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A major portion of migration flows are temporary, ending with return migration to the home country or migration to a third country. However, the propensity to return differs according to migrants' countries of origin. This paper presents a discussion of the theoretical approaches to return migration and a brief survey of the actual flows in Denmark. We then present a panel data set of immigrants to Denmark covering the years 1986 -1995 and run a number of logit models where return migration is related to individual background factors. Our research shows significant differences in decisions of return depending on country of origin, age at entry, education, and family ties. One of the most important determinants is found to be a migrant's success, or lack thereof, regarding labour market integration. This research suggests that policy instruments such as education, training, and temporary wage subsidies could play an important role in controlling the interaction between immigration and labour market forces. 88Jensen and Pedersen

This paper compares income inequality and income mobility in the Scandinavian countries and the United States during the 1980's. The results demonstrate that inequality is greater in the United States than in the Scandinavian countries and that the ranking of countries with respect to inequality remains unchanged when the accounting period of income is extended from one to 11 years. The pattern of mobility turns out to be remarkably similar despite major differences in labor market and social policies between the Scandinavian countries and the United States.Keywords: Income inequality, income mobility. JEL classification: D31Acknowledgement The authors would like to thank the discussant Stephen P Jenkins for helpful comments. We also thank Stephen E. Rhody and seminar pa rticipants at the University of Michigan and the University of Western Ontario.

The standard chronic wound assessment method based on visual examination is potentially inaccurate and also represents a significant clinical workload. Hence, computer-based systems providing quantitative wound assessment may be valuable for accurately monitoring wound healing status, with the wound area the best suited for automated analysis. Here, we present a novel approach, using support vector machines (SVM) to determine the wound boundaries on foot ulcer images captured with an image capture box, which provides controlled lighting and range. After superpixel segmentation, a cascaded two-stage classifier operates as follows: in the first stage, a set of k binary SVM classifiers are trained and applied to different subsets of the entire training images dataset, and incorrectly classified instances are collected. In the second stage, another binary SVM classifier is trained on the incorrectly classified set. We extracted various color and texture descriptors from superpixels that are used as input for each stage in the classifier training. Specifically, color and bag-of-word representations of local dense scale invariant feature transformation features are descriptors for ruling out irrelevant regions, and color and wavelet-based features are descriptors for distinguishing healthy tissue from wound regions. Finally, the detected wound boundary is refined by applying the conditional random field method. We have implemented the wound classification on a Nexus 5 smartphone platform, except for training which was done offline. Results are compared with other classifiers and show that our approach provides high global performance rates (average sensitivity = 73.3%, specificity = 94.6%) and is sufficiently efficient for a smartphone-based image analysis.

Diabetic foot ulcers represent a significant health issue. Currently, clinicians and nurses mainly base their wound assessment on visual examination of wound size and healing status, while the patients themselves seldom have an opportunity to play an active role. Hence, a more quantitative and cost-effective examination method that enables the patients and their caregivers to take a more active role in daily wound care potentially can accelerate wound healing, save travel cost and reduce healthcare expenses. Considering the prevalence of smartphones with a high-resolution digital camera, assessing wounds by analyzing images of chronic foot ulcers is an attractive option. In this paper, we propose a novel wound image analysis system implemented solely on the Android smartphone. The wound image is captured by the camera on the smartphone with the assistance of an image capture box. After that, the smartphone performs wound segmentation by applying the accelerated mean-shift algorithm. Specifically, the outline of the foot is determined based on skin color, and the wound boundary is found using a simple connected region detection method. Within the wound boundary, the healing status is next assessed based on red-yellow-black color evaluation model. Moreover, the healing status is quantitatively assessed, based on trend analysis of time records for a given patient. Experimental results on wound images collected in UMASS-Memorial Health Center Wound Clinic (Worcester, MA) following an Institutional Review Board approved protocol show that our system can be efficiently used to analyze the wound healing status with promising accuracy.