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A remarkable phenomenon in probability theory is that of universality – that many seemingly unrelated probability distributions, which ostensibly involve large numbers of unknown parameters, can end up converging to a universal law that may only depend on a small handful of parameters. One of the most famous examples of the universality phenomenon is the central limit theorem; another rich source of examples comes from random matrix theory, which is one of the areas of my own research.

Analogous universality phenomena also show up in empirical distributions – the distributions of a statistic ${X}$ from a large population of “real-world” objects. Examples include Benford’s law, Zipf’s law, and the Pareto distribution (of which the Pareto principle or 80-20 law is a special case). These laws govern the asymptotic distribution of many statistics ${X}$ which

• (i) take values as positive numbers;
• (ii) range over many different orders of magnitude;
• (iiii) arise from a complicated combination of largely independent factors (with different samples of ${X}$ arising from different independent factors); and
• (iv) have not been artificially rounded, truncated, or otherwise constrained in size.

Examples here include the population of countries or cities, the frequency of occurrence of words in a language, the mass of astronomical objects, or the net worth of individuals or corporations. The laws are then as follows:

• Benford’s law: For ${k=1,\ldots,9}$, the proportion of ${X}$ whose first digit is ${k}$ is approximately ${\log_{10} \frac{k+1}{k}}$. Thus, for instance, ${X}$ should have a first digit of ${1}$ about ${30\%}$ of the time, but a first digit of ${9}$ only about ${5\%}$ of the time.
• Zipf’s law: The ${n^{th}}$ largest value of ${X}$ should obey an approximate power law, i.e. it should be approximately ${C n^{-\alpha}}$ for the first few ${n=1,2,3,\ldots}$ and some parameters ${C, \alpha > 0}$. In many cases, ${\alpha}$ is close to ${1}$.
• Pareto distribution: The proportion of ${X}$ with at least ${m}$ digits (before the decimal point), where ${m}$ is above the median number of digits, should obey an approximate exponential law, i.e. be approximately of the form ${c 10^{-m/\alpha}}$ for some ${c, \alpha > 0}$. Again, in many cases ${\alpha}$ is close to ${1}$.

Benford’s law and Pareto distribution are stated here for base ${10}$, which is what we are most familiar with, but the laws hold for any base (after replacing all the occurrences of ${10}$ in the above laws with the new base, of course). The laws tend to break down if the hypotheses (i)-(iv) are dropped. For instance, if the statistic ${X}$ concentrates around its mean (as opposed to being spread over many orders of magnitude), then the normal distribution tends to be a much better model (as indicated by such results as the central limit theorem). If instead the various samples of the statistics are highly correlated with each other, then other laws can arise (for instance, the eigenvalues of a random matrix, as well as many empirically observed matrices, are correlated to each other, with the behaviour of the largest eigenvalues being governed by laws such as the Tracy-Widom law rather than Zipf’s law, and the bulk distribution being governed by laws such as the semicircular law rather than the normal or Pareto distributions).

To illustrate these laws, let us take as a data set the populations of 235 countries and regions of the world in 2007 (using the CIA world factbook); I have put the raw data here. This is a relatively small sample (cf. my previous post), but is already enough to discern these laws in action. For instance, here is how the data set tracks with Benford’s law (rounded to three significant figures):

 ${k}$ Countries Number Benford prediction 1 Angola, Anguilla, Aruba, Bangladesh, Belgium, Botswana, Brazil, Burkina Faso, Cambodia, Cameroon, Chad, Chile, China, Christmas Island, Cook Islands, Cuba, Czech Republic, Ecuador, Estonia, Gabon, (The) Gambia, Greece, Guam, Guatemala, Guinea-Bissau, India, Japan, Kazakhstan, Kiribati, Malawi, Mali, Mauritius, Mexico, (Federated States of) Micronesia, Nauru, Netherlands, Niger, Nigeria, Niue, Pakistan, Portugal, Russia, Rwanda, Saint Lucia, Saint Vincent and the Grenadines, Senegal, Serbia, Swaziland, Syria, Timor-Leste (East-Timor), Tokelau, Tonga, Trinidad and Tobago, Tunisia, Tuvalu, (U.S.) Virgin Islands, Wallis and Futuna, Zambia, Zimbabwe 59 (${25.1\%}$) 71 (${30.1\%}$) 2 Armenia, Australia, Barbados, British Virgin Islands, Cote d’Ivoire, French Polynesia, Ghana, Gibraltar, Indonesia, Iraq, Jamaica, (North) Korea, Kosovo, Kuwait, Latvia, Lesotho, Macedonia, Madagascar, Malaysia, Mayotte, Mongolia, Mozambique, Namibia, Nepal, Netherlands Antilles, New Caledonia Norfolk Island, Palau, Peru, Romania, Saint Martin, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Slovenia, Sri Lanka, Svalbard, Taiwan, Turks and Caicos Islands, Uzbekistan, Vanuatu, Venezuela, Yemen 44 (${18.7\%}$) 41 (${17.6\%}$) 3 Afghanistan, Albania, Algeria, (The) Bahamas, Belize, Brunei, Canada, (Rep. of the) Congo, Falkland Islands (Islas Malvinas), Iceland, Kenya, Lebanon, Liberia, Liechtenstein, Lithuania, Maldives, Mauritania, Monaco, Morocco, Oman, (Occupied) Palestinian Territory, Panama, Poland, Puerto Rico, Saint Kitts and Nevis, Uganda, United States of America, Uruguay, Western Sahara 29 (${12.3\%}$) 29 (${12.5\%}$) 4 Argentina, Bosnia and Herzegovina, Burma (Myanmar), Cape Verde, Cayman Islands, Central African Republic, Colombia, Costa Rica, Croatia, Faroe Islands, Georgia, Ireland, (South) Korea, Luxembourg, Malta, Moldova, New Zealand, Norway, Pitcairn Islands, Singapore, South Africa, Spain, Sudan, Suriname, Tanzania, Ukraine, United Arab Emirates 27 (${11.4\%}$) 22 (${9.7\%}$) 5 (Macao SAR) China, Cocos Islands, Denmark, Djibouti, Eritrea, Finland, Greenland, Italy, Kyrgyzstan, Montserrat, Nicaragua, Papua New Guinea, Slovakia, Solomon Islands, Togo, Turkmenistan 16 (${6.8\%}$) 19 (${7.9\%}$) 6 American Samoa, Bermuda, Bhutan, (Dem. Rep. of the) Congo, Equatorial Guinea, France, Guernsey, Iran, Jordan, Laos, Libya, Marshall Islands, Montenegro, Paraguay, Sierra Leone, Thailand, United Kingdom 17 (${7.2\%}$) 16 (${6.7\%}$) 7 Bahrain, Bulgaria, (Hong Kong SAR) China, Comoros, Cyprus, Dominica, El Salvador, Guyana, Honduras, Israel, (Isle of) Man, Saint Barthelemy, Saint Helena, Saint Pierre and Miquelon, Switzerland, Tajikistan, Turkey 17 (${7.2\%}$) 14 (${5.8\%}$) 8 Andorra, Antigua and Barbuda, Austria, Azerbaijan, Benin, Burundi, Egypt, Ethiopia, Germany, Haiti, Holy See (Vatican City), Northern Mariana Islands, Qatar, Seychelles, Vietnam 15 (${6.4\%}$) 12 (${5.1\%}$) 9 Belarus, Bolivia, Dominican Republic, Fiji, Grenada, Guinea, Hungary, Jersey, Philippines, Somalia, Sweden 11 (${4.5\%}$) 11 (${4.6\%}$)

Here is how the same data tracks Zipf’s law for the first twenty values of ${n}$, with the parameters ${C \approx 1.28 \times 10^9}$ and ${\alpha \approx 1.03}$ (selected by log-linear regression), again rounding to three significant figures:

 ${n}$ Country Population Zipf prediction Deviation from prediction 1 China 1,330,000,000 1,280,000,000 ${+4.1\%}$ 2 India 1,150,000,000 626,000,000 ${+83.5\%}$ 3 USA 304,000,000 412,000,000 ${-26.3\%}$ 4 Indonesia 238,000,000 307,000,000 ${-22.5\%}$ 5 Brazil 196,000,000 244,000,000 ${-19.4\%}$ 6 Pakistan 173,000,000 202,000,000 ${-14.4\%}$ 7 Bangladesh 154,000,000 172,000,000 ${-10.9\%}$ 8 Nigeria 146,000,000 150,000,000 ${-2.6\%}$ 9 Russia 141,000,000 133,000,000 ${+5.8\%}$ 10 Japan 128,000,000 120,000,000 ${+6.7\%}$ 11 Mexico 110,000,000 108,000,000 ${+1.7\%}$ 12 Philippines 96,100,000 98,900,000 ${-2.9\%}$ 13 Vietnam 86,100,000 91,100,000 ${-5.4\%}$ 14 Ethiopia 82,600,000 84,400,000 ${-2.1\%}$ 15 Germany 82,400,000 78,600,000 ${+4.8\%}$ 16 Egypt 81,700,000 73,500,000 ${+11.1\%}$ 17 Turkey 71,900,000 69,100,000 ${+4.1\%}$ 18 Congo 66,500,000 65,100,000 ${+2.2\%}$ 19 Iran 65,900,000 61,600,000 ${+6.9\%}$ 20 Thailand 65,500,000 58,400,000 ${+12.1\%}$

As one sees, Zipf’s law is not particularly precise at the extreme edge of the statistics (when ${n}$ is very small), but becomes reasonably accurate (given the small sample size, and given that we are fitting twenty data points using only two parameters) for moderate sizes of ${n}$.

This data set has too few scales in base ${10}$ to illustrate the Pareto distribution effectively – over half of the country populations are either seven or eight digits in that base. But if we instead work in base ${2}$, then country populations range in a decent number of scales (the majority of countries have population between ${2^{23}}$ and ${2^{32}}$), and we begin to see the law emerge, where ${m}$ is now the number of digits in binary, the best-fit parameters are ${\alpha \approx 1.18}$ and ${c \approx 1.7 \times 2^{26} / 235}$:

 ${m}$ Countries with ${\geq m}$ binary digit populations Number Pareto prediction 31 China, India 2 1 30 ” 2 2 29 “, United States of America 3 5 28 “, Indonesia, Brazil, Pakistan, Bangladesh, Nigeria, Russia 9 8 27 “, Japan, Mexico, Philippines, Vietnam, Ethiopia, Germany, Egypt, Turkey 17 15 26 “, (Dem. Rep. of the) Congo, Iran, Thailand, France, United Kingdom, Italy, South Africa, (South) Korea, Burma (Myanmar), Ukraine, Colombia, Spain, Argentina, Sudan, Tanzania, Poland, Kenya, Morocco, Algeria 36 27 25 “, Canada, Afghanistan, Uganda, Nepal, Peru, Iraq, Saudi Arabia, Uzbekistan, Venezuela, Malaysia, (North) Korea, Ghana, Yemen, Taiwan, Romania, Mozambique, Sri Lanka, Australia, Cote d’Ivoire, Madagascar, Syria, Cameroon 58 49 24 “, Netherlands, Chile, Kazakhstan, Burkina Faso, Cambodia, Malawi, Ecuador, Niger, Guatemala, Senegal, Angola, Mali, Zambia, Cuba, Zimbabwe, Greece, Portugal, Belgium, Tunisia, Czech Republic, Rwanda, Serbia, Chad, Hungary, Guinea, Belarus, Somalia, Dominican Republic, Bolivia, Sweden, Haiti, Burundi, Benin 91 88 23 “, Austria, Azerbaijan, Honduras, Switzerland, Bulgaria, Tajikistan, Israel, El Salvador, (Hong Kong SAR) China, Paraguay, Laos, Sierra Leone, Jordan, Libya, Papua New Guinea, Togo, Nicaragua, Eritrea, Denmark, Slovakia, Kyrgyzstan, Finland, Turkmenistan, Norway, Georgia, United Arab Emirates, Singapore, Bosnia and Herzegovina, Croatia, Central African Republic, Moldova, Costa Rica 123 159

Thus, with each new scale, the number of countries introduced increases by a factor of a little less than ${2}$, on the average. This approximate doubling of countries with each new scale begins to falter at about the population ${2^{23}}$ (i.e. at around ${4}$ million), for the simple reason that one has begun to run out of countries. (Note that the median-population country in this set, Singapore, has a population with ${23}$ binary digits.)

These laws are not merely interesting statistical curiosities; for instance, Benford’s law is often used to help detect fraudulent statistics (such as those arising from accounting fraud), as many such statistics are invented by choosing digits at random, and will therefore deviate significantly from Benford’s law. (This is nicely discussed in Robert Matthews’ New Scientist article “The power of one“; this article can also be found on the web at a number of other places.) In a somewhat analogous spirit, Zipf’s law and the Pareto distribution can be used to mathematically test various models of real-world systems (e.g. formation of astronomical objects, accumulation of wealth, population growth of countries, etc.), without necessarily having to fit all the parameters of that model with the actual data.

Being empirically observed phenomena rather than abstract mathematical facts, Benford’s law, Zipf’s law, and the Pareto distribution cannot be “proved” the same way a mathematical theorem can be proved. However, one can still support these laws mathematically in a number of ways, for instance showing how these laws are compatible with each other, and with other plausible hypotheses on the source of the data. In this post I would like to describe a number of ways (both technical and non-technical) in which one can do this; these arguments do not fully explain these laws (in particular, the empirical fact that the exponent ${\alpha}$ in Zipf’s law or the Pareto distribution is often close to ${1}$ is still quite a mysterious phenomenon), and do not always have the same universal range of applicability as these laws seem to have, but I hope that they do demonstrate that these laws are not completely arbitrary, and ought to have a satisfactory basis of mathematical support. Read the rest of this entry »