1. Introduction
Constrained economic circumstances and stagnant growth of the health sector have led many developing countries to con-sider cost recovery as a means of financing health care pro-duction. This paper assesses the impact of an experiment in health care cost recovery, preceded or accompanied by quality improvements, conducted in three districts in Niger. Using a model of the illness and treatment-seeking process, we analyze and discuss the policy implications of the results. In May 1993, cost recovery was substantially augmented in two of the three study districts in Niger.1,2In Say district, a direct method of payment was implemented for outpatient treatment at government facilities, with charges of 200 FCFA (equivalent to US$0.66 at the time of the test) per episode of treatment for adults and 100 FCFA ($0.33) per episode for children under 5 years. In Boboye district, over 100 km east of Say, an indirect method of payment was implemented. Revenues were gener-ated primarily through a regional tax of 200 FCFA per house-hold, earmarked specifically for financing health care, with moderate fees of 50 FCFA ($0.16) per episode of treatment for adults and 25 FCFA ($0.08) for children. The revenues so generated in Boboye, from fees and taxes, were pooled at the district level to create a district fund that was managed by health committees comprising representatives of the district’s population.2 In both Boboye and Say, the handicapped, schoolchildren, prisoners, soldiers and indigents were exempted from paying the taxes and fees. The population
paying taxes and fees thus knew their contributions were being used to finance health care. In Illela district, over 400 km north-east of Say, existing low levels of cost recovery were maintained, and the only sources of revenue for public health facilities were traditional sources of government finance (direct and indirect taxes and tariffs), not collected through the health sector. Illela thus serves naturally as a control site. It is important to note that in addition to the substantial expansion of cost sharing, diagnostic-treatment protocols, the availability of essential drugs and health management systems were improved at government health facilities.3In Boboye region, improvements in diagnostic and treatment protocols preceded the cost-recovery experiment by three years, while in Say the two took place more or less simul-taneously. Specifically, four interventions were implemented to improve efficiency at public health facilities: 4(1) improve-ment in drug availability; (2) training of health personnel in the use of standard diagnosis and treatment protocols; (3) strengthening management capacity; and (4) improving supervisory and managerial capacity.
The observed changes in treatment patterns in the two experimental districts can thus be attributed to both cost recovery and the beneficial effects of facility enhancements. The impacts that we document are those of the combined effect of cost sharing and quality enhancements, not pure demand or pure quality effects. Since there were no major changes in either pricing policy or quality in the control site
The impact of financing and quality changes on health care
demand in Niger
MUKESH CHAWLA1AND RANDALL P ELLIS2
1Department of Population and International Health, Harvard School of Public Health, Boston, MA, USA and 2Department of Economics, Boston University, Boston, MA, USA
This paper assesses the demand effects of a cost recovery and quality improvement pilot study conducted in Niger in 1993. Direct user charges and indirect insurance payments were implemented in government health care facilities in different parts of the country, and were preceded or accompanied by quality changes in these facilities. Decision-making by patients is modelled as a three-stage process of reporting an illness, seeking treatment and choice of provider; and multinomial nested logit techniques are used to estimate the parameters of the decision-tree. Overall, the results give a reasonably favourable impression of the policy changes. In neither case is there evidence of serious reductions in access or increases in cost. Particularly notable is that despite an increase in formal user charges, the observed decline in rates of visits is statisti-cally insignificant, suggesting the success of measures to improve quality of health care in public facilities. The observed increase in the probability of formal visits in the district with indirect payments is also strik-ing. Both contrast with the control region of Illela, where neither user charges were introduced nor were any efforts made to improve quality. The data suggest that higher utilization of formal care, probably due to improvements in quality, outweighed the decrease in utilization that may have come about due to introduc-tion of cost recovery, so that the net effect of the policy changes was an increase in utilizaintroduc-tion. Quality con-siderations appear to be important in ensuring the long-term success of cost sharing.
of Illela district, we use data from Illela to infer how the intro-duction of price and quality improvements in Say and Boboye affected demand.
Previous studies on the demand for health care have typically found small price effects. In a study of demand for health care in rural Malaysia, Heller5 found that total annual medical visits were not significantly influenced by prices. Similarly, Akin et al.6also concluded that prices were not an important determinant of demand for medical care in a rural region of the Philippines. In a study of the demand for health care in rural Cote d’Ivoire, Gertler and van der Gaag7 found the utilization of health services to be price unelastic. In a study that analyzes the determinants of demand for health care in urban Bolivia, Ii8found that though demand for medical care is responsive to changes in prices, the price elasticities tend to be very low.
Other studies have found that cost recovery has a negative impact on utilization of health services. In a study of the effect of a price increase in health facilities in Zaire, de Bethune et al.9found a decrease in utilization after an abrupt increase in prices. Waddington and Enyimayew10found a general fall in health facility utilization following a price increase in the Ashanti-Akim district in Ghana. Mwabu et al.11estimated that utilization of health facilities dropped by 38% during 1989–90 in the four facilities they examined, though Huber12attributes much of the drop in demand to insufficient use of exemptions. However, there is some evidence of improvement in access to health facilities following increases in both costs and quality of care, as found by Litvack and Bodart13in Cameroon.
The remainder of this paper is organized as follows. The data used are introduced briefly in section 2. Section 3 discusses the methodology, section 4 presents the results, and the paper ends with a discussion in section 5.
2. The data
The analysis in this paper uses data from household surveys1–3 conducted both before and after the policy change, with an interval of 12 months between the two. Data were collected in the three districts of Say, Boboye and Illela, which were chosen by the Ministry of Health, Government of Niger. The first set of surveys was done in October–November 1992, 6 months before the introduction of the cost-recovery systems, while the second surveys were carried out in October– November 1993, 6 months after the intervention.
The sampling criteria used was based on the population Census of 1988, and data on the distribution of health facili-ties in Niger in 1988. Each of the selected districts was divided into two parts: one part had a health facility, for which five census districts (‘grappes’) were included, while the other part had no health facility, for which 29 census districts were included. A three-stage stratified cluster design was used to select households in each of these census districts.
The surveys collected data on 612 households in each census district in each of the two periods. A total of 14 410 indi-viduals (1836 households) were interviewed in the first stage
and 13 051 individuals (1836 households) were interviewed in the second stage. Not all the households interviewed in the first stage were interviewed in the second stage. This analysis combines the data from the two sets of surveys in all three study sites.
3. Methodology
Two statistical methods were used: univariate comparisons of sample statistics across regions and over time, and a multi-variate nested logit model of the decision process underlying individual illness and treatment seeking. For the nested logit specification, the decision tree modelled is as shown in Figure 1. Although usefully thought of as a sequential decision, the assumed specification only imposes restrictions on the corre-lation structure of the error terms affecting different choices. It is consistent with all stages of decisions being made simul-taneously.14,15
The first stage of the decision tree is the decision to report an illness. This first stage of the decision process is important in order to distinguish whether observed changes in patterns of treatment-seeking reflect differences in illness (possibly due to environmental factors that are unrelated to the policy change in the short run) or differences in treatment-seeking behaviour once ill, which may be influenced by price and quality changes. Although in the long run differences in rates of illness could be due to changes in cost sharing and quality at government facilities, in the short run significant changes are unlikely. We model this stage as dependent upon indi-vidual and household level demographic variables, dummy variables for the region in which an individual lives, average price, travel time, and drug availability on dummy variables for the policy changes in each region, and on a term called the inclusive value, which picks up the expected utility of seeking treatment should one report an illness.i
The second stage of the decision tree is the decision to seek treatment from a formal provider or healer conditional on reporting an illness. For this study, we define formal providers to include hospitals, medical centres, medical posts, rural dis-pensaries, maternity hospitals and private clinics. Because the use of private clinics is extremely rare in our sample (with fewer than 25 cases in our sample of 27 357 individuals) we
Figure 1. The decision tree for the patient treatment-seeking process
were unable to model this choice separately. We chose instead to group them together with the formal public facili-ties, even though they need not have changed prices in line with the policy experiment. ‘Informal providers’ are defined to include traditional practitioners (healer) and ‘others’ (which include friends, other relatives, and pharmacies). Because both formal and informal treatment at home (i.e. treatment by persons other than family members) are rela-tively rare, and many people treated at home also receive treatment outside of the home, we did not attempt to model home treatment separately from treatment outside the home. Hence for this study the decision to ‘seek treatment’ includes the possibility of having a formal provider (i.e. doctor, midwife or nurse) or informal provider (traditional healer or ‘other’) treat the patient in his or her home. Treatment-seeking behaviour is modelled similarly to the illness report-ing decision, as dependreport-ing upon the same set of demographic variables, average price, drug availability and waiting time, regional dummies and pre- and post-policy-change dummy variables. We also include an inclusive value that captures the expected value of treatment by formal and informal pro-viders.
The third stage of the decision tree is the decision to seek formal rather than informal treatment. A sizeable proportion of the population in all districts paid and visited traditional healers and other informal providers, hence any change in the rates of treatment by these informal providers is important in understanding the total financial burdens on individuals. Variables included in this last stage are the same as for the previous two stages.
4. Results
Base line survey results
From May through October 1992, baseline surveys were con-ducted in 1813 households containing 14 359 individuals. Summary numbers from these household surveys are shown in Table 1. The three districts show considerable similarity in many demographic variables, with virtually identical aver-ages for age, proportion male, marital status and levels of secondary education. The three districts do differ in several dimensions. Household sizes are smaller in Illela than the other two districts, and our income measure (average esti-mated monthly consumption expenditures) is slightly lower in Illela and higher in Boboye than the sample average. Although days of illness are similar in the three districts, both the percentage reporting illness in the base period and the percentage seeking treatment are significantly lower in Say in the base period. Treatment in the formal sector is quite rare in all three districts, with only 2.7% of individuals seeking treatment from a formal provider in the base period. Our price measure was highest in Illela (1951 FCFA), relative to Say (679) and Boboye (792). Reported drug availability was similar in Say and Boboye, and slightly worse than in Illela, before the policy change. Travel times were slightly worse in Say than in Illela and Boboye.
Overall, the three regions were broadly similar prior to the policy changes, although the Say sample appears to be slightly healthier and that of Boboye slightly sicker than the control site of Illela. Bearing these initial differences in mind, the
Table 1. Sample characteristics in districts of Say, Boboye and Illela, Niger, before policy change, all individuals
Say Boboye Illela Total
Number of individuals 4685 5566 4108 14 359 Income (’000 FCFA) 12.95 14.97 11.57 13.34 (13.8) (18.41) (12.95) (15.61) Reported illness 0.1372 0.2432 0.2186 0.2016 (0.344) (0.4291) (0.4133) (0.4012) Days ill 11.19 11.301 1.98 11.36 (10.6) (10.36) (10.41) (10.44) Seeking treatment 0.0783 0.1886 0.1188 0.1326 (0.268) (0.3913) (0.3236) (0.3392) Treatment at home 0.0156 0.0099 0.0088 0.0114 (0.123) (0.0989) (0.0932) (0.1062) Treatment outside home 0.0201 0.0456 0.0314 0.0332
(0.140) (0.2087) (0.1744) (0.1792) Treatment by healer 0.0094 0.0129 0.0122 0.0115
(0.096) (0.1130) (0.1097) (0.1069) Formal treatment 0.0188 0.0377 0.0241 0.0276
(0.135) (0.1906) (0.1534) (0.1639) Total expenditures on illness 396.5 788.3 795.0 662.5
(1400.0) (2540.0) (2863.0) (2350.0) Price/1000 0.679 0.792 0.1951 0.1087 (1.089) (0.764) (2.983) (1.860) Drug availability 2.03 2.05 2.38 2.14 (0.568) (0.537) (0.420) (0.539) Travel time 149.0 122.0 123.0 130.9 (109.0) (79.7) (83.2) (92.1)
sample provides a nice setting for a controlled experiment for the impact of the two forms of cost sharing.
Descriptive comparison of policy impact in the three districts
Table 2 provides summary statistics of key variables before and after the policy changes in each of the three regions for all individuals seeking treatment of any kind. The first numbers to notice are the large changes in the number of people seeking treatment of any kind. In Say, where a direct form of payment was implemented, the number of people seeking treatment increased by 40%, from 367 to 514 indi-viduals. In Boboye, where predominantly indirect payment was implemented, the number seeking treatment declined by 26%, from 1050 to 778 individuals. In the control site of Illela, the number seeking treatment increased by 9%, from 488 to 532 individuals.
The percentage seeking treatment at home only was highest, but declined most sharply, in Say (17% pre, 12% post). In Boboye it fell marginally (5% pre and 4% post) and rose mar-ginally in Illela (7% pre and 8% post). The percentage seeking treatment from formal providers decreased from 23 to 21% in Say, increased from 20 to 23% in Boboye, and fell sharply in the control region of Illela, from 19 to 12%. The average price of formal treatment fell in each of the three study regions, with Say recording a fall of 14%, Boboye 36% and Illela almost 66%. Drug availability rose substantially in Boboye, followed by Say, but fell in Illela. Travel time increased marginally in Boboye and Say, and fell marginally in Illela.
The descriptive data provide some insights into the changes in access, use of formal providers and prices – although for a variety of reasons, it would be hasty to attribute these changes entirely to cost recovery and quality improvement in the facil-ities.iiFirst, since the percentage of population seeking care
from a formal provider is very small to begin with (about 2.7% in the base period), it is unlikely that any government policy changes in the formal sector would bring about a marked change in the overall treatment-seeking behaviour in a 1-year period. Second, other factors affecting treatment-seeking behaviour could have changed in the year between the preliminary baseline survey and the final survey, though it is unlikely that such changes could affect behaviour in a period as short as one year.
We note that between the baseline and final survey years, the number of patients seeking any form of treatment fell by 4%, with this fall being recorded in Boboye district only. Of all the three districts, the percentage of patients seeking treatment from formal providers increased solely in Boboye district, where an indirect payment system was introduced, despite the fact that quality improvement in the form of increased drug availability was recorded in both Say and Boboye. Like Say, Boboye also recorded a fall in the percentage of patients seeking treatment at home. In contrast, the control region of Illela experienced an increase in the percentage of patients seeking treatment at home, and a fall in the percentage of patients seeking treatment from formal providers. Notwith-standing the caveat mentioned in the preceding paragraph, all these results are too consistent with the changes in cost recov-ery and quality protocols in the three districts for us not to relate government policy changes to treatment-seeking behaviour.
Finally, we find it striking that the average price of formal treatment went down in each of the three study regions, even in Say where the policy change was to increase prices at the public facilities. We note that even the new fees charged in Say (200 FCFA per episode for adults, 100 FCFA for chil-dren) were well below the average reported payments in Say and Boboye even before the policy intervention. One prob-able explanation is that substantial payments were being made for medicines and consultations by formal providers
Table 2. Sample characteristics in the districts of Say, Boboye and Illela pre- and post-policy change, individuals seeking treatment only
Say Boboye Illela
–––––––––––––––––––––––––––– –––––––––––––––––––––––––––– ––––––––––––––––––––––––––––
pre post pre post pre post
Number of individuals 367 514 1050 778 488 532 Treatment at home 0.1717 0.1226 0.0505 0.0373 0.0697 0.0771
(0.3776) (0.3283) (0.219) (0.1896) (0.2549) (0.2669) Treatment outside home 0.2343 0.2043 0.2371 0.2429 0.2561 0.1561
(0.4242) (0.4036) (0.4255) (0.4291) (0.437) (0.3632) Treatment by healer 0.1008 0.0661 0.0648 0.0373 0.0984 0.695 (0.3015) (0.2488) (0.2462) (0.1896) (0.2982) (0.2546) Formal treatment 0.2262 0.2082 0.1981 0.2301 0.1947 0.1241 (0.4189) (0.4064) (0.3988) (0.4212) (0.3964) (0.3299) Price/1000 0.794 0.685 0.864 0.553 2.101 0.717 (1.080) (1.151) (0.790) (0.667) (2.737) (1.266) Drug availability 2.08 2.35 2.01 2.64 2.3 2.20 (0.545) (0.412) (0.507) (0.271) (0.40) (0.58) Travel time 129 139 118 121 127 125 (112.0) (0.103) (82.7) (80.0) (85.6) (83.1)
before the introduction of user fees and insurance systems, even though the public system was in principle free. With the improvements in drug availability pursuant to the quality improvements, the fall in average price may well be picking up a saving in expenditure on drugs. Thus, average prices fell markedly in Boboye, the district that recorded the highest improvement in drug availability. We cannot, however, explain the big fall in prices in Illela.
Results from a logit model of the decision process
The preceding analysis has identified major patterns of treat-ment and change using univariate comparisons of means. In this section we report the results of estimating a discrete choice (logit) model of the decision process underlying treat-ment decision.
Results from the three-stage nested logit model are presented in Table 3. The decision to report an illness is modelled in the first stage of the decision tree, where the choice is ‘reporting an illness’.iiiIndividuals belonging to large families are less likely to report an illness, as indicated by the negative and sig-nificant coefficient on household size. Married people and females are more likely to report an illness. Ethnic group is not a significant predictor of illness. The coefficients on income, price, drug availability and travel time cannot be used to infer their effect on the probability of reporting an illness because the model also includes interactions between income and the price, drug availability and travel time vari-ables. We note that the coefficient on drug availability alone, and price interacted with income, are each significant, sug-gesting that there is a relationship between these three vari-ables and reporting an illness. Even after controlling for
Table 3. Nested logit results for the treatment-seeking process (t-statistics in parenthesis)
Reporting illness Seeking treatment Formal versus informal Household size –0.0366a –0.00483 0.00972 (10.398) (0.699) (1.058) Old age 0.106 0.330a –0.284a (1.659) (2.797) (1.587) Adult –0.399a –0.0304 –0.0298 (7.996) (0.284) (0.187) Sex: male –0.888a –0.0241 0.438 (3.878) (0.080) (1.299) Married 0.300a –0.0873 0.131 (5.964) (0.839) (0.829) Secondary school –0.0291 0.367 0.314 (0.212) (1.008) (1.079) Non-Zarma –0.0360 –0.338a –0.0942 (0.747) (3.678) (0.798) Household income 0.000315 –0.0236a 0.00782 (0.069) (2.423) (0.828) Say district –0.547a –0.0456 0.0584 (8.837) (0.404) (0.307) Boboye district 0.244a 0.818a –0.290b (3.019) (6.597) (1.576) Price/1000 0.0160 –0.0724a –0.0141 (1.250) (2.722) (0.348) Drug availability –0.136a 0.0300 –0.298a (3.192) (0.365) (2.569) Travel time 0.000287 0.000464 –0.0123a (0.875) (0.648) (11.714) Price*Income 0.00197a 0.00496a 0.00131 (2.532) (2.571) (0.686) Travel time*Income 0.0000233b 0.0000363 0.00000790 (1.879) (1.190) (0.193) Drug availability*Income 0.0469a 0.0133a –0.00168 (2.443) (3.086) (0.406) Say district*Post dummy 0.473a 0.122 0.0607
(7.910) (1.099) (0.343) Boboye district*Post dummy –0.250a 0.120 0.556a
(4.537) (1.008) (3.834) Illela district*Post dummy 0.119b 0.292a –0.605a
(1.854) (2.781) (3.103) Inclusive value –0.140 –1.542a
(0.897) (3.789)
Log-likelihood –13 607.54 –3 348.64 –1 593.70 Sample size –27 357 15 618 –3 722
demographics and observed prices and drug availability, the two district dummies for Say and Boboye are statistically sig-nificant. The probability of illness being reported is negative and significant in the district of Say, while the reverse holds for Boboye. These coefficients changed significantly after the new payment system was introduced in Say and Boboye, perhaps picking up the impact of unobserved changes in facil-ity qualfacil-ity not captured in our drug availabilfacil-ity measure. Con-sistent with the absence of any changes in the structure of delivery in Illela, there was no change in the probability of reporting illness in the control region of Illela. The coefficient on the inclusive value is statistically insignificant, suggesting that the decision to report an illness is independent of whether the individual seeks treatment for this illness. The decision to seek treatment conditional on reporting an illness is modelled in the second level of the decision tree, where the choice variable is ‘seeking treatment’ versus ‘not seeking treatment’. Consistent with the results found in Ellis and Mwabu,14few of the demographic variables are statisti-cally significant. Older people are more likely to seek treat-ment when ill than adults or children, and non-Zarma ethnic groups are less likely to seek treatment. The coefficient on price is negative and statistically significant, and the inter-action term between income and price is positive and signifi-cant. Together these two parameters indicate that individuals from families with high incomes are less responsive to prices than individuals from families with low incomes. The income variable, not interacted with any others, is significant and negative, but cannot be easily interpreted in the presence of the interaction terms. We defer a discussion of income until the next section where we use simulations to identify meaningful patterns. The coefficient on the inclusive value is negative and significant. This implies that individuals that are more likely to use formal treatment are less likely to seek any treatment, which is contrary to expectations but not implaus-ible.
The third level of choice modelled is between formal and informal treatment, with the choice variable being ‘formal
treatment’. Perhaps in part because the sample size is smaller than in the previous stages, none of the individual demo-graphic variables are significant. The price term is negative, and the price–income interaction term is positive, both as theory would predict, but the coefficients are statistically insignificant. Drug availability is negative, and the coefficient on the interaction term between drug availability and income is also negative but insignificant. In Boboye, where an indirect payment system (tax) was introduced, the region–period interaction variable has a significant positive coefficient, indi-cating a shift toward formal treatment. The Illela district dummy interacted with the post-period dummy is negative and significant, suggesting a shift away from formal treatment in the region even after controlling for prices and drug avail-ability. Together the three regional–time period interaction dummy coefficients suggest that the cost recovery/quality enhancements in Say and Boboye had modest negative and positive effects on seeking formal treatment, while the control region experienced a sharp decline in rates of formal treatment.
Simulation results
As highlighted in the previous section, the income, price, drug availability and travel time results from nested logit models are not readily interpreted in terms of their policy signifi-cance. Therefore, we used the estimated parameters from our three-stage nested logit model to simulate the probabilities of each of the three decision stages. Results from our simu-lations are shown in Tables 4, 5 and 6. These simusimu-lations are based on a hypothetical individual with average character-istics for all variables except for the variables being simulated in each part of the table. The indirect effects of the variables through the inclusive values are taken into account in the simulations. Comparisons of the probabilities can be used to assess the magnitude of the policy impacts and how indi-viduals of different incomes were affected. For each simu-lation, we used the 10th, 50th and 90th percentiles of the variable of interest. Hence, as shown in Table 4, we simulated probabilities for income levels of 2300, 7800, and 28 800
Table 4. Simulated probabilities of seeking formal treatment conditional on seeking any treatment: income level and price Income percentile Level Price
–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– 10th percentile 50th percentile 90th percentile
Income (’000 FCFA) 2 317 2568
10th 2.3 11.1% 11.0% 10.8%
Median 7.8 11.3% 11.3% 11.2%
90th 28.8 12.4% 12.5% 13.1%
Probability of seeking treatment conditional on reporting illness
Income (’000 FCFA) 2 317 2568
10th 2.3 52.3% 51.9% 48.5%
Median 7.8 53.9% 53.7% 51.8%
90th 28.8 59.9% 60.4% 64.1%
Probability of reporting illness
Income (’000 FCFA) 2 317 2568
10th 2.3 23.4% 23.5% 24.3%
Median 7.8 23.8% 24.0% 25.3%
FCFA, and prices of 2317 and 2568 FCFA. In Table 5 we sim-ulated the probabilities for income percentiles and for per-centiles of average drug availability by grappe: 1.6, 2.3 and 3.0. Table 6 shows how the gross impact of the policy changes affected each income level in each of the three districts, taking into account changes in average prices, drug avail-ability and the post-policy-period dummy variables.
The simulations in Table 4 summarize the interactions between price and income on the three decisions: reporting illness, seeking treatment and seeking formal treatment. The first part of the table indicates that neither price nor income seem to have much of an impact on the decision to seek formal treatment conditional on seeking any treatment. Prob-abilities range from 10.85 for a high price, low income proba-bility to 12.4% on the low price, high income simulation. The second part of Table 4 indicates that the probability of seeking any treatment is more responsive to prices and income levels. The simulations suggest an approximate 7.5% increase in the probability of seeking any treatment as income
varies, and an approximate 4% increase as the price is increased. Changes in price responsiveness are striking: for low income levels, the probability of seeking any treatment declines as prices are increased, while for high income levels, the probability is predicted to increase with prices. Although contrary to usual economic theory, this is consistent with inadequately controlling for facility quality variation, which may be correlated with prices and highly desired by high-income individuals.
The bottom half of Table 4 presents simulations for the probability of reporting treatment. Consistent with expec-tations, this probability is virtually unaffected by prices, but increases modestly with income.
Table 5 presents a similar set of simulations for various levels of drug availability, each considered for three income levels. The probability of seeking formal treatment is negatively related to the average drug availability, and does not show a meaningful difference in responsiveness across income levels.
Table 5. Simulated probabilities of seeking formal treatment conditional on seeking any treatment: income level and drug availability Income percentile Level Drug availability
–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– 10th percentile 50th percentile 90th percentile
Income (’000 FCFA) 1.63 2.29 3.0
10th 2.3 13.0% 10.9% 9.0%
Median 7.8 13.5% 11.3% 9.2%
90th 28.8 15.3% 12.6% 10.1%
Probability of seeking treatment conditional on reporting illness
Income (’000 FCFA) 1.63 2.29 3.0
10th 2.3 50.0% 51.0% 52.1%
Median 7.8 51.0% 53.2% 55.6%
90th 28.8 54.8% 61.5% 68.2%
Probability of reporting illness
Income (’000 FCFA) 1.63 2.29 3.0
10th 2.3 25.3% 23.7% 22.0%
Median 7.8 25.9% 24.3% 22.6%
90th 28.8 28.4% 26.7% 25.0%
Table 6. Simulated probabilities of seeking formal treatment conditional on seeking any treatment, pre- and post-policy change, by income Income percentile Level Say Boboye Illela
––––––––––––––––––––––––– ––––––––––––––––––––––––– ––––––––––––––––––––––––– pre (%) post (%) pre (%) post (%) pre (%) post (%)
10th 2.3 11.3 11.1 10.4 15.7 12.3 6.6
Median 7.8 11.6 11.4 10.7 16.1 12.7 6.8 90th 28.8 13.1 12.6 12.2 17.8 13.0 6.9
Probability of seeking treatment conditional on reporting illness
10th 2.3 53.5 51.3 50.4 54.5 49.9 58.2 Median 7.8 55.6 53.8 52.5 56.7 51.9 60.4 90th 28.8 63.3 62.4 60.3 65.2 59.8 68.4
Probability of reporting illness
10th 2.3 22.8 31.2 24.9 19.8 23.8 25.1 Median 7.8 23.4 31.8 25.6 20.3 24.4 25.7 90th 28.8 26.0 34.5 28.3 22.3 27.1 28.1
Drug availability has a large effect on the decision to seek formal treatment, and causes nearly a 14% increase in the probability of seeking any treatment for the highest income level, versus only a 2.1% difference for the low income level. Income and drug availability predict a difference of more than 18% in the probability of seeking any treatment. The final section of Table 5 indicates that higher drug availability appears to reduce the rate of reported illness, with an approximate 3% reduction in the probability of reporting illness at a 90th versus 10th percentile level of drug avail-ability.
Table 6 summarizes the total impact of the policy change, including changes in prices, drug availability and unobserved variables captured by the district-level dummies interacted with the post-period dummy. This table provides the best picture of the overall impact of the policy changes on indi-viduals with different household incomes, holding constant all other observed characteristics. The first part of Table 6 indicates that the probability of seeking formal care con-ditional on seeking any care was virtually unchanged for all income levels in Say, increased approximately 5% in Boboye, and decreased approximately 6% in Illela. In each district the reduction in use of formal treatment when any treatment is sought is relatively independent of income levels.
The second part of Table 6 indicates that the policy changes also had a large impact on the probability of seeking any treatment when ill. There is a small decrease (2%) in this probability in Say, roughly a 4% increase in Boboye, and a 4% increase in Illela. These changes are similar across differ-ent income levels.
The third section of Table 6 indicates the probability of reporting illness in each of the three districts, before and after the policy change, for each income level. The simulations indicate large increases in Say (9%), reductions in Boboye (5%), and a small increase in Illela (1%). With only three regions and two time periods, we cannot detect whether these large changes in rates of reported illness are related to the policy changes or due to exogenous influences, such as epi-demics.
Several findings from the simulations are worth contrasting with previous literature. Price effects are significant, but rela-tively small, noticeably smaller than the income effects on the impact of seeking formal treatment and seeking any treat-ment. Households with the lowest incomes have roughly a 2% lower probability of seeking formal treatment, when seeking any treatment, than households in the highest income level, a difference that is small in comparison to other studies of other regions.14,16 On the other hand, income is more strongly related to the probability of seeking any treatment, with roughly a 10% change between low and high income levels.
5. Discussion
The probability of a patient visiting a formal provider increased in Boboye following the policy changes, even though these changes meant a greater financial outlay for the
residents of Boboye in terms of a moderate fee-for-service and an indirect tax specifically earmarked for financing health care. This result could have been brought about by one or both of the following reasons. First, the policy changes also included significant quality enhancements which, in the case of Boboye, began three years before the changes in health care financing were implemented. The quality-improvement initiatives included increased drug availability as well as improved management, supervision and training. The proba-bility of visiting formal providers is likely to have been posi-tively affected by these quality enhancements. Secondly, after incurring an ex-ante fixed cost in terms of the indirect tax, the marginal cost of 50 FCFA per visit to a formal provider is low, and demand is likely to increase irrespective of any other changes that might have taken place. In absence of complete data on the quality variables and on out-of-pocket expendi-tures before the policy changes, we are unable to unequivo-cally attribute the higher utilization rates to either the quality effect or to the moral hazard effect; both of these could poten-tially have brought about the observed increases in visits to formal providers. In any case, the observed changes in rates of visits were not due to changes in reported illness, which actually declined in Boboye.
The probability of visiting a formal provider did not change significantly in Say, where a fee of 200 FCFA was introduced. Unlike Boboye, quality changes in Say were introduced more or less simultaneously with the financing changes. In contrast, the probability of treatment by a formal provider when ill decreased significantly in the control region of Illela, where there were no cost-recovery or quality-improvement initiat-ives.
The introduction of cost recovery and improvements in quality of care in Boboye and Say changed the probabilities of seeking treatment by informal providers in the desirable direction. Individuals reporting an illness in the two experi-mental regions were more likely to visit formal providers and less likely to be treated only at home or by healers and other informal providers. In contrast, in the control region of Illela, individuals were more likely to report treatment at home or by informal providers after the policy change.
As far as reported illness is concerned, both the univariate analysis and logit model indicate that rates of reported illness increased substantially in Say, decreased in Boboye and were virtually unchanged in Illela. Because of the short time elapsed since changes in cost recovery and quality improve-ments, together with the fact that rates of visits were increas-ing or unchanged in the two experimental regions, it seems implausible that these changes can be attributed to the policy changes. They do suggest the need for further monitoring of this important trend, however.
Overall, the results give a reasonably favourable impression of the policy changes. In neither case is there evidence of serious reduction in access or increase in cost. Particularly notable is the fact that in Say, with moderate cost sharing, the observed decline in rates of visits is statistically insignificant. The observed increase in the probability of formal visits in Boboye is also striking. Both contrast with the control region
of Illela, where visit rates fell substantially even when there was no change in price.
An important caveat in using the above results to guide wide-spread health sector reforms is that while many of the observed changes lend positive support to the quality-enhancement and cost-recovery initiatives in Niger, the absolute magnitudes are rather low. Moreover, in the absence of any data on the informal provision sectors during this period of changes, it is difficult to examine any interactions between formal and informal care. This remains an important area for future research.
Endnotes
iMore formally, let S
ijbe the set of alternatives from which the
consumer chooses at stage igiven that he is currently at node j, and let Xijbe the vector of explanatory variables used at stage inode jof
the decision tree to predict choice of alternative j, and let flibe the
vector of utility weights assigned to this vector Xij. The probability
that the consumer will choose alternative j eSijcan be written as
Pr(j = j*) = exp(Xij* bi+ riIij*)/ [Sexp(Xi,kbi+ riIik)}.
k eSi,j
Note that in addition to the Xijvariables, the selection of alternative jat stage idepends on the ‘inclusive value’, Iij, weighted by the utility
weight ri. The inclusive value is defined recursively as
Iij= ln{[Sexp(Xi+1,kbi+1+ ri+1Ii+1,k)}.
k eSi+1,j
The inclusive value for nodejof the model is the denominator of the logit expression used to make choices at node j. Under the assump-tion that the errors follow the generalized extreme value distri-bution, this expression is equal to the expected utility derived from making the subsequent choices. Note that if there are no further choices to be made, then the inclusive value is zero. For a good dis-cussion of logit models, see Train.15
ii We are grateful to an anonymous referee for drawing our
attention to this.
iii The variable ‘reporting an illness’ captures incidence of illness
as reported by the individuals interrogated by the survey team.
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Acknowledgements
Research support for paper is gratefully acknowledged from USAID as part of the Health Financing and Sustainability project conducted by Abt Associates, Inc. We are grateful to Abdo Yazbeck, Ricardo Bitran, Francois Diop and two anonymous referees for helpful com-ments on the paper. Any errors remain our own.
Biographies
Mukesh Chawla is the Senior Health Economist with the Inter-national Health Systems Group at the Harvard School of Public Health. His research interests include health care financing, health systems reform, policy analysis, and project design and evaluation in health.
Randall Ellis is a Professor in the Economics Department at Boston University specializing in modelling the interaction between supply and demand side incentives on health markets. His research interests span both US and international health topics.
Correspondence: Mukesh Chawla, Department of Population and International Health, Harvard School of Public Health, Boston, MA 02115, USA.
