Media Coverage and Charitable Giving After the 2004 Tsunami

Media coverage of humanitarian crises is widely believed to influence charitable giving, yet this assertion has received little empirical scrutiny. Using Internet donations after the 2004 tsunami as a case study in a tobit framework, we show that media coverage of disasters increases charitable donations, with an additional minute of nightly news coverage increasing donations by 0.036 standard deviations from the mean. We repeat the analysis using instrumental variables in a tobit model to account for endogeneity, and the estimates are unchanged. We also show that the magnitude and sign of media impact vary by news source and relief agency.

Incumbency Reconsidered: Prospects, Strategic Entry, and Incumbent Quality in U.S. House Elections

Efforts to estimate the magnitude of the incumbency effect in U.S. House elections and assess its political meaning have been complicated by two omitted-variables problems. First, in the absence of an adequate measure of incumbent prospects, estimates of the magnitude of the incumbency effect fail to control for selection effects associated with the decision incumbents make about whether to run for reelection. Strategic incumbents enter races they think they can win and withdraw when they expect to lose. The consequence is an upward bias in estimates of incumbents’ electoral advantages. Second, the normative implications of high reelection rates cannot be assessed without measuring incumbent quality, since a possible explanation for their electoral success is that incumbents are of high quality and doing a good job. We propose a strategy for measuring incumbent prospects and quality, demonstrate the strategic nature of incumbent and challenger entry, re-estimate the incumbency effect, and show that incumbent quality has an impact on electoral outcomes. Our conclusion is that incumbents’ advantages in House elections have been over estimated while the positive basis of incumbent safety is typically under appreciated.

Estimating Brazilian monthly GDP: a state-space approach

This paper has several original contributions. The rst is to employ a superior interpolation method that enables to estimate, nowcast and forecast monthly Brazilian GDP for 1980-2012 in an integrated way; see Bernanke, Gertler and Watson (1997, Brookings Papers on Economic Activity). Second, along the spirit of Mariano and Murasawa (2003, Journal of Applied Econometrics), we propose and test a myriad of interpolation models and interpolation auxiliary series all coincident with GDP from a business-cycle dating point of view. Based on these results, we nally choose the most appropriate monthly indicator for Brazilian GDP. Third, this monthly GDP estimate is compared to an economic activity indicator widely used by practitioners in Brazil- the Brazilian Economic Activity Index - (IBC-Br). We found that the our monthly GDP tracks economic activity better than IBC-Br. This happens by construction, since our state-space approach imposes the restriction (discipline) that our monthly estimate must add up to the quarterly observed series in any given quarter, whichmay not hold regarding IBC-Br. Moreover, our method has the advantage to be easily implemented: it only requires conditioning on two observed series for estimation, while estimating IBC-Br requires the availability of hundreds of monthly series. Third, in a nowcasting and forecasting exercise, we illustrate the advantages of our integrated approach. Finally, we compare the chronology of recessions of our monthly estimate with those done elsewhere.

Dentistry scientometric analysis: A comparative study between Brazil and other most productive countries in the area

This research aims at performing a comparative study between the Brazilian scientific production in Dentistry, from 2000 to 2009 and countries that contribute with at least 2 % of the world's scientific production indexed in the Scopus database. More specifically, we intend to assess the annual Brazilian scientific production by comparing it to the other countries', analyze the Brazilian and other countries' publications in journals with higher impact factors, as well as to highlight the scientific production from these countries and its international visibility, measured by its total and by its average of citations and normalized citation index per year, by comparing the countries, and to compare the index h of such countries. As work procedure, the SCImago Journal and Country Rank was used as source, identifying the group of producing countries in the Dentistry area from 1996 to 2009. From a total of 136 countries, 13 were highlighted as the most productive, each one of them accounting for at least 2 % the worldwide scientific production in the area. The following indicators were raised for each country: number of produced documents, total of citations, self-citations, average of citations per document and index h. We verified that Brazil is the only country in Latin America that is pictured among the most productive ones in the Dentistry area. We observed that Brazil presents a growing visibility and impact in the international scenery, what suggests that its production is constantly consolidating, with Brazilian scientific recognition in the main vehicles of dissemination in the area. © 2012 Akadémiai Kiadó, Budapest, Hungary.

Produção científica dos Programas de Pós-Graduação de excelência no Brasil: colaboração internacional e impacto na área de Zootecnia

This research aimed to study the international scientific collaboration, through co-authorship, and the impact of full papers published in journals Qualis A1 and A2 of Graduate Programs in Animal Science of excellence (2007-2009). Through the indicators of production of graduate evaluation from CAPES, the research gathered the scientific production of three graduate programs, adding up to 125 articles. The citations received were gathered from the SCOPUS database and the impact of journals from the SCImago Journal & Country Rank. In order to investigate the correlation among the indicators analyzed, Pearson’s linear correlation coefficients were calculated. It was observed that the Impact Factor (IF) of the journal may influence the number of citations and that there is a weak trend for association of IFs with the number of coauthor countries. It was not observed a statistically significant correlation between the number of collaborating countries and citations received by the article.

Geração de indicadores de produção e citação científica em revistas de Ciência da Informação: estudo aplicado à base de dados BRAPCI

Pós-graduação em Ciência da Informação - FFC

The endangered species Brycon orbignyanus: genetic analysis and definition of priority areas for conservation

Currently, biodiversity is threatened by several factors often associated with human population growth and the extension of areas occupied by human activity. In particular, freshwater fish fauna is affected by overfishing, deforestation, water pollution, introduction of exotic species and habitat fragmentation promoted by hydroelectric dams, among other environmental impact factors. Several action plans to preserve ichthyofauna biodiversity have been adopted; however, these plans frequently cover only a small number of species, and decisions are often made without strong scientific support. This study aimed to evaluate the genetic aspects of wild groups of Brycon orbignyanus, an endangered fish species, using microsatellites and D-loop regions to identify the genetic structure of the samples and to establish priority areas for conservation based on the genetic patterns of this species. The results indicated that the samples showed levels of genetic variability compatible with others studies with Neotropical fishes. However, the results obtained in the analysis of molecular variance (AMOVA) for microsatellites (F (ST) = 0.258) and D-loop (F (ST) = 0.234) and the interpopulation fixation index revealed that B. orbignyanus was structured in different subpopulations in the La Plata River basin; the areas with better environmental conditions also showed subgroups with higher rates of genetic variability. Future conservation actions addressing these sites should consider two different management units: the complex formed by the Ivinhema River, Upper Parana, Camargo Port and Ilha Grande groups; and the complex formed by the Verde River and Sucuriu River groups.

Indicadores cientométricos normalizados: um estudo na produção científica brasileira internacional (1996 a 2011)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Índice h de las revistas españolas de Historia según Google Scholar Metrics (2007-2011)

[ES] La aparición de Google Scholar Metrics en abril de 2012 como nuevo sistema de evaluación bibliométrica de revistas científicas a partir del recuento de las citas bibliográficas que estas han recibido en Google Scholar, abre nuevas posibilidades para la medición del impacto de las revistas de humanidades. El objetivo de este trabajo es comprobar el alcance de este producto, a partir del análisis de la cobertura que muestra de las revistas españolas de Historia. Las búsquedas bibliográficas se efectuaron entre el 5 y 6 de diciembre de 2012. Se han identificado 69 revistas, cifra que representa tan solo un 24% de las revistas españolas de Historia. Los valores del índice h alcanzados por las revistas son minúsculos lo que hace que el ranking carezca de poder discriminatorio. Se propone un cambio en el diseño de Google Scholar Metrics para adaptarse a los patrones de producción y citación de la Historia, en particular, y las Humanidades, en general.

MULTIPLE DISEASES IN CARRIER PROBABILITY ESTIMATION: ACCOUNTING FOR SURVIVING ALL CANCERS OTHER THAN BREAST AND OVARY IN BRCAPRO

Mendelian models can predict who carries an inherited deleterious mutation of known disease genes based on family history. For example, the BRCAPRO model is commonly used to identify families who carry mutations of BRCA1 and BRCA2, based on familial breast and ovarian cancers. These models incorporate the age of diagnosis of diseases in relatives and current age or age of death. We develop a rigorous foundation for handling multiple diseases with censoring. We prove that any disease unrelated to mutations can be excluded from the model, unless it is sufficiently common and dependent on a mutation-related disease time. Furthermore, if a family member has a disease with higher probability density among mutation carriers, but the model does not account for it, then the carrier probability is deflated. However, even if a family only has diseases the model accounts for, if the model excludes a mutation-related disease, then the carrier probability will be inflated. In light of these results, we extend BRCAPRO to account for surviving all non-breast/ovary cancers as a single outcome. The extension also enables BRCAPRO to extract more useful information from male relatives. Using 1500 familes from the Cancer Genetics Network, accounting for surviving other cancers improves BRCAPRO’s concordance index from 0.758 to 0.762 (p = 0.046), improves its positive predictive value from 35% to 39% (p < 10−6) without impacting its negative predictive value, and improves its overall calibration, although calibration slightly worsens for those with carrier probability < 10%. Copyright c 2000 John Wiley & Sons, Ltd.

Inequity Measures for Evaluations of Environmental Justice: A Case Study of Close Proximity to Highways in NYC

Assessments of environmental and territorial justice are similar in that both assess whether empirical relations between the spatial arrangement of undesirable hazards (or desirable public goods and services) and socio-demographic groups are consistent with notions of social justice, evaluating the spatial distribution of benefits and burdens (outcome equity) and the process that produces observed differences (process equity. Using proximity to major highways in NYC as a case study, we review methodological issues pertinent to both fields and discuss choice and computation of exposure measures, but focus primarily on measures of inequity. We present inequity measures computed from the empirically estimated joint distribution of exposure and demographics and compare them to traditional measures such as linear regression, logistic regression and Theil’s entropy index. We find that measures computed from the full joint distribution provide more unified, transparent and intuitive operational definitions of inequity and show how the approach can be used to structure siting and decommissioning decisions.

TRIO LOGIC REGRESSION - DETECTION OF SNP - SNP INTERACTIONS IN CASE-PARENT TRIOS

Statistical approaches to evaluate higher order SNP-SNP and SNP-environment interactions are critical in genetic association studies, as susceptibility to complex disease is likely to be related to the interaction of multiple SNPs and environmental factors. Logic regression (Kooperberg et al., 2001; Ruczinski et al., 2003) is one such approach, where interactions between SNPs and environmental variables are assessed in a regression framework, and interactions become part of the model search space. In this manuscript we extend the logic regression methodology, originally developed for cohort and case-control studies, for studies of trios with affected probands. Trio logic regression accounts for the linkage disequilibrium (LD) structure in the genotype data, and accommodates missing genotypes via haplotype-based imputation. We also derive an efficient algorithm to simulate case-parent trios where genetic risk is determined via epistatic interactions.

Error models for microarray intensities

We derive the additive-multiplicative error model for microarray intensities, and describe two applications. For the detection of differentially expressed genes, we obtain a statistic whose variance is approximately independent of the mean intensity. For the post hoc calibration (normalization) of data with respect to experimental factors, we describe a method for parameter estimation.

DECOMPOSITION OF REGRESSION ESTIMATORS TO EXPLORE THE INFLUENCE OF "UNMEASURED" TIME-VARYING CONFOUNDERS

In environmental epidemiology, exposure X and health outcome Y vary in space and time. We present a method to diagnose the possible influence of unmeasured confounders U on the estimated effect of X on Y and to propose several approaches to robust estimation. The idea is to use space and time as proxy measures for the unmeasured factors U. We start with the time series case where X and Y are continuous variables at equally-spaced times and assume a linear model. We define matching estimator b(u)s that correspond to pairs of observations with specific lag u. Controlling for a smooth function of time, St, using a kernel estimator is roughly equivalent to estimating the association with a linear combination of the b(u)s with weights that involve two components: the assumptions about the smoothness of St and the normalized variogram of the X process. When an unmeasured confounder U exists, but the model otherwise correctly controls for measured confounders, the excess variation in b(u)s is evidence of confounding by U. We use the plot of b(u)s versus lag u, lagged-estimator-plot (LEP), to diagnose the influence of U on the effect of X on Y. We use appropriate linear combination of b(u)s or extrapolate to b(0) to obtain novel estimators that are more robust to the influence of smooth U. The methods are extended to time series log-linear models and to spatial analyses. The LEP plot gives us a direct view of the magnitude of the estimators for each lag u and provides evidence when models did not adequately describe the data.

Underestimation of Standard Errors in Multi-Site Time Series Studies

Multi-site time series studies of air pollution and mortality and morbidity have figured prominently in the literature as comprehensive approaches for estimating acute effects of air pollution on health. Hierarchical models are generally used to combine site-specific information and estimate pooled air pollution effects taking into account both within-site statistical uncertainty, and across-site heterogeneity. Within a site, characteristics of time series data of air pollution and health (small pollution effects, missing data, highly correlated predictors, non linear confounding etc.) make modelling all sources of uncertainty challenging. One potential consequence is underestimation of the statistical variance of the site-specific effects to be combined. In this paper we investigate the impact of variance underestimation on the pooled relative rate estimate. We focus on two-stage normal-normal hierarchical models and on under- estimation of the statistical variance at the first stage. By mathematical considerations and simulation studies, we found that variance underestimation does not affect the pooled estimate substantially. However, some sensitivity of the pooled estimate to variance underestimation is observed when the number of sites is small and underestimation is severe. These simulation results are applicable to any two-stage normal-normal hierarchical model for combining information of site-specific results, and they can be easily extended to more general hierarchical formulations. We also examined the impact of variance underestimation on the national average relative rate estimate from the National Morbidity Mortality Air Pollution Study and we found that variance underestimation as much as 40% has little effect on the national average.