Risk. The word has become so common today that we give it little consideration. Everyone uses it, from the Main Street to Wall Street. The problem, of course, is that so few people understand it. Few understand it, and even fewer know how to evaluate it. Just this morning I read an article regarding infrastructure risk produced by a popular consulting firm, and it became obvious in the second paragraph that the author had little concept of risk, let alone how to evaluate it. It quickly became my biased opinion that he had likely always worn wingtips, while having never laced a pair of muddy boots. Unfortunately, however, a lack of understanding does not preclude these persons from using the word risk, nor serving as an “expert” in the field. I suppose this is how the definition of expert came to be—a fool from out of town.
Before we begin our discussion, though, let’s be clear; we are referring to risk engineering here, not the infamous risk management that has permeated society in recent years. In other words, we are not referring to risk management from a mere insurance perspective at this point. We are referring to risk engineering—the analyses, assessments, and evaluations behind risk management. Providing adequate insurance to cover the risk requires comparatively little thought; analyzing, modeling, and forecasting risk is another construct altogether, and thus, should be conducted by only the savviest among us. In terms of infrastructure risk, that person must understand engineering, construction, research, statistics, probability, economics, and operations research, just to begin understanding risk. So, I cringe when I hear so-called experts throw around the word risk as if they were ordering breakfast. As they say, it is one thing to be ignorant and quite another to open your mouth and prove it.
So, in terms of infrastructure investment, what is risk, and how do we evaluate it? Well, risk is the likelihood of specific events and their impacts on variables of concern. So, risk engineering is the analysis of impacts derived by or through specific events on variables of concern. Just as we would view this situation from a research perspective, we gage dependent constructs against independent constructs. But also, as with scientific research, we cannot gage construct against construct; constructs must be operationalized into variables, first. Considering this issue from an example we can more easily understand, suppose you are mandated to develop a rubric through which you can measure the intelligence of your employees. How would you go about such an endeavor? I mean, intelligence defines who we are to some degree; it determines our possibilities—again, to some degree. So, obviously it is important. So, what is intelligence? How will you define it? And, how will you measure it? Is it defined by who occupies the corner office? Is it defined by who generates the most revenue for the company? Is it defined by who has the highest level of education? Or, is intelligence determined by who scores the highest on some employment entrance exam. Of course not. While these loose descriptives may serve as outcomes of intelligence, they certainly do not define intelligence. As such, we must first define, as a rather weak definition of the term operationalize, our constructs into variables. We must define exactly what we will consider as specific events, as well as upon which variables they may act that we consider important. Now, if you got all of that, we are ready to move forward.
Now suppose you are tasked with determining the financial risks associated with a PPP, or P3, client wanting to construct a large infrastructure project; P3 projects are good initiatives for us to pick on as they have various players with similar, differing, and competing risks that must be addressed long before the go-no-go decision is rendered. Broadly consider a few of the risks. Long planning processes with endless regulatory issues, environmental and mitigation concerns, engineering misjudgments and errors, unforeseen construction cost overruns and escalations, safety concerns and even construction deaths; and this is only the construction side of the equation. What about poor forecasts regarding technical output generated through the project? Or operational issues that occur with maintaining profitability? Then there are funding issues—capital outlay, operating, and expansion. Risks are endless with large initiatives such as this, and we have yet to consider the economics side of the equation. What happens when the project was constructed in a developing country as a means of not only providing say, electricity to an underserved region, but with the idea that electricity consumption may serve as a catalyst for increased economic output, such as increases in GDP, jobs, incomes, and tax generation, and such does not occur as planned? Then again, in this example, we still have not considered corruption via people tapping into electricity somewhere along the various paths of distribution; the volumes of under the table money-swaps that often occur on large infrastructure projects, especially in developing countries; or the probability of interruptions from war or terrorism.
As such, each risk must be independently analyzed, modeled, and forecasted, and considered in some form of cost—or equal unit—so they can be compared against benefits. Of course, another way to compare risks with unequal units is to standardize data using z-scores, or even some kind of rate, e.g. cost per capita or cost per MW. Along this same note, remember to not only use like units, but when forecasting data associated with some form of currency, make sure to use nominal dollars (values that are in current dollars), real dollars (values that are adjusted for inflation or other escalations or de-escalations), chained dollars (values in real dollars from a given year), or the like across all risks and benefits for equal consideration.
Obviously, a brief article such as this could never capture all risks associated with large endeavors. To this end, there are two fundamental components to investment risk in terms of evaluation; the cost side and the benefit side. Consider the relationship as you would in terms of feasibility—a cost and a benefit. If the benefit-cost ratio (BC) is greater than 1, we consider the project feasible. From a risk perspective, if the reward outweighs the risk, we consider moving forward with the project. If not, we consider alternatives. The problem, of course, remains that persons charged with rendering these decisions oversimplify the analyses, with no idea how to simultaneously incorporate into the analytical mix both financial variables and economic variables. As such, these same persons make what should be an arduous undertaking overly simple. Thus, I suppose it remains no wonder why so many financial endeavors fail to deliver the planned results and leave owners lost in a quandary of ambiguity.
About the Author
Herbert M Barber, Jr, PhD, PhD is a respected author, engineer, economist, researcher, and expert in financial and economic performance. Over the last 30 years, Dr. Barber has provided advisory and consulting in engineering economic systems as it relates to the implementation of large economic endeavors in industry and infrastructure across multiple countries. He is a seasoned scientific researcher with a keen understanding regarding the statistical and econometric effect and causality large financial and economic endeavors have on companies, governments, industries, and economies around the world in both developing and developed economies. Prior to assuming his role with Xicon Economics, Dr. Barber served in leadership positions with Seminole Southern, Fluor Corporation, and Jacobs Engineering.
About Xicon Economics
Xicon Economics brings intellectual rigor, objectivity, and real-world experience together to solve complex engineering, economic, and financial problems in an effort of increasing financial and economic output. Whether calculating the economic and financial feasibility of constructing a new high-speed rail system, analyzing policy changes in various regulatory agencies, mining smart grid data to develop real options valuations, or developing advanced energy algorithms, Xicon Economics stands ready to make a difference.
More simply, we leverage our backgrounds in the hard sciences to grow entities in the private and public sectors in an effort of growing economies while serving as experts in economics, research, and statistics. Our specific expertise in these areas can be found by visiting www.xiconeconomics.com.