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    • COVID-19 Business Impact Center
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      Cold Call
      A podcast featuring faculty discussing cases they've written and the lessons they impart.
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      • 05 Jan 2021
      • Cold Call Podcast

      Using Behavioral Science to Improve Well-Being for Social Workers

      For child and family social workers, coping with the hardships of children and parents is part of the job. But that can cause a lot of stress. Is it possible for financially constrained organizations to improve social workers’ well-being using non-cash rewards, recognition, and other strategies from behavioral science? Assistant Professor Ashley Whillans describes the experience of Chief Executive Michael Sanders’ at the UK’s What Works Centre for Children’s Social Care, as he led a research program aimed at improving the morale of social workers in her case, “The What Works Centre: Using Behavioral Science to Improve Social Worker Well-being.”  Open for comment; 0 Comment(s) posted.

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      Bojinov, I. IavorRemove Bojinov, I. Iavor →

      Page 1 of 3 Results
      • 22 Oct 2020
      • Working Paper Summaries

      Estimating Causal Effects in the Presence of Partial Interference Using Multivariate Bayesian Structural Time Series Models

      by Fiammetta Menchetti and Iavor Bojinov

      A case study of an Italian supermarket introducing a new pricing policy—in which it reduced prices on some brands—offers managers a new approach to reduce uncertainty. The approach is flexible and can be applied to different business problems.

      • 06 Oct 2020
      • Working Paper Summaries

      Design and Analysis of Switchback Experiments

      by Iavor I. Bojinov, David Simchi-Levi, and Jinglong Zhao

      This paper presents a framework for managers to design and run switchback experiments.

      • 06 Apr 2020
      • Working Paper Summaries

      A General Theory of Identification

      by Iavor Bojinov and Guillaume Basse

      Statistical inference teaches us how to learn from data, whereas identification analysis explains what we can learn from it. This paper proposes a simple unifying theory of identification, encouraging practitioners to spend more time thinking about what they can estimate from the data and assumptions before trying to estimate it.

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