The Politics of Forecast Bias- Forecaster Effect and Other Effects in New York City Revenue Forecasting

Please create a Case Study question (sample below) for the below article above and ask 2-3 questions related to the article above.  Some another similar scenario to ask the questions.

Budget – Chapter 1 – Excellent Questions.

In Bland chapter 1 (page 4) he writes that “A well-designed revenue structure — one that promotes fairness and market neutrality and is administratively cost-effective — is a city’s or county’s most effective tool for attracting and retaining business investment. In fact, tax incentives may be unnecessary if a community’s leadership has focused its efforts on building a sound tax structure.”

This brought to my mind the negotiations between the city of Anaheim and the LA Angels regarding renovations of Anaheim stadium. After the City Council approved a deal framework where the Angels would pay $150M to renovate the stadium but receive a 1$/yr lease on the parking lot (allowing them to  recoup some of their renovation costs by developing some of the land around the lot), Mayor Tom Tait objected and wanted both parties to share in the development profits of the land. The land was appraised at $225M if developed. This ultimately stalled negotiations and the Angels walked away, leaving their future in the city in doubt. Councilwoman Lucille Kring (who will oppose Tait in November’s mayoral election) criticized the Mayor’s position, accusing him of wanting to make a quick buck on …generic development” at the price of losing the Angels, a “unique part of our heritage.” (LA Times, 9/26/14).

Which is the better designed revenue structure – revenue from “generic development” or that generated by having a major sports franchise in the city? Is the “incentive” of letting a team recoup their expense of paying for stadium renovation off a free lease of city-owned property balanced out (or outweighed) by the dollars the team remaining in the city will generate for the city? Is this strictly a cost-benefit analysis for the city, or does the non-monetary value of preserving city heritage factor into the decision also? Is the Councilwoman merely playing politics or is there substance to her position?

C?2012 Public Financial Publicat ions, I nc.
The Politics of Forecast Bias: Forecaster Effect
and Other Effects in New York City Revenue
Forecasting
DA N I E L W. W I L L I A M S
This paper examines the impact of forecasters, horizons, revenue categories, and
forecast timing in relation to decision m aking on forecast bias or accuracy. The
significant findings are: for the m ost part forecasters tend to report forecasts that
are similar rather than competitive. Forecast bias (underforecasting) increases over
longer horizons; consequently claims of structural budget deficit are suspect, as
an assertion of structural deficit requires that a reliable forecast of revenue shows
continuous shortfall compared with a reliable forecast of expenditures. There is
an overforecasting bias in property tax, possibly reflecting demand for services.
There is an underforecasting forecast bias in two revenue categories, all other
taxes and federal categorical grants, resulting in a net total underforecasting bias
for the city’s revenue. There appears to be a period effect (forecasts in June are
substantially biased), but this effect requires further study. The study suggests
further examination of the bias associated with revenue categories, time within the
budget cycle, and forecast horizon.
INTRODUCTION
As long ago as Government Budgeting (Burkhead 1956), it has been observed t hat gov-ernment forecasters often underestimate revenue. In fact, N ew York City consistently un-derforecasts revenue w ith forecasts in the earlier years—sometimes roughly accurate, but
almost never in excess of actual revenues. Table 1 also s hows that in the past decade, un-derforecasting has been pervasive across revenue sources. Treat ing N ew York City as a
fo r e c a s t i n g c a s e s t u d y, t h i s p a p e r e x a m i n e s fo r e c a s t d e t a i l s t o d i s c o v e r c h a r a c t e r i s t i c s t h a t
predict underforecasting bias.
There have been many studies substantiating the notion of revenue underforecasting
by subnational governments in t he United Stat es. Voorhees (2006) provides an extensive
Daniel W. Williams is a Associate Professor, Baruch College, New York, NY 10010. He can be reached at
[email protected],PhilJoyce,JonathanJustice
and T had C alabrese for comments on prior versions of this paper.
Williams / The Politics of Forecast Bias 1
TA B L E 1
End of Year Revenue Variance from Beginning of Year Budgeted Revenue (Forecast as
Accepted in the Appropriation Process) and Proportionate Shares of Revenue
FY Taxes All own source Federal and State aid All revenue
2001 6.2% 9.2% 5.0% 7.8%
2002 ?4.3% 6.5% 13.7% 3.8%
2003 2.0% ?5.4% 15.2% 5.5%
2004 8.5% 8.1% 11.2% 9.1%
2005 14.1% 1 2.7% 11.2% 12.7%
2006 12.6% 12.2% 0.2% 8.4%
2007 16.4% 1 5.9% 2.9% 11.9%
2008 5.8% 4.9% 5.0% 5.9%
2009 ?1.2% ?2.5% 6.8% 2.5%
2010 5.7% 9.3% 6.2% 6.5%
2011 3.7% 5.7% 5.0% 4.5%
All years 6.3% 7.0% 7.5% 7.1%
Proportions 50.9%
?
10.9% 28.8%** 18.0%
?
Ta x e s : 2 2 . 7 % p r o p e r t y t a x , 3 8 . 2 % a l l o t h e r .
**
Fe d e r a l 1 0 . 8 % , S t a t e 1 8 . 0 % .
Source:Compiled by the author from the NYC Comprehensive Annual Financial Reports 2001–2011.
Source of Proportions: Compiled by the author from reports from the New York City Office of Management and
Budget based on average revenue over Fiscal Years 2005 through 2009.
review of the recent literat ure, reporting that underestimation bias varies substantially f rom
study to study. Rubin ( 1987) reports that poor jurisdictions tend to overestimate revenue.
In one study comparing the US Office of Management and Budget and Congressional
Budget Office, it was deter mined that competing forecasters exhibit similar biases rather
than providing competing independent forecasts (Krause and Douglas 2006).
Choate and Thompson (1988, 1990), Paleologou (2005), and Rodgers and Joyce ( 1996)
fo c u s o n t h e p o l i t i c a l c h a r a c t e r o f fo r e c a s t i n g . C h o a t e a n d T h o m p s o n a r g u e t h a t i t i s t h e
principal (the political decision maker), not the agent (the t echnical forecaster), who selects
the underforecast. They argue that forecast bias is not strictly a consequence of t he widely
believed budget officers’ risk aversion, and suggest that underforecasting plays a role in tax
policy. Paleologou shows that in the United Kingdom forecasting bias is associated with
the party in power. Rodgers and Joyce s uggest there are complex f actors some of which are
rat i o n a l i n a p o l i t i c a l c o n t ex t a n d t h e y c a l l fo r f u r t h e r wo rk t o ex p l a i n va r i at i o n i n fo re c a s t
errors.
This paper examines revenue forecasting diff erently t han past studies. F irst, rather than
looking at one, or even t wo, revenue forecasters, t his paper examines five s eparat e forecast-ers of N ew York City revenue over a five-year period. By examining a larger number of
fo r e c a s t e r s, i t i s p o s s i b l e t o e v a l u a t e w h e t h e r t h e i n s t i t u t i o n a l m o t i v a t i o n a n d p r e s e n c e o f
competition affects revenue forecasting accuracy and the direction of bias.
2PublicBudgeting&Finance/Winter2012
Second, the paper examines whether t here are differences in forecasting accuracy by
revenue t ypes and forecast timing. Revenue cat egories m ight be associat ed with bias because
they have separate political principals, and, from a more technical perspective, some are
easier to forecast than others. B udget cycle timing could be relat ed to bias because bias m ay
play a beneficial role in some stages of budget negotiation.
Typically, studies of forecast bias are excessively high level, providing little insight into
when and where forecast bias actually arises. T his study takes an alternative approach look-ing at substantial detail to deter mine where bias occurs. Forecast bias cannot be ameliorated
when such details are unknown. T he questions asked below reflect many well-known sources
of real world forecast concerns.
The s pecific questions that will be examined in this paper are:
1. Is there a forecaster eff ect on revenue forecasting? Revenue conservers may be m ore
interested in underforecasting while program advocat es may prefer overforecasting.
2. Is there a revenue cat egory eff ect on revenue forecasting? There are at least t wo reasons
for revenue category eff ect. F irst, some sources of revenue are more easily forecast
than others. For example, property tax is s ubject to only a small uncertainty related
to noncompliance and an even smaller uncertainty related t o net change in taxable
property. Second, some taxes m ay be less palat able to the electorat e and may, therefore,
lead to behavioral effects for forecasters or their managers.
3. Is there a forecast horizon effect on revenue forecasting? It is well known that forecast
accuracy diminishes over time (Makridakis et al. 1982, 1993; Makridakis and Hibon
2000; Makridakis, Hibon, and Moser 1979). I f forecasts are biased, the eff ect siz e of the
bias should, likewise, increase over time. This possibility is of particular significance
related t o forecasts that may be used in discussions of structural balance.
4. Is there an eff ect related t o t he month of origin of t he revenue forecast? The m onth
of origin is related t o t he political cycle. As the budget calendar progresses, concrete
commitments resultant from the forecast become hardened. Thus, later period bias
is more difficult to reverse. I f bias is a deliberat e political ploy, t hen early forecasts
should be more biased than later forecasts.
5. Is there an eff ect related t o repetition of t he revenue forecast in the s ame cycle? A s
with the previous question, repeat forecasting may allow for early period bias, which
may only appear with forecasters who off er multiple forecasts in the s ame cycle.
These questions relate directly to the reliability and use of forecasts and the need for
forecasts, as well as addressing significant gaps that exist in t he current research. In the
subsequent sections, each question is discussed in m ore detail.
This paper is significant because revenue forecasting is a principal controlling force in
budget m aking in subnational governments in t he United Stat es. E lected officials of all s orts
are loath to be seen to increase taxes or any visible revenue devices. Forecasting provides
estimates for the continuing effect of already established revenue devices for t he budget
year and future years. Elected officials may constrain expenditures w ithin t hese estimates
Williams / The Politics of Forecast Bias 3
and may, in some instances, change the direction of f uture year expenditures because of
these forecasts (Crain and Muris 1995; Gar man, Haggard, and Willis 2001; Heller 1997;
MacManus and Pammer 1990; Pressman 2004; Rose 1985; Weaver 1986).
I s T h e re a Forec a s t e r E f fe c t ?
Jurisdictions of substantial s iz e s ometimes have multiple revenue forecasts m ade by distinc-tive forecast units such as a legislative unit and an executive unit; but t he resultant forecasts
may not be treated as competitive. I nstead they may be used as inputs for a consensus fore-cast, a common device for reconciling forecasts.
1
This consensus forecast may be the only
publically released forecast. Where there are competitive forecasts, t he publically available
data may be limited.
In New York City there are many government sponsored forecasts, but no consensus
fo r e c a s t . T h e N e w Yo r k C i t y C h a r t e r p r o v i d e s t h a t t h e m ayo r ’s fo r e c a s t d e t e r m i n e s t h e
revenue estimate of all revenue sources except property tax, which t he city council can
adjust by adjusting the t ax rate. So, consensus forecasting is not required by t he political
process. This provides an opportunity to examine forecast bias.
The t heory of bias proposed here is that political decision makers and senior management
prefer bias and communicate this preference to forecasters, not that the particular algorithms
used by forecasters are more susceptible to bias than alternatives not used. All the New
York City forecasters use essentially t he same sorts of models, primarily regression or
ARIMA t ype econometric m odels; t he exception is the property tax, which can be computed
deter ministically. Some lesser revenue categories may be estimated with simpler models.
All forecasters examined here forecast the entire city budget. Aggregat ion of cat egories
(discussed lat er) is completed to make the forecasts parallel.
The forecasters include:
1. New York City Office of Management and Budget (NYCOMB), which reports to a
deputy m ayor and ultimately to the m ayor. This is the charter m andated deter minative
fo r e c a s t .
2. The F inancial C ontrol B oard (FCB), which was created after the financial crisis of
the 1970s. The explicit purpose of the FCB is to prevent the city from overspending
its revenue.
3. The N ew York City Comptroller (NYCC), one of three citywide elective positions
and is considered a competitor of t he mayor. In the 2009 election and other earlier
elections the comptroller was among the m ayoral candidates, sometimes opposed to
the current mayor.
4. The Independent Budget Office (IBO), which was created in a significant charter
revision in 1989. This charter change resulted in shifting budget power to the city
1. The author served on a technical panel for a consensus prison population forecast between three bodies
wh e n e m p l oy e d w i t h a s t at e g ov e r n m e n t .
4PublicBudgeting&Finance/Winter2012
council and the IBO was creat ed as technical advisory staff to the city council and to
the public. As the city council does not publish a forecast, the IBO forecast is the best
av a i l a b l e p r o x y f o r t h e c i t y c o u n c i l f o r e c a s t .
5. New York State Deputy C omptroller for New York C ity (DCNYC) represents the
state’s interest and in some ways has a s imilar f unction to the FCB. H alf of t he state’s
population lives within New York City and far more than half of the state’s revenue is
derived f rom activity w ithin N ew York City, s o t he state comptroller is also engaged
in maintaining oversight of the city revenue estimates.
During recent years each of these entities has made some infor m at ion about its fore-cast available to the public via the internet.
2
These publically available data provide the
opportunity to examine relative forecast practice of the five forecasters.
Should t here be a forecaster eff ect? B y forecaster eff ect, it is intended t hat t here is a
statistically s ignificant observable difference in s ystematic error associat ed with the s ource
of the forecast (the entity t hat produces the forecast). There are three reasons why t here
might be such a difference:
1. Among the five forecasters, t wo, N YCOMB and FCB, are revenue conservers (Bland
2007)—if there is a bias for underforecasting, they should exhibit this bias the most;
two, IBO and NYCC, represent demand for s ervices—IBO as a proxy for city council
and NYCC as a political competitor to the mayor—so they should reflect less under-forecasting bias; and one, DCNYC, is indeter m inat e. This reason is the m ost explicitly
political: revenue conservers such as NYCOMB and the FCB may be s eeking t o s up-press expenditures in order to build surpluses as a hedge against f uture uncertainty or
to hold dow n f uture t axes as argued by C hoate and Thompson (1988, 1990). Repre-sentat ives of demand, such as IBO and NYCC, s hould prefer t o find revenue s o t hat
ex p e n d i t u r e p rog r a m s c a n b e f u n d e d ( B r e t s c h n e i d e r, S t r a u s s m a n , a n d M u l l i n s 1 9 8 8 ) .
2. It is not an effective use of public resources to conduct five forecasts that get t he
same biased answer. So, forecasters should be expected to find diff erent answers. T he
existence of no forecaster eff ect should raise the s uspicion that the public is spending
too much m oney on forecasting. The existence of rat ional bias is well documented
in forecasting literature (Batchelor 2007; Butler and Lang 1991; Laster, Bennett, and
Geoum 1999). The specific rational bias suggested here, coming to different results,
is sometimes included in this literature, although it is poorly understood. The reason
offered here—the continued appearance of usefulness—is no more speculative than
those offered in the existing literature.
2. These websites include: NYC OMB budget publications at http://www.nyc.gov/html/omb/html/
publications/publications.shtml, F CB at http://www.fcb.state.ny.us/, IBO at http://www.ibo.nyc.ny.
us/, NYCC at http://www.comptroller.nyc.gov/bureaus/bud/, and DCNYC at http://www.osc.state.
ny.us/osdc/index.htm. These websites are revised rather frequently, so these URLs are to top level com-ponents of these websites. Actual data and reports are w ithin these sites.
Williams / The Politics of Forecast Bias 5
3. The forecasters may have differing levels of access t o contextual infor m at ion about
diff erent components of t heir forecast. For example, DCNYC m ay have more insight
into the funding of state categorical grants to the city. Such infor mation advantages
should result in s ome s ystematic differences in the forecasts.
There are also reasons why t here should be no forecaster eff ect.
1. As argued by K rause and Douglas (2006), forecasters may s eek the s ame answer rat her
than differences. This result is a safe strategy because the future is uncertain, leaving
opportunity for variance between forecast and actual. When t he same variance can be
at t r i b u t e d t o o n e ’s “ c o m p e t i t o r, ” i t a p p e a r s o n e i s s t i l l d o i n g a r e a s o n ab l y g o o d j o b.
Herding behavior has been documented in other uses of forecasting as well (Clement
and Tse 2005; Olsen 1996).
2. To the degree t hat it is documented on t heir various websites and reports, t hese entities
use the same basic approach to forecasting, mostly econometric modeling. To some
degree they are simply second guessing NYCOMB’s parameter choices. NYCOMB
produces a s et of input economic factor forecasts, which are not necessarily reforecast
by the other entities. I n addition, for “minor” cat egories various forecasters s imply
accept the NYCOMB forecast as their own.
3
Considering the similarity of method and
high interdependence, it is unlikely t hat t he forecasts will be substantially different.
3. Some data deficiencies that will be discussed in a subsequent section led to imputing
some values. T hus, even where there m ay be diff erences, t he data may be insufficient
to find these differences.
Hypothesis 1 = There is a forecaster eff ect that is biased to underforecasting (negat ive
coefficients) for NYCOMB and FCB, neutral or biased to over forecasting
(positive coefficients) for IBO and NYCC, and uncertain for DCNYC.
Because of the conflicting expectations, there is no clear expectation that
the data will be consistent with this hypothesis.
Is There a Revenue Category Effect on Forecasting?
Five revenue cat egories are examined, property tax, all other taxes, miscellaneous and other
city revenue, federal cat egorical grants, and state categorical grants. This selection is m ade
as a compromise between excessive aggregat ion and excessive use of imputat ion as s ome
forecasters do not report in even t his detail for all years examined.
Table 1 show s t hat property tax is 22.7 percent of all revenue, the s econd largest cat egory.
It is of particular interest because city council has a role in s etting the forecast by changing
3. No interviews were conducted w ith the various forecast entities. T hese entities were contacted for
data files that were not available on their websites. In the ensuing conversation some anecdotal infor mation
was volunteered. T his infor mation should not be treated as systematically collected. Despite the contacts, no
additional data were provided beyond that which could be found on the websites.
6PublicBudgeting&Finance/Winter2012
the t ax rate. A lso, property tax is especially simple to forecast as it is tax rat e t imes base ( ad-justed for properties coming on or going off the tax roll and updated for current assessment)
times compliance rat e ( all by cat egory). A lthough there is a small amount of uncertainty
in tax base and compliance rate, within the existing stock of New York property, only the
compliance rat e can change more than a t iny amount. B ecause it is hard t o attribute vari-ance in property tax forecast to technical difficulties and because it is controlled, in part, by
representatives of demand, the budget year forecast of property tax s hould either have no
bias or positive (over forecasting) bias.
Ta b l e 1 s h o w s t h a t a l l o t h e r t a x e s i s t h e l a r g e s t c a t e g o r y o f r e v e n u e a t 3 8 . 2 p e r c e n t . I t
is a residual of all t axes minus property taxes and includes personal income tax, general
corporation tax, banking corporation tax, unincorporated business tax, sale and use tax,
commercial rent t ax, real property transfer t ax, mortgage recording t ax, utility t ax, cigarette
tax, hotel tax, all other tax, and tax audit revenue. It would be desirable to compare the
fo r e c a s t s a t t h e l i n e l e v e l , b u t s o m e fo r e c a s t e r s d o n o t e v e n p r o v i d e t h e s i m p l e d i s t i n c t i o n
between property taxes and all other. To m inimiz e t he need for imputing values, only
the t wo tax cat egories are included. Because t he forecast of this category is controlled by
NYCOMB, a revenue conserver, and can involve many difficult forecasts, it is anticipat ed
that this category will show an underforecasting (negative) bias.
Table 1 show s t hat m iscellaneous and other city revenue is the s econd smallest category
at 10.9 percent. It is an aggregat e of t wo reported cat egories: (1) m iscellaneous, where the
city reports most such ow n s ource revenue as licenses, fines, interest earnings, and so forth.
This category is clearly reported by nearly all forecasters for nearly all years. (2) A ll other
city revenue, where t he city reports any other revenue except f ederal and stat e cat egorical
grants. This category is quite small.
4
As the forecasts of these cat egories are controlled by
NYCOMB, it is anticipated that this category will show an underforecasting (negative) bias.
However, this anticipat ion is s omewhat uncertain.
The smallest revenue cat egory reported in Table 1 is f ederal categorical grants, at
10.2 percent. This category refers to funds in four federal grant categories: Community
Development, Social Services, E ducat ion, and other. New York C ity estimates how much it
will receive in these grants cat egories. The city’s interest in forecasting these grants is m ixed.
Anticipating the receipt of these grants m ay lead the city t o commit t o expenditures t hat
it may later find itself having to fund with local revenue. However, that is not always the
case. Some of these grants may serve as simple pass-throughs or other devices that do not
commit the city to anything in particular should they fail to materializ e; failing to anticipate
receipt of such funds would have t he effect of reducing t he probability of t he overall s ocial
well-being of t he city. U nderforecasting of federal grants could lead t he grantor entities
to believe the city does not really need the f unds requested in grant applicat ions; however,
4. All other city revenue includes federal and state unrestricted grants, other categorical grants interfund
ag r e e m e n t s, d i s a l l ow a n c e o f c at e g o r i c a l g r a n t s ( a n e g at i v e e n t r y ) , a n d a d j u s t m e n t fo r i n t e r c i t y r e v e nu e ( a
negative entry, which is a revenue line in miscellaneous category). It is apparent that the standard “forecast”
for disallowance of categorical grants is set at $15 million every year. B y combining the two larger categories,
intercity revenue is netted out of the entire forecast and actuals, thus reducing irrelevant variability.
Williams / The Politics of Forecast Bias 7
mitigating against t his is t he difficulty of t racking f rom particular grants t o t he city revenue
fo r e c a s t b e c a u s e o f t h e a g g r e g a t i o n o f l i n e s t o t h e fo r e c a s t a n d d i f f e r e n c e s i n fi s c a l y e a r s
and the fact that many, perhaps most, of the funding is supplied through for mula grants
that have little t o do w ith city requests. D ue to the m ixed nature of these concerns, t here is
no clear direction t o any possible bias in t he revenue forecast.
Stat e cat egorical grants account for t he remaining 18 percent of t he revenue. This category
includes funds in Social Services, Education, Higher Education, Department of Health and
Mental Hygiene, and other. New York C ity estimates how much it will receive in these
grants cat egories. Considerat ions with state grants are similar t o t hose of f ederal grants,
thus there is no clear direction t o any possible bias in t he revenue forecast.
Hypothesis 2 = There is a revenue eff ect that is negative for all city source categories
exc e p t p ro p e r t y ( wh i c h i s u n c e r t a i n ) a n d u n c e r t a i n fo r f e d e r a l a n d s t at e
categorical grants.
Is There a Forecast Horizon Effect on Forecasting?
Almost universally, previous research has found that forecast accuracy deteriorat es over
time (Fildes et al. 1998; Gardner and McKenzie 1985; Makridakis et al. 1993; Makridakis
and Hibon 2000; Smith and Sincich 1991). N ew York City’s forecasts are for next year and
three out years. As the initial hypothesis is t hat t he forecast is biased, t he out years s hould
have a negat ive coefficient t hat reflects t he effect siz e of the bias over t ime. The possibility
of a forecast bias in out years is of s ubstantial interest. Since fiscal crisis of the 1970s, N ew
Yo r k C i t y h a s e x i s t e d u n d e r a p e r p e t u a l c l o u d o f “ s t r u c t u r a l d e fi c i t , ” t h a t i s , t h e e x p e c t a t i o n
that future year expenditure obligat ions are s ystematically higher t han future year revenues
(Chen and Barbaro 2008; Cooper 2004; Levy 1996; Rohatyn 1994; Shefter 1992). If there
is an out year revenue forecast bias, t his structural deficit may be illusory, although this
cannot be fully deter mined without also examining the expenditure forecasts. If, as asserted
at t h e b e g i n n i n g o f t h i s p a p e r, t h e s e r e v e nu e fo r e c a s t s a r e b i a s e d t o w a r d u n d e r fo r e c a s t i n g
and if forecast accuracy deteriorat es over time, it can be expected that the bias w ill become
more pronounced over time.
Hypothesis 3 = Underforecasting (negat ive) of revenue is predicted to increase with the
fo r e c a s t h o r i z o n .
Is There an Effect Related to the Month of Origin of t he Forecast?
The forecasts examined in this study are reported in May, June, and July of each year. May
and some June forecasts are related to the budget request. July and other June forecasts
relate to the final revenue estimates before city council makes its final budget decision. If,
as it is sometimes believed, the m ayor holds back s ome revenue for budget negotiations
8PublicBudgeting&Finance/Winter2012
with the city council, there m ay be a positive eff ect (comparat ive positive bias) for forecasts
made later in the process.
Hypothesis 4 = There is a month of origin eff ect that is negative (underforecast) for May,
uncertain for June, and positive for July (increase over earlier periods).
Is There an Effect Related to Repetition of Forecast Reporting in the Same C ycle?
Closely relat ed to month of origin is t he issuance of t wo forecasts in the s ame cycle. T he
first forecast, reported in May or June, is t he opening offer in t he budget negotiation. The
second forecast reflects postnegotiation adjustments and may have a positive eff ect (relat ive
increase).
Hypothesis 5 = The second forecast for the same fiscal years is predicted to be higher.
Data
The dat a used in t his analysis are collected from reports posted on t he websites as identified
in footnote 2. T he reports require considerable preanalysis before they can be examined
with respect to the central question of this study. N YCOMB and IBO report t heir forecasts.
However, these forecasts are divided into two parts. T he larger part of t hese forecasts is
in revenue cat egories, such as property tax, sales t ax, etc. A smaller part is in proposals
that are expected to pass at the city council session at the time of the forecast. Forecasts
of these proposals must be recategoriz ed to fit the revenue categories used in this study
before comparison with actual revenue outcomes.
5
After these adjustments and in order to
be parallel w ith t he other forecasters, t he NYCOMB forecast is summariz ed in five groups,
property tax, all other taxes, miscellaneous and other city revenue, f ederal categorical grants,
and state categorical grants. A f urther difficulty w ith t he IBO forecast is that in the first year
of these data, 2003, IBO aggregates federal and state categorical grants. To impute values,
the N YCOMB value federal cat egorical grants is imputed to IBO and the difference between
the aggregate values and the NYCOMB values are treated as the IBO value state categorical
grants. This procedure m ay have the eff ect of understating the difference between IBO and
NYCOMB in federal categorical grants while overstating it in state categorical grants.
The NYCC, FCB, and DCNYC report “risks,” which is to say how much they think NY-COMB’s forecast is incorrect in particular lines. These risks are converted back to forecasts
by adding or subtracting t hem compared with the appropriate NYCOMB forecast.
6
These
forecasters also do not necessarily report all the cat egories of t his analysis. Particularly, the
5. There is a report made by NYC OMB that assigns proposal forecasts to revenue categories; however,
efforts to obtain this report were unsuccessful. Most proposals are easily categoriz ed; however, there may be
some small error due to this lack of access.
6. Despite the ter m “risk,” these are alternate competing forecasts. The “risk” is the difference between
the NYC OMB’s forecast and competing forecast.
Williams / The Politics of Forecast Bias 9
FCB and DCNYC do not always report property taxes, so the aggregat e of all taxes must
be decomposed following the same procedure as described with IBO above. The variance
between NYCOMB and these other forecasters may be understated w ith respect to property
tax while overstated w ith respect to all other taxes. While this is unfortunate, property tax is
aspecial caseasdiscussedabove anditisdesirable toperformsomeseparateanalysis even
i