The adoption of e-government promises efficiency in the delivery of government services to citizens across various sectors of the economy. Due to this, most Global North countries have advanced in the deployment of e-government for improving public-service delivery. Unfortunately, most African countries, including Ghana, are still lagging in e-government development. This study examined e-government development across African Union member states. It explored the role of e-government in the reform of public administration and governance, focusing on Ghana as a case study. Using a mixed-method approach, the study analyzed secondary data of key e-government indicators using the TOPSIS method. This helped underscore the transformative impact on public administration and governance by using primary data via interviews. The results show advanced progress in some African countries, including Ghana, due to aligned digital strategies with national policies. Also, technology integration is evident in Ghana’s public sector and is reshaping public administration and governance. The study recommends that to achieve the long-term sustainability of these advancements, interagency collaboration and data-sharing mechanisms between the public and private sectors should be strengthened, while all forms of silos should be broken to promote the delivery of services. This study enhances public-service delivery by identifying areas needing both improvement and allocation of resources for shaping e-government policy development.

This paper examines the current No-Claim Discount (NCD) system used in Ghana’s auto insurance market as inefficient and outmoded and, therefore, proposes an alternative optimal Bonus-Malus System (BMS) intended to meet the present market conditions and demand. It appears that the existing BMS fails to acknowledge the frequency and severity of policyholders’ claims in its design. We minimized the auto insurance portfolios’ risk through Bayesian estimation and found that the risk is well fitted by gamma, with the claim distribution modeled by the negative binomial law with the expected number of claims (a priori) as 14%. The models presented in this paper recognize the longevity of accident-free driving and fully reward higher discounts to policyholders from the second year when the true characteristics of the hidden risks posed to the pool have been ascertained. The BMS finally constructed using the net premium principle is very optimal and has reasonable punishment and rewards for both good and bad drivers, which could also be useful in other developing economies.

This study examined risk exposure and auto insurance premium determinants in Ghana. We analysed an existing data set of 23 434 policies (without claims = 84.1%, policies with claims = 15.9%; comprehensive policies = 48.0%, third-party policies = 52.0%) applying the Autoregressive Distributed Lag (ARDL) model, controlling for driver demographics, value of car, and car usage variables. Findings indicate policyholders’ age significantly determine premiums charges. Additionally, auto seating capacity significantly influenced third-party rather than comprehensive premiums, and auto’s cubic capacity had no significant impact on premium charges. Pricing system impact premiums were influenced by policyholders’ characteristics more than variables from the insured vehicle. These findings suggest that policyholders’ age (novice drivers) and vehicles with many occupants increases auto insurers risk exposure.

To better understand the actual rating variables that affects Auto insurance Policyholders’ premium, this paper attempts to provide empirical evidence to justify which ones are significant and needed to be considered by insurers by adopting the autoregressive distributed lag (ARDL) model. In satisfying all the conditions for ARDL application, unit root, Heteroskedasticity, normality, dynamic stability and serial correlation tests were conducted. We estimate the effects of each rating variable on Premium taking into consideration whether the policy of the insured is Third-Party or Comprehensive. These rating variables in the ARDL model serves as the independent variables that establishes the short and long-run relationships between them and the Premium as the dependent variable. The results suggest that not all the classical rating variables used in the market significantly impact Premium. Whiles, to some extent, we found a varying degree of variables impact on Premium depending on the insurance type, the autos cubic capacity, which plays a cogent role on the basic Premium in Ghana, is insignificant. Also, policyholders’ age characteristics are statistically significant but are excluded in the premium calculations. Thus, this paper shows the need to consider all the other possible rating variables, including policyholders’ age into the Ghanaian insurance pricing system, whiles autos cubic capacity considering the weight it put on the basic Premium should be re-examined. This would help to obtain a financially balanced and optimal pricing system for policyholders.

Available statistics indicates that about 90% of all claims or accident in Ghana is caused by human behavior. Therefore, policyholders’ errors are categorized depending on the severity and extend of casualties caused as a result of misinterpretation of road traffic control devices based on their education levels. Hence, in order to ascertain all the possible causes within the human element to reduce the increasing trend of yearly claims, this study report on the influence of education levels on accident/claims frequency and severity drawing upon a purposive sample of 203 policyholders who have experienced at least one accident in a year using structural equation modeling (SEM). The findings from our regression weights gave enough evidence to reject most of our hypotheses with few ones being supported. This study provides enough evidence that education generally to perspective policyholders influence accidents/claims occurrence. However, in terms of education levels of policyholders, we did not have enough evidence in support of any of these levels either causing or reducing claims/accident frequency. Besides accident/claim frequency, we extended our regression analysis on claim severity and also included some well know auto insurance rating factors to ascertain their impacts on accident frequency. Consequently, it was revealed that most of the severe claims or accidents that results into deaths and serious injuries on yearly basis are caused by policyholders or drivers with medium level of education in Ghana with its frequency driving mostly by rating factors such as the vehicle’s age, cubic capacity, mileage, etc.

In the rapidly evolving economic landscape of Ghana, understanding the intricate interdependencies between macroeconomic variables is pivotal for informed policymaking and strategic economic planning. The study employed network analysis to enhance our comprehension of Ghana's macroeconomic dynamics. Data was sourced from the world development indicators. Initially, a statistical network was constructed to represent the interconnections between Ghana's principal macroeconomic variables using partial correlation matrix, offering a visual and analytical perspective of their relationships. Subsequently, centrality measures and other network analysis tools were utilized to identify and quantify the influence of key economic indicators within this network. Results showed that Exports, Inflation, Exchange rate, Gross Domestic Saving, Manufacturing and Gross National Expenditure played a significant role in the network. However, Agriculture and Imports were identified as most influential variables with high centrality scores across all centrality measures. Finally, Exponential Random Graph Model was employed to provide a comparative baseline, shedding light on the uniqueness or randomness of the observed interrelationships. The significant parameters in the model include the presence of edges between nodes and the presence of generalized geodesic triads (gwesp), which capture the tendency for nodes to form connections based on common neighbors. The findings also revealed that there is a probability of 16.19% for a relationship to exist between two macroeconomic variables if they are both connected to the same third variable.