Olivier Gergaud

Morgane Laouenan

Etienne Wasmer
email: etienne.wasmer@sciencespo.fr

Sarah Asset

Nicolas Britton

Jean-Benoît Eyméoud

Jessica Flakne

Simon Fredon

Valentine Watrin

Meradj Aghdam

Mathis Forman

Florentin Cognie

Blaise Leclair

Exploring a database of "notable people" (3000BCE-2015AD) Version 1.0.1 ⇤

This paper describes a database of 1,243,776 notable people and 7,184,575 locations (Geolinks) associated with them throughout human history (3000BCE-2015AD). We first describe in details the various approaches and procedures adopted to extract the relevant information from their Wikipedia biographies and then analyze the database. Ten main facts emerge.

1. There has been an exponential growth over time of the database, with more than 60% of notable people still living in 2015, with the exception of a relative decline of the cohort born in the XVIIth century and a local minimum between 1645 and 1655. 2. The average lifespan has increased by 20 years, from 60 to 80 years, between the cohort born in 1400AD and the one born in 1900AD.

3. The share of women in the database follows a U-shape pattern, with a minimum in the XVIIth century and a maximum at 25% for the most recent cohorts.

4. The fraction of notable people in governance occupations has decreased while the fraction in occupations such as arts, literature/media and sports has increased over the centuries; sports caught up to arts and literature for cohorts born in 1870 but remained at the same level until the 1950s cohorts; and eventually sports came to dominate the database after 1950.

Era and AD to Anno Domini.

† KEDGE Business School and LIEPP, Sciences Po. Email: olivier.gergaud@kedgebs.com ‡ CNRS, Centre d'Economie de la Sorbonne and U. Paris 1 and LIEPP, Sciences Po. Email: morgane.laouenan@univ-paris1.fr

There is currently a growing number of datasets allowing for the documentation of historical facts. A recent approach has focused particularly on historical individuals, who we call in this text notable people. This approach was pioneered by [START_REF] Schich Maximilian | A network framework of cultural history[END_REF]. The authors automatically collected the years and locations of birth and death for 150,000 notable people in history using Freebase, a Google-owned knowledge database. de la [START_REF] David | The longevity of famous people from Hammurabi to Einstein[END_REF] built a sample of 300,000 famous people born between Hammurabi's epoch and 1879, Einstein's birth year from Index Bio-bibliographicus Notorum Hominum, to estimate the timing of improvements in longevity and its role in economic growth. Recently, [START_REF] Yu | Pantheon 1.0, a manually verified dataset of globally famous biographies[END_REF] also used Freebase and assembled a manually verified dataset of 11,341 biographies existing in more than 25 languages in Wikipedia. Our paper extends these approaches. We compile the largest possible database of notable people rather than focusing only on "very famous" individuals, because we are ultimately interested in detecting the statistically significant local economic impact of these individuals. It actually turns out that weighting individuals with measures of their impact does not make a big difference, which ex post justifies our collection of information on hundreds of thousands of lesser known artists, business people and local rulers, famous enough to have been listed and described somewhere on the internet or in various rankings, but yet left out of the vast majority of internet sources. To this end, we use two different, yet complementary approaches to obtain names of and information on notable people. One is also based on Freebase, and allows us to collect information at a large scale for 938,000 individual profiles over 4,000 years of human history, after a careful examination of homonyms and the elimination of duplicates. We refer to this method as "top-down", since the information on names is centralized in Freebase. The second method is based on a systematic search from various categories in Wikipedia pages to identify notable people and is refered to as a "bottom-up" approach. This results in a list of about 1 million individuals that considerably overlaps with the "top-down" approach, and eventually adds another 280,000 names to the Freebase results. These individuals are then matched with their respective Wikipedia biographies in English, from which we extract a large amount of biographical information through a careful and manually verified semantic analysis. We categorize people according to gender, nationality, and their three main activities/occupations according to an ad hoc classification system consistent throughout history. Other key information such as birth year, death year, birth place, place of death and most importantly all geographical linkages (GeoLinks thereafter) mentioned in the biography in between birth and death, have been systematically collected where available. A total of 3.5 million geographical linkages have been gathered and analyzed. We also attempt to measure these individuals' impact on various economic outcomes. The visibility of each individual's page is probably a better proxy of his or her impact than the number of pages viewed over the past years (the alternative approach pionneered by [START_REF] Yu | Pantheon 1.0, a manually verified dataset of globally famous biographies[END_REF]. Therefore, we use a simple impact measure compiled using a combination of the number of words in the Wikipedia page and the number of languages into which the page has been translated. The implied ranking is disclosed in the text and its Appendix, both in an overall ranking and in rankings by categories. Alternatives are explored and correlations between them analyzed. We then match notable people with cities using a unique global historical population database composed of new Census data from 17 countries on 5 continents, the Urbanisation Hub-Bairoch-Bosker city population data between 800AD -1800AD, the Lincoln population data 1794 -2005 for 24 other cities in the world, and the United Nations (UN) population database since 1950. For each country and within each period, we select the 30 largest cities at a time for countries with a landsize of less than approximately 300,000 square kilometers and for the 50 largest cities for larger countries with a landsize larger than 300,000 square kilometers. The landsize threshold is more time invariant than any other population criterion and the number of cities retained permits that 50% of 3.5 million locations assembled in the database over the period 800AD-2015AD lie within 50km of the nearest city. In the last period of the sample (1950AD-2015AD), the UN population database even allows us to have 50% of the locations within 13km of the nearest city. In this paper, we carefully document the data search and procedures to extract the relevant information. We then explore the datasets, provide a number of descriptive stylized facts and finally provide descriptive, non-causal associations of the role of notable people on city growth.

1 Data collection 1.1 A list of individuals from Freebase then matched with Wikipedia pages ("top-down" approach)

his/her birth date (if available). As of January 2015, when we accessed the list, Freebase contained precisely 3,440,707 notable people. However, many names were duplicates, or mistakenly added. A subset of nearly 407,000 irrelevant observations, corresponding either to pure html codes, movie titles, ships such as HMS Titanic, names in non-Latin alphabets (overall, this eliminates around 3,000 names that appear in Chinese/Korean characters, in Greek or Cyrillic alphabets) or even numbers instead of full regular names have been identified and then automatically dropped out of the initial dataset. This step leads to a sample of 3,033,469 notable people.

2. We then match these 3 million names with their corresponding Wikipedia pages in English. The matching process was more easily facilitated when using the birth date, where available, especially for homonyms. Overall, 46% (1,391,718 observations) include a birth year. The matching process achieved a lower success rate for people with no such birth information, i.e. when the matching process was based on the full name only (see next point). Missing birth dates represent 54% of the sample (1,641,751 observations).

3. There are several homonyms in the database. When Wikipedia detects homonyms it either provides a list of names with birth dates1 or directly takes the user to the page of the most famous homonym e.g. for Ray Charles (https://en.wikipedia.org/wiki/Ray_Charles), via the following sentence and link : "For other uses, see Ray Charles (disambiguation). In this second case the link refers to a list of homonyms with a link to each one's respective page. In both cases, in order to capture as many homonyms as possible from Freebase we match both the full name and the birth year. For homonyms with no birth information in Freebase, we only consider the page corresponding to the most famous homonym instead of collecting all pages mentioned in the list described before (first case). Among the individuals with birth year information, approximately 30% do not have a Wikipedia page in English, 868,266 have a unique link to a page in English, while 154,349 are not matched. Among these pages, 76,000 must be disambiguated as they contain more than one link. This leads to a first group of names. Among individuals with no birth year information, 313,308 come up with a unique link to a Wikipedia page in English, but only 106,646 refer to an individual (and not to a list of homonyms, concepts, etc.). Of these, 37,854 are not duplicates with respect to the first group detailed above (with birth year information) and are therefore included in the final sample. At the end of the process, 964,245 individuals are matched with their Wikipedia biography, which we downloaded as of December 2015.

1.2 A list of individuals using Wikipedia categories ("bottom-up" approach)

To be exhaustive and consistent, we also use Wikipedia directly, which classifies most individual pages by birth date. A secondary independent dataquest is run automatically from birth dates between 1500BCE to 2015AD; a complete list of categories is assembled; virtually all of these categories include additional lists of people. Each of these is scanned to obtain more names with a page in English. We obtain a total of 1,176,812 names including 897,310 names in common with names identified using the "top-down" approach and 279,531 distinctly new names.

We then merge the two databases ("top down" and "bottom up") using the url of each individual

(

Individual characteristics

In this subsection we analyze the source code of each Wikipedia page to extract basic information about dates and locations for birth and death, occupations, citizenship and gender. These individual characteristics are either identified from the Infobox (fixed-format table at the top right-hand corner of biographies), from the Abstract, or from the Categories section. 398,830 (36.05%) individuals have no Abstract and therefore we extract information from the Main text.

Figure 2 shows the different parts of the Wikipedia page of Ray Charles. See his full page in the Appendix. Birth and Death Dates Information on an individual's date of birth and death is typically available in three different sections of the Wikipedia biography: i) Infobox, ii) Abstract/Main text and iii)

Categories section, which is located at the bottom of the page. We therefore get a maximum of three birth dates per individual of which 1,063,637 come from the Categories section, 669,405 were found in the Infobox and 951,147 were extracted from the Abstract/Main text. Some are misreported but the alternative birth dates allow for a correction: among observations without missing dates, the concordance rate between birth dates coming from the same biography is at around 97.3%. Similarly, we found 498,065 dates of death in the Categories section, 232,584 in the Infobox and 383,088 in the Abstract/Main text. The concordance rate between dates of death coming from the same biography is at around 98.4%. We end up with 1,073,585 birth years and 499,980 death years. Missing death years correspond either to an unreported death year or to an individual still living in 2015 (a large fraction of our database : 59%). Note also that we have 144,804 individuals with no birth information available regardless of the three sources used. Among those individuals, 21,581 have information available on their death.

Places of Birth and Death

Places of birth and death may both be found in the Infobox and/or in the main body of the text (Main text). We first analyze the information coming from the Infobox and then consider the Main text as an alternative source of information when either the Infobox is missing or the relevant information has not been detected in the Infobox. In this case, we use keywords such as "born in/at" or "died in/at" to find place of birth and death. Most locations of an individual's birth and death are associated to a latitude and a longitude (hereafter we use the word geocoded), which we then extract.

Citizenship/Gender Information about citizenship is usually present in the Infobox and in the Abstract/Main text. We use both sources in order to minimize the number of missing values. On rare occasions, when there is more than one citizenship/country mentioned in the Abstract, we consider the citizenship appearing first as that individual's citizenship. After thorough manual verification, it appears that this information always appears first in the Abstract section (as illustrated below in Ray Charles' biography).

For gender we also use the Abstract/Main text and check for the presence of pronouns (he/she) and possessive adjectives (his/her) in the text. We consider a person to be female (male) if "she"/ "her" ("he"/ "his") is found in the summary. In case we detect both masculine and feminine pronouns or possessive adjectives, we select the first pronoun that appears as the one identifying the person's gender. This method did not prove efficient for short biographies which do not contain any pronouns or possessive adjectives. Overall, women account for only 15.7% of the sample, while men represent the largest share (78.5%) and we failed to identify the gender information for only 5.8% of all biographies analyzed. However, a careful visual inspection of this latter category indicates that missing genders are predominantly males.

Occupations We determine occupations by locating linking verbs such as "was a"/"is a"/"was the"/"is the" in the Main text: for instance, "Ray Charles was an American singer, songwriter, musician, and composer ". Occupations are then grouped into 6 categories: Academics (studies, education), Entertainment (arts, literature/media, sports), Entrepreneur (business, inventor, worker), Family, Governance 1 and2 provide a few summary statistics of the entire database. See the full list of occupations collected and sorted in the Appendix. Table 3 summarizes the information that has been collected so far and informs us about the location where the information was found in the Wikipedia biography.

Visibility Factors Various "influence weights" are built, based on specific page characteristics combining the length of the page (in words), the number of translations of the page and some additional information such as the number of footnotes, categories, headlines, links, etc. Futher details about these are provided in Section 2 below. As argued in the introduction, many other sources, depending on the type of study envisaged can serve as a proxy for the visibility factor. Our measures fit well our purpose, which is to detect the local economic impact of the individuals present in the database.

The number of pages viewed in recent years is a potential indicator that has been favored by [START_REF] Yu | Pantheon 1.0, a manually verified dataset of globally famous biographies[END_REF], and would be very useful if we were interested in studying contemporary individuals (living artists, athletes). The number of internet pages (backlinks) linking to a Wikipedia biography could also be an indicator. The latter information would be interesting per se as a measure of the relevance of these biographies. For our purpose, the information on the length and complexity of a Wikipedia biography and the number of links leading outwards to the Internet is still the most appropriate: we presume that individuals who have a Wikipedia biography and another biography elsewhere on the internet have had precisely the impact we attempt to detect. Of course, we may want to compare an individual's visibility relative to others in their respective birth cohort where relevant.

Geographical Wikipedia linkages (GeoLinks)

An important improvement of the present work compared to other articles quoted above is our in-depth and careful analysis of GeoLinks present in individual Wikipedia biographies. This analysis provides detailed and reliable information about these different places where famous people live (lived) and/or interact with (interacted with) over the course of their lifetime2 .

To collect such information we copy all hyperlinks found in each page and make a distinction between links found in the abstract and links coming from the Main text. Hyperlinks, by definition, lead to other Wikipedia Pages (Wikilinks), which we extract and parse. Wikilinks with geographical coordinates (longitude/latitude), are potential GeoLinks such as Albany, Georgia where Ray Charles was born. We discard countries, regions or provinces that are locations with geographical coordinates as our goal is to match notable people with places at the city level (see below). There are also pages containing coordinates that do not correspond to locations, but to individuals. These pages provide the coordinates of their resting/burial place. For instance, the wikilink Ronald Reagan appears in Charles' Wikipedia page. Charles performed for Reagan's second inauguration in 1985. Ronald Reagan is considered by our code as a location because his page provides the coordinates of his resting place (Ronald Reagan Presidential Library, Simi Valley, California, 34.25899°N 118.82043°W). We can identify these wikilinks that contain coordinates but which are not locations and therefore exclude them. A GeoLink may not necessarily point to places where famous people at some point moved to, lived in or even just visited. After careful verification of hundreds of cases3 , it appears that a significant fraction of these GeoLinks refer to parents/family, or to a place where they lived, originated from, etc. This information might be relevant for some uses, but is not relevant in analyses of an individual's direct impact, so we keep both but separate them out, distinguishing between two types of GeoLinks: those having a connection with family and those with no obvious link to family, using keywords such as "sister", "father", "brother", etc. contained in the same sentence to allocate them into the first category.

Other GeoLinks refer to post-mortem events and can safely be dropped out of the sample. These are quite frequent for artists (e.g. links to museums where exhibitions of their work take place post-mortem ) or scientists (e.g. honorary awards, buildings named in his/her honor). We consider a location to be post-mortem whenever a GeoLink is pointed out after an individual's date of death and is present in a sentence where keywords such as "died/buried/..." are present.

Table 4 shows that among all 7,184,455, Wikilinks extracted 26% correspond to either countries or regions, 0.5% refers to a person instead of a location, 5.7% are places visited by family members and 5.1% are post-mortem locations. These cases are not mutually exclusive. In all, we detected 4,878,455 proper and usable GeoLinks.

Places are either located in the Abstract or in the Main text. Therefore, in order to keep a chronological path of places visited, we keep either the Abstract or the Main text depending on the number of places contained in both sections. We extract places from the section containing the largest number of GeoLinks. The Abstract is chosen arbitrarily in the case of a tie. On average, individuals have 5.7

GeoLinks in the Main text and only 2.4 in the Abstract.

We detail here the places visited by Ray Charles as it was identified by our automatic detection procedure. Charles was born in Georgia and grew up in Florida before reaching cities like Seattle, Philadelphia, New York and Los Angeles. Along with Table 5 providing the GeoLinks, Figure 5 in Appendix shows the screenshots of the different sections of his Wikipedia page including GeoLinks.

Figure 3 shows the map of places visited by Ray Charles according to his Wikipedia page. It is important to report that not all GeoLinks refer to city names. Some point to cultural events, museums, opera houses, theaters, universities, schools, stadiums or sports events such as Olympic games, etc.. Those links have coordinates and are added to GeoLinks. In this example, Ray Charles had two contacts with the city of St. Augustine, Florida, through a school first (Florida School for the Deaf and the Blind) and a radio station (WFOY) based in the same city. This is important information to consider as sampled individuals have had contact with these cities through these institutions. We show here, as another example, the trajectories of a person known for having been very mobile over the course of his life: Desiderius Erasmus (1466-1536AD) in Figure 4. Erasmus was born near Rotterdam (Netherlands: 1) with some controversy about whether he originated from Gouda (Netherlands: 2).

Erasmus has an indirect (weaker) link with at least two different GeoLinks. First, with the city of Zevenberger (Netherlands: 3) which is the place of birth of his grandfather and second with the city of Deventer (Netherlands: 4) where his oldest brother went to a famous school. Both places are familyrelated GeoLinks and are therefore in blue on the map. As previously explained, these two locations have been dropped from the list of GeoLinks. 1900AD;1901; see for instance the different maps of Europe and the world in Appendix A. We also use, in the econometric analysis of later sections, 66 Time Periods of varying length constructed as follows: individuals born before 1AD are divided into three groups, due to sample size (born before 500BCE, born between 501BCE and 250BCE, born between 249BCE and 0); periods of 50 years between 1AD and 1700AD; periods of 25 years between 1701AD and 1800AD and periods of ten years between 1801AD and 2015AD. These varying lengths of time capture the "accelerating history" phenomenon and reduce the disparities in the number of individuals across time periods.

The most important methodological choice here is to attribute to each GeoLink one or two of these Time Periods, as follows. Information on birth and death allows us to calculate the lifespan of each individual. We also estimate the mean lifespan according to each birth year or each Time Period, for both male and female individuals. When either the birth or death information is missing, we therefore impute lifespan based on the relevant Time Period of the gender category of the individual. Once done, we count the number of GeoLinks for that individual and use it to calculate the steps of a grid of the entire lifespan, as a proxy for the time elapsed between two successive GeoLinks. For people still alive in 2015, we use their current age in 2015 rather than their lifespan. Then, if a GeoLink is estimated to start in year a and end in year b, we assign that GeoLink to Time Period n if any year in the interval (a, b) intersects that Time Period . The first GeoLink of a given individual should in principle be his or her birthplace and the last GeoLink should be his or her place of death. This might not always be the case, however, and in hundreds of manually verified cases, the GeoLinks were sometimes indicating high-schools or other places. For these people, we therefore add a first GeoLink with the birthplace when available in the Infobox, or a last GeoLink with the place of death when available from the same source.

Validity of the extraction procedure

The extraction procedure presented above has been checked by a series of manual verifications based on 4 randomly selected sub-samples of extracted biographies (853 in total). Both the individual characteristics (birth and death dates, places of birth and death, citizenship, gender, occupation) and GeoLinks automatically extracted by our code have been cross verified. At each round of verification, the code was modified and re-run on the entire sample. We present in Table 6 detailed statistics about the different iterations. The rate of errors on variables is generally decreasing (and ends up being lower than 7%) and the one on GeoLinks is limited (lower than 10%). Further improvements of the code are envisaged to improve this systematic detection procedure. Data scientists and statisticians have introduced the concept of random dataset in which any information is subject to error and is potentially weighted by a probability of error. This will be our next step in future versions of the work. In the remaining sections we present some stylized facts as well as some correlations obtained between the various statistics generated by our Wikipedia extraction procedure and city population data as another ex post check of the quality of the database.

Average longevity

Longevity is expressed in years and computed as the difference between year deceased and year of birth, each of these itself estimated (see Section 1) from all available information on birth and death coming from Wikipedia categories, the Infobox and the Abstract/Main text. We first present the overall distribution for all years, then for the most recent period. For individuals born after 1900 and for whom a death has been recorded, lifespan is necessarily declining over time. For individuals born in 2000, average lifespan must be necessarily computed using 2015 as a reference year along with his/her birth year. As found in de la Croix and Licandro (2015), we observe that the steady improvements in longevity start with cohorts born around 1600.

Note: After 1900, a majority of individuals is still alive and the series is unreported, since by construction life duration decreases to zero as years of birth are closer to 2015. Interestingly we see from Figure 7a and 7d that the lifespan variance across individuals also decreased over the sample period; although it rose again after 1830, presumably because of an increase in people of an older age.

Average visibility: all individuals

The large number of individuals in the database is a distinctive feature of our work, as compared to previous attempts. This large number necessarily implies that most individuals have little visibility on the web. Yet, they may contribute to the social, economic or cultural development of their area of residence.

We start by providing an overview of the distribution of the number of words and translations of the biography. All distributions, Freebase and Wikipedia, are very skewed. Pages that were collected using

Freebase are on average longer and more translated than these extracted directly from Wikipedia following the "bottom-up" approach. Indeed, the additional pages from the Wikipedia search bring many individuals with little visibility. Another interesting finding is the time evolution of these two variables: as time goes on, they tend to decrease suggesting that history has kept memory of the most notable figures, while more recent individuals are more numerous and on average less impactful. out that the most basic combination leads to relatively sensible rankings. Indeed, the log of the product of the number of translations (+1) times the number of words in the Wikipedia biography leads to an indicator, called visibility, which is represented in Figure 9a. An alternative ranking is composed as the first index (visibility) multiplied by the log of the sum of all of the other variables described in the previous paragraph. The correlation between both indicators is 0.90 in levels and 0.88 in logs. The second distribution (Figure 9b) is more symmetrical than the one based on the basic visibility index positively skewed. The distribution in Figure 9 is based on visibility and will be used in the rest of the paper as it best reflects the skewness of visibilty in the database (already captured by the fact that we use logs; in levels, the distribution is extremely skewed leftward). It is also no surprise that visibility is higher when individuals are selected from Freebase given that they have shorter pages and fewer translations, as indicated above (Figure 9c); and when they belong to older cohorts (Figure 9d).

Visibility: women

Over time, an interesting pattern emerges, a U-shape curve, with a local minimum at around 1700 (Figure 10a). This might be attributable to a composition effect (e.g. fewer and fewer individuals in the Family group and more and more in the Artists and Sports categories). However, unreported graphs, available from authors upon request, reveal that the U-shape pattern emerges for all six groups of occupations. At the end of the observation period, the female share is at around 0.25. We see also that females are less visible than males; there is a clear first order stochastic dominance in Figure 10b as expressed by the c.d.f. of visibility.

Evolution of occupations

We now investigate the share and visibility of our different occupations. As indicated, we have three levels of aggregation for occupations. Table 9 shows the distribution of the highest level of aggregation (six categories).

Figure 11a shows the evolution of these categories over time, for people born before 1990 (e.g. being at least 25 y.o.). The series sum up to slightly more than 1 since an individual may be in more than one category (e.g. Academics and Entrepreneur; the only exclusion is that an individual cannot be in Governance and another category). As visible, the post-1950 period sees the rise of the "Entertainment category", which by far dominates the database. The Governance category, most present until the beginning of the XIXth century, decreases after the 1840's cohort and drops further after 1950 (mechanically since these are shares, but gross numbers also decrease).

The middle part of the graph shows the evolution of categories for the intermediate level of aggregation (15 categories). In particular, it shows that Sports rose in two periods: for the birth cohorts between 1850 and 1870, with the emergence of sports contests in the second half of the XIXth century culminating with the first modern Olympic Games in 1896 and the first Tour de France in 1904. Interestingly, as Figure 11c shows, there has been a race between Sports and Art and Literature/Media between birth cohorts 1860 to 1950 with the final victory of Sports after 1950. The Figure 11b shows the cumulative density of the visibility index retained; most activities are similar in terms of prominence except Family, which tends to be over-represented at large visibility levels.

Figure 11: Share of occupations and creative occupations throughout history

Detailed lists of individuals in the database: top people and examples in various places

We now present the list of individuals in various categories.

Geographical density through history

Finally, the sample is predominantly drawn from the Western World (Europe and North America) with, however, a rise of other continents, mostly Asia and Latin America over the sample period (see Figure 12a). The three main European countries have a share that declines over the centuries (Figure 12b). Also note the over-representation of the United Kingdom as compared to France and Germany in the database. Interestingly, Figure 12c shows that individuals from less represented countries tend to have higher visibility, due to selection of the sample.

Figure 12: Geographical composition of the sample 3 Facts on geographical mobility

Between birth and death

In the latter sample, the median distance between birth and death is 268 kilometers, the mean is 1522km.

Various percentiles are represented in Table 12. There is no systematic difference across visibility levels, and the very top end of the visibility distribution is even associated with lower distances between locations of birth and death. With regards to secular evolutions, one obtains the minimum of distances from the individuals born in the middle-ages, with those born before the 6th century having slightly higher distances (this may be a composition effect rather than a trend affecting the overall population born in these periods). After 1500AD, distances start increasing again with a particularly large increase in median distances and a larger increase in top 10% distances due to the existence of settlements in the new continents.

Identified international mobility

One recovers the full addresses, including the country information of all geocodes in the database (current location, places of birth and death when available). The country of origin corresponds to borders in 2015. We round up after the third digit the geocoordinates of all GeoLinks including places of birth and death. We obtain a grand total of 276,677 unique locations identified in the database. Of those, we match a total of 259,109 locations with a country and most of the time a full address, using the command geocode3 and a specific application, Google Maps Geocoding API. We then match these countries to the original locations. Of all non-unique geolinks present in the database, one is able to match 4,859,007 with a country. As for the individual's database, the U.S. represents the largest share (1,831,479 lines), followed by the UK (821,807 lines), then Germany, Canada, France, Italy, etc. The total number of countries, including overseas territories such as Mayotte or Saint-Pierre et Miquelon, islands such as Turks and Caicos Islands, U.S. Minor Outlands, Guam etc. is 248. Only 35,421 geocodes are returned as "not found" by the Google App. We finally create a variable "move_country" if the country of birth differs from the country of the current geolink, and none of the birth and current countries are missing or "not found". In the database, 19.7% of identified geolinks with a country have a country of birth different from the country of the individual's current location.

In time, we obtain a U-shape with a positive and accelerating trend from 1960 to 2015, as Figure 14a shows. The right panel, however, shows that this is more due to a composition effect, the "Entertainment" category being more internationally mobile and having a growing share in the sample as time goes on. Nevertheless, all categories exhibit a rising fraction of country-to-country mobility. After the urban population data was collected, city names were geocoded using the Google Maps API and matched with the trajectories of notable people. In order to obtain historical census data, multiple methods of data extraction were used. When downloadable digitized census records were not available, web scraping (for 6 countries) and optical character recognition (OCR for 3 countries) helped extract the required data. Certain manipulations on the extracted data were required, such as aggregating city data to urban areas, matching city names across datasets, and combining male/female portions of urban populations. More information on these different methods, but also on official links used to access the data, city definitions, dates, number of observations and years available and more are reported in Table 18. . In a few instances, city limits and definitions changed over time, due to land organization differences, evolving urban administrative regions, etc. An example is the UK where data are consistent between 1801 and 1911, but inconsistent with the period 1921-1961. In the longitudinal analyis, we treat these two samples a set of distinct cities (e.g. an unbalanced panel) to avoir dealing with non-comparable datasets. A list including all manipulations performed in the data extraction process is included in the Online appendix. Official census records have been collected for 2 countries in Asia (India and Japan), 11 countries in Europe (Austria, Belgium, Denmark, France, Germany, Italy, Netherlands, Portugal, Russia, Switzerland, United Kingdom), 2 countries in America (Canada and United States) and 2 countries in Oceania (Australia, New Zealand). Information for 65,087 different cities around the world has been gathered. Countries have been chosen both for the primary role they played over the course of history and the availability of official census records for these countries.

Data collection

To complement the Census dataset over the same period, we use a database compiled by the Lincoln Institute of Land Policy which provides historical urban population data for 30 major urban agglomerations from 1794 to 2005. The data is reported in intervals of 20 or 25 years and was used to fill gaps of missing data where city limits permitted. This database comprises the following cities sorted by continent and sub-region: Africa (Accra, Algiers, Cairo, Johannesburg, Lagos, Nairobi), Asia (Bangkok, Beijing, Kolkata, Mumbai, Shanghai, Tehran, Tel Aviv), Central America (Guatemala City, Mexico City), Eurasia (Istanbul, Moscow), Europe (Warsaw), Middle East (Jeddah, Kuwait City), South America (Buenos Aires, Santiago, Sao Paulo) and Southeast Asia (Manila). We only used information concerning cities for which we failed to find reliable census data and thus did not consider Lincoln data for Chicago, Los Angeles, Paris, London, Sydney, or Tokyo. Using Census years (typically every 10 years), we linearly interpolate population for each city in the databse to obtain yearly population and then collapse city population into periods of time of 10 years intervals (e.g. 1801-1810, 1811-1821 etc to 2001-2010). 2013) include more than 10,000 inhabitants and are generally defined as urban areas, where "suburbs (faubourgs) surrounding the center" are included. Bosker et al. updated Bairoch's database by scanning recent literature concerning the major cities covered. In particular, they updated all cities which during a point in history were larger than 60,000 inhabitants. This led to a number of important revisions of population records concerning Muslim cities in medieval Spain but also for the cities of Palermo, Paris, Bruges, and London. Although these revisions decrease the total number of cities covered, the remaining observations are thought to be more reliable. Bosker et al. (2013) additionally added Middle Eastern and North African cities to Bairoch et al. (1988), which previously only included European cities. This allowed us to obtain historical urban population data on a total of 116 cities coming primarily from the countries of Egypt, Turkey, the former Yugoslavia, Saudi Arabia, Oman, Yemen, Israel, Iraq, Lebanon, Libya, Tunisia, Algeria, and Morocco. 4Using available years (typically every 50 years or 100 years before 1000AD), we linearly interpolate population for each city in the databse to obtain yearly population and then collapse city population into our Time Periods of length of 50 or 25 years intervals (801-850 to 1651-1700, then 1701-1725, 1726-1750, etc.).

Forward extension 1950AD-2010AD: The United Nations database

A database provided by the United Nations' Department of Economic and Social Affairs gives urban agglomeration data from 1950 to 2010 at five year intervals. The definition of an urban agglomeration in this source references an urban area with over 300,000 inhabitants. This database was very useful since the data available from census records decreased steadily starting in the mid-20th century, as seen in Figure16 b below. The other advantage of this dataset compared to any other source of information is its worldwide coverage. In all, this database contains population data for 1,692 cities. We collapse the data into ten year intervals: 1951-1960, etc. Keeping only the largest 50 cities in coutries larger than 300,000 sq. km and the largest 30 cities in other countries (or less if fewer cities where avaialble), we end up with a panel of cities linearly increasing from 800AD to 1800AD (Source: Bairoch-Bosker) and increasing by steps (as larger countries enter the database with a first Census) from 1800 to 1950. See Figure 16b. After 1950, the Census database decreases since the last Census available online varies from country to country (e.g. in the United Kingdom, the database terminates in 1961). We use after 1950 the UN database and its 1,692 cities.

Distances to a large city ; at birth, at death, and in between

From now on, the city database is limited to the top 50 or 30 cities (for which population is available) in each country. We match the GeoLinks to the three nearest cities of this database period by period and only when population is available in that period. We use the geonear command, from geocodes of individuals' locations and of cities. It also returns distances in kilometers based on geodetic distances, "using a mathematical model of the earth" as specified in the description of the command. Figure 17 shows on the left hand side the cumulative density of distances of the third three cities. The Bairoch database covers well the individual's locations: over the period 800-1800AD, 60 percents of GeoLinks are located within a radius of 50 kilometers around a city in the database. Another 30% of GeoLinks are within 50 kilometers of a second nearest city, and 17% are within 50 kilometers of the third nearest city. Similarly, 50% of individuals over the period 1800-1939 are within 50 kilometers of the closest city. Finally, the last period of the sample, based on the UN database, is the best matched: 80% of GeoLinks are within 50 kilometers of the closest city. On the right part of the graph, we represent the cumulative distance to the closest city by occupation. They do not differ much for the Census and the UN database of cities, but differ according to occupation over the period 800-1800, Academics and Entertainment (Arts, Literature and Media only over this period) being significantly more likely to be close to a big city.

Facts on city rank, visibility and occupations

The last observations lead to a study of the relations between city size (or rank) and occupation and visibility. It can be seen from Figure 18 (left panels) that there is no systematic correlation between city rank in a country and the degree of visibility (in logs). For the top four cities, it turns out that the c.d.f. of log visibility are quite close to each other and the lowest c.d.f. is that of the first city and 4.4 Creating city averages of the share of notable people across occupations

The next stage is to collapse the GeoLinks database into cities for each period analyzed. We do so for all locations for which the estimated age of the individuals is above 15 years. Instead of using an arbitrary distance threshold for the potential influence of an individual on a city, we use a continuous measure. The decay factor is e dist/33 where distance is measured in kilometers. This decay factor implies that only individuals exactly located in the center of the city have a full influence. At 10 kilometers from the centroid of the city, their influence is 0.73. At 20 kilometers, it is 0.50; at 50 kilometers, it is 0.38, etc. The procedure is replicated successively for the closest, second closest and third closest city: an individual placed at 20 kilometers of two cities presumably influences that city twice, but with weight 0.50. We can also weight individuals according to their visibility. We use four different weighting procedures. The first one is to give equal weight to all individuals. The second one is to weight individuals by their percentile (in %) multiplied by 2 and divided by 100 (the median individual therefore has weight 1, the top person a weight of 2 and the bottom person a weight of 0). A third one is the square of the previous weight. The last one directly uses the visibility measures. That measure is very skewed and ranges from approximately 8000 to 10 7 . On average, over the period 775-2005, we represent in Table 19 the following weighted number of notable people.

A specific focus on the Entertainement category (Arts, Literature/Media, as well as Sports after 1850) is interesting. We calculate in Table 20 the shares of "efficient" units of notable people according to the population rank within the country. The different weights do not change much the pattern, except when weights are linear in visibilty. It also appears that the fraction continuously increases over time, from one fifth over the period 800-1800 to one third over the period 1800-1939 and finally one half after 1950. Figure 19 shows that the correlation between city size and the share of Entertainement is positive. We split the sample into pre-1725 and post 1871 to avoid the coexistence of cities from Bairoch sample (where population is at the agglomeration level) and from the Census sample (where population is at the city level). Variables are in log.

Facts on the correlations between city growth and the number of notable people

We now proceed to correlation analysis. Our database is organized into time periods, as indicated above: periods of 50 years between 800 and 1700; of 25 years between 1700 and 1800; and 10 years between 1800 and 1939. We exclude the post WWII period from the analysis. The typical regression is the growth rate of a given variable from one period to the next (population of a city, visibility of notable people in that city, number or shares of notable people in various occupations in the city) on their own lags (first and second lags), and the lags of other explanatory variables. All regressions are in logs on both sides of the equation and coefficients thus should be interpreted as elasticities. Doing so, we can get a sense of the intrinsic dynamics of the dependent variable (whether there are cycles or regression-to-the-mean) and the lagged cross-correlation between variables. Although it may be tempting to discuss Granger-causality here, we avoid it and only describe coefficients as reflecting the patterns of dynamic cross-correlations. In the Table, t refers to the aggregate time periods of varying length (50, 25 or 10 years).

Table 21: City growth regressions once we account for a time period fixed effect in the regression (Equation ( 3)). Visibility, lagged by two periods is significant at the 5% level but the three variables considered are not siginificant overall. We also find that population lagged by one period has a positive impact on its own growth, but a negative impact when lagged by two periods, indicating a pattern of cycles of length two periods.

Table 22 shows that reciprocally, the visibility of notable people is not impacted by the lags of population at least as long as the effect of time periods is neutralized. We also find strong persistence of the visibility variable, which positively depends on its first and second lags.

We now study the effect of the number and shares of notable people in each occupation. A key issue here is the respective role of Governance and Entertainment. We restrict our analysis to the period 1800-1939, future versions will study the full period of analysis. Over this time period, we distinguish between two groups of countries, Anglo-Saxon countries (United States, United Kingdom, Australia,

( New Zealand and Canada) and non-Anglo-Saxon countries. It is noteworthy that at the city level, the samples differ regarding the share of each occupation, with, in particular, a higher share of Governance in Anglo-Saxon countries compensated by a lower share of the Entertainment category. This is not the case on other dimensions such as population and years available, as reported .

We use four specifications where individuals are not weighted, weighted by their percentile of visibility or its square, or finally weighted by their visibility indicator in level. Results are reported in Table 24.

Regarding numbers (in logs), not surprisingly, most coefficients are positive, for each of the samples. Interestingly, the (lagged) share of Governance is either negative and significant in the first four columns or positive but marginally significant. Lagged Entertainment is positive, but typically not significant. Finally, and probably the most robust result, the (lagged) share of Entrepreneur is the most positive and significant variable in both sub-samples, with elasticities between 0.03 and 0.01: the first column tells us that a 10% increase in the lagged number of Entrepreneurs is associated (in a non-causal way) with an additional current period population growth of 0.29 percentage points.

A breakdown of the Entertainment category into Sports on the one hand, and Arts/Lit on the other hand, delivers a positive impact of the latter, as a relatively robust result. See Table 25. It remains to be verified and its causal value must not be claimed here.

We finally replicate the analysis using the share of occupations instead of using logs, we select the share of governance as the reference category, since the sum of shares comes to 1 by construction. Further, lagged shares of notable people do not provide information on their total number in each city, contrary to the previous table. We therefore further control by their total number, regardless of the category. This number is weighted according to the shares in each specification. Again, Table 26 shows that the lagged share of Entrepreneurs is the most significant variable, especially for Anglo-Saxon countries, while the lagged log number of notable people matters for growth in non Anglo-Saxon countries. In the

Dependent variable

Figure 2 :

 2 Figure 2: Example of a Wikipedia page: Ray Charles

Figure 3 :

 3 Figure 3: Automatically identified GeoLinks of Ray Charles

Figure 4 :

 4 Figure 4: Automatically identified GeoLinks of Desiderius Erasmus (1466-1536AD)

Figure 6 :

 6 Figure 6: Cohort size in the database of individuals, Freebase/Wikipedia samples merged (first three charts) or kept separated bottom left chart

Figure 7 :

 7 Figure 7: Longevity in years

Figure 8 :

 8 Figure 8: Components of visibility: distributions of number of translations of pages and number of words, time evolution and correlations

Figure 9 :

 9 Figure 9: Visibility indices

Figure 13 :

 13 Figure 13: Average distance between birth place and current location

Figure 14 :

 14 Figure 14: Country to country mobility

Figure 15 :

 15 Figure 15: Timeline of sources used

  4.1.2 Backward extension 800AD-1800AD: The Urbanisation Hub-Bairoch-Bosker databaseTo account for city population from 800 to 1800, we used an urban population database compiled initially byBairoch et al. (1988) and further revised byBosker et al. (2013). The maximum number of

Figure 16 :

 16 Figure 16: Number of cities by source of data (UH-Bairoch-Bosker ; Census ), only retaining the largest 30/50 cities in each country

Figure 17 :

 17 Figure 17: Distances between individual's locations and nearest cities in the three samples: Bairoch-Bosker ; Census ; United Nation Database

Figure 18 :

 18 Figure 18: Links between city rank and visibility. Left charts: ranking within countries ; right charts: wolrd ranking. Top: Bairoch city dataset (800-1800AD); Middle: Census city datasets ( 1800-1939AD) ; bottom : UN city dataset (1950-2000AD)

  in parentheses ; *** p<0.01, ** p<0.05, * p<0.1.

Figure A. 1 :Figure

 1 Figure A.1: Number of individuals by places (before 500)

Table 1 :

 1 Occupations (level of aggregation A -six categories)

	Occupation A1		Frequency Share
	Academics (studies, education)			110,610	9.0
	Entertainment (arts, literature/media, sports)		736,626	60.0
	Entrepreneur (business, inventor, worker)		59,028	4.8
	Family			11,813	1.0
	Governance (law, military, religious, politics, nobility)	285,514	23.3
	Other			23,712	1.9
	Total		1,227,303	100.0
	Occupation B1	Frequency Share
	Arts	241,042	19.6
	Business	48,974	4.0
	Education	36,542	3.0
	Family	11,813	1.0
	Inventor	4,783	0.4
	Law	28,585	2.3
	Literature/media	103,421	8.4
	Military	40,516	3.3
	Nobility	20,013	1.6
	Other	23,712	1.9
	Politics	160,696	13.1
	Religious	35,104	2.9
	Sports	392,163	32.0
	Studies	74,068	6.0
	Worker	5,271	0.4
	Total	1,226,703	100.0

Table 2 :

 2

Occupations (level of aggregation B -15 categories) (law, military, religious, politics, nobility) and Other. Tables

  Shares for death dates and death places are computed using the number of dead people only, i.e. 509,181 observations.

	Individual variables	Location within the Wikipedia biography	Available
	Birth & Death Dates	Abstract/Main text + Categories + Infobox	1,073,585 & 499,980
	Birth & Death Places	Main text + Infobox	842,926 (67.77%) & 235,514 (46.25%)
	Occupation	Abstract/Main text	1,183,971 (95.19%)
	Gender	Abstract/Main text	1,149,899 (92.45%)
	Citizenship	Abstract/Main text + Infobox	1,090,190 (87.65%)

Table 3 :

 3 Number of individuals with available information

Table 4 :

 4 Number of identified GeoLinks

Table 5 :

 5 GeoLinks of Ray Charles

  1.5 Matching procedure of GeoLinks to Time Periods

	For subsequent descriptive analyses, we often group individuals into aggregate time categories. At the
	highest agregation level we use 5 large historical periods (<500AD; 501-1500AD; 1501-1700AD; 1701-

Table 7 :

 7 Statistics on birth year and death year

		GeoLinks	Individuals	% Residual errors on individual characteristics errors on places % Residual
	Round 1	452	114	24.5	20.1
	Round 2	711	113	35.3	14.3
	Round 3	849	152	13.8	11.4
	Round 4	1316	81	6.17	9.12
	Table 6: Manual verifications: performance statistics in each round
	2 Facts on notable people
	2.1 Size of cohorts in the sample
	The sample is unbalanced across birth years, with an exponential growth of the sample size over time.
	The first individual in the database was born in 2285BCE, but the median individual was born in 1943
	AD (column I). Only 1% (1099 individuals) were born before 1330. The composition of individuals
	not selected from Freebase ("top-down" approach) but from Wikipedia ("bottom-up" approach) is not
	very different in terms of years of birth. The size of a cohort for a given year in the database varies
	from 0 or 1 to as much as 157,735 in the most recent years.

Table 8 :

 8 Number of words per page, translations and other visibility indicators

Table 9 :

 9

	Occupation	Obs	Mean	Std. Dev. Min	Max	P1	P10	P25	P50	P75	P90	P99
	Entertainment 698,704 1937.053	85.996	-1557 2015 1610 1877 1921 1958 1979 1988
	Academics	130,075 1886.951	145.864	-1570 2015 1428 1803 1870 1921 1947 1963
	Entrepreneur	76,262 1879.818	108.782	-550 2015 1510 1777 1841 1907 1949 1966
	Family	21,338 1767.293	323.474	-1398 2015 191 1422 1737 1886 1945 1972
	Governance	303,263 1848.069	205.971	-2285 2015 862 1709 1833 1905 1946 1962
	Other	39,721 1893.812	151.736	-915 2015 1298 1814 1882 1922 1959 1979

Summary statistics: birth year by occupations Figure 10: Share of women in different occupations throughout history

  The top of each table lists the top 20 individuals in the category, and then sample individuals in the top decile, top quartile, median, third quartile and last decile, to give an overview of the composition of the database. All other categories are reported in Appendix.

	Cat. rank	Ov. rank	Name	Birth	Death	Citizenship	Occupation B1	Occupation B2
			Top 20 individuals
	1	3	Jesus	0	30	.	Family	religious
	2	5	Napoleon Bonaparte	1769	1821	France	military	politics
	3	6	Winston Churchill	1874	1965	England	politics	politics
	4	9	Adolf Hitler	1889	1945	Austria	politics	politics
	5	10	Joseph Stalin	1878	1953	Russia	politics	politics
	6	12	Mahatma Gandhi	1869	1948	India	politics	politics
	7	13	Mahomet	570	632	.	religious	Other
	8	14	William Shakespeare	1564	1616	England	lit	lit
	9	18	Alexander The Great	-356	-323	Greece	nobility	nobility
	10	19	Mustafa Kemal Atatürk	1881	1938	Turkey	military	military
	11	20	Franklin D. Roosevelt	1882	1945	US	politics	politics
	12	21	Abraham Lincoln	1809	1865	US	politics	politics
	13	23	George Washington	1732	1799	US	politics	military
	14	26	Charlie Chaplin	1889	1977	England	arts	arts
	15	27	Vladimir Lenin	1870	1924	Russia	politics	politics
	16	28	Charles de Gaulle	1890	1970	France	lit	politics
	17	30	Albert Einstein	1879	1955	Germany	studies	studies
	18	32	Karl Marx	1818	1883	Germany	studies	studies
	19	41	Theodore Roosevelt	1858	1919	US	politics	lit
	20	43	Vincent Van Gogh	1853	1890	Netherlands	arts	arts
			Top decile (random sample)
	25019	101995	Karl Freiherr Von Müffling	1775	1851	Germany	military
	25021	102007	Custodio García Rovira	1780	1816	Spain	politics	arts
	25020	102008	Jacques de Billy	1602	1679	France	religious	studies
	25023	102023	Antoine-Vincent Arnault	1766	1834	France	arts	arts
	25022	102026	Ebbo	775	851	Germany	religious	religious
			First quartile (random sample)
	62554	268965	Charles L. Hutchinson	1854	1924	US	business	business
	62553	268967	Emil Johann Lambert Heinricher	1856	1934	Austria	studies	studies
	62550	268969	George Douglas, 16th Earl of Morton	1761	1827	US	Family	nobility
	62552	268977	Paul Morgan (actor)	1886	1938	Austria	arts	arts
	62551	268983	Emil Barth	1879	1941	Germany	politics	worker
			Median (random sample)
	125104	591207	Edmund Frederick Erk	1872	1953	US	politics	politics
	125102	591276	Ripley Hitchcock	1857	1918	US	lit	arts
	125106	591293	Alexis Lesieur Desaulniers	1837	1918	Canada	law	politics
	125105	591336	Anna Maria Hilfeling	1713	1783	Sweden	arts	arts
	125103	591347	Edward Garrard Marsh	1783	1862	England	lit	religious
			Third quartile (random sample)
	187655	931626	William Cole (scholar)	1753	1806	England	education	education
	187656	931829	A. W. Andrews	1868	1959	England	studies	lit
	187658	931846	Thomas Nash (Newfoundland)	1765	1810	Ireland	worker	Other
	187657	931911	Hugh Aiken Bayne	1870	1954	US	Family	law
	187654	931912	Samuel Backhouse	1554	1626	England	business	politics
			Last decile (random sample)
	225187	1121555	Edward Hopkins (politician)	1675	1736	Ireland	politics	education
	225185	1121647	Braxton Lloyd	1886	1947	US	studies	politics
	225188	1121709	Stephen Furniss	1875	1952	Canada	politics	politics
	225189	1121712	Ida Holterhoff Holloway	1865	1950	US	arts	arts
	225186	1121786	Robert Henry Blosset	1776	1823	England	law	military

Table 10 :

 10 Individuals born before 1891

	Cat. rank	Ov. rank	Name	Birth	Death	Citizenship	Occupation B1	Occupation B2
				Top 20 individuals
	1	1	Barack Obama	1961		US	politics	education
	2	2	Ronald Reagan	1911	2004	US	politics	arts
	3	4	George W. Bush	1946		US	politics	business
	4	7	Nelson Mandela	1918	2013	South Africa	politics	politics
	5	8	Michael Jackson	1958	2009	US	arts	arts
	6	11	John F. Kennedy	1917	1963	US	politics	politics
	7	15	Pope Francis	1936		Argentina	religious	religious
	8	1 6	C r i s t i a n o R o n a l d o	1 9 8 5		P o r t u g a l	s p o r t s	s p o r t s
	9	17	Pope John Paul Ii	1920	2005	Poland	politics	religious
	10	22	Roger Federer	1981		Switzerland	sports	sports
	11	24	Lionel Messi	1987		Argentina	sports	sports
	12	25	Novak Djokovic	1987		Serbia	sports	sports
	13	29	Hillary Rodham Clinton	1947		US	politics	politics
	14	31	Bill Clinton	1946		US	politics	politics
	15	33	Pope Benedict Xvi	1927		Germany	politics	religious
	16	34	Che Guevara	1928	1967	Argentina	politics	politics
	17	35	Elvis Presley	1935	1977	US	arts	arts
	18	36	Mao Zedong	1893	1976	China	politics	politics
	19	37	Hugo Chávez	1954	2013	Venezuela	politics	politics
	20	38	Rafael Nadal	1986		Spain	sports	sports
			Examples at the top decile
	81954	113355	Evan Jenkins (politician)	1960		US	politics	politics
	81956	113358	Anton Golotsutskov	1985		Russia	sports	sports
	81957	113361	Alphonse Leweck	1981		Luxembourg	sports	sports
	81953	113365	Lazar Ristovski	1952		Serbia	arts	arts
			Examples at the first quartile
	204889	283176	José Greci	1941		Italy	lit	arts
	204888	283204	Valeria Solarino	1979		Italy	arts	arts
	204887	283206	Mark Preston	1968		Australia	business	business
	204886	283212	Lev Dobriansky	1918	2008	.	education	education
			Examples at the Median
	409775	567684	Jeanette Lunde	1972		Norway	sports	sports
	409774	567752	Cristian Andrés Campozano	1985		Argentina	sports	sports
	409776	567799	Clifford Peeples	1970		Ireland	religious	politics
	409773	567823	Stephen Adams (business)	1937		US	business	business
			Examples at the third quartile
	614663	875000	Dorice Reid (baseball)	1929		US	sports	sports
	614660	875029	Birger Wernerfelt	1951		Denmark	studies	business
	614661	875091	Les Phillips	1963		England	sports	sports
	614662	875145	Casey Henwood	1980		New Zealand	sports	sports
			Examples at the last decile
	737592	1080475	Philip Gardiner	1946		Australia	politics	politics
	737595	1080596	James A. Andersen	1924		US	politics	law
	737596	1080687	Jeanette Kuvin Oren	1961		US	arts	arts
	737594	1080777	Bruce Martyn	1930		US	sports	lit
	737593	1080969	Claude Legris	1956		Canada	sports	sports

Table 11 :

 11 Individuals born after 1891

Table 12 :

 12 Distance from birth to death, in kilometers, all sample and by periods of history

	Period	Obs	Mean	Std. Dev. Min	Max		P1	P25	P50		P75	P99
	Before 500AD	3066	676.468	796.802	0	3212.737	0	3.721 316.725 1125.775 3212.737
	501-1500AD	32522	505.117	1432.714	0	14715.14	0	0	100.131 389.473 8857.269
	1501-1700AD	49669	926.074	2113.387	0	16684.94	0	22.546 156.78	515.041 9988.019
	1701-1900AD 707193 1650.343 3252.779	0	19821.38	0	56.313 318.805 1334.502 16970.39
	1901-2015	640993 1729.322 3059.098	0	19852.35	0	54.353 368.549 1808.116 14567.91
	Missing	27952	1561.39	3139.575	0	19805.96	0	.729	189.147 1118.294 15859.45
	Period	Obs	Mean Std. Dev. Min Max P1 P25 P50 P75 P99
	Before 500AD	10,560	16.517	17.283	0	101	0	5	10	23
	501-1500AD	79,229	16.156	20.182	0	161	0	5	10	19	110
	1501-1700AD	135,133	11.674	18.754	0	277	0	4	7	12
	1701-1900AD 1,281,826 10.872	12.377	0	218	0	5	8	12
	1901-2015AD 3,087,414 14.403	30.951	0	534	0	3	7	13	158
	Missing	439,980	7.534	17.147	0	344	0	2	4	8

Table 13 :

 13 Number of GeoLinks per individual (Full sample and by periods)

	3.2 Summary Statistics
	Visibility Perc.	Obs	Mean Std. Dev. Min Max P1 P25 P50 P75 P99
	All	3,987,369 8.942	16.467	0	479	0	3	6	10	66
	90	368,459	19.581	35.094	0	355	0	5	9	18	196
	95	449,297	29.623	51.754	0	534	1	8		28	277
	99	190,854	35.729	38.114	0	306	4	14		43	194
	99.9	38,163	45.877	28.566	0	158	7	25		59	142

Based on the methodology explained above in Section 1, we can compute distances bewteen birthplace and any GeoLink, and provide summary statistics.

Table 14 :

 14 Number of GeoLinks per individual (most visible individuals)

	Period	Obs	Mean	Std. Dev. Min	Max	P1	P25	P50	P75	P99
	Before 500AD	4,732	1029.195 1654.817	0	13248.77	0	46.953 497.799 1284.738 9102.902
	501-1500AD	45,551	729.829	1696.815	0	18083.47	0	31.204 197.733 595.974 9121.377
	1501-1700AD	83,482	951.713	2183.658	0	19581.95	0	11.268 150.668 557.502 10522.65
	1701-1900AD 1,007,984 1618.402	3312.81	0	19826.06	0	9.897 246.536 1179.298 16908.33
	1901-2015	2,542,850 1805.327 3365.142	0	20012.04	0	1.816 259.298 1671.164 15985.91
	Missing	144,790	1837.369 3468.855	0	19843.5	0	.021	179.772 1621.88 15745.76

Table 15 :

 15 Distance from birth to any identified GeoLink per individual (in km, Full sample, by periods)

	Visib. Perc.	Obs	Mean	Std. Dev. Min	Max	P1	P25	P50	P75	P99
	All	2,903,945 1559.643 3199.134	0	20,012.04	0	.092	176.389 1207.587 16,287.4
	90	313,248	1970.395 3459.575	0	19,789.02	0	19.559 389.453 1874.146 16,365.76
	95	401,799	2286.674 3681.198	0	19,941.56	0	67.235 549.752 2549.724 16640.06
	99	175,629	2567.837 3781.461	0	19,936.87	0	145.157 740.887 3337.66	16453.98
	99.9	34,768	2573.893 3672.483	0	19,243.99	0	171.841 785.636 3473.262 15,716.43

Table 16 :

 16

Distance from birth to any identified GeoLink in individual's life, in km (most visible individuals)

Table 17 :

 17 Most common country-to-country trajectories

	Trajectories Origin Destination	#	Mean Percentile Visib.
	1	U K	U S A	35,061	64.5%
	2	C A N	U S A	28,202	60.0%
	3	G E R	U S A	15,541	66.5%
	4	U S	C A N	13,816	58.0%
	5	U K	A U S	13,719	49.6%
	6	U K	F R A	9,702	67.7%
	7	I R E	U K	9,636	61.1%
	8	U S A	F R A	8,005	69.1%
	9	A U S	U K	7,706	64.4%
	10	ITA	US	7,098	71.5%

  4.1.1 Period 1800-2010: Census records and Lincoln InstituteWe collected and assembled detailed and official Census records. Census collections started in the early-to-mid-19th century. Census records are available primarily from National Statistical Institutes and cover a larger number of 'cities': 36,541 communes in France, 9,148 communes in Italy, 8,915 urban centers in Canada, and 1,008 incorporated cities in the United States, etc.

Table 18 :

 18 Census data: sources and main characteristicsEuropean cities in the revised version of the database amounts to 677 cities in the year 1800. All cities in Bosker at al. (

Table 19 :

 19 Number of notable people by city/period weighted by the distance (periods 16-63 or 775AD-2005AD)

Table 20 :

 20 Share of population in entertainment in cities, by cells of cities/period

Figure 19: Entertainment and Population

  In the Table, t refers to the aggregate time periods of varying length (50, 25 or 10 years).

			2)	(3)	(4)	(5)	(6)
	Variables		Mean Log Visibility of Notable People
	Log of population	-0.180***	-0.091***	-0.034	-	-	-
		(0.033)	(0.031)	(0.033)
	Log of population (t 1)	0.389***	0.139***	0.001	0.151***	0.021	-0.046
		(0.053)	(0.050)	(0.052)	(0.030)	(0.028)	(0.029)
	Log of population (t 2)	-0.291***	-0.094***	-0.027	-0.222***	-0.059**	-0.013
		(0.030)	(0.028)	(0.029)	(0.027)	(0.025)	(0.025)
	Log of visibility (t 1)	0.320***	0.147***	0.114***	0.322***	0.147***	0.114***
		(0.010)	(0.010)	(0.010)	(0.010)	(0.010)	(0.010)
	Log of visibility (t 2)	0.189***	0.040***	0.027***	0.194***	0.041***	0.027***
		(0.009)	(0.009)	(0.010)	(0.009)	(0.009)	(0.010)
	Constant	5.422***	12.303***	8.603***	5.260***	12.217***	8.569***
		(0.164)	(0.254)	(0.190)	(0.161)	(0.252)	(0.188)
	City Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes
	Time Period Fixed Effects	No	No	Yes	No	No	Yes
	Observations	11,141	11,141	11,141	11,141	11,141	11,141
	R-squared	0.699	0.742	0.756	0.698	0.742	0.756
	Clustered Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.

Table 22 :

 22 Visibility Index Regressions

		Anglo-saxon countries Non anglo-saxon countries
	Mean year in the sample	1879	1887
	Q25 City population	15 615	23 029
	Median City population	48 663	47 071
	Q75 City population	127 183	95 186
	Share governance	0.438 (0.167)	0.368 (0.193)
	Share entertainment	0.310 (0.160)	0.357 (0.190)
	Number of cities	252	633
	Number of cities / time periods	1,291	3,474

Table 23 :

 23 Sample statistics over the sample period

Table 24 :

 24 City growth regressions based on the number of notable people per category

			⇥ words
			transl.
	City Population Growth Rate	Non Anglo-Saxon countries	⇥ pctile/100) (2 ⇥ pctile/100) 2
			(2
			Unweighted
			⇥ words
			transl.
	City Population Growth Rate	Anglo-Saxon countries	⇥ pctile/100) (2 ⇥ pctile/100) 2
			(2
			Unweighted
	Dependent variable	Sample	Weights	Log of population (t 1)

  Table, t refers to the aggregate time periods of varying length (50, 25 or 10 years).

Table 25 :

 25 City growth regressions based on the number of notable people per category

			transl. ⇥ words	0.290***	(0.019)	-0.372***	(0.020)	-0.003	(0.006)	0.001	(0.009)	0.032*	(0.017)	0.018	(0.021)	-0.066***	(0.023)	-	0.003***	(0.001)	0.994***	(0.100)	Yes	Yes	3,474	0.580
	City Population Growth Rate	Non Anglo-Saxon countries	⇥ pctile/100) (2 ⇥ pctile/100) 2	0.286*** 0.287***	(0.019) (0.019)	-0.371*** -0.371***	(0.020) (0.020)	0.009 0.008	(0.013) (0.013)	0.017 0.016	(0.017) (0.016)	0.040 0.046*	(0.028) (0.027)	0.032 0.041	(0.055) (0.052)	-0.080* -0.063	(0.047) (0.044)	--	0.010*** 0.009***	(0.002) (0.002)	1.047*** 1.041***	(0.101) (0.101)	Yes Yes	Yes Yes	3,474 3,474	0.579 0.579
	City Population Growth Rate	Sample Anglo-Saxon countries	Weights Unweighted (2 ⇥ pctile/100) (2 ⇥ pctile/100) 2 transl. ⇥ words Unweighted (2	Log of population (t 1) 0.228*** 0.231*** 0.236*** 0.249*** 0.286***	(0.024) (0.025) (0.025) (0.024) (0.019)	Log of population (t 2) -0.367*** -0.373*** -0.376*** -0.379*** -0.371***	(0.021) (0.021) (0.021) (0.021) (0.020)	Share entertainment (t 1) 0.062 0.076 0.070 0.004 0.007	(0.064) (0.057) (0.048) (0.012) (0.013)	Share academics (t 1) 0.223** 0.173* 0.149** 0.021 0.017	(0.111) (0.091) (0.073) (0.016) (0.017)	Share entrepreneur (t 1) 0.361*** 0.297*** 0.227*** -0.004 0.028	(0.092) (0.081) (0.070) (0.019) (0.028)	Share family (t 1) 0.434 0.404 0.310 0.006 0.011	(0.348) (0.290) (0.235) (0.046) (0.058)	Share other (t 1) 0.745*** 0.367** 0.212 0.045 -0.109**	(0.217) (0.186) (0.153) (0.037) (0.049)	Share governance (ref. category) -----	Log number of notable people (t 1) -0.013 -0.011 -0.010 -0.003 0.010***	(0.010) (0.010) (0.009) (0.002) (0.002)	Constant 1.675*** 1.692*** 1.685*** 1.616*** 1.055***	(0.117) (0.119) (0.119) (0.118) (0.101)	City Fixed Effects Yes Yes Yes Yes Yes	Time Period Fixed Effects Yes Yes Yes Yes Yes	Observations 1,291 1,291 1,291 1,291 3,474	R-squared 0.788 0.786 0.784 0.781 0.580	Clustered Standard errors in parentheses ; *** p<0.01, ** p<0.05, * p<0.1.	In the Table, t refers to the aggregate time periods of varying length (50, 25 or 10 years).

Table 26 :

 26 City growth regressions based on the shares of notable people per category

Table A

 A

	Cat. rank Cat. rank Ov. rank Ov. rank Cat. rank Ov. rank Cat. rank Ov. rank Cat. rank Ov. rank Cat. rank Ov. rank Cat. rank Ov. rank Cat. rank Ov. rank Cat. rank Ov. rank Cat. rank Ov. rank	Name Name Name Name Name Name Name Name Name Name		Birth Birth Death Death Citizenship Citizenship Occupation B1 Occupation B1 Occupation B2 Occupation B2 Birth Death Citizenship Occupation B1 Occupation B2 Birth Death Citizenship Occupation B1 Occupation B2 Birth Death Citizenship Occupation B1 Occupation B2 Birth Death Citizenship Occupation B1 Occupation B2 Birth Death Citizenship Occupation B1 Occupation B2 Birth Death Citizenship Occupation B1 Occupation B2 Birth Death Citizenship Occupation B1 Occupation B2 Birth Death Citizenship Occupation B1 Occupation B2
	Cat. rank Cat. rank Ov. rank Ov. rank Cat. rank Ov. rank		Name Name Name		Birth Birth Death Death Birth Death Citizenship Citizenship Citizenship Occupation B1 Occupation B1 Occupation B2 Occupation B2 Occupation B1 Occupation B2
	Cat. rank Cat. rank	Ov. rank Ov. rank		Name Name		Birth Birth	Death Death	Citizenship Citizenship	Occupation B1 Occupation B1	Occupation B2 Occupation B2
								Top 20 individuals Top 20 individuals Top 20 individuals Top 20 individuals Top 20 individuals Top 20 individuals Top 20 individuals Top 20 individuals Top 20 individuals Top 20 individuals
	1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1 2 3 4 5 6 7 8 9 10 10 1 2 3 4 5 6 7 8 9 11 12 13 14 15 16 17 18 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 1 1 1 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 6 6 6 6 7 7 7 7 8 8 8 8 9 9 9 9 10 10 10 10 10 1 2 3 4 5 6 7 8 9 11 11 11 11 11 12 12 12 12 12 13 13 13 13 13 14 14 14 14 14 15 15 15 15 15 16 16 16 16 16 17 17 17 17 17 18 18 18 18 18 19 19 19 19 19 20 20 20 20	32 189 193 423 667 698 894 1024 1668 2062 2200 2279 2688 2761 2876 2913 3365 3825 4242 4265 1 6 22 24 25 3 8 72 93 97 99 102 106 108 123 127 136 165 180 183 186 1 1 4 2 6 43 44 57 118 139 1 4 1 144 152 158 159 163 217 250 260 268 287 8 35 56 5 9 6 6 6 8 74 78 80 87 94 96 98 107 109 113 131 132 135 145 1 2 4 5 6 7 9 10 11 12 17 20 21 23 27 28 29 31 14 70 75 81 90 1 1 4 120 121 1 4 1 142 170 225 239 263 267 281 288 303 304 308 30 32 45 49 54 58 63 70 75 77 90 101 130 133 140 146 147 160 170 174 5 19 23 42 79 104 210 218 219 237 295 299 316 325 334 340 341 378 393 418 52 57 84 89 134 160 255 285 404 410 449 632 633 733 754 760 910 1016 1311 3 18 65 29 29 13 15 17 33 46 77 100 112 115 169 238 272 321 351 376 380 385 422 440 121 60 167 55 55 247 69 280 5 9 69 496 88 351 69 103 Arthur Wellesley, 1st Duke of Wellington Karl Marx David Hume John Maynard Keynes Milton Friedman Friedrich Hayek Muhammad Yunus Paul Krugman Joseph Stiglitz Amartya Sen Peter Kropotkin Paul Samuelson Chanakya Gary Becker Murray Rothbard Kenneth Arrow Leonid Hurwicz Alan Greenspan Ben Bernanke Shaukat Aziz Ronald Coase Top 20 individuals 1818 1711 1883 1912 1899 1940 1953 1943 1933 1842 1915 -370 1930 1926 1921 1917 1926 1953 1949 1910 C r i s t i a n o R o n a l d o 1 9 8 5 Roger Federer 1981 Lionel Messi 1987 Novak Djokovic 1987 R a f a e l N a d a l 1 9 8 6 Serena Williams 1981 Bobby Fischer 1943 Michael Schumacher 1969 David Beckham 1975 Pelé 1940 Michael Phelps 1985 Michael Jordan 1963 Maria Sharapova 1987 Diego Maradona 1960 Francesco Totti 1976 Andy Murray 1987 Fernando Alonso 1981 Muhammad Ali 1942 Usain Bolt 1986 Top 20 individuals C h a r l i e C h a p l i n 1 8 8 9 1 9 7 7 1883 1776 1946 2006 1992 1921 2009 -283 2014 1995 2008 2013 2008 Vincent Van Gogh 1853 1890 Johann Sebastian Bach 1685 1750 Leonardo da Vinci 1452 1519 R a b i n d r a n a t h T a g o r e 1 8 6 1 1 9 4 1 Ludwig Van Beethoven 1770 1827 Richard Wagner 1813 1883 M a r k T w a i n 1 8 3 5 1 9 1 0 Pablo Picasso 1881 1973 Frédéric Chopin 1810 1849 Wolfgang Amadeus Mozart 1756 1791 Michelangelo 1475 1564 Frank Lloyd Wright 1867 1959 Giuseppe Verdi 1813 1901 Antoni Gaudí 1852 1926 Paul Gauguin 1848 1903 Pyotr Ilyich Tchaikovsky 1840 1893 Rembrandt 1606 1669 Michael Jackson 1958 2009 Elvis Presley 1935 1977 Paul McCartney 1942 L a d y G a g a 1 9 8 6 B o b D y l a n 1 9 4 1 F r a n k S i n a t r a 1 9 1 5 1 9 9 8 Akira Kurosawa 1910 1998 Celine Dion 1968 Madonna (entertainer) 1958 Marilyn Monroe 1926 1962 Whitney Houston 1963 2012 Beyoncé Knowles 1981 Taylor Swift 1989 Eminem 1972 John Lennon 1940 1980 Meryl Streep 1949 Stanley Kubrick 1928 1999 Angelina Jolie 1975 Rihanna 1988 Steven Spielberg 1946 Top 20 individuals Barack Obama 1961 Ronald Reagan 1911 2004 George W. Bush 1946 Napoleon Bonaparte 1769 1821 Winston Churchill 1874 1965 Nelson Mandela 1918 2013 Adolf Hitler 1889 1945 Joseph Stalin 1878 1953 John F. Kennedy 1917 1963 Mahatma Gandhi 1869 1948 Pope John Paul Ii 1920 2005 Franklin D. Roosevelt 1882 1945 Abraham Lincoln 1809 1865 George Washington 1732 1799 Vladimir Lenin 1870 1924 Charles de Gaulle 1890 1970 Hillary Rodham Clinton 1947 Bill Clinton 1946 William Shakespeare 1564 1616 Friedrich Nietzsche 1844 1900 Jean-Jacques Rousseau 1712 1778 Franz Kafka 1883 1924 Voltaire 1694 1778 R a b i n d r a n a t h T a g o r e 1 8 6 1 1 9 4 1 Charles Dickens 1812 1870 Confucius -551 -479 M a r k T w a i n 1 8 3 5 1 9 1 0 Fyodor Dostoyevsky 1821 1881 Avicenna 980 1037 Oscar Wilde 1854 1900 Victor Hugo 1802 1885 Leo Tolstoy 1828 1910 George Bernard Shaw 1856 1950 James Joyce 1882 1941 Rudyard Kipling 1865 1936 Miguel de Cervantes 1547 1616 Baruch Spinoza 1632 1677 Jules Verne 1828 1905 Albert Einstein 1879 1955 Karl Marx 1818 1883 Sigmund Freud 1856 1939 Isaac Newton 1642 1727 Immanuel Kant 1724 1804 Charles Darwin 1809 1882 Galileo Galilei 1564 1642 Friedrich Nietzsche 1844 1900 Jean-Jacques Rousseau 1712 1778 Søren Kierkegaard 1813 1855 Voltaire 1694 1778 Bertrand Russell 1872 1970 Gottfried Wilhelm Von Leibniz 1646 1716 Carl Linnaeus 1707 1778 Alan Turing 1912 1954 Neil Armstrong 1930 2012 Socrates -470 -390 Alexander Graham Bell 1847 1922 Avicenna 980 1037 Marie Curie 1867 1934 Napoleon Bonaparte 1769 1821 Netherlands Germany Scotland England US Austria Bangladesh US US India Russia US India US US US Poland US US Pakistan England P o r t u g a l Switzerland Argentina Serbia S p a i n US US Germany England Brazil US US Russia Argentina Italy Scotland Spain US Jamaica E n g l a n d Germany Italy I n d i a Germany Germany U S Spain Poland . Italy US Italy Spain France Russia Netherlands US US England U S U S U S Japan Canada US US US US US US England US US US Barbados US US US US France England South Africa Austria Russia US India Poland US US US Russia France US US England Germany . Austria France I n d i a England China U S Russia . Ireland France Russia Ireland Ireland England Spain Netherlands France Germany Germany Austria England Germany England Italy Germany . Denmark France England Germany Sweden England US Greece England . Poland France Mustafa Kemal Atatürk 1881 1938 Turkey George Washington 1732 1799 US Dwight D. Eisenhower 1890 1969 US Ulysses S. Grant 1822 1885 US Osama Bin Laden 1957 2011 Saudi Arabia Chiang Kai-Shek 1887 1975 China Bashar Al-Assad 1965 Syria Erwin Rommel 1891 1944 Germany Francisco Franco 1892 1975 Spain 1769 1852 Ireland Douglas Macarthur 1880 1964 US Pervez Musharraf 1943 Pakistan Nawaz Sharif 1949 Pakistan Ion Antonescu 1882 1946 Romania Frederick II of Prussia 1712 1786 Germany Oliver Cromwell 1599 1658 England Simón Bolívar 1783 1830 Venezuela Hosni Mubarak 1928 Egypt William Henry Harrison 1773 1841 US Top 20 individuals Steve Jobs 1955 2011 US Leonardo da Vinci 1452 1519 Italy Christopher Columbus 1451 1506 Italy Nikola Tesla 1856 1943 Austria Thomas Edison 1847 1931 US Alexander Graham Bell 1847 1922 England Hernán Cortés 1485 1547 Spain James Cook 1728 1779 England Ibn Battuta 1304 1369 . James Cameron 1954 Canada Edmund Hillary 1919 2008 New Zealand Ferdinand Magellan 1480 1521 Portugal Fridtjof Nansen 1861 1930 Norway Alfred Russel Wallace 1823 1913 England Roald Amundsen 1872 1928 Norway Guglielmo Marconi 1874 1937 Italy James Watt 1736 1819 Scotland Dmitri Mendeleev 1834 1907 Russia Tim Berners-Lee 1955 England Jesus 0 30 . Top 20 individuals Alexander The Great -350 -320 Greece Thomas Jefferson 1743 1826 US Hillary Rodham Clinton 1947 US Hillary Rodham Clinton 1947 US Mahomet 570 632 . Pope Francis 1936 Argentina Pope John Paul Ii 1920 2005 Poland Pope Benedict Xvi 1927 Germany Martin Luther 1483 1546 Germany Søren Kierkegaard 1813 1855 Denmark Augustine of Hippo 354 430 . Thomas Aquinas 1225 1274 Italy Mary (mother of Jesus) -18 41 . 14th Dalai Lama 1935 . Joseph Smith 1805 1844 US Mother Teresa 1910 1997 . Ruhollah Khomeini 1902 1989 Iran Umar Farooq 588 644 . Joan of Arc 1412 1431 France Abdullah of Saudi Arabia 1924 2015 Saudi Arabia Dante Alighieri 1265 1321 Italy Pope John Xxiii 1881 1963 Italian Anselm of Canterbury 1033 1109 . Confucius -550 -470 . Charlemagne 740 814 . John Adams 1735 1826 US Margaret Thatcher 1925 2013 England Margaret Thatcher 1925 2013 England Isaac Asimov 1920 1992 US Elizabeth Ii 1926 England William Howard Taft 1857 1930 US L a d y G a g a 1 9 8 6 U S Elizabeth Ii 1926 England Martin Heidegger 1889 1976 Germany inventor studies studies studies studies studies business studies studies studies studies studies education studies studies studies studies studies studies studies studies s p o r t s sports sports sports s p o r t s sports sports sports sports sports sports sports sports sports sports sports sports sports sports a r t s arts arts l i t arts arts l i t arts arts arts arts arts arts arts arts arts arts arts arts arts a r t s a r t s a r t s arts arts arts arts arts arts arts arts arts arts arts arts arts arts politics politics politics military politics politics politics politics politics politics politics politics politics politics politics lit politics politics lit studies studies lit lit l i t lit education l i t lit lit lit lit lit lit lit lit lit studies lit studies studies studies studies studies studies studies studies studies studies lit studies studies studies business studies studies studies lit studies military military politics politics politics Other politics politics military politics military military politics politics military nobility military military military politics business inventor inventor inventor inventor studies inventor inventor inventor arts sports inventor inventor studies inventor inventor inventor studies studies Family nobility law politics politics religious religious politics politics religious studies religious religious religious religious religious religious religious religious religious lit Family religious education nobility law politics politics lit nobility law a r t s nobility studies Louis XIV of France 1638 1715 France nobility Umar Farooq 588 644 . religious Elizabeth Ii 1926 England nobility Angela Merkel 1954 Germany politics 592 J. Robert Oppenheimer 1904 1967 US studies 119 Elizabeth I of England 1533 1603 England nobility 369 Chester A. Arthur 1829 1886 US law 72 Serena Williams 1981 US sports 115 Mary (mother of Jesus) -18 41 . religious 599 Karl Popper 1902 1994 England studies 122 Diana, Princess of Wales 1961 1997 England Family 487 Thomas More 1478 1535 England law 78 Celine Dion 1968 Canada arts 116 Yulia Tymoshenko 1960 Ukraine politics 686 Pierre-Simon Laplace 1749 1827 France education 125 Constantine The Great 272 337 Italy nobility 629 Muhammad Ali Jinnah 1876 1948 Pakistan law 80 Madonna (entertainer) 1958 US arts 119 Elizabeth I of England 1533 1603 England nobility 793 Talcott Parsons 1902 1979 US studies 128 Genghis Khan 1160 1227 Mongolia Other 634 Al-Ghazali 1058 1111 . religious 8 7 M a r i l y n M o n r o e 1 9 2 6 1 9 6 2 U S a r t s 122 Diana, Princess of Wales 1961 1997 England Family nobility studies studies Other studies studies business education education studies studies studies studies education studies lit studies business business lit lit s p o r t s sports sports sports s p o r t s sports Other sports sports sports sports sports sports sports sports sports sports sports sports a r t s arts arts arts a r t s arts arts a r t s arts arts Family arts arts arts arts arts arts arts arts arts arts a r t s a r t s a r t s arts arts arts arts arts arts arts arts arts arts lit arts arts lit education arts business politics politics politics politics politics politics politics religious politics politics military politics politics politics politics lit lit lit lit studies a r t s lit lit a r t s lit studies lit lit lit lit lit lit lit lit lit studies studies studies studies studies studies studies lit lit religious studies studies studies studies studies studies Other inventor studies studies politics military military military military military military military military military politics military military military lit military politics politics politics military inventor arts inventor business business inventor nobility inventor lit inventor inventor inventor studies inventor politics business business inventor inventor nobility politics politics politics religious Other religious religious religious education religious studies religious Family studies politics religious politics law nobility religious religious religious religious nobility lit politics politics lit politics a r t s education nobility law studies education Family politics sports Family education nobility studies arts politics education Family politics arts Family politics nobility law a r t s education 795 Emily Dickinson 1830 1886 US lit 1 4 3 A k b a r 1 5 4 2 1 6 0 5 . n o b i l i t y n o b i l i t y 641 Michelle Obama 1964 US law lit 94 Whitney Houston 1963 2012 US arts arts 129 Benazir Bhutto 1953 2007 Pakistan politics politics education 894 Paul Krugman 1953 US studies 161 Queen Victoria 1819 1901 England nobility nobility 670 Maximilien de Robespierre 1758 1794 France law politics 96 Beyoncé Knowles 1981 US arts arts 161 Queen Victoria 1819 1901 England nobility nobility education 1024 Joseph Stiglitz 1943 US studies 182 Nicholas II of Russia 1868 1918 Russia nobility nobility 767 Hassan Rouhani 1948 Iran politics law 98 Taylor Swift 1989 US arts arts 212 Sarah Palin 1964 US politics lit education 1065 Ralph Waldo Emerson 1803 1882 US lit 194 Charles, Prince of Wales 1948 England Family nobility 908 Muhammad Zia-Ul-Haq 1924 1988 Pakistan politics law 103 Angela Merkel 1954 Germany politics studies 231 Catherine The Great 1729 1796 Russia politics Other education 1101 Euripides -480 -400 Greece lit 196 Henry VIII of England 1491 1547 England nobility 925 Ehud Olmert 1945 Israel politics law 113 Meryl Streep 1949 US arts arts 242 Indira Gandhi 1917 1984 India politics politics education 1138 Heraclitus -530 -470 Greece studies 200 Muawiyah I 602 680 Syria nobility nobility 1043 Jeremy Bentham 1748 1832 England studies law 115 Mary (mother of Jesus) -18 41 . religious Family 259 Aung San Suu Kyi 1945 Myanmar politics politics education 1207 Golda Meir 1898 1978 Israel education 209 Trajan 53 117 Italy nobility politics 1239 Rudy Giuliani 1944 US law business 116 Yulia Tymoshenko 1960 Ukraine politics politics 272 Mother Teresa 1910 1997 . religious religious politics 1216 Ibn Taymiyyah 1263 1328 . education 215 Haile Selassie 1892 1975 Ethiopia nobility nobility 1257 Rick Santorum 1958 US law politics 119 Elizabeth I of England 1533 1603 England nobility Family 276 Corazon Aquino 1933 2009 . politics lit religious 1264 Robert H. Goddard 1882 1945 US business 255 Hernán Cortés 1485 1547 Spain inventor nobility 1540 Mohamed Elbaradei 1942 Egypt law education 122 Diana, Princess of Wales 1961 1997 England Family nobility 298 Marine Le Pen 1968 France politics politics education 1493 Robert Baden-Powell, 1st Baron Baden-Powell 1857 1941 . education 258 Nero 37 68 Italy nobility nobility 1573 Montesquieu 1689 1755 France law lit 123 Maria Sharapova 1987 . sports sports 306 Katharine Hepburn 1907 2003 US arts politics politics 1556 Origen 185 254 . education 278 Hadrian 76 138 Italy nobility inventor 1636 Alben W. Barkley 1877 1956 US law politics 129 Benazir Bhutto 1953 2007 Pakistan politics politics 322 Dilma Rousseff 1947 Brazil studies politics religious 1558 Aaron Copland 1900 1990 US arts education 292 Marcus Aurelius 121 180 Italy nobility studies 1703 Sonia Sotomayor 1954 US law law 132 Angelina Jolie 1975 US arts arts 356 Audrey Hepburn 1929 1993 England arts politics
	20	20 19 20	19 20 20	188 33 34 1438 391 421 1724		Kobe Bryant Raphael Gustav Mahler Pope Benedict Xvi Che Guevara Giacomo Casanova Petrarch	Top decile (random sample) 1978 1483 1520 1860 1911 Top decile (random sample) 1927 1928 1967 Top decile (random sample) Top decile (random sample) Top decile (random sample) 1725 1798 Top decile (random sample) 1304 Top decile (random sample) Top decile (random sample) Top decile (random sample) Top decile (random sample)	US Austria Italy Germany Argentina Italy 1374 Italy	sports arts arts politics politics inventor education	sports arts arts religious politics lit	lit
	269 270 271 272 273 40816 40815 40812 40814 4397 4398 4400 19149 19150 19152 19151 19153 22359 22360 22361 2056 2055 2057 2058 2059 9211 9212 9213 9214 9215 5737 5738 5739 5740 5741 931 932 933 934 4698 3564 4330 19519 4048 4699 4700 4702 4701 6776 223037 98711 98722 99463 99511 99995 125315 125320 125321 125329 109405 109411 109450 83815 83822 83830 83832 83849 155389 155414 155423 83546 83561 83604 83722 83781 112329 112353 112378 112395 112401 128776 128786 128795 128826 128829 90515 90576 90597 90617 118972 36235 258340 115708 119361 118996 119042 119133 119136 3565 36268 4331 258402 19522 115715 4049 119535 6777 223149 3566 36271 4332 258418 19521 115719 4050 119610 6778 223153 3567 36283 4333 258440 19520 115722 4051 119618 6779 223202 3568 36300 4334 258475 19523 115728 4052 119619		Jean Fourastié Franz Hermann Schulze-Delitzsch Iuliu Winkler Ladislaus Bortkiewicz Steve Keen Top decile (random sample) 1907 1990 1808 1883 1964 1868 1931 1953 Rudolf Bester 1983 Gimax 1938 Mario Ghella 1929 Nikolaj Ehlers 1996 Top decile (random sample) Henry Lytton 1865 1936 Adolphe d'Ennery 1811 1899 Marguerite Durand 1864 1936 Uhm Ji-Won 1977 South Korea France Germany Romania Russia Australia Namibia Italy Italy Denmark England France France Satoshi Urushihara 1966 Japan Jeremy Jordan (stage actor) 1984 US Yasumi Matsuno 1965 Japan Eileen Joyce 1908 1991 Australia Top decile (random sample) M. Kulasegaran 1957 Malaysia Daniel Pfeiffer 1975 US Gheorghe Cristescu 1882 1973 Romania Mikha'il Na'ima 1889 1988 Lebanon Christopher Anstey 1724 1805 England Joseph Glanvill 1636 1680 England Paul Fort 1872 1960 France Jacques Bainville 1879 1936 France Liu E 1857 1909 China Stanley Coren 1942 . Franciscus Junius (the younger) 1591 1677 Germany Thomas Thomson (chemist) 1773 1852 Scotland Boris Rybakov 1908 2001 Russia Franco Lucchini 1917 1943 Italy Paul Behncke 1869 1937 Germany Thomas Ewing, Jr. 1829 1896 US Angelo d 27arrigo 1961 2006 Italy Amédée Mouchez 1821 1892 France Top decile (random sample) Sámuel Teleki 1845 1916 Hungary Jacob Aaron Westervelt 1800 1879 . Otto Schmitt 1913 1998 US Royal Rife 1888 1971 US José Correia da Serra 1750 1823 Portugal Top decile (random sample) Euthydemus I -200 . Ramón J. Cárcano 1860 1946 Argentina Laura Riding 1901 1991 US Katrín Jakobsdóttir 1976 Iceland Tadeusz Isakowicz-Zaleski 1956 Poland Valerio Valeri 1883 1963 Italy Mahasena of Anuradhapura 301 Sri Lanka Yehuda Alharizi 1165 1225 Spain Michael Jensen 1939 US Ivan Stephen of Bulgaria 1373 Serbia András Tasnádi Nagy 1882 1956 Hungary Alice Rohrwacher 1982 Italy Yordanka Fandakova 1962 Bulgaria Robert B. Wilson 1937 US Kakinomoto No Hitomaro 662 710 Japan Malcolm McCusker 1938 Australia Jan Lehane 1941 Australia Diana E. H. Russell 1938 South Africa Antoine-Jean Saint-Martin 1791 1832 France Richard Woodville, 1st Earl Rivers 1405 1469 England Lloyd L. Gaines 1911 Canada Fleur East 1987 England Jacqueline Auriol 1917 2000 France Anne Morelli 1948 Belgium politics studies studies business studies studies sports sports sports sports arts arts arts arts arts arts sports arts politics politics politics lit lit lit lit studies lit education business studies studies military military law military military inventor inventor inventor inventor religious nobility law lit religious religious nobility religious education Family politics arts politics studies lit law sports politics education nobility law arts military military lit studies studies studies lit sports sports sports sports arts lit lit arts arts arts arts arts politics politics politics lit lit studies studies lit studies studies studies studies studies military military military education military inventor lit business arts nobility studies lit politics studies lit religious religious studies nobility law lit politics education nobility Other sports lit education nobility politics arts studies studies education George, Duke of Bavaria 1455 1503 Germany nobility Family John Trevor (speaker) 1630 1717 Wales law politics Tatjana Patitz 1966 Germany arts arts Vera Kobalia 1981 Georgia politics politics
	40813 4399 22362 935 6780 223239 125331 109456 155436 91126		First quartile (random sample) Marcell Deák-Nagy 1992 Gioseffo Guami 1542 1611 First quartile (random sample) Augusto del Noce 1910 1989 First quartile (random sample) First quartile (random sample) First quartile (random sample) Jozef Murgaš 1864 1929 First quartile (random sample) Sa'id Akhtar Rizvi 1927 2002 Italy Hungary Italy Slovakia Tanzania inventor sports arts studies education First quartile (random sample) First quartile (random sample) First quartile (random sample) First quartile (random sample)	sports arts politics arts	lit
	676 677 678 680 679 102033 102035 102034 102036 10996 10994 10995 10997 47876 47875 47874 47878 47877 55900 55901 55902 55899 5139 5141 5140 5142 5143 23030 23031 23032 23034 23033 14344 14345 14347 14346 14348 2331 2332 2333 2334 11748 8913 10829 48804 10124 11749 11752 11751 11750 16944 444491 292055 292200 292649 293982 294001 300267 300269 300284 300290 250344 250366 250385 250434 232537 232545 232561 232567 232575 378806 378850 378871 378916 225273 225296 225312 225331 225344 278169 278270 278304 278321 278329 324354 324380 324442 324501 324515 255786 256008 256020 256092 285318 128857 522365 301638 338478 285324 285366 285377 285382 8914 128859 10830 522398 48805 301644 10123 338502 16942 444520 8915 128869 10828 522442 48802 301645 10126 338556 16943 444530 8917 128914 10831 522539 48801 301668 10125 338624 16945 444552 8916 128887 10832 522549 48803 301716 10127 338672		Alvin Saunders Johnson Peyton Young Cecilia Rouse Takashi Negishi Georg Friedrich Sartorius First quartile (random sample) 1874 1971 1945 1963 1933 1765 1828 Ben Eaves 1982 Harald Smith 1879 1977 Ginger Molloy 1937 Paul Gruber 1965 First quartile (random sample) Ádám Récsey 1775 1852 William Thoms 1803 1885 Twm o'r Nant 1739 1810 Henri Gagnebin 1886 1977 Oleg Strizhenov 1929 Meg Mundy 1915 Go Eun-Mi 1976 South Korea US US US Japan Germany US Norway New Zealand US Hungary England Wales Belgium Russia England Jamal Rahimov 1987 Azerbaijan Andrea Rost 1962 Hungary First quartile (random sample) Anthony Enahoro 1923 2010 Nigeria Jorge del Prado Chávez 1910 1999 Peru Vera Chirwa 1932 Malawi Salvador María del Carril 1798 1883 Argentina Minamoto no Shunrai 1055 1129 Japan Dulduityn Danzanravjaa 1803 1856 Mongolia Jean-Baptiste-Antoine Suard 1733 1817 France Sara Jeannette Duncan 1861 1922 Canada Thomas Muir (mathematician) 1844 1934 Scotland Susanne Nyström 1982 Sweden Peter Nicholson (architect) 1765 1844 England Albert Pilát 1903 1974 Czech Republic David G. Hartwell 1941 US Alfred Twardecki 1962 Poland Friedrich Von Hollmann 1842 1913 Germany Focko Ukena 1360 1435 . Eric Plant 1890 1950 Australia Toto Koopman 1908 1991 Indonesia Pierre Segrétain 1909 1950 France First quartile (random sample) Aaron Seigo 1975 Canada David Wilhelm 1956 . Gerard Unger 1942 Netherlands Michael J. Freeman 1947 US Franz Ludwig Von Erthal 1730 1795 . First quartile (random sample) Louis de Silvestre 1675 1760 France Patrick Redmond 1966 England Josephine Meckseper 1964 Germany Brigitte Douay 1947 France Carlo Domenico del Carretto 1454 1514 Italy Sam Pollard 1864 1915 England Leo Soekoto 1920 1995 . Antonius Maria Bodewig 1839 1915 Germany Ida Bieler US Turan-Shah 1180 Turkey Jerome J. Shestack 1923 2011 US Livia Zita 1984 Hungary Christine Boyer 1771 1800 . Jacob Chandy 1910 2007 India Fergus of Galloway 1161 Scotland Hippolyte Rolin 1804 1883 Belgium Anne Rosellini US Linda Jeffrey 1958 Canada Arthur W. Barton 1899 1976 . Li Lianying 1848 1911 China Filippo Mancuso 1922 2011 Italy Brunilde Sismondo Ridgway 1929 Italy Mary Kim Titla 1960 US Konstantin Posse 1847 1928 Russia Alexander Mourousis 1816 Moldova Amédée Dunois 1878 1945 France Griedge Mbock Bathy 1995 France Veronica Palm 1973 Sweden	studies sports studies studies studies sports sports sports sports arts lit arts arts arts arts arts sports arts politics politics law law lit lit lit lit studies sports arts studies lit studies military military military military military business inventor arts inventor religious arts lit arts politics religious religious religious religious arts nobility law arts Family studies nobility law arts politics business nobility law studies lit studies nobility law sports politics	Other studies education studies studies sports sports sports sports politics arts lit arts arts arts arts arts arts politics politics politics politics Family arts studies lit lit studies studies studies studies studies nobility politics military arts military inventor politics inventor business nobility law arts politics religious religious lit religious Other nobility law arts religious education nobility politics arts politics education nobility politics arts law education education nobility lit sports politics
								Median (random sample) Median (random sample) Median (random sample) Median (random sample) Median (random sample) Median (random sample) Median (random sample) Median (random sample) Median (random sample) Median (random sample) Median (random sample) Median (random sample) Median (random sample) Median (random sample) Median (random sample)
	1354 1353 1355 1356 1357 204067 204070 204069 204068 21989 21990 21991 21992 95750 95752 95751 95753 111799 111800 111801 111802 10280 10279 10281 10282 10283 46063 46064 46062 46065 28689 28690 28691 28692 28693 4663 4664 4665 4666 23499 23498 23501 23500 33886 741355 566073 566131 566691 566822 567479 611202 611203 611214 611266 502347 502381 502591 502592 495326 495333 495339 495405 691320 691396 691464 691483 496058 496073 496492 496516 496618 550855 550857 550877 550900 631898 631971 632121 632126 632306 580149 580213 580260 580468 590055 590072 590103 590229 17830 356840 21657 814976 97603 611748 20247 661532 33888 741395 17829 356780 21661 815002 97607 611753 20251 661538 33885 741481 17828 356866 21659 815010 97604 611756 20248 661549 33887 741538 17830 356932 21658 815099 97606 611763 20250 661585 17831 356909 21660 815158 97605 611778 20249 661613		Svetlana Kirdina Gabibulla Rabadanovich Khasaev Maria Kiwanuka Jan Kregel Claudio Demattè Iosif Anisim Ivette María Arantxa Sanchis Hamid Reza Fathi Peter Ferdinand Funck William Adams Delano Adam Eberle Louis Round Wilson Tully Satre Claire Falkenstein Rick Krebs Henry Johnson (guitarist) Robert S. Hall Jørgen Flood Jessie Gruman Zhu Shaolian John Paget (author) Tudur Aled Edmond Tarbé Des Sablons Rosauro Almario Étienne Weill-Raynal George Molnar (philosopher) Josselyn Van Tyne Gheorghe Pintilie Clayton Oscar Person Edward Everett Smith Francois Baby (politician) Mohammad Hossein Jalali James Bruce Joseph Reynolds (congressman) Pierre de Sales Laterrière Alexis Nihon Richard K. Diran James H. Kelley Brian Hennessy (bishop) Nick Ribush Soulmother of Küssnacht James Bilsborrow Michael Stumpf Isabelle Romée Martin S. Ackerman Nancy Buchanan Karen Tandy George Kennion Tsuneharu Takeda James M. Burns (judge) Kelly Cassidy Sally Huffer Floyd Graham Werner Ewald William Travers Jerome Suzie Faulkner Helene Moszkiewiez Gong Xiantian Kim Myeong-Won Robert H. Johnson Kaitlin Cochran Maria Pilar Riba Font Antoni Jan Ostrowski A. David Mazzone Merle Goldman Tané Matsukata	1955 1951 1955 1944 1942 1975 1990 1980 1788 1874 1804 1876 1989 1908 1949 1954 1879 1792 1953 1887 1808 1460 1838 1886 1887 1934 1902 1902 1922 1861 1859 2004 1960 1832 1979 1997 1941 1867 2014 1929 1892 1525 1900 1933 1982 1991 1957 1985 1990 1931 1768 1852 1732 1791 1785 1864 1740 1815 1902 1980 1949 1833 1912 1919 1997 1577 1862 1931 1970 1377 1458 1932 1993 1946 1845 1922 Denmark Russia Russia Uganda US Italy Romania Spain India Iran US . US US US . US US Norway . China England Wales France Philippines France Hungary US Russia Canada US Canada Iran Russia US France Belgium US US US . Switzerland England . . . US US England 1944 Japan 1924 2001 US US 1965 US politics studies studies studies studies studies sports sports sports sports arts arts arts studies arts arts sports arts politics business Other business worker lit lit lit studies studies studies studies lit law military military Family business inventor inventor inventor inventor religious religious Other religious education Family law arts Other education politics law politics 1902 1974 US arts 1914 1993 Germany Other 1859 1934 US law 1979 Australia sports 1920 1998 . military 1944 . education 1534 1602 . politics 1916 2011 US lit 1987 US sports 1944 Spain politics 1782 1845 Poland nobility 1928 2004 US law 1931 US studies 1918 1989 Japan Other	studies education business studies Other sports sports sports sports arts business arts arts arts arts arts arts politics politics politics politics lit lit lit lit lit education arts politics studies military politics politics military military politics business arts business religious Other religious religious nobility business arts law studies nobility law politics politics education nobility politics sports politics education nobility law sports politics education business law education politics
							Third quartile (random sample) Third quartile (random sample) Third quartile (random sample) Third quartile (random sample) Third quartile (random sample) Third quartile (random sample) Third quartile (random sample) Third quartile (random sample) Third quartile (random sample) Third quartile (random sample) Third quartile (random sample) Third quartile (random sample) Third quartile (random sample) Third quartile (random sample) Third quartile (random sample)
	2030 2031 2032 2033 2034 306104 306102 306101 306103 32985 32984 32987 32986 143629 143628 143627 143625 143626 167699 167700 167702 167701 15422 15419 15420 15421 15423 69094 69095 69097 69093 69096 43034 902733 855631 855881 856632 856888 856947 944207 944237 944369 944402 835167 835236 835288 835328 804086 Bill Richardson (footballer born 1943) Dattatreya Gopal Karve Thomas Mayer (American economist) Alfredo Salazar Ray Major Wim Driehuis Karl Esleeck Orian Landreth Joe Blythe Last decile (random sample) 1898 1967 1927 2015 1961 1943 1903 1952 1904 1996 1943 1881 Sydney Mitchell 1856 1930 Umberto Coromaldi 1870 1948 Olive Mudie-Cooke 1890 1925 Lucy Isabella Buckstone 1859 1893 Last decile (random sample) Brett Goldsmith 1961 804119 Peter Gvozdják 1965 804147 Carl Von Hanno 1901 1953 804188 John Wylie (musician) 1974 804303 George Dahl 1894 1987 992734 Richard Bright (politician) 1822 1878 992832 Roswell G. Ham 1891 1983 992906 Jeffrey Herbst 1961 993044 Ashley Goldsworthy Last decile (random sample) 846629 Edward Anthony (photographer) 1819 1888 846660 Kathleen Hawkins 1883 1981 846688 Elizabeth Wynne Fremantle 1778 1857 846723 John E. Tullidge 1806 1873 846970 Frank Sayers 1763 1817 874446 Henry Jay Forman 874460 Vandana Singh 874497 John Tosh 874619 John Roberton (1776) 1776 1840 874638 Philip D. Morgan 1949 Ira Jones 1923 2004 43035 902735 Paterson Fraser 1907 2001 43037 902766 Kent Foster 1937 43038 902802 Cornelius Coffey 1903 1994 43036 902961 Earle D. Chesney 1900 1966 6994 890906 Dudley Bradstreet 1711 1763 6995 891735 Ira P. DeLoache 1879 1965 6996 892276 Wilhelm Brenneke 1865 1951 6997 892363 Silas C. Overpack 1842 1918 Last decile (random sample) 35249 893066 Harold Hyde-Lees 1890 1963 35251 893091 Hyacinth (Jacek) Gulski 1847 1911 35250 893134 Keshavananda Brahmachari 1942 35248 893220 David Saperstein (rabbi) 1947 35247 893224 Henry Marshall (bishop of Salford) 1884 1955 50831 971470 Thomas H. Makiyama 1928 2005 Netherlands India US . . US US England England Scotland Italy England England Australia Slovakia Norway US US England US US Australia US New Zealand . US England US India England Scotland England . . Canada US . Ireland US Germany . England Poland India US Italy US 50828 971554 Kerreen Reiger Australia sports studies studies studies studies studies education sports sports sports arts arts arts arts arts arts arts arts politics education politics politics lit lit lit arts lit education studies studies studies studies lit military military military religious inventor business inventor business religious business religious religious religious sports education 26743 716750 William St Clair of Roslin 1778 . nobility 32489 1040410 Ian West (Australian politician) 1951 Australia politics 146406 913747 Marion Goldman US education 30372 931559 Caroline Keer 1857 1928 England military 50829 971607 Keith Vivian Alexander New Zealand education 26744 716907 Giovanni dalle Carceri 1358 . nobility 32486 1040427 Andrew Bridge (lawyer) US law 146409 913916 Lila Rose Kaplan 1980 US lit 30371 931577 Anna Walker (civil servant) 1951 England politics 50830 971716 Thomas Chase (educator) 1827 1892 US education 26745 716936 Robert Logan of Restalrig 1550 1606 Scotland nobility 32490 1040629 Tony Randerson New Zealand military 146408 913923 Suzanne Goldenberg 1962 Canada lit 30373 931612 Mina Adampour 1987 Norway lit 26746 716956 Ludwig Spindler 1910 1944 Germany military 32487 1040718 John Batiuk 1923 2005 Canada politics 146405 913949 Claire Keelan England arts 30374 931680 Rosamond Carr 1912 2006 US politics Last decile (random sample) 26747 716971 Thihathura II of Ava 1474 1501 . nobility 32488 1040933 William Cliffe 1558 England religious 146407 914089 María Dolores 22Mary 22 Tarrero-Serrano 1924 2010 Cuba politics 30375 932156 Romola Sinha 1913 2010 India politics	education education education studies education sports sports sports sports arts arts arts arts politics arts politics arts arts politics politics studies politics Other business Family lit lit studies lit education politics inventor military military military education military politics inventor inventor inventor religious religious religious law education nobility law religious studies lit nobility politics arts politics business nobility law lit politics education nobility law arts lit religious nobility law Family politics
	367323 39584 201240 8394 60997 1116884 1125362 1096396 1161643 1087816		Last decile (random sample) 1883 1961 Giovan Giacomo Dalla Corna George Barron 1480 1560 Last decile (random sample) Solomon Quetsch 1798 1856 Last decile (random sample) Last decile (random sample) Last decile (random sample) Eric Drew Last decile (random sample) Víctor Manuel Gutiérrez 1922 Last decile (random sample) Last decile (random sample) Last decile (random sample) Last decile (random sample)	England Italy Austria . 1966 Guatemala inventor sports arts religious education	sports business politics lit politics
	2437 2438 2439 2441 2440 367324 367322 367321 367325 39582 39581 39583 172352 172353 172354 172351 172350 201241 201242 201239 18505 18507 18506 18504 18503 82912 82913 82916 82915 82914 51644 1071958 1046320 1048363 1048843 1048902 1048976 1125650 1125676 1125800 1125810 1096495 1096614 1096908 1027662 1027847 1027862 1027987 1028035 1161787 1161914 1161972 1062973 1063028 1063130 1063168 1063290 1079380 1079474 1079528 1079630 1079633 51642 1072099 51645 1072143 51643 1072323 51641 1072475 8395 1088485 8396 1088951 8398 1089168 8397 1089289 42300 1084730 42297 1084790 42301 1084799 42298 1084862 42299 1084911 60993 1116955 60994 1117025 32092 1026091 38984 1160517 175688 1109637 36450 1133938 60996 1117251 32094 1026099 38985 1160793 175689 1110020 36446 1134072 60995 1117294 32095 1026134 38987 1160914 175690 1109848 36448 1134362 32093 1026249 38986 1160918 175691 1110065 36449 1134369 32096 1026616 38988 1161388 175692 1110086 36447 1134454	.1: Family & Other 1926 2007 1943 1925 2002 1946 1941 1995 1958 1991 1878 1952 England US Poland Japan US India Canada Russia Scotland Romania 1868 1952 Scotland Mary Elizabeth Turner Salter Gaspar Roca Henryk Kierzkowski Ken-Ichi Inada Edward Wolff Reetika Khera Don Lisbon Aleksandr Dementyev Gordon Mair Vlad Negoitescu Arthur Hampson James Leslie Findlay 1856 1938 US Jamie Redfern 1957 England Lenin M. Sivam 1974 Canada Paul Meehan 1938 England Chad Connell 1983 Canada Anindita Nayar 1988 India François-Xavier Méthot 1796 1853 Canada Mukunda Ram Choudhury India William Nash (Manitoba politician) 1846 1917 Canada Jean Middlemass 1833 1919 England Samuel Johnson Jr. 1757 1836 England Lydia Mackenzie Falconer Miller 1812 1876 England William C. McClintock 1845 US John Shirreff 1759 1818 Scotland Rod Coutts Canada Robert Bruegmann . Oreste Piro 1954 Argentine Molly Worthen 1981 US Taylor Carman US Willoughby Williams 1802 US George M. Cox 1892 1977 . Richard O 27Farrell 1757 . William Murray Threipland 1866 1942 England Arthur Alphin US Jerome Wheelock 1834 1902 US Stephen White (programmer) 1969 Canada A. J. R. Russell-Wood 1940 2010 Brazil Nellie Zabel Willhite 1892 1991 US Deborah VanAmerongen US George E. Hibbard 1924 1991 US Erasmus Stourton 1603 1658 . Philip Remler US Thomas Bache (judge) 1410 Italy El-Gorashy 1942 1964 . Sig Andrusking 1913 1994 US James Neuberger 1949 . Des Adam 1945 . Claudia Dain US Angie Mentink 1973 . Achsah Guibbory US Abdulaziz Mohammed Majid Al-Farsi 1976 . Charles A. Cooke 1848 1917 US Oksana Ryabinicheva 1990 Russia Kim White US Barry Evans (rugby union) 1962 England politics lit studies studies studies studies sports sports sports sports sports arts arts lit arts sports arts arts business politics law lit lit lit lit lit education studies studies studies studies military military lit military military inventor business studies inventor politics religious religious politics religious education sports politics business lit sports military studies studies studies education studies sports sports sports sports arts military arts arts arts arts arts arts politics law politics lit education familyB lit lit studies arts studies lit education politics lit military military military inventor inventor inventor Other religious arts Other religious education nobility law lit education education lit nobility politics law sports sports business education sports Rick Nevin US politics nobility William A. Denning 1817 1856 US law politics Mary Kay (landscape photographer) Greece lit arts Majaji 350 South Africa politics nobility education Robert III de Stuteville 1186 England nobility nobility Lefteris Zagoritis 1956 Greece law politics Monica Buck US Other Other Danielle Moore 1946 US education politics law Thomas Farrer, 2nd Baron Farrer 1859 1940 . nobility Family T. Veeraswamy India politics law Valentyna Brik 1985 Ukraine sports sports Mariam Mfaki 1946 2015 Tanzania politics politics
					Table A.2: Economists (excluding the governance category) Table A.3: Individuals in Sports category Table A.4: Arts/Litt born before 1890 Table A.5: Arts/Litt born after 1890 Table A.6: Governance Table A.7: Litterature born before 1891 Table A.8: Science Table A.9: Military Table A.10: Inventors Table A.11: Religion Table A.12: Education Table A.13: Nobility Table A.14: Law Table A.15: Women, all categories

Table A

 A Table A.19: Share of the different occupations (B1) for occupation Academics A.4 Share of occupations, details Table A.38: Share of the different occupations (C1) for occupation Worker

	Occupation B1 Arts Literature/media Sports Total Table A.18: Share of the different occupations (B1) for occupation Entertainment Frequency Share 241,042 32.7 103,421 14.0 392,163 53.2 736,626 100.0 Occupation B1 Frequency Share Education 36,542 33.0 Studies 74,068 67.0 Total 110,610 Occupation C1 Frequency Share Occupation C1 Frequency Share Occupation B1 Frequency Share Family 11,813 Business 13,632 27.8 Politician 69,058 43.0 100.0 Total 11,813 Engineer 5,890 12.0 Elected 6,871 4.3 100.0 Table A.21: Share of the different occupations (B1) for occupation Family Occupation B1 Frequency Share Law 28,585 10.0 Military 40,516 14.2 Entrepreneur 3,707 7.6 Pioneer 3,108 6.3 Merchant 2,721 5.6 Farmer 2,431 5.0 Executive 2,147 4.4 Owner 2,068 4.2 Chairman 2,038 4.2 Bank 1,589 3.2 Representative 6,324 3.9 President 6,299 Occupation C1 Frequency Share 3.9 Democrat 4,924 Historian 6,541 8.8 3.1 Republican 4,730 Physician 5,500 7.4 2.9 Political 4,590 Mathematician 5,073 6.8 2.9 Minister 4,544 Scientist 4,170 5.6 2.8 Diplomat 4,426 Physicist 4,043 5.5 2.8 Conservative 2,933 Economist 3,302 4.5 1.8 Philosopher 3,005 4.1 100.0 Nobility 20,013 7.0 Politics 160,696 56.4 Table A.25: Share of the different occupations (C1) for occupation Business Chemist 2,684 3.6 Table A.34: Share of the different occupations (C1) for occupation Politics Astro 2,302 3.1
	Religious Botanist	35,104 2,231	12.3 3.0
	Total Occupation C1 Frequency Share 284,914 100.0 Occupation C1 Frequency Share
	College Church Table A.22: Share of the different occupations (B1) for occupation Governance 3,972 10.9 3,134 8.9 Professor 12,806 35.0 Scholar 4,776 13.1 Bishop 7,321 20.9 Priest 3,686 10.5 Table A.37: Share of the different occupations (C1) for occupation Studies
	Academic Clergy	3,517 2,190	9.6 6.2
	Educator Theologian	2,708 1,921	7.4 5.5
	Occupation B1 Frequency Share University 2,400 6.6 Archbishop 1,354 3.9
	Other Teacher Cardinal	23,712 2,346 1,294	100.0 6.4 3.7
	Total Education Rabbi	23,712 1,125 1,254	100.0 3.1 3.6
	Dean Missionary	956 1,239	2.6 3.5
	Principal Jesuit Table A.23: Share of the different occupations (B1) for occupation Other 761 2.1 1,119 3.2 Occupation C1 Frequency Share
	Table A.26: Share of the different occupations (C1) for occupation Education Sailor 2,137 40.5 Table A.35: Share of the different occupations (C1) for occupation Religious Engraver 842 16.0
	Occupation B1 Frequency Share Business 48,974 83.0 Inventor 4,783 8.1 Worker 5,271 8.9 Total 59,028 100.0 Occupation C1 Frequency Share Actor 27,523 11.4 Art 22,858 9.5 Actress 22,414 9.3 Singer 20,697 8.6 Film 20,439 8.5 Painter 17,484 7.3 Music 15,200 6.3 Composer 11,246 Architect 7,705 Jazz 5,251 Only child 86 0.7 Boxer 6,368 1.6 2.2 Widow 139 1.2 Athlete 6,697 1.7 3.2 Marriage 156 1.3 Racing 6,931 1.8 4.7 Occupation C1 Frequency Share Son 4,879 41.3 Child 2,920 24.7 Daughter of 1,713 14.5 Wife of 1,203 10.2 Grandson 225 1.9 Mother of 203 1.7 Married to 167 1.4 Worker 779 14.8 Occupation C1 Frequency Share Football 155,681 Potter 248 4.7 39.7 Cricket 19,037 Brewer 169 3.2 4.9 Baseball 18,180 Slave 162 3.1 4.6 Ice hockey 16,198 Fisher 120 2.3 4.1 Rugby 15,222 Jeweller 118 2.2 3.9 Basket 12,993 Metallurgist 91 1.7 3.3 Sport 7,114 1.8 Forester 79 1.5
	Table A.20: Share of the different occupations (B1) for occupation Entrepreneur Table A.24: Share of the different occupations (C1) for occupation Arts Table A.27: Share of the different occupations (C1) for occupation Family Table A.36: Share of the different occupations (C1) for occupation Sports

.16: Women in governance Figure A.8: Full Page -Ray Charles

E.g. for John Martin we get : https://en.wikipedia.org/wiki/John_Martin

We will use thereafter either the present or preterit tense to talk about present and past linkages respectively.

These verifications are available from authors upon request.

We have 842,356 individuals with geocoded places of birth, and 264,572 individuals with a geocoded place of death. Note that some of them have a birth year or a death year missing. Overall, we obtain geocoded information on both places of birth and death for 227,223 individuals. To check whether the final sample is biased toward more prominent people, we compare the distributions of visibility across samples.

More details about this revision are available in the data appendix accessible from http://bit.ly/2029bhk

Anne Le Page and the IT Department in Sciences Po. We benefited from very useful discussions with Pierre-Henri Bono and with the participants to the Seventh Conference on Cultural and Media Economics organized by the French Ministry support from the LIEPP (ANR-11-LABX-0091, ANR

this is true over the three sub-samples (800-1800AD, 1800-1939AD and post 1950). For instance, in the sample of Census data , the correlation coefficient between log visibility and city ranks from 1 to 50 is -0.08 and -0.07 if limited to the first 4 cities. When the rank of the city is calculated for all countries, as in the right part of the Figure, things are different (e.g. over the period 1800-1939AD London, New York, Paris, Beijing are ranked as first cities in the world, etc.), the pattern is different. Individuals in the first city have higher visibility, and have lower visibility in the second, fourth, and then third cities in the world. The correlation coefficient between that rank from 1 to 4 is now positive, equal to 0.17. However, the correlation between city rank and visibility of individuals is negative again in the post 1950 period where the biggest cities in the world are in located in developing countries (bottom right chart).

Conclusion

This paper is a first step into using long-run historical data from the Internet to conduct an economic analysis of the city growth. Future iterations will eliminate remaining errors in data and extend the empirical analysis beyond correlations to provide an assessement of causal links.

A.2 People by Occupations

A.5 Census data manipulations

Here we present the most significant manipulations that were needed to compile the Census database presented above. For the United Kingdom: the population of the urban agglomeration of Greater London was added to the second dataset used spanning the time period from 1921-1961. All municipal boroughs within the geographic borders of Greater London were then removed to avoid a double count in population. For the Netherlands a) Male and female populations were combined for the years 1795, 1830, 1849, 1859, 1889, 1869, 1899. b) Certain city and province names were modified to facilitate census record merging. For example, capital letters and hyphens were removed in all city names. Also, province names whose cardinal direction was not specified were modified after city locations were verified. For example, in some cases, the province of Holland was replaced by Nordholland. For Belgium: a) The population of cities within the borders of the urban agglomeration of Brussels (e.g. Uccle, Ixelles, etc.) was combined to yield one observation labeled Brussels (total). The urban area of Brussels was also retained and labeled as Brussels (urban area). Last, for Portugal we decided to consider Lisboa and Porto instead of their respective urban areas Grande Lisboa and Grande Porto respectively.