H) Found Struct Exterior 158 1 159 0.6%

(J) Other Construction 122 1 123 0.8%

(K) Food Beverage Tobacco 29 1 30 3.3%

(L) Apparel Textile 6 0 6 0.0%

(M) Paper Manufac turing 4 1 5 20.0%

(N) Printing And Related 17 6 23 26.1%

(O) Furniture And Related 4 1 5 20.0%

(P) Machinery 3 2 5 40.0%

(Q) Elec trical Equipment 8 0 8 0.0%

(R) Other Mfg 63 4 67 6.0%

(S) Truck Trans 74 4 78 5.1%

(T) Water Trans 41 2 43 4.7%

(U) Other Transport & Utils 24 1 25 4.0%

(W) Information 31 3 34 8.8%

(X) Wholesale Trade 21 3 24 12.5%

(Y) Retail Trade 85 2 87 2.3%

(Z) Fin,Ins,RE 20 0 20 0.0%

(Z1) Administrative Svces 43 3 46 6.5%

(Z2) Health/Social Assist 18 0 18 0.0%

(Z3) Accomodation/Food Svces 27 0 27 0.0%

(Z4) Other Services 128 3 131 2.3%

Total 1,432 40 1,472 2.7%

Source: PBGC, Form 5500 filings.
 6 

status, ranging from neither endangered nor critical (green) to currently receiving 

assistance from the PBGC (black). For mass-withdrawn plans, the risk status refers 

to the year in which the mass withdrawal occurred; for the others, it refers to the 

most current period for which a Form 5500 filing is available. 

Figure 1. Mass-Withdrawn Plans and Their Connections through Common 

Significant Contributors 

Figure 1 illustrates several issues. 

1. First, while six of the 40 plans that mass-withdrew were in critical and 

declining status (purple circles), most were financially stronger: 24 plans 

were in critical status (red circles) and 10 were even in neither endangered 

nor critical status (green circles).3 This raises questions about the potential 

3

 Critical and declining status (purple) was introduced on the Form 5500 in 2015, 

i.e., plans that experienced a mass withdrawal prior to 2015 could not be in the 

24

Mass-withdrawn plans

19

2

34

14

12 26

11

19

33

30

10

9

7

3

1

16

5

21

18

35

27

23

40 25

20

38

36

32

33

29

22

17

6

8

15

31

4

13

28

37

39

Receiving PBGC assistance

Critical and declining

Endangered

Neither endangered nor critical

Critical
 7 

perception that mass withdrawals tend to occur in financially weak plans. That 

said, a mass withdrawal does tend to foreshadow the need for the PBGC to 

step in; based on the most recently available information (not shown in 

Figure 1), 34 of the 40 mass-withdrawn plans are currently receiving PBGC 

assistance, and five of the remaining six experienced the mass withdrawal 

only recently. (The exception is a plan whose participating employers masswithdrew in 2012; it initially deteriorated, but recovered and is currently in 

the green zone.) 

2. Second, only two pairs of mass-withdrawn plans share a common significant 

contributor: Plans 3 and 33 have one contributor in common, as do plans 12 

and 34.4 In other words, there is little historical evidence that a mass 

withdrawal may trigger the mass withdrawal of a related plan, at least insofar 

relations are measured through common contributing employers. The mass 

withdrawals of plans 3 and 33 took place six months apart during 2014, 

whereas the mass withdrawal of plan 34 (February 2013) preceded that of 

plan 12 (December 2016) by almost four years. 

3. Third, while only two pairs of mass-withdrawn plans share a contributing 

employer, many more ties exist with plans that remain operational.5 Further 

strengthening ties, some plans shared two or three significant contributors. In 

other words, many employers that used to contribute to a plan that 

experienced a mass withdrawal are currently contributing to other plans, 

raising the potential for contagion. 

We close this section with two observations to place the incidence and consequences 

of mass withdrawals in context. 

Between October 2011 and July 2018, 40 multiemployer plans experienced a mass 

withdrawal, i.e., on average approximately six mass withdrawals per year. For 

reporting periods ending in 2017, 1,378 multiemployer plans filed a Form 5500. Mass 

withdrawals thus affected roughly 6/1,378 = 0.4% of multiemployer plans per year. 

For comparison, roughly 0.8% of single-employer plan sponsors filed for bankruptcy 

each year. In other words, mass withdrawals are not common, and their incidence is 

roughly half that of corporate bankruptcies. Contagion of mass withdrawals has been 

very limited in recent history. 

Based on computer code provided by the PBGC, we estimate that the PBGC provided 

financial assistance to at least 115 plans in 2017. Above we found that 34 of the 40 

mass-withdrawn plans are among those 115 plans, i.e., 81 plans (70%) are receiving 

assistance without having experienced a mass withdrawal. This is an overestimate, 

since we only accounted for mass withdrawals since 2011. Still, it appears that mass 

purple zone. The data contain 16 plans that mass-withdrew in 2015–2018; three 

were in the green zone, seven in the red zone, and six in the purple zone. 

4

 Not only do these plan-pairs share a contributing employer, they are also in the 

same industry: Information (plans 3 and 33) and Truck Transportation (plans 12 and 

34). 

5

 Plan 26, which experienced a mass withdrawal in September 2012, is tied to a plan 

that has long received PBGC assistance after mass withdrawing in February 2001. 

One could consider this pair as potentially reflective of contagion, but the lag—more 

than 11 years—was quite long. 
 8 

withdrawals are by no means the only route to financial assistance, and quite 

possibly not even the most common route. 

3. TIES AMONG MULTIEMPLOYER PLANS 

We now turn to quantitative evidence on ties among multiemployer plans, 

irrespective of any mass withdrawal. The purpose of this exercise is to gauge the 

potential for contagion, should a mass withdrawal occur. As in the previous section, 

the focus is on common industry ties and on common significant contributing 

employers. We further document the financial strength of connected multiemployer 

plans. 

Common Industry 

Table 2 documents the number of plans and plan participants in the main industry 

sectors (Construction, Retail, Transportation, and Other). More than one-half of plans 

(53%) are in the Construction industry, and they cover 36% of participants in 

multiemployer plans. Plans in the Retail and Transportation sectors cover 14% and 

15% of participants, respectively. 

Table 2. Plans, Participants, and Funding by Industry Sector (2017) 

 

Administrators and trustees of multiemployer plans may be expected to closely 

watch developments around other plans in their industry. As discussed in the 

previous section, mass withdrawals have been limited, and spill-over effects of mass 

withdrawals to other plans have been rare. However, based on the large numbers of 

plans in each industry, the potential for future contagion to other plans within the 

same industry is present. 

Table 2 also shows the aggregate funded ratio, defined as the ratio of total assets to 

total liabilities. (It is an aggregate ratio, not an average of plan-specific funded 

ratios.) Assets and liabilities are based on Schedule MB lines 2a (current value of 

assets) and 2b(4) (RPA ’94 current liability).6 In the aggregate, plans are funded at 

40%–43% of liabilities in each of the four sectors. Total unfunded liabilities, defined 

as the difference between liabilities and assets, are $317 billion for Construction 

plans, $46 billion for Retail plans, $132 billion for Transportation plans, and $156 

6

 Plans that did not file a Schedule MB in 2017 are included in the number of plans 

and participants, but their assets and liabilities are excluded from the table. Lack of a 

Schedule MB generally indicates that the plan is receiving PBGC assistance. 

Number of Plans

Number of 

Participants

Aggregate 

Funded

Unfunded 

Liabilities

Count Percent Count (m) Percent Ratio ($bn)

Construction 730 53.0% 3.85 36.3% 43.1% 317.3

Retail 80 5.8% 1.53 14.4% 41.6% 46.4

Transportation 140 10.2% 1.57 14.8% 40.5% 132.2

Other 428 31.1% 3.65 34.4% 42.6% 156.2

Total 1,378 100.0% 10.59 100.0% 42.4% 652.1

Source: Form 5500 filings.
 9 

billion for Other plans. In other words, the financial consequences of a potential 

cascade of mass withdrawals would be dire. 

The following graphs shed light on the financial status of plans in each of the four 

industry sectors. Figure 2 shows the plan-level distribution of risks status. 

Construction plans are relatively healthy, with two-thirds in the green zone and only 

2% currently receiving PBGC assistance. At the other end of the spectrum are Retail 

plans, with just 38% in the green zone, 35% in critical or critical and declining 

status, and 21% currently receiving PBGC assistance. (A small number of plans are 

seriously endangered; they are combined here with the endangered category.) 

Figure 2. Distribution of Risk Status, by Industry (Weighted by Plans, 2017) 

Similarly, Figure 3 shows the distribution of risk status, weighted by plan 

participants. Construction plans are again relatively financially strong. Separately, 

the black slices are thinner than at the plan level, suggesting that PBGC-assisted 

plans are below-average in size. Indeed, while the 122 PBGC-assisted plans comprise 

8.9% of all multiemployer plans, they cover only 1.5% of total participants.7

7

 For this purpose, plans that did not file a Schedule MB are assumed to be receiving 

PBGC assistance. There were seven such plans in 2017. Four had recently 

experienced a mass withdrawal, two others reported zero contributing employers, 

and the remaining plan is very small, with just nine participants. 

49 7 14 13 18

48 10 14 18 10

38 6 24 11 21

66 12 15 5 2

0% 20% 40% 60% 80% 100%

Other

Transportation

Retail

Construction

Source: Form 5500 filings.

Percentages may not sum to 100% because of rounding.
 10 

Figure 3. Distribution of Risk Status, by Industry (Weighted by Plan 

Participants, 2017) 

Finally, Figure 4 shows the distribution of risk status, weighted by unfunded 

liabilities.8 The general pattern is consistent with Figure 2 and Figure 3, with 

relatively few Transportation plans and relatively many Retail plans in critical or 

worse status. However, these patterns hide the severity of potential plan failures, as 

discussed next. 

8

 Recall that this metric is based on Schedule MB, which is rarely filed by plans that 

are receiving PBGC assistance. As a result, the black-colored fraction of unfunded 

liabilities is negligibly small. 

55 2 25 14 3

54 5 4 36 0

45 4 38 11 2

62 22 15 10

0% 20% 40% 60% 80% 100%

Other

Transportation

Retail

Construction

Source: Form 5500 filings.

Percentages may not sum to 100% because of rounding.
 11 

Figure 4. Distribution of Risk Status, by Industry (Weighted by Unfunded 

Liabilities, 2017) 

Consider again participants and unfunded liabilities by industry (Table 2). Perparticipant unfunded liabilities vary substantially across industries. They further vary 

by risk status, as shown in Figure 5. While the figures above suggest that plans in 

the Retail sector are in relatively weak financial health, their per-participant 

unfunded liabilities are lower than that in the Construction and Transportation 

sectors. For example, per-participant unfunded liabilities of plans in critical or worse 

status are about $112,000 (Construction), $39,000 (Retail), $113,000 

(Transportation), and $37,000 (Other). 

60 6 17 16 0

38 7 5 50

32 5 49 15

56 22 21 1

0% 20% 40% 60% 80% 100%

Other

Transportation

Retail

Construction

Source: Form 5500 filings.

Percentages may not sum to 100% because of rounding.
 12 

Figure 5. Per-Participant Unfunded Liabilities, by Industry and Risk Status 

(2017) 

Common Contributing Employers 

Multiemployer plan contagion may further arise when multiple plans share the same 

contributing employers. As explained above (page 5), Schedule R identifies the EIN 

and name of employers that contributed significantly (5% or more of the total). This 

section provides more detail on such employers and the ties they create among 

multiemployer plans. 

On average, the 1,378 multiemployer plans in the analysis listed 3.4 significant 

contributors; 212 plans did not list any significant contributor. The total number of 

unique contributors was 3,500. Table 3 shows the number of multiemployer plans to 

which any employer contributed. 

$0

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

Construction Retail Transportation Other

Source: Form 5500 filings.
 13 

Table 3. Number of Multiemployer Plans in Which Significant Employers 

Participate (2017) 

Most employers (2,970 employers, or 84.9%) contributed significantly to only one 

multiemployer plan. The remaining 530 employers contributed to at least two and as 

many as 33 different plans, thereby generating links among those plans that may 

elevate susceptibility to multiemployer contagion effects. 

Consider pairs of plans with a common contributor. As shown in Table 3, 338 

employers contributed to two plans each and thus generated 338 plan-pairs. Also, 89 

employers contributed to three plans each, thereby generating 89 x 3 = 267 planpairs.9

 In total, employers that contributed to multiple plans generated 2,781 planpairs. Some of those pairs are duplicates. For example, both employers X and Y may 

have contributed to plans A and B. Since plan-pair A-B has two contributors in 

common, its ties may be stronger than those of a plan-pair that appears only once. 

The number of common contributors may thus be a measure of a plan-pair’s 

susceptibility to multiemployer contagion. Table 4 shows the distribution of number 

of contributors to unique plan-pairs. 

9

 Suppose an employer contributed to plans A, B, and C. These form three plan-pairs 

(A-B, A-C, and B-C). More generally, k plans with a common contributor generate 

k(k-1)/2 plan-pairs. 

Number 

of plans Frequency Percent

1 2,970 84.9%

2 338 9.7%

3 89 2.5%

4 45 1.3%

5 26 0.7%

6 11 0.3%

7 5 0.1%

8 6 0.2%

9 2 0.1%

10 1 0.0%

11 1 0.0%

12 3 0.1%

25 2 0.1%

33 1 0.0%

Total 3,500 100.0%

Source: Form 5500 filings.
 14 

Table 4. Number of Employers Contributing Significantly to the Same Pair of 

Plans (2017) 

Most plan-pairs (92.3%) have only a single common significant contributor, but 

154+43+14+2 = 213 plan-pairs share multiple contributors. Two pairs share as 

many as five contributors each.10

Contagion risks may depend not only on the intensity of ties across plans (Table 4), 

but also on their financial strength. Table 5 shows the distribution of financial 

strength of plan-pairs with one or more common contributors. The plurality of planpairs (33.7%) consist of two plans that both are in neither Endangered nor Critical 

status (the “Green” zone). To the extent that mass withdrawals (and thus contagion) 

is linked to poor financial health, then contagion risks are particularly elevated for 

plan-pairs in which both plans are financially weak, i.e., the entries in the lower 

portion of Table 5. 

10 The first plan-pair with five common contributors are sponsored by unions for 

electrical workers, i.e., in the Construction industry. The second plan-pair with five 

common contributors are sponsored by the same union for newspaper workers, and 

both plans are in the Information (Other) industry. Both quintuple-connected planpairs consist of a green-zone and a purple-zone plan. 

Number of

employers Frequency Percent

1 2,568 92.3%

2 154 5.5%

3 43 1.5%

4 14 0.5%

5 2 0.1%

Total 2,781 100.0%

Source: Form 5500 filings.
 15 

Table 5. Risk Status of Plan-Pairs with a Common Significant Contributor 

(2017) 

Also see Figure 6, which graphically represents the information in Table 5. In 

addition to depicting the contributor linkages between plans of different risk types, it 

also shows the proportion of plans that are not affected by linkages. 

Risk Status of 

Connec ted Plans

Number of 

Plan Pairs Percent

Green Green 937 33.7%

Green Yellow 383 13.8%

Green Red 643 23.1%

Green Purple 226 8.1%

Green Black 9 0.3%

Yellow Yellow 54 1.9%

Yellow Red 172 6.2%

Yellow Purple 40 1.4%

Yellow Black 3 0.1%

Red Red 160 5.8%

Red Purple 101 3.6%

Red Black 3 0.1%

Purple Purple 47 1.7%

Purple Black 3 0.1%

Total 2,781 100.0%

Source: Form 5500 filings.
 16 

Figure 6. Connections across Plans through Common Significant 

Contributors (2017) 

4. INTERVIEW WITH A MULTIEMPLOYER PLAN 

TRUSTEE 

In order to understand what types of concerns arise due to unfunded liabilities, we 

interviewed the former treasurer at a well-known contributing employer and current 

trustee of several of the nation’s largest multiemployer plans. In his various roles, he 

was and is responsible for approving funding, monitoring unfunded liabilities and 

estimating potential future losses in benefits to participants. As a representative of 

one the key contributing employers of several multiemployer plans, our interviewee 

brings first-hand perspective to the issues at hand. 

Employer’s Access to and Cost of Funding 

In the interviewee’s case, unfunded liabilities had an adverse impact on his 

employer’s access and cost of capital. In his capacity as the treasurer, he met with 

all three major credit rating agencies twice a year to apprise them of the contributing 
 17 

employer’s exposure to multiemployer plans. This exposure had a substantial impact 

on the financial metrics used to determine the credit rating of the employer. 

This observation is independently supported by industry research conducted by us. 

We find that as the unfunded pension liability of plan contributors increases, they 

face capital funding issues in the form of credit downgrades and specific restrictions 

in the form of covenants required by lenders. Both these actions are publicized by a 

ratings agency (typically behind a paywall) or by the contributors in regulatory filings 

to the Securities and Exchange Commission (SEC). 

The three major credit ratings agencies —Fitch Ratings, Inc., Moody's Investors 

Service, Inc., and S&P Global Ratings—all take into account a contributor’s 

multiemployer pension obligations when determining a credit rating. A review of 

historical credit rating changes for companies with the largest exposure to pension 

obligations (measured as contributions as a percentage of Cash Flow from 

Operations) shows that pension exposure is accounted for in credit ratings and thus 

affects the cost and availability of capital. 

We also find that lenders typically include multiemployer plan-related provisions to 

ensure contributors do not face any future liabilities related to plan underfunding. 

Contributors to plans that are already underfunded may have to pay higher 

borrowing costs to compensate lenders for plan-related risks they already carry. 

They may also have to negotiate with a smaller group of lenders and/or offer more 

collateral, both of which may increase borrowing costs. Our review found numerous 

examples of lending agreements with conditions related to multiemployer plan 

underfunding. 

Other Observations 

Unique funding arrangements can be negotiated with plan administrators. In this 

case, the interviewee’s employer negotiated an appropriate withdrawal liability that 

allowed them to decouple their employees and retired employees from an 

increasingly distressed multiemployer plan and move them to a new plan. This action 

was taken to ensure that their employees and former employees did not suffer the 

consequences of belonging to a financially deteriorating plan with an increasing 

number of orphaned participants—participants for whom no employer has an 

obligation to make contributions. He also mentioned that another plan resisted 

attempts to spin off his employer’s workers, because the plan wanted to avoid 

preferential treatment of certain plan participants. 

Small sponsors are particularly vulnerable to unfunded liabilities in multiemployer 

plans. Voluntary withdrawal from a multiemployer plan may trigger withdrawal 

liabilities that can be many times the value of a contributing employer’s firm. For this 

reason, many of the plans are forced to stay and contribute to a plan. Our 

interviewee suggested more lenient rules on withdrawal liabilities aimed specifically 

at small-sized sponsors could alleviate this burden. 
 18 

5. A POTENTIAL THEORETICAL FRAMEWORK FOR 

CONTAGION 

Literature 

This section briefly summarizes contagion modeling in finance and economics, but 

starting from public health. Insofar we are aware, none of the literature directly 

addresses mass withdrawal issues in multiemployer pension plans, but we find some 

analogies. 

In epidemiology, two types of models exist. Deterministic models were developed as 

early as 1927. They describe the number of susceptible individuals, the number 

infected and the number recovered (e.g., Miller 2009). The reproductive ratio R଴

measures the number of secondary infections arising from a single individual during 

his or her infectious lifetime. R଴ < 1 suggests that only a finite number of susceptibles 

will be become infected and the contagion will die out, whereas R଴ > 1 triggers an 

epidemic. More recently, network-based models have been developed that are 

stochastic in nature. They capture the uncertainty and variability that is inherent in 

real-life epidemics (e.g., Imran et al., 2013). Academics have tried to apply these 

models to finance (e.g., Demiris et al., 2012; Caccioli et al., 2014). 

In finance, much of the effort on contagion is on its empirical measurement (Demiris 

et al., 2012). Existing literature has studied and measured intra-industry effects of 

contagion. Staum (2012) writes that firms in the same industry can be perceived as 

less creditworthy because of their similarity to a distressed firm and “contagion can 

spread among firms that use the same assets as collateral for borrowing, in a 

phenomenon that combines funding liquidity and asset-market liquidity.” Lang et al. 

(1992) find that the weighted average stock portfolio of competitors fell by 1% in 

reaction to a bankruptcy announcement while highly-leveraged competitors lost 

3.2%. Using a database of credit default swaps (CDS), Jorion et al. (2006) find that 

CDS spreads for competitors increase after a Chapter 11 bankruptcy, indicating 

effects of contagion. Conversely, they find that competitors benefit through smaller 

CDS spreads following a Chapter 7 liquidation. 

Hertzel et al. (2008) find that contagion effects can extend along the supply chain to 

suppliers and customers and eventually to industry competitors. Along similar lines, 

Helwege and Zhang (2014) argue that the negative externalities of bankruptcy stem 

from both counterparty exposure and market signals. Counterparty exposure results 

from direct business ties: The failure of one partner can have direct negative 

financial consequences for the others. Counterparties may be suppliers that are not 

paid; a different example is the collapse of Lehman Brothers, which cascaded 

throughout the financial industry as highly leveraged firms were exposed to the 

same assets. A bankruptcy may also send a signal about the cash flow of 

competitors in the same market. The initial failure can lower the value of other firms 

in the industry. For both counterparty exposure and information contagion, the 

effects were found to be strongest for financially weak firms. Benmelech et al. 

(2014) confirmed the role of financial strength in a different setting, namely effects 

of bankruptcies of large retail chains on neighboring stores. 

In short, negative externalities of a bankruptcy may play out (1) through a financial 

shock to business partners and (2) from signals about financial headwinds in the 

industry. Turning to multiemployer plans, it appears that the mass withdrawal of one 
 19 

plan would not generate a financial shock to other plans. However, the argument for 

information contagion is more applicable. Communication among plan sponsors, 

administrators, and trustees is likely robust. If one plan experiences a mass 

withdrawal and its formerly participating employers escape with limited liability 

payments or other reasonably palatable consequences, news may spread and 

instigate a cascade of mass withdrawals. 

Individual Withdrawals 

Before discussing mass withdrawals, consider the factors that an individual employer 

may evaluate when deciding whether to completely withdraw from a multiemployer 

plan. 

 Union demands and employee relations. Companies that consider withdrawing 

from a multiemployer plan may run into union opposition during the collective 

bargaining process. Withdrawal from a multiemployer plan generally implies 

that employees no longer accrue benefits. Also, employees who were not yet 

vested lose the opportunity to become vested. Employees will likely want to 

be compensated for their reduced retirement benefits, such as through 

employer contributions to a (newly established) 401(k) plan. Indeed, for 

single-employer plans, we found that employers (at least partially) 

compensated employees for DB plan freezes through expanded DC plan 

coverage, higher DC plan contributions, and expanded sponsorship of life 

insurance and long-term disability benefits.11 Such benefits would, of course, 

involve additional on-going expenses. 

 On-going payments. While remaining in the plan, the employer needs to 

make regular contributions to the plan, as negotiated in a collective 

bargaining agreement. A withdrawal would trigger a withdrawal liability, 

which is typically payable in installments that are roughly equal to historical 

contributions. In the short term, the effect on cash flow is therefore likely 

small (but see below). Depending on the funding status of the plan, the 

withdrawal liability can be very substantial, but the payment period is capped 

at 20 years. Employers in the Construction and Entertainment industries are 

generally exempt from paying withdrawal liability if they no longer perform 

work in the same geographical area. 

 Potential increase in future contributions. To avert insolvency of poorly funded 

plans, employers may face increased contribution rates when their next 

collective bargaining agreement is negotiated. A withdrawal would avoid such 

an increase. 

 Potential government bail-out. Over time, some politicians have attempted to 

address funding issues with multiemployer plans. For example, in July 2019 

the House of Representatives approved a $48.5 billion package that offers 

forgivable loans to the most troubled plans.12 While this particular measure 

11 Michael Brien and Constantijn Panis (2013): “Matching Form 5500 Employee 

Benefit Plan Filings by Employer.” Available at 

https://www.dol.gov/sites/dolgov/files/EBSA/researchers/analysis/retirement/matchi

ng-form-5500-employee-benefit-plan-filings-by-employer.pdf. 

12 “The Plan to Save Truckers’ and Miners’ Pensions Is Running Out of Time.” Wall 

Street Journal, July 25, 2019. 
 20 

may not find sufficient support in the Senate, the likelihood that Congress will 

provide assistance to multiemployer plans is not negligible. By withdrawing 

from a plan, employers forgo the option of benefitting from such a measure. 

In short, the trade-offs that employers face depend on the financial status of the 

plan, the costs of alternative employee benefits, and the probability and terms of a 

future bail-out. 

To complicate matters, the effect of a plan’s financial status may not be monotonic. 

As a plan’s financial condition weakens, the prospect of future contribution increases 

to keep the plan afloat provides an incentive to withdraw, but at the same time, the 

withdrawal liability rises and deters any withdrawal. 

Mass Withdrawals 

The trade-offs for a mass withdrawal are similar to those for an individual 

withdrawal, with a few differences. 

 No cap on withdrawal payments. The 20-year cap on withdrawal liability 

payments generally does not apply to mass withdrawals. Payments may be 

due into perpetuity if their present value falls short of the withdrawal liability. 

 Potential additional liability. The withdrawal liability may be higher if so-called 

reallocation liability rules apply. 

 Nonforfeitable benefits only. Benefits are automatically reduced to benefits 

that are nonforfeitable, which may imply a reduced withdrawal liability. 

 Employee relations. If plan insolvency is likely, benefits may need to be 

reduced further to the PBGC guaranty (or the level that can be paid with the 

plan’s available resources, if higher). A benefit reduction will likely undermine 

the employer-employee relationship. 

 Strength through numbers. Employers’ position to negotiate exit settlement 

terms with plan trustees may be strengthened in a mass withdrawal because 

of their numerosity. 

Contagion of Mass Withdrawals 

A potential theoretical framework for modeling contagion of mass withdrawals starts 

with a model for mass withdrawals. ME-PIMS currently predicts mass withdrawals on 

the basis of six factors: 

1. Plan size (tied to the number of active and total participants), 

2. Ratio of active to inactive participants, 

3. Ratio of assets to benefit payments and expenses, 

4. Ratio of the market value of assets to vested liabilities, 

5. Ratio of current year to previous year contributions, and 

6. Ratio of credit balance to employer contributions. 

The second through sixth factors all relate to a plan’s financial status. As discussed 

above, its effect may not be monotonic, and separate controls for components of 

financial status may help the model. For example, the ratio of active to inactive 

participants affects future contribution hikes to keep the plan afloat (if remaining in 
 21 

the plan) and the cost of sponsoring a 401(k) plan for active employees (if 

withdrawing). At the same time, it may make sense to combine some factors. For 

example, the withdrawal liability itself and the required periodic withdrawal 

payments are potentially more directly relevant than the components that determine 

such amounts. 

Insofar we are aware, the current model of mass withdrawals in ME-PIMS does not 

account for industry. However, given special withdrawal penalty rules for the 

Construction and Entertainment sectors, industry may well be a strong predictor.13

Up to this point, the discussion applies to individual and mass withdrawals, but not to 

contagion of mass withdrawals. Many factors that may trigger multiple mass 

withdrawals are in fact included in the economic factors discussed above. For 

example: 

 Industry pressure. Multiemployer plans in the same industry share many 

relevant factors. An industry may shrink due to off-shoring; unionized firms 

may be squeezed by the introduction of non-union competition; or an industry 

may boom due to new export opportunities, low cost of capital, etc. All these 

factors translate into economic incentives that can be controlled for in a 

redesigned mass withdrawal model. 

 Potential government bail-outs. A policy proposal that aims to shore up plans 

in a certain industry applies equally to multiple plans. While it is difficult to 

capture in a model the option value of any prospect of industry-wide financial 

relief, its effect applies to each of multiple plans individually, not as a 

contagion effect. 

What remains is potential contagion due to information effects. A mass withdrawal 

can send at least two types of information to employers in other multiemployer 

plans. 

First, a mass withdrawal can bring some clarity to the various trade-offs that 

employers face. While the trade-offs discussed above are generally clear at a 

conceptual level, it may be difficult to quantify them with reasonable precision. How 

will workers respond to the loss of benefits? Were negotiated withdrawal liabilities 

higher or lower than expected, such as perhaps due to a reallocation liability or 

interpretation of industry exemptions? Additional clarity on these types of issues 

translates into better decision making; it can trigger additional mass withdrawals, 

but can also swing the cost-benefit analysis away from mass withdrawals. 

Second, unexpected consequences of a mass withdrawal may materially affect the 

decision-making process. For example, suppose a mass withdrawal is expected to 

trigger debilitating withdrawal liabilities which would threaten the continued 

existence of many companies. Firms would be unlikely to proceed with such a mass 

withdrawal, but suppose it occurs anyway. If a public outcry ensues and results in 

political measures to support firms that face debilitating withdrawal payments, the 

13 It is our understanding that a withdrawal without a withdrawal liability is, 

technically, not a withdrawal. However, for practical purposes, such withdrawals 

have similar implications for plans as legal withdrawals. It stands to reason for MEPIMS to define a mass withdrawal based on economic rather than legal criteria. 
 22 

potential for contagion is realistic. Unexpectedly favorable consequences create a 

moral hazard—an incentive to take additional risks once losses are covered by 

insurance. This moral hazard is compounded by the moral hazard that is intrinsic in 

the bankruptcy system: the downside potential of financial gambles is limited, 

whereas the upside is not. 

In sum, contagion of mass withdrawals can result from the spread of information 

about unexpectedly favorable consequences of a mass withdrawal. A potential way to 

incorporate this into ME-PIMS is to generate a stochastic “surprise” shock when MEPIMS simulates its first mass withdrawal. That stochastic shock should subsequently 

propagate to multiple plans and affect their probability of experiencing a mass 

withdrawal. It could transmit just to plans that share a common contributor, or to all 

plans in an industry, or perhaps even to all plans nationwide, depending on how fast 

one believes information spreads. Suppose the probability of a mass withdrawal is 

modeled as a logistic regression; the stochastic shock could serve as an intercept 

shift. The shock should last multiple years, possibly for the remainder of the 

simulation horizon. The model would then generate withdrawal probabilities that, 

ceteris paribus, are persistently lower or higher in some scenarios than in others. 

6. CONCLUSION 

This report characterized historical mass withdrawals from multiemployer plans, 

assessed the potential for contagion effects from common ties among plans, and 

developed a theoretical framework for modeling contagion effects in ME-PIMS. 

Recent history has seen relatively few mass withdrawals: annually, roughly 0.4% of 

multiemployer plans experienced a mass withdrawal from 2011 to 2018. Contagion 

of mass withdrawals to plans that shared a common significant contributor has been 

largely absent; just 4 out of the 40 plans that experienced a mass withdrawal were 

connected through a single common contributing employer. There is, however, 

ample potential for contagion, with many plans in the same industry and many 

clusters of plans that share common contributors. 

We identified factors that may play a role in decisions about individual and mass 

withdrawals. While such factors may be common to employers that participate in 

multiple plans in a certain industry, they are applicable to each employer 

individually. Even if multiple plans were to experience a mass withdrawal due to 

certain shared circumstances, the withdrawals would be driven by individual decision 

making rather than causal contagion effects from a particular mass withdrawal. We 

identified an exception in the form of information, revealed by a particular mass 

withdrawal, about unexpectedly favorable consequences of a mass withdrawal. 

Finally, we suggested a parsimonious way to incorporate information contagion into 

ME-PIMS. 
 23 

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This document is the Part 2 Report of the Multiemployer Contagion Component of 

Contract 16PBGC18C0023 (Pension Insurance Modeling System MAP-21 Peer Review 

Service). 

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