willingness to pay for child survival: results of a national survey in central african republic

Pergmon S0277-9536(96)00015-9

Soc. Sci. Med. Vol. 43, No. 6, pp. 985-998, 1996 Copyright © 1996 Elsevier Science Ltd

Printed in Great Britain. All rights reserved 0277-9536/96 $15.00 + 0.00

WILLINGNESS TO PAY FOR CHILD SURVIVAL: RESULTS OF A NATIONAL SURVEY IN CENTRAL AFRICAN

REPUBLIC

M A R C I A WEAVER, l R O B E R T N D A M O B I S S I , 2 R U T H K O R N F I E L D , 3 C E S A I R E B L E W A N E , 4 A N T O I N E SATHE, 4 M I C H A E L C H A P K O , 5 N I C H O L A S BENDJE,~,I~

E M M A N U E L N G U E M B I 6 and J A C Q U E S S E N W A R A - D E F I O B O N A 4

t University of Washington, 1202 E. Pike St., 4#1132 Seattle, WA 98122-3934, U.S.A., 2Ministry of Economics, Plan, Statistics and International Cooperation, Central African Republic, Bangui, 3Consultant, Missoula, MT 59801, U.S.A. 4Ministry of Public Health and Population, Berberati, Central African Republic, SSeattle Veterans Affairs Medical Center, University of Washington, U.S.A.,

6 University of Bangui, Central African Republic

Abstract--Many policy-makers and health economists are interested in designing and implementing user fee/quality improvement programs in public facilities in Sub-Saharan Africa on a national scale. This research addresses two design issues for a national program: (1) to what extent would user fees finance the costs of quality improvements, and (2) whether a uniform program could be implemented throughout the country or the user fees should differ between urban and rural areas or across health regions.

A national survey was conducted to determine the population's willingness and ability to pay for seven quality improvements: (1) facility maintenance, (2) supervision of personnel, and drugs to treat (3) diarrheal diseases, (4) acute respiratory infections (ARI), (5) malaria, (6) intestinal parasites, and (7) sexually transmitted diseases (STDs).

Willingness to pay for quality improvements was measured by contingent valuation techniques in which subjects were asked about expenditures for care at government facilities under a hypothetical user fee/quality improvement program. Ability to pay was measured by monthly expenditures for health care as a percentage of monthly household consumption.

The majority of the population was willing to pay the cost of the quality improvements, which ranged from U.S. $0.40 to U.S. $4.00. Estimates of the percentage of the population that was willing to pay the cost of the quality improvements ranged from 81% for facility maintenance (an improvement with the lowest cost) to 64% for drugs to treat ARI (the improvement with the highest cost). The median willingness to pay ranged from U.S. $7.98 for drugs to treat malaria to U.S. $16.61 for drugs to treat diarrheal diseases.

Willingness to pay was greater in rural areas than in urban areas. It was also greater in Health Region I than in Health Regions IV and V,

The population was able to pay the estimated cost of all seven quality improvements. Median monthly health care expenditures per episode of illness was 2.6% of median monthly household consumption. In comparison, the estimated cost of the quality improvements ranged from 0.2 to 2.4% median monthly household consumption.

The national user fee/quality improvement program has good prospects for financing the quality improvements because the majority of the population is willing to pay the estimated costs of the quality improvements and more than half of the population is willing to pay substantially more than the costs. It also appears that the user fees should differ between urban and rural areas and across some health regions. Copyright © 1996 Elsevier Science Ltd

Key words--user fees, quality of care, child survival, contingent valuation, health care finance

INTRODUCTION

Many policy-makers and health economists are interested in designing and implementing user fee programs in public facilities in Sub-Saharan Africa on a national scale. Although considerable evidence has been amassed in the last decade to demonstrate that user fees can finance some of the costs of health care [1], most of the evidence is from private facilities or demonstrat ion projects in public facilities. There is little information about the design and implemen- tation of national user fee programs in public facilities, and even less information on national

programs that combine user fees with improvements in the quality of care at public facilities, with the possible exception of recent work in Kenya [2] and Cameroon [3].

The research reported below was undertaken as part of the design of a national user fee/quality improvement program for the Ministry of Public Health and Population (MPHP) in the Central African Republic. The national program seeks to sustain the successes of their award-winning child survival program [4], with the seven following quality improvements: (1) facility maintenance, (2) supervision of personnel, and drugs to treat (3) diarrheal

985

986 Marcia Weaver et al.

diseases, (4) malaria, (5) STDs, (6) ARI, and (7) intestinal parasites.

Policy-makers needed information on the population's willingness and ability to pay for the quality improvements to know whether or not the user fees would finance all of the quality improvements.

Policy-makers also needed information on differences in willingness to pay for the quality improvements between urban and rural areas and across health regions to know whether a uniform program could be implemented throughout the country or the program should vary between urban and rural areas or across health regions. (A health region is an administrative unit of the MPHP that oversees two to four 'prefectures'.) There are two hypo- theses that would suggest that a user fee/quality improvement program should be different between urban and rural areas. Residents of urban areas may have higher incomes that would increase their willingness to pay for health care. Or, residents of urban areas may have better access to private facilities and pharmacies that would decrease their willingness to pay for drugs at public facilities. There are similar differences in income and access across health regions.

To provide this information, we explicitly modeled and estimated the relationship between user fees and the quality of care. The estimates were based on data from a national survey that measured willingness to pay for quality improvements by contingent valuation methods in which subjects were asked about expenditures for care at government facilities under a hypothetical user fee/quality improvement program. Ability to pay was measured by monthly expenditures for health care as a percentage of monthly household consumption.

The following article is divided into five additional sections. In the Methods section, we show a model of the relationship between user fees and quality of care and describe the measures of willingness and ability to pay for quality improvements. In the Sample section, we summarize the sample selection process and define the unit of analysis for each of the measures. In the Results section, we present the results. In the Discussion, we discuss the policy implications of the results and research issues. The final section gives a brief conclusion.

METHODS

Model of the relationship of user fees and quality of c a r e

Formally, willingness to pay is the demand curve for health care represented by the negative relationship between price and quantity of care demanded, as shown with the curve D(P,L) in Fig. 1. The price of care is denoted by P, the quality of care is denoted by L, and the quantity of care is denoted by Q. In the absence of user fees, public facilities are represented

Price of ~ L Treatment per Episode of Illness

PI )

Po j \ QI Qo

Quantity of Treatments

Fig. 1. Demand curve showing negative relationship of price and quantity.

in Fig. 1 by the price Po, and the quantity of care demanded is Qo.

A user fee of P 1 will cause the quantity of care demanded to decrease to Q 1. This is the negative relationship of price and quantity that has been observed in countries such as Ghana [5], Swaziland [6], and South Africa [7], when user fees are introduced in the absence of an improvement in the quality of care.

The effect of the quality of care is represented on this two-dimensional graph by a shift in the demand curve. In Fig. 2, an increase in the quality of care will cause the demand curve to shift to the right as shown with the curve D'(P,L'). A user fee of P 1 introduced in combination with a quality improvement can cause the quantity of care demanded to increase to Q ' I . In this case, the negative effect of an increase in price was less than the positive effect of the increase in quality, so the net effect was an increase in the quantity of care demanded. This is the combined effect of user fees and quality improvements that has been observed in countries such as Cameroon [3], Niger [8], and Mall [9]. In other cases, the net effect could be a decrease in the quantity of care demanded.

The practical question for policy-makers is what combination of prices and quality improvements will lead to the desired level of utilization of public facilities? To answer this question, it is necessary to estimate the demand curve D(P,L) and predict bow

Price of Treatment per ~isoae of Illness

PI ~i (p,L,)

i\ Po QI Qo Q'I

~mtlty of TreatmentB

Fig. 2. Shift in demand curve following improved quality of c a r e .

Willingness to pay for child survival 987

it will shift with a change in quality, or estimate the demand curve D'(P,L'). We sought to estimate D'(P,L') with the contingent valuation method described below.

Contingent valuation measures of willingness to pay

Although most people would agree that the quality of health care is important, if an interviewer simply asks someone to state an amount he/she is willing to pay, the person may not give an answer that reflects his/her true value. The contingent valuation method is used in survey and questionnaire design to help a respondent identify and quantify how much he/she values a good in a way that does not bias his/her answer. As outlined in Mitchell and Carson [10], potential biases in questions include: (I) incentives to willfully misrepresent responses either by engaging in strategic behavior (to have a more than representative influence on population estimates) or by complying with the pre- sumed expectations of the sponsor or interviewer, (2) information that is interpreted by respondents to imply a 'correct' value, and (3) information that is either incorrect or interpreted incorrectly by the respondent.

The contingent valuation method was originally developed for measuring the value of environmental goods, such as damage from oil spills. Despite over 100 studies ([10], pp. 308-315), the contingent valuation method continues to be controversial in environmental economics, especially when it is used to assess the passive use values of public goods, which by definition cannot be measured with market data. The absence of comparable market data is the reason that policy-makers must rely on the method, and why the method has been the subject of careful scrutiny. A panel of economic experts commissioned by the National Oceanic and Atmos- pheric Administration (NOAA) recently published a report that summarizes the controversy and presents guidelines for contingent valuation surveys [! 1]. There is more information about these guidelines in the Discussion.

The contingent valuation method has also been used in health economics to measure the value of anti-hypertensive therapy [12, 13], in vitro fertilization [14] and water services in developing countries [15, 16]. In contrast to the applications in environmental economics, the method has been used in health economics to measure willingness to pay for new procedures or products. In cases where the new procedures or products are adopted, there are good prospects for testing the external validity of the method with subsequent market data.

For this research, a referendum scenario ([10], pp. 97-105) was used in which the interviewer described a quality improvement to the respondent, and then asked the respondent if he/she would pay

a specific price for it. For example, the contingent valuation question about facility maintenance was:

Given that your health facility is not well-maintained, without beds, mattresses, a delivery table, doors, etc., would you agree to pay $0.40 per episode of illness for maintenance of the health facility nearest you? Which of the following responses best represents your opinion?

1. I would not hesitate 2. I would go into debt to pay 3. I would pay if I had enough money 4. I would not pay

Responses one and two were considered 'yes' and responses three and four were considered 'no. ' The contingent valuation questions for all seven of the quality improvements are in the Appendix.

These contingent valuation questions were similar to the process of purchasing health care at a facility or a pharmacy where there is a single price and no bargaining. They were hypothetical questions, however, in the sense that the quality improvements had not yet been implemented and the respondent did not actually spend money.

The questions were carefully designed to insure that the respondents understood hypothetical questions and could identify the quality improvements that were offered to them. We consulted a local expert in socio-linguistics who verified that a cognitive framework for thinking hypothetically existed in Sango, the national language of the Central African Republic. We explored a variety of ways of present- ing the quality improvements, such as showing respondents drug packages, before determining that the verbal descriptions were adequate to identify them. The process of designing the questionnaire included: (1) translating the questionnaire into Sango, (2) conducting six focus groups in three distinct geographic regions of the country, and (3) pretesting the questionnaire at both an urban and rural site [17].

To estimate the demand curve for higher quality care denoted by D'(P,L') in Fig. 2, it was necessary to ask about several different prices for each quality improvement. There were five versions of the questionnaire, each with a different set of prices. One of the five versions was randomly assigned to a respondent. The prices for the seven quality improvements for each version of the questionnaire are presented in Table I.

The prices in Version A were based on the estimated cost of the quality improvements and they were the lowest prices. The cost of drugs were estimated with the prices of generic drugs purchased in bulk and include a 40% mark-up for transportation [18].

Statistical analysis Willingness to pay, as measured by the contingent

valuation method, was analyzed with a technique for the referendum scenario developed by Cameron [19]. The technique is based on the willingness to pay


Table 1. Payments proposed for each quality improvement by version of the questionnaire (in U.S.$)'

Version Quality improvement A B C D E

Knowledge of personnel 0.40 2.00 4.00 8.00 20.00 Facility maintenance 0.40 2.00 4.00 8.00 20.00 Drugs

Treatment of diarrhea 0.60 3.00 6.00 12.00 30.00 Treatment of ARI 4.00 20.00 40.00 80.00 120.00 Treatment of malaria 0.60 3.00 6.00 12.00 30.00 Treatment of intestinal parasites 1.20 6.00 12.00 24.00 36.00 Treatment of STDs 2.00 10.00 20.00 40.00 60.00

aF CFA amounts are converted to U.S.$ at an exchange rate of 250 F CFA per U.S.S.

function itself, so the results were easy to interpret. The technique is reviewed briefly below.

Let willingness to pay be a function of a vector of independent variables such as dummy variables for urban residence or health region. For individual i it is:

WTPi = Xi 'A + ui (1)

where WTPi is willingness to pay, Xi is a vector of independent variables, A is a vector of unknown parameters, and ui is a random term that has a logistic distribution with mean 0 and variance c.

WTPi was not observed, but the responses to the contingent valuation questions revealed whether or not the price quoted in the question, ti, was more or less than WTPi. The responses can be represented by an indicator variable I l i , where:

I l i = 1 if WTPi > ti (2) I l i = 0 otherwise.

and

Prob (Il i = 1) = Prob (WTPi > t i) = Prob (ui > ti -- Xi'A) (3)

Prob (I l i = 1 )= 1 - P r o b [ui /k l < - (ti - X i ' A ) / k 1]

Cameron showed that equation (3) can be used to write a censored logit likelihood function and described a convenient method for obtaining estimates of - I l k I and A / k 1 with conventional packaged logit algorithms when ti is among the independent variables. Estimates of - l / k l can then be used to identify A:

A = - ( A / k 1 ) / ( - 1/k 1) (4)

When the logit is estimated with only the vector of t and a vector ones, the estimate of A for the constant term is the median WTP. Cameron also showed how to calculate the correct standard error for A.

For this application, the policy variables represented gross differences across rural and urban residents, or among regions. The median WTP for rural, urban, and Bangui residents were estimated with a vector of t, a vector of ones, and dummy variables for urban residents, and residents of Bangui as regressors. The median WTP for each health

region were estimated with a vector of t, a vector of ones, and dummy variables for regions II, III, IV, and V. Estimates with independent variables that are usually related to health expenditures, such as total household consumption, education, and health status, to check the theoretical validity of the contingent valuation method are reported in [20].

The comparison of medians between rural and urban residents and among health regions is based on a two-sample median test, which is a non-parametric test to determine if two samples have the same median. The two samples are combined, and the median for the joint sample is calculated. The test statistics are based on counts of the number of observations in each sample that are above and below the joint median [21].

There is a limitation to WTP estimation technique, but it was not necessarily a problem for our analysis. In our data, WTP appeared to be log-normally distributed, so the natural log of ti was used as a regressor. With these estimates, the mean and median of WTP diverged, and only the median WTP was estimated accurately. This limitation was not a problem for our analysis, because the median was mean- ingful to policy-makers as the amount that at least half of the population will pay, whereas the mean did not have a comparable policy application. Also, WTP was compared to current health care expenditures which are also log-normally distributed and for which the median was the preferred descriptive statistic. It should be noted, however, that the mean WTP is the correct measure for cost-benefit analysis o f programs; the mean multiplied by the number of household is the total WTP for the population.

Abili ty to pay fo r health care

There is no objective guideline for the amount that people are able to pay for health care. In the United States more than 14% of national income is spent on health care, but popular opinion seems to be that this percentage is too high. There is even some indication that 7.5% of personal income is considered a burden, because the United States government allows income tax deductions for health care expenditures in excess of that amount.

In the absence of an objective guideline, we pro- pose using monthly health care expenditures per

Willingness to pay for child survival

episode of illness as a percentage of monthly household consumption. This guideline would mean that cost per episode of illness for the user fee/quality improvement program would be the same as the current cost per episode of illness.

Health care expenditures are also a measure of willingness to pay, and this interpretation is developed in [20]. In using health care expenditures in the guideline for ability to pay, it should be noted that there are reasons to expect health care expenditures to differ from the contingent valuation measure of willingness to pay. Health care expenditures are a minimum amount or 'lower bound estimate' of the amount that the respondents were willing to pay for health care. In other words, in some cases the respondents may have been willing to pay much more, but we only know that they were willing to pay at least the price charged by the facility.

Household consumption is a better denominator in ability to pay than income because it is less subject to temporary fluctuations [22], and for developing countries it includes the value of subsistence agricultural production [23]. In addition it was possible to obtain more complete information about consumption, because respondents were more willing to share information about consumption than income [24, 25].

For health care expenditures, the interviewer asked for information on utilization and expenditures for health care during the month prior to the interview for each member of the household who had been ill. The interviewer asked questions about utilization of modern and traditional care, and then asked about the distance traveled, expenditures for transportation, and expenditures for health care at an exhaus- tive list of private and public facilities and licensed and unlicensed pharmacies.

The measure of household consumption included market goods and the products of agriculture, hunting and fishing that were consumed. The interviewer asked the respondent about the amount and frequency of expenses during the month prior to the interview for 26 types of expenditures, including food, clothing, electricity, and family ceremonies. They also asked about market goods that were consumed but not purchased, such as the meat that a butcher brought home to his family. Then the interviewer asked about the consumption of 11 com- mon crops, 13 animals, and fish. The supervisors of the interviewers collected information on the prices of the crops, animals and fish in each census tract in the national sample so that a dollar value could be calculated for the quantities consumed.

SAMPLE

Sample selection

This survey was conducted in a nationally representative sample of 1263 households, that were selected on the basis of information on census tracts

989

Table 2. Comparison of the distribution of the stratified sample to that of the population by sample strata

Percentage Percentage Variable of sample of population Sample strata

Resident of Bangui 17 17 (451,690 inhabitants) Resident of towns with more 37 19 than 5000 inhabitants Resident of village with less 47 64 than 5000 inhabitants

from the 1989 census. Sixteen households were selected from each of 79 census tracts [26]. The census tracts were randomly selected during the design phase of the survey from a listing of the number of persons per census tract by administrative unit (prefecture, sub-prefecture, and commune) [27]. The 16 households within each census tract were randomly selected by the supervisor of the team of interviewers during the field phase of the survey [28].

The census tracts were selected from three strata: (1) Bangui (the capital city, which is 10 times larger than the next largest town), (2) towns with more than 5000 residents (excluding Bangui), and (3) villages with less than 5000 residents. The stratified sample included more census tracts in urban areas (excluding Bangui) than in rural areas, because households in urban areas have higher health care expenditures that require larger samples for accurate estimates. The accuracy of the survey data was improved at no extra cost by selecting disproportionately more census tracts in urban areas and fewer in rural areas.

The comparison of the unweighted stratified sample to the population by strata is presented in Table 2. All of the results have been weighted to correct for the sample stratifications as well as for differences in the number of households in each census tract.

A comparison of the weighted sample to the population by health region is presented in Table 3.

Unit of analysis

Two data sets were constructed for two different types of observations from the national survey data: (1) households and (2) members of households who had been ill during the month prior to the interview.

The contingent valuation analyses were conducted with the household as the unit of analysis. The respondent in every household in the national sample answered the contingent valuation questions and

Table 3. Distribution of the sample across health regions

Percentage Percentage Health region of sample of population Region I 35 36 Region II 19 18 Region lIl 20 20 Region IV 14 12 Region V I 1 13


Table 4. Percentage of the respondents who were willing to pay the estimated cost of quality of care improvements

Quality improvement

Percentage of Estimated respondents who were

cost willing to pay at least (U.S.$) the estimated cost

Facility maintenance 0.40 81 Knowledge of personnel 0.40 75 Drugs to treat

Malaria 0.60 77 STD 2.00 66 Intestinal parasite 1.20 76 ARI 4.00 64 Diarrhea 0.60 74

Sample size 251

provided the information on monthly household consumption.

The analyses of health care expenditures were conducted with the individual who had been ill as the unit of analysis. Fifty-five percent of the households in the national survey had at least one member who had been ill during the previous month. The respondent provided information about the health care utilization and expenditures during the month prior to the interview of each member who had been ill.

In most cases, the respondent was the head of the household. To insure that the head of the household would be available, the team of interviewers always arrived at the census tract the evening before the interviews were conducted. That evening the chief of the village or neighborhood announced the arrival of the team and asked the heads of all households to stay at home in the mornings until the interviews were completed. In some cases the heads of household were unable to wait for the interviewers and the respondent was a spouse. In other cases, the head of household was an elderly person who was not fam-

iliar with the details of the household finances, and the respondent was an adult who was responsible for household finances.

RESULTS

Willingness to pay for health care

Median wllh'ngness to pay for each o f the seven quality improvements. The contingent valuation measure of willingness to pay was used to answer two questions. What percentage of the population was willing to pay the estimated cost of the quality improvements? What was the price that at least 50% of the population was willing to pay for the quality improvements?

To answer the first question, Table 4 shows the results of the contingent valuation questions for the 251 respondents who were asked if they would be willing to pay the price that was equal to the estimated cost of the quality improvements (Version A). As shown, a high percentage of the sample was willing to pay the estimated cost of the quality improvements. The percentages ranged from a high of 81% for facility maintenance to a low of 64% for drugs to treat ARIs.

To answer the second question, Table 5 shows the calculated median amount that the population was willing to pay for the quality improvements. Columns 1 and 2 show the regression coefficients for the vector of ones and the vector of t. Column 3 is the calcu- lation from equation (4) to identify the constant term. Column 4 is the exponent of the constant term, because all estimates are with logs. The median was the amount that at least 50% of the population was willing to pay. As shown, the medians ranged from a high of U.S. $16.61 for drugs to treat diarrheal diseases to a low of U.S. $7.98 for drugs to treat malaria.

Table 5. Estimated coefficients* and median amount respondents were willing to pay for quality of care improvements (in U.S.$)t

(4) Median amount

(2) respondents were (1) Price willing to pay

Quality Constant coefficient (3) exponent of (3) improvement (A/k 1) ( - l/k I) - (1)/(2) (U.S.$)

Facility maintenance 1.104 - 0.457 2.417 11.21 (0.350) (0.049)

Knowledge personnel 0.878 - 0.390 2.255 9.53 (0.331) (0.047)

Drugs to treat Malaria 1.050 - 0.505 2.077 7.98

(0.358) (0.048) STD 1.273 - 0.545 2.337 10.35

(0.431) (0.052) Intestinal parasite 1.283 - 0.542 2.368 10.67

(0.419) (0.053) ARI 1.443 - 0.529 2.728 15.30

(0.459) (0.051) Diarrhea 1.114 - 0.396 2.810 16.61

(0.355) (0.048) Sample size 1263

*Standard errors of the estimated coefficients are in parentheses. °fF CFA amounts are converted to U.S.$ at an exchange of 250 F CFA per U.S.S.


Table 6. Reasons for refusal to pay for quality of care improvements

991

Reason for refusal (%)

Would pay if I had Pr ice Government Can use Will not condone Quality money/Do not have too should traditional illness of paying improvement enough money high provide medicine for cure Other

Facility maintenance 91.5 6.2 0.6 1.0 (n = 479)

Knowledge of personnel 87.4 4.9 6.6 0.2 (n = 519)

Drugs to treat Malaria 84.1 12.4 0.5 2. I

(n = 577) STD 68.6 13.4 0.1 3.7

(n = 692) Intestinal parasite 79.1 15.4 0.3 2.0

(n = 608) ARI 74.8 22.0 0.5 1.2

(n = 737) Diarrhea 86.6 10.1 0.5 0.2

(n = 506)

12.1

0.7

1.0

0.9

2.0

0.6

1.5

2.7

Comparison o f the contingent valuation measures across quality improvements. The differences in results across quali ty improvements suggest tha t the respondents viewed each quali ty improvement somewhat differently. To unders t and these differences, respondents who refused to pay for a quali ty im- p rovement were asked why he/she refused. The reasons for the refusal are presented in Table 6. As shown, by far the most c o m m o n reasons for all of the quali ty improvements were "would pay if I had money /do not have enough money ," and "price too high."

There were interest ing differences in the reasons for refusal across quali ty improvements , which suggest tha t the respondents distinctly identified each one. For improving the knowledge of personnel (the improvemen t with the second lowest median willingness to pay), 6.6% of the respondents who refused to pay though t tha t it was the "gove rnmen t ' s responsi- bility." This was the only quali ty improvement on which the government and /o r donors were spending a significant amount , and raises the issue of whether or no t the respondents unders tood tha t their response should include bo th taxes and out-of-pocket expenditures. For drugs to t reat STDs, 12.1% of the respondents who refused to pay would not condone the illness by paying for the cure. For intestinal

parasites, malaria, and STDs, 2% or more of the respondents who refused to pay could "use tra- di t ional medicine."

Comparison of the contingent valuation measures between urban and rural areas. The price tha t residents of rural areas were willing to pay was higher than the price tha t residents of u rban areas were willing to pay. The compar i son of median willingness to pay between u r b a n and rural residents is presented in Table 7. Asterisks next to a median for u rban residents mean tha t it was significantly different f rom the median for rural residents at s tandard levels of significance (P < 0.05). The lower median price for u rban residents was significantly different for all seven quali ty improvements .

The price tha t residents of Bangui were willing to pay was higher than the price tha t residents of u rban areas (excluding Bangui) were willing to pay for some, but not all quali ty improvements . In Table 7, asterisks next to the median for residents of Bangui mean tha t it was significantly different f rom the median for u rban residents (excluding Bangui). The higher median price for residents of Bangui was significantly different for two quality improvements: drugs to t reat STDs and diarrhea.

Comparison o f the contingent valuation measures across health regions. The compar ison of median

Table 7. Comparison of the median willingness to pay* (in U.S.$) as measured by contingent valuation methods between urban and rural areas

Villages with Towns with Quality fewer than 5000 more than 5000 improvement residents residents Bangui

Facility maintenance 18.49 5.22*** 3.82 Knowledge of personnel 17.57 3.91 * ** 2.34 Drugs to treat

Malaria 10.75 3.74"** 6.13 STD 12.98 4.85"** 7.16"** Intestinal parasite 14.72 5.75*** 5.99 ARI 20.95 7.05*** 10.86 Diarrhea 23.40 5.38*** 17.20**

*The comparison is based on a two-sample median test. See section II.C. tF CFA amounts are converted to U.S.$ at an exchange rate of 250 F. CFA per

U.S.S. Significance level 0.01 denoted ***, 0.05 denoted **


Table 8. Comparison of median willingness to pay* (in U.S.$) as measured by contingent valuation methods across health regions

Quality Health region improvement I II III IV V Facility maintenance 9.97 41.77"** 18.78 5.92 2.58*** Knowledge of personnel 6.99 25.24*** 7.27 4.86 1.47"** Drugs to treat

Malaria 10.56 9.80 16.89 3.17"** 2.63*** STD 11.66 13.34 24.25*** 7.95 1.89"** Intestinal parasite 14.53 11.73 20.61 5.48*** 3.34*** ARI 20.72 24.02 23.03 9.14"** 4.13"** Diarrhea 30.48 12.84"* 36.96 9.18"** 2.38***

*The comparison is based on a two-sample median test. tF CFA amounts are converted to U.S.$ at an exchange rate of 250 F CFA per U.S.S. Significance level 0.01 denoted ***, 0.05 denoted **

willingness to pay across health regions is presented in Table 8, where the median of health region I was the standard for the comparison. Residents of Bangui were included with region I. Asterisks next to a median for regions II through V mean that it was significantly different from the median for residents of region I.

The median prices that residents of regions IV and V were willing to pay was lower than the median prices that residents of region I were willing to pay. As shown in Table 8, prices were significantly different from region I for four out of seven quality improvements for region IV and for all seven quality improvements for region V.

Ability to pay for health care

Utilization o f health care. Table 9 presents the utilization of health care by household members who were ill during the month prior to the interview. As shown, 76% of those who were ill sought modern care and 47% sought traditional care. One-third of the people who sought modem care also sought traditional care.

People who sought modern care chose among three types of care: (1) public facilities, (2) private facilities, and (3) purchasing drugs without visiting a facility. Forty-one percent of the people who were ill sought care from a public facility, and more than half of them (24% of those who were ill) also purchased drugs from either a licensed or unlicensed pharmacy. Fifteen percent sought care from a private facility. Sixteen percent purchased drugs directly from either a licensed or unlicensed pharmacy without visiting a health care practitioner. These estimates are identical to the official statistics for Central African Republic [29] and comparable to survey data from Cameroon [3, p. 377].

Table 9. Use of health care during month prior to interview Percentage

Type of care of sample Person sought modern care 76 Person visited public facility 41 Person visited private facility 15 Person purchased drugs without visiting facility 16 Person sought traditional care 47 Sample size 922

Median expenditures for health care. Table 10 presents median health care expenditures for all people who sought modem care for four categories of expenditures: (1) facilities, (2) licensed or unlicensed pharmacies, (3) total for modern care, which was the sum of expenditures for transportation and at facilities and pharmacies, and (4) total for all care, which was the sum of expenditures for modem and traditional care. Note that these statistics include people who had zero expenditures for one or more of the categories [30]. As shown in Table 10, median total expenditures for all people who sought modem care was U.S. $4.34. These current expenditures were more than the estimated cost of every one of the seven quality improvements described above in relation to the contingent valuation measures.

Comparing expenditures between the subsamples of people who visited public and private facilities, the difference in their total expenditures for modem care was only U.S. $2.00, despite larger differences in their expenditures at facilities and pharmacies. As shown in Table 10, median expenditures at facilities for people who visited a private facility was U.S. $4.40, but for people who visited a public facility it was zero. Most people who visited a private facility purchased their drugs directly from the private facility [31]. Median expenditures at pharmacies for people who visited a public facility was U.S. $2.60, but for people who visited a private facility it was zero.

The sample population spent a total of U.S. $1395 in private facilities and U.S. $1512 in public facilities or 48% and 52%, respectively, of total expenditures at facilities during the month before the interview [32]. They spent a total of U.S. $7033 at pharmacies or 71% of total expenditures for modem care.

Monthly househoM consumption. Mean monthly household consumption during the month prior to the interview was U.S. $307 and the median was U.S. $182 [33].

Mean monthly consumption can be converted to annual per capita consumption, which is comparable to annual per capita income. Monthly consumption is multiplied by 12 to obtain annual consumption and it is divided by 6.9 (the mean number of people per household). The estimated annual per capita consumption of U.S. $534 is somewhat higher than the


Table 10. Health care expenditures per episode of illness (in U.S.$*) during month prior to the interview

Drugs from Total for Total for Sample Facility pharmacy modern care all care Person sought modern care 0 0.54 4.00 4.34

(n = 699) Person visited public facility 0 2.60 6.00 6.00

(n = 378) Person visited private facility 4.40 0 8.00 8.00

(n = 142) Person purchased drugs without 2.40 3.20 3.20

visiting facility (n = 146) *F CFA amounts are converted to U.S.$ at an exchange rate of 250 F CFA per U.S.S.

993

World Bank's estimated 1991 per capita income of U.S. $390 [34],

Table 11 presents the composition of consumption in terms of cash expenditures, market goods that were not purchased, and the products of agriculture, hunting and fishing. For readers who are interested in ability to pay measured by willingness to pay as a percentage of cash expenditures, it can be readily calculated with the median cash expenditures presented in Table 11.

Ability to pay. Table 12 presents median monthly expenditures for all care per episode of illness as a percentage of median monthly household consumption. As shown, median total expenditures for all people who sought modern care is 2.6% of monthly household consumption. Within the subsamples, total expenditures range from 1.9% for people who purchased drugs without visiting a facility to 4.8% for people who visited a private facility.

Cost of the quality improvements and the contingent valuation measure of willingness to pay as a percentage of monthly household consumption. The results above can be presented as a percentage of monthly household consumption to answer two questions. What is the estimated cost of the quality improvements as a percentage of median monthly household consumption? What is the median willingness to pay as a percentage of median monthly household consumption?

The answers to both of these questions are presented in Table 13. As shown in column 2, the estimated cost of five of the seven quality improvements was less than 1% of median monthly household consumption. The exceptions were drugs to treat STDs and ARI, for which the estimated costs were 1.2 and 2.4%, respectively, of median monthly household consumption.

Table 11. Total monthly household consumption (in U.S.$*)

Variable Mean Median

Monthly household consumption 307.40 181.88 Cash 267.34 134.80 Noncash 8.78 0 Agricultural production consumed 24.54 15.00 Hunting consumed 3.95 0 Fishing consumed 2.80 0

*F CFA amounts are converted to U.S.$ at an exchange rate of 250 F CFA per U.S.S.

As shown in column 3, median willingness to pay ranges from 4.4 to 9.1% of median monthly household consumption. The percentage was highest for drugs to treat diarrhea, because it was the quality improvements with the highest median willingness to pay.

DISCUSSION

The user fee program has good prospects for financing the estimated costs of all seven of the proposed quality improvements

Using the contingent valuation measures, a high percentage of the respondents were willing to pay a price equal to the estimated cost of the quality improvement. More than 75% were willing to pay the estimated cost of the five least expensive improvements, including: facility maintenance, improving the knowledge of personnel, and drugs to treat malaria, intestinal parasites and diarrhea. About 65% were willing to pay the estimated cost of the two more expensive quality improvements: drugs to treat STDs and ARI.

Although these results suggest that the user fee/quality improvement program has good prospects for success, the estimated costs will have to be subsidized for 25-35% of the population. The measures of the median willingness to pay suggest that it may be possible to finance some of the costs of that population through cross-subsidies. For every quality improvement, the median willingness to pay was at least U.S. $8.00 more than the estimated cost. This fact does not mean that the MPHP should charge the median amount, because it would only serve half of the population. It does mean that the MPHP has the option of charging some people more than the estimated cost and using the extra revenue

Table 12. Ability to pay for health care based on current total health care expenditure

Type of care

Median total expenditure per episode of illness

as percentage of median total consumption

Person sought modern care 2.6 Person visited public facility 3.6 Person visited private facility 4.8 Person purchased drugs without 1.9

visiting a facility


Table 13. Ability to pay for health care based on contingent valuation measures of willingness to pay

Quality improvement

Estimated Median amount respondents as a percentage were willing to pay as a of median total percentage of median total consumption consumption

Facility maintenance 0.2 6.1 Knowledge of personnel 0.2 5.2 Drugs to treat

Malaria 0.4 4.4 STD 1.2 5.7 Intestinal parasite 0.7 5.9 ARI 2.4 8.4 Diarrhea 0.4 9.1

to subsidize the care of other people. This option should be used very cautiously for reasons that will become clear in the following sub-section.

Note that the emphasis on a user fee/quality improvement program reflects the policy of the government of Central African Republic, and does not imply that such a program is more appropriate than a program financed by taxes in all countries or under all circumstances.

Willingness to pay was greater in rural areas than in urban areas

The contingent valuation questions specify quality improvements at the nearest public facility. Residents of rural areas who have less access to a variety of facilities and pharmacies may be willing to pay more at the nearest public facility than residents of urban areas who have greater access to more choices of facilities and pharmacies.

To the extent that willingness to pay was greater in rural than in urban areas, it may be possible to charge higher user fees at public facilities in rural areas. Although we do not advocate charging a price equal to the median willingness to pay, when the costs of quality improvements (such as transportation or in- ventory costs) are higher in rural than in urban areas, the results suggest that the residents of rural areas are willing to pay the additional costs. In contrast, the public facilities in urban areas must keep their prices close to the estimated costs in order to be competitive with private pharmacies.

Willingness to pay is clearly lower in health regions I V and V than in health region I

Successfully implementing the user fee program in regions IV and V may require subsidies for a higher percentage of the population than in other regions. At least half of the population will be able to pay the estimated cost of the quality improvements, however, because the median of the contingent valuation measures of willingness to pay and median current expenditures in regions IV and V are greater than the estimated costs.

Some of the differences in willingness to pay across health regions reflect the differences in consumption across regions. Median monthly consumption was

more than twice as high in region I than in regions III, IV and V. The differences in consumption were significant at a high level of significance (P < 0.01) for both average monthly consumption and the average of the natural log of monthly consumption [35]. Further, cash expenditure as a percentage of consumption was highest in region I where it is 88% and lowest in regions IV and V, where it was 62% and 60%, respectively.

When current expenditures as a percentage o f consumption are used as a guideline, the population will be able to pay the estimated cost o f all seven quality improvements proposed for the user fee program

Median current expenditures for everyone who used modern care was 2.6% of consumption. In contrast, the estimated cost of the seven quality improvements ranged from 0.2 to 2.4% of consumption. Although the total user fee per episode of illness may include fees for facility maintenance, knowledge of personnel and drugs, the total estimated cost per episode of illness would range from 0.8 to 2.8% of total consumption, and would exceed the guideline only in the case of drugs to treat ARIs.

Although the term "ability to pay" pervades policy debates about user fee/quality improvement programs, this is one of the first attempts to give an operational definition to it. As a guideline, the median current expenditures per episode includes a broader range of illnesses than the user fee/quality improvement program. According to the respondents, 33.5% of the episodes of illness were for the five illnesses in the quality improvement program, and 49% were for severe cases. This guideline, however, is an initial standard for future efforts to define ability to pay.

Note that this guideline does not imply that total expenditures for health care could not increase. As shown in Figs 1 and 2, expenditures at public facilities could increase from zero to P1 x Q'I if people used more health care under the new program for additional types of illnesses or less severe episodes. Increases in health care expenditure would come from substitutions away from other consumption goods or savings.

Willingness to pay for child survival 995

The contingent valuation survey and questionnaire design for this study met most of the NOAA guidelines

The NOAA report included guidelines for an ideal contingent valuation survey, and noted that survey did not have to meet each of the guidelines

to be considered reliable [l l , pp. 4608-4609]. Although the guidelines were published more than a year after our survey was completed, it is worth noting that it met most of the guidelines. The survey guidelines it met included: (l) a stratified, random, cluster sample with adequate sample size [36], (2) 100% response rate, (3) face-to-face interviews, (4) careful pretesting, and (5) publication of the questionnaire. The questionnaire guidelines it met included: (1) referendum scenario, (2) accurate description of the program, (3) follow-up questions on why people were not willing to pay, (4) willingness to pay format, and (4) tests for the theoretical validity.

The tests for theoretical validity are reported in [20], as well as comparisons to current health care expenditures to test the convergent validity of the contingent valuation measures. Note the results of one of the main tests of theoretical validity: how willingness to pay varies with income. The income elasticity of willingness to pay was similar to the income elasticity of current expenditures for health care. For example, the income elasticity of willingness to pay for facility maintenance was 0.91. This compares favorably to the income elasticity of current health care expenditures, which was 1.08 for total expenditures and 0.7 for expenditures at health facilities.

The survey did not comply with all of the NOAA guidelines. One questionnaire guideline it did not meet was including an "I don't know" or "no answer" option. In future surveys, this guideline would be easy to meet. Also, in this application with contingent valuation questions about more than one quality improvement, there was the potential for an anchoring problem; the bid on the first question may have affected the answer to the second. For future surveys, it would be possible to test for the anchoring problem during the pretest by changing the order of the questions in different versions of the questionnaire.

Two other recommendations for future surveys are: (1) include the incidence of the illnesses, and (2) clarify that willingness to pay includes both tax and out-of-pocket expenditures. Although it is unlikely that misperception of risk was a problem for this research because the illnesses covered by the user fee/quality improvement program occurred fre- quently, respondents should be reminded of the incidence of the illness and consequently the frequency of expenditures. The distinction between total and out- of-pocket expenditures was not relevant for six of the seven quality improvements, for which government and donor expenditures were negligible, but it may

have caused some confusion for the knowledge of personnel question.

CONCLUSION

We have demonstrated a model and a method of estimating the relationship between user fees and the quality of care. We have also demonstrated how the estimates can be used in the design of a national user fee/quality improvement program.

In the Central African Republic, the estimates from a national survey showed the national user fee/quality improvement program has good prospects for financing the quality improvements. User fees equal to the cost of the quality improvements would finance the quality improvements for 65-75% of the population. The estimates also showed that there were limited prospects for cross-subsidization when the willingness to pay exceeded the cost of the quality improvements.

The estimates also suggest that the national program should be implemented differently between urban and rural areas and across health regions rather than uniformly throughout the country. In particular, it may be possible to charge somewhat higher user fees at public facilities in rural areas to cover the additional costs, such as transportation and inventories, that may be required to serve those areas. In contrast, user fees at public facilities in urban areas should not exceed the cost of the quality improvements, because of competition from private facilities and pharmacies. In addition, it may be necessary to charge somewhat lower user fees in regions IV and V, and possibly to plan to subsidize the user fee/quality improvement program in those regions.

Acknowledgements--The authors would like to thank Ri- cardo Bitran, who served as the Health Finance and SUs- tainability (HFS) Director of Applied Research, and Marty Makinen, who served as the HFS Technical Director for advice on technical issues. We thank Frank Kerber, who served as the Agency for International Development (AID) Liaison Officer for CAR, Bob Hellyer and Bob Emrey of AID, Karen Hawkins of the Centers for Disease Control and Prevention and John Novak and Jim Setzer who served successively as the HFS Task Managers for CAR. Special thanks go to Fatou Gueye and Daba Paquita who served as administrative assistants to the HFS project during the survey. We would also like to acknowledge ~he hard work and dedication of all the interviewers, chauffeurs, and data coding and entry personnel. Finally, we would like to thank the two anonymous reviewers for several suggestions.

A summary of the July 1993 version of these results was presented at the American Public Health Association meet~ ings held in San Francisco, October 24-28, 1993.

This research was supported by the Health Finance and Sustainability (HFS) Project funded by AID (USAID project DPE-5974-Z-00-9026-00). The authors are solely responsible for the contents, which do not represent the official position of Abt Associates Inc. the government of CAR or AID.


REFERENCES

I. For a synthesis of cost recovery schemes in Africa, most of which involve user fees for drugs, see World Bank, Africa Technical Department, Population, Health and Nutrition Department. Pharmaceutical Expenditures and Cost Recovery Schemes in Sub-Saharan Africa. Technical Working Paper No. 4, 1992.

2. Collins D. H. and Hussein I. M. Financial Sustainabil- ity of Health Programs in Kenya: The Role of Cost Sharing. Oral Presentation at the 121st Annual Meeting of the American Public Health Association, 1993.

3. Litvak J. I. and Bodart C. User fees plus quality equals improved access to health care: results of a field exper- iment in Cameroon. Soc. Sci. Med. 37(3), 369, 1993.

4. The Ministry of Public Health and Social Affairs in Central African Republic received a National Council on International Health award in June 1990 for their child survival program.

5, Waddington C. J. and Enyimayew K. A. A Price to pay: the impact of user charges in Ashanti-Akim District, Ghana. Int. J. Hlth Planning Management 4, 25, 43, 1989.

6. Yoder R. A. Are people willing and able to pay for health services? Soc. Sci. Med. 29, 36, 1989.

7. Frankish J. G. Day hospital fees and accessibility of essential health services. South African Medical J. 70, 286, 1986.

8. Ministere de la Sante Publique du Niger et la Co- operation Medicale Beige. Financement des Medica- ments par la Population: Experience de Tibiri (Dosso). Bilan Final. Projet RESSFOP, 1990.

9. Ministere de la Sante Publique et des Affaires Sociales du Mali. Projet Magasins-Sante. Rapport Annuel 1988. Projet FED et Medecins Sans Frontieres-Belgique, 1988.

10. Mitchell R. C. and Carson R. T. Using Surveys to Value Public Goods: the Contingent Valuation Method, pp, 235-259. Resources for the Future. Washington, DC, 1989.

11. United States Department of Commerce. National Oceanic and Atmospheric Administration. Natural re- source damage assessments under the Oil Pollution Act of 1990. Federal Register 58(10), 4601, January 15, 1993.

12. Johannesson M., Jonsson B. and Borgquist L. Willing- ness to pay for antihypertensive therapy--results of a Swedish pilot study. J. Hlth Economics 10, 461, 1991.

13. Johannesson M., Johansson P.-O., Kristrom B. and Gerdtham U.-G. Willingness to pay for antihyperten- five therapy--further results. J. Hlth Economics 12, 95, 1993.

14. Neumann P. and Johannesson M. The willingness to pay for in vitro fertilization: a pilot study using contingent valuation. Medical Care 32, 686, 1994.

15. Whittington D., Briscoe J., Mu X. and Barron W. Estimating the willingness to pay for water services in developing countries: a case study in the use of contingent valuation surveys in Southern Haiti. Economic Development Cultural Change 293, 1990.

16. Whittington D., Smith V. K., Okorafor A., Okore A. and Liu J. L. Giving respondents time to think in contingent valuation studies: a developing country application. J. Environ. Economics Management 22, 205, 1992.

17. For more information on the survey design process, see Weaver M., Bendji N., Blewane C., Kornfield R. and Sathe A. Survey of Expenditures and Willingness to Pay for Health Care: Bangui, Central African Republic. Health Finance and Sustainability Project Technical Note, 1992.

18. Barker B. Central African Republic. Health Finance and Sustainability Project Trip Report, 1992.

19. Cameron T. A. A new paradigm for valuing non-market goods using Referendum Data: maximum likelihood estimation by censored logistic regression. J. Environ. Economics Management 15, 355, 1988.

20. Weaver M., Ndamobissi R., Kornfield R., Chapko M., Blewane C., Sathe A. and Ngueretia L. P. Willingness to Pay for Health Care: A Comparison of Contingent Valuation and Traditional Economic Methods. Health Finance and Sustainability Project Technical Report, 1993.

21. Marija J. Norusis/SPSS Inc. SPSS/PC + V2.0 Base Manual. p. B-184. SPSS Inc., Chicago, IL, 1988.

22. Friedman M. The Theory of the Consumption Function. Princeton University Press. Princeton, N J, 1957.

23. Gertler P. and van der Gaag J. The Willingness to Pay for Medical Care: Evidence from Two Developing Countries, p. 80. Johns Hopkins University Press for the World Bank, Baltimore, 1990.

24. Abel-Smith B. and Rawal P. Can the poor afford 'free' health services? A case study of Tanzania. Hlth Policy Planning 7, 331, 1993.

25. Weaver M. User fees and patient behavior: evidence from Niamey National Hospital in Niger. Hth Policy Planning 10, 350, 1995.

26. The survey design included 1280 households from 80 census tracts, but a team of interviewers was unable to conduct the survey in one census tract in Health Region II.

27. There were separate listings for urban (towns with more than 5000 residents including Bangui) and rural (villages with less than 5000 residents) census tracts. For urban census tracts, a sampling interval was obtained by dividing the total number of individuals in urban census tracts by 29 (36% of 80 census tracts). For urban census tracts except Bangui, the sampling interval was reduced by one-half, because the sample was stratified to over- represent these households. A starting point for the selection of census tracts was selected as a random number between one and the sampling interval. The first census tract was the one in which the individual who corresponded to the starting point lived. The sampling interval was added to the starting point, and the second census tract was the one in which the individual who corresponded to that sum lived.

The procedure for rural households was similar, with the exception that the sampling interval was obtained by dividing the total number of individuals in rural census tracts by 38.

28. The supervisor of the team of interviewers had the number of households for each of the census tracts where his/her team conducted interviews. The sampling interval within census tracts was obtained by dividing the number of households per census tract by 16 (the number of households interviewed in each census tract). A starting point for the selection of households was selected at random from pieces of folded paper num- bered between one and the sampling interval.

In rural census tracts where there was more than one village per census tract, the supervisor of the team of interviewers also had information on the number of households per village. The number of households selected from each village was proportional to its share of the number of households in the census tract. The sampling interval within the village was obtained by dividing the total number of households in the village by the number of households to be interviewed in that village. A starting point was selected as described above.

29. There were 516 outpatient visits and hospitalizations in one month or an estimated 6192 visits and hospitalizations in one year or 0.71 per person for the sample population of the 8715 people (1268 households multiplied by 6.9, the mean number of persons per house-

Willingness to pay

hold). In comparison, the official statistics are that there were 1,521,759 visits and hospitalizations in 1988 or 0.71 per person for a population of 2,463,616. For the health statistics see Republique Centrafricaine, Minis- tere de la Sante Publique et des Affaires Sociales. Bulletin Annuel d'lnformation Sanitaire Annee 1989, pp. 40-42. The official statistics may over-report utilization of public facilities and under-report utilization of private facilities.

30. It is somewhat unorthodox to include zeros in health expenditure statistics. Standard practice in the United States is to report the percentage of people who were ill that used a facility and the average expenditures among users. This practice is based on the premise that utilization is synonymous with expenditures. In developing countries, the relationship between utilization and expenditures is not as straightforward. For example, people who use a public facility may or may not have expenditures at that facility.

The question is: what group of people who are ill is the relevant comparison to the respondents to the contingent valuation questions? First consider who the respondents to the contingent valuation questions in the national sample are. Ninety-eight percent of the respondents in the national sample said that they would use modern care if the quality improvements were insti- tuted. So, the 20-36% of people who refused to pay the cost of the seven quality improvements include the 2% of people who would not use modern care, some people who would use modem care only if it was free, and people who would pay for modern care but less than the cost of the quality improvements. The percentage of people who would not use modem care seems negligible. We do not know the breakdown of those who would pay zero and those who would pay more than zero but less than the cost.

The issue then is whether the comparison group should be between all people who were ill and used modern care or only those who had expenditures. Given that the contingent valuation respondents included all people who would use modem care, the comparison presented here is of all people who used modern care including those who had zero expenditures. This comparison assumes that the majority of people who refused to pay the cost of the quality improvements would use modern care only if it was free. In fact, 20% of the people who had modern expenditures had zero expenditures. This is comparable to the 20-36% who refused to pay the cost of the quality improvements if the absence of expenditures does not reflect differences between the severity of the actual illness and the illnesses that would be treated by the quality improvements (or other fac- tors).

31. Two percent of the people who were ill visited a private facility and purchased drugs from a pharmacy.

32. A private communication from an anonymous reviewer states that the World Bank reports that 38% of health care expenditure in the Central African Republic are for private providers of care. If the World Bank statistic is for expenditures at facilities, our estimate of 48% is somewhat higher. If the World Bank statistic is for total expenditures for modern care and the definition of providers excludes pharmacies, our estimate of 14% is much lower; if it includes pharmacies, our estimate of 85% is much higher. The World Bank report has not been released to the public, so it was not possible to reconcile differences in definitions and statistics.

33. As with health care expenditures, all of the analyses were conducted with the median; the distribution of consumption is skewed by a few households with unusually high consumption and the median is a more representative statistic under these circumstances.

for child survival 997

34. World Bank. World Development Report, p. 238. Oxford University Press, New York, 1993.

There are two explanations for the difference: (I) World Bank estimates are less comparable and reliable in countries with high levels of subsistence farming (p. 308), and (2) the consumption data were collected right before the planting season when the market prices may be higher than average.

35. The natural log of consumption is the preferred variable in analysis of variance because the natural log trans- formation produces a more normal distribution when the distribution of consumption is skewed by a few households with unusually high consumption.

36. The confidence interval for the estimate depends on whether the estimate was an extreme value (e.g. 20 or 80%) or a central value (e.g. 50%). For example, a sample size of 250 for each version of the questionnaire results in a confidence interval of + 5 % for an estimate of 20 or 80%, and a confidence interval of less + 10% for an estimate of 50%.

APPENDIX

Contingent Valuation Questions

INTRODUCTION 42. I would like to ask you some questions about the quality

of health care in the public health facilities, specifically about: 1. maintenance of the health facilities 2. the technical knowledge of the health personnel 3. availability of drugs Supposing that the health facility the nearest to you has all of the attributes at all times, would you bring a someone who is ill for treatment at that facility? 1. yes 2. no []

I would like to make some proposals about your willingness to pay for health care per episode of illness. These proposals will help us to understand the value that you attach to health care.

For each question, would you please tell us which of the

[]

following responses best represents your opinion: 1. I would not hesitate 2. I would go into debt to pay 3. I would pay if I had enough money 4. I would not pay VERSION

HEALTH FACILITY MAINTENANCE 43. Given that your health facility is not well-maintained,

without beds, mattresses, a delivery table, doors, etc., would you agree to pay 1000 F CFA per episode of illness for maintenance of the health facility nearest you? Which of the following responses best represents your opinion? 1. I would not hesitate I--I 2. I would go into debt to pay 3. I would pay if I had enough money 4. I would not pay

44. IF RESPONDENT DOES NOT AGREE TO PAY, ASK Why would you not pay?

IMPROVEMENT IN THE KNOWLEDGE OF HEALTH PERSONNEL 45. In order to improve the knowledge of the health

personnel, would you agree to contribute X F CFA per episode of illness at the nearest health facility for their supervision and training? Which of the following responses best represents your opinion? 1. I would not hesitate [] 2. I would go into debt to pay 3. I would pay if I had enough money 4. I would not pay


46. IF RESPONDENT DOES NOT AGREE TO PAY, 50. ASK: Why would you not pay?

AVAILABILITY OF DRUGS 47. If your child has diarrhea today, would you agree to pay

X F CFA for drugs to treat it? Which of the following responses best represents your opinion? 1. I would not hesitate [] 2. I would go into debt to pay 3. I would pay if I had enough money 51. 4. 1 would not pay

48. If your child has a respiratory infection, would you agree to pay X F CFA for drugs to treat it? Which of the following responses best represents your opinion? 1. 1 would not hesitate [] 2. I would go into debt to pay 3. I would pay if I had enough money 4. I would not pay 52.

49. If a member of your household has malaria, would you accept to pay X F C F A for drugs to treat it? Which of the following responses best represents your opinion? 1. I would not hesitate [] 2. I would go into debt to pay 3. I would pay if I had enough money 4. I would not pay

If a member of your household has an intestinal infection, would you accept to pay X F CFA for drugs to treat it? Which of the following responses best represents your opinion? I. I would not hesitate [] 2. I would go into debt to pay 3. I would pay if I had enough money 4. I would not pay

If a member of your household has a sexually transmitted disease, would you accept to pay X F CFA for drugs to treat it? Which of the following responses best represents your opinion? 1. I would not hesitate [] 2. I would go into debt to pay 3. I would pay if I had enough money 4. I would not pay

IF THE RESPONDENT DOES NOT AGREE TO PAY FOR DRUGS, ASK THE FOLLOWING: Why would you not pay to treat these illnesses? 1. Diarrhea 2. Respiratory infections 3. Malaria 4. Intestinal illnesses 5. STD

willingness to pay for child survival: results of a national survey in central african republic

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