Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. CEREQ, Marseille). Our econometric approach is based on a semi-structural three-equations model, which is identified thanks to some exclusion restrictions. We exploit in particular exogenous variations in the earnings returns associated with the majors across the business cycle, in order to identify the causal effect of expected earnings on the probability of choosing a given major. Relying on a threecomponent mixture distribution, we account for correlation between the unobserved individual-specific terms affecting the preferences for the majors, the unobserved individualspecific factors entering the equation determining the length of studies within each major, and that affecting the labor market earnings equation. Following Arcidiacono and Jones (2003), we use the EM algorithm with a sequential maximization step to produce consistent parameter estimates. Simulating for each given major a 10 percent increase in the expected earnings suggests that expected earnings have a statistically significant but quantitatively small impact on the allocation of students across majors. Terms of use: Documents in D I S C U S S I O N P A P E R S E R I E SJEL Classification: J24, C35, D84

[fre] Cet article est consacré à l’estimation des effets du travail salarié des étudiants sur leur réussite universitaire et leur décision de poursuite d’études. L’analyse repose sur des échantillons extraits des enquêtes Emploi . conduites par l’Insee de 1992 à 2002. Ces échantillons sont restreints aux personnes en cours d’études initiales à l’université et préparant un diplôme universitaire de premier ou de second cycle (Deug, licence ou maîtrise). Sont exclus de l’analyse les étudiants dont l’emploi va de pair avec les études, en particulier les apprentis sous contrat et les stagiaires en formation. Les modèles estimés sont des modèles de type Probit à deux équations simultanées, la première expliquant l’occupation d’un emploi salarié par l’étudiant, la seconde sa réussite à l’examen de fin d’année, conjointement . avec sa décision de poursuite des études pour l’un des modèles. Le temps de travail salarié est pris en compte en distinguant, dans un des modèles, les emplois de moins ou plus de 16 heures par semaine. Les résultats montrent que l’occupation d’un emploi régulier réduit significativement la probabilité de réussite à l’examen de fin d’année universitaire. S’ils ne travaillaient pas, les étudiants salariés auraient une probabilité plus élevée de 43 points de réussir leur année. Une analyse complémentaire montre que le cumul emploi-études n’a pas d’effet significatif sur la probabilité de poursuivre les études l’année suivante, quels que soient la filière et le niveau des études. [eng] This article reports an estimation of the effects of students’ paid employment on their success at university and their decision to pursue their studies. Our analysis is based on samples extracted from INSEE Labour Force Surveys conducted between 1992 and 2002. The samples are confined to students who have begun their university studies and are preparing a first-or second-stage degree (French DEUG or B. A. or M. A. equivalent). We exclude students whose jobs are linked to their studies, particularly apprentices under contract and interns in training. We estimate probit models with two simultaneous equations: the first explains the student’s paid employment; the second explains the student’s success in the year-end exam and —in one of the models— jointly with the decision to continue his or her studies. The models incorporate working time in paid employment: one of the models distinguishes between jobs requiring more or less than 16 hours a week. The results show that holding a steady job significantly reduces the probability of passing exams at the end of the academic year. If they did not work, students in paid employment would have a 43-point-higher probability of completing their academic year successfully. An additional analysis shows that the job-plus-studies combination does not significantly influence the probability of pursuing one’s studies in the following year, regardless of program and academic level. [ger] In diesem Artikel werden die Auswirkungen einer lohnabhängigen Erwerbstätigkeit der Studenten auf den ...

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract A model of labour supply is developed in which individuals face restrictions on hours choices. Observed hours reflect both the distribution of preferences and the distribution of offers. In this framework the choice set is limited and observed hours may not appear to satisfy the revealed preference conditions for 'rational' choice. We show first that when the offer distribution is known, preferences can be identified. Terms of use: Documents inWe then show that, where preferences are known, the offer distribution can be fully recovered. We also develop conditions for identification of both preferences and the offer distribution. We illustrate this approach in a labour supply setting with nonlinear budget constraints. The occurrence of non-linearities in the budget constraint can directly reveal restrictions on choices. This framework is then used to study the labour supply choices of a large sample of working age mothers in the UK, accounting for nonlinearities in the tax and welfare benefit system, fixed costs of work and restrictions on hours choices. * We thank seminar participants at Sciences-Po for their comments and the data archive UK and IPUMS for data access.

This paper evaluates the French RAR program (Réseaux Ambition Réussite or Ambition Success Networks), a junior high school program started in 2006 which intended to concentrate means and funds on well-chosen disadvantaged junior high schools. We use the criteria of eligibility to estimate a regression discontinuity model. For evaluated junior high schools, the increase in per-pupil teaching hours, and the decrease in class size are disappointing. Our results also suggest that the program may have had a negative effect on teacher and pupil enrollment. Both the proportion of older teachers and the proportion of poorly qualified teachers have increased and the pupils' achievement has decreased in Grade 9, the final year of junior high school. The RAR program has increased the disparity of teachers' characteristics and of pupils' ability between schools.

Introduction: Thromboembolic events (TEs) are one of the most prevalent complications in patients (pts) with polycythemia vera (PV). This real-world evidence study of the US OPTUM database evaluated the incidence of TEs in hydroxyurea (HU)-treated PV pts who either switched to ruxolitinib (RUX) after initial treatment (Tx) with HU (HU-RUX group) or continued HU Tx without switching (HU-alone group). Machine learning was then used to build a precise and scientifically robust model to predict the occurrence of TEs in PV pts with/without a history of TEs and HU failure (defined by either European LeukemiaNet [ELN] hematologic criteria or TEs). Methods: The OPTUM database comprises claims data and electronic medical records from 90 million pts (2007-2017, median stay in the database=7 years), including 69,464 PV pts. To avoid any selection bias during comparison, only pts treated prior to the RUX market launch were included in the HU-alone group (HU-RUX, n=81; HU-alone, n=195). Due to unavailability of Tx duration, time difference between the first and the last prescription was used as a proxy, and overall Tx duration was matched in both groups. TEs were assessed before Tx initiation in both groups. For HU-RUX pts, it was also assessed while on HU (median duration 27 months) and on RUX (median duration 14 months). For HU-alone pts, it was assessed during the first 27 months of Tx (any pt included in the analysis was treated for longer than this due to duration matching) and during remaining period of Tx (median duration 14 months). TEs were identified by either a restrictive definition (a list of ICD codes containing keywords from the RESPONSE study was automatically generated and manually curated) or a less restrictive one (list of ICD codes was manually expanded to include any TEs matching those from the GEMFIN study). PV pts who were exclusively treated with HU for ≥6 months were selected (n=2057) for modeling. Outcomes to be predicted were TEs in the 12 months following the end of the 6-month HU Tx period, and HU failure within 3 months of Tx. A logistic regression model was used for prediction. The baseline features extracted from the database included median lab parameters (3-6 months after HU initiation), history of thrombosis prior to primary diagnosis of PV, sociological features (age, gender), comorbidities, and concomitant medications (from inpatient/outpatient tables). Performance assessment methods included Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) in early stages and confusion matrix in later stages; the findings were converted to clinically interpretable decision-tree classification algorithms. Results: Based on the extensive definition, the annual incidence of TEs in the HU-RUX and HU-alone groups, respectively, was 9% and 7% before HU initiation, which increased to 17% and 13% on HU Tx. The small difference in baseline incidence may reflect residual differences between the two groups. After a median duration of 14 months, the incidence of TEs decreased to 15% in pts who switched to RUX vs an increase to 20% in pts who continued HU Tx. A similar trend was observed using less restrictive definition (Figure 1). This definition resulted in a substantially increased incidence of TEs and a decreased predictive power of the machine-learning model. Using modeling, decision trees were developed to predict the occurrence of TEs in PV pts with/without a history of TEs. Lymphocyte percentage (<17%) and red cell distribution width (RDW; <15%) were predictors in pts without a history of TEs, whereas lymphocyte percentage (>13%) and platelet count (>393x103/µL) were predictors in pts with a history of TEs (Figure 2). Based on the decision tree developed to predict HU failure, phlebotomy-dependent pts with >15% RDW had a higher risk of HU failure within 3 months of Tx (Figure 3). Conclusions: A reduction in the incidence of TEs was observed in pts switching to RUX vs those who continued HU Tx. Based on the findings from this machine-learning model in PV pts, phlebotomy dependency and RDW were indicated as predictors of HU Tx failure within 3 months, whereas lymphocyte percentage+platelet count and lymphocyte percentage+RDW were predictors of incidence of TEs in pts with and without a history of TEs, respectively. Non-adjustment of the results for antiplatelet/anticoagulant Tx was a study limitation. Further validation of this machine-learning model is planned in other European databases. Disclosures Verstovsek: Celgene: Consultancy, Research Funding; Gilead: Research Funding; Promedior: Research Funding; CTI BioPharma Corp: Research Funding; Genetech: Research Funding; Protaganist Therapeutics: Research Funding; Constellation: Consultancy; Pragmatist: Consultancy; Incyte: Research Funding; Roche: Research Funding; NS Pharma: Research Funding; Blueprint Medicines Corp: Research Funding; Novartis: Consultancy, Research Funding; Sierra Oncology: Research Funding; Pharma Essentia: Research Funding; Astrazeneca: Research Funding; Ital Pharma: Research Funding. De Stefano:Celgene: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau; Alexion: Consultancy, Honoraria, Speakers Bureau. Heidel:Novartis: Consultancy, Honoraria, Research Funding; Celgene: Consultancy; CTI: Consultancy. Zuurman:Novartis Pharma B.V.: Employment. Zaiac:Novartis: Employment, Equity Ownership. Bigan:Novartis: Consultancy. Ruhl:Novartis: Consultancy. Meier:Novartis: Consultancy. Kiladjian:Celgene: Consultancy; Novartis: Honoraria, Research Funding; AOP Orphan: Honoraria, Research Funding.