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Does State Pension Fund Management Matter?Siona Listokin
November 24, 2005
WORKING DRAFT – DO NOT QUOTE
AbstractThis paper examines how the management composition of public employee retirement systems affects the decision to increase retiree benefits. The results suggest that management boards that are dominated by political appointees or elected officials (“political boards”) increase benefits more than boards that are dominated by state employee representatives (“non-political boards”). In contrast to previous studies regarding pension fund management, state fiscal balance and general financial health are included, as is the role of the legislative body’s control over the retirement system. Political boards appear to be more sensitive the state’s financial health, and raise benefits more during “flush” years. Political boards are similarly sensitive to the state election cycle.s and the state’s fiscal balance than non-political boards. The paper explores possible explanations for the counterintuitive results.
I. Introduction
Public employee retirement systems cover over 14 million state and local government employees with
retirement and disability benefits.1 The 2,600 systems at the state and local level have combined assets
of almost $3 trillion in cash and investments; their combined equity holdings account for about 12% of
total outstanding US equity value (Ilkiw 2003). In an environment where state budgets face shortfalls
in excess of $200 billion, state retirement systems often account for large portions of state fiscal
planning, and annual obligations can amount to 5-15% of state deficits (Census 2002).
Who runs these financial behemoths? Management schemes vary, though most state systems have a
board of trustees that oversee general asset investment, benefits and disability arrangements. The
retirement board trustees are appointed in a variety of ways and have different qualifications; roughly,
trustees are either active or retired members of the retirement system (“non-political trustees”) or
appointed by the state government or voters (“political trustees”). Given the amount of money these
officers control and the relative freedom under which they operate, the governance of public employee
retirement systems is an important issue for state sponsor government, system beneficiaries and state
voters. This paper studies whether fund governance affects the provision of benefit increases. In
particular, I examine whether trustees chosen by system members behave differently than trustees
chosen by voters or the state government body, and the extent to which political operators are sensitive
to the state political and fiscal environment when making their benefit decisions.
Pension fund governance and board composition has been highlighted in a number of previous studies.
The vast majority of these papers focus on the relationship between board composition and investment
1 State employee retirement systems are also called state pensions and funds. I use these terms interchangeably.
1
performance or fund solvency, and the studies are decidedly inconclusive (a thorough review of this
literature will follow). I shift the focus from the asset management side to the benefit side of
retirement system decision making. I can thus bypass many of the difficulties that plague previous
studies – such as endogenous asset mixes and investment restrictions – and better examine the effect
of pension fund governance.
My results suggest that board composition does influence the decision to increase benefits. Namely,
boards that are dominated by political trustees increase benefits more often and by a greater amount.
In addition, political trustees are sensitive to both state gubernatorial election years and shifts in state
fiscal balances. There is tentative support that political boards in Democratic states raise benefits
more than political boards in Republican states.
This study makes a number of contributions. First, it reexamines the question of public pension
system board influence in a setting that can offer more conclusive results. Second, it advances the
study of how the choice of managerial control of public retirement systems are influenced by political
and fiscal factors. More generally, the paper highlights the forces and incentives that may impact
management officers in charge of public moneys.
The paper proceeds as follows. Section 2 reviews the relevant literature, and section 3 describes the
relevant factors about public pension funds. Section 4 describes the data and methodology. Results
are presented in section 5 and section 6 concludes.
II. Literature Review
This study is related to two areas of research, the first on governance and agency and the second on
pension funds specifically.
Empirical and theoretical studies on corporate and public agency governance focus on the separation
between ownership and control. Relevant shareholders provide resources (mostly financial) to a
manager, whom they may not be able to properly monitor thereafter. Thus the principal-agent
problem arises, in this case that investors cannot be sure that managers are not spending their money
unwisely or otherwise acting opportunistically (see Jensen and Meckling, 1979; Shleifer and Vishny,
1997 offer a survey of corporate governance). Agency theory has had success in describing the
governance structures and contract arrangements that help solve the principal-agent problem given the
level of possible monitoring and differences between the two actors (theoretical and empirical
2
examples include Williamson, 1971; Fama and Jensen, 1983b; Masten and Saussier, 2000; Bajari and
Tadelis, 2002).2
Agency theory in political models of organization examines the same principles of agent self-interest
and conflicts of interest between principals (mostly constituents) and agents (representatives) (March,
1962; Pfeffer, 1981). The mechanisms through which principals try to solve the agent problem are
through appointment types, voting and negotiations. In the case of public pension fund governance,
the fund trustees are the agents, who may have political career concerns that result in increasing
benefits too much or too often with regards to the health of the fund.
The logic of principal-agent theory suggests that elected officials should desire reelection and are
inclined to behave as their constituents desire. A full review of a state pension fund’s principals
(stakeholders) and agents (trustees) follows in the next section; suffice to say now that the
stakeholders include current public employees, former public employees, the state government, and
state taxpayers. Uncertainty and goal divergence creates agency problems, as when politically
motivated actions on the part of the trustees are not in the fund’s overall best interests, 3 or when fund
beneficiaries have exclusive control of the board and are not subject to outside vigilance from
taxpayers.
Beneficiary trustees, whose constituents are a well defined and fairly homogenous group as members
of the pension system, arguably have clearer voter interests than other trustees. These interests would
include high benefit levels and fund solvency. Political trustees, in contrast, have varied constituent
groups. This condition is known as common agency, and opens the door to special interest group
maneuvering (c.f. Berheim and Whinston, 1986; Grossman and Helpman, 2001). Trustees elected by
the entire state may be sensitive to non-public employee voters’ concerns that higher benefits lead to
higher taxes in the future; these trustees would also be aware of the public employee special interest
group. Finally, trustees that are appointed by the governor may be highly sensitive to the governor’s
interests.
The major set of empirical studies about pension fund governance focuses on principal-agent
considerations where fund trustees may have differing interests than the vast majority of the system’s
stakeholders. (Impavido and Hess (2003) outline the agency considerations relevant to pension fund
governance.) Empirical research on public pension fund governance is somewhat unique in that there
2 Note that the governance literature focuses on the question, described by Jensen (1983) as “why [do] certain contractual relations arise?” This study examines the opposite mechanism: how does certain contractual/governance structures affect outcomes?3 A well cited example is the case of a former political trustee member of the NYC pension fund board who publicized her corporate governance activism while part of the board of trustees, even as she was criticized for acting outside of the fund’s interests (see Romano 2001).
3
is more varied work on international pension systems than US public funds, and as noted the work to
date often focuses on fund performance and solvency. Impavido (2002) gives a thorough review of
both the international and US domestic literature. Many of the conclusions in this literature suggest a
correlation between countries with poor governance records (e.g., high levels of corruption) and poor
investment returns (see, for example, Iglesias and Pacios 2000). Despite a fairly robust theoretical
understanding of where agency problems might impact public pension fund governance and outcomes
and papers that focus on international pension systems, empirical studies of US public system
governance remain inconclusive.4
The major thrust of this empirical research has been conducted by the Pension Research Council at
Wharton (for example, Mitchell and Smith 1994; Yang and Mitchell 2004; Mitchell and Hsin 1997;
Useem and Mitchell 2000), and the release of longitudinal PENDAT survey data has greatly increased
the available information regarding public pension board governance, investment behavior and
restrictions, and actuarial reviews. However, there remain very few papers that utilize the longitudinal
aspect of the data in their pension studies, and the vast majority of these studies focus on the
management and investment practices’ effects on funding flow and fund performance. Yang and
Mitchell (2004) provide a thorough review of both the cross-sectional and panel data papers; I will
highlight important conclusions and remaining questions.
Useem and Mitchell (2000) show that pension governance affects asset allocation and independent
financial management. They find that non-political boards are associated with less external
investment management and less equity investment. The Useem and Mitchell study is interesting
because it mainly uses fund management practices – rather than fund financial performance – as its
dependent variables (though they do look at financial performance as well, with no results). Their
study, however, is empirically limited in that it examines only one year (1993), and cannot distinguish
between asset allocation targets that are set outside of the purview of the board of trustees and those
targets that are set expressly by the board. Useem and Mitchell do limit their sample to funds that do
not have constitutional restrictions on investments. Pension systems, however, are subject to
numerous outside restrictions, such as pension charter in-state investment targets. Their paper is
illustrative of the difficulties in studying pension funding levels and investment performance, which
may be endogenously determined – since asset targets may have been set along with the board. (Other
examples of papers that encounter these difficulties include Inman 1986; Mark 1997; and Coronado et
al. 2003.) Given the difficulties involved in relating pension fund governance to funding and
investment performance, the question remains: does pension fund governance affect outcomes, and
how might board composition influence fund decisions?
4 Romano (1993), Useem and Mitchell (2000) and Mitchell and Hsin (1997) offer a stark example of this contradictory conclusions. Using variations of the same survey (over different years), the papers show negative correlation between political trustees and fund performance, no significant correlation between board composition and performance, and positive political trustee correlation with performance, respectively.
4
For all the inconsistencies in the studies focusing on fund financial performance, there is only one
study I have found that touches on fund governance and benefit decisions (Murphy and Van Nuys
1994). These authors hypothesize that beneficiary trustees are relatively risk averse and value income
stability highly, and make benefit decisions that enhance the security of benefits (p3). Their paper
suggests that a 10% increase in the number of non-political trustees on the board translates into an
average annual benefit decrease of $852 – a surprisingly large result. The Murphy and Van Nuys
paper, too, is fraught with measurement problems, including a small sample size and an assumption
that there are no state-specific characteristics that may drive part of the results. Another limitation of
their study – and one that the authors point out themselves – is that their dependent variable only
includes the retirement benefit, while my data includes other areas of benefit increases such as health
and disability benefits. (More on this construct follows in the section describing data.) The Murphy
and Van Nuys paper suggests a positive correlation between political board composition and benefit
levels, but due to the nature of their data they are unable to conclude that there is causality.
Further areas of study regarding benefit levels and increases remain unexplored, such as how
exogenous market and fiscal conditions impact boards’ benefit decisions. In papers unrelated to
benefit decisions, Mark (1997), Chaney et al. (2002) and Schneider and Damanpour (2002) examine
how exogenous factors, such as stock market conditions and state budget balances, relate to funding
levels, though they do not relate these outside conditions to governance. I have not seen a study that
examines the effects of exogenous political factors, such as political party control and electoral
competitiveness, on either funding or benefit behavior.5
To summarize, a good deal of work has explored the influence of public pension fund governance on
funding levels and investment performance. Perhaps due to data limitations, these studies are
inconclusive and often directly contradictory; questions fundamentally remain as to the effect of board
composition on system outcomes. System benefit decisions are particularly understudied, as is the
impact of exogenous factors. In his 2002 World Bank survey, Impavido concludes that “(However,) a
direct link between governance and performance cannot be established with US data… A few results
are contradictory, like the impact of the size and composition of the board… Further research is
clearly needed.” (p 6)
III. Public Pension Funds
The first incarnation of US public pension schemes were intended primarily for army personnel in the
mid-19th century (March, 1962). State pension systems began with teacher and firefighter systems,
and truly grew in the 1930s by which time, state and local public pension outlays totaled $106 million
5 Schneider and Damanpour (2002) do look at the percentage of voters in a given state.
5
(March 1980). In 2003, state and local plans held around $3 trillion in assets (Ilkiw 2003), and there
are 2,600 systems at the state and local level (GAO, 1996). While public retirement systems were
originally intended as forms of social welfare programs, today they have evolved into an integral
incentive to work for the government. Public employment offers a number of attractions, including
health care, job stability and retirement benefits (Lowenstein 2005). In the US, a state or local
government employee who has fulfilled his/her system's requirements receives an average of about
$1,400 a month (Census 2000).
The vast majority of public pension funds are run as defined benefit systems, meaning that the sponsor
(e.g. state public schools) and sometimes the public employee make regular contributions to the fund,
which is invested (saved) and from which benefits are paid to current retirees. Retirement benefits are
calculated by a schedule of factors, such as tenure, salary and retirement age, and are generally
updated for cost-of-living adjustments (COLAs) through formulas that are connected to CPI. The
upshot is that benefits are well established -- though often confusing to calculate -- while contributions
by the government sponsor can vary over the lifetime of an employee's tenure.
Fund health is determined through the plan's funding flow. The funding flow is the ratio of
accumulated assets to the pension benefit obligation (PBO). It is thus considered a good “stock”
measure, in that the cumulative financial health of the systems is considered, rather than a measure of
the fund’s ability to pay the following year’s requirements (Mitchell and Smith 1994). Both
components of the funding flow measure (assets and PBO) can be difficult to determine, especially
because actuarial assumptions are not clearly outlined. One stark example is the discrepancy between
reported funding flow, which measures about $300 billion in the red for all state and local systems,
and private assessments of public pensions’ funding flow, which can measure up to $460 billion in
deficit (Barclay’s Global Investment 2005). The difference between these measures lies in either
interest rate assumptions or definitions of the PBO. In general, the PBO is composed of obligations:
1) pledged to currently retired employees; 2) vested to terminated employees; 3) vested to active
employees; 4) payable to non-vested active employees who may vest in the future; and 5) those that
will be earned by current workers resulting from future salary increases (Mitchell and Smith 1994).
A fund is said to be underfunded if the assets are not sufficient to cover the projected liabilities, and
will instead rely on future contributions (i.e., heading towards pure pay-as-you-go), surprise increases
in investment returns, or other sources of increased assets (e.g., tax increases). A funding ratio of zero
corresponds to a pure pay-as-you-go system; a funding ratio of one means that the fund's current assets
are sufficient to cover the present value of projected liabilities. Severely underfunded systems must
adjust contributions or asset allocation in order to cover obligations.
Stakeholders
6
The structure of these defined benefit systems means that former public employees drawing benefits
are not the only population with a vested interest in public pension funds. Current government
employees have a stake in both their future retirement benefits and current contribution obligations.
Current and future public employees are known as the fund beneficiaries. The pension sponsor,
responsible for employer contributions and future liabilities, is also a stakeholder, as are the officers in
charge of the fund. Every state pension fund in my sample is protected by either state law or
constitutional provision in that the state is obligated by statute to meet its obligations. States can issue
government bonds, raise taxes or shift other government revenue to the retirement systems if the funds
face insolvency; whatever the method, liabilities must be met. All taxpayers are therefore stakeholders,
as residual claimants of the public pension systems.
While insolvency requiring emergency bailout by the state is rare, underfunded plans are not. In 1993,
close to 75% of all state pension plans were underfunded; about half were underfunded in 2001. Part
of the fluctuations in funding flow is determined by equity performance. As more and more public
pension funds increase their asset allocations to the stock market, they become more sensitive to
movements in the market.
Defined benefit plans include provisions for benefit increases. The vast majority of benefit increases
are COLAs. While these are occasionally tweaked -- for example, changing from an adjustment equal
to annual CPI up to 3% to a fixed increase of 2% every 2 years -- they are generally stable and do not
significantly vary on average over time. Benefits can also be changed for a host of other reasons (for
example, to make public employment more desirable, under political pressure). I am interested in
these ad hoc benefit increases.6
Fund Management
While there is variation in the organizational and management structure of public pension funds, most
follow the same general formation. Executive officers and administrative staff are responsible for
programs and day to day activities. The retirement systems vary in administration and investment of
retirement fund assets, and membership and benefit issues. The vast majority of these pension systems
have a board of trustees that make benefit decisions. There is considerably more variation in
investment control decisions. About 60% of the funds place investments under the control of the board
of trustees, though the extent of trustee involvement in actual investment decisions appears to vary
greatly. Other arrangements include investment and benefit committees, a public equivalent to a chief
investment officer at private pension funds and additional decision structures.
6 Excluding COLA differences between systems allows me to study the specific issue of increasing benefits, but results in a dependent variable that does not fully capture benefit changes. I will discuss this issue in a later section.
7
The board of trustees consists of an average of 10 officers, though size varies from 5 to 17 officers in
my sample. Board members are chosen by gubernatorial appointment, elections by the general
population, or elections by current and future public retirees. I designate trustees that are appointed or
elected by the state as political trustees, and those chosen by the fund members are beneficiary
trustees. Many boards have a combination of trustee types, thus ensuring that sponsor citizens (e.g.,
state voters) and fund beneficiaries are both represented. The trustee types seem to differ along a
number of dimensions, including tenure, career path and expertise. While I do not have
comprehensive data, I checked the board tenure for the current boards of 26 of the retirement systems.
This rough examination shows that the average tenure of beneficiary trustees is about 5 years longer
than political trustees, and about a fifth of the beneficiary trustees have been involved in fund
management for more than 20 years. The differentiation in time span may be a factor in their benefit
decisions, and I explore this possibility later in the paper.
The decision making process varies by fund. Here too, I do not have comprehensive data on
procedural rules, and the information has been culled from retirement system information packets and
board meeting minutes. Of this sample, I found a few funds that have formal voting minimums for
budget proposals (e.g. benefit increases), requiring a majority.7 In the systems where I did not see a
voting minimum (although it may exist), I examined meeting minutes to learn about the group
decision making process. While discussions are obviously highly condensed in published meeting
minutes, the votes recorded are overwhelmingly unanimous, suggesting that a consensus view is
determined at some point during the meeting. Although I cannot make strong conclusions about the
overall board procedures, my inspection supports the study in that different board composition may
impact decision making, either through formal voting or in reaching consensus.
What is the board's legal fiduciary responsibility? Private fund management boards are obligated to act
in the interests of the fund beneficiaries by the Employee Retirement Income Security Act of 1974
(ERISA). Public pension funds do not have an analogous federal law mandating fiduciary
responsibility (Romano 1993). Many states, however, have varying degrees of ERISA-like statutes for
the fund board. One popular guideline for public board decision making is the "prudent man" standard,
which holds that "a fiduciary must discharge his or her duties with the care, skill, prudence and
diligence that a prudent person acting in a like capacity would use in the conduct of an enterprise of
like character and aims." There are differences across government sponsors regarding the strength of
their fiduciary responsibilities, and certainly no standard across systems.
IV. Data and Methodology
7 Alabama Employee Retirement System, for example, requires approval by 8 of 14 trustee members.
8
My data comes from a number of sources. A significant portion of the structural elements of the
systems comes from the PENDAT survey. The PENDAT survey is a biannual longitudinal study of
state, county, municipal, city, district and other local pension systems in the US administered by the
Public Pension Coordinating Council. It includes detailed information about board composition, term
limits and legislative restrictions, as well as reliable funding flow reports. I supplement the PENDAT
survey with the Public Fund Survey, the Wisconsin Comparative Study of Major Public Employee
Retirement Systems, and the State and Local Pension Exchange. Remaining data is collected from the
National Association of State Retirement Administrators (NASERS), the National Conference of
Public Employee Retirement Systems, the National Council on Teacher Retirement, and the Census
Bureau. Where possible, I use system websites and conversations with system employees to confirm
and fill in data. Benefit data is double checked and supplemented with survey data from RV Kuhns
and Wilshire Capital.
The data sample consists of state employee retirement systems over the decade between 1993 and
2003. As the data is only collected every other year, that results in six years of information between
1993 and 2003. Given the difficulties of the previous studies, I restrict my data set to those state and
special systems whose board of trustees control benefit decisions. This reduces the number of systems
available in my sample by about 40%, however the study cannot be conducted reliably otherwise.
Variables
Dependent Variable
I am primarily interested in the determinants of both the incidence and extent of benefit increases, and
therefore require an accurate measure of changes in system benefits. Benefit changes, however, can
be difficult to calculate for a variety of reasons, most notably the wide range of norms used in benefit
formulas. I use three different variables as constructs of benefit increases as robustness checks.
My primary dependent variable is year over year change of self-reported “accumulated benefit earned
as a % of final average salary (FAS).” This number is reported for different levels of years of service
(5, 10, 20 and 30 years of service); I use the percentage from 30 years of service because that is the
longest and most frequent FAS formula. The unique aspect of this measure is that it does not measure
benefits to current retirees, but rather the benefit calculation for active and retired members (in other
words, it measures a benefit increase for retirees and for active member obligations going forward).
This is a crucial, and hard to find, measure. The downside of this variable is that it does not
differentiate between FAS calculations.8 However, insofar as the percentage of FAS changes from
system to system it can still be an accurate measure of the extent of a benefit increase.
8 Common FAS calculation variation occurs in whether it is an average of the previous year, average of the previous five years, the average of the highest year in the previous five years, and other “compensation” components that are included in FAS.
9
My second measure of benefit changes is simply a binary variable that equals one if the system raised
benefits during that year. While this construct only gives an indication of whether there was a benefit
increase, it is self-reported in the PENDAT survey and its reliability is strong. The benefit increases
here are ad hoc increases, meaning they are outside of changes to COLAs.
Finally, for a third measure I take the year over year change in PBO, adjusted for the previous year’s
actual benefit expenditures. This measure makes a number of assumptions, most prominently that the
system did not significantly change its hiring procedures from year to year.
For the years 1995-2003, I have a breakdown of the benefit expenditures into four components:
service (retirement payments to retirees), disability (payments to disabled retirees and active
members), health (partial health benefit payments to retirees and active members), and survivor
benefits (payments to deceased partners of retirees and active members). While only the service
measure is dedicated to retiree benefit payments – the main focus of this study – the remaining three
categories are directed primarily at retired members. The data in year 1993 is only given in aggregate
form. I therefore include all categories in my measure in order to get the most accurate metric of true
benefit changes, and to keep the measure consistent throughout my study period. I adjust for inflation
(all expenditures are baselined to 1993 dollars).
Independent Variables
My primary explanatory variable measures the board composition. I use a binary variable set to one if
the board of trustees of a pension system is composed of more than 50% political board types. 9 Ex
officio and appointed trustees are considered political trustees, whereas the board members chosen by
beneficiary members are considered beneficiary trustees. This board data was collected from
PENDAT and pension system websites. Personal data collection and review was especially vital for
“special” state systems, namely those beyond the teachers’ and general state employees’ retirement
funds, as the PENDAT data tended to replicate board composition in these instances.
I control for the systems’ level of benefits and fund solvency in the previous year. Controlling for
benefit level is important in order to determine what drives the changes in benefits. Perhaps boards
change benefits when the benefit level is low, in which case board composition would be less of a
factor. To measure the level of benefits, I divide that year’s benefit expenditures by the number of
benefit recipients.
Next, I control for fund solvency using the funding flow measure explained previously. The funding
flow is an important control because solvency could reasonably be a major determinant of benefit
9 Note that since I do not have decision rules for each of the funds – and it certainly seems that many boards do not use majority rules – the choice of 50% composition is not necessary. I choose the majority level as a cutoff to suggest that majorities can drive consensus and influence outcomes.
10
changes. For example, one could imagine a fund deciding to increase benefits when the fund is
overfunded, and to keep benefits steady when the system is underfunded. For the funding flow, I use
the PENDAT data to get the systems’ pension benefit obligation (PBO) and current level of assets at
market price. I divide the PBO by the market value of total assets to get the funding flow. This
measure is cross-checked with actuarial reports of funding flow levels and lagged one time period.10
In order to consider the ongoing tradeoff between salary and retirement benefits, I control for monthly
salary. This data comes from the Census Bureau’s state statistical abstracts, where the average public
employee salary (for either March or October of every year) is broken down by job function. The
categories included in the census match nearly all of the system-types in my data (for example, the
Census differentiates between teachers, judges, government employees, and judiciary). For states that
have only one state system, I use the average state monthly salary. (Note: I am only able to do this
because the census data differentiates between state, city, county, and other governmental employees.)
I also control for certain state characteristics in given years. I control for per-capita income in order to
control for general increases in state welfare that could influence benefit decisions. I proxy for state
employee service quality with a density measure of the number of state employees per 1,000 state
citizens. Both of these variables are collected from the Census Bureau.
Descriptive statistics can be found in Table 1. The PENDAT survey covers many more public
retirement systems than I use in my data set; only the relevant set of systems are included in this
table.11 There are 163 systems that qualify for inclusion. Of these, 27% have a board that is
dominated (50% or more) by political trustees. It is interesting to note that while I have defined a
political board as one composed of more than 50% political trustees, my results are similar when my
definition is looser; I can relax the political cutoff to 40% without significantly changing my results.
This may be a consequence from the “consensus” style of decision making on boards mentioned
previously; the results are not driven by majority rule by political trustees, but rather a strong presence
of political trustees on the board.
The descriptive statistics highlight the relative frequency of benefit increases, which occur in 70% of
the system-year observations. This may be a result of frequent “tweaks” to the benefit formulations.
The other primary measure of benefit increases changes less often, but the average change is large (9%
change). For this and other reasons, I focus my identification on the continuous variable, as it seems
to be a more realistic measure of benefit changes in retirement systems.
10 Note that this funding flow is in fact a stock measure and not a flow measure (despite its name), meaning that it measures the systems ability to fund its projected liabilities. Flow measures the system’s ability to pay for the current year’s obligations. Funding flow is the accepted name in private and public actuarial calculations.11 I do not include city, county and municipal systems. I only include systems where the board has control over benefits.
11
The data also indicate that 65% of the retirement funds require state legislature approval for its annual
budget. This legislative process can complicate my study, in that final approval for benefit increases
may not lie with the board of trustees, but with the state sponsor government. Benefit decisions at the
board level may be influenced by the board’s expectations of final approval by the legislature;
alternatively, political boards may have more success in pushing through benefit increase decisions
through the government. This will be an area of future research, in order to get a fuller picture of the
relationship between board recommendations and legislative approval. For the moment, this study
highlights the marginal effect of board composition on benefit decisions.
Methodology
Using probit and OLS regression analysis, I first seek to determine if boards that are dominated by
political operators are more likely to raise benefits and/or increase benefits by more than boards
dominated by fund members, controlling for the fund’s lagged level of benefits, lagged level of system
salary, the fund’s lagged solvency, and state characteristics, such as income level and the number of
public employees per 1,000 citizens. This base result is difficult to identify, since there may be
unobservable system characteristics that affect the benefit decisions systematically, which would bias
the results. For example, suppose the California public school teachers had undue influence over all of
the board members of CALSTRS (The California School Teachers Retirement System). The
CALSTRS benefit decisions would thus be partly a product of the unique relationship between
teachers and board members, and this effect would persist over the decade under study. I would not be
able to identify this type of relationship in my data set, and so results that suggest a board-type effect
may in fact be a system-relationship effect.
Generally, there are two main ways to control for these system effects. The first is to include system
fixed effects (basically, dummy variables for each system) in the identification, which would capture
any unique, systematic fund characteristics that could influence benefit decisions. The second general
method would be to look at systems that have experienced a board-type change. If there was a change
in benefit behavior within systems that had changed their board, it would suggest that the benefit
decisions are in fact a result of the board-type and not the systems. Both these methods, however,
require that pension system board-types change during the period I am studying. Board-type changes
are incredibly rare, however, and after I drop systems that do not meet base requirements for this
study, I have only six board changes.
I use state fixed effects in order to get at the question of whether political board-types raise benefits
more than beneficiary board-types. The state fixed effects control for unobserved state-specific, time-
invariant characteristics that would influence benefit decisions. To the extent that systems within a
certain state would share the unique characteristics that could influence benefit decisions, the state
12
fixed effects can capture these unobserved effects. In the CALSTRS example, this method would
control for those unique aspects of California that would give state public employees undue influence
over their respective pension system boards. To the extent that the California teachers’ special
relationship with their pension is shared across other California state employees, state fixed effects
would be sufficient. The main question is whether the unobservable variables are unique to California
or CALSTRS.
The baseline estimation for the OLS regression model is:
(1)
where i denotes a unique system, are system control variables, are state control variables,
are year fixed effects and are state fixed effects. This model seeks to determine if board
composition affects benefit increases. In order to go beyond this basic “effect” question, I examine to
what extent, and under what conditions, the board composition influences benefits. I look at the board
impact under different political party dominance (proxied by gubernatorial affiliation), during
gubernatorial election years, and under varying levels of state fiscal health and decline. For these
estimations, I am looking for complementarities between these factors and the board composition. For
example, in the case of gubernatorial elections, I test: 1) whether board composition matters to benefit
decisions regardless of whether the state faces an election year or not; 2) whether benefit increases are
different during election years versus non-election years; and 3) whether political boards are more
sensitive to election years in their benefit decisions than non-political boards. The third effect is
perhaps the most interesting, as it serves to confirm the baseline results and gives a contextual picture
to the manners in which board composition is important. If there is an interaction, then it suggests that
political boards are susceptible to electoral pressure to raise benefits.
The interaction estimations are: (2)
where state factor is either a political party dummy, an election year dummy, or the state
surplus/deficit (which vary by year).
V. Results
Does board composition affect benefit increases?
Table 2 shows the difference in means between political boards and non-political boards. The table
suggests that there are differences in benefit increase levels between the two types of boards: the
13
average benefit change is higher by about 4-5 percentage points with political boards, and this
difference is significant at the 1% level.12 The difference is not significant for the binary benefit
variable. While these univariate results cannot be conclusive, they do suggest that we can differentiate
benefit behavior by board composition.
Baseline multivariate results are found in table 3. Columns 1-3 use a fixed effects regression on
benefit changes. This identification has the largest sample and the strongest results. Regardless of the
state and system controls used, the results show that political boards increase benefit increases by over
3 percentage points more than non-political boards. The previous year’s benefit levels have a negative
influence on benefit increase, supporting the view that the benefit increases are higher when the base
level of monthly benefits is low. This effect is large and significant, corresponding to a 5 to 9
percentage point lower benefit increase for every increase of $100 in the previous year’s average
benefit level.13
Column 3 differs from column 2 only in that it excludes the six retirement systems that actually
changed their board composition during the decade. This extra estimation serves as a check that the
results are not purely driven by these six board changes; the results show very little change when the
relevant systems are not included.
Columns 4 and 5 use a probit model with the binary dependent variable equal to one if the system
increased benefits in a given year. The coefficient on the board type dummy is significant but small,
corresponding to about a 1% increase in the probability of a benefit increase when the board is
dominated by political operators. Columns 6 and 7 use the PBO extrapolation of benefits, and results
are not significant.
The baseline results in table 3 demonstrate that board composition is an important factor in the extent
of benefit increases. These results correspond with Murphy and Van Nuys’ (1994) conclusion that
greater beneficiary representation on retirement fund boards leads to lower benefits and less increases.
The conclusion may seem counterintuitive: why would political boards raise benefits more than boards
dominated by beneficiaries? After all, beneficiary trustees are current or future retirees themselves,
and they are chosen by government employees. It is reasonable to assume that the beneficiaries are
aligned in their desire to increase benefits. Murphy and Van Nuys hypothesize that beneficiaries are
more conservative than political trustees, and are more concerned about the long term solvency of the
fund. Another factor may be the difference in career paths of the two types of trustees. (I elaborate on
12 To clarify: percentage differences refer to level of benefit increases. In this case, there is a 4% difference between political and non-political boards; it refers to the difference between, for example, a 2% benefit increase and an 6% benefit increase. 13 This effect is actually less extreme than it may seem, as benefits average about $1,400 monthly for state employees. An increase of $100 would be a substantial step.
14
this in the discussion section. Without better controls for system characteristics, this result may be
driven by omitted variables bias.) To compare the motivations of political and non-political boards, it
is helpful to get a picture of what drives the political boards to increase benefits. With this
information, I may be able to better explain why political boards increase benefits more than their
beneficiary trustee counterparts. I therefore examine outside conditions that may influence the
political boards to test the sensitivity of board composition.
Is there a difference in board composition impact based on state political party?
Table 4 begins this examination by looking at the role of state political party in benefit decisions and
board composition. Perhaps political board’s proclivity to increase benefits more than non-political
boards is only true when Republicans or Democrats hold the gubernatorial power. To test this
possibility, I run the same regression as in the previous slide (with reported benefit changes as the
dependent variable) and split the samples into “Democratic” and “Republican” states. The political
party of the state is designated as the governor’s political party. The results show that regardless of
political party, political boards still raise benefits more than non-political boards. There is a
significant difference, however, in the extent of the benefit increment between Republican and
Democratic states. In states with a Republican governor, political boards increase benefits by about
1.5% more than non-political boards; the difference between board types grows to 5.5% in states with
a Democratic governor. The difference between these coefficients is significant at the 99% confidence
level. Likewise, lagged benefits have a greater negative effect in Republican states than in Democratic
states.
The results suggest there is a greater “political governance” effect in Democratic states than
Republican. Political boards in states with a Democratic governor behave significantly differently
than political boards in states with a Republican governor, in their differentiation from non-political
boards. Many factors may drive this result. Public employee unions traditionally have stronger ties to
and support from Democratic politicians, and political boards in Democratic states may be more
receptive to using their managerial control in response to union requests for higher benefits (see, for
example Zander 1962 or Shaffer 2002). This explanation, however, does not fully explain why
political boards in Democratic states would be so different than non-political boards.
Do election years change board composition impact on benefit increases?
A state’s dominant political party seems to influence the extent of the disparity between board types,
which suggests that the political environment is an important part of my study. In order to examine
other areas of political variation, I look at the effect of gubernatorial electoral cycles on benefit
increases, and how board composition interacts with election year influences. These results are found
in table 6. The first column shows the results for the full data sample. Benefit increases are 1% higher
during election years, regardless of board type. Further, benefit increases are 0.06% higher when both
15
the board is political and it is an election year (at a 90% confidence level). In other words, political
boards raise benefits more than non-political boards, and this effect is more pronounced during
election years. While the 0.06% effect may seem economically small, recall that public pension
funds’ benefit expenditures can be huge. In 2001, this increase corresponds to about a $15 million
increase in annual benefit expenditures for the average pension system; for CALPERS, the figure
translates into about a $250 million in added annual benefit expenditures. This election effect is
interesting, for both its direct and indirect implications. First, it may indicate that benefit increases are
election tools, as they are more pronounced in election years than non-election years. Second, the
positive interaction effect between elections and political boards suggests that political boards are
more sensitive to political factors such as elections than non-political boards. This may not be a
surprising deduction, but it is now supported by statistical evidence.
Columns 2 and 3 compare the election effect between states that are politically competitive and states
that are not politically competitive. A state is defined as politically competitive if there was a switch
in gubernatorial political party (e.g., in New Jersey the governor position went from a Democrat to a
Republican in 1994) or if at least one gubernatorial election had less than a 15% vote split (e.g., in
North Dakota in 2002, a Democratic gubernatorial candidate won with 52% of the vote, compared
with a 48% vote capture by the Republican candidate). There is not a significant difference in the
election or interaction effect; however, the board composition effect is significantly different between
the two types of state political competitiveness. I cannot draw conclusions from this result. I run a
similar comparison in columns 4 and 5, this time between Democratic and Republican states. Again,
there is not a statistically significant difference between the two types of states and the election effect.
Do state finances change board composition impact on benefit increases?
In addition to political factors, I also check if the state balance complements the board composition
effect. The results can be found in table 6, and suggest that while the state fiscal balance may not
influence benefit increases on its own, political boards do increase benefits by a greater amount than
non-political boards, and this effect is positively sensitive to the state fiscal balance. This interaction
effect is smaller than the election year effect noted earlier, but it does indicate that political boards
may be aware of the state’s fiscal affairs when deciding to increase benefits.
VI. Discussion
My present results suggest an interesting relationship between public pension board composition and
the decision to increase benefits. Public pension funds with boards that are dominated by political
trustees are more likely to increase benefits and increase benefits by more a greater amount than
boards that are dominated by beneficiary trustees. In addition, my study suggests that pension fund
16
boards are influenced by electoral cycles, political party dominance, and state fiscal levels. These
exogenous factors have not been examined in previous studies of pension fund governance.
While it seems clear that political boards are influenced to some degree by political considerations and
fiscal realities, it is still unclear why political boards raise benefits more than non-political boards.
Recall that I have defined non-political boards as those boards that are dominated by beneficiary
representatives. One might expect beneficiaries to have strong preferences for higher benefits, and
their representatives on the board of trustees would have strong incentives to increase benefits
significantly. Why do political boards increase benefits more than beneficiary boards?
In their 1994 paper, Murphy and Van Nuys suggest that beneficiary trustees “represent the interests of
active and retired state workers, who are likely to be more risk averse than the population at large, and
facing the prospect of living off of their retirement checks (pg 31).” The authors go further and
propose that beneficiaries are more interested in secure inflation adjustments than benefit increases
(recall: inflation adjustments are not included in my benefit increase variable). Murphy and Van Nuys
show preliminary correlations between beneficiary trustees and higher COLAs, subject to the data
complications mentioned in the literature review. The conclusion they draw is that beneficiaries prefer
high, secure income streams rather than occasionally large jumps in their benefits that cannot be
foreseen. If so, it may be that beneficiary boards increase benefits less in exchange for higher cost of
living adjustments, while political boards prefer the more unpredictable benefit increases as they can
be used opportunistically. In my conversations with employees of two public employee unions
(American Federation of State, County and Municipal Employees, and Communication Workers of
America), this “exchange” possibility was supported: they suggested that variations in COLA
formulas can be a tool of benefit increases that beneficiaries may prefer.
Another possibility to explain why political boards increase benefits more than non-political boards
involves the differing career paths of trustees. While accurate data is unavailable, a quick check of the
retirement systems’ websites indicate that many of the beneficiary trustees are “lifers,” in that they
have been trustees for well over a decade. The political trustees, on the other hand, have shorter time
spans on the boards, partly as a natural consequence of the state electoral process (e.g., the political
trustees can get voted out of office for issues unrelated to the pension fund). The differentiation in
career focus may naturally make beneficiary trustee more forward thinking since he expects to remain
on the board in the future, while political trustees are concerned with a shorter, electorally driven, time
period.
These possibilities present interesting areas for future data collection and research. In addition, there
are certain caveats in interpreting my results thus far. First, my measures of benefit increases are
imperfect. The reported formulaic changes, taken at an average level, may not be an accurate measure
17
of the increased costs or employee windfalls of the benefit increases. While I have tried to alleviate
these weaknesses by using three measures of the construct, I cannot be certain that my results capture
what I intend. Also, the level of state employer contributions is not accounted for in my data. Just as
the pension boards in my sample have control over benefit levels, they also control the rate at which
the state employers (e.g. public schools or police departments) must contribute to the fund. Thus,
boards could choose to fully fund their benefit increases with contribution increases. Alternatively,
boards could choose to pass the financial burden of the benefit increases to the future. My results
cannot distinguish between these highly different alternatives. It would be of great empirical benefit
to have data about contribution levels; I could then determine if board composition affects the choice
to “pay now or pay later.”14
I would like to get a clearer picture of the legislative approval process. To what extent do the boards
control benefit decisions if legislative approval is required for annual retirement system budgets?
Previous studies have used different measures of the board control; in one paper, a board composed
completely of political trustees with control over benefits is considered a “legislative” control instance,
while other papers consider the same case as being “board” control. Ultimate control may be a soft
variable, in that some boards have a tacit understanding of the extent of their control pending
legislative approval. One AFSCME employee suggested that even in cases where the board must go
to the legislature for approval, the trustees are responsible for projections and recommendations.
The conclusions of this paper lead to a number of implications. The most general is that pension fund
governance matters, at least insofar as the decision to increase benefits. As pension funds continue to
be plagued with massive underfunding yet repeatedly raise benefits, it is important to understand that
the management structure found in the retirement systems’ boards of trustees can impact the extent of
benefit increases. Additionally, the study suggests that concerns outside of fund solvency influence
the governance effect (indeed, fund solvency did not seem to lead to benefit increases at all). As a
result of runaway benefit burdens, many states are looking to shut down their defined benefit pension
systems in favor of defined contribution programs, where benefits are determined by investment
returns. (See, for example, the public employee retirement systems in Alaska and Michigan, which
have closed their programs to new employees. Arizona, California, Colorado, Ohio, Montana and
South Carolina are moving toward defined contribution, or seriously floated the idea.) In essence,
these states are choosing to wrest control over benefit increases away from trustee boards or
legislatures, despite massive upheaval costs. As other states consider how to deal with ever-escalating
benefits, it is paramount to understand the governance dynamics that led to the present state. A recent
New York Times Magazine article summarizes the implications of continued ignorance about this
issue: “The average voter doesn’t take notice when the legislature debates the benefits levels of
14 Contribution data is available as a PENDAT survey file. I hope to receive this data, but I cannot say for certain if it will vary.
18
firemen, teachers and the like… So benefits keep rising… [Eventually], you start firing cops and
teachers (NYT Magazine, 10/31/05).”
19
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22
Appendix
Table 1: Descriptive Statistics
N (unique observations) Mean Median SD
Systems 163Board of Trustees
Beneficiary Trustees (%) 163 0.28 0.47 0.31Political Trustees (%) 163 0.47 0.33 0.25Board Type (=1 if political) 163 0.27 0 0.44Trustee Term 57 4.23 4.0 0.87
Benefits
Benefit Increase Dummy (=1 if increase) 369 0.7 1 0.46Percent Benefit Change 949 1.09 0 0.20COLA Change 516 0.27 0.28 0.28Current Benefit Level 949 1550 1359 1334
System FinancialsFunding Flow 949 0.91 0.91 0.78Assets (market value, $mil) 848 10,304 5,163 12,869
Procedures
Benefits Gauranteed by Statute 97 0.87 1 0.34System Budget Approved by Legislature 97 0.65 1 0.48State Vote Required for Change in Benefits 97 0.03 0 0.17
Table 2: Means Differences
Political Board Non-Political Board ttest
Benefit Increase Dummy (=1 if increase ) 0.7283 0.6963 1.6230
Benefit Change 1.0123 1.0078 3.4550***
COLA Change 0.0300 0.0221 3.0952***
Assets (at market, in $mil) 10976 8767 1.4346
Average Monthly Earnings 3106 2969 2.8951***
Means
23
(1) (2) (4) (5) (6) (7)
Dependent VariableReported Benefit
ChangesReported Benefit
Changes
Benefit Increase Dummy (=1 if
increase )
Benefit Increase Dummy (=1 if
increase )Adjusted PBO
ChangeAdjusted PBO
Change
Estimation Regression Regression Probit Probit Regression Regression
Independent Variables
Board Type (=1 if political) 0.0348*** 0.0347*** 0.0072*** 0.0083** 0.0183 0.0191*(0.0080) (0.0079) (0.0027) (0.0028) (0.0118) (0.0118)
Benefits (Lagged) -0.0846*** -0.0596** -0.1450* 0.4393 -0.1796 -0.2351(0.0013) (0.0237) (0.0624) (0.1749) (0.5889) (0.6390)
Funding Flow (Lagged) 0.1880 0.1908 -0.0093 -0.0251 0.3028* 0.2912(0.1564) (0.1562) ('(0.0454) (0.0494) (0.1753) (0.1801)
Average monthly salary (Lagged) 0.0011 -0.0006 0.0042***(0.0009) (0.0004) (0.0012)0.0082 0.0158 0.3707
(0.1394) (0.1434) (0.4354)0.0091 -0.0199** 0.00121
(0.0070) (0.0073) (0.0072)
Year Fixed Effects Y Y Y Y Y YState Fixed Effects Y Y Y Y Y Y
Observations 949 848 269 250 516 501Systems 163 143 52 52 97 97States 46 45 18 18 41 41Years 6 6 5 5 6 6
R-squared 0.28 0.29 0.23 0.29 0.45 0.46Predicted x bar (for probits) 0.6930 0.7007Notes: Robust standard errors reported in parantheses. * refers to significance at 10% level, ** at 5%, and *** at 1%Reported probit results are marginal effects for small changes in continuous variables and discrete changes in binary variables
Table 3: Baseline Results
State Per Capita Income Level (Lagged)State Public Employee Density (Lagged)
24
(1) (2)
Dependent VariableReported Benefit
ChangesReported Benefit
Changes
Estimation Regression RegressionRepublican Democrat
Independent Variables
Board Type (=1 if political) 0.0157* 0.0550***(0.0094) (0.0122)
Benefits (Lagged) -0.0793*** -0.0458**(0.0039) (0.0050)
Funding Flow (Lagged) 0.0733 0.2449(0.1593) (0.2245)
Average monthly salary (Lagged) 0.0036 0.00090.0024 0.00110.0061 0.0136
(1.0082) 0.20890.0019 0.0062
(0.0145) 0.0075
Year Fixed Effects Y YState Fixed Effects Y Y
Observations 508 340Systems 96 72States 30 21Years 6 6
R-squared 0.22 0.37Notes: Robust standard errors reported in parantheses. * refers to significance at 10% level, ** at 5%, and *** at 1%States that changed party during this time: CA, MS, NE, NH, NJ, NV, VA, WVDifference in Board Type coefficient is significant at 1% (z=2.55)
Table 4: Baseline Results
State Per Capita Income Level (Lagged)State Public Employee Density (Lagged)
Is there a difference between Democrat Political Boards and Republican Political Boards?
25
(1) (2) (3)
Dependent Variable
Reported Benefit Changes
Reported Benefit Changes
Reported Benefit Changes
Condition Republican Democrat
Independent Variables
Board Type (=1 if political) 0.032*** 0.0448*** 0.0203*(0.0093) (0.0143) (0.0121)
Benefits (Lagged) -0.0819*** -0.0885*** -0.0718***(0.0152) (0.0224) (0.0203)
Funding Flow (Lagged) 0.0012 0.0031 0.0013(0.0018) (0.0027) (0.0019)
Per Capita Balance Change 0.0005 0.0017*** 0.00120.0003 (0.0000) (0.0014)
Balance*Board Type 0.0048*** 0.0002** 0.0032(0.0016) (0.0001) (0.0057)
State and System Variables+ Y Y YYear Fixed Effects Y Y YState Fixed Effects Y Y Y
Observations 848 508 340Systems 143 96 72States 45 30 21Years 6 6 6
R-squared 0.23 0.3 0.23Notes: Robust standard errors reported in parantheses. * refers to significance at 10% level, ** at 5%, and *** at 1%+ Note: Per Capita Income is Not included In the State Variables
Table 5: Marginal State Fiscal Balance Effects
Is there a marginal difference in political board behavior complimented by changes in state's fiscal level?
26
(1) (2) (3) (4) (5)
Dependent VariableReported Benefit
ChangesReported Benefit
ChangesReported Benefit
ChangesReported Benefit
ChangesReported Benefit
Changes
State Type N/APolitically
CompetitiveNot Politically Competitive Republican Democrat
Independent Variables
Board Type (=1 if political) 0.0349*** 0.0343*** 0.0265*** 0.0195** 0.0512**(0.0083) (0.0110) (0.0093) (0.0093) (0.0284)
Benefits (Lagged) -0.1193** -0.0634*** -0.0811** -0.1059*** -0.0693(0.0475) (0.1910) (0.0089) (0.0389) (0.3367)
Funding Flow (Lagged) 0.1900 -0.0016 0.0024 -0.009 0.3124(0.1562) (0.0011) (0.0026) (0.1330) (0.2406)
Election (=1 if election year) 0.0109** 0.0182*** 0.0129*** 0.0141* 0.0215**(0.0047) (0.0027) (0.0027) (0.0024) (0.0095)0.0006* 0.0005*** 0.0008* 0.0004* 0.0011(0.0003) (0.0002) (0.0004) (0.0002) (0.0008)
State and System Variables Y Y Y Y YYear Fixed Effects Y Y Y Y YState Fixed Effects Y Y Y Y Y
Observations 848 390 458 508 340Systems 143 65 78 96 72States 45 21 24 30 21Years 6 6 6 6 6
R-squared 0.33 0.23 0.19 0.28 0.47Notes: Robust standard errors reported in parantheses. * refers to significance at 10% level, ** at 5%, and *** at 1%
Notes on Magnitude: a 0.0008 marginal interaction effect translates into an average of $16.22million in annual benefit expenditures for a given system. For CALPERS, that translatesinto $259 million in added annual benefit expenditures.
A state is defined as politically competitive if a) there is gubernatorial party switch or b) at least one gubernatorial election had less than a 15% split
Table 6: Marginal Election EffectsIs there a marginal difference in political board behavior during election years?
Election*Board Type
27