How have global population and GDP per capita changed overhuman history?
Author: Toby Newberry
1. Introduction
2. Methods & Data
3. Analysis & Discussion
3.1 Global Population
3.2 Global GDP per capita
4. Conclusions
5. References

Introduction
This report surveys existing research on global population and global economic growth overthe course of human history. These features are important from a longtermist perspective
because they may inform forecasts of the likely future trajectory of human civilisation, andhelp us understand the relative effectiveness of different levers on civilisational progress. We
can also use them to form a more accurate picture of how human civilisation has developed
so far. They help us understand how many lives transpired in any given historical period, andthe conditions in which they were lived.
My primary aim in writing this report was to identify the most accurate and extensive
population and growth datasets available. I also had occasion to address a number ofrelated questions. On the subject of global population, these questions included:●●●●

Whether history of global population growth had just two important shifts, one at theagricultural revolution and one at the industrial revolution;
Whether the adoption of agriculture increased the population growth rate;
What rates of population growth were typical for hunter-gatherers, early
agriculturalists, and populations after the industrial revolution; andHow many people in total lived before the agricultural revolution.

On the subject of global economic growth, the most important additional question I
considered was whether the history of economic growth can accurately be characterised ashaving only a single important transition, during the industrial revolution.

2. Methods & Data
I first conducted a broad search for potentially relevant sources. I looked for both datasets ofglobal population or economic growth, and commentary on the relative merits of thesedatasets. I then read through the commentary to determine which dataset experts consider
the most reliable. Finally, I summarised and wrote up the results.
For global population, I considered six datasets.1 These were Clark (1967), McEvedy &Jones (1978), Biraben (1980), the UN (1999), HYDE 3.2 (2017), and Kaneda & Haub (2021).
This section briefly describes each dataset, and makes some comments on other candidates
that I didn’t consider as closely. The next section discusses the relative merits of thesedatasets in greater detail.
Clark (1967) is a book-length discussion of global demographics and land use, which
includes a range of estimates of the world population from 14 CE through to 1975. The
approach taken is what I would describe as ‘educated guess-work’, which is broadly thesame approach taken in all six candidate datasets. Helpfully, Durand (1977) includes a tablecontaining Clark’s estimates, and is easier to access than Clark’s book.
McEvedy & Jones (1978) is another book-length treatment of global population questions. Its
focus is almost exclusively on population estimates, and the book mostly consists of
estimates of the populations of different parts of the world with graphs. The time period
covered is BC 400 to AD 2000, and the method is also educated guess-work.
The remaining four datasets are more succinct. Biraben (1980) is a short paper that includes
estimates of global population from BC 400 to AD 1970. UN (1999) is a report containing theUN’s internal estimates of global population, which cover the period from BC 1 to projections
for AD 2200. HYDE 3.2 (2017) refers to a database of global historical information, including
population estimates from BC 10,00 to AD 2015. Finally, Kaneda & Haub (2021) is a set ofglobal population estimates ranging from BC 50,000 to AD 2050, and produced by The
Population Reference Bureau, an independent research centre for demographic questions.
In addition to these six candidate datasets, there are several other sets of estimates that are
sometimes cited as authoritative, but that I didn’t look into as closely. These include Durand(1977), Livi-Bacci (1992), Kremer (1993), and Maddison (2001). I didn’t look into thesebecause they are all essentially concatenations of the six datasets previously mentioned.
Durand (1977) gives a table summarising estimates of global population, including Clark(1967) among other early attempts, but does not make novel estimates. Livi-Bacci (1992)
uses the figures from Biraben (1980) for earlier dates, and UN figures for more recent dates(Livi-Bacci, p25)). Kremer (1993) primarily uses estimates from McEvedy & Jones (1978),supplemented with UN figures from 1920 onwards, and several estimates from Deevey(1960) for populations pre-10,000 BCE (Kremer, p 683). Maddison (2001) does containsome independent estimates, but uses McEvedy & Jones (1978) to ‘fill holes’ in the data,
and draws heavily on their work for all pre-1500 estimates (Maddison (2001), p 230).
For questions of global economic growth, I considered two candidate datasets: De Long
(1998) and the Maddison Project (ongoing). As far as I can tell, these are the only attempts
to estimate global economic growth over the long run using something close to best practicetechniques. De Long (1998) estimates global economic growth from BC 1 million to AD
2000. His method involves combining long run population estimates from Kremer (1993) with
1

Our World in Data provided a useful stepping off point for this part of the inquiry.

Angus Maddison’s estimates of GDP per capita for the 19th and 20th centuries. Essentially,
De Long correlates these two sets of figures, then uses the correlation to retrodict GDP per
capita for early history. The Maddison Project is an ongoing research initiative based at the
University of Groningen, with the aim of continuing and updating Angus Maddison’s
estimates of economic performance indicators. It includes estimates of GDP per capita
beginning in AD 1, divided into geographic regions.

3. Analysis & Discussion
3.1 Global Population
There are essentially three ways to estimate the historical population: using genetic
evidence, using archaeological evidence, or making educated guesses.2 In practice,
however, the first two methods do not yield results that are sufficiently reliable, or sufficiently
comprehensive, to be useful in the context of this inquiry.
Genetic evidence can be used to estimate historical populations as follows. First, youestimate the dates of origin for a variety of mutations in the current gene pool, by looking atfactors like the geographic spread of those mutations. You then estimate the relative
contributions that different time periods made to the current gene pool. Together with some
additional assumptions, these estimates can then be converted to estimates of population.
To give an illustrative example, if you found that many present-day mutations date to aroundBC 20,000, while only a small number date to around BC 15,000, this could imply that the
global population declined during that period. However, as noted, this method is not yet
sophisticated enough to give results that are suitable for addressing the large scalequestions considered here.
The archaeological approach is comparatively straightforward, and hinges on the idea thatlarger populations will tend to leave larger archaeological footprints. Simply put, if there weremany more people living at time A rather than time B, we would expect to find a greater
density of bones, tools, pots, or other archaeological residue dating from time A.
Unfortunately, this approach faces an array of methodological challenges, including the factthat some sorts of deposits tend to disappear over time, and that the size of anarchaeological footprint is sensitive to many other factors as well as population. Some ofthese challenges are surveyed in Williams (2012). More generally, the archaeological record
can support some population estimates about specific regions at specific times, but is too
patchy to support estimates of global population over many millennia.
M estimates in the literature use educated guess-work. At its best, this approach involves
estimating global population from many regional estimates, drawing on domain experts or
published figures for the regional estimates, and making principled adjustments in light ofqualitative arguments. At its worst, this method involves fudging figures to match proposed
historical narratives, making non-robust guesses, or making highly questionable inferential

2

My discussion of these methods draws heavily on notes made on these subjects by Ben Garfinkel
and David Roodman.

leaps. As an example of the latter, McEvedy & Jones (1978) estimate the global population
before BC 10,000 based partly on the density of present-day gorilla populations (p13-14).
More commonly, a central problem with this approach is that it usually involves making small
modifications to previous estimates, which are themselves modified versions of even earlier
estimates. Because of this, many relatively recent estimates actually incorporate figures from
40-100 years ago, and very few, if any, of the sets of estimates can properly be considered
independent. As a more acute form of the problem, many authors use at least some of the
figures from McEvedy & Jones (1978) (e.g. Kremer (1993), Maddison (2001), HYDE 3.2(2017)), while McEvedy & Jones themselves lean heavily on estimates made decades
earlier (especially Russell (1948)).
A further problem with the guess-work approach is that questions of global population were
somewhat politicised throughout the 20th century. For example, it is possible that population
estimates were influenced by concerns about overpopulation or the idea that certain parts of
the world were ‘terra nullius’ before European conquest.
In light of these problems, one conclusion of this report is that the quality of all long-run
estimates of the global population is surprisingly poor. While there are some topics about
which we can make robust claims over long timescales, such as claims about Earth’s climatebased on evidence from ice-cores, global population is not yet among these topics. Thisshould serve as a caveat that the specific claims made below come with a high degree of
uncertainty. I should also note that many of the authors mentioned so far are fully aware of
the speculative nature of their work. McEvedy & Jones even joke that on rare occasions they
just “pulled figures out of the sky,” (McEvedy & Jones, 1978, p10).
Of the six candidate datasets given in the previous section, I think HYDE 3.2 (2017) provides
the most accurate estimates of global population over the period 5,000 BCE to the presentday. The reasons for this verdict are as follows.
Both Clark (1967) and Biraben (1980) suffer from more severe qualitative problems than dothe other datasets. Clark (1967) has been the subject of several reasonable critiques. For
instance, Durand (1977) grades all of Clark’s population estimates on an A to D scale, andassigns most of them D grades, meaning they are based on assumed rates of population
growth, rather than any specific data relating to the time period or region in question. With
the exception of estimates for the population of China, none of Clark’s estimates from before
1750 receives a grade higher than C (see Durand (1977), p255 for an explanation of his
grading system). Maddison (2001) also compares Clark and Biraben’s estimatesunfavourably to McEvedy & Jones (1978), noting that they are less detailed andwell-documented, and involve judgements with which he disagrees. Biraben (1980), for hispart, is relatively clear in stating that his estimates should not be considered properly
authoritative:“We had neither the means nor the time to go over all the existing documentation inorder to obtain new complementary data. We therefore limited ourselves to collating
the published figures for each country; then, taking into consideration what is known
about the economic, political, and medical history of each country, we drew a curvethrough these figures and extended it into the past.” (Biraben, 1980, p655-656).

Both Kaneda & Haub (2021) and the UN (1999) suffer from issues of transparency; inneither case was I able adequately to account for the sources of the relevant figures. The
UN (1999) includes a table of population estimates going back to AD 1, and cites several
other pieces of work by the UN. However, in consulting these other sources I was unable tofind population estimates going back further than AD 1950, and the origin of the earlier
figures remains mysterious. Kaneda & Haub (2021) offer some insight into their
methodology, which was to produce population estimates for certain benchmark years, andthen combine these with birth rate estimates to generate a continuous series. However, as
the source for their historical population estimates they cite Dudley Poston Jr, ademographer from Texas A&M University, and do not offer details on how Poston Jr. came tohis estimates.3
McEvedy & Jones (1978) and HYDE 3.2 (2017) are very similar datasets. In fact, the HYDE
dataset is essentially identical to McEvedy & Jones, except that it incorporates more robust
data from the UN for the mid 20th century onwards, and supplements some earlier figureswith independent subnational estimates. This last comment is slightly opaque, in that I was
unable to work out exactly which estimates it refers to. That said, my view is that on balance
the HYDE data is slightly preferable to McEvedy & Jones, if only because it includes morereliable recent figures. Both HYDE and McEvedy & Jones are preferable to the other fourcandidate datasets, for the reasons given above.
One question that can be asked about global population concerns transitions between
different growth modes. Specifically, it is often suggested that the history of global population
growth contains two important shifts, one occurring at the dawn of agriculture, and one at theindustrial revolution.
The HYDE 3.2 data broadly supports this claim, but there are a number of clarifications
worth making. First, the claim that the history of global population contains two transition
points, or two shifts between different growth modes, could be interpreted as implying apiecewise structure to the data. This is not the interpretation I am taking. Instead, I
understand it, the claim is that the history of global population looks more or less like it has
three parts, with transitions between those parts occurring around the time of the dawn ofagriculture, and around the time of the industrial revolution. This is what the HYDE 3.2 datashows. In particular, it shows that global population was approximately level before aroundBC 10,000, rising quite slowly between BC 10,000 and around AD 1700, and rising sharply
from that point on. Note, however, that this does not necessarily imply that ‘something
special’ happened during the transition times.
The first transition, occurring at the dawn of agriculture, is also well-supported by evidence
from other sources. In archaeology, for example, this period is known as the ‘neolithic
demographic transition’, and there is evidence for a sharp increase in birth rate4 following theadvent of agriculture (see, e.g. Bocquet-Appel & Bar-Yosef, (2008), Bocquet-Appel (2002),
3

They cite the UN and previous work by the Population Reference Bureau for more recent figures(from about 1950 onwards).
4
Note that an increase in birth rate does not necessarily imply an increase in population, since it could
be offset by an increase in mortality. I take it that evidence for the former without additional evidence
for the latter should still be considered evidence in favour of a population increase.

Bocquet-Appel & Paz de Miguel Ibanez (2002)). There is also direct genetic evidence for a
sharp increase in population at this time. For example, Gignoux, Henn, & Mountain (2011)
find that “the invention of agriculture facilitated a fivefold increase in population growth
relative to more ancient expansions of hunter-gatherers.”
The second transition, occurring at the industrial revolution, is less well-supported byadditional evidence, but still appears plausible. The second graph shown in Section 4, whichuses the HYDE 3.2 data, displays a sharp uptick in global population around AD 1750. In
addition, at least one prominent economist has publicly endorsed the view that there was an“unprecedented increase in population growth during the early stages of industrialisation,”(Galor, (2005)).
There are several other questions about global population that were mentioned in the
introduction, and which can be answered using the HYDE data together with otherresources. To begin with, the HYDE data indicates that the population growth rate washigher after the advent of agriculture than in the hunter-gatherer era. More specifically, thedata shows essentially no sustained growth in population for hunter-gatherers, with typicalannual growth of around 0.1% for agriculturalists.5 One caveat to this is that pre-agricultural
populations almost certainly rose and fell; that is, the lack of growth should not be taken tomean that the pre-agricultural population was constant. After the industrial revolution, thepopulation growth rate rose again to between 0 and 2% per year, with a typical rate ofaround 1%.
HYDE 3.2 also includes estimates of typical population densities for agriculturalists andpeople living after the industrial revolution, which are a helpful complement to the estimatesof growth rate. These figures are summarised in the table below, though it should be notedthat the density estimates for hunter-gatherers come from McEvedy & Jones (1978), andshould be considered highly uncertain.

% growth (annual)

Population density(people/km^-2)

Hunter-gatherers

Typical: 0%
Typical: 0.1
Range: not enough reliable Range: 0.0 to 3.5
data

Agriculturalists

Typical: 0.1%
Range: -0.06 to 0.5%

Typical: 0.6
Range: 0.03 to 6.0

Post-industrial revolution

Typical: 1%
Range: 0 to 2.2%

Typical: 20
Range: 6 to 56

We might also wonder about the total number of people who have ever lived. While the
HYDE 3.2 data offers estimates of the total number who have lived since around 10,000
BCE, it is silent on the question of how many people lived before the agricultural revolution.
5

Here I am interpreting ‘agriculturalists’ quite broadly, as anyone living between 10,000 BCE and1750 CE. This is so that the three categories are jointly-exhaustive: ‘hunter-gatherers’ covers theperiod before 10,000 BCE, and ‘post-industrial revolution’ covers the period after 1750 CE.

Fortunately, this question is addressed by Kaneda & Haub (2021), who estimate that the
total number of people who lived between BC 190,000 and BC 10,000 was between 8 and 9
billion. Note again that this figure is a personal estimate made by a sociology professor, andI am unsure of the precise methodology at work. Note also that the cutoff date of 190,000
BCE is somewhat arbitrary, since there is not a well-defined point at which pre-humanancestors became fully-fledged Homo sapiens.

3.2 Global GDP per capita
Of the two candidate datasets for global GDP per capita, my view is that the ex-Nordhaus
series included in De Long (1998) is the most accurate. The reasons for this are as follows.
The Maddison Project data has several positive features. In particular, their estimates areabout 25 years more recent than De Long’s, and appear to represent considerably more
person-hours of research; they have been updated relatively regularly since 1995. However,there are two features of the dataset that together make it unsuitable for our purposes. First,
the Maddison Project estimates do not extend any earlier than AD 1. This would not beespecially problematic on its own, since we could simply use De Long’s data for earlier
years. Unfortunately, the Maddison Project data is also separated into geographic
subregions, and there is no clear and robust way to convert its figures into an estimate ofworld GDP. One might think these subregions could simply be summed up, but the result of
doing so has some unusual features6 that imply the geographic regions are notcomprehensive (that is, there might be ‘interstitial regions’ that do not have estimates
included in the original dataset, and which change over time).
De Long (1998) in fact consists of three separate datasets. One of these is based on
Maddison’s modelling assumptions7, one on De Long’s own model, and one on De Long’s
model with an additional important assumption (specified below). Notably, Maddison’s model
assumes that “pre-1500 GDP per capita was constant at near-Malthusian bio-cultural
subsistence.” De Long describes his own model as a “fitted relationship between population
growth and Maddison-concept GDP per capita.” As noted in Section 2, he uses Maddison’s
GDP estimates from 1820 onwards to establish a correlation between population and GDP
per capita, and then extends this backwards in time using population estimates from Kremer(1993). The additional assumption included in the third dataset is that GDP per capita should
be multiplied by a factor of four for the period from 1800 onwards. This is to account for thefact that the range of goods and services on which one can spend money has increased
over time - though De Long admits the precise implementation is somewhat arbitrary.
In my view, the De Long model without the additional assumption, which he calls ‘theex-Nordhaus series’, is the most accurate of the three. The Maddison series can be
excluded on the grounds that it assumes a particular answer to most of the question without
6

For example, it shows a sharp uptick around 1250 CE, followed by a slow downward trend until
around 1700. As noted, these appear to be an artefact of the method, rather than genuine features of
the trend in GDP per capita over time.
7
These are as follows: “...assume that GDP per capita was constant in Asia and Africa from1500-1820, grew at 0.1 percent per year from 1500-1820 in Latin America and Eastern Europe, andgrew at 0.2 percent per year from 1500-1820 in Western Europe… [and] that pre-1500 GDP per
capita was constant at near-Malthusian bio-cultural subsistence.”

argument. I have two reasons for preferring De Long’s unmodified series to the series withthe additional assumption. First, as stated, the four-fold multiplication is openly arbitrary. I
therefore think it is more forthright to present the data without the extra assumption, andsimply note that a possible limitation of the method is that it ignores ‘Nordhaus effects’.
Second, I do not find the explanation for these effects entirely convincing. De Long writes
that he “would be extremely unhappy if [he] were handed [his] current income, told that [he]could spend it on goods at current prices, but that [he] was prohibited from buying anythingthat was not made before 1800.” This strikes me as misleading, in that the relevant
comparison should also include De Long having had his memories replaced with someone
who grew up around 1800 (say). More generally, I think the intuition that the present
cornucopia of goods and services effectively translates to greater GDP per capita assumes alow degree of hedonic adaptation. It is not obvious to me that today’s relative abundance
actually means people live at a higher quality of life.
As with global population, one interesting question that can be asked of the data on global
economic growth concerns transition points. In particular, we might wonder whether thehistory of global economic growth contains only a single transition point, occurring at theindustrial revolution. On the basis of both the De Long data and the Maddison Project
estimates, I think this claim is justified. Graphs of GDP per capita over time tend to be mostly
flat until around 1800, when they have a sharp uptick. This is true of the global data, as well
as most country-specific data (see the Maddison Project (ongoing) and Broadberry (2013)).
Importantly, it contrasts with observed trends in global population. In looking at the graphs ofglobal population in the next section, and extending the first graph as a flat line to the left, it
is clear that ‘something changed around BC 10,000, and perhaps something changed again
around 1800’. In looking at the graph of global economic growth, on the other hand, there isno equivalent transition around BC 10,000.
Another question one might have about global economic growth concerns what sort of model
best fits with the estimated data-points. For example, it could be that the estimated andknown data best fits with an exponential model, a hyperbolic model, a series of exponentials,
or something else entirely. I will not attempt a comprehensive answer to this question here,
but instead note that there is a reasonable and ongoing debate about it, and sketch some ofthe positions in that debate. In brief, Oded Galor’s Unified Growth Theory holds that thehistory of economic growth consists of two phases, the first involving a long period ofMalthusian stagnation, followed by a period of sustained economic growth beginning at theindustrial revolution (see, e.g. Galor (2005)). Robin Hanson has argued for a view in which
the growth rate increased during the agricultural and industrial revolutions, but was on
average neither increasing nor decreasing for the intervening millennia. He also presents amodel of long-term growth as a series of three exponentials, corresponding to the
pre-agricultural, agricultural, and industrial eras (Hanson, (2000)). Kremer (1993) makes theclaim that the economic growth rate has risen hyperbolically, or ‘in proportion to the size ofthe economy’.8 More recently, Ben Garfinkel conducted a useful investigation of Kremer’scase (Garfinkel, (2020)). David Roodman has also done technical work on similar questions,
incorporating stochasticity into a base-hyperbolic model (Roodman, 2020).

8

Note that these theories refer to growth, or total output, rather than GDP per capita.

4. Conclusions
Based on the reasoning given above, my preferred graphs of global population use the
HYDE 3.2 dataset, and look like this:The first graph covers the pre-industrial period, and exhibits the neolithic transition describedin Section 3.1. If we imagine extending the graph as a horizontal line leftwards,9 then it looks
as if the graph begins its gradual upward trajectory shortly after 10,000 BC. Together, thetwo graphs also capture the idea that our information about global population is highly
uncertain until about 1900, from which year we have more reliable census data.10
The second graph slightly obscures the transition at the industrial revolution, since it uses a
different x-axis. However, this second transition does show up as a sharp uptick on the far
right of the graph when the full dataset is displayed, as below.

Using the ex-Nordhaus series from De Long (1998), I think the most accurate graph of world
GDP per capita over time looks like this:9

Here I assume there were no extremely large populations of early humans of which we are unaware.
Here is an explanation of the uncertainty ranges: “These uncertainty ranges were partly based on
the ranges we could find in literature and partly on our own expert judgement and should be treatedwith care. The uncertainty range A is cautiously estimated at 1 % in 2000 CE, 5 % in 1900 CE, 10 %in 1800 CE, 25 % in 1700 CE, 50 % in 850 CE, 75 % in 1 CE and 95 % in 10 000 BCE.,” (Goldewijk etal, (2017), p943). Uncertainty range B is simply twice range A.
10Each of these minor gridlines on the horizontal axis indicates 500 years, so that the sharp
rise on the right hand side of the graph occurs at approximately 1750 CE.
Along with these graphs and their associated datasets, the foregoing discussion reached anumber of other conclusions. In particular:(1) the quality of all long-run estimates of global population is surprisingly poor;(2) it is fair to characterise the history of world population growth as containing two
important transitions, one occurring at the dawn of agriculture and one at theindustrial revolution;(3) It is fair to characterise the history of growth in GDP per capita as containing one
important transition, occurring at the industrial revolution; and(4) between 8 and 9 billion people lived before the agricultural revolution.

5. References
Biraben (1980): Biraben, J. N., ‘An Essay Concerning Mankind’s Demographic Evolution’,
Journal of Human Evolution, Vol. 9, No. 8, 1980.
Bocquet-Appel (2002): Bocquet-Appel, J-P., ‘Paleoanthropological Traces of a Neolithic
Demographic Transition’, Current Anthropology, Vol. 43, No. 4, 2002.
Bocquet-Appel & Bar-Yosef (2008): Bocquet-Appel & Bar-Yosef, The Neolithic Demographic
Transition and its Consequences, Springer Science+Business Media, 2008.
Bocquet-Appel & de Miguel Ibáñez (2002): Bocquet-Appel, J-P., and de Miguel Ibáñez, M P.,‘Demografía de la difusión neolítica en Europa y lo datos paleoantropológicos’, Saguntum,Vol. 5, 2002.
Clark (1967): Clark, C., Population Growth and Land Use, The Macmillan Press Limited,
London and Basingstoke, 1967.
Deevey (1960): Deevey, E. S., ‘The Human Population,’ Scientific American, CCIII, 1960.

De Long (1998): De Long, J. B., ‘Estimates of World GDP, One Million B.C. – Present’,
Department of Economics, UC Berkeley, 1998.
Durand (1977): Durand, J., ‘Historical Estimates of World Population: An Evaluation’,
Population and Development Review, Vol. 3, No. 3, 1977.
Galor (2005): Galor, O., ‘From stagnation to growth: Unified Growth Theory’, in P. Aghion &S. Durlauf (Eds.), Handbook of Economic Growth, Amsterdam: Elsevier, 2005.
Garfinkel (2020): Garfinkel, B., ‘Does Economic History Point Toward a Singularity?’,
Effective Altruism Forum, 2020.
Gignoux, Henn, & Mountain (2011): Gignoux, Henn, & Mountain, ‘Rapid, global demographic
expansions after the origins of agriculture’, Proceedings of the National Academy of
Sciences, Vol. 108, No. 15, 2011.
HYDE (2017): Goldewijk et al, ‘Anthropogenic land use estimates for the Holocene – HYDE
3.2’, Earth System Science Data, No. 9, 2017.
Kaneda & Haub (2021): Kaneda & Haub, ‘How Many People Have Ever Lived on Earth?’,Population Reference Bureau, 2021.
Kremer (1993): Kremer, M., ‘Population Growth and Technological Change: One Million B.C.
to 1990’, Quarterly Journal of Economics, August 1993.
Livi-Bacci (1992): Livi-Bacci, M., A Concise History of World Population, Blackwell
Publishers Ltd, 1992.
Maddison (2001): Maddison, A., The World Economy: A Millennial Perspective, Paris:
OECD, 2001.
The Maddison Project (ongoing): Maddison Project Database 2020, University of Groningen,accessed athttps://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2020.
McEvedy & Jones (1978): McEvedy & Jones, Atlas of World Population History, Facts on
File, New York, 1978.
Our World in Data: Roser, M., Ritchie, H., and Ortiz-Ospina, E., ‘World Population Growth’,
accessed at https://ourworldindata.org/world-population-growth.
Roodman (2020): Roodman, D., ‘On the probability distribution of long-term changes in thegrowth rate of the global economy: An outside view,’ Open Philanthropy, 2020.
Russell (1948): Russell, J., British Medieval Population, The University of New Mexico
Press, 1948.

The UN (1999): The UN, ‘The World at Six Billion’, Population Division, Department of
Economics and Social Affairs, 1999.
Williams (2012): Williams, A., ‘The use of summed radiocarbon probability distributions inarchaeology: a review of methods,’ Journal of Archaeological Science, Vol. 39, Issue 3,2012.