Shahryar Minhas bio photo

Shahryar Minhas

Assistant Professor, Michigan State University

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fAid

  • Keeping Friends Close, but Enemies Closer: Foreign Aid Responses to Natural Disasters – with Cindy Cheng. Revise and Resubmit.
    • Despite a robust body of evidence that suggests that aid donors are largely motivated by strategic considerations when dispensing aid, anecdotal evidence suggests that countries often receive substantial humanitarian aid following natural disasters, even from strategic opponents. In this paper, we square this seeming incongruity by arguing that natural disasters can serve to, at least temporarily, emphasize the humanitarian, as opposed to the political or economic, aspects of a bilateral relationship. We argue that natural disaster severity increases the likelihood of donors giving humanitarian aid to strategic adversaries in the short term. However, we find that in the long-term, donors are still more likely to pursue strategic self-interest by increasing civil society aid to strategic adversaries following a natural disaster. We test our findings using a new measure of strategic interest which we argue improves upon existing measures by accounting for third-order dependencies in dyadic data. Our findings suggest that social context matters in short-term humanitarian aid allocation. They also have important implications for the future of foreign aid allocations more generally, as the number of natural disasters is likely to rise, not fall, with changing climate conditions.




influencenetworks

  • Measuring Influence in Tensors of Conflict – with Peter D. Hoff and Michael D. Ward.
    • Measuring influence and determining what drives it are persistent questions in political science and in network analysis more generally. Herein we focus on the domain of international relations. Our major substantive question is: How can we determine what characteristics make an actor influential? To address the topic of influence, we build on a multilinear tensor regression framework (MLTR) that captures influence relationships using a tensor generalization of a vector autoregression model. Influence relationships in that approach are captured in a pair of n x n matrices and provide measurements of how the network actions of one actor may influence the future actions of another. A limitation of the MLTR and earlier latent space approaches is that there are no direct mechanisms through which to explain why a certain actor is more or less influential than others. Our new framework, social influence regression, provides a way to statistically model the influence of one actor on another as a function of characteristics of the actors. Thus we can move beyond just estimating that an actor influences another to understanding why. To highlight the utility of this approach, we apply it to studying monthly-level conflictual events between countries as measured through the Integrated Crisis Early Warning System (ICEWS) event data project.




carbon

  • A Latent Factor Approach to Measuring State Preferences – with Max Gallop.
    • State preferences play an important, yet under discussed role in international politics. This is in large part because actually observing and measuring these preferences is impossible. In general, scholars have tried to infer preferences using either UN voting or alliance behavior. The two most notable measures of state preferences that have flowed from this research area are ideal point (Bailey et al., 2015) and S Scores (Signorino & Ritter, 1999). The basis of both these models is a spatial weighting scheme that has proven useful but discounts higher-order effects that might be present in relational data structures such as UN voting and alliances. We begin by arguing that both alliances and UN voting are simply examples of the multiple layers upon which states interact with one another. To estimate a measure of state preferences from this multilayer structure, we introduce a latent factor model that provides a reduced-rank approximation of the main patterns across the layers. Our new measure of preferences plausibly describes important state relations, and yields important insights on the relationship between preferences, democracy, and international conflict. Most importantly, a model of conflict that uses this measure of state preferences decisively outperforms models using extant measures when it comes to predicting conflict in an out of sample context.




chinawho

  • Who Are in Charge, Who Do I Work With, and Who Are My Friends: A Latent Space Approach to Understanding Elite Coappearances in China – with Narisong Huhe & Max Gallop.
    • How ruling elite arrange and maintain their power-sharing is key to our understanding of authoritarian politics. We propose a latent space framework to systematically analyze the dynamics of elite power-sharing in authoritarian regimes. We also introduce a novel dataset tracking appearances of elite Chinese Community Party (CCP) members at political events. Our new framework and data allow us to disentangle three key aspects of CCP elite power-sharing: (1) who are in charge, (2) who do I work with, and (3) who are my friends. Using a latent factor network analysis of approximately 10,000 appearance records of over 200 top CCP elites from 2013 to 2017, we empirically assess these three questions by computing elites' total appearances, dyadic coappearances, and their distance in a latent social space. We test how well these three indicators fare at predicting elites' appointments to the leading small groups (LSGs) of the CCP Central Committee and the Central Government, and from that analysis are able to highlight the need to account for the indirect ties elites share.




netsmatter

  • Taking Dyads Seriously – with Cassy Dorff, Max Gallop, Margaret Foster, Howard Liu, Juan Tellez, & Michael D. Ward.
    • Much of international relations scholarship concerns dyads. Dyadic hypotheses and especially dyadic data abound. Standard modeling approaches fail to adequately capture the data generating process behind dyadic events and processes. As a result, they suffer from biased coefficients and poorly calibrated standard errors. We introduce a regression-based approach, the Additive and Multiplicative Effects (AME) model, that better accounts for the inherent dependencies in dyadic data. First, we conduct a simulation to highlight how the model captures dependencies and show that accounting for these processes improves our ability to conduct inference on dyadic data. Second, we compare the AME model to approaches used in three prominent studies from recent international relations scholarship. For each study, we find that compared to AME, the modeling approach used performs notably worse at capturing the data generating process. Further, conventional methods misstate the effect of key variables and the uncertainty in these effects. Finally, AME dominates standard approaches in terms of out-of-sample fit. In sum, our work shows the consequences of failing to take the dependencies inherent to dyadic data seriously. Most importantly, by better modeling the data generating process underlying political phenomena, the AME framework improves scholars’ ability to conduct inferential analyses.




antiTrustDiffusion

  • Diffusion of Antitrust Laws in Open Economies – with Tim Büthe.
    • Antitrust law seeks to prevent the accumulation and abuse of market power by prohibiting cartels, bid-rigging, and similar anticompetitive behavior. A deliberately pro-market policy, antitrust has been shown to result in lower prices, more innovation, and greater efficiency. Antitrust enforcement, however, is inherently political - and has the potential for abuse - in that it entails the use of state power to constrain and possibly redistribute private economic power. Until 1990, antitrust was virtually entirely the domain of advanced capitalist democracies with strong rule-of-law traditions and professional public bureaucracies. Since then, the number of jurisdictions with antitrust laws has grown rapidly, from some thirty to more than one-hundred-and-thirty today. Most of the existing literature on this strikingly rapid diffusion has focused on the domestic conditions that are conducive to the adoption of such laws, such as political and economic liberalization. Even the few papers that consider factors such as international trade or World Bank advocacy of antitrust law adoption, model the enactment of antitrust laws as an independent decision for each country. Such models fail to capture causally important aspects of the way in which antitrust has diffused through the international political-economic system due to interdependencies in state decision-making. To analyze the global diffusion of antitrust empirically, we use an original dataset on not only when states adopted antitrust laws but also their content. This allows us to assess various mechanisms through which antitrust provisions diffuse across borders using an event history framework with spatial lags.