Local participation and not unemployment explains the M5S result in the South

The abundance of economic data and the scarcity of social data with a comparable level of granularity is a problem for the quantitative analysis of social phenomena. I argue that this fundamental problem has misguided the analysis of the electoral results of the Five Star Movement (M5S) and its interpretation. In this article, I provide statistical evidence suggesting that — in the South — unemployment is not associated with the exceptional increase in the M5S support and that local participation is a stronger predictor of support than most of the demographics.

What happened

The 2018 Italian general elections (elections, since both the Chamber of Deputies and the Senate, were renewed) saw

  1. a significant increase in the number of votes for two parties, the Five Start Movement (M5S) and the League (formerly Northern League),

and

  1. an increase in the importance geography as an explanatory dimension for the distribution of votes.

The following two maps show where the M5S and the League have increased electoral support from 2013 to 2018. (Electoral data are always data for the election of the Chamber of Deputies).

Vote difference: 2018-2013 (a few communes have not reported all the results, notably Rome)

 

The geographic pattern is quite simple. The M5S has increased its support in the South and maintained its votes in the North, the League has significantly strengthened its support in the North but has also collected votes in the South, where it had virtually no support. The third and the fourth most voted parties, the Democratic Party (PD) and Berlusconi’s Forza Italia (FI), have lost votes almost everywhere. If we map the results of the four parties side-by-side with the same scale, the PD and FI almost faded into the background.

Votes in the 2018 General elections

Yet, major metropolitan areas do not always follow the national trend. If Naples unambiguously voted M5S, Turin, Milan and Rome did saw the Democratic Party as the most voted party in the wealthiest districts.

Votes in the 2018 General elections (Clock-wise from top-left: Turin, Milan, Naples, Rome)

The density of the distribution of results at the commune and sub-commune level in the macro regions indicates that if the M5S electorally dominates in the South and in the two major islands, the League is the most popular party in the North.

Distribution of votes at commune or sub-commune level

The territoriality of the results, especially along the North-South dimension, makes the analysis especially complicated. This because the strong result of the League in the North and of the M5S in the South might simplistically suggest that immigration (which is much stronger in the North) explains the League’s result in the North and unemployment and poverty (stronger in the South) explain the M5S’s result in the South. This reading is especially attractive since immigration and the M5S proposal to introduce a guaranteed minim income have dominated the campaign.

(more…)

Tuesday, 20 March 2018

tweets


Twitter: frbailo

links


blogroll


RSS r-bloggers.com

  • RDCOMClient : A Simple Libor IRS Pricing with OIS Discounting
    This post shows a simple example which uses the RDCOMClient R package. As an example, A Libor IRS pricing with OIS discounting is presented with the help of a VBA macro code from Mikael Katajamäki's source with proper citation (this should be always... Continue reading: RDCOMClient : A Simple Libor IRS Pricing with OIS Discounting
  • parallel grid search cross-validation using `crossvalidation`
    parallel grid search cross-validation using `crossvalidation`. Continue reading: parallel grid search cross-validation using `crossvalidation`
  • Working with tree-based hierarchies using data.tree
    Lately I tried to visualize an hierarchy with Tableau Desktop. The problem was that the hierarchy had a variable depth because it was tree-based. Each row had an id and a parent_id. Normally hierarchies in Tableau are defined by pulling some fields together, such as product category, product group ... Continue reading: Working with tree-based […]
  • How to Calculate Mean Absolute Error in R
    Mean Absolute Error in R, when we do modeling always need to measure the accuracy of the model fit. The mean absolute error (MAE)... The post How to Calculate Mean Absolute Error in R appeared first on finnstats. Continue reading: How to Calculate Mean Absolute Error in R
  • Is it worth the weight?
    Intro Oh man, I did it again. Grab a coffee, this is going to be a long one. Weights got me confused. The justification for using weights seems simple enough; if you’re working with a sample in which one (or more) strata are over(under)-represented, you should compute ... Continue reading: Is it worth the weight?

RSS Simply Statistics

  • Streamline - tidy data as a service
    Tldr: We started a company called Streamline Data Science https://streamlinedatascience.io/ that offers tidy data as a service. We are looking for customers, partnerships and employees as we scale up after closing our funding round! Most of my career, I have worked in the muck of data cleaning. In the world of genomics, a lot of […]
  • The Four Jobs of the Data Scientist
    In 2019 I wrote a post about The Tentpoles of Data Science that tried to distill the key skills of the data scientist. In the post I wrote: When I ask myself the question “What is data science?” I tend to think of the following five components. Data science is (1) the application of design […]
  • Palantir Shows Its Cards
    File this under long-term followup, but just about four years ago I wrote about Palantir, the previously secretive but now soon to be public data science company, and how its valuation was a commentary on the value of data science more generally. Well, just recently Palantir filed to go public and therefore submitted a registration […]

RSS Statistical Modeling, Causal Inference, and Social Science