The changing structure of school to work transition in Brazil

: This paper provides an in-depth analysis of the changing structure and consequences of the links between the educational system and labor markets in Brazil in the past 30 years. Using the linkages methodology proposed by DiPrete et al. (2017) and Elbers (2021), we identify changes in education-occupation linkage that were offset by Brazil’s rapid educational expansion, distinguishing between changes in rates and changes in the structure of school-to-work linkages. We find that the overall compositional shift towards higher educational levels did not have substantial effects on occupational allocation, yet the trends for younger and older workers, as well as for workers with different fields of study in the tertiary system, were fundamentally different. Our findings suggest that the features of educational expansion in Brazil – i

The findings of the school to work literature are based mostly in the experience of industrialized societies. In these contexts, once primary and secondary education became nearly universal, scholars started to pay attention to the ways that post-secondary education expansion was accompanied by the differentiation and inequality of educational systems (Wolbers 2007). In common, scholars highlighted the importance for the school to work literature to consider the heterogeneity of educational credentials among workers with the same levels of education, but with specializations in different fields of study (Gerber and Cheung 2008 (Torche and Costa-Ribeiro 2012). Moreover, in the last three decades, the steady expansion of tertiary education in Brazil has amplified the heterogeneity of education and labor markets experiences between older and younger workers within the country (Santos, Lima and Carvalhaes 2020).
In this paper, we examine these trends through an investigation of how the linkages between the education and labor markets changed in Brazil between 1991 and 2010. Specifically, we investigate the changes in how a diverse set of educational credentials connect workers to certain occupations in the last 30 years. We implement this analysis by adopting the linkages methodology proposed by DiPrete et al (2017) and Elbers (2021). Together, these papers propose that the analysis of school-towork transitions should be studied as a segregation problem. These scholars define strong linkage for any specific credential as occurring when individuals with that credential are more strongly clustered in a We explain these results in detail below. We start by presenting the main institutional features and changes that happened in the education and labor markets in Brazil in the past 30 years.

Education: institutional characteristics and recent expansion
Since 1996, the Brazilian education system consists of eight years of elementary school, divided into the initial four years of primary education, the last four years of lower secondary education, and the three years of upper secondary education or high school (Stanek 2013). Formally, the Brazilian system provides a vocational education option at the upper secondary level, but few students attend these schools (in the age group from 15-20, Although there are many mandatory requirements for general education, and schools throughout the country must provide the same group of disciplines, the quality of the education offered varies greatly between schools (Alves, Soares and Xavier 2016). Commonly, the great divide is between public and private institutions, which in elementary education represent 85% and 15% of the system, respectively. The standardization of the quality of educational provision is thus very low. In Table 1, below, we use the standard comparative categories used by the school to work literature to classify Brazil's educational system.

Labor market: occupational distribution and main institutional features
For the past 30 years, Brazil's workforce has been mostly concentrated in urban areas (94%) and distributed across the following activities: 42% in service activities, 19% in industrial activities, 18% in farming, 13.6% in trade and 8.3% in other activities. 6 As can be seen in Figure 2

Methods and data
To understand the ways that school to work On the other hand, if the conditional occupational distribution for an educational credential g is very different from the overall occupational distribution in the labor force, then the educational credential leads to strong linkage: In this case, the educational credential helps predicting the occupation of a worker. This is the intuition that is captured in the formula for L g .
To arrive at a summary measure of linkage for the entire labor force, we take a weighted average of L g across all educational credentials: The weights are given by the sizes of the educational categories, and naturally ∑ g p. g = 1. In rely on a procedure fully described in Elbers (2021).
Due to space constraints, the full methodology cannot be explained here, but the basic idea is to create counterfactual scenarios in which (a) the margins are fixed, but the association structure is allowed to vary, and (b) the association structure is fixed, but the margins are allowed to vary. The counterfactual M values of these scenarios are then used to isolate the change that is due to changing margins and the changing association structure.
To run these analyses, we use data from the 1990, 2000 and 2010 Brazilian Censuses. We divided workers into five educational levels: access to schooling, the completion or not of primary education, as well as obtaining secondary and tertiary degrees. More specifically, we coded "No School" workers who had never been to school, "Primary Incomplete" those who attended the educational system but did not complete primary education, "Primary complete" includes graduates of primary education and those that started high school but left it, and "High School diploma" and "Tertiary Education" includes workers that finished and obtained these levels of education. We coded Fields of Study following the Census definition.
Occupational classifications were harmonized using both the three-digit and one-digit International Standard of Occupations (Isco-88).

Results: 30 years of linkages measures
We start with a baseline analysis of total linkage

Sources of change: marginal and structural components
As seen in Figure 3, total linkage has remained remarkably stable between 1991 and 2010. Figure   4 and   This shows that a large part of the decline is due to the Isco major group "Professionals." Especially for this group, then, the educational distribution of workers has become more diverse.

Final remarks
Has workers to prepare better for the labor market?
Our findings reinforce the point made by many stratification scholars that improving educational outcomes in Brazil will involve much more than just a change in the quantity of education (Salata 2018).
It will also need to address educational quality and inequality in the specific fields of study that distinct social groups have access to. In this paper, our main goal has been to construct quantitative measures for thirty years of school-to-work transitions for Brazil that would allow for a detailed understanding of the heterogenous impact of educational expansion for occupational outcomes. As such, we demonstrated that educational expansion in Brazil is leading to an overall weaker association between educational credentials and occupational destinations, but that this trend is very different for younger and older workers, and for workers with distinct educational credentials.
One limitation of our study is that it is insensitive to how gender stratifies linkage strength. In Brazil, women have more years of schooling than men, mainly due to higher completion rates in secondary and tertiary education (Beltrão and Alves 2009). Yet, women are segregated in occupations with lower labor market returns and clustered in fields of study that lead to lower returns (Silveira and Leão 2021).
We hope that the methods and findings of this paper will provide a promising research direction to better understand the processes generating gender wage gaps and occupational segregation in Brazil.