Evaluating Policy Lapse Behavior on Life Insurance Policyholders in Digos City

Evaluating Policy Lapse Behavior on Life Insurance Policyholders in Digos City

Baoilan, Aizelle S., Barret, Dorothy Zoe D., Bohol, Laica Jane N., Bulala, Francine May C., Calderon, Gracel Maye D., Conoman, Clariz Nina P., Fuentes, Jimkyl Klovis R., Mazo, Hillary Grace B., Renegado, Nicole Bernadette A., Aquino, Michael P., Flores, Apple Joy

Cor Jesu College, Inc, Digos City

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90400294

Received: 14 March 2025; Accepted: 21 March 2025; Published: 12 May 2025

ABSTRACT

Policy lapse behavior on life insurance threatens the financial stability of insurers and compromises the security of policyholders. Many policyholders discontinue their policies due to demographic and behavioral factors, leading to financial losses for insurers and reduced protection for individuals. Policy lapses not only affect the financial sustainability of insurance providers but also undermine the long-term financial planning and security of families who depend on these policies for protection. This study investigates the determinants of policy lapse behavior among life insurance policyholders in Digos City using a quantitative descriptive-comparative approach. Data from 152 respondents were analyzed through descriptive statistics and one-way ANOVA to assess the impact of demographic factors on policy lapse tendencies. The findings show that gender, occupation, age and income have minimal influence on lapse behavior. However, educational backgrounds play a more significant role, those with lower educational attainment exhibiting higher lapse rates. Respondents with vocational or undergraduate education demonstrated a higher likelihood of lapsing, emphasizing the role of education in policy lapse decisions. The results indicated that policyholders with lower educational attainment are more prone to policy lapses due to financial constraints, lack of awareness, and varying risk perceptions. The study concludes that targeted educational interventions can enhance financial literacy and promote long-term policy retention. To mitigate policy lapses, insurers should focus on improving awareness of the benefits of maintaining life insurance and provide regular policy reminders. Strengthening financial education and offering personalized communication strategies can help reduce lapse rates and improve financial security for policyholders while ensuring the sustainability of the insurance industry.

Keywords: Policy lapse behavior, demographic factors, life insurance, policyholders, Philippines

INTRODUCTION

Life insurance is a contract between an insurance company and a policyholder, where the policyholder agrees to pay a specified amount in exchange for an insurance premium. Many individuals rely on life insurance for financial protection. A large number of families depend on life insurance for financial and retirement security. However, there is still uncertainty surrounding the factors that affect life insurance premiums, such as lifestyle choices, health history, and occupation, and whether sharing personal details can result in higher costs. Following the recent pandemic, there was a global increase in life insurance policy holding (Wang, 2024), with some seeing significant growth. Despite this promising potential, concerns about the risks associated with insurance companies have led to an increase in policy lapses over time.

In the global context, young individuals increasingly purchase life insurance as their financial obligations evolve. People aged between 20 and 30 score 33.7% higher than those aged 40 and above (Rajkumar & Sugapriya, 2023). However, the policy lapse rate is notably higher among younger age groups, reflecting changing financial priorities and life circumstances. According to Dash (2018), younger individuals, particularly those 24 and under, have high lapse rates (50%) due to their preference for immediate benefits like critical illness coverage, while older, loss-averse individuals tend to maintain ownership but not at higher rates than younger groups. According to Koju et al. (2020), the life insurance industry in Nepal reported a mean lapse rate of 23.91% due to financial difficulties, administrative issues, underperformance, complex processes, trust concerns, and agent practices. The authors further highlighted age as a moderating factor in lapse decisions, specifically.

The relationship between demographic factors and life insurance policy lapse behavior is influenced by various socio-economic elements. Additionally, individuals within the 20-30 years old age bracket in Chitwan Nepal, which constitutes 26.7 percent of the population, are more likely to experience lapses in insurance coverage due to the influence of family decisions rather than their own personal choices (Bista & Upadhyay, 2023). According to the study of Ctisti et al. (2023), policyholders are most likely to raise the chance of lapsing the policy by 12% due to the shortage of money and liquidity needs for personal projects, forcing the policyholders to lapse their life insurance policies as a last resort. Individuals within this age group often face significant financial obligations, such as aspirations for homeownership, which can lead to financial stress. This financial pressure may influence their decisions regarding life insurance, potentially resulting in lapses. However, the specific reasons behind this behavior and the broader socio-economic influences that contribute to these choices require further investigation. A study by Koijen et al. (2024) found a strong connection between economic conditions and policy lapses. Policyholders from financially vulnerable groups— like lower-income households and minority households with average incomes, are more likely to let their policies lapse during economic downturns. Surprisingly, during the pandemic, policy lapses actually declined. With heightened awareness of mortality, more people held onto their policies, which ended up benefiting insurers’ profits.

In the ASEAN context, understanding the factors influencing life insurance policy lapses is essential for both consumers and providers. A study in Myanmar by Htet et al. (2022) highlights that economic conditions, insurance literacy, and cultural attitudes shape policyholder behavior. Unemployed individuals and those with lower education levels have high lapse rates (87% and 66%, respectively), signifies that unemployment and lower educational attainment contribute on lapsing policies; whereas professionals and highly educated individuals prioritize insurance for its risk protection, returns, and loan benefits (Rajkumar & Sugapriya, 2023; Lim & Tan, 2019; Chirimubwe et al., 2018). The COVID-19 pandemic further disrupted the industry, causing a decline in first-year premium income and new policies (Yadav & Suryavanshi, 2021). Despite Myanmar’s low insurance penetration rate (below 1%), improved literacy and positive perceptions can drive purchases, while superstitions contribute to policy lapses. Additionally, life events such as marriage, homeownership, and retirement significantly influence insurance purchases, with education and marital status being key determinants (Vongphachanh et al., 2022; Frauendorf, 2020). Conversely, being single, renting, financial instability, and poor insurer communication reduce the likelihood of maintaining policies.

In the Philippines, life insurance is necessary for financial security and risk management, as life insurance helps cover funeral costs, alleviating financial burdens on grieving families. According to Venkatesan and Jacob (2019), motivations for purchasing life insurance policies include saving for future returns and providing financial security for families. Furthermore, life insurance supports educational expenses, ensuring children’s schooling continues, and can be designated for this purpose. For business owners, key person insurance protects against losses by enabling smooth operations if a key employee passes away (Why Life Insurance Is Essential for Filipino Families, 2023). Insurance awareness has increased as the economy grows leading to more significant changes in the insurance industry of the country. According to Przybytniowski (2017) and the Insurance Commission’s 2023 report (Insurance Commission, 2024), these young professionals are highly inclined towards variable Insurance Life products, backed up by a low daily cost. Sanjay and Tiwari (2024) further emphasize that saving motives and financial literacy significantly influence purchase intentions for life insurance. The hope to cover their financial future was established early in their careers.

In Digos City, where the study was conducted, demographic characteristics such as age groups, income levels, and family dynamics. Significantly impact the lapse behavior of life insurance policies in the city. Younger policyholders tend to experience higher lapse rates due to financial pressures such as education costs or starting families, as they often prioritize immediate financial needs over long-term investments. On the other hand, older policyholders, particularly those nearing retirement, are more likely to hold on to their policies, recognizing their importance for family security (Personal Communication, 2024). Income levels also influence lapse behavior, as low-income families may face greater difficulty in maintaining premium payments during times of economic hardship, leading to higher lapse rates. Family structure further affects behavior, with families who have dependents more likely to retain coverage, seeing it as essential for securing their family’s future beyond retirement, whereas single individuals may not feel the same need. As Digos City continues to grow economically and socially, it is important for the insurance industry to closely examine these factors to improve retention strategies.

While most of the research data on lapse rates in insurance policies do exist, there is an essential gap in knowing the differential influence of various behaviors on these rates in life insurance within the local setting. Most studies focus on single aspects, like premium pricing or customer service, but few look at how these elements work together with demographic factors and overall customer experience. This study aims to bridge these gaps by evaluating different policy lapse behaviors. It provides a clearer picture of what drives policyholders to let their insurance lapse, especially in insurance companies located in Digos City, Davao del sur.

The theoretical framework for this study is anchored in Transaction Cost Theory (Coase, 1970). The Transaction Cost Theory (TCT), later developed by Williamson (1981), examines why life insurance policyholders allow their policies to expire, with a particular emphasis on the behavioral and demographic factors that influence these choices.  TCT explains that individuals seek to minimize transaction expenses, including search, negotiation, and enforcement costs. In life insurance, policyholders may let policies lapse if the perceived costs outweigh the benefits. Considering this framework, demographic factors-including income, age, and education-add to these costs because obviously, lower-income people would believe that they bear higher premium burdens, while younger people view an insurance policy as less crucial; however, information and decision costs would remain larger with more educated folks.

Statement of the Problem

This research aimed to determine the significant difference on policy lapse behavior among policy lapse behavior among policyholders in insurance companies, by using the quantitative descriptive comparative. Specifically, it seeks to answer the following research questions.

1. What is the demographic profile of life insurance policyholders in terms of:

1.1 Gender;

1.2 Education;

1.3 Occupation;

1.4 Monthly Income; and

1.5 Age?

2. What is the level of policy lapse behavior according to each demographic group?
3. Is there a significant difference in policy lapse behavior among life insurance policyholders in Digos City when grouped by gender, education, occupation, monthly income, and age?

Hypothesis

To answer the problems listed in the preceding section objectively, the given null hypothesis was formulated:

H0: There is no significant difference in policy lapse behavior among life insurance policyholders in Digos City when grouped by gender, education, occupation, monthly income, and age.

Significance of the Study

This study will contribute to credible research resources and courses on factors that influence the behavior of policy lapse rates in the insurance industry. It will also contribute to core aspects that are associated with the professional field, such as the following:

Insurance Commission. The findings can be used by this government agency responsible for regulating and overseeing the insurance industry, as they can inform policies that enhance consumer protection, improve financial literacy, and promote industry stability by addressing factors contributing to policy lapses.

Life Insurance Companies. This study will guide top-level management in mitigating the damages caused by lapse risks and in crafting strategies as to how they would be able to cope with the threats posed to the business.

Insurance Advisers and Brokers. As they portray a professional figure in the insurance industry, this study will be a tool that will help guide their future clients in choosing the most suitable policy based on their situation, needs, and risk tolerance.

Policyholders. This study aims to inform policyholders about what factors they could significantly contribute to Insurers. This study will help assess their ability to manage the obligations of owning an insurance plan.

 Future Researchers. This study can be used as a research resource in the future as a basis for their future studies. It will provide additional knowledge and information as they explore the literature on insurance policies and factors that affect lapse rates.

Scope and Delimitation

This study explores variations in policy lapse behavior among insurance policyholders, analyzing the impact of demographic factors such as age, monthly income, occupation, gender, and education. It aims to identify significant patterns and relationships between these variables and policy discontinuation. The respondents for this study are life insurance policyholders in Digos City, whose demographic information will provide valuable insights into the patterns and causes of lapses in life insurance policies.

This study recognized various limitations that affected the interpretation and analysis of the results. First, the respondents were limited solely to life insurance policyholders from Insurance Companies in Digos City and have at least 3 months of policy lapse. Second, the study used quota sampling technique, a non-probability technique where participants are chosen by the researcher according to specific attributes. This technique guaranteed that they reflect specific characteristics proportional to their frequency in the population, which might have influenced how the results were interpreted. Third, this study utilized a descriptive comparative research design, which proved effective for examining differences between groups that vary in a specific characteristic, allowing for an analysis of their outcomes or traits. Finally, the study solely relied on quantitative data to evaluate the relationship between the variables. These limitations should be considered when interpreting the results, as they might have affected the scope and significance, and accuracy of the conclusions reached.

Definition of Terms

The terms listed below are defined based on their specific usage and relevance within the context of this study.

Life Insurance Companies. These are financial institutions that provide financial protection to policyholders in exchange for premium payments. They function as intermediaries that manage risk through actuarial methods to ensure financial sustainability (Barucci, 2020). In this present study, life insurance companies were examined in terms of their role in financial markets, their impact on policyholder security, and their contributions to economic growth.

Policyholders. These are individuals who hold insurance policies and navigate financial decisions related to their coverage, often influenced by their level of insurance literacy and financial circumstances (Khera et al., 2022). In this study, policyholders were analyzed based on their health insurance literacy, financial hardship, and decision-making behaviors, which impact their ability to maintain insurance coverage.

Policy Lapse Behavior. This refers to the tendency of policyholders to discontinue or fail to renew their life insurance policies before maturity, leading to a lapse in coverage. It is a key issue in the insurance industry, as high lapse rates can affect insurer profitability and risk management (Haçarız, Kleinow, & Macdonald, 2024). This study evaluates policy lapse behavior among life insurance policyholders, focusing on the factors influencing lapsation and its impact on both insurers and policyholders.

Policy Lapse Rates. This is an actuarial measure that quantifies the percentage of life insurance policies that are not renewed within a specific period. Lapse rates are influenced by demographic and economic factors and can impact the financial stability of insurers (Society of Actuaries, 2023). In this study, policy lapse rates were examined to assess their implications for insurance companies, policyholder retention strategies, and overall market stability.

METHODS

This chapter outlines the methods used in conducting the study. It covers aspects such as research design, respondents, sampling method, sources of data, procedures for data collection, measurement techniques, methods of analysis and interpretation, as well as ethical considerations.

Research Design

This study employs a descriptive-comparative research design to analyze the potential relationships between variables by comparing groups with distinct characteristics. According to Maheshwari (2018), causal-comparative research, also known as ex post facto research, examines cause-and-effect relationships between an independent and a dependent variable without direct manipulation, as the researcher studies variables after they have already occurred. Goodrick (2014) highlights that comparative research focuses on identifying similarities, differences, and patterns across different contexts to enhance understanding of specific behaviors or trends.

In this study, the descriptive-comparative approach is applied to evaluate policy lapse behavior among life insurance policyholders in Digos by comparing different groups based on demographic profile. This method enables the identification of key determinants influencing policy lapses, providing valuable insights into policyholder retention and strategies for reducing policy terminations.

Respondents

The respondents of this study were 152 life insurance policyholders in Digos City who currently hold active life insurance policies from various providers with a lapse of at least three months, policyholders with less than three months of payment lapsation are excluded. The target respondents were randomly selected from the insurance companies’ population of life insurance policyholders with lapses as they provided essential information that was analyzed and interpreted to form the basis of the findings in this research study. This study was conducted at the City of Digos. Respondents were asked to answer the Google form with the questionnaires presented.

The exclusion criteria for this study include life insurance policyholders from insurance companies outside Digos City those who have lapses of less than three months and those who do not have any outstanding life insurance premiums. These respondents were excluded because they did not meet the specific requirements for the study, as their experiences and circumstances may not provide relevant or comparable data to the intended focus of the research. The aim is to ensure that the respondents selected for the study are those who have significant lapses in their life insurance premiums, as their insights will contribute meaningfully to understanding the factors and implications associated with such lapses.

Sampling Technique

This study utilized a quota sampling procedure, a non-probability sampling method that uses the following steps to obtain a sample from a population (Bobbitt, 2021). A total of 152 participants were gathered for this study, ensuring a comprehensive analysis. The inclusion of key subgroups, such as age, occupation, education, monthly income, and gender, reflects the varied demographic makeup of Digos City, ensuring that each subgroup is proportionally represented in the sample.

Respondents were randomly selected from the population of life insurance policyholders in Digos City who had experienced a policy lapse of at least three months. The selection process did not target specific subgroups based on demographic or policy-related factors; instead, it aimed to capture a diverse and representative sample of policyholders from specified insurance companies. While the study did not implement stratified sampling, the fully randomized approach helped reduce selection bias and ensured that the sample reflected the broader population. This method enhanced the study’s validity by allowing for a comprehensive analysis of policy lapse behavior without imposing constraints on participant selection. By adopting this approach, the study provided an unbiased exploration of the factors influencing policy lapses among life insurance policyholders in Digos City.

Data Gathering Procedure

Researchers utilized data-gathering procedures as a methodological approach to collect relevant information for their studies. It helped the researchers draw valid conclusions and support informed decision-making. (Harvard Business School, 2021).

Specifically, the researchers carried out the following steps to collect the data required for the study:

1. The researchers sought permission and approval from the authorized figure from insurance companies from Digos City, together with the validation from the research teacher to conduct this study. After the request was approved, the researchers then proceeded to execute the objective to survey respondents who have a policy lapse on the records from the two identified investment companies.
2. Before the full data collection process, a pilot test was conducted to assess the clarity and reliability of the survey questions. This helped ensure that respondents could comprehend the items and that the instrument effectively captured the necessary data. Any necessary revisions were made based on the feedback from the pilot test.
3. For the main data collection, respondents received a Google Form containing the survey questionnaire, along with a consent form explaining the study’s purpose, potential risks, assurances of confidentiality and anonymity, the participants’ right to ask questions, and the option to withdraw at any time if they experienced discomfort. The Google Form had a time limit of ten minutes to ensure timely and focused responses.
4. The respondents were informed that their responses were recorded. The record response was utilized for the precise collection of numerical data. After the data was verified, it was used as the basis to conclude.
5. The collected data were organized, analyzed, and interpreted using appropriate statistical methods. Throughout the study, the researchers strictly adhered to ethical guidelines to ensure integrity and compliance with established research standards.

Measure

The study adopted a survey questionnaire to examine the difference between the demographic profiles of life insurance policyholders and their policy lapse behavior. The questionnaire was structured into five sections, each tailored to meet the study’s specific objectives. The first section collected demographic information of the respondents, including age, gender, occupation, education, and their monthly income. This data was used to examine the factors that can affect the lapse behavior of the policyholders.

The following sections focused on customer service and company trust, policy features and financial burden, agent behavior and communication issues, and customer behavior and procrastination of the policyholders using a structured questionnaire. These sections were measured through the adapted questionnaire from Mtonga (2021). The researchers used modified questionnaires, which were validated through a pilot test, to collect relevant data for achieving the study’s objectives. A sample of 30 participants is selected for the pilot test. This assists in evaluating various factors that influence the policy lapse rate in life insurance companies, including Customer Service and Company Trust, Policy Features and Financial Burden, Agent Behavior and Communication Issues, and Customer Behavior and Procrastination. The survey is disseminated to respondents using online Google Forms.

To validate and ensure reliability, the researchers conducted a pilot test study with 30 respondents (N=30) in the form of a digital survey (Google Forms) to the target population. The Policy Lapse Behavior indicator had a Cronbach’s Alpha of 0.883. The results of the pilot test indicated that the Cronbach’s Alpha values for all variables met the standard threshold, ensuring the reliability of the questionnaires.

Table 1. Results and Interpretation of Reliability Test

Table 2 presents an interpretation guide to determine the level of policy lapse behavior based on respondents’ demographic profiles. It categorizes the likelihood of policy lapse behavior into five descriptive ratings ranging from Very Low to Very High based on the mean scores within specific ranges. This table helps in understanding how strongly demographic factors influence respondents’ tendency to exhibit policy lapse behavior.

Table 2. Interpretation of the Level of Policy Lapse Behavior Based on Demographic Profile

Analysis and Interpretation

To analyze and interpret the collected data effectively, this study employed statistical tools aligned with its statement of the problem, addressing specific sub-problems. The primary methods included calculating the mean and standard deviation, as well as utilizing the independent-sample t-test and ANOVA. These methods were instrumental in quantifying the levels and relationships among the variables under investigation, providing an important foundation for analysis.

Mean. The mean was calculated to determine the average levels of policy lapse behavior among life insurance policyholders in Digos City. This analysis aimed to assess the factors influencing policy lapse decisions and their impact on the overall behavior of policyholders in the region. Hurley and Tenny (2022) defined the mean as the sum of values in a sample divided by the number of values, offering an accurate summary of overall levels within the sample. This metric can be used to summarize the central tendency of the dataset, providing an overall value.

Standard Deviation (S.D.). The standard deviation was used to help assess the variability or consistency of the data around the mean. According to Omda and Sergent (2022), standard deviation quantified the extent to which individual data points differed from the mean, indicating response variability. A high S.D. suggested considerable variation in policyholders’ behavior toward policy lapsation, while a low S.D. implied more uniform responses among them. This analysis helped determine whether certain groups of policyholders in Digos City exhibited more consistent or diverse tendencies in their policy lapse behavior.

Independent-sample t-Test. The Independent Sample t-Test was conducted to determine whether there is a statistically significant difference in policy lapse behavior between two distinct groups of insurance policyholders in Digos City. This test compares the means of two independent groups to determine if differences are significant or due to chance. As explained by Kim (2015), the Independent Sample t-Test is an effective statistical tool for analyzing the mean comparison between two separate groups. In this study, the test was applied to examine variations in policy lapse behavior based on factors such as demographics, policy type, or income level. By comparing the policy lapse rates of different groups, this analysis aimed to determine whether certain characteristics influenced policyholders’ likelihood of discontinuing their insurance coverage.

Analysis of Variance (ANOVA). The Analysis of Variance (ANOVA) was employed to examine the differences in policy lapse behavior among insurance policyholders in Digos City across multiple independent groups. As cited by Carpenter (2024), ANOVA works by analyzing the levels of variance within more than two groups through samples taken from each of them. This statistical method was applied to determine whether significant variations exist between the means of different groups categorized based on factors such as demographics, policy types, or behavioral tendencies.

Ethical Considerations

This study places significant importance on ethical considerations to protect the rights of respondents of the study. In any field of research, the “dignity, rights, safety, and well-being of participants” should always be prioritized (UK Department of Health, 2002). A research study involving human participants must be ethical (British Psychological Society, 2006). The researchers adhered to these ethical considerations to fulfill our responsibility to protect the participants. In addition to this study being subject to ethical approval by the research panel in the class, the researchers ensured that the following ethical considerations are addressed.

Anonymity is a form of confidentiality that specifically ensures the participants’ identities are kept hidden, such as names and addresses (Saunders et al., 2014). As researchers, they are responsible for keeping the identities of their participants anonymous. To that end, they all agreed that they personally ensured that the participants’ names, addresses, and other private information were never disclosed to anyone outside the group.

Confidentiality is a crucial aspect of this study, with a strong commitment to protecting the privacy of the information shared by participants. This dedication requires careful planning and strict adherence to established research protocols, as outlined in the work of Tudy and Tudy (2016). Thus, the researchers prioritized protecting the participants’ privacy rights, ensuring that sensitive information remained confidential.

Informed Consent. In obtaining informed consent, potential participants were provided with thorough information about the study, including its benefits and possible risks. After receiving the detailed explanation, participants were invited to decide whether they wish to take part in the research. As they agreed, they were asked to sign an informed consent form that outlined the study’s objectives. The form clearly explained that participants have the right to withdraw from the study at any time, without consequence. Only those who fully agreed and completed the informed consent process were included as participants in the study.

RESULTS AND DISCUSSION

This chapter deals with the presentation, analysis, and interpretation of data. The first part describes the demographic profile of life insurance policyholders in Digos City. The second part presents the levels of policy lapse behavior among the respondents, analyzed in relation to the demographic profile. The third part presents the significant differences in policy lapse behavior of life insurance policyholders, based on their demographic profile. This analysis identifies how various demographic variables may influence the likelihood of policy lapses.

Level of Demographic Profile of Life Insurance Policyholders in Digos City

The demographic profile of life insurance policyholders provides insight into the characteristics of individuals who invest in life insurance policies. It explores the characteristics of gender, age, education, occupation, and monthly income. These factors play a crucial role in determining an individual’s financial priorities and ability to maintain insurance policies. To accomplish this, data was derived from the respondents’ responses to determine the distribution of policyholders across these categories. Understanding the demographic composition allows for a deeper analysis of policyholder behavior, particularly in terms of financial stability and decision-making.

Table 3. Level of Demographic Profile of Life Insurance Policyholders in Digos City

Table 3 presents the demographic profile of life insurance policyholders in Digos City, showing that 72% of respondents are female (110 respondents) and 28% are male (42 respondents). In terms of age, the largest groups are those aged 31-40 (35 respondents, 23%) and 41-50 (52 respondents, 34%), followed by those aged 21-30 (35 respondents, 23%), 51-60 (16 respondents, 11%), and over 60 (12 respondents, 8%). Regarding education, most respondents have a Bachelor’s Degree (69 respondents, 45%), followed by 45 undergraduates (30%), 26 high school graduates (17%), 9 with a Master’s Degree (6%), and 3 with vocational training (2%). In terms of occupation, the majority are self-employed (82 respondents, 54%), followed by private sector employees (37 respondents, 24%) and government employees (33 respondents, 22%). As for monthly income, 50% earn below ₱15,000 (76 respondents), 25% earn between ₱15,001 – ₱20,000 (38 respondents), 20% earn between ₱20,001 – ₱40,000 income bracket (30 respondent), while only 5% earn above ₱40,000 (8 respondents).

In a study by Cristi et al. (2022), they concluded that demographic and household factors are the significant drivers of life insurance policy lapses. This aligns with a study by Mtonga (2021) that highlights demographic characteristics such as age, gender, education, employment, and marital status as essential components in policy lapse behavior. Similarly, Srbinoski (2020) concluded that demographic and social aspects are significant factors affecting policyholder’s policy lapse behavior.

Level of Policy Lapse Behavior of Life Insurance Policyholders Based on Demographic Profile

The primary objective of this study is to evaluate the level of policy lapse behavior of life insurance policyholders based on demographic profiles. Specifically, this study examined their demographic profiles based on these 5 indicators: gender, age, education, occupation, and their monthly income. To achieve this research objective, the data was collected through descriptive statistics with the mean and standard deviation employed to determine the overall level of policy lapse behavior. This analysis aims to provide a clear understanding of how different demographic factors influence the likelihood of life insurance policyholders engaging in policy lapse behavior.

Table 4. Level of Policy Lapse Behavior of Life Insurance Policyholders Based on Demographic Profile

Table 4 illustrates the level of policy lapse behavior among life insurance policyholders in Digos City, categorized by gender, age, education, occupation, and monthly income. For gender, both males (M = 3.24, SD = 0.70) and females (M = 3.17, SD = 0.62) exhibit moderate levels of policy lapse behavior which means that gender somewhat influences policy lapse behavior. In terms of age, individuals under 20 (M = 2.84, SD = 1.19) display a moderate level, while those aged 51-60 (M = 3.43, SD = 0.63) show a high level of policy lapse behavior which means that age significantly influences policy lapse behavior. The rest of the age groups exhibit moderate levels, ranging from 3.04 to 3.22. Regarding education, individuals with a vocational background (M = 3.53, SD = 0.50) and undergraduates (M = 3.44, SD = 0.62) have high levels of policy lapse behavior, meaning that education significantly influences policy lapse behavior. In contrast, other education levels which include high school, bachelor’s, and master’s degrees show moderate levels ranging from 3.00 to 3.28. For occupation, government employees (M = 3.33, SD = 0.63) exhibit moderate policy lapse behavior, similar to private sector employees (mean = 3.17, SD = 0.63) and self-employed individuals (M = 3.14, SD = 0.64) which means that occupation is not a significant factor in influencing policy lapse behavior. Finally, monthly income does not show significant variation in policy lapse behavior. All income groups (below ₱15,000, ₱15,001 – ₱20,000, ₱20,001 – ₱40,000, above ₱40,000) demonstrate moderate levels of policy lapse behavior, with mean values ranging from 3.09 to 3.24 which means that monthly income does not significantly influence policy lapse behavior.

Contrary to our results, findings by Shamsuddin (2022), who identified gender differences in lapse behavior, men are more likely to lapse policies due to risk-taking, while women retain them for financial security. The study also found men lapse more under financial strain, whereas women with inherited or long-term policies are less likely to do so. Similarly, Mtonga (2021) found age, gender, and employment type significantly influence lapse decisions, contradicting this study’s findings.  Males were more prone to lapses, possibly because they were primary earners with higher financial responsibilities (Kayarthakdka et al. 2023).

The findings of age support the study of Friedberg et al. (2023), that policyholders around the age of 60 often let their policies lapse before their death, forfeiting all benefits, highlighting that as policyholders grew older, policy lapses become increasingly high. As supported by Cristi et al. (2023), who found that policyholders aged 41-50 are 12% more likely to lapse their policies due to financial commitments often prioritizing personal projects over premium payments. Sirak (2015) supports this, noting that age alone does not significantly impact lapse rates when income, wealth, and contract age are constant. Moreover, the study of Fang and Kung (2021) also found that lapsation is influenced by idiosyncratic shocks when policyholders are younger.

The indicated findings above, anchored to the study of Giri and Chatterjee (2024) assert that a lack of awareness of life insurance leads to poor management of life insurance, highlighting the need for better education and communication on insurance products to reduce lapsation rates. Moreover, Frauendorf (2020) determined that education is one of the factors that significantly influences life insurance ownership as it enhances financial literacy, enabling informed decisions and risk management. This also aligns with Mtonga (2021) who found that policyholders with a tertiary education have a better understanding of the value of life insurance leading to purchase but are also more prone to letting it lapse.

These findings align with the study of Kayarthakdka et al. (2023) found business owners are more likely to lapse policies than salaried employees due to irregular income, debt, and financial uncertainty, increasing lapse probability by one percent. Policyholders aged 41-50 were also more likely to lapse, likely due to family financial pressures. Moreover, as policyholders age, lapsation is driven more by income, health, and bequest motives. These results contrast with Kozarevic and Hodzic (2021), who identified income as the key factor in policy purchasing, and Bhatia et al. (2024), who found income stability encourages policy retention.

Difference between Policy Lapse Behavior of Life Insurance Policyholders based on Gender

This study aims to assess the level of policy lapse behavior among life insurance policyholders concerning gender. Specifically, it examines variations in lapse behavior between male and female policyholders. To achieve this, data was gathered and analyzed using an independent-sample t-test. This analysis aims to provide additional insight into how gender affects policyholders’ tendency to lapse their life insurance policies.

Table 5. Difference between Policy Lapse Behavior of Life Insurance Policyholders based on Gender

Table 5 shows the result of the comparative analysis using an independent t-test between the gender of life insurance policyholders with their policy lapse behavior. It can be noted that the p-value is 0.183 which is greater than the level of significance set at 0.05. It implies that there is no significant difference between male and female life insurance policyholders in terms of policy lapse behavior. Hence, the study failed to reject the null hypothesis. This suggests that gender does not significantly influence the likelihood of policy lapse behavior among life insurance policyholders. Since the p-value is 0.183, which is higher than the significance level of 0.05, the study concludes that there is no statistically significant difference in policy lapse behavior between male and female policyholders. Therefore, the null hypothesis is not rejected, meaning that gender does not play a critical role in determining whether policyholders allow their life insurance policies to lapse. This finding implies that life insurance companies may not need to design specific strategies based on gender when addressing policy lapse behaviors. Instead, other demographic factors or individual circumstances might be more relevant in understanding policyholder behavior and guiding retention strategies.

This finding aligns with the studies of Hurwitz (2024), which stated that there was not much difference in the policy lapse behavior of male and female life insurance policyholders. It was argued that factors such as income and educational background as well as financial literacy have been well proven to have much influence on the lapse behavior of the policies and more than gender itself. Additionally, Koijen (2022) identified age and income as the primary determinants of policy lapse, while gender was found to have no significant impact. Collectively, these findings reinforce the argument that policy lapse behavior is primarily driven by socio-economic factors, such as financial literacy and economic conditions, rather than demographic attributes like gender. This implies that rather than adjusting their strategies solely based on gender, insurers should enhance financial education and provide products that address particular financial needs.

Difference between Policy Lapse Behavior of Life Insurance Policyholders based on Age

The primary objective of this study is to understand how age correlated with life insurance customers’ tendency to let their policies lapse. The analysis examined age groups and used ANOVA to measure policy lapse rates. ANOVA identifies statistically significant differences between age groups. The findings, presented in Table 6, highlight how age influences the likelihood of policy lapse among Digos City policyholders. The ANOVA results show which age groups exhibit significant variations in lapse rates, helping in understanding which groups are more likely to continue their policies.

Table 6. Difference between Policy Lapse Behavior of Life Insurance Policyholders based on Age

Table 6 presents the results of a one-way analysis of variance (ANOVA) comparing the policy lapse behavior of life insurance policyholders when grouped according to age. When compared according to age, the analysis reveals that it does not significantly affect the policy lapse behavior of respondents. With an F-statistic of 0.753 and a p-value of 0.585, which exceeds the 0.05 significance level, the null hypothesis is not rejected. This indicates that there is no significant difference in policy lapse behavior across the different age groups, indicating that age alone does not serve as a decisive factor in policy lapse behavior. While individuals may be at different stages of life, their decision to discontinue a policy is not directly linked to their age.

Similarly, Katole’s (2012) as cited by Giri and Chatterjee (2021) study concluded that an individual’s age does not significantly influence the amount of life insurance purchased. Also, a study by Sharma and Paliwal (2017) entails that, while age may be considered in some contexts however, it does not significantly impact the decision-making process for all individuals when it comes to life insurance policies. Additionally, Gottlieb and Smetters (2016) emphasized that multiple factors contribute to a policyholder’s decision to lapse their insurance. Age is not the sole determining factor, as other factors that influence a lapse. Thus, this further backs up the notion that age does not lead policyholders to discontinue their insurance. In the analysis of Sirak (2015), it was found that policyholder age does not influence the lapse rate once income (along with proxies for wealth) and contract age are controlled. This suggests that age alone is not a significant factor in determining whether a policyholder will allow their insurance policy to lapse. Instead, financial factors such as income and wealth, as well as the duration of the contract, appear to play a more critical role in influencing lapse behavior.

Difference between Policy Lapse Behavior of Life Insurance Policyholders based on Education

This study aims to examine how education level correlates with policy lapse behavior. The analysis investigated various education levels and employed ANOVA to assess lapse rates. ANOVA is used to determine whether significant differences exist between groups. The results, presented in Table 7, revealed how education level influences the likelihood of policy lapse among policyholders. The ANOVA findings identified which education levels show notable variations in lapse behavior, helping to understand the impact of education on policy maintenance.

Table 7. Difference between Policy Lapse Behavior of Life Insurance Policyholders based on Education

Table 7 explored the relationship between education level and policy lapse behavior, shedding light on how varying levels of education impact an individual’s likelihood of maintaining coverage. The analysis reveals a significant outcome, indicating that the F-statistic for education is 3.385, and the p-value is 0.011, which is less than the 0.05 significance level. As a result, the null hypothesis is rejected, indicating that there is a significant difference in policy lapse behavior based on education level. This suggests that individuals with different levels of education may experience policy lapses for different reasons, possibly due to differences in financial knowledge, understanding of policy terms, or awareness of the long-term benefits of maintaining coverage. The results imply that the lack of education or financial literacy may contribute to higher policy lapses. Addressing this gap through education or targeted communication could help reduce lapses and improve policy retention, particularly among less-educated groups.

This result supports the study of Capricho (2021), that individuals with higher financial knowledge are more likely to purchase life insurance, linking education and financial understanding to retention behavior.  Further supporting this, Giri (2018) found that individuals earning less than $10,000 annually have a significantly higher policy lapse rate of 71.4%. Thus, it shows unemployed individuals and those with lower education levels exhibit high lapse rates, while professionals and higher-educated individuals are more likely to invest in insurance, valuing benefits like risk protection, returns, and loan options (Rajkumar & Sugapriya, 2023; Lim & Tan, 2019; Chirimubwe et al., 2018) supporting the study findings that education-level push policyholders on lapses. Furthermore, these findings also support the relevance of Transaction Cost Theory, as discussed in the first chapter. According to the theory, a person’s education level influences their ability to understand financial products and make informed decisions. Individuals with lower levels of education may struggle to fully comprehend the terms, benefits, and risks of insurance or investment products.

Differences between Policy Lapse Behavior of Life Insurance Policyholders based on Occupation

This study examines how occupation influences the likelihood of life insurance customers letting their policies lapse. By analyzing different occupational categories, the research used ANOVA to evaluate lapse rates. ANOVA is employed to identify significant differences across occupation groups. The findings, presented in Table 8, showed how occupation affects the probability of policy lapse among Digos City policyholders. The ANOVA results highlight which occupational groups demonstrate significant variations in lapse rates, providing valuable insights into which groups are more likely to maintain their policies.

Table 8. Difference between Policy Lapse Behavior of Life Insurance Policyholders based on Occupation

Table 8, when grouped by occupation, the results also indicate no significant effect on policy lapse behavior. The F-statistic for occupation is 1.005, with a p-value of 0.369, which is well above the 0.05 significance level. As a result, the null hypothesis is not rejected, implying that occupation does not have a statistically significant impact on policy lapse behavior. Therefore, there appears to be no notable difference in lapse behavior based on occupation, indicating that other factors may play a more influential role in determining lapse rates.

A previous study by Annamalah (2013) as cited by Srivastava and Jadid (2020) found that income and education are positively related to life insurance demand while age, number of children, occupation, and working spouse were found to be insignificant. This aligns with the results of the current study, where the p-value for occupation is 0.369, which exceeds the standard significance level of 0.05. This is further supported by Legas and Seid (2024), who also revealed that demographic factors, such as age, gender, education, and religion, do not significantly influence consumer behavior. Therefore, the results indicate that occupation does not have a statistically significant impact on insurance uptake.  According to Sirak (2015), the analysis highlights the significant impact of changes in employment status on policy lapses. Specifically, the study reveals that transitions to unemployment lead to a sharp increase in lapse rates, rising by more than 75%. This finding emphasizes the importance of occupation as a key factor in determining the likelihood of policyholders allowing their insurance policies to lapse. The results, derived from the Cox proportional hazards model and confirmed by pooled panel logit regression, underline employment status’s crucial role in lapse behavior, particularly during economic distress and rising unemployment.

Differences between Policy Lapse Behavior of Life Insurance Policyholders based on Monthly Income

This study investigated how monthly income influenced the likelihood of life insurance customers allowing their policies to lapse. The analysis considers different income brackets, ANOVA to assess lapse rates. ANOVA is used to identify significant differences across income groups. The results, shown in Table 9, reveal how monthly income influences the probability of policy lapse among Digos City policyholders. The ANOVA findings highlight which income groups show significant differences in lapse rates, offering insights into which groups are more likely to continue their policies.

Table 9. Difference between Policy Lapse Behavior of Life Insurance Policyholders based on Monthly Income

In Table 9, it discusses the association when respondents were grouped by monthly income, the analysis also found no significant effect on policy lapse behavior. The F-statistic for monthly income is 0.416, and the p-value is 0.742, both of which are significantly greater than the 0.05 significance level. Therefore, the null hypothesis is not rejected, suggesting that policy lapse behavior is not directly influenced by income level, implying that financial constraints alone may not be the primary driver of policy lapses.

These findings align with Dragos et al. (2017), which found no significant relationship between income distribution and life insurance demand across 32 European countries. This is further supported by Reck et al. (2023), who used an automated Lasso approach and found income to be an insignificant factor in lapse rates. Similarly, large-scale actuarial studies have reached the same conclusion. Additionally, behavioral research suggests that policy lapse is driven more by psychological factors than financial constraints. Emerald (2023) found that trust in insurers, policy misunderstandings, and psychological biases often drive lapsation rather than income limitations. This is in line with SCOR (2022), which examined lapse behavior during the COVID-19 pandemic and found that fluctuations were influenced more by uncertainty and changing risk perceptions than by income variations alone. These findings collectively indicate that while financial circumstances may affect an individual’s ability to maintain insurance, income level itself is not a direct determinant of policy lapse behavior. Conversely, large-scale actuarial studies have failed to establish a significant link between income and policy lapse behavior. The Milliman VALUES 2018 GLWB Industry Lapse Study, which analyzed data from approximately 3 million policyholders, did not identify income as a major factor in lapse decisions (Milliman, 2018). Instead, policyholders’ behavior appeared to be influenced by contract terms and benefit structures rather than their financial standing. A subsequent study by Milliman (2020), which reviewed over 18 million policy year records, further confirmed that income level did not play a defining role in lapse behavior. These findings indicate that while financial circumstances may influence an individual’s ability to maintain insurance, they do not serve as a direct determinant of lapse probability.

Summary

This study aimed to assess the policy lapse behavior among 152 policyholders of life insurance companies in Digos City and examine the differences in their lapse behavior. This study utilized a quota sampling technique and examined the policy lapse behavior among 152 respondents who are policyholders in life insurance companies in Digos City. The research addressed the assessment of the differences between the demographic profiles of policyholders and their policy lapse behaviors.

Through data collection from the respondents, the study employed one-way ANOVA to analyze the gathered information. In terms of gender, both males (M = 3.24, SD = 0.70) and females (M = 3.17, SD = 0.62) displayed moderate levels of policy lapse behavior, suggesting gender was not a significant factor. Regarding age, individuals under 20 (M = 2.84, SD = 1.19) showed a moderate level, while those aged 51-60 (M = 3.43, SD = 0.63) had a high level, indicating age-influenced policy lapse behavior variably. Other age groups ranged from 3.04 to 3.22, showing moderate levels. For education, individuals with a vocational background (M = 3.53, SD = 0.50) and undergraduates (M = 3.44, SD = 0.62) had high levels of policy lapse behavior, whereas those with high school, bachelor’s, and master’s degrees showed moderate levels (ranging from 3.00 to 3.28). In terms of occupation, government employees (M = 3.33, SD = 0.63), private sector employees (M = 3.17, SD = 0.63), and self-employed individuals (M = 3.14, SD = 0.64) exhibited moderate policy lapse behavior, meaning occupation did not significantly influence this behavior. Finally, monthly income did not show significant variation, as all income groups (below ₱15,000, ₱15,001 – ₱20,000, ₱20,001 – ₱40,000, above ₱40,000) demonstrated moderate levels, with means ranging from 3.09 to 3.24.

In summary, the study provides significant insights into the factors influencing policy lapse behavior among employees. It shows that gender, occupation, age, and income have little impact. Education, however, appear to play a more substantial role, with younger and less educated individuals displaying higher levels of policy lapse behavior. These findings suggest the need for targeted interventions based on demographic factors to reduce policy lapses within the workforce.

CONCLUSION

After a thorough investigation of the variables involved in this study, the following conclusions are drawn:

1. The majority of policyholders are female (72%), while 28% are male. Education levels vary, with 45% holding a bachelor’s degree. In terms of occupation, 54% are self-employed, 24% work in the private sector, and 22% are government employees. Half of the policyholders earn below ₱15,000 monthly, while only 5% earn above ₱40,000. Age distribution is broad, with the largest group (34%) falling within the 41-50 age range.
2. Policy lapse behavior is generally moderate across all demographic profiles. However, it is higher among policyholders in Digos City aged 51-60 and individuals with vocational or undergraduate education. This suggests that these groups are more prone to lapsing their policies.
3. There is no significant difference in policy lapse behavior based on gender, age, occupation, and monthly income. On the other hand, education had a significant impact, with those holding a vocational or undergraduate education showing higher lapse behavior. This indicates that policyholders with lower educational attainment are more prone to policy lapses due to financial constraints, lack of awareness, and varying risk perceptions.

RECOMMENDATIONS

Based on the study’s insights into policy lapse behavior among life insurance policyholders in Digos City, several recommendations are suggested:

1. Life insurance companies are in the role of providing financial protection. Therefore, it is crucial for them to assess the educational attainment of policyholders and to ensure they are capable and informed enough about their insurance types before selling them. Thus, they should approach individuals with higher educational attainment as they are more likely to recognize the importance of retaining their policies and the consequences of lapses. This helps avoid the risk of lapses and ensures systematic management and understanding of policy terms, ultimately enhancing customer satisfaction and long-term retention.
2. Insurance advisers and brokers should focus on proactive communication and client education, helping policyholders understand the importance of insurance and the benefits of their specific policies. This approach is effective when targeting individuals with higher educational attainment, as they often have more stable financial situations and may be more receptive to financial planning advice. By doing so, advisers can empower clients to make informed decisions, leading to greater overall satisfaction, and build more long-term relationships while reducing policy lapses. Additionally, advisers should tailor their policy recommendations to fit each client’s unique needs and risk tolerance. Those with higher educational backgrounds may require specialized coverage due to their professions or assets, ensuring that they receive the best possible coverage.
3. Government and Regulatory Agencies such as Insurance Commission could take into account several suggestions based on the study’s findings to increase the retention of life insurance policies. These include encouraging consumer education to guarantee a better understanding of life insurance and putting in place stronger policyholder protection measures, such as improved communication regarding policy renewals and grace periods. The government can also provide insurance education seminars to have a better background regarding insurance policies. To keep policyholders, regulators could also push insurers to provide flexible payment plans and loyalty rewards, while making sure that underwriting procedures are impartial and open. Lastly, customized solutions for high-risk populations and post-pandemic issues may guarantee greater access to reasonably priced insurance and lower lapses brought on by monetary difficulties.
4. Policyholders play a crucial role in managing their insurance policies responsibly. Taking that into consideration, it is important that they should prioritize maintaining their coverage, and expand their awareness about the consequences of policy lapses, including the risks to financial security. To ensure continuous coverage, it is best to set reminders for premium payments to avoid missed deadlines. Regularly reviewing the policy terms and benefits is crucial to ensure that they still align with their needs and preferences. Additionally, policyholders should also seek professional help such as insurance advisers, consulting how to better manage their insurance obligations and to ensure they remain protected against unforeseen events.
5. Future researchers should carry out more studies on the psychological and economic factors that determine this phenomenon. Moreover, they should assess the long-term effects of various policy structures on lapse rates and policyholder retention to allow for data-driven decisions to improve retention strategies overall. Additionally, future studies should explore other possible variables or factors, aside from the demographic profile, that could affect policy lapse behavior, such as customer satisfaction, policy features, and the level of communication between policyholders and insurance providers. This broader scope will help to provide a more comprehensive understanding of the factors influencing policy lapses.

ACKNOWLEDGMENT

1. The researchers would like to extend their wholehearted gratitude to everyone who gave their unwavering support throughout the entire process of conducting this research.
2. They also wish to convey their utmost thankfulness to Almighty God for granting them strength, wisdom, and perseverance throughout this research endeavor. His guidance has been instrumental in shaping their understanding and providing clarity during moments of uncertainty.
3. The researchers extend their sincere gratitude to Goodlife Insurance and St. Peter Life Plan for their invaluable cooperation and support throughout the course of this study. Their willingness to provide essential data, insights, and resources has greatly contributed to the depth and accuracy of this research.
4. The researchers sincerely thank their research teacher and statistician, Mr. Cleford Jay D. Bacan, MAEd-MT, for his expertise, guidance, and patience throughout the study. His dedication was crucial to its success. They also appreciate their research adviser, Mr. Michael P. Aquino, CTT, MRITax, for his insightful feedback and valuable suggestions that enhanced the clarity of this research.
5. Furthermore, they are grateful to their family for granting the necessary support and encouragement to conduct this study. Special thanks are also extended to the teachers of Cor Jesu College, whose generosity and support played a vital role in making this research possible.

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