Background: Public health insurance schemes can offer households financial protection against health care costs and help to resolve inequality in health care provision. The current study evaluates the impact of the Nigerian National Health Insurance Scheme (NHIS) in reducing financial hardship for a sample of Nigerian households working in the health and higher education sectors. The data allows us to examine the variation in the financial protection effects across different income groups and explore differences in standard of living by households with coverage and those without.Methods: Data was gathered in Akwa Ibom state, Nigeria. A cluster sampling technique is used to compare participants and non-participants in the NHIS and within this, identify equivalent groups with regard to household characteristics such as education level, income and household composition. A propensity score matching approach examines variations in out-of-pocket expenditure (OOPE), catastrophic health expenditure (CHE) and number of household assets across the insured and uninsured groups controlling for cofounding factors.Results: The likelihood of experiencing CHE for a household that is insured is estimated to be 82% lower than that of an insured household, even after controlling for our variety of observable characteristics. OOPEs are ₦50,000 lower in households with insurance compared to those without. We additionally find a significant difference in standard of living, as measured by household asset ownership across the insured and non-insured groupsConclusions: There is a statistically and practically significant association between participation in the NHIS scheme and household financial protection. This provides support to policy-makers seeking to design and extend equitable health-financing policies.
Insurance industry provides insurance protection/financial guarantee to the insuring public in a given economy say Nigeria. The existence of risk allowed impetus for the insurance industry. Firms, governments and households act in ways that mitigate the adverse effects of risks on their assets, operations and social responsibilities. Insurance contracts are among the risk management choices of the persons exposed to risks. In Nigeria, the insurance industry is not too popular and needs to build trust and confidence among the insuring public. Information and Communication technology innovations that are changing the manner of doing things have potentials of facilitating, boasting trust and keeping insurance industry relevant. These can be realized via efficient insurance service delivery and innovations. This study using scooping approach aims at examining the level of information and communication technology (ICT) applications in Nigeria insurance industry, and promoting the ICTs in the industry. The study while identifying favourable demography and sustained innovations in ICT in Nigeria recommends sustained interest of the stakeholders towards adoption of ICT in Nigeria insurance industry.
The importance of timely detection, classification and response to anomalies on petroleum products pipeline (PPP) have attracted pragmatic researches in recent times. There is need for efficient monitoring and detection of activities on PPP to guide leak detections and remedy decisions. This paper develops an intelligent hybrid system, driven by discrete event system specification (DEVS) and adaptive neuro-fuzzy inference system (ANFIS) for detection and classification of activities on PPP. A dataset comprising 330 records was used for training, validation and testing of the system. Result of sensitivity test shows that inlet pressure, inlet temperature, inlet volume and outlet volume have cumulative significance of 71.72% on flowrate of PPP. Hybrid learning algorithm was observed to converge faster than the back propagation algorithm in the detection of pipeline activities. ANFIS hybrid learning algorithm with training and testing errors of 0.11980 and 0.010233 yielded a correlation of 0.916 between the computed and the desired output and produced optimal consequent parameters to boost the intelligence of DEVS. A testing error of 0.0303 was observed in the evaluation of DEVS-ANFIS system on 33 test data sample, 32 precise detections were made with one incorrect detection, this gives 96.97% level of confidence in the DEVS-ANFIS model for detection, classification and localization of PPP activities.
Governments in developing economies are continuously seeking ways to increase the number of people that have access to formal financial services. However, literature on why households in developing economies are excluded from the formal credit sector is scarce. Thus, this study examines the link between household characteristics and the choice of credit provider using unique household-level primary data from the Niger Delta region. A binomial logistic regression model based on relevant household characteristics is developed for estimation. The results show that the number of dependents and income of a household, as well as education level and age of household head, is relevant in understanding the choice of the credit provider. Strikingly, the finding that the probability of borrowing from the informal sector increases with household distance from a formal lender at a decreasing rate suggests the significance of cost associated with traveling to the nearest bank on the choice of a lender and the presence of information asymmetry in the credit market of the region. Overall, the study raises important implications to inform credit market policies and practices in the region.
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