Election Frauds, Postelection Legal Challenges and Geography in Mexico

Post developed by Yioryos Nardis in coordination with Walter Mebane.

ICYMI (In Case You Missed It), the following work was presented at the 2015 Annual Meeting of the American Political Science Association (APSA).  The presentation, titled “Election Frauds, Postelection Legal Challenges and Geography in Mexico,” was a part of the session “Detecting and Concealing Patterns in Data” on Saturday September 5th, 2015.

Political Science and Statistics Professor and Center for Political Studies faculty member Walter Mebane previously examined electoral fraud in Russia. Professor Mebane, in collaboration with Research Assistant Jonathan Wall, now turns his focus to Mexico and the Presidential elections of 2006 and 2012.

This new research by Mebane and Wall investigates if the numbers of casillas (i.e. ballot boxes) and votes challenged and nullified in Mexico reflect political strategies or genuine election irregularities. In the 2006 elections in Mexico, nullification petitions by runners up were filed against 21% of ballot boxes (27,109/130,788), even though only .56% of votes (237,736/41,791,322) were actually nullified. Similarly, in the 2012 elections, 22% of ballot boxes (32,151/143,132) were challenged and .38% of votes nullified (184,725/49,087,466).

Mebane and Wall examine how the types of nullification claims relate to ballot-box level measures of election fraud and whether the reasons cited for the challenges are uniform across the two elections and/or geography. That is, are complaints geographically clustered or does a complaint depend on geographically clustered frauds?

Ballot-box level measures of election fraud are estimated using casilla vote count data. Hotspot analysis is used to show how nullification petition challenges to casillas are distributed across geography. This technique identifies which locations have local means that are higher than the overall average values and which have local means that are lower than the overall average. A redder color indicates a cluster of locations with higher than average values, and a bluer color indicates a cluster of locations with lower than average values. Grey indicates a cluster of locations that does not differ significantly from the overall mean.

Figure 1 represents nullification complaints of type corresponding to willful misconduct or error in the vote count in the two Presidential elections, and Figure 2 represents incremental fraud probabilities for nullification complaints. Comparing Figure 2 to Figure 1 indicates that the pattern of geographic clustering for the incremental fraud probabilities does not correspond well with the pattern for nullification complaints.

Figure 1: Nullification complaints of type corresponding to willful misconduct or error in the vote count, Mexico, 2006 and 2012 Presidential election Casillas.
Seccion (precint) geographic cluster hotspots

Figure 1aFigure 1b

Figure 2: Incremental fraud, Mexico, 2006 and 2012 Presidential election casillas.
Seccion (precint) geographic cluster hotspots

Figure 2aFigure 2b

In 2006 there are more widespread regions with clusters of casillas having above average frequencies of complaints than there are regions in which there are clusters of casillas with above average incremental fraud probability values. Some of the above average type clusters overlap with above average incremental fraud clusters, but more than half do not. In 2012 on the other hand, we observe the opposite. Clusters of casillas with above average incremental fraud probabilities are much more prevalent than are clusters of casillas with above average frequencies of complaints.

Such patterns indicate that it is unlikely that the relationship between incremental fraud probabilities and the incidence of complaints are positively related. This therefore suggests that the occurrence of nullification petitions is related to the strategic and tactical incentives of political parties.

To read the full paper please visit: https://drive.google.com/open?id=0By8J0EDg6IC3MDgxOWlUVGdfakE

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