Pareto principle

principle · origin: practice · evidence: not directly tested

In short

The Pareto principle says that, among causes that contribute to the same effect, a few of them produce most of the effect. The popular version is “80% of results come from 20% of causes”. The phenomenon is real and shows up in very different fields, from wealth to crime. The exact numbers, though, differ from case to case, sometimes a lot. The useful idea is to look for the “few that matter a lot”, not to assume it is always exactly 20%.

What it says

Where the name comes from. The principle was not formulated by Pareto. Joseph Juran, a quality-control specialist, tells the story in a 1975 article whose title says it all: “The Non-Pareto Principle; Mea Culpa”. Juran writes that he himself named the phenomenon after Pareto. He describes it as the case where, in any population contributing to a common effect, relatively few contributors account for the bulk of the effect: the “vital few and trivial many”. He later admitted he had used the wrong name.

Juran’s summary:

• Many people have observed the phenomenon over time, each in their own field.
• The economist Vilfredo Pareto observed it in the distribution of wealth and proposed a mathematical law for the distribution of income.
• Max Lorenz invented the cumulative curve used to show it graphically.
• Juran seems to have been the first to treat it as a “universal” principle, valid in many fields, and he was also the one who gave it Pareto’s name.

What the maths says. The physicist Mark Newman (2005) explains where such imbalances come from. Many quantities (wealth, city populations, website visits) roughly follow a power law. For this kind of distribution, the share of the total held by the top depends on a single number, the exponent. Newman calculates that, for US wealth, about 80% would be in the hands of the richest 20%. He also shows, however, that in the data he analyses the top 20% of websites get about two-thirds of visits, and the largest 10% of US cities hold about 60% of the population. The same kind of imbalance, but with different proportions.

Outside economics too. The criminologist David Weisburd proposed a “law of crime concentration”: a small share of streets accounts for a large share of crime. Gill, Wooditch and Weisburd (2017) summarize the evidence:

• In Seattle, 5.1% of street segments accounted for 50% of crime.
• In Tel Aviv, 4.5% of segments also accounted for 50%.
• In Weisburd’s (2015) analysis across several cities, the share of streets producing half of all crime falls within a narrow band of about 4 percentage points.
• In a suburban city in Minnesota, Gill et al. found even greater concentration: 2% of segments produced 50% of crime.

So the concentration is strong and stable, but in a “5/50” form, not “20/80”.

Example

The example Juran started from is quality control. When you analyse a product’s defects, you often find that a few types of defect cause most of the losses. Fixing those first gains you more than spreading the effort equally across all of them.

An example built for this text: you have 30 tasks on your list. Ask yourself which 3–5 of them would change the week’s outcome the most. There is no guarantee they are exactly 20%, but there are likely to be a few that matter much more than the rest.

How to apply it

The steps below are a practical approach we propose.

1. Measure before you assume. List the causes (clients, tasks, defect types, expenses) and note how much each contributes to the effect. Sort them in descending order and see what share of the total the top few add up to. The real ratio might be 60/20 or 50/5, not 80/20.
2. Start with the “vital few”. If you find strong concentration, put your effort there first.
3. Don’t ignore the rest. What isn’t at the top isn’t necessarily useless. The principle only says where most of the effect is, not that the rest doesn’t matter.
4. Link it to “good enough”. If the last part of the result takes a disproportionate share of the effort, it is sometimes reasonable to stop earlier. See the entry on maximizing and satisficing.

Limits and nuances

• “80/20” is not a law of nature. Newman (2005) shows that the ratio depends on the shape of the distribution. In the studies summarized by Gill et al. (2017), half of all crime falls on 2% to about 8% of streets, not 80% on 20%. 80/20 is a memorable approximation for some cases, not a general result.
• The two numbers don’t have to add up to 100. 80 and 20 measure different things (effects and causes). Ratios like 50/5 or 90/10 are just as possible.
• Not every distribution is lopsided. The principle appears where contributions are very unequal. Where contributions are similar, looking for the “vital few” leads nowhere.
• It is not a psychology finding. It is a regularity observed in economics, quality, criminology and other fields. Applying it to personal productivity is an extrapolation, not something tested in the studies cited here.
• The top can change. Gill et al. (2017) note that whether the same streets stay at the top year after year is less studied than the stability of the percentage. Likewise, the clients or tasks that matter most today may be different tomorrow.

Sources

• Joseph M. Juran (1975). The Non-Pareto Principle; Mea Culpa
• M. E. J. Newman (2005). Power laws, Pareto distributions and Zipf's law
• David Weisburd (2015). The Law of Crime Concentration and the Criminology of Place
• Charlotte Gill, Alese Wooditch, David Weisburd (2017). Testing the “Law of Crime Concentration at Place” in a Suburban Setting: Implications for Research and Practice

See also: Diminishing returns, Maximizing vs satisficing, Via negativa