This is an opportunity for our PhD student to practice job talk seminar

Eleonora will present paper: Bad Science

Fields: Political

Supervisors: Thiemo Fetzer, Sascha Becker, Andreas Stegmann

Chang Wang, a visiting student at WBS, presenting at the CAGE AMES Workshop. It will be a hybrid format workshop.

Title: The environmental benefits of electricity industry restructuring in China

Abstract: Ownership mixing vs. vertical unbundling Ownership mixing and vertical unbundling are the two pending electricity industry restructuring reforms in many developing countries, while few studies compared the two reforms, or explored the relative environmental effects of them. In this paper, we empirically examine the effects of ownership mixing and vertical unbundling on electricity generation firms’ SO2 emission intensity. We exploit both the ownership mixing and vertical unbundling reforms in China and use a difference-in-differences (DD) strategy to deal with identification. We rely on a unique firm-level dataset that contains comprehensive information on firms’ pollution records, production activities, and electricity generation. We document that ownership mixing, while achieving the main reform purpose of increased access to electricity and generation efficiency, also has unintended environmental benefits of significantly and substantially reducing firms’ air pollution intensity. The underlying channels are firms’ increased electricity generation and better enforcement of environmental regulations. Vertical unbundling, on the other hand, has no significant effects on firms’ air pollution. Our findings document an effective and environmentally friendly pathway for electricity industry restructuring in developing countries.

This is a hybrid format via MS Teams (https://teams.microsoft.com/l/meetup-join/19%3aeafd670486bc4ed6be08da79cf05d9ca%40thread.tacv2/1654765963206?context=%7b%22Tid%22%3a%2209bacfbd-47ef-4465-9265-3546f2eaf6bc%22%2c%22Oid%22%3a%22282d13eb-4c12-413a-b24a-e55af45e44c1%22%7d)

Title: Causality: a Decision-Theoretic Foundation

Abstract: We propose a decision-theoretic model akin to that of Savage that is useful for defining causal effects. Within this framework, we define what it means for a decision maker (DM) to act as if the relation between two variables is causal. Next, we provide axioms on preferences that are equivalent to the existence of a (unique) directed acyclic graph (DAG) that represents the DM's preferences. The notion of representation has two components: the graph factorizes the conditional independence properties of the DM's subjective beliefs, and arrows point from cause to effect. Finally, we explore the connection between our representation and models used in the statistical causality literature (for example, that of Pearl).