Sexual orientation and full-time monthly earnings, by public and private sector: evidence from Swedish register data

In this paper we explore annual earnings as well as full-time monthly earnings differentials resulting from sexual orientation. We observe that gay males are at an earnings disadvantage compared to male heterosexuals regardless of which earnings measure we use. This earnings disadvantage is found to be larger when we compare gay and heterosexual males who are working full-time. In addition, the disadvantage is larger in the private than in the public sector. Lesbians, however, earn more than heterosexual females. This earnings advantage is considerably smaller when we study full-time monthly rather than annual earnings but an earnings advantage for lesbians at the top of the earnings distribution is documented regardless of which earnings measure we use. In addition, lesbians are doing better than female heterosexuals in the public sector. To sum up, the results indicate that gay males face obstacles on the labor market that hinder them from reaching top-level positions and high earnings. The earnings advantage observed for lesbians is likely to stem from the fact that lesbians devote more time to market work than heterosexual females do.     

Refugees and Elections: The Effects of Syrians on Voting Behavior in Turkey

In the wake of the Syrian civil war, more than three million people have fled to Turkey, which now hosts the largest refugee population on earth. Making up 4.42 per cent of the total population in Turkey as of February 2018, Syrian refugees are nevertheless spread unevenly within its borders. The ratio of refugees to the local population ranges from as high as 99per cent in the city of Kilis to as low as 0.05per cent in Sinop. This article presents findings from the empirical study of the effect of this geographical variation on election outcomes in Turkey, after the arrival of major refugee populations in 2012. Drawing on a unique subnational dataset and ordinary least squares (OLS), generalized least squares (GLS), and difference‐in‐differences (DiD) regressions, the study compares cities hosting few refugees (control group) with cities with large refugee populations (treatment group) to determine whether significant differences in voting patterns emerged. Our findings show a negative, but insignificant, impact on the incumbent party. The findings have policy implications for Turkey as well as any country that experiences a considerable flow of refugees.     

A population sample registry for public health survey investigations: methods,lessons, and successes

Public health relies on sample surveys to monitor the health of populations and investigate factors that contribute to population health and illness. Such investigations require surveying individuals who are selected in ways that support statistical inference to the population. Faced with rising costs and declining participation, survey researchers are exploring economical ways to recruit samples that validly represent larger populations. The current report describes an innovative resource, the Colorado Adult Population Sample–Survey Research Registry, which maintains survey access to respondents from a probability-based state health survey. We describe recruitment and retention strategies, explore representativeness, report several studies that used the registry, and discuss future directions.     

IS ECONOMICS A HOUSE DIVIDED? ANALYSIS OF CITATION NETWORKS

We investigate divisions within the citation network in economics using citation data between 1990 and 2010. We consider all partitions of top institutions into two equal‐sized clusters and pick the one that minimizes cross‐cluster citations. The strongest division is much stronger than could be expected to be found under idiosyncratic citation patterns and is consistent with the reputed freshwater/saltwater division in macroeconomics. The division is stable over time but varies across the fields of economics. (JEL A11, D85, I23)     

A penalized likelihood method for nonseparable space–time generalized additive models

In this paper, we study space–time generalized additive models. We apply the penalyzed likelihood method to fit generalized additive models (GAMs) for nonseparable spatio-temporal correlated data in order to improve the estimation of the response and smooth terms of GAMs. The results show that our space–time generalized additive models estimated response and smooth terms reasonable well, and in addition, the mean squared error, mean absolute deviation and coverage intervals improved considerably compared to the classic GAM. An application on particulate matter concentration in the North-Italian region of Piemonte is also presented.     

Semi-doubly optimal concentric circles fitting with presence of heteroscedasticity

Previous studies focus on homogeneous and isotropic assumptions about the noisy data. Many methods have been developed recently for fitting concentric circles to data. In this paper, these statistical assumptions have been relaxed. To the best of our knowledge, only one iterative method has been recently developed. Due to its complexity, no such algorithm is available to compute the reliable maximum likelihood estimator (MLE). Accordingly, we have developed four new methods that outperform the existing methods including the orthogonal distance regression (ODR). We also discuss which of these methods is superior according to the four principles: statistical efficiency, accuracy, robustness, and computational efficiency. Numerical experiments on synthetic and real images have been conducted to validate our findings.     

Inferences on the ratio of two generalized variances: independent and correlated cases

Statistical inferences about the dispersion of multivariate population are determined by generalized variance. In this article, we consider constructing a confidence interval and testing the hypotheses about the ratio of two independent generalized variances, and the ratio of two dependent generalized variances in two multivariate normal populations. In the case of independence, we first propose a computational approach and then obtain an approximate approach. In the case of dependence, we give an approach using the concepts of generalized confidence interval and generalized p value. In each case, simulation studies are performed for comparing the methods and we find satisfactory results. Practical examples are given for each approach.     

Normalité asymptotique d'estimateurs convergents du mode conditionnel

We state sufficient conditions for asymptotic normality of convergent estimates of the conditional mode, irrespective of data dependence, and give an application to α-mixing stationary processes.     

FDA: strong consistency of the kNN local linear estimation of the functional conditional density and mode

In this paper we present a new estimator of the conditional density and mode when the co-variables are of functional kind. This estimator is a combination of both, the k-Nearest Neighbours procedure and the functional local linear estimation. Then, for each statistical parameter (conditional density or mode), results concerning the strong consistency and rate of convergence of the estimators are presented. Finally, their performances, for finite sample sizes, are illustrated by using simulated data.