This study examines the impact of institutional quality on foreign direct investment (F.D.I.) by categorising the countries as developed or developing. We measured institutional quality by the sum of control of corruption and rule of law indicators. We provide evidence that institutional quality positively and significantly impacts F.D.I. in developed countries; specifically, we find that a one standard deviation change in governance significantly affects F.D.I. by a factor of 0.2225 (using common law and the lagged values of the independent variables as instruments). Ceteris paribus, the results for the developing countries demonstrate that the institutional quality impact is insignificant because of the weak structure of institutions. Result findings strongly support the significance of governance indicators in attracting F.D.I. inflows. From our results, we infer that the relevance of governance indicators tends to be a key point in attracting F.D.I. inflows.

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This study investigates the impact of external debt and Foreign direct investment (FDI) on economic growth in Tanzania using time series data from 1971-2011. The empirical analysis was based on ARDL model and the Bounds test approach of co-integration as advocated by Pesaran et al (2001) to test for long-run equilibrium relationship. The results show that, in the long-run debt promote economic growth in Tanzania. However, foreign direct investment exhibits a negative impact on economic growth. While in the short-run, the results indicate that there is no directional causality either between external debts (PD) and economic growth (RGDP) or between FDI_INFL and economic growth (RGDP).

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This paper examines the drivers behind China's economic growth. In particular, it focuses on the channels of knowledge spillovers: human capital and openness to trade and foreign direct investment. The specific features of the study include using the most recent comprehensive panel data consisting of 29 provinces during the period 1994-2006 and performing unit root and cointegration tests in the panel data framework. The paper finds that human capital, trade and FDI are the significant determinants of total factor productivity, but their importance varies with technological levels of provinces. These findings have important policy implications.openness, human capital, economic growth, panel data,

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A firm's top manager and a government official may be connected due to special circumstances. This social relationship or political connection may provide industrial polluters with protection or a "pollution shelter" which could lead to severe environmental deterioration. This paper aims to examine the link between political connections and firms' pollution discharges by using Chinese data. Empirical results show that political connections are the institutional origin for firms to adopt strategic pollution discharges. Government officials who are young, of low education, promoted locally and in office for a relatively long time are more likely to build political connections with polluters. This phenomenon may lead to inadequate enforcement of regulation and emission control. The pollution discharge of politically connected firms also varies considerably due to firm heterogeneity. This study also shows that pollution shelter effects caused by political connections are more obvious in the central and western regions, prefecture cities and capital-intensive industries.

Focusing on each country’s topmost destination/origin migration relation with other countries, this study builds top1 destination networks and top1 origin networks in order to understand their skeletal construction and community dynamics. Each top1 network covers approximately 50% of the complete migrant network stock for each decade between 1960 and 2000. We investigate the community structure by implementing the Girvan-Newman algorithm and compare the number of components and communities to illustrate their differences. We find that (i) both top1 networks (origin and destination) exhibited communities with a clear structure and a surprising evolution, although 80% edges persist between each decade; (ii) top1 destination networks focused on developed countries exhibiting shorter paths and preferring more advance countries, while top1 origin networks focused both on developed as well as more substantial developing nations that presented a longer path and more stable groups; (iii) only few countries have a decisive influence on community evolution of both top1 networks. USA took the leading position as a destination country in top1 destination networks, while China and India were the main Asian emigration countries in top1 origin networks; European countries and the Russian Federation played an important role in both.