This mixed-methods study investigated factors influencing health insurance demand in Nigeria using behavioral economic principles and hypothetical purchase tasks. Study 1 employed quantitative analysis of demand patterns across open and closed economy conditions, while Study 2 used a focus group discussion following completion of a purchase task to explore participants’ decision-making processes and reasoning. The exponential demand model (Koffarnus et al., 2015) fit the data well, with a median R² = 0.87 (IQR = 0.81–0.92), indicating strong predictive validity across demographic subgroups. These findings also support the use of hypothetical purchase tasks as validated tools for understanding consumer demand in developing economies (Reed et al., 2022; Khan, 2020). Aggregate demand curves revealed typical patterns of decreasing consumption as price increased, consistent with established behavioral economic models.

One of the most informative quantitative findings was the identification of P_max, the price at which demand shifts from inelastic to elastic. For example, the low-income subgroup had a P_max of ₦5,710 ($3.81 USD), closely matching focus group participants’ stated cutoff of ₦5,500 ($3.67 USD). While P_max varied widely across subgroups (from ₦6,141 [$4.10 USD] for females to over ₦74,000 [$49.33 USD] for younger participants), this alignment in the low-income group suggests that, for certain populations, behavioral economic estimates can correspond closely to self-reported affordability limits.

Qualitative findings also revealed a “reverse price sensitivity,” where some participants rejected extremely low-priced insurance options due to quality concerns. While the aggregate demand curves from Study 1 did not show a clear drop in purchases at very low prices, Study 2’s focus group discussions clarified why some participants rejected inexpensive options. Participants worried that very cheap insurance would provide inadequate coverage or poor-quality care, with one noting a preference to “pay a reasonable amount of money … so that I can be sure that I’m not getting Paracetamol or Vitamin C.” This finding aligns with research revealing how quality perceptions influence decision-making in health markets (Casabianca et al., 2022) and suggests that insurance pricing must balance affordability with quality signaling, as prices that are too low may actually reduce demand by suggesting poor value.

Taken together, these findings point to a potential “pricing corridor” for low-to middle-income participants (roughly ₦3,000 to ₦17,500; approximately $2 to $12 USD at the time of data collection) where premiums may be high enough to signal quality but still within the range of relatively inelastic demand. This corridor should not be interpreted as universal, given the substantial variation in P_max across demographic groups, but it offers a useful starting point for designing targeted premium structures in Nigeria’s health insurance market.

Contrary to the theoretical expectation that consumers should respond differently when alternatives are available versus when they are not (Gilroy et al., 2018; Hursh, 2014; Reed et al., 2013), no significant differences emerged between open and closed economy conditions in demand intensity or price sensitivity. Focus group discussions revealed why this may have occurred: participants already operate within a complex ecosystem of healthcare alternatives, regardless of the experimental condition they were assigned. Even when instructed to imagine that formal insurance was their only option (closed condition), participants’ real-world knowledge of alternatives, including self-medication, over-the-counter pharmaceuticals, traditional medicine, and out-of-pocket payments, likely influenced their responses. As one participant noted, many Nigerians have “given up on hospitals because they feel they cannot afford it” and instead rely on pharmacists, medicinal herbs, and self-treatment.

Skepticism towards Nigeria’s National Health Insurance Scheme (NHIS), which is a system characterized by inefficient service delivery, inadequate infrastructure, and resource mismanagement (Eze et al., 2024), may have further eroded the salience of the experimental manipulation. This interpretation aligns with a previous study showing that only half of respondents expressed willingness to consider alternatives to the NHIS due to dissatisfaction with service delivery (Kofoworola et al., 2020). Taken together, this finding has important implications for how researchers model choice contexts in developing economies and highlights the need to consider broader institutional and cultural factors when applying behavioral economic frameworks (Kunreuther & Pauly, 2015; Madrian, 2014).

A related qualitative finding was that most participants accessed insurance through institutions rather than individual purchase, consistent with Adekunle and Vincent (2024). Employment-based coverage was common among formal sector workers, while students accessed care through mandatory health fees incorporated into tuition, and NHIS family-based coverage expired at age 18. This creates a critical transition point where coverage lapses, especially for young adults, and suggests that individual market approaches may be less effective than employer-sponsored or government-sponsored programs in achieving broad coverage (Onwujekwe et al., 2011).

Both age and income significantly influenced health insurance demand, revealing important patterns for understanding target populations. Younger participants demonstrated higher demand intensity when insurance was free or low-cost, but also showed greater price sensitivity as costs increased. The results also support findings by Boonen et al. (2016), who noted that younger adults are more sensitive to the price of health insurance, whereas older adults are more sensitive to quality considerations. Recent evidence from Nigeria also shows that young adults will take up coverage when it is available and accessible (Effiong et al., 2025). This, however, differs from literature suggesting that young adults are less likely to purchase insurance due to perceived invulnerability (Cantiello et al., 2015; Nachiappan et al., 2022).

Low-income participants similarly showed higher demand intensity at zero cost compared to their higher-income counterparts, which aligns with findings by Topan et al. (2024), who demonstrated that income level significantly influences willingness to pay for health insurance. This pattern becomes clearer when considering that low-income individuals face the greatest financial vulnerability to health shocks and have the most to gain from financial protection (Erlangga et al., 2019). These individuals often lack robust social support networks and cannot rely on family resources during health crises, making formal insurance coverage particularly valuable as a safety net (Böhnke & Link, 2017; Lubbers et al., 2020). Additionally, low-income participants may be more familiar with the financial burden of out-of-pocket healthcare payments, having experienced these costs directly, leading them to opt for insurance enrollment as a precautionary measure (Anaba et al., 2022). The high demand among younger and lower-income populations suggests that subsidized programs targeting these groups could achieve high uptake rates. This aligns with international evidence on the effectiveness of targeted subsidies in achieving universal health coverage (Schoen et al., 2010; Hussain et al., 2024).

Female participants exhibited higher demand intensity and greater price sensitivity compared to males. This finding aligns with previous research showing that women are more likely to enroll in health insurance (Agyemang-Duah, 2024; Antabe et al., 2025) and may reflect several factors. Women typically have higher healthcare utilization rates throughout their lives (Kaiser Family Foundation, 2021), particularly related to reproductive health, making insurance coverage more immediately valuable. Additionally, women often serve as healthcare decision-makers for their families (Matoff-Stepp et al., 2014), potentially increasing their awareness of healthcare costs and the value of insurance protection.

Participants’ risk-taking behaviors revealed complex relationships with health insurance demand. Those who gambled showed lower price sensitivity than non-gamblers, suggesting that risk-tolerant individuals may be more willing to pay higher premiums. This finding has two possible explanations. First, gambling behavior is associated with various health problems and declining health outcomes (Butler et al., 2020; Jazaeri & Habil, 2012; Latvala et al., 2019), potentially making gamblers more aware of their need for healthcare access. The behavioral tendency toward risk-taking, combined with declining health outcomes among frequent gamblers, may increase their reliance on medical care and consequently their willingness to secure access to treatment, even if it involves higher costs (Dąbrowska et al., 2017). Second, individuals comfortable with financial risk-taking in gambling contexts may view health insurance premiums as an acceptable wager against future health problems, or alternatively, may view health insurance as a way to secure health protection while continuing to engage in risky behaviors (Butler et al., 2020).

Similarly, participants with alcohol consumption history showed higher demand intensity compared to non-users. This pattern likely reflects their increased vulnerability to alcohol-related health conditions and higher expected healthcare utilization, as people with alcohol-related diagnoses incur significantly higher healthcare costs due to conditions such as heart disease, stroke, liver disease, and certain cancers (Ozluk, 2022). The higher demand for health insurance may also be explained by the concept of moral hazard, as discussed by Azagba et al. (2021), who found a significant relationship between health insurance coverage and heavy drinking rates.

Interestingly, religious and political beliefs showed no statistically significant effects on health insurance demand. The lack of religious influence was particularly surprising, given previous research suggesting that faith-based beliefs about divine providence might reduce demand for insurance (Adewole et al., 2015). However, quantitative analyses in the present study revealed that more religious participants actually had higher demand intensity, suggesting that religious individuals may view health insurance as not conflicting with faith-based beliefs. This finding also differs from Özden et al. (2024), whose study found that individuals without specific religious affiliations were more likely to purchase insurance.

Focus group participants valued health insurance primarily for financial protection and improved access to care, particularly in the context of Nigeria’s high inflation and rising healthcare costs, consistent with findings by Erlangga et al. (2019) who emphasized the financial protection offered by public health insurance. As one participant noted, malaria treatment costs had doubled between 2022 and 2024, making insurance coverage increasingly valuable for managing routine healthcare expenses. This finding also aligns with Adams (2024), who highlighted how economic stagnation and social inequalities in Nigeria collectively constrain access to essential healthcare services. That said, value perceptions were tempered by concerns with inefficiencies in the healthcare system. Focus group participants noted that having insurance did not guarantee faster service at government hospitals, a concern that reflects broader challenges across the six World Health Organization health system pillars (Oleribe et al., 2019). These structural issues including delayed care, workforce constraints, medicine shortages, financing gaps, and weak governance, undermine insurance’s perceived benefits, particularly in rural areas (Abah, 2023).

Views on government-mandated health insurance were mixed. While some acknowledged its potential to improve coverage and reduce reliance on unsafe alternatives such as unregulated medications and traditional remedies, others feared that corruption, political interference, and implementation inequities would limit its effectiveness. These concerns echo Bashar et al. (2025), who identified governance, market efficiency, and organizational health as priorities for expanding participation in Nigerian health insurance schemes. The findings suggest that successful insurance expansion requires concurrent improvements in government transparency and accountability to build public trust.

This research makes a significant contribution by demonstrating the successful application of hypothetical purchase tasks (HPTs) to health insurance demand in a developing economy context, extending the validated use of these methods beyond their traditional applications (e.g., Murphy & MacKillop, 2006). The strong model fits in a Nigerian sample support the cross-cultural validity of behavioral economic demand models and demonstrates the value of mixed-methods approaches for understanding health insurance demand. Study 2’s sequential design, where participants completed the hypothetical purchase task before engaging in focus group discussions, allowed for rich reflection on actual choice processes and provided context for interpreting the quantitative patterns observed in Study 1. This approach addresses calls for integrating behavioral economics with qualitative research to better understand decision-making processes (Beatty et al., 2023; Matjasko et al., 2016; Tewogbola et al., 2025).

Several areas warrant further investigation. First, future research should explore how alternative healthcare options (traditional medicine, self-medication, informal networks) function as substitutes for formal insurance in different economic contexts. Second, longitudinal studies could examine how insurance demand changes as individuals transition between institutional coverage types (family to individual, student to employee). Finally, experimental research could test whether the reverse price sensitivity finding replicates in other contexts and commodities, as this has important implications for behavioral economic theory.

The current research has limitations to consider when interpreting its findings. The quantitative component (Study 1) faced challenges related to sample size and data quality. After excluding participants with incomplete or missing responses, 28.3% of the original dataset was removed, reducing the final sample to 76 participants. This high exclusion rate may have introduced selection bias, as participants who completed all survey components may differ systematically from those who provided incomplete responses. Additionally, the use of crowdsourcing for participant recruitment, while practical for accessing Nigerian participants, represents a non-probability sampling method that may not accurately represent the broader Nigerian population, particularly those without reliable internet access or familiarity with online surveys (Cornesse et al., 2020). The binary response format of the hypothetical purchase task, while appropriate for aggregate analysis, prevented the estimation of individual-level demand parameters (Q₀ and α), limiting our ability to examine individual differences in demand characteristics. Furthermore, several subgroup analyses were compromised by non-convergence issues, particularly for cannabis and nicotine users, due to insufficient sample sizes in these categories. This prevented comprehensive analysis of substance use effects on health insurance demand.

The qualitative component (Study 2) was limited by its small sample size of five participants, which restricts the depth and generalizability of insights obtained from the focus group discussion (Vasileiou et al., 2018). The use of virtual focus groups via Zoom, while necessary due to geographical constraints, introduced technical challenges including network connectivity issues that occasionally prevented full participant engagement simultaneously (Nehls et al., 2015). Additionally, the participants were predominantly from similar geographic and socioeconomic backgrounds, which may have limited the diversity of perspectives captured and potentially overlooked important regional or cultural variations in health insurance attitudes within Nigeria (Gross et al., 2022). Finally, the study was conducted during a specific economic period in Nigeria, and the findings may not generalize to different economic conditions or policy environments. The rapid inflation mentioned by participants, for example, may have influenced price sensitivity in ways that might not persist under more stable economic conditions.