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Adam Leive Health, Nutrition, and Population The World Bank

Coping with Out-of-Pocket Health Payments: Applications of Engel Curves and Two Part Models in Six African Countries . Adam Leive Health, Nutrition, and Population The World Bank. Outline. Objectives Data Source Methodology Engel Curve analysis Two-Part Model Results Discussion.

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Adam Leive Health, Nutrition, and Population The World Bank

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  1. Coping with Out-of-Pocket Health Payments: Applications of Engel Curves and Two Part Models in Six African Countries Adam Leive Health, Nutrition, and Population The World Bank

  2. Outline • Objectives • Data Source • Methodology • Engel Curve analysis • Two-Part Model • Results • Discussion

  3. Objective • To analyze how African households modify consumption to finance out-of-pocket health payments (OOP) • Questions: • Which goods are protected and which are sacrificed? • Does absolute spending on basic goods (food, education, housing) decrease as OOP rises? • Are there patterns across countries?

  4. Data Source • World Health Survey 2003 • Cross section data • Household questionnaire • Countries: Burkina Faso, Chad, Kenya, Senegal, Zambia, Zimbabwe • Sample sizes range between 3,355 (Senegal) to 4,928 (Burkina Faso)

  5. Expenditure Variables: Labels, Definitions, and Sample Means CTP=capacity to pay, defined as non-subsistence spending

  6. Coping and Demographic Variables: Labels, Definitions, and Sample Means

  7. The Zero Expenditure Problem

  8. Methodology – Engel Curves • Which goods are protected and which are sacrificed • Engel Curve Analysis • Estimate in Working-Leser form • sih = αih + β1log(TEXPh) + β2CATA2h + β3CATA3h + β4CATA4h + Chγ+ Xhλ + uih where s is the expenditure share of good i for household h,C is the set of coping strategy dummy variables (SAVE, BORROW, SELL, COPE-OTHER), X is the set of demographic controls, and u is a random error term. • Estimation by 3SLS and SURE • Hausman test to compare 3SLS vs. SURE and 3SLS vs. 2SLS

  9. Methodology – Two Part Model (2PM) • Does absolute spending on basic goods (food, education, housing) decrease as OOP rises? • 2PM: E[ y | x] = Pr(y > 0 | x) * E[ y | y > 0, x] • logit specification for hurdle • Log-OLS or Generalized Linear Modeling (GLM) for second part (level) • Specification testing • Heteroskedasticity in log-scale errors • White Test • Determining Variance function of GLM • modified Park tests • Accuracy of prediction • RESET on hurdle • Modified Hosmer-Lemeshow on level and full 2PM

  10. Methodology – Average Partial Effects • Partial Effects in 2PM ∂E[ y | x ] = E[ y | xd = 1] − E[ y | xd = 0] ∂xd = (Pr(y > 0 | xd = 1) - Pr(y > 0 | xd = 0))*E[y | y > 0 | xd = 0] + Pr(y > 0 | xd = 0)(E[y | y > 0 | xd = 1] - E[y | y > 0 | xd = 0]) • Primary variables of interest are CATA dummy variables

  11. Engel curves results – Food share and Other share ** = significant at 1% level * = significant at 5% level

  12. Engel curves results – Education share and Housing share ** = significant at 1% level * = significant at 5% level

  13. 2PM results – APEs for Food expenditure Estimates in local currency units. Standard deviations in parentheses ** = coefficient from level equation significant at 1% level * = coefficient from level equation significant at 5% level

  14. 2PM results – APEs for Education expenditure and Housing expenditure Estimates in local currency units. Standard deviations in parentheses. ** = coefficient from level equation significant at 1% level * = coefficient from level equation significant at 5% level

  15. Discussion – Identifying Patterns in Consumption Modification • Households protect food and sacrifice other goods at higher CATA levels • Absolute values of food, education, and housing decrease at increasingly higher CATA levels • These patterns exist for most countries studied • Consumption modification of basic goods is most threatened at highest CATA level (40% threshold) • At intermediate levels of OOP (CATA2 and CATA3), signs of estimates vary more and fewer are significant compared to CATA4

  16. Discussion – Measurement of Financial Protection • How well do the CATA variables reflect financial protection? • Does CATA1 reflect those that are the most financially protected and CATA4 the least? • Heterogeneity within OOP = 0 • OOP = 0 because household does not get sick • OOP = 0 because household cannot pay • Heterogeneity within CATA3: 30% of $1,000,000 leaves a lot more left than 30% of $100 • However, same is true for CATA4 where consumption modification does occur

  17. CATA2 and CATA3 are concentrated among the rich

  18. Discussion – Policy Implications • Making the case for health insurance • Importance of multi-sectoral approach towards financial protection • Effectively targeting poor and those financially vulnerable to health shocks

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