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X-ORIGINAL-URL:https://crest.science
X-WR-CALDESC:Events for CREST
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TZID:Europe/Helsinki
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TZOFFSETFROM:+0200
TZOFFSETTO:+0300
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DTSTART:20250330T010000
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DTSTART;VALUE=DATE:20250602
DTEND;VALUE=DATE:20250607
DTSTAMP:20260711T002626
CREATED:20250210T145600Z
LAST-MODIFIED:20250210T145600Z
UID:17880-1748822400-1749254399@crest.science
SUMMARY:2025 E4C Summer School: Environmental Data Collection and Analysis for the Social Sciences
DESCRIPTION:☀️Summer School 2025 announcement! \nThe Energy4Climate (E4C) is launching the next edition of the Summer School. This year it is about “Environmental Data Collection and Analysis for the Social Sciences” in collaboration with CREST – Center for Research in Economics and Statistics. \nThis years participants will explore methods and applications related to climate\, biodiversity\, mobility\, and societal impacts through discussions and hands-on sessions. This event offers an opportunity to learn\, exchange ideas\, and connect with researchers in the field. \nWhat to expect?\n✔️ Lectures by leading researchers\n✔️ Small-group tutorials and hands-on sessions to apply data science tools and methods\n✔️ Real-world case studies on climate\, biodiversity\, mobility\, and societal impacts \nMore details coming soon. \n📍 At École Polytechnique\, Palaiseau\, France\n📆 From 02 to 06 june 2025\n🎯 Open to PhDs and research-oriented master’s students \nSave the date now and subscribe to stay informed about the latest news from this summer school! \n
URL:https://crest.science/event/2025-e4c-summer-school-environmental-data-collection-and-analysis-for-the-social-sciences/
CATEGORIES:Conferences and Workshops,Economics,Finance-Insurance,Sociology,Statistics
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DTSTART;TZID=Europe/Helsinki:20250602T121500
DTEND;TZID=Europe/Helsinki:20250602T133000
DTSTAMP:20260711T002626
CREATED:20250321T102511Z
LAST-MODIFIED:20250526T073615Z
UID:17982-1748866500-1748871000@crest.science
SUMMARY:Diego DARUICH  (University of Southern California (Marshall)) "Zoning Out Opportunities: Exploring the Child Development Impact of Zoning Laws"
DESCRIPTION:[vc_row][vc_column][vc_column_text]Macro seminar\nTime : 12h15 – 13h30 \nDate : 02th  June 2025 \nSalle 3001 \nDiego DARUICH (University of Southern California (Marshall)) “Zoning Out Opportunities: Exploring the Child Development Impact of Zoning Laws” \nAbstract: \nAn emerging body of literature demonstrates that neighborhoods significantly impact the long-term outcomes of children. A crucial factor in designing successful neighborhood-based policy interventions is understanding the elasticity of housing supply Chyn-Daruich (Forthcoming). This paper investigates the effects of housing supply restrictions by integrating neighborhood effects into a GE overlapping-generations model. The model accounts for endogenous housing supply\, neighborhood quality\, location choice\, and child development. \nImportantly\, housing production requires paying construction taxes that are increasing in the produced amount — and such convexity is determined by local housing regulations. Less restrictive regulations enhance supply and decrease house prices (e.g.\, favoring developments such as apartment buildings). Households hold houses as part of their assets\, and consequently\, rich households may oppose less restrictive regulations as this decreases the value of their wealth — a short-term loss. However\, loosening restrictions can make housing more affordable\, facilitating greater access for children to reside in neighborhoods conducive to their future potential — a long-term benefit. Furthermore\, the impact of housing regulation on neighborhood demographics may also influence neighborhood quality effects\, thereby affecting child development outcomes of all children. \nEstimated using US data\, the model is used to evaluate policies that reduce housing supply restrictions in line with the observed cross-sectional variation across US cities. Preliminary results suggest that restrictive housing regulation does hinder long-run child-development and welfare. In future steps\, transition dynamics and political economy results will be obtained. \nOrganizer : Suzanne BELLUE \n
URL:https://crest.science/event/diego-daruich-university-of-southern-california-marshall-t-b-a/
CATEGORIES:Macroeconomics,Seminars
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DTSTART;TZID=Europe/Helsinki:20250602T160000
DTEND;TZID=Europe/Helsinki:20250602T171500
DTSTAMP:20260711T002626
CREATED:20250526T143243Z
LAST-MODIFIED:20250527T134628Z
UID:18092-1748880000-1748884500@crest.science
SUMMARY:Davide VIVIANO (Harvard University) - "Program Evaluation with Remotely Sensed Outcomes and Evidence Aggregation"
DESCRIPTION:Paris Econometrics Seminar \nTime: 04:00 pm – 05:15 pm\nDate: 2nd of June\nRoom : 3001 \n  \nDavide VIVIANO (Harvard University) – “Program Evaluation with Remotely Sensed Outcomes and Evidence Aggregation” \nAbstract : \nIn the first part of this talk\, I will discuss issues about data combination in the presence of remotely sensed outcomes. Economists often estimate treatment effects in experiments using remotely sensed variables (RSVs)\, e.g. satellite images or mobile phone activity\, in place of directly measured economic outcomes. A common practice is to use an observational sample to train a predictor of the economic outcome from the RSV\, and then to use its predictions as the outcomes in the experiment. We show that this method is biased whenever the RSV is post-outcome\, i.e. if variation in the economic outcome causes variation in the RSV. In program evaluation\, changes in poverty or environmental quality cause changes in satellite images\, but not vice versa. As our main result\, we nonparametrically identify the treatment effect by formalizing the intuition that underlies common practice: the conditional distribution of the RSV given the outcome and treatment is stable across the samples. Based on our identifying formula\, we find that the efficient representation of RSVs for causal inference requires three predictions rather than one. Valid inference does not require any rate conditions on RSV predictions\, justifying the use of complex deep learning algorithms with unknown statistical properties. We re-analyze the effect of an anti-poverty program in India using satellite images.” \n  \nOrganizers:\nElia Lapenta – CREST/ENSAE \nSponsors:\nCREST \n
URL:https://crest.science/event/https-dviviano-github-io/
CATEGORIES:Paris Econometrics Seminar,Seminars
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