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DTSTART:20240331T010000
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DTSTART;TZID=Europe/Helsinki:20240122T121500
DTEND;TZID=Europe/Helsinki:20240122T133000
DTSTAMP:20260711T012007
CREATED:20240104T091527Z
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SUMMARY:Ihsaan Bassier (LSE)  "Collective Bargaining and Spillovers in Local Labour Markets"
DESCRIPTION:Applied Seminar \nTime: 12:15 pm – 13:30 pm\nDate: 22th of January\nRoom : 3001 \n  \nIhsaan Bassier (LSE) “Collective Bargaining and Spillovers in Local Labour Markets” \nAbstract : How does collective bargaining affect the broader wage structure? How are such spillovers transmitted? In imperfectly competitive labour markets\, a rise in wages in the covered sector can improve the outside options of workers in the noncovered sector. I use a decade of wage agreements matched with worker-level data in South Africa to study the effects of sharp changes in collectively bargained wages in an event-study framework. Observed wages in covered firms rise sharply\, and within-firm wage inequality declines. I use interfirm worker flows as a measure of distance to test for spillovers\, which I motivate with a model where wage changes are transmitted via outside options to nearby firms. Bilateral worker flows correlate with a wide range of firm characteristics\, capturing firm links which are poorly predicted by industry and location. I show that firms with higher flows to covered firms differentially increase wages more\, with an implied cross-wage elasticity of about 0.8. This is higher than comparable estimates in the literature because I am able to identify the labour market segments empirically relevant to wage spillovers. Firm profit margins decline\, as predicted by the model. A microdata simulation suggests that spillovers double the effects of collective bargaining agreements on the full wage distribution. \nOrganizer: Sara SIGNORELLI \n
URL:https://crest.science/event/ihsaan-bassier-lse-collective-bargaining-and-spillovers-in-local-labour-markets/
CATEGORIES:Applied Seminar,Seminars
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BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20240122T140000
DTEND;TZID=Europe/Helsinki:20240122T151500
DTSTAMP:20260711T012008
CREATED:20240104T125100Z
LAST-MODIFIED:20240104T125100Z
UID:16414-1705932000-1705936500@crest.science
SUMMARY:Eugène Ndiaye (Apple Research) - Finite Sample Confidence Sets with "Minimal" Assumptions on the Distribution
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:15 pm\nDate: 22th January of 2024\nPlace : 3001 \n  \nEugène Ndiaye (Apple Research) – Finite Sample Confidence Sets with “Minimal” Assumptions on the Distribution \n  \nAbstract: \nIf you predict a label y of a new object with $\hat{y}$\, how confident are you that ”y = $\hat{y}$”? The conformal prediction method provides an elegant framework for answering such a question by establishing a confidence set for an unobserved response of a feature vector based on previous similar observations of responses and features. This is performed without assumptions about the distribution of the data. While providing strong coverage guarantees\, computing conformal prediction sets requires adjusting a predictive model to an augmented dataset considering all possible values that the unobserved response can take\, and proceeding to select the most likely ones. For a regression problem where y is a continuous variable\, it typically requires an infinite number of model fits; which is usually infeasible. By assuming a little more regularity in the underlying prediction models\, I will describe some of the techniques that make the calculations feasible. Along similar lines\, it can be assumed that we are working with a parametric model that explains the relation between input and output variables. Consequently\, a natural question arises as to whether a confidence interval on the ground truth parameter of the model can be constructed\, also without assumptions on the distribution of the data. In this presentation\, I will provide a partial answer to the question with some preliminary results. This is mostly an ongoing project\, with still a lot of open questions that I am eager to discuss and share insights for future work. \n  \npage web https://eugenendiaye.github.io/ \n  \n  \nOrganizers:\nCristina BUTUCEA (CREST)\, Anna KORBA (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST)\nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/eugene-ndiaye-apple-research-finite-sample-confidence-sets-with-minimal-assumptions-on-the-distribution/
CATEGORIES:Statistics
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DTSTART;TZID=Europe/Helsinki:20240122T150000
DTEND;TZID=Europe/Helsinki:20240122T163000
DTSTAMP:20260711T012008
CREATED:20240119T095638Z
LAST-MODIFIED:20240119T095638Z
UID:16499-1705935600-1705941000@crest.science
SUMMARY:Eugène NDIAYE (Apple Research) "Finite Sample Confidence Sets with "Minimal" Assumptions on the Distribution"
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 3:00 pm – 4:00 pm\nDate: 22th January 2024\nPlace: 3001 \n  \nEugène NDIAYE (Apple Research) “Finite Sample Confidence Sets with “Minimal” Assumptions on the Distribution“ \n  \n Abstract: \nIf you predict a label y of a new object with $\hat{y}$\, how confident are you that ”y = $\hat{y}$”? The conformal prediction method provides an elegant framework for answering such a question by establishing a confidence set for an unobserved response of a feature vector based on previous similar observations of responses and features. This is performed without assumptions about the distribution of the data. While providing strong coverage guarantees\, computing conformal prediction sets requires adjusting a predictive model to an augmented dataset considering all possible values that the unobserved response can take\, and proceeding to select the most likely ones. For a regression problem where y is a continuous variable\, it typically requires an infinite number of model fits; which is usually infeasible. By assuming a little more regularity in the underlying prediction models\, I will describe some of the techniques that make the calculations feasible. Along similar lines\, it can be assumed that we are working with a parametric model that explains the relation between input and output variables. Consequently\, a natural question arises as to whether a confidence interval on the ground truth parameter of the model can be constructed\, also without assumptions on the distribution of the data. In this presentation\, I will provide a partial answer to the question with some preliminary results. This is mostly an ongoing project\, with still a lot of open questions that I am eager to discuss and share insights for future work. \n  \nOrganizers: \nKarim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST) \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/eugene-ndiaye-apple-research-finite-sample-confidence-sets-with-minimal-assumptions-on-the-distribution-2/
CATEGORIES:Seminars,Statistics
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