Adoption Of Insurance Research Articles

Impact of Technological Changes on Health Insurance Adoption in Rural Area

This study examines attitudes, actions, and willingness of rural residents to sign up for and pay for health insurance. It explores national health programs, statistical assessments of health insurance in rural populations, and responses to rural communities' attitudes and behaviors towards health insurance. Access to quality healthcare is a basic human right, but rural residents face increasing challenges due to rising healthcare costs. Health insurance can help address these issues. However, awareness, perception, and behavior related to health insurance are still limited among rural residents. Barriers such as high delivery costs and lack of awareness hinder insurance adoption in rural areas. Education on the importance of health insurance and providing financial stability for major illnesses and hospitalization are crucial. This study aims to evaluate rural residents' perceptions, actions, and willingness to sign up for and pay for health insurance, alongside analyzing national health programs and schemes.

Drivers of Health Insurance Coverage in Low Income Settlements: A Case of Kibera Informal Settlement, Nairobi County, Kenya

Insurance is critical for any country's economic growth and development to be sustainable. In rural areas of Kenya, among the illiterate, unemployed, poor, and vulnerable in society, health insurance adoption is extremely low. This paper focuses on determining the drivers for health insurance uptake among low-income populations in Kibera, Nairobi County. Semi structured questionnaires were administered to households located in Kibera informal settlements to collect data. The Yamane (1967) formula was used to select the sample size. Simple random sampling was adopted in selecting 399 respondents. Both descriptive and inferential analysis were conducted. The binary probit regression model was employed. Health insurance ownership was used as the dependent variable while age, gender, marital status, household size, school attainment, occupation, income levels, religion, cultural beliefs, frequency of hospital visits, cost of premiums, and pre-existing illness were used as the independent variables. Findings revealed that only 27.6 percent of the respondents had health insurance coverage. The findings further revealed that occupation, income levels, and religion significantly influence health insurance ownership among low-income populations in Kibera, Nairobi. Public health insurance cover was the most owned type of health insurance scheme. Recommendations were highlighted for the ministry of health and other relevant stakeholders to raise knowledge on the various health insurance options available and improve flexibility of the products to drive insurance uptake. In addition, national and county governments need to develop programmes and policies that could empower households in the informal settlements. Further, it is necessary for the government to subsidise the cost of acquiring public health insurance covers since this would encourage uptake among low-income families. The ministry of health should also work in conjunction with various religious denominations to campaign for uptake of health insurance among the poor.

Factors Affecting Health Insurance Adoption and Awareness in Uttar Pradesh, India: A Comprehensive Analysis

Factors Affecting Health Insurance Adoption and Awareness in Uttar Pradesh, India: A Comprehensive Analysis

Drivers of potential policyholders’ uptake of insurance in Kenya using Random Forest

The low adoption of insurance by potential policyholders in developing countries like Kenya is a cause for concern for insurers, regulators, and other marketing stakeholders. To effectively design targeted marketing strategies to boost insurance adoption, it is crucial to determine the factors that affect insurance uptake among potential policyholders. In this study, the 2021 FinAccess Survey, which interviewed sampled individuals above 16 years in Kenya and machine learning techniques, including Random Forest, XGBoost, and Logistic Regression, were utilized to uncover the factors driving insurance uptake and the reasons for the low adoption of insurance among potential policyholders. Random Forest was the most robust model of the three classifiers based on Kappa score, recall score, F1 score, precision, and area under the operating characteristic curve (approaching 1). The paper explores eight reasons why people currently do not have insurance policies. The results indicated that affordability was the primary driver of uptake with 68.67% of having expressed a desire to possess insurance but are unable to afford it. The highest level of education being the next most significant factor. Cultural and religious beliefs and mistrust of insurance providers were found to have a minimal impact on uptake. These findings imply that offering affordable insurance products and conducting awareness campaigns are critical to increase insurance adoption.

Agricultural insurance as a climate risk adaptation strategy in developing countries: a case of Nigeria

ABSTRACT Despite the potential benefits of agricultural insurance in helping farmers adapt to climate risks, its uptake among smallholder farmers remains limited. This study analyses the drivers of awareness and adoption of agricultural insurance in Nigeria to better understand the adoption process. 1,080 farming households were surveyed across six agro-ecological zones in Nigeria, covering areas with different socio-economic characteristics of farmers and levels of climate risk. Data were collected through face-to-face interviews between October 2020 and February 2021. The results show that more than half of the farmers were unaware of agricultural insurance. Logit regression results show that education, herd size, access to a bank, weather information, and flood experience positively influence awareness and adoption of agricultural insurance. In addition to low awareness, the main barriers to adoption are lack of knowledge about the effectiveness of insurance, difficulty in affording insurance, and farmers’ low level of trust in insurance providers. Late payment of claims and inadequate compensation were the main challenges faced by adopters of agricultural insurance. Raising awareness and helping farmers to assess the effectiveness of agricultural insurance, as well as developing a supportive institutional environment, would help to build a well-functioning insurance market. Key policy insights To increase the uptake of agricultural insurance, it is first necessary to raise awareness among farmers. Government agencies should consider monitoring and sanctioning insurance agencies and companies that fail to comply with contractual agreements to promote and ensure prompt payment of due compensation; to increase transparency of insurance providers’ performance; and to increase confidence in insurance providers. We recommend using different premium payment methods, adjusting land policies, improving access to weather information, and increasing access to bank credit for smallholder farmers to increase farmers’ motivation to use insurance.

Uncertainty in the drylands: Rethinking in/formal insurance from pastoral East Africa

Amidst climatic and economic volatility, agricultural development and climate adaptation policies have increasingly turned to weather microinsurance to manage uncertainties, particularly in dryland pastoral and agricultural settings. While the political embrace of insurance has been cause for concern amongst those who fear insurance will undermine embedded coping mechanisms and moral economies, economists have puzzled over low insurance adoption rates amongst target populations. This article argues for an approach that scrutinizes insurance in relation to dynamic social practices and norms for responding to uncertainty. We employ this approach to investigate pastoralists’ encounters with index-based livestock insurance in Northern Kenya and Southern Ethiopia. Drawing on interview, ethnographic, and survey data, we demonstrate how insurance is understood within larger moral economies and collective imaginaries for living with and managing uncertainty in the drylands. Relational understandings shape pastoralists’ participation in risk-sharing arrangements, collective and individual decisions about livestock insurance purchase, and eventual uses of insurance payouts. Payouts also support a broad array of social reproductive purposes and investments in social and political life. As we conclude, these findings upset the binary between formal and informal insurance, revealing how “formal” index insurance must be negotiated with embedded social affiliations, rights, obligations, and understandings of uncertainty.

Protecting land and livelihood under climate risks: What hinders crop insurance adoption?

Small-scale agriculturists in developing countries face continuous weather-related risks, which are exacerbated by the growing issues of climate change (See Shah et al., 2018 for Pakistan; Zakaria et al. (2020) for Sub Sahara Africa). Despite efforts to introduce formal crop insurance at a subsidized rate by many governments, including in India, insurance penetration rates remain low in less developed countries (LDCs). One reason for this is continued reliance on informal credit as a risk-coping mechanism in poorer regions in times of agricultural distress. This prompts the question as to why farmers rely on these methods when apparently less expensive formal crop insurance is available. To answer, we investigate the link between the conditionalities of informal credit and the decision to adopt crop insurance together with other factors such as farmers’ socioeconomic class, education, and attitude to risk. Thereby, we derive policy implications for enhancing insurance penetration among poor farmers. A theoretical model is first constructed to provide a motivation for the empirical relation examined. An analysis of the decision to adopt crop insurance and the extent of insurance coverage availed is undertaken using a two-part fractional logistic regression with crop insurance data from India (provided by the National Sample Survey Office). The results demonstrate that informal interest rates are significantly and positively correlated with the probability of taking up crop insurance, and disparities between economic and social classes in the adoption of crop insurance exist. Although the ills of usurious informal interest rates are often highlighted (Bhaduri, 1997), this indirect positive effect on crop insurance penetration has not been established heretofore. The exercise also looks at the possibility of loss expected by a farmer and their insurance adoption behaviour by relating it to Prospect Theory (Kanheman, Tversky, 1979).

Impact of government risk communication on residents’ decisions to adopt earthquake insurance: Evidence from a field survey in China

This study employs a field survey of 640 rural households in a seismically active area of China to examine the impact of government risk communication intervention on the adoption of earthquake insurance. Four treatments were performed, and they were accepted in various forms depending on the level of risk communication. Findings indicate that door-to-door risk communication from the government motivates residents to buy earthquake insurance. Furthermore, after receiving tailored risk communication, low-income groups and ethnic minorities demonstrate greater willingness to pay for insurance. These findings have implications for how comprehensive frameworks for managing disaster risk should be created in developing nations faced with escalating natural disaster hazards.

The role of peer imitation in agricultural index insurance adoption: Findings from lab‐in‐the‐field experiments in Kyrgyzstan

AbstractFrequent climate shocks require farmers in developing countries to increase their resilience. Although index insurance is often discussed as a promising climate adaptation strategy, take‐up rates are still low. This study primarily explores the role of peer behavior as peer imitation in the demand for three marketable and unsubsidized crop index insurance options. Furthermore, the influence of trust and understanding is investigated. We collected data in lab‐in‐the‐field experiments among farmers in Kyrgyzstan, where index insurance is planned for imminent implementation. Applying ordered logit estimations, our results show significant and strong peer imitation effects. Imitation attitudes decrease with own insurance experience and received insurance payouts, but intensify with peer size, insurance trust and practical insurance understanding. While trust robustly increases index insurance adoption, understanding effects only gain significance in the dynamic perspective. These findings underline the importance of community‐based extension treatments and trust toward the uptake of innovative agricultural technologies in the first steps of dissemination.

Systematic process for crop insurance development: area-yield rice insurance with machine learning technology implementation in Thailand

PurposeThis paper presents the systematic process framework to develop the suitable crop insurance for each agriculture farming region which has individual differences of associated crop, climate condition, including applicable technology to be implemented in crop insurance practice. This paper also studies the adoption of new insurance scheme to assess the willingness to join crop insurance program.Design/methodology/approachCrop insurance development has been performed through IDDI conceptual framework to illustrate the specific crop insurance diagram. Area-yield insurance as a type of index-based insurance advantages on reducing basis risk, adverse selection and moral hazard. This paper therefore aims to develop area-yield crop insurance, at a provincial level, focusing on rice insurance scheme for the protection of flood. The diagram demonstrates the structure of area-yield rice insurance associates with selected machine learning algorithm to evaluate indemnity payment and premium assessment applicable for Jasmine 105 rice farming in Ubon Ratchathani province. Technology acceptance model (TAM) is used for new insurance adoption testing.FindingsThe framework produces the visibly informative structure of crop insurance. Random Forest is the algorithm that gives high accuracy for specific collected data for rice farming in Ubon Ratchathani province to evaluate the rice production to calculate an indemnity payment. TAM shows that the level of adoption is high.Originality/valueThis paper originates the framework to generate the viable crop insurance that suitable to individual farming and contributes the idea of technology implementation in the new service of crop insurance scheme.

Does weather index-based insurance adoption influence Cocoa Output? An endogenous swicth regression approach

ABSTRACT Agriculture is the most weather-dependent and risky sector where it is unpredictable when a season will be profitable during harvest. Droughts and floods may increase the risk of weather-related catastrophes in agriculture. Weather index Insurance has been widely recommended for decades to control weather risk. 600 Ashanti cocoa producers were surveyed to see how weather index insurance adoption affects cocoa output in Ghana. The endogenous switching regression (ESR) technique coupled with probit models were employed in this study. Cocoa bean production was 30.8 percent whiles only 15.8% of cocoa farmers knew about weather index insurance. The average annual farm income, off-farm, and remittances were 7.40, 7.26, and 6.61, respectively. Weather index insurance adoption and cocoa output were positively influenced by cocoa farm age, gender, education, awareness, and access to finance. We found that weather index insurance negatively influences cocoa output in Ghana due to inadequate information on insurance among farmers, lack of education, lack of awareness, and access to credit. Our findings will help Ghana Agricultural Insurance Pool and the government improve accessibility, affordability, and awareness for cocoa growers in the region and beyond. The findings will add to weather index insurance literature and improve agriculture risk.

Modeling Vehicle Insurance Adoption by Automobile Owners: A Hybrid Random Forest Classifier Approach

This study presents a novel hybrid framework combining feature selection, oversampling, and machine learning (ML) to improve the prediction performance of vehicle insurance. The framework addresses the class imbalance problem in binary classification tasks by employing principal component analysis for feature selection, the synthetic minority oversampling technique for oversampling, and the random forest ML classifier for prediction. The results demonstrate that the proposed hybrid framework outperforms the conventional approach and achieves better accuracy. The purpose of this study is to provide insurance managers and practitioners with novel insights into how to improve prediction accuracy and decrease financial risks for the insurance industry.

Determining factors and impact of household income on dairy cattle insurance in Nepal

Nepalese agriculture insurance market is dominated by livestock sector and cattle are the most popular class of animal in livestock production system. This study was conducted to identify the determining factors and impact of household income on dairy cattle insurance. This study was based on the field survey conducted in Chitwan and Nawalparasi districts of Nepal. A total of 160 households were sampled using a stratified simple random sampling technique. Data were collected using a pre-tested interview schedule and focus group discussion (FGD) in 2017. The result of t-test showed annual household income, income from milk sell, and average cattle holding significantly higher in cattle insurer farmers than that of non-insurers. A probit model was used to assess the factors affecting the decision for the adoption of cattle insurance. The probit model revealed that cattle breed, access to loan, income from livestock, and number of cattle had a positive and significant relationship with the adoption of cattle insurance whereas, household size and district dummy had a negative and significant relationship with the adoption of cattle insurance. Income regression function revealed that the adoption of insurance, the number of cattle, and the district of the respondents had a positive and significant relation with income from livestock. The instrumental variable model adopted to combat endogeneity bias showed that adoption of insurance and district of the respondents had a positive and significant relation with income from livestock. Although the results are farmers specific, findings have implications for policymakers regarding promotion of improved breeds and facilitation of farm credit.

Adoption of Cyber Insurance in Malaysian Organisations

As businesses have embraced more and more technology, cyber risk has become one of the essential components of a risk management initiative that seeks to mitigate and analyze the multitude of new risks. One risk mitigation process is investing in cyber insurance to safeguard the organisations’ assets from cyber threats by transferring them to the insurer. For businesses to decide on cyber insurance, choosing the right insurance plan and policy is crucial. To do so, they need to consider the factors of its implementation. Therefore, this paper aims to investigate the adoption of cyber insurance in Malaysian organisations, thus further exploring the organisations' perceptions of the factors of adopting cyber insurance. A qualitative study is conducted to understand and describe Malaysian organisations’ needs, obstacles, and processes related to cyber insurance adoption by interviewing cybersecurity professionals and cyber insurance personnel to explore valuable insight into the existing knowledge. 30% of the respondents have adopted cyber insurance. While 40% of the respondents were unsure, the other 30% did not adopt cyber insurance in their organisation respectively. These insights include the factors in adopting cyber insurance among Malaysian organisations, which might be crucial for informing future research and practice in the cyber insurance industry. The novelty of this research is the overview of knowledge gaps and perceptions related to the adoption of cyber insurance in Malaysian organisations. Also, this research aims to consider the advantages of the relevant obstacles that influence the decision makers to adopt cyber insurance.

Return insurance in a competitive market: Benefiting the high-quality or low-quality retailer?

Return shipping insurance (return insurance), as emerging insurance on many online platforms (e.g., Taobao.com and JD.com), develops rapidly in China over the past decade. Many retailers on the platforms purchase return insurance for consumers to reduce consumers’ hassle cost, while some retailers abandon return insurance. To investigate the retailers’ divergence on the adoption of the return insurance, we build a duopoly model in which two competing online retailers with different quality levels sell products to consumers who have uncertainty about their satisfaction. Both retailers can choose whether to purchase return insurance at different premiums. Our finding shows that as long as the increase in the consumers’ expected utility is higher than the cost of premium, the retailer is better off offering return insurance, independent of his competitor’s decision. Then, when the premium for the high-quality product is sufficiently low and the premium for the low-quality product is sufficiently high, the retailer with the high-quality product purchases return insurance for consumers and the retailer with the low-quality product abandons return insurance. Moreover, when both retailers offer return insurance, the retailer with the high-quality product may reduce or increase the retail price and the retailer with the low-quality product always increases the retail price. Finally, we examine the interaction between the retailers’ decisions on quality levels and the adoption of return insurance, and show that both retailers may improve quality levels when offering return insurance.

Machine Learning Models for Predicting Romanian Farmers’ Purchase of Crop Insurance

Considering the large size of the agricultural sector in Romania, increasing the crop insurance adoption rate and identifying the factors that drive adoption can present a real interest in the Romanian market. The main objective of this research was to identify the performance of machine learning (ML) models in predicting Romanian farmers’ purchase of crop insurance based on crop-level and farmer-level characteristics. The data set used contains 721 responses to a survey administered to Romanian farmers in September 2021, and includes both characteristics related to the crop as well as farmer-level socio-demographic attributes, perception about risk, perception about insurers and knowledge about agricultural insurance. Various ML algorithms have been implemented, and among the approaches developed, the Multi-Layer Perceptron Classifier (MLP) and the Linear Support Vector Classifier (SVC) outperform the other algorithms in terms of overall accuracy. Tree-based ensembles were used to identify the most prominent features, which included the farmer’s general perception of risk, their likelihood of engaging in risky behaviour, as well as their level of knowledge about crop insurance. The models implemented in this study could be a useful tool for insurers and policymakers for predicting potential crop insurance ownership.

Influencing factors of the extent, timing, and pattern of online insurance adoption

Influencing factors of the extent, timing, and pattern of online insurance adoption

Becoming Entitled: Relief, Unemployment, and Reform during the Great Depression

Becoming Entitled: Relief, Unemployment, and Reform during the Great Depression

Assessing benefits of crop insurance vis-a-vis irrigation in Indian agriculture

This study has assessed the impact of crop insurance vis-à-vis irrigation on farm income and its higher-order moments. The study utilizes nationally representative farm survey data from India and applies a multinomial endogenous switching regression technique to estimate these measures' relative income and risk benefits. Four key findings have emerged from this analysis. First, farmers' decisions on risk management and adaptation measures are influenced by the historical exposure to climatic shocks, resource endowments, institutional credit and social safety nets for food security and employment. Second, crop insurance and irrigation improve farm income and reduce farmers' exposure to downside risk. Third, the income and risk benefits are bigger in the case of their joint adoption. Fourth, there is spatial heterogeneity in the benefits of these—crop insurance is relatively more effective at higher levels of rainfall, while the converse is true for irrigation. The key implication of these findings is that greater adoption of crop insurance requires designing cost-effective insurance products considering the adaptation benefits of irrigation and other such farm practices that reduce farmers' exposure to downside risk besides causing an improvement in farm income.

Barriers and enablers to adoption of cyber insurance in developing countries: An exploratory study of Malaysian organizations

Cyber insurance presents a compelling value proposition for organizations wanting to reduce risk exposure and recover losses from incidents. However, the adoption rate of cyber insurance outside first-world countries has been relatively low. In this paper we focus on the interesting case of Malaysia – a nation with a low rate of cyber insurance adoption despite its ranking among the top ten countries with the highest level of commitment to cybersecurity. We conducted an exploratory study to investigate the key barriers and enablers to cyber insurance adoption in Malaysia. Following a hybrid approach of deductive and inductive reasoning, we reviewed the literature to identify barriers and enablers to the adoption of cyber insurance. Subsequently, we conducted eight semi-structured interviews with experts from seven Malaysian organizations. Based on the qualitative and quantitative analysis of interviews, we developed a ranked list of twelve barriers and seven enablers to the adoption of cyber insurance. Our study makes two key contributions. First, it contributes to cyber insurance literature by providing the most comprehensive explanation to date for the low rate of cyber insurance adoption in developing countries. Second, our ranked lists provide prioritized guidance to cyber insurance stakeholders (e.g. government, regulatory authorities, insurance providers, and insurance policyholders) on the specific barriers to overcome and enablers to leverage in order to improve the adoption of cyber insurance in Malaysia.