Volume 14, Issue 2

This study examines the economic consequence of terrorism in Sub-Saharan Africa (SSA) with a panel of 38 countries between 2010 and 2020. Specifically, the study examined the impact of terrorism on economic growth, international tourism demand, income inequality, unemployment rate, and inflation rate. The study employed the two-step system GMM and quantile regression for the estimation. The results show that terrorism has had a significant negative impact on economic growth in SSA. Similarly, the study results indicate that terrorism impacts negatively on international tourism demand, widens the income gap, and increases the rate of unemployment and inflation in SSA. Furthermore, the two governance institution indicators employed in the study (government effectiveness and control of corruption) shows a significant negative impact on economic growth in the zone, while credit to the private sector and human capital had performed abysmally in the zones’ growth trajectory. These findings were further supported by the quantile regression results. The study employed both system GMM model and quantile regression in the analysis. The use of these two different methods makes the results obtained to be robust and efficient for policy purpose, which no other author has done before now.

The study involved 1240 women covering the ten administrative regions of Ghana. The aim was to study the prevalence of maternal mortality and its distribution across the ten regions based on given demographic factors. Three main analytical methods were used: Direct estimation method, Fay-Herriot method and Binary Logistic regression. The study revealed that prevalence of maternal mortality in Ghana was high among women engaged in manual works, rural dwellers, women living without partners and women living in pro-poor families. The Northern region had the highest number of maternal deaths followed by the Western region whilst the Ashanti and the Upper East regions had the lowest number of maternal deaths. There is therefore the need for Ghana Health Service to engage all stakeholders in checking maternal mortality in the country.

Russia’s aggression on Ukraine has attracted worldwide attention. The relations between Russia’s aggression and NATO’s expansion are being debated with conflicting viewpoints. Based on weekly Google Trends data from 5 March 2017 to 13 February 2022, this study creatively creates a series of time series variables to measure the public agenda in the US. Based on time series modellings, this study finds that the U.S. public believes that there are bi-directional relations between NATO’s expansion and Russia’s aggressiveness. In particular, Russia’s aggressiveness is driven by both NATO’s expansion and Russia’s imperialism. NATO’s expansion is driven by Russia’s foreign policy, Russia’s aggressiveness, and Russia’s imperialism. These conclusions predicted the U.S. government’s policy towards this war. This study contributes to policymakers by providing solid empirical evidence on the U.S. public’s opinion on this important issue and to academia as well by presenting a (still) new quantitative method in international relations. By employing this method, we can leverage readily accessible high-frequency time series data to gain valuable insights into U.S. public opinion, potentially enabling people to predict U.S. foreign policies.

Recent advances in medical instruments, information technology, and unprecedented data sharing allowed scientists to investigate, trace, and monitor the COVID-19 pandemic faster than any previous outbreak. This extraordinary speed makes COVID-19 a medical revolution that causes some unprecedented analyses, discussions, and models. Modeling the dependence between the number of tests and the positivity rate is one of these new issues. Using four classes of copulas (Clayton, Frank, Gumbel, and FGM), this study is the first attempt tom model the dependency. The estimation of the parameters of the copulas is obtained using the maximum likelihood method. To evaluate the goodness of fit of the copulas, we calculate AIC. The computations are conducted on Matlab R2015b, R 4.0.3, Maple 2018a, and EasyFit 5.6. Findings indicate that at the beginning of a typical epidemic, the number of tests is relatively low and the proportion of positivity is high. As time passes, the number of tests increases, and the positivity rate decreases. The epidemic peaks are occasions that violate the stated general rule –due to the early growth of the number of tests. Also, during both peak and non-peak times, the rising number of tests is accompanied by decreasing the positivity rate. We find that the proportion of positivity is more proportional than the number of tests to the number of infected cases. Therefore, the changes in the positivity rate can be considered a representative of the level of the spreading. Approaching zero positivity rate is a good criterion to scale the success of a healthcare system in fighting against an epidemic. Accordingly, the number and accuracy of tests can play a vital role in the quality level of epidemic data.

We investigate the effectiveness of five weighting and matching techniques, including propensity score matching (PSM), in improving covariate balance and reducing bias when estimating treatment effects in finite-sample situations through Monte Carlo simulations. King and Nielsen (2019. “Why Propensity Scores Should Not Be Used for Matching.” Political Analysis 27 (4): 435–54) argue that pruning observations based on PSM with 1-to-1 greedy matching can worsen rather than improve covariate balance and can increase the bias in the estimates of treatment effects. In our simulations, we observed this phenomenon not only in PSM with 1-to-1 greedy matching but also in other covariate balancing techniques that King and Nielsen (2019. “Why Propensity Scores Should Not Be Used for Matching.” Political Analysis 27 (4): 435–54) recommend as better matching methods, i.e. Mahalanobis distance matching (MDM) and coarsened exact matching (CEM). Regardless of the weighting/matching techniques and the data generation processes in this study, our findings indicate that matching and weighting under extreme caliper or cut-point settings does not improve covariate balance. In addition, once a substantially improved covariate balance is achieved in a given sample, the estimated bias tends to worsen slightly as the covariate balance continues to improve. Moreover, our simulation results suggest that OLS with proper covariates reduces selection bias as well as other weighting and matching methods. The results suggest that when analyzing observational data, it is important to avoid looking for a one-size-fits-all estimator and to identify the appropriate nonexperimental estimator carefully for the sample by thoroughly investigating the available data’s characteristics.