The willingness to pay for flood insurance in mekong river delta

INTRODUCTION

Problem statement

Natural disasters, exacerbated by climate change, have led to significant losses in human life and property, both directly and indirectly The frequency of these disasters is on the rise, with the IPCC reporting in 2007 that global temperatures have increased by approximately 0.76°C and sea levels have risen by about 20 centimeters since 1900.

Natural disasters are increasingly causing significant damage, particularly in areas that are already vulnerable to such events, a trend that is exacerbated by climate change (Pielke et al., 2005).

The Mekong River Delta, situated at the lower reaches of the Mekong River Basin, faces seasonal flooding challenges due to high flow rates exceeding 65,000 m³/s during the wet season and its low-lying terrain Research by the Vietnam Academy for Water Resources indicates that between 1991 and 2009, the area affected by annual floods has expanded from 1.6 million to 2 million hectares (To & Tang, 2011).

Every year, local residents in the Mekong River Delta prepare meticulously for the flood season to mitigate potential damage In 2011, the Vietnam Mekong River Delta Project, supported by the United Nations, aimed to enhance flood resistance for impoverished households This initiative involved reinforcing homes, inspecting flood protection infrastructure, conducting aid drills, and providing essential medicines to prioritized groups Despite these thorough preparations, the 2011 flood resulted in over 1,000 billion VND in property damage, affecting 27,000 hectares of rice and vegetable crops, with 10,000 hectares completely destroyed, along with nearly 12,000 hectares of fruit-bearing land also inundated.

The statistical data of flood damage in the Mekong River Delta from 1990s to 2000s shows that flood damages in downstream Mekong River Delta become abnormal.

Figure 1.1 Flood damages in the Mekong River Delta from 1990s to 2000s

Source: Collect from Nguyen, 2006; Dao & Bui, 2009; MRC, 2011; MRC, 2012

Recovery in the Mekong River Delta following floods is a prolonged process, largely due to residents' reliance on aid from government and humanitarian organizations This support is often inconsistent, highlighting the importance of self-sufficiency For instance, after the severe flooding in 2011, local communities faced a wait of at least one year to receive full assistance from the International Federation of Red Cross.

Figure 1.2 Disbursement process of IFRC funding contribution for Floods in MRD

Source: Report of International Federation Red Cross, 2013

Post-flood relief efforts in the Mekong Delta fall short of fully covering agricultural costs, as farmers typically invest around 15 million dong per hectare in their farms Despite the government's attempt to increase post-disaster subsidies to 5 million dong per hectare for paddy after the devastating 2011 floods, this amount only compensates for approximately 30% of the total agricultural investment (Ngoc Anh, 2011).

Natural disasters, such as floods, significantly heighten the financial burdens and default risks faced by farmers, leading to the potential threat of double liabilities Due to financial constraints, many farmers resort to purchasing agricultural inputs on credit, often at inflated prices payable post-harvest Typically, farmers and agents establish a four-month debit purchase agreement; however, if farmers fail to repay the loan by the due date, the outstanding debt incurs a monthly interest rate of 3-4% (Ngo, 2013).

Farmers face the persistent risk of accumulating debt despite having access to bank loans Specifically, rice farmers can secure loans of approximately 1 million VND per 1,000 square meters, while the average expenses for rice cultivation and harvesting range between 2.2 million VND and 2.5 million VND per 1,000 square meters.

1000� 2 , and transferring value is about 40-50 million VND (Ngo, 2013) Therefore, occurrence of flood might induce farmer to fall into debt piling up.

In the 2000s, the Mekong River Delta played a crucial role in Vietnam's agriculture, contributing over 48% of the nation's food production and 85% of its rice exports (To & Tang, 2011) However, projections by the Flood Prevention Agency in HCMC indicate that by 2030, around 45% of the Delta's area may face severe salinity issues and flooding, potentially resulting in losses of up to $17 billion (2008) The construction and operation of numerous dams in the Mekong River Basin have left Vietnam vulnerable to unpredictable flood discharge and prevention challenges For instance, heavy rainfall can lead to significant dam releases, triggering a domino effect across the entire network of 12 dams, which poses a substantial threat to the Delta's future and the country's overall stability (Huynh & Phan, 2015).

Adapting to floods, or "Living with floods," is essential for maximizing the benefits of flooding while preserving Vietnam's rice granary In addition to flood prevention infrastructure, disaster insurance plays a crucial role in this adaptation strategy For developing countries, investing in catastrophe insurance is a wise choice, as it helps reduce damage and mitigates the risk of falling into disaster-induced poverty Well-designed insurance not only aids in the adaptation process but also enhances risk management and recovery efforts following adverse events Furthermore, flood insurance alleviates the financial burden on governments during recovery, enabling communities to return to normalcy more swiftly In cases of severe natural disasters, insurance companies can distribute risk by using premiums collected from other households to compensate those affected.

Market principles can enhance the effectiveness of private insurance companies in implementing risk-reduction strategies compared to public entities (Priest, 1996) By incentivizing climate-adaptable construction designs and offering premium discounts, insurance policies can motivate households to engage in risk-reduction activities For instance, in areas prone to seasonal flooding, insurers might exclude coverage for wooden floors, thereby encouraging the use of flood-resistant materials (Thieken et al., 2006) A post-2002 flood survey in Germany revealed that insurance buyers are more attentive to flood mitigation efforts than non-buyers (Thieken et al., 2006), ultimately helping to lessen recovery costs and the adverse impacts of natural disasters.

Flood insurance and agricultural insurance are relatively new to Vietnamese farmers, contributing only 0.015% to total insurance revenue, as reported by the Ministry of Finance Since 2011, these insurance products have been in a pilot phase, hindered by challenges such as the lack of defined insured objects and risks, diverse local disaster conditions, and limited technical and information technology infrastructure Additional obstacles like ambiguity, adverse selection, moral hazard, and correlated risks further deter the growth of the private insurance market Consequently, researching the demand for flood and agricultural insurance in disaster-prone areas is essential for the advancement of these insurance products and the effectiveness of insurance companies.

The demand for flood insurance has been extensively studied in developed countries using insurance statistics (Kunreuther et al., 2009; Michel-Kerjan & Kousky, 2010) However, this approach is not applicable in regions like Vietnam, where the disaster insurance market is still developing Consequently, utilizing primary data is often recommended (Aliagha et al., 2015; Brouwer & Akter, 2010; Brouwer et al., 2013; Reynaud et al., 2012) While some studies have employed the Choice Experiment Model to explore potential flood insurance markets, they still contain significant errors despite offering valuable insights.

In Vietnam, flood insurance remains a novel product for farmers, making the choice experiment method particularly relevant for assessing demand While two studies have explored flood insurance demand in Central Vietnam using the Choice Experiment Model, they failed to address errors from prior research and overlooked the influence of local residents' perspectives on their insurance needs Additionally, there is a notable gap in studies focusing on flood insurance demand in the Mekong River Delta.

Facilitating the disaster insurance sector in developing countries such as Vietnam is essential Understanding the willingness-to-pay (WTP) of individuals in disaster-prone areas, along with the obstacles affecting their WTP, is crucial for enhancing insurance accessibility and effectiveness in these regions.

Research objectives

This study utilized Choice Experiments to assess the willingness to pay (WTP) for flood insurance among farmers in the Mekong River Delta, aiming to evaluate their financial commitment to mitigating flood risks.

Firstly, we estimate the impacts of flood insurance attributes on the utility of farmers in Mekong River Delta.

The development of the flood insurance market and local irrigation services faces significant challenges, including ambiguity, adverse selection, and charity hazard These issues impact the effectiveness of irrigation improvements and access to pumping stations Previous studies highlight that local farmers' perceptions of their vulnerability, fear of flooding, government accountability, and the quality of local irrigation services play a crucial role in shaping these challenges.

We will assess the willingness-to-pay of local farmers for various attribute levels and specific flood insurance packages, considering different combinations of policy types and providers based on the estimated results.

Fourthly, we determine the variation of probability of WTP of Mekong River Delta farmers who are willing to pay for flood insurance with the changes in premium level.

Finally, we would like to present some appropriate suggestions to insurance companies and policymakers.

Scope of the study

This study was conducted in October 2015, focusing on data collected from three districts in the Mekong River Delta: Gao Giong, Phu Loc, and Tan Cong Chi These areas have experienced significant impacts from flooding and other natural disasters in recent years.

Structure of this thesis

This thesis is structured into five chapters, beginning with Chapter 2, which explores theories related to choice experiments and the challenges associated with flood insurance, alongside a review of empirical studies on the demand for flood insurance and similar products Chapter 3 details the data collection methods and methodologies employed in the study In Chapter 4, the focus shifts to the interpretation and discussion of the statistical findings and empirical results Finally, Chapter 5 presents the conclusions drawn from the study, along with recommendations for flood insurance companies.

Previous studies without using RUM

2.1.1 Researches on agricultural insurance using secondary data or combine with primary data

Despite the significant potential of agricultural insurance to alleviate government burdens, such as reducing damage and preventing disaster-induced poverty in developing regions, empirical research on its demand remains limited (Barnett et al., 2008) Most studies have concentrated on the North American market and other developed nations, often relying on secondary data (Atreya et al., 2015; Dumm et al., 2012) or combining it with raw data (Enjolral et al., 2012; Sherrick et al., 2003).

A study analyzing data from 135 counties in Georgia between 1978 and 2010 revealed that economic and demographic factors significantly influence flood insurance purchasing, while flood mitigation assistance does not (Atreya, 2015) Building on this, Dumm et al (2012) combined this approach with insights from Volkman-Wise (2012) regarding the representative heuristic's impact on residents' risk assessment behaviors The findings indicate that recent natural disasters positively affect disaster insurance demand, although this influence diminishes over time, leading to an underestimation of risks prior to such events.

Researches not applying RUM on agricultural insurance using primary data

In developing countries where agricultural insurance is still emerging, the lack of secondary data makes surveys essential for assessing the potential for crop and disaster insurance In Malaysia, factors influencing the demand for flood insurance include demographics, exposure levels, resilience, adaptability, and residents' perceptions of vulnerability (Aliagha et al., 2015) However, this study is relatively basic and fails to identify significant issues within the insurance market or recommend specific flood insurance packages.

In the insurance market, information asymmetries can lead to market failures like adverse selection and moral hazard To address these issues, research has expanded the application of Expected Utility Maximization within theoretical frameworks, while also accounting for heterogeneity Studies, such as the one conducted by Smith and Baquet (1996), have examined how adverse selection influences individual risk perception of premiums Their findings, based on a survey in Montana, highlight the importance of understanding these dynamics in the insurance industry.

In 1996, Heckman's two-stage estimation procedures were utilized to analyze agricultural insurance decisions, incorporating a probit model for participation and OLS for coverage levels Various factors influenced local farmers' decisions to participate in insurance Adverse selection led to differing coverage-level choices between farmers with positive expected returns and those with negative expected returns, indicating that premiums are ineffective in reducing the loss ratio.

A study by Ye et al (2015) applied the Expected Utility Maximization and Random Utility Theorem to investigate how perceptions of insurance contracts influence farmers' participation decisions and demand for multi-peril insurance The research highlights that farmers' participation is affected by insurance premiums, perceptions, and indirect utility, with a simultaneous correlation between insurance participation and perception analyzed through simultaneous equations However, econometric analysis of survey data from Hubei, China reveals that local farmers possess a low perception of insurance contracts, despite long-term government subsidies, which may explain the lack of strong evidence for learning-by-doing in the insurance market.

The demand for flood insurance among farmers tends to rise following severe flooding events, particularly when factors such as risk behaviors, mitigation and adaptation efforts, financial constraints, psychological influences, and information gaps are taken into account (Turner et al., 2014) To better understand the risk attitudes of farmers, a lottery experiment was conducted, as capturing these behaviors is challenging In Pakistan, the Expected Utility Maximization framework was utilized, employing a Probit Model with a binary insurance choice experiment as the dependent variable, to explore the determinants influencing flood insurance participation (Turner et al., 2014).

Although using Binary Probit Models in Expected Utility Maximization framework is quite effective in finding the determinant of flood or crop insurance participation, it does not consider

The decision to participate in insurance is significantly influenced by various attributes, as potential clients often have unrealistic expectations about insurance packages When actual flood insurance options are presented, these clients may develop negative perceptions or feel disillusioned To address this challenge, some research has utilized the Contingent Value Method (CVM) to assess the demand for agricultural insurance products effectively.

Contingent Value Method was used to obtain the WTP for breeding-sow insurance (Wan, 2014).

In a survey, respondents utilized a payment card method that displayed calculated premiums, coverage options, and the premium-to-coverage ratio to select their preferred combination If participants struggled to find the ideal choice, they were prompted with open-ended questions for further insights The Tobit Model was employed due to the always-positive nature of the willingness to pay (WTP) for premium and coverage, revealing that the average WTP and coverage levels significantly exceeded current figures (Wan, 2014) However, a key limitation of the Contingent Valuation Method (CVM) is the challenge of accurately determining the maximum WTP from a single question, which can introduce substantial bias Additionally, constraints in presenting scenarios and the inability to consider simultaneous changes in insurance contracts have led researchers to seek improved methodologies.

Random utility model (RUM) and applications

Classical economic theory posits that consumers strive to maximize their self-interest, which is reflected in heterogeneous preferences theories through a utility function U(x), where x represents the quantity of goods consumed within a budget constraint This constraint can be expressed as px ≤ a, with p denoting the price vector and a representing income The resulting demand is formulated as x = d(a, p) + ε, where ε accounts for measurement errors in consumer behavior Such disturbances may arise from random factors affecting the objectives or constraints of economic agents (Griliches, 1975).

In 1927, Thurstone's law of comparative judgment proposed that individuals respond to stimuli by selecting the option that offers the highest level of stimulus Each alternative, denoted as j, is characterized by an objective level and a random error term, influencing the decision-making process.

In 1960, Thurstone's study on stimulus levels was adapted for economics, leading to the development of the Random Utility Maximization Model This model incorporates the effects of random factors on both binary and multiple choice probabilities in the context of utility maximization (Marschak, 1960).

Marschak expressed as utility level will be � � = � � +� � = � � � +� � , where � � and � � also were considered as systematic component and random component respectively.

In 1966, Lancaster introduced the "new theory of consumer demand," which transformed standard microeconomic theory by suggesting that consumers seek to acquire the characteristics of goods rather than the goods themselves This approach enables researchers to analyze the desired attributes of consumers and estimate demand curves for new products more effectively Building on Lancaster's theory, McFadden expanded the study by incorporating both the quantity of goods and their characteristics into the systematic component of demand analysis.

In McFadden's 1978 study, the systematic component of utility for an alternative j is represented by the equation U_j = β_0j + β_1X_1j + β_2X_2j + + β_kX_kj, where X_kj denotes the k-th attribute level of alternative j, β_k represents the marginal utility of that attribute, and β_0j is the alternative-specific constant reflecting preference independent of attributes The error term follows a Gumbel distribution, denoted as ε ~ G(μ, σ), with μ and σ being the location and scale parameters, respectively Ultimately, the alternative with the highest utility level is selected.

In the binary choice, the utility function U(x) of vector x is random utility indicator when:

In the multiple-choice, the utility function U(x) of vector x is random utility indicator when:

To sum up, the probability of choosing alternative j:

�=1� � � The coefficients of the utility functions are estimated by maximizing the log-likelihood function:

Where: � �� the choice of individual i on alternative j (1 = chosen)

The Random Utility Model incorporates a random factor influenced by unobserved heterogeneity, including variables like experience, preferences, and information sources regarding attributes This randomness leads to the development of choice probability models based on specific parameters It is assumed that the unobserved elements of consumer characteristics are correlated with the observed ones, contributing to the formation of subjective perceptions Consequently, there exists a continuous random field index that bridges unobserved and observed characteristics.

2.2.2Researches applying RUM on agricultural insurance

RUM effectively addresses the potential biases and drawbacks of CVM by assessing the maximum willingness to pay (WTP) through comparisons of various insurance contract drafts Additionally, offering well-structured insurance package options enhances the evaluation process, leading to more accurate insights into consumer preferences.

∑ help potential clients to have a clear visualization Due to many advantages, Random Utility Model is very suitable to study about the demand of agricultural insurances.

Holistic insurance, similar to flood insurance, is a relatively new and complex product, prompting the application of Random Utility Models (RUM) to gauge consumer demand (Nganje et al., 2008) Utilizing the Choice Experiment methodology, a survey was conducted after carefully testing attribute levels and employing D-optimal main effects to select alternatives, which is a significant strength of Nganje’s research Inadequate attention to this step could lead to misleading results and create false expectations among potential flood insurance customers Many prior studies failed to test the scale of levels or account for local farmers' income, relying instead on historical averages (Mercadé, 2009) Additionally, some studies established inappropriate and unrealistic attribute levels, particularly concerning premiums and coverage (Brouwer et al., 2013; Reynaud et al., 2012).

Surveys typically utilize questionnaires that gather demographic data and contingent valuation (CV) questions Respondents are presented with choice cards featuring three insurance packages, allowing them to select the option they find most suitable or opt for "none of them" (Nganje et al., 2008) To minimize confusion and encourage rational decision-making, some studies limit each choice card to two insurance packages along with the "none of them" option (Mercadé, 2009; Brouwer & Akter, 2010; Brouwer et al., 2013; Reynaud et al., 2012; Opiyo et al., 2014; Liesivaara & Myyra, 2014).

Holistic insurance integrates crop and health insurance, addressing varying levels of coverage for farmers It is designed around four scenarios that farmers may encounter: experiencing a farm disaster with good or bad health, or maintaining good or bad health with a thriving farm Each insurance package typically comprises five distinct attributes, tailored to meet the diverse needs of farmers (Nganje et al., 2008).

Table 2.1 Attributes in the insurance package in Nganje’s study

Type of insurance RAH: revenue assurance, farm selects target revenue health and dental.

AGRH: adjusted gross revenue guarantees farm historical revenue health, dental and vision.

MPCIH: multi-perial crop insurance coverage for yield-related losses and health.

Implementing agency Government, cooperative, private

Many attributes in Nganje’s study (2008) can be found in other studies, though some authors used the different names, the definition is the same.

Types of insurance mainly mention about the combination between crop insurance and health insurance with the difference in level of subjects insured Some author named this attribute

In Nganje's (2008) study, the types of insurance available are tailored to reflect individual household situations and their specific needs, with families having medical histories often opting for AGRH, while risk-averse households may prefer MPCIH Importantly, these insurance types adhere to the principle of mutually exclusive alternatives in Random Utility Model (RUM) analysis However, numerous studies on flood insurance demand have breached this fundamental RUM rule by incorporating mutually inclusive policies, including property damage, crop damage, health damage, and unemployment income insurance (Brouwer & Akter, 2010; Reynaud et al., 2012), as well as yield and rainfall insurance (Jorgensen & Termansen, 2015).

Implementing agencies are often referred to as "providers" in various studies, and it is crucial that their attributes remain consistent to avoid confusing potential clients Authors should present uniform levels based on the proportion of business owners, such as government and private companies, or the types of products offered by insurance and micro-credit companies Inconsistent levels among provider attributes can adversely impact study results, highlighting the importance of clarity and consistency in research (Brouwer et al., 2013; Reynaud et al., 2012; Opiyo et al., 2014; Brouwer & Akter, 2010).

The term "coverage level" typically refers to the percentage of damage insured, but it is more accurately described as "indemnity insured level" (Nganje et al., 2008) However, using this percentage as an attribute may fail to emphasize the importance of encouraging insurance buyers to engage in mitigation activities Instead, it is more effective to utilize the term "deductible," which represents the portion of the loss that is not compensated and must be borne by the buyer (Liesivaara & Myyra, 2014) This shared responsibility for crop failure can serve as a motivating factor for farmers to actively participate in mitigation efforts.

“deductibles” into insurance package is a good contribution, but this have yet to be applied in flood insurance studies.

Flood insurance demand studies define coverage as the maximum financial compensation farmers can receive when their productivity falls below a specified threshold in the event of a disaster.

Research indicates significant shortcomings in the scale of insurance cover attributes, particularly in flood-prone areas Brouwer (2010) highlighted that insurance coverage is inadequate, with flood damages reaching up to $738 per household while the lowest recorded damage is $224, leaving medium-level damages uninsured Additionally, some studies have employed vague classifications like "low" and "high" without providing specific regulations on coverage levels (Opiyo et al., 2014).

Challenges of disaster insurance market

Flood insurance companies looking to expand in the Mekong River Delta face several challenges, including a lack of mandatory participation, ambiguity in policies, adverse selection, and charity hazard (Botzen, 2010) These factors significantly hinder the demand for flood insurance in the region.

Ambiguity i s the difficulty in assessing accurately the probabilities of flood and its potential damages, predicting these issues requires a lot investment and many technical methods (Botzen,

2010) Moreover, the combination of climate change phenomena and variation of socio- economic characteristics contributes to make prediction to be more complicated (IPCC, 2007).

Simulation scenarios of climate change and socio-economic trends are essential for assessing future risks, particularly in understanding the magnitude and patterns of floods and other adverse events Historical data and statistical methods play a crucial role in this analysis (Saunders and Lea, 2008; Schmidt et al., 2010) However, these methods are limited by their reliance on past data, and despite advancements in computer-based techniques like catastrophe models, predictions remain uncertain (Grossi & Kunreuther, 2005) Consequently, the challenge of accurately pricing flood insurance persists, as ambiguity in natural disaster outcomes has yet to be fully addressed.

2.3.1.2The impact of ambiguity on flood insurance demand

22Empirical study found that the more ambiguous adverse events are, the more insurance premium would be charged (Kunreuther, 1996) The flood insurance companies have to set high premium

Natural disaster insurance often comes with higher premiums compared to other insurance types, like fire insurance, due to the uncertain severity and frequency of such events (Botzen, 2010) This elevated cost can lead to a decrease in demand for flood insurance.

The ambiguity and low probability of flooding can lead to significant underinsurance issues Research indicates that individuals often underestimate flood risks and neglect vital information, resulting in a lack of insurance coverage (Sunstein, 2002; Kunreuther et al., 2009) After experiencing a severe flood, many believe that such an event is unlikely to recur for another century, a phenomenon encapsulated by the term "100-year return." This perception, combined with the uncertainty surrounding flood risks, causes some clients to view flood insurance as an ineffective investment, leading them to abandon their insurance policies (Kunreuther et al., 1978).

Experiencing significant losses from low-probability risks, such as severe floods, can enhance people's awareness and visualization of these events Consequently, this heightened awareness may lead to an increased demand for safety and assurance, resulting in a higher willingness to pay (WTP) for flood insurance Therefore, establishing a high premium for flood insurance could be a prudent approach.

2.3.1.3Vulnerability perspective is a good method the capture the impact of ambiguity on flood insurance demand

Local farmers' awareness of flood threats is crucial, especially when potential flood damages are often ambiguous and unpredictable If farmers acknowledge these risks, the uncertainty surrounding floods will not hinder the growth of the flood insurance market.

The increasing popularity of media is heightening public awareness of vulnerability, significantly influencing insurance purchasing decisions As socio-economic developments, like population growth and improved living standards, make individuals more conscious of their susceptibility to adverse events, households with more members and assets recognize their heightened risk Additionally, social progress has raised awareness about vulnerabilities linked to environmental issues, such as rising greenhouse gas levels and global warming Consequently, perceptions of increased flood risk in local areas are driving a greater demand for flood insurance and a willingness to pay more for coverage.

A study by Botzen and Bergh (2008) reveals that adopting a vulnerability perspective significantly enhances the willingness to pay (WTP) for flood insurance, indicating that this approach may be more effective for insurance providers than relying solely on historical data This suggests that understanding the impact of ambiguous risk can lead to better demand for flood insurance.

In the Mekong River Delta, residents rely heavily on media, particularly TV and radio, to stay informed about weather conditions and natural disasters like floods and windstorms Despite their annual preparations to mitigate flood damage, significant losses remain unpredictable The 2011 flood, despite efforts from the United Nations and local governments to enhance flood resistance through infrastructure improvements and aid provisions, resulted in over 1,000 billion VND in damages, affecting 27,000 hectares of rice and vegetables, with 10,000 hectares completely destroyed This highlights the critical need for farmers in the region to understand their vulnerability to flooding, emphasizing the importance of assessing how this perspective influences their demand for flood insurance.

In addition to employing a vulnerability perspective to assess the effects of ambiguity, we also account for the improvements in irrigation and the accessibility of pumping stations for respondents, as these elements significantly influence the demand for flood insurance.

2.3.2.1Definition of adverse selection and its impact on flood insurance demand

Adverse selection is a phenomenon where insurance is primarily offered to high-risk individuals at exorbitant premiums This issue arises because the demand for insurance correlates positively with the expected risks faced by individuals, and premiums are meant to reflect these risk expectations However, information asymmetries complicate this process, as potential buyers often have a clearer understanding of their risks than insurance companies, making it challenging for insurers to categorize customers accurately When premiums are based on expected average losses, insurers may struggle with insufficient capital, leading them to charge higher premiums exclusively to high-risk or risk-averse individuals, thus perpetuating adverse selection (Botzen & van den Bergh, 2008).

2.3.2.2Reason for using risk perception or fear of individuals to determine adverse selection issue

A study reveals that even if insurance companies assess flood risks for different customer groups, issues stemming from information asymmetry persist Individuals’ natural risk perception, often based on intuition, diverges from the predictions made by insurance companies (Slovic, 1987; Slovic, 2000) According to Kunreuther's (2001) research, emotional responses, such as fear, significantly influence insurance purchases over rational decision-making Furthermore, in the context of government relief, those who perceive higher risks are more likely to buy insurance, while individuals who believe they face lower risks may depend on assistance from government or charitable organizations (Kim and Schlesinger).

2005) Therefore, risk perceptions of individual plays an important role in making decision about mitigation and choosing self-protective measures (Burn, 1999; Flynn et al, 1999).

Rothschild and Stiglitz (1976) propose that the insurance market can achieve equilibrium by distinguishing between high-risk and low-risk individuals in the context of natural disaster insurance Understanding individual risk perceptions and fears is crucial for identifying potential clients' risk categories, which can effectively address the issue of adverse selection However, there is a lack of studies examining how fear influences willingness to pay (WTP) for flood insurance, and research on individual risk perceptions regarding floods remains underexplored, yielding mixed results (Peacock et al., 2005).

Previous studies on flood risk perception have examined the impacts of socio-economic development, household characteristics, and the aftermath of past disasters Additionally, geographical factors, such as proximity to hazards, have been considered in assessing flood risk perceptions Overall, individuals' anxiety regarding flood risks tends to have positive implications.

Flood risk maps reveal a significant correlation between individual fear of floods and expert evaluations, as noted by Seigrist and Gutscher (2006) However, variations in these perceptions are observed across different regions, indicating that local factors influence individual responses to flood risks (Brilly & Polic, 2005).

Demand for flood insurance

Natural disasters in the Mekong River Delta annually inflict significant damage on agricultural production, leaving local farmers reliant on subsidies and humanitarian aid to cope with rising cultivation costs Despite being identified as a promising market for insurance companies since 2013, agricultural insurance products, particularly flood insurance, remain largely in the pilot phase (Pham, 2015) Given that flood insurance is a relatively new offering for farmers in the region, employing choice experiments to assess its viability is an appropriate approach.

In the insurance sector, studies have traditionally relied on statistical data to assess the demand for flood insurance (Kunreuther et al., 2009; Michel-Kerjan & Kousky, 2010) Recently, researchers have employed the Choice Experiment Model to explore potential markets for flood insurance However, while these studies offer valuable insights, they also contain several notable errors that need to be addressed.

The advantages of Choice Experiment compared to Contingent Value Method

In addition to the Choice Experiment (CE), the Contingent Value Method (CVM) is a widely utilized approach among economists CVM is a non-market valuation technique designed to assess individual preferences for public goods, environmental quality, and other specific commodities (Carson et al., 2005).

The Choice Experiment (CE), also known as the Random Utility Model (RUM), offers a significant advantage by enabling the simultaneous assessment of how changes in insurance contract terms, such as premium levels and attributes, as well as environmental factors, influence insurance decisions In contrast, the Contingent Valuation Method (CVM) limits the variety of periods and circumstances that can be presented to respondents.

The Contingent Valuation Method (CVM) can introduce significant bias by determining the maximum willingness to pay (WTP) based on a single question In contrast, Choice Experiments (CE) address this limitation by assessing the maximum WTP through a comparative analysis of various insurance contract terms.

The Choice Experiment method is preferred for assessing the impact of various attributes, obstacles, and household characteristics on the decision to purchase flood insurance, instead of utilizing the Contingent Value Method.

General model

From alternative j, respondent can obtains utility as: � � = � � + � � (Marschak, 1960) With:� � : systematic component, can be observed by researchers.

� � : random component, cannot be observed by researchers.

The systematic component does not only mention about quantity of good but also about the characteristics of goods (McFadden, 1987)

� �� is attribute k level of alternative j,

� � is the marginal utility of attribute k,

� 0� is the alternative specific constant.

� � : the random variable with Gumbel distribution

Estimation

The Conditional Logit Model is commonly used to analyze individual decision-making among various alternatives, emphasizing the characteristics of each option and the overall set available to the individual This model helps formulate hypotheses regarding choices, suggesting that individuals will select the alternative that offers the highest utility or systematic component The systematic component is influenced by the attributes of the alternatives, as defined by a specific function.

� �� = Pr(� �� > � �� ) with all k not equal to j The probability that alternative j could be chosen by individual n (� �� ).

• � �� = Pr (j is chosen among C) = Pr (� ��� �� >� �� , ∀ � ≠ �, �, � ∈ �)

In the Conditional Logit Model, while the influence of a unit of Z remains constant across alternatives, the values of the independent variables must vary among the options If Z does not exhibit variation across alternatives, it will not impact the choice probabilities, as the differences in characteristic values are essential for determining these probabilities.

The coefficients of the utility functions can be estimated by maximizing this log likelihood function:

After derivative, this log likelihood function will be:

When the log likelihood function is maximized, its first derivative equal to zero So the values of β estimated by maximum log likelihood function obey first-order condition.

N is the size of sample, with some arrangements and mathematical operations:

In this equation, the left-hand side represents the observed average value of the z attribute in the alternatives selected by respondents (�̅), while the right-hand side indicates the predicted average value of the z attribute in the alternatives anticipated to be chosen (�̂) Therefore, at the point of maximum likelihood estimates, the observed average is equal to the predicted average (�̅ = �̂).

The coefficients (β) derived from maximum likelihood estimation ensure that the average predicted values (�̅) align with the observed averages (�̂) This allows for the replication of observed averages through the model using maximum likelihood estimation This characteristic remains consistent even when incorporating alternative specific constants, where a dummy variable for alternative j is utilized to represent the utility of alternative i in capturing sample shares.

Another feature is reflected in the first-order condition:

As residual is differential between the choice of respondents (y nj ) and choice probability of alternative j (P nj ), ∑ n

In maximum likelihood estimations, the covariance of residuals and independent variables, represented by (y ij − P nj )Z ij, is minimized by the coefficients (βs) of independent variables, ensuring no correlation exists between them This principle is similarly applicable to the first-order conditions of logit models as it is in linear regression models.

To solve the first-order condition equations, the method of moments estimator is utilized, which relies on moment conditions or correlations between variables and modeling errors (Train, 2003) This approach leverages a sample with characteristics similar to the population, ensuring the assumption of exogenous variables, meaning there is no correlation between the independent variables and the residuals Consequently, the chosen estimates are significant, and the relationship between residuals and independent variables in the sample is absent In summary, these estimates facilitate the regression of the model on an analog sample of the population, where the covariance is zero.

3.4.2Estimation on Subset of Alternatives

This study involves a substantial number of combinations, totaling 63 alternatives The research team randomly selected combinations for each choice card, which includes two alternatives and an option for "none of them." Given that each alternative can be chosen for a subset, the utility function must be estimated for these subsets in addition to the overall sample It is assumed that each alternative has an equal probability of selection.

The probability that alternative j will be chosen in subset H given denotes as �(�|�) Subset or choice cards H given to respondent must contain alternative j, if �(�|�) > 0 If the subset

H does not contain j, q( H | j ) = 0 ∀ H In the full set, the probability that alternative j is selected is � ��

The primary objective of estimation is to assess the likelihood of selecting alternative j, given that the research group has chosen subset H for respondent n This likelihood is referred to as conditional probability (P(j|H)).

The nominator represents the joint probability of selecting subset H and the respondent choosing alternative j, while the denominator indicates the probability of selecting subset H from all possible alternatives This relationship allows us to express conditional probability effectively.

In our research, we randomly selected alternatives for each subset, leading us to assume that any alternative \( j \) within subset \( H \) shares the same probability \( P(j|H) \) This implies that the probability of choosing alternative \( j \) when a respondent selects \( j \) is equivalent to the probability of choosing \( j \) when selecting any alternative \( k \) in subset \( H \) This principle, known as the "uniform conditioning property," allows us to simplify the expression of \( P(j|H) \) by canceling out \( P(k|H) \) (McFadden, 1978) Consequently, \( P(j|H) \) can be represented as a logit function based on the alternatives available in subset \( H \) for respondent \( n \).

) Under this property, the conditional log likelihood function (CLL):

The maximum conditional log likelihood (CLL) function, when applied to a subset K of respondents, produces estimators that are consistent with those derived from the maximum log likelihood (LL) function However, the CLL estimator is less efficient because it omits information that the LL function incorporates.

Description of variables

3.5.1Description of all attributes and levels

Table 3.1 Attributes of flood insurance packages

Variable Notation Insurance Attribute Level

Insurance Policy ������ ��� This attribute allows respondents to choose kinds of disaster causing damages that the buyers will be compensated.

Flood Flood and inundation Flood and windstorm Flood, windstorm and inundation

Provider �������� ��� Which types of business owner will provides the insurance packages.

Foreign company Private company Corporation

In an insurance policy, coverage refers to the maximum compensation an insured individual can receive for damages caused by specified disasters, after deducting the applicable deductible.

(%) ���������� ��� This attribute is the percent of damage that insurance buyers have to bear if disasters occur.

� ����� ��� The amount of money that insurance buyers have to pay per farming season for insurance company.

15,000 VND30,000 VND40,000 VND50,000 VND65,000 VND

An insurance policy is essential for helping clients select the types of disasters for which they will receive compensation Previous research indicates that providing a list of potential disaster damages can enhance clients' understanding of insurance packages (Nganje et al., 2008; Mercadé et al., 2009) The research team aimed to address the shortcomings of earlier studies by avoiding the use of exclusive insurance types in their analysis (Brouwer).

& Akter, 2010; Reynaud et al., 2012) Levels of insurance policy include flood, flood and thunderstorm, flood and inundation, flood, thunderstorm and inundation.

In this study, the introduction of flood insurance necessitates identifying the preferred types of providers (Nganje et al., 2008; Brouwer & Akter, 2010; Reynaud et al., 2012; Brouwer et al., 2013; Opiyo et al., 2014) To enhance clarity and consistency, the research team categorized providers based on business ownership, initially including state-owned, foreign, and private companies However, preliminary surveys revealed that some farmers exhibited a strong preference for state-owned companies, leading to a phenomenon termed "dominant choice," where respondents predominantly selected state-owned insurance plans To address this issue, the study replaced state-owned companies with corporations that have state participation, effectively resolving the dominant choice problem Consequently, the final provider categories in this research are foreign companies, private companies, and corporations.

The cover represents the maximum payout that insurance buyers can receive after deducting the deductible, helping insurance providers understand their payment limits For farmers, knowing this maximum indemnity amount is crucial for calculating cultivation costs and potential profits Previous studies have confused respondents by using percentages of damage to describe coverage levels, leading to misunderstandings (Reynaud et al., 2012; Nganje et al., 2008; Opiyo et al.).

In order to maintain intelligibility regarding insurance coverage, specific monetary levels were established, as noted by Liesivaara and Myyra (2014) The research team utilized statistics on flood damages and agricultural costs associated with paddy fields to determine appropriate coverage levels Farmers typically invest between 4 million and 5 million VND in their paddy fields each farming season Consequently, three coverage levels were set at 2,000,000 VND, 3,000,000 VND, and 4,000,000 VND per 1,000 square meters for each farming season.

Deductible attributes was used to capture the demand for crop insurance in the study of Liesivaara

According to Myyra (2014), there is a lack of disaster insurance studies that incorporate deductibles as a key attribute Utilizing deductibles is beneficial, as it promotes shared responsibility between providers and farmers, motivating them to minimize flood damage and other disaster impacts This study examines two deductible levels: 10 percent and 25 percent.

A key attribute that captures the interest of many respondents is the premium associated with agricultural insurance packages Previous studies have consistently highlighted this aspect; however, some researchers presented premium levels as percentages, leading to confusion among respondents (Reynaud et al., 2012; Nganje et al.).

2008) The research team tried to use a specific amount of money as levels of premium

2009; Liesivaara & Myyra, 2014) Besides, the statistic results about farming profit per

1000� 2 from initial surveys was utilized to maintain the suitability of premium levels Profit per 1000� 2 are around 450,000 VND The levels of this attribute are 15 000, 30 000, 40 000, 50 000, 65 000

3.5.2Description of variables used to capture challenges for flood insurance market development

The development of the flood insurance market faces three key challenges: ambiguity, adverse selection, and charity hazard By examining farmers' perspectives on vulnerability, fear of flooding, and government responsibility for post-disaster recovery, we can better understand their influence on flood insurance demand These perspectives are represented as dummy variables, allowing us to analyze their impact on the utility that potential flood insurance buyers may experience.

This article examines the influence of local farmers' perceptions of flood vulnerability on the development of the flood insurance market in the Mekong River Delta It highlights that if farmers acknowledge the threat of floods despite their ambiguity and unpredictability, this recognition could facilitate market growth Key variables analyzed include the interactions between flood vulnerability perception and both policy and premium rates Additionally, the study addresses adverse selection by assessing how farmers' fear of floods affects their decision-making, with variables focusing on the interaction between fear of flood and insurance policy and premium Lastly, the concept of charity hazard is explored through farmers' views on government responsibility and post-flood relief, incorporating variables that examine the interaction between charity hazard and insurance policy and premium.

The demand for flood insurance and the utility experienced by buyers are influenced by the interplay between various perspectives and the specific benefits offered by different policy types and premiums Understanding this interaction is crucial for assessing how these factors shape consumer behavior in the insurance market.

Table 3.2 Variables used to capture impacts of challenges for flood insurance development

Variable Notation Definition Perceptive Levels

This variable is used to capture the impacts of flood ambiguity.

It reflects the perceptive of flood vulnerability of respondent �.

High or extremely high flood vulnerability = 1 Low or very flood vulnerability = 0

This variable is used to capture the effects of adverse selection.

It reflects whether respondents have the anxiety or fear of flood damages or not

Fear of flood = 1 Not fear of flood = 0

This variable is used to capture the impacts of charity hazard.

This variable reflects whether respondents depend so much or hazard their recovery after flood on government

Hazard the post-flood recovery on government = 1, or not = 0

This variable is used to capture the impacts of irrigation improvement.

It reflects whether respondent recognizes that irrigation in local area are improved or not

This variable is used to capture the impacts of pumping station accessibility.

This variable reflects whether respondent’s fields can access to pumping to pumping stations or not

Having accessibility to pumping stations = 1

Table 3.3 The interactions between perceptive about flood insurance challenges and attributes

Interaction between flood vulnerability perceptive and utility of flood insurance policy types

This variable reflects the impact of ambiguity on utility of flood insurance policy types

Interaction between flood vulnerability perceptive and utility of premium levels

� � ��� This variable reflects the impact of ambiguity on utility of premium levels

Interaction between fear of flood and utility of flood insurance policy types

� ���� ��� This variable reflects the impact of adverse selection on utility of flood insurance policy types

Interaction between fear of flood and utility of premium levels

This variable reflects the impact of adverse selection on utility of premium levels

Interaction between charity hazard and utility of flood insurance policy types

� ���� ��� This variable reflects the impact of charity hazard on utility of flood insurance policy types

Interaction between charity hazard and utility of premium levels

This variable reflects the impact charity hazard on utility of premium levels

Interaction between irrigation improvement and utility of flood insurance policy types

This variable reflects the impact of irrigation improvement on utility of flood insurance policy types

Interaction between irrigation improvement and utility of premium levels

This variable reflects the impact of irrigation improvement on utility of premium levels

Interaction between accessibility to pumping stations and utility of flood insurance policy types

This variable reflects the impact of pumping station accessibility on utility of flood insurance policy types

Interaction between accessibility to pumping stations and utility of premium levels

� � ��� This variable reflects the impact of accessibility to pumping stations on utility of premium levels

Empirical models

3.6.1Empirical model with only attribute variables

� ��� denotes the indirect utility of individual n who has chosen alternative j.

Insurance policies can cover various types of disaster damage, including those caused by floods, thunderstorms, and inundation We anticipate that policies offering coverage for multiple disaster scenarios will have a greater positive impact on the utility for insurance buyers Specifically, policies that include protection against all three types of disasters are expected to deliver the most significant benefits, while those covering only flood damage are likely to provide the least positive impact.

The chosen insurance provider for respondent n can be a foreign company, a private company, or a joint stock company Analyzing the coefficients of these providers will enable us to identify the most preferred options among them.

����� ��� is the maximum amount of compensation j for natural damage that insurance companies will pay for insured n, after minus the deductible (VND/1000� 2 / Farming season)

We expected this attribute will have positive impact on the utility of flood insurance buyers.

The percentage of damage that insurance buyer n must bear, denoted as j (%), indicates the shared burden between the insurance buyer and provider This attribute is anticipated to negatively affect the demand for flood insurance.

� � ��� is the insurance premium j that buyer n have to pay for insurance company (VND/1000� 2 / Farming season) We expected that premium will have the negative relationship with flood insurance demand.

� ��� is the unobserved random term.

3.6.2 Empirical model with attribute variables and their interaction with non- attribute variables

The Mekong River Delta lacks mandatory flood insurance participation, which poses challenges to the development of the flood insurance market, including risk uncertainty, adverse selection, and charity hazard (Botzen & van de Bergh, 2008) To understand the effects of these challenges, researchers examined local farmers' perceptions and how they interacted with various flood insurance attributes (Botzen, 2010) The findings from the model reveal how the marginal utilities of these attributes fluctuate in response to the identified challenges.

In this model, the expected relationships between attributes and flood insurance demand are similar to the basic model above.

There are expectation for interaction between flood insurance attribute and flood insurance market development challenges or irrigation improvement or accessibility to pumping stations in the circumstance t:

Flood vulnerability significantly influences the demand for flood insurance, as it reflects the potential impacts of ambiguous flood risks This relationship suggests that higher flood vulnerability may lead to increased interest in various types of insurance policies Consequently, the interaction between flood vulnerability and insurance policy types is likely to be positive, indicating a correlation between perceived risk and insurance uptake.

(������������� ��� ) and premium are expected to be positive.

Adverse selection is influenced by the fear of flooding, which we anticipate will positively correlate with the demand for flood insurance This fear is expected to interact positively with various types of insurance policies, such as those specifically designed for flood coverage Furthermore, we expect the interaction between the fear of flooding and insurance premiums to also show a positive relationship.

Local farmers who rely on government support for post-flood recovery are less inclined to purchase flood insurance This dependency on government assistance can diminish their perception of personal responsibility, ultimately affecting their willingness to invest in flood protection measures.

�ℎ�����ℎ����� ��� , the coefficients of interactions of charity hazard (�ℎ�����ℎ����� ��� ) and flood insurance policy types (������ ��� ) might produce negative sign The coefficient of

�ℎ�����ℎ����� ��� ������ � ��� are expected to be negative.

Improvements in irrigation and access to pumping stations are anticipated to decrease the demand for flood insurance, akin to the concept of charity hazard Consequently, the interaction coefficients with various insurance policy types are expected to be negative.

� � ��� are expected to be negative.

3.7 Calculation of Willingness-to-Pay (WTP) for specific insurance packages and probability of buying specify insurance packages with the changes in premium

3.7.1 Calculation of Willingness to Pay (WTP) for each attribute and for specific insurance packages

The WTP for each attribute of choice is

A comprehensive flood insurance package consists of five essential components: a flood insurance policy, a reliable provider, coverage amount (measured in VND per 1,000 square meters per farming season), deductibles (expressed as a percentage), and the premium cost (also in VND per 1,000 square meters per farming season) It is crucial to eliminate any unrealistic insurance options to ensure effective protection.

48 specific insurance packages in the survey.

Initial calculations show that the cover and deductible have minimal impact on the willingness to pay (WTP) of local farmers Detailed graphs illustrating the probability of WTP at various cover and premium levels can be found in the appendix Consequently, the focus is on calculating WTP for specific insurance packages that feature different combinations of policy types and providers, specifically those with a cover of 2 million VND and a deductible of 25 percent These calculations not only provide insights into farmers' WTP for flood insurance but also highlight how policy types and providers influence their willingness to pay.

Table 3.4 The insurance packages are used to calculate the WTP

The attributes of insurance packages are used to calculate the WTP

1 Flood Corporation 2 million VND 25 percent

2 Flood and Inundation Corporation 2 million VND 25 percent

3 Flood and Windstorm Corporation 2 million VND 25 percent

Windstorm Corporation 2 million VND 25 percent

5 Flood Foreign companies 2 million VND 25 percent

6 Flood and Inundation Foreign companies 2 million VND 25 percent

7 Flood and Windstorm Foreign companies 2 million VND 25 percent

Windstorm Foreign companies 2 million VND 25 percent

9 Flood Private companies 2 million VND 25 percent

10 Flood and Inundation Private companies 2 million VND 25 percent

11 Flood and Windstorm Private companies 2 million VND 25 percent

Windstorm Private companies 2 million VND 25 percent

In the basic model, calculation of Willingness to Pay (WTP) for specific insurance packages will be

� ∗ = � 0 + � 1 (Flood; Flood and thunderstorm; Flood and inundation; Flood, thunderstorm and inundation) + � 2 (Corporations; Foreign company; Private company) + � 3 (2 million VND; 3 million VND; 4 million VND) + � 4 (10%; 25%)

The second model examines how the development of the flood insurance market, challenges in irrigation improvement, and access to pumping stations influence individuals' willingness to pay (WTP) for flood insurance By incorporating individual characteristics (Z) of respondents, this model effectively captures the varying impacts on their WTP for specific insurance packages.

∗ ∗ ∗ will equal the quotient of dividend (� � ) and (−� $ ).

The total utility derived from flood insurance packages is the cumulative marginal utility of all attributes, excluding the premium cost This utility represents the benefit that a flood insurance buyer would experience if they were not required to pay the premium Conversely, the marginal utility of the premium itself is negative, indicating that the cost detracts from the overall utility received from the insurance.

The equation illustrates the relationship between various types of flooding events, including flood, windstorm, and inundation, with a focus on their impact on corporations, both domestic and foreign Financial implications are highlighted, with amounts ranging from 2 million VND to 4 million VND and percentages of 10% to 25% This analysis underscores the significance of understanding the interplay between these natural disasters and their effects on private companies.

� ���� + � 10 (Flood; Flood and windstorm; Flood and inundation; Flood, windstorm and inundation)* ��ℎ�����ℎ����� +

� 10 (Flood; Flood and windstorm; Flood and inundation; Flood, windstorm and inundation)* ����������� + � 12 (Flood; Flood and windstorm; Flood and inundation; Flood, windstorm and inundation)* ���������������

3.7.2Probability of buying specify insurance packages with the changes in premium levels

Based on the basic model, there is the calculation of probability of buying specify insurance packages with the changes in premium levels

This study analyzes the willingness to pay (WTP) for specific flood insurance packages and their attributes to identify optimal insurance plans By examining the probability of purchasing these packages in relation to varying premium levels, we can determine which flood insurance options are most viable for investment and launch in the Mekong River Delta.

To gather data for this study, we conducted a survey in three randomly selected districts—Gao Giong, Phu Loc, and Tan Cong Chi—located in the Mekong River Delta, all of which have experienced flooding in the past Households for the survey were chosen randomly.

Calculation of Willingness-to-Pay (WTP) for specific insurance packages, and probability

3.7.1 Calculation of Willingness to Pay (WTP) for each attribute and for specific insurance packages

The WTP for each attribute of choice is

A comprehensive flood insurance package consists of five essential components: a flood insurance policy, a reliable provider, coverage amount (VND/1000 for 2 farming seasons), deductibles (%), and premium costs (VND/1000 for 2 farming seasons) It is crucial to eliminate unrealistic insurance options to ensure effective protection against flood risks.

48 specific insurance packages in the survey.

Initial calculations reveal that the cover and deductible have minimal impact on the willingness to pay (WTP) of local farmers The appendix includes graphs illustrating the probability of WTP across various cover and premium levels Consequently, the focus shifts to calculating WTP for specific insurance packages that feature different combinations of policy types and providers This analysis specifically evaluates insurance packages with a cover of 2 million VND and a deductible of 25 percent In addition to providing insights into WTP for flood insurance, these calculations demonstrate how policy types and providers influence farmers' WTP.

Table 3.4 The insurance packages are used to calculate the WTP

Descriptive statistics

The survey included 226 respondents, with 72 participants from Gao Giong district, 73 from Phu Loc district, and 81 from Tan Cong Chi district.

In the Mekong River Delta, 39% of survey respondents have completed only primary school, while approximately 35.1% have education levels below primary school The majority of participants are male, reflecting the traditional role of fathers and grandfathers as the primary breadwinners in their families Additionally, most respondents are aged between 30 and over 50 years.

A significant challenge in the flood insurance market development is highlighted by the perceptions of farmers in the Mekong River Delta Despite 77.88% of farmers residing in irrigation improvement areas and 83.63% having access to pumping stations, a staggering 87.61% anticipate the return of severe floods, which they believe will put their property and families at high or extremely high risk Additionally, an overwhelming 89.38% of these farmers express fear and anxiety regarding the possibility of severe flooding.

In a study of 4,050 observations, only 16% opted not to purchase any insurance plan When respondents were presented with one of the six choice cards again for consistency checking, approximately 56.5% (2,340 observations) provided consistent answers in their second choice, while 11% selected the "none of them" option The analysis was conducted for both the entire sample and a subsample of participants who exhibited consistent responses in the follow-up question.

Figure 4.1 Statistical result of percentage of policy types chosen

The survey results indicate a clear preference among respondents for insurance policies that cover triple disaster damages, with a selection probability of 28% This preference rises to 30% when focusing solely on those who provided consistent answers in the second choice In contrast, the insurance policy that covers only flood damages is the least favored, with a selection probability of just 12%.

The appeal of insurance policies for floods and storms is only slightly greater than that for floods and inundation, with a selection probability of 22 percent for the former, exceeding the latter by just 1 percent.

In a recent analysis, it was found that the likelihood of selecting an insurance policy for flood and storm coverage stands at 22%, while the probability for policies covering floods and inundation is slightly higher at 24%.

Figure 4.2 Statistical result of percentage of providers chosen

In the Mekong River Delta of Vietnam, corporations with government participation are the most favored, boasting a selection probability of 44%, reflecting the residents' strong trust in the government Private companies follow in preference with a 24% likelihood of being chosen, while foreign companies rank lowest, with only a 16% probability of selection.

In a sample of 2,340 observations where the second choice remained consistent, the overall findings remained stable However, the likelihood of selecting a corporation increased to 48%, while the probability of choosing a foreign company stood at 25%.

Figure 4.3 Statistical result of percentage of deductibles rates chosen

The discrepancy between the probabilities of selecting different deductible levels may not be immediately apparent, yet it contradicts our expectations Typically, we anticipate that insurance packages with lower deductible levels would be favored over those with higher deductibles.

The insurance package whose the deductible is 10% level has the same probability of being selected with those whose the deductible at 20% level It is approximately 42%.

Surprisingly, our findings indicate that insurance packages with lower deductibles are not overwhelmingly preferred over those with higher deductibles In this analysis, the 20% deductible option was chosen by 45% of participants, only slightly surpassing the 10% deductible option by a mere 1% This suggests that there is minimal distinction in preference between the two deductible levels.

Figure 4.4 Statistical result of percentage of cover level chosen

Despite expectations, the insurance package offering the highest coverage level is not the most preferred choice among consumers This trend can be attributed to the fact that packages with maximum coverage typically come with higher premiums Instead, the average coverage level of three million VND is favored, with a selection probability of 34%.

When analyzing observations with identical responses in the second choice, the probability stands at 36% The likelihood of selecting a coverage level of two million VND is 29%, which is 8% higher than the probability for a coverage level of four million VND This highlights a significant distinction in selection probability between the two coverage levels.

Figure 4.5 Statistical result of percentage of premium levels chosen

Contrary to our initial expectation that higher premiums would correlate with a lower probability of selection, the statistical analysis reveals a more nuanced outcome Overall, the two highest premiums exhibit a lower likelihood of being chosen compared to the three lowest premiums This trend remains largely consistent, even when focusing solely on observations where respondents provided the same answer for their second choice.

The premium priced at 30,000 VND has the highest selection probability at 24%, followed closely by the 15,000 VND premium When analyzing responses with consistent answers in the second selection, the likelihood of choosing the 30,000 VND premium rises by 6% Additionally, the 40,000 VND premium shows a greater selection probability compared to the 15,000 VND option Notably, the average selection probability increases from 19% to 20% when focusing solely on observations with matching responses in the second choice.

Bivariate analysis

Table 4.1 Amount of observations choosing alternatives in each order of choice cards

The order of Choice card

In the survey, each respondent was presented with six choice cards, revealing no clear preference for either A or B alternatives This indicates that participants made their selections without significant consideration Furthermore, the frequency of respondents opting for the "none of them" choice was notably low.

Survey results reveal that policy and provider attributes are prioritized by respondents, with only 15 indicating that premium is their main concern This suggests that farmers may not place significant emphasis on premium, which could explain why their willingness to pay (WTP) levels exceed expectations.

Figure 4.6 The statistic results of the most interested attribute of farmers

4.2.2 Bivariate analysis about the effects of personal perspectives and externalities on flood insurance purchasing decision

Figure 4.7 The percentages of observations choosing to purchase flood insurance

Figure 4.8 Amount of observations divided according to respondents’ perception

This study aims to assess the effects of ambiguity, adverse selection, charity hazard, and improvements in irrigation and access to pumping stations on the livelihoods of farmers in the Mekong River Delta By leveraging insights from previous research, we will gather and analyze local farmers' perceptions to evaluate the impacts of these factors on their utility.

In our sample, 87.6% of farmers demonstrate a strong perception of flood vulnerability, leading to a corresponding increase in their demand for flood insurance Notably, 30% of those who perceive high vulnerability opt to purchase flood insurance, which is 8% higher than the rate among those with low vulnerability perceptions While these findings align with our expectations, the influence of ambiguity on the utility of insurance buyers appears to be minimal, suggesting that the ambiguity surrounding floods may have a limited effect on the development of flood insurance.

Farmers often exhibit a significant reliance on government aid, particularly in the context of natural disasters, as they strongly believe that it is the government's responsibility to provide subsidies for recovery efforts.

In the Mekong River Delta, the presence of charity hazards appears to have an insignificant impact on flood insurance demand Among 3,582 observations relying on governmental charity for post-flood recovery, only 29.2 percent opted to purchase flood insurance Interestingly, the percentage of individuals choosing to buy flood insurance in the non-charity hazard group is slightly higher, by just 3 percent, compared to those in the charity hazard group This suggests that the expected negative effect of charity hazards on flood insurance demand may not hold true in this context.

Despite expectations that anxiety and fear of flood damages would drive farmers to purchase flood insurance, statistics reveal otherwise Only 29 percent of those expressing concerns about flood risks opted for insurance, a figure that mirrors the percentage of those without such fears who also chose to buy coverage This suggests that fear and anxiety may not significantly influence local farmers' decisions to purchase flood insurance, alleviating concerns about adverse selection in the market.

In the Mekong River Delta, local residents often contribute to irrigation construction alongside government support, with the hope that enhanced irrigation will decrease the need for flood insurance Statistics indicate that the proportion of residents living in areas with irrigation who opt for flood insurance is slightly lower than those without such protection However, despite improvements in irrigation infrastructure, the demand for flood insurance among local farmers in the Mekong River Delta may not significantly diminish.

Accessibility to pumping stations significantly influences farmers' decisions regarding flood insurance purchases In a study of 3,168 observations involving paddy fields with access to pumping stations, only 28.6% opted for flood insurance In contrast, among those without access, 30.2% chose to buy flood insurance, indicating a 1.6% higher rate in the non-accessible group This suggests that the availability of pumping stations reduces the likelihood of farmers purchasing flood insurance.

The statistics on irrigation construction and access to pumping stations indicate that many of these facilities are located in vulnerable areas, which may explain their limited effectiveness Additionally, the existing irrigation systems have not fully safeguarded the paddy fields of local farmers, highlighting the need for improved infrastructure and protection measures.

The details about the effects of personal perspectives and externalities on choosing flood insurance attributes are included in Appendix.

Empirical results

This section will outline the findings from Model 1 and Model 2 Model 1 focuses solely on how various attributes affect the utility perceived by buyers, while Model 2 additionally examines the influences of these attributes alongside the challenges faced in the development of flood insurance.

Table 4.2 Estimate results of models

All observations Only observations having consistent answer

In the first column, there is the basic model We investigate the impacts of flood insurance attributes on utility or purchasing decision of respondents.

Insurance policies, excluding those that solely cover flood damages, significantly enhance the utility for farmers Among these, the “Flood, Inundation, Windstorm” insurance package has the greatest impact, increasing respondents' utility by 1.31 Additionally, choosing policies that cover both “Flood and Windstorm” or “Flood and Inundation” can boost utility by 0.796 and 0.718, respectively.

Controlling for the interaction between proxy variables related to the challenges in the flood insurance market and various insurance attributes diminishes the utility for buyers across different insurance policies The coefficients associated with these policies are not statistically significant, indicating a lack of meaningful impact Specifically, the coefficients for policies that cover only flood damages or both flood and windstorm damages become negative, while the coefficient for policies that encompass triple disasters decreases to 0.392.

Choosing insurance packages from corporations increases the utility of farmers in the Mekong River Delta by 1.093 compared to those offered by private companies Notably, there is no significant difference in utility between foreign and private companies, highlighting farmers' preference for corporate providers When controlling for factors such as charity hazard, fear, vulnerability, irrigation infrastructure, and access to pumping stations, the initial findings were reaffirmed Specifically, flood insurance provided by corporations enhances utility by 1.129 when compared to private company offerings.

Deductibles serve as a reminder for insurance buyers about the importance of disaster prevention, highlighting the concept of shared responsibility While we anticipated that higher deductible levels would negatively impact the utility perceived by flood insurance buyers, our findings indicate that this relationship is not statistically significant Consequently, it appears that deductibles do not significantly influence the purchasing decisions of buyers This suggests that farmers in the Mekong River Delta recognize and are willing to share the financial burden with flood insurance providers.

The study reveals a positive correlation between cover levels and farmers' utility, with statistically significant results Despite the seemingly small coefficients measured in million dong, their impact on the likelihood of selecting alternatives and changes in utility levels is substantial Specifically, an increase of one million dong in cover level leads to a utility increase of 0.126 Furthermore, when accounting for factors such as irrigation structures, charity hazards, fear, and vulnerability, the findings remain consistent with the initial model.

The relationship between premiums and farmers' utility is generally negative, but only the basic model shows statistically significant results The small coefficient magnitudes and the use of 1000 VND as a unit indicate that these effects are minimal; for instance, a 1000 VND increase in premium results in only a -0.01 reduction in utility for insurance buyers When considering the challenges in the flood insurance market, such as accessibility to pumping stations and irrigation, the impact of premiums on the utility of insurance buyers becomes negligible Consequently, premium levels do not significantly influence farmers' decisions to purchase insurance, which helps explain why some respondents exhibit a willingness to pay (WTP) for flood insurance that exceeds expectations.

Charity hazard refers to the underinsurance of individuals who rely heavily on anticipated government assistance or charity organizations, as highlighted by Raschky & Hannemann (2007) This phenomenon affects flood insurance demand by creating a market failure on the demand side, as individuals perceive government responsibility for post-flood relief In this study, the interaction between charity hazard and insurance premiums is expected to yield a negative coefficient, indicating a decrease in flood insurance demand and willingness to pay Although the regression results show that this interaction is not statistically significant, certain policy types in conjunction with charity hazard demonstrate significant positive coefficients This suggests that while charity hazard may not be a critical issue in the Mekong River Delta, farmers still believe in the government's role in post-flood recovery, albeit acknowledging that current subsidies are insufficient.

The fear of flood damage significantly influences farmers' decisions to purchase insurance, as it enhances their overall utility This study examines the interaction between fear of flood and insurance policy types, as well as fear of flood and premium costs However, the findings reveal that fear of disaster does not have a joint effect on the utility of local farmers, nor does it significantly impact their demand for flood insurance in the Mekong River Delta Consequently, adverse selection may not impede the growth of the flood insurance market in this region.

The ambiguity surrounding floods significantly influences the demand for flood insurance by shaping perceptions among local farmers regarding their vulnerability to potential flood damages If farmers acknowledge the threat posed by floods, this uncertainty will not hinder the development of the flood insurance market Key variables in this study include the interaction between flood vulnerability perception and insurance policy, as well as the interaction between vulnerability perception and premium costs The findings reveal no significant change in the utility derived from premium attributes due to disaster vulnerability; however, the perceived vulnerability can enhance the marginal utility of insurance policies Specifically, the perception of vulnerability may increase the utility of purchasing flood insurance that covers triple disaster damages by 1.94, while policies that include "flood and inundation" or "flood and windstorm" may boost buyer utility by 1.28.

1.67 respectively, under the impact of vulnerability perception Interestingly, vulnerability perception may cause the marginal utility of insurance covering only flood damages to raise by 1.72 Therefore, vulnerability perception might have significant impacts on farmer insurance purchasing decision and farmers’ utility, or the ambiguity of flood might not prevent the development of flood insurance in Mekong River Delta significantly But we should still take precaution.

In the Mekong River Delta, irrigation structures like dykes and drainage ditches, funded by local households, have led to a decreased demand for flood insurance To assess the impact of irrigation improvements, local farmers' perceptions were analyzed, incorporating variables such as the interaction between irrigation perception and policy, as well as premiums Despite expectations, the effects of irrigation construction on the marginal utility of insurance policies and premiums were found to be insignificant in some cases The findings indicate that irrigation development has only slightly reduced the utility of flood insurance policies by 0.797, suggesting that it has not fully mitigated flood and inundation damages to local paddy fields.

In the Mekong River Delta, agricultural pumping stations are crucial for preventing flooding and drought in paddy fields, but their accessibility may diminish the perceived utility of insurance attributes among local farmers Our study analyzed farmers' perceptions of pumping station accessibility and its impact on insurance utility Key variables included the interaction between pumping station access and insurance policy, as well as premium costs Findings revealed that farmers with accessible pumping stations experienced a reduction in utility from flood-related insurance coverage, with decreases of 1.365 for flood, 1.259 for flood and inundation, 1.138 for flood and windstorm, and 1.122 for triple disasters While accessibility to pumping stations may slightly increase utility by 0.017 when flood insurance premiums rise by 1000 VND, this increase is outweighed by the overall reduction in policy utility Consequently, easier access to pumping stations is likely to decrease the demand for flood insurance.

Previous studies have identified the impacts of income and household characteristics such as education and gender (Aliagha et al., 2015; Wan, 2014; Enjolras et al., 2012; Smith & Baquet, 1996) However, our model estimates, which control for these household characteristics, reveal that household income, family size, and respondent age do not significantly affect the marginal utility of attributes Notably, the interaction coefficient between income and insurance premium is zero and statistically insignificant Consequently, the results controlling for household characteristics are presented in the appendix rather than the main content.

Our regression analysis, which focused on observations with consistent second-choice responses, revealed that the estimation results align closely with previous models In both the basic model and the model accounting for perceptions of challenges in the flood insurance market, the coefficients were slightly larger, indicating a significant impact of adverse selection on the marginal utility of premiums and policies covering only flood damage This suggests that our initial findings are robust, demonstrating that farmers who seriously consider purchasing flood insurance and exhibit consistency in their decision-making are likely to derive greater utility from it, potentially leading them to pay more for coverage compared to others.

Conclusion remark

Since Mekong River Delta Located in the downstream of Mekong River Basin, annually local farmers have to face with damages caused by flood and other natural disasters (To & Tang,

Following severe floods, farmers in the Mekong River Delta often rely on government and charitable aid, which is frequently insufficient and delayed This overreliance on post-disaster assistance poses risks to agricultural stability, especially as many farmers face significant debts To enhance self-recovery efforts, it is crucial to introduce flood insurance and assess the demand for such products However, there is a notable lack of studies evaluating the demand and willingness to pay for flood insurance in the Mekong River Delta, highlighting a gap in research both in Vietnam and globally.

This study utilized the Random Utility Model to analyze the demand for flood insurance packages in the Mekong River Delta, while also addressing the challenges faced in developing the flood insurance market in the region The willingness to pay (WTP) for flood insurance was meticulously evaluated through a series of steps, drawing on data from a 2015 survey that included 4,050 observations The estimation results yielded several key findings that contribute to understanding the dynamics of flood insurance demand in this vulnerable area.

To effectively develop the flood insurance market, it is crucial to manage various obstacles, as all attributes, except for deductibles, significantly influence the utility experienced by buyers While the expected negative relationship with deductibles is observed, it lacks statistical significance Buyers may derive higher utility from flood insurance packages offered by corporations or policies that encompass more than just flood damage This finding remains consistent even when analyzing a sample of individuals with stable preferences.

The challenges faced in the development of flood insurance are better understood through local farmers' perceptions of flood vulnerability, government responsibilities, and the accessibility of irrigation and pumping stations By examining these perceptions, we can assess how they influence decisions to purchase flood insurance While current challenges may diminish the significance of policy and premium attributes for buyers, the role of corporate providers remains important In the Mekong River Delta, the perception of flood vulnerability actually increases the demand for flood insurance Interestingly, fear significantly impacts farmers' purchasing decisions, suggesting that adverse selection is not a major concern in this region Certain policy types may offer greater utility under the influence of charity hazards, indicating that these concerns should not deter flood insurance development However, the accessibility of pumping stations can exacerbate the negative effects of premiums on insurance utility, and some policy types may see a notable decline in utility due to improvements in irrigation and pumping station access Overall, pumping stations in the Mekong River Delta play a crucial role in mitigating flood, inundation, and windstorm damages.

After assessing the willingness to pay (WTP) for various attributes in flood insurance packages, we calculated the WTP for specific insurance options that combine different policy types and providers Currently, the average WTP for flood insurance along the Mekong River is determined based on these evaluations.

Delta farmers demonstrate a willingness to pay (WTP) of approximately 70,000 VND per 1,000 square meters for flood insurance during the farming season The findings reveal that both policy and provider significantly influence farmers' WTP Notably, insurance packages that cover damages from triple disasters and are offered by corporations are likely to attract the highest WTP, estimated at around 170,000 VND.

VND/1000� 2 /Farming season While local farmers do not willing to pay for flood insurance packages provided by foreign or private companies and covering only flood damages.

The findings are crucial for flood insurance companies looking to enter the Mekong River Delta market, as they provide insights for developing profitable flood insurance packages and determining appropriate pricing Additionally, policymakers can utilize these results to promote and support flood insurance initiatives in the Mekong River Delta and surrounding rural areas.

Policy implications

The Mekong River Delta presents significant potential for the development of a flood insurance market, indicating that insurance companies should actively participate Profitability in this sector can be enhanced by offering tailored flood insurance packages Research reveals that local farmers have a strong reliance on insurance corporations, particularly with government involvement, and they prefer flood insurance that covers either triple or double disaster damages Additionally, the optimal pricing for flood insurance is suggested to be around 50,000.

(VND/1000� 2 / Farming season) to 120,000 (VND/1000� 2 / Farming season).

To enhance the post-disaster self-recovery capabilities of farmers in the Mekong River Delta, policymakers must facilitate the involvement of insurance companies in the market and effectively implement the existing flood insurance development initiatives.

Promoting collaboration between state insurance companies and private or foreign firms is essential, as market principles can enhance the effectiveness of private insurers in implementing risk-reduction strategies compared to their public counterparts (Priest, 1996).

Limitations

Despite trying to provide the most completed result, this study still had some limitations that further studies can overcome.

This study aims to address the challenges in developing the flood insurance market; however, its impacts may not be comprehensively assessed (Botzen, 2010) The evaluation primarily reflects the perspectives of local farmers, lacking the incorporation of statistical and quantitative data to substantiate their experiences.

This study has not yet addressed the impact of geographical location and socio-economic characteristics on household risk exposure, despite the fact that these factors significantly influence perceptions of flood risk (Botzen, 2010).

Finally, using cross section data to analyses behaviors models might have some restrictions in controlling the changing overtime of taste and outside impacts.

To enhance the development of the flood insurance market, future research should focus on evaluating the challenges by leveraging statistical and quantitative data This approach will help assess farmers' experiences while considering geographical locations and socio-economic factors.

Using cross-sectional data to analyze behavioral models may have limitations due to external factors that change over time Future research should utilize panel data to enhance the robustness of this study, particularly in evaluating the willingness to pay (WTP) for flood insurance.

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APPENDIX A: Questions are used from the survey

Besides the choice cards whose sample was introduced in the Methodology, this study also used the data obtained from following questions.

1/ Do severe floods have potentiality to occur in this region? a) absolutely have no potentiality b) have no potentiality c) have potentiality d) absolutely have potentiality

2/ If the severe floods occur, whether your family is vulnerable easily or not? a) It is absolutely not b) It is not c) It is d) It is easy to be vulnerable

3/ In raining season, do you have anxiety about flood risk? a) I absolutely do not b) I do not c) I do d) I absolutely do

4/ In raining reason, do you fear of flood damages? a) I absolutely do not b) I do not c) I do d) I absolutely do

5/ Your perspective about irrigation improvement in your region? a) Decrease/worse b) Unchanged c) Increase/better

6/ Your perspective about accessibility to pumping station of your field? a) Decrease/worse b) Unchanged c) Increase/better

7/ Do you agree that government could compensate a part of damages caused by flood? a) Absolutely disagree b) Disagree c) Agree d) Absolutely agree

8/ Do you agree that government have responsibility to compensate for flood damages and cope with flood? a) Absolutely disagree b) Disagree c) Agree d) Absolutely agree

Appendix C: The statistic results about impacts of challenges

Appendix D: The variation of WTP for flood insurance probability, with difference levels of cover

Table: With different levels of coverage, the probability of WTP for flood insurance packages

Table: The probability of WTP, when the challenges are controlled

Appendix E: The variation of WTP for flood insurance probability, with difference levels of deductible rate

Table: With different levels of deductibles, the probability of WTP for flood insurance packages

Table: the probability of WTP, when the challenges are controlled

Appendix F: the regression result of models controlling the impacts of challenges and household characteristics

All observations Only observations having consistent answer

Appendix G: The regression result in Stata of models

Figure: The regression result of basic model based on the whole sample

Figure: The regression result of basic model based on the whole sample with the impact of challenges for the development of flood insurance market.

Figure: The regression result of basic model based on the sample of observations whose tastes are consistent.

Figure: The regression result of basic model based on the whole sample with the impact of challenges for the development of flood insurance market.

Appendix H: The regression result are obtained from applying

Nested Logit Model in Stata