Explaining Policy Ties in Presidential Congresses: A Network Analysis of Bill Initiation Data

Abstract

Policy networks formed by co‐authoring and co‐sponsoring bills reflect one of the most important types of connection legislators develop while in office. We expect that in presidential countries, the probability of a tie between two legislators should be influenced by partisan membership, territorial linkages and the policy areas in which they develop expertise. Given the complex nature of relational data and the particular characteristics of bill initiation networks, we propose a new approach – bootstrapping an exponential graph model using augmented data reflective of the frequency of ties – to address the challenges of thinning dense networks.

Transgovernmental Networks and Cooperation in the Global South

“…Homophily refers to the tendency of actors who share similar characteristics to work together, as expressed by the adage "birds of a feather, flock together" (McPherson, et al 2001). Past research reveals homophily to be present in many collaborative networks, similar to the one studied in this article (McPherson, et al 2001;Maoz 2012;Kinne 2013;Videras 2013;Alemán and Calvo 2013). If bureaucrats' priority is to advance their skills, they will want to seek out partners who have superior training.…”

The Nationalization of Legislative Collaboration: Territory, Partisanship, and Policymaking in Argentina

“…Unlike other types of relational data such as friendship or group membership, cosponsorship networks provide information both about the existence of a relationship and the relative frequency of such relationship (Alemán and Calvo 2013). Because the counts observed in cosponsorship data are meaningful, it is important to take advantage of such data rather than just assume away differences by reducing all counts to a single value expressing a tie.…”

“…10 We conduct two different types of analyses that extract information on the frequency of reported ties: first, we (i) augment our data using synthetic draws from the observed frequencies of ties in the original network of cosponsors (Alemán and Calvo 2013). As more frequent ties in the original affiliation matrix increase the probability of observing a tie in the synthetic data, we bootstrap ERGM estimates from 1000 network draws and retrieve estimates of the model.…”

“…We build upon a bourgeoning literature that in the past few years has sought to understand cosponsorship networks as expressions of public joint stances of policy preferences (Cranmer and Desmarais 2011;Alemán 2009;Alemán and Calvo 2013;Fowler 2006;Tam Cho and Fowler 2010). We consider cosponsorship networks as reflective of the policy intent of lawmakers, who connect with peers that share similar interests in policy areas or jurisdictions (Alemán and Calvo 2013); and measure the nationalization of legislators' policy intent as a function of the probability of coauthoring or cosponsoring legislation in Congress.…”

“…We consider cosponsorship networks as reflective of the policy intent of lawmakers, who connect with peers that share similar interests in policy areas or jurisdictions (Alemán and Calvo 2013); and measure the nationalization of legislators' policy intent as a function of the probability of coauthoring or cosponsoring legislation in Congress. To study these cosponsorship networks, we estimate exponential random graph models (ERGM) of legislative collaboration over 25 years of congressional politics in Argentina, distinguishing personal, partisan, and district level effects as well as variations over time.…”