On July 1, over 50 million Mexicans went to the polls to elect the next President of the Republic. The official count showed the Institutional Revolutionary Party (PRI) candidate, Enrique Peña Nieto, as winning with 38.21% of the vote. He was followed by Democratic Revolucionary Party (PRD) candidate Andrés Manuel López Obrador, who received 31.59% of the vote and National Action Party (PAN) candidate Josefina Vázquez Mota with 25.41% of the vote.

Electoral geography is a tool we use to visualize the overarching factors that divide Mexican society and motivate the citizens to express their distinct electoral preferences. Using a statistical analysis that encompasses the more than 66 thousand electoral sections based on the 2010 census cartography (created through a noble and intense effort by the IFE and INEGI), this report discusses which factors best explain voting behavior on election day. Notably, our analysis differs from those based on exit polls, which more accurately reflect the actual vote, but by their nature do not contain enough questions to assess in more depth the determinants behind that vote.(En): On June 1, over 50 million Mexicans went to the polls to elect the next President of the Republic. The official count showed the Institutional Revolutionary Party (PRI) candidate, Enrique Peña Nieto, as winning with 38.21% of the vote. He was followed by Democratic Revolucionary Party (PRD) candidate Andrés Manuel López Obrador, who received 31.59% of the vote and National Action Party (PAN) candidate Josefina Vázquez Mota with 25.41% of the vote.

One of the great advantages of working with electoral section data is that they reflect voting decisions of millions of voters. The risk associated with census data and exit polls is that they represent aggregated rather than individualized data because people’s votes are secret. With aggregated data, we cannot be absolutely certain that what happens in the aggregate also applies to all individuals within the group. Nevertheless, the possibility for error is diminished as we work with a large number of jurisdiction units that are highly disaggregated. 

(Es): El pasado primero de julio más de 50 millones de mexicanos acudieron a las urnas para elegir al futuro Presidente de la República. El conteo nacional reflejó al candidato del Partido Revolucionario Institucional (PRI), Enrique Peña Nieto (EPN), como el ganador de la contienda electoral al recibir 38.21% de los votos. En segundo lugar se ubicó el candidato del Partido de la Revolución Democrática (PRD), Andrés Manuel López Obrador (AMLO) con 31.59% de los votos, seguido por la candidata del Partido Acción N, Josefina Vázquez Mota (JVM), con el 25.41% de los votos.

La geografía electoral permite visualizar de manera contundente los factores que dividen a la sociedad mexicana y motivan a los ciudadanos a expresar preferencias electorales distintas. Por medio de un análisis estadístico de las más de 66 mil secciones electorales y con base en la cartografía de los datos censales de 2010 (creada en un esfuerzo notable del Instituto Federal Electoral y el Instituto Nacional de Estadística y Geografía), este reporte discute qué factores explican los resultados del comportamiento electoral tal como sucedió el día de las elecciones. Cabe destacar que nuestro análisis difiere de los que se basan en las encuestas de salida, las cuales reflejan con más precisión el voto efectivo, pero por su naturaleza no contienen suficientes preguntas que permitan evaluar en manera más profunda las determinantes del voto.

Una de las grandes ventajas de trabajar con los datos de las secciones electorales es que éstos reflejan las decisiones efectivas de millones de votantes. El riesgo de utilizar datos censales y votos efectivos es que nos vemos obligados a trabajar con datos agregados, pues el voto individual es secreto. El problema de la agregación es que no se puede tener certeza absoluta de que lo que sucede en el agregado también lo sea para los individuos que constituyen el conjunto. Sin embargo, las posibilidades de error se ven disminuidas al trabajar con un número tan grande de unidades jurisdiccionales que son sumamente desagregadas.

Merit‐based incentives are a topic of growing interest in labor economics due to their potential to increase performance for private and public employees. Following this argument, such pay schemes have been applied in numerous countries to provide incentives to teachers and schools based on their students’ achievement scores and other performance metrics. However, because of the multi-task, multi-principal and multi-period nature of education, they present several caveats. Observational and experimental research provides ambiguous conclusions about their impact. This paper contributes to this literature by evaluating the effects of the Mexican PECD, a program that since 2010 has provided salary bonuses to teachers in primary and secondary public schools based on their national standardized tests scores. Nearly 30,000 schools and 300,000 teachers have benefited from this program in its first implementation. Given its characteristics, the PECD provides an ideal ground for a Regression Discontinuity Design (RDD) and for a Multiple Rating‐Score Discontinuity (MRSD). Combining these quasi‐experimental techniques with a Hierarchical Linear Model (HLM), I show that the effect of this program on student performance is null. If any, this program seems to harm student performance; this negative effect is more evident for indigenous schools.