2018 Italian general election: Details on my simulation

This article describes the simulation behind the app that you find here

This simulation of the results for the 2018 general election is based on the results from the last two national elections (the Italian parliament election in 2013 and the European Parliament election 2014) and national polls conducted until 16 February 2018. The simulation is based on one assumption, which is reasonable but not necessarily realistic: the relative territorial strength of parties is stable. From this assumption derives that if the national support for a party (as measured by national voting intention polls) varies, it varies consistently and proportionally everywhere. A rising tide lifts all boats and vice versa. The assumption has some empirical justification. If we compare the difference from the national support (in percentage) for each district in 2013 and 2014 we see a significant correlation, especially in the major parties.

Votes to party in the 2018 Chamber districts


Tuesday, 27 February 2018

Quick analysis of the Italian referendum results

The 2016 Italian referendum torpedoed the constitutional reform presented by the government presided by Matteo Renzi (41). According to the final count, which includes 1.2 million votes cast overseas, the reform was rejected by almost 60% of the voters.

Three parties played a predominant role during the electoral campaign: the ruling Democraric Party (PD), leaded by the chief of government Renzi, the Five Star Movement (M5S), founded and leaded by Beppe Grillo (68), and the Lega Nord (LN), leaded by Matteo Salvini (43). The fourth Italian party, Forza Italia, for different reasons – including the health of Silvio Berlusconi (80) – played a minor role.


Monday, 5 December 2016

Cosa possiamo imparare dal M5S

Leggo e rispondo al post di Massimo Mantellini (Il M5S, il wifi e il principio di precauzione) in cui si evidenzia con preoccupazione come il Movimento abbia portato in Parlamento, dunque in qualche modo legittimandole, posizioni anti-scientifiche; un “pensiero tossico, banale e a suo modo inattaccabile, che nuoce al Paese intero”.

Il Movimento Cinque Stelle con un bacino elettorale che si aggira tra il 25 e il 30% (8.5-10 milioni di persone) è necessariamente complesso in termini di rappresentanza demografica e di diversità di opinione. Considerando un astensionismo del 25%, se vi trovate in fila al supermercato delle 10 persone che vi precedono circa due votano M5S. Purtroppo questa complessità raramente traspare nelle narrazioni giornalistiche, e chi fa informazione tende (troppo) spesso a preferire i tratti caricaturali (da cappello di carta stagnola o da gita in Corea del Nord, per intenderci). Ma questo tipo di informazione è sbagliata: primo perché distorce nella semplificazione, secondo perché incoraggia comportamenti macchiettistici, grotteschi e sbracati da parte di chi sedendo in istituzioni affollate cerca visibilità.


Friday, 22 July 2016

Road to Rome: The organisational and political success of the M5S

The Five Star Movement (M5S) obtained two major victories in the second round of municipal elections on 19 June 2016 in Rome and Turin. Rome attracted the most international attention but it is M5S’ victory in Turin that is likely the most consequential for them and other European anti-establishment parties.

In Rome, a municipality with 2.8 million people and an annual budget of €5 billon, Virginia Raggi (age 37) gained doubled the votes of her contender Roberto Giachetti (age 55). In Turin, a city with a population of 900,000 and an annual budget of €1.69 billion, Chiara Appendino (age 31) outstripped Piero Fassino (age 66) by about 10 percentage points.

Continue reading on Pop Politics Aus

Friday, 8 July 2016

Explicit semantic analysis with R

Explicit semantic analysis (ESA) was proposed by Gabrilovich and Markovitch (2007) to compute a document position in a high-dimensional concept space. At the core, the technique compares the terms of the input document with the terms of documents describing the concepts estimating the relatedness of the document to each concept. In spatial terms if I know the relative distance of the input document from meaningful concepts (e.g. ‘car’, ‘Leonardo da Vinci’, ‘poverty’, ‘electricity’), I can infer the meaning of the document relatively to explicitly defined concepts because of the document’s position in the concept space.


Tuesday, 26 April 2016

Italy’s Five Star Movement – a spectral analysis of its political composition

To talk about identity and soul of the Five Star Movement (M5S) is not only politically contentious but also practically challenging because of the different axes (at least three) along which the M5S has been developing: the vertical top-down axis from Beppe Grillo to his followers (and sympathising voters), the horizontal axis connecting thousands of militants across the country to local, flexible and loosely organised meetups, and finally the cloudy axis linking Internet users through the different online communicative platforms pertaining to the Movement. The academic literature and the media have been prevalently interested in mapping the provenance of votes. I will try here to show some data also on the position of the M5S derived from its 2013 electoral program and the political background of both the onsite and online activists of the Movement.

But let’s first start briefly introducing the trajectory of a movement that vehemently refuses to be called a party or to be associated with any traditional political identity.

Continue reading on the blog of the WZB.

Tuesday, 12 May 2015

NDVI, risk assessment and developing countries

The Normalized Difference Vegetation Index (NDVI) estimates the greenness of plants covering the surface of the Earth by measuring the light reflected by the vegetation into space. The main idea behind the NDVI is that visible and near-infrared light is absorbed in different proportions by healthy and unhealthy plants: a green plant will reflect 50% of the near infrared-light it receives and only 8% of the visible light while an unhealthy plant will reflect respectively 40% and 30%. NDVI can then be used to quantitatively compare vegetation conditions across time and space (and indeed is quite widely used, a Google Scholar search on NDVI produced 60,500 hits).


Thursday, 14 February 2013

Eyes on Guatemala

The Economist has published an article on malnutrition in Guatemala. Hunger is not new in the country, with half of the children population not eating enough Guatemala is the six-worst country in the world, but in some Maya communities children chronic malnutrition can reach 75% (the Economist says 80%). These figures are astonishing, especially because the problem is not food scarcity.

But this as well is hardly new. It was 1981 when Amartya Sen published his Poverty and Famines: An Essay on Entitlement and Deprivation demonstrating that hunger is mostly caused by inequality rather than scarcity. There is no lack of food in Guatemala if you have the money to buy it. In Guatemala City is taking place, as we speak, the 14th Festival Gastronómico Internacional so it seems difficult to talk about a famine or about an emergency (according to the Longman Dictionary an emergency is “an unexpected and dangerous situation that must be dealt with immediately”). The problem is the lack of a functioning state. Because a state cannot function with tax revenues estimated at just 10% of GDP.

Democracy is highly unrepresentative in Guatemala. Who should push for a better redistribution of resources has no voice. National newspapers point constantly the finger at the government (presidency, parliament, judiciary) in a impressive campaign of delegitimation. The Rosenberg tape was just part of it. I’m not defending the government, but saying that criticising it and attempting to systematically destroy its credibility are not quite the same thing. While the headlines cover crime, corruption and hunger the real battle within the country is on the tax reform. A battle that so far every government has badly lost.

Friday, 28 August 2009

Back into Poverty

Increase in food prices has pushed back into poverty at least 100 million people in 2008 and, according to the United Nations Standing Committee on Nutrition (here, p. 60),

erase at least four years of progress towards the Millennium Development Goal (MDG) 1 target for the reduction of poverty. The household level consequences of this crisis are most acutely felt in LIFDCs [Low-Income Food-Deficit Countries] where a 50% rise in staple food prices causes a 21% increase in total food expenditure, increasing these from 50 to 60% of income. In a high income country this rise in prices causes a 6% rise in retail food expenditure with income expenditure on food rising from 10 to 11%. FAO estimates that food price rises have resulted in at least 50 million more people becoming hungry in 2008, going back to the 1970 figures.

According to the World Bank (here) this means that between 200,000 and 400,000 more children will died every year for malnutrition until 2015.

Thursday, 18 June 2009

Selva Amazónica, More Valuable Standing Than Felled

An article published on Science this week analyzes the development of the region across the Amazon deforestation frontier. In three words: boom and bust. It means that comparing the Human Development Index of different classes of Brazilian municipalities, from prefrontier municipalities to heavily post frontier deforested municipalities, you can see how the HDI relatively grows in the first phase of the deforestation (on the frontier line) and relatively declines when deforestation is completed.  In other words,

when the median HDI of each class is plotted against deforestation extent, a boom-and-bust pattern becomes apparent, which suggests that relative development levels increase rapidly in the early stages of deforestation and then decline as the frontier advances. Hence, although municipalities with active deforestation had development levels that approached the overall Brazilian median, pre- and postfrontier HDI values were substantially lower and statistically indistinguishable from each other (P > 0.9). These results are robust to the particular thresholds used to define the frontier classes. A boom-and-bust pattern is also found for each of the HDI subindices: standard of living, literacy, and life expectancy.

This strongly suggests that the poor have no choice but to exploit every resource available. It is difficult to think that farmers or loggers do not see that they are compromising their very own future. They simply have no choice. The challenge is giving them a choice.

Friday, 12 June 2009


Twitter: frbailo



RSS r-bloggers.com

  • xts 0.10-2 on CRAN
    This xts release contains mostly bugfixes, but there are a few noteworthy features. Some of these features were added in version 0.10-1, but I forgot to blog about it. Anyway, in no particular order: endpoints() gained sub-second accuracy on Windows (#202)! na.locf.xts() now honors 'x' and 'xout' arguments by dispatching to the next method (#215). Thanks […]
  • RcppSMC 0.2.1: A few new tricks
    A new release, now at 0.2.1, of the RcppSMC package arrived on CRAN earlier this afternoon (and once again as a very quick pretest-publish within minutes of submission). RcppSMC provides Rcpp-based bindings to R for the Sequential Monte Carlo Templat...
  • Exploring the underlying theory of the chi-square test through simulation – part 1
    Kids today are so sophisticated (at least they are in New York City, where I live). While I didn’t hear about the chi-square test of independence until my first stint in graduate school, they’re already talking about it in high school. When my kids came home and started talking about it, I did what I […]
  • R Tip: Use stringsAsFactors = FALSE
    R tip: use stringsAsFactors = FALSE. R often uses a concept of factors to re-encode strings. This can be too early and too aggressive. Sometimes a string is just a string. Sigmund Freud, it is often claimed, said: “Sometimes a cigar is just a cigar.” To avoid problems delay re-encoding of strings by using stringsAsFactors […]
  • RcppClassicExamples 0.1.2
    Per a CRAN email sent to 300+ maintainers, this package (just like many others) was asked to please register its S3 method. So we did, and also overhauled a few other packagaging standards which changed since the previous uploads in December of 2012 ...

RSS Simply Statistics

  • What do Fahrenheit, comma separated files, and markdown have in common?
    Anil Dash asked people what their favorite file format was. David Robinson replied: CSV is similar to Markdown. No one global standard (though there are attempts) but a damn good attempt at "Whatever humans think it is at a glance, they're probably right"— David Robinson (@drob) February 8, 2018 His tweet reminded me a lot […]
  • Some datasets for teaching data science
    In this post I describe the dslabs package, which contains some datasets that I use in my data science courses. A much discussed topic in stats education is that computing should play a more prominent role in the curriculum. I strongly agree, but I think the main improvement will come from bringing applications to the […]
  • A non-comprehensive list of awesome things other people did in 2017
    Editor’s note: For the last few years I have made a list of awesome things that other people did (2016,2015, 2014, 2013). Like in previous years I’m making a list, again right off the top of my head. If you know of some, you should make your own list or add it to the comments! […]

RSS Statistical Modeling, Causal Inference, and Social Science

  • Wanna know what happened in 2016? We got a ton of graphs for you.
    The paper’s called Voting patterns in 2016: Exploration using multilevel regression and poststratification (MRP) on pre-election polls, it’s by Rob Trangucci, Imad Ali, Doug Rivers, and myself, and here’s the abstract: We analyzed 2012 and 2016 YouGov pre-election polls in order to understand how different population groups voted in the 2012 and 2016 elections. We […]
  • The New England Journal of Medicine wants you to “identify a novel clinical finding”
    Mark Tuttle writes: This is worth a mention in the blog. At least they are trying to (implicitly) reinforce re-analysis and re-use of data. Apparently, some of the re-use efforts will be published, soon. My reply: I don’t know enough about medical research to make any useful comments here. But there’s one bit that raises […]
  • What are the odds of Trump’s winning in 2020?
    Kevin Lewis asks: What are the odds of Trump’s winning in 2020, given that the last three presidents were comfortably re-elected despite one being a serial adulterer, one losing the popular vote, and one bringing race to the forefront? My reply: Serial adulterer, poor vote in previous election, ethnicity . . . I don’t think […]