Dynamics of the formal and informal labour in Brazil: occupational and earnings mobility

Francieli Tonet Maciel (Department of Economics, Center for Development and Regional Planning (Cedeplar), Federal University of Minas Gerais, Brazil)
Ana Maria Hermeto C. Oliveira (Department of Economics, Center for Development and Regional Planning (Cedeplar), Federal University of Minas Gerais, Brazil)

International Journal of Development Issues

ISSN: 1446-8956

Publication date: 3 April 2018

Abstract

Purpose

The purpose of this paper is to discuss recent dynamics of the Brazilian labour market, by analysing occupational mobility patterns, specially the transitions between formal and informal labour, and verify the earnings mobility resulting from these transitions, separately by gender.

Design/methodology/approach

The changes in the mobility patterns are analysed by performing an estimation of the transition probabilities between different occupational status between 2002 and 2012, using a multinomial logit model and the microdata from the Monthly Employment Survey (PME). The earnings mobility is analysed by using quantile regressions.

Findings

The results indicate a high degree of mobility from unemployment to formal employment in the period but suggest the persistence of mobility patterns. Women are better off in the period, but only among individuals with better attributes. The earnings mobility results, for women and men, suggest an increase in valuation of the formal labour relatively to informality (informal salaried employment and self-employment), especially at the bottom of the earnings distribution.

Originality/value

The paper contributes to a better understanding of recent changes in occupational mobility patterns between formal and informal labour and the earnings mobility underlying these patterns, accounting for the differences along the earnings distribution and gender issues. That is, it allows identify which groups of workers benefited more from the formalisation process to infer about trends in formal–informal dynamics over the period and discuss the challenges in conducting policies to promote inclusive and quality employment.

Keywords

Citation

Maciel, F. and Oliveira, A. (2018), "Dynamics of the formal and informal labour in Brazil: occupational and earnings mobility", International Journal of Development Issues, Vol. 17 No. 1, pp. 28-54. https://doi.org/10.1108/IJDI-07-2017-0129

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Publisher

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

Literature on the dynamics of labour market informality is wide and covers a variety of theoretical and conceptual approaches because of the complexity of relations between formal and informal economies. Although informality constitutes a structural characteristic of emerging and developing economies, it has also gained global importance in different contexts, even in economies with structured labour markets, renewing interest and fostering a permanent debate about the theme.

A starting point for studies that use the segmentation hypothesis to explain the wage differentials between formal and informal sectors, especially for the literature of the developing countries, is the dual labour market theory (Doeringer and Piore, 1971; Piore, 1972; Reich et al., 1973; and Vietorisz and Harrison, 1973). Although there are different meanings of the concept of segmentation, the key hypothesis is that there are mechanisms of wage determination, which vary according to the labour market segment, and there is rationing of jobs in the formal sector. The emergence of the debate about the informal sector was motivated by International Labour Organization (ILO) studies in the 1970s[1], this debate consisted of a variety of theoretical approaches which resulted in different interpretations of the phenomena. In Latin America, in particular, studies of Souza and Tokman (1976), Tokman (1977) and Souza (1980) stressed the emergence and expansion of the informal sector because of the workforce surplus not absorbed by the formal sector.

In the 1980s, in a context of growing importance of informal activities in different economic and social scenarios, predominant approaches (Portes et al., 1989) emphasised a redefinition of production relations through the connection between formal and informal activities, because of decentralisation and greater flexibility in production, increasing subcontracting relations, especially in developed countries. Others studies, based on the De Soto et al.’s (1986) work, focussed on the association between informality and illegality, highlighting activities outside legal and institutional frameworks and the inadequacy of regulatory frameworks to restrict the expansion of productive informal activities. The occupational strategy approach in the 1990s considers informality as “voluntary”, analysing the opportunity cost of working informally (Fields, 1990; Maloney, 1999). From this point of view, part of the informal sector would reflect an efficient allocation of labour, translating the worker’s deliberate choice.

After 30 years of the introduction of the concept of “informal sector”, the recent literature has changed the terminology, using the broader concept of “informal economy” to capture the dynamic, heterogeneous and complex aspects of a phenomenon that cannot be reduced to a “sector”, in the sense of a specific group of the industry or economic activity (ILO, 2002). The “informal economy” includes a diverse group of workers and companies that operate informally. They differ either by the type of production unit or by occupation status and have in common the lack of recognition within the legal and regulatory frameworks, or under the ILO terms, the “decent work deficits”. Moreover, these workers and companies have as a remarkable feature the high degree of vulnerability.

This process of structural changes was dubbed “informal process” (Cacciamali, 2000). The author argues that this process in Latin America, during the 1990s, walked hand in hand with the diverse possibilities of occupational insertion, as contracts and work relations were reshaped in the formal sector (i.e. the reorganisation of the salaried labour) and other survival strategies such as self-employment. In Brazil and other Latin American countries, where the conditions of a social welfare system have not yet been entirely developed, imposing some resistance to the informal process, trade liberalisation not only dismantled the existing productive base, contracting the salaried employment level, but also stimulated a link of the formal and informal sectors.

Despite the expansion of informality in the 1990s, a reversal of these trends began in the 2000s, marked by a significant growth of overall employment and a lower incidence of informal labour in a significant number of countries in Latin America. In particular, the unemployment rate for the region fell steadily from nearly 11 per cent in 2000 to reach 6.3 per cent in 2013 (ILO, 2016). Although labour informality continues to be one of the region’s distinctive characteristics, its incidence has fallen in many countries during the period –Brazil, Argentina, Paraguay, Ecuador, Peru, Bolivia, Uruguay, among others (Maurizio, 2014). According to data from Brazilian Demographic Census 2000-2010, urban informality decreased by 19 per cent, concomitantly associated with the growth of formal employment. These improvements in the labour market outcomes of the region contrast with employment trends in many advanced economies, such as France, UK, Italy, Greece and Portugal, where employment rates decreased and the incidence of non-standard employment increased, especially between 2007 and 2010 (ILO, 2012).

The development of better conditions particularly in the Brazilian labour market derives from a number of economic, institutional and political factors, which caused not only the increase in labour demand but also the strengthening of the formal employment relations. Furthermore, there were profound and rapid changes in the composition of the workforce because of factors such as aging, rising educational levels and rising women’s labour participation. In this scenario, issues related to occupational mobility dynamics emerge. In a structurally heterogeneous market, changes in the allocation patterns between formal and informal labour could reflect changes in the relative composition between groups, as well as in the earnings differentials between them.

In this sense, it is important to discuss this dynamics in a context of increasing formal labour, as the 2000s. To what extent did increase in formal labour impact different groups of workers to change the conditions of selection between formal and informal labour? In other words, was there a change in the determinants of mobility between formal and informal labour over the period? What are the earnings returns resulting from these occupational transitions? Thus, this paper aims to analyse changes in occupational mobility patterns, specially the transitions between formal and informal labour, and verify the earnings mobility resulting from these transitions, separately by gender. We consider two categories of informal labour: informal salaried employment (without a formal contract) and self-employment. The analysis by gender is important because of the differences of participation in informality, mainly because of the weight of domestic work among Brazilian women.

We use microdata from the Monthly Employment Survey (PME) of the Brazilian Census Bureau (IBGE) for 2002-2012. PME is a household sample survey of longitudinal nature that yields transitions of each individual in consecutive pairs of years. The changes in the mobility patterns are analysed by performing an estimation of the transition probabilities between different occupational status over the period, using a multinomial logit model. To analyse the earnings mobility, we estimate equations for earnings variation resulting from these transitions, using quantile regressions.

This paper comprises five sections apart from this introduction. Section 2 provides a brief review of the empirical context. Section 3 presents the database and methodology. Section 4 presents some descriptive statistics about the transitions between different occupational status and the variation in the earnings related to these transitions. Section 5 discusses the estimated results. Finally, Section 6 concludes the paper.

2. Empirical context

Empirical evidence on informality in different countries and contexts support different theoretical perspectives. Some noteworthy studies that support the dual labour market theory, by estimating wage differentials, are Souza (1980) and Uthoff (1983) for developing countries and DOsterman (1975) and Dickens and Lang (1985) for the USA.

An alternative dualistic approach, advanced by Maloney’s (1999) study of Mexico, is that workers do not “queue up” for jobs in the formal sector. The author uses transition matrices and a multinomial logit analysis. A few years later, Bosh and Maloney (2010) analysed the labour market dynamics in Brazil, Argentina and Mexico during the 1990s and concluded that while self-employment seems to be a voluntary choice, informal salaried work appears to be the result of lack of better opportunities, supporting the queuing view.

In the context of developed countries, many studies examine entry and exit flows in self-employment. Blanchflower (2000), for the Organisation for Economic Co-operation and Development (OECD) countries, argues that the likelihood of being self-employed is higher among men, for those with lower education and increases with age. Taylor (2004) shows that gender, occupation of parents and hours of work are important aspects of self-employment entry, while gender, occupation and duration of self-employment are important determinants of self-employment exit in Britain. Henley (2004) for Britain, Parker and Robson (2004) for OECD countries and Taylor (2011) for some EU countries also suggest a considerable degree of persistence in self-employment.

In the Brazilian literature, there is no consensus regarding existence of segmentation in the labour market, either from the mobility perspective or from the perspective of the earnings differentials between sectors. Curi and Menezes-Filho (2004) find a high mobility among workers in the informal sector and a significantly higher probability of transition to the formal sector than the probability of transition in the opposite direction, for the period between1984 and 2000. In contrast, Soares (2004) reports the existence of segmentation in the Brazilian labour market, as the tests he conducted, using data from 1990, do not reject the jobs rationing hypothesis in the formal sector. Blacks, women, illiterate, young people and workers who were informal in the last job are the workers with greater barriers to be selected from the queue.

Curi and Menezes-Filho’s (2006) results indicate a significant reduction in earnings differentials, which suggest that segmentation is not a major issue in Brazil. Fontes and Pero (2008) and Fontes (2009), on the other hand, claim that there has been an increase in earnings differentials between formal and informal labour, i.e. earnings associated to the transition from informal to formal labour have gained importance overtime, while transitioning from formal to informal implies a reduction on earnings.

To evaluate the most recent period of increase in the formal labour in Brazil and Argentina, Maurizio (2014) analyses the transition probabilities to formal employment in both the countries, between 2003 and 2011, and shows that the transition probabilities vary according to different groups or categories or workers. In particular, middle aged highly educated men have higher chances of mobility towards formal employment, demonstrating the existence of barriers to mobility in both the countries.

The lack of consensus in the Brazilian literature as to whether there is segmentation in the labour market is, in part, because of the different methods and definitions of the concept of “informal labour”, but mostly it reflects the structural heterogeneity of the Brazilian labour market. Moreover, particularly in the 2000s, the empirical literature has failed to encompass the full range of changes:

  • transitions between different status of occupation, also considering the transitions to and from the unemployment and inactivity (out of labour force) status, as it is crucial to address not only the formalisation but also the reduction in unemployment;

  • the returns from these transitions along the earnings distribution; and

  • persistence of the gender gap.

In this sense, this paper aims to contribute to a better understanding of recent changes in occupational mobility patterns between formal and informal labour and the earnings mobility underlying these patterns, accounting for the differences along the earnings distribution and gender issues. That is, identifying which groups of workers benefited more from the formalisation process to infer about trends in formal–informal dynamics over the period 2002-2012.

3. Methodology

3.1 Database

Database consists of microdata from the Monthly Employment Survey (PME) of the Brazilian Census Bureau (IBGE) for 2002-2012. PME is a household sample survey of the metropolitan labour market in Brazil, whose primary goal is to provide monthly indicators of employment and unemployment. This survey has a rotating sample panel structure: each panel corresponds to a number of selected households, which are split into rotational interviewed groups in a specific week of the month. Each group of households is interviewed for four consecutive months, leaves the sample for eight months and returns to another wave of four monthly interviews. Despite the limited coverage of PME on the national territory, the behaviour of metropolitan labour markets signals general conditions of functioning of the economy, and the longitudinal nature of this survey allows the transitional analysis. The mobility of workers between formal and informal labour and its determinants is an example, as the sample scheme allows the generation of transitions of each individual in consecutive pairs of years.

Data are restricted to the population between the ages of 25 and 59, living in metropolitan areas covered by the survey. The perspective adopted in the present paper is that the concept of informality, especially in Brazil, encompasses various kinds of insertion in the labour market. Although different categories under the heading of informality have many important differences, their common feature is the vulnerability in employment and income and the absence of salaried relations, of long-term working contracts and of the possibility of representation in negotiations. Therefore, we consider two categories of informal labour: informal salaried employment (domestic and non-domestic) and the self-employment. Informal salaried employment comprehends employees not covered by the current labour laws (without a formal working contract), while self-employment includes individuals working in their own business without employees[2]. Formal labour includes salaried employees with a formal working contract (domestic and non-domestic) and government employees. Furthermore, unemployment and inactivity (out of labour force) status were included in the transition analysis.

3.2 Mobility analysis methods

Analysis of changes in mobility patterns of workforce were conducted by estimating a multinomial logit model for the probabilities of transition between different occupational status for 2002-2012. The longitudinal nature of PME microdata allows an understanding of the dynamics of the allocation process between formal and informal labour over the period. Moreover, as there may be significant differences between individuals’ mobility pattern, differences in the changes in earnings resulting from these occupational transitions are expected.

3.2.1 Occupational mobility.

We define transitions between possible occupational status for each individual, that is, for every pair of years over the period 2002-2003, 2003-2004, 2004-2005, 2005-2006, 2006-2007, 2007-2008, 2008-2009, 2009-2010, 2010-2011 and 2011-2012, from the occupational status where the individual is in the initial period. Considering five initial statuses, namely, inactivity (out of labour force), unemployment, formal labour, informal salaried employment and self-employment, we have from each of them four possible transitions in addition to immobility. Therefore, for every pair of years, we estimated five multinomial logistic regressions, for women and men separately, from the initial condition:

  1. from the formal labour: continue in the initial status, transition to informal salaried employment, transition to self-employment, transition to unemployment and transition to inactivity;

  2. from the informal salaried employment: continue in the initial status, transition to formal labour, transition to self-employment, transition to unemployment and transition to inactivity;

  3. from the self-employment: continue in the initial status, transition to formal labour, transition to informal salaried employment, transition to unemployment and transition to inactivity;

  4. from the unemployment: continue in the initial status, transition to formal labour, transition to informal salaried labour, transition to self-employment and transition to inactivity; and

  5. from the inactivity (out of labour force): continue in the initial status, transition to formal labour, transition to informal salaried employment, transition to self-employment and transition to unemployment.

The multinomial logit model is used when the outcome variable is a categorical and multinomial result, i.e. when it presents m alternatives. The model specifies:

(1) pij=exp(xiβj)l=1mxiβj,j=1,,m
where pij represents the choice probability of j alternative by individual i; xi is a vector of observable characteristics, represented by the following variables: race, age, education level and metropolitan area; and βj is a set of parameters. This model ensures that 0 < pij < 1 and l=1mpij=1. To ensure the identification of the model, βj is set as zero for one of the categories, and then we interpret the coefficients with respect to that category of reference. Thus, the model defined in equation (1) implies that:
(2) Pr(yi=j|yi=jou1)=Pr(yi=j)Pr(yi=j)+Pr(yi=1)=exp(xiβj)1+exp(xiβj),
defining β1 = 0 and the cancellation of l=1mexp(xiβj) in the numerator and denominator. β̂j can be seen as a parameter of a binary logit model between j and alternative 1. A positive coefficient, however, means that there is greater probability of choice of j than alternative 1. In the case of transitions between occupational status defined here, the category of reference is the immobility, so all transition alternatives are interpreted with respect to the non-transition.

The estimation of the transition probabilities conditioned to a range of individual characteristics (personal and regional attributes) allows us to verify whether there are barriers to workers mobility. Furthermore, as the estimation follows the same individual overtime, it is possible to analyse the changes in the mobility patterns, i.e. trends of formal–informal dynamics in the period.

3.2.2 Earnings mobility.

We address the earnings mobility hypothesis based on the comparison of changes in earnings from a period to another, related to the occupational mobility relatively to the changes in case of non-transition, i.e. returns to mobility relatively to immobility. Therefore, we estimate equations for the earnings variation between 2002 and 2012 conditioned to the transitions, from each occupational status in the initial period, and to the set of control variables, using quantile regressions.

The quantile regression model introduced by Koenker and Bassett (1978) proceeds by taking sample quantiles of a random variable Y with distribution function F(Y). Then, the θth sample quantile, 0 < θ < 1, is defined as any value that minimises the weighted sum of absolute errors:

(3) minb[i=yibθ|yib|+i=yi<b(1θ)|yib|]

Substituting b by a linear function of covariates, the θth quantile regression may be defined as:

(4) minβ1n[i=yixiβθ|yixiβ|+i=yi<xiβ(1θ)|yixiβ|]

The earnings equation in the quantile regression can be written as:

(5) yi=xiβθ+uθi,
where uθixi(βoβθ) + ui, and the θth conditional quantile of y on x corresponds to Quantθ(yi | xi) = xiβθ, such that Quantθ(uθ | x) = 0. Therefore, the equation for the earnings variation conditioned to the transitions, for each occupational status in the initial period, and to a set of control variables, for women and men separately, can be defined as follows:
(6) lnΔyi=xiδθ+ΔTiβθ+εiθ
where Δyi = ytyt–1 and represents the variation, between t−1 and t, in the log hourly earnings for each individual in the θth quantile of the earnings distribution, so that the θth conditional quantile of Δy on x and ΔT corresponds to Qunatθy | x, ΔT) = xδθ + Δθ, such that Quantθ(εθ | x, ΔT) = 0; δ is a vector of estimated coefficients associated to a set of explanatory variables, represented by race, age, education level and metropolitan area. Finally, β is a vector of coefficients of the categorical variable of transition, T, and provides the difference in the earnings differentials, between t−1 and t, for each transition relatively to non-transition (reference category). That is, T is a categorical variable of multinomial choice and represents each possible transition, from the each initial status. As the categories are mutually exclusive, they should be compared directly with the reference category.

4. Mobility patterns: a descriptive analysis

This section overviews the mobility patterns of the Brazilian labour force, female and male, between 2002 and 2012. More specifically, some characteristics of the transitions between different occupational status and of the earnings variation conditioned to these statuses are presented. Figure 1 shows the sample composition by occupational status and gender. Among women, there was an increase of the relative participation of formal labour and a decrease of inactivity and unemployment. The share of inactive and unemployed female has reduced roughly 18 and 37 per cent, respectively, while the share of formal workers female has increased 33.5 per cent over the period. The proportion of informal workers female (informal salaried and self-employed workers) has not shown a significant change.

Regarding men’s labour, there is also a relative increase in formal labour, unlike women this was accompanied by a reduction in the proportion of all other statuses. The variable that presented the greatest reduction was unemployment, around 53 per cent, while the share of formal workers has risen by 18 per cent. It is possible to note that, although women have experienced higher growth of participation in formal labour, female participation is much lower than male participation in this status. Moreover, the relevance of inactivity and informal salaried employment is higher among women than among men, while self-employment is more significant among men.

The transitions between the possible occupational status for each individual have been defined from the occupational condition in the initial period. Thus, to obtain the first insights about the occupational mobility pattern, we constructed transition matrices, for women and men – Figure 2. There is, in both cases, a low level of mobility from the initial condition of inactivity, and this pattern has remained practically unchanged over the period. Among men, the proportion of inactive individuals that continue in the same status is lower than among women, i.e. men present higher mobility, although a small increase in the stay rate in status is observed. There is, however, mainly among women, a modest movement of upward transition towards formal labour and an opposite movement mainly towards unemployment.

The transitions from the initial condition of unemployment present a more significant dynamic of occupational flows. There is a growing mobility towards formal labour and a simultaneous reduction in the stay in unemployment, both among women and men. Among women, transition rates to formal labour are lower than among men, and a significant proportion of women leaves unemployment towards inactivity. While in the first period (2002-2003), only 15 per cent of unemployed women have moved to formal labour, in the last period (2011-2012) this proportion has reached to 27 per cent. For men, however, these transition rates have increased from 25 to 45 per cent.

On the other hand, concerning transitions from the initial status of formal labour, the lack of mobility predominates. A significant part of formal workers, women and men, has remained in the same status over the period. Actually, the stay rate presented a small elevation during the period. In this sense, the period is favourable not only to the entry into formal labour but also to the permanence of those who were already in this condition.

Concerning transitions from informal salaried employment, we find that, for both female and male labour, there is no trend of change in the mobility pattern during the period, as the stay rate in the original condition is predominant, mainly among women. For men, however, there is an increase of the transitions rate to formal labour. With respect to transitions from the initial status of self-employment, in turn, the proportion of workers that have moved to others status is even lower than that observed for informal salaried employment. In this case, unlike the previous one, staying in the original condition is more common among men than among women.

The results suggest that the dynamics of formalisation over the period is more associated with the flow from unemployment to formal labour than with the flow from informality to formal labour. Although a reasonable flow from informal salaried to formal labour is verified, there is no trend of increasing flows over the period, mainly among women. Regarding self-employment, the mobility trend is even less relevant. In this sense, there is no evidence of a significant improvement in the degree of mobility from informality to formality.

Taking into account the transitions between occupational status by gender, race and education, the individuals (women and men) who have moved from their initial occupational condition to other occupational status were those who were initially unemployed – Table I. The transition rates from unemployment to formal labour are higher for white than for black people and for those with 11 or more years of schooling, whereas the opposite occurs for the transition rates to informal salaried employment and self-employment. For men, these differences are more significant by education level than race. Furthermore, men present transition rates to formal higher labour than women do, whereas women present a relatively high transition rate from unemployment to inactivity.

For the transitions from inactivity, among men and women, there is a high rate of continuing in the same status regardless of race or education level. Regarding transition rates from formal labour, there is also a high degree of continuing in the same status, which is slightly higher among white than among black women and men and for people with a higher education level. The differences by race and education are more significant for the transitions from informal salaried employment, with transitions to formal labour significantly higher among white and more educated individuals. Moreover, the transition rates from informal salaried employment and self-employment to formal labour is higher for men compared to women. Among women, the transition rates from self-employment to formal labour are relatively low and for all groups the mobility to inactivity is more relevant, whereas among men, the continue rate in self-employment is relatively high.

From the observed statistics for occupational transitions, the earnings mobility derived from these transitions are investigated. Table II shows that moving from any initial status to formal labour yields the highest returns in terms of average earnings, both among women and men. For individuals into the labour force in the initial period, it was observed that, among women, the transition from informal salaried employment to formal labour presents the highest variation in average earnings, while among men, those who have remained in formal labour have presented higher returns.

When considered the individuals in the initial situation of unemployment or inactivity, there is in fact the earnings average for each occupational destination, not the variation, as earnings in the initial condition are zero. For women and men, the returns in the mobility towards formal labour are higher for the transition from inactivity than from unemployment. Except for the transition from informal salaried employment, in all other transitions towards formal labour, the returns are higher for men than for women.

Thus, the analysed statistics suggest that the expansion of the formal labour is a positive phenomenon because it not only represents a greater coverage of the social security system and labour rights but also induces the higher earnings. Despite the differences in the mobility pattern by gender, race and education, the formality represents a way to leave the precarious labour, especially from informal salaried employment, and it is particularly important for the transition from unemployment to formal labour, which is the largest flow observed during the period.

5. Results and discussion

5.1 Evolution of the occupational mobility pattern

This section presents the estimates for the transition probabilities between different occupational status over the period. For every pair of years between 2002 and 2012, we estimated five multinomial logistic regressions, for women and men separately, from the initial occupational status. As the main objective of the study is to examine the changes in the allocation process between formal and informal labour, we present, in particular, the estimates for the transitions towards formal labour, informal salaried employment and self-employment, for the beginning and the end of the period[3].

Tables III and IV present the estimates for women and men, respectively, from each initial status of occupation, between 2002-2003 and 2011-2012. For women, with respect to the mobility to formal labour, we find that, both in the beginning and the end of the period, the probabilities of transitioning from unemployment are higher for white than for black women, suggesting that there was no change in this differential between them over the period. Concerning age, from the initial status of informal salaried employment and inactivity, the transition probabilities are higher for women aged between 25 and 34. Education level (years of schooling) increases the transition probabilities from informal salaried employment and inactivity.

When we analyse the transition probabilities to informal salaried employment, we find that these probabilities from formal labour and self-employment, in the last period, are higher for black than for white women. Concerning age, from inactivity status, younger women are more likely to move to informal salaried labour than older women. With respect to the education level, the coefficients, in the first-time interval, for transitions from formal labour, self-employment and unemployment indicate that the chances of mobility decrease as years of study increase, contrary to it was observed for transitions to formal labour.

Regarding transitions to self-employment, we observe that the changes of mobility from formal labour and unemployment increase with age, justifying the hypothesis that self-employment is an alternative for older women and, therefore, with more difficulty of insertion in the labour market. The only exception is the transition from inactivity, where the probability is lower for older women.

For male workforce, we find that the younger men (25-34 years) are more likely of transitioning to formal labour, from the self-employment and inactivity, over the period. The coefficients for education are significant only for men in the initial status of inactivity and indicate that the chances of transitioning increase with years of schooling. Similarly, for the transition probabilities to informal salaried employment, the coefficients indicate that the probabilities of transitioning from the self-employment and inactivity decrease as the age increases.

Finally, for the transitions to self-employment, the changes of mobility from informal salaried employment increase with age, while the chances of mobility from inactivity decrease with age, similarly to what has been observed for women. Concerning education, the coefficients show that the transition probabilities from formal labour decrease as the education level increases and that this pattern seems to have not changed over the period.

In general, estimates indicate that the younger the population (within the established age range) and the higher its educational level, the more likely it is to move to formal labour, while the opposite is true regarding education for the mobility to informality (informal salaried employment and self-employment) and concerning age for the mobility to self-employment. The differences by race are most important among women and for the mobility between formal and informal salaried employment, where there is an advantage for white women towards formality, while the opposite occurs towards informality.

The mobility patterns over the period are better defined by using an approach of extreme profiles based on predicted transition probabilities, to identify population groups that are better off from the formalisation process and groups that are most vulnerable. This approach allows us to verify whether employment conditions in the Brazilian labour market have changed significantly. The extreme profiles are defined by control variables (Table V).

Figure 3 illustrates the predicted probabilities of transitioning to formal labour, derived from multinomial logistics regressions, by the initial status and extreme profiles. For women and men, these probabilities indicate a high probability of continuing in the formal labour over the period. However, there is an important difference with respect to the profiles. Individuals whose profile is “low” present lower probability of continuing as formally employed than individuals with “high” profile and a considerable variability over the period, especially women, suggesting a higher vulnerability of them.

For the transition probabilities from other initial status to formal labour, the difference is more significant, i.e. the chances of women and men mobility whose profile is “low” is much lower than the chances of those whose profile is “high”. For women and men with “high” profiles, the transition probabilities from informal salaried employment to formal labour are relatively high, but do not present variation over the period, while for the transition probabilities from self-employment, inactivity (out of labour force) and unemployment, there was an increase during the period.

Among women, the transition probability from unemployment to formal labour presented the greatest variation in the period, while among men the chance of transitioning from self-employment showed the highest elevation. However, in the latter case, there is a great variability over the period, suggesting a higher vulnerability of self-employed workers when compared to the unemployed. In this sense, there is an upward trend in the probability of transitioning from unemployment to formal labour in the period, mainly among women.

Thus, the share of the population that has most benefited by the growth of the formal labour in the past decade were those who were initially unemployed. This result is reasonable, as it has been a period of stable economic growth and employment generation. In particular, white and young women and men, with college degree and living in the metropolitan region of São Paulo, constitute the share of the population who mostly benefited. For black and older women, with low education levels and living in the metropolitan region of Recife, the transition probabilities to formal labour, from any initial status, did not change over the period, not exceeding 10 per cent. Among men with the same profile, some variation was observed, particularly for the transition probabilities from informal salaried employment. However, these changes do not indicate a trend. Regarding profile called “modal”, used as a control, the probabilities present intermediate values between the extreme profiles, confirming the evidence of selective mobility to formal labour.

The results are similar, in part, to the evidence found by Maurizio (2014) for the transitions probabilities to formal labour in Brazil and Argentina. The author’s analysis shows that, in both the countries, the initially unemployed are more likely to move to formal labour over the period. Furthermore, the formalisation process has not been homogeneous across the groups. Prime-aged workers, between 25 and 44 years, men, with higher education have particularly benefited from this improvement in the working conditions.

5.2 Earnings mobility

The earnings mobility analysis is carried by the comparison between changes in earnings, from a period to another, because of the transitions between occupational status is relative to the changes in case of non-transition. Quantile regressions for earnings variations between 2002 and 2012 were estimated, separately for women and men (Appendix 1). Table VI shows the coefficients related to occupational transition variables along the earnings distribution.

Among women, the transitions from formal labour to informal salaried employment and self-employment represent reductions in earnings only up to 25th quantile, while for the highest quantiles there are positive returns, with a non-significant variation in the median. Among men, the results are similar, but the variation in the median is also negative. For both men and women, the reduction and the increase in earnings resulting from transitions are higher at the two extremes of the distribution. Furthermore, these variations are more important for the mobility to self-employment.

For the transitions from informal salaried employment, the mobility to formal labour implies an increase in earnings, for women and men, in all quantiles of the distribution (except for the 25th quantile) relative to immobility. Women present higher returns at the bottom of the earnings distribution, while among men, returns are bigger for those who earn more than the median, and returns are higher than for women. Earnings returns resulting from this type of transition, vis-à-vis the losses for the transition in the opposite direction, ratify the precariousness of informal salaried employment when compared with formal labour at the bottom of the distribution. The mobility from informal salaried employment to self-employment, for men and women, presents a reduction in earnings in the lower quantiles and an increase in the upper quantiles of the distribution. This result indicates that the informal salaried employment presented higher returns than self-employment at the bottom of the distribution over the period, while the opposite occurs at the top of the distribution, and in both the cases, these differentials are higher among women.

Among women and men, the coefficients regarding transition from self-employment to formal labour are not significant up to 25th quantile. However, there are positive and growing returns for the upper half of the distribution (above the median). Comparing to the transition in the opposite direction, i.e. from the formal labour to self-employment, there was a relative gain for the formality at the bottom of the earnings distribution in the period. For the transition to informal salaried employment, coefficients for women indicate positive and statistically significant returns only above the median, while for men, in the lowest quantiles the returns are negative. When compared to the transitions in the opposite direction, these results indicate that the informal salaried employment compensated the self-employment at the bottom of the earnings distribution, while at the top of the earnings distribution the self-employment had relative gains over the period.

Thereby, although positive returns for all types of transitions in the highest quantiles of the earnings distribution were verified, for women the most important earnings refer to the transition from formal labour to self-employment, suggesting that at the top of the distribution, there are higher relative returns to self-employment. For men, the opposite occurs, as the transition from self-employment to formal labour shows higher relative returns in the period. However, in the higher quantiles, the differences in returns are smaller than the differences in the lower quantiles of the distribution. At the bottom and median of the distribution, both for women and men, the results suggest an increase in valuation of the formal labour relatively to informality (informal salaried employment and self-employment) in the period, i.e. earnings variations resulting from mobility between formal and informal occupation are bigger for transitions to formality. Therefore, there are relevant differences regarding returns for these transitions along the earnings distribution.

The mobility from unemployment and inactivity to formal and informal labour represents positive earnings returns. However, women and men in all quantiles of the distribution present higher earnings from mobility to formal labour than to informal salaried employment or self-employment, i.e. formality compensates informality. Comparing informal salaried employment and self-employment, the first one presents higher earnings at the bottom and median of the distribution, mainly among women, while above the median the earnings are bigger for the transitions to self-employment. In all cases, the returns to mobility are higher for men than for women.

6. Concluding remarks

The aim of this paper was to discuss informality dynamics in the Brazilian labour market between 2002 and 2012, by analysing the changes in occupational mobility patterns, specially the transitions between formal and informal labour, and and verify the earnings mobility resulting from these transitions, separately by gender. The results for the mobility analysis between occupational status indicate that the share of the population has most benefited by the growth of the formal labour in the past decade were those who were initially unemployed, i.e. this group presented the higher probability of transition to formal labour over the period. This result is reasonable, as it has been a period of stable economic growth and employment generation.

However, the results indicate that this process has not reached the different profiles of workers with the same intensity, i.e. individuals with “better” attributes, particularly white and younger women and men, with higher education and living in wealthier regions, are more likely to be formally employed. For this profile, there is no significant difference by gender, and women are better off in the period. On the other hand, at the extreme profile, where people’s attributes are less valued by the market, such as being black, older, lower educated and living in poorer regions, the transition probabilities to formal labour are very low and do not present significant change over the period. In this aspect, the situation of women was relatively worse. The profile used as a control (modal) shows that, to the extent the attributes move away from the “high” profile and approximate of the “low” profile, the chances of entry in the formal labour are reduced.

Although the transition probability from informality to formality is higher than the transition probability in the opposite direction, pointing to the absence of restrictions to mobility, according the literature (Curi and Menezes-Filho, 2004, 2006), the results suggest that this type of mobility is also selective. That is, the probability of transitioning from an informal occupation (informal salaried employment and self-employment) to a formal occupation is significantly higher for individuals with better attributes. In this sense, the evidences not only corroborate with the hypothesis of the existence of barriers to mobility towards formal labour but also indicate the persistence of such mobility patterns during the period.

The earnings mobility, or returns, resulting from the transitions between occupational status strengthens the pattern of selectivity, in particular for the mobility to formal labour, as these returns are higher for the workforce at the top of the earnings distribution. On the other hand, at the bottom of the earnings distribution, the mobility to formal labour represents better working and income conditions, as there was an increase in valuation of the formality relatively to informality (informal salaried employment and self-employment). The evolution of the educational level and the real gain in the value of the minimum wage, in the past decade, could justify the earnings mobility at the bottom of the distribution, as wages in the formal labour are attached to this value of reference.

It should be noted that there is a range of possible advancement for the study of the dynamics of the labour market. An alternative is expanding the analysis to other age profiles, below and above the age range considered in this paper, as informality takes distinct importance among both young people and elderly, and the movements of entry and exit of the labour market are deeply related to life cycle. Disaggregation of the salaried employment between domestic and non-domestic work is another possibility of improvement because of the weight of informality in domestic activities, especially for female labour. The mobility by economic activity, occupation category and public and private sector, also represents an option for the deepening of the research, enabling a more detailed view on the evolution of labour relations according the productive and occupational structure. In this sense, there are several possibilities of future advancements.

Figures

Sample composition by occupational status and gender, ages 25-59, Metropolitan Brazil, 2002-2012 (percentages)

Figure 1.

Sample composition by occupational status and gender, ages 25-59, Metropolitan Brazil, 2002-2012 (percentages)

Transition matrices, women, men, Metropolitan Brazil, 2002-2012 (percentages)

Figure 2.

Transition matrices, women, men, Metropolitan Brazil, 2002-2012 (percentages)

Predicted transition probabilities to formal labour, by extreme profiles, women, men, 2002-2012

Figure 3.

Predicted transition probabilities to formal labour, by extreme profiles, women, men, 2002-2012

Transition matrices by gender, race and education, Metropolitan Brazil, 2002-2012 (percentages)

Variable/Occupational status Women Men
OLF UN F INF SE OLF UN F INF SE
Race
White
 Inactivity (OLF) 80.67 3.54 5.81 5.07 4.91 70.09 4.7 11.98 5.26 7.97
 Unemployment 37.74 21.74 21.42 13.9 5.21 19.19 19.73 32.66 14 14.42
 Formal 7.33 2.13 83.19 5.42 1.93 3.65 2.22 86.4 4.45 3.28
 Informal salaried 15.14 4.18 24.96 48.54 7.17 6.55 4.14 31.76 42.15 15.4
 Self-employment 19.72 2.06 8.72 9.47 60.03 6.05 2.06 12.03 9.32 70.54
Black
 Inactivity (OLF) 77.02 5.11 5.31 6.46 6.10 65.01 6.05 12.41 6.51 10.02
 Unemployment 37.96 24.57 16.37 14.94 6.16 17.33 22.57 30.59 14.54 14.97
 Formal 8.06 2.95 80.11 6.63 2.25 4.34 2.69 84.5 4.55 3.91
 Informal salaried 16.4 5.06 19.09 52.9 6.55 7.87 5.06 29.56 41.15 16.36
 Self-employment 21.7 2.98 6.68 10.68 57.96 7.39 2.89 11.64 9.19 68.89
Years of schooling
Less than 11
 Inactivity (OLF) 80.47 3.68 3.99 6.23 5.64 69.05 4.49 10.48 5.95 10.03
 Unemployment 40.24 22.52 13.11 17.39 6.75 18.26 19.92 27.57 15.8 18.45
 Formal 10.53 2.79 75.96 7.85 2.87 5.07 2.64 82.54 5.11 4.64
 Informal salaried 18.05 4.32 15.17 55.57 6.89 7.89 4.82 26.52 42.1 18.67
 Self-employment 22.52 2.59 5.91 10.57 58.42 6.95 2.44 10.54 9.06 71.01
11 or more
 Inactivity (OLF) 77.57 4.83 7.14 5.2 5.26 66.05 6.24 13.97 5.8 7.94
 Unemployment 36.37 23.9 21.84 12.71 5.19 18.04 22.2 34.64 13.25 11.86
 Formal 6.68 2.34 83.88 5.3 1.8 3.35 2.31 87.11 4.21 3.02
 Informal salaried 13.66 4.96 28.2 46.39 6.79 6.6 4.34 34.02 41.49 13.54
 Self-employment 19.13 2.39 9.27 9.59 59.62 6.45 2.49 13.18 9.49 68.4
Notes:

Status: F = Formal; INF = Informal; SE = Self-employment; UN = Unemployment; and OLF = Out of labour force (inactivity)

Source: IBGE, PME, 2002-2012

Earnings variation, by occupational transition and gender, ages 25-59, Metropolitan Brazil, 2002-2012

Occupational status Women Men
Formal Informal salaried Self-employment Formal Informal salaried Self-employment
Formal 1.02 0.70 0.57 1.16 0.34 0.36
Informal salaried 1.16 0.56 0.85 1.14 0.55 0.93
Self-employment 0.44 0.33 0.36 0.81 0.35 0.55
Unemployment 6.79 4.92 4.76 7.91 5.73 5.38
Inactivity 8.60 5.31 5.33 9.34 6.99 6.38

Source: IBGE, PME 2002-2012

Multinomial logit estimates for transitions to formal and informal jobs, and self-employment, by origin statusa, women, metropolitan Brazil, 2002-2003 and 2011-2012

Variables INF - F SE - F F - INF SE - INF F - SE INF-SE
2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012
Race 0.282* 0.215 0.008 0.016 −0.143 −0.214* −0.021 −0.484* −0.195 0.231 0.169 0.261
Age group
35-49 −0.298* −0.226 −0.043 −0.233 −0.199 −0.094 0.089 −0.054 0.438* 0.432* 0.089 0.231
50-59 −0.450* −0.838*** −0.398 −0.602** −0.112 −0.136 −0.840** −0.222 0.594* 0.602* 0.001 −0.196
Years of schooling
4-7 0.285 0.407 −0.759 14.397 −0.382 0.031 −1.308** 14.963 −0.343 13.146 −0.126 1.494
8-10 0.997** 0.96 −0.98 14.5 −0.53 −0.3 −1.311** 14.927 −0.502 13.395 0.504 2.148*
11-14 1.036** 1.312** −0.832 14.868 −0.934** −0.564 −1.358** 15.041 −0.824 12.91 0.007 2.030*
15−+ 1.738*** 2.040*** 0.926 15.972 −1.013** −0.573 −0.78 15.366 −1.708*** 12.052 −0.307 1.206
Metropolitan Area
RMRE −0.375 0.256 0.409 0.167 0.491* 0.435* 0.03 0.225 0.901*** 0.108 −0.109 0.402
RMSA 0.315 0.356 0.008 0.078 0.241 0.233 −0.165 −0.227 0.138 0.288 0.197 0.306
RMBH 0.406* 0.039 0.251 0.218 0.311 0.350* −0.749* −0.123 −0.113 −0.176 0.163 −0.242
RMRJ 0.041 −0.556** 0.743* −0.137 0.636*** −0.045 0.12 −0.107 0.269 0.207 −0.113 −0.31
RMPOA 0.066 0.510** 1.184*** 0.306 0.021 0.049 0.185 0.1 −0.375 −0.467 0.007 0.567*
Constant −1.474*** −1.856*** −1.305* −16.417 −1.635*** −2.195*** 0.009 −16.595 −2.779*** −17.052 −1.693*** −3.984***
UN-F OLF-F UN-INF OLF-INF UN-SE OLF-SE
2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012
Race 0.600* 0.544* −0.299* −0.13 0.242 0.412 −0.113 −0.035 0.071 0.425 −0.067 −0.083
Age group
35-49 −0.132 −0.239 −0.802*** −0.832*** 0.248 −0.194 −0.385*** −0.482*** 1.115*** 0.338 0.032 0.22
50-59 −15.798 −0.603 −1.391*** −1.772*** 0.55 0.703 −1.133*** −1.145*** 1.652** 1.373* −0.442*** −0.408**
Years of schooling
4-7 −1.849* −0.299 0.108 0.488 −1.527 −1.016 0.666* 0.112 15.502 11.527 0.09 0.46
8-10 −1.71 0.162 0.627* 0.565 −1.898* −1.207 0.647* 0.151 15.333 9.672 0.379 0.607
11-14 −1.476 0.167 0.661* 1.019** −2.020* −1.669 0.619* −0.129 15.196 11.003 0.206 0.539
15−+ −1.15 0.568 1.487*** 1.535*** −2.463** −1.696 −0.056 −0.311 −1.005 10.798 −0.388 0.179
RM
RMRE 0.331 0.638 −0.703** −0.11 0.762* 0.286 −0.386* 0.13 0.769 −0.273 0.467** 0.335
RMSA 0.191 −0.619 −0.379 0.038 0.722* −0.269 −0.195 −0.148 0.668 0.427 0.825*** 0.462*
RMBH 0.363 0.356 0.253 0.108 0.488 0.265 −0.08 0.158 0.4 −0.158 0.501*** 0.036
RMRJ 0.259 −0.729* 0.021 −0.308* 0.49 −0.01 0.073 −0.661*** 0.466 −0.295 0.285 −0.365*
RMPOA 0.835* 0.244 0.503** 0.365* 0.749* −0.077 −0.018 −0.155 0.229 0.118 0.103 0.117
Constant 0.616 0.208 −2.522*** −2.253*** 0.688 0.978 −2.556*** −1.862*** −17.48 −12.854 −2.842*** −3.200***
Notes:

Reference categories: blacks, age group 25-34, 0-3 years of schooling; RMSP= Metropolitan Region of São Paulo;

*

p < 0.05;

**

p < 0.01;

***

p < 0.001;

a

Initial status: F = Formal; INF = Informal; SE = Self-employment; UN = Unemployment; and OLF = Out of LABOUR force (inactivity)

Source: IBGE, PME, 2002-2012

Multinomial logit estimates for transitions to formal and informal jobs, and self-employment, by origin statusa, men, metropolitan Brazil, 2002-2003 and 2011-2012

Variables INF-F SE-F F-INF SE-INF F-SE INF-SE
2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012
Race −0.362* 0.236 −0.397* 0.124 −0.122 0.035 −0.181 −0.068 0.061 0.109 −0.298 0.299
Age group
35-49 0.227 −0.019 −0.330* −0.582*** −0.217 −0.394*** −0.301* −0.426* 0.228 0.038 0.646*** 0.594**
50-59 −0.264 −0.31 −0.918*** −1.268*** −0.278 −0.458** −0.923*** −0.705*** 0.462* −0.056 0.743** 0.685**
Years of schooling
4-7 0.6 0.112 −0.033 0.46 −0.1 0.183 0.008 1.824 −0.486 −0.07 0.836* 0.891
8-10 0.947* 0.384 0.299 0.774 −0.404 0.188 0.331 1.479 −0.838** −0.496 0.636 1.176
11-14 0.765* 0.358 0.223 0.968 −0.279 −0.048 0.186 1.763 −0.957** −0.519 0.806 0.849
15-+ 1.360*** 0.713 0.601 1.963** 0.072 −0.002 0.633 2.618* −1.366*** −1.233* 0.175 0.315
Metropolitan Area
RMRE −0.053 0.436 0.335 0.561* 0.168 0.101 0.124 0.212 0.906*** 0.297 −0.114 −0.175
RMSA 0.024 0.115 −0.076 0.464 −0.163 0.355 −0.333 −0.506 0.417 0.661** −0.013 −0.309
RMBH 0.332 0.649** 0.269 0.306 0.233 0.206 −0.189 −0.206 0.540** 0.493** 0.261 0.282
RMRJ −0.242 −0.221 0.417 −0.519* 0.168 −0.02 0.05 −0.464* 0.317 −0.05 −0.131 −0.668**
RMPOA 0.188 0.205 0.415 0.141 0.012 0.026 −0.575* −0.084 0.305 0.193 0.39 0.216
Constant −0.879* −0.622 −1.517*** −2.193*** −2.372*** −2.976*** −1.330*** −3.305** −2.625*** −2.958*** −1.710*** −2.019**
UN - F OLF - F UN - INF OLF - INF UN - SE OLF-SE
2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012 2002-2003 2011-2012
Race −0.032 −0.1 −0.207 −0.15 0.2 0.311 −0.443* −0.197 −0.103 −0.267 −0.31 −0.174
Age group
De 35-49 −0.373 −0.429 −0.714*** −0.790*** −0.146 −0.451 −1.090*** −1.160*** 0.404 0.307 −0.431* −0.405
De 50-59 −0.697* −0.041 −2.010*** −2.137*** −0.638 −0.885 −1.911*** −1.664*** 0.452 1.192* −0.962*** −0.572**
Years of schooling
De 4-7 −0.305 −14.061 1.158** 1.167** −0.647 −14.275 0.695 1.362* 0.164 −15.185 0.733* 1.530**
De 8-10 −0.059 −13.159 1.319*** 1.256** −1.247 −14.592 0.543 1.653** 0.067 −14.067 0.202 1.509**
De 11-14 −0.299 −13.541 1.132** 1.368*** −0.668 −15.059 0.413 1.172 −0.048 −14.423 0.264 1.374**
15-+ 0.461 −14.016 1.194** 1.853*** −0.138 −15.975 0.911 1.760** −0.526 −16.198 −0.716 0.63
RM
RMRE −0.109 −0.072 −0.510* 0.353 0.281 0.156 −0.638* 0.993** 0.2 −0.628 −0.111 0.799**
RMSA 0.292 −0.557 −0.247 0.611* −0.213 −0.79 −0.391 1.169** 0.143 −0.474 −0.162 0.826*
RMBH 1.128*** 0.44 −0.396 0.405 0.764* 0.416 −0.045 0.435 0.736* 0.378 −0.006 0.513
RMRJ 0.650* 0.041 0.148 −0.196 0.595 0.654 0.086 0.266 0.994** 0.082 0.125 0.096
RMPOA 1.338** 0.441 −0.076 0.222 0.919 0.236 −0.111 0.68 1.282** −0.341 0.027 0.262
Constant 0.172 14.952 −1.282** −1.909*** 0.056 14.748 −1.088* −3.221*** −0.854 14.487 −1.178*** −3.293***
Notes:

Reference categories: blacks, age group 25-34, 0-3 years of schooling; RMSP= Metropolitan Region of São Paulo.

*

p < 0.05;

**

p < 0.01;

***

p < 0.001;

a

Initial status: F = Formal; INF = Informal; SE = Self-employment; UN = Unemployment; and OLF = Out of labour force (inactivity)

Source: IBGE, PME, 2002-2012

Extreme patterns, by observable attributes

Variables “High” Mobility “Low” Mobility “Modal”
Race Whites Blacks Whites
Age group 25-34 50-59 35-49
Educational level 15+ 0-3 11-14
Metropolitan area São Paulo Recife São Paulo

Source: IBGE, PME, 2002-2012

Quantile regression estimates for the variation in log earnings/hour, by occupational transition and gender, 2002-2012

Transitions Gender q10 q25 q50 q75 q90
Formal–Informal Women −0.235 −0.115 −0.012* 0.060 0.146
Men −0.303 −0.182 −0.036 0.011* 0.079
Formal–Self-employed Women −0.666 −0.301 −0.012* 0.149 0.341
Men −0.481 −0.292 −0.049 0.082 0.209
Informal–Formal Women 0.067 0.000* 0.049 0.054 0.065
Men 0.040 0.009* 0.085 0.121 0.180
Informal–Self-employed Women −0.380 −0.192 −0.018* 0.119 0.233
Men −0.182 −0.128 0.028 0.106 0.183
Self-employed–Formal Women −0.099* −0.016* 0.102 0.148 0.260
Men 0.000* 0.003* 0.097 0.139 0.225
Self-employed–Informal Women −0.052* −0.018* 0.065 0.105 0.201
Men −0.105 −0.047 0.000* 0.057 0.102
Unemployed–Formal Women 1.022 1.334 1.605 1.869 2.169
Men 1.214 1.437 1.733 2.023 2.408
Unemployed–Informal Women 0.616 0.945 1.346 1.687 2.003
Men 0.799 1.054 1.415 1.765 2.178
Unemployed–Self-employed Women 0.037* 0.616 1.192 1.715 2.139
Men 0.616 1.022 1.328 1.745 2.226
Out of labour force–Formal Women 1.022 1.350 1.666 2.063 2.696
Men 1.155 1.460 1.820 2.22 2.750
Out of labour force–Informal Women 0.462 0.904 1.346 1.795 2.226
Men 0.681 1.081 1.457 1.954 2.408
Out of labour force–Self-employed Women 0.057* 0.616 1.309 1.820 2.359
Men 0.434 0.973 1.427 2.003 2.408
Notes:

Complete estimates are in Appendix;

*

coefficients not significant at 1%

Source: IBGE, PME, 2002-2012

Quantile regression estimates for the variation in log earnings/hour, by occupational transition and gender, 2002-2012

Women Men
F INF SE UN OLF F INF SE UN OLF
Quantile Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. Coeff.
q10
Race −0.015 −0.011 0.035 0.000 −0.000*** −0.022** −0.033 −0.019 −0.000 −0.000*
Age group
35-49 −0.042*** −0.040 −0.053* −0.000 −0.000*** −0.041*** −0.031* −0.000 −0.000 −0.000***
50-59 −0.068*** −0.000 −0.143*** 0.000 −0.000*** −0.071*** −0.071*** −0.032 −0.000 −0.000***
Years of schooling
4-7 −0.018 0.064 −0.020 0.336* 0.000 −0.033 −0.009 0.045 0.327** 0.000
8-10 −0.064* 0.074 −0.045 0.336** 0.000 −0.064** −0.040 0.039 0.365*** 0.000
11-14 −0.113*** 0.091 −0.057 0.336** 0.000 −0.091*** −0.097 0.000 0.365*** 0.000
15+ −0.268*** 0.076 −0.094 0.336** 0.000** −0.204*** −0.168** −0.037 0.365*** 0.000
RM
RMRE −0.075*** −0.052 −0.153*** 0.000 −0.000* −0.053*** −0.096 −0.146*** −0.220*** −0.000*
RMSA −0.021 0.016 −0.059 0.000 −0.000 −0.020 −0.074 −0.068* −0.145** −0.000
RMBH −0.004 −0.047 0.002 −0.000 0.000* 0.002 −0.030 −0.036 −0.000 −0.000
RMRJ 0.135*** 0.161*** 0.290*** −0.000 −0.000** 0.184*** 0.152*** 0.300*** 0.000 −0.000***
RMPOA −0.024 −0.064* 0.045 −0.000 0.000* 0.013 −0.094* 0.032 −0.000 −0.000
Constant −0.137*** −0.512*** −0.620*** −0.336** 0.000 −0.204*** −0.278*** −0.569*** −0.365*** 0.000***
q25
Race 0.000* −0.000 −0.000 0.000 −0.000* 0.000 0.000 −0.003 −0.000 0.000
Age group
35-49 −0.000 −0.000 −0.038* −0.000 0.000*** −0.000 −0.003 −0.000 −0.000 −0.000***
50-59 −0.003 0.000 −0.075*** −0.000 0.000*** −0.000 −0.009 −0.022* 0.000 −0.000***
Years of schooling
4-7 −0.036* 0.058* −0.051 0.207* −0.000 −0.000*** 0.000 0.000 0.261** 0.000
8-10 −0.039* 0.058* −0.038 0.207* −0.000 −0.000*** −0.009 0.000 0.284*** 0.000
11-14 −0.039* 0.058* −0.061 0.207* −0.000 −0.000*** −0.009 −0.003 0.284*** 0.000
15+ −0.077*** 0.058* −0.101*** 0.207* −0.000 −0.008** −0.054** 0.000 0.284*** 0.000
RM
RMRE −0.037*** −0.012 −0.126*** −0.000 −0.000* −0.026*** −0.035 −0.082*** −0.128*** −0.000
RMSA 0.030** 0.038* −0.026 −0.000 −0.000*** 0.010 0.037 −0.018 0.000 −0.000
RMBH 0.004 −0.010 −0.031 −0.000 −0.000* 0.000 0.006 −0.035** −0.000 −0.000
RMRJ 0.059*** 0.134*** 0.200*** −0.000 −0.000 0.087*** 0.086*** 0.182*** −0.000 −0.000
RMPOA −0.021*** −0.031 −0.009 0.000 −0.000*** 0.000 −0.059*** −0.007 0.000 −0.000
Constant −0.020 −0.192*** −0.162*** −0.207* −0.000 −0.087*** −0.083*** −0.182*** −0.284*** 0.000***
q50
Race −0.005 −0.022** −0.000 −0.000 −0.000* 0.000 0.000 −0.000 0.000 0.000
Age group
35-49 −0.017*** −0.003 −0.016 0.000 0.000** −0.025*** −0.038*** −0.000 0.000 −0.000***
50-59 −0.017*** −0.010 −0.016 0.000 0.000 −0.025*** −0.038*** −0.000 0.018 −0.000***
Years of schooling
4-7 −0.023*** −0.010 −0.099** 0.000 0.000 −0.009 −0.015 0.000 0.163** 0.000
8-10 −0.031*** −0.023 −0.099** 0.000 0.000 −0.009 −0.020 0.000 0.181** 0.000
11-14 −0.040*** −0.023 −0.099*** 0.000 −0.000 −0.021* −0.020 0.000 0.181** 0.000
15+
RM −0.075*** −0.050*** −0.115*** 0.333*** −0.000 −0.045*** −0.047* 0.000 0.655*** 0.000
RMRE 0.055*** 0.077*** 0.067* −0.000*** −0.000 0.047*** 0.050*** 0.126*** −0.018 −0.000
RMSA 0.033*** 0.047*** 0.025 −0.000*** −0.000*** 0.028*** 0.047** 0.069*** −0.000 −0.000
RMBH 0.060*** 0.044*** 0.053** −0.000*** −0.000 0.057*** 0.064*** 0.077*** −0.000 −0.000
RMRJ −0.005 0.009 −0.016 −0.000*** −0.000*** −0.013* −0.037*** 0.000 −0.000 −0.000**
RMPOA 0.037*** 0.043*** 0.033 −0.000** −0.000** 0.034*** −0.006 0.065*** 0.018 −0.000
Constant 0.102*** 0.081*** 0.131*** −0.000 0.000 0.083*** 0.095*** 0.000 −0.181** 0.000***
q75
Race 0.004 −0.026** −0.006 0.000 −0.000* 0.008 0.013 −0.000 0.014 0.000
Age group
35-49 −0.016*** 0.002 −0.028 0.000 0.000*** −0.016*** −0.039*** 0.000 −0.000 −0.000***
50-59 −0.019** −0.008 −0.063** −0.000 0.000* −0.018** −0.028* −0.000 0.034 −0.000***
Years of schooling
4-7 −0.016 −0.028 −0.071** −0.000 0.000 −0.007 −0.027 0.000 0.014 0.000
8-10 −0.001 −0.049 −0.105** −0.000 0.000 0.010 −0.016 −0.000 0.034 −0.000
11-14 0.006 −0.046 −0.105** 0.000 0.000 0.018 0.001 0.000 0.034 0.000
15+
RM 0.038*** 0.011 −0.174** 0.762*** 0.000 0.022 −0.025 −0.024 0.896*** 0.000
RMRE 0.071*** 0.109*** 0.107** −0.000 0.000 0.052*** 0.093*** 0.134*** −0.020 0.000
RMSA 0.028*** 0.010 0.065* −0.000 0.000 0.018* 0.041 0.069** −0.020 0.000
RMBH 0.063*** 0.059*** 0.105*** 0.000 0.000** 0.052*** 0.068*** 0.069*** −0.014 0.000**
RMRJ −0.061*** −0.103*** −0.139*** −0.000 0.000 −0.084*** −0.126*** −0.113*** −0.020 −0.000
RMPOA 0.031*** 0.059*** 0.033 −0.000 0.000* 0.018*** 0.021 0.064** 0.069* 0.000
Constant 0.237*** 0.373*** 0.539*** −0.000 −0.000 0.258*** 0.336*** 0.336*** −0.014 0.000*
q90
Race 0.013 −0.039* −0.027 −0.000 −0.000*** 0.015* 0.056** −0.007 0.000 0.000
Age group
35-49 −0.012 0.003 −0.045** 0.000 0.000 −0.007 −0.050*** 0.004 −0.000 −0.000***
50-59 0.007 −0.000 −0.095** −0.000 0.000 0.011 0.013 0.018 0.048 −0.000***
Years of schooling
4-7 0.005 −0.047 −0.134* 0.000 0.000 0.007 −0.082 −0.039 −0.000 −0.000
8-10 0.025 −0.113** −0.161** 0.000 0.000 0.033 −0.081 −0.025 −0.000 −0.000
11-14 0.070* −0.068 −0.185*** 0.000 0.000 0.068* −0.039 −0.003 −0.000 −0.000
15+ 0.173*** 0.053 −0.124 1.003*** 0.000 0.151*** −0.019 0.009 1.028*** 0.288***
RM
RMRE 0.084*** 0.131*** 0.141** −0.000 −0.000*** 0.057*** 0.178*** 0.186*** 0.000 −0.000
RMSA 0.053*** 0.030 0.157*** −0.000 −0.000*** 0.055*** 0.137** 0.138*** 0.000 −0.000
RMBH 0.048*** 0.054** 0.088* 0.000 −0.000*** 0.046*** 0.077** 0.066** −0.000 0.000
RMRJ −0.118*** −0.190*** −0.210*** −0.000 −0.000*** −0.127*** −0.169*** −0.210*** 0.000 −0.000
RMPOA 0.024* 0.046 0.018 0.000 −0.000 0.014 −0.004 0.001 −0.000 0.000
Constant 0.415*** 0.748*** 1.053*** −0.000 −0.000 0.457*** 0.644*** 0.724*** 0.000 0.000***
N 60.349.000 19.414.000 13.982.000 7.009.000 77.830.000 77.747.000 13.914.000 23.509.000 6.017.000 20.345.000
Notes:
*

p < 0.05;

**

p < 0.01; and

***

p < 0.001;

Reference categories: blacks, age group 25-34, 0-3 years of schooling; RMSP= Metropolitan Region of São Paulo; Coefficients related to occupational transition variables are in Table VI; Initial status: F = Formal; INF-Informal; SE = Self-employment; UN = Unemployment; OLF = Out of labour force (inactivity)

Source: IBGE, PME, 2002-2012

Notes

1.

From the Report of the Global Employment Mission – ILO (2002) and Hart (1973).

2.

The Brazilian literature does not include liberal professionals among self-employment, as their work is characteristically knowledge-intensive – Cacciamali (2000).

3.

Complete estimates, for women and men separately, and for every pair of years, are available for review directly with the authors.

Appendix

Table AI

References

Blanchflower, D.G. (2000), “Self-employment in OECD countries”, Labour Economics, Vol. 7 No. 5, pp. 471-505.

Bosh, M. and Maloney, W. (2010), “Comparative analysis of labor market dynamics using markov processes: an application to informality”, Labor Economics, Vol. 17 No. 4, pp. 621-631.

Cacciamali, M.C. (2000), “Globalização e processo de informalidade”, Economia e Sociedade, Vol. 9 No. 1, pp. 153-174.

Curi, A.Z. and Menezes-Filho, N.A. (2004), “Os Determinantes das Transições Ocupacionais no Mercado de trabalho brasileiro”, paper presented at the XXXII Encontro Nacional de Economia, João Pessoa, 7-10 December.

Curi, A.A. and Menezes-Filho, N.A. (2006), “O mercado de trabalho brasileiro é segmentado? alterações no perfil da informalidade e dos diferenciais de salários nas décadas de 1980 e 1990”, Estudos Econômicos (São Paulo), Vol. 36 No. 4, pp. 867-899.

De Soto, H., Ghersi, E. and Ghibellini, M. (1986), “El Otro Sendero: La Revolución Informal”, Editorial El Barranco, Lima.

Dickens, W.T. and Lang, K. (1985), “A test of dual labour market theory”, American Economic Review, Vol. 75 No. 4, pp. 1-22.

Doeringer, P.B. and Piore, M.J. (1971), Internal Labor Markets and Manpower Analysis, Lexington Books, Lexington.

Fields, G.S. (1990), “Labor Market Modelling and the Urban Informal Sector: theory and evidence”, in Turnham, D., Salomé, B. and Schwarz, A. (Eds), The Informal Sector Revisited, OECD, Paris, pp. 49-69.

Fontes, A. (2009), Ensaios Sobre Informalidade No Brasil, Thesis (PhD in Economics) – Instituto De Economia, Universidade Federal do Rio de Janeiro, Rio de Janeiro.

Fontes, A. and Pero, V.L. (2008), “Segmentação do mercado de trabalho e mobilidade de renda entre 2002 e 2007”, paper presented at the XXXVI Encontro Nacional de Economia, Salvador, 9-10 December.

Hart, K. (1973), “Informal income opportunities and urban development in Ghana”, The Journal of Modern African Studies, Vol. 11 No. 1, pp. 61-89.

Henley, A. (2004), “Self-employment status: the role of state dependence and initial circumstances”, Small Business Economics, Vol. 22 No. 1, pp. 67-82.

ILO (2002), Decent Work and the Informal Economy: Report VI”, in 90th International Labour Conference, International Labour Organization, Geneva.

International Labour Organization (1972), Employment, Incomes and Equality: A Strategy for Increasing Productive Employment in Kenya, International Labour Office (ILO), Geneva.

International Labour Organization (2012), World of Work Report: Better Jobs for a Better Economy, International Labour Office (ILO), Geneva.

International Labour Organization (2016), What works: Active Labour Market Policies in Latin America and the Caribbean, International Labour Office (ILO), Geneva.

Koenker, R. and Bassett, G. Jr (1978), “Regression quantiles”, Econometrica, Vol. 46 No. 1, pp. 33-50.

Maloney, W.F. (1999), “Does informality imply segmentation in urban labor markets? Evidence from sectoral transitions in Mexico”, World Bank Economic Review, Vol. 13 No. 2, pp. 275-302.

Maurizio, R. (2014), “Labour formalization and declining inequality in Argentina and Brazil in 2000s: a dynamic approach”, ILO Research Paper n. 9, International Labour Organization, Geneva.

Osterman, P. (1975), “An empirical study of labor market segmentation”, Industrial and Labor Relations Review, Vol. 28 No. 4, pp. 508-523.

Parker, S.C. and Robson, M.T. (2004), “Explaining international variation in self-employment: evidence from a panel of OEDC countries”, Southern Economic Journal, Vol. 71 No. 2, pp. 287-301.

Portes, A., Castells, M. and Benton, L.A. (1989), The Informal Economy: Studies in Advanced and Less Developed Countries, Johns Hopkins University Press, Baltimore.

Reich, M., Gordon, D.M. and Edward, R.C. (1973), “Dual labor markets: a theory of labor market segmentation”, American Economic Review, Vol. 63 No. 2, pp. 359-365.

Soares, F.V. (2004), “Do informal workers queue for formal jobs in Brazil?”, Discussion Paper n. 1021, IPEA, Brasília.

Souza, P.R. (1980), Emprego, Salários e Pobreza: Hucitec, Funcamp, Campinas, São Paulo.

Souza, P.T. and Tokman, V.E. (1976), “The informal urban sector in Latin America”, International Labour Review, Vol. 114 No. 3, pp. 355-365.

Piore, M.J. (1972), “Notes for a Theory of Labor Markets Stratification”, Working Paper No. 95, Institute of Technology, Cambridge, MA.

Taylor, M. (2004), “Self-employment in Britain: when, who and why?”, Swedish Economic Policy Review, Vol. 11, pp. 139-173.

Taylor, M. (2011), “Self-employment flows and persistence: a European comparative analisys”, ISER Working Paper Series No. 2011-26, Institute for Social and Economic Research (ISER), Colchester, Essex.

Tokman, V.E. (1977), “An exploration into the nature of informal-formal sector interrelationships”, Monograph on Employment No. 2, PREALC/OIT, Lima.

Uthoff, A. (1983), “Subempleo, segmentación, movilidad ocupacional y distribución del ingreso del trabajo. El caso del gran Santiago em 1969 y 1978”, Estudios De Economía, Vol. 10 No. 1, pp. 113-146.

Vietorisz, T. and Harrison, B. (1973), “The formal-informal labor market segmentation hypothesis revisited”, Brazilian Review of Econometrics, Vol. 30 No. 2, pp. 311-334.

Corresponding author

Francieli Tonet Maciel can be contacted at: francieli.tonet@gmail.com