In developing countries, the cost of health care financing is one of the biggest social challenges and out-of-pocket payments present significant drains on household spending patterns, making health insurance one of the best social interventions . However, health financing is challenged by a number of problems such as public under-funding, high user fees and informal payments, causing the poor to be deprived of health care .
In Ghana, the legal framework for mandatory health insurance was promulgated in 2004 through an Act of Parliament, Act 650. While the establishment of an insurance scheme was in fulfilment of political promise made by the then ruling government during the 2000 general elections campaign, it was more of a response to a need to improve equity and access to health services in the face of increasing cost of health care.
The WHO  concept of equity in health financing states that individuals must be given the needed service based on the ability to pay. This therefore calls for a system of financing that pools risks and resources from a greater majority to protect the most vulnerable against cost of illness [4–6]. Thus, making health insurance in poor countries a reflection of social justice .
In spite of the perceived successes of the health insurance scheme in Ghana, especially in terms of the number of subscribers, the major objective of achieving equity is believed to be failing, given the low enrolment of the poor [8–10]. Unhealthy poor people, especially women, stand a higher risk of being denied access to health care because of their ability to afford. Equity in health insurance is therefore intended to avoid preclusion of people who are vulnerable due to their socioeconomic conditions as well as health states .
A number of theoretical propositions have been expounded in the health insurance literature to explain decision-making processes towards subscriptions to health insurance in poor settings. Schneider  provided one of such seminal reviews of theoretical paradigms on the subject. Among the variants of decision making models Schneider  espoused are consumer choice, expected utility, state-dependent utility, prospect, cumulative prospect, endowment/status quo/veil of experience, regret and disappointment, time preferences, poverty and social capital. Whereas all the decision making models provide us with several intuitive explanations to why people buy or do not buy insurance, the time preferences model, the state-dependent utility model and the poverty model help us to examine the influence of certain socioeconomic and demographic variables on insurance subscription among Ghanaian women. In this regard, this paper is nested within theoretical triangulation , thus examining the data from multiple-perspectives. Time preference decision model is posited around present and future assessment of health needs based on present circumstances. Thus, if people value future than present consumptions, current consumption could be deferred for investments in the future, which could be a decision to be insured whereas preference for the present could constrain deferring present consumption for the future. According to the World Bank  cited in Schneider , those with high future time preference would purchase insurance. The state-dependent utility approach assumes that individuals engage in introspection of their subjective health state before purchasing health insurance. If an individual considers his/her risk of illness to be low, there is less likelihood of purchasing insurance. On the other hand, if perceived risk of illness is high, a person would subscribe to an insurance provider. The poverty position of Schneider’s decision making is simply concerned with ability to pay high premiums. When individuals and households consider premiums to be out of their reach, then they are unlikely to be insured.
From spatial perspective, geographic targeting (GT)  provides a useful strategy for identifying people who need insurance most based on physical location. Geographic targeting is a population level measure more than individual level measure. It is often premised on the need to identify populations who reside in specific locations with comparatively higher levels of poverty. GT is often combined with welfare or wealth status measures as a way of identifying populations whose needs are not being served adequately . Although a useful conceptual construct for identifying the poor for interventions, GT can also lead to outflows to the rich, who are usually not the targets for interventions [14–16].
This paper contributes to the debate on purchasing of health insurance by examining the interactive effect of spatial location and household wealth status with special emphasis on women. Our focus on women is justified by the fact that this population, in the context of a developing nation, faces more health challenges than men, particularly as a result of reproductive health risks. By this, we do not seek to negate the health needs of men; just that women constitute a special population in our context. A recent study by Mensah et al.  provides us with additional good reasons to focus on women. Mensah and his colleagues found that, between insured and uninsured women, those insured reported better reproductive health outcomes than the uninsured and that the achievement of the Millennium Development Goals could be accelerated by up-scaling health insurance coverage. Our emphasis on women in the reproductive ages (15–49) will therefore enable us to highlight the characteristics of the women who could be targeted for interventions. Apart from the population of interest, it is only the study by Akazili et al. , to the best of our knowledge, which used a nationally representative data (Ghana Living Standards Survey-2005). All the other studies we identified did not use nationally representative samples [6–8, 17–19]. Our use of a nationally representative data therefore widens the scope in analysing the characteristics of insured and uninsured women.
This study proceeds on the assumption that there are no significant interactive effects of household wealth status and spatial location on health insurance subscription among women in Ghana. Yet we are also mindful of the fact that there are other hypothetically pertinent dynamics which influence whether an individual would insure or remain uninsured. With respect to our dataset, the following variables: education, age, religion, partner’s education, autonomy (determined by health decision making, large and small household purchases, see ) are controlled for in our analysis.