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This study is concerned with a problem in measuring citation impact with the aid of percentile data, which arises from the assignment of publications (or the journals in which the publications have appeared) by Thomson Reuters for the Web of Science to more than one subject category. If there is more than one subject category for a publication, it is initially unclear which category is to be used to create the reference set for the calculation of the percentile. This paper seeks to address these issues.

In this study the author would like to look at whether the calculation of differences between the citation impact of research institutions is affected by whether the minimum (the maximum percentile), the maximum (the minimum percentile), the mean or the median impact (percentile) for the different subject categories is used. The study is based on a sample of percentile data for three research institutions (n=4,232).

The result of the comparison of citation impact of the three institutions remains very similar for all the calculation methods, but on a different level.

It is the first study, which investigates how far it makes a difference in the comparison of the citation impact of three different research institutes whether – with multiple assignments of subject categories to one publication – the minimum, the maximum, the mean or the median inverted percentile is used. An answer to the question is very relevant since different methods are used in practical application. For example, the web-based research evaluation tool InCites uses the minimum percentile.

As an alternative to the standard p and np charts along with their various modifications, beta control charts are used in the literature for monitoring proportion data. These charts in general use average of proportions to set up the control limits assuming in-control parameters known. The purpose of the paper is to propose a control chart for detecting shift(s) in the percentiles of a beta distributed process monitoring scheme when in-control parameters are unknown. Such situations arise when specific percentile of proportion of conforming or non-conforming units is the quality parameter of interest.

Parametric bootstrap method is used to develop the control chart for monitoring percentiles of a beta distributed process when in-control parameters are unknown. Extensive Monte Carlo simulations are conducted for various combinations of percentiles, false-alarm rates and sample sizes to evaluate the in-control performance of the proposed bootstrap control charts in terms of average run lengths (ARL). The out-of-control behavior and performance of the proposed bootstrap percentile chart is thoroughly investigated for several choices of shifts in the parameters of beta distribution. The proposed chart is finally applied to two skewed data sets for illustration.

The simulated values of in-control ARL are found to be closer to the theoretical results implying that the proposed chart for percentiles performs well with both positively and negatively skewed data. Also, the out-of-control ARL values for the percentiles decrease sharply with both downward and upward small, medium and large shifts in the parameters. The phenomenon indicates that the chart is effective in detecting shifts in the parameters. However, the speed of detection of shifts varies depending on the type of shift, the parameters and the percentile being considered. The proposed chart is found to be effective in comparison to the Shewhart-type chart and bootstrap-based unit gamma chart.

It is worthwhile to mention that the beta control charts proposed in the literature use average of proportion to set up the control limits. However, in practice, specific percentile of proportion of conforming or non-conforming items should be more useful as the quality parameter of interest than average. To the best of our knowledge, no research addresses beta control chart for percentiles of proportion in the literature. Moreover, the proposed control chart assumes in-control parameters to be unknown, and hence captures additional variability introduced into the monitoring scheme through parameter estimation. In this sense, the proposed chart is original and unique.

We examine the relationship between the business cycle and poverty for the period from 1960 to 2008 using income data from the Current Population Survey and consumption data from the Consumer Expenditure Survey. This new evidence on the relationship between macroeconomic conditions and poverty is of particular interest, given recent changes in antipoverty policies that have placed greater emphasis on participation in the labor market and in-kind transfers. We look beyond official poverty, examining alternative income poverty and consumption poverty, which have conceptual and empirical advantages as measures of the well-being of the poor. We find that both income and consumption poverty are sensitive to macroeconomic conditions. A 1 percentage point increase in unemployment is associated with an increase in the after-tax income poverty rate of 0.9–1.1 percentage points in the long run, and an increase in the consumption poverty rate of 0.3–1.2 percentage points in the long run. The evidence on whether income is more responsive to the business cycle than consumption is mixed. Income poverty does appear to be more responsive using national level variation, but consumption poverty is often more responsive to unemployment when using regional variation. Low percentiles of both income and consumption are sensitive to macroeconomic conditions, and in most cases, low percentiles of income appear to be more responsive than low percentiles of consumption.

This study aims to analyse the consumption inequality between farm and non-farm households in rural Vietnam, using the data from the 2016 Vietnam household living standards survey.

The present paper applies the “recentered influence functions (RIF)” in “Oaxaca-Blinder (OB)” type decomposition as proposed by Firpo et al. (2018) to allow for the flexible distribution of the outcome variables and the non-randomness of non-farm employment that violates the classical linearity assumption.

Non-farm households have significantly higher per capita consumption expenditure than farm households for the entire distribution. The gap in expenditure is large at low percentiles and narrowing with higher percentiles. At 10th percentile, the gap is estimated at 27.1%, but it is decreasing to 11.1% at 90th percentile. Most of the gaps are explained by the differences in the observed characteristics between farm and non-farm households such as ethnicity, education, income, internal transmittances and household composition. Non-farm households are endowed with more productive factors that result in higher per capita consumption expenditure.

Gaps in ethnicity and education are found to be key predictors of the inequality in consumption expenditures between farm and non-farm households, then, government policies that are aimed at increasing access to non-farm employment and education for ethnic minorities and for rural poor households are pathways to improve rural household welfare and hence reduce inequality.

The study aims to provide a quick-and-robust multifactorial screening technique for early detection of statistically significant effects that could influence a product's life-time performance.

The proposed method takes advantage of saturated fractional factorial designs for organizing the lifetime dataset collection process. Small censored lifetime data are fitted to the Kaplan–Meier model. Low-percentile lifetime behavior that is derived from the fitted model is used to screen for strong effects. A robust surrogate profiler is employed to furnish the predictions.

The methodology is tested on a difficult published case study that involves the eleven-factor screening of an industrial-grade thermostat. The tested thermostat units are use-rate accelerated to expedite the information collection process. The solution that is provided by this new method suggests as many as two active effects at the first decile of the data which improves over a solution provided from more classical methods.

To benchmark the predicted solution with other competing approaches, the results showcase the critical first decile part of the dataset. Moreover, prediction capability is demonstrated for the use-rate acceleration condition.

The technique might be applicable to projects where the early reliability improvement is studied for complex industrial products.

The proposed methodology offers a range of features that aim to make the product reliability profiling process faster and more robust while managing to be less susceptible to assumptions often encountered in classical multi-parameter treatments.

We analyze the evolution of the gender wage gap in Mexico between 1989 and 2012, a period in which skill-biased technological change accelerated. We deviate from most prior work investigating the gap across the wage distribution. We find substantial gender wage convergence in the decade of the 2000s at the mean and, more markedly, at the upper and lower ends of the wage distribution, alongside little change in the median wage gap. The gender wage gap at the 90th percentile was largely eliminated by the year 2012 and, at the 10th percentile, it narrowed by a fourth of its 1990 level. This narrowing of gender inequality in wages occurred alongside a narrowing of inequality in wages within each gender group. The share of college-educated women relative to men in the work force grew substantially over the two decades, and they sorted disproportionately into brain-intensive occupations, where the gender wage gap fell sharply. The wage return to being in a brain-intensive occupation was, in both periods, greater for women; it declined for men while rising for women during the 2000s. Our findings demonstrate how structural economic change may interact with a biologically premised comparative advantage of women in brain-intensive occupations to raise their relative wages. Our results also underline the relevance of studying changes across the wage distribution.

This paper aims to empirically investigate the market-to-book/return on equity valuation model.

The authors use a worldwide commodities sector panel of 6,323 firms from 69 countries with annual observations from 1999 to 2010 to estimate panel ordinary least squares (OLS), instrumental variables (IV) and quantile regressions. They also measure the impact of return on equity on market-to-book uncovering value versus growth and positive versus negative profitability dimensions.

The new evidence is that the impact of return on equity on market-to-book is time-varying and declining across the years in the sample. There is positive and strong persistence in the market-to-book of companies in this sector worldwide, but value stocks are more persistent than growth stocks. The coefficient of return on equity is positive at the 10th percentile of the market-to-book, but it becomes negative for growth stocks at 90th percentiles. Conditional on negative profitability, the coefficient of return on equity on market-to-book is negative for growth stocks. The effect of the S&P500 volatility index (VIX) is negative, significant and large in magnitude, but declines in absolute value, as the quantiles increase toward the upper 90th percentile.

The commodities sector is important for countries that depend on it for development.

The paper provides a rich panel data approach, and the market-to-book/return on equity valuation model is naturally applied to the commodities sector, as this sector tends to have more tangibles relative to intangibles.

Competitions in high-performance sports are based on the premise of fairness and the absence of advantages for participants. Therefore, several metrics were created to divide participants into categories and neutralize the advantages of some athletes. The division by sex is justified in the face of differences in body development between men and women due to testosterone levels. Faced with the difficulties of determining a single unequivocal criterion for determining sex, major sports entities periodically change the rules that guide the inclusion criteria to compete in the female category. The purpose of this study is to assess whether changes in gender metric rules bring female sports performance closer to male performance, reducing the equality of conditions for female competitors.

This is a retrospective study that compared female and male results from the past 5 Olympic games in the 100-metre dash, high jump and javelin throw.

The performance in the men's category in the 100-metre dash was superior to the women's in the Olympic Games in Sydney by 8.78% (± 0.16), in Athens by 9.88% (± 0.21), in Beijing by 10.11% (± 0.29), in London by 9.25% (± 0.59) and in Rio de Janeiro by 8.6% (± 0.23). The male high jump was higher than the female by 17.24% (± 1.18) in Sydney, 16.61% (± 1.83) in Athens, 17.01% (± 1.79) in Beijing, 15.47% (± 1.96 higher) in London and 19.67% (± 1.09) in Rio de Janeiro. In the javelin throw, the male superiority was 34.87% (± 2.35) in Sydney, 29.88% (± 4.15) in Athens, 31.87% (± 4.15) in Beijing, 29.44% (± 3.24) in London and 32.31% (± 2.69) in Rio de Janeiro.

As limitations of the study, this study mentions the lack of hormonal dosage of the athletes involved, the non-evaluation of other modalities with different dynamics of execution and muscular requirements and the non-evaluation of the rules in sexual metrics during the qualifying stages for the games.

In this manuscript, the authors show that interference of the International Olympic Committee in the sex metric influences the athletic performance of women in some sports. Rules that facilitate participation of transgender athletes, or with sexual differentiation disorder and other forms of hyperandrogenism, improve female athletic performance overall.

The authors believe that this manuscript is appropriate for publication by International Journal of Human Rights in Healthcare because it is an original paper that fits the goals of the publication. This manuscript creates a paradigm for future policies that rule the sex determination metrics in competitive sports.

Waist circumference (WC) is a better predictor than total body fat for cardiovascular disease (CVD) risk factors and a sensitive marker of abdominal obesity for both adults and children. The purpose of this paper is to provide, for the first time, percentile curves of WC in children from Thessaloniki, northern Greece.

In total, 607 children (324 boys‐283 girls) aged 7‐15 years participated in the study. Sex‐specific descriptive statistics for three age groups (7‐9, 10‐12 and 13‐15) and smoothed percentiles curves of WC were derived and presented.

WC increased with age in both boys and girls. Boys had higher mean values of WC in two age groups (10‐12 and 13‐15 years) compared with girls, while girls showed a higher mean value in the age group of 7‐9 years compared with boys. Percentile levels were higher for girls above the 90th percentile for the age groups of 7‐9 and 10‐12 years. WC correlated closely with body mass index (BMI) (r = 0.90 vs 0.89, for boys and girls, respectively). Reference curves of WC were provided for the first time. Considering the lack of such reference curves and also the high incidence of pediatric obesity in northern Greece's children, the paper aims to help identify and prevent early obesity risk factors associated with high WC values.

The paper informs nutritionists and clinical dietitians of new data of percentile ranges regarding waist circumference in a pediatric population.

The purpose of this paper is to design a modified chain sampling plan for assuring the product percentile life where the lifetime follows Weibull or generalized exponential distributions (GEDs). In order to reduce the cost of inspection when implementing the proposed modified chain sampling plan, it is also considered the economic aspect of designing of proposed plan in this paper.

The authors have designed the proposed plan on the basis of two points on the operating characteristic (OC) curve approach. The optimization problem is used to determine the plan parameters of the proposed plan so that the specified values of producer’s risk and consumer’s risk are satisfied simultaneously.

The results we have obtained, confirm that the proposed plan will be very effective in reducing the sample size rather than other existing sampling plans. The OC curves of proposed plan, chain sampling plan and zero acceptance number single sampling plan show that the performance of proposed plan in discriminating the good and poor quality lots is better than other two plans. In this paper, it is proved that the value of number of preceding lots required for current lot disposition plays an important role.

The proposed modified chain sampling plan for assuring the percentile lifetime of the products under Weibull or GEDs is not available in the literature. The proposed plan can be used in all the manufacturing industries to assure the product percentile lifetime with minimum sample size as well as minimum cost.