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Published in: Eurasian Business Review 3/2022

05-07-2022 | Regular Article

Routine-biased technological change and wage inequality: do workers’ perceptions matter?

Authors: Silvia Vannutelli, Sergio Scicchitano, Marco Biagetti

Published in: Eurasian Business Review | Issue 3/2022

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Abstract

The Routine-Biased Technological Change (RBTC) has been regarded as a relatively novel technology-based explanation of social changes affecting job and wage polarization. In this paper, we investigate wage inequality between routine and non-routine workers along the wage distribution in Italy. Thanks to unique survey data, we can estimate the wage differential using both the actual and the perceived level of routine intensity of jobs to classify workers. We adopt semi-parametric decomposition techniques to quantify the importance of worker characteristics in explaining the gaps. We also employ non-parametric techniques to account for self-selection bias. We find evidence of a significant U-shaped pattern in the wage gap, according to both definitions, with non-routine workers always earning significantly more than routine workers. Results show that worker characteristics fully explain the gap in the case of perceived routine, while they account for no more than 50% of the gap across the distribution in the case of actual routine. Thus, the results highlight the importance of taking into account workers’ perceptions to reduce the set of omitted vaiables when analyzing determinants of wage inequality.
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Footnotes
1
In line with Rohrbach-Schmidt and Tiemann (2013) we use the terms “subjective” and “self-defined/self-assessed” interchangeably to define the definition of routine at the worker’s level and the term “objective” to define the actual level of routine at the occupational level.
 
2
Sticky-floor refers to a situation in which the 10th percentile wage gap is higher than the estimated wage gap at the 50th percentile. Glass ceiling refers to a situation in which the 90th percentile wage gap is higher than the estimated wage gap at the 50th percentile.
 
3
Also Stinebrickner et al., (2019a; 2019b) using job-level information from the the Berea Panel Study, find that current job-tasks significantly determine wages in the US.
 
4
Even though it is possible that interviewees may have not the same understanding of the term as the RBTC, this is the general problem with self-assessed measures, while the main advantage is precisely capturing real individual perceptions, without any mediating factor.
 
5
The self-assessed approach has a subjective nature and consists in asking workers about the educational requirements set by the firm to get the job or the level required for the job, according to their view, and comparing it with their actual level of education. For example, in the OECD’s Survey of Adult Skills (PIAAC), individuals are asked, relative to their own education, what level of education they think would be necessary to satisfactorily do their job. In a similar way, we follow a subjective approach by asking workers if their work can be considered routine.
 
6
Many kernel functions can be used to the scope. In our exercise we chose the Gaussian kernel evaluated at (\(w - w_{i}\)) given the bandwidth h. Our choice of the kernel is due to its property of monotonicity of peaks and valleys w.r.t. changes in the smoothing parameters, which proves to be useful when comparing distributions (Sheather, 2004). For what concerns the bandwidth, our choice has fallen on the Cross Validation (CV) method: it is suitable as there is no need to make assumptions about the smoothness to which the unknown density belongs (Loader, 1999).
 
7
The US O*Net database is based on the Dictionary of Occupational Titles (DOT hereafter), which since 1939 has provided information on occupations, with a specific focus on the skills required in the public employment service. The O*Net is based on the Standard Occupational Classification (SOC), providing for each elementary occupation variables on knowledge, skills, abilities, and tasks. The key dimensions included in the O*Net are the following: worker characteristics: permanent characteristics affecting workers’ performance as well as their propensity to acquire knowledge and skills; worker requirements: workers’ characteristics measured by means of experience and education; experience: characteristics mostly related to past work experience; occupation: a large set of variables referring to requirements and specific features of the various occupations.
 
8
See also Barbieri et al. (2021), Bonacini et al. (2021) and Cirillo et al. (2020).
 
9
In line with the literature (Autor and Dorn, 2013; Firpo et al., 2013; Goos et al., 2014) we calculate the RTI assuming rank-stability of tasks for the short-time span, since occupational task requirements are likely to be time invariant or to change at a very slow rate, thus leaving the rank of occupations substantially invariant.
 
10
These figures differ from those presented in the regression analyses as many individuals are discarded for lack of characteristics.
 
11
We included this variable because it is the only way to consider the household's wealth: therefore, the ability to make ends meet could be high even in the absence of sufficient wages. By the way, we used a different specification of the model by excluding this variable and results hold. Results are available from the authors upon request.
 
12
In the regression analysis many observations (i.e. many individuals) are discarded because of lack of information on used covariates. This is why the number of observations in the regressions is different from that in the Table 4.
 
13
There could be some variables which could suffer from potential endogeneity: in particular, this could be the case of the variables “make ends meet”, “stress” and “training”: nonetheless, both the regression analyses and the Oaxaca decomposition do not show significant differences when these variables are discarded. This is to be interpreted as a sign of weak or virtually absent endogeneity. This “leave-them-out” approach has been chosen due to the lack of feasible instruments within our cross-sectional database. In the case of the Oaxaca decomposition, the wage differential explained by endowments is 77% of the total when those variables are included and only slightly decrease to 73% when those are excluded. Results of these analyses are available upon request.
 
14
It should be noted that methods like RIF-regressions are not unproblematic due to their strong assumptions. As argued in Kassenboehmer and Sinning (2014) RIF-regression implies that the respective feature of the outcome distribution depends on the marginal distribution of the covariates only through their mean.
 
15
The clusterization of standard errors at the occupational level does not produce significant changes in the estimates. Results are available from the authors upon request.
 
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Metadata
Title
Routine-biased technological change and wage inequality: do workers’ perceptions matter?
Authors
Silvia Vannutelli
Sergio Scicchitano
Marco Biagetti
Publication date
05-07-2022
Publisher
Springer International Publishing
Published in
Eurasian Business Review / Issue 3/2022
Print ISSN: 1309-4297
Electronic ISSN: 2147-4281
DOI
https://doi.org/10.1007/s40821-022-00222-3

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