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Occupational Classifications and Derived Status Indices: Challenges and New Approaches 1
| Dr Christoph Homuth (Leibniz Institute for Educational Trajectories)
Professor Corinna Kleinert (Leibniz Institute for Educational Trajectories)
Dr Ann-Christin Bächmann (Leibniz Institute for Educational Trajectories)
|Wednesday 19 July, 14:00 - 15:00
Occupations are important indicators of economic, cultural, and social capital, social status, prestige and class, as well as predictors of future life chances and security. They are associated with particular working conditions, gender types, interests and tasks, as well as with macro-level phenomena such as occupational segregation, segmentation, and closure.
Therefore, it is essential to gather valid and reliable information on occupations in surveys, which then is coded into occupational classifications (e.g., ISCO) that allow examining occupations and deriving social status indicators (e.g., ISEI, EGP).
Measuring and analyzing occupations and derived indices is challenging: coding in surveys is resource-intensive and error-prone due to occupational complexity and incorrect or incomplete data. Hence, automation methods are searched for, which have to be adapted to new survey modes. Different data sources (e.g., survey and administrative data) might produce different measurement errors. Occupational classifications and derived indices might be quickly outdated in times of rapid occupational and social change. Different cultural, economic, and institutional settings challenge the harmonization and comparison of occupational classifications and status indices in international comparative research.
In this session, we aim to bring together researchers interested in:
- advancements in occupational coding (e.g., automatic coding, machine learning)
- new ways of collecting occupational information, especially in the context of changing
survey modes (e.g., more online surveys or app-based data collection)
- innovative measurement approaches to collecting information on established occupational
classifications and scales
- adaptation of and innovation in occupational classifications
- temporal stability of occupational classifications
- cross-national comparisons and harmonization of classifications and indices
- comparisons of occupational codes in linked data sets (e.g., survey and administrative
data) to assess data quality
- other theoretical or methodological aspects related to occupational classifications and
derived status indices
Keywords: Occupations, Coding, Occupational Classifications, Status Indices
Ms Nadine Bachbauer (Leibniz Institute for Educational Trajectories) - Presenting Author
Mr Gregor Lampel (Leibniz Institute for Educational Trajectories)
Prestige scores and socioeconomic indexes are important variables for a wide range of studies about employment biographies, career development, social position etc. Most of the used scales – for example ISEI, SIOPS – are based on the occupation. This is collected as an open text and coded afterwards or is already classified during the data collection. Therefore occupational coding respective the quality of the underlying information is very important. NEPS-SC6-ADIAB is a linked data product which contains survey data and administrative data and therefore provides the opportunity to compare the occupational information of the same individuals using two data sources collected from different perspectives. On the one hand, the administrative data consists of social security notifications which have to be done in a yearly manner by the employers and, on the other hand, the National Educational Panel Study which consists of the survey of employees. The linkage of these data sources is done on the personal level. Thus, the information about occupation and the resulting social position can be compared directly. This study wants to answer the question, whether upgrades are less well reflected in the administrative data than in the survey data. Employers' reporting of occupations is not relevant for social security, and thus partly subject to errors. It is assumed that especially occupational changes within an establishment are not reported. In contrast, it is assumed that employees accurately report career changes, especially upgrades, in terms of social prestige and socioeconomic status. So, we want to show that a typical career is represented in the administrative data with few large position jumps, since within-job upgrades are often not reported, and a more or less continuous increase is observable in the survey data.
Dr Britta Matthes (Institute for Employment Research) - Presenting Author
Occupations are classified in order to systematically reduce the diversity of occupations and divide them into categories that are as homogeneous as possible within and as heterogeneous as possible among themselves. The new development of the Classification of Occupations 2010 (KldB 2010) is therefore based on a theory-driven empirical analysis of the task similarity of occupations. However, it was already determined during this redesign that a regular review of the occupational classification is necessary, especially because new occupations emerge and occupations can change in such a way that they should be assigned to a different classification unit. In addition, there are always legislative occasions, such as the reform of the nursing professions, which make it necessary to revise the classification structure. On the other hand, updating the occupational classification leads to breaks that make comparability over longer periods difficult or even impossible. Against this background, the presentation will first briefly present the theoretical and empirical basis for the development of the KldB 2010 and report which adjustments have been made in the 2020 version. Subsequently, the example of measuring occupational mobility will be used to discuss the balancing act between the need for adjustments in occupational classification on the one hand and the resulting consequences for empirical analyses on the other.
Ms Ana Santiago-Vela (Federal Institute for Vocational Education and Training) - Presenting Author
Occupations are central for sociological labour market research and have consistently played a prominent role in analyses of social inequality and working conditions. The reliable measurement of occupations is an essential prerequisite to provide researchers with high-quality data sets. Nevertheless, the use of different data sources to capture occupational information may induce inconsistencies affecting the data quality. Whereas there is evidence on inconsistent measures of the level of education in administrative and survey data, comparisons of occupational information in linked data are missing. Thereby, the aim of this paper is to compare occupational codes in administrative data with survey self‑reported occupations in order to assess the data quality of occupational information. By using a unique linked dataset with information on occupational classifications that have been provided by employers and employees, we are able to assess potential differences in reporting occupational information between employers (administrative data) and employees (self-reports from survey data). We used data from the BIBB/BAuA-Employment Survey 2018 (a representative dataset of persons in active employment in Germany where the measurement of occupations was a primary purpose of data collection), which were linked to administrative data from social security records provided by the German Federal Institute for Employment Research (ADIAB). The mechanism of gathering the data is different between data sources: whereas employers are required to report detailed information on all employees, including information on occupational classifications, occupational information in the survey data based on employees’ text answers (respondents’ job). Our results show that occupational information is different in the administrative and survey data, whereas inconsistencies are more likely, the more disaggregated the occupational classification is. Moreover, requirement levels of jobs also differ between data sources. In addition, our results shed light on regional, firm, individual and occupation-specific factors affecting inconsistent occupational information.