Author: Spencer Graves
Date: 2026-01-09

# Identifying MaddisonData leaders

#### Spencer Graves

#### 2026-01-09

## Intro

This vignette documents now to identify the countries with max `gdppc` for each year, extract from that the major technology leaders, and plot them with annotations.

## `MaddisonLeaders`

    library(MaddisonData)
    MadLdrs <- MaddisonLeaders()
    (MadLdrsSum <- summary(MadLdrs, 'yearBegin'))

    ##     ISO yearBegin yearEnd   n           p
    ## ITA ITA         1    1501   3 0.001998668
    ## IRQ IRQ       730    1000 271 1.000000000
    ## CHN CHN      1090    1150  61 1.000000000
    ## GBR GBR      1252    1898  91 0.140649150
    ## FRA FRA      1276    1374  19 0.191919192
    ## ESP ESP      1278    1348  50 0.704225352
    ## SWE SWE      1304    1509  13 0.063106796
    ## NLD NLD      1349    1807 447 0.973856209
    ## BEL BEL      1500    1500   1 1.000000000
    ## AUS AUS      1853    1891  17 0.435897436
    ## NZL NZL      1873    1874   2 1.000000000
    ## USA USA      1882    1990  58 0.532110092
    ## CHE CHE      1931    1934   4 1.000000000
    ## QAT QAT      1950    2022  45 0.616438356
    ## KWT KWT      1953    1957   5 1.000000000
    ## ARE ARE      1965    1984   5 0.250000000
    ## LUX LUX      1991    1995   5 1.000000000
    ## NOR NOR      1996    2002   7 1.000000000

The Netherlands led the world in `gdppc` for 97 percent of the years between 1349 and 1807. Other major technology leaders since then have been England / Great Britain / the United Kingdom (`GBR`) and `USA` – and maybe others like Singapore. Let’s redo this starting from 1349 and excluding petrostates Qatar (`QAT`), Kuwait (`KWT`), and United Arab Emirates (`ARE`), plus Norway (`NOR`), which owes a substantial portion of their wealth to democratic management of North Sea oil.

    MadDat1349 <- subset(MaddisonData, (year > 1348) &
                           !(ISO %in% c('QAT', 'KWT', 'ARE', 'NOR') ))
    MadLdrs1349 <- MaddisonLeaders(data=MadDat1349)
    (MadLdrsSum1349 <- summary(MadLdrs1349, 'yearBegin'))

    ##     ISO yearBegin yearEnd   n          p
    ## NLD NLD      1349    1807 447 0.97385621
    ## FRA FRA      1357    1374   7 0.38888889
    ## ITA ITA      1451    1501   2 0.03921569
    ## SWE SWE      1468    1509   2 0.04761905
    ## BEL BEL      1500    1500   1 1.00000000
    ## GBR GBR      1808    1898  67 0.73626374
    ## AUS AUS      1853    1891  17 0.43589744
    ## NZL NZL      1873    1874   2 1.00000000
    ## USA USA      1882    1990  92 0.84403670
    ## CHE CHE      1931    2009   6 0.07594937
    ## LUX LUX      1991    2008  18 1.00000000
    ## SGP SGP      2010    2022  13 1.00000000

Singapore (`SGP`) has replaced Norway as the current leader, according to the Maddison project data. The Wikipedia article on “List of countries by GDP (PPP) per capita” notes that data from the US Central Intelligence Agency report `gdppc` number for Monaco (`MCO`) and Liechtenstein (`LIE`) higher than Singapore and Norway. However, they are tiny countries with populations roughly 40,000 each without broad-based economies and are not included in `MaddisonData`. Luxembourg (`LUX`) has a population under a million. Let’s redo this analysis without `LUX`.

First, however, lets check on the early years for which data on Holland are available.

    NLDdat <- subset(MaddisonData, ISO=='NLD')
    head(NLDdat)

    ## # A tibble: 6 × 4
    ##   ISO    year gdppc   pop
    ##   <chr> <dbl> <dbl> <dbl>
    ## 1 NLD       1   NA    200
    ## 2 NLD    1000   NA    300
    ## 3 NLD    1348 1405.    NA
    ## 4 NLD    1349 1460.    NA
    ## 5 NLD    1350 1631.    NA
    ## 6 NLD    1351 1714.    NA

`MaddisonData` on Holland starts with year 1, then skips to 1000, then to 1348 before Holland becomes the leader in 1349.

Now let’s refine the analysis of `GDPpc` leaders, as indicated above.

    MadDat1349a <- subset(MaddisonData, (year > 1348) &
                           !(ISO %in% c('QAT', 'KWT', 'ARE', 'NOR', 'LUX') ))
    MadLdrs1349a <- MaddisonLeaders(data=MadDat1349a)
    (MadLdrsSum1349a <- summary(MadLdrs1349a, 'yearBegin'))

    ##     ISO yearBegin yearEnd   n          p
    ## NLD NLD      1349    1807 447 0.97385621
    ## FRA FRA      1357    1374   7 0.38888889
    ## ITA ITA      1451    1501   2 0.03921569
    ## SWE SWE      1468    1509   2 0.04761905
    ## BEL BEL      1500    1500   1 1.00000000
    ## GBR GBR      1808    1898  67 0.73626374
    ## AUS AUS      1853    1891  17 0.43589744
    ## NZL NZL      1873    1874   2 1.00000000
    ## USA USA      1882    2005 107 0.86290323
    ## CHE CHE      1931    2009   9 0.11392405
    ## SGP SGP      2010    2022  13 1.00000000

The Netherlands (`NLD`) was the leader for 97 percent of the years between 1349 and 1807, according to `MaddisonData`. Then England / Great Britain / the United Kingdom (`GBR`) led for 74 percent of the years between 1808 and 1898. Then `USA` led for 84 percent of the years between 1882 and 1990 with Australia (`AUS`), New Zealand (`NZL`) and Switzerland (`CHE`) leading for the remaining 16 percent of those years. Luxembourg (`LUX`) led between 1991 and 2008, then Switzerland (`CHE`) led for 2009, then Singapore (`SGP`) between 2008 and 2022.

How much did `gdppc` fall for `NLD` between 1807 and 1808?

    (NLD1808 <- subset(NLDdat, year %in% 1807:1808))

    ## # A tibble: 2 × 4
    ##   ISO    year gdppc   pop
    ##   <chr> <dbl> <dbl> <dbl>
    ## 1 NLD    1807 3863.    NA
    ## 2 NLD    1808 2632     NA

    (dNLD1808 <- diff(log(NLD1808$gdppc)))

    ## [1] -0.3836341

    expm1(dNLD1808)

    ## [1] -0.3186193

For simplicity, we focus on `NLD`, `GBR`, `USA`, and `SGP`. A few other countries (`AUS`, `NZL`, `CHE`, and `LUX`) led for so few years in this period that including them in this plot would likely add more complexity than information and make it harder to understand the big picture.

    NLD_SGP <- subset(MadDat1349, ISO %in% c('NLD', 'GBR', 'USA', 'SGP'))
    NLD_SGPsum <- MaddisonLeaders(data=NLD_SGP)
    summary(NLD_SGPsum, 'yearBegin')

    ##     ISO yearBegin yearEnd   n         p
    ## NLD NLD      1349    1807 459 1.0000000
    ## GBR GBR      1808    1934  80 0.6299213
    ## USA USA      1880    2006 119 0.9370079
    ## SGP SGP      2007    2022  16 1.0000000

    (NLD_SGP0 <- ggplotPath(y='gdppc', group='ISO', data=NLD_SGP, scaley=1000))

The line for the Netherlands shows a dramatic decline between 1807 and 1808. Before speculating further, let’s check the data sources used by `MaddisonData`:

    NLDrefs <- getMaddisonSources('NLD')

The prior to 1808 these data are for Holland [Van Zanden and van Leeuwen (2012)]. The more recent data are for the Netherlands [Smits et al. (2000)], of which Holland is only a part. That transition was during the Napoleonic wars, and the Netherlands became part of France for part of those times.

We want multiple annotations on this plot: - The `NLD` line as “Holland” (at `ggppc` = roughly $5K in 1600) and “Netherlands” (at `gdppc` = roughly $4K in 1900). - ‘English Civil War’, 1642-1652, during which King Charles I was decapitated (in 1649), after which `gdppc` for `GBR` began to increase; we could see that more clearly in a separate plot zooming in on that particular time.
- Queen Ann (1702-1714), who reigned over substantial turbulence in `gdppc` and was followed by slower but still impressive growth on `gdppc` relative to the economic stagnation before Charles I lost his head. - War of 1812 (1812-1815). - American Civil War (1861-1865). - `WW1` (1914-1918). - Herbert Hoover (1929-1933). - Franklin Roosevelt (1933-1945). - `WW2` (1939-1945). - Ronald Reagan. - The first presidency of Donald Trump. - Joe Biden.

    x0 <- yr(c('1642-01-04', '1702-03-08', '1812-06-18', '1861-04-12',
               '1914-07-28', '1929-03-04', '1933-03-04', '1939-09-01'))
    x1 <- yr(c('1651-09-03', '1714-08-01', '1815-02-17', '1865-05-26',
               '1918-11-11', '1933-03-04', '1945-04-12', '1945-09-02'))
    Vlines <- sort(unique(c(x0, x1)))
    attr(Vlines, 'color') <- c(rep('grey', 10), 'red', 'green4', 'grey',
                               'green4', 'grey')
    Hlines = c(1, 3, 5, 10, 30, 50)
    Lbls <- data.frame(x=c(1500, 1600, 1740, 1870, (x0+x1)/2, 1985),
                       y=c(1.35,  5.7, 1.65,  3.5, 13, rep(15, 4), 36, 21, 64, 8),
        label=c('UK', 'Holland', 'US', 'Netherlands', 'English civil war',
                'Queen Ann', 'War of 1812', 'American Civil War', 'WW1', 'Hoover',
                'FDR', 'WW2', 'Singapore'),
        srt=c(0, 0, 40, 50, rep(90, 8), 87),
        col=c('red', 'orange', 'blue', 'orange', 'red', rep('grey', 4), 'red',
              'green4', 'grey', 'darkolivegreen4') )

    (NLD_SGP1 <- ggplotPath(y='gdppc', group='ISO', data=NLD_SGP, scaley=1000,
                            ylab='GDP per capita (2011 K$ PPP)',
                            hlines=Hlines, vlines=Vlines, labels=Lbls,
                            fontsize=20,
                            color=c('red', 'orange', 'darkolivegreen4', 'blue'),
                            linetype=c(1:2, 1, 1)))

Save.

    svg('NLD_SGP.svg')
    NLD_SGP1
    dev.off()

This figure needs to acknowledge Bolt and Van Janden (2024) for the Maddison Data generally, Van Zanden, J. L. and van Leeuwen, B. (2012) for the data on Holland 1348–1807 and the Netherlands 1808-1913, Smits et al (2000) for the data on the Netherlands 1800-1913, Broadberry et al. (2015) for the data on England 1252–1700 and on Great Britain until 1870, and Sugimoto (2011) for Singapore to 2007.

    (GBRrefs <- getMaddisonSources('GBR'))

    ##   ISO               years
    ## 1                   2008-
    ## 2                   1990-
    ## 3             1, .., 2022
    ## 4 GBR                   1
    ## 5 GBR 1252–1700 (England)
    ## 6 GBR           1700–1870
    ##                                                                                                                                                                                                                 source
    ## 1    GDP pc(2008-): Total Economy Database (TED) of the Conference Board for all countries included in TED [https://www.conference-board.org/topics/total-economy-database]. Otherwise UN national accounts statistics
    ## 2 population(1990-):Total Economy Database (TED) of the Conference Board for all countries included in TED [https://www.conference-board.org/topics/total-economy-database]. Otherwise UN national accounts statistics
    ## 3                                                   Jutta Bolt and Jan Luiten Van Zanden (2024) "Maddison style estimates of the evolution of the world economy: A new 2023 update", Journal of Economic Surveys, 1-41
    ## 4                                                        Scheidel, W. and Friesen, S. J., ‘The size of the economy and the distribution of income in the Roman Empire’, Journal of Roman Studies, 99 (2009), pp. 61–91
    ## 5                                                              Broadberry, S.N., B. Campbell, A. Klein, M. Overton and B. van Leeuwen (2015), British Economic Growth 1270-1870 Cambridge: Cambridge University Press.
    ## 6                                                              Broadberry, S.N., B. Campbell, A. Klein, M. Overton and B. van Leeuwen (2015), British Economic Growth 1270-1870 Cambridge: Cambridge University Press.

    (USArefs <- getMaddisonSources('USA'))

    ##   ISO       years
    ## 1           2008-
    ## 2           1990-
    ## 3     1, .., 2022
    ## 4 USA 1650 - 1790
    ## 5 USA 1790 - 1870
    ## 6 USA   1800-1830
    ##                                                                                                                                                                                                                        source
    ## 1           GDP pc(2008-): Total Economy Database (TED) of the Conference Board for all countries included in TED [https://www.conference-board.org/topics/total-economy-database]. Otherwise UN national accounts statistics
    ## 2        population(1990-):Total Economy Database (TED) of the Conference Board for all countries included in TED [https://www.conference-board.org/topics/total-economy-database]. Otherwise UN national accounts statistics
    ## 3                                                          Jutta Bolt and Jan Luiten Van Zanden (2024) "Maddison style estimates of the evolution of the world economy: A new 2023 update", Journal of Economic Surveys, 1-41
    ## 4         McCusker, John J., ‘Colonial Statistics’, Historical Statistics of the United States: Earliest Time to the Present, in S. B. Carter, S. S. Gartner, M. R. Haineset al. New York, Cambridge University Press. V-671.
    ## 5 Sutch, R. (2006). National Income and Product. Historical Statistics of the United States: Earliest Time to the Present, in S. B. Carter, S. S. Gartner, M. R. Haineset al. New York, Cambridge University Press III-23-25.
    ## 6                                                     Prados de la Escosura, L. (2009). “Lost Decades? Economic Performance in Post-Independence Latin America,” Journal of Latin America Studies 41: 279–307. (updated data)

    (SGPrefs <- getMaddisonSources('SGP'))

    ##   ISO       years
    ## 1           2008-
    ## 2           1990-
    ## 3     1, .., 2022
    ## 4 SGP   1900–1959
    ##                                                                                                                                                                                                                                                               source
    ## 1                                                  GDP pc(2008-): Total Economy Database (TED) of the Conference Board for all countries included in TED [https://www.conference-board.org/topics/total-economy-database]. Otherwise UN national accounts statistics
    ## 2                                               population(1990-):Total Economy Database (TED) of the Conference Board for all countries included in TED [https://www.conference-board.org/topics/total-economy-database]. Otherwise UN national accounts statistics
    ## 3                                                                                                 Jutta Bolt and Jan Luiten Van Zanden (2024) "Maddison style estimates of the evolution of the world economy: A new 2023 update", Journal of Economic Surveys, 1-41
    ## 4 Sugimoto, I. (2011), Economic growth of Singapore in the twentieth century: historical GDP estimates and empirical investigations, Economic Growth Centre Research Monograph ser., 2, http://www.worldscibooks.com/economics/7858.html (accessed on 30 Jan. 2013).

## Bibliography

Daron Acemoğlu; Simon Johnson (2023). Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity. PublicAffairs. Wikidata Q125292212. ISBN 978-1-5417-0253-0.

Jutta Bolt; Jan Luiten van Zanden (2024). “Maddison style estimates of the evolution of the world economy: A new 2023 update”. Journal of Economic Surveys: 1-41. doi:10.1111/ .12618. Wikidata Q126723821. ISSN 0950-0804.

Stephen Broadberry; Bruce M. S. Campbell; Alexander Klein; Mark Overton; Bas van Leeuwen (2015). British Economic Growth, 1270–1870. `doi:10.1017/CBO9781107707603`. Wikidata Q57945943. ISBN 978-1-107-70760-3.

Susan B. Carter; S. S. Gartner; M. R. Haineset (2006), Historical statistics of the United States : earliest times to the present, Wikidata Q137660377

Spencer Graves (24 November 2025), MaddisonData: Maddison Project Data, Wikidata Q137660514

John J. McCusker (2006). “Colonial Statistics”. Historical statistics of the United States : earliest times to the present V: 671. Wikidata Q137669937.

Reece Peck (2016). “Usurping the usable past: How Fox News remembered the Great Depression during the Great Recession”. Journalism. Wikidata Q135527962. ISSN 1464-8849.

Walter Scheidel; Steven J. Friesen (November 2009). “The Size of the Economy and the Distribution of Income in the Roman Empire”. The Journal of Roman Studies 99: 61. doi:10.3815/007543509789745223. Wikidata Q55878109. ISSN 0075-4358.

Jan-Pieter Smits; Edwin Horlings; Jan Luiten van Zanden (2000), Dutch GDP and its Components 1800-1913 (PDF), Wikidata Q137669960

Ichiro Sugimoto (2011), Economic growth of Singapore in the twentieth century: historical GDP estimates and empirical investigations (PDF), Wikidata Q137669987

R. Sutch (2006). “National Income and Product”. Historical statistics of the United States : earliest times to the present III: 23-25. Wikidata Q137670038.

Jan Luiten van Zanden; Bas van Leeuwen (2012). “Persistent but not consistent: the growth of national income in Holland 1347–1807”. Explorations in Economic History 49 (2): 119-130. Wikidata Q137670058. ISSN 0014-4983.