Flashpoint: May 2013
By Peter Sells
In researching this column, I came across a paper published in 2012 by the Institute on Municipal Finance and Governance of the Munk School of Global Affairs at the University of Toronto.
By Peter Sells
In researching this column, I came across a paper published in 2012 by the Institute on Municipal Finance and Governance of the Munk School of Global Affairs at the University of Toronto. The paper Economies of Scale in Fire and Police Services in Ontario, by PhD candidate Adam Found, set forth to determine what size municipality, received fire and police services at the lowest cost for its size.
Found ran a very detailed statistical analysis using data for the years 2005 through 2008. His results suggest that “the lowest fire costs are achieved by medium-sized towns and large townships, where the population is about 20,000 residents.”
The political/organizational appetite for a study such as this is well understood across the Canadian fire service. All governments have been under constant pressure to create efficiencies, whether through service reductions or amalgamations/consolidations. Between 1995 and 2001, successive Ontario governments passed legislation that led to the amalgamation of hundreds of municipalities. As stated by Found, the over-arching assumptions were that fewer and larger municipalities would reduce municipal bureaucracy and inefficiency, realize cost savings from economies of scale, and provide clear lines of accountability by capturing costs and benefits within the same jurisdiction.
Found has described the economies of scale very well with respect to costs, but what of benefits? I believe that there is a false assumption that economies of scale are the same as efficiencies of scale. We all want more bang for the buck. Economies of scale deliver for fewer bucks. Efficiencies of scale deliver more bang. In other words, bucks are the input into the municipal machine; bang is the output.
The only specific output measures mentioned by Found are average response time and emergency calls per household. However, my attention was drawn to his note that “more emergency calls per household and lower response times will represent higher service levels delivered.”
Let’s take those assessments one at a time. Found notes elsewhere in the paper that “if residents with strong preferences for low emergency response times tend to live in large cities, higher fire service costs would be observed for larger municipalities.” I would tend to characterize those residents, or at least the small subset of them who actually base their choice of residence on fire protection, as those with strong preferences for lower insurance premiums. Still, there is no doubt that faster response comes at a price.
It is the statement that “more emergency calls per household will represent higher service levels” that I call into question. A fire department isn’t better simply because it is busier; in fact, I would suggest the opposite.
Found also states his assumption that “full-time fire departments are better able to deliver public education and fire prevention services compared with volunteer fire departments.” I don’t think that the assumption is necessarily true of all full-time or all volunteer fire departments, but as a general statement about services provided by better-funded larger versus lesser-funded smaller organizations, I will agree with it in principle. However, there is a tendency in performance measurement to confuse activity with results. It is inconsistent to describe a fire department that is better able to deliver public-education and fire-prevention services as desirable and also describe more emergency calls per household as a higher level of service. Logically, better fire-prevention or public-education services should result in fewer fires and therefore fewer emergency calls. Found’s study was very well constructed, but needed to have deeper fire-service perspective and a broader scope.
With access to historical data only, I crunched some numbers to see if I could determine what size municipality gets the most effective outputs (more bang). Working with data for 144 fire departments, covering 92 per cent of Ontario’s population, I determined that, with respect to the average annual number of structure fires per capita, there is a general efficiency of scale as population increases, with the minimum (best bang) at two million residents. The average annual number of fire injuries per capita was essentially flat across the board, with an extremely slight efficiency as population increased. Finally, correcting for the greatly divergent property values across Ontario, the estimated structural fire loss per $100,000 of property assessment was analyzed, with the best bang being indicated for a population of 1.25 million.
Conclusion? Just like when buying a car, a computer or a free-agent defenceman, when it comes to fire service you can shop on price or you can shop on performance.
Retired District Chief Peter Sells writes, speaks and consults on
fire service management and professional development across North
America and internationally. He holds a B.Sc. from the University of
Toronto and an MBA from the University of Windsor. He sits on the
advisory council of the Institution of Fire Engineers, Canada branch.
Peter is president of NivoNuvo Consulting, Inc, specializing in
fire-service management. Contact him at firstname.lastname@example.org and
follow him on Twitter at @NivoNuvo.