# Voting Twice in France

February 19, 2014
By

(This article was first published on Freakonometrics » R-english, and kindly contributed to R-bloggers)

On the Monkey Cage blog, Baptiste Coulmont (a.k.a. @coulmont) recently uploaded a post entitled “You can vote twice ! The many political appeals of proxy votes in France“, coauthored with Joël Gombin (a.k.a. @joelgombin), and myself. The study was initially written in French as mentioned in a previous post. Baptiste posted additional information on his blog (http://coulmont.com/blog/…) and I also wanted to post some lines of code, to mention a model that was not used in that study (more complex to analyze, but more realistic, and with the same conclusions). The econometric study is based on aggregated voted, with a possible ecological misinterpretation.

• Regression Model: Possible Explanatory Variables

The first idea was to model proxies using a binomial regression, per pooling station $P_i\sim\mathcal{B}(N_i,p_i)$ where $P_i$ denote the number of proxy vote, per station $i$, and $N_i$ denotes the number of voters. Proportion $p_i$ can be a function of possible explanatory variables (on Baptiste’s blog there are additional information about the datasets, obtained from insee.fr and opendata.paris.fr)

```> bt1=read.table("paris2007-pres-t1.csv",header=TRUE,sep=";")
> bv\$BV=bv\$BVCOM
> baset1=merge(bt1,bv,by="BV")
> baset2=merge(bt2,bv,by="BV")
> baset1\$LOGEMENT=baset1\$PROPRIO+baset1\$LOCNONHLM+baset1\$LOCHLM+baset1\$GRATUIT
> baset2\$LOGEMENT=baset2\$PROPRIO+baset2\$LOCNONHLM+baset2\$LOCHLM+baset2\$GRATUIT```

For instance, assume that $p_i$ is a function of the proportion of owner of the place people live in, denoted $X_i$ in the neighborhood of the pooling station,

```> variable="PROPRIO"
> reference="LOGEMENT"
> baset1\$taux=baset1[,variable]/baset1[,reference]
> baset2\$taux=baset2[,variable]/baset2[,reference]```

We can consider a logistic regression

$p_i=h(X_i)=\frac{\exp[\beta_0+\beta_1 X_i]}{1+\exp[\beta_0+\beta_1 X_i]}$

or a logistic regression with splines, if we do not want to assume a linear model

$p_i=\tilde h(X_i)=\frac{\exp[s(X_i)]}{1+\exp[s(X_i)]}$

With cubic splines, the code is

```> b=hist(baset1\$taux,plot=FALSE)
> library(splines)
> regt1=glm(PROCURATIONS/INSCRITS~bs(taux,6),family=binomial,weights=INSCRITS,data=baset1)
> regt2=glm(PROCURATIONS/INSCRITS~bs(taux,6),family=binomial,weights=INSCRITS,data=baset2)
> u=seq(min(baset1\$taux)+.015,max(baset1\$taux)-.015,by=.001)
> ND=data.frame(taux=u)
> ug=seq(0,max(baset1\$taux)+.05,by=.001)
> pt1=predict(regt1,newdata=ND,se=TRUE,type="response")
> pt2=predict(regt2,newdata=ND,se=TRUE,type="response")
> library(RColorBrewer)
> CL=brewer.pal(6, "RdBu")
> plot(ug,ug*1,col="white",xlab=nom,ylab="Taux de procuration",
+ ylim=c(0,.1))
> for(i in 1:(length(b\$breaks)-1)){
+ polygon(b\$breaks[i+c(0,0,1,1)],c(0,b\$counts[i],b\$counts[i],0)
+ /max(b\$counts)*.05,col="light yellow",border=NA)}
> polygon(c(u,rev(u)),c(pt1\$fit+2*pt1\$se.fit,rev(pt1\$fit-2*pt1\$se.fit)),
+ border=NA,density=30,col=CL[4])```

while a standard logistic regression would be

```> lines(u,pt1\$fit,col=CL[6],lwd=2)
> polygon(c(u,rev(u)),c(pt2\$fit+2*pt2\$se.fit,rev(pt2\$fit-2*pt2\$se.fit)),
+ border=NA,density=30,col=CL[3])
> lines(u,pt2\$fit,col=CL[1],lwd=2)
> regt1l=glm(PROCURATIONS/INSCRITS~taux,family=binomial,weights=INSCRITS,data=baset1)
> regt2l=glm(PROCURATIONS/INSCRITS~taux,family=binomial,weights=INSCRITS,data=baset2)
> ND=data.frame(taux=ug)
> pt1l=predict(regt1l,newdata=ND,se=TRUE,type="response")
> pt2l=predict(regt2l,newdata=ND,se=TRUE,type="response")
> lines(ug,pt1l\$fit,col=CL[5],lty=2)
> lines(ug,pt2l\$fit,col=CL[2],lty=2)
> legend(0,.1,c("Second Tour","Premier Tour"),col=CL,
+ lwd=2,lty=1,border=NA)```

Here it is (the confidence region is for the spline regression) with on blue the first round of the Presidential election, and in red, the second round (in France, it’s a two-round system)

(the legend of the y axis is not correct). We can consider as explanatory variable the rate of H.L.M., low-cost housing or council housing,

If I like the graph, unfortunately, the interpretation of coefficient $\beta_1$ might be complicated

```> summary(regt1l)

Call:
glm(formula = PROCURATIONS/INSCRITS ~ taux, family = binomial,
data = baset1, weights = INSCRITS)

Deviance Residuals:
Min        1Q    Median        3Q       Max
-12.9549   -1.5722    0.0319    1.6292   13.1303

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.70811    0.01516  -244.6   <2e-16 ***
taux         1.49666    0.04012    37.3   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 12507  on 836  degrees of freedom
Residual deviance: 11065  on 835  degrees of freedom
AIC: 15699

Number of Fisher Scoring iterations: 4

> summary(regt2l)

Call:
glm(formula = PROCURATIONS/INSCRITS ~ taux, family = binomial,
data = baset2, weights = INSCRITS)

Deviance Residuals:
Min        1Q    Median        3Q       Max
-15.4872   -1.7817   -0.1615    1.6035   12.5596

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.24272    0.01230 -263.61   <2e-16 ***
taux         1.45816    0.03266   44.65   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 9424.7  on 836  degrees of freedom
Residual deviance: 7362.3  on 835  degrees of freedom
AIC: 12531

Number of Fisher Scoring iterations: 4```

So we did consider a standard linear regression model, for the proxy rate, per station,

$\frac{P_i}{N_i}=\beta_0+\beta_1 X_i+\varepsilon_i$

(again, either a model with splines, or a standard linear model). The code is

```> regt1=lm(PROCURATIONS/INSCRITS~bs(taux,6),weights=INSCRITS,data=baset1)
> regt2=lm(PROCURATIONS/INSCRITS~bs(taux,6),weights=INSCRITS,data=baset2)
> u=seq(min(baset1\$taux)+.015,max(baset1\$taux)-.015,by=.001)
> ND=data.frame(taux=u)
> ug=seq(0,max(baset1\$taux)+.05,by=.001)
> pt1=predict(regt1,newdata=ND,se=TRUE,type="response")
> pt2=predict(regt2,newdata=ND,se=TRUE,type="response")
> library(RColorBrewer)
> CL=brewer.pal(6, "RdBu")
> plot(ug,ug*1,col="white",xlab=nom,ylab="Taux de procuration",
+ ylim=c(0,.1))
> for(i in 1:(length(b\$breaks)-1)){
+ polygon(b\$breaks[i+c(0,0,1,1)],c(0,b\$counts[i],b\$counts[i],0)
+ /max(b\$counts)*.05,col="light yellow",border=NA)}
> polygon(c(u,rev(u)),c(pt1\$fit+2*pt1\$se.fit,rev(pt1\$fit-2*pt1\$se.fit)),
+ border=NA,density=30,col=CL[4])
> lines(u,pt1\$fit,col=CL[6],lwd=2)
> polygon(c(u,rev(u)),c(pt2\$fit+2*pt2\$se.fit,rev(pt2\$fit-2*pt2\$se.fit)),
+ border=NA,density=30,col=CL[3])
> lines(u,pt2\$fit,col=CL[1],lwd=2)
> regt1l=lm(PROCURATIONS/INSCRITS~taux,weights=INSCRITS,data=baset1)
> regt2l=lm(PROCURATIONS/INSCRITS~taux,weights=INSCRITS,data=baset2)
> ND=data.frame(taux=ug)
> pt1l=predict(regt1l,newdata=ND,se=TRUE,type="response")
> pt2l=predict(regt2l,newdata=ND,se=TRUE,type="response")
> lines(ug,pt1l\$fit,col=CL[5],lty=2)
> lines(ug,pt2l\$fit,col=CL[2],lty=2)
> legend(0,.1,c("Second Tour","Premier Tour"),col=CL,
+ lwd=2,lty=1,border=NA)```

Here is again the evolution as a function of the rate of owner of their homes,

The graph is rather close to the one before, and here, the interpretation of the summary table is more conventional,

```> summary(regt1l)

Call:
lm(formula = PROCURATIONS/INSCRITS ~ taux, data = baset1, weights = INSCRITS)

Weighted Residuals:
Min      1Q  Median      3Q     Max
-1.9994 -0.2926  0.0011  0.3173  3.2072

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.021268   0.001739   12.23   <2e-16 ***
taux        0.054371   0.004812   11.30   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.646 on 835 degrees of freedom
Multiple R-squared:  0.1326,	Adjusted R-squared:  0.1316
F-statistic: 127.7 on 1 and 835 DF,  p-value: < 2.2e-16

> summary(regt2l)

Call:
lm(formula = PROCURATIONS/INSCRITS ~ taux, data = baset2, weights = INSCRITS)

Weighted Residuals:
Min      1Q  Median      3Q     Max
-2.9029 -0.4148 -0.0338  0.4029  3.4907

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.033909   0.001866   18.17   <2e-16 ***
taux        0.079749   0.005165   15.44   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.6934 on 835 degrees of freedom
Multiple R-squared:  0.2221,	Adjusted R-squared:  0.2212
F-statistic: 238.4 on 1 and 835 DF,  p-value: < 2.2e-16```

We have used those codes to produce the graphs mentioned in the post. But before mentioning the residuals of the multiple model we considered, I wanted to share some awesome code that produce maps (I can say that those codes are awesome since Baptiste wrote most of them).

• Visualization of Residuals on a Map of Paris

To plot the neighborhood of the pooling stations, one more time the post on Baptiste’s blog, explains how the shapefile was obtained from cartelec.net

```> library(maptools)
> library(rgdal)
> library(classInt)

To visualize the proxy rate (the average of round one and round two), here is the code

```> elec=data.frame()
> elec=cbind(bt1\$BV,(bt1\$PROCURATIONS+bt2\$PROCURATIONS),(bt1\$EXPRIMES+bt2\$EXPRIMES))
> colnames(elec)=c("BV","PROCURATIONS","EXPRIMES")
> elec=as.data.frame(elec)
> elec\$BV=bt1\$BV```

To get nice colors, function of the rates, we use

```> m=match(paris\$BUREAU,elec\$BV)
> plotvar=100*elec\$PROCURATIONS/elec\$EXPRIMES
> nclr=7
> plotclr=brewer.pal(nclr,"RdYlBu")[nclr:1]
>(plotvar[m], nclr, style="fisher",dataPrecision=1)
> colcode=findColours(class, plotclr)```

and finally

```> par(mar=c(1,1,1,1))
> plot(paris,col=colcode,border=colcode)
> legend(656274.9, 6867308,legend=names(attr(colcode,"table")),
+ fill=attr(colcode, "palette"), cex=1, bty="n",
+ title="Frequence procurations (%)")```

If we consider a model with three explanatory variable, to explain the proxy rate,

```> regt1=lm(PROCURATIONS/INSCRITS~I(POP65P/POP)+
+ I(PROPRIO/LOGEMENT)+I(CS3/POP1564),weights=INSCRITS,data=baset1)```

we can plot the residuals using

```> m=match(paris\$BUREAU,elec\$BV)
> plotvar=100*residuals(regt1)
> nclr=7
> plotclr=brewer.pal(nclr,"RdYlBu")[nclr:1]
>(plotvar[m], nclr, style="fisher",dataPrecision=1)
> colcode=findColours(class, plotclr)
> par(mar=c(1,1,1,1))
> plot(paris,col=colcode,border=colcode)
> legend(656274.9, 6867308,legend=names(attr(colcode,"table")),
+ fill=attr(colcode, "palette"), cex=1, bty="n",title="Residus")```

It might not be a pure random spatial noise… But we could not get better with our small set of covariates.

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