"Last-place Aversion": Evidence and Redistributive Implications

Why do low-income individuals often oppose redistribution? We hypothesize that an aversion to being in "last place" undercuts support for redistribution, with low-income individuals punishing those slightly below themselves to keep someone "beneath" them. In laboratory experiments, we find support for "last-place aversion" in the contexts of risk aversion and redistributive preferences. Participants choose gambles with the potential to move them out of last place that they reject when randomly placed in other parts of the distribution. Similarly, in money- transfer games, those randomly placed in second-to-last place are the least likely to costlessly give money to the player one rank below. Last-place aversion predicts that those earning just above the minimum wage will be most likely to oppose minimum-wage increases as they would no longer have a lower-wage group beneath them, a prediction we confirm using survey data.

The determinants of election to the United Nations Security Council

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11127-013-0096-4.The United Nations Security Council (UNSC) is the foremost international body responsible for the maintenance of international peace and security. Members vote on issues of global importance and consequently receive perks—election to the UNSC predicts, for instance, World Bank and IMF loans. But who gets elected to the UNSC? Addressing this question empirically is not straightforward as it requires a model that allows for discrete choices at the regional and international levels; the former nominates candidates while the latter ratifies them. Using an original multiple discrete choice model to analyze a dataset of 180 elections from 1970 to 2005, we find that UNSC election appears to derive from a compromise between the demands of populous countries to win election more frequently and a norm of giving each country its turn. We also find evidence that richer countries from the developing world win election more often, while involvement in warfare lowers election probability. By contrast, development aid does not predict election

Human Rights Shaming Through INGOs and Foreign Aid Delivery

Does the ``shaming" of human rights violations influence foreign aid delivery decisions across OECD donor countries? We examine the effect of shaming, defined as targeted negative attention by human rights international nongovernmental organizations (INGOs), on donor decisions about how to deliver bilateral aid. We argue that INGO shaming of recipient countries leads donor governments, on average, to ``bypass" the recipient government in favor of non-state aid delivery channels, including international and local NGOs and international organizations (IOs). However, we expect this relationship to be conditional on a donor country's position in the international system. Minor power countries have limited influence in global affairs and are therefore more able to centrally promote human rights in their foreign policy. Major power countries, on the other hand, shape world politics and often confront ``realpolitik" concerns that may require government-to-government aid relations in the presence of INGO shaming. We expect aid officials of minor donor countries to be more likely to condition aid delivery decisions on human rights shaming than their counterparts of major donor countries. Using compositional data analysis, we test our argument using originally collected data on human rights shaming events in a time-series cross-sectional framework from 2004 to 2010. We find support for our hypotheses: On average, OECD donor governments increase the proportion of bypass when INGOs shame the recipient government. When differentiating between donor types we find that this finding holds for minor but not for major powers. These results add to both our understanding of the influences of aid allocation decision-making and our understanding of the role of INGOs on foreign-policy

Protein Sequence Alignment Analysis by Local Covariation: Coevolution Statistics Detect Benchmark Alignment Errors

The use of sequence alignments to understand protein families is ubiquitous in molecular biology. High quality alignments are difficult to build and protein alignment remains one of the largest open problems in computational biology. Misalignments can lead to inferential errors about protein structure, folding, function, phylogeny, and residue importance. Identifying alignment errors is difficult because alignments are built and validated on the same primary criteria: sequence conservation. Local covariation identifies systematic misalignments and is independent of conservation. We demonstrate an alignment curation tool, LoCo, that integrates local covariation scores with the Jalview alignment editor. Using LoCo, we illustrate how local covariation is capable of identifying alignment errors due to the reduction of positional independence in the region of misalignment. We highlight three alignments from the benchmark database, BAliBASE 3, that contain regions of high local covariation, and investigate the causes to illustrate these types of scenarios. Two alignments contain sequential and structural shifts that cause elevated local covariation. Realignment of these misaligned segments reduces local covariation; these alternative alignments are supported with structural evidence. We also show that local covariation identifies active site residues in a validated alignment of paralogous structures. Loco is available at https://sourceforge.net/projects/locoprotein/files

Using Structure to Explore the Sequence Alignment Space of Remote Homologs

Protein structure modeling by homology requires an accurate sequence alignment between the query protein and its structural template. However, sequence alignment methods based on dynamic programming (DP) are typically unable to generate accurate alignments for remote sequence homologs, thus limiting the applicability of modeling methods. A central problem is that the alignment that is “optimal” in terms of the DP score does not necessarily correspond to the alignment that produces the most accurate structural model. That is, the correct alignment based on structural superposition will generally have a lower score than the optimal alignment obtained from sequence. Variations of the DP algorithm have been developed that generate alternative alignments that are “suboptimal” in terms of the DP score, but these still encounter difficulties in detecting the correct structural alignment. We present here a new alternative sequence alignment method that relies heavily on the structure of the template. By initially aligning the query sequence to individual fragments in secondary structure elements and combining high-scoring fragments that pass basic tests for “modelability”, we can generate accurate alignments within a small ensemble. Our results suggest that the set of sequences that can currently be modeled by homology can be greatly extended