Knowledge that Transforms

The gCube System - AquaMaps Species View Portlet<br> --------------------------------------------------<br> <br> Species view explorer portlet for AquaMaps suite<br> <br> <br> This software is part of the gCube Framework (https://www.gcube-system.org/): an<br> open-source software toolkit used for building and operating Hybrid Data<br> Infrastructures enabling the dynamic deployment of Virtual Research Environments<br> by favouring the realisation of reuse oriented policies.<br> <br> The projects leading to this software have received funding from a series of <br> European Union programmes including: <br> * the Sixth Framework Programme for Research and Technological Development - <br> DILIGENT (grant no. 004260); <br> * the Seventh Framework Programme for research, technological development and <br> demonstration - D4Science (grant no. 212488), D4Science-II (grant no. <br> 239019),ENVRI (grant no. 283465), EUBrazilOpenBio (grant no. 288754), iMarine <br> (grant no. 283644); <br> * the H2020 research and innovation programme - BlueBRIDGE (grant no. 675680), <br> EGIEngage (grant no. 654142), ENVRIplus (grant no. 654182), Parthenos (grant <br> no. 654119), SoBigData (grant no. 654024);<br> <br> <br> Version<br> --------------------------------------------------<br> <br> 1.3.3-4.0.0-130288 (2016-11-27)<br> <br> Please see the file named "changelog.xml" in this directory for the release notes.<br> <br> <br> <br> Authors<br> --------------------------------------------------<br> <br> * Fabio Sinibaldi (fabio.sinibaldi-AT-isti.cnr.it) Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" - CNR, Pisa (Italy). <br> <br> Maintainers<br> -----------<br> <br> * Fabio Sinibaldi (fabio.sinibaldi-AT-isti.cnr.it) Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" - CNR, Pisa (Italy). <br> <br> <br> <br> Download information<br> --------------------------------------------------<br> <br> Source code is available from SVN: <br> http://svn.research-infrastructures.eu/public/d4science/gcube/trunk/portlets/user/aquamapsspeciesview<br> <br> Binaries can be downloaded from the gCube website: <br> https://www.gcube-system.org/<br> <br> Installation<br> --------------------------------------------------<br> <br> Installation documentation is available on-line in the gCube Wiki:<br> https://wiki.gcube-system.org/gcube/index.php/AquaMaps_Suite<br> <br> Documentation <br> --------------------------------------------------<br> <br> Documentation is available on-line in the gCube Wiki:<br> https://wiki.gcube-system.org/gcube/index.php/AquaMaps_Suite<br> https://wiki.gcube-system.org/gcube/index.php/AquaMaps_Suite<br> <br> <br> Support <br> --------------------------------------------------<br> <br> Bugs and support requests can be reported in the gCube issue tracking tool:<br> https://support.d4science.org/projects/gcube/<br> <br> <br> Licensing<br> --------------------------------------------------<br> <br> This software is licensed under the terms you may find in the file named "LICENSE" in this directory.<br>

We use variation in historical state centralization to examine the long-term impact of institutions on cultural norms. The Kuba Kingdom, established in Central Africa in the early 17th century by King Shyaam, had more developed state institutions than the other independent villages and chieftaincies in the region. It had an unwritten constitution, separation of political powers, a judicial system with courts and juries, a police force, a military, taxation, and significant public goods provision. Comparing individuals from the Kuba Kingdom to those from just outside the Kingdom, we find that centralized formal institutions are associated with weaker norms of rule following and a greater propensity to cheat for material gain. This finding is consistent with recent models where endogenous investments to inculcate values in children decline when there is an increase in the effectiveness of formal institutions that enforce socially desirable behavior. Consistent with such a mechanism, we find that Kuba parents believe it is less important to teach children values related to rule-following behaviors.

The impact of insurer competition on welfare, negotiated provider prices, and premiums in the U.S. private health care industry is theoretically ambiguous. Reduced competition may increase the premiums charged by insurers and their payments made to hospitals. However, it may also strengthen insurers' bargaining leverage when negotiating with hospitals, thereby generating offsetting cost decreases. To understand and measure this trade-off, we estimate a model of employer-insurer and hospital-insurer bargaining over premiums and reimbursements, household demand for insurance, and individual demand for hospitals using detailed California admissions, claims, and enrollment data. We simulate the removal of both large and small insurers from consumers' choice sets. Although consumer welfare decreases and premiums typically increase, we find that premiums can fall upon the removal of a small insurer if an employer imposes effective premium constraints through negotiations with the remaining insurers. We also document substantial heterogeneity in hospital price adjustments upon the removal of an insurer, with renegotiated price increases and decreases of as much as 10% across markets.

This paper proposes an empirical model of network formation, combining strategic and random networks features. Payoffs depend on direct links, but also link externalities. Players meet sequentially at random, myopically updating their links. Under mild assumptions, the network formation process is a potential game and converges to an exponential random graph model (ERGM), generating directed dense networks. I provide new identification results for ERGMs in large networks: if link externalities are nonnegative, the ERGM is asymptotically indistinguishable from an Erdős–Renyi model with independent links. We can identify the parameters only when at least one of the externalities is negative and sufficiently large. However, the standard estimation methods for ERGMs can have exponentially slow convergence, even when the model has asymptotically independent links. I thus estimate parameters using a Bayesian MCMC method. When the parameters are identifiable, I show evidence that the estimation algorithm converges in almost quadratic time.

This paper examines how sales force impacts competition and equilibrium prices in the context of a privatized pension market. We use detailed administrative data on fund manager choices and worker characteristics at the inception of Mexico's privatized social security system, where fund managers had to set prices (management fees) at the national level, but could select sales force levels by local geographic areas. We develop and estimate a model of fund manager choice where sales force can increase or decrease customer price sensitivity. We find exposure to sales force lowered price sensitivity, leading to inelastic demand and high equilibrium fees. We simulate oft proposed policy solutions: a supply‐side policy with a competitive government player and a demand‐side policy that increases price elasticity. We find that demand‐side policies are necessary to foster competition in social safety net markets with large segments of inelastic consumers.

A principal wishes to screen an agent along several dimensions simultaneously. The agent has quasilinear preferences that are additively separable across the various components. We consider a robust version of the principal’s problem, in which she knows the marginal distribution of each component of the agent’s type, but does not know the joint distribution. Any mechanism is evaluated by its worst-case expected profit, over all joint distributions consistent with the known marginals. We show that the optimum for the principal is simply to screen along each component separately. This result does not require any assumptions (such as single-crossing) on the structure of preferences within each component. Applications of the model include monopoly pricing and dynamic taxation. This paper has greatly benefited from conversations with Florian Scheuer, as well as helpful comments from (in random order) Richard Holden, Dawen Meng, Andy

We study from both a theoretical and an empirical perspective how a network of military alliances and enmities affects the intensity of a conflict. The model combines elements from network theory and from the politico-economic theory of conflict. We obtain a closed-form characterization of the Nash equilibrium. Using the equilibrium conditions, we perform an empirical analysis using data on the Second Congo War, a conflict that involves many groups in a complex network of informal alliances and rivalries. The estimates of the fighting externalities are then used to infer the extent to which the conflict intensity can be reduced through (i) dismantling specific fighting groups involved in the conflict; (ii) weapon embargoes; (iii) interventions aimed at pacifying animosity among groups. Finally, with the aid of a random utility model, we study how policy shocks can induce a reshaping of the network structure.

The availability of high frequency financial data has generated a series of estimators based on intra-day data, improving the quality of large areas of financial econometrics. However, estimating the standard error of these estimators is often challenging. The root of the problem is that traditionally, standard errors rely on estimating a theoretically derived asymptotic variance, and often this asymptotic variance involves substantially more complex quantities than the original parameter to be estimated. Standard errors are important: they are used to assess the precision of estimators in the form of confidence intervals, to create “feasible statistics” for testing, to build forecasting models based on, say, daily estimates, and also to optimize the tuning parameters. The contribution of this paper is to provide an alternative and general solution to this problem, which we call Observed Asymptotic Variance. It is a general nonparametric method for assessing asymptotic variance (AVAR). It provides consistent estimators of AVAR for a broad class of integrated parameters Θ = ∫ θt dt, where the spot parameter process θ can be a general semimartingale, with continuous and jump components. The observed AVAR is implemented with the help of a two-scales method. Its construction works well in the presence of microstructure noise, and when the observation times are irregular or asynchronous in the multivariate case. The methodology is valid for a wide variety of estimators, including the standard ones for variance and covariance, and also for more complex estimators, such as, of leverage effects, high frequency betas, and semivariance.

We assess the empirical content of equilibrium models of labor market sorting based on unobserved (to economists) characteristics. In particular, we show theoretically that all parameters of the classic model of sorting based on absolute advantage in Becker, 1973 with search frictions can be nonparametrically identified using only matched employer?employee data on wages and labor market transitions. In particular, these data are sufficient to nonparametrically estimate the output of any individual worker with any given firm. Our identification proof is constructive and we provide computational algorithms that implement our identification strategy given the limitations of the available data sets. Finally, we add on-the-job search to the model, extend the identification strategy, and apply it to a large German matched employer?employee data set to describe detailed patterns of sorting and properties of the production function.

This paper develops characterizations of identified sets of structures and structural features for complete and incomplete models involving continuous or discrete variables.Multiple values of unobserved variables can be associated with particular combinations of observed variables.This can arise when there are multiple sources of heterogeneity, censored or discrete endogenous variables, or inequality restrictions on functions of observed and unobserved variables.The models generalize the class of incomplete instrumental variable (IV) models in which unobserved variables are singlevalued functions of observed variables.Thus the models are referred to as generalized IV (GIV) models, but there are important cases in which instrumental variable restrictions play no significant role.Building on a definition of observational equivalence for incomplete models the development uses results from random set theory that guarantee that the characterizations deliver sharp bounds, thereby dispensing with the need for case-by-case proofs of sharpness.The use of random sets defined on the space of unobserved variables allows identification analysis under mean and quantile independence restrictions on the distributions of unobserved variables conditional on exogenous variables as well as under a full independence restriction.The results are used to develop sharp bounds on the distribution of valuations in an incomplete model of English auctions, improving on the pointwise bounds available until now.Application of many of the results of the paper requires no familiarity with random set theory.