Conference Agenda

*Purpose:* Literature is exchanged across nations and languages. The exchange is very unequal, however, and prioritizes a handful of mostly Western countries. Existing research is split over the question of whether these inequalities are driven by the prestige of national literary traditions, economic infrastructures, political support, or cultural proximity. To advance the debate, this presentation leverages newly collected data on N = 147,443 translations of literary works to reconstruct the network of translation flows between 32 European countries for the time period 2018 to 2020. *Methods:* The effects of literary prestige, economic infrastructure, political support, and cultural proximity on translation flows were estimated with a combination of Generalized Additive Models (GAMs) and the Quadratic Assignment Procedure (QAP). Data on over 10,000 writers were compiled from eight encyclopedias of world literature to construct a comprehensive measure of literary prestige. Flexible P-splines were used to control for nonlinear effects of country size that otherwise pose the risk of confounding analyses of ecological units such as countries. *Results:* Countries with a prestigious national literature and a powerful publishing industry were found to have more out-translations but fewer in-translations. Surprisingly, countries with higher state investment into culture generated fewer out-translations. Countries with higher cultural proximity did not exchange more translations. *Contributions:* This presentation contributes significantly to ongoing research on the factors that (re)produce the unequal representation of national cultures in transnational exchanges, showing that symbolic status orders and unequal economic infrastructures contribute to asymmetric cultural exchange.

Why do some countries impose sanctions while others refrain, even when faced with similar geopolitical pressures? This paper critically examines how affinity, broadly conceptualized across political, economic, and cultural dimensions, influences the likelihood of sanction imposition. While extant research focuses primarily on direct material and strategic interests, this paper posits that deeper, structurally embedded ties shape the likelihood of sanctioning. Countries with stronger affinity through political preferences, economic links, or cultural bonds are less likely to impose sanctions on one another due to inherent preferential attachment and the cost of disrupting these interdependencies. Methodologically, this paper applies an advanced statistical network analysis method called the Temporal Exponential Random Graph Model (TERGM) to assess how affinity structures shape sanctions over time. The TERGM enables a rigorous analysis of how sanctions evolve within broader geopolitical and geoeconomic systems, capturing the role of affinity in mitigating coercive statecraft. By integrating affinity as a multidimensional factor into the study of global sanctions, this research advances theoretical debates on statecraft, interdependence, and international coercion, offering new insights into how structural ties moderate geopolitical contestation and influence the stability of international cooperation.

Although prior research conceptualizes the multinational enterprise (MNE) as a network of organizational units located in different countries, extant knowledge of how the MNE’s network evolves is limited. Consistent with studies highlighting the important role of political relations for an MNE’s decision to enter or exit a host country, we examine whether the political relations between countries in the MNE’s network (rather than solely between the MNE’s home and host country) influence the evolution of the MNE’s network. We test our theory by analyzing data on the host countries of 1,126 Japanese MNEs with a stochastic actor-oriented model (SAOM). We find that the political relations of host countries in the MNE’s network determine whether a focal host country becomes or stays part of the MNE’s network. This study advances prior research which largely examines the role of the political relations between the MNE’s home and a host country for an MNE’s decision to enter or exit a host country, by showing that, in fact, it is the political relations between the host country and other host countries in the MNE’s network which determine the MNE’s network evolution. Without accounting for this effect, conclusions on how political relations affect an MNE’s network evolution may be flawed and misleading. Methodologically, using a SAOM helps to overcome model misspecifications in current research because it allows for modelling interdependencies in an MNE’s network evolution. These interdependencies are not accounted for in commonly applied regression models, leading to potentially biased estimates.