De quoi parle-t-on exactement, lorsqu’il est question de données objectives, ou de « données dures »? Les roches sont dures, assurément, mais les données? L’encre sur le papier et les électrons dans un disque dur sont tout sauf durs. (En fait, on parle alors de « version électronique », ce qui est loin de la notion de dureté.)

Si vous devez employer une métaphore, essayez plutôt les nuages dans le ciel : on les voit clairement de loin, mais ils sont opaques vus de près. Ils sont insaisissables. On parle de données « dures » pour se donner l’illusion d’avoir transformé quelque chose de concret en chiffres. Cet homme, là-bas, ce n’est pas Simon : c’est un 4,7 sur l’échelle d’un psychologue. Cette compagnie n’a pas seulement connu du succès : elle a vendu 49 milliards de gadgets. N’est-ce pas limpide, ainsi?

Les données subjectives, aussi appelées données « molles », peuvent quant à elles être ambiguës, floues et incertaines —du moins vues de loin. Il faut généralement faire appel à notre jugement pour les interpréter; à l’instar de Simon, elles ne peuvent même pas être transmises de façon électronique. En fait, certaines données subjectives ne valent guère mieux que les rumeurs, les racontars ou les impressions (par exemple, cette rumeur qui circule à savoir que ces gadgets seraient défectueux).

Ainsi, les dés sont pipés. Les données dures gagnent à tout coup… du moins, jusqu’à ce qu’elles entrent en contact avec cette matière molle qui constitue nos cerveaux, qui existent dans notre société subjective. Il est donc avisé de réfléchir au ventre mou des données dures.

1. Les données objectives peuvent être trop générales. De façon isolée, elles peuvent être stériles, si ce n’est impuissantes. « Peu importe ce que je lui disais, s’est plainte l’une des participantes de la célèbre étude de Kinsey sur le comportement sexuel des hommes, il me regardait simplement droit dans les yeux en demandant : “combien de fois¹” ». Est-ce vraiment tout ce qu’il y a à dire? (En premier lieu, qu’est-ce qui constitue exactement une « fois »? Et pour qui?)

Les données dures, ou objectives, peuvent servir de base pour une description, mais qu’en est-il des explications? D’accord, les ventes de gadgets ont augmenté. Mais pourquoi? Parce que le marché était en expansion? (On peut probablement associer un chiffre à ce phénomène.) Parce que le principal concurrent prenait des décisions idiotes? (Impossible de chiffrer cette affirmation, il ne s’agit que de rumeurs.) Parce que notre gestion était excellente? (Notre direction aime bien cette explication, aussi subjective soit-elle.) Serait-ce plutôt parce que la compagnie a rogné sur la qualité pour réduire ses prix? (Essayez d’obtenir les données qui prouveront cela.) Tout ceci laisse entendre que nous avons généralement besoin des données subjectives pour expliquer les données objectives : par exemple, les rumeurs sur les activités du principal compétiteur ou les ouï-dire sur la qualité des produits de notre propre usine.

2. Les données objectives peuvent être trop agrégées. Comment ces données objectives sont-elles présentées? On ne les reçoit pas chaque fois qu’un gadget est vendu, mais additionnées pour former un seul chiffre : les ventes totales. Il en va de même pour le proverbial résultat financier : l’ensemble de la compagnie y est décrit en un seul et unique chiffre. Pensez à toute la vie qui se perd dans ce chiffre, et à toute la réalité. Il n’y a pas de mal à voir la forêt plutôt que la somme des arbres… à moins que vous ne soyez dans l’industrie forestière. Les gestionnaires de cette industrie doivent aussi connaître les arbres. Trop de gestion se fait depuis un hélicoptère, d’où les arbres ont l’apparence d’un tapis verdoyant.

3. Une grande partie des données objectives arrive trop tard. L’information a besoin de temps pour « durcir ». Ne vous laissez pas berner par la vitesse de tous ces électrons qui filent sur Internet. Premièrement, les événements doivent être notés comme des « faits » (ce qui peut prendre du temps), avant d’être agrégés dans des rapports, qui doivent également probablement suivre un calendrier prédéterminé (comme la fin d’un trimestre). À ce moment-là, les clients qui sont déjà dégoûtés de la qualité des gadgets auront probablement déjà opté pour ceux de la compétition. La rumeur peut avoir déjà annoncé cela, de façon subjective, et le bouche-à-oreille l’a propagé, rapidement. Dans l’univers des données subjectives, cela a toutefois peu de poids.

4. Finalement, une quantité étonnante de données objectives n’est simplement pas fiable. Ils ont fière allure, tous ces petits chiffres sur leur bel écran. Mais d’où proviennent-ils? Soulevez la roche des données objectives et jetez un œil à ce qui fourmille en dessous. Les organismes publics sont très attachés à la collecte de statistiques : ils les recueillent, les additionnent, les élèvent au plus haut degré, prennent leur racine cubique et préparent de merveilleux diagrammes. Mais ce qu’il ne faut jamais oublier, c’est que chacun de ces chiffres provient au départ du [gardien du village], qui a simplement noté ce qui lui chantait² ».

Et ce n’est pas vrai uniquement pour les organismes publics. La plupart des organisations sont obnubilées par les chiffres. Toutefois, qui se préoccupe de vérifier ce que le gardien a consigné, particulièrement en cette époque d’automatisation? Ou encore des chiffres du gestionnaire en quête d’avancement? Avez-vous déjà rencontré un chiffre qui ne pouvait pas être traficoté : le compte d’objets rejetés dans une usine, le compte de citations dans une université (vous n’avez qu’à vous citer vous-même), ou même le proverbial résultat financier d’une entreprise? Par ailleurs, même si les faits enregistrés étaient fiables en premier lieu, quelque chose se perd généralement dans le processus de quantification. Les nombres sont arrondis, des erreurs sont commises et des nuances se perdent³.  

N’allez pas croire qu’il s’agit ici d’un plaidoyer pour se départir des données objectives. Cela n’aurait pas plus de sens que de vouloir se départir des données subjectives. Il s’agit plutôt d’un plaidoyer pour que nous cessions d’être obnubilés par les mesures. Nous savons tous comment utiliser les données objectives pour vérifier des intuitions subjectives. Eh bien, pourquoi n’utiliserions-nous pas nos intuitions subjectives pour vérifier des faits objectifs (apprécier les chiffres « à l’œil »)?

Qu’en est-il en fin de compte? Une vieille plaisanterie dit que si vous croisez quelqu’un [d’un pays que je ne peux nommer], giflez-le. Il saura pourquoi. Bon, si vous croisez un chiffre, remettez-le en question. Vous comprendrez pourquoi.

© Henry Mintzberg 2015. En fait, j’ai déjà ébauché ces idées bien avant la venue d’Internet (Impediments to the Use of Management Information [monographie de la National Association of Accountants [É.-U.] et de la Société des comptables en management du Canada [Canada], 1975]) LIEN, et je les ai adaptées dans de nombreux ouvrages depuis lors. TWOGS connexes : “If you can’t measure it, you had better manage it”; “How National Happiness became gross”; “Downsizing as 21st Century bloodletting”; “Productive and Destructive Productivity”; et “What could possibly be wrong with efficiency? Plenty”.

Traduction par Nathalie Tremblay

________________________________

¹ Tiré de A. Kaplan, The Conduct of Inquiry (Chandler, 1964).

² Attribué à Josiah Stamp, 1929, cité dans Michael D. Maltz, Bridging Gaps in Police Crime Data : A Discussion Paper from the BJS Fellows Program, Washington, DC, Bureau of Justice Statistics, 1999.

³ Dans sa chronique « Statistics and planning » [Statistiques et planification], destinée au British Air Ministry durant la Seconde Guerre mondiale (Planning in Practice: Essays in Aircraft Planning in Wartime [Cambridge, Cambridge University Press, 1950]), Ely Devons a écrit que la collecte de telles données était extrêmement difficile et délicate, exigeant un « haut niveau d’habileté », alors qu’elle « était traitée… comme un travail inférieur, dégradant et routinier qu’on pouvait confier aux moins efficaces des employés de bureau » (p. 134). Des erreurs se sont glissées dans les données de toutes sortes de manières, ne serait-ce qu’en considérant des mois comme normaux alors qu’ils comprenaient des jours fériés, par exemple. « Les chiffres étaient souvent utilisés seulement comme une façon utile de résumer des jugements et des approximations » (p. 155). Ils étaient parfois même développés par le biais de « marchandage statistique ». Mais « une fois qu’un nombre était mis de l’avant… personne n’était capable d’utiliser des arguments rationnels pour démontrer qu’il était faussé » (p. 155). « Une fois que ces nombres étaient appelés “statistiques”, ils gagnaient l’autorité et le caractère sacré de la Sainte Écriture » (p. 155).

The tiny kingdom of Bhutan, wedged between Tibet and India, became famous for coming up with Gross National Happiness (GNH), thanks to its king. This was not your usual king (see the photo). Before voluntarily ceding power to democratic elections, he decreed an increase in the country’s forest cover, had every child in the country learn English, and introduced Gross National Happiness.

The retired king of Bhutan with his four wives, all sisters. (Photo of a Bhutanese placemat.)

GNH resonated with people around the world who were fed up with Gross National Product (GNP). As Robert Kennedy commented:

Gross National Product counts air pollution and cigarette advertising.…It counts the destruction of the redwood…and the television programs which glorify violence.…Yet [it] does not allow for the health of our children, the quality of their education or the joy of their play.…It measures everything in short, except that which makes life worthwhile.¹

GNH was based on four “pillars”: good governance, sustainable development, preservation and promotion of culture, and environmental conservation. These were elaborated in nine “domains,” which included health, education, psychological well-being, and community vitality. Simple enough.

Curious about this GNH and loving mountains, I visited Bhutan in 2006. Two things struck me in discussions with a number of the country’s knowledgeable people. First, they had no idea how to measure much of GNH; second, this didn’t much matter because the country seemed to be behaving true to its precepts. As a BBC reporter put it, GNH had become “a way of life” in Bhutan—a poor country where life seemed to be rather pleasant.

Not long after this, economists descended on Bhutan to fix GNH, even though it wasn’t broken. After all, if the Bhutanese didn’t measure GNH, how could they possibly manage it? Soon each of the nine domains had “its own weighted and unweighted GNH index…analyzed using…72 indicators.…Mathematical formulas have even been developed to reduce happiness to its tiniest component parts.”² One survey, which took five to six hours to complete, “included about 750 variables.”³ These technocrats attended to the gross all right, but how about the happiness?

Critics of GNH have challenged its subjective judgments. “Economics professor Deirdre McCloskey criticizes such measurements as unscientific…making the analogy that society could not ‘base physics on asking people whether today was “hot, nice, or cold.”’⁴ If only education, culture, and happiness were as measurable as the temperature. I wonder if the greater threat to GNH has come from the enemies who want to eradicate it or the friends who want to measure it.

In 2013, not long after all this measuring, Tshering Tobgay, who had studied with economist Michael Porter at Harvard Business School, became prime minister. Soon he was claiming that GNH “distracted [some people] from the real business at hand,”⁵ namely “the bottom line…that we have to work harder.”⁶ This he could understand, in contrast to GNH, which he found “very difficult,” in fact, “complicating stuff for me.”⁷

F. Scott Fitzgerald claimed that “the test of a first-rate intelligence is the ability to hold two opposed ideas in mind at the same time and still retain the ability to function.”⁸ To any economist or prime minister who cannot handle measurement and happiness at the same time, let me suggest that you drop the measurement and celebrate the happiness.

© Henry Mintzberg 2018. To appear in “Bedtime Stories for Managers” (Berrett-Koehler, forthcoming in February 2019); a similar version appeared in this blog on 25 September 2015.  Our International Masters Program for Managers (impm.org) is for those managers who can function holding two opposed ideas in mind at the same time.

Follow this TWOG on Twitter @mintzberg141, or receive the blogs directly in your inbox by subscribing here. To help disseminate these blogs, we also have a Facebook page and a LinkedIn page.

____________________________________

¹ Robert F. Kennedy, “Remarks at the University of Kansas” (speech, Lawrence, KS, March 18, 1968).

² Seth Mydans, “Recalculating Happiness in a Himalayan Kingdom,” New York Times, May 6, 2009.

³ “2010 Survey Results: Results of the Second Nationwide 2010 Survey on Gross National Happiness,” accessed August 4, 2018.

⁴ “ACM: Cultural Marxism: The Highest Stage of RW Brakin’ 2 Eclecpapertic Bugaboo,” Daily Kos, March 22, 2015.

⁵ “Bhutan’s ‘Gross National Happiness’ Masks Problems, Says New Prime Minister,” Telegraph, August 2, 2013.

⁶ Gardiner Harris, “Index of Happiness? Bhutan’s New Leader Prefers More Concrete Goals,” New York Times, October 4, 2013.

⁷ “Bhutan’s ‘Gross National Happiness’ Masks Problems.”

⁸ F. Scott Fitzgerald, “Part I: The Crack-Up,” Esquire, February 1936 (reprinted March 7, 2017).

Modified version of blog posted 10 October 2014 and, 9 September 2015.

Efficiency is like motherhood. It gets us the greatest bang for the buck, to use an old military expression. Herbert Simon, winner of one of those non-Nobel prizes in economics, called efficiency a value-free, completely neutral concept. You decide what benefits you want; efficiency gets you them at the least possible cost. Who could possibly argue with that?

Me, for one.

I list below a couple of things that are efficient. Ask yourself what am I referring to—the first words that pop into your head.

A restaurant is efficient.

Did you think about speed of service? Most people do. Few think about the quality of the food. Is that the way you chose your restaurants?

My house is efficient.

Energy consumption always comes out way ahead. Tell me: who ever bought a house for its energy consumption, compared with, say, its design, or its location?

What’s going on here? It’s quite obvious as soon as we realize it. When we hear the word efficiency we zero in―subconsciously―on the most measurable criteria, like speed of service or consumption of energy. Efficiency means measurable efficiency. That’s not neutral at all, since it favors what can best be measured. And herein lies the problem, in three respects:

1. Because costs are usually easier to measure than benefits, efficiency often reduces to economy: cutting measurable costs at the expense of less measurable benefits. Think of all those governments that have cut the costs of health care or education while the quality of those services have deteriorated. (I defy anyone to come up with an adequate measure of what a child really learns in a classroom.) How about those CEOs who cut budgets for research so that they can earn bigger bonuses right away, or the student who found all sorts of ways to make an orchestra more efficient.

2. Because economic costs are typically easier to measure than social costs, efficiency can actually result in an escalation of social costs. Making a factory or a school more efficient is easy, so long as you don’t care about the air polluted or the minds turned off learning. I’ll bet the factory that collapsed in Bangladesh was very efficient.

3. Because economic benefits are typically easier to measure than social benefits, efficiency drives us toward an economic mindset that can result in social degradation. In a nutshell, we are efficient when we eat fast food instead of good food.

So beware of efficiency, and of efficiency experts, as well as of efficient education, heath care, and music, even efficient factories. Be careful too of balanced scorecards, because, while inclusion of all kinds of factors may be well intentioned, the dice are loaded in favor of those that can most easily be measured.

By the way, twitter is efficient. Only 140 characters! This blog is less so.

References

Herbert A. Simon Administrative Behavior: Second Edition (Macmillan, 1957, page 14).

This TWOG derives from my article “A Note on the Dirty Word Efficiency”, Interfaces (October, 1982: 101-105)

© 2017s Henry Mintzberg

Follow this TWOG on Twitter @mintzberg141, or receive the blogs directly in your inbox by subscribing here. To help disseminate these blogs, we also have a Facebook page and a LinkedIn page.

Photo credit: Ian Thompson, CC BY-SA 2.0

“It is a well-known axiom that what is not measured can’t be managed” (Kaplan and Porter in the opening of their 2011 Harvard Business Review article “How to solve the cost crisis in health care”). This is well-known all right, and false, not to mention downright silly.

Who ever successfully measured culture, leadership, even the potential for a truly new product? Can none of these thus be managed? Did Kaplan and Porter measure the effectiveness of their own recommendations? Indeed, who has even tried to measure the performance of measurement itself, aside from assuming that it is marvelous? And how about measuring the performance of management? (Don’t tell me that increase in share price does this for the CEO. See “The tricky task of measuring managers.”) I guess, therefore, measurement and management can’t be managed.

Guess what? They can. We just have to understand that many of the things that matter most in organizations (and in life) cannot be measured, yet they have to be managed, whether personally or organizationally. Certainly we have to measure what we can; we just cannot allow ourselves to be mesmerized by measurement―which we so often are.

In this article, Kaplan and Porter (2011) provide a list of seven steps “to estimate the total costs of treating...patient populations”:

1.    Select the medical condition [specifying the possible “complications and comorbidities"]

2.    Define the care delivery value chain…which charts the principal activities

3.    Develop process maps for each activity

4.    Obtain time estimates for each process

5.    Estimate the cost of supplying patient care resources

6.    Estimate the capacity of each resource and calculate the capacity cost rate

7.    Calculate the total cost of patient care

Don’t look for:

8.    Include the costs of doing all this.

But you can get a sense of it by reading the authors’ example of a knee replacement, for which 77 activities are listed.¹ Multiply this by elbows, hips, brains, hearts and minds, etc., factor in the frequency of improvements in these activities, which may come one at a time, and you have to wonder if analysts will soon outnumber clinicians in health care.

But the direct costs of their efforts are not the only costs. How about the costs of the distractions to the clinicians―for example, by having to record so much data―plus the costs of the political battles that ensue over who is measuring what, how, where, when, and for whom. Analysts see measurements as objective; contrast this with the political blood spilled over determining them.

Imagine if analysts put themselves through the same scrutiny as some do everyone else. In other words, imagine if they analyzed themselves. Maybe then, instead, we would get more of the following:

© Henry Mintzberg 2016. Excerpted from my new book Managing the Myths of Health Care (forthcoming in 2017).

Follow this TWOG on Twitter @mintzberg141, or receive the blogs directly in your inbox by subscribing here. To help disseminate these blogs, we now also have a Facebook page and a LinkedIn page.

Last week I wrote that I will be writing TWOGs more bi-weekly, and revisiting some earlier ones in between. This is the first of those.

First posted October 10 2014

Efficiency is like motherhood. It gets us the greatest bang for the buck, to use an old military expression. Herbert Simon, winner of one of those non-Nobel prizes in economics, called efficiency a value-free, completely neutral concept. You decide what benefits you want; efficiency gets you them at the least possible cost. Who could possibly argue with that?

Me, for one.

I list below a couple of things that are efficient. Ask yourself what am I referring to—the first words that pop into your head.

A restaurant is efficient.

Did you think about speed of service? Most people do. Few think about the quality of the food. Is that the way you chose your restaurants?

My house is efficient.

Energy consumption always comes out way ahead. Tell me: who ever bought a house for its energy consumption, compared with, say, its design, or its location?

What’s going on here? It’s quite obvious as soon as we realize it. When we hear the word efficiency we zero in―subconsciously―on the most measurable criteria, like speed of service or consumption of energy. Efficiency means measurable efficiency. That’s not neutral at all, since it favors what can best be measured. And herein lies the problem, in three respects:

1. Because costs are usually easier to measure than benefits, efficiency often reduces to economy: cutting measurable costs at the expense of less measurable benefits. Think of all those governments that have cut the costs of health care or education while the quality of those services have deteriorated. (I defy anyone to come up with an adequate measure of what a child really learns in a classroom.) How about those CEOs who cut budgets for research so that they can earn bigger bonuses right away, or the student who found all sorts of ways to make an orchestra more efficient. This week, on the news in Canada, we are hearing about railroads that are determined to be more efficient, while overworked engineers are reporting that they have been falling asleep at the switch. Very efficient this.

2. Because economic costs are typically easier to measure than social costs, efficiency can actually result in an escalation of social costs. Making a factory or a school more efficient is easy, so long as you don’t care about the air polluted or the minds turned off learning. I’ll bet the factory that collapsed in Bangladesh was very efficient.

3. Because economic benefits are typically easier to measure than social benefits, efficiency drives us toward an economic mindset that can result in social degradation. In a nutshell, we are efficient when we eat fast food instead of good food.

So beware of efficiency, and of efficiency experts, as well as efficient education, heath care, and music, even efficient factories. Be careful too of balanced scorecards, because while including all kinds of criteria may be well intentioned, the dice are loaded in favor of those that can most easily be measured.

By the way, twitter is efficient. Only 140 characters!

References

Herbert A. Simon Administrative Behavior: Second Edition (Macmillan, 1957, page 14).

This TWOG derives from my article “A Note on the Dirty Word Efficiency”, Interfaces (October, 1982: 101-105)

© 2014 Henry Mintzberg

What exactly are “hard data”? Rocks are hard, but data? Ink on paper or electrons in a “hard drive” are hardly hard. (Indeed, the latter are often called “soft copy.”)

If you must have a metaphor, try clouds in the sky. You can see them clearly from a distance, but up close they are obscure. You can poke your hand through them and feel nothing. “Hard” is the illusion of having turned something real into a number. That guy over there is not Simon, but 4.7 on some psychologist’s scale. The company didn’t just do well; it sold 49 trillion widgets. Isn’t that clear enough?

Soft data, in contrast, can be fuzzy, ambiguous, subjective—at least from a distance. They usually require judgment; like Simon, they can’t even be transmitted electronically. In fact, sometimes they are no more than gossip, hearsay, impression (for example, the rumor going around that most of those widgets are proving defective).

So the dice are loaded. Hard data win every time, at least until they hit the mushy brains of us human beings, living in our soft societies. Hence we had better consider the soft underbelly of these hard data.

1. Hard data can be too general. Alone, they can even be sterile, if not impotent. “No matter what I told him,” complained one of the subjects of Kinsey’s famous study of sexual behavior in the human male, “he just looked at me straight in the eye and asked ‘How many times?”’¹ A little bit of the nuance lost, no? (For starters, what exactly constitutes a “time”? And whose?)

Hard data may provide the basis for description, but often not for explanation. So the sales went up. Why? Because the market was expanding? (You can probably get a number on that.) Because a key competitor was doing dumb things? (No numbers on that, just more gossip.) Because our own management was brilliant?  (That’s objective! …says the management.) Or else because it lowered quality to cut the price? (Try to get the data on that.) All this suggests that we usually need soft data to explain what’s behind the hard numbers—for example, hearsay about what the competitor is doing, gossip about quality in our own factory.

2. Hard data can be too aggregated. How are these hard data presented? Not widget-by-widget. Usually all the widgets are added up to provide one number: total sales. Likewise with that quintessential bottom line: the whole company wrapped up in that one number. Think of all the life lost in that number, and all the reality. It is fine to see the forest from the trees…unless you are in the lumber business. Most managers are in the lumber business: they also need to know about the trees. Too much managing happens in a helicopter, where the trees look like a green carpet.

3. Much hard data arrives too late. Information takes time to “harden.” Don’t be fooled by the speed of those electrons racing around the Internet. Happenings first have to be recorded as “facts”—that can take time—and then aggregated into reports, which may have to await some predetermined schedule (like the end of a quarter). By then, fed up with the quality, the customers may have run off with the competitor. The gossip may have indicated this first, softly, and the grapevine may have carried it around, quickly. But in a world of hard data, that hardly counts.

4. Finally, a surprising amount of hard data is not reliable. They look good, all those definitive little numbers on a pretty screen. But where did they come from? Lift up the rock over hard data and have a look at what’s crawling underneath. “Public agencies are very keen on amassing statistics—they collect them, add them, raise them to the n^th power, take the cube root and prepare wonderful diagrams. But what you just never forget is that every one of those figures comes in the first instance from the village watchman, who just puts down what he damn pleases.”²

And not only public agencies. Most organizations these days are obsessed with the numbers. Yet who goes back to find out what the watchmen put down, especially today when he is some kind of automaton? Or what some manager in search of a promotion put down? Have you ever met a number that could not be gamed—a reject count in a factory or a citation count in a university (just cite your own articles), let alone that quintessential “bottom line”? Moreover, even if the recorded facts were reliable in the first instance, something is always lost in the process of quantification. Numbers get rounded up, mistakes get made, nuances get lost.³  

All of this is not a plea for getting rid of hard data. That makes no more sense than getting rid of soft data. It is a plea to cease being mesmerized by the measures. We all know about using hard facts to check out the soft hunches. Well, how about using soft hunches to check out the hard facts (“eyeballing” the numbers)?

So what’s the bottom line? There’s an old joke that if you meet [someone from a country I can't mention], hit him in the face. He’ll know why. Well, if you meet a number, challenge it. You’ll find out why.

             ____________________________________________________

© Henry Mintzberg 2015. In fact, I sketched out these ideas long ago, before the Internet descended upon us (Impediments to the Use of Management Information [monograph of the National Association of Accountants [U.S.] and Society of Industrial Accountants [Canada], 1975]) LINK, and revised them in various publications ever since. Related TWOGS include: “If you can’t measure it, you had better manage it”; “How National Happiness became gross”; “Downsizing as 21^st Century bloodletting”; “Productive and Destructive Productivity”; and “What could possibly be wrong with efficiency? Plenty”.  

¹ From A. Kaplan The Conduct of Inquiry (Chandler, 1964)

² Attributed to Sir Josiah Stamp 1928, cited in Maltz, M. D. (1997) Bridging Gaps in Police Crime Data: Discussion Paper, BJS Fellow Program, Bureau of Justice Statistics.

³ In his account of “statistics and planning” in the British Air Ministry during World War II, Ely Devons wrote that the collection of such data was extremely difficult and subtle, demanding “a high degree of skill,” yet it “was treated . . . as inferior, degrading and routine work on which the most inefficient clerical staff could best be employed” (p. 134). Errors entered the data in all kinds of ways, even just treating months as normal although all included some holiday or other. “Figures were often merely a useful way of summing up judgment and guesswork.” Sometimes they were even “developed through ‘statistical bargaining.’ But ‘once a figure was put forward . . . no one was able by rational argument to demonstrate that it was wrong.” And when those figures were called “statistics,” they acquired the authority and sanctity of Holy Writ.” (E. Devons Planning in Practice: Essays in Aircraft Planning in War-time, Cambridge University Press, 1950:155)

The tiny kingdom of Bhutan, wedged between Tibet and India, became famous for Gross National Happiness (GNH), thanks to its king. This was not your usual king.  Before voluntarily ceding power to democratic elections, he decreed an increase in the country’s forest cover, had every kid in the country learning English, and in 1972 introduced Gross National Happiness. GNH resonated with people around the world who were fed up with Gross National Product (GNP). As Robert Kennedy commented in a 1968 speech:

Gross National Product counts air pollution and cigarette advertising…. It counts the destruction of the redwood…and the television programs which glorify violence… Yet [it] does not allow for the health of our children, the quality of their education or the joy of their play. …it measures everything in short, except that which makes life worthwhile.¹

GNH stood on four “pillars”: Good Governance, Sustainable Development, Preservation and Promotion of Culture, and Environmental Conservation, elaborated in nine “domains”, including health, education, psychological wellbeing, and community vitality.  

I was curious about this GNH, and being a fan of mountains, I visited Bhutan, in 2006. Two things struck me in discussions with a number of the country’s knowledgeable people. First, they had no idea how to measure much of GNH, and second, this did not matter because the country seemed to be behaving true to its precepts. As a BBC reporter put it, GNH had become “a way of life” in Bhutan—a poor country where life seemed to be rather pleasant.

Not long after, the technocrats descended on Bhutan, to fix GNH. After all, if the Bhutanese didn’t measure GNH, how could they possibly manage it?² Soon each of the nine domains had “its own weighted and un-weighted GNH index...analyzed using...72 indicators.... Mathematical formulas have even been developed to reduce happiness to its tiniest component parts”³ One survey, which took 5-6 hours to complete, “included about 750 variables.”⁴ All this sure took care of gross, but how about happiness?

Critics, especially in the economics profession, have challenged the subjective judgments of GNH. (For the objective judgments of GNP, reread the Kennedy quote.) “Economics professor Deirdre McCloskey criticizes such measurements as unscientific…making the analogy that society could not ‘base physics on asking people whether today was 'hot, nice, or cold’’’.”⁵ If only education, culture, and wellbeing were as measurable as the temperature. (The scientific pretentions of economists have been referred to as “physics envy”.) It’s tough to tell who have been the greater threat to GNH: friends who want to measure it or enemies who want to eradicate it.

Not long after all this measuring, in 2013, Tshering Tobgay, who had studied with economist Michael Porter of the Harvard Business School, became prime minister. He soon claimed that GNH has “distracted [some people] from the real business at hand”: “The bottom line is that we have to work harder” (italics added). What the current king of Bhutan describes as “development with values”, including “kindness, equality, and humanity”⁶, the current prime minister finds “very difficult”, in fact “complicating stuff for me.”⁷ Which stuff—happiness? Or measurement?

F. Scott Fitzgerald claimed that “"The test of a first-rate intelligence is the ability to hold two opposing ideas in mind at the same time and still retain the ability to function." Tshering Tobgay was one of the people I met when I visited Bhutan. I don’t recall him waxing eloquent about GNH, but he certainly seemed to be intelligent. So maybe the problem is that even first-rate intelligence can’t handle three ideas at the same time that seem to be opposing—in this case, economics, happiness, and measurement. But why not cut the measurement, and keep the happiness?  

The retired king of Bhutan with his four wives, all sisters.

© Henry Mintzberg 2015  HM teaches in impm.org, a masters program for functioning managers who can hold two important ideas in mind at the same time.

______________________________

¹ www.jfklibrary.org

² See the TWOG “If you can’t measure it, you’d better manage it.” Others related to this topic include “Can the World Economic Forum deal with the world’s social problems?” and “There is no Nobel Prize in economics…and why that matters.”

³ Mydans, S. (2009, May 6) Recalculating Happiness in a Himalayan Kingdom. Thim- phu Journal. Retrieved from nytimes.com

⁴ Gross National Happiness, 2010 Survey www.grossnationalhappiness.com

⁵  wikipedia.org  accessed 23 June 2015

⁶ http://www.gnhcentrebhutan.org/what-is-gnh/

⁷ Quoting from The Telegraph, “Bhutan’s ‘Gross National Happiness’ masks problems, says new prime minister”, 2 August 2013, and Gardiner Harris, “Index of Happiness? Bhutan’s new leader prefers more concrete goals”, New York Times, 4 October 2013.

I’m a Canadian who got tired of listening to economists telling us how unproductive was our economy. This was going on while our economy was doing exceptionally well, thank you, far better than the exceptionally productive American economy. Can there be something unproductive about productivity?

Yes there can. I came to the conclusion that there are two kinds of productivity, one productive, the other destructive. The problem is that economists can’t tell the difference.

Economists measure the ratio of production outputs to labor inputs, and when that looks good, they declare an economy to be productive. The assumption is that workers have been better trained, superior machinery has been purchased, improved practices have been introduced in company operations, and so on. This is no doubt the case for a certain amount of productivity. But not all, not by a long shot: the unproductive side of productivity has been on the rise for years.

While economists study statistics in the air, companies engage in practices on the ground. As I shall discuss in a later TWOG (about the soft underbelly of hard data), statistics can be dangerous when their users don’t understand where they have come from. Consider this not-quite-hypothetical example.

You are the CEO of a manufacturing company, determined to make it the most productive one around. Here’s what to do: fire everybody in the factory and ship customer orders from stock. Sales will continue while working hours go down. Ask any economist: that’s productive! It’s great for the company too, until, of course, it runs out of stock.

You may find this example a bit extreme—accountants do, after all, keep track of inventories, for all to see. Well then, consider all those big companies that have fired thousands of workers, not in fear of going bankrupt, but because they did not make the numbers that the stock market analysts expected. For these companies to maintain their sales, and keep those inventories up, the workers left behind have had to work that much harder, and probably for lower wages at that. Productive this is too, on the backs of those workers. (More on this “downsizing” as bloodletting in next week’s TWOG.)

There are other ways to realize this kind of productivity, which are less likely to be noticed, let alone measured, by accountants, economists, and stock market analysts: cut research, reduce maintenance, diminish product quality. All save money immediately even if they trash the company eventually.

Add up all these schemes by so many companies and you have an economy that is running out of stock. And a society that is running out of time.¹

Remember those downsized employees, even if the companies don’t: out of budget, out of mind. They are not out of society. These are human beings, not just human resources. They did nothing wrong—except to be in the wrong place at the wrong time. In an economy that is so productive, many of them can’t find new jobs, and some of them get sick, and families break down. That’s hardly productive. (Economists have a fancy word for this: externalities. The companies create the costs; the society pays the consequences. More on this too in a later TWOG.)

And how about those human resources left behind in the companies, who have to make up for their departed colleagues? Who are they to complain: they should be thankful that they have a job in such an economy, even if it was brought to its knees by these very practices. So the best thing for them to do is lay low—after all, they could be next. Can you think of a better way to kill culture and community in a company?

Why, then, do such companies survive? Well, if their competitors are doing the same thing, there’s no problem. (An awful lot of companies are doing the same thing.) And if they are not doing the same thing, then these companies can use their new found money to buy these competitors—imagine all the productivity that can be squeezed out of them. Apparently monopolies are productive too.

And when such companies run out of such gimmicks to make themselves more productive, they can turn to a whole host of political activities—for example, lobbying governments and bribing politicians to enact legislation that can save them from their own productivity. Remember “too big to fail”? (The Supreme Court of the United States has now declared bribing through political donations to be legal.)

Let me tell you: all of this is a lot easier—and quicker for executives hot for their bonuses—than investing in workers and improving processes. Thus do productive companies survive while productive societies collapse.

© Henry Mintzberg 2015. HM is co-founder of CoachingOurselves, a positively productive way of developing human managers. (http://www.coachingourselves.com)

Efficiency is like motherhood. It gets us the greatest bang for the buck, to use an old military expression. Herbert Simon, winner of one of those non-Nobel prizes in economics (more on that in a later TWOG), called efficiency a value-free, completely neutral concept. You decide what benefits you want; efficiency gets you them at the least possible cost. Who could possibly argue with that?

Me, for one.

I list below a couple of things that are efficient. Ask yourself what am I referring to—the first words that pop into your head.

A restaurant is efficient.

Did you think about speed of service? Most people do. Few think about the quality of the food. Is that the way you chose your restaurants?

My house is efficient.

Energy consumption always comes out way ahead. Tell me: who ever bought a house for its energy consumption, compared with, say, its design, or its location?

What’s going on here? It’s quite obvious as soon as we realize it. When we hear the word efficiency we zero in―subconsciously―on the most measurable criteria, like speed of service or consumption of energy. Efficiency means measurable efficiency. That’s not neutral at all, since it favors what can best be measured. And herein lies the problem, in three respects:

1. Because costs are usually easier to measure than benefits, efficiency often reduces to economy: cutting measurable costs at the expense of less measurable benefits. Think of all those governments that have cut the costs of health care or education while the quality of those services have deteriorated. (I defy anyone to come up with an adequate measure of what a child really learns in a classroom.) How about those CEOs who cut budgets for research so that they can earn bigger bonuses right away, or the student in last week’s TWOG who found all sorts of ways to make an orchestra more efficient. This week, on the news in Canada, we are hearing about railroads that are determined to be more efficient, while overworked engineers are reporting that they have been falling asleep at the switch. Very efficient this.

2. Because economic costs are typically easier to measure than social costs, efficiency can actually result in an escalation of social costs. Making a factory or a school more efficient is easy, so long as you don’t care about the air polluted or the minds turned off learning. I’ll bet the factory that collapsed in Bangladesh was very efficient.

3. Because economic benefits are typically easier to measure than social benefits, efficiency drives us toward an economic mindset that can result in social degradation. In a nutshell, we are efficient when we eat fast food instead of good food.

So beware of efficiency, and of efficiency experts, as well as efficient education, heath care, and music, even efficient factories. Be careful too of balanced scorecards, because while including all kinds of criteria may be well intentioned, the dice are loaded in favor of those that can most easily be measured.

By the way, twitter is efficient. Only 140 characters!

References

Herbert A. Simon Administrative Behavior: Second Edition (Macmillan, 1957, page 14).

This TWOG derives from my article “A Note on the Dirty Word Efficiency”, Interfaces (October, 1982: 101-105)

© 2014 Henry Mintzberg