If user fees for health have been so vilified (including in comments on this blog), why are we bringing the subject up again? Because new evidence calls into question the prevailing view, namely that removing user fees leads to: (i) increased use of health services and hence to (ii) improved health outcomes. Confirming (i), the recent literature shows that (ii) does not always follow.
Raising the price of a good or service has two effects: it reduces demand and increases supply. In the case of user fees for health, it was thought that paying for a service also makes people use it more appropriately (you don’t go to the doctor for minor ailments) and value it more than if they obtained it for free. The new empirical evidence shows that this reasoning is incorrect [people use bed-nets just as much, even if they get it for free]. On the supply side, though, the evidence is consistent with the view that payments to providers generate incentives for better performance.
Unfortunately, in many African countries, user fees were not paid directly to health facilities or frontline providers. They went into the central-government’s coffers and therefore played no role in incentivizing providers. And since we now know that they didn’t help people use health services better, user fees helped no one. Removing them was a good idea. But instead of replacing them with systems that held providers accountable for performance, they were replaced with—nothing. This neglect was not benign.
Three recent studies, using rigorous empirical methods, provide valuable insights on the effects of changes in user fees on health care use and health outcomes.
1. Jessica Cohen and Pascaline Dupas undertook a field experiment in Kenya where some people had to buy bed-nets (a user fee) and others got it for free. They show that: (a) take-up dropped by 60 percentage points when the price increased from 0 to 60 cents; and (b) families were just as likely to use (hang) the net if they got it for free as if they paid for it. In their words: “We find no psychological effect of price or the act of paying on usage.”
2. In a randomized control trial that looks at both use and outcomes, Tarozzi and others find that free distribution dramatically increased “previous night usage rates”, but had no impact on outcomes. In their words: “Most strikingly, we find that neither micro-loans nor free distribution led to improvements in malaria and anemia prevalence, measured using blood tests.” The authors conjecture that the disconnect may have come from insufficient bed-net coverage, as the outcome benefits begin once more than 80 percent of households start using the product, something that their usage rates fell short of.
3. Ansah and colleagues in Ghana randomly assigned 2194 households with 2592 children into those who continued to pay the normal user-fees and those who received free primary care including drugs. The results?
a.There was a substitution away from informal to formal care (chemical sellers to primary care clinics) for households with lower user-fees. This is the usual evidence for user-fee reductions.
b.There were no impacts on health outcomes. In the words of the authors: “The primary outcome of moderate anemia was detected in 37 (3.1%) children in the control and 26 (3.2%) in the intervention arm…Mean Hb concentration, severe anemia, parasite prevalence and anthropometric measurements were similar in each group.”
In short, use increased but outcomes did not.
What’s going on?
It turns out that the two statements “there was a massive increase in use” and “there was no increase in health outcomes” are not only consistent but likely to be the norm when there are substitutes.
Consider the following example.
Jhanta likes apples and every apple a day he eats keeps the doctor away. At his supermarket, there are two types of apples—green apples and red apples–and he likes them both (exactly) equally. His decision rule is then very simple—he buys whichever one is cheaper.
On Monday of the first week of May, the green apple cost $1 and the red apple $0.99. Jhanta bought 7 red apples.
On Monday, the second week of May, the green apple cost $0.99 and the red apple $1. Jhanta bought 7 green apples.
Note what happened: The price of the green apple decreased by 1 cent from $1 to $0.99. Yet, the use of green apples went up by an amazing 7 units. Replace green apples with formal public health care and the price with user-fees and you have the basic result: A small reduction in user fees (imagine the price went down because the government subsidized green apples) led to a dramatic increase in use. But would this increase health outcomes? Not at all. Irrespective of which week we look at, Jhanta bought (and ate) 7 apples, successfully keeping the doctor away for an additional 7 days in each case.
The point is that interpreting changes in use and their relationship with changes in health outcomes as a result of price reductions depends not only on the service whose price you changed, but the availability of close substitutes. In the Ghana case, households in the study substituted formal care for informal care; the impact on outcomes depends on the quality comparison between the two. Economics (and common sense) tell us that the closer the two types of services were in quality, the more price sensitive people would be. But the closer they were in quality, the less of an impact the switch would have on outcomes.
What do we know about substitutes for formal public care in the health market of low-income countries? Until recently, there was no study of quality among different types of providers in the health market that could answer the following simple question: Suppose the same patient with the same set of symptoms (say, chest pain) went to representative samples of different types of providers, what would be the accuracy of the treatment that he/she received? (This question is conceptually different from many studies of public and private care that focus on the knowledge of health care providers or specific components of their practice.)
A recent study in Health Affairs by Das and others tries to rectify this gap in our knowledge. They trained 22 standardized patients (each for 150 hours or more) to present the same symptoms to providers in the public and private sectors and providers with and without training in rural and urban India. They report that 67 percent of health care providers who were sampled reported no medical qualifications at all, and more surprisingly, 63 percent of interactions in the public sector also took place with clinical staff without any medical training—a consequence of high absence among doctors.
“What’s more, we found only small differences between trained and untrained doctors in such areas as adherence to clinical checklists. Correct diagnoses were rare, incorrect treatments were widely prescribed, and adherence to clinics checklists was higher in private than in public clinics”
Although the resulting small sample of interactions with formally trained doctors in the public sector does not allow them to statistically verify the claim, quality as measured through adherence to clinical checklists was higher among informally trained private sector providers relative to formally trained public sector doctors. They speculate that this is not because of equally poor training, but due to incentives. Informal sector care was not only a close substitute; it was probably better than formal sector care!
By throwing the price incentives of user-fees for providers out with the disincentives of user-fees for users, we may well have thrown the baby out with the bath water. We need to strengthen systems of basic accountability on the supply-side for health care in low-income countries.
This is easier said than done. It most likely involves a process rather than a one-shot solution. A number of different accountability systems—ranging from administrative and peer accountability to pure price incentives—could be introduced, but each will require considerable experimentation, learning and tweaking. Vouchers or simple results-based financing schemes may not be sufficient, largely because they replace the dense information that the market uses for pricing with crude administrative price setting. Although even basic incentives can help improve use and clinical quality, it can also go terribly wrong: For instance, if an insurance scheme prices hysterectomies too high relative to what it was before, health care providers will increase the use of unnecessary hysterectomies.
We can debate how this can be done, but the first step is setting up an institutional framework that allows these experiments to be carried out and for the results of such experimentation to feedback into the evidence-base of the policy maker. Without this basic feedback system, we are likely to get it wrong—again.