|Methods and techniques|
Indirect loss is a loss resulting from a peril but not caused directly and immediately by that peril( e.g.,loss of property due to fire is a direct loss, while the loss of rental income due to the fire would be an indirect loss. Indirect loss tend to be intangible and difficult to quantify. It will often exceed the direct losses by a wide margin, and will continue to be felt for a long time after the disaster is over. For example, a study of the share prices of 15 firms which survived disasters showed that it took an average of around 50 days for the share prices to get back to the level they had been prior to the event. A low share price may seem to be a rather distant problem for a company to have, but it could have a very real effect on the company's efforts to recover, for example, by making it more difficult to secure lines of credit.
Indirect losses are modeled using economical or statistical approaches. Indirect loss levels are often either considered a function of the direct damage level to the structures or are considered a function of the economic sectors. Various models allow calculating the potential indirect losses. These models range from simple linear input-output models examining the interdependencies of sectors, to nonlinear general equilibrium-based systems that attempts to include all relevant changes to the economy after an event.
It is important to understand the timing of economic disruptions that trigger indirect losses in order to plan for efficient emergency responses and to assess the cost-effectiveness of alternate mitigation strategies. The committee recommends that a microsimulation model be developed to create a timeline of regional commercial and industrial closures. Other models that should be devised include a formal restoration model and a comprehensive indirect loss model.
The most important indirect losses
The most important losses to be considered are therefore those which are consequential or indirect. Indirect losses:
- Loss of confidence by customers, employees and shareholders
- Loss of market reputation and market share
- Breaches of regulations or legal requirements
- Penalties payable for late or non-delivery
- Loss of data leading to loss of management control
Categories of losses
All firms and individuals face uncertainties caused by the possibility of loss. A risk management program is an organized method for dealing with risks. The program begins with identification and measurement of exposures to loss. The second step is to choose an approach from among the risk management alternatives and then to implement the decision. The third step is reevaluating and updating previous decisions. The identification process begins with the recognition of four categories of losses:
- direct losses of property, such as the loss of a machine in a fire
- indirect losses of income, such as the loss of income if a useiness cannot operate because its most important machine burned
- liability losses, such as being sued for negligently injuring a customer
- loss of key personnel, such as the loss of a research scientist who has been responsible for several important inventions.
An estimate of the total potential loss that given peril could cause is an important step in the risk management program. The maximum possible loss is the damage that could be caused under the worst possible circumstances. The maximum probable loss is the most likely maximum amount of damage a firm might sustain from a loss given normal circumstances.
- A. Hawker 2010, p.219
- G. Michel 2017, p.144
- The Impacts of Natural Disasters: A Framework for Loss Estimation 1999, p.47
- A. Hawker 2010, p.219
- M.S. Dorfman 1998, p.63-64
- Dorfman M.S., (1998), Wprowadzenie do zarządzania ryzykiem i ubezpieczeń, Prentice Hall, Little Rock.
- Hawker A., (2010), Security and Control in Information Systems: A Guide for Business and Accounting, Psychology Press, New York.
- Michel G., (2017), Risk Modeling for Hazards and Disasters, Elsevier, Amsterdam.
- Impacts of Natural Disasters: A Framework for Loss Estimation, (1999), National Academies Press, Washington.
Author: Karolina Urbańczyk