de Oliveira, Angela C. M., John M. Spraggon, and Matthew J. Denny (2016) "Instrumenting Beliefs in Threshold Public Goods." PLoS ONE 11(2): e0147043. doi:10.1371/journal.pone.0147043

Understanding the causal impact of beliefs on contributions in Threshold Public Goods (TPGs) is particularly important since the social optimum can be supported as a Nash Equilibrium and best-response contributions are a function of beliefs. Unfortunately, investigations of the impact of beliefs on behavior are plagued with endogeneity concerns. We create a set of instruments by cleanly and exogenously manipulating beliefs without deception. Tests indicate that the instruments are valid and relevant. Perhaps surprisingly, we fail to find evidence that beliefs are endogenous in either the one-shot or repeated-decision settings. TPG allocations are determined by a base contribution and beliefs in a one shot-setting. In the repeated-decision environment, once we instrument for first-round allocations, we find that second-round allocations are driven equally by beliefs and history. Moreover, we find that failing to instrument prior decisions overstates their importance.

[article], [supporting information], [replication data], [BibTeX]

ben-Aaron, James, Matthew J. Denny, Bruce A. Desmarais, Hanna Wallach (2016) "Transparency by Conformity: A Field Experiment Evaluating Openness in Local Governments." Public Administration Review (Accepted). SSRN preprint

Sunshine laws establishing government transparency are ubiquitous in the United States; however, the intended degree of openness is often unclear or unrealized. Although researchers have identified characteristics of government organizations or officials that affect the fulfillment of public records requests, they have not considered the influence that government organizations have on each other. This picture of independently acting organizations does not accord with the literature on diffusion in public policy and administration. In this article, we present a field experiment to test whether a county government's fulfillment of a public records request is influenced by the knowledge that its peers have already complied. We argue that knowledge of peer compliance should (1) induce competitive pressures to comply and (2) resolve legal ambiguity in favor of compliance. We find evidence of peer conformity effects both in the time to initial response and in the rate of complete request fulfillment.


Working Papers

Assessing the Consequences of Text Preprocessing Decisions (2016)

We consider the impact of preprocessing decisions, a crucial first step in all text-as-data investigations. We note that grounded practical help on feature selection for the mostly unsupervised context in which social scientists work is scant: instead, advice is lifted from the supervised literature with little thought as to whether it is optimal, or even appropriate, for the tasks at hand. Worryingly, as we show with real data, substantive inferences and model performance for unsupervised approaches are not generally robust to perturbations of common preprocessing steps. We introduce a statistical procedure and easy-to-use software — preText — allowing scholars to examine the sensitivity of their findings under alternate preprocessing regimes. For a range of datasets, we show that while some steps are mostly harmless, researchers should be cautious about others, since they transform the data in ways likely to lead the unwary down different "forking paths" of inference.

The Importance of Generative Models for Assessing Network Structure (2016)

Network representations, theories, and methods have emerged as a powerful framework for examining political phenomena at a systems level. This article illustrates the importance of specifying and testing theories about the structure of political networks at multiple levels, and the potential pitfalls of failing to model the processes by which these networks were generated. These pitfalls range from missing important alternative explanations for an observed network structure, to specifying a model which does not test the hypotheses that derive from the researcher’s theory. It then combines these insights into a general framework for assessing the structural properties of political networks. The framework is applied to re-specify an existing political science study that seeks to engage with network concepts. I find that the approach taken in this paper can dramatically affect inferences about the structure of a political system.

Stochastic Weighted Graphs: Flexible Model Specification and Simulation (2015)

An important consideration in the study of networks is how to simulate and model the edges of a weighted graph. The generalized exponential random graph model (GERGM) was recently developed for this purpose. The GERGM specifies a joint distribution for an exponential family of graphs with continuous-valued edge weights. However, current estimation algorithms for the GERGM only allow inference on a restricted family of model specifications. To address this issue, we develop a Metropolis-Hastings method that can efficiently estimate any GERGM specification thereby significantly extending the family of weighted graphs that can be modeled under the GERGM framework. We show that new flexible model specifications are capable of avoiding likelihood degeneracy and efficiently capturing network motifs in applications where such models were not previously available. We demonstrate the utility of the new class of GERGMs through application to two real network data sets, and we further assess the effectiveness of our proposed methodology by simulating non-degenerate model specifications from the well-studied two-stars model. The results of our study suggest that our flexible class of GERGM specifications provide an effective means for statistical inference of weighted graphs. A working R version of the GERGM code is available in the supplement and will be incorporated in the xergm CRAN package.

Influence in the United States Senate (2015)

Interpersonal influence plays an important role in determining what legislation will see action the United States Congress. Building on theories that conceptualize a legislator’s influence as an individual property, I cast influence in a relational framework, recognizing that influence is exercised through legislators’ social and political networks. I develop a novel measure of legislative influence using temporal patterns in bill cosponsorship data as an instrument to infer a latent network of influence relationships between legislators. I then validate the measure of legislative influence I derive from these networks in an extension of a recently published study and in the context of predicting bill advancement. I find that my measure performs like an effective measure of interpersonal influence in Congress.

Graph Compartmentalization (2014)

This article introduces a concept and measure of graph compartmentalization. This new measure allows for principled comparison between graphs of arbitrary structure, unlike existing measures such as graph modularity. The proposed measure is invariant to graph size and number of groups and can be calculated analytically, facilitating measurement on very large graphs. I also introduce a block model generative process for compartmentalized graphs as a benchmark on which to validate the proposed measure. Simulation results demonstrate improved performance of the new measure over modularity in recovering the degree of compartmentalization of graphs simulated from the generative model. I also explore an application to the measurement of political polarization.



Complex Stochastic Weighted Graphs: Flexible Specification and Simulation

Presented at the International Conference on Computational Social Science , Helsinki Finland, 2015.


Topic-Conditioned Hierarchical Latent Space Models for Text-Valued Networks

Presented at PolMeth XXXI , University of Georgia, 2014.


Influence in the United States Senate

Presented at PolNet , McGill University, 2014.


An Analytic Measure of Graph Compartmentalization

Presented at UMass Amherst Computational Social Science Initiative Poster Session, 2014


Community Structure and Its Implications for the Evolution of Cooperation

Sunbelt XXXII, annual conference of the International Network for Social Network Analysis (INSNA), Redondo Beach CA, 2012.


Assessing the Consequences of Text Preprocessing Decisions

Presented at New Directions in Analyzing Text as Data , Northeastern University, 2016.

The Generalized Exponential Random Graph Model for Weighted Networks

Presented at the 9th Annual Political Networks Conference , Washington University St. Louis, 2016.

The Importance of Generative Models for Assessing Network Structure

Presented at the 9th Annual Political Networks Conference , Washington University St. Louis, 2016.

Content-Conditioned Hierarchical Latent Space Models for Textual Communication Networks

Presented at the International Conference on Computational Social Science , Helsinki Finland, 2015.

Inferring Latent Influence Diffusion Networks in The United States Senate

Presented at the International Conference on Computational Social Science , Helsinki Finland, 2015.