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16-10-2024 | Original Paper

Occupational specialization, skills, and urban wage premium in Brazil

Authors: Everlândia de Souza Silva, Diana Lúcia Gonzaga da Silva, Roberta de Moraes Rocha

Published in: The Annals of Regional Science | Issue 4/2024

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Abstract

We set out to verify how local occupational specialization, according to different categories of workers’ skills (cognitive, social, and manual), can impact the individual wage differential in Brazil. We estimate the effects of occupational specialization by skills from the wage equation using instrumental variables and worker and firm characteristics. The main contribution of the study is to decompose the effects of specialization of skills in relation to urban conditions, such as population density. We rely on individual microdata for Brazil for the period 2009–2018. We found a wage premium for cognitive and social specializations, benefitting workers in occupations that are intensive in these skills. The results of the specialization wage premium, with the control of the endogeneity of the local choice, indicated that the Ordinary Least Squares estimates were underestimated. In the decomposition analysis, density negatively affected wage premiums for cognitive and social specializations. The results suggest that the negative effect of competition in denser labor markets can offset the positive interaction externalities. When controlling for worker and firm fixed effects, density no longer affects the specialization wage premium. This result suggests that worker-firm matching is more relevant than density in explaining occupational specialization wage premiums. The results for Brazil indicate the importance of policies that promote the quality of worker-firm matching.

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Appendix
Available only for authorised users
Footnotes
1
Wheaton and Lewis (2002) and Rossi-Hansberg et al. (2019) define occupational specialization as the number of workers in an occupation in the metropolitan region divided by the total number of workers in the same region. In our study, occupational specialization is categorized by skill type (cognitive, social, and manual).
 
2
Most studies classify occupations by cognitive, social, and manual skills when examining skill wage differentials in urban areas (Florida et al. 2011; Bacolod et al. 2009; Neves Júnior 2018; Barufi et al. 2016).
 
3
While Ehrl and Monasterio (2020) concentration measure detects the average amount of labor with certain skills in a municipality, the specialization measures allow us to determine the proportion of workers in each skill category within the regional unit, i.e., we assess how specialized the Population Arrangement is in each skill, enabling us to better understand the effects of externalities.
 
4
Ehrl and Monasterio (2020) use the local fixed effect estimated in the wage equation as the dependent variable and the skill indicator as the explanatory variable for the decomposition model. In this sense, the study uses local cross-sectional data to estimate the decomposition model.
 
5
Sharing is a positive externality that arises from the size of cities, allowing for more efficient use of local infrastructure, the variety of suppliers of intermediate goods, or workers with similar skills. On the other hand, the larger cities improve the quality of worker-firm matching. Finally, learning corresponds to knowledge spillovers and diffusion of ideas and innovations in large cities (Duranton and Puga 2004; Puga 2010).
 
6
We obtained the elasticity by regressing the log of average local real wages in Brazil against the log of density. The resulting estimate (0.039) was multiplied by 100 to obtain the percentage effect of doubling the density. Combes et al. (2008) performed this regression using data from local labor markets in France.
 
7
The IBGE (2016) defined 294 Population Arrangements in the country, composed of 953 municipalities.
 
8
Ehrl and Monasterio (2020) classify cognitive and manual skills as routine and non-routine to analyze the routinization of occupations. Our study uses general cognitive and manual skills because our goal is not to analyze the routinization of occupations, but rather the urban wage premium of specialization by skill.
 
9
The IBGE (2016) defined 294 Population Arrangements in the country, composed of 953 municipalities. The Arrangements are classified into large, medium, and small arrangements, where a population of less than 100,000 is considered small, a population between 100,000 and 750,000 inhabitants is considered medium, and a population greater than 750,000 is considered large. In Brazil, there are 189 small, 81 medium, and 24 large arrangements. Thus, out of a total of 5565 municipalities, only 953 belong to these Population Arrangements, representing 55.7% of the resident population in 2010. In addition, the IBGE (2016) further defined Medium and Large Urban Concentrations. Medium concentrations are composed of isolated municipalities (which do not form arrangements) and Population Arrangements that have from 100,000 to 750,000 inhabitants, while large concentrations have populations that exceed 750,000 inhabitants. Considering all the Population Arrangements and the isolated municipalities of the medium and large concentrations, we analyzed 376 geographic units in this study.
 
10
Maciente (2013) made the CBO (Brazilian Classification of Occupations) and ONET occupations compatible to extract a measure of the skill level required for each occupation in Brazil. For more details on how Maciente (2013) constructed the skill content of each occupation, see Barufi et al. (2016) and Neves Júnior (2018).
 
11
As examples, we have: the occupation “Bioengineer” is classified as intensive in cognitive skills; the occupation “Sign painter” is classified as intensive in manual skills; and the occupation “Microcredit agent” is classified as intensive in social skills. Thus, of the 2609 occupations: 755 are classified as intensive in cognitive skills; 685 as intensive in social skills; and 1169 as intensive in manual skills. The complete classification for the 2609 occupations is available on request from the authors.
 
12
The classification of all variables used in this research is in Table 3 of the Appendix.
 
13
The identification strategy for Eq. (1) assumes the use of IV to address the endogeneity of occupational specialization caused by workers’ location choices by skill. According to the literature, individual fixed effects may arise from workers’ skills. We estimate Eq. (1) separately for each skill category. Thus, we indirectly control the worker skill sorting by estimating the wage equation for skill groups. On the other hand, the wage decomposition strategy (from Eq. 2) controls for the unobserved heterogeneity of workers and firms to account for the matching effect in the labor market, as suggested in the literature (Combes et al. 2008; Silva and Azzoni 2021).
 
14
As a robustness test, endogeneity is also considered using the Bartik instrument. The results are in the Appendix.
 
15
We used the fixed effects of the worker-firm pairs (\({\varphi }_{ij}\)) generated from identifiers for each combination of worker and firm. In this case, we obtain a new pair (worker/firm) for each worker who changes firms. From this point on, the worker-firm fixed effect is called matching.
 
16
At the 2023 price level.
 
17
A statistical test of correlation between the variables of occupational specialization of skills was performed. These results are present in a table in the Appendix. It appears that there is a negative correlation of − 0.9563 between social and manual occupational specialization.
 
18
This is a preliminary result to evaluate the association between the logarithm of local density and the logarithm of the local average wage for the period between 2009 and 2018, without controls. This empirical exercise carried out for Brazil was based on Combes et al. (2008). In this study, density is the total population of the Population Arrangement divided by its area.
 
19
In a log-log econometric model, a 1% increase in the explanatory variable produces a 1% increase in the dependent variable, so the estimate measures an elasticity. In this study, we interpreted the occupational specialization wage premium as a wage elasticity. In some parts of the text, we used the 100% increase in specialization instead of 1% to obtain the elasticity. In the latter case, the specialization elasticity estimates should be multiplied by 100.
 
20
The identification strategy adopted in this study requires the mobility of workers between firms and Population Arrangements and the mobility of firms between Population Arrangements across the panel, by skill group (cognitive, social, and manual). By looking at the mobility of workers between firms (17.07%, 28.13%, and 31.26%) and of workers (4.43%, 5.18%, and 8.13%) and firms (0.78%, 0.81%, and 0.71%) across Population Arrangements, the identification conditions are satisfied by this study.
 
21
All results obtained by estimating the model in Eq. (1) are available in the Appendix.
 
22
Due to computational constraints, the skill specialization premium decomposition analysis was conducted while incorporating a random sample of 1% of the databases from the previous section. We found mobility of workers by skill (cognitive, social, and manual) among firms (27.82%, 40.37%, and 44.43%), mobility of workers among Population Arrangements (7.12%, 7.05%, and 10.71%), and mobility of firms among Population Arrangements (0.65%, 0.49%, and 0.61%) for the random samples obtained. Thus, the samples are considered to be representative and the identification conditions are satisfied for the samples.
 
23
The results for the first stage, Eq. (2), of the decomposition of the effects of skill specialization on local wages are in the Appendix tables.
 
24
The base year 1994 was considered because it represents a minimum lag of 15 years from the first year of the database (2009). This allows for greater credibility and exogeneity of the instrument in regard to recent local shocks.
 
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Metadata
Title
Occupational specialization, skills, and urban wage premium in Brazil
Authors
Everlândia de Souza Silva
Diana Lúcia Gonzaga da Silva
Roberta de Moraes Rocha
Publication date
16-10-2024
Publisher
Springer Berlin Heidelberg
Published in
The Annals of Regional Science / Issue 4/2024
Print ISSN: 0570-1864
Electronic ISSN: 1432-0592
DOI
https://doi.org/10.1007/s00168-024-01318-6