Effects of Foreign Direct Investment on Inclusive Growth and Employment

This dissertation studies the effects of foreign direct investment (FDI) on inclusive growth and employment. The first chapter examines the conditions under which FDI can effectively lead to inclusive growth. By using a fixed effects regression with annual data for 67 countries from 1990 to 2015, we find that FDI has a positive effect on inclusive growth when there is a sufficiently large manufacturing sector and infrastructure base in the host country. We also indirectly find that FDI has a positive effect on inclusive growth when the host country has a large service sector. These not very optimistic results emphasize the critical importance of the host country’s absorptive capacity. A smaller technological or knowledge gap with the foreign firms is required for FDI to lead to more linkages and spillovers, and ultimately job creation for the poor. The second chapter looks at the effect of manufacturing FDI on manufacturing employment in Sub-Saharan African countries, by using annual data for 16 manufacturing industry sectors in 15 SSA countries from 2003 to 2018. In the first analysis, we find that manufacturing FDI has a positive effect on manufacturing employment at the industry sector level. In the second analysis, we look at how the effect of manufacturing FDI on manufacturing employment differs by groups of industry sectors. The results show that the effect of manufacturing FDI on employment creation varies by industry sector groups; automotive related industries create the most, followed by business machines/electronics related industries, and lastly metals/minerals related industries. The result reflects both direct and indirect employment effects via spillovers and forward and backward linkages

When Does Foreign Direct Investment Lead to Inclusive Growth?

Foreign Direct Investment (FDI) is widely considered among the most effective instruments for the promotion of economic development. However, not all FDI leads to inclusive economic growth, lifting the welfare of the poorest groups in developing countries. This paper examines the conditions under which FDI can effectively lead to inclusive growth. By using a fixed effects regression with annual data for 68 countries from 1990 to 2015, we find that FDI has the most positive effect on inclusive growth when there is a sufficiently large manufacturing sector and a developed enough infrastructure base in the host country. These not very optimistic results emphasize the critical importance of the host country’s absorptive capacity. A smaller technological or knowledge gap with the foreign firms is required for FDI to lead to more linkages and spillovers, and ultimately job creation for the poor. The results cast doubt on development strategies that rely on FDI as a sufficient policy for inclusive growt

Geospatial Clustering Analysis on Drug Abuse Emergencies

The epidemic of drug abuse is a serious public health issue in the U.S. The number of overdose deaths involving prescription opioids and illicit drugs has continuously increased over the last few years. This study aims to develop a geospatial model that identifies geospatial clusters in terms of socioeconomic and demographic characteristics with an unsupervised machine learning algorithm. Then, we suggest the most important features affecting heroin overdose both negatively and positively. The findings of this study may inform policymakers about strategies to mitigate the drug overdose crisis

Geospatial Clustering Analysis on Drug Abuse Emergencies

The epidemic of drug abuse is a serious public health issue in the U.S. The number of overdose deaths involving prescription opioids and illicit drugs has continuously increased over the last few years. This study aims to develop a geospatial model that identifies geospatial clusters in terms of socioeconomic and demographic characteristics with an unsupervised machine learning algorithm. Then, we suggest the most important features affecting heroin overdose both negatively and positively. The findings of this study may inform policymakers about strategies to mitigate the drug overdose crisis

Visit-to-Visit Blood Pressure Variability and Sleep Architecture

Visit‐to‐visit blood pressure (BP) variability (BPV) is an independent risk factor of cardiovascular disease (CVD). Sleep architecture characterizes the distribution of different stages of sleep and may be important in CVD development. We examined the association between visit‐to‐visit BPV and sleep architecture using in‐lab polysomnographic data from 3,565 patients referred to an academic sleep center. BPV was calculated using the intra‐individual coefficient of variation of BP measures collected 12 months before the sleep study. We conducted multiple linear regression analyses to assess the association of systolic and diastolic BPV with sleep architecture—rapid eye movement (REM) and non‐rapid eye movement (NREM) sleep duration. Our results show that systolic BPV was inversely associated with REM sleep duration (p = .058). When patients were divided into tertile groups based on their BPV, those in the third tertile (highest variability) spent 2.7 fewer minutes in REM sleep than those in the first tertile (lowest variability, p = .032), after adjusting for covariates. We did not find an association of systolic BPV with other measures of sleep architecture. Diastolic BPV was not associated with sleep architecture either. In summary, our study showed that greater systolic BPV was associated with lower REM sleep duration. Future investigation is warranted to clarify the directionality, mechanism, and therapeutic implications

Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach

Objectives The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea. Methods A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable time-series model. Results The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI. Conclusions Implementing a multicenter-based time-series classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies

A Mixed Integer Programming Model for Patient Appointment in an Infusion Center

Infusion centers in hospitals are experiencing higher demand, resulting in substantially long patient wait times. Also, scheduling chemotherapy is not a straightforward task due to several factors such as constraints on patient appointments for infusion and doctor visits. Limited resource capacity in an infusion center and physician unavailability are also important factors that increase appointment scheduling complexity. In this study, we develop a Mixed-Integer Programming (MIP) model based on real work flow data of an infusion center and generate balanced appointment schedules for chemotherapy. The aim of the proposed model is to reduce patient's wait times and the makespan which will improve the satisfaction levels for timely services and will bring more balanced resource utilization, resulting in cost reduction