The Nigerian population has a predominantly rural populace and over 90% of the population do not have access to the National Health Insurance scheme due to the highly informal nature of the economy. The use of the Community Based Health Insurance Scheme (CBHIS) is vital to health status especially among pregnant women given that the Nigerian economy is the second largest contributor to global under-five and maternal deaths. This paper examined the Willingness to pay (WTP) for the use of the CBHIS and premium amounts WTP in rural parts of Lagos State.

The study made use of the probit model to examine factors that affect the WTP and measures of central tendency to determine the premium charge that pregnant women are WTP for use of the scheme. Data was obtained from a cross section of 350 pregnant women in three different CBHIS centers in Lagos State

Results of the study suggest that income, employment status, household size, marital status and distance to the CBHIS significantly determine the WTP for the use of the CBHIS. Use of the CBHIS would be relatively high when monthly premium is set at N 500. The average amounts that pregnant women are WTP for use of the scheme was about N1,186.40 (US $6.02) per month.

Efforts meant to raise the use of the CBHIS in rural communities by pregnant women, should incorporate strategies that will reduce premium payment below existing rate and create enlightenment of benefits of the scheme particularly for women in self-employment.

Financing health-care financing is one of the most challenging problems facing the world’s poorest populations especially in developing countries. This is because while over 90% of the global burden of disease is borne by approximately 80% of the world’s poor, only about 11% of global health spending is targeted at the poor [1]. In other to mitigate the negative effects of low health care spending on the poor, risk sharing agreements through the use of tax funds, formal insurance schemes and other forms of mandatory and voluntary financing is veritable. Another is the use of direct user fees. While these are important, they are not often easily and effectively implemented in resource poor African countries due to weak institutional arrangements and other deficiencies. Whereas some African countries; Ghana, Burkina Faso and Uganda have been able to institute social health insurance schemes (SHICs) that cover both the formal and informal sectors, many others have not been able to do so [2]. In Nigeria, the National Health Insurance Scheme (NHIS) is associated with formal sector employment which requires regular contributions compatible with formal sector earnings. The scheme does not cover individuals in the informal sector who predominantly live in rural areas. Due to this limitation, the Community Based Health Insurance (CBHI) was advocated as a transitional mechanism to achieving universal coverage for health particularly for rural dwellers [3].

While most studies have examined premium payment source for the CBHIS and factors that influence the WTP for the use of the scheme, the use especially by pregnant women living in the rural areas has received less attention [2, 4-10]. In Nigeria, the role of the CBHIS in accessibility to health care services for pregnant women is paramount due to high maternal and child mortality figures in the country. The country is the second largest contributor to maternal and child deaths. About 14% of the global maternal mortality and 13% of the under-five deaths are related to Nigeria. This figure is quite disproportionate with evidence that the country has approximately 2% of the global population figures [11-14]. This study therefore sought to examine the factors that influence the WTP for the use of the CBHIS by pregnant women in rural communities. Focus in rural areas is further strengthened on the argument that the population is predominantly a rural populace. The study provides estimates of premium amounts which women are WTP for the use of the scheme. Findings of the study will provide insights on measures that can be adopted for enhanced participation and better health outcome from increased health access and use during pregnancy.

Theoretical explanations of individual’s behavior towards making choices particularly in contingent valuations are rooted in behavioural theories; planned behaviour and norm activation theory. The Theory of Planned Behaviour (TPB), argues that an individual’s intention to perform a behavior is influenced by a combination of behavioural attitudes, beliefs, subjective norms or otherwise opinions of others who play vital roles and perceived behavioural control over an action [15]. Each of these factors is directly determined by the individual’s values, emotions, intelligence, age, gender, race, ethnicity, education, income, religion, experience and media exposure. The Norm-Activation theory explains the reasons why altruistic behaviour occurs in some situations and not in others [16]. It alludes altruistic attitudes to factors such as awareness of need, situational responsibility, efficacy and ability [17-18].

Each of these theories is used as benchmark in the analysis of contingent valuations. Most studies elicit people’s WTP for healthcare interventions through contingent valuation surveys so that the benefits of those interventions can be valued in monetary terms [19-22]. Contingent valuation studies have mainly adopted the closed ended format in which the respondents are asked whether or not they will be WTP a specified price for a given health insurance package. In this case responses can be categorized as a binary variable assuming the value of one for positive answer and zero otherwise. The open ended format on the other hand is sometimes adopted to elicit information directly for maximum amounts individuals are WTP [7, 23-24]. Several studies have examined the WTP for health insurance as a form of demand for health care. In rural India for instance, individuals WTP for the use of health insurance scheme was examined as a binary response variable. The results showed that insurance/savings schemes especially life insurance as opposed to saving schemes, accounted mainly for rural dwellers WTP for rural health scheme [25].

In a similar study, the CVM was used to compare household heads WTP for CBHIs in Burkina Faso. The average monetary amounts that household heads are WTP for the CBHIs was twice the mean per capita amount they are WTP for the entire household. Older individuals, females, poor persons and those with less education showed lower WTP than those who are young, male, rich and persons with more education. The WTP for CBHIs was between $3.17 and $425 for an individual and $ 8.6 and $13.03 per household in a year [4].

A related study in rural Iran revealed that households are WTP an average of $277 per month for health insurance [5]. In Ethiopia, the mean medical expenses people were WTP for CBHI was as low as $22.46 per three-month period mainly because of high poverty figures in the country. This suggests that income significantly influences the choice to make use of the CBHIS [9].

Studies have showed that other factors influencing the WTP for CBHIS mainly include age, gender, the level of educational attainment, family size, marital status, geographical location and employment type. In terms of age groups, individuals between 30 and 39 years are argued to have more WTP for the use of the scheme than other age brackets [2]. Studies showed that males, persons with higher educational qualification and families with large household size, are more WTP for use of the scheme than females, individuals with no-formal education and smaller households [7-8, 10]. Higher preference for the scheme is seen among rural dwellers and farmers especially those in self-employment [8, 10].

Overall, empirical evidence provides findings for the WTP for CBHIS in relation to payment mechanism, out of pocket, saving scheme and on the determinants of the use of the scheme. Less attention is given to the WTP for the use of the scheme by pregnant women living in rural communities in Nigeria. The argument for this area of research rests on poor statistics for maternal and child health as well as access to health care facilities in the rural areas. Health care payment which is mainly out of pocket would be less burdensome with the use of less expensive payment mechanism particularly through health insurance. This study hence provides evidence on factors that would affect the WTP for the use of the CBHIS in Nigeria. The CBHIS is a community based insurance programme and hence operates in the rural areas where majority of the Nigerian population resides [26].

Methods

The study was conducted in Lagos State. The state is located in the South west geo-political zone with population density of about 2,607 [27]. It is known as the nerve centre of the country having the largest concentration of industries, financial institutions and major seaports in Nigeria. Overall, there are 20 LGA in Lagos State. Estimate of total population figures in 2016 was approximately 21 million [27-28].

Study population was drawn from communities in Lagos State where the CBHIS is practiced; Awoyaya and Iberekodo mutual health plans in Ibeju-Lekki LGA, Ikosi-Isheri mutual health association in Kosofe LGA and Ajeromi-Ifelodun mutual health association, in Ajeromi-Ifelodun LGA. Two of the selected CBHIS centres; Ikosi-Isheri and Ajeromi-Ifelodun are located in the mainland metropolis and the other two; Awoyaya and Iberekodo mutual health plans, are on the island. Using the 2006 census figures, total population in each LGA are 117,793 (males: 60,729, females: 57,064) in Ibeju-Lekki, 682,772 (males: 358,935, females: 323,837) in Kosofe and 687,316 (males: 352,273, females: 335,043) in Ajeromi-Ifelodun [27]. Due to difficulty in obtaining population figures for the selected communities, the sample size was drawn based on the female population for each local government. Female population figures are selected in line with study interest on the WTP for CBHIS among pregnant women. A minimum sample size (n) = 300 (rounded up to 350) was obtained using the formula where, , (at 90% confidence interval) and margin error d of 0.03. Using population proportion p= 0.001, 0.004 and 0.005 for Ibeju Lekki, Kosofe and Ajeromi-ifelodum LGA respectively, minimum samples were consecutively 30, 120 and 150. The conventional 5% value for P was not used because of the large sample size it generates given available population data [29-30]. Choice of population proportion chosen was based on percentage values of female population in each LGA relative to total female population in the three LGAs. The sample size of 300 was rounded up to 350 following selected female population proportion values in each LGA. Samples from the LGA’s were 54, 129 and 167 for Ibeju Lekki, Kosofe and Ajeromi-ifelodum LGA respectively.

The CBHIS requires payment of a premium of N1, 200 per household. This covers a maximum of six household members; father, mother, and four children. Premium payment is N 600 for single individuals. Each enrollee is made to pay N 50 consultation fees per visit to control for demand side moral hazard. Users of the scheme are required to pay premium charge not later than the seventh day of the month otherwise, penalty fee of additional 20 per cent premium is required.

A structured pretested questionnaire was used for data collection. The questionnaire was pretested on 30 randomly selected households in each LGA using the selected P values. The questionnaire assessed the social economic characteristics of respondents and the WTP for use of the CBHIS. A total of 350 pretested questionnaires were administered randomly. Assessment of WTP was based on the close ended question format. Respondents were asked to state whether they are WTP the prevailing amount as premium charge for use of the CBHIS. To determine average amounts WTP per month, respondents were required to tick within a range of value from N 500 to N 3,000. The selected range is meant to capture distribution of persons WTP below the required charge of N 600 for single individuals and those who can afford payment beyond the existing charge of N 1,200.

Only pregnant women in the adult population who are permanent residents in the community were considered. The simple random sampling technique was used to select the required number of women. Questionnaires were randomly administered on the day of antenatal visit to the health centre. The interviewer occasionally interpreted the questions to the respondents in the local dialect (which is Yoruba). Information from field survey was coded using Statistical Package for Social Sciences (SPSS) version 20.0, and estimations were carried out using STATA 13.

The theoretical framework of the study is premised on the theory of planned behaviour. In line with the supposition of the theory, the WTP for CBHIS was modelled as a function of behavioural attitude, subjective norm and perceived behavioural control of the individual. These variables are measured using trust, membership and income respectively [15]. Other covariates are also considered in the study. The model specification for WTP for an individual i is given as:

1

Where, = Trust;

= Membership Association; = Income and = Other covariates.

Other covariates considered in the model include; employment type, household size, age, husband’s educational attainment, marital status, religion, household size and distance to the CHBIS from place of residence.

For the purpose of estimation, the model specification used in this study is given as

2

The variables are measured in categorical forms. Where, the willingness to pay for CBHIs, Trust in governments program, ; Association membership, : Income, . Nature of employment,: Household size, : Religion, , ; Level of Education attainment, : Marital status. : distance to CBHI centre in meters and is the error term. Equation 2 is analyzed as a probit model which lends itself naturally to the use of the maximum likelihood estimation. The probit model is one of the three existing approaches considered in the analysis of a binary choice dependent variable. Other methods include the logit and the Linear Probability Model (LPM), [31]. Unlike the LPM, the probit and logit model guarantee that the probability of an event occurring will yield results that lie between zero and one. Choice of the probit over the logit and vice versa is subjective and depends on the assumed distribution of the data [31].

The study examined premium paid (P) for CBHIs as:

3

Where represents total number of individuals who are WTP for the use of CBHIS and indicates the total amount individuals are WTP for the use of the scheme.

Results

Responses were received from all the 350 questionnaires administered. Results for summary statistics of the variables used in the study, factors affecting the WTP and the premium amounts pregnant women are WTP are presented in tables 1, 2 and 3 respectively. We also show some post estimation analysis for model specification; checking for collinearity. Variables used in the study are basically categorical with mean values from summary statistics showing the percentage composition of each variable.

Table 1: Summary Statistics of Variables

There are more pregnant women (65%) who are WTP for the use of the CBHIS than those who are not. Approximately 40% of the study sample have some trust in the CBHIS scheme and most of them (62%) are members of a thrift society. Individuals who earn above N 24, 000 per month are relatively more in the study sample (31%) compared to other income groups and are mainly engaged in self-employment work type. Most of the women are from households with 2 to 5 persons (54%). Age distribution reflects 13 to 35 years for most persons in the study. Figures for educational distribution reveal that there are more women with no formal education (23%) than those with primary education (15%). Those with secondary and tertiary education are about 23% and 39% respectively. Most respondents in the study are from a monogamous family (56%). Approximately 29% of respondents reside less than 100 meters away from the location of the CBHIS.

Table 2 presents the probit regression estimates of the WTP for use of the CBHIS. As shown in the table, the model consisted of 5 variables that were significantly associated with the WTP for use of the CBHIS. The most determining factor of the WTP for CBHIS was household size. The WTP for use of the scheme dropped with increase in household size. Individuals who are from households with less than two persons had highest WTP with approximately 99% likelihood compared to those from households with 10 and above number of persons.

Table 2: Distribution of Per Capita Health Expenditure by Region by Financing Agents ($)

Variable	Estimates
Trust: No trust is the reference category
Trust (Persons who trust in the effectiveness of the CBHIS scheme)	-0.03(0.037)
Association membership: Non membership as reference category
Association Membership	-0.004(0.035)
Income: Above N24,000 is the reference category
Income less than N5,000	0.504(0.254)*
Income N5000-9,999	0.93(0.131)***
Income N10,000-24,000	0.507(0.333)**
Employment: Wage employment is the reference category
Unemployed	-0.147(0.088)*
Self-employed	-0.414(0.213)**
Household size: Reference category 10 and above number of persons
Household size less than 2	0.99(0.001)***
Household size 2_5	0.939(0.058)***
Household size 6_9	0.801(0.186)***
Religion: Reference category Islam
Religion Christian	-0.006(0.045)
Age in years: Reference category above 46 years
Age in years (13-35)	-0.128(0.123)
Age in years (36-46)	-0.078(0.075)
Education: Reference category Tertiary education
No Formal education	-0.065(0.052)
Primary education	-0.052(0.033)
Secondary education	-0.052(0.034)
Marital Status: Reference category single
Married Monogamous	0.106(0.083)*
Marred Polygamous	0.223(0.19)*
Married loose union	0.484(0.388)*
Distance from home to CBHIS :Reference 1,000 meters and above
Distance less than 100 meters	-0.129(0.07)*
Distance 101- 400 meters	0.05(0.066)
Distance 401-999 meters	0.108(0.059)
Diagnostics
LR chi2(19) = 33.950 Prob >chi2=0.019 Psuedo R2 0.348 Observation 119
Model specification
Collinearity test Mean variance inflation factor(Mean VIF)=5.24

Source: Author’s computation Notes: 1: Marginal effects of coefficients are reported with standard error values in brackets. 2: *, ** and *** indicate statistical significance at 10%, 5% and 1% levels, respectively

Income was also a significant determinant of the WTP for use of the CBHIS. Pregnant women receiving income between N5, 000 and N9, 9000 are relatively more likely to pay for use of the CBHIS. Such women have approximately 93% likelihood of paying for the use of the CBHIS compared to those earning above N24, 000. Marital status was also a significant factor influencing the WTP for CBHIS. Women who are married; monogamous, polygamous or loose union are more WTP for use of the CBHIS than single women. Those in a loose union show highest WTP for use of the CBHIS among the various grouping of marital status. They are about 48% more likely to pay for use of the CBHIS relative to those who are single. The employment status of the pregnant woman was also a significant determinant of the WTP for the use of the CBHIS. Women who are in self-employment had relatively highest less likelihood of the WTP. Those in self-employment are about 41% less likely to pay for use of the CBHIS relative to those in wage employment. Unemployed women are about 15% less likely to pay for use of the scheme. Distance from place of residence to the CBHIS also significantly affected the WTP, especially for women who reside very close to the centre. Pregnant women who live less than 100m away from the location of the CBHIS showed approximately 13% less likelihood of paying for use of the scheme. The likelihood test statistics show overall model fit with significant probability values at 5% and the VIF value of 5.24 annuls concerns for the problem of multicollinearity as it is less than the threshold value of 10 [31]. The results for premium amounts pregnant women are WTP for the use of the CBHIS are shown in Table 3.

Table 3: Premium amounts WTP for CBHIs

Amounts WTP monthly to benefit from the CBHIS in N	Average N (X)	Frequency (F)	Percentage	Total of amounts WTP (FX)
at the rate of 500	500	107.00	46.93	53500
600-2000	1,300	75.00	32.89	97500
2001-3000	2,500	37.00	16.23	92500
above 3000	3,000	9.00	3.95	27000
Total	7,300	228	100	270500

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