Contrary to expectations, actively-managed ETFs have also gained popularity since the SEC’s approval of such ETFs in March 2008. As of today, 223 actively- managed ETFs are registered with the SEC, totaling $51.7 billion of assets under management with smart beta ETFs being one of the fastest growing investment instruments. Hyung will contribute to the literature by analyzing the performance actively-managed ETFs, their management fee structure, and investor base characteristics, as well as the interplay with passive strategies and alternative active strategies. Hyung will examine the implications of the growth in actively-managed ETFs on the underlying constituents’ security lending conditions to gain insights about the interplay of limits to arbitrage with stock market pricing efficiency.

In a world of complex choices, consumers may make uninformed decisions even when all relevant information is readily available. A common policy suggestion is to aid consumers by making information more accessible, simple, and standardized. The goal of this project is to study the general equilibrium effects of the provision of simple information. Specifically, the focus is on a nationwide, mandatory labeling policy, advocated by the Israeli Ministry of Health, to promote healthy nutrition. Oren Reshef develops a simple conceptual framework to formalize the mechanisms governing consumers’ and firms’ behavior examining consumers’ understanding and usage of the simplified labels and whether demand for healthier products changes as a result. Oren focuses on the unintended consequences of the policy, especially those affecting individual information acquisition and demand for nutritional dimensions not explicitly included in the labeling. Additionally, he studies the supply side response by examining how the shift in incentives drives product innovation, reformulation, and readjustment of pricing strategies. Finally, he derives the aggregated welfare implications.

Increasingly ubiquitous online social interactions have nourished the advent and ongoing marketing innovations to engage consumers through social media in many industries. Firms leverage customers’ social influences for retaining and growing their customer assets. In particular, they face a choice on what conditions to impose on the social aspect. We answer the research question of how social promotion affects consumer choice and firm outcomes. Specifically, we devise a two-level randomization framework to assess the social advertising effectiveness in terms of market share, consumer spending, and firm profitability. Through a field experiment, we show that while the immediate effect of sharing is to reduce sales of the promoted products but the overall effect is just the opposite; the leads created with sharing more than make up for the lost sales due to increasing customer costs of obtaining the deal. When forced to share, customers substitute and purchase other similar products instead. Moreover, despite potential deeper connections among customers in smaller cities, those from big cities exhibit stronger lead generation potential.

Using survey data and email data from a mid-sized organization, he is establishing the linkages between the cognitive manifestations of cultural fit– that is, how someone thinks about how they fit into an organization based on self-reports- -and the behavioral manifestations– based on linguistic similarity between a person and her interlocutors in internal email communications. He is using the tools of computational linguistics and machine learning to identify the “linguistic signature” of cognitive cultural fit so that we can impute self-reported cultural fit for people and time periods where these data are not available. This will enable him to transform a one-time survey into a longitudinal assessment, which will in turn enable him to examine the dynamic interplay between cognitive and behavioral cultural fit and its consequences for individual performance.

This is a project that aims to account for a complex empirical pattern within a unified framework that can speak to the data. The pattern involves cross-city differentials: vast inequality in wages and property prices, and differences in the human capital that on average flocks to different cities. But in addition, individual human capital and wages differ within cities. The understanding of these patterns require a general equilibrium framework, which Santiago has developed, and one that is estimable. In order to estimate the role of the key forces at play in driving the various inequalities in the U.S., Santiago has launched into fascinating data work. On the one hand, he has collected data on wages, city amenities, and property values in 50 metropolitan areas in the U.S., and analyzed those data to first describe the overall patterns, and then used the data to estimate his model. In addition, Santiago has worked with a leading online brain training company (Lumosity) to obtain from them their user data. Santiago has succeeded and is now in the process of analyzing it. These data are exciting because they include millions of observations of users all over the country, that among other things allow us to produce an “IQ map” that helps us better understand the distribution of talent over space. These data complement his analysis of the human capital distribution across cities, but they have great additional potential. An obvious challenge in using these data is the fact that they reflect self-selection into using a brain-training service. Santiago has deployed state-of- the-art selection techniques to undo these selection effects, which tells me there is truly fantastic potential for the use of these data.