There is a positive association between education and longevity. Individuals with a university degree tend to live longer than high school graduates who, in turn, live longer than those with compulsory education. These differences are neither larger nor smaller in Sweden than in other European countries, despite its ambitious welfare-state policies. Furthermore, educational differences in longevity are growing, especially among women. In this thesis I look at the structural, individual and behavioral processes which generate and maintain the educational gradient in mortality. This is done by compiling theoretical insights and empirical research from a range of scientific disciplines. In doing so, this thesis aims to contribute to a more comprehensive understanding of the educational gradient in mortality. Several factors contribute to the association between education and health. Social and biological processes initiated in early life influence both educational achievement and adult health. Education helps individuals become more effective as agents by fostering generic skills such as information-gathering and decision-making. This aspect of education, learned effectiveness, promotes control and health regardless of available resources and prevailing conditions. Education thus has a direct influence on health. Education also indirectly influences health by giving access to better occupational positions and higher incomes, as well as by promoting social capital and healthy habits. The empirical section of the thesis consists of four separate quantitative studies using register data. Three of the studies use Swedish national register data while one uses register data from 18 European populations. The results indicate that widening income inequalities in mortality have contributed to a widening of educational inequalities in mortality, since education is a determinant of income. Both alcohol and smoking contribute to educational inequalities in longevity, but smoking has played an especially pronounced role in the widening of inequalities among women. Smoking represents a significant part of the explanation as to why women with low education have experienced smaller gains in life expectancy than the rest of the population. The results also indicate that the general trend towards more well-educated populations has contributed to the widening educational inequalities in mortality in Europe and that education is a stronger predictor of mortality among low income-earners than among the rest of the population. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.</p>

This diploma thesis deals with registers of state administration in term of data quality. The main objective is to analyze the ways how to evaluate data quality and to apply appropriate method to data in business register. Analysis of possibilities of data cleansing and data quality improving and proposal of solution of found inaccuracy in business register is another objective. The last goal of this paper is to analyze approaches how to set identifier of persons and to choose suitable key for identification of persons in registers of state administration. The thesis is divided into several parts. The first one includes introduction into the sphere of registers of state administration. It closely analyzes several selected registers especially in terms of which data contain and how they are updated. Description of legislation changes, which will come into operation in the middle of year 2010, is great contribution of this part. Special attention is dedicated to the impact of these changes from data quality point of view. Next part deals with problems of legal and physical entities identifiers. This section contains possible solution how to identify entities in data from registers. Third part analyzes ways how to determine data quality. Method called data profiling is closely described and applied to extensive data quality analysis of business register. Correct metadata and information about incorrect data are the outputs of this analysis. The last chapter deals with possibilities how to solve data quality problems. There are proposed and compared three variations of solution. The paper as a whole represents compact material how to solve problems with effective using of data contained in registers of state administration. Nevertheless, proposed solutions and described approaches can be used in many other projects which deal with data quality.

The purpose of this master thesis was to investigate if using both X-ray images and patient's register data could increase the performance of a neural network in discrimination of two types of fractures in the thigh bone, called atypical femoral fractures (AFF) and normal femoral fractures (NFF). We also examined and evaluated how the fusion of the two data types could be done and how different types of fusion affect the performance. Finally, we evaluated how the number of variables in the register data affect a network's performance. Our image dataset consisted of 1,442 unique images from 580 patients (16.85% of the images were labelled AFF corresponding to 15.86% of the patients). Since the dataset is very imbalanced, sensitivity is a prioritized evaluation metric. The register data network was evaluated using five different versions of register data parameters: two (age and sex), seven (binary and non-binary) and 44 (binary and non-binary). Having only age and sex as input resulted in a classifier predicting all samples to class 0 (NFF), for all tested network architectures. Using a certain network structure (celled register data model 2), in combination with the seven non-binary parameters outperforms using both two and 44 (both binary and non-binary) parameters regarding mean AUC and sensitivity. Highest mean accuracy is obtained by using 44 non-binary parameters. The seven register data parameters have a known connection to AFF and includes age and sex. The network with X-ray images as input uses a transfer learning approach with a pre-trained ResNet50-base. This model performed better than all the register data models, regarding all considered evaluation metrics.        Three fusion architectures were implemented and evaluated: probability fusion (PF), feature fusion (FF) and learned feature fusion (LFF). PF concatenates the prediction provided from the two separate baseline models. The combined vector is fed into a shallow neural network, which are the only trainable part in this architecture. FF fuses a feature vector provided from the image baseline model, with the raw register data parameters. Prior to the concatenation both vectors were normalized and the fused vector is then fed into a shallow trainable network. The final architecture, LFF, does not have completely frozen baseline models but instead learns two separate feature vectors. These feature vectors are then concatenated and fed into a shallow neural network to obtain a final prediction. The three fusion architectures were evaluated twice: using seven non-binary register data parameters, or only age and sex. When evaluated patient-wise, all three fusion architectures using the seven non-binary parameters obtain higher mean AUC and sensitivity than the single modality baseline models. All fusion architectures with only age and sex as register data parameters results in higher mean sensitivity than the baseline models. Overall, probability fusion with the seven non-binary parameters results in the highest mean AUC and sensitivity, and learned feature fusion with the seven non-binary parameters results in the highest mean accuracy.

Unemployment at young age is a negative life event that has been found to have scarring effects for future life outcomes, especially when continuing long-term. Understanding precursors for long-term unemployment in early adulthood is important to be able to target policy interventions in critical junctures in the life course. Paths to unemployment are complex and a comprehensive outlook on the most important factors and mechanisms is difficult to obtain. This study proposes a data-driven, exploratory approach for studying individual and family level factors during ages 0-24, that predict long-term unemployment at the age of 25-30. A supervised machine learning approach was applied to understand associations deriving from longitudinal, individual-level administrative data from a full birth cohort in Finland. The data comprise information about physical and social wellbeing, life course events, as well as demographics, including the parents of the cohort members. Potential predictors were chosen from the data based on theories and previous research, and used to train a model aiming to correctly classify unemployed individuals. A CART algorithm was used to build a classification tree that reveals important variables, ranges of them as well as combinations of factors that together are predictive of long-term unemployment. A random forest algorithm was used to build several trees producing smoothed predictions that reduce overfitting of one tree. CARTs and random forest models were compared to each other to understand how they perform in a research task predicting life outcomes. Both individual and family level factors were found to be predictive of the outcome. Combinations of variables such as GPA lower than ~7.5, ego’s low education level, late work history start, depressive disorders and low parental education and income levels were found to be particularly predictive of unemployment. CART models correctly classified up to 87% of the unemployed, while misclassifying 70% of the employed and having 45% overall accuracy. Testing for CART model stability, finding consistency across several tree models improved robustness. Random forest correctly predicted up to 59% of the unemployed, while also correctly classifying 65% of the employed and producing robust results. The two algorithms together provided valuable insight for better understanding factors contributing to unemployment. The study shows promise for classification tree based methods in studying life course and life outcomes.

In the modern industrialized society, a long commuting time is becoming more and more common. However, commuting results in a number of different costs, for example, external costs such as congestion and pollution as well as internal costs such as individual time consumption. On the other hand, increased commuting opportunities offer welfare gains, for example via larger local labor markets. The length of the commute that is acceptable to the workers is determined by the workers' preferences and the compensation opportunities in the labor market. In this thesis the value of travel time or commuting time changes, has been empirically analyzed in four self-contained essays. First, a large set of register data on the Swedish labor market is used to analyze the commuting time changes that follow residential relocations and job relocations. The average commuting time is longer after relocation than before, regardless of the type of relocation. The commuting time change after relocation is found to differ substantially with socio-economic characteristics and these effects also depend on where the distribution of commuting time changes is evaluated. The same data set is used in the second essay to estimate the value of commuting time (VOCT). Here, VOCT is estimated as the trade-off between wage and commuting time, based on the effects wage and commuting time have on the probability of changing jobs. The estimated VOCT is found to be relatively large, in fact about 1.8 times the net wage rate. In the third essay, the VOCT is estimated on a different type of data, namely data from a stated preference survey. Spouses of two-earner households are asked to individually make trade-offs between commuting time and wage. The subjects are making choices both with regard to their own commuting time and wage only, as well as when both their own commuting time and wage and their spouse's commuting time and wage are simultaneously changed. The results show relatively high VOCT compared to other studies. Also, there is a tendency for both spouses to value the commuting time of the wife highest. Finally, the presence of hypothetical bias in a value of time experiment without scheduling constraints is tested. The results show a positive but not significant hypothetical bias. By taking preference certainty into account, positive hypothetical bias is found for the non-certain subjects.

Nationella och internationella kunskapsmätningar i matematik visar likartade resultat för flickor och pojkar. Trots det visar statistiken att flickor erhåller systematiskt högre slutbetyg. Denna studie undersöker huruvida betyg tjänar som likvärdigt mått på kunskap hos flickor och pojkar i gymnasiets matematikämne. Detta görs dels utifrån en kvantitativ ansats och dels utifrån en enkätstudie. Med utgångspunkt i nationell registerdata (SCB) för slutbetyg och resultat på nationella provet undersöks, medelst fyra olika analysmetoder, könsskillnader med avseende på kurs, skolform och län. Studien visar att flickor generellt erhåller högre slutbetyg än pojkar i relation till resultatet på nationella provet, vilket bekräftar tidigare forskning. Vidare påvisar analysen särskilt stora diskrepanser på betygsnivå C och högre; i matematikkurser på yrkesförberedande program; i senare kurser inom samtliga program; i Västernorrlands, Västmanlands, Gotlands och Kalmar län; liksom i fristående skolor. Korrelationsanalys tydliggör hur nationella provet utgör en mindre del av betygsunderlaget för flickor jämfört med pojkar. Dessutom avslöjar analysen ett omvänt samband mellan könsbetingad relativ prestation på nationella provet och avvikelse i slutbetyget. Enkätstudien undersöker bedömningspraktiken hos matematiklärare. Filtrering på lärarens kön, ålder, program och skolform, har tillämpats. Resultatet tyder på systematiska skillnader i bedömningspraktik mellan olika lärarkategorier, vilket innebär att betygssättningen kan brista i likvärdighet. Skillnader har påvisats mellan, i första hand, lärare på yrkesprogram och naturvetenskapliga program, såväl som mellan lärare i kommunala och fristående skolor. Också lärarens kön och ålder tycks ha viss betydelse. Studien avslutas med en diskussion kring möjliga lösningar. / National and international assessments in mathematics show similar results for girls and boys. Despite this, statistics show that girls receive systematically higher final grades. This study examines whether grades serve as an equivalent measure of knowledge of girls and boys in high school mathematics. This is done partly on the basis of a quantitative approach and partly on the basis of a survey. Based on national register data (Statistics Sweden) for final grades and results of national tests, using four different methods of analysis, gender differences with respect to course, school form and county, are examined. The study shows that girls generally get a higher final grade than boys in relation to their results on the national test, confirming previous research. Furthermore, the analysis shows particularly large discrepancies at grade C and higher; in mathematics courses on vocational programs; in later courses within all programs; in V¨asternorrland, V¨astmanland, Gotland and Kalmar County; as well as in independent schools. Correlation analysis clarifies how the national test constitutes a smaller part of the assessment basis for girls compared to boys. The analysis also reveals an inverse relationship between gender dependent relative performance on the national test and the final grade deviation. The survey examines the assessment practice among mathematics teachers. Filtering on the teacher’s gender, age, program and school form has been applied. The result suggests systematic differences in assessment practice between different teacher categories, implying that grades can break in equality. Differences have been shown between, primarily, teachers in vocational programs and science programs, as well as between teachers in municipal and independent schools. Also the teacher’s gender and age seems to be of some importance. The study concludes with a discussion about possible solutions.