This study examines whether or not Sweden’s education reform in 1977, which established new university colleges in selected urban municipalities, had a spillover effect on people’s migration decisions toward rural municipalities and, if so, how that effect varied across demographic groups. To test this, a difference-in-differences method with a fixed effect setting is used in combination with Coarsened Exact Matching to create the best-matched control group. The results suggest that the study cannot draw a general conclusion that the education reform impacted people’s migration decisions toward rural regions in Sweden. Nonetheless, the results demonstrate the significance of geographic distance and individual characteristics in explaining migration decisions in these regions. Following the reform, fewer individuals between 15 and 24 years old are moving into rural municipalities with the effects being small but statistically significant. Furthermore, fewer individuals between 15 and 24 years old as well as 55 and 64 years old leave rural municipalities, but the effects are statistically weak to explain the true impact on migration. Finally, suggestions for future research on the subject are presented at the end.

In this thesis we develop methods for dealing with missing data in a univariate response variable when estimating regression parameters. Missing outcome data is a problem in a number of applications, one of which is follow-up studies. In follow-up studies data is collected at two (or more) occasions, and it is common that only some of the initial participants return at the second occasion. This is the case in Paper II, where we investigate predictors of decline in self reported health in older populations in Sweden, the Netherlands and Italy. In that study, around 50% of the study participants drop out. It is common that researchers rely on the assumption that the missingness is independent of the outcome given some observed covariates. This assumption is called data missing at random (MAR) or ignorable missingness mechanism. However, MAR cannot be tested from the data, and if it does not hold, the estimators based on this assumption are biased. In the study of Paper II, we suspect that some of the individuals drop out due to bad health. If this is the case the data is not MAR. One alternative to MAR, which we pursue, is to incorporate the uncertainty due to missing data into interval estimates instead of point estimates and uncertainty intervals instead of confidence intervals. An uncertainty interval is the analog of a confidence interval but wider due to a relaxation of assumptions on the missing data. These intervals can be used to visualize the consequences deviations from MAR have on the conclusions of the study. That is, they can be used to perform a sensitivity analysis of MAR. The thesis covers different types of linear regression. In Paper I and III we have a continuous outcome, in Paper II a binary outcome, and in Paper IV we allow for mixed effects with a continuous outcome. In Paper III we estimate the effect of a treatment, which can be seen as an example of missing outcome data.

With increasing external and internal pressure to increase retention and graduation rates in select colleges along with increasing numbers of college-going populations over time, student affairs professionals have responded with a variety of programs to support students' transition to college. This study sought to examine freshman students in science majors went on academic probation at the end of their first semester. If these students did not raise their GPAs quickly, they faced academic dismissal from the institution. Consequently, the institution would not be able to retain them, and ultimately, they would not graduate. Managerial professionals at the institution created, implemented, and evaluated an intervention in the form of a success course for these students to help get them back on track, retain them, and ultimately graduate from the institution. The literatures drawn upon for this study included retention theory, probationary student behaviors and attitudes, interventions, success courses, fear appeal theories, academic capitalism, and institutional isomorphism. The study employed tests including chi-square, logistic regressions, and differences-in-differences fixed effects regressions to identify the differences and effects on performance, persistence, and graduation rates of the treatment and comparison groups. The findings of this study showed significant differences between the persistence and graduation rates of the treatment and control groups, and regression effects showed a short-term causal effect on performance as well as significant likelihoods of persisting and graduating within four or five years. Recommendations for further improvements to interventions are discussed in the final chapter.

This diploma thesis analyzes the impact of education on unemployment of citizens of the Czech Republic. It utilizes data from the ISSP international survey between years 1994-2012. The main assumption of this work is that education reduces the likelihood of individual's unemployment. This hypothesis is confirmed by using the Probit method, in several models accompanied also by the instrumental variable approach. The instrumental variable method helps to identify the causal effect of education on unemployment. Results show that education has greater impact on women. Each additional year of schooling reduces the likelihood of a woman's unemployment by 1 percentage point and likelihood of a man's unemployment by 0.75 percentage points. In additional models, education is measured by highest reached level rather than years of schooling. Results confirm that the probability of unemployment is lower for people with higher level of education than for others.

The Principal Stratification method estimates a causal intervention effect by taking account of subjects' differences in participation, adherence or compliance. The current Principal Stratification method has been mostly used in randomized intervention trials with randomization at a single (individual) level with subjects who were randomly assigned to either intervention or control condition. However, randomized intervention trials have been conducted at group level instead of individual level in many scientific fields. This is so called "two-level randomization", where randomization is conducted at a group (second) level, above an individual level but outcome is often observed at individual level within each group. The incorrect inferences may result from the causal modeling if one only considers the compliance from individual level, but ignores it or be determine it from group level for a two-level randomized trial. The Principal Stratification method thus needs to be further developed to address this issue. To extend application of the Principal Stratification method, this research developed a new methodology for causal inferences in two-level intervention trials which principal stratification can be formed by both group level and individual level compliance. Built on the original Principal Stratification method, the new method incorporates a range of alternative methods to assess causal effects on a population when data on exposure at the group level are incomplete or limited, and are data at individual level. We use the Gatekeeper Training Trial, as a motivating example as well as for illustration. This study is focused on how to examine the intervention causal effect for schools that varied by level of adoption of the intervention program (Early-adopter vs. Later-adopter). In our case, the traditional Exclusion Restriction Assumption for Principal Stratification method is no longer hold. The results show that the intervention had a stronger impact on Later-Adopter group than Early-Adopter group for all participated schools. These impacts were larger for later trained schools than earlier trained schools. The study also shows that the intervention has a different impact on middle and high schools.

No description available.

The global pandemic Covid-19 caused an inevitable impact on economic growth and public health. Policymakers were forced to opt for the zero or the non-zero strategy to ease the economic effects and stop the spreading of infection. Previous literature on the matter strikingly agreed that the zero strategy was optimal. This paper’s primary purpose is to analyse the impact of zero and non-zero strategies on economic growth by comparing the members of the Organisation for Economic Co-operation and Development (OECD). The empirical methodology utilised in this paper constitutes the traditional Difference in Difference (DiD) design in a two-way fixed effects framework. The dataset contained 38 OECD member countries during the period 2015 to 2021. The countries were assigned to a treatment group and a control group based on the chosen strategy. The main results contradict previous literature and presented a significant and negative relationship between the zero strategy and gross domestic product per capita growth. In the case of future global pandemics, these findings can facilitate the choice of action aiming to mitigate the economic effects.

No description available.

This thesis contributes to the field of causal inference, where the main interest is to estimate the effect of a treatment on some outcome. At its core, causal inference is an exercise in controlling for imbalance (differences) in covariate distributions between the treated and the controls, as such imbalances otherwise can bias estimates of causal effects. Imbalance on observed covariates can be handled through matching, where treated and controls with similar covariate distributions are extracted from a data set and then used to estimate the effect of a treatment. The first paper of this thesis describes and investigates a matching design, where a data-driven algorithm is used to discretise a covariate before matching. The paper also gives sufficient conditions for if, and how, a covariate can be discretised without introducing bias. Balance is needed for unobserved covariates too, but is more difficult to achieve and verify. Unobserved covariates are sometimes replaced with correlated counterparts, usually referred to as proxy variables. However, just replacing an unobserved covariate with a correlated one does not guarantee an elimination of, or even reduction of, bias. In the second paper we formalise proxy variables in a causal inference framework and give sufficient conditions for when they lead to nonparametric identification of causal effects. The third and fourth papers both concern estimating the effect an enhanced cooperation between the Swedish Social Insurance Agency and the Public Employment Service has on reducing sick leave. The third paper is a study protocol, where the matching design used to estimate this effect is described. The matching was then also carried out in the study protocol, before the outcome for the treated was available, ensuring that the matching design was not influenced by any estimated causal effects. The third paper also presents a potential proxy variable for unobserved covariates, that is used as part of the matching. The fourth paper then carries out the analysis described in the third paper, and uses an instrumental variable approach to test for unobserved confounding not captured by the supposed proxy variable.

This thesis examines the output-variability relationship and causal relationships among the inflation, the output growth and their uncertainties for the Central and Eastern European region during the period of time that covers the economic crisis of 2008. We apply the bivariate GARCH(1,1) model with the constant conditional correlation covariance matrix to obtain conditional variances that proxy the two uncertainties and use Granger causality test to determine the causal effects among four variables. We come up with a number of interesting results. First, we did not find statistical evidence neither for the inflation-output variability relationship nor for the Phillips curve. Second, we uncovered support for the positive causal effect of the inflation on its uncertainty and negative causal effect for the reverse direction. Additionally, we also found some support for the indirect negative causal effect of the inflation on the output growth. These results support the policy of low and stable inflation in the countries. Finally, we showed that crisis has a significant impact on the results, changing the behaviour of conditional variances and causal effects among the variables. Powered by TCPDF (www.tcpdf.org)