Big Data Applications in Labor Economics, Part B: Volume 52B

Cover of Big Data Applications in Labor Economics, Part B

Table of contents

(8 chapters)
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Abstract

We construct a novel dataset of Canadian online job postings, classified by occupation. The data, provided by Indeed, an online job board, represents vacancies advertised by employers across Canada. We have classified these job postings into standard occupations using text analytics. This dataset has been used to study changes in the demand for jobs linked to digitalization over the COVID-19 pandemic. To this end, we leverage time-series and cross-sectional variations in COVID-19 containment policies, examining their impact on jobs broadly related to digitalization. Our findings reveal that vacancies in digital production jobs increased more substantially than in traditional jobs during the reopening phases. However, no substantial differences were observed when considering different types of vacancies according to the use of digital technologies (i.e., occupations at low risk of automation or those that allow remote work). Overall, our results do not support the popular idea that the COVID-19 pandemic marked a significant turning point in digitalization trends, but rather document a modest shift in this direction.

Abstract

Outside of Europe and the United States, the knowledge on skills dynamics is scarce due to a lack of data. We therefore assess whether online data on vacancies and applications to a job board can help fill this gap. We propose a taxonomy with three broad categories – cognitive, socioemotional, and manual skills – and 14 commonly observed subcategories, which we define based on unique skills identified through keywords and expressions. The taxonomy is comprehensive but succinct, suitable for developing and emerging economies, and adapted for online data. We then develop a text-mining approach to implement the taxonomy. Based on Uruguayan data from the job board BuscoJobs, we find that our model is able to assign skills to 64% of applicants' employment spells and 94% of vacancies. While online data are usually skewed toward highly qualified work, we show that our data include meaningful numbers of vacancies and applicants of intermediate and even lower qualification levels. Our approach relies on data that are currently available in many countries, thereby allowing for country-specific analysis that does not assume that occupational skills are constant across countries. This is key as we find considerable differences between our findings and those using US O-NET data. Finally, we end with an illustration of how our approach can inform the analysis of skills dynamics. To our knowledge, we are the first to explore this approach in the context of emerging economies.

Abstract

This study provides new evidence on skill requirements in the labor market and shows to what extent skill demand is associated with wages and vacancy duration. In a sample of more than 1.5 million job postings administered by the Austrian public employment service, I identify the most common skill requirements mentioned in job descriptions. Accounting for a broad set of detailed job characteristics, there exists a robust association between the number of skill requirements and wages. In particular, jobs with many skill requirements pay substantially higher wages. While I estimate large effects for managerial and analytical skills, associations with most soft skills are small. Employers also need longer to fill vacancies with many skill requirements. Robustness tests show that measurement error is unlikely to explain these results and that the estimates can be replicated using vacancy postings from another job board.

Abstract

This article shows how different data sources can be combined to learn about the evolution of gender norms over time. First, data on job advertisements from 1950 up to 2020 reveal that there was a significant change among Swiss employers' stated preferences regarding their prospective employees' gender. More specifically, the proportion of gender-neutral job posts increased from five to almost 95% within the observation period. To further corroborate and contextualize this finding, I complement it with time series on the relative frequency of several specific queries, such as equality between men and women, from Google's German language book corpus. These additional series are broadly consistent with the evolution of the share of gender-neutral job posts. However, it also appears that there are two distinct narratives, one concerned with the personal sphere, identity, and intimate relationships, the other with the political and public realm. Interestingly, the narrative on personal relations set off considerably earlier than the change in the proportion of gender-neutral job ads. Overall, the evidence from the different data series shows that gender norms have changed substantively, yet in a complex manner, over the past decades.

Abstract

We study the demand for skills by using text analysis methods on job descriptions in a large volume of ads posted on an online Indian job portal. We make use of domain-specific unlabeled data to obtain word vector representations (i.e., word embeddings) and discuss how these can be leveraged for labor market research. We start by carrying out a data-driven categorization of required skill words and construct gender associations of different skill categories using word embeddings. Next, we examine how different required skill categories correlate with log posted wages as well as explore how skills demand varies with firm size. We find that female skills are associated with lower posted wages, potentially contributing to observed gender wage gaps. We also find that large firms require a more extensive range of skills, implying that complementarity between female and male skills is greater among these firms.

Abstract

This article presents new evidence on anticompetitive practices in the franchise sector. Drawing from a corpus of Franchise Disclosure Documents (FDDs) filed by 3,716 franchise brands in years 2011–2023 (partial), I report new information on franchise brands' use of interfirm nonsolicitation (“no poach”) clauses barring recruitment between firms, no hire clauses barring employment, and franchisor requirements that franchisees use employee noncompete clauses barring workers from joining competitors. Regulatory actions that restricted the enforceability of anticompetitive clauses began to appear in FDDs in 2018. While nonsolicitation and no hire clauses have declined in use, the use of noncompetes remained stable over time. While prior evidence on anticompetitive practices largely draws from individual complaints, survey data, and limited hand-coded samples, this article spotlights new methods for finding barriers to worker mobility in large, unstructured text corpora.

Abstract

This article combines 530 digitized Franchise Disclosure Documents and standard contracts with employer-identified job ads from Burning Glass Technologies to establish stylized facts about franchising labor markets and their relation to the vertical restraints and contractual provisions that limit the autonomy of franchisees vis a vis their franchisors. We report novel findings about the application of vertical restraints like Resale Price Maintenance, Exclusive Dealing, and No-poaching Restrictions, among many others, to a low wage workforce. A legal regime that favors the franchising business model incentivizes franchisees to profit at the expense of workers and to limit egalitarian tendencies operating in the workplace.

Cover of Big Data Applications in Labor Economics, Part B
DOI
10.1108/S0147-9121202552B
Publication date
2024-12-10
Book series
Research in Labor Economics
Editors
Series copyright holder
Emerald Publishing Limited
ISBN
978-1-83608-713-7
eISBN
978-1-83608-712-0
Book series ISSN
0147-9121