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DSS News
D. J. Power, Editor
June 9, 2002 -- Vol. 3, No. 12
A Bi-Weekly Publication of DSSResources.COM
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Check the article by Joe Herlihy at DSSResources.COM
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Featured:
* DSS Wisdom
* Ask Dan! - Can DSS improve state government tax revenue projections?
* Spreadsheet-based DSS Tip - Hide Unused Rows and Columns
* What's New at DSSResources.COM
* DSS News Releases
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Check Dan Power's new book, Decision Support Systems:
Concepts and Resources for Managers. Get information at
http://www.dssresources.com/dssbookstore/power02.html .
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This newsletter has more than 850 subscribers from
50 countries. Please forward this newsletter to people interested in
Decision Support Systems or suggest they visit DSSResources.COM.
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DSS Wisdom
According to Haynes and Henry (1974) in their book on Managerial
Economics, "In appraising the basic four-step procedure for forecasting
-- constructing a model, estimating the parameters, choosing values for
the exogenous variables, and simulating the future -- several possible
weak links are obvious. First, there is the possibility of incorrectly
specifying the model. ... A second possible weakness relates to any
attempt to extrapolate into the future based on past experience. A
change in the economy cannot be measured immediately ... A third
possible weak link in the procedure involves choice of values for
exogenous variables. ... Perhaps the most important positive feature of
econometric model forecasting is that it requires the forecaster to
completely specify the assumptions underlying his forecasts. ... Another
strength of econometric models is their capability for simulation of a
great variety of possible environments." (p. 132)
from Haynes, W. W. and W. R. Henry. Managerial Economics: Analysis and
Cases (3rd edition). Dallas, TX: Business Publications, Inc., 1974.
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Ask Dan!
by Daniel J. Power
Can DSS improve state government tax revenue projections?
In the United States, state governments forecast tax revenues, prepare
spending budgets based on the forecast, and then track and monitor tax
receipts and expenditures. This past year has been especially difficult
for the forecasters. Here in Iowa we have experienced major tax revenue
shortfalls. Rather than raising taxes to meet budgeted needs, our state
legislature has cut budgets. This Ask Dan! briefly examines why state
tax revenue projections are so "bad" this year, the revenue estimating
decision process, and then the role, if any, DSS play and could play in
tax revenue projections. Web resources have been invaluable for
collecting information for this Ask Dan!, but I would welcome comments
from those with "first hand" knowledge of using DSS in revenue
estimating.
People in many states in the United States are asking why state tax
revenue projections are so "bad". One reads many explanations. The
shortfall is caused by the national recession. The terrorist attacks
have reduced consumer spending and hence sales taxes. More people are
unemployed, personal income is down and hence there has been a much
"higher-than-expected" increase in income tax refunds. There is a
continuing Medicaid shortfall. And there is a ripple effect theory ...
an unexpected effect of the federal government's stimulus package and
changes in depreciation schedules was a major reduction in state
corporate income taxes. It sounds like part of the blame falls to errors
in specifying a revenue estimating model. Model-driven DSS should be
able to assist forecasters in examining impacts of changes in state and
federal tax laws and the impact of changes in personal income on tax
refunds and hence the impact on tax revenues.
What about process problems? Here in Iowa we have a 3 person revenue
estimating board. These "experts" are supposed to take the politics out
of tax revenue projections. It is not clear however how they arrive at
their forecasts. A state legislator told me they don't use any decision
support systems. The revenue numbers continue to fall and the shortfalls
seem to only get worse.
Lynn Okamoto, a staff writer for the Des Moines Register, reported May
8, 2002 the "Off-the-mark revenue estimates over the past year have led
to criticism of the Revenue Estimating Conference, the three-person
panel that lawmakers rely upon to determine the size of the state budget
pie." Iowa Governor, Tom Vilsack, recently replaced his appointee to the
panel with banker Holmes Foster. Foster noted in an interview with
Okamoto, "This isn't an exact science. I would characterize it as a very
intelligent guess."
What is the revenue estimating decision process? IT DEPENDS! Processes
vary among the states. In Michigan, a Revenue Estimating Conference is
held each January and it is a major part of the budget process.
According to a document at the State of Michigan Web site
(http://www.michigan.gov), during the conference, "national and state
economic indicators are used to formulate an accurate prediction of
revenue available for appropriation in the upcoming fiscal year." The
Web page also notes "The principal participants in the conference are
the State Budget Director and the Directors of the Senate and House
Fiscal Agencies or their respective designees. Other participants may
include the Governor and senior officials from the Department of
Treasury." A second Revenue Estimating Conference takes place in June of
each year.
In Florida, the following seven step revenue estimating process is used
(http://www.fgfoa.org):
1. The process begins with a national economic forecast.
2. The national economic forecast establishes the basic parameters for
making a state economic forecast.
3. Once a particular level of state economic activity is agreed upon,
state revenues can be forecast.
4. Demographic and other relevant data are taken into consideration.
5. The principals use the adopted national and state economic forecasts
to derive forecasts of state revenue collections.
6. For the General Revenue Fund, separate forecasts are made for each
major tax source.
7. The forecasts for each separate tax source are debated, and a
consensus forecast is agreed upon.
In Tennessee, the "revenue estimating process generally starts twelve
months before a fiscal year begins. Revenue collections are tracked on a
monthly basis, and this information, along with specific long-run
forecasts of individual sectors of the economy, is used to form the
basis for the next fiscal year's estimated revenue collections.
Preliminary estimates are supplied to the Department of Finance and
Administration in mid-summer by the Department of Revenue and the
University of Tennessee Center for Business and Economic Research. Tax
estimates are recalculated in October and November and refined in
December and January for inclusion in the Governor's Budget Document
(from www.state.tn.us/finance/bud/overview/revsource.pdf).
According to a web page at the State of Tennessee Web site, their
revenue estimating process incorporates the "Good Practices in Revenue
Estimating" endorsed by the National Association of State Budget
Officers and the Federation of Tax Administrators. The web page notes
this "requires the use of national and state economic forecasts,
development of an official revenue estimate, monitoring and monthly
reporting on revenue collections, and revision of estimates when
appropriate."
In Rhode Island, a Revenue Estimating Conference develops a consensus
forecast (cf., http://www.budget.state.ri.us).
In general, estimators are supposed to take into account state tax law
changes, federal tax law changes, and updated economic factors. In some
U.S. states, the revenue estimating authority is in the executive branch
(e.g., Treasurer, Comptroller, or revenue collection agency). In other
states, authority has been placed in a consensus conference process.
What role, if any, do Decision Support Systems play in tax revenue
projections? The Federation of Tax Administrators
(http://www.taxadmin.org) holds an annual Revenue Estimation and Tax
Research Conference. At the 2001 conference in Minneapolis, the agenda
included working sessions on Dynamic Estimation and Modeling, Data
Base/Blurring Techniques, Tax Expenditure Reporting/Estimation
Techniques, and Revenue Forecasting. Michael Lipsman of the Iowa
Department of Revenue and Finance presented a paper titled "Hunting for
Leading Indicators: The Iowa Experience". Reece Womack, Oklahoma Tax
Commission, presented "A Survey on the Use of Multivariate Time series
Analysis in State Revenue Forecasting." William Witzleben, New York
Dept. of Taxation and Finance, presented "Corporate Tax Simulations --
Modeling and Distributional Analysis." From what I can tell some tax
administrators are using data and models to support revenue estimating.
The sophistication of the DSS is however unclear.
The American Economics Group (www.americaneconomics.com) sells a
software product called RevCast™ for State and Local Government Revenue
Forecasting. Their marketing materials claim it can help prepare
accurate monthly forecasts of taxes and other revenue, link revenue
expectations to forecasts of the regional economy, track monthly revenue
against target forecast ranges, receive early warning of revenue
shortfalls or surpluses and reduce revenue-estimating volatility.
RevCast™ sounds almost too good to be true. If you are using the
product, let me know how it is working.
What's going wrong with the processes, models, and Decision Support
Systems? Some possibilities ... the forecasters and decision makers may
be
1) making consensus estimates based on limited information.
2) relying too much on simple moving average forecasts.
3) using simplistic revenue models that need major revisions or
incorrectly specifying the revenue model (cf., Haynes and Henry, 1974).
4) using incorrect data or data that is outdated.
5) failing to recognize the economy has fundamentally changed (cf., Haynes
and Henry, 1974).
6) reading tea leaves instead of using a crystal ball; perhaps they need
Crystal Ball 2000 (the Excel add-in at http://crystalball.com).
Building a new model-driven Decision Support System or improving current
DSS won't eliminate the current revenue shortfalls ... all Decision
Support Systems can do is give decision makers better, more timely
estimates so they can prepare better budgets, respond faster to changes
in the economy and tax laws, and hopefully reduce the impact of future
revenue shortfalls or make proactive changes to reduce any shortfalls.
As Haynes and Henry (1974) note, "forecasting is both an art and a
science (p. 135)." Let's not forget the science of forecasting.
References
American Economics Group at http://www.americaneconomics.com/rev.html
Haynes, W. W. and W. R. Henry. Managerial Economics: Analysis and Cases
(3rd edition). Dallas, TX: Business Publications, Inc., 1974.
Michigan.Gov Office of the Governor FAQ, "What is the purpose of the
Revenue Estimating Conference?", at URL
http://www.michigan.gov/gov/1,1431,7-103-786-2659--F,00.html
O'Kain, S. "Consensus Estimating Process - Overview," presented at 2001
Florida Government Finance Officers Association Annual Conference,
http://www.fgfoa.org/conf_2001/SteveOCainLCIRReveueEstimatingPrograms.pdf.
Okamoto, L. "New, lower revenue estimate keeps budget nightmare alive."
Des Moines Register newspaper, 05/08/2002,
http://desmoinesregister.com/news/stories/c4780934/18134495.html
State of Tennessee, "State Tax Revenues", at URL
www.state.tn.us/finance/bud/overview/revsource.pdf.
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Spreadsheet-based DSS Tip
Hide Unused Rows and Columns
Users can get lost navigating the more than 16 million cells in an Excel
worksheet. For practical and aesthetic reasons hide the unused rows and
columns. Rodney Powell in a posting at beyondtechnology.com explained
the steps to "hide" rows:
1. Select the row header just beneath the used area of your spreadsheet,
where you want to start hiding rows.
2. Press Ctrl + Shift + Down Arrow. This will highlight everything from
your selected row through the bottom of the worksheet.
3. From the worksheet's Format menu, choose Row, then Hide.
Follow the same basic steps to hide columns. Check
http://www.beyondtechnology.com/tips003.shtml
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What's New at DSSResources.COM
06/02/2002 Posted article by Herlihy, J. "Simulation software: A
powerful way to improve organizational decision making",
DSSResources.COM, 06/02/2002, URL
http://dssresources.com/papers/dssarticles.html.
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