Patrick T. Brandt

What Do Transnational Terrorists Target? Has It Changed? Are We Safer?

This article utilizes Bayesian Poisson changepoint regression models to demonstrate how transnational terrorists adjusted their target choices in response to target hardening. In addition, changes in the collective tastes of terrorists and their sponsorship have played a role in target selection over time. For each of four target types— officials, military, business, and private parties—the authors identify the number of regimes and the probable predictors of the events. Regime changes are tied to the rise of modern transnational terrorism, the deployment of technological barriers, the start of state sponsorship, and the dominance of the fundamentalists. The authors also include two sets of covariates—logistical outcome and victim’s nature—to better explain the dynamics. As other targets have been fortified and terrorists have sought greater carnage, private parties have become the preferred target type. In recent years, terrorists have increasingly favored people over property for all target types. Moreover, authorities have been more successful at stopping attacks against officials and the military, thereby motivating terrorists to attack business targets and private parties.

Real Time, Time Series Forecasting of Inter- and Intra-State Political Conflict

We propose a framework for forecasting and analyzing regional and international conflicts. It generates forecasts that (1) are accurate but account for uncertainty, (2) are produced in (near) real time, (3) capture actors’ simultaneous behaviors, (4) incorporate prior beliefs, and (5) generate policy contingent forecasts. We combine the CAMEO event-coding framework with Markov-switching and Bayesian vector autoregression models to meet these goals. Our example produces a series of forecasts for material conflict between the Israelis and Palestinians for 2010. Our forecast is that the level of material conflict between these belligerents will increase in 2010, compared to 2009.

The dynamics of reciprocity, accountability, and credibility

Do public opinion dynamics play an important role in understanding conflict trajectories between democratic governments and other rival groups? The authors interpret several theories of opinion dynamics as competing clusters of contemporaneous causal links connoting reciprocity, accountability, and credibility. They translate these clusters into four distinct Bayesian structural time series models fit to events data from the Israeli—Palestinian conflict with variables for U.S. intervention and Jewish public opinion about prospects for peace. A credibility model, allowing Jewish public opinion to influence U.S., Palestinian, and Israeli behavior within a given month, fits best. More pacific Israeli opinion leads to more immediate Palestinian hostility toward Israelis. This responses direction suggests a negative feedback mechanism in which low-level conflict is maintained and momentum toward either all-out war or dramatic peace is slowed. In addition, a forecasting model including Jewish public opinion is shown to forecast ex ante better than a model without this variable.

WHEN AND HOW THE FIGHTING STOPS: EXPLAINING THE DURATION AND OUTCOME OF CIVIL WARS

Previous research has shown that the duration of a civil war is in part a function of how it ends: in government victory, rebel victory, or negotiated settlement. We present a model of how protagonists in a civil war choose to stop fighting. Hypotheses derived from this theory relate the duration of a civil war to its outcome as well as characteristics of the civil war and the civil war nation. Findings from a competing risk model reveal that the effects of predictors on duration vary according to whether the conflict ended in government victory, rebel victory, or negotiated settlement.

Terrorist attack and target diversity : Changepoints and their drivers

Terrorists choose from a wide variety of targets and attack methods. Unlike past literature, this article investigates how diversity in target choice and attack modes among domestic and transnational terrorists has evolved and changed over the past 40 years. Changes in the practice of homeland security, which affects the marginal costs of target–attack combinations, and changes in the dominant terrorist influence at the global level, which affects the marginal benefits of target–attack combinations, drive the changepoints. Our empirical analysis relies on count data drawn from the Global Terrorism Database (GTD) for 1970–2010 that distinguishes between domestic and transnational terrorist incidents. Given the data-intensity requirements of our methods, the study is necessarily from a global perspective. A Bayesian Reversible Jump Markov chain Monte Carlo (RJMCMC) changepoint analysis is applied to identify arrival rate changes in both domestic and transnational terrorism. The changepoints in these aggregate series are then matched with those of the subset time series for attack modes (e.g. assassinations and bombings) and target types (e.g. officials and private parties). The underlying drivers of these changepoints are then identified. The article also calculates a Herfindahl index of attack diversity for the aggregate and component domestic and transnational terrorism time series for the entire period and during four subperiods. The variation in both domestic and transnational terrorist attacks has generally fallen over the last four decades; nevertheless, this diversity still remains high. Bombings of private parties have become the preferred target–attack combination for both transnational and domestic terrorists. This combination is the hardest-to-defend target–attack combination and requires the most homeland security resources. Policymakers can use these and other results to focus their counter-terrorism measures.

Democratic accountability in open economies

We analyze democratic accountability in open economies based on different hypotheses about political evaluations and government responsiveness. Specifically, we assess whether citizens primarily rely on government policies or if they focus on economic outcomes resulting from these policies to evaluate governments. Our empirical analysis relies on Bayesian structural vector autoregression models for the British economy, aggregate monthly measures of public opinion, and economic evaluations from 1984 to 2006. We find that voters continuously monitor and strongly respond contemporaneously to changes in monetary and fiscal policies, but less to changes in macroeconomic outcomes. Voters also respond to policies differently when institutions change. When the Bank of England became politically independent, citizens shifted their attention toward fiscal policy, and the role of monetary policy in their evaluations decreased significantly. Finally, politicians respond to voting behavior by adjusting their policies in a sensible way. When vote intentions and approval decrease, the government reacts to the public by adjusting fiscal policy and, before the Bank of England became independent, also monetary policy.

Current and future Disability-Adjusted Life Years (DALYs) of Salmonella and Campylobacter in Belgium

Salmonellosis and campylobacteriosis are major foodborne diseases caused by consumption of contaminated raw pork or poultry. Symptomatic disease is mainly characterized by gastroenteritis, although immunoreactive complications also occur, especially among the immunocompromised. Although these diseases cause the highest number of confirmed foodborne bacterial infections in Belgium, their real and future population health impact remains unknown. The objectives of this study were to estimate and forecast the number of Salmonella and Campylobacter cases in Belgium from 2012 to 2020 and to calculate the corresponding number of Disability-Adjusted Life Years. The time series of laboratory-confirmed Salmonella cases ranged from 2001 to 2012, and that of Campylobacter cases from 1993 to 2013. We developed a Bai and Perron two breakpoint model to design salmonellosis time series taking into account the multiple changes in Salmonella spp. transmission and control. We developed a dynamic linear model for campylobacteriosis because the data showed a stochastic drift, varied locally and suffered from parameter stability issues around the seasonal component of the data. We calculated DALYs using standard formulas. The salmonellosis forecast showed a continued decline after 2005. The average monthly number of salmonellosis cases was 264 in 2012 and predicted to be 212 in 2020 (Standard Deviation [SD] 87). Salmonellosis caused 173 DALYs (95% Uncertainty Interval [UI] 33-433) per 100,000 in 2012 and 177 DALYs (95% UI 32-430) per 100,000 in 2020. The average monthly number of campylobacteriosis cases was 633 in 2012, with the predictions showing an upward trend until 2020 to an average of 1081 (SD 311) cases per month. Campylobacteriosis caused 25 DALYs (95% UI 9-60) per 100,000 in 2012 and 40 DALYs (95% UI 14-101) per 100,000 in 2020. Assuming a constant environment, the burden of salmonellosis will stay stable and the burden of campylobacteriosis may almost double until 2020.

Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides

Executions in Texas from 1994–2005 do not deter homicides, contrary to the results of Land et al. (2009). We find that using different models—based on pre-tests for unit roots that correct for earlier model misspecifications—one cannot reject the null hypothesis that executions do not lead to a change in homicides in Texas over this period. Using additional control variables, we show that variables such as the number of prisoners in Texas may drive the main drop in homicides over this period. Such conclusions however are highly sensitive to model specification decisions, calling into question the assumptions about fixed parameters and constant structural relationships. This means that using dynamic regressions to account for policy changes that may affect homicides need to be done with significant care and attention.

APART: Automatic Political Actor Recommendation in Real-time

Extracting actor data from news reports is important when generating event data. Hand-coded dictionaries are used to code actors and actions. Manually updating dictionaries for new actors and roles is costly and there is no automated method. We propose a dynamic frequency-based actor ranking algorithm with partial string matching for new actor-role detection, based on similar actors in the CAMEO dictionary. This is compared to a graph-based weighted label propagation baseline method. Results show our method outperforms the alternatives.

Long-Lasting Insecticide Net Ownership, Access and Use in Southwest Ethiopia: A Community-Based Cross-Sectional Study

Introduction: A large proportion of the Ethiopian population (approximately 68%) lives in malaria risk areas. Millions of long-lasting insecticide treated nets (LLINs) have been distributed as part of the malaria prevention and control strategy in the country. This study assessed the ownership, access and use of LLNs in the malaria endemic southwest Ethiopia. Methods: A community-based cross-sectional study was conducted in southwest Ethiopia during October–November 2015, including 836 households from sixteen villages around Gilgel-Gibe dam area. Indicators of ownership, access and use of LLINs were derived following the Roll Back Malaria (RBM) guidelines. Factors associated with failure for both LLIN access and use were analysed at household level using a multivariate logistic regression model. Results: The proportion of households with at least one LLIN was 82.7% (95% CI: 80.0, 85.1). However, only 68.9% (95% CI: 65.6, 71.9) had enough LLINs to cover all family members (with ≥one LLIN for every two persons). While 75.3% (95% CI: 68.4, 83.0) of the population was estimated to have accessed to LLINs, only 63.8% (95% CI: 62.3, 65.2) reported to have used a LLIN the previous night. The intra-household gap (i.e., households owning at least one LLIN, but unable to cover all family members) and the behavioral gap (i.e., household members who did not sleep under a LLIN despite having access to one) were 16.8% and 10.5%, respectively. Age, marital status and education of household heads, as well as household size and cooking using firewood were associated with the access to enough LLINs within households. Decreased access to LLINs at households was the main determinant for not achieving ≥80% household members sleeping under a LLIN the previous night. Other associated factors were household size and education level of household head. Conclusions: LLIN coverage levels in study villages remain below national targets of 100% for ownership and 80% for use. The access to enough LLINs within the households is the main restriction of LLIN use in the study area.