The first question is more important historically than it is today. Currently, of 32 major league teams only six are located within the same metropolitan area. The Oakland and San Francisco markets probably overlap but the United States Census Bureau conveniently identifies them as separate metropolitan areas and I’m willing to go with that. In the original 16-team leagues of 1900 that stayed fixed into the 1950s, however, no less than 11 teams competed financially on the same turf with two of three New York-based teams participating in the same league.

Although some teams may have had geographic strongholds within their metropolitan realms, as I suppose Brooklyn must have and I’ve always heard the White Sox and the Cubs do (dividing the north and south sides of Chicago), the only way I could see to account for split markets was to divide the metropolitan population of each by the number of teams present. Thus New York, Chicago, and Los Angeles in the present were divided by two as were Boston, Chicago, Philadelphia, and St. Louis of the past. New York to 1958 was divided by three to account for the Dodgers, Giants, and Yankees.

The first observation to make is that even when thus divided, teams in the biggest cities have considerably larger populations on which to draw. The smallest single team markets in the long-standing 16-team league were always smaller than the partial populations in leading cities. Aside from St. Louis, which was the smallest market of all when divided in two and shared equally between the Browns and Cardinals, who generally played in the same stadium, Cincinnati was always the smallest National League market and Detroit or Washington held up the American League.

The major changes resulting from the division of markets are the elevation of Pittsburgh by a narrow margin to most populous of the National League markets in the 1900s and 1910s, and of Detroit to the top of the American League in the 1940s. Both teams did well under those circumstances with the Honus Wagner-led Pirates winning considerably more games than any other team in the National League in the first decade of the century and finishing fourth in the second. The Tigers of the forties were the third best team in junior circuit.

For the most part, though, the rank of markets is not greatly changed by splitting them. A third of the New York market was more than any other team had to work with in the National League from 1920 to 1950. In the American League, it was enough to make the Yankees pre-eminent throughout. The major statistical influence is to lower the populations associated with the generally good performance of all three teams. The consequence for the regression analysis I outlined in my previous post is to marginally improve the correlations between market population and winning, and significantly increase the number of wins associated with increased market population:

Although correlations are generally a bit stronger, the correlation for the National League is negative for three decades running from the 1940s through the 1960s. The correlation coefficient is essentially the same for the sixties but it is a substantial turnaround for the fifties, presumably because the well performing Dodgers and Giants markets are recast from being the largest to the fourth and fifth positions (the Dodgers and Giants count their 1958 and 1959 seasons in Los Angeles and San Francisco, respectively, and the Giants fall back because San Francisco was and remains considerably smaller than New York). Otherwise, it is notable that slopes are steeper in every case but one between the two leagues, which follows from the narrower range of populations.

Another thing that can be done with regression equations and which J. C. Bradbury does in his book, which I referenced in my previous post, is to predict the performance that each team should have given the population that supports it. The following tables give the results for all teams in both leagues by decade. The all-time over performers ironically enough are the San Francisco but previously New York Giants and the (ugh) New York Yankees. The underperformers are the Phillies, who shared their market for about half of their “modern” existence and who hold the world professional record for losses with over 10,000, and the big city Los Angeles Angels of Anaheim. Those who put more weight on a really long losing tradition, however, may prefer the Baltimore Orioles, who did most of their damage as the St. Louis Browns, and the Chicago White who carried a curse from 1919 on. The all-time stretch of futility goes to the Phillies, who contrived to underperform their market by nearly 20 games per year for three decades from the 1920s through the 1940s. Their current dynasty is going to have to last to dig out of the resulting hole.

My Reds have been a consistent if fairly modest overachiever with a particularly notable performance in the seventies when they illuminated my adolescence and young adulthood, after hooking me as a seven year old in 1961, and taking me on a bit of a roller coaster ride through the sixties. As the following table shows the Reds were a whopping 15.3 wins over par as determined by the regression equation for National League performance in the seventies, which is exceeded only by the Cubs of the 1900s, who spent the last half of the twentieth century going in the other direction. Over their history from 1900, the Reds are a more modest 1.5 wins over expectations. The best smaller city performer, in fact, is Ohio’s other team, the Cleveland Indians. Although the Indians were league doormats throughout my youth and have only won two World Series in their history, they have actually been a model of consistency, in the top half of the league outside of the seventies and eighties, and several times breathing down the neck of the dominant Yankees. Their record of 4.0 wins over par, however, is handily exceeded by both the Yankees and the Giants.

Whether or not this means anything is open to question. National League teams in the sixties are actually being discounted wins for population and the generally mediocre correlations suggest that population has never been the determining factor. Whether their large market area has allowed the Yankees to sustain good management through most of their history is open to question. Certainly, most of the New York teams have done well but the performance of the Mets, Chicago’s two teams, and the Angels suggests that franchises with large fan bases do not inevitably succeed. Small city dynasties such as the Orioles, who led the American League in wins through the sixties and seventies; the Pirates of the 1900s and the Reds of the seventies, who dominated the National League; and the Cardinals, who led in the forties, eighties, and the decade just ended also test the point. They account for the seven decades of 22 that teams representing metropolitan areas in the bottom half of either league won the most games.

The importance of the current situation is the growing feeling that the strength of larger markets in 2000s is rooted in the structure of baseball as it has evolved with limited revenue sharing, no salary cap, and the prevalence of free agency. Larger communities have always had a bit of an advantage, as a century plus of analysis suggests, but it hasn’t often risen to the current level. The most comparable decade is the 1950s when the New York teams in both leagues dominated a collection of dying northeastern markets. The result was the redistribution of franchises from historic over-served markets, including New York, to new and growing areas.

Today’s conditions are arguably the opposite. Nearly all viable markets are now served and the three that have more than one team, are more than able to support two franchises. The pressure now is on the smaller single team cities like Kansas City, Milwaukee, and Cincinnati. It seems unlikely that any will lose their team. The Brewers are not that bad and drew over 3 million last year, and the Royals and Reds are drawing surprisingly well given their extended periods of failure.

The question still nags that they may never ever be contenders again because they lack the resources to compete. The Brewers have cycled up for a couple of years but now appear to be sliding back. The Indians also appeared to be making a move a few years ago but have since given away two of the best pitchers in baseball because they couldn’t afford them, handing one to the Brewers, who could only manage to rent C. C. Sabathia before turning him over to the Yankees. The powers that be do little to help. The fact is they seem to like it. Boston-New York games pull in big TV ratings and keep the big city writers humming. Another Oakland-Cincinnati World Series may not be a rational business goal.

On the other hand, enough has to be dangled to keep the small market fans interested. The Reds and the Royals draw enough customers to keep their ballparks painted (I’ve never been to either place but I understand that both Great American Ballpark and Kaufmann Stadium are lovely) but their attendance is in the bottom two of their respective leagues. It was much better for both when they won and if it slips much farther in either case there could be problems. If Major League Baseball wants the Yankees to continue to have a geographically distributed collection of punching bags, Bud Selig, who ought to know a thing or two about small markets, may have to do a bit more to help the little guys.

Bradbury writes on a variety of issues from the non-existence of lefthanded catchers to the positive contributions of pitching coach Leo Mazzone to the superiority of statistics over scouting as a method to identify baseball talent. The essay in his book that naturally interested me the most, however, is called “The Big City v. the Small City Problem.” In it Bradbury applies simple linear regression to assess the correlation between the population of the market served by each major league team and its performance on the field. His analysis is based on average wins per season by all major league teams from 1995 through 2004 related to metropolitan population over the same period.

Bradbury finds what I would expect. Teams playing in larger communities typically do better than their rivals drawing on smaller markets. It is not surprising given that larger markets offer the potential for considerably more revenue, particularly in an era when teams rely as much on their cable contracts and merchandise sales to pay the freight as they do on gate receipts (which also favour teams in larger centres). It is also not surprising when the perennial powerhouse of Major League baseball, the New York Yankees, hail from North America’s largest city.

As a fan of baseball’s longest standing small market team, the Cincinnati Reds, as well as a demographic analyst, my interest runs deeper than Bradbury’s I think. I immediately wondered if there was any difference between the National League, of which the Reds are members and which has been less clearly dominated by a specific team, and the American League, over which the Yankees have long reigned. I also wondered if there was ever a better time in which the small enjoyed a more even playing field.

Expanding Bradbury’s study was not that hard. I knew Baseball Reference.com had posted on-line the records of every major league season from the founding of the National League in 1876. It did not take long to find a convenient site with the historic populations of American metropolitan areas back to 1900. I decided to do my analysis from 1901 because the population data were readily available and that was the year that the American and National Leagues merged to begin what is generally regarded as “the modern era” of baseball’s history.

With the data assembled, it is also not hard to do regression analysis. In fact, Excel will provide the key parameters of a linear regression equation as easily as it will add up a row of numbers and it took me a few minutes to obtain the correlation between wins and population for every National League and American League season from 1901 to 2008, 214 equations in all. A couple more minutes and I had the same for the decades from the 19 oughts to the 20 oughts. In each case, the equation is in the form

y = a +bx.

where

y = the dependent variable (i.e., expected number of wins)

a = the intercept or the number of wins that would be expected in a market with no population whatsoever

b = the slope or the number of additional wins expected for each 1,000 additional residents in the market

x = the independent variable (i.e., population of each team’s metropolitan market in 1,000s)

The equation is also accompanied by a correlation coefficient (r²), which provides an estimate of the explanatory value of the equation as well as the direction of the presumed relationship.

First of all, the National and American Leagues do differ as I supposed, at least to a degree. In 39 of 107 (36.1 per cent) National League seasons, the smaller market teams actually out performed their big brothers as indicated by the slope of regression equation and the sign of the correlation coefficient, which was negative in each case. For the American League, only 22 of 107 seasons (20.4 per cent) showed a negative relationship. Indeed, the average equations for each league further reflect this difference:

The average correlation coefficient for the National League suggests a weak to almost negligible relationship between population and wins. The intercept is also quite close to the average number of wins available, which is just under 78 and the slope is extremely shallow. The explanatory value of the American League equation is substantially higher, although hardly overwhelming. The same can be said for the intercept and the slope of the equation both of which suggest that population has had a more meaningful role in American League success.

The predictive value of the equations improves considerably when numbers are averaged by decades:

The strongest correlations were in the 1950s when the Yankees won eight of ten pennants in the American League and the Dodgers and Giants won six between them before their departure for the West Coast (where the Dodgers won one more in 1959). The weakest were in the first decade of the nineteenth century when the leagues were generally less settled and the twenties before the Yankees got their chokehold on the American League. In fact, the correlation between population and wins was stronger in the American League for every decade after the twenties than before, with the exception of the 1970s.

Other notable decades are the 1940s, when population appears to have had no bearing whatsoever on performance in the National League and the 1960s when it was negatively correlated. The forties were arguably another unsettled period with the influence of the war, although population seems to have had plenty of influence in the American League. The perverse sixties, on the other hand, are attributable to the presence of an expansion team in the form of the abysmal Mets occupying the National League’s largest market augmented by the poor performance of the Cubs in the nation’s Second City. It is the only equation of 22 with a negative slope and correlation.

The current era rates among the leading “big market” decades of all time, ranking third for the National League and second for the American League. For the American League the keys have been not only the continued dominance of the Yankees but also strong performances by the Angels in Los Angeles, the Chicago White Sox, and the Boston Red Sox. In the National League, the top teams, St. Louis and Atlanta, represent largish cities, and the third best, the Dodgers, are located in the largest market. The Mets have also done reasonably despite some awful ups and downs. It is also notable that the very worst performers were from the smallest markets in both leagues including Pittsburgh, Cincinnati, Kansas City, and Tampa Bay. In times past, one or two teams of this sort usually held out as a mini-dynasty in the shadow of the Yankees, Giants, and Dodgers. For our current decade the Oakland Athletics have held the role not as occasional winners but as contending also rans, good enough to support a book on the managerial revolution that has kept them within hailing distance of the mountain top.

Small market teams have always had their trials as exemplified by the Reds, whose history is most familiar to me. In 1901, John T. Brush traded future Hall of Famer and hometown boy Christy Mathewson from the Reds to the New York Giants for expired Hall of Famer Amos Rusie before transferring his financial interests from the Queen City to the Empire State. In the early thirties, Powell Crosley bought the Reds out of bankruptcy and saved them by inventing night baseball. They were also in trouble in the 1950s, trailing the league in attendance immediately before their surprise pennant in 1961 and in the mid-1960s when moving the franchise to San Diego or Denver was seriously discussed before the advent of the Big Red Machine. In 1981, the Reds were robbed of a pennant by the stupid split season format adopted to deal with a strike.

These are the travails of the less powerful, exaggerated by the belief of baseball’s administrators and even the media that success in large markets makes for stronger leagues. The Reds and the Expos have been screwed out of pennants they should have won not the Yankees or the Red Sox. Innumerable New York writers have expounded on the glories of the fifties when only one World Series was played without a New York team (and even that one had a former New York team), notwithstanding that the experience was so boring for even New Yorkers that crowds dwindled through the decade. No one, it seems, values the variety of the sixties and seventies when the pennant was shared, and the small market Orioles and Reds demonstrated the virtues of cultivating players within a well managed organization. Baseball attendance rose during those years.

Most certainly, Zipf’s notion that the largest city in a country typically has twice as many residents as the second largest city, three times as many as the third largest, and n times as many as the nth largest is a valuable analytical tool. Adherence to Zipf’s line is an excellent measure of the evenness or unevenness of the geographic distribution of population. Application to a time series provides a telling picture of the evolution of settlement patterns.

In addition to assessing Zipf’s law against major urban areas in the United States and Canada, I also tried it out on the historic populations of Nova Scotia’s counties, which I discussed in the blog that preceded my thoughts on Zipf. I have Nova Scotia data for urban centres but their boundaries have changed frequently and substantially over the years so that they are less suitable than the province’s counties as a means to assess the changing distribution of our population. As I pointed out previously, no change has been made to the boundaries of Nova Scotia’s counties since Confederation.

For each Census from 1871, I ranked Nova Scotia’s 18 counties by population. I then calculated the deviation of each from the population predicted for its rank by Zipf’s Law. For example, whereas the most populous county in 1931, Halifax, had a population of 100,204, Zipf would predict that the second largest county by population, Cape Breton should have a population of 50,102. Given that Cape Breton in 1931 had a population of 92,502, it was out by more than a little. Its total deviation from Zipf’s expected population was 40,400 or 84.6 per cent.

My overall measure is simply the average of the absolute values of these deviations for all 17 counties in each Census, shown in the following table. In all of the Censuses listed, Halifax was the largest county. Until 1911, the smallest was Queens. After 1911, the mantle of tininess passed to Victoria County in Cape Breton. As I noted in my blog on the counties, the population of the smallest has generally fallen while the size of Halifax’s population has relentlessly risen.

Deviation from Zipf, on the other hand, fell consistently to 1961 at which point it began to rise again. The restoration of this measure to its past heights does not, however, reflect a return to a traditional settlement pattern. On the contrary, Nova Scotia passed through Zipf in 1961 and kept on going.

Size of Largest County and Deviation from Zipf’s Law, Nova Scotia Counties, 1871-2006

Prior to 1961, the province’s population was relatively evenly distributed. In 1871, the evenness with which people were spread across Nova Scotia’s territory was remarkable. From 1871 and probably before, its population steadily gathered in Cape Breton County as well as Halifax County. By 1961, however, Cape Breton began its slow decline, while the growth of Halifax accelerated. Whereas Cape Breton was much larger than Zipf would have predicted through the early and middle years of the twentieth century, it is now much smaller and Nova Scotia’s population is much more concentrated. The average deviation of Nova Scotia’s counties from Zipf’s predictions, which fell from 184.0 per cent in 1871 to just 12.2 per cent in 1961, has now risen back to 40.6 per cent.

Absolute Deviation from Zipf’s Law, Nova Scotia Counties, 1871-2006

In all probability it is going to continue to rise as Halifax continues to absorb population from across its hinterland. The hinterland from which Halifax is drawing is, in fact, larger than Nova Scotia. It is most of Atlantic Canada. Halifax is probably not the primary destination of migrants from the French-speaking norther half of New Brunswick, who are mostly attracted to Moncton. It also competes with Saint John/Fredericton for English speakers in southern New Brunswick, St. John’s for migrating Newfoundlanders, and Charlottetown for footloose Prince Edward Islanders. Halifax, however, is winning the battle for the mobile.

At the Atlantic level, in fact, the changing populations of major cities are conforming more and more closely to Zipf. The current rank order of the seven largest Census Metropolitan Areas (CMAs) and Census Agglomerations (CAs) is Halifax followed by St. John’s, Newfoundland; Saint John, New Brunswick; Moncton, New Brunswick; Cape Breton, Nova Scotia; Fredericton, New Brunswick; and Charlottetown, Prince Edward Island. The rank order of population for all seven was the same in 2001, although in 1996, Cape Breton was slightly ahead of Moncton. My projections for the next 15 years based on trends in the 2001 to 2006 period suggest this ranking will continue to be fixed.

Population, Major CMAs and CAs, Atlantic Canada, 1996-2021

The following table provides average deviations for each of these urban centres for the past Census years 1996, 2001, and 2006, and for the predicted future censuses of 2011, 2016, and 2021. Clearly, Atlantic Canada’s major urban centre are falling into Zipf’s line with Halifax at the head. From 1996 to 2006, the average deviation from Zipf’s law fell from more than 27 per cent to barely more than 16 per cent. My projections for the seven centres in question, furthermore, suggest that they will progress further toward Zipf’s ideal, with an average deviation of less than 10 per cent expected by 2021.

Average Absolute Deviation from Zipf’s Law, Major CMAs and CAs, Atlantic Canada, 1996-2021

Whether the fall in overall deviation will continuie is hard to say. Based on my projections, the second and third place cities (i.e., St. John’s and Saint John) appear to be passing through a period of almost perfect fit to Zipf’s distribution and beginning to diverge by falling somewhat behind my expectations for Halifax as you can see in the following bar chart. The fourth through seventh place cities are also off Halifax’s pace but, with the exception of the seventh (i.e., Charlottetown), all are somewhat bigger than Zipf would predict and are all gradually dropping into line. In the longer run, I would expect, Halifax to surge well ahead of the position suggested by Zipf to even greater prominence in the Atlantic region, which will increase the average absolute deviation, which I am using as my primary measure of fit. That is not at all a knock on the six smaller urban regions behind Halifax. Its just an acknowledgement of the strengths that have put Nova Scotia’s capital in the lead and are likely to benefit it for some time to come.

Percentage Deviation from Zipf’s Law, Top Seven CMAs and CAs, Atlantic Canada, 1996-2021

According to Strogatz, Zipf’s Law describes:

… [a] striking regularity in the size distribution of cities. He noticed that if you tabulate the biggest cities in a given country and rank them according to their populations, the largest city is always about twice as big as the second largest, and three times as big as the third largest, and so on. In other words, the population of a city is, to a good approximation, inversely proportional to its rank. Why this should be true, no one knows.

My immediate reaction to any such powerful and readily verifiable statement as this is to check it out, which I did within a few minutes of finishing the column. On the Web I found that those who like Zipf’s Law, like Strogatz, like it a lot. Many responded within a few days of Strogatz’s column with examples of city size distributions from varied countries that demonstrate or test Zipf’s construct. A good one can be found at the Infrastructurist blog site. Another site I found provides a compilation of cities by size for most of the major countries in the world. The data are a bit old (the Canadian populations are from 1991, the American numbers from 1994) but amply justify the authors’ faith in Zipf.

The prevalence of opinion and hard data didn’t stop me though. I immediately downloaded the list of Canadian Census Metropolitan Areas (CMAs) from the Statistics Canada Web site and the list of Stantard Metropolitan Statistical Areas from the United States Census Bureau site and tested Zipf north of the Rio Grande. The results are a bit different between Canada and the United States but neither undermines Zipf much for anyone but the dedicatedly literal.

The figure following illustrates the relative size of the ten largest CMAs in Canada and the ten largest SMSAs in the United States with the curve predicted by Zipf. The fit is pretty good in both cases with the Canadian metropoli arranged a bit erratically around Zipf’s curve but invariably close to it, while their American equivalents describe a curve that fits closely to Zipf’s slope but which lies significantly above the Zipf line. While the average deviation of the top ten Canadian urban centres from Zipf’s prediction (16.5 per cent) is considerably less than their American counterparts (73.4 per cent), the US distribution would be remarkably close to the Zipfian ideal if we could only get New York up to 24 million souls.

Ten Largest Canadian CMAs, 2006, and Ten Largest US SMSAs, 2003, Relative to Zipf’s Law

When we plot all 33 of Canada’s CMAs, the resulting image really is remarkable. Although the average deviation goes up slightly to 19.3 per cent, the visual fit is outstanding. The same can be said for the distribution of Ame rican SMSAs, all 364 of which are larger than predicted by Zipf but whose overall distribution closely follows his expected curve. The only noticeable difference is the tendency of US SMSAs to be above the Zipf line, particularly for mid-sized urban areas.

All Canadian CMAs Relative to Zipf’s Law, 2006

All US SMSAs Relative to Zipf’s Law, 2003

The relationship that Zipf’s Law illuminates is not hard to observe. Most countries and regions are dominated by a single large city or urban region. The reasons aren’t that hard to fathom either. Large scale is a well-recognized economic advantage. Once a community has the edge in size the benefits accumulate. All roads do tend to lead to Rome and once they do young people are inclined to follow them. Theorists have suggested that Zipf Law holds because the associations of many people in large places draw many more people to them. They also suggest that the rich get richer as the thriving economies of populous communities attract the downtrodden. Most certainly the bigger the place the more familiar it is and the more obvious it likely becomes to those who can’t find a good time, an education, a marriage partner,  or a job where they grew up.

Nova Scotia Counties, 1865

Nova Scotia Counties, 2009

Census Populations, Nova Scotia Counties, 1871-2006

The first thing I noticed was how much more evenly distributed the province’s population was in 1871 than today. No county had more than 15 per cent of the province’s population and the smallest, Queens, had 2.7 per cent. In 2006, Halifax had 40.8 per cent of the province’s population and seven counties had smaller shares than Queens did in 1871. The smallest, Victoria, had just 0.8 per cent.

Cape Breton had less than half the population of Halifax at Confederation but was within 10 percentage points (90.3 per cent) by 1941. It has since fallen back to just 28.4 per cent. Cape Breton Island, which peaked at 25.5 per cent of Nova Scotia’s population in 1941, has fallen to just 15.6 per cent. Halifax, in contrast, has increased its share steadily from 14.7 per cent to its current 40.8 per cent.

Just four counties recorded their highest Census population since Confederation in 2006. Only Halifax has gained population in every Census since 1871. The three rural counties of Cape Breton have lost population steadily since 1881. Four rural counties reached their nadir in 1931 but all four have recovered. The most notable is Hants, which has more than doubled its population in the years since. Guysborough began a sustained decline in 1901 and now has less than half the population that it had at that time. Cumberland began to lose population in 1921 and now has barely more than three-quarters of its peak population. Antigonish lost population steadily from Confederation to 1931 but gained population in every Census thereafter until the most recent one in 2006. Other counties have mostly peaked since 1981 but only the four now at their historic peak, gained population from 2001 to 2006.

Nova Scotia itself has increased its population nearly as steadily as Halifax, taking a backward step only once, in 2001 when the province lost just over 1,000 residents. Its population is now at its highest point in history, which of course means its population density is at its peak with 17.26 persons per square kilometer. Many areas of the province, however, have much lower population densities than in the past. The title of Nova Scotia’s least populated county has been exchanged periodically between Victoria County and Guysborough County, which covers the extreme eastern tip of the Nova Scotia mainland. Victoria has held the distinction for 13 of the 17 Censuses listed in the following table but Guysborourgh edged Victoria in 1891 and since regaining the title in 1996 has widened the gap to nearly half a person less per square kilometer.

Population Densities (persons per square kilometer), Nova Scotia Counties, 1871-2006

Halifax County has not always been the most densely settled area of Nova Scotia. In 1871, Richmond, Antigonish, and Cape Breton were all more tightly packed than Halifax. It’s astonishing to think that Richmond, which is now eleventh among Nova Scotia’s 17 counties in terms of population density, was once the leader. It is probably even more amazing to realize that Halifax only surpassed Cape Breton by this measure in 1981, less than 30 years ago.

Over this period, the population density in the province’s most sparsely settled county has more often than not fallen further. It is now 56.7 per cent of the minimum in 1871. The maximum population density recorded for any county has risen between every Census but one (1961 to 1971) and is now nearly six times the 1871 level. Not surprisingly, the standard deviation, which measure the dispersion in each distribution of population densities,  has risen relentlessly and now stands at more than six times its 1871 level.

What Nova Scotia’s post-Confederation demographic history illustrates very nicely is the process of urbanization. The settlement pattern of the province was surprisingly uniform when it joined with Upper and Lower Canada, and New Brunswick in 1867. I notice that on my job all the time when I work in rocky areas on the edge of Halifax that were once farmed or in rural communities where clients mention cabins, roadways, and former industries “in the woods” or try to develop ideas to revive communities that once supported schools at all three levels and had a downtown with a post office but are now occupied by a few hundred senior citizens. Population has been steadily drawn to the cities. At one time it was drawn to Sydney and the cluster of towns around it that now constitute Cape Breton Regional Municipality (for practical purposes the former Cape Breton County) as well as Halifax, but for nearly 50 years now it has largely gone to Halifax alone. In fact, since 1971, Halifax has begun to spillover into the adjacent counties of Hants, Colchester, and Kings, which share the distinction with Halifax of being the only counties that grew from 2001 to 2006.

The process of urbanization, furthermore, has had several interesting steps. The five counties that reached their nadir by 1931 included three that relied heavily on agriculture (Annapolis, Antigonish, and Hants). The other two, Shelburne and Queens were primarily oriented to the fishery. The four counties that have reached bottom since 1951 are in the province’s northeast: the three rural counties of Cape Breton and Guysborough all of which have relied on a mixture of resource industries. Many other rural resource areas are also losing population. This includes three former coal mining counties (i.e., Cape Breton, Pictou, and Cumberland) that generally grew well after Confederation but all of which peaked by 1981.

As Nova Scotia enters an era in which it seems very likely that it will eventually experience a decline in population, Halifax seems likely to maintain its strong position. As the seat of provincial government, a major centre for the delivery of Federal government services, and as the financial and service centre of Atlantic Canada, but especially as the site of several universities, Halifax seems well-positioned to maintain and expand its leadership. Other counties with universities and major educational institutions (Antigonish, Kings, and Colchester) should also be stable. For the remainder of the province, the future depends on the ability of residents in each locality to reposition themselves as the resource industries on which they have traditionally relied continue to decline. The environment and amenities of Nova Scotia offer a promising foundation for resort development and tourism industry on which many will likely have to rely along with repositioned resource-based businesses and niche knowledge-based operations.

I was never much of a customer. Only in the most desperate circumstances was I willing to pay 3 cents extra per litre to have a chat about the weather and the finish on my SUV. Lots of my neighbours, though, valued the experience highly and not only paid extra for their gas but also to have the various whines and clunks coming from their vehicles checked out. The closure gave rise to a newspaper article to which several of these folks supplied nostalgic quotes concerning the passing of an era in which attendants wore uniforms and pulled out hankies to scrub bug splatter off the driver-side windshield.

It was a pretty big dent in neighbourhood retail complex for sure  – about one-third of it up in smoke to be precise. We had a small seafood restaurant until a couple of years ago. Now we have a convenience store and a large yacht supplies store. Judging by its ever decreasing stock, the convenience store is next on – or should I say “off” – the block. It shares a building with the yacht store, which I suspect will move into its space as soon as the milk and girlie magazines are removed.

The convenience store is, interestingly, the victim of the same gas stations that killed our neighbourhood gas station: the massive ones that have 16 pumps and a convenience store overseen by a teenager or other minimum wage worker. According to the newspaper article these new operations pump at least three or four times more product than our former local station. In the lulls, they sell pop, snacks, lottery tickets, and all the other stuff that the convenience store handles at minimal extra cost to management. The convenience store is open from 6 or 7 am to midnight, seven days a week to provide us with near constant access to its vital products; however, the gas bars are open 24/7 for the same purpose. Frequently, these operations also include a small restaurant, albeit normally a franchised fast food operation. That was the business that left our ‘hood first.

The loss of a local business always gives rise to a tear but the truth is the restaurant simply moved to a more productive location in the “restaurant district” about a kilometer to the east on the way to downtown. The gas station was, of course, owned by one of the largest corporate entities in the world. The convenience store is owned by the Sobey’s organization, which is held by Nova Scotia’s most successful business family. When it closes, its tiny market share will probably be absorbed by an Irving station owned by the only family in Atlantic Canada that’s even richer.

As an urban planner, I hear a lot of lamentation about retailing. When the convenience store closes, there will be no place where we can cross paths with the neighbours. We will continue to have two yacht clubs, where I can meet with my neighbours who sail. That’s why we are going to have a yacht supplies store that will soon have 5,000 square feet, although you wonder if that’s for sure when they are doing half their business on the Internet. I think they will remain though, if only because there will always be a large group who buy their anti-fouling paint in May even though they should know that they will need it in October, and because winches burn out at the most inconvenient times and when they do most of us will pay any price to replace them rather than lose a week from our four-month sailing season (slight exaggeration).

As you may notice if you read the archive of this blog, I am more of an analyst than an interventionist, which I suppose is odd for a planner. I can’t see how any of this can be stopped. I’m also reluctant to see how much of it should be stopped. Gas stations, after all, are one of a small number of land uses specified by Jane Jacobs in the Life and Death of Great American Cities as unsuitable for incorporation in neighbourhoods.

There is now a huge hole in front of our old station where hydrogeologists are prospecting for environmental damage that I am sure Shell or, possibly, their insurers are praying is confined to their property. If the land gets a clean bill of health, the former station operator told the newspaper that he hopes to re-establish a service operation that will sustain a parking lot full of vehicles for another several years.

I can live with that if it happens. Our main street isn’t the type of Main Street that Jane Jacobs was thinking of anyway. She was writing of something like the Danforth in Toronto where people live in apartments above stores that front neighbourhoods laid out behind them. Residents of the apartments and the neighbourhoods in back walk along these streets dropping into pubs and restaurants and, if they want, buying furniture and other stuff that we get at the mall and, nowadays, the power centre.

Our street, by contrast, is a long and windy one going uphill and down on its way to a lovely former fishing village called Purcell’s Cove. At spots, it’s beautiful. You can see a couple of lakes at some points, the ocean at others. It goes through residential areas that range from modest to spectacular. There are two yacht clubs and a world class park. The commercial area is a little island at the beginning. We’ve had a bunch of retail and service operations come and go, including video stores (2), convenience stores (2), a dry cleaner/tailor, a pie store, and a tanning place that has grown into a salon/spa. These businesses fed off residents of a substantial hinterland of suburbs and fishing villages that was forced to pass by them on every trip to what was once “the city.”

In the long run I’m betting the yacht store and, maybe, the spa will be our retail complex. People say we need a café or pub in which to rally in the manner of turn of the last century Viennese intellectuals but on three occasions when the developers of the townhouse/apartment subdivision in which I live have sought approval for a strip mall opposite the existing yacht store/convenience store, residents have reacted like it was 1848. The developers being realists about the planning/political process and, probably, the future form of retail, have happily conceded the point in return for increased residential development that’s now on hold thanks to more neighbourhood resistance and, I suppose, the recession.

My point is that economic, environmental concerns, and “the people” are all driving our little retail complex to concentration. We are a specialized neighbourhood and it appears to me that we will ultimately support one specialized store, although there may be enough rich people in the big houses to support a bigger and better spa. Retailing has always been dynamic. In ancient times, small producers brought tiny collections of wares to markets that they attended until they sold out or gave up. Gradually, middle men were introduced and they added more and more lines. Customers could come and see everything they wanted just about any time they wanted it. Those who couldn’t make it could get a catalogue and order the products they wanted. The products would be delivered to the customer’s door by a wagon or a truck.

As more people moved closer to the store, though, catalogue circulation dropped off, everyone could drive to the mall and get what they wanted under a single roof. Eventually the mall got so big, however, that it seems like customers began to think that they might as well drive between the stores, especially if that allowed the stores to be bigger with still more stuff and lower prices to boot. Now, though, it looks like the catalogue is about to make a comeback in electronic form and, for the most part, completely separate from the store. If you want a soccer shirt from your favorite team in the Mexican league, you can have it in a week – home, away, or third jersey – delivered to your doorstep or, maybe, your superbox (don’t they make so much sense not only as a place to keep the mail dry and secure but also as a place to meet the neighbours?).

Where it will all go, is a not entirely predictable but also not entirely unpredictable. Several retail categories are obviously endangered. Some products that have become electronic rather than physical entities are already disappearing from streetfronts. CD stores are the leading example but I’ll be very surprised if videos don’t follow them into oblivion. I’m guessing book and magazine stores will follow for the same reason, given that the migration of their product lines to the Web has already played a part in the decimation of convenience stores. I’m also willing to guess that a variety of specialized products and services will go the same route. I expect the number of bank branches to continue to decline, for example.

Well-defined products like china or silverware are other obvious ecommerce targets. A few years ago I wanted to augment a small number of place servings of cutlery that I acquired on the occasion of my marriage in the early 1980s. I couldn’t find the pattern in town so I resorted to the Web. The original pattern had been spec’ed for the Wedding Register because it happened to be on sale at a substantial discount at the time of the blessed event; however, I found a warehouse in Kansas City offering eight place settings plus serving spoons, etc. for a price that would barely have bought two place settings from Birks 15 years before. I couldn’t believe it was the same thing until it arrived at my door three or four days later.

As much as I enjoyed the experience of going to a store and being served by a knowledgeable middle-aged woman who dressed better for her job than I ever have for mine, it can’t beat getting exactly what you want for 20 per cent of the price. Its just too good to deny and it suggests to me that the future of retail is a few very, very large stores selling the small number of things that we really need to see, feel, and put on before we buy, or that we want for immediate or next to immediate consumption. That’s food and liquor, clothes, hardware, and a few other things, including gasoline so long as we need it. Even in the remaining categories, don’t be surprised if the demand adapts to the mode of supply. There is a reason that fashion has shifted so strongly to logo wear and other branded products that have a guaranteed look that circumvents the need for direct inspection. Its particularly helpful if you are willing to wear your pants halfway down your ass to start with.

It may be as David Foot has suggested that our tolerance for cavernous warehouse stores with concrete floors will wane as we all get older and our knees giveout. That may encourage boutique operations to fill up inexpensive retail spaces abandoned by record stores, video outlets, convenience stores, and so on but it may just as likely lead to further migration to the Internet so as to avoid leaving the condo altogether. It could also lead to a revival of coffee shops (or, maybe, that’s continued proliferation) to provide the meeting places lost to the decline of the bazaar, altough there are lots of examples of the Internet serving that role beyond the prying eyes of nosy neighbours. Hard to say, I think.

What I do think is that we planners have got to re-think the glue that will hold communities together. Retail is important. I don’t think its going to disappear. I think it going to be more concentrated although and I can’t see its relevance increasing except possibly among the elderly — who will admittedly be increasing in number. If I’m betting, though, I’ve already said that I think this is a very modest antidote to the more pervasive tug of the Internet. For planners, I think this adds to the reasons that we have cultivated for a century that allow communities to locate anywhere and land uses to separate from each other.

The question that follows is how much to fight it. Many planners seem to start from the perspective that the market is in a constant state of failure. I don’t think so myself. I’ve always seen retail as a better mechanism for tying together a community than parks and community centres, precisely because the usefulness of stores is confirmed by the willingness of consumers to pay full price (as opposed to acquiesing to a government paying it for them). While we could have a lengthy discussion about the role of roadways and other public infrastructure in supporting retail districts, people go to them and reach into their pockets to consume voluntarily.

Good Commercial Districts and Good Parks are Good Neighbours

It has always been my feeling that the best urban areas combine good commercial opportunities with quality public amenities but the challenge of creating these places will grow as commercial networks move into cyberspace. Its not likely that planners with only modest influence that is generally confined to the local level will be able to steer this global trend. With much less of this secret ingredient to go into the plans for public places, planners are going to have to search for new recipes for cohesive communities.

I’ve probably attended about half a dozen David Foot lectures. Before he published the book that made him a househould name among readers of McLean’s magazine, he was a regular on the Canadian planning circuit. He is an excellent and entertaining speaker. Although he is  primarily a Canadian phenomenon, Foot is actually an Australian and he knows how to use his accent and jolly Down Under demeanor to good effect. Dr. Foot’s other great strength is to make demography seem not just more relevant that we may have thought but, in fact, the most important factor influencing our lives.Foot is, more than any thing, a popularizer and that is a very important role. Without him, many Canadians — Baby Boomers included — wouldn’t know what the Baby Boom was. Even fewer would be aware of the Baby Bust. For those who have read him closely, he has also clearly defined when the Baby Boom was (i.e., 1946 to 1966 in Canada), and when the Echo Boom followed (1980 to 1995). He has also provided a host of insights to the implications of these phenomena.

On the other hand, many still don’t appreciate that the consequences of an aging population are coming as opposed to being here. Although I began this blog with Foot’s famous statement that demography explains “two-thirds” of everything, I’d have to admit that’s not the quantity I’d put on it. Half might make more sense. Tw0-thirds makes the audience perk up for sure but its pretty hard to dismiss the influences of the environment and technology, and to ignore that politics and the economy and politics are the result of inter-play as much as cause and effect.

A good example of Foot’s overplay is his first example in his chapter on “Demographic Investing” in Boom, Bust and Echo. Foot refers to the touting of the Canadian firm Lens Crafters by a financial advisor friend of his, who recognized that as Baby Boomers advanced past 40, the demand for Lens Crafters primary product, glasses, would grow. Makes good sense but Foot himself pointed out that Lens Crafters stock was essentially stagnant until the mid-1990s when its price shot up not because of a sudden surge in purchases of eyewear but because its parent, US Shoe, became the subject of a takeover bid by Italian eyeglass manufacturer Luxottica Group Spa. Now its pretty hard to see Lens Crafters as a demographic play at all because they are part of an eyewear conglomerate selling such youth oriented brands as Oakley and Ray Ban, along with tie ins to high fashion frame designers and EyeMed Vision Care, a leading managed eye care provider and insurer.

Luxottica Stock Price, New York Stock Exchange, 1990-2009

I won’t pretend to understand Luxottica Group’s financials. It looks like they’ve been going down precipitously for two or three years but, like the rest of the market, have bounced up a bit in the last couple of months. Regardless, the connection of the company to our demographic future is a little hard to discern now though. Basically, like a lot of big corporate players, they have spread themselves over all market segments, presumably to foster growth and to cover their asses in case another baby boom is about to commence.

Frankly, I don’t know whether Lens Crafters or Luxottica is a good or a bad investment on demographic or other grounds. I have a feeble retirement savings plan that I only look at when I want to get depressed. What I am trying to suggest is that demography has been submerged by financial hedging and the stock price appears to have fluctuated more because of management than any thing else. Maybe the thought of selling bifocals in Prada frames drew Luxottica to the Lens Crafters flame but that wouldn’t explain their acquisition of those youth-oriented sunglass brands.

In the mean time, the long-term future of corrective lens themselves may be in question. One segment Luxottica appears not to have addressed is vision correction, which I suspect has to be undermining the business of making and prescirbing lens, which I would assume is the core of the Lens Crafters operation. I appreciate that corrective operations are not the answer for everyone but my 80-year old father stopped wearing glasses in his seventies thanks to a trip to the Lasik clinic. I’m considering the same and, if I’m able to do so, my biannual purchases will be subtracted from the eyewear market. I may, of course, buy some Oakleys at that point since I won’t have to wear glasses to see across my yard.

If, indeed, demography is two-thirds of everything, the other third in the case of Lens Crafters is covering business management (its purchase by Luxottica), the economy (its fluctuations along with the rest of the market), and technology/innovation (the advent of surgical vision correction). You could toss in fashion to boot when you reflect on the sales drivers of Oakley, Ray Ban, and (for sure) Prada. Its just a lot more complicated, which of course doesn’t work for a popularizer like Foot but is a very pertinent criticism of a book with the subtitle “How to Profit from the Coming Demographic Shift.”

I’ve wanted to make that criticism of Foot for a while; in fact, I imply it in the home page of this site after quoting Foot.  I’m pretty sure in one of the early talks I heard from him he explained East Germany’s success in the Montreal Olympics in terms of the preponderence of twenty somethings in the Democratic Republic in the mid-1970s. I’ll be damned if I can find a reference to that proposition anywhere now, but I can certainly think of another reason why Ulrike Richter made a double bronze medalist out of Halifax’s Nancy Garapick. I’ll let Foot off the hook there until someone can corroborate my faint memory. For now its just an opportunity to make reference to the injustice done to Garapick, who should frankly be given Richter’s medals.

Digression aside, I am a David Foot acolyte. I don’t like him quite as much as Bill James but, as I’ve said, Foot is a very fine speaker and a good writer. Like James, also, he is has the surprisingly rare knack of turning numbers into ideas and explaining the ideas with clarity. Whether demography counts for two-thirds, 75 per cent, or 1 in 5, it is important. It affects the size and form of markets and the shape of demand very damn near everything. It is, also, as Foot was the first to make clear to me, one of the few things about the future of which we can be reasonably certain. As such, it is a baseline consideration for more than two-thirds of everything form cola sales to the need for schools and arenas to the fate of the planet. In that respect, Foot may actually be under doing it.

The June 1976 issue of Plan Canada was interesting in and of itself because it was a special edition on “population research.” In addition to Professor Cherukupalle’s article, it contains an interesting overview article by Larry Bourne called “Priorities for Demographic Policy Research” (pp. 83-88), which reflects not only an environment of growth but also a rising awareness of coming stabilization combined with concern about climate change (global cooling and related loss in Canada’s agricultural productivity, if you can believe it). Cherukupalle addressed the implications of this stabilization for practicing planners preparing projections and forecasts, a group that I was soon to join.

As demographic decllne began to dawn across the western world, Cherukupalle recognized that old approaches would not do any longer:

The contention of this [i.e., Cherukupalle's] article is that the context of the exercise is now quite different. The emphasis on the ‘management’ of growth, as opposed to that of the accommodation of growth calls for far more precise techniques of projection, or alternatively cybernetic determination, of both the size and the composition of future population.

She noted that practitioners to that time were accustomed to preparing liberal forecasts recognizing that accommodation of growth provided a wide margin of error given that overestimation would be absorbed quickly enough — if a projection or forecast for 15 years hence proved to be excessive, the expected population level was generally achieved in 20 or 25 years. In addition, she noted, the consequences of underestimation in terms of inadequate service and congestion, were far more serious than providing a little more than was needed.

She placed the challenges to precision in two categories: methodological and institutional. On the methodological side, she noted the challenge faced by planners dealing with smaller areas with open boundaries that increase the difficulty of estimating future migration. On the institutional side, she pointed to the ‘generalist’ bias of planners who were not well-equipped to apply complex mathematical models even when the models were what she quaintly called ‘mechanized.’ Planners she suggested did not have the sophisticated technical knowledge to address the underlying judgements needed to operationalize such models.

In this context Cherukupalle assessed the ‘planner’s tool kit,’ which she divided into three categories as follows:

1. The Demographer’s Approach – Essentially, the cohort-survival method, which she noted had been supported by careful statistical record keeping dating from the 1920s.
2. The ‘Your-guess-is-as-good-as-mine’ Approach – Projection methods, including apportionment methods through which a local projection was developed as a constant or changing share of a national level projection.
3. The ‘Holding Capacity’ Approach — A variety of methods then popular with planners and still applied by which the ability of an area to absorb population was calculated and planned for.

Then and since I have ranked the merit of these techniques as Cherukupalle listed them. The holding capacity approach is certainly valid when projecting the population of a small area like a subdivision but it can be downright scary when applied to a municipal unit or a region. For the first half of my planning career, the Halifax-Dartmouth region in which I have practiced was governed by a 1976 land use plan designed to accommodate 435,000 people by 1991, the calculated capacity within a “regional development boundary” defined by the same plan. According to the 2006 Census, we are still 75,000 to 85,000 short of that number, although land was assembled and developed in the 1970s and 80s to provide for the explosion. The result was urban sprawl, fiscal imbalance, and the waste of money and non-renewable resources. While the mitigating influence of declining household size made some sense of huge suburban/exurban land assemblies, so many other options were available that could have provided more appropriate housing in a more cost-effective settlement pattern, if the real future had been addressed instead of a futuristic vision.

In another Atlantic Canadian instance with which I am familiar, a well-established engineering firm designed the water system for an utterly moribund coal mining town based on the assumption that the community would be developed at its then prevailing density across its entire vacant area. The resulting estimate of the town’s future population was ten times its current population and would have made the town one of the half dozen largest municipalities in the region. The community has lost population in every Census since and currently has about ten per cent fewer residents than at the time its future “population potential” was overestimated. I haven’t checked to see what they are paying per capita for their over-sized water supply.

Projection methods can be quite a bit more effective. They are however a bit distasteful as they are designed and applied to be easier rather than better, and because they do not give key details about the composition of population that are often more important than the total numbers. In addition, most projection methods are inherently flawed in situations in which circumstances are about to change, since they rest on the fundamental assumption that the future will be similar to the past.

The demographer’s approach or the cohort-survivial method has always seemed more attractive because it takes into account the individual components of growth (i.e., births and deaths) and provides important detail about sex and age composition that is normally absent from projections developed by other methods. Because the fundamental influences on cohort-survival are available and accurately recorded, it is also the perfect method to deal with changing demographic circumstances. The major problems presented by the technique are the estimation of migration and the relative effort required to apply the method.

These problems, I have always thought were well worth trying to overcome. The answer I believed was in mechanization or, nowadays, computerization. When calculations had to be done manually or even with a calculator, the cohort-survival method is not so much difficult to apply as tedious. Even in the 1970s when age groups were only broken down up to the age of 70, a single iteration of the cohort-survival method using five-year age cohorts required over 30 calculations, assuming the analyst wasn’t bothering to project birth or death rates, or add in the effects of migration. Developing even a 15-year projection must have taken a week’s work. At about that time, I had a job preparing 20-year projections of municipal populations in Eastern Ontario on which basis water and sewer rates were set. Suffice to say I never contemplated using the cohort-survival method for any aspect of this work.

In my first blog on my influences, about Bill James, I noted how little progress has been made in improving planning methods and models since the advent of the personal computer.  The cohort-survival method is a leading example of this astonishing inertia. I remember a spreadsheet that was circulated in the United States in the 1980s, I think as a result of an article in what was then the Journal of the American Institute of Planners (now JAPA). I got a copy but, as I also recall, it was difficult to understand and employed different rates for black and Hispanic populations that were not relevant to Canada. Canada Mortgage and Housing Corporation also produced and, for awhile, marketed its Potential Housing Demand or PHD model, which still seems to be used by the Corporation and by some municipalities, but is hardly widespread. I’ve developed and refined cohort-survival models through several iterations in BASIC, QikBASIC, and several spreadsheet programs. I’m sure there are others out there, who have done the same but I am still startled by the crudeness of a lot of local demographic analysis and the mystery that still appears to surround application of the cohort-survival method.

The concerns that Cherukupalle raised, in other words, are with us still. Planners don’t really understand the best methods of demographic projection and forecasting, and aren’t comfortable applying them or interpreting the results. We have, however, fully entered the period of slow growth/decline that Cherukupalle saw coming and the challenges for predction that it presents are real. I don’t see it as a crisis but if we want a sustainable future, we have to avoid the waste inherent in over projection. For that we need sharper instruments more precisely applied. We are soon going to be in need of more considered methodologies for the developing world as well as the developed world, given that many countries in the Middle and Far East, and even Latin America are going through the same process of declining birth rates.

As I too have my influences, I thought I’d trot out a few for consideration so that my readers can move on or stick around accordingly. To start with, I refer to David Foot in the Introduction to this blog. You can hardly be a Canadian interested in demography and not be influenced by Dr. Foot. I also owe a debt to Dr. Nirmala Cherukupalle, who taught me briefly at Queens University and reinforced the importance of accuracy in projection. 

The first influence I want to discuss, however, is my favorite writer and one of North America’s most famous statisticians: baseball prognosticator Bill James. James was the subject of the very second article I ever published (“A Whole New Ball Game,” Plan Canada, Volume 26, Number 1, March 1986, pp. 10-11, 25), in which I expressed my hope that the analytical approach that James applied to baseball might inspire planners.

It was a bit odd because James was and is an economist who applied his training to his real interest and developed a suite of methods that at the time I wrote about him were beginning to influence views on baseball management. James was a leading proponent of what is called sabermetrics, named after the Society of American Baseball Research, which was formed to research and assess baseball statistics first made widely available through the 1968 publication of the Baseball Encyclopedia. I have an original copy that my father bought for me in recognition of my middling accomplishments in high school and it would qualify as my personal most read book of the 1970s. Bill James’ Abstracts — at least the published ones as opposed to the early mimeographed ones — were my most read books of the eighties.

What James and other sabermetricians active at the time were doing was digging behind conventional statistical measures and developing new techniques to measure what really mattered on a baseball diamond. James was in the forefront because on top of being an insightful statistician, he was a terrific, frequently very funny writer, who could not only grind out numbers but could also write insightfully on the short-term benefits of hallucinogenic drugs in light of the success of the 1979 Pittsburgh Pirates.

There are hundreds of observations by James on life, statistics, and research methods that are well worth the consideration planners, demographers, planners, and people in general. In the 20 plus years since I wrote about him, James has furthermore enjoyed success that more often than not has eluded prognosticators in the dry profession from which he sprung. Despite considerable resistance and even disdain within baseball management during the 1980s and early 1990s, his ideas were gradually adopted. Most notably, the Oakland A’s made statistical analysis a cornerstone of their management approach in the past ten years, and their ability to accomplish more on the field with less in the bank converted the baseball business, with a considerable assist from George F. Will’s book Moneyball. Even before the endorsement of the A’s and Will, James was getting hired by agents and teams to assist in player rating for arbitration hearings and other negotiations. In 2002, he was hired by the Boston Red Sox and assisted Theo Epstein, who at 28 years of age was named General Manager of the Boston Red Sox in 2004, to win the Sox’ first two World Series since they sold Babe Ruth in 1918.

I’m not too sure exactly what James does for the Red Sox, some sort of analysis of player performance to assist the field manager to deploy his team effectively and to help Epstein to figure out who to keep and who to get rid of I supose, but this is what Epstein has said about his influence:

The thing that stands out for me is Bill’s humility. He was an outsider, self-publishing invisible truths about baseball while the Establishment ignored him. Now 25 years later, his ideas have become part of the foundation of baseball strategy. But where’s Bill? Where’s the gloating? Where’s the publicist? He’s like somebody outlining the Internet in the ’80s and watching silently as it comes to pass.

I don’t really care if James is as humble as Christ or more bombastic than Chad Ocho Cinco. What matters to me is that Epstein and many otheers recognize that James has amply demonstrated that his methods — statistical methods — are an effective tool. I can think of a lot of reasons why analysis of this type has fallen into such disrepute. There’s that stupid aphorism about damn lies and statistics for one thing. There is also the fact that most people find math tougher than English. And there is the further fact that economists appear so often to do such a bad job of predicting what will happen with the economy.

James, however, is the counterpoint. Before him, notwithstanding the astonishing statistical database maintained by the sport, baseball people for the most part went by the seats of their pants. They weren’t total Luddites about this. Kevin Kerrane’s Dollar Sign on the Muscle gives a good insight into the methods by which scouts rate and record the “five tools” that normally define the potential of a baseball player (i.e., hitting for average, hitting for power, running speed, arm strength, and fielding ability). Its a quantitative approach too but it relies heavily on the feel of experience over the measurement of performance.

As James has amply demonstrated time and again, many a 6′ 4″, 220 lb. stud with afterburners on his arms and legs does not necessarily live up to his assets. Observation is valuable but it can also be deceiving, particularly when it is formed and reinforced by group think. Ultimately, James wanted to know how the big guy actually performed and, in particular, how he performed relative to others who could be given the opportunity instead of him. By no means was James simplistic about this. He was among the first to recognize that baseball parks often had a big impact on how a player performed and that a key issue in assessing players was to equalize for the different places that they played their games. He also recognized, as did some others, that many of the measures used to track baseball performance were not all that they were cracked up to be. He recognized, for example, that the Little League chant, “a walk’s as good as a hit” was actually true and that on base percentage ((hits + walks)/at bats) was a better measure of a hitter’s contribution than the traditional batting average (hits alone/at bats).  He also recognized that more complex measures were often needed with the constant goal being to determine what players, management and strategy contributed to winning.

By no means am I James biggest fan. I’ve read most everything he’s written but a quick Google cruise with his name will throw up literally thousands of largely male North Americans who have actually read everything and strive to out do each other in their devotion. I am just one modest member of the cult, who sees value in James’s approach in my professional field where it should much more easily have a home. A fair number of planners are economists or statisticians and most of us, like me, have at least taken a fair number of courses in economics and/or statistics. For most of my career, however, these disciplines have been anything but ascendent in my profession.

Planning is now seems to be primarily a combination of animation and consultation techniques combined with collection of prescriptions like Smart Growth developed by other professions (including economists) that we accept and promulgate largely on faith. Its not that I disagree with consultation. I most certainly wouldn’t want to return to the 1950s approach when planners influenced by Howard Roark school of modern architecture ran roughshod over “slums” so as to provide a better life for the residents, whether those residents liked it or not. I do think, though, that consultation has its own set of problems, particularly when its practitioners choose to be ciphers who refuse to commit to ideas of their own.

The particularly sad part of planners’ desertion of method is that the tools for applying quantitative tehniques have been so much improved. Obviously, the last 30 years have seen a computer revolution that have made the refinement and appplication of quantitative methods infinitely easier. That revolution has been key to the success of sabermetrics. Personal computers on every desk and in every home have allowed James and many, many others to look at issues that interested them that they weren’t allowed to take near the university mainframe. We planners, on the other hand, have always been considered to have a legitimate call on institutional resources and can even get paid to apply them to the issues that concern us. We’ve made big advances with GIS, for sure but when I look over my copy of the 1974 edition of Donald Krueckeberg and Arthur Silvers’ Urban Planning Analysis: Methods and Models, I do not see one method or model presented that has been supplanted by a superior technique in three and a half decades.

My fixation has been demography but I could also see benefit in planners taking more interest in the techniques of transportation planners and the lessons they provide in human behavior. I wonder, in the same vein, if we couldn’t develop a better statistical understanding of pedestrian movement, parking lot usage, community facilities location, and a variety of other areas in which our communities perform less well than they should. There is also the area of building design and densification, which we slavishly endorse but in which a whole collection of poorly understood but quantifiable trade offs are made. For example, while I may save gasoline living an apartment building next to the office tower where I work, what energy is consumed by elevators, security lighting, and mechanical HVAC systems? Its exactly the kind of question, it seems to me, that once intrigued James about baseball and spawned his niche profession and still ought to motivate mine.

The issues arising from declining growth, according to Longman’s article are things like finding enough soldiers for the military, and enough nurses and doctors to take care of all the old folks who are becoming the majority throughout the developed world and are apparently poised to become the majority in such unlikely places as Mexico and the Middle East. As Longman notes, this stands in direct contrast to the hand-wringing of the preceding generation characterized by Paul Ehrlich’s Population Bomb, which in 1968 posited an unsustainable future in which the planet would be overwhelmed by humans doubling their numbers every couple of decades.

Given that I would normally assert that population numbers and composition are pretty easy to predict, especially over the course of a 20 to 30-year period, this is a remarkable reversal. Although we are still looking ahead to 50 years or so of worldwide population growth, the solution to yesterday’s impending disaster is becoming the primary challenge of the future. How the heck are so few going to support so many, and how can society progress with a preponderance of doddering old fogies watching CSI re-runs at the retirement villa?

I don’t really agree with Longman, anymore than I agreed with Ehrlich in the day (when I was in high school). When the Baby Boom swept the Western world after World War II, the technology of birth control was being rapidly refined as was the sociocultural framework that subsequently encouraged its application. Mixed with other changes that have made children much less valuable if not completely irrelevant to the workforce, and women at least as valuable as workers as family caregivers, and we have an unprecedented sea change that is spreading to traditional societies that not so long ago seemed headed to Malthusian self-destruction illustrated by the graphic of world population change below. The fact that current trends may leave us a bit short on military personnel and with an admittedly major health care challenge seems minor by contrast.

World Population (logarithmic scale), 10,000 BC-

World Population (logarithmic scale), 10,000 BC-

This blog, in any case, is not about these big issues but the juxtaposition of yesterday’s and today’s issues, did make me think about macro-demographic policy, which both Ehrlich and Longman advocated or advocate. Here at Demography+ our concern is with the prediction of local populations as a basis for determining the need for local infrastructure and services. In that regard, the declining number of babies and the increasing number of elderly, as well as the increase or decrease of population, are certainly of the utmost importance but, at the same time, are not something we would expect to control or even significantly influence.

Policy and prognostication can influence demography. The writings of Ehrlich and others were most certainly a factor in the defusing of the population bomb, although I am confident that they pale by comparison to improvements in birth control and the emancipation of women in the workplace. Longman and others will likewise have an impact, although their viewpoint will battle with Ehrlich’s offspring: the wide array of environmentalists who will not be satisfied by until the global footprint of the human race is severely reduced. This includes Ehrlich himself, who has recently published The Dominant Animal: Human Evolution and the Environment (Island Press, June 30 2008).

At this point, I am an agnostic. I have enough concern for the environment to be reluctant to support baby birthing programs. At the same time, I think the increasing participation of women in the paid workforce and the related tendency to smaller families that is now characteristic of most of North America and Europe, and appears to be influencing even the most traditional societies of the Third World, is here for the long-term. Octomom, aside, women who have 20 or more babies as quite a few once did are likely a curiosity of the past, as are awards for the accomplishment.

It seems to me that we are in a transitional stage to a new equilibrium. Certainly, this will create challenges but it is hard to see the care of massive numbers of seniors as any more daunting than the education of the very same group 50 years ago. The population of the world is going to continue to grow and the pace of that growth is pretty much known. Around the middle of the century, population will stop growing and the environment may well get a break, although I personally hope we will have found effective institutional and technological remedies long before that.

Where I do hope this will put individual human beings is in a position to make free reproductive/demographic choices. If we can be simultaneously satisfied that we not only no longer face a demographic catastrophe of over population, but also that we do not have to go forth and multiply to perpetuate the human race, reproductive decisions can be undertaken on the basis of individual satisfaction rather than social obligation. Child-rearing under such circumstances has to be more materially and emotionally satisfying, and a new reproductive equilibrium may well be achieved in 50 years or so. For our part at Demography+, we’ll watch like Chauncey Gardner and try to keep provisions for population in our neck of the woods in line with its numbers and form.