Ricardian equivalence for sub-national states

The authors test Ricardian equivalence within an endogenous growth model for U.S. states, which have high rates of migration relative to most countries. Results are consistent with both Ricardian equivalence and endogenous growth, despite the relative ease of migration. Increases in productive government expenditures increase long-run real growth by the same amount, for example, whether financed by taxes or bonds. State rules limiting the use of bond financing may play a role in supporting Ricardian equivalence. The study provides the first explicit test of Ricardian equivalence for sub-national states in the context of an endogenous growth model.endogenous growth

Life’s shared dependence on water

The ecocentric world view holds that non-human life has intrinsic value – a worth that is independent of any benefits that may be derived from such lives by humans. Exemplifying this, a salmon matters for reasons that are immeasurably greater than simply representing a target for anglers or a potential flavour on a human tongue. A fundamental tenet of the ecocentric philosophy is that moral standing permeates beyond the merely human world and into wider nature. Furthermore, this world view foregrounds the unfolding mass extinction of life on Earth as the moral and existential arch-crisis of our time. This arch-crisis is being driven, in turn, by an array of interconnected emergencies that include, among others, rapid anthropogenic climate change and diminishing freshwater supplies. In the case of water, shifting rainfall patterns and increasing pressures on abstraction to support a growing human population are causing suffering, and rendering landscapes unliveable, to humans and non-humans alike. For life is united in its dependence on water. This shared elemental need offers a potential touchpoint for citizens, both younger and older, to develop a sense of kinship with non-human others and to become more ecocentric in their value systems. Ultimately, a groundswell of ecocentric concern will help generate policies and foster practices that support broad socio-ecological justice in water usage and in other domains of our interconnected lives as Earth-kin

Granting political representation to non-humans

This commentary discusses the representation of individuals versus populations — human and nonhuman, present and future — in Treves et al.’s proposed trusteeship for futurity. Terminological questions are also discussed

An Economist’s Guide to Heaven

This paper is the first to offer an economic model of God and humanity as optimizing agents in the context of concrete belief archetypes (religious ‘contracts’) in Judeo-Christian theology. Data support the model’s unique predictions, despite their otherwise counterintuitive, unlikely nature. For example, the model requires that in one belief archetype, ‘good works’ not increase with strength of faith, as one might otherwise expect, and that what appears may be God’s dominant contract precisely balances divine penalties for reneging on promises with incentives to seek divine ‘gifts’—an equivalence supported in the data.economics;religion

Exact reconstruction of gene regulatory networks using compressive sensing.

BackgroundWe consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the network's sparseness.ResultsFor the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. We demonstrate that it is possible to use the coherence distribution to guide biological experiment design effectively. By collecting a more informative dataset, the proposed method helps reduce the cost of experiments. For each problem, a set of numerical examples is presented.ConclusionsThe method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies

The rising share of nonmarital births: A response to Ermisch, Martin, and Wu

We are flattered that our recent paper in Demography, GSS (2006), has attracted such close attention from Ermisch Martin and Wu (EMW). In this response we appreciate the opportunity to expand on several key aspects of our paper, but see no reason to substantially revise any of our major conclusions based on EMW comments. Reading EMW, one might think we had proposed the demographic equivalent of Newton’s second law of thermodynamics – the existence of a universal phenomenon, manifest in identical form in all places, for all groups, during all times periods, regardless of circumstances. It will be helpful, then, to review briefly the central points in GSS before turning to the major EMW comments, along with our responses.fertility, illegitimacy ratio, marriage, nonmarital fertility ratio, nonmarital births

Joint estimation of multiple related biological networks

Graphical models are widely used to make inferences concerning interplay in multivariate systems. In many applications, data are collected from multiple related but nonidentical units whose underlying networks may differ but are likely to share features. Here we present a hierarchical Bayesian formulation for joint estimation of multiple networks in this nonidentically distributed setting. The approach is general: given a suitable class of graphical models, it uses an exchangeability assumption on networks to provide a corresponding joint formulation. Motivated by emerging experimental designs in molecular biology, we focus on time-course data with interventions, using dynamic Bayesian networks as the graphical models. We introduce a computationally efficient, deterministic algorithm for exact joint inference in this setting. We provide an upper bound on the gains that joint estimation offers relative to separate estimation for each network and empirical results that support and extend the theory, including an extensive simulation study and an application to proteomic data from human cancer cell lines. Finally, we describe approximations that are still more computationally efficient than the exact algorithm and that also demonstrate good empirical performance.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS761 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org