An editorial on plagiarism

In January this year, we were confronted with a case of plagiarism. One paper that had been submitted last year by a certain person turned out to be written by three other persons. It was presented by the three true authors at a conference in 2010, where they distributed copies of their paper. One of the reviewers of the paper informed us about that fact. We asked the three authors for a copy, which turned out to be identical with the submission, except for a few minor details. When confronted with these facts, the person who had submitted the paper was unable to give us a satisfactory explanation. This is a case of serious scientific misconduct. The editors and the publisher of Demographic Research cannot and will not accept any form of plagiarism. Nor will we accept any other form of misconduct in science, including fabrication, falsification, or other practices that seriously deviate from those that are commonly accepted within the scientific community for proposing, conducting, or reporting research. With Long et al. ("Responding to possible plagiarism", Science 6 March 2009), we are of the opinion that the responsibility for research integrity ultimately lies in the hands of the scientific community: educators, students, authors, and those who provide peer reviews. Journal editors must take appropriate action and verify the originality of suspected manuscripts. The Office of Research Integrity provides useful guidelines (http://ori.dhhs.gov/). We have decided that any future submission to Demographic Research that lists the plagiarist as an author or co-author will be rejected automatically.

TFR Predictions Based on Brownian Motion Theory

In stochastic population forecasts, the predictive distribution of the TFR is of centralconcern. Common time series models can be used to predict the TFR and itsmoments on the short run (up to 10 or 20 years), but on the long run (40-50 years)they result in excessively wide prediction intervals. The aim of this study is toanalyse and apply a time series model for the TFR, which restricts the predictedvalues to a certain pre-specified interval.I will model the time series of log TFR-values as a Brownian motion with absorbingupper barrier. I will give and analyse expressions for the predictive distribution of the log of the TFR assuming itfollows a Brownian motion with absorbing ceiling; expressions for the first and second moments of the predictive distribution ofthe log of the TFR.When the log of the TFR follows a random walk with absorbing ceiling, I find thatthe second moment of the predictive distribution for the long-run TFR in Norwayis insensitive for ceiling levels beyond a threshold of approximately 3.4 childrenper woman. This conclusion holds for a fairly broad range of innovation variances.If the log of the TFR follows a random walk, sample paths that exceed approximately3.4 children per woman may be rejected when simulating future fertility in Westerncountries. This will not have any major effect on the width of the long-termpredictive distribution

The Effects of Changing Marital Status Patterns on Social Security Expenditures in the Netherlands, 1985-2050

Projections of expenditures for old-age pensions, survivor pensions, and disability pensions were made for the period 1985-2050 on the basis of future developments in the population structure by age, sex, and marital status. Five demographic scenarios were formulated: (i) a Benchmark scenario, with demographic rates kept constant at their 1980-84 level; (ii) a Fertility scenario, with a rise of the Total Fertility Rate (TFR) towards replacement level; (iii) a Mortality scenario, with reductions in mortality rates of 30 percent for females, and 45 percent for males; (iv) a Western scenario, which combines extreme demographic conditions of several West European countries: a TFR of 1.28, proportions never-marrying of one-third, one-third of all marriages ending in divorce, and male and female life expectancies of 74 and 81 years, respectively; and a Realistic scenario, which is the only one to include international migration, and which corresponds closely to the official population forecasts for the Netherlands. Two pension scenarios and two labor market scenarios were combined with the demographic scenarios. The current pension system, with its flat benefit rate, was combined with all five demographic scenarios. Also, the consequences of the system which was in use in the Federal Republic of Germany in 1985 were traced. Finally, the impact of high female labor force participation, and a rise in the average age at retirement were investigated. The results indicate that changes in demographic conditions (e.g. a fertility rise, or a persistent influx of immigrants) cannot prevent increases in and funding problems for pension expenditures in the Netherlands. Linking pension benefits to the labor market history of the individuals concerned brings no relief either. However, raising the average age at retirement would, to a large extent, avoid funding problems for old-age pensions for a great deal, with a large amount of overfunding in the short run

Why population forecasts should be probabilistic - illustrated by the case of Norway

Deterministic population forecasts do not give an appropriate indication of forecast uncertainty. Forecasts should be probabilistic, rather than deterministic, so that their expected accuracy can be assessed. We review three main methods to compute probabilistic forecasts, namely time series extrapolation, analysis of historical forecast errors, and expert judgement. We illustrate, by the case of Norway up to 2050, how elements of these three methods can be combined when computing prediction intervals for a population’s future size and age-sex composition. We show the relative importance for prediction intervals of various sources of variance, and compare our results with those of the official population forecast computed by Statistics Norway.cohort component method, forecast errors, forecasting, simulation, stochastic population forecast, time series, uncertainty

Drones on the Rise: Societal Misperceptions of Small Unmanned Aircraft Systems (sUAS)

Throughout the past decade, Small Unmanned Aircraft Systems (sUAS) have been on the rise in both the civilian and military sectors. It is forecasted that in the near future they will create thousands of jobs and billions in tax revenue due to their ability to execute difficult and hazardous tasks safely, efficiently, and cost effectively. However, one current issue with the proliferation of the technology is a shortage of skilled employees due to a lack of education and common negative public misperceptions associated with them. To investigate this, responses from a mixed methods survey will be analyzed. Within the survey, questions such as the participants age, education level, current knowledge of sUAS, and their interest in learning more about the technology were asked. The new knowledge we hope to create is a clearer understanding about the challenges and barriers regarding public perceptions on sUAS. The examination of data may reveal how stakeholders can better communicate to the public in hopes of building a skilled and educated work force. One approach to changing misperceptions about drones is through formal and informal educational initiatives, which can engage the public. The research will propose opportunities for higher education to play a role in educating the public through (1) aviation focused after-school programs, (2) transdisciplinary/interdisciplinary courses and programs incorporating aviation, (3) the establishment of aviation minors and aviation university-level electives, (4) the development of informal aviation programs working with museums, and (5) facilitating summer aviation camps for high school students, to name a few