Simulating the Effects of a Green Payment Program on the Diffusion Rate of a Conservation Technology

The decision to adopt a potentially profitable but unfamiliar conservation technology is cast in a multi-period Bayesian framework. Specifically, dairy farmers who are both risk-averse and susceptible to peer group influence progressively learn about the true impact of adopting reduced phosphorus dairy diets on their income distributions as they repeatedly experiment with this new technology. Empirically calibrated simulations are used to examine the effects of a voluntary green payment program on the rate of technological diffusion. Results suggest that (a) green payments can accelerate learning and produce significant, permanent changes in behavior relatively quickly and for a reasonable cost; (b) shorter contracts offering larger incentives may be more cost-effective when learning plays an important role in behavioral change; and (c) unknown prior beliefs can reduce the efficacy of a green payment program, implying efforts to verify these priors or to ensure against them by increasing the payment level may be worthwhile.Environmental Economics and Policy,

A sparse multinomial probit model for classification

A recent development in penalized probit modelling using a hierarchical Bayesian approach has led to a sparse binomial (two-class) probit classifier that can be trained via an EM algorithm. A key advantage of the formulation is that no tuning of hyperparameters relating to the penalty is needed thus simplifying the model selection process. The resulting model demonstrates excellent classification performance and a high degree of sparsity when used as a kernel machine. It is, however, restricted to the binary classification problem and can only be used in the multinomial situation via a one-against-all or one-against-many strategy. To overcome this, we apply the idea to the multinomial probit model. This leads to a direct multi-classification approach and is shown to give a sparse solution with accuracy and sparsity comparable with the current state-of-the-art. Comparative numerical benchmark examples are used to demonstrate the method

Processing Data from Social Dilemma Experiments: A Bayesian Comparison of Parametric Estimators

Observed choices in Social Dilemma Games usually take the form of bounded integers. We propose a doubly-truncated count data framework to process such data. We compare this framework to past approaches based on ordered outcomes and truncated continuous densities using Bayesian estimation and model selection techniques. We find that all three frameworks (i) support the presence of unobserved heterogeneity in individual decision-making, and (ii) agree on the ranking of regulatory treatment effects. The count data framework exhibits superior efficiency and produces more informative predictive distributions for outcomes of interest. The continuous framework fails to allocate adequate probability mass to boundary outcomes, which are often of pivotal importance in these games.Social dilemma games; Hierarchical modeling; Bayesian simulation; Common property resource

A Priori Relevance Based On Quality and Diversity Of Social Signals

International audienceSocial signals (users' actions) associated with web resources (documents) can be considered as an additional information that can play a role to estimate a priori importance of the resource. In this paper, we are particularly interested in: first, showing the impact of signals diversity associated to a resource on information retrieval performance; second, studying the influence of their social networks origin on their quality. We propose to model these social features as prior that we integrate into language model. We evaluated the effectiveness of our approach on IMDb dataset containing 167438 resources and their social signals collected from several social networks. Our experimental results are statistically significant and show the interest of integrating signals diversity in the retrieval process

It goes with the territory: Ownership across spatial boundaries.

Previous studies have shown that people are faster to process objects that they own as compared with objects that other people own. Yet object ownership is embedded within a social environment that has distinct and sometimes competing rules for interaction. Here we ask whether ownership of space can act as a filter through which we process what belongs to us. Can a sense of territory modulate the well-established benefits in information processing that owned objects enjoy? In 4 experiments participants categorized their own or another person’s objects that appeared in territories assigned either to themselves or to another. We consistently found that faster processing of self-owned than other-owned objects only emerged for objects appearing in the self-territory, with no such advantage in other territories. We propose that knowing whom spaces belong to may serve to define the space in which affordances resulting from ownership lead to facilitated processing

Inflation targets and endogenous wage markups in a New Keynesian model

Empirical contributions show that wage re-negotiations take place while expiring contracts are still in place. This is captured by assuming that nominal wages are pre-determined. As a consequence, wage setters act as Stackelberg leaders, whereas in the typical New Keynesian model the wage-setting rule implies that they play a Nash game. We present a DSGE New Keynesian model with pre-determined wages and money entering the representative household's utility function and show how these assumptions are sufficient to identify an inverse relationship between the inflation target and the wage markup (and thus employment) both in the short and the long run. This is due to the complementary effects that wage claims and the inflation target have on money holdings. Model estimates suggest that a moderate long-run inflation rate generates non-negligible output gains.E52, E58, J51, E24

Farm Structural Change in German Regions - An Empirical Analysis using Micro and Macro Data

General economic developments as well as recent fundamental changes in the Common Agricultural Policy will likely impact significantly on the European farm structure. Although a decline of total farm numbers continues to be the general observation, important differences occur across regions and farm types. These differentiated developments and their determinants are of high relevance for policy impact assessment at the regional level. The main objective of the analysis provided in this paper is to empirically identify whether regionally specific characteristics account for differences in regional farm structure development. This is exemplarily shown for German FADN regions. As methodological approach a combined time series, cross-sectional Markov chain analysis is applied. The non-stationary Markov model is estimated via generalised cross-entropy estimation technique with the transition probabilities being represented as multinomial logit functions of explanatory variables and their coefficients. Prior information on the transition probabilities is obtained from observed single farm movements of the FADN sample.Farm structure, Markov model, Germany, Farm Management,

The Definition and Choice of Environmental Commodities for Nonmarket Valuation

Economic analyses of nature must somehow define the “environmental commodities” to which values are attached. This paper articulates a set of principles to guide the choice and interpretation of nonmarket commodities. We describe how complex natural systems can be decomposed consistent with what can be called “ecological production theory.” Ecological production theory--like conventional production theory--distinguishes between biophysical inputs, process, and outputs. We argue that a systems approach to the decomposition and presentation of natural commodities can inform and possibly improve the validity of nonmarket environmental valuation studies. We raise concerns about the interpretation, usefulness, and accuracy of benefit estimates derived without reference to ecological production theory.nonmarket valuation, stated preference, revealed preference, commodities, endpoints

Spatial Guilds in the Serengeti Food Web Revealed by a Bayesian Group Model

Food webs, networks of feeding relationships among organisms, provide fundamental insights into mechanisms that determine ecosystem stability and persistence. Despite long-standing interest in the compartmental structure of food webs, past network analyses of food webs have been constrained by a standard definition of compartments, or modules, that requires many links within compartments and few links between them. Empirical analyses have been further limited by low-resolution data for primary producers. In this paper, we present a Bayesian computational method for identifying group structure in food webs using a flexible definition of a group that can describe both functional roles and standard compartments. The Serengeti ecosystem provides an opportunity to examine structure in a newly compiled food web that includes species-level resolution among plants, allowing us to address whether groups in the food web correspond to tightly-connected compartments or functional groups, and whether network structure reflects spatial or trophic organization, or a combination of the two. We have compiled the major mammalian and plant components of the Serengeti food web from published literature, and we infer its group structure using our method. We find that network structure corresponds to spatially distinct plant groups coupled at higher trophic levels by groups of herbivores, which are in turn coupled by carnivore groups. Thus the group structure of the Serengeti web represents a mixture of trophic guild structure and spatial patterns, in contrast to the standard compartments typically identified in ecological networks. From data consisting only of nodes and links, the group structure that emerges supports recent ideas on spatial coupling and energy channels in ecosystems that have been proposed as important for persistence.Comment: 28 pages, 6 figures (+ 3 supporting), 2 tables (+ 4 supporting