In this study, I examine the employment effects on average earnings and duration to work during a 45 month period, after receiving social assistance (SA) in October 2019, compared to receiving unemployment insurance (UI) the same month. A distinction is made between two treatment groups; receiving SA in addition to UI (treatment I) and receiving SA (treatment II). Using propensity score matching (PSM), I estimate the average treatment effects on the treated on earnings, as well as duration to work by using the Kaplan-Meier survival estimator with the matched observations. I use newly released Swedish administrative micro level data of individuals’ monthly labour market status (BAS) between 2020-2023, from Statistics Sweden. During this thesis process, where Statistics Sweden allowed me data access, I was allowed an additional year of data, for 2019. Results showed that the inflow of SA recipients in October 2019, on average had 25.5 percent lower earnings between November 2019-July 2023, compared to the inflow of UI recipients the same month. In addition, the inflow of SA recipients in October 2019, on average spent 4 months longer in unemployment, compared to those receiving UI the same month. However, results were insignificant when comparing effects between the inflow of those receiving SA in addition to UI in October 2019 with the inflow of UI recipients the same month. Results for this group were insignificant for both employment outcomes; average earnings and duration to work.

In this study, I examine the employment effects on average earnings and duration to work during a 45 month period, after receiving social assistance (SA) in October 2019, compared to receiving unemployment insurance (UI) the same month. A distinction is made between two treatment groups; receiving SA in addition to UI (treatment I) and receiving SA (treatment II). Using propensity score matching (PSM), I estimate the average treatment effects on the treated on earnings, as well as duration to work by using the Kaplan-Meier survival estimator with the matched observations. I use newly released Swedish administrative micro level data of individuals’ monthly labour market status (BAS) between 2020-2023, from Statistics Sweden. During this thesis process, where Statistics Sweden allowed me data access, I was allowed an additional year of data, for 2019. Results showed that the inflow of SA recipients in October 2019, on average had 25.5 percent lower earnings between November 2019-July 2023, compared to the inflow of UI recipients the same month. In addition, the inflow of SA recipients in October 2019, on average spent 4 months longer in unemployment, compared to those receiving UI the same month. However, results were insignificant when comparing effects between the inflow of those receiving SA in addition to UI in October 2019 with the inflow of UI recipients the same month. Results for this group were insignificant for both employment outcomes; average earnings and duration to work.

This thesis examines the evolution of income inequality in Canada from 1997 to 2019 through the Gini coefficient and the share of income of the top 10% of income earners. These metrics are then used to evaluate whether there are any associations between income inequality and CO2 emissions. The results reveal that the Gini coefficient is negatively associated with CO2 emissions; however, no definitive conclusions can be drawn about the effect of income share. The implications of the results for the effect of economic policies (i.e., redistributive) on national climate commitments are then discussed. / Graduate

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24288号 / 医博第4904号 / 新制||医||1061(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 山本 洋介, 教授 中本 裕士, 教授 森田 智視 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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Hydraulic losses in sewer pipes are caused by wall roughness, blockages and in-pipe sedimentation. Hydraulic resistance is a key parameter that is used to account for the hydraulic energy losses and predict the sewer system propensity to flood. Unfortunately, there are no objective methods to measure the hydraulic resistance in live sewers. A common method to estimate the hydraulic resistance of a sewer is to analyse collected CCTV images and then to compare them against a number of suggested hydraulic roughness values published in the Sewer Rehabilitation Manual. This thesis reports on the development of a novel, non-invasive acoustic method and instrumentation to measure the hydraulic roughness in partially filled pipes under various structural and operational conditions objectively. This research presents systematic laboratory and field studies of the hydraulic and surface water wave characteristics, of shallow water flows in a sewer pipes with the presence of local and distributed roughness, in order to relate them to some fundamental properties of the acoustic field measured in the vicinity of the flow surface. The results of this thesis indicate that for the local roughness the energy content of the reflected acoustic signal is an indicator of the pipe head loss and hydraulic roughness. In the case of the distributed roughness, the variation in the temporal and frequency characteristics of the propagated sound wave can be related empirically to the mean flow depth, mean velocity, wave standard deviation and hydraulic roughness.

Background: Asset prices, investment choices, and market mood can all be greatly impacted by macroeconomic factors and risk perception. Therefore, for investors, portfolio managers, policymakers, and regulators looking to negotiate the complexity of financial markets, knowing how macroeconomic factors affect risk is crucial. Objective: This study delves into the intricate relationship between macroeconomic indicators and market risk propensity, offering a comprehensive analysis of both cross-sectional and panel data. Focusing on key factors such as inflation, interest rates, exchange rates, and the Index of Industrial Production (IPP), this study explore their multifaceted impacts on market risk dynamics. Methods: To reveal the complex linkages that determine risk-taking behaviors and affect business outcomes, the study uses sophisticated econometric methodologies. Results: According to our research, inflation has a significant impact on investor sentiment and corporate profitability. Reduced profit margins and increased market risk are the results of higher inflation. Similar to this, interest rates become an important variable that affects borrowing costs, investment options, and the level of competition on the stock market. Exchange rate fluctuations, which are a key component of the global financial landscape, have been shown to have an effect on investor returns, corporate operations, and dynamics of international trade, which in turn shapes market risk. Additionally, this research reveals the complex relationship between the IPP and stock market performance, wherein good growth in industrial output signifies an expansion of the economy and investor confidence, which in turn affects demand for and the price of stocks. In contrast, a drop in the IPP denotes an economic slowdown and increased market risk. The paper also discusses the unusual impact of the COVID-19 pandemic on international financial markets, emphasizing the interaction between pandemic-induced uncertainty, exchange rate changes, and monetary policy reactions to produce novel market risk dynamics. Conclusion: In conclusion, this study offers a thorough grasp of the interactions between macroeconomic data and market risk inclination. For investors, companies, and politicians looking to comprehend the complexity of the global economic landscape, make educated decisions, and successfully manage financial risks, these insights are essential. Keyword: Propensity Risk, Stock Market, Inflation, Exchange rate and IPP.

Heterogeneity in the school education quality plays an important role for those who want to pursue a bachelor's degree in Peru since access to higher education is highly correlated with socioeconomic status. In that sense, an intervention for disadvantaged students took place for the first time in 2022 and was constrained to the assessment of a scholarship called “Beca 18”, the biggest scholarship that the public institution called PRONABEC addresses every year since 2012. The intervention included additional tools for a group of applicants: (i) full-time online classes for 2 to 4 months; (ii) an electronic device with an internet connection; and (iii) the admission exam payment fully covered up to 2 times. The objective of this thesis is to evaluate the effectiveness of this intervention in increasing the likelihood of the treated to access higher education through the 2022 “Beca 18” scholarship process. As the treatment was not randomly assigned, a control group was estimated using the Propensity Score Matching methodology based on individual characteristics. Results showed that there is no statistically significant effect of the intervention in the treated applicants and invite to re-evaluate its design and implementation.

Bipartite experiments have recently emerged as a focal point in causal inference. In these experiments, treatment is administered to one set of units, while outcomes of interest are gauged on a distinct set of units. Such experiments are especially valuable in scenarios where pronounced interference effects transpire between units on a bipartite network. For instance, in market experiments, designating treatment at the seller level and assessing outcomes at the buyer level (or vice-versa) can lead to causal models that more accurately reflect the inherent interference between buyers and sellers. Although bipartite experiments can enhance the precision of causal effect estimations in specific contexts, it's imperative to conduct the analysis judiciously to avoid introducing undue bias through the network. Drawing from the generalized propensity score literature, we demonstrate that it's feasible to achieve unbiased estimates of causal effects for bipartite experiments, given a conventional set of assumptions. Furthermore, we delve into the formulation of confidence sets with accurate coverage probabilities. By employing a bipartite graph from a publicly accessible dataset previously explored in bipartite experiment studies, we illustrate, via simulations, a notable reduction in bias and augmented coverage. / Statistics

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