The Causal Links between FDI and Economic Growth in Developing Countries: A Case Study of the Republic of Rwanda

The study examines the short-run and long-run causality running from real economic growth to real foreign direct investment inflows (RFDI). Other variables such as education (involving combination of primary, secondary and tertiary enrolment as a proxy to education), real development finance, unskilled labour, to real RFDI inflows are included in the study. The time series data covering the period of 1983 -2013 are examined. First, I applied Augmented Dicky-Fuller (ADF) technique to test for unit root in variables. Findings shows all variables integrated of order one [I(1)]. Thereafter, Johansen Co-integration Test (JCT) was conducted to establish the relationship among variables. Both trace and maximum Eigen value at 5% level of significance indicate 3 co-integrated equations. Vector error correction method (VECM) was applied to capture short and long-run causality running from education, economic growth, real development finance, and unskilled labour to real foreign direct investment inflows in the Republic of Rwanda. Findings shows no short-run causality running from education, real development finance, real GDP and unskilled labour to real FDI inflows, however there were existence of long-run causality. This can be interpreted that, in the short-run; education, development finance, finance and economic growth does not influence inflows of foreign direct investment in Rwanda; but it does in long-run. From the policy perspective, the Republic of Rwanda should focus more on long term goal of investing in education to improve human capital, undertake policy reforms that promotes economic growth, in addition to promoting good governance to attract development finance – especially from Nordics countries (particularly Norway and Denmark).

Uses of agent-based modeling for health communication: the TELL ME case study

Government communication is an important management tool during a public health crisis, but understanding its impact is difficult. Strategies may be adjusted in reaction to developments on the ground and it is challenging to evaluate the impact of communication separately from other crisis management activities. Agent-based modeling is a well-established research tool in social science to respond to similar challenges. However, there have been few such models in public health. We use the example of the TELL ME agent-based model to consider ways in which a non-predictive policy model can assist policy makers. This model concerns individuals’ protective behaviors in response to an epidemic, and the communication that influences such behavior. Drawing on findings from stakeholder workshops and the results of the model itself, we suggest such a model can be useful: (i) as a teaching tool, (ii) to test theory, and (iii) to inform data collection. We also plot a path for development of similar models that could assist with communication planning for epidemics.