Unit data from the recent round of National Family Health Survey (NFHS-4) conducted during 2015-16 has been used for the analysis. NFHS 4 is the fourth in the series of Demographic Health Survey (DHS) in India that aimed to provide reliable estimates of utilization of maternal and child health services, contraception, nutrition etc. along with the socio-economic and demographic condition of the households. A total of 601,509 households, 699,686 ever-married women in the age group 15–49 and 112,122 men in the age group 15–54 were successfully interviewed across all states and union territories of India. The NFHS-4 for the first time included a set of policy relevant question on OOP payment on delivery care (defined as the expenditure net of reimbursement) for the last birth delivered at a health centre and reimbursement under Janani Suraksha Yojana (JSY). Findings from the survey along with sampling design, methodology, and results are available in the national report [35].
Unit data from the kids file has been used, which provides details of births to mothers during five years preceding the survey. A total of 259,627 births were reported of which 190,898 were last births and 148,645 were conducted in the health centres (institutional delivery). The unit data was cleaned for factual errors on OOP payment before the analysis. The details and procedure for data cleaning are available elsewhere [36].
Statistical Analysis
Descriptive statistics, Benefit Incidence Analysis (BIA), and Concentration Index (CI), and Concentration Curve (CC) are used in the analysis.
Benefit Incidence Analysis
To determine the distribution of benefits received by various socio-economic groups using public health services for delivery care, Benefit Incidence Analysis has been used. One of the difficulties in benefit incidence analyses is obtaining the cost of services. In the absence of cost of services, the modal value of OOP payment for delivery has been used in literature [43]. However, we have used the median value in our study since a significant proportion of women reported zero OOP (varying from 7–10% across the wealth quintile) of delivery at the accredited private health centres (JSY under NHM programme has such provision) and so the modal value for OOP payments became zero. Further, to examine the robustness of the result obtained from median value, we have also estimated benefit incidence using mean value. We have estimated the benefit incidence of a particular group j utilizing service i (institutional delivery) from public health centres. The OOP payment in private health centres has been taken as synonymy to cost of services. In case of maternal care, most of the health insurances in India do not provide any coverage/reimbursement and so OOP is equivalent to household expenditure. In case, no charge was levied, the OOP payment was considered as zero.
Mathematically, the formula for calculating Benefit Incidence is as follows:
The following steps have been used in the study.
i. Computing wealth quintile (population ranked by wealth) as a measure of socio-economic status.
ii. Estimating the utilization rate for delivery care in public health centres for each quintile.
iii. Estimating net subsidy at public health centres for each quintile (obtained by deducting the median OOP payment on delivery care in public health centres from median OOP payment in private health centres)
iv. Estimating individual subsidy for each quintile by multiplying the net subsidy with the utilization rate.
v. Calculating Benefit Incidence for each quintile by taking percentage share of individual subsidy.
OOP payment and Cost of service on institutional delivery
We have computed the OOP payment by quintile for mothers delivering at public health centres. Further, NFHS 4 does not provide any information on the actual cost of delivery care at the public health centre. Hence in line with previous literature, we have used OOP payment on delivery care in private health centres as the proxy for the actual cost of delivery care in public health centres [44-45].
Concentration Index (CI) and Concentration Curve (CC)
To examine the economic inequality in the utilization of delivery care services from public/private health centre, CC and CI were used. CC and CI are the commonly used measures by researchers across the globe to measure health inequality [24, 49-50]. The inequality is graphically represented through CC and plots the cumulative proportion of the population (ranked by wealth) against the cumulative proportions of the population utilizing delivery care services from public/private health centres. If CC overlaps with the line of equality, then the extent of utilization of services from public/private health centres is evenly distributed across the wealth group. However, if CC lies above the line of equality, it implies a pro-poor concentration of utilization of delivery care services while if CC lies below the line of equality, it implies a pro-rich concentration in utilization of delivery care services. CI is defined as twice the area between the CC and the line of equality. The value of CI ranges from -1 to +1, with a zero value of CI suggesting equal distribution of utilization of services across the wealth group. The negative value of CI signifies a pro-poor distribution of utilization of delivery care services while a positive value of CI signifies a pro-rich distribution.