Trade, migration, poverty and inequality: A global perspective

Trade and migration have been extensively studied in a variety of disciplines but their inter relationship is still arguable. It is crucial to understand how trade and migration are linked, since they are important elements in a modern, globalized economy. This thesis presents three interconnected studies on trade and migration that make use of newly estimated international migration flows data, to understand these relationships and the potential contributions of trade and migration to global poverty and inequality reduction. The first study examines the bi-directional relationship between trade and migration using international bilateral trade and migration flows data within a seemingly unrelated regression gravity model framework. The study finds that trade and migration are complements, which means that larger migration flows are associated with higher trade flows, and vice versa. Although these results do not definitively demonstrate causality, they suggest that, if world trade decreases due to countries implementation on current protectionist policies, migration flows might also be likely to decline. The second study extends the investigation by examining the causal relationship between trade and migration. The study uses a novel instrumental variables strategy, utilising World Trade Organisation (WTO) affiliation and average tariff rates as instruments within a gravity model framework, to overcome endogeneity in the regression model. The results suggest that trade is a causal driver of migration. This means that migration flows would increase following an increase in trade flows. The third study investigates whether international trade or migration has a larger effect on global extreme poverty and inequality. Again, an instrumental variables approach is used to address any endogeneity problems. The results suggest that trade has a larger potential impact on reducing extreme poverty and inequality. Overall, the outcomes of this thesis are important to policy makers in countries where growing migration is a political issue, and to origin countries that want to restrict the mobility of migrants and reduce the brain drain from their countries. It suggests that how countries treat their borders influences poverty and income inequality outcomes. This thesis suggests that trade provides a greater impact than migration on extreme poverty as well as inequality. Therefore, a country might want to allow freer trade by reducing their national border barriers, in order to reduce extreme poverty and inequality

Does financial efficiency modify CO2 emission? Using panel ARDL-PMG in the case of five selected ASEAN countries

Financial efficiency reduces carbon emissions by optimising resource usage, encouraging innovation and investment in low-carbon technology and solutions, and increasing transparency and accountability. This study examined the short- and long-term equilibrium relationships between CO2 emissions, financial efficiency, GDP, and energy consumption in five ASEAN nations from 1980 to 2020. Data stationarity was tested using the panel unit root test. The Autoregression Distribution Lag Pooled Mean Group (ARDL-PMG) model is best for empirical research because the data are long time series. The ARDL-PMG model shows that all variables affect CO2 emissions in the short term. Gross domestic product per capita and energy use affect CO2 emissions but not financial efficiency over time

Does financial efficiency modify CO2 emission? Using panel ARDL-PMG in the case of five selected ASEAN countries

Financial efficiency reduces carbon emissions by optimising resource usage, encouraging innovation and investment in low-carbon technology and solutions, and increasing transparency and accountability. This study examined the short- and long-term equilibrium relationships between CO2 emissions, financial efficiency, GDP, and energy consumption in five ASEAN nations from 1980 to 2020. Data stationarity was tested using the panel unit root test. The Autoregression Distribution Lag Pooled Mean Group (ARDL-PMG) model is best for empirical research because the data are long time series. The ARDL-PMG model shows that all variables affect CO2 emissions in the short term. Gross domestic product per capita and energy use affect CO2 emissions but not financial efficiency over time