|Volume 57 (4) — October 2008
Economic valuation and application of services
As discussed in Elements for Life (2007), published in conjunction with the WMO International Conference on Secure and Sustainable Living: Social and Economic Benefits of Weather, Climate and Water Services (Madrid, Spain, 19-23 March 2007), there are several reasons for assessing the economic value of hydrometeorological services (Lazo 2007).
This leads to a discussion of appropriate methods for economic assessment. As part of an effort to build a foundation for the use of economic research and applications, we describe a resource we are developing—the Primer on Economics for National Meteorological and Hydrological Services (see box on previous page). We will be releasing this primer as an introductory overview of benefit-cost analysis to encourage the use of appropriate economic methods for assessing hydrometeorological programmes.
The economy and economics
No less important than the collection of water data for water resources assessment is the archiving and dissemination of water data. Those preparing assessments must be able to access the wide array of hydrological and related data needed to evaluate water availability and trends and to do so in such a way as to simplify the data-analysis process.
Measuring the economic impact of hydrometeorological services and information typically involves assessing the impact of hydrometeorological events or forecasts of events on specific economic sectors such as trans-portation, energy or agriculture. Changes in measures of output, employment, revenue or taxes are presented as the economic impacts of these events or forecasts.
Although these measures do result in useful information, we would like to make a distinction between “the economy” and “economics.” Merriam-Webster (www.m-w.com) defines economy as “the structure or conditions of economic life in a country, area or period; also: an economic system”. The term economy, then, is usually construed as the productive system of a country or region, and economic impacts are interpreted as disturbances to productive activity. Output, employment, revenue or taxes are all related to productive activity—but they do not necessarily indicate changes in societal well-being.
In what seems to follow the same conceptual meaning focusing on production, economics is defined as “a social science concerned chiefly with description and analysis of the production, distribution, and consumption of goods and services” (www.m-w.com). Digging deeper, however, we find that social sciences are concerned with understanding “the institutions and functioning of human society and with the interpersonal relationships of individuals as members of society (www.m-w.com)”. As a field of study of human behaviour, economics extends well beyond the productive activities of an economy; economics as a social science considers the full range of impacts on individuals, firms and society. This includes changes in public goods, environmental effects, health impacts, population distributions, vulnerable populations and all other aspects of individual and societal welfare. Welfare economics is the area of economics specifically concerned with the overall welfare of society, including economic efficiency and income distribution.
Focusing only on the economy as a system of production can bias decisions toward purely monetary/economic outcomes and neglect adequate consideration of overall societal welfare. According to Lazo et al., 2007(a):
“The distinction between measures of economic activity and measures of economic welfare is important. Measures of activity, even if expressed in monetary units (e.g., output), do not tell us the value of the activity. In other words, these measures do not tell us what people would be willing to pay for that activity. Welfare measures, on the other hand, are specifically designed to quantify what people are willing to pay for something. As a result, welfare measures of benefits are appropriately compared to the costs that people pay for those benefits.”
To achieve one of WMO’s stated goals—“a strategic approach to the implementation of the PWSP (Public Weather Services Programme) that would help NMHSs to realize a quantum change in the delivery of products and services”6—we encourage continuing to develop a focus on societal welfare rather than the more limited conception of maximizing “economic” measures.
Hydrometeorological services rely on data, and great care, effort and expense are put into observing, assimilating, manipulating, creating and disseminating data. In essence, the fundamental function of hydro-meteorological services can be charac-terized as the collection and transformation of data into information, e.g. transforming observations into forecasts. The hydrometeorological community does an incredible job in this complex effort.
But data on damage from hydro-meteorological events, although of considerable importance to the hydrometeorological community, receive little attention. We do not address this topic with particular expertise but, instead, from a position of concern about the quality of damage data we have identified while updating the National Center for Atmospheric Research (NCAR) Extreme Weather Sourcebook (a collection of data on severe weather events in the USA available at www.sip.ucar.edu/sourcebook/index.jsp). As we worked to update damage data in this resource from 1999 to 2006, we dug deeper into the sources of these data and looked at how damage from hydrometeorological events are assessed in the USA.
As an example, the National Weather Service (NWS) has built “Storm Data”, which is probably the primary source of damage data used in the USA (see www.ncdc.noaa.gov/oa/climate/sd/). Under NWS Storm Data, guidelines for calculating hail damage to a structure’s roof7, only the cost of the new roofing material is considered as damages. The NWS uses this approach, which precludes any consideration of the labour required for repairing damaged structures, to calculate damage from almost all hydrometeorological events in the USA.
On the other hand, for hurricane damage, the insurance industry supplies data on insured losses, which are then doubled and reported—by the NWS and others—as the damage from a hurricane. Because insurance data for hurricanes include the costs of labour for replacing damaged property, this information more closely represents the total real cost of repairing or replacing damaged property. Doubling these numbers is an attempt to account for damage to uninsured property and undercounted damages. For a similar incident, then, an approach deriving damage data from insurance industry information would yield a higher damage estimate than the approach used by the NWS.
Perhaps unaware of the limits of damage data, some researchers have undertaken analysis of available disaster damage data to argue that there have or have not been changes in weather-, climate- and water-related impacts on society over relatively long periods of time. It is difficult to put much confidence in this type of analysis when the underlying data on damage are of questionable quality. Furthermore, to the extent that decision-makers use storm-impact information, there should be concern about their ability to make fully informed decisions. As stated in the supporting material for Bouwer et al., Table S2 (2007): “Because of issues related to data quality, the stochastic nature of extreme event impacts, length of time series, and various societal factors present in the disaster loss record, it is still not possible to determine the portion of the increase in damage that might be attributed to climate change due to GHG [greenhouse gas] emissions”. One of the policy recommendations from Bouwer et al. is “We recommend the creation of an open-source disaster database according to agreed-upon standards.”
Numerous experts have assessed loss estimation (see box, previous page). These documents discuss appropriate conceptual and theoretical frameworks for assessing loss from natural disasters and hydrometeorological events which are largely based on accepted economic theory of social welfare measurement. In addition, assessing societal losses requires valid and reliable economic analysis of costs and benefits of these events, using methods not particularly different from those we discuss in the next section. As a result, we feel that, within readily available literature, the issues surrounding the need for higher-quality damage data are well identified and that a conceptual and theoretical framework for assessing damage already exists. We question, though, whether there is an adequate understanding of the importance of collecting reliable damage data within the hydrometeorological community. We also doubt that it is adequately understood that the currently available damage data are of questionable quality. In the USA at least, the public weather service (NWS) is the agency currently collecting and reporting damage data. We perceive that the agency is investing inadequate resources to ensure that this is undertaken in a reliable and consistent manner.
To encourage and increase capacity in economic methods, we are completing a document titled Primer on Economics for National Meteorological and Hydrological Services (Lazo et al., 2007(b))8. This primer, which covers economic theory, methods and applications, is mainly for members of the weather community (it is available for download at
Given that weather forecasts are quasi-public goods9, the economic value of most weather forecasting services is not directly observed in the market. For this reason, it is difficult to determine the economic value of improvements in weather forecasting. In the primer, we offer guidance on the theories, methods and applications that can be applied to valuing projects or programmes that improve hydrometeorological forecasts.
The primer focuses on a step-by-step approach to benefit-cost analysis. Figure 1 from the primer (reproduced on the preceding page) outlines these basic steps, which are discussed in more detail at a level accessible to non-economists in the primer itself. An important part of any valuation effort, as indicated on the right-hand side of Figure 1, is making connections with stakeholders. In the NMHS context, stakeholders are typically the users of the information that is to be produced by the programme under consideration, but decision-makers and different parties within the NMHS itself are stakeholders as well.
BOUWER, L.M., R.P. CROMPTON, E. FAUST, P. HÖPPE and R.A. PIELKE Jr., 2007. Confronting disaster losses. Science 318: 753.
EBI, K.L., T.J. TEISBERG, L.S. KALKSTEIN, L. ROBINSON and R. WEIHER, 2004: Heat watch/warning systems save lives: estimating costs and benefits for Philadelphia 1995–98. B. Am. Meteorol. Soc. August, 1067–1073.
Elements for Life, 2007: A Publication for the International Conference on Secure and Sustainable Living. Tudor-Rose, Leicester, United Kingdom.
LARSEN, P.H., M. LAWSON, J.K. LAZO and D.M. WALDMAN, 2007: Sensitivity of the US Economy to Weather. National Center for Atmospheric Research, Boulder, Colorado, USA.
LAZO, J.K. 2007: Economics of weather impacts and weather forecasts. In: Elements for Life:, Tudor Rose , Leicester, United Kingdom.
LAZO, J.K. and L. CHESTNUT, 2002: Economic Value of Current and Improved Weather Forecasts in the US Household Sector. Stratus Consulting, Boulder, Colorado. USA.
LAZO, J.K., M.L. HAGENSTAD, K.P. COONEY, J.L. HENDERSON and J.S. RICE, 2003: Benefit Analysis for NOAA High Performance Computing System for Research Applications. Stratus Consulting, Boulder, Colorado. USA.
LAZO, J.K., T.J. TEISBERG and R.F. WEIHER, 2007(a): Methodologies for assessing economic benefits of national meteorological and hydrological services. Elements for Life, Chapter 9, Tudor Rose, Leicester, United Kingdom.
LAZO, J.K., R.S. RAUCHER, T.J. TEISBERG, C.J. WAGNER and R.F. WEIHER, 2007(b): Primer on Economics for National Meteorological and Hydrological Services. US Voluntary Cooperation Program Contribution managed by the NWS International Activities Office and NCAR Societal Impacts Program. National Center for Atmospheric Research, Boulder, Colorado, USA.
TEISBERG, T.J., R.F. WEIHER and A. KHOTANZAD, 2005: The economic value of temperature forecasts in electricity generation. B. Am. Meteorol. Soc. 86(12): 1765–1771