Public works projects. In public works and large scale construction or engineering projects – where uncertainty mostly (only) concerns cost, a simplified scenario analysis is often used.

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Public Works Projects
Posted By S@R On September 3, 2009 @ 7:57 am In Other topics | No Comments
Table of contents for Scenario analysis
1. The fallacies of Scenario analysis [1]
2. Public Works Projects
It always takes longer than you expect, even when you take into account Hofstadter’s Law.
(Hofstadter,1999)
In public works and large scale construction or engineering projects – where uncertainty mostly (only)
concerns cost, a simplified scenario analysis is often used.
Costing Errors
An excellent study carried out by Flyvberg, Holm and Buhl (Flyvbjerg, Holm, Buhl2002) address the
serious questions surrounding the chronic costing errors in public works projects. The purpose was to
identify typical deviation from budget and the specifics of the major causes for these deviations:
The main findings from the study reported in their article – all highly significant and most likely
conservative -are as follows:
• In 9 out of 10 transportation infrastructure projects, costs are underestimated.
For a randomly selected project, the probability of actual costs being larger than estimated costs is
0.86. The probability of actual costs being lower than or equal to estimated costs is only 0.14.
• For all project types, actual costs are on average 28% higher than estimated costs.
• Cost underestimation:
- exists across 20 nations and 5 continents: appears to be a global phenomena.
- has not decreased over the past 70 years: no improvement in cost estimate accuracy.
- cannot be excused by error: seems best explained by strategic misrepresentation, i.e. the
planned, systematic distortion or misstatement of facts inn the budget process. (Jones,
Euske,1991)
Demand Forecast Errors
The demand forecasts only adds more errors to the final equation (Flyvbjerg, Holm, Buhl, 2005):
• 84 percent of rail passenger forecasts are wrong by more than ±20 percent.
• 50 percent of road traffic forecasts are wrong by more than ±20 percent.
• Errors in traffic forecasts are found in the 14 nations and 5 continents covered by the study.
• Inaccuracy is constant for the 30-year period covered: no improvement over time.
The Machiavellian Formulae
Adding the cost and demand errors to other uncertain effects, we get :
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2. Machiavelli’s Formulae:
Overestimated revenues + Overvalued development effects – Underestimated cost – Undervalued
environmental impact = Project Approval (Flyvbjerg, 2007)
Cost Projections
Transportation infrastructure projects do not appear to be more prone to cost underestimation than
are other types of large projects like: power plants, dams, water distribution, oil and gas extraction,
information technology systems, aerospace systems, and weapons systems.
All of the findings above should be considered forms of risk. As has been shown in cost engineering
research, poor risk analysis account for many project cost overruns.
Two components of errors in the cost estimate can easily be identified (Bertisen, 2008):
• Economic components: these errors are the result of incorrectly forecasted exchange rates,
inflation rates of unit prices, fuel prices, or other economic variables affecting the realized
nominal cost. Many of these variables have positive skewed distribution. This will then feed
through to positive skewness in the total cost distribution.
• Engineering components: this relates to errors both in estimating unit prices and in the
required quantities. There may also be an over- or underestimation of the contingency needed
to capture excluded items. Costs and quantity errors are always limited on the downside.
However, there is no limit to costs and quantities on the upside, though. For many cost and
quantity items, there is also a small probability of a “catastrophic event”, which would
dramatically increase costs or quantities.
When combining these factors the result is likely to be a positive skewed cost distribution, with many
small and large under run and overrun deviations (from most likely value) joined by a few very large
or catastrophic overrun deviations.
Since the total cost (distribution) is positively skewed, expected cost can be considerably higher than
the calculated most likely cost.
We will have these findings as a backcloth when we examine the Norwegian Ministry of Finance’s
guidelines for assessing risk in public works (Ministry of Finance, 2008, pp 3) (Total uncertainty equal
to the sum of systematic and unsystematic uncertainty):
Interpreting the guidelines, we find the following assumption and advices:
1. Unsystematic risk cancels out looking at large portfolios of projects.
2. All systematic risk is perfectly correlated to the business cycle.
3. Total cost approximately normal distributed.
Since total risk is equal to the sum of systematic and unsystematic risk will, by the 2nd
assumption,
unsystematic risks comprise all uncertainty not explained by the business cycle. That is it will be
comprised of all uncertainty in planning, mass calculations etc. and production of the project.
It is usually in these tasks that the projects inherent risks later are revealed. Based on the above
studies it is reasonable to believe that the unsystematic risk have a skewed distribution and is located
in its entirety on the positive part of the cost axis i.e. it will not cancel out even in a portfolio of
projects.
The 2nd
assumption that all systematic risk is perfectly correlated to the business cycle is a convenient
one. It opens for a simple summation of percentiles (10%/90%) for all cost variables to arrive at total
cost percentiles. (see previous post in this series)
The effect of this assumption is that the risk model becomes a perverted one, with only one stochastic
variable. All the rest can be calculated from the outcomes of the “business cycle” distribution.
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3. Now we know that delivery time, quality and prices for all equipment, machinery and raw materials
are dependent on the activity level in all countries demanding or producing the same items. So, even
if there existed a “business cycle” for every item (and a measure for it) these cycles would not
necessarily be perfectly synchronised and thus prove false the assumption.
The 3rd
assumption implies either that all individual cost distributions are “near normal” or that they
are independent and identically-distributed with finite variance, so that the central limit theorem can
be applied.
However, the individual cost distributions will be the product of unit price, exchange rate and quantity
so even if the elements in the multiplication has a normal distribution, the product will not have a
normal distribution.
Claiming the central limit theorem is also a no-go since the cost elements by the 2nd
assumption is
perfectly correlated, they can not be independent.
All experience and every study concludes that the total cost distribution does not have a normal
distribution. The cost distribution evidently is positively skewed with fat tails whereas the normal
distribution is symmetric with thin tails.
The solution to all this is to establish a proper simulation model for every large project and do the
Monte Carlo simulation necessary to establish the total cost distribution, and then calculate the risks
involved.
“If we arrive, as our forefathers did, at the scene of battle inadequately equipped, incorrectly trained
and mentally unprepared, then this failure will be a criminal one because there has been ample
warning” — (Elliot-Bateman, 1967)
Bertisen, J., Davis, Graham A. (2008). Bias and error in mine project capital cost estimation..
Engineering Economist, 01-APR-08
Elliott-Bateman, M. (1967). Defeat in the East: the mark of Mao Tse-tung on war. London: Oxford
University Press.
Flyvbjerg Bent (2007), Truth and Lies about Megaprojects, Inaugural speech, Delft University of
Technology, September 26.
Flyvbjerg, Bent, Mette K. Skamris Holm, and Søren L. Buhl (2002), “Underestimating Costs in Public
Works Projects: Error or Lie?” Journal of the American Planning Association, vol. 68, no. 3, 279-295.
Flyvbjerg, Bent, Mette K. Skamris Holm, and Søren L. Buhl (2005), “How (In)accurate Are Demand
Forecasts in Public Works Projects?” Journal of the American Planning Association, vol. 71, no. 2, 131-
146.
Hofstadter, D., (1999). Gödel, Escher, Bach. New York: Basic Books
Jones, L.R., K.J. Euske (1991).Strategic Misrepresentation in Budgeting. Journal of Public
Administration Research and Theory, 1(4), 437-460.
Ministry of Finance, (Norway) (2008,). Systematisk usikkerhet. Retrieved July 3, 2009, from The
Concept research programme Web site: http://www.ivt.ntnu.no/bat/pa/forskning/Concept/KS-
ordningen/Dokumenter/Veileder%20nr%204%20Systematisk%20usikkerhet%2011_3_2008.pdf
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