Blue Ribbons and Ontario Universities

 

Ontario has made a number of announcements regarding its post-secondary education sector.  First, it extended its tuition freeze for a third year.  As you might recall, going into the pandemic, Ontario reduced domestic student tuition fees by ten percent and since then has held them frozen meaning that after three years of time and inflation, in real terms tuition has been reduced by at least twenty percent.  Though not emphasized this week, Ontario’s government grants to its universities also remain largely frozen.  As a result, with flat domestic enrollment and frozen funding, at the margin, Ontario university revenues have only been growing because they have been recruiting international students making them increasingly reliant on what could be a volatile source of revenue.

 

However, the provincial government is concerned enough about the future sustainability of its universities that it has also announced a Blue Ribbon Panel to “provide advice and recommendations to the Minister of Colleges and Universities to help keep the post-secondary education sector financially strong and focused on providing the best student experience possible. ” A blue ribbon panel or committee is generally a group of “exceptional” and “accomplished” people who are brought together to study a particularly vexing problem and bring their expertise to bear on providing solutions.  It is several notches below a Royal Commission but designed to bring a semblance of non-partisan expert advice to a problem.  This panel is expected to report back in summer of 2023.

 

Now it has already been remarked that there are no faculty representatives on this Blue Ribbon Panel.  However, it would be churlish to say the least to infer that the provincial government in any way thinks that university or college faculty are neither exceptional or accomplished.  In trying to choose faculty representatives for such a committee, one opens a can of worms larger than the universe.  If the provincial government had selected someone from the humanities, professional schools and sciences would have complained they were being neglected.  If a scientist, the humanities would have been outraged. If they chose a faculty member from a large university, the small universities would have complained.  If they had picked an economist, there would have been a chorus of criticism charging that the outcome was predetermined by selecting a minion of the capitalist neoconservative hegemony. You get the picture.

 

So in the end, the government chose a panel from people that it sees as leaders without a direct current stake in universities to study the problem.  There are no faculty nor do  there appear to be any currently serving university administrators on the panel either, though they all have links to post-secondary education in one form or another as well as board and community experience.   There is a member with student experience.  And there is past faculty and administrative experience in the case of Bonnie Patterson and Alan Harrison – who incidentally is also an economist who was at McMaster teaching at the time I was there in the 1980s.  There does seem to be an emphasis on CEO types with some financial experience and also strong representation from the new age of e-learning and other types of perceived innovative practices in education with CEOs from E-Campus Ontario and Contact North.

 

So, what will the panel decide? Well, that is to be decided obviously though I suspect e-learning and micro-credentials is one direction they are likely to emphasize. That will not please some universities who are trying to bring everyone back to the before times with full in person learning and on campus presences including night classes.  However, this is motivated as much for pedagogical reasons as it is for financial ones rooted in the need to fill parking lots, residences, and cafeterias with paying customers.  

 

The crux of the problem is that the provincial government thinks universities should be training people and providing marketable skills and is not happy where the money seems to be going.  Parents and students think that universities are supposed to provide the ticket to a career and lifestyle and do not seem to think the tuition fees worth what they are getting – though they still insist their kids go to university.  Essentially, the provincial government and the public do not perceive they are getting value for money especially given what appear to outsiders to be high paying cushy jobs for university faculty and staff. 

 

Meanwhile, university faculty believe they are independent researchers and scholars, building minds, and extending the frontiers of knowledge while university administrators seem to be conflicted players of whack-a-mole - negotiating the competing demands of government, parents and students, donors, and faculty and staff.  As the old university adage about where the money should go goes, faculty like new faculty hires, Deans like new programs, while University Presidents like new buildings.  So, the interim solution by government has been a grant and tuition freeze which universities have got around by bringing in more international students who can be charged as much as the market will bear.

 

Needless to say, this is not a scenario for long-term sustainability.  Demographics suggest that domestic enrollment in Ontario has peaked and will remain flat for some time to come.  Thus, without an increase in tuition fees, domestic students will not lead to increased revenues.  Moreover, domestic students want a flexibility in their learning environments – i.e., online learning – that seems to be at odds with the preferences of many university administrators and faculty.  Bringing in more international students is also not a stable long-term solution given that at any time that tap could shut.  And then there is the question that if the Ontario public university system is something Ontario does not want to pay more for either via public funds or private (more tuition) and is increasingly geared to international students, then why should it be as large as it is?

 

This last question is the uncomfortable one but needs to be asked given the increasing financial stress Ontario universities are facing especially in the shadow of the Laurentian bankruptcy.  Does Ontario have too many public universities given domestic demand? That is a question the Blue Ribbon Panel will inevitably have to answer.  Perhaps there should be mergers and rationalizations culminating in several province wide campuses – A University of Southwestern Ontario, A University of Eastern Ontario, a University of Northern Ontario and then a half dozen or so fully comprehensive research universities?  Should some universities be merged with community colleges to create Polytechnics?  Should there be a provincial E-University to satisfy the demand for flexible credentials earned online?  But then what of the rest of the system?

 

In the wake of the Laurentian debacle, the provincial government has nevertheless been creating new small financially weaker universities left right and center so it appears they are not too concerned that there may be too many universities.  Moreover, all communities with their own current university campuses will scream if their university is no longer a “real” university or does not offer the range of programs they are used to having.  Just don’t ask them to pay for it.  The political cost of major change is high and as a result there is unlikely to be any major change. 

 

My guess, is that along with keeping all the current players in Ontario’s university system, the end game is going to be the creation of assorted new online learning options independent of the current system and perhaps even new targeted private micro-universities that will provide the programs the provincial governments thinks should be offered.  This is in keeping with the provincial government policy towards its post-secondary sector of the last thirty years that has allowed colleges to evolve into perceived lower cost universities and universities expand their physical footprints without much thought as to what might happen down the road. 

 

Some of this is already underway and what the Blue Ribbon Panel may offer is some way of moving forward in a transitioning post-pandemic university environment that is still moving towards an unknown equilibrium.  At minimum, it will provide a justification for what the government wants to do. On the other hand, the panel may surprise everyone – including the government - with their recommendations which is why governments do not necessarily follow what Blue Ribbon Panels or Royal Commissions for that matter, suggest they do.

 

 

 

Is Canada’s Labour Shortage Actually a Productivity Slump?

 

Despite what seems to be supply side issues of staff shortages, rising demand and inflation in the wake of the pandemic, at least one contrarian view is that Canada’s labour shortage is an illusion. University of Waterloo economist Mikal Skuterud in a recent Globe Oped noted that despite perceptions of a labour shortage, Canadian labour force participation was identical to what it was in October 2018 at 65.7 percent and the absolute size of the labour force at 20.8 million is the largest it has ever been.  The “shortage” may indeed also be a result of the demand for workers in the post pandemic surge growing faster than their numbers.  Indeed, if one looks at the health sector, the supply of physicians and nurses per capita is the largest it has ever been but the post pandemic surge in dealing with postponed surgeries and procedures has been overwhelming.

 

However, the problem may be worse than you might think.  Not only is the size of the labour force the largest is has ever been but so is total employment.  If you look at the number of people employed – producing labour units so to speak - it is three percent higher than it was in 2019.  Employment did plunge in 2020 as a result of the pandemic shutdowns but it has since rebounded dramatically – by over 9 percent since 2020.  As the accompanying figure illustrates, employment is indeed the highest it has ever been.  [Data Sources: Statistics Canada, Table 14100393 Labour force characteristics, annual and 

V62471340 Canada [11124]; Gross domestic product at market prices]

 

 

However, despite more people working than ever before, the output response has been shall we say a bit sluggish?  While employment grew by over 9 percent from 2020 to 2022 as pandemic recovery set in, real GDP (in $2012 constant dollars) grew by less than 9 percent.  As a result, output per employed person has actually declined since 2020.  From 2020 to 2022 real GDP per employed person actually fell by just over one third of one percent.

 

Something has happened over the course of the pandemic that seems to have affected the productivity of Canadian workers.  Perhaps the long shutdown resulted in a deterioration of human capital and skills?  Perhaps the retirement of so many experienced workers and their replacement by less experienced entry level workers has led to output disruptions as new workers learn by doing?  Or, after the trauma of the pandemic, everyone wants more work life balance and as a result we are simply not working as hard as we used to?  Is this simply the aggregate effects of “quiet quitting?”

 

Such slowdowns in output per employed person are not unique to the pandemic era and based on the chart have occurred before – for example during the Great Recession and also between 2014 and 2016.  History suggests that we do recover from these “productivity” slumps and based on past performance one would expect the same over the next couple of years.  The disruption of the pandemic will take a number of years to fully work its way through the economy and the social fabric of the country.  The bigger problem in terms of productivity is if this time things are going to be different, and the productivity slowdown becomes a permanent feature.  Given that Canada has had economic productivity issues for decades, this latest iteration of an old issue is disconcerting to say the least. 

Police Month Continues! Police Spending in Ontario Municipalities

 

It appears that police month is continuing here on Northern Economist as I came across police spending data from the latest BMA Municipal Report  - the 2022 edition.  The BMA reports provides estimates of net policing costs per capita both excluding and including capital cost amortization as well as per $100,000 dollars of assessment.  These costs vary quite a bit and the BMA report has a preamble to the police cost data explaining that such costs can vary a great deal depending on the daily inflow and outflow of non-residents and commuters, the policing of specialized facilities in a municipality such as casinos or airports as well as demographic characteristics, the urban rural mix, service levels and the complexity of crimes. 

 

 

 

Having noted all that, Figure 1 provides a chart ranking policing costs per capita for Ontario’s thirty largest municipalities and they range from a high of $461 per capita for Thunder Bay to a low of 258 dollars per capita for Milton, Burlington and Oakville, which of course all share the costs via the Halton Regional Police Service. Indeed, a number of these other communities have the same per capita net costs because they are part of a regional service – Durham Region (Clarington, Ajax, Whitby, Oshawa, Pickering), York Region (Vaughan, Richmond Hill, Markham, Waterloo Region (Kitchener, Waterloo, Cambridge), Niagara Region (St. Catharines, Niagara Falls).  

 

 

 

In trying to find out what might account for these differences, the simplest starting point is a plot of net per capita costs against the number of officers per 100,000 population as provided in Figure 2.  It is a positive relationship with each additional officer per 100,000 resulting in an addition of nearly 2 dollars per capita in policing costs.  These are policing costs and are not simply the cost of an officer but include all the costs and support associated with that officer.  As you will recall in an earlier post, police forces also have a fairly large number of civilian employees as support.  However, simply stating that having more officers is going to cost more money is not terribly helpful in trying to explain differences across municipalities. 

 

 

 

Figure 3 plots the net costs per capita against the area of the municipality in square kilometers.  For most of these police forces, as area rises, the per capita cost actually falls though it starts to rise for very large municipal areas.   The plot vaguely resembles a u-shaped cost curve but it is ultimately not very satisfying chart because there is not really much in the middle to support the fitted curve.  The three communities on the far right who make up the upward sloping part of the curve are Hamilton, Ottawa and Greater Sudbury.

 

 

 

Figure 4 looks at the relationship between policing costs per capita and the community crime severity (Using the 2021 Crime Severity Index from Statistics Canada).  Once the polynomial curve is fitted to these points, one can say with a certain degree of confidence that there is an positive relationship between crime severity and police costs per capita but it is somewhat non-linear with one peak at a crime index of 50 and another at about 90.  Why this relationship might be bi-modal is either statistical luck of the draw with this data set or the subject of future research.

 

 

 

 

Finally, Figure 5 plots police costs per capita against total population to see if there are any economy of scale type relationship that might emerge.  The u-shaped curve that emerges is misleading because only Toronto is responsible for pulling up that curve,  For the rest of the communities, as population rises, per capita policing costs come down.  In a sense, throwing Toronto into the mix is not useful because it is so much larger than any other municipality in Ontario. 

 

I guess, the ultimate insight would be to run a regression with some of these variables as determinants of per capita policing costs.  Table 1 below presents the results of a very simple regression of net police costs per capita on municipal area (square kilometers), total population, Crime Severity Index in 2021 and regional fixed effects variables with the GTA/Central as the omitted comparison category.  Policing costs per capita do appear to be significant and positively related at the 5 percent level with Crime Severity and being in northern Ontario.  Population also seems to be a positive driver but again that is likely being driven by Toronto.  Still, it could be that really large populations create much more complex crime environments that really drive up police costs per capita.

 

 

So, in the end, what drives police costs in Ontario? Crime Severity is likely a factor but regional differences as captured by regional dummy variables also seems to be of some importance.  Obviously, more research is needed on this topic but that is time intensive and assumes that someone actually wants an answer.

Policing in northern Ontario's cities: Some Stats

 

It does seem to have turned into Police Month here on Northern Economist given that the first four February posts have been reposting of a series I did for the Fraser Institute Blog on policing in Ontario.  Well, to add to that, some charts, and statistics on policing in the five major northern Ontario cities: Thunder Bay, Timmins, Greater Sudbury, Sault Ste. Marie, and North Bay. The data is from Statistics Canada - Table 35100077 - Police personnel and selected crime statistics, municipal police services, annually.  These provide an overview of police resources in these cities and perhaps the best way to start off is with an overview of crime.  

 

 

 

Figure 1 plots the Crime Severity Index for these five cities over the period from 2000 to 2021.  Crime Severity was on a downswing in all five of these cities from about 2010 to 2015 and since then appears to have increased.  Between 2015 and 2021, crime severity increases 19 percent in Thunder Bay, 61 percent in Timmins, 78 percent in Sault Ste. Marie, 48 percent in Sudbury and 67 percent in North Bay.  While in 2010, crime severity was highest in Thunder Bay,  by 2021 Thunder Bay’s crime severity had been surpassed by Timmins, Sault Ste. Marie, and North Bay.

 

 

 

Figure 2 plots the number of municipal police officers for each of these five cities and the largest forces are in Sudbury, followed by Thunder Bay, Sault Ste. Marie, North Bay and then Timmins. In 2021, Sudbury reported 257 officers while Thunder Bay had 238, the Sault 136, North Bay 98, and Timmins 81.  Between 2000 and 2021, all of these police forces grew but since 2015 the Timmins force has actually shrunk 5 percent while the Sault and Sudbury remained flat and Thunder Bay and North Bay grew 7 percent each. 

 

 

 

Of course, police forces also employ civilian and other personnel to support the officers and Figure 3 plots the total number of these employees for the 2000 to 2021 period also.  Relative to 2015, these have increased though after a lengthy prior period of staying flat.  However, the largest increases appear to characterize Sudbury and the Sault.  Between 2015 and 2021 the number of civilian and other personnel employed by police forces grew 10 percent for each of Thunder Bay and Timmins, 24 percent in North Bay, 42 percent in the Sault and 43 percent in Greater Sudbury. The ratios of supporting civilian staff to police officers vary across these communities.  For example, Thunder Bay has 1 civilian staffer per 2.4 police officers while Sudbury has 1 civilian staffer per 1.6 officers.  

 

 

 

Another comparison is provided in Figure 4  for  police officers adjusted for population, that is police officers per 100,000 population and suggests an overall long-term increase in all of these communities from 2000 to 2021.  The most officers per 100,000 population in 2021 is in Thunder Bay at 200 per 100,000 followed by Timmins at 192, then the Sault at 177, North Bay at 170 and Greater Sudbury at 152.  Since 2015, the number of officers per 100,000 has grown 5 percent in Thunder Bay, 6 percent in North Bay, stayed flat in the Sault, and actually declined by 2 percent in each of Timmins and Sudbury.

 

 

 

Finally, what is the relationship between policing and crime in these five northern Ontario cities.  Well, figures 5 and 6 plot two relationships.  Figure 5 plots the Crime Severity Index against police officers per 100,000 and puts in a linear trend and it shows a positive correlation between crime severity and police officers per 100,000 population with a fair amount of variation around the trend.  In terms of interpreting the result, it could mean that communities with more crime severity need more police officers, or it could be that communities with more police officers are able to report more crimes.  However, if one wants a better indicator of effectiveness, then figure 6 may be more helpful.  It plots the relationship between weighted clearance rates and the number of police officers per 100,000.  Clearance rates are the proportion of crimes that are cleared, that is a charge laid.  A weighted clearance rate attaches a higher weight to the clearance of more serious crimes such as homicides. The chart shows a positive correlation between clearance rates and the number of officers per 100,000 also with a fair amount of variation around the trend. 

 

 

 

So, what is the takeaway from all this?  First, policing resources and crime severity vary a great deal even within these five communities.  Some of them have seen substantial surges in crime severity since 2015.  Second, police forces not only employ police officers, but they have substantial civilian staff also and some of them have more support staff than others. An important question is if these additional staff are able to free up officers for more police work and how effective their supporting roles are, but this question is difficult to assess without a lot more analysis with more confounding factors considered.  Third, as limited an analysis as it is, having more police officers per 100,000 is correlated with higher clearance rates though how many convictions ultimately result is also something that requires more data. 

 

One wonders if any of these municipalities have actually done any analysis of the data they must obviously have that examines their staffing and the relationship between staffing, crime rates and crime severity and clearance rates.  One imagines that all of these municipalities have in place staff with statistics training to do data analytics that can then be used to assess their own needs and performance as well as make the case for resources when the need.  I am actually surprised there was not more mention of things like "more officers improve clearance rates" in much of the public discussion reported on police requests for hiring.  Or, for that matter that large proportions of many police forces are nearing retirement age and you need to plan for replacements.  On the other hand, I may simply have an over active imagination and assume a lot about the abilities of public sector entities to make effective cases on their own behalf.

Policing and Crime in Ontario, Part 4: estimating needs

 This post originally appeared on the Fraser Institute Blog February 7th. This is the fourth and final post in the series.

In estimating policing needs across Ontario municipalities, one approach is to estimate the determinants of police resources then compute a predicted staffing level. Using the results for the police regression estimated in the third post in this series, we can construct an estimate of police resources per 100,000 for each community based on community characteristics such as available property tax resources, population density and regional variation, and then compare actual staffing with what would be predicted by the regression.

The chart and table below present the results of this exercise and plot the actual number of police per 100,000 versus the predicted for each municipality, and rank the results by the size of the difference between the two amounts.

 

 

 

 

 

Brantford has the largest difference with 182 actual officers per 100,000 and a predicted level of 163 resulting, for 19 more officers per 100,000 than the model would predict. It’s followed by Oakville (16 more), Windsor (14 more) and Thunder Bay (13 more). Indeed, of these 30 municipalities, about half have more officers per 100,000 than predicted, ranging from Brantford (19) to Niagara Falls and Hamilton (approximately 1 each). Toronto is also just above its predicted staffing at about one officer more per 100,000 than predicted. The remaining municipalities have staffing below what the model would predict, ranging from about one officer per 100,000 less for Ottawa and Richmond Hill to Oshawa (12) and Kitchener (14).

Of course, these are estimates and there can be other extenuating factors that affect police staffing and hiring in respective municipalities as well as the weight of historical staffing patterns. For example, Windsor is a border city, with cross-border demands and traffic, and an entry point into Canada that requires more policing while Thunder Bay has long-standing issues with high homicide rates, which absorb substantial investigative resources. One also wonders if the presence of casinos in some of these cities may lead to a need for more resources. Moreover, like the rest of the labour force, police forces are aging and some of the proposed hiring may reflect replacements of retirements rather than overall staffing increases.

And for those municipalities that are part of regional police arrangements, the results provide an interesting comparison of what their population sizes suggest their policing resources should be and what they’re getting via a regional arrangement.

For example, Oakville, Burlington and Milton based on the Halton Region staffing all are assigned 117 officers per 100,000 population by the statistics. The predicted staffing per 100,000 is 101 for Oakville, 120 for Burlington and 113 for Milton. Whether this is reflected in actual day-to-day operations or is simply a statistical artefact is an interesting question. It’s also interesting that even though some municipalities are near the bottom in terms of actual police officers per 100,000 population relative to other large Ontario municipalities, they still have more officers than predicted.

However, these results may assist in revisiting the cases of Toronto, Hamilton, Sudbury and Thunder Bay mentioned at the outset of this series. In the case of Toronto, there are long-standing narratives that the police force is either overstaffed or understaffed. Moreover, this debate has occurred against a background of recent rising crime and rising policing costs, more complicated policing needs, issues of racism and defunding, calls for alternate investment in areas like homelessness or mental health, and responses to evolving events such as the recent violence on Toronto public transit. Hamilton has issues similar to Toronto and in the evolving debate over its proposed increases in the policing budget there’s the recent news that Hamilton had a historic drop in homicides in 2022. Does this mean that Hamilton’s police force is so effective that it does not need more officers? Or that Hamilton just got lucky in 2022 and crime rates are more random than one might imagine?

Making resource decisions in a heated, emotionally or politically charged debate environment driven by the events of the moment is not always the best policy approach. While empirical evidence is but one piece of the decision-making process, both Toronto and Hamilton appear to be very close to what the determinants in the models would predict their staffing levels to be, suggesting that other factors notwithstanding, at the very least, better deployment of existing resources may be something worth considering.

Sudbury, on the other hand has eight fewer officers per 100,000 population than the model would predict while Thunder Bay already has 14 more officers per 100,000 than one might expect. A simplistic interpretation of these results given their size would be that Sudbury should go ahead and hire more while Thunder Bay should not. However, if Sudbury is currently able to achieve its policing goals with fewer resources, then it should not automatically feel compelled to ramp them up. As for Thunder Bay, the picture there’s probably more complicated than even a regression equation can possibly imagine, but that still does not mean better deployment of existing resources should not be a complement to whatever else is deemed necessary.

The takeaway from all this is that policing and public safety is complex and complicated and more effort should be made to acquire evidence to support decision-making. The types of results are simply one piece of evidence that can go into resource allocation decisions at budget time, and municipal ratepayers and their city councils should make wise use of all available information. This is especially the case given the large increases in tax rates that seem to be marking municipal budget season in Ontario this year.