ERC FUNDED PROJECTS

Historically, people around the world have demanded democratic institutions. Such democratic movements propel political change and also determine economic outcomes. In this project, we ask, how do political preferences, beliefs, and second-order beliefs shape the strategic decision to participate in a movement demanding democracy? Existing scholarship is unsatisfactory because it is conducted ex post: preferences, beliefs, and behavior have converged to a new equilibrium. In contrast, we examine a democratic movement in real time, studying the ongoing democracy movement in Hong Kong. Our study is composed of four parts. In Part 1, we collect panel survey data from Hong Kong university students, a particularly politically active subpopulation. We collect data on preferences, behavior, beliefs, and second-order beliefs using incentivized and indirect elicitation to encourage truthful reporting. We analyze the associations among these variables to shed light on the drivers of participation in the democracy movement. In Part 2, we exploit experimental variation in the provision of information to study political coordination. Among participants in the panel survey, we provide information regarding the preferences and beliefs of other students. We examine whether exposure to information regarding peers causes students to update their beliefs and change their behavior. In Part 3, we extend the analysis in Part 1 to a nationally representative sample of Hong Kong citizens. To do so, we have added a module regarding political preferences, beliefs, and behavior (including incentivized questions and questions providing cover for responses to politically sensitive topics) to the HKPSSD panel survey. In Part 4, we study preferences for redistribution – plausibly a central driver for demands for political rights – among Hong Kong citizens and mainland Chinese. We examine how these preferences differ across populations, as well as their link to support for democracy.

1 494 647 €

Start date: 2017-03-01, End date: 2022-02-28

Cooperation is essential for the functioning of the economy and society. Thus, with inappropriate mechanisms to harness self-interest by aligning it with the common good, the outcome of social and economic interaction can be bleak and even catastrophic. Recent advances in computer technology lead to radical innovation in market design and trading strategies. This creates both, new challenges and exciting opportunities for “engineering cooperation”. This project uses the economic engineering approach (as advocated by Alvin Roth) to address some of the most pressing cooperation problems of modern markets and societies. I propose three work packages, each using innovative experimental methods and (behavioral) game theory in order to address a specific challenge: The first one studies the design of electronic reputation mechanisms that promote cooperation in the digital world. Previous research has shown that mechanisms to promote trust on the Internet are flawed. Yet, there is little empirical and normative guidance on how to repair these systems, and engineer better ones. The second studies the design of mechanisms that avoid arms races for speed in real-time financial and electricity market trading. Traders use algorithmic sniping strategies, even when they are collectively wasteful and seriously threatening market liquidity and stability. Yet, little is known about the robust properties of alternative market designs to eliminate sniping. The third one studies how to design modern markets that align with ethical considerations. People sometimes have a distaste for certain kinds of modern transactions, such as reciprocal kidney exchange and buying pollution rights. Yet, little is known about the underlying nature and robustness of this distaste. My project will generate important knowledge to improve the functioning of modern markets, and at the same time open new horizons in the sciences of cooperation and of “behavioral economic engineering”.

1 155 104 €

Start date: 2018-03-01, End date: 2023-02-28

Many markets are characterized by illicit or illegal behavior by agents. To the extent that the empirical economic framework does not incorporate these unobserved actions or control for them in estimation, the resulting models are likely to be mis-specified. Naturally, if the models do not contain all elements relevant for decision making, then predictions based on the estimates will be misleading, which could result in incorrect policy recommendations. This project directly addresses three situations in which unobserved behavior plays a crucial role. The first concerns markets where consumers engage in illicit behavior. These markets are prevalent in society as they constitute the market for illicit drugs, which is estimated at more than $300 billion per year (UN, 2012). The second concerns markets where firms make strategic decisions in the presence of an unidentified competitor - a counterfeiter, where the global value of counterfeit products rivals that of illegal drugs (OECD, 2007). The third concerns situations where firms use legal tools for illegal purposes, for which the impact is challenging to quantify and one goal of this project. In each area, the project (i) develops state-of-the-art empirical models that incorporate illicit behaviors, (ii) proposes novel estimation methods that can be used to detect illegal behavior, and (iii) provides evidence that the proposed methodology is feasible and the data are sufficient to estimate the models. Incorporating and estimating unobserved behavior in a variety of settings is an ambitious undertaking. However, it is vital as a key objective of the proposal is to provide policy makers with tangible tools that accurately reflect the unobserved nature of these markets. Given the global significance of illicit markets, the novel concepts proposed, and the focus on policy, this project has the potential to make a sizable impact, both in and beyond academia, representing an ambitious but worthwhile pursuit.

1 212 934 €

Start date: 2017-09-01, End date: 2022-08-31

With the advent of genome-scale sequencing, molecular phylogeny, which reconstructs gene trees from homologous sequences, has reached an impasse. Instead of answering open questions, new genomes have reignited old debates. The problem is clear, gene trees are not species trees, each is the unique result of series of evolutionary events. If, however, we model these differences in the context of a common species tree, we can access a wealth of information on genome evolution and the diversification of species that is not available to traditional methods. For example, as horizontal gene transfer (HGT) can only occur between coexisting species, HGTs provide information on the order of speciations. When HGT is rare, lineage sorting can generate incongruence between gene trees and the dating problem can be formulated in terms of biologically meaningful parameters (such as population size), that are informative on the rate of evolution and hence invaluable to molecular dating. My first goal is to develop methods that systematically extract information on the pattern and timing of genomic evolution by explaining differences between gene trees. This will allow us to, for the first time, reconstruct a dated tree of life from genome-scale data. We will use parallel programming to maximise the number of genomes analysed. My second goal is to apply these methods to open problems, e.g.: i) to resolve the timing of microbial evolution and its relationship to Earth history, where the extreme paucity of fossils limits the use of molecular dating methods, by using HGT events as “molecular fossils”; ii) to reconstruct rooted phylogenies from complete genomes and harness phylogenetic incongruence to answer long standing questions, such as the of diversification of animals or the position of eukaryotes among archaea; and iii) for eukaryotic groups such as Fungi, where evidence of significant amounts of HGT is emerging our methods will also allow the quantification of the extent of HGT.

1 453 542 €

Start date: 2017-07-01, End date: 2022-06-30