What Do Panel Studies Tell Us About a Deterrent Effect of Capital Punishment? A Critique of the Literature

Objectives

We provide a critical review of empirical research on the deterrent effect of capital punishment that makes use of state and, in some instances, county-level, panel data.

Methods

We present the underlying behavioral model that presumably informs the specification of panel data regressions, outline the typical model specification employed, discuss current norms regarding “best-practice” in the analysis of panel data, and engage in a critical review.

Results

The connection between the theoretical reasoning underlying general deterrence and the regression models typically specified in this literature is tenuous. Many of the papers purporting to find strong effects of the death penalty on state-level murder rates suffer from basic methodological problems: weak instruments, questionable exclusion restrictions, failure to control for obvious factors, and incorrect calculation of standard errors which in turn has led to faulty statistical inference. The lack of variation in the key underlying explanatory variables and the heavy influence exerted by a few observations in state panel data regressions is a fundamental problem for all panel data studies of this question, leading to overwhelming model uncertainty.

Conclusions

We find the recent panel literature on whether there is a deterrent effect of the death penalty to be inconclusive as a whole, and in many cases uninformative. Moreover, we do not see additional methodological tools that are likely to overcome the multiple challenges that face researchers in this domain, including the weak informativeness of the data, a lack of theory on the mechanisms involved, and the likely presence of unobserved confounders.     

Network Governance as a Mechanism for Responding to Internet Violence

Jones, Hesterly and Borgatti (1997) proposed a general theory of network governance (GTNG), identifying conditions under which network governance is likely to develop and detailing four social mechanisms that protect and promote resource exchange and collaborative action within a network. This article applies their theory to the emerging problem of swatting—internet-based spoofing attacks that compel a SWAT response to a selected innocent victim. As this problem has not been addressed through traditional policy or law enforcement mechanisms, networks have emerged to respond to these attacks. The author details the parallels between these emergent networks and Jones et al.’s theoretical framework.     

Early Impacts of the Affordable Care Act on Health Insurance Coverage in Medicaid Expansion and Non‐Expansion States

The Affordable Care Act (ACA) aimed to achieve nearly universal health insurance coverage in the United States through a combination of insurance market reforms, mandates, subsidies, health insurance exchanges, and Medicaid expansions, most of which took effect in 2014. This paper estimates the causal effects of the ACA on health insurance coverage in 2014 using data from the American Community Survey. We utilize difference‐in‐difference‐in‐differences models that exploit cross‐sectional variation in the intensity of treatment arising from state participation in the Medicaid expansion and local area pre‐ACA uninsured rates. This strategy allows us to identify the effects of the ACA in both Medicaid expansion and non‐expansion states. Our preferred specification suggests that, at the average pre‐treatment uninsured rate, the full ACA increased the proportion of residents with insurance by 5.9 percentage points compared to 2.8 percentage points in states that did not expand Medicaid. Private insurance expansions from the ACA were due to increases in both employer‐provided and non‐group coverage. The coverage gains from the full ACA were largest for those without a college degree, non‐whites, young adults, unmarried individuals, and those without children in the home. We find no evidence that the Medicaid expansion crowded out private coverage.     

Intraparty Hostility: Social Identity,Subidentity, and the Hostile Media Effect in a Contested Primary

The hostile media effect (HME) has generally been tested in terms of in-groups and out-groups, with a “neutral” story in between. This ignores the nature of many social groups as comprising subgroups, often but not always sharing feelings of connectedness and purpose. In cases when bounded subgroups are at odds with one another, HME provides little guidance. A contested partisan primary provides such a case. This study takes identity centrality, candidate favorability, and perceived social network homogeneity as measures of partisanship and involvement, hypothesizing relationships between each and perceived bias against one’s candidate and party. Findings show that markers of candidate-focused social identity predict greater perceived bias against one’s candidate during the 2016 primary season, while party-focused identity fails to predict perceived bias against one’s party. This suggests that candidate support identity overrides plain partisanship during primaries, supporting concern that a heated primary might damage general election party unity. Subsequent postconvention findings suggest that the salience of candidate-focused identity fades, while homogeneity of one’s network regarding party support helps to make perceived hostility toward one’s party identity more salient. However, as campaigns become more candidate-centered, the contestation between nested candidate and party identities may grow fiercer.     

Preelection poll accuracy and bias in the 2016 U.S. general elections

ABSTRACT

This report examines accuracy and bias in national- and state-level preelection polls conducted during the 2016 U.S. general election cycle. Overall, national polls in 2016 were somewhat more accurate than in 2012, but statewide polls were less accurate. Patterns across the board suggest polls underestimated Republican support in the presidential, U.S. Senate and gubernatorial races. Nevertheless, these biases were generally statistically insignificant, suggesting significant bias in preelection polls was scarce in 2016.     

Don't Forget to Vote: Text Message Reminders as a Mobilization Tool

Current explanations of effective voter mobilization strategies maintain that turnout increases only when a potential voter is persuaded to participate through increased social connectedness. The connectedness explanation does not take into account, however, that registered voters, by registering, have already signaled their interest in voting. The theory presented in this article predicts that impersonal, noticeable   messages can succeed in increasing the likelihood that a registered voter will turn out by reminding the recipient that Election Day is approaching. Text messaging is examined as an example of an impersonal, noticeable communication to potential voters. A nationwide field experiment (n = 8,053) in the 2006 election finds that text message reminders produce a statistically significant 3.0 percentage point increase in the likelihood of voting. While increasing social connectedness has been shown to positively affect voter turnout, the results of this study, in combination with empirical evidence from prior studies, suggest that connectedness is not a necessary condition for a successful mobilization campaign. For certain voters, a noticeable reminder is sufficient to drive them to the polls.