Ethan Cohen-Cole

Bio: Ethan Cohen-Cole is an academic researcher from Federal Reserve Bank of Boston. The author has contributed to research in topics: Bankruptcy & Earnings. The author has an hindex of 19, co-authored 65 publications receiving 1607 citations. Previous affiliations of Ethan Cohen-Cole include Federal Reserve System & University of Maryland, College Park.

Detecting implausible social network effects in acne, height, and headaches : longitudinal analysis

Abstract: Objective To investigate whether “network effects” can be detected for health outcomes that are unlikely to be subject to network phenomena. Design Statistical analysis common in network studies, such as logistic regression analysis, controlled for own and friend’s lagged health status. Analyses controlled for environmental confounders. Setting Subsamples of the National Longitudinal Study of Adolescent Health (Add Health). Participants 4300 to 5400 male and female adolescents who nominated a friend in the dataset and who were both longitudinally surveyed. Measurements Health outcomes, including headache severity, acne severity, and height self reported by respondents in 1994-5, 1995-6, and 2000-1. Results Significant network effects were observed in the acquisition of acne, headaches, and height. A friend’s acne problems increased an individual’s odds of acne problems (odds ratio 1.62, 95% confidence interval 0.91 to 2.89). The likelihood that an individual had headaches also increased with the presence of a friend with headaches (1.47, 0.93 to 2.33); and an individual’s height increased by 20% of his or her friend’s height (0.18, 0.15 to 0.26). Each of these results was estimated by using standard methods found in several publications. After adjustment for environmental confounders, however, the results become uniformly smaller and insignificant. Conclusions Researchers should be cautious in attributing correlations in health outcomes of close friends to social network effects, especially when environmental confounders are not adequately controlled for in the analysis.

Credit Card Redlining

Abstract: This paper evaluates the presence of racial disparities in the issuance of consumer credit. Using a database of credit histories, I link location-based information on race with individual credit files. After controlling for place-specific factors such as housing vacancy rates and general population demographics, I find qualitatively large differences in the amount of credit offered to similarly qualified applicants living in black versus white areas. High data quality allows distinguishing between issuer-provision (supply) and utilization of credit (demand). Additional estimates using information on payday lending provide support for idea that issuers condition lending on location.

Model Uncertainty and the Deterrent Effect of Capital Punishment

Abstract: The reintroduction of capital punishment in 1976 that ended the four-year moratorium on executions generated by the Supreme Court in the 1972 decision Furman v. Georgia has permitted researchers to employ state-level heterogeneity in the use of capital punishment to study deterrent effects. However, no scholarly consensus exists as to their magnitude. A key reason that this has occurred is that the use of alternative models across studies produces differing estimates of the deterrent effect. Because differences across models are not well motivated by theory, the deterrence literature is plagued by model uncertainty. We argue that the analysis of deterrent effects should explicitly recognize the presence of model uncertainty in drawing inferences. We describe methods for addressing model uncertainty and apply them to understand the disparate findings between two major studies in the deterrence literature, finding that evidence of deterrent effects appears, while not nonexistent, weak.

Systemic Risk and Network Formation in the Interbank Market

Abstract: We propose a novel mechanism to facilitate understanding of systemic risk in financial markets. The literature on systemic risk has focused on two mechanisms, common shocks and domino-like sequential default. Our approach is a formal model that provides an intellectual combination of the two by looking at how shocks propagate through a network of interconnected banks. Transmission in our model is not based on default. Instead, we provide a simple microfoundation of banks’ profitability based on classic competition incentives. As competitors’ lending quantities change, both for closely connected ones and the whole market, banks adjust their own lending decisions as a result, generating a ‘transmission’ of shocks through the system. Our approach permits us to measure both the degree that shocks are amplified by the network structure and the manner in which losses and gains are shared. We provide a unique equilibrium characterization of a static model, and embed this model into a full dynamic model of network formation with n agents. Because we have an explicit characterization of equilibrium behavior, we have a tractable way to bring the model to the data. Indeed, our measures of systemic risk capture the propagation of shocks in a wide variety of contexts; that is, it can explain the pattern of behavior both in good times as well as in crisis.

The Spread of Obesity in a Large Social Network over 32 Years

Abstract: Background The prevalence of obesity has increased substantially over the past 30 years. We performed a quantitative analysis of the nature and extent of the person-to-person spread of obesity as a possible factor contributing to the obesity epidemic. Methods We evaluated a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. The bodymass index was available for all subjects. We used longitudinal statistical models to examine whether weight gain in one person was associated with weight gain in his or her friends, siblings, spouse, and neighbors. Results Discernible clusters of obese persons (body-mass index [the weight in kilograms divided by the square of the height in meters], ≥30) were present in the network at all time points, and the clusters extended to three degrees of separation. These clusters did not appear to be solely attributable to the selective formation of social ties among obese persons. A person’s chances of becoming obese increased by 57% (95% confidence interval [CI], 6 to 123) if he or she had a friend who became obese in a given interval. Among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40% (95% CI, 21 to 60). If one spouse became obese, the likelihood that the other spouse would become obese increased by 37% (95% CI, 7 to 73). These effects were not seen among neighbors in the immediate geographic location. Persons of the same sex had relatively greater influence on each other than those of the opposite sex. The spread of smoking cessation did not account for the spread of obesity in the network. Conclusions Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties. These findings have implications for clinical and public health interventions.

Experimental evidence of massive-scale emotional contagion through social networks

Abstract: Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others' positive experiences constitutes a positive experience for people.

Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach

Abstract: This paper examines the robustness of explanatory variables in cross-country economic growth regressions. It employs a novel approach, Bayesian Averaging of Classical Estimates (BACE), which constructs estimates as a weighted average of OLS estimates for every possible combination of included variables. The weights applied to individual regressions are justified on Bayesian grounds in a way similar to the well-known Schwarz criterion. Of 32 explanatory variables we find 11 to be robustly partially correlated with long-term growth and another five variables to be marginally related. Of all the variables considered, the strongest evidence is for the initial level of real GDP per capita.

Systemic Risk and Stability in Financial Networks

Abstract: We provide a framework for studying the relationship between the financial network architecture and the likelihood of systemic failures due to contagion of counterparty risk. We show that financial contagion exhibits a form of phase transition as interbank connections increase: as long as the magnitude and the number of negative shocks affecting financial institutions are sufficiently small, more "complete" interbank claims enhance the stability of the system. However, beyond a certain point, such interconnections start to serve as a mechanism for propagation of shocks and lead to a more fragile financial system. We also show that, under natural contracting assumptions, financial networks that emerge in equilibrium may be socially inefficient due to the presence of a network externality: even though banks take the effects of their lending, risk-taking and failure on their immediate creditors into account, they do not internalize the consequences of their actions on the rest of the network.

A Survey of Statistical Network Models

Abstract: Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active "network community" and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online "networking communities" such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.