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Using dogesr to work with Venetian doges tenures

JJ Merelo

2023-08-21

Introduction

Since Venetian doges were elected for life, it was apparently a conscious design by the electoral college (composed, after a series of lot drawings and designations of 41 nobles from the Maggior Consiglio) to choose them in such a way that their terms would not last too long (Smith, Crowley, and Leguizamon 2021). Using data from dogesr (Merelo-Guervós 2022), we will, in this vignette, see to what extent that happened and if there was some evolution during the time the Republic of Venice existed

Set up

library(dogesr)
data("doges")

This will import the data from the dogesr package into the data.doges data frame. We are interested in the Doges and Years variables. Let’s create a data frame out of that:

doges.all.years <- data.frame( doge = data.doges$Doge, years = data.doges$Years)

Several doges only were able to stay alive during the same year

knitr::kable(doges.all.years[doges.all.years$years == 0,]$doge, col.names=NULL)
Michele Morosini
Nicolo Donato
Francesco Corner

And quite a few during one year or less

doges.one.year <- unique(doges.all.years[doges.all.years$years <= 1,])
knitr::kable(doges.one.year[order(doges.one.year$years),],col.names=NULL)
71 Michele Morosini 0
107 Nicolo Donato 0
115 Francesco Corner 0
2 Domenico Leone 1
3 Felice Cornicula 1
4 Teodato 1
5 Gioviano 1
6 Giovanni Fabriciaco 1
8 Galla Gaulo 1
19 Pietro I Candiano 1
28 Vitale Candiano 1
58 Marino Zorzi 1
63 Marino Faliero 1
65 Giovanni Gradenigo 1
80 Nicolo Marcello 1
85 Marco Barbarigo 1
93 Marcantonio Trivisan 1
100 Sebastiano Venier 1
109 Francesco Contarini 1
111 Nicolo Contarini 1
114 Carlo Contarini 1
117 Giovanni Pesaro 1
131 Marco Foscarini 1

So it effectively looks like most doges effectively had a short shelf life. As a matter of fact, the doge that staid in power the longest was

knitr::kable(doges.all.years[which.max(doges.all.years$years),])
doge years
75 Francesco Foscari 34

Francesco Foscari was elected when he was 50, defeating Pietro Loredan. After being in power for 34 years, he was the only doge forced to abdicate after the serrata or new rules electing the doges.

We can see whether this has any kind of influence using now several functions provided by the dogesr package next.

Analyzing the evolution of the effective terms of the doges using doges.years

This can be analyzed a but further with a function provided by dogesr, doges.years. This filters part of the original data, leaving only data needed to work with the doges’ terms (or years)

data("doges.years")
knitr::kable(head(doges.years[order(-doges.years$Years),],10))
Doge Century Start End Family.doge Years
68 Francesco Foscari 15 1423 1457 Foscari 34
33 Domenico I Contarini 11 1041 1071 Contarini 30
16 Pietro Tradonico 9 837 864 Tradonico 27
20 Pietro Tribuno 9 888 912 Pietro 24
45 Pietro Ziani 13 1205 1229 Ziani 24
10 Maurizio Galbaio 8 764 787 Galbaio 23
52 Pietro Gradenigo 13 1289 1311 Gradenigo 22
21 Orso II Participazio 10 912 932 Participazio 20
46 Jacopo Tiepolo 13 1229 1249 Tiepolo 20
78 Leonardo Loredan 16 1501 1521 Loredan 20

First thing we can note is that, among the top 10, there is only one, Leonardo Loredan, who was elected after the aforementioned Francesco Foscari. This was, according to everyone, an unexpected event (Wikipedia contributors 2022), and he was chosen by the minimum needed to get elected. So he was really an outlier. The distribution in bins of 5 years, below, shows how infrequent was a term of more than 10 years.

In fact, doges’ years of ruling were as short as the life of the common dog.

library(ggplot2)
ggplot(doges.years, aes(x=Years))+geom_histogram(binwidth=5)
This histogram reproduces, with a fixed bin size, Figure 1 in (Smith, Crowley, and Leguizamon 2021).
This histogram reproduces, with a fixed bin size, Figure 1 in (Smith, Crowley, and Leguizamon 2021).

The distribution is skewed because it includes the first years of the institution. This is the distribution by centuries

doges.years$Century <- as.factor(doges.years$Century)
ggplot(doges.years, aes(x=Century,y=Years))+geom_boxplot()

There seems to be a clear difference before and after the thirteenth century; there is also a return to longer tenures by the end of the Republic, in the 18th century. Was there a real difference?

doges.years$pre.serrata <- TRUE
doges.years[doges.years$Start >= 1297,]$pre.serrata <- FALSE
means <- aggregate(Years ~  pre.serrata, doges.years, mean)
medians <- aggregate(Years ~  pre.serrata, doges.years, median)
ggplot(doges.years, aes(x=pre.serrata, y=Years))+geom_boxplot(notch=T)+
  stat_summary(fun=mean, geom="point", shape=20, size=3, color="red", fill="red") +
  geom_text(data = means, aes(label = round(Years, 2), y = Years + 2), size = 3) + 
  geom_text(data = medians, aes(label = round(Years, 2), y = Years - 1), size = 3)

This difference is significant, as indicated by the Wilcoxon test

wilcox.test(doges.years[doges.years$pre.serrata == T,]$Years,   doges.years[doges.years$pre.serrata == F,]$Years )
#> 
#>  Wilcoxon rank sum test with continuity correction
#> 
#> data:  doges.years[doges.years$pre.serrata == T, ]$Years and doges.years[doges.years$pre.serrata == F, ]$Years
#> W = 2467.5, p-value = 0.001425
#> alternative hypothesis: true location shift is not equal to 0

Conclusions

As indicated by (Smith, Crowley, and Leguizamon 2021), there seems to be evidence that supports the existence of “informal” limits on the terms of doges of the the Republic of Venice. In their paper they indicate 1172 as a departure point for the term “limits”, when the Maggior Consiglio was formed. According to the figure above, there does not seem to be a significant difference between the XII and XIII century in terms of tenure. However, 1297 does seem to be a watershed event, with all terms after that being significantly different to terms prior to it. We can conclude, then, that the Serrata, closing of the Maggior Consiglio to all but a few patrician families, was in fact the event that marked the shift to electing older doges.

This vignette also shows how, using dogesr, it becomes significantly easier for everyone with the skill to work with the R language to reproduce or work upon results related to life terms and other Venetian doges data.

References

Merelo-Guervós, J. J. 2022. “What Is a Good Doge? Analyzing the Patrician Social Network of the Republic of Venice.” arXiv. https://doi.org/10.48550/ARXIV.2209.07334.
Smith, Daniel J, George R Crowley, and J Sebastian Leguizamon. 2021. “Long Live the Doge? Death as a Term Limit on Venetian Chief Executives.” Public Choice 188 (3): 333–59.
Wikipedia contributors. 2022. Leonardo LoredanWikipedia, the Free Encyclopedia.” https://en.wikipedia.org/w/index.php?title=Leonardo_Loredan&oldid=1111394557.

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