The main objective of our analysis is to investigate the causes of shifts of the Beveridge curve in Great Britain, the Netherlands and Sweden. In chapter 2., we will outline the model which is the basis for our analysis. The cornerstone of our theoretical framework regarding the long-run relationship between unemployment and vacancies otherwise known as the Beveridge curve is the matching process. We will describe how certain features such as structural mismatch, the relative attractiveness of benefit provisions and changes in search intensity and search effectiveness of the unemployed, could theoretically affect the Beveridge curve. In order to analyse a possible shift of the Beveridge curve, time series analysis will be used. In chapter 3., we describe the patterns of the relevant data series. Also, we describe the significance of the long-term unemployment problem in Great Britain and the Netherlands. In the following chapter we describe the characteristics of the disability arrangements in the three relevant countries. We do this in order to explain how the disability arrangements have affected the unemployment patterns in one of our sample countries. The focal point is the existence of a hidden unemployment component in the disability stock. In order to estimate the Beveridge curve for each country, we will use the instrumental variables technique. In chapter 5., after first having tested for the suitability of our econometric practice regarding the data series in the context of the theory of cointegration, we will present and discuss several model specifications regarding the Beveridge curve. We will also test for the sensitivy of our main results to variations in data and estimation method. Also, we present models of the British and Dutch long-term unemployment patterns. In chapter 6., we will discuss the most relevant results and compare the British, Dutch and Swedish labour market experiences. Conclusions are presented in the final chapter.

Thesis (M.S.)--University of Delaware, 2008. / Principal faculty advisor: Thomas Ilvento, Dept. of Food & Resource Economics. Includes bibliographical references.

The purpose of this thesis is to examine if there is a difference between Swedish urban and rural regions in terms of job-matching efficiency. The thesis employs the Beveridge curve with unemployment rate as the dependent variable as a framework and a longitudinal dataset covering 60 regions and the period 1998-2015. Two aspects of the job-matching efficiency are considered; the determinants of unemployment and the temporal changes in the job-matching efficiency. Considering the determinants of unemployment, some differences between urban and rural regions are detected. The results indicate that the mean age of a region’s population is negatively related to the unemployment rate while the share of women in the labor force is positively related in both types of regions. According to the Beveridge curve, this implies that the job-matching efficiency increases with a higher mean age while a higher share of women in the labor force decreases the matching efficiency. However, both variables are significantly stronger related to the unemployment rate in urban regions. Education is found to be positively associated with unemployment rate in urban regions while insignificant in rural ones. Lastly, no major difference between the two types of regions regarding the changes or position of the Beveridge curve are found. This implies that the job-matching efficiency is similar and change simultaneously in both urban and rural regions.

Thesis (Ph. D.)--University of California, San Diego, 2004. / Vita. Includes bibliographical references (leaves 121-125).

This paper investigates the relationship between vacant job positions and unemployed workers, illustrated by the Beveridge curve, a tool for observing the matching process and the condition of a labour market. The Swedish case is studied together with its three largest labour market regions, i.e., Stockholm-, Västra Götaland- and Skåne county. A comparison opens up a discussion of whether local labour markets with similar characteristics located in different parts of the country behave similarly or in what way they distinguish. Furthermore, these three regions are expected to influence the Swedish Beveridge curve to a larger extent, which is also examined in the paper. In addition, the effect of an exogenous shock, such as the building of the Öresund Bridge, expanding the labour market of Skåne county by connection to another metropolitan area, Copenhagen, is studied. This is done by comparing the matching efficiency before and after the bridge is opened. Moreover, the effect in Skåne is then analysed in accordance with the same period for the other regions included, to get an indication of whether the bridge alone provides a change in matching efficiency or if changes are connected to national events that influence all regions similarly.  The analysis is based on monthly data from year 1996-2020, collected from the Swedish Public Employment service and Statistics Sweden, primarily. Graphical illustrations of the Beveridge curve in combination with OLS regressions provide concluding results that the Beveridge curves for the three regional labour markets observed are shaped rather similarly and experience shifts and movements during the same time points, generally. Skåne county is the exception and experience more horizontal and vertical movements compared to Stockholm- and Västra Götaland county and the Swedish average. Furthermore, there are statistically significant estimates ensuring the negative relationship between unemployment- and vacancy rate, i.e., a downward sloping Beveridge curve for all regions. Not enough evidence on the effect of the Öresund Bridge on the matching efficiency of Skåne county is provided to present a valid conclusion regarding this topic.

Denna studie undersöker användningen av maskininlärningsmodeller för att predicera arbetskraftstrender inom hälso- och sjukvården i Sverige. Med hjälp av en linjär regressionmodell, en Gradient Boosting Regressor-modell och en Exponential Smoothing-modell syftar forskningen för detta arbete till att ge viktiga insikter för underlaget till makroekonomiska överväganden och att ge en djupare förståelse av Beveridge-kurvan i ett sammanhang relaterat till hälso- och sjukvårdssektorn. Trots vissa utmaningar med datan är målet att förbättra noggrannheten och effektiviteten i beslutsfattandet rörande arbetsmarknaden. Resultaten av denna studie visar maskininlärningspotentialen i predicering i ett ekonomiskt sammanhang, även om inneboende begränsningar och etiska överväganden beaktas. / This study examines the use of machine learning models to predict workforce trends in the healthcare sector in Sweden. Using a Linear Regression model, a Gradient Boosting Regressor model, and an Exponential Smoothing model the research aims to grant needed insight for the basis of macroeconomic considerations and to give a deeper understanding of the Beveridge Curve in the healthcare sector’s context. Despite some challenges with data, the goal is to improve the accuracy and efficiency of the policy-making around the labor market. The results of this study demonstrates the machine learning potential in the forecasting within an economic context, although inherent limitations and ethical considerations are considered.