Abstract
The overall objective of this work is to capture some of the reflexes created by such processes in the relationship between the conditions of access to the labor market and the territorial dynamics of the metropolis of Rio de Janeiro. In other words, we intend to analyze the relationships between the processes of social division of the metropolitan territory and the conditions governing the access to opportunities in the labor market. We will seek to test to what extent the location of individuals and social groups in a socio-spatial structure characterized by tendencies to residential segregation and territorial segmentation impact on the quality of employment (occupational fragility) and on the possibilities of transforming the very job opportunity in resources from the labor market (income).
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Notes
- 1.
Research Report carried out as part of the project titled Observatório das Metrópoles: Territory, Social Cohesion and Democratic Governance related to Metropolization, Intra-metropolitan Dynamics and National Territory. According to this study, the Metropolitan Region of Rio de Janeiro (MRRJ) concentrates 6.4% of the national population, 9.7% of the aggregate income and 10% of technological capacity. Indeed, these same indicators for the metropolis of São Paulo are as follows: 10.5, 18 and 22%, respectively. In terms of the productive capacity of the exporting and innovative enterprises the scenario is even more contrasting: while the Metropolitan Region of São Paulo owns 19% of the value of the industrial transformation of these enterprises, the Metropolitan Region of Rio de Janeiro has participated with just 5.8%.
- 2.
This concept has origins in the works of Kain (1968, 1969) and it was designed to describe the effects on the access to employment opportunities and on the wages of African American workers resulting from the decrease in the choices of the place of residence on the grounds of racial discrimination and peripheral dispersion of jobs which were concentrated in central areas in the past.
- 3.
In the original text it is área de expansão demográfica (area of demographic expansion) whose initials are AED.
- 4.
For such a procedure we used the Statab software.
- 5.
These differentials and other indicators are in the table annexed.
- 6.
For the purposes of this study, we grouped the adults aged 25–59 years on the basis of the occupations considered fragile. Therefore, we considered fragile the following occupations indicated by the Census variable “position in the main occupation”: (1) self-employed employee—not a social security contributor; (2) domestic employee with labor card signed; (3) domestic employee without labor card signed; and (4) domestic employee without labor card signed and who is not a social security contributor of the compulsory social welfare system.
- 7.
For a detailed description of the logistic model, see Annex 9.1.
- 8.
In Portuguese it is população economicamente ativa and its initials are PEA.
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Appendices
Annex 9.1
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Description of the regression models used
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1.
Logistic regression model
As we are working with a dichotomous variable (whose values are: absence (0) or presence (1) of risk) such as the “occupational fragility”, we chose to use the logistic model as regression model since it is more suitable for this type of distribution of the response variable. The logistic regression model is given by the equation which is commonly known as logarithm of advantages, where β is the vector of the estimated parameters of explanatory variables and π is the probability of the individual in a situation of occupational fragility.
As the logistic regression model is applicable mostly to low-impact phenomena in the population, which is not our case, we performed a correction called “relative risk” (Zhang and Yu 1998), given by the formula RR = OR/((1 − π i ) + (π i × OR), being RR the relative risk and OR the odds ratio, given by the formula.
To test the significance of the estimated parameters, we will use Wald’s statistics that is given by, which, for large samples, is distributed as, or. With this correction, we prevent distorted estimates of the parameters. The results can be understood as percentage of the effect of an explanatory variable on the response variable in relation to the reference group that is given in the model, which is a contribution to risk if the sign is positive and a protection if the sign is negative.
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2.
Multiple linear regression model
For the explanation of the “income per hour worked” variable based on the selected explanatory variables, we used the multiple linear regression model since the response variable chosen has a continuous distribution. Due to its asymmetric distribution, we applied a transformation given by the logarithm. The multiple linear regression model is given by the formula, where y is the response variable, X is the matrix with the values observed by the explanatory variables, is the vector of parameters corresponding to the effect of each explanatory variable and is the matrix of random error (Charnet et al. 1999).
To test the model adequacy, we used the adjusted coefficient of determination (adjusted R 2) which is obtained by the formula, where n corresponds to the number of explanatory variables and p corresponds to the number of estimated parameters. The test of significance of the parameters is given by the expression. The least squares estimator of the parameters is given by. The result of the parameters estimation gives us a measure of the contribution of each explanatory variable for the distribution of the response variable. In the case of the transformation of the response variable by the logarithm we can tell of a relative contribution of each explanatory variable in relation to the variation in the response variable.
Annex 9.2
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de Queiroz Ribeiro, L.C., Rodrigues, J.M., Corrêa, F.S. (2017). Segregation and Occupational Inequalities. In: de Queiroz Ribeiro, L. (eds) Urban Transformations in Rio de Janeiro. The Latin American Studies Book Series. Springer, Cham. https://doi.org/10.1007/978-3-319-51899-2_9
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