Public Sector Unions, Partisanship, and Pensions in the U.S. States
Carolyn Abott∗
Princeton University
September 20, 2015
Abstract
Proper funding of state-sponsored pension liabilities has become politically controversial over the last seven years but has posed both practical and theoretical problems
for far longer. This paper investigates the determination of liability and asset levels
of these pension funds by focusing on specific policies that have lead to the expansion and contraction of funding levels since 1995. Building on theory I outline in a
separate paper and an original dataset constructed from state personnel and pension
benefit data, I demonstrate that the roles that legislative and executive partisanship
play and do not play in these policy decisions is not straight forward and strongly
dependent upon the specific policy being analyzed. Importantly, public sector unions
are not substantively responsible for contributing to state fiscal solvency woes, at least
via pensions, and often act as bulwarks against poor pension performance when paired
with friendly co-partisan state governments.
∗
This work would not have been possible without the help of Nolan McCarty and a generous grant from
the Russell Sage Foundation. Additional thanks go to Alex Bolton, Chuck Cameron, Brandice Canes-Wrone,
Ben Fifield, Mary Kroeger, and Lauren Mattioli for helpful feedback throughout the development of this
project.
1
1
Introduction
Open any major national newspaper today and chances are you will be able to find some
horror story about the fiscal ravages of public sector pension plans. Over the last ten
years, so the story goes, the combination of a weak financial market (Baker 2011) and state
governments’ failure of political will and fiscal responsibility has decimated the assets of
hundreds of defined benefit plans across the country. Coupled with expanding liabilities for
greedy, out-of-touch public sector workers, the state-run public sector pension plans have
become an intense financial, political, and fiscal burden on already struggling state budgets.
New Jersey’s pension crisis, for example, is a classic example of the backroom political
maneuvering meant to short-change the retirement systems by putting off costs to the future.
As Lyman & Walsh (2014) relates, New Jersey began diverting pension payments as far back
as 1992. Most infamously egregious of these diversions was Governor Christine Whitman’s
use of the payments to subsidize a 30 percent income tax decrease in 1994. Three years later
trouble was clearly on the horizon; but rather than increase contributions to the pension fund,
Whitman issued $2.75 billion in pension obligation bonds to pay off the liabilities (Mysak
2014). As a result, New Jersey became the first state in the union to be accused of securities
fraud by the Securities and Exchange Commission in 2010 (Walsh 2010). Governor Chris
Christie promised to make sweeping overhauls to the pension system in 2011, but according
to Lyman & Walsh (2014),
...while the pension cuts helped lower the cost of the system, the state also created
a new, 38-year funding schedule that began with no payment for one year. That
was followed by a seven-year interlude, called “the ramp” during which the state
would gradually work its way up to proper funding. Under the law, New Jersey
does not have to start making the annual contributions that its actuaries say are
required until 2018; it will have until 2048 to pay down its unfunded liabilities.
But by 2018, the state itself forecasts, its system will have become shakier, with a
funded ratio of just 52.3 percent, down from 2010, because its contributions will
have trailed far behind the cost of the plan during the seven-year “ramp.” That
missing money must be made up, with interest, in subsequent years, meaning the
overhaul will have increased the long-term cost of the system.
Though New Jersey’s experience is illuminating, we do not know in a systematic way who
or what is responsible for decisions to expand or contract liabilities and decisions to expand
or contract assets - policies that directly bear on both the short-run popularity and long-run
2
sustainability of pension funds and state budgets. In this work, I assemble a more complete
picture of why some states have been able to keep liabilities and assets on an even keel while
others are circling the fiscal drain. I do this by examining individual policy decisions relating
to the status of pension fiscal positions and analyzing each of them separately.
There are many moving parts that go into understanding public sector pension health.
As in many studies of public budgets and public sector debt levels, taking a holistic, topdown approach to analyzing public sector pensions is - though well-motivated - not a terribly
enlightening exercise (for an excellent summary of the complications inherent in a macro approach to budgets, see Baumgartner et al. (2014)). As such, it makes little sense to talk
broadly about pensions when in fact it is their component parts that should be evaluated
and analyzed. The two main elements of all public sector pensions are how much the pension
owes its current and future beneficiaries (the liabilities) and how much the pension has saved
up already to pay these beneficiaries (the assets). Taken separately, these two components
have distributional implications for different classes of taxpayers (pension beneficiaries vs.
non-beneficiaries), but taken together they have distributional implications for both different
classes and different generations of taxpayers. For example, if pension liabilities are high
relative to total public expenditures, then taxpayer resources are being diverted from other
parts of the budget to the retirement compensation of public employees (as in Kiewiet &
McCubbins (2014)). Of course, taxpayers receive services in return for providing pension
benefits to public workers, and this choice of high liabilities may be a rational one on the
part of the voters via their elected officials. On the other hand, if pension liabilities are
high relative to pension assets (irrespective of their levels relative to the rest of the budget), creating a low funding level, the state government is essentially generating debt that
future taxpayers will need to pay back on behalf of their predecessors or else face the possibility of insolvency and fiscal crisis (see Abott (2015) for a discussion of contemporaneous
repercussions of running large gaps in pension funding).
To understand why some states, in some years, are better or worse at funding their
pension promises, then, we have to look at why liabilities are high relative to assets (and,
conversely, why they are low in other state-years). A number of policy decisions go into the
determination of pension liability levels, asset levels, and, consequently, funding levels. The
most crucial policy choices affecting real pension funding levels1 are:
• actual (realized) investment returns on assets,
1
I emphasize the term real here to contrast it with reported funding levels. As explained below, the
pension numbers used in this paper are constructed as objective, comparable measures, as compared to the
self-reported and potentially biased figures commonly used in other analyses.
3
• employee contribution rates,
• employer contribution rates,
• payments made into the pension fund by the state legislature,
• benefit generosity (including the benefit replacement rate,2 the Cost of Living Adjustment, vesting periods for employees, and eligibility requirements for employees),
• and state personnel policy (size of the public workforce and salary levels).
With these policy choices in mind, Munnell et al. (2015) found that between 2001 and 2013,
poor investment returns accounted for the vast bulk of the increase in underfundedness of
state and local pensions - for both the well funded plans and the poorly funded plans. This
likely reflects the impact of the Great Recession and accompanying financial crisis that hit
almost all pension plans quite hard, and was almost certainly apolitical in nature (that is, to
the extent that states had any control over the disappointing investment returns). What did
differ by the quality of fundedness of the plans, however, was employer contributions, employee contributions, state payments to the pension funds, and changes in benefit structures:
for pensions that were relatively well funded, low contributions and payments contributed
less to increases in unfunded liabilities than in plans that were poorly funded, while benefit
changes contributed more to increases in unfunded liabilities in well funded plans than in
poorly funded plans (on average, they actually contributed to decreases in unfunded liabilities in those plans). To make this clearer, consider the two examples Munnell et al. use.
Over the period between 2001 and 2013, Georgia’s Teachers’ Retirement System (TRS) was
one of the best funded plans in Munnell et al. ’s sample; New Jersey’s Teachers’ Retirement
System was one of the worst. Both pensions, however, saw their unfunded liabilities increase
over time. Both plans also experienced poor investment returns relative to expectations.
Inadequate employee and employer contributions and payments from the state legislature
contributed to 18% of the increase in the Georgia TRS unfunded liability, but contributed
to a whopping 47% of the increase in the New Jersey TRS unfunded liability. Conversely,
modest benefit contractions reduced Georgia’s unfunded liability by 4% but greater benefit
2
Defined benefit pension annuities are almost always a function of the number of years worked by the
pensioner, a weighted average of her final salary, and the benefit replacement rate. Most often the calculation
for determining the annuities is Number of Years Worked × Final Salary × Replacement Rate, so that the
replacement rate is usually thought of as the multiplier necessary for a pensioner to receive 100% of their
working wage given a lifetime of normal employment. For instance, if we assume that normal full-time
employment lasts for forty years, the replacement rate necessary for a retiree to receive her entire final year
salary upon retirement would be 2.5%.
4
cuts in New Jersey helped to offset their unfunded liability by 20%. In other words: the
worst funded plans are often the ones with drastically inadequate contributions and payments, and often attempt to correct for this shortfall by enacting large benefit cuts. The
better funded plans contribute more appropriately and regularly to their asset pool, and are
not forced to reduce benefits to public sector workers when the going gets rough.
Given the patterns that Munnell et al. found in their sample of pensions, the next
step is to ask why some states have mustered the political will to contribute sufficiently to
their pension funds, and why others have not. Similarly, why have some states been able
to keep both benefit generosity and funding levels at a relatively steady level while others
have failed over the same time period? This paper suggests that partisan politics, interest
group behavior, and political institutional structures all play a strong role in these policy
outcomes.
Altogether, this work helps considerably to further our understanding of policymaking
in general, and budget policy and debt management, in particular. First, it gives us new
insight into how interest groups and partisans balance budgetary and political tradeoffs.
As I show, Republicans do not enact broad-based cuts in pensions, just as Democrats do
not use every tool at their disposal to bolster them. My evidence, instead, suggests that
highly unionized workforces - often working in concert with their Democratic sympathizers
- are more likely to encourage good faith towards the solvency of their pension plans in
certain situations, but not in all. When displaying restraint and moderation in pension
benefit demands furthers unions’ political objectives (i.e., by keeping at-risk Democrats in
office), the decision to choose solvency over benefits is easy. When Republicans are in charge,
however, unions tend to forgo solvency in order to secure greater benefits or avoid to benefit
cuts. Second, this work furthers our understanding of how public sector unions are unique to
the political process, and how they differ from other types of interest groups. While previous
studies of public sector unions tend to underscore their ability to organize and lobby, this
work emphasizes the fact that public unions are actually employed by the very people they
hope to influence. I provide evidence that unions recognize this unique dynamic, and care
about how their influence on policy ultimately affects electoral outcomes.
5
2
Pension fundedness, public sector unions, and partisan government
Robust theoretical predictions about how interest groups (e.g., unions), governments, or
voters should respond to the underfunding of public sector pension plans are few and far
between (Anzia & Moe 2015c, 2013). On the one hand, unions want to extract as many
concessions as they can from their employers and tax payers by expanding liabilities3 , but
as rational interest groups, they should also care about the stability and fundedness of the
plan, and the possibility that the pension may go bankrupt if mismanaged badly enough4 .
In other words, unions care about the financial positions of their pension funds - how many
assets are socked away relative to the size of their benefits. In a perfect world, unions would
expand assets at a rate comparable with their liabilities. In the real world, however, unions
face scarcity and may be forced to make trade offs between expanding their liabilities and
maintaining a given net financial position of the pensions.
Governments, both Republican and Democrat, may also have conflicting incentives for
adequately funding state pension plans. Because long-term liabilities do not enter the general
budgets of states, governments can use pensions as types of piggy banks - both for legal
reasons (to circumvent balanced budget laws) and for political reasons (to increase spending
without increasing taxes, a win-win for everyone). As The Institute for Truth in Accounting
(2009) explains,
Because state budgets are calculated using cash-based measures, only the pension
contributions paid to the plans are included in state budgets. The budgets only
include the pension contributions legislators decide they want to pay...[they] have
nothing to do with the amount of retirement benefits earned by the workers
during the budget period. Consequently a state budget calculation may not
recognize billions of dollars of retirement costs incurred each year, yet the state
is deemed balanced even though current revenue is not set aside to adequately
fund these promises. (p. 30)
But again, on the other hand, if state pensions were to be so underfunded as to provoke
crisis, there is little doubt that the government would be held electorally responsible (the
3
Or liabilities per worker.
While it is possible that public unions never believed that they could get the short end of the stick if a
state ran into financial trouble, contemporary events in New Jersey, Rhode Island, Detroit, and elsewhere
have made clear that pensioners may be at risk of abrogated contracts and do not necessarily have the first
lien on a bankrupt municipality.
4
6
Greek government debt crisis of 2009-2015 is a case in point). The ramifications of a state
pension becoming insolvent would not only be felt by state workers; the economic and
fiscal consequences would be widespread, and likely not even contained to the state within
which the crisis occurred. Abott (2015), for instance, finds that states with poor pension
funding are likely to receive worse credit ratings from Standard and Poor’s, which in turn
has implications for the states’ cost of borrowing and, by extension, their ability to provide
adequate public services relative to tax revenue.
Partisan affiliations may also color the government’s incentives to fund or not fund the
state pensions adequately. Republicans might hold an ideological commitment to adequately
fund the plans, as not doing so is the moral equivalent of running perpetual deficits.5
Democrats, however, may also want to keep the plans well funded in order to keep their
union-member constituents happy.
As such, theoretical predictions about funding rates of public sector pensions funds are
murky. Part of the reason for this is the cross-cutting of incentives for multiple actors, but
an equally significant part of the reason for this has to do with the fact that funding rates
are a consequence of many, many decisions made by both employee and employer. Here
I try to break down the funding ratios into smaller, more testable components of financial
and political decisions. Many of the decisions made regarding the fiscal and financial health
of the state pension funds are done at the legislative level, by statute. All of the policy
choices I address in this paper fall under the legal purview of the state legislature. For these
policies, the primary negotiations take place in a traditional lawmaking environment and
amongst individual legislators or across parties, though the governor can influence policy
outcomes through the executive’s veto power and as as an agenda setter (this is especially
important when it comes to budgetary decisions, which almost always originate with the
state executive). Unions also have the ability to influence the final outcome of the law much
in the same way that any type of interest group can influence policymaking, via campaign
contributions and electoral strength.
For these reasons, then, it is reasonable to expect a straightforward relationship between
Democrats and the strong public sector unions that donate to them and vote for them. The
stronger the union constituency - in both number and financial strength - the more generous
we would expect Democrats to be, and the less concerned about the ramifications of shifting
resources towards public sector pensions. Previous scholars have found little evidence that
5
Certain Republicans might also hold an ideological commitment to running specific sectors of the government out of business, but my work assumes that no actors want to purposefully force a public entity into
bankruptcy.
7
this is true, however (Anzia & Moe 2013, 2015b, Kiewiet 2010), raising questions about what
exactly is transpiring across the political landscape that would shift the “normal” motives and
incentives of actors. Anzia & Moe (2015b) argue that pension policy was simply not salient
enough until the Great Recession to force an opposition group to coalesce around the issue of
generous unfunded pension benefits. I argue, however, that the reason pension policy was not
particularly salient until the Great Recession was because unions and Democrats were able
to police themselves, to a certain extent. As discussed above, the Great Recession decimated
asset levels and funding ratios for all state pension plans more or less in a nondiscriminatory
fashion (Munnell et al. 2015). This piqued the public’s interest, and for good reason. Prior
to the Great Recession, however, funding levels were more likely to rise and fall in line with
politically controllable outcomes, like changes in benefit generosity and contribution rates.
Some states, in some years, were better at maintaining funding levels than others. If all
states and partisans were equally profligate, as Anzia and Moe suggest, the Great Recession
would not have been necessary to pique the public’s interest in the way they depict.
Part of the reason for the lack of previous findings may have do with the unique trade-off
that public sector unions face. Public sector unions posses not only the ability to lobby for
preferable legislation but to actually influence who their future employers are (Moe 2006).
This trade-off that public sector unions face is quite distinct from that of a traditional
interest group. Union efforts to mobilize voters and sabotage unfriendly partisans’ policy
ambitions influence elections (as do traditional interest groups’ efforts), but the winners
of the union-influenced elections control the employment environment of the immediate
future. This puts unions in a precarious situation: if their preferred boss (which is likely
a Democrat, considering the overall pattern of public sector union contributions in Figures
1 and 2 and the storied history of private sector union voting behavior (Freeman 2003))
does not or cannot pass union-preferred policy because of conflicting voter preferences or
budget constraints, does the union throw their financial and electoral resources behind an
effort to ram the legislation through anyway? Or do they moderate their behavior and
recognize their “linked fate” with the Democratic party and the fact that a policy adjustment
might be preferable to a fiscal crisis and a Republican-controlled government in the next
electoral cycle? Elected officials use policy somewhat regularly to create new types of politics
(Anzia & Moe 2015a, Flavin & Hartney 2015, Hacker & Pierson 2014). By passing laws
favorable to public sector collective bargaining, Democrats were essentially able to create
a new, electorally powerful constituency in the form of the public sector union. There is
good reason to imagine that unions are sophisticated enough to be playing the same game
8
as their managerial counterparts, and are even more motivated to prevent Repubicans from
taking office if it means the repeal of such protections in an effort to entirely eliminate their
constituency (consider Republican Governor Scott Walker’s legislation in Wisconsin in 2011).
Given this logic, we have two directly competing hypotheses:
Constituency hypothesis: Democratic governors and Democratic-controlled
legislatures will be more likely to increase pension benefits and allocate more pension funding from the general budget, relative to their Republican counterparts.
They will be even more likely to do so when unions are relatively strong.
Linked fate hypothesis: Stronger public sector unions will be more successful
in achieving labor-friendly pension policies. When strong unions are coupled
with strong Democratic government, however, both parties will choose to show
restraint in passing labor-friendly pension policy that impinges upon non-public
sector worker voter welfare.
Figure 1: AFSCME Campaign Contributions to State Elections by Year and Party
Figure 1: Breakdown of AFSCME’s partisan giving habits over time. Data from National Institute on Money
in State Politics (1995-2012)
.
The last clause in the second hypothesis is important. Though there is little evidence that
voters pay close attention to public pension policymaking, any unfunded (or under-funded)
pension policy will - by construction - either shift public goods spending away from nonworkers or increase the state’s debt load (which, again, is also likely to have contemporaneous
9
Figure 2: AFSCME Campaign Contributions to State Elections by State and Party
Figure 2: Breakdown of AFSCME’s partisan giving habits across states. Data from National Institute on
Money in State Politics (1995-2012)
.
consequences via credit ratings and bond markets (Abott 2015)). Both of these outcomes
are the result of either zero-sum or negative-sum interactions amongst workers, non-worker
voters, and future taxpayers, and are inherently deleterious to the welfare of taxpayer-voters
who are not employed by the state. As such, simple retrospective voting a la Fiorina (1978)
should be adequate for motivating unions and strong Democratic government to restrain
themselves from gutting state budgets in favor of unfunded pension expansions. If Democrats
perform economically poorly while in office, or find themselves needing to raise taxes, voters
will simply replace them with Republicans. Neither the incumbent Democrats nor the unions
dependent upon their beneficence want to see this happen.
Lastly, these hypotheses are unique to public sector unions, rather than public sector
workers in general, because of the implicit collective action problem unions have been able
to overcome as evidenced by their mere existence. While this paper remains agnostic about
how, exactly, groups of workers are able to form unions and solve Olson’s collective action
problem, it has been shown that, in conjunction with friendly labor laws, they have been
able to do so (Flavin & Hartney 2015) with relatively great success. Because non-unionized
state workers have not been able to overcome the collective action problem, they cannot
identify with their co-partisans in the same way that unionized state workers can. Nor
can they overcome the costs of putting on shows of solidarity and taking time out of their
individual lives to lobby the government. The third hypothesis, then, is that the sheer
10
concentration of state workers will have no impact on pension policy outcomes or partisan
behavior, independent of their unionization status.
Collective action hypothesis: High state worker concentrations do not influence pension policy independently of the state’s unionization rate or the campaign
donation behavior of state unions.
In sum, I argue against previous findings that partisan and interest group politics play
little role in public sector pension policymaking. Instead, I claim that pension policy much like budgetary policy - is incredibly politically fraught on all levels. State spending on
pensions means not spending on other public goods or not spending in the future. Whether
Democratic state governments and unions collude to expand pension liabilities in an effort to
placate constituencies and/or fulfill ideological commitments (in what Anzia & Moe (2015b)
call “polarization politics”) or whether they recognize that they are both playing a repeated
game of electoral politics in which forbearance has its virtues6 - and that the union vote
is not enough to secure an unpopular Democrat’s incumbency - remains to be seen in the
empirical section below.
3
Data
Below I describe the key dependent, independent, and control variables used in the analysis
and their sources.
3.1
Dependent variables
Statutory changes to benefit replacement rates, Cost of Living Adjustments (COLAs), employee contribution rates, and employer contribution rates
The main dependent variable in my analysis is an indicator of how much the state government expands or contracts liabilities in a given calendar year. The National Conference of
State Legislatures (National Conference of State Legislatures (NCSL) 1999-2011) provides
extensive summaries of all passed state legislation relating to pension reform for each year
from 1999 to 2011. Data were hardcoded as {−1, 1} to reflect whether a state passed legislation that would result in an eventual contraction or expansion of liabilities, or 0 if they
6
This analysis is influenced by and adapted from Rodden (2006)’s model of fiscal bailouts in federal
systems.
11
passed no legislation in the individual policy arena. Figure 3 summarizes average legislative
policy over time.
It is important to make sure that my main dependent variable is an indicator of the
net expansion or contraction. If a state were to pass legislation that raised the benefit
replacement rate, for instance, but also passed legislation increasing employee contribution
rates in order to offset the replacement rate increase, the state would not, on net, be liable
for any increase in pension obligations. If state workers pay for their pensions solely through
their own contributions7 and the interest earned on already existing assets, the government
should have no qualms about increasing the generosity of replacement rates or COLAs. It
is only when workers receive expanded benefits that do not “pay for themselves” that the
state and the voters should sit up and take notice, intervening if they deem necessary.
To understand this logic better, imagine that state pensions are actually semi-sovereign
subnational governments, much like a local school district.8 State workers are the sole beneficiaries, taxpayers, voters, and ruling authority in this government. Their semi-sovereign
pension government, however, exists in a federal system where the state government has far
greater resources at its disposal, including a much larger tax base,9 and can transfer funds
to the pension at its own discretion. The need to transfer funds to pension plans, above and
beyond their normal rate of intergovernmental transference, will be dictated by the extent
to which liabilities and assets (or public goods and taxes, to continue the metaphor) are
mismatched. When a pension finds itself in a poor funding position, they have two options:
either undergo a potentially painful adjustment to the liability and asset side of their ledgers,
or hope for a state “bailout” of their semi-sovereign government. If pensions choose the latter option, state governments can then either acquiesce and give the pension the necessary
transfer, or allow the pension to go belly up (“default”).
The metaphor gets a bit messy because state governments actually have some control
over the liability and asset growth of this semi-sovereign subnational, since it is the state
legislature that sets benefit and contribution rates. Nevertheless, I argue that state workers
- at least when organized into cohesive bargaining and lobbying units in the form of unions
7
Here I consider employer contributions to be the theoretical equivalent of employee contributions, since
- holding all else equal - this is a form of compensation that employees would otherwise receive as wages. If
I reconstruct the dependent variable by removing employer contributions from consideration, the results do
not change.
8
This section is influenced by Rodden (2006).
9
A kink in this analogy is that the pensions actually have considerably more borrowing power than
the state government, since pensions can (and do) run excessive gaps in their funding levels while state
governments are constitutionally and statutorily constrained in their ability to issue debt and run deficits.
12
- have considerable say over these decisions. If state workers want to expand their benefits
but plan to pay for the expansion themselves via increased contributions, state governments
will evaluate such a move as having no impact on the likelihood of a fiscal crisis, and thus no
impact on the likelihood that they will have to bail out the pension fund. For this reason, I
construct the first dependent variable as the count of passed legislation that increased pension benefit generosity (with passed legislation that decreased pension generosity coded as
a negative count), less the count of passed legislation that increased employee or employer
contribution rates. In my data, this variable takes on the value {−4, −3, −2, −1, 0, 1, 2, 3}.
Holding the current funding status of the pension plan constant, a negative value of the
variable lowers bailout and default risk and forces state employees to take a cut in their welfare. A positive value is a net benefit for employees but at the cost of increasing bailout and
default risk. Under what circumstances would workers be willing to lower their net benefits
in exchange for greater fiscal responsibility? If we believe workers are rational agents, this
would happen only when workers suffer costs associated with the alternatives - bailouts and
defaults.
Actual contribution rates
Net changes in pension benefits are indicators of when a pension plan acknowledges (net contraction) or does not acknowledge (net expansion) the threat of fiscal stress. Net expansions
imply that the costs of receiving a bailout from the state or suffering from a default do not
outweigh the benefits of greater pension generosity for workers. Net contractions imply the
opposite. What has not been discussed, however, is what would make a state more likely to
bail out the pension via ad hoc legislative contributions rather than allow them to default.
To understand this dynamic I constructed a second dependent variable that uses data on
payments made by the state legislature to the pension plans.
Contribution rates are typically defined as the size of a state’s payment to the pension fund relative to the Actuarially Required Contribution (ARC). The ARC, in turn, is
(supposed to be) defined as the yearly payment that would be needed to fully amortize the
Unfunded Actuarial Accrued Liability (UAAL). There is no federal law in place that requires
states to either pick an accurate ARC or to contribute 100% of the ARC. As such, states
have a strong incentive to underestimate the ARC and report inflated contribution rates,
especially if they believe this information is important to voters or state employees. To get
around this reporting bias, I collected data that could be used to more accurately judge the
13
0.4
Figure 3: Average Pension Reform Involving Benefit Changes Across States, by Year
0.0
-0.4
-0.2
Average Legislation Change
0.2
Employer Contributions
Employee Contributions
Benefits
COLA
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Year
Figure 3: Increases (decreases) on the graph correspond with increases (decreases) in benefit allowances,
increases (decreases) in COLAs, and decreases (increases) in contribution rates, all of which are expected to
expand (contract) liabilities and worsen (improve) the pensions’ fiscal positions.
fundeness of a state plan.10 As Figure 4 shows, the self-reported ARC is clustered around
the low end of the distribution, especially relative to my revised ARC measure. Figure 5
10
Please see the appendix for a full description of this process.
14
emphasizes this point by comparing the distributions of self-reported and revised contribution rates - the median contribution rate, according to states’ own records, was 100%! My
numbers tell a different story. Below I briefly outline the process through which I obtained
the self-reported ARC and contribution data, and how I revised them to more accurately
reflect policy choices.
Self-reported ARC and contribution rates were first obtained from The Pew Center on
the States (2010) for all 50 states and the years 1997-2008. Using this data I was then able
to back out exactly how much the state paid into their pension funds in a given year: ARC
× Contribution Rate = Actual Payment.
Next, I used my revised liability and asset data to construct a measure of what I call
the “aggregate UAAL,” which is simply the sum of the UAALs for all pension plans in a
given state. This is mathematically constructed as UAAL = Liabilities − Assets, and is an
indication of how much the state needs to meet its future and current obligations.
In order to get a sense of how many dollars a state would have to contribute on a yearly
basis, over 30 years, in order to pay off this UAAL, I then needed to calculate the revised
ARC. This number is calculated much in the same way a prospective homeowner would
calculate a possible mortgage payment. The interest rate that the state is “borrowing” at
is the rate of return that the assets would have received had the UAAL not existed - the
opportunity cost of not having enough assets, in other words. For this calculation, I used
the rate of return that was equal to the rate of return I assumed to revise the actuarial value
of assets (AVA) calculations (rather than the plans’ assumed rate of return), or 6.5%. To
construct the revised ARC, then,
i(1 + i)n
(1 + i)n − 1
0.065(1.065)30
= UAAL ×
(1.065)30 − 1
ARC = UAAL ×
≈ UAAL × 0.077
From here, calculating the actual contribution rates of state legislatures over my sample period is straightforward. I simply divided the actual payment amount (backed out from Pew’s
data) by the revised ARC, so that Revised Contribution Rate = Actual Payment/Revised ARC.
As an additional analysis and robustness check, I also calculate payments as a percentage
of general expenditures. This measure is constructed by dividing actual payments by general
15
Figure 4: Distribution of Self-Reported vs. Revised ARC, Per Capita
0.003
0.000
0.001
0.002
Density
0.004
0.005
0.006
Self-Reported
Revised
Median
0
500
1000
1500
2000
2500
Dollars per Capita
Figure 4: Distributions of self-reported and revised Actuarially Required Contribution (ARC) normalized
by population. This measure represents the amount of dollars per resident that a state needs to contribute
to the pension fund annually over thirty years in order to fully amortize the unfunded liabilities. Data from
The Pew Center on the States (2010) and the author’s calculations.
.
expenditures of the state budget (collected from the United States Census Bureau (19952012)). This is a helpful variable to look at if we believe that unions or governmental actors
might not be sure of the true value of the revised ARC or UAAL, but can more easily observe
payments relative to expenditures. Figure 6 summarizes average payment history over time.
16
Figure 5: Distribution of Self-Reported vs. Revised Contribution Rates
0
1
2
3
Density
4
5
6
Self-Reported
Revised
Median
0
1
2
3
4
Ratio
Figure 5: Distributions of self-reported and revised legislative contribution rates. This rate is the percentage
of the ARC actually paid off by the legislature in a given year. Data from The Pew Center on the States
(2010) and the author’s calculations.
.
3.2
Independent variables
Public sector unionization rates and AFSCME campaign contributions
State public sector unionization rates were calculated from survey data taken from the Cur17
0.030
Average Payment ($ Millions)
Ratio of Avg. Payment to Self-Reported Liabs
Ratio of Avg. Payment to Revised Liabs
0.010
0.015
0.020
0.025
Ratio of Average Payment to Liabilities
1000
900
800
0.005
700
500
0.000
600
Average Payment ($ Millions)
1100
1200
Figure 4: Average State Legislature Payment into Pension Fund Across States, by Year
1997
1999
2001
2003
2005
2007
Year
Figure 4: Average payment from state legislature into pension funds across states, by year. Left-hand axis
corresponds with absolute average payment, while the right-hand axis corresponds with the ratio of average
payment to different measures of pension liabilities.
rent Population Survey (United States Census Bureau and Bureau of Labor Statistics 19952012). This rate measures the percent of state government workers who belong to a union,
and is a good indicator of how well organized workers are as a group. The better organized
18
employees are, the easier it will be for them to affect policy that is relevant to them via lobbying efforts and electoral mobilization. On the other hand, better organized employees are
also more liable to perceive their “linked fate” with Democratic politicians and to encourage
moderated demands when friendly co-partisans are in office.
Similarly, the financial strength of public sector unions is also indicative of their level of
organization. Though not a perfect measure of the financial capacity of state public sector
unions, the amount of dollars donated to state political entities by the American Federation
of State, County, and Municipal Employees (AFSCME) serves to approximate this power.
The data comes from the National Institute on Money in State Politics (1995-2012) and
covers political races in all fifty states from 1995 to 2012. A potential source of trouble
with using AFSCME contribution data is that they essentially only donate to Democratic
candidates. This fact lends support for the purported link between the Democratic party and
state public sector unions, but it also means that many states receive little to no campaign
donations over the sample period. Figures 1 and 2 present this visually. To compensate for
the inflated zeros, I transform the data so that the operative variable is Logged Donations
= ln(Donations + 1).
Overall union strength is likely a combination of strength in sheer numbers and strength
in financial resources. Recognizing this, I normalize both the public sector unionization
rates and logged donations and combine them into a (non-weighted) single index. Doing so
also alleviates any concerns about collinearity. Using the variables separately in my analysis
yields similar results as using my index, though unionization rates appear to play a bigger
role in certain policy arenas while campaign contributions appear to be more important in
others. A larger discussion of this is contained in the Appendix.
State worker percentages
The percentage of workers employed by the state relative to all full-time private and publicsector employees was also calculated from CPS survey data (United States Census Bureau
and Bureau of Labor Statistics 1995-2012). This measure is distinct from the unionization
rate discussed above as it in no way serves to capture the level of organization of the state
workforce. It does, however, serve as a proxy for the electoral strength of public sector employees, and the potential threat that they pose to uncooperative politicians at the ballot box.
Collective bargaining protections and right to strike
In order to control for unobserved state specific variables like hostility or friendliness to labor
19
in general, I collected data from Valletta & Freeman (1988)’s Public Sector Collective Bargaining Law Dataset and updated years not covered. The collective bargaining protections
variable can take on any integer from 0 to 6, where 0 is a state with no collective bargaining
provision at all and 6 is a state law that makes the duty to enage in collective bargaining
explicit. Similarly, the right to strike in a state ranges from no provision to permitted, and
can take on two other intermediary values (prohibited with explicit penalties and prohibited
with no penalties specified). For this analysis, I combined these two variables into an overall
“bargaining” index where I simply sum the individual measures. Again, this cuts down on
the collinearity of the model without sacrificing analytical parsimony or qualitatively changing the results.
Governor partisanship and legislative partisanship
Party of the governor and party composition of the state legislatures were taken from National Conference of State Legislatures (NCSL) (1999-2011). Party composition is operationalized here by the percentage of Democrats in the state legislature. Substituting party
control in the upper or lower house of the state legislature does not qualitatively change my
results.
3.3
Control variables
Historical policy and political data
Because policy might have a natural “cap” to the degree of expansion or contraction a state
can possibly tolerate, I control for lagged changes in policy as well as the number of times
the governorship changed party since 1963 and the standard deviation (across time, within
a state) of my Democratic legislator measure above. Governorship party data were collected
by the author.
Election data
Years that gubernatorial elections took place in a given state were collected by the author.
Unemployment and Gross State Product (GSP)
The unemployment rate for each state-year was obtained from the Bureau of Labor Statistics
(United States Bureau of Labor Statistics 1995-2012); real and nominal GSP comes from
the Bureau of Economic Analysis (United States Bureau of Economic Analaysis 1995-2012).
20
Total debt, general revenue, general expenditures, and legality of fiscal year deficit carryover
All state budget data were obtained from the Census of Governments Annual Survey of State
Government Finances (United States Census Bureau 1995-2012), while the legal indicator
was collected from Hou & Smith (2006).
Aggregate funding ratios of state pension plans
Data used to construct unbiased funding ratios were collected by the author from individual
CAFRs. Please see the Appendix for a full discussion of the measure.
4
4.1
Analysis
Pension policy
I now turn to the analysis of my data and tests of the hypotheses laid out above. I begin with an illustrative example drawing on New Jersey’s pension experience. New Jersey
has had a fair amount of legislative turnover within my data. The state began my sample
period in 2000 with Democrats controlling neither house in the legislature and occupying
only 42% of all seats. Ten years later, however, New Jersey Democrats controlled both
houses and commanded nearly 60% of seats. At the same time, the concentration of public workers employed by the state remained relatively constant, hovering around 3.5%, as
did the strength of the state workers’ unions. Over this period, New Jersey’s unions fell
consistently into the top quarter of the strongest organizations in the country (New York’s,
on average, were the strongest, while Mississippi’s were the weakest). As Table 1 depicts,
expansionary pension policy appears to be correlated with lower numbers of Democrats in
the legislature. Expanded benefit replacement rates and reductions in both employer and
employee contribution rates were allowed to pass the state legislature by unified Republican
government in 2001. This is likely due to the remarkable influence of New Jersey’s public
employees unions. As Democrats regained control of the legislature, however, New Jersey
began rolling back some of the generosity of previous Republican administrations, cutting
benefits twice and increasing contribution requirements for both employees and employers.
Even after accounting for yearly trends, as I do in Table 2, it is clear that there is an inverse
relationship between expansionary policy and Democratic control.
This preliminary example appears to support my Linked Fate Hypothesis in which strong
unions moderate their policy demands when Democrats are in office, and provides no evidence
21
Table 1: New Jersey’s Pension Legislation, 1999-2010
Year
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Union Strength
1.04
1.12
1.22
1.22
1.04
1.24
1.31
1.29
1.38
1.28
1.47
1.23
Dems in Office
0.40
0.43
0.43
0.62
0.62
0.58
0.58
0.58
0.58
0.59
0.59
0.59
Net Leg.
0
1
3
0
1
0
0
0
-2
0
-1
-1
Type of Leg.
Employee CR(-)
Benefits(+), Employer CR(-), Employee CR(-)
Employer CR(-)
Benefits(-), Employee CR(+)
Employer CR(+)
Benefits(-)
Table 1: Union Strength is calculated as an index on [0, 2] and is constructed from the degree of unionization
in the state-year and the amount of campaign contributions given by AFSCME and its local chapters to state
elections. Dems in Office is the ratio of Democrats in the state legislature relative to total seats. Net Leg.
is the count of expansionary pension policy laws passed in the state-year, less the number of contractionary
pension laws. Rows in red are periods of Republican gubernatorial administrations while rows in blue are
periods of Democratic administrations.
Table 2: New Jersey’s Pension Legislation Net of Country-Wide Yearly Average, 1999-2010
Year
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Union Strength
1.04
1.12
1.22
1.22
1.04
1.24
1.31
1.29
1.38
1.28
1.47
1.23
Dems in Office
0.40
0.42
0.42
0.60
0.60
0.57
0.57
0.58
0.58
0.59
0.59
0.59
Net Leg.
-0.16
0.84
2.64
-0.16
1.12
0.18
0.32
0.22
-1.78
0.14
-0.88
-0.55
Table 2: Net Leg. is the count of expansionary pension policy laws passed in the state-year, less the number
of contractionary pension laws, and less the average pension legislation of the entire country in that year.
22
Table 3: New Jersey Legislative Contributions to Pension Fund, 2000-2008
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
Union Strength
1.12
1.22
1.22
1.04
1.24
1.31
1.29
1.38
1.28
Dems in Office
0.42
0.42
0.60
0.60
0.57
0.57
0.58
0.58
0.59
Contribution Ratio
1.186
0.06
0.01
0.02
0.03
0.07
0.13
0.25
0.27
Payment/Gen. Expend. (%)
1.05
0.32
0.05
0.09
0.28
0.61
1.31
3.88
4.50
Table 3: Contribution ratio is calculated as the ratio of the state’s payment to the revised ARC. Note that the
percent paid out relative to general expenditures increases drastically over time after falling off considerably.
This is not due to a drop in expenditures (which grew 54% from 2001 to 2008) but to a ramp-up in the
UAAL (which ballooned over 400% over the same period). Actual payments grew over a staggering 2000%.
I cross-referenced these figures with news stories because they seemed so outrageous that it was possible my
figures might be off by an order of magnitude (or two). These numbers are indeed correct.
for my Constituency Hypothesis. Culling inferences about patterns of legislative contribution
behavior, however, is more difficult. My data tell a complicated story. As depicted in Table
3, New Jersey Republicans started off the sample strong by contributing over 100% of the
ARC in 2000. This number dipped precipitously immediately after, however, and only really
began to recover in 2007 when the contribution climbed to about a quarter of what was
needed to fully fund the pension over 30 years. As such, New Jersey’s case is helpful to
consider, but still only anecdotal. I now turn to a more systematic investigation of pension
policy in the states.
I begin with an analysis of my first dependent variable, net pension legislation. Most
observations of the data take on a value of zero in the data (approximately 80%). Because
zero-inflated models for count data with negative values do not exist (to the best of my
knowledge), however, I use ordered probit models for most of the following analysis. As
a robustness check, I also contrast my main findings with results from linear models and
rare events logit models. I use the latter model by splitting my legislation data into two
groups - net expansions and net contractions of liabilities. I code the binary net expansion
variable as 1 if the underlying variable is greater than zero, and the binary net contraction
variable as 1 if the underlying variable is less than zero. I then use a rare events logit to
analyze what is likely to predict an expansion, and what is likely to predict a contraction,
23
relative to passing neutral policy or no policy at all. Using this method I can contrast action
to inaction, but cannot contrast contraction to expansion. While less than ideal, it is true
that most states are probably not weighing all of the paired choices between contraction,
expansion, and neutrality (or inaction), but rather they are considering either to contract or
not contract, or to expand or not expand - not whether to expand or contract. Either way,
however, the results of this robustness check are in line with my findings from the ordered
probit models, and is are contained in the Appendix.
The probit is estimated as follows:
yst =



−4







−3







−2







if zst ≤ µ1 ,



0







1







2





3
if µ4 < zst ≤ µ5 ,
if µ1 < zst ≤ µ2 ,
if µ2 < zst ≤ µ3 ,
−1 if µ3 < zst ≤ µ4 ,
if µ5 < zst ≤ µ6 ,
if µ6 < zst ≤ µ7 ,
if µ7 < zst
where zst = β1 · Union Strengthst + β2 · Dem Govst + β3 · Avg Demsst
+ β4 · Union Strengthst · Dem Govst + β5 · Union Strengthst · Avg Demsst
+ β6 · State Worker Ratest + β7 · Bargaining Indexs + γ Xst + δs + κt + st
Xst is a vector of control variables that vary by state and year and δs and κt control for
fixed effects invariant to year and state, respectively. The Constituency Hypothesis requires
that β2 > 0 and/or β3 > 0. The strong version of the hypothesis additionally requires
that β4 > 0 and/or β5 > 0. The Linked Fate Hypothesis, on the other hand, requires that
β1 > 0 and that either or both β4 , β5 < 0. Finally, the Collective Action Hypothesis states
that the coefficient on state worker concentration, β6 , be zero.
Estimating this regression is not straightforward. The biggest hurdle researchers often
face when studying sub-national policymaking is the lack of variation both across states and
within states (or other sub-national units). In the case of public unionization strength, the
within-state variation (across time) is much smaller relative to across-state variation (within
a given year). Most studies, for this reason, often neglect to include state fixed effects,
making interpretation suspect. Adding controls and region fixed effects often ameliorates
24
some concerns, but the threat of omitting unobserved variables that might explain a given
policy is always lurking in the background of these models. In order to get around concerns
of omitted variable bias in an ordered probit or ordinary least squares model (full pooling)
and power issues in an OLS state fixed effects model11 (no pooling), I use a multilevel mixed
effects model that acts as a kind of compromise between the two extremes (partial pooling),
taking into account both baseline differences unique to that year or state (“fixed effects”) and
the fact that these baseline differences can be variable due to sampling or noise (“random
effects”) (Gelman & Hill 2006). In this way, effects on the dependent variable attributable to
unobservable variables are largely mitigated without completely overwhelming large crossstate (relative to within-state) differences in independent variables.
Table 4 presents estimates from five regressions analyzing the effect of my independent
variables and the hypotheses discussed above. The first model pools all observations together
into a standard ordered probit, without controlling for economic factors, previous policy, or
historical political circumstances. The second model is a mixed or multilevel model in which
I control for state fixed and random effects and add a dummy variable indicating whether
the observations occur before or after the Great Recession. The third model adds year
fixed and random effects, while the fourth controls for historical factors that might influence
policy. The last model adds a host of economic control variables described in the previous
section. In every one of the models, the coefficient on Union Strength is statistically and
substantively significantly greater than zero while the coefficient on Ratio of Dems * Union
Strength is statistically and substantively significantly less than zero, in line with the Linked
Fate Hypothesis. While Dem. Gov. * Union Strength is not statistically significant, this is
not terribly surprising considering that pension legislation is primarily within the purview
of the state legislature. Similarly, State Worker Concentration is statistically insignificant
in all but the last of the models, suggesting that the Collective Action Hypothesis is also at
least partially correct. Furthermore, swapping out the state worker variable for the union
strength variable in Table 5 does not make any of the independent variables statistically
significant, adding evidence that the hypothesis is correct. In fact, the only statistically
significant relationship in the regression is unemployment’s strongly negative influence on
generous policy (which is standard across all of the models). In no specification is the
presence of a Democratic governor or large concentrations of Democrats in the legislature
statistically significant, suggesting that the Constituency Hypothesis does not hold water.
To see how the raw coefficients from the probits translate into probabilities of policy
11
Ordered factor models cannot incorporate fixed effects, generally speaking.
25
Table 4: Ordred Probit Regression on Net Expansionary Pension Legislation
Ratio of Democrats in Legislature
Union Strength
Democratic Governor
Bargaining Index
State Worker Concentration
Ratio of Dems * Union Strength
Dem. Gov. * Union Strength
Model 1
0.20
(0.65)
0.75∗∗
(0.37)
−0.30
(0.19)
−0.01
(0.02)
2.43
(2.75)
−1.41∗∗
(0.66)
0.13
(0.20)
Model 2
0.19
(0.65)
0.72∗
(0.37)
−0.31
(0.19)
−0.01
(0.02)
2.54
(2.76)
−1.36∗∗
(0.66)
0.15
(0.20)
Model 3
0.07
(0.65)
0.67∗
(0.38)
−0.30
(0.19)
−0.02
(0.02)
3.24
(2.80)
−1.23∗
(0.68)
0.16
(0.20)
547
547
X
X
547
X
X
X
Std. Dev. of Dems in State Legislature
No. of Gov. Party Switches since 1963
Lagged Pension Policy
Std. Dev. Dems * Ratio of Dems
Party Switches * Dem. Gov.
Num. obs.
State FE/RE
Post-2008 Dummy
Year FE/RE
Controls
Table 4: Control variables have not been reported for purposes of brevity.
26
∗∗∗
p < 0.01,
Model 4 Model 5
−0.09
0.68
(1.07)
(1.17)
0.84∗∗
1.34∗∗∗
(0.42)
(0.50)
−0.16
−.020
(0.39)
(.040)
−0.02
−0.06∗
(0.03)
(0.03)
3.88
8.27∗∗
(2.96)
(4.08)
−1.42∗ −2.34∗∗∗
(0.77)
(0.86)
0.08
0.14
(0.21)
(0.22)
−1.13
−1.70
(9.39)
(10.18)
−0.05
−0.03
(0.04)
(0.05)
0.00
−0.05
(0.08)
(0.08)
8.15
7.17
(17.62)
(19.05)
−.01
−0.02
(0.07)
(0.07)
487
487
X
X
X
X
X
X
X
∗∗
p < 0.05, ∗ p < 0.1
Table 5: Ordred Probit Regression on Net Expansionary Pension Legislation
Model 6
Ratio of Democrats in Legislature
−1.65
(1.40)
State Worker Concentration
−1.34
(13.04)
Democratic Governor
−0.06
(0.47)
Bargaining Index
−0.05
(0.03)
Union Strength
0.20
(0.19)
Ratio of Dems * State Worker Concentration
12.56
(22.10)
Dem. Gov. * State Worker Concentration
3.07
(5.87)
Std. Dev. of Dems in State Legislature
1.19
(10.43)
No. of Gov. Party Switches since 1963
0.00
(0.05)
Lagged Pension Policy
−0.01
(0.08)
Std. Dev. Dems * Ratio of Dems
4.77
(19.73)
Party Switches * Dem. Gov.
−0.05
(0.07)
Num. obs.
487
State FE/RE
X
Post-2008 Dummy
X
Year FE/REk
X
Controls
X
Table 5: Control variables have not been reported for purposes of brevity.
27
∗∗∗
p < 0.01,
∗∗
p < 0.05, ∗ p < 0.1
change, consider Figure 5 which plots the probabilities of observing a given number of net
policy changes as union strength increases. The left plot shows the effect of unions in
an unfavorable partisan environment where Republicans hold a large majority of legislative
seats (here, around 65%). The right plot shows what happens if Democrats gain control (also
around 65%). While extreme policy changes are in both cases quite rare, it is clear from
the inverted slopes that under Republican control, increases in union strength are associated
with increased likelihood of passing moderate increases in policy (and decreased likelihood
of passing moderate decreases in policy), while under Democratic control, the reverse is true.
Figure 5: Predicted Probabilities vs. Union Strength
2
3
-1
0
1
2
3
0.6
0.0
0.2
0.4
Predicted Probability
0.6
0.4
0.0
0.2
Predicted Probability
-4
-3
-2
0.8
-1
0
1
0.8
-4
-3
-2
Dem. Legislature, Repub. Governor
1.0
1.0
Repub. Legislature, Repub. Governor
0.0
0.5
1.0
1.5
0.0
Union Strength Index
0.5
1.0
1.5
Union Strength Index
Figure 5: Effects plotted from Model 1 of Table 4. The figure on the left plots probabilities from a model
with a low percentage (bottom quarter of the sample) of Democrats in the state legislature while the figure
on the right does so with a high percentage (third quarter of the sample) of Democrats. Party of the governor
takes on the median value in the sample, which is Republican. Other variables are held at their mean.
Changes in the likelihood of policy change are also strongly related to changes in the
28
percent of Democrats in the legislature, so long as union strength is high. Consider Figure 6.
The left panel shows probabilities from a model in which unions are relatively weak. There
is essentially no relationship between the presence of Democrats in the state legislature and
pension policy. Moderate policy changes are again more likely than extreme policy changes,
but neutral and no policy changes are far and away the most probable outcome, regardless of
partisan control. On the other hand, the right panel makes it clear that partisans can have
a very strong impact on policy if unions are a large political presence. As more and more
Democrats populate the legislature under regimes of high unionization rates and considerable
union campaign activity, the more and more likely that the government will pass moderate
decreases in pension policy, and the less likely that it will pass increases in policy.
4.2
Ad hoc legislative contributions, or: how Democrats learned to love bailouts
Bailout is probably an unfair term to pensioners and prospective pensioners if the term
connotes reckless financial practice. The state, at the end of the day, is the sole guarantor
of state pension funds - not the pensioners, and not their local employers (be they school
districts, counties, or state agencies). Nevertheless, in my framework, state payouts to
pension funds are in fact considered bailouts in the sense that they are being provided to
a sub-state entity that should - in theory - be able to pay for their benefits solely through
employee contributions, employer contributions, and gains in their asset portfolios. When
the pension fails to do so and receives transfers from the flow of general state revenue and
the taxpayers at large, they are receiving net benefits that are coming from a non-Pareto
transaction. It is in this sense that I use the term “bailout.”
I test the idea that the size and likelihood of these bailouts are also the result of partisan
and interest group politics by estimating the following regression:
yst = β1 · Union Strengthst + β2 · Dem Govst + β3 · Avg Demsst
+ β4 · Union Strengthst · Dem Govst + β5 · Union Strengthst · Avg Demsst
+ β6 · State Worker Ratest + β7 · Bargaining Indexs + γ Xst + δs + κt + st
where Xst is again a vector of economic and historical control variables that may influence
budgetary allocations made by the governor and state legislature. Note that here I include
the lagged revised funding ratio of the pension plan (so as to avoid endogeneity by including
the contemporaneous value) and in Models 9-10 and 13-14 the contemporaneous value of the
pension legislation variable from above. The former controls for the fiscal status of the plan
29
Figure 6: Predicted Probabilities vs. Democratic Seats
1.0
-1
0
1
2
3
-1
0
1
2
3
0.6
0.0
0.2
0.4
Predicted Probability
0.6
0.4
0.0
0.2
Predicted Probability
-4
-3
-2
0.8
-4
-3
-2
High Union Strength, Repub. Governor
0.8
1.0
Low Union Strength, Repub. Governor
0.2
0.4
0.6
0.8
1.0
0.2
Ratio of Democratic Seats
0.4
0.6
0.8
1.0
Ratio of Democratic Seats
Figure 6: Effects plotted from Model 1 of Table 4. The figure on the left plots probabilities from a model
with low union strength (bottom quarter of the sample) while the figure on the right does so with high union
strength (third quarter of the sample). Party of the governor takes on the median value in the sample, which
is Republican. Other variables are held at their mean.
- where we would expect better funded plans to receive greater contributions as in Munnell
et al. (2015) - while the latter controls for the permissiveness of the state government,
in general. We might expect, for example, that if the state has recently passed unfunded
benefit expansions, it may believe that it must make a transfer to the plan to pay for it.
While this would still be considered a “bailout” within my framework, I am also interested in
understanding what would happen if the pension suffered some sort of plausibly exogenous
shock that forced the state government to decide whether or not they should bail out the
fund or enforce austerity via cuts or statutory contribution rate increases.12
12
The Great Recession would have been an ideal shock for this purpose, but two issues make this a difficult
30
The models contained in Table 6 and 7 are a series of linear fixed effects and mixed model
regressions that test whether Democrats might be more liable to provide bailouts to pension
funds, and whether strong unions are more able to obtain them. The dependent variable in
Models 6-10 is the ratio of the actual state legislative payment relative to the revised ARC.
In Models 11-14 I re-scale this variable by subtracting the ratio in a given state-year from the
state’s average contribution rate over the time period.13 This second specification controls
for baseline expectations that unions and workers may have about the degree to which the
state government will subsidize their pensions even in the absence of fiscal strain. Finally, as
a robustness check, Models 15 and 16 in Table 8 use payments as a percentage of the state’s
general expenditures as the dependent variable.
As can be seen from Tables 6-8, the statistical significance of having a Democratic governor in office is remarkably stable across the model specifications. In the models with partial
and full controls, Democratic governors increase the contribution rates of their states by upwards of 40 percentage points.14 Even without controls, Democratic governors provide large
and statistically significant bumps to contribution rates. This is also true when considering
deviations from mean contributions rather than actual contributions. Clearly, Democratic
governors are more likely to transfer general use funds to pensions rather than have the
pensions raise their own contribution rates or cut benefits. It is not terribly surprising that
the executive plays a larger role here than the legislature, and a larger role than in passing
pension legislation, as the budget is often commandeered by the governor’s agenda setting
power. In most states, the governor proposes the initial budget, and it is up to a legislative
committee to agree upon changes or to introduce it as is. For this reason, the governor
possesses a much stronger presence in budgetary policymaking than in pension benefit policymaking. Also note in Table 6 that the lagged funding ratio is positively associated with
ad hoc contribution ratios of the legislature, in line with Munnell et al. (2015).
These results suggest a curious thing: Democratic governors are more likely to “bail out”
a state pension fund by transferring general use funds, but highly unionized workforces in
conjunction with Democratic legislatures are less likely to pass expansionary policy that
endeavor. The first is that the financial crisis did not hit all of the state pension funds equally because of
differences in portfolio risk exposure and differences in tax capacity (school districts that have to rely solely
on sales taxes, for instance, probably suffered more in revenue loss than those that could count on property
taxes). Secondly, and more importantly, is that The Pew Center on the States (2010) stopped collecting
payment data after 2008. While I can examine other areas of pension policy post-Great Recession, I would
need to obtain legislative records from after 2008 to analyze ad hoc contribution behavior.
13
This rescaling is irrelevant for Model 6 due to the state fixed effects that absorb all baseline differences.
14
This is also true in a regular OLS model with state and year fixed effects and full controls.
31
Table 6: Linear Regression on Revised Contribution Rates
Ratio of Democrats in Legislature
Union Strength
Democratic Governor
Model 6
0.44
(0.46)
0.28
(0.27)
0.15∗∗
(0.07)
Bargaining Index
State Worker Concentration
Ratio of Dems * Union Strength
Dem. Gov. * Union Strength
−2.32
(2.64)
−0.60
(0.49)
−0.07
(0.07)
Model 7
0.85∗∗
(0.36)
0.14
(0.22)
0.13∗
(0.07)
0.02
(0.02)
−2.11
(1.80)
−0.60
(0.39)
−0.12
(0.08)
Model 8
0.59∗
(0.35)
0.20∗
(0.21)
0.14∗∗
(0.06)
0.01
(0.02)
−1.15
(1.79)
−0.46
(0.38)
−0.09
(0.07)
445
X
X
445
X
X
X
Std. Dev. of Dems in State Legislature
No. of Gov. Party Switches since 1963
Pension Policy
Lagged Funding Ratio
Std. Dev. Dems * Ratio of Dems
Party Switches * Dem. Gov.
Num. obs.
State FE/RE
Post-2008 Dummy
Year FE/RE
State & Year FE
Controls
445
Model 9
−0.12
(0.51)
−0.03
(0.19)
0.41∗∗∗
(0.11)
0.01
(0.01)
0.45
(1.51)
−0.07
(0.34)
−0.09
(0.06)
−4.20
(3.91)
−0.01
(0.02)
0.03
(0.02)
0.28∗∗∗
(0.10)
8.41
(7.00)
−0.05∗∗∗
(0.02)
366
X
X
X
Model 10
0.05
(0.54)
0.05
(0.20)
0.41∗∗∗
(0.11)
0.01
(0.01)
−1.81
(1.75)
−0.12
(0.35)
−0.09
(0.06)
−2.36
(4.04)
−0.02
(0.02)
0.03∗
(0.02)
0.24∗∗
(0.10)
5.67
(7.21)
−0.05∗∗∗
(0.02)
366
X
X
X
X
X
Table 6: Control variables have not been reported for purposes of brevity.
32
∗∗∗
p < 0.01,
∗∗
∗
p < 0.05, p < 0.1
Table 7: Linear Regression on Deviations from State-Mean Contribution Rates
Ratio of Democrats in Legislature
Union Strength
Democratic Governor
Bargaining Index
State Worker Concentration
Ratio of Dems * Union Strength
Dem. Gov. * Union Strength
Model 11
0.91∗
(0.48)
0.20
(0.28)
0.13∗
(0.07)
0.08∗∗
(0.04)
−4.76∗
(2.68)
−0.84
(0.52)
−0.10
(0.08)
Model 12
0.49
(0.44)
0.30
(0.26)
0.15∗∗
(0.07)
0.06
(0.04)
−2.65
(2.46)
−0.65
(0.47)
−0.07
(0.07)
445
X
X
445
X
X
X
Std. Dev. of Dems in State Legislature
No. of Gov. Party Switches since 1963
Pension Policy
Lagged Funding Ratio
Std. Dev. Dems * Ratio of Dems
Party Switches * Dem. Gov.
Num. obs.
State FE/RE
Post-2008 Dummy
Year FE/RE
Controls
Table 7: Control variables have not been reported for purposes of brevity.
33
∗∗∗
Model 13
−0.97
(0.88)
−0.01
(0.25)
0.42∗∗∗
(0.11)
0.06
(0.04)
−1.35
(2.25)
−0.09
(0.44)
−0.07
(0.06)
−13.59∗
(7.39)
0.06
(0.07)
0.03
(0.02)
0.19∗
(0.11)
14.86
(10.41)
−0.05∗∗∗
(0.02)
366
X
X
X
p < 0.01,
∗∗
Model 14
−0.78
(0.89)
0.09
(0.25)
0.45∗∗∗
(0.12)
0.05
(0.04)
−1.21
(2.32)
−0.21
(0.44)
−0.08
(0.06)
−11.63
(7.60)
0.05
(0.08)
0.03∗
(0.02)
0.15
(0.11)
12.73
(10.47)
−0.05∗∗∗
(0.02)
366
X
X
X
X
p < 0.05, ∗ p < 0.1
Table 8: Linear Regression on Legislative Pension Payments as a Percentage of General
Expenditures
Ratio of Democrats in Legislature
Union Strength
Democratic Governor
Model 15
0.40
(1.63)
−0.72
(0.97)
0.59∗∗
(0.23)
Bargaining Index
State Worker Concentration
Ratio of Dems * Union Strength
Dem. Gov. * Union Strength
15.54∗
(9.31)
3.30∗
(1.74)
−0.65∗∗
0.26
Std. Dev. of Dems in State Legislature
No. of Gov. Party Switches since 1963
Pension Policy
Lagged Funding Ratio
Std. Dev. Dems * Ratio of Dems
Party Switches * Dem. Gov.
Num. obs.
State FE/RE
Post-2008 Dummy
Year FE/RE
State & Year FE
Controls
465
Model 16
3.34
(3.18)
−0.01
(1.06)
0.91∗
(0.53)
−0.24∗∗
(0.10)
8.16
(9.38)
1.30
(1.87)
−0.65∗∗
(0.28)
18.49
(23.41)
−0.01
(0.16)
−0.01
(0.09)
0.09
(0.42)
−27.04
(39.55)
−0.08
(0.09)
382
X
X
X
X
X
Table 8: Control variables have not been reported for purposes of brevity.
34
∗∗∗
p < 0.01,
∗∗
p < 0.05, ∗ p < 0.1
would lead states into situations that would make a bail out necessary in the first place. Do
unions recognize this dynamic? And if not - would they be less likely to allow contractionary
policy if they knew Democratic governors would foot the bill if things got out of hand? The
fact that Pension Policy is mildly positive and statistically significant in Models 10 and 14
suggests that unions might know that the state will feel obligated to pay for otherwise unfunded policy expansions, but interacting Pension Policy with Union Strength or Democratic
Governor does not change the results of the analysis nor does it yield statistically significant
estimates on the interacted variables. Though this finding does not confirm the Constituency
Hypothesis, it does cast some doubt on the Linked Fate Hypothesis as it relates to governors
and unions. It is possible that Democratic governors have been able to credibly commit that
they will not bail out the pensions if overly generous, unfunded benefits get out of hand, but
their past behavior should be a clue to the unions that they will cave if necessary. Below I
discuss the Linked Fate Hypothesis further.
4.3
Further evidence of “linked fate”
Though not necessarily appealing to conventional wisdom, it appears that the Linked Fate
Hypothesis is more realistic than the Constituency Hypothesis. To bolster this claim, however, I need evidence to show that (1) unions care about electing Democrats and (2) that
Democrats are likely to lose elections if they vote for generous pension benefits that directly
decrease the welfare of regular voters. The fact that AFSCME donates so overwhelmingly
to state Democratic candidates and parties is reasonable evidence that (1) is probably true
(Bolton (2014) also shows that public sector unions give overwhelmingly to Democratic rather
than Republican federal candidates). Below I offer evidence of (2), showing that turnover in
state partisan control is strongly influenced by pension policy legislative behavior and that,
further, unions can do little to mitigate the fallout from passing overly generous policy once
it gets to the electoral stage.
Table 9 reports estimates of the effects that pension and other budgetary policy has
on the change in Democratic shares of legislative seats in the year after an election. As
the results indicate, Democrats are punished for expansionary pension policy, holding the
ratio of Democrats in office constant during the period in which legislation was passed.
From this regression, however, it is not clear if voters are punishing Democrats because
they attribute expansionary policy to them, regardless of who is in office when the policy
was passed, or if they are punishing Democrats because they actually passed the legislation.
Table 10 clarifies this issue by examining the likelihood of control of the upper house of
35
the state legislature switching after an election. Because the regression controls for who
was in control of the upper house prior to the election, there is no question that voters are
actually punishing the legislators who were responsible for the expansionary policy. Both
Models 17 and 18 confirm, however, that union strength alone is not adequate for ensuring
the electoral success of Democratic candidates. In these specifications, union strength is in
fact completely unrelated to Democratic success at the polls. This should not be all that
surprising - even in states with the highest concentration of unionized state workers (Hawaii,
Alaska, Delaware, Washington), unionized employees make up to no more than 2-7% of all
full time public and private sector employees. That is hardly enough to win an election, even
in cases where the race might be very close. While unions may make ripples in Democratic
primary elections, where turnout is much lower and endorsements are far more important
(Anzia 2011), they have little to no significant influence in general elections.
Finally, Table 11 shows us that governors are clearly not being punished for expansionary
pension policy. Whether this is because voters recognize that policy is under the legal
purview of the legislature, or because the partisanship of governors is not systematically
associated with patterns of policy change, is unclear. In general, however, unions appear to
play a greater role in gubernatorial elections than state senate elections. Though incumbent
Democrats are more likely to be kicked out of office than incumbent Republicans, strong
unions can help mitigate the effects of this bias by throwing their electoral and financial
weight behind the Democratic candidate. In situations in which the Republican is the
incumbent, the presence of unions actually makes the Democrat more likely to win than the
sitting governor, all else equal. Note, however, that the analysis demonstrates a key piece
of conventional political wisdom that all governors already know: the fastest way out of the
Governor’s Mansion is not through the front door but by raising taxes.
36
Table 9: Mixed Model Linear Regression on Change in Concentration of Legislative
Democrats
Lagged Pension Policy
Lagged Union Strength
Lagged Ratio of Democrats in Office
Lagged Union Strength * Dems
Incumbent Democratic Governor
Lagged % Change in Per Capita General Expenditures
Lagged % Change in Per Capita Taxes
Lagged % Change in Per Capita Debt
Lagged Change in S&P’s Credit Rating
Num. obs.
State FE/RE
Year FE/RE
Controls
Model 17
−0.02∗∗∗
(0.01)
0.01
(0.04)
−0.08
(0.08)
−0.01
(0.08)
0.00
(0.01)
0.40∗∗
(0.16)
0.12
(0.08)
−0.04
(0.06)
−0.00
(0.02)
107
X
X
X
Table 9: Variables are one-year lags unless otherwise specified. Control variables include economic indicators
in addition to the lagged levels of the lagged change variables above. They have not been reported for purposes
of brevity. ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1
37
Table 10: Mixed Model Logit Regression on Change in Partisan Control of the State Senate
Lagged Pension Policy
Lagged Union Strength
Lagged Democratic Senate Control
Lagged Union Strength * Dem. Sen. Control
Incumbent Democratic Governor
Lagged % Change in Per Capita General Expenditures
Lagged % Change in Per Capita Taxes
Lagged % Change in Per Capita Debt
Lagged Change in S&P’s Credit Rating
Num. obs.
State FE/RE
Year FE/RE
Controls
Model 18
1.85∗∗
(0.86)
4.39∗
(2.34)
6.08∗
(3.28)
−4.17
(2.62)
1.54
(0.97)
−10.90
(14.40)
3.02
(7.53)
−2.55
(4.76)
0.02
(1.35)
107
X
X
X
Table 10: Variables are one-year lags unless otherwise specified. Control variables include economic indicators
in addition to the lagged levels of the lagged change variables above. They have not been reported for purposes
of brevity. ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1
38
Table 11: Mixed Model Logit Regression on Change in Party of the Governor
Lagged Pension Policy
Lagged Union Strength
Incumbent Democratic Governor
Lagged Union Strength * Incumbent Dem. Governor
Lagged % Change in Per Capita General Expenditures
Lagged % Change in Per Capita Taxes
Lagged % Change in Per Capita Debt
Lagged Change in S&P’s Credit Rating
Num. obs.
State FE/RE
Year FE/RE
Controls
Model 19
0.10
(0.35)
1.53∗∗
(0.76)
2.59∗∗
(1.06)
−2.64∗∗∗
(1.00)
0.45
(7.80)
8.39∗∗
(3.79)
−0.19
(2.61)
−.060
(0.88)
111
X
X
X
Table 11: Variables are one-year lags unless otherwise specified. Control variables include economic indicators
in addition to the lagged levels of the lagged change variables above. They have not been reported for purposes
of brevity. ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1
39
5
Conclusion
Public sector unions are interest groups - but they are not just interest groups. Public
sector unions enjoy the unique advantage of being able to both lobby for and determine
policy outcomes. This is most obviously true in the case of public worker pensions. In
this paper I have analyzed an understudied facet of interest group and budgetary politics the way in which public sector unions and their partisan compatriots can shape the fiscal
environment of the state through pension policy. I proposed two key possible scenarios:
that Democrats and strong unions would work together to expand pensions at the expense
of other spending, or that Democrats and strong unions would recognize that unfunded
policy expansions might lead to voter backlash and a subsequent removal of Democrats from
office. Using my original data on pension finance and union strength, I found that the latter
scenario is far more plausible. I provided strong support for the claim that public unions and
legislative Democrats recognize their “linked fate” - that what is bad for the goose is bad
for the gander. Unions might enjoy a benefit expansion paid for by the voters, but chances
are that they will not enjoy it as much as they will regret having Republicans as their new
bosses, especially if that means cuts in general spending - leading to decreases in hiring and
salary bumps -, the stripping of collective bargaining protections, or the passage of other
hostile labor laws.
In addition to examining the passage of pension policy statutes, I also investigated the
effect that partisan governments and unions have on ad hoc transfers from general revenue
to pension funds. I found strong evidence that Democratic governors are more generous with
their transfer allocations, and that states with better funded pensions are likely to transfer
more - and to continue to transfer more - than states with poorly funded pensions. This is
in line with Munnell et al. (2015) but also with the idea that states that have bailed out
pensions in the past are unable to commit to not bailing them out in the future. A well
funded pension plan is not the worst imaginable scenario for a bail out game; but it does
decrease the welfare of regular voters to the extent that transfers to pensions come out of
revenue that would be otherwise spent to increase public services or to decrease taxes.
Finally, I also provided evidence that Democratic legislators are strongly punished at
the polls for increasing unfunded pension benefits while in office. This further supports the
Linked Fate Hypothesis in that there are clear and strong consequences for Democrats if
they appear too accommodating to public employees. Democratic governors, on the other
hand, manage to avoid much of the blame, although they are punished significantly if they
40
are forced to raise taxes. This suggests that while there is no simple link between bail outs
and governors at the polls, the secondary effects of a bail out - such as raising taxes on the
voters at large - loom large in the electoral arena.
41
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44
A
Appendix
Appendix A
One of the greatest challenges of this larger project was to construct a data set that accurately
reflected the liability and asset status of states’ public pension plans. With the help of
a grant from the Russell Sage Foundation, Nolan McCarty and I set out to collect the
Comprehensive Annual Financial Reports (CAFRs) of 128 state funded15 pension plans over
17 years. Following a procedure outlined by Novy-Marx & Rauh (2011) that they used to
revise 2009 liabilities, we used these CAFRs to hard-code data on benefit levels, cost of
living adjustments, salary structure, employee age and experience distributions, and current
annuitant numbers and allowances. Using these (relatively) few data we were then able
to construct a standardized measure of liabilities across pension membership tiers, plans,
states, and years. Again following Novy-Marx & Rauh we used the Accumulated Benefit
Obligation (ABO) as a measure of plan liability, the most conservative of the standard
actuarial formulations. This obligation is essentially a measure of what the plan would owe
if all of the employees stopped working today. An easy analogy would be if the employer
(i.e., the school system in the case of a teachers pension plan) went belly-up today, but
was still responsible for paying off the pension benefits that their employees had accrued.
This construction of the liability is convenient because it makes the minimum number of
assumptions necessary to get a picture of the plan’s obligations - we do not need to guess
about termination rates or wage increases, for instance. Current annuitants would continue
receiving what they receive today, while active employees would receive only what they have
already earned up to today, beginning when they reach normal retirement age 16 .
Once the data is collected, the construction of the ABO happens in four main stages:
1. Calculate averages of active workers and annuitants by age and years of service per
year across regions (west, midwest, south, northeast) and/or across plan types (judges,
general/mixed, teachers, police and firemen, elected officials)
15
Plans that were at least partially funded by contributions from the state legislature were included in
the collection. Municipal plans that receive no revenue from the state government were not included, even
if they were administered by the state. Many large and prominent plans were excluded using this standard
(including Teachers’ Retirement System of New York City and Detroit’s municipal pension funds), but the
idea is to look at funds that are the direct and clear responsibility of the state legislature, even if there are
scenarios in which the state bails out local funds.
16
Assumed here to be 60. I plan to rerun the simulations assuming that police and firemen retire at 55,
teachers at 65, and everyone else at 60.
45
2. Construct distributions (weights)
3. Calculate individual ABOs
• Match plan-year to weights by region and year and/or plan types and year
• ABO for active members = plan-year benefit factor * years of service * weight for
age-service salary cell * plan-year average salary
• ABO for annuitants = weight for age benefit cell * plan-year average benefit
4. Calculate discounted plan ABOs
• Obtain life expectancy data (mortality tables)
• Calculate expected years of remaining annuity payments
• Pick discount rate17 and COLA18
• Calculate individual discounted ABOs
• Plan discounted ABO = (discounted indiviudal ABO * weight for age-service
employee cell * plan-year total active workforce size) + (discounted annuitant
ABO * weight for age annuitant cell * plan-year total annuitants)
Depending on the level of analysis, the plan ABO can be left as is or again aggregated up
to the state level.
We also re-adjusted pension assets such that their actuarial value reflected a more reasonable expected rate of return. We collected the market value of net plan assets for each
pension-year and then calculated their adjusted actuarial value such that
A1 = A0 + rA0 + s(M1 − (A0 + rA0 ))
where At is the actuarial value in period t, Mt is the market value in period t, r is the
expected rate of return on the assets, and s is the smoothing factor (the percent of the
difference between market value and expected actuarial value that is incorporated into the
17
We used the January-of-next-year average of the 10-year Treasury rate, but this is a hotly contested
issue. Most plans use the expected return on their assets as a discount rate on their liabilities (which has no
basis in economic theory or actuarial practice, but has been encoded in the GASB’s list of recommendations),
often at a much-too high rate of 7 or 8 percent. The GASB recently revised their recommendations to suggest
that state pension plans use a discount rate (and expected rate of return) that at least partially reflects a
high-quality tax-exempt municipal bond rate.
18
We used annual CPI inflation.
46
current actuarial value). For the most recent calculations, I used a 6.5% rate of return and
the standard actuarial assumption of a 20% smoothing rate.
All of the assumptions made to revise the actuarial value of pension liabilities and assets
are essentially subjective. Some assumptions are more reasonable (i.e., lowering the discount
rate) than others (i.e., raising the discount rate), but I cannot claim I am more justified
in picking the 10 year Treasury rate over a high grade tax-exempt municipal bond rate,
especially when the conversation is still going on (Elliott 2010). As such, this project does
not seek to establish an authoritative measure of state pension liabilities, assets, or overall
funding levels; I do not make any claims that plan x needs to raise y dollars in revenue
in order to avert financial collapse. What this project does seek to establish, however, is a
pecking order of plans’ financial health. While I cannot say with certainty that Texas’s state
pensions were only funded at a woeful 37.8% in 2011 (versus the more reasonable 83.0%
the state claimed), I can say with certainty that it was 1) not funded as well as the state
claimed, 2) better funded than Kentucky (the worst offender for that year, with a revised
20.6% funding level (versus a 50.6% state-reported funding level), and 3) not as well funded as
Hawaii (52.2% revised funding level, 59.4% self-reported). By standardizing assumptions and
calculations across plans, states, and years and preserving an ordinal relationship between
liabilities, assets, and funding levels, I may not be able to say with absolute certainty how
bad a state’s financial position is, but I hope to be able to understand why some states are
in one worse situations than others.
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