This study reviews the competition and productivity of the U.S. freight transportation industry for the past 41 years. This study investigated the trends of HHI market concentration index values and labor productivity values of rail and truck sectors and tried to find any relationships between the two values in the separate periods before and after the abolishment of the ICC. This study also investigated how the existence of a regulatory body impacted productivity of the freight transportation industry by using a Cobb Douglas production function on annual financial statement data in the U.S. stock exchange market. This study found that: while the truck sector became more competitive after the abolishment of the ICC, the rail sector became less competitive, both sectors had a strong positive correlation between HHI and labor productivity, and the ICC’s abolishment resulted in positive changes of total factor productivity for the truck sector only.

Background: Despite the societal progress made in recent decades, a gender gap in entrepreneurship persists. In Europe, there are fewer female entrepreneurs than male entrepreneurs. This issue is significant as female entrepreneurship substantially impacts economic development and social well-being. In this paper, we will investigate how different socioeconomic and individual factors affect female total entrepreneurial activity in European countries. Through our research, we aim to provide recommendations to increase the number of female entrepreneurs, remove gender-based barriers, and foster societal progress and economic growth in Europe. Purpose: The purpose of this study is to investigate the impact of socioeconomic and individual factors on total entrepreneurial activity for women in European countries. We will specifically analyze the impacts of government support, cultural approval, insufficient access to finance, networking, training & education, and fear of failure on female entrepreneurship. Method: Using publicly available GEM data, we use a panel data study of 31 European countries and seven years, from 2013 to 2019, to investigate how socioeconomic and individual factors influence female total entrepreneurial activity. By utilizing the Hausman test and LM test, we statistically determine that the Random Effects Model (REM) is the most appropriate model to predict our data. Conclusion: The results of the REM show that both Networking and Training & Education have significant positive effects on female total entrepreneurial activity in European countries. We recommend implementing entrepreneurship education in middle or high school curricula to expose students to entrepreneurship experiences and encourage young women to pursue entrepreneurship as a career. Additionally, we recommend tailoring government policies and programs specifically to women to support female entrepreneurial activities.

Establishing causal relationships is arguably the most important task of the social sciences. While the relationship between the social sciences and the concept of causality has been rocky, the randomized experiment gives us a concrete definition of a causal effect as the difference in outcomes due to the researcher's intervention. However, many interesting questions cannot be easily examined using experiments. Feasibility and ethics limit the use of randomized experiments in some situations and retrospective questions, i.e., working from the observed outcome to uncover the cause, require a different logic. Observational studies in which we observe pairs of variables without any intervention lend themselves to such situations but come with many difficulties. That is, it is not immediately clear whether an observed relationship between two variables is due to a true causal effect, or whether the relationship is due to other common causes. Panel data describe repeated observations of the same units over time. They offer a powerful framework for approaching causal questions with observational data. Panel analysis allows us to essentially use each unit as their own control. In an experiment, random assignment to either treatment and control group makes both groups equal on all characteristics. Similarly, if we compare the same individual pre- and post-treatment, then the two are equal at least on the things that do not change over time, such as sex, date of birth, nationality, etc. Structural equation modeling (SEM) is a group of statistical methods for assessing relationships between variables, often at the latent (unobserved) variable level. The use of SEM for panel analysis allows for a great deal of flexibility. Latent variables can be incorporated to account for measurement error and rule out alternative models. This dissertation focuses on the use of panel data in SEM for causal analysis. It comprises an introduction, four main chapters and a conclusion. After a short introduction (Chapter 1) outlining the goals and scope of the dissertation, Chapter 2 provides an overview of the topic of causality in the social sciences. Since the randomized experiment is often not feasible in social research, special emphasis has been placed on non-experimental, i.e., observational data. The chapter outlines some competing views on causality with non-experimental data, then discusses the two currently dominant frameworks for causal analysis, potential outcomes and directed graphs. It goes on to outline empirical methods and notes their compatibility with SEM. Chapter 3 discusses how panel data can be used to deal with unobserved time-invariant heterogeneity, i.e., stable characteristics that might normally confound analyses. It attempts to show in detail how basic panel regression in SEM works. It also discusses some issues that are not normally addressed outside of SEM, e.g., measurement error in observed variables, effects that change over time, model comparisons, etc. This discussion of the more basic panel regression setup provides a sort of basis for the more complex discussion in the following chapters. Chapter 4 compares and contrasts several ways to model dynamic processes, where the outcome at a particular point in time may affect future outcomes or even the presumed cause later on. It shows that popular recently proposed modeling techniques have much do to with their older counterparts. In fact, the newer modeling techniques do not seem to offer benefit with regards to estimating the causal effects of interest. The chapter focuses on arguably common situations in which the newer techniques may have serious drawbacks. Chapter 5 provides an applied example. It looks to better assess the causal effect of environmental attitudes on environmental behaviour (mobility, consumption, willingness to sacrifice). It touches on many of the aspects from the previous chapters, including the use of latent variables for constructs that are not directly observable, unobserved time-invariant confounders, state dependence (feedback from outcome to outcome), and reverse causality (feedback from outcome to cause). It shows that failure to account for time-invariant confounders leads to biased estimates of the effect of attitudes on behaviour. After controlling for these factors, the effects disappear in terms of mobility and consumption behaviour: when a person's attitudes become more positive, their behaviour does not become more environmentally-friendly. There is, however, a fairly robust effect of attitudes on willingness to sacrifice, even after controlling for unobserved time-invariant confounders, state dependence and reverse causality. This suggests changing attitudes do affect willingness to make sacrifices, holding potential time-invariant confounders, outcome to outcome feedback (essentially habits), as well as some time-varying confounders constant. Finally, Chapter 6 summarizes the previous chapters and provides an outlook for future work.:1. Introduction 2. Causal Inference in the Social Sciences 3. A Closer Look at Random and Fixed Effects Panel Regression in Structural Equation Modeling Using lavaan 4. Equivalent Approaches to Dealing with Unobserved Heterogeneity in Cross-Lagged Panel Models? 5. Re-Examining the Effect of Environmental Attitudes on Behaviour in a Panel Setting 6. Conclusion

This thesis investigates the potential relationship between support for radical right-wing parties and the increased employment rates of women in member states of the European Union. While previous research has predominantly focused on immigration, by virtue of grievances, as a driver of radical right-wing support, this study explores whether increases in the employment ratio of women can also be perceived as a threat and contribute to this support. The rationale for examining this assumption is rooted in the understanding of radical right-wing parties as Männerparteien, being primarily supported and led by men. By analyzing time-series cross-sectional data from thirteen EU member states over twenty time periods, this study reveals a positive correlation between the employment rates of women and support for radical right-wing parties. This finding aligns with the concept of group threat theory, suggesting that increases in the employment ratio of women can indeed be perceived as a threat. The finding highlights the complex dynamics and potential resistance that arise when traditional power dynamics are challenged, emphasizing the need for comprehensive approaches to address these barriers and promote gender equality. The study also reveals a positive correlation between support for radical right-wing parties and the attainment of advanced education for men, as well as a negative correlation between radical right-wing support and increased GDP. The findings offer valuable insights to previous research and lay the groundwork for future studies in various research fields by yielding further insights into the group dynamics between men and women and by proposing the use of additional explanatory variables in explaining radical right-wing support.

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Västvärlden har de senaste åren upplevt hög och stigande inflation, något som varit ovanligt de senaste decennierna. Ekonomisk tillväxt är av stort intresse för alla länder, och inflationens effekt på tillväxten var välstuderat under 90-talet. Därefter har bidragen varit färre, men är återigen intressant att analysera med bakgrund av rådande inflationsnivåer. Studiens syfte är att undersöka om inflation påverkar real BNP-tillväxt negativt på lång sikt. Det görs genom att med paneldata från OECD undersöka fem länder i en IV-regression mellan 1973-1984 och under en elvaårsperiod med start fem år efter respektive lands införande av ett inflationsmål. Resultatet visar att inflation haft en statistiskt signifikant negativ kausal effekt på den reala BNP-tillväxten i dessa länder och tidsperioder. Det är i linje med stor del av tidigare forskning på området. / The western world has during the last couple of years witnessed high and rising inflation, which has been of rare occurrence during the last decades. Economic growth is of great interest all over the world, and during the nineties the subject of the effects inflation has on growth was well studied. Thereafter the contributions have been fewer. Given the recent levels of inflation this is once again an interesting area of study. The purpose of this paper is to examine if inflation affects real GDP growth negatively in the long run. This is made possible through the usage of panel data, collected from the OECD, where five countries are included in an IV-regression during 1973-1984 as well as the eleven year period occurring five years after the introduction of inflation targeting. The results show that the negative causal effect inflation has had on real GDP growth in these countries and time periods is of statistical significance. That is in line with a major part of the published research on the subject.

This study examines the impact of fiscal deficits on economic growth in developing countries. Based on deduction from the relevant theoretical and empirical literature, the study tests the following hypotheses regarding the impact of fiscal deficits on economic growth. First, fiscal deficits have significant positive or negative impact on economic growth in developing countries. Second, the impact of fiscal deficits on economic growth depends on the size of deficits as a percentage of GDP – that is, there is a non-linear relationship between fiscal deficits and economic growth. Third, the impact of fiscal deficits on economic growth depends on the ways in which deficits are financed. Fourth, the impact of fiscal deficits on economic growth depends on what deficit financing is used for. The study also examines whether there are any significant regional differences in terms of the relationship between fiscal deficits and economic growth in developing countries. The study uses panel data for thirty-one developing countries covering the period 1972- 2001, which is analysed based on the econometric estimation of a dynamic growth model using the Arellano and Bond (1991) generalised method of moments (GMM) technique. Overall, the results suggest the following. First, fiscal deficits per se have no any significant positive or negative impact on economic growth. Second, by contrast, when the deficit is substituted by domestic and foreign financing, we find that both domestic and foreign financing of fiscal deficits exerts a negative and statistically significant impact on economic growth with a lag. Third, we find that both categories of economic classification of government expenditure, namely, capital and current expenditure, have no significant impact on economic growth. When government expenditure is disaggregated on the basis of a functional classification, the results suggest that spending on education, defence and economic services have positive but insignificant impact on growth, while spending on health and general public services have positive and significant impact. Fourth, in terms of regional differences with regard to the estimated relationships, the study finds that, while there are some regional differences between the four different regions represented in our sample of thirty-one developing countries - namely, Asia and the Pacific, Latin America and the Caribbean, Middle East and North Africa, and Sub-Saharan Africa – these differences are not statistically significant. On the basis of these findings, the study concludes that fiscal deficits per se are not necessarily good or bad for economic growth in developing countries; how the deficits are financed and what they are used for matters. In addition, the study concludes that there are no statistically significant regional differences in terms of the relationship between fiscal deficits and economic growth in developing countries.

There is a lack of consensus on the empirical findings in research examining the so-called ‘guns and butter’ argument – does military expenditure crowd out social expenditure or not? Feminist scholars have been arguing that militarism, affects women’s welfare in a negative way. This study investigates whether countries' increase in military expenditure leads to a deterioration in women's health and education, based on the guns and butter argument. The study intends to find out what a possible correlation looks like for two different models, one that represents the impact on women's health and another that represents the impact on women's education, by using maternal mortality and school enrollment (with a gender parity index) as dependent variables. The survey is conducted for a sampling of countries in Africa, the Middle East, and South Asia for the period 1988 to 2020. The main findings of this study indicate that increased military expenditure leads to increased maternal mortality, however, the study finds no linear relationship between military expenditure and girls' school enrollment. The study also finds no correlation between health care, education expenditure and maternal mortality. Nevertheless, it indicates that education expenditure has a positive effect on girls’ school enrollment, and health care expenditure has a negative effect on girls’ school registration.

Foreign direct investment (FDI) is played a vital role for boosting up the economies of developing countries. Hence, it is necessary to know the factors that determines the flows of FDI in the developing countries. This study has attempted to investigate how different factors affect the inflow of foreign direct investment in South Asian Countries. To attain the objective this study has collected data on the respective variables for 45 years and considered seven countries. The relationship between different economic variables and their overall impact on FDI inflows have been examined through various panel models like basic pooled OLS estimation, entity fixed effect model, time fixed effect estimation and random effect model. The outcome of this study is that GDP of the country is the main factor behind the FDI inflows in South Asian countries.