by William Hooke, AMS Policy Program Director
from the AMS Project Living on the Real World
In the early 1900’s, my grandfather faced a challenge at work. Though only a teenager, he was foreman in a foundry making cast-iron bathtubs in Chattanooga, Tennessee. His company was struggling. A large number of the bathtubs they produced were defective – so badly flawed they had to scrap them. They were losing money. What to do?
My grandfather was a baseball fan.[1] He solved the problem the way a baseball fan would. He got a big blackboard. He hung it on the foundry wall. He wrote every workman’s name on it. Next to each name he started keeping a tally: how many passable bathtubs had that worker produced that week? And what was his batting average? Of all the workers, who was the best that week? The Top Tubber? The MVP?
The workers reconnected with their competitive side. Almost overnight, the foundry’s output shot up. Defects went down. No one had to be threatened with loss of a job. No one had to be offered any more pay. Morale improved. All that was needed? A scorecard.[2]
Maybe we can scale this up. If we want to reduce disaster losses, why shouldn’t we start by keeping score? We certainly do this in other areas. Take the economy. Think GDP growth, month by month, or year on year. Unemployment statistics. Balance of trade. Housing sales. Inflation. Consumer confidence. These and other figures make the news on a regular basis. They drive financial markets. They trigger public discussion and debate. And as they do, they help us improve national economic performance. Increasingly, such figures drive the environmental discussion as well: the number of high-ozone days per year in cities; fish stock statistics; global temperatures; atmospheric carbon dioxide concentrations and emissions. And it doesn’t stop there. Metrics, measures of success, performance and results, and indicators permeate the whole of the public policy discussion. OSHA tracks accidents in the workplace, seeking to reduce them. In all those public schools opening today, we’ll test students and compare their reading and math scores with kids from other schools in the United States and abroad. It is a preoccupation both nationally and internationally. The nomenclature changes from field to field, or from administration to administration, but the idea is the same. We hope to get better at things we measure.
No idea is completely new. This notion, or something like it, has already come up in the hazards arena. Take the StormReady Program run by the National Weather Service. Counties, communities, Indian nations, universities, and other groups or organizations – all can qualify by meeting certain criteria. For the most part, these criteria focus on preparedness…how alert is the community in question to NWS warnings? How ready are they to respond? The Institute for Business and Home Safety, a group of insurers, has its Fortified program, which recognizes homes that have been constructed to a higher, safer standard than the normal building codes. In earlier posts, we talked about the disaster loss figures tallied annually by Munich Reinsurance.
All well and good. But it reminds me of another family vignette. When my dad was in his late seventies, he had a blood workup at the clinic. Looking at the lab results, the doctor said, “Mr. Hooke, you have the blood of a 26-year-old!” “That proves,” my father replied, “that you’re measuring the wrong things.”
It’s also important to measure not just what is easy to measure, but what matters most. Suppose, on that blackboard, my grandfather had tallied attendance? Or who arrived earliest? Or stayed the latest? Performance in those areas would have improved. And it might well have helped the foundry, but only indirectly. Something similar happens in baseball. Statistics tempt athletes to focus on their individual batting averages, or runs batted in. But over the past half-century, managers have started refining those measures…what happens to a player’s batting average when men are on base? How about RBI’s when the game is still in doubt? And what really counts is not the number of runs but the “W.” That’s the bottom line for the team.
When we apply this test to hazards and the above examples we see some work still remains. It’s important for communities to be ready to react to oncoming storms. But maybe we should be engineering our homes and towns so there’s less need for evacuation or taking special shelter. The Fortified program addresses this to some extent, but doesn’t address the community-wide performance of critical infrastructure, and the extent of business disruption. How should we capture these dimensions in our scorecard? Consider the drought and heat wave of 1980. Water levels were so low in the Mississippi River basin that barges couldn’t ply their trade. The result was a spike in the cost of getting coal to utilities all through the Midwest. Farmers and ranchers in the southern tier of states lost something like twenty billion dollars of livestock and poultry. But as one economist told me at the time, this didn’t show up in national GDP figures, because farmers in the northern tier of states received higher prices for their cattle and chickens that year. So, in terms of the national accounts, there was little or no net loss to the economy. !!! But there was a $20B transfer payment from the southern states to the northern states. And GDP was an inadequate measure of the turmoil and disruption for many Americans that year. [Something similar happens in flooding, or windstorm damages. The property loss may be compensated in part by a construction boom.]
The last serious look at these questions – what to measure? How? Why? – may date back more than a decade. (Know of more recent comprehensive studies? Please let me know!) In the late 1990’s, under the auspices of the National Academies of Science National Research Council (NAS/NRC), Robert E. Litan of the Brookings Institution led a Committee on Assessing the Costs of Natural Disasters. They wrote a report: The Impacts of Natural Disasters: A Framework for Loss Estimation. The findings and recommendations included a look at the diverse kinds of direct loss that are incurred, a call for greater attention to indirect losses such as unemployment and business disruptions, encouragement for more uniform, standardized measurement of losses across different hazards, and much more. The Committee recommended that the Bureau of Economic Analysis of the U.S. Department of Commerce, working with other federal agencies such as FEMA, might take the lead in establishing the needed data base. They noted: “researchers and experts in disaster loss estimation could benefit from a standardized data base that would enable them to improve estimates of both the direct and indirect losses of disasters. These improvements in turn would assist policymakers in their efforts to devise policies to reduce the losses caused by future disasters.” [emphasis added][3]
So as we all return to work after Labor Day, let’s remember our forebears – those foundry men and others. And in every workplace, whether we’re building cars or reducing disaster losses, let’s keep an eye on the score.
[1]one of a long line! Three generations of my family spent this past Sunday afternoon at a minor league game.[2] Did my grandfather use this incident as a springboard to a distinguished business career? Did he go on to invent the science of quality control? No, he became a college French professor. But that’s another story…
[3]There’s a connection here to earlier posts: the need for more research in valuation, and the special role that could be played by the Department of Commerce.
My grandfather was a baseball fan.[1] He solved the problem the way a baseball fan would. He got a big blackboard. He hung it on the foundry wall. He wrote every workman’s name on it. Next to each name he started keeping a tally: how many passable bathtubs had that worker produced that week? And what was his batting average? Of all the workers, who was the best that week? The Top Tubber? The MVP?
The workers reconnected with their competitive side. Almost overnight, the foundry’s output shot up. Defects went down. No one had to be threatened with loss of a job. No one had to be offered any more pay. Morale improved. All that was needed? A scorecard.[2]
Maybe we can scale this up. If we want to reduce disaster losses, why shouldn’t we start by keeping score? We certainly do this in other areas. Take the economy. Think GDP growth, month by month, or year on year. Unemployment statistics. Balance of trade. Housing sales. Inflation. Consumer confidence. These and other figures make the news on a regular basis. They drive financial markets. They trigger public discussion and debate. And as they do, they help us improve national economic performance. Increasingly, such figures drive the environmental discussion as well: the number of high-ozone days per year in cities; fish stock statistics; global temperatures; atmospheric carbon dioxide concentrations and emissions. And it doesn’t stop there. Metrics, measures of success, performance and results, and indicators permeate the whole of the public policy discussion. OSHA tracks accidents in the workplace, seeking to reduce them. In all those public schools opening today, we’ll test students and compare their reading and math scores with kids from other schools in the United States and abroad. It is a preoccupation both nationally and internationally. The nomenclature changes from field to field, or from administration to administration, but the idea is the same. We hope to get better at things we measure.
No idea is completely new. This notion, or something like it, has already come up in the hazards arena. Take the StormReady Program run by the National Weather Service. Counties, communities, Indian nations, universities, and other groups or organizations – all can qualify by meeting certain criteria. For the most part, these criteria focus on preparedness…how alert is the community in question to NWS warnings? How ready are they to respond? The Institute for Business and Home Safety, a group of insurers, has its Fortified program, which recognizes homes that have been constructed to a higher, safer standard than the normal building codes. In earlier posts, we talked about the disaster loss figures tallied annually by Munich Reinsurance.
All well and good. But it reminds me of another family vignette. When my dad was in his late seventies, he had a blood workup at the clinic. Looking at the lab results, the doctor said, “Mr. Hooke, you have the blood of a 26-year-old!” “That proves,” my father replied, “that you’re measuring the wrong things.”
It’s also important to measure not just what is easy to measure, but what matters most. Suppose, on that blackboard, my grandfather had tallied attendance? Or who arrived earliest? Or stayed the latest? Performance in those areas would have improved. And it might well have helped the foundry, but only indirectly. Something similar happens in baseball. Statistics tempt athletes to focus on their individual batting averages, or runs batted in. But over the past half-century, managers have started refining those measures…what happens to a player’s batting average when men are on base? How about RBI’s when the game is still in doubt? And what really counts is not the number of runs but the “W.” That’s the bottom line for the team.
When we apply this test to hazards and the above examples we see some work still remains. It’s important for communities to be ready to react to oncoming storms. But maybe we should be engineering our homes and towns so there’s less need for evacuation or taking special shelter. The Fortified program addresses this to some extent, but doesn’t address the community-wide performance of critical infrastructure, and the extent of business disruption. How should we capture these dimensions in our scorecard? Consider the drought and heat wave of 1980. Water levels were so low in the Mississippi River basin that barges couldn’t ply their trade. The result was a spike in the cost of getting coal to utilities all through the Midwest. Farmers and ranchers in the southern tier of states lost something like twenty billion dollars of livestock and poultry. But as one economist told me at the time, this didn’t show up in national GDP figures, because farmers in the northern tier of states received higher prices for their cattle and chickens that year. So, in terms of the national accounts, there was little or no net loss to the economy. !!! But there was a $20B transfer payment from the southern states to the northern states. And GDP was an inadequate measure of the turmoil and disruption for many Americans that year. [Something similar happens in flooding, or windstorm damages. The property loss may be compensated in part by a construction boom.]
The last serious look at these questions – what to measure? How? Why? – may date back more than a decade. (Know of more recent comprehensive studies? Please let me know!) In the late 1990’s, under the auspices of the National Academies of Science National Research Council (NAS/NRC), Robert E. Litan of the Brookings Institution led a Committee on Assessing the Costs of Natural Disasters. They wrote a report: The Impacts of Natural Disasters: A Framework for Loss Estimation. The findings and recommendations included a look at the diverse kinds of direct loss that are incurred, a call for greater attention to indirect losses such as unemployment and business disruptions, encouragement for more uniform, standardized measurement of losses across different hazards, and much more. The Committee recommended that the Bureau of Economic Analysis of the U.S. Department of Commerce, working with other federal agencies such as FEMA, might take the lead in establishing the needed data base. They noted: “researchers and experts in disaster loss estimation could benefit from a standardized data base that would enable them to improve estimates of both the direct and indirect losses of disasters. These improvements in turn would assist policymakers in their efforts to devise policies to reduce the losses caused by future disasters.” [emphasis added][3]
So as we all return to work after Labor Day, let’s remember our forebears – those foundry men and others. And in every workplace, whether we’re building cars or reducing disaster losses, let’s keep an eye on the score.
[1]one of a long line! Three generations of my family spent this past Sunday afternoon at a minor league game.[2] Did my grandfather use this incident as a springboard to a distinguished business career? Did he go on to invent the science of quality control? No, he became a college French professor. But that’s another story…
[3]There’s a connection here to earlier posts: the need for more research in valuation, and the special role that could be played by the Department of Commerce.