Representation by law? Gender quotas in Brazil’s elections

On January 1st, 2013, 7,646 women took office as members of the local legislatures in more than five thousand municipalities in Brazil. 665 women were also elected as mayors in these municipalities, marking the largest number of women to enter local office in Brazil’s history.

Is this latest achievement part of the trend started two years ago, when on January 1st, 2011, Dilma Rousseff took office as President of the country? After all, in 2010, not only did Brazil elect its first female president, Marina Silva also gained the largest vote share of any third-runner for the presidency since re-democratization in 1989. Or, are these numbers the direct effect of the gender quota law enacted in 2009? This law requires that a minimum of 30%, of women be on party lists for proportional elections (local, state, and federal legislators). This 2009 law, applied for the first time in the 2012 elections, made a similar 1997 gender quota law more effective by forcing parties to actually enlist women to their tickets.

Examining only the absolute number of elected women in Brazil can be misleading, however. Indeed, careful examination suggests that the proportion of elected women has only risen slightly despite the more effective enforcement of the quota law.


The graphs above tell us part of the story. We can see that the proportion of elected women still remains significantly less than elected men. The first graph indicates that  even though the number of female candidates for local chambers has risen sharply (due to the enforcement of the quota law), the proportion of elected local female legislators is still very small.  Similarly, the proportion of elected state and federal deputies is remarkably stable even through there has been a rise in the number of candidates in the 2010 elections, as shown in the second graph. Given that the gender quota does not affect majoritarian elections, it is a bit surprising that the proportion of elected female mayors has been rising more rapidly than the proportion of elected female local legislators.

One possible explanation for the under-performance of women in elections for local chambers is the lack of resources and support provided by the parties, which recruit women simply in order to formally reach the threshold demanded by the law. This is a difficult hypothesis to test but the graphs above shed some light on it. For instance, the number of female candidates who identified as “housewives” increased in the 2012 elections. This may reflect the greater influence the Electoral Justice had on parties to obey the gender quota law, leading parties to enroll female candidates who were related to existing male candidates.

The gender quota law provides a necessary first step towards equal gender representation. Nevertheless, making sure women have spots on party lists does not guarantee that they will have the resources or access to other factors necessary to get elected.  Like other types of affirmative action, quotas tackle issues of inequality by guaranteeing access of underprivileged groups to the arenas in which they are systematically under-represented (these arenas could be the realm of elections, universities or jobs, among others). Yet, whether this type of affirmative action proves ultimately effective hinges upon empirical and normative assessments.


Affirmative action in Brazil: the challenges of racial classification

It’s old news that Brazil is enacting social quotas – both socioeconomic and racial – for public higher education. In my earlier post, I detailed the impact this sort of policy could have on the quality of higher education.  However, before I had the chance to write a follow-up to that post, a new piece of legislation began being drafted to introduce affirmative action to the civil service.

This is not the first policy of its kind in Brazil. Yet, it is too soon to discuss the implications and effects of this law. Regardless of the final shape the bill takes, any affirmative action will have to grapple with the basic issue of identification of the beneficiaries.

In Brazil, racial classification has always been a contentious topic. For many decades, the government refused to even collect racial information, arguing that race was not a salient issue on this side of the Americas.  However, even if one agrees that there is racial discrimination in Brazil, and that part of the country’s huge inequality hinges upon race and not only class and education, the issue of racial classification is not something to be quickly dismissed. A recent New York Times  forum, for instance, shows very different perspectives.

On the one hand, Peter Fry, a leading anthropologist, argues: “[…], unlike the U.S., the majority of Brazilians do not classify themselves neatly into blacks and whites. In Brazil, therefore, eligibility for racial quotas is always a problem.”

On the other hand, Antonio Sergio Guimarães, a leading sociologist fights back:

Perhaps the biggest challenge in Brazil is the temptation to introduce a systematic verification of self-declared color or race to prevent fraud in affirmative action programs. Race and color are social constructs. It is impossible to define their borders scientifically. Passing is something inherent to this kind of classification. It can be motivated by selfish economic protection or by political altruistic reasons. The fear of fraud must be restrained to give a chance to these programs to flourish.

Ultimately, these scholars seem to be discussing an empirical and methodological issue of racial classification with wide implications for redistribution. Despite the known complexities of racial classification, much analysis relies on a single self-classification based on fixed, mutually exclusive, choices.

Bailey, Loveman and Muniz (2012) present an interesting analysis of Brazil’s racial make-up and racial inequality, taking different racial classification schemes into consideration:

They demonstrate that very different pictures of Brazil’s racial make-up are created depending on which scheme is followed. Comparing the most extreme cases, Brazil could be either 70.4% or 40.7% White. Beneficiaries of affirmative action could either comprise 29.6% or 59.3% of the population. These are hugely different percentages.

Furthermore, these different measurements are not necessarily robust.  Even if more than one measure is used, there is still a lot of incongruence.

In their paper, they go on to convincingly show that different measures also imply different mappings of income inequality between those groups. Their findings do not necessarily challenge the finding that Blacks are, on average, worse off than Whites, but they do bring more precise, rigorous, and contextual evidence to support that claim. In any case, these findings do not mean that race should be disregarded and that it does not influence social interactions in Brazil. They argue that these different measures provide more evidence that race is a multi-dimensional social construct and should be analyzed as such – there is no “true race” to be measured.

But, what do these findings tell us when discussing redistributive policies based on race? Do these inconsistencies hinder any systematic implementation of affirmative action? Or are inconsistencies (and, to some extent, fraud) a “lesser-evil”, with affirmative action a good idea despite these issues? The recent policies seem to have embraced affirmative action despite these problematic measurement issues. The consequences of these choices are still to be fully understood.

Affirmative Action in Brazil: The Country of Racial Inequality Battles the Country of Racial Democracy

On August 29th, President Dilma Rousseff of Brazil signed into law a policy that would require public federal universities to reserve at least half of their admission spots for students who had attended public high schools. The law also dictates that there should be quotas based on the racial composition of the state in which the university is located; that is, the number of students admitted should mirror the racial composition of the state.

This law has generated a lot of debate, with the introduction of the racial quota proving particularly controversial. While the debate isn’t new, this latest development marks a major milestone. Last April, the Supreme Court ruled in favor of racial quotes at University of Brasília (UnB), finally burying the much-used argument that racial quotas were unconstitutional, in turn paving the way for this law.

Most students in public federal universities, which are usually of better quality than private universities, come from private elementary and high schools. In four years, Brazilian federal universities will be very different in terms of the demographics of their students, particularly in comparison to the situation prior to 2003, before the first experiment with affirmative action and the expansion of federal universities through a policy of restructuring of higher public education (REUNI).

Now that the social quotas have been enacted by law, universities have four years to adapt to the changes. The effect of the law will certainly vary depending on the field of study – medicine, law and engineering are usually more competitive than other undergraduate majors. But what can we generally expect as a result of this development? In particular, will quotas “lower the quality” of teaching and research in public universities?

There are as many ways to answer this question as there are ways to measure the “quality” of schools and students. Based on the experience of the Federal University of Bahia (UFBa), Antonio Sergio Guimarães et al. collected data and provided analysis on three measures: absolute performance, relative performance, and dropout rates (if you’re interested, there are many others that you can find mentioned here).

Absolute performance is a standardized measure based on coursework grades. Relative performance assesses the development of the students: do students improve their relative positions from entrance admission scores to coursework scores? In other words, are students climbing up the “educational ladder” while in college, are the entrance positions relatively stable over time, or are students’ performances exacerbating the entrance score differences?

Based on these measures, Guimarães et al. compare three types of students: non-beneficiaries (students that did not fill quotas requirements), non-effective beneficiaries (students that, even though they fulfilled the quota requirements, would have been admitted without quotas), and effective beneficiaries (students that were admitted because of the quotas).

They show that, as expected, effective beneficiaries indeed are in much worse socioeconomic positions than the other types of students. Also, effective beneficiaries have, on average, worse absolute performance, as the graph below shows (vertical axis gives the absolute performance, blue and green ticks present the estimate for non-beneficiaries and non-effective beneficiaries, and yellow ticks are for effective beneficiaries):

In terms of relative performance, the authors find that effective beneficiaries perform, on average, similar to other types of students. And, based on a few measures of relative performance, they clearly outperform both the non-beneficiaries and the non-effective beneficiaries.

This point deserves greater attention. It could be argued that effective beneficiaries outperform other students in terms of relative performance because they have more room for improvement given their lower entrance exam scores. The authors acknowledge this point and, based on a series of analyses, attempt to measure the degree to which effective beneficiaries were able to rise to the challenge and keep up with other types of students, accounting for this initial improvement. Their analysis shows that 50% of effective beneficiaries, on average, improved their performance in college (compared to the entrance scores) and rose to the same level as other types of students. In low demand majors, this percentage is as high as 75% and in high demand majors, the percentage is about 40%.

Furthermore, even though it takes longer for effective beneficiaries to graduate (many of them have to work while attending school), they dropout at lower rates than other types of students. This is demonstrated in the graph below (colors are the same as above, blue for non-beneficiaries, green for non-effective beneficiaries, and yellow for effective beneficiaries):

The answer, then, to the fear that social and racial quotas will lower the “quality” of public universities is a cautious “no”. There are many threats to the future of public higher education in Brazil, but, based on the pieces of evidence we have, social quotas do not seem to be a particularly threatening one.