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 1.000000000The 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 1.00000000Singapore (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. NAMaddisonData 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 1.00000000The 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] -0.3836341expm1(dNLD1808)## [1] -0.3186193For 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
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