Long before Donald Trump declared criticisms of his ineptitude to be fake news, and Silvio Berlusconi evaded criticisms on account of his opponents being the Communists, there was Georges Ernest Boulanger. In 1887, the French government was quite startled to discover that General Boulanger had received over 100,000 write-in votes in an election for the Seine. Their astonishment wasn’t because this number was particularly high, but rather because Boulanger wasn’t even a candidate. And just like we can trace the popularity of the two aforementioned leaders to growing political frustrations, Boulanger rose to prominence over the frustrations with the unstable government during the Third Republic of France.

In this period Boulanger laid out his signature Triple R plan. France, he envisioned, would get revenge over Germany for the Franco-Prussian war of 1870, revise the constitution, and restore the monarchy. With these views he was a political outsider, and when authorities moved his post from Paris to the provinces, a crowd of 10 thousand protested with signs that read “He will be back.” And after Boulanger was expelled from the army over politics — all the while the corruption of his contemporaries was exposed — his popularity only grew. The fervor continued to build as a popular song emerged. It was called “Boulanger is the One We Need.”

Despite not being a legal candidate, Boulanger was voted to the Chamber. Many feared that he could eventually rise to become a dictator. He did not, however, have the authority to pass legislation, so his time was instead spent on his public image. You could probably imagine him going to the newspapers and saying “You should see my crowds. I have the biggest crowds.”

What came next was a real scare to Boulanger’s political opponents. In January 1889, he won the seat of deputy of Paris with 84,000 more votes than the next guy. Historians believe he probably would have been successful in a coup d’etat had he acted immediately, but he procrastinated, and his political opponents built up a legal case against him. That takes us to the months preceding the September 1889 elections, which in Durkheim’s words “ended the Boulanger agitation.” It is in these numbers that the real story begins.

This section explains how I set up the data for an interrupted time series on Durkheim’s classic data set. To follow along, grab the csv file and fire up R Studio. Otherwise, you can skip below to see how I visualized the data.

# Setting up a Time Series with Control suicide <- read.csv("suicide038.csv") # Just the boulanger bou <- suicide[suicide$Event == "Boulanger Agitation",] # Time markers (these are the months of the year) bou$time <- rep(seq(1:8), 3) # Creating the anomie variable bou$anomie <- c(rep(0, 8), rep(0, 3), 1, 1, rep(0,3), rep(0,8)) # Create a time variable for both the Agitation Year and the Control bou$time <- c(seq(1:8), seq(1:8), seq(1:8)) # Create a level variable for both WV and the control group. # The intervention is at 4 bou$level <- rep(c(0,1,0,1,0,1), times = c(3, 5, 3, 5, 3, 5)) # Creating variable for the Boulanger year bou$yr1889 <- rep(c(0,1,0), times = c(8, 8, 8)) # Creating a variable for trend (5 months each) bou$trend <- c(rep(0,3), 1:5, rep(0,3), 1:5, rep(0,3), 1:5) # Creating Boulanger-specific variables bou$setime <- c(rep(0, 8), 1:8, rep(0,8)) bou$selevel <- c(rep(0,8), rep(0,3), rep(1, 5), rep(0,8)) bou$setrend <- c(rep(0,8), rep (0,3), 1:5, rep(0,8)) # Comparing only 1888 and 1889 bou2 <- bou[1:16,]

Now that we have the data set up, we can visualize it.

It is important to note that there is some seasonality that influences the suicide rates from month to month. Many people mistakenly believe that suicide rates increase over the winter months. It is precisely the opposite. That is a topic for another blog post.

Now, just by eyeballing this, I can’t tell if it is statistically significant. Let’s construct an OLS model to check to see. Durkheim’s theory of anomie will be included in the model.

# Creating a model model_ols <- lm(SP1M ~ yr1889 + time + trend + level + setime + setrend + anomie, data = bou2)

More on anomie in a minute. But first, let’s get our model straight. Durkheim writes

“The Chamber is dissolved at the beginning of August; the excitement of the election period begins at once and lasts to the end of September, the time of the elections. An abrupt decrease of 12 per cent, compared with the corresponding month of 1888, occurs in August and lasts into September but stops abruptly in October when the struggle is ended.”

The easiest way to isolate the election period for statistical analysis, while dealing with the seasonality problem is to cut off the analysis to 6 months rather than 8. Here is how to do that in R:

# Durkheim says the excitement is over at the end of September first <- bou[1:6,] second <- bou[9:14,] third <- bou[17:22,] # Combining 1888 and 1889 together for analysis an2 <- rbind(first, second)

# And now let's construct a new model model_ols2 <- lm(SP1M ~ yr1889 + time + trend + setime + setrend + anomie + level, data = an2)

Alright, but what exactly is anomie? (pronounced an-oh-me)

Durkheim thought of anomie as something that occurs in many forms. The two main examples he gives are economic anomie, the root cause of which is the division of labor and matrimonial anomie that results from the institution of divorce. What all the examples have in common is some new way of thinking weakens the normative power of the old way of thinking.

In the book Suicide, Durkheim investigates the power of marriage in preventing suicides. He concluded it was due to the strength of the ties between husband and wife, and the power of marriage to stop the man from searching for more romantic partners. The normative power was even stronger when the couple had a child. He hypothesized that a higher degree of integration in family life would lead to lower suicide rates. Except this wasn’t the case in countries that had introduced divorce in the late 19th century. There was no benefit to being married — in terms of suicide — once divorce became an option.

Anomie doesn’t, however, refer to normlessness itself, but rather to a sort of passion that results from it. In Durkheim’s view of human nature, known as homo duplex, there is a side to man that is passionate, and another side that is thoughtful and contemplative of the external world.

All of Durkheim’s theories on suicide are based on an emotional mechanism. Fatalistic and Anomic suicide are both high passion suicides. Take away the power and hold of marriage on the individual, and the suicide that results, theoretically, is highly passionate. Perhaps the best way to think about it is Icarus flying too close to the sun.

With France so unstable during the Third Republic, it makes sense that Durkheim would base his theories around stability of societal structures. Boulanger represented instability, and the agitation surrounding his rise appears to have contributed to suicides. However, Durkheim’s argument focuses instead on how the feeling of resolution following the ousting of Boulanger restored some social order and prevented many of these sorts of passionate suicides that would have occurred from ever occurring.

What Durkheim is doing is making a counterfactual claim. If it wasn’t for this event, suicides would have been higher. We have the statistical tools to see if he was right. First, let’s visualize the counterfactual. In time series analysis, these appear as dotted lines.

# First plot the raw data points for 1889 plot(an2$time[7:12], an2$SP1M[7:12], ylab="Suicides Per Million Inhabitants", ylim=c(400, 1000), main="How the 1889 Election Influenced Suicide Rates in France", xlab="Month", pch=20, col="red", xaxt="n") # Add x-axis month labels axis(1, at=1:6, labels= an2$Month[1:6]) # Label the chamber dissolving abline(v=3.5,lty=2) # Label the election abline(v=5.5,lty=2) # Add in the points for the control points(an2$time[1:6], an2$SP1M[1:6], col="blue", pch=20) points(an2$time[1:6], an2$SP1M[1:6], col="black", pch=21) points(an2$time[7:12], an2$SP1M[7:12], col="black", pch=21) # Plot the first line segment lines(an2$time[7:10], fitted(model_ols2)[7:10], col="red",lwd=2) # Plot the second line segment lines(an2$time[10:12], fitted(model_ols2)[10:12], col="red",lwd=2) # Add the counterfactual for the intervention group segments(3, model_ols2$coef[1] + model_ols2$coef[3] * 3 + model_ols2$coef[2] + model_ols2$coef[5] * 3, 6, model_ols2$coef[1] + model_ols2$coef[3] * 6 + model_ols2$coef[2] + model_ols2$coef[5] * 6, lty=2,col='red',lwd=2) # Plot the first line segment for the control group lines(an2$time[1:6], fitted(model_ols2)[1:6], col="blue",lwd=2) # Add the counterfactual for the control group segments(1, model_ols2$coef[1]+ model_ols2$coef[3], 6, model_ols2$coef[1] + model_ols2$coef[3] * 6, lty=2,col='blue',lwd=2) # Add in a legend legend(x=1, y=550, legend=c("1888","1889"), col=c("blue","red"),pch=20) # Add a box to show the higher intensity rect(3.5, 0, 5.5, 5000, border = NA, col= '#00000011') # Add a box to show the lighter intensity rect(4, 0, 5.5, 5000, border = NA, col= '#00000011')

By comparing the predicted value of our model to the counterfactual, we can estimate how many suicides were prevented in the month of September.

# Predicted value 2 months after chambers dissolved pred <- fitted(model_ols2)[11] # Estimate the counterfactuals at 5 months (2 after chambers dissolved) cfac <- model_ols2$coef[1] + model_ols2$coef[3] * 5 + model_ols2$coef[2] + model_ols2$coef[5] * 5 + model_ols2$coef[4] * 2 + model_ols2$coef[8]

Now that we have the prediction and the counterfactual, we can just do some simple calculations

# Absolute change at 5 months pred - cfac 79.52174 lives per million saved in September 1889 # Relative change at 5 months (pred-cfac) / cfac Decrease of 11.57%

By comparing to a counterfactual, we estimate that — remarkably — as many as 80 deaths from suicide were prevented as the Boulanger agitation came to a close.

Further Reading

This type of approach to sociology was what got me hooked to begin with. I would strongly recommend checking out a recent paper by Peter Bearman and Mark Anthony Hoffman of Columbia University. It is called Bringing Anomie Back In

Feel free to examine the data further, and see if you can construct a better model. I would be very interested to improve on this analysis.

Share your thoughts in the comments!