THE INFLUENCE OF STATE POLITICS ON STATE RAINY DAY FUNDS
by
NATHANIEL F. KELLEY
ERIN L. BORRY, COMMITTEE CHAIR
AKHLAQUE HAQUE
COLE D. TARATOOT
A THESIS
Submitted to the graduate faculty of The University of Alabama at Birmingham,
in partial fulfillment of the requirements for the degree of
Master of Public Administration
BIRMINGHAM, ALABAMA
2015
ABSTRACT
States often find themselves facing fiscal hardships due to such issues as their
own revenue volatility or through federal recessions. The methods taken by the states
have varied over the years, but rainy day funds are becoming one of the leading
mechanisms for coping with economic disparity. As more state officials look to rainy day
funds as a solution, the field for research regarding rainy day funds has expanded. The
existing studies have focused on the best way to build and maintain these types of funds,
but there has been little information put forth that helps understand how they work. This
study looks at the influence that state political ideologies have in the formation and
upkeep of rainy day funds by analyzing state fiscal behavior from the years of 2002 to
2013. The study found that there appear to be patterns of growth in rainy day fund
balances, but the regression model did not support the hypotheses. The future research of
rainy day funds will need to pay attention to the implications and data limitations when
conducted similar studies, as these might have played a larger role in the results than was
anticipated.
ii
TABLE OF CONTENTS
ABSTRACT ........................................................................................................................ ii
LIST OF TABLES .............................................................................................................. v
CHAPTER
1 INTRODUCTION TO RAINY DAY FUNDS............................................................... 1
2 THE EXISTING LITERATURE .................................................................................... 6
Rainy Day Funds: The Basics ........................................................................................ 6
Purposes of Rainy Day Funds ..................................................................................... 8
Rainy Day Funds: The Logistics.................................................................................... 9
Types of Rainy Day Funds ......................................................................................... 9
RDF Regulations: Deposits....................................................................................... 11
RDF Regulations: Withdrawals ................................................................................ 13
State Finances and Rainy Day Funds........................................................................... 14
History of the States and Rainy Day Funds .............................................................. 16
State Finances and Political Influences........................................................................ 18
State Officials and RDFs .......................................................................................... 19
Political Business Cycles and Misuse of RDFs ........................................................ 21
Fiscally Liberal vs. Fiscally Conservative State Politics and RDFs ......................... 22
State Citizen Ideologies and RDFs ........................................................................... 24
Conclusion ................................................................................................................... 24
3 DATA AND METHODS ............................................................................................. 25
Data and Sources......................................................................................................... 25
Consumer Price Index ............................................................................................... 26
Rainy Day Fund Percentage...................................................................................... 26
State Legislative Partisanship ................................................................................... 27
State Government Ideology ...................................................................................... 27
Citizen Ideology ........................................................................................................ 28
Methods....................................................................................................................... 29
4 RESULTS AND DISCUSSION ................................................................................... 34
iii
RDF: Percentage by Year ........................................................................................... 34
Legislature and RDFs ................................................................................................. 36
RDF: Percentage by Year and State............................................................................ 36
Regression Model: Tests of Hypotheses ..................................................................... 38
5 CONCLUSION ............................................................................................................. 43
Revisiting the Research Questions............................................................................... 43
Study Limitations and Implications ............................................................................. 44
Time and Economic Cycles ...................................................................................... 45
RDF Components...................................................................................................... 46
CBPP RDF Percentage Formatting ........................................................................... 47
Specific Purpose Savings Accounts .......................................................................... 49
Money Transfers ....................................................................................................... 49
Partisanship vs. Ideology .......................................................................................... 49
Potential Future Research ............................................................................................ 50
Conclusion .................................................................................................................... 51
REFERENCES ................................................................................................................. 52
iv
LIST OF TABLES
Table
Page
Table 3.1: Consumer Price Index Conversion Chart…………………………………….37
Table 3.2: Average State Government Ideology by State...…………………..………….38
Table 3.3: Average Citizen Ideology by State………………..………………………….39
Table 4.1: RDF Percent Breakdown by Year……………………………………………47
Table 4.2: RDF Percent and RDF Dollar by Legislature………………………………...48
Table 4.3: Regression…………………………………………………………………….49
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CHAPTER 1
INTRODUCTION TO RAINY DAY FUNDS
Throughout the history of the United States, American culture has been one that
relies on innovation to prove its resilience against the threats to society. Because of its
shaky and unpredictable nature, it can be argued that one of the most vulnerable aspects
of the United States is its economic infrastructure. While this is common knowledge,
what is not often realized is how state governments are all directly impacted by the
trickle-down effect of a federal fiscal crisis. When state governments do not properly
budget, or when revenues are not as high as predicted, the ability to maintain government
functions is put at risk. There are two main ways out of a scenario such as this: a federal
bailout or accessing an account with the sole purpose of allowing the state government to
carry on with its everyday tasks. This type of account is called a Rainy Day Fund (RDF)
or a Budget Stabilization Fund (BSF). In this study, an RDF refers to a savings account
used by the states to combat unexpected fiscal shocks. These RDFs acquire money
through various methods, but the states deposit money annually. For the states trying to
stay afloat while facing their own economic problems during a national fiscal crisis, the
options are becoming geared more towards RDFs as the most practical solution.
Meagan Jordan (2006, para.1) cites Zahradnik and Johnson (2002) when she says
that rainy day funds “are the most common method of state revenue stabilization and are
used to address cyclical (as opposed to structural) deficits.” Revenue stabilization simply
1
means balancing the expenditure amount to remain below or equal to the revenues
coming in to the state. In other words, money should be placed by the states into this type
of fund when economic times are good and money taken out during harder times to keep
deficits at bay.
State politics play a role in the creation of RDFs through the state legislatures
creating the policy needed for updating rainy day funds. While there is a relationship
between politics and state budgeting, the question is how much do these policy-makers
actually affect a state’s preparation for fiscal hardship? An ideal measure for this would
be to look at the states’ fiscal health before and after the Great Recession.
The states were barely able to budget during the Great Recession, leaving hardly
any money to recover from the left-over deficit. Liz Farmer from Governing (2014)
quotes Brenna Erford from the Pew Charitable Trust: “A lot of states had been budgeting
in perpetual crisis mode during the Recession and some of that has continued into the
recovery and beyond” (Farmer, 2014, para. 4). Farmer furthers this by saying that the
collective amount of states’ reserves in 2008 (a total of $60 billion) was met by a doubled
deficit amount in 2009 (Farmer, 2014, para. 4). In response to this, Erford proclaims that
Pew’s focus has intensified in getting the states to look at their long-term economic
status.
Despite this massive deficit, there are states that have taken measures to ensure
that the economic damage that they receive is as minimal as possible. One such example
is Virginia. Reid Wilson from the Washington Post (2014, para. 3) claims that Virginia is
one of the best states at saving for the worst-case scenario. Michael Cassidy and Sara
Okos from The Commonwealth Institute (2011, p. 2) say that Virginia has used around
2
$1.13 billion from their RDF, just since 2008. “This amount closed approximately 15
percent of the total budget gap occurring over the course of the FY2008-2010 period of
economic downturn” (Cassidy and Okos, 2011, p. 2). Cassidy and Okos (2011, p. 2) also
state that, because of the effectiveness of their RDF, Virginia had more flexibility in how
the state handled the spending cuts required to reach the budgetary shortfalls from the
Great Recession.
One of the issues that hurt the states leading up to the Great Recession was that
even if they saved up to the maximum amount allowed within their RDFs, these “caps”
were not realistically large enough to help weather the Great Recession. Caps, or cap
limits, are restrictions placed on an RDF that dictate the maximum amount of money that
can be in an RDF account and are usually created though legislative policy. Michael
Martz from the Roanoke Times (2014) references a report from the Pew Charitable Trust
in saying that there were many states that saved up to the maximum level allowed by the
state’s RDF policies prior to the Great Recession, but that these amounts were still not
enough to support the states (Martz, 2014).
However, Martz says that Virginia addressed this policy implication by raising the
RDF cap limit from 10 % to 15% in 2010. Martz furthers this by stating “[t]he state could
have had almost $600 million more in savings to balance its budget during the recession
under the higher cap” (Martz, 2014). Cassidy and Okos (2014, para. 2) note that through
changing its RDF cap limit by passing legislation, Virginia’s Constitution was amended,
allowing the state to save higher amounts during future economically strong years. Being
able to update policy in order to maintain fiscal security is one of the links between state
legislatures and state finances.
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Similarly to how statutes are unique to each state, all RDFs are state-specific as
there is no national RDF. Due to the differences of the RDFs from state to state, there is
not a one-size-fits-all model for adequate depositing and upkeep. There are common
standards that are shared by different states, but these are sporadic and lack any real
connection between them. The main focus of this research is the relationship between
state legislative politics, state finances, and state RDFs. It is understandable that a wealth
of information regarding RDFs does not exist because RDFs are fairly new. Virginia, for
example, added their RDF in 1993 and was one of the first states to implement one
(Martz, 2014). In particular, there are underexplored topics related to them that might
explain their implementation. Given that most states have these funds, it is logical to
question what influences their implementation. This thesis seeks to explore the following
research questions:
1) What patterns are seen among states and rainy day funds?
2) How do state politics affect the size of states’ rainy day funds?
In order to find answers to these research questions, data were collected from various
sources such as the National Association of State Budget Officers (NASBO), the Center
on Budget and Policy Priorities (CBPP), and the National Conference of State
Legislatures (NCSL).
This first chapter of this thesis introduced the general concept of rainy day funds
as well as the research questions that this thesis seeks to answer. Chapter Two presents
the existing literature pertaining to the purposes, types, and regulations of RDFs. It also
addresses the interaction that RDFs have with state finances and political influences, such
4
as fiscal typologies and political business cycles. The hypotheses of this study can also be
found in Chapter Two. Chapter Three discusses data sources, as well as the statistical
methods utilized to test these hypotheses. Chapter Four discusses the descriptive statistics
and results while also offering discussion in regards to those findings. Lastly, Chapter
Five ties this thesis together by presenting a final view of the research questions, what
limitations are involved in the production of the findings, and topics for plausible future
research regarding rainy day funds.
5
CHAPTER 2
THE EXISTING LITERATURE
The literature pertaining to RDFs has been found to mainly discuss their
efficiency at helping states cope with Recessions. RDFs are a fairly new topic to research,
so the main focus has been on if they truly help states in financial recovery, rather than
what might influence RDFs. Due to the unseasoned age of RDF implementation, it is
expected that there will be large gaps in literature. These gaps in the research have
formed the topic that this study will be exploring: whether state politics influence state
rainy day funds. The following literature review helps illustrate this gap. First, I address
the fundamentals of RDFs, including what they are, what they are used for, and their use
by states. Then, this review turns to what is known about the relationship between state
politics and finances. Finally, I present the study’s hypotheses.
Rainy Day Funds: The Basics
The rainy day fund is budgetary method for storing money in a separate account
when economic times are good so as to create a safety net for the times when money is
sparse. Traditionally, this form of savings has been used overtime for an array of
purposes, such as for disaster relief, but modern times have given way to the creation of
policies that specify how the money is to be spent, often times requiring stringent
6
protocol for removing the money (Burson, 2013, para. 4). It should be noted that “rainy
day funds” are connoted in this present research as “RDF” (or RDFs).
Rainy day funds have recently become exceedingly important for the upkeep of
state’s budgets against their own fiscal situations and of the economic conditions of the
federal government (Alm and Sjoquist, 2014, p.17). When a state has excess money, they
can either save the money or spend it. Due to the unpredictability of the American
economy, government policy groups and financial organizations (such as the CBPP,
NASBO, and the NCSL) have all recommended that this excess money be placed into a
rainy day fund. The purpose behind establishing a separate account strictly for savings
(and not for spending) is to make sure that each state is able to annually maintain a
balanced budget. When a state has adequately maintained their RDFs, its likelihood of
being able to carry on with mandatory operations during a fiscal crisis is greatly
increased. It should be noted that there are not federal regulations in place that monitor
state usage of RDFs, thus requiring states to maintain RDFs at their own volition.
While each state has their own unique RDF system, it is the differences between
each RDF that make them successes or failures. Some states have RDFs that are better
structured than others. Mitchell and Stansel (2008, p. 438) reference Sobel and Holcombe
(1996) in that simply having an RDF does not guarantee a safety net during fiscal crises,
but that it is the characteristics of that RDF that matter. These characteristics refer to the
methods for depositing and withdrawing funds, the locations from which the money
comes, the group or person that controls the account, if the fund stands alone or is tied to
another account (such as another general savings account), the minimum amount that can
be in the RDF, and the cap level placed on the maximum amount of money in the RDF.
7
Purposes of Rainy Day Funds
Rainy day funds have been utilized in different ways as each state sees the need to
create one. Wagner and Elder (2013) write that RDFs are tools that can be utilized by
lawmakers to combat countercyclical fiscal policy as well as to operate as a functioning
system when government policy development is stagnant. The CBPP (2014, para. 9)
points out that the focus of RDFs “is to help maintain state support for education, health
care, transportation, and other services that promote economic growth and meet
residents’ needs.” One of the main reasons for needing an RDF is to have security against
revenue volatility. Often enough, people will see a large sum of money set aside and
wonder why it is not being used for the present issues. This type of thinking comes from
misunderstanding that the money is technically earmarked for fiscal crises. According to
the Tax Policy Center, “[s]tates most often use their rainy day funds in times of budget
deficit-every state except Vermont has some sort of requirement to balance its budget
each year” (Rueben and Rosenberg, 2009, para. 4).
While it is not common, some states use their saved RDF money to deal with any
form of disaster (James Smith, 2006, para. 1). However, this is not universally accepted
since funding for disasters should be planned on, and therefore, inserted into the budget
before the RDF receives any form of deposit. Smith (2006) points out that the governor
of most states already has funding for emergencies. Smith also mentions that cities have
money set aside for snow plowing, but if all of the funding in those accounts does not get
used, it sits in the account or goes to other sources. Further, spending money for disaster
relief, such as for snow plowing, is not viewed in the same manner as unnecessarily
8
spending for city/state growth or development. Mitchell and Stansel (2008, p. 435) say
“[s]tates that restrain spending growth during expansionary years and implement strong
rainy day fund withdrawal rules are likely to face less severe fiscal crises during
recessions.” Overall, there appears to be discrepancy amongst the literature: some
scholars suggest that states should not use RDFs for anything other than fiscal stability,
while others say that using an RDF for any type of disaster relief is different—and more
acceptable—than spending for city/state development. In all, this converges to a central
idea—that it is imperative to reduce RDF spending by large amounts in order to make
sure that money is available for the states to operate when they (or the federal
government) are in an economic crisis.
Rainy Day Funds: The Logistics
Types of Rainy Day Funds
Pinpointing an exact description of the different types of RDFs can be a difficult
task because these funds take on many different forms and purposes. For example, both
Alabama and California have two rainy day funds (CBPP, 2014). Alabama has an
Education Trust Fund and the General Fund Trust Fund, with caps set at 20 percent and
10 percent of the state’s overall budget, respectively. In California, the “Budget
Stabilization Account” is capped at five percent and its “Special Fund for Economic
Uncertainties” has no cap (CBPP, 2014).
Some states have multiple RDFs and some of those RDFs may even have special
purposes. Jean Burson (2013, para. 2) makes note that there are multiple reasons for
states having different rainy day funds: “In many, limited budget tools hamper
9
lawmakers. In others, the reasons have been institutionalized into their constitutions and
policies. In some states, a polarized political climate bedevils budget reforms.” Thus,
Burson suggests that specialized RDFs are sometimes created as a way around previous
legislation or as a way to bypass political polarization that could keep needed fiscal
policy changes from occurring.
A main reason as to why a variety of RDF formats exist is that there is not a onesize-fits-all model for states to follow. This contemporary sentiment has become widely
accepted by many scholars and practitioners, one of which is Phillip G. Joyce (2001).
Joyce states that there is “…no justification for a “one size fits all” approach; each state
should design policies based on its own peculiar needs” (2001, p. 1). The CBPP says that
the size of a state’s RDF depends on how vulnerable a state’s economy is to the
fluctuation of revenues and expenditures (CBPP, 2014). They further this by referencing
the Pew Charitable Trust: “states that depend heavily on more volatile revenue sources
such as oil and gas taxes should consider higher caps in order to maintain a larger budget
cushion” (2014). The literature overall has stated that even though there is a general
recommended RDF percent for states to strive to reach, there is not a practical method for
applying a one-size-fits-all model to all of the states. While the majority of states have a
standard RDF, often called a General Reserve Fund, states will often tailor their RDF (or
add a secondary customized RDF) based on the needs of the state, such as the state’s
level of revenue volatility.
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RDF Regulations: Deposits
Deposits are when a state places money into an RDF account. Depending on the
state, the deposit amount will either be added at the beginning or the end of the fiscal year
based on how the state’s budgetary system is set up. An example of this is Connecticut,
which has funds deposited to its RDF based on having a year-end surplus. The Pew
Charitable Trusts (2014) states that it was not just the Great Recession of the mid-2000’s
that dealt a hefty blow to the fiscal affairs of the states, but that the states have
continuously held insufficient reserves in the previous recessions. They attribute this to
the “…statutory limits on the total size of reserves and rules for deposits that make saving
a low budget priority” (Pew, 2014).
Since the states’ RDFs are not federally funded, the money has to come from the
state’s budgets. However, there are different methods for how a state chooses to deposit
money into their fund. The CBPP (2014) lists these deposit formats ranging anywhere
from a required budget allocation, year-end surpluses, a range of economic formulas
(such as based on personal income growth), appropriations, tobacco funds, oil and gas
funds/revenues, and taxes. These policies are often indicative of the economic state of the
both the states and the country. Pew compiles these types of deposit methods into five
categories based on surplus, static deposits, revenue forecast error, required balance, or
by appropriation.
Having the RDF deposit based on a surplus means that this amount is based on
“extra” money left over at the end of the fiscal year. There are both positive and negative
aspects for this method. Pew (2014, p. 7) says that since this money comes from what is
left over, it is often in a low position on the budgetary priority list. Pew uses Minnesota
11
as an example: “By putting funds into savings before state legislators begin creating a
budget, Minnesota effectively prioritizes savings ahead of other expenditures” (Pew,
2014, p. 8). Pew notes that some states such as Georgia and Nebraska place the complete
amount of surplus into their RDFs. Other states will divide this surplus amount and place
some within the RDF while directing the rest of the money to be used elsewhere.
A second form of saving is called static, or ad hoc, depositing and is based on a
percentage of state revenues. This is more of a primitive system, as it does not take into
account the up-and-down fluctuations that happen over time (Pew, 2014, p. 8). These
states are more likely to run into issues when trying to modify the amount of money that
should be deposited as a way of dodging fiscal shocks. Pew (2014) gives the example of
lawmakers in Missouri that created a temporary secondary fund that collected some of
the revenues from 2015. While this shows that modern lawmakers see the significance in
having an effective savings account, this was only a one-time modification that shows the
lack of adaptability within this type of system.
Forecast error is a form of depositing into an RDF by means of the fiscal
differences that occur between the annual projected revenues against the annual actual
revenues. The benefit of this method is that if a downturn is predicted but does not
happen, the excess money predicted must still be placed into that account. However, if a
growth is predicted and does not occur, there is no requirement to save. Pew (2014)
points out that New Jersey, one of the states that uses this method, had any empty RDF
for five consecutive years. This type of depositing seems to be fully utilized when the
state has an accurate awareness of their revenue cycles and encounters little budget
volatility.
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Depositing due to a system of appropriations is another platform used by various
states (Pew, 2014, p. 8). The framework this is based on comes down to complete
legislative discretion, which can either hinder or bolster the states’ RDFs. Pew notes
“This strategy has had mixed results: Wyoming is one of the states with the most saved to
manage uncertainties – while Arkansas has a history of inadequate reserves” (Pew, 2014,
p. 8, footnote 38). The main analysis surrounding this type of savings is that in can be
inconsistent and a politically charged battleground depending on the partisan cooperation
of the legislatures.
One last method for depositing comes in the form of a formula-based system and
is used by five states (Pew, 2014, p. 6). Rose (2008, p. 159) defines this system as one in
which “transfers are made automatically when personal income growth exceeds a predetermined threshold, or in a fixed amount each year.” This type of savings, as noted by
Pew, is one of the stronger forms as it directly ties the amount deposited to the
fluctuation, or volatility, seen within their main sources of revenue.
RDF Regulations: Withdrawals
Withdrawals are when a state removes money from a RDF account. There are
restrictions put in place to govern how a state legislature or an official can remove money
from the RDFs. Similarly to how the rules of depositing money into RDFs vary from
state-to-state, the practices for withdrawing money are also unique to each state. The
basic concept is that states will either have an unrestrictive path of lenient policies
towards withdrawing funds or that they will have a nearly impossible task in trying to
gain access to the RDF though a supermajority approval (Burson, 2013, para. 7). It can be
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quite difficult to find a medium between the two that allows for the removal of funds
without doing so in a risky manner. If a state has lenient policies for withdrawals,
temptation to use this funding for societal developmental can arise for state leaders.
Mitchel and Stansel (2008) declare that the states place themselves in fiscal binds by
spending this money in ways in which it was not intended.
The more common and modern approach is to have a two-thirds majority
approval in each house for a withdrawal (Pew, 2014, p. 9). However, if there is a political
disagreement on this issue or if the restrictions are too fortified, the RDF might become a
source of inaccessible money. (Pew, p. 9) Pew mentions the case of Missouri, “whose
fund hasn’t been accessed since the 1990s, with the last withdrawal being used to address
devastation caused by floods rather than an economic downturn” (Pew, p. 9). Balancing
between deposits and withdrawals on an annual basis can be a cumbersome task, but it is
one in which prevents the states from facing fiscal hardships with empty pockets.
State Finances and Rainy Day Funds
There are many justifications for the states’ implementation and continuous use of
RDFs. Pew (2014, p. 2) notes that the fiscal hardships on states over the past ten years
have occurred due to a variety of reasons. These reasons include transitions in the
predictability of income tax patterns, the shrinking of the sales tax base paralleled to the
changing consumer market, and states being dependent on tax revenue that is determined
by the global demand (Pew, 2014, p. 2). A large factor here is the source of a state’s
revenue and the sensitivity of the state to this volatility. Revenue volatility refers to the
spikes and dips in annual revenue. While fluctuations can be generically planned on, the
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size of the low and highs is not truly predictable. The period of financial stability before
or after an economic depression occurs is deemed fiscal normalcy. The cause of a state
being removed from fiscal normalcy usually is the result of a fiscal shock, or the negative
financial “hits” that a state takes as the result of unexpected economic events, such as an
economic depression.
To combat these unpredictable rhythms in revenue, Pew (2014, p. 2) states
“Budget stabilization funds are vital to managing these unexpected swings.” Even though
the number of states adopting RDFs was rapidly on the rise in the late 2000’s, the caps
placed on these RDF accounts do not adequately allow the states to utilize them in a
manner to efficiently offset the complete fiscal damage caused by the shortages from the
early 2000’s (Pew, 2014, p. 2). Windfall revenues can also be the source of self-inflicted
damage, as states will often use this money thinking that it is going to permanently
remain fixed at this level rather than fluctuating. Windfalls are infrequent or single
occurrence revenues that are unexpected monetary boosts (Pew, 2014, p. 2). They can be
beneficial, especially for savings, but should not be used for the creation of long lasting
programs, as these funds are not continuous. The CBPP (2014) says that future budget
imbalances can occur due to spending one-time funds for expanding programs or creating
permanent tax cuts. This allows for holes in the budget to occur later on in the manner of
over-spending. Pew (2014, p. 2) recommends using the knowledge on state-specific
swing revenues as a way to customize the best regulations for caps and deposits.
Before looking at individual state RDF information, it is important to note that
there are differing theories regarding the exact methodology a state should use for
determining how much to save in an RDF. In the past, the CBPP recommended that each
15
state should deposit amounts equal to 5% of the state’s overall revenues or expenditures.
The modern recommendation is 15%, which is needed to offset the financial blows that
are being seen in recent years (CBPP, 2011). The RDF amount varies from state to state
because it is a percentage based on each state’s expenditures. The Fiscal Survey of the
States (produced by NASBO) displays and distinguishes which of the 50 states adhere to
that recommended level, those that fall below the level, and those that are well-above the
savings level.
History of the States and Rainy Day Funds
Until the last few decades, there has been little utilitarian perspective by the states
regarding RDFs. Collectively, the states began to add and modify budget stabilization
funds, or RDFs, to their budgetary processes as each economic cycle hit a depression and
damaged the states’ finances. Jordan (2006, para. 1) references previous authors stating,
“There were only 12 states with rainy day funds in 1982, 38 in 1989, and 44 by 1997
(Sobel and Holcombe, 1996; Joyce, 2001; Douglas and Gaddie, 2002).” This is
significant due to the states’ observable increased interest over time regarding RDF
methods. As of 2013, 47 states have implemented RDFs.
While just at half of the states with RDFs have them as a general Budget
Stabilization Fund (BSF), the others either have a special purpose for it or have more than
one RDF. For example, Alaska has two, the Constitutional Budget Reserve Fund and the
statutory Budget Reserve Fund, neither of which have a cap (CBPP, 2014). As mentioned
earlier, Alabama and California both have two RDFs but different cap limits. The other
states that have two RDFs include Iowa, New York, Oregon, South Carolina, and Utah.
16
Vermont, however, has three along with a “rainy day reserve.” That some states have
more than one RDF shows the development of thinking by the states regarding them. It
would appear that the economic fall-outs of the 1990’s and then of the mid-2000’s
reinforced the notion that saving was no longer voluntary, but required if a state wanted
to persevere through fiscally exhaustive years.
The most recent state to adopt a RDF was Arkansas. Initially, Arkansas had a
system—the Arkansas Revenue Stabilization Act of 1945, or ARSA—that responded to
financial fluctuations, but the money did not come from a separate fund. Jordan states
that the ARSA “responds to revenue fluctuations by prioritizing fund distributions”
(Jordan, 2006, p. 104). Using Arkansas’ recent implementation of an RDF as an example,
more weight is given to the notion that RDFs are budgetary tools states are just starting to
understand and to develop as mechanisms against fiscal shock. While Arkansas was the
most recent state to formulate an RDF, the states without RDFs have other ways for
dealing with fiscal setbacks.
According to the Center on Budget and Policy Priorities (2014), there are four
states that do not have any sort of rainy day fund: Colorado, Montana, Kansas, and
Illinois. However, these states have their own measures for combating revenue volatility.
For example, the Colorado Legislative Council Staff states that Colorado’s General Fund
Reserve is labeled as outside of the realm of RDFs “due to its size and the requirement
that it be replenished each year” (Watkins, 2010, p. 5). The argument for its legitimacy is
based on the fund being used for budget stabilization purposes after economic downturns
have occurred. However, it does not have any guidelines for withdrawals or deposits
minus that the Reserve must be replenished based on that years “statutorily-defined
17
percentage of General Fund appropriations” (Watkins, p. 5). Even though Colorado
considers itself to have an RDF, the discrepancy comes from the RDF being discredited
as a real RDF by the CBPP (CBPP, 2014). In spite of this discrepancy, Colorado is
included in the 47 states with RDFs because its balances are reported to NASBO each
year.
Similarly, James Carroll notes that Montana uses a trust (funded by a coalseverance tax) that can only be withdrawn under the approval of a three-fourths majority
from both legislative houses. Carroll also states that Kansas, which uses a more simplistic
approach, requires that 7.5 percent of their general fund be left over annually (Carroll,
nd). Civic-fed.org notes that, according to the Commission on Government Forecasting
and Accountability (COGFA), Illinois set up the Budget Stabilization Fund in May 2000
but it is deemed to be non-existent because the state has yet to have excess funds
available to be placed into this account. Illinois is, however, making a sophisticated effort
to boost its RDF efficiency. Passed in 2014, the Illinois Revenue Volatility Study Act is
their route towards achieving this goal. According to Pew, “Illinois is only the third state
to statutorily require a volatility study specifically intended to inform rainy day fund
policy” (Pew Trust, 2014).
State Finances and Political Influences
The overall research regarding the influence that state government politics have on RDFs
focuses on whether or not RDFs are susceptible to being influenced. We know that state
lawmakers create and develop state government policies. Alm and Sjoquist say “[t]o our
knowledge, there is no study that examines recent state government trends and offers
18
explanations for these trends” (Alm & Sjoquist, 2014, p. 165). The ability of state
governments to effectively pass legislation is often determined by the political
environment (Burson, 2012). This information available about state finance and politics
can help us determine if potential relationships exist that might explain these trends.
Berry, Ringquist, Fording, and Hanson (1998, p. 327) put forth a statement
connecting states and politics: “Democracy requires a strong correspondence between
popular preferences, the ideological orientations of elected representatives, and
government policies. Many scholars focus on these connections at the state level in
American politics.” This means that there is a strong relationship between state
government and politics while being significant because budgetary policy is a large part
of state government operations. Chang, Kim, and Ying (2009, p. 344) say that “[s]tudies
on the effect of politics on the economy at the state-level are few but growing.” There is
worth in looking at the existing literature between state governments (both the executive
and legislative branches) and state finances because it sheds light onto the effect that state
politics might have on RDFs. It should be noted that the studies that look at the link
between state governments and state RDFs look at the executive branch and legislative
branch collectively. The reason for this is that while states widely vary in their RDF
policy, the governor and the legislature are both involved in the RDF policy process. If a
relationship between a state’s economy and its government can be found amongst the
literature, it can then be most-likely assumed that a relationship of some sort exists
between a state’s RDF and its political ideology.
State Officials and RDFs
19
Thus far, one of the most relevant sources for information regarding the states’
political influence over its budgetary habits comes from Shanna Rose (2008). Rose
(2008, p. 154) states that the current literature pertaining to RDFs centers around its
economic functionality, not on RDFs susceptibility to state politics. In her study, Rose
asks “[d]o politicians in the U.S. states manipulate rainy day funds…?” (2008, p. 170).
Using the findings of her study, Rose responds to this question by saying that the answer
to this question appears to be yes. Since we can say that politicians appear to have an
impact on RDFs, we are interested in the driving motivators for this manipulation.
One theory, called the opportunistic theory, was formulated by Nordhaus (1975)
and Lindbeck (1976) and suggests that politicians use the states’ funding at their
discretion as a way to bolster their chances of re-election (Chang et al., 2009, p. 343).
The second theory, called the partisan theory, was created by Hibbs (1977) who presumes
that the decisions made by politicians come from the ideologies of their party (Chang et
al., 2009, p. 343). While Chang et al. (2009) declare that the partisan theory is supported
by empirical evidence, they also state that Klein (1996) found evidence arguing for the
contrary: that there is more evidence supporting the opportunistic theory of the political
business cycle. On the other hand, the findings that Chang et al. (2009, p. 351) put forth
“contradict the recent works which find no connection between state-level economic
conditions and gubernatorial elections.”
In support of the partisan influence on state-level economics, Alt and Lowry
(1994) find that the political differences in state-level spending and budgeting usually are
influenced by whichever party is in control. In other words, there are academic studies
that both support and refute the influence of state politics on state finances. However,
20
Chang et al. (2009, p. 351) state that while definitive evidence exists for both the
opportunistic theory and for the partisan theory (in reference to the national-level and the
state-level), state government spending increases under a Democratic governor and
decreases under a Republican governor. Thus, the data show that political stances of
elected officials do have some sort of relevance to state budgetary practices. While the
literature answers that politics do influence state spending, the gap in the literature is
whether or not the type of political orientation of a state influences the size and
effectiveness of its RDF.
Political Business Cycles and Misuse of RDFs
One of the more controversial aspects of state-level use of RDFs is the misuse of
RDFs by elected officials. Wilson (2014, para. 3) puts bluntly that RDFs are “not a piggy
bank for politicians too lethargic to find cost savings in an otherwise free-spending fiscal
environment.” Politicians will often attempt to use RDFs during election years so as to
dodge having to cut spending or to raise taxes. Shanna Rose (2008) labels this as
“mismanagement of budget stabilization funds.” She makes note of the “political
business cycle,” where politicians will make policy changes right before elections take
place, often using rainy day funds as a way to fund these policies. She later says that if a
surplus of rainy day fund money is available, it is not uncommon to sidestep high savings
for this money and then for it to be used “to finance pet projects instead” (Rose, 2008, p.
154). Another topic that Rose states is that state officials often play “shell games,” which
she defines as “temporarily moving money in and out of funds” (2008, p. 160). She says
21
that this is more likely to happen during election years and with RDFs that have lenient
withdrawal policies (2008, p. 160).
However, Carlsen (1997) tests monetary policy against the timing of elections and
states that there is not a consistent relationship between the two and the political business
cycle. While there are some discrepancies presented from various studies, the overall
literature here shows that a correlation is present between politicians and RDFs through
their manipulation of it as seen through the political business cycle.
Fiscally Liberal vs. Fiscally Conservative State Politics and RDFs
The relationship of interest here is between state partisan politics and RDFs;
specifically, how politics influence the RDF balances and how these RDF percentages
match up to the recommended savings. According to Rose (2008, p. 155), “[t]he political
manipulation of rainy day funds should be more pronounced under unified partisan
control than under divided government.” This translates into that the political influence
on RDFs should be more likely to occur when the state government is of the same
political composition. “As for political considerations, the actions that a state might take
to recover are likely to differ based on the political orientation of the state government”
(Alm & Sjoquist, 2014, p. 168). The literature appears to collectively say that the political
ideology of a state is likely to determine their fiscal policies.
The terminology of fiscally liberal and fiscally conservative has been loosely
defined. However, to maintain clarity for RDF analysis, fiscally conservative refers to big
saving and little spending; conversely, fiscally liberal as little (if any) long and short term
saving while having a mindset of big spending. “To me, fiscal conservatism means
22
balancing budgets—not running deficits that the next generation can’t afford… And most
importantly, being a fiscal conservative means preparing for the inevitable economic
downturns—and by all indications, we’ve got one coming” (Michael Bloomberg, 2007,
para. 22). Summed up, Bloomberg was making the point that being fiscally conservative
is saving for the unpredictable and “inevitable” fiscal crises. In a similar statement,
Chang et al. (2009, p. 343) make reference to Hibbs (1977), which “presumes that
Democrats pay more attention to promoting expansionary policies at the cost of
inflationary pressure” (p. 343).
Broken down to the basics, this declares that Democrats spend more—which is
the modern concept of being fiscally liberal. Rose (2008, p.172) references Alt and
Lowry (1994) in saying that information pertaining to politics and budget policy within
the U.S. states proposes “that Democratic incumbents prefer to spend more than
Republican incumbents, all else equal.” This is furthered by the other part of Hibbs’
theory that Chang et al. (2009, p. 343) discuss: “Republicans are more concerned with
keeping inflation in check at the cost of some unemployment.” Alm and Sjoquist say that
they expect Republican states less probable to raise taxes (2014, p. 168); however, their
findings regarding this assumption contradicted their expectations. This puts forth the
polar opposite of high spending and is what has come to be viewed as being fiscally
conservative: low spending and high cuts.
Given the patterns of spending according to partisanship, we expect to see
conservative (Republican) state governments have higher savings amounts in their RDFs
and for liberal (Democratic) state governments to have lower RDF savings. Therefore, we
can observe the following hypotheses regarding Republican versus Democratic states:
23
H1: Fiscally conservative state governments will have a higher rainy day fund
percentage than fiscally liberal state governments.
State Citizen Ideologies and RDFs
While Rose (2008) makes the strongest correlation regarding the influence that
the politicians have on rainy day funds, it does not actually address the topic of the
overall political ideology of the citizens within the states. Based on expectations relating
to state government ideologies, we infer that citizen ideology will also have an impact on
state RDFs. Therefore, we will say that since the citizens elect the state legislatures we
can assume that they share similar ideological perspectives.
H2: States with a more conservative citizenry will have a higher rainy day fund
percentage than states that have a more liberal citizenry.
Conclusion
Overall, this chapter has presented a guide for understanding how RDFs work by
examining the literature and studies that pertain to RDFs. The literature pertaining to the
history of the states’ and their RDFs gives insight on the growing academic interest in
how the states use their RDFs. It is also meant to show that a framework is present for
finding a potential relationship between the political nature of a state and how it
structures its RDF policy. Overall, these topics aim at exposing the gaps in the literature
regarding the influence that state level politics have on state RDFs.
24
CHAPTER 3
DATA AND METHODS
The data used to inform this study come from several sources and pertain to state
finances, state partisanship and ideology, and citizen ideology. Collectively, data from
these sources help form the variables and scales used to test hypotheses regarding the
influence of state legislatures on state rainy day funds.
Data and Sources
In this study, the units of analysis are states with rainy day funds, which totals to
47 states.1 However, the data regarding the states are mixed and come from different
sources. To start, data on state budgets were collected. Annual data on state budgets
dating back to 1972 can be found within the Fiscal Survey of the States, which is
published by the National Association of State Budget Officers (NASBO). This data
include each state’s actual RDF amount, its annual beginning balance, annual revenues,
annual expenditures, and its end balance. NASBO releases two “Fiscal Surveys of the
States” for each year, a spring edition and a fall edition. The fall editions contain
estimated figures while the spring editions use actual figures. As a result, the spring
surveys are most accurate and data were collected from those.
1
The states without rainy day funds include Montana, Kansas, and Illinois.
25
Consumer Price Index
[Insert Table 3.1 here]
In order to have an accurate portrayal of states’ fiscal developments over time,
dollar figures collected from the Fiscal Surveys must be converted to account for
inflation. To do this, I used the consumer price index (CPI) of all urban consumers in the
United States. The information used in this study came from Statistia: The Statistics
Portal (2015). According to Statistia (2015, para. 2), The United States Bureau of Labor
Statistics defines the CPI as “a measure of the average change over time in the prices paid
by urban consumers for a market basket of consumer goods and services.” All of the
years from 2002 to 2012 were converted by using the CPI number for the year 2013. In
order to do this, I divided the CPI number for each year by the base CPI number for 2013.
Once each year’s CPI number is calculated, all dollar figures are multiplied by that CPI.
The CPI conversion numbers used for each year can be found in Table 3.1. I used the
2013 CPI number to convert each state’s actual RDF amount, its annual beginning
balance, annual revenues, annual expenditures, and its end balance.
Rainy Day Fund Percentage
The CBPP provides a recommended percentage of a state’s annual expenditures
be placed in their RDF. The CBPP has published a variety of reports over the years (2014
is the most recent) where the initial recommended percentage in 2005 was an annual 5%
of a state’s overall expenditures for that year. This recommended percent has increased to
26
15% in more recent years. To get this percentage, a state’s RDF balance amount is
divided by the amount of expenditures that year. Using this percent, I was able to
categorize each state’s RDF percent according to the CBPP’s recommendation. Using
the initial recommendation of 5% as a low point and 15% as a high point, I coded each
state accordingly.
State Legislative Partisanship
To measure political influences, I collected data on partisanship and ideology. For
partisanship, I collected data on each state’s legislative composition from the National
Conference of State Legislature’s “Partisan Composition of State Legislators 20022014.” This dataset specified each state’s legislative composition for the years 20022014. I gathered information on each state’s lower and upper legislative chambers. Each
state was assigned an R (Republican), D (Democrat), or S (Split) to indicate the
combined legislative composition for each year. Split is in reference to having a split
legislature: one chamber is majority one party and the other chamber being majority of
the opposite party. Nebraska was not included in the legislative composition data of this
study because it has a unicameral system, meaning that it only has one legislative entity.
State Government Ideology
[Insert Table 3.2 here.]
For political ideology, I collected data from the “revised 1960-2010 citizen
ideology series.” This series includes data regarding each state’s political ideologies as
27
well as their citizen ideologies. They create this ideology based off of five of the major
actors within state governments (Berry et al., 1998, p. 332), which include the state
governor, two majority parties, and the two houses that make up the legislature. They
give equal weight to the governor and legislature, with both chambers having equal
strength; however, Berry et al. mention that the parties within one of the chambers are
factored in by means of distribution of power. They further explain this by saying that
even if the difference is small, the more dominant group is considered the majority. Berry
et al. (1998, p. 332) state that this relationship “can be used to determine the power of the
Democratic and republican delegations within a state’s lower and upper chambers in any
year, using data on party seat shares.”
It should be noted that Berry et al. (1998) use two separate measures for state
government ideologies.2 The indictor that was used for state government is “NOMINATE
measure of state government ideology,” in which the “Common-Space”3 scores are used.
The NOMINATE measure is composed “of ideal points for members of Congress from
the same state as a proxy” (Berry et al., 1998). Berry et al. make note that the state
partisan control data come from Carl Klarner (2003). The common space scores used in
the NOMINATE measure are provided by Keith Poole (1998).
Citizen Ideology
[Insert Table 3.3 here.]
2
The other indicator is called “ADA/COPE.” However, Berry et al (1998). write that researchers should
use the NOMINATE version because of its higher levels of validity through differing tests.
3
These are congressional ideology scores and can be found at http://voteview.com/basic.htm.
28
In order to look at the influence of citizen ideology on rainy day funds, citizen
ideology scores produced by Berry et al. (1998) were also collected. The purpose of the
citizen ideological scale is to get another perspective on state political ideologies by using
election results. The citizen ideology variable rates the ideological positions of state
legislators through estimations based on members of Congress (Berry et al., 1998, p.
332). Each state is then given a rating based on the 0-100 ideological scale. The scale is
formed by first identifying each member of Congress’ ideological stance by looking at
the ratings that of interest groups (Berry et al., 1998). They then place those ratings on
the corresponding district incumbents and then estimate an ideological score for the
incumbent’s challenger. The results from that election should reflect the ideological
nature of the citizens per district, which are then turned into an average for the state. The
average citizen ideology scores are found in table 3.3.
Methods
In order to test whether there is a causal relationship between state and citizen
political ideologies and rainy day fund percentages (a state’s RDF dollar amount divided
by/ its expenditures amount), an ordinary least squares regression model was conducted.
Because political ideology data (Berry et al., 1998) is only available until 2010, the
regression model in this research includes the years 2002 to 2010. This regression
includes both state and citizen political ideology variables and yearly ending budget
balance as a control variable. The reason for including the ending balances is that the
state’s with larger budgets and ending balances may drive the differences seen within the
fund balances.
29
In addition to the regression model, a variety of descriptive statistics were run in
the Statistical Package for the Social Sciences (SPSS). The descriptive statistics use data
from 2002 to 2013, as all of the data are available for these years. Descriptive data allows
us to consider RDF saving patterns over time, in general, and account for different
legislative compositions.
30
Table 3.1: Consumer Price Index Conversion Chart
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
CPI Number for
Conversion
.77
.79
.81
.84
.87
.89
.92
.92
.94
.97
.99
1.00
* This table comes from my calculations made from data on the Statista webpage.
31
Table 3.2: Average State Government Ideology from 2002-2013 by State
State
Alaska
Alabama
Arizona
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Iowa
Idaho
Illinois
Indiana
Kentucky
Louisiana
Massachusetts
Maryland
Maine
Michigan
Minnesota
Missouri
Mississippi
North Carolina
State Government Ideology
Mean Std. Dev. State
39.414
5.095 North Dakota
49.159
12.829 Nebraska
37.710
21.679 New Hampshire
59.214
17.164 New Jersey
42.887
27.593 New Mexico
57.314
1.813 Nevada
69.014
5.821 New York
16.488
0.472 Ohio
21.785
23.113 Oklahoma
68.725
8.344 Oregon
68.683
9.450 Pennsylvania
13.382
1.129 Rhode Island
77.561
16.265 South Carolina
31.969
17.834 South Dakota
50.192
15.158 Tennessee
57.343
17.388 Texas
80.740
11.001 Utah
70.393
18.125 Virginia
79.909
3.534 Vermont
63.251
16.872 Washington
40.530
10.183 Wisconsin
36.369
17.099 West Virginia
52.917
13.564 Wyoming
75.514
1.682
32
Mean Std. Dev.
27.975
3.891
24.389
3.507
52.881
25.828
78.987
11.829
78.958
8.555
35.084
6.823
59.696
17.578
39.889
19.500
50.166
9.855
75.351
8.178
56.034
10.272
72.551
0.617
16.498
13.615
24.543
4.556
58.895
9.458
17.950
16.241
15.705
1.186
55.412
4.121
72.621
3.743
78.680
6.497
56.019
18.472
78.611
8.953
47.024
13.285
Table 3.3: Average Citizen Ideology from 2002-2013 by State
State
Alaska
Alabama
Arizona
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Iowa
Idaho
Illinois
Indiana
Kentucky
Louisiana
Massachusetts
Maryland
Maine
Michigan
Minnesota
Missouri
Mississippi
North Carolina
Mean
48.715
36.045
46.205
59.494
50.728
74.997
67.447
49.059
40.91
77.686
50.79
25.521
61.231
47.73
38.189
38.967
82.403
67.749
74.09
59.322
57.235
50.047
39.006
51.086
Citizen Ideology
Std. Dev. State
12.648 North Dakota
9.629 Nebraska
3.345 New Hampshire
2.903 New Jersey
3.145 New Mexico
9.338 Nevada
9.991 New York
5.696 Ohio
4.476 Oklahoma
4.851 Oregon
4.624 Pennsylvania
7 Rhode Island
4.844 South Carolina
4.318 South Dakota
13 Tennessee
5.231 Texas
6.59 Utah
5.761 Virginia
8.883 Vermont
5.902 Washington
6.044 Wisconsin
6.397 West Virginia
5.325 Wyoming
5.787
33
Mean Std. Dev.
59.9
7.151
32.163
9.816
49.947
6.812
64.538
6.667
59.878
4.836
52.289
4.479
71.518
6.965
53.345
6.329
27.053
5.439
63.172
6.229
59.557
6.983
82.819
3.244
46.097
2.644
49.474
5.224
42.252
5.864
44.924
3.317
30.467
4.074
50.519
5.775
87.265
4.953
58.499
6.089
55.31
4.29
65.332
8.795
27.868
3.888
CHAPTER 4
RESULTS AND DISCUSSION
The states’ implementation of more effective RDF methodologies should not be
surprising, especially when looking at the monetary damage taken by the states over the
past two decades. The damage done to the states’ economies could have been dampened
through a controlled, yet aggressive RDF savings plan that placed constraints upon
uncontrolled state spending. RDF savings strategies have grown in rapid spikes over the
past decade paralleled by a decline in the number of states without functional RDF
systems. In this chapter, I present descriptive data to show patterns on RDF savings as
well as present the results of the regression model run to test my hypotheses.
RDF: Percentage by Year
[Insert Table 4.1 here.]
Strictly speaking about the states’ RDF growth (not in relation to the states’
political systems), the numbers are staggering. Table 4.1, which looks at the states’ RDF
patterns from 2002-2013, shows that these developments happened in spurts, but not
necessarily random. For example, the number of states in 2002 that held an RDF
percentage above 5% was only at four in total. However, this number jumps to 17 states
in 2013, just 11 years later. This is extremely significant in that it raises questions as to
what could cause such a steep jump in such a short span.
34
The other factor regarding this data is an increase of states that are between 5%
and 15% during the years of 2005 and 2006.This is prefaced by a smaller measure (but
one still of importance) showing a rise from two states in 2004 to eight states in 2005.
Even though we see a rise of states with below 5% in their RDFs at the tail end of the
2000s, we also see a rise in the number of states that have an RDF percentage of above
15%. In 2002, only one state’s RDF fund balance exceeded 15% of its expenditures;
however, by the end of 2013, this number rose to five states. This suggests, along with
the other patterns in the data, that there is growing savings-trend amongst the states.
In 2002, there were three states at the recommended level of between 5% and
15%. Following 2002, there was an increase through 2007 to 19 states and then a gradual
drop to 12 states in 2013. However, the most surprising piece of information from this
data is the continuous rise in the number of states that are above 15% RDF savings. The
number of states in 2002 above the recommended level was just one, but rises to a total of
five states by 2010. While a rise of just four states may not seem as dramatic as the other
categories, it should be noted that this specific growth’s importance is that most states
have preventative legislation that keeps states’ RDFs capped at a certain limit. Therefore,
a policy change may be required for a state to jump from one type of percentage tier to
another. Due to both the strict rules monitoring RDFs, as well as the often-stagnant
politics of the state, the implementation of new RDF policy would likely be both lengthy
and complicated. However, the data in Table 4.1 clearly shows some sort of influence
that goes above both legislative policy and state politics. This previous statement is given
weight by looking at the starting number of states for the lower and middle percentiles to
the ending number of states within those same percentiles.
35
Legislature and RDFs
[Insert Table 4.2 here.]
Table 4.2 shows an aggregate RDF percent mean and RDF dollar mean collected
for each state’s legislative composition. The overall mean of the RDF percent values is
7%. However, this does not account for the trends observed regarding widespread
development. The 7% supports the notion that an overwhelming amount of states have
been well behind on their levels of RDF savings. In the 12 years tested, democratic
legislatures averaged an RDF percent of 3% and republican legislatures averaged a value
of 8%. While surprising, the states with split legislatures (bi-partisan based legislatures)
yielded the highest results at 13%. It should be noted that some states might have higher
levels of savings than are recorded, but transfers out of RDF accounts are not consistently
documented.
RDF: Percentage by Year and State
Other data regarding the annual breakdown of each state’s RDF percentages helps
us to see which states stayed in their original categories versus those that boosted their
RDF savings. Furthermore, this data shows the individual timeline of each state for fiscal
development. This can give a better idea of why these numbers had major changes,
especially when looking at the years of 2004, 2005, 2006, and 2013. The reason for
pointing out these four years is that they indicate a national trend of shifting from low
RDF savings to middle and high RDF savings. For instance, the majority of the states that
36
have RDFs above 15% consist of Wyoming, Arkansas, North Dakota, West Virginia, and
Texas. These five states that hold above 15% RDFs are consistent in RDF savings upkeep
since each fund’s establishment. This presents a possible argument that once a state is
able to see the benefits of a higher amount of RDF savings, they may maintain this level.
North Dakota is an example of a state moving from below 5%, to building up to above
10%, and then a final rise to the third tier of RDF savings. New Mexico, North Dakota,
Oklahoma, Iowa, and Utah all represent states that held very low (if any) savings prior to
2006. These are the states that help explain the jump seen between 2004 and 2006
regarding the states that move from below 5% to being between 5% and 15%.
The data also show the difference between each state’s mean RDF percentage in
2002 and its mean RDF percentage in 2013. The findings here further explain the
numbers presented in Table 4.1 regarding the changing RDF climate across the country.
The first states worth mentioning are those with a large increase over the 12 years (some
with incredibly high numbers and others with more realistic numbers). These states
include New Mexico, Arkansas, North Dakota, Texas, West Virginia, and Wyoming.
While Alaska has an immensely high growth of 121.46%, it has had the highest RDF
percentage by a wide margin for every year. The other states mentioned, like Wyoming
and North Dakota, are great examples of those that have seen the value of a proper RDF
savings and held a steady growth in these funds over the observed 12 years (a rise of
42.84% and 21.74%, respectively). New Mexico yields the most surprising results in that
it had no RDF until 2005, in which it incorporated an enormous amount of 11.17%.
States with a negative change include Alabama, Florida, Georgia, Hawaii, Illinois,
Maryland, Mississippi, Nevada, Ohio, South Dakota, and Virginia. While none of these
37
states’ percentages decreased dramatically, it does raise the question as to why the same
time period produced some states adding massive amounts to RDF savings while other
states went negative. Also worth mentioning are the states that had below 1% of RDF
growth: Arkansas, Delaware, Indiana, Massachusetts, Montana, Missouri, New
Hampshire, New Jersey, New York, Tennessee, Pennsylvania, Washington, and
Wisconsin. The results regarding those states associated with negative changes versus
those with minimal changes does give us some feedback. First, it helps to explain why
the overall mean of the states’ RDFs appear to be so low, with just a growth of 5.75%
from 2002 to 2013. There is a recent trend regarding which states have made avid efforts
at increasing their RDF amounts. This information can be extremely useful in future
studies that look at states’ RDFs beyond the year of 2013 because it shows exact patterns
of state’s RDF progress up until this point. From this data, we can see that there is a
developmental trend that builds speed over time and is just now gaining that momentum.
Regression Model: Tests of Hypotheses
The goal of the first hypothesis was to determine if more fiscally conservative
states had higher RDF percentages than fiscally liberal states. The second hypotheses set
was to determine if states that had a more conservative citizenry were more likely to have
higher RDFs than states with a more liberal citizenry. The expectations were that both
fiscally conservative states and states with more conservative citizenry would have higher
RDFs. In order to test this, an OLS regression was used.
[Insert Table 4.3 here.]
38
The results from the regression model can be seen in table 4.3, which shows that
there is not a significant relationship seen between the RDF percentage and each state’s
annual ending balance, its citizenry ideology, and its state ideology. Though not
significant, these relationships can be explained in terms of changes in values. Results
show that the RDF percent decreases by .0000146 when the end balance goes up by 1 and
that the RDF percent decreases by .0007308 when the state ideology goes up by 1. The
RDF percent also has a decrease of .0000593 when the citizen ideology goes up by 1. The
RDF percent is predicted to be at .1100542 when the ending balance, citizenry ideology,
and state ideology are all equal to 0. As the table shows, the P values for all the variables
are way above 5%: the ending balance is 41%, the citizenry ideology is 93%, and the
state ideology is 29%. Because the P values for each of the variables are above the 5%
threshold, we must accept the null hypotheses. The R-Squared value tells us that 1.97%
of the variation in the dependent variable is explained by the independent variables in the
model. This shows that the regression model explains very little about the dependent
variable. However, this does mean that there is a great deal explaining the dependent
variable that is not incorporated in this study.
The results discussed in this section are critical in helping to understand the
relationships seen between the variables of state RDFs, state financial patterns, and state
politics. While the data come from a variety of sources, they help to illustrate an overall
picture regarding the significance of rainy day funds and the role that state politics plays
in their overall implementation and development over time.
39
Table 4.1: RDF Percent Breakdown by Year
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
RDF Percentage Scale
Below 5% 5%-15% Above 15%
42
3
1
42
2
2
41
2
3
36
8
2
27
18
1
24
19
3
25
17
4
31
11
4
33
8
5
35
7
4
32
10
4
29
12
5
40
Table 4.2: RDF Percent and RDF Dollar by Legislature
Legislature
Democratic
Republican
Split
Total
RDF Percent
Mean
Std. Dev.
0.035
0.039
0.081
0.151
0.134
0.451
0.071
0.217
RDF Dollar
Mean
Std. Dev.
253.61
673.77
486.55
1009.12
914.24
2846.87
460.03
1442.146
41
Table 4.3: Regression
Rainy Day Fund
Std.
Coefficients
Percent
Errors
CPI End Balance
-0.0000146 0.0000174
Citizenry Ideology
-0.0000593 0.0006645
State Ideology
-0.0007308
0.000689
Constant
0.1100542 0.0490408
N = 423; R-Squared = 0.0197
42
P Value 0.406
0.929
0.294
0.03
CHAPTER 5
CONCLUSION
The original purpose of this thesis was to explore rainy day funds and to
determine if they are influenced by state politics. Specifically, this research investigated
whether or not political ideologies played a role in the size and patterns of each state’s
rainy day fund. As the majority of existing research and knowledge on rainy day funds
relating to state politics pertains to the politicians, this left an area to be explored
regarding the influence of state political ideologies. While the results from this study on
the influence of state politics over RDFs show a non-significant causal relationship
between the two, it does show that a dense quantity of RDF information is still
unexplained.
Revisiting the Research Questions
This thesis explored the following research questions:
1) What patterns are seen among states and rainy day funds?
2) How do state politics affect the size of states’ rainy day funds?
In regards to the first question, there were many patterns found among the states and
RDFs. A huge boost can be seen between 2004 and 2006 of states having below five
percent to having between five percent and 15 percent. This finding is significant as it
43
shows an evolution of the RDF concept. While not tested in this study, it is quite possible
that this is a result of learning from past economic depressions. Other patterns show that
over the last decade six states have had large growth in the amounts of their RDF savings
and 15 states have had mild increases. Similarly, the results show the states that kept
below a five percent RDF in 2005 and 2006 were not likely to boost their RDF amounts
by much, if any, or the next seven years.
The answer to the second research question is that there was not a significant
relationship found regarding the influence of state politics on rainy day funds in this
specific model. Furthermore it was found that RDFs were not influenced by any sort of
political ideology, as represented by the analysis of the citizen’s political preferences of
each state. This is valuable because it helps to narrow the research on what might actually
cause RDF implementation and why there has been such an increase in the amount of
money placed in RDFs.
Study Limitations and Implications
Due to RDFs being young in terms of states’ usage, the extent of the implications
within the relationship between state politics and RDFs are still being discovered. In this
study, there were some implications that future research should be aware of in conducted
similar studies. While the results from the regression show no significant relationship
between the RDF percentage and each state’s ideologies, the question remains as to why
these relationships were not found to be significant despite the fact that literature shows
politics do influence state financial policy (Rose, 2008, p. 170).
44
Even though a directly causal relationship was not found, there might be other
avenues of research that are exposed through the limitations of the study. These
implications focus on time and economic cycles, RDF components, the CBPP RDF
percentage format, specific purpose savings accounts, and money transfers.
Time and Economic Cycles
While rainy day funds are by no means new to research, the lasting impacts of
them are still being observed. One of the major limitations of this study is that it looks at
a 12-year time period and not at the entire time length in which rainy day funds have
been implemented and consistently utilized. Therefore, it does not incorporate all the data
from the start of each state’s RDFs. Along the same lines, the years of 2014 and 2015
were not included so these results are slightly antiquated.
The time period might be one such reason as it looked at a relatively small
numbers of years. While the implementation of RDFs has occurred over the past few
decades, the massive growth of these funds has really only happened since the start of the
2000’s. Therefore, when we look at the state’s RDF percentages over time as an
aggregate mean, these numbers appear to be significantly lower than expected. This
implication might be corrected by looking at just the years of the Great Recession and
those years that follow it (around 2005-2015). Furthermore, some states began to boost
their RDFs before the Recession hit, while others stayed at lower percentages. An
analysis looking at the current fiscal health of the states that boosted their RDFs before
the Recession versus those states that didn’t boost them might help to explain whether or
not RDFs would be beneficial to all states.
45
Economic cycles might have an effect on how much money states were saving
versus spending. It could be helpful to compare how the states faired during recessions
when they had weak RDFs versus how they faired after the Great Recession when many
of the states boosted their RDFs. If some of the states were acutely aware of how their
own budget cycle matched up to the federal economic cycle, this might explain why
some of the states boosted their RDFs so highly while others did not.
Other limitations involve the citizen and state ideological data, which are only
available until 2010. The implications here are that the findings for the descriptive
analysis were based on a timeline ending with 2013, not 2010, so a three year gap occurs
between the regression model and the descriptive analysis. The regression analysis might
have been different (though unlikely) if the data up to 2013 were available regarding the
ideologies. From this, we can see that time might have been more significant of a factor
than it was incorporated within this study.
RDF Components
While recognizing that time might have been a much larger factor in the
relationship of states and RDFs than was accounted for, the main focus here is on why
state politics appeared to have such a weak role in the relationship. Through the literature,
we have found that politics do influence RDFs through state fiscal policy (Rose, 2008, p.
170e). Therefore, we can say that politics have an effect, but we are not able to observe in
what manner it affects RDFs.
This study considered political influences on RDF balances, but politics may have
a much larger influence on the components that make up an RDF, such as the cap limits,
46
the deposits, and the withdrawals from the account, rather than the RDF balance itself.
This is feasible since lawmakers create the ceiling of an RDF account and that many
states require a 2/3 majority approval from the legislature for a withdrawal (Burson,
2013, para. 4). Also, we know that the elected officials are responsible for the creation of
specific types of RDFs, such as one for general education.
In regards to the deposit amount, we know that the legislative branch also
annually determines the budget. This is an important component because the CBPP’s
recommended percentage is based off of a state’s annual expenditures, which is usually
put forth during the annual budget creation. This all pertains to the implication that
possibly state politics influence RDFs through the mechanisms that define the RDF. For
example, the influence of politics on RDFs could be found to affect the states’ deposit
and withdrawal policies.
CBPP RDF Percentage Formatting
Ultimately, the states do not have to take the advice from CBPP regarding the
recommended amounts of saving. This opens the discussion on whether or not RDFs are
actually needed or effective for all states. The literature presented information on two
fronts: the first is that there are three states that do not use RDFs, but have other
mechanisms for dealing with economic troubles. A question that should be asked would
be are RDFs absolutely necessary if another method has been proven to work?
The second point that the literature brings up is that there is discrepancy over if
there should be a one-size-fits-all model or if the states should create their own method
based on its unique set of needs. This study operated under the assumption that all states
47
with operational RDFs used the one-size-fits-all model (the CBPP recommended
percentage of 15%) but there are some limitations to this assumption. Because the
expectation was to see liberals below this percent and conservatives above it, it is
assumed that this is the best method for looking at the way that political ideologies affect
states’ fiscal behavior. However, the results could be very different if a scale was used
that also measured the states’ revenue volatility or how much a state was impacted by
external economic issues (such as the Great Recession). The best way to try and correct
this implication would most likely be to determine if other RDF measures have been
created and if they yield accurate results.
While the CBPP suggests using a percentage of a state’s annual expenditures as
the proper methodology for determining a state’s RDF percent, a question to ask would
be whether or not the findings would have changed if the RDF percentage was based on
revenues, beginning balances, or ending balances. It is logical to assume that these
percentages would change. It might be helpful to do a comparison of the RDF actual
dollar amount against the expenditure amount (both as percentages from the overall
annual budget) as a way to measure a state’s savings habits versus its spending habits.
Additionally, it is unclear as to whether or not the CBPP recommended 15% is the
most practical number for all states. The reason for this is that so few states actually
bumped up to this percentage number, but they seem to be on the way back to recovery.
Some states might not have the ability to go to this number or, at least, not to do it in such
a quick transition timeframe. This is a topic that future research might be able to answer.
48
Specific Purpose Savings Accounts
It is important to make note that some states have more than one savings account.
These often have specific purposes, such as a General Education Fund, but it is not made
clear in the data obtained from NASBO’s “Fiscal Survey of the States” if these are
included in the Budget Stabilization Fund. It should also be noted that these special funds
may only be allowed to be used for the purposes in which they were designed. Thus, this
research assumed that the states did have access to all RDF accounts, regardless of
whether they actually do or do not have access to these accounts.
Money Transfers
As mentioned in the literature, “shell games” often occur by state politicians
(Rose, 2008, p. 160). This refers to the withdrawal and depositing of money from
different accounts and is usually not documented. The implication that arises from this is
that the RDF dollar amounts used to calculate the RDF percentage might not have
originally included these “shell game” numbers. Without this consideration, an RDF
percent might be higher (or lower) than what was actually reported, thus potentially
skewing the correlation between a state’s RDF percentage and the influence of a state’s
political orientation.
Partisanship vs. Ideology
This study analyzed data of both state government partisanship as well as of state
and citizen ideologies. However, the regression model strictly looked at the ideologies
49
variables, rather than that of partisanship. We did see patterns within the descriptive
statistics regarding the partisanship variables, such as the increase in RDFs over time, so
it is possible that this variable might have had more of an impact regarding the
relationship with politics and RDFs than the findings that were found between RDFs and
state and citizen ideologies.
Potential Future Research
The future of RDFs is unpredictable, but it is certain that the research regarding
them will continue. One plausible area of future research could conduct a similar study to
this one, but on a different time scale. It might also look at the patterns of financial
behavior states have during recessions or during economic cycles in general. It would be
beneficial to know if the spike of states implementing RDFs between 2005 and 2006 was
coincidental or if they foresaw the economic disaster that was about to transpire.
Research could also be done looking to see if RDFs spiked before previous recessions.
Some other topics for future research could include why the states that have split
legislatures have higher RDF percentages than democratic or republican legislatures.
Further research could be done looking at specific RDF types and seeing if patterns exist
between their individual growths versus other RDF types. There is a vast amount of
unexplored topics relating to the RDFs in general that might help explain what all truly
influences RDFs.
The information collected from this study can be utilized in a number of ways.
First, it looks at modern data from the last decade’s economic buildup, end, and recovery
from the Great Recession. Also, it looks at all states with true RDFs (minus Nebraska)
50
and whether or not their political parties have a generic influence over spending and
saving. Therefore, this might be a useful tool for those working in state and local
government to get a better idea about how to handle recovery from fiscal crises. It also
offers a stepping-stone for future research regarding state politics and RDFs. Even though
a significant relationship was not proven to exist between state politics and RDFs, this
study shows just how extensive and complex rainy day funds are and there is a massive
amount of influences on RDFs that have not yet been charted.
Conclusion
This study has sought to build off of the existing literature of rainy day funds by
exploring other avenues of research that have yet to be conducted. The literature prior to
this study looked at the relationships between politics and state budgeting, as well as the
influence of politicians on RDFs. This thesis, however, has taken the first steps in
determining the level of influence that state political ideologies have on the amounts and
effectiveness of state rainy day funds. While the findings of this study were not
significant, they are vital in the process of helping to understand how RDFs work. Also,
the implications mentioned above provide viable topics for future studies as well as to
inform researchers of inherent problems within the relevant data. As states continue to
implement more aggressive savings methods, the research into what affects RDFs will
become more detailed as well.
51
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