"people who had health insurance coverage did not see any significant difference in their total real OOP health expenditures, relative to people without any health insurance"
Livemint has an op-ed on National Health Protection Scheme (NHPS), otherwise known as Aayushmaan Bharat. The article argues that NHPS is a good idea and suggests ways to ensure smooth implementation. It is a flimsy defense of NHPS.
The article concludes that NHPS is a good idea based on the following arguments.
1. "Out-of-pocket payments for healthcare services are very high in our country (about 70%, according to the National Sample Survey Office, 2014), which causes impoverishment to nearly 7% of our population."
2. Evidence from Karnataka's Vajpayee Aarogyashree programme lowered mortality for covered diseases and erased rich-poor disparities in concerned mortality rates. It also lowered out of pocket expenditure.
3. "Existing evidence shows that providing insurance to the poor not only saves lives but is also “cost-effective.
The above three points are either inappropriate or incomplete arguments in defence of NHPS, far from terming it as a step in the right direction.
1. There is no debate on the fact that the out-of-pocket expenditure on health care is huge in India. But, National Health Accounts data points out that 42% of the total out-of-pocket spending (OOP) is used towards buying medicines.
Given this scenario, a simple and significant step to reduce OOP would be to make all medicines free. Expenditure on NHPS with no coverage for medicines, while still requiring people to buy medicines for other conditions too, may not be of much use.
It is, in fact, pointed out in one of the author's own paper.
2. While the authors agree that not every insurance programme has been successful, the cited programme VAS is not a representative example. A host of other programmes have failed but one can give a benefit of the doubt regarding the implementation quality. It thus brings us to the next aspect - the range of conditions and cost-effectiveness.
3. Surprisingly, the article claims the existence of evidence which shows that insurance programmes are cost-effective. The evidence on the same is to the contrary. Insurance programmes are anything but cost-effective. Starting from the US where they spend 18% of GDP but still don't get timely and quality care to the Indian data, insurance programmes are known for NOT being cost-effective, as Gulzar Natarajan points out.
4. The argument for primary and secondary healthcare as a prerequisite for good public health system is well taken but the budget allocation to the same is not proportional to its importance. The right question to ask is - assuming that there is an increased spending on health care, where should the increased funding go? Of course, if it ends up being the case that the overall expenditure on health care is NOT increased but money is rearranged towards NHPS, its a lost battle.
Overall, with no increased funding for primary and secondary care, with no coverage for medicines (42% of OOP), and with our existing weak state capacity, the defence for NHPS is flimsy.
The way out is to retain private insurance coverage for terminal illnesses with expensive treatments like cancer, but increase the spending significantly and use a bulk of it towards primary and secondary health care.
RS TV has conducted a TV discussion on education reform, in the context of government’s decision to reduce the syllabus by half.
The ideas and analysis presented in the discussion is symbolic of everything that’s wrong with the education reform discourse in India.
Consider two key points raised the discussion — “low salary of teachers” as the binding constraint of teacher quality, and a “library movement” as “the solution”.
The reasoning is that talented people aren’t becoming teachers because of the low pay, as compared to the corporates. It seems an obvious reason, so obvious that it clouds our vision preventing us to look beyond. But if we take a step back and probe this further, a different picture emerges.
As I have argued in my book, one needs to ask three questions
- If teacher salary is the issue, what’s the “comparative outcome” of teachers of different grades, receiving different salaries?
- In a counterfactual scenario, if teacher salary is the binding constraint, then increasing teacher salary should have significant effect on the outcomes.
- Finally, what’s the teacher salary in India, compared to our per-capita income?
Some people have asked me for my thoughts on a career in public policy. I am synthesizing these in the post below.
The essence of my suggestion is that
the decision on a career in public policy should be based on 3 factors — nature of the person, nature of the problem that the person is interested in, and the available path.
In the following post, I first describe what I mean by each of those 3 factors mentioned above and I put the various combinations arising from these 3 factors in form of a matrix at the end of the post. If you directly want to jump to the matrix, please feel free to do so.
I. Nature of the person
The “nature” of a person has wide connotations but in our present context, we can categorize people into three broad categories. These categories are illustrative and there can be overlaps between them.
- Problem Solvers: These people are obsessed with the policy problem. They would go to any extent to solve the problem. If they need to do a massive protest, they would do it. If they need to enter politics, they would do it. If they need to sit and research for years, they would do it. In essence, they are path agnostic. They will figure out their path. They don’t need advice! Such people are analogous to entrepreneurs in business.
- Career-ists: Some people are interested in policy sector but they don’t want to take risks. They would like to pursue it more as a career, with stability. Such people are categorised in “career-ists” group. Such people are akin to MBAs, who want to be in the private sector but don’t want to take the risk of becoming an entrepreneur. They get an MBA, enter the corporate world and climb the ladder.
- Satisfaction-seekers: Such people are seeking satisfaction, something beyond the materialistic pleasures of life. Often, they want immediate tangible outcomes. For instance, the joy of donating money to a poor person.
Just to be clear, there’s no judgment involved in categorizing people. It’s just a description of people’s nature as it is.
Labels: Policy Careers
Government introduced private insured health-care for the poor up to Rs.5 lakh per year. I have only few things to say.
After spending 18% of its GDP on health-care, US’s health system is no better than some public funded systems like Thailand. This is in a country with higher implementation capacity than India’s.
By weakening public health system through inaction and making a complete shift to insurance model, we might be moving planning our own disaster. More on this later.
Azim Premji University recently conducted a longitudinal survey of schools following two types of pedagogy — Activity Based Learning, and usual textbook based pedagogy. It says the following
The study assessed the ability of students to recognize characters in the local languages, and while students could recognize the root alphabet, they struggled to identify the compound character formed by joining “a” to the root character. While 32% students in Palghar could recognize the compound character, only 22% in Yadgir could recognize the same.
The lead researcher of the study says
Often, poor learning outcomes are attributed to lack of diversity in curricula, this research shows that even an activity-based curriculum couldn’t help the students as the teachers were ill-prepared and lacked the pedagogical tools required to teach Indian languages
How do we interpret the results of the study?
- Is it the issue with pedagogy — teachers lacking pedagogical tools?
- Is it the issue of systemic capacity/constraints (Gulzar calls them ecosystem-constraints) that constrain teachers’ efforts or overlook their lack of efforts?
Now, we largely know that family characteristics and environment have a significant impact on a child’s career trajectory; in some cases, these factors even trumping the quality of schools they attend.
What is it about the poor families that makes children from these backgrounds less advantageous?
This is an important question because if the answer is good instruction at school, then fixing school will do the job. But, if the answer is that child’s disadvantages also arise due to out-of-school environment, then we need to expand the spectrum of our efforts.
My theory, as I blogged earlier is that the culture of expectations and exposure to opportunities in higher-income family environment, differentiate them from that of low-income family environment.
Culture of expectations is present in poor families too, who see education to get out of poverty but the exposure to opportunities is important. Such exposure does two things. One, it illustrates the wide range of possibilities and also removes mental barriers in process of deciding “what to do”. Two, the exposure demonstrates high likelihood of success building the confidence. Role models play an important role here. Prof. Anirudh Krishna has demonstrated its importance in Indian context.
Apart from income, the other social factors like gender, race, caste also are important.
Now, we have a new paper adding new piece of information to this analysis.
Raj Chetty et al. in his recent paper throws light on this question — what is it about the high-income families that makes their environment different from that of low-income families? [Read this for the summary of findings]
First things first, Chetty et al, demonstrate that there is a difference in the likelihood of success, based on income.
children from high-income (top 1%) families are ten times as likely to become inventors as those from below-median income families. There are similarly large gaps by race and gender.
To what extent is this difference due to the innate ability of students? The answer is — not much.
Differences in innate ability, as measured by test scores in early childhood, explain relatively little of these gaps.
Next, they explore the channels that drive the high likelihood of success in high-income families. They argue that the “exposure to innovation” is the key driving factor. They demonstrate it as follows
Growing up in a neighborhood or family with a high innovation rate in a specific technology class leads to a higher probability of patenting in exactly the same technology class.
The fact that exposure to specific technology leads to success in the same technology class demonstrates that exposure is the key driver.
There are gender-differences in this.
exposure effects are gender-specific: girls are more likely to become inventors in a particular technology class if they grow up in an area with more female inventors in that technology class
Overall, there are two lessons from this new piece of research.
- Exposure to innovation or broad possibilities in general is the differentiating factor between low-income and high-income families. So, policies trying to bridge the income gaps, should focus on creating exposure to the kids. Role models and technology are important tools in this process.
- The closer the role models to the socio-economic characteristics of the child, the better.
At a given point of time, there might not be enough role models for all local communities. One idea can be to pick up the students with promising potential from each local community, give them all possible support and demonstrate their success. It’s a long term project, but it will have impact in the long-term.
It may seem vague and abstract idea but it’s important to do this as part of education reform. If not, we are “losing many Einsteins”.
I like the analogy of “oil drilling” here. We take huge pain to explore all over the earth to mine the oil and create value out of it. Education is something similar. The human minds are mines of intelligence, waiting to be explored. The more we nurture them, the better for us. We might be losing out a lot by the virtue of not exploiting this valuable resource!
I have been recently exploring health sector. Unlike education, where everyone has seen schools from inside and have experienced, medical sector is a black box.
The excellent TV Series, House M.D, along with conversations with few doctors helped me realize two key aspects of medical diagnosis
- The key job of a doctor is to diagnose the disease with minimum symptoms.
- The key constraint in step 1 is the cost of medical tests.
Technology can help enhance doctors’ performance in both the steps.
Let me explain what the two aspects mean, before discussing the role of technology.
1) Job of a doctor: Suppose a patient goes with a headache and stomach ache. What does it mean for the doctor?
A headache can be due to numerous reasons and so can be a stomach ache. Further, headache and stomach ache may not just be stand-alone aspects, they can be symptoms of an underlying major disease.
The doctor’s job now is to use these signs to narrow down the possible diseases that explain these symptoms. This is essentially a mental exercise of matching these signs with diseases that the doctor is aware of! It’s called “differential diagnosis”
Currently, doctors do it using their memory. The problems with this are
a) doctors may not be aware of all possible diseases
b) even if they are aware, they can’t do the huge matching exercise
c) even if the doctors are aware, they might subconsciously discard some diseases because the probability of their occurrence is too low!
As one can see, this is an extremely inefficient process prone to high errors. It’s too much to expect doctors to remember many possible diseases, symptoms and match them instantly.
The key to improving it essentially involves two parameters — doctor’s memory and capacity of match-making process.
We may note that these exact things — memory and processing — are the two aspects that the computers are really good at! Hence, technology would be of a great help here.
A technology tool here would be one where the doctor inputs the symptoms and the tool does the matching and throws out the possible diseases!
This can improve doctors’ efficiency multiple times. It is primarily an IT challenge.