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A regression kink design (RKD or RK design) can be used to identify casual effects in settings where the regressor of interest is a kinked function of an assignment variable. In this chapter, we apply an RKD approach to study the effect of unemployment benefits on the duration of joblessness in Austria, and discuss implementation issues that may arise in similar settings, including the use of bandwidth selection algorithms and bias-correction procedures. Although recent developments in nonparametric estimation (Calonico, Cattaneo, & Farrell, 2014; Imbens & Kalyanaraman, 2012) are sometimes interpreted by practitioners as pointing to a default estimation procedure, we show that in any given application different procedures may perform better or worse. In particular, Monte Carlo simulations based on data-generating processes that closely resemble the data from our application show that some asymptotically dominant procedures may actually perform worse than “sub-optimal” alternatives in a given empirical application.

Liability of foreignness (LOF) has been one of the central constructs in the field of international business and management. Over the past two decades, a significant body of theoretical and empirical research has accumulated, theorizing on the sources of these LOFs, investigating their magnitude, and prescribing approaches to mitigate these disadvantages. However, much of this research is almost exclusively related to firms expanding their products, services, and operations to other countries as part of their global expansion. The difficulties firms face in foreign product markets is just one dimension of the costs they can face in their attempts to secure resources abroad.

We expand the domain of the LOF construct to include liabilities faced by firms accessing foreign capital markets in light of the increasing integration of capital markets. We identify four sources of LOF in capital markets: regulatory costs, information costs, unfamiliarity costs, and costs arising out of cultural differences. Based on an extensive review of “home bias” in equity markets, we propose four strategies to erase the legitimacy deficits that firms encounter in foreign capital markets: bonding, signaling, adoption of business practices isomorphic with the host country, and certifications and endorsements by third parties. We also offer suggestions for operationalizing and measuring LOF in capital markets as well as several directions for advancing further research on LOF in the context of capital markets.

This chapter develops a novel bootstrap procedure to obtain robust bias-corrected confidence intervals in regression discontinuity (RD) designs. The procedure uses a wild bootstrap from a second-order local polynomial to estimate the bias of the local linear RD estimator; the bias is then subtracted from the original estimator. The bias-corrected estimator is then bootstrapped itself to generate valid confidence intervals (CIs). The CIs generated by this procedure are valid under conditions similar to Calonico, Cattaneo, and Titiunik’s (2014) analytical correction – that is, when the bias of the naive RD estimator would otherwise prevent valid inference. This chapter also provides simulation evidence that our method is as accurate as the analytical corrections and we demonstrate its use through a reanalysis of Ludwig and Miller’s (2007) Head Start dataset.

The asymptotic bias and variance of a general class of local polynomial estimators of M-regression functions are studied over the whole compact support of the multivariate covariate under a minimal assumption on the support. The support assumption ensures that the vicinity of the boundary of the support will be visited by the multivariate covariate. The results show that like in the univariate case, multivariate local polynomial estimators have good bias and variance properties near the boundary. For the local polynomial regression estimator, we establish its asymptotic normality near the boundary and the usual optimal uniform convergence rate over the whole support. For local polynomial quantile regression, we establish a uniform linearization result which allows us to obtain similar results to the local polynomial regression. We demonstrate both theoretically and numerically that with our uniform results, the common practice of trimming local polynomial regression or quantile estimators to avoid “the boundary effect” is not needed.

Despite an increase in research that examines the media's selection of protest events for coverage, two areas of study have been left undeveloped. First, the type of protest examined is limited to common forms of the demonstration (march, vigil, rally). A second drawback of this literature is its focus on mass audience newspapers. The goal of the current study is to address these two issues by comparing coverage of a previously ignored form of protest, the strike, across two different media sources, the mass audience New York Times and the Daily Labor Report, a newspaper which targets industry and labor leaders and garners its revenue from subscriptions, not advertising. Due to specific differences between the two newspapers (primarily readership and revenue base), it is expected that certain strike characteristics (industry) will play a greater role in the New York Times’ selection of strikes than in the Daily Labor Report. Using government data to construct the population of events, I find that both newspapers select strikes in a manner that resembles coverage of other forms of protest. Important variables include size, length, and disruptiveness. The main difference between the two newspapers is the New York Time's attention to strikes in industries that affect the public and consumers and its strong regional bias. These findings indicate that not only do similar media selection processes work for both protest and strikes, but also that, despite some differences, media type did not affect selection greatly.

The global slack hypothesis is central to the discussion of the trade-offs that monetary policy faces in an increasingly more integrated world. The workhorse New Open Economy Macro (NOEM) model of Martínez-García and Wynne (2010), which fleshes out this hypothesis, shows how expected future local inflation and global slack affect current local inflation. In this chapter, I propose the use of the orthogonalization method of Aoki (1981) and Fukuda (1993) on the workhorse NOEM model to further decompose local inflation into a global component and an inflation differential component. I find that the log-linearized rational expectations model of Martínez-García and Wynne (2010) can be solved with two separate subsystems to describe each of these two components of inflation.

I estimate the full NOEM model with Bayesian techniques using data for the United States and an aggregate of its 38 largest trading partners from 1980Q1 until 2011Q4. The Bayesian estimation recognizes the parameter uncertainty surrounding the model and calls on the data (inflation and output) to discipline the parameterization. My findings show that the strength of the international spillovers through trade – even in the absence of common shocks – is reflected in the response of global inflation and is incorporated into local inflation dynamics. Furthermore, I find that key features of the economy can have different impacts on global and local inflation – in particular, I show that the parameters that determine the import share and the price-elasticity of trade matter in explaining the inflation differential component but not the global component of inflation.

Consistent research highlighting their utility for documenting historical protest events find social movement scholars relying heavily on newspapers. Simultaneously, research consistently finds racial bias in the media. Together, these findings suggest that scholars’ reliance on mainstream media accounts of protest by minority groups could lead to inaccurate histories and explanations. This chapter compares reports of a six-year-long protest case featuring African American activists found in both a mainstream media source, the New York Times, and two New York-based African American newspapers, the New York Amsterdam News and the New York Age, which were then triangulated with data from archival manuscript collections. Doing so revealed considerable and important differences. The ethnic press reported more protest events than the New York Times, which contained descriptive bias reflecting existing racial stereotypes and effectively silenced activists. These findings suggest that social movement scholars focusing on minority activists should engage in both ethnic and mainstream press accounts of protest events and political activity to ensure accurate descriptions of events and activist sentiments.