1. 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

2. Outline Objectives Data Source Methodology Results Discussion
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
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 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

6. 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

7. 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%

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
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

12. 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

13. 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

14. 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

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