Impact evaluation methods in public economics : a brief introduction to randomized evaluations and comparison with other methods

Recent years have seen a large expansion in the use of rigorous impact evaluation techniques. Increasingly, public administrations are collaborating with academic economists and other quantitative social scientists to apply such rigorous methods to the study of public finance. These developments allow for more reliable measurements of the effects of different policy options on the behavioral responses of citizens, firm owners, or public officials. They can help decision makers in tax administrations, public procurement offices, and other public agencies design programs informed by well-founded evidence. This article provides an introductory overview of the most frequently used impact evaluation methods. It is aimed at facilitating communication and collaboration between practitioners and academics by introducing key vocabulary and concepts used in rigorous impact evaluation methods, starting with randomized controlled trials and comparing them with other methods ranging from simple pre–post analysis to difference-in-differences, matching estimations, and regression discontinuity designs

When Can We Trust Population Thresholds in Regression Discontinuity Designs?

A recent literature has used variation just around deterministic legislative population thresholds to identify the causal effects of institutional changes. This paper reviews the use of regression discontinuity designs using such population thresholds. Our concern involves three arguments: (1) simultaneous exogenous (co-)treatment, (2) simultaneous endogenous choices and (3) manipulation and precise control over population measures. Revisiting the study by Egger and Koethenbuerger (2010), who analyse the relationship between council size and government spending, we present new evidence that these three concerns do matter for causal analysis. Our results suggest that empirical designs using population thresholds are only to be used with utmost care and confidence in the precise institutional setting

Computing Highly Correlated Positions Using Mutual Information and Graph Theory for G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are a superfamily of seven transmembrane-spanning proteins involved in a wide array of physiological functions and are the most common targets of pharmaceuticals. This study aims to identify a cohort or clique of positions that share high mutual information. Using a multiple sequence alignment of the transmembrane (TM) domains, we calculated the mutual information between all inter-TM pairs of aligned positions and ranked the pairs by mutual information. A mutual information graph was constructed with vertices that corresponded to TM positions and edges between vertices were drawn if the mutual information exceeded a threshold of statistical significance. Positions with high degree (i.e. had significant mutual information with a large number of other positions) were found to line a well defined inter-TM ligand binding cavity for class A as well as class C GPCRs. Although the natural ligands of class C receptors bind to their extracellular N-terminal domains, the possibility of modulating their activity through ligands that bind to their helical bundle has been reported. Such positions were not found for class B GPCRs, in agreement with the observation that there are not known ligands that bind within their TM helical bundle. All identified key positions formed a clique within the MI graph of interest. For a subset of class A receptors we also considered the alignment of a portion of the second extracellular loop, and found that the two positions adjacent to the conserved Cys that bridges the loop with the TM3 qualified as key positions. Our algorithm may be useful for localizing topologically conserved regions in other protein families