The effects of gender segregation at the establishment level: an empirical analysis using a panel of matched employer-employee data

This paper studies the relationship between segregation of women across establish- ments and the wages of males and females. To investigate this issue empirically we use a panel of matched employer-employee data from Brazil. Various longitudinal models are used to assess the wage impact of establishment gender segregation. Overall, the results indicate that the e ect of establishment female proportion on the wages of males and females is negative. We also compare these longitudinal results with cross-section estimates, which are the usual ones obtained in the related literature. This com- parison suggests that unmeasured, time-invariant worker- and establishment-speci c e ects are correlated with the establishment female composition.

Genetics of anthracnose panel canker disease resistance and its relationship with yield and growth characters in half-sib progenies of rubber tree (Hevea brasiliensis)

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

Infant mortality in Brazil, 1980-2000: A spatial panel data analysis

Background: Infant mortality is an important measure of human development, related to the level of welfare of a society. In order to inform public policy, various studies have tried to identify the factors that influence, at an aggregated level, infant mortality. The objective of this paper is to analyze the regional pattern of infant mortality in Brazil, evaluating the effect of infrastructure, socio-economic, and demographic variables to understand its distribution across the country. Methods: Regressions including socio-economic and living conditions variables are conducted in a structure of panel data. More specifically, a spatial panel data model with fixed effects and a spatial error autocorrelation structure is used to help to solve spatial dependence problems. The use of a spatial modeling approach takes into account the potential presence of spillovers between neighboring spatial units. The spatial units considered are Minimum Comparable Areas, defined to provide a consistent definition across Census years. Data are drawn from the 1980, 1991 and 2000 Census of Brazil, and from data collected by the Ministry of Health (DATASUS). In order to identify the influence of health care infrastructure, variables related to the number of public and private hospitals are included. Results: The results indicate that the panel model with spatial effects provides the best fit to the data. The analysis confirms that the provision of health care infrastructure and social policy measures (e. g. improving education attainment) are linked to reduced rates of infant mortality. An original finding concerns the role of spatial effects in the analysis of IMR. Spillover effects associated with health infrastructure and water and sanitation facilities imply that there are regional benefits beyond the unit of analysis. Conclusions: A spatial modeling approach is important to produce reliable estimates in the analysis of panel IMR data. Substantively, this paper contributes to our understanding of the physical and social factors that influence IMR in the case of a developing country.

Determinants of government consumption expenditure in developing countries : a panel data analysis

The paper focuses on the recent pattern of government consumption expenditure in developing countries and estimates the determinants which have influenced government expenditure. Using a panel data set for 111 developing countries from 1984 to 2004, this study finds evidence that political and institutional variables as well as governance variables significantly influence government expenditure. Among other results, the paper finds new evidence of Wagner's law which states that peoples' demand for service and willingness to pay is income-elastic hence the expansion of public economy is influenced by the greater economic affluence of a nation (Cameron1978). Corruption is found to be influential in explaining the public expenditure of developing countries. On the contrary, size of the economy and fractionalization are found to have significant negative association with government expenditure. In addition, the study finds evidence that public expenditure significantly shrinks under military dictatorship compared with other form of governance.

Evolution over time of heavy vehicle volume in toll roads: A dynamic panel data to identify key explanatory variables in Spain

Improving the knowledge of demand evolution over time is a key aspect in the evaluation of transport policies and in forecasting future investment needs. It becomes even more critical for the case of toll roads, which in recent decades has become an increasingly common device to fund road projects. However, literature regarding demand elasticity estimates in toll roads is sparse and leaves some important aspects to be analyzed in greater detail. In particular, previous research on traffic analysis does not often disaggregate heavy vehicle demand from the total volume, so that the specific behavioral patternsof this traffic segment are not taken into account. Furthermore, GDP is the main socioeconomic variable most commonly chosen to explain road freight traffic growth over time. This paper seeks to determine the variables that better explain the evolution of heavy vehicle demand in toll roads over time. To that end, we present a dynamic panel data methodology aimed at identifying the key socioeconomic variables that explain the behavior of road freight traffic throughout the years. The results show that, despite the usual practice, GDP may not constitute a suitable explanatory variable for heavy vehicle demand. Rather, considering only the GDP of those sectors with a high impact on transport demand, such as construction or industry, leads to more consistent results. The methodology is applied to Spanish toll roads for the 1990?2011 period. This is an interesting case in the international context, as road freight demand has experienced an even greater reduction in Spain than elsewhere, since the beginning of the economic crisis in 2008.

Explaining light vehicle demand evolution in interurban toll roads: a dynamic panel data analysis in Spain

Tolls have increasingly become a common mechanism to fund road projects in recent decades. Therefore, improving knowledge of demand behavior constitutes a key aspect for stakeholders dealing with the management of toll roads. However, the literature concerning demand elasticity estimates for interurban toll roads is still limited due to their relatively scarce number in the international context. Furthermore, existing research has left some aspects to be investigated, among others, the choice of GDP as the most common socioeconomic variable to explain traffic growth over time. This paper intends to determine the variables that better explain the evolution of light vehicle demand in toll roads throughout the years. To that end, we establish a dynamic panel data methodology aimed at identifying the key socioeconomic variables explaining changes in light vehicle demand over time. The results show that, despite some usefulness, GDP does not constitute the most appropriate explanatory variable, while other parameters such as employment or GDP per capita lead to more stable and consistent results. The methodology is applied to Spanish toll roads for the 1990?2011 period, which constitutes a very interesting case on variations in toll road use, as road demand has experienced a significant decrease since the beginning of the economic crisis in 2008.

Performance determinants For Swedish Financial Monetary Institutions: Panel Data evidence between 2010-2014.

Due to the rapid changes that governs the Swedish financial sector such as financial deregulations and technological innovations, it is imperative to examine the extent to which the Swedish Financial institutions had performed amid these changes. For this to be accomplish, the work investigates what are the determinants of performance for Swedish Financial Monetary Institutions? Assumptions were derived from theoretical and empirical literatures to investigate the authenticity of this research question using seven explanatory variables. Two models were specified using Returns on Asset (ROA) and Return on Equity (ROE) as the main performance indicators and for the sake of reliability and validity, three different estimators such as Ordinary Least Square (OLS), Generalized Least Square (GLS) and Feasible Generalized Least Square (FGLS) were employed. The Akaike Information Criterion (AIC) was also used to verify which specification explains performance better while performing robustness check of parameter estimates was done by correcting for standard errors. Based on the findings, ROA specification proves to have the lowest Akaike Information Criterion (AIC) and Standard errors compared to ROE specification. Under ROA, two variables; the profit margins and the Interest coverage ratio proves to be statistically significant while under ROE just the interest coverage ratio (ICR) for all the estimators proves significant. The result also shows that the FGLS is the most efficient estimator, then follows the GLS and the last OLS. when corrected for SE robust, the gearing ratio which measures the capital structure becomes significant under ROA and its estimate become positive under ROE robust. Conclusions were drawn that, within the period of study three variables (ICR, profit margins and gearing) shows significant and four variables were insignificant. The overall findings show that the institutions strive to their best to maximize returns but these returns were just normal to cover their costs of operation. Much should be done as per the ASC theory to avoid liquidity and credit risks problems. Again, estimated values of ICR and profit margins shows that a considerable amount of efforts with sound financial policies are required to increase performance by one percentage point. Areas of further research could be how the individual stochastic factors such as the Dupont model, repo rates, inflation, GDP etc. can influence performance.

Leaf-, Panel- And Latex-expressed Sequenced Tags From The Rubber Tree (hevea Brasiliensis) Under Cold-stressed And Suboptimal Growing Conditions: The Development Of Gene-targeted Functional Markers For Stress Response.

Hevea brasiliensis is a native species of the Amazon Basin of South America and the primary source of natural rubber worldwide. Due to the occurrence of South American Leaf Blight disease in this area, rubber plantations have been extended to suboptimal regions. Rubber tree breeding is time-consuming and expensive, but molecular markers can serve as a tool for early evaluation, thus reducing time and costs. In this work, we constructed six different cDNA libraries with the aim of developing gene-targeted molecular markers for the rubber tree. A total of 8,263 reads were assembled, generating 5,025 unigenes that were analyzed; 912 expressed sequence tags (ESTs) represented new transcripts, and two sequences were highly up-regulated by cold stress. These unigenes were scanned for microsatellite (SSR) regions and single nucleotide polymorphisms (SNPs). In total, 169 novel EST-SSR markers were developed; 138 loci were polymorphic in the rubber tree, and 98 % presented transferability to six other Hevea species. Locus duplication was observed in H. brasiliensis and other species. Additionally, 43 SNP markers in 13 sequences that showed similarity to proteins involved in stress response, latex biosynthesis and developmental processes were characterized. cDNA libraries are a rich source of SSR and SNP markers and enable the identification of new transcripts. The new markers developed here will be a valuable resource for linkage mapping, QTL identification and other studies in the rubber tree and can also be used to evaluate the genetic variability of other Hevea species, which are valuable assets in rubber tree breeding.

Post Hoc Analysis Of The Patricia Randomized Trial Of The Efficacy Of Human Papillomavirus Type 16 (hpv-16)/hpv-18 As04-adjuvanted Vaccine Against Incident And Persistent Infection With Nonvaccine Oncogenic Hpv Types Using An Alternative Multiplex Type-specific Pcr Assay For Hpv Dna.

The efficacy of the human papillomavirus type 16 (HPV-16)/HPV-18 AS04-adjuvanted vaccine against cervical infections with HPV in the Papilloma Trial against Cancer in Young Adults (PATRICIA) was evaluated using a combination of the broad-spectrum L1-based SPF10 PCR-DNA enzyme immunoassay (DEIA)/line probe assay (LiPA25) system with type-specific PCRs for HPV-16 and -18. Broad-spectrum PCR assays may underestimate the presence of HPV genotypes present at relatively low concentrations in multiple infections, due to competition between genotypes. Therefore, samples were retrospectively reanalyzed using a testing algorithm incorporating the SPF10 PCR-DEIA/LiPA25 plus a novel E6-based multiplex type-specific PCR and reverse hybridization assay (MPTS12 RHA), which permits detection of a panel of nine oncogenic HPV genotypes (types 16, 18, 31, 33, 35, 45, 52, 58, and 59). For the vaccine against HPV types 16 and 18, there was no major impact on estimates of vaccine efficacy (VE) for incident or 6-month or 12-month persistent infections when the MPTS12 RHA was included in the testing algorithm versus estimates with the protocol-specified algorithm. However, the alternative testing algorithm showed greater sensitivity than the protocol-specified algorithm for detection of some nonvaccine oncogenic HPV types. More cases were gained in the control group than in the vaccine group, leading to higher point estimates of VE for 6-month and 12-month persistent infections for the nonvaccine oncogenic types included in the MPTS12 RHA assay (types 31, 33, 35, 45, 52, 58, and 59). This post hoc analysis indicates that the per-protocol testing algorithm used in PATRICIA underestimated the VE against some nonvaccine oncogenic HPV types and that the choice of the HPV DNA testing methodology is important for the evaluation of VE in clinical trials. (This study has been registered at ClinicalTrials.gov under registration no. NCT00122681.).

Variabilidade do regime de monções sobre o Brasil: o clima presente e projeções para um cenário com 2xCO2 usando o modelo MIROC

Este trabalho investiga a variabilidade do Sistema de Monções da América do Sul (SMAS) sobre o Brasil com particular interesse na região do cerrado brasileiro. O início, final e total de precipitação durante as monções de verão são examinados utilizando estimativas de precipitação por satélite (pêntadas) do Global Precipitation Climatology Project (GPCP) entre 1979-2004. Analogamente, as características do regime de monção simuladas pelo modelo climático global acoplado MIROC (Model for interdisciplinary Research on Climate) do IPCC (Intergovernmental Panel for Climate Change) são examinadas em dois cenários distintos: o clima do século XX (1981-2000) e o clima em uma condição com o dobro da concentração atual de CO2 (2xCO2) na atmosfera (2061-2080). Mostra-se que a variabilidade espacial do início da monção de verão sobre o cerrado na simulação do clima do século XX pelo MIROC corresponde bem às observações. Além disso, há indicação de uma mudança das caudas da distribuição sazonal da precipitação no Cerrado para um cenário com 2xCO2, comparativamente com o clima presente. Este resultado sugere uma mudança na probabilidade de ocorrência de eventos extremos (secos ou úmidos) em um cenário com 2xCO2 sobre o cerrado, o que de acordo com o MIROC, indica uma maior exposição da região às conseqüências de possíveis mudanças climáticas resultantes do aumento de gases de efeito estufa.

Per capita versus adult-equivalent estimates of calorie availability in household budget surveys

This study aims to estimate an adult-equivalent scale for calorie requirements and to determine the differences between adult-equivalent and per capita measurements of calorie availability in the Brazilian population. The study used data from the 2002-2003 Brazilian Household Budget Survey. The calorie requirement for a reference adult individual was based on the mean requirements for adult males and females (2,550kcal/day). The conversion factors were defined as the ratios between the calorie requirements for each age group and gender and that of the reference adult. The adult-equivalent calorie availability levels were higher than the per capita levels, with the largest differences in rural and low-income households. Differences in household calorie availability varied from 22kcal/day (households with adults and an adolescent) to 428kcal/day (households with elderly individuals), thus showing that per capital measurements can underestimate the real calorie availability, since they overlook differences in household composition.

Genetic trend estimates of meat quality traits in a male broiler line

The present research was conducted to estimate the genetic trends for meat quality traits in a male broiler line. The traits analyzed were initial pH, pH at 6 h after slaughter, final pH, initial range of falling pH, final range of falling pH, lightness, redness, yellowness, weep loss, drip loss, shrink loss, and shear force. The number of observations varied between 618 and 2125 for each trait. Genetic values were obtained by restricted maximum likelihood, and the numerator relationship matrix had 107,154 animals. The genetic trends were estimated by regression of the broiler average genetic values with respect to unit of time (generations), and the average genetic trend was estimated by regression coefficients. Generally, for the traits analyzed, small genetic trends were obtained, except for drip loss and shear force, which were higher. The small magnitude of the trends found could be a consequence of the absence of selection for meat quality traits in the line analyzed. The estimates of genetic trends obtained were an indication of an improvement in the meat quality traits in the line analyzed, except for drip loss.

Multi-trait and random regression mature weight heritability and breeding value estimates in Nelore cattle

Mature weight breeding values were estimated using a multi-trait animal model (MM) and a random regression animal model (RRM). Data consisted of 82 064 weight records from 8 145 animals, recorded from birth to eight years of age. Weights at standard ages were considered in the MM. All models included contemporary groups as fixed effects, and age of dam (linear and quadratic effects) and animal age as covariates. In the RRM, mean trends were modelled through a cubic regression on orthogonal polynomials of animal age and genetic maternal and direct and maternal permanent environmental effects were also included as random. Legendre polynomials of orders 4, 3, 6 and 3 were used for animal and maternal genetic and permanent environmental effects, respectively, considering five classes of residual variances. Mature weight (five years) direct heritability estimates were 0.35 (MM) and 0.38 (RRM). Rank correlation between sires' breeding values estimated by MM and RRM was 0.82. However, selecting the top 2% (12) or 10% (62) of the young sires based on the MM predicted breeding values, respectively 71% and 80% of the same sires would be selected if RRM estimates were used instead. The RRM modelled the changes in the (co) variances with age adequately and larger breeding value accuracies can be expected using this model.

Estimates of genetic trend for carcass traits in a commercial broiler line

Data from the slaughter of 24,001 chickens that were part of a selection program for the production of commercial broilers were used to estimate genetic trend for absolute carcass (CW), breast meat (BRW), and leg (LW) weights, and relative carcass (CY), breast meat (BRY), and leg (LY) weights. The components of (co) variance and breeding values of individuals were obtained by the restricted maximum likelihood method applied to animal models. The relationship matrix was composed of 132,442 birds. The models included as random effects, maternal additive genetic and permanent environmental for CW, BRW, LW, CY, and BRY, and only maternal permanent environmental for LY, besides the direct additive genetic and residual effects, and as fixed effects, hatch week, parents' mating group and sex. The estimates of genetic trend were obtained by average regression of breeding value on generation, and the average genetic trend was estimated by regression coefficients. The genetic trends for CW (+ 6.0336 g/generation), BRW (+ 3.6723 g/generation), LW (+ 1.5846 g/generation), CY (+ 0.1195%/generation), and BRY (+ 0.1388%/generation) were positive, and they were in accordance with the objectives of the selection program for these traits. The genetic trend for LY(-0.0019%/generation) was negative, possibly due to the strong emphasis on selection for BRY and the negative correlations between these two traits.

[O/Fe] ESTIMATES FOR CARBON-ENHANCED METAL-POOR STARS FROM NEAR-INFRARED SPECTROSCOPY

We report on oxygen abundances determined from medium-resolution near-infrared spectroscopy for a sample of 57 carbon-enhanced metal-poor (CEMP) stars selected from the Hamburg/ESO Survey. The majority of our program stars exhibit oxygen-to-iron ratios in the range +0.5 < [O/Fe]< + 2.0. The [O/Fe] values for this sample are statistically compared to available high-resolution estimates for known CEMP stars as well as to high-resolution estimates for a set of carbon-normal metal-poor stars. Carbon, nitrogen, and oxygen abundance patterns for a sub-sample of these stars are compared to yield predictions for very metal-poor asymptotic giant branch (AGB) abundances in the recent literature. We find that the majority of our sample exhibit patterns that are consistent with previously studied CEMP stars having s-process-element enhancements and thus have very likely been polluted by carbon- and oxygen-enhanced material transferred from a metal-poor AGB companion.