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Enregistrement W2952731636 · doi:10.2307/j.ctvbj7k3q.10

Wage discrimination at career entry in Switzerland:

2016· book-chapter· en· W2952731636 sur OpenAlexaboutno aff
Kathrin Bertschy

Notice bibliographique

RevueVerlag Barbara Budrich eBooks · 2016
Typebook-chapter
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueLabor market dynamics and wage inequality
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWageLabour economicsDemographic economicsPsychologyEconomicsSociology

Résumé

récupéré en direct d'OpenAlex

In recent decades, it has been apparent that the educational and occupational pathways of women and men have been converging. Women, in particular, have benefited from the expansion of education and, with respect to the achievement of general education certificates, have advanced to the same level as men. While the educational attainment gap is closing, the gender gap in the workplace stubbornly persists. Our analysis shows that wage discrimination is not the result of different career paths, but already exists at career entry. “Productivity” variables like formal qualifications and skills do not have any direct impact on wages. Also, different values (e. g. extrinsic or intrinsic work orientation) or different risk preferences are unlikely to influence the wages at career entry. We conclude that the early wage discrimination is partly due to the persistent gender segregation in the Swiss labor market and education system, which stimulate early gender-typed occupational career decisions. Findings show a wage discrimination of 7.3% at career entry. Young women have lower earnings due to lower salaries in traditional female occupations and because of wage discrimination in gender-mixed occupations as well as in typically male professions. In highly segregated jobs, women in typical male-jobs often “choose” or get allocated to specific work contents that go together with lower compensation; men do the contrary in typical female-jobs. These niche activities are economically inefficient because training qualifies women for better paid activities. Young women do not choose or are not allocated to these activities they have trained for. Possible reasons for early wage gaps Unexplainable wage differentials at career entry might be slightly overestimated, and also within our data set. However, the majority of the inexplicable differences in wages are likely the result of discrimination. One possibility is the so-called “statistical” gender discrimination. Companies assume that sooner or later, women tend to reduce their working hours. From the start, these companies consciously or unconsciously pay lower salaries to women, assign them lower paying jobs, or do not admit them the same development in wages as for men. In sociological and economic theory, this effect is referred to as statistical discrimination, because a specific behavior (the one of the companies) is derived from observed probabilities (women reduce their employment more often than men due to parenting). This behavior of the companies discriminates against all women, including those not complying with this assessment and those not intending to reduce their workload, with or without children. Generous family-friendly policies, such as long maternity leaves and/ or part-time work protections, made it possible for more women to work. But they can also enforce statistical discrimination: Woman are more likely to work in low segment, lower paid jobs and less likely to be managers in countries with generous family-friendly policies, as shown by Blau and Kahn (2013) in a study comparing 22 countries. To avoid statistical discrimination, policies should be devised gender neutral. This is apparent in countries/regions including Iceland, Sweden and Quebec, where parental leave policies encourage both men and women to take time off for a new child. Apart from measures against education and labor market segmentation, gender-neutral state policies could thus help to close the early gender wage gap. Secondly, solidarity among men is also likely to play a role. In sociological theory, this behavior is known as discrimination theory, which states that “the same” or “the like” is preferred. As there are many more men than women in superior positions and as such, men more often make employment or wage decisions, they may prefer applicants or employees with similar characteristics, for example. This preferential treatment can also be unconsciously done. Implications Implications of gender wage gap on work and family and the persistence of gender inequalities can be manifold. In order to maximize household-production, young professionals in couple households quickly divide the paid and house work according to the traditional pattern Analyses with TREE-Data show that young couples divide household labor and paid work based on hourly wages. An unequal division of labor will be reinforced over time due to the different, statistically unjustifiable wage developments for men and women. From an economic point of view, and with regard to an optimal allocation of human resources, this development is economically inefficient. The lower incomes of women at career entry also cause these economically problematic dynamic trends. The interaction of all these effects is jointly responsible for the persistence of gender inequalities.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,449
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,209
Écart entre enseignants0,180 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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