Transforming Naturally Occurring Text Data Into Economic Statistics: The Case of Online Job Vacancy Postings

Turrell, Arthur; Speigner, Bradley; Djumalieva, Jyldyz; Copple, David; Thurgood, James

doi:10.3386/w25837

Cited by 19 publications

(12 citation statements)

References 27 publications

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“…The use of such data, in fact, requires the resolution of problems linked to definition and data collection (duplication, selection of stable and credible sources, removing non‐work ads, selection of active ads etc. ), the transformation from OJA flows to stocks (Garasto et al, 2021; Turrell et al, 2019) and issues such as non‐representativeness and lack of coverage at the population level (Couper, 2013; Fan et al, 2014; Japec et al, 2015; Kureková et al, 2015; Tam & Clarke, 2015).…”

Section: Discussionmentioning

confidence: 99%

Do job vacancies variations anticipate employment variations by sector? Some preliminary evidence from Italy

Lovaglio

2021

Labour

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Government policy has placed increasing emphasis on the need for robust labour market projections. The job vacancy rate is a key indicator of the state of the economy underpinning most monetary policy decisions. However, its variation over time is rarely studied in relation to employment variations, especially at the sectoral level. The present paper assesses whether changes in the number of vacancies from quarter to quarter are a leading anticipator of employment variation in certain economic sectors over the previous decade in Italy, using multivariate time‐series tools (the vector autoregressive and error correction models) with Eurostat data. As robustness checks for integration order and cointegration, we compare traditional critical values with those provided by response surface models. To the best of our knowledge, no previous study has evaluated this relationship using Italian data over the last decade. The results demonstrate that percentage changes in numbers employed (occupied persons) react to percentage changes in vacancies (one‐quarter lagged), but not vice versa, indicating that variations of vacancies are weakly exogenous. The fastest short‐term adjustment from disequilibrium is seen in the construction industry, whereas the manufacturing and the information and communication technology sectors demonstrate the strongest long‐run relationships among variations. This suggests that the matching rates – the likelihood that a vacancy is filled – are higher for these than for other sectors, as a result of developments in recruitment technology for professional figures of such industries.

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Section: Discussionmentioning

confidence: 99%

Do job vacancies variations anticipate employment variations by sector? Some preliminary evidence from Italy

Lovaglio

2021

Labour

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“…This suggests to define as post-sampling weight balancing the quarterly stock of vacancies by industry according to online vacancies towards the quarterly stock of vacancies by industry according to the JVS: this produces a set of "post-sampling" weights for each quarter and industry, that can be assigned to each vacancy or vacancy distributions (by relevant auxiliary variables, such as NUTS, ISCO, NACE, Quarters and possible interactions). Recent works [16,17] adopt such kind of posts-stratification. Over or under-representation (for univariate or two-way or three way interactions) in online vacancies can be easily assessed by the ratio between percentage distributions of online counts and poststratified ones: If the ratio is higher than 1, it means that a certain category (industry, occupation) is likely to be over-represented in the online job adverts dataset, whereas the opposite is true with ratio is less than 1.…”

Section: Discussionmentioning

confidence: 99%

Occupations and Skills in Demand from Web-Based Job Vacancies

Lovaglio

2021

SIAM

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Online job portals collecting web vacancies have become important media for job demand and supply matching. They also represent a growing research area for the application of analytical methods to study the labour market using innovative data sources. Both the Knowledge Discovery in Databases approach and mixed supervised and unsupervised text mining approaches were typically applied to retrieve occupations associated with each web vacancy (ISCO classification up to level 4) and related skills. In the present paper we apply this method to a population of online web vacancies collected for three countries (Italy, UK and Germany) collected over a quarter in 2019, within an international project, to demonstrate the potentiality of informative power of such approach that can be considered as promising strategy providing effective support for decision making of several stakeholders such as government organizations, analysts, and recruitment agencies, as they allow for timely and fine-grained representations of complex labour market dynamics, in terms of trends, occupations, and skills. Finally, problems of representativeness that affect online vacancies are briefly discussed and possible approaches are proposed.

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“…As a consequence, using online job vacancies as a data source for calculating common labour market indicators (such as number of vacancies, labour market tightness, degree of skill mismatch) has become a relatively common practice for scholars and practitioners (Japec and Lyberg 2020;Štefánik, Lyócsa, and Bilka [2022]; Turrell et al 2019). Interesting micro-level applications of online labour market data, beyond skills analysis, include those focusing on the value of the migration experience in employers' demands (Kureková and Žilinčíková, 2018); the role of occupational mismatch in explaining the productivity puzzle (Turrell et al 2021); the relationship between firm credit crunch and employee job search behaviour (Gortmaker, Jeffers, and Lee 2021); discrimination against women in the labour market (Kuhn and Shen 2013); or links between the introduction of unemployment benefits, job searches and job postings during the Great Recession in the US (Marinescu 2017).…”

Section: Online Data In Labour Market Research: Trends and Characteri...mentioning

confidence: 99%

“…For example, it is generally rarely the case that the representative dataset would contain information about sector and occupation, coded in a way that allows direct comparison with the online data. It follows that a reweighting strategy based on sectoral representation will align the online data closely with the representative source, in terms of the representation of individual sectors; but this will only address the difference in the studied occupations, to the extent that this difference is caused by different sectoral coverage (Turrell et al 2019). In the previous parts of this paper we emphasized other problems with applying weights to online job vacancy data: mainly, the essential difference between measuring stocks/matched jobs versus flows/unmatched jobs, in (representative) employment statistics and in online vacancy data, respectively.…”

Section: Statistical Techniquesmentioning

confidence: 99%

Methodological issues related to the use of online labour market data

Fabo¹,

Kureková²

2022

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This report provides a mapping of existing research that employs online labour market data, covering both online job vacancies (demand side) and online applicant data (CVs) (supply side). We discuss and assess a variety of tools and empirical methods that have been used to address specific disadvantages of this data, such as non-representativeness or fluctuations in data quantity and structure; these may be due to external shocks, such as the COVID-19 pandemic. We find that while this research field has expanded rapidly, including with respect to geographical coverage, many empirical studies do not engage with the methodological aspects and weaknesses of online labour market data and take them at face value. We highlight that there are legitimate research approaches, which are inductive in nature, focused on discovering patterns and trends in underlying data. These are by definition less concerned with generalizability of findings, as they have different objectives. For this body of research, online labour market data open new avenues for understanding developments in labour markets. We also argue that biases in online labour market data emerge due to multiple factors. With respect to the order of discrepancies between online labour market data and representative data sources, these are typically not paramount. Different techniques have been adopted to deal with the non-representativeness problem, such as statistical techniques; adapting the research questions and research focus to the quality of data; and use of mixed methods, including qualitative methods, to increase the robustness of results.

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Transforming Naturally Occurring Text Data Into Economic Statistics: The Case of Online Job Vacancy Postings

Cited by 19 publications

References 27 publications

Do job vacancies variations anticipate employment variations by sector? Some preliminary evidence from Italy

Do job vacancies variations anticipate employment variations by sector? Some preliminary evidence from Italy

Occupations and Skills in Demand from Web-Based Job Vacancies

Methodological issues related to the use of online labour market data

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