Augmenting the availability of historical GDP per capita estimates through machine learning
Edited by Marshall Burke, Stanford University, Stanford, CA; received January 31, 2024; accepted August 9, 2024 by Editorial Board Member Ronald D. Lee
Significance
The scarcity of historical GDP per capita data limits our ability to explore questions of long-term economic development. Here, we introduce a machine learning method using detailed data on famous biographies to estimate the historical GDP per capita of hundreds of regions in Europe and North America. Our model generates accurate out-of-sample estimates (R2 = 90%) that quadruple the availability of historical GDP per capita data and correlate positively with proxies of economic output such as urbanization, body height, well-being, and church building activity. We use these estimates to reproduce the reversal of fortunes experienced by southern and northern Europe and the historical role played by Atlantic ports. These findings show that machine learning can effectively augment the historical availability of economic data.
Abstract
Can we use data on the biographies of historical figures to estimate the GDP per capita of countries and regions? Here, we introduce a machine learning method to estimate the GDP per capita of dozens of countries and hundreds of regions in Europe and North America for the past seven centuries starting from data on the places of birth, death, and occupations of hundreds of thousands of historical figures. We build an elastic net regression model to perform feature selection and generate out-of-sample estimates that explain 90% of the variance in known historical income levels. We use this model to generate GDP per capita estimates for countries, regions, and time periods for which these data are not available and externally validate our estimates by comparing them with four proxies of economic output: urbanization rates in the past 500 y, body height in the 18th century, well-being in 1850, and church building activity in the 14th and 15th century. Additionally, we show our estimates reproduce the well-known reversal of fortune between southwestern and northwestern Europe between 1300 and 1800 and find this is largely driven by countries and regions engaged in Atlantic trade. These findings validate the use of fine-grained biographical data as a method to augment historical GDP per capita estimates. We publish our estimates with CI together with all collected source data in a comprehensive dataset.
Data, Materials, and Software Availability
We publish our out-of-sample estimates together with the collected source data on countries (27, 28) and regions (29, 58–65) in a comprehensive dataset comprising 5,700 observations (1,336 source data observations, and 4,364 out-of-sample estimates). For the out-of-sample estimates, we provide 90 percent CI. Also, we publish the code to ensure reproducibility of our results. Data and code are available at https://github.com/philmkoch/historicalGDPpc.
Acknowledgments
We acknowledge the support of the Agence Nationale de la Recherche grant number ANR-19-P3IA-0004, the European Union and the European Research Executive Agency under the Horizon EU project LearnData 101086712, Institute for Advanced Study in Toulouse funding from the French National Research Agency (ANR) under grant ANR-17-EURE-0010 (Investissements d’Avenir program), and the European Lighthouse of AI for Sustainability [grant number 101120237-HORIZON-CL4-2022-HUMAN-02].
Author contributions
P.K., V.S., and C.A.H. designed research; P.K. performed research; P.K. analyzed data; C.A.H. supervised the project; and P.K., V.S., and C.A.H. wrote the paper.
Competing interests
The authors declare no competing interest.
Supporting Information
Appendix 01 (PDF)
- Download
- 7.42 MB
References
1
S. Athey, “The impact of machine learning on economics” in The Economics of Artificial Intelligence: An Agenda, National Bureau of Economic Research Conference Report, A. Agrawal, J. Gans, A. Goldfarb, Eds. (The University of Chicago Press, National Bureau of Economic Research, Chicago, IL, 2019), pp. 507–547.
2
S. Athey, G. W. Imbens, Machine learning methods that economists should know about. Annu. Rev. Econ. 11, 685–725 (2019).
3
N. Jean et al., Combining satellite imagery and machine learning to predict poverty. Science 353, 790–794 (2016).
4
D. Ahn et al., A human-machine collaborative approach measures economic development using satellite imagery. Nat. Commun. 14, 6811 (2023).
5
G. Chi, H. Fang, S. Chatterjee, J. E. Blumenstock, Microestimates of wealth for all low- and middle-income countries. Proc. Natl. Acad. Sci. U.S.A. 119, e2113658119 (2022).
6
J. V. Henderson, A. Storeygard, D. N. Weil, Measuring economic growth from outer space. Am. Econ. Rev. 102, 994–1028 (2012).
7
C. Robinson, F. Hohman, B. Dilkina, “A deep learning approach for population estimation from satellite imagery” in Proceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities (Association for Computing Machinery, New York, NY, 2017), pp. 47–54.
8
F. R. Stevens, A. E. Gaughan, C. Linard, A. J. Tatem, Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PLoS One 10, e0107042 (2015).
9
M. C. Hansen et al., High-resolution global maps of 21st-Century forest cover change. Science 342, 850–853 (2013).
10
E. Rolf et al., A generalizable and accessible approach to machine learning with global satellite imagery. Nat. Commun. 12, 4392 (2021).
11
M. Burke, A. Driscoll, D. B. Lobell, S. Ermon, Using satellite imagery to understand and promote sustainable development. Science 371, eabe8628 (2021).
12
L. Yu et al., Meta-discoveries from a synthesis of satellite-based land-cover mapping research. Int. J. Remote Sens. 35, 4573–4588 (2014).
13
C. A. Hidalgo, B. Klinger, A.-L. Barabási, R. Hausmann, The product space conditions the development of nations. Science 317, 482–487 (2007).
14
F. L. Pinheiro, D. Hartmann, R. Boschma, C. A. Hidalgo, The time and frequency of unrelated diversification. Res. Policy 51, 104323 (2021), https://doi.org/10.1016/j.respol.2021.104323.
15
S. Poncet, F. S. de Waldemar, Product relatedness and firm exports in China. World Bank Econ. Rev. 29, 579–605 (2015).
16
C. A. Hidalgo, Economic complexity theory and applications. Nat. Rev. Phys. 3, 92–113 (2021).
17
J. L. van Zanden, B. van Leeuwen, Persistent but not consistent: The growth of national income in Holland 1347–1807. Explor. Econ. Hist. 49, 119–130 (2012).
18
S. N. Broadberry, B. M. S. Campbell, A. Klein, M. Overton, B. van Leeuwen, British Economic Growth, 1270–1870 (Cambridge University Press, 2015).
19
N. Palma, J. Reis, From convergence to divergence: Portuguese economic growth, 1527–1850. J. Econ. Hist. 79, 477–506 (2019).
20
P. Malanima, The long decline of a leading economy: GDP in central and northern Italy, 1300–1913. Eur. Rev. Econ. Hist. 15, 169–219 (2011).
21
U. Pfister, Economic growth in Germany, 1500–1850. J. Econ. Hist. 82, 1071–1107 (2022).
22
M. Malinowski, J. L. van Zanden, Income and its distribution in preindustrial Poland. Cliometrica 11, 375–404 (2017).
23
C. Álvarez-Nogal, L. P. De La Escosura, The rise and fall of Spain (1270–1850). Econ. Hist. Rev. 66, 1–37 (2013).
24
O. Krantz, Swedish GDP 1300–1560: A tentative estimate (Lund Papers in Economic History, General Issues 152, 2017).
25
L. Ridolfi, Six centuries of real wages in france from Louis IX to Napoleon III: 1250–1860. J. Econ. Hist. 79, 589–627 (2019).
26
L. Schön, O. Krantz, New Swedish historical national accounts since the 16th century in constant and current prices (Lund Papers in Economic History, General Issues 140, 2015).
27
J. Bolt, J. L. van Zanden, Maddison style estimates of the evolution of the world economy. A new 2020 update (Maddison-Project Working Paper WP-15, 2020).
28
J. Bolt, J. L. van Zanden, The Maddison Project: Collaborative research on historical national accounts: The Maddison Project. Econ. Hist. Rev. 67, 627–651 (2014).
29
C. Alvarez-Nogal, L. P. De La Escosura, The decline of Spain (1500–1850): Conjectural estimates. Eur. Rev. Econ. Hist. 11, 319–366 (2007).
30
K. Enflo, A. Missiaia, Regional GDP estimates for Sweden, 1571–1850. Hist. Methods J. Quant. Interdiscip. Hist. 51, 115–137 (2018).
31
M. Laouenan et al., A cross-verified database of notable people, 3500BC-2018AD. Sci. Data 9, 290 (2022).
32
A. Z. Yu, S. Ronen, K. Hu, T. Lu, C. A. Hidalgo, Pantheon 1.0, a manually verified dataset of globally famous biographies. Sci. Data 3, 150075 (2016).
33
J. Mokyr, “Long-term economic growth and the history of technology” in Handbook of Economic Growth, (Elsevier, 2005), pp. 1113–1180.
34
U. Akcigit, J. Grigsby, T. Nicholas, The rise of Amercian ingenuity: Innovation and inventors of the Golden Age (NBER Working Paper 23047, 2017).
35
C. MacLaurin, P. Murdoch, An Account of Sir Isaac Newton’s Philosophical Discoveries: In Four Book (Printed for A. Millar, ed. 2, 1750).
36
M. Schich et al., A network framework of cultural history. Science 345, 558–562 (2014).
37
J. Mokyr, Mobility, Creativity, and Technological Development: David Hume, Immanuel Kant and the Economic Development of Europe. [Preprint] (2005). Available at: https://faculty.wcas.northwestern.edu/jmokyr/Berlin.PDF. (Accessed 7 January 2023).
38
J. O’Hagan, K. J. Borowiecki, Birth location, migration, and clustering of important composers: Historical patterns. Hist. Methods J. Quant. Interdiscip. Hist. 43, 81–90 (2010).
39
P. Koch, V. Stojkoski, C. A. Hidalgo, The role of immigrants, emigrants, and locals in the historical formation of European knowledge agglomerations. Reg. Stud. 58, 1659–1673 (2023), https://doi.org/10.1080/00343404.2023.2275571.
40
C. M. Cipolla, The diffusion of innovations in early modern Europe. Comp. Stud. Soc. Hist. 14, 46–52 (1972).
41
E. Miguelez, A. Morrison, Migrant inventors as agents of technological change. J. Technol. Transf. 48, 669–692 (2022).
42
D. Bahar, P. Choudhury, H. Rapoport, Migrant inventors and the technological advantage of nations. Res. Policy 49, 103947 (2020).
43
S. Bernstein, R. Diamond, A. Jiranaphawiboon, T. McQuade, B. Pousada, The contribution of high-skilled immigrants to innovation in the United States (NBER Working Paper Series w30797, 2022), https://doi.org/10.3386/w30797.
44
E. Hornung, Immigration and the diffusion of technology: The huguenot Diaspora in Prussia. Am. Econ. Rev. 104, 84–122 (2014).
45
I. Ganguli, Immigration and ideas: What did Russian scientists “Bring” to the United States? J. Labor Econ. 33, S257–S288 (2015).
46
D. Diodato, A. Morrison, S. Petralia, Migration and invention in the age of mass migration. J. Econ. Geogr. 22, 477–498 (2022).
47
K. J. Borowiecki, K. Graddy, Immigrant artists: Enrichment or displacement? J. Econ. Behav. Organ. 191, 785–797 (2021).
48
K. J. Borowiecki, Are composers different? Historical evidence on conflict-induced migration (1816–1997). Eur. Rev. Econ. Hist. 16, 270–291 (2012).
49
D. Acemoglu, S. Johnson, J. Robinson, The rise of Europe: Atlantic trade, institutional change, and economic growth. Am. Econ. Rev. 95, 546–579 (2005).
50
A. M. de Pleijt, J. L. van Zanden, Accounting for the “Little Divergence”: What drove economic growth in pre-industrial Europe, 1300–1800? Eur. Rev. Econ. Hist. 20, 387–409 (2016).
51
A. Henriques, N. Palma, Comparative European institutions and the little divergence, 1385–1800. J. Econ. Growth 28, 259–294 (2022).
52
M. Fochesato, Origins of Europe’s north-south divide: Population changes, real wages and the ‘little divergence’ in early modern Europe. Explor. Econ. Hist. 70, 91–131 (2018).
53
R. C. Allen, The great divergence in European wages and prices from the middle ages to the first world war. Explor. Econ. Hist. 38, 411–447 (2001).
54
E. Buringh, The population of European cities from 700 to 2000: Social and economic history. Res. Data J. Humanit. Soc. Sci. 6, 1–18 (2021).
55
J. Baten, M. Blum, Why are you tall while others are short? Agricultural production and other proximate determinants of global heights. Eur. Rev. Econ. Hist. 18, 144–165 (2014).
56
A. Rijpma, “A composite view of well-being since 1820” in How Was Life? J. L. Van Zanden, J. Baten, M. Mira d’Ercole, A. Rijpma, M. P. Timmer, Eds. (OECD, 2014), pp. 249–269.
57
E. Buringh, B. M. S. Campbell, A. Rijpma, J. L. Van Zanden, Church building and the economy during Europe’s ‘Age of the Cathedrals’, 700–1500 CE. Explor. Econ. Hist. 76, 101316 (2020).
58
K. Enflo, M. Henning, L. Schön, “Swedish regional GDP 1855–2000: Estimations and general trends in the Swedish regional system” in Research in Economic History, (Emerald Group Publishing, 2014), pp. 47–89.
59
N. Delefortrie, J. Morice, Les revenus départementaux en 1864 et en 1954. Population 15, 721 (1960).
60
M. P. Squicciarini, N. Voigtländer, Human capital and industrialization: Evidence from the age of enlightenment. Q. J. Econ. 130, 1825–1883 (2015).
61
F. Geary, T. Stark, Regional GDP in the UK, 1861–1911: New estimates: Regional GDP. Econ. Hist. Rev. 68, 123–144 (2015).
62
Office for National Statistics, Historical regional GDP 1968 to 1970 and 1971 to 1996. Office for National Statistics. https://www.ons.gov.uk/economy/grossvalueaddedgva/adhocs/005458historicalregionalgdp1968to1970and1971to1996. Deposited 10 March 2016.
63
E. Felice, The roots of a dual equilibrium: GDP, productivity, and structural change in the Italian regions in the long run (1871–2011). Eur. Rev. Econ. Hist. 23, 499–528 (2018).
64
M. Badia-Miró, J. Guilera, P. Lains, Reconstruction of the Regional GDP of Portugal, 1890–1980 (UB Economic Working Paper 12/280, 2012).
65
E. Buyst, Reversal of Fortune in a small, open economy: Regional GDP in Belgium, 1896–2000. SSRN Electron. J. (2009), https://doi.org/10.2139/ssrn.1586762.
66
Eurostat, Gross domestic product (GDP) at current market prices by NUTS 2 regions. Eurostat. https://doi.org/10.2908/NAMA_10R_2GDP. Deposited 21 February 2023.
67
Office for National Statistics, Regional gross domestic product: All ITL regions. Office for National Statistics. https://www.ons.gov.uk/economy/grossdomesticproductgdp/datasets/regionalgrossdomesticproductallnutslevelregions. Deposited 30 May 2022.
68
Bureau of Economic Analysis, Gross domestic product by metropolitan area. Bureau of Economic Analysis. https://www.bea.gov/news/2018/gross-domestic-product-metropolitan-area-2017. Deposited 18 September 2018.
69
Statistics Canada, Metropolitan gross domestic product. Statistics Canada. https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3610042301. Deposited 10 November 2014.
70
State Statistics Services Ukraine, Baлoвий peгioнaльний пpoдyкт. Derzhstat. https://www.ukrstat.gov.ua/operativ/operativ2008/vvp/vrp/vrp2008_u.htm. Deposited 17 December 2013.
71
Belstat, Gross regional product at current prices. National Statistical Committee of the Republic of Belarus. http://dataportal.belstat.gov.by/osids/indicator-info/10202100055. Deposited 1 February 2023.
72
Rosstat, Gross regional product at current basic prices per capita (1998–2019). Rosstat Federal State Statistics Service. https://eng.rosstat.gov.ru/storage/mediabank/jp03tDOh/dusha98-19e%20rev.xlsx. Deposited 26 February 2021.
73
L. Prados De La Escosura, International comparisons of real product, 1820–1990: An alternative data set. Explor. Econ. Hist. 37, 1–41 (2000).
74
M. J. Klasing, P. Milionis, Quantifying the evolution of world trade, 1870–1949. J. Int. Econ. 92, 185–197 (2014).
75
K. Pomeranz, The Great Divergence: China, Europe, and the Making of the Modern World Economy, First Princeton Classics (Princeton University Press, ed. Paperback, 2021).
76
R. Inklaar, H. de Jong, J. Bolt, J. L. Van Zanden, Rebasing “Maddison”: New income comparisons and the shape of long-run economic development (Groningen Growth and Development Centre Research Memorandum GD-174, 2018).
77
C. Hube, “Bias in Wikipedia” in Proceedings of the 26th International Conference on World Wide Web Companion—WWW ‘17 Companion, International World Wide Web Conferences Steering Committee, Eds. (ACM Press, Perth, Australia, 2017), pp. 717–721.
78
M. Dittus, M. Graham, Mapping Wikipedia’s geolinguistic contours. Digit. Cult. Soc. 5, 147–164 (2019).
79
U. Pfeil, P. Zaphiris, C. S. Ang, Cultural differences in collaborative authoring of Wikipedia. J. Comput. Mediat. Commun. 12, 88–113 (2006).
80
E. S. Callahan, S. C. Herring, Cultural bias in Wikipedia content on famous persons. J. Am. Soc. Inf. Sci. Technol. 62, 1899–1915 (2011).
81
A. Halavais, D. Lackaff, An analysis of topical coverage of Wikipedia. J. Comput. Mediat. Commun. 13, 429–440 (2008).
82
T. Chesney, An empirical examination of Wikipedia’s credibility. First Monday (2006), https://doi.org/10.5210/fm.v11i11.1413.
83
J. Giles, Internet encyclopaedias go head to head. Nature 438, 900–901 (2005).
84
M. Serafinelli, G. Tabellini, Creativity over time and space: A historical analysis of European cities. J. Econ. Growth 27, 1–43 (2022).
85
D. De La Croix, O. Licandro, The longevity of famous people from Hammurabi to Einstein. J. Econ. Growth 20, 263–303 (2015).
86
C. A. Hidalgo, R. Hausmann, The building blocks of economic complexity. Proc. Natl. Acad. Sci. U.S.A. 106, 10570–10575 (2009).
87
P.-A. Balland et al., The new paradigm of economic complexity. Res. Policy 51, 104450 (2022).
88
V. Stojkoski, Z. Utkovski, L. Kocarev, The impact of services on economic complexity: Service sophistication as route for economic growth. PLoS One 11, e0161633 (2016).
89
P. Koch, Economic complexity and growth: Can value-added exports better explain the link? Econ. Lett. 198, 109682 (2021).
90
G. Domini, Patterns of specialization and economic complexity through the lens of universal exhibitions, 1855–1900. Explor. Econ. Hist. 83, 101421 (2022), https://doi.org/10.1016/j.eeh.2021.101421.
91
R. Hausmann et al., The Atlas of Economic Complexity: Mapping Paths to Prosperity (Center for International Development, Harvard University and Harvard Kennedy School and Macro Connections, MIT and Massachusetts Institute of Technology, 2011).
92
V. Stojkoski, P. Koch, C. A. Hidalgo, Multidimensional economic complexity and inclusive green growth. Commun. Earth Environ. 4, 130 (2023).
93
I. M. Weber, G. Semieniuk, T. Westland, J. Liang, What you exported matters: Persistence in productive capabilities across two eras of globalization. UMass Amherst Economic Department Working Paper 2021–02 (2021).
94
D. Hartmann, M. R. Guevara, C. Jara-Figueroa, M. Aristarán, C. A. Hidalgo, Linking economic complexity, institutions, and income inequality. World Dev. 93, 75–93 (2017).
95
A. Lapatinas, The effect of the Internet on economic sophistication: An empirical analysis. Econ. Lett. 174, 35–38 (2019).
96
J. P. Romero, C. Gramkow, Economic complexity and greenhouse gas emissions. World Dev. 139, 105317 (2021).
97
H. Zou, T. Hastie, Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 301–320 (2005).
98
P. Bairoch, J. Batou, P. Chèvre, La population des villes européennes de 800 à 1850 (Librairie Droz, 1988).
99
J. E. Dittmar, Information technology and economic change: The impact of the printing press. Q. J. Econ. 126, 1133–1172 (2011).
100
J. B. De Long, A. Shleifer, Princes and merchants: European city growth before the industrial revolution. J. Law Econ. 36, 671–702 (1993).
101
J. Mokyr, C. Ó. Gráda, Height and health in the United Kingdom 1815–1860: Evidence from the East India Company Army. Explor. Econ. Hist. 33, 141–168 (1996).
102
H. J. Brinkman, J. W. Drukker, B. Slot, Height and income: A new method for the estimation of historical national income series. Explor. Econ. Hist. 25, 227–264 (1988).
103
R. H. Steckel, Height and per capita income. Hist. Methods J. Quant. Interdiscip. Hist. 16, 1–7 (1983).
104
D. Acemoglu, D. Cantoni, S. Johnson, J. A. Robinson, The consequences of radical reform: The French revolution. Am. Econ. Rev. 101, 3286–3307 (2011).
105
J. C. Smith, Nuremberg, A Renaissance City, 1500–1618 (University of Texas Press, ed. 1, 1983).
106
J. C. Smith, Netherlandish artists and art in renaissance Nuremberg. Simiolus Neth. Q. Hist. Art 20, 153 (1990).
107
S. O. Becker, L. Woessmann, Was Weber wrong? A human capital theory of protestant economic history. Q. J. Econ. 124, 531–596 (2009).
108
S. O. Becker, S. Pfaff, J. Rubin, Causes and consequences of the protestant reformation. Explor. Econ. Hist. 62, 1–25 (2016).
109
D. Cantoni, The economic effects of the protestant reformation: Testing the Weber hypothesis in the German lands. J. Eur. Econ. Assoc. 13, 561–598 (2015).
110
R. C. Nash, Urbanization in the Colonial South: Charleston, South Carolina, as a case study. J. Urban Hist. 19, 3–29 (1992).
111
A. L. Slap, F. Towers, D. R. Goldfield, Eds. Confederate Cities: The Urban South during the Civil War Era (The University of Chicago Press, 2015).
112
A. S. Pereira, The opportunity of a disaster: The economic impact of the 1755 Lisbon earthquake. J. Econ. Hist. 69, 466 (2009).
113
L. Shapley, “A value for n-person games” in Contributions to the Theory of Games (AM-28), H. W. Kuhn, A. W. Tucker, Eds. (Princeton University Press, 1953), vol. 2, pp. 307–317.
114
S. M. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30 (2017), pp. 4768–4777.
115
B. Rozemberczki et al., The shapley value in machine learning. arXiv [Preprint] (2022). https://arxiv.org/abs/2202.05594 (Accessed 8 August 2023).
116
G. H. Golub, C. Reinsch, “Singular value decomposition and least squares solutions” in Handbook for Automatic Computation, F. L. Bauer et al., Eds. (Springer, Berlin Heidelberg, 1971), pp. 134–151.
Information & Authors
Information
Published in
Cover image: Pictured are rescue tigers (Panthera tigris) at In-Sync Exotics Wildlife Rescue and Educational Center in Wylie, Texas. Ellie Armstrong, Jazlyn Mooney et al. explored the genomic diversity of the privately owned captive tiger population in the United States based on genomic sequencing of 138 individuals. The study population had ancestry from all six extant wild tiger subspecies and measures of genetic diversity and inbreeding that were comparable to wild tiger populations. The results could help inform tiger conservation efforts and illegal trafficking investigations. See the article by Armstrong, Mooney et al. e2402924121. Image credit: Keri Ostermann (In-Sync Exotics Wildlife Rescue and Educational Center, Wylie, TX).
Classifications
Copyright
Copyright © 2024 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
Data, Materials, and Software Availability
We publish our out-of-sample estimates together with the collected source data on countries (27, 28) and regions (29, 58–65) in a comprehensive dataset comprising 5,700 observations (1,336 source data observations, and 4,364 out-of-sample estimates). For the out-of-sample estimates, we provide 90 percent CI. Also, we publish the code to ensure reproducibility of our results. Data and code are available at https://github.com/philmkoch/historicalGDPpc.
Submission history
Received: January 31, 2024
Accepted: August 9, 2024
Published online: September 16, 2024
Published in issue: September 24, 2024
Change history
September 24, 2024: The article text has been updated to correct a typographical error. Previous version (September 16, 2024)
Keywords
Acknowledgments
We acknowledge the support of the Agence Nationale de la Recherche grant number ANR-19-P3IA-0004, the European Union and the European Research Executive Agency under the Horizon EU project LearnData 101086712, Institute for Advanced Study in Toulouse funding from the French National Research Agency (ANR) under grant ANR-17-EURE-0010 (Investissements d’Avenir program), and the European Lighthouse of AI for Sustainability [grant number 101120237-HORIZON-CL4-2022-HUMAN-02].
Author Contributions
P.K., V.S., and C.A.H. designed research; P.K. performed research; P.K. analyzed data; C.A.H. supervised the project; and P.K., V.S., and C.A.H. wrote the paper.
Competing Interests
The authors declare no competing interest.
Notes
This article is a PNAS Direct Submission. M.B. is a guest editor invited by the Editorial Board.
Although PNAS asks authors to adhere to United Nations naming conventions for maps (https://www.un.org/geospatial/mapsgeo), our policy is to publish maps as provided by the authors.
Authors
Metrics & Citations
Metrics
Citation statements
Altmetrics
Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
View Options
View options
PDF format
Download this article as a PDF file
DOWNLOAD PDFLogin options
Check if you have access through your login credentials or your institution to get full access on this article.
Personal login Institutional LoginRecommend to a librarian
Recommend PNAS to a LibrarianPurchase options
Purchase this article to access the full text.