Gender stereotypes in politics: what changes when a woman becomes the local political leader?

This study documents how the presence of a woman in an executive political role affects the gender stereotype of women in politics. We use Brazilian electoral data and restrict our focus to close mayoral races (using an RDD design) in which the top two candidates are of opposite sexes. Our most important result was a reduction in the number of candidates and votes for female mayoral candidates after a woman is elected, regardless of her eligibility status for reelection. This negative result is linked only to the position of mayor and not to other political positions (councilor, state or federal deputy). In addition, our results may be interpreted as evidence that voters do not use their update on women as local leaders to change their beliefs on women’s ability to run for other political positions. Finally, female mayors do not appear to have a role model effect on younger cohorts of women. We also note that our results are not influenced by differences in mayoral policies (generally and specifically for women), which could influence voters’ gender stereotypes.

Changes in test scores distribution for students of the fourth grade in Brazil: a relative distribution analysis for the years 1997 to 2005

To assess the quality of school education, much of educational research is concerned with comparisons of test scores means or medians. In this paper, we shift this focus and explore test scores data by addressing some often neglected questions. In the case of Brazil, the mean of test scores in Math for students of the fourth grade has declined approximately 0,2 standard deviation in the late 1990s. But what about changes in the distribution of scores? It is unclear whether the decline was caused by deterioration in student performance in upper and/or lower tails of the distribution. To answer this question, we propose the use of the relative distribution method developed by Handcock and Morris (1999). The advantage of this methodology is that it compares two distributions of test scores data through a single distribution and synthesizes all the differences between them. Moreover, it is possible to decompose the total difference between two distributions in a level effect (changes in median) and shape effect (changes in shape of the distribution). We find that the decline of average-test scores is mainly caused by a worsening in the position of all students throughout the distribution of scores and is not only specific to any quantile of distribution.