Suchergebnisse

This is the final version as it appears for the citation: Webb, Clayton and Soren Jordan. 2022. "Avoiding, and Learning From, Mistakes Made by Junior Scholars Teaching Political Methodology." PS: Political Science & Politics 55 (1): 236-241. DOI:10.1017/S1049096521001189 The page numbers here are consistent with the published version. Corresponding author: Clayton Webb (webb767@ku.edu)

AbstractThe prevailing academic consensus holds that economic hardship does not motivate terrorism. We argue that this academic consensus is misguided because it assumes a single causal pathway connecting the economy to terrorism. In addition, most tests rely on national-level macroeconomic measures of economic performance that are not well suited to capturing individual-level decision-making processes that motivate people to engage in political violence. We argue that shifts in economic performance have heterogeneous effects on terrorist activity. The suffering caused by economic hardship energizes pre-existing grievances and generates feelings of anger and resentment toward the government, making affected individuals susceptible to violent radicalization. Economic crises also increase opportunities for terrorist recruitment by weakening institutions for coping with the consequences of sharp economic downturns. On the other hand, the economic losses caused by crises reduce the resources available to terrorist groups. These competing pressures are difficult to observe at the national level and are not equally reflected in all measures of economic performance. We test these arguments using a novel dataset of terrorist attacks and terrorist crimes in the Russian federal subjects between 2008 and 2016. We find evidence to support opportunity- and resource-based arguments for terrorism. These findings suggest a need to rethink the academic consensus on terrorism and a need to problematize the theoretical and empirical approaches that brought us to the prevailing consensus.

Why does the public care more about some terrorist attacks than others? In recent years, there has been a wave of terrorist attacks carried out by similar terrorist organizations, but these attacks have produced disparate public responses. Existing research shows that terrorist attacks are more traumatic for people who live near terrorist targets, but this research cannot explain differences in public attitudes about attacks occurring in other countries. We argue that threat perceptions are shaped by the physical and personal proximity of terrorist attacks. The identities of the victims are rarely known. People impute the characteristics of victims based on the country where the attack occurred. These perceived identities determine the empathy people feel toward victims and affect perceptions of terrorist threats. People feel a greater sense of vulnerability when attacks occur near their borders. We test these arguments using a series of online experiments. We find that the location of the attack and the race and nationality of the victims drive threat perceptions.

Existing theories of foreign policy opinion formation tend to treat elites as a black-box category for members of the nonpublic. This misses important nuances in public perceptions of elites. We argue that elite vocation serves as an important source cue, signaling elite access to information and elite knowledge that can be brought to bear on that information. We use a survey experiment to evaluate our hypotheses comparing four types of elites: elected officials, academics, career professionals, and members of the media. We find that, even accounting for partisanship, people still evaluate elites as knowledgeable and credible. There are also important differences in public perceptions of elites that should be accounted for in our theories of opinion formation. These findings have important implications for the in vogue death of expertise argument as well as research on public perceptions of foreign policy and public opinion formation.

AbstractDebate on the use of lagged dependent variables has a long history in political science. The latest contribution to this discussion is Wilkins (2018, Political Science Research and Methods, 6, 393–411), which advocates the use of an ADL(2,1) model when there is serial dependence in the outcome and disturbance. While this specification does offer some insurance against serially correlated disturbances, this is never the best (linear unbiased estimator) approach and should not be pursued as a general strategy. First, this strategy is only appropriate when the data-generating process (DGP) actually implies a more parsimonious model. Second, when this is not the DGP—e.g., lags of the predictors have independent effects—this strategy mischaracterizes the dynamic process. We clarify this issue and detail a Wald test that can be used to evaluate the appropriateness of the Wilkins approach. In general, we argue that researchers need to always: (i) ensure models are dynamically complete and (ii) test whether more restrictive models are appropriate.

Pesaran, Shin, and Smith (2001) (PSS) proposed a bounds procedure for testing for the existence of long run cointegrating relationships between a unit root dependent variable ($y_{t}$) and a set of weakly exogenous regressors$\boldsymbol{x}_{t}$when the analyst does not know whether the independent variables are stationary, unit root, or mutually cointegrated processes. This procedure recognizes the analyst's uncertainty over the nature of the regressors but not the dependent variable. When the analyst is uncertain whether$y_{t}$is a stationary or unit root process, the test statistics proposed by PSS are uninformative for inference on the existence of a long run relationship (LRR) between$y_{t}$and$\boldsymbol{x}_{t}$. We propose the long run multiplier (LRM) test statistic as a means of testing for LRRs without knowing whether the series are stationary or unit roots. Using stochastic simulations, we demonstrate the behavior of the test statistic given uncertainty about the univariate dynamics of both$y_{t}$and$\boldsymbol{x}_{t}$, illustrate the bounds of the test statistic, and generate small sample and approximate asymptotic critical values for the upper and lower bounds for a range of sample sizes and model specifications. We demonstrate the utility of the bounds framework for testing for LRRs in models of public policy mood and presidential success.

AbstractA fundamental challenge facing applied time‐series analysts is how to draw inferences about long‐run relationships (LRR) when we are uncertain whether the data contain unit roots. Unit root tests are notoriously unreliable and often leave analysts uncertain, but popular extant methods hinge on correct classification. Webb, Linn, and Lebo (WLL; 2019) develop a framework for inference based on critical value bounds for hypothesis tests on the long‐run multiplier (LRM) that eschews unit root tests and incorporates the uncertainty inherent in identifying the dynamic properties of the data into inferences about LRRs. We show how the WLL bounds procedure can be applied to any fully specified regression model to solve this fundamental challenge, extend the results of WLL by presenting a general set of critical value bounds to be used in applied work, and demonstrate the empirical relevance of the LRM bounds procedure in two applications.

In this article, we highlight three points. First, we counter Grant and Lebo's claim that the error correction model (ECM) cannot be applied to stationary data. We maintain that when data are properly stationary, the ECM is an entirely appropriate model. We clarify that for a model to be properly stationary, it must be balanced. Second, we contend that while fractional integration techniques can be useful, they also have important weaknesses, especially when applied to many time series typical in political science. We also highlight two related but often ignored complications in time series: low power and overfitting. We argue that the statistical tests used in time-series analyses have little power to detect differences in many of the sample sizes typical in political science. Moreover, given the small sample sizes, many analysts overfit their time-series models. Overfitting occurs when a statical model describes random error or noise instead of the underlying relationship. We argue that the results in the Grant and Lebo replications could easily be a function of overfitting.