State Revenue Forecasting Practices: Accuracy, Transparency, and Political Acceptance

A Volcker Alliance Project Paper

March 18, 2017

ASPA 2017 Annual Conference

Emily Franklin

Public Finance Fellow

Center for State and Local Finance

Andrew Young School of Policy Studies

Georgia State University

efranklin8@student.gsu.edu

Carolyn Bourdeaux

Associate Professor

Director, Center for State and Local Finance

Andrew Young School of Policy Studies

Georgia State University

Alex Hathaway

Public Finance Fellow

Center for State and Local Finance

Andrew Young School of Policy Studies

Georgia State University

Special thanks to the Volcker Alliance for their research support and permission to use

their data in this analysis.

DRAFT: Please do not cite without permission of the authors.

ABSTRACT

This paper will discuss the budget practices around revenue forecasting. Recent research has

discussed how revenue forecasting is not just a matter of accuracy but also is important for

transparency and political acceptance of revenue forecasts as a guide to the budget process. At

the same time, recent research has pointed out there is significant variation in “consensus

forecasting” practices. This paper will look at the diversity of revenue forecasting processes

across the 15 states and assess the extent to which they have proven to be accurate, transparent,

and politically accepted between FY15 and FY16.

INTRODUCTION

The state revenue forecast sets the tenor for budget deliberations in the U.S. states.

Because most states operate under a balanced budget constraint, the revenue forecast establishes

a foundation for fiscal discipline since it sets a cap on state spending. Almost every state has

evolved to have a unique process around revenue estimation and scholars have long debated

whether there are different processes or methodologies that are more accurate, more likely to

lead to broad political acceptance and generally provide a firmer foundation for fiscal discipline.

The academic literature on these different processes is vast and by no means conclusive

regarding best practices.

This paper briefly reviews the literature on revenue forecasting and then draws on a

dataset produced as part of the Volcker Alliance Truth and Integrity in Government Finance

project to examine in detail the revenue forecasting processes in 15 southeastern states. In

particular the paper assesses the process and actors involved in forecasting, the methodological

approach, the accuracy of the forecast, and ultimately, the political acceptance of the forecast.

The analysis draws on the rich detail the researchers have available for each state to explore

some of the more interesting state practices around forecasting.

LITERATURE REVIEW

Forecasting Processes

One of the most vibrant debates in the literature on state revenue forecasting centers

around the process associated with revenue forecasting. Revenue forecasting is roughly divided

into three types: executive, consensus and separate (where the legislative and executive branch

and even the different parties within the legislative branch develop separate forecasts).

1 Perhaps the most widely recommended process is a consensus forecast.2 3 4 5 According

to the National Association of State Budget Officers (NASBO), a consensus revenue forecast is a

“revenue projection developed in agreement through an official forecasting group representing

both the executive and legislative branches.”6 However, these categories mask wide variations,

particularly around consensus forecasting, which can involve a number of different combinations

of legislative and executive policy-makers, including involvement by legislative and executive

staff, elected officials or even elected officials explicitly from opposing political parties as well

as involvement by non-partisan external parties such as academics. A common question is the

extent to which these process differences are linked to accuracy, transparency or more recently,

political acceptance of the forecast.

Accuracy

Because revenue collections are affected by state, national and global economic

conditions, they are difficult to predict. Researchers have looked at both the association of

accuracy with consensus forecasting process as well as whether different techniques of

forecasting produce more accurate results. There is extensive literature showing that combining

independent forecasts increases accuracy.7 Updating revenue forecasts as close to the start of the

fiscal year as possible has also been shown to increase accuracy.8 Some studies have found that

consensus revenue forecasting improves the accuracy of forecasts, though it is not clear why it

improves accuracy. Consensus forecasts may lower the percentage of forecast “misses”.9 10 11 12

One possible reason is the process often involves a number of experts on different parts of the

economy, who can provide a wealth of economic information, as well as ensure the forecasting

method is less politically influenced. In addition, if different forecasts are combined during the

process of forming the official forecast, then accuracy may increase. Voorhees (2004), however,

found less of an effect on accuracy after controlling for frequency of state’s forecasts,

independent expert input and other factors.13 Boyd and Dadayan (2014) agree that consensus

revenue forecasting can help insulate the forecasting process from undue political influences, but

their study did not show that it contributed to the accuracy of the forecast.14

A further dimension to the revenue forecasting debate is the extent to which the forecast

is subject to political influence. Several studies have found forecasting errors increased due to

politically opportunistic behavior when governors have a balanced-budget requirement, when the

forecasting group is linked with the government, or when elected officials have longer term

limits or long-term unified government. The creation of independent groups with knowledge of

the forecasting domain, however, reduces forecasting error because they reduce error due to

human bias or political expediency. 15 16 17 18 In contrast, Mikesell and Ross (2014) found that

the Indiana revenue forecast process (which has not only executive and legislative consensus, but

also Republican and Democrat party representation) outperformed various “naïve” revenue

forecasting processes and had the further benefit of building political consensus around the

forecast. Certain fiscal policies can also predict more transparency around the revenue estimation

process. For example, Rose & Smith (2011) found that states that created budget stabilization

funds (BSFs), with policies governing how much expected surplus would be put into the account

and how much could be taken out and used for other purposes, also decreased their revenue

forecast bias.19

Transparency

The level of transparency surrounding the estimation process is another relevant aspect of

revenue forecasting. The Government Finance Officers Association (GFOA) recommends

governments are upfront about their forecasting philosophies. For instance, some governments

prefer to make “conservative” forecasts, which may make balancing the budget more difficult,

but reduce the risk of a shortfall. Other governments try to make an “objective” forecast, which

seeks to be as close to actual revenue collections as possible. These forecasts make it easier to

balance the budget, but also increase the risk of a shortfall. The GFOA also recommends that

governments clearly present underlying assumptions and methodologies in the final budget

document.20 The Organisation for Economic Co-operation and Development (OECD) and the

International Monetary Fund (IMF) recommend that key economic assumptions be disclosed in

budget documents, as well. Such assumptions can include gross domestic product (GDP)

growth, the employment and unemployment rates, and inflation. Furthermore, an analysis of the

impact of these macroeconomic trends on the budget should be publicly available.21 22 However,

McNichol, Lav and Leachman (2015) found that most states do not clearly tie these

underpinning economic assumptions to the revenue forecast. A few states, such as Alabama, do

not publish these assumptions at all.23

Different factors predict the level of transparency around the revenue forecast. Alt,

Lassen and Rose (2006) provide some anecdotal accounts of states adopting consensus revenue

forecasting in an effort to increase fiscal transparency, such as Delaware and Rhode Island.

These transparency efforts were generally adopted in periods of fiscal stress, split governments

or a recent party turnover in the governor’s office.24

Political Acceptance

Recently, Mikesell and Ross (2014) raised the issue of political contention around the

revenue forecast, pointing out that both the executive and legislative branches need to be

involved in the process in order to build consensus and reduce conflict over the final estimate. 25

Ironically, other researchers have called for de-politicizing the revenue forecasting process for

this very same reason.26 27 28 29 30 There are a number of studies with anecdotal accounts of states

adopting consensus forecasts for political reasons;31 32 33 however, the researchers were unable to

locate a study that looks at this issue on a national scale. States seem to have adopted a mixture

of both approaches. As of 2014, as many as 24 states have adopted some form of consensus

forecast, while 36 include non-government experts in their forecasting process. 18 states have

both a consensus forecast and non-governmental experts in their forecasting body.34

This analysis examines three key aspects of revenue forecasting in 15 southeastern states:

the level of accuracy, the level of transparency, and the level of political acceptance. Though

there is evidence that combined forecasts and independent expert participation increases

accuracy, the verdict is still out on whether or not a consensus forecast increases accuracy.

Regarding transparency, the GFOA and others recommend that states publish detailed

methodologies with underlying macroeconomic assumptions along with their forecasts. There

are anecdotal accounts of consensus forecasting increasing fiscal transparency. An interesting

question is: are states with consensus forecasts more likely to publish detailed forecast

methodologies and underlying macroeconomic assumptions because they are more likely to

involve external experts in making the forecast? Finally, regarding political acceptance and the

revenue forecast, there are a number of studies that describe states’ adoption of consensus

forecasting processes for political reasons; however, we were unable to find a study that looked

at this issue on a national scale. While recognizing the “endogeneity” of a consensus forecast to

inter-party political conflict, a further question of interest is whether there is any evidence that

consensus forecasts are more likely to be accepted as legitimate by policy-makers than a pure

executive forecast or a forecast where the legislative and executive branch each act

independently.

Using data compiled for the Truth and Integrity in Government Finance project by the

Volcker Alliance, this paper examines state revenue forecasting budget practices and the extent

to which they reflect the best practices described above. While the data is limited, the paper also

assesses whether there is any evidence of a relationship between these best practices and the

extent to which the forecast is accurate and viewed as legitimate by different political actors. The

research then draws on some rich contextual detail and case study material available to the

researchers to further examine these issues.

METHODS

The Volcker Alliance’s project involved organizing a group of universities to conduct a

survey of budget and fiscal practices of all fifty states for the fiscal years 2015, 2016 and 2017.

The project was organized around assessing whether and to what extent states are facing

structural deficits and several questions focused on revenue forecasting processes, multi-year

revenue and expenditure forecasts, revenue growth projection rationales, and midyear budget

adjustments. There were 29 questions in total, but, for this paper, the analysis focuses on the data

gathered for the following five questions:

1. Does the state disclose consensus revenue forecasts in budget documents?

2. Does the state disclose multi-year revenue forecasts (at least 3 years) in budget

documents?

3. Does the state disclose multiyear expenditure forecasts (at least 3 years) in budget

documents?

4. Does the state reasonably support revenue growth projections at time of initial

budget?

5. Was there a need for a meaningful (i.e., greater than 1%) midyear budget

adjustment?

Since all states were still in the middle of FY2017 at the time of this analysis, only

FY2015 and FY2016 responses were considered. For the purposes of the project, budget

documents were considered the Governor’s recommended budget, enacted appropriations bills,

as well as supporting documents such as legislative analysis of the budget and presentations or

revenue forecasts produced in tandem with the budget. Consensus revenue forecasts were

defined as forecasts with both executive and legislative participation. Additionally, for five

states, Maryland, Virginia, North Carolina, South Carolina, and Georgia, the analysis draws on

more detailed case study information on the shifts in the revenue estimate during budget

development, adoption and implementation. Because questions of accuracy and transparency

cannot be entirely addressed based on the Volcker project questions, the analysis also

supplements this information with material collected from the National Association of State

Budget Officers (NASBO) Fiscal Survey of the States as well as more in depth reviews of

revenue forecasting and budget documents across all of the states to examine the methodology in

the forecasting process as well as participation in consensus and executive forecasting processes.

RESULTS

Forecasting Processes in the Southeastern States

This section discusses different aspects of the revenue forecasting processes in the

southeastern states, including the number of states with consensus forecasts, types of

membership in the states’ forecasting groups (executive, legislative, non-partisan), and multi-

year forecasts.

[Table 1: Forecasting Processes in the Southeastern States]

Table 1 describes the membership of the states’ forecasting groups and whether or not

they include executive, legislative or non-partisan members. Most southeastern states have a

consensus revenue forecast-type process (10 out of 15 states); however, four have executive

driven processes. Only Alabama has a forecast developed separately by the executive and

legislative branches.

As noted earlier, states define “consensus” in different ways with varying degrees of non-

partisan, staff and elected official participation and even the executive dominated processes have

different external and staff advisement arrangements. In some states, such as Maryland, only

non-partisan staff are involved in the revenue forecasting process, although the process is still

considered consensus because the staff are from the executive as well as the legislative research

offices. Other consensus, staff driven processes include Florida, Kentucky, North Carolina, and

Mississippi. Virginia has an interesting hybrid process with two committees working on the

forecast. The first committee is a staff or “expert” committee, the Joint Advisory Board of

Economists (JABE). This committee works on the detailed methodology behind the forecast and

produces several options for consideration by elected policy-makers. The Governor’s Advisory

Council on Revenue Estimates (GACRE) includes legislative leadership as well as the Governor

and they ultimately vote on the revenue estimate to use.

No southeastern state statutorily requires the participation of both Republican and

Democrat politicians. In Delaware, however, some representation from each party is generally

included. After an initial review, Delaware appears to be the only state to explicitly include

minority party members in its forecasting group, although other states may have multi-party

representation if the legislative majorities in either Chamber are from the opposing party.

All fifteen states include the governor or some other executive membership, such as an

executive budget office, in their forecasting group. Georgia and West Virginia appear to be the

only states that exclusively rely on the Governor and his or her direct staff to develop the

revenue estimate. Oklahoma is an executive driven process but includes leadership from across

the executive branch, including the Lieutenant Governor and Attorney General.

The majority also include some sort of legislative membership, whether elected officials

or professional staff from legislative research offices. Only a very few include parties from

outside government. These include Virginia’s JABE and GARCE and the Delaware forecasting

group, which have citizen appointees and Louisiana and Alabama which have academic

appointees.

Accuracy of Southeastern States’ Revenue Forecasts

This section discusses the accuracy of revenue forecasts in the fifteen southeastern U.S.

states. Table 2 shows midyear adjustments to the budget, forecasting errors and accuracy grades.

[Table 2: Accuracy of Southeastern States’ Revenue Forecasts]

Table 2 shows the response to the Volcker Alliance survey assessment on whether a state

made a mid-year adjustment greater than one percent for FY2015 and FY2016. This is paired

with a fairly objective assessment of forecast accuracy pairing the general fund forecast with the

general fund year-end actual collections from NASBO. The table shows the forecasting error as a

raw percentage, which shows whether the state over or under estimated revenues, and absolute

value, which helps compare the size of the forecast “miss.” To more easily compare the

forecasting errors, we simply assigned a letter grade with errors between 0 and 1 receiving an

“A”, errors between 1 percent and 2 percent a “B”, errors between 2 and 3 percent a “C”, errors

between 3 and 4 a “D”, and errors above 4 percent an “F”. The grades are the same regardless of

whether the error is positive or negative. While an “A” grade signals a more accurate forecast, an

“F” grade signals a less accurate forecast, Willoughby and Guo (2008) considered less than five

percent forecasting error to be a relatively accurate forecast and a Pew-Rockefeller report on

revenue forecasts generally found that the mean forecast error was around 3 percent.35 36 States

with “D”s and “F”s are outside of the average forecast error.

While some important considerations around revenue forecast accuracy will be

considered later, just taking these forecasts at face value, in FY2015, the average forecast error

(the absolute value of the percentage difference between the forecast general fund revenues and

actuals) for the 15 states was two percent. In FY2016, the forecast error was 2.5 percent. This

average suggests the southeastern states on average are beating the forecast errors predicted by

the Pew-Rockefeller report -- even including states that are intentionally low-balling their

forecasts. FY2016 appears to be particularly affected by serious revenue declines in the energy

sector dependent states of Oklahoma, Louisiana, and West Virginia, but also by some large

misses to the positive in Georgia, South Carolina and Tennessee. The standard deviation for the

actual to forecast percentages is 3.72 for 2016 while only 2.58 for 2015.

Drawing on the more detailed data from various case studies suggests some important

considerations when simply taking the difference between forecast and actual revenues at face

value. For instance, in the 2015 legislative session Georgia passed transportation legislation that

increased tax and fee revenues by $870 million in FY2016. However, the state did not add these

new revenues to their budget until the middle of the fiscal year 2016, which makes the error rate

seem like 7.38 percent.

Additionally, Georgia annually adds a one percent of net revenue increase each year to

K-12 education in the mid-year budget out of the revenue shortfall reserve. The state essentially

pre-funds this by lowballing the revenue estimate in order to force the state to reserve this

amount the year prior. Finally, Georgia’s governor publicly committed to building a $2 billion

reserve fund before he left office in 2018. Georgia’s reserve is replenished from any surplus

year-end funds, so the Governor has further low-balled the revenue estimate to force sufficient

surpluses to rebuild the reserve. None of this is explicit in any budget documents so there is no

way to know what the “true” underlying state forecast actually is. While Georgia does not win

points for transparency, the state’s overly conservative revenue estimates do not necessarily

reflect poor forecasting capacity, but an intentional effort to significantly underestimate the

revenues.

Of the other southeastern states, South Carolina and Tennessee also appear to be similarly

substantially underestimating revenues. Like Georgia, South Carolina has certain designated

current year uses for prior year reserves, in their case a reserve for capital outlay. The state

actually uses this capital outlay reserve as a first resort rainy day fund prior to tapping their

revenue shortfall reserve. However, according to a budget official, South Carolina does not make

conservative revenue estimates in order to replenish this fund, but to avoid mid-year budget cuts

and year-end deficits.1

On the other end of the spectrum, six of the fifteen states over-projected their revenues,

and four over projected by greater than one percent in FY2015. Not surprisingly, these four

states were also forced to make negative mid-year adjustments of greater than one percent.2

Louisiana, Oklahoma and West Virginia were affected by unanticipated declines in the energy

sectors, and in FY2016 face even more dramatic overestimates of revenues; however, Virginia is

an anomaly. In 2016, the state gets an A for its revenue forecast, and is the only state to swing

from an F to an A. The challenge in Virginia is that the state appears to have intentionally gone

into FY2015, the second year in its biennium, knowing that it would overshoot its revenue

1 Interview with Executive Budget Office official, South Carolina Department of Administration 2 Two other states also recorded making mid-year adjustments greater than one percent, even though revenues were tracking “to the good.” Arkansas’s mid-year adjustments are actually pre-planned. As part of the budget process, the state adopts a set of mid-year spending priorities if the state is on track to make budget. If the revenues track below estimate, these second tier priorities are put on hold for the fiscal year. Georgia actually is similar in that it has a pre-planned mid-year adjustment for K-12 growth out of the revenue shortfall reserve, which is required to be one percent of prior year revenues. Additionally, in FY2016 the state added in the tax revenues from the transportation tax. Depending on whether one counts these adjustments, then the state also made a greater than one percent mid-year adjustment like Arkansas. Tennessee continues to be an anomaly.

estimate. According to one account, the state had to formally overshoot its revenue estimate in

order to access its reserves.3 The state statute stipulates that the state can only access the revenue

shortfall reserve “if the general fund revenues appropriated exceed the forecast by more than two

percent.” Despite passing an adjusted FY2015 budget in June of 2014 and making significant

provision for an anticipated shortfall, the legislature did not formally lower the revenue estimate

until after an August 2014 revised revenue estimate (which in turn, was legally triggered by

problems with the FY2014 revenue estimate). The legislature then formally inserted the reserves

into the revised budget in December of 2014. In detail, the state ended the regular Session

without a budget due to a budget impasse, and the impasse continued into a Special Session. By

May, it was clear that the state would not meet its forecast for FY2014, which, on July 1st, would

legally trigger a new forecast for the FY2014-16 biennial budget period. Because the state did

not want to use a revised forecast in adopting its new budget, the impasse ended, and a budget

was adopted based on the old revenue forecast from December 2013. Now the forecast would not

be revised until after the start of the new fiscal year, allowing the state to reopen the newly

adopted budget and use the Revenue Stabilization Fund. Virginia’s constitution does not allow

the use of the Revenue Stabilization Fund in building a budget, which is why it was imperative to

adopt the new budget using the December 2013 revenue estimate.4

Given these variations in managing the revenue estimate, the metric of “forecast error”

may be something of a misnomer for many states. Rather than reflecting any lack of internal

capacity or methodological problems, forecasting error at the state level may be more

appropriately characterized as a problem of transparency or strategies for management of legal

and institutional arrangements. Revenue forecasts to some degree have a unique role in the

3 Interview with Budget Director, Virginia House of Delegates. 4 Interview with Staff Director, Virginia House Appropriations Committee.

budget process that goes beyond accuracy around expected revenues. For instance, in Georgia,

the balanced budget requirements are tied to the revenue forecast. Since the executive

unilaterally sets the forecast, the forecast is used as a policy lever to bound legislative

expenditures rather than as a device to actually communicate anticipated revenues. As noted in

the literature, a further confounding factor is that some states explicitly adopt an accuracy

approach, while others explicitly try to undershoot the estimate.

Given that many of the D and F scoring states either face problems like difficult to

predict energy prices or have revenue forecasts that do not accurately reflect their actual revenue

expectations, it is difficult to assess the relationship between forecasting process (e.g., consensus,

etc.) and outcome. A brief look at the states that appear to produce forecasts that are intended to

accurately reflect expected revenues shows no evidence of any clear linkage between a

consensus forecast and accuracy of the forecast. The states that received A or B grades in

FY2015 and FY2016 include Alabama, Arkansas, Delaware, Florida, Maryland and Mississippi;

however, these include a mix of forecast process types. Four out of the six have a consensus

process, but the committees working on the process are quite varied. Arkansas has an executive

forecast and Alabama has a “separated” process. Obviously, a more sophisticate quantitative

analysis is warranted, but as noted above, such an analysis would need to carefully consider

actual revenue expectations relative to the reported revenue forecast.

Transparency of Southeastern States’ Revenue Forecasts

This section discusses the extent to which the assumptions and methodology

underpinning the state revenue forecast are not explained, explained at a high level, but without a

clear connection to the forecast, and explained at a high level and clearly linked to the final

forecast.

[Table 3: Transparency of Southeastern States’ Revenue Forecasts]

In order to create Table 3, the researchers looked at the official revenue forecasting

documents for each of the fifteen southeastern states, searching specifically for information on

general macroeconomic trends in the forecasting document. Such trends could include housing

starts, employment rates, and US gross domestic product (GDP). The researchers wanted to see

if states were following best practice as recommended by GFOA and others by linking these

macroeconomic trends to the forecast. For instance, the state might link the income tax forecast

to personal income forecasts. If the document did not contain such information, the state received

an “X” in the column labeled “No Macroeconomic Information Explaining the Forecast”. If the

forecasting document did contain information on macroeconomic trends, but did not make clear

connections between those trends and the state forecast, the state received an “X” in the column

labeled “General Macroeconomic Trends without a Clear Link to the Forecast”. Finally, if the

state did clearly link the forecast to general macroeconomic trends, then it received an “X” in the

column labeled “Detailed Methodology with Direct Links to the Forecast”.

Four of the 15 states received an “X” in the first column, “No Macroeconomic

Information Explaining the Forecast”. These states only included a chart with the forecast

numbers, with no explanation of how these estimates were reached. Nine of the fifteen states

received an “X” in the second column, “General Macroeconomic Trends Without a Clear Link to

the Forecast”. These states included narrative about macroeconomic trends with a general

description of how they may affect the state economy; however, they did not detail their

methodology for how these trends brought them to their forecast number.

Only Virginia and Florida received an “X” in the third column, “Detailed Methodology

with Direct Links to the Forecast”. These states clearly linked the forecast numbers with these

trends or included detail on their methodology for how the macroeconomic trends brought them

to their forecast. For example, Florida’s estimate of revenue from the ad valorem tax is directly

linked to new construction and other important variables that affect that source of revenue. All of

these statistics are available online, so it is fairly easy to see how the ad valorem revenue

estimate was arrived at by the revenue estimating group. Virginia includes its revenue source

calculations in “The Economic Outlook and Revenue Forecast,” prepared by the Virginia

Department of Taxation for one of its forecasting groups, the Governor’s Advisory Council on

Revenue Estimates. These calculations show directly how the estimate for each revenue source

was developed.5

While the results of the analysis are interesting from a normative perspective, there is no

clear interrelationship between accuracy, consensus forecasts or transparency around the

methods that went into the forecast. Both Florida and Virginia have consensus forecasts. Florida

received a “B” accuracy grade in fiscal year 2015 and an “A” in 2016. Virginia received an “F”

in fiscal year 2015, but an “A” in fiscal year 2016; however, as noted earlier, it does not appears

that the revenue forecast in the budget actually reflected revenue expectations. That being said,

Virginia did struggle during the FY2015 year to arrive at a solid forecast, significantly

downgrading a forecast that ultimately turned out to be close to what the state had initially

predicted prior to the start of the fiscal year. But the state also did a significant and public

analysis of how the revenue forecast missed and what steps the state would take going forward to

avoid such errors. By way of contrast, Alabama does not include the macroeconomic

assumptions underlying its forecast, but it received “A” ’s in both years studied. Generally, most

5 It is pretty clear from Table 3 that different universities evaluating this question for the Volcker Alliance used different decision criteria when assessing whether states provided a reasonable rationale for their revenue estimates. Because the GFOA and others consider linking macroeconomic trends to the forecast to be best practice, the Volcker Alliance might consider using these decision rules when answering Question 4 (“Does the state reasonably support revenue growth projections at time of initial budget?”).

states seem comfortable providing general economic trends and then a forecast that is loosely

associated with these trends. This holds for executive states and consensus forecasting states.

However, the rigor around the Virginia and Florida processes is certainly appealing, and a more

expanded analysis of rigor, transparency and accuracy is warranted.

Political Acceptance of Southeastern States’ Revenue Forecasts

A further issue raised in Mikesell and Ross (2014) is political acceptance surrounding the

revenue forecasts in the southeastern states. The analysis looked in detail at five states, four of

which had some form of consensus forecast, and one of which had an executive forecast. While

it is difficult to prove a negative, there was no obvious evidence of the revenue estimate being

challenged in any of the states, even those with some intense partisan political conflict such as

occurred in Maryland and Virginia – certainly not in the open way that is visible at the national

level. The case studies of Maryland, Virginia, North Carolina, South Carolina and Georgia all

included a review of the revenue estimates as they moved across through the budget approval

process and all executive and legislative documents in the years studied mapped directly back to

the formal revenue estimate. In the case of Maryland and Virginia, where the legislature and

Governor were in different parties, the legislature did reverse a number of the Governor’s

proposed initiatives and newspaper articles as well as some budget documents reflect significant

conflict.6

Additionally, based on an initial review of news articles from the fiscal years 2015 to

2017, the researchers did not find any evidence of contested forecasts in any of the southeastern

states and conversations with researchers on the other southeastern teams found no evidence of a

contested estimate, although Alabama may bear further scrutiny. Further research on revenue

6 Add citations from Maryland 90 Day Summary and articles from VA about Medicaid expansion conflict and possibly the session summaries.

estimation during highly charged periods such as the Great Recession may shed further light on

whether different processes are more effective at building consensus; additionally other regions

of the country may have different insights.

CONCLUSION

The state revenue forecast is an important aspect of the budget-forming process. In a

balanced budget environment, the forecast establishes a critical constraint on expenditures.

Because forecasts are affected by global, national and state economic conditions, revenue

collections are notoriously difficult to forecast. As Dadayan and Boyd (2014) found, states are

having a hard time making accurate forecasts even after the end of the recession.37 While

accuracy is important, it is also important that the revenue forecast is transparent and politically

acceptable. As Mikesell and Ross (2014) commented, revenue “forecast accuracy is irrelevant if

the budget process does not respect the forecast as a resource constraint.”38 In other words, a

forecasting process that lacks transparency and is likely to be unaccepted by political

stakeholders can be harmful to the state budget-making process, regardless of the forecast’s

accuracy.

Using data compiled for the Truth and Integrity in Government Finance project by the

Volcker Alliance, this paper examined revenue forecasting practices in the southeast and

generally found that on average the forecasts were more accurate than prior research would have

led us to anticipate. That being said, the 2015 and 2016 budget years were not particularly

volatile. Additionally, some of the case analysis of the circumstances around some forecasts

suggest that these cannot always be taken at face value: the forecasts exist in institutional as well

as political frameworks and for a variety of reasons, the anticipated revenues are not always the

same as the formal forecast. This finding is important when considering embarking on a larger

quantitative analysis assessing forecast accuracy or using forecast accuracy as an independent

variable. While certainly not determinative, there was little evidence that consensus forecasts

were consistently associated with improved accuracy.

In terms of transparency, only two states draw a clear line between economic

assumptions and the actual revenue forecast. Most states present some generic economic trends

and the forecast not explicitly connected to these trends. The rigor of states that clearly explain

their models is refreshing but more research would be required to validate their accuracy.

Last, when examining political acceptance or the legitimacy of the forecast, there was

little evidence that any of the forecasts faced a significant challenge, regardless of the

methodology or the process around the forecast. Again, this analysis is by no means

determinative but simply adds another observation or data point to broader theory. As observed

in the literature review, it is quite possible that the revenue forecasting process is endogenous to

political conflict – so high conflict situations lead to processes that help resolve the conflict,

whether this be consensus forecasts or other strategies.

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1 McNichol, Elizabeth. 2014. Improving State Revenue Forecasting: Best Practices for a More Trusted and Reliable Revenue Estimate. Center on Budget and Policy Priorities. 2 Mikesell, John L. State Revenue Forecasting in the State of Indiana. 2008. In Government Budget Forecasting: Theory and Practice. ed. Jinping Sun and Thomas D. Lynch. 142: 415-429. Boca Raton, FL: CRC Press. 3 Klay, William Earle. And Joseph A. Vonasek. 2008. Consensus Forecasting for Budgeting in Theory and Practice. In Government Budget Forecasting: Theory and Practice. ed. Jinping Sun and Thomas D. Lynch. 142: 379-391. Boca Raton, FL: CRC Press. 4 Sun, Jinping. Forecast Evaluation: A Case Study. 2008. In Government Budget Forecasting: Theory and Practice. ed. Jinping Sun and Thomas D. Lynch. 142: 223-240. Boca Raton, FL: CRC Press. 5 Tebbs, Jeffrey M. 2009. Breaking The Stalemate: A Proposal for a Consensus Revenue Forecasting Process. Connecticut Voices for Children. 6 McNichol, Elizabeth. 2014. Improving State Revenue Forecasting: Best Practices for a More Trusted and Reliable Revenue Estimate. Center on Budget and Policy Priorities. 7 Clemen, Robert T. 1989. Combining forecasts: A review and annotated bibliography. International Journal of Forecasting. 5(1989): 559-583 8 Boyd, Donald J. and Lucy Dadayan. 2014. State Tax Revenue Forecasting Accuracy. Rockefeller Institute. 9 Willoughby, Katherine G. and Hai Guo. The State of the Art: Revenue Forecasting in U.S. State Governments. In Government Budget Forecasting: Theory and Practice. ed. Jinping Sun and Thomas D. Lynch. 142: 27-42. Boca Raton, FL: CRC Press. 10 Qiao, Yuhua. Use of Consensus Revenue Forecasting in U.S. State Governments. In Government Budget Forecasting: Theory and Practice. ed. Jinping Sun and Thomas D. Lynch. 142: 393-413. Boca Raton, FL: CRC Press. 11 Klay, William Earle. And Joseph A. Vonasek. 2008. Consensus Forecasting for Budgeting in Theory and Practice. In Government Budget Forecasting: Theory and Practice. ed. Jinping Sun and Thomas D. Lynch. 142: 379-391. Boca Raton, FL: CRC Press. 12 Wong, John D. and Carl D. Ekstrom. 2008. Consensus Revenue Estimating in State Government: A Case of What Works in Kansas. In Government Budget Forecasting: Theory and Practice. ed. Jinping Sun and Thomas D. Lynch. 142: 431-455. Boca Raton, FL: CRC Press. 13 Voorhees, William R. 2004. More Is Better: Consensual Forecasting and State Revenue Forecast Error. International Journal of Public Administration. 27(8-9): 651-671 14 Boyd, Donald J. and Lucy Dadayan. 2014. State Tax Revenue Forecasting Accuracy. Rockefeller Institute. 15 Elaine Deschamps. 2003. The impact of institutional change on forecast accuracy: A case study of budget forecasting in Washington State. International Journal of Forecasting. 2003. 16 Buettner, Thiess and Bjoern Kauder. 2015. Political biases despite external expert participation? An empirical analysis of tax revenue forecasts in Germany. Public Choice (2015): 164:287–307 17 Boylan, Richard T. 2008. Political Distortions in State Forecasts. Public Choice. 136 (2008): 411–427 18 Krause, George. A, David E. Lewis, James W. Douglas. 2013. Politics Can Limit Policy Opportunism in Fiscal Institutions: Evidence from Official General Fund Revenue Forecasts in the American States. Journal of Policy Analysis and Management. 32(2): 271-295 (2013) 19 Rose, Shanna and Daniel L. Smith. Budget Slack, Institutions, and Transparency. 2012. Public Administration Review. 72(2): 187-195 (2012) 20 Government Finance Officers Association (GFOA). Financial Forecasting in the Budget Preparation Process. 2014. Accessed on March 7, 2017. 21 Organisation for Economic Co-operation and Development (OECD). Best Practices for Budget Transparency. 2002. OECD Journal on Budgeting. 22 International Monetary Fund (IMF). Code of Good Practices on Fiscal Transparency. 2007.

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23 McNichol, Elizabeth, Iris Lav, and Michael Leachman. 2015. Better State Budget Planning Can Help Build Healthier Economies. Center on Budget and Policy Priorities Policy Futures. 24 Alt, James E., David Dreyer Lassen and Shanna Rose. The Causes of Fiscal Transparency: Evidence from the U.S. States. 2006. IMF Staff Papers. 53: 30-57 (2006) 25 Mikesell, John L. and Justin M. Ross. 2014. State Revenue Forecasts and Political Acceptance: The Value of Consensus Forecasting in the Budget Process. Public Administration Review. 74(2): 188-203 26 Elaine Deschamps. 2003. The impact of institutional change on forecast accuracy: A case study of budget forecasting in Washington State. International Journal of Forecasting. 2003. 27 Krause, George A., David E. Lewis and James W. Douglas. 2006. Political Appointments, Civil Service Systems, and Bureaucratic Competence: Organizational Balancing and Executive Branch Revenue Forecasts in the American States. American Journal of Political Science. 50(3): 770–787 28 Buettner, Thiess and Bjoern Kauder. 2015. Political biases despite external expert participation? An empirical analysis of tax revenue forecasts in Germany. Public Choice (2015): 164:287–307 29 Boylan, Richard T. 2008. Political Distortions in State Forecasts. Public Choice. 136 (2008): 411–427 30 Krause, George. A, David E. Lewis, James W. Douglas. 2013. Politics Can Limit Policy Opportunism in Fiscal Institutions: Evidence from Official General Fund Revenue Forecasts in the American States. Journal of Policy Analysis and Management. 32(2): 271-295 (2013) 31 Mikesell, John L. and Justin M. Ross. 2014. State Revenue Forecasts and Political Acceptance: The Value of Consensus Forecasting in the Budget Process. Public Administration Review. 74(2): 188-203 32 Wong, John D. and Carl D. Ekstrom. 2008. Consensus Revenue Estimating in State Government: A Case of What Works in Kansas. In Government Budget Forecasting: Theory and Practice. ed. Jinping Sun and Thomas D. Lynch. 142: 431-455. Boca Raton, FL: CRC Press. 33 Alt, James E., David Dreyer Lassen and Shanna Rose. The Causes of Fiscal Transparency: Evidence from the U.S. States. 2006. IMF Staff Papers. 53: 30-57 (2006) 34 McNichol, Elizabeth. 2014. Improving State Revenue Forecasting: Best Practices for a More Trusted and Reliable Revenue Estimate. Center on Budget and Policy Priorities. 35 Willoughby, Katherine G. and Hai Guo. The State of the Art: Revenue Forecasting in U.S. State Governments. In Government Budget Forecasting: Theory and Practice. ed. Jinping Sun and Thomas D. Lynch. 142: 27-42. Boca Raton, FL: CRC Press. 36 Boyd, Donald J. and Lucy Dadayan. 2014. State Tax Revenue Forecasting Accuracy. Rockefeller Institute. 37 Boyd, Donald J. and Lucy Dadayan. 2014. State Tax Revenue Forecasting Accuracy. Rockefeller Institute. 38 Mikesell, John L. and Justin M. Ross. 2014. State Revenue Forecasts and Political Acceptance: The Value of Consensus Forecasting in the Budget Process. Public Administration Review. 74(2): 188-203