Uses and Misuses of Price's Law or Pareto Principle

The notion that a five percent to 10 percent reduction in force at a large organization might be "productivity-neutral" or even "productivity-positive" rests on the premise that large organizations eventually suffer from organizational entropy or “slack.”

In massive corporations, the individual contribution of an individual is notoriously difficult to measure, if it can be measured at all.

The Pareto Principle (80/20 rule) suggests that 80 percent of the value is produced by 20 percent of the employees. If this holds true, a 10-percent layoff that misses the "vital 20 percent" would, mathematically, have a negligible impact on total output.

Price's Law likewise suggests that half of organizational output is created by just 10 percent of workers.

But the idea can be carried too far. In other words, Pareto might suggest  where value is concentrated, but it does not tell us what parts of the value chain, in what quantities, we can try to remove.

In other words, complex products often require many value chain contributions whose contributions are outside the “80 percent of value” attribution, but are still essential for product success. 

The logic of eliminating most of the other value chain elements outside the “80 percent of value” only works only when inputs are independent and optional. That is rarely, if ever, the case for most products. 

For example, a particular  product ships only if every step is completed. So even low-value steps are non-optional constraints. 

Also, some value chain operations represent option value or risk reduction. They might not show up in the completed product, but might instead provide protection against product failure. 

Quality assurance efforts, regulatory compliance or maintenance might not be direct value creators, but might be necessary to deliver a final product. 

So many functions might be structurally necessary but individually have low marginal impact. And even that does not help address the question of staffing levels to support such processes. 

Were that not the case, competitive markets would force firms toward the Pareto-suggestion of minimal staffing. And we do not see that. 

Study / Source	Domain	Key Finding	Implication for Workforce
Pareto principle overview	Operations / Engineering	80% of outcomes driven by ~20% of causes	Output concentration is real
Pareto in supply chains (Slimstock)	Supply chain	20% of products drive most revenue	Inventory focus is uneven
Lean operations Pareto usage	Manufacturing	Majority of defects from small set of causes	Useful for prioritization
Juran Pareto analysis guide	Quality management	“Vital few and useful many” distinction	Many low-impact roles still necessary
IMD Pareto analysis strategy article	Strategy	Pareto helps focus leadership effort, not eliminate complexity	Tool for prioritization, not simplification
Value chain simulation research (VCS)	Manufacturing systems	Output depends on interdependent processes across cost, quality, delivery	System requires multiple linked roles
Pareto distribution empirical study (arXiv)	Economics/statistics	Heavy-tailed distributions common in real systems	Inequality of contribution is structural, not eliminative

That noted, Pareto does make sense for:

• Prioritizing improvements (fix top 20 percent of defects)

• Sales focus (top 20 percent of customers or products)

• Time allocation (focus on high-leverage activities)

Still, Pareto notwithstanding, large organizations might often be less productive than imagined because of:

• Social Loafing: In large groups, individuals often work less hard than they would alone because their lack of effort is easily hidden by the group's overall performance

• Bureaucratic Friction: Beyond a certain size, organizations require "coordinators for the coordinators." Removing a layer of these roles can actually speed up decision-making, allowing the remaining staff to be more productive because they spend less time in meetings.

So when an organization has excess personnel, it can often absorb a reduction in force without losing core output, effectively "trimming the fat" to improve the output-per-employee ratio.

Study / Source	Key Focus	Source Link
Love & Nohria (2005)	Reducing Slack: Found that downsizing improves performance specifically when the firm has "excessive" resources (high slack).	Read at ResearchGate
Cascio, Young, & Morris (1997)	Financial Consequences: A landmark study showing that layoffs alone rarely boost ROA, but asset restructuring combined with cuts does.	Read at ResearchGate
Guthrie & Datta (2008)	Industry Context: Demonstrates that the negative impact of layoffs is significantly higher in "knowledge-intensive" (R&D) industries.	Read at ResearchGate
McKinsey & Company	The Productivity Imperative: Analyzes how technology and "de-layering" (removing management tiers) can boost service-sector productivity.	Download PDF (McKinsey)
Zyglidopoulos (2005)	Corporate Reputation: Examines how the market and stakeholders perceive downsizing as a signal of "efficiency-seeking" behavior.	Read at ResearchGate

The caveats are several.

Guthrie and Datta warn that in organizations with high innovation requirements, a 10-percent cut can remove critical "intangible assets" whose loss will not be seen until later. 

The Cascio study suggests that many firms fail to see long-term productivity growth after layoffs because they lose the ability to innovate

In other words, if the ideal is “cutting fat but not muscle,” the danger is “cutting some muscle as well.”.

If “productivity” is defined as total output divided by total input, then a smaller denominator “automatically” raises productivity, assuming output remains the same. 

The argument is that many large firms have enough "operational slack" (excess resources) that a five-percent to 10-percent  cut acts as a "forcing function," requiring the remaining staff to automate or abandon low-value tasks.

Large organizations are complex, so it might not be easy to determine how much, and where, to make layoffs. If one assumes every existing function actually is essential, then an “across the board” approach actually makes some sense.

The organization keeps the function, but possibly operates more efficiently. 

Long Tail Doesn't Apply?

As observers have started to track sales of digital goods more closely, some apparently-contradictory evidence has started to appear about the "long tail" theory of sales in the digital domain. The concept: cheap digital distribution changes the retail sales function, allowing profitable sales of low-volume titles or items on a scale not possible in a physical distribution strategy. Most observers instinctively would agree. 

The key prediction has been that online distribution would allow niche businesses, content and goods to thrive in a digital distribution context impossible to sustain in a physical distribution context. As commonly understood, perhaps an additional 20 percent increase in volume should be feasible, as well as a change in "mass culture" that would fragment demand. 

But some sales data contradicts the notion. A new study by Will Page, chief economist of the MCPS-PRS Alliance, a music royalty collection organization, suggests that online sales success still relies on big hits. The study found that 80 per cent of all revenue came from around 52,000 tracks. For albums, of the 1.23 million available, only 173,000 were ever bought, meaning 85 per cent did not sell a single copy all year.

Frankly, the long tail now appears, in one sense, as the triumph of hope over experience. Many "wanted" online distribution to change purchasing patterns. The notion was that once huge variety was available, tastes would change. It isn't so clear why it is assumed "tastes" will change with different distribution. It is clear why fulfillment will change with cheaper distribution. But efficient, or better, distribution still should result in a "long tail" of demand that is the same as a distribution-induced "short tail" of demand. 

The reason derives from the theory itself. The idea behind the "long tail" is not actually new, and dates back to an Italian economist, Vilfredo Pareto, who in 1906 coined the Pareto Principle, popularly known as the 80-20 rule. Basically, the idea is that in much of life and nature,  roughly 80 percent of the effects come from 20 percent of the causes.

The same concept is known as Bradford's Law. 

As implied by the theory of the long tail, online distribution should allow retailers to sell small volumes of hard-to-find items, instead of selling a smaller number of highly-popular items. Most people can grasp that. What isn't so clear is why that expected distribution curve will be a "new" Pareto curve, instead of validating the existing Pareto curve. 

Under any normal set of circumstances, a Pareto distribution is what one would expect to see. Some have pointed to music sales at Rhapsody, an online music service. Of the 735,000 items for sale, 39,000 account for 78 percent of sales, while 796,000 titles represent 22 percent of sales. 

Likewise, Netflix data suggests 20 percent of total rentals are of "tail" or low-volume titles, while 80 percent of rentals are basically "hit movies" one would expect most people to be interested in. 

Is that confirmation of the operation of a Pareto distribution? Yes. Does it represent incremental sales of 22 percent that might not occur in a physical distribution scenario? Yes. Are the results unexpected? Not if one expects to see a Pareto distribution. 

Is the idea wrong, or useless? Not really. A Pareto distribution can assume a 70-30 pattern, for example, suggesting a bigger role for niche products than before. That represents an important shift of opportunity for providers of niche services and products because of online or Web distribution. 

But the long tail might not mean a revolution. Forrester Research, for example, estimates seven percent of retail sales in 2008 will have been made online, up from 3.2 percent in 2007. What does that mean? Most sales follow a Pareto curve: 97 percent of things sold still are sold the traditional way. There will be further shifts, of course.

But Pareto would suggest online sales will settle in at around 20 percent of total sales, at best, on a sustainable basis. 

Pareto Theorem, or 80/20 Rule, Applies to Telecom Attackers as Well

This is a good illustration of the Pareto theorem, which states that 80 percent of instances or outcomes in business or nature come from 20 percent of the cases or effort. The Pareto theorem is popularly known as the 80/20 rule . 

Of the 83 challengers in 20 telecom markets analyzed by Bain & Company, only (22 percent) grew both their revenue and free cash flow and increased their share of profit from 2010 to 2017.

source: Bain and Company 

That is a nearly-perfect example of the predicted Pareto pattern. 

source: IP Carrier 

Vilfredo Pareto, an Italian economist, was studying the distribution of wealth in1906. What he found was a distribution most people would commonly understand as the "80/20 rule," where a disproportionate share of results come from 20 percent of actions. The Pareto distribution has been found widely in the physical and human worlds. It applies, for example, to the sizes of human settlements (few cities, many hamlets/villages). It fits the file size of Internet traffic (many smaller files, few larger ones).

It describes the distribution of oil reserves (a few large fields, many small fields) and jobs assigned supercomputers (a few large ones, many small ones). It describes the price returns on individual stocks. It likely holds for total returns from stock investments over a span of several years, as most observers point out that most of the gain, and most of the loss in a typical portfolio comes from changes on just a few days a year.

The Pareto distribution is what one finds when examining the sizes of sand particles, meteorites or numbers of species per genus, areas burnt in forest fires, casualty losses: general liability, commercial auto, and workers compensation.

The Pareto distribution also fits sales of music from online music stores and mass market retailer market share. The viewership of a single video over time fits the Pareto curve. Pareto describes the distribution of social networking sites. It describes the readership of books and the lifecycle value of telecom customers.

No Bandwidth Hogs?

Some would argue there is no "exaflood" and no such thing as a "bandwidth hog." 

I have no more detailed data from any Internet service provider than anybody else does, so I doubt anybody can prove or disprove the thesis definitively. But I also have no reason to think the usage curve will be anything other than a Pareto distribution, since so many common distributions in the physical and business world conform to such a distribution.

Vilfredo Pareto, an Italian economist, was studying the distribution of wealth in1906. What he found was a distribution most people would commonly understand as the "80/20 rule," where a disproportionate share of results come from 20 percent of actions. The Pareto distribution has been found widely in the physical and human worlds. It applies, for example, to the sizes of human settlements (few cities, many hamlets/villages). It fits the file size of Internet traffic (many smaller files, few larger ones).

It describes the distribution of oil reserves (a few large fields, many small fields) and jobs assigned supercomputers (a few large ones, many small ones). It describes the price returns on individual stocks. It likely holds for total returns from stock investments over a span of several years, as most observers point out that most of the gain, and most of the loss in a typical portfolio comes from changes on just a few days a year.

The Pareto distribution is what one finds when examining the sizes of sand particles, meteorites or numbers of species per genus, areas burnt in forest fires, casualty losses: general liability, commercial auto, and workers compensation.

The Pareto distribution also fits sales of music from online music stores and mass market retailer market share. The viewership of a single video over time fits the Pareto curve. Pareto describes the distribution of social networking sites. It describes the readership of books and the lifecycle value of telecom customers.

So knowing nothing else than that the Pareto distribution is so widely represented in the physical world and in business, I would expect to see the same sort of distribution in bandwidth consumption. As applied to users of bandwidth, Pareto would predict that a small number of users in fact do consumer a disproportionate share of bandwidth.

I certainly can't say for sure, but would be highly surprised if in fact a Pareto distribution does not precisely describe bandwidth consumption.

Pareto Theorem Suggests Where and Why Millimeter Wave Spectrum Will be Useful

Pareto distributions--often colloquially referred to as the “80/20 rule.--are common in business, technology and nature.

Most of us are familiar with the 80/20 rule, which suggests that roughly 80 percent of value or outcomes are generated by about 20 percent of actions. Formally, it is the Pareto theorem. 

Virtually nobody would be surprised if told that the highest data demand in the U.K. mobile services market comes from areas such as London, Manchester or Glasgow, which are major population centers. 

What might be more surprising is that cell site data demand is about as disparate as the population data would suggest. According to Ofcom, the U.K. communications regulatory body, the largest 20 cities, containing 32 percent of the total U.K. population, cover about 2.4 percent of the surface area. 

source: Ofcom 

In fact, cell locations and data usage tend to show a Pareto distribution. Pareto would suggest that about 80 percent of mobile data usage is generated by 20 percent of the locations. 

source: Medium 

Pareto applies to most aspects of the connectivity, data center or computing businesses. It even applies to revenue generated by mobile cell sites. Half of mobile revenue is driven from traffic on about 10 percent of sites. Fully 80 percent of revenue is driven by activity on just 30 percent of cell sites. 

source: Ericsson 

Pareto also applies to mobile operator and telco revenue, profits, accounts and cost.  

source: Telco Strategies

That is clear in the distribution of customer accounts, ranked by revenue potential.

source: B2B International

source: Ofcom 

That Pareto distribution of data usage also shows where and why millimeter wave spectrum will prove useful. The skewing of data demand in a relatively small number of dense, urban areas suggests millimeter wave’s capacity advantages will prove most valuable there, as Verizon has argued. 

source: Verizon 

Don't Let Any "Good" Be the Enemy of the "Greatest Good"

source: Rype

The Pareto distribution can be seen in one’s personal or business life, in business strategy or business performance. The fundamental principle is that effort and outcomes are non-linear. A small number of inputs or instances drive most of the outputs or results.

The practical implication for communications or app providers is that a relatively small number of decisions and priorities actually matter, where it comes to making a transition from legacy to next generation business models.

The corollary is that there are a many “good” or “useful” or “helpful” things any service or app provider can do, but which should not be done, to concentrate on the few areas where breakthroughs are possible.

In other words, the temptation to “do” any number of helpful things actually can be detrimental to strategic success, which requires intense concentration on a relative handful of decisions, investments and effort.

There are always lots of useful or helpful things a company might do, to support its business. Many of those things actually will deliver a measurable result. But most will fail to help a company make a strategic breakthrough.

So saying “no” to most of those helpful things can be a prerequisite for focusing effort on a few matters that can decisively change a company’s future.

The areas in which Pareto applies are rather large. The Pareto theorem is the underlying principle of the “long tail” approach to freemium pricing, for example, where basic versions of a product are free, and users then pay incrementally more for additional features.

The Pareto rule can guide resource allocation, the principle being that there is some allocation or resources that makes a person or an organization better off, while not harming existing persons or the organization itself.

The principle is popularly understand as the 80/20 rule, which stipulates that about 20 percent of effort produces 80 percent of results.

The Gini coefficient essentially follows Pareto distribution patterns as well, and describes national income inequality patterns as well.

In the United States, the number of homes without a broadband connection follows a Pareto distribution.

It illustrates the law of diminishing returns. The cost of building access loops generally follow Pareto rules, for example. The inverse of the Pareto distribution is that a small number of instances produce most of the “per-line” access cost.

In other words, a small number of remote locations represent a disproportionate share of network cost, based on cost per mile.

source: National Broadband Plan

source: National Broadband Plan

80/20 Has Many Implications

One should not underestimate the importance of the Pareto theorem, commonly known as the “80/20 rule,” where 80 percent of results come from 20 percent of instances.  

One example are financial returns from the Standard and Poors 500 index, over the period 1989 to 2015, when just 20 percent of stocks accounted for 100 percent of index gains.

Put another way, 80 percent of stocks in the index actually had a collective return of zero percent.

source: A Wealth of Common Sense

In communication or other markets, the Pareto theorem matters because it tends to describe the broad structure of markets, as well as the generation of revenue and profits within each market.

In the Indian mobile services business, just 17 percent of customers generate 60 percent of revenues, for example.

The fundamental principle is that effort and outcomes are non-linear. A small number of inputs or instances drive most of the outputs or results.

The practical implication for communications or app providers is that a relatively small number of decisions and priorities actually matter, where it comes to making a transition from legacy to next generation business models.

The corollary is that there are a many “good” or “useful” or “helpful” things any service or app provider can do, but which should not be done, to concentrate on the few areas where breakthroughs are possible.

source: Rype

The Pareto rule can guide resource allocation, the principle being that there is some allocation or resources that makes a person or an organization better off, while not harming existing persons or the organization itself.

In other words, at some point, additional effort will produce diminishing returns.

The Gini coefficient essentially follows Pareto distribution patterns as well, and describes national income inequality patterns as well.

In the United States, the number of homes without a broadband connection follows a Pareto distribution.

It illustrates the law of diminishing returns. The cost of building access loops generally follow Pareto rules, for example. The inverse of the Pareto distribution is that a small number of instances produce most of the “per-line” access cost.

In other words, a small number of remote locations represent a disproportionate share of network cost, based on cost per mile.

source: National Broadband Plan

source: National Broadband Plan

In practical sense, the Pareto theorem suggests that a small number of actions actually drive most of the actual organization results. 

In other words, the temptation to “do” any number of helpful things actually can be detrimental to strategic success, which requires intense concentration on a relative handful of decisions, investments and effort.

There are always lots of useful or helpful things a company might do, to support its business. Many of those things actually will deliver a measurable result. But most will fail to help a company make a strategic breakthrough.

So saying “no” to most of those helpful things can be a prerequisite for focusing effort on a few matters that can decisively change a company’s future.