Modeling international diffusion: Inferential benefits and methodological challenges, with an application to international tax competition

Although scholars recognize that time-series-cross-section data typically correlate across both time and space, they tend to model temporal dependence directly, often by lags of dependent variables, but to address spatial interdependence solely as a nuisance to be “corrected” by FGLS or to which to be “robust” in standard-error estimation (by PCSE). We explore the inferential benefits and methodological challenges of directly modeling international diffusion, one form of spatial dependence. To this end, we first identify two substantive classes of modern comparative-and-international-political-economy (C&IPE) theoretical models—(context-conditional) open-economy comparative political-economy (CPE) models and international political-economy (IPE) models, which imply diffusion (along with predecessors, closed-economy CPE and orthogonal open-economy CPE)—and then we evaluate the relative performance of three estimators—non-spatial OLS, spatial OLS, and spatial 2SLS—for analyzing empirical models corresponding to these two modern alternative theoretical visions from spatially interdependent data. Finally, we offer a substantive application of the spatial 2SLS approach in what we call a spatial error-correction model of international tax competition. -- Obwohl Wissenschaftler wissen, dass Zeitreihenquerschnittsdaten sowohl über die Zeit als auch über den Raum korreliert sind, neigen sie dazu, die zeitliche Abhängigkeit direkt zu modellieren, z. B. durch Zeitabstände der abhängigen Variablen. Die räumliche Abhängigkeit jedoch wird als ein Ärgernis angesehen, welches durch FGLS ‚korrigiert’ wird oder ‚robust’ gemacht wird in Standard- Abweichungs-Schätzungen (durch PCSE). Wir untersuchen methodologische Herausforderungen und die Nutzen für Schlussfolgerungen aus einer direkten Modellierung internationaler Diffusion als einer Form der räumlichen Abhängigkeit. Zu diesem Zweck identifizieren wir zuerst zwei inhaltliche Hauptklassen theoretischer Modelle der modernen ‚Vergleichenden und Internationalen Politischen Ökonomie“, nämlich Modelle der (kontextbezogenen) Vergleichenden Politischen Ökonomie Offener Volkwirtschaften und Modelle der Internationalen Politischen Ökonomie. Diese bilden Diffusion ab, ebenso wie die Vorläufermodelle der Vergleichenden Politischen Ökonomie geschlossener Volkswirtschaften und gegensätzlich offener Volkswirtschaften. Zweitens bewerten wir die relative Performanz von drei Schätzern – nicht-räumliche OLS, räumliche OLS und räumliche 2SLS. Schließlich wenden wir den Ansatz des räumlichen 2SLS in einem von uns so genannten ‚Spatial Error Correction’-Modell des internationalen Steuerwettbewerbs an.International Tax Competition,Panel Models,Policy Diffusion,Political Economy,Spatial Interdependence

Appointing ministers to multiparty cabinets

How does intra-party competition affect governance in multiparty cabinets? For a long time scholars have recognized that intra-party competition and the strength of factions can affect governance through the selection of cabinet min- isters or through policy negotiations among coalition partners. Yet, there has been very little, if any, quantitative work to test these expectations, primar- ily due to lack of data that could either measure party cohesion or ministerial types. Using novel data on both accounts, this paper investigates how intra- party ideological cohesion affects ministerial appointments in four European countries with multiparty governments: Germany, the Netherlands, Sweden and Ireland. We make two important contributions in this paper. First, we pro- vide a theory of ministerial appointments predicting that when there is intra- party conflict over policy, more ideologically extreme ministers are appointed. This prediction holds even in multiparty cabinets, going against one’s expec- tations that more moderate ministers should be appointed in multiparty cab- inets Second, utilizing unique data on ministers’ background, we show that intra-party conflict predicts the appointments of ministers with more extreme policy preferences

Modeling History Dependence in Network-Behavior Coevolution

Spatial interdependence--the dependence of outcomes in some units on those in others--is substantively and theoretically ubiquitous and central across the social sciences. Spatial association is also omnipresent empirically. However, spatial association may arise from three importantly distinct processes: common exposure of actors to exogenous external and internal stimuli, interdependence of outcomes/behaviors across actors (contagion), and/or the putative outcomes may affect the variable along which the clustering occurs (selection). Accurate inference about any of these processes generally requires an empirical strategy that addresses all three well. From a spatial-econometric perspective, this suggests spatiotemporal empirical models with exogenous covariates (common exposure) and spatial lags (contagion), with the spatial weights being endogenous (selection). From a longitudinal network-analytic perspective, we can identify the same three processes as potential sources of network effects and network formation. From that perspective, actors\u27 self-selection into networks (by, e.g., behavioral homophily) and actors\u27 behavior that is contagious through those network connections likewise demands theoretical and empirical models in which networks and behavior coevolve over time. This paper begins building such modeling by, on the theoretical side, extending a Markov type-interaction model to allow endogenous tie-formation, and, on the empirical side, merging a simple spatial-lag logit model of contagious behavior with a simple p-star logit model of network formation, building this synthetic discrete-time empirical model from the theoretical base of the modified Markov type-interaction model. One interesting consequence of network-behavior coevolution--identically: endogenous patterns of spatial interdependence--emphasized here is how it can produce history-dependent political dynamics, including equilibrium phat and path dependence (Page 2006). The paper explores these implications, and then concludes with a preliminary demonstration of the strategy applied to alliance formation and conflict behavior among the great powers in the first half of the twentieth century

Network Selection and Path-Dependent Coevolution

Scholars have increasingly become aware that actors’ self-selection into networks (e.g., homophily) is an important determinant of network-tie formation. Such self-selection adds methodological complexity to the empirical evaluation of the effects of network ties on individual behavior. Moreover, the endogenous network formation implies that network-tie structures and actors’ behavior “coevolve” over time. Therefore, in longitudinal network studies, it is very crucial for scholars to understand the nature of coevolutionary dynamics in the data, in order to explain the network-formation and the behavioral-decision-making mechanisms accurately. In this project, we claim that one of the most important aspects of the coevolutionary dynamic is its connection with history dependence. By history dependence, we primarily focus on what Page (2006) defines as “phat” and path dependence. We first establish theoretically that systems with coevolution can easily generate multiple equilibria (i.e., the steady states of the system), using a simple Markov type-interaction model that allows for endogenous tie formation. The potential of multiple equilibria posits an important and very difficult empirical question--how sensitive are equilibrium distributions (over types) to the past states? More simply put, to what extent does history matter? What is at stake in this question is not trivial. If history matters for an equilibrium attained in the society, then we can also analyze the potential policy interventions that could change the path of the social process such that it would lead to a socially optimal equilibrium. As for the empirical strategy, we start with developing a discrete-time Markov model, combining a spatial-logit and p-star model to evaluate the empirical significance of coevolutionary dynamics in the data. The strength of this empirical approach is in its direct connection with the theoretical Markov interaction model, and can provide a foundation for developing statistical tests for history dependence generated by coevolution

Modeling International Diffusion: Inferential Benefits and Methodological Challenges, with an Application to International Tax Competition

Although scholars recognize that time-series-cross-section data typically correlate across both time and space, they tend to model temporal dependence directly, often by lags of dependent variables, but to address spatial interdependence solely as a nuisance to be “corrected” by FGLS or to which to be “robust” in standard-error estimation (by PCSE). We explore the inferential benefits and methodological challenges of directly modeling international diffusion, one form of spatial dependence. To this end, we first identify two substantive classes of modern comparative-and-international-political-economy (C&IPE) theoretical models—(context-conditional) open-economy comparative political-economy (CPE) models and international political-economy (IPE) models, which imply diffusion (along with predecessors, closed-economy CPE and orthogonal open-economy CPE)—and then we evaluate the relative performance of three estimators—non-spatial OLS, spatial OLS, and spatial 2SLS—for analyzing empirical models corresponding to these two modern alternative theoretical visions from spatially interdependent data. Finally, we offer a substantive application of the spatial 2SLS approach in what we call a spatial error-correction model of international tax competition.Obwohl Wissenschaftler wissen, dass Zeitreihenquerschnittsdaten sowohl über die Zeit als auch über den Raum korreliert sind, neigen sie dazu, die zeitliche Abhängigkeit direkt zu modellieren, z. B. durch Zeitabstände der abhängigen Variablen. Die räumliche Abhängigkeit jedoch wird als ein Ärgernis angesehen, welches durch FGLS ‚korrigiert’ wird oder ‚robust’ gemacht wird in Standard- Abweichungs-Schätzungen (durch PCSE). Wir untersuchen methodologische Herausforderungen und die Nutzen für Schlussfolgerungen aus einer direkten Modellierung internationaler Diffusion als einer Form der räumlichen Abhängigkeit. Zu diesem Zweck identifizieren wir zuerst zwei inhaltliche Hauptklassen theoretischer Modelle der modernen ‚Vergleichenden und Internationalen Politischen Ökonomie“, nämlich Modelle der (kontextbezogenen) Vergleichenden Politischen Ökonomie Offener Volkwirtschaften und Modelle der Internationalen Politischen Ökonomie. Diese bilden Diffusion ab, ebenso wie die Vorläufermodelle der Vergleichenden Politischen Ökonomie geschlossener Volkswirtschaften und gegensätzlich offener Volkswirtschaften. Zweitens bewerten wir die relative Performanz von drei Schätzern – nicht-räumliche OLS, räumliche OLS und räumliche 2SLS. Schließlich wenden wir den Ansatz des räumlichen 2SLS in einem von uns so genannten ‚Spatial Error Correction’-Modell des internationalen Steuerwettbewerbs an

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease