Socio-economic inequality in catastrophic health expenditure among households in India: A decomposition analysis

Abstract

The paper investigates the socioeconomic-related inequality in the incidence of catastrophic health expenditure (CHE) over the decade (2004–2014). We tested four hypotheses: (1) whether the incidence of CHE has changed over the decade? (2) Whether the inequality in the incidence of CHE is significantly pro-rich or pro-poor? (3) What are the major determinants contributing to inequality in CHE? and (4) Whether the change in various socio-economic determinants and health policies affects inequality over the decade? We have used two rounds of surveys: NSSO (2004 and 2014) and IHDS (2004–2005 and 2011–2012), and we adopted the generalized linear model with probit link function, concentration index (CI), and Oaxaca decomposition method for the study. The post-estimation decomposition method determines the major contributing factors to inequality and as well as its relative importance to the total inequality. We find that the mean out-of-pocket health expenditure has increased over the decade. Richer household is found to be spending more on inpatient care while the poor spend more on outpatient care. There has been a slight decrease in the incidences of CHE for outpatient care, but has significantly increase in inpatient care, particularly more towards the poor household. However, the inequality in incidences of CHE for inpatient care has decreased while in the case of outpatient care it has increased over the decade. The absence of any effective health insurance coverage, household size, income, and regional differences are found to be major contributing factors towards inequality in the incidence of CHE.

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Fig. 1

Notes

  1. 1.

    In 2010, globally it is estimated that 808 million people incurred catastrophic health expenditure.

  2. 2.

    It represents that the 9 equivalent years loss of healthy life through years lived with disease and disability.

  3. 3.

    Disability-adjusted life years are the sum of years of loss of life due to premature death (YLL) and years of loss of healthy life due to disease and disability (YLD).

  4. 4.

    IHDS is a multi-topic nationally representative survey in India. This survey has been conducted by National Council of Applied Economic Research (NCAER), New Delhi, jointly with University of Maryland. The first round of interviews was completed in 2004–2005 with the sample of 41,554 households and second round was done in 2011-12 with 42,152 households.

  5. 5.

    The closest data set on food expenditure (FE) corresponding to NSSO 60th round is NSSO 64th round. The problem is the consumption tier in 64th round (2004) is bifurcated under two classifications and extracting the FE was quite complex, so to avoid unnecessary complexity we have used IHDS data to derive the households’ basic FE as accurately as possible across quintile, sector and state. We expect the average value of FE over these three segments would reflect approximately true value of actual FE.

  6. 6.

    The NSSO is stratified multi-stage sampling; the details of the sampling methodology followed in both the rounds may be examined in the reports (GoI 2006, 2016).

  7. 7.

    https://fred.stlouisfed.org/.

  8. 8.

    Hence European Scale UMPCE is calculated as follows, \( {\text{UMPCE}}_{\text{ES}} = \frac{\text{HC}}{{\left[ {1 + 0.7\left( {N_{\text{A}} - 1} \right) + 0.5N_{\text{C}} } \right]}}, \) where HC is household monthly consumption, NA is number of adult members in household (>14 years of age), and NC number of children in household (≤14 years of age) (http://www.childlineindia.org.in/child-in-india.htm).

  9. 9.

    The standard concentration index in continuous function is defined as \( {\text{CI}} = 1 - 2\int\limits_{0}^{1} {Q_{\text{h}} \left( p \right){\text{d}}p} \) while in discrete function it is defined as \( {\text{CI}} = \frac{2}{n\mu }\left[ {\mathop \sum \limits_{i = 1}^{n} h_{i} r_{i} } \right] - 1. \)

  10. 10.

    The fractional rank of individual i: \( r_{t} = \sum\limits_{j = 0}^{i - 1} {\omega_{j} + \frac{1}{2}\omega_{i} } \), ωi is the sample weight scaled sum to one and ω0 is equal to zero.

  11. 11.

    While for discrete independent variables we used standard CI, from footnote (10).

  12. 12.

    In-patient OOP is calculated for 365 days while out-patient OOP is calculated for 15 days recall period.

  13. 13.

    We are not accounting the differential in quality and quantity with respect to private health services. We assume the standard health practice in the public sector is sufficient for comparability.

  14. 14.

    Proportion of CHE for inpatient is \( \frac{\text{NHE}}{{12*{\text{UMHCE}}}} \times 100 \), where NHE is the net health expenditure, and UMHCE is uniform monthly household consumption expenditure.

    The proportion of CHE for outpatient is \( \frac{\text{NHE}}{{\left( {1/2} \right)*{\text{UMHCE}}}} \times 100 \), where NHE is the net health expenditure, and UMHCE is uniform monthly household consumption expenditure.

  15. 15.

    The ratio of household with CHE in the fifth quintile to the first quintile.

  16. 16.

    Taking NSS 60th round as base.

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Acknowledgements

We are indebted to the Associate Editor, Shreekant Gupta and the anonymous referee for their valuable comments. All remaining errors are ours.

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Akhtar, A., Ahmad, N. & Roy Chowdhury, I. Socio-economic inequality in catastrophic health expenditure among households in India: A decomposition analysis. Ind. Econ. Rev. 55, 339–369 (2020). https://doi.org/10.1007/s41775-020-00093-3

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Keywords

  • Socio-economic inequality
  • Catastrophic health expenditure
  • Concentration index
  • Oaxaca decomposition method

JEL classification

  • I14
  • C2