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 Results Discussion Engel Curve analysis Two-Part Model Results Discussion

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?

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)

Expenditure Variables: Labels, Definitions, and Sample Means Variable Label Definition Burkina Faso Kenya Chad Senegal Zambia Zimbabwe TEXP Total expenditure without OOP 41,278 7,478 40,119 102,861 223,372 76,912 FOOD Food expenditure 25,002 3,346 33,084 61,974 140,371 46,023 EDUCATION  Education expenditure 3,527 1,554 1,653 5,753 19,059 8,741 HOUSE  Housing expenditure 5,225 2,042 26,707 26,622 7,878 OOP  OOP expenditure 5,137 640 2,811 9,592 3,934 3,444 OTHER  Expenditure on all other goods 7,228 926 3,339 8,427 37,320 14,271 CATA2 =1 if 0.1≤OOP/CTP<0.2 =0 otherwise  0.09 0.08 0.03 0.07 0.04 CATA3 =1 if 0.2≤OOP/CTP<0.4 =0 otherwise 0.11 0.05 CATA4 =1 if 0.4≤OOP/CTP =0 otherwise 0.21 0.13 0.17 0.14 0.06 CTP=capacity to pay, defined as non-subsistence spending

Coping and Demographic Variables: Labels, Definitions, and Sample Means Burkina Faso Kenya Chad Senegal Zambia Zimbabwe BORROW = 1 if household borrowed to pay OOP =0 otherwise 0.18 0.13 0.07 0.14 0.03 SAVE = 1 if household used savings to pay OOP = 0 otherwise 0.01 0.04 SELL = 1 if household sold assets to pay OOP 0.25 0.1 0.05 0.08 INS insurance pay OOP COPE- OTHER other strategy to pay OOP 0.09 0.02 URBAN = 1 if household lives in urban area 0.41 0.32 0.48 0.36 EQSIZE (Household size)0.56 2.57 2.15 2.37 3.11 2.49 SAGE518  Share of household between age 5 and 18 0.31 0.27 0.30 0.34 AGE  Age of household head 43.18 42.7 41.51 51.13 42.57 44.22 AGE2  AGE2/100 21.04 20.77 19.88 28.34 20.3 22.24

The Zero Expenditure Problem Percentage of Observations with Zero Values by Expenditure Item Country   FOOD EDUCATION HOUSE OTHER OOP Burkina Faso 1% 81% 50% 52% 46% Chad 5% 83% 67% 72% Kenya 54% 21% 56% Senegal 4% 63% 27% 59% Zambia 73% 44% 47% 77% Zimbabwe 3% 58% 39% 57% 82%

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

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

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

Engel curves results – Food share and Other share Coefficients on CATA variables by share equation Food Other Country CATA2 CATA3 CATA4   Burkina Faso -0.037** -0.039** -0.006 0.039** 0.011 -0.120** Chad -0.121** -0.094** 0.057** 0.061** 0.051** -0.029** Kenya 0.005 -0.029* 0.034* -0.013 0.002 -0.030** Senegal 0.022 -0.001 0.032 0.001 Zambia 0.006 0.023 0.131** -0.02 -0.060** -0.115** Zimbabwe -0.019 0.127** 0.003 -0.056** -0.111** ** = significant at 1% level * = significant at 5% level

Engel curves results – Education share and Housing share Coefficients on CATA variables by share equation Education Housing Country CATA2 CATA3 CATA4   Burkina Faso -0.002 0.001 0.027** 0.029** 0.100** Chad 0.035** 0.034** -0.014** 0.025* 0.009 -0.014* Kenya -0.014 0.004 -0.013 0.022* 0.024* 0.008 Senegal -0.011 0.002 0.017* -0.016 -0.018 Zambia 0.015 -0.004 0.006 0.023* -0.012 Zimbabwe -0.019 0.035* 0.038* ** = significant at 1% level * = significant at 5% level

2PM results – APEs for Food expenditure Country   CATA2 CATA3 CATA4 Burkina Faso -108 (131) -2,591 * (983) -5,344 **(2,068) Chad -181 (70) -1,302 (713) -7,467 (2,494) Kenya -61 * (45) -485 (302) -186 (120) Senegal 6,133 (2,357) -197 (335) -1,810 (812) Zambia -9,882 (5,082) -17,151 (8,344) 1,135 (562) Zimbabwe -5,120 (2,908) -13,565 (7,856) -7,821 (4,597) 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

2PM results – APEs for Education expenditure and Housing expenditure   EDUCATION HOUSE Country CATA2 CATA3 CATA4 Burkina Faso 329 (413) -1,007 ** (2,045) -3,164 ** (5,211) 1,113* (1,555) 470 (973) -1,338 * (1,731) Chad 1,741 (1,879) 1,852 (1,945) -453 ** (1,051) 1,806 * (2,526) 116 (1,492) -1,353 ** (2,322) Kenya -655** (1,414) -970 ** (1,970) -2,045 ** (3,644) -42 (181) -212 * (533) -849 ** (1,610) Senegal 2,078 (730) 1,498 (843) -3,094 (3,183) 2,397 (1,798) -4,535 (5,866) -20,497 ** (11,035) Zambia -18,328 (253,045) -23,534 (310,078) -57,599** (700,355) -2,195 (6,759) -6,661** (10,666) -16,766 ** (17,727) Zimbabwe 1,322 (1,668) -1,925* (2,211) -5,811 (5,507) 3,575 (1,993) 1,808 (626) -2,448 (1,380) 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

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

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

CATA2 and CATA3 are concentrated among the rich

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